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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2009.04971 | Shashanka Ubaru | Shashanka Ubaru, Lior Horesh, Guy Cohen | Dynamic graph and polynomial chaos based models for contact tracing data
analysis and optimal testing prescription | 4 figures | Journal of Biomedical Informatics, Volume 122, October 2021,
103901 | 10.1016/j.jbi.2021.103901 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this study, we address three important challenges related to disease
transmissions such as the COVID-19 pandemic, namely, (a) providing an early
warning to likely exposed individuals, (b) identifying individuals who are
asymptomatic, and (c) prescription of optimal testing when testing capacity is
limited. First, we present a dynamic-graph based SEIR epidemiological model in
order to describe the dynamics of the disease propagation. Our model considers
a dynamic network that accounts for the interactions between individuals over
time, such as the ones obtained by manual or automated contact tracing, and
uses a diffusion-reaction mechanism to describe the state dynamics. This
dynamic graph model helps identify likely exposed/infected individuals to whom
we can provide early warnings, even before they display any symptoms and/or are
asymptomatic. Moreover, when the testing capacity is limited compared to the
population size, reliable estimation of individual's health state and disease
transmissibility using epidemiological models is extremely challenging. Thus,
estimation of state uncertainty is paramount for both eminent risk assessment,
as well as for closing the tracing-testing loop by optimal testing
prescription. Therefore, we propose the use of arbitrary Polynomial Chaos
Expansion, a popular technique used for uncertainty quantification, to
represent the states, and quantify the uncertainties in the dynamic model. This
design enables us to assign uncertainty of the state of each individual, and
consequently optimize the testing as to reduce the overall uncertainty given a
constrained testing budget. These tools can also be used to optimize vaccine
distribution to curb the disease spread when limited vaccines are available. We
present a few simulation results that illustrate the performance of the
proposed framework, and estimate the impact of incomplete contact tracing data.
| [
{
"created": "Thu, 10 Sep 2020 16:24:35 GMT",
"version": "v1"
},
{
"created": "Thu, 17 Sep 2020 14:22:16 GMT",
"version": "v2"
},
{
"created": "Fri, 16 Oct 2020 21:18:48 GMT",
"version": "v3"
},
{
"created": "Fri, 10 Sep 2021 16:37:53 GMT",
"version": "v4"
}
] | 2021-09-13 | [
[
"Ubaru",
"Shashanka",
""
],
[
"Horesh",
"Lior",
""
],
[
"Cohen",
"Guy",
""
]
] | In this study, we address three important challenges related to disease transmissions such as the COVID-19 pandemic, namely, (a) providing an early warning to likely exposed individuals, (b) identifying individuals who are asymptomatic, and (c) prescription of optimal testing when testing capacity is limited. First, we present a dynamic-graph based SEIR epidemiological model in order to describe the dynamics of the disease propagation. Our model considers a dynamic network that accounts for the interactions between individuals over time, such as the ones obtained by manual or automated contact tracing, and uses a diffusion-reaction mechanism to describe the state dynamics. This dynamic graph model helps identify likely exposed/infected individuals to whom we can provide early warnings, even before they display any symptoms and/or are asymptomatic. Moreover, when the testing capacity is limited compared to the population size, reliable estimation of individual's health state and disease transmissibility using epidemiological models is extremely challenging. Thus, estimation of state uncertainty is paramount for both eminent risk assessment, as well as for closing the tracing-testing loop by optimal testing prescription. Therefore, we propose the use of arbitrary Polynomial Chaos Expansion, a popular technique used for uncertainty quantification, to represent the states, and quantify the uncertainties in the dynamic model. This design enables us to assign uncertainty of the state of each individual, and consequently optimize the testing as to reduce the overall uncertainty given a constrained testing budget. These tools can also be used to optimize vaccine distribution to curb the disease spread when limited vaccines are available. We present a few simulation results that illustrate the performance of the proposed framework, and estimate the impact of incomplete contact tracing data. |
1502.07555 | Daniel Merkle | Jakob L. Andersen, Christoph Flamm, Daniel Merkle, Peter F. Stadler | Support for Eschenmoser's Glyoxylate Scenario | null | null | null | null | q-bio.MN cs.FL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A core topic of research in prebiotic chemistry is the search for plausible
synthetic routes that connect the building blocks of modern life such as
sugars, nucleotides, amino acids, and lipids to "molecular food sources" that
have likely been abundant on Early Earth. In a recent contribution, Albert
Eschenmoser emphasised the importance of catalytic and autocatalytic cycles in
establishing such abiotic synthesis pathways. The accumulation of intermediate
products furthermore provides additional catalysts that allow pathways to
change over time. We show here that generative models of chemical spaces based
on graph grammars make it possible to study such phenomena is a systematic
manner. In addition to repro- ducing the key steps of Eschenmoser's hypothesis
paper, we discovered previously unexplored potentially autocatalytic pathways
from HCN to glyoxylate. A cascading of autocatalytic cycles could efficiently
re-route matter, distributed over the combinatorial complex network of HCN
hydrolysation chemistry, towards a potential primordial metabolism. The
generative approach also has it intrinsic limitations: the unsupervised
expansion of the chemical space remains infeasible due to the exponential
growth of possible molecules and reactions between them. Here in particular the
combinatorial complexity of the HCN polymerisation and hydrolysation networks
forms the computational bottleneck. As a consequence, guidance of the
computational exploration by chemical experience is indispensable.
| [
{
"created": "Thu, 26 Feb 2015 14:02:25 GMT",
"version": "v1"
}
] | 2015-02-27 | [
[
"Andersen",
"Jakob L.",
""
],
[
"Flamm",
"Christoph",
""
],
[
"Merkle",
"Daniel",
""
],
[
"Stadler",
"Peter F.",
""
]
] | A core topic of research in prebiotic chemistry is the search for plausible synthetic routes that connect the building blocks of modern life such as sugars, nucleotides, amino acids, and lipids to "molecular food sources" that have likely been abundant on Early Earth. In a recent contribution, Albert Eschenmoser emphasised the importance of catalytic and autocatalytic cycles in establishing such abiotic synthesis pathways. The accumulation of intermediate products furthermore provides additional catalysts that allow pathways to change over time. We show here that generative models of chemical spaces based on graph grammars make it possible to study such phenomena is a systematic manner. In addition to repro- ducing the key steps of Eschenmoser's hypothesis paper, we discovered previously unexplored potentially autocatalytic pathways from HCN to glyoxylate. A cascading of autocatalytic cycles could efficiently re-route matter, distributed over the combinatorial complex network of HCN hydrolysation chemistry, towards a potential primordial metabolism. The generative approach also has it intrinsic limitations: the unsupervised expansion of the chemical space remains infeasible due to the exponential growth of possible molecules and reactions between them. Here in particular the combinatorial complexity of the HCN polymerisation and hydrolysation networks forms the computational bottleneck. As a consequence, guidance of the computational exploration by chemical experience is indispensable. |
2102.13469 | Abhishek Singh | Abhishek Narain Singh | The unmasking of Mitochondrial Adam and Structural Variants larger than
point mutations as stronger candidates for traits, disease phenotype and sex
determination | null | null | null | null | q-bio.GN cs.DC q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Background: Structural Variations, SVs, in a genome can be linked to a
disease or characteristic phenotype. The variations come in many types and it
is a challenge, not only determining the variations accurately, but also
conducting the downstream statistical and analytical procedure. Method:
Structural variations, SVs, with size 1 base-pair to 1000s of base-pairs with
their precise breakpoints and single-nucleotide polymorphisms, SNPs, were
determined for members of a family. The genome was assembled using optimal
metrics of ABySS and SOAPdenovo assembly tools using paired-end DNA sequence.
Results: An interesting discovery was the mitochondrial DNA could have paternal
leakage of inheritance or that the mutations could be high from maternal
inheritance. It is also discovered that the mitochondrial DNA is less prone to
SVs re-arrangements than SNPs, which propose better standards for determining
ancestry and divergence between races and species over a long-time frame. Sex
determination of an individual is found to be strongly confirmed using calls of
nucleotide bases of SVs to the Y chromosome, more strongly determined than
SNPs. We note that in general there is a larger variance -and thus the standard
deviation, in the sum of SVs nucleotide compared to sum of SNPs of an
individual when compared to reference sequence, and thus SVs serve as a
stronger means to characterize an individual for a given trait or phenotype or
to determine sex. The SVs and SNPs in HLA loci would also serve as a medical
transformation method for determining the success of an organ transplant for a
patient, and predisposition to diseases apriori.
| [
{
"created": "Wed, 24 Feb 2021 15:23:43 GMT",
"version": "v1"
}
] | 2021-03-01 | [
[
"Singh",
"Abhishek Narain",
""
]
] | Background: Structural Variations, SVs, in a genome can be linked to a disease or characteristic phenotype. The variations come in many types and it is a challenge, not only determining the variations accurately, but also conducting the downstream statistical and analytical procedure. Method: Structural variations, SVs, with size 1 base-pair to 1000s of base-pairs with their precise breakpoints and single-nucleotide polymorphisms, SNPs, were determined for members of a family. The genome was assembled using optimal metrics of ABySS and SOAPdenovo assembly tools using paired-end DNA sequence. Results: An interesting discovery was the mitochondrial DNA could have paternal leakage of inheritance or that the mutations could be high from maternal inheritance. It is also discovered that the mitochondrial DNA is less prone to SVs re-arrangements than SNPs, which propose better standards for determining ancestry and divergence between races and species over a long-time frame. Sex determination of an individual is found to be strongly confirmed using calls of nucleotide bases of SVs to the Y chromosome, more strongly determined than SNPs. We note that in general there is a larger variance -and thus the standard deviation, in the sum of SVs nucleotide compared to sum of SNPs of an individual when compared to reference sequence, and thus SVs serve as a stronger means to characterize an individual for a given trait or phenotype or to determine sex. The SVs and SNPs in HLA loci would also serve as a medical transformation method for determining the success of an organ transplant for a patient, and predisposition to diseases apriori. |
1404.2886 | Christian Hilbe | Christian Hilbe, Arne Traulsen, Bin Wu, Martin A. Nowak | Zero-determinant alliances in multiplayer social dilemmas | null | null | 10.1073/pnas.1407887111 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Direct reciprocity and conditional cooperation are important mechanisms to
prevent free riding in social dilemmas. But in large groups these mechanisms
may become ineffective, because they require single individuals to have a
substantial influence on their peers. However, the recent discovery of the
powerful class of zero-determinant strategies in the iterated prisoner's
dilemma suggests that we may have underestimated the degree of control that a
single player can exert. Here, we develop a theory for zero-determinant
strategies for multiplayer social dilemmas, with any number of involved
players. We distinguish several particularly interesting subclasses of
strategies: fair strategies ensure that the own payoff matches the average
payoff of the group; extortionate strategies allow a player to perform above
average; and generous strategies let a player perform below average. We use
this theory to explore how individuals can enhance their strategic options by
forming alliances. The effects of an alliance depend on the size of the
alliance, the type of the social dilemma, and on the strategy of the allies:
fair alliances reduce the inequality within their group; extortionate alliances
outperform the remaining group members; but generous alliances increase
welfare. Our results highlight the critical interplay of individual control and
alliance formation to succeed in large groups.
| [
{
"created": "Thu, 10 Apr 2014 17:44:40 GMT",
"version": "v1"
}
] | 2014-11-05 | [
[
"Hilbe",
"Christian",
""
],
[
"Traulsen",
"Arne",
""
],
[
"Wu",
"Bin",
""
],
[
"Nowak",
"Martin A.",
""
]
] | Direct reciprocity and conditional cooperation are important mechanisms to prevent free riding in social dilemmas. But in large groups these mechanisms may become ineffective, because they require single individuals to have a substantial influence on their peers. However, the recent discovery of the powerful class of zero-determinant strategies in the iterated prisoner's dilemma suggests that we may have underestimated the degree of control that a single player can exert. Here, we develop a theory for zero-determinant strategies for multiplayer social dilemmas, with any number of involved players. We distinguish several particularly interesting subclasses of strategies: fair strategies ensure that the own payoff matches the average payoff of the group; extortionate strategies allow a player to perform above average; and generous strategies let a player perform below average. We use this theory to explore how individuals can enhance their strategic options by forming alliances. The effects of an alliance depend on the size of the alliance, the type of the social dilemma, and on the strategy of the allies: fair alliances reduce the inequality within their group; extortionate alliances outperform the remaining group members; but generous alliances increase welfare. Our results highlight the critical interplay of individual control and alliance formation to succeed in large groups. |
2403.14181 | Djshwar Lateef Dr. | Djshwar Dhahir Lateef and Nawroz Abdul-razzak Tahir | Genetic diversity of barley accessions and their response under abiotic
stresses using different approaches | null | null | 10.13140/RG.2.2.30027.81447 | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | In this investigation, five separate experiments were carried out. The first
experiments were examined the molecular characteristics of 59 barley accessions
collected from different regions in Iraq using three different molecular
markers (ISSR, CDDP, and Scot). A total of 391 amplified polymorphic bands were
generated using forty-four ISSR, nine CDDP, and twelve Scot primers, which they
totally observed 255, 35, and 101 polymorphic bands respectively. The mean
values of PIC for ISSR, CDDP, and Scot markers were 0.74, 0.63, and 0.80,
respectively, indicating the efficiency of the underlying markers in detecting
polymorphic status among the studied barley accessions. Based on the respective
markers, the barley accessions were classified and clustered into two main
groups using the UPGMA and population structure analysis. Results of claustral
analyses showed that the variation patterns corresponded with the geographical
distribution of barley accessions.
| [
{
"created": "Thu, 21 Mar 2024 07:11:13 GMT",
"version": "v1"
}
] | 2024-03-22 | [
[
"Lateef",
"Djshwar Dhahir",
""
],
[
"Tahir",
"Nawroz Abdul-razzak",
""
]
] | In this investigation, five separate experiments were carried out. The first experiments were examined the molecular characteristics of 59 barley accessions collected from different regions in Iraq using three different molecular markers (ISSR, CDDP, and Scot). A total of 391 amplified polymorphic bands were generated using forty-four ISSR, nine CDDP, and twelve Scot primers, which they totally observed 255, 35, and 101 polymorphic bands respectively. The mean values of PIC for ISSR, CDDP, and Scot markers were 0.74, 0.63, and 0.80, respectively, indicating the efficiency of the underlying markers in detecting polymorphic status among the studied barley accessions. Based on the respective markers, the barley accessions were classified and clustered into two main groups using the UPGMA and population structure analysis. Results of claustral analyses showed that the variation patterns corresponded with the geographical distribution of barley accessions. |
1901.05010 | Thomas P Wytock | Thomas P. Wytock and Adilson E. Motter | Predicting Growth Rate from Gene Expression | 26 pages, 10 figures, 6 tables, code is available at
https://github.com/twytock/MI-POGUE | Proc. Natl. Acad. Sci. USA 116(2), 367-372 (2019) | 10.1073/pnas.1808080116 | null | q-bio.CB physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Growth rate is one of the most important and most complex phenotypic
characteristics of unicellular microorganisms, which determines the genetic
mutations that dominate at the population level, and ultimately whether the
population will survive. Translating changes at the genetic level to their
growth rate consequences remains a subject of intense interest, since such a
mapping could rationally direct experiments to optimize antibiotic efficacy or
bioreactor productivity. In this paper, we directly map transcriptional
profiles to growth rates by gathering published gene-expression data from
Escherichia coli and Saccharomyces cerevisiae with corresponding growth-rate
measurements. Using a machine-learning technique called k-nearest-neighbors
regression, we build a model which predicts growth rate from gene expression.
By exploiting the correlated nature of gene expression and sparsifying the
model, we capture 81% of the variance in growth rate of the E. coli dataset
while reducing the number of features from over 4,000 to nine. In S.
cerevisiae, we account for 89% of the variance in growth rate while reducing
from over 5,500 dimensions to 18. Such a model provides a basis for selecting
successful strategies from among the combinatorial number of experimental
possibilities when attempting to optimize complex phenotypic traits like growth
rate.
| [
{
"created": "Tue, 15 Jan 2019 19:00:03 GMT",
"version": "v1"
}
] | 2019-01-23 | [
[
"Wytock",
"Thomas P.",
""
],
[
"Motter",
"Adilson E.",
""
]
] | Growth rate is one of the most important and most complex phenotypic characteristics of unicellular microorganisms, which determines the genetic mutations that dominate at the population level, and ultimately whether the population will survive. Translating changes at the genetic level to their growth rate consequences remains a subject of intense interest, since such a mapping could rationally direct experiments to optimize antibiotic efficacy or bioreactor productivity. In this paper, we directly map transcriptional profiles to growth rates by gathering published gene-expression data from Escherichia coli and Saccharomyces cerevisiae with corresponding growth-rate measurements. Using a machine-learning technique called k-nearest-neighbors regression, we build a model which predicts growth rate from gene expression. By exploiting the correlated nature of gene expression and sparsifying the model, we capture 81% of the variance in growth rate of the E. coli dataset while reducing the number of features from over 4,000 to nine. In S. cerevisiae, we account for 89% of the variance in growth rate while reducing from over 5,500 dimensions to 18. Such a model provides a basis for selecting successful strategies from among the combinatorial number of experimental possibilities when attempting to optimize complex phenotypic traits like growth rate. |
2102.03836 | Antonio Bianconi Prof. | Gaetano Campi, Maria Vittoria Mazziotti, Antonio Valletta, Giampietro
Ravagnan, Augusto Marcelli, Andrea Perali, Antonio Bianconi | Metastable states in plateaus and multi-wave epidemic dynamics of
Covid-19 spreading in Italy | 14 pages, 5 figures | Scientific Reports 11, 12412 (2021) | 10.1038/s41598-021-91950-5 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The control of Covid 19 epidemics by public health policy in Italy during the
first and the second epidemic waves has been driven by using reproductive
number Rt(t) to identify the supercritical (percolative), the subcritical
(arrested), separated by the critical regime. Here we show that to quantify the
Covid-19 spreading rate with containment measures (CSRwCM) there is a need of a
3D expanded parameter space phase diagram built by the combination of Rt(t) and
doubling time Td(t). In this space we identify the dynamics of the Covid-19
dynamics Italy and its administrative Regions. The supercritical regime is
mathematically characterized by i) the power law of Td vs. [Rt(t)-1] and ii)
the exponential behaviour of Td vs. time, either in the first and in the second
wave. The novel 3D phase diagram shows clearly metastable states appearing
before and after the second wave critical regime. for loosening quarantine and
tracing of actives cases. The metastable states are precursors of the abrupt
onset of a next nascent wave supercritical regime. This dynamic description
allows epidemics predictions needed by policymakers to activate
non-pharmaceutical interventions (NPIs), a key issue for avoiding economical
losses, reduce fatalities and avoid new virus variant during vaccination
campaign
| [
{
"created": "Sun, 7 Feb 2021 16:28:33 GMT",
"version": "v1"
}
] | 2021-06-25 | [
[
"Campi",
"Gaetano",
""
],
[
"Mazziotti",
"Maria Vittoria",
""
],
[
"Valletta",
"Antonio",
""
],
[
"Ravagnan",
"Giampietro",
""
],
[
"Marcelli",
"Augusto",
""
],
[
"Perali",
"Andrea",
""
],
[
"Bianconi",
"Antoni... | The control of Covid 19 epidemics by public health policy in Italy during the first and the second epidemic waves has been driven by using reproductive number Rt(t) to identify the supercritical (percolative), the subcritical (arrested), separated by the critical regime. Here we show that to quantify the Covid-19 spreading rate with containment measures (CSRwCM) there is a need of a 3D expanded parameter space phase diagram built by the combination of Rt(t) and doubling time Td(t). In this space we identify the dynamics of the Covid-19 dynamics Italy and its administrative Regions. The supercritical regime is mathematically characterized by i) the power law of Td vs. [Rt(t)-1] and ii) the exponential behaviour of Td vs. time, either in the first and in the second wave. The novel 3D phase diagram shows clearly metastable states appearing before and after the second wave critical regime. for loosening quarantine and tracing of actives cases. The metastable states are precursors of the abrupt onset of a next nascent wave supercritical regime. This dynamic description allows epidemics predictions needed by policymakers to activate non-pharmaceutical interventions (NPIs), a key issue for avoiding economical losses, reduce fatalities and avoid new virus variant during vaccination campaign |
1703.04145 | Mihai Alexandru Petrovici | Mihai A. Petrovici, Anna Schroeder, Oliver Breitwieser, Andreas
Gr\"ubl, Johannes Schemmel, Karlheinz Meier | Robustness from structure: Inference with hierarchical spiking networks
on analog neuromorphic hardware | accepted at IJCNN 2017 | International Joint Conference on Neural Networks (IJCNN), 2017 | 10.1109/IJCNN.2017.7966123 | null | q-bio.NC cs.NE stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How spiking networks are able to perform probabilistic inference is an
intriguing question, not only for understanding information processing in the
brain, but also for transferring these computational principles to neuromorphic
silicon circuits. A number of computationally powerful spiking network models
have been proposed, but most of them have only been tested, under ideal
conditions, in software simulations. Any implementation in an analog, physical
system, be it in vivo or in silico, will generally lead to distorted dynamics
due to the physical properties of the underlying substrate. In this paper, we
discuss several such distortive effects that are difficult or impossible to
remove by classical calibration routines or parameter training. We then argue
that hierarchical networks of leaky integrate-and-fire neurons can offer the
required robustness for physical implementation and demonstrate this with both
software simulations and emulation on an accelerated analog neuromorphic
device.
| [
{
"created": "Sun, 12 Mar 2017 17:29:11 GMT",
"version": "v1"
}
] | 2017-07-12 | [
[
"Petrovici",
"Mihai A.",
""
],
[
"Schroeder",
"Anna",
""
],
[
"Breitwieser",
"Oliver",
""
],
[
"Grübl",
"Andreas",
""
],
[
"Schemmel",
"Johannes",
""
],
[
"Meier",
"Karlheinz",
""
]
] | How spiking networks are able to perform probabilistic inference is an intriguing question, not only for understanding information processing in the brain, but also for transferring these computational principles to neuromorphic silicon circuits. A number of computationally powerful spiking network models have been proposed, but most of them have only been tested, under ideal conditions, in software simulations. Any implementation in an analog, physical system, be it in vivo or in silico, will generally lead to distorted dynamics due to the physical properties of the underlying substrate. In this paper, we discuss several such distortive effects that are difficult or impossible to remove by classical calibration routines or parameter training. We then argue that hierarchical networks of leaky integrate-and-fire neurons can offer the required robustness for physical implementation and demonstrate this with both software simulations and emulation on an accelerated analog neuromorphic device. |
1706.08138 | Maurizio De Pitt\`a | Valeri Matrosov, Susan Gordleeva, Natalia Boldyreva, Eshel Ben-Jacob,
Alexey Semyanov, Victor Kazantsev and Maurizio De Pitt\`a | Emergence of regular and complex calcium oscillations by inositol
1,4,5-trisphosphate signaling in astrocytes | 19 pages (24 pages with References), 1 table, 5 figures, book chapter | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We use tools of bifurcation theory to characterize dynamics of
astrocytic~IP$_3$ and~Ca$^{2+}$ for different~IP$_3$ regimes from a
mathematical point of view. We do so following a bottom-up approach, starting
from a compact, well-stirred astrocyte model to first identify
characteristic~IP$_3$ pathways whereby~Ca$^{2+}$ (and~IP$_3$) dynamics
"bifurcate", namely change from stable (constant) concentration levels, to
oscillatory dynamics. Then we extend our analysis to the elemental case of two
astrocytes, coupled by~IP$_3$ diffusion mediated by gap junction channels,
putting emphasis on the mechanisms of emergence of chaotic oscillations.
Finally, we complete our analysis discussing spatiotemporal~Ca$^{2+}$ dynamics
in a spatially-extended astrocyte model, gaining insights on the possible
physical mechanisms whereby random Ca$^{2+}$~generation could be orchestrated
into robust, spatially-confined intracellular~Ca$^{2+}$ oscillations.
| [
{
"created": "Sun, 25 Jun 2017 16:39:03 GMT",
"version": "v1"
}
] | 2017-06-27 | [
[
"Matrosov",
"Valeri",
""
],
[
"Gordleeva",
"Susan",
""
],
[
"Boldyreva",
"Natalia",
""
],
[
"Ben-Jacob",
"Eshel",
""
],
[
"Semyanov",
"Alexey",
""
],
[
"Kazantsev",
"Victor",
""
],
[
"De Pittà",
"Maurizio",
... | We use tools of bifurcation theory to characterize dynamics of astrocytic~IP$_3$ and~Ca$^{2+}$ for different~IP$_3$ regimes from a mathematical point of view. We do so following a bottom-up approach, starting from a compact, well-stirred astrocyte model to first identify characteristic~IP$_3$ pathways whereby~Ca$^{2+}$ (and~IP$_3$) dynamics "bifurcate", namely change from stable (constant) concentration levels, to oscillatory dynamics. Then we extend our analysis to the elemental case of two astrocytes, coupled by~IP$_3$ diffusion mediated by gap junction channels, putting emphasis on the mechanisms of emergence of chaotic oscillations. Finally, we complete our analysis discussing spatiotemporal~Ca$^{2+}$ dynamics in a spatially-extended astrocyte model, gaining insights on the possible physical mechanisms whereby random Ca$^{2+}$~generation could be orchestrated into robust, spatially-confined intracellular~Ca$^{2+}$ oscillations. |
1911.02406 | Homayoun Valafar | Paul Shealy, Homayoun Valafar | Aligning Multiple Protein Structures using Biochemical and Biophysical
Properties | BioComp 2009, 7 pages | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Aligning multiple protein structures can yield valuable information about
structural similarities among related proteins, as well as provide insight into
evolutionary relationships between proteins in a family. We have developed an
algorithm (msTALI) for aligning multiple protein structures using biochemical
and biophysical properties, including torsion angles, secondary structure,
hydrophobicity, and surface accessibility. The algorithm is a progressive
alignment algorithm motivated by popular techniques from multiple sequence
alignment. It has demonstrated success in aligning the major structural regions
of a set of proteins from the s/r kinase family. The algorithm was also
successful at aligning functional residues of these proteins. In addition, the
algorithm was also successful in aligning seven members of the acyl carrier
protein family, including both experimentally derived as well as
computationally modeled structures.
| [
{
"created": "Wed, 6 Nov 2019 14:08:11 GMT",
"version": "v1"
}
] | 2019-11-07 | [
[
"Shealy",
"Paul",
""
],
[
"Valafar",
"Homayoun",
""
]
] | Aligning multiple protein structures can yield valuable information about structural similarities among related proteins, as well as provide insight into evolutionary relationships between proteins in a family. We have developed an algorithm (msTALI) for aligning multiple protein structures using biochemical and biophysical properties, including torsion angles, secondary structure, hydrophobicity, and surface accessibility. The algorithm is a progressive alignment algorithm motivated by popular techniques from multiple sequence alignment. It has demonstrated success in aligning the major structural regions of a set of proteins from the s/r kinase family. The algorithm was also successful at aligning functional residues of these proteins. In addition, the algorithm was also successful in aligning seven members of the acyl carrier protein family, including both experimentally derived as well as computationally modeled structures. |
2009.03758 | Roman Martin | Roman Martin, Thomas Hackl, Georges Hattab, Matthias G. Fischer and
Dominik Heider | MOSGA: Modular Open-Source Genome Annotator | null | Bioinformatics 36(22-23) 2020 5514-5515 | 10.1093/bioinformatics/btaa1003 | null | q-bio.GN | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The generation of high-quality assemblies, even for large eukaryotic genomes,
has become a routine task for many biologists thanks to recent advances in
sequencing technologies. However, the annotation of these assemblies - a
crucial step towards unlocking the biology of the organism of interest - has
remained a complex challenge that often requires advanced bioinformatics
expertise. Here we present MOSGA, a genome annotation framework for eukaryotic
genomes with a user-friendly web-interface that generates and integrates
annotations from various tools. The aggregated results can be analyzed with a
fully integrated genome browser and are provided in a format ready for
submission to NCBI. MOSGA is built on a portable, customizable, and easily
extendible Snakemake backend, and thus, can be tailored to a wide range of
users and projects. We provide MOSGA as a publicly free available web service
at https://mosga.mathematik.uni-marburg.de and as a docker container at
registry.gitlab.com/mosga/mosga:latest. Source code can be found at
https://gitlab.com/mosga/mosga
| [
{
"created": "Tue, 8 Sep 2020 13:51:42 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Sep 2020 19:33:25 GMT",
"version": "v2"
}
] | 2021-04-07 | [
[
"Martin",
"Roman",
""
],
[
"Hackl",
"Thomas",
""
],
[
"Hattab",
"Georges",
""
],
[
"Fischer",
"Matthias G.",
""
],
[
"Heider",
"Dominik",
""
]
] | The generation of high-quality assemblies, even for large eukaryotic genomes, has become a routine task for many biologists thanks to recent advances in sequencing technologies. However, the annotation of these assemblies - a crucial step towards unlocking the biology of the organism of interest - has remained a complex challenge that often requires advanced bioinformatics expertise. Here we present MOSGA, a genome annotation framework for eukaryotic genomes with a user-friendly web-interface that generates and integrates annotations from various tools. The aggregated results can be analyzed with a fully integrated genome browser and are provided in a format ready for submission to NCBI. MOSGA is built on a portable, customizable, and easily extendible Snakemake backend, and thus, can be tailored to a wide range of users and projects. We provide MOSGA as a publicly free available web service at https://mosga.mathematik.uni-marburg.de and as a docker container at registry.gitlab.com/mosga/mosga:latest. Source code can be found at https://gitlab.com/mosga/mosga |
1304.1834 | Yongtao Guan | Yongtao Guan | Detecting the structure of haplotypes, local ancestry and excessive
local European ancestry in Mexicans | 28 pages, 12 figures | null | null | null | q-bio.QM q-bio.PE stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a two-layer hidden Markov model to detect structure of haplotypes
for unrelated individuals. This allows modeling two scales of linkage
disequilibrium (one within a group of haplotypes and one between groups),
thereby taking advantage of rich haplotype information to infer local ancestry
for admixed individuals. Our method outperforms competing state-of-art methods,
particularly for regions of small ancestral track lengths. Applying our method
to Mexican samples in HapMap3, we found five coding regions, ranging from $0.3
-1.3$ megabase (Mb) in lengths, that exhibit excessive European ancestry
(average dosage > 1.6). A particular interesting region of 1.1Mb (with average
dosage 1.95) locates on Chromosome 2p23 that harbors two genes, PXDN and MYT1L,
both of which are associated with autism and schizophrenia. In light of the low
prevalence of autism in Hispanics, this region warrants special attention. We
confirmed our findings using Mexican samples from the 1000 genomes project. A
software package implementing methods described in the paper is freely
available at \url{http://bcm.edu/cnrc/mcmcmc}
| [
{
"created": "Fri, 5 Apr 2013 23:24:42 GMT",
"version": "v1"
}
] | 2013-04-09 | [
[
"Guan",
"Yongtao",
""
]
] | We present a two-layer hidden Markov model to detect structure of haplotypes for unrelated individuals. This allows modeling two scales of linkage disequilibrium (one within a group of haplotypes and one between groups), thereby taking advantage of rich haplotype information to infer local ancestry for admixed individuals. Our method outperforms competing state-of-art methods, particularly for regions of small ancestral track lengths. Applying our method to Mexican samples in HapMap3, we found five coding regions, ranging from $0.3 -1.3$ megabase (Mb) in lengths, that exhibit excessive European ancestry (average dosage > 1.6). A particular interesting region of 1.1Mb (with average dosage 1.95) locates on Chromosome 2p23 that harbors two genes, PXDN and MYT1L, both of which are associated with autism and schizophrenia. In light of the low prevalence of autism in Hispanics, this region warrants special attention. We confirmed our findings using Mexican samples from the 1000 genomes project. A software package implementing methods described in the paper is freely available at \url{http://bcm.edu/cnrc/mcmcmc} |
2302.04154 | J. C. Phillips | J. C. Phillips | Evolution of Two Membrane Protein Sequences and Functions | 7 pages, two figures | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | TRPC(3,6) are two ~ 930 amino acid membrane proteins that form calcium
permeant cation channels. Here we examine the differences between mammals and
oviparous species. Our method is based on the concept of evolution towards
criticality, a general concept we have previously applied to many proteins,
especially in describing the evolution of pandemic sequences through natural
selection.
