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A DNA-binding domain (DBD) is an independently folded protein domain that contains at least one structural motif that recognizes double- or single-stranded DNA. A DBD can recognize a specific DNA sequence (a recognition sequence) or have a general affinity to DNA. Some DNA-binding domains may also include nucleic acids in their folded structure.
== Function ==
One or more DNA-binding domains are often part of a larger protein consisting of further protein domains with differing function. The extra domains often regulate the activity of the DNA-binding domain. The function of DNA binding is either structural or involves transcription regulation, with the two roles sometimes overlapping.
DNA-binding domains with functions involving DNA structure have biological roles in DNA replication, repair, storage, and modification, such as methylation.
Many proteins involved in the regulation of gene expression contain DNA-binding domains. For example, proteins that regulate transcription by binding DNA are called transcription factors. The final output of most cellular signaling cascades is gene regulation.
The DBD interacts with the nucleotides of DNA in a DNA sequence-specific or non-sequence-specific manner, but even non-sequence-specific recognition involves some sort of molecular complementarity between protein and DNA. DNA recognition by the DBD can occur at the major or minor groove of DNA, or at the sugar-phosphate DNA backbone (see the structure of DNA). Each specific type of DNA recognition is tailored to the protein's function. For example, the DNA-cutting enzyme DNAse I cuts DNA almost randomly and so must bind to DNA in a non-sequence-specific manner. But, even so, DNAse I recognizes a certain 3-D DNA structure, yielding a somewhat specific DNA cleavage pattern that can be useful for studying DNA recognition by a technique called DNA footprinting.
Many DNA-binding domains must recognize specific DNA sequences, such as DBDs of transcription factors that activate specific genes, or those of enzymes that modify DNA at specific sites, like restriction enzymes and telomerase. The hydrogen bonding pattern in the DNA major groove is less degenerate than that of the DNA minor groove, providing a more attractive site for sequence-specific DNA recognition.
The specificity of DNA-binding proteins can be studied using many biochemical and biophysical techniques, such as gel electrophoresis, analytical ultracentrifugation, calorimetry, DNA mutation, protein structure mutation or modification, nuclear magnetic resonance, x-ray crystallography, surface plasmon resonance, electron paramagnetic resonance, cross-linking and microscale thermophoresis (MST).
== DNA-binding protein in genomes ==
A large fraction of genes in each genome encodes DNA-binding proteins (see Table). However, only a rather small number of protein families are DNA-binding. For instance, more than 2000 of the ~20,000 human proteins are "DNA-binding", including about 750 Zinc-finger proteins.
== Types ==
=== Helix-turn-helix ===
Originally discovered in bacteria, the helix-turn-helix motif is commonly found in repressor proteins and is about 20 amino acids long. In eukaryotes, the homeodomain comprises 2 helices, one of which recognizes the DNA (aka recognition helix). They are common in proteins that regulate developmental processes.
=== Helix-hairpin-helix ===
The helix-hairpin-helix is found in proteins that interact with DNA in a non-sequence-specific manner. It consists of two anti-parallel alpha-helices connected by a short hairpin loop. The two alpha-helices are packed at an acute angle of ~25–50° that dictates the characteristic pattern of hydrophobicity in the sequences, while other DNA-binding structures like the helix-turn-helix motif, which is also formed by a pair of helices, can be easily distinguished by the packing of the helices at an almost right angle.
=== Zinc finger ===
The zinc finger domain is mostly found in eukaryotes, but some examples have been found in bacteria. The zinc finger domain is generally between 23 and 28 amino acids long and is stabilized by coordinating zinc ions with regularly spaced zinc-coordinating residues (either histidines or cysteines). The most common class of zinc finger (Cys2His2) coordinates a single zinc ion and consists of a recognition helix and a 2-strand beta-sheet. In transcription factors these domains are often found in arrays (usually separated by short linker sequences) and adjacent fingers are spaced at 3 basepair intervals when bound to DNA.
=== Leucine zipper ===
The basic leucine zipper (bZIP) domain is found mainly in eukaryotes and to a limited extent in bacteria. The bZIP domain contains an alpha helix with a leucine at every 7th amino acid. If two such helices find one another, the leucines can interact as the teeth in a zipper, allowing dimerization of two proteins. When binding to the DNA, basic amino acid residues bind to the sugar-phosphate backbone while the helices sit in the major grooves. It regulates gene expression.
=== Winged helix ===
Consisting of about 110 amino acids, the winged helix (WH) domain has four helices and a two-strand beta-sheet.
=== Winged helix-turn-helix ===
The winged helix-turn-helix (wHTH) domain SCOP 46785 is typically 85-90 amino acids long. It is formed by a 3-helical bundle and a 4-strand beta-sheet (wing).
=== Helix-loop-helix ===
The basic helix-loop-helix (bHLH) domain is found in some transcription factors and is characterized by two alpha helices (α-helixes) connected by a loop. One helix is typically smaller and due to the flexibility of the loop, allows dimerization by folding and packing against another helix. The larger helix typically contains the DNA-binding regions.
=== HMG-box ===
HMG-box domains are found in high mobility group proteins which are involved in a variety of DNA-dependent processes like replication and transcription. They also alter the flexibility of the DNA by inducing bends. The domain consists of three alpha helices separated by loops.
=== Wor3 domain ===
Wor3 domains, named after the White–Opaque Regulator 3 (Wor3) in Candida albicans arose more recently in evolutionary time than most previously described DNA-binding domains and are restricted to a small number of fungi.
=== OB-fold domain ===
The OB-fold is a small structural motif originally named for its oligonucleotide/oligosaccharide binding properties. OB-fold domains range between 70 and 150 amino acids in length. OB-folds bind single-stranded DNA, and hence are single-stranded binding proteins.
OB-fold proteins have been identified as critical for DNA replication, DNA recombination, DNA repair, transcription, translation, cold shock response, and telomere maintenance.
== Unusual ==
=== Immunoglobulin fold ===
The immunoglobulin domain (InterPro: IPR013783) consists of a beta-sheet structure with large connecting loops, which serve to recognize either DNA major grooves or antigens. Usually found in immunoglobulin proteins, they are also present in Stat proteins of the cytokine pathway. This is likely because the cytokine pathway evolved relatively recently and has made use of systems that were already functional, rather than creating its own.
=== B3 domain ===
The B3 DBD (InterPro: IPR003340, SCOP 117343) is found exclusively in transcription factors from higher plants and restriction endonucleases EcoRII and BfiI and typically consists of 100-120 residues. It includes seven beta sheets and two alpha helices, which form a DNA-binding pseudobarrel protein fold.
=== TAL effector ===
TAL effectors are found in bacterial plant pathogens of the genus Xanthomonas and are involved in regulating the genes of the host plant in order to facilitate bacterial virulence, proliferation, and dissemination. They contain a central region of tandem 33-35 residue repeats and each repeat region encodes a single DNA base in the TALE's binding site. Within the repeat it is residue 13 alone that directly contacts the DNA base, determining sequence specificity, while other positions make contacts with the DNA backbone, stabilising the DNA-binding interaction. Each repeat within the array takes the form of paired alpha-helices, while the whole repeat array forms a right-handed superhelix, wrapping around the DNA-double helix. TAL effector repeat arrays have been shown to contract upon DNA binding and a two-state search mechanism has been proposed whereby the elongated TALE begins to contract around the DNA beginning with a successful Thymine recognition from a unique repeat unit N-terminal of the core TAL-effector repeat array.
Related proteins are found in bacterial plant pathogen Ralstonia solanacearum, the fungal endosymbiont Burkholderia rhizoxinica and two as-yet unidentified marine-microorganisms. The DNA binding code and the structure of the repeat array is conserved between these groups, referred to collectively as the TALE-likes.
== See also ==
For a structural classification of DNA-binding-domains presents in land plant genomes, see
Comparison of nucleic acid simulation software
== References ==
== External links ==
DBD database of predicted transcription factors Kummerfeld SK, Teichmann SA (January 2006). "DBD: a transcription factor prediction database". Nucleic Acids Research. 34 (Database issue): D74-81. doi:10.1093/nar/gkj131. PMC 1347493. PMID 16381970. Uses a curated set of DNA-binding domains to predict transcription factors in all completely sequenced genomes
Table of DNA-binding motifs
DNA+Footprinting at the U.S. National Library of Medicine Medical Subject Headings (MeSH)
DNA-Binding+Proteins at the U.S. National Library of Medicine Medical Subject Headings (MeSH)
DNA-binding domains in PROSITE | Wikipedia/DNA-binding_domain |
Protein tyrosine phosphatases (EC 3.1.3.48, systematic name protein-tyrosine-phosphate phosphohydrolase) are a group of enzymes that remove phosphate groups from phosphorylated tyrosine residues on proteins:
[a protein]-tyrosine phosphate + H2O = [a protein]-tyrosine + phosphate
Protein tyrosine (pTyr) phosphorylation is a common post-translational modification that can create novel recognition motifs for protein interactions and cellular localization, affect protein stability, and regulate enzyme activity. As a consequence, maintaining an appropriate level of protein tyrosine phosphorylation is essential for many cellular functions. Tyrosine-specific protein phosphatases (PTPase; EC 3.1.3.48) catalyse the removal of a phosphate group attached to a tyrosine residue, using a cysteinyl-phosphate enzyme intermediate. These enzymes are key regulatory components in signal transduction pathways (such as the MAP kinase pathway) and cell cycle control, and are important in the control of cell growth, proliferation, differentiation, transformation, and synaptic plasticity.
== Functions ==
Together with tyrosine kinases, PTPs regulate the phosphorylation state of many important signalling molecules, such as the MAP kinase family.
PTPs are increasingly viewed as integral components of signal transduction cascades, despite less study and understanding compared to tyrosine kinases.
PTPs have been implicated in regulation of many cellular processes, including, but not limited to:
Cell growth
Cellular differentiation
Mitotic cycles
Oncogenic transformation
Receptor endocytosis
== Classification ==
=== By mechanism ===
PTP activity can be found in four protein families.
Links to all 107 members of the protein tyrosine phosphatase family can be found in the template at the bottom of this article.
==== Class I ====
The class I PTPs, are the largest group of PTPs with 99 members, which can be further subdivided into
38 classical PTPs
21 receptor tyrosine phosphatases
17 nonreceptor-type PTPs
61 VH-1-like or dual-specific phosphatases (DSPs)
11 MAPK phosphatases (MPKs)
3 Slingshots
3 PRLs
4 CDC14s
19 atypical DSPs
5 phosphatase and tensin homologs (PTENs)
16 myotubularins
Dual-specificity phosphatases (dTyr and dSer/dThr) dual-specificity protein-tyrosine phosphatases. Ser/Thr and Tyr dual-specificity phosphatases are a group of enzymes with both Ser/Thr (EC 3.1.3.16) and tyrosine-specific protein phosphatase (EC 3.1.3.48) activity able to remove the serine/threonine or the tyrosine-bound phosphate group from a wide range of phosphoproteins, including a number of enzymes that have been phosphorylated under the action of a kinase. Dual-specificity protein phosphatases (DSPs) regulate mitogenic signal transduction and control the cell cycle.
LEOPARD syndrome, Noonan syndrome, and metachondromatosis are associated with PTPN11.
Elevated levels of activated PTPN5 negatively affects synaptic stability and plays a role in Alzheimer's disease, Fragile X syndrome, schizophrenia, and Parkinson's disease. Decreased levels of PTPN5 has been implicated in Huntington's disease, brain ischemia, alcohol use disorder, and stress disorders. Together these findings indicate that only at optimal levels of PTPN5 is synaptic function unimpaired.
==== Class II ====
LMW (low-molecular-weight) phosphatases, or acid phosphatases, act on tyrosine phosphorylated proteins, low-MW aryl phosphates and natural and synthetic acyl phosphates.
The class II PTPs contain only one member, low-molecular-weight phosphotyrosine phosphatase (LMPTP).
==== Class III ====
Cdc25 phosphatases (dTyr and/or dThr)
The Class III PTPs contains three members, CDC25 A, B, and C
==== Class IV ====
These are members of the HAD fold and superfamily, and include phosphatases specific to pTyr and pSer/Thr as well as small molecule phosphatases and other enzymes. The subfamily EYA (eyes absent) is believed to be pTyr-specific, and has four members in human, EYA1, EYA2, EYA3, and EYA4. This class has a distinct catalytic mechanism from the other three classes.
=== By location ===
Based on their cellular localization, PTPases are also classified as:
Receptor-like, which are transmembrane receptors that contain PTPase domains. In terms of structure, all known receptor PTPases are made up of a variable-length extracellular domain, followed by a transmembrane region and a C-terminal catalytic cytoplasmic domain. Some of the receptor PTPases contain fibronectin type III (FN-III) repeats, immunoglobulin-like domains, MAM domains, or carbonic anhydrase-like domains in their extracellular region. In general, the cytoplasmic region contains two copies of the PTPase domain. The first seems to have enzymatic activity, whereas the second is inactive.
Non-receptor (intracellular) PTPases
=== Common elements ===
All PTPases, other than those of the EYA family, carry the highly conserved active site motif C(X)5R (PTP signature motif), employ a common catalytic mechanism, and possess a similar core structure made of a central parallel beta-sheet with flanking alpha-helices containing a beta-loop-alpha-loop that encompasses the PTP signature motif. Functional diversity between PTPases is endowed by regulatory domains and subunits.
== Expression pattern ==
Individual PTPs may be expressed by all cell types, or their expression may be strictly tissue-specific. Most cells express 30% to 60% of all the PTPs, however hematopoietic and neuronal cells express a higher number of PTPs in comparison to other cell types. T cells and B cells of hematopoietic origin express around 60 to 70 different PTPs. The expression of several PTPS is restricted to hematopoietic cells, for example, LYP, SHP1, CD45, and HePTP. The expression of PTPN5 is restricted to the brain, and differs between brain regions, with no expression in the cerebellum.
== References ==
== Sources ==
== External links ==
PTP Summary and Relevant Publications at Monash University
Protein-Tyrosine-Phosphatase at the U.S. National Library of Medicine Medical Subject Headings (MeSH)
EC 3.1.3.48 | Wikipedia/Protein_tyrosine_phosphatase |
In molecular biology, fibrous proteins or scleroproteins are one of the three main classifications of protein structure (alongside globular and membrane proteins). Fibrous proteins are made up of elongated or fibrous polypeptide chains which form filamentous and sheet-like structures. This kind of protein can be distinguished from globular protein by its low solubility in water. In contrast, globular proteins are spherical and generally soluble in water, performing dynamic functions like enzymatic activity or transport. Such proteins serve protective and structural roles by forming connective tissue, tendons, bone matrices, and muscle fiber.
Fibrous proteins consist of many families including keratin, collagen, elastin, fibrin or spidroin. Collagen is the most abundant of these proteins which exists in vertebrate connective tissue including tendon, cartilage, and bone.
== Biomolecular structure ==
A fibrous protein forms long protein filaments, which are shaped like rods or wires. Fibrous proteins are structural or storage proteins that are typically inert and water-insoluble. A fibrous protein occurs as an aggregate due to hydrophobic side chains that protrude from the molecule.
A fibrous protein's peptide sequence often has limited residues with repeats; these can form unusual secondary structures, such as a collagen helix. The structures often feature cross-links between chains (e.g., cys-cys disulfide bonds between keratin chains).
Fibrous proteins tend not to denature as easily as globular proteins.
Miroshnikov et al. (1998) are among the researchers who have attempted to synthesize fibrous proteins.
== References ==
== External links ==
Scleroproteins at the U.S. National Library of Medicine Medical Subject Headings (MeSH) | Wikipedia/Scleroprotein |
Sickle cell disease (SCD), also simply called sickle cell, is a group of inherited haemoglobin-related blood disorders. The most common type is known as sickle cell anemia. Sickle cell anemia results in an abnormality in the oxygen-carrying protein haemoglobin found in red blood cells. This leads to the red blood cells adopting an abnormal sickle-like shape under certain circumstances; with this shape, they are unable to deform as they pass through capillaries, causing blockages. Problems in sickle cell disease typically begin around 5 to 6 months of age. A number of health problems may develop, such as attacks of pain (known as a sickle cell crisis) in joints, anemia, swelling in the hands and feet, bacterial infections, dizziness and stroke. The probability of severe symptoms, including long-term pain, increases with age. Without treatment, people with SCD rarely reach adulthood but with good healthcare, median life expectancy is between 58 and 66 years. All of the major organs are affected by sickle cell disease. The liver, heart, kidneys, gallbladder, eyes, bones, and joints can be damaged from the abnormal functions of the sickle cells and their inability to effectively flow through the small blood vessels.
Sickle cell disease occurs when a person inherits two abnormal copies of the β-globin gene that makes haemoglobin, one from each parent. Several subtypes exist, depending on the exact mutation in each haemoglobin gene. An attack can be set off by temperature changes, stress, dehydration, and high altitude. A person with a single abnormal copy does not usually have symptoms and is said to have sickle cell trait. Such people are also referred to as carriers. Diagnosis is by a blood test, and some countries test all babies at birth for the disease. Diagnosis is also possible during pregnancy.
The care of people with sickle cell disease may include infection prevention with vaccination and antibiotics, high fluid intake, folic acid supplementation, and pain medication. Other measures may include blood transfusion and the medication hydroxycarbamide (hydroxyurea). In 2023, new gene therapies were approved involving the genetic modification and replacement of blood forming stem cells in the bone marrow.
As of 2021, SCD is estimated to affect about 7.7 million people worldwide, directly causing an estimated 34,000 annual deaths and a contributory factor to a further 376,000 deaths. About 80% of sickle cell disease cases are believed to occur in Sub-Saharan Africa. It also occurs to a lesser degree among people in parts of India, Southern Europe, West Asia, North Africa and among people of African origin (sub-Saharan) living in other parts of the world. The condition was first described in the medical literature by American physician James B. Herrick in 1910. In 1949, its genetic transmission was determined by E. A. Beet and J. V. Neel. In 1954, it was established that carriers of the abnormal gene are protected to some degree against malaria.
== Signs and symptoms ==
Signs of sickle cell disease usually begin in early childhood. The severity of symptoms can vary from person to person, as can the frequency of crisis events. Sickle cell disease may lead to various acute and chronic complications, several of which have a high mortality rate.
=== First events ===
When SCD presents within the first year of life, the most common problem is an episode of pain and swelling in the child's hands and feet, known as dactylitis or "hand-foot syndrome." Pallor, jaundice, and fatigue can also be early signs due to anaemia resulting from sickle cell disease.
In children older than 2 years, the most common initial presentation is a painful episode of a generalized or variable nature, while a slightly less common presentation involves acute chest pain. Dactylitis is rare or almost never occurs in children over the age of 2.
=== Critical events ===
==== Vaso-occlusive crisis ====
Also termed "sickle cell crisis" or "sickling crisis", the vaso-occlusive crisis (VOC) manifests principally as extreme pain, most often affecting the chest, back, legs and/or arms. The underlying cause is sickle-shaped red blood cells that obstruct capillaries and restrict blood flow to an organ, resulting in ischaemia, pain, necrosis, and often organ damage. The frequency, severity, and duration of these crises vary considerably. Milder crises can be managed with nonsteroidal anti-inflammatory drugs. For more severe crises, patients may require inpatient management for intravenous opioids. Vaso-occlusive crisis involving organs such as the penis or lungs are considered an emergency and treated with red blood cell transfusions.
A VOC can be triggered by anything which causes blood vessels to constrict; this includes physical or mental stress, cold, and dehydration. "After HbS deoxygenates in the capillaries, it takes some time (seconds) for HbS polymerization and the subsequent flexible-to-rigid transformation. If the transit time of RBC through the microvasculature is longer than the polymerization time, sickled RBC will lodge in the microvasculature."
==== Splenic sequestration crisis ====
The spleen is especially prone to damage in SCD due to its role as a blood filter. A splenic sequestration crisis, also known as a spleen crisis, is a medical emergency that occurs when sickled red blood cells block the spleen's filter mechanism, causing the spleen to swell and fill with blood. The accumulation of red blood cells in the spleen results in a sudden drop in circulating hemoglobin and potentially life-threatening anemia. Symptoms include pain on the left side, swollen spleen (which can be detected by palpation), fatigue, dizziness, irritability, rapid heartbeat, or pale skin. It most commonly affects young children, the median age of first occurrence is 1.4 years. By the age of 5 years repeated instances of sequestration cause scarring and eventual atrophy of the spleen.
Treatment is supportive, with blood transfusion if hemoglobin levels fall too low. Full or partial splenectomy may be necessary. Long term consequences of a loss of spleen function are increased susceptibility to bacterial infections.
==== Acute chest syndrome ====
Acute chest syndrome is caused by a VOC which affects the lungs, possibly triggered by infection or by emboli which have circulated from other organs. Symptoms include wheezing, chest pain, fever, pulmonary infiltrate (visible on x-ray), and hypoxemia. After sickling crisis (see above) it is the second-most common cause of hospitalization and it accounts for about 25% of deaths in patients with SCD. Most cases present with vaso-occlusive crises, and then develop acute chest syndrome.
==== Aplastic crisis ====
Aplastic crises are instances of an acute worsening of the patient's baseline anaemia, producing pale appearance, fast heart rate, and fatigue. This crisis is normally triggered by parvovirus B19, which directly affects production of red blood cells by invading the red cell precursors and multiplying in and destroying them. Parvovirus infection almost completely prevents red blood cell production for two to three days (red cell aplasia). In normal individuals, this is of little consequence, but the shortened red cell life of SCD patients results in an abrupt, life-threatening situation. Reticulocyte count drops dramatically during the disease (causing reticulocytopenia), red cell production lapses, and the rapid destruction of existing red cells leads to acute and severe anemia. This crisis takes four to seven days to resolve. Most patients can be managed supportively; some need a blood transfusion.
=== Complications ===
Sickle cell anaemia can lead to various complications including:
Increased risk of severe bacterial infections is due to loss of functioning spleen tissue. These infections are typically caused by bacteria such as Streptococcus pneumoniae and Haemophilus influenzae. Daily penicillin prophylaxis is the most commonly used treatment during childhood, with some haematologists continuing treatment indefinitely. Patients benefit from routine vaccination for S. pneumoniae.
Stroke can result from blockage of blood vessels in the brain, causing numbness, confusion, or weakness which may be long lasting. Silent stroke causes no immediate symptoms, but is associated with damage to the brain. Silent stroke is probably five times as common as symptomatic stroke. About 10–15% of children with SCD have strokes, with silent strokes predominating in the younger patients.
Cholelithiasis (gallstones) and cholecystitis may result from excessive bilirubin production and precipitation due to prolonged haemolysis.
Avascular necrosis (aseptic bone necrosis) of the hip and other major joints may occur as a result of ischaemia.
Priapism and infarction of the penis
Osteomyelitis (bacterial bone infection) as a result of damage to the spleen, commonly caused by either Staphylococcus aureus or species of Salmonella.
Chronic kidney failure due to sickle-cell nephropathy manifests itself with hypertension, protein loss in the urine, loss of red blood cells in urine and worsened anaemia. If it progresses to end-stage kidney failure, it carries a poor prognosis.
Leg ulcers are relatively common in SCD and can be disabling.
In eyes, background retinopathy, proliferative retinopathy, vitreous haemorrhages, and retinal detachments can result in blindness. Regular annual eye checks are recommended.
During pregnancy, intrauterine growth restriction, spontaneous abortion, and pre-eclampsia
Chronic pain: Even in the absence of acute vaso-occlusive pain, many patients have unreported chronic pain.
Pulmonary hypertension (increased pressure on the pulmonary artery) can lead to strain on the right ventricle and a risk of heart failure; typical symptoms are shortness of breath, decreased exercise tolerance, and episodes of syncope. 21% of children and 30% of adults have evidence of pulmonary hypertension when tested; this is associated with reduced walking distance and increased mortality.
Cardiomyopathy and left ventricular diastolic dysfunction caused by fibrosis or scarring of cardiac tissues. This also contributes to pulmonary hypertension, decreased exercise capacity, and arrhythmias.
== Genetics ==
Hemoglobin is an oxygen-binding protein, found in erythrocytes, which transports oxygen from the lungs (or in the fetus, from the placenta) to the tissues. Each molecule of hemoglobin comprises 4 protein subunits, referred to as globins. Normally, humans have:-
hemoglobin F (Fetal hemoglobin, HbF), consisting of two alpha (α-globin) and two gamma (γ-globin) chains. This dominates during development of the fetus and until about 6 weeks of age. Afterwards, haemoglobin A dominates throughout life.
hemoglobin A, (Adult hemoglobin, HbA) which consists of two alpha and two beta (β-globin) chains. This is the most common human hemoglobin tetramer, accounting for over 97% of the total red blood cell hemoglobin in normal adults.
hemoglobin A2, (HbA2) is a second form of adult hemoglobin and is composed of two alpha and two delta (δ-globin) chains. This hemoglobin typically makes up 1–3% of hemoglobin in adults.
β-globin is encoded by the HBB gene on human chromosome 11; mutations in this gene produce variants of the protein which are implicated with abnormal hemoglobins. The mutation which causes sickle cell disease results in an abnormal hemoglobin known as hemoglobin S (HbS), which replaces HbA in adults. The human genome contains a pair of genes for β-globin; in people with sickle cell disease, both genes are affected and the erythropoietic cells in the bone marrow will only create HbS. In people with sickle cell trait, only one gene is abnormal; erythropoiesis generates a mixture of normal HbA and sickle HbS. The person has very few if any symptoms of sickle cell disease but carries the gene and can pass it on to their children.
Sickle cell disease has an autosomal recessive pattern of inheritance from parents. Both copies of the affected gene must carry the same mutation (homozygous condition) for a person to be affected by an autosomal recessive disorder. An affected person usually has unaffected parents who each carry one mutated gene and one normal gene (heterozygous condition) and are referred to as genetic carriers; they may not have any symptoms. When both parents have the sickle cell trait, any given child has a 25% chance of sickle cell disease; a 25% chance of no sickle cell alleles, and a 50% chance of the heterozygous condition (see diagram).
There are several different haplotypes of the sickle cell gene mutation, indicating that it probably arose spontaneously in different geographic areas. The variants are known as Cameroon, Senegal, Benin, Bantu, and Saudi-Asian. These are clinically important because some are associated with higher HbF levels, e.g., Senegal and Saudi-Asian variants, and tend to have milder disease.
The gene defect is a single nucleotide mutation of the β-globin gene, which results in glutamate being substituted by valine at position 6 of the β-globin chain. Hemoglobin S with this mutation is referred to as HbS, as opposed to the normal adult HbA. Under conditions of normal oxygen concentration this causes no apparent effects on the structure of haemoglobin or its ability to transport oxygen around the body. However, the deoxy form of HbS has an exposed hydrophobic patch which causes HbS molecules to join to form long inflexible chains. Under conditions of low oxygen concentration in the bloodstream, such as exercise, stress, altitude or dehydration, HbS polymerization forms fibrous precipitates within the red blood cell. In people homozygous for the sickle cell mutation, the presence of long-chain polymers of HbS distort the shape of the red blood cell from a smooth, doughnut-like shape to the sickle shape, making it fragile and susceptible to blocking or breaking within capillaries.
In people heterozygous for HbS (carriers of sickle cell disease), the polymerisation problems are minor because the normal allele can produce half of the haemoglobin. Sickle cell carriers have symptoms only if they are deprived of oxygen (for example, at altitude) or while severely dehydrated.
=== Malaria ===
SCD is most prevalent in areas which have historically been associated with endemic malaria. The sickle cell trait provides a carrier with a survival advantage against malaria fatality over people with normal hemoglobin in regions where malaria is endemic.
Infection with the malaria parasite affects asymptomatic carriers of the abnormal hemoglobin gene differently from patients with full SCD. Carriers (heterozygous for the gene) who catch malaria are less likely to suffer from severe symptoms than people with normal hemogolobin. SCD patients (homozygous for the gene) are similarly less likely to become infected with malaria; however once infected they are more likely to develop severe and life-threatening anemia.
The impact of sickle cell anemia on malaria immunity illustrates some evolutionary trade-offs that have occurred because of endemic malaria. Although the shorter life expectancy for those with the homozygous condition would tend to disfavour the trait's survival, the trait is preserved in malaria-prone regions because of the benefits provided by the heterozygous form; an example of natural selection.
Due to the adaptive advantage of the heterozygote, the disease is still prevalent, especially among people with recent ancestry in malaria-stricken areas, such as Africa, the Mediterranean, India, and the Middle East. Malaria was historically endemic to southern Europe, but it was declared eradicated in the mid-20th century, with the exception of rare sporadic cases.
The malaria parasite has a complex lifecycle and spends part of it in red blood cells. There are two mechanisms which protect sickle cell carriers from malaria. One is that the parasite is hindered from growing and reproducing in a carrier's red blood cells; another is that a carrier's red cells show signs of damage when infected, and are detected and destroyed as they pass through the spleen.
== Pathophysiology ==
Under conditions of low oxygen concentration, HBS polymerises to form long strands within the red blood cell (RBC). These strands distort the shape of the cell and after a few seconds cause it to adopt an abnormal, inflexible sickle-like shape. This process reverses when oxygen concentration is raised and the cells resume their normal biconcave disc shape. If sickling takes place in the venous system, after blood has passed through the capillaries, it has no effect on the organs and the RBCs can unsickle when they become oxygenated in the lungs. Repeated switching between sickle and normal shapes damages the membrane of the RBC so that it eventually becomes permanently sickled.
Normal red blood cells are quite elastic and have a biconcave disc shape, which allows the cells to deform to pass through capillaries. In sickle cell disease, low oxygen tension promotes red blood cell sickling and repeated episodes of sickling damage the cell membrane and decrease the cell's elasticity. These cells fail to return to normal shape when normal oxygen tension is restored. As a consequence, these rigid blood cells are unable to deform as they pass through narrow capillaries, leading to vessel occlusion and ischaemia.
Cells which have become sickled are detected as they pass through the spleen and are destroyed. In young children with SCD, the accumulation of sickled cells in the spleen can result in splenic sequestration crisis. In this, the spleen becomes engorged with blood, depriving the general circulation of blood cells and leading to severe anemia. The spleen initially becomes noticeably swollen but the lack of a healthy blood flow through the organ culminates in scarring of the spleen tissues and eventually death of the organ, generally before the age of 5 years.
The actual anaemia of the illness is caused by haemolysis, the destruction of the red cells, because of their shape. Although the bone marrow attempts to compensate by creating new red cells, it does not match the rate of destruction. Healthy red blood cells typically function for 90–120 days, but sickled cells only last 10–20 days.
The rapid breakdown of RBC's in SCD results in the release of free heme into the bloodstream exceeding the capacity of the body's protective mechanisms. Although heme is an essential component of hemoglobin, it is also a potent oxidative molecule. Free heme is also an alarmin - a signal of tissue damage or infection, which triggers defensive responses in the body and increases the risk of inflammation and vaso-occlusive events.
== Diagnosis ==
=== Prenatal and newborn screening ===
Checking for SCD begins during pregnancy, with a prenatal screening questionnaire which includes, among other things, a consideration of health issues in the child's parents and close relatives. During pregnancy, genetic testing can be done on either a blood sample from the fetus or a sample of amniotic fluid. During the first trimester of pregnancy, chorionic villus sampling (CVS) is also a technique used for SCD prenatal diagnosis. A routine heel prick test, in which a small sample of blood is collected a few days after birth, is used to check conclusively for SCD as well as other inherited conditions.
=== Tests ===
Where SCD is suspected, a number of tests can be used. Often a simpler, cheaper test is applied first with a more complex test such as DNA analysis used to confirm a positive result.
Two tests which are specific for SCD:
A blood smear is a thin layer of blood smeared on a glass microscope slide and then stained in such a way as to allow the various blood cells to be examined microscopically. This technique can be used to visually detect sickled cells, however it does not detect sickle carriers.
A solubility test relies on the fact that HbS is less soluble than normal hemoglobin (HbA); it is highly reliable but does not distinguish between full SCD and carrier status.
Tests which can be used for SCD as well as for other hemoglobinopathies:
Hemoglobin electrophoresis is a test that can detect different types of hemoglobin. Hemoglobin is extracted from the red cells, then introduced into a porous gel and subjected to an electrical field. This separates the normal and abnormal types of hemoglobin which can then be identified and quantified.
Isoelectric focusing (IEF) is a technique that can be used to diagnose sickle cell disease and other hemoglobinopathies. The technique separates molecules based on their isoelectric point, or the pH at which they have no net electrical charge. IEF uses an electric charge to separate and identify different types of hemoglobin, which become focused into sharp stationary bands. The technique can distinguish many types of abnormal hemoglobin.
High-performance liquid chromatography (HPLC) is reliable, fully automated, and able to distinguish most types of sickle cell disease including heterozygous, The method separates and quantifies hemoglobin fractions by measuring their rate of flow through a column of absorbent material.
DNA analysis using polymerase chain reaction (PCR), to amplify small samples of DNA. Variants of PCR used to diagnose SCD include Amplification-refractory mutation system (ARMS) and Allele-Specific Recombinase Polymerase Amplification. These tests can identify subtypes of SCD as well as combination hemoglobinopathies.
== Genetic counseling ==
Genetic counselling is the process by which people with a hereditary disorder are advised of the probability of transmitting it and the ways in which this may be prevented or ameliorated.
People who are known carriers of the disease or at risk of having a child with sickle cell anemia may undergo genetic counseling. Genetic counselors work with families to discuss the benefits, limitations, and logistics of genetic testing options as well as the potential impact of testing and test results on the individual. Counselling is best given before a child is conceived, and a number of possible courses could be suggested. These include adoption, the use of eggs or sperm from a healthy donor, and in-vitro fertilisation (IVF) when combined with pre-implantation genetic diagnosis of the embryos.
== Treatment ==
=== Management ===
There are a number of precautions which can help reduce the risk of developing a sickling crisis. Lifestyle behaviours include maintaining good hydration and avoiding physical stress or exhaustion. Since sickling can be triggered by low oxygen levels, people with SCD should avoid high altitudes such as high mountains or flying in unpressurised aircraft. People with SCD should avoid alcohol and smoking, as alcohol can cause dehydration and smoking can trigger acute chest syndrome. Stress can also trigger a sickle cell crisis, so relaxation techniques like breathing exercises can help.
Pneumococcal infection is a leading cause of death among children with SCD; penicillin is recommended daily during the first 5 years of life in order to minimise the risk of infection.
Dietary supplementation of folic acid is sometimes recommended, on the basis that it facilitates the creation of new red blood cells and may reduce anemia. A Cochrane review of its use in 2016 found "the effect of supplementation on anaemia and any symptoms of anaemia remains unclear" due to a lack of medical evidence.
People with SCD are recommended to receive all vaccinations which are recommended by health authorities in order to avoid serious infection which might trigger a sickling crisis.
Hydroxyurea was the first approved drug for the treatment of SCD, which has been shown to decrease the number and severity of attacks and possibly increase survival time. This is achieved, in part, by reactivating fetal haemoglobin production in place of the haemoglobin S that causes sickling. Hydroxyurea lowers the expression of adhesion molecules on endothelial and red blood cells, which lowers the chance of small vessel blockages. Additionally, it encourages the release of nitric oxide, which enhances blood flow and inhibits the formation of clots. Hydroxyurea had previously been used as a chemotherapy agent, and some concern exists that long-term use may be harmful. A Cochrane review in 2022 found a weak evidence base for its use in SCD.
Voxelotor was received accelerated approval as a treatment for SCD in the United States in 2019, and was approved by the European Medicines Agency (EMA) in 2021. In trials, it had been shown to have disease-modifying potential by increasing hemoglobin levels and decreasing hemolysis indicators However, following an increased risk of vaso-occlusive seizures and death observed in registries and clinical trials, the manufacturer, Pfizer, withdrew it from the market worldwide.
=== Blood transfusion ===
A simple blood transfusion can be used to treat SCD when hemoglobin levels drop too low, or to prepare for an operation or pregnancy. It can also be used to protect against long-term complications, or to reduce the risk of stroke. The simple, or top-up transfusion is a procedure in which healthy blood cells from a donor are infused into the patient's bloodstream. This benefits by alleviating anemia and increasing oxygen levels in the tissues, reducing the risk of sickling and relieving sickling symptoms. A simple transfusion can be used to treat SCD when hemoglobin levels drop too low, or to prepare for an operation or pregnancy. It can also be used to protect against long-term complications, or to reduce the risk of stroke.
An exchange transfusion is a procedure in which blood is removed from the body, then processed to extract sickled cells, which are replaced by healthy red blood cells from a donor. The treated blood, including white cells and plasma, is then returned to the patient. Exchange transfusions are likely to be needed in an emergency, in severe cases of SCD, or to support a mother during pregnancy.
=== Stroke prevention ===
Transcranial Doppler ultrasound (TCD) can detect children with sickle cell that have a high risk for stroke. The ultrasound test detects blood vessels partially obstructed by sickle cells by measuring the rate of blood into the brain, as blood flow velocity is inversely related to arterial diameter, and consequently, high blood-flow velocity is correlated with narrowing of the arteries.
In children, preventive RBC transfusion therapy has been shown to reduce the risk of first stroke or silent stroke when transcranial Doppler ultrasonography shows abnormal cerebral blood flow. In those who have sustained a prior stroke event, it also reduces the risk of recurrent stroke and additional silent strokes.
=== Vaso-occlusive crisis ===
Most people with sickle cell disease have intensely painful episodes called vaso-occlusive crises (VOC). However, the frequency, severity, and duration of these crises vary tremendously. In a VOC, the circulation of blood vessels is obstructed by sickled red blood cells, causing ischemic injuries to the tissues, inflammation and pain. Recurrent episodes may cause irreversible organ damage.
The most common and obvious symptom of a VOC is pain, which may be felt anywhere in the body but most commonly in the limbs and back. The degree of pain varies from mild to severe. Home treatment options include bedrest and hydration, and pain control using over-the-counter medication such as paracetamol or ibuprofen. More severe cases may require prescription opioids such as codeine or morphine for pain control.
In 2019, crizanlizumab, a monoclonal antibody targeting P-selectin, was approved in the United States to reduce the frequency of vaso-occlusive crisis in those 16 years and older. It had also been approved in the UK and Europe, but in both cases authorisation was subsequently withdrawn because of poor evidence of its effectiveness.
=== Acute chest syndrome ===
Acute chest syndrome is caused by vaso-occlusion occurring in the lungs. As with a VOC, treatment includes pain control and hydration. Antibiotics are required because there is a severe risk of pulmonary infection, and oxygen supplementation for hypoxia. Blood transfusion may also be required, or exchange transfusion in severe cases.
=== Treating avascular necrosis ===
When treating avascular necrosis of the bone in people with sickle cell disease, the aim of treatment is to reduce or stop the pain and maintain joint mobility. Treatment options include resting the joint, physical therapy, pain-relief medicine, joint-replacement surgery, or bone grafting.
=== Psychological therapy ===
Psychological therapies such as patient education, cognitive therapy, behavioural therapy, and psychodynamic psychotherapy, that aim to complement current medical treatments, require further research to determine their effectiveness.
== Stem cell treatments ==
Hematopoietic stem cells (HSC) are cells in the bone marrow that can develop into all types of blood cells, including red blood cells, white blood cells, and platelets. There are two possible ways to treat SCD and some other hemoglobinopathies by targeting HSCs. Since 1991, a small number of patients have received bone marrow transplants from healthy matched donors, although this procedure has a high level of risk. More recently, it has become possible to use CRISPR gene editing technology to modify the patient's own HSCs in a way that reduces or eliminates the production of sickle hemoglobin HbS and replaces it with a non-sickling form of hemoglobin.
All stem cell treatments must involve myeloablation of the patients' bone marrow in order to remove HSCs containing the faulty gene. This requires high doses of chemotherapy agents with side effects such as sickness and tiredness. A long hospital stay is necessary after infusion of the replacement HSCs while the cells take up residence in the bone marrow and start to make red blood cells with the stable form of haemoglobin.
=== Gene therapy ===
Gene therapy was first trialled in 2014 on a single patient, and followed by clinical trials in which a number of patients were successfully treated. In 2023, both exagamglogene autotemcel (Casgevy) and lovotibeglogene autotemcel (Lyfgenia) were approved for the treatment of sickle cell disease. Kendric Cromer in October 2024 became the first commercial case in the USA to receive gene therapy and was discharged from Children's National Hospital. The one-off gene-editing therapy, Casgevy, also known as Exa-cel, is to be offered to patients on the National Health Service (NHS) in England as from 2025.
Both Casgevy and Lyfgenia work by first harvesting the patient's HSCs, then using CRISPR gene editing to modify their DNA in the laboratory. In parallel with this, the person with sickle cell disease's bone marrow is put through a myeloablation procedure to destroy the remaining HSCs. The treated cells are then infused back into the patient where they colonise the bone marrow and eventually resume production of blood cells. Casgevy works by editing the BCL11A gene, which normally inhibits the production of hemoglobin F (fetal hemoglobin) in adults. The edit has the effect of increasing production of HbF, which is not prone to sickling. Lyfgenia introduces a new gene for T87Q-globin which coexists with the sickling beta-globin but reduces the incidence of sickling.
=== Hematopoietic stem cell transplantation ===
Hematopoietic stem cell transplantation (HSCT) involves replacing the dysfunctional stem cells from a person with sickle cell disease with healthy cells from a well-matched donor. Finding a well matched donor is essential to the process' success. Different types of donors may be suitable and include umbilical cord blood, human leukocyte antigen (HLA) matched relatives, or HLA matched donors that are not related to the person being treated. Risks associated with HSCT can include graft-versus host disease, failure of the graft, and other toxicity related to the transplant.
== Prognosis ==
Sickle cell disease is most prevalent in sub-saharan Africa. In areas without healthcare infrastructure, it is estimated that between 50% and 90% of children born with the disease die before the age of 5 years.
In contrast, life expectancy in the United States in 2010–2020 was 43 years and in the UK 67 years.
== Epidemiology ==
The HbS gene can be found in every ethnic group. The highest frequency of sickle cell disease is found in tropical regions, particularly sub-Saharan Africa, tribal regions of India, and the Middle East. About 80% of sickle cell disease cases are believed to occur in Sub-Saharan Africa. Migration of substantial populations from these high-prevalence areas to low-prevalence countries in Europe has dramatically increased in recent decades and in some European countries, sickle cell disease has now overtaken more familiar genetic conditions such as haemophilia and cystic fibrosis. In 2015, it resulted in about 114,800 deaths.
Sickle cell disease occurs more commonly among people whose ancestors lived in tropical and subtropical sub-Saharan regions where malaria is or was common. Where malaria is common, carrying a single sickle cell allele (trait) confers a heterozygote advantage; humans with one of the two alleles of sickle cell disease show less severe symptoms when infected with malaria.
This condition is inherited in an autosomal recessive pattern, which means both copies of the gene in each cell have mutations. The parents each carry one copy of the mutated gene, but they typically do not show signs and symptoms of the condition.
=== Africa ===
Three-quarters of sickle cell cases occur in Africa. A WHO report dated 2006 estimated that around 2% of newborns in Nigeria were affected by sickle cell anaemia, giving a total of 150,000 affected children born every year in Nigeria alone. The carrier frequency ranges between 10 and 40% across equatorial Africa, decreasing to 1–2% on the North African coast and <1% in South Africa. In the West African countries of Ghana and Nigeria, the frequencies can vary from 15 to 30%. In the whole of Nigeria, 24% of the population carries the gene, and 20 per 1000 newborns are born with the disease, or 150 000 annually.
Uganda has the fifth-highest sickle cell disease burden in Africa. One study indicates that 20 000 babies per year, or 0.7% of the total, are born with sickle cell disease, and 13.3% carry the trait. In Uganda, carrier frequency of the trait varies strongly across tribal lines: among the Baamba, it reaches 45%.
=== United States ===
The number of people with the disease in the United States is about 100,000 (one in 3,300), mostly affecting Americans of sub-Saharan African descent. In the United States, about one out of 365 African-American children and one in every 16,300 Hispanic-American children have sickle cell anaemia. The life expectancy for men with SCD is approximately 42 years of age while women live approximately six years longer. An additional 2 million are carriers of the sickle cell trait. Most infants with SCD born in the United States are identified by routine neonatal screening. As of 2016 all 50 states include screening for sickle cell disease as part of their newborn screen. The newborn's blood is sampled through a heel-prick and is sent to a lab for testing. The baby must have been eating for a minimum of 24 hours before the heel-prick test can be done. Some states also require a second blood test to be done when the baby is two weeks old to ensure the results.
Sickle cell anemia is the most common genetic disorder among African Americans. Approximately 8% are carriers and 1 in 375 are born with the disease. Patient advocates for sickle cell disease have complained that it gets less government and private research funding than similar rare diseases such as cystic fibrosis, with researcher Elliott Vichinsky saying this shows racial discrimination or the role of wealth in health care advocacy. Overall, without considering race, approximately 1.5% of infants born in the United States are carriers of at least one copy of the mutant (disease-causing) gene.
=== France ===
As a result of population growth in African-Caribbean regions of overseas France and immigration from North and sub-Saharan Africa to mainland France, sickle cell disease has become a major health problem in France. SCD has become the most common genetic disease in the country, with an overall birth prevalence of one in 2,415 in mainland France, ahead of phenylketonuria (one in 10,862), congenital hypothyroidism (one in 3,132), congenital adrenal hyperplasia (one in 19,008) and cystic fibrosis (one in 5,014) for the same reference period.
Since 2000, neonatal screening of SCD has been performed at the national level for all newborns defined as being "at-risk" for SCD based on ethnic origin (defined as those born to parents originating from sub-Saharan Africa, North Africa, the Mediterranean area (South Italy, Greece, and Turkey), the Arabic peninsula, the French overseas islands, and the Indian subcontinent).
=== United Kingdom ===
In the United Kingdom, between 12,000 and 15,000 people are thought to have sickle cell disease with an estimated 250,000 carriers of the condition in England alone. As the number of carriers is only estimated, all newborn babies in the UK receive a routine blood test to screen for the condition. Due to many adults in high-risk groups not knowing if they are carriers, pregnant women and both partners in a couple are offered screening so they can get counselling if they have the sickle cell trait. In addition, blood donors from those in high-risk groups are also screened to confirm whether they are carriers and whether their blood filters properly. Donors who are found to be carriers are informed and their blood, while often used for those of the same ethnic group, is not used for those with sickle cell disease who require a blood transfusion.
=== West Asia ===
In Saudi Arabia, about 4.2% of the population carry the sickle cell trait and 0.26% have sickle cell disease. The highest prevalence is in the Eastern province, where approximately 17% of the population carry the gene and 1.2% have sickle cell disease.
In 2005, Saudi Arabia introduced a mandatory premarital test including HB electrophoresis, which aimed to decrease the incidence of SCD and thalassemia.
In Bahrain, a study published in 1998 that covered about 56,000 people in hospitals in Bahrain found that 2% of newborns have sickle cell disease, 18% of the surveyed people have the sickle cell trait, and 24% were carriers of the gene mutation causing the disease. The country began screening of all pregnant women in 1992, and newborns started being tested if the mother was a carrier. In 2004, a law was passed requiring couples planning to marry to undergo free premarital counseling. These programs were accompanied by public education campaigns.
=== India and Nepal ===
Sickle cell disease is common in some ethnic groups of central India, where the prevalence has ranged from 9.4 to 22.2% in endemic areas of Madhya Pradesh, Rajasthan, and Chhattisgarh. It is also endemic among Tharu people of Nepal and India; however, they have a sevenfold lower rate of malaria despite living in a malaria infested zone.
=== Caribbean Islands ===
In Jamaica, 10% of the population carry the sickle cell gene, making it the most prevalent genetic disorder in the country.
== History ==
The first modern report of sickle cell disease may have been in 1846, where the autopsy of an executed runaway slave was discussed; the key finding was the absence of the spleen. Reportedly, African slaves in the United States exhibited resistance to malaria, but were prone to leg ulcers. The abnormal characteristics of the red blood cells, which later lent their name to the condition, was first described by Ernest E. Irons (1877–1959), intern to Chicago cardiologist and professor of medicine James B. Herrick (1861–1954), in 1910. Irons saw "peculiar elongated and sickle-shaped" cells in the blood of a man named Walter Clement Noel, a 20-year-old first-year dental student from Grenada. Noel had been admitted to the Chicago Presbyterian Hospital in December 1904 with anaemia. Noel was readmitted several times over the next three years for "muscular rheumatism" and "bilious attacks" but completed his studies and returned to the capital of Grenada (St. George's) to practice dentistry. He died of pneumonia in 1916 and is buried in the Catholic cemetery at Sauteurs in the north of Grenada. Shortly after the report by Herrick, another case appeared in the Virginia Medical Semi-Monthly with the same title, "Peculiar Elongated and Sickle-Shaped Red Blood Corpuscles in a Case of Severe Anemia." This article is based on a patient admitted to the University of Virginia Hospital on 15 November 1910. In the later description by Verne Mason in 1922, the name "sickle cell anemia" is first used. Childhood problems related to sickle cells disease were not reported until the 1930s, despite the fact that this cannot have been uncommon in African-American populations.
Memphis physician Lemuel Diggs, a prolific researcher into sickle cell disease, first introduced the distinction between sickle cell disease and trait in 1933, although until 1949, the genetic characteristics had not been elucidated by James V. Neel and E.A. Beet. 1949 was the year when Linus Pauling described the unusual chemical behaviour of haemoglobin S, and attributed this to an abnormality in the molecule itself. The molecular change in HbS was described in 1956 by Vernon Ingram. The late 1940s and early 1950s saw further understanding in the link between malaria and sickle cell disease. In 1954, the introduction of haemoglobin electrophoresis allowed the discovery of particular subtypes, such as HbSC disease.
Large-scale natural history studies and further intervention studies were introduced in the 1970s and 1980s, leading to widespread use of prophylaxis against pneumococcal infections amongst other interventions. Bill Cosby's Emmy-winning 1972 TV movie, To All My Friends on Shore, depicted the story of the parents of a child with sickle cell disease. The 1990s had the development of hydroxycarbamide, and reports of cure through bone marrow transplantation appeared in 2007.
Some old texts refer to it as drepanocytosis.
== Society and culture ==
=== United States ===
Sickle cell disease is frequently contested as a disability. Effective 15 September 2017, the U.S. Social Security Administration issued a Policy Interpretation Ruling providing background information on sickle cell disease and a description of how Social Security evaluates the disease during its adjudication process for disability claims.
In the US, there are stigmas surrounding SCD that discourage people with SCD from receiving necessary care. These stigmas mainly affect people of African American and Latin American ancestries, according to the National Heart, Lung, and Blood Institute. People with SCD experience the impact of stigmas of the disease on multiple aspects of life including social and psychological well-being. Studies have shown that those with SCD frequently feel as though they must keep their diagnosis a secret to avoid discrimination in the workplace and also among peers in relationships. In the 1960s, the US government supported initiatives for workplace screening for genetic diseases in an attempt to be protective towards people with SCD. By having this screening, it was intended that employees would not be placed in environments that could potentially be harmful and trigger SCD.
=== Uganda ===
Uganda has the 5th highest sickle cell disease (SCD) burden in the world. In Uganda, social stigma exists for those with sickle cell disease because of the lack of general knowledge of the disease. The general gap in knowledge surrounding sickle cell disease is noted among adolescents and young adults due to the culturally sanctioned secrecy about the disease. While most people have heard generally about the disease, a large portion of the population is relatively misinformed about how SCD is diagnosed or inherited. Those who are informed about the disease learned about it from family or friends and not from health professionals. Failure to provide the public with information about sickle cell disease results in a population with a poor understanding of the causes of the disease, symptoms, and prevention techniques. The differences, physically and socially, that arise in those with sickle cell disease, such as jaundice, stunted physical growth, and delayed sexual maturity, can also lead them to become targets of bullying, rejection, and stigma.
==== Rate of sickle cell disease in Uganda ====
The data compiled on sickle cell disease in Uganda has not been updated since the early 1970s. The deficiency of data is due to a lack of government research funds, even though Ugandans die daily from SCD. Data shows that the trait frequency of sickle cell disease is 20% of the population in Uganda. It is also estimated that about 25,000 Ugandans are born each year with SCD and 80% of those people do not live past five years old. SCD also contributes 25% to the child mortality rate in Uganda. The Bamba people of Uganda, located in the southwest of the country, carry 45% of the gene which is the highest trait frequency recorded in the world. The Sickle Cell Clinic in Mulago is only one sickle cell disease clinic in the country and on average sees 200 patients a day.
==== Misconceptions about sickle cell disease ====
The stigma around the disease is particularly bad in regions of the country that are not as affected. For example, Eastern Ugandans tend to be more knowledgeable of the disease than Western Ugandans, who are more likely to believe that sickle cell disease resulted as a punishment from God or witchcraft. Other misconceptions about SCD include the belief that it is caused by environmental factors but, in reality, SCD is a genetic disease. There have been efforts throughout Uganda to address the social misconceptions about the disease. In 2013, the Uganda Sickle Cell Rescue Foundation was established to spread awareness of sickle cell disease and combat the social stigma attached to the disease. In addition to this organization's efforts, there is a need for the inclusion of sickle cell disease education in preexisting community health education programs in order to reduce the stigmatization of sickle cell disease in Uganda.
==== Social isolation of people with sickle cell disease ====
The deeply rooted stigma of SCD from society causes families to often hide their family members' sick status for fear of being labeled, cursed, or left out of social events. Sometimes in Uganda, when it is confirmed that a family member has sickle cell disease, intimate relationships with all members of the family are avoided. The stigmatization and social isolation people with sickle cell disease tend to experience is often the consequence of popular misconceptions that people with SCD should not socialize with those free from the disease. This mentality robs people with SCD of the right to freely participate in community activities like everyone else SCD-related stigma and social isolation in schools, especially, can make a life for young people living with sickle cell disease extremely difficult. For school-aged children living with SCD, the stigma they face can lead to peer rejection. Peer rejection involves the exclusion from social groups or gatherings. It often leads the excluded individual to experience emotional distress and may result in their academic underperformance, avoidance of school, and occupational failure later in life. This social isolation is also likely to negatively impact people with SCD's self-esteem and overall quality of life.
Mothers of children with sickle cell disease tend to receive disproportionate amounts of stigma from their peers and family members. These women will often be blamed for their child's diagnosis of SCD, especially if SCD is not present in earlier generations, due to the suspicion that the child's poor health may have been caused by the mother's failure to implement preventative health measures or promote a healthy environment for her child to thrive. The reliance on theories related to environmental factors to place blame on the mother reflects many Ugandans’ poor knowledge of how the disease is acquired as it is determined by genetics, not environment. Mothers of children with sickle cell disease are also often left with very limited resources to safeguard their futures against the stigma of having SCD. This lack of access to resources results from their subordinating roles within familial structures as well as the class disparities that hinder many mothers' ability to satisfy additional childcare costs and responsibilities.
Women living with SCD who become pregnant often face extreme discrimination and discouragement in Uganda. These women are frequently branded by their peers as irresponsible for having a baby while living with sickle cell disease or even engaging in sex while living with SCD. The criticism and judgement these women receive, not only from healthcare professionals but also from their families, often leaves them feeling alone, depressed, anxious, ashamed, and with very little social support. Most pregnant women with SCD also go on to be single mothers as it is common for them to be left by their male partners who claim they were unaware of their partner's SCD status. Not only does the abandonment experienced by these women cause emotional distress for them, but this low level of parental support can be linked to depressive symptoms and overall lower quality of life for the child once they are born.
=== United Kingdom ===
In 2021 many patients were found to be afraid to visit hospitals, so purchased pain relief to treat themselves outside the NHS. They were often waiting a long time for pain relief, and sometimes suspected of "drugs-seeking" behaviour. Delays to treatment, failure to inform the hospital haematology team and poor pain management had caused deaths. Specialist haematology staff preferred to work in bigger, teaching hospitals, leading to shortages of expertise elsewhere. In 2021, the NHS initiated its first new treatment in 20 years for Sickle Cell. This involved the use of Crizanlizumab, a drug given via transfusion drips, which reduces the number of visits to A&E by sufferers. The treatment can be accessed, via consultants, at any of ten new hubs set up around the country. In the same year, however, an All-Party Parliamentary Group produced a report on Sickle Cell and Thalassaemia entitled 'No-one is listening'. Partly in response to this, on 19 June 2022, World Sickle Cell Day, the NHS launched a campaign called "Can you tell it's sickle cell?". The campaign had twin aims. One was to increase awareness of the key signs and symptoms of the blood disorder so that people would be as alert to signs of a sickle cell crisis as they are to an imminent heart attack or stroke. The second aim was to set up a new training programme to help paramedics, Accident and Emergency staff, carers and the general public to care effectively for sufferers in crisis.
== References ==
== Further reading ==
== External links == | Wikipedia/Sickle_cell_disease |
Translational neuroscience is the field of study which applies neuroscience research to translate or develop into clinical applications and novel therapies for nervous system disorders. The field encompasses areas such as deep brain stimulation, brain machine interfaces, neurorehabilitation and the development of devices for the sensory nervous system such as the use of auditory implants, retinal implants, and electronic skins.
== Classification ==
Translational neuroscience research is categorized into stages of research, which are classified using a five tier system (T0-T4), beginning with basic science research and ending with the public health applications of basic scientific discoveries. While once considered a linear progression from basic science to public health application, translational research, and translational neuroscience in particular, is now regarded as a cyclic, where public health needs inform basic science research, which then works to discover the mechanisms of public health issues and works towards clinical and public health implementation.
The stages of translational neuroscience research are as follows:
T0: Basic science research
T1: Preclinical research
T2: Clinical research or Clinical neuroscience
T3: Clinical implementation
T4: Public health
== Methods ==
=== Electrophysiology ===
Electrophysiology is used within translational neuroscience as a means of studying the electric properties of neurons in animal models as well as to investigate the properties of human neurological dysfunction. Techniques used in animal models, such as patch-clamp recordings, have been used to investigate how neurons respond to pharmacological agents. Electroencephalography (EEG) and magnetoencephalography (MEG) are both used to measure electrical activity in the human brain, and can be used in clinical settings to localize the source of neurological dysfunction in conditions such as epilepsy, and can also be used in a research setting to investigate the differences in electrical activity in the brain between normal and neurologically dysfunctional individuals.
=== Neuroimaging ===
Neuroimaging comprises a variety of techniques used to observe the activity or the structures of, or within, the nervous system. Positron emission tomography (PET) has been used in animal models, such as non-human primate and rodent, to identify and target molecular mechanisms of neurological disease, and to study the neurological impact of pharmacological drug addiction. Similarly, functional magnetic resonance imaging (fMRI) has been used to investigate the neurological mechanisms of pharmacological drug addiction, the neurological mechanisms of mood and anxiety disorders in elderly populations, and the neurological mechanisms of disorders such as schizophrenia.
=== Gene therapy ===
Gene therapy is the delivery of nucleic acid as a treatment for a disorder. In translational neuroscience, gene therapy is the delivery of nucleic acid as a treatment for a neurological disorder. Gene therapy has been proven effective at treating a variety of disorders, including neurodegenerative disorders such as Parkinson's disease (PD) and Alzheimer's disease (AD), in rodent and non-human primate models, and in humans, via the application of neurotrophic factors, such as nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), and glial cell line-derived neurotrophic factor (GDNF), and via the application of enzymes such as glutamic acid decarboxylase (GAD), which commonly use adeno-associated viruses (AAV) as a vector.
=== Stem cells ===
Stem cells, particularly induced pluripotent stem cells (iPSCs), are utilized in translational neuroscience research as not only a treatment for nervous system disorders, but also as the source for models of neural dysfunction. For example, due to the central nervous system's limited regenerative abilities, human embryonic stem cells (hESCs), a type of pluripotent stem cell, has been used as a replacement for damaged neurons, a novel approach that involves the surgical transplantation of fetal stem cells
== Applications ==
=== Neurodevelopmental disorders ===
Neurodevelopmental disorders are characterized as disorders where the development of the nervous system was disrupted, and encompasses disorders such as learning disabilities, autism spectrum disorders (ASD), epilepsy, and certain neuromuscular disorders. Translational neuroscience research involves efforts to uncover the molecular mechanisms for these disorders and work towards cures in patient populations. Additionally, translational neuroscience research has focused on elucidating the cause of neurodevelopmental disorders, whether it be genetic, environmental, or a combination of both, as well as tactics for prevention, if possible.
=== Neurodegenerative disorders ===
Neurodegenerative disorders are a result of neuronal loss of function over time which lead to cell death. Examples of neurodegenerative disorders include Alzheimer's disease, Parkinson's disease, and Huntington's disease. The focus of translational neuroscience research is to investigate the molecular mechanisms for these disorders, and to investigate the mechanisms of drug delivery to treat these disorders, including an investigation into the impact of the blood-brain barrier on drug delivery, and the role of the body's immune system in neurodegenerative disorders.
== See also ==
Translational medicine
Knowledge transfer
== References == | Wikipedia/Translational_neuroscience |
Biological neuron models, also known as spiking neuron models, are mathematical descriptions of the conduction of electrical signals in neurons. Neurons (or nerve cells) are electrically excitable cells within the nervous system, able to fire electric signals, called action potentials, across a neural network. These mathematical models describe the role of the biophysical and geometrical characteristics of neurons on the conduction of electrical activity.
Central to these models is the description of how the membrane potential (that is, the difference in electric potential between the interior and the exterior of a biological cell) across the cell membrane changes over time. In an experimental setting, stimulating neurons with an electrical current generates an action potential (or spike), that propagates down the neuron's axon. This axon can branch out and connect to a large number of downstream neurons at sites called synapses. At these synapses, the spike can cause the release of neurotransmitters, which in turn can change the voltage potential of downstream neurons. This change can potentially lead to even more spikes in those downstream neurons, thus passing down the signal. As many as 95% of neurons in the neocortex, the outermost layer of the mammalian brain, consist of excitatory pyramidal neurons, and each pyramidal neuron receives tens of thousands of inputs from other neurons. Thus, spiking neurons are a major information processing unit of the nervous system.
One such example of a spiking neuron model may be a highly detailed mathematical model that includes spatial morphology. Another may be a conductance-based neuron model that views neurons as points and describes the membrane voltage dynamics as a function of trans-membrane currents. A mathematically simpler "integrate-and-fire" model significantly simplifies the description of ion channel and membrane potential dynamics (initially studied by Lapique in 1907).
== Biological background, classification, and aims of neuron models ==
Non-spiking cells, spiking cells, and their measurement
Not all the cells of the nervous system produce the type of spike that defines the scope of the spiking neuron models. For example, cochlear hair cells, retinal receptor cells, and retinal bipolar cells do not spike. Furthermore, many cells in the nervous system are not classified as neurons but instead are classified as glia.
Neuronal activity can be measured with different experimental techniques, such as the "Whole cell" measurement technique, which captures the spiking activity of a single neuron and produces full amplitude action potentials.
With extracellular measurement techniques, one or more electrodes are placed in the extracellular space. Spikes, often from several spiking sources, depending on the size of the electrode and its proximity to the sources, can be identified with signal processing techniques. Extracellular measurement has several advantages:
It is easier to obtain experimentally;
It is robust and lasts for a longer time;
It can reflect the dominant effect, especially when conducted in an anatomical region with many similar cells.
Overview of neuron models
Neuron models can be divided into two categories according to the physical units of the interface of the model. Each category could be further divided according to the abstraction/detail level:
Electrical input–output membrane voltage models – These models produce a prediction for membrane output voltage as a function of electrical stimulation given as current or voltage input. The various models in this category differ in the exact functional relationship between the input current and the output voltage and in the level of detail. Some models in this category predict only the moment of occurrence of the output spike (also known as "action potential"); other models are more detailed and account for sub-cellular processes. The models in this category can be either deterministic or probabilistic.
Natural stimulus or pharmacological input neuron models – The models in this category connect the input stimulus, which can be either pharmacological or natural, to the probability of a spike event. The input stage of these models is not electrical but rather has either pharmacological (chemical) concentration units, or physical units that characterize an external stimulus such as light, sound, or other forms of physical pressure. Furthermore, the output stage represents the probability of a spike event and not an electrical voltage.
Although it is not unusual in science and engineering to have several descriptive models for different abstraction/detail levels, the number of different, sometimes contradicting, biological neuron models is exceptionally high. This situation is partly the result of the many different experimental settings, and the difficulty to separate the intrinsic properties of a single neuron from measurement effects and interactions of many cells (network effects).
Aims of neuron models
Ultimately, biological neuron models aim to explain the mechanisms underlying the operation of the nervous system. However, several approaches can be distinguished, from more realistic models (e.g., mechanistic models) to more pragmatic models (e.g., phenomenological models). Modeling helps to analyze experimental data and address questions. Models are also important in the context of restoring lost brain functionality through neuroprosthetic devices.
== Electrical input–output membrane voltage models ==
The models in this category describe the relationship between neuronal membrane currents at the input stage and membrane voltage at the output stage. This category includes (generalized) integrate-and-fire models and biophysical models inspired by the work of Hodgkin–Huxley in the early 1950s using an experimental setup that punctured the cell membrane and allowed to force a specific membrane voltage/current.
Most modern electrical neural interfaces apply extra-cellular electrical stimulation to avoid membrane puncturing, which can lead to cell death and tissue damage. Hence, it is not clear to what extent the electrical neuron models hold for extra-cellular stimulation (see e.g.).
=== Hodgkin–Huxley ===
The Hodgkin–Huxley model (H&H model)
is a model of the relationship between the flow of ionic currents across the neuronal cell membrane and the membrane voltage of the cell. It consists of a set of nonlinear differential equations describing the behavior of ion channels that permeate the cell membrane of the squid giant axon. Hodgkin and Huxley were awarded the 1963 Nobel Prize in Physiology or Medicine for this work.
It is important to note the voltage-current relationship, with multiple voltage-dependent currents charging the cell membrane of capacity Cm
C
m
d
V
(
t
)
d
t
=
−
∑
i
I
i
(
t
,
V
)
.
{\displaystyle C_{\mathrm {m} }{\frac {dV(t)}{dt}}=-\sum _{i}I_{i}(t,V).}
The above equation is the time derivative of the law of capacitance, Q = CV where the change of the total charge must be explained as the sum over the currents. Each current is given by
I
(
t
,
V
)
=
g
(
t
,
V
)
⋅
(
V
−
V
e
q
)
{\displaystyle I(t,V)=g(t,V)\cdot (V-V_{\mathrm {eq} })}
where g(t,V) is the conductance, or inverse resistance, which can be expanded in terms of its maximal conductance ḡ and the activation and inactivation fractions m and h, respectively, that determine how many ions can flow through available membrane channels. This expansion is given by
g
(
t
,
V
)
=
g
¯
⋅
m
(
t
,
V
)
p
⋅
h
(
t
,
V
)
q
{\displaystyle g(t,V)={\bar {g}}\cdot m(t,V)^{p}\cdot h(t,V)^{q}}
and our fractions follow the first-order kinetics
d
m
(
t
,
V
)
d
t
=
m
∞
(
V
)
−
m
(
t
,
V
)
τ
m
(
V
)
=
α
m
(
V
)
⋅
(
1
−
m
)
−
β
m
(
V
)
⋅
m
{\displaystyle {\frac {dm(t,V)}{dt}}={\frac {m_{\infty }(V)-m(t,V)}{\tau _{\mathrm {m} }(V)}}=\alpha _{\mathrm {m} }(V)\cdot (1-m)-\beta _{\mathrm {m} }(V)\cdot m}
with similar dynamics for h, where we can use either τ and m∞ or α and β to define our gate fractions.
The Hodgkin–Huxley model may be extended to include additional ionic currents. Typically, these include inward Ca2+ and Na+ input currents, as well as several varieties of K+ outward currents, including a "leak" current.
The result can be at the small end of 20 parameters which one must estimate or measure for an accurate model. In a model of a complex system of neurons, numerical integration of the equations are computationally expensive. Careful simplifications of the Hodgkin–Huxley model are therefore needed.
The model can be reduced to two dimensions thanks to the dynamic relations which can be established between the gating variables. it is also possible to extend it to take into account the evolution of the concentrations (considered fixed in the original model).
=== Perfect Integrate-and-fire ===
One of the earliest models of a neuron is the perfect integrate-and-fire model (also called non-leaky integrate-and-fire), first investigated in 1907 by Louis Lapicque. A neuron is represented by its membrane voltage V which evolves in time during stimulation with an input current I(t) according
I
(
t
)
=
C
d
V
(
t
)
d
t
{\displaystyle I(t)=C{\frac {dV(t)}{dt}}}
which is just the time derivative of the law of capacitance, Q = CV. When an input current is applied, the membrane voltage increases with time until it reaches a constant threshold Vth, at which point a delta function spike occurs and the voltage is reset to its resting potential, after which the model continues to run. The firing frequency of the model thus increases linearly without bound as input current increases.
The model can be made more accurate by introducing a refractory period tref that limits the firing frequency of a neuron by preventing it from firing during that period. For constant input I(t)=I the threshold voltage is reached after an integration time tint=CVthr/I after starting from zero. After a reset, the refractory period introduces a dead time so that the total time until the next firing is tref+tint . The firing frequency is the inverse of the total inter-spike interval (including dead time). The firing frequency as a function of a constant input current, is therefore
f
(
I
)
=
I
C
V
t
h
+
t
r
e
f
I
.
{\displaystyle \,\!f(I)={\frac {I}{C_{\mathrm {} }V_{\mathrm {th} }+t_{\mathrm {ref} }I}}.}
A shortcoming of this model is that it describes neither adaptation nor leakage. If the model receives a below-threshold short current pulse at some time, it will retain that voltage boost forever - until another input later makes it fire. This characteristic is not in line with observed neuronal behavior. The following extensions make the integrate-and-fire model more plausible from a biological point of view.
=== Leaky integrate-and-fire ===
The leaky integrate-and-fire model, which can be traced back to Louis Lapicque, contains a "leak" term in the membrane potential equation that reflects the diffusion of ions through the membrane, unlike the non-leaky integrate-and-fire model. The model equation looks like
C
m
d
V
m
(
t
)
d
t
=
I
(
t
)
−
V
m
(
t
)
R
m
{\displaystyle C_{\mathrm {m} }{\frac {dV_{\mathrm {m} }(t)}{dt}}=I(t)-{\frac {V_{\mathrm {m} }(t)}{R_{\mathrm {m} }}}}
where Vm is the voltage across the cell membrane and Rm is the membrane resistance. (The non-leaky integrate-and-fire model is retrieved in the limit Rm to infinity, i.e. if the membrane is a perfect insulator). The model equation is valid for arbitrary time-dependent input until a threshold Vth is reached; thereafter the membrane potential is reset.
For constant input, the minimum input to reach the threshold is Ith = Vth / Rm. Assuming a reset to zero, the firing frequency thus looks like
f
(
I
)
=
{
0
,
I
≤
I
t
h
[
t
r
e
f
−
R
m
C
m
log
(
1
−
V
t
h
I
R
m
)
]
−
1
,
I
>
I
t
h
{\displaystyle f(I)={\begin{cases}0,&I\leq I_{\mathrm {th} }\\\left[t_{\mathrm {ref} }-R_{\mathrm {m} }C_{\mathrm {m} }\log \left(1-{\tfrac {V_{\mathrm {th} }}{IR_{\mathrm {m} }}}\right)\right]^{-1},&I>I_{\mathrm {th} }\end{cases}}}
which converges for large input currents to the previous leak-free model with the refractory period. The model can also be used for inhibitory neurons.
The most significant disadvantage of this model is that it does not contain neuronal adaptation, so that it cannot describe an experimentally measured spike train in response to constant input current. This disadvantage is removed in generalized integrate-and-fire models that also contain one or several adaptation-variables and are able to predict spike times of cortical neurons under current injection to a high degree of accuracy.
=== Adaptive integrate-and-fire ===
Neuronal adaptation refers to the fact that even in the presence of a constant current injection into the soma, the intervals between output spikes increase. An adaptive integrate-and-fire neuron model combines the leaky integration of voltage V with one or several adaptation variables wk (see Chapter 6.1. in the textbook Neuronal Dynamics)
τ
m
d
V
m
(
t
)
d
t
=
R
I
(
t
)
−
[
V
m
(
t
)
−
E
m
]
−
R
∑
k
w
k
{\displaystyle \tau _{\mathrm {m} }{\frac {dV_{\mathrm {m} }(t)}{dt}}=RI(t)-[V_{\mathrm {m} }(t)-E_{\mathrm {m} }]-R\sum _{k}w_{k}}
τ
k
d
w
k
(
t
)
d
t
=
−
a
k
[
V
m
(
t
)
−
E
m
]
−
w
k
+
b
k
τ
k
∑
f
δ
(
t
−
t
f
)
{\displaystyle \tau _{k}{\frac {dw_{k}(t)}{dt}}=-a_{k}[V_{\mathrm {m} }(t)-E_{\mathrm {m} }]-w_{k}+b_{k}\tau _{k}\sum _{f}\delta (t-t^{f})}
where
τ
m
{\displaystyle \tau _{m}}
is the membrane time constant, wk is the adaptation current number, with index k,
τ
k
{\displaystyle \tau _{k}}
is the time constant of adaptation current wk, Em is the resting potential and tf is the firing time of the neuron and the Greek delta denotes the Dirac delta function. Whenever the voltage reaches the firing threshold the voltage is reset to a value Vr below the firing threshold. The reset value is one of the important parameters of the model. The simplest model of adaptation has only a single adaptation variable w and the sum over k is removed.
Integrate-and-fire neurons with one or several adaptation variables can account for a variety of neuronal firing patterns in response to constant stimulation, including adaptation, bursting, and initial bursting. Moreover, adaptive integrate-and-fire neurons with several adaptation variables are able to predict spike times of cortical neurons under time-dependent current injection into the soma.
=== Fractional-order leaky integrate-and-fire ===
Recent advances in computational and theoretical fractional calculus lead to a new form of model called Fractional-order leaky integrate-and-fire. An advantage of this model is that it can capture adaptation effects with a single variable. The model has the following form
I
(
t
)
−
V
m
(
t
)
R
m
=
C
m
d
α
V
m
(
t
)
d
α
t
{\displaystyle I(t)-{\frac {V_{\mathrm {m} }(t)}{R_{\mathrm {m} }}}=C_{\mathrm {m} }{\frac {d^{\alpha }V_{\mathrm {m} }(t)}{d^{\alpha }t}}}
Once the voltage hits the threshold it is reset. Fractional integration has been used to account for neuronal adaptation in experimental data.
=== 'Exponential integrate-and-fire' and 'adaptive exponential integrate-and-fire' ===
In the exponential integrate-and-fire model, spike generation is exponential, following the equation:
d
V
d
t
−
R
τ
m
I
(
t
)
=
1
τ
m
[
E
m
−
V
+
Δ
T
exp
(
V
−
V
T
Δ
T
)
]
.
{\displaystyle {\frac {dV}{dt}}-{\frac {R}{\tau _{m}}}I(t)={\frac {1}{\tau _{m}}}\left[E_{m}-V+\Delta _{T}\exp \left({\frac {V-V_{T}}{\Delta _{T}}}\right)\right].}
where
V
{\displaystyle V}
is the membrane potential,
V
T
{\displaystyle V_{T}}
is the intrinsic membrane potential threshold,
τ
m
{\displaystyle \tau _{m}}
is the membrane time constant,
E
m
{\displaystyle E_{m}}
is the resting potential, and
Δ
T
{\displaystyle \Delta _{T}}
is the sharpness of action potential initiation, usually around 1 mV for cortical pyramidal neurons. Once the membrane potential crosses
V
T
{\displaystyle V_{T}}
, it diverges to infinity in finite time. In numerical simulation the integration is stopped if the membrane potential hits an arbitrary threshold (much larger than
V
T
{\displaystyle V_{T}}
) at which the membrane potential is reset to a value Vr . The voltage reset value Vr is one of the important parameters of the model. Importantly, the right-hand side of the above equation contains a nonlinearity that can be directly extracted from experimental data. In this sense the exponential nonlinearity is strongly supported by experimental evidence.
In the adaptive exponential integrate-and-fire neuron the above exponential nonlinearity of the voltage equation is combined with an adaptation variable w
τ
m
d
V
d
t
=
R
I
(
t
)
+
[
E
m
−
V
+
Δ
T
exp
(
V
−
V
T
Δ
T
)
]
−
R
w
{\displaystyle \tau _{m}{\frac {dV}{dt}}=RI(t)+\left[E_{m}-V+\Delta _{T}\exp \left({\frac {V-V_{T}}{\Delta _{T}}}\right)\right]-Rw}
τ
d
w
(
t
)
d
t
=
−
a
[
V
m
(
t
)
−
E
m
]
−
w
+
b
τ
δ
(
t
−
t
f
)
{\displaystyle \tau {\frac {dw(t)}{dt}}=-a[V_{\mathrm {m} }(t)-E_{\mathrm {m} }]-w+b\tau \delta (t-t^{f})}
where w denotes the adaptation current with time scale
τ
{\displaystyle \tau }
. Important model parameters are the voltage reset value Vr, the intrinsic threshold
V
T
{\displaystyle V_{T}}
, the time constants
τ
{\displaystyle \tau }
and
τ
m
{\displaystyle \tau _{m}}
as well as the coupling parameters a and b. The adaptive exponential integrate-and-fire model inherits the experimentally derived voltage nonlinearity of the exponential integrate-and-fire model. But going beyond this model, it can also account for a variety of neuronal firing patterns in response to constant stimulation, including adaptation, bursting, and initial bursting. However, since the adaptation is in the form of a current, aberrant hyperpolarization may appear. This problem was solved by expressing it as a conductance.
=== Adaptive Threshold Neuron Model ===
In this model, a time-dependent function
θ
(
t
)
{\displaystyle \theta (t)}
is added to the fixed threshold,
v
t
h
0
{\displaystyle v_{th0}}
, after every spike, causing an adaptation of the threshold. The threshold potential,
v
t
h
{\displaystyle v_{th}}
, gradually returns to its steady state value depending on the threshold adaptation time constant
τ
θ
{\displaystyle \tau _{\theta }}
. This is one of the simpler techniques to achieve spike frequency adaptation. The expression for the adaptive threshold is given by:
v
t
h
(
t
)
=
v
t
h
0
+
∑
θ
(
t
−
t
f
)
f
=
v
t
h
0
+
∑
θ
0
exp
[
−
(
t
−
t
f
)
τ
θ
]
f
{\displaystyle v_{th}(t)=v_{th0}+{\frac {\sum \theta (t-t_{f})}{f}}=v_{th0}+{\frac {\sum \theta _{0}\exp \left[-{\frac {(t-t_{f})}{\tau _{\theta }}}\right]}{f}}}
where
θ
(
t
)
{\displaystyle \theta (t)}
is defined by:
θ
(
t
)
=
θ
0
exp
[
−
t
τ
θ
]
{\displaystyle \theta (t)=\theta _{0}\exp \left[-{\frac {t}{\tau _{\theta }}}\right]}
When the membrane potential,
u
(
t
)
{\displaystyle u(t)}
, reaches a threshold, it is reset to
v
r
e
s
t
{\displaystyle v_{rest}}
:
u
(
t
)
≥
v
t
h
(
t
)
⇒
v
(
t
)
=
v
rest
{\displaystyle u(t)\geq v_{th}(t)\Rightarrow v(t)=v_{\text{rest}}}
A simpler version of this with a single time constant in threshold decay with an LIF neuron is realized in to achieve LSTM like recurrent spiking neural networks to achieve accuracy nearer to ANNs on few spatio temporal tasks.
=== Double Exponential Adaptive Threshold (DEXAT) ===
The DEXAT neuron model is a flavor of adaptive neuron model in which the threshold voltage decays with a double exponential having two time constants. Double exponential decay is governed by a fast initial decay and then a slower decay over a longer period of time. This neuron used in SNNs through surrogate gradient creates an adaptive learning rate yielding higher accuracy and faster convergence, and flexible long short-term memory compared to existing counterparts in the literature. The membrane potential dynamics are described through equations and the threshold adaptation rule is:
v
t
h
(
t
)
=
b
0
+
β
1
b
1
(
t
)
+
β
2
b
2
(
t
)
{\displaystyle v_{th}(t)=b_{0}+\beta _{1}b_{1}(t)+\beta _{2}b_{2}(t)}
The dynamics of
b
1
(
t
)
{\displaystyle b_{1}(t)}
and
b
2
(
t
)
{\displaystyle b_{2}(t)}
are given by
b
1
(
t
+
δ
t
)
=
p
j
1
b
1
(
t
)
+
(
1
−
p
j
1
)
z
(
t
)
δ
(
t
)
{\displaystyle b_{1}(t+\delta t)=p_{j1}b_{1}(t)+(1-p_{j1})z(t)\delta (t)}
,
b
2
(
t
+
δ
t
)
=
p
j
2
b
2
(
t
)
+
(
1
−
p
j
2
)
z
(
t
)
δ
(
t
)
{\displaystyle b_{2}(t+\delta t)=p_{j2}b_{2}(t)+(1-p_{j2})z(t)\delta (t)}
,
where
p
j
1
=
exp
[
−
δ
t
τ
b
1
]
{\displaystyle p_{j1}=\exp \left[-{\frac {\delta t}{\tau _{b1}}}\right]}
and
p
j
2
=
exp
[
−
δ
t
τ
b
2
]
{\displaystyle p_{j2}=\exp \left[-{\frac {\delta t}{\tau _{b2}}}\right]}
.
Further, multi-time scale adaptive threshold neuron model showing more complex dynamics is shown in.
== Stochastic models of membrane voltage and spike timing ==
The models in this category are generalized integrate-and-fire models that include a certain level of stochasticity. Cortical neurons in experiments are found to respond reliably to time-dependent input, albeit with a small degree of variations between one trial and the next if the same stimulus is repeated. Stochasticity in neurons has two important sources. First, even in a very controlled experiment where input current is injected directly into the soma, ion channels open and close stochastically and this channel noise leads to a small amount of variability in the exact value of the membrane potential and the exact timing of output spikes. Second, for a neuron embedded in a cortical network, it is hard to control the exact input because most inputs come from unobserved neurons somewhere else in the brain.
Stochasticity has been introduced into spiking neuron models in two fundamentally different forms: either (i) a noisy input current is added to the differential equation of the neuron model; or (ii) the process of spike generation is noisy. In both cases, the mathematical theory can be developed for continuous time, which is then, if desired for the use in computer simulations, transformed into a discrete-time model.
The relation of noise in neuron models to the variability of spike trains and neural codes is discussed in Neural Coding and in Chapter 7 of the textbook Neuronal Dynamics.
=== Noisy input model (diffusive noise) ===
A neuron embedded in a network receives spike input from other neurons. Since the spike arrival times are not controlled by an experimentalist they can be considered as stochastic. Thus a (potentially nonlinear) integrate-and-fire model with nonlinearity f(v) receives two inputs: an input
I
(
t
)
{\displaystyle I(t)}
controlled by the experimentalists and a noisy input current
I
n
o
i
s
e
(
t
)
{\displaystyle I^{\rm {noise}}(t)}
that describes the uncontrolled background input.
τ
m
d
V
d
t
=
f
(
V
)
+
R
I
(
t
)
+
R
I
noise
(
t
)
{\displaystyle \tau _{m}{\frac {dV}{dt}}=f(V)+RI(t)+RI^{\text{noise}}(t)}
Stein's model is the special case of a leaky integrate-and-fire neuron and a stationary white noise current
I
n
o
i
s
e
(
t
)
=
ξ
(
t
)
{\displaystyle I^{\rm {noise}}(t)=\xi (t)}
with mean zero and unit variance. In the subthreshold regime, these assumptions yield the equation of the Ornstein–Uhlenbeck process
τ
m
d
V
d
t
=
[
E
m
−
V
]
+
R
I
(
t
)
+
R
ξ
(
t
)
{\displaystyle \tau _{m}{\frac {dV}{dt}}=[E_{m}-V]+RI(t)+R\xi (t)}
However, in contrast to the standard Ornstein–Uhlenbeck process, the membrane voltage is reset whenever V hits the firing threshold Vth . Calculating the interval distribution of the Ornstein–Uhlenbeck model for constant input with threshold leads to a first-passage time problem. Stein's neuron model and variants thereof have been used to fit interspike interval distributions of spike trains from real neurons under constant input current.
In the mathematical literature, the above equation of the Ornstein–Uhlenbeck process is written in the form
d
V
=
[
E
m
−
V
+
R
I
(
t
)
]
d
t
τ
m
+
σ
d
W
{\displaystyle dV=[E_{m}-V+RI(t)]{\frac {dt}{\tau _{m}}}+\sigma \,dW}
where
σ
{\displaystyle \sigma }
is the amplitude of the noise input and dW are increments of a Wiener process. For discrete-time implementations with time step dt the voltage updates are
Δ
V
=
[
E
m
−
V
+
R
I
(
t
)
]
Δ
t
τ
m
+
σ
τ
m
y
{\displaystyle \Delta V=[E_{m}-V+RI(t)]{\frac {\Delta t}{\tau _{m}}}+\sigma {\sqrt {\tau _{m}}}y}
where y is drawn from a Gaussian distribution with zero mean unit variance. The voltage is reset when it hits the firing threshold Vth .
The noisy input model can also be used in generalized integrate-and-fire models. For example, the exponential integrate-and-fire model with noisy input reads
τ
m
d
V
d
t
=
E
m
−
V
+
Δ
T
exp
(
V
−
V
T
Δ
T
)
+
R
I
(
t
)
+
R
ξ
(
t
)
{\displaystyle \tau _{m}{\frac {dV}{dt}}=E_{m}-V+\Delta _{T}\exp \left({\frac {V-V_{T}}{\Delta _{T}}}\right)+RI(t)+R\xi (t)}
For constant deterministic input
I
(
t
)
=
I
0
{\displaystyle I(t)=I_{0}}
it is possible to calculate the mean firing rate as a function of
I
0
{\displaystyle I_{0}}
. This is important because the frequency-current relation (f-I-curve) is often used by experimentalists to characterize a neuron.
The leaky integrate-and-fire with noisy input has been widely used in the analysis of networks of spiking neurons. Noisy input is also called 'diffusive noise' because it leads to a diffusion of the subthreshold membrane potential around the noise-free trajectory (Johannesma, The theory of spiking neurons with noisy input is reviewed in Chapter 8.2 of the textbook Neuronal Dynamics.
=== Noisy output model (escape noise) ===
In deterministic integrate-and-fire models, a spike is generated if the membrane potential V(t) hits the threshold
V
t
h
{\displaystyle V_{th}}
. In noisy output models, the strict threshold is replaced by a noisy one as follows. At each moment in time t, a spike is generated stochastically with instantaneous stochastic intensity or 'escape rate'
ρ
(
t
)
=
f
(
V
(
t
)
−
V
t
h
)
{\displaystyle \rho (t)=f(V(t)-V_{th})}
that depends on the momentary difference between the membrane voltage V(t) and the threshold
V
t
h
{\displaystyle V_{th}}
. A common choice for the 'escape rate'
f
{\displaystyle f}
(that is consistent with biological data) is
f
(
V
−
V
t
h
)
=
1
τ
0
exp
[
β
(
V
−
V
t
h
)
]
{\displaystyle f(V-V_{th})={\frac {1}{\tau _{0}}}\exp[\beta (V-V_{th})]}
where
τ
0
{\displaystyle \tau _{0}}
is a time constant that describes how quickly a spike is fired once the membrane potential reaches the threshold and
β
{\displaystyle \beta }
is a sharpness parameter. For
β
→
∞
{\displaystyle \beta \to \infty }
the threshold becomes sharp and spike firing occurs deterministically at the moment when the membrane potential hits the threshold from below. The sharpness value found in experiments is
1
/
β
≈
4
m
V
{\displaystyle 1/\beta \approx 4mV}
which means that neuronal firing becomes non-negligible as soon as the membrane potential is a few mV below the formal firing threshold.
The escape rate process via a soft threshold is reviewed in Chapter 9 of the textbook Neuronal Dynamics.
For models in discrete time, a spike is generated with probability
P
F
(
t
n
)
=
F
[
V
(
t
n
)
−
V
t
h
]
{\displaystyle P_{F}(t_{n})=F[V(t_{n})-V_{th}]}
that depends on the momentary difference between the membrane voltage V at time
t
n
{\displaystyle t_{n}}
and the threshold
V
t
h
{\displaystyle V_{th}}
. The function F is often taken as a standard sigmoidal
F
(
x
)
=
0.5
[
1
+
tanh
(
γ
x
)
]
{\displaystyle F(x)=0.5[1+\tanh(\gamma x)]}
with steepness parameter
γ
{\displaystyle \gamma }
, similar to the update dynamics in artificial neural networks. But the functional form of F can also be derived from the stochastic intensity
f
{\displaystyle f}
in continuous time introduced above as
F
(
y
n
)
≈
1
−
exp
[
y
n
Δ
t
]
{\displaystyle F(y_{n})\approx 1-\exp[y_{n}\Delta t]}
where
y
n
=
V
(
t
n
)
−
V
t
h
{\displaystyle y_{n}=V(t_{n})-V_{th}}
is the threshold distance.
Integrate-and-fire models with output noise can be used to predict the peristimulus time histogram (PSTH) of real neurons under arbitrary time-dependent input. For non-adaptive integrate-and-fire neurons, the interval distribution under constant stimulation can be calculated from stationary renewal theory.
=== Spike response model (SRM) ===
main article: Spike response model
The spike response model (SRM) is a generalized linear model for the subthreshold membrane voltage combined with a nonlinear output noise process for spike generation. The membrane voltage V(t) at time t is
V
(
t
)
=
∑
f
η
(
t
−
t
f
)
+
∫
0
∞
κ
(
s
)
I
(
t
−
s
)
d
s
+
V
r
e
s
t
{\displaystyle V(t)=\sum _{f}\eta (t-t^{f})+\int \limits _{0}^{\infty }\kappa (s)I(t-s)\,ds+V_{\mathrm {rest} }}
where tf is the firing time of spike number f of the neuron, Vrest is the resting voltage in the absence of input, I(t-s) is the input current at time t-s and
κ
(
s
)
{\displaystyle \kappa (s)}
is a linear filter (also called kernel) that describes the contribution of an input current pulse at time t-s to the voltage at time t. The contributions to the voltage caused by a spike at time
t
f
{\displaystyle t^{f}}
are described by the refractory kernel
η
(
t
−
t
f
)
{\displaystyle \eta (t-t^{f})}
. In particular,
η
(
t
−
t
f
)
{\displaystyle \eta (t-t^{f})}
describes the reset after the spike and the time course of the spike-afterpotential following a spike. It therefore expresses the consequences of refractoriness and adaptation. The voltage V(t) can be interpreted as the result of an integration of the differential equation of a leaky integrate-and-fire model coupled to an arbitrary number of spike-triggered adaptation variables.
Spike firing is stochastic and happens with a time-dependent stochastic intensity (instantaneous rate)
f
(
V
−
ϑ
(
t
)
)
=
1
τ
0
exp
[
β
(
V
−
ϑ
(
t
)
)
]
{\displaystyle f(V-\vartheta (t))={\frac {1}{\tau _{0}}}\exp[\beta (V-\vartheta (t))]}
with parameters
τ
0
{\displaystyle \tau _{0}}
and
β
{\displaystyle \beta }
and a dynamic threshold
ϑ
(
t
)
{\displaystyle \vartheta (t)}
given by
ϑ
(
t
)
=
ϑ
0
+
∑
f
θ
1
(
t
−
t
f
)
{\displaystyle \vartheta (t)=\vartheta _{0}+\sum _{f}\theta _{1}(t-t^{f})}
Here
ϑ
0
{\displaystyle \vartheta _{0}}
is the firing threshold of an inactive neuron and
θ
1
(
t
−
t
f
)
{\displaystyle \theta _{1}(t-t^{f})}
describes the increase of the threshold after a spike at time
t
f
{\displaystyle t^{f}}
. In case of a fixed threshold, one sets
θ
1
(
t
−
t
f
)
=
0
{\displaystyle \theta _{1}(t-t^{f})=0}
. For
β
→
∞
{\displaystyle \beta \to \infty }
the threshold process is deterministic.
The time course of the filters
η
,
κ
,
θ
1
{\displaystyle \eta ,\kappa ,\theta _{1}}
that characterize the spike response model can be directly extracted from experimental data. With optimized parameters the SRM describes the time course of the subthreshold membrane voltage for time-dependent input with a precision of 2mV and can predict the timing of most output spikes with a precision of 4ms. The SRM is closely related to linear-nonlinear-Poisson cascade models (also called Generalized Linear Model). The estimation of parameters of probabilistic neuron models such as the SRM using methods developed for Generalized Linear Models is discussed in Chapter 10 of the textbook Neuronal Dynamics.
The name spike response model arises because, in a network, the input current for neuron i is generated by the spikes of other neurons so that in the case of a network the voltage equation becomes
V
i
(
t
)
=
∑
f
η
i
(
t
−
t
i
f
)
+
∑
j
=
1
N
w
i
j
∑
f
′
ε
i
j
(
t
−
t
j
f
′
)
+
V
r
e
s
t
{\displaystyle V_{i}(t)=\sum _{f}\eta _{i}(t-t_{i}^{f})+\sum _{j=1}^{N}w_{ij}\sum _{f'}\varepsilon _{ij}(t-t_{j}^{f'})+V_{\mathrm {rest} }}
where
t
j
f
′
{\displaystyle t_{j}^{f'}}
is the firing times of neuron j (i.e., its spike train);
η
i
(
t
−
t
i
f
)
{\displaystyle \eta _{i}(t-t_{i}^{f})}
describes the time course of the spike and the spike after-potential for neuron i; and
w
i
j
{\displaystyle w_{ij}}
and
ε
i
j
(
t
−
t
j
f
′
)
{\displaystyle \varepsilon _{ij}(t-t_{j}^{f'})}
describe the amplitude and time course of an excitatory or inhibitory postsynaptic potential (PSP) caused by the spike
t
j
f
′
{\displaystyle t_{j}^{f'}}
of the presynaptic neuron j. The time course
ε
i
j
(
s
)
{\displaystyle \varepsilon _{ij}(s)}
of the PSP results from the convolution of the postsynaptic current
I
(
t
)
{\displaystyle I(t)}
caused by the arrival of a presynaptic spike from neuron j with the membrane filter
κ
(
s
)
{\displaystyle \kappa (s)}
.
=== SRM0 ===
The SRM0 is a stochastic neuron model related to time-dependent nonlinear renewal theory and a simplification of the Spike Response Model (SRM). The main difference to the voltage equation of the SRM introduced above is that in the term containing the refractory kernel
η
(
s
)
{\displaystyle \eta (s)}
there is no summation sign over past spikes: only the most recent spike (denoted as the time
t
^
{\displaystyle {\hat {t}}}
) matters. Another difference is that the threshold is constant. The model SRM0 can be formulated in discrete or continuous time. For example, in continuous time, the single-neuron equation is
V
(
t
)
=
η
(
t
−
t
^
)
+
∫
0
∞
κ
(
s
)
I
(
t
−
s
)
d
s
+
V
r
e
s
t
{\displaystyle V(t)=\eta (t-{\hat {t}})+\int _{0}^{\infty }\kappa (s)I(t-s)\,ds+V_{\mathrm {rest} }}
and the network equations of the SRM0 are
V
i
(
t
∣
t
^
i
)
=
η
i
(
t
−
t
^
i
)
+
∑
j
w
i
j
∑
f
ε
i
j
(
t
−
t
^
i
,
t
−
t
f
)
+
V
r
e
s
t
{\displaystyle V_{i}(t\mid {\hat {t}}_{i})=\eta _{i}(t-{\hat {t}}_{i})+\sum _{j}w_{ij}\sum _{f}\varepsilon _{ij}(t-{\hat {t}}_{i},t-t^{f})+V_{\mathrm {rest} }}
where
t
^
i
{\displaystyle {\hat {t}}_{i}}
is the last firing time neuron i. Note that the time course of the postsynaptic potential
ε
i
j
{\displaystyle \varepsilon _{ij}}
is also allowed to depend on the time since the last spike of neuron i to describe a change in membrane conductance during refractoriness. The instantaneous firing rate (stochastic intensity) is
f
(
V
−
ϑ
)
=
1
τ
0
exp
[
β
(
V
−
V
t
h
)
]
{\displaystyle f(V-\vartheta )={\frac {1}{\tau _{0}}}\exp[\beta (V-V_{th})]}
where
V
t
h
{\displaystyle V_{th}}
is a fixed firing threshold. Thus spike firing of neuron i depends only on its input and the time since neuron i has fired its last spike.
With the SRM0, the interspike-interval distribution for constant input can be mathematically linked to the shape of the refractory kernel
η
{\displaystyle \eta }
. Moreover the stationary frequency-current relation can be calculated from the escape rate in combination with the refractory kernel
η
{\displaystyle \eta }
. With an appropriate choice of the kernels, the SRM0 approximates the dynamics of the Hodgkin-Huxley model to a high degree of accuracy. Moreover, the PSTH response to arbitrary time-dependent input can be predicted.
=== Galves–Löcherbach model ===
The Galves–Löcherbach model is a stochastic neuron model closely related to the spike response model SRM0 and the leaky integrate-and-fire model. It is inherently stochastic and, just like the SRM0, it is linked to time-dependent nonlinear renewal theory. Given the model specifications, the probability that a given neuron
i
{\displaystyle i}
spikes in a period
t
{\displaystyle t}
may be described by
P
r
o
b
(
X
t
(
i
)
=
1
∣
F
t
−
1
)
=
φ
i
(
∑
j
∈
I
W
j
→
i
∑
s
=
L
t
i
t
−
1
g
j
(
t
−
s
)
X
s
(
j
)
,
t
−
L
t
i
)
,
{\displaystyle \mathop {\mathrm {Prob} } (X_{t}(i)=1\mid {\mathcal {F}}_{t-1})=\varphi _{i}{\Biggl (}\sum _{j\in I}W_{j\rightarrow i}\sum _{s=L_{t}^{i}}^{t-1}g_{j}(t-s)X_{s}(j),~~~t-L_{t}^{i}{\Biggl )},}
where
W
j
→
i
{\displaystyle W_{j\rightarrow i}}
is a synaptic weight, describing the influence of neuron
j
{\displaystyle j}
on neuron
i
{\displaystyle i}
,
g
j
{\displaystyle g_{j}}
expresses the leak, and
L
t
i
{\displaystyle L_{t}^{i}}
provides the spiking history of neuron
i
{\displaystyle i}
before
t
{\displaystyle t}
, according to
L
t
i
=
sup
{
s
<
t
:
X
s
(
i
)
=
1
}
.
{\displaystyle L_{t}^{i}=\sup\{s<t:X_{s}(i)=1\}.}
Importantly, the spike probability of neuron
i
{\displaystyle i}
depends only on its spike input (filtered with a kernel
g
j
{\displaystyle g_{j}}
and weighted with a factor
W
j
→
i
{\displaystyle W_{j\to i}}
) and the timing of its most recent output spike (summarized by
t
−
L
t
i
{\displaystyle t-L_{t}^{i}}
).
== Didactic toy models of membrane voltage ==
The models in this category are highly simplified toy models that qualitatively describe the membrane voltage as a function of input. They are mainly used for didactic reasons in teaching but are not considered valid neuron models for large-scale simulations or data fitting.
=== FitzHugh–Nagumo ===
Sweeping simplifications to Hodgkin–Huxley were introduced by FitzHugh and Nagumo in 1961 and 1962. Seeking to describe "regenerative self-excitation" by a nonlinear positive-feedback membrane voltage and recovery by a linear negative-feedback gate voltage, they developed the model described by
r
c
l
d
V
d
t
=
V
−
V
3
/
3
−
w
+
I
e
x
t
τ
d
w
d
t
=
V
−
a
−
b
w
{\displaystyle {\begin{aligned}{rcl}{\dfrac {dV}{dt}}&=V-V^{3}/3-w+I_{\mathrm {ext} }\\\tau {\dfrac {dw}{dt}}&=V-a-bw\end{aligned}}}
where we again have a membrane-like voltage and input current with a slower general gate voltage w and experimentally-determined parameters a = -0.7, b = 0.8, τ = 1/0.08. Although not derivable from biology, the model allows for a simplified, immediately available dynamic, without being a trivial simplification. The experimental support is weak, but the model is useful as a didactic tool to introduce dynamics of spike generation through phase plane analysis. See Chapter 7 in the textbook Methods of Neuronal Modeling.
=== Morris–Lecar ===
In 1981, Morris and Lecar combined the Hodgkin–Huxley and FitzHugh–Nagumo models into a voltage-gated calcium channel model with a delayed-rectifier potassium channel represented by
C
d
V
d
t
=
−
I
i
o
n
(
V
,
w
)
+
I
d
w
d
t
=
φ
⋅
w
∞
−
w
τ
w
{\displaystyle {\begin{aligned}C{\frac {dV}{dt}}&=-I_{\mathrm {ion} }(V,w)+I\\{\frac {dw}{dt}}&=\varphi \cdot {\frac {w_{\infty }-w}{\tau _{w}}}\end{aligned}}}
where
I
i
o
n
(
V
,
w
)
=
g
¯
C
a
m
∞
⋅
(
V
−
V
C
a
)
+
g
¯
K
w
⋅
(
V
−
V
K
)
+
g
¯
L
⋅
(
V
−
V
L
)
{\displaystyle I_{\mathrm {ion} }(V,w)={\bar {g}}_{\mathrm {Ca} }m_{\infty }\cdot (V-V_{\mathrm {Ca} })+{\bar {g}}_{\mathrm {K} }w\cdot (V-V_{\mathrm {K} })+{\bar {g}}_{\mathrm {L} }\cdot (V-V_{\mathrm {L} })}
. The experimental support of the model is weak, but the model is useful as a didactic tool to introduce dynamics of spike generation through phase plane analysis. See Chapter 7 in the textbook Methods of Neuronal Modeling.
A two-dimensional neuron model very similar to the Morris-Lecar model can be derived step-by-step starting from the Hodgkin-Huxley model. See Chapter 4.2 in the textbook Neuronal Dynamics.
=== Hindmarsh–Rose ===
Building upon the FitzHugh–Nagumo model, Hindmarsh and Rose proposed in 1984 a model of neuronal activity described by three coupled first-order differential equations:
d
x
d
t
=
y
+
3
x
2
−
x
3
−
z
+
I
d
y
d
t
=
1
−
5
x
2
−
y
d
z
d
t
=
r
⋅
(
4
(
x
+
8
5
)
−
z
)
{\displaystyle {\begin{aligned}{\frac {dx}{dt}}&=y+3x^{2}-x^{3}-z+I\\{\frac {dy}{dt}}&=1-5x^{2}-y\\{\frac {dz}{dt}}&=r\cdot (4(x+{\tfrac {8}{5}})-z)\end{aligned}}}
with r2 = x2 + y2 + z2, and r ≈ 10−2 so that the z variable only changes very slowly. This extra mathematical complexity allows a great variety of dynamic behaviors for the membrane potential, described by the x variable of the model, which includes chaotic dynamics. This makes the Hindmarsh–Rose neuron model very useful, because it is still simple, allows a good qualitative description of the many different firing patterns of the action potential, in particular bursting, observed in experiments. Nevertheless, it remains a toy model and has not been fitted to experimental data. It is widely used as a reference model for bursting dynamics.
=== Theta model and quadratic integrate-and-fire ===
The theta model, or Ermentrout–Kopell canonical Type I model, is mathematically equivalent to the quadratic integrate-and-fire model which in turn is an approximation to the exponential integrate-and-fire model and the Hodgkin-Huxley model. It is called a canonical model because it is one of the generic models for constant input close to the bifurcation point, which means close to the transition from silent to repetitive firing.
The standard formulation of the theta model is
d
θ
(
t
)
d
t
=
(
I
−
I
0
)
[
1
+
cos
(
θ
)
]
+
[
1
−
cos
(
θ
)
]
{\displaystyle {\frac {d\theta (t)}{dt}}=(I-I_{0})[1+\cos(\theta )]+[1-\cos(\theta )]}
The equation for the quadratic integrate-and-fire model is (see Chapter 5.3 in the textbook Neuronal Dynamics )
τ
m
d
V
m
(
t
)
d
t
=
(
I
−
I
0
)
R
+
[
V
m
(
t
)
−
E
m
]
[
V
m
(
t
)
−
V
T
]
{\displaystyle \tau _{\mathrm {m} }{\frac {dV_{\mathrm {m} }(t)}{dt}}=(I-I_{0})R+[V_{\mathrm {m} }(t)-E_{\mathrm {m} }][V_{\mathrm {m} }(t)-V_{\mathrm {T} }]}
The equivalence of theta model and quadratic integrate-and-fire is for example reviewed in Chapter 4.1.2.2 of spiking neuron models.
For input
I
(
t
)
{\displaystyle I(t)}
that changes over time or is far away from the bifurcation point, it is preferable to work with the exponential integrate-and-fire model (if one wants to stay in the class of one-dimensional neuron models), because real neurons exhibit the nonlinearity of the exponential integrate-and-fire model.
== Sensory input-stimulus encoding neuron models ==
The models in this category were derived following experiments involving natural stimulation such as light, sound, touch, or odor. In these experiments, the spike pattern resulting from each stimulus presentation varies from trial to trial, but the averaged response from several trials often converges to a clear pattern. Consequently, the models in this category generate a probabilistic relationship between the input stimulus to spike occurrences. Importantly, the recorded neurons are often located several processing steps after the sensory neurons, so that these models summarize the effects of the sequence of processing steps in a compact form
=== The non-homogeneous Poisson process model (Siebert) ===
Siebert modeled the neuron spike firing pattern using a non-homogeneous Poisson process model, following experiments involving the auditory system. According to Siebert, the probability of a spiking event at the time interval
[
t
,
t
+
Δ
t
]
{\displaystyle [t,t+\Delta _{t}]}
is proportional to a non-negative function
g
[
s
(
t
)
]
{\displaystyle g[s(t)]}
, where
s
(
t
)
{\displaystyle s(t)}
is the raw stimulus.:
P
spike
(
t
∈
[
t
′
,
t
′
+
Δ
t
]
)
=
Δ
t
⋅
g
[
s
(
t
)
]
{\displaystyle P_{\text{spike}}(t\in [t',t'+\Delta _{t}])=\Delta _{t}\cdot g[s(t)]}
Siebert considered several functions as
g
[
s
(
t
)
]
{\displaystyle g[s(t)]}
, including
g
[
s
(
t
)
]
∝
s
2
(
t
)
{\displaystyle g[s(t)]\propto s^{2}(t)}
for low stimulus intensities.
The main advantage of Siebert's model is its simplicity. The shortcomings of the model is its inability to reflect properly the following phenomena:
The transient enhancement of the neuronal firing activity in response to a step stimulus.
The saturation of the firing rate.
The values of inter-spike-interval-histogram at short intervals values (close to zero).
These shortcomings are addressed by the age-dependent point process model and the two-state Markov Model.
=== Refractoriness and age-dependent point process model ===
Berry and Meister studied neuronal refractoriness using a stochastic model that predicts spikes as a product of two terms, a function f(s(t)) that depends on the time-dependent stimulus s(t) and one a recovery function
w
(
t
−
t
^
)
{\displaystyle w(t-{\hat {t}})}
that depends on the time since the last spike
ρ
(
t
)
=
f
(
s
(
t
)
)
w
(
t
−
t
^
)
{\displaystyle \rho (t)=f(s(t))w(t-{\hat {t}})}
The model is also called an inhomogeneous Markov interval (IMI) process. Similar models have been used for many years in auditory neuroscience. Since the model keeps memory of the last spike time it is non-Poisson and falls in the class of time-dependent renewal models. It is closely related to the model SRM0 with exponential escape rate. Importantly, it is possible to fit parameters of the age-dependent point process model so as to describe not just the PSTH response, but also the interspike-interval statistics.
=== Linear-nonlinear Poisson cascade model and GLM ===
The linear-nonlinear-Poisson cascade model is a cascade of a linear filtering process followed by a nonlinear spike generation step. In the case that output spikes feed back, via a linear filtering process, we arrive at a model that is known in the neurosciences as Generalized Linear Model (GLM). The GLM is mathematically equivalent to the spike response model SRM) with escape noise; but whereas in the SRM the internal variables are interpreted as the membrane potential and the firing threshold, in the GLM the internal variables are abstract quantities that summarizes the net effect of input (and recent output spikes) before spikes are generated in the final step.
=== The two-state Markov model (Nossenson & Messer) ===
The spiking neuron model by Nossenson & Messer produces the probability of the neuron firing a spike as a function of either an external or pharmacological stimulus. The model consists of a cascade of a receptor layer model and a spiking neuron model, as shown in Fig 4. The connection between the external stimulus to the spiking probability is made in two steps: First, a receptor cell model translates the raw external stimulus to neurotransmitter concentration, and then, a spiking neuron model connects neurotransmitter concentration to the firing rate (spiking probability). Thus, the spiking neuron model by itself depends on neurotransmitter concentration at the input stage.
An important feature of this model is the prediction for neurons firing rate pattern which captures, using a low number of free parameters, the characteristic edge emphasized response of neurons to a stimulus pulse, as shown in Fig. 5. The firing rate is identified both as a normalized probability for neural spike firing and as a quantity proportional to the current of neurotransmitters released by the cell. The expression for the firing rate takes the following form:
R
fire
(
t
)
=
P
spike
(
t
;
Δ
t
)
Δ
t
=
[
y
(
t
)
+
R
0
]
⋅
P
0
(
t
)
{\displaystyle R_{\text{fire}}(t)={\frac {P_{\text{spike}}(t;\Delta _{t})}{\Delta _{t}}}=[y(t)+R_{0}]\cdot P_{0}(t)}
where,
P0 is the probability of the neuron being "armed" and ready to fire. It is given by the following differential equation:
P
˙
0
=
−
[
y
(
t
)
+
R
0
+
R
1
]
⋅
P
0
(
t
)
+
R
1
{\displaystyle {\dot {P}}_{0}=-[y(t)+R_{0}+R_{1}]\cdot P_{0}(t)+R_{1}}
P0 could be generally calculated recursively using the Euler method, but in the case of a pulse of stimulus, it yields a simple closed-form expression.
y(t) is the input of the model and is interpreted as the neurotransmitter concentration on the cell surrounding (in most cases glutamate). For an external stimulus it can be estimated through the receptor layer model:
y
(
t
)
≃
g
gain
⋅
⟨
s
2
(
t
)
⟩
,
{\displaystyle y(t)\simeq g_{\text{gain}}\cdot \langle s^{2}(t)\rangle ,}
with
⟨
s
2
(
t
)
⟩
{\displaystyle \langle s^{2}(t)\rangle }
being a short temporal average of stimulus power (given in Watt or other energy per time unit).
R0 corresponds to the intrinsic spontaneous firing rate of the neuron.
R1 is the recovery rate of the neuron from the refractory state.
Other predictions by this model include:
1) The averaged evoked response potential (ERP) due to the population of many neurons in unfiltered measurements resembles the firing rate.
2) The voltage variance of activity due to multiple neuron activity resembles the firing rate (also known as Multi-Unit-Activity power or MUA).
3) The inter-spike-interval probability distribution takes the form a gamma-distribution like function.
== Pharmacological input stimulus neuron models ==
The models in this category produce predictions for experiments involving pharmacological stimulation.
=== Synaptic transmission (Koch & Segev) ===
According to the model by Koch and Segev, the response of a neuron to individual neurotransmitters can be modeled as an extension of the classical Hodgkin–Huxley model with both standard and nonstandard kinetic currents. Four neurotransmitters primarily influence the CNS. AMPA/kainate receptors are fast excitatory mediators while NMDA receptors mediate considerably slower currents. Fast inhibitory currents go through GABAA receptors, while GABAB receptors mediate by secondary G-protein-activated potassium channels. This range of mediation produces the following current dynamics:
I
A
M
P
A
(
t
,
V
)
=
g
¯
A
M
P
A
⋅
[
O
]
⋅
(
V
(
t
)
−
E
A
M
P
A
)
{\displaystyle I_{\mathrm {AMPA} }(t,V)={\bar {g}}_{\mathrm {AMPA} }\cdot [O]\cdot (V(t)-E_{\mathrm {AMPA} })}
I
N
M
D
A
(
t
,
V
)
=
g
¯
N
M
D
A
⋅
B
(
V
)
⋅
[
O
]
⋅
(
V
(
t
)
−
E
N
M
D
A
)
{\displaystyle I_{\mathrm {NMDA} }(t,V)={\bar {g}}_{\mathrm {NMDA} }\cdot B(V)\cdot [O]\cdot (V(t)-E_{\mathrm {NMDA} })}
I
G
A
B
A
A
(
t
,
V
)
=
g
¯
G
A
B
A
A
⋅
(
[
O
1
]
+
[
O
2
]
)
⋅
(
V
(
t
)
−
E
C
l
)
{\displaystyle I_{\mathrm {GABA_{A}} }(t,V)={\bar {g}}_{\mathrm {GABA_{A}} }\cdot ([O_{1}]+[O_{2}])\cdot (V(t)-E_{\mathrm {Cl} })}
I
G
A
B
A
B
(
t
,
V
)
=
g
¯
G
A
B
A
B
⋅
[
G
]
n
[
G
]
n
+
K
d
⋅
(
V
(
t
)
−
E
K
)
{\displaystyle I_{\mathrm {GABA_{B}} }(t,V)={\bar {g}}_{\mathrm {GABA_{B}} }\cdot {\tfrac {[G]^{n}}{[G]^{n}+K_{\mathrm {d} }}}\cdot (V(t)-E_{\mathrm {K} })}
where ḡ is the maximal conductance (around 1S) and E is the equilibrium potential of the given ion or transmitter (AMDA, NMDA, Cl, or K), while [O] describes the fraction of open receptors. For NMDA, there is a significant effect of magnesium block that depends sigmoidally on the concentration of intracellular magnesium by B(V). For GABAB, [G] is the concentration of the G-protein, and Kd describes the dissociation of G in binding to the potassium gates.
The dynamics of this more complicated model have been well-studied experimentally and produce important results in terms of very quick synaptic potentiation and depression, that is fast, short-term learning.
The stochastic model by Nossenson and Messer translates neurotransmitter concentration at the input stage to the probability of releasing neurotransmitter at the output stage. For a more detailed description of this model, see the Two state Markov model section above.
== HTM neuron model ==
The HTM neuron model was developed by Jeff Hawkins and researchers at Numenta and is based on a theory called Hierarchical Temporal Memory, originally described in the book On Intelligence. It is based on neuroscience and the physiology and interaction of pyramidal neurons in the neocortex of the human brain.
== Applications ==
Spiking Neuron Models are used in a variety of applications that need encoding into or decoding from neuronal spike trains in the context of neuroprosthesis and brain-computer interfaces such as retinal prosthesis: or artificial limb control and sensation. Applications are not part of this article; for more information on this topic please refer to the main article.
== Relation between artificial and biological neuron models ==
The most basic model of a neuron consists of an input with some synaptic weight vector and an activation function or transfer function inside the neuron determining output. This is the basic structure used for artificial neurons, which in a neural network often looks like
y
i
=
φ
(
∑
j
w
i
j
x
j
)
{\displaystyle y_{i}=\varphi \left(\sum _{j}w_{ij}x_{j}\right)}
where yi is the output of the i th neuron, xj is the jth input neuron signal, wij is the synaptic weight (or strength of connection) between the neurons i and j, and φ is the activation function. While this model has seen success in machine-learning applications, it is a poor model for real (biological) neurons, because it lacks time-dependence in input and output.
When an input is switched on at a time t and kept constant thereafter, biological neurons emit a spike train. Importantly, this spike train is not regular but exhibits a temporal structure characterized by adaptation, bursting, or initial bursting followed by regular spiking. Generalized integrate-and-fire models such as the Adaptive Exponential Integrate-and-Fire model, the spike response model, or the (linear) adaptive integrate-and-fire model can capture these neuronal firing patterns.
Moreover, neuronal input in the brain is time-dependent. Time-dependent input is transformed by complex linear and nonlinear filters into a spike train in the output. Again, the spike response model or the adaptive integrate-and-fire model enables to prediction of the spike train in the output for arbitrary time-dependent input, whereas an artificial neuron or a simple leaky integrate-and-fire does not.
If we take the Hodkgin-Huxley model as a starting point, generalized integrate-and-fire models can be derived systematically in a step-by-step simplification procedure. This has been shown explicitly for the exponential integrate-and-fire model and the spike response model.
In the case of modeling a biological neuron, physical analogs are used in place of abstractions such as "weight" and "transfer function". A neuron is filled and surrounded with water-containing ions, which carry electric charge. The neuron is bound by an insulating cell membrane and can maintain a concentration of charged ions on either side that determines a capacitance Cm. The firing of a neuron involves the movement of ions into the cell, that occurs when neurotransmitters cause ion channels on the cell membrane to open. We describe this by a physical time-dependent current I(t). With this comes a change in voltage, or the electrical potential energy difference between the cell and its surroundings, which is observed to sometimes result in a voltage spike called an action potential which travels the length of the cell and triggers the release of further neurotransmitters. The voltage, then, is the quantity of interest and is given by Vm(t).
If the input current is constant, most neurons emit after some time of adaptation or initial bursting a regular spike train. The frequency of regular firing in response to a constant current I is described by the frequency-current relation, which corresponds to the transfer function
φ
{\displaystyle \varphi }
of artificial neural networks. Similarly, for all spiking neuron models, the transfer function
φ
{\displaystyle \varphi }
can be calculated numerically (or analytically).
== Cable theory and compartmental models ==
All of the above deterministic models are point-neuron models because they do not consider the spatial structure of a neuron. However, the dendrite contributes to transforming input into output. Point neuron models are valid description in three cases. (i) If input current is directly injected into the soma. (ii) If synaptic input arrives predominantly at or close to the soma (closeness is defined by a length scale
λ
{\displaystyle \lambda }
introduced below. (iii) If synapse arrives anywhere on the dendrite, but the dendrite is completely linear. In the last case, the cable acts as a linear filter; these linear filter properties can be included in the formulation of generalized integrate-and-fire models such as the spike response model.
The filter properties can be calculated from a cable equation.
Let us consider a cell membrane in the form of a cylindrical cable. The position on the cable is denoted by x and the voltage across the cell membrane by V. The cable is characterized by a longitudinal resistance
r
l
{\displaystyle r_{l}}
per unit length and a membrane resistance
r
m
{\displaystyle r_{m}}
. If everything is linear, the voltage changes as a function of timeWe introduce a length scale
λ
2
=
r
m
/
r
l
{\displaystyle \lambda ^{2}={r_{m}}/{r_{l}}}
on the left side and time constant
τ
=
c
m
r
m
{\displaystyle \tau =c_{m}r_{m}}
on the right side. The cable equation can now be written in its perhaps best-known form:
The above cable equation is valid for a single cylindrical cable.
Linear cable theory describes the dendritic arbor of a neuron as a cylindrical structure undergoing a regular pattern of bifurcation, like branches in a tree. For a single cylinder or an entire tree, the static input conductance at the base (where the tree meets the cell body or any such boundary) is defined as
G
i
n
=
G
∞
tanh
(
L
)
+
G
L
1
+
(
G
L
/
G
∞
)
tanh
(
L
)
{\displaystyle G_{in}={\frac {G_{\infty }\tanh(L)+G_{L}}{1+(G_{L}/G_{\infty })\tanh(L)}}}
,
where L is the electrotonic length of the cylinder, which depends on its length, diameter, and resistance. A simple recursive algorithm scales linearly with the number of branches and can be used to calculate the effective conductance of the tree. This is given by
G
D
=
G
m
A
D
tanh
(
L
D
)
/
L
D
{\displaystyle \,\!G_{D}=G_{m}A_{D}\tanh(L_{D})/L_{D}}
where AD = πld is the total surface area of the tree of total length l, and LD is its total electrotonic length. For an entire neuron in which the cell body conductance is GS and the membrane conductance per unit area is Gmd = Gm / A, we find the total neuron conductance GN for n dendrite trees by adding up all tree and soma conductances, given by
G
N
=
G
S
+
∑
j
=
1
n
A
D
j
F
d
g
a
j
,
{\displaystyle G_{N}=G_{S}+\sum _{j=1}^{n}A_{D_{j}}F_{dga_{j}},}
where we can find the general correction factor Fdga experimentally by noting GD = GmdADFdga.
The linear cable model makes several simplifications to give closed analytic results, namely that the dendritic arbor must branch in diminishing pairs in a fixed pattern and that dendrites are linear. A compartmental model allows for any desired tree topology with arbitrary branches and lengths, as well as arbitrary nonlinearities. It is essentially a discretized computational implementation of nonlinear dendrites.
Each piece, or compartment, of a dendrite, is modeled by a straight cylinder of arbitrary length l and diameter d which connects with fixed resistance to any number of branching cylinders. We define the conductance ratio of the ith cylinder as Bi = Gi / G∞, where
G
∞
=
π
d
3
/
2
2
R
i
R
m
{\displaystyle G_{\infty }={\tfrac {\pi d^{3/2}}{2{\sqrt {R_{i}R_{m}}}}}}
and Ri is the resistance between the current compartment and the next. We obtain a series of equations for conductance ratios in and out of a compartment by making corrections to the normal dynamic Bout,i = Bin,i+1, as
B
o
u
t
,
i
=
B
i
n
,
i
+
1
(
d
i
+
1
/
d
i
)
3
/
2
R
m
,
i
+
1
/
R
m
,
i
{\displaystyle B_{\mathrm {out} ,i}={\frac {B_{\mathrm {in} ,i+1}(d_{i+1}/d_{i})^{3/2}}{\sqrt {R_{\mathrm {m} ,i+1}/R_{\mathrm {m} ,i}}}}}
B
i
n
,
i
=
B
o
u
t
,
i
+
tanh
X
i
1
+
B
o
u
t
,
i
tanh
X
i
{\displaystyle B_{\mathrm {in} ,i}={\frac {B_{\mathrm {out} ,i}+\tanh X_{i}}{1+B_{\mathrm {out} ,i}\tanh X_{i}}}}
B
o
u
t
,
p
a
r
=
B
i
n
,
d
a
u
1
(
d
d
a
u
1
/
d
p
a
r
)
3
/
2
R
m
,
d
a
u
1
/
R
m
,
p
a
r
+
B
i
n
,
d
a
u
2
(
d
d
a
u
2
/
d
p
a
r
)
3
/
2
R
m
,
d
a
u
2
/
R
m
,
p
a
r
+
…
{\displaystyle B_{\mathrm {out,par} }={\frac {B_{\mathrm {in,dau1} }(d_{\mathrm {dau1} }/d_{\mathrm {par} })^{3/2}}{\sqrt {R_{\mathrm {m,dau1} }/R_{\mathrm {m,par} }}}}+{\frac {B_{\mathrm {in,dau2} }(d_{\mathrm {dau2} }/d_{\mathrm {par} })^{3/2}}{\sqrt {R_{\mathrm {m,dau2} }/R_{\mathrm {m,par} }}}}+\ldots }
where the last equation deals with parents and daughters at branches, and
X
i
=
l
i
4
R
i
d
i
R
m
{\displaystyle X_{i}={\tfrac {l_{i}{\sqrt {4R_{i}}}}{\sqrt {d_{i}R_{m}}}}}
. We can iterate these equations through the tree until we get the point where the dendrites connect to the cell body (soma), where the conductance ratio is Bin,stem. Then our total neuron conductance for static input is given by
G
N
=
A
s
o
m
a
R
m
,
s
o
m
a
+
∑
j
B
i
n
,
s
t
e
m
,
j
G
∞
,
j
.
{\displaystyle G_{N}={\frac {A_{\mathrm {soma} }}{R_{\mathrm {m,soma} }}}+\sum _{j}B_{\mathrm {in,stem} ,j}G_{\infty ,j}.}
Importantly, static input is a very special case. In biology, inputs are time-dependent. Moreover, dendrites are not always linear.
Compartmental models enable to include nonlinearities via ion channels positioned at arbitrary locations along the dendrites. For static inputs, it is sometimes possible to reduce the number of compartments (increase the computational speed) and yet retain the salient electrical characteristics.
== Conjectures regarding the role of the neuron in the wider context of the brain principle of operation ==
=== The neurotransmitter-based energy detection scheme ===
The neurotransmitter-based energy detection scheme suggests that the neural tissue chemically executes a Radar-like detection procedure.
As shown in Fig. 6, the key idea of the conjecture is to account for neurotransmitter concentration, neurotransmitter generation, and neurotransmitter removal rates as the important quantities in executing the detection task, while referring to the measured electrical potentials as a side effect that only in certain conditions coincide with the functional purpose of each step. The detection scheme is similar to a radar-like "energy detection" because it includes signal squaring, temporal summation, and a threshold switch mechanism, just like the energy detector, but it also includes a unit that emphasizes stimulus edges and a variable memory length (variable memory). According to this conjecture, the physiological equivalent of the energy test statistics is neurotransmitter concentration, and the firing rate corresponds to neurotransmitter current. The advantage of this interpretation is that it leads to a unit-consistent explanation which allows for bridge between electrophysiological measurements, biochemical measurements, and psychophysical results.
The evidence reviewed in suggests the following association between functionality to histological classification:
Stimulus squaring is likely to be performed by receptor cells.
Stimulus edge emphasizing and signal transduction is performed by neurons.
Temporal accumulation of neurotransmitters is performed by glial cells. Short-term neurotransmitter accumulation is likely to occur also in some types of neurons.
Logical switching is executed by glial cells, and it results from exceeding a threshold level of neurotransmitter concentration. This threshold crossing is also accompanied by a change in neurotransmitter leak rate.
Physical all-or-non movement switching is due to muscle cells and results from exceeding a certain neurotransmitter concentration threshold on muscle surroundings.
Note that although the electrophysiological signals in Fig.6 are often similar to the functional signal (signal power/neurotransmitter concentration / muscle force), there are some stages in which the electrical observation differs from the functional purpose of the corresponding step. In particular, Nossenson et al. suggested that glia threshold crossing has a completely different functional operation compared to the radiated electrophysiological signal and that the latter might only be a side effect of glia break.
== General comments regarding the modern perspective of scientific and engineering models ==
The models above are still idealizations. Corrections must be made for the increased membrane surface area given by numerous dendritic spines, temperatures significantly hotter than room-temperature experimental data, and nonuniformity in the cell's internal structure. Certain observed effects do not fit into some of these models. For instance, the temperature cycling (with minimal net temperature increase) of the cell membrane during action potential propagation is not compatible with models that rely on modeling the membrane as a resistance that must dissipate energy when current flows through it. The transient thickening of the cell membrane during action potential propagation is also not predicted by these models, nor is the changing capacitance and voltage spike that results from this thickening incorporated into these models. The action of some anesthetics such as inert gases is problematic for these models as well. New models, such as the soliton model attempt to explain these phenomena, but are less developed than older models and have yet to be widely applied.
Modern views regarding the role of the scientific model suggest that "All models are wrong but some are useful" (Box and Draper, 1987, Gribbin, 2009; Paninski et al., 2009).
Recent conjecture suggests that each neuron might function as a collection of independent threshold units. It is suggested that a neuron could be anisotropically activated following the origin of its arriving signals to the membrane, via its dendritic trees. The spike waveform was also proposed to be dependent on the origin of the stimulus.
== External links ==
Neuronal Dynamics: from single neurons to networks and models of cognition (W. Gerstner, W. Kistler, R. Naud, L. Paninski, Cambridge University Press, 2014). In particular, Chapters 6 - 10, html online version.
Spiking Neuron Models (W. Gerstner and W. Kistler, Cambridge University Press, 2002)
== See also ==
Binding neuron
Bayesian approaches to brain function
Brain-computer interfaces
Free energy principle
Models of neural computation
Neural coding
Neural oscillation
Quantitative models of the action potential
Spiking neural network
== References == | Wikipedia/Biological_neuron_model |
The British Neuroscience Association (BNA) is a scientific society with around 2,500 members. Starting out as an informal gathering of scientists meeting at the Black Horse Public House in London to discuss brain-related topics (the 'London Black Horse Group'), on 23 February 1968 it was formerly established as the Brain Research Association, and subsequently relaunched as the British Neuroscience Association in 1997.
The BNA is the largest UK organisation of its kind, supporting and promoting neuroscience and neuroscientists.
== Charitable objects ==
It is a registered charity (number 1103852), with charitable objects as follows:
‘To preserve and protect health and advance public education in neurosciences related to health and disease (in particular but not exclusively) by:’
Promoting on a multidisciplinary basis the study of the development structure and function of the nervous system in health and disease.
Promoting the dissemination of information to all those interested in the neurosciences and related disciplines by means of lectures, discussions, meetings and reports from time to time obtained from such researchers.
Advising as far as possible on issues in neurosciences related to health and disease.
Endeavouring to increase public awareness and understanding of neuroscience research in health and disease.
Assisting in the training of neuroscientists and other professionals engaged in neuroscience teaching and research.
Representing the interests of neuroscience researchers and promoting the case for the advancement of neuroscience research in the United Kingdom to government, to agencies providing research funding and to bodies engaged in science administration, regulation and standards.
The BNA is a member of the International Brain Research Organization (IBRO), the Federation of European Neuroscience Societies (FENS), and the Royal Society of Biology (RSB).
== Publications ==
The BNA publishes a peer-reviewed scientific journal, Brain and Neuroscience Advances with Jeffrey W. Dalley (Cambridge University) and Kate Baker as co-editors-in-chief. It also publishes the BNA Bulletin membership magazine.
== Events ==
The headline event of the BNA is the biennial 'Festival of Neuroscience'. The festivals are unique in bringing together multiple people and organisations with a shared interested in neuroscience - societies, charities, companies, scientists, clinicians and members of the public too.
== References ==
== External links ==
Official website | Wikipedia/British_Neuroscience_Association |
Cellular neuroscience is a branch of neuroscience concerned with the study of neurons at a cellular level. This includes morphology and physiological properties of single neurons. Several techniques such as intracellular recording, patch-clamp, and voltage-clamp technique, pharmacology, confocal imaging, molecular biology, two photon laser scanning microscopy and Ca2+ imaging have been used to study activity at the cellular level. Cellular neuroscience examines the various types of neurons, the functions of different neurons, the influence of neurons upon each other, and how neurons work together.
== Neurons and glial cells ==
Neurons are cells that are specialized to receive, propagate, and transmit electrochemical impulses. In the human brain alone, there are over eighty billion neurons. Neurons are diverse with respect to morphology and function. Thus, not all neurons correspond to the stereotypical motor neuron with dendrites and myelinated axons that conduct action potentials. Some neurons such as photoreceptor cells, for example, do not have myelinated axons that conduct action potentials. Other unipolar neurons found in invertebrates do not even have distinguishing processes such as dendrites. Moreover, the distinctions based on function between neurons and other cells such as cardiac and muscle cells are not helpful. Thus, the fundamental difference between a neuron and a nonneuronal cell is a matter of degree.
Another major class of cells found in the nervous system are glial cells. These cells are only recently beginning to receive attention from neurobiologists for being involved not just in nourishment and support of neurons, but also in modulating synapses. For example, Schwann cells, which are a type of glial cell found in the peripheral nervous system, modulate synaptic connections between presynaptic terminals of motor neuron endplates and muscle fibers at neuromuscular junctions.
== Neuronal function ==
One prominent characteristic of many neurons is excitability. Neurons generate electrical impulses or changes in voltage of two types: graded potentials and action potentials. Graded potentials occur when the membrane potential depolarizes and hyperpolarizes in a graded fashion relative to the amount of stimulus that is applied to the neuron. An action potential on the other hand is an all-or-none electrical impulse. Despite being slower than graded potentials, action potentials have the advantage of traveling long distances in axons with little or no decrement. Much of the current knowledge of action potentials comes from squid axon experiments by Sir Alan Lloyd Hodgkin and Sir Andrew Huxley.
== Action potential ==
The Hodgkin–Huxley model of an action potential in the squid giant axon has been the basis for much of the current understanding of the ionic bases of action potentials. Briefly, the model states that the generation of an action potential is determined by two ions: Na+ and K+. An action potential can be divided into several sequential phases: threshold, rising phase, falling phase, undershoot phase, and recovery. Following several local graded depolarizations of the membrane potential, the threshold of excitation is reached, voltage-gated sodium channels are activated, which leads to an influx of Na+ ions. As Na+ ions enter the cell, the membrane potential is further depolarized, and more voltage-gated sodium channels are activated. Such a process is also known as a positive feedback loop. As the rising phase reaches its peak, voltage-gated Na+ channels are inactivated whereas voltage-gated K+ channels are activated, resulting in a net outward movement of K+ ions, which re-polarizes the membrane potential towards the resting membrane potential. Repolarization of the membrane potential continues, resulting in an undershoot phase or absolute refractory period. The undershoot phase occurs because, unlike voltage-gated sodium channels, voltage-gated potassium channels inactivate much more slowly. Nevertheless, as more voltage-gated K+ channels become inactivated, the membrane potential recovers to its normal resting steady state.
== Structure and formation of synapses ==
Neurons communicate with one another via synapses. Synapses are specialized junctions between two cells in close apposition to one another. In a synapse, the neuron that sends the signal is the presynaptic neuron and the target cell receives that signal is the postsynaptic neuron or cell. Synapses can be either electrical or chemical. Electrical synapses are characterized by the formation of gap junctions that allow ions and other organic compound to instantaneously pass from one cell to another. Chemical synapses are characterized by the presynaptic release of neurotransmitters that diffuse across a synaptic cleft to bind with postsynaptic receptors. A neurotransmitter is a chemical messenger that is synthesized within neurons themselves and released by these same neurons to communicate with their postsynaptic target cells. A receptor is a transmembrane protein molecule that a neurotransmitter or drug binds. Chemical synapses are slower than electrical synapses.
== Neurotransmitter transporters, receptors, and signaling mechanisms ==
After neurotransmitters are synthesized, they are packaged and stored in vesicles. These vesicles are pooled together in terminal boutons of the presynaptic neuron. When there is a change in voltage in the terminal bouton, voltage-gated calcium channels embedded in the membranes of these boutons become activated. These allow Ca2+ ions to diffuse through these channels and bind with synaptic vesicles within the terminal boutons. Once bounded with Ca2+, the vesicles dock and fuse with the presynaptic membrane, and release neurotransmitters into the synaptic cleft by a process known as exocytosis. The neurotransmitters then diffuse across the synaptic cleft and bind to postsynaptic receptors embedded on the postsynaptic membrane of another neuron. There are two families of receptors: ionotropic and metabotropic receptors. Ionotropic receptors are a combination of a receptor and an ion channel. When ionotropic receptors are activated, certain ion species such as Na+ enter the postsynaptic neuron, which depolarizes the postsynaptic membrane. If more of the same type of postsynaptic receptors are activated, then more Na+ will enter the postsynaptic membrane and depolarize cell. Metabotropic receptors on the other hand activate second messenger cascade systems that result in the opening of ion channel located some place else on the same postsynaptic membrane. Although slower than ionotropic receptors that function as on-and-off switches, metabotropic receptors have the advantage of changing the cell's responsiveness to ions and other metabolites, examples being gamma amino-butyric acid (inhibitory transmitter), glutamic acid (excitatory transmitter), dopamine, norepinephrine, epinephrine, melanin, serotonin, melatonin, endorphins, dynorphins, nociceptin, and substance P.
Postsynaptic depolarizations can either transmit excitatory or inhibitory neurotransmitters. Those that release excitatory vesicles are referred to as excitatory postsynaptic potential (EPSP). Alternatively, inhibitory vesicles stimulate postsynaptic receptors such as to allow Cl− ions to enter the cell or K+ ions to leave the cell, which results in an inhibitory postsynaptic potential (IPSP). If the EPSP is dominant, the threshold of excitation in the postsynaptic neuron may be reached, resulting in the generation of an action potential in the neuron(s) in turn postsynaptic to it, propagating the signal.
== Synaptic plasticity ==
Synaptic plasticity is the process whereby strengths of synaptic connections are altered. For example, long-term changes in synaptic connection may result in more postsynaptic receptors being embedded in the postsynaptic membrane, resulting in the strengthening of the synapse. Synaptic plasticity is also found to be the neural mechanism that underlies learning and memory. The basic properties, activity and regulation of membrane currents, synaptic transmission and synaptic plasticity, neurotransmission, neuroregensis, synaptogenesis and ion channels of cells are a few other fields studied by cellular neuroscientists. Tissue, cellular and subcellular anatomy are studied to provide insight into mental retardation at the Mental Retardation Research Center MRRC Cellular Neuroscience Core. Journals such as Frontiers in Cellular Neuroscience and Molecular and Cellular Neuroscience are published regarding cellular neuroscientific topics.
== See also ==
Action potential
Calcium concentration microdomains
Cell biology
Cell signaling
Chemical synapse
Dendrite
Hair cells
IKK2
Neuroendocrinology
Neuropharmacology
Pyramidal cells
Soliton model
Synaptotropic hypothesis
== References == | Wikipedia/Cellular_neuroscience |
The Morris–Lecar model is a biological neuron model developed by Catherine Morris and Harold Lecar to reproduce the variety of oscillatory behavior in relation to Ca++ and K+ conductance in the muscle fiber of the giant barnacle . Morris–Lecar neurons exhibit both class I and class II neuron excitability.
== History ==
Catherine Morris (b. 24 December 1949) is a Canadian biologist. She won a Commonwealth scholarship to study at Cambridge University, where she earned her PhD in 1977. She became a professor at the University of Ottawa in the early 1980s. As of 2015, she is an emeritus professor at the University of Ottawa. Harold Lecar (18 October 1935 – 4 February 2014) was an American professor of biophysics and neurobiology at the University of California Berkeley. He graduated with his PhD in physics from Columbia University in 1963.
== Experimental method ==
The Morris–Lecar experiments relied on the voltage clamp method established by Keynes et al. (1973).
Large specimens of the barnacle Balanus nubilus (Pacific Bio-Marine Laboratories Inc., Venice, California) were used. The barnacle was sawed into lateral halves, and the depressor scutorum rostralis muscles were carefully exposed. Individual fibers were dissected, the incision starting from the tendon. The other end of the muscle was cut close to its attachment on the shell and ligatured. Isolated fibers were either used immediately or kept for up to 30 min in standard artificial seawater (ASW; see below) before use. Experiments were carried out at room temperature of 22 C.
== The principal assumptions underlying the Morris–Lecar model ==
Among the principal assumptions are these:
Equations apply to a spatially iso-potential patch of membrane. There are two persistent (non-inactivating) voltage-gated currents with oppositively biased reversal potentials. The depolarizing current is carried by Na+ or Ca2+ ions (or both), depending on the system to be modeled, and the hyperpolarizing current is carried by K+.
Activation gates follow changes in membrane potential sufficiently rapidly that the activating conductance can instantaneously relax to its steady-state value at any voltage.
The dynamics of the recovery variable can be approximated by a first-order linear differential equation for the probability of channel opening.
== Physiological description ==
The Morris–Lecar model is a two-dimensional system of nonlinear differential equations. It is considered a simplified model compared to the four-dimensional Hodgkin–Huxley model.
Qualitatively, this system of equations describes the complex relationship between membrane potential and the activation of ion channels within the membrane: the potential depends on the activity of the ion channels, and the activity of the ion channels depends on the voltage. As bifurcation parameters are altered, different classes of neuron behavior are exhibited. τN is associated with the relative time scales of the firing dynamics, which varies broadly from cell to cell and exhibits significant temperature dependency.
Quantitatively:
C
d
V
d
t
=
I
−
g
L
(
V
−
V
L
)
−
g
C
a
M
s
s
(
V
−
V
C
a
)
−
g
K
N
(
V
−
V
K
)
d
N
d
t
=
N
s
s
−
N
τ
N
{\displaystyle {\begin{aligned}C{\frac {dV}{dt}}&~=~I-g_{\mathrm {L} }(V-V_{\mathrm {L} })-g_{\mathrm {Ca} }M_{\mathrm {ss} }(V-V_{\mathrm {Ca} })-g_{\mathrm {K} }N(V-V_{\mathrm {K} })\\[5pt]{\frac {dN}{dt}}&~=~{\frac {N_{\mathrm {ss} }-N}{\tau _{N}}}\end{aligned}}}
where
M
s
s
=
1
2
⋅
(
1
+
tanh
[
V
−
V
1
V
2
]
)
N
s
s
=
1
2
⋅
(
1
+
tanh
[
V
−
V
3
V
4
]
)
τ
N
=
1
/
(
φ
cosh
[
V
−
V
3
2
V
4
]
)
{\displaystyle {\begin{aligned}M_{\mathrm {ss} }&~=~{\frac {1}{2}}\cdot \left(1+\tanh \left[{\frac {V-V_{1}}{V_{2}}}\right]\right)\\[5pt]N_{\mathrm {ss} }&~=~{\frac {1}{2}}\cdot \left(1+\tanh \left[{\frac {V-V_{3}}{V_{4}}}\right]\right)\\[5pt]\tau _{N}&~=~1/\left(\varphi \cosh \left[{\frac {V-V_{3}}{2V_{4}}}\right]\right)\end{aligned}}}
Note that the Mss and Nss equations may also be expressed as Mss = (1 + exp[−2(V − V1) / V2])−1 and Nss = (1 + exp[−2(V − V3) / V4])−1, however most authors prefer the form using the hyperbolic functions.
