id stringlengths 9 13 | submitter stringlengths 4 48 | authors stringlengths 4 9.62k | title stringlengths 4 343 | comments stringlengths 2 480 ⌀ | journal-ref stringlengths 9 309 ⌀ | doi stringlengths 12 138 ⌀ | report-no stringclasses 277 values | categories stringlengths 8 87 | license stringclasses 9 values | orig_abstract stringlengths 27 3.76k | versions listlengths 1 15 | update_date stringlengths 10 10 | authors_parsed listlengths 1 147 | abstract stringlengths 24 3.75k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1309.7382 | Genevieve Erwin | Genevieve D. Erwin, Rebecca M. Truty, Dennis Kostka, Katherine S.
Pollard, John A. Capra | Integrating diverse datasets improves developmental enhancer prediction | 33 pages, 7 figures | null | 10.1371/journal.pcbi.1003677 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene-regulatory enhancers have been identified by many lines of evidence,
including evolutionary conservation, regulatory protein binding, chromatin
modifications, and DNA sequence motifs. To integrate these different
approaches, we developed EnhancerFinder, a novel method for predicting
developmental enhancers and their tissue specificity. EnhancerFinder uses a
two-step multiple-kernel learning approach to integrate DNA sequence motifs,
evolutionary patterns, and thousands of diverse functional genomics datasets
from a variety of cell types and developmental stages. We trained
EnhancerFinder on hundreds of experimentally verified human developmental
enhancers from the VISTA Enhancer Browser, in contrast to histone mark or
sequence-based enhancer definitions commonly used. We comprehensively evaluated
EnhancerFinder, and found that our integrative approach improves enhancer
prediction accuracy over previous approaches that consider a single type of
data. Our evaluation highlights the importance of considering information from
many tissues when predicting specific types of enhancers. We find that VISTA
enhancers active in embryonic heart are easier to predict than enhancers active
in several other tissues due to their uniquely high GC content. We applied
EnhancerFinder to the entire human genome and predicted 84,301 developmental
enhancers and their tissue specificity. These predictions provide specific
functional annotations for large amounts of human non-coding DNA, and are
significantly enriched near genes with annotated roles in their predicted
tissues and hits from genome-wide association studies. We demonstrate the
utility of our enhancer predictions by identifying and validating a novel
cranial nerve enhancer in the ZEB2 locus. Our genome-wide developmental
enhancer predictions will be freely available as a UCSC Genome Browser track.
| [
{
"created": "Fri, 27 Sep 2013 22:36:34 GMT",
"version": "v1"
}
] | 2015-06-17 | [
[
"Erwin",
"Genevieve D.",
""
],
[
"Truty",
"Rebecca M.",
""
],
[
"Kostka",
"Dennis",
""
],
[
"Pollard",
"Katherine S.",
""
],
[
"Capra",
"John A.",
""
]
] | Gene-regulatory enhancers have been identified by many lines of evidence, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a novel method for predicting developmental enhancers and their tissue specificity. EnhancerFinder uses a two-step multiple-kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and thousands of diverse functional genomics datasets from a variety of cell types and developmental stages. We trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser, in contrast to histone mark or sequence-based enhancer definitions commonly used. We comprehensively evaluated EnhancerFinder, and found that our integrative approach improves enhancer prediction accuracy over previous approaches that consider a single type of data. Our evaluation highlights the importance of considering information from many tissues when predicting specific types of enhancers. We find that VISTA enhancers active in embryonic heart are easier to predict than enhancers active in several other tissues due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and hits from genome-wide association studies. We demonstrate the utility of our enhancer predictions by identifying and validating a novel cranial nerve enhancer in the ZEB2 locus. Our genome-wide developmental enhancer predictions will be freely available as a UCSC Genome Browser track. |
0802.2683 | Jason Locasale W | Jason W. Locasale | Signal duration and the time scale dependence of signal integration in
biochemical pathways | 27 pages, 4 figures | null | null | null | q-bio.MN q-bio.SC | http://creativecommons.org/licenses/by/3.0/ | Signal duration (e.g. the time scales over which an active signaling
intermediate persists) is a key regulator of biological decisions in myriad
contexts such as cell growth, proliferation, and developmental lineage
commitments. Accompanying differences in signal duration are numerous
downstream biological processes that require multiple steps of biochemical
regulation. Here, we present an analysis that investigates how simple
biochemical motifs that involve multiple stages of regulation can be
constructed to differentially process signals that persist at different time
scales. We compute the dynamic gain within these networks and resulting power
spectra to better understand how biochemical networks can integrate signals at
different time scales. We identify topological features of these networks that
allow for different frequency dependent signal processing properties. Our
studies suggest design principles for why signal duration in connection with
multiple steps of downstream regulation is a ubiquitous control motif in
biochemical systems.
| [
{
"created": "Tue, 19 Feb 2008 17:01:22 GMT",
"version": "v1"
}
] | 2008-02-20 | [
[
"Locasale",
"Jason W.",
""
]
] | Signal duration (e.g. the time scales over which an active signaling intermediate persists) is a key regulator of biological decisions in myriad contexts such as cell growth, proliferation, and developmental lineage commitments. Accompanying differences in signal duration are numerous downstream biological processes that require multiple steps of biochemical regulation. Here, we present an analysis that investigates how simple biochemical motifs that involve multiple stages of regulation can be constructed to differentially process signals that persist at different time scales. We compute the dynamic gain within these networks and resulting power spectra to better understand how biochemical networks can integrate signals at different time scales. We identify topological features of these networks that allow for different frequency dependent signal processing properties. Our studies suggest design principles for why signal duration in connection with multiple steps of downstream regulation is a ubiquitous control motif in biochemical systems. |
2311.12143 | Ariel Amir | Ido Golding and Ariel Amir | Gene expression in growing cells: A biophysical primer | Submitted to Reviews of Modern Physics | null | null | null | q-bio.SC cond-mat.stat-mech q-bio.CB | http://creativecommons.org/licenses/by/4.0/ | Cell growth and gene expression, essential elements of all living systems,
have long been the focus of biophysical interrogation. Advances in single-cell
methods have invigorated theoretical studies into these processes. However,
until recently, there was little dialog between the two areas of study. Most
theoretical models for gene regulation assumed gene activity to be oblivious to
the progression of the cell cycle between birth and division. But there are
numerous ways in which the periodic character of all cellular observables can
modulate gene expression. The molecular factors required for transcription and
translation increase in number during the cell cycle, but are also diluted due
to the continuous increase in cell volume. The replication of the genome
changes the dosage of those same cellular players but also provides competing
targets for regulatory binding. Finally, cell division reduces their number
again, and so forth. Stochasticity is inherent to all these biological
processes, manifested in fluctuations in the synthesis and degradation of new
cellular components as well as the random partitioning of molecules at each
cell division. The notion of gene expression as stationary is thus hard to
justify. In this review, we survey the emerging paradigm of cell-cycle
regulated gene expression, with an emphasis on the global expression patterns
rather than gene-specific regulation. We discuss recent experimental reports
where cell growth and gene expression were simultaneously measured in
individual cells, providing first glimpses into the coupling between the two.