| [
{
"created": "Tue, 7 Feb 2023 18:21:11 GMT",
"version": "v1"
}
] | 2023-02-09 | [
[
"Phillips",
"J. C.",
""
]
] | TRPC(3,6) are two ~ 930 amino acid membrane proteins that form calcium permeant cation channels. Here we examine the differences between mammals and oviparous species. Our method is based on the concept of evolution towards criticality, a general concept we have previously applied to many proteins, especially in describing the evolution of pandemic sequences through natural selection. |
2111.13101 | Raphael Yuster | Sagi Snir, Osnat Weissberg, Raphael Yuster | On the quartet distance given partial information | null | null | null | null | q-bio.PE cs.DM math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Let $T$ be an arbitrary phylogenetic tree with $n$ leaves. It is well-known
that the average quartet distance between two assignments of taxa to the leaves
of $T$ is $\frac 23 \binom{n}{4}$. However, a longstanding conjecture of
Bandelt and Dress asserts that $(\frac 23 +o(1))\binom{n}{4}$ is also the {\em
maximum} quartet distance between two assignments. While Alon, Naves, and
Sudakov have shown this indeed holds for caterpillar trees, the general case of
the conjecture is still unresolved. A natural extension is when partial
information is given: the two assignments are known to coincide on a given
subset of taxa. The partial information setting is biologically relevant as the
location of some taxa (species) in the phylogenetic tree may be known, and for
other taxa it might not be known. What can we then say about the average and
maximum quartet distance in this more general setting? Surprisingly, even
determining the {\em average} quartet distance becomes a nontrivial task in the
partial information setting and determining the maximum quartet distance is
even more challenging, as these turn out to be dependent of the structure of
$T$. In this paper we prove nontrivial asymptotic bounds that are sometimes
tight for the average quartet distance in the partial information setting. We
also show that the Bandelt and Dress conjecture does not generally hold under
the partial information setting. Specifically, we prove that there are cases
where the average and maximum quartet distance substantially differ.
| [
{
"created": "Thu, 25 Nov 2021 14:38:33 GMT",
"version": "v1"
}
] | 2021-11-29 | [
[
"Snir",
"Sagi",
""
],
[
"Weissberg",
"Osnat",
""
],
[
"Yuster",
"Raphael",
""
]
] | Let $T$ be an arbitrary phylogenetic tree with $n$ leaves. It is well-known that the average quartet distance between two assignments of taxa to the leaves of $T$ is $\frac 23 \binom{n}{4}$. However, a longstanding conjecture of Bandelt and Dress asserts that $(\frac 23 +o(1))\binom{n}{4}$ is also the {\em maximum} quartet distance between two assignments. While Alon, Naves, and Sudakov have shown this indeed holds for caterpillar trees, the general case of the conjecture is still unresolved. A natural extension is when partial information is given: the two assignments are known to coincide on a given subset of taxa. The partial information setting is biologically relevant as the location of some taxa (species) in the phylogenetic tree may be known, and for other taxa it might not be known. What can we then say about the average and maximum quartet distance in this more general setting? Surprisingly, even determining the {\em average} quartet distance becomes a nontrivial task in the partial information setting and determining the maximum quartet distance is even more challenging, as these turn out to be dependent of the structure of $T$. In this paper we prove nontrivial asymptotic bounds that are sometimes tight for the average quartet distance in the partial information setting. We also show that the Bandelt and Dress conjecture does not generally hold under the partial information setting. Specifically, we prove that there are cases where the average and maximum quartet distance substantially differ. |
1411.3709 | Hannes Svardal | Hannes Svardal, Claus Rueffler and Joachim Hermisson | A general condition for adaptive genetic polymorphism in temporally and
spatially heterogeneous environments | Accepted for publication in Theoretical Population Biology | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Both evolution and ecology have long been concerned with the impact of
variable environmental conditions on observed levels of genetic diversity
within and between species. We model the evolution of a quantitative trait
under selection that fluctuates in space and time, and derive an analytical
condition for when these fluctuations promote genetic diversification. As
ecological scenario we use a generalized island model with soft selection
within patches in which we incorporate generation overlap. We allow for
arbitrary fluctuations in the environment including spatio-temporal
correlations and any functional form of selection on the trait. Using the
concepts of invasion fitness and evolutionary branching, we derive a simple and
transparent condition for the adaptive evolution and maintenance of genetic
diversity. This condition relates the strength of selection within patches to
expectations and variances in the environmental conditions across space and
time. Our results unify, clarify, and extend a number of previous results on
the evolution and maintenance of genetic variation under fluctuating selection.
Individual-based simulations show that our results are independent of the
details of the genetic architecture and on whether reproduction is clonal or
sexual. The onset of increased genetic variance is predicted accurately also in
small populations in which alleles can go extinct due to environmental
stochasticity.
| [
{
"created": "Thu, 13 Nov 2014 20:52:56 GMT",
"version": "v1"
},
{
"created": "Fri, 14 Nov 2014 09:43:10 GMT",
"version": "v2"
}
] | 2014-11-17 | [
[
"Svardal",
"Hannes",
""
],
[
"Rueffler",
"Claus",
""
],
[
"Hermisson",
"Joachim",
""
]
] | Both evolution and ecology have long been concerned with the impact of variable environmental conditions on observed levels of genetic diversity within and between species. We model the evolution of a quantitative trait under selection that fluctuates in space and time, and derive an analytical condition for when these fluctuations promote genetic diversification. As ecological scenario we use a generalized island model with soft selection within patches in which we incorporate generation overlap. We allow for arbitrary fluctuations in the environment including spatio-temporal correlations and any functional form of selection on the trait. Using the concepts of invasion fitness and evolutionary branching, we derive a simple and transparent condition for the adaptive evolution and maintenance of genetic diversity. This condition relates the strength of selection within patches to expectations and variances in the environmental conditions across space and time. Our results unify, clarify, and extend a number of previous results on the evolution and maintenance of genetic variation under fluctuating selection. Individual-based simulations show that our results are independent of the details of the genetic architecture and on whether reproduction is clonal or sexual. The onset of increased genetic variance is predicted accurately also in small populations in which alleles can go extinct due to environmental stochasticity. |
2301.04416 | Josh Williams | Josh Williams, Ali Ozel, Uwe Wolfram | pyssam -- a Python library for statistical modelling of biomedical shape
and appearance | 5 pages, 3 figures, Journal of Open Source Software submission | null | null | null | q-bio.QM cs.CV eess.IV | http://creativecommons.org/licenses/by/4.0/ | pyssam is a Python library for creating statistical shape and appearance
models (SSAMs) for biological (and other) shapes such as bones, lungs or other
organs. A point cloud best describing the anatomical 'landmarks' of the organ
are required from each sample in a small population as an input. Additional
information such as landmark gray-value can be included to incorporate joint
correlations of shape and 'appearance' into the model. Our library performs
alignment and scaling of the input data and creates a SSAM based on covariance
across the population. The output SSAM can be used to parameterise and quantify
shape change across a population. pyssam is a small and low dependency codebase
with examples included as Jupyter notebooks for several common SSAM
computations. The given examples can easily be extended to alternative
datasets, and also alternative tasks such as medical image segmentation by
incorporating a SSAM as a constraint for segmented organs.
| [
{
"created": "Wed, 11 Jan 2023 11:50:44 GMT",
"version": "v1"
}
] | 2023-01-12 | [
[
"Williams",
"Josh",
""
],
[
"Ozel",
"Ali",
""
],
[
"Wolfram",
"Uwe",
""
]
] | pyssam is a Python library for creating statistical shape and appearance models (SSAMs) for biological (and other) shapes such as bones, lungs or other organs. A point cloud best describing the anatomical 'landmarks' of the organ are required from each sample in a small population as an input. Additional information such as landmark gray-value can be included to incorporate joint correlations of shape and 'appearance' into the model. Our library performs alignment and scaling of the input data and creates a SSAM based on covariance across the population. The output SSAM can be used to parameterise and quantify shape change across a population. pyssam is a small and low dependency codebase with examples included as Jupyter notebooks for several common SSAM computations. The given examples can easily be extended to alternative datasets, and also alternative tasks such as medical image segmentation by incorporating a SSAM as a constraint for segmented organs. |
1902.03507 | Sebastian Schreiber | Sebastian J. Schreiber | When do factors promoting balanced selection also promote population
persistence? A demographic perspective on Gillespie's SAS-CFF model | 19 pages, 2 figures | null | null | null | q-bio.PE math.DS math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Classical stochastic demography predicts that environmental stochasticity
reduces population growth rates and, thereby, can increase extinction risk. In
contrast, the SAS-CFF model demonstrates that environmental stochasticity can
promote genetic diversity. Extending the SAS-CFF to account for demography, I
examine the simultaneous effects of environmental stochasticity on genetic
diversity and population persistence. Consistent with Gillespie's analysis, if
the log-fitness function is concave and allelic responses to the environment
are not perfectly correlated, then per-capita growth rates of rare alleles are
positive and genetic diversity is maintained in the sense of stochastic
persistence i.e. allelic frequencies tend to stay away from zero almost-surely
and in probability. Alternatively, if the log-fitness function is convex, then
per-capita growth rates of rare alleles are negative and an allele
asymptotically fixates with probability one. If the population's low-density,
per-capita growth rate is positive, then the population persists in the sense
of stochastic persistence, else it goes asymptotically extinct with probability
one. In contrast to per-capita growth rates of rare alleles, the population's
per-capita growth rate is a decreasing function of the concavity of the
log-fitness function. Moreover, when the log-fitness function is concave,
allelic diversity increases the population's per-capita growth rate while
decreasing the per-capita growth rate of rare alleles, and environmental
stochasticity increases the per-capita growth rate of rare alleles but
decreases the population's per-capita growth rate. Collectively, these results
(i) highlight how mechanisms promoting population persistence may be at odds
with mechanisms promoting genetic diversity, and (ii) provide conditions under
which population persistence relies on existing standing genetic variation.
| [
{
"created": "Sat, 9 Feb 2019 22:38:18 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Jul 2019 00:27:10 GMT",
"version": "v2"
}
] | 2019-07-25 | [
[
"Schreiber",
"Sebastian J.",
""
]
] | Classical stochastic demography predicts that environmental stochasticity reduces population growth rates and, thereby, can increase extinction risk. In contrast, the SAS-CFF model demonstrates that environmental stochasticity can promote genetic diversity. Extending the SAS-CFF to account for demography, I examine the simultaneous effects of environmental stochasticity on genetic diversity and population persistence. Consistent with Gillespie's analysis, if the log-fitness function is concave and allelic responses to the environment are not perfectly correlated, then per-capita growth rates of rare alleles are positive and genetic diversity is maintained in the sense of stochastic persistence i.e. allelic frequencies tend to stay away from zero almost-surely and in probability. Alternatively, if the log-fitness function is convex, then per-capita growth rates of rare alleles are negative and an allele asymptotically fixates with probability one. If the population's low-density, per-capita growth rate is positive, then the population persists in the sense of stochastic persistence, else it goes asymptotically extinct with probability one. In contrast to per-capita growth rates of rare alleles, the population's per-capita growth rate is a decreasing function of the concavity of the log-fitness function. Moreover, when the log-fitness function is concave, allelic diversity increases the population's per-capita growth rate while decreasing the per-capita growth rate of rare alleles, and environmental stochasticity increases the per-capita growth rate of rare alleles but decreases the population's per-capita growth rate. Collectively, these results (i) highlight how mechanisms promoting population persistence may be at odds with mechanisms promoting genetic diversity, and (ii) provide conditions under which population persistence relies on existing standing genetic variation. |
1802.02937 | Jerome Laurin | Caroline Pin-Barre (LAMHESS), Annabelle Constans, Jeanick Brisswalter,
Christophe Pellegrino, J\'er\^ome Laurin (ISM) | Effects of high vs moderate-intensity training on neuroplasticity and
functional recovery after focal ischemia | null | American Heart Association, 2017, 48 (10), pp.2855 - 2864 | 10.1161/STROKEAHA.117.017962 | null | q-bio.NC q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background and Purpose: This study was designed to compare the effects of
high-intensity interval training (HIT) and moderate-intensity continuous
training (MOD) on functional recovery and cerebral plasticity during the first
2 weeks following cerebral ischemia. Methods: Rats were randomized as follows:
Control (n=15), SHAM (n=9), MCAO (n=13), MCAO-D1 (n=7), MOD (n=13) and HIT
(n=13). Incremental tests were performed at day 1 (D1) and 14 (D14) to identify
the running speed associated with the lactate threshold (SLT) and the maximal
speed (Smax). Functional tests were performed at D1, D7 and D14. Microglia
form, cytokines, p75NTR, KCC2 and NKCC1 expression were made at D15.
Results-HIT was more effective to improve the endurance performance than MOD
and induced a fast recovery of the impaired forelimb grip force. The Iba-1
positive cells with amoeboid form and the pro- and anti-inflammatory cytokine
expression were lower in HIT group, mainly in the ipsilesional hemisphere. A
p75NTR overexpression is observed on the ipsilesional side together with a
restored NKCC1/KCC2 ratio on the contralesional side. Conclusions-Low-volume
HIT based on lactate threshold appears to be more effective after cerebral
ischemia than work-matched MOD to improve aerobic fitness, grip strength and
might promote cerebral plasticity.
| [
{
"created": "Thu, 8 Feb 2018 15:56:19 GMT",
"version": "v1"
}
] | 2018-02-09 | [
[
"Pin-Barre",
"Caroline",
"",
"LAMHESS"
],
[
"Constans",
"Annabelle",
"",
"ISM"
],
[
"Brisswalter",
"Jeanick",
"",
"ISM"
],
[
"Pellegrino",
"Christophe",
"",
"ISM"
],
[
"Laurin",
"Jérôme",
"",
"ISM"
]
] | Background and Purpose: This study was designed to compare the effects of high-intensity interval training (HIT) and moderate-intensity continuous training (MOD) on functional recovery and cerebral plasticity during the first 2 weeks following cerebral ischemia. Methods: Rats were randomized as follows: Control (n=15), SHAM (n=9), MCAO (n=13), MCAO-D1 (n=7), MOD (n=13) and HIT (n=13). Incremental tests were performed at day 1 (D1) and 14 (D14) to identify the running speed associated with the lactate threshold (SLT) and the maximal speed (Smax). Functional tests were performed at D1, D7 and D14. Microglia form, cytokines, p75NTR, KCC2 and NKCC1 expression were made at D15. Results-HIT was more effective to improve the endurance performance than MOD and induced a fast recovery of the impaired forelimb grip force. The Iba-1 positive cells with amoeboid form and the pro- and anti-inflammatory cytokine expression were lower in HIT group, mainly in the ipsilesional hemisphere. A p75NTR overexpression is observed on the ipsilesional side together with a restored NKCC1/KCC2 ratio on the contralesional side. Conclusions-Low-volume HIT based on lactate threshold appears to be more effective after cerebral ischemia than work-matched MOD to improve aerobic fitness, grip strength and might promote cerebral plasticity. |
q-bio/0403025 | Joseph Rushton Wakeling | Joseph Rushton Wakeling | Adaptivity and `Per learning' | 8 pages, 4 figures. To appear in a special Per Bak memorial issue of
Physica A | Physica A 340 (2004) 766-773 | 10.1016/j.physa.2004.05.028 | null | q-bio.NC cond-mat.dis-nn nlin.AO physics.bio-ph | null | One of the key points addressed by Per Bak in his models of brain function
was that biological neural systems must be able not just to learn, but also to
adapt--to quickly change their behaviour in response to a changing environment.
I discuss this in the context of various simple learning rules and adaptive
problems, centred around the Chialvo-Bak `minibrain' model [Neurosci. 90 (1999)
1137--1148].
| [
{
"created": "Wed, 17 Mar 2004 16:03:14 GMT",
"version": "v1"
}
] | 2010-01-21 | [
[
"Wakeling",
"Joseph Rushton",
""
]
] | One of the key points addressed by Per Bak in his models of brain function was that biological neural systems must be able not just to learn, but also to adapt--to quickly change their behaviour in response to a changing environment. I discuss this in the context of various simple learning rules and adaptive problems, centred around the Chialvo-Bak `minibrain' model [Neurosci. 90 (1999) 1137--1148]. |
0707.1407 | Stefan Bornholdt | Stefan Braunewell and Stefan Bornholdt | Reliability of genetic networks is evolvable | 5 pages, 3 figures | null | 10.1103/PhysRevE.77.060902 | null | q-bio.MN | null | Control of the living cell functions with remarkable reliability despite the
stochastic nature of the underlying molecular networks -- a property presumably
optimized by biological evolution. We here ask to what extent the property of a
stochastic dynamical network to produce reliable dynamics is an evolvable
trait. Using an evolutionary algorithm based on a deterministic selection
criterion for the reliability of dynamical attractors, we evolve dynamical
networks of noisy discrete threshold nodes. We find that, starting from any
random network, reliability of the attractor landscape can often be achieved
with only few small changes to the network structure. Further, the evolvability
of networks towards reliable dynamics while retaining their function is
investigated and a high success rate is found.
| [
{
"created": "Tue, 10 Jul 2007 19:56:10 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Braunewell",
"Stefan",
""
],
[
"Bornholdt",
"Stefan",
""
]
] | Control of the living cell functions with remarkable reliability despite the stochastic nature of the underlying molecular networks -- a property presumably optimized by biological evolution. We here ask to what extent the property of a stochastic dynamical network to produce reliable dynamics is an evolvable trait. Using an evolutionary algorithm based on a deterministic selection criterion for the reliability of dynamical attractors, we evolve dynamical networks of noisy discrete threshold nodes. We find that, starting from any random network, reliability of the attractor landscape can often be achieved with only few small changes to the network structure. Further, the evolvability of networks towards reliable dynamics while retaining their function is investigated and a high success rate is found. |
2205.10308 | Annika Hagemann | Annika Hagemann, Marcel Stephan Kehl, Jonas Dehning, F. Paul Spitzner,
Johannes Niediek, Michael Wibral, Florian Mormann, Viola Priesemann | Intrinsic timescales of spiking activity in humans during wakefulness
and sleep | preprint | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Information processing in the brain requires integration of information over
time. Such an integration can be achieved if signals are maintained in the
network activity for the required period, as quantified by the intrinsic
timescale. While short timescales are considered beneficial for fast responses
to stimuli, long timescales facilitate information storage and integration. We
quantified intrinsic timescales from spiking activity in the medial temporal
lobe of humans. We found extended and highly diverse timescales ranging from
tens to hundreds of milliseconds, though with no evidence for differences
between subareas. Notably, however, timescales differed between sleep stages
and were longest during slow wave sleep. This supports the hypothesis that
intrinsic timescales are a central mechanism to tune networks to the
requirements of different tasks and cognitive states.
| [
{
"created": "Fri, 20 May 2022 17:20:48 GMT",
"version": "v1"
}
] | 2022-05-23 | [
[
"Hagemann",
"Annika",
""
],
[
"Kehl",
"Marcel Stephan",
""
],
[
"Dehning",
"Jonas",
""
],
[
"Spitzner",
"F. Paul",
""
],
[
"Niediek",
"Johannes",
""
],
[
"Wibral",
"Michael",
""
],
[
"Mormann",
"Florian",
"... | Information processing in the brain requires integration of information over time. Such an integration can be achieved if signals are maintained in the network activity for the required period, as quantified by the intrinsic timescale. While short timescales are considered beneficial for fast responses to stimuli, long timescales facilitate information storage and integration. We quantified intrinsic timescales from spiking activity in the medial temporal lobe of humans. We found extended and highly diverse timescales ranging from tens to hundreds of milliseconds, though with no evidence for differences between subareas. Notably, however, timescales differed between sleep stages and were longest during slow wave sleep. This supports the hypothesis that intrinsic timescales are a central mechanism to tune networks to the requirements of different tasks and cognitive states. |
1908.08647 | Andrew Francis | Michael Hendriksen and Andrew Francis | Tree-metrizable HGT networks | 26 pages, 13 figures | null | null | null | q-bio.PE math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phylogenetic trees are often constructed by using a metric on the set of taxa
that label the leaves of the tree. While there are a number of methods for
constructing a tree using a given metric, such trees will only display the
metric if it satisfies the so-called "four point condition", established by
Buneman in 1971. While this condition guarantees that a unique tree will
display the metric, meaning that the distance between any two leaves can be
found by adding the distances on arcs in the path between the leaves, it
doesn't exclude the possibility that a phylogenetic network might also display
the metric. This possibility was recently pointed out and "tree-metrized"
networks --- that display a tree metric --- with a single reticulation were
characterized. In this paper, we show that in the case of HGT (horizontal gene
transfer) networks, in fact there are tree-metrized networks containing many
reticulations.
| [
{
"created": "Fri, 23 Aug 2019 03:15:07 GMT",
"version": "v1"
}
] | 2019-08-26 | [
[
"Hendriksen",
"Michael",
""
],
[
"Francis",
"Andrew",
""
]
] | Phylogenetic trees are often constructed by using a metric on the set of taxa that label the leaves of the tree. While there are a number of methods for constructing a tree using a given metric, such trees will only display the metric if it satisfies the so-called "four point condition", established by Buneman in 1971. While this condition guarantees that a unique tree will display the metric, meaning that the distance between any two leaves can be found by adding the distances on arcs in the path between the leaves, it doesn't exclude the possibility that a phylogenetic network might also display the metric. This possibility was recently pointed out and "tree-metrized" networks --- that display a tree metric --- with a single reticulation were characterized. In this paper, we show that in the case of HGT (horizontal gene transfer) networks, in fact there are tree-metrized networks containing many reticulations. |
2105.00469 | Harry Clifford MSci DPhil | Adnan Akbar, Andrey Solovyev, John W Cassidy, Nirmesh Patel, Harry W
Clifford | DRIVE: Machine Learning to Identify Drivers of Cancer with
High-Dimensional Genomic Data & Imputed Labels | Submission to the 33rd Conference on Neural Information Processing
Systems (NeurIPS 2019) | null | null | null | q-bio.GN cs.LG | http://creativecommons.org/licenses/by/4.0/ | Identifying the mutations that drive cancer growth is key in clinical
decision making and precision oncology. As driver mutations confer selective
advantage and thus have an increased likelihood of occurrence, frequency-based
statistical models are currently favoured. These methods are not suited to
rare, low frequency, driver mutations. The alternative approach to address this
is through functional-impact scores, however methods using this approach are
highly prone to false positives. In this paper, we propose a novel combination
method for driver mutation identification, which uses the power of both
statistical modelling and functional-impact based methods. Initial results show
this approach outperforms the state-of-the-art methods in terms of precision,
and provides comparable performance in terms of area under receiver operating
characteristic curves (AU-ROC). We believe that data-driven systems based on
machine learning, such as these, will become an integral part of precision
oncology in the near future.
| [
{
"created": "Sun, 2 May 2021 13:27:31 GMT",
"version": "v1"
}
] | 2021-05-04 | [
[
"Akbar",
"Adnan",
""
],
[
"Solovyev",
"Andrey",
""
],
[
"Cassidy",
"John W",
""
],
[
"Patel",
"Nirmesh",
""
],
[
"Clifford",
"Harry W",
""
]
] | Identifying the mutations that drive cancer growth is key in clinical decision making and precision oncology. As driver mutations confer selective advantage and thus have an increased likelihood of occurrence, frequency-based statistical models are currently favoured. These methods are not suited to rare, low frequency, driver mutations. The alternative approach to address this is through functional-impact scores, however methods using this approach are highly prone to false positives. In this paper, we propose a novel combination method for driver mutation identification, which uses the power of both statistical modelling and functional-impact based methods. Initial results show this approach outperforms the state-of-the-art methods in terms of precision, and provides comparable performance in terms of area under receiver operating characteristic curves (AU-ROC). We believe that data-driven systems based on machine learning, such as these, will become an integral part of precision oncology in the near future. |
1506.08596 | Tatiana T. Marquez-Lago | Zach Hensel and Tatiana T. Marquez-Lago | Cell-cycle-synchronized, oscillatory expression of a negatively
autoregulated gene in E. coli | 52 pages, 4 figures, 12 supplementary figures | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Engineering genetic networks to be both predictable and robust is a key
challenge in synthetic biology. Synthetic circuits must reliably function in
dynamic, stochastic and heterogeneous environments, and simple circuits can be
studied to refine complex gene-regulation models. Although robust behaviours
such as genetic oscillators have been designed and implemented in prokaryotic
and eukaryotic organisms, a priori genetic engineering of even simple networks
remains difficult, and many aspects of cell and molecular biology critical to
engineering robust networks are still inadequately characterized. Particularly,
periodic processes such as gene doubling and cell division are rarely
considered in gene regulatory models, which may become more important as
synthetic biologists utilize new tools for chromosome integration. We studied a
chromosome-integrated, negative-feedback circuit based upon the bacteriophage
{\lambda} transcriptional repressor Cro and observed strong, feedback-dependent
oscillations in single-cell time traces. This finding was surprising due to a
lack of cooperativity, long delays or fast protein degradation. We further show
that oscillations are synchronized to the cell cycle by gene duplication, with
phase shifts predictably correlating with estimated gene doubling times.
Furthermore, we characterized the influence of negative feedback on the
magnitude and dynamics of noise in gene expression. Our results show that
cell-cycle effects must be accounted for in accurate, predictive models for
even simple gene circuits. Cell-cycle-periodic expression of {\lambda} Cro also
suggests an explanation for cell-size dependence in lysis probability and an
evolutionary basis for site-specific {\lambda} integration.
| [
{
"created": "Mon, 29 Jun 2015 12:00:24 GMT",
"version": "v1"
}
] | 2015-06-30 | [
[
"Hensel",
"Zach",
""
],
[
"Marquez-Lago",
"Tatiana T.",
""
]
] | Engineering genetic networks to be both predictable and robust is a key challenge in synthetic biology. Synthetic circuits must reliably function in dynamic, stochastic and heterogeneous environments, and simple circuits can be studied to refine complex gene-regulation models. Although robust behaviours such as genetic oscillators have been designed and implemented in prokaryotic and eukaryotic organisms, a priori genetic engineering of even simple networks remains difficult, and many aspects of cell and molecular biology critical to engineering robust networks are still inadequately characterized. Particularly, periodic processes such as gene doubling and cell division are rarely considered in gene regulatory models, which may become more important as synthetic biologists utilize new tools for chromosome integration. We studied a chromosome-integrated, negative-feedback circuit based upon the bacteriophage {\lambda} transcriptional repressor Cro and observed strong, feedback-dependent oscillations in single-cell time traces. This finding was surprising due to a lack of cooperativity, long delays or fast protein degradation. We further show that oscillations are synchronized to the cell cycle by gene duplication, with phase shifts predictably correlating with estimated gene doubling times. Furthermore, we characterized the influence of negative feedback on the magnitude and dynamics of noise in gene expression. Our results show that cell-cycle effects must be accounted for in accurate, predictive models for even simple gene circuits. Cell-cycle-periodic expression of {\lambda} Cro also suggests an explanation for cell-size dependence in lysis probability and an evolutionary basis for site-specific {\lambda} integration. |
q-bio/0409008 | Eugene Korotkov | Andrew A. Laskin, Nikolai A. Kudryashov, Konstantin G.Skryabin, Eugene
V. Korotkov | Latent periodicity of serine-threonine and tyrosine protein kinases and
another protein families | 27 pages, 3 figures, 6 tables, 57 references | Molekularnuya Biologiya (Russian), vol.39, N3, 2005 | null | null | q-bio.BM | null | We identified latent periodicity in catalytic domains of approximately 85% of
serine/threonine and tyrosine protein kinases. Similar results were obtained
for other 22 protein domains. We also designed the method of noise
decomposition, which is aimed to distinguish between different periodicity
types of the same period length. The method is to be used in conjunction with
the cyclic profile alignment, and this combination is able to reveal
structure-related or function-related patterns of latent periodicity. Possible
origins of the periodic structure of protein kinase active sites are discussed.
Summarizing, we presume that latent periodicity is the common property of many
catalytic protein domains.
| [
{
"created": "Mon, 6 Sep 2004 12:48:17 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Laskin",
"Andrew A.",
""
],
[
"Kudryashov",
"Nikolai A.",
""
],
[
"Skryabin",
"Konstantin G.",
""
],
[
"Korotkov",
"Eugene V.",
""
]
] | We identified latent periodicity in catalytic domains of approximately 85% of serine/threonine and tyrosine protein kinases. Similar results were obtained for other 22 protein domains. We also designed the method of noise decomposition, which is aimed to distinguish between different periodicity types of the same period length. The method is to be used in conjunction with the cyclic profile alignment, and this combination is able to reveal structure-related or function-related patterns of latent periodicity. Possible origins of the periodic structure of protein kinase active sites are discussed. Summarizing, we presume that latent periodicity is the common property of many catalytic protein domains. |
1911.02294 | Steven Lade | Steven J. Lade and Brian H. Walker and L. Jamila Haider | Resilience as pathway diversity: Linking systems, individual and
temporal perspectives on resilience | 1 box, 1 table, 4 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Approaches to understanding resilience from psychology and sociology
emphasise individuals' agency but obscure systemic factors. Approaches to
understanding resilience stemming from ecology emphasise system dynamics such
as feedbacks but obscure individuals. Approaches from both psychology and
ecology examine the actions or attractors available in the present, but neglect
how actions taken now can affect the configuration of the social-ecological
system in the future. Here, we propose an extension to resilience theory, which
we label 'pathway diversity', that links existing individual, systems and
temporal theories of resilience. In our theory of pathway diversity, resilience
is greater if more actions are currently available and can be maintained or
enhanced into the future. Using a toy model of an agricultural
social-ecological system, we show how pathway diversity could deliver a
context-sensitive method of assessing resilience and guiding planning. Using a
toy state-and-transition model of a poverty trap, we show how pathway diversity
is generally consistent with existing definitions of resilience and can
illuminate long-standing questions about normative and descriptive resilience.