=== Variables ===
V : membrane potential
N : recovery variable: the probability that the K+ channel is conducting
=== Parameters and constants ===
I : applied current
C : membrane capacitance
gL, gCa, gK : leak, Ca++, and K+ conductances through membranes channel
VL, VCa, VK : equilibrium potential of relevant ion channels
V1, V2, V3, V4: tuning parameters for steady state and time constant
φ: reference frequency
== Bifurcations ==
Bifurcation in the Morris–Lecar model have been analyzed with the applied current I, as the main bifurcation parameter and φ, gCa, V3, V4 as secondary parameters for phase plane analysis.
== See also ==
Computational neuroscience
Biological neuron model
Hodgkin–Huxley model
FitzHugh–Nagumo model
Neural oscillations
Nonlinear dynamics
Quantitative models of the action potential
== References ==
== External links ==
A Morris–Lecar simulator
Scholarpedia: Morris–Lecar Model
Catherine Morris – Research Profile | Wikipedia/Morris–Lecar_model |
The FitzHugh–Nagumo model (FHN) describes a prototype of an excitable system (e.g., a neuron).
It is an example of a relaxation oscillator because, if the external stimulus
I
ext
{\displaystyle I_{\text{ext}}}
exceeds a certain threshold value, the system will exhibit a characteristic excursion in phase space, before the variables
v
{\displaystyle v}
and
w
{\displaystyle w}
relax back to their rest values.
This behaviour is a sketch for neural spike generations, with a short, nonlinear elevation of membrane voltage
v
{\displaystyle v}
, diminished over time by a slower, linear recovery variable
w
{\displaystyle w}
representing sodium channel reactivation and potassium channel deactivation, after stimulation by an external input current.
The equations for this dynamical system read
v
˙
=
v
−
v
3
3
−
w
+
R
I
e
x
t
{\displaystyle {\dot {v}}=v-{\frac {v^{3}}{3}}-w+RI_{\rm {ext}}}
τ
w
˙
=
v
+
a
−
b
w
.
{\displaystyle \tau {\dot {w}}=v+a-bw.}
The FitzHugh–Nagumo model is a simplified 2D version of the Hodgkin–Huxley model which models in a detailed manner activation and deactivation dynamics of a spiking neuron.
In turn, the Van der Pol oscillator is a special case of the FitzHugh–Nagumo model, with
a
=
b
=
0
{\displaystyle a=b=0}
.
== History ==
It was named after Richard FitzHugh (1922–2007) who suggested the system in 1961 and Jinichi Nagumo et al. who created the equivalent circuit the following year.
In the original papers of FitzHugh, this model was called Bonhoeffer–Van der Pol oscillator (named after Karl-Friedrich Bonhoeffer and Balthasar van der Pol) because it contains the Van der Pol oscillator as a special case for
a
=
b
=
0
{\displaystyle a=b=0}
. The equivalent circuit was suggested by Jin-ichi Nagumo, Suguru Arimoto, and Shuji Yoshizawa.
== Qualitative analysis ==
Qualitatively, the dynamics of this system is determined by the relation between the three branches of the cubic nullcline and the linear nullcline.
The cubic nullcline is defined by
v
˙
=
0
↔
w
=
v
−
v
3
/
3
+
R
I
e
x
t
{\displaystyle {\dot {v}}=0\leftrightarrow w=v-v^{3}/3+RI_{ext}}
.
The linear nullcline is defined by
w
˙
=
0
↔
w
=
(
v
+
a
)
/
b
{\displaystyle {\dot {w}}=0\leftrightarrow w=(v+a)/b}
.
In general, the two nullclines intersect at one or three points, each of which is an equilibrium point. At large values of
v
2
+
w
2
{\displaystyle v^{2}+w^{2}}
, far from origin, the flow is a clockwise circular flow, consequently the sum of the index for the entire vector field is +1. This means that when there is one equilibrium point, it must be a clockwise spiral point or a node. When there are three equilibrium points, they must be two clockwise spiral points and one saddle point.
If the linear nullcline pierces the cubic nullcline from downwards then it is a clockwise spiral point or a node.
If the linear nullcline pierces the cubic nullcline from upwards in the middle branch, then it is a saddle point.
The type and stability of the index +1 can be numerically computed by computing the trace and determinant of its Jacobian:
(
t
r
,
det
)
=
(
1
−
b
/
τ
−
v
2
,
(
v
2
−
1
)
b
/
τ
+
1
/
τ
)
{\displaystyle (tr,\det )=(1-b/\tau -v^{2},(v^{2}-1)b/\tau +1/\tau )}
The point is stable iff the trace is negative. That is,
v
2
>
1
−
b
/
τ
{\displaystyle v^{2}>1-b/\tau }
.
The point is a spiral point iff
4
det
−
t
r
2
>
0
{\displaystyle 4\det -tr^{2}>0}
. That is,
(
τ
v
2
−
b
−
τ
)
2
<
4
τ
{\displaystyle (\tau v^{2}-b-\tau )^{2}<4\tau }
.
The limit cycle is born when a stable spiral point becomes unstable by Hopf bifurcation.
Only when the linear nullcline pierces the cubic nullcline at three points, the system has a separatrix, being the two branches of the stable manifold of the saddle point in the middle.
If the separatrix is a curve, then trajectories to the left of the separatrix converge to the left sink, and similarly for the right.
If the separatrix is a cycle around the left intersection, then trajectories inside the separatrix converge to the left spiral point. Trajectories outside the separatrix converge to the right sink. The separatrix itself is the limit cycle of the lower branch of the stable manifold for the saddle point in the middle. Similarly for the case where the separatrix is a cycle around the right intersection.
Between the two cases, the system undergoes a homoclinic bifurcation.
Gallery figures: FitzHugh-Nagumo model, with
a
=
0.7
,
τ
=
12.5
,
R
=
0.1
{\displaystyle a=0.7,\tau =12.5,R=0.1}
, and varying
b
,
I
e
x
t
{\displaystyle b,I_{ext}}
. (They are animated. Open them to see the animation.)
== See also ==
== References ==
== Further reading ==
== External links ==
FitzHugh–Nagumo model on Scholarpedia
Interactive FitzHugh-Nagumo. Java applet, includes phase space and parameters can be changed at any time.
Interactive FitzHugh–Nagumo in 1D. Java applet to simulate 1D waves propagating in a ring. Parameters can also be changed at any time.
Interactive FitzHugh–Nagumo in 2D. Java applet to simulate 2D waves including spiral waves. Parameters can also be changed at any time.
Java applet for two coupled FHN systems Options include time delayed coupling, self-feedback, noise induced excursions, data export to file. Source code available (BY-NC-SA license). | Wikipedia/FitzHugh–Nagumo_model |
A motor neuron (or motoneuron), also known as efferent neuron is a neuron whose cell body is located in the motor cortex, brainstem or the spinal cord, and whose axon (fiber) projects to the spinal cord or outside of the spinal cord to directly or indirectly control effector organs, mainly muscles and glands. There are two types of motor neuron – upper motor neurons and lower motor neurons. Axons from upper motor neurons synapse onto interneurons in the spinal cord and occasionally directly onto lower motor neurons. The axons from the lower motor neurons are efferent nerve fibers that carry signals from the spinal cord to the effectors. Types of lower motor neurons are alpha motor neurons, beta motor neurons, and gamma motor neurons.
A single motor neuron may innervate many muscle fibres and a muscle fibre can undergo many action potentials in the time taken for a single muscle twitch. Innervation takes place at a neuromuscular junction and twitches can become superimposed as a result of summation or a tetanic contraction. Individual twitches can become indistinguishable, and tension rises smoothly eventually reaching a plateau.
Although the word "motor neuron" suggests that there is a single kind of neuron that controls movement, this is not the case. Indeed, upper and lower motor neurons—which differ greatly in their origins, synapse locations, routes, neurotransmitters, and lesion characteristics—are included in the same classification as "motor neurons." Essentially, motor neurons, also known as motoneurons, are made up of a variety of intricate, finely tuned circuits found throughout the body that innervate effector muscles and glands to enable both voluntary and involuntary motions. Two motor neurons come together to form a two-neuron circuit. While lower motor neurons start in the spinal cord and go to innervate muscles and glands all throughout the body, upper motor neurons originate in the cerebral cortex and travel to the brain stem or spinal cord. It is essential to comprehend the distinctions between upper and lower motor neurons as well as the routes they follow in order to effectively detect these neuronal injuries and localise the lesions.
== Development ==
Motor neurons begin to develop early in embryonic development, and motor function continues to develop well into childhood. In the neural tube cells are specified to either the rostral-caudal axis or ventral-dorsal axis. The axons of motor neurons begin to appear in the fourth week of development from the ventral region of the ventral-dorsal axis (the basal plate). This homeodomain is known as the motor neural progenitor domain (pMN). Transcription factors here include Pax6, OLIG2, Nkx-6.1, and Nkx-6.2, which are regulated by sonic hedgehog (Shh). The OLIG2 gene being the most important due to its role in promoting Ngn2 expression, a gene that causes cell cycle exiting as well as promoting further transcription factors associated with motor neuron development.
Further specification of motor neurons occurs when retinoic acid, fibroblast growth factor, Wnts, and TGFb, are integrated into the various Hox transcription factors. There are 13 Hox transcription factors and along with the signals, determine whether a motor neuron will be more rostral or caudal in character. In the spinal column, Hox 4-11 sort motor neurons to one of the five motor columns.
== Anatomy and physiology ==
=== Upper motor neurons ===
Upper motor neurons originate in the motor cortex located in the precentral gyrus. The cells that make up the primary motor cortex are Betz cells, which are giant pyramidal cells. The axons of these cells descend from the cortex to form the corticospinal tract. Corticomotorneurons project from the primary cortex directly onto motor neurons in the ventral horn of the spinal cord. Their axons synapse on the spinal motor neurons of multiple muscles as well as on spinal interneurons. They are unique to primates and it has been suggested that their function is the adaptive control of the hands including the relatively independent control of individual fingers. Corticomotorneurons have so far only been found in the primary motor cortex and not in secondary motor areas.
=== Nerve tracts ===
Nerve tracts are bundles of axons as white matter, that carry action potentials to their effectors. In the spinal cord these descending tracts carry impulses from different regions. These tracts also serve as the place of origin for lower motor neurons. There are seven major descending motor tracts to be found in the spinal cord:
Lateral corticospinal tract
Rubrospinal tract
Lateral reticulospinal tract
Vestibulospinal tract
Medial reticulospinal tract
Tectospinal tract
Anterior corticospinal tract
=== Lower motor neurons ===
Lower motor neurons are those that originate in the spinal cord and directly or indirectly innervate effector targets. The target of these neurons varies, but in the somatic nervous system the target will be some sort of muscle fiber. There are three primary categories of lower motor neurons, which can be further divided in sub-categories.
According to their targets, motor neurons are classified into three broad categories:
Somatic motor neurons
Special visceral motor neurons
General visceral motor neurons
==== Somatic motor neurons ====
Somatic motor neurons originate in the central nervous system, project their axons to skeletal muscles (such as the muscles of the limbs, abdominal, and intercostal muscles), which are involved in locomotion. The three types of these neurons are the alpha efferent neurons, beta efferent neurons, and gamma efferent neurons. They are called efferent to indicate the flow of information from the central nervous system (CNS) to the periphery.
Alpha motor neurons innervate extrafusal muscle fibers, which are the main force-generating component of a muscle. Their cell bodies are in the ventral horn of the spinal cord and they are sometimes called ventral horn cells. A single motor neuron may synapse with 150 muscle fibers on average. The motor neuron and all of the muscle fibers to which it connects is a motor unit. Motor units are split up into 3 categories:
Slow (S) motor units stimulate small muscle fibers, which contract very slowly and provide small amounts of energy but are very resistant to fatigue, so they are used to sustain muscular contraction, such as keeping the body upright. They gain their energy via oxidative means and hence require oxygen. They are also called red fibers.
Fast fatiguing (FF) motor units stimulate larger muscle groups, which apply large amounts of force but fatigue very quickly. They are used for tasks that require large brief bursts of energy, such as jumping or running. They gain their energy via glycolytic means and hence do not require oxygen. They are called white fibers.
Fast fatigue-resistant motor units stimulate moderate-sized muscles groups that do not react as fast as the FF motor units, but can be sustained much longer (as implied by the name) and provide more force than S motor units. These use both oxidative and glycolytic means to gain energy.
In addition to voluntary skeletal muscle contraction, alpha motor neurons also contribute to muscle tone, the continuous force generated by noncontracting muscle to oppose stretching. When a muscle is stretched, sensory neurons within the muscle spindle detect the degree of stretch and send a signal to the CNS. The CNS activates alpha motor neurons in the spinal cord, which cause extrafusal muscle fibers to contract and thereby resist further stretching. This process is also called the stretch reflex.
Beta motor neurons innervate intrafusal muscle fibers of muscle spindles, with collaterals to extrafusal fibres. There are two types of beta motor neurons: Slow Contracting- These innervate extrafusal fibers. Fast Contracting- These innervate intrafusal fibers.
Gamma motor neurons innervate intrafusal muscle fibers found within the muscle spindle. They regulate the sensitivity of the spindle to muscle stretching. With activation of gamma neurons, intrafusal muscle fibers contract so that only a small stretch is required to activate spindle sensory neurons and the stretch reflex. There are two types of gamma motor neurons: Dynamic- These focus on Bag1 fibers and enhance dynamic sensitivity. Static- These focus on Bag2 fibers and enhance stretch sensitivity.
Regulatory factors of lower motor neurons
Size Principle – this relates to the soma of the motor neuron. This restricts larger neurons to receive a larger excitatory signal in order to stimulate the muscle fibers it innervates. By reducing unnecessary muscle fiber recruitment, the body is able to optimize energy consumption.
Persistent Inward Current (PIC) – recent animal study research has shown that constant flow of ions such as calcium and sodium through channels in the soma and dendrites influence the synaptic input. An alternate way to think of this is that the post-synaptic neuron is being primed before receiving an impulse.
After Hyper-polarization (AHP) – A trend has been identified that shows slow motor neurons to have more intense AHPs for a longer duration. One way to remember this is that slow muscle fibers can contract for longer, so it makes sense that their corresponding motor neurons fire at a slower rate.
==== Special visceral motor neurons ====
These are also known as branchial motor neurons, which are involved in facial expression, mastication, phonation, and swallowing. Associated cranial nerves are the oculomotor, abducens, trochlear, and hypoglossal nerves.
==== General visceral motor neurons ====
These motor neurons indirectly innervate cardiac muscle and smooth muscles of the viscera ( the muscles of the arteries): they synapse onto neurons located in ganglia of the autonomic nervous system (sympathetic and parasympathetic), located in the peripheral nervous system (PNS), which themselves directly innervate visceral muscles (and also some gland cells).
In consequence, the motor command of skeletal and branchial muscles is monosynaptic involving only one motor neuron, either somatic or branchial, which synapses onto the muscle. Comparatively, the command of visceral muscles is disynaptic involving two neurons: the general visceral motor neuron, located in the CNS, synapses onto a ganglionic neuron, located in the PNS, which synapses onto the muscle.
All vertebrate motor neurons are cholinergic, that is, they release the neurotransmitter acetylcholine. Parasympathetic ganglionic neurons are also cholinergic, whereas most sympathetic ganglionic neurons are noradrenergic, that is, they release the neurotransmitter noradrenaline. (see Table)
=== Neuromuscular junctions ===
A single motor neuron may innervate many muscle fibres and a muscle fibre can undergo many action potentials in the time taken for a single muscle twitch. As a result, if an action potential arrives before a twitch has completed, the twitches can superimpose on one another, either through summation or a tetanic contraction. In summation, the muscle is stimulated repetitively such that additional action potentials coming from the somatic nervous system arrive before the end of the twitch. The twitches thus superimpose on one another, leading to a force greater than that of a single twitch. A tetanic contraction is caused by constant, very high frequency stimulation - the action potentials come at such a rapid rate that individual twitches are indistinguishable, and tension rises smoothly eventually reaching a plateau.
The interface between a motor neuron and muscle fiber is a specialized synapse called the neuromuscular junction. Upon adequate stimulation, the motor neuron releases a flood of acetylcholine (Ach) neurotransmitters from synaptic vesicles bound to the plasma membrane of the axon terminals. The acetylcholine molecules bind to postsynaptic receptors found within the motor end plate. Once two acetylcholine receptors have been bound, an ion channel is opened and sodium ions are allowed to flow into the cell. The influx of sodium into the cell causes depolarization and triggers a muscle action potential. T tubules of the sarcolemma are then stimulated to elicit calcium ion release from the sarcoplasmic reticulum. It is this chemical release that causes the target muscle fiber to contract.
In invertebrates, depending on the neurotransmitter released and the type of receptor it binds, the response in the muscle fiber could be either excitatory or inhibitory. For vertebrates, however, the response of a muscle fiber to a neurotransmitter can only be excitatory, in other words, contractile. Muscle relaxation and inhibition of muscle contraction in vertebrates is obtained only by inhibition of the motor neuron itself. This is how muscle relaxants work by acting on the motor neurons that innervate muscles (by decreasing their electrophysiological activity) or on cholinergic neuromuscular junctions, rather than on the muscles themselves.
=== Synaptic input to motor neurons ===
Motor neurons receive synaptic input from premotor neurons. Premotor neurons can be 1) spinal interneurons that have cell bodies in the spinal cord, 2) sensory neurons that convey information from the periphery and synapse directly onto motoneurons, 3) descending neurons that convey information from the brain and brainstem. The synapses can be excitatory, inhibitory, electrical, or neuromodulatory. For any given motor neuron, determining the relative contribution of different input sources is difficult, but advances in connectomics have made it possible for fruit fly motor neurons. In the fly, motor neurons controlling the legs and wings are found in the ventral nerve cord, homologous to the spinal cord. Fly motor neurons vary by over 100X in the total number of input synapses. However, each motor neuron gets similar fractions of its synapses from each premotor source: ~70% from neurons within the VNC, ~10% from descending neurons, ~3% from sensory neurons, and ~6% from VNC neurons that also send a process up to the brain. The remaining 10% of synapses come from neuronal fragments that are unidentified by current image segmentation algorithms and require additional manual segmentation to measure.
== See also ==
Betz cell
Central chromatolysis
Motor dysfunction
Motor neuron disease
Nerve
Sensory nerve
Motor nerve
Afferent nerve fiber
Efferent nerve fiber
Sensory neuron
== References ==
== Sources ==
Sherwood, L. (2001). Human Physiology: From Cells to Systems (4th ed.). Pacific Grove, CA: Brooks-Cole. ISBN 0-534-37254-6.
Marieb, E. N.; Mallatt, J. (1997). Human Anatomy (2nd ed.). Menlo Park, CA: Benjamin/Cummings. ISBN 0-8053-4068-8. | Wikipedia/Motor_neuron |
Sensory neuroscience is a subfield of neuroscience which explores the anatomy and physiology of neurons that are part of sensory systems such as vision, hearing, and olfaction. Neurons in sensory regions of the brain respond to stimuli by firing one or more nerve impulses (action potentials) following stimulus presentation. How is information about the outside world encoded by the rate, timing, and pattern of action potentials? This so-called neural code is currently poorly understood and sensory neuroscience plays an important role in the attempt to decipher it. Looking at early sensory processing is advantageous since brain regions that are "higher up" (e.g. those involved in memory or emotion) contain neurons which encode more abstract representations. However, the hope is that there are unifying principles which govern how the brain encodes and processes information. Studying sensory systems is an important stepping stone in our understanding of brain function in general.
== Typical experiments ==
A typical experiment in sensory neuroscience involves the presentation of a series of relevant stimuli to an experimental subject while the subject's brain is being monitored. This monitoring can be accomplished by noninvasive means such as functional magnetic resonance imaging (fMRI) or electroencephalography (EEG), or by more invasive means such as electrophysiology, the use of electrodes to record the electrical activity of single neurons or groups of neurons. fMRI measures changes in blood flow which related to the level of neural activity and provides low spatial and temporal resolution, but does provide data from the whole brain. In contrast,
Electrophysiology provides very high temporal resolution (the shapes of single spikes can be resolved) and data can be obtained from single cells. This is important since computations are performed within the dendrites of individual neurons.
=== Single neuron experiments ===
In most of the central nervous system, neurons communicate exclusively by sending each other action potentials, colloquially known as "spikes". It is therefore thought that all of the information a sensory neuron encodes about the outside world can be inferred by the pattern of its spikes. Current experimental techniques cannot measure individual spikes noninvasively. Such encoding includes the Dual Process Theory and how we think in a conscious and unconscious manner.
A typical single neuron experiment will consist of isolating a neuron (that is, navigating the neuron until the experimenter finds a neuron which spikes in response to the type of stimulus to be presented, and (optionally) determining that all of the spikes observed indeed come from a single neuron), then presenting a stimulus protocol. Because neural responses are inherently variable (that is, their spiking pattern may depend on more than just the stimulus which is presented, although not all of this variability may be true noise, since factors other than the presented stimulus may affect the sensory neuron under study), often the same stimulus protocol is repeated many times to get a feel for the variability a neuron may have. One common analysis technique is to study the neuron's average time-varying firing rate, called its post stimulus time histogram or PSTH.
== Receptive field estimation ==
One major goal of sensory neuroscience is to try to estimate the neuron's receptive field; that is, to try to determine which stimuli cause the neuron to fire in what ways. One common way to find the receptive field is to use linear regression to find which stimulus characteristics typically caused neurons to become excited or depressed. Since the receptive field of a sensory neuron can vary in time (i.e. latency between the stimulus and the effect it has on the neuron) and in some spatial dimension (literally space for vision and somatosensory cells, but other "spatial" dimensions such as the frequency of a sound for auditory neurons), the term spatio temporal receptive field or STRF is often used to describe these receptive fields.
=== Natural stimuli ===
One recent trend in sensory neuroscience has been the adoption of natural stimuli for the characterization of sensory neurons. There is good reason to believe that there has been evolutionary pressure on sensory systems to be able to represent natural stimuli well, so sensory systems may exhibit the most relevant behaviour in response to natural stimuli. The adoption of natural stimuli in sensory neuroscience has been slowed by the fact that the mathematical descriptions of natural stimuli tend to be more complex than of simplified artificial stimuli such as simple tones or clicks in audition or line patterns in vision. Free software is now available to help neuroscientists interested in estimating receptive fields cope with the difficulty of using natural stimuli.
Sensory neuroscience is also used as a bottom-up approach to studying consciousness. For example, visual sense and representation has been studied by Crick and Koch (1998), and experiments have been suggested in order to test various hypotheses in this research stream.
== See also ==
Efficient coding hypothesis
Multisensory integration
== References == | Wikipedia/Sensory_neuroscience |
In neuroscience, functional specialization is a theory which suggests that different areas in the brain are specialized for different functions. It is opposed to the anti-localizationist theories and brain holism and equipotentialism.
== Historical origins ==
Phrenology, created by Franz Joseph Gall (1758–1828) and Johann Gaspar Spurzheim (1776–1832) and best known for the idea that one's personality could be determined by the variation of bumps on their skull, proposed that different regions in one's brain have different functions and may very well be associated with different behaviours. Gall and Spurzheim were the first to observe the crossing of pyramidal tracts, thus explaining why lesions in one hemisphere are manifested in the opposite side of the body. However, Gall and Spurzheim did not attempt to justify phrenology on anatomical grounds. It has been argued that phrenology was fundamentally a science of race. Gall considered the most compelling argument in favor of phrenology the differences in skull shape found in sub-Saharan Africans and the anecdotal evidence (due to scientific travelers and colonists) of their intellectual inferiority and emotional volatility. In Italy, Luigi Rolando carried out lesion experiments and performed electrical stimulation of the brain, including the Rolandic area.
Phineas Gage became one of the first lesion case studies in 1848 when an explosion drove a large iron rod completely through his head, destroying his left frontal lobe. He recovered with no apparent sensory, motor, or gross cognitive deficits, but with behaviour so altered that friends described him as "no longer being Gage," suggesting that the damaged areas are involved in "higher functions" such as personality. However, Gage's mental changes are usually grossly exaggerated in modern presentations.
Subsequent cases (such as Broca's patient Tan) gave further support to the doctrine of specialization.
In the XX century, in the process of treating epilepsy, Wilder Penfield produced maps of the location of various functions (motor, sensory, memory, vision) in the brain.
== Major theories of the brain ==
Currently, there are two major theories of the brain's cognitive function. The first is the theory of modularity. Stemming from phrenology, this theory supports functional specialization, suggesting the brain has different modules that are domain specific in function. The second theory, distributive processing, proposes that the brain is more interactive and its regions are functionally interconnected rather than specialized. Each orientation plays a role within certain aims and tend to complement each other (see below section `Collaboration´).
=== Modularity ===
The theory of modularity suggests that there are functionally specialized regions in the brain that are domain specific for different cognitive processes. Jerry Fodor expanded the initial notion of phrenology by creating his Modularity of the Mind theory. The Modularity of the Mind theory indicates that distinct neurological regions called modules are defined by their functional roles in cognition. He also rooted many of his concepts on modularity back to philosophers like Descartes, who wrote about the mind being composed of "organs" or "psychological faculties". An example of Fodor's concept of modules is seen in cognitive processes such as vision, which have many separate mechanisms for colour, shape and spatial perception.
One of the fundamental beliefs of domain specificity and the theory of modularity suggests that it is a consequence of natural selection and is a feature of our cognitive architecture. Researchers Hirschfeld and Gelman propose that because the human mind has evolved by natural selection, it implies that enhanced functionality would develop if it produced an increase in "fit" behaviour. Research on this evolutionary perspective suggests that domain specificity is involved in the development of cognition because it allows one to pinpoint adaptive problems.
An issue for the modular theory of cognitive neuroscience is that there are cortical anatomical differences from person to person. Although many studies of modularity are undertaken from very specific lesion case studies, the idea is to create a neurological function map that applies to people in general. To extrapolate from lesion studies and other case studies this requires adherence to the universality assumption, that there is no difference, in a qualitative sense, between subjects who are intact neurologically. For example, two subjects would fundamentally be the same neurologically before their lesions, and after have distinctly different cognitive deficits. Subject 1 with a lesion in the "A" region of the brain may show impaired functioning in cognitive ability "X" but not "Y", while subject 2 with a lesion in area "B" demonstrates reduced "Y" ability but "X" is unaffected; results like these allow inferences to be made about brain specialization and localization, also known as using a double dissociation.
The difficulty with this theory is that in typical non-lesioned subjects, locations within the brain anatomy are similar but not completely identical. There is a strong defense for this inherent deficit in our ability to generalize when using functional localizing techniques (fMRI, PET etc.). To account for this problem, the coordinate-based Talairach and Tournoux stereotaxic system is widely used to compare subjects' results to a standard brain using an algorithm. Another solution using coordinates involves comparing brains using sulcal reference points. A slightly newer technique is to use functional landmarks, which combines sulcal and gyral landmarks (the groves and folds of the cortex) and then finding an area well known for its modularity such as the fusiform face area. This landmark area then serves to orient the researcher to the neighboring cortex.
Future developments for modular theories of neuropsychology may lie in "modular psychiatry". The concept is that a modular understanding of the brain and advanced neuro-imaging techniques will allow for a more empirical diagnosis of mental and emotional disorders. There has been some work done towards this extension of the modularity theory with regards to the physical neurological differences in subjects with depression and schizophrenia, for example. Zielasek and Gaeble have set out a list of requirements in the field of neuropsychology in order to move towards neuropsychiatry:
To assemble a complete overview of putative modules of the human mind
To establish module-specific diagnostic tests (specificity, sensitivity, reliability)
To assess how far individual modules, sets of modules or their connections are affected in certain psychopathological situations
To probe novel module-specific therapies like the facial affect recognition training or to retrain access to context information in the case of delusions and hallucinations, in which "hyper-modularity" may play a role
Research in the study of brain function can also be applied to cognitive behaviour therapy. As therapy becomes increasingly refined, it is important to differentiate cognitive processes in order to discover their relevance towards different patient treatments. An example comes specifically from studies on lateral specialization between the left and right cerebral hemispheres of the brain. The functional specialization of these hemispheres are offering insight on different forms of cognitive behaviour therapy methods, one focusing on verbal cognition (the main function of the left hemisphere) and the other emphasizing imagery or spatial cognition (the main function of the right hemisphere). Examples of therapies that involve imagery, requiring right hemisphere activity in the brain, include systematic desensitization and anxiety management training. Both of these therapy techniques rely on the patient's ability to use visual imagery to cope with or replace patients symptoms, such as anxiety. Examples of cognitive behaviour therapies that involve verbal cognition, requiring left hemisphere activity in the brain, include self-instructional training and stress inoculation. Both of these therapy techniques focus on patients' internal self-statements, requiring them to use vocal cognition. When deciding which cognitive therapy to employ, it is important to consider the primary cognitive style of the patient. Many individuals have a tendency to prefer visual imagery over verbalization and vice versa. One way of figuring out which hemisphere a patient favours is by observing their lateral eye movements. Studies suggest that eye gaze reflects the activation of cerebral hemisphere contralateral to the direction. Thus, when asking questions that require spatial thinking, individuals tend to move their eyes to the left, whereas when asked questions that require verbal thinking, individuals usually move their eyes to the right. In conclusion, this information allows one to choose the optimal cognitive behaviour therapeutic technique, thereby enhancing the treatment of many patients.
==== Areas representing modularity in the brain ====
===== Fusiform face area =====
One of the most well known examples of functional specialization is the fusiform face area (FFA). Justine Sergent was one of the first researchers that brought forth evidence towards the functional neuroanatomy of face processing. Using positron emission tomography (PET), Sergent found that there were different patterns of activation in response to the two different required tasks, face processing verses object processing. These results can be linked with her studies of brain-damaged patients with lesions in the occipital and temporal lobes. Patients revealed that there was an impairment of face processing but no difficulty recognizing everyday objects, a disorder also known as prosopagnosia. Later research by Nancy Kanwisher using functional magnetic resonance imaging (fMRI), found specifically that the region of the inferior temporal cortex, known as the fusiform gyrus, was significantly more active when subjects viewed, recognized and categorized faces in comparison to other regions of the brain. Lesion studies also supported this finding where patients were able to recognize objects but unable to recognize faces. This provided evidence towards domain specificity in the visual system, as Kanwisher acknowledges the Fusiform Face Area as a module in the brain, specifically the extrastriate cortex, that is specialized for face perception.
===== Visual area V4 and V5 =====
While looking at the regional cerebral blood flow (rCBF), using PET, researcher Semir Zeki directly demonstrated functional specialization within the visual cortex known as visual modularity, first in the monkey and then in the human visual brain. He localized regions involved specifically in the perception of colour and vision motion, as well as of orientation (form). For colour, visual area V4 was located when subjects were shown two identical displays, one being multicoloured and the other shades of grey. This was further supported from lesion studies where individuals were unable to see colours after damage, a disorder known as achromatopsia. Combining PET and magnetic resonance imaging (MRI), subjects viewing a moving checker board pattern verses a stationary checker board pattern located visual area V5, which is now considered to be specialized for vision motion. (Watson et al., 1993) This area of functional specialization was also supported by lesion study patients whose damage caused cerebral motion blindness, a condition now referred to as cerebral akinetopsia
===== Frontal lobes =====
Studies have found the frontal lobes to be involved in the executive functions of the brain, which are higher level cognitive processes. This control process is involved in the coordination, planning and organizing of actions towards an individual's goals. It contributes to such things as one's behaviour, language and reasoning. More specifically, it was found to be the function of the prefrontal cortex, and evidence suggest that these executive functions control processes such as planning and decision making, error correction and assisting overcoming habitual responses. Miller and Cummings used PET and functional magnetic imaging (fMRI) to further support functional specialization of the frontal cortex. They found lateralization of verbal working memory in the left frontal cortex and visuospatial working memory in the right frontal cortex. Lesion studies support these findings where left frontal lobe patients exhibited problems in controlling executive functions such as creating strategies. The dorsolateral, ventrolateral and anterior cingulate regions within the prefrontal cortex are proposed to work together in different cognitive tasks, which is related to interaction theories. However, there has also been evidence suggesting strong individual specializations within this network. For instance, Miller and Cummings found that the dorsolateral prefrontal cortex is specifically involved in the manipulation and monitoring of sensorimotor information within working memory.
===== Right and left hemispheres =====
During the 1960s, Roger Sperry conducted a natural experiment on epileptic patients who had previously had their corpora callosa cut. The corpus callosum is the area of the brain dedicated to linking both the right and left hemisphere together. Sperry et al.'s experiment was based on flashing images in the right and left visual fields of his participants. Because the participant's corpus callosum was cut, the information processed by each visual field could not be transmitted to the other hemisphere. In one experiment, Sperry flashed images in the right visual field (RVF), which would subsequently be transmitted to the left hemisphere (LH) of the brain. When asked to repeat what they had previously seen, participants were fully capable of remembering the image flashed. However, when the participants were then asked to draw what they had seen, they were unable to. When Sperry et al. flashed images in the left visual field (LVF), the information processed would be sent to the right hemisphere (RH) of the brain. When asked to repeat what they had previously seen, participants were unable to recall the image flashed, but were very successful in drawing the image. Therefore, Sperry concluded that the left hemisphere of the brain was dedicated to language as the participants could clearly speak the image flashed. On the other hand, Sperry concluded that the right hemisphere of the brain was involved in more creative activities such as drawing.
===== Parahippocampal place area =====
Located in the parahippocampal gyrus, the parahippocampal place area (PPA) was coined by Nancy Kanwisher and Russell Epstein after an fMRI study showed that the PPA responds optimally to scenes presented containing a spatial layout, minimally to single objects and not at all to faces. It was also noted in this experiment that activity remains the same in the PPA when viewing a scene with an empty room or a room filled with meaningful objects. Kanwisher and Epstein proposed "that the PPA represents places by encoding the geometry of the local environment". In addition, Soojin Park and Marvin Chun posited that activation in the PPA is viewpoint specific, and so responds to changes in the angle of the scene. In contrast, another special mapping area, the retrosplenial cortex (RSC), is viewpoint invariant or does not change response levels when views change. This perhaps indicates a complementary arrangement of functionally and anatomically separate visual processing brain areas.
===== Extrastriate body area =====
Located in the lateral occipitotemporal cortex, fMRI studies have shown the extrastriate body area (EBA) to have selective responding when subjects see human bodies or body parts, implying that it has functional specialization. The EBA does not optimally respond to objects or parts of objects but to human bodies and body parts, a hand for example. In fMRI experiments conducted by Downing et al. participants were asked to look at a series of pictures. These stimuli includes objects, parts of objects (for example just the head of a hammer), figures of the human body in all sorts of positions and types of detail (including line drawings or stick men), and body parts (hands or feet) without any body attached. There was significantly more blood flow (and thus activation) to human bodies, no matter how detailed, and body parts than to objects or object parts.
=== Distributive processing ===
The cognitive theory of distributed processing suggests that brain areas are highly interconnected and process information in a distributed manner.
A remarkable precedent of this orientation is the research of Justo Gonzalo on brain dynamics where several phenomena that he observed could not be explained by the traditional theory of localizations. From the gradation he observed between different syndromes in patients with different cortical lesions, this author proposed in 1952 a functional gradients model, which permits an ordering and an interpretation of multiple phenomena and syndromes. The functional gradients are continuous functions through the cortex describing a distributed specificity, so that, for a given sensory system, the specific gradient, of contralateral character, is maximum in the corresponding projection area and decreases in gradation towards more "central" zone and beyond so that the final decline reaches other primary areas.
As a consequence of the crossing and overlapping of the specific gradients, in the central zone where the overlap is greater, there would be an action of mutual integration, rather nonspecific (or multisensory) with bilateral character due to the corpus callosum. This action would be maximum in the central zone and minimal towards the projection areas. As the author stated (p. 20 of the English translation) "a functional continuity with regional variation is then offered, each point of the cortex acquiring different properties but with certain unity with the rest of the cortex. It is a dynamic conception of quantitative localizations".
A very similar gradients scheme was proposed by Elkhonon Goldberg in 1989
Other researchers who provide evidence to support the theory of distributive processing include Anthony McIntosh and William Uttal, who question and debate localization and modality specialization within the brain.
McIntosh's research suggests that human cognition involves interactions between the brain regions responsible for processes sensory information, such as vision, audition, and other mediating areas like the prefrontal cortex. McIntosh explains that modularity is mainly observed in sensory and motor systems, however, beyond these very receptors, modularity becomes "fuzzier" and you see the cross connections between systems increase. He also illustrates that there is an overlapping of functional characteristics between the sensory and motor systems, where these regions are close to one another. These different neural interactions influence each other, where activity changes in one area influence other connected areas. With this, McIntosh suggest that if you only focus on activity in one area, you may miss the changes in other integrative areas. Neural interactions can be measured using analysis of covariance in neuroimaging. McIntosh used this analysis to convey a clear example of the interaction theory of distributive processing. In this study, subjects learned that an auditory stimulus signalled a visual event. McIntosh found activation (an increase blood flow), in an area of the occipital cortex, a region of the brain involved in visual processing, when the auditory stimulus was presented alone. Correlations between the occipital cortex and different areas of the brain such as the prefrontal cortex, premotor cortex and superior temporal cortex showed a pattern of co-variation and functional connectivity.
Uttal focusses on the limits of localizing cognitive processes in the brain. One of his main arguments is that since the late 1990s, research in cognitive neuroscience has forgotten about conventional psychophysical studies based on behavioural observation. He believes that current research focusses on the technological advances of brain imaging techniques such as MRI and PET scans. Thus, he further suggest that this research is dependent on the assumptions of localization and hypothetical cognitive modules that use such imaging techniques to pursuit these assumptions. Uttal's major concern incorporates many controversies with the validly, over-assumptions and strong inferences some of these images are trying to illustrate. For instance, there is concern over the proper utilization of control images in an experiment. Most of the cerebrum is active during cognitive activity, therefore the amount of increased activity in a region must be greater when compared to a controlled area. In general, this may produce false or exaggerated findings and may increase potential tendency to ignore regions of diminished activity which may be crucial to the particular cognitive process being studied. Moreover, Uttal believes that localization researchers tend to ignore the complexity of the nervous system. Many regions in the brain are physically interconnected in a nonlinear system, hence, Uttal believes that behaviour is produced by a variety of system organizations.
=== Collaboration ===
The two theories, modularity and distributive processing, can also be combined. By operating simultaneously, these principles may interact with each other in a collaborative effort to characterize the functioning of the brain. Fodor himself, one of the major contributors to the modularity theory, appears to have this sentiment. He noted that modularity is a matter of degrees, and that the brain is modular to the extent that it warrants studying it in regards to its functional specialization. Although there are areas in the brain that are more specialized for cognitive processes than others, the nervous system also integrates and connects the information produced in these regions. In fact, the proposed distributive scheme of the functional cortical gradientes by J. Gonzalo already tries to join both concepts modular and distributive: regional heterogeneity should be a definitive acquisition (maximum specificity in the projection paths and primary areas), but the rigid separation between projection and association areas would be erased through the continuous functions of gradient.
The collaboration between the two theories not only would provide a more unified perception and understanding of the world but also make available the ability to learn from it.
== See also ==
Neural processing for individual categories of objects
Grandmother cell
Modularity of mind
== References == | Wikipedia/Functional_specialization_(brain) |
Integrative neuroscience is the study of neuroscience that works to unify functional organization data to better understand complex structures and behaviors. The relationship between structure and function, and how the regions and functions connect to each other. Different parts of the brain carrying out different tasks, interconnecting to come together allowing complex behavior. Integrative neuroscience works to fill gaps in knowledge that can largely be accomplished with data sharing, to create understanding of systems, currently being applied to simulation neuroscience: Computer Modeling of the brain that integrates functional groups together.
== Overview ==
The roots of integrative neuroscience originated from the Rashevsky-Rosen school of relational biology that characterizes functional organization mathematically by abstracting away the structure (i.e., physics and chemistry). It was further expanded by Chauvet who introduced hierarchical and functional integration.
Hierarchical integration is structural involving spatiotemporal dynamic continuity in Euclidean space to bring about functional organization, viz.
Hierarchical organization + Hierarchical integration = Functional organization
However, functional integration is relational and as such this requires a topology not restricted to Euclidean space, but rather occupying vector spaces This means that for any given functional organization the methods of functional analysis enable a relational organization to be mapped by the functional integration, viz.
Functional organization + Functional integration = Relational Organization
Thus hierarchical and functional integration entails a "neurobiology of cognitive semantics" where hierarchical organization is associated with the neurobiology and relational organization is associated with the cognitive semantics. Relational organization throws away the matter; "function dictates structure", hence material aspects are entailed, while in reductionism the causal nexus between structure and dynamics entails function that obviates functional integration because the causal entailment in the brain of hierarchical integration is absent from the structure.
If integrative neuroscience is studied from the viewpoint of functional organization of hierarchical levels then it is defined as causal entailment in the brain of hierarchical integration. If it is studied from the viewpoint of relational organization then it is defined as semantic entailment in the brain of functional integration.
It aims to present studies of functional organization of particular brain systems across scale through hierarchical integration leading to species-typical behaviors under normal and pathological states. As such, integrative neuroscience aims for a unified understanding of brain function across scale.
Spivey's continuity of mind thesis extends integrative neuroscience to the domain of continuity psychology.
== Motivation ==
With data building up, it ends up in its respective specializations with very little overlap. With the creation of a standardized integrated database of neuroscience data, lead to statical models that would otherwise not be possible, for example, understanding and treating psychiatric disorders.
It provides a framework for linking the great diversity of specializations within contemporary neuroscience, including
Molecular neuroscience – genetic and cellular aspects of brain function
Neuroanatomy – connections, networks, neurotransmitter systems
Behavioral neuroscience – the overt consequences of neural activity
Systems neuroscience – description of sensory and motors systems
Developmental neuroscience – structural and functional changes during maturation
Cognitive neuroscience – channels and stages of sensory processing, including memory
Mathematical neuroscience – quantitative simulation and emulation of neuronal and brain function
Clinical observations – evidence that can be gleaned from brain dysfunction
This diversity is inevitable, yet has arguably created a void: neglect of the primary role of the nervous system in enabling the animal to survive and prosper. Integrative neuroscience aims to fill this perceived void.
== Experimental methods ==
Identifying different brain regions through correlation and causal methods, combine to contribute an overall brain function and location map. Using different data collected from different methods combine to create a better interconnected and integrative understanding of the brain.
=== Correlation ===
The relationship between brain states and behavioral states. Observed through spatial and temporal differences. That pin point places in the brain affected by an action or stimuli, and the timing of the response. Tools used for this include fMRI and EEG, more information below.
==== Functional magnetic resonance imaging ====
Functional magnetic resonance imaging (fMRI) measures blood oxygen dependent response (BOLD), using magnetic resonance to observe blood oxygenated areas. Active areas are associated with increased blood flow, presenting a correlation relationship. The spatial localization of fMRI allows accurate information down to the nuclei and Brodmann areas. Certain activities such as the visual system are so rapid lasting only fractions of seconds, while other brain functions can take days or months such as memory. fMRI measures in the frame of seconds, making it difficult to measure extremely fast processes.
==== Electroencephalography ====
Electroencephalography (EEG) allows you to see the electrical activity of the brain over time, can only measure presented stimuli responses, stimuli the experimenter presents. it uses electrode sensors places on the surface on the skull to measure synchronous neuron firing. It can not be certain activity is caused by stimuli only a correlation between a given function and brain area. EEG measures overall changes in wide regions, lacking specificity.
=== Causal ===
Brain activity is directly caused by stimulation of a specific region, as proven through experimentation.
==== TMS ====
TMS (Transcranial magnetic stimulation) uses a magnetic coil releasing a burst of magnetic field that activated activity in a specific brain area. It is useful in exciting a specific area in the cortex and recording the MEPs (Motor Evoked Potentials) that occurs as a result. It gives certain causal relationships, but is limited to the cortex making it impossible to reach any deeper than the surface of the brain.
==== Lesions studies ====
When patients have natural lesions, it is an opportunity to watch how a lesion in a given region affects functionality. Or in non-human experimentation, lesions can be created by removing sections of the brain. These methods are not reversible, unlike brain studying techniques, and does not accurately show what that section of the brain are disabled due to the disruption of homeostasis in the brain. With a permeate lesion, the brain chemically adjusted and restores homeostasis Relying on natural occurrences has little control over variables such as location and size. And in cases with damage in multiple areas, differentiation is not certain with lack of mass data.
==== Electrode stimulation ====
Cortical Stimulation Mapping, invasive brain surgery that probes at area of the cortex to relate different regions to function. Typically occurs during open brain surgery where electrodes are inserted in areas and observations are made. This method is limited by number of patients having open brain surgery that consent to such experimentation, and to what area of the brain is being operated on. Also performed in mice with full range over the brain.
== Applications ==
=== Human Brain Project ===
Since the 'decade of the brain' there has been an explosion of insights into the brain and their application in most areas of medicine. With this explosion, the need for integration of data across studies, modalities and levels of understanding is increasingly recognized. A concrete exemplar of the value of large-scale data sharing has been provided by the Human Brain Project.
=== Medical ===
The importance of large-scale integration of brain information for new approaches to medicine has been recognized. Rather than relying mainly on symptom information, a combination of brain and gene information may ultimately be required for understanding what treatment is best suited to which individual person.
=== Behavioral ===
There is also work studying empathy and social behavior trends to better understand how empathy plays a role in behavioral science, and how the brain responds to empathy, produces empathy, and develops empathy over time. Combining these functional units and the social behavior and impact work to create a better understanding of the complex behaviors that create the human experience.
== References ==
== External links ==
Journal of the International Neuropsychological Society
Journal of Integrative Neuroscience Archived 2019-08-03 at the Wayback Machine | Wikipedia/Integrative_neuroscience |
Neuroscience is the scientific study of the nervous system (the brain, spinal cord, and peripheral nervous system), its functions, and its disorders. It is a multidisciplinary science that combines physiology, anatomy, molecular biology, developmental biology, cytology, psychology, physics, computer science, chemistry, medicine, statistics, and mathematical modeling to understand the fundamental and emergent properties of neurons, glia and neural circuits. The understanding of the biological basis of learning, memory, behavior, perception, and consciousness has been described by Eric Kandel as the "epic challenge" of the biological sciences.
The scope of neuroscience has broadened over time to include different approaches used to study the nervous system at different scales. The techniques used by neuroscientists have expanded enormously, from molecular and cellular studies of individual neurons to imaging of sensory, motor and cognitive tasks in the brain.
== History ==
The earliest study of the nervous system dates to ancient Egypt. Trepanation, the surgical practice of either drilling or scraping a hole into the skull for the purpose of curing head injuries or mental disorders, or relieving cranial pressure, was first recorded during the Neolithic period. Manuscripts dating to 1700 BC indicate that the Egyptians had some knowledge about symptoms of brain damage.
Early views on the function of the brain regarded it to be a "cranial stuffing" of sorts. In Egypt, from the late Middle Kingdom onwards, the brain was regularly removed in preparation for mummification. It was believed at the time that the heart was the seat of intelligence. According to Herodotus, the first step of mummification was to "take a crooked piece of iron, and with it draw out the brain through the nostrils, thus getting rid of a portion, while the skull is cleared of the rest by rinsing with drugs."
The view that the heart was the source of consciousness was not challenged until the time of the Greek physician Hippocrates. He believed that the brain was not only involved with sensation—since most specialized organs (e.g., eyes, ears, tongue) are located in the head near the brain—but was also the seat of intelligence. Plato also speculated that the brain was the seat of the rational part of the soul. Aristotle, however, believed the heart was the center of intelligence and that the brain regulated the amount of heat from the heart. This view was generally accepted until the Roman physician Galen, a follower of Hippocrates and physician to Roman gladiators, observed that his patients lost their mental faculties when they had sustained damage to their brains.
Abulcasis, Averroes, Avicenna, Avenzoar, and Maimonides, active in the Medieval Muslim world, described a number of medical problems related to the brain. In Renaissance Europe, Vesalius (1514–1564), René Descartes (1596–1650), Thomas Willis (1621–1675) and Jan Swammerdam (1637–1680) also made several contributions to neuroscience.
Luigi Galvani's pioneering work in the late 1700s set the stage for studying the electrical excitability of muscles and neurons. In 1843 Emil du Bois-Reymond demonstrated the electrical nature of the nerve signal, whose speed Hermann von Helmholtz proceeded to measure, and in 1875 Richard Caton found electrical phenomena in the cerebral hemispheres of rabbits and monkeys. Adolf Beck published in 1890 similar observations of spontaneous electrical activity of the brain of rabbits and dogs. Studies of the brain became more sophisticated after the invention of the microscope and the development of a staining procedure by Camillo Golgi during the late 1890s. The procedure used a silver chromate salt to reveal the intricate structures of individual neurons. His technique was used by Santiago Ramón y Cajal and led to the formation of the neuron doctrine, the hypothesis that the functional unit of the brain is the neuron. Golgi and Ramón y Cajal shared the Nobel Prize in Physiology or Medicine in 1906 for their extensive observations, descriptions, and categorizations of neurons throughout the brain.
In parallel with this research, in 1815 Jean Pierre Flourens induced localized lesions of the brain in living animals to observe their effects on motricity, sensibility and behavior. Work with brain-damaged patients by Marc Dax in 1836 and Paul Broca in 1865 suggested that certain regions of the brain were responsible for certain functions. At the time, these findings were seen as a confirmation of Franz Joseph Gall's theory that language was localized and that certain psychological functions were localized in specific areas of the cerebral cortex. The localization of function hypothesis was supported by observations of epileptic patients conducted by John Hughlings Jackson, who correctly inferred the organization of the motor cortex by watching the progression of seizures through the body. Carl Wernicke further developed the theory of the specialization of specific brain structures in language comprehension and production. Modern research through neuroimaging techniques, still uses the Brodmann cerebral cytoarchitectonic map (referring to the study of cell structure) anatomical definitions from this era in continuing to show that distinct areas of the cortex are activated in the execution of specific tasks.
During the 20th century, neuroscience began to be recognized as a distinct academic discipline in its own right, rather than as studies of the nervous system within other disciplines. Eric Kandel and collaborators have cited David Rioch, Francis O. Schmitt, and Stephen Kuffler as having played critical roles in establishing the field. Rioch originated the integration of basic anatomical and physiological research with clinical psychiatry at the Walter Reed Army Institute of Research, starting in the 1950s. During the same period, Schmitt established a neuroscience research program within the Biology Department at the Massachusetts Institute of Technology, bringing together biology, chemistry, physics, and mathematics. The first freestanding neuroscience department (then called Psychobiology) was founded in 1964 at the University of California, Irvine by James L. McGaugh. This was followed by the Department of Neurobiology at Harvard Medical School, which was founded in 1966 by Stephen Kuffler.
In the process of treating epilepsy, Wilder Penfield produced maps of the location of various functions (motor, sensory, memory, vision) in the brain. He summarized his findings in a 1950 book called The Cerebral Cortex of Man. Wilder Penfield and his co-investigators Edwin Boldrey and Theodore Rasmussen are considered to be the originators of the cortical homunculus.
The understanding of neurons and of nervous system function became increasingly precise and molecular during the 20th century. For example, in 1952, Alan Lloyd Hodgkin and Andrew Huxley presented a mathematical model for the transmission of electrical signals in neurons of the giant axon of a squid, which they called "action potentials", and how they are initiated and propagated, known as the Hodgkin–Huxley model. In 1961–1962, Richard FitzHugh and J. Nagumo simplified Hodgkin–Huxley, in what is called the FitzHugh–Nagumo model. In 1962, Bernard Katz modeled neurotransmission across the space between neurons known as synapses. Beginning in 1966, Eric Kandel and collaborators examined biochemical changes in neurons associated with learning and memory storage in Aplysia. In 1981 Catherine Morris and Harold Lecar combined these models in the Morris–Lecar model. Such increasingly quantitative work gave rise to numerous biological neuron models and models of neural computation.
As a result of the increasing interest about the nervous system, several prominent neuroscience organizations have been formed to provide a forum to all neuroscientists during the 20th century. For example, the International Brain Research Organization was founded in 1961, the International Society for Neurochemistry in 1963, the European Brain and Behaviour Society in 1968, and the Society for Neuroscience in 1969. Recently, the application of neuroscience research results has also given rise to applied disciplines as neuroeconomics, neuroeducation, neuroethics, and neurolaw.
Over time, brain research has gone through philosophical, experimental, and theoretical phases, with work on neural implants and brain simulation predicted to be important in the future.
== Modern neuroscience ==
The scientific study of the nervous system increased significantly during the second half of the twentieth century, principally due to advances in molecular biology, electrophysiology, and computational neuroscience. This has allowed neuroscientists to study the nervous system in all its aspects: how it is structured, how it works, how it develops, how it malfunctions, and how it can be changed.
For example, it has become possible to understand, in much detail, the complex processes occurring within a single neuron. Neurons are cells specialized for communication. They are able to communicate with neurons and other cell types through specialized junctions called synapses, at which electrical or electrochemical signals can be transmitted from one cell to another. Many neurons extrude a long thin filament of axoplasm called an axon, which may extend to distant parts of the body and are capable of rapidly carrying electrical signals, influencing the activity of other neurons, muscles, or glands at their termination points. A nervous system emerges from the assemblage of neurons that are connected to each other in neural circuits, and networks.
The vertebrate nervous system can be split into two parts: the central nervous system (defined as the brain and spinal cord), and the peripheral nervous system. In many species—including all vertebrates—the nervous system is the most complex organ system in the body, with most of the complexity residing in the brain. The human brain alone contains around one hundred billion neurons and one hundred trillion synapses; it consists of thousands of distinguishable substructures, connected to each other in synaptic networks whose intricacies have only begun to be unraveled. At least one out of three of the approximately 20,000 genes belonging to the human genome is expressed mainly in the brain.
Due to the high degree of plasticity of the human brain, the structure of its synapses and their resulting functions change throughout life.
Making sense of the nervous system's dynamic complexity is a formidable research challenge. Ultimately, neuroscientists would like to understand every aspect of the nervous system, including how it works, how it develops, how it malfunctions, and how it can be altered or repaired. Analysis of the nervous system is therefore performed at multiple levels, ranging from the molecular and cellular levels to the systems and cognitive levels. The specific topics that form the main focus of research change over time, driven by an ever-expanding base of knowledge and the availability of increasingly sophisticated technical methods. Improvements in technology have been the primary drivers of progress. Developments in electron microscopy, computer science, electronics, functional neuroimaging, and genetics and genomics have all been major drivers of progress.
Advances in the classification of brain cells have been enabled by electrophysiological recording, single-cell genetic sequencing, and high-quality microscopy, which have combined into a single method pipeline called patch-sequencing in which all three methods are simultaneously applied using miniature tools. The efficiency of this method and the large amounts of data that is generated has allowed researchers to make some general conclusions about cell types; for example that the human and mouse brain have different versions of fundamentally the same cell types.
=== Molecular and cellular neuroscience ===
Basic questions addressed in molecular neuroscience include the mechanisms by which neurons express and respond to molecular signals and how axons form complex connectivity patterns. At this level, tools from molecular biology and genetics are used to understand how neurons develop and how genetic changes affect biological functions. The morphology, molecular identity, and physiological characteristics of neurons and how they relate to different types of behavior are also of considerable interest.
Questions addressed in cellular neuroscience include the mechanisms of how neurons process signals physiologically and electrochemically. These questions include how signals are processed by neurites and somas and how neurotransmitters and electrical signals are used to process information in a neuron. Neurites are thin extensions from a neuronal cell body, consisting of dendrites (specialized to receive synaptic inputs from other neurons) and axons (specialized to conduct nerve impulses called action potentials). Somas are the cell bodies of the neurons and contain the nucleus.
Another major area of cellular neuroscience is the investigation of the development of the nervous system. Questions include the patterning and regionalization of the nervous system, axonal and dendritic development, trophic interactions, synapse formation and the implication of fractones in neural stem cells, differentiation of neurons and glia (neurogenesis and gliogenesis), and neuronal migration.
Computational neurogenetic modeling is concerned with the development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes, on the cellular level (Computational Neurogenetic Modeling (CNGM) can also be used to model neural systems).
=== Neural circuits and systems ===
Systems neuroscience research centers on the structural and functional architecture of the developing human brain, and the functions of large-scale brain networks, or functionally-connected systems within the brain. Alongside brain development, systems neuroscience also focuses on how the structure and function of the brain enables or restricts the processing of sensory information, using learned mental models of the world, to motivate behavior.
Questions in systems neuroscience include how neural circuits are formed and used anatomically and physiologically to produce functions such as reflexes, multisensory integration, motor coordination, circadian rhythms, emotional responses, learning, and memory. In other words, this area of research studies how connections are made and morphed in the brain, and the effect it has on human sensation, movement, attention, inhibitory control, decision-making, reasoning, memory formation, reward, and emotion regulation.
Specific areas of interest for the field include observations of how the structure of neural circuits effect skill acquisition, how specialized regions of the brain develop and change (neuroplasticity), and the development of brain atlases, or wiring diagrams of individual developing brains.
The related fields of neuroethology and neuropsychology address the question of how neural substrates underlie specific animal and human behaviors. Neuroendocrinology and psychoneuroimmunology examine interactions between the nervous system and the endocrine and immune systems, respectively. Despite many advancements, the way that networks of neurons perform complex cognitive processes and behaviors is still poorly understood.
=== Cognitive and behavioral neuroscience ===
Cognitive neuroscience addresses the questions of how psychological functions are produced by neural circuitry. The emergence of powerful new measurement techniques such as neuroimaging (e.g., fMRI, PET, SPECT), EEG, MEG, electrophysiology, optogenetics and human genetic analysis combined with sophisticated experimental techniques from cognitive psychology allows neuroscientists and psychologists to address abstract questions such as how cognition and emotion are mapped to specific neural substrates. Although many studies hold a reductionist stance looking for the neurobiological basis of cognitive phenomena, recent research shows that there is an interplay between neuroscientific findings and conceptual research, soliciting and integrating both perspectives. For example, neuroscience research on empathy solicited an interdisciplinary debate involving philosophy, psychology and psychopathology. Moreover, the neuroscientific identification of multiple memory systems related to different brain areas has challenged the idea of memory as a literal reproduction of the past, supporting a view of memory as a generative, constructive and dynamic process.
Neuroscience is also allied with the social and behavioral sciences, as well as with nascent interdisciplinary fields. Examples of such alliances include neuroeconomics, decision theory, social neuroscience, and neuromarketing to address complex questions about interactions of the brain with its environment. A study into consumer responses for example uses EEG to investigate neural correlates associated with narrative transportation into stories about energy efficiency.
=== Computational neuroscience ===
Questions in computational neuroscience can span a wide range of levels of traditional analysis, such as development, structure, and cognitive functions of the brain. Research in this field utilizes mathematical models, theoretical analysis, and computer simulation to describe and verify biologically plausible neurons and nervous systems. For example, biological neuron models are mathematical descriptions of spiking neurons which can be used to describe both the behavior of single neurons as well as the dynamics of neural networks. Computational neuroscience is often referred to as theoretical neuroscience.
=== Neuroscience and medicine ===
==== Clinical neuroscience ====
Neurology, psychiatry, neurosurgery, psychosurgery, anesthesiology and pain medicine, neuropathology, neuroradiology, ophthalmology, otolaryngology, clinical neurophysiology, addiction medicine, and sleep medicine are some medical specialties that specifically address the diseases of the nervous system. These terms also refer to clinical disciplines involving diagnosis and treatment of these diseases.
Neurology works with diseases of the central and peripheral nervous systems, such as amyotrophic lateral sclerosis (ALS) and stroke, and their medical treatment. Psychiatry focuses on affective, behavioral, cognitive, and perceptual disorders. Anesthesiology focuses on perception of pain, and pharmacologic alteration of consciousness. Neuropathology focuses upon the classification and underlying pathogenic mechanisms of central and peripheral nervous system and muscle diseases, with an emphasis on morphologic, microscopic, and chemically observable alterations. Neurosurgery and psychosurgery work primarily with surgical treatment of diseases of the central and peripheral nervous systems.
Neuroscience underlies the development of various neurotherapy methods to treat diseases of the nervous system.
==== Translational research ====
Recently, the boundaries between various specialties have blurred, as they are all influenced by basic research in neuroscience. For example, brain imaging enables objective biological insight into mental illnesses, which can lead to faster diagnosis, more accurate prognosis, and improved monitoring of patient progress over time.
Integrative neuroscience describes the effort to combine models and information from multiple levels of research to develop a coherent model of the nervous system. For example, brain imaging coupled with physiological numerical models and theories of fundamental mechanisms may shed light on psychiatric disorders.
Another important area of translational research is brain–computer interfaces (BCIs), or machines that are able to communicate and influence the brain. They are currently being researched for their potential to repair neural systems and restore certain cognitive functions. However, some ethical considerations have to be dealt with before they are accepted.
== Major branches ==
Modern neuroscience education and research activities can be very roughly categorized into the following major branches, based on the subject and scale of the system in examination as well as distinct experimental or curricular approaches. Individual neuroscientists, however, often work on questions that span several distinct subfields.
== Careers in neuroscience ==
Source:
=== Bachelor's Level ===
=== Master's Level ===
=== Advanced Degree ===
== Neuroscience organizations ==
The largest professional neuroscience organization is the Society for Neuroscience (SFN), which is based in the United States but includes many members from other countries. Since its founding in 1969 the SFN has grown steadily: as of 2010 it recorded 40,290 members from 83 countries. Annual meetings, held each year in a different American city, draw attendance from researchers, postdoctoral fellows, graduate students, and undergraduates, as well as educational institutions, funding agencies, publishers, and hundreds of businesses that supply products used in research.
Other major organizations devoted to neuroscience include the International Brain Research Organization (IBRO), which holds its meetings in a country from a different part of the world each year, and the Federation of European Neuroscience Societies (FENS), which holds a meeting in a different European city every two years. FENS comprises a set of 32 national-level organizations, including the British Neuroscience Association, the German Neuroscience Society (Neurowissenschaftliche Gesellschaft), and the French Société des Neurosciences. The first National Honor Society in Neuroscience, Nu Rho Psi, was founded in 2006. Numerous youth neuroscience societies which support undergraduates, graduates and early career researchers also exist, such as Simply Neuroscience and Project Encephalon.
In 2013, the BRAIN Initiative was announced in the US. The International Brain Initiative was created in 2017, currently integrated by more than seven national-level brain research initiatives (US, Europe, Allen Institute, Japan, China, Australia, Canada, Korea, and Israel) spanning four continents.
=== Public education and outreach ===
In addition to conducting traditional research in laboratory settings, neuroscientists have also been involved in the promotion of awareness and knowledge about the nervous system among the general public and government officials. Such promotions have been done by both individual neuroscientists and large organizations. For example, individual neuroscientists have promoted neuroscience education among young students by organizing the International Brain Bee, which is an academic competition for high school or secondary school students worldwide. In the United States, large organizations such as the Society for Neuroscience have promoted neuroscience education by developing a primer called Brain Facts, collaborating with public school teachers to develop Neuroscience Core Concepts for K-12 teachers and students, and cosponsoring a campaign with the Dana Foundation called Brain Awareness Week to increase public awareness about the progress and benefits of brain research. In Canada, the Canadian Institutes of Health Research's (CIHR) Canadian National Brain Bee is held annually at McMaster University.
Neuroscience educators formed a Faculty for Undergraduate Neuroscience (FUN) in 1992 to share best practices and provide travel awards for undergraduates presenting at Society for Neuroscience meetings.
Neuroscientists have also collaborated with other education experts to study and refine educational techniques to optimize learning among students, an emerging field called educational neuroscience. Federal agencies in the United States, such as the National Institute of Health (NIH) and National Science Foundation (NSF), have also funded research that pertains to best practices in teaching and learning of neuroscience concepts.
== Engineering applications of neuroscience ==
=== Neuromorphic computer chips ===
Neuromorphic engineering is a branch of neuroscience that deals with creating functional physical models of neurons for the purposes of useful computation. The emergent computational properties of neuromorphic computers are fundamentally different from conventional computers in the sense that they are complex systems, and that the computational components are interrelated with no central processor.
One example of such a computer is the SpiNNaker supercomputer.
Sensors can also be made smart with neuromorphic technology. An example of this is the Event Camera's BrainScaleS (brain-inspired Multiscale Computation in Neuromorphic Hybrid Systems), a hybrid analog neuromorphic supercomputer located at Heidelberg University in Germany. It was developed as part of the Human Brain Project's neuromorphic computing platform and is the complement to the SpiNNaker supercomputer, which is based on digital technology. The architecture used in BrainScaleS mimics biological neurons and their connections on a physical level; additionally, since the components are made of silicon, these model neurons operate on average 864 times (24 hours of real time is 100 seconds in the machine simulation) that of their biological counterparts.
Recent advances in neuromorphic microchip technology have led a group of scientists to create an artificial neuron that can replace real neurons in diseases.
== Nobel prizes related to neuroscience ==
== See also ==
== References ==
== Further reading ==
== External links ==
Neuroscience on In Our Time at the BBC
Neuroscience Information Framework (NIF)
American Society for Neurochemistry
British Neuroscience Association (BNA)
Federation of European Neuroscience Societies
Neuroscience Online (electronic neuroscience textbook)
HHMI Neuroscience lecture series - Making Your Mind: Molecules, Motion, and Memory Archived 2013-06-24 at the Wayback Machine
Société des Neurosciences
Neuroscience For Kids | Wikipedia/Brain_science |
Nutritional neuroscience is the scientific discipline that studies the effects various components of the diet such as minerals, vitamins, protein, carbohydrates, fats, dietary supplements, synthetic hormones, and food additives have on neurochemistry, neurobiology, behavior, and cognition.
Research on nutritional mechanisms and their effect on the brain shows they are involved in almost every facet of neurological functioning, including alterations in neurogenesis, neurotrophic factors, neural pathways and neuroplasticity, throughout the life cycle.
Relatively speaking, the brain consumes an immense amount of energy in comparison to the rest of the body. The human brain is approximately 2% of the human body mass and uses 20–25% of the total energy expenditure. Therefore, mechanisms involved in the transfer of energy from foods to neurons are likely to be fundamental to the control of brain function. Insufficient intake of selected vitamins, or certain metabolic disorders, affect cognitive processes by disrupting the nutrient-dependent processes within the body that are associated with the management of energy in neurons, which can subsequently affect neurotransmission, synaptic plasticity, and cell survival.
== Minerals ==
Deficiency or excess of essential minerals (e.g. iron, zinc, copper, and magnesium) can disrupt brain development and neurophysiology to affect behavior. Furthermore, minerals have been implicated in the pathophysiology of neurodegenerative diseases including Alzheimer's dementia.
=== Iron ===
Iron is essential for several critical metabolic enzymes and a deficiency of this mineral can disrupt brain development. For, example chronic marginal iron affects dopamine metabolism and myelin fatty acid composition and behavior in mice. In rats a marginal iron deficiency that does not cause anemia disrupted axon growth in the auditory nerve affecting auditory brainstem latency without major changes in myelination. In rhesus macaques, prenatal iron deficiency disrupts emotional behavior and polymorphisms that reduce the expression of monoamine oxidase interact with gestational iron deficiency to exacerbate the response to a stressful situation leading to increased aggressiveness. Inexpensive and effective iron supplementation is an available preventive strategy recommended by the World Health Organization. However, iron supplementation can exacerbate malaria infection. Therefore, individuals receiving iron supplementation in malaria-endemic areas must be carefully monitored.
=== Zinc ===
Zinc is essential for the structure and function of thousands of proteins critical for the function of every cell. Zinc can also serve as a neurotransmitter in the brain, thus a deficiency of this mineral can clearly disrupt development as well as neurophysiology. For example, zinc deficiency during early development impairs neurogenesis leading to memory impairments. However, zinc deficiency later in life can disrupt appetite and cause depression-like behavior. However, it is important to consider copper intake relative to zinc supplementation because excess zinc can disrupt copper absorption.
==== Deficiency ====
Conservative estimates suggest that 25% of the world's population is at risk of zinc deficiency.
Hypozincemia is usually a nutritional deficiency, but can also be associated with malabsorption, diarrhea, acrodermatitis enteropathica, chronic liver disease, chronic renal disease, sickle cell disease, diabetes, malignancy, pyroluria, and other chronic illnesses. It can also occur after bariatric surgery, heavy metal exposure and tartrazine.
Zinc deficiency is typically the result of inadequate dietary intake of zinc, disease states that promote zinc losses, or physiological states that require increased zinc. Populations that consume primarily plant-based diets that are low in bioavailable zinc often have zinc deficiencies. Diseases or conditions that involve intestinal malabsorption promote zinc losses. Fecal losses of zinc caused by diarrhea are one contributing factor, often common in developing countries. Changes in intestinal tract absorbability and permeability due, in part, to viral, protozoal, and bacteria pathogens may also encourage fecal losses of zinc. Physiological states that require increased zinc include periods of growth in infants and children as well as in mothers during pregnancy.
===== Anorexia =====
Zinc deficiency may cause a decrease in appetite which can degenerate into anorexia or anorexia nervosa. Appetite disorders, in turn, cause malnutrition and, notably, inadequate zinc intake. Anorexia itself is a cause of zinc deficiency, thus leading to a vicious cycle: the worsening of anorexia worsens the zinc deficiency. A 1994 randomized, double-blind, placebo-controlled trial showed that zinc (14 mg per day) doubled the rate of body mass increase in the treatment of anorexia nervosa.
===== Cognitive and motor function impairment =====
Cognitive and motor function may also be impaired in zinc deficient children. Zinc deficiency can interfere with many organ systems especially when it occurs during a time of rapid growth and development when nutritional needs are high, such as during infancy. In animal studies, rats who were deprived of zinc during early fetal development exhibited increased emotionality, poor memory, and abnormal response to stress which interfered with performance in learning situations. Zinc deprivation in monkeys showed that zinc deficient animals were emotionally less mature, and also had cognitive deficits indicated by their difficulty in retaining previously learned problems and in learning new problems. Human observational studies show weaker results. Low maternal zinc status has been associated with less attention during the neonatal period and worse motor functioning. In some studies, supplementation has been associated with motor development in very low birth weight infants and more vigorous and functional activity in infants and toddlers.
Plasma zinc level has been associated with many psychological disorders. However, the nature of this relationship remains unclear in most instances. An increasing amount of evidence suggests that zinc deficiency could play a causal role in the etiology of depression. Indeed, zinc supplementation has been reported to improve measures of depression in randomized double blind placebo controlled trials.
=== Copper ===
==== Deficiency ====
The neurodegenerative syndrome of copper deficiency has been recognized for some time in ruminant animals, in which it is commonly known as "swayback". The disease involves a nutritional deficiency in the trace element copper. Copper is ubiquitous and daily requirement is low making acquired copper deficiency very rare. Copper deficiency can manifest in parallel with vitamin B12 and other nutritional deficiencies. The most common cause of copper deficiency is a remote gastrointestinal surgery, such as gastric bypass surgery, due to malabsorption of copper, or zinc toxicity. On the other hand, Menkes disease is a genetic disorder of copper deficiency involving a wide variety of symptoms that is often fatal.
===== Neurological presentation =====
Copper deficiency can cause a wide variety of neurological problems including, myelopathy, peripheral neuropathy, and optic neuropathy.
===== Myelopathy =====
Affected individuals typically present difficulty walking (gait difficulty) caused by sensory ataxia (irregular muscle coordination) due to dorsal column dysfunction or degeneration of the spinal cord (myelopathy). Patients with ataxic gait have problems balancing and display an unstable wide walk. They often feel tremors in their torso, causing side way jerks and lunges.
In brain MRI, there is often an increased T2 signalling at the posterior columns of the spinal cord in patients with myelopathy caused by copper deficiency. T2 signalling is often an indicator of some kind of neurodegeneration. There are some changes in the spinal cord MRI involving the thoracic cord, the cervical cord or sometimes both. Copper deficiency myelopathy is often compared to subacute combined degeneration (SCD). Subacute combined degeneration is also a degeneration of the spinal cord, but instead vitamin B12 deficiency is the cause of the spinal degeneration. SCD also has the same high T2 signalling intensities in the posterior column as copper deficient patient in MRI imaging.
===== Peripheral neuropathy =====
Another common symptom of copper deficiency is peripheral neuropathy, which is numbness or tingling that can start in the extremities and can sometimes progress radially inward towards the torso. In an Advances in Clinical Neuroscience & Rehabilitation (ACNR) published case report, a 69-year-old patient had progressively worsened neurological symptoms. These symptoms included diminished upper limb reflexes with abnormal lower limb reflexes, sensation to light touch and pin prick was diminished above the waist, vibration sensation was lost in the sternum, and markedly reduced proprioception or sensation about the self's orientation. Many people with the neurological effects of copper deficiency complain about very similar or identical symptoms as the patient. This numbness and tingling poses danger for the elderly because it increases their risk of falling and injuring themselves. Peripheral neuropathy can become very disabling leaving some patients dependent on wheel chairs or walking canes for mobility if there is lack of correct diagnosis. Rarely can copper deficiency cause major disabling symptoms. The deficiency will have to be present for an extensive amount of time until such disabling conditions manifest.
===== Optic neuropathy =====
Some patients with copper deficiency have shown signs of vision and color loss. The vision is usually lost in the peripheral views of the eye. The bilateral vision loss is usually very gradual. An optical coherence tomography (OCT) shows some nerve fiber layer loss in most patients, suggesting the vision loss and color vision loss was secondary to optic neuropathy or neurodegeneration.
==== Toxicity ====
Copper toxicity can occur from excessive supplement use, eating acid foods cooked in uncoated copper cookware, exposure to excess copper in drinking water, or as the result of an inherited metabolic disorder in the case of Wilson's disease. A significant portion of the toxicity of copper comes from its ability to accept and donate single electrons as it changes oxidation state. This catalyzes the production of very reactive radical ions, such as hydroxyl radical in a manner similar to Fenton chemistry. This catalytic activity of copper is used by the enzymes with which it is associated, thus is only toxic when unsequestered and unmediated. This increase in unmediated reactive radicals is generally termed oxidative stress, and is an active area of research in a variety of diseases where copper may play an important but more subtle role than in acute toxicity.
Some of the effects of aging may be associated with excess copper. In addition, studies have found that people with mental illnesses, such as schizophrenia, had heightened levels of copper in their systems. However, it is unknown at this stage whether the copper contributes to the mental illness, whether the body attempts to store more copper in response to the illness, or whether the high levels of copper are the result of the mental illness.
===== Alzheimer's disease =====
Elevated free copper levels exist in Alzheimer's disease. Copper and zinc are known to bind to amyloid beta proteins in Alzheimer's disease.
=== Manganese ===
Manganese is a component of some enzymes and stimulates the development and activity of other enzymes. Manganese superoxide dismutase (MnSOD) is the principal antioxidant in mitochondria. Several enzymes activated by manganese contribute to the metabolism of carbohydrates, amino acids, and cholesterol.
Deficiency of manganese causes skeletal deformation in animals and inhibits the production of collagen in wound healing. On the other hand, manganese toxicity is associated with neurological complications.
==== Toxicity ====
Manganese poisoning is a toxic condition resulting from chronic exposure to manganese and first identified in 1837 by James Couper.
===== Presentation =====
Chronic exposure to excessive Mn levels can lead to a variety of psychiatric and motor disturbances, termed manganism. Generally, exposure to ambient Mn air concentrations in excess of 5 mg Mn/m3 can lead to Mn-induced symptoms.
In initial stages of manganism, neurological symptoms consist of reduced response speed, irritability, mood changes, and compulsive behaviors. Upon protracted exposure symptoms are more prominent and resemble those of idiopathic Parkinson's disease, as which it is often misdiagnosed, although there are particular differences in both the symptoms (nature of tremors, for example), response to drugs such as levodopa, and affected portion of the basal ganglia. Symptoms are also similar to Lou Gehrig's disease and multiple sclerosis.
===== Causes =====
Manganism has become an active issue in workplace safety as it has been the subject of numerous product liability lawsuits against manufacturers of arc welding supplies. In these lawsuits, welders have accused the manufacturers of failing to provide adequate warning that their products could cause welding fumes to contain dangerously high manganese concentrations that could lead welders to develop manganism. Companies employing welders are also being sued, for what colloquially is known as "welders' disease". However, studies fail to show any link between employment as a welder and manganism (or other neurological problems).
Manganism is also documented in reports of illicit methcathinone manufacturing. This is due to manganese being a byproduct of methcathinone synthesis if potassium permanganate is used as an oxidiser. Symptoms include apathy, bradykinesia, gait disorder with postural instability, and spastic-hypokinetic dysarthria. Another street drug sometimes contaminated with manganese is the so-called "Bazooka", prepared by free-base methods from cocaine using manganese carbonate.
Reports also mention such sources as contaminated drinking water, and fuel additive methylcyclopentadienyl manganese tricarbonyl (MMT), which on combustion becomes partially converted into manganese phosphates and sulfate that go airborne with the exhaust, and manganese ethylene-bis-dithiocarbamate (Maneb), a pesticide.
===== Pathological mechanisms =====
Manganese may affect liver function, but the threshold of acute toxicity is very high. On the other hand, more than 95% of manganese is eliminated by biliary excretion. Any existing liver damage may slow this process, increasing its concentration in blood plasma. The exact neurotoxic mechanism of manganese is uncertain but there are clues pointing at the interaction of manganese with iron, zinc, aluminum, and copper. Based on a number of studies, disturbed iron metabolism could underlie the neurotoxic action of manganese.
It participates in Fenton reactions and could thus induce oxidative damage, a hypothesis corroborated by the evidence from studies of affected welders. A study of the exposed workers showed that they have significantly fewer children. This may indicate that long-term accumulation of manganese affects fertility. Pregnant animals repeatedly receiving high doses of manganese bore malformed offspring significantly more often compared to controls. Manganism mimics Schizophrenia. It is found in large quantities in paint and steelmaking.
===== Treatment =====
The current mainstay of manganism treatment is levodopa and chelation with EDTA. Both have limited and at best transient efficacy. Replenishing the deficit of dopamine with levodopa has been shown to initially improve extrapyramidal symptoms, but the response to treatment goes down after 2 or 3 years, with worsening condition of the same patients noted even after 10 years since last exposure to manganese. Enhanced excretion of manganese prompted by chelation therapy brings its blood levels down but the symptoms remain largely unchanged, raising questions about efficacy of this form of treatment.
Increased ferroportin protein expression in human embryonic kidney (HEK293) cells is associated with decreased intracellular Mn concentration and attenuated cytotoxicity, characterized by the reversal of Mn-reduced glutamate uptake and diminished lactate dehydrogenase (LDH) leakage.
===== Locations =====
The Red River Delta near Hanoi has high levels of manganese or arsenic in the water. Approximately 65 percent of the region's wells contain high levels of arsenic, manganese, selenium and barium. This was also published in the Proceedings of the National Academy of Sciences.
=== Magnesium ===
Magnesium is necessary for the function of many metabolic enzymes and also serves as a key regulator of calcium channels involved in neurotransmission (e.g. NMDA receptor). Magnesium supplementation facilitates nerve regeneration after injury. Although unpolished grains contain magnesium, phytic acid in grains can inhibit its absorption. Leafy greens are an excellent source of magnesium.
== Vitamins ==
Deficiency or excess intake of many vitamins can affect the brain contributing to developmental and degenerative diseases.
=== Vitamin A ===
Vitamin A is an essential nutrient for mammals which takes form in either retinol or the provitamin beta-Carotene. It helps regulation of cell division, cell function, genetic regulation, helps enhance the immune system, and is required for brain function, chemical balance, growth and development of the central nervous system and vision.
==== Learning memory ====
In an experiment by Chongqing Medical University pregnant rats were either plentiful in vitamin A or were of a vitamin A deficiency (VAD) due to their diet. The offspring of these rats were then tested in a water maze at 8 weeks old and it was found the VAD offspring had a harder time finishing the maze which helps show that these rats, even while having a deficiency from in utero, have more problems with learning memory. Young rats in a separate study by the same university also showed impaired long-term potentiation in the hippocampus when they were VAD which shows neuronal impairment. When the patient is VAD for too long, the effects of the damage to the hippocampus can be irreversible.
==== Spatial memory ====
Vitamin A affects spatial memory most of the time because the size of the nuclei in hippocampal neurons are reduced by approximately 70% when there is a deficiency which affects a person's abilities for higher cognitive function. In a study by the University of Cagliari, Italy, VAD rats had more trouble learning a Radial arm maze than rats who had normal levels of the vitamin. The healthy rats were able to correctly solve the maze within the 15-day training period and other rats that were once deficient but had vitamin A restored to normal levels were also able to solve it. Here it was found that the retinoid receptors which help transport vitamin A were of normal function.
==== Prevention, treatment and symptoms ====
Eating foods high in vitamin A or taking dietary supplements, retinol or retinal will prevent a deficiency. The foods highest in vitamin A are any pigmented fruits and vegetables and leafy green vegetables also provide beta-Carotene.
There can be symptoms of fat loss and a reduction of any weight gain that would be considered normal for an individual, especially developmental weight gains such as in infants which would occur if the infant was deprived of vitamin A in utero and/or if it was deprived postnatal for an extensive period of time. The deficiency can also cause conditions such as blindness or night blindness, also known as nyctalopia. Night blindness is due to the inability to regenerate rhodopsin in the rods which is needed in dim light in order to see properly.
A treatment of supplements of retinoic acid which is a part of vitamin A can help replenish levels and help bring learning to normal, but after 39 weeks this is ineffective even if the treatment is daily because it will not bring the retinoid hypo-signalling back to normal.
==== Relationship with zinc ====
Zinc is needed to maintain normal vitamin A levels in blood plasma. It also helps vitamin A become metabolized by the liver. However evidence suggests that when someone is deficient in both vitamin A and zinc, memory is more improved when just vitamin A is increased than when just zinc is increased. Of course memory has the largest improvement when both are increased. When one of these nutrients is not balanced, the other is most likely to be affected because they rely on each other for proper functioning in learning.
=== Thiamin (vitamin B1) ===
Vitamin B1, also known as thiamine, is a coenzyme essential for the metabolism of carbohydrates. This vitamin is important for the facilitation of glucose use, thus ensuring the production of energy for the brain, and normal functioning of the nervous system, muscles, and heart.
Thiamine is found in all living tissues, and is uniformly distributed throughout mammalian nervous tissue, including the brain and spinal cord. Metabolism and coenzyme function of the vitamin suggest a distinctive function for thiamin within the nervous system.
The brain retains its thiamine content in the face of a vitamin-deficient diet with great tenacity, as it is the last of all nervous tissues studied to become depleted. A 50% reduction of thiamine stores in rats becomes apparent after only 4 days of being put on a thiamine-deficient diet. However, polyneuritic signs do not begin to appear until about 4 or 5 weeks have passed. Similar results have been found in human subjects.
==== Deficiencies ====
The body has only small stores of B1; accordingly, there is risk of deficiency if the level of intake is reduced only for a few weeks. Thiamin deficiency during critical periods of early development can disrupts neurogenesis in animal models. Lack of thiamin later in life causes the disease known as beriberi. There are two forms of beriberi: "wet", and "dry". Dry beriberi is also known as cerebral beriberi. Characteristics of wet beriberi include prominent edema and cardiac involvement, whereas dry beriberi is mainly characterized by a polyneuritis.
In industrialized nations, thiamine deficiency is a clinically significant problem in individuals with chronic alcoholism or other disorders that interfere with normal ingestion of food. Thiamine deficiency within developed nations tends to manifest as Wernicke–Korsakoff syndrome. Chronic alcoholism can disrupt thiamin absorption and thiamin deficiency contributes to neurodegeneration and memory loss in alcoholics known as Wernicke's encephalopathy. Individuals with chronic alcoholism may fall short on minimum daily requirements of thiamine in part due to anorexia, erratic eating habits, lack of available food, or a combination of any of these factors. Thiamine deficiency has been reported in up to 80% of alcoholic patients due to inadequate nutritional intake, reduced absorption, and impaired utilization of thiamine. Alcohol, in combination with its metabolite acetaldehyde, interacts with thiamine utilization at the molecular level during transport, diphosphorylation, and modification processes. For this reason, chronic alcoholics may have insufficient thiamine for maintenance of normal brain function, even with seemingly adequate dietary intake.
==== Symptoms ====
Clinical signs of B1 deficiency include mental changes such as apathy, decrease in short-term memory, confusion, and irritability. Moderate deficiency in thiamine may reduce growth in young populations, in increase chronic illness in both young and middle-aged adults. In addition, moderate deficiency of thiamine may increase rates of depression, dementia, falls, and fractures in old age.
The lingering symptoms of neuropathy associated with cerebral beriberi are known as Korsakoff's syndrome, or the chronic phase of Wernicke-Korsakoff's. Wernicke encephalopathy is a neurological disorder resulting from a deficiency in thiamine, sharing the same predominant features of cerebral beriberi, as characterized by ocular abnormalities, ataxia of gait, a global state of confusion, and neuropathy. The state of confusion associated with Wernicke's may consist of apathy, inattention, spatial disorientation, inability to concentrate, and mental sluggishness or restlessness. Clinical diagnosis of Wernicke's disease cannot be made without evidence of ocular disturbance, yet these criteria may be too rigid. Korsakoff's likely represents a variation in the clinical manifestation of Wernicke encephalopathy, as they both share similar pathological origin.
Korsakoff's syndrome is often characterized by confabulation, disorientation, and profound amnesia. Characteristics of the neuropathology are varied, but generally consist of bilaterally symmetrical midline lesions of brainstem areas, including the mammillary bodies, thalamus, periaqueductal region, hypothalamus, and the cerebellar vermis.
==== Treatment ====
Immediate treatment of Wernicke encephalopathy involves the administration of intravenous thiamine, followed with long-term treatment and prevention of the disorder through oral thiamine supplements, alcohol abstinence, and a balanced diet. Improvements in brain functioning of chronic alcoholics may occur with abstinence-related treatment, involving the discontinuation of alcohol consumption and improved nutrition. Wernicke's encephalopathy is life-threatening if left untreated. However, a rapid reversal of symptoms may result from prompt administration of thiamine.
==== Prevention ====
Fortification of flour is practiced in some countries to replace the thiamine lost during processing. However, this method has been criticized for missing the target population of chronic alcoholics, who are most at risk for deficiency. Alternative solutions have suggested the fortification of alcoholic beverages with thiamine.
Ingesting a diet rich in thiamine may stave off the adverse effects of deficiency. Foods providing rich sources of thiamine include unrefined grain products, ready-to-eat cereals, meat (especially pork), dairy products, peanuts, legumes, fruits and eggs.
=== Niacin (vitamin B3) ===
Vitamin B3, also known as niacin, includes both nicotinamide as well as nicotinic acid, both of which function in many biological oxidization and reduction reactions within the body. These functions include the biochemical degradation of carbohydrates, fats and proteins. Niacin is also involved in the synthesis of fatty acids and cholesterol, which are known mediators of brain biochemistry, and in effect, of cognitive function.
Sufficient niacin intake is either obtained from diet, or synthesized from the amino acid tryptophan.
==== Deficiencies ====
Severe niacin deficiency typically manifests itself as the disease pellagra. Synthesis of B3 from tryptophan involves vitamin B2 and B6, so deficiencies in either of these nutrients can lead to niacin deficiency. An excess of leucine, an essential amino acid, in the diet can also interfere with tryptophan conversion and subsequently result in a B3 deficiency.
Pellagra is most common to populations within developing countries in which corn is the dietary staple. The disease has virtually disappeared from industrialized countries, yet still appears in India and parts of China and Africa. This is in part due to the bound form of niacin that unprocessed corn contains, which is not readily absorbed into the human body. The processes involved in making corn tortillas, can release the bound niacin into a more absorbable form. Pellagra is not problematic in countries which traditionally prepare their corn in this way, but is a problem in other countries where unprocessed corn is main source of caloric intake.
Though pellagra predominantly occurs in developing countries, sporadic cases of pellagra may be observed within industrialized nations, primarily in chronic alcoholics and patients living with functional absorption complications.
==== Symptoms ====
Pellagra is classically characterized by four 4 "D's": diarrhea, dermatitis, dementia, and death. Neuropsychiatric manifestations of pellagra include headache, irritability, poor concentration, anxiety, hallucinations, stupor, apathy, psychomotor unrest, photophobia, tremor, ataxia, spastic paresis, fatigue, and depression. Symptoms of fatigue and insomnia may progress to encephalopathy characterized by confusion, memory loss, and psychosis.
Those affected by pellagra may undergo pathological alterations in the nervous system. Findings may include demylenation and degeneration of various affected parts of the brain, spinal cord, and peripheral nerves.
==== Treatment ====
Prognosis of deficiency is excellent with treatment. Without, pellagra will gradually progress and lead to death within 4–5 years, often a result of malnutrition from prolonged diarrhea, or complications as caused by concurrent infections or neurological symptoms. Symptoms of pellagra can be cured with exogenous administration of nicotinic acid or nicotinamide.
Flushing occurs in many patients treated therapeutically with nicotinic acid, and as a result, nicotinamide holds more clinical value as it is not associated with the same uncomfortable flushing. The adult dose of nicotinamide is 100 mg taken orally every 6 hours until resolution of major acute symptoms, followed with oral administration of 50 mg every 8–12 hours until skin lesions heal. For children, treatment involves oral ingestion of 10–15 mg of nicotinamide, depending on weight, every 6 hours until signs and symptoms are resolved. Severe cases require 1 gram every 3–4 hours, administered parenterally.
Oral nicotinamide has been promoted as an over-the-counter drug for the treatment of Alzheimer's dementia. Conversely, no clinically significant effect has been found for the drug, as nicotinamide administration has not been found to promote memory functions in patients with mild to moderate dementia of either Alzheimer's, vascular, or fronto-temporal types. This evidence suggests that nicotinamide may treat dementia as related to pellagra, but administration does not effectively treat other types of dementia.
==== Prevention ====
The best method of prevention is to eat foods rich in B3. Generally, this involves the intake of a protein-rich diet. Foods that contain high concentrations of niacin in the free form include beans and organ meat, as well as enriched grain and cereal products. While niacin is present in corn and other grains, the bioavailability of the nutrient is much less than it is in protein-rich sources. Different methods of processing corn may result in a higher degree of bioavailability of the vitamin.
Though treatment with niacin does little to alter the effects of Alzheimer's dementia, niacin intake from foods is inversely associated with the disease.
=== Folate (vitamin B9) ===
Folate deficiency can disrupt neurulation and neurogenesis. Maternal folic acid intake around the time of conception prevents neural tube defects. Furthermore, folic acid intake was recently associated with incidence of autism. Enriched white flour is fortified with folic acid in the United States and many other countries. However the European Union does not have mandatory folic acid fortification. Although the protective effects of folic acid are well documented, there remains legitimate concern that fortification could lead to toxic levels in a subset of the population. For example, elevated levels of folic acid can interact with vitamin B12 deficiency to cause neurodegeneration. Furthermore, folic acid and iron can interact to exacerbate malaria.
Folic acid is the most oxidized and stable form of folate, and can also be referred to as vitamin B9. It rarely occurs naturally in foods, but it is the form used in vitamin supplements as well as fortified food products.
Folate coenzymes are involved in numerous conversion processes within the body, including DNA synthesis and amino acid interconversions. Folate and vitamin B12 play a vital role in the synthesis of S-adenosylmethionine, which is of key importance in the maintenance and repairment of all cells, including neurons. In addition, folate has been linked to the maintenance of adequate brain levels of cofactors necessary for chemicals reactions that lead to the synthesis of serotonin and catecholamine neurotransmitters.
Folate has a major, but indirect role in activities which help to direct gene expression and cell proliferation. These activities occur at a greatly increased rate during pregnancy, and depend on adequate levels of folate within blood plasma.
Concentrations of blood plasma folate and homocysteine concentrations are inversely related, such that an increase in dietary folate decreases homocysteine concentration. Thus, dietary intake of folate is a major determinant of homocysteine levels within the body.
Autoantibodies against folate receptor alpha have been found in up to 75% of children with autism.
==== Deficiencies ====
Folate deficiency most commonly arises from insufficient folate intake from the diet, but may also stem from inefficient absorption or metabolic utilization of folate, usually a result of genetic variation. The relationship between folate and B12 is so interdependent that deficiency in either vitamin can result in megaloblastic anemia, characterized by organic mental change.
The process of neural tube transformation into structures that will eventually develop into the central nervous system is known as neurulation, the success of which is dependent on the presence of folate within the body. This process begins in the human approximately 21 days after conception, and is completed by 28 days. Thus, a woman may not even be aware of her pregnancy by the time the process of neurulation is complete, potentially causing severe consequences in the development of the fetus.
Functional problems in the absorption and utilization of vitamins may also play a role in folate deficiencies within the elderly.
==== Symptoms ====
The link between levels of folate and altered mental function is not large, but is sufficient to suggest a causal association. Deficiency in folate can cause an elevation of homocysteine within the blood, as the clearance of homocysteine requires enzymatic action dependent on folate, and to a lesser extent, vitamins B6 and B12. Elevated homocysteine has been associated with increased risk of vascular events, as well as dementia.
Differences lie in the presentation of megaloblastic anemia induced by either folate or B12 deficiency. Megaloblastic anemia related to deficiency in B12 generally results in peripheral neuropathy, whereas folate-related anemia often results in affective, or mood disorders. Neurological effects are not often associated with folate-related megaloblastic anemia, although demyelinating disorders may eventually present. In one study, mood disturbances were recorded for the majority of patients presenting with megaloblastic anemia in the absence of B12 deficiency. In addition, folate concentrations within blood plasma have been found to be lower in patients with both unipolar and bipolar depressive disorders when compared with control groups. In addition, depressive groups with low folate concentrations responded less well to standard antidepressant therapy than did those with normal levels within plasma. However, replication of these findings are less robust.
The role of folic acid during pregnancy is vital to normal development of the nervous system in the fetus. A deficiency in folate levels of a pregnant woman could potentially result in neural tube disorder, a debilitating condition in which the tubes of the central nervous system do not fuse entirely. NTDs are not to be confused with spina bifida, which does not involve neural elements. Neural tube defects can present in a number of ways as a result of the improper closure at various points of the neural tube. The clinical spectrum of the disorder includes encephalocele, craniorachischisis, and anencephaly. In addition, these defects can also be classified as open, if neural tissue is exposed or covered only by membrane, or can be classified as closed, if the tissue is covered by normal skin.
Intake of the vitamin has been linked to deficits in learning and memory, particularly within the elderly population. Elderly people deficient in folate may present with deficits in free recall and recognition, which suggests that levels of folate may be related to efficacy of episodic memory.
==== Prevention ====
Because neurulation may be completed before pregnancy is recognized, it is recommended that women capable of becoming pregnant take about 400μg of folic acid from fortified foods, supplements, or a combination of the two in order to reduce the risk of neural tube defects. These major anomalies in the nervous system can be reduced by 85% with systematic folate supplementation occurring before the onset of pregnancy.
The incidence of Alzheimer's and other cognitive diseases has been loosely connected to deficiencies in folate. It is recommended for the elderly to consume folate through food, fortified or not, and supplements in order to reduce risk of developing the disease.
Good sources of folate include liver, ready-to-eat breakfast cereals, beans, asparagus, spinach, broccoli, and orange juice.
=== Choline ===
Choline is an important methyl donor involved in one-carbon metabolism that also becomes incorporated into phospholipids and the neurotransmitter acetylcholine. Because of its role in cellular synthesis, choline is an important nutrient during the prenatal and early postnatal development of offspring as it contributes heavily to the development of the brain. A study found that rats that were given supplements of choline in utero or in the weeks following birth had superior memories. The changes appeared to be a result of physical changes to the hippocampus, the area of the brain responsible for memory. Furthermore, choline can reduce some of the deleterious effects of folate deficiency on neurogenesis.
While choline during development is important, adult levels of choline are also important. Choline has been shown to increase the synthesis and release of acetylcholine from neurons, which in turn increases memory. A double-blind study was conducted using normal college students (no neurological disorders). Results showed that twenty-five grams of phosphatidylcholine (another form of choline) created a significant improvement in explicit memory, measured by a serial learning task, however this improvement may be attributed to the improvement of slow learners. Another study found that a single ten-gram oral dose of choline, given to normal volunteers (again, without neurological disorders) significantly decreased the number of trials needed to master a serial-learning word test. This increase in memory is particularly beneficial to memory loss experienced by old age. A study conducted on rats who, like humans, had an age-related loss of memory were tested on how choline affected memory. The results showed that rats who had a chronic low-choline diet showed greater memory loss then their same-age control counterparts, while rats who had choline-enriched diets showed a diminished memory loss compared to both the choline-low diet and control rat groups. Furthermore, young rats who were choline-deficient performed as poorly on memory tasks as older rats while older rats that were given choline supplements performed as well as three-month-old rats.
==== Deficiencies and treatments ====
Despite the wide range of foods that choline is found in, studies have shown that the mean choline intake of men, women and children are below the Adequate Intake levels. It is important to note that not enough choline is naturally produced by the body, so diet is an important factor. People who consume a large quantity of alcohol may be at an increased risk for choline deficiency. Sex and age also plays a role, with premenopausal females being less sensitive to choline deficiency than either males or postmenopausal females. This has been theorized to be a result of premenopausal women having an increased ability to synthesize choline in some form, which has been confirmed in studies on rats. In such instances of deficiency, choline supplements or (if able) dietary changes may be beneficial. Good sources of choline include liver, milk, eggs and peanuts. There is further evidence to suggest that choline supplements can be used to treat people who have neurological disorders as well we memory defects. Oral doses of CDP-choline (another form of choline) given to elderly subjects with memory deficits, but without dementia, for four weeks showed improved memory in free recall tasks, but not in recognition tests. In a second study, patients with early Alzheimer-type dementia were treated with twenty-give gram doses of phosphatidylcholine every day for six months. Slightly improvements were shown in memory tests compared to the placebo control group. Other studies conducted did not find any such improvement.
=== Cobalamin (vitamin B12) ===
Also known as cobalamin, B12 is an essential vitamin necessary for normal blood formation. It is also important for the maintenance of neurological function and psychiatric health. The absorption of B12 into the body requires adequate amounts of intrinsic factor, the glycoprotein produced in the parietal cells of the stomach lining. A functioning small intestine is also necessary for the proper metabolism of the vitamin, as absorption occurs within the ileum.
B12 is produced in the digestive tracts of all animals, including humans. Thus, animal-origin food is the only natural food source of vitamin B12 However, synthesis of B12 occurs in the large intestine, which is past the point of absorption that occurs within the small intestine. As such, vitamin B12 must be obtained through diet.
==== Deficiencies ====
Unlike other B vitamins which are not stored in the body, B12 is stored in the liver. Because of this, it may take 5–10 years before a sudden dietary B12 deficiency will become apparent in a previously healthy adult. B12 deficiency, also known as hypocobalaminemia, often results from complications involving absorption into the body.
B12 deficiency is often associated with pernicious anemia, as it is the most common cause. Pernicious anemia results from an autoimmune disorder which destroys the cells that produce intrinsic factor within the stomach lining, thereby hindering B12 absorption. B12 absorption is important for the subsequent absorption of iron, thus, people with pernicious anemia often present with typical symptoms of anemia, such as pale skin, dizziness, and fatigue.
Among those at highest risk for B12 deficiency are the elderly population, as 10-15% of people aged 60+ may present with some form of hypocobalaminemia. High rates of deficiency in the elderly commonly results from the decrease of functional absorption of B12, as production of intrinsic factor declines with age. However, pernicious anemia is the most common cause of B12 deficiency in North American and European populations.
Those affected by various gastrointestinal diseases may also be at risk for deficiency as a result of malabsorption. These diseases may affect production of intrinsic factor in the stomach, or of pancreatic bile. Diseases that involve disorders of the small intestine, such as celiac disease, Crohn's disease and ileitis, may also reduce B12 absorption. For example, people with celiac disease may damage the microvilli within their small intestines through the consumption of gluten, thereby inhibiting absorption of B12 as well as other nutrients.
A diet low in B12, whether voluntary or not, can also cause symptoms of hypocobalaminemia. Many rich sources of B12 come from animal meats and by-products. Populations in developing countries may not have access to these foods on a consistent basis, and as a result may become deficient in B12. In addition, vegans, and to a lesser extent vegetarians, are at risk for consuming a diet low in cobalamin as they voluntarily abstain from animal sources of B12. A combination of these two scenarios may increase prevalence of cobalamin deficit. For instance, B12 deficiency is problematic in India, where the majority of the population is vegetarian and the scarcity of meat consumption is common for omnivores as well.
==== Symptoms ====
An assortment of neurological effects can be observed in 75-90% of individuals of any age with clinically observable B12 deficiency. Cobalamin deficiency manifestations are apparent in the abnormalities of the spinal cord, peripheral nerves, optic nerves, and cerebrum. These abnormalities involve a progressive degeneration of myelin, and may be expressed behaviourally through reports of sensory disturbances in the extremities, or motor disturbances, such as gait ataxia. Combined myelopathy and neuropathy are prevalent within a large percentage of cases. Cognitive changes may range from loss of concentration to memory loss, disorientation, and dementia. All of these symptoms may present with or without additional mood changes. Mental symptoms are extremely variable, and include mild disorders of mood, mental slowness, and memory defect. Memory defect encompasses symptoms of confusion, severe agitation and depression, delusions and paranoid behaviour, visual and auditory hallucinations, urinary and fecal incontinence in the absence of overt spinal lesions, dysphasia, violent maniacal behaviour, and epilepsy. It has been suggested that mental symptoms could be related to a decrease in cerebral metabolism, as caused by the state of deficiency. All of these symptoms may present with or without additional mood changes.
Mild to moderate cases of pernicious anemia may show symptoms of bleeding gums, headache, poor concentration, shortness of breath, and weakness. In severe cases of pernicious anemia, individuals may present with various cognitive problems such as dementia, and memory loss.
It is not always easy to determine whether B12 deficiency is present, especially within older adults. Patients may present with violent behaviour or more subtle personality changes. They may also present with vague complaints, such as fatigue or memory loss, that may be attributed to normative aging processes. Cognitive symptoms may mimic behaviour in Alzheimer's and other dementias as well. Tests must be run on individuals presenting with such signs to confirm or negate cobalamin deficiency within the blood.
==== Treatment ====
Patients deficient in B12 despite normal absorption functionality may be treated through oral administration of at least 6 mg of the vitamin in pill form. Patients who have irreversible causes of deficiency, such as pernicious anemia or old age, will need lifelong treatment with pharmacological doses of B12. Strategy for treatment is dependent on the patient's level of deficiency as well as their level of cognitive functioning. Treatment for those with severe deficiency involves 1000 mg of B12 administered intramuscularly daily for one week, weekly for one month, then monthly for the rest of the patient's life. Daily oral supplementation of B12 mega-doses may be sufficient in reliable patients, but it is imperative that the supplementation be continued on a lifelong basis as relapse may occur otherwise.
The progression of neurological manifestations of cobalamin deficiency is generally gradual. As a result, early diagnosis is important or else irreversible damage may occur. Patients who become demented usually show little to no cognitive improvement with the administration of B12.
A deficiency in folate may produce anemia similar to the anemia resulting from B12 deficiency. There is risk that folic acid administered to those with B12 deficiency may mask anemic symptoms without solving the issue at hand. In this case, patients would still be at risk for neurological deficits associated with B12 deficiency-related anemia, which are not associated with anemia related to folate deficiency.
==== Prevention ====
In addition to meeting intake requirements through food consumption, supplementation of diet with vitamin B12 is seen as a viable preventive measure for deficiency. It has been recommended for the elderly to supplement 50 mcg a day in order to prevent deficit from occurring.
Animal protein products are a good source of B12, particularly organ meats such as kidney or liver. Other good sources are fish, eggs, and dairy products. It is suggested that vegans, who consume no animal meat or by-products, supplement their diet with B12. While there are foods fortified with B12 available, some may be mislabelled in an attempt to boost their nutritional claims. Products of fermentation, such as algae extracts and sea vegetables, may be labelled as sources of B12, but actually contain B12 analogues which compete for the absorption of the nutrient itself. In order to get adequate amounts of the vitamin, orally administered pills or fortified foods such as cereals and soy milk, are recommended for vegans.
=== Vitamin D ===
Vitamin D is an essential regulator of the vitamin D receptor that controls gene transcription during development. The vitamin D receptor is strongly expressed in the substantia nigra. Accordingly, vitamin D deficiency can disrupt neurogenesis leading to altered dopamine signaling and increased exploratory behavior in rats. This is considered a rodent model of the schizophrenia phenotype and vitamin D deficiency has been proposed as an explanation for the increased incidence of schizophrenia among children that were conceived during winter months. A Finnish study found that vitamin D supplement use is associated with reduced risk of schizophrenia.
Vitamin D deficiency may also have downstream effects on neurosteriod synthesis. Vitamin D supplementation appears to increase both free and total testosterone levels in healthy men, with serum vitamin D levels between 70-80 nmol/L having been shown to maximize testosterone production in overweight men. 5a-Androstanediol, a metabolite of testosterone, is a potent positive allosteric modulator of the GABAA receptor. The synthesis of 5a-Androstanediol is generally correlated with that of testosterone production.
== Lipids ==
=== Fat ===
Fatty acids are necessary for the synthesis of cell membranes neurotransmitters and other signaling molecules. While excessive fat intake can be harmful, deficiency of essential fatty acids can disrupt neurodevelopment and synaptic plasticity.
==== Saturated fat ====
Consuming large amounts of saturated fat can negatively affect the brain. Eating foods with saturated fats elevates the level of cholesterol and triglycerides in the body. Studies have shown that high levels of triglycerides strongly link with mood problems such as depression, hostility and aggression. This may occur because high triglyceride levels decrease the amount of oxygen that blood can carry to the brain. The American Heart Association recommends that people consume no more than 16g of saturated fat daily. Common sources of saturated fat are meat and dairy products.
==== Essential fatty acids ====
There are two kinds of essential fatty acids that people must consume (omega-3 and omega-6). Many academics recommend a balanced amount of omega-3s and omega-6s. However, some estimate that Americans consume twenty times more omega-6s than omega-3s. There is a theory that an imbalance of essential fatty acids may lead to mental disorders such as depression, hyperactivity and schizophrenia, but it still lacking evidences. An omega-3 deficient diet increases omega-6 levels in the brain disrupting endocannabinoid signaling in the prefrontal cortex and nucleus accumbens contributing to anxiety and depression-like behaviors in mice. Sources of omega-3 include flax seeds, chia seeds, walnuts, sea vegetables, green leafy vegetables, and cold water fish. Sources of omega-6 include walnuts, hazelnuts; sunflower, safflower, corn, and sesame oils.
=== Cholesterol ===
While cholesterol is essential for membranes and steroid hormones, excess cholesterol affects blood flow impairing cognitive function in vascular dementia.
== Carbohydrates ==
Studies have shown that learning and memory improve after consuming carbohydrates. There are two kinds of carbohydrates people consume: simple and complex. Simple carbohydrates are often found in processed foods and release sugar into the bloodstream quickly after consumption. Complex carbohydrates are digested more slowly and therefore cause sugar to be released into the bloodstream more slowly. Good sources of complex carbohydrates are whole-grain breads, pasta, brown rice, oatmeal, and potatoes. It is recommended that people consume more complex carbohydrates because consuming complex carbohydrates will cause the level of sugar in the bloodstream to be more stable, which will cause less stress hormones to be released. Consuming simple carbohydrates may cause the levels of sugar in the bloodstream to rise and fall, which can cause mood swings.
=== Low carbohydrate ketogenic diets ===
The ketone body beta-hydroxybutyrate is a fuel source for the brain during times of fasting when blood glucose levels fall. Although the mechanism is not understood, it is well established that eating a diet low in carbohydrates can be therapeutic for children with epilepsy. This is likely a result of ketone bodies providing an alternative fuel source to glucose for neuronal function. Furthermore, a ketogenic diet can be beneficial for dementia patients. Medium-chain triglycerides can stimulate ketone synthesis and coconut oil is a rich source of medium chain triglycerides that several anecdotal reports suggest can improve cognitive function in Alzheimer's type dementia patients.
== Protein ==
When protein is consumed, it is broken down into amino acids. These amino acids are used to produce many things like neurotransmitters, enzymes, hormones, and chromosomes. Proteins known as complete proteins contain all eight of the essential amino acids. Meat, cheese, eggs, and yogurt are all examples of complete proteins. Incomplete proteins contain only some of the eight essential amino acids and it is recommended that people consume a combination of these proteins. Examples of incomplete proteins include nuts, seeds, legumes, and grains. When animals are fed a diet deficient in essential amino acids, uncharged tRNAs accumulate in the anterior piriform cortex signaling diet rejection [105]. The body normally interconverts amino acids to maintain homeostasis, but muscle protein can be catabolized to release amino acids during conditions of amino acid deficiency. Disruption of amino acid metabolism can affect brain development and neurophysiology to affect behavior. For example, fetal protein deficiency decreases the number of neurons in the CA1 region of the hippocampus.
=== Glutamate ===
Glutamate is a proteinogenic amino acid and neurotransmitter, though it is perhaps publicly best known in its sodium salt form: monosodium glutamate (MSG). It is also a flavor on its own, producing the umami or savory flavor found in many fermented foods such as cheese. As an amino acid it acts as a source of fuel for various cellular functions and as a neurotransmitter. Glutamate operates as an excitatory neurotransmitter and is released when a nerve impulse excites a glutamate producing cell. This in turn binds to neurons with glutamate receptors, stimulating them.
==== Deficiencies and treatments ====
Glutamate is a nutrient that is extremely difficult to be deficient in, as, being an amino acid, it is found in every food that contains protein. Additionally it is found, as previously mentioned, in fermented foods and in foods containing monosodium glutamate. As such, good sources of glutamate include meat, fish, dairy products and a wide array of other foods. Glutamate is also absorbed extremely efficiently by the intestines. However, there are instances of glutamate deficiency occurring, but only in cases where genetic disorders are present. One such example is Glutamate formiminotransferase deficiency and can cause either minor or profound physical and intellectual disabilities, depending on the severity of the condition. This disorder is extremely rare however, as only twenty people have so far been identified with this condition. Glutamate, while critically important in the body also acts as an excitotoxin in high concentrations not normally found outside of laboratory conditions, although it can occur following brain injury or spinal cord injury.
=== Phenylalanine ===
L-Phenylalanine is biologically converted into L-tyrosine, another one of the DNA-encoded amino acids, and beta-phenethylamine. L-tyrosine in turn is converted into L-DOPA, which is further converted into dopamine, norepinephrine (noradrenaline), and epinephrine (adrenaline). The latter three are known as the catecholamines. Phenethylamine is further converted into N-methylphenethylamine. Phenylalanine uses the same active transport channel as tryptophan to cross the blood–brain barrier, and, in large quantities, interferes with the production of serotonin.
==== Phenylketonuria ====
Toxic levels of phenylalanine accumulate in the brains of patients with phenylketonuria leading to severe brain damage and intellectual disability. To prevent brain damage, these individuals can restrict dietary phenylalanine intake by avoiding protein and supplementing their diet with essential amino acids.
== See also ==
Nutrition and cognition
Impact of nutrition on intelligence
Orthomolecular psychiatry
== References == | Wikipedia/Nutritional_neuroscience |
Principles of Neural Science is a neuroscience textbook edited by Columbia University professors Eric R. Kandel, James H. Schwartz, and Thomas M. Jessell. First published in 1981 by McGraw-Hill, the original edition was 468 pages, and has now grown to 1646 pages on the sixth edition. The second edition was published in 1985, third in 1991, fourth in 2000. The fifth was published on October 26, 2012 and included Steven A. Siegelbaum and A.J. Hudspeth as editors. The sixth and latest edition was published on March 8, 2021.
== Authors ==
=== Editors ===
Kandel was one of the recipients of the 2000 Nobel Prize in Physiology or Medicine. He is currently a professor of biochemistry, molecular biophysics, physiology, cellular biophysics, and psychiatry at Columbia University. He is a senior investigator at the Howard Hughes Medical Institute and a recipient of the National Medal of Science.
Schwartz was a professor of physiology, cellular biophysics, neurology, and psychiatry at Columbia University.
Jessell became an editor of the book starting from the third edition. He was a professor of biochemistry and molecular biophysics at Columbia University, and an investigator at the Howard Hughes Medical Institute.
Hudspeth is a professor of sensory neuroscience at Rockefeller University. He is also an investigator at the Howard Hughes Medical Institute.
Siegelbaum is Chair of the Department of Neuroscience at Columbia University and is also an investigator at the Howard Hughes Medical Institute.
=== Contributors ===
Including the editors—all of whom also contributed to individual chapters in the book—there are a total of 45 authors of this text. Included among them are several notable researchers and physicians. Several authors are also highly decorated scientists, including Nobel laureate Linda B. Buck and renowned neurophysiologist Roger M. Enoka.
== Content ==
Principles of Neural Science is often assigned as a textbook for many undergraduate and graduate/medical neuroscience and neurobiology courses. The book attempts to introduce every aspect of the most modern understanding of the brain. The sixth edition is divided into sixty-four chapters, organized into nine parts:
Part I: Overall Perspective
Part II: Cell and Molecular Biology of Cells of the Nervous System
Part III: Synaptic Transmission
Part IV: Perception
Part V: Movement
Part VI: The Biology of Emotion, Motivation, and Homeostasis
Part VII: Development and the Emergence of Behavior
Part VIII: Learning, Memory, Language and Cognition
Part IX: Diseases of the Nervous System
== References ==
== Sources ==
Kandel ER, Schwartz JH, Jessell TM 1991. Principles of Neural Science, 3rd ed. Appleton & Lange. ISBN 0-8385-8068-8
Kandel ER, Schwartz JH, Jessell TM 2000. Principles of Neural Science, 4th ed. McGraw-Hill, New York. ISBN 0-8385-7701-6
Kandel ER, Schwartz JH, Jessell TM 2012, Siegelbaum SA, Hudspeth AJ. Principles of Neural Science, 5th ed. McGraw-Hill, New York. ISBN 0-07-139011-1
Kandel ER, Koester JD, Mack SH 2021, Siegelbaum SA. Principles of Neural Science, 6th ed. McGraw-Hill, New York. ISBN 978-1-25-964224-1 | Wikipedia/Principles_of_Neural_Science |
Global neurosurgery is a field at the intersection of public health and clinical neurosurgery. It aims to expand provision of improved and equitable neurosurgical care globally.
== Definition and history ==
Global neurosurgery is "the clinical and public health practice of neurosurgery with the primary purpose of ensuring timely, safe, and affordable neurosurgical care to all who need it." The term global neurosurgery was first used in 1995 by Canadian neurosurgeon Dwight Parkinson to describe comprehensive clinical neurosurgery care in Manitoba; however, the field as defined today was born in the mid-2010s. The modern definition of global neurosurgery was born from a combination of global health and neurosurgery. Hence, global neurosurgery is conceived as a subspecialty of global health within global surgery.
== Burden of diseases amenable to neurosurgery ==
Around 22.6 million people are affected by diseases amenable to neurosurgery each year, and 13.8 million require surgical intervention. The burden of diseases amenable to neurosurgery is disproportionately distributed globally, with low- and middle-income countries bearing more than 78.1% of cases. Low- and middle-income countries lack the workforce, infrastructure, funding, and data needed to address the disease burden. High-income country patients, especially in rural areas and from economically-disadvantaged backgrounds, face unique challenges in accessing safe, timely, and affordable neurosurgical care. For this reason, most global neurosurgery work has focused on access to care in low- and middle-incomce countries despite the global nature of disparities in accessing neurosurgical care.
== Practice ==
Global neurosurgery practice involves advocacy, education, policy, research, and service delivery. The components of global neurosurgery practice are interdependent but global neurosurgeons tend to focus their practice on one or two of them. This trend has allowed for specialization within the field and greater collaboration between individuals and institutions.
=== Advocacy ===
Advocacy efforts happen at the international, regional, and local levels and in collaboration with health initiatives that share similar goals with global neurosurgery - universal health coverage and sustainable development. Internationally, global neurosurgery advocacy groups participate in high-level health policy events like the World Health Assembly and the United Nations General Assembly. Global neurosurgery advocates have contributed to numerous high-level decisions including folate fortification, detection and management of congenital malformations, and injury prevention. Locally, global neurosurgery advocacy groups are constituted of health workers and other patient advocates. These groups affect local decision making but they are equally active internationally. Many local advocacy groups are members of international advocacy groups like the G4 Alliance, People and Organisations United for Spina Bifida and Hydrocephalus (PUSH!) Global Alliance, and International Federation Spina Bifida and Hydrocephalus (IFSBH). Local global neurosurgery advocacy groups work within these international organizations to coordinate advocacy efforts regionally and globally.
=== Education ===
Global neurosurgery education focuses on two aspects. First, global neurosurgery educators train specialists to serve under-resourced regions. The training focuses primarily on safe and quality service delivery within underserved communities. These global neurosurgery education efforts can be divided into non-specialized and specialized training. Non-specialized training or education for task-sharing/-shifting targets non-specialized healthcare workers such as general surgeons, clinical officers, and general practitioners. Non-specialized training is especially important in increasing access to essential and emergency neurosurgical care rapidly. Non-specialized training, unlike specialized training, can be done in shorter periods, with larger cohorts, and with fewer resources. Specialized neurosurgery training can last anywhere from a few months to 8 years depending on the training level. Postgraduate medical fellowships in one of the neurosurgical subspecialties are open to graduate neurosurgery residents/registrars and can last between three and 24 months. On the other hand, neurosurgery residencies last between 4 and 8 years.
The other focus of global neurosurgery education is fellowships that introduce trainees to global and public health concepts. Global neurosurgery fellowships are relatively new but increasingly popular with institutions like Cambridge, Cornell, Duke, Harvard, and the University of Cape Town offering specialized training.
=== Policy ===
Global neurosurgeons contribute significantly to the design and implementation of health policies that improve access to safe, timely, and affordable neurosurgical care globally. Prime examples of global neurosurgery policy efforts include the comprehensive health policy guidelines for traumatic brain and spine injuries and for spina bifida and hydrocephalus. The comprehensive policy guidelines address challenges that affect the patient continuum of care and suggest solutions for every component of the healthcare system. These documents were designed for policymakers in areas with a large burden of diseases amenable to neurosurgery. Traumatic brain and spine injuries were chosen because they constitute more than 47.1% of the global neurosurgical disease burden while hydrocephalus and spina bifida were chosen for their deleterious impact on children.
=== Research ===
Research is an indispensable aspect of global neurosurgery practice called academic global neurosurgery. Academic global neurosurgery has a broad focus and uses concepts from epidemiology, health economics, health policy, health services, health systems, implementation & dissemination science, and patient safety & quality improvement research. Academic global neurosurgery's exponential growth since 2016 is the result of increased interest and support from the neurosurgical community characterized by the creation of an ad-hoc committee within the World Federation of Neurosurgical Societies, publication of special issues in reputable peer-reviewed journals, creation of a specialized journal, and the creation of global neurosurgery centers. Academic global neurosurgery identifies challenges to accessing neurosurgical care and proposes solutions that increase access to care. The evidence generated by academic global neurosurgery informs the other aspects of global neurosurgery practice.
=== Service delivery ===
Service delivery is the oldest component of global neurosurgery practice and can be traced back to the colonial era when surgeons would deliver care in colonies. Global neurosurgery aims to reduce barriers to essential and emergency neurosurgery procedures such as those needed for acute stroke, neural tube defects, traumatic brain injuries, and traumatic spine injuries.
Low- and middle-income country patients have worse outcomes than their high-income country counterparts because they regularly face barriers to accessing timely and safe neurosurgical care.
The workforce deficit in low- and middle-income countries constitutes a significant barrier to receiving care. Although former colonies have trained local neurosurgeons since their independence, the neurosurgical workforce density in many low- and middle-income countries remains below the World Federation of Neurosurgical Societies' recommendation of 1 neurosurgeon per 200,000 people. In addition, the majority of low- and middle income countries have geographical disparities in the neurosurgical workforce with most neurosurgeons working in urban areas whereas the majority of people in these countries are rural-dwellers. In addition, surgical non-governmental organizations from high-income countries help fill the service delivery gap in some low- and middle-income countries. Although most neurosurgical non-governmental organizations offer short-term service delivery in low- and middle-income countries, some like CURE International offer long-term care.
The neurosurgical workforce in low- and middle-income countries has increased gradually in the past decade thanks to targeted efforts from the global neurosurgery community. For example, the World Federation of Neurosurgical Societies supports the training of aspiring neurosurgeons from understaffed countries through scholarships at accredited centers in Africa, Asia, and South America.
Young neurosurgeons from under-resourced regions who have been trained in advanced neurosurgical techniques report their patients do not get safe and timely care because of inadequate infrastructure. Access to neurosurgical infrastructure can be assessed summarily using the World Federation of Neurosurgical Societies facility three-tier classification or using hospital assessment tools. The World Federation of Neurosurgical Societies facility three-tier classification groups facilities into level 1 (equipment for emergency neurosurgery procedures), level 2 (equipment to perform basic microneurosurgical procedures), and level 3 (equipment for complex and advanced neurosurgery).
== References == | Wikipedia/Global_neurosurgery |
Large-scale brain networks (also known as intrinsic brain networks) are collections of widespread brain regions showing functional connectivity by statistical analysis of the fMRI BOLD signal or other recording methods such as EEG, PET and MEG. An emerging paradigm in neuroscience is that cognitive tasks are performed not by individual brain regions working in isolation but by networks consisting of several discrete brain regions that are said to be "functionally connected". Functional connectivity networks may be found using algorithms such as cluster analysis, spatial independent component analysis (ICA), seed based, and others. Synchronized brain regions may also be identified using long-range synchronization of the EEG, MEG, or other dynamic brain signals.
The set of identified brain areas that are linked together in a large-scale network varies with cognitive function. When the cognitive state is not explicit (i.e., the subject is at "rest"), the large-scale brain network is a resting state network (RSN). As a physical system with graph-like properties, a large-scale brain network has both nodes and edges and cannot be identified simply by the co-activation of brain areas. In recent decades, the analysis of brain networks was made feasible by advances in imaging techniques as well as new tools from graph theory and dynamical systems.
The Organization for Human Brain Mapping has created the Workgroup for HArmonized Taxonomy of NETworks (WHATNET) group to work towards a consensus regarding network nomenclature. WHATNET conducted a survey in 2021 which showed a large degree of agreement about the name and topography of three networks: the "somato network", the "default network" and the "visual network", while other networks had less agreement. Several issues make the work of creating a common atlas for networks difficult: some of these issues are the variability of spatial and time scales, variability across individuals, and the dynamic nature of some networks.
Some large-scale brain networks are identified by their function and provide a coherent framework for understanding cognition by offering a neural model of how different cognitive functions emerge when different sets of brain regions join together as self-organized coalitions. The number and composition of the coalitions will vary with the algorithm and parameters used to identify them. In one model, there is only the default mode network and the task-positive network, but most current analyses show several networks, from a small handful to 17. The most common and stable networks are enumerated below. The regions participating in a functional network may be dynamically reconfigured.
Disruptions in activity in various networks have been implicated in neuropsychiatric disorders such as depression, Alzheimer's, autism spectrum disorder, schizophrenia, ADHD and bipolar disorder.
== Commonly identified networks ==
Because brain networks can be identified at various different resolutions and with various different neurobiological properties, there is currently no universal atlas of brain networks that fits all circumstances. Uddin, Yeo, and Spreng proposed in 2019 that the following six networks should be defined as core networks based on converging evidences from multiple studies to facilitate communication between researchers.
=== Default mode (medial frontoparietal) ===
The default mode network is active when an individual is awake and at rest. It preferentially activates when individuals focus on internally-oriented tasks such as daydreaming, envisioning the future, retrieving memories, and theory of mind. It is negatively correlated with brain systems that focus on external visual signals. It is the most widely researched network.
=== Salience (midcingulo-insular) ===
The salience network consists of several structures, including the anterior (bilateral) insula, dorsal anterior cingulate cortex, and three subcortical structures which are the ventral striatum, substantia nigra/ventral tegmental region. It plays the key role of monitoring the salience of external inputs and internal brain events. Specifically, it aids in directing attention by identifying important biological and cognitive events.
This network includes the ventral attention network, which primarily includes the temporoparietal junction and the ventral frontal cortex of the right hemisphere. These areas respond when behaviorally relevant stimuli occur unexpectedly. The ventral attention network is inhibited during focused attention in which top-down processing is being used, such as when visually searching for something. This response may prevent goal-driven attention from being distracted by non-relevant stimuli. It becomes active again when the target or relevant information about the target is found.
=== Attention (dorsal frontoparietal) ===
This network is involved in the voluntary, top-down deployment of attention. Within the dorsal attention network, the intraparietal sulcus and frontal eye fields influence the visual areas of the brain. These influencing factors allow for the orientation of attention.
=== Control (lateral frontoparietal) ===
This network initiates and modulates cognitive control and comprises 18 sub-regions of the brain. There is a strong correlation between fluid intelligence and the involvement of the fronto-parietal network with other networks.
Versions of this network have also been called the central executive (or executive control) network and the cognitive control network.
=== Sensorimotor or somatomotor (pericentral) ===
This network processes somatosensory information and coordinates motion. The auditory cortex may be included.
=== Visual (occipital) ===
This network handles visual information processing.
== Other networks ==
Different methods and data have identified several other brain networks, many of which greatly overlap or are subsets of more well-characterized core networks.
Limbic
Auditory
Right/left executive
Cerebellar
Spatial attention
Language
Lateral visual
Temporal
Visual perception/imagery
== See also ==
Complex network
Neural network (biology)
== References == | Wikipedia/Large-scale_brain_network |
Molecular neuroscience is a branch of neuroscience that observes concepts in molecular biology applied to the nervous systems of animals. The scope of this subject covers topics such as molecular neuroanatomy, mechanisms of molecular signaling in the nervous system, the effects of genetics and epigenetics on neuronal development, and the molecular basis for neuroplasticity and neurodegenerative diseases. As with molecular biology, molecular neuroscience is a relatively new field that is considerably dynamic.
== Locating neurotransmitters ==
In molecular biology, communication between neurons typically occurs by chemical transmission across gaps between the cells called synapses. The transmitted chemicals, known as neurotransmitters, regulate a significant fraction of vital body functions. It is possible to anatomically locate neurotransmitters by labeling techniques. It is possible to chemically identify certain neurotransmitters such as catecholamines by fixing neural tissue sections with formaldehyde. This can give rise to formaldehyde-induced fluorescence when exposed to ultraviolet light. Dopamine, a catecholamine, was identified in the nematode C. elegans by using this technique.
Immunocytochemistry, which involves raising antibodies against targeted chemical or biological entities, includes a few other techniques of interest. A targeted neurotransmitter could be specifically tagged by primary and secondary antibodies with radioactive labeling in order to identify the neurotransmitter by autoradiography. The presence of neurotransmitters (though not necessarily the location) can be observed in enzyme-linked immunocytochemistry or enzyme-linked immunosorbent assays (ELISA) in which substrate-binding in the enzymatic assays can induce precipitates, fluorophores, or chemiluminescence. In the event that neurotransmitters cannot be histochemically identified, an alternative method is to locate them by their neural uptake mechanisms.
== Voltage-gated ion channels ==
Excitable cells in living organisms have voltage-gated ion channels. These can be observed throughout the nervous system in neurons. The first ion channels to be characterized were the sodium and potassium ion channels by A.L. Hodgkin and A.F. Huxley in the 1950s upon studying the giant axon of the squid genus Loligo. Their research demonstrated the selective permeability of cellular membranes, dependent on physiological conditions, and the electrical effects that result from these permeabilities to produce action potentials.
=== Sodium ion channels ===
Sodium channels were the first voltage-gated ion channels to be isolated in 1984 from the eel Electrophorus electricus by Shosaku Numa. The pufferfish toxin tetrodotoxin (TTX), a sodium channel blocker, was used to isolate the sodium channel protein by binding it using the column chromatography technique for chemical separation. The amino acid sequence of the protein was analyzed by Edman degradation and then used to construct a cDNA library which could be used to clone the channel protein. Cloning the channel itself allowed for applications such as identifying the same channels in other animals.
Sodium channels are known for working in concert with potassium channels during the development of graded potentials and action potentials. Sodium channels allow an influx of Na+ ions into a neuron, resulting in a depolarization from the resting membrane potential of a neuron to lead to a graded potential or action potential, depending on the degree of depolarization.
=== Potassium ion channels ===
Potassium channels come in a variety of forms, are present in most eukaryotic cells, and typically tend to stabilize the cell membrane at the potassium equilibrium potential. As with sodium ions, graded potentials and action potentials are also dependent on potassium channels. While influx of Na+ ions into a neuron induce cellular depolarization, efflux of K+ ions out of a neuron causes a cell to repolarize to resting membrane potential. The activation of potassium ion channels themselves are dependent on the depolarization resulting from Na+ influx during an action potential.
As with sodium channels, the potassium channels have their own toxins that block channel protein action. An example of such a toxin is the large cation, tetraethylammonium (TEA), but it is notable that the toxin does not have the same mechanism of action on all potassium channels, given the variety of channel types across species. The presence of potassium channels was first identified in Drosophila melanogaster mutant flies that shook uncontrollably upon anesthesia due to problems in cellular repolarization that led to abnormal neuron and muscle electrophysiology. Potassium channels were first identified by manipulating molecular genetics (of the flies) instead of performing channel protein purification because there were no known high-affinity ligands for potassium channels (such as TEA) at the time of discovery.
=== Calcium ion channels ===
Calcium channels are important for certain cell-signaling cascades as well as neurotransmitter release at axon terminals. A variety of different types of calcium ion channels are found in excitable cells. As with sodium ion channels, calcium ion channels have been isolated and cloned by chromatographic purification techniques. It is notable, as with the case of neurotransmitter release, that calcium channels can interact with intracellular proteins and plays a strong role in signaling, especially in locations such as the sarcoplasmic reticulum of muscle cells.
== Receptors ==
Various types of receptors can be used for cell signaling and communication and can include ionotropic receptors and metabotropic receptors. These cell surface receptor types are differentiated by the mechanism and duration of action with ionotropic receptors being associated with fast signal transmission and metabotropic receptors being associated with slow signal transmission. Metabotropic receptors happen to cover a wide variety of cell-surface receptors with notably different signaling cascades.
=== Ionotropic receptors ===
Ionotropic receptors, otherwise known as ligand-gated ion channels, are fast acting receptors that mediate neural and physiological function by ion channel flow with ligand-binding. Nicotinic, GABA, and Glutamate receptors are among some of the cell surface receptors regulated by ligand-gated ion channel flow. GABA is the brain's main inhibitory neurotransmitter and glutamate is the brain's main excitatory neurotransmitter.
==== GABA receptors ====
GABAA and GABAC receptors are known to be ionotropic, while the GABAB receptor is metabotropic. GABAA receptors mediate fast inhibitory responses in the central nervous system (CNS) and are found on neurons, glial cells, and adrenal medulla cells. It is responsible for inducing Cl− ion influx into cells, thereby reducing the probability that membrane depolarization will occur upon the arrival of a graded potential or an action potential. GABA receptors can also interact with non-endogenous ligands to influence activity. For example, the compound diazepam (marketed as Valium) is an allosteric agonist which increases the affinity of the receptor for GABA. The increased physiological inhibitory effects resulting from increased GABA binding make diazepam a useful tranquilizer or anticonvulsant (antiepileptic drugs). On the other hand, GABA receptors can also be targeted by decreasing Cl− cellular influx with the effect of convulsants like picrotoxin. The antagonistic mechanism of action for this compound is not directly on the GABA receptor, but there are other compounds that are capable of allosteric inactivation, including T-butylbicyclophorothionate (TBPS) and pentylenetetrazole (PZT).
Compared with GABAA, GABAC receptors have a higher affinity for GABA, they are likely to be longer-lasting in activity, and their responses are likely to be generated by lower GABA concentrations.
==== Glutamate receptors ====
Ionotropic glutamate receptors can include NMDA, AMPA, and kainate receptors. These receptors are named after agonists that facilitate glutamate activity. NMDA receptors are notable for their excitatory mechanisms to affect neuronal plasticity in learning and memory, as well as neuropathologies such as stroke and epilepsy. NDMA receptors have multiple binding sites just like ionotropic GABA receptors and can be influenced by co-agonists such the glycine neurotransmitter or phencyclidine (PCP). The NMDA receptors carry a current by Ca2+ ions and can be blocked by extracellular Mg2+ ions depending on voltage and membrane potential. This Ca2+ influx is increased by excitatory postsynaptic potentials (EPSPs) produced by NMDA receptors, activating Ca2+-based signaling cascades (such as neurotransmitter release). AMPA generate shorter and larger excitatory postsynaptic currents than other ionotropic glutamate receptors.
==== Nicotinic ACh receptors ====
Nicotinic receptors bind the acetylcholine (ACh) neurotransmitter to produce non-selective cation channel flow that generates excitatory postsynaptic responses. Receptor activity, which can be influenced by nicotine consumption, produces feelings of euphoria, relaxation, and inevitably addiction in high levels.
=== Metabotropic receptors ===
Metabotropic receptors, are slow response receptors in postsynaptic cells. Typically these slow responses are characterized by more elaborate intracellular changes in biochemistry. Responses of neurotransmitter uptake by metabotropic receptors can result in the activation of intracellualar enzymes and cascades involving second messengers, as is the case with G protein-linked receptors. Various metabotropic receptors can include certain glutamate receptors, muscarinic ACh receptors, GABAB receptors, and receptor tyrosine kinases.
==== G protein-linked receptors ====
The G protein-linked signaling cascade can significantly amplify the signal of a particular neurotransmitter to produce hundreds to thousands of second messengers in a cell. The mechanism of action by which G protein-linked receptors cause a signaling cascade is as follows:
Neurotransmitter binds to the receptor
The receptor undergoes a conformational change to allow G-protein complex binding
GDP is exchanged with GTP upon G protein complex binding to the receptor
The α-subunit of the G protein complex is bound to GTP and separates to bind with a target protein such as adenylate cyclase
The binding to the target protein either increases or decreases the rate of second messenger (such as cyclic AMP) production
GTPase hydrolyzes the α-subunit so that is bound to GDP and the α-subunit returns to the G protein complex inactive
== Neurotransmitter release ==
Neurotransmitters are released in discrete packets known as quanta from the axon terminal of one neuron to the dendrites of another across a synapse. These quanta have been identified by electron microscopy as synaptic vesicles. Two types of vesicles are small synaptic vessicles (SSVs), which are about 40-60nm in diameter, and large dense-core vesicles (LDCVs), electron-dense vesicles approximately 120-200nm in diameter. The former is derived from endosomes and houses neurotransmitters such as acetylcholine, glutamate, GABA, and glycine. The latter is derived from the Golgi apparatus and houses larger neurotransmitters such as catecholamines and other peptide neurotransmitters. Neurotransmitters are released from an axon terminal and bind to postsynaptic dendrites in the following procession:
Mobilization/recruitment of synaptic vesicle from cytoskeleton
Docking of vesicle (binding) to presynaptic membrane
Priming of vesicle by ATP (relatively slow step)
Fusion of primed vesicle with presynaptic membrane and exocytosis of the housed neurotransmitter
Uptake of neurotransmitters in receptors of a postsynaptic cell
Initiation or inhibition of action potential in postsynaptic cell depending on whether the neurotransmitters are excitatory or inhibitory (excitatory will result in depolarization of the postsynaptic membrane)
=== Neurotransmitter release is calcium-dependent ===
Neurotransmitter release is dependent on an external supply of Ca2+ ions which enter axon terminals via voltage-gated calcium channels. Vesicular fusion with the terminal membrane and release of the neurotransmitter is caused by the generation of Ca2+ gradients induced by incoming action potentials. The Ca2+ ions cause the mobilization of newly synthesized vesicles from a reserve pool to undergo this membrane fusion. This mechanism of action was discovered in squid giant axons. Lowering intracellular Ca2+ ions provides a direct inhibitory effect on neurotransmitter release. After release of the neurotransmitter occurs, vesicular membranes are recycled to their origins of production. Calcium ion channels can vary depending on the location of incidence. For example, the channels at an axon terminal differ from the typical calcium channels of a cell body (whether neural or not). Even at axon terminals, calcium ion channel types can vary, as is the case with P-type calcium channels located at the neuromuscular junction.
== Neuronal gene expression ==
=== Sex differences ===
Differences in sex determination are controlled by sex chromosomes. Sex hormonal releases have a significant effect on sexual dimorphisms (phenotypic differentiation of sexual characteristics) of the brain. Recent studies seem to suggest that regulating these dimorphisms has implications for understanding normal and abnormal brain function. Sexual dimorphisms may be significantly influenced by sex-based brain gene expression which varies from species to species.
Animal models such as rodents, Drosophila melanogaster, and Caenorhabditis elegans, have been used to observe the origins and/or extent of sex bias in the brain versus the hormone-producing gonads of an animal. With the rodents, studies on genetic manipulation of sex chromosomes resulted in an effect on one sex that was completely opposite of the effect in the other sex. For example, a knockout of a particular gene only resulted in anxiety-like effects in males. With studies on D. menlanogaster it was found that a large brain sex bias of expression occurred even after the gonads were removed, suggesting that sex bias could be independent of hormonal control in certain aspects.
Observing sex-biased genes has the potential for clinical significance in observing brain physiology and the potential for related (whether directly or indirectly) neurological disorders. Examples of
diseases with sex biases in development include Huntington's disease, cerebral ischemia, and Alzheimer's disease.
=== Epigenetics of the brain ===
Many brain functions can be influenced at the cellular and molecular level by variations and changes in gene expression, without altering the sequence of DNA in an organism. This is otherwise known as epigenetic regulation. Examples of epigenetic mechanisms include histone modifications and DNA methylation. Such changes have been found to be strongly influential in the incidence of brain disease, mental illness, and addiction. Epigenetic control has been shown to be involved in high levels of plasticity in early development, thereby defining its importance in the critical period of an organism. Examples of how epigenetic changes can affect the human brain are as follows:
Higher methylation levels in rRNA genes in the hippocampus of the brain results in a lower production of proteins and thus limited hippocampal function can result in learning and memory impairment and resultant suicidal tendencies.
In a study comparing genetic differences between healthy people and psychiatric patients 60 different epigenetic markers associated with brain cell signaling were found.
Environmental factors such as child abuse appears to cause the expression of an epigenetic tag on glucocorticoid receptors (associated with stress responses) that was not found in suicide victims. This is an example of experience-dependent plasticity.
Environmental enrichment in individuals is associated with increased hippocampal gene histone acetylation and thus improved memory consolidation (notably spatial memory).
== Molecular mechanisms of neurodegenerative diseases ==
=== Excitotoxicity and glutamate receptors ===
Excitotoxicity is phenomenon in which glutamate receptors are inappropriately activated. It can be caused by prolonged excitatory synaptic transmission in which high levels of glutamate neurotransmitter cause excessive activation in a postsynaptic neuron that can result in the death of the postsynaptic neuron. Following brain injury (such as from ischemia), it has been found that excitotoxicity is a significant cause of neuronal damage. This can be understandable in the case where sudden perfusion of blood after reduced blood flow to the brain can result in excessive synaptic activity caused by the presence of increased glutamate and aspartate during the period of ischemia.
=== Alzheimer's disease ===
Alzheimer's disease is the most common neurodegenerative disease and is the most common form of dementia in the elderly. The disorder is characterized by progressive loss of memory and various cognitive functions. It is hypothesized that the deposition of amyloid-β peptide (40-42 amino acid residues) in the brain is integral in the incidence of Alzheimer's disease. Accumulation is purported to block hippocampal long-term potentiation. It is also possible that a receptor for amyloid-β oligomers could be a prion protein.
=== Parkinson's disease ===
Parkinson's disease is the second most common neurodegenerative disease after Alzheimer's disease. It is a hypokinetic movement basal ganglia disease caused by the loss of dopaminergic neurons in the substantia nigra of the human brain. The inhibitory outflow of the basal ganglia is thus not decreased, and so upper motor neurons, mediated by the thalamus, are not activated in a timely manner. Specific symptoms include rigidity, postural problems, slow movements, and tremors. Blocking GABA receptor input from medium spiny neurons to reticulata cells, causes inhibition of upper motor neurons similar to the inhibition that occurs in Parkinson's disease.