While the experimental findings, not surprisingly, differ among genes and
organisms, several theoretical models have emerged that attempt to reconcile
these differences and form a unifying framework for understanding gene
expression in growing cells.
| [
{
"created": "Mon, 20 Nov 2023 19:45:22 GMT",
"version": "v1"
}
] | 2023-11-22 | [
[
"Golding",
"Ido",
""
],
[
"Amir",
"Ariel",
""
]
] | Cell growth and gene expression, essential elements of all living systems, have long been the focus of biophysical interrogation. Advances in single-cell methods have invigorated theoretical studies into these processes. However, until recently, there was little dialog between the two areas of study. Most theoretical models for gene regulation assumed gene activity to be oblivious to the progression of the cell cycle between birth and division. But there are numerous ways in which the periodic character of all cellular observables can modulate gene expression. The molecular factors required for transcription and translation increase in number during the cell cycle, but are also diluted due to the continuous increase in cell volume. The replication of the genome changes the dosage of those same cellular players but also provides competing targets for regulatory binding. Finally, cell division reduces their number again, and so forth. Stochasticity is inherent to all these biological processes, manifested in fluctuations in the synthesis and degradation of new cellular components as well as the random partitioning of molecules at each cell division. The notion of gene expression as stationary is thus hard to justify. In this review, we survey the emerging paradigm of cell-cycle regulated gene expression, with an emphasis on the global expression patterns rather than gene-specific regulation. We discuss recent experimental reports where cell growth and gene expression were simultaneously measured in individual cells, providing first glimpses into the coupling between the two. While the experimental findings, not surprisingly, differ among genes and organisms, several theoretical models have emerged that attempt to reconcile these differences and form a unifying framework for understanding gene expression in growing cells. |
1112.4331 | Baruch Meerson | Omer Gottesman and Baruch Meerson | Multiple extinction routes in stochastic population models | 10 pages, 8 figures | Phys. Rev. E 85, 021140 (2012) | 10.1103/PhysRevE.85.021140 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Isolated populations ultimately go extinct because of the intrinsic noise of
elementary processes. In multi-population systems extinction of a population
may occur via more than one route. We investigate this generic situation in a
simple predator-prey (or infected-susceptible) model. The predator and prey
populations may coexist for a long time but ultimately both go extinct. In the
first extinction route the predators go extinct first, whereas the prey thrive
for a long time and then also go extinct. In the second route the prey go
extinct first causing a rapid extinction of the predators. Assuming large
sub-population sizes in the coexistence state, we compare the probabilities of
each of the two extinction routes and predict the most likely path of the
sub-populations to extinction. We also suggest an effective three-state master
equation for the probabilities to observe the coexistence state, the
predator-free state and the empty state.
| [
{
"created": "Mon, 19 Dec 2011 13:21:56 GMT",
"version": "v1"
},
{
"created": "Sun, 29 Jan 2012 15:25:47 GMT",
"version": "v2"
}
] | 2015-06-03 | [
[
"Gottesman",
"Omer",
""
],
[
"Meerson",
"Baruch",
""
]
] | Isolated populations ultimately go extinct because of the intrinsic noise of elementary processes. In multi-population systems extinction of a population may occur via more than one route. We investigate this generic situation in a simple predator-prey (or infected-susceptible) model. The predator and prey populations may coexist for a long time but ultimately both go extinct. In the first extinction route the predators go extinct first, whereas the prey thrive for a long time and then also go extinct. In the second route the prey go extinct first causing a rapid extinction of the predators. Assuming large sub-population sizes in the coexistence state, we compare the probabilities of each of the two extinction routes and predict the most likely path of the sub-populations to extinction. We also suggest an effective three-state master equation for the probabilities to observe the coexistence state, the predator-free state and the empty state. |
1112.0639 | Maurizio De Pitta' | Maurizio De Pitt\`a, Vladislav Volman, Hugues Berry, Eshel Ben-Jacob | A tale of two stories: astrocyte regulation of synaptic depression and
facilitation | 93 pages, manuscript+supplementary text, 10 main figures, 11
supplementary figures, 1 table | PLoS Comput. Biol. (2011) 7(12): e1002293 | 10.1371/journal.pcbi.1002293 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Short-term presynaptic plasticity designates variations of the amplitude of
synaptic information transfer whereby the amount of neurotransmitter released
upon presynaptic stimulation changes over seconds as a function of the neuronal
firing activity. While a consensus has emerged that changes of the synapse
strength are crucial to neuronal computations, their modes of expression in
vivo remain unclear. Recent experimental studies have reported that glial
cells, particularly astrocytes in the hippocampus, are able to modulate
short-term plasticity but the underlying mechanism is poorly understood. Here,
we investigate the characteristics of short-term plasticity modulation by
astrocytes using a biophysically realistic computational model. Mean-field
analysis of the model unravels that astrocytes may mediate counterintuitive
effects. Depending on the expressed presynaptic signaling pathways, astrocytes
may globally inhibit or potentiate the synapse: the amount of released
neurotransmitter in the presence of the astrocyte is transiently smaller or
larger than in its absence. But this global effect usually coexists with the
opposite local effect on paired pulses: with release-decreasing astrocytes most
paired pulses become facilitated, while paired-pulse depression becomes
prominent under release-increasing astrocytes. Moreover, we show that the
frequency of astrocytic intracellular Ca2+ oscillations controls the effects of
the astrocyte on short-term synaptic plasticity. Our model explains several
experimental observations yet unsolved, and uncovers astrocytic
gliotransmission as a possible transient switch between short-term paired-pulse
depression and facilitation. This possibility has deep implications on the
processing of neuronal spikes and resulting information transfer at synapses.
| [
{
"created": "Sat, 3 Dec 2011 09:36:12 GMT",
"version": "v1"
}
] | 2011-12-06 | [
[
"De Pittà",
"Maurizio",
""
],
[
"Volman",
"Vladislav",
""
],
[
"Berry",
"Hugues",
""
],
[
"Ben-Jacob",
"Eshel",
""
]
] | Short-term presynaptic plasticity designates variations of the amplitude of synaptic information transfer whereby the amount of neurotransmitter released upon presynaptic stimulation changes over seconds as a function of the neuronal firing activity. While a consensus has emerged that changes of the synapse strength are crucial to neuronal computations, their modes of expression in vivo remain unclear. Recent experimental studies have reported that glial cells, particularly astrocytes in the hippocampus, are able to modulate short-term plasticity but the underlying mechanism is poorly understood. Here, we investigate the characteristics of short-term plasticity modulation by astrocytes using a biophysically realistic computational model. Mean-field analysis of the model unravels that astrocytes may mediate counterintuitive effects. Depending on the expressed presynaptic signaling pathways, astrocytes may globally inhibit or potentiate the synapse: the amount of released neurotransmitter in the presence of the astrocyte is transiently smaller or larger than in its absence. But this global effect usually coexists with the opposite local effect on paired pulses: with release-decreasing astrocytes most paired pulses become facilitated, while paired-pulse depression becomes prominent under release-increasing astrocytes. Moreover, we show that the frequency of astrocytic intracellular Ca2+ oscillations controls the effects of the astrocyte on short-term synaptic plasticity. Our model explains several experimental observations yet unsolved, and uncovers astrocytic gliotransmission as a possible transient switch between short-term paired-pulse depression and facilitation. This possibility has deep implications on the processing of neuronal spikes and resulting information transfer at synapses. |
q-bio/0407039 | Jie Liang | T. Andrew Binkowski, Bhaskar DasGupta, and Jie Liang | Order independent structural alignment of circularly permuted proteins | 5 pages, 3 figures, Accepted by IEEE-EMBS 2004 Conference Proceedings | null | 10.1109/IEMBS.2004.1403795 | null | q-bio.BM | null | Circular permutation connects the N and C termini of a protein and
concurrently cleaves elsewhere in the chain, providing an important mechanism
for generating novel protein fold and functions. However, their in genomes is
unknown because current detection methods can miss many occurances, mistaking
random repeats as circular permutation. Here we develop a method for detecting
circularly permuted proteins from structural comparison. Sequence order
independent alignment of protein structures can be regarded as a special case
of the maximum-weight independent set problem, which is known to be
computationally hard. We develop an efficient approximation algorithm by
repeatedly solving relaxations of an appropriate intermediate integer
programming formulation, we show that the approximation ratio is much better
then the theoretical worst case ratio of $r = 1/4$. Circularly permuted
proteins reported in literature can be identified rapidly with our method,
while they escape the detection by publicly available servers for structural
alignment.