Our results show that pathway diversity advances both theoretical understanding
and practical tools for building resilience.
| [
{
"created": "Wed, 6 Nov 2019 10:37:40 GMT",
"version": "v1"
}
] | 2019-11-07 | [
[
"Lade",
"Steven J.",
""
],
[
"Walker",
"Brian H.",
""
],
[
"Haider",
"L. Jamila",
""
]
] | Approaches to understanding resilience from psychology and sociology emphasise individuals' agency but obscure systemic factors. Approaches to understanding resilience stemming from ecology emphasise system dynamics such as feedbacks but obscure individuals. Approaches from both psychology and ecology examine the actions or attractors available in the present, but neglect how actions taken now can affect the configuration of the social-ecological system in the future. Here, we propose an extension to resilience theory, which we label 'pathway diversity', that links existing individual, systems and temporal theories of resilience. In our theory of pathway diversity, resilience is greater if more actions are currently available and can be maintained or enhanced into the future. Using a toy model of an agricultural social-ecological system, we show how pathway diversity could deliver a context-sensitive method of assessing resilience and guiding planning. Using a toy state-and-transition model of a poverty trap, we show how pathway diversity is generally consistent with existing definitions of resilience and can illuminate long-standing questions about normative and descriptive resilience. Our results show that pathway diversity advances both theoretical understanding and practical tools for building resilience. |
1708.03499 | Reinhard B\"urger | Reinhard B\"urger | Two-locus clines on the real line with a step environment | null | Theoretical Population Biology 117, 1-22 (2017) | 10.1016/j.tpb.2017.08.002 | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The shape of allele-frequency clines maintained by migration-selection
balance depends not only on the properties of migration and selection, but also
on the dominance relations among alleles and on linkage to other loci under
selection. We investigate a two-locus model in which two diallelic, recombining
loci are subject to selection caused by an abrupt environmental change. The
habitat is one-dimensional and unbounded, selection at each locus is modeled by
step functions such that in one region one allele at each locus is advantageous
and in the other deleterious. We admit an environmentally independent,
intermediate degree of dominance at both loci, including complete dominance.
First, we derive an explicit expression for the single-locus cline with
dominance, thus generalizing classical results by Haldane (1948). We show that
the slope of the cline in the center (at the step) or, equivalently, the width
of the cline, is independent of the degree of dominance. Second, under the
assumption of strong recombination relative to selection and migration, the
first-order approximations of the allele-frequency clines at each of the loci
and of the linkage disequilibrium are derived. This may be interpreted as the
quasi-linkage-equilibrium approximation of the two-locus cline. Explicit
asymptotic expressions for the clines are deduced as $x\to\pm\infty$. For
equivalent loci, explicit expressions for the whole clines are derived. The
influence of dominance and of linkage on the slope of the cline in the center
and on a global measure of steepness are investigated. This global measure
reflects the influence of dominance. Finally, the accuracy of the
approximations and the dependence of the shape of the two-locus cline on the
full range of recombination rates is explored by numerical integration of the
underlying system of partial differential equations.
| [
{
"created": "Fri, 11 Aug 2017 10:34:00 GMT",
"version": "v1"
}
] | 2018-12-18 | [
[
"Bürger",
"Reinhard",
""
]
] | The shape of allele-frequency clines maintained by migration-selection balance depends not only on the properties of migration and selection, but also on the dominance relations among alleles and on linkage to other loci under selection. We investigate a two-locus model in which two diallelic, recombining loci are subject to selection caused by an abrupt environmental change. The habitat is one-dimensional and unbounded, selection at each locus is modeled by step functions such that in one region one allele at each locus is advantageous and in the other deleterious. We admit an environmentally independent, intermediate degree of dominance at both loci, including complete dominance. First, we derive an explicit expression for the single-locus cline with dominance, thus generalizing classical results by Haldane (1948). We show that the slope of the cline in the center (at the step) or, equivalently, the width of the cline, is independent of the degree of dominance. Second, under the assumption of strong recombination relative to selection and migration, the first-order approximations of the allele-frequency clines at each of the loci and of the linkage disequilibrium are derived. This may be interpreted as the quasi-linkage-equilibrium approximation of the two-locus cline. Explicit asymptotic expressions for the clines are deduced as $x\to\pm\infty$. For equivalent loci, explicit expressions for the whole clines are derived. The influence of dominance and of linkage on the slope of the cline in the center and on a global measure of steepness are investigated. This global measure reflects the influence of dominance. Finally, the accuracy of the approximations and the dependence of the shape of the two-locus cline on the full range of recombination rates is explored by numerical integration of the underlying system of partial differential equations. |
1506.05538 | Qingming Tang | Qingming Tang, Sheng Wang, Jian Peng, Jianzhu Ma and Jinbo Xu | Bermuda: Bidirectional de novo assembly of transcripts with new insights
for handling uneven coverage | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: RNA-seq has made feasible the analysis of a whole set of
expressed mRNAs. Mapping-based assembly of RNA-seq reads sometimes is
infeasible due to lack of high-quality references. However, de novo assembly is
very challenging due to uneven expression levels among transcripts and also the
read coverage variation within a single transcript. Existing methods either
apply de Bruijn graphs of single-sized k-mers to assemble the full set of
transcripts, or conduct multiple runs of assembly, but still apply graphs of
single-sized k-mers at each run. However, a single k-mer size is not suitable
for all the regions of the transcripts with varied coverage. Contribution: This
paper presents a de novo assembler Bermuda with new insights for handling
uneven coverage. Opposed to existing methods that use a single k-mer size for
all the transcripts in each run of assembly, Bermuda self-adaptively uses a few
k-mer sizes to assemble different regions of a single transcript according to
their local coverage. As such, Bermuda can deal with uneven expression levels
and coverage not only among transcripts, but also within a single transcript.
Extensive tests show that Bermuda outperforms popular de novo assemblers in
reconstructing unevenly-expressed transcripts with longer length, better
contiguity and lower redundancy. Further, Bermuda is computationally efficient
with moderate memory consumption.
| [
{
"created": "Thu, 18 Jun 2015 03:06:55 GMT",
"version": "v1"
}
] | 2015-06-19 | [
[
"Tang",
"Qingming",
""
],
[
"Wang",
"Sheng",
""
],
[
"Peng",
"Jian",
""
],
[
"Ma",
"Jianzhu",
""
],
[
"Xu",
"Jinbo",
""
]
] | Motivation: RNA-seq has made feasible the analysis of a whole set of expressed mRNAs. Mapping-based assembly of RNA-seq reads sometimes is infeasible due to lack of high-quality references. However, de novo assembly is very challenging due to uneven expression levels among transcripts and also the read coverage variation within a single transcript. Existing methods either apply de Bruijn graphs of single-sized k-mers to assemble the full set of transcripts, or conduct multiple runs of assembly, but still apply graphs of single-sized k-mers at each run. However, a single k-mer size is not suitable for all the regions of the transcripts with varied coverage. Contribution: This paper presents a de novo assembler Bermuda with new insights for handling uneven coverage. Opposed to existing methods that use a single k-mer size for all the transcripts in each run of assembly, Bermuda self-adaptively uses a few k-mer sizes to assemble different regions of a single transcript according to their local coverage. As such, Bermuda can deal with uneven expression levels and coverage not only among transcripts, but also within a single transcript. Extensive tests show that Bermuda outperforms popular de novo assemblers in reconstructing unevenly-expressed transcripts with longer length, better contiguity and lower redundancy. Further, Bermuda is computationally efficient with moderate memory consumption. |
2112.03978 | Mikail Khona | Mikail Khona, Ila R. Fiete | Attractor and integrator networks in the brain | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | In this review, we describe the singular success of attractor neural network
models in describing how the brain maintains persistent activity states for
working memory, error-corrects, and integrates noisy cues. We consider the
mechanisms by which simple and forgetful units can organize to collectively
generate dynamics on the long time-scales required for such computations. We
discuss the myriad potential uses of attractor dynamics for computation in the
brain, and showcase notable examples of brain systems in which inherently
low-dimensional continuous attractor dynamics have been concretely and
rigorously identified. Thus, it is now possible to conclusively state that the
brain constructs and uses such systems for computation. Finally, we look ahead
by highlighting recent theoretical advances in understanding how the
fundamental tradeoffs between robustness and capacity and between structure and
flexibility can be overcome by reusing and recombining the same set of modular
attractors for multiple functions, so they together produce representations
that are structurally constrained and robust but exhibit high capacity and are
flexible.
| [
{
"created": "Tue, 7 Dec 2021 20:39:56 GMT",
"version": "v1"
},
{
"created": "Thu, 9 Dec 2021 19:16:18 GMT",
"version": "v2"
},
{
"created": "Wed, 2 Mar 2022 03:22:58 GMT",
"version": "v3"
}
] | 2022-03-03 | [
[
"Khona",
"Mikail",
""
],
[
"Fiete",
"Ila R.",
""
]
] | In this review, we describe the singular success of attractor neural network models in describing how the brain maintains persistent activity states for working memory, error-corrects, and integrates noisy cues. We consider the mechanisms by which simple and forgetful units can organize to collectively generate dynamics on the long time-scales required for such computations. We discuss the myriad potential uses of attractor dynamics for computation in the brain, and showcase notable examples of brain systems in which inherently low-dimensional continuous attractor dynamics have been concretely and rigorously identified. Thus, it is now possible to conclusively state that the brain constructs and uses such systems for computation. Finally, we look ahead by highlighting recent theoretical advances in understanding how the fundamental tradeoffs between robustness and capacity and between structure and flexibility can be overcome by reusing and recombining the same set of modular attractors for multiple functions, so they together produce representations that are structurally constrained and robust but exhibit high capacity and are flexible. |
2107.14471 | Sandeep Juneja | Sandeep Juneja, Daksh Mittal | Potential 3rd COVID Wave in Mumbai: Scenario Analysis | 22 pages, 23 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The second wave of Covid-19 that started in mid-February 2021 in Mumbai is
now subsiding. Increasingly the focus amongst the policy makers and general
public is on the potential third wave. Due to uncertainties regarding emergence
of new variants and reinfections, instead of projecting our best guess
scenario, in this report we conduct an extensive scenario analysis for Mumbai
and track peak fatalities in the coming months in each of these scenarios. Our
key conclusions are - As per our model, about 80% of Mumbai population has been
exposed to Covid-19 by June 1, 2021. Under the assumption that all who are
exposed have immunity against further infection, it is unlikely that Mumbai
would see a large third wave. It is the reinfections that may lead to a large
wave. Reinfections could occur because of declining antibodies amongst the
infected as well as by variants that can break through the immunity provided by
prior infections. Even under a reasonably pessimistic scenario we observe the
resulting peak to be no larger than that under the second wave. We further
observe that under the scenario where the reinfections are mild so that they
affect the fatality figures negligibly, where the new variants (beyond the
existing delta variant) have a mild impact, as the city opens up, we observe a
small wave in the coming months. However, if by then the vaccine coverage is
extensive, this wave will be barely noticeable. We also plot $R_t$, the
infection growth rate at time $t$, and highlight some interesting observations.
| [
{
"created": "Fri, 30 Jul 2021 07:47:33 GMT",
"version": "v1"
}
] | 2021-08-02 | [
[
"Juneja",
"Sandeep",
""
],
[
"Mittal",
"Daksh",
""
]
] | The second wave of Covid-19 that started in mid-February 2021 in Mumbai is now subsiding. Increasingly the focus amongst the policy makers and general public is on the potential third wave. Due to uncertainties regarding emergence of new variants and reinfections, instead of projecting our best guess scenario, in this report we conduct an extensive scenario analysis for Mumbai and track peak fatalities in the coming months in each of these scenarios. Our key conclusions are - As per our model, about 80% of Mumbai population has been exposed to Covid-19 by June 1, 2021. Under the assumption that all who are exposed have immunity against further infection, it is unlikely that Mumbai would see a large third wave. It is the reinfections that may lead to a large wave. Reinfections could occur because of declining antibodies amongst the infected as well as by variants that can break through the immunity provided by prior infections. Even under a reasonably pessimistic scenario we observe the resulting peak to be no larger than that under the second wave. We further observe that under the scenario where the reinfections are mild so that they affect the fatality figures negligibly, where the new variants (beyond the existing delta variant) have a mild impact, as the city opens up, we observe a small wave in the coming months. However, if by then the vaccine coverage is extensive, this wave will be barely noticeable. We also plot $R_t$, the infection growth rate at time $t$, and highlight some interesting observations. |
2104.00145 | Becket Ebitz | R. Becket Ebitz and Benjamin Y. Hayden | The population doctrine in cognitive neuroscience | 35 pages, 3 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | A major shift is happening within neurophysiology: a population doctrine is
drawing level with the single-neuron doctrine that has long dominated the
field. Population-level ideas have so far had their greatest impact in motor
neuroscience, but they hold great promise for resolving open questions in
cognition as well. Here, we codify the population doctrine and survey recent
work that leverages this view to specifically probe cognition. Our discussion
is organized around five core concepts that provide a foundation for
population-level thinking: (1) state spaces, (2) manifolds, (3) coding
dimensions, (4) subspaces, and (5) dynamics. The work we review illustrates the
progress and promise that population neurophysiology holds for cognitive
neuroscience$-$for delivering new insight into attention, working memory,
decision-making, executive function, learning, and reward processing.
| [
{
"created": "Wed, 31 Mar 2021 22:25:16 GMT",
"version": "v1"
},
{
"created": "Tue, 13 Jul 2021 19:13:23 GMT",
"version": "v2"
}
] | 2021-07-15 | [
[
"Ebitz",
"R. Becket",
""
],
[
"Hayden",
"Benjamin Y.",
""
]
] | A major shift is happening within neurophysiology: a population doctrine is drawing level with the single-neuron doctrine that has long dominated the field. Population-level ideas have so far had their greatest impact in motor neuroscience, but they hold great promise for resolving open questions in cognition as well. Here, we codify the population doctrine and survey recent work that leverages this view to specifically probe cognition. Our discussion is organized around five core concepts that provide a foundation for population-level thinking: (1) state spaces, (2) manifolds, (3) coding dimensions, (4) subspaces, and (5) dynamics. The work we review illustrates the progress and promise that population neurophysiology holds for cognitive neuroscience$-$for delivering new insight into attention, working memory, decision-making, executive function, learning, and reward processing. |
1212.5383 | Indrani Bose | Mainak Pal, Amit Kumar Pal, Sayantari Ghosh and Indrani Bose | Early signatures of regime shifts in gene expression dynamics | 13 Pages, 12 Figures, revtex4-1, Published version | Phys. Biol. 10 (2013) 036010 | 10.1088/1478-3975/10/3/036010 | null | q-bio.MN cond-mat.stat-mech q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, a large number of studies have been carried out on the early
signatures of sudden regime shifts in systems as diverse as ecosystems,
financial markets, population biology and complex diseases. Signatures of
regime shifts in gene expression dynamics are less systematically investigated.
In this paper, we consider sudden regime shifts in the gene expression dynamics
described by a fold-bifurcation model involving bistability and hysteresis. We
consider two alternative models, Models 1 and 2, of competence development in
the bacterial population B. subtilis and determine some early signatures of the
regime shifts between competence and noncompetence. We use both deterministic
and stochastic formalisms for the purpose of our study. The early signatures
studied include the critical slowing down as a transition point is approached,
rising variance and the lag-1 autocorrelation function, skewness and a ratio of
two mean first passage times. Some of the signatures could provide the
experimental basis for distinguishing between bistability and excitability as
the correct mechanism for the development of competence.
| [
{
"created": "Fri, 21 Dec 2012 10:22:50 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Apr 2013 11:28:17 GMT",
"version": "v2"
},
{
"created": "Thu, 16 May 2013 08:02:38 GMT",
"version": "v3"
}
] | 2015-06-12 | [
[
"Pal",
"Mainak",
""
],
[
"Pal",
"Amit Kumar",
""
],
[
"Ghosh",
"Sayantari",
""
],
[
"Bose",
"Indrani",
""
]
] | Recently, a large number of studies have been carried out on the early signatures of sudden regime shifts in systems as diverse as ecosystems, financial markets, population biology and complex diseases. Signatures of regime shifts in gene expression dynamics are less systematically investigated. In this paper, we consider sudden regime shifts in the gene expression dynamics described by a fold-bifurcation model involving bistability and hysteresis. We consider two alternative models, Models 1 and 2, of competence development in the bacterial population B. subtilis and determine some early signatures of the regime shifts between competence and noncompetence. We use both deterministic and stochastic formalisms for the purpose of our study. The early signatures studied include the critical slowing down as a transition point is approached, rising variance and the lag-1 autocorrelation function, skewness and a ratio of two mean first passage times. Some of the signatures could provide the experimental basis for distinguishing between bistability and excitability as the correct mechanism for the development of competence. |
2101.12151 | Takashi Odagaki | Takashi Odagaki | Self-organization of oscillation in an epidemic model for COVID-19 | 11 pages, 6 figures | null | 10.1016/j.physa.2021.125925 | null | q-bio.PE physics.med-ph physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | On the basis of a compartment model, the epidemic curve is investigated when
the net rate $\lambda$ of change of the number of infected individuals $I$ is
given by an ellipse in the $\lambda$-$I$ plane which is supported in
$[I_{\ell}, I_h]$. With $a \equiv (I_h - I_{\ell})/(I_h + I_{\ell})$, it is
shown that (1) when $a < 1$ or $I_{\ell} >0$, oscillation of the infection
curve is self-organized and the period of the oscillation is in proportion to
the ratio of the difference $ (I_h - I_{\ell})$ and the geometric mean
$\sqrt{I_h I_{\ell}}$ of $I_h$ and $I_{\ell}$, (2) when $a = 1$, the infection
curve shows a critical behavior where it decays obeying a power law function
with exponent $-2$ in the long time limit after a peak, and (3) when $a > 1$,
the infection curve decays exponentially in the long time limit after a peak.
The present result indicates that the pandemic can be controlled by a measure
which makes $I_{\ell} < 0$.
| [
{
"created": "Wed, 27 Jan 2021 02:09:10 GMT",
"version": "v1"
},
{
"created": "Tue, 16 Feb 2021 11:45:55 GMT",
"version": "v2"
}
] | 2021-04-07 | [
[
"Odagaki",
"Takashi",
""
]
] | On the basis of a compartment model, the epidemic curve is investigated when the net rate $\lambda$ of change of the number of infected individuals $I$ is given by an ellipse in the $\lambda$-$I$ plane which is supported in $[I_{\ell}, I_h]$. With $a \equiv (I_h - I_{\ell})/(I_h + I_{\ell})$, it is shown that (1) when $a < 1$ or $I_{\ell} >0$, oscillation of the infection curve is self-organized and the period of the oscillation is in proportion to the ratio of the difference $ (I_h - I_{\ell})$ and the geometric mean $\sqrt{I_h I_{\ell}}$ of $I_h$ and $I_{\ell}$, (2) when $a = 1$, the infection curve shows a critical behavior where it decays obeying a power law function with exponent $-2$ in the long time limit after a peak, and (3) when $a > 1$, the infection curve decays exponentially in the long time limit after a peak. The present result indicates that the pandemic can be controlled by a measure which makes $I_{\ell} < 0$. |
1306.5228 | Anatoly Sorokin | S.G.Kamzolova, P.M.Beskaravainy, A.A.Osypov, T.R.Dzhelyadin,
E.A.Temlyakova and A.A.Sorokin | Electrostatic map of T7 DNA. Comparative analysis of functional and
electrostatic properties of T7 RNA polymerase specific promoters | This is an Author's Original Manuscript of an article submitted for
consideration in the Journal of Journal of Biomolecular Structure & Dynamics | null | 10.1080/07391102.2013.819298 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The entire T7 bacteriophage genome contains 39937 base pairs (Database NCBI
RefSeq N1001604). Here, electrostatic potential distribution around double
helical T7 DNA was calculated by Coulomb method using the computer program of
Sorokin A.A. Electrostatic profiles of 17 promoters recognized by T7 phage
specific RNA polymerase were analyzed. It was shown that electrostatic profiles
of all T7 RNA polymerase specific promoters can be characterized by distinctive
motifs which are specific for each promoter class. Comparative analysis of
electrostatic profiles of native T7 promoters of different classes demonstrates
that T7 RNA polymerase can differentiate them due to their electrostatic
features.
| [
{
"created": "Mon, 24 Jun 2013 11:07:00 GMT",
"version": "v1"
}
] | 2013-06-25 | [
[
"Kamzolova",
"S. G.",
""
],
[
"Beskaravainy",
"P. M.",
""
],
[
"Osypov",
"A. A.",
""
],
[
"Dzhelyadin",
"T. R.",
""
],
[
"Temlyakova",
"E. A.",
""
],
[
"Sorokin",
"A. A.",
""
]
] | The entire T7 bacteriophage genome contains 39937 base pairs (Database NCBI RefSeq N1001604). Here, electrostatic potential distribution around double helical T7 DNA was calculated by Coulomb method using the computer program of Sorokin A.A. Electrostatic profiles of 17 promoters recognized by T7 phage specific RNA polymerase were analyzed. It was shown that electrostatic profiles of all T7 RNA polymerase specific promoters can be characterized by distinctive motifs which are specific for each promoter class. Comparative analysis of electrostatic profiles of native T7 promoters of different classes demonstrates that T7 RNA polymerase can differentiate them due to their electrostatic features. |
1904.04544 | Ulisse Ferrari | Oleksandr Sorochynskyi, St\'ephane Deny, Olivier Marre, Ulisse Ferrari | Predicting synchronous firing of large neural populations from
sequential recordings | null | null | null | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A major goal in neuroscience is to understand how populations of neurons code
for stimuli or actions. While the number of neurons that can be recorded
simultaneously is increasing at a fast pace, in most cases these recordings
cannot access a complete population. In particular, it is hard to
simultaneously record all the neurons of the same type in a given area. Recent
progress have made possible to profile each recorded neuron in a given area
thanks to genetic and physiological tools, and to pool together recordings from
neurons of the same type across different experimental sessions. However, it is
unclear how to infer the activity of a full population of neurons of the same
type from these sequential recordings. Neural networks exhibit collective
behaviour, e.g. noise correlations and synchronous activity, that are not
directly captured by a conditionally-independent model that would just put
together the spike trains from sequential recordings. Here we show that we can
infer the activity of a full population of retina ganglion cells from
sequential recordings, using a novel method based on copula distributions and
maximum entropy modeling. From just the spiking response of each ganglion cell
to a repeated stimulus, and a few pairwise recordings, we could predict the
noise correlations using copulas, and then the full activity of a large
population of ganglion cells of the same type using maximum entropy modeling.
Remarkably, we could generalize to predict the population responses to
different stimuli and even to different experiments. We could therefore use our
method to construct a very large population merging cells' responses from
different experiments. We predicted synchronous activity accurately and showed
it grew substantially with the number of neurons. This approach is a promising
way to infer population activity from sequential recordings in sensory areas.
| [
{
"created": "Tue, 9 Apr 2019 08:56:04 GMT",
"version": "v1"
}
] | 2019-04-10 | [
[
"Sorochynskyi",
"Oleksandr",
""
],
[
"Deny",
"Stéphane",
""
],
[
"Marre",
"Olivier",
""
],
[
"Ferrari",
"Ulisse",
""
]
] | A major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing at a fast pace, in most cases these recordings cannot access a complete population. In particular, it is hard to simultaneously record all the neurons of the same type in a given area. Recent progress have made possible to profile each recorded neuron in a given area thanks to genetic and physiological tools, and to pool together recordings from neurons of the same type across different experimental sessions. However, it is unclear how to infer the activity of a full population of neurons of the same type from these sequential recordings. Neural networks exhibit collective behaviour, e.g. noise correlations and synchronous activity, that are not directly captured by a conditionally-independent model that would just put together the spike trains from sequential recordings. Here we show that we can infer the activity of a full population of retina ganglion cells from sequential recordings, using a novel method based on copula distributions and maximum entropy modeling. From just the spiking response of each ganglion cell to a repeated stimulus, and a few pairwise recordings, we could predict the noise correlations using copulas, and then the full activity of a large population of ganglion cells of the same type using maximum entropy modeling. Remarkably, we could generalize to predict the population responses to different stimuli and even to different experiments. We could therefore use our method to construct a very large population merging cells' responses from different experiments. We predicted synchronous activity accurately and showed it grew substantially with the number of neurons. This approach is a promising way to infer population activity from sequential recordings in sensory areas. |
1304.3351 | Fan Zhang | Fan Zhang, Ruoyan Chen, Dongbing Liu, Xiaotian Yao, Guoqing Li, Yabin
Jin, Chang Yu, Yingrui Li and Lachlan Coin | YHap: software for probabilistic assignment of Y haplogroups from
population re-sequencing data | 2 pages 3 tables | null | null | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Y haplogroup analyses are an important component of genealogical
reconstruction, population genetic analyses, medical genetics and forensics.
These fields are increasingly moving towards use of low-coverage, high
throughput sequencing. However, there is as yet no software available for using
sequence data to assign Y haplogroup groups probabilistically, such that the
posterior probability of assignment fully reflects the information present in
the data, and borrows information across all samples sequenced from a
population. YHap addresses this problem.
| [
{
"created": "Thu, 11 Apr 2013 16:01:38 GMT",
"version": "v1"
},
{
"created": "Fri, 10 May 2013 09:49:20 GMT",
"version": "v2"
}
] | 2013-05-13 | [
[
"Zhang",
"Fan",
""
],
[
"Chen",
"Ruoyan",
""
],
[
"Liu",
"Dongbing",
""
],
[
"Yao",
"Xiaotian",
""
],
[
"Li",
"Guoqing",
""
],
[
"Jin",
"Yabin",
""
],
[
"Yu",
"Chang",
""
],
[
"Li",
"Yingrui",
... | Y haplogroup analyses are an important component of genealogical reconstruction, population genetic analyses, medical genetics and forensics. These fields are increasingly moving towards use of low-coverage, high throughput sequencing. However, there is as yet no software available for using sequence data to assign Y haplogroup groups probabilistically, such that the posterior probability of assignment fully reflects the information present in the data, and borrows information across all samples sequenced from a population. YHap addresses this problem. |
1811.02861 | Sven Goedeke | Felipe Yaroslav Kalle Kossio, Sven Goedeke, Benjamin van den Akker,
Borja Ibarz, Raoul-Martin Memmesheimer | Growing Critical: Self-Organized Criticality in a Developing Neural
System | 6 pages, 4 figures, supplemental material: 10 pages, 7 figures | Phys. Rev. Lett. 121(5), 058301, 2018 | 10.1103/PhysRevLett.121.058301 | null | q-bio.NC cond-mat.dis-nn nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Experiments in various neural systems found avalanches: bursts of activity
with characteristics typical for critical dynamics. A possible explanation for
their occurrence is an underlying network that self-organizes into a critical
state. We propose a simple spiking model for developing neural networks,
showing how these may "grow into" criticality. Avalanches generated by our
model correspond to clusters of widely applied Hawkes processes. We
analytically derive the cluster size and duration distributions and find that
they agree with those of experimentally observed neuronal avalanches.
| [
{
"created": "Wed, 7 Nov 2018 12:47:11 GMT",
"version": "v1"
}
] | 2018-11-08 | [
[
"Kossio",
"Felipe Yaroslav Kalle",
""
],
[
"Goedeke",
"Sven",
""
],
[
"Akker",
"Benjamin van den",
""
],
[
"Ibarz",
"Borja",
""
],
[
"Memmesheimer",
"Raoul-Martin",
""
]
] | Experiments in various neural systems found avalanches: bursts of activity with characteristics typical for critical dynamics. A possible explanation for their occurrence is an underlying network that self-organizes into a critical state. We propose a simple spiking model for developing neural networks, showing how these may "grow into" criticality. Avalanches generated by our model correspond to clusters of widely applied Hawkes processes. We analytically derive the cluster size and duration distributions and find that they agree with those of experimentally observed neuronal avalanches. |
1312.3070 | Aur \'elien Sikora | Aur\'elien Sikora, Javier Ram\'on-Azc\'on, Kyongwan Kim, Kelley
Reaves, Hikaru Nakazawa, Mitsuo Umetsu, Izumi Kumagai, Tadafumi Adschiri,
Hitoshi Shiku, Tomokazu Matsue, Wonmuk Hwang and Winfried Teizer | Molecular Motor-Powered Shuttles along Multi-walled Carbon Nanotube
Tracks | 19 pages, 4 figures | null | 10.1021/nl4042388 | null | q-bio.BM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As a complementary tool to nanofluidics, biomolecular based transport is
envisioned for nanotechnological devices. We report a new method for guiding
microtubule shuttles on multi-walled carbon nanotube tracks, aligned by
dielectrophoresis on a functionalized surface. In the absence of electric field
and in fluid flow, alignment is maintained. The directed translocation of
kinesin propelled microtubules has been investigated using fluorescence
microscopy. To our knowledge, this is the first demonstration of microtubules
gliding along carbon nanotubes.
| [
{
"created": "Wed, 11 Dec 2013 08:20:06 GMT",
"version": "v1"
}
] | 2015-06-18 | [
[
"Sikora",
"Aurélien",
""
],
[
"Ramón-Azcón",
"Javier",
""
],
[
"Kim",
"Kyongwan",
""
],
[
"Reaves",
"Kelley",
""
],
[
"Nakazawa",
"Hikaru",
""
],
[
"Umetsu",
"Mitsuo",
""
],
[
"Kumagai",
"Izumi",
""
],
... | As a complementary tool to nanofluidics, biomolecular based transport is envisioned for nanotechnological devices. We report a new method for guiding microtubule shuttles on multi-walled carbon nanotube tracks, aligned by dielectrophoresis on a functionalized surface. In the absence of electric field and in fluid flow, alignment is maintained. The directed translocation of kinesin propelled microtubules has been investigated using fluorescence microscopy. To our knowledge, this is the first demonstration of microtubules gliding along carbon nanotubes. |
0912.4060 | Daniel Soudry | Daniel Soudry, Ron Meir | History dependent dynamics in a generic model of ion channels - an
analytic study | Several small modifications and corrections were made to last
version, due to reviewer comments. The only major change - we added two
further sub-sections in Methods (sections 4.1.4 and 4.3.3) and one section
(G) in the Supplementary Information. | Front.Comput.Neurosci.4:3.(2010) | 10.3389/fncom.2010.00003 | null | q-bio.SC q-bio.NC q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent experiments have demonstrated that the timescale of adaptation of
single neurons and ion channel populations to stimuli slows down as the length
of stimulation increases; in fact, no upper bound on temporal time-scales seems
to exist in such systems. Furthermore, patch clamp experiments on single ion
channels have hinted at the existence of large, mostly unobservable,
inactivation state spaces within a single ion channel. This raises the question
of the relation between this multitude of inactivation states and the observed
behavior. In this work we propose a minimal model for ion channel dynamics
which does not assume any specific structure of the inactivation state space.