=== Huntington's disease ===
Huntington's disease is a hyperkinetic movement basal ganglia disease caused by lack of normal inhibitory inputs from medium spiny neurons of the basal ganglia. This poses the opposite effects of those associated with Parkinson's disease, including inappropriate activation of upper motor neurons. As with the GABAergic mechanisms observed in relation to Parkinson's disease, a GABA agonist injected into the substantia nigra pars reticulata decreases inhibition of upper motor neurons, resulting in ballistic involuntary motor movements, similar to symptoms of Huntington's disease.
== References == | Wikipedia/Molecular_neuroscience |
The neuron doctrine is the concept that the nervous system is made up of discrete individual cells, a discovery due to decisive neuro-anatomical work of Santiago Ramón y Cajal and later presented by, among others, H. Waldeyer-Hartz. The term neuron (spelled neurone in British English) was itself coined by Waldeyer as a way of identifying the cells in question. The neuron doctrine, as it became known, served to position neurons as special cases under the broader cell theory evolved some decades earlier. He appropriated the concept not from his own research but from the disparate observation of the histological work of Albert von Kölliker, Camillo Golgi, Franz Nissl, Santiago Ramón y Cajal, Auguste Forel and others.
== Historical context ==
Theodor Schwann proposed in 1839 that the tissues of all organisms are composed of cells. Schwann was expanding on the proposal of his good friend Matthias Jakob Schleiden the previous year that all plant tissues were composed of cells. The nervous system stood as an exception. Although nerve cells had been described in tissue by numerous investigators including Jan Purkinje, Gabriel Valentin, and Robert Remak, the relationship between the nerve cells and other features such as dendrites and axons was not clear. The connections between the large cell bodies and smaller features could not be observed, and it was possible that neurofibrils would stand as an exception to cell theory as non-cellular components of living tissue. Technical limitations of microscopy and tissue preparation were largely responsible. Chromatic aberration, spherical aberration and the dependence on natural light all played a role in limiting microscope performance in the early 19th century. Tissue was typically lightly mashed in water and pressed between a glass slide and cover slip. There was also a limited number of dyes and fixatives available prior to the middle of the 19th century.
A landmark development came from Camillo Golgi who invented a silver staining technique in 1873 which he called la reazione nera (black reaction), but more popularly known as Golgi stain or Golgi method. Using this technique nerve cells with their highly branched dendrites and axon could be clearly visualised against a yellow background. Unfortunately Golgi described the nervous system as a continuous single network, in support of a notion called reticular theory. It was reasonable at the time because under light microscope the nerve cells are merely a mesh of single thread. Santiago Ramón y Cajal started investigating nervous system in 1887 using Golgi stain. In the first issue of the Revista Trimestral de Histología Normal y Patológica (May, 1888) Ramón y Cajal reported that the nerve cells were not continuous in the brain of birds. Ramón y Cajal's discovery was the decisive evidence for the discontinuity of nervous system and the presence of large number of individual nerve cells. Golgi refused to accept the neuron theory and hung on to the reticular theory. Golgi and Ramón y Cajal were jointly awarded the 1906 Nobel Prize for Physiology or Medicine, but the controversy between the two scientists continued. The matter was finally resolved in the 1950s with the development of electron microscopy by which it was unambiguously demonstrated that nerve cells were individual cells interconnected through synapses to form a nervous system, thereby validating the neuron theory.
== Elements ==
Neuron theory is an example of consilience where low level theories are absorbed into higher level theories that explain the base data as part of higher order structure. As a result, the neuron doctrine has multiple elements, each of which were the subject of low level theories, debate, and primary data collection. Some of these elements are imposed by the necessity of cell theory that Waldeyer was trying to use to explain the direct observations, and other elements try to explain observations so that they are compatible with cell theory.
Neural units
The brain is made up of individual units that contain specialized features such as dendrites, a cell body, and an axon.
Neurons are cells
These individual units are cells as understood from other tissues in the body.
Specialization
These units may differ in size, shape, and structure according to their location or functional specialization.
Nucleus is key
The nucleus is the trophic center for the cell. If the cell is divided only the portion containing the nucleus will survive.
Nerve fibers are cell processes
Nerve fibers are outgrowths of nerve cells.
Cell division
Nerve cells are generated by cell division.
Contact
Nerve cells are connected by sites of contact and not cytoplasmic continuity. Waldeyer himself was neutral on this point, and strictly speaking the neuron doctrine does not depend upon this element. The heart is an example of excitable tissue where the cells connect via cytoplasmic continuity and yet is perfectly consistent with cell theory. This is true of other examples such as connections between horizontal cells of the retina, or the Mauthner cell synapse in goldfish.
Law of dynamic polarization
Although the axon can conduct in both directions, in tissue there is a preferred direction for transmission from cell to cell. Later elements that were not included by Waldeyer, but were added in the following decades.
Synapse
A barrier to transmission exists at the site of contact between two neurons that may permit transmission.
Unity of transmission
If a contact is made between two cells, then that contact can be either excitatory or inhibitory, but will always be of the same type.
Dale's law
Each nerve terminal releases a single type of transmitter.
== Update ==
While the neuron doctrine is a central tenet of modern neuroscience, recent studies suggest that there are notable exceptions and important additions to our knowledge about how neurons function.
Electrical synapses are more common in the central nervous system than previously thought. Thus, rather than functioning as individual units, in some parts of the brain large ensembles of neurons may be active simultaneously to process neural information. Electrical synapses are formed by gap junctions that allow molecules to directly pass between neurons, creating a cytoplasm-to-cytoplasm connection, known as a syncytium.
Furthermore, the phenomenon of cotransmission, in which more than one neurotransmitter is released from a single presynaptic terminal (contrary to Dale's law), contributes to the complexity of information transmission within the nervous system.
== References ==
Bullock, T.H.; Bennett, M.V.L.; Johnston, D.; Josephson, R.; Marder, E.; Fields, R.D. (2005). "The Neuron Doctrine, Redux". Science. 310 (5749): 791–793. doi:10.1126/science.1114394. PMID 16272104. S2CID 170670241.
== External links ==
The discovery of the neuron | Wikipedia/Neuron_doctrine |
Biomedical sciences are a set of sciences applying portions of natural science or formal science, or both, to develop knowledge, interventions, or technology that are of use in healthcare or public health. Such disciplines as medical microbiology, clinical virology, clinical epidemiology, genetic epidemiology, and biomedical engineering are medical sciences. In explaining physiological mechanisms operating in pathological processes, however, pathophysiology can be regarded as basic science.
Biomedical Sciences, as defined by the UK Quality Assurance Agency for Higher Education Benchmark Statement in 2015, includes those science disciplines whose primary focus is the biology of human health and disease and ranges from the generic study of biomedical sciences and human biology to more specialised subject areas such as pharmacology, human physiology and human nutrition. It is underpinned by relevant basic sciences including anatomy and physiology, cell biology, biochemistry, microbiology, genetics and molecular biology, pharmacology, immunology, mathematics and statistics, and bioinformatics. As such the biomedical sciences have a much wider range of academic and research activities and economic significance than that defined by hospital laboratory sciences. Biomedical Sciences are the major focus of bioscience research and funding in the 21st century.
== Roles within biomedical science ==
A sub-set of biomedical sciences is the science of clinical laboratory diagnosis. This is commonly referred to in the UK as 'biomedical science' or 'healthcare science'. There are at least 45 different specialisms within healthcare science, which are traditionally grouped into three main divisions:
specialisms involving life sciences
specialisms involving physiological science
specialisms involving medical physics or bioengineering
== Life sciences specialties ==
Molecular toxicology
Molecular pathology
Blood transfusion science
Cervical cytology
Clinical biochemistry
Clinical embryology
Clinical immunology
Clinical pharmacology and therapeutics
Electron microscopy
External quality assurance
Haematology
Haemostasis and thrombosis
Histocompatibility and immunogenetics
Histopathology and cytopathology
Molecular genetics and cytogenetics
Molecular biology and cell biology
Microbiology including mycology
Bacteriology
Tropical diseases
Phlebotomy
Tissue banking/transplant
Virology
== Physiological science specialisms ==
== Physics and bioengineering specialisms ==
== Biomedical science in the United Kingdom ==
The healthcare science workforce is an important part of the UK's National Health Service. While people working in healthcare science are only 5% of the staff of the NHS, 80% of all diagnoses can be attributed to their work.
The volume of specialist healthcare science work is a significant part of the work of the NHS. Every year, NHS healthcare scientists carry out:
nearly 1 billion pathology laboratory tests
more than 12 million physiological tests
support for 1.5 million fractions of radiotherapy
The four governments of the UK have recognised the importance of healthcare science to the NHS, introducing the Modernising Scientific Careers initiative to make certain that the education and training for healthcare scientists ensures there is the flexibility to meet patient needs while keeping up to date with scientific developments.
Graduates of an accredited biomedical science degree programme can also apply for the NHS' Scientist training programme, which gives successful applicants an opportunity to work in a clinical setting whilst also studying towards an MSc or Doctoral qualification.
== Biomedical Science in the 20th century ==
At this point in history the field of medicine was the most prevalent sub field of biomedical science, as several breakthroughs on how to treat diseases and help the immune system were made. As well as the birth of body augmentations.
=== 1910s ===
In 1912, the Institute of Biomedical Science was founded in the United Kingdom. The institute is still standing today and still regularly publishes works in the major breakthroughs in disease treatments and other breakthroughs in the field 117 years later. The IBMS today represents approximately 20,000 members employed mainly in National Health Service and private laboratories.
=== 1920s ===
In 1928, British Scientist Alexander Fleming discovered the first antibiotic penicillin. This was a huge breakthrough in biomedical science because it allowed for the treatment of bacterial infections.
In 1926, the first artificial pacemaker was made by Australian physician Dr. Mark C. Lidwell. This portable machine was plugged into a lighting point. One pole was applied to a skin pad soaked with strong salt solution, while the other consisted of a needle insulated up to the point and was plunged into the appropriate cardiac chamber and the machine started. A switch was incorporated to change the polarity. The pacemaker rate ranged from about 80 to 120 pulses per minute and the voltage also variable from 1.5 to 120 volts.
=== 1930s ===
The 1930s was a huge era for biomedical research, as this was the era where antibiotics became more widespread and vaccines started to be developed. In 1935, the idea of a polio vaccine was introduced by Dr. Maurice Brodie. Brodie prepared a died poliomyelitis vaccine, which he then tested on chimpanzees, himself, and several children. Brodie's vaccine trials went poorly since the polio-virus became active in many of the human test subjects. Many subjects had fatal side effects, paralyzing, and causing death.
=== 1940s ===
During and after World War II, the field of biomedical science saw a new age of technology and treatment methods. For instance in 1941 the first hormonal treatment for prostate cancer was implemented by Urologist and cancer researcher Charles B. Huggins. Huggins discovered that if you remove the testicles from a man with prostate cancer, the cancer had nowhere to spread, and nothing to feed on thus putting the subject into remission. This advancement lead to the development of hormonal blocking drugs, which is less invasive and still used today. At the tail end of this decade, the first bone marrow transplant was done on a mouse in 1949. The surgery was conducted by Dr. Leon O. Jacobson, he discovered that he could transplant bone marrow and spleen tissues in a mouse that had both no bone marrow and a destroyed spleen. The procedure is still used in modern medicine today and is responsible for saving countless lives.
=== 1950s ===
In the 1950s, we saw innovation in technology across all fields, but most importantly there were many breakthroughs which led to modern medicine. On 6 March 1953, Dr. Jonas Salk announced the completion of the first successful killed-virus Polio vaccine. The vaccine was tested on about 1.6 million Canadian, American, and Finnish children in 1954. The vaccine was announced as safe on 12 April 1955.
== See also ==
Biomedical research institution Austral University Hospital
== References ==
== External links ==
Extraordinary You: Case studies of Healthcare scientists in the UK's National Health Service
National Institute of Environmental Health Sciences
The US National Library of Medicine
National Health Service | Wikipedia/Biomedical_sciences |
I of the Vortex: From Neurons to Self is a popular science book by the Colombian neuroscientist Rodolfo Llinás, published in February 2002 by MIT Press. and whose Spanish edition features a prologue by his friend, Nobel laureate Gabriel García Márquez.
The book is considered a best seller for scientific dissemination and won the "best health book" award at the International Latino Book Award Fair in BookExpo America 2013, in New York, and according to Google Scholar has received more than 1000 citations.
== Content ==
The book traces the history of neuroscience in its search for trying to explain the functioning of the mind and the brain. In addition, the author includes some of his ideas and research, published in international research journals, but prepared for a general public.
== See also ==
Human evolution
Neuroscience
== References ==
== External links ==
NYU Medical Center Rodolfo Llinás
Librería Norma
The MIT Press: I of the vortex | Wikipedia/I_of_the_vortex:_from_neurons_to_self |
Biological neuron models, also known as spiking neuron models, are mathematical descriptions of the conduction of electrical signals in neurons. Neurons (or nerve cells) are electrically excitable cells within the nervous system, able to fire electric signals, called action potentials, across a neural network. These mathematical models describe the role of the biophysical and geometrical characteristics of neurons on the conduction of electrical activity.
Central to these models is the description of how the membrane potential (that is, the difference in electric potential between the interior and the exterior of a biological cell) across the cell membrane changes over time. In an experimental setting, stimulating neurons with an electrical current generates an action potential (or spike), that propagates down the neuron's axon. This axon can branch out and connect to a large number of downstream neurons at sites called synapses. At these synapses, the spike can cause the release of neurotransmitters, which in turn can change the voltage potential of downstream neurons. This change can potentially lead to even more spikes in those downstream neurons, thus passing down the signal. As many as 95% of neurons in the neocortex, the outermost layer of the mammalian brain, consist of excitatory pyramidal neurons, and each pyramidal neuron receives tens of thousands of inputs from other neurons. Thus, spiking neurons are a major information processing unit of the nervous system.
One such example of a spiking neuron model may be a highly detailed mathematical model that includes spatial morphology. Another may be a conductance-based neuron model that views neurons as points and describes the membrane voltage dynamics as a function of trans-membrane currents. A mathematically simpler "integrate-and-fire" model significantly simplifies the description of ion channel and membrane potential dynamics (initially studied by Lapique in 1907).
== Biological background, classification, and aims of neuron models ==
Non-spiking cells, spiking cells, and their measurement
Not all the cells of the nervous system produce the type of spike that defines the scope of the spiking neuron models. For example, cochlear hair cells, retinal receptor cells, and retinal bipolar cells do not spike. Furthermore, many cells in the nervous system are not classified as neurons but instead are classified as glia.
Neuronal activity can be measured with different experimental techniques, such as the "Whole cell" measurement technique, which captures the spiking activity of a single neuron and produces full amplitude action potentials.
With extracellular measurement techniques, one or more electrodes are placed in the extracellular space. Spikes, often from several spiking sources, depending on the size of the electrode and its proximity to the sources, can be identified with signal processing techniques. Extracellular measurement has several advantages:
It is easier to obtain experimentally;
It is robust and lasts for a longer time;
It can reflect the dominant effect, especially when conducted in an anatomical region with many similar cells.
Overview of neuron models
Neuron models can be divided into two categories according to the physical units of the interface of the model. Each category could be further divided according to the abstraction/detail level:
Electrical input–output membrane voltage models – These models produce a prediction for membrane output voltage as a function of electrical stimulation given as current or voltage input. The various models in this category differ in the exact functional relationship between the input current and the output voltage and in the level of detail. Some models in this category predict only the moment of occurrence of the output spike (also known as "action potential"); other models are more detailed and account for sub-cellular processes. The models in this category can be either deterministic or probabilistic.
Natural stimulus or pharmacological input neuron models – The models in this category connect the input stimulus, which can be either pharmacological or natural, to the probability of a spike event. The input stage of these models is not electrical but rather has either pharmacological (chemical) concentration units, or physical units that characterize an external stimulus such as light, sound, or other forms of physical pressure. Furthermore, the output stage represents the probability of a spike event and not an electrical voltage.
Although it is not unusual in science and engineering to have several descriptive models for different abstraction/detail levels, the number of different, sometimes contradicting, biological neuron models is exceptionally high. This situation is partly the result of the many different experimental settings, and the difficulty to separate the intrinsic properties of a single neuron from measurement effects and interactions of many cells (network effects).
Aims of neuron models
Ultimately, biological neuron models aim to explain the mechanisms underlying the operation of the nervous system. However, several approaches can be distinguished, from more realistic models (e.g., mechanistic models) to more pragmatic models (e.g., phenomenological models). Modeling helps to analyze experimental data and address questions. Models are also important in the context of restoring lost brain functionality through neuroprosthetic devices.
== Electrical input–output membrane voltage models ==
The models in this category describe the relationship between neuronal membrane currents at the input stage and membrane voltage at the output stage. This category includes (generalized) integrate-and-fire models and biophysical models inspired by the work of Hodgkin–Huxley in the early 1950s using an experimental setup that punctured the cell membrane and allowed to force a specific membrane voltage/current.
Most modern electrical neural interfaces apply extra-cellular electrical stimulation to avoid membrane puncturing, which can lead to cell death and tissue damage. Hence, it is not clear to what extent the electrical neuron models hold for extra-cellular stimulation (see e.g.).
=== Hodgkin–Huxley ===
The Hodgkin–Huxley model (H&H model)
is a model of the relationship between the flow of ionic currents across the neuronal cell membrane and the membrane voltage of the cell. It consists of a set of nonlinear differential equations describing the behavior of ion channels that permeate the cell membrane of the squid giant axon. Hodgkin and Huxley were awarded the 1963 Nobel Prize in Physiology or Medicine for this work.
It is important to note the voltage-current relationship, with multiple voltage-dependent currents charging the cell membrane of capacity Cm
C
m
d
V
(
t
)
d
t
=
−
∑
i
I
i
(
t
,
V
)
.
{\displaystyle C_{\mathrm {m} }{\frac {dV(t)}{dt}}=-\sum _{i}I_{i}(t,V).}
The above equation is the time derivative of the law of capacitance, Q = CV where the change of the total charge must be explained as the sum over the currents. Each current is given by
I
(
t
,
V
)
=
g
(
t
,
V
)
⋅
(
V
−
V
e
q
)
{\displaystyle I(t,V)=g(t,V)\cdot (V-V_{\mathrm {eq} })}
where g(t,V) is the conductance, or inverse resistance, which can be expanded in terms of its maximal conductance ḡ and the activation and inactivation fractions m and h, respectively, that determine how many ions can flow through available membrane channels. This expansion is given by
g
(
t
,
V
)
=
g
¯
⋅
m
(
t
,
V
)
p
⋅
h
(
t
,
V
)
q
{\displaystyle g(t,V)={\bar {g}}\cdot m(t,V)^{p}\cdot h(t,V)^{q}}
and our fractions follow the first-order kinetics
d
m
(
t
,
V
)
d
t
=
m
∞
(
V
)
−
m
(
t
,
V
)
τ
m
(
V
)
=
α
m
(
V
)
⋅
(
1
−
m
)
−
β
m
(
V
)
⋅
m
{\displaystyle {\frac {dm(t,V)}{dt}}={\frac {m_{\infty }(V)-m(t,V)}{\tau _{\mathrm {m} }(V)}}=\alpha _{\mathrm {m} }(V)\cdot (1-m)-\beta _{\mathrm {m} }(V)\cdot m}
with similar dynamics for h, where we can use either τ and m∞ or α and β to define our gate fractions.
The Hodgkin–Huxley model may be extended to include additional ionic currents. Typically, these include inward Ca2+ and Na+ input currents, as well as several varieties of K+ outward currents, including a "leak" current.
The result can be at the small end of 20 parameters which one must estimate or measure for an accurate model. In a model of a complex system of neurons, numerical integration of the equations are computationally expensive. Careful simplifications of the Hodgkin–Huxley model are therefore needed.
The model can be reduced to two dimensions thanks to the dynamic relations which can be established between the gating variables. it is also possible to extend it to take into account the evolution of the concentrations (considered fixed in the original model).
=== Perfect Integrate-and-fire ===
One of the earliest models of a neuron is the perfect integrate-and-fire model (also called non-leaky integrate-and-fire), first investigated in 1907 by Louis Lapicque. A neuron is represented by its membrane voltage V which evolves in time during stimulation with an input current I(t) according
I
(
t
)
=
C
d
V
(
t
)
d
t
{\displaystyle I(t)=C{\frac {dV(t)}{dt}}}
which is just the time derivative of the law of capacitance, Q = CV. When an input current is applied, the membrane voltage increases with time until it reaches a constant threshold Vth, at which point a delta function spike occurs and the voltage is reset to its resting potential, after which the model continues to run. The firing frequency of the model thus increases linearly without bound as input current increases.
The model can be made more accurate by introducing a refractory period tref that limits the firing frequency of a neuron by preventing it from firing during that period. For constant input I(t)=I the threshold voltage is reached after an integration time tint=CVthr/I after starting from zero. After a reset, the refractory period introduces a dead time so that the total time until the next firing is tref+tint . The firing frequency is the inverse of the total inter-spike interval (including dead time). The firing frequency as a function of a constant input current, is therefore
f
(
I
)
=
I
C
V
t
h
+
t
r
e
f
I
.
{\displaystyle \,\!f(I)={\frac {I}{C_{\mathrm {} }V_{\mathrm {th} }+t_{\mathrm {ref} }I}}.}
A shortcoming of this model is that it describes neither adaptation nor leakage. If the model receives a below-threshold short current pulse at some time, it will retain that voltage boost forever - until another input later makes it fire. This characteristic is not in line with observed neuronal behavior. The following extensions make the integrate-and-fire model more plausible from a biological point of view.
=== Leaky integrate-and-fire ===
The leaky integrate-and-fire model, which can be traced back to Louis Lapicque, contains a "leak" term in the membrane potential equation that reflects the diffusion of ions through the membrane, unlike the non-leaky integrate-and-fire model. The model equation looks like
C
m
d
V
m
(
t
)
d
t
=
I
(
t
)
−
V
m
(
t
)
R
m
{\displaystyle C_{\mathrm {m} }{\frac {dV_{\mathrm {m} }(t)}{dt}}=I(t)-{\frac {V_{\mathrm {m} }(t)}{R_{\mathrm {m} }}}}
where Vm is the voltage across the cell membrane and Rm is the membrane resistance. (The non-leaky integrate-and-fire model is retrieved in the limit Rm to infinity, i.e. if the membrane is a perfect insulator). The model equation is valid for arbitrary time-dependent input until a threshold Vth is reached; thereafter the membrane potential is reset.
For constant input, the minimum input to reach the threshold is Ith = Vth / Rm. Assuming a reset to zero, the firing frequency thus looks like
f
(
I
)
=
{
0
,
I
≤
I
t
h
[
t
r
e
f
−
R
m
C
m
log
(
1
−
V
t
h
I
R
m
)
]
−
1
,
I
>
I
t
h
{\displaystyle f(I)={\begin{cases}0,&I\leq I_{\mathrm {th} }\\\left[t_{\mathrm {ref} }-R_{\mathrm {m} }C_{\mathrm {m} }\log \left(1-{\tfrac {V_{\mathrm {th} }}{IR_{\mathrm {m} }}}\right)\right]^{-1},&I>I_{\mathrm {th} }\end{cases}}}
which converges for large input currents to the previous leak-free model with the refractory period. The model can also be used for inhibitory neurons.
The most significant disadvantage of this model is that it does not contain neuronal adaptation, so that it cannot describe an experimentally measured spike train in response to constant input current. This disadvantage is removed in generalized integrate-and-fire models that also contain one or several adaptation-variables and are able to predict spike times of cortical neurons under current injection to a high degree of accuracy.
=== Adaptive integrate-and-fire ===
Neuronal adaptation refers to the fact that even in the presence of a constant current injection into the soma, the intervals between output spikes increase. An adaptive integrate-and-fire neuron model combines the leaky integration of voltage V with one or several adaptation variables wk (see Chapter 6.1. in the textbook Neuronal Dynamics)
τ
m
d
V
m
(
t
)
d
t
=
R
I
(
t
)
−
[
V
m
(
t
)
−
E
m
]
−
R
∑
k
w
k
{\displaystyle \tau _{\mathrm {m} }{\frac {dV_{\mathrm {m} }(t)}{dt}}=RI(t)-[V_{\mathrm {m} }(t)-E_{\mathrm {m} }]-R\sum _{k}w_{k}}
τ
k
d
w
k
(
t
)
d
t
=
−
a
k
[
V
m
(
t
)
−
E
m
]
−
w
k
+
b
k
τ
k
∑
f
δ
(
t
−
t
f
)
{\displaystyle \tau _{k}{\frac {dw_{k}(t)}{dt}}=-a_{k}[V_{\mathrm {m} }(t)-E_{\mathrm {m} }]-w_{k}+b_{k}\tau _{k}\sum _{f}\delta (t-t^{f})}
where
τ
m
{\displaystyle \tau _{m}}
is the membrane time constant, wk is the adaptation current number, with index k,
τ
k
{\displaystyle \tau _{k}}
is the time constant of adaptation current wk, Em is the resting potential and tf is the firing time of the neuron and the Greek delta denotes the Dirac delta function. Whenever the voltage reaches the firing threshold the voltage is reset to a value Vr below the firing threshold. The reset value is one of the important parameters of the model. The simplest model of adaptation has only a single adaptation variable w and the sum over k is removed.
Integrate-and-fire neurons with one or several adaptation variables can account for a variety of neuronal firing patterns in response to constant stimulation, including adaptation, bursting, and initial bursting. Moreover, adaptive integrate-and-fire neurons with several adaptation variables are able to predict spike times of cortical neurons under time-dependent current injection into the soma.
=== Fractional-order leaky integrate-and-fire ===
Recent advances in computational and theoretical fractional calculus lead to a new form of model called Fractional-order leaky integrate-and-fire. An advantage of this model is that it can capture adaptation effects with a single variable. The model has the following form
I
(
t
)
−
V
m
(
t
)
R
m
=
C
m
d
α
V
m
(
t
)
d
α
t
{\displaystyle I(t)-{\frac {V_{\mathrm {m} }(t)}{R_{\mathrm {m} }}}=C_{\mathrm {m} }{\frac {d^{\alpha }V_{\mathrm {m} }(t)}{d^{\alpha }t}}}
Once the voltage hits the threshold it is reset. Fractional integration has been used to account for neuronal adaptation in experimental data.
=== 'Exponential integrate-and-fire' and 'adaptive exponential integrate-and-fire' ===
In the exponential integrate-and-fire model, spike generation is exponential, following the equation:
d
V
d
t
−
R
τ
m
I
(
t
)
=
1
τ
m
[
E
m
−
V
+
Δ
T
exp
(
V
−
V
T
Δ
T
)
]
.
{\displaystyle {\frac {dV}{dt}}-{\frac {R}{\tau _{m}}}I(t)={\frac {1}{\tau _{m}}}\left[E_{m}-V+\Delta _{T}\exp \left({\frac {V-V_{T}}{\Delta _{T}}}\right)\right].}
where
V
{\displaystyle V}
is the membrane potential,
V
T
{\displaystyle V_{T}}
is the intrinsic membrane potential threshold,
τ
m
{\displaystyle \tau _{m}}
is the membrane time constant,
E
m
{\displaystyle E_{m}}
is the resting potential, and
Δ
T
{\displaystyle \Delta _{T}}
is the sharpness of action potential initiation, usually around 1 mV for cortical pyramidal neurons. Once the membrane potential crosses
V
T
{\displaystyle V_{T}}
, it diverges to infinity in finite time. In numerical simulation the integration is stopped if the membrane potential hits an arbitrary threshold (much larger than
V
T
{\displaystyle V_{T}}
) at which the membrane potential is reset to a value Vr . The voltage reset value Vr is one of the important parameters of the model. Importantly, the right-hand side of the above equation contains a nonlinearity that can be directly extracted from experimental data. In this sense the exponential nonlinearity is strongly supported by experimental evidence.
In the adaptive exponential integrate-and-fire neuron the above exponential nonlinearity of the voltage equation is combined with an adaptation variable w
τ
m
d
V
d
t
=
R
I
(
t
)
+
[
E
m
−
V
+
Δ
T
exp
(
V
−
V
T
Δ
T
)
]
−
R
w
{\displaystyle \tau _{m}{\frac {dV}{dt}}=RI(t)+\left[E_{m}-V+\Delta _{T}\exp \left({\frac {V-V_{T}}{\Delta _{T}}}\right)\right]-Rw}
τ
d
w
(
t
)
d
t
=
−
a
[
V
m
(
t
)
−
E
m
]
−
w
+
b
τ
δ
(
t
−
t
f
)
{\displaystyle \tau {\frac {dw(t)}{dt}}=-a[V_{\mathrm {m} }(t)-E_{\mathrm {m} }]-w+b\tau \delta (t-t^{f})}
where w denotes the adaptation current with time scale
τ
{\displaystyle \tau }
. Important model parameters are the voltage reset value Vr, the intrinsic threshold
V
T
{\displaystyle V_{T}}
, the time constants
τ
{\displaystyle \tau }
and
τ
m
{\displaystyle \tau _{m}}
as well as the coupling parameters a and b. The adaptive exponential integrate-and-fire model inherits the experimentally derived voltage nonlinearity of the exponential integrate-and-fire model. But going beyond this model, it can also account for a variety of neuronal firing patterns in response to constant stimulation, including adaptation, bursting, and initial bursting. However, since the adaptation is in the form of a current, aberrant hyperpolarization may appear. This problem was solved by expressing it as a conductance.
=== Adaptive Threshold Neuron Model ===
In this model, a time-dependent function
θ
(
t
)
{\displaystyle \theta (t)}
is added to the fixed threshold,
v
t
h
0
{\displaystyle v_{th0}}
, after every spike, causing an adaptation of the threshold. The threshold potential,
v
t
h
{\displaystyle v_{th}}
, gradually returns to its steady state value depending on the threshold adaptation time constant
τ
θ
{\displaystyle \tau _{\theta }}
. This is one of the simpler techniques to achieve spike frequency adaptation. The expression for the adaptive threshold is given by:
v
t
h
(
t
)
=
v
t
h
0
+
∑
θ
(
t
−
t
f
)
f
=
v
t
h
0
+
∑
θ
0
exp
[
−
(
t
−
t
f
)
τ
θ
]
f
{\displaystyle v_{th}(t)=v_{th0}+{\frac {\sum \theta (t-t_{f})}{f}}=v_{th0}+{\frac {\sum \theta _{0}\exp \left[-{\frac {(t-t_{f})}{\tau _{\theta }}}\right]}{f}}}
where
θ
(
t
)
{\displaystyle \theta (t)}
is defined by:
θ
(
t
)
=
θ
0
exp
[
−
t
τ
θ
]
{\displaystyle \theta (t)=\theta _{0}\exp \left[-{\frac {t}{\tau _{\theta }}}\right]}
When the membrane potential,
u
(
t
)
{\displaystyle u(t)}
, reaches a threshold, it is reset to
v
r
e
s
t
{\displaystyle v_{rest}}
:
u
(
t
)
≥
v
t
h
(
t
)
⇒
v
(
t
)
=
v
rest
{\displaystyle u(t)\geq v_{th}(t)\Rightarrow v(t)=v_{\text{rest}}}
A simpler version of this with a single time constant in threshold decay with an LIF neuron is realized in to achieve LSTM like recurrent spiking neural networks to achieve accuracy nearer to ANNs on few spatio temporal tasks.
=== Double Exponential Adaptive Threshold (DEXAT) ===
The DEXAT neuron model is a flavor of adaptive neuron model in which the threshold voltage decays with a double exponential having two time constants. Double exponential decay is governed by a fast initial decay and then a slower decay over a longer period of time. This neuron used in SNNs through surrogate gradient creates an adaptive learning rate yielding higher accuracy and faster convergence, and flexible long short-term memory compared to existing counterparts in the literature. The membrane potential dynamics are described through equations and the threshold adaptation rule is:
v
t
h
(
t
)
=
b
0
+
β
1
b
1
(
t
)
+
β
2
b
2
(
t
)
{\displaystyle v_{th}(t)=b_{0}+\beta _{1}b_{1}(t)+\beta _{2}b_{2}(t)}
The dynamics of
b
1
(
t
)
{\displaystyle b_{1}(t)}
and
b
2
(
t
)
{\displaystyle b_{2}(t)}
are given by
b
1
(
t
+
δ
t
)
=
p
j
1
b
1
(
t
)
+
(
1
−
p
j
1
)
z
(
t
)
δ
(
t
)
{\displaystyle b_{1}(t+\delta t)=p_{j1}b_{1}(t)+(1-p_{j1})z(t)\delta (t)}
,
b
2
(
t
+
δ
t
)
=
p
j
2
b
2
(
t
)
+
(
1
−
p
j
2
)
z
(
t
)
δ
(
t
)
{\displaystyle b_{2}(t+\delta t)=p_{j2}b_{2}(t)+(1-p_{j2})z(t)\delta (t)}
,
where
p
j
1
=
exp
[
−
δ
t
τ
b
1
]
{\displaystyle p_{j1}=\exp \left[-{\frac {\delta t}{\tau _{b1}}}\right]}
and
p
j
2
=
exp
[
−
δ
t
τ
b
2
]
{\displaystyle p_{j2}=\exp \left[-{\frac {\delta t}{\tau _{b2}}}\right]}
.
Further, multi-time scale adaptive threshold neuron model showing more complex dynamics is shown in.
== Stochastic models of membrane voltage and spike timing ==
The models in this category are generalized integrate-and-fire models that include a certain level of stochasticity. Cortical neurons in experiments are found to respond reliably to time-dependent input, albeit with a small degree of variations between one trial and the next if the same stimulus is repeated. Stochasticity in neurons has two important sources. First, even in a very controlled experiment where input current is injected directly into the soma, ion channels open and close stochastically and this channel noise leads to a small amount of variability in the exact value of the membrane potential and the exact timing of output spikes. Second, for a neuron embedded in a cortical network, it is hard to control the exact input because most inputs come from unobserved neurons somewhere else in the brain.
Stochasticity has been introduced into spiking neuron models in two fundamentally different forms: either (i) a noisy input current is added to the differential equation of the neuron model; or (ii) the process of spike generation is noisy. In both cases, the mathematical theory can be developed for continuous time, which is then, if desired for the use in computer simulations, transformed into a discrete-time model.
The relation of noise in neuron models to the variability of spike trains and neural codes is discussed in Neural Coding and in Chapter 7 of the textbook Neuronal Dynamics.
=== Noisy input model (diffusive noise) ===
A neuron embedded in a network receives spike input from other neurons. Since the spike arrival times are not controlled by an experimentalist they can be considered as stochastic. Thus a (potentially nonlinear) integrate-and-fire model with nonlinearity f(v) receives two inputs: an input
I
(
t
)
{\displaystyle I(t)}
controlled by the experimentalists and a noisy input current
I
n
o
i
s
e
(
t
)
{\displaystyle I^{\rm {noise}}(t)}
that describes the uncontrolled background input.
τ
m
d
V
d
t
=
f
(
V
)
+
R
I
(
t
)
+
R
I
noise
(
t
)
{\displaystyle \tau _{m}{\frac {dV}{dt}}=f(V)+RI(t)+RI^{\text{noise}}(t)}
Stein's model is the special case of a leaky integrate-and-fire neuron and a stationary white noise current
I
n
o
i
s
e
(
t
)
=
ξ
(
t
)
{\displaystyle I^{\rm {noise}}(t)=\xi (t)}
with mean zero and unit variance. In the subthreshold regime, these assumptions yield the equation of the Ornstein–Uhlenbeck process
τ
m
d
V
d
t
=
[
E
m
−
V
]
+
R
I
(
t
)
+
R
ξ
(
t
)
{\displaystyle \tau _{m}{\frac {dV}{dt}}=[E_{m}-V]+RI(t)+R\xi (t)}
However, in contrast to the standard Ornstein–Uhlenbeck process, the membrane voltage is reset whenever V hits the firing threshold Vth . Calculating the interval distribution of the Ornstein–Uhlenbeck model for constant input with threshold leads to a first-passage time problem. Stein's neuron model and variants thereof have been used to fit interspike interval distributions of spike trains from real neurons under constant input current.
In the mathematical literature, the above equation of the Ornstein–Uhlenbeck process is written in the form
d
V
=
[
E
m
−
V
+
R
I
(
t
)
]
d
t
τ
m
+
σ
d
W
{\displaystyle dV=[E_{m}-V+RI(t)]{\frac {dt}{\tau _{m}}}+\sigma \,dW}
where
σ
{\displaystyle \sigma }
is the amplitude of the noise input and dW are increments of a Wiener process. For discrete-time implementations with time step dt the voltage updates are
Δ
V
=
[
E
m
−
V
+
R
I
(
t
)
]
Δ
t
τ
m
+
σ
τ
m
y
{\displaystyle \Delta V=[E_{m}-V+RI(t)]{\frac {\Delta t}{\tau _{m}}}+\sigma {\sqrt {\tau _{m}}}y}
where y is drawn from a Gaussian distribution with zero mean unit variance. The voltage is reset when it hits the firing threshold Vth .
The noisy input model can also be used in generalized integrate-and-fire models. For example, the exponential integrate-and-fire model with noisy input reads
τ
m
d
V
d
t
=
E
m
−
V
+
Δ
T
exp
(
V
−
V
T
Δ
T
)
+
R
I
(
t
)
+
R
ξ
(
t
)
{\displaystyle \tau _{m}{\frac {dV}{dt}}=E_{m}-V+\Delta _{T}\exp \left({\frac {V-V_{T}}{\Delta _{T}}}\right)+RI(t)+R\xi (t)}
For constant deterministic input
I
(
t
)
=
I
0
{\displaystyle I(t)=I_{0}}
it is possible to calculate the mean firing rate as a function of
I
0
{\displaystyle I_{0}}
. This is important because the frequency-current relation (f-I-curve) is often used by experimentalists to characterize a neuron.
The leaky integrate-and-fire with noisy input has been widely used in the analysis of networks of spiking neurons. Noisy input is also called 'diffusive noise' because it leads to a diffusion of the subthreshold membrane potential around the noise-free trajectory (Johannesma, The theory of spiking neurons with noisy input is reviewed in Chapter 8.2 of the textbook Neuronal Dynamics.
=== Noisy output model (escape noise) ===
In deterministic integrate-and-fire models, a spike is generated if the membrane potential V(t) hits the threshold
V
t
h
{\displaystyle V_{th}}
. In noisy output models, the strict threshold is replaced by a noisy one as follows. At each moment in time t, a spike is generated stochastically with instantaneous stochastic intensity or 'escape rate'
ρ
(
t
)
=
f
(
V
(
t
)
−
V
t
h
)
{\displaystyle \rho (t)=f(V(t)-V_{th})}
that depends on the momentary difference between the membrane voltage V(t) and the threshold
V
t
h
{\displaystyle V_{th}}
. A common choice for the 'escape rate'
f
{\displaystyle f}
(that is consistent with biological data) is
f
(
V
−
V
t
h
)
=
1
τ
0
exp
[
β
(
V
−
V
t
h
)
]
{\displaystyle f(V-V_{th})={\frac {1}{\tau _{0}}}\exp[\beta (V-V_{th})]}
where
τ
0
{\displaystyle \tau _{0}}
is a time constant that describes how quickly a spike is fired once the membrane potential reaches the threshold and
β
{\displaystyle \beta }
is a sharpness parameter. For
β
→
∞
{\displaystyle \beta \to \infty }
the threshold becomes sharp and spike firing occurs deterministically at the moment when the membrane potential hits the threshold from below. The sharpness value found in experiments is
1
/
β
≈
4
m
V
{\displaystyle 1/\beta \approx 4mV}
which means that neuronal firing becomes non-negligible as soon as the membrane potential is a few mV below the formal firing threshold.
The escape rate process via a soft threshold is reviewed in Chapter 9 of the textbook Neuronal Dynamics.
For models in discrete time, a spike is generated with probability
P
F
(
t
n
)
=
F
[
V
(
t
n
)
−
V
t
h
]
{\displaystyle P_{F}(t_{n})=F[V(t_{n})-V_{th}]}
that depends on the momentary difference between the membrane voltage V at time
t
n
{\displaystyle t_{n}}
and the threshold
V
t
h
{\displaystyle V_{th}}
. The function F is often taken as a standard sigmoidal
F
(
x
)
=
0.5
[
1
+
tanh
(
γ
x
)
]
{\displaystyle F(x)=0.5[1+\tanh(\gamma x)]}
with steepness parameter
γ
{\displaystyle \gamma }
, similar to the update dynamics in artificial neural networks. But the functional form of F can also be derived from the stochastic intensity
f
{\displaystyle f}
in continuous time introduced above as
F
(
y
n
)
≈
1
−
exp
[
y
n
Δ
t
]
{\displaystyle F(y_{n})\approx 1-\exp[y_{n}\Delta t]}
where
y
n
=
V
(
t
n
)
−
V
t
h
{\displaystyle y_{n}=V(t_{n})-V_{th}}
is the threshold distance.
Integrate-and-fire models with output noise can be used to predict the peristimulus time histogram (PSTH) of real neurons under arbitrary time-dependent input. For non-adaptive integrate-and-fire neurons, the interval distribution under constant stimulation can be calculated from stationary renewal theory.
=== Spike response model (SRM) ===
main article: Spike response model
The spike response model (SRM) is a generalized linear model for the subthreshold membrane voltage combined with a nonlinear output noise process for spike generation. The membrane voltage V(t) at time t is
V
(
t
)
=
∑
f
η
(
t
−
t
f
)
+
∫
0
∞
κ
(
s
)
I
(
t
−
s
)
d
s
+
V
r
e
s
t
{\displaystyle V(t)=\sum _{f}\eta (t-t^{f})+\int \limits _{0}^{\infty }\kappa (s)I(t-s)\,ds+V_{\mathrm {rest} }}
where tf is the firing time of spike number f of the neuron, Vrest is the resting voltage in the absence of input, I(t-s) is the input current at time t-s and
κ
(
s
)
{\displaystyle \kappa (s)}
is a linear filter (also called kernel) that describes the contribution of an input current pulse at time t-s to the voltage at time t. The contributions to the voltage caused by a spike at time
t
f
{\displaystyle t^{f}}
are described by the refractory kernel
η
(
t
−
t
f
)
{\displaystyle \eta (t-t^{f})}
. In particular,
η
(
t
−
t
f
)
{\displaystyle \eta (t-t^{f})}
describes the reset after the spike and the time course of the spike-afterpotential following a spike. It therefore expresses the consequences of refractoriness and adaptation. The voltage V(t) can be interpreted as the result of an integration of the differential equation of a leaky integrate-and-fire model coupled to an arbitrary number of spike-triggered adaptation variables.
Spike firing is stochastic and happens with a time-dependent stochastic intensity (instantaneous rate)
f
(
V
−
ϑ
(
t
)
)
=
1
τ
0
exp
[
β
(
V
−
ϑ
(
t
)
)
]
{\displaystyle f(V-\vartheta (t))={\frac {1}{\tau _{0}}}\exp[\beta (V-\vartheta (t))]}
with parameters
τ
0
{\displaystyle \tau _{0}}
and
β
{\displaystyle \beta }
and a dynamic threshold
ϑ
(
t
)
{\displaystyle \vartheta (t)}
given by
ϑ
(
t
)
=
ϑ
0
+
∑
f
θ
1
(
t
−
t
f
)
{\displaystyle \vartheta (t)=\vartheta _{0}+\sum _{f}\theta _{1}(t-t^{f})}
Here
ϑ
0
{\displaystyle \vartheta _{0}}
is the firing threshold of an inactive neuron and
θ
1
(
t
−
t
f
)
{\displaystyle \theta _{1}(t-t^{f})}
describes the increase of the threshold after a spike at time
t
f
{\displaystyle t^{f}}
. In case of a fixed threshold, one sets
θ
1
(
t
−
t
f
)
=
0
{\displaystyle \theta _{1}(t-t^{f})=0}
. For
β
→
∞
{\displaystyle \beta \to \infty }
the threshold process is deterministic.
The time course of the filters
η
,
κ
,
θ
1
{\displaystyle \eta ,\kappa ,\theta _{1}}
that characterize the spike response model can be directly extracted from experimental data. With optimized parameters the SRM describes the time course of the subthreshold membrane voltage for time-dependent input with a precision of 2mV and can predict the timing of most output spikes with a precision of 4ms. The SRM is closely related to linear-nonlinear-Poisson cascade models (also called Generalized Linear Model). The estimation of parameters of probabilistic neuron models such as the SRM using methods developed for Generalized Linear Models is discussed in Chapter 10 of the textbook Neuronal Dynamics.
The name spike response model arises because, in a network, the input current for neuron i is generated by the spikes of other neurons so that in the case of a network the voltage equation becomes
V
i
(
t
)
=
∑
f
η
i
(
t
−
t
i
f
)
+
∑
j
=
1
N
w
i
j
∑
f
′
ε
i
j
(
t
−
t
j
f
′
)
+
V
r
e
s
t
{\displaystyle V_{i}(t)=\sum _{f}\eta _{i}(t-t_{i}^{f})+\sum _{j=1}^{N}w_{ij}\sum _{f'}\varepsilon _{ij}(t-t_{j}^{f'})+V_{\mathrm {rest} }}
where
t
j
f
′
{\displaystyle t_{j}^{f'}}
is the firing times of neuron j (i.e., its spike train);
η
i
(
t
−
t
i
f
)
{\displaystyle \eta _{i}(t-t_{i}^{f})}
describes the time course of the spike and the spike after-potential for neuron i; and
w
i
j
{\displaystyle w_{ij}}
and
ε
i
j
(
t
−
t
j
f
′
)
{\displaystyle \varepsilon _{ij}(t-t_{j}^{f'})}
describe the amplitude and time course of an excitatory or inhibitory postsynaptic potential (PSP) caused by the spike
t
j
f
′
{\displaystyle t_{j}^{f'}}
of the presynaptic neuron j. The time course
ε
i
j
(
s
)
{\displaystyle \varepsilon _{ij}(s)}
of the PSP results from the convolution of the postsynaptic current
I
(
t
)
{\displaystyle I(t)}
caused by the arrival of a presynaptic spike from neuron j with the membrane filter
κ
(
s
)
{\displaystyle \kappa (s)}
.
=== SRM0 ===
The SRM0 is a stochastic neuron model related to time-dependent nonlinear renewal theory and a simplification of the Spike Response Model (SRM). The main difference to the voltage equation of the SRM introduced above is that in the term containing the refractory kernel
η
(
s
)
{\displaystyle \eta (s)}
there is no summation sign over past spikes: only the most recent spike (denoted as the time
t
^
{\displaystyle {\hat {t}}}
) matters. Another difference is that the threshold is constant. The model SRM0 can be formulated in discrete or continuous time. For example, in continuous time, the single-neuron equation is
V
(
t
)
=
η
(
t
−
t
^
)
+
∫
0
∞
κ
(
s
)
I
(
t
−
s
)
d
s
+
V
r
e
s
t
{\displaystyle V(t)=\eta (t-{\hat {t}})+\int _{0}^{\infty }\kappa (s)I(t-s)\,ds+V_{\mathrm {rest} }}
and the network equations of the SRM0 are
V
i
(
t
∣
t
^
i
)
=
η
i
(
t
−
t
^
i
)
+
∑
j
w
i
j
∑
f
ε
i
j
(
t
−
t
^
i
,
t
−
t
f
)
+
V
r
e
s
t
{\displaystyle V_{i}(t\mid {\hat {t}}_{i})=\eta _{i}(t-{\hat {t}}_{i})+\sum _{j}w_{ij}\sum _{f}\varepsilon _{ij}(t-{\hat {t}}_{i},t-t^{f})+V_{\mathrm {rest} }}
where
t
^
i
{\displaystyle {\hat {t}}_{i}}
is the last firing time neuron i. Note that the time course of the postsynaptic potential
ε
i
j
{\displaystyle \varepsilon _{ij}}
is also allowed to depend on the time since the last spike of neuron i to describe a change in membrane conductance during refractoriness. The instantaneous firing rate (stochastic intensity) is
f
(
V
−
ϑ
)
=
1
τ
0
exp
[
β
(
V
−
V
t
h
)
]
{\displaystyle f(V-\vartheta )={\frac {1}{\tau _{0}}}\exp[\beta (V-V_{th})]}
where
V
t
h
{\displaystyle V_{th}}
is a fixed firing threshold. Thus spike firing of neuron i depends only on its input and the time since neuron i has fired its last spike.
With the SRM0, the interspike-interval distribution for constant input can be mathematically linked to the shape of the refractory kernel
η
{\displaystyle \eta }
. Moreover the stationary frequency-current relation can be calculated from the escape rate in combination with the refractory kernel
η
{\displaystyle \eta }
. With an appropriate choice of the kernels, the SRM0 approximates the dynamics of the Hodgkin-Huxley model to a high degree of accuracy. Moreover, the PSTH response to arbitrary time-dependent input can be predicted.
=== Galves–Löcherbach model ===
The Galves–Löcherbach model is a stochastic neuron model closely related to the spike response model SRM0 and the leaky integrate-and-fire model. It is inherently stochastic and, just like the SRM0, it is linked to time-dependent nonlinear renewal theory. Given the model specifications, the probability that a given neuron
i
{\displaystyle i}
spikes in a period
t
{\displaystyle t}
may be described by
P
r
o
b
(
X
t
(
i
)
=
1
∣
F
t
−
1
)
=
φ
i
(
∑
j
∈
I
W
j
→
i
∑
s
=
L
t
i
t
−
1
g
j
(
t
−
s
)
X
s
(
j
)
,
t
−
L
t
i
)
,
{\displaystyle \mathop {\mathrm {Prob} } (X_{t}(i)=1\mid {\mathcal {F}}_{t-1})=\varphi _{i}{\Biggl (}\sum _{j\in I}W_{j\rightarrow i}\sum _{s=L_{t}^{i}}^{t-1}g_{j}(t-s)X_{s}(j),~~~t-L_{t}^{i}{\Biggl )},}
where
W
j
→
i
{\displaystyle W_{j\rightarrow i}}
is a synaptic weight, describing the influence of neuron
j
{\displaystyle j}
on neuron
i
{\displaystyle i}
,
g
j
{\displaystyle g_{j}}
expresses the leak, and
L
t
i
{\displaystyle L_{t}^{i}}
provides the spiking history of neuron
i
{\displaystyle i}
before
t
{\displaystyle t}
, according to
L
t
i
=
sup
{
s
<
t
:
X
s
(
i
)
=
1
}
.
{\displaystyle L_{t}^{i}=\sup\{s<t:X_{s}(i)=1\}.}
Importantly, the spike probability of neuron
i
{\displaystyle i}
depends only on its spike input (filtered with a kernel
g
j
{\displaystyle g_{j}}
and weighted with a factor
W
j
→
i
{\displaystyle W_{j\to i}}
) and the timing of its most recent output spike (summarized by
t
−
L
t
i
{\displaystyle t-L_{t}^{i}}
).
== Didactic toy models of membrane voltage ==
The models in this category are highly simplified toy models that qualitatively describe the membrane voltage as a function of input. They are mainly used for didactic reasons in teaching but are not considered valid neuron models for large-scale simulations or data fitting.
=== FitzHugh–Nagumo ===
Sweeping simplifications to Hodgkin–Huxley were introduced by FitzHugh and Nagumo in 1961 and 1962. Seeking to describe "regenerative self-excitation" by a nonlinear positive-feedback membrane voltage and recovery by a linear negative-feedback gate voltage, they developed the model described by
r
c
l
d
V
d
t
=
V
−
V
3
/
3
−
w
+
I
e
x
t
τ
d
w
d
t
=
V
−
a
−
b
w
{\displaystyle {\begin{aligned}{rcl}{\dfrac {dV}{dt}}&=V-V^{3}/3-w+I_{\mathrm {ext} }\\\tau {\dfrac {dw}{dt}}&=V-a-bw\end{aligned}}}
where we again have a membrane-like voltage and input current with a slower general gate voltage w and experimentally-determined parameters a = -0.7, b = 0.8, τ = 1/0.08. Although not derivable from biology, the model allows for a simplified, immediately available dynamic, without being a trivial simplification. The experimental support is weak, but the model is useful as a didactic tool to introduce dynamics of spike generation through phase plane analysis. See Chapter 7 in the textbook Methods of Neuronal Modeling.
=== Morris–Lecar ===
In 1981, Morris and Lecar combined the Hodgkin–Huxley and FitzHugh–Nagumo models into a voltage-gated calcium channel model with a delayed-rectifier potassium channel represented by
C
d
V
d
t
=
−
I
i
o
n
(
V
,
w
)
+
I
d
w
d
t
=
φ
⋅
w
∞
−
w
τ
w
{\displaystyle {\begin{aligned}C{\frac {dV}{dt}}&=-I_{\mathrm {ion} }(V,w)+I\\{\frac {dw}{dt}}&=\varphi \cdot {\frac {w_{\infty }-w}{\tau _{w}}}\end{aligned}}}
where
I
i
o
n
(
V
,
w
)
=
g
¯
C
a
m
∞
⋅
(
V
−
V
C
a
)
+
g
¯
K
w
⋅
(
V
−
V
K
)
+
g
¯
L
⋅
(
V
−
V
L
)
{\displaystyle I_{\mathrm {ion} }(V,w)={\bar {g}}_{\mathrm {Ca} }m_{\infty }\cdot (V-V_{\mathrm {Ca} })+{\bar {g}}_{\mathrm {K} }w\cdot (V-V_{\mathrm {K} })+{\bar {g}}_{\mathrm {L} }\cdot (V-V_{\mathrm {L} })}
. The experimental support of the model is weak, but the model is useful as a didactic tool to introduce dynamics of spike generation through phase plane analysis. See Chapter 7 in the textbook Methods of Neuronal Modeling.
A two-dimensional neuron model very similar to the Morris-Lecar model can be derived step-by-step starting from the Hodgkin-Huxley model. See Chapter 4.2 in the textbook Neuronal Dynamics.
=== Hindmarsh–Rose ===
Building upon the FitzHugh–Nagumo model, Hindmarsh and Rose proposed in 1984 a model of neuronal activity described by three coupled first-order differential equations:
d
x
d
t
=
y
+
3
x
2
−
x
3
−
z
+
I
d
y
d
t
=
1
−
5
x
2
−
y
d
z
d
t
=
r
⋅
(
4
(
x
+
8
5
)
−
z
)
{\displaystyle {\begin{aligned}{\frac {dx}{dt}}&=y+3x^{2}-x^{3}-z+I\\{\frac {dy}{dt}}&=1-5x^{2}-y\\{\frac {dz}{dt}}&=r\cdot (4(x+{\tfrac {8}{5}})-z)\end{aligned}}}
with r2 = x2 + y2 + z2, and r ≈ 10−2 so that the z variable only changes very slowly. This extra mathematical complexity allows a great variety of dynamic behaviors for the membrane potential, described by the x variable of the model, which includes chaotic dynamics. This makes the Hindmarsh–Rose neuron model very useful, because it is still simple, allows a good qualitative description of the many different firing patterns of the action potential, in particular bursting, observed in experiments. Nevertheless, it remains a toy model and has not been fitted to experimental data. It is widely used as a reference model for bursting dynamics.
=== Theta model and quadratic integrate-and-fire ===
The theta model, or Ermentrout–Kopell canonical Type I model, is mathematically equivalent to the quadratic integrate-and-fire model which in turn is an approximation to the exponential integrate-and-fire model and the Hodgkin-Huxley model. It is called a canonical model because it is one of the generic models for constant input close to the bifurcation point, which means close to the transition from silent to repetitive firing.
The standard formulation of the theta model is
d
θ
(
t
)
d
t
=
(
I
−
I
0
)
[
1
+
cos
(
θ
)
]
+
[
1
−
cos
(
θ
)
]
{\displaystyle {\frac {d\theta (t)}{dt}}=(I-I_{0})[1+\cos(\theta )]+[1-\cos(\theta )]}
The equation for the quadratic integrate-and-fire model is (see Chapter 5.3 in the textbook Neuronal Dynamics )
τ
m
d
V
m
(
t
)
d
t
=
(
I
−
I
0
)
R
+
[
V
m
(
t
)
−
E
m
]
[
V
m
(
t
)
−
V
T
]
{\displaystyle \tau _{\mathrm {m} }{\frac {dV_{\mathrm {m} }(t)}{dt}}=(I-I_{0})R+[V_{\mathrm {m} }(t)-E_{\mathrm {m} }][V_{\mathrm {m} }(t)-V_{\mathrm {T} }]}
The equivalence of theta model and quadratic integrate-and-fire is for example reviewed in Chapter 4.1.2.2 of spiking neuron models.
For input
I
(
t
)
{\displaystyle I(t)}
that changes over time or is far away from the bifurcation point, it is preferable to work with the exponential integrate-and-fire model (if one wants to stay in the class of one-dimensional neuron models), because real neurons exhibit the nonlinearity of the exponential integrate-and-fire model.
== Sensory input-stimulus encoding neuron models ==
The models in this category were derived following experiments involving natural stimulation such as light, sound, touch, or odor. In these experiments, the spike pattern resulting from each stimulus presentation varies from trial to trial, but the averaged response from several trials often converges to a clear pattern. Consequently, the models in this category generate a probabilistic relationship between the input stimulus to spike occurrences. Importantly, the recorded neurons are often located several processing steps after the sensory neurons, so that these models summarize the effects of the sequence of processing steps in a compact form
=== The non-homogeneous Poisson process model (Siebert) ===
Siebert modeled the neuron spike firing pattern using a non-homogeneous Poisson process model, following experiments involving the auditory system. According to Siebert, the probability of a spiking event at the time interval
[
t
,
t
+
Δ
t
]
{\displaystyle [t,t+\Delta _{t}]}
is proportional to a non-negative function
g
[
s
(
t
)
]
{\displaystyle g[s(t)]}
, where
s
(
t
)
{\displaystyle s(t)}
is the raw stimulus.:
P
spike
(
t
∈
[
t
′
,
t
′
+
Δ
t
]
)
=
Δ
t
⋅
g
[
s
(
t
)
]
{\displaystyle P_{\text{spike}}(t\in [t',t'+\Delta _{t}])=\Delta _{t}\cdot g[s(t)]}
Siebert considered several functions as
g
[
s
(
t
)
]
{\displaystyle g[s(t)]}
, including
g
[
s
(
t
)
]
∝
s
2
(
t
)
{\displaystyle g[s(t)]\propto s^{2}(t)}
for low stimulus intensities.
The main advantage of Siebert's model is its simplicity. The shortcomings of the model is its inability to reflect properly the following phenomena:
The transient enhancement of the neuronal firing activity in response to a step stimulus.
The saturation of the firing rate.
The values of inter-spike-interval-histogram at short intervals values (close to zero).
These shortcomings are addressed by the age-dependent point process model and the two-state Markov Model.
=== Refractoriness and age-dependent point process model ===
Berry and Meister studied neuronal refractoriness using a stochastic model that predicts spikes as a product of two terms, a function f(s(t)) that depends on the time-dependent stimulus s(t) and one a recovery function
w
(
t
−
t
^
)
{\displaystyle w(t-{\hat {t}})}
that depends on the time since the last spike
ρ
(
t
)
=
f
(
s
(
t
)
)
w
(
t
−
t
^
)
{\displaystyle \rho (t)=f(s(t))w(t-{\hat {t}})}
The model is also called an inhomogeneous Markov interval (IMI) process. Similar models have been used for many years in auditory neuroscience. Since the model keeps memory of the last spike time it is non-Poisson and falls in the class of time-dependent renewal models. It is closely related to the model SRM0 with exponential escape rate. Importantly, it is possible to fit parameters of the age-dependent point process model so as to describe not just the PSTH response, but also the interspike-interval statistics.
=== Linear-nonlinear Poisson cascade model and GLM ===
The linear-nonlinear-Poisson cascade model is a cascade of a linear filtering process followed by a nonlinear spike generation step. In the case that output spikes feed back, via a linear filtering process, we arrive at a model that is known in the neurosciences as Generalized Linear Model (GLM). The GLM is mathematically equivalent to the spike response model SRM) with escape noise; but whereas in the SRM the internal variables are interpreted as the membrane potential and the firing threshold, in the GLM the internal variables are abstract quantities that summarizes the net effect of input (and recent output spikes) before spikes are generated in the final step.
=== The two-state Markov model (Nossenson & Messer) ===
The spiking neuron model by Nossenson & Messer produces the probability of the neuron firing a spike as a function of either an external or pharmacological stimulus. The model consists of a cascade of a receptor layer model and a spiking neuron model, as shown in Fig 4. The connection between the external stimulus to the spiking probability is made in two steps: First, a receptor cell model translates the raw external stimulus to neurotransmitter concentration, and then, a spiking neuron model connects neurotransmitter concentration to the firing rate (spiking probability). Thus, the spiking neuron model by itself depends on neurotransmitter concentration at the input stage.
An important feature of this model is the prediction for neurons firing rate pattern which captures, using a low number of free parameters, the characteristic edge emphasized response of neurons to a stimulus pulse, as shown in Fig. 5. The firing rate is identified both as a normalized probability for neural spike firing and as a quantity proportional to the current of neurotransmitters released by the cell. The expression for the firing rate takes the following form:
R
fire
(
t
)
=
P
spike
(
t
;
Δ
t
)
Δ
t
=
[
y
(
t
)
+
R
0
]
⋅
P
0
(
t
)
{\displaystyle R_{\text{fire}}(t)={\frac {P_{\text{spike}}(t;\Delta _{t})}{\Delta _{t}}}=[y(t)+R_{0}]\cdot P_{0}(t)}
where,
P0 is the probability of the neuron being "armed" and ready to fire. It is given by the following differential equation:
P
˙
0
=
−
[
y
(
t
)
+
R
0
+
R
1
]
⋅
P
0
(
t
)
+
R
1
{\displaystyle {\dot {P}}_{0}=-[y(t)+R_{0}+R_{1}]\cdot P_{0}(t)+R_{1}}
P0 could be generally calculated recursively using the Euler method, but in the case of a pulse of stimulus, it yields a simple closed-form expression.
y(t) is the input of the model and is interpreted as the neurotransmitter concentration on the cell surrounding (in most cases glutamate). For an external stimulus it can be estimated through the receptor layer model:
y
(
t
)
≃
g
gain
⋅
⟨
s
2
(
t
)
⟩
,
{\displaystyle y(t)\simeq g_{\text{gain}}\cdot \langle s^{2}(t)\rangle ,}
with
⟨
s
2
(
t
)
⟩
{\displaystyle \langle s^{2}(t)\rangle }
being a short temporal average of stimulus power (given in Watt or other energy per time unit).
R0 corresponds to the intrinsic spontaneous firing rate of the neuron.
R1 is the recovery rate of the neuron from the refractory state.
Other predictions by this model include:
1) The averaged evoked response potential (ERP) due to the population of many neurons in unfiltered measurements resembles the firing rate.
2) The voltage variance of activity due to multiple neuron activity resembles the firing rate (also known as Multi-Unit-Activity power or MUA).
3) The inter-spike-interval probability distribution takes the form a gamma-distribution like function.
== Pharmacological input stimulus neuron models ==
The models in this category produce predictions for experiments involving pharmacological stimulation.
=== Synaptic transmission (Koch & Segev) ===
According to the model by Koch and Segev, the response of a neuron to individual neurotransmitters can be modeled as an extension of the classical Hodgkin–Huxley model with both standard and nonstandard kinetic currents. Four neurotransmitters primarily influence the CNS. AMPA/kainate receptors are fast excitatory mediators while NMDA receptors mediate considerably slower currents. Fast inhibitory currents go through GABAA receptors, while GABAB receptors mediate by secondary G-protein-activated potassium channels. This range of mediation produces the following current dynamics:
I
A
M
P
A
(
t
,
V
)
=
g
¯
A
M
P
A
⋅
[
O
]
⋅
(
V
(
t
)
−
E
A
M
P
A
)
{\displaystyle I_{\mathrm {AMPA} }(t,V)={\bar {g}}_{\mathrm {AMPA} }\cdot [O]\cdot (V(t)-E_{\mathrm {AMPA} })}
I
N
M
D
A
(
t
,
V
)
=
g
¯
N
M
D
A
⋅
B
(
V
)
⋅
[
O
]
⋅
(
V
(
t
)
−
E
N
M
D
A
)
{\displaystyle I_{\mathrm {NMDA} }(t,V)={\bar {g}}_{\mathrm {NMDA} }\cdot B(V)\cdot [O]\cdot (V(t)-E_{\mathrm {NMDA} })}
I
G
A
B
A
A
(
t
,
V
)
=
g
¯
G
A
B
A
A
⋅
(
[
O
1
]
+
[
O
2
]
)
⋅
(
V
(
t
)
−
E
C
l
)
{\displaystyle I_{\mathrm {GABA_{A}} }(t,V)={\bar {g}}_{\mathrm {GABA_{A}} }\cdot ([O_{1}]+[O_{2}])\cdot (V(t)-E_{\mathrm {Cl} })}
I
G
A
B
A
B
(
t
,
V
)
=
g
¯
G
A
B
A
B
⋅
[
G
]
n
[
G
]
n
+
K
d
⋅
(
V
(
t
)
−
E
K
)
{\displaystyle I_{\mathrm {GABA_{B}} }(t,V)={\bar {g}}_{\mathrm {GABA_{B}} }\cdot {\tfrac {[G]^{n}}{[G]^{n}+K_{\mathrm {d} }}}\cdot (V(t)-E_{\mathrm {K} })}
where ḡ is the maximal conductance (around 1S) and E is the equilibrium potential of the given ion or transmitter (AMDA, NMDA, Cl, or K), while [O] describes the fraction of open receptors. For NMDA, there is a significant effect of magnesium block that depends sigmoidally on the concentration of intracellular magnesium by B(V). For GABAB, [G] is the concentration of the G-protein, and Kd describes the dissociation of G in binding to the potassium gates.
The dynamics of this more complicated model have been well-studied experimentally and produce important results in terms of very quick synaptic potentiation and depression, that is fast, short-term learning.
The stochastic model by Nossenson and Messer translates neurotransmitter concentration at the input stage to the probability of releasing neurotransmitter at the output stage. For a more detailed description of this model, see the Two state Markov model section above.
== HTM neuron model ==
The HTM neuron model was developed by Jeff Hawkins and researchers at Numenta and is based on a theory called Hierarchical Temporal Memory, originally described in the book On Intelligence. It is based on neuroscience and the physiology and interaction of pyramidal neurons in the neocortex of the human brain.
== Applications ==
Spiking Neuron Models are used in a variety of applications that need encoding into or decoding from neuronal spike trains in the context of neuroprosthesis and brain-computer interfaces such as retinal prosthesis: or artificial limb control and sensation. Applications are not part of this article; for more information on this topic please refer to the main article.
== Relation between artificial and biological neuron models ==
The most basic model of a neuron consists of an input with some synaptic weight vector and an activation function or transfer function inside the neuron determining output. This is the basic structure used for artificial neurons, which in a neural network often looks like
y
i
=
φ
(
∑
j
w
i
j
x
j
)
{\displaystyle y_{i}=\varphi \left(\sum _{j}w_{ij}x_{j}\right)}
where yi is the output of the i th neuron, xj is the jth input neuron signal, wij is the synaptic weight (or strength of connection) between the neurons i and j, and φ is the activation function. While this model has seen success in machine-learning applications, it is a poor model for real (biological) neurons, because it lacks time-dependence in input and output.
When an input is switched on at a time t and kept constant thereafter, biological neurons emit a spike train. Importantly, this spike train is not regular but exhibits a temporal structure characterized by adaptation, bursting, or initial bursting followed by regular spiking. Generalized integrate-and-fire models such as the Adaptive Exponential Integrate-and-Fire model, the spike response model, or the (linear) adaptive integrate-and-fire model can capture these neuronal firing patterns.
Moreover, neuronal input in the brain is time-dependent. Time-dependent input is transformed by complex linear and nonlinear filters into a spike train in the output. Again, the spike response model or the adaptive integrate-and-fire model enables to prediction of the spike train in the output for arbitrary time-dependent input, whereas an artificial neuron or a simple leaky integrate-and-fire does not.
If we take the Hodkgin-Huxley model as a starting point, generalized integrate-and-fire models can be derived systematically in a step-by-step simplification procedure. This has been shown explicitly for the exponential integrate-and-fire model and the spike response model.
In the case of modeling a biological neuron, physical analogs are used in place of abstractions such as "weight" and "transfer function". A neuron is filled and surrounded with water-containing ions, which carry electric charge. The neuron is bound by an insulating cell membrane and can maintain a concentration of charged ions on either side that determines a capacitance Cm. The firing of a neuron involves the movement of ions into the cell, that occurs when neurotransmitters cause ion channels on the cell membrane to open. We describe this by a physical time-dependent current I(t). With this comes a change in voltage, or the electrical potential energy difference between the cell and its surroundings, which is observed to sometimes result in a voltage spike called an action potential which travels the length of the cell and triggers the release of further neurotransmitters. The voltage, then, is the quantity of interest and is given by Vm(t).
If the input current is constant, most neurons emit after some time of adaptation or initial bursting a regular spike train. The frequency of regular firing in response to a constant current I is described by the frequency-current relation, which corresponds to the transfer function
φ
{\displaystyle \varphi }
of artificial neural networks. Similarly, for all spiking neuron models, the transfer function
φ
{\displaystyle \varphi }
can be calculated numerically (or analytically).
== Cable theory and compartmental models ==
All of the above deterministic models are point-neuron models because they do not consider the spatial structure of a neuron. However, the dendrite contributes to transforming input into output. Point neuron models are valid description in three cases. (i) If input current is directly injected into the soma. (ii) If synaptic input arrives predominantly at or close to the soma (closeness is defined by a length scale
λ
{\displaystyle \lambda }
introduced below. (iii) If synapse arrives anywhere on the dendrite, but the dendrite is completely linear. In the last case, the cable acts as a linear filter; these linear filter properties can be included in the formulation of generalized integrate-and-fire models such as the spike response model.
The filter properties can be calculated from a cable equation.
Let us consider a cell membrane in the form of a cylindrical cable. The position on the cable is denoted by x and the voltage across the cell membrane by V. The cable is characterized by a longitudinal resistance
r
l
{\displaystyle r_{l}}
per unit length and a membrane resistance
r
m
{\displaystyle r_{m}}
. If everything is linear, the voltage changes as a function of timeWe introduce a length scale
λ
2
=
r
m
/
r
l
{\displaystyle \lambda ^{2}={r_{m}}/{r_{l}}}
on the left side and time constant
τ
=
c
m
r
m
{\displaystyle \tau =c_{m}r_{m}}
on the right side. The cable equation can now be written in its perhaps best-known form:
The above cable equation is valid for a single cylindrical cable.
Linear cable theory describes the dendritic arbor of a neuron as a cylindrical structure undergoing a regular pattern of bifurcation, like branches in a tree. For a single cylinder or an entire tree, the static input conductance at the base (where the tree meets the cell body or any such boundary) is defined as
G
i
n
=
G
∞
tanh
(
L
)
+
G
L
1
+
(
G
L
/
G
∞
)
tanh
(
L
)
{\displaystyle G_{in}={\frac {G_{\infty }\tanh(L)+G_{L}}{1+(G_{L}/G_{\infty })\tanh(L)}}}
,
where L is the electrotonic length of the cylinder, which depends on its length, diameter, and resistance. A simple recursive algorithm scales linearly with the number of branches and can be used to calculate the effective conductance of the tree. This is given by
G
D
=
G
m
A
D
tanh
(
L
D
)
/
L
D
{\displaystyle \,\!G_{D}=G_{m}A_{D}\tanh(L_{D})/L_{D}}
where AD = πld is the total surface area of the tree of total length l, and LD is its total electrotonic length. For an entire neuron in which the cell body conductance is GS and the membrane conductance per unit area is Gmd = Gm / A, we find the total neuron conductance GN for n dendrite trees by adding up all tree and soma conductances, given by
G
N
=
G
S
+
∑
j
=
1
n
A
D
j
F
d
g
a
j
,
{\displaystyle G_{N}=G_{S}+\sum _{j=1}^{n}A_{D_{j}}F_{dga_{j}},}
where we can find the general correction factor Fdga experimentally by noting GD = GmdADFdga.
The linear cable model makes several simplifications to give closed analytic results, namely that the dendritic arbor must branch in diminishing pairs in a fixed pattern and that dendrites are linear. A compartmental model allows for any desired tree topology with arbitrary branches and lengths, as well as arbitrary nonlinearities. It is essentially a discretized computational implementation of nonlinear dendrites.
Each piece, or compartment, of a dendrite, is modeled by a straight cylinder of arbitrary length l and diameter d which connects with fixed resistance to any number of branching cylinders. We define the conductance ratio of the ith cylinder as Bi = Gi / G∞, where
G
∞
=
π
d
3
/
2
2
R
i
R
m
{\displaystyle G_{\infty }={\tfrac {\pi d^{3/2}}{2{\sqrt {R_{i}R_{m}}}}}}
and Ri is the resistance between the current compartment and the next. We obtain a series of equations for conductance ratios in and out of a compartment by making corrections to the normal dynamic Bout,i = Bin,i+1, as
B
o
u
t
,
i
=
B
i
n
,
i
+
1
(
d
i
+
1
/
d
i
)
3
/
2
R
m
,
i
+
1
/
R
m
,
i
{\displaystyle B_{\mathrm {out} ,i}={\frac {B_{\mathrm {in} ,i+1}(d_{i+1}/d_{i})^{3/2}}{\sqrt {R_{\mathrm {m} ,i+1}/R_{\mathrm {m} ,i}}}}}
B
i
n
,
i
=
B
o
u
t
,
i
+
tanh
X
i
1
+
B
o
u
t
,
i
tanh
X
i
{\displaystyle B_{\mathrm {in} ,i}={\frac {B_{\mathrm {out} ,i}+\tanh X_{i}}{1+B_{\mathrm {out} ,i}\tanh X_{i}}}}
B
o
u
t
,
p
a
r
=
B
i
n
,
d
a
u
1
(
d
d
a
u
1
/
d
p
a
r
)
3
/
2
R
m
,
d
a
u
1
/
R
m
,
p
a
r
+
B
i
n
,
d
a
u
2
(
d
d
a
u
2
/
d
p
a
r
)
3
/
2
R
m
,
d
a
u
2
/
R
m
,
p
a
r
+
…
{\displaystyle B_{\mathrm {out,par} }={\frac {B_{\mathrm {in,dau1} }(d_{\mathrm {dau1} }/d_{\mathrm {par} })^{3/2}}{\sqrt {R_{\mathrm {m,dau1} }/R_{\mathrm {m,par} }}}}+{\frac {B_{\mathrm {in,dau2} }(d_{\mathrm {dau2} }/d_{\mathrm {par} })^{3/2}}{\sqrt {R_{\mathrm {m,dau2} }/R_{\mathrm {m,par} }}}}+\ldots }
where the last equation deals with parents and daughters at branches, and
X
i
=
l
i
4
R
i
d
i
R
m
{\displaystyle X_{i}={\tfrac {l_{i}{\sqrt {4R_{i}}}}{\sqrt {d_{i}R_{m}}}}}
. We can iterate these equations through the tree until we get the point where the dendrites connect to the cell body (soma), where the conductance ratio is Bin,stem. Then our total neuron conductance for static input is given by
G
N
=
A
s
o
m
a
R
m
,
s
o
m
a
+
∑
j
B
i
n
,
s
t
e
m
,
j
G
∞
,
j
.
{\displaystyle G_{N}={\frac {A_{\mathrm {soma} }}{R_{\mathrm {m,soma} }}}+\sum _{j}B_{\mathrm {in,stem} ,j}G_{\infty ,j}.}
Importantly, static input is a very special case. In biology, inputs are time-dependent. Moreover, dendrites are not always linear.
Compartmental models enable to include nonlinearities via ion channels positioned at arbitrary locations along the dendrites. For static inputs, it is sometimes possible to reduce the number of compartments (increase the computational speed) and yet retain the salient electrical characteristics.
== Conjectures regarding the role of the neuron in the wider context of the brain principle of operation ==
=== The neurotransmitter-based energy detection scheme ===
The neurotransmitter-based energy detection scheme suggests that the neural tissue chemically executes a Radar-like detection procedure.
As shown in Fig. 6, the key idea of the conjecture is to account for neurotransmitter concentration, neurotransmitter generation, and neurotransmitter removal rates as the important quantities in executing the detection task, while referring to the measured electrical potentials as a side effect that only in certain conditions coincide with the functional purpose of each step. The detection scheme is similar to a radar-like "energy detection" because it includes signal squaring, temporal summation, and a threshold switch mechanism, just like the energy detector, but it also includes a unit that emphasizes stimulus edges and a variable memory length (variable memory). According to this conjecture, the physiological equivalent of the energy test statistics is neurotransmitter concentration, and the firing rate corresponds to neurotransmitter current. The advantage of this interpretation is that it leads to a unit-consistent explanation which allows for bridge between electrophysiological measurements, biochemical measurements, and psychophysical results.
The evidence reviewed in suggests the following association between functionality to histological classification:
Stimulus squaring is likely to be performed by receptor cells.
Stimulus edge emphasizing and signal transduction is performed by neurons.
Temporal accumulation of neurotransmitters is performed by glial cells. Short-term neurotransmitter accumulation is likely to occur also in some types of neurons.
Logical switching is executed by glial cells, and it results from exceeding a threshold level of neurotransmitter concentration. This threshold crossing is also accompanied by a change in neurotransmitter leak rate.
Physical all-or-non movement switching is due to muscle cells and results from exceeding a certain neurotransmitter concentration threshold on muscle surroundings.
Note that although the electrophysiological signals in Fig.6 are often similar to the functional signal (signal power/neurotransmitter concentration / muscle force), there are some stages in which the electrical observation differs from the functional purpose of the corresponding step. In particular, Nossenson et al. suggested that glia threshold crossing has a completely different functional operation compared to the radiated electrophysiological signal and that the latter might only be a side effect of glia break.
== General comments regarding the modern perspective of scientific and engineering models ==
The models above are still idealizations. Corrections must be made for the increased membrane surface area given by numerous dendritic spines, temperatures significantly hotter than room-temperature experimental data, and nonuniformity in the cell's internal structure. Certain observed effects do not fit into some of these models. For instance, the temperature cycling (with minimal net temperature increase) of the cell membrane during action potential propagation is not compatible with models that rely on modeling the membrane as a resistance that must dissipate energy when current flows through it. The transient thickening of the cell membrane during action potential propagation is also not predicted by these models, nor is the changing capacitance and voltage spike that results from this thickening incorporated into these models. The action of some anesthetics such as inert gases is problematic for these models as well. New models, such as the soliton model attempt to explain these phenomena, but are less developed than older models and have yet to be widely applied.
Modern views regarding the role of the scientific model suggest that "All models are wrong but some are useful" (Box and Draper, 1987, Gribbin, 2009; Paninski et al., 2009).
Recent conjecture suggests that each neuron might function as a collection of independent threshold units. It is suggested that a neuron could be anisotropically activated following the origin of its arriving signals to the membrane, via its dendritic trees. The spike waveform was also proposed to be dependent on the origin of the stimulus.
== External links ==
Neuronal Dynamics: from single neurons to networks and models of cognition (W. Gerstner, W. Kistler, R. Naud, L. Paninski, Cambridge University Press, 2014). In particular, Chapters 6 - 10, html online version.
Spiking Neuron Models (W. Gerstner and W. Kistler, Cambridge University Press, 2002)
== See also ==
Binding neuron
Bayesian approaches to brain function
Brain-computer interfaces
Free energy principle
Models of neural computation
Neural coding
Neural oscillation
Quantitative models of the action potential
Spiking neural network
== References == | Wikipedia/Biological_neuron_models |
A mathematical model is an abstract description of a concrete system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modeling. Mathematical models are used in applied mathematics and in the natural sciences (such as physics, biology, earth science, chemistry) and engineering disciplines (such as computer science, electrical engineering), as well as in non-physical systems such as the social sciences (such as economics, psychology, sociology, political science). It can also be taught as a subject in its own right.
The use of mathematical models to solve problems in business or military operations is a large part of the field of operations research. Mathematical models are also used in music, linguistics, and
philosophy (for example, intensively in analytic philosophy). A model may help to explain a system and to study the effects of different components, and to make predictions about behavior.
== Elements of a mathematical model ==
Mathematical models can take many forms, including dynamical systems, statistical models, differential equations, or game theoretic models. These and other types of models can overlap, with a given model involving a variety of abstract structures. In general, mathematical models may include logical models. In many cases, the quality of a scientific field depends on how well the mathematical models developed on the theoretical side agree with results of repeatable experiments. Lack of agreement between theoretical mathematical models and experimental measurements often leads to important advances as better theories are developed. In the physical sciences, a traditional mathematical model contains most of the following elements:
Governing equations
Supplementary sub-models
Defining equations
Constitutive equations
Assumptions and constraints
Initial and boundary conditions
Classical constraints and kinematic equations
== Classifications ==
Mathematical models are of different types:
Linear vs. nonlinear. If all the operators in a mathematical model exhibit linearity, the resulting mathematical model is defined as linear. A model is considered to be nonlinear otherwise. The definition of linearity and nonlinearity is dependent on context, and linear models may have nonlinear expressions in them. For example, in a statistical linear model, it is assumed that a relationship is linear in the parameters, but it may be nonlinear in the predictor variables. Similarly, a differential equation is said to be linear if it can be written with linear differential operators, but it can still have nonlinear expressions in it. In a mathematical programming model, if the objective functions and constraints are represented entirely by linear equations, then the model is regarded as a linear model. If one or more of the objective functions or constraints are represented with a nonlinear equation, then the model is known as a nonlinear model.Linear structure implies that a problem can be decomposed into simpler parts that can be treated independently and/or analyzed at a different scale and the results obtained will remain valid for the initial problem when recomposed and rescaled.Nonlinearity, even in fairly simple systems, is often associated with phenomena such as chaos and irreversibility. Although there are exceptions, nonlinear systems and models tend to be more difficult to study than linear ones. A common approach to nonlinear problems is linearization, but this can be problematic if one is trying to study aspects such as irreversibility, which are strongly tied to nonlinearity.
Static vs. dynamic. A dynamic model accounts for time-dependent changes in the state of the system, while a static (or steady-state) model calculates the system in equilibrium, and thus is time-invariant. Dynamic models typically are represented by differential equations or difference equations.
Explicit vs. implicit. If all of the input parameters of the overall model are known, and the output parameters can be calculated by a finite series of computations, the model is said to be explicit. But sometimes it is the output parameters which are known, and the corresponding inputs must be solved for by an iterative procedure, such as Newton's method or Broyden's method. In such a case the model is said to be implicit. For example, a jet engine's physical properties such as turbine and nozzle throat areas can be explicitly calculated given a design thermodynamic cycle (air and fuel flow rates, pressures, and temperatures) at a specific flight condition and power setting, but the engine's operating cycles at other flight conditions and power settings cannot be explicitly calculated from the constant physical properties.
Discrete vs. continuous. A discrete model treats objects as discrete, such as the particles in a molecular model or the states in a statistical model; while a continuous model represents the objects in a continuous manner, such as the velocity field of fluid in pipe flows, temperatures and stresses in a solid, and electric field that applies continuously over the entire model due to a point charge.
Deterministic vs. probabilistic (stochastic). A deterministic model is one in which every set of variable states is uniquely determined by parameters in the model and by sets of previous states of these variables; therefore, a deterministic model always performs the same way for a given set of initial conditions. Conversely, in a stochastic model—usually called a "statistical model"—randomness is present, and variable states are not described by unique values, but rather by probability distributions.
Deductive, inductive, or floating. A deductive model is a logical structure based on a theory. An inductive model arises from empirical findings and generalization from them. The floating model rests on neither theory nor observation, but is merely the invocation of expected structure. Application of mathematics in social sciences outside of economics has been criticized for unfounded models. Application of catastrophe theory in science has been characterized as a floating model.
Strategic vs. non-strategic. Models used in game theory are different in a sense that they model agents with incompatible incentives, such as competing species or bidders in an auction. Strategic models assume that players are autonomous decision makers who rationally choose actions that maximize their objective function. A key challenge of using strategic models is defining and computing solution concepts such as Nash equilibrium. An interesting property of strategic models is that they separate reasoning about rules of the game from reasoning about behavior of the players.
== Construction ==
In business and engineering, mathematical models may be used to maximize a certain output. The system under consideration will require certain inputs. The system relating inputs to outputs depends on other variables too: decision variables, state variables, exogenous variables, and random variables. Decision variables are sometimes known as independent variables. Exogenous variables are sometimes known as parameters or constants. The variables are not independent of each other as the state variables are dependent on the decision, input, random, and exogenous variables. Furthermore, the output variables are dependent on the state of the system (represented by the state variables).
Objectives and constraints of the system and its users can be represented as functions of the output variables or state variables. The objective functions will depend on the perspective of the model's user. Depending on the context, an objective function is also known as an index of performance, as it is some measure of interest to the user. Although there is no limit to the number of objective functions and constraints a model can have, using or optimizing the model becomes more involved (computationally) as the number increases. For example, economists often apply linear algebra when using input–output models. Complicated mathematical models that have many variables may be consolidated by use of vectors where one symbol represents several variables.
=== A priori information ===
Mathematical modeling problems are often classified into black box or white box models, according to how much a priori information on the system is available. A black-box model is a system of which there is no a priori information available. A white-box model (also called glass box or clear box) is a system where all necessary information is available. Practically all systems are somewhere between the black-box and white-box models, so this concept is useful only as an intuitive guide for deciding which approach to take.
Usually, it is preferable to use as much a priori information as possible to make the model more accurate. Therefore, the white-box models are usually considered easier, because if you have used the information correctly, then the model will behave correctly. Often the a priori information comes in forms of knowing the type of functions relating different variables. For example, if we make a model of how a medicine works in a human system, we know that usually the amount of medicine in the blood is an exponentially decaying function, but we are still left with several unknown parameters; how rapidly does the medicine amount decay, and what is the initial amount of medicine in blood? This example is therefore not a completely white-box model. These parameters have to be estimated through some means before one can use the model.
In black-box models, one tries to estimate both the functional form of relations between variables and the numerical parameters in those functions. Using a priori information we could end up, for example, with a set of functions that probably could describe the system adequately. If there is no a priori information we would try to use functions as general as possible to cover all different models. An often used approach for black-box models are neural networks which usually do not make assumptions about incoming data. Alternatively, the NARMAX (Nonlinear AutoRegressive Moving Average model with eXogenous inputs) algorithms which were developed as part of nonlinear system identification can be used to select the model terms, determine the model structure, and estimate the unknown parameters in the presence of correlated and nonlinear noise. The advantage of NARMAX models compared to neural networks is that NARMAX produces models that can be written down and related to the underlying process, whereas neural networks produce an approximation that is opaque.
==== Subjective information ====
Sometimes it is useful to incorporate subjective information into a mathematical model. This can be done based on intuition, experience, or expert opinion, or based on convenience of mathematical form. Bayesian statistics provides a theoretical framework for incorporating such subjectivity into a rigorous analysis: we specify a prior probability distribution (which can be subjective), and then update this distribution based on empirical data.
An example of when such approach would be necessary is a situation in which an experimenter bends a coin slightly and tosses it once, recording whether it comes up heads, and is then given the task of predicting the probability that the next flip comes up heads. After bending the coin, the true probability that the coin will come up heads is unknown; so the experimenter would need to make a decision (perhaps by looking at the shape of the coin) about what prior distribution to use. Incorporation of such subjective information might be important to get an accurate estimate of the probability.
=== Complexity ===
In general, model complexity involves a trade-off between simplicity and accuracy of the model. Occam's razor is a principle particularly relevant to modeling, its essential idea being that among models with roughly equal predictive power, the simplest one is the most desirable. While added complexity usually improves the realism of a model, it can make the model difficult to understand and analyze, and can also pose computational problems, including numerical instability. Thomas Kuhn argues that as science progresses, explanations tend to become more complex before a paradigm shift offers radical simplification.
For example, when modeling the flight of an aircraft, we could embed each mechanical part of the aircraft into our model and would thus acquire an almost white-box model of the system. However, the computational cost of adding such a huge amount of detail would effectively inhibit the usage of such a model. Additionally, the uncertainty would increase due to an overly complex system, because each separate part induces some amount of variance into the model. It is therefore usually appropriate to make some approximations to reduce the model to a sensible size. Engineers often can accept some approximations in order to get a more robust and simple model. For example, Newton's classical mechanics is an approximated model of the real world. Still, Newton's model is quite sufficient for most ordinary-life situations, that is, as long as particle speeds are well below the speed of light, and we study macro-particles only. Note that better accuracy does not necessarily mean a better model. Statistical models are prone to overfitting which means that a model is fitted to data too much and it has lost its ability to generalize to new events that were not observed before.
=== Training, tuning, and fitting ===
Any model which is not pure white-box contains some parameters that can be used to fit the model to the system it is intended to describe. If the modeling is done by an artificial neural network or other machine learning, the optimization of parameters is called training, while the optimization of model hyperparameters is called tuning and often uses cross-validation. In more conventional modeling through explicitly given mathematical functions, parameters are often determined by curve fitting.
=== Evaluation and assessment ===
A crucial part of the modeling process is the evaluation of whether or not a given mathematical model describes a system accurately. This question can be difficult to answer as it involves several different types of evaluation.
==== Prediction of empirical data ====
Usually, the easiest part of model evaluation is checking whether a model predicts experimental measurements or other empirical data not used in the model development. In models with parameters, a common approach is to split the data into two disjoint subsets: training data and verification data. The training data are used to estimate the model parameters. An accurate model will closely match the verification data even though these data were not used to set the model's parameters. This practice is referred to as cross-validation in statistics.
Defining a metric to measure distances between observed and predicted data is a useful tool for assessing model fit. In statistics, decision theory, and some economic models, a loss function plays a similar role. While it is rather straightforward to test the appropriateness of parameters, it can be more difficult to test the validity of the general mathematical form of a model. In general, more mathematical tools have been developed to test the fit of statistical models than models involving differential equations. Tools from nonparametric statistics can sometimes be used to evaluate how well the data fit a known distribution or to come up with a general model that makes only minimal assumptions about the model's mathematical form.
==== Scope of the model ====
Assessing the scope of a model, that is, determining what situations the model is applicable to, can be less straightforward. If the model was constructed based on a set of data, one must determine for which systems or situations the known data is a "typical" set of data. The question of whether the model describes well the properties of the system between data points is called interpolation, and the same question for events or data points outside the observed data is called extrapolation.
As an example of the typical limitations of the scope of a model, in evaluating Newtonian classical mechanics, we can note that Newton made his measurements without advanced equipment, so he could not measure properties of particles traveling at speeds close to the speed of light. Likewise, he did not measure the movements of molecules and other small particles, but macro particles only. It is then not surprising that his model does not extrapolate well into these domains, even though his model is quite sufficient for ordinary life physics.
==== Philosophical considerations ====
Many types of modeling implicitly involve claims about causality. This is usually (but not always) true of models involving differential equations. As the purpose of modeling is to increase our understanding of the world, the validity of a model rests not only on its fit to empirical observations, but also on its ability to extrapolate to situations or data beyond those originally described in the model. One can think of this as the differentiation between qualitative and quantitative predictions. One can also argue that a model is worthless unless it provides some insight which goes beyond what is already known from direct investigation of the phenomenon being studied.
An example of such criticism is the argument that the mathematical models of optimal foraging theory do not offer insight that goes beyond the common-sense conclusions of evolution and other basic principles of ecology. It should also be noted that while mathematical modeling uses mathematical concepts and language, it is not itself a branch of mathematics and does not necessarily conform to any mathematical logic, but is typically a branch of some science or other technical subject, with corresponding concepts and standards of argumentation.
== Significance in the natural sciences ==
Mathematical models are of great importance in the natural sciences, particularly in physics. Physical theories are almost invariably expressed using mathematical models. Throughout history, more and more accurate mathematical models have been developed. Newton's laws accurately describe many everyday phenomena, but at certain limits theory of relativity and quantum mechanics must be used.
It is common to use idealized models in physics to simplify things. Massless ropes, point particles, ideal gases and the particle in a box are among the many simplified models used in physics. The laws of physics are represented with simple equations such as Newton's laws, Maxwell's equations and the Schrödinger equation. These laws are a basis for making mathematical models of real situations. Many real situations are very complex and thus modeled approximately on a computer, a model that is computationally feasible to compute is made from the basic laws or from approximate models made from the basic laws. For example, molecules can be modeled by molecular orbital models that are approximate solutions to the Schrödinger equation. In engineering, physics models are often made by mathematical methods such as finite element analysis.
Different mathematical models use different geometries that are not necessarily accurate descriptions of the geometry of the universe. Euclidean geometry is much used in classical physics, while special relativity and general relativity are examples of theories that use geometries which are not Euclidean.
== Some applications ==
Often when engineers analyze a system to be controlled or optimized, they use a mathematical model. In analysis, engineers can build a descriptive model of the system as a hypothesis of how the system could work, or try to estimate how an unforeseeable event could affect the system. Similarly, in control of a system, engineers can try out different control approaches in simulations.
A mathematical model usually describes a system by a set of variables and a set of equations that establish relationships between the variables. Variables may be of many types; real or integer numbers, Boolean values or strings, for example. The variables represent some properties of the system, for example, the measured system outputs often in the form of signals, timing data, counters, and event occurrence. The actual model is the set of functions that describe the relations between the different variables.
== Examples ==
One of the popular examples in computer science is the mathematical models of various machines, an example is the deterministic finite automaton (DFA) which is defined as an abstract mathematical concept, but due to the deterministic nature of a DFA, it is implementable in hardware and software for solving various specific problems. For example, the following is a DFA M with a binary alphabet, which requires that the input contains an even number of 0s:
M
=
(
Q
,
Σ
,
δ
,
q
0
,
F
)
{\displaystyle M=(Q,\Sigma ,\delta ,q_{0},F)}
where
Q
=
{
S
1
,
S
2
}
,
{\displaystyle Q=\{S_{1},S_{2}\},}
Σ
=
{
0
,
1
}
,
{\displaystyle \Sigma =\{0,1\},}
q
0
=
S
1
,
{\displaystyle q_{0}=S_{1},}
F
=
{
S
1
}
,
{\displaystyle F=\{S_{1}\},}
and
δ
{\displaystyle \delta }
is defined by the following state-transition table:
The state
S
1
{\displaystyle S_{1}}
represents that there has been an even number of 0s in the input so far, while
S
2
{\displaystyle S_{2}}
signifies an odd number. A 1 in the input does not change the state of the automaton. When the input ends, the state will show whether the input contained an even number of 0s or not. If the input did contain an even number of 0s,
M
{\displaystyle M}
will finish in state
S
1
,
{\displaystyle S_{1},}
an accepting state, so the input string will be accepted.
The language recognized by
M
{\displaystyle M}
is the regular language given by the regular expression 1*( 0 (1*) 0 (1*) )*, where "*" is the Kleene star, e.g., 1* denotes any non-negative number (possibly zero) of symbols "1".
Many everyday activities carried out without a thought are uses of mathematical models. A geographical map projection of a region of the earth onto a small, plane surface is a model which can be used for many purposes such as planning travel.
Another simple activity is predicting the position of a vehicle from its initial position, direction and speed of travel, using the equation that distance traveled is the product of time and speed. This is known as dead reckoning when used more formally. Mathematical modeling in this way does not necessarily require formal mathematics; animals have been shown to use dead reckoning.
Population Growth. A simple (though approximate) model of population growth is the Malthusian growth model. A slightly more realistic and largely used population growth model is the logistic function, and its extensions.
Model of a particle in a potential-field. In this model we consider a particle as being a point of mass which describes a trajectory in space which is modeled by a function giving its coordinates in space as a function of time. The potential field is given by a function
V
:
R
3
→
R
{\displaystyle V\!:\mathbb {R} ^{3}\!\to \mathbb {R} }
and the trajectory, that is a function
r
:
R
→
R
3
,
{\displaystyle \mathbf {r} \!:\mathbb {R} \to \mathbb {R} ^{3},}
is the solution of the differential equation:
−
d
2
r
(
t
)
d
t
2
m
=
∂
V
[
r
(
t
)
]
∂
x
x
^
+
∂
V
[
r
(
t
)
]
∂
y
y
^
+
∂
V
[
r
(
t
)
]
∂
z
z
^
,
{\displaystyle -{\frac {\mathrm {d} ^{2}\mathbf {r} (t)}{\mathrm {d} t^{2}}}m={\frac {\partial V[\mathbf {r} (t)]}{\partial x}}\mathbf {\hat {x}} +{\frac {\partial V[\mathbf {r} (t)]}{\partial y}}\mathbf {\hat {y}} +{\frac {\partial V[\mathbf {r} (t)]}{\partial z}}\mathbf {\hat {z}} ,}
that can be written also as
m
d
2
r
(
t
)
d
t
2
=
−
∇
V
[
r
(
t
)
]
.
{\displaystyle m{\frac {\mathrm {d} ^{2}\mathbf {r} (t)}{\mathrm {d} t^{2}}}=-\nabla V[\mathbf {r} (t)].}
Note this model assumes the particle is a point mass, which is certainly known to be false in many cases in which we use this model; for example, as a model of planetary motion.
Model of rational behavior for a consumer. In this model we assume a consumer faces a choice of
n
{\displaystyle n}
commodities labeled
1
,
2
,
…
,
n
{\displaystyle 1,2,\dots ,n}
each with a market price
p
1
,
p
2
,
…
,
p
n
.
{\displaystyle p_{1},p_{2},\dots ,p_{n}.}
The consumer is assumed to have an ordinal utility function
U
{\displaystyle U}
(ordinal in the sense that only the sign of the differences between two utilities, and not the level of each utility, is meaningful), depending on the amounts of commodities
x
1
,
x
2
,
…
,
x
n
{\displaystyle x_{1},x_{2},\dots ,x_{n}}
consumed. The model further assumes that the consumer has a budget
M
{\displaystyle M}
which is used to purchase a vector
x
1
,
x
2
,
…
,
x
n
{\displaystyle x_{1},x_{2},\dots ,x_{n}}
in such a way as to maximize
U
(
x
1
,
x
2
,
…
,
x
n
)
.
{\displaystyle U(x_{1},x_{2},\dots ,x_{n}).}
The problem of rational behavior in this model then becomes a mathematical optimization problem, that is:
max
U
(
x
1
,
x
2
,
…
,
x
n
)
{\displaystyle \max \,U(x_{1},x_{2},\ldots ,x_{n})}
subject to:
∑
i
=
1
n
p
i
x
i
≤
M
,
{\displaystyle \sum _{i=1}^{n}p_{i}x_{i}\leq M,}
x
i
≥
0
for all
i
=
1
,
2
,
…
,
n
.
{\displaystyle x_{i}\geq 0\;\;\;{\text{ for all }}i=1,2,\dots ,n.}
This model has been used in a wide variety of economic contexts, such as in general equilibrium theory to show existence and Pareto efficiency of economic equilibria.
Neighbour-sensing model is a model that explains the mushroom formation from the initially chaotic fungal network.
In computer science, mathematical models may be used to simulate computer networks.
In mechanics, mathematical models may be used to analyze the movement of a rocket model.
== See also ==
== References ==
== Further reading ==
=== Books ===
Aris, Rutherford [ 1978 ] ( 1994 ). Mathematical Modelling Techniques, New York: Dover. ISBN 0-486-68131-9
Bender, E.A. [ 1978 ] ( 2000 ). An Introduction to Mathematical Modeling, New York: Dover. ISBN 0-486-41180-X
Gary Chartrand (1977) Graphs as Mathematical Models, Prindle, Webber & Schmidt ISBN 0871502364
Dubois, G. (2018) "Modeling and Simulation", Taylor & Francis, CRC Press.
Gershenfeld, N. (1998) The Nature of Mathematical Modeling, Cambridge University Press ISBN 0-521-57095-6 .
Lin, C.C. & Segel, L.A. ( 1988 ). Mathematics Applied to Deterministic Problems in the Natural Sciences, Philadelphia: SIAM. ISBN 0-89871-229-7
Models as Mediators: Perspectives on Natural and Social Science edited by Mary S. Morgan and Margaret Morrison, 1999.
Mary S. Morgan The World in the Model: How Economists Work and Think, 2012.
=== Specific applications ===
Papadimitriou, Fivos. (2010). Mathematical Modelling of Spatial-Ecological Complex Systems: an Evaluation. Geography, Environment, Sustainability 1(3), 67–80. doi:10.24057/2071-9388-2010-3-1-67-80
Peierls, R. (1980). "Model-making in physics". Contemporary Physics. 21: 3–17. Bibcode:1980ConPh..21....3P. doi:10.1080/00107518008210938.
An Introduction to Infectious Disease Modelling by Emilia Vynnycky and Richard G White.
== External links ==
General reference
Patrone, F. Introduction to modeling via differential equations, with critical remarks.
Plus teacher and student package: Mathematical Modelling. Brings together all articles on mathematical modeling from Plus Magazine, the online mathematics magazine produced by the Millennium Mathematics Project at the University of Cambridge.
Philosophical
Frigg, R. and S. Hartmann, Models in Science, in: The Stanford Encyclopedia of Philosophy, (Spring 2006 Edition)
Griffiths, E. C. (2010) What is a model? | Wikipedia/Mathematical_Modeling |
Neurosurgery or neurological surgery, known in common parlance as brain surgery, is the medical specialty that focuses on the surgical treatment or rehabilitation of disorders which affect any portion of the nervous system including the brain, spinal cord, peripheral nervous system, and cerebrovascular system. Neurosurgery as a medical specialty also includes non-surgical management of some neurological conditions.
== Education and context ==
In different countries, there are different requirements for an individual to legally practice neurosurgery, and there are varying methods through which they must be educated. In most countries, neurosurgeon training requires a minimum period of seven years after graduating from medical school.
=== United Kingdom ===
In the United Kingdom, students must gain entry into medical school. The MBBS qualification (Bachelor of Medicine, Bachelor of Surgery) takes four to six years depending on the student's route. The newly qualified physician must then complete foundation training lasting two years; this is a paid training program in a hospital or clinical setting covering a range of medical specialties including surgery. Junior doctors then apply to enter the neurosurgical pathway. Unlike most other surgical specialties, it currently has its own independent training pathway which takes around eight years (ST1-8); before being able to sit for consultant exams with sufficient amounts of experience and practice behind them. Neurosurgery remains consistently amongst the most competitive medical specialties in which to obtain entry.
=== United States ===
In the United States, a neurosurgeon must generally complete four years of undergraduate education, four years of medical school, and seven years of residency (PGY-1-7). Most, but not all, residency programs have some component of basic science or clinical research. Neurosurgeons may pursue additional training in the form of a fellowship after residency, or, in some cases, as a senior resident in the form of an enfolded fellowship. These fellowships include pediatric neurosurgery, trauma/neurocritical care, functional and stereotactic surgery, surgical neuro-oncology, radiosurgery, neurovascular surgery, skull-base surgery, peripheral nerve and complex spinal surgery. Fellowships typically span one to two years. In the U.S., neurosurgery is a very small, highly competitive specialty, constituting only 0.5 percent of all physicians.
== History ==
Neurosurgery, or the premeditated incision into the head for pain relief, has been around for thousands of years, but notable advancements in neurosurgery have only come within the last hundred years.
=== Ancient ===
The Incas appear to have practiced a procedure known as trepanation since before European colonization. During the Middle Ages in Al-Andalus from 936 to 1013 AD, Al-Zahrawi performed surgical treatments of head injuries, skull fractures, spinal injuries, hydrocephalus, subdural effusions and headache. During the Roman Empire, doctors and surgeons performed neurosurgery on depressed skull fractures. Simple forms of neurosurgery were performed on King Henri II in 1559, after a jousting accident with Gabriel Montgomery fatally wounded him. Ambroise Paré and Andreas Vesalius, both experts in their field at the time,
attempted their own methods, to no avail, in curing Henri. In China, Hua Tuo created the first general anaesthesia called mafeisan, which he used on surgical procedures on the brain.
=== Modern ===
History of tumor removal: In 1879, after locating it via neurological signs alone, Scottish surgeon William Macewen (1848–1924) performed the first successful brain tumor removal. On November 25, 1884, after English physician Alexander Hughes Bennett (1848–1901) used Macewen's technique to locate it, English surgeon Rickman Godlee (1849–1925) performed the first primary brain tumor removal, which differs from Macewen's operation in that Bennett operated on the exposed brain, whereas Macewen operated outside of the "brain proper" via trepanation. On March 16, 1907, Austrian surgeon Hermann Schloffer became the first to successfully remove a pituitary tumor.
Lobotomy: also known as leucotomy, was a form of psychosurgery, a neurosurgical treatment of mental disorders that involves severing connections in the brain's prefrontal cortex. The originator of the procedure, Portuguese neurologist António Egas Moniz, shared the Nobel Prize for Physiology or Medicine of 1949. Some patients improved in some ways after the operation, but complications and impairments – sometimes severe – were frequent. The procedure was controversial from its initial use, in part due to the balance between benefits and risks. It is mostly rejected as a treatment now and non-compliant with patients' rights.
History of electrodes in the brain: In 1878, Richard Caton discovered that electrical signals transmitted through an animal's brain. In 1950 Jose Delgado invented the first electrode that was implanted in an animal's brain (bull), using it to make it run and change direction. In 1972 the cochlear implant, a neurological prosthetic that allowed deaf people to hear was marketed for commercial use. In 1998 researcher Philip Kennedy implanted the first Brain Computer Interface (BCI) into a human subject.
A survey done in 2010 on 100 most cited works in neurosurgery shows that the works mainly cover clinical trials evaluating surgical and medical therapies, descriptions of novel techniques in neurosurgery, and descriptions of systems classifying and grading diseases.
=== Modern surgical instruments ===
The main advancements in neurosurgery came about as a result of highly crafted tools. Modern neurosurgical tools, or instruments, include chisels, curettes, dissectors, distractors, elevators, forceps, hooks, impactors, probes, suction tubes, power tools, and robots. Most of these modern tools have been in medical practice for a relatively long time. The main difference of these tools in neurosurgery, were the precision in which they were crafted. These tools are crafted with edges that are within a millimeter of desired accuracy. Other tools, such as handheld power saws and robots, have only recently been commonly used inside of a neurological operating room. As an example, the University of Utah developed a device for computer-aided design / computer-aided manufacturing (CAD-CAM) which uses an image-guided system to define a cutting tool path for a robotic cranial drill.
== Organised neurosurgery ==
The World Federation of Neurosurgical Societies (WFNS), founded in 1955, in Switzerland, as a professional, scientific, non governmental organization, is composed of 130 member societies: consisting of 5 Continental Associations (AANS, AASNS, CAANS, EANS and FLANC), 6 Affiliate Societies, and 119 National Neurosurgical Societies, representing some 50,000 neurosurgeons worldwide. It has a consultative status in the United Nations. The official Journal of the Organization is World Neurosurgery. The other global organisations being the World Academy of Neurological Surgery (WANS) and the World Federation of Skull Base Societies (WFSBS).
== Main divisions ==
General neurosurgery involves most neurosurgical conditions including neuro-trauma and other neuro-emergencies such as intracranial hemorrhage. Most level 1 hospitals have this kind of practice.
Specialized branches have developed to cater to special and difficult conditions. These specialized branches co-exist with general neurosurgery in more sophisticated hospitals. To practice advanced specialization within neurosurgery, additional higher fellowship training of one to two years is expected from the neurosurgeon.
Some of these divisions of neurosurgery are:
Vascular neurosurgery includes clipping of aneurysms and performing carotid endarterectomy (CEA).
Stereotactic neurosurgery, functional neurosurgery, and epilepsy surgery (the latter includes partial or total corpus callosotomy – severing part or all of the corpus callosum to stop or lessen seizure spread and activity, and the surgical removal of functional, physiological and/or anatomical pieces or divisions of the brain, called epileptic foci, that are operable and that are causing seizures, and also the more radical and rare partial or total lobectomy, or even hemispherectomy – the removal of part or all of one of the lobes, or one of the cerebral hemispheres of the brain; those two procedures, when possible, are also very, very rarely used in oncological neurosurgery or to treat very severe neurological trauma, such as stab or gunshot wounds to the brain)
Oncological neurosurgery also called neurosurgical oncology; includes pediatric oncological neurosurgery; treatment of benign and malignant central and peripheral nervous system cancers and pre-cancerous lesions in adults and children (including, among others, glioblastoma multiforme and other gliomas, brain stem cancer, astrocytoma, pontine glioma, medulloblastoma, spinal cancer, tumors of the meninges and intracranial spaces, secondary metastases to the brain, spine, and nerves, and peripheral nervous system tumors)
Skull base surgery
Spinal neurosurgery
Peripheral nerve surgery
Pediatric neurosurgery (for cancer, seizures, bleeding, stroke, cognitive disorders or congenital neurological disorders)
=== Commonly performed surgeries ===
According to an analysis by the American College of Surgeons National Surgical Quality Improvement Program (NSQIP), the most common surgeries performed by neurosurgeons in between 2006 and 2014 were the following:
Anterior cervical discectomy and fusion (ACDF)
Craniotomy for brain tumor (CBT)
Discectomy
Laminectomy
Posterolateral lumbar fusion (PLF)
== Neuropathology ==
Neuropathology is a specialty within the study of pathology focused on the disease of the brain, spinal cord, and neural tissue. This includes the central nervous system and the peripheral nervous system. Tissue analysis comes from either surgical biopsies or post mortem autopsies. Common tissue samples include muscle fibers and nervous tissue. Common applications of neuropathology include studying samples of tissue in patients who have Parkinson's disease, Alzheimer's disease, dementia, Huntington's disease, amyotrophic lateral sclerosis, mitochondria disease, and any disorder that has neural deterioration in the brain or spinal cord.
=== History ===
While pathology has been studied for millennia only within the last few hundred years has medicine focused on a tissue- and organ-based approach to tissue disease. In 1810, Thomas Hodgkin started to look at the damaged tissue for the cause. This was conjoined with the emergence of microscopy and started the current understanding of how the tissue of the human body is studied.
== Neuroanesthesia ==
Neuroanesthesia is a field of anesthesiology which focuses on neurosurgery. Anesthesia is not used during the middle of an "awake" brain surgery. Awake brain surgery is where the patient is conscious for the middle of the procedure and sedated for the beginning and end. This procedure is used when the tumor does not have clear boundaries and the surgeon wants to know if they are invading on critical regions of the brain which involve functions like talking, cognition, vision, and hearing. It will also be conducted for procedures which the surgeon is trying to combat epileptic seizures.
=== History ===
The physician Hippocrates (460–370 BCE) made accounts of using different wines to sedate patients while trepanning. In 60 CE, Dioscorides, a physician, pharmacologist, and botanist, detailed how mandrake, henbane, opium, and alcohol were used to put patients to sleep during trepanning. In 972 CE, two brother surgeons in Paramara, now India, used "samohine" to sedate a patient while removing a small tumor, and awoke the patient by pouring onion and vinegar in the patient's mouth. The combination of carbon dioxide, hydrogen, and nitrogen, was a form of neuroanesthesia adopted in the 18th century and introduced by Humphry Davy.
== Neurosurgery methods ==
Various Imaging methods are used in modern neurosurgery diagnosis and treatment. They include computer assisted imaging computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), magnetoencephalography (MEG), and stereotactic radiosurgery. Some neurosurgery procedures involve the use of intra-operative MRI and functional MRI.
In conventional neurosurgery the neurosurgeon opens the skull, creating a large opening to access the brain. Techniques involving smaller openings with the aid of microscopes and endoscopes are now being used as well. Methods that utilize small craniotomies in conjunction with high-clarity microscopic visualization of neural tissue offer excellent results. However, the open methods are still traditionally used in trauma or emergency situations.
Microsurgery is utilized in many aspects of neurological surgery. Microvascular techniques are used in EC-IC bypass surgery and in restoration carotid endarterectomy. The clipping of an aneurysm is performed under microscopic vision. Minimally-invasive spine surgery utilizes microscopes or endoscopes. Procedures such as microdiscectomy, laminectomy, and artificial disc replacement rely on microsurgery.
Using stereotaxy neurosurgeons can approach a minute target in the brain through a minimal opening. This is used in functional neurosurgery where electrodes are implanted or gene therapy is instituted with high level of accuracy as in the case of Parkinson's disease or Alzheimer's disease. Using the combination method of open and stereotactic surgery, intraventricular hemorrhages can potentially be evacuated successfully. Conventional surgery using image guidance technologies is also becoming common and is referred to as surgical navigation, computer-assisted surgery, navigated surgery, stereotactic navigation. Similar to a car or mobile Global Positioning System (GPS), image-guided surgery systems, like Curve Image Guided Surgery and StealthStation, use cameras or electromagnetic fields to capture and relay the patient's anatomy and the surgeon's precise movements in relation to the patient, to computer monitors in the operating room. These sophisticated computerized systems are used before and during surgery to help orient the surgeon with three-dimensional images of the patient's anatomy including the tumor. Real-time functional brain mapping has been employed to identify specific functional regions using electrocorticography (ECoG)
Minimally invasive endoscopic surgery is commonly utilized by neurosurgeons when appropriate. Techniques such as endoscopic endonasal surgery are used in pituitary tumors, craniopharyngiomas, chordomas, and the repair of cerebrospinal fluid leaks. Ventricular endoscopy is used in the treatment of intraventricular bleeds, hydrocephalus, colloid cyst and neurocysticercosis. Endonasal endoscopy is at times carried out with neurosurgeons and ENT surgeons working together as a team.
Repair of craniofacial disorders and disturbance of cerebrospinal fluid circulation is done by neurosurgeons who also occasionally team up with maxillofacial and plastic surgeons. Cranioplasty for craniosynostosis is performed by pediatric neurosurgeons with or without plastic surgeons.
Neurosurgeons are involved in stereotactic radiosurgery along with radiation oncologists in tumor and AVM treatment. Radiosurgical methods such as Gamma knife, Cyberknife and Novalis Radiosurgery are used as well.
Endovascular neurosurgery utilize endovascular image guided procedures for the treatment of aneurysms, AVMs, carotid stenosis, strokes, and spinal malformations, and vasospasms. Techniques such as angioplasty, stenting, clot retrieval, embolization, and diagnostic angiography are endovascular procedures.
A common procedure performed in neurosurgery is the placement of ventriculo-peritoneal shunt (VP shunt). In pediatric practice this is often implemented in cases of congenital hydrocephalus. The most common indication for this procedure in adults is normal pressure hydrocephalus (NPH).
Neurosurgery of the spine covers the cervical, thoracic and lumbar spine. Some indications for spine surgery include spinal cord compression resulting from trauma, arthritis of the spinal discs, or spondylosis. In cervical cord compression, patients may have difficulty with gait, balance issues, and/or numbness and tingling in the hands or feet. Spondylosis is the condition of spinal disc degeneration and arthritis that may compress the spinal canal. This condition can often result in bone-spurring and disc herniation. Power drills and special instruments are often used to correct any compression problems of the spinal canal. Disc herniations of spinal vertebral discs are removed with special rongeurs. This procedure is known as a discectomy. Generally once a disc is removed it is replaced by an implant which will create a bony fusion between vertebral bodies above and below. Instead, a mobile disc could be implanted into the disc space to maintain mobility. This is commonly used in cervical disc surgery. At times instead of disc removal a Laser discectomy could be used to decompress a nerve root. This method is mainly used for lumbar discs. Laminectomy is the removal of the lamina of the vertebrae of the spine in order to make room for the compressed nerve tissue.
Surgery for chronic pain is a sub-branch of functional neurosurgery. Some of the techniques include implantation of deep brain stimulators, spinal cord stimulators, peripheral stimulators and pain pumps.
Surgery of the peripheral nervous system is also possible, and includes the very common procedures of carpal tunnel decompression and peripheral nerve transposition. Numerous other types of nerve entrapment conditions and other problems with the peripheral nervous system are treated as well.
== Conditions ==
Conditions treated by neurosurgeons include, but are not limited to:
Meningitis and other central nervous system infections including abscesses
Spinal disc herniation
Cervical spinal stenosis and Lumbar spinal stenosis
Hydrocephalus
Head trauma (brain hemorrhages, skull fractures, etc.)
Spinal cord trauma
Traumatic injuries of peripheral nerves
Tumors of the spine, spinal cord and peripheral nerves
Intracerebral hemorrhage, such as subarachnoid hemorrhage, interdepartmental, and intracellular hemorrhages
Some forms of drug-resistant epilepsy
Some forms of movement disorders (advanced Parkinson's disease, chorea) – this involves the use of specially developed minimally invasive stereotactic techniques (functional, stereotactic neurosurgery) such as ablative surgery and deep brain stimulation surgery
Intractable pain of cancer or trauma patients and cranial/peripheral nerve pain
Some forms of intractable psychiatric disorders
Vascular malformations (i.e., arteriovenous malformations, venous angiomas, cavernous angiomas, capillary telangectasias) of the brain and spinal cord
Moyamoya disease
== Recovery ==
=== Postoperative pain ===
Pain following brain surgery can be significant and may lengthen recovery, increase the amount of time a person stays in the hospital following surgery, and increase the risk of complications following surgery. Severe acute pain following brain surgery may also increase the risk of a person developing a chronic post-craniotomy headache. Approaches to treating pain in adults include treatment with nonsteroidal anti‐inflammatory drugs (NSAIDs), which have been shown to reduce pain for up to 24 hours following surgery. Low-quality evidence supports the use of the medications dexmedetomidine, pregabalin or gabapentin to reduce post-operative pain. Low-quality evidence also supports scalp blocks and scalp infiltration to reduce postoperative pain. Gabapentin or pregabalin may also decrease vomiting and nausea following surgery, based on very low-quality medical evidence.
== Notable neurosurgeons ==
Saleem Abdulrauf – developed "awake" craniotomy for complex aneurysms and vascular malformations.
John R. Adler – Stanford University neurosurgeon who invented the Cyberknife.
Alim-Louis Benabid – known as one of the developers of deep brain stimulation surgery for movement disorder.
Ben Carson – retired pediatric neurosurgeon from Johns Hopkins Hospital, pioneer in hemispherectomy, and pioneer in the separation of craniopagus twins (joined at the head); former 2016 Republican Party presidential candidate, and former United States Secretary of Housing and Urban Development under the Presidency of Donald Trump.
Harvey Cushing – known as one of the fathers of modern Neurosurgery.
Walter Dandy – known as one of the founding fathers of modern Neurosurgery.
Christopher Duntsch – Former neurosurgeon who killed or maimed nearly every patient he operated on before being incarcerated.
Victor Horsley – known as the first neurosurgeon.
Lars Leksell – Swedish neurosurgeon who developed the Gamma Knife.
Wirginia Maixner – pediatric neurosurgeon at Melbourne's Royal Children's Hospital. Primarily known for separating conjoined Bangladeshi twins, Trishna and Krishna.
Henry Marsh – leading English neurosurgeon and pioneer of neurosurgical advancements in Ukraine
Frank Henderson Mayfield – invented the Mayfield skull clamp.
B. K. Misra – First neurosurgeon in the world to perform image-guided surgery for aneurysms, first in South Asia to perform stereotactic radiosurgery, first in India to perform awake craniotomy and laparoscopic spine surgery.
Karin Muraszko – first woman to occupy a chair of neurosurgery at an American medical school (University of Michigan).
Hirotaro Narabayashi – a pioneer of stereotactic Neurosurgery.
Ayub K. Ommaya – invented the Ommaya reservoir.
Wilder Penfield – known as one of the founding fathers of modern neurosurgery, and pioneer of epilepsy Neurosurgery.
Ludvig Puusepp – known as one of the founding fathers of modern neurosurgery, world's first professor of neurosurgery.
Joseph Ransohoff – known for his pioneering use of medical imaging and catheterization in neurosurgery, and for founding the first neurosurgery intensive care unit.
Majid Samii – pioneer of cerebello-pontine angle tumor surgery. World Federation of Neurosurgical Societies coined a medal of honor bearing Samii's name which would be given to outstanding neurosurgeons every two years.
Juliet Sekabunga Nalwanga – Uganda's first female neurosurgeon.
Hermann Schloffer invented transsphenoidal surgery in 1907.
Robert Wheeler Rand – along with Theodore Kurze, MD was among the first to introduce the surgical microscope into neurosurgical procedures in 1957 and published first textbook on Microneurosurgery in 1969.
Robert J. White – Established the Vatican's Commission on Biomedical Ethics in 1981 after his appointment to the Pontifical Academy of Sciences and was famous for his head transplants on living monkeys.
Gazi Yaşargil – known as the father of microneurosurgery.
== Bioethics in neurosurgery ==
Neurosurgery is a part of practical medicine and the only specialty that involves invasive intervention in the activity of the living brain. The brain ensures the structural and functional integrity of the body and the implementation of all the main life processes of the body. Therefore, neurosurgery faces a wide range of bioethical issues and a significant selection of the latest treatment technologies.
Neurosurgery has the following applied scientific and ethical problems:
Ethical and legal aspects of clinical research;
Αxiological deficit due to professional deformation and professional burnout;
Limited access to expensive medical services;
The industry-specific problem of "medical error" due to the complexity of neurosurgical pathologies and the huge number of possible technologies and tools for their treatment;
Controversial bioethical and legal issues of surgery for the treatment of psychiatric diseases;
Bioethical discussions regarding the instrumentation of reconstructive surgery, through the use of experimental technologies;
Debatable bioethical issues of improving human brain activity with the help of artificial implants, for instance neurocomponents (artificial impulse quasi-neurons);
Cyborgization in transhumanism meaning;
Ethical issue of standardization of research protocols for testing neuroengineering means of nerve tissue regeneration in order to improve the implementation of experimental research results in clinical practice.
== See also ==
== References == | Wikipedia/Neurosurgery |
The development of the nervous system, or neural development (neurodevelopment), refers to the processes that generate, shape, and reshape the nervous system of animals, from the earliest stages of embryonic development to adulthood. The field of neural development draws on both neuroscience and developmental biology to describe and provide insight into the cellular and molecular mechanisms by which complex nervous systems develop, from nematodes and fruit flies to mammals.
Defects in neural development can lead to malformations such as holoprosencephaly, and a wide variety of neurological disorders including limb paresis and paralysis, balance and vision disorders, and seizures, and in humans other disorders such as Rett syndrome, Down syndrome and intellectual disability.
== Vertebrate brain development ==
The vertebrate central nervous system (CNS) is derived from the ectoderm—the outermost germ layer of the embryo. A part of the dorsal ectoderm becomes specified to neural ectoderm – neuroectoderm that forms the neural plate along the dorsal side of the embryo. This is a part of the early patterning of the embryo (including the invertebrate embryo) that also establishes an anterior-posterior axis. The neural plate is the source of the majority of neurons and glial cells of the CNS. The neural groove forms along the long axis of the neural plate, and the neural plate folds to give rise to the neural tube. This process is known as neurulation. When the tube is closed at both ends it is filled with embryonic cerebrospinal fluid. As the embryo develops, the anterior part of the neural tube expands and forms three primary brain vesicles, which become the forebrain (prosencephalon), midbrain (mesencephalon), and hindbrain (rhombencephalon). These simple, early vesicles enlarge and further divide into the telencephalon (future cerebral cortex and basal ganglia), diencephalon (future thalamus and hypothalamus), mesencephalon (future colliculi), metencephalon (future pons and cerebellum), and myelencephalon (future medulla). The CSF-filled central chamber is continuous from the telencephalon to the central canal of the spinal cord, and constitutes the developing ventricular system of the CNS. Embryonic cerebrospinal fluid differs from that formed in later developmental stages, and from adult CSF; it influences the behavior of neural precursors. Because the neural tube gives rise to the brain and spinal cord any mutations at this stage in development can lead to fatal deformities like anencephaly or lifelong disabilities like spina bifida. During this time, the walls of the neural tube contain neural stem cells, which drive brain growth as they divide many times. Gradually some of the cells stop dividing and differentiate into neurons and glial cells, which are the main cellular components of the CNS. The newly generated neurons migrate to different parts of the developing brain to self-organize into different brain structures. Once the neurons have reached their regional positions, they extend axons and dendrites, which allow them to communicate with other neurons via synapses. Synaptic communication between neurons leads to the establishment of functional neural circuits that mediate sensory and motor processing, and underlie behavior.
== Induction ==
During early embryonic development of the vertebrate, the dorsal ectoderm becomes specified to give rise to the epidermis and the nervous system; a part of the dorsal ectoderm becomes specified to neural ectoderm to form the neural plate which gives rise to the nervous system. The conversion of undifferentiated ectoderm to neuroectoderm requires signals from the mesoderm. At the onset of gastrulation presumptive mesodermal cells move through the dorsal blastopore lip and form a layer of mesoderm in between the endoderm and the ectoderm. Mesodermal cells migrate along the dorsal midline to give rise to the notochord that develops into the vertebral column. Neuroectoderm overlying the notochord develops into the neural plate in response to a diffusible signal produced by the notochord. The remainder of the ectoderm gives rise to the epidermis. The ability of the mesoderm to convert the overlying ectoderm into neural tissue is called neural induction.
In the early embryo, the neural plate folds outwards to form the neural groove. Beginning in the future neck region, the neural folds of this groove close to create the neural tube. The formation of the neural tube from the ectoderm is called neurulation. The ventral part of the neural tube is called the basal plate; the dorsal part is called the alar plate. The hollow interior is called the neural canal, and the open ends of the neural tube, called the neuropores, close off.
A transplanted blastopore lip can convert ectoderm into neural tissue and is said to have an inductive effect. Neural inducers are molecules that can induce the expression of neural genes in ectoderm explants without inducing mesodermal genes as well. Neural induction is often studied in Xenopus embryos since they have a simple body plan and there are good markers to distinguish between neural and non-neural tissue. Examples of neural inducers are the molecules noggin and chordin.
When embryonic ectodermal cells are cultured at low density in the absence of mesodermal cells they undergo neural differentiation (express neural genes), suggesting that neural differentiation is the default fate of ectodermal cells. In explant cultures (which allow direct cell-cell interactions) the same cells differentiate into epidermis. This is due to the action of BMP4 (a TGF-β family protein) that induces ectodermal cultures to differentiate into epidermis. During neural induction, noggin and chordin are produced by the dorsal mesoderm (notochord) and diffuse into the overlying ectoderm to inhibit the activity of BMP4. This inhibition of BMP4 causes the cells to differentiate into neural cells. Inhibition of TGF-β and BMP (bone morphogenetic protein) signaling can efficiently induce neural tissue from pluripotent stem cells.
== Regionalization ==
In a later stage of development the superior part of the neural tube flexes at the level of the future midbrain—the mesencephalon, at the mesencephalic flexure or cephalic flexure. Above the mesencephalon is the prosencephalon (future forebrain) and beneath it is the rhombencephalon (future hindbrain).
The alar plate of the prosencephalon expands to form the telencephalon which gives rise to the cerebral hemispheres, whilst its basal plate becomes the diencephalon. The optical vesicle (which eventually become the optic nerve, retina and iris) forms at the basal plate of the prosencephalon.
== Patterning ==
In chordates, dorsal ectoderm forms all neural tissue and the nervous system. Patterning occurs due to specific environmental conditions - different concentrations of signaling molecules
=== Dorsoventral axis ===
The ventral half of the neural plate is controlled by the notochord, which acts as the 'organiser'. The dorsal half is controlled by the ectoderm plate, which flanks either side of the neural plate.
Ectoderm follows a default pathway to become neural tissue. Evidence for this comes from single, cultured cells of ectoderm, which go on to form neural tissue. This is postulated to be because of a lack of BMPs, which are blocked by the organiser. The organiser may produce molecules such as follistatin, noggin and chordin that inhibit BMPs.
The ventral neural tube is patterned by sonic hedgehog (Shh) from the notochord, which acts as the inducing tissue. Notochord-derived Shh signals to the floor plate, and induces Shh expression in the floor plate. Floor plate-derived Shh subsequently signals to other cells in the neural tube, and is essential for proper specification of ventral neuron progenitor domains. Loss of Shh from the notochord and/or floor plate prevents proper specification of these progenitor domains. Shh binds Patched1, relieving Patched-mediated inhibition of Smoothened, leading to activation of the Gli family of transcription factors (GLI1, GLI2, and GLI3).
In this context Shh acts as a morphogen - it induces cell differentiation dependent on its concentration. At low concentrations it forms ventral interneurons, at higher concentrations it induces motor neuron development, and at highest concentrations it induces floor plate differentiation. Failure of Shh-modulated differentiation causes holoprosencephaly.
The dorsal neural tube is patterned by BMPs from the epidermal ectoderm flanking the neural plate. These induce sensory interneurons by activating Sr/Thr kinases and altering SMAD transcription factor levels.
=== Rostrocaudal (Anteroposterior) axis ===
Signals that control anteroposterior neural development include FGF and retinoic acid, which act in the hindbrain and spinal cord. The hindbrain, for example, is patterned by Hox genes, which are expressed in overlapping domains along the anteroposterior axis under the control of retinoic acid. The 3′ (3 prime end) genes in the Hox cluster are induced by retinoic acid in the hindbrain, whereas the 5′ (5 prime end) Hox genes are not induced by retinoic acid and are expressed more posteriorly in the spinal cord. Hoxb-1 is expressed in rhombomere 4 and gives rise to the facial nerve. Without this Hoxb-1 expression, a nerve similar to the trigeminal nerve arises.
== Neurogenesis ==
Neurogenesis is the process by which neurons are generated from neural stem cells and progenitor cells. Neurons are 'post-mitotic', meaning that they will never divide again for the lifetime of the organism.
Epigenetic modifications play a key role in regulating gene expression in differentiating neural stem cells and are critical for cell fate determination in the developing and adult mammalian brain. Epigenetic modifications include DNA cytosine methylation to form 5-methylcytosine and 5-methylcytosine demethylation. DNA cytosine methylation is catalyzed by DNA methyltransferases (DNMTs). Methylcytosine demethylation is catalyzed in several sequential steps by TET enzymes that carry out oxidative reactions (e.g. 5-methylcytosine to 5-hydroxymethylcytosine) and enzymes of the DNA base excision repair (BER) pathway.
== Neuronal migration ==
Neuronal migration is the method by which neurons travel from their origin or birthplace to their final position in the brain. There are several ways they can do this, e.g. by radial migration or tangential migration. Sequences of radial migration (also known as glial guidance) and somal translocation have been captured by time-lapse microscopy.
=== Radial ===
Neuronal precursor cells proliferate in the ventricular zone of the developing neocortex, where the principal neural stem cell is the radial glial cell. The first postmitotic cells must leave the stem cell niche and migrate outward to form the preplate, which is destined to become Cajal–Retzius cells and subplate neurons. These cells do so by somal translocation. Neurons migrating with this mode of locomotion are bipolar and attach the leading edge of the process to the pia. The soma is then transported to the pial surface by nucleokinesis, a process by which a microtubule "cage" around the nucleus elongates and contracts in association with the centrosome to guide the nucleus to its final destination.
Radial glial cells, whose fibers serve as a scaffolding for migrating cells and a means of radial communication mediated by calcium dynamic activity, act as the main excitatory neuronal stem cell of the cerebral cortex or translocate to the cortical plate and differentiate either into astrocytes or neurons. Somal translocation can occur at any time during development.
Subsequent waves of neurons split the preplate by migrating along radial glial fibres to form the cortical plate. Each wave of migrating cells travel past their predecessors forming layers in an inside-out manner, meaning that the youngest neurons are the closest to the surface. It is estimated that glial guided migration represents 90% of migrating neurons in human and about 75% in rodents.
=== Tangential ===
Most interneurons migrate tangentially through multiple modes of migration to reach their appropriate location in the cortex. An example of tangential migration is the movement of interneurons from the ganglionic eminence to the cerebral cortex. One example of ongoing tangential migration in a mature organism, observed in some animals, is the rostral migratory stream connecting subventricular zone and olfactory bulb.
=== Axophilic ===
Many neurons migrating along the anterior-posterior axis of the body use existing axon tracts to migrate along; this is called axophilic migration. An example of this mode of migration is in GnRH-expressing neurons, which make a long journey from their birthplace in the nose, through the forebrain, and into the hypothalamus. Many of the mechanisms of this migration have been worked out, starting with the extracellular guidance cues that trigger intracellular signaling. These intracellular signals, such as calcium signaling, lead to actin and microtubule cytoskeletal dynamics, which produce cellular forces that interact with the extracellular environment through cell adhesion proteins to cause the movement of these cells.
=== Multipolar ===
There is also a method of neuronal migration called multipolar migration. This is seen in multipolar cells, which in the human, are abundantly present in the cortical intermediate zone. They do not resemble the cells migrating by locomotion or somal translocation. Instead these multipolar cells express neuronal markers and extend multiple thin processes in various directions independently of the radial glial fibers.
== Neurotrophic factors ==
The survival of neurons is regulated by survival factors, called trophic factors. The neurotrophic hypothesis was formulated by Victor Hamburger and Rita Levi Montalcini based on studies of the developing nervous system. Victor Hamburger discovered that implanting an extra limb in the developing chick led to an increase in the number of spinal motor neurons. Initially he thought that the extra limb was inducing proliferation of motor neurons, but he and his colleagues later showed that there was a great deal of motor neuron death during normal development, and the extra limb prevented this cell death. According to the neurotrophic hypothesis, growing axons compete for limiting amounts of target-derived trophic factors and axons that fail to receive sufficient trophic support die by apoptosis. It is now clear that factors produced by a number of sources contribute to neuronal survival.
Nerve Growth Factor (NGF): Rita Levi Montalcini and Stanley Cohen purified the first trophic factor, Nerve Growth Factor (NGF), for which they received the Nobel Prize. There are three NGF-related trophic factors: BDNF, NT3, and NT4, which regulate survival of various neuronal populations. The Trk proteins act as receptors for NGF and related factors. Trk is a receptor tyrosine kinase. Trk dimerization and phosphorylation leads to activation of various intracellular signaling pathways including the MAP kinase, Akt, and PKC pathways.
CNTF: Ciliary neurotrophic factor is another protein that acts as a survival factor for motor neurons. CNTF acts via a receptor complex that includes CNTFRα, GP130, and LIFRβ. Activation of the receptor leads to phosphorylation and recruitment of the JAK kinase, which in turn phosphorylates LIFRβ. LIFRβ acts as a docking site for the STAT transcription factors. JAK kinase phosphorylates STAT proteins, which dissociate from the receptor and translocate to the nucleus to regulate gene expression.
GDNF: Glial derived neurotrophic factor is a member of the TGFb family of proteins, and is a potent trophic factor for striatal neurons. The functional receptor is a heterodimer, composed of type 1 and type 2 receptors. Activation of the type 1 receptor leads to phosphorylation of Smad proteins, which translocate to the nucleus to activate gene expression.
== Synapse formation ==
=== Neuromuscular junction ===
Much of our understanding of synapse formation comes from studies at the neuromuscular junction. The transmitter at this synapse is acetylcholine. The acetylcholine receptor (AchR) is present at the surface of muscle cells before synapse formation. The arrival of the nerve induces clustering of the receptors at the synapse. McMahan and Sanes showed that the synaptogenic signal is concentrated at the basal lamina. They also showed that the synaptogenic signal is produced by the nerve, and they identified the factor as Agrin. Agrin induces clustering of AchRs on the muscle surface and synapse formation is disrupted in agrin knockout mice. Agrin transduces the signal via MuSK receptor to rapsyn. Fischbach and colleagues showed that receptor subunits are selectively transcribed from nuclei next to the synaptic site. This is mediated by neuregulins.
In the mature synapse each muscle fiber is innervated by one motor neuron. However, during development, many of the fibers are innervated by multiple axons. Lichtman and colleagues have studied the process of synapses elimination. This is an activity-dependent event. Partial blockage of the receptor leads to retraction of corresponding presynaptic terminals. Later they used a connectomic approach, i.e., tracing out all the connections between motor neurons and muscle fibers, to characterize developmental synapse elimination on the level of a full circuit. Analysis confirmed the massive rewiring, 10-fold decrease in the number of synapses, that takes place as axons prune their motor units but add more synaptic areas at the NMJs with which they remain in contact.
=== CNS synapses ===
Agrin appears not to be a central mediator of CNS synapse formation and there is active interest in identifying signals that mediate CNS synaptogenesis. Neurons in culture develop synapses that are similar to those that form in vivo, suggesting that synaptogenic signals can function properly in vitro. CNS synaptogenesis studies have focused mainly on glutamatergic synapses. Imaging experiments show that dendrites are highly dynamic during development and often initiate contact with axons. This is followed by recruitment of postsynaptic proteins to the site of contact. Stephen Smith and colleagues have shown that contact initiated by dendritic filopodia can develop into synapses.
Induction of synapse formation by glial factors: Barres and colleagues made the observation that factors in glial conditioned media induce synapse formation in retinal ganglion cell cultures. Synapse formation in the CNS is correlated with astrocyte differentiation suggesting that astrocytes might provide a synaptogenic factor. The identity of the astrocytic factors is not yet known.
Neuroligins and SynCAM as synaptogenic signals: Sudhof, Serafini, Scheiffele and colleagues have shown that neuroligins and SynCAM can act as factors that induce presynaptic differentiation. Neuroligins are concentrated at the postsynaptic site and act via neurexins concentrated in the presynaptic axons. SynCAM is a cell adhesion molecule that is present in both pre- and post-synaptic membranes.
=== Assembly of neural circuits ===
The processes of neuronal migration, differentiation and axon guidance are generally believed to be activity-independent mechanisms and rely on hard-wired genetic programs in the neurons themselves. Research findings however have implicated a role for activity-dependent mechanisms in mediating some aspects of these processes such as the rate of neuronal migration, aspects of neuronal differentiation and axon pathfinding. Activity-dependent mechanisms influence neural circuit development and are crucial for laying out early connectivity maps and the continued refinement of synapses which occurs during development. There are two distinct types of neural activity we observe in developing circuits -early spontaneous activity and sensory-evoked activity. Spontaneous activity occurs early during neural circuit development even when sensory input is absent and is observed in many systems such as the developing visual system, auditory system, motor system, hippocampus, cerebellum and neocortex.
Experimental techniques such as direct electrophysiological recording, fluorescence imaging using calcium indicators and optogenetic techniques have shed light on the nature and function of these early bursts of activity. They have distinct spatial and temporal patterns during development and their ablation during development has been known to result in deficits in network refinement in the visual system. In the immature retina, waves of spontaneous action potentials arise from the retinal ganglion cells and sweep across the retinal surface in the first few postnatal weeks. These waves are mediated by neurotransmitter acetylcholine in the initial phase and later on by glutamate. They are thought to instruct the formation of two sensory maps- the retinotopic map and eye-specific segregation. Retinotopic map refinement occurs in downstream visual targets in the brain-the superior colliculus (SC) and dorsal lateral geniculate nucleus (LGN). Pharmacological disruption and mouse models lacking the β2 subunit of the nicotinic acetylcholine receptor has shown that the lack of spontaneous activity leads to marked defects in retinotopy and eye-specific segregation.
Recent studies confirm that microglia, the resident immune cell of the brain, establish direct contacts with the cell bodies of developing neurons, and through these connections, regulate neurogenesis, migration, integration and the formation of neuronal networks in an activity-dependent manner.
In the developing auditory system, developing cochlea generate bursts of activity which spreads across the inner hair cells and spiral ganglion neurons which relay auditory information to the brain. ATP release from supporting cells triggers action potentials in inner hair cells. In the auditory system, spontaneous activity is thought to be involved in tonotopic map formation by segregating cochlear neuron axons tuned to high and low frequencies. In the motor system, periodic bursts of spontaneous activity are driven by excitatory GABA and glutamate during the early stages and by acetylcholine and glutamate at later stages. In the developing zebrafish spinal cord, early spontaneous activity is required for the formation of increasingly synchronous alternating bursts between ipsilateral and contralateral regions of the spinal cord and for the integration of new cells into the circuit. Motor neurons innervating the same twitch muscle fibers are thought to maintain synchronous activity which allows both neurons to remain in contact with the muscle fiber in adulthood. In the cortex, early waves of activity have been observed in the cerebellum and cortical slices. Once sensory stimulus becomes available, final fine-tuning of sensory-coding maps and circuit refinement begins to rely more and more on sensory-evoked activity as demonstrated by classic experiments about the effects of sensory deprivation during critical periods.