| [
{
"created": "Thu, 29 Jul 2004 17:55:32 GMT",
"version": "v1"
}
] | 2016-11-17 | [
[
"Binkowski",
"T. Andrew",
""
],
[
"DasGupta",
"Bhaskar",
""
],
[
"Liang",
"Jie",
""
]
] | Circular permutation connects the N and C termini of a protein and concurrently cleaves elsewhere in the chain, providing an important mechanism for generating novel protein fold and functions. However, their in genomes is unknown because current detection methods can miss many occurances, mistaking random repeats as circular permutation. Here we develop a method for detecting circularly permuted proteins from structural comparison. Sequence order independent alignment of protein structures can be regarded as a special case of the maximum-weight independent set problem, which is known to be computationally hard. We develop an efficient approximation algorithm by repeatedly solving relaxations of an appropriate intermediate integer programming formulation, we show that the approximation ratio is much better then the theoretical worst case ratio of $r = 1/4$. Circularly permuted proteins reported in literature can be identified rapidly with our method, while they escape the detection by publicly available servers for structural alignment. |
2209.03229 | Guo-Wei Wei | Guo-Wei Wei | Topological AI forecasting of future dominating viral variants | 5 pages, 2 figures | SIAM 2022 | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | The understanding of the mechanisms of SARS-CoV-2 evolution and transmission
is one of the greatest challenges of our time. By integrating artificial
intelligence (AI), viral genomes isolated from patients, tens of thousands of
mutational data, biophysics, bioinformatics, and algebraic topology, the
SARS-CoV-2 evolution was revealed to be governed by infectivity-based natural
selection. Two key mutation sites, L452 and N501 on the viral spike protein
receptor-binding domain (RBD), were predicted in summer 2020, long before they
occur in prevailing variants Alpha, Beta, Gamma, Delta, Kappa, Theta, Lambda,
Mu, and Omicron. Recent studies identified a new mechanism of natural
selection: antibody resistance. AI-based forecasting of Omicron's infectivity,
vaccine breakthrough, and antibody resistance was later nearly perfectly
confirmed by experiments. The replacement of dominant BA.1 by BA.2 in later
March was predicted in early February. On May 1, 2022, persistent
Laplacian-based AI projected Omicron BA.4 and BA.5 to become the new dominating
COVID-19 variants. This prediction became reality in late June. Topological AI
models offer accurate prediction of mutational impacts on the efficacy of
monoclonal antibodies (mAbs).
| [
{
"created": "Wed, 7 Sep 2022 15:33:54 GMT",
"version": "v1"
}
] | 2022-09-08 | [
[
"Wei",
"Guo-Wei",
""
]
] | The understanding of the mechanisms of SARS-CoV-2 evolution and transmission is one of the greatest challenges of our time. By integrating artificial intelligence (AI), viral genomes isolated from patients, tens of thousands of mutational data, biophysics, bioinformatics, and algebraic topology, the SARS-CoV-2 evolution was revealed to be governed by infectivity-based natural selection. Two key mutation sites, L452 and N501 on the viral spike protein receptor-binding domain (RBD), were predicted in summer 2020, long before they occur in prevailing variants Alpha, Beta, Gamma, Delta, Kappa, Theta, Lambda, Mu, and Omicron. Recent studies identified a new mechanism of natural selection: antibody resistance. AI-based forecasting of Omicron's infectivity, vaccine breakthrough, and antibody resistance was later nearly perfectly confirmed by experiments. The replacement of dominant BA.1 by BA.2 in later March was predicted in early February. On May 1, 2022, persistent Laplacian-based AI projected Omicron BA.4 and BA.5 to become the new dominating COVID-19 variants. This prediction became reality in late June. Topological AI models offer accurate prediction of mutational impacts on the efficacy of monoclonal antibodies (mAbs). |
2202.11144 | Dushyant Sahoo | Dushyant Sahoo, Mathilde Antoniades, Cynthia H.Y. Fu, and Christos
Davatzikos | Robust Hierarchical Patterns for identifying MDD patients: A Multisite
Study | null | null | null | null | q-bio.QM cs.LG eess.IV | http://creativecommons.org/licenses/by/4.0/ | Many supervised machine learning frameworks have been proposed for disease
classification using functional magnetic resonance imaging (fMRI) data,
producing important biomarkers. More recently, data pooling has flourished,
making the result generalizable across a large population. But, this success
depends on the population diversity and variability introduced due to the
pooling of the data that is not a primary research interest. Here, we look at
hierarchical Sparse Connectivity Patterns (hSCPs) as biomarkers for major
depressive disorder (MDD). We propose a novel model based on hSCPs to predict
MDD patients from functional connectivity matrices extracted from resting-state
fMRI data. Our model consists of three coupled terms. The first term decomposes
connectivity matrices into hierarchical low-rank sparse components
corresponding to synchronous patterns across the human brain. These components
are then combined via patient-specific weights capturing heterogeneity in the
data. The second term is a classification loss that uses the patient-specific
weights to classify MDD patients from healthy ones. Both of these terms are
combined with the third term, a robustness loss function to improve the
reproducibility of hSCPs. This reduces the variability introduced due to site
and population diversity (age and sex) on the predictive accuracy and pattern
stability in a large dataset pooled from five different sites. Our results show
the impact of diversity on prediction performance. Our model can reduce
diversity and improve the predictive and generalizing capability of the
components. Finally, our results show that our proposed model can robustly
identify clinically relevant patterns characteristic of MDD with high
reproducibility.
| [
{
"created": "Tue, 22 Feb 2022 19:40:32 GMT",
"version": "v1"
}
] | 2022-02-24 | [
[
"Sahoo",
"Dushyant",
""
],
[
"Antoniades",
"Mathilde",
""
],
[
"Fu",
"Cynthia H. Y.",
""
],
[
"Davatzikos",
"Christos",
""
]
] | Many supervised machine learning frameworks have been proposed for disease classification using functional magnetic resonance imaging (fMRI) data, producing important biomarkers. More recently, data pooling has flourished, making the result generalizable across a large population. But, this success depends on the population diversity and variability introduced due to the pooling of the data that is not a primary research interest. Here, we look at hierarchical Sparse Connectivity Patterns (hSCPs) as biomarkers for major depressive disorder (MDD). We propose a novel model based on hSCPs to predict MDD patients from functional connectivity matrices extracted from resting-state fMRI data. Our model consists of three coupled terms. The first term decomposes connectivity matrices into hierarchical low-rank sparse components corresponding to synchronous patterns across the human brain. These components are then combined via patient-specific weights capturing heterogeneity in the data. The second term is a classification loss that uses the patient-specific weights to classify MDD patients from healthy ones. Both of these terms are combined with the third term, a robustness loss function to improve the reproducibility of hSCPs. This reduces the variability introduced due to site and population diversity (age and sex) on the predictive accuracy and pattern stability in a large dataset pooled from five different sites. Our results show the impact of diversity on prediction performance. Our model can reduce diversity and improve the predictive and generalizing capability of the components. Finally, our results show that our proposed model can robustly identify clinically relevant patterns characteristic of MDD with high reproducibility. |
2201.08941 | Jingwen Zhang | Jingwen Zhang, Qing Liu, Haorui Zhang, Michelle Dai, Qianqian Song,
Defu Yang, Guorong Wu, Minghan Chen | Uncovering the System Vulnerability and Criticality of Human Brain under
Dynamical Neuropathological Events in Alzheimer's Disease | null | null | null | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Despite the striking efforts in investigating neurobiological
factors behind the acquisition of amyloid-\b{eta} (A), protein tau (T), and
neurodegeneration ([N]) biomarkers, the mechanistic pathways of how AT[N]
biomarkers spreading throughout the brain remain elusive. Objectives: To
disentangle the massive heterogeneities in AD progressions and identify
vulnerable/critical brain regions to AD pathology. Methods: In this work, we
characterized the interaction of AT[N] biomarkers and their propagation across
brain networks using a novel bistable reaction-diffusion model, which allows us
to establish a new systems biology underpinning of Alzheimer's disease (AD)
progression. We applied our model to large-scale longitudinal neuroimages from
the ADNI database and studied the systematic vulnerability and criticality of
brains. Results: Our model yields long term prediction that is statistically
significant linear correlated with temporal imaging data, produces clinically
consistent risk prediction, and captures the Braak-like spreading pattern of
AT[N] biomarkers in AD development. Conclusion: Our major findings include (i)
tau is a stronger indicator of regional risk compared to amyloid, (ii) temporal
lobe exhibits higher vulnerability to AD-related pathologies, (iii) proposed
critical brain regions outperform hub nodes in transmitting disease factors
across the brain, and (iv) comparing the spread of neuropathological burdens
caused by amyloid-\b{eta} and tau diffusions, disruption of metabolic balance
is the most determinant factor contributing to the initiation and progression
of Alzheimer's disease.