The model is simple enough to render an analytical study possible. This leads
to a clear and concise explanation of the experimentally observed exponential
history-dependent relaxation in sodium channels in a voltage clamp setting, and
shows that their recovery rate from slow inactivation must be voltage
dependent. Furthermore, we predict that history-dependent relaxation cannot be
created by overly sparse spiking activity. While the model was created with ion
channel populations in mind, its simplicity and genericalness render it a good
starting point for modeling similar effects in other systems, and for scaling
up to higher levels such as single neurons which are also known to exhibit
multiple time scales.
| [
{
"created": "Sun, 20 Dec 2009 22:47:39 GMT",
"version": "v1"
},
{
"created": "Wed, 28 Apr 2010 18:59:27 GMT",
"version": "v2"
}
] | 2010-04-29 | [
[
"Soudry",
"Daniel",
""
],
[
"Meir",
"Ron",
""
]
] | Recent experiments have demonstrated that the timescale of adaptation of single neurons and ion channel populations to stimuli slows down as the length of stimulation increases; in fact, no upper bound on temporal time-scales seems to exist in such systems. Furthermore, patch clamp experiments on single ion channels have hinted at the existence of large, mostly unobservable, inactivation state spaces within a single ion channel. This raises the question of the relation between this multitude of inactivation states and the observed behavior. In this work we propose a minimal model for ion channel dynamics which does not assume any specific structure of the inactivation state space. The model is simple enough to render an analytical study possible. This leads to a clear and concise explanation of the experimentally observed exponential history-dependent relaxation in sodium channels in a voltage clamp setting, and shows that their recovery rate from slow inactivation must be voltage dependent. Furthermore, we predict that history-dependent relaxation cannot be created by overly sparse spiking activity. While the model was created with ion channel populations in mind, its simplicity and genericalness render it a good starting point for modeling similar effects in other systems, and for scaling up to higher levels such as single neurons which are also known to exhibit multiple time scales. |
2205.09637 | Oliver Jensen | Oliver E. Jensen, Christopher K. Revell | Couple stresses and discrete potentials in the vertex model of cellular
monolayers | 8 figures, 1 table | Biomech. Model. Mechanobiol. (2022) | 10.1007/s10237-022-01620-2 | null | q-bio.TO cond-mat.soft | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The vertex model is widely used to simulate the mechanical properties of
confluent epithelia and other multicellular tissues. This inherently discrete
framework allows a Cauchy stress to be attributed to each cell, and its
symmetric component has been widely reported, at least for planar monolayers.
Here we consider the stress attributed to the neighbourhood of each tricellular
junction, evaluating in particular its leading-order antisymmetric component
and the associated couple stresses, which characterise the degree to which
individual cells experience (and resist) in-plane bending deformations. We
develop discrete potential theory for localised monolayers having disordered
internal structure and use this to derive the analogues of Airy and Mindlin
stress functions. These scalar potentials typically have broad-banded spectra,
highlighting the contributions of small-scale defects and boundary-layers to
global stress patterns. An affine approximation attributes couple stresses to
pressure differences between cells sharing a trijunction, but simulations
indicate an additional role for non-affine deformations.
| [
{
"created": "Thu, 19 May 2022 15:59:06 GMT",
"version": "v1"
},
{
"created": "Thu, 21 Jul 2022 16:44:13 GMT",
"version": "v2"
}
] | 2022-10-10 | [
[
"Jensen",
"Oliver E.",
""
],
[
"Revell",
"Christopher K.",
""
]
] | The vertex model is widely used to simulate the mechanical properties of confluent epithelia and other multicellular tissues. This inherently discrete framework allows a Cauchy stress to be attributed to each cell, and its symmetric component has been widely reported, at least for planar monolayers. Here we consider the stress attributed to the neighbourhood of each tricellular junction, evaluating in particular its leading-order antisymmetric component and the associated couple stresses, which characterise the degree to which individual cells experience (and resist) in-plane bending deformations. We develop discrete potential theory for localised monolayers having disordered internal structure and use this to derive the analogues of Airy and Mindlin stress functions. These scalar potentials typically have broad-banded spectra, highlighting the contributions of small-scale defects and boundary-layers to global stress patterns. An affine approximation attributes couple stresses to pressure differences between cells sharing a trijunction, but simulations indicate an additional role for non-affine deformations. |
1210.6482 | Steven Watterson | Ozgur Akman, Steven Watterson, Andrew Parton, Nigel Binns, Andrew
Millar and Peter Ghazal | Digital clocks: simple Boolean models can quantitatively describe
circadian systems | 26 pages plus nine figures (in separate files) | Journal of the Royal Society: Interface 9: 74. 2365-2382 (2012) | 10.1098/rsif.2012.0080 | null | q-bio.MN q-bio.CB q-bio.QM q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The gene networks that comprise the circadian clock modulate biological
function across a range of scales, from gene expression to performance and
adaptive behaviour. The clock functions by generating endogenous rhythms that
can be entrained to the external 24-h day?night cycle, enabling organisms to
optimally time biochemical processes relative to dawn and dusk. In recent
years, computational models based on differential equations have become useful
tools for dissecting and quantifying the complex regulatory relationships
underlying the clock's oscillatory dynamics. However, optimizing the large
parameter sets characteristic of these models places intense demands on both
computational and experimental resources, limiting the scope of in silico
studies. Here, we develop an approach based on Boolean logic that dramatically
reduces the parametrization, making the state and parameter spaces finite and
tractable. We introduce efficient methods for fitting Boolean models to
molecular data, successfully demonstrating their application to synthetic time
courses generated by a number of established clock models, as well as
experimental expression levels measured using luciferase imaging. Our results
indicate that despite their relative simplicity, logic models can (i) simulate
circadian oscillations with the correct, experimentally observed phase
relationships among genes and (ii) flexibly entrain to light stimuli,
reproducing the complex responses to variations in daylength generated by more
detailed differential equation formulations. Our work also demonstrates that
logic models have sufficient predictive power to identify optimal regulatory
structures from experimental data. By presenting the first Boolean models of
circadian circuits together with general techniques for their optimization, we
hope to establish a new framework for the systematic modelling of complex
clocks.
| [
{
"created": "Wed, 24 Oct 2012 10:52:30 GMT",
"version": "v1"
}
] | 2012-10-25 | [
[
"Akman",
"Ozgur",
""
],
[
"Watterson",
"Steven",
""
],
[
"Parton",
"Andrew",
""
],
[
"Binns",
"Nigel",
""
],
[
"Millar",
"Andrew",
""
],
[
"Ghazal",
"Peter",
""
]
] | The gene networks that comprise the circadian clock modulate biological function across a range of scales, from gene expression to performance and adaptive behaviour. The clock functions by generating endogenous rhythms that can be entrained to the external 24-h day?night cycle, enabling organisms to optimally time biochemical processes relative to dawn and dusk. In recent years, computational models based on differential equations have become useful tools for dissecting and quantifying the complex regulatory relationships underlying the clock's oscillatory dynamics. However, optimizing the large parameter sets characteristic of these models places intense demands on both computational and experimental resources, limiting the scope of in silico studies. Here, we develop an approach based on Boolean logic that dramatically reduces the parametrization, making the state and parameter spaces finite and tractable. We introduce efficient methods for fitting Boolean models to molecular data, successfully demonstrating their application to synthetic time courses generated by a number of established clock models, as well as experimental expression levels measured using luciferase imaging. Our results indicate that despite their relative simplicity, logic models can (i) simulate circadian oscillations with the correct, experimentally observed phase relationships among genes and (ii) flexibly entrain to light stimuli, reproducing the complex responses to variations in daylength generated by more detailed differential equation formulations. Our work also demonstrates that logic models have sufficient predictive power to identify optimal regulatory structures from experimental data. By presenting the first Boolean models of circadian circuits together with general techniques for their optimization, we hope to establish a new framework for the systematic modelling of complex clocks. |
2004.14943 | Selvaakumar Chellasamy | Selvaa Kumar C, Senthil Arun Kumar, Haiyan Wei | A computational insight of the improved nicotine binding with
ACE2-SARS-CoV-2 complex with its clinical impact | 11 pages, 3 figures | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Smokers being witnessed with the mild adverse clinical symptoms of
SARS-CoV-2, the in-silico study is intended to explore the effect of nicotine
binding to the soluble angiotensin converting enzyme II (ACE2) receptor with or
without SARS-CoV-2 binding. Nicotine established a stable interaction with the
conserved amino acid residues: Asp382, Gly405, His378 and Tyr385 through His401
of the soluble ACE2 that seals its interaction with the INS1. Also, nicotine
binding has significantly reduced the affinity score of ACE2 with INS1 to -12.6
kcal/mol (versus -15.7 kcal/mol without nicotine) and the interface area to
1933.6 square Angstrom (versus 2057.3 square Angstrom without nicotine).
Nicotine exhibited a higher binding affinity score with ACE2-SARS-CoV-2 complex
with -6.33 kcal/mol (Vs -5.24 kcal/mol without SARS-CoV-2) and a lowered
inhibitory contant value of 22.95 micromolar (Vs 151.69 micromolar without
SARS-CoV). Eventhough ACE2 is not a potential receptor for nicotine binding in
the healthy people, in COVID19 patients, it may exhibit better binding affinity
with the ACE2 receptor. In overall, nicotines strong preference for
ACE2-SARS-CoV-2 complex might drastically reduce the SARS-CoV-2 virulence by
intervening the ACE2 conserved residues interaction with the spike (S1) protein
of SARS-CoV-2.
| [
{
"created": "Thu, 30 Apr 2020 16:49:23 GMT",
"version": "v1"
}
] | 2020-05-01 | [
[
"C",
"Selvaa Kumar",
""
],
[
"Kumar",
"Senthil Arun",
""
],
[
"Wei",
"Haiyan",
""
]
] | Smokers being witnessed with the mild adverse clinical symptoms of SARS-CoV-2, the in-silico study is intended to explore the effect of nicotine binding to the soluble angiotensin converting enzyme II (ACE2) receptor with or without SARS-CoV-2 binding. Nicotine established a stable interaction with the conserved amino acid residues: Asp382, Gly405, His378 and Tyr385 through His401 of the soluble ACE2 that seals its interaction with the INS1. Also, nicotine binding has significantly reduced the affinity score of ACE2 with INS1 to -12.6 kcal/mol (versus -15.7 kcal/mol without nicotine) and the interface area to 1933.6 square Angstrom (versus 2057.3 square Angstrom without nicotine). Nicotine exhibited a higher binding affinity score with ACE2-SARS-CoV-2 complex with -6.33 kcal/mol (Vs -5.24 kcal/mol without SARS-CoV-2) and a lowered inhibitory contant value of 22.95 micromolar (Vs 151.69 micromolar without SARS-CoV). Eventhough ACE2 is not a potential receptor for nicotine binding in the healthy people, in COVID19 patients, it may exhibit better binding affinity with the ACE2 receptor. In overall, nicotines strong preference for ACE2-SARS-CoV-2 complex might drastically reduce the SARS-CoV-2 virulence by intervening the ACE2 conserved residues interaction with the spike (S1) protein of SARS-CoV-2. |
1202.4278 | Manuela Capello | M. Capello, M. Soria, G. Potin, P. Cotel and L. Dagorn | Role of current and daylight variations on small-pelagic fish
aggregations around a coastal FAD from accurate acoustic tracking | 16 pages, 5 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We monitored twelve acoustically-tagged small pelagic fish (Selar
crumenophthalmus) around a floating object in shallow water, playing the role
of a coastal fish aggregating device (FAD). We characterized the response of
the tagged-fish aggregation to varying current strengths and daylight. We found
that the current induced a displacement of the aggregation upstream of the FAD,
at distances that were increasing with the current strength. We gave evidence,
of an expansion and a higher coordination in the aggregation at dusk, with
increasing swimming speed, distances among congeners and alignment. We
discussed possible scenarios where fish polarization increases at dusk and
proposed complementary measurements in future experiments that could confirm
our findings.
| [
{
"created": "Mon, 20 Feb 2012 10:12:50 GMT",
"version": "v1"
}
] | 2012-02-21 | [
[
"Capello",
"M.",
""
],
[
"Soria",
"M.",
""
],
[
"Potin",
"G.",
""
],
[
"Cotel",
"P.",
""
],
[
"Dagorn",
"L.",
""
]
] | We monitored twelve acoustically-tagged small pelagic fish (Selar crumenophthalmus) around a floating object in shallow water, playing the role of a coastal fish aggregating device (FAD). We characterized the response of the tagged-fish aggregation to varying current strengths and daylight. We found that the current induced a displacement of the aggregation upstream of the FAD, at distances that were increasing with the current strength. We gave evidence, of an expansion and a higher coordination in the aggregation at dusk, with increasing swimming speed, distances among congeners and alignment. We discussed possible scenarios where fish polarization increases at dusk and proposed complementary measurements in future experiments that could confirm our findings. |
1911.00511 | Tu\u{g}ba \"Onal-S\"uzek | Talip Zengin, Tu\u{g}ba \"Onal-S\"uzek | Analysis of Genomic and Transcriptomic Variations as Prognostic
Signature for Lung Adenocarcinoma | 46 pages | null | 10.1186/s12859-020-03691-3 | null | q-bio.GN stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Lung cancer is the leading cause of the largest number of deaths worldwide
and lung adenocarcinoma (LUAD) is the most common form of lung cancer. In this
study, we carried out an integrated meta-analysis of the mutations including
single-nucleotide variations (SNVs), the copy number variations (CNVs), RNA-seq
and clinical data of patients with LUAD downloaded from The Cancer Genome Atlas
(TCGA). We integrated significant SNV and CNV genes, differentially expressed
genes (DEGs) and the DEGs in active subnetworks to construct a prognosis
signature. Cox proportional hazards model (LOOCV) with Lasso penalty was used
to identify the best gene signature among different gene categories. The
patients in both training and test data were clustered into high-risk and
low-risk groups by using risk scores of the patients calculated based on
selected gene signature. We generated a 12-gene signature (DEPTOR, ZBTB16,
BCHE, MGLL, MASP2, TNNI2, RAPGEF3, SGK2, MYO1A, CYP24A1, PODXL2, CCNA1) for
overall survival prediction. The survival time of high-risk and low-risk groups
was significantly different. This 12-gene signature could predict prognosis and
they are potential predictors for the survival of the patients with LUAD.
| [
{
"created": "Fri, 1 Nov 2019 14:31:34 GMT",
"version": "v1"
},
{
"created": "Wed, 4 Dec 2019 13:56:54 GMT",
"version": "v2"
}
] | 2020-10-02 | [
[
"Zengin",
"Talip",
""
],
[
"Önal-Süzek",
"Tuğba",
""
]
] | Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma (LUAD) is the most common form of lung cancer. In this study, we carried out an integrated meta-analysis of the mutations including single-nucleotide variations (SNVs), the copy number variations (CNVs), RNA-seq and clinical data of patients with LUAD downloaded from The Cancer Genome Atlas (TCGA). We integrated significant SNV and CNV genes, differentially expressed genes (DEGs) and the DEGs in active subnetworks to construct a prognosis signature. Cox proportional hazards model (LOOCV) with Lasso penalty was used to identify the best gene signature among different gene categories. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. We generated a 12-gene signature (DEPTOR, ZBTB16, BCHE, MGLL, MASP2, TNNI2, RAPGEF3, SGK2, MYO1A, CYP24A1, PODXL2, CCNA1) for overall survival prediction. The survival time of high-risk and low-risk groups was significantly different. This 12-gene signature could predict prognosis and they are potential predictors for the survival of the patients with LUAD. |
1404.0015 | Ariel Amir | Ariel Amir and Sven van Teeffelen | Getting into shape: how do rod-like bacteria control their geometry? | invited review, to appear in special issue of "Systems and Synthetic
Biology" | Systems and synthetic biology 8,3, 227 (2014) | 10.1007/s11693-014-9143-9 | null | q-bio.SC cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rod-like bacteria maintain their cylindrical shapes with remarkable precision
during growth. However, they are also capable to adapt their shapes to external
forces and constraints, for example by growing into narrow or curved
confinements. Despite being one of the simplest morphologies, we are still far
from a full understanding of how shape is robustly regulated, and how bacteria
obtain their near-perfect cylindrical shapes with excellent precision. However,
recent experimental and theoretical findings suggest that cell-wall geometry
and mechanical stress play important roles in regulating cell shape in rod-like
bacteria. We review our current understanding of the cell wall architecture and
the growth dynamics, and discuss possible candidates for regulatory cues of
shape regulation in the absence or presence of external constraints. Finally,
we suggest further future experimental and theoretical directions, which may
help to shed light on this fundamental problem.
| [
{
"created": "Mon, 31 Mar 2014 20:00:23 GMT",
"version": "v1"
}
] | 2016-02-10 | [
[
"Amir",
"Ariel",
""
],
[
"van Teeffelen",
"Sven",
""
]
] | Rod-like bacteria maintain their cylindrical shapes with remarkable precision during growth. However, they are also capable to adapt their shapes to external forces and constraints, for example by growing into narrow or curved confinements. Despite being one of the simplest morphologies, we are still far from a full understanding of how shape is robustly regulated, and how bacteria obtain their near-perfect cylindrical shapes with excellent precision. However, recent experimental and theoretical findings suggest that cell-wall geometry and mechanical stress play important roles in regulating cell shape in rod-like bacteria. We review our current understanding of the cell wall architecture and the growth dynamics, and discuss possible candidates for regulatory cues of shape regulation in the absence or presence of external constraints. Finally, we suggest further future experimental and theoretical directions, which may help to shed light on this fundamental problem. |
q-bio/0508025 | Per Arne Rikvold | Per Arne Rikvold (Florida State Univ.) | Self-optimization, community stability, and fluctuations in two
individual-based models of biological coevolution | 26 pages, 12 figures. Discussion of early-time dynamics added. J.
Math. Biol., in press | J. Math. Biol. 55, 653-677 (2007) | 10.1007/s00285-007-0101-y | null | q-bio.PE cond-mat.stat-mech nlin.AO | null | We compare and contrast the long-time dynamical properties of two
individual-based models of biological coevolution. Selection occurs via
multispecies, stochastic population dynamics with reproduction probabilities
that depend nonlinearly on the population densities of all species resident in
the community. New species are introduced through mutation. Both models are
amenable to exact linear stability analysis, and we compare the analytic
results with large-scale kinetic Monte Carlo simulations, obtaining the
population size as a function of an average interspecies interaction strength.
Over time, the models self-optimize through mutation and selection to
approximately maximize a community fitness function, subject only to
constraints internal to the particular model. If the interspecies interactions
are randomly distributed on an interval including positive values, the system
evolves toward self-sustaining, mutualistic communities. In contrast, for the
predator-prey case the matrix of interactions is antisymmetric, and a nonzero
population size must be sustained by an external resource. Time series of the
diversity and population size for both models show approximate 1/f noise and
power-law distributions for the lifetimes of communities and species. For the
mutualistic model, these two lifetime distributions have the same exponent,
while their exponents are different for the predator-prey model. The difference
is probably due to greater resilience toward mass extinctions in the food-web
like communities produced by the predator-prey model.
| [
{
"created": "Fri, 19 Aug 2005 19:59:29 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Mar 2006 22:08:23 GMT",
"version": "v2"
},
{
"created": "Thu, 3 May 2007 22:24:20 GMT",
"version": "v3"
}
] | 2011-11-10 | [
[
"Rikvold",
"Per Arne",
"",
"Florida State Univ."
]
] | We compare and contrast the long-time dynamical properties of two individual-based models of biological coevolution. Selection occurs via multispecies, stochastic population dynamics with reproduction probabilities that depend nonlinearly on the population densities of all species resident in the community. New species are introduced through mutation. Both models are amenable to exact linear stability analysis, and we compare the analytic results with large-scale kinetic Monte Carlo simulations, obtaining the population size as a function of an average interspecies interaction strength. Over time, the models self-optimize through mutation and selection to approximately maximize a community fitness function, subject only to constraints internal to the particular model. If the interspecies interactions are randomly distributed on an interval including positive values, the system evolves toward self-sustaining, mutualistic communities. In contrast, for the predator-prey case the matrix of interactions is antisymmetric, and a nonzero population size must be sustained by an external resource. Time series of the diversity and population size for both models show approximate 1/f noise and power-law distributions for the lifetimes of communities and species. For the mutualistic model, these two lifetime distributions have the same exponent, while their exponents are different for the predator-prey model. The difference is probably due to greater resilience toward mass extinctions in the food-web like communities produced by the predator-prey model. |
0905.1893 | Serena Bradde | S. Bradde, A. Braunstein, H. Mahmoudi, F. Tria, M. Weigt and R.
Zecchina | Aligning graphs and finding substructures by a cavity approach | 5 pages, 4 figures | 2010 Europhys. Lett. 89 37009 | 10.1209/0295-5075/89/37009 | null | q-bio.QM cond-mat.stat-mech cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new distributed algorithm for aligning graphs or finding
substructures within a given graph. It is based on the cavity method and is
used to study the maximum-clique and the graph-alignment problems in random
graphs. The algorithm allows to analyze large graphs and may find applications
in fields such as computational biology. As a proof of concept we use our
algorithm to align the similarity graphs of two interacting protein families
involved in bacterial signal transduction, and to predict actually interacting
protein partners between these families.
| [
{
"created": "Tue, 12 May 2009 16:09:47 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Apr 2010 13:40:11 GMT",
"version": "v2"
}
] | 2010-04-02 | [
[
"Bradde",
"S.",
""
],
[
"Braunstein",
"A.",
""
],
[
"Mahmoudi",
"H.",
""
],
[
"Tria",
"F.",
""
],
[
"Weigt",
"M.",
""
],
[
"Zecchina",
"R.",
""
]
] | We introduce a new distributed algorithm for aligning graphs or finding substructures within a given graph. It is based on the cavity method and is used to study the maximum-clique and the graph-alignment problems in random graphs. The algorithm allows to analyze large graphs and may find applications in fields such as computational biology. As a proof of concept we use our algorithm to align the similarity graphs of two interacting protein families involved in bacterial signal transduction, and to predict actually interacting protein partners between these families. |
1809.08199 | Alejandro Rodr\'iguez-Gonz\'alez | Ernestina Menasalvas Ruiz, Juan Manuel Tu\~nas, Guzm\'an Bermejo,
Consuelo Gonzalo Mart\'in, Alejandro Rodr\'iguez-Gonz\'alez, Massimiliano
Zanin, Cristina Gonz\'alez de Pedro, Marta Mendez, Olga Zaretskaia, Jes\'us
Rey, Consuelo Parejo, Juan Luis Cruz Bermudez, Mariano Provencio | Profiling lung cancer patients using electronic health records | 14 pages, 12 figures | Journal of Medical Systems (2018) 42:126 | 10.1007/s10916-018-0975-9 | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | If Electronic Health Records contain a large amount of information about the
patients condition and response to treatment, which can potentially
revolutionize the clinical practice, such information is seldom considered due
to the complexity of its extraction and analysis. We here report on a first
integration of an NLP framework for the analysis of clinical records of lung
cancer patients making use of a telephone assistance service of a major Spanish
hospital. We specifically show how some relevant data, about patient
demographics and health condition, can be extracted; and how some relevant
analyses can be performed, aimed at improving the usefulness of the service. We
thus demonstrate that the use of EHR texts, and their integration inside a data
analysis framework, is technically feasible and worth of further study.
| [
{
"created": "Tue, 18 Sep 2018 16:19:59 GMT",
"version": "v1"
}
] | 2018-09-24 | [
[
"Ruiz",
"Ernestina Menasalvas",
""
],
[
"Tuñas",
"Juan Manuel",
""
],
[
"Bermejo",
"Guzmán",
""
],
[
"Martín",
"Consuelo Gonzalo",
""
],
[
"Rodríguez-González",
"Alejandro",
""
],
[
"Zanin",
"Massimiliano",
""
],
[
... | If Electronic Health Records contain a large amount of information about the patients condition and response to treatment, which can potentially revolutionize the clinical practice, such information is seldom considered due to the complexity of its extraction and analysis. We here report on a first integration of an NLP framework for the analysis of clinical records of lung cancer patients making use of a telephone assistance service of a major Spanish hospital. We specifically show how some relevant data, about patient demographics and health condition, can be extracted; and how some relevant analyses can be performed, aimed at improving the usefulness of the service. We thus demonstrate that the use of EHR texts, and their integration inside a data analysis framework, is technically feasible and worth of further study. |
2210.14961 | Arvind Seshan | Arvind Seshan | A Neural Network Based Automated IFT-20 Sensory Neuron Classifier for
Caenorhabditis elegans | This article has been removed by arXiv administrators because the
submitter did not have the authority to grant the license at the time of
submission | null | null | null | q-bio.NC cs.LG | http://creativecommons.org/licenses/by/4.0/ | Determining neuronal identity in imaging data is an essential task in
neuroscience, facilitating the comparison of neural activity across organisms.
Cross-organism comparison, in turn, enables a wide variety of research
including whole-brain analysis of functional networks and linking the activity
of specific neurons to behavior or environmental stimuli. The recent
development of three-dimensional, pan-neuronal imaging with single-cell
resolution within Caenorhabditis elegans has brought neuron identification,
tracking, and activity monitoring all within reach. The nematode C. elegans is
often used as a model organism to study neuronal activity due to factors such
as its transparency and well-understood nervous system. The principal barrier
to high-accuracy neuron identification is that in adult C. elegans, the
position of neuronal cell bodies is not stereotyped. Existing approaches to
address this issue use genetically encoded markers as an additional identifying
feature. For example, the NeuroPAL strain uses multicolored fluorescent
reporters. However, this approach has limited use due to the negative effects
of excessive genetic modification. In this study, I propose an alternative
neuronal identification technique using only single-color fluorescent images. I
designed a novel neural network based classifier that automatically labels
sensory neurons using an iterative, landmark-based neuron identification
process inspired by the manual annotation procedures that humans employ. This
design labels sensory neurons in C. elegans with 91.61% accuracy.
| [
{
"created": "Mon, 24 Oct 2022 00:17:26 GMT",
"version": "v1"
}
] | 2022-11-02 | [
[
"Seshan",
"Arvind",
""
]
] | Determining neuronal identity in imaging data is an essential task in neuroscience, facilitating the comparison of neural activity across organisms. Cross-organism comparison, in turn, enables a wide variety of research including whole-brain analysis of functional networks and linking the activity of specific neurons to behavior or environmental stimuli. The recent development of three-dimensional, pan-neuronal imaging with single-cell resolution within Caenorhabditis elegans has brought neuron identification, tracking, and activity monitoring all within reach. The nematode C. elegans is often used as a model organism to study neuronal activity due to factors such as its transparency and well-understood nervous system. The principal barrier to high-accuracy neuron identification is that in adult C. elegans, the position of neuronal cell bodies is not stereotyped. Existing approaches to address this issue use genetically encoded markers as an additional identifying feature. For example, the NeuroPAL strain uses multicolored fluorescent reporters. However, this approach has limited use due to the negative effects of excessive genetic modification. In this study, I propose an alternative neuronal identification technique using only single-color fluorescent images. I designed a novel neural network based classifier that automatically labels sensory neurons using an iterative, landmark-based neuron identification process inspired by the manual annotation procedures that humans employ. This design labels sensory neurons in C. elegans with 91.61% accuracy. |
1705.05603 | Luca Ambrogioni | Luca Ambrogioni, Max Hinne, Marcel van Gerven and Eric Maris | GP CaKe: Effective brain connectivity with causal kernels | null | null | null | null | q-bio.NC stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A fundamental goal in network neuroscience is to understand how activity in
one region drives activity elsewhere, a process referred to as effective
connectivity. Here we propose to model this causal interaction using
integro-differential equations and causal kernels that allow for a rich
analysis of effective connectivity. The approach combines the tractability and
flexibility of autoregressive modeling with the biophysical interpretability of
dynamic causal modeling. The causal kernels are learned nonparametrically using
Gaussian process regression, yielding an efficient framework for causal
inference. We construct a novel class of causal covariance functions that
enforce the desired properties of the causal kernels, an approach which we call
GP CaKe. By construction, the model and its hyperparameters have biophysical
meaning and are therefore easily interpretable. We demonstrate the efficacy of
GP CaKe on a number of simulations and give an example of a realistic
application on magnetoencephalography (MEG) data.
| [
{
"created": "Tue, 16 May 2017 09:07:13 GMT",
"version": "v1"
}
] | 2017-05-17 | [
[
"Ambrogioni",
"Luca",
""
],
[
"Hinne",
"Max",
""
],
[
"van Gerven",
"Marcel",
""
],
[
"Maris",
"Eric",
""
]
] | A fundamental goal in network neuroscience is to understand how activity in one region drives activity elsewhere, a process referred to as effective connectivity. Here we propose to model this causal interaction using integro-differential equations and causal kernels that allow for a rich analysis of effective connectivity. The approach combines the tractability and flexibility of autoregressive modeling with the biophysical interpretability of dynamic causal modeling. The causal kernels are learned nonparametrically using Gaussian process regression, yielding an efficient framework for causal inference. We construct a novel class of causal covariance functions that enforce the desired properties of the causal kernels, an approach which we call GP CaKe. By construction, the model and its hyperparameters have biophysical meaning and are therefore easily interpretable. We demonstrate the efficacy of GP CaKe on a number of simulations and give an example of a realistic application on magnetoencephalography (MEG) data. |
2106.00172 | Sang-Yoon Kim | Sang-Yoon Kim and Woochang Lim | Population and Individual Firing Behaviors in Sparsely Synchronized
Rhythms in The Hippocampal Dentate Gyrus | arXiv admin note: text overlap with arXiv:2105.06057 | null | null | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate population and individual firing behaviors in sparsely
synchronized rhythms (SSRs) in a spiking neural network of the hippocampal
dentate gyrus (DG). The main encoding granule cells (GCs) are grouped into
lamellar clusters. In each GC cluster, there is one inhibitory (I) basket cell
(BC) along with excitatory (E) GCs, and they form the E-I loop. Winner-take-all
competition, leading to sparse activation of the GCs, occurs in each GC
cluster. Such sparsity has been thought to enhance pattern separation performed
in the DG. During the winner-take-all competition, SSRs are found to appear in
each population of the GCs and the BCs through interaction of excitation of the
GCs with inhibition of the BCs. Sparsely synchronized spiking stripes appear
successively with the population frequency $f_p~ (= 13$ Hz) in the raster plots
of spikes. We also note that excitatory hilar mossy cells (MCs) control the
firing activity of the GC-BC loop by providing excitation to both the GCs and
the BCs. SSR also appears in the population of MCs via interaction with the GCs
(i.e., GC-MC loop). Population behaviors in the SSRs are quantitatively
characterized in terms of the synchronization measures. In addition, we
investigate individual firing activity of GCs, BCs, and MCs in the SSRs.