Contemporary diffusion-weighted MRI techniques may also uncover the macroscopic process of axonal development. The connectome can be constructed from diffusion MRI data: the vertices of the graph correspond to anatomically labelled gray matter areas, and two such vertices, say u and v, are connected by an edge if the tractography phase of the data processing finds an axonal fiber that connects the two areas, corresponding to u and v. Numerous braingraphs, computed from the Human Connectome Project can be downloaded from the http://braingraph.org site. The Consensus Connectome Dynamics (CCD) is a remarkable phenomenon that was discovered by continuously decreasing the minimum confidence-parameter at the graphical interface of the Budapest Reference Connectome Server. The Budapest Reference Connectome Server (http://connectome.pitgroup.org) depicts the cerebral connections of n=418 subjects with a frequency-parameter k: For any k=1,2,...,n one can view the graph of the edges that are present in at least k connectomes. If parameter k is decreased one-by-one from k=n through k=1 then more and more edges appear in the graph, since the inclusion condition is relaxed. The surprising observation is that the appearance of the edges is far from random: it resembles a growing, complex structure, like a tree or a shrub (visualized on the animation on the left).
It is hypothesized in that the growing structure copies the axonal development of the human brain: the earliest developing connections (axonal fibers) are common at most of the subjects, and the subsequently developing connections have larger and larger variance, because their variances are accumulated in the process of axonal development.
== Synapse elimination ==
Several motorneurons compete for each neuromuscular junction, but only one survives until adulthood. Competition in vitro has been shown to involve a limited neurotrophic substance that is released, or that neural activity infers advantage to strong post-synaptic connections by giving resistance to a toxin also released upon nerve stimulation. In vivo, it is suggested that muscle fibres select the strongest neuron through a retrograde signal or that activity-dependent synapse elimination mechanisms determine the identity of the "winning" axon at a motor endplate.
== Mapping ==
Brain mapping can show how an animal's brain changes throughout its lifetime. As of 2021, scientists mapped and compared the whole brains of eight C. elegans worms across their development on the neuronal level and the complete wiring of a single mammalian muscle from birth to adulthood.
== Adult neurogenesis ==
Neurogenesis also occurs in specific parts of the adult brain.
== See also ==
== References ==
== External links ==
Neural Development (peer-reviewed open access journal).
Translating Neurodevelopmental Time Across Mammalian Species
The Child's Developing Brain
Brain Development
How poverty might change the brain
The Teenage Brain | Wikipedia/Neuronal_migration |
A neural network, also called a neuronal network, is an interconnected population of neurons (typically containing multiple neural circuits). Biological neural networks are studied to understand the organization and functioning of nervous systems.
Closely related are artificial neural networks, machine learning models inspired by biological neural networks. They consist of artificial neurons, which are mathematical functions that are designed to be analogous to the mechanisms used by neural circuits.
== Overview ==
A biological neural network is composed of a group of chemically connected or functionally associated neurons. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive. Connections, called synapses, are usually formed from axons to dendrites, though dendrodendritic synapses and other connections are possible. Apart from electrical signalling, there are other forms of signalling that arise from neurotransmitter diffusion.
Artificial intelligence, cognitive modelling, and artificial neural networks are information processing paradigms inspired by how biological neural systems process data. Artificial intelligence and cognitive modelling try to simulate some properties of biological neural networks. In the artificial intelligence field, artificial neural networks have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents (in computer and video games) or autonomous robots.
Neural network theory has served to identify better how the neurons in the brain function and provide the basis for efforts to create artificial intelligence.
== History ==
The preliminary theoretical base for contemporary neural networks was independently proposed by Alexander Bain (1873) and William James (1890). In their work, both thoughts and body activity resulted from interactions among neurons within the brain.
For Bain, every activity led to the firing of a certain set of neurons. When activities were repeated, the connections between those neurons strengthened. According to his theory, this repetition was what led to the formation of memory. The general scientific community at the time was skeptical of Bain's theory because it required what appeared to be an inordinate number of neural connections within the brain. It is now apparent that the brain is exceedingly complex and that the same brain “wiring” can handle multiple problems and inputs.
James' theory was similar to Bain's; however, he suggested that memories and actions resulted from electrical currents flowing among the neurons in the brain. His model, by focusing on the flow of electrical currents, did not require individual neural connections for each memory or action.
C. S. Sherrington (1898) conducted experiments to test James' theory. He ran electrical currents down the spinal cords of rats. However, instead of demonstrating an increase in electrical current as projected by James, Sherrington found that the electrical current strength decreased as the testing continued over time. Importantly, this work led to the discovery of the concept of habituation.
McCulloch and Pitts (1943) also created a computational model for neural networks based on mathematics and algorithms. They called this model threshold logic. These early models paved the way for neural network research to split into two distinct approaches. One approach focused on biological processes in the brain and the other focused on the application of neural networks to artificial intelligence.
The parallel distributed processing of the mid-1980s became popular under the name connectionism. The text by Rumelhart and McClelland (1986) provided a full exposition on the use of connectionism in computers to simulate neural processes.
Artificial neural networks, as used in artificial intelligence, have traditionally been viewed as simplified models of neural processing in the brain, even though the relation between this model and brain biological architecture is debated, as it is not clear to what degree artificial neural networks mirror brain function.
== Neuroscience ==
Theoretical and computational neuroscience is the field concerned with the analysis and computational modeling of biological neural systems.
Since neural systems are intimately related to cognitive processes and behaviour, the field is closely related to cognitive and behavioural modeling.
The aim of the field is to create models of biological neural systems in order to understand how biological systems work. To gain this understanding, neuroscientists strive to make a link between observed biological processes (data), biologically plausible mechanisms for neural processing and learning (neural network models) and theory (statistical learning theory and information theory).
=== Types of models ===
Many models are used; defined at different levels of abstraction, and modeling different aspects of neural systems. They range from models of the short-term behaviour of individual neurons, through models of the dynamics of neural circuitry arising from interactions between individual neurons, to models of behaviour arising from abstract neural modules that represent complete subsystems. These include models of the long-term and short-term plasticity of neural systems and their relation to learning and memory, from the individual neuron to the system level.
=== Connectivity ===
In August 2020 scientists reported that bi-directional connections, or added appropriate feedback connections, can accelerate and improve communication between and in modular neural networks of the brain's cerebral cortex and lower the threshold for their successful communication. They showed that adding feedback connections between a resonance pair can support successful propagation of a single pulse packet throughout the entire network. The connectivity of a neural network stems from its biological structures and is usually challenging to map out experimentally. Scientists used a variety of statistical tools to infer the connectivity of a network based on the observed neuronal activities, i.e., spike trains. Recent research has shown that statistically inferred neuronal connections in subsampled neural networks strongly correlate with spike train covariances, providing deeper insights into the structure of neural circuits and their computational properties.
== Recent improvements ==
While initially research had been concerned mostly with the electrical characteristics of neurons, a particularly important part of the investigation in recent years has been the exploration of the role of neuromodulators such as dopamine, acetylcholine, and serotonin on behaviour and learning.
Biophysical models, such as BCM theory, have been important in understanding mechanisms for synaptic plasticity, and have had applications in both computer science and neuroscience.
== See also ==
Adaptive resonance theory
Biological cybernetics
Cognitive architecture
Cognitive science
Connectomics
Cultured neuronal networks
Parallel constraint satisfaction processes
Wood Wide Web
== References == | Wikipedia/Neural_network_(biology) |
Neurotherapy is medical treatment that implements systemic targeted delivery of an energy stimulus or chemical agents to a specific neurological zone in the body to alter neuronal activity and stimulate neuroplasticity in a way that develops (or balances) a nervous system in order to treat different diseases, restore and/or to improve patients' physical strength, cognitive functions, and overall health.
== Definition ==
A consensus in the academic community considers this notion within limitations of the contemporary meaning of neuromodulation, which is "the alteration of nerve activity through targeted delivery of a stimulus, such as electrical stimulation or chemical agents, to specific neurological sites in the body" (see Neuromodulation). While neurotherapy may have a broader meaning, its modern definition focuses exclusively on technological methods that exert an energy-based impact on the development of the balanced nervous system in order to address symptom control and cure several
conditions. The definition of neurotherapy relies on evolving scientific concepts from different fields of knowledge, ranging from physics to neuroscience. Four central concepts that underlie the knowledge of neurotherapy are defined here:
=== Energy stimulus ===
Energy, as the ability to do work, cannot be created or destroyed; it can only be transformed from one form to another (the law of conservation of energy). There are different form of energy. Such forms of energy as radiant energy carried by electromagnetic radiation, electrical energy and magnetic energy, are of interest to neurotherapy. Medical devices for neuromodulation exert electrical, magnetic, and/or electromagnetic energy to treat mental and physical health disorders in patients.
=== Synaptic plasticity ===
Synaptic plasticity, a particular type of neuroplasticity is the ability of the nervous system to modify the intensity of interneuronal relationships (synapses), to establish new ones and to eliminate some. This property allows the nervous system to modify its structure and functionality in a more or less lasting way and dependent on the events that influence them such as experience or neuromodulation.
=== Neuroplasticity ===
Brain plasticity refers to the ability of the brain to modify its structure and functionality depending on the activity of its neurons, related for example to stimuli received from the external environment, in reaction to traumatic lesions or pathological changes and in relation to the development process of the individual or neuromodulation.
=== A balanced nervous system ===
In the balanced nervous system with required cognitive functions, the sympathetic (SNS) and parasympathetic nervous systems (PNS) operate in synergy while opposing each other. Stimulation of the SNS boosts body activity and attention: it raises heart rate and blood pressure. In contrast, stimulation of the PNS is the rest
and digest state: it reduces blood pressure and heart rate. The nervous system interplays with the immune system. Through these interactions, the nervous and immune systems ensure the nervous system maintains immune homeostasis.
== Medical uses ==
According to the International Neuromodulation Society, neuromodulation-based therapy "addresses symptom control through nerve stimulation" in the following condition categories:
Chronic pain
Movement disorders
Epilepsy
Psychiatric disorders
Brain injury / Stroke
Cardiovascular disorders
Gastrointestinal disorders
Genitourinary and colorectal disorders
Sensory deficits
== Types ==
Neurotherapy, as many medical therapy, is based on knowledge from conventional medicine, relying on scientific approach and evidence-based practice. However, some neuromodulation techniques are still attributed to alternative medicine (healthcare procedures "not readily integrated into the dominant healthcare
model") because of their novelty and lack of evidence to support them. The wide range of
neurotherapy techniques can be divided into three groups based on the application of energy stimulus:
=== Electric energy ===
Auditory brainstem implant
Cranial electrotherapy stimulation
Deep brain stimulation
Electrical brain stimulation
Electroanalgesia
Electroconvulsive therapy (ECT)
Functional electrical stimulation (FES)
Hypoglossal nerve stimulation
Neurofeedback
Microcurrent electrical neuromuscular stimulator
Occipital nerve stimulation (ONS)
Percutaneous tibial nerve stimulation (PTNS)
Peripheral nerve stimulation
Sacral nerve stimulation (SNS) / sacral neuromodulation (SNM)
Transcranial direct current stimulation (tDCS)
Transcranial alternating current stimulation (tACS)
Transcranial pulsed current stimulation (tPCS) )
Transcranial random noise stimulation (tRNS)
Transcutaneous electrical nerve stimulation (TENS)
Vagus nerve stimulation
=== Magnetic energy ===
Magnet therapy
Magnetic resonance therapy
Repetitive transcranial magnetic stimulation (rTMS)
Transcranial magnetic stimulation
=== Electromagnetic radiation ===
Acoustic photonic intellectual neurostimulation (APIN)
Light therapy (phototherapy)
Ultraviolet light therapy
PUVA therapy
Photodynamic therapy
Photothermal therapy
Pulsed electromagnetic field therapy
Cytoluminescent therapy
Blood irradiation therapy
Laser therapy
Low level laser therapy
Transcranial pulsed electromagnetic fields (tPEMF) [2]
== Mechanisms ==
Origins behind the way that an external energy stimulus alters neuronal activity and stimulates neuroplasticity during various artificial neurostimulation techniques are still under discussion. It is important to note that electrical and magnetic energy are two forms of energy that are closely interconnected: a moving charge induces electrical and magnetic fields. Electrical current creates a magnetic field, and a magnetic field induces an electrical charge movement. Neurons are electrically active cells. Neuronal oscillations have a dual role in synapsis: they are affected by spiking inputs and, in turn, impact the timing of spike outputs. Because of the above facts, both electrical and magnetic fields may induce electrical currents in neuronal circuits. Therefore, similar mechanisms of altered neuronal activity may underlie different neuromodulation techniques that use electrical, magnetic, or electromagnetic energy in treatment.
A variety of hypotheses try to explain the mechanisms that contribute to synaptic activity during neurostimulation. According to an influential position, electrical and magnetic fields may alter Ca2+ and Na+ channel activity. The voltage-gated Ca2+ channels are the primary conduits for the Ca2+ ions that cause a confluence of neurotransmitter-containing vesicles with the presynaptic membrane. The altered activity of Ca2+ and Na+ channel changes the timing and strength of synaptic output, contributing to neuronal excitability.
Another perspective hypothesis stands that electromagnetic fields increase in adenosine receptors release that facilitates neuronal communication. Because A(2A) adenosine receptors control the release of other neurotransmitters (e.g., glutamate and dopamine), this contributes to adjusting neuronal functions.
According to the natural neurostimulation hypothesis, energy stimuli induce mitochondrial stress and micro vascular vasodilation. These promote increasing Adenosine triphosphate (ATP) protein and oxygenation, inducing synaptic strength. This position explains neuromodulation from different scale levels: from interpersonal dynamics to nonlocal neuronal coupling. According to natural neurostimulation, the innate natural mechanism of physical interactions between the mother and embryo ensures the balanced development of the embryonic nervous system. The drivers of these interactions, the electromagnetic properties of the mother's heart, enable brain waves to interact between the mother's and fetal nervous systems. The electromagnetic and acoustic oscillations of the mother's heart converge the neuronal activity of both nervous systems in an ensemble, shaping harmony from a cacophony of separate oscillations. These interactions synchronize brain oscillations, influencing neuroplasticity in the fetus. During the mother's intentional actions with her environment, these interchanges provide hints to the fetus's nervous system, binding synaptic activity with relevant stimuli. This hypothesis posits that the physiological processes of mitochondrial stress induction (affecting neuronal plasticity) and vasodilation, which cooperatively increase microvascular blood flow and tissue oxygenation, are the basis of the natural neurostimulation. It is also thought to be a foundation of many non-invasive artificial neuromodulation techniques. Because if the mother-fetus interactions allow the child's nervous system to grow with adequate biological sentience, similar (while scaling) environmental interactions can heal the damaged nervous system in adults.
== History ==
While neurotherapy is a relatively young medical treatment in conventional Western biomedicine (that relies on a scientific approach and evidence-based practice), different age-old cultural practices of traditional Indian, Egyptian, and Chinese medicine have been using neuromodulation elements thousands of years ago. Before the basic processes of neurotherapy were scientifically studied, humans used the electrical properties of animals for therapeutic purposes. The Egyptians used the Nile catfish (Synodontis batensoda and Malapterurus electricus) to stimulate tissue electrically, according to an interpretation of frescoes in the tomb of the architect Ti at Saqqara, Egypt. The first documented use of electrical stimulation for pain relief dates back to 46 AD when Scribonius Largus of the ancient Roman Empire used the electric properties of torpedo fish to relieve headaches.
Scientific studies of neuromodulation began in 1745, when German physician De Haen published “a number of cases of spasmodic, paralytic and other nervous affections cured by electricity”.
The first implementation of electrocutical apparatus in hospital medical treatment recorded in Middlesex Hospital of London in 1767.
In 1870, German physicians Gustav Fritsch and Eduard Hitzig reported the modulation of brain activity in dogs by electrical stimulation of the motor cortex.
In 1924, the German psychiatrist Hans Berger attached electrodes to the scalp and detected small currents in the brain.
In the mid-20th century, the scientific study of neuromodulation in humans expanded significantly. Neurologist Professor Spiegel and neurosurgeon Professor Weissys of Temple University presented a stereotactic device to perform "ablation procedures" in humans; "intraoperative electrical stimulation" was introduced to test
the brain's target zone before surgery in 1947. In the 1950s, Professor Heath reported about subcortical stimulation with precise descriptions of behavioral changes. In 1967, Dr. Norm Shealy from Western Reserve Medical School presented “the first dorsal column stimulator for pain control”. It was developed based on the Gate Theory of Wall and
Melzack, which stated that pain transmissions from tiny nerve fibers would be blocked if competing transmissions were made along larger sensory nerve fibers.
In 1987, the team of neurosurgeons/neurologists Professor Benabid and Professor Pollak and their colleagues (Grenoble, France) published results on this topic about thalamic Deep Brain Stimulation.
== See also ==
International Neuromodulation Society
Neuromodulation
Neurostimulation
Neurotechnology
Non-invasive cerebellar stimulation
== References == | Wikipedia/Neurotherapy |
Psychosurgery, also called neurosurgery for mental disorder (NMD), is the neurosurgical treatment of mental disorders. Psychosurgery has always been a controversial medical field. The modern history of psychosurgery begins in the 1880s under the Swiss psychiatrist Gottlieb Burckhardt. The first significant foray into psychosurgery in the 20th century was conducted by the Portuguese neurologist Egas Moniz who, during the mid-1930s, developed the operation known as leucotomy. The practice was enthusiastically taken up in the United States by the neuropsychiatrist Walter Freeman and the neurosurgeon James W. Watts who devised what became the standard prefrontal procedure and named their operative technique lobotomy, although the operation was called leucotomy in the United Kingdom. In spite of the award of the Nobel Prize to Moniz in 1949, the use of psychosurgery declined during the 1950s. By the 1970s the standard Freeman-Watts type of operation was very rare, but other forms of psychosurgery, although used on a much smaller scale, survived. Some countries have abandoned psychosurgery altogether; in others, for example the US and the UK, it is only used in a few centres on small numbers of people with depression or obsessive-compulsive disorder (OCD).
In some countries it is also used in the treatment of schizophrenia and other disorders.
Psychosurgery is a collaboration between psychiatrists and neurosurgeons. During the operation, which is carried out under a general anaesthetic and using stereotactic methods, a small piece of brain is destroyed or removed. The most common types of psychosurgery in current or recent use are anterior capsulotomy, cingulotomy, subcaudate tractotomy and limbic leucotomy. Lesions are made by radiation, thermo-coagulation, freezing or cutting. About a third of patients show significant improvement in their symptoms after operation. Advances in surgical technique have greatly reduced the incidence of death and serious damage from psychosurgery; the remaining risks include seizures, incontinence, decreased drive and initiative, weight gain, and cognitive and affective problems.
Currently, interest in the neurosurgical treatment of mental illness is shifting from ablative psychosurgery (where the aim is to destroy brain tissue) to deep brain stimulation (DBS) where the aim is to stimulate areas of the brain with implanted electrodes.
== Medical uses ==
All the forms of psychosurgery in use today (or used in recent years) target the limbic system, which involves structures such as the amygdala, hippocampus, certain thalamic and hypothalamic nuclei, prefrontal and orbitofrontal cortex, and cingulate gyrus—all connected by fibre pathways and thought to play a part in the regulation of emotion. There is no international consensus on the best target site.
Anterior cingulotomy was first used by Hugh Cairns in the UK, and developed in the US by H.T. Ballantine Jr. In recent decades it has been the most commonly used psychosurgical procedure in the US. The target site is the anterior cingulate cortex; the operation disconnects the thalamic and posterior frontal regions and damages the anterior cingulate region.
Anterior capsulotomy was developed in Sweden, where it became the most frequently used procedure. It is also used in Scotland and Canada. The aim of the operation is to disconnect the orbitofrontal cortex and thalamic nuclei by inducing a lesion in the anterior limb of internal capsule.
Subcaudate tractotomy was the most commonly used form of psychosurgery in the UK from the 1960s to the 1990s. It targets the lower medial quadrant of the frontal lobes, severing connections between the limbic system and supra-orbital part of the frontal lobe.
Limbic leucotomy is a combination of subcaudate tractotomy and anterior cingulotomy. It was used at Atkinson Morley Hospital London in the 1990s and also at Massachusetts General Hospital.
Amygdalotomy, which targets the amygdala, was developed as a treatment for aggression by Hideki Narabayashi in 1961 and is still used occasionally, for example at the Medical College of Georgia.
There is debate about whether deep brain stimulation (DBS) should be classed as a form of psychosurgery.
=== Effectiveness ===
Success rates for anterior capsulotomy, anterior cingulotomy, subcaudate tractotomy, and limbic leucotomy in treating depression and OCD have been reported as between 25 and 70 percent. The quality of outcome data is poor and the Royal College of Psychiatrists in their 2000 report concluded that there were no simple answers to the question of modern psychosurgery's clinical effectiveness; studies suggested improvements in symptoms following surgery but it was impossible to establish the extent to which other factors contributed to this improvement. Research into the effects of psychosurgery has not been able to overcome a number of methodological problems, including the problems associated with non-standardised diagnoses and outcome measurements, the small numbers treated at any one centre, and positive publication bias. Controlled studies are very few in number and there have been no placebo-controlled studies. There are no systematic reviews or meta-analyses.
Modern techniques have greatly reduced the risks of psychosurgery, although risks of adverse effects still remain. Whilst the risk of death or vascular injury has become extremely small, there remains a risk of seizures, fatigue, and personality changes following operation.
A 2012 follow-up study of eight depressed patients who underwent anterior capsulotomy in Vancouver, Canada, classified five of them as responders at two to three years after surgery. Results on neuropsychological testing were unchanged or improved, although there were isolated deficits and one patient was left with long-term frontal psychobehavioral changes and fatigue. One patient, aged 75, was left mute and akinetic for a month following surgery and then developed dementia.
== By country ==
=== China ===
In China, psychosurgical operations which make a lesion in the nucleus accumbens are used in the treatment of drug and alcohol dependence. Stereotactic surgery is used to locate and damage the target.
Psychosurgery is also used in the treatment of schizophrenia, depression, and other mental disorders. One patient diagnosed with schizophrenia underwent as many as 10 surgeries, without effect on the condition but leaving him with a partially limp right arm and slurred speech. The use of psychosurgery in China has been criticised in the West.
According to the Wall Street Journal, psychosurgery for drug addiction is banned in China since 2004, but other forms of the surgery were not as of 2007. Science reports that psychosurgery was only allowed for refractory OCD, depression, and brain disorders since 2008, and that neurosurgeons were pushing to reverse the ban in 2011.
The ban appears to have been lifted for schizophrenia some time before 2017, when People's Daily Online reposted an article about psychosurgery for schizophrenia in Shanghai from Xinmin Evening News. In 2024, Chinese scholars published the Chinese Expert Consensus on Surgical Treatment of Mental Illnesses (2024), with intervention method and targets and evidence/recommendation levels listed for several conditions.
=== India ===
India had an extensive psychosurgery programme until the 1980s, using it to treat addiction, and aggressive behaviour in adults and children, as well as depression and OCD.
Cingulotomy and capsulotomy for depression and OCD continue to be used, for example at the BSES MG Hospital in Mumbai.
=== Japan ===
In Japan the first lobotomy was performed in 1939 and the operation was used extensively in mental hospitals. However, psychosurgery fell into disrepute in the 1970s, partly due to its use on children with behavioural problems.
=== Australia and New Zealand ===
In the 1980s there were 10–20 operations a year in Australia and New Zealand. The number had decreased to one or two a year by the 1990s. In Victoria, there were no operations between 2001 and 2006, but between 2007 and 2012 the Victoria Psychosurgery Review Board dealt with 12 applications, all of them for DBS.
=== Europe ===
In the 20-year period 1971–1991 the Committee on Psychosurgery in the Netherlands and Belgium oversaw 79 operations. Since 2000 there has been only one centre in Belgium performing psychosurgery, carrying out about 8 or 9 operations a year (some capsulotomies and some DBS), mostly for OCD.
In France about five people a year were undergoing psychosurgery in the early 1980s.
In 2005 the Health Authority recommended the use of ablative psychosurgery and DBS for OCD.
In the early 2000s in Spain about 24 psychosurgical operations (capsulotomy, cingulotomy, subcaudate tractotomy, and hypothalamotomy) a year were being performed. OCD was the most common diagnosis, but psychosurgery was also being used in the treatment of anxiety and schizophrenia, and other disorders.
In the UK between the late 1990s and 2009 there were just two centres using psychosurgery: a few stereotactic anterior capsulotomies are performed every year at the University Hospital of Wales, Cardiff, while anterior cingulotomies are carried out by the Advanced Interventions Service at Ninewells Hospital, Dundee. The patients have diagnoses of depression, obsessive-compulsive disorder, and anxiety. Ablative psychosurgery was not performed in England between the late 1990s and 2009, although a couple of hospitals have been experimenting with DBS. In 2010, Frenchay Hospital in Bristol performed an anterior cingulotomy on a woman who had previously undergone DBS.
In Russia in 1998 the Institute of the Human Brain (Russian Academy of Sciences) started a programme of stereotactic cingulotomy for the treatment of drug addiction. About 85 people, all under the age of 35, were operated on annually. In the Soviet Union, leucotomies were used for the treatment of schizophrenia in the 1940s, but the practice was prohibited by the Ministry of Health in 1950.
=== North America ===
In the United States, the Massachusetts General Hospital has a psychosurgery program. Operations are also performed at a few other centres.
In Mexico, psychosurgery is used in the treatment of anorexia and aggression.
In Canada, anterior capsulotomies are used in the treatment of depression and OCD.
=== South America ===
Venezuela has three centres performing psychosurgery. Capsulotomies, cingulotomies and amygdalotomies are used to treat OCD and aggression.
== History ==
=== Early psychosurgery ===
Evidence of trepanning (or trephining)—the practice of drilling holes in the skull—has been found in a skull from a Neolithic burial site in France, dated to about 5100 BC although it was also used to treat brain cranial trauma. There have also been archaeological finds in South America, while in Europe trepanation was carried out in classical and medieval times.
The first systematic attempt at psychosurgery is commonly attributed to the Swiss psychiatrist Gottlieb Burckhardt. In December 1888 Burckhardt operated on the brains of six patients (one of whom died a few days after the operation) at the Préfargier Asylum, cutting out a piece of cerebral cortex. He presented the results at the Berlin Medical Congress and published a report, but the response was hostile and he did no further operations.
Early in the 20th century, Russian neurologist Vladimir Bekhterev and Estonian neurosurgeon Ludvig Puusepp operated on three patients with mental illness, with discouraging results.
=== 1930s–1950s ===
Although there had been earlier attempts to treat psychiatric disorders with brain surgery, it was Portuguese neurologist Egas Moniz who was responsible for introducing the operation into mainstream psychiatric practice. He also coined the term psychosurgery. Moniz developed a theory that people with mental illnesses, particularly "obsessive and melancholic cases", had a disorder of the synapses which allowed unhealthy thoughts to circulate continuously in their brains. Moniz hoped that by surgically interrupting pathways in their brain he could encourage new healthier synaptic connections. In November 1935, under Moniz's direction, surgeon Pedro Almeida Lima drilled a series of holes on either side of a woman's skull and injected ethanol to destroy small areas of subcortical white matter in the frontal lobes. After a few operations using ethanol, Moniz and Almeida Lima changed their technique and cut out small cores of brain tissue. They designed an instrument which they called a leucotome and called the operation a leucotomy (cutting of the white matter). After twenty operations, they published an account of their work. The reception was generally not friendly but a few psychiatrists, notably in Italy and the US, were inspired to experiment for themselves.
In the US, psychosurgery was taken up and zealously promoted by neurologist Walter Freeman and neurosurgeon James Watts. They started a psychosurgery program at George Washington University in 1936, first using Moniz's method but then devised a method of their own in which the connections between the prefrontal lobes and deeper structures in the brain were severed by making a sweeping cut through a burr hole on either side of the skull. They called their new operation a lobotomy.
Freeman went on to develop a new form of lobotomy which could be dispensed without the need for a neurosurgeon. He hammered an ice pick-like instrument, an orbitoclast, through the eye socket and swept through the frontal lobes. The transorbital or "ice pick" lobotomy was done under local anesthesia or using electroconvulsive therapy to render the patient unconscious and could be performed in mental hospitals lacking surgical facilities. Such was Freeman's zeal that he began to travel around the nation in his own personal van, which he called his "lobotomobile", demonstrating the procedure in psychiatric hospitals. Freeman's patients included 19 children, one of whom was 4 years old.
The 1940s saw a rapid expansion of psychosurgery, in spite of the fact that it involved a significant risk of death and severe personality changes. By the end of the decade, up to 5000 psychosurgical operations were being carried out annually in the US. In 1949, Moniz was awarded the Nobel Prize for Physiology or Medicine.
Beginning in the 1940s various new techniques were designed in the hope of reducing the adverse effects of the operation. These techniques included William Beecher Scoville's orbital undercutting, Jean Talairach's anterior capsulotomy, and Hugh Cairn's bilateral cingulotomy. Stereotactic techniques made it possible to place lesions more accurately, and experiments were done with alternatives to cutting instruments such as radiation. Psychosurgery nevertheless went into rapid decline in the 1950s, due to the introduction of new drugs and a growing awareness of the long-term damage caused by the operations, as well as doubts about its efficacy. By the 1970s, the standard or transorbital lobotomy had been replaced with other forms of psychosurgical operations.
=== 1960s to the present ===
During the 1960s and 1970s, psychosurgery became the subject of increasing public concern and debate, culminating in the US with congressional hearings. Particularly controversial in the United States was the work of Harvard neurosurgeon Vernon Mark and psychiatrist Frank Ervin, who carried out amygdalotomies in the hope of reducing violence and "pathologic aggression" in patients with temporal lobe seizures and wrote a book entitled Violence and the Brain in 1970. The National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research in 1977 endorsed the continued limited use of psychosurgical procedures. Since then, a few facilities in some countries, such as the US, have continued to use psychosurgery on small numbers of patients. In the US and other Western countries, the number of operations has further declined over the past 30 years, a period during which there had been no major advances in ablative psychosurgery.
== Ethics ==
Psychosurgery has a controversial history, and despite modifications, still raises serious questions about benefit, risks, and the adequacy with which consent is obtained. Its continued use is defended by references to the "therapeutic imperative" to do something in the case of psychiatric patients who have not responded to other forms of treatment, and the evidence that some patients see improvement in their symptoms following surgery. There remain however problems concerning the rationale, indications and efficacy of psychosurgery, and the results of the operation raise questions of "identity, spirit, relationships, integrity and human flourishing".
== Individuals who underwent psychosurgery ==
Lena Zavaroni (1963–1999), Scottish child star and singer who had suffered from anorexia and depression for many years, underwent a stereotactic anterior capsulotomy at the University of Wales Hospital in Cardiff in 1999. She died of pneumonia three weeks later.
Josef Hassid: Polish violin prodigy who died at 26 following psychosurgery.
Rosemary Kennedy: Walter Freeman's most famous patient and sister of President John F. Kennedy. She was left with permanent mental incapacity as a result of the procedure, unable to speak or walk.
Rose Williams: Sister of Tennessee Williams.
Howard Dully: One of Walter Freeman's youngest patients, author of My Lobotomy (2007).
== See also ==
History of psychosurgery in the United Kingdom
Wylie McKissock
Elliot Valenstein
The Terminal Man (film)
== References ==
== External links ==
New England Journal of Medicine article Archived 2010-05-29 at the Wayback Machine
Brain surgery to cure the mind - BBC Radio 4 documentary on modern psychosurgery
"Shedding Light on Shadowland" - in-depth essay exploring the propagation of the Frances Farmer lobotomy legend | Wikipedia/Psychosurgery |
Neuroscience is a peer-reviewed scientific journal of neuroscience. It was established in 1976 with P.G. Kostyuk, Rodolfo Llinás, and A.D. Smith as founding editors-in-chief and originally published by Pergamon Press. The current editor-in-chief is Juan Lerma Gómez (Spanish National Research Council). The journal is published by Elsevier on behalf of the International Brain Research Organization (IBRO).
The journal continues the IBRO News section formerly published in Brain Research.
== Abstracting and indexing ==
The journal is abstracted and indexed in:
According to the Journal Citation Reports, Neuroscience has a 2020 impact factor of 3.590.
== Notable articles ==
As of 2018, the following articles are the most downloaded according to the publisher's data:
Keeler, J.F.; Pretsell, D.O.; Robbins, T.W. (2014). "Functional implications of dopamine D1 vs. D2 receptors: A 'prepare and select' model of the striatal direct vs. indirect pathways". Neuroscience. 282: 156–175. doi:10.1016/j.neuroscience.2014.07.021. PMID 25062777.
Hernández SE (2018). "Gray Matter and Functional Connectivity in Anterior Cingulate Cortex are Associated with the State of Mental Silence During Sahaja Yoga Meditation". Neuroscience. 371: 395–406. doi:10.1016/j.neuroscience.2017.12.017. hdl:10234/175002. PMID 29275207.
Humpel, C (2015). "Organotypic brain slice cultures: A review". Neuroscience. 305: 86–98. doi:10.1016/j.neuroscience.2015.07.086. PMC 4699268. PMID 26254240.
Dalley, J (2012). "Dopamine, serotonin and impulsivity" (PDF). Neuroscience. 215: 42–58. doi:10.1016/j.neuroscience.2012.03.065. PMID 22542672.
Zorina-Lichtenwalter, K (2016). "Genetic predictors of human chronic pain conditions". Neuroscience. 338: 36–62. doi:10.1016/j.neuroscience.2016.04.041. PMID 27143481.
== References ==
== External links ==
Official website | Wikipedia/Neuroscience_(journal) |
Cultural neuroscience is a field of research that focuses on the interrelation between a human's cultural environment and neurobiological systems. The field particularly incorporates ideas and perspectives from related domains like anthropology, psychology, and cognitive neuroscience to study sociocultural influences on human behaviors. Such impacts on behavior are often measured using various neuroimaging methods, through which cross-cultural variability in neural activity can be examined.
Cultural neuroscientists study cultural variation in mental, neural and genomic processes as a means of articulating the bidirectional relationship of these processes and their emergent properties using a variety of methods. Researchers in cultural neuroscience are motivated by two fundamentally intriguing, yet still unanswered, questions on the origins of human nature and human diversity: how do cultural traits (e.g., values, beliefs, practices) shape neurobiology (e.g., genetic and neural processes) and behavior, and how do neurobiological mechanisms (e.g., genetic and neural processes) facilitate the emergence and transmission of cultural traits?
The idea that complex behavior results from the dynamic interaction of genes and cultural environment is not new; however, cultural neuroscience represents a novel empirical approach to demonstrating bidirectional interactions between culture and biology by integrating theory and methods from cultural psychology, neuroscience and neurogenetics.
Similar to other interdisciplinary fields such as social neuroscience, cognitive neuroscience, affective neuroscience, and neuroanthropology, cultural neuroscience aims to explain a given mental phenomenon in terms of a synergistic product of mental, neural and genetic events. In particular, cultural neuroscience shares common research goals with social neuroscientists examining how neurobiological mechanisms (e.g., mirror neurons), facilitate cultural transmission, (e.g., imitative learning) and neuroanthropologists examining how embedded culture, as captured by cross-species comparison and ethnography, is related to brain function. Cultural neuroscience also shares intellectual goals with critical neuroscience, a field of inquiry that scrutinizes the social, cultural, economic and political contexts and assumptions that underlie behavioral and brain science research as it is practiced today.
Research in cultural neuroscience has practical relevance to transcultural psychiatry, business and technology as well as broader implications for global public policy issues such as population health disparities, bioethics, globalization, immigration, interethnic ideology and international relations.
== Previous cross-cultural research ==
While the field of cultural neuroscience may still be growing, there are studies conducted by various researchers that have looked at cross-cultural similarities and differences in human attention, visual perception, and the understanding of others and the self. Previous behavioral research has focused on the cultural differences in perception, particularly between people from East Asian and Western regions. The results from these studies have suggested that East Asians focus their visual perception more on the backgrounds and contexts of their environment, while Westerners focus on individual stimuli/objects. To further explore these findings, more research was done to specifically look at the neurological similarities and differences in attention and visual perception of people in East Asian and Western cultures.
Results from a 2008 study by Hedden et al. support the previous findings by showing how East Asians require more attention than Americans for individually processing objects. Brain regions more focused on attention, such as areas in the parietal and prefrontal lobes as well as the inferior parietal lobule and precentral gyrus, were found to be highly active in East Asian subjects compared to American subjects, during individual object processing. A visual perception study conducted by Gutchess et al. in 2006, also found neurological differences between Chinese and American subjects as they completed tasks of encoding images of individual objects, backgrounds, and objects with backgrounds. The fMRI results from the study presented that during visual processing of objects, there was greater neural activity in the middle temporal gyri, right superior temporal gyri, and superior parietal lobules of the American subjects than that of the Chinese subjects. Such results indicate a focus on object processing among Westerners compared to East Asians. Insignificant differences in neural activity between subjects were found during the visual processing of images with backgrounds.
People from East Asian and Western cultures were also studied to learn more about cross-cultural differences in understanding both the self and other people. Findings from a 1991 study by Markus and Kitayama presented that people from Eastern cultures view the self in relation to others in their community, while people from Western cultures have a more independent perspective of the self. A 2007 fMRI study observed differences in activity in the ventromedial prefrontal cortex, a brain region highly active during self perception, when Western and Chinese subjects were thinking about themselves versus when they were thinking about their mothers. The results interestingly showed that there was still activity in the ventral medial prefrontal cortices of Chinese subjects even when they thought about their mothers, while activity was only detected in American subjects when they thought about themselves.
A different study conducted by psychologist Joan Chiao found that due to cultural differences, East Asians are more likely to suffer from depression than Americans. She found that East Asians are more likely to carry the short allele of the serotonin transporter gene (STG) which leads to depression while Americans carry the long allele which doesn't lead to depression. Yet due to difference in cultural structure they found that collectivist societies are more likely to find happiness than individual societies.
Another study done by psychologists Nalini Ambady and Jonathan Freeman showed a difference in brain activity between Japanese and Americans when shown different body posture. They found that the reward circuitry in the limbic system would light up when Japanese participants saw submissive body posture while the reward circuitry would activate when Americans saw dominant body posture.
== Culture differences in visual stimuli ==
Cultural differences exist in the ventral visual cortex and many studies have shown this. In a study conducted in 2005 they found that East Asians were more likely to keep their eyes focused on background scenes than westerners who would instead focus more on the central object such as a giraffe in a savanna. In a similar 2006 study it showed that in congruence to the difference in society structure westerners showed more activation in object processing regions, including the bilateral middle temporal gyrus, left superior parietal gyrus, and right superior temporal gyrus, although no activation differences were observed in context-processing regions such as the hippocampus. However, there has been some research contradicting cultural bias in the oculomotor control such as one conducted in 2007 by Rayner, Li, Williams, Cave, and Well who failed to find evidence that East Asians focus more on context although they did find evidence that they are more likely to focus less on central objects. In a different study they focused more on difference in attention towards faces. They proved that Americans focus more broadly on the entire face such as both the eyes and mouth while Asians focus more on a single part, such as the mouth. The authors point out that this happens due to gaze avoidance in east Asian culture as a way of politeness. In 2008, another study focusing on context showed that East Asians were more likely to include greater details and background when taking photographs of a model when they were free to set the zoom function of the camera as they saw fit. In 2003, a group of researchers used the Frame-Line Test and asked the participants to draw a line of either exactly the same length as the one showed or one that was proportional in size. Americans were more accurate in the absolute task, suggesting better memory for the exact or absolute size of the focal object, but East Asians were more accurate in the relative (proportional) task, suggesting better memory for contextual relationships. In a later study conducted by the same group they found a pattern within the cultures when processing emotions. East Asians were less likely to know the difference between fear and disgust than Americans when sampling faces.
Many studies conducted proves that constant repetition in a certain skill has an effect on brain activity. For example, in a 2000 study they showed that taxi drivers in London showed larger gray matter in the posterior hippocampi than the average civilian. A different study in 2004 showed that those who know how to juggle have an increase in volume of the cortical tissue in the bilateral midtemporal area and left posterior intraparietal sulcus.
The findings from many neuroimaging studies reflect the behavioral patterns observed in previous anthropological and cultural research. Such comparisons that were made between particular behavioral and neural activity across different cultures, have already provided the scientific community with more insight into the cultural influences on human behavior.
== See also ==
== References ==
== Further reading ==
Books
Wexler, B.E. (2006). Brain and Culture: Neurobiology, Ideology and Social Change. MIT Press, Cambridge. ISBN 978-0-262-73193-5
Reviews
Iacoboni, M.; Dapretto, M. (2006). "The mirror neuron system and the consequences of its dysfunction". Nature Reviews Neuroscience. 7 (12): 942–951. doi:10.1038/nrn2024. PMID 17115076. S2CID 9463011.
Articles
Begley, Sharon. "How Different Cultures Shape the Brain". The Daily Beast. Retrieved 26 September 2011.
Chen, C. S.; Burton, M.; Greenberger, E.; Dmitrieva, J. (1999). "Population migration and the variation of dopamine D4 receptor (DRD4) allele frequencies around the globe" (PDF). Evolution and Human Behavior. 20 (5): 309–324. Bibcode:1999EHumB..20..309C. doi:10.1016/s1090-5138(99)00015-x. S2CID 12754148.
Chiao, J. Y.; Blizinsky, K. D. (2009). "Culture-gene coevolution of individualism-collectivism and the serotonin transporter gene". Proceedings of the Royal Society B. 277 (1681): 529–37. doi:10.1098/rspb.2009.1650. PMC 2842692. PMID 19864286.
Kim, H. S.; Sherman, D. K.; Taylor, S. E.; Sasaki, J. Y.; Chu, T. Q.; Ryu, C.; Suh, E. M.; Xu, J. (2010). "Culture, serotonin receptor polymorphism and locus of attention". Social Cognitive and Affective Neuroscience. 5 (2–3): 212–218. doi:10.1093/scan/nsp040. PMC 2894665. PMID 19736291.
Seligman, R.; Kirmayer, L. J. (2008). "Dissociative experience and cultural neuroscience: narrative, metaphor and mechanism". Culture, Medicine and Psychiatry. 32 (1): 31–64. doi:10.1007/s11013-007-9077-8. PMC 5156567. PMID 18213511. | Wikipedia/Cultural_neuroscience |
A neurodegenerative disease is caused by the progressive loss of neurons, in the process known as neurodegeneration. Neuronal damage may also ultimately result in their death. Neurodegenerative diseases include amyotrophic lateral sclerosis, multiple sclerosis, Parkinson's disease, Alzheimer's disease, Huntington's disease, multiple system atrophy, tauopathies, and prion diseases. Neurodegeneration can be found in the brain at many different levels of neuronal circuitry, ranging from molecular to systemic. Because there is no known way to reverse the progressive degeneration of neurons, these diseases are considered to be incurable; however research has shown that the two major contributing factors to neurodegeneration are oxidative stress and inflammation. Biomedical research has revealed many similarities between these diseases at the subcellular level, including atypical protein assemblies (like proteinopathy) and induced cell death. These similarities suggest that therapeutic advances against one neurodegenerative disease might ameliorate other diseases as well.
Within neurodegenerative diseases, it is estimated that 55 million people worldwide had dementia in 2019, and that by 2050 this figure will increase to 139 million people.
== Specific disorders ==
The consequences of neurodegeneration can vary widely depending on the specific region affected, ranging from issues related to movement to the development of dementia.
=== Alzheimer's disease ===
Alzheimer's disease (AD) is a chronic neurodegenerative disease that results in the loss of neurons and synapses in the cerebral cortex and certain subcortical structures, resulting in gross atrophy of the temporal lobe, parietal lobe, and parts of the frontal cortex and cingulate gyrus. It is the most common neurodegenerative disease. Even with billions of dollars being used to find a treatment for Alzheimer's disease, no effective treatments have been found. Within clinical trials stable and effective AD therapeutic strategies have a 99.5% failure rate. Reasons for this failure rate include inappropriate drug doses, invalid target and participant selection, and inadequate knowledge of pathophysiology of AD. Currently, diagnoses of Alzheimer's is subpar, and better methods need to be utilized for various aspects of clinical diagnoses. Alzheimer's has a 20% misdiagnosis rate.
AD pathology is primarily characterized by the presence of amyloid plaques and neurofibrillary tangles. Plaques are made up of small peptides, typically 39–43 amino acids in length, called amyloid beta (also written as A-beta or Aβ). Amyloid beta is a fragment from a larger protein called amyloid precursor protein (APP), a transmembrane protein that penetrates through the neuron's membrane. APP appears to play roles in normal neuron growth, survival and post-injury repair. APP is cleaved into smaller fragments by enzymes such as gamma secretase and beta secretase. One of these fragments gives rise to fibrils of amyloid beta which can self-assemble into the dense extracellular amyloid plaques.
=== Parkinson's disease ===
Parkinson's disease (PD) is the second most common neurodegenerative disorder. It typically manifests as bradykinesia, rigidity, resting tremor and posture instability. The crude prevalence rate of PD has been reported to range from 15 per 100,000 to 12,500 per 100,000, and the incidence of PD from 15 per 100,000 to 328 per 100,000, with the disease being less common in Asian countries.
PD is primarily characterized by death of dopaminergic neurons in the substantia nigra, a region of the midbrain. The cause of this selective cell death is unknown. Notably, alpha-synuclein-ubiquitin complexes and aggregates are observed to accumulate in Lewy bodies within affected neurons. It is thought that defects in protein transport machinery and regulation, such as RAB1, may play a role in this disease mechanism. Impaired axonal transport of alpha-synuclein may also lead to its accumulation in Lewy bodies. Experiments have revealed reduced transport rates of both wild-type and two familial Parkinson's disease-associated mutant alpha-synucleins through axons of cultured neurons. Membrane damage by alpha-synuclein could be another Parkinson's disease mechanism.
The main known risk factor is age. Mutations in genes such as α-synuclein (SNCA), leucine-rich repeat kinase 2 (LRRK2), glucocerebrosidase (GBA), and tau protein (MAPT) can also cause hereditary PD or increase PD risk. While PD is the second most common neurodegenerative disorder, problems with diagnoses still persist. Problems with the sense of smell is a widespread symptom of Parkinson's disease (PD), however, some neurologists question its efficacy. This assessment method is a source of controversy among medical professionals. The gut microbiome might play a role in the diagnosis of PD, and research suggests various ways that could revolutionize the future of PD treatment.
=== Huntington's disease ===
Huntington's disease (HD) is a rare autosomal dominant neurodegenerative disorder caused by mutations in the huntingtin gene (HTT). HD is characterized by loss of medium spiny neurons and astrogliosis. The first brain region to be substantially affected is the striatum, followed by degeneration of the frontal and temporal cortices. The striatum's subthalamic nuclei send control signals to the globus pallidus, which initiates and modulates motion. The weaker signals from subthalamic nuclei thus cause reduced initiation and modulation of movement, resulting in the characteristic movements of the disorder, notably chorea. Huntington's disease presents itself later in life even though the proteins that cause the disease works towards manifestation from their early stages in the humans affected by the proteins. Along with being a neurodegenerative disorder, HD has links to problems with neurodevelopment.
HD is caused by polyglutamine tract expansion in the huntingtin gene, resulting in the mutant huntingtin. Aggregates of mutant huntingtin form as inclusion bodies in neurons, and may be directly toxic. Additionally, they may damage molecular motors and microtubules to interfere with normal axonal transport, leading to impaired transport of important cargoes such as BDNF. Huntington's disease currently has no effective treatments that would modify the disease.
=== Multiple sclerosis ===
Multiple sclerosis (MS) is a chronic debilitating demyelinating disease of the central nervous system, caused by an autoimmune attack resulting in the progressive loss of myelin sheath on neuronal axons. The resultant decrease in the speed of signal transduction leads to a loss of functionality that includes both cognitive and motor impairment depending on the location of the lesion. The progression of MS occurs due to episodes of increasing inflammation, which is proposed to be due to the release of antigens such as myelin oligodendrocyte glycoprotein, myelin basic protein, and proteolipid protein, causing an autoimmune response. This sets off a cascade of signaling molecules that result in T cells, B cells, and macrophages to cross the blood-brain barrier and attack myelin on neuronal axons leading to inflammation. Further release of antigens drives subsequent degeneration causing increased inflammation. Multiple sclerosis presents itself as a spectrum based on the degree of inflammation, a majority of patients experience early relapsing and remitting episodes of neuronal deterioration following a period of recovery. Some of these individuals may transition to a more linear progression of the disease, while about 15% of others begin with a progressive course on the onset of multiple sclerosis. The inflammatory response contributes to the loss of the grey matter, and as a result current literature devotes itself to combatting the auto-inflammatory aspect of the disease. While there are several proposed causal links between EBV and the HLA-DRB1*15:01 allele to the onset of MS – they may contribute to the degree of autoimmune attack and the resultant inflammation – they do not determine the onset of MS.
=== Amyotrophic lateral sclerosis ===
Amyotrophic lateral sclerosis (ALS), commonly referred to Lou Gehrig's disease, is a rare neurodegenerative disorder characterized by the gradual loss of both upper motor neurons (UMNs) and lower motor neurons (LMNs). Although initial symptoms may vary, most patients develop skeletal muscle weakness that progresses to involve the entire body. The precise etiology of ALS remains unknown. In 1993, missense mutations in the gene encoding the antioxidant enzyme superoxide dismutase 1 (SOD1) were discovered in a subset of patients with familial ALS. More recently, TAR DNA-binding protein 43 (TDP-43) and Fused in Sarcoma (FUS) protein aggregates have been implicated in some cases of the disease, and a mutation in chromosome 9 (C9orf72) is thought to be the most common known cause of sporadic ALS. Early diagnosis of ALS is harder than with other neurodegenerative diseases as there are no highly effective means of determining its early onset. Currently, there is research being done regarding the diagnosis of ALS through upper motor neuron tests. The Penn Upper Motor Neuron Score (PUMNS) consists of 28 criteria with a score range of 0–32. A higher score indicates a higher level of burden present on the upper motor neurons. The PUMNS has proven quite effective in determining the burden that exists on upper motor neurons in affected patients.
Independent research provided in vitro evidence that the primary cellular sites where SOD1 mutations act are located on astrocytes. Astrocytes then cause the toxic effects on the motor neurons. The specific mechanism of toxicity still needs to be investigated, but the findings are significant because they implicate cells other than neuron cells in neurodegeneration.
=== Batten disease ===
Batten disease is a rare and fatal recessive neurodegenerative disorder that begins in childhood. Batten disease is the common name for a group of lysosomal storage disorders known as neuronal ceroid lipofuscinoses (NCLs) – each caused by a specific gene mutation, of which there are thirteen. Since Batten disease is quite rare, its worldwide prevalence is about 1 in every 100,000 live births. In North America, NCL3 disease (juvenile NCL) typically manifests between the ages of 4 and 7. Batten disease is characterized by motor impairment, epilepsy, dementia, vision loss, and shortened lifespan. A loss of vision is common first sign of Batten disease. Loss of vision is typically preceded by cognitive and behavioral changes, seizures, and loss of the ability to walk. It is common for people to establish cardiac arrhythmias and difficulties eating food as the disease progresses. Batten disease diagnosis depends on a conflation of many criteria: clinical signs and symptoms, evaluations of the eye, electroencephalograms (EEG), and brain magnetic resonance imaging (MRI) results. The diagnosis provided by these results are corroborated by genetic and biochemical testing. It is only in recent years that more models have been created to expedite the research process for methods to treat Batten disease.
=== Creutzfeldt–Jakob disease ===
Creutzfeldt–Jakob disease (CJD) is a prion disease that is characterized by rapidly progressive dementia. Misfolded proteins called prions aggregate in brain tissue leading to nerve cell death. Variant Creutzfeldt–Jakob disease (vCJD) is the infectious form that comes from the meat of a cow that was infected with bovine spongiform encephalopathy, also called mad cow disease.
== Risk factors ==
=== Aging ===
The greatest risk factor for neurodegenerative diseases is aging. Mitochondrial DNA mutations as well as oxidative stress both contribute to aging. Many of these diseases are late-onset, meaning there is some factor that changes as a person ages for each disease. One constant factor is that in each disease, neurons gradually lose function as the disease progresses with age. It has been proposed that DNA damage accumulation provides the underlying causative link between aging and neurodegenerative disease. About 20–40% of healthy people between 60 and 78 years old experience discernable decrements in cognitive performance in several domains including working, spatial, and episodic memory, and processing speed.
=== Infections ===
A study using electronic health records indicates that 45 (with 22 of these being replicated with the UK Biobank) viral exposures can significantly elevate risks of neurodegenerative disease, including up to 15 years after infection.
== Mechanisms ==
=== Genetics ===
Many neurodegenerative diseases are caused by genetic mutations, most of which are located in completely unrelated genes. In many of the different diseases, the mutated gene has a common feature: a repeat of the CAG nucleotide triplet. CAG codes for the amino acid glutamine. A repeat of CAG results in a polyglutamine (polyQ) tract. Diseases associated with such mutations are known as trinucleotide repeat disorders.
Polyglutamine repeats typically cause dominant pathogenesis. Extra glutamine residues can acquire toxic properties through a variety of ways, including irregular protein folding and degradation pathways, altered subcellular localization, and abnormal interactions with other cellular proteins. PolyQ studies often use a variety of animal models because there is such a clearly defined trigger – repeat expansion. Extensive research has been done using the models of nematode (C. elegans), and fruit fly (Drosophila), mice, and non-human primates.
Nine inherited neurodegenerative diseases are caused by the expansion of the CAG trinucleotide and polyQ tract, including Huntington's disease and the spinocerebellar ataxias.
=== Epigenetics ===
The presence of epigenetic modifications for certain genes has been demonstrated in this type of pathology. An example is FKBP5 gene, which progressively increases its expression with age and has been related to Braak staging and increased tau pathology both in vitro and in mouse models of AD.
=== Protein misfolding ===
Several neurodegenerative diseases are classified as proteopathies as they are associated with the aggregation of misfolded proteins. Protein toxicity is one of the key mechanisms of many neurodegenrative diseases.
alpha-synuclein: can aggregate to form insoluble fibrils in pathological conditions characterized by Lewy bodies, such as Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. Alpha-synuclein is the primary structural component of Lewy body fibrils. In addition, an alpha-synuclein fragment, known as the non-Abeta component (NAC), is found in amyloid plaques in Alzheimer's disease.
tau: hyperphosphorylated tau protein is the main component of neurofibrillary tangles in Alzheimer's disease; tau fibrils are the main component of Pick bodies found in behavioral variant frontotemporal dementia.
amyloid beta: the major component of amyloid plaques in Alzheimer's disease.
prion: main component of prion diseases and transmissible spongiform encephalopathy.
=== Intracellular mechanisms ===
==== Protein degradation pathways ====
Parkinson's disease and Huntington's disease are both late-onset and associated with the accumulation of intracellular toxic proteins. Diseases caused by the aggregation of proteins are known as proteopathies, and they are primarily caused by aggregates in the following structures:
cytosol, e.g. Parkinson's and Huntington's
nucleus, e.g. Spinocerebellar ataxia type 1 (SCA1)
endoplasmic reticulum (ER), (as seen with neuroserpin mutations that cause familial encephalopathy with neuroserpin inclusion bodies)
extracellularly excreted proteins, amyloid-beta in Alzheimer's disease
There are two main avenues eukaryotic cells use to remove troublesome proteins or organelles:
ubiquitin–proteasome: protein ubiquitin along with enzymes is key for the degradation of many proteins that cause proteopathies including polyQ expansions and alpha-synucleins. Research indicates proteasome enzymes may not be able to correctly cleave these irregular proteins, which could possibly result in a more toxic species. This is the primary route cells use to degrade proteins.
Decreased proteasome activity is consistent with models in which intracellular protein aggregates form. It is still unknown whether or not these aggregates are a cause or a result of neurodegeneration.
autophagy–lysosome pathways: a form of programmed cell death (PCD), this becomes the favorable route when a protein is aggregate-prone meaning it is a poor proteasome substrate. This can be split into two forms of autophagy: macroautophagy and chaperone-mediated autophagy (CMA).
macroautophagy is involved with nutrient recycling of macromolecules under conditions of starvation, certain apoptotic pathways, and if absent, leads to the formation of ubiquinated inclusions. Experiments in mice with neuronally confined macroautophagy-gene knockouts develop intraneuronal aggregates leading to neurodegeneration.
chaperone-mediated autophagy defects may also lead to neurodegeneration. Research has shown that mutant proteins bind to the CMA-pathway receptors on lysosomal membrane and in doing so block their own degradation as well as the degradation of other substrates.
==== Membrane damage ====
Damage to the membranes of organelles by monomeric or oligomeric proteins could also contribute to these diseases. Alpha-synuclein can damage membranes by inducing membrane curvature, and cause extensive tubulation and vesiculation when incubated with artificial phospholipid vesicles.
The tubes formed from these lipid vesicles consist of both micellar as well as bilayer tubes. Extensive induction of membrane curvature is deleterious to the cell and would eventually lead to cell death. Apart from tubular structures, alpha-synuclein can also form lipoprotein nanoparticles similar to apolipoproteins.
==== Mitochondrial dysfunction ====
The most common form of cell death in neurodegeneration is through the intrinsic mitochondrial apoptotic pathway. This pathway controls the activation of caspase-9 by regulating the release of cytochrome c from the mitochondrial intermembrane space. Reactive oxygen species (ROS) are normal byproducts of mitochondrial respiratory chain activity. ROS concentration is mediated by mitochondrial antioxidants such as manganese superoxide dismutase (SOD2) and glutathione peroxidase. Over production of ROS (oxidative stress) is a central feature of all neurodegenerative disorders. In addition to the generation of ROS, mitochondria are also involved with life-sustaining functions including calcium homeostasis, PCD, mitochondrial fission and fusion, lipid concentration of the mitochondrial membranes, and the mitochondrial permeability transition. Mitochondrial disease leading to neurodegeneration is likely, at least on some level, to involve all of these functions.
There is strong evidence that mitochondrial dysfunction and oxidative stress play a causal role in neurodegenerative disease pathogenesis, including in four of the more well known diseases Alzheimer's, Parkinson's, Huntington's, and amyotrophic lateral sclerosis.
Neurons are particularly vulnerable to oxidative damage due to their strong metabolic activity associated with high transcription levels, high oxygen consumption, and weak antioxidant defense.
==== DNA damage ====
The brain metabolizes as much as a fifth of consumed oxygen, and reactive oxygen species produced by oxidative metabolism are a major source of DNA damage in the brain. Damage to a cell's DNA is particularly harmful because DNA is the blueprint for protein production and unlike other molecules it cannot simply be replaced by re-synthesis. The vulnerability of post-mitotic neurons to DNA damage (such as oxidative lesions or certain types of DNA strand breaks), coupled with a gradual decline in the activities of repair mechanisms, could lead to accumulation of DNA damage with age and contribute to brain aging and neurodegeneration. DNA single-strand breaks are common and are associated with the neurodegenerative disease ataxia-oculomotor apraxia. Increased oxidative DNA damage in the brain is associated with Alzheimer's disease and Parkinson's disease. Defective DNA repair has been linked to neurodegenerative disorders such as Alzheimer's disease, amyotrophic lateral sclerosis, ataxia telangiectasia, Cockayne syndrome, Parkinson's disease and xeroderma pigmentosum.
==== Axonal transport ====
Axonal swelling, and axonal spheroids have been observed in many different neurodegenerative diseases. This suggests that defective axons are not only present in diseased neurons, but also that they may cause certain pathological insult due to accumulation of organelles. Axonal transport can be disrupted by a variety of mechanisms including damage to: kinesin and cytoplasmic dynein, microtubules, cargoes, and mitochondria. When axonal transport is severely disrupted a degenerative pathway known as Wallerian-like degeneration is often triggered.
=== Programmed cell death ===
Programmed cell death (PCD) is death of a cell in any form, mediated by an intracellular program. This process can be activated in neurodegenerative diseases including Parkinson's disease, amytrophic lateral sclerosis, Alzheimer's disease and Huntington's disease. PCD observed in neurodegenerative diseases may be directly pathogenic; alternatively, PCD may occur in response to other injury or disease processes.
==== Apoptosis (type I) ====
Apoptosis is a form of programmed cell death in multicellular organisms. It is one of the main types of programmed cell death (PCD) and involves a series of biochemical events leading to a characteristic cell morphology and death.
Extrinsic apoptotic pathways: Occur when factors outside the cell activate cell surface death receptors (e.g., Fas) that result in the activation of caspases-8 or -10.
Intrinsic apoptotic pathways: Result from mitochondrial release of cytochrome c or endoplasmic reticulum malfunctions, each leading to the activation of caspase-9. The nucleus and Golgi apparatus are other organelles that have damage sensors, which can lead the cells down apoptotic pathways.
Caspases (cysteine-aspartic acid proteases) cleave at very specific amino acid residues. There are two types of caspases: initiators and effectors. Initiator caspases cleave inactive forms of effector caspases. This activates the effectors that in turn cleave other proteins resulting in apoptotic initiation.
==== Autophagic (type II) ====
Autophagy is a form of intracellular phagocytosis in which a cell actively consumes damaged organelles or misfolded proteins by encapsulating them into an autophagosome, which fuses with a lysosome to destroy the contents of the autophagosome. Because many neurodegenerative diseases show unusual protein aggregates, it is hypothesized that defects in autophagy could be a common mechanism of neurodegeneration.
==== Cytoplasmic (type III) ====
PCD can also occur via non-apoptotic processes, also known as Type III or cytoplasmic cell death. For example, type III PCD might be caused by trophotoxicity, or hyperactivation of trophic factor receptors. Cytotoxins that induce PCD can cause necrosis at low concentrations, or aponecrosis (combination of apoptosis and necrosis) at higher concentrations. It is still unclear exactly what combination of apoptosis, non-apoptosis, and necrosis causes different kinds of aponecrosis.
=== Transglutaminase ===
Transglutaminases are human enzymes ubiquitously present in the human body and in the brain in particular.
The main function of transglutaminases is bind proteins and peptides intra- and intermolecularly, by a type of covalent bonds termed isopeptide bonds, in a reaction termed transamidation or crosslinking.
Transglutaminase binding of these proteins and peptides make them clump together. The resulting structures are turned extremely resistant to chemical and mechanical disruption.
Most relevant human neurodegenerative diseases share the property of having abnormal structures made up of proteins and peptides.
Each of these neurodegenerative diseases have one (or several) specific main protein or peptide. In Alzheimer's disease, these are amyloid-beta and tau. In Parkinson's disease, it is alpha-synuclein. In Huntington's disease, it is huntingtin.
Transglutaminase substrates:
Amyloid-beta, tau, alpha-synuclein and huntingtin have been proved to be substrates of transglutaminases in vitro or in vivo, that is, they can be bonded by trasglutaminases by covalent bonds to each other and potentially to any other transglutaminase substrate in the brain.
Transglutaminase augmented expression:
It has been proved that in these neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, and Huntington's disease) the expression of the transglutaminase enzyme is increased.
Presence of isopeptide bonds in these structures:
The presence of isopeptide bonds (the result of the transglutaminase reaction) have been detected in the abnormal structures that are characteristic of these neurodegenerative diseases.
Co-localization:
Co-localization of transglutaminase mediated isopeptide bonds with these abnormal structures has been detected in the autopsy of brains of patients with these diseases.
== Management ==
The process of neurodegeneration is not well understood, so the diseases that stem from it have, as yet, no cures.
=== Animal models in research ===
In the search for effective treatments (as opposed to palliative care), investigators employ animal models of disease to test potential therapeutic agents. Model organisms provide an inexpensive and relatively quick means to perform two main functions: target identification and target validation. Together, these help show the value of any specific therapeutic strategies and drugs when attempting to ameliorate disease severity. An example is the drug Dimebon by Medivation, Inc. In 2009 this drug was in phase III clinical trials for use in Alzheimer's disease, and also phase II clinical trials for use in Huntington's disease. In March 2010, the results of a clinical trial phase III were released; the investigational Alzheimer's disease drug Dimebon failed in the pivotal CONNECTION trial of patients with mild-to-moderate disease. With CONCERT, the remaining Pfizer and Medivation Phase III trial for Dimebon (latrepirdine) in Alzheimer's disease failed in 2012, effectively ending the development in this indication.
In another experiment using a rat model of Alzheimer's disease, it was demonstrated that systemic administration of hypothalamic proline-rich peptide (PRP)-1 offers neuroprotective effects and can prevent neurodegeneration in hippocampus amyloid-beta 25–35. This suggests that there could be therapeutic value to PRP-1.
=== Other avenues of investigation ===
Protein degradation offers therapeutic options both in preventing the synthesis and degradation of irregular proteins. There is also interest in upregulating autophagy to help clear protein aggregates implicated in neurodegeneration. Both of these options involve very complex pathways that we are only beginning to understand.
The goal of immunotherapy is to enhance aspects of the immune system. Both active and passive vaccinations have been proposed for Alzheimer's disease and other conditions; however, more research must be done to prove safety and efficacy in humans.
A current therapeutic target for the treatment of Alzheimer's disease is the protease β-secretase, which is involved in the amyloidogenic processing pathway that leads to the pathological accumulation of proteins in the brain. When the gene that encodes for amyloid precursor protein (APP) is spliced by α-secretase rather than β-secretase, the toxic protein β amyloid is not produced. Targeted inhibition of β-secretase can potentially prevent the neuronal death that is responsible for the symptoms of Alzheimer's disease.
== See also ==
Amyloid
JUNQ and IPOD
Neurodegeneration with brain iron accumulation
Prevention of dementia
== References == | Wikipedia/Neurodegenerative_disease |
The Hodgkin–Huxley model, or conductance-based model, is a mathematical model that describes how action potentials in neurons are initiated and propagated. It is a set of nonlinear differential equations that approximates the electrical engineering characteristics of excitable cells such as neurons and muscle cells. It is a continuous-time dynamical system.
Alan Hodgkin and Andrew Huxley described the model in 1952 to explain the ionic mechanisms underlying the initiation and propagation of action potentials in the squid giant axon. They received the 1963 Nobel Prize in Physiology or Medicine for this work.
== Basic components ==
The typical Hodgkin–Huxley model treats each component of an excitable cell as an electrical element (as shown in the figure). The lipid bilayer is represented as a capacitance (Cm). Voltage-gated ion channels are represented by electrical conductances (gn, where n is the specific ion channel) that depend on both voltage and time. Leak channels are represented by linear conductances (gL). The electrochemical gradients driving the flow of ions are represented by voltage sources (En) whose voltages are determined by the ratio of the intra- and extracellular concentrations of the ionic species of interest. Finally, ion pumps are represented by current sources (Ip). The membrane potential is denoted by Vm.
Mathematically, the current flowing into the capacitance of the lipid bilayer is written as
I
c
=
C
m
d
V
m
d
t
{\displaystyle I_{c}=C_{m}{\frac {{\mathrm {d} }V_{m}}{{\mathrm {d} }t}}}
and the current through a given ion channel is the product of that channel's conductance and the driving potential for the specific ion
I
i
=
g
i
(
V
m
−
V
i
)
{\displaystyle I_{i}={g_{i}}(V_{m}-V_{i})\;}
where
V
i
{\displaystyle V_{i}}
is the reversal potential of the specific ion channel.
Thus, for a cell with sodium and potassium channels, the total current through the membrane is given by:
I
=
C
m
d
V
m
d
t
+
g
K
(
V
m
−
V
K
)
+
g
N
a
(
V
m
−
V
N
a
)
+
g
l
(
V
m
−
V
l
)
{\displaystyle I=C_{m}{\frac {{\mathrm {d} }V_{m}}{{\mathrm {d} }t}}+g_{K}(V_{m}-V_{K})+g_{Na}(V_{m}-V_{Na})+g_{l}(V_{m}-V_{l})}
where I is the total membrane current per unit area, Cm is the membrane capacitance per unit area, gK and gNa are the potassium and sodium conductances per unit area, respectively, VK and VNa are the potassium and sodium reversal potentials, respectively, and gl and Vl are the leak conductance per unit area and leak reversal potential, respectively. The time dependent elements of this equation are Vm, gNa, and gK, where the last two conductances depend explicitly on the membrane voltage (Vm) as well.
== Ionic current characterization ==
In voltage-gated ion channels, the channel conductance is a function of both time and voltage (
g
n
(
t
,
V
)
{\displaystyle g_{n}(t,V)}
in the figure), while in leak channels,
g
l
{\displaystyle g_{l}}
, it is a constant (
g
L
{\displaystyle g_{L}}
in the figure). The current generated by ion pumps is dependent on the ionic species specific to that pump. The following sections will describe these formulations in more detail.
=== Voltage-gated ion channels ===
Using a series of voltage clamp experiments and by varying extracellular sodium and potassium concentrations, Hodgkin and Huxley developed a model in which the properties of an excitable cell are described by a set of four ordinary differential equations. Together with the equation for the total current mentioned above, these are:
I
=
C
m
d
V
m
d
t
+
g
¯
K
n
4
(
V
m
−
V
K
)
+
g
¯
Na
m
3
h
(
V
m
−
V
N
a
)
+
g
¯
l
(
V
m
−
V
l
)
,
{\displaystyle I=C_{m}{\frac {{\mathrm {d} }V_{m}}{{\mathrm {d} }t}}+{\bar {g}}_{\text{K}}n^{4}(V_{m}-V_{K})+{\bar {g}}_{\text{Na}}m^{3}h(V_{m}-V_{Na})+{\bar {g}}_{l}(V_{m}-V_{l}),}
d
n
d
t
=
α
n
(
V
m
)
(
1
−
n
)
−
β
n
(
V
m
)
n
{\displaystyle {\frac {dn}{dt}}=\alpha _{n}(V_{m})(1-n)-\beta _{n}(V_{m})n}
d
m
d
t
=
α
m
(
V
m
)
(
1
−
m
)
−
β
m
(
V
m
)
m
{\displaystyle {\frac {dm}{dt}}=\alpha _{m}(V_{m})(1-m)-\beta _{m}(V_{m})m}
d
h
d
t
=
α
h
(
V
m
)
(
1
−
h
)
−
β
h
(
V
m
)
h
{\displaystyle {\frac {dh}{dt}}=\alpha _{h}(V_{m})(1-h)-\beta _{h}(V_{m})h}
where I is the current per unit area and
α
i
{\displaystyle \alpha _{i}}
and
β
i
{\displaystyle \beta _{i}}
are rate constants for the i-th ion channel, which depend on voltage but not time.
g
¯
n
{\displaystyle {\bar {g}}_{n}}
is the maximal value of the conductance. n, m, and h are dimensionless probabilities between 0 and 1 that are associated with potassium channel subunit activation, sodium channel subunit activation, and sodium channel subunit inactivation, respectively. For instance, given that potassium channels in squid giant axon are made up of four subunits which all need to be in the open state for the channel to allow the passage of potassium ions, the n needs to be raised to the fourth power. For
p
=
(
n
,
m
,
h
)
{\displaystyle p=(n,m,h)}
,
α
p
{\displaystyle \alpha _{p}}
and
β
p
{\displaystyle \beta _{p}}
take the form
α
p
(
V
m
)
=
p
∞
(
V
m
)
/
τ
p
{\displaystyle \alpha _{p}(V_{m})=p_{\infty }(V_{m})/\tau _{p}}
β
p
(
V
m
)
=
(
1
−
p
∞
(
V
m
)
)
/
τ
p
.
{\displaystyle \beta _{p}(V_{m})=(1-p_{\infty }(V_{m}))/\tau _{p}.}
p
∞
{\displaystyle p_{\infty }}
and
(
1
−
p
∞
)
{\displaystyle (1-p_{\infty })}
are the steady state values for activation and inactivation, respectively, and are usually represented by Boltzmann equations as functions of
V
m
{\displaystyle V_{m}}
. In the original paper by Hodgkin and Huxley, the functions
α
{\displaystyle \alpha }
and
β
{\displaystyle \beta }
are given by
α
n
(
V
m
)
=
0.01
(
10
−
V
)
exp
(
10
−
V
10
)
−
1
α
m
(
V
m
)
=
0.1
(
25
−
V
)
exp
(
25
−
V
10
)
−
1
α
h
(
V
m
)
=
0.07
exp
(
−
V
20
)
β
n
(
V
m
)
=
0.125
exp
(
−
V
80
)
β
m
(
V
m
)
=
4
exp
(
−
V
18
)
β
h
(
V
m
)
=
1
exp
(
30
−
V
10
)
+
1
{\displaystyle {\begin{array}{lll}\alpha _{n}(V_{m})={\frac {0.01(10-V)}{\exp {\big (}{\frac {10-V}{10}}{\big )}-1}}&\alpha _{m}(V_{m})={\frac {0.1(25-V)}{\exp {\big (}{\frac {25-V}{10}}{\big )}-1}}&\alpha _{h}(V_{m})=0.07\exp {\bigg (}-{\frac {V}{20}}{\bigg )}\\\beta _{n}(V_{m})=0.125\exp {\bigg (}-{\frac {V}{80}}{\bigg )}&\beta _{m}(V_{m})=4\exp {\bigg (}-{\frac {V}{18}}{\bigg )}&\beta _{h}(V_{m})={\frac {1}{\exp {\big (}{\frac {30-V}{10}}{\big )}+1}}\end{array}}}
where
V
=
V
r
e
s
t
−
V
m
{\displaystyle V=V_{rest}-V_{m}}
denotes the negative depolarization in mV.
In many current software programs
Hodgkin–Huxley type models generalize
α
{\displaystyle \alpha }
and
β
{\displaystyle \beta }
to
A
p
(
V
m
−
B
p
)
exp
(
V
m
−
B
p
C
p
)
−
D
p
{\displaystyle {\frac {A_{p}(V_{m}-B_{p})}{\exp {\big (}{\frac {V_{m}-B_{p}}{C_{p}}}{\big )}-D_{p}}}}
In order to characterize voltage-gated channels, the equations can be fitted to voltage clamp data. For a derivation of the Hodgkin–Huxley equations under voltage-clamp, see. Briefly, when the membrane potential is held at a constant value (i.e., with a voltage clamp), for each value of the membrane potential the nonlinear gating equations reduce to equations of the form:
m
(
t
)
=
m
0
−
[
(
m
0
−
m
∞
)
(
1
−
e
−
t
/
τ
m
)
]
{\displaystyle m(t)=m_{0}-[(m_{0}-m_{\infty })(1-e^{-t/\tau _{m}})]\,}
h
(
t
)
=
h
0
−
[
(
h
0
−
h
∞
)
(
1
−
e
−
t
/
τ
h
)
]
{\displaystyle h(t)=h_{0}-[(h_{0}-h_{\infty })(1-e^{-t/\tau _{h}})]\,}
n
(
t
)
=
n
0
−
[
(
n
0
−
n
∞
)
(
1
−
e
−
t
/
τ
n
)
]
{\displaystyle n(t)=n_{0}-[(n_{0}-n_{\infty })(1-e^{-t/\tau _{n}})]\,}
Thus, for every value of membrane potential
V
m
{\displaystyle V_{m}}
the sodium and potassium currents can be described by
I
N
a
(
t
)
=
g
¯
N
a
m
(
V
m
)
3
h
(
V
m
)
(
V
m
−
E
N
a
)
,
{\displaystyle I_{\mathrm {Na} }(t)={\bar {g}}_{\mathrm {Na} }m(V_{m})^{3}h(V_{m})(V_{m}-E_{\mathrm {Na} }),}
I
K
(
t
)
=
g
¯
K
n
(
V
m
)
4
(
V
m
−
E
K
)
.
{\displaystyle I_{\mathrm {K} }(t)={\bar {g}}_{\mathrm {K} }n(V_{m})^{4}(V_{m}-E_{\mathrm {K} }).}
In order to arrive at the complete solution for a propagated action potential, one must write the current term I on the left-hand side of the first differential equation in terms of V, so that the equation becomes an equation for voltage alone. The relation between I and V can be derived from cable theory and is given by
I
=
a
2
R
∂
2
V
∂
x
2
,
{\displaystyle I={\frac {a}{2R}}{\frac {\partial ^{2}V}{\partial x^{2}}},}
where a is the radius of the axon, R is the specific resistance of the axoplasm, and x is the position along the nerve fiber. Substitution of this expression for I transforms the original set of equations into a set of partial differential equations, because the voltage becomes a function of both x and t.
The Levenberg–Marquardt algorithm is often used to fit these equations to voltage-clamp data.
While the original experiments involved only sodium and potassium channels, the Hodgkin–Huxley model can also be extended to account for other species of ion channels.
=== Leak channels ===
Leak channels account for the natural permeability of the membrane to ions and take the form of the equation for voltage-gated channels, where the conductance
g
l
e
a
k
{\displaystyle g_{leak}}
is a constant. Thus, the leak current due to passive leak ion channels in the Hodgkin-Huxley formalism is
I
l
=
g
l
e
a
k
(
V
−
V
l
e
a
k
)
{\displaystyle I_{l}=g_{leak}(V-V_{leak})}
.
=== Pumps and exchangers ===
The membrane potential depends upon the maintenance of ionic concentration gradients across it. The maintenance of these concentration gradients requires active transport of ionic species. The sodium-potassium and sodium-calcium exchangers are the best known of these. Some of the basic properties of the Na/Ca exchanger have already been well-established: the stoichiometry of exchange is 3 Na+: 1 Ca2+ and the exchanger is electrogenic and voltage-sensitive. The Na/K exchanger has also been described in detail, with a 3 Na+: 2 K+ stoichiometry.
== Mathematical properties ==
The Hodgkin–Huxley model can be thought of as a differential equation system with four state variables,
V
m
(
t
)
,
n
(
t
)
,
m
(
t
)
{\displaystyle V_{m}(t),n(t),m(t)}
, and
h
(
t
)
{\displaystyle h(t)}
, that change with respect to time
t
{\displaystyle t}
. The system is difficult to study because it is a nonlinear system, cannot be solved analytically, and therefore has no closed-form solution. However, there are many numerical methods available to analyze the system. Certain properties and general behaviors, such as limit cycles, can be proven to exist.
=== Center manifold ===
Because there are four state variables, visualizing the path in phase space can be difficult. Usually two variables are chosen, voltage
V
m
(
t
)
{\displaystyle V_{m}(t)}
and the potassium gating variable
n
(
t
)
{\displaystyle n(t)}
, allowing one to visualize the limit cycle. However, one must be careful because this is an ad-hoc method of visualizing the 4-dimensional system. This does not prove the existence of the limit cycle.
A better projection can be constructed from a careful analysis of the Jacobian of the system, evaluated at the equilibrium point. Specifically, the eigenvalues of the Jacobian are indicative of the center manifold's existence. Likewise, the eigenvectors of the Jacobian reveal the center manifold's orientation. The Hodgkin–Huxley model has two negative eigenvalues and two complex eigenvalues with slightly positive real parts. The eigenvectors associated with the two negative eigenvalues will reduce to zero as time t increases. The remaining two complex eigenvectors define the center manifold. In other words, the 4-dimensional system collapses onto a 2-dimensional plane. Any solution starting off the center manifold will decay towards the center manifold. Furthermore, the limit cycle is contained on the center manifold.
=== Bifurcations ===
If the injected current
I
{\displaystyle I}
were used as a bifurcation parameter, then the Hodgkin–Huxley model undergoes a Hopf bifurcation. As with most neuronal models, increasing the injected current will increase the firing rate of the neuron. One consequence of the Hopf bifurcation is that there is a minimum firing rate. This means that either the neuron is not firing at all (corresponding to zero frequency), or firing at the minimum firing rate. Because of the all-or-none principle, there is no smooth increase in action potential amplitude, but rather there is a sudden "jump" in amplitude. The resulting transition is known as a canard.
== Improvements and alternative models ==
The Hodgkin–Huxley model is regarded as one of the great achievements of 20th-century biophysics. Nevertheless, modern Hodgkin–Huxley-type models have been extended in several important ways:
Additional ion channel populations have been incorporated based on experimental data.
The Hodgkin–Huxley model has been modified to incorporate transition state theory and produce thermodynamic Hodgkin–Huxley models.
Models often incorporate highly complex geometries of dendrites and axons, often based on microscopy data.
Conductance-based models similar to Hodgkin–Huxley model incorporate the knowledge about cell types defined by single cell transcriptomics.
Stochastic models of ion-channel behavior, leading to stochastic hybrid systems.
The Poisson–Nernst–Planck (PNP) model is based on a mean-field approximation of ion interactions and continuum descriptions of concentration and electrostatic potential.
Several simplified neuronal models have also been developed (such as the FitzHugh–Nagumo model), facilitating efficient large-scale simulation of groups of neurons, as well as mathematical insight into dynamics of action potential generation.
== See also ==
== References ==
== Further reading ==
== External links ==
Interactive Javascript simulation of the HH model Runs in any HTML5 – capable browser. Allows for changing the parameters of the model and current injection.
Interactive Java applet of the HH model Parameters of the model can be changed as well as excitation parameters and phase space plottings of all the variables is possible.
Direct link to Hodgkin–Huxley model and a Description in BioModels Database
Neural Impulses: The Action Potential In Action by Garrett Neske, The Wolfram Demonstrations Project
Interactive Hodgkin–Huxley model by Shimon Marom, The Wolfram Demonstrations Project
ModelDB A computational neuroscience source code database containing 4 versions (in different simulators) of the original Hodgkin–Huxley model and hundreds of models that apply the Hodgkin–Huxley model to other channels in many electrically excitable cell types.
Several articles about the stochastic version of the model and its link with the original one. | Wikipedia/Hodgkin–Huxley_model |
A clinical research associate (CRA), also called a clinical monitor or trial monitor, is a health-care professional who performs many activities related to medical research, particularly clinical trials. Clinical research associates work in various settings, such as pharmaceutical companies, medical research institutes and government agencies. Depending on the jurisdiction, different education and certification requirements may be necessary, although not usually required, to practice as a clinical research associate. The main tasks of the CRA are defined by good clinical practice guidelines for monitoring clinical trials, such as those elaborated by the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. A CRA would subsequently grow into a Feasibility Leader, Study Start up Leader, Project Manager, and Project Director at a Pharmaceutical company or a contract research organization. A CRA is usually required to possess an academic degree in Life Sciences and needs to have a good knowledge of good clinical practice and local regulations.
== Overview ==
The main function of a clinical research associate is to monitor clinical trials. The CRA may work directly with the sponsor company of a clinical trial, as an independent freelancer or for a contract research organization (CRO). A clinical research associate ensures compliance with the clinical trial protocol, checks clinical site activities, makes on-site visits, reviews case report forms (CRFs), and communicates with clinical research coordinators. Clinical research associates also "ensure the protection of the rights, safety and well being of human study subjects." Additionally, a CRA must "make certain that the scientific integrity of the data collected is protected and verified" and "ensure that adverse events are correctly documented and reported."
== Certification and practice ==
=== Canada ===
The Canadian Association of Clinical Research Specialists (CACRS) is a federally registered professional association in Canada (Reg. #779602-1). The CACRS is a not-for-profit organization that promotes and advocates on behalf of its members in the field of Clinical Research and Clinical Trials. The CACRS has a comprehensive accreditation program including the Clinical Research Specialist (CRS) designation, which is a professional title conferred by passing a qualifying exam. Applicants holding a doctorate degree in medicine or science are required 2 years of prior experience whereas bachelor's degree holders are required 3 years of prior experience prior to taking the qualifying exam.
=== European Union ===
In the European Union, the practice guidelines for CRAs are part of EudraLex.
=== India ===
In India, a CRA requires knowledge about New drugs and Clinical trials Rules, 2019 along with Drugs and Cosmetics Act, 1940 and Drugs and Cosmetics Rules, 1945.
=== United States ===
In the United States, the rules of good clinical practice are codified in Title 21 of the Code of Federal Regulations. CNNMoney listed Clinical Research Associate at #4 on their list of the "Best Jobs in America" in 2012, with a median salary of $90,700.
The Society of Clinical Research Associates (SOCRA) is a non-profit organization that is "dedicated to the continuing education and development of clinical research professionals". The Society of Clinical Research Associates (SOCRA) has developed an International Certification Program in order to create an internationally accepted standard of knowledge, education, and experience by which CRPs will be recognized as Certified Clinical Research Professionals (CCRP®s) in the clinical research community. The standards upon which this certification program is based have been set forth by this organization to promote recognition and continuing excellence in the ethical conduct of clinical trials. SOCRA provides training, continuing education, and a certification program. A CRA who is certified through SOCRA's certification program receives the designation of a Certified Clinical Research Professional (CCRP®).
The Association of Clinical Research Professionals (ACRP) provides a certification for CRAs, specific to the job function performed. The ACRP offers the designation of Certified Clinical Research Associate (CCRA®). In order to become accredited as a CCRA®, the Clinical Research Associate must pass a CCRA® examination in addition to meeting other specific requirements. Before taking the exam, the potential applicant must show that they "work independently of the investigative staff conducting the research at the site or institution," in order to ensure that the person will not have the opportunity to alter any data. The applicant must also show that they have worked a required number of hours in accordance with study protocols and Good Clinical Practices, including making sure that adverse drug reactions are reported and all necessary documentation is completed. The number of hours that must be completed performing these activities is based on the level of education achieved; for example, someone who has only graduated from high school must perform 6,000 hours, but a registered nurse, person with a bachelor's, masters, or doctorate of medicine degree must only perform 3,000 hours. The ACRP's CRA certification program is accredited by the National Commission for Certifying Agencies (NCCA), the accrediting body of the Institute for Credentialing Excellence.
== References ==
== External links ==
Association of Clinical Research Professionals (United States and United Kingdom)
Certified Clinical Research Professionals (United States)
Canadian Association of Clinical Research Specialists
Clinical Research Association of Canada (Canada)
Clinical Research Society - Certified Clinical Research Associate
ICH Guidelines
Society of Clinical Research Associates (United States) | Wikipedia/Clinical_research_associate |
A neural network, also called a neuronal network, is an interconnected population of neurons (typically containing multiple neural circuits). Biological neural networks are studied to understand the organization and functioning of nervous systems.
Closely related are artificial neural networks, machine learning models inspired by biological neural networks. They consist of artificial neurons, which are mathematical functions that are designed to be analogous to the mechanisms used by neural circuits.
== Overview ==
A biological neural network is composed of a group of chemically connected or functionally associated neurons. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive. Connections, called synapses, are usually formed from axons to dendrites, though dendrodendritic synapses and other connections are possible. Apart from electrical signalling, there are other forms of signalling that arise from neurotransmitter diffusion.
Artificial intelligence, cognitive modelling, and artificial neural networks are information processing paradigms inspired by how biological neural systems process data. Artificial intelligence and cognitive modelling try to simulate some properties of biological neural networks. In the artificial intelligence field, artificial neural networks have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents (in computer and video games) or autonomous robots.
Neural network theory has served to identify better how the neurons in the brain function and provide the basis for efforts to create artificial intelligence.
== History ==
The preliminary theoretical base for contemporary neural networks was independently proposed by Alexander Bain (1873) and William James (1890). In their work, both thoughts and body activity resulted from interactions among neurons within the brain.
For Bain, every activity led to the firing of a certain set of neurons. When activities were repeated, the connections between those neurons strengthened. According to his theory, this repetition was what led to the formation of memory. The general scientific community at the time was skeptical of Bain's theory because it required what appeared to be an inordinate number of neural connections within the brain. It is now apparent that the brain is exceedingly complex and that the same brain “wiring” can handle multiple problems and inputs.
James' theory was similar to Bain's; however, he suggested that memories and actions resulted from electrical currents flowing among the neurons in the brain. His model, by focusing on the flow of electrical currents, did not require individual neural connections for each memory or action.
C. S. Sherrington (1898) conducted experiments to test James' theory. He ran electrical currents down the spinal cords of rats. However, instead of demonstrating an increase in electrical current as projected by James, Sherrington found that the electrical current strength decreased as the testing continued over time. Importantly, this work led to the discovery of the concept of habituation.
McCulloch and Pitts (1943) also created a computational model for neural networks based on mathematics and algorithms. They called this model threshold logic. These early models paved the way for neural network research to split into two distinct approaches. One approach focused on biological processes in the brain and the other focused on the application of neural networks to artificial intelligence.
The parallel distributed processing of the mid-1980s became popular under the name connectionism. The text by Rumelhart and McClelland (1986) provided a full exposition on the use of connectionism in computers to simulate neural processes.
Artificial neural networks, as used in artificial intelligence, have traditionally been viewed as simplified models of neural processing in the brain, even though the relation between this model and brain biological architecture is debated, as it is not clear to what degree artificial neural networks mirror brain function.
== Neuroscience ==
Theoretical and computational neuroscience is the field concerned with the analysis and computational modeling of biological neural systems.
Since neural systems are intimately related to cognitive processes and behaviour, the field is closely related to cognitive and behavioural modeling.
The aim of the field is to create models of biological neural systems in order to understand how biological systems work. To gain this understanding, neuroscientists strive to make a link between observed biological processes (data), biologically plausible mechanisms for neural processing and learning (neural network models) and theory (statistical learning theory and information theory).
=== Types of models ===
Many models are used; defined at different levels of abstraction, and modeling different aspects of neural systems. They range from models of the short-term behaviour of individual neurons, through models of the dynamics of neural circuitry arising from interactions between individual neurons, to models of behaviour arising from abstract neural modules that represent complete subsystems. These include models of the long-term and short-term plasticity of neural systems and their relation to learning and memory, from the individual neuron to the system level.
=== Connectivity ===
In August 2020 scientists reported that bi-directional connections, or added appropriate feedback connections, can accelerate and improve communication between and in modular neural networks of the brain's cerebral cortex and lower the threshold for their successful communication. They showed that adding feedback connections between a resonance pair can support successful propagation of a single pulse packet throughout the entire network. The connectivity of a neural network stems from its biological structures and is usually challenging to map out experimentally. Scientists used a variety of statistical tools to infer the connectivity of a network based on the observed neuronal activities, i.e., spike trains. Recent research has shown that statistically inferred neuronal connections in subsampled neural networks strongly correlate with spike train covariances, providing deeper insights into the structure of neural circuits and their computational properties.
== Recent improvements ==
While initially research had been concerned mostly with the electrical characteristics of neurons, a particularly important part of the investigation in recent years has been the exploration of the role of neuromodulators such as dopamine, acetylcholine, and serotonin on behaviour and learning.
Biophysical models, such as BCM theory, have been important in understanding mechanisms for synaptic plasticity, and have had applications in both computer science and neuroscience.
== See also ==
Adaptive resonance theory
Biological cybernetics
Cognitive architecture
Cognitive science
Connectomics
Cultured neuronal networks
Parallel constraint satisfaction processes
Wood Wide Web
== References == | Wikipedia/Neural_networks_(biology) |
Computational neurogenetic modeling (CNGM) is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This area brings together knowledge from various scientific disciplines, such as computer and information science, neuroscience and cognitive science, genetics and molecular biology, as well as engineering.
== Levels of processing ==
=== Molecular kinetics ===
Models of the kinetics of proteins and ion channels associated with neuron activity represent the lowest level of modeling in a computational neurogenetic model. The altered activity of proteins in some diseases, such as the amyloid beta protein in Alzheimer's disease, must be modeled at the molecular level to accurately predict the effect on cognition. Ion channels, which are vital to the propagation of action potentials, are another molecule that may be modeled to more accurately reflect biological processes. For instance, to accurately model synaptic plasticity (the strengthening or weakening of synapses) and memory, it is necessary to model the activity of the NMDA receptor (NMDAR). The speed at which the NMDA receptor lets Calcium ions into the cell in response to Glutamate is an important determinant of Long-term potentiation via the insertion of AMPA receptors (AMPAR) into the plasma membrane at the synapse of the postsynaptic cell (the cell that receives the neurotransmitters from the presynaptic cell).
=== Genetic regulatory network ===
In most models of neural systems neurons are the most basic unit modeled. In computational neurogenetic modeling, to better simulate processes that are responsible for synaptic activity and connectivity, the genes responsible are modeled for each neuron.
A gene regulatory network, protein regulatory network, or gene/protein regulatory network, is the level of processing in a computational neurogenetic model that models the interactions of genes and proteins relevant to synaptic activity and general cell functions. Genes and proteins are modeled as individual nodes, and the interactions that influence a gene are modeled as excitatory (increases gene/protein expression) or inhibitory (decreases gene/protein expression) inputs that are weighted to reflect the effect a gene or protein is having on another gene or protein. Gene regulatory networks are typically designed using data from microarrays.
Modeling of genes and proteins allows individual responses of neurons in an artificial neural network that mimic responses in biological nervous systems, such as division (adding new neurons to the artificial neural network), creation of proteins to expand their cell membrane and foster neurite outgrowth (and thus stronger connections with other neurons), up-regulate or down-regulate receptors at synapses (increasing or decreasing the weight (strength) of synaptic inputs), uptake more neurotransmitters, change into different types of neurons, or die due to necrosis or apoptosis. The creation and analysis of these networks can be divided into two sub-areas of research: the
gene up-regulation that is involved in the normal functions of a neuron, such as growth, metabolism, and synapsing; and the effects of mutated genes on neurons and cognitive functions.
=== Artificial neural network ===
An artificial neural network generally refers to any computational model that mimics the central nervous system, with capabilities such as learning and pattern recognition. With regards to computational neurogenetic modeling, however, it is often used to refer to those specifically designed for biological accuracy rather than computational efficiency. Individual neurons are the basic unit of an artificial neural network, with each neuron acting as a node. Each node receives weighted signals from other nodes that are either excitatory or inhibitory. To determine the output, a transfer function (or activation function) evaluates the sum of the weighted signals and, in some artificial neural networks, their input rate. Signal weights are strengthened (long-term potentiation) or weakened (long-term depression) depending on how synchronous the presynaptic and postsynaptic activation rates are (Hebbian theory).
The synaptic activity of individual neurons is modeled using equations to determine the temporal (and in some
cases, spatial) summation of synaptic signals, membrane potential, threshold for action potential
generation, the absolute and relative refractory period, and optionally ion receptor channel kinetics and Gaussian noise (to increase biological accuracy by incorporation of random elements). In addition to connectivity, some types of artificial neural networks, such as spiking neural networks, also model the distance between neurons, and its effect on the synaptic weight (the strength of a synaptic transmission).
=== Combining gene regulatory networks and artificial neural networks ===
For the parameters in the gene regulatory network to affect the neurons in the artificial neural network as intended there must be some connection between them. In an organizational context, each node (neuron) in the artificial neural network has its own gene regulatory network associated with it. The weights (and in some networks, frequencies of synaptic transmission to the node), and the resulting membrane potential of the node (including whether an action potential is produced or not), affect the expression of different genes in the gene regulatory network. Factors affecting connections between neurons, such as synaptic plasticity, can be modeled by inputting the values of synaptic activity-associated genes and proteins to a function that re-evaluates the weight of an input from a particular neuron in the artificial neural network.
=== Incorporation of other cell types ===
Other cell types besides neurons can be modeled as well. Glial cells, such as astroglia and microglia, as well as endothelial cells, could be included in an artificial neural network. This would enable modeling of diseases where pathological effects may occur from sources other than neurons, such as Alzheimer's disease.
== Factors affecting choice of artificial neural network ==
While the term artificial neural network is usually used in computational neurogenetic modeling to refer to models of the central nervous system meant to possess biological accuracy, the general use of the term can be applied to many gene regulatory networks as well.
=== Time variance ===
Artificial neural networks, depending on type, may or may not take into account the timing of inputs. Those that do, such as spiking neural networks, fire only when the pooled inputs reach a membrane potential is reached. Because this mimics the firing of biological neurons, spiking neural networks are viewed as a more biologically accurate model of synaptic activity.
=== Growth and shrinkage ===
To accurately model the central nervous system, creation and death of neurons should be modeled as well. To accomplish this, constructive artificial neural networks that are able to grow or shrink to adapt to inputs are often used. Evolving connectionist systems are a subtype of constructive artificial neural networks (evolving in this case referring to changing the structure of its neural network rather than by mutation and natural selection).
=== Randomness ===
Both synaptic transmission and gene-protein interactions are stochastic in nature. To model biological nervous systems with greater fidelity some form of randomness is often introduced into the network. Artificial neural networks modified in this manner are often labeled as probabilistic versions of their neural network sub-type (e.g., pSNN).
=== Incorporation of fuzzy logic ===
Fuzzy logic is a system of reasoning that enables an artificial neural network to deal in non-binary and linguistic variables. Biological data is often unable to be processed using Boolean logic, and moreover accurate modeling of the capabilities of biological nervous systems requires fuzzy logic. Therefore, artificial neural networks that incorporate it, such as evolving fuzzy neural networks (EFuNN) or Dynamic Evolving Neural-Fuzzy Inference Systems (DENFIS), are often used in computational neurogenetic modeling. The use of fuzzy logic is especially relevant in gene regulatory networks, as the modeling of protein binding strength often requires non-binary variables.
=== Types of learning ===
Artificial Neural Networks designed to simulate of the human brain require an ability to learn a variety of tasks that is not required by those designed to accomplish a specific task. Supervised learning is a mechanism by which an artificial neural network can learn by receiving a number of inputs with a correct output already known. An example of an artificial neural network that uses supervised learning is a multilayer perceptron (MLP). In unsupervised learning, an artificial neural network is trained using only inputs. Unsupervised learning is the learning mechanism by which a type of artificial neural network known as a self-organizing map (SOM) learns. Some types of artificial neural network, such as evolving connectionist systems, can learn in both a supervised and unsupervised manner.
== Improvement ==
Both gene regulatory networks and artificial neural networks have two main strategies for improving their accuracy. In both cases the output of the network is measured against known biological data using some function, and subsequent improvements are made by altering the structure of the network. A common test of accuracy for artificial neural networks is to compare some parameter of the model to data acquired from biological neural systems, such as from an EEG. In the case of EEG recordings, the local field potential (LFP) of the artificial neural network is taken and compared to EEG data acquired from human patients. The relative intensity ratio (RIRs) and fast Fourier transform (FFT) of the EEG are compared with those generated by the artificial neural networks to determine the accuracy of the model.
=== Genetic algorithm ===
Because the amount of data on the interplay of genes and neurons and their effects is not enough to construct a rigorous model,
evolutionary computation is used to optimize artificial neural networks and gene regulatory networks, a common technique being the genetic algorithm. A genetic algorithm is a process that can be used to refine models by mimicking the process of natural selection observed in biological ecosystems. The primary advantages are that, due to not requiring derivative information, it can be applied to black box problems and multimodal optimization. The typical process for using genetic algorithms to refine a gene
regulatory network is: first, create a population; next, to create offspring via a crossover operation and
evaluate their fitness; then, on a group chosen for high fitness, simulate mutation via a mutation operator;
finally, taking the now mutated group, repeat this process until a desired level of fitness is demonstrated.
=== Evolving systems ===
Methods by which artificial neural networks may alter their structure without simulated mutation and fitness selection have been developed. A dynamically evolving neural network is one approach, as the creation of new connections and new neurons can
be modeled as the system adapts to new data. This enables the network to evolve in modeling accuracy without simulated natural selection. One method by which dynamically evolving networks may be optimized, called evolving layer neuron aggregation, combines neurons with sufficiently similar input weights into one neuron. This can take place during the training of the network, referred to as online aggregation, or between periods of training, referred to as offline aggregation. Experiments have suggested that offline aggregation is more efficient.
== Potential applications ==
A variety of potential applications have been suggested for accurate computational neurogenetic models, such as simulating genetic diseases, examining the impact of potential treatments, better understanding of learning and cognition, and development of hardware able to interface with neurons.
The simulation of disease states is of particular interest, as modeling both the neurons and their genes and proteins allows linking genetic mutations and protein abnormalities to pathological effects in the central nervous system. Among those diseases suggested as being possible targets of computational neurogenetic modeling based analysis are epilepsy, schizophrenia, mental retardation, brain aging and Alzheimer's disease, and Parkinson's disease.
== See also ==
Memristor
== References ==
== External links ==
http://ecos.watts.net.nz/Algorithms/ | Wikipedia/Computational_neurogenetic_modeling |
Consumer neuroscience is the combination of consumer research with modern neuroscience. The goal of the field is to find neural explanations for consumer behaviors in individuals both with or without disease.
== Consumer research ==
Consumer research has existed for more than a century and has been well established as a combination of sociology, psychology, and anthropology, and popular topics in the field revolve around consumer decision-making, advertising, and branding. For decades, however, consumer researchers had never been able to directly record the internal mental processes that govern consumer behavior; they always were limited to designing experiments in which they alter the external conditions in order to view the ways in which changing variables may affect consumer behavior (examples include changing the packaging or changing a subject’s mood). With the integration of neuroscience with consumer research, it is possible to go directly into the brain to discover the neural explanations for consumer behavior. The ability to record brain activity with electrodes and advances in neural imaging technology make it possible to determine specific regions of the brain that are responsible for critical behaviors involved in consumption.
Consumer neuroscience is similar to neuroeconomics and neuromarketing, but subtle, yet distinct differences exist between them. Neuroeconomics is more of an academic field while neuromarketing and consumer neuroscience are more of an applied science. Neuromarketing focuses on the study of various marketing techniques and attempts to integrate neuroscience knowledge to help improve the efficiency and effectiveness of said marketing strategies. Consumer neuroscience is unique among the three because the main focus is on the consumer and how various factors affect individual preferences and purchasing behavior.
== Advertising ==
=== Advertising and emotion ===
Studies of emotion are crucial to advertising research as it has been shown that emotion plays a significant role in ad memorization. Classically in advertising research, the theory has been that emotion and ratio are represented in different regions of the brain, but neuroscience may be able to disprove this theory by showing that the ventromedial prefrontal cortex and the striatum play a role in bilateral emotion processing.
The attractiveness of the advertisements correlates with specific changes in brain activity in various brain regions including the medial prefrontal cortex, posterior cingulate, nucleus accumbens and higher-order visual cortices. This may represent an interaction between the perceived attractiveness of the ad by the consumer and the emotions expressed by the people pictured in the advertisement. It has been suggested that ads that use people with positive emotions are perceived as attractive while ads using exclusively text or depicting people with neutral expressions may generally be viewed as unattractive. Unattractive ads activate the anterior insula, which plays a role in the processing of negative emotions. Both attractive and unattractive ads have been shown to be more memorable than ads described as ambiguously attractive, but more research is needed to determine how this translates to the overall brand perception in the eyes of the consumer and how this may impact future purchasing behavior.
=== Mental processing of advertisements ===
There are various studies that have been conducted to research the question of how consumers process and store the information presented in advertisements. Television commercials with scene durations lasting longer than 1.5 seconds have been shown to be more memorable one week later than scenes that last less than 1.5 seconds, and scenes that produce the quickest electrical response in the left frontal hemisphere have been shown to be more memorable as well. It has been suggested that the transfer of visual advertising inputs from short term memory to long term memory may take place in the left hemisphere, and highly memorable ads can be created by producing the fastest responses in the left hemisphere. However, these theories have been renounced by some who believe that the research findings may be attributed to extraneous and unmeasured factors. There is also evidence to suggest that a front to back difference in processing speed may be more influential on ad memorization than left to right differences.
Research has shown that there are certain periods of commercials that are far more significant for the consumer in terms of establishing advertising effects. These short segments are referred to as “branding moments” and are thought to be the most engaging parts of the commercial. These moments can be identified using an EEG and analyzing alpha waves (8–13 Hz), beta waves (13–30 Hz) and theta waves (4–7 Hz). These results may suggest that the strength of a commercial with regard to its effect on the consumer can be evaluated by the strength of its unique branding moments, helping brands create more engaging and effective AR campaigns.
In addition, research has also found that a consequence of curiosity, in terms of advertising, is that an unsatisfied curiosity can lead to indulgent consumption in any domain.
=== Affective vs. cognitive ads ===
Affective advertising (using comedy, drama, suspense, etc.) activates the amygdala, the orbitofrontal cortices, and the brainstem whereas cognitive advertising (strict facts) mainly activates the posterior parietal cortex and the superior prefrontal cortices. Ambler and Burne in 1999 created the Memory-Affect-Cognition (MAC) theory to explain the processes involved in decision making. According to the theory, the majority of decisions are habitual and do not require affect or cognition; they require memory only. Most of the remaining decisions only require memory and affect; they do not require cognition. The main use for cognition is in the form of rationalization following a particular action, however, there are occasional instances in which memory, affect and cognition are all used in conjunction, such as during a debate about a particular choice. The above findings suggest a correlation exists between ad memorization and the degree of affective content within the advertisement, but it is still unclear how this translates to brand memory.
== Branding ==
=== Brand associations ===
Much of consumer research is devoted to studying the effect of brand associations on consumer preferences and how they manifest into brand memories. Brand memories can be defined as “everything that exists in the minds of customers with respect to a brand (e.g. thoughts, feelings, experiences, images, perceptions, beliefs and attitudes)”. Several studies have indicated there is not a designated area of the brain devoted to brand recognition. Studies have shown that different areas of the brain are activated when exposed to a brand as opposed to a person, and decisions regarding the evaluation of brands in different product categories activate the area of the brain responsible for semantic object processing rather than areas involved with the judgment of people. These two findings suggest that brands are not processed by the brain in the same manner as human personalities, indicating that personality theory cannot be used to explain brand preferences.
=== Consumer neuroscience explains brand loyalty ===
In a study of fMRI scans of loyal and less loyal customers it was found that in the case of loyal customers the presence of a particular brand serves as a reward during choice tasks, but less loyal customers do not exhibit the same reward pathway. It was also found that loyal customers had greater activation in the brain areas concerned with emotion and memory retrieval suggesting that loyal customers develop an affective bond with a particular brand, which serves as the primary motivation for repeat purchases.
Brand loyalty has been shown to be the result of changes in neural activity in the striatum, which is part of the human action reward system. In order to become brand loyal the brain must make a decision of brand A over brand B, a process which relies on the brain to make predictions based upon expected reward and then evaluate the results to learn loyalty. The brain is required to remember both positive and negative outcomes of previous brand choices in order to accurately be able to make predictions regarding the expected outcome of future brand decisions. For example, a helpful salesman or a discount in price may serve as a reward to encourage future customer loyalty. It is thought that the amygdala and striatum are the two most prominent structures for predicting the outcomes of decisions, and that the brain learns to better predict in part by establishing a larger neural network in these structures.
For recently-formed brand relationships, there is greater self-reported emotional arousal. Over time, that self-reported emotional arousal decreases and inclusion increases. When tested through skin conductance, increased emotional arousal for recently formed close relationships was found, but not for already established close brand relationships. Also, an association was found between insula activation (a brain area connected to urging, addiction, loss aversion, and interpersonal love), and established close relationships.
Research shows that brand betrayal is neuro-physiologically different from brand dissatisfaction. Brand betrayal is associated with feelings of psychological loss, self-castigation over previous brand support, anger from indignation, and rumination. Thus, compared with brand dissatisfaction, brand betrayal is likely to be more harmful to both the brand and the person’s relationship with the brand. This makes brand betrayal more difficult for marketers to deflect, with longer-lasting consequences.
In an attempt to model how the brain learns, a temporal difference learning algorithm has been developed which takes into account expected reward, stimuli presence, reward evaluation, temporal error, and individual differences. As yet this is a theoretical equation, but it may be solved in the near future.
=== How branding affects consumers ===
Brands serve to connect consumers to the products they are purchasing either by establishing an emotional connection or by creating a particular image. It has been shown that when consumers are forced to choose an item from a group in which a familiar brand is present the choice is much easier than when consumers are forced to choose from a group of entirely unfamiliar brands. One MRI study found that there was significantly increased activation in the brain reward centers including the orbitofrontal cortex, the ventral striatum and the anterior cingulate when consumers were looking at sports cars as compared to sedans (presumably because the status symbol associated with sports cars is rewarding in some way). Many corporations have conducted similar MRI studies to investigate the effect of their brand on consumers including Delta Air Lines, General Motors, Home Depot, Hallmark, and Motorola but the results have not been made public.
A study by McClure et al. investigated the difference in branding between Coca-Cola and Pepsi. The study found that when the two drinks were tasted blind there was no difference in consumer preference between the brands. Both drinks produced equal activation in the ventromedial prefrontal cortex, which is thought to be activated because the taste is rewarding. When the subjects were informed of the brand names the consumers preferred Coke, and only Coke activated the ventromedial prefrontal cortex, suggesting that drinking the Coke brand is rewarding beyond simply the taste itself. More subjects preferred Coke when they knew it was Coke than when the taste testing was anonymous, which demonstrates the power of branding to influence consumer behavior. There was also significant activation in the hippocampus and dorsolateral prefrontal cortex when subjects knew they were drinking Coke. These brain structures are known to play a role in memory and recollection, which indicates they are helping the subjects to connect their present drinking experience to previous brand associations. The study proposes that there are two separate processes contributing to consumer decision making: the ventromedial prefrontal cortex responds to sensory inputs and the hippocampus and dorsolateral prefrontal cortex recall previous associations to cultural information. According to the results of this study, the Coke brand has much more firmly established itself as a rewarding experience.
== Packaging ==
Consumer neuroscience research has also invested in how firms package their goods, how designers apply principles of aesthetics to package design, and how consumers neurophysiologically respond to packaged goods. One such finding is that the reaction time of a consumer's choice is significantly increased when the product has aesthetic packaging. Similarly, aesthetic packaging also leads to a product being chosen over a product in standard packaging, even if the standard-packaged product is from a well-known brand and is less expensive.
When packaging is deemed aesthetic, there is an increase in activation in the nucleus accumbens and the ventromedial prefrontal cortex.
== Purchasing ==
Research in consumer buying has focused on the identification of processes that contribute to an individual making a purchase. The brain does not contain a “buy button”, but rather recruits several processes during choice tasks, and studies report that the prefrontal cortex is heavily involved in limiting the emotions expressed during impulse buying. Reducing the effect of these executive control areas of the brain may contribute to changes in purchasing behavior, for example music may lead to reduced cognitive control which is why it has been shown to correlate with a higher percentage of unplanned purchases.
=== Purchasing process ===
Several MEG studies have been conducted to measure the neuronal correlates associated with decision making in order to investigate the underlying processes governing purchasing. The studies suggest that decisions involved with purchasing can be seen as occurring in two halves. The first half is concerned with memory recall and problem identification and recognition. The second half is associated with the purchasing decision itself; familiar brands produce different brain patterns than do nonfamiliar brands. The right parietal cortex is activated when consumers choose a familiar brand, which indicates the choice is at least partially intentional and behavior is influenced by prior experiences.
=== Familiar vs. unfamiliar purchases ===
When consumers select less well known products or products that are completely unfamiliar, several areas of the brain are activated to help with the decision making process that are not activated when consumers select more well known products. There is an increased synchronization between the right dorsolateral cortices (associated with consideration of multiple sources of information), there is increased activity in the right orbitofrontal cortex (associated with evaluation of rewards) and there is increased activity in the left inferior frontal cortex (associated with silent vocalization). Activation in these brain structures indicates that the decision between less well known products is difficult in some way. MEG findings also suggest that even repetitive daily shopping that is apparently simple actually relies on very complex neural mechanisms.
== Associated areas of the brain ==
=== Ventromedial prefrontal cortex ===
It has been shown that the ventromedial prefrontal cortex is heavily involved in decisions regarding brand-related preferences and individuals with damage to this region of the brain do not demonstrate normal brand-preference behavior. People with damage to the ventromedial prefrontal cortex have also been found to be more easily influenced by misleading advertisement.
=== Amygdala and striatum ===
It is thought that the amygdala and striatum are the two most prominent structures for predicting the outcomes of decisions, and that the brain learns to better make predictions in part by establishing a larger neural network in these structures.
=== Hippocampus and dorsolateral prefrontal cortex ===
The hippocampus and dorsolateral prefrontal cortex help consumers recall previous associations with cultural information and cultural expectations. These associations with prior information serve to modify consumer behavior and influence purchasing decisions.
== Real-world applications ==
Consumer research provides a real-world application for neuroscience studies. Consumer studies help neuroscience to learn more about how healthy and unhealthy brain functions differ, which may assist in discovering the neural source of consumption-related dysfunctions and treat a variety of addictions. Additionally, studies are currently underway to investigate the neural mechanism of “anchoring”, which has been thought to contribute to obesity because people are more influenced by the behaviors of their peers than an internal standard. Discovering a neural source of anchoring may be the key to preventing behaviors that typically lead to obesity.
== Limitations ==
Most of the consumer neuroscience studies involving brain scanning techniques have been conducted in medical or technological environments where such brain imaging devices are present. This is not a realistic environment for consumer decision making and may serve to skew the data relative to consumer decision making in normal consumer environments.
Testing underlying neurophysiological principles is extraordinarily difficult from an experimental setup standpoint simply because it is unclear exactly how various factors are perceived in the human mind. An extremely comprehensive understanding of the neuroscientific testing techniques to be used is required to be able to establish proper controls and create an environment such that test subjects are not inadvertently exposed to unwanted stimuli that may bias results.
There are many concerns over the value and the potential usage of consumer neuroscience data. The potential for enhanced consumer welfare is certainly present but equally present is the potential for the information to be used inappropriately for individual gain. The reaction to emerging study results in both the public and the media remains to be seen.
In its current state, consumer neuroscience research is a compilation of only loosely related subjects that is unable, at this point, to produce any collective conclusions.
== References ==
== See also ==
Neuromarketing
Neuromanagement | Wikipedia/Consumer_neuroscience |
The neuroscience of religion, also known as "neurotheology" or "spiritual neuroscience," seeks to explain the biological and neurological processes behind religious experience. Researchers in this field study correlations of the biological neural phenomena, in addition to subjective experiences of spirituality, in order to explain how brain activity functions in response to religious and spiritual practices and beliefs. This contrasts with the psychology of religion, which studies the behavioral responses to religious practices. Some people do warn of the limitations of neurotheology, as they worry that it may simplify the socio-cultural complexity of religion down to neurological factors.
Researchers that study the field of the neuroscience of religion use a formulation of scientific techniques to understand the correlations between brain pathways in response to spiritually based stimuli. The is used interdisciplinary with neurological and evolutionary studies in order to understand the broader subjective experiences under which traditionally categorized spiritual or religious practices are organized. This is done through a multilateral approach of scientific and cultural studies. Such studies include but is not limited to fMRI and EEG scans, theological studies, and anthropological studies. By using these approaches, researchers can better understand how spirituality and religion affect the chemistry of human brains and in turn how brain activity may affect experiences of transcendence and spirituality.
== Terminology ==
=== Neurotheology ===
Aldous Huxley coined the term "neurotheology" for the first time in his utopian novel Island. In this, he described the discipline as a combination of cognitive neuroscience of religious experience and spirituality. The term has also been used in a less scientific context, but rather as a subcategory of philosophy. In some cases, according to the mainstream scientific community, this is considered as a pseudoscience.
=== Biocultural ===
In Armin W. Geertz article on Brain, Body and Culture: A Biocultural Theory of Religion, the term "biocultural" refers to the simultaneous intersection of humans as both biological and cultural animals. In his article, Geertz discusses the connection between the human brain and the rest of the body, stating that the brain does not work independently, but rather in unison with other sense organs in the body. Essentially, arguing that the "cognition functions in the embodiment of the brain." With this, he says that religio-spiritual practices (such as dancing, chanting, or the use of psychoactive substances) that engage the other senses, have physical effects on brain chemistry. This varies cross-culturally, as different cultural and religious practices engage in different methods to induce senses divine transcendence. This, in turn, demonstrates the connection between biology and cultural contexts, since neither are uniform.
=== Religion ===
Spiritual practices and religious rituals have been around for hundreds of thousands of years with some dating as far back as 300,000 in the Rising Star Cave with the discovery of Homo Naledi. Dave Vliegenthart's article Can Neurotheology Explain Religion? aims at answering the question of neurotheology as a legitimate way of explaining religious experiences. In this he defines the term "religion" as a "state of consciousness in which reality is deemed religious and thought and experienced through the lens of a particular human mind-set." This is categorized under feelings of intuition, higher or altered states of consciousness, or a connection to a divine being. Through attempts to achieve religious ecstasy, people have tried to connect to divine or ethereal beings as a way to breed human connection in addition to achieving higher wisdom. This goal of attaining eternal knowledge or harmony with the universe is demonstrated cross culturally, as mentioned above in Geertz's work on biocultural studies.
=== Consciousness ===
According to an article in Scientific American, "consciousness" is everything a person experiences: a personal sense of reality based on experiences of one's own real life events. The article discusses how neuronal correlates of consciousness and the neurological process that go behind the brain's formations of conscious thinking, saying how the senses relay information through the spinal cord to the cerebellum in order to translate physical experience into neurological interpretation. For hundreds of thousands of years humans have been trying to find ways to alter their states of consciousness. This varies widely across cultural groups, religious practices, and more so when looking from individual to individual. In Ancient Greece, maenads would attempt this by ecstatic and frenzied dance. In Sufi Mysticism, also known as Rumism, there is a similar practice of the whirling dervishes where spinning in circles to music is done in order to create a connection with the define. In some more extreme cases, may include forms of asceticism such as fasting, celibacy, or extreme isolation.
== History, Developments, and Theoretical Work ==
In an attempt to focus and clarify what was a growing interest in this field, 1994 educator and businessman Laurence O. McKinney published the first book on the subject, titled Neurotheology: Virtual Religion in the 21st Century. In addition to being written for a popular audience, it also promoted in the theological journal Zygon. According to McKinney, "neurotheology" sources the basis of religious inquiry in relatively recent developmental neurophysiology. McKinney's theory emphasizes how pre-frontal development in humans creates an illusion of chronological time as a fundamental part of normal adult cognition past the age of three. The inability of the adult brain to retrieve earlier images experienced by an infantile brain creates questions such as "Where did I come from?" and "Where does it all go?" He suggests that this neurological process led to the creation of various religious explanations. Moreover, studies behind the experience of death as a peaceful regression into timelessness as the brain dies won praise from readers as varied as writer Arthur C. Clarke, eminent theologian Harvey Cox, and the Dalai Lama and sparked a new interest in the field. Similarly, radical Catholic theologian Eugen Drewermann developed a two-volume critique of traditional conceptions of God and the soul in which he reinterpreted religion based on contemporary neuroscientific research.
The neuroscientist Andrew B. Newberg has claimed that "intensely focused spiritual contemplation triggers an alteration in the activity of the brain that leads one to perceive transcendent religious experiences as solid, tangible reality. In other words, the sensation that Buddhists call oneness with the universe." The orientation area requires sensory input to do its calculus. "If you block sensory inputs to this region, as you do during the intense concentration of meditation, you prevent the brain from forming the distinction between self and not-self," says Newberg. With no information from the senses arriving, the left orientation area cannot find any boundary between the self and the world. As a result, the brain seems to have no choice but "to perceive the self as endless and intimately interwoven with everyone and everything." "The right orientation area, equally bereft of sensory data, defaults to a feeling of infinite space. The meditators feel that they have touched infinity." Still, it has also been argued "that neurotheology should be conceived and practiced within a theological framework."
== Experimental Work ==
In 1969, British biologist Alister Hardy founded a Religious Experience Research Centre (RERC) at Oxford after retiring from his post as Linacre Professor of Zoology. Citing William James's The Varieties of Religious Experience (1902), he set out to collect first-hand accounts of numinous experiences. He was awarded the Templeton Prize before his death in 1985. His successor David Hay suggested in God's Biologist: A Life of Alister Hardy (2011) that the RERC later dispersed as investigators turned to newer techniques of scientific investigation.
=== Magnetic Stimulation Studies ===
During the 1980s Michael Persinger stimulated the temporal lobes of human subjects with a weak magnetic field using an apparatus that popularly became known as the "God helmet" and reported that many of his subjects claimed to experience a "sensed presence" during stimulation. This work has been criticised,
though some researchers have published a replication of one God Helmet experiment.
Granqvist et al. claimed that Persinger's work was not double-blind. Participants were often graduate students who knew what sort of results to expect, and there was the risk that the experimenters' expectations would be transmitted to subjects by unconscious cues. The participants were frequently given an idea of the purpose of the study by being asked to fill in questionnaires designed to test their suggestibility to paranormal experiences before the trials were conducted. Granqvist et al. failed to replicate Persinger's experiments double-blinded, and concluded that the presence or absence of the magnetic field had no relationship with any religious or spiritual experience reported by the participants, but was predicted entirely by their suggestibility and personality traits. Following the publication of this study, Persinger et al. dispute this. One published attempt to create a "haunted room" using environmental "complex" electromagnetic fields based on Persinger's theoretical and experimental work did not produce the sensation of a "sensed presence" and found that reports of unusual experiences were uncorrelated with the presence or absence of these fields. As in the study by Granqvist et al., reports of unusual experiences were instead predicted by the personality characteristics and suggestibility of participants. One experiment with a commercial version of the God helmet found no difference in response to graphic images whether the device was on or off.
=== Neuropsychology and Neuroimaging ===
The first researcher to note and catalog the abnormal experiences associated with temporal lobe epilepsy (TLE) was neurologist Norman Geschwind, who noted a set of religious behavioral traits associated with TLE seizures. These include hypergraphia, hyperreligiosity, reduced sexual interest, fainting spells, and pedantism, often collectively ascribed to a condition known as Geschwind syndrome.
Vilayanur S. Ramachandran explored the neural basis of the hyperreligiosity seen in TLE using the galvanic skin response (GSR), which correlates with emotional arousal, to determine whether the hyperreligiosity seen in TLE was due to an overall heightened emotional state or was specific to religious stimuli. Ramachandran presented two subjects with neutral, sexually arousing and religious words while measuring GSR. Ramachandran was able to show that patients with TLE showed enhanced emotional responses to the religious words, diminished responses to the sexually charged words, and normal responses to the neutral words. This study was presented as an abstract at a neuroscience conference and referenced in Ramachandran's book, Phantoms in the Brain, which was not published as a peer-reviewed scientific article.
Research by Mario Beauregard at the University of Montreal, using fMRI on Carmelite nuns, has purported to show that religious and spiritual experiences include several brain regions and not a single 'God spot'. As Beauregard has said, "There is no God spot in the brain. Spiritual experiences are complex, like intense experiences with other human beings." The neuroimaging was conducted when the nuns were asked to recall past mystical states, not while actually undergoing them; "subjects were asked to remember and relive (eyes closed) the most intense mystical experience ever felt in their lives as a member of the Carmelite Order."
A 2011 study by researchers at the Duke University Medical Center found hippocampal atrophy is associated with older adults who report life-changing religious experiences, as well as those who are "born-again Protestants, Catholics, and those with no religious affiliation".
A 2016 study using fMRI found "a recognizable feeling central to ... (Mormon)... devotional practice was reproducibly associated with activation in nucleus accumbens, ventromedial prefrontal cortex, and frontal attentional regions. Nucleus accumbens activation preceded peak spiritual feelings by 1–3 s and was replicated in four separate tasks. ... The association of abstract ideas and brain reward circuitry may interact with frontal attentional and emotive salience processing, suggesting a mechanism whereby doctrinal concepts may come to be intrinsically rewarding and motivate behavior in religious individuals."
=== Psychopharmacology ===
Some scientists working in the field hypothesize that the basis of spiritual experience arises in neurological physiology. Speculative suggestions have been made that an increase of N,N-dimethyltryptamine levels in the pineal gland contribute to spiritual experiences. It has also been suggested that stimulation of the temporal lobe by psychoactive ingredients of magic mushrooms mimics religious experiences. This hypothesis has found laboratory validation with respect to psilocybin.
== See also ==
Bicameral mentality
Cognitive science of religion
Psychedelic crisis
Religion and schizophrenia
Scholarly approaches to mysticism
Transpersonal psychology
== References ==
== Further reading ==
Begley, Sharon (7 May 2001). "Your Brain on Religion: Mystic visions or brain circuits at work?". Newsweek. Archived from the original on 2 December 2005 – via Center for Cognitive Liberty & Ethics.
Hitt, Jack (1 November 1999). "This Is Your Brain on God". Wired.
Neher, Andrew (1990). The Psychology of Transcendence (2nd ed.). Dover. ISBN 0-486-26167-0.
Newberg, Andrew B. (1999). The Mystical Mind: Probing the Biology of Religious Experience. Minneapolis: Fortress Press. ISBN 0-8006-3163-3.
McNamara, Patrick (2009). The Neuroscience of Religious Experience. Cambridge: Cambridge University Press. ISBN 978-0-521-88958-2.
Powell, Victoria (2007). "Neurotheology: With God in Mind". Clinically Psyched. Archived from the original on 14 June 2013.
Roberts, Thomas B. (2006). "Chemical Input — Religious Output: Entheogens". In McNamara, Robert (ed.). Where God and Science Meet: The Psychology of Religious Experience. Vol. 3. Westport, CT: Praeger Publishers. ISBN 978-0-275-98791-6.
Runehov, Anne L. C. (2007). Sacred or Neural? The Potential of Neuroscience to Explain Religious Experience. Göttingen: Vandenhoeck and Ruprecht. ISBN 978-3-525-56980-1.
Vliegenthart, Dave. "Can Neurotheology Explain Religion?" Archiv Für Religionspsychologie / Archive for the Psychology of Religion 33, no. 2 (2011): 137–71. http://www.jstor.org/stable/23919331.
Geertz, Armin W. "Brain, Body and Culture: A Biocultural Theory of Religion." Method & Theory in the Study of Religion, vol. 22, no. 4, 2010, pp. 304–21. JSTOR, http://www.jstor.org/stable/23555751.
Taylor, Jill Bolte. "My Stroke of Insight." TED Talks, 2019.
== External links ==
God on the Brain - programme summary at BBC
Mystical Brain at National Film Board of Canada | Wikipedia/Neuroscience_of_religion |
Clinical neuroscience is a branch of neuroscience that focuses on the scientific study of fundamental mechanisms that underlie diseases and disorders of the brain and central nervous system. It seeks to develop new ways of conceptualizing and diagnosing such disorders and ultimately of developing novel treatments.
A clinical neuroscientist is a scientist who has specialized knowledge in the field. Not all clinicians are clinical neuroscientists. Clinicians and scientists -including psychiatrists, neurologists, clinical psychologists, neuroscientists, and other specialists—use basic research findings from neuroscience in general and clinical neuroscience in particular to develop diagnostic methods and ways to prevent and treat neurobiological disorders. Such disorders include addiction, Alzheimer's disease, amyotrophic lateral sclerosis, anxiety disorders, attention deficit hyperactivity disorder, autism, bipolar disorder, brain tumors, depression, Down syndrome, dyslexia, epilepsy, Huntington's disease, multiple sclerosis, neurological AIDS, neurological trauma, pain, obsessive-compulsive disorder, Parkinson's disease, schizophrenia, sleep disorders, stroke and Tourette syndrome.
While neurology, neurosurgery and psychiatry are the main medical specialties that use neuroscientific information, other specialties such as cognitive neuroscience, neuroradiology, neuropathology, ophthalmology, otorhinolaryngology, anesthesiology and rehabilitation medicine can contribute to the discipline. Integration of the neuroscience perspective alongside other traditions like psychotherapy, social psychiatry or social psychology will become increasingly important.
== One Mind for Research ==
The "One Mind for Research" forum was a convention held in Boston, Massachusetts on May 23–25, 2011 that produced the blueprint document A Ten-Year Plan for Neuroscience: From Molecules to Brain Health. Leading neuroscience researchers and practitioners in the United States contributed to the creation of this document, in which 17 key areas of opportunities are listed under the Clinical Neuroscience section. These include the following:
Rethinking curricula to break down intellectual silos
Training translational neuroscientists and clinical investigators
Investigating biomarkers
Improving psychiatric diagnosis
Developing a “Framingham Study of Brain Disorders” (i.e. longitudinal cohort for central nervous system disease)
Identifying developmental risk factors and producing effective interventions
Discovering new treatments for pain, including neuropathic pain
Treating disorders of neural signaling and pathological synchrony
Treating disorders of immunity or inflammation
Treating metabolic and mitochondrial disorders
Developing new treatments for depression
Treating addictive disorders
Improving treatment of schizophrenia
Preventing and treating cerebrovascular disease
Achieving personalized medicine
Understanding shared mechanisms of neurodegeneration
Advancing anesthesia
In particular, it advocates for better integrated and scientifically driven curricula for practitioners, and it recommends that such curricula be shared among neurologists, psychiatrists, psychologists, neurosurgeons and neuroradiologists.
Given the various ethical, legal and societal implications for healthcare practitioners arising from advances in neuroscience, the University of Pennsylvania inaugurated the Penn Conference on Clinical Neuroscience and Society in July 2011.
== Similarities between Other Fields of Neuroscience ==
=== Neuropsychology ===
As byproducts of Neuroscience, they do not share the same objective as their parent (Neuroscience), as such focus on specific fields. While Clinical Neuroscience is more focused on the anatomy and how the brain would react to specific types of disorders and how to prevent them. Clinical Neuropsychology is more focused on how the brain functions and understands behaviors.) Yet both of these fields can be both applied to aiding and preventing mental disorders, alongside the diagnosing of brain disorders and assessing cognitive and mental behaviors.
Scientists who research Neuropsychology are able to assist and aid subjects in a clinical manner rather than biological. Treating people they come to as patients rather than subjects in a way. Neuropsychology is research intensive, requiring existential knowledge in the field of Psychology. Most Neuropsychologists have acquired their Doctoral Degree's due to how research extensive the topic may be, making the field extremely competitive in the job market.
Progress into Neuropsychology is equivalent to becoming a therapist if not equally time investing.
=== Neuropsychiatry ===
Neuropsychiatry is a field that connects the mind and the brain, looking at how both affect each other. It combines ideas from both neurology (the study of the brain and nervous system) and psychiatry (the study of mental health), and focuses on treating problems related to thinking, emotions, and behavior that come from brain disorders.
Rather than focusing on just one part of a problem (like a specific brain issue or mental health symptom), neuropsychiatry takes a broader approach. It recognizes that many brain disorders, like Parkinson’s or Alzheimer's, can affect mood or thinking, and many mental health conditions, like depression or schizophrenia, have a neurological aspect too.
Neuropsychology serves people from all across the entire age, making it feasible to all across the world. Helping us identify developmental concerns within infants and other ages around childhood.
== See also ==
Behavioral neurology
Neuropsychiatry
Neuropsychology
Society for Neuroscience
Cognitive neuroscience
== References == | Wikipedia/Clinical_neuroscience |
Models of neural computation are attempts to elucidate, in an abstract and mathematical fashion, the core principles that underlie information processing in biological nervous systems, or functional components thereof. This article aims to provide an overview of the most definitive models of neuro-biological computation as well as the tools commonly used to construct and analyze them.
== Introduction ==
Due to the complexity of nervous system behavior, the associated experimental error bounds are ill-defined, but the relative merit of the different models of a particular subsystem can be compared according to how closely they reproduce real-world behaviors or respond to specific input signals. In the closely related field of computational neuroethology, the practice is to include the environment in the model in such a way that the loop is closed. In the cases where competing models are unavailable, or where only gross responses have been measured or quantified, a clearly formulated model can guide the scientist in designing experiments to probe biochemical mechanisms or network connectivity.
In all but the simplest cases, the mathematical equations that form the basis of a model cannot be solved exactly. Nevertheless, computer technology, sometimes in the form of specialized software or hardware architectures, allow scientists to perform iterative calculations and search for plausible solutions. A computer chip or a robot that can interact with the natural environment in ways akin to the original organism is one embodiment of a useful model. The ultimate measure of success is however the ability to make testable predictions.
== General criteria for evaluating models ==
=== Speed of information processing ===
The rate of information processing in biological neural systems are constrained by the speed at which an action potential can propagate down a nerve fibre. This conduction velocity ranges from 1 m/s to over 100 m/s, and generally increases with the diameter of the neuronal process. Slow in the timescales of biologically-relevant events dictated by the speed of sound or the force of gravity, the nervous system overwhelmingly prefers parallel computations over serial ones in time-critical applications.
=== Robustness ===
A model is robust if it continues to produce the same computational results under variations in inputs or operating parameters introduced by noise. For example, the direction of motion as computed by a robust motion detector would not change under small changes of luminance, contrast or velocity jitter. For simple mathematical models of neuron, for example the dependence of spike patterns on signal delay is much weaker than the dependence on changes in "weights" of interneuronal connections.
=== Gain control ===
This refers to the principle that the response of a nervous system should stay within certain bounds even as the inputs from the environment change drastically. For example, when adjusting between a sunny day and a moonless night, the retina changes the relationship between light level and neuronal output by a factor of more than
10
6
{\displaystyle 10^{6}}
so that the signals sent to later stages of
the visual system always remain within a much narrower range of amplitudes.
=== Linearity versus nonlinearity ===
A linear system is one whose response in a specified unit of measure, to a set of inputs considered at once, is the sum of its responses due to the inputs considered individually.
Linear systems are easier to analyze mathematically and are a persuasive assumption in many models including the McCulloch and Pitts neuron, population coding models, and the simple neurons often used in Artificial neural networks. Linearity may occur in the basic elements of a neural circuit such as the response of a postsynaptic neuron, or as an emergent property of a combination of nonlinear subcircuits. Though linearity is often seen as incorrect, there has been recent work suggesting it may, in fact, be biophysically plausible in some cases.
== Examples ==
A computational neural model may be constrained to the level of biochemical signalling in individual neurons or it may describe an entire organism in its environment. The examples here are grouped according to their scope.
=== Models of information transfer in neurons ===
The most widely used models of information transfer in biological neurons are based on analogies with electrical circuits. The equations to be solved are time-dependent differential equations with electro-dynamical variables such as current, conductance or resistance, capacitance and voltage.
==== Hodgkin–Huxley model and its derivatives ====
The Hodgkin–Huxley model, widely regarded as one of the great achievements of 20th-century biophysics, describes how action potentials in neurons are initiated and propagated in axons via voltage-gated ion channels. It is a set of nonlinear ordinary differential equations that were introduced by Alan Lloyd Hodgkin and Andrew Huxley in 1952 to explain the results of voltage clamp experiments on the squid giant axon. Analytic solutions do not exist, but the Levenberg–Marquardt algorithm, a modified Gauss–Newton algorithm, is often used to fit these equations to voltage-clamp data.
The FitzHugh–Nagumo model is a simplication of the Hodgkin–Huxley model. The Hindmarsh–Rose model is an extension which describes neuronal spike bursts. The Morris–Lecar model is a modification which does not generate spikes, but describes slow-wave propagation, which is implicated in the inhibitory synaptic mechanisms of central pattern generators.
==== Solitons ====
The soliton model is an alternative to the Hodgkin–Huxley model that claims to explain how action potentials are initiated and conducted in the form of certain kinds of solitary sound (or density) pulses that can be modeled as solitons along axons, based on a thermodynamic theory of nerve pulse propagation.
==== Transfer functions and linear filters ====
This approach, influenced by control theory and signal processing, treats neurons and synapses as time-invariant entities that produce outputs that are linear combinations of input signals, often depicted as sine waves with a well-defined temporal or spatial frequencies.
The entire behavior of a neuron or synapse are encoded in a transfer function, lack of knowledge concerning the exact underlying mechanism notwithstanding. This brings a highly developed mathematics to bear on the problem of information transfer.
The accompanying taxonomy of linear filters turns out to be useful in characterizing neural circuitry. Both low- and high-pass filters are postulated to exist in some form in sensory systems, as they act to prevent information loss in high and low contrast environments, respectively.
Indeed, measurements of the transfer functions of neurons in the horseshoe crab retina according to linear systems analysis show that they remove short-term fluctuations in input signals leaving only the long-term trends, in the manner of low-pass filters. These animals are unable to see low-contrast objects without the help of optical distortions caused by underwater currents.
=== Models of computations in sensory systems ===
==== Lateral inhibition in the retina: Hartline–Ratliff equations ====
In the retina, an excited neural receptor can suppress the activity of surrounding neurons within an area called the inhibitory field. This effect, known as lateral inhibition, increases the contrast and sharpness in visual response, but leads to the epiphenomenon of Mach bands. This is often illustrated by the optical illusion of light or dark stripes next to a sharp boundary between two regions in an image of different luminance.
The Hartline-Ratliff model describes interactions within a group of p photoreceptor cells. Assuming these interactions to be linear, they proposed the following relationship for the steady-state response rate
r
p
{\displaystyle r_{p}}
of the given p-th photoreceptor in terms of the steady-state response rates
r
j
{\displaystyle r_{j}}
of the j surrounding receptors:
r
p
=
|
[
e
p
−
∑
j
=
1
,
j
≠
p
n
k
p
j
|
r
j
−
r
p
j
o
|
]
|
{\displaystyle r_{p}=\left|\left[e_{p}-\sum _{j=1,j\neq p}^{n}k_{pj}\left|r_{j}-r_{pj}^{o}\right|\right]\right|}
.
Here,
e
p
{\displaystyle e_{p}}
is the excitation of the target p-th receptor from sensory transduction
r
p
j
o
{\displaystyle r_{pj}^{o}}
is the associated threshold of the firing cell, and
k
p
j
{\displaystyle k_{pj}}
is the coefficient of inhibitory interaction between the p-th and the jth receptor. The inhibitory interaction decreases with distance from the target p-th receptor.
==== Cross-correlation in sound localization: Jeffress model ====
According to Jeffress, in order to compute the location of a sound source in space from interaural time differences, an auditory system relies on delay lines: the induced signal from an ipsilateral auditory receptor to a particular neuron is delayed for the same time as it takes for the original sound to go in space from that ear to the other. Each postsynaptic cell is differently delayed and thus specific for a particular inter-aural time difference. This theory is equivalent to the mathematical procedure of cross-correlation.
Following Fischer and Anderson, the response of the postsynaptic neuron to the signals from the left and right ears is given by
y
R
(
t
)
−
y
L
(
t
)
{\displaystyle y_{R}\left(t\right)-y_{L}\left(t\right)}
where
y
L
(
t
)
=
∫
0
τ
u
L
(
σ
)
w
(
t
−
σ
)
d
σ
{\displaystyle y_{L}\left(t\right)=\int _{0}^{\tau }u_{L}\left(\sigma \right)w\left(t-\sigma \right)d\sigma }
y
R
(
t
)
=
∫
0
τ
u
R
(
σ
)
w
(
t
−
σ
)
d
σ
{\displaystyle y_{R}\left(t\right)=\int _{0}^{\tau }u_{R}\left(\sigma \right)w\left(t-\sigma \right)d\sigma }
and
w
(
t
−
σ
)
{\displaystyle w\left(t-\sigma \right)}
represents the delay function. This is not entirely correct and a clear eye is needed to put the symbols in order.
Structures have been located in the barn owl which are consistent with Jeffress-type mechanisms.
==== Cross-correlation for motion detection: Hassenstein–Reichardt model ====
A motion detector needs to satisfy three general requirements: pair-inputs, asymmetry and nonlinearity. The cross-correlation operation implemented asymmetrically on the responses from a pair of photoreceptors satisfies these minimal criteria, and furthermore, predicts features which have been observed in the response of neurons of the lobula plate in bi-wing insects.
The master equation for response is
R
=
A
1
(
t
−
τ
)
B
2
(
t
)
−
A
2
(
t
−
τ
)
B
1
(
t
)
{\displaystyle R=A_{1}(t-\tau )B_{2}(t)-A_{2}(t-\tau )B_{1}(t)}
The HR model predicts a peaking of the response at a particular input temporal frequency. The conceptually similar Barlow–Levick model is deficient in the sense that a stimulus presented to only one receptor of the pair is sufficient to generate a response. This is unlike the HR model, which requires two correlated signals delivered in a time ordered fashion. However the HR model does not show a saturation of response at high contrasts, which is observed in experiment. Extensions of the Barlow-Levick model can provide for this discrepancy.
==== Watson–Ahumada model for motion estimation in humans ====
This uses a cross-correlation in both the spatial and temporal directions, and is related to the concept of optical flow.
=== Anti-Hebbian adaptation: spike-timing dependent plasticity ===
Tzounopoulos, T; Kim, Y; Oertel, D; Trussell, LO (2004). "Cell-specific, spike timing-dependent plasticities in the dorsal cochlear nucleus". Nat Neurosci. 7 (7): 719–725. doi:10.1038/nn1272. PMID 15208632. S2CID 17774457.
Roberts, Patrick D.; Portfors, Christine V. (2008). "Design principles of sensory processing in cerebellum-like structures". Biological Cybernetics. 98 (6): 491–507. doi:10.1007/s00422-008-0217-1. PMID 18491162. S2CID 14393814.
=== Models of sensory-motor coupling ===
==== Neurophysiological metronomes: neural circuits for pattern generation ====
Mutually inhibitory processes are a unifying motif of all central pattern generators. This has been demonstrated in the stomatogastric (STG) nervous system of crayfish and lobsters. Two and three-cell oscillating networks based on the STG have been constructed which are amenable to mathematical analysis, and which depend in a simple way on synaptic strengths and overall activity, presumably the knobs on these things. The mathematics involved is the theory of dynamical systems.