| [
{
"created": "Sat, 22 Jan 2022 01:55:01 GMT",
"version": "v1"
},
{
"created": "Wed, 25 May 2022 18:09:25 GMT",
"version": "v2"
},
{
"created": "Mon, 21 Aug 2023 05:30:07 GMT",
"version": "v3"
}
] | 2023-08-22 | [
[
"Zhang",
"Jingwen",
""
],
[
"Liu",
"Qing",
""
],
[
"Zhang",
"Haorui",
""
],
[
"Dai",
"Michelle",
""
],
[
"Song",
"Qianqian",
""
],
[
"Yang",
"Defu",
""
],
[
"Wu",
"Guorong",
""
],
[
"Chen",
"Min... | Background: Despite the striking efforts in investigating neurobiological factors behind the acquisition of amyloid-\b{eta} (A), protein tau (T), and neurodegeneration ([N]) biomarkers, the mechanistic pathways of how AT[N] biomarkers spreading throughout the brain remain elusive. Objectives: To disentangle the massive heterogeneities in AD progressions and identify vulnerable/critical brain regions to AD pathology. Methods: In this work, we characterized the interaction of AT[N] biomarkers and their propagation across brain networks using a novel bistable reaction-diffusion model, which allows us to establish a new systems biology underpinning of Alzheimer's disease (AD) progression. We applied our model to large-scale longitudinal neuroimages from the ADNI database and studied the systematic vulnerability and criticality of brains. Results: Our model yields long term prediction that is statistically significant linear correlated with temporal imaging data, produces clinically consistent risk prediction, and captures the Braak-like spreading pattern of AT[N] biomarkers in AD development. Conclusion: Our major findings include (i) tau is a stronger indicator of regional risk compared to amyloid, (ii) temporal lobe exhibits higher vulnerability to AD-related pathologies, (iii) proposed critical brain regions outperform hub nodes in transmitting disease factors across the brain, and (iv) comparing the spread of neuropathological burdens caused by amyloid-\b{eta} and tau diffusions, disruption of metabolic balance is the most determinant factor contributing to the initiation and progression of Alzheimer's disease. |
2102.05892 | Laurent Najman | Quentin Garrido (LIGM, HCI), Sebastian Damrich (HCI), Alexander
J\"ager (HCI), Dario Cerletti (HCI), Manfred Claassen, Laurent Najman (LIGM),
Fred Hamprecht (HCI) | Visualizing hierarchies in scRNA-seq data using a density tree-biased
autoencoder | null | Bioinformatics, Oxford University Press (OUP), In press | null | null | q-bio.QM cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Single cell RNA sequencing (scRNA-seq) data makes studying the
development of cells possible at unparalleled resolution. Given that many
cellular differentiation processes are hierarchical, their scRNA-seq data is
expected to be approximately tree-shaped in gene expression space. Inference
and representation of this tree-structure in two dimensions is highly desirable
for biological interpretation and exploratory analysis.Results:Our two
contributions are an approach for identifying a meaningful tree structure from
high-dimensional scRNA-seq data, and a visualization method respecting the
tree-structure. We extract the tree structure by means of a density based
minimum spanning tree on a vector quantization of the data and show that it
captures biological information well. We then introduce DTAE, a tree-biased
autoencoder that emphasizes the tree structure of the data in low dimensional
space. We compare to other dimension reduction methods and demonstrate the
success of our method both qualitatively and quantitatively on real and toy
data.Availability: Our implementation relying on PyTorch and Higra is available
at https://github.com/hci-unihd/DTAE.
| [
{
"created": "Thu, 11 Feb 2021 08:48:48 GMT",
"version": "v1"
},
{
"created": "Fri, 4 Feb 2022 08:39:09 GMT",
"version": "v2"
},
{
"created": "Fri, 22 Apr 2022 08:59:25 GMT",
"version": "v3"
}
] | 2022-04-25 | [
[
"Garrido",
"Quentin",
"",
"LIGM, HCI"
],
[
"Damrich",
"Sebastian",
"",
"HCI"
],
[
"Jäger",
"Alexander",
"",
"HCI"
],
[
"Cerletti",
"Dario",
"",
"HCI"
],
[
"Claassen",
"Manfred",
"",
"LIGM"
],
[
"Najman",
"L... | Motivation: Single cell RNA sequencing (scRNA-seq) data makes studying the development of cells possible at unparalleled resolution. Given that many cellular differentiation processes are hierarchical, their scRNA-seq data is expected to be approximately tree-shaped in gene expression space. Inference and representation of this tree-structure in two dimensions is highly desirable for biological interpretation and exploratory analysis.Results:Our two contributions are an approach for identifying a meaningful tree structure from high-dimensional scRNA-seq data, and a visualization method respecting the tree-structure. We extract the tree structure by means of a density based minimum spanning tree on a vector quantization of the data and show that it captures biological information well. We then introduce DTAE, a tree-biased autoencoder that emphasizes the tree structure of the data in low dimensional space. We compare to other dimension reduction methods and demonstrate the success of our method both qualitatively and quantitatively on real and toy data.Availability: Our implementation relying on PyTorch and Higra is available at https://github.com/hci-unihd/DTAE. |
2211.08096 | Meng Huang | Meng Huang (1), Xiucai Ye (1 and 2), Tetsuya Sakurai (1 and 2) ((1)
University of Tsukuba, (2) Center for Artificial Intelligence Research in
University of Tsukuba) | Unveiling interpretable development-specific gene signatures in the
developing human prefrontal cortex with ICGS | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | In this paper, to unveil interpretable development-specific gene signatures
in human PFC, we propose a novel gene selection method, named Interpretable
Causality Gene Selection (ICGS), which adopts a Bayesian Network (BN) to
represent causality between multiple gene variables and a development variable.