Individual GCs exhibit random spike skipping, leading to a multi-peaked
inter-spike-interval histogram, which is well characterized in terms of the
random phase-locking degree. On the other hand, both BCs and MCs show
"intrastripe" burstings within stripes, together with "interstripe" random
spike skipping. MC loss may occur during epileptogenesis. With decreasing the
fraction of the MCs, changes in the population and individual firings in the
SSRs are also studied. Finally, quantitative association between the
population/individual firing behaviors in the SSRs and the winner-take-all
competition is discussed.
| [
{
"created": "Tue, 1 Jun 2021 01:42:34 GMT",
"version": "v1"
}
] | 2021-06-02 | [
[
"Kim",
"Sang-Yoon",
""
],
[
"Lim",
"Woochang",
""
]
] | We investigate population and individual firing behaviors in sparsely synchronized rhythms (SSRs) in a spiking neural network of the hippocampal dentate gyrus (DG). The main encoding granule cells (GCs) are grouped into lamellar clusters. In each GC cluster, there is one inhibitory (I) basket cell (BC) along with excitatory (E) GCs, and they form the E-I loop. Winner-take-all competition, leading to sparse activation of the GCs, occurs in each GC cluster. Such sparsity has been thought to enhance pattern separation performed in the DG. During the winner-take-all competition, SSRs are found to appear in each population of the GCs and the BCs through interaction of excitation of the GCs with inhibition of the BCs. Sparsely synchronized spiking stripes appear successively with the population frequency $f_p~ (= 13$ Hz) in the raster plots of spikes. We also note that excitatory hilar mossy cells (MCs) control the firing activity of the GC-BC loop by providing excitation to both the GCs and the BCs. SSR also appears in the population of MCs via interaction with the GCs (i.e., GC-MC loop). Population behaviors in the SSRs are quantitatively characterized in terms of the synchronization measures. In addition, we investigate individual firing activity of GCs, BCs, and MCs in the SSRs. Individual GCs exhibit random spike skipping, leading to a multi-peaked inter-spike-interval histogram, which is well characterized in terms of the random phase-locking degree. On the other hand, both BCs and MCs show "intrastripe" burstings within stripes, together with "interstripe" random spike skipping. MC loss may occur during epileptogenesis. With decreasing the fraction of the MCs, changes in the population and individual firings in the SSRs are also studied. Finally, quantitative association between the population/individual firing behaviors in the SSRs and the winner-take-all competition is discussed. |
1811.09326 | Fabian Spill | Jorge Escribano, Michelle B. Chen, Emad Moeendarbary, Xuan Cao, Vivek
Shenoy, Jose Manuel Garcia-Aznar, Roger D. Kamm, Fabian Spill | Balance of Mechanical Forces Drives Endothelial Gap Formation and May
Facilitate Cancer and Immune-Cell Extravasation | 25 pages, 28 supplementary pages, 5 figures, 15 supplementary figures | null | 10.1371/journal.pcbi.1006395 | null | q-bio.CB physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The formation of gaps in the endothelium is a crucial process underlying both
cancer and immune cell extravasation, contributing to the functioning of the
immune system during infection, the unfavorable development of chronic
inflammation and tumor metastasis. Here, we present a stochastic-mechanical
multiscale model of an endothelial cell monolayer and show that the dynamic
nature of the endothelium leads to spontaneous gap formation, even without
intervention from the transmigrating cells. These gaps preferentially appear at
the vertices between three endothelial cells, as opposed to the border between
two cells. We quantify the frequency and lifetime of these gaps, and validate
our predictions experimentally. Interestingly, we find experimentally that
cancer cells also preferentially extravasate at vertices, even when they first
arrest on borders. This suggests that extravasating cells, rather than
initially signaling to the endothelium, might exploit the autonomously forming
gaps in the endothelium to initiate transmigration.
| [
{
"created": "Thu, 22 Nov 2018 23:18:41 GMT",
"version": "v1"
}
] | 2019-06-19 | [
[
"Escribano",
"Jorge",
""
],
[
"Chen",
"Michelle B.",
""
],
[
"Moeendarbary",
"Emad",
""
],
[
"Cao",
"Xuan",
""
],
[
"Shenoy",
"Vivek",
""
],
[
"Garcia-Aznar",
"Jose Manuel",
""
],
[
"Kamm",
"Roger D.",
""
... | The formation of gaps in the endothelium is a crucial process underlying both cancer and immune cell extravasation, contributing to the functioning of the immune system during infection, the unfavorable development of chronic inflammation and tumor metastasis. Here, we present a stochastic-mechanical multiscale model of an endothelial cell monolayer and show that the dynamic nature of the endothelium leads to spontaneous gap formation, even without intervention from the transmigrating cells. These gaps preferentially appear at the vertices between three endothelial cells, as opposed to the border between two cells. We quantify the frequency and lifetime of these gaps, and validate our predictions experimentally. Interestingly, we find experimentally that cancer cells also preferentially extravasate at vertices, even when they first arrest on borders. This suggests that extravasating cells, rather than initially signaling to the endothelium, might exploit the autonomously forming gaps in the endothelium to initiate transmigration. |
2404.08600 | Lyle Poley | Lyle Poley, Tobias Galla, Joseph W. Baron | Interaction networks in persistent Lotka-Volterra communities | 10 pages, 6 figures plus appendix | null | null | null | q-bio.PE cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A central concern of community ecology is the interdependence between
interaction strengths and the underlying structure of the network upon which
species interact. In this work we present a solvable example of such a feedback
mechanism in a generalised Lotka-Volterra dynamical system. Beginning with a
community of species interacting on a network with arbitrary degree
distribution, we provide an analytical framework from which properties of the
eventual `surviving community' can be derived. We find that highly-connected
species are less likely to survive than their poorly connected counterparts,
which skews the eventual degree distribution towards a preponderance of species
with low degree, a pattern commonly observed in real ecosystems. Further, the
average abundance of the neighbours of a species in the surviving community is
lower than the community average (reminiscent of the famed friendship paradox).
Finally, we show that correlations emerge between the connectivity of a species
and its interactions with its neighbours. More precisely, we find that
highly-connected species tend to benefit from their neighbours more than their
neighbours benefit from them. These correlations are not present in the initial
pool of species and are a result of the dynamics.
| [
{
"created": "Fri, 12 Apr 2024 16:53:25 GMT",
"version": "v1"
}
] | 2024-04-15 | [
[
"Poley",
"Lyle",
""
],
[
"Galla",
"Tobias",
""
],
[
"Baron",
"Joseph W.",
""
]
] | A central concern of community ecology is the interdependence between interaction strengths and the underlying structure of the network upon which species interact. In this work we present a solvable example of such a feedback mechanism in a generalised Lotka-Volterra dynamical system. Beginning with a community of species interacting on a network with arbitrary degree distribution, we provide an analytical framework from which properties of the eventual `surviving community' can be derived. We find that highly-connected species are less likely to survive than their poorly connected counterparts, which skews the eventual degree distribution towards a preponderance of species with low degree, a pattern commonly observed in real ecosystems. Further, the average abundance of the neighbours of a species in the surviving community is lower than the community average (reminiscent of the famed friendship paradox). Finally, we show that correlations emerge between the connectivity of a species and its interactions with its neighbours. More precisely, we find that highly-connected species tend to benefit from their neighbours more than their neighbours benefit from them. These correlations are not present in the initial pool of species and are a result of the dynamics. |
1811.09588 | Liane Gabora | Liane Gabora and Mike Unrau | Social Innovation and the Evolution of Creative, Sustainable Worldviews | 14 pages; 5 figures | In Lebuda, I. & Glaveanu, V. (Eds.) The Palgrave Handbook of
Social Creativity Research (pp. 541-558). Palgrave Macmillan (2018) | null | null | q-bio.NC q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ideas that we forge creatively as individuals and groups build on one
another in a manner that is cumulative and adaptive, forming open-ended
lineages across space and time. Thus, human culture is believed to evolve. The
pervasiveness of cross-domain creativity--as when a song inspires a
painting--would appear indicative of discontinuities in cultural lineages.
However, if what evolves through culture is our worldviews--the webs of
thoughts, ideas, and attitudes that constitutes our way of seeing being in the
world--then the problem of discontinuities is solved. The state of a worldview
can be affected by information assimilated in one domain, and this
change-of-state can be expressed in another domain. In this view, the gesture,
narrative, or artifact that constitutes a specific creative act is not what is
evolving; it is merely the external manifestation of the state of an evolving
worldview. Like any evolutionary process, cultural evolution requires a balance
between novelty, via the generation of variation, and continuity, via the
preservation of variants that are adaptive. In cultural evolution, novelty is
generated through creativity, and continuity is provided by social learning
processes, e.g., imitation. Both the generative and imitative aspects of
cultural evolution are affected by social media. We discuss the trajectory from
social ideation to social innovation, focusing on the role of
self-organization, renewal, and perspective-taking at the individual and social
group level.
| [
{
"created": "Fri, 23 Nov 2018 18:23:32 GMT",
"version": "v1"
},
{
"created": "Fri, 5 Jul 2019 22:14:21 GMT",
"version": "v2"
}
] | 2019-07-09 | [
[
"Gabora",
"Liane",
""
],
[
"Unrau",
"Mike",
""
]
] | The ideas that we forge creatively as individuals and groups build on one another in a manner that is cumulative and adaptive, forming open-ended lineages across space and time. Thus, human culture is believed to evolve. The pervasiveness of cross-domain creativity--as when a song inspires a painting--would appear indicative of discontinuities in cultural lineages. However, if what evolves through culture is our worldviews--the webs of thoughts, ideas, and attitudes that constitutes our way of seeing being in the world--then the problem of discontinuities is solved. The state of a worldview can be affected by information assimilated in one domain, and this change-of-state can be expressed in another domain. In this view, the gesture, narrative, or artifact that constitutes a specific creative act is not what is evolving; it is merely the external manifestation of the state of an evolving worldview. Like any evolutionary process, cultural evolution requires a balance between novelty, via the generation of variation, and continuity, via the preservation of variants that are adaptive. In cultural evolution, novelty is generated through creativity, and continuity is provided by social learning processes, e.g., imitation. Both the generative and imitative aspects of cultural evolution are affected by social media. We discuss the trajectory from social ideation to social innovation, focusing on the role of self-organization, renewal, and perspective-taking at the individual and social group level. |
1110.4444 | Andrew Noble | Andrew E. Noble, Alan Hastings, and William F. Fagan | A Multivariate Moran Process with Lotka-Volterra Phenomenology | 5 pages, 2 figures, Supplemental Material appended, accepted to
Physical Review Letters | null | 10.1103/PhysRevLett.107.228101 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For a population with any given number of types, we construct a new
multivariate Moran process with frequency-dependent selection and establish,
analytically, a correspondence to equilibrium Lotka-Volterra phenomenology.
This correspondence, on the one hand, allows us to infer the phenomenology of
our Moran process based on much simpler Lokta-Volterra phenomenology, and on
the other, allows us to study Lotka-Volterra dynamics within the finite
populations of a Moran process. Applications to community ecology, population
genetics, and evolutionary game theory are discussed.
| [
{
"created": "Thu, 20 Oct 2011 05:08:23 GMT",
"version": "v1"
}
] | 2015-05-30 | [
[
"Noble",
"Andrew E.",
""
],
[
"Hastings",
"Alan",
""
],
[
"Fagan",
"William F.",
""
]
] | For a population with any given number of types, we construct a new multivariate Moran process with frequency-dependent selection and establish, analytically, a correspondence to equilibrium Lotka-Volterra phenomenology. This correspondence, on the one hand, allows us to infer the phenomenology of our Moran process based on much simpler Lokta-Volterra phenomenology, and on the other, allows us to study Lotka-Volterra dynamics within the finite populations of a Moran process. Applications to community ecology, population genetics, and evolutionary game theory are discussed. |
1601.06248 | Takuya Koumura | Takuya Koumura and Kazuo Okanoya | Automatic recognition of element classes and boundaries in the birdsong
with variable sequences | null | null | 10.1371/journal.pone.0159188 | null | q-bio.NC cs.LG cs.SD | http://creativecommons.org/licenses/by/4.0/ | Researches on sequential vocalization often require analysis of vocalizations
in long continuous sounds. In such studies as developmental ones or studies
across generations in which days or months of vocalizations must be analyzed,
methods for automatic recognition would be strongly desired. Although methods
for automatic speech recognition for application purposes have been intensively
studied, blindly applying them for biological purposes may not be an optimal
solution. This is because, unlike human speech recognition, analysis of
sequential vocalizations often requires accurate extraction of timing
information. In the present study we propose automated systems suitable for
recognizing birdsong, one of the most intensively investigated sequential
vocalizations, focusing on the three properties of the birdsong. First, a song
is a sequence of vocal elements, called notes, which can be grouped into
categories. Second, temporal structure of birdsong is precisely controlled,
meaning that temporal information is important in song analysis. Finally, notes
are produced according to certain probabilistic rules, which may facilitate the
accurate song recognition. We divided the procedure of song recognition into
three sub-steps: local classification, boundary detection, and global
sequencing, each of which corresponds to each of the three properties of
birdsong. We compared the performances of several different ways to arrange
these three steps. As results, we demonstrated a hybrid model of a deep neural
network and a hidden Markov model is effective in recognizing birdsong with
variable note sequences. We propose suitable arrangements of methods according
to whether accurate boundary detection is needed. Also we designed the new
measure to jointly evaluate the accuracy of note classification and boundary
detection. Our methods should be applicable, with small modification and
tuning, to the songs in other species that hold the three properties of the
sequential vocalization.
| [
{
"created": "Sat, 23 Jan 2016 07:57:56 GMT",
"version": "v1"
}
] | 2016-09-28 | [
[
"Koumura",
"Takuya",
""
],
[
"Okanoya",
"Kazuo",
""
]
] | Researches on sequential vocalization often require analysis of vocalizations in long continuous sounds. In such studies as developmental ones or studies across generations in which days or months of vocalizations must be analyzed, methods for automatic recognition would be strongly desired. Although methods for automatic speech recognition for application purposes have been intensively studied, blindly applying them for biological purposes may not be an optimal solution. This is because, unlike human speech recognition, analysis of sequential vocalizations often requires accurate extraction of timing information. In the present study we propose automated systems suitable for recognizing birdsong, one of the most intensively investigated sequential vocalizations, focusing on the three properties of the birdsong. First, a song is a sequence of vocal elements, called notes, which can be grouped into categories. Second, temporal structure of birdsong is precisely controlled, meaning that temporal information is important in song analysis. Finally, notes are produced according to certain probabilistic rules, which may facilitate the accurate song recognition. We divided the procedure of song recognition into three sub-steps: local classification, boundary detection, and global sequencing, each of which corresponds to each of the three properties of birdsong. We compared the performances of several different ways to arrange these three steps. As results, we demonstrated a hybrid model of a deep neural network and a hidden Markov model is effective in recognizing birdsong with variable note sequences. We propose suitable arrangements of methods according to whether accurate boundary detection is needed. Also we designed the new measure to jointly evaluate the accuracy of note classification and boundary detection. Our methods should be applicable, with small modification and tuning, to the songs in other species that hold the three properties of the sequential vocalization. |
1212.2172 | Kieran Sharkey | Kieran J. Sharkey, Istvan Z. Kiss, Robert R. Wilkinson, Peter L. Simon | Exact equations for SIR epidemics on tree graphs | 33 pages, 7 figures | null | 10.1007/s11538-013-9923-5 | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider Markovian susceptible-infectious-removed (SIR) dynamics on
time-invariant weighted contact networks where the infection and removal
processes are Poisson and where network links may be directed or undirected. We
prove that a particular pair-based moment closure representation generates the
expected infectious time series for networks with no cycles in the underlying
graph. Moreover, this ``deterministic'' representation of the expected
behaviour of a complex heterogeneous and finite Markovian system is
straightforward to evaluate numerically.
| [
{
"created": "Mon, 10 Dec 2012 19:21:05 GMT",
"version": "v1"
},
{
"created": "Sat, 7 Sep 2013 15:12:55 GMT",
"version": "v2"
},
{
"created": "Wed, 18 Dec 2013 14:50:32 GMT",
"version": "v3"
}
] | 2013-12-19 | [
[
"Sharkey",
"Kieran J.",
""
],
[
"Kiss",
"Istvan Z.",
""
],
[
"Wilkinson",
"Robert R.",
""
],
[
"Simon",
"Peter L.",
""
]
] | We consider Markovian susceptible-infectious-removed (SIR) dynamics on time-invariant weighted contact networks where the infection and removal processes are Poisson and where network links may be directed or undirected. We prove that a particular pair-based moment closure representation generates the expected infectious time series for networks with no cycles in the underlying graph. Moreover, this ``deterministic'' representation of the expected behaviour of a complex heterogeneous and finite Markovian system is straightforward to evaluate numerically. |
1809.02511 | \'Aine Byrne | \'Aine Byrne, Daniele Avitabile, Stephen Coombes | A next generation neural field model: The evolution of synchrony within
patterns and waves | null | Phys. Rev. E 99, 012313 (2019) | 10.1103/PhysRevE.99.012313 | null | q-bio.NC math.DS nlin.PS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural field models are commonly used to describe wave propagation and bump
attractors at a tissue level in the brain. Although motivated by biology, these
models are phenomenological in nature. They are built on the assumption that
the neural tissue operates in a near synchronous regime, and hence, cannot
account for changes in the underlying synchrony of patterns. It is customary to
use spiking neural network models when examining within population
synchronisation. Unfortunately, these high dimensional models are notoriously
hard to obtain insight from. In this paper, we consider a network of
$\theta$-neurons, which has recently been shown to admit an exact mean-field
description in the absence of a spatial component. We show that the inclusion
of space and a realistic synapse model leads to a reduced model that has many
of the features of a standard neural field model coupled to a further dynamical
equation that describes the evolution of network synchrony. Both Turing
instability analysis and numerical continuation software are used to explore
the existence and stability of spatio-temporal patterns in the system. In
particular, we show that this new model can support states above and beyond
those seen in a standard neural field model. These states are typified by
structures within bumps and waves showing the dynamic evolution of population
synchrony.
| [
{
"created": "Fri, 7 Sep 2018 14:39:34 GMT",
"version": "v1"
}
] | 2019-01-16 | [
[
"Byrne",
"Áine",
""
],
[
"Avitabile",
"Daniele",
""
],
[
"Coombes",
"Stephen",
""
]
] | Neural field models are commonly used to describe wave propagation and bump attractors at a tissue level in the brain. Although motivated by biology, these models are phenomenological in nature. They are built on the assumption that the neural tissue operates in a near synchronous regime, and hence, cannot account for changes in the underlying synchrony of patterns. It is customary to use spiking neural network models when examining within population synchronisation. Unfortunately, these high dimensional models are notoriously hard to obtain insight from. In this paper, we consider a network of $\theta$-neurons, which has recently been shown to admit an exact mean-field description in the absence of a spatial component. We show that the inclusion of space and a realistic synapse model leads to a reduced model that has many of the features of a standard neural field model coupled to a further dynamical equation that describes the evolution of network synchrony. Both Turing instability analysis and numerical continuation software are used to explore the existence and stability of spatio-temporal patterns in the system. In particular, we show that this new model can support states above and beyond those seen in a standard neural field model. These states are typified by structures within bumps and waves showing the dynamic evolution of population synchrony. |
2111.05369 | Tanja Zerenner | Tanja Zerenner, Francesco Di Lauro, Masoumeh Dashti, Luc Berthouze,
Istvan Z. Kiss | Probabilistic predictions of SIS epidemics on networks based on
population-level observations | null | null | null | null | q-bio.PE math.DS stat.AP | http://creativecommons.org/licenses/by/4.0/ | We predict the future course of ongoing susceptible-infected-susceptible
(SIS) epidemics on regular, Erd\H{o}s-R\'{e}nyi and Barab\'asi-Albert networks.
It is known that the contact network influences the spread of an epidemic
within a population. Therefore, observations of an epidemic, in this case at
the population-level, contain information about the underlying network. This
information, in turn, is useful for predicting the future course of an ongoing
epidemic. To exploit this in a prediction framework, the exact high-dimensional
stochastic model of an SIS epidemic on a network is approximated by a
lower-dimensional surrogate model. The surrogate model is based on a
birth-and-death process; the effect of the underlying network is described by a
parametric model for the birth rates. We demonstrate empirically that the
surrogate model captures the intrinsic stochasticity of the epidemic once it
reaches a point from which it will not die out. Bayesian parameter inference
allows for uncertainty about the model parameters and the class of the
underlying network to be incorporated directly into probabilistic predictions.
An evaluation of a number of scenarios shows that in most cases the resulting
prediction intervals adequately quantify the prediction uncertainty. As long as
the population-level data is available over a long-enough period, even if not
sampled frequently, the model leads to excellent predictions where the
underlying network is correctly identified and prediction uncertainty mainly
reflects the intrinsic stochasticity of the spreading epidemic. For predictions
inferred from shorter observational periods, uncertainty about parameters and
network class dominate prediction uncertainty. The proposed method relies on
minimal data and is numerically efficient, which makes it attractive either as
a standalone inference and prediction scheme or in conjunction with other
methods.
| [
{
"created": "Tue, 9 Nov 2021 19:20:58 GMT",
"version": "v1"
}
] | 2021-11-11 | [
[
"Zerenner",
"Tanja",
""
],
[
"Di Lauro",
"Francesco",
""
],
[
"Dashti",
"Masoumeh",
""
],
[
"Berthouze",
"Luc",
""
],
[
"Kiss",
"Istvan Z.",
""
]
] | We predict the future course of ongoing susceptible-infected-susceptible (SIS) epidemics on regular, Erd\H{o}s-R\'{e}nyi and Barab\'asi-Albert networks. It is known that the contact network influences the spread of an epidemic within a population. Therefore, observations of an epidemic, in this case at the population-level, contain information about the underlying network. This information, in turn, is useful for predicting the future course of an ongoing epidemic. To exploit this in a prediction framework, the exact high-dimensional stochastic model of an SIS epidemic on a network is approximated by a lower-dimensional surrogate model. The surrogate model is based on a birth-and-death process; the effect of the underlying network is described by a parametric model for the birth rates. We demonstrate empirically that the surrogate model captures the intrinsic stochasticity of the epidemic once it reaches a point from which it will not die out. Bayesian parameter inference allows for uncertainty about the model parameters and the class of the underlying network to be incorporated directly into probabilistic predictions. An evaluation of a number of scenarios shows that in most cases the resulting prediction intervals adequately quantify the prediction uncertainty. As long as the population-level data is available over a long-enough period, even if not sampled frequently, the model leads to excellent predictions where the underlying network is correctly identified and prediction uncertainty mainly reflects the intrinsic stochasticity of the spreading epidemic. For predictions inferred from shorter observational periods, uncertainty about parameters and network class dominate prediction uncertainty. The proposed method relies on minimal data and is numerically efficient, which makes it attractive either as a standalone inference and prediction scheme or in conjunction with other methods. |
1101.2103 | Tim Spencer | T. J. Spencer, I. Halliday, C. M. Care, S. H. Cartmell and L. A.
Hidalgo-Bastida | Numerical Solution of a Complete Formulation of Flow in a Perfusion
Bone-Tissue Bioreactor Using Lattice Boltzmann Equation Method | 9 pages, 3 figures | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We report the key findings from numerical solutions of a model of transport
within an established perfusion bioreactor design. The model includes a
complete formulation of transport with fully coupled convection-diffusion and
scaffold cell attachment. It also includes the experimentally determined
internal (Poly-L-Lactic Acid (PLLA)) scaffold boundary, together with the
external vessel and flow-port boundaries. Our findings, obtained using parallel
lattice Boltzmann equation method, relate to (i) whole-device, steady-state
flow and species distribution and (ii) the properties of the scaffold. In
particular the results identify which elements of the problem may be addressed
by coarse grained methods such as the Darcy approximation and those which
require a more complete description. The work demonstrates that appropriate
numerical modelling will make a key contribution to the design and development
of large scale bioreactors.
| [
{
"created": "Tue, 11 Jan 2011 11:49:03 GMT",
"version": "v1"
}
] | 2011-01-12 | [
[
"Spencer",
"T. J.",
""
],
[
"Halliday",
"I.",
""
],
[
"Care",
"C. M.",
""
],
[
"Cartmell",
"S. H.",
""
],
[
"Hidalgo-Bastida",
"L. A.",
""
]
] | We report the key findings from numerical solutions of a model of transport within an established perfusion bioreactor design. The model includes a complete formulation of transport with fully coupled convection-diffusion and scaffold cell attachment. It also includes the experimentally determined internal (Poly-L-Lactic Acid (PLLA)) scaffold boundary, together with the external vessel and flow-port boundaries. Our findings, obtained using parallel lattice Boltzmann equation method, relate to (i) whole-device, steady-state flow and species distribution and (ii) the properties of the scaffold. In particular the results identify which elements of the problem may be addressed by coarse grained methods such as the Darcy approximation and those which require a more complete description. The work demonstrates that appropriate numerical modelling will make a key contribution to the design and development of large scale bioreactors. |
2111.07137 | Hans-Christof Gasser | Hans-Christof Gasser, Georges Bedran, Bo Ren, David Goodlett, Javier
Alfaro, Ajitha Rajan | Interpreting BERT architecture predictions for peptide presentation by
MHC class I proteins | 10 pages | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The major histocompatibility complex (MHC) class-I pathway supports the
detection of cancer and viruses by the immune system. It presents parts of
proteins (peptides) from inside a cell on its membrane surface enabling
visiting immune cells that detect non-self peptides to terminate the cell. The
ability to predict whether a peptide will get presented on MHC Class I
molecules helps in designing vaccines so they can activate the immune system to
destroy the invading disease protein. We designed a prediction model using a
BERT-based architecture (ImmunoBERT) that takes as input a peptide and its
surrounding regions (N and C-terminals) along with a set of MHC class I (MHC-I)
molecules. We present a novel application of well known interpretability
techniques, SHAP and LIME, to this domain and we use these results along with
3D structure visualizations and amino acid frequencies to understand and
identify the most influential parts of the input amino acid sequences
contributing to the output. In particular, we find that amino acids close to
the peptides' N- and C-terminals are highly relevant. Additionally, some
positions within the MHC proteins (in particular in the A, B and F pockets) are
often assigned a high importance ranking - which confirms biological studies
and the distances in the structure visualizations.
| [
{
"created": "Sat, 13 Nov 2021 16:01:36 GMT",
"version": "v1"
}
] | 2021-11-16 | [
[
"Gasser",
"Hans-Christof",
""
],
[
"Bedran",
"Georges",
""
],
[
"Ren",
"Bo",
""
],
[
"Goodlett",
"David",
""
],
[
"Alfaro",
"Javier",
""
],
[
"Rajan",
"Ajitha",
""
]
] | The major histocompatibility complex (MHC) class-I pathway supports the detection of cancer and viruses by the immune system. It presents parts of proteins (peptides) from inside a cell on its membrane surface enabling visiting immune cells that detect non-self peptides to terminate the cell. The ability to predict whether a peptide will get presented on MHC Class I molecules helps in designing vaccines so they can activate the immune system to destroy the invading disease protein. We designed a prediction model using a BERT-based architecture (ImmunoBERT) that takes as input a peptide and its surrounding regions (N and C-terminals) along with a set of MHC class I (MHC-I) molecules. We present a novel application of well known interpretability techniques, SHAP and LIME, to this domain and we use these results along with 3D structure visualizations and amino acid frequencies to understand and identify the most influential parts of the input amino acid sequences contributing to the output. In particular, we find that amino acids close to the peptides' N- and C-terminals are highly relevant. Additionally, some positions within the MHC proteins (in particular in the A, B and F pockets) are often assigned a high importance ranking - which confirms biological studies and the distances in the structure visualizations. |
2111.01351 | Yonghui Xu | Xiaofang Sun, Xiangwei Zheng, Yonghui Xu, Lizhen Cui and Bin Hu | Major Depressive Disorder Recognition and Cognitive Analysis Based on
Multi-layer Brain Functional Connectivity Networks | null | International Workshop on AI for Cognitive and Physical Frailty
Workshop in Conjunction with IJCAI 2021 (AIF-IJCAI'21) | null | null | q-bio.NC cs.LG | http://creativecommons.org/licenses/by/4.0/ | On the increase of major depressive disorders (MDD), many researchers paid
attention to their recognition and treatment. Existing MDD recognition
algorithms always use a single time-frequency domain method method, but the
single time-frequency domain method is too simple and is not conducive to
simulating the complex link relationship between brain functions. To solve this
problem, this paper proposes a recognition method based on multi-layer brain
functional connectivity networks (MBFCN) for major depressive disorder and
conducts cognitive analysis. Cognitive analysis based on the proposed MBFCN
finds that the Alpha-Beta1 frequency band is the key sub-band for recognizing
MDD. The connections between the right prefrontal lobe and the temporal lobe of
the extremely depressed disorders (EDD) are deficient in the brain functional
connectivity networks (BFCN) based on phase lag index (PLI). Furthermore,
potential biomarkers by the significance analysis of depression features and
PHQ-9 can be found.
| [
{
"created": "Tue, 2 Nov 2021 03:24:43 GMT",
"version": "v1"
}
] | 2021-11-03 | [
[
"Sun",
"Xiaofang",
""
],
[
"Zheng",
"Xiangwei",
""
],
[
"Xu",
"Yonghui",
""
],
[
"Cui",
"Lizhen",
""
],
[
"Hu",
"Bin",
""
]
] | On the increase of major depressive disorders (MDD), many researchers paid attention to their recognition and treatment. Existing MDD recognition algorithms always use a single time-frequency domain method method, but the single time-frequency domain method is too simple and is not conducive to simulating the complex link relationship between brain functions. To solve this problem, this paper proposes a recognition method based on multi-layer brain functional connectivity networks (MBFCN) for major depressive disorder and conducts cognitive analysis. Cognitive analysis based on the proposed MBFCN finds that the Alpha-Beta1 frequency band is the key sub-band for recognizing MDD. The connections between the right prefrontal lobe and the temporal lobe of the extremely depressed disorders (EDD) are deficient in the brain functional connectivity networks (BFCN) based on phase lag index (PLI). Furthermore, potential biomarkers by the significance analysis of depression features and PHQ-9 can be found. |
2007.07039 | James Hague | Jonathan Keelan and James P. Hague | The role of vascular complexity on optimal junction exponents | null | null | null | null | q-bio.TO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine the role of complexity on arterial tree structures, determining
globally optimal vessel arrangements using the Simulated AnneaLing Vascular
Optimization (SALVO) algorithm, which we have previously used to reproduce
features of cardiac and cerebral vasculatures. Fundamental biophysical
understanding of complex vascular structure has applications to modelling of
cardiovascular diseases, and for improved representations of vasculatures in
large artificial tissues. In order to progress in-silico methods for growing
arterial networks, we need to understand the stability of computational
arterial growth algorithms to complexity, variations in physiological
parameters such as tissue demand, and underlying assumptions regarding the
value of junction exponents. We determine the globally optimal structure of
two-dimensional arterial trees; analysing sensitivity of tree morphology and
optimal bifurcation exponent to physiological parameters. We find that, for
physiologically relevant simulation parameters, arterial structure is stable,
whereas optimal junction exponents vary. We conclude that the full complexity
of arterial trees is essential for determining the fundamental properties of
vasculatures. These results are important for establishing that
optimisation-based arterial growth algorithms are stable against uncertainties
in physiological parameters, while identifying that optimal bifurcation
exponents (a key parameter for many arterial growth algorithms) are sensitive
to complexity and the boundary conditions dictated by organs.