==== Feedback and control: models of flight control in the fly ====
Flight control in the fly is believed to be mediated by inputs from the visual system and also the halteres, a pair of knob-like organs which measure angular velocity. Integrated computer models of Drosophila, short on neuronal circuitry but based on the general guidelines given by control theory and data from the tethered flights of flies, have been constructed to investigate the details of flight control.
==== Cerebellum sensory motor control ====
Tensor network theory is a theory of cerebellar function that provides a mathematical model of the transformation of sensory space-time coordinates into motor coordinates and vice versa by cerebellar neuronal networks. The theory was developed by Andras Pellionisz and Rodolfo Llinas in the 1980s as a geometrization of brain function (especially of the central nervous system) using tensors.
== Software modelling approaches and tools ==
=== Neural networks ===
In this approach the strength and type, excitatory or inhibitory, of synaptic connections are represented by the magnitude and sign of weights, that is, numerical coefficients
w
′
{\displaystyle w'}
in front of the inputs
x
{\displaystyle x}
to a particular neuron. The response of the
j
{\displaystyle j}
-th neuron is given by a sum of nonlinear, usually "sigmoidal" functions
g
{\displaystyle g}
of the inputs as:
f
j
=
∑
i
g
(
w
j
i
′
x
i
+
b
j
)
{\displaystyle f_{j}=\sum _{i}g\left(w_{ji}'x_{i}+b_{j}\right)}
.
This response is then fed as input into other neurons and so on. The goal is to optimize the weights of the neurons to output a desired response at the output layer respective to a set given inputs at the input layer. This optimization of the neuron weights is often performed using the backpropagation algorithm and an optimization method such as gradient descent or Newton's method of optimization. Backpropagation compares the output of the network with the expected output from the training data, then updates the weights of each neuron to minimize the contribution of that individual neuron to the total error of the network.
=== Genetic algorithms ===
Genetic algorithms are used to evolve neural (and sometimes body) properties in a model brain-body-environment system so as to exhibit some desired behavioral performance. The evolved agents can then be subjected to a detailed analysis to uncover their principles of operation. Evolutionary approaches are particularly useful for exploring spaces of possible solutions to a given behavioral task because these approaches minimize a priori assumptions about how a given behavior ought to be instantiated. They can also be useful for exploring different ways to complete a computational neuroethology model when only partial neural circuitry is available for a biological system of interest.
=== NEURON ===
The NEURON software, developed at Duke University, is a simulation environment for modeling individual neurons and networks of neurons. The NEURON environment is a self-contained environment allowing interface through its GUI or via scripting with hoc or python. The NEURON simulation engine is based on a Hodgkin–Huxley type model using a Borg–Graham formulation. Several examples of models written in NEURON are available from the online database ModelDB.
== Embodiment in electronic hardware ==
=== Conductance-based silicon neurons ===
Nervous systems differ from the majority of silicon-based computing devices in that they resemble analog computers (not digital data processors) and massively parallel processors, not sequential processors. To model nervous systems accurately, in real-time, alternative hardware is required.
The most realistic circuits to date make use of analog properties of existing digital electronics (operated under non-standard conditions) to realize Hodgkin–Huxley-type models in silico.
=== Retinomorphic chips ===
== See also ==
== References ==
== External links ==
Neural Dynamics at NSI – Web page of Patrick D Roberts at the Neurological Sciences Institute
Dickinson Lab – Web page of the Dickinson group at Caltech which studies flight control in Drosophila | Wikipedia/Models_of_neural_computation |
Nanoneuroscience is an interdisciplinary field that integrates nanotechnology and neuroscience. One of its main goals is to gain a detailed understanding of how the nervous system operates and, thus, how neurons organize themselves in the brain. Consequently, creating drugs and devices that are able to cross the blood brain barrier (BBB) are essential to allow for detailed imaging and diagnoses. The blood brain barrier functions as a highly specialized semipermeable membrane surrounding the brain, preventing harmful molecules that may be dissolved in the circulation blood from entering the central nervous system.
The main two hurdles for drug-delivering molecules to access the brain are size (must have a molecular weight < 400 Da) and lipid solubility. Physicians hope to circumvent difficulties in accessing the central nervous system through viral gene therapy. This often involves direct injection into the patient's brain or cerebral spinal fluid. The drawback of this therapy is that it is invasive and carries a high risk factor due to the necessity of surgery for the treatment to be administered. Because of this, only 3.6% of clinical trials in this field have progressed to stage III since the concept of gene therapy was developed in the 1980s.
Another proposed way to cross the BBB is through temporary intentional disruption of the barrier. This method was first inspired by certain pathological conditions that were discovered to break down this barrier by themselves, such as Alzheimer's disease, Parkinson's disease, stroke, and seizure conditions.
== Nanoparticles ==
Nanoparticles are unique from macromolecules because their surface properties are dependent on their size, allowing for strategic manipulation of these properties (or, "programming") by scientists that would not be possible otherwise. Likewise, nanoparticle shape can also be varied to give a different set of characteristics based on the surface area to volume ratio of the particle.
Nanoparticles have promising therapeutic effects when treating neurodegenerative diseases. Oxygen reactive polymer (ORP) is a nano-platform programmed to react with oxygen and has been shown to detect and reduce the presence of reactive oxygen species (ROS) formed immediately after traumatic brain injuries. Nanoparticles have also been employed as a "neuroprotective" measure, as is the case with Alzheimer's disease and stroke models. Alzheimer's disease results in toxic aggregates of the amyloid beta protein formed in the brain. In one study, gold nanoparticles were programmed to attach themselves to these aggregates and were successful in breaking them up. Likewise, with ischemic stroke models, cells in the affected region of the brain undergo apoptosis, dramatically reducing blood flow to important parts of the brain and often resulting in death or severe mental and physical changes. Platinum nanoparticles have been shown to act as ROS, serving as "biological antioxidants" and significantly reducing oxidation in the brain as a result of stroke. Nanoparticles can also lead to neurotoxicity and cause permanent BBB damage either from brain oedema or from unrelated molecules crossing the BBB and causing brain damage. This proves further long term in vivo studies are needed to gain enough understanding to allow for successful clinical trials.
One of the most common nano-based drug delivery platforms is liposome-based delivery. They are both lipid-soluble and nano-scale and thus are permitted through a fully functioning BBB. Additionally, lipids themselves are biological molecules, making them highly biocompatible, which in turn lowers the risk of cell toxicity. The bilayer that is formed allows the molecule to fully encapsulate any drug, protecting it while it is travelling through the body. One drawback to shielding the drug from the outside cells is that it no longer has specificity, and requires coupling to extra antibodies to be able to target a biological site. Due to their low stability, liposome-based nanoparticles for drug delivery have a short shelf life.
Targeted therapy using magnetic nanoparticles (MNPs) is also a popular topic of research and has led to several stage III clinical trials. Invasiveness is not an issue here because a magnetic force can be applied from the outside of a patient's body to interact and direct the MNPs. This strategy has been proven successful in delivering brain-derived neurotropic factor, a naturally occurring gene thought to promote neurorehabilitation, across the BBB.
== Nano-imaging tools ==
The visualization of neuronal activity is of key importance in neuroscience. Nano-imaging tools with nanoscale resolution help in these areas. These optical imaging tools are PALM and STORM which helps visualize nanoscale objects within cells. So far, these imaging tools revealed the dynamic behavior and organization of the actin cytoskeleton inside the cells, which will assist in understanding how neurons probe their involvement during neuronal outgrowth and in response to injury, and how they differentiate axonal processes and characterization of receptor clustering and stoichiometry at the plasma inside the synapses, which are critical for understanding how synapses respond to changes in neuronal activity. These past works focused on devices for stimulation or inhibition of neural activity, but the crucial aspect is the ability for the device to simultaneously monitor neural activity. The major aspect that is to be improved in the nano imaging tools is the effective collection of the light as a major problem is that biological tissue are dispersive media that do not allow a straightforward propagation and control of light. These devices use nanoneedle and nanowire for probing and stimulation.
== Nanowires ==
Nanowires are artificial nano- or micro-sized "needles" that can provide high-fidelity electrophysiological recordings if used as microscopic electrodes for neuronal recordings. Nanowires are an attractive as they are highly functional structures that offer unique electronic properties that are affected by biological/chemical species adsorbed on their surface; mostly the conductivity. This conductivity variance depending on chemical species present allows enhanced sensing performances. Nanowires are also able to act as non-invasive and highly local probes. These versatility of nanowires makes it optimal for interfacing with neurons due to the fact that the contact length along the axon (or the dendrite projection crossing a nanowires) is just about 20 nm.
== References == | Wikipedia/Nanoneuroscience |
Functional neuroimaging is the use of neuroimaging technology to measure an aspect of brain function, often with a view to understanding the relationship between activity in certain brain areas and specific mental functions. It is primarily used as a research tool in cognitive neuroscience, cognitive psychology, neuropsychology, and social neuroscience.
== Overview ==
Common methods of functional neuroimaging include
Positron emission tomography (PET)
Functional magnetic resonance imaging (fMRI)
Electroencephalography (EEG)
Magnetoencephalography (MEG)
Functional near-infrared spectroscopy (fNIRS)
Single-photon emission computed tomography (SPECT)
Functional ultrasound imaging (fUS)
PET, fMRI, fNIRS and fUS can measure localized changes in cerebral blood flow related to neural activity. These changes are referred to as activations. Regions of the brain which are activated when a subject performs a particular task may play a role in the neural computations which contribute to the behaviour. For instance, widespread activation of the occipital lobe is typically seen in tasks which involve visual stimulation (compared with tasks that do not). This part of the brain receives signals from the retina and is believed to play a role in visual perception.
Other methods of neuroimaging involve recording of electrical currents or magnetic fields, for example EEG and MEG. Different methods have different advantages for research; for instance, MEG measures brain activity with high temporal resolution (down to the millisecond level), but is limited in its ability to localize that activity. fMRI does a much better job of localizing brain activity for spatial resolution, but with a much lower time resolution while functional ultrasound (fUS) can reach an interesting spatio-temporal resolution (down to 100 micrometer, 100 milliseconds, at 15 MHz in preclinical models) but is also limited by the neurovascular coupling.
Recently, Magnetic particle imaging has been proposed as a new sensitive imaging technique that has sufficient temporal resolution for functional neuroimaging based on the increase of cerebral blood volume. First pre-clinical trials have successfully demonstrated functional imaging in rodents.
== Functional neuroimaging topics ==
The measure used in a particular study is generally related to the particular question being addressed. Measurement limitations vary amongst the techniques. For instance, MEG and EEG record the magnetic or electrical fluctuations that occur when a population of neurons is active. These methods are excellent for measuring the time-course of neural events (on the order of milliseconds,) but generally bad at measuring where those events happen. PET and fMRI measure changes in the composition of blood near a neural event. Because measurable blood changes are slow (on the order of seconds), these methods are much worse at measuring the time-course of neural events, but are generally better at measuring the location.
Traditional "activation studies" focus on determining distributed patterns of brain activity associated with specific tasks. However, scientists are able to more thoroughly understand brain function by studying the interaction of distinct brain regions, as a great deal of neural processing is performed by an integrated network of several regions of the brain. An active area of neuroimaging research involves examining the functional connectivity of spatially remote brain regions. Functional connectivity analyses allow the characterization of interregional neural interactions during particular cognitive or motor tasks or merely from spontaneous activity during rest. FMRI and PET enable creation of functional connectivity maps of distinct spatial distributions of temporally correlated brain regions called functional networks. Several studies using neuroimaging techniques have also established that posterior visual areas in blind individuals may be active during the performance of nonvisual tasks such as Braille reading, memory retrieval, and auditory localization as well as other auditory functions.
A direct method to measure functional connectivity is to observe how stimulation of one part of the brain will affect other areas. This can be done noninvasively in humans by combining transcranial magnetic stimulation with one of the neuroimaging tools such as PET, fMRI, or EEG. Massimini et al. (Science, September 30, 2005) used EEG to record how activity spreads from the stimulated site. They reported that in non-REM sleep, although the brain responds vigorously to stimulation, functional connectivity is much attenuated from its level during wakefulness. Thus, during deep sleep, "brain areas do not talk to each other".
Functional neuroimaging draws on data from many areas other than cognitive neuroscience and social neuroscience, including other biological sciences (such as neuroanatomy and neurophysiology), physics and maths, to further develop and refine the technology.
== Critique and careful interpretation ==
Functional neuroimaging studies have to be carefully designed and interpreted with care.
Statistical analysis (often using a technique called statistical parametric mapping) is often needed so that the different sources of activation within the brain can be distinguished from one another.
This can be particularly challenging when considering processes which are difficult to conceptualise or have no easily definable task associated with them (for example belief and consciousness).
Functional neuroimaging of interesting phenomena often gets cited in the press.
In one case a group of prominent functional neuroimaging researchers felt compelled to write a letter to New York Times in response to an op-ed article about a study of so-called neuropolitics. They argued that some of the interpretations of the study were "scientifically unfounded".
The Hastings Center issued a report in March 2014 entitled "Interpreting Neuroimages: An Introduction to the Technology and Its Limits", with articles by leading neuroscientists and bioethicists. The report briefly explains neuroimaging technologies and mostly critiques, but also somewhat defends, their current state, import and prospects.
== See also ==
== References ==
== Further reading ==
Cabeza, R., & Kingstone, K. (eds.) (2006). Handbook of Functional Neuroimaging of Cognition. MIT Press.
Cacioppo, J.T., Tassinary, L.G., & Berntson, G. G. (2007). Handbook of Psychophysiology. Cambridge University Press.
Hillary, F.G., & DeLuca, J. (2007). Functional Neuroimaging in Clinical Populations.
Kanwisher, N., & Duncan, J. (2004). Functional Neuroimaging of Visual Cognition.
Silbersweig, D., & Stern, E. (2001). Functional Neuroimaging and Neuropsychology Fundamentals and Practice.
Thatcher, R, W. (1994). Functional Neuroimaging: Technical Foundations.
== External links ==
The American Society of Neuroimaging (ASN).
UCLA Neuroimaging Training Program.
BrainMapping.org, a free BrainMapping community information portal
The Whole Brain Atlas @ Harvard | Wikipedia/Functional_neuroimaging |
The development of the nervous system, or neural development (neurodevelopment), refers to the processes that generate, shape, and reshape the nervous system of animals, from the earliest stages of embryonic development to adulthood. The field of neural development draws on both neuroscience and developmental biology to describe and provide insight into the cellular and molecular mechanisms by which complex nervous systems develop, from nematodes and fruit flies to mammals.
Defects in neural development can lead to malformations such as holoprosencephaly, and a wide variety of neurological disorders including limb paresis and paralysis, balance and vision disorders, and seizures, and in humans other disorders such as Rett syndrome, Down syndrome and intellectual disability.
== Vertebrate brain development ==
The vertebrate central nervous system (CNS) is derived from the ectoderm—the outermost germ layer of the embryo. A part of the dorsal ectoderm becomes specified to neural ectoderm – neuroectoderm that forms the neural plate along the dorsal side of the embryo. This is a part of the early patterning of the embryo (including the invertebrate embryo) that also establishes an anterior-posterior axis. The neural plate is the source of the majority of neurons and glial cells of the CNS. The neural groove forms along the long axis of the neural plate, and the neural plate folds to give rise to the neural tube. This process is known as neurulation. When the tube is closed at both ends it is filled with embryonic cerebrospinal fluid. As the embryo develops, the anterior part of the neural tube expands and forms three primary brain vesicles, which become the forebrain (prosencephalon), midbrain (mesencephalon), and hindbrain (rhombencephalon). These simple, early vesicles enlarge and further divide into the telencephalon (future cerebral cortex and basal ganglia), diencephalon (future thalamus and hypothalamus), mesencephalon (future colliculi), metencephalon (future pons and cerebellum), and myelencephalon (future medulla). The CSF-filled central chamber is continuous from the telencephalon to the central canal of the spinal cord, and constitutes the developing ventricular system of the CNS. Embryonic cerebrospinal fluid differs from that formed in later developmental stages, and from adult CSF; it influences the behavior of neural precursors. Because the neural tube gives rise to the brain and spinal cord any mutations at this stage in development can lead to fatal deformities like anencephaly or lifelong disabilities like spina bifida. During this time, the walls of the neural tube contain neural stem cells, which drive brain growth as they divide many times. Gradually some of the cells stop dividing and differentiate into neurons and glial cells, which are the main cellular components of the CNS. The newly generated neurons migrate to different parts of the developing brain to self-organize into different brain structures. Once the neurons have reached their regional positions, they extend axons and dendrites, which allow them to communicate with other neurons via synapses. Synaptic communication between neurons leads to the establishment of functional neural circuits that mediate sensory and motor processing, and underlie behavior.
== Induction ==
During early embryonic development of the vertebrate, the dorsal ectoderm becomes specified to give rise to the epidermis and the nervous system; a part of the dorsal ectoderm becomes specified to neural ectoderm to form the neural plate which gives rise to the nervous system. The conversion of undifferentiated ectoderm to neuroectoderm requires signals from the mesoderm. At the onset of gastrulation presumptive mesodermal cells move through the dorsal blastopore lip and form a layer of mesoderm in between the endoderm and the ectoderm. Mesodermal cells migrate along the dorsal midline to give rise to the notochord that develops into the vertebral column. Neuroectoderm overlying the notochord develops into the neural plate in response to a diffusible signal produced by the notochord. The remainder of the ectoderm gives rise to the epidermis. The ability of the mesoderm to convert the overlying ectoderm into neural tissue is called neural induction.
In the early embryo, the neural plate folds outwards to form the neural groove. Beginning in the future neck region, the neural folds of this groove close to create the neural tube. The formation of the neural tube from the ectoderm is called neurulation. The ventral part of the neural tube is called the basal plate; the dorsal part is called the alar plate. The hollow interior is called the neural canal, and the open ends of the neural tube, called the neuropores, close off.
A transplanted blastopore lip can convert ectoderm into neural tissue and is said to have an inductive effect. Neural inducers are molecules that can induce the expression of neural genes in ectoderm explants without inducing mesodermal genes as well. Neural induction is often studied in Xenopus embryos since they have a simple body plan and there are good markers to distinguish between neural and non-neural tissue. Examples of neural inducers are the molecules noggin and chordin.
When embryonic ectodermal cells are cultured at low density in the absence of mesodermal cells they undergo neural differentiation (express neural genes), suggesting that neural differentiation is the default fate of ectodermal cells. In explant cultures (which allow direct cell-cell interactions) the same cells differentiate into epidermis. This is due to the action of BMP4 (a TGF-β family protein) that induces ectodermal cultures to differentiate into epidermis. During neural induction, noggin and chordin are produced by the dorsal mesoderm (notochord) and diffuse into the overlying ectoderm to inhibit the activity of BMP4. This inhibition of BMP4 causes the cells to differentiate into neural cells. Inhibition of TGF-β and BMP (bone morphogenetic protein) signaling can efficiently induce neural tissue from pluripotent stem cells.
== Regionalization ==
In a later stage of development the superior part of the neural tube flexes at the level of the future midbrain—the mesencephalon, at the mesencephalic flexure or cephalic flexure. Above the mesencephalon is the prosencephalon (future forebrain) and beneath it is the rhombencephalon (future hindbrain).
The alar plate of the prosencephalon expands to form the telencephalon which gives rise to the cerebral hemispheres, whilst its basal plate becomes the diencephalon. The optical vesicle (which eventually become the optic nerve, retina and iris) forms at the basal plate of the prosencephalon.
== Patterning ==
In chordates, dorsal ectoderm forms all neural tissue and the nervous system. Patterning occurs due to specific environmental conditions - different concentrations of signaling molecules
=== Dorsoventral axis ===
The ventral half of the neural plate is controlled by the notochord, which acts as the 'organiser'. The dorsal half is controlled by the ectoderm plate, which flanks either side of the neural plate.
Ectoderm follows a default pathway to become neural tissue. Evidence for this comes from single, cultured cells of ectoderm, which go on to form neural tissue. This is postulated to be because of a lack of BMPs, which are blocked by the organiser. The organiser may produce molecules such as follistatin, noggin and chordin that inhibit BMPs.
The ventral neural tube is patterned by sonic hedgehog (Shh) from the notochord, which acts as the inducing tissue. Notochord-derived Shh signals to the floor plate, and induces Shh expression in the floor plate. Floor plate-derived Shh subsequently signals to other cells in the neural tube, and is essential for proper specification of ventral neuron progenitor domains. Loss of Shh from the notochord and/or floor plate prevents proper specification of these progenitor domains. Shh binds Patched1, relieving Patched-mediated inhibition of Smoothened, leading to activation of the Gli family of transcription factors (GLI1, GLI2, and GLI3).
In this context Shh acts as a morphogen - it induces cell differentiation dependent on its concentration. At low concentrations it forms ventral interneurons, at higher concentrations it induces motor neuron development, and at highest concentrations it induces floor plate differentiation. Failure of Shh-modulated differentiation causes holoprosencephaly.
The dorsal neural tube is patterned by BMPs from the epidermal ectoderm flanking the neural plate. These induce sensory interneurons by activating Sr/Thr kinases and altering SMAD transcription factor levels.
=== Rostrocaudal (Anteroposterior) axis ===
Signals that control anteroposterior neural development include FGF and retinoic acid, which act in the hindbrain and spinal cord. The hindbrain, for example, is patterned by Hox genes, which are expressed in overlapping domains along the anteroposterior axis under the control of retinoic acid. The 3′ (3 prime end) genes in the Hox cluster are induced by retinoic acid in the hindbrain, whereas the 5′ (5 prime end) Hox genes are not induced by retinoic acid and are expressed more posteriorly in the spinal cord. Hoxb-1 is expressed in rhombomere 4 and gives rise to the facial nerve. Without this Hoxb-1 expression, a nerve similar to the trigeminal nerve arises.
== Neurogenesis ==
Neurogenesis is the process by which neurons are generated from neural stem cells and progenitor cells. Neurons are 'post-mitotic', meaning that they will never divide again for the lifetime of the organism.
Epigenetic modifications play a key role in regulating gene expression in differentiating neural stem cells and are critical for cell fate determination in the developing and adult mammalian brain. Epigenetic modifications include DNA cytosine methylation to form 5-methylcytosine and 5-methylcytosine demethylation. DNA cytosine methylation is catalyzed by DNA methyltransferases (DNMTs). Methylcytosine demethylation is catalyzed in several sequential steps by TET enzymes that carry out oxidative reactions (e.g. 5-methylcytosine to 5-hydroxymethylcytosine) and enzymes of the DNA base excision repair (BER) pathway.
== Neuronal migration ==
Neuronal migration is the method by which neurons travel from their origin or birthplace to their final position in the brain. There are several ways they can do this, e.g. by radial migration or tangential migration. Sequences of radial migration (also known as glial guidance) and somal translocation have been captured by time-lapse microscopy.
=== Radial ===
Neuronal precursor cells proliferate in the ventricular zone of the developing neocortex, where the principal neural stem cell is the radial glial cell. The first postmitotic cells must leave the stem cell niche and migrate outward to form the preplate, which is destined to become Cajal–Retzius cells and subplate neurons. These cells do so by somal translocation. Neurons migrating with this mode of locomotion are bipolar and attach the leading edge of the process to the pia. The soma is then transported to the pial surface by nucleokinesis, a process by which a microtubule "cage" around the nucleus elongates and contracts in association with the centrosome to guide the nucleus to its final destination.
Radial glial cells, whose fibers serve as a scaffolding for migrating cells and a means of radial communication mediated by calcium dynamic activity, act as the main excitatory neuronal stem cell of the cerebral cortex or translocate to the cortical plate and differentiate either into astrocytes or neurons. Somal translocation can occur at any time during development.
Subsequent waves of neurons split the preplate by migrating along radial glial fibres to form the cortical plate. Each wave of migrating cells travel past their predecessors forming layers in an inside-out manner, meaning that the youngest neurons are the closest to the surface. It is estimated that glial guided migration represents 90% of migrating neurons in human and about 75% in rodents.
=== Tangential ===
Most interneurons migrate tangentially through multiple modes of migration to reach their appropriate location in the cortex. An example of tangential migration is the movement of interneurons from the ganglionic eminence to the cerebral cortex. One example of ongoing tangential migration in a mature organism, observed in some animals, is the rostral migratory stream connecting subventricular zone and olfactory bulb.
=== Axophilic ===
Many neurons migrating along the anterior-posterior axis of the body use existing axon tracts to migrate along; this is called axophilic migration. An example of this mode of migration is in GnRH-expressing neurons, which make a long journey from their birthplace in the nose, through the forebrain, and into the hypothalamus. Many of the mechanisms of this migration have been worked out, starting with the extracellular guidance cues that trigger intracellular signaling. These intracellular signals, such as calcium signaling, lead to actin and microtubule cytoskeletal dynamics, which produce cellular forces that interact with the extracellular environment through cell adhesion proteins to cause the movement of these cells.
=== Multipolar ===
There is also a method of neuronal migration called multipolar migration. This is seen in multipolar cells, which in the human, are abundantly present in the cortical intermediate zone. They do not resemble the cells migrating by locomotion or somal translocation. Instead these multipolar cells express neuronal markers and extend multiple thin processes in various directions independently of the radial glial fibers.
== Neurotrophic factors ==
The survival of neurons is regulated by survival factors, called trophic factors. The neurotrophic hypothesis was formulated by Victor Hamburger and Rita Levi Montalcini based on studies of the developing nervous system. Victor Hamburger discovered that implanting an extra limb in the developing chick led to an increase in the number of spinal motor neurons. Initially he thought that the extra limb was inducing proliferation of motor neurons, but he and his colleagues later showed that there was a great deal of motor neuron death during normal development, and the extra limb prevented this cell death. According to the neurotrophic hypothesis, growing axons compete for limiting amounts of target-derived trophic factors and axons that fail to receive sufficient trophic support die by apoptosis. It is now clear that factors produced by a number of sources contribute to neuronal survival.
Nerve Growth Factor (NGF): Rita Levi Montalcini and Stanley Cohen purified the first trophic factor, Nerve Growth Factor (NGF), for which they received the Nobel Prize. There are three NGF-related trophic factors: BDNF, NT3, and NT4, which regulate survival of various neuronal populations. The Trk proteins act as receptors for NGF and related factors. Trk is a receptor tyrosine kinase. Trk dimerization and phosphorylation leads to activation of various intracellular signaling pathways including the MAP kinase, Akt, and PKC pathways.
CNTF: Ciliary neurotrophic factor is another protein that acts as a survival factor for motor neurons. CNTF acts via a receptor complex that includes CNTFRα, GP130, and LIFRβ. Activation of the receptor leads to phosphorylation and recruitment of the JAK kinase, which in turn phosphorylates LIFRβ. LIFRβ acts as a docking site for the STAT transcription factors. JAK kinase phosphorylates STAT proteins, which dissociate from the receptor and translocate to the nucleus to regulate gene expression.
GDNF: Glial derived neurotrophic factor is a member of the TGFb family of proteins, and is a potent trophic factor for striatal neurons. The functional receptor is a heterodimer, composed of type 1 and type 2 receptors. Activation of the type 1 receptor leads to phosphorylation of Smad proteins, which translocate to the nucleus to activate gene expression.
== Synapse formation ==
=== Neuromuscular junction ===
Much of our understanding of synapse formation comes from studies at the neuromuscular junction. The transmitter at this synapse is acetylcholine. The acetylcholine receptor (AchR) is present at the surface of muscle cells before synapse formation. The arrival of the nerve induces clustering of the receptors at the synapse. McMahan and Sanes showed that the synaptogenic signal is concentrated at the basal lamina. They also showed that the synaptogenic signal is produced by the nerve, and they identified the factor as Agrin. Agrin induces clustering of AchRs on the muscle surface and synapse formation is disrupted in agrin knockout mice. Agrin transduces the signal via MuSK receptor to rapsyn. Fischbach and colleagues showed that receptor subunits are selectively transcribed from nuclei next to the synaptic site. This is mediated by neuregulins.
In the mature synapse each muscle fiber is innervated by one motor neuron. However, during development, many of the fibers are innervated by multiple axons. Lichtman and colleagues have studied the process of synapses elimination. This is an activity-dependent event. Partial blockage of the receptor leads to retraction of corresponding presynaptic terminals. Later they used a connectomic approach, i.e., tracing out all the connections between motor neurons and muscle fibers, to characterize developmental synapse elimination on the level of a full circuit. Analysis confirmed the massive rewiring, 10-fold decrease in the number of synapses, that takes place as axons prune their motor units but add more synaptic areas at the NMJs with which they remain in contact.
=== CNS synapses ===
Agrin appears not to be a central mediator of CNS synapse formation and there is active interest in identifying signals that mediate CNS synaptogenesis. Neurons in culture develop synapses that are similar to those that form in vivo, suggesting that synaptogenic signals can function properly in vitro. CNS synaptogenesis studies have focused mainly on glutamatergic synapses. Imaging experiments show that dendrites are highly dynamic during development and often initiate contact with axons. This is followed by recruitment of postsynaptic proteins to the site of contact. Stephen Smith and colleagues have shown that contact initiated by dendritic filopodia can develop into synapses.
Induction of synapse formation by glial factors: Barres and colleagues made the observation that factors in glial conditioned media induce synapse formation in retinal ganglion cell cultures. Synapse formation in the CNS is correlated with astrocyte differentiation suggesting that astrocytes might provide a synaptogenic factor. The identity of the astrocytic factors is not yet known.
Neuroligins and SynCAM as synaptogenic signals: Sudhof, Serafini, Scheiffele and colleagues have shown that neuroligins and SynCAM can act as factors that induce presynaptic differentiation. Neuroligins are concentrated at the postsynaptic site and act via neurexins concentrated in the presynaptic axons. SynCAM is a cell adhesion molecule that is present in both pre- and post-synaptic membranes.
=== Assembly of neural circuits ===
The processes of neuronal migration, differentiation and axon guidance are generally believed to be activity-independent mechanisms and rely on hard-wired genetic programs in the neurons themselves. Research findings however have implicated a role for activity-dependent mechanisms in mediating some aspects of these processes such as the rate of neuronal migration, aspects of neuronal differentiation and axon pathfinding. Activity-dependent mechanisms influence neural circuit development and are crucial for laying out early connectivity maps and the continued refinement of synapses which occurs during development. There are two distinct types of neural activity we observe in developing circuits -early spontaneous activity and sensory-evoked activity. Spontaneous activity occurs early during neural circuit development even when sensory input is absent and is observed in many systems such as the developing visual system, auditory system, motor system, hippocampus, cerebellum and neocortex.
Experimental techniques such as direct electrophysiological recording, fluorescence imaging using calcium indicators and optogenetic techniques have shed light on the nature and function of these early bursts of activity. They have distinct spatial and temporal patterns during development and their ablation during development has been known to result in deficits in network refinement in the visual system. In the immature retina, waves of spontaneous action potentials arise from the retinal ganglion cells and sweep across the retinal surface in the first few postnatal weeks. These waves are mediated by neurotransmitter acetylcholine in the initial phase and later on by glutamate. They are thought to instruct the formation of two sensory maps- the retinotopic map and eye-specific segregation. Retinotopic map refinement occurs in downstream visual targets in the brain-the superior colliculus (SC) and dorsal lateral geniculate nucleus (LGN). Pharmacological disruption and mouse models lacking the β2 subunit of the nicotinic acetylcholine receptor has shown that the lack of spontaneous activity leads to marked defects in retinotopy and eye-specific segregation.
Recent studies confirm that microglia, the resident immune cell of the brain, establish direct contacts with the cell bodies of developing neurons, and through these connections, regulate neurogenesis, migration, integration and the formation of neuronal networks in an activity-dependent manner.
In the developing auditory system, developing cochlea generate bursts of activity which spreads across the inner hair cells and spiral ganglion neurons which relay auditory information to the brain. ATP release from supporting cells triggers action potentials in inner hair cells. In the auditory system, spontaneous activity is thought to be involved in tonotopic map formation by segregating cochlear neuron axons tuned to high and low frequencies. In the motor system, periodic bursts of spontaneous activity are driven by excitatory GABA and glutamate during the early stages and by acetylcholine and glutamate at later stages. In the developing zebrafish spinal cord, early spontaneous activity is required for the formation of increasingly synchronous alternating bursts between ipsilateral and contralateral regions of the spinal cord and for the integration of new cells into the circuit. Motor neurons innervating the same twitch muscle fibers are thought to maintain synchronous activity which allows both neurons to remain in contact with the muscle fiber in adulthood. In the cortex, early waves of activity have been observed in the cerebellum and cortical slices. Once sensory stimulus becomes available, final fine-tuning of sensory-coding maps and circuit refinement begins to rely more and more on sensory-evoked activity as demonstrated by classic experiments about the effects of sensory deprivation during critical periods.
Contemporary diffusion-weighted MRI techniques may also uncover the macroscopic process of axonal development. The connectome can be constructed from diffusion MRI data: the vertices of the graph correspond to anatomically labelled gray matter areas, and two such vertices, say u and v, are connected by an edge if the tractography phase of the data processing finds an axonal fiber that connects the two areas, corresponding to u and v. Numerous braingraphs, computed from the Human Connectome Project can be downloaded from the http://braingraph.org site. The Consensus Connectome Dynamics (CCD) is a remarkable phenomenon that was discovered by continuously decreasing the minimum confidence-parameter at the graphical interface of the Budapest Reference Connectome Server. The Budapest Reference Connectome Server (http://connectome.pitgroup.org) depicts the cerebral connections of n=418 subjects with a frequency-parameter k: For any k=1,2,...,n one can view the graph of the edges that are present in at least k connectomes. If parameter k is decreased one-by-one from k=n through k=1 then more and more edges appear in the graph, since the inclusion condition is relaxed. The surprising observation is that the appearance of the edges is far from random: it resembles a growing, complex structure, like a tree or a shrub (visualized on the animation on the left).
It is hypothesized in that the growing structure copies the axonal development of the human brain: the earliest developing connections (axonal fibers) are common at most of the subjects, and the subsequently developing connections have larger and larger variance, because their variances are accumulated in the process of axonal development.
== Synapse elimination ==
Several motorneurons compete for each neuromuscular junction, but only one survives until adulthood. Competition in vitro has been shown to involve a limited neurotrophic substance that is released, or that neural activity infers advantage to strong post-synaptic connections by giving resistance to a toxin also released upon nerve stimulation. In vivo, it is suggested that muscle fibres select the strongest neuron through a retrograde signal or that activity-dependent synapse elimination mechanisms determine the identity of the "winning" axon at a motor endplate.
== Mapping ==
Brain mapping can show how an animal's brain changes throughout its lifetime. As of 2021, scientists mapped and compared the whole brains of eight C. elegans worms across their development on the neuronal level and the complete wiring of a single mammalian muscle from birth to adulthood.
== Adult neurogenesis ==
Neurogenesis also occurs in specific parts of the adult brain.
== See also ==
== References ==
== External links ==
Neural Development (peer-reviewed open access journal).
Translating Neurodevelopmental Time Across Mammalian Species
The Child's Developing Brain
Brain Development
How poverty might change the brain
The Teenage Brain | Wikipedia/Developmental_neuroscience |
Golgi's method is a silver staining technique that is used to visualize nervous tissue under light microscopy. The method was discovered by Camillo Golgi, an Italian physician and scientist, who published the first picture made with the technique in 1873. It was initially named the black reaction (la reazione nera) by Golgi, but it became better known as the Golgi stain or later, Golgi method.
Golgi staining was used by Spanish neuroanatomist Santiago Ramón y Cajal (1852–1934) to discover a number of novel facts about the organization of the nervous system, inspiring the birth of the neuron doctrine. Ultimately, Ramón y Cajal improved the technique by using a method he termed "double impregnation". Ramón y Cajal's staining technique, still in use, is called Cajal's stain.
== Mechanism ==
The cells in nervous tissue are densely packed, and little information on their structures and interconnections can be obtained if all the cells are stained. Furthermore, the thin filamentary extensions of neural cells, including the axon and the dendrites of neurons, are too slender and transparent to be seen with normal staining techniques. Golgi's method stains a limited number of cells at random in their entirety. The mechanism by which this happens is still largely unknown. Dendrites, as well as the cell soma, are clearly stained in brown and black and can be followed in their entire length, which allowed neuroanatomists to track connections between neurons and to make visible the complex networking structure of many parts of the brain and spinal cord.
Golgi's staining is achieved by impregnating aldehyde-fixed nervous tissue with potassium dichromate and silver nitrate. Cells thus stained are filled by microcrystallization of silver chromate.
== Technique ==
According to SynapseWeb, this is the recipe for Golgi's staining technique:
Immerse a block (approx. 10x5 mm) of formaldehyde-fixed (or paraformaldehyde- glutaraldehyde-perfused) brain tissue into a 2% aqueous solution of potassium dichromate for 2 days
Dry the block shortly with filter paper.
Immerse the block into a 2% aqueous solution of silver nitrate for another 2 days.
Cut sections approx. 20–100 μm thick.
Dehydrate quickly in ethanol, clear and mount (e.g., into Depex or Enthalan).
This technique has since been refined to substitute the silver precipitate with gold by immersing the sample in gold chloride then oxalic acid, followed by removal of the silver by sodium thiosulphate. This preserves a greater degree of fine structure with the ultrastructural details marked by small particles of gold.
== Quote ==
Ramón y Cajal said of the Golgi method:
I expressed the surprise which I experienced upon seeing with my own eyes the wonderful revelatory powers of the chrome-silver reaction and the absence of any excitement in the scientific world aroused by its discovery.
Recuerdos de mi vida, Vol. 2, Historia de mi labor científica. Madrid: Moya, 1917, p. 76.
== References ==
== External links ==
Photomicrograph of a cortex cell stained with Golgi's. IHC Image Gallery.
Golgi impregnations. Images of the brain of flies.
Visualization of dendritic spines using Golgi Method. SynapseWeb. Includes a time-lapse study of Golgi impregnation.
Berrebi, Albert: Cell Biology of Neurons: Structure and Methods of Study. (in PDF)
Stained brain slice images which include the "Golgi-stained neurons" at the BrainMaps project | Wikipedia/Golgi's_method |
Magnetoencephalography (MEG) is a functional neuroimaging technique for mapping brain activity by recording magnetic fields produced by electrical currents occurring naturally in the brain, using very sensitive magnetometers. Arrays of SQUIDs (superconducting quantum interference devices) are currently the most common magnetometer, while the SERF (spin exchange relaxation-free) magnetometer is being investigated for future machines. Applications of MEG include basic research into perceptual and cognitive brain processes, localizing regions affected by pathology before surgical removal, determining the function of various parts of the brain, and neurofeedback. This can be applied in a clinical setting to find locations of abnormalities as well as in an experimental setting to simply measure brain activity.
== History ==
MEG signals were first measured by University of Illinois physicist David Cohen in 1968, before the availability of the SQUID, using a copper induction coil as the detector. To reduce the magnetic background noise, the measurements were made in a magnetically shielded room. The coil detector was barely sensitive enough, resulting in poor, noisy MEG measurements that were difficult to use. Later, Cohen built a much better shielded room at MIT, and used one of the first SQUID detectors, just developed by James E. Zimmerman, a researcher at Ford Motor Company, to again measure MEG signals. This time the signals were almost as clear as those of EEG. This stimulated the interest of physicists who had been looking for uses of SQUIDs. Subsequent to this, various types of spontaneous and evoked MEGs began to be measured.
At first, a single SQUID detector was used to successively measure the magnetic field at a number of points around the subject's head. This was cumbersome, and, in the 1980s, MEG manufacturers began to arrange multiple sensors into arrays to cover a larger area of the head. Present-day MEG arrays are set in a helmet-shaped vacuum flask that typically contain 300 sensors, covering most of the head. In this way, MEGs of a subject or patient can now be accumulated rapidly and efficiently.
Recent developments attempt to increase portability of MEG scanners by using spin exchange relaxation-free (SERF) magnetometers. SERF magnetometers are relatively small, as they do not require bulky cooling systems to operate. At the same time, they feature sensitivity equivalent to that of SQUIDs. In 2012, it was demonstrated that MEG could work with a chip-scale atomic magnetometer (CSAM, type of SERF). More recently, in 2017, researchers built a working prototype that uses SERF magnetometers installed into portable individually 3D-printed helmets, which they noted in interviews could be replaced with something easier to use in future, such as a bike helmet.
== The basis of the MEG signal ==
Synchronized neuronal currents induce weak magnetic fields. The brain's magnetic field, measuring at 10 femtotesla (fT) for cortical activity and 103 fT for the human alpha rhythm, is considerably smaller than the ambient magnetic noise in an urban environment, which is on the order of 108 fT or 0.1 μT. The essential problem of biomagnetism is, thus, the weakness of the signal relative to the sensitivity of the detectors, and to the competing environmental noise.
The MEG (and EEG) signals derive from the net effect of ionic currents flowing in the dendrites of neurons during synaptic transmission. In accordance with Maxwell's equations, any electrical current will produce a magnetic field, and it is this field that is measured. The net currents can be thought of as current dipoles, i.e. currents with a position, orientation, and magnitude, but no spatial extent. According to the right-hand rule, a current dipole gives rise to a magnetic field that points around the axis of its vector component.
To generate a signal that is detectable, approximately 50,000 active neurons are needed. Since current dipoles must have similar orientations to generate magnetic fields that reinforce each other, it is often the layer of pyramidal cells, which are situated perpendicular to the cortical surface, that gives rise to measurable magnetic fields. Bundles of these neurons that are orientated tangentially to the scalp surface project measurable portions of their magnetic fields outside of the head, and these bundles are typically located in the sulci. Researchers are experimenting with various signal processing methods in the search for methods that detect deep brain (i.e., non-cortical) signal, but no clinically useful method is currently available.
It is worth noting that action potentials do not usually produce an observable field, mainly because the currents associated with action potentials flow in opposite directions and the magnetic fields cancel out. However, action fields have been measured from peripheral nerve system.
== Magnetic shielding ==
Since the magnetic signals emitted by the brain are on the order of a few femtoteslas, shielding from external magnetic signals, including the Earth's magnetic field, is necessary. Appropriate magnetic shielding can be obtained by constructing rooms made of aluminium and mu-metal for reducing high-frequency and low-frequency noise, respectively.
=== Magnetically shielded room (MSR) ===
A magnetically shielded room (MSR) model consists of three nested main layers. Each of these layers is made of a pure aluminium layer plus a high-permeability ferromagnetic layer, similar in composition to molybdenum permalloy. The ferromagnetic layer is supplied as 1 mm sheets, while the innermost layer is composed of four sheets in close contact, and the outer two layers are composed of three sheets each. Magnetic continuity is maintained by overlay strips. Insulating washers are used in the screw assemblies to ensure that each main layer is electrically isolated. This helps eliminate radio frequency radiation, which would degrade SQUID performance. Electrical continuity of the aluminium is also maintained by aluminium overlay strips to ensure AC eddy current shielding, which is important at frequencies greater than 1 Hz. The junctions of the inner layer are often electroplated with silver or gold to improve conductivity of the aluminium layers.
=== Active shielding system ===
Active systems are designed for three-dimensional noise cancellation. To implement an active system, low-noise fluxgate magnetometers are mounted at the center of each surface and oriented orthogonally to it. This negatively feeds a DC amplifier through a low-pass network with a slow falloff to minimize positive feedback and oscillation. Built into the system are shaking and degaussing wires. Shaking wires increase the magnetic permeability, while the permanent degaussing wires are applied to all surfaces of the inner main layer to degauss the surfaces. Moreover, noise cancellation algorithms can reduce both low-frequency and high-frequency noise. Modern systems have a noise floor of around 2–3 fT/Hz0.5 above 1 Hz.
== Source localization ==
=== The inverse problem ===
The challenge posed by MEG is to determine the location of electric activity within the brain from the induced magnetic fields outside the head. Problems such as this, where model parameters (the location of the activity) have to be estimated from measured data (the SQUID signals) are referred to as inverse problems (in contrast to forward problems where the model parameters (e.g. source location) are known and the data (e.g. the field at a given distance) is to be estimated.) The primary difficulty is that the inverse problem does not have a unique solution (i.e., there are infinite possible "correct" answers), and the problem of defining the "best" solution is itself the subject of intensive research. Possible solutions can be derived using models involving prior knowledge of brain activity.
The source models can be either over-determined or under-determined. An over-determined model may consist of a few point-like sources ("equivalent dipoles"), whose locations are then estimated from the data. Under-determined models may be used in cases where many different distributed areas are activated ("distributed source solutions"): there are infinitely many possible current distributions explaining the measurement results, but the most likely is selected. Localization algorithms make use of given source and head models to find a likely location for an underlying focal field generator.
One type of localization algorithm for overdetermined models operates by expectation-maximization: the system is initialized with a first guess. A loop is started, in which a forward model is used to simulate the magnetic field that would result from the current guess. The guess is adjusted to reduce the discrepancy between the simulated field and the measured field. This process is iterated until convergence.
Another common technique is beamforming, wherein a theoretical model of the magnetic field produced by a given current dipole is used as a prior, along with second-order statistics of the data in the form of a covariance matrix, to calculate a linear weighting of the sensor array (the beamformer) via the Backus-Gilbert inverse. This is also known as a linearly constrained minimum variance (LCMV) beamformer. When the beamformer is applied to the data, it produces an estimate of the power in a "virtual channel" at the source location.
The extent to which the constraint-free MEG inverse problem is ill-posed cannot be overemphasized. If one's goal is to estimate the current density within the human brain with say a 5mm resolution then it is well established that the vast majority of the information needed to perform a unique inversion must come not from the magnetic field measurement but rather from the constraints applied to the problem. Furthermore, even when a unique inversion is possible in the presence of such constraints said inversion can be unstable. These conclusions are easily deduced from published works.
=== Magnetic source imaging ===
The source locations can be combined with magnetic resonance imaging (MRI) images to create magnetic source images (MSI). The two sets of data are combined by measuring the location of a common set of fiducial points marked during MRI with lipid markers and marked during MEG with electrified coils of wire that give off magnetic fields. The locations of the fiducial points in each data set are then used to define a common coordinate system so that superimposing the functional MEG data onto the structural MRI data ("coregistration") is possible.
A criticism of the use of this technique in clinical practice is that it produces colored areas with definite boundaries superimposed upon an MRI scan: the untrained viewer may not realize that the colors do not represent a physiological certainty, not because of the relatively low spatial resolution of MEG, but rather some inherent uncertainty in the probability cloud derived from statistical processes. However, when the magnetic source image corroborates other data, it can be of clinical utility.
=== Dipole model source localization ===
A widely accepted source-modeling technique for MEG involves calculating a set of equivalent current dipoles (ECDs), which assumes the underlying neuronal sources to be focal. This dipole fitting procedure is non-linear and over-determined, since the number of unknown dipole parameters is smaller than the number of MEG measurements. Automated multiple dipole model algorithms such as multiple signal classification (MUSIC) and multi-start spatial and temporal modeling (MSST) are applied to the analysis of MEG responses. The limitations of dipole models for characterizing neuronal responses are (1) difficulties in localizing extended sources with ECDs, (2) problems with accurately estimating the total number of dipoles in advance, and (3) dependency on dipole location, especially depth in the brain.
=== Distributed source models ===
Unlike multiple-dipole modeling, distributed source models divide the source space into a grid containing a large number of dipoles. The inverse problem is to obtain the dipole moments for the grid nodes. As the number of unknown dipole moments is much greater than the number of MEG sensors, the inverse solution is highly underdetermined, so additional constraints are needed to reduce ambiguity of the solution. The primary advantage of this approach is that no prior specification of the source model is necessary. However, the resulting distributions may be difficult to interpret, because they only reflect a "blurred" (or even distorted) image of the true neuronal source distribution. The matter is complicated by the fact that spatial resolution depends strongly on various parameters such as brain area, depth, orientation, number of sensors etc.
=== Independent component analysis (ICA) ===
Independent component analysis (ICA) is another signal processing solution that separates different signals that are statistically independent in time. It is primarily used to remove artifacts such as blinking, eye muscle movement, facial muscle artifacts, cardiac artifacts, etc. from MEG and EEG signals that may be contaminated with outside noise. However, ICA has poor resolution of highly correlated brain sources.
== Use in the field ==
In research, MEG's primary use is the measurement of time courses of activity. MEG can resolve events with a precision of 10 milliseconds or faster, while functional magnetic resonance imaging (fMRI), which depends on changes in blood flow, can at best resolve events with a precision of several hundred milliseconds. MEG also accurately pinpoints sources in primary auditory, somatosensory, and motor areas. For creating functional maps of human cortex during more complex cognitive tasks, MEG is most often combined with fMRI, as the methods complement each other. Neuronal (MEG) and hemodynamic fMRI data do not necessarily agree, in spite of the tight relationship between local field potentials (LFP) and blood oxygenation level-dependent (BOLD) signals. MEG and BOLD signals may originate from the same source (though the BOLD signals are filtered through the hemodynamic response).
MEG is also being used to better localize responses in the brain. The openness of the MEG setup allows external auditory and visual stimuli to be easily introduced. Some movement by the subject is also possible as long as it does not jar the subject's head. The responses in the brain before, during, and after the introduction of such stimuli/movement can then be mapped with greater spatial resolution than was previously possible with EEG. Psychologists are also taking advantage of MEG neuroimaging to better understand relationships between brain function and behavior. For example, a number of studies have been done comparing the MEG responses of patients with psychological troubles to control patients. There has been great success isolating unique responses in patients with schizophrenia, such as auditory gating deficits to human voices. MEG is also being used to correlate standard psychological responses, such as the emotional dependence of language comprehension.
Recent studies have reported successful classification of patients with multiple sclerosis, Alzheimer's disease, schizophrenia, Sjögren's syndrome, chronic alcoholism, facial pain and thalamocortical dysrhythmias. MEG can be used to distinguish these patients from healthy control subjects, suggesting a future role of MEG in diagnostics.
A large part of the difficulty and cost of using MEG is the need for manual analysis of the data. Progress has been made in analysis by computer, comparing a patient's scans with those drawn from a large database of normal scans, with the potential to reduce cost greatly.
=== Brain connectivity and neural oscillations ===
Based on its perfect temporal resolution, magnetoencephalography (MEG) is now heavily used to study oscillatory activity in the brain, both in terms of local neural synchrony and cross-area synchronisation. As an example for local neural synchrony, MEG has been used to investigate alpha rhythms in various targeted brain regions, such as in visual or auditory cortex. Other studies have used MEG to study the neural interactions between different brain regions (e.g., between frontal cortex and visual cortex). Magnetoencephalography can also be used to study changes in neural oscillations across different stages of consciousness, such as in sleep.
=== Focal epilepsy ===
The clinical uses of MEG are in detecting and localizing pathological activity in patients with epilepsy, and in localizing eloquent cortex for surgical planning in patients with brain tumors or intractable epilepsy. The goal of epilepsy surgery is to remove the epileptogenic tissue while sparing healthy brain areas. Knowing the exact position of essential brain regions (such as the primary motor cortex and primary sensory cortex, visual cortex, and areas involved in speech production and comprehension) helps to avoid surgically induced neurological deficits. Direct cortical stimulation and somatosensory evoked potentials recorded on electrocorticography (ECoG) are considered the gold standard for localizing essential brain regions. These procedures can be performed either intraoperatively or from chronically indwelling subdural grid electrodes. Both are invasive.
Noninvasive MEG localizations of the central sulcus obtained from somatosensory evoked magnetic fields show strong agreement with these invasive recordings. MEG studies assist in clarification of the functional organization of primary somatosensory cortex and to delineate the spatial extent of hand somatosensory cortex by stimulation of the individual digits. This agreement between invasive localization of cortical tissue and MEG recordings shows the effectiveness of MEG analysis and indicates that MEG may substitute invasive procedures in the future.
=== Fetal ===
MEG has been used to study cognitive processes such as vision, audition, and language processing in fetuses and newborns. Only two bespoke MEG systems, designed specifically for fetal recordings, operate worldwide. The first was installed at the University of Arkansas in 2000, and the second was installed at the University of Tübingen in 2008. Both devices are referred to as SQUID arrays for reproductive assessment (SARA) and utilize a concave sensor array whose shape compliments the abdomen of a pregnant woman. Fetal recordings of cortical activity are feasible with a SARA device from a gestational age of approximately 25 weeks onward until birth. Although built for fetal recordings, SARA systems can also record from infants placed in a cradle head-first toward the sensory array. A third high density custom-made unit with similar whole abdomen coverage has been installed in 2002 at the University of Kansas Medical Center to assess fetal electrophysiology. While only a small number of devices worldwide are capable of fetal MEG recordings as of 2023, the proliferation of optically pumped magnetometers for MEG in neuroscience research will likely result in a greater number of research centers capable of recording and publishing fetal MEG data in the near future.
=== Traumatic brain injury ===
MEG can be used to identify traumatic brain injury, which is particularly common among soldiers exposed to explosions. Such injuries are not easily diagnosed by other methods, as the symptoms (e.g. sleep disturbances, memory problems) overlap with those from frequent co-comorbidities such as post-traumatic stress disorder (PTSD).
== Comparison with related techniques ==
MEG has been in development since the 1960s but has been greatly aided by recent advances in computing algorithms and hardware, and promises improved spatial resolution coupled with extremely high temporal resolution (better than 1 ms). Since the MEG signal is a direct measure of neuronal activity, its temporal resolution is comparable with that of intracranial electrodes.
MEG complements other brain activity measurement techniques such as electroencephalography (EEG), positron emission tomography (PET), and fMRI. Its strengths consist in independence of head geometry compared to EEG (unless ferromagnetic implants are present), non-invasiveness, use of no ionizing radiation, as opposed to PET and high temporal resolution as opposed to fMRI.
=== MEG in comparison to EEG ===
Although EEG and MEG signals originate from the same neurophysiological processes, there are important differences. Magnetic fields are less distorted than electric fields by the skull and scalp, which results in a better spatial resolution of the MEG. Whereas scalp EEG is sensitive to both tangential and radial components of a current source in a spherical volume conductor, MEG detects only its tangential components. Scalp EEG can, therefore, detect activity both in the sulci and at the top of the cortical gyri, whereas MEG is most sensitive to activity originating in sulci. EEG is, therefore, sensitive to activity in more brain areas, but activity that is visible in MEG can also be localized with more accuracy.
Scalp EEG is sensitive to extracellular volume currents produced by postsynaptic potentials. MEG detects intracellular currents associated primarily with these synaptic potentials because the field components generated by volume currents tend to cancel out in a spherical volume conductor. The decay of magnetic fields as a function of distance is more pronounced than for electric fields. Therefore, MEG is more sensitive to superficial cortical activity, which makes it useful for the study of neocortical epilepsy. Finally, MEG is reference-free, while scalp EEG relies on a reference that, when active, makes interpretation of the data difficult.
== See also ==
== References ==
== Further reading == | Wikipedia/Magnetoencephalography |
Hydrolyzed vegetable protein (HVP) products are foodstuffs obtained by the hydrolysis of protein, and have a meaty, savory taste similar to broth (bouillon).
Regarding the production process, a distinction can be made between acid-hydrolyzed vegetable protein (aHVP), enzymatically produced HVP, and other seasonings, e.g., fermented soy sauce. Hydrolyzed vegetable protein products are particularly used to round off the taste of soups, sauces, meat products, snacks, and other dishes, as well as for the production of ready-to-cook soups and bouillons.
== History ==
Food technologists have long known that protein hydrolysis produces a meat bouillon-like odor and taste. Hydrolysates have been a part of the human diet for centuries, notably in the form of fermented soy sauce, or Shoyu. Shoyu, traditionally made from wheat and soy protein, has been produced in Japan for over 1,500 years, following its introduction from mainland China. The origins of producing these materials through the acid hydrolysis of protein (aHVP) can be traced back to the scarcity and economic challenges of obtaining meat extracts during the Napoleonic wars.
In 1831, Berzelius obtained products having a meat bouillon taste when hydrolysing proteins with hydrochloric acid. Julius Maggi produced acid-catalyzed hydrolyzed vegetable protein industrially for the first time in 1886.
In 1906, Fischer found that amino acids contributed to the specific taste. In 1954, D. Phillips found that the bouillon odor required the presence of proteins containing threonine. Another important substance that gives a characteristic taste is glutamic acid.
== Manufacture ==
Almost all products rich in protein are suitable for the production of HVP. Today, it is made mainly from protein resources of vegetable origin, such as defatted oil seeds (soybean meal, grapeseed meal) and protein from maize (Corn gluten meal), wheat (gluten), pea, and rice. The process and the feedstock determines the organoleptic properties of the end product. Proteins consist of chains of amino acids joined through amide bonds. When subjected to hydrolysis (hydrolyzed), the protein is broken down into its component amino acids.
In aHVP, hydrochloric acid is used for hydrolysis. The remaining acid is then neutralized by mixing with an alkali such as sodium hydroxide, which leaves behind table salt, which comprises up to 20% of the final product (acid-hydrolyzed vegetable protein, aHVP).
In enzymatic HVP (eHVP), proteases are used to break down the proteins under a more neutral pH and lower temperatures. The amount of salt is greatly reduced.
Because of the different processing conditions, the two types of HVP have different sensory profiles. aHVP is usually dark-brown in color and has a strong savory flavor, whereas eHVP usually is lighter in color and has a mild savory flavor.
=== Acid hydrolysis ===
Acid hydrolysates are produced from various edible protein sources, with soy, corn, wheat, and casein being the most common. For the production of aHVP, the proteins are hydrolyzed by cooking with a diluted (15–20%) hydrochloric acid, at a temperature between 90 and 120 °C for up to 8 hours. After cooling, the hydrolysate is neutralized with either sodium carbonate or sodium hydroxide to a pH of 5 to 6. During hydrolysis, extraneous polymeric material known as humin, which forms from the interaction of carbohydrate and protein fragments, is generated and subsequently removed by filtration and then further refined.
The source of the raw material, concentration of the acid, the temperature of the reaction, the time of the reaction, and other factors can all affect the organoleptic properties of the final product. Activated carbon treatment can be employed to remove both flavor and color components, to the required specification. Following a final filtration, the aHVP may, depending upon the application, be fortified with additional flavoring components. Thereafter, the product can be stored as a liquid at 30–40% dry matter, or alternatively it may be spray dried or vacuum dried and further used as a food ingredient.
One hundred pounds (45kg) of material containing 60% protein will yield 100 pounds of aHVP, which contains approximately 40 pounds (18 kg) of salt. This salt gain occurs during the neutralization step.
=== Enzymatic hydrolysis ===
For the production process of enzymatic HVP, enzymes are used to break down the proteins. To break down the protein to amino acids, proteases are added to the mixture of defatted protein and water. Due to the sensitivity of enzymes to a specific pH, either an acid or a base is added to match the optimum pH. Depending on the activity of the enzymes, up to 24 hours are needed to break down the proteins. The mixture is heated to inactivate the enzymes and then filtered to remove the insoluble carbohydrates (humin).
Since no salt is formed during the production process, manufacturers may add salt to eHVP preparations to extend shelf life or to provide a product similar to conventional aHVP. A vendor source states that salt is conventionally added before eHVP production to control microbial growth. With acid-tolerant enzymes, some of the salt can be replaced with a small amount of an acid (patent literature mentioning the acid-tolerant enzyme suggests a reaction pH of 4) to reduce sodium content.
==== Enzymes used ====
A commonly used protease mixture is "Flavourzyme", extracted from Aspergillus oryzae, the mold used for soy sauce production. This mixture contains both endo- and exo-peptidases.
The endopeptidase Alcalase may also be used, but without an exopeptidase it tends to generate a bitter flavor. As a result, it should be used with a companion exopeptidase. A commercial exopeptidase produced for this purpose is "Protana Prime", a mixture with both leucine aminopeptidase and carboxypeptidase D activity.
Beyond proteolysis, the amount of umami taste can also be increased by adding a glutaminase, which converts glutamine to glutamate. Commercial options include "Protana Boost" and others.
== Composition ==
Liquid aHVP typically contains 55% water, 16% salt, 25% organic substances (thereof 20% protein (amino acids) analyzed as about 3% total nitrogen and 2% amino nitrogen).
Many amino acids have either a bitter or sweet taste. In many commercial processes, nonpolar amino acids such as L-leucine and L-isoleucine are often removed to create hydrolysates with a more mellow and less bitter character. D-tryptophan, D-histidine, D-phenylalanine, D-tyrosine, D-leucine, L-alanine, and glycine are known to be sweet, while bitterness is associated with L-tryptophan, L-phenylalanine, L-tyrosine, and L-leucine. When not specified explicitly, the chirality of an amino acid is assumed to be L-, the form found in natural proteins. However, the D-forms do occur in natural food materials in smaller amounts, and the harsh chemical condition of aHVP production is known to flip a small amount of molecules to the D-form. Modern aHVP production has a step for removing tyrosine and leucine from the hydrolysate.
Tyrosine is an amino acid susceptible to halogenation during hydrolysis with HCl. Lysine is stable under standard acid hydrolysis, but during heat treatment, the side-chain amino group can react with other compounds, such as reducing sugars, producing Maillard products.
The organoleptic properties of HVP is determined not only by amino acid composition, but also by the various aroma-bearing substances other than the amino acids created during the production of both aHVP and eHVP. Aromas can be formed via amino acid decomposition, Maillard reaction, sugar cyclization, and lipid oxidation. A complex mix of aromas similar to butter, meat, bone stock, wood smoke, lovage and many other substances can be produced, depending on reaction conditions (time, temperature, hydrolysis method, additional feedstock such as xylose and spices).
According to the European Code of Practice for Bouillons and Consommés, hydrolyzed protein products intended for retail sale correspond to these characteristics:
Specific gravity at 20°C min.: 1.22
Total nitrogen min.: 4% (on dry matter)
Amino nitrogen min.: 1.3% (on dry matter)
Sodium chloride max.: 50% (on dry matter)
== Use ==
When foods are produced by canning, freezing, or drying, some flavor loss is almost inevitable. Manufacturers can use HVP to make up for it. Therefore, HVP is used in a wide variety of products, such as in the spice, meat, fish, fine-food, snack, flavor, and soup industries.
== Safety ==
=== 3-MCPD ===
3-MCPD, a carcinogen in rodents and a suspected human carcinogen, is created during acid-hydrolysis as glycerol released from lipid (e.g. triglycerides) reacts with hydrochloric acid. Legal limits have been set to keep aHVP products safe for human consumption. aHVP manufacturers can reduce the amount of 3-MCPD to acceptable limits by (1) careful control of reaction time and temperature (2) timely neutralization of hydrochloric acid, optionally extending to an alkaline hydrolysis step to destroy any 3-MCPD already formed (3) replacement of hydrochloric acid with other acids such as sulfuric acid.
=== As an allergen ===
Whether hydrolyzed vegetable protein is an allergen or not is contentious.
According to European law, wheat and soy are subject to allergen labelling in terms of Regulation (EU) 1169/2011 on food information to consumers. Since wheat and soy used for the production of HVP are not exempted from allergen labelling for formal reasons, HVP produced by using those raw materials has to be labelled with a reference to wheat or soy in the list of ingredients.
Nevertheless, strong evidence indicates at least aHVP is not allergenic, since proteins are degraded to single amino acids which are not likely to trigger an allergic reaction. A 2010 study has shown that aHVP does not contain detectable traces of proteins or IgE-reactive peptides. This provides strong evidence that aHVP is very unlikely to trigger an allergic reaction to people who are intolerant or allergic to soy or wheat. Earlier peer-reviewed animal studies done in 2006 also indicate that soy-hypersensitive dogs do not react to soy hydrolysate, a proposed protein source for soy-sensitive dogs.
There are reports of a cosmetic-grade aHVP, Glupearl 19S (GP19S), inducing anaphylaxis when present in soap. Unlike food aHVP, this Japanese wheat aHVP is only very mildly hydrolyzed. The unusual chemical condition makes GP19S more allergenic than pure gluten. Newer regulations for cosmetic hydrolyzed wheat protein have been developed in response, requiring an average molecular mass of less than 3500 Da – about 35 residues long. In theory, "an allergen must have at least 2 IgE-binding epitopes, and each epitope must be at least 15 amino acid residues long, to trigger a type 1 hypersensitivity reaction." Experiments also show that this degree of hydrolysis is sufficient to not trigger IgE binding from GP19S-allergic patients.
Allergenicity of eHVP depends on the specific food source and the enzyme used. Alcalase is able to render chickpea and green pea completely non-immunoreactive but papain only achieves partial reduction. Alcalase is also unable to make white beans non-reactive due to the antinutritional factors preventing complete digestion. Alcalase, but not "Flavourzyme" (a commercial Aspergillus oryzae protease blend for eHVP production), is able to make roasted peanut non-reactive.
== Regulation ==
aHVP is not acceptable in the production of natural flavors in the EU, but eHVP is.
== See also ==
Hydrolyzed protein
MSG
== References == | Wikipedia/Acid-hydrolyzed_vegetable_protein |
The Food Information and Control Agency (Spanish: Agencia de Información y Control Alimentarios, AICA), known between 1988 and 2014 as the Olive Oil Agency, is the Spanish Department of Agriculture, Fisheries and Food autonomous agency responsible for managing the information and control systems of the olericulture, dairy and other markets that the Ministry determines; the control of compliance with the Food Chain Improvement Act of 2013 and the official control of Protected Designations of Origin and Geographical Indications whose territorial scope extends to more than one autonomous community, before the commercialization.
== History ==
=== Origins: olive oil ===
The origin of the AICA is in 1987. This year, Law 28/1987, of December 11, was approved by the Cortes Generales, which created a new public agency: the Agency for Olive Oil (AAO). This brief regulation created the new body based on the European mandates provided for in Regulations 2262/1984, of the Council, of July 17, and 27/1985, of the Commission, of January 4, which establishes the need for Member States to create autonomous control agencies for the aid granted to the olive oil sector. It was attached to the Ministry of Agriculture, Fisheries and Food and its director had the organic level of deputy director-general. In September 1988, its Statute was approved, which put the AAO into operation.
=== Food Chain Act: agency reform ===
In 2013, the Law on measures to improve the functioning of the food chain (Food Chain Act) was approved, and the first additional provision of this law transformed the Agency for Olive Oil into the Food Information and Control Agency (AICA) after expanding its functions not only to the olive markets that it already supervised, but also to others such as dairy, wine and any other that was assigned to it.. The renewed agency began to perform his de facto functions on January 1, 2014 and its internal rules were approved in April 2014.
Following protests from the primary sector in 2020, the minister of Agriculture, Luis Planas, pledged, among other things, to strengthen the Agency to ensure compliance with the Food Chain Act. As a result of this commitment, at the end of 2020 the Ministry presented a budget for 2021 with an increase of two million euros compared to the 2018 budget (still in force in 2020), an increase of almost 32%.
In the same vein, in December 2021 the Cortes Generales approved an amendment of the Food Chain Act. With regard to the AICA, on the one hand, it was granted full authority to access the digital registry in which the food contracts signed with the primary producers and their groups, as well as their modifications, are registered to carry out the pertinent checks within the scope of their powers. On the other hand, with the aim of promoting efficiency in management and legal certainty, the decision-making power about minor pecuniary sanctions (when they do not exceed 100,000 euros) is transferred from the Director-General for the Food Industry to the Director of the Food Information and Control Agency.
Likewise, the 2021 law declares AICA as the National Execution Authority, being the highest national body responsible for ensuring compliance with the Food Chain Act and the contact point between Spain and the European Commission for these matters.
== Organization chart ==
The Agency is structured through an executive body and an advisory body:
The executive body of the agency is the Director of the Food Information and Control Agency. The director has the rank of deputy director-general and he or she heads the agency and, as such, it directs and represents it.
The Secretary-General, responsible for the management of human resources, internal regime, legal regime, financial and economic regime and the management and maintenance of the State Register of Good Commercial Practices in Food Contracting.
The Technical Unit for Market Information and Inspection, which is responsible for obtaining data and its analysis and processing, as well as the planning and execution of the controls and the evaluation of its results.
The Technical Unit for Monitoring the Chain, which is responsible for monitoring, evaluation and control of food contracts and commercial practices in the food chain, as well as the promotion of fair business practices.
The advisory body is the Advisory Council, a collective organ composed by representatives from the General State Administration, from the Autonomous Communities and from those actors of the food chain who are interested, including consumers.
The Chairperson of the Advisory Council is the Secretary-General for Agriculture and Food and the Deputy Chair is the Director-General for the Food Industry, both officials from the Ministry.
Among the members of the council, there is the director of the Agency and representatives from the departments of Agriculture, Economy, Finance, from the Spanish Agency for Food Safety and Nutrition, from the National Commission on Markets and Competition and from cooperatives, associations and other social organizations, of the autonomous communities and of the Spanish Council on Consumers and Users.
== List of directors ==
Since the agency's creation in 1988, these are the persons who held the position of director:
Vicente Fernández Lobato (1988–1995)
Julio Blanco Gómez (1995–1997)
Álvaro González Coloma Pascua (1997–2003)
Valentín Almansa Sahagún (2003–2004)
Carlos Sánchez Laín (2004–2013)
José Miguel Herrero (2014–2018)
Gema Hernández Maroñas (2018–)
== References == | Wikipedia/Food_Information_and_Control_Agency |
Nutritional rating systems are used to communicate the nutritional value of food in a more-simplified manner, with a ranking (or rating), than nutrition facts labels. A system may be targeted at a specific audience. Rating systems have been developed by governments, non-profit organizations, private institutions, and companies. Common methods include point systems to rank (or rate) foods based on general nutritional value or ratings for specific food attributes, such as cholesterol content. Graphics and symbols may be used to communicate the nutritional values to the target audience.
== Types ==
=== Glycemic index ===
Glycemic index is a ranking of how quickly food is metabolized into glucose when digested. It compares available carbohydrates gram-for-gram in foods to provide a numerical, evidence-based index of postprandial (post-meal) blood sugar level. The concept was introduced in 1981. The glycemic load of food is a number which estimates how much a food will raise a person's blood glucose level.
=== Guiding Stars ===
Guiding Stars is a patented food-rating system which rates food based on nutrient density with a scientific algorithm. Foods are credited with vitamins, minerals, dietary fiber, whole grains and Omega-3 fatty acids, and discredited for saturated fat, trans fats, and added sodium (salt) and sugar.
Rated foods are tagged with one, two or three stars, with three stars the best ranking. The program began at Hannaford Supermarkets in 2006, and is found in over 1,900 supermarkets in Canada and the US. Guiding Stars has expanded into public schools, colleges and hospitals.
The evidence-based, proprietary algorithm is based on the dietary guidelines and recommendations of regulatory and health organizations, including the US Food and Drug Administration and Department of Agriculture and the World Health Organization. The algorithm was developed by a scientific advisory panel composed of experts in nutrition and health from Dartmouth College, Harvard University, Tufts University, the University of North Carolina, and other colleges.
=== Health Star Rating System ===
The Health Star Rating System (HSR) is an Australian and New Zealand Government initiative that assigns health ratings to packaged foods and beverages. Ratings scale by half star increments between half a star up to five stars, with the higher the rating, the healthier the product. A calculator uses nutritional information such as total sugar, sodium, energy and other variants to obtain a rating for the product. Points are added for "healthy" nutrients such as fibres, proteins and vegetable matter whilst points are deducted for "unhealthy" nutrients that have been scientifically linked to chronic health disease, such as fats and sugars.
=== Nutri-Score ===
Nutri-Score is a nutrition label guide recommended by the European Commission and World Health Organization. It is a 5-color nutrition label selected by the French government in March 2017 for display on food products to facilitate consumer understanding of nutrient composition. It relies on the computation of a nutrient profiling system derived from the United Kingdom Food Standards Agency score.
A Nutri-Score for a particular food item is given one of five color-coded letters, with 'A' (enlarged letter, dark green) as a score indicating excellent nutrient composition, and 'E' (dark orange) as a low-rated, nutrient-poor score. The calculation of the score involves seven different parameters of nutrient content per 100 g of food typically displayed on food packages. High content of fruits and vegetables, dietary fiber, and protein promote a higher score, while high content of calories, sugar, saturated fat, and sodium promote a detrimental score.
=== NutrInform ===
NutrInform is an Italian alternative to Nutri-Score, backed by the country's Ministry of Agricultural, Food and Forestry Policies.
=== Nutripoints ===
Nutripoints is a food-rating system which places foods on a numerical scale based on their overall nutritional value. The method is based on an analysis of 26 positive factors (such as vitamins, minerals, protein and fiber) and negative factors (such as cholesterol, saturated fat, sugar and sodium) relative to calories. The Nutripoint score of the food is the result. The higher the value, the more nutrition per calorie (nutrient-dense) and the fewest negative factors exist in the food.
Nutripoints was developed by Doctor of Public Health Roy E. Vartabedian during the 1980s and was released in 1990 with his book, Nutripoints, which was published in thirteen countries in ten languages. The food-rating system is part of a program to help people measure, balance, and upgrade their diet for improvement in well-being. The system rates over 3,600 foods, from apples and oranges to fast foods and brand-name products.
=== Points Food System ===
WeightWatchers developed the Points Food System for use with its Flex Plan. The system's primary objective is to maintain a healthy weight and to track weight loss or gain over time. It is designed to allow users to eat any food, tracking the number of points for each food consumed.
Members try to keep to their points target for a given time within a given range, which is personalized based on the member's height, weight and other factors (such as gender). A weekly points allowance is established to provide for special occasions and occasional overindulgences.
=== Naturally Nutrient Rich ===
Developed by Adam Drewnowski of the University of Washington, the Naturally Nutrient Rich system is based on mean-percentage Daily Values for 14 nutrients in food with 2,000 calories. It proposes to assign nutrient-density values to foods within and across food groups. The score allows consumers to identify and select nutrient-dense foods, permitting flexibility in discretionary calories consumed.
=== Traffic light rating system ===
== Past systems ==
=== NuVal ===
The overall nutritional quality index was a nutritional-rating system developed at the Yale-Griffin Prevention Research Center. It assigned foods a score between 1 and 100 which reflected overall nutrition relative to calories consumed. Marketed as NuVal, it was widely adopted in United States grocery stores before it was discontinued in 2017 amid accusations of conflicts of interest and for its refusal to publish the scoring algorithm. Scoring inconsistencies occurred, in which processed foods scored higher than canned fruits and vegetables.
=== Smart Choices Program ===
Launched late in 2009, the Smart Choices Program (SCP) was a rating system developed by a coalition of companies from the food industry. The criteria for rating food products used 18 different attributes. The system had varying levels of acceptability based on 16 types of food which allowed for wide discretion in the selection of foods to include in the program. The program was discontinued in October 2009 after sharp criticism for including products such as Froot Loops, Lucky Charms, and Frosted Flakes as Smart Choices.
On August 19, 2009, the FDA wrote a letter to SCP manager saying: "FDA and FSIS would be concerned if any FOP labeling systems used criteria that were not stringent enough to protect consumers against misleading claims, were inconsistent with the Dietary Guidelines for Americans, or had the effect of encouraging consumers to choose highly processed foods and refined grains instead of fruits, vegetables, and whole grains." SCP was suspended in 2009 after the FDA's announcement that they will be addressing both on front-of- package and on-shelf systems. SCP Chair Mike Hughes said: "It is more appropriate to postpone active operations and channel our information and learning to the agency to support their initiative."
== See also ==
Eating disorder
5 A Day
Healthy eating pyramid
List of diets
Insulin index
Satiety value
== References ==
== External links ==
2020 Changes to the Nutrition Facts Label in the United States, US Food and Drug Administration, 30 August 2019 | Wikipedia/Nutritional_rating_systems |
Minamata disease (Japanese: 水俣病, Hepburn: Minamata-byō) is a neurological disease caused by severe mercury poisoning. Signs and symptoms include ataxia, numbness in the hands and feet, general muscle weakness, loss of peripheral vision, and damage to hearing and speech. In extreme cases, insanity, paralysis, coma, and death follow within weeks of the onset of symptoms. A congenital form of the disease affects fetuses, causing microcephaly, extensive cerebral damage, and symptoms similar to those seen in cerebral palsy.
Minamata disease was first discovered in the city of Minamata, Kumamoto Prefecture, Japan, in 1956. It was caused by the release of methylmercury in the industrial wastewater from a chemical factory owned by the Chisso Corporation, which continued from 1932 to 1968. It has also been suggested that some of the mercury sulfate in the wastewater was also metabolized to methylmercury by bacteria in the sediment. This highly toxic chemical bioaccumulated and biomagnified in shellfish and fish in Minamata Bay and the Shiranui Sea, which, when eaten by the local population, resulted in mercury poisoning. The poisoning and resulting deaths of both humans and animals continued for 36 years, while Chisso and the Kumamoto prefectural government did little to prevent the epidemic. The animal effects were severe enough in cats that they came to be named as having "dancing cat fever."
As of March 2001, 2,265 victims had been officially recognized as having Minamata disease and over 10,000 had received financial compensation from Chisso. By 2004, Chisso had paid $86 million in compensation, and in the same year was ordered to clean up its contamination. On March 29, 2010, a settlement was reached to compensate as-yet uncertified victims.
A second outbreak of Minamata disease occurred in Niigata Prefecture in 1965. The original Minamata disease and Niigata Minamata disease are considered two of the Four Big Pollution Diseases of Japan.
== 1908–1955 ==
In 1908, the Chisso Corporation first opened a chemical factory in Minamata, Kumamoto Prefecture, located on the west coast of the southern island of Kyūshū. Initially producing fertilisers, the factory followed the nationwide expansion of Japan's chemical industry, branching out into production of acetylene, acetaldehyde, acetic acid, vinyl chloride, and octanol, among others. The Minamata factory became the most advanced in all of Japan, both before and after World War II. The waste products resulting from the manufacture of these chemicals were released into Minamata Bay through the factory wastewater. These pollutants had a deleterious environmental impact. Fisheries were damaged in terms of reduced catches; in response, Chisso reached two separate compensation agreements with the fishery cooperative in 1926 and 1943.
The rapid expansion of the Chisso factory spurred on the local economy, and as the company prospered so did Minamata. This fact, combined with the lack of other industry, meant that Chisso had great influence in the city. At one point, over half of the tax revenue of Minamata City authority came from Chisso and its employees, and the company and its subsidiaries were responsible for creating a quarter of all jobs in Minamata. The city was even dubbed Chisso's "castle town," in reference to the capital cities of feudal lords who ruled Japan during the Edo period.
The Chisso factory first started acetaldehyde production in 1932, with 210 tons that year. In 1951, production had jumped to 6,000 tons and eventually peaked at 45,245 tons in 1960. The factory's output historically amounted to between a quarter and a third of Japan's total acetaldehyde production. The chemical reaction used to produce the acetaldehyde employed mercury sulfate as a catalyst. Starting in August 1951, the co-catalyst was changed from manganese dioxide to ferric sulfide. A side reaction of this catalytic cycle led to the production of a significant amount (about 5% of the outflow) of the organic mercury compound methylmercury. As a result of the catalyst change, this highly toxic compound was released into Minamata Bay regularly between 1951 and 1968, when this production method was finally discontinued.
== 1956–1959 ==
On 21 April 1956, a 5 year
-old girl was examined at Chisso's factory hospital in Minamata. The physicians were puzzled by her symptoms: difficulty walking, difficulty speaking, and convulsions. Two days later, her younger sister also began to exhibit the same symptoms and she, too, was hospitalised. The girls' mother informed doctors that her neighbour's daughter was also experiencing similar problems. After a house-to-house investigation, eight further patients were discovered and hospitalised. On 1 May, the hospital director reported an "epidemic of an unknown disease of the central nervous system" to the local public health office, marking the official discovery of Minamata disease.
To investigate the epidemic, the city government and various medical practitioners formed the Strange Disease Countermeasures Committee at the end of May 1956. Owing to the localised nature of the disease, it was initially suspected to be contagious; patients were isolated and their homes disinfected as a precaution. Although contagion was later disproved, this initial response contributed to the stigmatisation and discrimination experienced by Minamata survivors from the local community. During its investigations, the Committee uncovered surprising anecdotal evidence of the strange behaviour of cats and other wildlife in the areas surrounding patients' homes. Reports of cats convulsing, going mad and dying started around 1950. Locals called it the "cat dancing disease" in reference to the cats' erratic movements. Crows had fallen from the sky, seaweed no longer grew on the sea bed, and fish floated dead on the surface of the sea. As the extent of the outbreak was understood, the committee invited researchers from Kumamoto University (or Kumadai) to help in the research effort.
The Kumamoto University Research Group was formed on 24 August 1956. Researchers from the School of Medicine began visiting Minamata regularly and admitted patients to the university hospital for extensive examination. A more complete picture of the symptoms exhibited by patients was gradually uncovered. The disease struck without any prior warning, with patients complaining of a loss of sensation and numbness in their hands and feet. They became unable to grasp small objects or fasten buttons. They could not run or walk without stumbling, their voices changed in pitch, and many patients complained of difficulties seeing, hearing, and swallowing. In general, these symptoms worsened and were followed by severe convulsions, coma, and eventually death. By October 1956, forty patients had been discovered, fourteen of whom had died - an alarming case fatality rate of 35%.
=== Finding the cause ===
Researchers from Kumadai also began to focus on the cause of the strange disease. They found that the victims, often members of the same family, were clustered in fishing hamlets along the shore of Minamata Bay. The staple food of victims was invariably fish and shellfish from Minamata Bay. The cats in these areas, who often ate scraps from the family table, presented with symptoms similar to humans. This led the researchers to believe that the outbreak was caused by some kind of food poisoning, with contaminated fish and shellfish being the prime suspects.
On 4 November, the research group announced its initial findings: "Minamata disease is rather considered to be poisoning by a heavy metal, presumably it enters the human body mainly through fish and shellfish."
=== Identification of mercury ===
As soon as the investigation identified a heavy metal as the causal substance, the wastewater from the Chisso factory was immediately suspected as the origin. The company's own tests revealed that its wastewater contained many heavy metals in concentrations sufficiently high enough to bring about serious environmental degradation, including lead, mercury, manganese, arsenic, thallium, and copper, plus the chalcogen selenium. Identifying which particular poison was responsible for the disease proved to be extremely difficult and time-consuming. During 1957 and 1958, many different theories were proposed by different researchers. At first, manganese was thought to be the causal substance due to the high concentrations found in fish and the organs of the deceased. Thallium, selenium, and a multiple contaminant theory were also proposed. In March 1958, visiting British neurologist Douglas McAlpine suggested that Minamata symptoms resembled those of organic mercury poisoning, which shifted the focus of the investigation to mercury.
In February 1959, researchers tested the waters of Minamata Bay for mercury. The results were shocking - high quantities of mercury were detected in fish, shellfish, and sludge from the bay. The highest concentrations centred around the Chisso factory wastewater canal in Hyakken Harbour, decreasing as it went out to sea, clearly identifying the plant as the source of contamination. At the mouth of the wastewater canal, a figure of 2 kg of mercury per ton of sediment was measured, a level so high that it would be economically viable to mine (Chisso later set up a subsidiary to reclaim and sell the mercury recovered from the sludge).
Hair samples were taken from the residents of Minamata and tested for mercury. In residents affected by the disease, the highest mercury level recorded was 705 parts per million (ppm), indicating very heavy exposure, and in asymptomatic Minamata residents, the highest level was 191 ppm. This compared to an average level of 4 ppm for people living outside the Minamata area.
On 12 November 1959, the Ministry of Health and Welfare's Minamata Food Poisoning Subcommittee published its results:
Minamata disease is a poisoning disease that affects mainly the central nervous system and is caused by the consumption of large quantities of fish and shellfish living in Minamata Bay and its surroundings, the major causative agent being some sort of organic mercury compound.
== 1959 ==
During the Kumadai investigation, the causal substance had been identified as a heavy metal and it was widely presumed that the Chisso factory was the source of the contamination. Chisso was coming under closer scrutiny and to deflect criticism, the wastewater output route was changed. Chisso knew of the environmental damage caused by its wastewater and was aware that it was the prime suspect in the investigation. Despite this, from September 1958, instead of discharging its waste into Hyakken Harbour (the focus of investigation and source of original contamination), it discharged wastewater directly into Minamata River. The immediate effect was the death of fish at the mouth of the river, and from that point on, new Minamata disease victims began to appear in other fishing villages up and down the coast of the Shiranui Sea as the pollution spread.
Chisso failed to co-operate with the Kumadai research team. It withheld information on its industrial processes, leaving researchers to speculate what products the factory was producing and by what methods. The Chisso factory's hospital director, Hajime Hosokawa, established a laboratory in the research division of the facility to carry out his own experiments into Minamata disease in July 1959. In one experiment, Hosokawa added factory wastewater to food that was then fed to healthy cats. Seventy-eight days into the experiment, cat 400 exhibited symptoms of Minamata disease, and pathological examinations confirmed a diagnosis of organic mercury poisoning. Chisso did not reveal these results to the investigators and ordered Hosokawa to stop his research.
In an attempt to undermine the Kumadai researchers' organic mercury theory, Chisso and other parties with a vested interest that the factory remain open (including the Ministry of International Trade and Industry and the Japan Chemical Industry Association) funded research into alternative causes of the disease, other than its own waste.
=== Compensation of fishermen and patients, 1959 ===
Polluting wastewater had damaged the fisheries around Minamata ever since the opening of the Chisso factory in 1908. The Minamata Fishing Cooperative had managed to win small payments of "sympathy money" from the company in 1926 and again in 1943, but after the outbreak of Minamata disease, the fishing situation became critical. Fishing catches declined by 91% between 1953 and 1957. The Kumamoto prefectural government issued a partial ban on the sale of fish caught in the heavily polluted Minamata Bay – but not an all-out ban, which would have legally obliged it to compensate the fishermen. The fishing cooperative protested against Chisso and angrily forced their way into the factory on 6 August and 12 August, demanding compensation. A committee was set up by Minamata Mayor Todomu Nakamura to mediate between the two sides, but this committee was stacked heavily in the company's favour. On 29 August, the fishing cooperative agreed to the mediation committee's proposal, stating: "In order to end the anxiety of the citizens, we swallow our tears and accept". Chisso paid the cooperative ¥20 million (US$183,477 — about US$1.7 million in 2021 value) and set up a ¥15 million ($137,608 — about 1.25 million today) fund to promote the recovery of fishing.
After Chisso changed the route of wastewater output in 1958, pollution had spread up and down the Shiranui Sea, damaging fisheries there as well. Emboldened by the success of the small Minamata cooperative, the Kumamoto Prefectural Alliance of Fishing Cooperatives also decided to seek compensation from Chisso. On 17 October, 1,500 fishermen from the alliance descended on the factory to demand negotiations. When this produced no results, the alliance members took their campaign to Tokyo, securing an official visit to Minamata by members of the Japanese Diet. During the visit on 2 November, alliance members forced their way into the factory and rioted, causing many injuries and ¥10 million ($100,000) in damage. The violence was covered widely in the media, bringing the nation's attention to Minamata for the first time since the outbreak began. Another mediation committee was set up, and an agreement was hammered out and signed on 17 December. Some ¥25 million of "sympathy money" was paid to the alliance, and a ¥65 million fishing recovery fund was established.
In 1959, those affected by Minamata disease were in a much weaker bargaining position than the fishermen. The recently formed Minamata Disease Patients Families Mutual Aid Society was much more divided than the fishing cooperatives. Patients' families were the victims of discrimination and ostracism from the local community. Local people felt that the company (and their city that depended upon it) was facing economic ruin. To some patients, this rejection from the community represented a greater fear than the disease itself. After staging a sit-in at the Chisso factory gates in November 1959, the patients asked Kumamoto Prefecture Governor Hirosaku Teramoto to include the patients' request for compensation with the mediation that was ongoing with the prefectural fishing alliance. Chisso agreed, and after a few weeks' further negotiation, another "sympathy money" agreement was signed. Patients who were certified by a Ministry of Health and Welfare committee would be compensated: adult patients received ¥100,000 ($917) per year; children ¥30,000 ($275) per year, and families of dead patients would receive a one-off ¥320,000 ($2935) payment.
=== Wastewater treatment ===
On 21 October 1959, Chisso was ordered by the Ministry of International Trade and Industry reroute its wastewater drainage from the Minamata River back to Hyakken Harbour, and to speed up the installation of wastewater treatment systems at the factory. Chisso installed a Cyclator purification system on 19 December 1959, and opened it with a special ceremony. Chisso's president Kiichi Yoshioka drank a glass of water supposedly treated through the Cyclator to demonstrate that it was safe. In fact, the wastewater from the factory, which the company knew still contained mercury and led to Minamata disease when fed to cats, was not being treated through the Cyclator at the time. Testimony at a later Niigata Minamata disease trial proved that Chisso knew the Cyclator to be completely ineffective: "The purification tank was installed as a social solution and did nothing to remove organic mercury."
The deception was successful, and almost all parties affected by Minamata disease were duped into believing that the factory's wastewater had been made safe from December 1959 onward. This widespread assumption meant that doctors were not expecting new patients to appear, resulting in numerous problems in the years to follow as the pollution continued. In most people's minds, the issue of Minamata disease had been resolved.
== 1959–1969 ==
The years between the first set of "sympathy money" agreements in 1959 and the start of the first legal action to be taken against Chisso in 1969 are often called the "ten years of silence." This was inaccurate - much activity on the part of the patients and fishermen took place during this period, but nothing had a significant impact on the actions of the company or the coverage of Minamata in the Japanese media.
=== Continued pollution ===
Despite the almost universal assumption to the contrary, the wastewater treatment facilities installed in December 1959 had no effect on the level of organic mercury being released into the Shiranui Sea. The pollution and the disease it caused continued to spread. The Kumamoto and Kagoshima prefectural governments conducted a joint survey in late 1960 and early 1961 to measure the level of mercury in the hair of people living around the Shiranui Sea. The results confirmed that organic mercury had spread all around the inland sea and that people were still being poisoned by contaminated fish. Hundreds of people were discovered to have levels greater than 50 ppm of mercury in their hair, the level at which people are likely to experience nerve damage. The highest result recorded was that of a woman from Goshonoura island whose sample had a mercury level of 920 ppm.
The prefectural governments did not publish the results and did nothing in response to these surveys. The participants who had donated hair samples were not informed of their results, even when they requested them. A follow-up study ten years later discovered that many had died from "unknown causes" during this time period.
=== Congenital Minamata disease ===
Local doctors and medical officials had noticed an abnormally high frequency of cerebral palsy and other infantile disorders in the Minamata area. In 1961, a number of medical professionals, including Masazumi Harada (later to be honored by the United Nations for his body of work on Minamata disease), set about re-examining children diagnosed with cerebral palsy. The symptoms of the children closely mirrored those of adult Minamata disease patients, but many of their mothers did not exhibit symptoms. The fact that these children had been born after the initial outbreak and had never been fed contaminated fish also led their mothers to believe they were not victims. At the time, the medical establishment believed the placenta would protect the foetus from toxins in the bloodstream, which is the case with most chemicals, but in the case of methylmercury the placenta removes it from the mother's bloodstream and concentrates it in the foetus. This was unknown at the time.
After several years of study and the autopsies of two children, the doctors announced that these children had an as-yet unrecognised congenital form of Minamata disease. The certification committee convened on 29 November 1962 and agreed that the two dead children and the sixteen children still alive should be certified as patients, and therefore liable for "sympathy money" from Chisso, in line with the 1959 agreement.
=== Outbreak of Niigata Minamata disease ===
Minamata disease broke out again in 1965, this time along the banks of the Agano River in Niigata Prefecture. The polluting factory (owned by Showa Denko) employed a chemical process using a mercury catalyst very similar to that used by Chisso in Minamata. As in Minamata, from the autumn of 1964 to the spring of 1965, cats living along the banks of the Agano River had been seen to go mad and die. Before long, patients appeared with identical symptoms to patients living on the Shiranui Sea, and the outbreak was made public on 12 June 1965. Researchers from the Kumamoto University Research Group and Hajime Hosokawa (who had retired from Chisso in 1962) used their experience from Minamata and applied it to the Niigata outbreak. In September 1966, a report was issued proving Showa Denko's pollution to be the cause of this second Minamata disease outbreak.
Unlike the patients in Minamata, the victims of Showa Denko's pollution lived a considerable distance from the factory and had no particular link to the company. As a result, the local community was much more supportive of patients' groups and a lawsuit was filed against Showa Denko in March 1968, only three years after discovery.
The events in Niigata catalysed a change in response to the original Minamata incident. The scientific research carried out in Niigata forced a re-examination of that done in Minamata, and the decision of Niigata patients to sue the polluting company allowed the same response to be considered in Minamata. Masazumi Harada has said that, "It may sound strange, but if this second Minamata disease had not broken out, the medical and social progress achieved by now in Kumamoto... would have been impossible."
Around this time, two other pollution-related diseases were also grabbing headlines in Japan. People with Yokkaichi asthma and itai-itai disease were forming citizens' groups and filed lawsuits against the polluting companies in September 1967 and March 1968, respectively. As a group, these diseases came to be known as the four big pollution diseases of Japan.
Slowly but surely, the mood in Minamata and Japan as a whole was shifting. Minamata patients found the public gradually becoming more receptive and sympathetic as the decade wore on. This culminated in 1968 with the establishment in Minamata of the Citizens' Council for Minamata Disease Countermeasures, which was to become the chief citizens' support group to the Minamata patients. A founding member of the citizens' council was Michiko Ishimure, a local housewife and poet who later that year published Pure Land, Poisoned Sea: Our Minamata disease, a book of poetic essays that received national acclaim.
== 1969–1973 ==
=== Official government recognition ===
Finally on 26 September 1968 – twelve years after the discovery of the disease (and four months after Chisso had stopped production of acetaldehyde using its mercury catalyst) – the Japanese government issued an official conclusion as to the cause of Minamata disease:
Minamata disease is a disease of the central nervous system, a poisoning caused by long-term consumption, in large amounts, of fish and shellfish from Minamata Bay. The causative agent is methylmercury. Methylmercury produced in the acetaldehyde acetic acid facility of Shin Nihon Chisso's Minamata factory was discharged in factory wastewater... Minamata disease patients last appeared in 1960, and the outbreak has ended. This is presumed to be because consumption of fish and shellfish from Minamata Bay was banned in the fall of 1957, and the fact that the factory had waste-treatment facilities in place from January 1960.
The conclusion contained many factual errors: eating fish and shellfish from other areas of the Shiranui Sea, not just Minamata Bay, could cause the disease; eating small amounts, as well as large amounts of contaminated fish over a long time also produced symptoms; the outbreak had not, in fact, ended in 1960 nor had mercury-removing wastewater facilities been installed in January 1960. Nevertheless, the government announcement brought a feeling of relief to a great many victims and their families. Many felt vindicated in their long struggle to force Chisso to accept responsibility for causing the disease and expressed thanks that their plight had been recognised by their social superiors. The struggle now focused on to what extent the victims should be compensated.
=== Struggle for a new agreement ===
In light of the government announcement, the patients of the Mutual Aid Society decided to ask for a new compensation agreement with Chisso and submitted the demand on 6 October. Chisso replied that it was unable to judge what would be fair compensation and asked the Japanese government to set up a binding arbitration committee to decide. This proposal split the members of the patients' society, many of whom were extremely wary of entrusting their fate to a third party, as they had done in 1959 with unfortunate results. At a meeting on the 5 April 1969, the opposing views within the society could not be reconciled and the organisation split into the pro-arbitration group and the litigation group (who decided to sue the company). That summer, Chisso sent gifts to the families who opted for arbitration rather than litigation.
An arbitration committee was duly set up by the Ministry of Health and Welfare on 25 April, but it took almost a year to draw up a draft compensation plan. A newspaper leak in March 1970 revealed that the committee would ask Chisso to pay only ¥2 million ($5,600) for dead patients and ¥140,000 to ¥200,000 ($390 to $560) per year to surviving patients. The arbitration group were dismayed by the sums on offer. They petitioned the committee, together with patients and supporters of the litigation group, for a fairer deal. The arbitration committee announced their compensation plan on 25 May in a disorderly session at the Ministry of Health and Welfare in Tokyo. Thirteen protesters were arrested.
Instead of accepting the agreement as they had promised, the arbitration group asked for increases. The committee was forced to revise its plan and the patients waited inside the ministry building for two days while they did so. The final agreement was signed on 27 May. Payments for deaths ranged from ¥1.7 million to ¥4 million ($4,700 to $11,100), one-time payments from ¥1 million to ¥4.2 million ($2,760 to $11,660) and annual payments between ¥170,000 and ¥380,000 ($470 to $1,100) for surviving patients. On the day of the signing, the Minamata Citizens' Council held a protest outside the Chisso factory gates. One of the Chisso trade unions held an eight-hour strike in protest at the poor treatment of the arbitration group by their own company.
The litigation group, representing 41 certified patients (17 already deceased) in 28 families, submitted their suit against Chisso in the Kumamoto District Court on 14 June 1969. The leader of the group, Eizō Watanabe (a former leader of the Mutual Aid Society), declared, "Today, and from this day forth, we are fighting against the power of the state." Those who decided to sue the company came under fierce pressure to drop their lawsuits. One woman was visited personally by a Chisso executive and harassed by her neighbours. She was blackballed by the community, her family's fishing boat used without permission, their fishing nets were cut, and human faeces were thrown at her in the street.
The litigation group and their lawyers were helped substantially by an informal national network of citizens' groups that had sprung up around the country in 1969. The Associations to Indict those Responsible for Minamata Disease were instrumental in raising awareness and funds for the lawsuit. The Kumamoto branch, in particular, was especially helpful to the case. In September 1969, they set up a Trial Research Group, which included law professors, medical researchers (including Harada), sociologists and even Michiko Ishimure to provide useful material to the lawyers to improve their legal arguments. Their report, Corporate Responsibility for Minamata Disease: Chisso's Illegal Acts, published in August 1970, formed the basis of the ultimately successful lawsuit.
The trial lasted almost four years. The litigation group's lawyers sought to prove Chisso's corporate negligence. Three main legal points had to be overcome to win the case. First, the lawyers had to show that methylmercury caused Minamata disease and that the company's factory was the source of pollution. The extensive research by Kumadai and the government's conclusion meant that this point was proved quite easily. Second, they needed to show that Chisso could and should have anticipated the effect of its wastewater and taken steps to prevent the tragedy (i.e., was the company negligent in its duty of care). Third, it had to disprove that the "sympathy money" agreement of 1959, which forbade the patients from claiming any further compensation, was a legally binding contract.
The trial heard from patients and their families, but the most important testimony came from Chisso executives and employees. The most dramatic testimony came from Hosokawa, who spoke on 4 July 1970 from his hospital bed where he was dying of cancer. Hosokawa explained his experiments with cats, including the infamous "cat 400", which developed Minamata disease after being fed factory wastewater. He also spoke of his opposition to the 1958 change in wastewater output route to Minamata River. Hosokawa's testimony was backed up by a colleague who also told how Chisso officials had ordered them to halt their cat experiments in the autumn of 1959. Hosokawa died three months after giving his testimony. Former factory manager Eiichi Nishida admitted that the company put profits ahead of safety, resulting in dangerous working conditions and a lack of care with mercury. Former Chisso President Kiichi Yoshioka admitted that the company promoted a theory of dumped World War II explosives, though it knew it to be unfounded.
The verdict handed down on 20 March 1973 represented a complete victory for the patients of the litigation group:
The defendant's factory was a leading chemical plant with the most advanced technology and ... should have assured the safety of its wastewater. The defendant could have prevented the occurrence of Minamata disease or at least have kept it at a minimum. We cannot find that the defendant took any of the precautionary measures called for in this situation whatsoever. The presumption that the defendant had been negligent from beginning to end in discharging wastewater from its acetaldehyde plant is amply supported. The defendant cannot escape liability for negligence.
The "sympathy money" agreement was found to be invalid and Chisso was ordered to make one-time payments of ¥18 million ($66,000) for each deceased patient and from ¥16 million to ¥18 million ($59,000 to $66,000) for each surviving patient. The total compensation of ¥937 million ($3.4 million) was the largest sum ever awarded by a Japanese court.
=== Uncertified patients' fight to be recognised ===
While the struggles of the arbitration and litigation groups against Chisso were continuing, a new group of individuals with Minamata disease emerged. To qualify for compensation under the 1959 agreement, patients had to be officially recognised by various ad hoc certification committees according to their symptoms. However, in an effort to limit the liability and financial burden on the company, these committees were sticking to a rigid interpretation of Minamata disease. They required that patients must exhibit all symptoms of Hunter-Russell syndrome – the standard diagnosis of organic mercury poisoning at the time, which originated from an industrial accident in the United Kingdom in 1940. The committee certified only patients exhibiting explicit symptoms of the British syndrome, rather than basing their diagnosis on the disease in Japan. This resulted in many applicants being rejected by the committee, leaving them confused and frustrated.
== Legacies ==
=== Epidemiology ===
As of March 2001, 2,265 victims had been officially certified and over 10,000 people had received financial compensation from Chisso, although they were not recognised as official victims. The issue of quantifying the impact of Minamata disease is complicated, as a full epidemiological study has never been conducted and patients were recognised only if they voluntarily applied to a certification council to seek financial compensation. Many individuals with Minamata disease faced discrimination and ostracism from the local community if they came out into the open about their symptoms. Some people feared the disease to be contagious, and many local people were fiercely loyal to Chisso, depending on the company for their livelihoods. In this atmosphere, those affected were reluctant to come forward and seek certification. Despite these factors, over 17,000 people have applied to the council for certification. Also, in recognising an applicant as having Minamata disease, the certification council qualified that patient to receive financial compensation from Chisso. For that reason, the council has always been under immense pressure to reject claimants and minimise the financial burden placed on Chisso. Rather than being a council of medical recognition, the decisions of the council were always affected by the economic and political factors surrounding Minamata and the Chisso corporation. Furthermore, compensation of the victims led to continued strife in the community, including unfounded accusations that some of the people who sought compensation did not actually have the disease. More properly, the impact should be called a criminal 'poisoning', not a clinical 'disease'. These forms of obfuscation are commonly experienced by 'environmental victims' in many countries.
In 1978, the National Institute for Minamata Disease was established in Minamata. It consists of four departments: The Department of Basic Medical Science, The Department of Clinical Medicine, The Department of Epidemiology and The Department of International Affairs and Environmental Sciences. In 1986, The Institute became a WHO Collaborating Centre for Studies on the Health Effects of Mercury Compounds. The Institute seeks to improve medical treatment of Minamata disease patients and conducts research on mercury compounds and their impact on organisms as well as potential detoxification mechanisms. In April, 2008 the Institute invented a method for absorbing gaseous mercury in order to prevent air pollution and enable recycling of the metal.
=== Environmental protection ===
The movement for redress by Minamata victims and activists and the national outrage their movement elicited played a central role in the rise of environmental protection in Japan. The 1970 session of the Japanese Diet became remembered as the "Pollution Diet", as the Japanese government took action under rising pressure from the Minamata disease movement as well as other major environmental catastrophes such as Yokkaichi asthma and itai-itai disease. Fourteen new environmental laws were passed in a single session, giving Japan what at the time were the most stringent environmental protection laws in the world. These new laws included a Water Pollution Act and nationwide regulations of toxic discharges. The "polluter pays" principle was introduced. A national Environmental Agency, which later developed into the Ministry of Environment, was founded in 1971. National governmental expenditures on environmental issues almost doubled between 1970 and 1975 and tripled at the local government level.
=== Democratizing effects ===
According to historian Timothy S. George, the environmental protests that surrounded the disease appeared to aid in the democratization of Japan. When the first cases were reported and subsequently suppressed, the rights of the victims were not recognised, and they were given no compensation. Instead, the affected were ostracised from their community due to ignorance about the disease, as people were afraid that it was contagious.
The people directly impacted by the pollution of Minamata Bay were not originally allowed to participate in actions that would affect their future. Disease victims, fishing families, and company employees were excluded from the debate. Progress occurred when Minamata victims were finally allowed to come to a meeting to discuss the issue. As a result, postwar Japan took a small step toward democracy.
Through the evolution of public sentiments, the victims and environmental protesters were able to acquire standing and proceed more effectively in their cause. The involvement of the press also aided the process of democratization because it caused more people to become aware of the facts of Minamata disease and the pollution that caused it. However, although the environmental protests did result in Japan becoming more democratized, it did not completely rid Japan of the system that first suppressed the fishermen and individuals with Minamata disease.
=== Popular culture ===
Toshiko Akiyoshi, touched by the plight of the fishing village, wrote a jazz suite, "Minamata", that was to be the central piece of the Toshiko Akiyoshi-Lew Tabackin Big Band's 1976 album on RCA, Insights. The piece was constructed in three parts, to musically reflect the tragedy – "Peaceful Village", "Prosperity & Consequence", and "Epilogue". Akiyoshi used Japanese vocalists to sing the Japanese lyrics of a tone poem that were part of the composition. The album won many awards in jazz circles, including Downbeat's best album award, largely on the strength of this piece, which brought some further attention to the tragedy.
The song "Kepone Factory" on Dead Kennedys' In God We Trust, Inc. makes reference to the disaster in its chorus.
The song "The Disease of the Dancing Cats" by the band Bush on the album The Science of Things is in reference to the disaster.
In 2021 a manga/comic book about Minamata disease was made with the cooperation of various disease suffers and long term helper and activist, Takeko Kato. The Scottish writer Sean Michael Wilson and Japanese artist Akiko Shimojima collaborated on the book, called The Minamata Story: an ecotragedy, which was published in English by Stonebridge Press, and went on to win two awards.
=== Visual documentation ===
Photographic documentation of Minamata started in the early 1960s. One photographer who arrived in 1960 was Shisei Kuwabara, straight from university and photo school, who had his photographs published in Weekly Asahi as early as May 1960. The first exhibition of his photographs of Minamata was held in the Fuji Photo Salon in Tokyo in 1962, and the first of his book-length anthologies, Minamata Disease, was published in Japan in 1965. He has returned to Minamata many times since.
A dramatic photographic essay by W. Eugene Smith brought world attention to Minamata disease. He and his Japanese wife lived in Minamata from 1971 to 1973. The most famous and striking photo of the essay, Tomoko and Mother in the Bath (1972), shows Ryoko Kamimura holding her severely deformed daughter, Tomoko, in a Japanese bath chamber. Tomoko was poisoned by methylmercury while still in the womb. The photo was very widely published. It was posed by Smith with the co-operation of Ryoko and Tomoko to dramatically illustrate the consequences of the disease. It has subsequently been withdrawn from circulation at the request of Tomoko's family, so does not appear in recent anthologies of Smith's works. Smith and his wife were extremely dedicated to the cause of the people with Minamata disease, closely documenting their struggle for recognition and right to compensation. Smith was himself attacked and seriously injured by Chisso employees in an incident in Goi, Ichihara city, near Tokyo on January 7, 1972, in an attempt to stop him from further revealing the issue to the world. The 54-year-old Smith survived the attack, but his sight in one eye deteriorated and his health never fully recovered before his death in 1978. Johnny Depp plays W. Eugene Smith in Minamata (2020) a drama based on the book written by Smith's wife.
Japanese photographer Takeshi Ishikawa, who assisted Smith in Minamata, has since exhibited his own photographs documenting the disease. His photographs cover the years 1971 to the present, with Minamata victims as his subjects.
The prominent Japanese documentary filmmaker Noriaki Tsuchimoto made a series of films, starting with Minamata: The Victims and Their World (1971) and including The Shiranui Sea (1975), documenting the incident and siding with the victims in their struggle against Chisso and the government.
Kikujiro Fukushima, a well-known Japanese photographer and journalist, published a series of photographs in 1980 concerning pollution in Japan, including Minamata disease. Some negatives of these photos are available on the website, and Kyodo News Images holds the rights to them.
=== Today ===
Minamata disease remains an important issue in contemporary Japanese society. Lawsuits against Chisso and the prefectural and national governments are still continuing and many regard the government responses to date as inadequate. The company's "historical overview" in its current website makes no mention of their role in the mass contamination of Minamata and the dreadful aftermath. Their 2004 Annual Report, however, reports an equivalent of about US$50 million (5,820 million yen) in "Minamata Disease Compensation Liabilities". From 2000 to 2003, the company also reported total compensation liabilities of over US$170 million. Their 2000 accounts also show that the Japanese and Kumamoto prefectural governments waived an enormous US$560 million in related liabilities. Their FY2004 and FY2005 reports refer to Minamata disease as "mad hatter's disease", a term coined from the mercury poisoning experienced by hat-makers of the last few centuries (cf. Erethism).
A memorial service was held at the Minamata Disease Municipal Museum on 1 May 2006 to mark 50 years since the official discovery of the disease. Despite bad weather, the service was attended by over 600 people, including Chisso chairman Shunkichi Goto and Environment Minister Yuriko Koike.
On Monday, March 29, 2010, a group of 2,123 uncertified victims reached a settlement with the government of Japan, the Kumamoto Prefectural government, and Chisso Corporation to receive individual lump-sum payments of 2.1 million yen and monthly medical allowances.
Most congenital patients are now in their forties and fifties and their health is deteriorating. Their parents, who are often their only source of care, are into their seventies or eighties or already deceased. Often, these patients find themselves tied to their own homes and the care of their family, effectively isolated from the local community. Some welfare facilities for patients do exist. One notable example is Hot House, a vocational training centre for congenital patients as well as other disabled people in the Minamata area. Hot House members are also involved in raising awareness of Minamata disease, often attending conferences and seminars as well as making regular visits to elementary schools throughout Kumamoto Prefecture.
== See also ==
Heavy metal poisoning
Minamata Convention on Mercury
Ontario Minamata disease
Mercury in fish
== References ==
== Further reading ==
"Minamata Disease: The History and Measures", The Ministry of the Environment, (2002), retrieved 17 January 2007
"Minamata Disease Archives" by the National Institute for Minamata Disease, retrieved 29 October 2006
Harada, Masazumi. (1972). Minamata Disease. Kumamoto Nichinichi Shinbun Centre & Information Center/Iwanami Shoten Publishers. ISBN 4-87755-171-9 C3036
George, S. Timothy. (2001). Minamata: Pollution and the Struggle for Democracy in Postwar Japan. Harvard University Press. ISBN 0-674-00785-9
Ui, Jun. (1992). Industrial Pollution in Japan. United Nations University Press. ISBN 92-808-0548-7. Chapter 4, section IV
Smith, W. E. and Smith, A. M. (1975). Minamata. Chatto & Windus, Ltd. (London), ISBN 0-7011-2131-9
Eto, K., Marumoto, M. and Takeya, M. (2010) "The pathology of methylmercury poisoning (Minamata disease)", retrieved 7 December 2013
Oiwa, Keibo. (2001). Rowing the Eternal Sea: The Story of a Minamata Fisherman. Rowman & Littlefield Publishers. ISBN 0-7425-0021-7
Steingraber, Sandra. (2001). Having Faith: An Ecologist Journey to Motherhood. Perseus Publishing. ISBN 0-425-18999-6
Approaches to Water Pollution Control, Minamata City, Kumamoto Prefecture
Allchin, Douglas. The Poisoning of Minamata
Saito, Hisashi. (2009). Niigata Minamata Disease: Methyl Mercury Poisoning in Niigata, Japan. Niigata Nippo.
Walker, Brett. (2010) "Toxic Archipelago: A History of Industrial Disease in Japan." University of Washington Press. ISBN 0-295-98954-8
Wilson, Sean Michael and Shimojima, Akiko. ’The Minamata Story’, manga (Stonebridge Press, 2021) ISBN 9781611720563
== External links ==
ATSDR – ToxFAQs: Mercury – Frequently asked questions about Mercury
National Institute for Minamata Disease
Minamata Disease: The History and Measures – The Ministry of the Environment's summary of Minamata disease
Soshisha – The Supporting Center for Minamata Disease and the Minamata Disease Museum
Aileen Archive – Copyright holder of W. Eugene Smith's Minamata photos
Photograph by W. Eugene Smith – Tomoko Uemura in Her Bath, 1972
Minamata disease – Chapter from Industrial Pollution in Japan by Dr Jun Ui
Toxic Archipelago: Industrial Pollution in Japan – A talk by Brett Walker, September 16, 2010
Minamata Timeline by Minamata City Council.
Minamata disease museum | Wikipedia/Minamata_disease |
A parasitic disease, also known as parasitosis, is an infectious disease caused by parasites. Parasites are organisms which derive sustenance from its host while causing it harm. The study of parasites and parasitic diseases is known as parasitology. Medical parasitology is concerned with three major groups of parasites: parasitic protozoa, helminths, and parasitic arthropods. Parasitic diseases are thus considered those diseases that are caused by pathogens belonging taxonomically to either the animal kingdom, or the protozoan kingdom.
== Terminology ==
Although organisms such as bacteria function as parasites, the usage of the term "parasitic disease" is usually more restricted. The three main types of organisms causing these conditions are protozoa (causing protozoan infection), helminths (helminthiasis), and ectoparasites. Protozoa and helminths are usually endoparasites (usually living inside the body of the host), while ectoparasites usually live on the surface of the host. Protozoa are single-celled, microscopic organisms that belong to the kingdom Protista. Helminths on the other hand are macroscopic, multicellular organisms that belong to the kingdom Animalia. Protozoans obtain their required nutrients through pinocytosis and phagocytosis. Helminths of class Cestoidea and Trematoda absorb nutrients, whereas nematodes obtain needed nourishment through ingestion. Occasionally the definition of "parasitic disease" is restricted to diseases due to endoparasites.
Some parasitic diseases can occur in either an acute or chronic form. The acute form is characterized by quicker and often more severe onset of symptoms. The chronic form is typically less severe but is life-long. Some parasites that cause chronic and acute manifestations in their respective diseases are:
Trypanosoma brucei rhodesiense (acute) / Trypanosoma brucei gambiense (chronic)
Trypanosoma cruzi
Clonorchis sinensis
Paragonimus westermani
== Transmission ==
=== Infection ===
Mammals can get parasites from contaminated food or water, bug bites, sexual contact, or contact with animals.
Some ways in which people may acquire parasitic infections are walking barefoot, inadequate disposal of feces, lack of hygiene, close contact with someone carrying specific parasites, and eating undercooked foods, unwashed fruits and vegetables or foods from contaminated regions. It is important to note that only at specific stages in a parasites life is it infectious. Contact with non-infective stages will not lead to infection.
Many parasites utilize vectors to infect hosts. Vectors are vessels for the parasite, and help the parasite infect its next host. Some examples of parasitic diseases that use vectors are malaria, Lyme disease, and leishmaniasis.
==== At-risk groups ====
Many parasitic diseases are concentrated in specific areas of the globe. Majority of these diseases are prevalent along the equator due to the warm temperatures. Therefore, people located in these areas are at greater risk of contracting the disease causing parasites.
Parasitic diseases are far more common among marginalized groups. Lack of indoor bathrooms and access to clean drinking water are only some of the risk factors faced. Additionally, in the United States being Hispanic or African-American have shown to be risk factors for specific parasitic diseases.
== Morbidity ==
=== Symptoms ===
Parasitic diseases can manifest in many different symptoms, with some being asymptomatic. Many of the symptoms of parasitic diseases are common among other ailments, such as food poisoning or the flu. This can cause correct diagnoses to take a while. The target organ(s) of the parasite typically dictates the symptoms experienced:
==== General ====
Fever
Ulcers/lesions
Death
Headache
Malaise
Anemia
Muscle Pain
==== Gastrointestinal Tract ====
Dysentery
Constipation
Abdominal pain
Vomiting
Nausea
Bloating
Anorexia
==== Lungs ====
Cough
Blood in sputum
Lesions
==== Skin ====
Local inflammation
Local dermatitis
Hives
Itching
Rash
== Treatment ==
=== Diagnosis ===
Different parasitic diseases require different diagnostic methods because different parasites have different diagnostic stages. Testing routes will often be determined by symptoms.
==== Testing ====
Stool Test
Blood Test
Colonoscopy/Endoscopy
X-ray/MRI/ CT Scan
=== Therapies ===
Parasitic infections can usually be treated with antiparasitic drugs. The use of viruses to treat infections caused by protozoa has been proposed.
== See also ==
Protozoan infection
Babesiosis
Giardiasis
== References ==
== External links == | Wikipedia/Parasitic_disease |
Variant Creutzfeldt–Jakob disease (vCJD), formerly known as new variant Creutzfeldt–Jakob disease (nvCJD) and referred to colloquially as "mad cow disease" or "human mad cow disease" to distinguish it from its BSE counterpart, is a fatal type of brain disease within the transmissible spongiform encephalopathy family. Initial symptoms include psychiatric problems, behavioral changes, and painful sensations. In the later stages of the illness, patients may exhibit poor coordination, dementia and involuntary movements. The length of time between exposure and the development of symptoms is unclear, but is believed to be years to decades. Average life expectancy following the onset of symptoms is 13 months.
It is caused by prions, which are misfolded proteins. Spread is believed to be primarily due to eating beef infected with bovine spongiform encephalopathy (BSE). Infection is also believed to require a specific genetic susceptibility. Spread may potentially also occur via blood products or contaminated surgical equipment. Diagnosis is by brain biopsy but can be suspected based on certain other criteria. It is different from typical Creutzfeldt–Jakob disease, though both are due to prions.
Treatment for vCJD involves supportive care. As of 2020, 178 cases of vCJD have been recorded in the United Kingdom, due to a 1990s outbreak, and 50 cases in the rest of the world. The disease has become less common since 2000. The typical age of onset is less than 30 years old. It was first identified in 1996 by the National CJD Surveillance Unit in Edinburgh, Scotland.
== Signs and symptoms ==
Initial symptoms include psychiatric problems, behavioral changes, and painful sensations. In the later stages of the illness, patients may exhibit poor coordination, dementia and involuntary movements. The length of time between exposure and the development of symptoms is unclear, but is believed to be years. Average life expectancy following the onset of symptoms is 13 months.
== Cause ==
=== Tainted beef ===
In Britain, the primary cause of vCJD has been eating beef tainted with bovine spongiform encephalopathy. A 2012 study by the Health Protection Agency found that around 1 in 2000 had abnormal prions present in appendix cells.
Jonathan Quick, instructor of medicine at the Department of Global Health and Social Medicine at Harvard Medical School, stated that bovine spongiform encephalopathy (BSE) is the first man-made epidemic, or "Frankenstein" disease, because a human decision to feed meat and bone meal to previously herbivorous cattle (as a source of protein) caused what was previously an animal pathogen to enter into the human food chain, and from there to begin causing humans to contract vCJD.
The risk of contracting vCJD from ingestion of cattle products has led to many countries banning the import of beef from countries where BSE has been known to occur, such as the ban on beef from the United States imposed by Japan, South Korea, Mexico, Canada, and other countries in 2003 immediately following the first reported case of BSE in American cattle. Stringent preventative and surveillance practices implemented since then to prevent the disease from entering the human and cattle food chains have caused some to conclude that such bans are unnecessary.
=== Blood products ===
As of 2018, evidence suggests that there may be prions in the blood of individuals with vCJD, but this is not the case in individuals with sporadic CJD.
In 2004, a report showed that vCJD can be transmitted by blood transfusions. The finding alarmed healthcare officials because a large epidemic of the disease could result in the near future. A blood test for vCJD infection is possible but is not yet available for screening blood donations. Significant restrictions exist to protect the blood supply. The UK government banned anyone who had received a blood transfusion since January 1980 from donating blood. Since 1999 there has been a ban in the UK for using UK blood to manufacture fractional products such as albumin.
Whilst these restrictions may go some way to preventing a self-sustaining epidemic of secondary infections, the number of infected blood donations is unknown and could be considerable. In June 2013 the government was warned that deaths, then at 176, could rise five-fold through blood transfusions.
On 28 May 2002, the United States Food and Drug Administration instituted a policy that excludes from blood donation anyone having spent at least six months in certain European countries (or three months in the United Kingdom) from 1980 to 1996. Given the large number of U.S. military personnel and their dependents residing in Europe, it was expected that over 7% of donors would be deferred due to the policy. Later changes to this policy first relaxed the restriction to a cumulative total of five years or more of civilian travel in European countries (six months or more if military) then, in 2022, removed it entirely.
In New Zealand, the New Zealand Blood Service (NZBS) in 2000 introduced measures to preclude permanently donors having resided in the United Kingdom (including the Isle of Man and the Channel Islands) for a total of six months or more between January 1980 and December 1996. The measure resulted in ten percent of New Zealand's active blood donors at the time becoming ineligible to donate blood. In 2003, the NZBS further extended restrictions to permanently preclude donors having received a blood transfusion in the United Kingdom since January 1980, and in April 2006, restrictions were further extended to include the Republic of Ireland and France. The restriction was rescinded in late February 2024.
Similar regulations are in place where anyone having spent more than six months for Germany or one year for France living in the UK between January 1980 and December 1996 is permanently banned from donating blood.
In Canada, individuals were formerly ineligible to donate blood or plasma if they had spent a cumulative total of three months or more in the mainland UK or its Crown Dependencies or six months or more in Saudi Arabia from January 1, 1980, through December 31, 1996. They were also ineligible if they had spent a cumulative total of five years or more in France or the Republic of Ireland from January 1, 1980, through 31 December 2001. These restrictions were removed by December 2023.
In Poland, anyone having spent cumulatively six months or longer between 1 January 1980 and 31 December 1996 in the UK, Ireland, or France is permanently barred from donating.
In France, anyone having lived or stayed in the United Kingdom a total of over one year between 1 January 1980 and 31 December 1996 is permanently barred from donating.
In the Czech Republic, anyone having spent more than six months in the UK or France between the years 1980 and 1996 or received transfusion in the UK after the year 1980 is not allowed to donate blood.
In Finland, anyone having lived or stayed in the mainland United Kingdom or its Crown Dependencies for a total of over six months between 1 January 1980 and 31 December 1996 is permanently barred from donating.
=== Sperm donation ===
In the U.S., the FDA has banned import of any donor sperm, motivated by a risk of variant Creutzfeldt–Jakob disease, inhibiting the once popular import of Scandinavian sperm. Despite this, the scientific consensus is that the risk is negligible, as there is no evidence Creutzfeldt–Jakob is sexually transmitted.
=== Occupational contamination ===
In France, the last two victims of variant Creutzfeldt–Jakob disease, who died in 2019 and 2021, were research technicians at the National Research Institute for Agriculture, Food and the Environment (INRAE). Emilie Jaumain, who died in 2019, at the age of 33, had been the victim of a work accident in 2010, during which she had pricked herself with a tool contaminated with infected brain. The efficacy of this route of contamination has been unambiguously demonstrated in primates. Pierrette C., who died in 2021, had been victim of the same type of work accident. After her diagnosis, a moratorium was initiated in all French laboratories on research activities on infectious prions. In March 2022, INRAE recognized the occupational cause of these two deaths. This raises serious questions about the safety of personnel in these laboratories. Indeed, inspections have noted serious failures in the protection of agents in the face of this deadly risk, and the long incubation period of this disease leads to fears of new cases in the future, hence great concern.
=== False link to consumption of squirrel brains ===
A 1997 article by the Lancet postulated that a connection existed between the consumption of squirrel brains and CJD. Media reporting on this myth was reignited in 2018 when an article by Live Science reported on a Rochester man who was alleged to have contracted vCJD from his consumption of squirrel brains.
Creutzfeldt-Jakob Disease and eating squirrel brains, the 1997 article by the Lancet has been almost entirely discredited due its lack of scientific evidence and false statistical assumptions. An article published by the New Yorker in the year 2000, shortly following the release of the 1997 Lancet article was one of the first to report on the lack of scientific footing for the Lancet's study. It reported that many issues revolved around the researchers conflating statistical correlation with epidemiological causation. This is especially important with a slow moving, and hard to trace disease like vCJD, a point emphasized in their statement that "the Lancet study blundered blithely into such statistical pitfalls".
One of the most direct and robust dismissals of the connection between consumption of squirrel brains and vCJD was a statement made in 2018 by the Creutzfeldt–Jakob Disease Foundation, addressing both the 1997 Lancet article and Live Science article. This statement was part of a press release, reading that “The previous ProMed commentary mentioned a 1997 Lancet report that hypothesized about a potential link between consumption of squirrel brains and CJD; there was no mention of vCJD in that report. Since that brief report, there has been no convincing evidence found suggesting that the consumption of squirrel meat, brain or otherwise, is a risk factor for any prion disease. While prion diseases have been identified in several other types of mammals, they have never been identified in squirrels. Without additional experimental or epidemiological evidence, a link between consumption of squirrel brain and human prion disease is unjustifiably speculative.”
An article by Democrat and Chronicle provided another primary source debunking this myth. This report included interviews with the epidemiologist overseeing the 2018 case reported on by Live Science. In a statement, Dr. Emil Lesho stated that “We never thought squirrel brains”.
Despite the fact that there is no scientific basis for this connection, much reporting surrounds both the 1997 Lancet article, and the 2018 Live Science article, most echoing their statements. Although eating the nervous tissue of any mammal is not recommended due to the risk of disease transmission, fundamentally, there is no robust scientific evidence that the consumption of squirrel brains presents a risk factor for vCJD at any level, or has ever caused a case of vCJD in humans.
== Mechanism ==
Despite the consumption of contaminated beef in the UK being high, vCJD has infected a small number of people. One explanation for this can be found in the genetics of people with the disease. The human PRNP protein which is subverted in prion disease can occur with either methionine or valine at amino acid 129, without any apparent physiological difference. Of the overall white population, about 40% have two methionine-containing alleles, 10% have two valine-containing alleles, and the other 50% are heterozygous at this position. Only a single person with vCJD tested was found to be heterozygous; most of those affected had two copies of the methionine-containing form. It is not yet known whether those unaffected are actually immune or only have a longer incubation period until symptoms appear. Studies in transgenetic mice indicate that all of these genotypes can be affected.
== Diagnosis ==
=== Definitive ===
Examination of brain tissue is required to confirm a diagnosis of variant CJD. The following confirmatory features should be present:
Numerous widespread kuru-type amyloid plaques surrounded by vacuoles in both the cerebellum and cerebrum – florid plaques.
Spongiform change and extensive prion protein deposition shown by immunohistochemistry throughout the cerebellum and cerebrum.
=== Suspected ===
Current age or age at death less than 55 years (a brain autopsy is recommended, however, for all physician-diagnosed CJD cases).
Psychiatric symptoms at illness onset and/or persistent painful sensory symptoms (frank pain and/or dysesthesia).
Dementia, and development ≥4 months after illness onset of at least two of the following five neurologic signs: poor coordination, myoclonus, chorea, hyperreflexia, or visual signs. (If persistent painful sensory symptoms exist, ≥4 months' delay in the development of the neurologic signs is not required).
A normal or an abnormal EEG, but not the diagnostic EEG changes often seen in classic CJD.
Duration of illness of over 6 months.
Routine investigations do not suggest an alternative, non-CJD diagnosis.
No history of getting human pituitary growth hormone or a dura mater graft from a cadaver.
No history of CJD in a first degree relative or prion protein gene mutation in the person.
=== Classification ===
vCJD is a separate condition from classic Creutzfeldt–Jakob disease (though both are caused by PrP prions). Both classic and variant CJD are subtypes of Creutzfeldt–Jakob disease. There are three main categories of CJD disease: sporadic CJD, hereditary CJD, and acquired CJD, with variant CJD being in the acquired group along with iatrogenic CJD. The classic form includes sporadic and hereditary forms. Sporadic CJD is the most common type.
ICD-10 has no separate code for vCJD and such cases are reported under the Creutzfeldt–Jakob disease code (A81.0).
== Epidemiology ==
The Lancet in 2006 suggested that it may take more than 50 years for vCJD to develop, from their studies of kuru, a similar disease in Papua New Guinea. The reasoning behind the claim is that kuru was possibly transmitted through cannibalism in Papua New Guinea when family members would eat the body of a dead relative as a sign of mourning. In the 1950s, cannibalism was banned in Papua New Guinea.
In the late 20th century, however, kuru reached epidemic proportions in certain Papua New Guinean communities, therefore suggesting that vCJD may also have a similar incubation period of 20 to 50 years. A critique to this theory is that while mortuary cannibalism was banned in Papua New Guinea in the 1950s, that does not necessarily mean that the practice ended. Fifteen years later Jared Diamond was informed by Papuans that the practice continued.
These researchers noticed a genetic variation in some people with kuru that has been known to promote long incubation periods. They have also proposed that individuals having contracted CJD in the early 1990s represent a distinct genetic subpopulation, with unusually short incubation periods for bovine spongiform encephalopathy (BSE). This means that there may be many more people with vCJD with longer incubation periods, which may surface many years later.
Prion protein is detectable in lymphoid and appendix tissue up to two years before the onset of neurological symptoms in vCJD. Large scale studies in the UK have yielded an estimated prevalence of 493 per million, higher than the actual number of reported cases. This finding indicates a large number of asymptomatic cases and the need to monitor.
== Society and culture ==
=== United Kingdom ===
The first human death from vCJD occurred in the United Kingdom; Wiltshire teenager Stephen Churchill died on 23 May 1995, aged 19. Researchers believe one in 2,000 people in the UK is a carrier of the disease, linked to eating contaminated beef. The survey provides the most robust prevalence measure to date—and identifies abnormal prion protein across a wider age group than found previously and in all genotypes, indicating "infection" may be relatively common. This new study examined over 32,000 anonymous appendix samples. Of these, 16 samples were positive for abnormal prion protein, indicating an overall prevalence of 493 per million population, or one in 2,000 people are likely to be carriers. No difference was seen in different birth cohorts (1941–1960 and 1961–1985), in both sexes, and there was no apparent difference in abnormal prion prevalence in three broad geographical areas. Genetic testing of the 16 positive samples revealed a higher proportion of valine homozygous (VV) genotype on the codon 129 of the gene encoding the prion protein (PRNP) compared with the general UK population. This also differs from the 176 people with vCJD, all of whom to date have been methionine homozygous (MM) genotype. The concern is that individuals with this VV genotype may be susceptible to developing the condition over longer incubation periods.
=== Human BSE Foundation ===
In 2000 a voluntary support group was formed by families of people who had died from vCJD. The goal was to support other families going through a similar experience. This support was provided through a National Helpline, a Carer's Guide, a website and a network of family befriending. The support groups had an internet presence at the turn of the 21st century. The driving force behind the foundation was Lester Firkins, whose young son had died from the disease.
In October 2000 the report of the government inquiry into BSE chaired by Lord Phillips was published. The BSE report criticised former Conservative Party Agriculture Ministers John Gummer, John MacGregor and Douglas Hogg. The report concluded that the escalation of BSE into a crisis was the result of intensive farming, particularly with cows being fed with cow and sheep remains. Furthermore, the report was critical of the way the crisis had been handled. There was a reluctance to consider the possibility that BSE could cross the species barrier. The government assured the public that British beef was safe to eat, with agriculture minister John Gummer famously feeding his daughter a burger. The British government were reactive more than proactive in response; the worldwide ban on all British beef exports in March 1996 was a serious economic blow.
The foundation had been calling for compensation to include a care package to help relatives look after those with vCJD. There have been widespread complaints of inadequate health and social services support. Following the Phillips Report in October 2001, the government announced a compensation scheme for British people affected with vCJD. The multi-million-pound financial package was overseen by the vCJD Trust.
A memorial plaque for those who have died due to vCJD was installed in central London in approximately 2000. It is located on the boundary wall of St Thomas' Hospital in Lambeth facing the Riverside Walk of Albert Embankment.
== See also ==
Jonathan Simms, a person who died from vCJD
Mepacrine
== References ==
== External links == | Wikipedia/Variant_Creutzfeldt–Jakob_disease |
Clinical nutrition centers on the prevention, diagnosis, and management of nutritional changes in patients linked to chronic diseases and conditions primarily in health care. Clinical in this sense refers to the management of patients, including not only outpatients at clinics and in private practice, but also inpatients in hospitals. It incorporates primarily the scientific fields of nutrition and dietetics. Furthermore, clinical nutrition aims to maintain a healthy energy balance, while also providing sufficient amounts of nutrients such as protein, vitamins, and minerals to patients.
== Dietary needs and disease processes ==
Normally, individuals obtain the necessary nutrients their bodies require through normal daily diets that process the foods accordingly within the body. Nevertheless, there are circumstances such as disease, distress, stress, and so on that may prevent the body from obtaining sufficient nutrients through diets alone. In such conditions, a dietary supplementation specifically formulated for their individual condition may be required to fill the void created by the specific condition. This can come in form of Medical Nutrition.
== Methods of nutrition ==
Among the routes of administration, the preferred means of nutrition is, if possible, oral administration. Alternatives include enteral administration (in nasogastric feeding) and intravenous (in parenteral nutrition).
== Clinical malnutrition ==
In the field of clinical nutrition, malnutrition has causes, epidemiology and management distinct from those associated with malnutrition that is mainly related to poverty.
The main causes of clinical malnutrition are:
Cachexia caused by diseases, injuries and/or aging
Difficulties with ingestion, such as stroke, paresis, dementia, depression, dysphagia
Clinical malnutrition may also be aggravated by iatrogenic factors, i.e., the inability of a health care entity to appropriately compensate for causes of malnutrition.
There are various definitions of clinical malnutrition. According to one of them, patients are defined as severely undernourished when meeting at least one of the following criteria: BMI < or = 20 kg/m2 and/or > or = 5% unintentional weight loss in the past month and/or > or = 10% unintentional weight loss in the past 6 months. By the same system, the patient is moderately undernourished if they met at least one of the following criteria: BMI 20.1–22 kg/m2 and/or 5-10% unintentional weight loss in the past six months.
== Medical nutrition therapy ==
Medical nutrition therapy (MNT) is the use of specific nutrition services to treat an illness, injury, or condition. It was introduced in 1994 by the American Dietetic Association to better articulate the nutrition therapy process. It involves the assessment of the nutritional status of the client and the actual treatment, which includes nutrition therapy, counseling, and the use of specialized nutrition supplements, devised and monitored by a medical doctor physician or registered dietitian nutritionist (RDN). Registered dietitians started using MNT as a dietary intervention for preventing or treating other health conditions that are caused by or made worse by unhealthy eating habits.
The role of MNT when administered by a physician or dietitian nutritionist (RDN) is to reduce the risk of developing complications in pre-existing conditions such as type 2 diabetes as well as ameliorate the effects any existing conditions such as high cholesterol. Many medical conditions either develop or are made worse by an improper or unhealthy diet. Similar to MNT is diabetes self-management training (DSMT) which is an education and training program by a healthcare professional rather than a personal treatment plan from a registered dietitian.
An example is the use of macronutrient preload in type 2 diabetes.
=== Administration ===
In most cases the use of Medical Nutrition is recommended within international and professional guidelines. It can be an integral part of managing acute and short-term diseases. It can also play a major role in supporting patients for extended periods of time and even for a lifetime in some special cases. Medical Nutrition is not meant to replace the treatment of disease but rather complement the normal use of drug therapies prescribed by physicians and other licensed healthcare providers.
Unlike Medical Foods which are defined by the U.S. Department of Health and Human Services Food and Drug Administration, within their Medical Foods Guidance Documents & Regulatory Information guide in section 5(b) of the Orphan Drug Act (21 U.S.C. 30ee (b) (3)); as "a food which is formulated to be consumed or administered enterally under the supervision of a physician and which is intended for the specific dietary management of a disease or condition for which distinctive nutritional requirements, based on recognized scientific principles, are established by medical evaluation,"
=== Advantages ===
The following advantages come with medical nutrition:
It is often very effective in treating type 1 or type 2 diabetes.
It can help one to live better at any age
=== Disadvantages ===
The following are some disadvantages of medical nutrition:
A patient may need to follow a strict diet to see benefits while using a medical nutrition plan.
Some forms of medical nutrition can be very expensive. A poor patient may not afford such.
== Journals ==
The American Journal of Clinical Nutrition is the highest-ranked journal in ISI's nutrition category.
== See also ==
European Society for Clinical Nutrition and Metabolism
Eating disorders
Medical food
Nutrition
Therapeutic food
== Notes and references ==
== External links ==
Academy of Nutrition and Dietetics | Wikipedia/Clinical_nutrition |
3-Phosphoglyceric acid (3PG, 3-PGA, or PGA) is the conjugate acid of 3-phosphoglycerate or glycerate 3-phosphate (GP or G3P). This glycerate is a biochemically significant metabolic intermediate in both glycolysis and the Calvin-Benson cycle. The anion is often termed as PGA when referring to the Calvin-Benson cycle. In the Calvin-Benson cycle, 3-phosphoglycerate is typically the product of the spontaneous scission of an unstable 6-carbon intermediate formed upon CO2 fixation. Thus, two equivalents of 3-phosphoglycerate are produced for each molecule of CO2 that is fixed. In glycolysis, 3-phosphoglycerate is an intermediate following the dephosphorylation (reduction) of 1,3-bisphosphoglycerate.: 14
== Glycolysis ==
In the glycolytic pathway, 1,3-bisphosphoglycerate is dephosphorylated to form 3-phosphoglyceric acid in a coupled reaction producing two ATP via substrate-level phosphorylation. The single phosphate group left on the 3-PGA molecule then moves from an end carbon to a central carbon, producing 2-phosphoglycerate. This phosphate group relocation is catalyzed by phosphoglycerate mutase, an enzyme that also catalyzes the reverse reaction.
Compound C00236 at KEGG Pathway Database. Enzyme 2.7.2.3 at KEGG Pathway Database. Compound C00197 at KEGG Pathway Database. Enzyme 5.4.2.1 at KEGG Pathway Database. Compound C00631 at KEGG Pathway Database.
Click on genes, proteins and metabolites below to link to respective articles.
== Calvin-Benson cycle ==
In the light-independent reactions (also known as the Calvin-Benson cycle), two 3-phosphoglycerate molecules are synthesized. RuBP, a 5-carbon sugar, undergoes carbon fixation, catalyzed by the rubisco enzyme, to become an unstable 6-carbon intermediate. This intermediate is then cleaved into two, separate 3-carbon molecules of 3-PGA. One of the resultant 3-PGA molecules continues through the Calvin-Benson cycle to be regenerated into RuBP while the other is reduced to form one molecule of glyceraldehyde 3-phosphate (G3P) in two steps: the phosphorylation of 3-PGA into 1,3-bisphosphoglyceric acid via the enzyme phosphoglycerate kinase (the reverse of the reaction seen in glycolysis) and the subsequent catalysis by glyceraldehyde 3-phosphate dehydrogenase into G3P. G3P eventually reacts to form the sugars such as glucose or fructose or more complex starches.: 156
== Amino acid synthesis ==
Glycerate 3-phosphate (formed from 3-phosphoglycerate) is also a precursor for serine, which, in turn, can create cysteine and glycine through the homocysteine cycle.
== Measurement ==
3-phosphoglycerate can be separated and measured using paper chromatography as well as with column chromatography and other chromatographic separation methods. It can be identified using both gas-chromatography and liquid-chromatography mass spectrometry and has been optimized for evaluation using tandem MS techniques.
== See also ==
2-Phosphoglyceric acid
Calvin-Benson cycle
Photosynthesis
Ribulose 1,5-bisphosphate
== References == | Wikipedia/3-Phosphoglycerate |
Bisphosphoglycerate mutase (EC 5.4.2.4, BPGM) is an enzyme expressed in erythrocytes and placental cells. It is responsible for the catalytic synthesis of 2,3-Bisphosphoglycerate (2,3-BPG) from 1,3-bisphosphoglycerate. BPGM has both a mutase and a phosphatase function, but these are much less active, in contrast to its glycolytic cousin, phosphoglycerate mutase (PGM), which favors these two functions, but can also catalyze the synthesis of 2,3-BPG to a lesser extent.
== Tissue distribution ==
Because the main function of bisphosphoglycerate mutase is the synthesis of 2,3-BPG, this enzyme is found only in erythrocytes and placental cells. In glycolysis, converting 1,3-BPG to 2,3-BPG bypasses the ATP-generating phosphoglycerate kinase reaction, consequently, no net ATP is generated when glucose is metabolized through the Luebering–Rapoport pathway. Since the main role of 2,3-BPG is to shift the equilibrium of hemoglobin toward the deoxy-state, its production is really only useful in the cells which contain hemoglobin, such as erythrocytes and placental cells.
== Function ==
1,3-BPG is formed as an intermediate in glycolysis. BPGM then takes this and converts it to 2,3-BPG, which serves an important function in oxygen transport. 2,3-BPG binds with high affinity to Hemoglobin, causing a conformational change that results in the release of oxygen. Local tissues can then pick up the free oxygen. This is also important in the placenta, where fetal and maternal blood come within such close proximity. With the placenta producing 2,3-BPG, a large amount of oxygen is released from nearby maternal hemoglobin, which can then dissociate and bind with fetal hemoglobin, which has a much lower affinity for 2,3-BPG.
== Structure ==
=== Overall ===
BPGM is a dimer composed of two identical protein subunits, each with its own active site. Each subunit consists six β-strands, β A-F, and ten α-helices, α 1–10. Dimerization occurs along the faces of β C and α 3 of both monomers. BPGM is roughly 50% identical to its PGM counterpart, with the main active-site residues conserved in nearly all PGMs and BPGMs.