The proposed ICGS method combines the positive instances-based contrastive
learning with a Variational AutoEncoder (VAE) to obtain this optimal BN
structure and use a Markov Blanket (MB) to identify gene signatures causally
related to the development variable. Moreover, the differential expression
genes (DEGs) are used to filter redundant genes before gene selection. In order
to identify gene signatures, we apply the proposed ICGS to the human PFC
single-cell transcriptomics data. The experimental results demonstrate that the
proposed method can effectively identify interpretable development-specific
gene signatures in human PFC. Gene ontology enrichment analysis and ASD-related
gene analysis show that these identified gene signatures reveal the key
biological processes and pathways in human PFC and have more potential for
neurodevelopment disorder cure. These gene signatures are expected to bring
important implications for understanding PFC development heterogeneity and
function in humans.
| [
{
"created": "Tue, 15 Nov 2022 12:27:26 GMT",
"version": "v1"
}
] | 2022-11-18 | [
[
"Huang",
"Meng",
"",
"1 and 2"
],
[
"Ye",
"Xiucai",
"",
"1 and 2"
],
[
"Sakurai",
"Tetsuya",
"",
"1 and 2"
]
] | In this paper, to unveil interpretable development-specific gene signatures in human PFC, we propose a novel gene selection method, named Interpretable Causality Gene Selection (ICGS), which adopts a Bayesian Network (BN) to represent causality between multiple gene variables and a development variable. The proposed ICGS method combines the positive instances-based contrastive learning with a Variational AutoEncoder (VAE) to obtain this optimal BN structure and use a Markov Blanket (MB) to identify gene signatures causally related to the development variable. Moreover, the differential expression genes (DEGs) are used to filter redundant genes before gene selection. In order to identify gene signatures, we apply the proposed ICGS to the human PFC single-cell transcriptomics data. The experimental results demonstrate that the proposed method can effectively identify interpretable development-specific gene signatures in human PFC. Gene ontology enrichment analysis and ASD-related gene analysis show that these identified gene signatures reveal the key biological processes and pathways in human PFC and have more potential for neurodevelopment disorder cure. These gene signatures are expected to bring important implications for understanding PFC development heterogeneity and function in humans. |
2108.06396 | Eric Sanchis | Eric Sanchis | Free Will: A New Formulation | 25th World Multi-Conference on Systemics, Cybernetics and
Informatics: WMSCI 2021, pp. 91-96, July 18 - 21, Virtual Conference | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Free will is sometimes summarised in the philosophical literature as the
subjective impression felt by an individual that he or she is the ultimate
source or cause of his or her own choices. The two most common arguments for
denying the existence of free will come from philosophy and neuroscience. The
first argument is the Consequence Argument. The second asserts that our
decisions are first made by the brain and only then become conscious to the
subject, taking away the control of the decision. The purpose of these two
arguments is to demonstrate that an individual cannot be the source or primary
cause of his or her choices. It is shown in this work that the concepts of
primary cause and primary source are not adequate to state a solid
characterisation of free will. A new formulation of this property is proposed
in which it is seen as a three-stage decision-making process implemented by an
individual to escape his or her own real or supposed alienation. This
decision-making process is represented in the form of a computer model called
the PSU (Predictability - Suspension - Unpredictability) model. The
compatibility of this new formulation of free will with the feeling it provides
and the analysis of various situations are then discussed.
| [
{
"created": "Thu, 5 Aug 2021 16:12:24 GMT",
"version": "v1"
}
] | 2021-08-17 | [
[
"Sanchis",
"Eric",
""
]
] | Free will is sometimes summarised in the philosophical literature as the subjective impression felt by an individual that he or she is the ultimate source or cause of his or her own choices. The two most common arguments for denying the existence of free will come from philosophy and neuroscience. The first argument is the Consequence Argument. The second asserts that our decisions are first made by the brain and only then become conscious to the subject, taking away the control of the decision. The purpose of these two arguments is to demonstrate that an individual cannot be the source or primary cause of his or her choices. It is shown in this work that the concepts of primary cause and primary source are not adequate to state a solid characterisation of free will. A new formulation of this property is proposed in which it is seen as a three-stage decision-making process implemented by an individual to escape his or her own real or supposed alienation. This decision-making process is represented in the form of a computer model called the PSU (Predictability - Suspension - Unpredictability) model. The compatibility of this new formulation of free will with the feeling it provides and the analysis of various situations are then discussed. |
1604.06660 | Lina Meinecke | Lina Meinecke and Markus Eriksson | Excluded volume effects in on- and off-lattice reaction-diffusion models | null | null | null | null | q-bio.QM q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mathematical models are important tools to study the excluded volume effects
on reaction-diffusion systems, which are known to play an important role inside
living cells. Detailed microscopic simulations with off-lattice Brownian
dynamics become computationally expensive in crowded environments. In this
paper we therefore investigate to which extent on-lattice approximations, so
called Cellular Automata models, can be used to simulate reactions and
diffusion in the presence of crowding molecules. We show that the diffusion is
most severely slowed down in the off-lattice model, since randomly distributed
obstacles effectively exclude more volume than those ordered on an artificial
grid. Crowded reaction rates can be both increased and decreased by the grid
structure and it proves important to model the molecules with realistic sizes
when excluded volume is taken into account. The grid artifacts increase with
increasing crowder density and we conclude that the computationally more
efficient on-lattice simulations are accurate approximations only for low
crowder densities.
| [
{
"created": "Fri, 22 Apr 2016 14:00:20 GMT",
"version": "v1"
}
] | 2016-04-25 | [
[
"Meinecke",
"Lina",
""
],
[
"Eriksson",
"Markus",
""
]
] | Mathematical models are important tools to study the excluded volume effects on reaction-diffusion systems, which are known to play an important role inside living cells. Detailed microscopic simulations with off-lattice Brownian dynamics become computationally expensive in crowded environments. In this paper we therefore investigate to which extent on-lattice approximations, so called Cellular Automata models, can be used to simulate reactions and diffusion in the presence of crowding molecules. We show that the diffusion is most severely slowed down in the off-lattice model, since randomly distributed obstacles effectively exclude more volume than those ordered on an artificial grid. Crowded reaction rates can be both increased and decreased by the grid structure and it proves important to model the molecules with realistic sizes when excluded volume is taken into account. The grid artifacts increase with increasing crowder density and we conclude that the computationally more efficient on-lattice simulations are accurate approximations only for low crowder densities. |
2302.07206 | Richard Naud | Richard Naud, Zachary Friedenberger, Katalin Toth | Silences, Spikes and Bursts: Three-Part Knot of the Neural Code | 15 pages, 4 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | When a neuron breaks silence, it can emit action potentials in a number of
patterns. Some responses are so sudden and intense that electrophysiologists
felt the need to single them out, labeling action potentials emitted at a
particularly high frequency with a metonym -- bursts. Is there more to bursts
than a figure of speech? After all, sudden bouts of high-frequency firing are
expected to occur whenever inputs surge. The burst coding hypothesis advances
that the neural code has three syllables: silences, spikes and bursts. We
review evidence supporting this ternary code in terms of devoted mechanisms for
burst generation, synaptic transmission and synaptic plasticity. We also review
the learning and attention theories for which such a triad is beneficial.
| [
{
"created": "Tue, 14 Feb 2023 17:30:05 GMT",
"version": "v1"
}
] | 2023-02-15 | [
[
"Naud",
"Richard",
""
],
[
"Friedenberger",
"Zachary",
""
],
[
"Toth",
"Katalin",
""
]
] | When a neuron breaks silence, it can emit action potentials in a number of patterns. Some responses are so sudden and intense that electrophysiologists felt the need to single them out, labeling action potentials emitted at a particularly high frequency with a metonym -- bursts. Is there more to bursts than a figure of speech? After all, sudden bouts of high-frequency firing are expected to occur whenever inputs surge. The burst coding hypothesis advances that the neural code has three syllables: silences, spikes and bursts. We review evidence supporting this ternary code in terms of devoted mechanisms for burst generation, synaptic transmission and synaptic plasticity. We also review the learning and attention theories for which such a triad is beneficial. |
1705.06529 | Michael Margaliot | Yoram Zarai, Michael Margaliot, Anatoly B. Kolomeisky | A Deterministic Model for One-Dimensional Excluded Flow with Local
Interactions | null | null | 10.1371/journal.pone.0182074 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Natural phenomena frequently involve a very large number of interacting
molecules moving in confined regions of space. Cellular transport by motor
proteins is an example of such collective behavior. We derive a deterministic
compartmental model for the unidirectional flow of particles along a
one-dimensional lattice of sites with nearest-neighbor interactions between the
particles. The flow between consecutive sites is governed by a soft simple
exclusion principle and by attracting or repelling forces between neighboring
particles. Using tools from contraction theory, we prove that the model admits
a unique steady-state and that every trajectory converges to this steady-state.