| [
{
"created": "Tue, 14 Jul 2020 13:56:17 GMT",
"version": "v1"
}
] | 2020-07-15 | [
[
"Keelan",
"Jonathan",
""
],
[
"Hague",
"James P.",
""
]
] | We examine the role of complexity on arterial tree structures, determining globally optimal vessel arrangements using the Simulated AnneaLing Vascular Optimization (SALVO) algorithm, which we have previously used to reproduce features of cardiac and cerebral vasculatures. Fundamental biophysical understanding of complex vascular structure has applications to modelling of cardiovascular diseases, and for improved representations of vasculatures in large artificial tissues. In order to progress in-silico methods for growing arterial networks, we need to understand the stability of computational arterial growth algorithms to complexity, variations in physiological parameters such as tissue demand, and underlying assumptions regarding the value of junction exponents. We determine the globally optimal structure of two-dimensional arterial trees; analysing sensitivity of tree morphology and optimal bifurcation exponent to physiological parameters. We find that, for physiologically relevant simulation parameters, arterial structure is stable, whereas optimal junction exponents vary. We conclude that the full complexity of arterial trees is essential for determining the fundamental properties of vasculatures. These results are important for establishing that optimisation-based arterial growth algorithms are stable against uncertainties in physiological parameters, while identifying that optimal bifurcation exponents (a key parameter for many arterial growth algorithms) are sensitive to complexity and the boundary conditions dictated by organs. |
1611.00666 | Carla Bosia Dr | Marco Del Giudice, Stefano Bo, Silvia Grigolon and Carla Bosia | On the role of extrinsic noise in microRNA-mediated bimodal gene
expression | 29 pages, 9 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several studies highlighted the relevance of extrinsic noise in shaping cell
decision making and differentiation in molecular networks. Experimental
evidences of phenotypic differentiation are given by the presence of bimodal
distributions of gene expression levels, where the modes of the distribution
often correspond to different physiological states of the system. We
theoretically address the presence of bimodal phenotypes in the context of
microRNA (miRNA)-mediated regulation. MiRNAs are small noncoding RNA molecules
that downregulate the expression of their target mRNAs. The nature of this
interaction is titrative and induces a threshold effect: below a given target
transcription rate no mRNAs are free and available for translation. We
investigate the effect of extrinsic noise on the system by introducing a
fluctuating miRNA-transcription rate. We find that the presence of extrinsic
noise favours the presence of bimodal target distributions which can be
observed for a wider range of parameters compared to the case with intrinsic
noise only and for lower miRNA-target interaction strength. Our results suggest
that combining threshold-inducing interactions with extrinsic noise provides a
simple and robust mechanism for obtaining bimodal populations not requiring
fine tuning. We furthermore characterise the protein distributions dependence
on protein half-life.
| [
{
"created": "Wed, 2 Nov 2016 16:18:53 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Apr 2017 12:03:12 GMT",
"version": "v2"
}
] | 2017-04-19 | [
[
"Del Giudice",
"Marco",
""
],
[
"Bo",
"Stefano",
""
],
[
"Grigolon",
"Silvia",
""
],
[
"Bosia",
"Carla",
""
]
] | Several studies highlighted the relevance of extrinsic noise in shaping cell decision making and differentiation in molecular networks. Experimental evidences of phenotypic differentiation are given by the presence of bimodal distributions of gene expression levels, where the modes of the distribution often correspond to different physiological states of the system. We theoretically address the presence of bimodal phenotypes in the context of microRNA (miRNA)-mediated regulation. MiRNAs are small noncoding RNA molecules that downregulate the expression of their target mRNAs. The nature of this interaction is titrative and induces a threshold effect: below a given target transcription rate no mRNAs are free and available for translation. We investigate the effect of extrinsic noise on the system by introducing a fluctuating miRNA-transcription rate. We find that the presence of extrinsic noise favours the presence of bimodal target distributions which can be observed for a wider range of parameters compared to the case with intrinsic noise only and for lower miRNA-target interaction strength. Our results suggest that combining threshold-inducing interactions with extrinsic noise provides a simple and robust mechanism for obtaining bimodal populations not requiring fine tuning. We furthermore characterise the protein distributions dependence on protein half-life. |
2207.07734 | Haiyi Mao | Haiyi Mao, Minxue Jia, Jason Xiaotian Dou, Haotian Zhang, Panayiotis
V. Benos | COEM: Cross-Modal Embedding for MetaCell Identification | 5 pages, 2 figures, ICML workshop on computational biology | null | null | null | q-bio.GN cs.AI cs.GL | http://creativecommons.org/licenses/by/4.0/ | Metacells are disjoint and homogeneous groups of single-cell profiles,
representing discrete and highly granular cell states. Existing metacell
algorithms tend to use only one modality to infer metacells, even though
single-cell multi-omics datasets profile multiple molecular modalities within
the same cell. Here, we present \textbf{C}ross-M\textbf{O}dal
\textbf{E}mbedding for \textbf{M}etaCell Identification (COEM), which utilizes
an embedded space leveraging the information of both scATAC-seq and scRNA-seq
to perform aggregation, balancing the trade-off between fine resolution and
sufficient sequencing coverage. COEM outperforms the state-of-the-art method
SEACells by efficiently identifying accurate and well-separated metacells
across datasets with continuous and discrete cell types. Furthermore, COEM
significantly improves peak-to-gene association analyses, and facilitates
complex gene regulatory inference tasks.
| [
{
"created": "Fri, 15 Jul 2022 20:17:50 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Jul 2022 03:10:31 GMT",
"version": "v2"
}
] | 2022-07-26 | [
[
"Mao",
"Haiyi",
""
],
[
"Jia",
"Minxue",
""
],
[
"Dou",
"Jason Xiaotian",
""
],
[
"Zhang",
"Haotian",
""
],
[
"Benos",
"Panayiotis V.",
""
]
] | Metacells are disjoint and homogeneous groups of single-cell profiles, representing discrete and highly granular cell states. Existing metacell algorithms tend to use only one modality to infer metacells, even though single-cell multi-omics datasets profile multiple molecular modalities within the same cell. Here, we present \textbf{C}ross-M\textbf{O}dal \textbf{E}mbedding for \textbf{M}etaCell Identification (COEM), which utilizes an embedded space leveraging the information of both scATAC-seq and scRNA-seq to perform aggregation, balancing the trade-off between fine resolution and sufficient sequencing coverage. COEM outperforms the state-of-the-art method SEACells by efficiently identifying accurate and well-separated metacells across datasets with continuous and discrete cell types. Furthermore, COEM significantly improves peak-to-gene association analyses, and facilitates complex gene regulatory inference tasks. |
2005.06239 | Janusz Szwabi\'nski | Joanna Janczura, Patrycja Kowalek, Hanna Loch-Olszewska, Janusz
Szwabi\'nski, and Aleksander Weron | Classification of particle trajectories in living cells: machine
learning versus statistical testing hypothesis for fractional anomalous
diffusion | 32 pages, 5 figures | Phys. Rev. E 102, 032402 (2020) | 10.1103/PhysRevE.102.032402 | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Single-particle tracking (SPT) has become a popular tool to study the
intracellular transport of molecules in living cells. Inferring the character
of their dynamics is important, because it determines the organization and
functions of the cells. For this reason, one of the first steps in the analysis
of SPT data is the identification of the diffusion type of the observed
particles. The most popular method to identify the class of a trajectory is
based on the mean square displacement (MSD). However, due to its known
limitations, several other approaches have been already proposed. With the
recent advances in algorithms and the developments of modern hardware, the
classification attempts rooted in machine learning (ML) are of particular
interest. In this work, we adopt two ML ensemble algorithms, i.e. random forest
and gradient boosting, to the problem of trajectory classification. We present
a new set of features used to transform the raw trajectories data into input
vectors required by the classifiers. The resulting models are then applied to
real data for G protein-coupled receptors and G proteins. The classification
results are compared to recent statistical methods going beyond MSD.
| [
{
"created": "Wed, 13 May 2020 10:25:00 GMT",
"version": "v1"
},
{
"created": "Fri, 10 Jul 2020 11:37:44 GMT",
"version": "v2"
}
] | 2020-09-09 | [
[
"Janczura",
"Joanna",
""
],
[
"Kowalek",
"Patrycja",
""
],
[
"Loch-Olszewska",
"Hanna",
""
],
[
"Szwabiński",
"Janusz",
""
],
[
"Weron",
"Aleksander",
""
]
] | Single-particle tracking (SPT) has become a popular tool to study the intracellular transport of molecules in living cells. Inferring the character of their dynamics is important, because it determines the organization and functions of the cells. For this reason, one of the first steps in the analysis of SPT data is the identification of the diffusion type of the observed particles. The most popular method to identify the class of a trajectory is based on the mean square displacement (MSD). However, due to its known limitations, several other approaches have been already proposed. With the recent advances in algorithms and the developments of modern hardware, the classification attempts rooted in machine learning (ML) are of particular interest. In this work, we adopt two ML ensemble algorithms, i.e. random forest and gradient boosting, to the problem of trajectory classification. We present a new set of features used to transform the raw trajectories data into input vectors required by the classifiers. The resulting models are then applied to real data for G protein-coupled receptors and G proteins. The classification results are compared to recent statistical methods going beyond MSD. |
2008.04783 | Erin Sparks | Ashley N. Hostetler, Rajdeep S. Khangura, Brian P. Dilkes, and Erin E.
Sparks | Bracing for sustainable agriculture: the development and function of
brace roots in members of Poaceae | null | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | Optimization of crop production requires root systems to function in water
uptake, nutrient use, and anchorage. In maize, two types of nodal
roots-subterranean crown and aerial brace roots function in anchorage and water
uptake and preferentially express multiple water and nutrient transporters.
Brace root development shares genetic control with juvenile-to-adult phase
change and flowering time. We present a comprehensive list of the genes known
to alter brace roots and explore these as candidates for QTL studies in maize
and sorghum. Brace root development and function may be conserved in other
members of Poaceae, however research is limited. This work highlights the
critical knowledge gap of aerial nodal root development and function and
suggests new focus areas for breeding resilient crops.
| [
{
"created": "Sat, 8 Aug 2020 20:20:21 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Oct 2020 20:56:11 GMT",
"version": "v2"
},
{
"created": "Tue, 1 Dec 2020 14:43:01 GMT",
"version": "v3"
}
] | 2020-12-02 | [
[
"Hostetler",
"Ashley N.",
""
],
[
"Khangura",
"Rajdeep S.",
""
],
[
"Dilkes",
"Brian P.",
""
],
[
"Sparks",
"Erin E.",
""
]
] | Optimization of crop production requires root systems to function in water uptake, nutrient use, and anchorage. In maize, two types of nodal roots-subterranean crown and aerial brace roots function in anchorage and water uptake and preferentially express multiple water and nutrient transporters. Brace root development shares genetic control with juvenile-to-adult phase change and flowering time. We present a comprehensive list of the genes known to alter brace roots and explore these as candidates for QTL studies in maize and sorghum. Brace root development and function may be conserved in other members of Poaceae, however research is limited. This work highlights the critical knowledge gap of aerial nodal root development and function and suggests new focus areas for breeding resilient crops. |
1505.01105 | Luiz Max Carvalho | Luiz Max Carvalho and Nuno Rodrigues Faria and Andres M. Perez and
Marc A. Suchard and Philippe Lemey and Waldemir de Castro Silveira and Andrew
Rambaut and Guy Baele | Spatio-temporal Dynamics of Foot-and-Mouth Disease Virus in South
America | 21 pages, 5 figures, sumitted to Virus Evolution
(http://ve.oxfordjournals.org/). Updated affiliations list | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although foot-and-mouth disease virus (FMDV) incidence has decreased in South
America over the last years, the pathogen still circulates in the region and
the risk of re-emergence in previously FMDV-free areas is a veterinary public
health concern. In this paper we merge environmental, epidemiological and
genetic data to reconstruct spatiotemporal patterns and determinants of FMDV
serotypes A and O dispersal in South America. Our dating analysis suggests that
serotype A emerged in South America around 1930, while serotype O emerged
around 1990. The rate of evolution for serotype A was significantly higher
compared to serotype O. Phylogeographic inference identified two well-connected
sub networks of viral flow, one including Venezuela, Colombia and Ecuador;
another including Brazil, Uruguay and Argentina. The spread of serotype A was
best described by geographic distances, while trade of live cattle was the
predictor that best explained serotype O spread. Our findings show that the two
serotypes have different underlying evolutionary and spatial dynamics and may
pose different threats to control programmes. Key-words: Phylogeography,
foot-and-mouth disease virus, South America, animal trade.
| [
{
"created": "Tue, 5 May 2015 18:18:14 GMT",
"version": "v1"
},
{
"created": "Mon, 1 Jun 2015 11:41:11 GMT",
"version": "v2"
}
] | 2015-06-02 | [
[
"Carvalho",
"Luiz Max",
""
],
[
"Faria",
"Nuno Rodrigues",
""
],
[
"Perez",
"Andres M.",
""
],
[
"Suchard",
"Marc A.",
""
],
[
"Lemey",
"Philippe",
""
],
[
"Silveira",
"Waldemir de Castro",
""
],
[
"Rambaut",
"... | Although foot-and-mouth disease virus (FMDV) incidence has decreased in South America over the last years, the pathogen still circulates in the region and the risk of re-emergence in previously FMDV-free areas is a veterinary public health concern. In this paper we merge environmental, epidemiological and genetic data to reconstruct spatiotemporal patterns and determinants of FMDV serotypes A and O dispersal in South America. Our dating analysis suggests that serotype A emerged in South America around 1930, while serotype O emerged around 1990. The rate of evolution for serotype A was significantly higher compared to serotype O. Phylogeographic inference identified two well-connected sub networks of viral flow, one including Venezuela, Colombia and Ecuador; another including Brazil, Uruguay and Argentina. The spread of serotype A was best described by geographic distances, while trade of live cattle was the predictor that best explained serotype O spread. Our findings show that the two serotypes have different underlying evolutionary and spatial dynamics and may pose different threats to control programmes. Key-words: Phylogeography, foot-and-mouth disease virus, South America, animal trade. |
2104.07939 | Philipp H\"ovel | Nikita Gutjahr, Philipp H\"ovel, Aline Viol | Controlling extended criticality via modular connectivity | 20 pages, 11 figure (9 in main text, 2 in Appendix) | null | null | null | q-bio.NC nlin.AO | http://creativecommons.org/licenses/by/4.0/ | Criticality has been conjectured as an integral part of neuronal network
dynamics. Operating at a critical threshold requires precise parameter tuning
and a corresponding mechanism remains an open question. Recent studies have
suggested that topological features observed in brain networks give rise to a
Griffiths phase, leading to power-laws in brain activity dynamics and the
operational benefits of criticality in an extended parameter region. Motivated
by growing evidence of neural correlates of different states of consciousness,
we investigate how topological changes affect the expression of a Griffiths
phase. We analyze the activity decay in modular networks using a
Susceptible-Infected-Susceptible propagation model and find that we can control
the extension of the Griffiths phase by altering intra- and intermodular
connectivity. We find that by adjusting system parameters, we can counteract
changes in critical behavior and maintain a stable critical region despite
changes in network topology. Our results give insight into how structural
network properties affect the emergence of a Griffiths phase and how its
features are linked to established topological network metrics. We discuss how
those findings can contribute to understand the observed changes in functional
brain networks. Finally, we indicate how our results could be useful in the
study of disease spreading.
| [
{
"created": "Fri, 16 Apr 2021 07:36:00 GMT",
"version": "v1"
}
] | 2021-04-19 | [
[
"Gutjahr",
"Nikita",
""
],
[
"Hövel",
"Philipp",
""
],
[
"Viol",
"Aline",
""
]
] | Criticality has been conjectured as an integral part of neuronal network dynamics. Operating at a critical threshold requires precise parameter tuning and a corresponding mechanism remains an open question. Recent studies have suggested that topological features observed in brain networks give rise to a Griffiths phase, leading to power-laws in brain activity dynamics and the operational benefits of criticality in an extended parameter region. Motivated by growing evidence of neural correlates of different states of consciousness, we investigate how topological changes affect the expression of a Griffiths phase. We analyze the activity decay in modular networks using a Susceptible-Infected-Susceptible propagation model and find that we can control the extension of the Griffiths phase by altering intra- and intermodular connectivity. We find that by adjusting system parameters, we can counteract changes in critical behavior and maintain a stable critical region despite changes in network topology. Our results give insight into how structural network properties affect the emergence of a Griffiths phase and how its features are linked to established topological network metrics. We discuss how those findings can contribute to understand the observed changes in functional brain networks. Finally, we indicate how our results could be useful in the study of disease spreading. |
2402.05216 | Giacomo Bertazzoli | Giacomo Bertazzoli, Carlo Miniussi, Petro Julkunen, Marta Bortoletto | TMS-EEG Reliability: Bridging the Gap to Clinical Use | 57 pages, 4 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Concurrent transcranial magnetic stimulation (TMS) and electroencephalography
(EEG), or TMS-EEG, holds the potential to broaden the clinical applications of
TMS beyond its traditional role in evaluating the cortico-spinal tract and
motor cortices. TMS-evoked potentials (TEPs) have emerged as valuable tools in
clinical research, enabling the assessment of cortical excitability and
effective connectivity between cortical regions in various psychiatric and
neurological disorders and are increasingly recognized as a promising candidate
biomarker for aiding in diagnosis and prognosis. Despite the well-established
diagnostic utility of TMS, the clinical implementation of TMS-EEG has yet to
meet the necessary standards. One critical aspect that often remains
unaddressed is the reliability of TEP measurements. In this context, we outline
the crucial reliability assessments required to determine the clinical
applicability of TEPs. Firstly, we conduct a comprehensive review of the
existing literature on reliability, encompassing both theoretical and
statistical considerations. Subsequently, we present the current state of
knowledge on TEP reliability. We emphasize the specific elements of reliability
that must be incorporated to facilitate a unified, evidence-derived assessment
of TMS-EEG as a clinical tool.
| [
{
"created": "Wed, 7 Feb 2024 19:44:46 GMT",
"version": "v1"
}
] | 2024-02-09 | [
[
"Bertazzoli",
"Giacomo",
""
],
[
"Miniussi",
"Carlo",
""
],
[
"Julkunen",
"Petro",
""
],
[
"Bortoletto",
"Marta",
""
]
] | Concurrent transcranial magnetic stimulation (TMS) and electroencephalography (EEG), or TMS-EEG, holds the potential to broaden the clinical applications of TMS beyond its traditional role in evaluating the cortico-spinal tract and motor cortices. TMS-evoked potentials (TEPs) have emerged as valuable tools in clinical research, enabling the assessment of cortical excitability and effective connectivity between cortical regions in various psychiatric and neurological disorders and are increasingly recognized as a promising candidate biomarker for aiding in diagnosis and prognosis. Despite the well-established diagnostic utility of TMS, the clinical implementation of TMS-EEG has yet to meet the necessary standards. One critical aspect that often remains unaddressed is the reliability of TEP measurements. In this context, we outline the crucial reliability assessments required to determine the clinical applicability of TEPs. Firstly, we conduct a comprehensive review of the existing literature on reliability, encompassing both theoretical and statistical considerations. Subsequently, we present the current state of knowledge on TEP reliability. We emphasize the specific elements of reliability that must be incorporated to facilitate a unified, evidence-derived assessment of TMS-EEG as a clinical tool. |
1706.03619 | Omid Rezania | Omid Rezania | Physics of the Brain-Schizophrenia | 6 pages | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Schizophrenic patients suffer from hallucination which its causality is not
yet fully understood. This paper attempts to approach this mystery from the
perspective of quantum mechanical theories. A novel approach has been adopted
to demonstrate the hallucination as a time evolution of percepts basis states
in the Hilbertian consciousness which are desynchronised from the time in real
world. The method also extends his approach to predict mind and brain
modulation through the correlation and coupling of consciousness and it reaches
a clinically hypothetical outcome of inducing consciousness into a brain which
is not conscious.
| [
{
"created": "Wed, 7 Jun 2017 22:20:00 GMT",
"version": "v1"
}
] | 2017-06-13 | [
[
"Rezania",
"Omid",
""
]
] | Schizophrenic patients suffer from hallucination which its causality is not yet fully understood. This paper attempts to approach this mystery from the perspective of quantum mechanical theories. A novel approach has been adopted to demonstrate the hallucination as a time evolution of percepts basis states in the Hilbertian consciousness which are desynchronised from the time in real world. The method also extends his approach to predict mind and brain modulation through the correlation and coupling of consciousness and it reaches a clinically hypothetical outcome of inducing consciousness into a brain which is not conscious. |
2005.10248 | Daniel Meyer | R. Daniel Meyer, Bohdana Ratitch, Marcel Wolbers, Olga Marchenko, Hui
Quan, Daniel Li, Chrissie Fletcher, Xin Li, David Wright, Yue Shentu, Stefan
Englert, Wei Shen, Jyotirmoy Dey, Thomas Liu, Ming Zhou, Norman Bohidar,
Peng-Liang Zhao, Michael Hale | Statistical Issues and Recommendations for Clinical Trials Conducted
During the COVID-19 Pandemic | Accepted for publication in Statistics in Biopharmaceutical Research.
40 pages | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The COVID-19 pandemic has had and continues to have major impacts on planned
and ongoing clinical trials. Its effects on trial data create multiple
potential statistical issues. The scale of impact is unprecedented, but when
viewed individually, many of the issues are well defined and feasible to
address. A number of strategies and recommendations are put forward to assess
and address issues related to estimands, missing data, validity and
modifications of statistical analysis methods, need for additional analyses,
ability to meet objectives and overall trial interpretability.
| [
{
"created": "Thu, 21 May 2020 00:26:06 GMT",
"version": "v1"
}
] | 2020-05-22 | [
[
"Meyer",
"R. Daniel",
""
],
[
"Ratitch",
"Bohdana",
""
],
[
"Wolbers",
"Marcel",
""
],
[
"Marchenko",
"Olga",
""
],
[
"Quan",
"Hui",
""
],
[
"Li",
"Daniel",
""
],
[
"Fletcher",
"Chrissie",
""
],
[
"... | The COVID-19 pandemic has had and continues to have major impacts on planned and ongoing clinical trials. Its effects on trial data create multiple potential statistical issues. The scale of impact is unprecedented, but when viewed individually, many of the issues are well defined and feasible to address. A number of strategies and recommendations are put forward to assess and address issues related to estimands, missing data, validity and modifications of statistical analysis methods, need for additional analyses, ability to meet objectives and overall trial interpretability. |
1112.3991 | Shuchen Zhu | Komi Messan, Kyle Smith, Shawn Tsosie, Shuchen Zhu, Sergei Suslov | Short and Long Range Population Dynamics of the Monarch | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The monarch butterfly annually migrates from central Mexico to southern
Canada. During recent decades, its population has been reduced due to human
interaction with their habitat. We examine the effect of herbicide usage on the
monarch butterfly's population by creating a system of linear and non-linear
ordinary differential equations that describe the interaction between the
monarch's population and its environment at various stages of migration: spring
migration, summer loitering, and fall migration. The model has various stages
that are used to describe the dynamics of the monarch butterfly population over
multiple generations. In Stage 1, we propose a system of coupled ordinary
differential equations that model the populations of the monarch butterflies
and larvae during spring migration. In Stage 2, we propose a predator-prey
model with age structure to model the population dynamics at the summer
breeding site. In Stages 3 and 4, we propose exponential decay functions to
model the monarch butterfly's fall migration to central Mexico and their time
at the overwintering site. The model is used to analyze the long-term behavior
of the monarch butterflies through numerical analysis, given data available in
the research literature.
| [
{
"created": "Fri, 16 Dec 2011 22:47:40 GMT",
"version": "v1"
}
] | 2011-12-20 | [
[
"Messan",
"Komi",
""
],
[
"Smith",
"Kyle",
""
],
[
"Tsosie",
"Shawn",
""
],
[
"Zhu",
"Shuchen",
""
],
[
"Suslov",
"Sergei",
""
]
] | The monarch butterfly annually migrates from central Mexico to southern Canada. During recent decades, its population has been reduced due to human interaction with their habitat. We examine the effect of herbicide usage on the monarch butterfly's population by creating a system of linear and non-linear ordinary differential equations that describe the interaction between the monarch's population and its environment at various stages of migration: spring migration, summer loitering, and fall migration. The model has various stages that are used to describe the dynamics of the monarch butterfly population over multiple generations. In Stage 1, we propose a system of coupled ordinary differential equations that model the populations of the monarch butterflies and larvae during spring migration. In Stage 2, we propose a predator-prey model with age structure to model the population dynamics at the summer breeding site. In Stages 3 and 4, we propose exponential decay functions to model the monarch butterfly's fall migration to central Mexico and their time at the overwintering site. The model is used to analyze the long-term behavior of the monarch butterflies through numerical analysis, given data available in the research literature. |
2008.09574 | William Bialek | William Bialek | What do we mean by the dimensionality of behavior? | Based in part on a presentation at the Physics of Behavior Virtual
Workshop (30 April 2020). Videos of the lectures and discussion are available
at https://www.youtube.com/watch?v=xSwWAgp2VdU | Proc Natl Acad Sci (USA) 119, e2021860119 (2022) | 10.1073/pnas.2021860119 | null | q-bio.NC cond-mat.stat-mech q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is growing effort in the "physics of behavior" that aims at complete
quantitative characterization of animal movements under more complex,
naturalistic conditions. One reaction to the resulting explosion of data is the
search for low dimensional structure. Here I try to define more clearly what we
mean by the dimensionality of behavior, where observable behavior may consist
either of continuous trajectories or sequences of discrete states. This
discussion also serves to isolate situations in which the dimensionality of
behavior is effectively infinite. I conclude with some more general
perspectives about the importance of quantitative phenomenology.
| [
{
"created": "Fri, 21 Aug 2020 16:34:11 GMT",
"version": "v1"
}
] | 2024-01-23 | [
[
"Bialek",
"William",
""
]
] | There is growing effort in the "physics of behavior" that aims at complete quantitative characterization of animal movements under more complex, naturalistic conditions. One reaction to the resulting explosion of data is the search for low dimensional structure. Here I try to define more clearly what we mean by the dimensionality of behavior, where observable behavior may consist either of continuous trajectories or sequences of discrete states. This discussion also serves to isolate situations in which the dimensionality of behavior is effectively infinite. I conclude with some more general perspectives about the importance of quantitative phenomenology. |
1803.02626 | PierGianLuca Porta Mana | PierGianLuca Porta Mana, Claudia Bachmann, Abigail Morrison | Inferring health conditions from fMRI-graph data | V1: 35 pages, 5 figures, 2 tables. V2: 36 pages, 5 figures, 2 tables;
partially rewritten all sections and added references. V3: Rewritten
introduction | null | null | null | q-bio.QM q-bio.NC stat.AP | http://creativecommons.org/licenses/by/4.0/ | Automated classification methods for disease diagnosis are currently in the
limelight, especially for imaging data. Classification does not fully meet a
clinician's needs, however: in order to combine the results of multiple tests
and decide on a course of treatment, a clinician needs the likelihood of a
given health condition rather than binary classification yielded by such
methods. We illustrate how likelihoods can be derived step by step from first
principles and approximations, and how they can be assessed and selected,
illustrating our approach using fMRI data from a publicly available data set
containing schizophrenic and healthy control subjects. We start from the basic
assumption of partial exchangeability, and then the notion of sufficient
statistics and the "method of translation" (Edgeworth, 1898) combined with
conjugate priors. This method can be used to construct a likelihood that can be
used to compare different data-reduction algorithms. Despite the
simplifications and possibly unrealistic assumptions used to illustrate the
method, we obtain classification results comparable to previous, more realistic
studies about schizophrenia, whilst yielding likelihoods that can naturally be
combined with the results of other diagnostic tests.
| [
{
"created": "Wed, 7 Mar 2018 12:58:46 GMT",
"version": "v1"
},
{
"created": "Mon, 19 Mar 2018 14:42:08 GMT",
"version": "v2"
},
{
"created": "Fri, 4 May 2018 10:48:02 GMT",
"version": "v3"
}
] | 2018-05-07 | [
[
"Mana",
"PierGianLuca Porta",
""
],
[
"Bachmann",
"Claudia",
""
],
[
"Morrison",
"Abigail",
""
]
] | Automated classification methods for disease diagnosis are currently in the limelight, especially for imaging data. Classification does not fully meet a clinician's needs, however: in order to combine the results of multiple tests and decide on a course of treatment, a clinician needs the likelihood of a given health condition rather than binary classification yielded by such methods. We illustrate how likelihoods can be derived step by step from first principles and approximations, and how they can be assessed and selected, illustrating our approach using fMRI data from a publicly available data set containing schizophrenic and healthy control subjects. We start from the basic assumption of partial exchangeability, and then the notion of sufficient statistics and the "method of translation" (Edgeworth, 1898) combined with conjugate priors. This method can be used to construct a likelihood that can be used to compare different data-reduction algorithms. Despite the simplifications and possibly unrealistic assumptions used to illustrate the method, we obtain classification results comparable to previous, more realistic studies about schizophrenia, whilst yielding likelihoods that can naturally be combined with the results of other diagnostic tests. |
1806.01915 | Loren Coquille | Modibo Diabate, Loren Coquille, Adeline Samson | Parameter estimation and treatment optimization in a stochastic model
for immunotherapy of cancer | major reorganisation of the paper and the reformulation of many
substantial parts | null | null | null | q-bio.PE q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Adoptive Cell Transfer therapy of cancer is currently in full development and
mathematical modeling is playing a critical role in this area. We study a
stochastic model developed by Baar et al. in 2015 for modeling immunotherapy
against melanoma skin cancer. First, we estimate the parameters of the
deterministic limit of the model based on biological data of tumor growth in
mice. A Nonlinear Mixed Effects Model is estimated by the Stochastic
Approximation Expectation Maximization algorithm. With the estimated
parameters, we head back to the stochastic model and calculate the probability
that the T cells all get exhausted during the treatment. We show that for some
relevant parameter values, an early relapse is due to stochastic fluctuations
(complete T cells exhaustion) with a non negligible probability. Then, focusing
on the relapse related to the T cell exhaustion, we propose to optimize the
treatment plan (treatment doses and restimulation times) by minimizing the T
cell exhaustion probability in the parameter estimation ranges.
| [
{
"created": "Tue, 5 Jun 2018 19:57:51 GMT",
"version": "v1"
},
{
"created": "Mon, 1 Apr 2019 10:41:47 GMT",
"version": "v2"
},
{
"created": "Fri, 6 Mar 2020 15:54:31 GMT",
"version": "v3"
}
] | 2020-03-09 | [
[
"Diabate",
"Modibo",
""
],
[
"Coquille",
"Loren",
""
],
[
"Samson",
"Adeline",
""
]
] | Adoptive Cell Transfer therapy of cancer is currently in full development and mathematical modeling is playing a critical role in this area. We study a stochastic model developed by Baar et al. in 2015 for modeling immunotherapy against melanoma skin cancer. First, we estimate the parameters of the deterministic limit of the model based on biological data of tumor growth in mice. A Nonlinear Mixed Effects Model is estimated by the Stochastic Approximation Expectation Maximization algorithm. With the estimated parameters, we head back to the stochastic model and calculate the probability that the T cells all get exhausted during the treatment. We show that for some relevant parameter values, an early relapse is due to stochastic fluctuations (complete T cells exhaustion) with a non negligible probability. Then, focusing on the relapse related to the T cell exhaustion, we propose to optimize the treatment plan (treatment doses and restimulation times) by minimizing the T cell exhaustion probability in the parameter estimation ranges. |
1902.06028 | Michael Saint-Antoine | Michael M. Saint-Antoine and Abhyudai Singh | Evaluating Pruning Methods in Gene Network Inference | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One challenge in gene network inference is distinguishing between direct and
indirect regulation. Some algorithms, including ARACNE and Phixer, approach
this problem by using pruning methods to eliminate redundant edges in an
attempt to explain the observed data with the simplest possible network
structure. However, we hypothesize that there may be a cost in accuracy to
simplifying the predicted networks in this way, especially due to the
prevalence of redundant connections, such as feed forward loops, in gene
networks. In this paper, we evaluate the pruning methods of ARACNE and Phixer,
and score their accuracy using receiver operating characteristic curves and
precision-recall curves. Our results suggest that while pruning can be useful
in some situations, it may have a negative effect on overall accuracy that has
not been previously studied. Researchers should be aware of both the advantages
and disadvantages of pruning when inferring networks, in order to choose the
best inference strategy for their experimental context.