=== Important residues ===
His11: the nucleophile of the 1,2-BPG to 1,3-BPG reaction. Rotates back and forth with the help of His-188 to get in an in-line position in order to attack the 1’ phosphate group.
His-188: involved in overall stability of protein, as well as hydrogen bonding to substrate, as His-11, which it pulls into its catalytic position.
Arg90: although not involved directly in binding, this positively charged residue is essential to overall stability of the protein. Can be substituted with Lysine with little effect on catalysis.
Cys23: has little effect on overall structure, but large effect on reactivity of the enzyme.
== Mechanism of catalysis ==
1,3-BPG binds to the active site, which causes a conformational change, in which the cleft around the active site closes in on the substrate, securely locking it in place. 1,3-BPG forms a large number of hydrogen bonds to the surrounding residues, many which are positively charged, severely restricting its mobility. Its rigidity suggests a very enthalpically driven association. Conformational changes cause His11 to rotate, partially aided by hydrogen bonding to His188. His11 is brought in–line with the phosphate group, and then goes through an SN2 mechanism in which His11 is the nucleophile that attacks the phosphate group. The 2’ hydroxy group then attacks the phosphate and removes it from His11, thereby creating 2,3-BPG.
== References ==
== Further reading ==
== External links ==
Bisphosphoglycerate Mutase at the U.S. National Library of Medicine Medical Subject Headings (MeSH)
EC 5.4.2.4 | Wikipedia/Bisphosphoglycerate_mutase |
Phosphoglycerate mutase 2 (PGAM2), also known as muscle-specific phosphoglycerate mutase (PGAM-M), is a phosphoglycerate mutase that, in humans, is encoded by the PGAM2 gene on chromosome 7.
Phosphoglycerate mutase (PGAM) catalyzes the reversible reaction of 3-phosphoglycerate (3-PGA) to 2-phosphoglycerate (2-PGA) in the glycolytic pathway. The PGAM is a dimeric enzyme containing, in different tissues, different proportions of a slow-migrating muscle (MM) isozyme, a fast-migrating brain (BB) isozyme, and a hybrid form (MB). This gene encodes muscle-specific PGAM subunit. Mutations in this gene cause muscle phosphoglycerate mutase deficiency, also known as glycogen storage disease X.[provided by RefSeq, Sep 2009]
== Structure ==
PGAM2 is one of two genes in humans encoding a PGAM subunit, the other being PGAM1.
=== Gene ===
The PGAM2 gene is composed of three exons of lengths spanning 454, 180, and 202 bp, separated by two introns of 103 bp and 5.6 kb. Located 29 bp upstream of the transcription start site is a TATA box-like element, and 40 bp upstream of this element is an inverted CCAAT box element (ATTGG). Despite its muscle-specific expression, no muscle-specific consensus sequences were identified in the 5'-untranslated region of human PGAM2, though one consensus sequence has been proposed in rat and chicken. Unlike PGAM1, which is present as several copies in the human genome, only one copy of PGAM2 is found in the genome, indicating that this gene arose from gene duplication of and subsequent modifications in the PGAM1 gene.
=== Protein ===
The isozyme encoded by PGAM2 spans 253 residues, which demonstrates highly sequence similarity (81% identity) to the protein PGAM1. Both form either homo- or heterodimers. The MM homodimer is found primarily in adult muscle, while the MB heterodimer, composed of a subunit from each isozyme, is found in the heart.
One key residue in the active site of PGAM2, lysine 100 (K100), is highly conserved across bacteria, to yeast, plant, and mammals, indicating its evolutionary importance. K100 directly contacts the substrate (3-PGA) and intermediate (2,3-PGA); however, the acetylation of this residue under normal cellular conditions neutralizes its positive charge and interferes with this binding.
== Mechanism ==
PGAM2 catalyzes the 3-PG-to-2-PG isomerization via a 2-step process:
a phosphate group from the phosphohistidine in the active site is transferred to the C-2 carbon of 3-PGA to form 2,3-bisphosglycerate (2,3-PGA), and then
the phosphate group linked to the C-3 carbon of 2,3-PG is transferred to the catalytic histidine to form 2-PGA and regenerate the phosphohistidine.
== Function ==
PGAM2 is one of two PGAM subunits found in humans and is predominantly expressed in adult muscle. Both isozymes of PGAM are glycolytic enzymes that catalyze the reversible conversion of 3-PGA to 2-PGA using 2,3-bisphosphoglycerate as a cofactor. Since both 3-PGA and 2-PGA are allosteric regulators of the pentose phosphate pathway (PPP) and glycine and serine synthesis pathways, respectively, PGAM2 may contribute to the biosynthesis of amino acids, 5-carbon sugar, and nucleotides precursors.
== Clinical significance ==
PGAM activity is upregulated in cancers, including lung cancer, colon cancer, liver cancer, breast cancer, and leukemia. One possible mechanism involves the deacetylation of residue K100 in the PGAM active site by sirtuin 2 (SIRT2) under conditions of oxidative stress. This deacetylation activates PGAM activity, resulting in increased NADPH production and cell proliferation, and thus tumor growth.
In a patient with intolerance for strenuous exercise and persistent pigmenturia, PGAM2 activity was found to be decreased relative to other glycolytic enzymes. This PGAM2 deficiency results in a metabolic myopathy (glycogenosis type X) and has been traced to mutations in the PGAM2 gene. Currently, four mutations have been identified from African-American, Caucasian, and Japanese families. One G-to-A transition mutation in codon 78 produced a truncated protein product, while mutations at codons 89 and 90 may have disrupted the active site and resulted in an inactive protein product. Meanwhile, two patients heterozygous for the G97D mutation presented with exercise intolerance and muscle cramps.
== Interactions ==
PGAM2 is known to interact with:
SIRT2.
== Interactive pathway map ==
Click on genes, proteins and metabolites below to link to respective articles.
== See also ==
Phosphoglycerate mutase
PGAM1
== References ==
This article incorporates text from the United States National Library of Medicine ([1]), which is in the public domain. | Wikipedia/Glycogen_storage_disease_type_X |
2,3-Bisphosphoglyceric acid (conjugate base 2,3-bisphosphoglycerate) (2,3-BPG), also known as 2,3-diphosphoglyceric acid (conjugate base 2,3-diphosphoglycerate) (2,3-DPG), is a three-carbon isomer of the glycolytic intermediate 1,3-bisphosphoglyceric acid (1,3-BPG).
D-2,3-BPG is present in human red blood cells (RBC; erythrocyte) at approximately 5 mmol/L. It binds with greater affinity to deoxygenated hemoglobin (e.g., when the red blood cell is near respiring tissue) than it does to oxygenated hemoglobin (e.g., in the lungs) due to conformational differences: 2,3-BPG (with an estimated size of about 9 Å) fits in the deoxygenated hemoglobin conformation (with an 11-Angstrom pocket), but not as well in the oxygenated conformation (5 Angstroms). It interacts with deoxygenated hemoglobin beta subunits and decreases the affinity for oxygen and allosterically promotes the release of the remaining oxygen molecules bound to the hemoglobin. Therefore, it enhances the ability of RBCs to release oxygen near tissues that need it most. 2,3-BPG is thus an allosteric effector.
Its function was discovered in 1967 by Reinhold Benesch and Ruth Benesch.
== Metabolism ==
2,3-BPG is formed from 1,3-BPG by the enzyme BPG mutase. It can then be broken down by 2,3-BPG phosphatase to form 3-phosphoglycerate. Its synthesis and breakdown are, therefore, a way around a step of glycolysis, with the net expense of one ATP per molecule of 2,3-BPG generated as the high-energy carboxylic acid-phosphate mixed anhydride bond is cleaved by 2,3-BPG phosphatase.
The normal glycolytic pathway generates 1,3-BPG, which may be dephosphorylated by phosphoglycerate kinase (PGK), generating ATP, or it may be shunted into the Luebering-Rapoport pathway, where bisphosphoglycerate mutase catalyzes the transfer of a phosphoryl group from C1 to C2 of 1,3-BPG, giving 2,3-BPG. 2,3-BPG, the most concentrated organophosphate in the erythrocyte, forms 3-PG by the action of bisphosphoglycerate phosphatase. The concentration of 2,3-BPG varies proportionately to the [H+].
There is a delicate balance between the need to generate ATP to support energy requirements for cell metabolism and the need to maintain appropriate oxygenation/deoxygenation status of hemoglobin. This balance is maintained by isomerisation of 1,3-BPG to 2,3-BPG, which enhances the deoxygenation of hemoglobin.
== Structural binding to hemoglobin ==
When 2,3-BPG binds to deoxyhemoglobin, it acts to stabilize the low oxygen affinity state (T state) of the oxygen carrier. It fits neatly into the cavity of the deoxy- conformation, exploiting the molecular symmetry and positive polarity by forming salt bridges with lysine and histidine residues in the β subunits of hemoglobin. The R state, with oxygen bound to a heme group, has a different conformation and does not allow this interaction. By itself, hemoglobin has sigmoid-like kinetics. In selectively binding to deoxyhemoglobin, 2,3-BPG stabilizes the T state conformation, making it harder for oxygen to bind hemoglobin and more likely to be released to adjacent tissues.
== Physiological effects ==
An increase in 2,3-BPG essentially facilitates the delivery of oxygen from hemoglobin in target tissues, at a cost of also making it somewhat more difficult for hemoglobin to take up oxygen in the lungs. This mechanisms makes maternal-fetal oxygenation more efficient, as fetal 2,3-BPG is lower than maternal levels, resulting in a higher uptake of oxygen by the fetal blood in the placenta.
2,3-BPG may also serve to physiologically counteract certain metabolic disturbances to the oxygen-hemoglobin dissociation curve. For example, at high altitudes, low atmospheric oxygen content of oxygen can cause hyperventilation and resultant metabolic alkalosis which causes an abnormal left-shift of the oxygen-hemoglobin dissociation curve, and this can be counteracted by an increase in 2,3-BPG. Traditional teaching has claimed that the physiologic increased 2,3-BPG seen at high altitudes is simply to make it easier for oxygen to be delivered in target tissues, but this mechanism by itself is refuted by the reasoning that the decreased oxygen affinity would also inhibit oxygen uptake in the lungs, and arguably result in a net decrease in total oxygen delivery to target tissues.
=== Maternal-fetal oxygenation ===
In pregnant women, there is a 30% increase in intracellular 2,3-BPG. This lowers the maternal hemoglobin affinity for oxygen, and therefore allows more oxygen to be offloaded to the fetus in the maternal uterine arteries. The fetus has a low sensitivity to 2,3-BPG, so its hemoglobin has a higher affinity for oxygen. Therefore, although the pO2 in the uterine arteries is low, the fetal umbilical artery (which carries deoxygenated blood) can still get oxygenated from them.
The increased maternal 2,3-BPG also causes a decreased affinity for oxygen takeup in the lungs, but this is usually compensated by a physiologic increased respiratory rate in pregnancy.
Fetal hemoglobin (HbF), on the other hand, exhibits a low affinity for 2,3-BPG, resulting in a higher binding affinity for oxygen. This increased oxygen-binding affinity relative to that of adult hemoglobin (HbA) is due to HbF's having two α/γ dimers as opposed to the two α/β dimers of HbA. The positive histidine residues of HbA β-subunits that are essential for forming the 2,3-BPG binding pocket are replaced by serine residues in HbF γ-subunits. Like that, histidine nº143 gets lost, so 2,3-BPG has difficulties in linking to the fetal hemoglobin, and it looks like the pure hemoglobin.
Increased binding affinity of fetal hemoglobin relative to HbA facilitates the passage of oxygen across the placental membrane from the mother to the fetus.
Differences between myoglobin (Mb), fetal hemoglobin (Hb F), adult hemoglobin (Hb A)
== Diseases related to 2,3-BPG ==
Hyperthyroidism
A 2004 study checked the effects of thyroid hormone on 2,3-BPG levels. The result was that the hyperthyroidism modulates in vivo 2,3-BPG content in erythrocytes by changes in the expression of phosphoglycerate mutase (PGM) and 2,3-BPG synthase.
This result shows that the increase in the 2,3-BPG content of erythrocytes observed in hyperthyroidism doesn’t depend on any variation in the rate of circulating hemoglobin, but seems to be a direct consequence of the stimulating effect of thyroid hormones on erythrocyte glycolytic activity.
Chronic anemia
Red cells increase their intracellular 2,3-BPG concentration as much as five times within one to two hours in patients with chronic anemia, when the oxygen carrying capacity of the blood is diminished. This results in a rightward shift of the oxygen dissociation curve and more oxygen being released to the tissues.
Chronic respiratory disease with hypoxia
Recently, scientists have found similarities between low amounts of 2,3-BPG with the occurrence of high altitude pulmonary edema at high altitudes.
== Hemodialysis ==
In a 1998 study, erythrocyte 2,3-BPG concentration was analyzed during the hemodialysis process. The 2,3-BPG concentration was expressed relative to the hemoglobin tetramer (Hb4) concentration as the 2,3-BPG/Hb4 ratio. Physiologically, an increase in 2,3-BPG levels would be expected to counteract the hypoxia that is frequently observed in this process. Nevertheless, the results show a 2,3-BPG/Hb4 ratio decreased. This is due to the procedure itself: mechanical stress on the erythrocytes is believed to cause the 2,3-BPG escape, which is then removed by hemodialysis. The concentrations of calcium, phosphate, creatinine, urea and albumin did not correlate significantly with the total change in 2,3-BPG/Hb4 ratio. However, the ratio sampled just before dialysis correlated significantly and positively with the total weekly dosage of erythropoietin (main hormone in erythrocyte formation) given to the patients.
== See also ==
Oxygen–hemoglobin dissociation curve
== References ==
== External links ==
A live model of the effect of changing 2,3 BPG on the oxyhaemoglobin saturation curve | Wikipedia/2,3-Bisphosphoglycerate |
E3 binding protein also known as pyruvate dehydrogenase protein X component, mitochondrial is a protein that in humans is encoded by the PDHX gene. The E3 binding protein is a component of the pyruvate dehydrogenase complex found only in eukaryotes. Defects in this gene are a cause of pyruvate dehydrogenase deficiency which results in neurological dysfunction and lactic acidosis in infancy and early childhood. This protein is also a minor antigen for antimitochondrial antibodies. These autoantibodies are present in nearly 95% of patients with primary biliary cholangitis, an autoimmune disease of the liver. In primary biliary cholangitis, activated T lymphocytes attack and destroy epithelial cells in the bile duct where this protein is abnormally distributed and overexpressed. Primary biliary cholangitis eventually leads to liver failure.
== Structure ==
The mRNA encoded by the human PDHX gene is approximately 2.5 kb in length, and expressed primarily in human skeletal and cardiac muscle tissues. The gene has been localized in humans to the 11th chromosome, with the specific location being 11p1.3.
The protein encoded by the human PDHX gene, also known as E3 binding protein (E3BP), is part of the pyruvate dehydrogenase complex, a required complex for cellular respiration that catalyzes the dehydration of pyruvate to Acetyl-CoA. The entire complex is 9.5 MDa in size, and has been described as 60-meric, meaning there are over 60 components that are assembled to make the entire complex. These subunits are conserved across many species, as the function of this complex is essential for the generation of ATP for all eukaryotes. The E3 binding protein directly interacts with the dihydrolipoamide transacetylase (E2) core, anchoring it to the complex. E3BP binds the I domain of E2 by its C-terminal I' domain. The composition of E2.E3BP was thought to be 60 E2 plus approximately 12 E3BP, however, equilibrium sedimentation and small angle x-ray scattering studies showed that the E3BP/E2 binding complex has a lower mass than the E2 subunit alone. Additionally, these studies showed that E3 binds to E2.E3BP outside the central dodecahedron of the PDH complex, and that this interaction creates a lower binding affinity for the E1 subunit. Together, these data support a substitution model, in which the smaller E3BP subunits replace the E2 subunits rather than adding to the 60-mer entire complex. The specific model illustrates 12 I domains of E2 being substituted by 12 I' domains of E3BP, thereby forming 6 dimer edges that are symmetrically located in the dodecahedron structure.
E3BP similarly binds to E3, having linker regions that connect an E3-binding domain and a lipoyl domain.
Crystallography of the complex has shown that, E3BD also binds to E3, though no significant conformational changes occur. In this binding, two E3 subunits come together to form the binding site. This has also shown that E3BP has residues that come into contact with the E3 component across its two-fold axis; this means that there is one binding site for this reaction on the E3 homodimer. Changing the central residues at the E3BD/E3 interface affect binding much more drastically than does changing peripheral residues. This data corroborates the theory of the existence of a “hot spot”. Specifically, there are three hydrophobic residues within the binding domain of E3BP - Pro133, Pro154, and Ile157 – that interact with the surface of both E3 polypeptide chains. This interaction is significantly stabilized by many ionic and hydrogen bonds that take place between the residues of three interacting polypeptide chains adjacent to the central hydrophobic patch. This specificity is most likely due to the lack of conformational flexibility of the binding fragment of E3BP and the complementary amino acid match with the E3 interface.
== Function ==
The pyruvate dehydrogenase (PDH) complex is located in the mitochondrial matrix and catalyzes the conversion of pyruvate to acetyl coenzyme A. The PDH complex thereby links glycolysis to the citric acid cycle. The PDH complex contains three catalytic subunits, E1, E2, and E3, two regulatory subunits, E1 kinase and E1 phosphatase, and a non-catalytic subunit, E3 binding protein (E3BP). This gene encodes the E3 binding protein subunit; also known as component X of the pyruvate dehydrogenase complex. This protein tethers E3 dimers to the E2 core of the PDH complex.
== Clinical significance ==
Mutations in the PDHX gene have been known to cause one form of pyruvate dehydrogenase deficiency. Pyruvate dehydrogenase deficiency is characterized by the buildup of a chemical called lactic acid in the body and a variety of neurological problems. Signs and symptoms of this condition usually first appear shortly after birth, and they can vary widely among affected individuals. The most common feature is a potentially life-threatening buildup of lactic acid (lactic acidosis), which can cause nausea, vomiting, severe breathing problems, and an abnormal heartbeat. People with pyruvate dehydrogenase deficiency usually have neurological problems as well. Most have delayed development of mental abilities and motor skills such as sitting and walking. Other neurological problems can include intellectual disability, seizures, weak muscle tone (hypotonia), poor coordination, and difficulty walking. Some affected individuals have abnormal brain structures, such as underdevelopment of the tissue connecting the left and right halves of the brain (corpus callosum), wasting away (atrophy) of the exterior part of the brain known as the cerebral cortex, or patches of damaged tissue (lesions) on some parts of the brain. Because of the severe health effects, many individuals with pyruvate dehydrogenase deficiency do not survive past childhood, although some may live into adolescence or adulthood.
While this deficiency primarily results in mutations in the E1 alpha subunit of the PDH complex, a few mutations have been identified in the PDX1 gene. Specific investigations of this gene have identified 78del85 and 965del59 mutations in a homozygous state, while some mutations could not be identified due to no PDHX mRNA being expressed in the individuals. In other cases, it has been reported that an entire exon, exon 10, was removed due to a gross deletion mutation; the mechanism for this has been theorized to be a mispairing, because there is an exact direct repeat, CCACTG, within the gene. Other large deletions (over 3900 bp) have been reported. E3BP, in coordination with the E2 subunit, has also been shown to be a secondary antigen for antimitochondrial antibodies and immune responses. The autoantibodies for this protein are present in the vast majority of patients with primary biliary cirrhosis, a chronic, progressive cholestatic liver disease that usually affects middle-aged women and eventually leads to liver failure.
== Interactive pathway map ==
Click on genes, proteins and metabolites below to link to respective articles.
== References ==
== Further reading ==
== External links ==
PDHX+protein,+human at the U.S. National Library of Medicine Medical Subject Headings (MeSH)
This article incorporates text from the United States National Library of Medicine, which is in the public domain. | Wikipedia/E3_binding_protein |
2,3-Bisphosphoglyceric acid (conjugate base 2,3-bisphosphoglycerate) (2,3-BPG), also known as 2,3-diphosphoglyceric acid (conjugate base 2,3-diphosphoglycerate) (2,3-DPG), is a three-carbon isomer of the glycolytic intermediate 1,3-bisphosphoglyceric acid (1,3-BPG).
D-2,3-BPG is present in human red blood cells (RBC; erythrocyte) at approximately 5 mmol/L. It binds with greater affinity to deoxygenated hemoglobin (e.g., when the red blood cell is near respiring tissue) than it does to oxygenated hemoglobin (e.g., in the lungs) due to conformational differences: 2,3-BPG (with an estimated size of about 9 Å) fits in the deoxygenated hemoglobin conformation (with an 11-Angstrom pocket), but not as well in the oxygenated conformation (5 Angstroms). It interacts with deoxygenated hemoglobin beta subunits and decreases the affinity for oxygen and allosterically promotes the release of the remaining oxygen molecules bound to the hemoglobin. Therefore, it enhances the ability of RBCs to release oxygen near tissues that need it most. 2,3-BPG is thus an allosteric effector.
Its function was discovered in 1967 by Reinhold Benesch and Ruth Benesch.
== Metabolism ==
2,3-BPG is formed from 1,3-BPG by the enzyme BPG mutase. It can then be broken down by 2,3-BPG phosphatase to form 3-phosphoglycerate. Its synthesis and breakdown are, therefore, a way around a step of glycolysis, with the net expense of one ATP per molecule of 2,3-BPG generated as the high-energy carboxylic acid-phosphate mixed anhydride bond is cleaved by 2,3-BPG phosphatase.
The normal glycolytic pathway generates 1,3-BPG, which may be dephosphorylated by phosphoglycerate kinase (PGK), generating ATP, or it may be shunted into the Luebering-Rapoport pathway, where bisphosphoglycerate mutase catalyzes the transfer of a phosphoryl group from C1 to C2 of 1,3-BPG, giving 2,3-BPG. 2,3-BPG, the most concentrated organophosphate in the erythrocyte, forms 3-PG by the action of bisphosphoglycerate phosphatase. The concentration of 2,3-BPG varies proportionately to the [H+].
There is a delicate balance between the need to generate ATP to support energy requirements for cell metabolism and the need to maintain appropriate oxygenation/deoxygenation status of hemoglobin. This balance is maintained by isomerisation of 1,3-BPG to 2,3-BPG, which enhances the deoxygenation of hemoglobin.
== Structural binding to hemoglobin ==
When 2,3-BPG binds to deoxyhemoglobin, it acts to stabilize the low oxygen affinity state (T state) of the oxygen carrier. It fits neatly into the cavity of the deoxy- conformation, exploiting the molecular symmetry and positive polarity by forming salt bridges with lysine and histidine residues in the β subunits of hemoglobin. The R state, with oxygen bound to a heme group, has a different conformation and does not allow this interaction. By itself, hemoglobin has sigmoid-like kinetics. In selectively binding to deoxyhemoglobin, 2,3-BPG stabilizes the T state conformation, making it harder for oxygen to bind hemoglobin and more likely to be released to adjacent tissues.
== Physiological effects ==
An increase in 2,3-BPG essentially facilitates the delivery of oxygen from hemoglobin in target tissues, at a cost of also making it somewhat more difficult for hemoglobin to take up oxygen in the lungs. This mechanisms makes maternal-fetal oxygenation more efficient, as fetal 2,3-BPG is lower than maternal levels, resulting in a higher uptake of oxygen by the fetal blood in the placenta.
2,3-BPG may also serve to physiologically counteract certain metabolic disturbances to the oxygen-hemoglobin dissociation curve. For example, at high altitudes, low atmospheric oxygen content of oxygen can cause hyperventilation and resultant metabolic alkalosis which causes an abnormal left-shift of the oxygen-hemoglobin dissociation curve, and this can be counteracted by an increase in 2,3-BPG. Traditional teaching has claimed that the physiologic increased 2,3-BPG seen at high altitudes is simply to make it easier for oxygen to be delivered in target tissues, but this mechanism by itself is refuted by the reasoning that the decreased oxygen affinity would also inhibit oxygen uptake in the lungs, and arguably result in a net decrease in total oxygen delivery to target tissues.
=== Maternal-fetal oxygenation ===
In pregnant women, there is a 30% increase in intracellular 2,3-BPG. This lowers the maternal hemoglobin affinity for oxygen, and therefore allows more oxygen to be offloaded to the fetus in the maternal uterine arteries. The fetus has a low sensitivity to 2,3-BPG, so its hemoglobin has a higher affinity for oxygen. Therefore, although the pO2 in the uterine arteries is low, the fetal umbilical artery (which carries deoxygenated blood) can still get oxygenated from them.
The increased maternal 2,3-BPG also causes a decreased affinity for oxygen takeup in the lungs, but this is usually compensated by a physiologic increased respiratory rate in pregnancy.
Fetal hemoglobin (HbF), on the other hand, exhibits a low affinity for 2,3-BPG, resulting in a higher binding affinity for oxygen. This increased oxygen-binding affinity relative to that of adult hemoglobin (HbA) is due to HbF's having two α/γ dimers as opposed to the two α/β dimers of HbA. The positive histidine residues of HbA β-subunits that are essential for forming the 2,3-BPG binding pocket are replaced by serine residues in HbF γ-subunits. Like that, histidine nº143 gets lost, so 2,3-BPG has difficulties in linking to the fetal hemoglobin, and it looks like the pure hemoglobin.
Increased binding affinity of fetal hemoglobin relative to HbA facilitates the passage of oxygen across the placental membrane from the mother to the fetus.
Differences between myoglobin (Mb), fetal hemoglobin (Hb F), adult hemoglobin (Hb A)
== Diseases related to 2,3-BPG ==
Hyperthyroidism
A 2004 study checked the effects of thyroid hormone on 2,3-BPG levels. The result was that the hyperthyroidism modulates in vivo 2,3-BPG content in erythrocytes by changes in the expression of phosphoglycerate mutase (PGM) and 2,3-BPG synthase.
This result shows that the increase in the 2,3-BPG content of erythrocytes observed in hyperthyroidism doesn’t depend on any variation in the rate of circulating hemoglobin, but seems to be a direct consequence of the stimulating effect of thyroid hormones on erythrocyte glycolytic activity.
Chronic anemia
Red cells increase their intracellular 2,3-BPG concentration as much as five times within one to two hours in patients with chronic anemia, when the oxygen carrying capacity of the blood is diminished. This results in a rightward shift of the oxygen dissociation curve and more oxygen being released to the tissues.
Chronic respiratory disease with hypoxia
Recently, scientists have found similarities between low amounts of 2,3-BPG with the occurrence of high altitude pulmonary edema at high altitudes.
== Hemodialysis ==
In a 1998 study, erythrocyte 2,3-BPG concentration was analyzed during the hemodialysis process. The 2,3-BPG concentration was expressed relative to the hemoglobin tetramer (Hb4) concentration as the 2,3-BPG/Hb4 ratio. Physiologically, an increase in 2,3-BPG levels would be expected to counteract the hypoxia that is frequently observed in this process. Nevertheless, the results show a 2,3-BPG/Hb4 ratio decreased. This is due to the procedure itself: mechanical stress on the erythrocytes is believed to cause the 2,3-BPG escape, which is then removed by hemodialysis. The concentrations of calcium, phosphate, creatinine, urea and albumin did not correlate significantly with the total change in 2,3-BPG/Hb4 ratio. However, the ratio sampled just before dialysis correlated significantly and positively with the total weekly dosage of erythropoietin (main hormone in erythrocyte formation) given to the patients.
== See also ==
Oxygen–hemoglobin dissociation curve
== References ==
== External links ==
A live model of the effect of changing 2,3 BPG on the oxyhaemoglobin saturation curve | Wikipedia/2,3-bisphosphoglycerate |
The biochemical systems equation is a compact equation of nonlinear differential equations for describing a kinetic model for any network of coupled biochemical reactions and transport processes.
The equation is expressed in the following form:
d
x
d
t
=
N
v
(
x
(
p
)
,
p
)
{\displaystyle {\dfrac {\bf {dx}}{dt}}={\bf {N}}{\bf {v}}({\bf {x}}(p),p)}
The notation for the dependent variable x varies among authors. For example, some authors use s, indicating species. x is used here to match the state space notation used in control theory but either notation is acceptable.
N
{\displaystyle {\bf {N}}}
is the stoichiometry matrix which is an
m
{\displaystyle m}
by
n
{\displaystyle n}
matrix of stoichiometry coefficient.
m
{\displaystyle m}
is the number of species and
n
{\displaystyle n}
the number of biochemical reactions. The notation for
N
{\displaystyle {\bf {N}}}
is also variable. In constraint-based modeling the symbol
N
{\displaystyle {\bf {N}}}
tends to be used to indicate 'stoichiometry'. However in biochemical dynamic modeling and sensitivity analysis,
N
{\displaystyle {\bf {N}}}
tends to be in more common use to indicate 'number'. In the chemistry domain, the symbol used for the stoichiometry matrix is highly variable though the symbols S and N have been used in the past.
v
{\displaystyle {\bf {v}}}
is an n-dimensional column vector of reaction rates, and
p
{\displaystyle p}
is a p-dimensional column vector of parameters.
== Example ==
Given the biochemical network:
X
o
⟶
v
1
x
1
⟶
v
2
x
2
⟶
v
3
x
3
⟶
v
4
X
1
{\displaystyle X_{o}{\stackrel {v_{1}}{\longrightarrow }}\ x_{1}{\stackrel {v_{2}}{\longrightarrow }}\ x_{2}{\stackrel {v_{3}}{\longrightarrow }}\ x_{3}{\stackrel {v_{4}}{\longrightarrow }}\ X_{1}}
where
X
o
{\displaystyle X_{o}}
and
X
1
{\displaystyle X_{1}}
are fixed species to ensure the system is open. The system equation can be written as:
N
=
[
1
−
1
+
0
+
0
0
+
1
−
1
+
0
0
+
0
+
1
−
1
]
,
{\displaystyle \mathbf {N} ={\begin{bmatrix}1&-1&{\phantom {+}}0&{\phantom {+}}0\\0&{\phantom {+}}1&-1&{\phantom {+}}0\\0&{\phantom {+}}0&{\phantom {+}}1&-1\\\end{bmatrix}},\ }
v
=
[
v
1
v
2
v
3
v
4
]
{\displaystyle \mathbf {v} ={\begin{bmatrix}v_{1}\\v_{2}\\v_{3}\\v_{4}\\\end{bmatrix}}}
So that:
[
d
x
1
d
t
d
x
2
d
t
d
x
3
d
t
d
x
4
d
t
]
=
[
1
−
1
+
0
+
0
0
+
1
−
1
+
0
0
+
0
+
1
−
1
]
{\displaystyle {\begin{bmatrix}{\dfrac {dx_{1}}{dt}}\\[4pt]{\dfrac {dx_{2}}{dt}}\\[4pt]{\dfrac {dx_{3}}{dt}}\\[4pt]{\dfrac {dx_{4}}{dt}}\\[4pt]\end{bmatrix}}={\begin{bmatrix}1&-1&{\phantom {+}}0&{\phantom {+}}0\\0&{\phantom {+}}1&-1&{\phantom {+}}0\\0&{\phantom {+}}0&{\phantom {+}}1&-1\\\end{bmatrix}}}
[
v
1
v
2
v
3
v
4
]
{\displaystyle {\begin{bmatrix}v_{1}\\v_{2}\\v_{3}\\v_{4}\\\end{bmatrix}}}
The elements of the rate vector will be rate equations that are functions of one or more species
x
i
{\displaystyle x_{i}}
and parameters, p. In the example, these might be simple mass-action rate laws such as
v
2
=
k
2
x
1
{\displaystyle v_{2}=k_{2}x_{1}}
where
k
2
{\displaystyle k_{2}}
is the rate constant parameter. The particular laws chosen will depend on the specific system under study. Assuming mass-action kinetics, the above equation can be written in complete form as:
[
d
x
1
d
t
d
x
2
d
t
d
x
3
d
t
d
x
4
d
t
]
=
[
1
−
1
+
0
+
0
0
+
1
−
1
+
0
0
+
0
+
1
−
1
]
{\displaystyle {\begin{bmatrix}{\dfrac {dx_{1}}{dt}}\\[4pt]{\dfrac {dx_{2}}{dt}}\\[4pt]{\dfrac {dx_{3}}{dt}}\\[4pt]{\dfrac {dx_{4}}{dt}}\\[4pt]\end{bmatrix}}={\begin{bmatrix}1&-1&{\phantom {+}}0&{\phantom {+}}0\\0&{\phantom {+}}1&-1&{\phantom {+}}0\\0&{\phantom {+}}0&{\phantom {+}}1&-1\\\end{bmatrix}}}
[
k
1
X
o
k
2
x
1
k
3
x
2
k
4
x
3
]
{\displaystyle {\begin{bmatrix}k_{1}X_{o}\\k_{2}x_{1}\\k_{3}x_{2}\\k_{4}x_{3}\\\end{bmatrix}}}
== Analysis ==
The system equation can be analyzed by looking at the linear response of the equation around the steady-state with respect to the parameter
p
{\displaystyle {\bf {p}}}
. At steady-state, the system equation is set to zero and given by:
0
=
N
v
(
x
(
p
)
,
p
)
{\displaystyle 0={\bf {N}}{\bf {v}}({\bf {x}}({\bf {p}}),{\bf {p}})}
Differentiating the equation with respect to
p
{\displaystyle {\bf {p}}}
and rearranging gives:
d
x
d
p
=
−
(
N
∂
v
∂
x
)
−
1
N
∂
v
∂
p
{\displaystyle {\dfrac {d{\bf {x}}}{d{\bf {p}}}}=-\left({\bf {N}}{\frac {\partial \mathbf {v} }{\partial \mathbf {x} }}\right)^{-1}{\bf {N}}{\frac {\partial \mathbf {v} }{\partial \mathbf {p} }}}
This derivation assumes that the stoichiometry matrix has full rank. If this is not the case, then the inverse won't exist.
=== Example ===
For example, consider the same problem from the previous section of a linear chain. The matrix
∂
v
∂
x
{\displaystyle {\frac {\partial \mathbf {v} }{\partial \mathbf {x} }}}
is the unscaled elasticity matrix:
E
=
[
∂
v
1
∂
x
1
⋯
∂
v
1
∂
x
m
⋮
⋱
⋮
∂
v
n
∂
x
1
⋯
∂
v
n
∂
x
m
]
.
{\displaystyle {\mathcal {E}}={\begin{bmatrix}{\dfrac {\partial v_{1}}{\partial x_{1}}}&\cdots &{\dfrac {\partial v_{1}}{\partial x_{m}}}\\\vdots &\ddots &\vdots \\{\dfrac {\partial v_{n}}{\partial x_{1}}}&\cdots &{\dfrac {\partial v_{n}}{\partial x_{m}}}\end{bmatrix}}.}
In this specific problem there are 3 species (
m
=
3
{\displaystyle m=3}
) and 4 reaction steps (
n
=
4
{\displaystyle n=4}
), the elasticity matrix is therefore a
m
×
n
=
3
by
4
{\displaystyle m\times n=3\ {\mbox{by}}\ 4}
matrix. However, a number of entries in the matrix will be zero. For example
∂
v
1
/
∂
x
3
{\displaystyle \partial v_{1}/\partial x_{3}}
will be zero since
x
3
{\displaystyle x_{3}}
has no effect on
v
1
{\displaystyle v_{1}}
. The matrix, therefore, will contain the following entries:
E
=
[
∂
v
1
∂
x
1
0
0
∂
v
2
∂
x
1
∂
v
2
∂
x
2
0
0
∂
v
3
∂
x
2
∂
v
3
∂
x
3
0
0
∂
v
4
∂
x
3
]
.
{\displaystyle {\mathcal {E}}={\begin{bmatrix}{\dfrac {\partial v_{1}}{\partial x_{1}}}&0&0\\{\dfrac {\partial v_{2}}{\partial x_{1}}}&{\dfrac {\partial v_{2}}{\partial x_{2}}}&0\\0&{\dfrac {\partial v_{3}}{\partial x_{2}}}&{\dfrac {\partial v_{3}}{\partial x_{3}}}\\0&0&{\dfrac {\partial v_{4}}{\partial x_{3}}}\\\end{bmatrix}}.}
The parameter matrix depends on which parameters are considered. In Metabolic control analysis, a common set of parameters are the enzyme activities. For the sake of argument, we can equate the rate constants with the enzyme activity parameters. We also assume that each enzyme,
k
i
{\displaystyle k_{i}}
, only can affect its own step and no other. The matrix
∂
v
∂
p
{\displaystyle {\frac {\partial \mathbf {v} }{\partial \mathbf {p} }}}
is the unscaled elasticity matrix with respect to the parameters. Since there are 4 reaction steps and 4 corresponding parameters, the matrix will be a 4 by 4 matrix. Since each parameter only affects one reaction, the matrix will be a diagonal matrix:
E
=
[
∂
v
1
∂
k
1
0
0
0
0
∂
v
2
∂
k
2
0
0
0
0
∂
v
3
∂
k
3
0
0
0
∂
v
4
∂
k
4
]
.
{\displaystyle {\mathcal {E}}={\begin{bmatrix}{\dfrac {\partial v_{1}}{\partial k_{1}}}&0&0&0\\0&{\dfrac {\partial v_{2}}{\partial k_{2}}}&0&0\\0&0&{\dfrac {\partial v_{3}}{\partial k_{3}}}&0\\0&0&&{\dfrac {\partial v_{4}}{\partial k_{4}}}\\\end{bmatrix}}.}
Since there are 3 species and 4 reactions, the resulting matrix
d
x
d
p
{\displaystyle {\frac {d{\bf {x}}}{d{\bf {p}}}}}
will be a 3 by 4 matrix
D
=
E
1
1
E
2
2
(
E
3
3
−
E
3
4
)
+
E
1
1
E
2
3
E
3
4
−
E
2
1
E
2
3
E
3
4
{\displaystyle D={\mathcal {E}}_{1}^{1}{\mathcal {E}}_{2}^{2}({\mathcal {E}}_{3}^{3}-{\mathcal {E}}_{3}^{4})+{\mathcal {E}}_{1}^{1}{\mathcal {E}}_{2}^{3}{\mathcal {E}}_{3}^{4}-{\mathcal {E}}_{2}^{1}{\mathcal {E}}_{2}^{3}{\mathcal {E}}_{3}^{4}}
{\displaystyle {\vphantom {}}}
d
x
d
p
=
1
D
[
E
k
1
1
(
E
2
2
(
E
3
3
−
E
3
4
)
+
E
2
3
E
3
4
)
−
E
2
3
E
3
4
E
k
2
2
E
2
1
E
k
1
1
(
E
3
3
−
E
3
4
)
E
1
1
E
k
2
2
(
E
3
3
−
E
3
4
)
E
2
1
E
2
3
E
k
1
1
E
1
1
E
2
3
E
k
2
2
{\displaystyle {\frac {d{\bf {x}}}{d{\bf {p}}}}={\frac {1}{D}}\left[{\begin{array}{ll}{\mathcal {E}}_{k_{1}}^{1}({\mathcal {E}}_{2}^{2}({\mathcal {E}}_{3}^{3}-{\mathcal {E}}_{3}^{4})+{\mathcal {E}}_{2}^{3}{\mathcal {E}}_{3}^{4})&-{\mathcal {E}}_{2}^{3}{\mathcal {E}}_{3}^{4}{\mathcal {E}}_{k_{2}}^{2}\\{\mathcal {E}}_{2}^{1}{\mathcal {E}}_{k_{1}}^{1}({\mathcal {E}}_{3}^{3}-{\mathcal {E}}_{3}^{4})&{\mathcal {E}}_{1}^{1}{\mathcal {E}}_{k_{2}}^{2}({\mathcal {E}}_{3}^{3}-{\mathcal {E}}_{3}^{4})\\{\mathcal {E}}_{2}^{1}{\mathcal {E}}_{2}^{3}{\mathcal {E}}_{k_{1}}^{1}&{\mathcal {E}}_{1}^{1}{\mathcal {E}}_{2}^{3}{\mathcal {E}}_{k_{2}}^{2}\\\end{array}}\right.}
E
2
2
E
3
4
E
k
3
3
E
2
2
E
3
3
E
k
4
4
E
3
4
E
k
3
3
(
E
1
1
−
E
2
1
)
E
3
3
E
k
4
4
(
E
2
1
−
E
1
1
)
E
1
1
E
2
2
E
k
3
3
−
E
k
4
4
(
E
1
1
(
E
2
2
−
E
2
3
)
+
E
2
1
E
2
3
)
]
{\displaystyle \qquad \qquad \qquad \quad \left.{\begin{array}{ll}{\mathcal {E}}_{2}^{2}{\mathcal {E}}_{3}^{4}{\mathcal {E}}_{k_{3}}^{3}&{\mathcal {E}}_{2}^{2}{\mathcal {E}}_{3}^{3}{\mathcal {E}}_{k_{4}}^{4}\\{\mathcal {E}}_{3}^{4}{\mathcal {E}}_{k_{3}}^{3}({\mathcal {E}}_{1}^{1}-{\mathcal {E}}_{2}^{1})&{\mathcal {E}}_{3}^{3}{\mathcal {E}}_{k_{4}}^{4}({\mathcal {E}}_{2}^{1}-{\mathcal {E}}_{1}^{1})\\{\mathcal {E}}_{1}^{1}{\mathcal {E}}_{2}^{2}{\mathcal {E}}_{k_{3}}^{3}&-{\mathcal {E}}_{k_{4}}^{4}({\mathcal {E}}_{1}^{1}({\mathcal {E}}_{2}^{2}-{\mathcal {E}}_{2}^{3})+{\mathcal {E}}_{2}^{1}{\mathcal {E}}_{2}^{3})\\\end{array}}\right]}
Each expression in the matrix describes how a given parameter influences the steady-state concentration of a given species. Note that this is the unscaled derivative. It is often the case that the derivative is scaled by the parameter and concentration to eliminate units as well as turn the measure into a relative change.
== Assumptions ==
The biochemical systems equation makes two key assumptions:
Species exist in a well-stirred reactor, so there are no spatial gradients.
Species concentrations are high enough so that stochastic effects are negligible
== See also ==
Stoichiometry matrix
Chemical reaction network theory
List of systems biology modeling software
== References == | Wikipedia/Biochemical_systems_equation |
Cancer systems biology encompasses the application of systems biology approaches to cancer research, in order to study the disease as a complex adaptive system with emerging properties at multiple biological scales. Cancer systems biology represents the application of systems biology approaches to the analysis of how the intracellular networks of normal cells are perturbed during carcinogenesis to develop effective predictive models that can assist scientists and clinicians in the validations of new therapies and drugs. Tumours are characterized by genomic and epigenetic instability that alters the functions of many different molecules and networks in a single cell as well as altering the interactions with the local environment. Cancer systems biology approaches, therefore, are based on the use of computational and mathematical methods to decipher the complexity in tumorigenesis as well as cancer heterogeneity.
Cancer systems biology encompasses concrete applications of systems biology approaches to cancer research, notably (a) the need for better methods to distill insights from large-scale networks, (b) the importance of integrating multiple data types in constructing more realistic models, (c) challenges in translating insights about tumorigenic mechanisms into therapeutic interventions, and (d) the role of the tumor microenvironment, at the physical, cellular, and molecular levels. Cancer systems biology therefore adopts a holistic view of cancer aimed at integrating its many biological scales, including genetics, signaling networks, epigenetics, cellular behavior, mechanical properties, histology, clinical manifestations and epidemiology. Ultimately, cancer properties at one scale, e.g., histology, are explained by properties at a scale below, e.g., cell behavior.
Cancer systems biology merges traditional basic and clinical cancer research with “exact” sciences, such as applied mathematics, engineering, and physics. It incorporates a spectrum of “omics” technologies (genomics, proteomics, epigenomics, etc.) and molecular imaging, to generate computational algorithms and quantitative models that shed light on mechanisms underlying the cancer process and predict response to intervention. Application of cancer systems biology include but are not limited to- elucidating critical cellular and molecular networks underlying cancer risk, initiation and progression; thereby promoting an alternative viewpoint to the traditional reductionist approach which has typically focused on characterizing single molecular aberrations.
== History ==
Cancer systems biology finds its roots in a number of events and realizations in biomedical research, as well as in technological advances. Historically cancer was identified, understood, and treated as a monolithic disease. It was seen as a “foreign” component that grew as a homogenous mass, and was to be best treated by excision. Besides the continued impact of surgical intervention, this simplistic view of cancer has drastically evolved. In parallel with the exploits of molecular biology, cancer research focused on the identification of critical oncogenes or tumor suppressor genes in the etiology of cancer. These breakthroughs revolutionized our understanding of molecular events driving cancer progression. Targeted therapy may be considered the current pinnacle of advances spawned by such insights.
Despite these advances, many unresolved challenges remain, including the dearth of new treatment avenues for many cancer types, or the unexplained treatment failures and inevitable relapse in cancer types where targeted treatment exists. Such mismatch between clinical results and the massive amounts of data acquired by omics technology highlights the existence of basic gaps in our knowledge of cancer fundamentals. Cancer Systems Biology is steadily improving our ability to organize information on cancer, in order to fill these gaps. Key developments include:
The generation of comprehensive molecular datasets (genome, transcriptome, epigenomics, proteome, metabolome, etc.)
The Cancer Genome Atlas data collection
Computational algorithms to extract drivers of cancer progression from existing datasets
Statistical and mechanistic modeling of signaling networks
Quantitative modeling of cancer evolutionary processes
Mathematical modeling of cancer cell population growth
Mathematical modeling of cellular responses to therapeutic intervention
Mathematical modeling of cancer metabolism
The practice of Cancer Systems Biology requires close physical integration between scientists with diverse backgrounds. Critical large-scale efforts are also underway to train a new workforce fluent in both the languages of biology and applied mathematics. At the translational level, Cancer Systems Biology should engender precision medicine application to cancer treatment.
== Resources ==
High-throughput technologies enable comprehensive genomic analyses of mutations, rearrangements, copy number variations, and methylation at the cellular and tissue levels, as well as robust analysis of RNA and microRNA expression data, protein levels and metabolite levels.
List of High-Throughput Technologies and the Data they generated, with representative databases and publications
== Approaches ==
The computational approaches used in cancer systems biology include new mathematical and computational algorithms that reflect the dynamic interplay between experimental biology and the quantitative sciences. A cancer systems biology approach can be applied at different levels, from an individual cell to a tissue, a patient with a primary tumour and possible metastases, or to any combination of these situations. This approach can integrate the molecular characteristics of tumours at different levels (DNA, RNA, protein, epigenetic, imaging) and different intervals (seconds versus days) with multidisciplinary analysis. One of the major challenges to its success, besides the challenge posed by the heterogeneity of cancer per se, resides in acquiring high-quality data that describe clinical characteristics, pathology, treatment, and outcomes and integrating the data into robust predictive models
== Applications ==
Modelling Cancer Growth and Development
Mathematical modeling can provide useful context for the rational design, validation and prioritization of novel cancer drug targets and their combinations. Network-based modeling and multi-scale modeling have begun to show promise in facilitating the process of effective cancer drug discovery. Using a systems network modeling approach, Schoerberl et al. identified a previously unknown, complementary and potentially superior mechanism of inhibiting the ErbB receptor signaling network. ErbB3 was found to be the most sensitive node, leading to Akt activation; Akt regulates many biological processes, such as proliferation, apoptosis and growth, which are all relevant to tumor progression. This target driven modelling has paved way for first of its kind clinical trials. Bekkal et al. presented a nonlinear model of the dynamics of a cell population divided into proliferative and quiescent compartments. The proliferative phase represents the complete cell cycle (G (1)-S-G (2)-M) of a population committed to divide at its end. The asymptotic behavior of solutions of the nonlinear model is analysed in two cases, exhibiting tissue homeostasis or tumor exponential growth. The model is simulated and its analytic predictions are confirmed numerically.
Furthermore, advances in hardware and software have enabled the realization of clinically feasible, quantitative multimodality imaging of tissue pathophysiology. Earlier efforts relating to multimodality imaging of cancer have focused on the integration of anatomical and functional characteristics, such as PET-CT and single-photon emission CT (SPECT-CT), whereas more-recent advances and applications have involved the integration of multiple quantitative, functional measurements (for example, multiple PET tracers, varied MRI contrast mechanisms, and PET-MRI), thereby providing a more-comprehensive characterization of the tumour phenotype. The enormous amount of complementary quantitative data generated by such studies is beginning to offer unique insights into opportunities to optimize care for individual patients. Although important technical optimization and improved biological interpretation of multimodality imaging findings are needed, this approach can already be applied informatively in clinical trials of cancer therapeutics using existing tools.
Cancer Genomics
Statistical and mechanistic modelling of cancer progression and development
Clinical response models / Modelling cellular response to therapeutic interventions
Sub-typing in Cancer.
Systems Oncology - Clinical application of Cancer Systems Biology
== National funding efforts ==
In 2004, the US National Cancer Institute launched a program effort on Integrative Cancer Systems Biology to establish Centers for Cancer Systems Biology that focus on the analysis of cancer as a complex biological system. The integration of experimental biology with mathematical modeling will result in new insights in the biology and new approaches to the management of cancer. The program brings clinical and basic cancer researchers together with researchers from mathematics, physics, engineering, information technology, imaging sciences, and computer science to work on unraveling fundamental questions in the biology of cancer.
== See also ==
Systems biology
Bioconductor
== References == | Wikipedia/Cancer_systems_biology |
Functional genomics is a field of molecular biology that attempts to describe gene (and protein) functions and interactions. Functional genomics make use of the vast data generated by genomic and transcriptomic projects (such as genome sequencing projects and RNA sequencing). Functional genomics focuses on the dynamic aspects such as gene transcription, translation, regulation of gene expression and protein–protein interactions, as opposed to the static aspects of the genomic information such as DNA sequence or structures. A key characteristic of functional genomics studies is their genome-wide approach to these questions, generally involving high-throughput methods rather than a more traditional "candidate-gene" approach.
== Definition and goals ==
In order to understand functional genomics it is important to first define function. In their paper Graur et al. define function in two possible ways. These are "selected effect" and "causal role". The "selected effect" function refers to the function for which a trait (DNA, RNA, protein etc.) is selected for. The "causal role" function refers to the function that a trait is sufficient and necessary for. Functional genomics usually tests the "causal role" definition of function.
The goal of functional genomics is to understand the function of genes or proteins, eventually all components of a genome. The term functional genomics is often used to refer to the many technical approaches to study an organism's genes and proteins, including the "biochemical, cellular, and/or physiological properties of each and every gene product" while some authors include the study of nongenic elements in their definition. Functional genomics may also include studies of natural genetic variation over time (such as an organism's development) or space (such as its body regions), as well as functional disruptions such as mutations.
The promise of functional genomics is to generate and synthesize genomic and proteomic knowledge into an understanding of the dynamic properties of an organism. This could potentially provide a more complete picture of how the genome specifies function compared to studies of single genes. Integration of functional genomics data is often a part of systems biology approaches.
== Techniques and applications ==
Functional genomics includes function-related aspects of the genome itself such as mutation and polymorphism (such as single nucleotide polymorphism (SNP) analysis), as well as the measurement of molecular activities. The latter comprise a number of "-omics" such as transcriptomics (gene expression), proteomics (protein production), and metabolomics. Functional genomics uses mostly multiplex techniques to measure the abundance of many or all gene products such as mRNAs or proteins within a biological sample. A more focused functional genomics approach might test the function of all variants of one gene and quantify the effects of mutants by using sequencing as a readout of activity. Together these measurement modalities endeavor to quantitate the various biological processes and improve our understanding of gene and protein functions and interactions.
=== At the DNA level ===
==== Genetic interaction mapping ====
Systematic pairwise deletion of genes or inhibition of gene expression can be used to identify genes with related function, even if they do not interact physically. Epistasis refers to the fact that effects for two different gene knockouts may not be additive; that is, the phenotype that results when two genes are inhibited may be different from the sum of the effects of single knockouts.
==== DNA/Protein interactions ====
Proteins formed by the translation of the mRNA (messenger RNA, a coded information from DNA for protein synthesis) play a major role in regulating gene expression. To understand how they regulate gene expression it is necessary to identify DNA sequences that they interact with. Techniques have been developed to identify sites of DNA-protein interactions. These include ChIP-sequencing, CUT&RUN sequencing and Calling Cards.
==== DNA accessibility assays ====
Assays have been developed to identify regions of the genome that are accessible. These regions of accessible chromatin are candidate regulatory regions. These assays include ATAC-seq, DNase-Seq and FAIRE-Seq.
=== At the RNA level ===
==== Microarrays ====
Microarrays measure the amount of mRNA in a sample that corresponds to a given gene or probe DNA sequence. Probe sequences are immobilized on a solid surface and allowed to hybridize with fluorescently labeled "target" mRNA. The intensity of fluorescence of a spot is proportional to the amount of target sequence that has hybridized to that spot and therefore to the abundance of that mRNA sequence in the sample. Microarrays allow for the identification of candidate genes involved in a given process based on variation between transcript levels for different conditions and shared expression patterns with genes of known function.
==== SAGE ====
Serial analysis of gene expression (SAGE) is an alternate method of analysis based on RNA sequencing rather than hybridization. SAGE relies on the sequencing of 10–17 base pair tags which are unique to each gene. These tags are produced from poly-A mRNA and ligated end-to-end before sequencing. SAGE gives an unbiased measurement of the number of transcripts per cell, since it does not depend on prior knowledge of what transcripts to study (as microarrays do).
==== RNA sequencing ====
RNA sequencing has taken over microarray and SAGE technology in recent years, as noted in 2016, and has become the most efficient way to study transcription and gene expression. This is typically done by next-generation sequencing.
A subset of sequenced RNAs are small RNAs, a class of non-coding RNA molecules that are key regulators of transcriptional and post-transcriptional gene silencing, or RNA silencing. Next-generation sequencing is the gold standard tool for non-coding RNA discovery, profiling and expression analysis.
==== Massively Parallel Reporter Assays (MPRAs) ====
Massively parallel reporter assays is a technology to test the cis-regulatory activity of DNA sequences. MPRAs use a plasmid with a synthetic cis-regulatory element upstream of a promoter driving a synthetic gene such as Green Fluorescent Protein. A library of cis-regulatory elements is usually tested using MPRAs, a library can contain from hundreds to thousands of cis-regulatory elements. The cis-regulatory activity of the elements is assayed by using the downstream reporter activity. The activity of all the library members is assayed in parallel using barcodes for each cis-regulatory element. One limitation of MPRAs is that the activity is assayed on a plasmid and may not capture all aspects of gene regulation observed in the genome.
==== STARR-seq ====
STARR-seq is a technique similar to MPRAs to assay enhancer activity of randomly sheared genomic fragments. In the original publication, randomly sheared fragments of the Drosophila genome were placed downstream of a minimal promoter. Candidate enhancers amongst the randomly sheared fragments will transcribe themselves using the minimal promoter. By using sequencing as a readout and controlling for input amounts of each sequence the strength of putative enhancers are assayed by this method.
==== Perturb-seq ====
Perturb-seq couples CRISPR mediated gene knockdowns with single-cell gene expression. Linear models are used to calculate the effect of the knockdown of a single gene on the expression of multiple genes.
=== At the protein level ===
==== Yeast two-hybrid system ====
A yeast two-hybrid screening (Y2H) tests a "bait" protein against many potential interacting proteins ("prey") to identify physical protein–protein interactions. This system is based on a transcription factor, originally GAL4, whose separate DNA-binding and transcription activation domains are both required in order for the protein to cause transcription of a reporter gene. In a Y2H screen, the "bait" protein is fused to the binding domain of GAL4, and a library of potential "prey" (interacting) proteins is recombinantly expressed in a vector with the activation domain. In vivo interaction of bait and prey proteins in a yeast cell brings the activation and binding domains of GAL4 close enough together to result in expression of a reporter gene. It is also possible to systematically test a library of bait proteins against a library of prey proteins to identify all possible interactions in a cell.
==== MS and AP/MS ====
Mass spectrometry (MS) can identify proteins and their relative levels, hence it can be used to study protein expression. When used in combination with affinity purification, mass spectrometry (AP/MS) can be used to study protein complexes, that is, which proteins interact with one another in complexes and in which ratios. In order to purify protein complexes, usually a "bait" protein is tagged with a specific protein or peptide that can be used to pull out the complex from a complex mix. The purification is usually done using an antibody or a compound that binds to the fusion part. The proteins are then digested into short peptide fragments and mass spectrometry is used to identify the proteins based on the mass-to-charge ratios of those fragments.
==== Deep mutational scanning ====
In deep mutational scanning, every possible amino acid change in a given protein is first synthesized. The activity of each of these protein variants is assayed in parallel using barcodes for each variant. By comparing the activity to the wild-type protein, the effect of each mutation is identified. While it is possible to assay every possible single amino-acid change due to combinatorics two or more concurrent mutations are hard to test. Deep mutational scanning experiments have also been used to infer protein structure and protein-protein interactions. Deep Mutational Scanning is an example of a multiplexed assays of variant effect (MAVEs), a family of methods that involve mutagenesis of a DNA-encoded protein or regulatory element followed by a multiplexed assay for some aspect of function. MAVEs enable the generation of ‘variant effect maps’ characterizing aspects of the function of every possible single nucleotide change in a gene or functional element of interest.
=== Mutagenesis and phenotyping ===
An important functional feature of genes is the phenotype caused by mutations. Mutants can be produced by random mutations or by directed mutagenesis, including site-directed mutagenesis, deleting complete genes, or other techniques.
==== Knock-outs (gene deletions) ====
Gene function can be investigated by systematically "knocking out" genes one by one. This is done by either deletion or disruption of function (such as by insertional mutagenesis) and the resulting organisms are screened for phenotypes that provide clues to the function of the disrupted gene. Knock-outs have been produced for whole genomes, i.e. by deleting all genes in a genome. For essential genes, this is not possible, so other techniques are used, e.g. deleting a gene while expressing the gene from a plasmid, using an inducible promoter, so that the level of gene product can be changed at will (and thus a "functional" deletion achieved).
==== Site-directed mutagenesis ====
Site-directed mutagenesis is used to mutate specific bases (and thus amino acids). This is critical to investigate the function of specific amino acids in a protein, e.g. in the active site of an enzyme.
==== RNAi ====
RNA interference (RNAi) methods can be used to transiently silence or knockdown gene expression using ~20 base-pair double-stranded RNA typically delivered by transfection of synthetic ~20-mer short-interfering RNA molecules (siRNAs) or by virally encoded short-hairpin RNAs (shRNAs). RNAi screens, typically performed in cell culture-based assays or experimental organisms (such as C. elegans) can be used to systematically disrupt nearly every gene in a genome or subsets of genes (sub-genomes); possible functions of disrupted genes can be assigned based on observed phenotypes.
==== CRISPR screens ====
CRISPR-Cas9 has been used to delete genes in a multiplexed manner in cell-lines. Quantifying the amount of guide-RNAs for each gene before and after the experiment can point towards essential genes. If a guide-RNA disrupts an essential gene it will lead to the loss of that cell and hence there will be a depletion of that particular guide-RNA after the screen. In a recent CRISPR-cas9 experiment in mammalian cell-lines, around 2000 genes were found to be essential in multiple cell-lines. Some of these genes were essential in only one cell-line. Most of genes are part of multi-protein complexes. This approach can be used to identify synthetic lethality by using the appropriate genetic background. CRISPRi and CRISPRa enable loss-of-function and gain-of-function screens in a similar manner. CRISPRi identified ~2100 essential genes in the K562 cell-line. CRISPR deletion screens have also been used to identify potential regulatory elements of a gene. For example, a technique called ScanDel was published which attempted this approach. The authors deleted regions outside a gene of interest(HPRT1 involved in a Mendelian disorder) in an attempt to identify regulatory elements of this gene. Gassperini et al. did not identify any distal regulatory elements for HPRT1 using this approach, however such approaches can be extended to other genes of interest.
=== Functional annotations for genes ===
==== Genome annotation ====
Putative genes can be identified by scanning a genome for regions likely to encode proteins, based on characteristics such as long open reading frames, transcriptional initiation sequences, and polyadenylation sites. A sequence identified as a putative gene must be confirmed by further evidence, such as similarity to cDNA or EST sequences from the same organism, similarity of the predicted protein sequence to known proteins, association with promoter sequences, or evidence that mutating the sequence produces an observable phenotype.
==== Rosetta stone approach ====
The Rosetta stone approach is a computational method for de-novo protein function prediction. It is based on the hypothesis that some proteins involved in a given physiological process may exist as two separate genes in one organism and as a single gene in another. Genomes are scanned for sequences that are independent in one organism and in a single open reading frame in another. If two genes have fused, it is predicted that they have similar biological functions that make such co-regulation advantageous.
== Bioinformatics methods for Functional genomics ==
Because of the large quantity of data produced by these techniques and the desire to find biologically meaningful patterns, bioinformatics is crucial to analysis of functional genomics data. Examples of techniques in this class are data clustering or principal component analysis for unsupervised machine learning (class detection) as well as artificial neural networks or support vector machines for supervised machine learning (class prediction, classification). Functional enrichment analysis is used to determine the extent of over- or under-expression (positive- or negative- regulators in case of RNAi screens) of functional categories relative to a background sets. Gene ontology based enrichment analysis are provided by DAVID and gene set enrichment analysis (GSEA), pathway based analysis by Ingenuity and Pathway studio and protein complex based analysis by COMPLEAT.
New computational methods have been developed for understanding the results of a deep mutational scanning experiment. 'phydms' compares the result of a deep mutational scanning experiment to a phylogenetic tree. This allows the user to infer if the selection process in nature applies similar constraints on a protein as the results of the deep mutational scan indicate. This may allow an experimenter to choose between different experimental conditions based on how well they reflect nature. Deep mutational scanning has also been used to infer protein-protein interactions. The authors used a thermodynamic model to predict the effects of mutations in different parts of a dimer. Deep mutational structure can also be used to infer protein structure. Strong positive epistasis between two mutations in a deep mutational scan can be indicative of two parts of the protein that are close to each other in 3-D space. This information can then be used to infer protein structure. A proof of principle of this approach was shown by two groups using the protein GB1.
Results from MPRA experiments have required machine learning approaches to interpret the data. A gapped k-mer SVM model has been used to infer the kmers that are enriched within cis-regulatory sequences with high activity compared to sequences with lower activity. These models provide high predictive power. Deep learning and random forest approaches have also been used to interpret the results of these high-dimensional experiments. These models are beginning to help develop a better understanding of non-coding DNA function towards gene-regulation.
== Consortium projects ==
=== The ENCODE project ===
The ENCODE (Encyclopedia of DNA elements) project is an in-depth analysis of the human genome whose goal is to identify all the functional elements of genomic DNA, in both coding and non-coding regions. Important results include evidence from genomic tiling arrays that most nucleotides are transcribed as coding transcripts, non-coding RNAs, or random transcripts, the discovery of additional transcriptional regulatory sites, further elucidation of chromatin-modifying mechanisms.
=== The Genotype-Tissue Expression (GTEx) project ===
The GTEx project is a human genetics project aimed at understanding the role of genetic variation in shaping variation in the transcriptome across tissues. The project has collected a variety of tissue samples (> 50 different tissues) from more than 700 post-mortem donors. This has resulted in the collection of >11,000 samples. GTEx has helped understand the tissue-sharing and tissue-specificity of eQTLs. The genomic resource was developed to "enrich our understanding of how differences in our DNA sequence contribute to health and disease."
=== The Atlas of Variant Effects Alliance ===
The Atlas of Variant Effects Alliance (AVE), founded in 2020, is an international consortium aiming to catalog the impact of all possible genetic variants for disease-related functional genomics by creating variant effect maps that reveal the function of every possible single nucleotide change in a gene or regulatory element. AVE is funded in part through the Brotman Baty Institute at the University of Washington and the National Human Genome Research Institute, via funding from the Center of Excellence in Genome Science grant (NHGRI RM1HG010461).
== See also ==
== References ==
== External links ==
European Science Foundation Programme on Frontiers of Functional Genomics
MUGEN NoE — Integrated Functional Genomics in Mutant Mouse Models
Nature insights: functional genomics
ENCODE | Wikipedia/Functional_genomics |
In biochemistry, metabolic control analysis (MCA) is a mathematical framework for describing
metabolic, signaling, and genetic pathways. MCA quantifies how variables, such as fluxes and species concentrations, depend on network parameters.
In particular, it is able to describe how network-dependent properties,
called control coefficients, depend on local properties called elasticities or elasticity coefficients.
MCA was originally developed to describe the control in metabolic pathways
but was subsequently extended to describe signaling and genetic networks. MCA has sometimes also been referred to as Metabolic Control Theory, but this terminology was rather strongly opposed by Henrik Kacser, one of the founders.
More recent work has shown that MCA can be mapped directly on to classical control theory and are as such equivalent.
Biochemical systems theory (BST) is a similar formalism, though with rather different objectives. Both are evolutions of an earlier theoretical analysis by Joseph Higgins.
Chemical reaction network theory is another theoretical framework that has overlap with both MCA and BST but is considerably more mathematically formal in its approach. Its emphasis is primarily on dynamic stability criteria and related theorems associated with mass-action networks. In more recent years the field has also developed a sensitivity analysis which is similar if not identical to MCA and BST.
== Control coefficients ==
A control coefficient
measures the relative steady state change in a system variable, e.g. pathway flux (J) or metabolite concentration (S), in response to a relative change in a parameter, e.g. enzyme activity or the steady-state rate (
v
i
{\displaystyle v_{i}}
) of step
i
{\displaystyle i}
. The two main control coefficients are the flux and concentration control coefficients. Flux control coefficients are defined by
C
v
i
J
=
(
d
J
d
p
p
J
)
/
(
∂
v
i
∂
p
p
v
i
)
=
d
ln
J
d
ln
v
i
{\displaystyle C_{v_{i}}^{J}=\left({\frac {dJ}{dp}}{\frac {p}{J}}\right){\bigg /}\left({\frac {\partial v_{i}}{\partial p}}{\frac {p}{v_{i}}}\right)={\frac {d\ln J}{d\ln v_{i}}}}
and concentration control coefficients by
C
v
i
S
=
(
d
S
d
p
p
S
)
/
(
∂
v
i
∂
p
p
v
i
)
=
d
ln
S
d
ln
v
i
{\displaystyle C_{v_{i}}^{S}=\left({\frac {dS}{dp}}{\frac {p}{S}}\right){\bigg /}\left({\frac {\partial v_{i}}{\partial p}}{\frac {p}{v_{i}}}\right)={\frac {d\ln S}{d\ln v_{i}}}}
.
=== Summation theorems ===
The flux control summation theorem was discovered independently by the Kacser/Burns group and the Heinrich/Rapoport group in the early 1970s and late 1960s. The flux control summation theorem implies that metabolic fluxes are systemic properties and that their control is shared by all reactions in the system. When a single reaction changes its control of the flux this is compensated by changes in the control of the same flux by all other reactions.
∑
i
C
v
i
J
=
1
{\displaystyle \sum _{i}C_{v_{i}}^{J}=1}
∑
i
C
v
i
s
=
0
{\displaystyle \sum _{i}C_{v_{i}}^{s}=0}
=== Elasticity coefficients ===
The elasticity coefficient measures the local response of an enzyme or other chemical reaction to changes in its environment. Such changes include factors such as substrates, products, or effector concentrations. For further information, please refer to the dedicated page at elasticity coefficients.
.
=== Connectivity theorems ===
The connectivity theorems are specific relationships between elasticities and control coefficients. They are useful because they highlight the close relationship between the kinetic properties of individual reactions and the system properties of a pathway. Two basic sets of theorems exists, one for flux and another for concentrations. The concentration connectivity theorems are divided again depending on whether the system species
S
n
{\displaystyle S_{n}}
is different from the local species
S
m
{\displaystyle S_{m}}
.
∑
i
C
i
J
ε
s
i
=
0
{\displaystyle \sum _{i}C_{i}^{J}\varepsilon _{s}^{i}=0}
∑
i
C
i
s
n
ε
s
m
i
=
0
n
≠
m
{\displaystyle \sum _{i}C_{i}^{s_{n}}\varepsilon _{s_{m}}^{i}=0\quad n\neq m}
∑
i
C
i
s
n
ε
s
m
i
=
−
1
n
=
m
{\displaystyle \sum _{i}C_{i}^{s_{n}}\varepsilon _{s_{m}}^{i}=-1\quad n=m}
== Response Coefficient ==
Kacser and Burns introduced an additional coefficient that described how a biochemical pathway would respond the external environment. They termed this coefficient the response coefficient and designated it using the symbol R. The response coefficient is an important metric because it can be used to assess how much a nutrient or perhaps more important, how a drug can influence a pathway. This coefficient is therefore highly relevant to the pharmaceutical industry.
The response coefficient is related to the core of metabolic control analysis via the response coefficient theorem, which is stated as follows:
R
m
X
=
C
i
X
ε
m
i
{\displaystyle R_{m}^{X}=C_{i}^{X}\varepsilon _{m}^{i}}
where
X
{\displaystyle X}
is a chosen observable such as a flux or metabolite concentration,
i
{\displaystyle i}
is the step that the external factor targets,
C
i
X
{\displaystyle C_{i}^{X}}
is the control coefficient of the target steps, and
ε
m
i
{\displaystyle \varepsilon _{m}^{i}}
is the elasticity of the target step with respect to the external factor
m
{\displaystyle m}
.
The key observation of this theorem is that an external factor such as a therapeutic drug, acts on the organism's phenotype via two influences: 1) How well the drug can affect the target itself through effective binding of the drug to the target protein and its effect on the protein activity. This effectiveness is described by the elasticity
ε
m
i
{\displaystyle \varepsilon _{m}^{i}}
and 2) How well do modifications of the target influence the phenotype by transmission of the perturbation to the rest of the network. This is indicated by the control coefficient
C
i
X
{\displaystyle C_{i}^{X}}
.
A drug action, or any external factor, is most effective when both these factors are strong. For example, a drug might be very effective at changing the activity of its target protein, however if that perturbation in protein activity is unable to be transmitted to the final phenotype then the effectiveness of the drug is greatly diminished.
If a drug or external factor,
m
{\displaystyle m}
, targets multiple sites of action, for example
n
{\displaystyle n}
sites, then the overall response in a phenotypic factor
X
{\displaystyle X}
, is the sum of the individual responses:
R
m
X
=
∑
i
=
1
n
C
i
X
ε
m
i
{\displaystyle R_{m}^{X}=\sum _{i=1}^{n}C_{i}^{X}\varepsilon _{m}^{i}}
== Control equations ==
It is possible to combine the summation with the connectivity theorems to obtain closed expressions that relate the control coefficients to the elasticity coefficients. For example, consider the simplest non-trivial pathway:
X
o
→
S
→
X
1
{\displaystyle X_{o}\rightarrow S\rightarrow X_{1}}
We assume that
X
o
{\displaystyle X_{o}}
and
X
1
{\displaystyle X_{1}}
are fixed boundary species so that the pathway can reach a steady state. Let the first step have a rate
v
1
{\displaystyle v_{1}}
and the second step
v
2
{\displaystyle v_{2}}
. Focusing on the flux control coefficients, we can write one summation and one connectivity theorem for this simple pathway:
C
v
1
J
+
C
v
2
J
=
1
{\displaystyle C_{v_{1}}^{J}+C_{v_{2}}^{J}=1}
C
v
1
J
ε
s
v
1
+
C
v
2
J
ε
s
v
2
=
0
{\displaystyle C_{v_{1}}^{J}\varepsilon _{s}^{v_{1}}+C_{v_{2}}^{J}\varepsilon _{s}^{v_{2}}=0}
Using these two equations we can solve for the flux control coefficients to yield
C
v
1
J
=
ε
s
2
ε
s
2
−
ε
s
1
{\displaystyle C_{v_{1}}^{J}={\frac {\varepsilon _{s}^{2}}{\varepsilon _{s}^{2}-\varepsilon _{s}^{1}}}}
C
v
2
J
=
−
ε
s
1
ε
s
2
−
ε
s
1
{\displaystyle C_{v_{2}}^{J}={\frac {-\varepsilon _{s}^{1}}{\varepsilon _{s}^{2}-\varepsilon _{s}^{1}}}}
Using these equations we can look at some simple extreme behaviors. For example, let us assume that the first step is completely insensitive to its product (i.e. not reacting with it), S, then
ε
s
v
1
=
0
{\displaystyle \varepsilon _{s}^{v_{1}}=0}
. In this case, the control coefficients reduce to
C
v
1
J
=
1
{\displaystyle C_{v_{1}}^{J}=1}
C
v
2
J
=
0
{\displaystyle C_{v_{2}}^{J}=0}
That is all the control (or sensitivity) is on the first step. This situation represents the classic rate-limiting step that is frequently mentioned in textbooks. The flux through the pathway is completely dependent on the first step. Under these conditions, no other step in the pathway can affect the flux. The effect is however dependent on the complete insensitivity of the first step to its product. Such a situation is likely to be rare in real pathways. In fact the classic rate limiting step has almost never been observed experimentally. Instead, a range of limitingness is observed, with some steps having more limitingness (control) than others.
We can also derive the concentration control coefficients for the simple two step pathway:
C
v
1
s
=
1
ε
s
2
−
ε
s
1
{\displaystyle C_{v_{1}}^{s}={\frac {1}{\varepsilon _{s}^{2}-\varepsilon _{s}^{1}}}}
C
v
2
s
=
−
1
ε
s
2
−
ε
s
1
{\displaystyle C_{v_{2}}^{s}={\frac {-1}{\varepsilon _{s}^{2}-\varepsilon _{s}^{1}}}}
== Three step pathway ==
Consider the simple three step pathway:
where
X
o
{\displaystyle X_{o}}
and
X
1
{\displaystyle X_{1}}
are fixed boundary species, the control equations for this pathway can be derived in a similar manner to the simple two step pathway although it is somewhat more tedious.
C
e
1
J
=
ε
1
2
ε
2
3
/
D
{\displaystyle C_{e_{1}}^{J}=\varepsilon _{1}^{2}\varepsilon _{2}^{3}/D}
C
e
2
J
=
−
ε
1
1
ε
2
3
/
D
{\displaystyle C_{e_{2}}^{J}=-\varepsilon _{1}^{1}\varepsilon _{2}^{3}/D}
C
e
3
J
=
ε
1
1
ε
2
2
/
D
{\displaystyle C_{e_{3}}^{J}=\varepsilon _{1}^{1}\varepsilon _{2}^{2}/D}
where D the denominator is given by
D
=
ε
1
2
ε
2
3
−
ε
1
1
ε
2
3
+
ε
1
1
ε
2
2
{\displaystyle D=\varepsilon _{1}^{2}\varepsilon _{2}^{3}-\varepsilon _{1}^{1}\varepsilon _{2}^{3}+\varepsilon _{1}^{1}\varepsilon _{2}^{2}}
Note that every term in the numerator appears in the denominator, this ensures that the flux control coefficient summation theorem is satisfied.
Likewise the concentration control coefficients can also be derived, for
S
1
{\displaystyle S_{1}}
C
e
1
S
1
=
(
ε
2
3
−
ε
2
2
)
/
D
{\displaystyle C_{e_{1}}^{S_{1}}=(\varepsilon _{2}^{3}-\varepsilon _{2}^{2})/D}
C
e
2
S
1
=
−
ε
2
3
/
D
{\displaystyle C_{e_{2}}^{S_{1}}=-\varepsilon _{2}^{3}/D}
C
e
3
S
1
=
ε
2
2
/
D
{\displaystyle C_{e_{3}}^{S_{1}}=\varepsilon _{2}^{2}/D}
And for
S
2
{\displaystyle S_{2}}
C
e
1
S
2
=
ε
1
2
/
D
{\displaystyle C_{e_{1}}^{S_{2}}=\varepsilon _{1}^{2}/D}
C
e
2
S
2
=
−
ε
1
1
/
D
{\displaystyle C_{e_{2}}^{S_{2}}=-\varepsilon _{1}^{1}/D}
C
e
3
S
2
=
(
ε
1
1
−
ε
1
2
)
/
D
{\displaystyle C_{e_{3}}^{S_{2}}=(\varepsilon _{1}^{1}-\varepsilon _{1}^{2})/D}
Note that the denominators remain the same as before and behave as a normalizing factor.
== Derivation using perturbations ==
Control equations can also be derived by considering the effect of perturbations on the system. Consider that reaction rates
v
1
{\displaystyle v_{1}}
and
v
2
{\displaystyle v_{2}}
are determined by two enzymes
e
1
{\displaystyle e_{1}}
and
e
2
{\displaystyle e_{2}}
respectively. Changing either enzyme will result in a change to the steady state level of
x
{\displaystyle x}
and the steady state reaction rates
v
{\displaystyle v}
. Consider a small change in
e
1
{\displaystyle e_{1}}
of magnitude
δ
e
1
{\displaystyle \delta e_{1}}
. This will have a number of effects, it will increase
v
1
{\displaystyle v_{1}}
which in turn will increase
x
{\displaystyle x}
which in turn will increase
v
2
{\displaystyle v_{2}}
. Eventually the system will settle to a new steady state. We can describe these changes by focusing on the change in
v
1
{\displaystyle v_{1}}
and
v
2
{\displaystyle v_{2}}
. The change in
v
2
{\displaystyle v_{2}}
, which we designate
δ
v
2
{\displaystyle \delta v_{2}}
, came about as a result of the change
δ
x
{\displaystyle \delta x}
. Because we are only considering small changes we can express the change
δ
v
2
{\displaystyle \delta v_{2}}
in terms of
δ
x
{\displaystyle \delta x}
using the relation
δ
v
2
=
∂
v
2
∂
x
δ
x
{\displaystyle \delta v_{2}={\frac {\partial v_{2}}{\partial x}}\delta x}
where the derivative
∂
v
2
/
∂
x
{\displaystyle \partial v_{2}/\partial x}
measures how responsive
v
2
{\displaystyle v_{2}}
is to changes in
x
{\displaystyle x}
. The derivative can be computed if we know the rate law for
v
2
{\displaystyle v_{2}}
. For example, if we assume that the rate law is
v
2
=
k
2
x
{\displaystyle v_{2}=k_{2}x}
then the derivative is
k
2
{\displaystyle k_{2}}
. We can also use a similar strategy to compute the change in
v
1
{\displaystyle v_{1}}
as a result of the change
δ
e
1
{\displaystyle \delta e_{1}}
. This time the change in
v
1
{\displaystyle v_{1}}
is a result of two changes, the change in
e
1
{\displaystyle e_{1}}
itself and the change in
x
{\displaystyle x}
. We can express these changes by summing the two individual contributions:
δ
v
1
=
∂
v
1
∂
e
1
δ
e
1
+
∂
v
1
∂
x
δ
x
{\displaystyle \delta v_{1}={\frac {\partial v_{1}}{\partial e_{1}}}\delta e_{1}+{\frac {\partial v_{1}}{\partial x}}\delta x}
We have two equations, one describing the change in
v
1
{\displaystyle v_{1}}
and the other in
v
2
{\displaystyle v_{2}}
. Because we allowed the system to settle to a new steady state we can also state that the change in reaction rates must be the same (otherwise it wouldn't be at steady state). That is we can assert that
δ
v
1
=
δ
v
2
{\displaystyle \delta v_{1}=\delta v_{2}}
. With this in mind we equate the two equations and write
∂
v
2
∂
x
δ
x
=
∂
v
1
∂
e
1
δ
e
1
+
∂
v
1
∂
x
δ
x
{\displaystyle {\frac {\partial v_{2}}{\partial x}}\delta x={\frac {\partial v_{1}}{\partial e_{1}}}\delta e_{1}+{\frac {\partial v_{1}}{\partial x}}\delta x}
Solving for the ratio
δ
x
/
δ
e
1
{\displaystyle \delta x/\delta e_{1}}
we obtain:
δ
x
δ
e
1
=
−
∂
v
1
∂
e
1
∂
v
2
∂
x
−
∂
v
1
∂
x
{\displaystyle {\frac {\delta x}{\delta e_{1}}}={\dfrac {-{\dfrac {\partial v_{1}}{\partial e_{1}}}}{{\dfrac {\partial v_{2}}{\partial x}}-{\dfrac {\partial v_{1}}{\partial x}}}}}
In the limit, as we make the change
δ
e
1
{\displaystyle \delta e_{1}}
smaller and smaller, the left-hand side converges to the derivative
d
x
/
d
e
1
{\displaystyle dx/de_{1}}
:
lim
δ
e
1
→
0
δ
x
δ
e
1
=
d
x
d
e
1
=
−
∂
v
1
∂
e
1
∂
v
2
∂
x
−
∂
v
1
∂
x
{\displaystyle \lim _{\delta e_{1}\rightarrow 0}{\frac {\delta x}{\delta e_{1}}}={\frac {dx}{de_{1}}}={\dfrac {-{\dfrac {\partial v_{1}}{\partial e_{1}}}}{{\dfrac {\partial v_{2}}{\partial x}}-{\dfrac {\partial v_{1}}{\partial x}}}}}
We can go one step further and scale the derivatives to eliminate units. Multiplying both sides by
e
1
{\displaystyle e_{1}}
and dividing both sides by
x
{\displaystyle x}
yields the scaled derivatives:
d
x
d
e
1
e
1
x
=
−
∂
v
1
∂
e
1
e
1
v
1
∂
v
2
∂
x
x
v
2
−
∂
v
1
∂
x
x
v
1
{\displaystyle {\frac {dx}{de_{1}}}{\frac {e_{1}}{x}}={\frac {-{\dfrac {\partial v_{1}}{\partial e_{1}}}{\dfrac {e_{1}}{v_{1}}}}{{\dfrac {\partial v_{2}}{\partial x}}{\dfrac {x}{v_{2}}}-{\dfrac {\partial v_{1}}{\partial x}}{\dfrac {x}{v_{1}}}}}}
The scaled derivatives on the right-hand side are the elasticities,
ε
x
v
{\displaystyle \varepsilon _{x}^{v}}
and the scaled left-hand term is the scaled sensitivity coefficient or concentration control coefficient,
C
e
x
{\displaystyle C_{e}^{x}}
C
e
1
x
=
ε
e
1
1
ε
x
2
−
ε
x
1
{\displaystyle C_{e_{1}}^{x}={\frac {\varepsilon _{e_{1}}^{1}}{\varepsilon _{x}^{2}-\varepsilon _{x}^{1}}}}
We can simplify this expression further. The reaction rate
v
1
{\displaystyle v_{1}}
is usually a linear function of
e
1
{\displaystyle e_{1}}
. For example, in the Briggs–Haldane equation, the reaction rate is given by
v
=
e
1
k
c
a
t
x
/
(
K
m
+
x
)
{\displaystyle v=e_{1}k_{cat}x/(K_{m}+x)}
. Differentiating this rate law with respect to
e
1
{\displaystyle e_{1}}
and scaling yields
ε
e
1
v
1
=
1
{\displaystyle \varepsilon _{e_{1}}^{v_{1}}=1}
.
Using this result gives:
C
e
1
x
=
1
ε
x
2
−
ε
x
1
{\displaystyle C_{e_{1}}^{x}={\frac {1}{\varepsilon _{x}^{2}-\varepsilon _{x}^{1}}}}
A similar analysis can be done where
e
2
{\displaystyle e_{2}}
is perturbed. In this case we obtain the sensitivity of
x
{\displaystyle x}
with respect to
e
2
{\displaystyle e_{2}}
:
C
e
2
x
=
−
1
ε
x
2
−
ε
x
1
{\displaystyle C_{e_{2}}^{x}=-{\frac {1}{\varepsilon _{x}^{2}-\varepsilon _{x}^{1}}}}
The above expressions measure how much enzymes
e
1
{\displaystyle e_{1}}
and
e
2
{\displaystyle e_{2}}
control the steady state concentration of intermediate
x
{\displaystyle x}
. We can also consider how the steady state reaction rates
v
1
{\displaystyle v_{1}}
and
v
2
{\displaystyle v_{2}}
are affected by perturbations in
e
1
{\displaystyle e_{1}}
and
e
2
{\displaystyle e_{2}}
. This is often of importance to metabolic engineers who are interested in increasing rates of production. At steady state the reaction rates are often called the fluxes and abbreviated to
J
1
{\displaystyle J_{1}}
and
J
2
{\displaystyle J_{2}}
. For a linear pathway such as this example, both fluxes are equal at steady-state so that the flux through the pathway is simply referred to as
J
{\displaystyle J}
. Expressing the change in flux as a result of a perturbation in
e
1
{\displaystyle e_{1}}
and taking the limit as before we obtain
C
e
1
J
=
ε
x
1
ε
x
2
−
ε
x
1
,
C
e
2
J
=
−
ε
x
1
ε
x
2
−
ε
x
1
{\displaystyle C_{e_{1}}^{J}={\frac {\varepsilon _{x}^{1}}{\varepsilon _{x}^{2}-\varepsilon _{x}^{1}}},\quad C_{e_{2}}^{J}={\frac {-\varepsilon _{x}^{1}}{\varepsilon _{x}^{2}-\varepsilon _{x}^{1}}}}
The above expressions tell us how much enzymes
e
1
{\displaystyle e_{1}}
and
e
2
{\displaystyle e_{2}}
control the steady state flux. The key point here is that changes in enzyme concentration, or equivalently the enzyme activity, must be brought about by an external action.
== Derivation using the systems equation ==
The control equations can also be derived in a more rigorous fashion using the systems equation:
d
x
d
t
=
N
v
(
x
(
p
)
,
p
)
{\displaystyle {\dfrac {\bf {dx}}{dt}}={\bf {N}}{\bf {v}}({\bf {x}}(p),p)}
where
N
{\displaystyle {\bf {N}}}
is the stoichiometry matrix,
x
{\displaystyle {\bf {x}}}
is a vector of chemical species, and
p
{\displaystyle {\bf {p}}}
is a vector of parameters (or inputs) that can influence the system. In metabolic control analysis the key parameters are the enzyme concentrations. This approach was popularized by Heinrich, Rapoport, and Rapoport and Reder and Mazat. A detailed discussion of this approach can be found in Heinrich & Schuster and Hofmeyr.
== Properties of a linear pathway ==
A linear biochemical pathway is a chain of enzyme-catalyzed reaction steps. The figure below shows a three step pathway, with intermediates,
S
1
{\displaystyle S_{1}}
and
. In order to sustain a steady-state, the boundary species
X
o
{\displaystyle X_{o}}
and
X
1
{\displaystyle X_{1}}
are fixed.
At steady-state the rate of reaction is the same at each step. This means there is an overall flux from X_o to X_1.
Linear pathways possess some well-known properties:
Flux control is biased towards the first few steps of the pathway. Flux control shifts more to the first step as the equilibrium constants become large.
Flux control is small at reactions close to equilibrium.
Assuming reversibly, flux control at a given step is proportional to the product of the equilibrium constants. For example, flux control at the second step in a three step pathway is proportional to the product of the second and third equilibrium constants.
In all cases, a rationale for these behaviors is given in terms of how elasticities transmit changes through a pathway.
== Metabolic control analysis software ==
There are a number of software tools that can directly compute elasticities and control coefficients:
COPASI (GUI)
PySCeS (Python)
SBW (GUI)
libroadrunner (Python)
VCell
== Relationship to Classical Control Theory ==
Classical Control theory is a field of mathematics that deals with the control of dynamical systems in engineered processes and machines. In 2004 Brian Ingalls published a paper that showed that classical control theory and metabolic control analysis were identical. The only difference was that metabolic control analysis was confined to zero frequency responses when cast in the frequency domain whereas classical control theory imposes no such restriction. The other significant difference is that classical control theory has no notion of stoichiometry and conservation of mass which makes it more cumbersome to use but also means it fails to recognize the structural properties inherent in stoichiometric networks which provide useful biological insights.
== See also ==
Branched pathways
Biochemical systems theory
Control coefficient (biochemistry)
Flux (metabolism)
Moiety conservation
Rate-limiting step (biochemistry)
== References ==
== External links ==
The Metabolic Control Analysis Web | Wikipedia/Metabolic_Control_Analysis |
BioSystems is a monthly peer-reviewed scientific journal covering experimental, computational, and theoretical research that links biology, evolution, and the information processing sciences. It was established in 1967 as Currents in Modern Biology by Robert G. Grenell and published by North-Holland Publishing Company out of Amsterdam until North-Holland merged with Elsevier in 1970. Grenell wrote of his purpose in founding the journal, It has become necessary to develop a new language of biology; a new mathematics, and to strip biological theory and experiment of their classical approaches, assumptions and limitations. It is such considerations which underlie the establishment of this journal.
In 1972 the journal was renamed Currents in Modern Biology: Bio Systems, which was shortened to BioSystems in 1974. Previous editors include J.P. Schadé, Alan W. Schwartz, Sidney W. Fox, Michael Conrad, Lynn Margulis, David B. Fogel, Gary B. Fogel, George Kampis, Francisco Lara-Ochoa, Koichiro Matsuno, Ray Paton, and W. Mike L. Holcombe.
According to the Journal Citation Reports, the journal has a 2023 impact factor of 2.0.
== Special issues ==
Special issues of BioSystems cover different aspects of theoretical and evolutionary biology.
=== Symbiogenesis ===
The special issue "Symbiogenesis and Progressive Evolution" (2021) is dedicated to Boris Kozo-Polyansky and Lynn Margulis and contains articles about these scientists, and also includes an annotated translation of the article by Konstantin Merezhkovsky, in which the concept of symbiogenesis was first outlined.
Mikhailovsky, George; Gordon, Richard; Igamberdiev, Abir U. (2021). "Editorial: Symbiogenesis and progressive evolution". Biosystems. 206. Bibcode:2021BiSys.20604429M. doi:10.1016/j.biosystems.2021.104429. PMID 33864879.
Kowallik, Klaus V.; Martin, William F. (2021). "The origin of symbiogenesis: An annotated English translation of Mereschkowsky's 1910 paper on the theory of two plasma lineages". Biosystems. 199. Bibcode:2021BiSys.19904281K. doi:10.1016/j.biosystems.2020.104281. PMC 7816216. PMID 33279568.
Sagan, Dorion (2021). "From Empedocles to Symbiogenetics: Lynn Margulis's revolutionary influence on evolutionary biology". Biosystems. 204. Bibcode:2021BiSys.20404386S. doi:10.1016/j.biosystems.2021.104386. PMID 33621579.
=== Biological computation ===
The special issue "Fundamental principles of biological computation: From molecular computing to biological complexity" (2022) is dedicated to the memory of one of the founders of computational biology Efim Liberman and contains his autobiography "On the way to a new science".
Shklovskiy-Kordi, Nikita E.; Matsuno, Koichiro; Marijuán, Pedro C.; Lgamberdiev, Abir U. (2022). "Editorial: Fundamental principles of biological computation: From molecular computing to evolutionary complexity". Biosystems. 219. Bibcode:2022BiSys.21904719S. doi:10.1016/j.biosystems.2022.104719.
=== Biological thermodynamics ===
The special issue "Biological Thermodynamics: Bridging the gap between physics and life" (2024) is dedicated to Ervin Bauer and contains biographical and theoretical articles about him, as well as English translations of his major works.
Igamberdiev, Abir U.; Müller, Miklós; Elek, Gábor; Mikhailovsky, George E.; Cottam, Ron (2024). "Biological thermodynamics: Bridging the gap between physics and life". Biosystems. 242. Bibcode:2024BiSys.24205258I. doi:10.1016/j.biosystems.2024.105258. PMID 38880329.
Müller, Miklós; Igamberdiev, Abir U. (2024). "The emergence of theoretical biology: Two fundamental works of Ervin Bauer (1890–1938) in English translation". Biosystems. 241. Bibcode:2024BiSys.24105201M. doi:10.1016/j.biosystems.2024.105201. PMID 38642880.
== Some other significant papers and articles ==
Miller, William B.; Baluška, František; Reber, Arthur S.; Slijepčević, Predrag (2025). "Biological mechanisms contradict AI consciousness: The spaces between the notes". Biosystems. 247. Bibcode:2025BiSys.24705387M. doi:10.1016/j.biosystems.2024.105387.
== Abstracting and indexing ==
The journal is abstracted and indexed in:
== References ==
== External links ==
Official website | Wikipedia/BioSystems |
Drug design, often referred to as rational drug design or simply rational design, is the inventive process of finding new medications based on the knowledge of a biological target. The drug is most commonly an organic small molecule that activates or inhibits the function of a biomolecule such as a protein, which in turn results in a therapeutic benefit to the patient. In the most basic sense, drug design involves the design of molecules that are complementary in shape and charge to the biomolecular target with which they interact and therefore will bind to it. Drug design frequently but not necessarily relies on computer modeling techniques. This type of modeling is sometimes referred to as computer-aided drug design. Finally, drug design that relies on the knowledge of the three-dimensional structure of the biomolecular target is known as structure-based drug design. In addition to small molecules, biopharmaceuticals including peptides and especially therapeutic antibodies are an increasingly important class of drugs and computational methods for improving the affinity, selectivity, and stability of these protein-based therapeutics have also been developed.
== Definition ==
The phrase "drug design" is similar to ligand design (i.e., design of a molecule that will bind tightly to its target). Although design techniques for prediction of binding affinity are reasonably successful, there are many other properties, such as bioavailability, metabolic half-life, and side effects, that first must be optimized before a ligand can become a safe and effective drug. These other characteristics are often difficult to predict with rational design techniques.
Due to high attrition rates, especially during clinical phases of drug development, more attention is being focused early in the drug design process on selecting candidate drugs whose physicochemical properties are predicted to result in fewer complications during development and hence more likely to lead to an approved, marketed drug. Furthermore, in vitro experiments complemented with computation methods are increasingly used in early drug discovery to select compounds with more favorable ADME (absorption, distribution, metabolism, and excretion) and toxicological profiles.
== Drug targets ==
A biomolecular target (most commonly a protein or a nucleic acid) is a key molecule involved in a particular metabolic or signaling pathway that is associated with a specific disease condition or pathology or to the infectivity or survival of a microbial pathogen. Potential drug targets are not necessarily disease causing but must by definition be disease modifying. In some cases, small molecules will be designed to enhance or inhibit the target function in the specific disease modifying pathway. Small molecules (for example receptor agonists, antagonists, inverse agonists, or modulators; enzyme activators or inhibitors; or ion channel openers or blockers) will be designed that are complementary to the binding site of target. Small molecules (drugs) can be designed so as not to affect any other important "off-target" molecules (often referred to as antitargets) since drug interactions with off-target molecules may lead to undesirable side effects. Due to similarities in binding sites, closely related targets identified through sequence homology have the highest chance of cross reactivity and hence highest side effect potential.
Most commonly, drugs are organic small molecules produced through chemical synthesis, but biopolymer-based drugs (also known as biopharmaceuticals) produced through biological processes are becoming increasingly more common. In addition, mRNA-based gene silencing technologies may have therapeutic applications. For example, nanomedicines based on mRNA can streamline and expedite the drug development process, enabling transient and localized expression of immunostimulatory molecules. In vitro transcribed (IVT) mRNA allows for delivery to various accessible cell types via the blood or alternative pathways. The use of IVT mRNA serves to convey specific genetic information into a person's cells, with the primary objective of preventing or altering a particular disease.
=== Drug discovery ===
==== Phenotypic drug discovery ====
Phenotypic drug discovery is a traditional drug discovery method, also known as forward pharmacology or classical pharmacology. It uses the process of phenotypic screening on collections of synthetic small molecules, natural products, or extracts within chemical libraries to pinpoint substances exhibiting beneficial therapeutic effects. This method is to first discover the in vivo or in vitro functional activity of drugs (such as extract drugs or natural products), and then perform target identification. Phenotypic discovery uses a practical and target-independent approach to generate initial leads, aiming to discover pharmacologically active compounds and therapeutics that operate through novel drug mechanisms. This method allows the exploration of disease phenotypes to find potential treatments for conditions with unknown, complex, or multifactorial origins, where the understanding of molecular targets is insufficient for effective intervention.
==== Rational drug discovery ====
Rational drug design (also called reverse pharmacology) begins with a hypothesis that modulation of a specific biological target may have therapeutic value. In order for a biomolecule to be selected as a drug target, two essential pieces of information are required. The first is evidence that modulation of the target will be disease modifying. This knowledge may come from, for example, disease linkage studies that show an association between mutations in the biological target and certain disease states. The second is that the target is capable of binding to a small molecule and that its activity can be modulated by the small molecule.
Once a suitable target has been identified, the target is normally cloned and produced and purified. The purified protein is then used to establish a screening assay. In addition, the three-dimensional structure of the target may be determined.
The search for small molecules that bind to the target is begun by screening libraries of potential drug compounds. This may be done by using the screening assay (a "wet screen"). In addition, if the structure of the target is available, a virtual screen may be performed of candidate drugs. Ideally, the candidate drug compounds should be "drug-like", that is they should possess properties that are predicted to lead to oral bioavailability, adequate chemical and metabolic stability, and minimal toxic effects. Several methods are available to estimate druglikeness such as Lipinski's Rule of Five and a range of scoring methods such as lipophilic efficiency. Several methods for predicting drug metabolism have also been proposed in the scientific literature.
Due to the large number of drug properties that must be simultaneously optimized during the design process, multi-objective optimization techniques are sometimes employed. Finally because of the limitations in the current methods for prediction of activity, drug design is still very much reliant on serendipity and bounded rationality.
== Computer-aided drug design ==
The most fundamental goal in drug design is to predict whether a given molecule will bind to a target and if so how strongly. Molecular mechanics or molecular dynamics is most often used to estimate the strength of the intermolecular interaction between the small molecule and its biological target. These methods are also used to predict the conformation of the small molecule and to model conformational changes in the target that may occur when the small molecule binds to it. Semi-empirical, ab initio quantum chemistry methods, or density functional theory are often used to provide optimized parameters for the molecular mechanics calculations and also provide an estimate of the electronic properties (electrostatic potential, polarizability, etc.) of the drug candidate that will influence binding affinity.
Molecular mechanics methods may also be used to provide semi-quantitative prediction of the binding affinity. Also, knowledge-based scoring function may be used to provide binding affinity estimates. These methods use linear regression, machine learning, neural nets or other statistical techniques to derive predictive binding affinity equations by fitting experimental affinities to computationally derived interaction energies between the small molecule and the target.
Ideally, the computational method will be able to predict affinity before a compound is synthesized and hence in theory only one compound needs to be synthesized, saving enormous time and cost. The reality is that present computational methods are imperfect and provide, at best, only qualitatively accurate estimates of affinity. In practice, it requires several iterations of design, synthesis, and testing before an optimal drug is discovered. Computational methods have accelerated discovery by reducing the number of iterations required and have often provided novel structures.
Computer-aided drug design may be used at any of the following stages of drug discovery:
hit identification using virtual screening (structure- or ligand-based design)
hit-to-lead optimization of affinity and selectivity (structure-based design, QSAR, etc.)
lead optimization of other pharmaceutical properties while maintaining affinity
In order to overcome the insufficient prediction of binding affinity calculated by recent scoring functions, the protein-ligand interaction and compound 3D structure information are used for analysis. For structure-based drug design, several post-screening analyses focusing on protein-ligand interaction have been developed for improving enrichment and effectively mining potential candidates:
Consensus scoring
Selecting candidates by voting of multiple scoring functions
May lose the relationship between protein-ligand structural information and scoring criterion
Cluster analysis
Represent and cluster candidates according to protein-ligand 3D information
Needs meaningful representation of protein-ligand interactions.
== Types ==
There are two major types of drug design. The first is referred to as ligand-based drug design and the second, structure-based drug design.
=== Ligand-based ===
Ligand-based drug design (or indirect drug design) relies on knowledge of other molecules that bind to the biological target of interest. These other molecules may be used to derive a pharmacophore model that defines the minimum necessary structural characteristics a molecule must possess in order to bind to the target. A model of the biological target may be built based on the knowledge of what binds to it, and this model in turn may be used to design new molecular entities that interact with the target. Alternatively, a quantitative structure-activity relationship (QSAR), in which a correlation between calculated properties of molecules and their experimentally determined biological activity, may be derived. These QSAR relationships in turn may be used to predict the activity of new analogs.
=== Structure-based ===
Structure-based drug design (or direct drug design) relies on knowledge of the three dimensional structure of the biological target obtained through methods such as x-ray crystallography or NMR spectroscopy. If an experimental structure of a target is not available, it may be possible to create a homology model of the target based on the experimental structure of a related protein. Using the structure of the biological target, candidate drugs that are predicted to bind with high affinity and selectivity to the target may be designed using interactive graphics and the intuition of a medicinal chemist. Alternatively, various automated computational procedures may be used to suggest new drug candidates.
Current methods for structure-based drug design can be divided roughly into three main categories. The first method is identification of new ligands for a given receptor by searching large databases of 3D structures of small molecules to find those fitting the binding pocket of the receptor using fast approximate docking programs. This method is known as virtual screening.
A second category is de novo design of new ligands. In this method, ligand molecules are built up within the constraints of the binding pocket by assembling small pieces in a stepwise manner. These pieces can be either individual atoms or molecular fragments. The key advantage of such a method is that novel structures, not contained in any database, can be suggested. A third method is the optimization of known ligands by evaluating proposed analogs within the binding cavity.
==== Binding site identification ====
Binding site identification is the first step in structure based design. If the structure of the target or a sufficiently similar homolog is determined in the presence of a bound ligand, then the ligand should be observable in the structure in which case location of the binding site is trivial. However, there may be unoccupied allosteric binding sites that may be of interest. Furthermore, it may be that only apoprotein (protein without ligand) structures are available and the reliable identification of unoccupied sites that have the potential to bind ligands with high affinity is non-trivial. In brief, binding site identification usually relies on identification of concave surfaces on the protein that can accommodate drug sized molecules that also possess appropriate "hot spots" (hydrophobic surfaces, hydrogen bonding sites, etc.) that drive ligand binding.
==== Scoring functions ====
Structure-based drug design attempts to use the structure of proteins as a basis for designing new ligands by applying the principles of molecular recognition. Selective high affinity binding to the target is generally desirable since it leads to more efficacious drugs with fewer side effects. Thus, one of the most important principles for designing or obtaining potential new ligands is to predict the binding affinity of a certain ligand to its target (and known antitargets) and use the predicted affinity as a criterion for selection.
One early general-purposed empirical scoring function to describe the binding energy of ligands to receptors was developed by Böhm. This empirical scoring function took the form:
Δ
G
bind
=
Δ
G
0
+
Δ
G
hb
Σ
h
−
b
o
n
d
s
+
Δ
G
ionic
Σ
i
o
n
i
c
−
i
n
t
+
Δ
G
lipophilic
|
A
|
+
Δ
G
rot
N
R
O
T
{\displaystyle \Delta G_{\text{bind}}=\Delta G_{\text{0}}+\Delta G_{\text{hb}}\Sigma _{h-bonds}+\Delta G_{\text{ionic}}\Sigma _{ionic-int}+\Delta G_{\text{lipophilic}}\left\vert A\right\vert +\Delta G_{\text{rot}}{\mathit {NROT}}}
where:
ΔG0 – empirically derived offset that in part corresponds to the overall loss of translational and rotational entropy of the ligand upon binding.
ΔGhb – contribution from hydrogen bonding
ΔGionic – contribution from ionic interactions
ΔGlip – contribution from lipophilic interactions where |Alipo| is surface area of lipophilic contact between the ligand and receptor
ΔGrot – entropy penalty due to freezing a rotatable in the ligand bond upon binding
A more general thermodynamic "master" equation is as follows:
Δ
G
bind
=
−
R
T
ln
K
d
K
d
=
[
Ligand
]
[
Receptor
]
[
Complex
]
Δ
G
bind
=
Δ
G
desolvation
+
Δ
G
motion
+
Δ
G
configuration
+
Δ
G
interaction
{\displaystyle {\begin{array}{lll}\Delta G_{\text{bind}}=-RT\ln K_{\text{d}}\\[1.3ex]K_{\text{d}}={\dfrac {[{\text{Ligand}}][{\text{Receptor}}]}{[{\text{Complex}}]}}\\[1.3ex]\Delta G_{\text{bind}}=\Delta G_{\text{desolvation}}+\Delta G_{\text{motion}}+\Delta G_{\text{configuration}}+\Delta G_{\text{interaction}}\end{array}}}
where:
desolvation – enthalpic penalty for removing the ligand from solvent
motion – entropic penalty for reducing the degrees of freedom when a ligand binds to its receptor
configuration – conformational strain energy required to put the ligand in its "active" conformation
interaction – enthalpic gain for "resolvating" the ligand with its receptor
The basic idea is that the overall binding free energy can be decomposed into independent components that are known to be important for the binding process. Each component reflects a certain kind of free energy alteration during the binding process between a ligand and its target receptor. The Master Equation is the linear combination of these components. According to Gibbs free energy equation, the relation between dissociation equilibrium constant, Kd, and the components of free energy was built.
Various computational methods are used to estimate each of the components of the master equation. For example, the change in polar surface area upon ligand binding can be used to estimate the desolvation energy. The number of rotatable bonds frozen upon ligand binding is proportional to the motion term. The configurational or strain energy can be estimated using molecular mechanics calculations. Finally the interaction energy can be estimated using methods such as the change in non polar surface, statistically derived potentials of mean force, the number of hydrogen bonds formed, etc. In practice, the components of the master equation are fit to experimental data using multiple linear regression. This can be done with a diverse training set including many types of ligands and receptors to produce a less accurate but more general "global" model or a more restricted set of ligands and receptors to produce a more accurate but less general "local" model.
== Examples ==
A particular example of rational drug design involves the use of three-dimensional information about biomolecules obtained from such techniques as X-ray crystallography and NMR spectroscopy. Computer-aided drug design in particular becomes much more tractable when there is a high-resolution structure of a target protein bound to a potent ligand. This approach to drug discovery is sometimes referred to as structure-based drug design. The first unequivocal example of the application of structure-based drug design leading to an approved drug is the carbonic anhydrase inhibitor dorzolamide, which was approved in 1995.
Another case study in rational drug design is imatinib, a tyrosine kinase inhibitor designed specifically for the bcr-abl fusion protein that is characteristic for Philadelphia chromosome-positive leukemias (chronic myelogenous leukemia and occasionally acute lymphocytic leukemia). Imatinib is substantially different from previous drugs for cancer, as most agents of chemotherapy simply target rapidly dividing cells, not differentiating between cancer cells and other tissues.
Additional examples include:
== Drug screening ==
Types of drug screening include phenotypic screening, high-throughput screening, and virtual screening. Phenotypic screening is characterized by the process of screening drugs using cellular or animal disease models to identify compounds that alter the phenotype and produce beneficial disease-related effects. Emerging technologies in high-throughput screening substantially enhance processing speed and decrease the required detection volume. Virtual screening is completed by computer, enabling a large number of molecules can be screened with a short cycle and low cost. Virtual screening uses a range of computational methods that empower chemists to reduce extensive virtual libraries into more manageable sizes.
== Case studies ==
== Criticism ==
It has been argued that the highly rigid and focused nature of rational drug design suppresses serendipity in drug discovery.
== See also ==
== References ==
== External links ==
Drug+Design at the U.S. National Library of Medicine Medical Subject Headings (MeSH)
[Drug Design Org](https://www.drugdesign.org/chapters/drug-design/) | Wikipedia/Structure-based_drug_design |
Neural coding (or neural representation) is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the neuronal responses, and the relationship among the electrical activities of the neurons in the ensemble. Based on the theory that
sensory and other information is represented in the brain by networks of neurons, it is believed that neurons can encode both digital and analog information.
== Overview ==
Neurons have an ability uncommon among the cells of the body to propagate signals rapidly over large distances by generating characteristic electrical pulses called action potentials: voltage spikes that can travel down axons. Sensory neurons change their activities by firing sequences of action potentials in various temporal patterns, with the presence of external sensory stimuli, such as light, sound, taste, smell and touch. Information about the stimulus is encoded in this pattern of action potentials and transmitted into and around the brain. Beyond this, specialized neurons, such as those of the retina, can communicate more information through graded potentials. These differ from action potentials because information about the strength of a stimulus directly correlates with the strength of the neurons' output. The signal decays much faster for graded potentials, necessitating short inter-neuron distances and high neuronal density. The advantage of graded potentials is higher information rates capable of encoding more states (i.e. higher fidelity) than spiking neurons.
Although action potentials can vary somewhat in duration, amplitude and shape, they are typically treated as identical stereotyped events in neural coding studies. If the brief duration of an action potential (about 1 ms) is ignored, an action potential sequence, or spike train, can be characterized simply by a series of all-or-none point events in time. The lengths of interspike intervals (ISIs) between two successive spikes in a spike train often vary, apparently randomly. The study of neural coding involves measuring and characterizing how stimulus attributes, such as light or sound intensity, or motor actions, such as the direction of an arm movement, are represented by neuron action potentials or spikes. In order to describe and analyze neuronal firing, statistical methods and methods of probability theory and stochastic point processes have been widely applied.
With the development of large-scale neural recording and decoding technologies, researchers have begun to crack the neural code and have already provided the first glimpse into the real-time neural code as memory is formed and recalled in the hippocampus, a brain region known to be central for memory formation. Neuroscientists have initiated several large-scale brain decoding projects.
== Encoding and decoding ==
The link between stimulus and response can be studied from two opposite points of view. Neural encoding refers to the map from stimulus to response. The main focus is to understand how neurons respond to a wide variety of stimuli, and to construct models that attempt to predict responses to other stimuli. Neural decoding refers to the reverse map, from response to stimulus, and the challenge is to reconstruct a stimulus, or certain aspects of that stimulus, from the spike sequences it evokes.
== Hypothesized coding schemes ==
A sequence, or 'train', of spikes may contain information based on different coding schemes. In some neurons the strength with which a postsynaptic partner responds may depend solely on the 'firing rate', the average number of spikes per unit time (a 'rate code'). At the other end, a complex 'temporal code' is based on the precise timing of single spikes. They may be locked to an external stimulus such as in the visual and auditory system or be generated intrinsically by the neural circuitry.
Whether neurons use rate coding or temporal coding is a topic of intense debate within the neuroscience community, even though there is no clear definition of what these terms mean.
=== Rate code ===
The rate coding model of neuronal firing communication states that as the intensity of a stimulus increases, the frequency or rate of action potentials, or "spike firing", increases. Rate coding is sometimes called frequency coding.
Rate coding is a traditional coding scheme, assuming that most, if not all, information about the stimulus is contained in the firing rate of the neuron. Because the sequence of action potentials generated by a given stimulus varies from trial to trial, neuronal responses are typically treated statistically or probabilistically. They may be characterized by firing rates, rather than as specific spike sequences. In most sensory systems, the firing rate increases, generally non-linearly, with increasing stimulus intensity. Under a rate coding assumption, any information possibly encoded in the temporal structure of the spike train is ignored. Consequently, rate coding is inefficient but highly robust with respect to the ISI 'noise'.
During rate coding, precisely calculating firing rate is very important. In fact, the term "firing rate" has a few different definitions, which refer to different averaging procedures, such as an average over time (rate as a single-neuron spike count) or an average over several repetitions (rate of PSTH) of experiment.
In rate coding, learning is based on activity-dependent synaptic weight modifications.
Rate coding was originally shown by Edgar Adrian and Yngve Zotterman in 1926. In this simple experiment different weights were hung from a muscle. As the weight of the stimulus increased, the number of spikes recorded from sensory nerves innervating the muscle also increased. From these original experiments, Adrian and Zotterman concluded that action potentials were unitary events, and that the frequency of events, and not individual event magnitude, was the basis for most inter-neuronal communication.
In the following decades, measurement of firing rates became a standard tool for describing the properties of all types of sensory or cortical neurons, partly due to the relative ease of measuring rates experimentally. However, this approach neglects all the information possibly contained in the exact timing of the spikes. During recent years, more and more experimental evidence has suggested that a straightforward firing rate concept based on temporal averaging may be too simplistic to describe brain activity.
==== Spike-count rate (average over time) ====
The spike-count rate, also referred to as temporal average, is obtained by counting the number of spikes that appear during a trial and dividing by the duration of trial. The length T of the time window is set by the experimenter and depends on the type of neuron recorded from and to the stimulus. In practice, to get sensible averages, several spikes should occur within the time window. Typical values are T = 100 ms or T = 500 ms, but the duration may also be longer or shorter (Chapter 1.5 in the textbook 'Spiking Neuron Models' ).
The spike-count rate can be determined from a single trial, but at the expense of losing all temporal resolution about variations in neural response during the course of the trial. Temporal averaging can work well in cases where the stimulus is constant or slowly varying and does not require a fast reaction of the organism — and this is the situation usually encountered in experimental protocols. Real-world input, however, is hardly stationary, but often changing on a fast time scale. For example, even when viewing a static image, humans perform saccades, rapid changes of the direction of gaze. The image projected onto the retinal photoreceptors changes therefore every few hundred milliseconds (Chapter 1.5 in )
Despite its shortcomings, the concept of a spike-count rate code is widely used not only in experiments, but also in models of neural networks. It has led to the idea that a neuron transforms information about a single input variable (the stimulus strength) into a single continuous output variable (the firing rate).
There is a growing body of evidence that in Purkinje neurons, at least, information is not simply encoded in firing but also in the timing and duration of non-firing, quiescent periods. There is also evidence from retinal cells, that information is encoded not only in the firing rate but also in spike timing. More generally, whenever a rapid response of an organism is required a firing rate defined as a spike-count over a few hundred milliseconds is simply too slow.
==== Time-dependent firing rate (averaging over several trials) ====
The time-dependent firing rate is defined as the average number of spikes (averaged over trials) appearing during a short interval between times t and t+Δt, divided by the duration of the interval. It works for stationary as well as for time-dependent stimuli. To experimentally measure the time-dependent firing rate, the experimenter records from a neuron while stimulating with some input sequence. The same stimulation sequence is repeated several times and the neuronal response is reported in a Peri-Stimulus-Time Histogram (PSTH). The time t is measured with respect to the start of the stimulation sequence. The Δt must be large enough (typically in the range of one or a few milliseconds) so that there is a sufficient number of spikes within the interval to obtain a reliable estimate of the average. The number of occurrences of spikes nK(t;t+Δt) summed over all repetitions of the experiment divided by the number K of repetitions is a measure of the typical activity of the neuron between time t and t+Δt. A further division by the interval length Δt yields time-dependent firing rate r(t) of the neuron, which is equivalent to the spike density of PSTH (Chapter 1.5 in ).
For sufficiently small Δt, r(t)Δt is the average number of spikes occurring between times t and t+Δt over multiple trials. If Δt is small, there will never be more than one spike within the interval between t and t+Δt on any given trial. This means that r(t)Δt is also the fraction of trials on which a spike occurred between those times. Equivalently, r(t)Δt is the probability that a spike occurs during this time interval.
As an experimental procedure, the time-dependent firing rate measure is a useful method to evaluate neuronal activity, in particular in the case of time-dependent stimuli. The obvious problem with this approach is that it can not be the coding scheme used by neurons in the brain. Neurons can not wait for the stimuli to repeatedly present in an exactly same manner before generating a response.
Nevertheless, the experimental time-dependent firing rate measure can make sense, if there are large populations of independent neurons that receive the same stimulus. Instead of recording from a population of N neurons in a single run, it is experimentally easier to record from a single neuron and average over N repeated runs. Thus, the time-dependent firing rate coding relies on the implicit assumption that there are always populations of neurons.
=== Temporal coding ===
When precise spike timing or high-frequency firing-rate fluctuations are found to carry information, the neural code is often identified as a temporal code. A number of studies have found that the temporal resolution of the neural code is on a millisecond time scale, indicating that precise spike timing is a significant element in neural coding. Such codes, that communicate via the time between spikes are also referred to as interpulse interval codes, and have been supported by recent studies.
Neurons exhibit high-frequency fluctuations of firing-rates which could be noise or could carry information. Rate coding models suggest that these irregularities are noise, while temporal coding models suggest that they encode information. If the nervous system only used rate codes to convey information, a more consistent, regular firing rate would have been evolutionarily advantageous, and neurons would have utilized this code over other less robust options. Temporal coding supplies an alternate explanation for the “noise," suggesting that it actually encodes information and affects neural processing. To model this idea, binary symbols can be used to mark the spikes: 1 for a spike, 0 for no spike. Temporal coding allows the sequence 000111000111 to mean something different from 001100110011, even though the mean firing rate is the same for both sequences, at 6 spikes/10 ms.
Until recently, scientists had put the most emphasis on rate encoding as an explanation for post-synaptic potential patterns. However, functions of the brain are more temporally precise than the use of only rate encoding seems to allow. In other words, essential information could be lost due to the inability of the rate code to capture all the available information of the spike train. In addition, responses are different enough between similar (but not identical) stimuli to suggest that the distinct patterns of spikes contain a higher volume of information than is possible to include in a rate code.
Temporal codes (also called spike codes ), employ those features of the spiking activity that cannot be described by the firing rate. For example, time-to-first-spike after the stimulus onset, phase-of-firing with respect to background oscillations, characteristics based on the second and higher statistical moments of the ISI probability distribution, spike randomness, or precisely timed groups of spikes (temporal patterns) are candidates for temporal codes. As there is no absolute time reference in the nervous system, the information is carried either in terms of the relative timing of spikes in a population of neurons (temporal patterns) or with respect to an ongoing brain oscillation (phase of firing). One way in which temporal codes are decoded, in presence of neural oscillations, is that spikes occurring at specific phases of an oscillatory cycle are more effective in depolarizing the post-synaptic neuron.
The temporal structure of a spike train or firing rate evoked by a stimulus is determined both by the dynamics of the stimulus and by the nature of the neural encoding process. Stimuli that change rapidly tend to generate precisely timed spikes (and rapidly changing firing rates in PSTHs) no matter what neural coding strategy is being used. Temporal coding in the narrow sense refers to temporal precision in the response that does not arise solely from the dynamics of the stimulus, but that nevertheless relates to properties of the stimulus. The interplay between stimulus and encoding dynamics makes the identification of a temporal code difficult.
In temporal coding, learning can be explained by activity-dependent synaptic delay modifications. The modifications can themselves depend not only on spike rates (rate coding) but also on spike timing patterns (temporal coding), i.e., can be a special case of spike-timing-dependent plasticity.
The issue of temporal coding is distinct and independent from the issue of independent-spike coding. If each spike is independent of all the other spikes in the train, the temporal character of the neural code is determined by the behavior of time-dependent firing rate r(t). If r(t) varies slowly with time, the code is typically called a rate code, and if it varies rapidly, the code is called temporal.
==== Temporal coding in sensory systems ====
For very brief stimuli, a neuron's maximum firing rate may not be fast enough to produce more than a single spike. Due to the density of information about the abbreviated stimulus contained in this single spike, it would seem that the timing of the spike itself would have to convey more information than simply the average frequency of action potentials over a given period of time. This model is especially important for sound localization, which occurs within the brain on the order of milliseconds. The brain must obtain a large quantity of information based on a relatively short neural response. Additionally, if low firing rates on the order of ten spikes per second must be distinguished from arbitrarily close rate coding for different stimuli, then a neuron trying to discriminate these two stimuli may need to wait for a second or more to accumulate enough information. This is not consistent with numerous organisms which are able to discriminate between stimuli in the time frame of milliseconds, suggesting that a rate code is not the only model at work.
To account for the fast encoding of visual stimuli, it has been suggested that neurons of the retina encode visual information in the latency time between stimulus onset and first action potential, also called latency to first spike or time-to-first-spike. This type of temporal coding has been shown also in the auditory and somato-sensory system. The main drawback of such a coding scheme is its sensitivity to intrinsic neuronal fluctuations. In the primary visual cortex of macaques, the timing of the first spike relative to the start of the stimulus was found to provide more information than the interval between spikes. However, the interspike interval could be used to encode additional information, which is especially important when the spike rate reaches its limit, as in high-contrast situations. For this reason, temporal coding may play a part in coding defined edges rather than gradual transitions.
The mammalian gustatory system is useful for studying temporal coding because of its fairly distinct stimuli and the easily discernible responses of the organism. Temporally encoded information may help an organism discriminate between different tastants of the same category (sweet, bitter, sour, salty, umami) that elicit very similar responses in terms of spike count. The temporal component of the pattern elicited by each tastant may be used to determine its identity (e.g., the difference between two bitter tastants, such as quinine and denatonium). In this way, both rate coding and temporal coding may be used in the gustatory system – rate for basic tastant type, temporal for more specific differentiation.
Research on mammalian gustatory system has shown that there is an abundance of information present in temporal patterns across populations of neurons, and this information is different from that which is determined by rate coding schemes. Groups of neurons may synchronize in response to a stimulus. In studies dealing with the front cortical portion of the brain in primates, precise patterns with short time scales only a few milliseconds in length were found across small populations of neurons which correlated with certain information processing behaviors. However, little information could be determined from the patterns; one possible theory is they represented the higher-order processing taking place in the brain.
As with the visual system, in mitral/tufted cells in the olfactory bulb of mice, first-spike latency relative to the start of a sniffing action seemed to encode much of the information about an odor. This strategy of using spike latency allows for rapid identification of and reaction to an odorant. In addition, some mitral/tufted cells have specific firing patterns for given odorants. This type of extra information could help in recognizing a certain odor, but is not completely necessary, as average spike count over the course of the animal's sniffing was also a good identifier. Along the same lines, experiments done with the olfactory system of rabbits showed distinct patterns which correlated with different subsets of odorants, and a similar result was obtained in experiments with the locust olfactory system.
==== Temporal coding applications ====
The specificity of temporal coding requires highly refined technology to measure informative, reliable, experimental data. Advances made in optogenetics allow neurologists to control spikes in individual neurons, offering electrical and spatial single-cell resolution. For example, blue light causes the light-gated ion channel channelrhodopsin to open, depolarizing the cell and producing a spike. When blue light is not sensed by the cell, the channel closes, and the neuron ceases to spike. The pattern of the spikes matches the pattern of the blue light stimuli. By inserting channelrhodopsin gene sequences into mouse DNA, researchers can control spikes and therefore certain behaviors of the mouse (e.g., making the mouse turn left). Researchers, through optogenetics, have the tools to effect different temporal codes in a neuron while maintaining the same mean firing rate, and thereby can test whether or not temporal coding occurs in specific neural circuits.
Optogenetic technology also has the potential to enable the correction of spike abnormalities at the root of several neurological and psychological disorders. If neurons do encode information in individual spike timing patterns, key signals could be missed by attempting to crack the code while looking only at mean firing rates. Understanding any temporally encoded aspects of the neural code and replicating these sequences in neurons could allow for greater control and treatment of neurological disorders such as depression, schizophrenia, and Parkinson's disease. Regulation of spike intervals in single cells more precisely controls brain activity than the addition of pharmacological agents intravenously.
==== Phase-of-firing code ====
Phase-of-firing code is a neural coding scheme that combines the spike count code with a time reference based on oscillations. This type of code takes into account a time label for each spike according to a time reference based on phase of local ongoing oscillations at low or high frequencies.
It has been shown that neurons in some cortical sensory areas encode rich naturalistic stimuli in terms of their spike times relative to the phase of ongoing network oscillatory fluctuations, rather than only in terms of their spike count. The local field potential signals reflect population (network) oscillations. The phase-of-firing code is often categorized as a temporal code although the time label used for spikes (i.e. the network oscillation phase) is a low-resolution (coarse-grained) reference for time. As a result, often only four discrete values for the phase are enough to represent all the information content in this kind of code with respect to the phase of oscillations in low frequencies. Phase-of-firing code is loosely based on the phase precession phenomena observed in place cells of the hippocampus. Another feature of this code is that neurons adhere to a preferred order of spiking between a group of sensory neurons, resulting in firing sequence.
Phase code has been shown in visual cortex to involve also high-frequency oscillations. Within a cycle of gamma oscillation, each neuron has its own preferred relative firing time. As a result, an entire population of neurons generates a firing sequence that has a duration of up to about 15 ms.
=== Population coding ===
Population coding is a method to represent stimuli by using the joint activities of a number of neurons. In population coding, each neuron has a distribution of responses over some set of inputs, and the responses of many neurons may be combined to determine some value about the inputs. From the theoretical point of view, population coding is one of a few mathematically well-formulated problems in neuroscience. It grasps the essential features of neural coding and yet is simple enough for theoretic analysis. Experimental studies have revealed that this coding paradigm is widely used in the sensory and motor areas of the brain.
For example, in the visual area medial temporal (MT), neurons are tuned to the direction of object motion. In response to an object moving in a particular direction, many neurons in MT fire with a noise-corrupted and bell-shaped activity pattern across the population. The moving direction of the object is retrieved from the population activity, to be immune from the fluctuation existing in a single neuron's signal. When monkeys are trained to move a joystick towards a lit target, a single neuron will fire for multiple target directions. However it fires the fastest for one direction and more slowly depending on how close the target was to the neuron's "preferred" direction. If each neuron represents movement in its preferred direction, and the vector sum of all neurons is calculated (each neuron has a firing rate and a preferred direction), the sum points in the direction of motion. In this manner, the population of neurons codes the signal for the motion. This particular population code is referred to as population vector coding.
Place-time population codes, termed the averaged-localized-synchronized-response (ALSR) code, have been derived for neural representation of auditory acoustic stimuli. This exploits both the place or tuning within the auditory nerve, as well as the phase-locking within each nerve fiber auditory nerve. The first ALSR representation was for steady-state vowels; ALSR representations of pitch and formant frequencies in complex, non-steady state stimuli were later demonstrated for voiced-pitch, and formant representations in consonant-vowel syllables.
The advantage of such representations is that global features such as pitch or formant transition profiles can be represented as global features across the entire nerve simultaneously via both rate and place coding.
Population coding has a number of other advantages as well, including reduction of uncertainty due to neuronal variability and the ability to represent a number of different stimulus attributes simultaneously. Population coding is also much faster than rate coding and can reflect changes in the stimulus conditions nearly instantaneously. Individual neurons in such a population typically have different but overlapping selectivities, so that many neurons, but not necessarily all, respond to a given stimulus.
Typically an encoding function has a peak value such that activity of the neuron is greatest if the perceptual value is close to the peak value, and becomes reduced accordingly for values less close to the peak value. It follows that the actual perceived value can be reconstructed from the overall pattern of activity in the set of neurons. Vector coding is an example of simple averaging. A more sophisticated mathematical technique for performing such a reconstruction is the method of maximum likelihood based on a multivariate distribution of the neuronal responses. These models can assume independence, second order correlations,
or even more detailed dependencies such as higher order maximum entropy models, or copulas.
==== Correlation coding ====
The correlation coding model of neuronal firing claims that correlations between action potentials, or "spikes", within a spike train may carry additional information above and beyond the simple timing of the spikes. Early work suggested that correlation between spike trains can only reduce, and never increase, the total mutual information present in the two spike trains about a stimulus feature. However, this was later demonstrated to be incorrect. Correlation structure can increase information content if noise and signal correlations are of opposite sign. Correlations can also carry information not present in the average firing rate of two pairs of neurons. A good example of this exists in the pentobarbital-anesthetized marmoset auditory cortex, in which a pure tone causes an increase in the number of correlated spikes, but not an increase in the mean firing rate, of pairs of neurons.
==== Independent-spike coding ====
The independent-spike coding model of neuronal firing claims that each individual action potential, or "spike", is independent of each other spike within the spike train.
==== Position coding ====
A typical population code involves neurons with a Gaussian tuning curve whose means vary linearly with the stimulus intensity, meaning that the neuron responds most strongly (in terms of spikes per second) to a stimulus near the mean. The actual intensity could be recovered as the stimulus level corresponding to the mean of the neuron with the greatest response. However, the noise inherent in neural responses means that a maximum likelihood estimation function is more accurate.
This type of code is used to encode continuous variables such as joint position, eye position, color, or sound frequency. Any individual neuron is too noisy to faithfully encode the variable using rate coding, but an entire population ensures greater fidelity and precision. For a population of unimodal tuning curves, i.e. with a single peak, the precision typically scales linearly with the number of neurons. Hence, for half the precision, half as many neurons are required. In contrast, when the tuning curves have multiple peaks, as in grid cells that represent space, the precision of the population can scale exponentially with the number of neurons. This greatly reduces the number of neurons required for the same precision.
==== Topology of population dynamics ====
Dimensionality reduction and topological data analysis, have revealed that the population code is constrained to low-dimensional manifolds, sometimes also referred to as attractors. The position along the neural manifold correlates to certain behavioral conditions like head direction neurons in the anterodorsal thalamic nucleus forming a ring structure, grid cells encoding spatial position in entorhinal cortex along the surface of a torus, or motor cortex neurons encoding hand movements and preparatory activity. The low-dimensional manifolds are known to change in a state dependent manner, such as eye closure in the visual cortex, or breathing behavior in the ventral respiratory column.
=== Sparse coding ===
The sparse code is when each item is encoded by the strong activation of a relatively small set of neurons. For each item to be encoded, this is a different subset of all available neurons. In contrast to sensor-sparse coding, sensor-dense coding implies that all information from possible sensor locations is known.
As a consequence, sparseness may be focused on temporal sparseness ("a relatively small number of time periods are active") or on the sparseness in an activated population of neurons. In this latter case, this may be defined in one time period as the number of activated neurons relative to the total number of neurons in the population. This seems to be a hallmark of neural computations since compared to traditional computers, information is massively distributed across neurons. Sparse coding of natural images produces wavelet-like oriented filters that resemble the receptive fields of simple cells in the visual cortex. The capacity of sparse codes may be increased by simultaneous use of temporal coding, as found in the locust olfactory system.
Given a potentially large set of input patterns, sparse coding algorithms (e.g. sparse autoencoder) attempt to automatically find a small number of representative patterns which, when combined in the right proportions, reproduce the original input patterns. The sparse coding for the input then consists of those representative patterns. For example, the very large set of English sentences can be encoded by a small number of symbols (i.e. letters, numbers, punctuation, and spaces) combined in a particular order for a particular sentence, and so a sparse coding for English would be those symbols.
==== Linear generative model ====
Most models of sparse coding are based on the linear generative model. In this model, the symbols are combined in a linear fashion to approximate the input.
More formally, given a k-dimensional set of real-numbered input vectors
ξ
→
∈
R
k
{\displaystyle {\vec {\xi }}\in \mathbb {R} ^{k}}
, the goal of sparse coding is to determine n k-dimensional basis vectors
b
1
→
,
…
,
b
n
→
∈
R
k
{\displaystyle {\vec {b_{1}}},\ldots ,{\vec {b_{n}}}\in \mathbb {R} ^{k}}
, corresponding to neuronal receptive fields, along with a sparse n-dimensional vector of weights or coefficients
s
→
∈
R
n
{\displaystyle {\vec {s}}\in \mathbb {R} ^{n}}
for each input vector, so that a linear combination of the basis vectors with proportions given by the coefficients results in a close approximation to the input vector:
ξ
→
≈
∑
j
=
1
n
s
j
b
→
j
{\displaystyle {\vec {\xi }}\approx \sum _{j=1}^{n}s_{j}{\vec {b}}_{j}}
.
The codings generated by algorithms implementing a linear generative model can be classified into codings with soft sparseness and those with hard sparseness. These refer to the distribution of basis vector coefficients for typical inputs. A coding with soft sparseness has a smooth Gaussian-like distribution, but peakier than Gaussian, with many zero values, some small absolute values, fewer larger absolute values, and very few very large absolute values. Thus, many of the basis vectors are active. Hard sparseness, on the other hand, indicates that there are many zero values, no or hardly any small absolute values, fewer larger absolute values, and very few very large absolute values, and thus few of the basis vectors are active. This is appealing from a metabolic perspective: less energy is used when fewer neurons are firing.
Another measure of coding is whether it is critically complete or overcomplete. If the number of basis vectors n is equal to the dimensionality k of the input set, the coding is said to be critically complete. In this case, smooth changes in the input vector result in abrupt changes in the coefficients, and the coding is not able to gracefully handle small scalings, small translations, or noise in the inputs. If, however, the number of basis vectors is larger than the dimensionality of the input set, the coding is overcomplete. Overcomplete codings smoothly interpolate between input vectors and are robust under input noise. The human primary visual cortex is estimated to be overcomplete by a factor of 500, so that, for example, a 14 x 14 patch of input (a 196-dimensional space) is coded by roughly 100,000 neurons.
Other models are based on matching pursuit, a sparse approximation algorithm which finds the "best matching" projections of multidimensional data, and dictionary learning, a representation learning method which aims to find a sparse matrix representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves.
==== Biological evidence ====
Sparse coding may be a general strategy of neural systems to augment memory capacity. To adapt to their environments, animals must learn which stimuli are associated with rewards or punishments and distinguish these reinforced stimuli from similar but irrelevant ones. Such tasks require implementing stimulus-specific associative memories in which only a few neurons out of a population respond to any given stimulus and each neuron responds to only a few stimuli out of all possible stimuli.
Theoretical work on sparse distributed memory has suggested that sparse coding increases the capacity of associative memory by reducing overlap between representations. Experimentally, sparse representations of sensory information have been observed in many systems, including vision, audition, touch, and olfaction. However, despite the accumulating evidence for widespread sparse coding and theoretical arguments for its importance, a demonstration that sparse coding improves the stimulus-specificity of associative memory has been difficult to obtain.
In the Drosophila olfactory system, sparse odor coding by the Kenyon cells of the mushroom body is thought to generate a large number of precisely addressable locations for the storage of odor-specific memories. Sparseness is controlled by a negative feedback circuit between Kenyon cells and GABAergic anterior paired lateral (APL) neurons. Systematic activation and blockade of each leg of this feedback circuit shows that Kenyon cells activate APL neurons and APL neurons inhibit Kenyon cells. Disrupting the Kenyon cell–APL feedback loop decreases the sparseness of Kenyon cell odor responses, increases inter-odor correlations, and prevents flies from learning to discriminate similar, but not dissimilar, odors. These results suggest that feedback inhibition suppresses Kenyon cell activity to maintain sparse, decorrelated odor coding and thus the odor-specificity of memories.
== See also ==
== References ==
== Further reading ==
Földiák P, Endres D, Sparse coding, Scholarpedia, 3(1):2984, 2008.
Dayan P & Abbott LF. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge, Massachusetts: The MIT Press; 2001. ISBN 0-262-04199-5
Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W. Spikes: Exploring the Neural Code. Cambridge, Massachusetts: The MIT Press; 1999. ISBN 0-262-68108-0
Olshausen, B. A.; Field, D. J. (1996). "Emergence of simple-cell receptive field properties by learning a sparse code for natural images". Nature. 381 (6583): 607–9. Bibcode:1996Natur.381..607O. doi:10.1038/381607a0. PMID 8637596. S2CID 4358477.
Tsien, JZ.; et al. (2014). "On initial Brain Activity Mapping of episodic and semantic memory code in the hippocampus". Neurobiology of Learning and Memory. 105: 200–210. doi:10.1016/j.nlm.2013.06.019. PMC 3769419. PMID 23838072. | Wikipedia/Neuroelectrodynamics |
Metabolic network modelling, also known as metabolic network reconstruction or metabolic pathway analysis, allows for an in-depth insight into the molecular mechanisms of a particular organism. In particular, these models correlate the genome with molecular physiology. A reconstruction breaks down metabolic pathways (such as glycolysis and the citric acid cycle) into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. In simplified terms, a reconstruction collects all of the relevant metabolic information of an organism and compiles it in a mathematical model. Validation and analysis of reconstructions can allow identification of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. This knowledge can then be applied to create novel biotechnology.
In general, the process to build a reconstruction is as follows:
Draft a reconstruction
Refine the model
Convert model into a mathematical/computational representation
Evaluate and debug model through experimentation
The related method of flux balance analysis seeks to mathematically simulate metabolism in genome-scale reconstructions of metabolic networks.
== Genome-scale metabolic reconstruction ==
A metabolic reconstruction provides a highly mathematical, structured platform on which to understand the systems biology of metabolic pathways within an organism. The integration of biochemical metabolic pathways with rapidly available, annotated genome sequences has developed what are called genome-scale metabolic models. Simply put, these models correlate metabolic genes with metabolic pathways. In general, the more information about physiology, biochemistry and genetics is available for the target organism, the better the predictive capacity of the reconstructed models. Mechanically speaking, the process of reconstructing prokaryotic and eukaryotic metabolic networks is essentially the same. Having said this, eukaryote reconstructions are typically more challenging because of the size of genomes, coverage of knowledge, and the multitude of cellular compartments. The first genome-scale metabolic model was generated in 1995 for Haemophilus influenzae. The first multicellular organism, C. elegans, was reconstructed in 1998. Since then, many reconstructions have been formed. For a list of reconstructions that have been converted into a model and experimentally validated, see http://sbrg.ucsd.edu/InSilicoOrganisms/OtherOrganisms.
== Drafting a reconstruction ==
=== Resources ===
Because the timescale for the development of reconstructions is so recent, most reconstructions have been built manually. However, now, there are quite a few resources that allow for the semi-automatic assembly of these reconstructions that are utilized due to the time and effort necessary for a reconstruction. An initial fast reconstruction can be developed automatically using resources like PathoLogic or ERGO in combination with encyclopedias like MetaCyc, and then manually updated by using resources like PathwayTools. These semi-automatic methods allow for a fast draft to be created while allowing the fine tune adjustments required once new experimental data is found. It is only in this manner that the field of metabolic reconstructions will keep up with the ever-increasing numbers of annotated genomes.
==== Databases ====
Kyoto Encyclopedia of Genes and Genomes (KEGG): a bioinformatics database containing information on genes, proteins, reactions, and pathways. The ‘KEGG Organisms’ section, which is divided into eukaryotes and prokaryotes, encompasses many organisms for which gene and DNA information can be searched by typing in the enzyme of choice.
BioCyc, EcoCyc, and MetaCyc: BioCyc Is a collection of 3,000 pathway/genome databases (as of Oct 2013), with each database dedicated to one organism. For example, EcoCyc is a highly detailed bioinformatics database on the genome and metabolic reconstruction of Escherichia coli, including thorough descriptions of E. coli signaling pathways and regulatory network. The EcoCyc database can serve as a paradigm and model for any reconstruction. Additionally, MetaCyc, an encyclopedia of experimentally defined metabolic pathways and enzymes, contains 2,100 metabolic pathways and 11,400 metabolic reactions (Oct 2013).
ENZYME: An enzyme nomenclature database (part of the ExPASy proteonomics server of the Swiss Institute of Bioinformatics). After searching for a particular enzyme on the database, this resource gives you the reaction that is catalyzed. ENZYME has direct links to other gene/enzyme/literature databases such as KEGG, BRENDA, and PUBMED.
BRENDA: A comprehensive enzyme database that allows for an enzyme to be searched by name, EC number, or organism.
BiGG: A knowledge base of biochemically, genetically, and genomically structured genome-scale metabolic network reconstructions.
metaTIGER: Is a collection of metabolic profiles and phylogenomic information on a taxonomically diverse range of eukaryotes which provides novel facilities for viewing and comparing the metabolic profiles between organisms.
==== Tools for metabolic modeling ====
Pathway Tools: A bioinformatics software package that assists in the construction of pathway/genome databases such as EcoCyc. Developed by Peter Karp and associates at the SRI International Bioinformatics Research Group, Pathway Tools has several components. Its PathoLogic module takes an annotated genome for an organism and infers probable metabolic reactions and pathways to produce a new pathway/genome database. Its MetaFlux component can generate a quantitative metabolic model from that pathway/genome database using flux-balance analysis. Its Navigator component provides extensive query and visualization tools, such as visualization of metabolites, pathways, and the complete metabolic network.
ERGO: A subscription-based service developed by Integrated Genomics. It integrates data from every level including genomic, biochemical data, literature, and high-throughput analysis into a comprehensive user friendly network of metabolic and nonmetabolic pathways.
KEGGtranslator: an easy-to-use stand-alone application that can visualize and convert KEGG files (KGML formatted XML-files) into multiple output formats. Unlike other translators, KEGGtranslator supports a plethora of output formats, is able to augment the information in translated documents (e.g., MIRIAM annotations) beyond the scope of the KGML document, and amends missing components to fragmentary reactions within the pathway to allow simulations on those. KEGGtranslator converts these files to SBML, BioPAX, SIF, SBGN, SBML with qualitative modeling extension, GML, GraphML, JPG, GIF, LaTeX, etc.
ModelSEED: An online resource for the analysis, comparison, reconstruction, and curation of genome-scale metabolic models. Users can submit genome sequences to the RAST annotation system, and the resulting annotation can be automatically piped into the ModelSEED to produce a draft metabolic model. The ModelSEED automatically constructs a network of metabolic reactions, gene-protein-reaction associations for each reaction, and a biomass composition reaction for each genome to produce a model of microbial metabolism that can be simulated using Flux Balance Analysis.