Analysis and simulations of the effect of the attracting and repelling forces
on this steady-state highlight the crucial role that these forces may play in
increasing the steady-state flow, and reveal that this increase stems from the
alleviation of traffic jams along the lattice. Our theoretical analysis
clarifies microscopic aspects of complex multi-particle dynamic processes.
| [
{
"created": "Thu, 18 May 2017 11:20:58 GMT",
"version": "v1"
}
] | 2017-11-01 | [
[
"Zarai",
"Yoram",
""
],
[
"Margaliot",
"Michael",
""
],
[
"Kolomeisky",
"Anatoly B.",
""
]
] | Natural phenomena frequently involve a very large number of interacting molecules moving in confined regions of space. Cellular transport by motor proteins is an example of such collective behavior. We derive a deterministic compartmental model for the unidirectional flow of particles along a one-dimensional lattice of sites with nearest-neighbor interactions between the particles. The flow between consecutive sites is governed by a soft simple exclusion principle and by attracting or repelling forces between neighboring particles. Using tools from contraction theory, we prove that the model admits a unique steady-state and that every trajectory converges to this steady-state. Analysis and simulations of the effect of the attracting and repelling forces on this steady-state highlight the crucial role that these forces may play in increasing the steady-state flow, and reveal that this increase stems from the alleviation of traffic jams along the lattice. Our theoretical analysis clarifies microscopic aspects of complex multi-particle dynamic processes. |
2108.06338 | Hui Liu | Lei Deng, Yibiao Huang, Xuejun Liu and Hui Liu | Graph2MDA: a multi-modal variational graph embedding model for
predicting microbe-drug associations | null | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Accumulated clinical studies show that microbes living in humans interact
closely with human hosts, and get involved in modulating drug efficacy and drug
toxicity. Microbes have become novel targets for the development of
antibacterial agents. Therefore, screening of microbe-drug associations can
benefit greatly drug research and development. With the increase of microbial
genomic and pharmacological datasets, we are greatly motivated to develop an
effective computational method to identify new microbe-drug associations. In
this paper, we proposed a novel method, Graph2MDA, to predict microbe-drug
associations by using variational graph autoencoder (VGAE). We constructed
multi-modal attributed graphs based on multiple features of microbes and drugs,
such as molecular structures, microbe genetic sequences, and function
annotations. Taking as input the multi-modal attribute graphs, VGAE was trained
to learn the informative and interpretable latent representations of each node
and the whole graph, and then a deep neural network classifier was used to
predict microbe-drug associations. The hyperparameter analysis and model
ablation studies showed the sensitivity and robustness of our model. We
evaluated our method on three independent datasets and the experimental results
showed that our proposed method outperformed six existing state-of-the-art
methods. We also explored the meaningness of the learned latent representations
of drugs and found that the drugs show obvious clustering patterns that are
significantly consistent with drug ATC classification. Moreover, we conducted
case studies on two microbes and two drugs and found 75\%-95\% predicted
associations have been reported in PubMed literature. Our extensive performance
evaluations validated the effectiveness of our proposed method.\
| [
{
"created": "Sat, 14 Aug 2021 07:33:05 GMT",
"version": "v1"
}
] | 2021-08-16 | [
[
"Deng",
"Lei",
""
],
[
"Huang",
"Yibiao",
""
],
[
"Liu",
"Xuejun",
""
],
[
"Liu",
"Hui",
""
]
] | Accumulated clinical studies show that microbes living in humans interact closely with human hosts, and get involved in modulating drug efficacy and drug toxicity. Microbes have become novel targets for the development of antibacterial agents. Therefore, screening of microbe-drug associations can benefit greatly drug research and development. With the increase of microbial genomic and pharmacological datasets, we are greatly motivated to develop an effective computational method to identify new microbe-drug associations. In this paper, we proposed a novel method, Graph2MDA, to predict microbe-drug associations by using variational graph autoencoder (VGAE). We constructed multi-modal attributed graphs based on multiple features of microbes and drugs, such as molecular structures, microbe genetic sequences, and function annotations. Taking as input the multi-modal attribute graphs, VGAE was trained to learn the informative and interpretable latent representations of each node and the whole graph, and then a deep neural network classifier was used to predict microbe-drug associations. The hyperparameter analysis and model ablation studies showed the sensitivity and robustness of our model. We evaluated our method on three independent datasets and the experimental results showed that our proposed method outperformed six existing state-of-the-art methods. We also explored the meaningness of the learned latent representations of drugs and found that the drugs show obvious clustering patterns that are significantly consistent with drug ATC classification. Moreover, we conducted case studies on two microbes and two drugs and found 75\%-95\% predicted associations have been reported in PubMed literature. Our extensive performance evaluations validated the effectiveness of our proposed method.\ |
2303.05174 | Valentin Schmutz | Valentin Schmutz, Johanni Brea, Wulfram Gerstner | Emergent rate-based dynamics in duplicate-free populations of spiking
neurons | 41 pages, 4 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Can Spiking Neural Networks (SNNs) approximate the dynamics of Recurrent
Neural Networks (RNNs)? Arguments in classical mean-field theory based on laws
of large numbers provide a positive answer when each neuron in the network has
many "duplicates", i.e. other neurons with almost perfectly correlated inputs.
Using a disordered network model that guarantees the absence of duplicates, we
show that duplicate-free SNNs can converge to RNNs, thanks to the concentration
of measure phenomenon. This result reveals a general mechanism underlying the
emergence of rate-based dynamics in large SNNs.
| [
{
"created": "Thu, 9 Mar 2023 11:11:25 GMT",
"version": "v1"
},
{
"created": "Mon, 29 Jan 2024 17:50:24 GMT",
"version": "v2"
},
{
"created": "Wed, 21 Feb 2024 12:27:12 GMT",
"version": "v3"
},
{
"created": "Mon, 13 May 2024 16:33:13 GMT",
"version": "v4"
}
] | 2024-05-14 | [
[
"Schmutz",
"Valentin",
""
],
[
"Brea",
"Johanni",
""
],
[
"Gerstner",
"Wulfram",
""
]
] | Can Spiking Neural Networks (SNNs) approximate the dynamics of Recurrent Neural Networks (RNNs)? Arguments in classical mean-field theory based on laws of large numbers provide a positive answer when each neuron in the network has many "duplicates", i.e. other neurons with almost perfectly correlated inputs. Using a disordered network model that guarantees the absence of duplicates, we show that duplicate-free SNNs can converge to RNNs, thanks to the concentration of measure phenomenon. This result reveals a general mechanism underlying the emergence of rate-based dynamics in large SNNs. |
2002.10441 | Brian Camley | Austin Hopkins and Brian A. Camley | Chemotaxis in uncertain environments: hedging bets with multiple
receptor types | null | Phys. Rev. Research 2, 043146 (2020) | 10.1103/PhysRevResearch.2.043146 | null | q-bio.CB cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Eukaryotic cells are able to sense chemical gradients in a wide range of
environments. We show that, if a cell is exposed to a highly variable
environment, it may gain chemotactic accuracy by expressing multiple receptor
types with varying affinities for the same signal, as found commonly in
chemotaxing cells like Dictyostelium. As environment uncertainty is increased,
there is a transition between cells preferring a single receptor type and a
mixture of types - hedging their bets against the possibility of an unfavorable
environment. We predict the optimal receptor affinities given a particular
environment. In chemotaxing, cells may also integrate their measurement over
time. Surprisingly, time-integration with multiple receptor types is
qualitatively different from gradient sensing by a single type -- cells may
extract orders of magnitude more chemotactic information than expected by naive
time integration. Our results show when cells should express multiple receptor
types to chemotax, and how cells can efficiently interpret the data from these
receptors.