| [
{
"created": "Sat, 16 Feb 2019 02:39:46 GMT",
"version": "v1"
},
{
"created": "Wed, 29 May 2019 06:01:30 GMT",
"version": "v2"
}
] | 2019-05-30 | [
[
"Saint-Antoine",
"Michael M.",
""
],
[
"Singh",
"Abhyudai",
""
]
] | One challenge in gene network inference is distinguishing between direct and indirect regulation. Some algorithms, including ARACNE and Phixer, approach this problem by using pruning methods to eliminate redundant edges in an attempt to explain the observed data with the simplest possible network structure. However, we hypothesize that there may be a cost in accuracy to simplifying the predicted networks in this way, especially due to the prevalence of redundant connections, such as feed forward loops, in gene networks. In this paper, we evaluate the pruning methods of ARACNE and Phixer, and score their accuracy using receiver operating characteristic curves and precision-recall curves. Our results suggest that while pruning can be useful in some situations, it may have a negative effect on overall accuracy that has not been previously studied. Researchers should be aware of both the advantages and disadvantages of pruning when inferring networks, in order to choose the best inference strategy for their experimental context. |
1305.0100 | Natalia Komarova | Leili Shahriyari and Natalia L. Komarova | Symmetric vs asymmetric stem cell divisions: an adaptation against
cancer? | null | null | 10.1371/journal.pone.0076195 | null | q-bio.CB q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditionally, it has been held that a central characteristic of stem cells
is their ability to divide asymmetrically. Recent advances in inducible genetic
labeling provided ample evidence that symmetric stem cell divisions play an
important role in adult mammalian homeostasis. It is well understood that the
two types of cell divisions differ in terms of the stem cells' flexibility to
expand when needed. On the contrary, the implications of symmetric and
asymmetric divisions for mutation accumulation are still poorly understood. In
this paper we study a stochastic model of a renewing tissue, and address the
optimization problem of tissue architecture in the context of mutant
production. Specifically, we study the process of tumor suppressor gene
inactivation which usually takes place as a sequence of two consecutive "hits",
and which is one of the most common patterns in carcinogenesis. We compare and
contrast symmetric and asymmetric (and mixed) stem cell divisions, and focus on
the rate at which double-hit mutants are generated. It turns out that
symmetrically-dividing cells generate such mutants at a rate which is
significantly lower than that of asymmetrically-dividing cells. This result
holds whether single-hit (intermediate) mutants are disadvantageous, neutral,
or advantageous. It is also independent on whether the carcinogenic double-hit
mutants are produced only among the stem cells or also among more
differentiated cells. We argue that symmetric stem cell divisions in mammals
could be an adaptation which helps delay the onset of cancers. We further
investigate the question of the optimal fraction of stem cells in the tissue,
and quantify the contribution of non-stem cells in mutant production. Our work
provides a hypothesis to explain the observation that in mammalian cells,
symmetric patterns of stem cell division seem to be very common.
| [
{
"created": "Wed, 1 May 2013 06:17:23 GMT",
"version": "v1"
}
] | 2014-03-05 | [
[
"Shahriyari",
"Leili",
""
],
[
"Komarova",
"Natalia L.",
""
]
] | Traditionally, it has been held that a central characteristic of stem cells is their ability to divide asymmetrically. Recent advances in inducible genetic labeling provided ample evidence that symmetric stem cell divisions play an important role in adult mammalian homeostasis. It is well understood that the two types of cell divisions differ in terms of the stem cells' flexibility to expand when needed. On the contrary, the implications of symmetric and asymmetric divisions for mutation accumulation are still poorly understood. In this paper we study a stochastic model of a renewing tissue, and address the optimization problem of tissue architecture in the context of mutant production. Specifically, we study the process of tumor suppressor gene inactivation which usually takes place as a sequence of two consecutive "hits", and which is one of the most common patterns in carcinogenesis. We compare and contrast symmetric and asymmetric (and mixed) stem cell divisions, and focus on the rate at which double-hit mutants are generated. It turns out that symmetrically-dividing cells generate such mutants at a rate which is significantly lower than that of asymmetrically-dividing cells. This result holds whether single-hit (intermediate) mutants are disadvantageous, neutral, or advantageous. It is also independent on whether the carcinogenic double-hit mutants are produced only among the stem cells or also among more differentiated cells. We argue that symmetric stem cell divisions in mammals could be an adaptation which helps delay the onset of cancers. We further investigate the question of the optimal fraction of stem cells in the tissue, and quantify the contribution of non-stem cells in mutant production. Our work provides a hypothesis to explain the observation that in mammalian cells, symmetric patterns of stem cell division seem to be very common. |
q-bio/0510054 | Reka Albert | Reka Albert | Scale- free networks in cell biology | Review article, 21 pages, 10 figures | Journal of Cell Science 118, 4947-4957 (2005) | null | null | q-bio.MN | null | A cell's behavior is a consequence of the complex interactions between its
numerous constituents, such as DNA, RNA, proteins and small molecules. Cells
use signaling pathways and regulatory mechanisms to coordinate multiple
processes, allowing them to respond to and adapt to an ever-changing
environment. The large number of components, the degree of interconnectivity
and the complex control of cellular networks are becoming evident in the
integrated genomic and proteomic analyses that are emerging. It is increasingly
recognized that the understanding of properties that arise from whole-cell
function require integrated, theoretical descriptions of the relationships
between different cellular components. Recent theoretical advances allow us to
describe cellular network structure with graph concepts, and have revealed
organizational features shared with numerous non-biological networks. How do we
quantitatively describe a network of hundreds or thousands of interacting
components? Does the observed topology of cellular networks give us clues about
their evolution? How does cellular networks' organization influence their
function and dynamical responses? This article will review the recent advances
in addressing these questions.
| [
{
"created": "Fri, 28 Oct 2005 16:47:56 GMT",
"version": "v1"
}
] | 2007-09-12 | [
[
"Albert",
"Reka",
""
]
] | A cell's behavior is a consequence of the complex interactions between its numerous constituents, such as DNA, RNA, proteins and small molecules. Cells use signaling pathways and regulatory mechanisms to coordinate multiple processes, allowing them to respond to and adapt to an ever-changing environment. The large number of components, the degree of interconnectivity and the complex control of cellular networks are becoming evident in the integrated genomic and proteomic analyses that are emerging. It is increasingly recognized that the understanding of properties that arise from whole-cell function require integrated, theoretical descriptions of the relationships between different cellular components. Recent theoretical advances allow us to describe cellular network structure with graph concepts, and have revealed organizational features shared with numerous non-biological networks. How do we quantitatively describe a network of hundreds or thousands of interacting components? Does the observed topology of cellular networks give us clues about their evolution? How does cellular networks' organization influence their function and dynamical responses? This article will review the recent advances in addressing these questions. |
1701.00390 | Paolo Del Giudice | Gabriel Baglietto, Guido Gigante and Paolo Del Giudice | Density-based clustering: A 'landscape view' of multi-channel neural
data for inference and dynamic complexity analysis | null | null | 10.1371/journal.pone.0174918 | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Simultaneous recordings from N electrodes generate N-dimensional time series
that call for efficient representations to expose relevant aspects of the
underlying dynamics. Binning the time series defines neural activity vectors
that populate the N-dimensional space as a density distribution, especially
informative when the neural dynamics performs a noisy path through metastable
states (often a case of interest in neuroscience); this makes clustering in the
N-dimensional space a natural choice. We apply a variant of the 'mean-shift'
algorithm to perform such clustering, and validate it on an Hopfield network in
the glassy phase, in which metastable states are uncorrelated from memory
attractors. The neural states identified as clusters' centroids are then used
to define a parsimonious parametrization of the synaptic matrix, which allows a
significant improvement in inferring the synaptic couplings from neural
activities. We next consider the more realistic case of a multi-modular spiking
network, with spike-frequency adaptation (SFA) inducing history-dependent
effects; we develop a procedure, inspired by Boltzmann learning but extending
its domain of application, to learn inter-module synaptic couplings so that the
spiking network reproduces a prescribed pattern of spatial correlations. After
clustering the activity generated by multi-modular spiking networks, we
represent their multi-dimensional dynamics as the symbolic sequence of the
clusters' centroids, which naturally lends itself to complexity estimates that
provide information on memory effects like those induced by SFA. To obtain a
relative complexity measure we compare the Lempel-Ziv complexity of the actual
centroid sequence to the one of Markov processes sharing the same transition
probabilities between centroids; as an illustration, we show that the
dependence of such relative complexity on the time scale of SFA.
| [
{
"created": "Mon, 2 Jan 2017 14:02:24 GMT",
"version": "v1"
}
] | 2017-07-05 | [
[
"Baglietto",
"Gabriel",
""
],
[
"Gigante",
"Guido",
""
],
[
"Del Giudice",
"Paolo",
""
]
] | Simultaneous recordings from N electrodes generate N-dimensional time series that call for efficient representations to expose relevant aspects of the underlying dynamics. Binning the time series defines neural activity vectors that populate the N-dimensional space as a density distribution, especially informative when the neural dynamics performs a noisy path through metastable states (often a case of interest in neuroscience); this makes clustering in the N-dimensional space a natural choice. We apply a variant of the 'mean-shift' algorithm to perform such clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are uncorrelated from memory attractors. The neural states identified as clusters' centroids are then used to define a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from neural activities. We next consider the more realistic case of a multi-modular spiking network, with spike-frequency adaptation (SFA) inducing history-dependent effects; we develop a procedure, inspired by Boltzmann learning but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations. After clustering the activity generated by multi-modular spiking networks, we represent their multi-dimensional dynamics as the symbolic sequence of the clusters' centroids, which naturally lends itself to complexity estimates that provide information on memory effects like those induced by SFA. To obtain a relative complexity measure we compare the Lempel-Ziv complexity of the actual centroid sequence to the one of Markov processes sharing the same transition probabilities between centroids; as an illustration, we show that the dependence of such relative complexity on the time scale of SFA. |
1708.00779 | Daniel Pouzzner | Daniel Pouzzner | Control of Functional Connectivity in Cerebral Cortex by Basal Ganglia
Mediated Synchronization | Expanded comparison to cerebellum (13.1); Discuss resonant
frequencies of cortico-subcortical loops (1.8, 3.2, 13.1.7); Expanded
treatment of neural noise and criticality (5.2, 11.4, 11.6, 12.7, 14.3.9);
Minor clarifications, expansions, and reorganization for readability
throughout; 306 new references | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Since the earliest electroencephalography experiments, large scale
oscillations have been observed in the mammalian brain. More recently, they
have been identified not only in the cerebral cortex and thalamus, but
pervasively in the healthy basal ganglia. The basal ganglia mediated
synchronization model, introduced here, implicates these oscillations in the
combination of cortical association mechanisms with stimulus-response and
reinforcement mechanisms in the basal ganglia. In the core mechanism of the
model, oscillatory patterns in cortex are selected by and routed through the
basal ganglia to the thalamus phase-coherently, then circulated back to widely
separated areas of cortex, synchronizing those areas and functionally
connecting them. Corticostriatal and striatonigral conduction delays are
crucial to this mechanism, and evidence suggests that these delays are
unusually long, and unusually varied, in arrangements that might facilitate
learning of useful time alignments and associated resonant frequencies. Other
structural arrangements in the basal ganglia show further specialization for
this role, with convergence in the inputs from cortex, and divergence in many
of the return paths to cortex, that systematically reflect corticocortical
anatomical connectivity. The basal ganglia also target the dopaminergic,
cholinergic, and serotonergic centers of the brainstem and basal forebrain, and
the reticular nucleus of the thalamus, structures broadly implicated in the
modulation of oscillatory network activity and expressions of plasticity. By
learning to coordinate these various output channels, the basal ganglia are
positioned to facilitate and synchronize activity in selected areas of cortex,
broadly impart selective receptivity, attenuate and disconnect interfering
activity, and recurrently process the resulting patterns of activity,
channeling cognition and promoting goal [...]
| [
{
"created": "Mon, 31 Jul 2017 20:44:26 GMT",
"version": "v1"
},
{
"created": "Fri, 10 Apr 2020 17:13:49 GMT",
"version": "v2"
}
] | 2020-04-13 | [
[
"Pouzzner",
"Daniel",
""
]
] | Since the earliest electroencephalography experiments, large scale oscillations have been observed in the mammalian brain. More recently, they have been identified not only in the cerebral cortex and thalamus, but pervasively in the healthy basal ganglia. The basal ganglia mediated synchronization model, introduced here, implicates these oscillations in the combination of cortical association mechanisms with stimulus-response and reinforcement mechanisms in the basal ganglia. In the core mechanism of the model, oscillatory patterns in cortex are selected by and routed through the basal ganglia to the thalamus phase-coherently, then circulated back to widely separated areas of cortex, synchronizing those areas and functionally connecting them. Corticostriatal and striatonigral conduction delays are crucial to this mechanism, and evidence suggests that these delays are unusually long, and unusually varied, in arrangements that might facilitate learning of useful time alignments and associated resonant frequencies. Other structural arrangements in the basal ganglia show further specialization for this role, with convergence in the inputs from cortex, and divergence in many of the return paths to cortex, that systematically reflect corticocortical anatomical connectivity. The basal ganglia also target the dopaminergic, cholinergic, and serotonergic centers of the brainstem and basal forebrain, and the reticular nucleus of the thalamus, structures broadly implicated in the modulation of oscillatory network activity and expressions of plasticity. By learning to coordinate these various output channels, the basal ganglia are positioned to facilitate and synchronize activity in selected areas of cortex, broadly impart selective receptivity, attenuate and disconnect interfering activity, and recurrently process the resulting patterns of activity, channeling cognition and promoting goal [...] |
2202.07468 | Yuto Omae | Yuto Omae, Makoto Sasaki, Jun Toyotani, Kazuyuki Hara, Hirotaka
Takahashi | Theoretical Analysis of SIRVVD Model to Provide Insight on the Target
Rate of COVID-19/SARS-CoV-2 Vaccination in Japan | 9 pages, 6 figures | IEEE Access, 2022 | 10.1109/ACCESS.2022.3168985 | null | q-bio.PE math.DS q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The effectiveness of the first and second dose vaccinations are different for
COVID-19; therefore, a
susceptible-infected-recovered-vaccination1-vaccination2-death (SIRVVD) model
that can represent the states of the first and second vaccination doses has
been proposed. By the previous study, we can carry out simulating the spread of
infectious disease considering the effects of the first and second doses of the
vaccination based on the SIRVVD model. However, theoretical analysis of the
SIRVVD Model is insufficient. Therefore, we obtained an analytical expression
of the infectious number, by treating the numbers of susceptible persons and
vaccinated persons as parameters. We used the solution to determine the target
rate of the vaccination for decreasing the infection numbers of the COVID-19
Delta variant (B.1.617) in Japan. Further, we investigated the target
vaccination rates for cases with strong or weak variants by comparison with the
COVID-19 Delta variant (B.1.617). This study contributes to the mathematical
development of the SIRVVD model and provides insight into the target rate of
the vaccination to decrease the number of infections.
| [
{
"created": "Sun, 13 Feb 2022 10:48:24 GMT",
"version": "v1"
}
] | 2022-05-02 | [
[
"Omae",
"Yuto",
""
],
[
"Sasaki",
"Makoto",
""
],
[
"Toyotani",
"Jun",
""
],
[
"Hara",
"Kazuyuki",
""
],
[
"Takahashi",
"Hirotaka",
""
]
] | The effectiveness of the first and second dose vaccinations are different for COVID-19; therefore, a susceptible-infected-recovered-vaccination1-vaccination2-death (SIRVVD) model that can represent the states of the first and second vaccination doses has been proposed. By the previous study, we can carry out simulating the spread of infectious disease considering the effects of the first and second doses of the vaccination based on the SIRVVD model. However, theoretical analysis of the SIRVVD Model is insufficient. Therefore, we obtained an analytical expression of the infectious number, by treating the numbers of susceptible persons and vaccinated persons as parameters. We used the solution to determine the target rate of the vaccination for decreasing the infection numbers of the COVID-19 Delta variant (B.1.617) in Japan. Further, we investigated the target vaccination rates for cases with strong or weak variants by comparison with the COVID-19 Delta variant (B.1.617). This study contributes to the mathematical development of the SIRVVD model and provides insight into the target rate of the vaccination to decrease the number of infections. |
1603.01846 | Jay Newby | Jay Newby and Jun Allard | First-passage time to clear the way for receptor-ligand binding in a
crowded environment | null | Phys. Rev. Lett. 116, 128101 (2016) | 10.1103/PhysRevLett.116.128101 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Certain biological reactions, such as receptor-ligand binding at cell-cell
interfaces and macromolecules binding to biopolymers, require many smaller
molecules crowding a reaction site to be cleared. Examples include the T cell
interface, a key player in immunological information processing. Diffusion sets
a limit for such cavitation to occur spontaneously, thereby defining a
timescale below which active mechanisms must take over. We consider $N$
independent diffusing particles in a closed domain, containing a sub-region
with $N_{0}$ particles, on average. We investigate the time until the
sub-region is empty, allowing a subsequent reaction to proceed. The first
passage time is computed using an efficient exact simulation algorithm and an
asymptotic approximation in the limit that cavitation is rare. In this limit,
we find that the mean first passage time is sub-exponential, $T \propto
e^{N_{0}}/N_{0}^2$. For the case of T cell receptors, we find that stochastic
cavitation is exceedingly slow, $10^9$ seconds at physiological densities,
however can be accelerated to occur within 5 second with only a four-fold
dilution.
| [
{
"created": "Sun, 6 Mar 2016 17:18:31 GMT",
"version": "v1"
}
] | 2016-03-30 | [
[
"Newby",
"Jay",
""
],
[
"Allard",
"Jun",
""
]
] | Certain biological reactions, such as receptor-ligand binding at cell-cell interfaces and macromolecules binding to biopolymers, require many smaller molecules crowding a reaction site to be cleared. Examples include the T cell interface, a key player in immunological information processing. Diffusion sets a limit for such cavitation to occur spontaneously, thereby defining a timescale below which active mechanisms must take over. We consider $N$ independent diffusing particles in a closed domain, containing a sub-region with $N_{0}$ particles, on average. We investigate the time until the sub-region is empty, allowing a subsequent reaction to proceed. The first passage time is computed using an efficient exact simulation algorithm and an asymptotic approximation in the limit that cavitation is rare. In this limit, we find that the mean first passage time is sub-exponential, $T \propto e^{N_{0}}/N_{0}^2$. For the case of T cell receptors, we find that stochastic cavitation is exceedingly slow, $10^9$ seconds at physiological densities, however can be accelerated to occur within 5 second with only a four-fold dilution. |
2402.04287 | Yeaju Kim | Yeaju Kim, Junggu Choi, Bora Kim, Yongwan Park, Jihyun Cha, Jongkwan
Choi, and Sanghoon Han | Association between Prefrontal fNIRS signals during Cognitive tasks and
College scholastic ability test (CSAT) scores: Analysis using a quantum
annealing approach | 42 pages, 11 tables | null | null | null | q-bio.NC cs.ET quant-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Academic achievement is a critical measure of intellectual ability, prompting
extensive research into cognitive tasks as potential predictors. Neuroimaging
technologies, such as functional near-infrared spectroscopy (fNIRS), offer
insights into brain hemodynamics, allowing understanding of the link between
cognitive performance and academic achievement. Herein, we explored the
association between cognitive tasks and academic achievement by analyzing
prefrontal fNIRS signals. A novel quantum annealer (QA) feature selection
algorithm was applied to fNIRS data to identify cognitive tasks correlated with
CSAT scores. Twelve features (signal mean, median, variance, peak, number of
peaks, sum of peaks, slope, minimum, kurtosis, skewness, standard deviation,
and root mean square) were extracted from fNIRS signals at two time windows
(10- and 60-second) to compare results from various feature variable
conditions. The feature selection results from the QA-based and XGBoost
regressor algorithms were compared to validate the former's performance. In a
three-step validation process using multiple linear regression models,
correlation coefficients between the feature variables and the CSAT scores,
model fitness (adjusted R2), and model prediction error (RMSE) values were
calculated. The quantum annealer demonstrated comparable performance to
classical machine learning models, and specific cognitive tasks, including
verbal fluency, recognition, and the Corsi block tapping task, were correlated
with academic achievement. Group analyses revealed stronger associations
between Tower of London and N-back tasks with higher CSAT scores. Quantum
annealing algorithms have significant potential in feature selection using
fNIRS data, and represents a novel research approach. Future studies should
explore predictors of academic achievement and cognitive ability.
| [
{
"created": "Tue, 6 Feb 2024 04:44:57 GMT",
"version": "v1"
}
] | 2024-02-08 | [
[
"Kim",
"Yeaju",
""
],
[
"Choi",
"Junggu",
""
],
[
"Kim",
"Bora",
""
],
[
"Park",
"Yongwan",
""
],
[
"Cha",
"Jihyun",
""
],
[
"Choi",
"Jongkwan",
""
],
[
"Han",
"Sanghoon",
""
]
] | Academic achievement is a critical measure of intellectual ability, prompting extensive research into cognitive tasks as potential predictors. Neuroimaging technologies, such as functional near-infrared spectroscopy (fNIRS), offer insights into brain hemodynamics, allowing understanding of the link between cognitive performance and academic achievement. Herein, we explored the association between cognitive tasks and academic achievement by analyzing prefrontal fNIRS signals. A novel quantum annealer (QA) feature selection algorithm was applied to fNIRS data to identify cognitive tasks correlated with CSAT scores. Twelve features (signal mean, median, variance, peak, number of peaks, sum of peaks, slope, minimum, kurtosis, skewness, standard deviation, and root mean square) were extracted from fNIRS signals at two time windows (10- and 60-second) to compare results from various feature variable conditions. The feature selection results from the QA-based and XGBoost regressor algorithms were compared to validate the former's performance. In a three-step validation process using multiple linear regression models, correlation coefficients between the feature variables and the CSAT scores, model fitness (adjusted R2), and model prediction error (RMSE) values were calculated. The quantum annealer demonstrated comparable performance to classical machine learning models, and specific cognitive tasks, including verbal fluency, recognition, and the Corsi block tapping task, were correlated with academic achievement. Group analyses revealed stronger associations between Tower of London and N-back tasks with higher CSAT scores. Quantum annealing algorithms have significant potential in feature selection using fNIRS data, and represents a novel research approach. Future studies should explore predictors of academic achievement and cognitive ability. |
q-bio/0510049 | Ashok Prasad | Yuko Hori, Ashok Prasad, and Jane' Kondev | Stretching short biopolymers by fields and forces | 10 pages, 7 figures | PHYSICAL REVIEW E 75, 041904 (2007) | 10.1103/PhysRevE.75.041904 | null | q-bio.BM cond-mat.soft | null | We study the mechanical properties of semiflexible polymers when the contour
length of the polymer is comparable to its persistence length. We compute the
exact average end-to-end distance and shape of the polymer for different
boundary conditions, and show that boundary effects can lead to significant
deviations from the well-known long-polymer results. We also consider the case
of stretching a uniformly charged biopolymer by an electric field, for which we
compute the average extension and the average shape, which is shown to be
trumpetlike. Our results also apply to long biopolymers when thermal
fluctuations have been smoothed out by a large applied field or force.
| [
{
"created": "Thu, 27 Oct 2005 05:16:15 GMT",
"version": "v1"
},
{
"created": "Fri, 6 Apr 2007 03:48:39 GMT",
"version": "v2"
}
] | 2013-05-29 | [
[
"Hori",
"Yuko",
""
],
[
"Prasad",
"Ashok",
""
],
[
"Kondev",
"Jane'",
""
]
] | We study the mechanical properties of semiflexible polymers when the contour length of the polymer is comparable to its persistence length. We compute the exact average end-to-end distance and shape of the polymer for different boundary conditions, and show that boundary effects can lead to significant deviations from the well-known long-polymer results. We also consider the case of stretching a uniformly charged biopolymer by an electric field, for which we compute the average extension and the average shape, which is shown to be trumpetlike. Our results also apply to long biopolymers when thermal fluctuations have been smoothed out by a large applied field or force. |
2006.03091 | Eitan Lerner | Eitan Lerner, Benjamin Ambrose, Anders Barth, Victoria Birkedal, Scott
C. Blanchard, Richard Borner, Thorben Cordes, Timothy D. Craggs, Taekjip Ha,
Gilad Haran, Thorsten Hugel, Antonino Ingargiola, Achillefs Kapanidis, Don C.
Lamb, Ted Laurence, Nam ki Lee, Edward A. Lemke, Emmanuel Margeat, Jens
Michaelis, Xavier Michalet, Daniel Nettels, Thomas-Otavio Peulen, Benjamin
Schuler, Claus A.M. Seidel, Hamid So-leimaninejad, Shimon Weiss | The FRET-based structural dynamics challenge -- community contributions
to consistent and open science practices | null | eLife 10 (2021) e60416 | 10.7554/eLife.60416 | null | q-bio.BM physics.bio-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Single-molecule F\"{o}rster resonance energy transfer (smFRET) has become a
mainstream technique for probing biomolecular structural dynamics. The rapid
and wide adoption of the technique by an ever-increasing number of groups has
generated many improvements and variations in the technique itself, in methods
for sample preparation and characterization, in analysis of the data from such
experiments, and in analysis codes and algorithms. Recently, several labs that
employ smFRET have joined forces to try to bring the smFRET community together
in adopting a consensus on how to perform experiments and analyze results for
achieving quantitative structural information. These recent efforts include
multi-lab blind-tests to assess the accuracy and precision of smFRET between
different labs using different procedures, the formal assembly of the FRET
community and development of smFRET procedures to be considered for entries in
the wwPDB. Here we delve into the different approaches and viewpoints in the
field. This position paper describes the current "state-of-the field", points
to unresolved methodological issues for quantitative structural studies,
provides a set of 'soft recommendations' about which an emerging consensus
exists, and a list of resources that are openly available. To make further
progress, we strongly encourage 'open science' practices. We hope that this
position paper will provide a roadmap for newcomers to the field, as well as a
reference for seasoned practitioners.
| [
{
"created": "Thu, 4 Jun 2020 18:27:18 GMT",
"version": "v1"
}
] | 2021-08-04 | [
[
"Lerner",
"Eitan",
""
],
[
"Ambrose",
"Benjamin",
""
],
[
"Barth",
"Anders",
""
],
[
"Birkedal",
"Victoria",
""
],
[
"Blanchard",
"Scott C.",
""
],
[
"Borner",
"Richard",
""
],
[
"Cordes",
"Thorben",
""
]... | Single-molecule F\"{o}rster resonance energy transfer (smFRET) has become a mainstream technique for probing biomolecular structural dynamics. The rapid and wide adoption of the technique by an ever-increasing number of groups has generated many improvements and variations in the technique itself, in methods for sample preparation and characterization, in analysis of the data from such experiments, and in analysis codes and algorithms. Recently, several labs that employ smFRET have joined forces to try to bring the smFRET community together in adopting a consensus on how to perform experiments and analyze results for achieving quantitative structural information. These recent efforts include multi-lab blind-tests to assess the accuracy and precision of smFRET between different labs using different procedures, the formal assembly of the FRET community and development of smFRET procedures to be considered for entries in the wwPDB. Here we delve into the different approaches and viewpoints in the field. This position paper describes the current "state-of-the field", points to unresolved methodological issues for quantitative structural studies, provides a set of 'soft recommendations' about which an emerging consensus exists, and a list of resources that are openly available. To make further progress, we strongly encourage 'open science' practices. We hope that this position paper will provide a roadmap for newcomers to the field, as well as a reference for seasoned practitioners. |
1502.04455 | Leonardo L. Gollo | Leonardo L. Gollo, Andrew Zalesky, R. Matthew Hutchison, Martijn van
den Heuvel, Michael Breakspear | Dwelling Quietly in the Rich Club: Brain Network Determinants of Slow
Cortical Fluctuations | 35 pages, 6 figures | Phil. Trans. R. Soc. B 370 : 20140165 (2015) | 10.1098/rstb.2014.0165 | null | q-bio.NC cond-mat.dis-nn nlin.CD nlin.PS physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For more than a century, cerebral cartography has been driven by
investigations of structural and morphological properties of the brain across
spatial scales and the temporal/functional phenomena that emerge from these
underlying features. The next era of brain mapping will be driven by studies
that consider both of these components of brain organization simultaneously --
elucidating their interactions and dependencies. Using this guiding principle,
we explored the origin of slowly fluctuating patterns of synchronization within
the topological core of brain regions known as the rich club, implicated in the
regulation of mood and introspection. We find that a constellation of densely
interconnected regions that constitute the rich club (including the anterior
insula, amygdala, and precuneus) play a central role in promoting a stable,
dynamical core of spontaneous activity in the primate cortex. The slow time
scales are well matched to the regulation of internal visceral states,
corresponding to the somatic correlates of mood and anxiety. In contrast, the
topology of the surrounding "feeder" cortical regions show unstable, rapidly
fluctuating dynamics likely crucial for fast perceptual processes. We discuss
these findings in relation to psychiatric disorders and the future of
connectomics.
| [
{
"created": "Mon, 16 Feb 2015 08:42:35 GMT",
"version": "v1"
}
] | 2016-05-06 | [
[
"Gollo",
"Leonardo L.",
""
],
[
"Zalesky",
"Andrew",
""
],
[
"Hutchison",
"R. Matthew",
""
],
[
"Heuvel",
"Martijn van den",
""
],
[
"Breakspear",
"Michael",
""
]
] | For more than a century, cerebral cartography has been driven by investigations of structural and morphological properties of the brain across spatial scales and the temporal/functional phenomena that emerge from these underlying features. The next era of brain mapping will be driven by studies that consider both of these components of brain organization simultaneously -- elucidating their interactions and dependencies. Using this guiding principle, we explored the origin of slowly fluctuating patterns of synchronization within the topological core of brain regions known as the rich club, implicated in the regulation of mood and introspection. We find that a constellation of densely interconnected regions that constitute the rich club (including the anterior insula, amygdala, and precuneus) play a central role in promoting a stable, dynamical core of spontaneous activity in the primate cortex. The slow time scales are well matched to the regulation of internal visceral states, corresponding to the somatic correlates of mood and anxiety. In contrast, the topology of the surrounding "feeder" cortical regions show unstable, rapidly fluctuating dynamics likely crucial for fast perceptual processes. We discuss these findings in relation to psychiatric disorders and the future of connectomics. |
1807.03059 | Sophie Beltran-Bech | Sylvine Durand, Fr\'ed\'eric Grandjean, Isabelle Giraud, Richard
Cordaux, Sophie Beltran-Bech, Nicolas Bech | Fine-scale population structure analysis in Armadillidium vulgare
(Isopoda: Oniscidea) reveals strong female philopatry | 23 pages (including 2 figures and one table) and two supplementary
files containing 5 pages with 3 tables S1 to S3 and one figure S1, Last two
authors have contributed equally to this study | Acta Oecologica, 2019 | 10.1016/j.actao.2019.103478 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the last decades, dispersal studies have benefited from the use of
molecular markers for detecting patterns differing between categories of
individuals and have highlighted sex-biased dispersal in several species. To
explain this phenomenon, several hypotheses implying mating systems,
intrasexual competition or sex-related handicaps have been proposed. In this
context, we investigated sex-biased dispersal in Armadillidium vulgare, a
terrestrial isopod with a promiscuous mating system. As a proxy for effective
dispersal, we performed a fine-scale investigation of the spatial genetic
structure in males and females, using individuals originating from five
sampling points located within 70 meters of each other. Based on microsatellite
markers and spatial autocorrelation analyses, our results revealed that while
males did not present a significant genetic structure at this geographic scale,
females were significantly and genetically more similar to each other when they
were collected in the same sampling point. As females invest more parental care
than males in A. vulgare, but also because this species is promiscuous and
males experience a high intrasexual competition, our results meet the
predictions of most classical hypotheses for sex-biased dispersal. We suggest
that widening dispersal studies to other isopods or crustaceans, differing in
their ecology or mating system and displaying varying levels of parental care,
might shed light on the processes underlying the evolution of sex-biased
dispersal.
| [
{
"created": "Mon, 9 Jul 2018 11:51:09 GMT",
"version": "v1"
},
{
"created": "Mon, 1 Apr 2019 12:51:39 GMT",
"version": "v2"
},
{
"created": "Tue, 25 Jun 2019 08:26:25 GMT",
"version": "v3"
},
{
"created": "Fri, 18 Oct 2019 13:23:22 GMT",
"version": "v4"
}
] | 2019-10-21 | [
[
"Durand",
"Sylvine",
""
],
[
"Grandjean",
"Frédéric",
""
],
[
"Giraud",
"Isabelle",
""
],
[
"Cordaux",
"Richard",
""
],
[
"Beltran-Bech",
"Sophie",
""
],
[
"Bech",
"Nicolas",
""
]
] | In the last decades, dispersal studies have benefited from the use of molecular markers for detecting patterns differing between categories of individuals and have highlighted sex-biased dispersal in several species. To explain this phenomenon, several hypotheses implying mating systems, intrasexual competition or sex-related handicaps have been proposed. In this context, we investigated sex-biased dispersal in Armadillidium vulgare, a terrestrial isopod with a promiscuous mating system. As a proxy for effective dispersal, we performed a fine-scale investigation of the spatial genetic structure in males and females, using individuals originating from five sampling points located within 70 meters of each other. Based on microsatellite markers and spatial autocorrelation analyses, our results revealed that while males did not present a significant genetic structure at this geographic scale, females were significantly and genetically more similar to each other when they were collected in the same sampling point. As females invest more parental care than males in A. vulgare, but also because this species is promiscuous and males experience a high intrasexual competition, our results meet the predictions of most classical hypotheses for sex-biased dispersal. We suggest that widening dispersal studies to other isopods or crustaceans, differing in their ecology or mating system and displaying varying levels of parental care, might shed light on the processes underlying the evolution of sex-biased dispersal. |
1708.04329 | Jeremie Kim | Jeremie S Kim, Damla Senol, Hongyi Xin, Donghyuk Lee, Saugata Ghose,
Mohammed Alser, Hasan Hassan, Oguz Ergin, Can Alkan, Onur Mutlu | GRIM-filter: fast seed filtering in read mapping using emerging memory
technologies | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Seed filtering is critical in DNA read mapping, a process where
billions of DNA fragments (reads) sampled from a donor are mapped onto a
reference genome to identify genomic variants of the donor. Read mappers 1)
quickly generate possible mapping locations (i.e., seeds) for each read, 2)
extract reference sequences at each of the mapping locations, and then 3) check
similarity between each read and its associated reference sequences with a
computationally expensive dynamic programming algorithm (alignment) to
determine the origin of the read. Location filters come into play before
alignment, discarding seed locations that alignment would have deemed a poor
match. The ideal location filter would discard all poor matching locations
prior to alignment such that there is no wasted computation on poor alignments.