MetaMerge: algorithm for semi-automatically reconciling a pair of existing metabolic network reconstructions into a single metabolic network model.
CoReCo: algorithm for automatic reconstruction of metabolic models of related species. The first version of the software used KEGG as reaction database to link with the EC number predictions from CoReCo. Its automatic gap filling using atom map of all the reactions produce functional models ready for simulation.
==== Tools for literature ====
PUBMED: This is an online library developed by the National Center for Biotechnology Information, which contains a massive collection of medical journals. Using the link provided by ENZYME, the search can be directed towards the organism of interest, thus recovering literature on the enzyme and its use inside of the organism.
=== Methodology to draft a reconstruction ===
A reconstruction is built by compiling data from the resources above. Database tools such as KEGG and BioCyc can be used in conjunction with each other to find all the metabolic genes in the organism of interest. These genes will be compared to closely related organisms that have already developed reconstructions to find homologous genes and reactions. These homologous genes and reactions are carried over from the known reconstructions to form the draft reconstruction of the organism of interest. Tools such as ERGO, Pathway Tools and Model SEED can compile data into pathways to form a network of metabolic and non-metabolic pathways. These networks are then verified and refined before being made into a mathematical simulation.
The predictive aspect of a metabolic reconstruction hinges on the ability to predict the biochemical reaction catalyzed by a protein using that protein's amino acid sequence as an input, and to infer the structure of a metabolic network based on the predicted set of reactions. A network of enzymes and metabolites is drafted to relate sequences and function. When an uncharacterized protein is found in the genome, its amino acid sequence is first compared to those of previously characterized proteins to search for homology. When a homologous protein is found, the proteins are considered to have a common ancestor and their functions are inferred as being similar. However, the quality of a reconstruction model is dependent on its ability to accurately infer phenotype directly from sequence, so this rough estimation of protein function will not be sufficient. A number of algorithms and bioinformatics resources have been developed for refinement of sequence homology-based assignments of protein functions:
InParanoid: Identifies eukaryotic orthologs by looking only at in-paralogs.
CDD: Resource for the annotation of functional units in proteins. Its collection of domain models utilizes 3D structure to provide insights into sequence/structure/function relationships.
InterPro: Provides functional analysis of proteins by classifying them into families and predicting domains and important sites.
STRING: Database of known and predicted protein interactions.
Once proteins have been established, more information about the enzyme structure, reactions catalyzed, substrates and products, mechanisms, and more can be acquired from databases such as KEGG, MetaCyc and NC-IUBMB. Accurate metabolic reconstructions require additional information about the reversibility and preferred physiological direction of an enzyme-catalyzed reaction which can come from databases such as BRENDA or MetaCyc database.
== Model refinement ==
An initial metabolic reconstruction of a genome is typically far from perfect due to the high variability and diversity of microorganisms. Often, metabolic pathway databases such as KEGG and MetaCyc will have "holes", meaning that there is a conversion from a substrate to a product (i.e., an enzymatic activity) for which there is no known protein in the genome that encodes the enzyme that facilitates the catalysis. What can also happen in semi-automatically drafted reconstructions is that some pathways are falsely predicted and don't actually occur in the predicted manner. Because of this, a systematic verification is made in order to make sure no inconsistencies are present and that all the entries listed are correct and accurate. Furthermore, previous literature can be researched in order to support any information obtained from one of the many metabolic reaction and genome databases. This provides an added level of assurance for the reconstruction that the enzyme and the reaction it catalyzes do actually occur in the organism.
Enzyme promiscuity and spontaneous chemical reactions can damage metabolites. This metabolite damage, and its repair or pre-emption, create energy costs that need to be incorporated into models. It is likely that many genes of unknown function encode proteins that repair or pre-empt metabolite damage, but most genome-scale metabolic reconstructions only include a fraction of all genes.
Any new reaction not present in the databases needs to be added to the reconstruction. This is an iterative process that cycles between the experimental phase and the coding phase. As new information is found about the target organism, the model will be adjusted to predict the metabolic and phenotypical output of the cell. The presence or absence of certain reactions of the metabolism will affect the amount of reactants/products that are present for other reactions within the particular pathway. This is because products in one reaction go on to become the reactants for another reaction, i.e. products of one reaction can combine with other proteins or compounds to form new proteins/compounds in the presence of different enzymes or catalysts.
Francke et al. provide an excellent example as to why the verification step of the project needs to be performed in significant detail. During a metabolic network reconstruction of Lactobacillus plantarum, the model showed that succinyl-CoA was one of the reactants for a reaction that was a part of the biosynthesis of methionine. However, an understanding of the physiology of the organism would have revealed that due to an incomplete tricarboxylic acid pathway, Lactobacillus plantarum does not actually produce succinyl-CoA, and the correct reactant for that part of the reaction was acetyl-CoA.
Therefore, systematic verification of the initial reconstruction will bring to light several inconsistencies that can adversely affect the final interpretation of the reconstruction, which is to accurately comprehend the molecular mechanisms of the organism. Furthermore, the simulation step also ensures that all the reactions present in the reconstruction are properly balanced. To sum up, a reconstruction that is fully accurate can lead to greater insight about understanding the functioning of the organism of interest.
== Metabolic stoichiometric analysis ==
A metabolic network can be broken down into a stoichiometric matrix where the rows represent the compounds of the reactions, while the columns of the matrix correspond to the reactions themselves. Stoichiometry is a quantitative relationship between substrates of a chemical reaction. In order to deduce what the metabolic network suggests, recent research has centered on a few approaches, such as extreme pathways, elementary mode analysis, flux balance analysis, and a number of other constraint-based modeling methods.
=== Extreme pathways ===
Price, Reed, and Papin, from the Palsson lab, use a method of singular value decomposition (SVD) of extreme pathways in order to understand regulation of a human red blood cell metabolism. Extreme pathways are convex basis vectors that consist of steady state functions of a metabolic network. For any particular metabolic network, there is always a unique set of extreme pathways available. Furthermore, Price, Reed, and Papin, define a constraint-based approach, where through the help of constraints like mass balance and maximum reaction rates, it is possible to develop a ‘solution space’ where all the feasible options fall within. Then, using a kinetic model approach, a single solution that falls within the extreme pathway solution space can be determined. Therefore, in their study, Price, Reed, and Papin, use both constraint and kinetic approaches to understand the human red blood cell metabolism. In conclusion, using extreme pathways, the regulatory mechanisms of a metabolic network can be studied in further detail.
=== Elementary mode analysis ===
Elementary mode analysis closely matches the approach used by extreme pathways. Similar to extreme pathways, there is always a unique set of elementary modes available for a particular metabolic network. These are the smallest sub-networks that allow a metabolic reconstruction network to function in steady state. According to Stelling (2002), elementary modes can be used to understand cellular objectives for the overall metabolic network. Furthermore, elementary mode analysis takes into account stoichiometrics and thermodynamics when evaluating whether a particular metabolic route or network is feasible and likely for a set of proteins/enzymes.
=== Minimal metabolic behaviors (MMBs) ===
In 2009, Larhlimi and Bockmayr presented a new approach called "minimal metabolic behaviors" for the analysis of metabolic networks. Like elementary modes or extreme pathways, these are uniquely determined by the network, and yield a complete description of the flux cone. However, the new description is much more compact. In contrast with elementary modes and extreme pathways, which use an inner description based on generating vectors of the flux cone, MMBs are using an outer description of the flux cone. This approach is based on sets of non-negativity constraints. These can be identified with irreversible reactions, and thus have a direct biochemical interpretation. One can characterize a metabolic network by MMBs and the reversible metabolic space.
=== Flux balance analysis ===
A different technique to simulate the metabolic network is to perform flux balance analysis. This method uses linear programming, but in contrast to elementary mode analysis and extreme pathways, only a single solution results in the end. Linear programming is usually used to obtain the maximum potential of the objective function that you are looking at, and therefore, when using flux balance analysis, a single solution is found to the optimization problem. In a flux balance analysis approach, exchange fluxes are assigned to those metabolites that enter or leave the particular network only. Those metabolites that are consumed within the network are not assigned any exchange flux value. Also, the exchange fluxes along with the enzymes can have constraints ranging from a negative to positive value (ex: -10 to 10).
Furthermore, this particular approach can accurately define if the reaction stoichiometry is in line with predictions by providing fluxes for the balanced reactions. Also, flux balance analysis can highlight the most effective and efficient pathway through the network in order to achieve a particular objective function. In addition, gene knockout studies can be performed using flux balance analysis. The enzyme that correlates to the gene that needs to be removed is given a constraint value of 0. Then, the reaction that the particular enzyme catalyzes is completely removed from the analysis.
=== Dynamic simulation and parameter estimation ===
In order to perform a dynamic simulation with such a network it is necessary to construct an ordinary differential equation
system that describes the rates of change in each metabolite's concentration or amount. To this end, a rate law, i.e., a kinetic equation that determines the rate of reaction based on the concentrations of all reactants is required for each reaction. Software packages that include numerical integrators, such as COPASI or SBMLsimulator, are then able to simulate the system dynamics given an initial condition. Often these rate laws contain kinetic parameters with uncertain values. In many cases it is desired to estimate these parameter values with respect to given time-series data of metabolite concentrations. The system is then supposed to reproduce the given data. For this purpose the distance between the given data set and the result of the simulation, i.e., the numerically or in few cases analytically obtained solution of the differential equation system is computed. The values of the parameters are then estimated to minimize this distance. One step further, it may be desired to estimate the mathematical structure of the differential equation system because the real rate laws are not known for the reactions within the system under study. To this end, the program SBMLsqueezer allows automatic creation of appropriate rate laws for all reactions with the network.
=== Synthetic accessibility ===
Synthetic accessibility is a simple approach to network simulation whose goal is to predict which metabolic gene knockouts are lethal. The synthetic accessibility approach uses the topology of the metabolic network to calculate the sum of the minimum number of steps needed to traverse the metabolic network graph from the inputs, those metabolites available to the organism from the environment, to the outputs, metabolites needed by the organism to survive. To simulate a gene knockout, the reactions enabled by the gene are removed from the network and the synthetic accessibility metric is recalculated. An increase in the total number of steps is predicted to cause lethality. Wunderlich and Mirny showed this simple, parameter-free approach predicted knockout lethality in E. coli and S. cerevisiae as well as elementary mode analysis and flux balance analysis in a variety of media.
== Applications of a reconstruction ==
Several inconsistencies exist between gene, enzyme, reaction databases, and published literature sources regarding the metabolic information of an organism. A reconstruction is a systematic verification and compilation of data from various sources that takes into account all of the discrepancies.
The combination of relevant metabolic and genomic information of an organism.
Metabolic comparisons can be performed between various organisms of the same species as well as between different organisms.
Analysis of synthetic lethality
Predict adaptive evolution outcomes
Use in metabolic engineering for high value outputs
Reconstructions and their corresponding models allow the formulation of hypotheses about the presence of certain enzymatic activities and the production of metabolites that can be experimentally tested, complementing the primarily discovery-based approach of traditional microbial biochemistry with hypothesis-driven research. The results these experiments can uncover novel pathways and metabolic activities and decipher between discrepancies in previous experimental data. Information about the chemical reactions of metabolism and the genetic background of various metabolic properties (sequence to structure to function) can be utilized by genetic engineers to modify organisms to produce high value outputs whether those products be medically relevant like pharmaceuticals; high value chemical intermediates such as terpenoids and isoprenoids; or biotechnological outputs like biofuels, or polyhydroxybutyrates also known as bioplastics.
Metabolic network reconstructions and models are used to understand how an organism or parasite functions inside of the host cell. For example, if the parasite serves to compromise the immune system by lysing macrophages, then the goal of metabolic reconstruction/simulation would be to determine the metabolites that are essential to the organism's proliferation inside of macrophages. If the proliferation cycle is inhibited, then the parasite would not continue to evade the host's immune system. A reconstruction model serves as a first step to deciphering the complicated mechanisms surrounding disease. These models can also look at the minimal genes necessary for a cell to maintain virulence. The next step would be to use the predictions and postulates generated from a reconstruction model and apply it to discover novel biological functions such as drug-engineering and drug delivery techniques.
== See also ==
Computational systems biology
Computer simulation
Flux balance analysis
Fluxomics
Metabolic control analysis
Metabolic flux analysis
Metabolic network
Metabolic pathway
Biochemical systems equation
Metagenomics
== References ==
== Further reading ==
Overbeek R, Larsen N, Walunas T, D'Souza M, Pusch G, Selkov Jr, Liolios K, Joukov V, Kaznadzey D, Anderson I, Bhattacharyya A, Burd H, Gardner W, Hanke P, Kapatral V, Mikhailova N, Vasieva O, Osterman A, Vonstein V, Fonstein M, Ivanova N, Kyrpides N. (2003) The ERGO genome analysis and discovery system. Nucleic Acids Res. 31(1):164-71
Whitaker, J.W., Letunic, I., McConkey, G.A. and Westhead, D.R. metaTIGER: a metabolic evolution resource. Nucleic Acids Res. 2009 37: D531-8.
== External links ==
ERGO
GeneDB
KEGG
PathCase Case Western Reserve University
BRENDA
BioCyc and Cyclone - provides an open source Java API to the pathway tool BioCyc to extract Metabolic graphs.
EcoCyc
MetaCyc
SEED
ModelSEED
ENZYME
SBRI Bioinformatics Tools and Software
TIGR
Pathway Tools
metaTIGER
Stanford Genomic Resources
Pathway Hunter Tool
IMG The Integrated Microbial Genomes system, for genome analysis by the DOE-JGI.
Systems Analysis, Modelling and Prediction Group at the University of Oxford, Biochemical reaction pathway inference techniques.
efmtool provided by Marco Terzer
SBMLsqueezer
Cellnet analyzer from Klamt and von Kamp
Copasi
gEFM A graph-based tool for EFM computation | Wikipedia/Metabolic_network_modelling |
Computer simulation is the running of a mathematical model on a computer, the model being designed to represent the behaviour of, or the outcome of, a real-world or physical system. The reliability of some mathematical models can be determined by comparing their results to the real-world outcomes they aim to predict. Computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics (computational physics), astrophysics, climatology, chemistry, biology and manufacturing, as well as human systems in economics, psychology, social science, health care and engineering. Simulation of a system is represented as the running of the system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions.
Computer simulations are realized by running computer programs that can be either small, running almost instantly on small devices, or large-scale programs that run for hours or days on network-based groups of computers. The scale of events being simulated by computer simulations has far exceeded anything possible (or perhaps even imaginable) using traditional paper-and-pencil mathematical modeling. In 1997, a desert-battle simulation of one force invading another involved the modeling of 66,239 tanks, trucks and other vehicles on simulated terrain around Kuwait, using multiple supercomputers in the DoD High Performance Computer Modernization Program.
Other examples include a 1-billion-atom model of material deformation; a 2.64-million-atom model of the complex protein-producing organelle of all living organisms, the ribosome, in 2005;
a complete simulation of the life cycle of Mycoplasma genitalium in 2012; and the Blue Brain project at EPFL (Switzerland), begun in May 2005 to create the first computer simulation of the entire human brain, right down to the molecular level.
Because of the computational cost of simulation, computer experiments are used to perform inference such as uncertainty quantification.
== Simulation versus model ==
A model consists of the equations used to capture the behavior of a system. By contrast, computer simulation is the actual running of the program that perform algorithms which solve those equations, often in an approximate manner. Simulation, therefore, is the process of running a model. Thus one would not "build a simulation"; instead, one would "build a model (or a simulator)", and then either "run the model" or equivalently "run a simulation".
== History ==
Computer simulation developed hand-in-hand with the rapid growth of the computer, following its first large-scale deployment during the Manhattan Project in World War II to model the process of nuclear detonation. It was a simulation of 12 hard spheres using a Monte Carlo algorithm. Computer simulation is often used as an adjunct to, or substitute for, modeling systems for which simple closed form analytic solutions are not possible. There are many types of computer simulations; their common feature is the attempt to generate a sample of representative scenarios for a model in which a complete enumeration of all possible states of the model would be prohibitive or impossible.
== Data preparation ==
The external data requirements of simulations and models vary widely. For some, the input might be just a few numbers (for example, simulation of a waveform of AC electricity on a wire), while others might require terabytes of information (such as weather and climate models).
Input sources also vary widely:
Sensors and other physical devices connected to the model;
Control surfaces used to direct the progress of the simulation in some way;
Current or historical data entered by hand;
Values extracted as a by-product from other processes;
Values output for the purpose by other simulations, models, or processes.
Lastly, the time at which data is available varies:
"invariant" data is often built into the model code, either because the value is truly invariant (e.g., the value of π) or because the designers consider the value to be invariant for all cases of interest;
data can be entered into the simulation when it starts up, for example by reading one or more files, or by reading data from a preprocessor;
data can be provided during the simulation run, for example by a sensor network.
Because of this variety, and because diverse simulation systems have many common elements, there are a large number of specialized simulation languages. The best-known may be Simula. There are now many others.
Systems that accept data from external sources must be very careful in knowing what they are receiving. While it is easy for computers to read in values from text or binary files, what is much harder is knowing what the accuracy (compared to measurement resolution and precision) of the values are. Often they are expressed as "error bars", a minimum and maximum deviation from the value range within which the true value (is expected to) lie. Because digital computer mathematics is not perfect, rounding and truncation errors multiply this error, so it is useful to perform an "error analysis" to confirm that values output by the simulation will still be usefully accurate.
== Types ==
Models used for computer simulations can be classified according to several independent pairs of attributes, including:
Stochastic or deterministic (and as a special case of deterministic, chaotic) – see external links below for examples of stochastic vs. deterministic simulations
Steady-state or dynamic
Continuous or discrete (and as an important special case of discrete, discrete event or DE models)
Dynamic system simulation, e.g. electric systems, hydraulic systems or multi-body mechanical systems (described primarily by DAE:s) or dynamics simulation of field problems, e.g. CFD of FEM simulations (described by PDE:s).
Local or distributed.
Another way of categorizing models is to look at the underlying data structures. For time-stepped simulations, there are two main classes:
Simulations which store their data in regular grids and require only next-neighbor access are called stencil codes. Many CFD applications belong to this category.
If the underlying graph is not a regular grid, the model may belong to the meshfree method class.
For steady-state simulations, equations define the relationships between elements of the modeled system and attempt to find a state in which the system is in equilibrium. Such models are often used in simulating physical systems, as a simpler modeling case before dynamic simulation is attempted.
Dynamic simulations attempt to capture changes in a system in response to (usually changing) input signals.
Stochastic models use random number generators to model chance or random events;
A discrete event simulation (DES) manages events in time. Most computer, logic-test and fault-tree simulations are of this type. In this type of simulation, the simulator maintains a queue of events sorted by the simulated time they should occur. The simulator reads the queue and triggers new events as each event is processed. It is not important to execute the simulation in real time. It is often more important to be able to access the data produced by the simulation and to discover logic defects in the design or the sequence of events.
A continuous dynamic simulation performs numerical solution of differential-algebraic equations or differential equations (either partial or ordinary). Periodically, the simulation program solves all the equations and uses the numbers to change the state and output of the simulation. Applications include flight simulators, construction and management simulation games, chemical process modeling, and simulations of electrical circuits. Originally, these kinds of simulations were actually implemented on analog computers, where the differential equations could be represented directly by various electrical components such as op-amps. By the late 1980s, however, most "analog" simulations were run on conventional digital computers that emulate the behavior of an analog computer.
A special type of discrete simulation that does not rely on a model with an underlying equation, but can nonetheless be represented formally, is agent-based simulation. In agent-based simulation, the individual entities (such as molecules, cells, trees or consumers) in the model are represented directly (rather than by their density or concentration) and possess an internal state and set of behaviors or rules that determine how the agent's state is updated from one time-step to the next.
Distributed models run on a network of interconnected computers, possibly through the Internet. Simulations dispersed across multiple host computers like this are often referred to as "distributed simulations". There are several standards for distributed simulation, including Aggregate Level Simulation Protocol (ALSP), Distributed Interactive Simulation (DIS), the High Level Architecture (simulation) (HLA) and the Test and Training Enabling Architecture (TENA).
== Visualization ==
Formerly, the output data from a computer simulation was sometimes presented in a table or a matrix showing how data were affected by numerous changes in the simulation parameters. The use of the matrix format was related to traditional use of the matrix concept in mathematical models. However, psychologists and others noted that humans could quickly perceive trends by looking at graphs or even moving-images or motion-pictures generated from the data, as displayed by computer-generated-imagery (CGI) animation. Although observers could not necessarily read out numbers or quote math formulas, from observing a moving weather chart they might be able to predict events (and "see that rain was headed their way") much faster than by scanning tables of rain-cloud coordinates. Such intense graphical displays, which transcended the world of numbers and formulae, sometimes also led to output that lacked a coordinate grid or omitted timestamps, as if straying too far from numeric data displays. Today, weather forecasting models tend to balance the view of moving rain/snow clouds against a map that uses numeric coordinates and numeric timestamps of events.
Similarly, CGI computer simulations of CAT scans can simulate how a tumor might shrink or change during an extended period of medical treatment, presenting the passage of time as a spinning view of the visible human head, as the tumor changes.
Other applications of CGI computer simulations are being developed to graphically display large amounts of data, in motion, as changes occur during a simulation run.
== In science ==
Generic examples of types of computer simulations in science, which are derived from an underlying mathematical description:
a numerical simulation of differential equations that cannot be solved analytically, theories that involve continuous systems such as phenomena in physical cosmology, fluid dynamics (e.g., climate models, roadway noise models, roadway air dispersion models), continuum mechanics and chemical kinetics fall into this category.
a stochastic simulation, typically used for discrete systems where events occur probabilistically and which cannot be described directly with differential equations (this is a discrete simulation in the above sense). Phenomena in this category include genetic drift, biochemical or gene regulatory networks with small numbers of molecules. (see also: Monte Carlo method).
multiparticle simulation of the response of nanomaterials at multiple scales to an applied force for the purpose of modeling their thermoelastic and thermodynamic properties. Techniques used for such simulations are Molecular dynamics, Molecular mechanics, Monte Carlo method, and Multiscale Green's function.
Specific examples of computer simulations include:
statistical simulations based upon an agglomeration of a large number of input profiles, such as the forecasting of equilibrium temperature of receiving waters, allowing the gamut of meteorological data to be input for a specific locale. This technique was developed for thermal pollution forecasting.
agent based simulation has been used effectively in ecology, where it is often called "individual based modeling" and is used in situations for which individual variability in the agents cannot be neglected, such as population dynamics of salmon and trout (most purely mathematical models assume all trout behave identically).
time stepped dynamic model. In hydrology there are several such hydrology transport models such as the SWMM and DSSAM Models developed by the U.S. Environmental Protection Agency for river water quality forecasting.
computer simulations have also been used to formally model theories of human cognition and performance, e.g., ACT-R.
computer simulation using molecular modeling for drug discovery.
computer simulation to model viral infection in mammalian cells.
computer simulation for studying the selective sensitivity of bonds by mechanochemistry during grinding of organic molecules.
Computational fluid dynamics simulations are used to simulate the behaviour of flowing air, water and other fluids. One-, two- and three-dimensional models are used. A one-dimensional model might simulate the effects of water hammer in a pipe. A two-dimensional model might be used to simulate the drag forces on the cross-section of an aeroplane wing. A three-dimensional simulation might estimate the heating and cooling requirements of a large building.
An understanding of statistical thermodynamic molecular theory is fundamental to the appreciation of molecular solutions. Development of the Potential Distribution Theorem (PDT) allows this complex subject to be simplified to down-to-earth presentations of molecular theory.
Notable, and sometimes controversial, computer simulations used in science include: Donella Meadows' World3 used in the Limits to Growth, James Lovelock's Daisyworld and Thomas Ray's Tierra.
In social sciences, computer simulation is an integral component of the five angles of analysis fostered by the data percolation methodology, which also includes qualitative and quantitative methods, reviews of the literature (including scholarly), and interviews with experts, and which forms an extension of data triangulation. Of course, similar to any other scientific method, replication is an important part of computational modeling
== In practical contexts ==
Computer simulations are used in a wide variety of practical contexts, such as:
analysis of air pollutant dispersion using atmospheric dispersion modeling
As a possible humane alternative to live animal testing in respect to animal rights.
design of complex systems such as aircraft and also logistics systems.
design of noise barriers to effect roadway noise mitigation
modeling of application performance
flight simulators to train pilots
weather forecasting
forecasting of risk
simulation of electrical circuits
Power system simulation
simulation of other computers is emulation.
forecasting of prices on financial markets (for example Adaptive Modeler)
behavior of structures (such as buildings and industrial parts) under stress and other conditions
design of industrial processes, such as chemical processing plants
strategic management and organizational studies
reservoir simulation for the petroleum engineering to model the subsurface reservoir
process engineering simulation tools.
robot simulators for the design of robots and robot control algorithms
urban simulation models that simulate dynamic patterns of urban development and responses to urban land use and transportation policies.
traffic engineering to plan or redesign parts of the street network from single junctions over cities to a national highway network to transportation system planning, design and operations. See a more detailed article on Simulation in Transportation.
modeling car crashes to test safety mechanisms in new vehicle models.
crop-soil systems in agriculture, via dedicated software frameworks (e.g. BioMA, OMS3, APSIM)
The reliability and the trust people put in computer simulations depends on the validity of the simulation model, therefore verification and validation are of crucial importance in the development of computer simulations. Another important aspect of computer simulations is that of reproducibility of the results, meaning that a simulation model should not provide a different answer for each execution. Although this might seem obvious, this is a special point of attention in stochastic simulations, where random numbers should actually be semi-random numbers. An exception to reproducibility are human-in-the-loop simulations such as flight simulations and computer games. Here a human is part of the simulation and thus influences the outcome in a way that is hard, if not impossible, to reproduce exactly.
Vehicle manufacturers make use of computer simulation to test safety features in new designs. By building a copy of the car in a physics simulation environment, they can save the hundreds of thousands of dollars that would otherwise be required to build and test a unique prototype. Engineers can step through the simulation milliseconds at a time to determine the exact stresses being put upon each section of the prototype.
Computer graphics can be used to display the results of a computer simulation. Animations can be used to experience a simulation in real-time, e.g., in training simulations. In some cases animations may also be useful in faster than real-time or even slower than real-time modes. For example, faster than real-time animations can be useful in visualizing the buildup of queues in the simulation of humans evacuating a building. Furthermore, simulation results are often aggregated into static images using various ways of scientific visualization.
In debugging, simulating a program execution under test (rather than executing natively) can detect far more errors than the hardware itself can detect and, at the same time, log useful debugging information such as instruction trace, memory alterations and instruction counts. This technique can also detect buffer overflow and similar "hard to detect" errors as well as produce performance information and tuning data.
== Pitfalls ==
Although sometimes ignored in computer simulations, it is very important to perform a sensitivity analysis to ensure that the accuracy of the results is properly understood. For example, the probabilistic risk analysis of factors determining the success of an oilfield exploration program involves combining samples from a variety of statistical distributions using the Monte Carlo method. If, for instance, one of the key parameters (e.g., the net ratio of oil-bearing strata) is known to only one significant figure, then the result of the simulation might not be more precise than one significant figure, although it might (misleadingly) be presented as having four significant figures.
== See also ==
== References ==
== Further reading ==
Young, Joseph and Findley, Michael. 2014. "Computational Modeling to Study Conflicts and Terrorism." Routledge Handbook of Research Methods in Military Studies edited by Soeters, Joseph; Shields, Patricia and Rietjens, Sebastiaan. pp. 249–260. New York: Routledge,
R. Frigg and S. Hartmann, Models in Science. Entry in the Stanford Encyclopedia of Philosophy.
E. Winsberg Simulation in Science. Entry in the Stanford Encyclopedia of Philosophy.
S. Hartmann, The World as a Process: Simulations in the Natural and Social Sciences, in: R. Hegselmann et al. (eds.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View, Theory and Decision Library. Dordrecht: Kluwer 1996, 77–100.
E. Winsberg, Science in the Age of Computer Simulation. Chicago: University of Chicago Press, 2010.
P. Humphreys, Extending Ourselves: Computational Science, Empiricism, and Scientific Method. Oxford: Oxford University Press, 2004.
James J. Nutaro (2011). Building Software for Simulation: Theory and Algorithms, with Applications in C++. John Wiley & Sons. ISBN 978-1-118-09945-2.
Desa, W. L. H. M., Kamaruddin, S., & Nawawi, M. K. M. (2012). Modeling of Aircraft Composite Parts Using Simulation. Advanced Material Research, 591–593, 557–560.
== External links ==
Guide to the Computer Simulation Oral History Archive 2003-2018 | Wikipedia/Computational_modeling |
DNA polymerase I (or Pol I) is an enzyme that participates in the process of prokaryotic DNA replication. Discovered by Arthur Kornberg in 1956, it was the first known DNA polymerase (and the first known of any kind of polymerase). It was initially characterized in E. coli and is ubiquitous in prokaryotes. In E. coli and many other bacteria, the gene that encodes Pol I is known as polA. The E. coli Pol I enzyme is composed of 928 amino acids, and is an example of a processive enzyme — it can sequentially catalyze multiple polymerisation steps without releasing the single-stranded template. The physiological function of Pol I is mainly to support repair of damaged DNA, but it also contributes to connecting Okazaki fragments by deleting RNA primers and replacing the ribonucleotides with DNA.
== Discovery ==
In 1956, Arthur Kornberg and colleagues discovered Pol I by using Escherichia coli (E. coli) extracts to develop a DNA synthesis assay. The scientists added 14C-labeled thymidine so that a radioactive polymer of DNA, not RNA, could be retrieved. To initiate the purification of DNA polymerase, the researchers added streptomycin sulfate to the E. coli extract. This separated the extract into a nucleic acid-free supernatant (S-fraction) and nucleic acid-containing precipitate (P-fraction). The P-fraction also contained Pol I and heat-stable factors essential for the DNA synthesis reactions. These factors were identified as nucleoside triphosphates, the building blocks of nucleic acids. The S-fraction contained multiple deoxynucleoside kinases. In 1959, the Nobel Prize in Physiology or Medicine was awarded to Arthur Kornberg and Severo Ochoa "for their discovery of the mechanisms involved in the biological synthesis of Ribonucleic acid and Deoxyribonucleic Acid."
== Structure and function ==
=== General structure ===
Pol I mainly functions in the repair of damaged DNA. Structurally, Pol I is a member of the alpha/beta protein superfamily, which encompasses proteins in which α-helices and β-strands occur in irregular sequences. E. coli DNA Pol I consists of multiple domains with three distinct enzymatic activities. Three domains, often referred to as thumb, finger and palm domain work together to sustain DNA polymerase activity. A fourth domain next to the palm domain contains an exonuclease active site that removes incorrectly incorporated nucleotides in a 3' to 5' direction in a process known as proofreading. A fifth domain contains another exonuclease active site that removes DNA or RNA in a 5' to 3' direction and is essential for RNA primer removal during DNA replication or DNA during DNA repair processes.
E. coli bacteria produces 5 different DNA polymerases: DNA Pol I, DNA Pol II, DNA Pol III, DNA Pol IV, and DNA Pol V.
=== Structural and functional similarity to other polymerases ===
In DNA replication, the leading DNA strand is continuously extended in the direction of replication fork movement, whereas the DNA lagging strand runs discontinuously in the opposite direction as Okazaki fragments. DNA polymerases also cannot initiate DNA chains so they must be initiated by short RNA or DNA segments known as primers. In order for DNA polymerization to take place, two requirements must be met. First of all, all DNA polymerases must have both a template strand and a primer strand. Unlike RNA, DNA polymerases cannot synthesize DNA from a template strand. Synthesis must be initiated by a short RNA segment, known as RNA primer, synthesized by Primase in the 5' to 3' direction. DNA synthesis then occurs by the addition of a dNTP to the 3' hydroxyl group at the end of the preexisting DNA strand or RNA primer. Secondly, DNA polymerases can only add new nucleotides to the preexisting strand through hydrogen bonding. Since all DNA polymerases have a similar structure, they all share a two-metal ion-catalyzed polymerase mechanism. One of the metal ions activates the primer 3' hydroxyl group, which then attacks the primary 5' phosphate of the dNTP. The second metal ion will stabilize the leaving oxygen's negative charge, and subsequently chelates the two exiting phosphate groups.
The X-ray crystal structures of polymerase domains of DNA polymerases are described in analogy to human right hands. All DNA polymerases contain three domains. The first domain, which is known as the "fingers domain", interacts with the dNTP and the paired template base. The "fingers domain" also interacts with the template to position it correctly at the active site. Known as the "palm domain", the second domain catalyses the reaction of the transfer of the phosphoryl group. Lastly, the third domain, which is known as the "thumb domain", interacts with double stranded DNA. The exonuclease domain contains its own catalytic site and removes mispaired bases. Among the seven different DNA polymerase families, the "palm domain" is conserved in five of these families. The "finger domain" and "thumb domain" are not consistent in each family due to varying secondary structure elements from different sequences.
=== Function ===
Pol I possesses four enzymatic activities:
A 5'→3' (forward) DNA-dependent DNA polymerase activity, requiring a 3' primer site and a template strand
A 3'→5' (reverse) exonuclease activity that mediates proofreading
A 5'→3' (forward) exonuclease activity mediating nick translation during DNA repair.
A 5'→3' (forward) RNA-dependent DNA polymerase activity. Pol I operates on RNA templates with considerably lower efficiency (0.1–0.4%) than it does DNA templates, and this activity is probably of only limited biological significance.
In order to determine whether Pol I was primarily used for DNA replication or in the repair of DNA damage, an experiment was conducted with a deficient Pol I mutant strain of E. coli. The mutant strain that lacked Pol I was isolated and treated with a mutagen. The mutant strain developed bacterial colonies that continued to grow normally and that also lacked Pol I. This confirmed that Pol I was not required for DNA replication. However, the mutant strain also displayed characteristics which involved extreme sensitivity to certain factors that damaged DNA, like UV light. Thus, this reaffirmed that Pol I was more likely to be involved in repairing DNA damage rather than DNA replication.
== Mechanism ==
In the replication process, RNase H removes the RNA primer (created by primase) from the lagging strand and then polymerase I fills in the necessary nucleotides between the Okazaki fragments (see DNA replication) in a 5'→3' direction, proofreading for mistakes as it goes. It is a template-dependent enzyme—it only adds nucleotides that correctly base pair with an existing DNA strand acting as a template. It is crucial that these nucleotides are in the proper orientation and geometry to base pair with the DNA template strand so that DNA ligase can join the various fragments together into a continuous strand of DNA. Studies of polymerase I have confirmed that different dNTPs can bind to the same active site on polymerase I. Polymerase I is able to actively discriminate between the different dNTPs only after it undergoes a conformational change. Once this change has occurred, Pol I checks for proper geometry and proper alignment of the base pair, formed between bound dNTP and a matching base on the template strand. The correct geometry of A=T and G≡C base pairs are the only ones that can fit in the active site. However, it is important to know that one in every 104 to 105 nucleotides is added incorrectly. Nevertheless, Pol I can fix this error in DNA replication using its selective method of active discrimination.
Despite its early characterization, it quickly became apparent that polymerase I was not the enzyme responsible for most DNA synthesis—DNA replication in E. coli proceeds at approximately 1,000 nucleotides/second, while the rate of base pair synthesis by polymerase I averages only between 10 and 20 nucleotides/second. Moreover, its cellular abundance of approximately 400 molecules per cell did not correlate with the fact that there are typically only two replication forks in E. coli. Additionally, it is insufficiently processive to copy an entire genome, as it falls off after incorporating only 25–50 nucleotides. Its role in replication was proven when, in 1969, John Cairns isolated a viable polymerase I mutant that lacked the polymerase activity. Cairns' lab assistant, Paula De Lucia, created thousands of cell free extracts from E. coli colonies and assayed them for DNA-polymerase activity. The 3,478th clone contained the polA mutant, which was named by Cairns to credit "Paula" [De Lucia]. It was not until the discovery of DNA polymerase III that the main replicative DNA polymerase was finally identified.
== Research applications ==
DNA polymerase I obtained from E. coli is used extensively for molecular biology research. However, the 5'→3' exonuclease activity makes it unsuitable for many applications. This undesirable enzymatic activity can be simply removed from the holoenzyme to leave a useful molecule called the Klenow fragment, widely used in molecular biology. In fact, the Klenow fragment was used during the first protocols of polymerase chain reaction (PCR) amplification until Thermus aquaticus, the source of a heat-tolerant Taq Polymerase I, was discovered in 1976. Exposure of DNA polymerase I to the protease subtilisin cleaves the molecule into a smaller fragment, which retains only the DNA polymerase and proofreading activities.
== See also ==
DNA polymerase II
DNA polymerase III
DNA polymerase V
== References == | Wikipedia/DNA_polymerase_I |
Phosphoglycerate kinase (EC 2.7.2.3) (PGK 1) is an enzyme that catalyzes the reversible transfer of a phosphate group from 1,3-bisphosphoglycerate (1,3-BPG) to ADP producing 3-phosphoglycerate (3-PG) and ATP :
1,3-bisphosphoglycerate + ADP ⇌ glycerate 3-phosphate + ATP
Like all kinases it is a transferase. PGK is a major enzyme used in glycolysis, in the first ATP-generating step of the glycolytic pathway. In gluconeogenesis, the reaction catalyzed by PGK proceeds in the opposite direction, generating ADP and 1,3-BPG.
In humans, two isozymes of PGK have been so far identified, PGK1 and PGK2. The isozymes have 87-88% identical amino acid sequence identity and though they are structurally and functionally similar, they have different localizations: PGK2, encoded by an autosomal gene, is unique to meiotic and postmeiotic spermatogenic cells, while PGK1, encoded on the X-chromosome, is ubiquitously expressed in all cells.
== Biological function ==
PGK is present in all living organisms as one of the two ATP-generating enzymes in glycolysis. In the gluconeogenic pathway, PGK catalyzes the reverse reaction. Under biochemical standard conditions, the glycolytic direction is favored.
In the Calvin cycle in photosynthetic organisms, PGK catalyzes the phosphorylation of 3-PG, producing 1,3-BPG and ADP, as part of the reactions that regenerate ribulose-1,5-bisphosphate.
PGK has been reported to exhibit thiol reductase activity on plasmin, leading to angiostatin formation, which inhibits angiogenesis and tumor growth. The enzyme was also shown to participate in DNA replication and repair in mammal cell nuclei.
The human isozyme PGK2, which is only expressed during spermatogenesis, was shown to be essential for sperm function in mice.
=== Interactive pathway map ===
Click on genes, proteins and metabolites below to link to respective articles.
== Structure ==
=== Overview ===
PGK is found in all living organisms and its sequence has been highly conserved throughout evolution. The enzyme exists as a 415-residue monomer containing two nearly equal-sized domains that correspond to the N- and C-termini of the protein. 3-phosphoglycerate (3-PG) binds to the N-terminal, while the nucleotide substrates, MgATP or MgADP, bind to the C-terminal domain of the enzyme. This extended two-domain structure is associated with large-scale 'hinge-bending' conformational changes, similar to those found in hexokinase. The two domains of the protein are separated by a cleft and linked by two alpha-helices. At the core of each domain is a 6-stranded parallel beta-sheet surrounded by alpha helices. The two lobes are capable of folding independently, consistent with the presence of intermediates on the folding pathway with a single domain folded. Though the binding of either substrate triggers a conformational change, only through the binding of both substrates does domain closure occur, leading to the transfer of the phosphate group.
The enzyme has a tendency to exist in the open conformation with short periods of closure and catalysis, which allow for rapid diffusion of substrate and products through the binding sites; the open conformation of PGK is more conformationally stable due to the exposure of a hydrophobic region of the protein upon domain closure.
=== Role of magnesium ===
Magnesium ions are normally complexed to the phosphate groups the nucleotide substrates of PGK. It is known that in the absence of magnesium, no enzyme activity occurs. The bivalent metal assists the enzyme ligands in shielding the bound phosphate group's negative charges, allowing the nucleophilic attack to occur; this charge-stabilization is a typical characteristic of phosphotransfer reaction. It is theorized that the ion may also encourage domain closure when PGK has bound both substrates.
== Mechanism ==
Without either substrate bound, PGK exists in an "open" conformation. After both the triose and nucleotide substrates are bound to the N- and C-terminal domains, respectively, an extensive hinge-bending motion occurs, bringing the domains and their bound substrates into close proximity and leading to a "closed" conformation. Then, in the case of the forward glycolytic reaction, the beta-phosphate of ADP initiates a nucleophilic attack on the 1-phosphate of 1,3-BPG. The Lys219 on the enzyme guides the phosphate group to the substrate.
PGK proceeds through a charge-stabilized transition state that is favored over the arrangement of the bound substrate in the closed enzyme because in the transition state, all three phosphate oxygens are stabilized by ligands, as opposed to only two stabilized oxygens in the initial bound state.
In the glycolytic pathway, 1,3-BPG is the phosphate donor and has a high phosphoryl-transfer potential. The PGK-catalyzed transfer of the phosphate group from 1,3-BPG to ADP to yield ATP can power the carbon-oxidation reaction of the previous glycolytic step (converting glyceraldehyde 3-phosphate to 3-phosphoglycerate).
== Regulation ==
The enzyme is activated by low concentrations of various multivalent anions, such as pyrophosphate, sulfate, phosphate, and citrate. High concentrations of MgATP and 3-PG activates PGK, while Mg2+ at high concentrations non-competitively inhibits the enzyme.
PGK exhibits a wide specificity toward nucleotide substrates. Its activity is inhibited by salicylates, which appear to mimic the enzyme's nucleotide substrate.
Macromolecular crowding has been shown to increase PGK activity in both computer simulations and in vitro environments simulating a cell interior; as a result of crowding, the enzyme becomes more enzymatically active and more compact.
== Disease relevance ==
Phosphoglycerate kinase (PGK) deficiency is an X-linked recessive trait associated with hemolytic anemia, mental disorders and myopathy in humans, depending on form – there exists a hemolytic form and a myopathic form. Since the trait is X-linked, it is usually fully expressed in males, who have one X chromosome; affected females are typically asymptomatic. The condition results from mutations in Pgk1, the gene encoding PGK1, and twenty mutations have been identified. On a molecular level, the mutation in Pgk1 impairs the thermal stability and inhibits the catalytic activity of the enzyme. PGK is the only enzyme in the immediate glycolytic pathway encoded by an X-linked gene. In the case of hemolytic anemia, PGK deficiency occurs in the erythrocytes. Currently, no definitive treatment exists for PGK deficiency.
PGK1 overexpression has been associated with gastric cancer and has been found to increase the invasiveness of gastric cancer cells in vitro. The enzyme is secreted by tumor cells and participates in the angiogenic process, leading to the release of angiostatin and the inhibition of tumor blood vessel growth.
Due to its wide specificity towards nucleotide substrates, PGK is known to participate in the phosphorylation and activation of HIV antiretroviral drugs, which are nucleotide-based.
== Human isozymes ==
== References ==
== External links ==
Phosphoglycerate+kinase at the U.S. National Library of Medicine Medical Subject Headings (MeSH)
Illustration at arizona.edu | Wikipedia/Phosphoglycerate_kinase_deficiency |
DNA polymerase eta (Pol η), is a protein that in humans is encoded by the POLH gene.
DNA polymerase eta is a eukaryotic DNA polymerase involved in the DNA repair by translesion synthesis. The gene encoding DNA polymerase eta is POLH, also known as XPV, because loss of this gene results in the disease xeroderma pigmentosum. Polymerase eta is particularly important for allowing accurate translesion synthesis of DNA damage resulting from ultraviolet radiation or UV.
== Function ==
This gene encodes a member of the Y family of specialized DNA polymerases. It copies undamaged DNA with a lower fidelity than other DNA-directed polymerases. However, it accurately replicates UV-damaged DNA; when thymine dimers are present, this polymerase inserts the complementary nucleotides in the newly synthesized DNA, thereby bypassing the lesion and suppressing the mutagenic effect of UV-induced DNA damage. This polymerase is thought to be involved in hypermutation during immunoglobulin class switch recombination.
=== Bypass of 8-oxoguanine ===
During DNA replication of the Saccharomyces cerevisiae chromosome, the oxidative DNA damage 8-oxoguanine triggers a switch to translesion synthesis by DNA polymerase eta. This polymerase replicates 8-oxoguanine with an accuracy (insertion of a cytosine opposite the 8-oxoguanine) of approximately 94%. Replication of 8-oxoguanine in the absence of DNA polymerase eta is less than 40%.
== Clinical significance ==
Mutations in this gene result in XPV, a variant type of xeroderma pigmentosum, characterized by sun sensitivity, elevated incidence of skin cancer, and at the cellular level, by delayed replication and hypermutability after UV-irradiation
== Interactions ==
POLH has been shown to interact with PCNA.
== References ==
== Further reading ==
== External links ==
GeneReviews/NIH/NCBI/UW entry on Xeroderma Pigmentosum
This article incorporates text from the United States National Library of Medicine, which is in the public domain. | Wikipedia/DNA_polymerase_eta |
DNA polymerase lambda, also known as Pol λ, is an enzyme found in all eukaryotes. In humans, it is encoded by the POLL gene.
== Function ==
Pol λ is a member of the X family of DNA polymerases. It is thought to resynthesize missing nucleotides during non-homologous end joining (NHEJ), a pathway of DNA double-strand break (DSB) repair. NHEJ is the main pathway in higher eukaryotes for repair of DNA DSBs. Chromosomal DSBs are the most severe type of DNA damage. During NHEJ, duplexes generated by the alignment of broken DNA ends usually contain small gaps that need to be filled in by a DNA polymerase. DNA polymerase lambda can perform this function.
The crystal structure of pol λ shows that, unlike the DNA polymerases that catalyze DNA replication, pol λ makes extensive contacts with the 5' phosphate of the downstream DNA strand. This allows the polymerase to stabilize the two ends of a double-strand break and explains how pol λ is uniquely suited for a role in non-homologous end joining.
In addition to NHEJ, pol λ can also participate in base excision repair (BER), where it provides backup activity in the absence of Pol β. BER is the major pathway for repair of small base damages resulting from alkylation, oxidation, depurination/depyrimidination, and deamination of DNA.
Besides its catalytic polymerase domain, pol λ has an 8 kDa domain and a BRCT domain. The 8 kDa domain has lyase activity that can remove a 5' deoxyribosephosphate group from the end of a strand break. The BRCT domain is a phosphopeptide binding domain that is common among DNA repair proteins and is likely involved in coordinating protein-protein interactions. Pol λ is structurally and functionally related to pol μ, another member of the X family that also participates in non-homologous end joining. Like pol μ, pol λ participates in V(D)J recombination, the process by which B-cell and T-cell receptor diversity is generated in the vertebrate immune system. Whereas pol μ is important for heavy-chain rearrangements, pol λ seems to be more important for light-chain rearrangements. The yeast Saccharomyces cerevisiae has a single homolog of both pol λ and pol μ called Pol4.
Translesion synthesis is a damage tolerance mechanism in which specialized DNA polymerases substitute for replicative polymerases in copying across DNA damages during replication. DNA polymerase lambda appears to be involved in translesion synthesis of abasic sites and 8-oxodG damages.
== Interactions ==
Pol λ has been shown to interact with PCNA.
== References == | Wikipedia/DNA_polymerase_lambda |
DNA Polymerase V (Pol V) is a polymerase enzyme involved in DNA repair mechanisms in bacteria, such as Escherichia coli. It is composed of a UmuD' homodimer and a UmuC monomer, forming the UmuD'2C protein complex. It is part of the Y-family of DNA Polymerases, which are capable of performing DNA translesion synthesis (TLS). Translesion polymerases bypass DNA damage lesions during DNA replication - if a lesion is not repaired or bypassed the replication fork can stall and lead to cell death. However, Y polymerases have low sequence fidelity during replication (prone to add wrong nucleotides). When the UmuC and UmuD' proteins were initially discovered in E. coli, they were thought to be agents that inhibit faithful DNA replication and caused DNA synthesis to have high mutation rates after exposure to UV-light. The polymerase function of Pol V was not discovered until the late 1990s when UmuC was successfully extracted, consequent experiments unequivocally proved UmuD'2C is a polymerase. This finding lead to the detection of many Pol V orthologs and the discovery of the Y-family of polymerases.
== Function ==
Pol V functions as a TLS (translesion DNA synthesis) polymerase in E. coli as part of the SOS response to DNA damage. When DNA is damaged regular DNA synthesis polymerases are unable to add dNTPs onto the newly synthesized strand. DNA Polymerase III (Pol III) is the regular DNA polymerase in E. coli. As Pol III stalls unable to add nucleotides to the nascent DNA strand, the cell becomes at risk of having the replication fork collapse and cell death to occur. Pol V TLS function depends on association with other elements of the SOS response, most importantly Pol V translesion activity is tightly dependent on the formation of RecA nucleoprotein filaments. Pol V can use TLS on lesions that block replication or miscoding lesions, which modify bases and lead to wrong base pairing. However, it is unable to translate through 5' → 3' backbone nick errors. Pol V also lacks exonuclease activity, thus rendering unable to proofread synthesis causing it to be error prone.
=== SOS Response ===
SOS response in E. coli attempts to alleviate the effect of a damaging stress in the cell. The role of Pol V in SOS response triggered by UV-radiation is described as follows:
Pol III stalls at lesion site.
DNA replication helicase DnaB continues to expand the replication fork creating single stranded DNA (ssDNA) segments ahead of from the lesion.
ssDNA binding proteins (SSBs) stabilize ssDNA.
RecA recruited and loaded onto ssDNA by RecFOR replacing SSBs. Formation of RecA nucleoprotein filament (RecA*).
RecA functions through mediator proteins to activate Pol V (see Regulation).
Pol V accesses 3'-OH of nascent DNA strand and extends strand past the lesion site.
Pol III resumes elongation.
== Regulation ==
Pol V is only expressed in the cell during the SOS response. It is very tightly regulated at different levels of protein expression and under different mechanisms to avoid its activity unless absolutely necessary for survival of the cell. Pol V's strict regulation stems from its poor replication fidelity, Pol V is highly mutagenic and it is used as a last resort in DNA repair mechanisms. As such, the expression of the UmuD'2C complex takes 45–50 minutes after UV radiation exposure.
=== Transcriptional regulation ===
Transcription of the SOS response genes is negatively regulated by the LexA repressor. LexA binds to the promoter of the UmuDC operon and inhibits gene transcription. DNA damage in the cell leads to the formation of RecA*. RecA* interacts with LexA and stimulates its proteolytic activity, which leads to the autocleavage of the repressor freeing the operon for transcription. The UmuDC operon is transcribed and translated into UmuC and UmuD.
==== Post-translational regulation ====
The formation of the UmuD'2C complex is limited by the formation of UmuD' from UmuD. UmuD is made of a polypeptide with 139 amino acid residues that form a stable tertiary structure, however it needs to be post-translationally modified to be in its active form. UmuD has self-proteolytic activity that is activated by RecA, it removes 24 amino acids at the N-terminus, turning it into UmuD'. UmuD' can form a homodimer and associate with UmuC to form the active UmuD'2C complex.
==== Functional regulation ====
UmuD'2C complex is inactive unless associated with RecA*. Pol V directly interacts with RecA* at the 3' tip of the nucleoprotein filament; this is the site of the nascent DNA strand where Pol V restarts DNA synthesis. Additionally, it has been shown that the REV1/REV3L/REV7 pathway is necessary for the TLS synthesis mediated by DNA polymerase V.
== References ==
== External links ==
Overview of all the structural information available in the PDB for UniProt: P0AG11 (E. coli Protein UmuD) at the PDBe-KB. | Wikipedia/DNA_polymerase_V |
DNA polymerase epsilon is a member of the DNA polymerase family of enzymes found in eukaryotes. It is composed of the following four subunits: POLE (central catalytic unit), POLE2 (subunit 2), POLE3 (subunit 3), and POLE4 (subunit 4). Recent evidence suggests that it plays a major role in leading strand DNA synthesis and nucleotide and base excision repair.
Research had conducted to study nucleotide excision repair DNA synthesis by DNA polymerase epsilon in the presence of PCNA (proliferating cell nuclear antigen), RFC (replication factor C) and RPA (replication protein A). Either DNA polymerase epsilon or DNA polymerase delta along with DNA ligase can be used to repair UV-damaged DNA. However, it is found that DNA polymerase delta require the presence of both RFC and PCNA in order in DNA repair. In addition, it only produces small amount of fractionated DNA ligated products. DNA polymerase epsilon proves to be best suited for nucleotide excision repair. DNA polymerase epsilon is independent of both PCNA and RFC, and produces mostly ligated DNA products. It is also found that under one condition where DNA polymerase epsilon require PCNA and RFC: nucleotide excision repair in the presence of single strand binding protein RPA. PCNA and RFC function as anchor and direct DNA polymerase epsilon onto the DNA template.
== References == | Wikipedia/DNA_polymerase_epsilon |
DNA polymerase III holoenzyme is the primary enzyme complex involved in prokaryotic DNA replication. It was discovered by Thomas Kornberg (son of Arthur Kornberg) and Malcolm Gefter in 1970. The complex has high processivity (i.e. the number of nucleotides added per binding event) and, specifically referring to the replication of the E.coli genome, works in conjunction with four other DNA polymerases (Pol I, Pol II, Pol IV, and Pol V). Being the primary holoenzyme involved in replication activity, the DNA Pol III holoenzyme also has proofreading capabilities that corrects replication mistakes by means of exonuclease activity reading 3'→5' and synthesizing 5'→3'. DNA Pol III is a component of the replisome, which is located at the replication fork.
== Components ==
The replisome is composed of the following:
2 DNA Pol III enzymes, each comprising α, ε and θ subunits. (It has been proven that there is a third copy of Pol III at the replisome.)
the α subunit (encoded by the dnaE gene) has the polymerase activity.
the ε subunit (dnaQ) has 3'→5' exonuclease activity.
the θ subunit (holE) stimulates the ε subunit's proofreading.
2 β units (dnaN) which act as sliding DNA clamps, they keep the polymerase bound to the DNA.
2 τ units (dnaX) which act to dimerize two of the core enzymes (α, ε, and θ subunits).
1 γ unit (also dnaX) which acts as a clamp loader for the lagging strand Okazaki fragments, helping the two β subunits to form a unit and bind to DNA. The γ unit is made up of 5 γ subunits which include 3 γ subunits, 1 δ subunit (holA), and 1 δ' subunit (holB). The δ is involved in copying of the lagging strand.
Χ (holC) and Ψ (holD) which form a 1:1 complex and bind to γ or τ. X can also mediate the switch from RNA primer to DNA.
== Activity ==
DNA polymerase III synthesizes base pairs at a rate of around 1000 nucleotides per second. DNA Pol III activity begins after strand separation at the origin of replication. Because DNA synthesis cannot start de novo, an RNA primer, complementary to part of the single-stranded DNA, is synthesized by primase (an RNA polymerase):
("!" for RNA, '"$" for DNA, "*" for polymerase)
-------->
* * * *
! ! ! ! _ _ _ _
_ _ _ _ | RNA | <--ribose (sugar)-phosphate backbone
G U A U | Pol | <--RNA primer
* * * * |_ _ _ _| <--hydrogen bonding
C A T A G C A T C C <--template ssDNA (single-stranded DNA)
_ _ _ _ _ _ _ _ _ _ <--deoxyribose (sugar)-phosphate backbone
$ $ $ $ $ $ $ $ $ $
=== Addition onto 3'OH ===
As replication progresses and the replisome moves forward, DNA polymerase III arrives at the RNA primer and begins replicating the DNA, adding onto the 3'OH of the primer:
* * * *
! ! ! ! _ _ _ _
_ _ _ _ | DNA | <--deoxyribose (sugar)-phosphate backbone
G U A U | Pol | <--RNA primer
* * * * |_III_ _| <--hydrogen bonding
C A T A G C A T C C <--template ssDNA (single-stranded DNA)
_ _ _ _ _ _ _ _ _ _ <--deoxyribose (sugar)-phosphate backbone
$ $ $ $ $ $ $ $ $ $
=== Synthesis of DNA ===
DNA polymerase III will then synthesize a continuous or discontinuous strand of DNA, depending if this is occurring on the leading or lagging strand (Okazaki fragment) of the DNA. DNA polymerase III has a high processivity and therefore, synthesizes DNA very quickly. This high processivity is due in part to the β-clamps that "hold" onto the DNA strands.
----------->
* * * *
! ! ! ! $ $ $ $ $ $ _ _ _ _
_ _ _ _ _ _ _ _ _ _| DNA | <--deoxyribose (sugar)-phosphate backbone
G U A U C G T A G G| Pol | <--RNA primer
* * * * * * * * * *|_III_ _| <--hydrogen bonding
C A T A G C A T C C <--template ssDNA (single-stranded DNA)
_ _ _ _ _ _ _ _ _ _ <--deoxyribose (sugar)-phosphate backbone
$ $ $ $ $ $ $ $ $ $
=== Removal of primer ===
After replication of the desired region, the RNA primer is removed by DNA polymerase I via the process of nick translation. The removal of the RNA primer allows DNA ligase to ligate the DNA-DNA nick between the new fragment and the previous strand. DNA polymerase I & III, along with many other enzymes are all required for the high fidelity, high-processivity of DNA replication.
== See also ==
Beta clamp
DNA polymerase
DNA replication
== References ==
== External links ==
Overview at Oregon State University
DNA+Polymerase+III at the U.S. National Library of Medicine Medical Subject Headings (MeSH)
Clamping down on pathogenic bacteria – how to shut down a key DNA polymerase complex | Wikipedia/DNA_polymerase_III_holoenzyme |
In enzymology, a protein-histidine pros-kinase (EC 2.7.13.1) is an enzyme that catalyzes the chemical reaction
ATP + protein L-histidine
⇌
{\displaystyle \rightleftharpoons }
ADP + protein Nπ-phospho-L-histidine
Thus, the two substrates of this enzyme are ATP and protein L-histidine, whereas its two products are ADP and protein Npi-phospho-L-histidine.
This enzyme belongs to the family of transferases, specifically those transferring a phosphate group to the sidechain of histidine residues in proteins (protein-histidine kinases). The systematic name of this enzyme class is ATP:protein-L-histidine Npi-phosphotransferase. Other names in common use include ATP:protein-L-histidine N-pros-phosphotransferase, histidine kinase, histidine protein kinase, protein histidine kinase, protein kinase (histidine), and HK2.
== References ==
Fujitaki JM, Fung G, Oh EY, Smith RA (1981). "Characterization of chemical enzymatic acid-labile phosphorylation of histone H4 using phosphorus-31 nuclear magnetic resonance". Biochemistry. 20 (12): 3658–64. doi:10.1021/bi00515a055. PMID 7196259.
Huebner VD, Matthews HR (1985). "Phosphorylation of histidine in proteins by a nuclear extract of Physarum polycephalum plasmodia". J. Biol. Chem. 260 (30): 16106–13. PMID 4066704. | Wikipedia/Protein-histidine_pros-kinase |
Cell Biochemistry and Biophysics is a peer-reviewed scientific journal covering all aspects of the biology of cells, especially their biochemistry and biophysics. It was established in 1979 as Cell Biophysics with Nicholas Catsimpoolas as founding editor-in-chief, obtaining its current name in 1996. The journal is published by Springer Science+Business Media and the editor-in-chief is Lawrence J. Berliner (University of Denver).
== Abstracting and indexing ==
The journal is abstracted and indexed in:
According to the Journal Citation Reports, the journal has a 2020 impact factor of 2.194.
== Peer review problems ==
In March 2015 the publisher placed the journal on hold after a pattern of "inappropriate and compromised peer review" was uncovered. Retraction Watch noted that the journal had retracted 16 articles in the preceding year that had been generated by the computer program SCIgen, as well as a further paper for plagiarism, although it was not stated whether these cases were related to the suspension.
== References ==
== External links ==
Official website | Wikipedia/Cell_Biochemistry_and_Biophysics |
A serine/threonine protein kinase (EC 2.7.11.-) is a kinase enzyme, in particular a protein kinase, that phosphorylates the OH group of the amino-acid residues serine or threonine, which have similar side chains. At least 350 of the 500+ human protein kinases are serine/threonine kinases (STK).
In enzymology, the term serine/threonine protein kinase describes a class of enzymes in the family of transferases, that transfer phosphates to the oxygen atom of a serine or threonine side chain in proteins. This process is called phosphorylation. Protein phosphorylation in particular plays a significant role in a wide range of cellular processes and is a very important post-translational modification.
The chemical reaction performed by these enzymes can be written as
ATP + a protein
⇌
{\displaystyle \rightleftharpoons }
ADP + a phosphoprotein
Thus, the two substrates of this enzyme are ATP and a protein, whereas its two products are ADP and phosphoprotein.
The systematic name of this enzyme class is ATP:protein phosphotransferase (non-specific).
== Function ==
Serine/threonine kinases play a role in the regulation of cell proliferation, programmed cell death (apoptosis), cell differentiation, and embryonic development.
== Selectivity ==
While serine/threonine kinases all phosphorylate serine or threonine residues in their substrates, they select specific residues to phosphorylate on the basis of residues that flank the phosphoacceptor site, which together comprise the consensus sequence. Since the consensus sequence residues of a target substrate only make contact with several key amino acids within the catalytic cleft of the kinase (usually through hydrophobic forces and ionic bonds), a kinase is usually not specific to a single substrate, but instead can phosphorylate a whole "substrate family" which share common recognition sequences. While the catalytic domain of these kinases is highly conserved, the sequence variation that is observed in the kinome (the subset of genes in the genome that encode kinases) provides for recognition of distinct substrates. Many kinases are inhibited by a pseudosubstrate that binds to the kinase like a real substrate but lacks the amino acid to be phosphorylated. When the pseudosubstrate is removed, the kinase can perform its normal function.
== EC numbers ==
Many serine/threonine protein kinases do not have their own individual EC numbers and use 2.7.11.1, "non-specific serine/threonine protein kinase". This entry is for any enzyme that phosphorylates proteins while converting ATP to ADP (i.e., ATP:protein phosphotransferases.) 2.7.11.37 "protein kinase" was the former generic placeholder and was split into several entries (including 2.7.11.1) in 2005. 2.7.11.70 "protamine kinase" was merged into 2.7.11.1 in 2004.
2.7.11.- is the generic level where all serine/threonine kinases should sit in.
== Types ==
Types include those acting directly as membrane-bound receptors (Receptor protein serine/threonine kinase) and intracellular kinases participating in Signal transduction. Of the latter, types include:
== Clinical significance ==
Serine/threonine kinase (STK) expression is altered in many types of cancer. Limited benefit of serine/threonine kinase inhibitors has been demonstrated in ovarian cancer but studies are ongoing to evaluate their safety and efficacy.
Serine/threonine protein kinase p90-kDa ribosomal S6 kinase (RSK) is in involved in development of some prostate cancers.
Raf inhibition has become the target for new anti-metastatic cancer drugs as they inhibit the MAPK cascade and reduce cell proliferation.
== See also ==
Protein serine/threonine phosphatase, enzyme for reverse process.
Pseudokinase, a protein without enzyme activity (pseudoenzyme). It can be related to proteins of this class.
ATM serine/threonine kinase, responsible for the disorder ataxia–telangiectasia.
== References ==
== External links ==
protein-serine-threonine+kinases at the U.S. National Library of Medicine Medical Subject Headings (MeSH)
KinCore (Kinase Conformational Resource) | Wikipedia/Serine/threonine-specific_protein_kinase |
DNA polymerase mu is a polymerase enzyme found in eukaryotes. In humans, this protein is encoded by the POLM gene.
== Function ==
Pol μ is a member of the X family of DNA polymerases. It participates in resynthesis of damaged or missing nucleotides during the non-homologous end joining (NHEJ) pathway of DNA repair. Pol μ interacts with Ku and DNA ligase IV, which also participate in NHEJ. It is structurally and functionally related to pol λ, and, like pol λ, pol μ has a BRCT domain that is thought to mediate interactions with other DNA repair proteins. Unlike pol λ, however, pol μ has the unique ability to add a base to a blunt end that is templated by the overhang on the opposite end of the double-strand break. Pol μ is also closely related to terminal deoxynucleotidyl transferase (TdT), a specialized DNA polymerase that adds random nucleotides to DNA ends during V(D)J recombination, the process by which B-cell and T-cell receptor diversity is generated in the vertebrate immune system. Like TdT, pol μ participates in V(D)J recombination, but only during light chain rearrangements. This is distinct from pol λ, which is involved in heavy chain rearrangements.
== POLM mutant mice ==
In polymerase mu mutant mice, hematopoietic cell development is defective in several peripheral and bone marrow cell populations with about a 40% decrease in bone marrow cell number that includes several hematopoietic lineages. Expansion potential of hematopoietic progenitor cells is also reduced. These characteristics correlate with reduced ability to repair double-strand breaks in hematopoietic tissue. Whole body gamma irradiation of polymerase mu mutant mice indicates that polymerase mu also has a role in double-strand break repair in other tissues unrelated to hematopoietic tissue. Thus polymerase mu has a significant role in maintaining genetic stability in hematopoietic and non-hematopoietic tissue.
== References ==
== External links ==
Overview of all the structural information available in the PDB for UniProt: Q9NP87 (Human DNA-directed DNA/RNA polymerase mu) at the PDBe-KB. | Wikipedia/DNA_polymerase_mu |
1,3-Bisphosphoglyceric acid (1,3-Bisphosphoglycerate or 1,3BPG) is a 3-carbon organic molecule present in most, if not all, living organisms. It primarily exists as a metabolic intermediate in both glycolysis during respiration and the Calvin cycle during photosynthesis. 1,3BPG is a transitional stage between glycerate 3-phosphate and glyceraldehyde 3-phosphate during the fixation/reduction of CO2. 1,3BPG is also a precursor to 2,3-bisphosphoglycerate which in turn is a reaction intermediate in the glycolytic pathway.
== Biological structure and role ==
1,3-Bisphosphoglycerate is the conjugate base of 1,3-bisphosphoglyceric acid. It is phosphorylated at the number 1 and 3 carbons. The result of this phosphorylation gives 1,3BPG important biological properties such as the ability to phosphorylate ADP to form the energy storage molecule ATP.
=== In glycolysis ===
Compound C00118 at KEGG Pathway Database. Enzyme 1.2.1.12 at KEGG Pathway Database. Compound C00236 at KEGG Pathway Database. Enzyme 2.7.2.3 at KEGG Pathway Database. Compound C00197 at KEGG Pathway Database.
As previously mentioned 1,3BPG is a metabolic intermediate in the glycolytic pathway. It is created by the exergonic oxidation of the aldehyde in G3P. The result of this oxidation is the conversion of the aldehyde group into a carboxylic acid group which drives the formation of an acyl phosphate bond. This is incidentally the only step in the glycolytic pathway in which NAD+ is converted into NADH. The formation reaction of 1,3BPG requires the presence of an enzyme called glyceraldehyde-3-phosphate dehydrogenase.
The high-energy acyl phosphate bond of 1,3BPG is important in respiration as it assists in the formation of ATP. The molecule of ATP created during the following reaction is the first molecule produced during respiration. The reaction occurs as follows;
1,3-bisphosphoglycerate + ADP ⇌ 3-phosphoglycerate + ATP
The transfer of an inorganic phosphate from the carboxyl group on 1,3BPG to ADP to form ATP is reversible due to a low ΔG. This is as a result of one acyl phosphate bond being cleaved whilst another is created. This reaction is not naturally spontaneous and requires the presence of a catalyst. This role is performed by the enzyme phosphoglycerate kinase. During the reaction phosphoglycerate kinase undergoes a substrate induced conformational change similar to another metabolic enzyme called hexokinase.
Because two molecules of glyceraldehyde-3-phosphate are formed during glycolysis from one molecule of glucose, 1,3BPG can be said to be responsible for two of the ten molecules of ATP produced during the entire process. Glycolysis also uses two molecules of ATP in its initial stages as a committed and irreversible step. For this reason glycolysis is not reversible and has a net produce of 2 molecules of ATP and two of NADH. The two molecules of NADH themselves go on to produce approximately 3 molecules of ATP each.
Click on genes, proteins and metabolites below to link to respective articles.
=== In the Calvin cycle ===
1,3-BPG has a very similar role in the Calvin cycle to its role in the glycolytic pathway. For this reason both reactions are said to be analogous. However the reaction pathway is effectively reversed. The only other major difference between the two reactions is that NADPH is used as an electron donor in the calvin cycle whilst NAD+ is used as an electron acceptor in glycolysis. In this reaction cycle 1,3BPG originates from 3-phosphoglycerate and is made into glyceraldehyde 3-phosphate by the action of specific enzymes.
Contrary to the similar reactions of the glycolytic pathway, 1,3BPG in the Calvin cycle does not produce ATP but instead uses it. For this reason it can be considered to be an irreversible and committed step in the cycle. The outcome of this section of the cycle is an inorganic phosphate is removed from 1,3BPG as a hydrogen ion and two electrons are added to the compound+.
In complete reverse of the glycolytic pathway reaction, the enzyme phosphoglycerate kinase catalyses the reduction of the carboxyl group of 1,3BPG to form an aldehyde instead. This reaction also releases an inorganic phosphate molecule which is subsequently used as energy for the donation of electrons from the conversion of NADPH to NADP+. Overseeing this latter stage of the reaction is the enzyme glyceraldehyde-phosphate dehydrogenase.
=== In oxygen transfer ===
During normal metabolism in human erythrocytes, ≈19% of the 1,3BPG produced does not go any further in the glycolytic pathway. It is instead shunted through the Luebering–Rapoport pathway involving the reduction of ATP in the red blood cells. During this alternate pathway it is made into a similar molecule called 2,3-bisphosphoglyceric acid (2,3BPG). 2,3BPG is used as a mechanism to oversee the efficient release of oxygen from hemoglobin. Levels of this 1,3BPG will raise in a patient's blood when oxygen levels are low as this is one of the mechanisms of acclimatization. Low oxygen levels trigger a rise in 1,3BPG levels which in turn raises the level of 2,3BPG which alters the efficiency of oxygen dissociation from hemoglobin.
== References ==
Alberts, Bruce; et al. (2001). Molecular Biology of the Cell. New York: Garland Science. ISBN 0-8153-4072-9.
Germann, William J.; Stanfield, Cindy L. (2002). Principles of Human Physiology. San Francisco: Benjamin Cummings. ISBN 0-8053-6056-5.
Stryer, Lubert; et al. (2002). Biochemistry (5th ed.). New York: W. H. Freeman. ISBN 0-7167-4684-0.
== External links ==
1,3BPG in Glycolysis and Fermentation
Medical Dictionary reference for 1,3BPG
1,3BPG enzyme mechanisms Archived 2013-04-14 at archive.today | Wikipedia/1,3-Bisphosphoglycerate |
DNA polymerase IV is a prokaryotic polymerase that is involved in mutagenesis and is encoded by the dinB gene. It exhibits no 3′→5′ exonuclease (proofreading) activity and hence is error prone. In E. coli, DNA polymerase IV (Pol 4) is involved in non-targeted mutagenesis. Pol IV is a Family Y polymerase expressed by the dinB gene that is switched on via SOS induction caused by stalled polymerases at the replication fork. During SOS induction, Pol IV production is increased tenfold and one of the functions during this time is to interfere with Pol III holoenzyme processivity. This creates a checkpoint, stops replication, and allows time to repair DNA lesions via the appropriate repair pathway. Another function of Pol IV is to perform translesion synthesis at the stalled replication fork like, for example, bypassing N2-deoxyguanine adducts at a faster rate than transversing undamaged DNA. Cells lacking dinB gene have a higher rate of mutagenesis caused by DNA damaging agents.
== Replication bypass of 8-oxoguanine ==
Reactive oxygen species are produced continuously during normal metabolism and these damage DNA. DNA polymerase IV can catalyze translesion synthesis across a variety of DNA damages including 8-oxoguanine, a major oxidative damage with high mutagenic potential. Upon chromosome duplication by replicative polymerases, unrepaired 8-oxoguanine tends to mispair with A, so that during the next round of replication a G:C to T:A transversion mutation is produced (G:C → 8-oxoG:C → 8-oxoG:A → T:A). However, when DNA polymerase IV intervenes to bypass the damage, it preferentially incorporates the correct nucleotide CTP opposite 8-oxoguanine with high efficiency, thus avoiding potential mutations (G:C → 8-oxoG:C → 8-oxoG:C → GC).
== References == | Wikipedia/DNA_polymerase_IV |
A protein kinase is a kinase which selectively modifies other proteins by covalently adding phosphates to them (phosphorylation) as opposed to kinases which modify lipids, carbohydrates, or other molecules. Phosphorylation usually results in a functional change of the target protein (substrate) by changing enzyme activity, cellular location, or association with other proteins. The human genome contains about 500 protein kinase genes and they constitute about 2% of all human genes. There are two main types of protein kinase. The great majority are serine/threonine kinases, which phosphorylate the hydroxyl groups of serines and threonines in their targets. Most of the others are tyrosine kinases, although additional types exist. Protein kinases are also found in bacteria and plants. Up to 30% of all human proteins may be modified by kinase activity, and kinases are known to regulate the majority of cellular pathways, especially those involved in signal transduction.
== Chemical activity ==
The chemical activity of a protein kinase involves removing a phosphate group from ATP and covalently attaching it to one of three amino acids that have a free hydroxyl group. Most kinases act on both serine and threonine, others act on tyrosine, and a number (dual-specificity kinases) act on all three. There are also protein kinases that phosphorylate other amino acids, including histidine kinases that phosphorylate histidine residues.
== Structure ==
Eukaryotic protein kinases are enzymes that belong to a very extensive family of proteins that share a conserved catalytic core. The structures of over 280 human protein kinases have been determined.
There are a number of conserved regions in the catalytic domain of protein kinases. In the N-terminal extremity of the catalytic domain there is a glycine-rich stretch of residues in the vicinity of a lysine amino acid, which has been shown to be involved in ATP binding. In the central part of the catalytic domain, there is a conserved aspartic acid, which is important for the catalytic activity of the enzyme.
== Serine/threonine-specific protein kinases ==
Serine/threonine protein kinases (EC 2.7.11.1) phosphorylate the OH group of serine or threonine (which have similar side chains). Activity of these protein kinases can be regulated by specific events (e.g., DNA damage), as well as numerous chemical signals, including cAMP/cGMP, diacylglycerol, and Ca2+/calmodulin.
One very important group of protein kinases are the MAP kinases (acronym from: "mitogen-activated protein kinases"). Important subgroups are the kinases of the ERK subfamily, typically activated by mitogenic signals, and the stress-activated protein kinases JNK and p38. While MAP kinases are serine/threonine-specific, they are activated by combined phosphorylation on serine/threonine and tyrosine residues. Activity of MAP kinases is restricted by a number of protein phosphatases, which remove the phosphate groups that are added to specific serine or threonine residues of the kinase and are required to maintain the kinase in an active conformation.
== Tyrosine-specific protein kinases ==
Tyrosine-specific protein kinases (EC 2.7.10.1 and EC 2.7.10.2) phosphorylate tyrosine amino acid residues, and like serine/threonine-specific kinases are used in signal transduction. They act primarily as growth factor receptors and in downstream signaling from growth factors. Some examples include:
Platelet-derived growth factor receptor (PDGFR)
Epidermal growth factor receptor (EGFR)
Insulin receptor and insulin-like growth factor 1 receptor (IGF1R)
Stem cell factor (SCF) receptor (also called c-kit, see the article on gastrointestinal stromal tumor).
=== Receptor tyrosine kinases ===
These kinases consist of extracellular domains, a transmembrane spanning alpha helix, and an intracellular tyrosine kinase domain protruding into the cytoplasm. They play important roles in regulating cell division, cellular differentiation, and morphogenesis. More than 50 receptor tyrosine kinases are known in mammals.
==== Structure ====
The extracellular domains serve as the ligand-binding part of the molecule, often inducing the domains to form homo- or heterodimers. The transmembrane element is a single α helix. The intracellular or cytoplasmic Protein kinase domain is responsible for the (highly conserved) kinase activity, as well as several regulatory functions.
==== Regulation ====
Ligand binding causes two reactions:
Dimerization of two monomeric receptor kinases or stabilization of a loose dimer. Many ligands of receptor tyrosine kinases are multivalent. Some tyrosine receptor kinases (e.g., the platelet-derived growth factor receptor) can form heterodimers with other similar but not identical kinases of the same subfamily, allowing a highly varied response to the extracellular signal.
Trans-autophosphorylation (phosphorylation by the other kinase in the dimer) of the kinase.
Autophosphorylation stabilizes the active conformation of the kinase domain. When several amino acids suitable for phosphorylation are present in the kinase domain (e.g., the insulin-like growth factor receptor), the activity of the kinase can increase with the number of phosphorylated amino acids; in this case, the first phosphorylation switches the kinase from "off" to "standby".
==== Signal transduction ====
The active tyrosine kinase phosphorylates specific target proteins, which are often enzymes themselves. An important target is the ras protein signal-transduction chain.
=== Receptor-associated tyrosine kinases ===
Tyrosine kinases recruited to a receptor following hormone binding are receptor-associated tyrosine kinases and are involved in a number of signaling cascades, in particular those involved in cytokine signaling (but also others, including growth hormone). One such receptor-associated tyrosine kinase is Janus kinase (JAK), many of whose effects are mediated by STAT proteins. (See JAK-STAT pathway.)
== Dual-specificity protein kinases ==
Some kinases have dual-specificity kinase activities. For example, MEK (MAPKK), which is involved in the MAP kinase cascade, is a both a serine/threonine and tyrosine kinase.
== Histidine-specific protein kinases ==
Histidine kinases are structurally distinct from most other protein kinases and are found mostly in prokaryotes as part of two-component signal transduction mechanisms. A phosphate group from ATP is first added to a histidine residue within the kinase, and later transferred to an aspartate residue on a 'receiver domain' on a different protein, or sometimes on the kinase itself. The aspartyl phosphate residue is then active in signaling.
Histidine kinases are found widely in prokaryotes, as well as in plants, fungi and eukaryotes. The pyruvate dehydrogenase family of kinases in animals is structurally related to histidine kinases, but instead phosphorylate serine residues, and probably do not use a phospho-histidine intermediate.
== Aspartic acid/glutamic acid-specific protein kinases ==
== Inhibitors ==
Deregulated kinase activity is a frequent cause of disease, in particular cancer, wherein kinases regulate many aspects that control cell growth, movement and death. Drugs that inhibit specific kinases are being developed to treat several diseases, and some are currently in clinical use, including Gleevec (imatinib) and Iressa (gefitinib).
Anthra(1,9-cd)pyrazol-6(2H)-one
Staurosporine
== Kinase assays and profiling ==
Drug developments for kinase inhibitors are started from kinase assays Archived 2014-11-26 at the Wayback Machine, the lead compounds are usually profiled for specificity before moving into further tests. Many profiling services are available from fluorescent-based assays to radioisotope based detections, and competition binding assays.
== References ==
== External links ==
Human and mouse protein kinases in UniProt: classification and index
Kinase.Com: Genomics, evolution and large-scale analysis of protein kinases (non-commercial).
KinMutBase: A registry of disease-causing mutations in protein kinase domains Archived 2022-06-15 at the Wayback Machine
KLIFS (Kinase-Ligand Interaction Fingerprints and Structures) Database -- analysis of kinase structures and kinase-inhibitor interactions
KinCore: the Kinase Conformation Resource: A web resource for protein kinase sequence, structure and phylogeny
Kinomer: A multilevel HMM library for the classification and functional annotation of eukaryotic protein kinases. | Wikipedia/Protein_kinase |
DNA polymerase delta (DNA Pol δ) is an enzyme complex found in eukaryotes that is involved in DNA replication and repair. The DNA polymerase delta complex consists of 4 subunits: POLD1, POLD2, POLD3, and POLD4. DNA Pol δ is an enzyme used for both leading and lagging strand synthesis. It exhibits increased processivity when interacting with the proliferating cell nuclear antigen (PCNA). As well, the multisubunit protein replication factor C, through its role as the clamp loader for PCNA (which involves catalysing the loading of PCNA on to DNA) is important for DNA Pol δ function.
== References ==
== External links ==
DNA+polymerase+delta at the U.S. National Library of Medicine Medical Subject Headings (MeSH)
This article incorporates text from the United States National Library of Medicine, which is in the public domain. | Wikipedia/DNA_polymerase_delta |
DNA polymerase alpha also known as Pol α is an enzyme complex found in eukaryotes that is involved in initiation of DNA replication. The DNA polymerase alpha complex consists of 4 subunits: POLA1, POLA2, PRIM1, and PRIM2.
Pol α has limited processivity and lacks 3′ exonuclease activity for proofreading errors. Thus it is not well suited to efficiently and accurately copy long templates (unlike Pol Delta and Epsilon). Instead, it plays a more limited role in replication. Pol α is responsible for the initiation of DNA replication at origins of replication (on both the leading and lagging strands) and during synthesis of Okazaki fragments on the lagging strand. The Pol α complex (pol α-DNA primase complex) consists of four subunits: the catalytic subunit POLA1, the regulatory subunit POLA2, and the small and the large primase subunits PRIM1 and PRIM2 respectively. Once primase has created the RNA primer, Pol α starts replication elongating the primer with ~20 nucleotides.
== Structure ==
DNA polymerase alpha, like DNA primase, contains iron-sulfur clusters, that are critical in electron transport that uses DNA itself to transfer electrons at very high speeds; this process is involved in detecting DNA damage, and may also be involved in a feedback between the primase complex and the DNA polymerase alpha.
== References ==
== External links ==
DNA+polymerase+alpha at the U.S. National Library of Medicine Medical Subject Headings (MeSH)
This article incorporates text from the United States National Library of Medicine, which is in the public domain. | Wikipedia/DNA_polymerase_alpha |
In enzymology, a protein-histidine tele-kinase (EC 2.7.13.2) is an enzyme that catalyzes the chemical reaction
ATP + protein L-histidine
⇌
{\displaystyle \rightleftharpoons }
ADP + protein Nτ-phospho-L-histidine
Thus, the two substrates of this enzyme are ATP and protein L-histidine, whereas its two products are ADP and protein Ntau-phospho-L-histidine.
This enzyme belongs to the family of transferases, specifically those transferring a phosphate group to the sidechain of histidine residues in proteins (protein-histidine kinases). The systematic name of this enzyme class is ATP:protein-L-histidine Ntau-phosphotransferase. Other names in common use include ATP:protein-L-histidine N-tele-phosphotransferase, histidine kinase, histidine protein kinase, protein histidine kinase, protein kinase (histidine), and HK3.
== References ==
Fujitaki JM, Fung G, Oh EY, Smith RA (1981). "Characterization of chemical and enzymatic acid-labile phosphorylation of histone H4 using phosphorus-31 nuclear magnetic resonance". Biochemistry. 20 (12): 3658–64. doi:10.1021/bi00515a055. PMID 7196259.
Huebner VD, Matthews HR (1985). "Phosphorylation of histidine in proteins by a nuclear extract of Physarum polycephalum plasmodia". J. Biol. Chem. 260 (30): 16106–13. doi:10.1016/S0021-9258(17)36207-5. PMID 4066704. | Wikipedia/Protein-histidine_tele-kinase |
DNA polymerase II (also known as DNA Pol II or Pol II) is a prokaryotic DNA-dependent DNA polymerase encoded by the PolB gene.
DNA Polymerase II is an 89.9-kDa protein and is a member of the B family of DNA polymerases. It was originally isolated by Thomas Kornberg in 1970, and characterized over the next few years. The in vivo functionality of Pol II is under debate, yet consensus shows that Pol II is primarily involved as a backup enzyme in prokaryotic DNA replication. The enzyme has 5′→3′ DNA synthesis capability as well as 3′→5′ exonuclease proofreading activity. DNA Pol II interacts with multiple binding partners common with DNA Pol III in order to enhance its fidelity and processivity.
== Discovery ==
DNA polymerase I was the first DNA-directed DNA polymerase to be isolated from E. coli. Several studies involving this isolated enzyme indicated that DNA pol I was most likely involved in repair replication and was not the main replicative polymerase. In order to better understand the in vivo role of DNA pol I, E. coli mutants deficient in this enzyme (termed Pol A1−) were generated in 1969 by De Lucia and Cairns. As characterized, this new mutant strain was more sensitive to ultraviolet light, corroborating the hypothesis that DNA pol I was involved in repair replication. The mutant grew at the same rate as the wild type, indicating the presence of another enzyme responsible for DNA replication. The isolation and characterization of this new polymerase involved in semiconservative DNA replication followed, in parallel studies conducted by several labs. The new polymerase was termed DNA polymerase II, and was believed to be the main replicative enzyme of E. coli for a time. DNA pol II was first crystallized by Anderson et al. in 1994.
In 2023 it was reported that ageing-related accelerated transcription causes Pol II to make more mistakes, leading to flawed copies that can cause numerous diseases.
== Structure ==
DNA Pol II is an 89.9 kD protein, composed of 783 amino acids, that is encoded by the polB (dinA) gene. A globular protein, DNA Pol II functions as a monomer, whereas many other polymerases will form complexes. There are three main sections of this monomer colloquially referred to as the palm, fingers, and thumb. This “hand” closes around a strand of DNA. The palm of the complex contains three catalytic residues that will coordinate with two divalent metal ions in order to function. DNA Pol II has a high quantity of copies in the cell, around 30-50, whereas the level of DNA Pol III in a cell is five times fewer.
=== Similarity to other group B polymerases ===
Most of the polymerases have been grouped into families based on similar structure and function. DNA Pol II falls into the Group B along with human DNA Pol α, δ, ϵ, and ζ. These are all homologs of RB69, 9°N-7, and Tgo. The other members of group B do have at least one other subunit which makes the DNA Pol II unique.
== Function ==
=== Confirmed ===
Polymerases all are involved with DNA replication in some capacity, synthesizing chains of nucleic acids. DNA replication is a vital aspect of a cell's proliferation. Without replicating its DNA, a cell cannot divide and share its genetic information to progeny. In prokaryotes, like E. coli, DNA Pol III is the major polymerase involved with DNA replication. While DNA Pol II is not a major factor in chromosome replication, it has other roles to fill.
DNA Pol II does participate in DNA replication. While it might not be as fast as DNA Pol III, it has some abilities that make it an effective enzyme. This enzyme has an associated 3′→5′ exonuclease activity along with primase activity. DNA Pol II is a high fidelity enzyme with a substitution error rate of ≤ 2×10−6 and a −1 frameshift error rate of ≤ 1×10−6. DNA Pol II can proofread and process mismatches caused by the Pol III. Banach-Orlowska et al. showed that DNA Pol II is involved with replication but it is strand dependent and preferentially replicates the lagging strand. A proposed mechanism suggests that when DNA Pol III stalls or becomes non-functional, then DNA Pol II is able to be specifically recruited to the replication point and continue replication.
There are many different ways that DNA can be damaged, from UV damage to oxidation, so it is logical that there are different types of polymerases to fix these damages. One important role that DNA Pol II is the major polymerase for the repairing of inter-strand cross-links. Interstrand cross-links are caused by chemicals such as nitrogen mustard and psoralen which create cytotoxic lesions. Repairing these lesions is difficult because both DNA strands have been damaged by the chemical agent and thus the genetic information on both strands is incorrect. The exact mechanism of how these interstrand cross-links are fixed is still being researched, but it is known that Pol II is highly involved.
=== Activity ===
DNA Pol II is not the most studied polymerase so there are many proposed functions of this enzyme which are all likely functions but are ultimately unconfirmed:
repair of DNA damaged by UV irradiation
replication restart in UV-irradiated E. coli
adaptive mutagenesis
long-term survival
== Mechanism ==
During DNA replication, base pairs are subject to damage in the sequence. A damaged sequence of DNA can cause replication to be stalled. In order to fix an error in the sequence, DNA Pol II catalyzes the repair of nucleotide base pairs. In vitro studies have shown that Pol II occasionally interacts with Pol III accessory proteins (β‐clamp and clamp loading complex) giving the Pol II access to the growing nascent strand. Concerning the function of DNA Pol II during DNA replication, this makes sense as any mistakes that Pol III produces will be in the growing strand and not the conservative strand. The N-terminal domain of DNA Pol II is responsible for the association and dissociation of the DNA strand to the catalytic subunit. There are most likely two sites in the N-terminal domain of DNA Pol II that recognize single-stranded DNA. One site(s) is responsible for recruiting single-stranded DNA to DNA Pol II and another site(s) is responsible for the dissociation of single-stranded DNA from DNA Pol II.
Upon binding of substrate, DNA Pol II binds nucleoside triphosphates to maintain the hydrogen bonded structure of DNA. The correct dNTP is then bound and the enzyme complex undergoes conformational changes of subdomains and amino acid residues. These conformational changes allow the rate of repair synthesis to be fast. The active site contains two Mg2+ ions that are stabilized by catalytic Aspartic Acids D419 and D547. Magnesium ions bind to DNA along with dNTP in the open state and coordinate conformational changes of active site amino acid residues in order for catalysis to take place (closed state). After magnesium ions are released, the enzyme returns to its open state.
== Species distribution ==
=== Prokaryotic ===
DNA Polymerase II is a member of the polymerase B family and supports Polymerase III in DNA replication moving from the 3′ end to the 5′ end. In the case when Polymerase III stalls during a replication error, Polymerase II can interrupt and excise the mismatched bases. Polymerase II has a much higher fidelity factor than Polymerase III, meaning that it is much less likely to create mispairings. Without Polymerase II's proofreading step, Polymerase III would extend the mispairings and thus create a mutation.
In addition to protecting from mutations that could be caused by Polymerase III, Polymerase II functions to protect against mutations caused by Polymerase IV. Polymerase IV is much more error prone than Polymerase II but also functions to repair mismatched base pairings starting from the 3′ end. Polymerase II protects the 3′ end from Polymerase IV and blocks it from acting. This protection will prevent the formation of mutations while the Polymerase II is functioning normally. If the Polymerase II is knocked out by a mutation or disabled by other factors, Polymerase IV will take its place to fix the mispaired bases.
=== Eukaryotic ===
While Polymerase II will not function naturally in conjunction with the eukaryotic members of Family B, it does share similar structural and functional motifs. The members of Family B include Polymerase α, ε, ζ, and δ. These polymerases all function to proofread the newly synthesized DNA in the 3′→5′ direction. These polymerases are capable of synthesizing DNA on both the leading and lagging strands. This class of polymerase tends to be very accurate which allows them to correct any mispairings that occur during DNA synthesis.
== Regulation ==
DNA Polymerase II is naturally abundant in the cell, which usually amounts to five times greater than the amount of Polymerase III. This greater abundance allows Polymerase II to overpower Polymerase III in the case of mispairings. This amount can be increased upon the inducement of the SOS response, which upregulates the polB gene so the amount of Polymerase II increases to about sevenfold greater. Although Polymerase II can work on both strands, it has been shown to prefer the lagging strand versus the leading strand.
== See also ==
DNA replication
== References == | Wikipedia/DNA_polymerase_II |
DNA polymerase beta, also known as POLB, is an enzyme present in eukaryotes. In humans, it is encoded by the POLB gene.
== Function ==
In eukaryotic cells, DNA polymerase beta (POLB) performs base excision repair (BER) required for DNA maintenance, replication, recombination, and drug resistance.
The mitochondrial DNA of mammalian cells is constantly under attack from oxygen radicals released during ATP production. Mammalian cell mitochondria contain an efficient base excision repair system employing POLB that removes some frequent oxidative DNA damages. POLB thus has a key role in maintaining the stability of the mitochondrial genome.
An analysis of the fidelity of DNA replication by polymerase beta in the neurons from young and very aged mice indicated that aging has no significant effect on the fidelity of DNA synthesis by polymerase beta. This finding was considered to provide evidence against the error catastrophe theory of aging.
=== Base excision repair ===
Cabelof et al. measured the ability to repair DNA damage by the BER pathway in tissues of young (4-month-old) and old (24-month-old) mice. In all tissues examined (brain, liver, spleen and testes) the ability to repair DNA damage declined significantly with age, and the reduction in repair capability correlated with decreased levels of DNA polymerase beta at both the protein and messenger RNA levels. Numerous investigators have reported an accumulation of DNA damage with age, especially in brain and liver. Cabelof et al. suggested that the inability of the BER pathway to repair damages over time may provide a mechanistic explanation for the frequent observations of DNA accumulation of damage with age.
== Regulation of expression ==
DNA polymerase beta maintains genome integrity by participating in base excision repair. Overexpression of POLB mRNA has been correlated with a number of cancer types, whereas deficiencies in POLB results in hypersensitivity to alkylating agents, induced apoptosis, and chromosomal breaking. Therefore, it is essential that POLB expression is tightly regulated.
POLB gene is upregulated by CREB1 transcription factor's binding to the cAMP response element (CRE) present in the promoter of the POLB gene in response to exposure to alkylating agents. POLB gene expression is also regulated at the post transcriptional level as the 3'UTR of the POLB mRNA has been shown to contain three stem-loop structures that influence gene expression. These three-stem loop structures are known as M1, M2, and M3, where M2 and M3 have a key role in gene regulation. M3 contributes to gene expression, as it contains the polyadenylation signal followed by the cleavage and polyadenylation site, thereby contributing to pre-mRNA processing. M2 has been shown to be evolutionary conserved, and, through mutagenesis, it was shown that this stem loop structure acts as a RNA destabilizing element.
In addition to these cis-regulatory elements present within the 3'UTR a trans-acting protein, HAX1 is thought to contribute to the regulation of gene expression. Yeast three-hybrid assays have shown that this protein binds to the stem loops within the 3'UTR of the POLB mRNA, however the exact mechanism in how this protein regulates gene expression is still to be determined.
== Interactions ==
DNA polymerase beta has been shown to interact with PNKP and XRCC1.
== See also ==
POLA1
POLA2
== References ==
== Further reading ==
== External links ==
Rfam entry for the stem loopII (M2) regulatory element in POLB | Wikipedia/DNA_polymerase_beta |
T7 DNA polymerase is an enzyme used during the DNA replication of the T7 bacteriophage. During this process, the DNA polymerase “reads” existing DNA strands and creates two new strands that match the existing ones. The T7 DNA polymerase requires a host factor, E. coli thioredoxin, in order to carry out its function. This helps stabilize the binding of the necessary protein to the primer-template to improve processivity by more than 100-fold, which is a feature unique to this enzyme. It is a member of the Family A DNA polymerases, which include E. coli DNA polymerase I and Taq DNA polymerase.
This polymerase has various applications in site-directed mutagenesis as well as a high-fidelity enzyme suitable for PCR. It has also served as the precursor to Sequenase, an engineered-enzyme optimized for DNA sequencing.
== Mechanism ==
=== Phosphoryl transfer ===
Figure 2. Nucleotidyl transfer by DNA polymerase.
T7 DNA polymerase catalyzes the phosphoryl transfer during DNA replication of the T7 phage. As shown in Figure 2, the 3’ hydroxyl group of a primer acts as a nucleophile and attacks the phosphodiester bond of nucleoside 5’-triphosphate (dTMP-PP). This reaction adds a nucleoside monophosphate into DNA and releases a pyrophosphate (PPi). Generally, the reaction is metal-dependent and cations such as Mg2+ are often present in the enzyme active site.
For T7 DNA polymerase, the fingers, palm and thumb (Figure 1) position the primer-template so that the 3’-end of the primer strand is positioned next to the nucleotide-binding site (located at the intersection of the fingers and thumb). The base pair formed between the nucleotide and the template base fits nicely into a groove between the fingers and the 3’-end of the primer. Two Mg2+ ions form an octahedral coordinate network with oxygen ligand and also bring the reactive primer hydroxyl and the nucleotide α-phosphate close together, thereby lowering the entropic cost of nucleophilic addition. The rate-limiting step in the catalytic cycle occurs after the nucleoside triphosphate binds and before it is incorporated into the DNA (corresponding to the closure of the fingers subdomain around the DNA and nucleotide).
=== Role of Mg2+ ions and amino acid residues in the active site ===
The amino acids present in the active site assist in creating a stabilizing environment for the reaction to proceed. Amino acids such as Lys522, Tyr526, His506 and Arg518 act as hydrogen bond donors. The backbone carbonyl of Ala476, Asp475 and Asp654 form coordinate bonds with the Mg2+ ions.
Asp475 and Asp654 form a bridge with the Mg2+ cations to orient them properly. The Mg2+ ion on the right (Figure 3) interacts with negatively charged oxygens of the alpha(α), beta(β) and gamma(γ) phosphates to align the scissile bond for the primer to attack. Even if there is no general base within the active site to deprotonate the primer hydroxyl, the lowered pka of the metal-bound hydroxyl favors the formation of the 3’-hydroxide nucleophile. Metal ions and Lys522 contact non-bridging oxygens on the α-phosphate to stabilize the negative charge developing on the α-phosphorus during bond formation with the nucleophile.
Moreover, the Lys522 sidechain also moves to neutralize the negatively charged pyrophosphate group. Tyr526, His506, Arg518 side chains and the oxygen from the backbone carbonyl group of Ala476 take part in the hydrogen bond network and assist in aligning the substrate for phosphoryl transfer.
== Accessory proteins ==
While phage T7 mediates DNA replication in very similar manner to higher organisms, T7 system is generally simpler compared to other replication systems. In addition to T7 DNA polymerase (also known as gp5), T7 replisome requires only four accessory proteins for proper function: host thioredoxin, gp4, gp2.5, and gp1.7.
=== Host thioredoxin ===
T7 polymerase by itself has a very low processivity. It dissociates from the primer-template after incorporating about 15 nucleotides. Upon infection of the host, T7 polymerase binds to host thioredoxin in 1:1 ratio. The hydrophobic interaction between thioredoxin and T7 polymerase helps to stabilize the binding of T7 polymerase to primer-template. In addition, the binding of thioredoxin increases T7 polymerase processivity to nearly 80-fold. The precise mechanism for how the thioredoxin-T7 polymerase complex is able to achieve such increase in processivity is still unknown. Binding of thioredoxin exposes a large number of basic amino acid residues in the thumb region of T7 polymerase. Several studies suggest that the electrostatic interaction between these positively charged basic residues with the negatively charged phosphate backbone of DNA and other accessory proteins is responsible for increased processivity in gp5/thioredoxin complex.
=== gp4 ===
gp4 is a hexameric protein containing two functional domains: helicase domain and primase domain.
The helicase domain unwinds double-stranded DNA to provide template for replication. The C-terminal tail of helicase domain contains several negatively charged acidic residues which make contact with the exposed basic residue of T7 polymerase/thioredoxin. These interactions help to load T7 polymerase/thioredoxin complex onto replication fork.
The primase domain catalyzes the synthesis of short oligoribonucleotides. These oligoribonucleotides, called primers, are complementary to the template strand and used to initiate DNA replication. In T7 system, primase domain of one subunit interacts with primase domain of adjacent subunit. This interaction between primase domains acts as a brake to stop helicase when needed, which ensure the leading stand synthesis in-pace with lagging stand synthesis.
=== gp2.5 ===
gp2.5 has similar function to single-stranded DNA binding protein. gp2.5 protects single-stranded DNA produced during replication and coordinates synthesis of leading and lagging strands through interaction between its acidic C-terminal tail and gp5/thioredoxin.
=== gp1.7 ===
gp1.7 is a nucleoside monophosphate kinase, which catalyzes the conversion of deoxynucleoside 5'-monophosphates to di and triphosphate nucleotides, which accounts for the sensitivity of T7 polymerase to dideoxynucleotides (see Sequenase below).
== Properties ==
=== Processivity ===
The primary gp5 subunit of T7 DNA Polymerase by itself has low processivity and dissociates from DNA after the incorporation of just a few nucleotides. In order to become efficiently processive, T7 DNA polymerase recruits host thioredoxin to form a thioredoxin-gp5 complex. Thioredoxin binds the thioredoxin binding domain of gp5 thereby stabilizes a flexible DNA binding region of gp5. The stabilization of this region of gp5 allosterically increases the amount of protein surface interaction with the duplex portion of the primer-template. The resulting thioredoxin-gp5 complex increases the affinity of T7 polymerase for the primer terminus by ~80-fold and acts processively around 800 nucleotide incorporation steps.
The mechanism adopted by T7 polymerase to achieve its processivity differs from many other polymerases in that it does not rely on a DNA clamp or a clamp loader. Instead, the T7 DNA polymerase complex requires only three proteins for processive DNA polymerization: T7 polymerase (gp5), Escherichia coli thioredoxin, and single-stranded DNA-binding protein gp2.5. Although these three proteins are the only ones required for template single-stranded DNA polymerization, in a native biological setting the thioredoxin-gp5 interacts with gp4 helicase, which provides single-stranded DNA template (figure 4). During leading strand synthesis thioredoxin-gp5 and gp4 form a high affinity complex increasing overall polymerase processivity to around 5 kb.
=== Exonuclease activity ===
T7 DNA polymerase possesses a 3’-5’ single and double stranded DNA exonuclease activity. This exonuclease activity is activated when a newly synthesized base does not correctly base-pair with the template strand. Excision of incorrectly incorporated bases acts as a proofreading mechanism thereby increasing the fidelity of T7 polymerase.
During early characterization of exonuclease activity, it was discovered that iron-catalyzed oxidation of T7 polymerase produced a modified enzyme with greatly reduced exonuclease activity. This discovery lead to the development and use of T7 Polymerase as a sequenase in early DNA sequencing methods.
The mechanism by which T7 DNA polymerase senses that a mismatched base has been incorporated is still a topic of study. However, some studies have provided evidence to suggesting that changes in tension of the template DNA strand caused by base-pair mismatch may induce exonuclease activation. Wuite et al. observed that applying tension of above 40 pN to the template DNA resulted in 100-fold increase in exonuclease activity.
== Applications ==
=== Strand extensions in site directed mutagenesis ===
Site-directed mutagenesis is a molecular biology method that is used to make specific and intentional changes to the DNA sequence of a gene and any gene products. The technique was developed at a time when the highest quality commercially available DNA polymerase for converting an oligonucleotide into a complete complementary DNA strand was the large (Klenow) fragment of E. coli DNA polymerase 1. However, ligation step can become an issue with oligonucleotide mutagenesis. That is when the DNA ligase operates inefficiently relative to the DNA polymerase, strand displacement of the oligonucleotide can reduce the mutant frequency. In the other hand, T7 DNA polymerase does not perform strand displacement synthesis; and thus, can be utilized to obtain high mutant frequencies for point mutants independent of ligation.
=== Second strand synthesis of cDNA ===
cDNA cloning is a major technology for analysis of the expression of genomes. The full-length first-strand can be synthesized through the commercially available reverse transcriptases. Synthesis of the second-strand was once a major limitation to cDNA cloning. Two groups of methods differing by the mechanism of initiation were developed to synthesize the second-strand. In a first group of methods, initiation of second-strand synthesis takes place within the sequence of the first strand. However, the digestion of the 3' end of the first strand is required and therefore results in the loss of the sequences corresponding to the 5'end of the mRNA. In a second group of methods, initiation of second-strand synthesis takes place outside the sequence of the first strand. This group of methods does not require digestion of the 3' end of the first strand. However, the limitation of this group of method lies upon the elongation. Cloning with T7 DNA polymerase helps overcome this limitation by allowing digestion of the poly(dT) tract during the second-strand synthesis reaction. Therefore, the size of the tract synthesized with terminal transferase is not required to be within a given size range and the resulting clones contain a tract of a limited size. Moreover, due to high 3’ exonuclease activity of T7 DNA polymerase, high yield of the full-length second-strand can be obtained.
=== Sequenase (DNA sequencing) ===
In Sanger sequencing, one of the major problem regarding DNA polymerases is the discrimination against dideoxynucleotides, the chain-terminating nucleotides. Most of known DNA polymerases strongly discriminate against ddNTP; and thus, a high ratio of ddNTP to dNTP must be used for efficient chain-termination. T7 DNA polymerase discriminates against ddNTP only several fold; and thereby, requires much lower concentration of ddNTP to provide high uniformity of DNA bands on the gel. However, its strong 3’-5’ exonuclease activity can disrupt the sequencing since when the concentration of dNTP falls, the exonuclease activity increases resulting in no net DNA synthesis or degradation of DNA. In order to use for DNA sequencing, T7 DNA polymerase has been modified to remove its exonuclease activity, either chemically (Sequenase 1.0) or by deletion of residues (Sequenase Version 2.0).
== References == | Wikipedia/T7_DNA_polymerase |
Pfu DNA polymerase is an enzyme found in the hyperthermophilic archaeon Pyrococcus furiosus, where it functions to copy the organism's DNA during cell division (thermostable DNA polymerase). In the laboratory setting, Pfu is used to amplify DNA in the polymerase chain reaction (PCR), where the enzyme serves the central function of copying a new strand of DNA during each extension step.
It is a family B DNA polymerase. It has an RNase H-like 3'-5' exonuclease domain, typical of B-family polymerase such as DNA polymerase II.
== Proofreading ability of Pfu polymerase ==
Pfu DNA polymerase has superior thermostability and proofreading properties compared with Taq DNA polymerase. Unlike Taq DNA polymerase, Pfu DNA polymerase possesses 3' to 5' exonuclease proofreading activity, meaning that as the DNA is assembled from the 5' end to 3' end, the exonuclease activity immediately removes nucleotides misincorporated at the 3' end of the growing DNA strand. Consequently, Pfu DNA polymerase-generated PCR fragments will have fewer errors than Taq-generated PCR inserts.
Commercially available Pfu typically results in an error rate of 1 in 1.3 million base pairs and can yield 2.6% mutated products when amplifying 1 kb fragments using PCR. However, Pfu is slower and typically requires 1–2 minutes per cycle to amplify 1kb of DNA at 72 °C. Using Pfu DNA polymerase in PCR reactions also results in blunt-ended PCR products.
Pfu DNA polymerase is hence superior to Taq DNA polymerase for techniques that require high-fidelity DNA synthesis, but can also be used in conjunction with Taq polymerase to obtain the fidelity of Pfu with the speed of Taq polymerase activity.
== History ==
Scientists led by Eric Mathur at the biotech company Stratagene, based in La Jolla, California, discovered Pfu DNA polymerase which exhibits significantly higher fidelity of replication than Taq DNA polymerase in 1991. They received patents for exonuclease-deficient Pfu and the full Pfu in 1996.
Other polymerases from Pyrococcus strains such as "Deep Vent" (Q51334) from strain GB-D and Pwo DNA polymerase have also seen use.
== References ==
== External links ==
Stratagene's Pfu U.S. Patents
Patent 5,489,523
Patent 5,545,552 | Wikipedia/Pfu_DNA_polymerase |
The management of HIV/AIDS normally includes the use of multiple antiretroviral drugs as a strategy to control HIV infection. There are several classes of antiretroviral agents that act on different stages of the HIV life-cycle. The use of multiple drugs that act on different viral targets is known as highly active antiretroviral therapy (HAART). HAART decreases the patient's total burden of HIV, maintains function of the immune system, and prevents opportunistic infections that often lead to death. HAART also prevents the transmission of HIV between serodiscordant same-sex and opposite-sex partners so long as the HIV-positive partner maintains an undetectable viral load.
Treatment has been so successful that in many parts of the world, HIV has become a chronic condition in which progression to AIDS is increasingly rare. Anthony Fauci, former head of the United States National Institute of Allergy and Infectious Diseases, has written, "With collective and resolute action now and a steadfast commitment for years to come, an AIDS-free generation is indeed within reach." In the same paper, he noted that an estimated 700,000 lives were saved in 2010 alone by antiretroviral therapy. As another commentary noted, "Rather than dealing with acute and potentially life-threatening complications, clinicians are now confronted with managing a chronic disease that in the absence of a cure will persist for many decades."
The United States Department of Health and Human Services and the World Health Organization (WHO) recommend offering antiretroviral treatment to all patients with HIV. Because of the complexity of selecting and following a regimen, the potential for side effects, and the importance of taking medications regularly to prevent viral resistance, such organizations emphasize the importance of involving patients in therapy choices and recommend analyzing the risks and the potential benefits.
The WHO has defined health as more than the absence of disease. For this reason, many researchers have dedicated their work to better understanding the effects of HIV-related stigma, the barriers it creates for treatment interventions, and the ways in which those barriers can be circumvented.
== Classes of medication ==
There are six classes of drugs, which are usually used in combination, to treat HIV infection. Antiretroviral (ARV) drugs are broadly classified by the phase of the retrovirus life-cycle that the drug inhibits. Typical combinations include two nucleoside reverse-transcriptase inhibitors (NRTI) as a "backbone" along with one non-nucleoside reverse-transcriptase inhibitor (NNRTI), protease inhibitor (PI) or integrase inhibitors (also known as integrase nuclear strand transfer inhibitors or INSTIs) as a "base".
=== Entry inhibitors ===
Entry inhibitors (or fusion inhibitors) interfere with binding, fusion and entry of HIV-1 to the host cell by blocking one of several targets. Maraviroc, enfuvirtide and Ibalizumab are available agents in this class. Maraviroc works by targeting CCR5, a co-receptor located on human helper T-cells. Caution should be used when administering this drug, however, due to a possible shift in tropism which allows HIV to target an alternative co-receptor such as CXCR4. Ibalizumab is effective against both CCR5 and CXCR4 tropic HIV viruses.
In rare cases, individuals may have a mutation in the CCR5 delta gene which results in a nonfunctional CCR5 co-receptor and in turn, a means of resistance or slow progression of the disease. However, as mentioned previously, this can be overcome if an HIV variant that targets CXCR4 becomes dominant. To prevent fusion of the virus with the host membrane, enfuvirtide can be used. Enfuvirtide is a peptide drug that must be injected and acts by interacting with the N-terminal heptad repeat of gp41 of HIV to form an inactive hetero six-helix bundle, therefore preventing infection of host cells.
=== Nucleoside/nucleotide reverse-transcriptase inhibitors ===
Nucleoside reverse-transcriptase inhibitors (NRTI) and nucleotide reverse-transcriptase inhibitors (NtRTI) are nucleoside and nucleotide analogues which inhibit reverse transcription. HIV is an RNA virus, so it can not be integrated into the DNA in the nucleus of the human cell unless it is first "reverse" transcribed into DNA. Since the conversion of RNA to DNA is not naturally done in the mammalian cell, it is performed by a viral protein, reverse transcriptase, which makes it a selective target for inhibition. NRTIs are chain terminators. Once NRTIs are incorporated into the DNA chain, their lack of a 3' OH group prevents the subsequent incorporation of other nucleosides. Both NRTIs and NtRTIs act as competitive substrate inhibitors. Examples of NRTIs include zidovudine, abacavir, lamivudine, emtricitabine, and of NtRTIs – tenofovir and adefovir.
=== Non-nucleoside reverse-transcriptase inhibitors ===
Non-nucleoside reverse-transcriptase inhibitors (NNRTI) inhibit reverse transcriptase by binding to an allosteric site of the enzyme; NNRTIs act as non-competitive inhibitors of reverse transcriptase. NNRTIs affect the handling of substrate (nucleotides) by reverse transcriptase by binding near the active site. NNRTIs can be further classified into 1st generation and 2nd generation NNRTIs. 1st generation NNRTIs include nevirapine and efavirenz. 2nd generation NNRTIs are etravirine and rilpivirine. HIV-2 is intrinsically resistant to NNRTIs.
=== Integrase inhibitors ===
Integrase inhibitors (also known as integrase nuclear strand transfer inhibitors or INSTIs) inhibit the viral enzyme integrase, which is responsible for integration of viral DNA into the DNA of the infected cell. There are several integrase inhibitors under clinical trial, and raltegravir became the first to receive FDA approval in October 2007. Raltegravir has two metal binding groups that compete for substrate with two Mg2+ ions at the metal binding site of integrase. As of early 2022, four other clinically approved integrase inhibitors are elvitegravir, dolutegravir, bictegravir, and cabotegravir.
=== Protease inhibitors ===
Protease inhibitors block the viral protease enzyme necessary to produce mature virions upon budding from the host membrane. Particularly, these drugs prevent the cleavage of gag and gag/pol precursor proteins. Virus particles produced in the presence of protease inhibitors are defective and mostly non-infectious. Examples of HIV protease inhibitors are lopinavir, indinavir, nelfinavir, amprenavir and ritonavir. Darunavir and atazanavir are recommended as first line therapy choices. Maturation inhibitors have a similar effect by binding to gag, but development of two experimental drugs in this class, bevirimat and vivecon, was halted in 2010. Resistance to some protease inhibitors is high. Second generation drugs have been developed that are effective against otherwise resistant HIV variants.
== Combination therapy ==
The life cycle of HIV can be as short as about 1.5 days from viral entry into a cell, through replication, assembly, and release of additional viruses, to infection of other cells. HIV lacks proofreading enzymes to correct errors made when it converts its RNA into DNA via reverse transcription. Its short life-cycle and high error rate cause the virus to mutate very rapidly, resulting in a high genetic variability. Most of the mutations either are inferior to the parent virus (often lacking the ability to reproduce at all) or convey no advantage, but some of them have a natural selection superiority to their parent and can enable them to slip past defenses such as the human immune system and antiretroviral drugs. The more active copies of the virus, the greater the possibility that one resistant to antiretroviral drugs will be made.
When antiretroviral drugs are used improperly, multi-drug resistant strains can become the dominant genotypes very rapidly. In the era before multiple drug classes were available (pre-1997), the reverse-transcriptase inhibitors zidovudine, didanosine, zalcitabine, stavudine, and lamivudine were used serially or in combination leading to the development of multi-drug resistant mutations.
In contrast, antiretroviral combination therapy defends against resistance by creating multiple obstacles to HIV replication. This keeps the number of viral copies low and reduces the possibility of a superior mutation. If a mutation that conveys resistance to one of the drugs arises, the other drugs continue to suppress reproduction of that mutation. With rare exceptions, no individual antiretroviral drug has been demonstrated to suppress an HIV infection for long; these agents must be taken in combinations in order to have a lasting effect. As a result, the standard of care is to use combinations of antiretroviral drugs. Combinations usually consist of three drugs from at least two different classes. This three drug combination is commonly known as a triple cocktail. Combinations of antiretrovirals are subject to positive and negative synergies, which limits the number of useful combinations.
Because of HIV's tendency to mutate, when patients who have started an antiretrovial regimen fail to take it regularly, resistance can develop. On the other hand, patients who take their medications regularly can stay on one regimen without developing resistance. This greatly increases life expectancy and leaves more drugs available to the individual should the need arise.
In 2000, drug companies have worked together to combine these complex regimens into single-pill fixed-dose combinations. More than 20 antiretroviral fixed-dose combinations have been developed. This greatly increases the ease with which they can be taken, which in turn increases the consistency with which medication is taken (adherence), and thus their effectiveness over the long-term.
=== Adjunct treatment ===
Although antiretroviral therapy has helped to improve the quality of life of people living with HIV, there is still a need to explore other ways to further address the disease burden. One such potential strategy that was investigated was to add interleukin 2 as an adjunct to antiretroviral therapy for adults with HIV. A Cochrane review included 25 randomized controlled trials that were conducted across six countries. The researchers found that interleukin 2 increases the CD4 immune cells, but does not make a difference in terms of death and incidence of other infections. Furthermore, there is probably an increase in side-effects with interleukin 2. The findings of this review do not support the use of interleukin 2 as an add-on treatment to antiretroviral therapy for adults with HIV.
== Treatment guidelines ==
=== Initiation of antiretroviral therapy ===
Antiretroviral drug treatment guidelines have changed over time. Before 1987, no antiretroviral drugs were available and treatment consisted of treating complications from opportunistic infections and malignancies. After antiretroviral medications were introduced, most clinicians agreed that HIV positive patients with low CD4 counts should be treated, but no consensus formed as to whether to treat patients with high CD4 counts.
In April 1995, Merck and the National Institute of Allergy and Infectious Diseases began recruiting patients for a trial examining the effects of a three drug combination of the protease inhibitor indinavir and two nucleoside analogs, illustrating the substantial benefit of combining two NRTIs with a new class of antiretrovirals, protease inhibitors, namely indinavir. Later that year David Ho became an advocate of this "hit hard, hit early" approach with aggressive treatment with multiple antiretrovirals early in the course of the infection. Later reviews in the late 90s and early 2000s noted that this approach of "hit hard, hit early" ran significant risks of increasing side effects and development of multidrug resistance, and this approach was largely abandoned. The only consensus was on treating patients with advanced immunosuppression (CD4 counts less than 350/μL). Treatment with antiretrovirals was expensive at the time, ranging from $10,000 to $15,000 a year.
The timing of when to start therapy has continued to be a core controversy within the medical community, though recent studies have led to more clarity. The NA-ACCORD study observed patients who started antiretroviral therapy either at a CD4 count of less than 500 versus less than 350 and showed that patients who started ART at lower CD4 counts had a 69% increase in the risk of death. In 2015 the START and TEMPRANO studies both showed that patients lived longer if they started antiretrovirals at the time of their diagnosis, rather than waiting for their CD4 counts to drop to a specified level.
Other arguments for starting therapy earlier are that people who start therapy later have been shown to have less recovery of their immune systems, and higher CD4 counts are associated with less cancer.
The European Medicines Agency (EMA) has recommended the granting of marketing authorizations for two new antiretroviral (ARV) medicines, rilpivirine (Rekambys) and cabotegravir (Vocabria), to be used together for the treatment of people with human immunodeficiency virus type 1 (HIV-1) infection. The two medicines are the first ARVs that come in a long-acting injectable formulation. This means that instead of daily pills, people receive intramuscular injections monthly or every two months.
The combination of Rekambys and Vocabria injection is intended for maintenance treatment of adults who have undetectable HIV levels in the blood (viral load less than 50 copies/ml) with their current ARV treatment, and when the virus has not developed resistance to certain class of anti-HIV medicines called non-nucleoside reverse transcriptase inhibitors (NNRTIs) and integrase strand transfer inhibitors (INIs).
==== Treatment as prevention ====
A separate argument for starting antiretroviral therapy that has gained more prominence is its effect on HIV transmission. ART reduces the amount of virus in the blood and genital secretions. This has been shown to lead to dramatically reduced transmission of HIV when one partner with a suppressed viral load (<50 copies/ml) has sex with a partner who is HIV negative. In clinical trial HPTN 052, 1763 serodiscordant heterosexual couples in nine countries were planned to be followed for at least 10 years, with both groups receiving education on preventing HIV transmission and condoms, but only one group getting ART. The study was stopped early (after 1.7 years) for ethical reasons when it became clear that antiviral treatment provided significant protection. Of the 28 couples where cross-infection had occurred, all but one had taken place in the control group, consistent with a 96% reduction in risk of transmission while on ART. The single transmission in the experimental group occurred early after starting ART before viral load was likely to be suppressed. Pre-exposure prophylaxis (PrEP) provides HIV-negative individuals with medication—in conjunction with safer-sex education and regular HIV/STI screenings—in order to reduce the risk of acquiring HIV. In 2011, the journal Science gave the Breakthrough of the Year award to treatment as prevention.
In July 2016 a consensus document was created by the Prevention Access Campaign which has been endorsed by over 400 organisations in 58 countries. The consensus document states that the risk of HIV transmission from a person living with HIV who has been undetectable for a minimum of six months is negligible to non-existent, with negligible being defined as "so small or unimportant to be not worth considering". The Chair of the British HIV Association (BHIVA), Chloe Orkin, stated in July 2017 that 'there should be no doubt about the clear and simple message that a person with sustained, undetectable levels of HIV virus in their blood cannot transmit HIV to their sexual partners.'
Furthermore, the PARTNER study, which ran from 2010 to 2014, enrolled 1166 serodiscordant couples (where one partner is HIV positive and the other is negative) in a study that found that the estimated rate of transmission through any condomless sex with the HIV-positive partner taking ART with an HIV load less than 200 copies/ml was zero.
In summary, as the WHO HIV treatment guidelines state, "The ARV regimens now available, even in the poorest countries, are safer, simpler, more effective and more affordable than ever before."
There is a consensus among experts that, once initiated, antiretroviral therapy should never be stopped. This is because the selection pressure of incomplete suppression of viral replication in the presence of drug therapy causes the more drug sensitive strains to be selectively inhibited. This allows the drug resistant strains to become dominant. This in turn makes it harder to treat the infected individual as well as anyone else they infect. One trial showed higher rates of opportunistic infections, cancers, heart attacks and death in patients who periodically interrupted their ART.
=== Guideline sources ===
There are several treatment guidelines for HIV-1 infected adults in the developed world (that is, those countries with access to all or most therapies and laboratory tests). In the United States there are both the International AIDS Society-USA (IAS-USA) (a 501(c)(3) not-for-profit organization in the US) as well as the US government's Department of Health and Human Services guidelines. In Europe there are the European AIDS Clinical Society guidelines.
For resource limited countries, most national guidelines closely follow the World Health Organization (WHO) guidelines.
==== Guidelines ====
The guidelines use new criteria to consider starting HAART, as described below. However, there remain a range of views on this subject and the decision of whether to commence treatment ultimately rests with the patient and his or her doctor.
The US DHHS guidelines (published April 8, 2015) state:
Antiretroviral therapy (ART) is recommended for all HIV-infected individuals to reduce the risk of disease progression.
ART also is recommended for HIV-infected individuals for the prevention of transmission of HIV.
Patients starting ART should be willing and able to commit to treatment and understand the benefits and risks of therapy and the importance of adherence. Patients may choose to postpone therapy, and providers, on a case-by-case basis, may elect to defer therapy on the basis of clinical and/or psychosocial factors.
The newest WHO guidelines (dated September 30, 2015) now agree and state:
Antiretroviral therapy (ART) should be initiated in everyone living with HIV at any CD4 cell count
==== Baseline resistance ====
Baseline resistance is the presence of resistance mutations in patients who have never been treated before for HIV. In countries with a high rate of baseline resistance, resistance testing is recommended before starting treatment; or, if the initiation of treatment is urgent, then a "best guess" treatment regimen should be started, which is then modified on the basis of resistance testing. In the UK, there is 11.8% medium to high-level resistance at baseline to the combination of efavirenz + zidovudine + lamivudine, and 6.4% medium to high level resistance to stavudine + lamivudine + nevirapine. In the US, 10.8% of one cohort of patients who had never been on ART before had at least one resistance mutation in 2005. Various surveys in different parts of the world have shown increasing or stable rates of baseline resistance as the era of effective HIV therapy continues. With baseline resistance testing, a combination of antiretrovirals that are likely to be effective can be customized for each patient.
=== Regimens ===
Most HAART regimens consist of three drugs: Two NRTIs ("backbone")+ a PI/NNRTI/INSTI ("base"). Initial regimens use "first-line" drugs with a high efficacy and low side-effect profile.
The US DHHS preferred initial regimens for adults and adolescents in the United States, as of April 2015, are:
tenofovir/emtricitabine and raltegravir (an integrase inhibitor)
tenofovir/emtricitabine and dolutegravir (an integrase inhibitor)
abacavir/lamivudine (two NRTIs) and dolutegravir for patients who have been tested negative for the HLA-B*5701 gene allele
tenofovir/emtricitabine, elvitegravir (an integrase inhibitor) and cobicistat (inhibiting metabolism of the former) in patients with good kidney function (gfr > 70)
tenofovir/emtricitabine, ritonavir, and darunavir (both latter are protease inhibitors)
Both efavirenz and nevirapine showed similar benefits when combined with NRTI respectively.
In the case of the protease inhibitor based regimens, ritonavir is used at low doses to inhibit cytochrome p450 enzymes and "boost" the levels of other protease inhibitors, rather than for its direct antiviral effect. This boosting effect allows them to be taken less frequently throughout the day. Cobicistat is used with elvitegravir for a similar effect but does not have any direct antiviral effect itself.
The WHO preferred initial regimen for adults and adolescents as of June 30, 2013, is:
tenofovir + lamivudine (or emtricitabine) + efavirenz
=== Special populations ===
==== Acute infection ====
In the first six months after infection HIV viral loads tend to be elevated and people are more often symptomatic than in later latent phases of HIV disease. There may be special benefits to starting antiretroviral therapy early during this acute phase, including lowering the viral "set-point" or baseline viral load, reduce the mutation rate of the virus, and reduce the size of the viral reservoir (See section below on viral reservoirs). The SPARTAC trial compared 48 weeks of ART vs 12 weeks vs no treatment in acute HIV infection and found that 48 weeks of treatment delayed the time to decline in CD4 count below 350 cells per ml by 65 weeks and kept viral loads significantly lower even after treatment was stopped.
Since viral loads are usually very high during acute infection, this period carries an estimated 26 times higher risk of transmission. By treating acutely infected patients, it is presumed that it could have a significant impact on decreasing overall HIV transmission rates since lower viral loads are associated with lower risk of transmission (See section on treatment as prevention). However an overall benefit has not been proven and has to be balanced with the risks of HIV treatment. Therapy during acute infection carries a grade BII recommendation from the US DHHS.
==== Children ====
HIV can be especially harmful to infants and children, with one study in Africa showing that 52% of untreated children born with HIV had died by age 2. By five years old, the risk of disease and death from HIV starts to approach that of young adults. The WHO recommends treating all children less than 5 years old, and starting all children older than 5 with stage 3 or 4 disease or CD4 <500 cells/ml. DHHS guidelines are more complicated but recommend starting all children less than 12 months old and children of any age who have symptoms.
As for which antiretrovirals to use, this is complicated by the fact that many children who are born to mothers with HIV are given a single dose of nevirapine (an NNRTI) at the time of birth to prevent transmission. If this fails it can lead to NNRTI resistance. Also, a large study in Africa and India found that a PI based regimen was superior to an NNRTI based regimen in children less than 3 years who had never been exposed to NNRTIs in the past. Thus the WHO recommends PI based regimens for children less than 3.
The WHO recommends for children less than 3 years:
abacavir (or zidovudine) + lamivudine + lopinivir + ritonivir
and for children 3 years to less than 10 years and adolescents <35 kilograms:
abacavir + lamivudine + efavirenz
US DHHS guidelines are similar but include PI based options for children > 3 years old.
A systematic review assessed the effects and safety of abacavir-containing regimens as first-line therapy for children between 1 month and 18 years of age when compared to regimens with other NRTIs. This review included two trials and two observational studies with almost eleven thousand HIV infected children and adolescents. They measured virologic suppression, death and adverse events. The authors found that there is no meaningful difference between abacavir-containing regimens and other NRTI-containing regimens. The evidence is of low to moderate quality and therefore it is likely that future research may change these findings.
==== Pregnant women ====
The goals of treatment for pregnant women include the same benefits to the mother as in other infected adults as well as prevention of transmission to her child. The risk of transmission from mother to child is proportional to the plasma viral load of the mother. Untreated mothers with a viral load >100,000 copies/ml have a transmission risk of over 50%. The risk when viral loads are < 1000 copies/ml are less than 1%. ART for mothers both before and during delivery and to mothers and infants after delivery are recommended to substantially reduce the risk of transmission. The mode of delivery is also important, with a planned Caesarian section having a lower risk than vaginal delivery or emergency Caesarian section.
HIV can also be detected in breast milk of infected mothers and transmitted through breast feeding. The WHO balances the low risk of transmission through breast feeding from women who are on ART with the benefits of breastfeeding against diarrhea, pneumonia and malnutrition. It also strongly recommends that breastfeeding infants receive prophylactic ART. In the US, the DHHS recommends against women with HIV breastfeeding.
==== Older adults ====
With improvements in HIV therapy, several studies now estimate that patients on treatment in high-income countries can expect a normal life expectancy. This means that a higher proportion of people living with HIV are now older and research is ongoing into the unique aspects of HIV infection in the older adult. There is data that older people with HIV have a blunted CD4 response to therapy but are more likely to achieve undetectable viral levels. However, not all studies have seen a difference in response to therapy. The guidelines do not have separate treatment recommendations for older adults, but it is important to take into account that older patients are more likely to be on multiple non-HIV medications and consider drug interactions with any potential HIV medications. There are also increased rates of HIV associated non-AIDS conditions (HANA) such as heart disease, liver disease and dementia that are multifactorial complications from HIV, associated behaviors, coinfections like hepatitis B, hepatitis C, and human papilloma virus (HPV) as well as HIV treatment.
==== Adults with depression ====
Many factors may contribute to depression in adults living with HIV, such as the effects of the virus on the brain, other infections or tumours, antiretroviral drugs and other medical treatment. Rates of major depression are higher in people living with HIV compared to the general population, and this may negatively influence antiretroviral treatment. In a systematic review, Cochrane researchers assessed whether giving antidepressants to adults living with both HIV and depression may improve depression. Ten trials, of which eight were done in high-income countries, with 709 participants were included. Results indicated that antidepressants may be better in improving depression compared to placebo, but the quality of the evidence is low and future research is likely to impact on the findings.
== Concerns ==
There are several concerns about antiretroviral regimens that should be addressed before initiating:
Intolerance: The drugs can have serious side-effects which can lead to harm as well as keep patients from taking their medications regularly.
Resistance: Not taking medication consistently can lead to low blood levels that foster drug resistance.
Cost: The WHO maintains a database of world ART costs which have dropped dramatically in recent years as more first line drugs have gone off-patent. A one pill, once a day combination therapy has been introduced in South Africa for as little as $10 per patient per month. One 2013 study estimated an overall cost savings to ART therapy in South Africa given reduced transmission. In the United States, new on-patent regimens can cost up to $28,500 per patient, per year.
Public health: Individuals who fail to use antiretrovirals as directed can develop multi-drug resistant strains which can be passed onto others.
== Response to therapy ==
=== Virologic response ===
Suppressing the viral load to undetectable levels (<50 copies per ml) is the primary goal of ART. This should happen by 24 weeks after starting combination therapy. Viral load monitoring is the most important predictor of response to treatment with ART. Lack of viral load suppression on ART is termed virologic failure. Levels higher than 200 copies per ml is considered virologic failure, and should prompt further testing for potential viral resistance.
Research has shown that people with an undetectable viral load are unable to transmit the virus through condomless sex with a partner of either gender. The 'Swiss Statement' of 2008 described the chance of transmission as 'very low' or 'negligible,' but multiple studies have since shown that this mode of sexual transmission is impossible where the HIV-positive person has a consistently undetectable viral load. This discovery has led to the formation of the Prevention Access Campaign are their 'U=U' or 'Undetectable=Untransmittable' public information strategy, an approach that has gained widespread support amongst HIV/AIDS-related medical, charitable, and research organisations. The studies demonstrating that U=U is an effective strategy for preventing HIV transmission in serodiscordant couples so long as "the partner living with HIV [has] a durably suppressed viral load" include: Opposites Attract, PARTNER 1, PARTNER 2, (for male–male couples) and HPTN052 (for heterosexual couples). In these studies, couples where one partner was HIV-positive and one partner was HIV-negative were enrolled and regular HIV testing completed. In total from the four studies, 4097 couples were enrolled over four continents and 151,880 acts of condomless sex were reported, there were zero phylogenetically linked transmissions of HIV where the positive partner had an undetectable viral load. Following this the U=U consensus statement advocating the use of 'zero risk' was signed by hundreds of individuals and organisations including the US CDC, British HIV Association and The Lancet medical journal. The importance of the final results of the PARTNER 2 study were described by the medical director of the Terrence Higgins Trust as "impossible to overstate", while lead author Alison Rodger declared that the message that "undetectable viral load makes HIV untransmittable ... can help end the HIV pandemic by preventing HIV transmission." The authors summarised their findings in The Lancet as follows:
Our results provide a similar level of evidence on viral suppression and HIV transmission risk for gay men to that previously generated for heterosexual couples and suggest that the risk of HIV transmission in gay couples through condomless sex when HIV viral load is suppressed is effectively zero. Our findings support the message of the U=U (undetectable equals untransmittable) campaign, and the benefits of early testing and treatment for HIV.
This result is consistent with the conclusion presented by Anthony S. Fauci, the Director of the National Institute of Allergy and Infectious Diseases for the U.S. National Institutes of Health, and his team in a viewpoint published in the Journal of the American Medical Association, that U=U is an effective HIV prevention method when an undetectable viral load is maintained.
=== Immunologic response ===
CD4 cell counts are another key measure of immune status and ART effectiveness. CD4 counts should rise 50 to 100 cells per ml in the first year of therapy. There can be substantial fluctuation in CD4 counts of up to 25% based on the time of day or concomitant infections. In one long-term study, the majority of increase in CD4 cell counts was in the first two years after starting ART with little increase afterwards. This study also found that patients who began ART at lower CD4 counts continued to have lower CD4 counts than those who started at higher CD4 counts. When viral suppression on ART is achieved but without a corresponding increase in CD4 counts it can be termed immunologic nonresponse or immunologic failure. While this is predictive of worse outcomes, there is no consensus on how to adjust therapy to immunologic failure and whether switching therapy is beneficial. DHHS guidelines do not recommend switching an otherwise suppressive regimen.
Innate lymphoid cells (ILC) are another class of immune cell that is depleted during HIV infection. However, if ART is initiated before this depletion at around 7 days post infection, ILC levels can be maintained. While CD4 cell counts typically replenish after effective ART, ILCs depletion is irreversible with ART initiated after the depletion despite suppression of viremia. Since one of the roles of ILCs is to regulate the immune response to commensal bacteria and to maintain an effective gut barrier, it has been hypothesized that the irreversible depletion of ILCs plays a role in the weakened gut barrier of HIV patients, even after successful ART.
== Salvage therapy ==
In patients who have persistently detectable viral loads while taking ART, tests can be done to investigate whether there is drug resistance. Most commonly a genotype is sequenced which can be compared with databases of other HIV viral genotypes and resistance profiles to predict response to therapy. Resistance testing may improve virological outcomes in those who have treatment failures. However, there is lack of evidence of effectiveness of such testing in those who have not done any treatment before.
If there is extensive resistance a phenotypic test of a patient's virus against a range of drug concentrations can be performed, but is expensive and can take several weeks, so genotypes are generally preferred. Using information from a genotype or phenotype, a regimen of three drugs from at least two classes is constructed that will have the highest probability of suppressing the virus. If a regimen cannot be constructed from recommended first line agents it is termed salvage therapy, and when six or more drugs are needed it is termed mega-HAART.
== Structured treatment interruptions ==
Drug holidays (or "structured treatment interruptions") are intentional discontinuations of antiretroviral drug treatment. As mentioned above, randomized controlled studies of structured treatment interruptions have shown higher rates of opportunistic infections, cancers, heart attacks and death in patients who took drug holidays. With the exception of post-exposure prophylaxis (PEP), treatment guidelines do not call for the interruption of drug therapy once it has been initiated.
== Adverse effects ==
Each class and individual antiretroviral carries unique risks of adverse side effects.
=== NRTIs ===
The NRTIs can interfere with mitochondrial DNA synthesis and lead to high levels of lactate and lactic acidosis, liver steatosis, peripheral neuropathy, myopathy and lipoatrophy. First-line NRTIs such as lamivudine/emtrictabine, tenofovir, and abacavir are less likely to cause mitochondrial dysfunction.
Mitochondrial Haplogroups(mtDNA), non pathologic mutations inherited from the maternal line, have been linked to the efficacy of CD4+ count following ART. Idiosyncratic toxicity with mtDNA haplogroup is also well studied (Boeisteril et al., 2007).
=== NNRTIs ===
NNRTIs are generally safe and well tolerated. The main reason for discontinuation of efavirenz is neuro-psychiatric effects including suicidal ideation. Nevirapine can cause severe hepatotoxicity, especially in women with high CD4 counts.
=== Protease inhibitors ===
Protease inhibitors (PIs) are often given with ritonavir, a strong inhibitor of cytochrome P450 enzymes, leading to numerous drug-drug interactions. They are also associated with lipodystrophy, elevated triglycerides and elevated risk of heart attack.
=== Integrase inhibitors ===
Integrase inhibitors (INSTIs) are among the best tolerated of the antiretrovirals with excellent short and medium term outcomes. Given their relatively new development there is less long term safety data. They are associated with an increase in creatinine kinase levels and rarely myopathy.
== Post-exposure prophylaxis (PEP) ==
When people are exposed to HIV-positive infectious bodily fluids either through skin puncture, contact with mucous membranes or contact with damaged skin, they are at risk for acquiring HIV. Pooled estimates give a risk of transmission with puncture exposures of 0.3% and mucous membrane exposures 0.63%. United States guidelines state that "feces, nasal secretions, saliva, sputum, sweat, tears, urine, and vomitus are not considered potentially infectious unless they are visibly bloody." Given the rare nature of these events, rigorous study of the protective abilities of antiretrovirals are limited but do suggest that taking antiretrovirals afterwards can prevent transmission. It is unknown if three medications are better than two. The sooner after exposure that ART is started the better, but after what period they become ineffective is unknown, with the US Public Health Service Guidelines recommending starting prophylaxis up to a week after exposure. They also recommend treating for a duration of four weeks based on animal studies. Their recommended regimen is emtricitabine + tenofovir + raltegravir (an INSTI). The rationale for this regimen is that it is "tolerable, potent, and conveniently administered, and it has been associated with minimal drug interactions." People who are exposed to HIV should have follow up HIV testing at 6, 12, and 24 weeks.
== Pregnancy planning ==
Women with HIV have been shown to have decreased fertility which can affect available reproductive options. In cases where the woman is HIV negative and the man is HIV positive, the primary assisted reproductive method used to prevent HIV transmission is sperm washing followed by intrauterine insemination (IUI) or in vitro fertilization (IVF). Preferably this is done after the man has achieved an undetectable plasma viral load. In the past there have been cases of HIV transmission to an HIV-negative partner through processed artificial insemination, but a large modern series in which followed 741 couples where the man had a stable viral load and semen samples were tested for HIV-1, there were no cases of HIV transmission.
For cases where the woman is HIV positive and the man is HIV negative, the usual method is artificial insemination. With appropriate treatment the risk of mother-to-child infection can be reduced to below 1%.
== History ==
Several buyers clubs sprang up since 1986 to combat HIV. The drug zidovudine (AZT), a nucleoside reverse-transcriptase inhibitor (NRTI), was not effective on its own. It was approved by the US FDA in 1987. The FDA bypassed stages of its review for safety and effectiveness in order to distribute this drug earlier. Subsequently, several more NRTIs were developed but even in combination were unable to suppress the virus for long periods of time and patients still inevitably died. To distinguish from this early antiretroviral therapy (ART), the term highly active antiretroviral therapy (HAART) was introduced. In 1996 two sequential publications in The New England Journal of Medicine by Hammer and colleagues and Gulick and colleagues illustrated the substantial benefit of combining two NRTIs with a new class of antiretrovirals, protease inhibitors, namely indinavir. This concept of three-drug therapy was quickly incorporated into clinical practice and rapidly showed impressive benefit with a 60% to 80% decline in rates of AIDS, death, and hospitalization. It would also create a new period of optimism at the 11th International AIDS Conference that was held in Vancouver that year.
As HAART became widespread, fixed dose combinations were made available to ease the administration. Later, the term combination antiretroviral therapy (cART) gained favor with some physicians as a more accurate name, not conveying to patients any misguided idea of the nature of the therapy. Today multidrug, highly effective regimens are long since the default in ART, which is why they are increasingly called simply ART instead of HAART or cART. This retronymic process is linguistically comparable to the way that the words electronic computer and digital computer at first were needed to make useful distinctions in computing technology, but with the later irrelevance of the distinction, computer alone now covers their meaning. Thus as "all computers are digital now", so "all ART is combination ART now." However, the names HAART and cART, reinforced by thousands of earlier mentions in medical literature still being regularly cited, also remain in use. In 1997, the new number of new HIV/AIDS cases in the United States would see its first significant decline at 47%, with credit going to the effectiveness of HAART.
== Research ==
People living with HIV can expect to live a nearly normal life span if able to achieve durable viral suppression on combination antiretroviral therapy. However this requires lifelong medication and will still have higher rates of cardiovascular, kidney, liver and neurologic disease. This has prompted further research towards a cure for HIV.
=== Patients cured of HIV infection ===
The so-called "Berlin patient" has been potentially cured of HIV infection and has been off of treatment since 2006 with no detectable virus. This was achieved through two bone marrow transplants that replaced his immune system with a donor's that did not have the CCR5 cell surface receptor, which is needed for some variants of HIV to enter a cell. Bone marrow transplants carry their own significant risks including potential death and was only attempted because it was necessary to treat a blood cancer he had. Attempts to replicate this have not been successful and given the risks, expense and rarity of CCR5 negative donors, bone marrow transplant is not seen as a mainstream option. It has inspired research into other methods to try to block CCR5 expression through gene therapy. A procedure zinc-finger nuclease-based gene knockout has been used in a Phase I trial of 12 humans and led to an increase in CD4 count and decrease in their viral load while off antiretroviral treatment. Attempt to reproduce this failed in 2016. Analysis of the failure showed that gene therapy only successfully treats 11–28% of cells, leaving the majority of CD4+ cells capable of being infected. The analysis found that only patients where less than 40% of cells were infected had reduced viral load. The gene therapy was not effective if the native CD4+ cells remained. This is the main limitation which must be overcome for this treatment to become effective.