| [
{
"created": "Mon, 24 Feb 2020 18:41:51 GMT",
"version": "v1"
},
{
"created": "Mon, 21 Sep 2020 22:14:30 GMT",
"version": "v2"
}
] | 2020-11-04 | [
[
"Hopkins",
"Austin",
""
],
[
"Camley",
"Brian A.",
""
]
] | Eukaryotic cells are able to sense chemical gradients in a wide range of environments. We show that, if a cell is exposed to a highly variable environment, it may gain chemotactic accuracy by expressing multiple receptor types with varying affinities for the same signal, as found commonly in chemotaxing cells like Dictyostelium. As environment uncertainty is increased, there is a transition between cells preferring a single receptor type and a mixture of types - hedging their bets against the possibility of an unfavorable environment. We predict the optimal receptor affinities given a particular environment. In chemotaxing, cells may also integrate their measurement over time. Surprisingly, time-integration with multiple receptor types is qualitatively different from gradient sensing by a single type -- cells may extract orders of magnitude more chemotactic information than expected by naive time integration. Our results show when cells should express multiple receptor types to chemotax, and how cells can efficiently interpret the data from these receptors. |
1905.02015 | Claus Vogl | Claus Vogl and Lynette Caitlin Mikula | Maximum likelihood (ML) estimators for scaled mutation parameters with a
strand symmetric mutation model in equilibrium | null | null | null | null | q-bio.PE stat.AP | http://creativecommons.org/licenses/by/4.0/ | With the multiallelic parent-independent mutation-drift model, the
equilibrium proportions of alleles are known to be Dirichlet distributed. A
special case is the biallelic model, in which the proportions are beta
distributed. A sample taken from these models is then Dirichlet-multinomially
or beta-binomially distributed, respectively. Maximum likelihood (ML)
estimators for the mutation parameters of the biallelic parent-independent
mutation model are available via an expectation maximization algorithm.
Assuming small scaled mutation rates, the distribution of a sample of size $M$
can be expanded in a Taylor series of first order. Then the ML estimators for
the two parameters in the biallelic model can be expressed using the site
frequency spectrum. In this article, we go beyond parent-independent mutation
and analyse a strand-symmetric mutation model with six scaled mutation
parameters that deviates from parent independent mutation and, generally, from
detailed balance. We derive ML estimators for these six parameters assuming
mutation-drift equilibrium and small scaled mutation rates. This is the first
time that ML estimators are provided for a mutation model more complex than
parent-independent mutation.
| [
{
"created": "Mon, 6 May 2019 13:05:47 GMT",
"version": "v1"
}
] | 2019-05-07 | [
[
"Vogl",
"Claus",
""
],
[
"Mikula",
"Lynette Caitlin",
""
]
] | With the multiallelic parent-independent mutation-drift model, the equilibrium proportions of alleles are known to be Dirichlet distributed. A special case is the biallelic model, in which the proportions are beta distributed. A sample taken from these models is then Dirichlet-multinomially or beta-binomially distributed, respectively. Maximum likelihood (ML) estimators for the mutation parameters of the biallelic parent-independent mutation model are available via an expectation maximization algorithm. Assuming small scaled mutation rates, the distribution of a sample of size $M$ can be expanded in a Taylor series of first order. Then the ML estimators for the two parameters in the biallelic model can be expressed using the site frequency spectrum. In this article, we go beyond parent-independent mutation and analyse a strand-symmetric mutation model with six scaled mutation parameters that deviates from parent independent mutation and, generally, from detailed balance. We derive ML estimators for these six parameters assuming mutation-drift equilibrium and small scaled mutation rates. This is the first time that ML estimators are provided for a mutation model more complex than parent-independent mutation. |
1211.3272 | Ramin Golestanian | Rachel R. Bennett and Ramin Golestanian (Oxford) | Emergent Run-and-Tumble Behavior in a Simple Model of Chlamydomonas with
Intrinsic Noise | 5 pages, 2 composite figures (made of 12 separate EPS files) | null | 10.1103/PhysRevLett.110.148102 | null | q-bio.CB cond-mat.soft cond-mat.stat-mech physics.bio-ph physics.flu-dyn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent experiments on the green alga Chlamydomonas that swims using
synchronized beating of a pair of flagella have revealed that it exhibits a
run-and-tumble behavior similar to that of bacteria such as E. Coli. Using a
simple purely hydrodynamic model that incorporates a stroke cycle and an
intrinsic Gaussian white noise, we show that a stochastic run-and-tumble
behavior could emerge, due to the nonlinearity of the combined
synchronization-rotation-translation dynamics. This suggests the intriguing
possibility that the alga might exploit nonlinear mechanics---as opposed to
sophisticated biochemical circuitry as used by bacteria---to control its
behavior.
| [
{
"created": "Wed, 14 Nov 2012 11:07:45 GMT",
"version": "v1"
}
] | 2015-06-12 | [
[
"Bennett",
"Rachel R.",
"",
"Oxford"
],
[
"Golestanian",
"Ramin",
"",
"Oxford"
]
] | Recent experiments on the green alga Chlamydomonas that swims using synchronized beating of a pair of flagella have revealed that it exhibits a run-and-tumble behavior similar to that of bacteria such as E. Coli. Using a simple purely hydrodynamic model that incorporates a stroke cycle and an intrinsic Gaussian white noise, we show that a stochastic run-and-tumble behavior could emerge, due to the nonlinearity of the combined synchronization-rotation-translation dynamics. This suggests the intriguing possibility that the alga might exploit nonlinear mechanics---as opposed to sophisticated biochemical circuitry as used by bacteria---to control its behavior. |
2303.10533 | Yang Chen | Yang Chen, Zhenyu Yang, Jingtong Zhao, Justus Adamson, Yang Sheng,
Fang-Fang Yin, Chunhao Wang | A Radiomics-Incorporated Deep Ensemble Learning Model for
Multi-Parametric MRI-based Glioma Segmentation | null | null | null | null | q-bio.QM cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We developed a deep ensemble learning model with a radiomics spatial encoding
execution for improved glioma segmentation accuracy using multi-parametric MRI
(mp-MRI). This model was developed using 369 glioma patients with a 4-modality
mp-MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. In each
modality volume, a 3D sliding kernel was implemented across the brain to
capture image heterogeneity: fifty-six radiomic features were extracted within
the kernel, resulting in a 4th order tensor. Each radiomic feature can then be
encoded as a 3D image volume, namely a radiomic feature map (RFM). PCA was
employed for data dimension reduction and the first 4 PCs were selected. Four
deep neural networks as sub-models following the U-Net architecture were
trained for the segmenting of a region-of-interest (ROI): each sub-model
utilizes the mp-MRI and 1 of the 4 PCs as a 5-channel input for a 2D execution.