Results: We propose a novel filtering algorithm, GRIM-Filter, optimized to
exploit emerging 3D-stacked memory systems that integrate computation within a
stacked logic layer, enabling processing-in-memory (PIM). GRIM-Filter quickly
filters locations by 1) introducing a new representation of coarse-grained
segments of the reference genome and 2) using massively-parallel in-memory
operations to identify read presence within each coarse-grained segment. Our
evaluations show that for 5% error acceptance rates, GRIM-Filter eliminates
5.59x-6.41x more false negatives and exhibits end-to-end speedups of
1.81x-3.65x compared to mappers employing the best previous filtering
algorithm.
| [
{
"created": "Mon, 14 Aug 2017 21:08:41 GMT",
"version": "v1"
}
] | 2017-08-16 | [
[
"Kim",
"Jeremie S",
""
],
[
"Senol",
"Damla",
""
],
[
"Xin",
"Hongyi",
""
],
[
"Lee",
"Donghyuk",
""
],
[
"Ghose",
"Saugata",
""
],
[
"Alser",
"Mohammed",
""
],
[
"Hassan",
"Hasan",
""
],
[
"Ergin",... | Motivation: Seed filtering is critical in DNA read mapping, a process where billions of DNA fragments (reads) sampled from a donor are mapped onto a reference genome to identify genomic variants of the donor. Read mappers 1) quickly generate possible mapping locations (i.e., seeds) for each read, 2) extract reference sequences at each of the mapping locations, and then 3) check similarity between each read and its associated reference sequences with a computationally expensive dynamic programming algorithm (alignment) to determine the origin of the read. Location filters come into play before alignment, discarding seed locations that alignment would have deemed a poor match. The ideal location filter would discard all poor matching locations prior to alignment such that there is no wasted computation on poor alignments. Results: We propose a novel filtering algorithm, GRIM-Filter, optimized to exploit emerging 3D-stacked memory systems that integrate computation within a stacked logic layer, enabling processing-in-memory (PIM). GRIM-Filter quickly filters locations by 1) introducing a new representation of coarse-grained segments of the reference genome and 2) using massively-parallel in-memory operations to identify read presence within each coarse-grained segment. Our evaluations show that for 5% error acceptance rates, GRIM-Filter eliminates 5.59x-6.41x more false negatives and exhibits end-to-end speedups of 1.81x-3.65x compared to mappers employing the best previous filtering algorithm. |
0710.4475 | Giuseppe Gaeta | Mariano Cadoni, Roberto De Leo, Sergio Demelio and Giuseppe Gaeta | Twist solitons in complex macromolecules: from DNA to polyethylene | New version substantially longer, with new applications to
Polyethylene. To appear in "International Journal of Non-Linear Mechanics" | null | 10.1016/j.ijnonlinmec.2008.03.010 | null | q-bio.BM | null | DNA torsion dynamics is essential in the transcription process; simple models
for it have been proposed by several authors, in particular Yakushevich (Y
model). These are strongly related to models of DNA separation dynamics such as
the one first proposed by Peyrard and Bishop (and developed by Dauxois, Barbi,
Cocco and Monasson among others), but support topological solitons. We recently
developed a ``composite'' version of the Y model, in which the sugar-phosphate
group and the base are described by separate degrees of freedom. This at the
same time fits experimental data better than the simple Y model, and shows
dynamical phenomena, which are of interest beyond DNA dynamics. Of particular
relevance are the mechanism for selecting the speed of solitons by tuning the
physical parameters of the non linear medium and the hierarchal separation of
the relevant degrees of freedom in ``master'' and ``slave''. These mechanisms
apply not only do DNA, but also to more general macromolecules, as we show
concretely by considering polyethylene.
| [
{
"created": "Wed, 24 Oct 2007 14:09:02 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Mar 2008 10:33:27 GMT",
"version": "v2"
}
] | 2009-11-13 | [
[
"Cadoni",
"Mariano",
""
],
[
"De Leo",
"Roberto",
""
],
[
"Demelio",
"Sergio",
""
],
[
"Gaeta",
"Giuseppe",
""
]
] | DNA torsion dynamics is essential in the transcription process; simple models for it have been proposed by several authors, in particular Yakushevich (Y model). These are strongly related to models of DNA separation dynamics such as the one first proposed by Peyrard and Bishop (and developed by Dauxois, Barbi, Cocco and Monasson among others), but support topological solitons. We recently developed a ``composite'' version of the Y model, in which the sugar-phosphate group and the base are described by separate degrees of freedom. This at the same time fits experimental data better than the simple Y model, and shows dynamical phenomena, which are of interest beyond DNA dynamics. Of particular relevance are the mechanism for selecting the speed of solitons by tuning the physical parameters of the non linear medium and the hierarchal separation of the relevant degrees of freedom in ``master'' and ``slave''. These mechanisms apply not only do DNA, but also to more general macromolecules, as we show concretely by considering polyethylene. |
q-bio/0312033 | Richard P. Sear | Richard P. Sear | Specific protein-protein binding in many-component mixtures of proteins | 13 pages, 3 figures (changes for v2 mainly notational - to be more in
line with notation in information theory literature) | Physical Biology v1, 53 (2004) | 10.1088/1478-3967/1/2/001 | null | q-bio.BM q-bio.MN | null | Proteins must bind to specific other proteins in vivo in order to function.
The proteins must bind only to one or a few other proteins of the of order a
thousand proteins typically present in vivo. Using a simple model of a protein,
specific binding in many component mixtures is studied. It is found to be a
demanding function in the sense that it demands that the binding sites of the
proteins be encoded by long sequences of bits, and the requirement for specific
binding then strongly constrains these sequences. This is quantified by the
capacity of proteins of a given size (sequence length), which is the maximum
number of specific-binding interactions possible in a mixture. This calculation
of the maximum number possible is in the same spirit as the work of Shannon and
others on the maximum rate of communication through noisy channels.
| [
{
"created": "Fri, 19 Dec 2003 18:47:55 GMT",
"version": "v1"
},
{
"created": "Fri, 6 Feb 2004 19:00:57 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Sear",
"Richard P.",
""
]
] | Proteins must bind to specific other proteins in vivo in order to function. The proteins must bind only to one or a few other proteins of the of order a thousand proteins typically present in vivo. Using a simple model of a protein, specific binding in many component mixtures is studied. It is found to be a demanding function in the sense that it demands that the binding sites of the proteins be encoded by long sequences of bits, and the requirement for specific binding then strongly constrains these sequences. This is quantified by the capacity of proteins of a given size (sequence length), which is the maximum number of specific-binding interactions possible in a mixture. This calculation of the maximum number possible is in the same spirit as the work of Shannon and others on the maximum rate of communication through noisy channels. |
1605.04492 | Jun Li | Martin Barron and Jun Li | Identifying and removing the cell-cycle effect from single-cell
RNA-Sequencing data | 12 pages, 5 figures | null | null | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Single-cell RNA-Sequencing (scRNA-Seq) is a revolutionary technique for
discovering and describing cell types in heterogeneous tissues, yet its
measurement of expression often suffers from large systematic bias. A major
source of this bias is the cell cycle, which introduces large within-cell-type
heterogeneity that can obscure the differences in expression between cell
types. The current method for removing the cell-cycle effect is unable to
effectively identify this effect and has a high risk of removing other
biological components of interest, compromising downstream analysis. We present
ccRemover, a new method that reliably identifies the cell-cycle effect and
removes it. ccRemover preserves other biological signals of interest in the
data and thus can serve as an important pre-processing step for many scRNA-Seq
data analyses. The effectiveness of ccRemover is demonstrated using simulation
data and three real scRNA-Seq datasets, where it boosts the performance of
existing clustering algorithms in distinguishing between cell types.
| [
{
"created": "Sun, 15 May 2016 02:23:23 GMT",
"version": "v1"
}
] | 2016-05-17 | [
[
"Barron",
"Martin",
""
],
[
"Li",
"Jun",
""
]
] | Single-cell RNA-Sequencing (scRNA-Seq) is a revolutionary technique for discovering and describing cell types in heterogeneous tissues, yet its measurement of expression often suffers from large systematic bias. A major source of this bias is the cell cycle, which introduces large within-cell-type heterogeneity that can obscure the differences in expression between cell types. The current method for removing the cell-cycle effect is unable to effectively identify this effect and has a high risk of removing other biological components of interest, compromising downstream analysis. We present ccRemover, a new method that reliably identifies the cell-cycle effect and removes it. ccRemover preserves other biological signals of interest in the data and thus can serve as an important pre-processing step for many scRNA-Seq data analyses. The effectiveness of ccRemover is demonstrated using simulation data and three real scRNA-Seq datasets, where it boosts the performance of existing clustering algorithms in distinguishing between cell types. |
2210.10051 | Yulia Khristoforova | Yulia Khristoforova, Lyudmila Bratchenko, Ivan Bratchenko | Combination of Raman spectroscopy and chemometrics: A review of recent
studies published in the Spectrochimica Acta, Part A: Molecular and
Biomolecular Spectroscopy Journal | 23 pages, 1 figure | null | null | null | q-bio.QM cs.LG physics.data-an physics.med-ph | http://creativecommons.org/licenses/by/4.0/ | Raman spectroscopy is a promising technique used for noninvasive analysis of
samples in various fields of application due to its ability for fingerprint
probing of samples at the molecular level. Chemometrics methods are widely used
nowadays for better understanding of the recorded spectral fingerprints of
samples and differences in their chemical composition. This review considers a
number of manuscripts published in the Spectrochimica Acta, Part A: Molecular
and Biomolecular Spectroscopy Journal that presented findings regarding the
application of Raman spectroscopy in combination with chemometrics to study
samples and their changes caused by different factors. In 57 reviewed
manuscripts, we analyzed application of chemometrics algorithms, statistical
modeling parameters, utilization of cross validation, sample sizes, as well as
the performance of the proposed classification and regression model. We
summarized the best strategies for creating classification models and
highlighted some common drawbacks when it comes to the application of
chemometrics techniques. According to our estimations, about 70% of the papers
are likely to contain unsupported or invalid data due to insufficient
description of the utilized methods or drawbacks of the proposed classification
models. These drawbacks include: (1) insufficient experimental sample size for
classification/regression to achieve significant and reliable results, (2) lack
of cross validation (or a test set) for verification of the
classifier/regression performance, (3) incorrect division of the spectral data
into the training and the test/validation sets; (4) improper selection of the
PC number to reduce the analyzed spectral data dimension.
| [
{
"created": "Tue, 18 Oct 2022 13:08:20 GMT",
"version": "v1"
}
] | 2022-10-20 | [
[
"Khristoforova",
"Yulia",
""
],
[
"Bratchenko",
"Lyudmila",
""
],
[
"Bratchenko",
"Ivan",
""
]
] | Raman spectroscopy is a promising technique used for noninvasive analysis of samples in various fields of application due to its ability for fingerprint probing of samples at the molecular level. Chemometrics methods are widely used nowadays for better understanding of the recorded spectral fingerprints of samples and differences in their chemical composition. This review considers a number of manuscripts published in the Spectrochimica Acta, Part A: Molecular and Biomolecular Spectroscopy Journal that presented findings regarding the application of Raman spectroscopy in combination with chemometrics to study samples and their changes caused by different factors. In 57 reviewed manuscripts, we analyzed application of chemometrics algorithms, statistical modeling parameters, utilization of cross validation, sample sizes, as well as the performance of the proposed classification and regression model. We summarized the best strategies for creating classification models and highlighted some common drawbacks when it comes to the application of chemometrics techniques. According to our estimations, about 70% of the papers are likely to contain unsupported or invalid data due to insufficient description of the utilized methods or drawbacks of the proposed classification models. These drawbacks include: (1) insufficient experimental sample size for classification/regression to achieve significant and reliable results, (2) lack of cross validation (or a test set) for verification of the classifier/regression performance, (3) incorrect division of the spectral data into the training and the test/validation sets; (4) improper selection of the PC number to reduce the analyzed spectral data dimension. |
1311.5557 | Esther Iba\~nez | Esther Ib\'a\~nez and Marta Casanellas | EM for phylogenetic topology reconstruction on non-homogeneous data | 1 main file: 6 Figures and 2 Tables. 1 Additional file with 2 Figures
and 2 Tables. To appear in "BCM Evolutionary Biology" | BMC Evolutionary Biology.2014, 14:132 | 10.1186/1471-2148-14-132 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: The reconstruction of the phylogenetic tree topology of four taxa
is, still nowadays, one of the main challenges in phylogenetics. Its
difficulties lie in considering not too restrictive evolutionary models, and
correctly dealing with the long-branch attraction problem. The correct
reconstruction of 4-taxon trees is crucial for making quartet-based methods
work and being able to recover large phylogenies.
Results: In this paper we consider an expectation-maximization method for
maximizing the likelihood of (time nonhomogeneous) evolutionary Markov models
on trees. We study its success on reconstructing 4-taxon topologies and its
performance as input method in quartet-based phylogenetic reconstruction
methods such as QFIT and QuartetSuite. Our results show that the method
proposed here outperforms neighbor-joining and the usual (time-homogeneous
continuous-time) maximum likelihood methods on 4-leaved trees with
among-lineage instantaneous rate heterogeneity, and perform similarly to usual
continuous-time maximum-likelihood when data satisfies the assumptions of both
methods.
Conclusions: The method presented in this paper is well suited for
reconstructing the topology of any number of taxa via quartet-based methods and
is highly accurate, specially regarding largely divergent trees and time
nonhomogeneous data.
| [
{
"created": "Thu, 21 Nov 2013 21:11:38 GMT",
"version": "v1"
},
{
"created": "Wed, 18 Jun 2014 07:38:36 GMT",
"version": "v2"
}
] | 2014-06-19 | [
[
"Ibáñez",
"Esther",
""
],
[
"Casanellas",
"Marta",
""
]
] | Background: The reconstruction of the phylogenetic tree topology of four taxa is, still nowadays, one of the main challenges in phylogenetics. Its difficulties lie in considering not too restrictive evolutionary models, and correctly dealing with the long-branch attraction problem. The correct reconstruction of 4-taxon trees is crucial for making quartet-based methods work and being able to recover large phylogenies. Results: In this paper we consider an expectation-maximization method for maximizing the likelihood of (time nonhomogeneous) evolutionary Markov models on trees. We study its success on reconstructing 4-taxon topologies and its performance as input method in quartet-based phylogenetic reconstruction methods such as QFIT and QuartetSuite. Our results show that the method proposed here outperforms neighbor-joining and the usual (time-homogeneous continuous-time) maximum likelihood methods on 4-leaved trees with among-lineage instantaneous rate heterogeneity, and perform similarly to usual continuous-time maximum-likelihood when data satisfies the assumptions of both methods. Conclusions: The method presented in this paper is well suited for reconstructing the topology of any number of taxa via quartet-based methods and is highly accurate, specially regarding largely divergent trees and time nonhomogeneous data. |
1704.04194 | Branko Dragovich | Branko Dragovich, Andrei Yu. Khrennikov, Nata\v{s}a \v{Z}. Mi\v{s}i\'c | Ultrametrics in the genetic code and the genome | 20 pages. Accepted for publication in Applied Mathematics and
Computation | Applied Mathematics and Computation 309 (2017) 359-358 | 10.1016/j.amc.2017.04.012 | null | q-bio.OT cs.IT math.IT math.MG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ultrametric approach to the genetic code and the genome is considered and
developed. $p$-Adic degeneracy of the genetic code is pointed out. Ultrametric
tree of the codon space is presented. It is shown that codons and amino acids
can be treated as $p$-adic ultrametric networks. Ultrametric modification of
the Hamming distance is defined and noted how it can be useful. Ultrametric
approach with $p$-adic distance is an attractive and promising trend towards
investigation of bioinformation.
| [
{
"created": "Sun, 9 Apr 2017 15:12:42 GMT",
"version": "v1"
}
] | 2017-05-16 | [
[
"Dragovich",
"Branko",
""
],
[
"Khrennikov",
"Andrei Yu.",
""
],
[
"Mišić",
"Nataša Ž.",
""
]
] | Ultrametric approach to the genetic code and the genome is considered and developed. $p$-Adic degeneracy of the genetic code is pointed out. Ultrametric tree of the codon space is presented. It is shown that codons and amino acids can be treated as $p$-adic ultrametric networks. Ultrametric modification of the Hamming distance is defined and noted how it can be useful. Ultrametric approach with $p$-adic distance is an attractive and promising trend towards investigation of bioinformation. |
2004.06477 | Babagana Modu | Babagana Modu, Nereida Polovina, Savas Konur | Agent-Based Modelling of Malaria Transmission Dynamics | null | null | null | null | q-bio.PE stat.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent statistics of malaria shows that over 200 million cases and estimated
deaths of nearly half a million occur globally. Africa alone accounts for
almost 90% of the cases. Several studies have been conducted to understand the
disease transmission dynamics. In particular, mathematical methods have been
frequently used to model and understand the disease dynamics and outbreak
patterns. Although, mathematical methods have provided good results for
homogeneous populations, these methods impose significant limitations for
studying malaria dynamics in heterogeneous populations, a result of various
factors, e.g. spatial and temporal fluctuations, social networks, human
movements pattern etc. This paper proposes an agent-based modelling approach
that permits modelling and analysing malaria dynamics for heterogenous
populations. Our approach is illustrated using the climate and demographic data
for the Tripura, Limpopo and Benin cities. Our agent-based simulation has been
validated against the reported cases of malaria collected in the cities
mentioned. Furthermore, the efficiency of the proposed model has been compared
with the mathematical model used as benchmark. A statistical test confirms the
proposed model is robust and has potential for predicting the peak seasons of
malaria. This potentially makes our methods a useful tool as an intervention
mechanism, which will have impact on hospitals, healthcare providers, health
organisations.
| [
{
"created": "Thu, 9 Apr 2020 08:04:29 GMT",
"version": "v1"
}
] | 2020-04-15 | [
[
"Modu",
"Babagana",
""
],
[
"Polovina",
"Nereida",
""
],
[
"Konur",
"Savas",
""
]
] | Recent statistics of malaria shows that over 200 million cases and estimated deaths of nearly half a million occur globally. Africa alone accounts for almost 90% of the cases. Several studies have been conducted to understand the disease transmission dynamics. In particular, mathematical methods have been frequently used to model and understand the disease dynamics and outbreak patterns. Although, mathematical methods have provided good results for homogeneous populations, these methods impose significant limitations for studying malaria dynamics in heterogeneous populations, a result of various factors, e.g. spatial and temporal fluctuations, social networks, human movements pattern etc. This paper proposes an agent-based modelling approach that permits modelling and analysing malaria dynamics for heterogenous populations. Our approach is illustrated using the climate and demographic data for the Tripura, Limpopo and Benin cities. Our agent-based simulation has been validated against the reported cases of malaria collected in the cities mentioned. Furthermore, the efficiency of the proposed model has been compared with the mathematical model used as benchmark. A statistical test confirms the proposed model is robust and has potential for predicting the peak seasons of malaria. This potentially makes our methods a useful tool as an intervention mechanism, which will have impact on hospitals, healthcare providers, health organisations. |
1804.10964 | Frederick Matsen IV | Branden J. Olson and Frederick A. Matsen IV | The Bayesian optimist's guide to adaptive immune receptor repertoire
analysis | in press, Immunological Reviews | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic modeling is fundamental to the statistical analysis of complex
data. In addition to forming a coherent description of the data-generating
process, probabilistic models enable parameter inference about given data sets.
This procedure is well-developed in the Bayesian perspective, in which one
infers probability distributions describing to what extent various possible
parameters agree with the data. In this paper we motivate and review
probabilistic modeling for adaptive immune receptor repertoire data then
describe progress and prospects for future work, from germline haplotyping to
adaptive immune system deployment across tissues. The relevant quantities in
immune sequence analysis include not only continuous parameters such as gene
use frequency, but also discrete objects such as B cell clusters and lineages.
Throughout this review, we unravel the many opportunities for probabilistic
modeling in adaptive immune receptor analysis, including settings for which the
Bayesian approach holds substantial promise (especially if one is optimistic
about new computational methods). From our perspective the greatest prospects
for progress in probabilistic modeling for repertoires concern ancestral
sequence estimation for B cell receptor lineages, including uncertainty from
germline genotype, rearrangement, and lineage development.
| [
{
"created": "Sun, 29 Apr 2018 16:52:07 GMT",
"version": "v1"
}
] | 2018-05-01 | [
[
"Olson",
"Branden J.",
""
],
[
"Matsen",
"Frederick A.",
"IV"
]
] | Probabilistic modeling is fundamental to the statistical analysis of complex data. In addition to forming a coherent description of the data-generating process, probabilistic models enable parameter inference about given data sets. This procedure is well-developed in the Bayesian perspective, in which one infers probability distributions describing to what extent various possible parameters agree with the data. In this paper we motivate and review probabilistic modeling for adaptive immune receptor repertoire data then describe progress and prospects for future work, from germline haplotyping to adaptive immune system deployment across tissues. The relevant quantities in immune sequence analysis include not only continuous parameters such as gene use frequency, but also discrete objects such as B cell clusters and lineages. Throughout this review, we unravel the many opportunities for probabilistic modeling in adaptive immune receptor analysis, including settings for which the Bayesian approach holds substantial promise (especially if one is optimistic about new computational methods). From our perspective the greatest prospects for progress in probabilistic modeling for repertoires concern ancestral sequence estimation for B cell receptor lineages, including uncertainty from germline genotype, rearrangement, and lineage development. |
1406.2074 | Padmini Rangamani | Jasmine Nirody and Padmini Rangamani | ATP concentration regulates enzyme kinetics | 14 pages, 5 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Adenosine 5'-triphosphate (ATP) is the nearly ubiquitous "energy currency" of
living organisms, and thus is a crucial participant in the majority of
enzymatic reactions. The standard models in enzyme kinetics generally ignore
the temporal dynamics of ATP because it is assumed to be present in large
excess. However, this assumption may not hold in many situations of cellular
stress where ATP concentrations may be comparable to substrate levels. Here, we
demonstrate the importance of ATP concentration on the dynamics of multi-enzyme
reactions by explicit consideration of ATP as a secondary substrate for an
enzyme. We apply our model to the mitogen-activated protein (MAP) kinase
cascade, which is involved in the regulation of a vast range of cellular
activities. We show that three fundamental features of this signaling network
--- (i) duration of response, (ii) signal amplification, and (iii)
ultrasensitivity to stimulus concentration --- are all dependent on ATP
concentration. Our results indicate that the concentration of ATP regulates the
response of the MAP kinase activation network, and potentially suggests another
possible mechanism for disruption of the cascade in pathogenic states.
| [
{
"created": "Mon, 9 Jun 2014 04:20:22 GMT",
"version": "v1"
}
] | 2014-06-10 | [
[
"Nirody",
"Jasmine",
""
],
[
"Rangamani",
"Padmini",
""
]
] | Adenosine 5'-triphosphate (ATP) is the nearly ubiquitous "energy currency" of living organisms, and thus is a crucial participant in the majority of enzymatic reactions. The standard models in enzyme kinetics generally ignore the temporal dynamics of ATP because it is assumed to be present in large excess. However, this assumption may not hold in many situations of cellular stress where ATP concentrations may be comparable to substrate levels. Here, we demonstrate the importance of ATP concentration on the dynamics of multi-enzyme reactions by explicit consideration of ATP as a secondary substrate for an enzyme. We apply our model to the mitogen-activated protein (MAP) kinase cascade, which is involved in the regulation of a vast range of cellular activities. We show that three fundamental features of this signaling network --- (i) duration of response, (ii) signal amplification, and (iii) ultrasensitivity to stimulus concentration --- are all dependent on ATP concentration. Our results indicate that the concentration of ATP regulates the response of the MAP kinase activation network, and potentially suggests another possible mechanism for disruption of the cascade in pathogenic states. |
1406.7511 | David Holcman | N. Hoze and D. Holcman | Residence times of receptors in dendritic spines analyzed by simulations
in empirical domains | 19 pages | null | 10.1016/j.bpj.2014.10.018 | null | q-bio.SC math.PR physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Analysis of high-density superresolution imaging of receptors reveal the
organization of dendrites at the nano-scale resolution. We present here
simulations in empirical live cell images, which allows converting local
information extracted from short range trajectories into simulations of long
range trajectories. Based on these empirical simulations, we compute the
residence time of an AMPA receptor (AMPAR) in dendritic spines that accounts
for receptors local interactions and geometrical organization. We report here
that depending on the type of the spine, the residence time varies from one to
five minutes. Moreover, we show that there exists transient organized
structures, previously described as potential wells that can regulate the
trafficking of AMPARs to dendritic spines.
| [
{
"created": "Sun, 29 Jun 2014 14:15:17 GMT",
"version": "v1"
}
] | 2015-06-22 | [
[
"Hoze",
"N.",
""
],
[
"Holcman",
"D.",
""
]
] | Analysis of high-density superresolution imaging of receptors reveal the organization of dendrites at the nano-scale resolution. We present here simulations in empirical live cell images, which allows converting local information extracted from short range trajectories into simulations of long range trajectories. Based on these empirical simulations, we compute the residence time of an AMPA receptor (AMPAR) in dendritic spines that accounts for receptors local interactions and geometrical organization. We report here that depending on the type of the spine, the residence time varies from one to five minutes. Moreover, we show that there exists transient organized structures, previously described as potential wells that can regulate the trafficking of AMPARs to dendritic spines. |
1903.11913 | Joachim Krug | Joachim Krug | Accessibility percolation in random fitness landscapes | 20 pages, 3 figures | In: Probabilistic Structures in Evolution (E. Baake and A.
Wakolbinger, eds.), EMS Press, Berlin, 2021, pp. 1-22 | 10.4171/ECR/17-1/1 | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The fitness landscape encodes the mapping of genotypes to fitness and
provides a succinct representation of possible trajectories followed by an
evolving population. Evolutionary accessibility is quantified by the existence
of fitness-monotonic paths connecting far away genotypes. Studies of
accessibility percolation use probabilistic fitness landscape models to explore
the emergence of such paths as a function of the initial fitness, the
parameters of the landscape or the structure of the genotype graph. This
chapter reviews these studies and discusses their implications for the
predictability of evolutionary processes.
| [
{
"created": "Thu, 28 Mar 2019 12:22:37 GMT",
"version": "v1"
},
{
"created": "Tue, 29 Jun 2021 16:21:29 GMT",
"version": "v2"
}
] | 2021-06-30 | [
[
"Krug",
"Joachim",
""
]
] | The fitness landscape encodes the mapping of genotypes to fitness and provides a succinct representation of possible trajectories followed by an evolving population. Evolutionary accessibility is quantified by the existence of fitness-monotonic paths connecting far away genotypes. Studies of accessibility percolation use probabilistic fitness landscape models to explore the emergence of such paths as a function of the initial fitness, the parameters of the landscape or the structure of the genotype graph. This chapter reviews these studies and discusses their implications for the predictability of evolutionary processes. |
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