After the "Berlin patient", two additional patients with both HIV infection and cancer were reported to have no traceable HIV virus after successful stem cell transplants. Virologist Annemarie Wensing of the University Medical Center Utrecht announced this development during her presentation at the 2016 "Towards an HIV Cure" symposium. However, these two patients are still on antiretroviral therapy, which is not the case for the Berlin patient. Therefore, it is not known whether or not the two patients are cured of HIV infection. The cure might be confirmed if the therapy were to be stopped and no viral rebound occurred.
In March 2019, a second patient, referred to as the "London Patient", was confirmed to be in complete remission of HIV. Like the Berlin Patient, the London Patient received a bone marrow transplant from a donor who has the same CCR5 mutation. He has been off antiviral drugs since September 2017, indicating the Berlin Patient was not a "one-off".
Alternative approaches aiming to mimic one's biological immunity to HIV through the absence or mutation of the CCR5 gene is being conducted in current research efforts. The efforts of which are done through the introduction of induced pluripotent stem cells that have been CCR5 disrupted through the CRISPR/Cas9 gene editing system.
=== Viral reservoirs ===
The main obstacle to complete elimination of HIV infection by conventional antiretroviral therapy is that HIV is able to integrate itself into the DNA of host cells and rest in a latent state, while antiretrovirals only attack actively replicating HIV. The cells in which HIV lies dormant are called the viral reservoir, and one of the main sources is thought to be central memory and transitional memory CD4+ T cells. In 2014 there were reports of the cure of HIV in two infants, presumably due to the fact that treatment was initiated within hours of infection, preventing HIV from establishing a deep reservoir. There is work being done to try to activate reservoir cells into replication so that the virus is forced out of latency and can be attacked by antiretrovirals and the host immune system. Targets include histone deacetylase (HDAC) which represses transcription and if inhibited can lead to increased cell activation. The HDAC inhibitors valproic acid and vorinostat have been used in human trials with only preliminary results so far.
=== Immune activation ===
Even with all latent virus deactivated, it is thought that a vigorous immune response will need to be induced to clear all the remaining infected cells. Strategies include using cytokines to restore CD4+ cell counts as well as therapeutic vaccines to prime immune responses. One such candidate vaccine is Tat Oyi, developed by Biosantech. This vaccine is based on the HIV protein tat. Animal models have shown the generation of neutralizing antibodies and lower levels of HIV viremia.
=== Sequential mRNA vaccine ===
HIV vaccine development is an active area of research and an important tool for managing the global AIDS epidemic. Research into a vaccine for HIV has been ongoing for decades with no lasting success for preventing infection. The rapid development, though, of mRNA vaccines to deal with the COVID-19 pandemic may provide a new path forward.
Like SARS-CoV-2, the virus that causes COVID-19, HIV has a spike protein. In retroviruses like HIV, the spike protein is formed by two proteins expressed by the Env gene. This viral envelope binds to the host cell's receptor and is what gains the virus entry into the cell. With mRNA vaccines, mRNA or messenger RNA, contains the instructions for how to make the spike protein. The mRNA is put into lipid-based nanoparticles for drug delivery. This was a key breakthrough in optimizing the efficiency and efficacy of in vivo delivery. When the vaccine is injected, the mRNA enters cells and joins up with a ribosome. The ribosome then translates the mRNA instructions into the spike protein. The immune system detects the presence of the spike protein and B cells, a type of white blood cell, begin to develop antibodies. Should the actual virus later enter the system, the external spike protein will be recognized by memory B cells, whose function is to memorize the characteristics of the original antigen. Memory B cells then produce the antibodies, hopefully destroying the virus before it can bind to another cell and repeat the HIV life cycle.
SARS-CoV-2 and HIV-1 have similarities—notably both are RNA viruses—but there are important differences. As a retrovirus, HIV-1 can insert a copy of its RNA genome into the host's DNA, making total eradication more difficult. The virus is also highly mutable making it a challenge for the adaptive immune system to develop a response. As a chronic infection, HIV-1 and the adaptive immune system undergo reciprocal selective pressures leading to the evolutionary arms race of coevolution.
Broadly neutralizing HIV-1 antibodies, or bnAbs, have been shown to attach to the Env spike protein envelope regardless of the specific HIV mutations. This bodes well for vaccine development. Complicating matters, though, naive B cells—mature B cells not yet exposed to any antigen and are the progenitors of bnAbs—are rare. Further, the mutation events needed to turn these B cells into bnAbs are also rare. Because of this, there is a growing consensus that an effective HIV vaccine will need to create not only humoral (antibody-mediated) immunity, but a T-cell-mediated immunity.
mRNA vaccines have advantages over traditional vaccines which may help deal with some of the challenges presented by the HIV virus. The mRNA in the vaccine only codes for the protein spike, not the whole virus, so the possibility of reverse transcription, where the virus copies its genetic material into the host's genome, is not present. Another advantage when compared to traditional vaccines is the speed of development. mRNA vaccines take months not years, which means a multipart sequential vaccine regime is possible.
Attempts to elicit an immune response that triggers broadly neutralizing antibodies (bnAbs) with a single vaccine dose have been unsuccessful. A multipart sequential mRNA vaccine regime, however, might guide the immune response in the right direction. The first shot triggers an immune response for the correct naive B cells. Later vaccinations encourage the development of these cells further, eventually turning them into memory b cells, and later into plasma cells, which can secrete the broadly neutralizing antibodies:
In essence, the sequential immunization approach represents an attempt to mimic Env evolution that would occur with natural infection.... In contrast to traditional prime/boost strategies, in which the same immunogen is used repeatedly for vaccination, the sequential immunization approach relies on a series of different immunogens with the goal of eventually inducing bnAb(s).
A Phase 1 clinical trial by Scripps Research and the International AIDS Vaccine Initiative of an mRNA vaccine showed that 97 percent of participants had the desired initial “priming” immune response of naive b cells. This is a positive result for developing the first shot in a vaccine sequence. Moderna is partnering with Scripps and the International AIDS Vaccine Initiative for a follow-up phase 1 clinical trial of an HIV mRNA vaccine (mRNA-1644) starting later in 2021.
== Drug advertisements ==
Direct-to-consumer and other advertisements for HIV drugs in the past were criticized for their use of healthy, glamorous models rather than typical people with HIV/AIDS. Usually, these people will present with debilitating conditions or illnesses as a result of HIV/AIDS. In contrast, by featuring people in unrealistically strenuous activities, such as mountain climbing; this proved to be offensive and insensitive to the suffering of people who are HIV positive. The US FDA reprimanded multiple pharmaceutical manufacturers for publishing such adverts in 2001, as the misleading advertisements harmed consumers by implying unproven benefits and failing to disclose important information about the drugs. Overall, some drug companies chose not to present their drugs in a realistic way, which consequently harmed the general public's ideas, suggesting that HIV would not affect you as much as suggested. This led to people not wanting to get tested, for fear of being HIV positive, because at the time (in the 1980s and 1990s particularly), having contracted HIV was seen as a death sentence, as there was no known cure. An example of such a case is Freddie Mercury, who died in 1991, aged 45, of AIDS-related pneumonia.
== Beyond medical management ==
The preamble to the World Health Organization's Constitution defines health as "a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity." Those living with HIV today are met with other challenges that go beyond the singular goal of lowering their viral load. A 2009 meta-analysis studying the correlates of HIV-stigma found that individuals living with higher stigma burden were more likely to have poorer physical and mental health. Insufficient social support and delayed diagnosis due to decreased frequency of HIV testing and knowledge of risk reduction were cited as some of the reasons. People living with HIV (PLHIV) have lower health related quality of life (HRQoL) scores than do the general population. The stigma of having HIV is often compounded with the stigma of identifying with the LGBTQ community or the stigma of being an injecting drug user (IDU) even though heterosexual sexual transmission accounts for 85% of all HIV-1 infections worldwide. AIDS has been cited as the most heavily stigmatized medical condition among infectious diseases. Part of the consequence of this stigma toward PLHIV is the belief that they are seen as responsible for their status and less deserving of treatment.
A 2016 study sharing the WHO's definition of health critiques its 90-90-90 target goal, which is part of a larger strategy that aims to eliminate the AIDS epidemic as a public health threat by 2030, by arguing that it does not go far enough in ensuring the holistic health of PLHIV. The study suggests that maintenance of HIV and AIDS should go beyond the suppression of viral load and the prevention of opportunistic infection. It proposes adding a 'fourth 90' addressing a new 'quality of life' target that would focus specifically on increasing the quality of life for those that are able to suppress their viral load to undetectable levels along with new metrics to track the progress toward that target. This study serves as an example of the shifting paradigm in the dynamics of the health care system from being heavily 'disease-oriented' to more 'human-centered'. Though questions remain of what exactly a more 'human-centered' method of treatment looks like in practice, it generally aims to ask what kind of support, other than medical support, PLHIV need to cope with and eliminate HIV-related stigmas. Campaigns and marketing aimed at educating the general public in order to reduce any misplaced fears of HIV contraction is one example. Also encouraged is the capacity-building and guided development of PLHIV into more leadership roles with the goal of having a greater representation of this population in decision making positions. Structural legal intervention has also been proposed, specifically referring to legal interventions to put in place protections against discrimination and improve access to employment opportunities. On the side of the practitioner, greater competence for the experience of people living with HIV is encouraged alongside the promotion of an environment of nonjudgment and confidentiality.
Psychosocial group interventions such as psychotherapy, relaxation, group support, and education may have some beneficial effects on depression in HIV positive people.
== Food insecurity ==
The successful treatment and management of HIV/AIDS is affected by a plethora of factors which ranges from successfully taking prescribed medications, preventing opportunistic infection, and food access etc. Food insecurity is a condition in which households lack access to adequate food because of limited money or other resources. Food insecurity is a global issue that has affected billions of people yearly, including those living in developed countries.
Food insecurity is a major public health disparity in the United States of America, which significantly affects minority groups, people living at or below the poverty line, and those who are living with one or more morbidity. As of December 31, 2017, there were approximately 126,742 people living with HIV/AIDS (PLWHA) in NYC, of whom 87.6% can be described as living with some level of poverty and food insecurity as reported by the NYC Department of Health on March 31, 2019. Having access to a consistent food supply that is safe and healthy is an important part in the treatment and management of HIV/AIDS. PLWHA are also greatly affected by food inequities and food deserts which causes them to be food insecure. Food insecurity, which can cause malnutrition, can also negatively impact HIV treatment and recovery from opportunistic infections. Similarly, PLWHA require additional calories and nutritionally support that require foods free from contamination to prevent further immunocompromising. Food insecurity can further exacerbate the progression of HIV/AIDS and can prevent PLWHA from consistently following their prescribed regimen, which will lead to poor outcomes.
It is imperative that these food insecurity among PLWHA are addressed and rectified to reduce this health inequity. It is important to recognized that socioeconomic status, access to medical care, geographic location, public policy, race and ethnicity all play a pivotal role in the treatment and management of HIV/AIDS. The lack of sufficient and constant income does limit the options for food, treatment, and medications. The same can be inferred for those who are among the oppressed groups in society who are marginalized and may be less inclined or encouraged to seek care and assistance. Endeavors to address food insecurity should be included in HIV treatment programs and may help improve health outcomes if it also focuses on health equity among the diagnosed as much as it focuses on medications. Access to consistently safe and nutritious foods is one of the most important facets in ensuring PLWHA are being provided the best possible care. By altering the narratives for HIV treatment so that more support can be garnered to reduce food insecurity and other health disparities mortality rates will decrease for people living with HIV/AIDS.
== See also ==
AV-HALT
Discovery and development of HIV-protease inhibitors
Discovery and development of non-nucleoside reverse-transcriptase inhibitors
Discovery and development of nucleoside and nucleotide reverse-transcriptase inhibitors
HIV capsid inhibition
== References ==
== Further reading ==
Strayer DS, Akkina R, Bunnell BA, Dropulic B, Planelles V, Pomerantz RJ, et al. (June 2005). "Current status of gene therapy strategies to treat HIV/AIDS". Molecular Therapy. 11 (6): 823–42. doi:10.1016/j.ymthe.2005.01.020. PMID 15922953.
== External links ==
HIVinfo at US Department of Health and Human Services | Wikipedia/Antiretroviral_drugs |
Agnatha (; from Ancient Greek ἀ- (a-) 'without' and γνάθος (gnáthos) 'jaws') or jawless fish is a paraphyletic infraphylum of animals in the subphylum Vertebrata of the phylum Chordata, characterized by the lack of jaws. The group consists of both living (cyclostomes such as hagfishes and lampreys) and extinct clades (e.g. conodonts and cephalaspidomorphs, among others). They are sister to vertebrates with jaws known as gnathostomes, who evolved from jawless ancestors during the early Silurian by developing folding articulations in the first pairs of gill arches.
Molecular data, both from rRNA and from mtDNA as well as embryological data, strongly supports the hypothesis that both groups of living agnathans, hagfishes and lampreys, are more closely related to each other than to jawed fish, forming the superclass Cyclostomi.
The oldest fossil agnathans appeared in the Cambrian. Living jawless fish comprise about 120 species in total. Hagfish are considered members of the subphylum Vertebrata, because they secondarily lost vertebrae; before this event was inferred from molecular and developmental data, the Craniata hypothesis was accepted (and is still sometimes used as a strictly morphological descriptor) to reference hagfish plus vertebrates.
== Metabolism ==
Agnathans are ectothermic, meaning they do not regulate their own body temperature. Agnathan metabolism is slow in cold water, and therefore they do not have to eat very much. They have no distinct stomach, but rather a long gut, more or less homogeneous throughout its length. Lampreys feed on carrion, as well as other fish and marine mammals, although some species are non-carnivorous. Anticoagulant fluids preventing blood clotting are injected into the host, causing the host to yield more blood. Hagfish are scavengers, eating mostly dead animals, although they have also been observed hunting. They use a row of sharp teeth to break down the animal. Because agnathan teeth are unable to move up and down it limits their possible food types.
== Morphology ==
In addition to the absence of jaws, modern agnathans are characterised by absence of paired fins; the presence of a notochord both in larvae and adults; and seven or more paired gill pouches. Lampreys have a light sensitive pineal eye (homologous to the pineal gland in mammals). All living and most extinct Agnatha do not have an identifiable stomach or any appendages. Fertilization and development are both external. There is no parental care in the Agnatha class. The Agnatha are ectothermic or cold-blooded, with a cartilaginous skeleton, and the heart contains 2 chambers.
=== Body covering ===
In modern agnathans, the body is covered in skin, with neither dermal or epidermal scales. The skin of hagfish has copious slime glands, the slime constituting their defense mechanism. The slime can sometimes clog up enemy fishes' gills, causing them to die. In direct contrast, many extinct agnathans sported extensive exoskeletons composed of either massive, heavy dermal armour or small mineralized scales.
=== Appendages ===
Almost all agnathans, including all extant agnathans, have no paired appendages, although most do have a dorsal or a caudal fin. Some fossil agnathans, such as osteostracans and pituriaspids, did have paired fins, a trait inherited in their jawed descendants.
== Reproduction ==
Fertilization in lampreys is external. Mode of fertilization in hagfishes is not known. Development in both groups probably is external. There is no known parental care. Not much is known about the hagfish reproductive process. It is believed that hagfish only have 30 eggs over a lifetime. There is very little of the larval stage that characterizes the lamprey. Lamprey are only able to reproduce once. Lampreys reproduce in freshwater riverbeds, working in pairs to build a nest and burying their eggs about an inch beneath the sediment. The resulting hatchlings go through four years of larval development before becoming adults.
== Evolution ==
Although a minor element of modern marine fauna, agnathans were prominent among the early fish in the early Paleozoic. Two types of Early Cambrian animal apparently having fins, vertebrate musculature, and gills are known from the early Cambrian Maotianshan shales of China: Haikouichthys and Myllokunmingia. They have been tentatively assigned to Agnatha by Janvier. A third possible agnathan from the same region is Haikouella. A possible agnathan that has not been formally described was reported by Simonetti from the Middle Cambrian Burgess Shale of British Columbia. Conodonts, a class of agnathans which arose in the early Cambrian, remained common enough until their extinction in the Triassic that their teeth (the only parts of them that were usually fossilized) are often used as index fossils from the late Cambrian to the Triassic.
Many Ordovician, Silurian, and Devonian agnathans were armored with heavy bony-spiky plates. The first armored agnathans—the ostracoderms, precursors to the bony fish and hence to the tetrapods (including humans)—are known from the middle Ordovician, and by the Late Silurian the agnathans had reached the high point of their evolution. Most of the ostracoderms, such as thelodonts, osteostracans, and galeaspids, were more closely related to the gnathostomes than to the surviving agnathans, known as cyclostomes. Cyclostomes apparently split from other agnathans before the evolution of dentine and bone, which are present in many fossil agnathans, including conodonts. Agnathans declined in the Devonian and never recovered.
Approximately 500 million years ago, two types of recombinatorial adaptive immune systems (AISs) arose in vertebrates. The jawed vertebrates diversify their repertoire of immunoglobulin domain-based T and B cell antigen receptors mainly through the rearrangement of V(D)J gene segments and somatic hypermutation, but none of the fundamental AIS recognition elements in jawed vertebrates have been found in jawless vertebrates. Instead, the AIS of jawless vertebrates is based on variable lymphocyte receptors (VLRs) that are generated through recombinatorial usage of a large panel of highly diverse leucine-rich-repeat (LRR) sequences. Three VLR genes (VLRA, VLRB, and VLRC) have been identified in lampreys and hagfish, and are expressed on three distinct lymphocytes lineages. VLRA+ cells and VLRC+ cells are T-cell-like and develop in a thymus-like lympho-epithelial structure, termed thymoids. VLRB+ cells are B-cell-like, develop in hematopoietic organs, and differentiate into "VLRB antibody"-secreting plasma cells.
== Classification ==
== Phylogeny ==
Phylogeny based on the work of Mikko Haaramo and Delsuc et al.
While the "Agnatha" Conodonta was indeed jawless, if it would have continued to live, its descendants would still be closer related to e.g. humans than to lampreys, and also contemporary it was closer related to the ancestor of humans. Due to such considerations, Agnatha can not be consolidated into a coherent grouping without either removing any non-Cyclostomata, or by including all Vertebrata thus rendering it into a junior synonym of Vertebrata.
The new phylogeny from Miyashita et al. (2019) is considered compatible with both morphological and molecular evidence.
== See also ==
Gnathostomata
Amphirhina, an alternate name for the above parallel, or sister, classification
Cyclostomata
== References == | Wikipedia/Jawless_vertebrates |
Marine vertebrates are vertebrates that live in marine environments, which include saltwater fish (including pelagic, coral and deep sea fish) and marine tetrapods (primarily marine mammals and marine reptiles, as well as semiaquatic clades such as seabirds). As a subphylum of chordates, all vertebrates have evolved a vertebral column (backbone) based around the embryonic notochord (which becomes the intervertebral discs), forming the core structural support of an internal skeleton, and also serves to enclose and protect the spinal cord.
Compared to other marine animals, marine vertebrates are distinctly more nektonic, and their aquatic locomotions rely mainly on propulsion by the tail and paired appendages such as fins, flippers and webbed limbs. Marine vertebrates also have a far more centralized nervous system than marine invertebrates, with most of the higher functions cephalized and monopolized by the brain; and most of them have evolved myelinated central and peripheral nerve system, which increases conduction speeds significantly. The combination of endoskeleton (which allows much larger body sizes for the same skeletal mass) and a more robust and efficient nervous system (which enables more acute perception and more sophisticated motor control) gives vertebrates much quicker body reactivity and behavioral adaptability, which have led to marine vertebrates dominating most of the higher-level niches in the marine ecosystems.
== Marine fish ==
Fish fall into two main groups: fish with bony internal skeletons and fish with cartilaginous internal skeletons. Fish anatomy and physiology generally includes a two-chambered heart, eyes adapted to seeing underwater, and a skin protected by scales and mucous. They typically breathe by extracting oxygen from water through gills. Fish use fins to propel and stabilise themselves in the water. Over 33,000 species of fish have been described as of 2017, of which about 20,000 are marine fish.
=== Jawless fish ===
Hagfish form a class of about 20 species of eel-shaped, slime-producing marine fish. They are the only known living animals that have a skull but no vertebral column. Lampreys form a superclass containing 38 known extant species of jawless fish. The adult lamprey is characterized by a toothed, funnel-like sucking mouth. Although they are well known for boring into the flesh of other fish to suck their blood, only 18 species of lampreys are actually parasitic. Together hagfish and lampreys are the sister group to vertebrates. Living hagfish remain similar to hagfish from around 300 million years ago. The lampreys are a very ancient lineage of vertebrates, though their exact relationship to hagfishes and jawed vertebrates is still a matter of dispute. Molecular analysis since 1992 has suggested that hagfish are most closely related to lampreys, and so also are vertebrates in a monophyletic sense. Others consider them a sister group of vertebrates in the common taxon of craniata.
Pteraspidomorphi is an extinct class of early jawless fish ancestral to jawed vertebrates. The few characteristics they share with the latter are now considered as primitive for all vertebrates.
=== Cartilaginous fish ===
Cartilaginous fish, such as sharks and rays, have jaws and skeletons made of cartilage rather than bone. Megalodon is an extinct species of shark that lived about 28 to 1.5 Ma. It looked much like a stocky version of the great white shark, but was much larger with fossil lengths reaching 20.3 metres (67 ft). Found in all oceans it was one of the largest and most powerful predators in vertebrate history, and probably had a profound impact on marine life. The Greenland shark has the longest known lifespan of all vertebrates, about 400 years.
=== Bony fish ===
Bony fish have jaws and skeletons made of bone rather than cartilage. About 90% of the world's fish species are bony fish. Bony fish also have hard, bony plates called operculum which help them respire and protect their gills, and they often possess a swim bladder which they use for better control of their buoyancy.
Bony fish can be further divided into those with lobe fins and those with ray fins. Lobe fins have the form of fleshy lobes supported by bony stalks which extend from the body. Lobe fins evolved into the legs of the first tetrapod land vertebrates, so by extension an early ancestor of humans was a lobe-finned fish. Apart from the coelacanths and the lungfishes, lobe-finned fishes are now extinct. The rest of the modern fish have ray fins. These are made of webs of skin supported by bony or horny spines (rays) which can be erected to control the fin stiffness.
== Marine tetrapods ==
A tetrapod (Greek for four feet) is a vertebrate with limbs (feet). Tetrapods evolved from ancient lobe-finned fishes about 400 million years ago during the Devonian Period when their earliest ancestors emerged from the sea and adapted to living on land. This change from a body plan for breathing and navigating in gravity-neutral water to a body plan with mechanisms enabling the animal to breath in air without dehydrating and move on land is one of the most profound evolutionary changes known. Tetrapods can be divided into four classes: amphibians, reptiles, birds and mammals.
Marine tetrapods are tetrapods that returned from land back to the sea again. The first returns to the ocean may have occurred as early as the Carboniferous Period whereas other returns occurred as recently as the Cenozoic, as in cetaceans, pinnipeds, and several modern amphibians.
=== Amphibians ===
Amphibians (Greek for both kinds of life) live part of their life in water and part on land. They mostly require fresh water to reproduce. A few inhabit brackish water, but there are no true marine amphibians. There have been reports, however, of amphibians invading marine waters, such as a Black Sea invasion by the natural hybrid Pelophylax esculentus reported in 2010.
=== Reptiles ===
Reptiles (Late Latin for creeping or crawling) do not have an aquatic larval stage, and in this way are unlike amphibians. Most reptiles are oviparous, although several species of squamates are viviparous, as were some extinct aquatic clades — the fetus develops within the mother, contained in a placenta rather than an eggshell. As amniotes, reptile eggs are surrounded by membranes for protection and transport, which adapt them to reproduction on dry land. Many of the viviparous species feed their fetuses through various forms of placenta analogous to those of mammals, with some providing initial care for their hatchlings.
Some reptiles are more closely related to birds than other reptiles, and many scientists prefer to make Reptilia a monophyletic group which includes the birds. Extant non-avian reptiles which inhabit or frequent the sea include sea turtles, sea snakes, terrapins, the marine iguana, and the saltwater crocodile. Currently, of the approximately 12,000 extant reptile species and sub-species, only about 100 of are classed as marine reptiles.
Except for some sea snakes, most extant marine reptiles are oviparous and need to return to land to lay their eggs. Apart from sea turtles, the species usually spend most of their lives on or near land rather than in the ocean. Sea snakes generally prefer shallow waters nearby land, around islands, especially waters that are somewhat sheltered, as well as near estuaries. Unlike land snakes, sea snakes have evolved flattened tails which help them swim.
Some extinct marine reptiles, such as ichthyosaurs, evolved to be viviparous and had no requirement to return to land. Ichthyosaurs resembled dolphins. They first appeared about 245 million years ago and disappeared about 90 million years ago. The terrestrial ancestor of the ichthyosaur had no features already on its back or tail that might have helped along the evolutionary process. Yet the ichthyosaur developed a dorsal and tail fin which improved its ability to swim. The biologist Stephen Jay Gould said the ichthyosaur was his favourite example of convergent evolution. The earliest marine reptiles arose in the Permian. During the Mesozoic many groups of reptiles became adapted to life in the seas, including ichthyosaurs, plesiosaurs, mosasaurs, nothosaurs, placodonts, sea turtles, thalattosaurs and thalattosuchians. Marine reptiles were less numerous after mass extinction at the end of the Cretaceous.
=== Birds ===
Marine birds are adapted to life within the marine environment. They are often called seabirds. While marine birds vary greatly in lifestyle, behaviour and physiology, they often exhibit striking convergent evolution, as the same environmental problems and feeding niches have resulted in similar adaptations. Examples include albatross, penguins, gannets, and auks.
In general, marine birds live longer, breed later and have fewer young than terrestrial birds do, but they invest a great deal of time in their young. Most species nest in colonies, which can vary in size from a few dozen birds to millions. Many species are famous for undertaking long annual migrations, crossing the equator or circumnavigating the Earth in some cases. They feed both at the ocean's surface and below it, and even feed on each other. Marine birds can be highly pelagic, coastal, or in some cases spend a part of the year away from the sea entirely. Some marine birds plummet from heights, plunging through the water leaving vapour-like trails, similar to that of fighter planes. Gannets plunge into the water at up to 100 kilometres per hour (60 mph). They have air sacs under their skin in their face and chest which act like bubble-wrap, cushioning the impact with the water.
The first marine birds evolved in the Cretaceous period, and modern marine bird families emerged in the Paleogene.
=== Mammals ===
Mammals (from Latin for breast) are characterised by the presence of mammary glands which in females produce milk for feeding (nursing) their young. There are about 130 living and recently extinct marine mammal species such as seals, dolphins, whales, manatees, sea otters and polar bears. They do not represent a distinct taxon or systematic grouping, but are instead unified by their reliance on the marine environment for feeding. Both cetaceans and sirenians are fully aquatic and therefore are obligate water dwellers. Seals and sea-lions are semiaquatic; they spend the majority of their time in the water, but need to return to land for important activities such as mating, breeding and molting. In contrast, both otters and the polar bear are much less adapted to aquatic living. Their diet varies considerably as well: some may eat zooplankton; others may eat fish, squid, shellfish, and sea-grass; and a few may eat other mammals.
In a process of convergent evolution, marine mammals such as dolphins and whales redeveloped their body plan to parallel the streamlined fusiform body plan of pelagic fish. Front legs became flippers and back legs disappeared, a dorsal fin reappeared and the tail morphed into a powerful horizontal fluke. This body plan is an adaptation to being an active predator in a high drag environment. A parallel convergence occurred with the now extinct ichthyosaur.
== See also ==
Marine habitat
Marine invertebrate
Marine life
== References == | Wikipedia/Marine_vertebrate |
Gnathostomata (; from Ancient Greek: γνάθος (gnathos) 'jaw' + στόμα (stoma) 'mouth') are jawed vertebrates. Gnathostome diversity comprises roughly 60,000 species, which accounts for 99% of all extant vertebrates, including all living bony fishes (both ray-finned and lobe-finned, including their terrestrial tetrapod relatives) and cartilaginous fishes, as well as extinct prehistoric fish such as placoderms and acanthodians. Most gnathostomes have retained ancestral traits like true teeth, a stomach, and paired appendages (pectoral and pelvic fins, limbs, wings, etc.). Other traits are elastin, horizontal semicircular canal of the inner ear, myelinated neurons, and an adaptive immune system which has discrete secondary lymphoid organs (spleen and thymus) and uses V(D)J recombination to create antigen recognition sites, rather than using genetic recombination in the variable lymphocyte receptor gene.
It is now assumed that Gnathostomata evolved from ancestors that already possessed two pairs of paired fins. Until recently these ancestors, known as antiarchs, were thought to have lacked pectoral or pelvic fins. In addition to this, some placoderms were shown to have a third pair of paired appendages, that had been modified to claspers in males and pelvic basal plates in females — a pattern not seen in any other vertebrate group. The jawless Osteostraci are generally considered the closest sister taxon of Gnathostomata.
Jaw development in vertebrates is likely a product of bending the first pair of gill arches. This development would help suck water into the mouth by the movement of the jaw, so that it would then pass over the gills via buccal pumping for gas exchange. The repetitive use of the newly formed jaw bones would eventually lead to the ability to bite in some gnathostomes.
Newer research suggests that a branch of placoderms was most likely the ancestor of present-day gnathostomes. A 419-million-year-old fossil of a placoderm named Entelognathus had a bony oral skeleton and anatomical details associated with cartilaginous and bony fish, demonstrating that the absence of a bony skeleton in cartilaginous fish is a derived trait. The fossil findings of primitive bony fishes such as Guiyu oneiros and Psarolepis, which lived contemporaneously with Entelognathus and had pelvic girdles more in common with placoderms than with other bony fish, show that it was a relative rather than a direct ancestor of the extant gnathostomes. It also indicates that spiny sharks and Chondrichthyes represent a single sister group to the bony fishes. Fossil findings of juvenile placoderms, which had true teeth that grew on the surface of the jawbone and had no roots, making them impossible to replace or regrow as they broke or wore down as they grew older, proves the common ancestor of all gnathostomes had teeth and place the origin of teeth along with, or soon after, the evolution of jaws.
Late Ordovician-aged microfossils of what have been identified as scales of either acanthodians or "spiny sharks", may mark Gnathostomata's first appearance in the fossil record. Undeniably unambiguous gnathostome fossils, mostly of primitive acanthodians, begin appearing by the early Silurian, and become abundant by the start of the Devonian.
== Classification ==
Gnathostomata is traditionally an infraphylum, broken into three top-level groupings: Chondrichthyes, or the cartilaginous fish; Placodermi, an extinct grade of armored fish; and Teleostomi, which includes the familiar classes of bony fish, birds, mammals, reptiles, and amphibians. Some classification systems have used the term Amphirhina. It is a sister group of the Agnatha (jawless fish).
== Evolution ==
The appearance of the early vertebrate jaw has been described as "a crucial innovation" and "perhaps the most profound and radical evolutionary step in the vertebrate history". Fish without jaws had more difficulty surviving than fish with jaws, and most jawless fish became extinct during the Triassic period. However studies of the cyclostomes, the jawless hagfishes and lampreys that did survive, have yielded little insight into the deep remodelling of the vertebrate skull that must have taken place as early jaws evolved.
The ancestor of all jawed vertebrates have gone through two rounds of whole genome duplication. The first happened before the gnathostome and cyclostome split, and appears to have been an autopolyploidy event (happened within the same species). The second occurred after the split, and was an allopolyploidy event (the result of hybridization between two lineages).
The customary view is that jaws are homologous to the gill arches. In jawless fishes a series of gills opened behind the mouth, and these gills became supported by cartilaginous elements. The first set of these elements surrounded the mouth to form the jaw. The upper portion of the second embryonic arch supporting the gill became the hyomandibular bone of jawed fish, which supports the skull and therefore links the jaw to the cranium. The hyomandibula is a set of bones found in the hyoid region in most fishes. It usually plays a role in suspending the jaws or the operculum in the case of teleosts.
While potentially older Ordovician records are known, the oldest unambigious evidence of jawed vertebrates are Qianodus and Fanjingshania from the early Silurian (Aeronian) of Guizhou, China around 439 million years ago, which are placed as acanthodian-grade stem-chondrichthyans.
== References ==
== External links ==
Tree of Life discussion of Gnathostomata
The Gill Arches: Meckel's Cartilage | Wikipedia/Jawed_vertebrate |
The taxonomy of the vertebrates presented by John Zachary Young in The Life of Vertebrates (1962) is a system of classification with emphasis on this group of animals.
== Phylum Chordata ==
Phylum Chordata [p. 24]
Subphylum 1. Hemichordata (e.g., Balanoglossus, Cephalodiscus, Rhabdopleura)
Subphylum 2. Cephalochordata (= Acrania) (e.g., Branchiostoma)
Subphylum 3. Tunicata (e.g., Ciona)
Subphylum 4. Vertebrata (= Craniata)
Superclass 1. Agnatha
Class 1. Cyclostomata
Class 2. †Cephalaspidomorphi (e.g., †Cephalaspis)
Class 3. †Pteraspidomorphi (e.g., †Pteraspis)
Class 4. †Anaspida (e.g., †Birkenia, †Jamoytius)
Superclass 2. Gnathostomata
Class 1. †Placodermi (e.g., †Acanthodes)
Class 2. Elasmobranchii
Class 3. Actinopterygii
Class 4. Crossopterygii
Class 5. Amphibia
Class 6. Reptilia
Class 7. Aves
Class 8. Mammalia
=== Subphylum Vertebrata (= Craniata) ===
==== Superclass Agnatha ====
Subphylum Vertebrata (= Craniata)
Superclass 1. Agnatha [p. 81]
Class 1. Cyclostomata
Order 1. Petromyzontia (e.g., Petromyzon, Lampetra, Entosphenus, Geotria, Mordacia)
Order 2. Myxinoidea (e.g., Myxine, Bdellostoma)
Class 2. †Osteostraci (e.g., †Cephalaspis, †Tremataspis)
Class 3. †Anaspida (e.g., †Birkenia, †Jamoytius)
Class 4. †Heterostraci (e.g., †Astraspis, †Pteraspis, †Drepanaspis)
Class 5. †Coelolepida (e.g., †Thelodus, †Lanarkia)
==== Superclass Gnathostomata ====
===== Class Elasmobranchii =====
Superclass 2. Gnathostomata
Class Elasmobranchii (= Chondrichthyes) [p. 175]
Subclass 1. Selachii
Order 1. †Cladoselachii (e.g., †Cladoselache, †Goodrichia)
Order 2. †Pleuracanthodii (e.g., †Pleuracanthus)
Order 3. Protoselachii (e.g., †Hybodiis, Heterodontus)
Order 4. Euselachii
Suborder 1. Pleurotremata
Division 1. Notidanoidea (e.g., Hexanchus, Chlamydoselache)
Division 2. Galeoidea (e.g., Scyliorhinus, Mustelus, Cetorhinus, Carcharodon)
Division 3. Squaloidea (e.g., Squalus, Squatina, Pristiophorus, Alopias)
Suborder 2. Hypotremata (e.g., Raja, Rhinobatis, Pristis, Torpedo, Trygon)
Subclass 2. Bradyodonti
Order 1. †Eubradyodonti (e.g., †Helodus)
Order 2. Holocephali (e.g., Chimaera)
===== Class Actinopterygii =====
Class Actinopterygii [p. 228]
Superorder 1. Chondrostei
Order 1. Palaeoniscoidei (e.g., †Cheirolepis, †Palaeoniscus, †Amphicentrum, †Platysomus, †Dorypterus, †Cleithrolepis, †Tarrasius, Polypterus [bichir])
Order 2. Acipenseroidei (e.g., †Chondrosteus, Acipenser [sturgeon], Polyodon [paddle-fish])
Order 3. Subholostei (e.g., †Ptycholepis)
Superorder 2. Holostei (e.g., †Acentrophorus, †Lepidotes, †Dapedius, †Microdon, Amia [bowfin], Lepisosteus [gar-pike])
Superorder 3. Teleostei
Order 1. Isospondyli (e.g., †Leptolepis, †Portheus, Clupea [herring], Salmo [trout])
Order 2. Ostariophysi (e.g., Cyprinus [carp], Tinea [tench], Silurus [catfish])
Order 3. Apodes (e.g., Anguilla [eel], Conger [conger eel])
Order 4. Mesichthyes (e.g., Esox [pike], Belone, Exocoetus [flying fish], Gasterosteus [stickle-back], Syngnathus [pipe-fish], Hippocampus [seahorse])
Order 5. Acanthopterygii (e.g., †Hoplopteryx, Zens [John Dory], Perca [perch], Labrus [wrasse], Uranoscopus [star gazer], Blennius [blenny], Gadus [whiting], Pleuronectes [plaice], Solea [sole], Lophius [angler-fish])
===== Class Crossopterygii =====
Class Crossopterygii [p. 268]
Order 1. Rhipidistia
Suborder 1. †Osteolepidoti (e.g., †Osteolepis, †Sauripterus, †Diplopterax, †Eusthenopteron)
Suborder 2. Coelacanthini (= Actinistia) (e.g., †Coelacanthus, †Undina, Latimeria)
Order 2. Dipnoi (e.g., †Dipterus, †Ceratodus, Neoceratodus, Protopterus, Lepidosiren)
===== Class Amphibia =====
Class Amphibia [p. 296]
Subclass 1. †Stegocephalia
Order 1. †Labyrinthodontia
Suborder 1. †Ichthyostegalia (e.g., †Ichthyostega, †Elpistostege)
Suborder 2. †Embolomeri (e.g., †Eogyrinus, †Loxomma)
Suborder 3. †Rhachitomi (e.g., †Eryops, †Cacops)
Suborder 4. †Stereospondyli (e.g., †Capitosaurus, †Buettneria)
Order 2. †Phyllospondyli (e.g., †Branchiosaurus)
Order 3. †Lepospondyli (e.g., †Dolichosoma, †Diplocaulus, †Microbrachis)
Order 4. †Adelospondyli (e.g., †Lysorophus)
Subclass 2. Urodela (= Caudata) (e.g., Molge, Salamandra, Ambystoma, Necturus)
Subclass 3. Anura (= Salientia) (e.g., †Miobatrachus, †Protobatrachiis, Rana, Bufo, Hyla, Pipa)
Subclass 4. Apoda (= Gymnophiona = Caecilia) (e.g., Ichthyophis, Typhlonectes)
===== Class Reptilia =====
Class Reptilia [p. 369]
Subclass 1. Anapsida
Order 1. †Cotylosauria (e.g., †Seymouria, †Captorhinus, †Diadectes)
Order 2. Chelonia (e.g., †Eunotosaurus, †Triassochelys, Chelys, Emys, Chelone, Testudo)
Subclass 2. †Synaptosauria
Order 1. †Protorosauria (e.g., †Araeoscelis, †Tanystropheus)
Order 2. †Sauropterygia (e.g., †Lariosaurus, †Pliosaurus, †Plesiosaurus, †Placodus)
Subclass 3. †Ichthyopterygia
Order 1. †Ichthyosauria (e.g., †Mixosaurus, †Ichthyosaurus)
Subclass 4. Lepidosauria
Order 1. †Eosuchia (e.g., †Youngina, †Prolacerta)
Order 2. Rhynchocephalia (e.g., †Homoesaurus, †Rhynchosaurus, Sphenodon [= Hatteria])
Order 3. Squamata
Suborder 1. Lacertilia (= Sauria)
Infraorder 1. Gekkota (e.g., Gecko)
Infraorder 2. Iguania (e.g., Iguana, Anolis, Phrynosoma, Draco, Lyriocephalus, Agama, Chamaeleo)
Infraorder 3. Scincomorpha (e.g., Lacerta, Scincus, Amphisbaena)
Infraorder 4. Anguimorpha (e.g., †Dolichosaurus, †Aigialosaurus, †Tylosaurus, Varanus, Lanthanotus, Anguis)
Suborder 2. Ophidia (= Serpentes) (e.g., †Palaeophis, Python, Natrix, Naja, Vipera)
Subclass 5. Archosauria
Order 1. †Pseudosuchia (= †Thecodontia) (e.g., †Euparkeria, †Saltoposuchus)
Order 2. †Phytosauria (e.g., †Phytosaurus, †Mystriosuchus)
Order 3. Crocodilia (e.g., †Protosuchus, Crocodilus, Alligator, Caiman, Gavialis)
Order 4. †Saurischia
Suborder 1. †Theropoda (e.g., †Compsognathus, †Ornitholestes, †Allosaurus, †Tyrannosaurus, †Struthiomimus)
Suborder 2. †Sauropoda (e.g., †Apatosaurus [= †Brontosaurus], †Diplodocus, †Yaleosaurus, †Plateosaurus, Brachiosaurus)
Order 5. †Ornithischia
Suborder 1. †Ornithopoda (e.g., †Camptosaurus, †Iguanodon, †Hadrosaurus)
Suborder 2. †Stegosauria (e.g., †Stegosaurus)
Suborder 3. †Ankylosauria (e.g., †Ankylosaurus, †Nodosaurus)
Suborder 4. †Ceratopsia (e.g., †Triceratops)
Order 6. †Pterosauria (e.g., †Rhamphorhynchus, †Pteranodon)
Subclass 6. †Synapsida [pp. 370, 533]
Order 1. †Pelycosauria (= †Theromorpha) (e.g., †Varanosaurus, †Edaphosaurus, †Dimetrodon)
Order 2. †Therapsida
Suborder 1. †Dicynodontia (e.g., †Galepus, †Moschops, †Dicynodon, †Kannemeyeria)
Suborder 2. †Theriodontia (e.g., †Cynognathus, †Scymnognathus, †Bauria, †Dromatherium, †Tritylodon, †Oligokyphus)
Order 3. †Mesosauria (= †Proganosauria) (e.g., †Mesosaurus)
===== Class Aves =====
Class Aves [p. 509]
Subclass 1. †Archaeornithes (e.g., †Archaeopteryx)
Subclass 2. Neornithes
Superorder 1. †Odontognathae (e.g., †Hesperornis, †Ichthyornis)
Superorder 2. Palaeognathae [ratites] (e.g., Struthio, Rhea, Dromiceius, Casuarius, †Dinornis, †Aepyornis, Apteryx, Tinamus)
Superorder 3. Impennae [penguins] (e.g., Spheniscus, Aptenodytes)
Superorder 4. Neognathae
Order 1. Gaviiformes [loons] (e.g., Gavia [loon])
Order 2. Colymbiformes [grebes] (e.g., Colymbus [= Podiceps] [grebe])
Order 3. Procellariiformes [petrels] (e.g., Fulmarus [petrel], Puffinus [shearwater], Diomedea [albatross])
Order 4. Pelecaniformes (e.g., Phalacrocorax [cormorant], Pelecanus [pelican], Sida [gannet])
Order 5. Ciconiiformes (e.g., Ciconia [stork], Ardea [heron], Phoenicopterus [flamingo])
Order 6. Anseriformes [ducks] (e.g., Anas [duck], Cygnus [swan])
Order 7. Falconiformes [hawks] (e.g., Falco [kestrel], Aquila [eagle], Buteo [buzzard], Neophron [vulture], Milvus [kite])
Order 8. Galliformes [game birds] (e.g., Gallus [fowl], Phasianus [pheasant], Perdix [partridge], Lagopus [grouse], Meleagris [turkey], Numida [guinea fowl], Pavo [peacock], Opisthocomus [hoatzin])
Order 9. Gruiformes [rails] (e.g., Fulica [coot], Gallinula [moorhen], Crex [corn-crake], Grus [crane], †Phororhacos, †Diatryma)
Order 10. Charadriiformes [waders and gulls] (e.g., Numenius [curlew], Capella [snipe], Calidris [sandpiper], Vanellus [lapwing], Scolopax [woodcock], Larus [gull], Uria [guillemot], Plautus [little auk])
Order 11. Columbiformes [pigeons] (e.g., Columba [pigeon], †Raphus [dodo])
Order 12. Cuculiformes [cuckoos] (e.g., Cuculus [cuckoo])
Order 13. Psittaciformes [parrots]
Order 14. Strigiformes [owls] (e.g., Athene [little owl], Tyto [farm owl], Strix [tawny owl])
Order 15. Caprimulgiformes [nightjars] (e.g., Caprimulgus [nightjar])
Order 16. Micropodiformes (e.g., Apus [swift], Trochilus [humming-bird])
Order 17. Coraciiformes (e.g., Merops [bee-eater], Alcedo [kingfisher])
Order 18. Piciformes [woodpeckers] (e.g., Picus [woodpecker])
Order 19. Passeriformes [perching birds] (e.g., Corvus [rook], Sturnus [starling], Fringilla [finch], Passer [house-sparrow], Alauda [lark], Anthus [pipit], Motacilla [wagtail], Certhia [tree-creeper], Parus [tit], Lanius [shrike], Sylvia [warbler], Turdus [thrush], Erithacus [British robin], Luscinia [nightingale], Prunella [hedge-sparrow], Troglodytes [wren], Hirundo [swallow])
===== Class Mammalia =====
Class Mammalia [p. 533]
Subclass 1. Eotheria
Order †Docodonta (e.g., †Morganucodon, †Docodon)
Order incertae sedis †Diarthrognathus
Subclass 2. Prototheria
Order Monotremata (e.g., Tachyglossus [= Echidna] [spiny anteater], Zaglossus [= Proechidna], Ornithorhynchus [platypus])
Subclass 3. †Allotheria
Order †Multituberculata (e.g., †Plagiaulax, †Ptilodus)
Subclass 4. Theria
Infraclass 1. †Pantotheria
Order 1. †Eupantotheria (e.g., †Amphitherium)
Order 2. †Symmetrodonta (e.g., †Spalacotherium)
Infraclass 2. Metatheria
Order Marsupialia
Infraclass 3. Eutheria (= Placentalia)
Order incertae sedis †Triconodonta (e.g., †Amphilestes, †Triconodon)
====== Infraclass Metatheria ======
Infraclass 2. Metatheria [p. 563]
Order Marsupialia
Superfamily 1. Didelphoidea (e.g., †Eodelphis, Didelphis [opossum], Chironectes [water opossum])
Superfamily 2. †Borhyaenoidea (e.g., †Thylacosmilus, †Borhyaena)
Superfamily 3. Dasyuroidea (e.g., Dasyurus [native cat], Sarcophilus [Tasmanian devil], Thylacinus [Tasmanian wolf], Myrmecobius [banded ant-eater], Notoryctes [marsupial mole], Sminthopsis [pouched mouse])
Superfamily 4. Perameloidea (e.g., Perameles [bandicoot])
Superfamily 5. Caenolestoidea (e.g., †Palaeothentes [= †Epanorthus], Caenolestes [opossum-rat])
Superfamily 6. Phalangeroidea (e.g., Trichosurus [Australian opossum], Petaurus [flying opossum], Phascolarctos [koala bear], Vombatus [wombat], Macropus [kangaroo], Bettongia [rat kangaroo], †Diprotodon, †Thylacoleo)
====== Infraclass Eutheria ======
Infraclass 3. Eutheria [p. 577]
Cohort 1. Unguiculata
Order 1. Insectivora [p. 581]
Order 2. Chiroptera [p. 585]
Order 3. Dermoptera
Order 4. †Taeniodonta
Order 5. †Tillodontia
Order 6. Edentata [p. 592]
Order 7. Pholidota
Order 8. Primates [p. 602]
Cohort 2. Glires [p. 653]
Order 1. Rodentia
Order 2. Lagomorpha
Cohort 3. Mutica [p. 666]
Order Cetacea
Cohort 4. Ferungulata
Superorder 1. Ferae [p. 679]
Order Carnivora
Superorder 2. Protungulata [p. 699]
Order 1. †Condylarthra
Order 2. †Notoungulata
Order 3. †Litopterna
Order 4. †Astrapotheria
Order 5. Tubulidentata
Superorder 3. Paenungulata [p. 706]
Order 1. Hyracoidea
Order 2. Proboscidea
Order 3. †Pantodonta
Order 4. †Dinocerata
Order 5. †Pyrotheria
Order 6. †Embrithopoda
Order 7. Sirenia
Superorder 4. Mesaxonia [p. 723]
Order Perissodactyla
Superorder 5. Paraxonia [p. 745]
Order Artiodactyla
====== Order Primates ======
Order 8. Primates [p. 602]
Suborder 1. Prosimii
Infraorder 1. Lemuriformes
Family 1. †Plesiadapidae (e.g., †Plesiadapis)
Family 2. †Adapidae (e.g., †Notharctus, †Adapis)
Family 3. Lemuridae (e.g., †Megaladapis, Lemur [common lemur])
Family 4. Indridae (e.g., Indri [indris])
Family 5. Daubentoniidae (e.g., Daubentonia [= Cheiromys] [aye-aye])
Infraorder 2. Lorisiformes
Family. Lorisidae (e.g., Loris [slender loris], Galago [bush baby], Perodicticus [potto])
Infraorder 3. Tarsiiformes
Family 1. †Anaptomorphidae (e.g., †Necrolemur, †Pseudoloris)
Family 2. Tarsiidae (e.g., Tarsius [tarsier])
Suborder 2. Anthropoidea
Superfamily 1. Ceboidea [New World monkeys]
Family 1. Callithricidae (e.g., Callithrix [= Hapale] [marmoset])
Family 2. Cebidae (e.g., †Homunculus, Cebus [capuchin], Ateles [spider monkey], Alouatta [howler monkey])
Superfamily 2. Cercopithecoidea
Family 1. †Parapithecidae (e.g., †Parapithecus)
Family 2. Cercopithecidae [Old World monkeys] (e.g., †Mesopithecus, Macaca [rhesus monkey, macaque], Papio [baboon], Mandrillus [mandrill], Cercopithecus [guenon], Presbytis [langur], Colobus, [guereza])
Superfamily 3. Hominoidea
Family 1. Pongidae apes (e.g., †Propliopithecus, †Pliopithecus, †Dryopithecus, †Oreopithecus †Australopithecus, †Proconsul, Hylobates gibbon, Pongo orangutan, Pan chimpanzee, Gorilla gorilla)
Family 2. Hominidae human (e.g., †Pithecanthropus [= †Sinanthropus] [Java and Pekin man], Homo [human ("all living races")])
== References == | Wikipedia/Taxonomy_of_the_vertebrates_(Young,_1962) |
Invertebrates are animals that neither develop nor retain a vertebral column (commonly known as a spine or backbone), which evolved from the notochord. It is a paraphyletic grouping including all animals excluding the chordate subphylum Vertebrata, i.e. vertebrates. Well-known phyla of invertebrates include arthropods, molluscs, annelids, echinoderms, flatworms, cnidarians, and sponges.
The majority of animal species are invertebrates; one estimate puts the figure at 97%. Many invertebrate taxa have a greater number and diversity of species than the entire subphylum of Vertebrata. Invertebrates vary widely in size, from 10 μm (0.0004 in) myxozoans to the 9–10 m (30–33 ft) colossal squid.
Some so-called invertebrates, such as the Tunicata and Cephalochordata, are actually sister chordate subphyla to Vertebrata, being more closely related to vertebrates than to other invertebrates. This makes the "invertebrates" paraphyletic, so the term has no significance in taxonomy.
== Etymology ==
The word "invertebrate" comes from the Latin word vertebra, which means a joint in general, and sometimes specifically a joint from the spinal column of a vertebrate. The jointed aspect of vertebra is derived from the concept of turning, expressed in the root verto or vorto, to turn. The prefix in- means "not" or "without".
== Taxonomic significance ==
The term invertebrates does not describe a taxon in the same way that Arthropoda, Vertebrata or Manidae do. Each of those terms describes a valid taxon, phylum, subphylum or family. "Invertebrata" is a term of convenience, not a taxon; it has very little circumscriptional significance except within the Chordata. The Vertebrata as a subphylum comprises such a small proportion of the Metazoa that to speak of the kingdom Animalia in terms of "Vertebrata" and "Invertebrata" has limited practicality. In the more formal taxonomy of Animalia other attributes that logically should precede the presence or absence of the vertebral column in constructing a cladogram, for example, the presence of a notochord. That would at least circumscribe the Chordata. However, even the notochord would be a less fundamental criterion than aspects of embryological development and symmetry or perhaps Bauplan.
Despite this, the concept of invertebrates as a taxon of animals has persisted for over a century among the laity, and within the zoological community and in its literature it remains in use as a term of convenience for animals that are not members of the Vertebrata. The following text reflects earlier scientific understanding of the term and of those animals which have constituted it. According to this understanding, invertebrates do not possess a skeleton of bone, either internal or external. They include hugely varied body plans. Many have fluid-filled, hydrostatic skeletons, like jellyfish or worms. Others have hard exoskeletons, outer shells like those of insects and crustaceans. The most familiar invertebrates include the Protozoa, Porifera, Coelenterata, Platyhelminthes, Nematoda, Annelida, Echinodermata, Mollusca and Arthropoda. Arthropoda include insects, crustaceans and arachnids.
== Number of extant species ==
By far the largest number of described invertebrate species are insects. The following table lists the number of described extant species for major invertebrate groups as estimated in the IUCN Red List of Threatened Species, 2014.3.
The IUCN estimates that 66,178 extant vertebrate species have been described, which means that over 95% of the described animal species in the world are invertebrates.
== Characteristics ==
The trait that is common to all invertebrates is the absence of a vertebral column (backbone): this creates a distinction between invertebrates and vertebrates. The distinction is one of convenience only; it is not based on any clear biologically homologous trait, any more than the common trait of having wings functionally unites insects, bats, and birds, or than not having wings unites tortoises, snails and sponges. Being animals, invertebrates are heterotrophs, and require sustenance in the form of the consumption of other organisms. With a few exceptions, such as the Porifera, invertebrates generally have bodies composed of differentiated tissues. There is also typically a digestive chamber with one or two openings to the exterior.
=== Morphology and symmetry ===
The body plans of most multicellular organisms exhibit some form of symmetry, whether radial, bilateral, or spherical. A minority, however, exhibit no symmetry. One example of asymmetric invertebrates includes all gastropod species. This is easily seen in snails and sea snails, which have helical shells. Slugs appear externally symmetrical, but their pneumostome (breathing hole) is located on the right side. Other gastropods develop external asymmetry, such as Glaucus atlanticus that develops asymmetrical cerata as they mature. The origin of gastropod asymmetry is a subject of scientific debate.
Other examples of asymmetry are found in fiddler crabs and hermit crabs. They often have one claw much larger than the other. If a male fiddler loses its large claw, it will grow another on the opposite side after moulting. Sessile animals such as sponges are asymmetrical alongside coral colonies (with the exception of the individual polyps that exhibit radial symmetry); Alpheidae claws that lack pincers; and some copepods, polyopisthocotyleans, and monogeneans which parasitize by attachment or residency within the gill chamber of their fish hosts).
==== Nervous system ====
Neurons differ in invertebrates from mammalian cells. Invertebrates cells fire in response to similar stimuli as mammals, such as tissue trauma, high temperature, or changes in pH. The first invertebrate in which a neuron cell was identified was the medicinal leech, Hirudo medicinalis.
Learning and memory using nociceptors have been described in the sea hare, Aplysia. Mollusk neurons are able to detect increasing pressures and tissue trauma.
Neurons have been identified in a wide range of invertebrate species, including annelids, molluscs, nematodes and arthropods.
==== Respiratory system ====
One type of invertebrate respiratory system is the open respiratory system composed of spiracles, tracheae, and tracheoles that terrestrial arthropods have to transport metabolic gases to and from tissues. The distribution of spiracles can vary greatly among the many orders of insects, but in general each segment of the body can have only one pair of spiracles, each of which connects to an atrium and has a relatively large tracheal tube behind it. The tracheae are invaginations of the cuticular exoskeleton that branch (anastomose) throughout the body with diameters from only a few micrometres up to 0.8 mm. The smallest tubes, tracheoles, penetrate cells and serve as sites of diffusion for water, oxygen, and carbon dioxide. Gas may be conducted through the respiratory system by means of active ventilation or passive diffusion. Unlike vertebrates, insects do not generally carry oxygen in their haemolymph.
A tracheal tube may contain ridge-like circumferential rings of taenidia in various geometries such as loops or helices. In the head, thorax, or abdomen, tracheae may also be connected to air sacs. Many insects, such as grasshoppers and bees, which actively pump the air sacs in their abdomen, are able to control the flow of air through their body. In some aquatic insects, the tracheae exchange gas through the body wall directly, in the form of a gill, or function essentially as normal, via a plastron. Despite being internal, the tracheae of arthropods are shed during moulting (ecdysis).
==== Hearing ====
=== Reproduction ===
Like vertebrates, most invertebrates reproduce at least partly through sexual reproduction. They produce specialized reproductive cells that undergo meiosis to produce smaller, motile spermatozoa or larger, non-motile ova. These fuse to form zygotes, which develop into new individuals. Others are capable of asexual reproduction, or sometimes, both methods of reproduction.
Extensive research with model invertebrate species such as Drosophila melanogaster and Caenorhabditis elegans has contributed much to our understanding of meiosis and reproduction. However, beyond the few model systems, the modes of reproduction found in invertebrates show incredible diversity. In one extreme example, it is estimated that 10% of orbatid mite species have persisted without sexual reproduction and have reproduced asexually for more than 400 million years.
==== Reproductive systems ====
=== Social interaction ===
Social behavior is widespread in invertebrates, including cockroaches, termites, aphids, thrips, ants, bees, Passalidae, Acari, spiders, and more. Social interaction is particularly salient in eusocial species but applies to other invertebrates as well.
Insects recognize information transmitted by other insects.
=== Phyla ===
The term invertebrates covers several phyla. One of these are the sponges (Porifera). They were long thought to have diverged from other animals early. They lack the complex organization found in most other phyla. Their cells are differentiated, but in most cases not organized into distinct tissues. Sponges typically feed by drawing in water through pores. Some speculate that sponges are not so primitive, but may instead be secondarily simplified. The Ctenophora and the Cnidaria, which includes sea anemones, corals, and jellyfish, are radially symmetric and have digestive chambers with a single opening, which serves as both the mouth and the anus. Both have distinct tissues, but they are not organized into organs. There are only two main germ layers, the ectoderm and endoderm, with only scattered cells between them. As such, they are sometimes called diploblastic.
The Echinodermata are radially symmetric and exclusively marine, including starfish (Asteroidea), sea urchins, (Echinoidea), brittle stars (Ophiuroidea), sea cucumbers (Holothuroidea) and feather stars (Crinoidea).
The largest animal phylum is also included within invertebrates: the Arthropoda, including insects, spiders, crabs, and their kin. All these organisms have a body divided into repeating segments, typically with paired appendages. In addition, they possess a hardened exoskeleton that is periodically shed during growth. Two smaller phyla, the Onychophora and Tardigrada, are close relatives of the arthropods and share some traits with them, excluding the hardened exoskeleton. The Nematoda, or roundworms, are perhaps the second largest animal phylum, and are also invertebrates. Roundworms are typically microscopic, and occur in nearly every environment where there is water. A number are important parasites. Smaller phyla related to them are the Kinorhyncha, Priapulida, and Loricifera. These groups have a reduced coelom, called a pseudocoelom. Other invertebrates include the Nemertea, or ribbon worms, and the Sipuncula.
Another phylum is Platyhelminthes, the flatworms. These were originally considered primitive, but it now appears they developed from more complex ancestors. Flatworms are acoelomates, lacking a body cavity, as are their closest relatives, the microscopic Gastrotricha. The Rotifera, or rotifers, are common in aqueous environments. Invertebrates also include the Acanthocephala, or spiny-headed worms, the Gnathostomulida, Micrognathozoa, and the Cycliophora.
Also included are two of the most successful animal phyla, the Mollusca and Annelida. The former, which is the second-largest animal phylum by number of described species, includes animals such as snails, clams, and squids, and the latter comprises the segmented worms, such as earthworms and leeches. These two groups have long been considered close relatives because of the common presence of trochophore larvae, but the annelids were considered closer to the arthropods because they are both segmented. Now, this is generally considered convergent evolution, owing to many morphological and genetic differences between the two phyla.
Among lesser phyla of invertebrates are the Hemichordata, or acorn worms, and the Chaetognatha, or arrow worms. Other phyla include Acoelomorpha, Brachiopoda, Bryozoa, Entoprocta, Phoronida, and Xenoturbellida.
== Classification ==
Invertebrates can be classified into several main categories, some of which are taxonomically obsolescent or debatable, but still used as terms of convenience. Each however appears in its own article at the following links.
Sponges (Porifera)
Comb jellies (Ctenophora)
Medusozoans and corals (Cnidaria)
Acoels (Xenacoelomorpha)
Flatworms (Platyhelminthes)
Bristleworms, earthworms and leeches (Annelida)
Insects, springtails, crustaceans, myriapods, chelicerates (Arthropoda)
Chitons, snails, slugs, bivalves, tusk shells, cephalopods (Mollusca)
Roundworms or threadworms (Nematoda)
Rotifers (Rotifera)
Tardigrades (Tardigrada)
Scalidophores (Scalidophora)
Lophophorates (Lophophorata)
Velvet worms (Onychophora)
Arrow worms (Chaetognatha)
Gordian worms or horsehair worms (Nematomorpha)
Ribbon worms (Nemertea)
Placozoa
Loricifera
Starfishes, sea urchins, sea cucumbers, sea lilies and brittle stars (Echinodermata)
Acorn worms, cephalodiscids and graptolites (Hemichordata)
Lancelets (Amphioxiformes)
Salps, pyrosomes, doliolids, larvaceans and sea squirts (Tunicata)
Cycliophora
== History ==
The earliest animal fossils are of invertebrates. 665-million-year-old fossils in the Trezona Formation at Trezona Bore, West Central Flinders, South Australia have been interpreted as being early sponges. Some paleontologists suggest that animals appeared much earlier, possibly as early as 1 billion years ago though they probably became multicellular in the Tonian. Trace fossils such as tracks and burrows found in the late Neoproterozoic Era indicate the presence of triploblastic worms, roughly as large (about 5 mm wide) and complex as earthworms.
Around 453 MYA, animals began diversifying, and many of the important groups of invertebrates diverged from one another. Fossils of invertebrates are found in various types of sediment from the Phanerozoic. Fossils of invertebrates are commonly used in stratigraphy.
=== Classification ===
Carl Linnaeus divided these animals into only two groups, the Insecta and the now-obsolete Vermes (worms). Jean-Baptiste Lamarck, who was appointed to the position of "Curator of Insecta and Vermes" at the Muséum National d'Histoire Naturelle in 1793, both coined the term "invertebrate" to describe such animals and divided the original two groups into ten, by splitting Arachnida and Crustacea from the Linnean Insecta, and Mollusca, Annelida, Cirripedia, Radiata, Coelenterata and Infusoria from the Linnean Vermes. They are now classified into over 30 phyla, from simple organisms such as sea sponges and flatworms to complex animals such as arthropods and molluscs.
==== Significance ====
Invertebrates are animals without a vertebral column. This has led to the conclusion that invertebrates are a group that deviates from the normal, vertebrates. This has been said to be because researchers in the past, such as Lamarck, viewed vertebrates as a "standard": in Lamarck's theory of evolution, he believed that characteristics acquired through the evolutionary process involved not only survival, but also progression toward a "higher form", to which humans and vertebrates were closer than invertebrates were. Although goal-directed evolution has been abandoned, the distinction of invertebrates and vertebrates persists to this day, even though the grouping has been noted to be "hardly natural or even very sharp." Another reason cited for this continued distinction is that Lamarck created a precedent through his classifications which is now difficult to escape from. It is also possible that some humans believe that, they themselves being vertebrates, the group deserves more attention than invertebrates. In any event, in the 1968 edition of Invertebrate Zoology, it is noted that "division of the Animal Kingdom into vertebrates and invertebrates is artificial and reflects human bias in favor of man's own relatives." The book also points out that the group lumps a vast number of species together, so that no one characteristic describes all invertebrates. In addition, some species included are only remotely related to one another, with some more related to vertebrates than other invertebrates (see Paraphyly).
== In research ==
For many centuries, invertebrates were neglected by biologists, in favor of big vertebrates and "useful" or charismatic species. Invertebrate biology was not a major field of study until the work of Linnaeus and Lamarck in the 18th century. During the 20th century, invertebrate zoology became one of the major fields of natural sciences, with prominent discoveries in the fields of medicine, genetics, palaeontology, and ecology. The study of invertebrates has also benefited law enforcement, as arthropods, and especially insects, were discovered to be a source of information for forensic investigators.
Two of the most commonly studied model organisms nowadays are invertebrates: the fruit fly Drosophila melanogaster and the nematode Caenorhabditis elegans. They have long been the most intensively studied model organisms, and were among the first life-forms to be genetically sequenced. This was facilitated by the severely reduced state of their genomes, but many genes, introns, and linkages have been lost. Analysis of the starlet sea anemone genome has emphasised the importance of sponges, placozoans, and choanoflagellates, also being sequenced, in explaining the arrival of 1,500 ancestral genes unique to animals. Invertebrates are also used by scientists in the field of aquatic biomonitoring to evaluate the effects of water pollution and climate change.
== See also ==
Invertebrate zoology
Invertebrate paleontology
Marine invertebrates
Pain in invertebrates
== References ==
== Further reading ==
== External links ==
A. R. Maggenti; S. Gardner (2005). Online Dictionary of Invertebrate Zoology. Archived from the original on 26 December 2018. Retrieved 7 September 2005.
Buglife (UK)
African Invertebrates | Wikipedia/Invertebrate |
Gnathostomata (; from Ancient Greek: γνάθος (gnathos) 'jaw' + στόμα (stoma) 'mouth') are jawed vertebrates. Gnathostome diversity comprises roughly 60,000 species, which accounts for 99% of all extant vertebrates, including all living bony fishes (both ray-finned and lobe-finned, including their terrestrial tetrapod relatives) and cartilaginous fishes, as well as extinct prehistoric fish such as placoderms and acanthodians. Most gnathostomes have retained ancestral traits like true teeth, a stomach, and paired appendages (pectoral and pelvic fins, limbs, wings, etc.). Other traits are elastin, horizontal semicircular canal of the inner ear, myelinated neurons, and an adaptive immune system which has discrete secondary lymphoid organs (spleen and thymus) and uses V(D)J recombination to create antigen recognition sites, rather than using genetic recombination in the variable lymphocyte receptor gene.
It is now assumed that Gnathostomata evolved from ancestors that already possessed two pairs of paired fins. Until recently these ancestors, known as antiarchs, were thought to have lacked pectoral or pelvic fins. In addition to this, some placoderms were shown to have a third pair of paired appendages, that had been modified to claspers in males and pelvic basal plates in females — a pattern not seen in any other vertebrate group. The jawless Osteostraci are generally considered the closest sister taxon of Gnathostomata.
Jaw development in vertebrates is likely a product of bending the first pair of gill arches. This development would help suck water into the mouth by the movement of the jaw, so that it would then pass over the gills via buccal pumping for gas exchange. The repetitive use of the newly formed jaw bones would eventually lead to the ability to bite in some gnathostomes.
Newer research suggests that a branch of placoderms was most likely the ancestor of present-day gnathostomes. A 419-million-year-old fossil of a placoderm named Entelognathus had a bony oral skeleton and anatomical details associated with cartilaginous and bony fish, demonstrating that the absence of a bony skeleton in cartilaginous fish is a derived trait. The fossil findings of primitive bony fishes such as Guiyu oneiros and Psarolepis, which lived contemporaneously with Entelognathus and had pelvic girdles more in common with placoderms than with other bony fish, show that it was a relative rather than a direct ancestor of the extant gnathostomes. It also indicates that spiny sharks and Chondrichthyes represent a single sister group to the bony fishes. Fossil findings of juvenile placoderms, which had true teeth that grew on the surface of the jawbone and had no roots, making them impossible to replace or regrow as they broke or wore down as they grew older, proves the common ancestor of all gnathostomes had teeth and place the origin of teeth along with, or soon after, the evolution of jaws.
Late Ordovician-aged microfossils of what have been identified as scales of either acanthodians or "spiny sharks", may mark Gnathostomata's first appearance in the fossil record. Undeniably unambiguous gnathostome fossils, mostly of primitive acanthodians, begin appearing by the early Silurian, and become abundant by the start of the Devonian.
== Classification ==
Gnathostomata is traditionally an infraphylum, broken into three top-level groupings: Chondrichthyes, or the cartilaginous fish; Placodermi, an extinct grade of armored fish; and Teleostomi, which includes the familiar classes of bony fish, birds, mammals, reptiles, and amphibians. Some classification systems have used the term Amphirhina. It is a sister group of the Agnatha (jawless fish).
== Evolution ==
The appearance of the early vertebrate jaw has been described as "a crucial innovation" and "perhaps the most profound and radical evolutionary step in the vertebrate history". Fish without jaws had more difficulty surviving than fish with jaws, and most jawless fish became extinct during the Triassic period. However studies of the cyclostomes, the jawless hagfishes and lampreys that did survive, have yielded little insight into the deep remodelling of the vertebrate skull that must have taken place as early jaws evolved.
The ancestor of all jawed vertebrates have gone through two rounds of whole genome duplication. The first happened before the gnathostome and cyclostome split, and appears to have been an autopolyploidy event (happened within the same species). The second occurred after the split, and was an allopolyploidy event (the result of hybridization between two lineages).
The customary view is that jaws are homologous to the gill arches. In jawless fishes a series of gills opened behind the mouth, and these gills became supported by cartilaginous elements. The first set of these elements surrounded the mouth to form the jaw. The upper portion of the second embryonic arch supporting the gill became the hyomandibular bone of jawed fish, which supports the skull and therefore links the jaw to the cranium. The hyomandibula is a set of bones found in the hyoid region in most fishes. It usually plays a role in suspending the jaws or the operculum in the case of teleosts.
While potentially older Ordovician records are known, the oldest unambigious evidence of jawed vertebrates are Qianodus and Fanjingshania from the early Silurian (Aeronian) of Guizhou, China around 439 million years ago, which are placed as acanthodian-grade stem-chondrichthyans.
== References ==
== External links ==
Tree of Life discussion of Gnathostomata
The Gill Arches: Meckel's Cartilage | Wikipedia/Jawed_vertebrates |
The Integrated Taxonomic Information System (ITIS) is an American partnership of federal agencies designed to provide consistent and reliable information on the taxonomy of biological species. ITIS was originally formed in 1996 as an interagency group within the US federal government, involving several US federal agencies, and has now become an international body, with Canadian and Mexican government agencies participating. The database draws from a large community of taxonomic experts. Primary content staff are housed at the Smithsonian National Museum of Natural History and IT services are provided by a US Geological Survey facility in Denver. The primary focus of ITIS is North American species, but many biological groups exist worldwide and ITIS collaborates with other agencies to increase its global coverage.
== Reference database ==
ITIS provides an automated reference database of scientific and common names for species. As of May 2016, it contains over 839,000 scientific names, synonyms, and common names for terrestrial, marine, and freshwater taxa from all biological kingdoms (animals, plants, fungi, and microbes). While the system does focus on North American species at minimum, it also includes many species not found in North America, especially among birds, fishes, amphibians, mammals, bacteria, many reptiles, several plant groups, and many invertebrate animal groups. Data presented in ITIS are considered public information, and may be freely distributed and copied, though appropriate citation is requested. ITIS is frequently used as the de facto source of taxonomic data in biodiversity informatics projects.
ITIS couples each scientific name with a stable and unique taxonomic serial number (TSN) as the "common denominator" for accessing information on such issues as invasive species, declining amphibians, migratory birds, fishery stocks, pollinators, agricultural pests, and emerging diseases. It presents the names in a standard classification that contains author, date, distributional, and bibliographic information related to the names. In addition, common names are available through ITIS in the major official languages of the Americas (English, French, Spanish, and Portuguese).
== Catalogue of Life ==
ITIS and its international partner, Species 2000, cooperate to annually produce the Catalogue of Life, a checklist and index of the world's species. The Catalogue of Life's goal was to complete the global checklist of 1.9 million species by 2011. As of May 2012, the Catalogue of Life has reached 1.4 million species—a major milestone in its quest to complete the first up-to-date comprehensive catalogue of all living organisms.
ITIS and the Catalogue of Life are core to the Encyclopedia of Life initiative announced May 2007. EOL will be built largely on various Creative Commons licenses.
== Legacy database ==
Of the ~714,000 (May 2016) scientific names in the current database, approximately 210,000 were inherited from the database formerly maintained by the National Oceanographic Data Center (NODC) of the US National Oceanic and Atmospheric Administration (NOAA). The newer material has been checked to higher standards of taxonomic credibility, and over half of the original material has been checked and improved to the same standard.
Building on efforts by Richard Swartz, Marvin Wass, and Donald Boesch in 1972 to establish an "intelligent" numeric coding system for taxonomy, the first edition of the NODC Taxonomic Code was published in 1977. Hard copy editions were published until 1984. Subsequent editions were published digitally until 1996. 1996 marked the release of NODC version 8, which served as a bridge to ITIS, which abandoned "intelligent" numeric codes in favor of more stable, but "un-intelligent" Taxonomic Serial Numbers.
== Standards ==
Biological taxonomy is not fixed, and opinions about the correct status of taxa at all levels, and their correct placement, are constantly revised as a result of new research. Many aspects of classification remain a matter of scientific judgment. The ITIS database is updated to take account of new research as it becomes available.
Records within ITIS include information about how far it has been possible to check and verify them. Its information should be checked against other sources where these are available, and against the primary research scientific literature where possible.
== Member agencies ==
Agriculture and Agri-Food Canada
Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO)
National Oceanic and Atmospheric Administration
National Park Service
NatureServe
Smithsonian Institution
United States Department of Agriculture
United States Environmental Protection Agency
United States Geological Survey
United States Fish and Wildlife Service
== See also ==
Encyclopedia of Life
PlantList
Wikispecies
World Register of Marine Species
== References ==
== External links ==
Official website – Integrated Taxonomic Information System (ITIS)
Canada Interface: Integrated Taxonomic Information System (ITIS*CA)
Mexico Interface: Sistema Integrado de Información Taxonómica (SIIT*MX) (archived link) | Wikipedia/Integrated_Taxonomic_Information_System |
In biochemistry, non-coded or non-proteinogenic amino acids are distinct from the 22 proteinogenic amino acids (21 in eukaryotes), which are naturally encoded in the genome of organisms for the assembly of proteins. However, over 140 non-proteinogenic amino acids occur naturally in proteins and thousands more may occur in nature or be synthesized in the laboratory. Chemically synthesized amino acids can be called unnatural amino acids. Unnatural amino acids can be synthetically prepared from their native analogs via modifications such as amine alkylation, side chain substitution, structural bond extension cyclization, and isosteric replacements within the amino acid backbone. Many non-proteinogenic amino acids are important:
intermediates in biosynthesis,
in post-translational formation of proteins,
in a physiological role (e.g. components of bacterial cell walls, neurotransmitters and toxins),
natural or man-made pharmacological compounds,
present in meteorites or used in prebiotic experiments (such as the Miller–Urey experiment),
might be important neurotransmitters, such as γ-aminobutyric acid, and
can play a crucial role in cellular bioenergetics, such as creatine.
== Definition by negation ==
Technically, any organic compound with an amine (–NH2) and a carboxylic acid (–COOH) functional group is an amino acid. The proteinogenic amino acids are a small subset of this group that possess a central carbon atom (α- or 2-) bearing an amino group, a carboxyl group, a side chain and an α-hydrogen levo conformation, with the exception of glycine, which is achiral, and proline, whose amine group is a secondary amine and is consequently frequently referred to as an imino acid for traditional reasons, albeit not an imino.
The genetic code encodes 20 standard amino acids for incorporation into proteins during translation. However, there are two extra proteinogenic amino acids: selenocysteine and pyrrolysine. These non-standard amino acids do not have a dedicated codon, but are added in place of a stop codon when a specific sequence is present, UGA codon and SECIS element for selenocysteine, UAG PYLIS downstream sequence for pyrrolysine. All other amino acids are termed "non-proteinogenic".
There are various groups of amino acids:
20 standard amino acids
22 proteinogenic amino acids
over 80 amino acids created abiotically in high concentrations
about 900 are produced by natural pathways
over 118 engineered amino acids have been placed into proteins
These groups overlap, but are not identical. All 22 proteinogenic amino acids are biosynthesised by organisms and some, but not all, of them also are abiotic (found in prebiotic experiments and meteorites). Some natural amino acids, such as norleucine, are misincorporated translationally into proteins due to infidelity of the protein-synthesis process. Many amino acids, such as ornithine, are metabolic intermediates produced biosynthetically, but not incorporated translationally into proteins. Post-translational modification of amino acid residues in proteins leads to the formation of many proteinaceous, but non-proteinogenic, amino acids. Other amino acids are solely found in abiotic mixes (e.g. α-methylnorvaline). Over 30 unnatural amino acids have been inserted translationally into proteins in engineered systems, yet are not biosynthetic.
== Nomenclature ==
In addition to the IUPAC numbering system to differentiate the various carbons in an organic molecule, by sequentially assigning a number to each carbon, including those forming a carboxylic group, the carbons along the side-chain of amino acids can also be labelled with Greek letters, where the α-carbon is the central chiral carbon possessing a carboxyl group, a side chain and, in α-amino acids, an amino group – the carbon in carboxylic groups is not counted. (Consequently, the IUPAC names of many non-proteinogenic α-amino acids start with 2-amino- and end in -ic acid.)
== Natural non-L-α-amino acids ==
Most natural amino acids are α-amino acids in the L configuration, but some exceptions exist.
=== Non-alpha ===
Some non-α-amino acids exist in organisms. In these structures, the amine group is displaced further from the carboxylic acid end of the amino acid molecule. Thus a β-amino acid has the amine group bonded to the second carbon away, and a γ-amino acid has it on the third. Examples include β-alanine, GABA, and δ-aminolevulinic acid.
The reason why α-amino acids are used in proteins has been linked to their frequency in meteorites and prebiotic experiments. An initial speculation on the deleterious properties of β-amino acids in terms of secondary structure turned out to be incorrect.
=== D-amino acids ===
Some amino acids contain the opposite absolute chirality, chemicals that are not available from normal ribosomal translation and transcription machinery. Most bacterial cells walls are formed by peptidoglycan, a polymer composed of amino sugars crosslinked with short oligopeptides bridged between each other. The oligopeptide is non-ribosomally synthesised and contains several peculiarities including D-amino acids, generally D-alanine and D-glutamate. A further peculiarity is that the former is racemised by a PLP-binding enzymes (encoded by alr or the homologue dadX), whereas the latter is racemised by a cofactor independent enzyme (murI). Some variants are present, in Thermotoga spp. D-Lysine is present and in certain vancomycin-resistant bacteria D-serine is present (vanT gene).
=== Without a hydrogen on the α-carbon ===
All proteinogenic amino acids have at least one hydrogen on the α-carbon. Glycine has two hydrogens, and all others have one hydrogen and one side-chain. Replacement of the remaining hydrogen with a larger substituent, such as a methyl group, distorts the protein backbone.
In some fungi α-aminoisobutyric acid is produced as a precursor to peptides, some of which exhibit antibiotic properties. This compound is similar to alanine, but possesses an additional methyl group on the α-carbon instead of a hydrogen. It is therefore achiral. Another compound similar to alanine without an α-hydrogen is dehydroalanine, which possesses a methylene sidechain. It is one of several naturally occurring dehydroamino acids.
=== Twin amino acid stereocentres ===
A subset of L-α-amino acids are ambiguous as to which of two ends is the α-carbon. In proteins a cysteine residue can form a disulfide bond with another cysteine residue, thus crosslinking the protein. Two crosslinked cysteines form a cystine molecule. Cysteine and methionine are generally produced by direct sulfurylation, but in some species they can be produced by transsulfuration, where the activated homoserine or serine is fused to a cysteine or homocysteine forming cystathionine. A similar compound is lanthionine, which can be seen as two alanine molecules joined via a thioether bond and is found in various organisms. Similarly, djenkolic acid, a plant toxin from jengkol beans, is composed of two cysteines connected by a methylene group. Diaminopimelic acid is both used as a bridge in peptidoglycan and is used a precursor to lysine (via its decarboxylation).
== Prebiotic amino acids and alternative biochemistries ==
In meteorites and in prebiotic experiments (e.g. Miller–Urey experiment) many more amino acids than the twenty standard amino acids are found, several of which are at higher concentrations than the standard ones. It has been conjectured that if amino acid based life were to arise elsewhere in the universe, no more than 75% of the amino acids would be in common. The most notable anomaly is the lack of aminobutyric acid.
=== Straight side chain ===
The genetic code has been described as a frozen accident and the reasons why there is only one standard amino acid with a straight chain, alanine, could simply be redundancy with valine, leucine and isoleucine. However, straight chained amino acids are reported to form much more stable alpha helices.
=== Chalcogen ===
Serine, homoserine, O-methylhomoserine and O-ethylhomoserine possess a hydroxymethyl, hydroxyethyl, O-methylhydroxymethyl and O-methylhydroxyethyl side chain; whereas cysteine, homocysteine, methionine and ethionine possess the thiol equivalents. The selenol equivalents are selenocysteine, selenohomocysteine, selenomethionine and selenoethionine. Amino acids with the next chalcogen down are also found in nature: several species such as Aspergillus fumigatus, Aspergillus terreus, and Penicillium chrysogenum in the absence of sulfur are able to produce and incorporate into protein tellurocysteine and telluromethionine.
== Expanded genetic code ==
== Roles ==
In cells, especially autotrophs, several non-proteinogenic amino acids are found as metabolic intermediates. However, despite the catalytic flexibility of PLP-binding enzymes, many amino acids are synthesised as keto acids (such as 4-methyl-2-oxopentanoate to leucine) and aminated in the last step, thus keeping the number of non-proteinogenic amino acid intermediates fairly low.
Ornithine and citrulline occur in the urea cycle, part of amino acid catabolism (see below).
In addition to primary metabolism, several non-proteinogenic amino acids are precursors or the final production in secondary metabolism to make small compounds or non-ribosomal peptides (such as some toxins).
=== Post-translationally incorporated into protein ===
Despite not being encoded by the genetic code as proteinogenic amino acids, some non-standard amino acids are nevertheless found in proteins. These are formed by post-translational modification of the side chains of standard amino acids present in the target protein. These modifications are often essential for the function or regulation of a protein; for example, in γ-carboxyglutamate the carboxylation of glutamate allows for better binding of calcium cations, and in hydroxyproline the hydroxylation of proline is critical for maintaining connective tissues. Another example is the formation of hypusine in the translation initiation factor EIF5A, through modification of a lysine residue. Such modifications can also determine the localization of the protein, for example, the addition of long hydrophobic groups can cause a protein to bind to a phospholipid membrane.
There is some preliminary evidence that aminomalonic acid may be present, possibly by misincorporation, in protein.
== Toxic analogues ==
Several non-proteinogenic amino acids are toxic due to their ability to mimic certain properties of proteinogenic amino acids, such as thialysine. Some non-proteinogenic amino acids are neurotoxic by mimicking amino acids used as neurotransmitters (that is, not for protein biosynthesis), including quisqualic acid, canavanine, caramboxin and azetidine-2-carboxylic acid.
Cephalosporin C has an α-aminoadipic acid (homoglutamate) backbone that is amidated with a cephalosporin moiety. Penicillamine is a therapeutic amino acid, whose mode of action is unknown.
Naturally-occurring cyanotoxins can also include non-proteinogenic amino acids. Microcystin and nodularin, for example, are both derived from ADDA, a β-amino acid.
== Taurine ==
Taurine is an amino sulfonic acid and not an amino carboxylic acid, however it is occasionally considered as such as the amounts required to suppress the auxotroph in certain organisms (such as cats) are closer to those of "essential amino acids" (amino acid auxotrophy) than of vitamins (cofactor auxotrophy).
The osmolytes, sarcosine and glycine betaine are derived from amino acids, but have a secondary and quaternary amine respectively.
== See also ==
Dicarboxylic acid
== Notes ==
== References == | Wikipedia/Non-proteinogenic_amino_acids |
In computational biology, protein pKa calculations are used to estimate the pKa values of amino acids as they exist within proteins. These calculations complement the pKa values reported for amino acids in their free state, and are used frequently within the fields of molecular modeling, structural bioinformatics, and computational biology.
== Amino acid pKa values ==
pKa values of amino acid side chains play an important role in defining the pH-dependent characteristics of a protein. The pH-dependence of the activity displayed by enzymes and the pH-dependence of protein stability, for example, are properties that are determined by the pKa values of amino acid side chains.
The pKa values of an amino acid side chain in solution is typically inferred from the pKa values of model compounds (compounds that are similar to the side chains of amino acids). See Amino acid for the pKa values of all amino acid side chains inferred in such a way. There are also numerous experimental studies that have yielded such values, for example by use of NMR spectroscopy.
The table below lists the model pKa values that are often used in a protein pKa calculation, and contains a third column based on protein studies.
== The effect of the protein environment ==
When a protein folds, the titratable amino acids in the protein are transferred from a solution-like environment to an environment determined by the 3-dimensional structure of the protein. For example, in an unfolded protein, an aspartic acid typically is in an environment which exposes the titratable side chain to water. When the protein folds, the aspartic acid could find itself buried deep in the protein interior with no exposure to solvent.
Furthermore, in the folded protein, the aspartic acid will be closer to other titratable groups in the protein and will also interact with permanent charges (e.g. ions) and dipoles in the protein. All of these effects alter the pKa value of the amino acid side chain, and pKa calculation methods generally calculate the effect of the protein environment on the model pKa value of an amino acid side chain.
Typically, the effects of the protein environment on the amino acid pKa value are divided into pH-independent effects and pH-dependent effects. The pH-independent effects (desolvation, interactions with permanent charges and dipoles) are added to the model pKa value to give the intrinsic pKa value. The pH-dependent effects cannot be added in the same straightforward way and have to be accounted for using Boltzmann summation, Tanford–Roxby iterations or other methods.
The interplay of the intrinsic pKa values of a system with the electrostatic interaction energies between titratable groups can produce quite spectacular effects such as non-Henderson–Hasselbalch titration curves and even back-titration effects.
The image on the right shows a theoretical system consisting of three acidic residues. One group is displaying a back-titration event (blue group).
== pKa calculation methods ==
Several software packages and webserver are available for the calculation of protein pKa values.
=== Using the Poisson–Boltzmann equation ===
Some methods are based on solutions to the Poisson–Boltzmann equation (PBE), often referred to as FDPB-based methods (FDPB stands for "finite difference Poisson–Boltzmann"). The PBE is a modification of Poisson's equation that incorporates a description of the effect of solvent ions on the electrostatic field around a molecule.
The H++ web server, the pKD webserver, MCCE2, Karlsberg+, PETIT and GMCT use the FDPB method to compute pKa values of amino acid side chains.
FDPB-based methods calculate the change in the pKa value of an amino acid side chain when that side chain is moved from a hypothetical fully solvated state to its position in the protein. To perform such a calculation, one needs theoretical methods that can calculate the effect of the protein interior on a pKa value, and knowledge of the pKa values of amino acid side chains in their fully solvated states.
=== Empirical methods ===
A set of empirical rules relating the protein structure to the pKa values of ionizable residues have been developed by Li, Robertson, and Jensen. These rules form the basis for the web-accessible program called PROPKA for rapid predictions of pKa values. A recent empirical pKa prediction program was released by Tan KP et.al. with the online server DEPTH web server.
=== Molecular dynamics (MD)-based methods ===
Molecular dynamics methods of calculating pKa values make it possible to include full flexibility of the titrated molecule.
Molecular dynamics based methods are typically much more computationally expensive, and not necessarily more accurate, ways to predict pKa values than approaches based on the Poisson–Boltzmann equation. Limited conformational flexibility can also be realized within a continuum electrostatics approach, e.g., for considering multiple amino acid sidechain rotamers. In addition, current commonly used molecular force fields do not take electronic polarizability into account, which could be an important property in determining protonation energies.
=== Determining pKa values from titration curves or free energy calculations ===
From the titration of protonatable group, one can read the so-called pKa1⁄2 which is equal to the pH value where the group is half-protonated (i.e. when 50% such groups would be protonated). The pKa1⁄2 is equal to the Henderson–Hasselbalch pKa (pKHHa) if the titration curve follows the Henderson–Hasselbalch equation. Most pKa calculation methods silently assume that all titration curves are Henderson–Hasselbalch shaped, and pKa values in pKa calculation programs are therefore often determined in this way. In the general case of multiple interacting protonatable sites, the pKa1⁄2 value is not thermodynamically meaningful. In contrast, the Henderson–Hasselbalch pKa value can be computed from the protonation free energy via
p
K
a
H
H
(
p
H
)
=
p
H
−
Δ
G
p
r
o
t
(
p
H
)
R
T
ln
10
{\displaystyle \mathrm {p} K_{\mathrm {a} }^{\mathrm {HH} }(\mathrm {pH} )=\mathrm {pH} -{\frac {\Delta G^{\mathrm {prot} }(\mathrm {pH} )}{\mathrm {RT} \ln 10}}}
and is thus in turn related to the protonation free energy of the site via
Δ
G
p
r
o
t
(
p
H
)
=
R
T
ln
10
(
p
H
−
p
K
a
H
H
)
{\displaystyle \Delta G^{\mathrm {prot} }(\mathrm {pH} )=\mathrm {RT} \ln 10\;(\mathrm {pH} -\mathrm {p} K_{\mathrm {a} }^{\mathrm {HH} })}
The protonation free energy can in principle be computed from the protonation probability of the group ⟨x⟩(pH) which can be read from its titration curve
Δ
G
p
r
o
t
(
p
H
)
=
−
R
T
ln
[
⟨
x
⟩
1
−
⟨
x
⟩
]
{\displaystyle \Delta G^{\mathrm {prot} }(\mathrm {pH} )=-\mathrm {RT} \ln \left[{\frac {\langle x\rangle }{1-\langle x\rangle }}\right]}
Titration curves can be computed within a continuum electrostatics approach with formally exact but more elaborate analytical or Monte Carlo (MC) methods, or inexact but fast approximate methods. MC methods that have been used to compute titration curves are Metropolis MC or Wang–Landau MC. Approximate methods that use a mean-field approach for computing titration curves are the Tanford–Roxby method and hybrids of this method that combine an exact statistical mechanics treatment within clusters of strongly interacting sites with a mean-field treatment of intercluster interactions.
In practice, it can be difficult to obtain statistically converged and accurate protonation free energies from titration curves if ⟨x⟩ is close to a value of 1 or 0. In this case, one can use various free energy calculation methods to obtain the protonation free energy such as biased Metropolis MC, free-energy perturbation, thermodynamic integration, the non-equilibrium work method or the Bennett acceptance ratio method.
Note that the pKHHa value does in general depend on the pH value.
This dependence is small for weakly interacting groups like well solvated amino acid side chains on the protein surface, but can be large for strongly interacting groups like those buried in enzyme active sites or integral membrane proteins.
While many protein pKa prediction methods are available, their accuracies often differ significantly due to subtle and often drastic differences in strategy.
== References ==
== External links ==
AccelrysPKA — Accelrys CHARMm based pKa calculation
H++ — Poisson–Boltzmann based pKa calculations
MCCE2 — Multi-Conformation Continuum Electrostatics (Version 2)
Karlsberg+ — pKa computation with multiple pH adapted conformations
PETIT — Proton and Electron TITration
GMCT — Generalized Monte Carlo Titration
DEPTH web server — Empirical calculation of pKa values using Residue Depth as a major feature | Wikipedia/Protein_pKa_calculations |
In chemistry, the rate equation (also known as the rate law or empirical differential rate equation) is an empirical differential mathematical expression for the reaction rate of a given reaction in terms of concentrations of chemical species and constant parameters (normally rate coefficients and partial orders of reaction) only. For many reactions, the initial rate is given by a power law such as
v
0
=
k
[
A
]
x
[
B
]
y
{\displaystyle v_{0}\;=\;k[\mathrm {A} ]^{x}[\mathrm {B} ]^{y}}
where
[
A
]
{\displaystyle [\mathrm {A} ]}
and
[
B
]
{\displaystyle [\mathrm {B} ]}
are the molar concentrations of the species
A
{\displaystyle \mathrm {A} }
and
B
,
{\displaystyle \mathrm {B} ,}
usually in moles per liter (molarity,
M
{\displaystyle M}
). The exponents
x
{\displaystyle x}
and
y
{\displaystyle y}
are the partial orders of reaction for
A
{\displaystyle \mathrm {A} }
and
B
{\displaystyle \mathrm {B} }
, respectively, and the overall reaction order is the sum of the exponents. These are often positive integers, but they may also be zero, fractional, or negative. The order of reaction is a number which quantifies the degree to which the rate of a chemical reaction depends on concentrations of the reactants. In other words, the order of reaction is the exponent to which the concentration of a particular reactant is raised. The constant
k
{\displaystyle k}
is the reaction rate constant or rate coefficient and at very few places velocity constant or specific rate of reaction. Its value may depend on conditions such as temperature, ionic strength, surface area of an adsorbent, or light irradiation. If the reaction goes to completion, the rate equation for the reaction rate
v
=
k
[
A
]
x
[
B
]
y
{\displaystyle v\;=\;k[{\ce {A}}]^{x}[{\ce {B}}]^{y}}
applies throughout the course of the reaction.
Elementary (single-step) reactions and reaction steps have reaction orders equal to the stoichiometric coefficients for each reactant. The overall reaction order, i.e. the sum of stoichiometric coefficients of reactants, is always equal to the molecularity of the elementary reaction. However, complex (multi-step) reactions may or may not have reaction orders equal to their stoichiometric coefficients. This implies that the order and the rate equation of a given reaction cannot be reliably deduced from the stoichiometry and must be determined experimentally, since an unknown reaction mechanism could be either elementary or complex. When the experimental rate equation has been determined, it is often of use for deduction of the reaction mechanism.
The rate equation of a reaction with an assumed multi-step mechanism can often be derived theoretically using quasi-steady state assumptions from the underlying elementary reactions, and compared with the experimental rate equation as a test of the assumed mechanism. The equation may involve a fractional order, and may depend on the concentration of an intermediate species.
A reaction can also have an undefined reaction order with respect to a reactant if the rate is not simply proportional to some power of the concentration of that reactant; for example, one cannot talk about reaction order in the rate equation for a bimolecular reaction between adsorbed molecules:
v
0
=
k
K
1
K
2
C
A
C
B
(
1
+
K
1
C
A
+
K
2
C
B
)
2
.
{\displaystyle v_{0}=k{\frac {K_{1}K_{2}C_{A}C_{B}}{(1+K_{1}C_{A}+K_{2}C_{B})^{2}}}.}
== Definition ==
Consider a typical chemical reaction in which two reactants A and B combine to form a product C:
A
+
2
B
⟶
3
C
.
{\displaystyle {\ce {{A}+ {2B}-> {3C}}}.}
This can also be written
−
A
−
2
B
+
3
C
=
0.
{\displaystyle -\mathrm {A} -2\mathrm {B} +3\mathrm {C} =0.}
The prefactors −1, −2 and 3 (with negative signs for reactants because they are consumed) are known as stoichiometric coefficients. One molecule of A combines with two of B to form 3 of C, so if we use the symbol [X] for the molar concentration of chemical X,
−
d
[
A
]
d
t
=
−
1
2
d
[
B
]
d
t
=
1
3
d
[
C
]
d
t
.
{\displaystyle -{\frac {d[\mathrm {A} ]}{dt}}=-{\frac {1}{2}}{\frac {d[\mathrm {B} ]}{dt}}={\frac {1}{3}}{\frac {d[\mathrm {C} ]}{dt}}.}
If the reaction takes place in a closed system at constant temperature and volume, without a build-up of reaction intermediates, the reaction rate
v
{\displaystyle v}
is defined as
v
=
1
ν
i
d
[
X
i
]
d
t
,
{\displaystyle v={\frac {1}{\nu _{i}}}{\frac {d[\mathrm {X} _{i}]}{dt}},}
where νi is the stoichiometric coefficient for chemical Xi, with a negative sign for a reactant.
The initial reaction rate
v
0
=
v
t
=
0
{\displaystyle v_{0}=v_{t=0}}
has some functional dependence on the concentrations of the reactants,
v
0
=
f
(
[
A
]
,
[
B
]
,
…
)
,
{\displaystyle v_{0}=f\left([\mathrm {A} ],[\mathrm {B} ],\ldots \right),}
and this dependence is known as the rate equation or rate law. This law generally cannot be deduced from the chemical equation and must be determined by experiment.
== Power laws ==
A common form for the rate equation is a power law:
v
0
=
k
[
A
]
x
[
B
]
y
⋯
{\displaystyle v_{0}=k[{\ce {A}}]^{x}[{\ce {B}}]^{y}\cdots }
The constant
k
{\displaystyle k}
is called the rate constant. The exponents, which can be fractional, are called partial orders of reaction and their sum is the overall order of reaction.
In a dilute solution, an elementary reaction (one having a single step with a single transition state) is empirically found to obey the law of mass action. This predicts that the rate depends only on the concentrations of the reactants, raised to the powers of their stoichiometric coefficients.
The differential rate equation for an elementary reaction using mathematical product notation is:
−
d
d
t
[
Reactants
]
=
k
∏
i
[
Reactants
i
]
{\displaystyle -{d \over dt}[{\text{Reactants}}]=k\prod _{i}[{\text{Reactants}}_{i}]}
Where:
−
d
d
t
[
Reactants
]
{\textstyle -{d \over dt}[{\text{Reactants}}]}
is the rate of change of reactant concentration with respect to time.
k is the rate constant of the reaction.
∏
i
[
Reactants
i
]
{\textstyle \prod _{i}[{\text{Reactants}}_{i}]}
represents the concentrations of the reactants, raised to the powers of their stoichiometric coefficients and multiplied together.
=== Determination of reaction order ===
==== Method of initial rates ====
The natural logarithm of the power-law rate equation is
ln
v
0
=
ln
k
+
x
ln
[
A
]
+
y
ln
[
B
]
+
⋯
{\displaystyle \ln v_{0}=\ln k+x\ln[{\ce {A}}]+y\ln[{\ce {B}}]+\cdots }
This can be used to estimate the order of reaction of each reactant. For example, the initial rate can be measured in a series of experiments at different initial concentrations of reactant
A
{\displaystyle {\rm {A}}}
with all other concentrations
[
B
]
,
[
C
]
,
…
{\displaystyle [{\rm {B],[{\rm {C],\dots }}}}}
kept constant, so that
ln
v
0
=
x
ln
[
A
]
+
constant
.
{\displaystyle \ln v_{0}=x\ln[{\ce {A}}]+{\textrm {constant}}.}
The slope of a graph of
ln
v
{\displaystyle \ln v}
as a function of
ln
[
A
]
{\displaystyle \ln[{\ce {A}}]}
then corresponds to the order
x
{\displaystyle x}
with respect to reactant
A
{\displaystyle {\rm {A}}}
.
However, this method is not always reliable because
measurement of the initial rate requires accurate determination of small changes in concentration in short times (compared to the reaction half-life) and is sensitive to errors, and
the rate equation will not be completely determined if the rate also depends on substances not present at the beginning of the reaction, such as intermediates or products.
==== Integral method ====
The tentative rate equation determined by the method of initial rates is therefore normally verified by comparing the concentrations measured over a longer time (several half-lives) with the integrated form of the rate equation; this assumes that the reaction goes to completion.
For example, the integrated rate law for a first-order reaction is
ln
[
A
]
=
−
k
t
+
ln
[
A
]
0
,
{\displaystyle \ln {[{\ce {A}}]}=-kt+\ln {[{\ce {A}}]_{0}},}
where
[
A
]
{\displaystyle [{\rm {A]}}}
is the concentration at time
t
{\displaystyle t}
and
[
A
]
0
{\displaystyle [{\rm {A]_{0}}}}
is the initial concentration at zero time. The first-order rate law is confirmed if
ln
[
A
]
{\displaystyle \ln {[{\ce {A}}]}}
is in fact a linear function of time. In this case the rate constant
k
{\displaystyle k}
is equal to the slope with sign reversed.
==== Method of flooding ====
The partial order with respect to a given reactant can be evaluated by the method of flooding (or of isolation) of Ostwald. In this method, the concentration of one reactant is measured with all other reactants in large excess so that their concentration remains essentially constant. For a reaction a·A + b·B → c·C with rate law
v
0
=
k
⋅
[
A
]
x
⋅
[
B
]
y
,
{\displaystyle v_{0}=k\cdot [{\rm {A}}]^{x}\cdot [{\rm {B}}]^{y},}
the partial order
x
{\displaystyle x}
with respect to
A
{\displaystyle {\rm {A}}}
is determined using a large excess of
B
{\displaystyle {\rm {B}}}
. In this case
v
0
=
k
′
⋅
[
A
]
x
{\displaystyle v_{0}=k'\cdot [{\rm {A}}]^{x}}
with
k
′
=
k
⋅
[
B
]
y
,
{\displaystyle k'=k\cdot [{\rm {B}}]^{y},}
and
x
{\displaystyle x}
may be determined by the integral method. The order
y
{\displaystyle y}
with respect to
B
{\displaystyle {\rm {B}}}
under the same conditions (with
B
{\displaystyle {\rm {B}}}
in excess) is determined by a series of similar experiments with a range of initial concentration
[
B
]
0
{\displaystyle [{\rm {B]_{0}}}}
so that the variation of
k
′
{\displaystyle k'}
can be measured.
=== Zero order ===
For zero-order reactions, the reaction rate is independent of the concentration of a reactant, so that changing its concentration has no effect on the rate of the reaction. Thus, the concentration changes linearly with time. The rate law for zero order reaction is
−
d
[
A
]
d
t
=
k
[
A
]
0
=
k
,
{\displaystyle -{d[A] \over dt}=k[A]^{0}=k,}
The unit of k is mol dm−3 s−1. This may occur when there is a bottleneck which limits the number of reactant molecules that can react at the same time, for example if the reaction requires contact with an enzyme or a catalytic surface.
Many enzyme-catalyzed reactions are zero order, provided that the reactant concentration is much greater than the enzyme concentration which controls the rate, so that the enzyme is saturated. For example, the biological oxidation of ethanol to acetaldehyde by the enzyme liver alcohol dehydrogenase (LADH) is zero order in ethanol.
Similarly, reactions with heterogeneous catalysis can be zero order if the catalytic surface is saturated. For example, the decomposition of phosphine (PH3) on a hot tungsten surface at high pressure is zero order in phosphine, which decomposes at a constant rate.
In homogeneous catalysis zero order behavior can come about from reversible inhibition. For example, ring-opening metathesis polymerization using third-generation Grubbs catalyst exhibits zero order behavior in catalyst due to the reversible inhibition that occurs between pyridine and the ruthenium center.
=== First order ===
A first order reaction depends on the concentration of only one reactant (a unimolecular reaction). Other reactants can be present, but their concentration has no effect on the rate. The rate law for a first order reaction is
−
d
[
A
]
d
t
=
k
[
A
]
,
{\displaystyle -{\frac {d[{\ce {A}}]}{dt}}=k[{\ce {A}}],}
The unit of k is s−1. Although not affecting the above math, the majority of first order reactions proceed via intermolecular collisions. Such collisions, which contribute the energy to the reactant, are necessarily second order. However according to the Lindemann mechanism the reaction consists of two steps: the bimolecular collision which is second order and the reaction of the energized molecule which is unimolecular and first order. The rate of the overall reaction depends on the slowest step, so the overall reaction will be first order when the reaction of the energized reactant is slower than the collision step.
The half-life is independent of the starting concentration and is given by
t
1
/
2
=
ln
(
2
)
k
{\textstyle t_{1/2}={\frac {\ln {(2)}}{k}}}
. The mean lifetime is τ = 1/k.
Examples of such reactions are:
2
N
2
O
5
⟶
4
NO
2
+
O
2
{\displaystyle {\ce {2N2O5 -> 4NO2 + O2}}}
[
CoCl
(
NH
3
)
5
]
2
+
+
H
2
O
⟶
[
Co
(
H
2
O
)
(
NH
3
)
5
]
3
+
+
Cl
−
{\displaystyle {\ce {[CoCl(NH3)5]^2+ + H2O -> [Co(H2O)(NH3)5]^3+ + Cl-}}}
H
2
O
2
⟶
H
2
O
+
1
2
O
2
{\displaystyle {\ce {H2O2 -> H2O + 1/2O2}}}
In organic chemistry, the class of SN1 (nucleophilic substitution unimolecular) reactions consists of first-order reactions. For example, in the reaction of aryldiazonium ions with nucleophiles in aqueous solution, ArN+2 + X− → ArX + N2, the rate equation is
v
0
=
k
[
ArN
2
+
]
,
{\displaystyle v_{0}=k[{\ce {ArN2+}}],}
where Ar indicates an aryl group.
=== Second order ===
A reaction is said to be second order when the overall order is two. The rate of a second-order reaction may be proportional to one concentration squared,
v
0
=
k
[
A
]
2
,
{\displaystyle v_{0}=k[{\ce {A}}]^{2},}
or (more commonly) to the product of two concentrations,
v
0
=
k
[
A
]
[
B
]
.
{\displaystyle v_{0}=k[{\ce {A}}][{\ce {B}}].}
As an example of the first type, the reaction NO2 + CO → NO + CO2 is second-order in the reactant NO2 and zero order in the reactant CO. The observed rate is given by
v
0
=
k
[
NO
2
]
2
,
{\displaystyle v_{0}=k[{\ce {NO2}}]^{2},}
and is independent of the concentration of CO.
For the rate proportional to a single concentration squared, the time dependence of the concentration is given by
1
[
A
]
=
1
[
A
]
0
+
k
t
.
{\displaystyle {\frac {1}{{\ce {[A]}}}}={\frac {1}{{\ce {[A]0}}}}+kt.}
The unit of k is mol−1 dm3 s−1.
The time dependence for a rate proportional to two unequal concentrations is
[
A
]
[
B
]
=
[
A
]
0
[
B
]
0
e
(
[
A
]
0
−
[
B
]
0
)
k
t
;
{\displaystyle {\frac {{\ce {[A]}}}{{\ce {[B]}}}}={\frac {{\ce {[A]0}}}{{\ce {[B]0}}}}e^{\left({\ce {[A]0}}-{\ce {[B]0}}\right)kt};}
if the concentrations are equal, they satisfy the previous equation.
The second type includes nucleophilic addition-elimination reactions, such as the alkaline hydrolysis of ethyl acetate:
CH
3
COOC
2
H
5
+
OH
−
⟶
CH
3
COO
−
+
C
2
H
5
OH
{\displaystyle {\ce {CH3COOC2H5 + OH- -> CH3COO- + C2H5OH}}}
This reaction is first-order in each reactant and second-order overall:
v
0
=
k
[
CH
3
COOC
2
H
5
]
[
OH
−
]
{\displaystyle v_{0}=k[{\ce {CH3COOC2H5}}][{\ce {OH-}}]}
If the same hydrolysis reaction is catalyzed by imidazole, the rate equation becomes
v
0
=
k
[
imidazole
]
[
CH
3
COOC
2
H
5
]
.
{\displaystyle v_{0}=k[{\text{imidazole}}][{\ce {CH3COOC2H5}}].}
The rate is first-order in one reactant (ethyl acetate), and also first-order in imidazole, which as a catalyst does not appear in the overall chemical equation.
Another well-known class of second-order reactions are the SN2 (bimolecular nucleophilic substitution) reactions, such as the reaction of n-butyl bromide with sodium iodide in acetone:
CH
3
CH
2
CH
2
CH
2
Br
+
NaI
⟶
CH
3
CH
2
CH
2
CH
2
I
+
NaBr
↓
{\displaystyle {\ce {CH3CH2CH2CH2Br + NaI -> CH3CH2CH2CH2I + NaBr(v)}}}
This same compound can be made to undergo a bimolecular (E2) elimination reaction, another common type of second-order reaction, if the sodium iodide and acetone are replaced with sodium tert-butoxide as the salt and tert-butanol as the solvent:
CH
3
CH
2
CH
2
CH
2
Br
+
NaO
t
−
Bu
⟶
CH
3
CH
2
CH
=
CH
2
+
NaBr
+
HO
t
−
Bu
{\displaystyle {\ce {{CH3CH2CH2CH2Br}+NaO{\mathit {t}}-Bu->{CH3CH2CH=CH2}+{NaBr}+HO{\mathit {t}}-Bu}}}
=== Pseudo-first order ===
If the concentration of a reactant remains constant (because it is a catalyst, or because it is in great excess with respect to the other reactants), its concentration can be included in the rate constant, leading to a pseudo–first-order (or occasionally pseudo–second-order) rate equation. For a typical second-order reaction with rate equation
v
0
=
k
[
A
]
[
B
]
,
{\displaystyle v_{0}=k[{\ce {A}}][{\ce {B}}],}
if the concentration of reactant B is constant then
v
0
=
k
[
A
]
[
B
]
=
k
′
[
A
]
,
{\displaystyle v_{0}=k[{\ce {A}}][{\ce {B}}]=k'[{\ce {A}}],}
where the pseudo–first-order rate constant
k
′
=
k
[
B
]
.
{\displaystyle k'=k[{\ce {B}}].}
The second-order rate equation has been reduced to a pseudo–first-order rate equation, which makes the treatment to obtain an integrated rate equation much easier.
One way to obtain a pseudo-first order reaction is to use a large excess of one reactant (say, [B]≫[A]) so that, as the reaction progresses, only a small fraction of the reactant in excess (B) is consumed, and its concentration can be considered to stay constant. For example, the hydrolysis of esters by dilute mineral acids follows pseudo-first order kinetics, where the concentration of water is constant because it is present in large excess:
CH
3
COOCH
3
+
H
2
O
⟶
CH
3
COOH
+
CH
3
OH
{\displaystyle {\ce {CH3COOCH3 + H2O -> CH3COOH + CH3OH}}}
The hydrolysis of sucrose (C12H22O11) in acid solution is often cited as a first-order reaction with rate
v
0
=
k
[
C
12
H
22
O
11
]
.
{\displaystyle v_{0}=k[{\ce {C12H22O11}}].}
The true rate equation is third-order,
v
0
=
k
[
C
12
H
22
O
11
]
[
H
+
]
[
H
2
O
]
;
{\displaystyle v_{0}=k[{\ce {C12H22O11}}][{\ce {H+}}][{\ce {H2O}}];}
however, the concentrations of both the catalyst H+ and the solvent H2O are normally constant, so that the reaction is pseudo–first-order.
=== Summary for reaction orders 0, 1, 2, and n ===
Elementary reaction steps with order 3 (called ternary reactions) are rare and unlikely to occur. However, overall reactions composed of several elementary steps can, of course, be of any (including non-integer) order.
Here
M
{\displaystyle {\rm {M}}}
stands for concentration in molarity (mol · L−1),
t
{\displaystyle t}
for time, and
k
{\displaystyle k}
for the reaction rate constant. The half-life of a first-order reaction is often expressed as t1/2 = 0.693/k (as ln(2)≈0.693).
=== Fractional order ===
In fractional order reactions, the order is a non-integer, which often indicates a chemical chain reaction or other complex reaction mechanism. For example, the pyrolysis of acetaldehyde (CH3CHO) into methane and carbon monoxide proceeds with an order of 1.5 with respect to acetaldehyde:
v
0
=
k
[
CH
3
CHO
]
3
/
2
.
{\displaystyle v_{0}=k[{\ce {CH3CHO}}]^{3/2}.}
The decomposition of phosgene (COCl2) to carbon monoxide and chlorine has order 1 with respect to phosgene itself and order 0.5 with respect to chlorine:
v
0
=
k
[
COCl
2
]
[
Cl
2
]
1
/
2
.
{\displaystyle v_{0}=k{\ce {[COCl2] [Cl2]}}^{1/2}.}
The order of a chain reaction can be rationalized using the steady state approximation for the concentration of reactive intermediates such as free radicals. For the pyrolysis of acetaldehyde, the Rice-Herzfeld mechanism is
Initiation
CH
3
CHO
⟶
⋅
CH
3
+
⋅
CHO
{\displaystyle {\ce {CH3CHO -> .CH3 + .CHO}}}
Propagation
⋅
CH
3
+
CH
3
CHO
⟶
CH
3
CO
⋅
+
CH
4
{\displaystyle {\ce {.CH3 + CH3CHO -> CH3CO. + CH4}}}
CH
3
CO
⋅
⟶
⋅
CH
3
+
CO
{\displaystyle {\ce {CH3CO. -> .CH3 + CO}}}
Termination
2
⋅
CH
3
⟶
C
2
H
6
{\displaystyle {\ce {2 .CH3 -> C2H6}}}
where • denotes a free radical. To simplify the theory, the reactions of the *CHO to form a second *CH3 are ignored.
In the steady state, the rates of formation and destruction of methyl radicals are equal, so that
d
[
⋅
CH
3
]
d
t
=
k
i
[
CH
3
CHO
]
−
k
t
[
⋅
CH
3
]
2
=
0
,
{\displaystyle {\frac {d[{\ce {.CH3}}]}{dt}}=k_{i}[{\ce {CH3CHO}}]-k_{t}[{\ce {.CH3}}]^{2}=0,}
so that the concentration of methyl radical satisfies
[
⋅
CH
3
]
∝
[
CH
3
CHO
]
1
2
⋅
{\displaystyle {\ce {[.CH3]\quad \propto \quad [CH3CHO]^{1/2}.}}}
The reaction rate equals the rate of the propagation steps which form the main reaction products CH4 and CO:
v
0
=
d
[
CH
4
]
d
t
|
0
=
k
p
[
⋅
CH
3
]
[
CH
3
CHO
]
∝
[
CH
3
CHO
]
3
2
{\displaystyle v_{0}={\frac {d[{\ce {CH4}}]}{dt}}|_{0}=k_{p}{\ce {[.CH3][CH3CHO]}}\quad \propto \quad {\ce {[CH3CHO]^{3/2}}}}
in agreement with the experimental order of 3/2.
== Complex laws ==
=== Mixed order ===
More complex rate laws have been described as being mixed order if they approximate to the laws for more than one order at different concentrations of the chemical species involved. For example, a rate law of the form
v
0
=
k
1
[
A
]
+
k
2
[
A
]
2
{\displaystyle v_{0}=k_{1}[A]+k_{2}[A]^{2}}
represents concurrent first order and second order reactions (or more often concurrent pseudo-first order and second order) reactions, and can be described as mixed first and second order. For sufficiently large values of [A] such a reaction will approximate second order kinetics, but for smaller [A] the kinetics will approximate first order (or pseudo-first order). As the reaction progresses, the reaction can change from second order to first order as reactant is consumed.
Another type of mixed-order rate law has a denominator of two or more terms, often because the identity of the rate-determining step depends on the values of the concentrations. An example is the oxidation of an alcohol to a ketone by hexacyanoferrate (III) ion [Fe(CN)63−] with ruthenate (VI) ion (RuO42−) as catalyst. For this reaction, the rate of disappearance of hexacyanoferrate (III) is
v
0
=
[
Fe
(
CN
)
6
]
2
−
k
α
+
k
β
[
Fe
(
CN
)
6
]
2
−
{\displaystyle v_{0}={\frac {{\ce {[Fe(CN)6]^2-}}}{k_{\alpha }+k_{\beta }{\ce {[Fe(CN)6]^2-}}}}}
This is zero-order with respect to hexacyanoferrate (III) at the onset of the reaction (when its concentration is high and the ruthenium catalyst is quickly regenerated), but changes to first-order when its concentration decreases and the regeneration of catalyst becomes rate-determining.
Notable mechanisms with mixed-order rate laws with two-term denominators include:
Michaelis–Menten kinetics for enzyme-catalysis: first-order in substrate (second-order overall) at low substrate concentrations, zero order in substrate (first-order overall) at higher substrate concentrations; and
the Lindemann mechanism for unimolecular reactions: second-order at low pressures, first-order at high pressures.
=== Negative order ===
A reaction rate can have a negative partial order with respect to a substance. For example, the conversion of ozone (O3) to oxygen follows the rate equation
v
0
=
k
[
O
3
]
2
[
O
2
]
−
1
{\displaystyle v_{0}=k{\ce {[O_3]^2}}{\ce {[O_2]^{-1}}}}
in an excess of oxygen. This corresponds to second order in ozone and order (−1) with respect to oxygen.
When a partial order is negative, the overall order is usually considered as undefined. In the above example, for instance, the reaction is not described as first order even though the sum of the partial orders is
2
+
(
−
1
)
=
1
{\displaystyle 2+(-1)=1}
, because the rate equation is more complex than that of a simple first-order reaction.
== Opposed reactions ==
A pair of forward and reverse reactions may occur simultaneously with comparable speeds. For example, A and B react into products P and Q and vice versa (a, b, p, and q are the stoichiometric coefficients):
a
A
+
b
B
↽
−
−
⇀
p
P
+
q
Q
{\displaystyle {\ce {{{\mathit {a}}A}+{{\mathit {b}}B}<=>{{\mathit {p}}P}+{{\mathit {q}}Q}}}}
The reaction rate expression for the above reactions (assuming each one is elementary) can be written as:
v
=
k
1
[
A
]
a
[
B
]
b
−
k
−
1
[
P
]
p
[
Q
]
q
{\displaystyle v=k_{1}[{\ce {A}}]^{a}[{\ce {B}}]^{b}-k_{-1}[{\ce {P}}]^{p}[{\ce {Q}}]^{q}}
where: k1 is the rate coefficient for the reaction that consumes A and B; k−1 is the rate coefficient for the backwards reaction, which consumes P and Q and produces A and B.
The constants k1 and k−1 are related to the equilibrium coefficient for the reaction (K) by the following relationship (set v=0 in balance):
k
1
[
A
]
a
[
B
]
b
=
k
−
1
[
P
]
p
[
Q
]
q
K
=
[
P
]
p
[
Q
]
q
[
A
]
a
[
B
]
b
=
k
1
k
−
1
{\displaystyle {\begin{aligned}&k_{1}[{\ce {A}}]^{a}[{\ce {B}}]^{b}=k_{-1}[{\ce {P}}]^{p}[{\ce {Q}}]^{q}\\[8pt]&K={\frac {[{\ce {P}}]^{p}[{\ce {Q}}]^{q}}{[{\ce {A}}]^{a}[{\ce {B}}]^{b}}}={\frac {k_{1}}{k_{-1}}}\end{aligned}}}
=== Simple example ===
In a simple equilibrium between two species:
A
↽
−
−
⇀
P
{\displaystyle {\ce {A <=> P}}}
where the reaction starts with an initial concentration of reactant A,
[
A
]
0
{\displaystyle {\ce {[A]0}}}
, and an initial concentration of 0 for product P at time t=0.
Then the equilibrium constant K is expressed as:
K
=
d
e
f
k
1
k
−
1
=
[
P
]
e
[
A
]
e
{\displaystyle K\ {\stackrel {\mathrm {def} }{=}}\ {\frac {k_{1}}{k_{-1}}}={\frac {\left[{\ce {P}}\right]_{e}}{\left[{\ce {A}}\right]_{e}}}}
where
[
A
]
e
{\displaystyle [{\ce {A}}]_{e}}
and
[
P
]
e
{\displaystyle [{\ce {P}}]_{e}}
are the concentrations of A and P at equilibrium, respectively.
The concentration of A at time t,
[
A
]
t
{\displaystyle [{\ce {A}}]_{t}}
, is related to the concentration of P at time t,
[
P
]
t
{\displaystyle [{\ce {P}}]_{t}}
, by the equilibrium reaction equation:
[
A
]
t
=
[
A
]
0
−
[
P
]
t
{\displaystyle {\ce {[A]_{\mathit {t}}=[A]0-[P]_{\mathit {t}}}}}
The term
[
P
]
0
{\displaystyle {\ce {[P]0}}}
is not present because, in this simple example, the initial concentration of P is 0.
This applies even when time t is at infinity; i.e., equilibrium has been reached:
[
A
]
e
=
[
A
]
0
−
[
P
]
e
{\displaystyle {\ce {[A]_{\mathit {e}}=[A]0-[P]_{\mathit {e}}}}}
then it follows, by the definition of K, that
[
P
]
e
=
k
1
k
1
+
k
−
1
[
A
]
0
{\displaystyle [{\ce {P}}]_{e}={\frac {k_{1}}{k_{1}+k_{-1}}}{\ce {[A]0}}}
and, therefore,
[
A
]
e
=
[
A
]
0
−
[
P
]
e
=
k
−
1
k
1
+
k
−
1
[
A
]
0
{\displaystyle \ [{\ce {A}}]_{e}={\ce {[A]0}}-[{\ce {P}}]_{e}={\frac {k_{-1}}{k_{1}+k_{-1}}}{\ce {[A]0}}}
These equations allow us to uncouple the system of differential equations, and allow us to solve for the concentration of A alone.
The reaction equation was given previously as:
v
=
k
1
[
A
]
a
[
B
]
b
−
k
−
1
[
P
]
p
[
Q
]
q
{\displaystyle v=k_{1}[{\ce {A}}]^{a}[{\ce {B}}]^{b}-k_{-1}[{\ce {P}}]^{p}[{\ce {Q}}]^{q}}
For
A
↽
−
−
⇀
P
{\displaystyle {\ce {A <=> P}}}
this is simply
−
d
[
A
]
d
t
=
k
1
[
A
]
t
−
k
−
1
[
P
]
t
{\displaystyle -{\frac {d[{\ce {A}}]}{dt}}=k_{1}[{\ce {A}}]_{t}-k_{-1}[{\ce {P}}]_{t}}
The derivative is negative because this is the rate of the reaction going from A to P, and therefore the concentration of A is decreasing. To simplify notation, let x be
[
A
]
t
{\displaystyle [{\ce {A}}]_{t}}
, the concentration of A at time t. Let
x
e
{\displaystyle x_{e}}
be the concentration of A at equilibrium. Then:
−
d
[
A
]
d
t
=
k
1
[
A
]
t
−
k
−
1
[
P
]
t
−
d
x
d
t
=
k
1
x
−
k
−
1
[
P
]
t
=
k
1
x
−
k
−
1
(
[
A
]
0
−
x
)
=
(
k
1
+
k
−
1
)
x
−
k
−
1
[
A
]
0
{\displaystyle {\begin{aligned}-{\frac {d[{\ce {A}}]}{dt}}&={k_{1}[{\ce {A}}]_{t}}-{k_{-1}[{\ce {P}}]_{t}}\\[8pt]-{\frac {dx}{dt}}&={k_{1}x}-{k_{-1}[{\ce {P}}]_{t}}\\[8pt]&={k_{1}x}-{k_{-1}({\ce {[A]0}}-x)}\\[8pt]&={(k_{1}+k_{-1})x}-{k_{-1}{\ce {[A]0}}}\end{aligned}}}
Since:
k
1
+
k
−
1
=
k
−
1
[
A
]
0
x
e
{\displaystyle k_{1}+k_{-1}=k_{-1}{\frac {{\ce {[A]0}}}{x_{e}}}}
the reaction rate becomes:
d
x
d
t
=
k
−
1
[
A
]
0
x
e
(
x
e
−
x
)
{\displaystyle {\frac {dx}{dt}}={\frac {k_{-1}{\ce {[A]0}}}{x_{e}}}(x_{e}-x)}
which results in:
ln
(
[
A
]
0
−
[
A
]
e
[
A
]
t
−
[
A
]
e
)
=
(
k
1
+
k
−
1
)
t
{\displaystyle \ln \left({\frac {{\ce {[A]0}}-[{\ce {A}}]_{e}}{[{\ce {A}}]_{t}-[{\ce {A}}]_{e}}}\right)=(k_{1}+k_{-1})t}
.
A plot of the negative natural logarithm of the concentration of A in time minus the concentration at equilibrium versus time t gives a straight line with slope k1 + k−1. By measurement of [A]e and [P]e the values of K and the two reaction rate constants will be known.
=== Generalization of simple example ===
If the concentration at the time t = 0 is different from above, the simplifications above are invalid, and a system of differential equations must be solved. However, this system can also be solved exactly to yield the following generalized expressions:
[
A
]
=
[
A
]
0
1
k
1
+
k
−
1
(
k
−
1
+
k
1
e
−
(
k
1
+
k
−
1
)
t
)
+
[
P
]
0
k
−
1
k
1
+
k
−
1
(
1
−
e
−
(
k
1
+
k
−
1
)
t
)
[
P
]
=
[
A
]
0
k
1
k
1
+
k
−
1
(
1
−
e
−
(
k
1
+
k
−
1
)
t
)
+
[
P
]
0
1
k
1
+
k
−
1
(
k
1
+
k
−
1
e
−
(
k
1
+
k
−
1
)
t
)
{\displaystyle {\begin{aligned}&\left[{\ce {A}}\right]={\ce {[A]0}}{\frac {1}{k_{1}+k_{-1}}}\left(k_{-1}+k_{1}e^{-\left(k_{1}+k_{-1}\right)t}\right)+{\ce {[P]0}}{\frac {k_{-1}}{k_{1}+k_{-1}}}\left(1-e^{-\left(k_{1}+k_{-1}\right)t}\right)\\[8pt]&\left[{\ce {P}}\right]={\ce {[A]0}}{\frac {k_{1}}{k_{1}+k_{-1}}}\left(1-e^{-\left(k_{1}+k_{-1}\right)t}\right)+{\ce {[P]0}}{\frac {1}{k_{1}+k_{-1}}}\left(k_{1}+k_{-1}e^{-\left(k_{1}+k_{-1}\right)t}\right)\end{aligned}}}
When the equilibrium constant is close to unity and the reaction rates very fast for instance in conformational analysis of molecules, other methods are required for the determination of rate constants for instance by complete lineshape analysis in NMR spectroscopy.
== Consecutive reactions ==
If the rate constants for the following reaction are
k
1
{\displaystyle k_{1}}
and
k
2
{\displaystyle k_{2}}
;
A
⟶
B
⟶
C
{\displaystyle {\ce {A -> B -> C}}}
, then the rate equation is:
For reactant A:
d
[
A
]
d
t
=
−
k
1
[
A
]
{\displaystyle {\frac {d[{\ce {A}}]}{dt}}=-k_{1}[{\ce {A}}]}
For reactant B:
d
[
B
]
d
t
=
k
1
[
A
]
−
k
2
[
B
]
{\displaystyle {\frac {d[{\ce {B}}]}{dt}}=k_{1}[{\ce {A}}]-k_{2}[{\ce {B}}]}
For product C:
d
[
C
]
d
t
=
k
2
[
B
]
{\displaystyle {\frac {d[{\ce {C}}]}{dt}}=k_{2}[{\ce {B}}]}
With the individual concentrations scaled by the total population of reactants to become probabilities, linear systems of differential equations such as these can be formulated as a master equation. The differential equations can be solved analytically and the integrated rate equations are
[
A
]
=
[
A
]
0
e
−
k
1
t
{\displaystyle [{\ce {A}}]={\ce {[A]0}}e^{-k_{1}t}}
[
B
]
=
{
[
A
]
0
k
1
k
2
−
k
1
(
e
−
k
1
t
−
e
−
k
2
t
)
+
[
B
]
0
e
−
k
2
t
k
1
≠
k
2
[
A
]
0
k
1
t
e
−
k
1
t
+
[
B
]
0
e
−
k
1
t
otherwise
{\displaystyle \left[{\ce {B}}\right]={\begin{cases}{\ce {[A]0}}{\frac {k_{1}}{k_{2}-k_{1}}}\left(e^{-k_{1}t}-e^{-k_{2}t}\right)+{\ce {[B]0}}e^{-k_{2}t}&k_{1}\neq k_{2}\\{\ce {[A]0}}k_{1}te^{-k_{1}t}+{\ce {[B]0}}e^{-k_{1}t}&{\text{otherwise}}\\\end{cases}}}
[
C
]
=
{
[
A
]
0
(
1
+
k
1
e
−
k
2
t
−
k
2
e
−
k
1
t
k
2
−
k
1
)
+
[
B
]
0
(
1
−
e
−
k
2
t
)
+
[
C
]
0
k
1
≠
k
2
[
A
]
0
(
1
−
e
−
k
1
t
−
k
1
t
e
−
k
1
t
)
+
[
B
]
0
(
1
−
e
−
k
1
t
)
+
[
C
]
0
otherwise
{\displaystyle \left[{\ce {C}}\right]={\begin{cases}{\ce {[A]0}}\left(1+{\frac {k_{1}e^{-k_{2}t}-k_{2}e^{-k_{1}t}}{k_{2}-k_{1}}}\right)+{\ce {[B]0}}\left(1-e^{-k_{2}t}\right)+{\ce {[C]0}}&k_{1}\neq k_{2}\\{\ce {[A]0}}\left(1-e^{-k_{1}t}-k_{1}te^{-k_{1}t}\right)+{\ce {[B]0}}\left(1-e^{-k_{1}t}\right)+{\ce {[C]0}}&{\text{otherwise}}\\\end{cases}}}
The steady state approximation leads to very similar results in an easier way.
== Parallel or competitive reactions ==
When a substance reacts simultaneously to give two different products, a parallel or competitive reaction is said to take place.
=== Two first order reactions ===
A
⟶
B
{\displaystyle {\ce {A -> B}}}
and
A
⟶
C
{\displaystyle {\ce {A -> C}}}
, with constants
k
1
{\displaystyle k_{1}}
and
k
2
{\displaystyle k_{2}}
and rate equations
−
d
[
A
]
d
t
=
(
k
1
+
k
2
)
[
A
]
{\displaystyle -{\frac {d[{\ce {A}}]}{dt}}=(k_{1}+k_{2})[{\ce {A}}]}
;
d
[
B
]
d
t
=
k
1
[
A
]
{\displaystyle {\frac {d[{\ce {B}}]}{dt}}=k_{1}[{\ce {A}}]}
and
d
[
C
]
d
t
=
k
2
[
A
]
{\displaystyle {\frac {d[{\ce {C}}]}{dt}}=k_{2}[{\ce {A}}]}
The integrated rate equations are then
[
A
]
=
[
A
]
0
e
−
(
k
1
+
k
2
)
t
{\displaystyle [{\ce {A}}]={\ce {[A]0}}e^{-(k_{1}+k_{2})t}}
;
[
B
]
=
k
1
k
1
+
k
2
[
A
]
0
(
1
−
e
−
(
k
1
+
k
2
)
t
)
{\displaystyle [{\ce {B}}]={\frac {k_{1}}{k_{1}+k_{2}}}{\ce {[A]0}}\left(1-e^{-(k_{1}+k_{2})t}\right)}
and
[
C
]
=
k
2
k
1
+
k
2
[
A
]
0
(
1
−
e
−
(
k
1
+
k
2
)
t
)
{\displaystyle [{\ce {C}}]={\frac {k_{2}}{k_{1}+k_{2}}}{\ce {[A]0}}\left(1-e^{-(k_{1}+k_{2})t}\right)}
.
One important relationship in this case is
[
B
]
[
C
]
=
k
1
k
2
{\displaystyle {\frac {{\ce {[B]}}}{{\ce {[C]}}}}={\frac {k_{1}}{k_{2}}}}
=== One first order and one second order reaction ===
This can be the case when studying a bimolecular reaction and a simultaneous hydrolysis (which can be treated as pseudo order one) takes place: the hydrolysis complicates the study of the reaction kinetics, because some reactant is being "spent" in a parallel reaction. For example, A reacts with R to give our product C, but meanwhile the hydrolysis reaction takes away an amount of A to give B, a byproduct:
A
+
H
2
O
⟶
B
{\displaystyle {\ce {A + H2O -> B}}}
and
A
+
R
⟶
C
{\displaystyle {\ce {A + R -> C}}}
. The rate equations are:
d
[
B
]
d
t
=
k
1
[
A
]
[
H
2
O
]
=
k
1
′
[
A
]
{\displaystyle {\frac {d[{\ce {B}}]}{dt}}=k_{1}{\ce {[A][H2O]}}=k_{1}'[{\ce {A}}]}
and
d
[
C
]
d
t
=
k
2
[
A
]
[
R
]
{\displaystyle {\frac {d[{\ce {C}}]}{dt}}=k_{2}{\ce {[A][R]}}}
, where
k
1
′
{\displaystyle k_{1}'}
is the pseudo first order constant.
The integrated rate equation for the main product [C] is
[
C
]
=
[
R
]
0
[
1
−
e
−
k
2
k
1
′
[
A
]
0
(
1
−
e
−
k
1
′
t
)
]
{\displaystyle {\ce {[C]=[R]0}}\left[1-e^{-{\frac {k_{2}}{k_{1}'}}{\ce {[A]0}}\left(1-e^{-k_{1}'t}\right)}\right]}
, which is equivalent to
ln
[
R
]
0
[
R
]
0
−
[
C
]
=
k
2
[
A
]
0
k
1
′
(
1
−
e
−
k
1
′
t
)
{\displaystyle \ln {\frac {{\ce {[R]0}}}{{\ce {[R]0-[C]}}}}={\frac {k_{2}{\ce {[A]0}}}{k_{1}'}}\left(1-e^{-k_{1}'t}\right)}
. Concentration of B is related to that of C through
[
B
]
=
−
k
1
′
k
2
ln
(
1
−
[
C
]
[
R
]
0
)
{\displaystyle [{\ce {B}}]=-{\frac {k_{1}'}{k_{2}}}\ln \left(1-{\frac {\ce {[C]}}{\ce {[R]0}}}\right)}
The integrated equations were analytically obtained but during the process it was assumed that
[
A
]
0
−
[
C
]
≈
[
A
]
0
{\displaystyle {\ce {[A]0}}-{\ce {[C]}}\approx {\ce {[A]0}}}
. Therefore, previous equation for [C] can only be used for low concentrations of [C] compared to [A]0
== Stoichiometric reaction networks ==
The most general description of a chemical reaction network considers a number
N
{\displaystyle N}
of distinct chemical species reacting via
R
{\displaystyle R}
reactions.
The chemical equation of the
j
{\displaystyle j}
-th reaction can then be written in the generic form
r
1
j
X
1
+
r
2
j
X
2
+
⋯
+
r
N
j
X
N
→
k
j
p
1
j
X
1
+
p
2
j
X
2
+
⋯
+
p
N
j
X
N
,
{\displaystyle r_{1j}{\ce {X}}_{1}+r_{2j}{\ce {X}}_{2}+\cdots +r_{Nj}{\ce {X}}_{N}{\ce {->[k_{j}]}}\ p_{1j}{\ce {X}}_{1}+\ p_{2j}{\ce {X}}_{2}+\cdots +p_{Nj}{\ce {X}}_{N},}
which is often written in the equivalent form
∑
i
=
1
N
r
i
j
X
i
→
k
j
∑
i
=
1
N
p
i
j
X
i
.
{\displaystyle \sum _{i=1}^{N}r_{ij}{\ce {X}}_{i}{\ce {->[k_{j}]}}\sum _{i=1}^{N}\ p_{ij}{\ce {X}}_{i}.}
Here
j
{\displaystyle j}
is the reaction index running from 1 to
R
{\displaystyle R}
,
X
i
{\displaystyle {\ce {X}}_{i}}
denotes the
i
{\displaystyle i}
-th chemical species,
k
j
{\displaystyle k_{j}}
is the rate constant of the
j
{\displaystyle j}
-th reaction and
r
i
j
{\displaystyle r_{ij}}
and
p
i
j
{\displaystyle p_{ij}}
are the stoichiometric coefficients of reactants and products, respectively.
The rate of such a reaction can be inferred by the law of mass action
f
j
(
[
X
]
)
=
k
j
∏
z
=
1
N
[
X
z
]
r
z
j
{\displaystyle f_{j}([\mathbf {X} ])=k_{j}\prod _{z=1}^{N}[{\ce {X}}_{z}]^{r_{zj}}}
which denotes the flux of molecules per unit time and unit volume. Here
(
[
X
]
)
=
(
[
X
1
]
,
[
X
2
]
,
…
,
[
X
N
]
)
{\displaystyle {\ce {([\mathbf {X} ])=([X1],[X2],\ldots ,[X_{\mathit {N}}])}}}
is the vector of concentrations. This definition includes the elementary reactions:
zero order reactions
for which
r
z
j
=
0
{\displaystyle r_{zj}=0}
for all
z
{\displaystyle z}
,
first order reactions
for which
r
z
j
=
1
{\displaystyle r_{zj}=1}
for a single
z
{\displaystyle z}
,
second order reactions
for which
r
z
j
=
1
{\displaystyle r_{zj}=1}
for exactly two
z
{\displaystyle z}
; that is, a bimolecular reaction, or
r
z
j
=
2
{\displaystyle r_{zj}=2}
for a single
z
{\displaystyle z}
; that is, a dimerization reaction.
Each of these is discussed in detail below. One can define the stoichiometric matrix
N
i
j
=
p
i
j
−
r
i
j
,
{\displaystyle N_{ij}=p_{ij}-r_{ij},}
denoting the net extent of molecules of
i
{\displaystyle i}
in reaction
j
{\displaystyle j}
. The reaction rate equations can then be written in the general form
d
[
X
i
]
d
t
=
∑
j
=
1
R
N
i
j
f
j
(
[
X
]
)
.
{\displaystyle {\frac {d[{\ce {X}}_{i}]}{dt}}=\sum _{j=1}^{R}N_{ij}f_{j}([\mathbf {X} ]).}
This is the product of the stoichiometric matrix and the vector of reaction rate functions.
Particular simple solutions exist in equilibrium,
d
[
X
i
]
d
t
=
0
{\displaystyle {\frac {d[{\ce {X}}_{i}]}{dt}}=0}
, for systems composed of merely reversible reactions. In this case, the rate of the forward and backward reactions are equal, a principle called detailed balance. Detailed balance is a property of the stoichiometric matrix
N
i
j
{\displaystyle N_{ij}}
alone and does not depend on the particular form of the rate functions
f
j
{\displaystyle f_{j}}
. All other cases where detailed balance is violated are commonly studied by flux balance analysis, which has been developed to understand metabolic pathways.
== General dynamics of unimolecular conversion ==
For a general unimolecular reaction involving interconversion of
N
{\displaystyle N}
different species, whose concentrations at time
t
{\displaystyle t}
are denoted by
X
1
(
t
)
{\displaystyle X_{1}(t)}
through
X
N
(
t
)
{\displaystyle X_{N}(t)}
, an analytic form for the time-evolution of the species can be found. Let the rate constant of conversion from species
X
i
{\displaystyle X_{i}}
to species
X
j
{\displaystyle X_{j}}
be denoted as
k
i
j
{\displaystyle k_{ij}}
, and construct a rate-constant matrix
K
{\displaystyle K}
whose entries are the
k
i
j
{\displaystyle k_{ij}}
.
Also, let
X
(
t
)
=
(
X
1
(
t
)
,
X
2
(
t
)
,
…
,
X
N
(
t
)
)
T
{\displaystyle X(t)=(X_{1}(t),X_{2}(t),\ldots ,X_{N}(t))^{T}}
be the vector of concentrations as a function of time.
Let
J
=
(
1
,
1
,
1
,
…
,
1
)
T
{\displaystyle J=(1,1,1,\ldots ,1)^{T}}
be the vector of ones.
Let
I
{\displaystyle I}
be the
N
×
N
{\displaystyle N\times N}
identity matrix.
Let
diag
{\displaystyle \operatorname {diag} }
be the function that takes a vector and constructs a diagonal matrix whose on-diagonal entries are those of the vector.
Let
L
−
1
{\displaystyle {\mathcal {L}}^{-1}}
be the inverse Laplace transform from
s
{\displaystyle s}
to
t
{\displaystyle t}
.
Then the time-evolved state
X
(
t
)
{\displaystyle X(t)}
is given by
X
(
t
)
=
L
−
1
[
(
s
I
+
diag
(
K
J
)
−
K
T
)
−
1
X
(
0
)
]
,
{\displaystyle X(t)={\mathcal {L}}^{-1}[(sI+\operatorname {diag} (KJ)-K^{T})^{-1}X(0)],}
thus providing the relation between the initial conditions of the system and its state at time
t
{\displaystyle t}
.
== See also ==
Michaelis–Menten kinetics
Molecularity
Petersen matrix
Reaction–diffusion system
Reactions on surfaces: rate equations for reactions where at least one of the reactants adsorbs onto a surface
Reaction progress kinetic analysis
Reaction rate
Reaction rate constant
Steady state approximation
Gillespie algorithm
Balance equation
Belousov–Zhabotinsky reaction
Lotka–Volterra equations
Chemical kinetics
== References ==
=== Books cited ===
== External links ==
Chemical kinetics, reaction rate, and order (needs flash player)
Reaction kinetics, examples of important rate laws (lecture with audio).
Rates of Reaction | Wikipedia/Rate_law |
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