The 4 softmax probability results given by the U-net ensemble were superimposed
and binarized by Otsu method as the segmentation result. Three ensemble models
were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor
(WT). The adopted radiomics spatial encoding execution enriches the image
heterogeneity information that leads to the successful demonstration of the
proposed deep ensemble model, which offers a new tool for mp-MRI based medical
image segmentation.
| [
{
"created": "Sun, 19 Mar 2023 02:16:55 GMT",
"version": "v1"
}
] | 2023-03-21 | [
[
"Chen",
"Yang",
""
],
[
"Yang",
"Zhenyu",
""
],
[
"Zhao",
"Jingtong",
""
],
[
"Adamson",
"Justus",
""
],
[
"Sheng",
"Yang",
""
],
[
"Yin",
"Fang-Fang",
""
],
[
"Wang",
"Chunhao",
""
]
] | We developed a deep ensemble learning model with a radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric MRI (mp-MRI). This model was developed using 369 glioma patients with a 4-modality mp-MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. In each modality volume, a 3D sliding kernel was implemented across the brain to capture image heterogeneity: fifty-six radiomic features were extracted within the kernel, resulting in a 4th order tensor. Each radiomic feature can then be encoded as a 3D image volume, namely a radiomic feature map (RFM). PCA was employed for data dimension reduction and the first 4 PCs were selected. Four deep neural networks as sub-models following the U-Net architecture were trained for the segmenting of a region-of-interest (ROI): each sub-model utilizes the mp-MRI and 1 of the 4 PCs as a 5-channel input for a 2D execution. The 4 softmax probability results given by the U-net ensemble were superimposed and binarized by Otsu method as the segmentation result. Three ensemble models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT). The adopted radiomics spatial encoding execution enriches the image heterogeneity information that leads to the successful demonstration of the proposed deep ensemble model, which offers a new tool for mp-MRI based medical image segmentation. |
2402.10638 | Dionn Hargreaves | Dionn Hargreaves, Sarah Woolner, Oliver E. Jensen | Relaxation and noise-driven oscillations in a model of mitotic spindle
dynamics | 7 figures, 2 tables, 2 online resources (movies) | null | null | null | q-bio.SC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | During cell division, the mitotic spindle moves dynamically through the cell
to position the chromosomes and determine the ultimate spatial position of the
two daughter cells. These movements have been attributed to the action of
cortical force generators which pull on the astral microtubules to position the
spindle, as well as pushing events by these same microtubules against the cell
cortex and plasma membrane. Attachment and detachment of cortical force
generators working antagonistically against centring forces of microtubules
have been modelled previously (Grill et al. 2005, Phys. Rev. Lett. 94:108104)
via stochastic simulations and mean-field Fokker-Planck equations (describing
random motion of force generators) to predict oscillations of a spindle pole in
one spatial dimension. Using systematic asymptotic methods, we reduce the
Fokker-Planck system to a set of ordinary differential equations (ODEs),
consistent with a set proposed by Grill et al., which can provide accurate
predictions of the conditions for the Fokker-Planck system to exhibit
oscillations. In the limit of small restoring forces, we derive an algebraic
prediction of the amplitude of spindle-pole oscillations and demonstrate the
relaxation structure of nonlinear oscillations. We also show how noise-induced
oscillations can arise in stochastic simulations for conditions in which the
mean-field Fokker-Planck system predicts stability, but for which the period
can be estimated directly by the ODE model and the amplitude by a related
stochastic differential equation that incorporates random binding kinetics.
| [
{
"created": "Fri, 16 Feb 2024 12:38:03 GMT",
"version": "v1"
},
{
"created": "Wed, 10 Jul 2024 16:14:00 GMT",
"version": "v2"
}
] | 2024-07-11 | [
[
"Hargreaves",
"Dionn",
""
],
[
"Woolner",
"Sarah",
""
],
[
"Jensen",
"Oliver E.",
""
]
] | During cell division, the mitotic spindle moves dynamically through the cell to position the chromosomes and determine the ultimate spatial position of the two daughter cells. These movements have been attributed to the action of cortical force generators which pull on the astral microtubules to position the spindle, as well as pushing events by these same microtubules against the cell cortex and plasma membrane. Attachment and detachment of cortical force generators working antagonistically against centring forces of microtubules have been modelled previously (Grill et al. 2005, Phys. Rev. Lett. 94:108104) via stochastic simulations and mean-field Fokker-Planck equations (describing random motion of force generators) to predict oscillations of a spindle pole in one spatial dimension. Using systematic asymptotic methods, we reduce the Fokker-Planck system to a set of ordinary differential equations (ODEs), consistent with a set proposed by Grill et al., which can provide accurate predictions of the conditions for the Fokker-Planck system to exhibit oscillations. In the limit of small restoring forces, we derive an algebraic prediction of the amplitude of spindle-pole oscillations and demonstrate the relaxation structure of nonlinear oscillations. We also show how noise-induced oscillations can arise in stochastic simulations for conditions in which the mean-field Fokker-Planck system predicts stability, but for which the period can be estimated directly by the ODE model and the amplitude by a related stochastic differential equation that incorporates random binding kinetics. |
1802.06275 | Maxime Woringer | Maxime Woringer and Xavier Darzacq | Protein motion in the nucleus: from anomalous diffusion to weak
interactions | null | Biochemical Society Transactions, BST20170310, 2018 | 10.1042/BST20170310 | null | q-bio.SC physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Understanding how transcription factors (TFs) regulate mammalian gene
expression in space and time is a central topic in biology. To activate a gene,
a TF has first to diffuse in the available space of the nucleus until it
reaches a target DNA sequence or protein (target site). This eventually results
in the recruitment of the whole transcriptional machinery.
All these processes take place in the mammalian nucleoplasm, a highly
organized and dynamic environment, in which some complexes transiently assemble
and break apart, whereas others appear more stable. This diversity of dynamic
behaviors arises from the number of biomolecules that make up the nucleoplasm
and their pairwise interactions. Indeed, interactions energies that span
several orders of magnitude, from covalent bounds to transient and dynamic
interactions can shape nuclear landscapes. Thus, the nuclear environment
determines how frequently and how fast a TF contacts its target site, and
indirectly gene expression. How exactly transient interactions are involved in
the regulation of TF diffusion is unclear, but are reflected by live cell
imaging techniques such as fluorescence correlation spectroscopy, fluorescence
recovery after photobleaching or single-particle tracking. Overall, the
macroscopic result of these microscopic interactions is almost always anomalous
diffusion, a phenomenon widely studied and modeled.
Here, we review the connections between the anomalous diffusion of a TF and
the microscopic organization of the nucleus, including recently described
topologically associated domains and dynamic phase-separated compartments. We
propose that anomalous diffusion found in single particle tracking (SPT) data
result from weak and transient interactions with dynamic nuclear substructures,
and that SPT data analysis would benefit form a better description of such
structures.
| [
{
"created": "Sat, 17 Feb 2018 19:06:23 GMT",
"version": "v1"
}
] | 2018-11-19 | [
[
"Woringer",
"Maxime",
""
],
[
"Darzacq",
"Xavier",
""
]
] | Understanding how transcription factors (TFs) regulate mammalian gene expression in space and time is a central topic in biology. To activate a gene, a TF has first to diffuse in the available space of the nucleus until it reaches a target DNA sequence or protein (target site). This eventually results in the recruitment of the whole transcriptional machinery. All these processes take place in the mammalian nucleoplasm, a highly organized and dynamic environment, in which some complexes transiently assemble and break apart, whereas others appear more stable. This diversity of dynamic behaviors arises from the number of biomolecules that make up the nucleoplasm and their pairwise interactions. Indeed, interactions energies that span several orders of magnitude, from covalent bounds to transient and dynamic interactions can shape nuclear landscapes. Thus, the nuclear environment determines how frequently and how fast a TF contacts its target site, and indirectly gene expression. How exactly transient interactions are involved in the regulation of TF diffusion is unclear, but are reflected by live cell imaging techniques such as fluorescence correlation spectroscopy, fluorescence recovery after photobleaching or single-particle tracking. Overall, the macroscopic result of these microscopic interactions is almost always anomalous diffusion, a phenomenon widely studied and modeled. Here, we review the connections between the anomalous diffusion of a TF and the microscopic organization of the nucleus, including recently described topologically associated domains and dynamic phase-separated compartments. We propose that anomalous diffusion found in single particle tracking (SPT) data result from weak and transient interactions with dynamic nuclear substructures, and that SPT data analysis would benefit form a better description of such structures. |
1705.03543 | Sayan Nag | Sayan Nag, Sayan Biswas, Sourya Sengupta, Shankha Sanyal, Archi
Banerjee, Ranjan Sengupta and Dipak Ghosh | Can Musical Emotion Be Quantified With Neural Jitter Or Shimmer? A Novel
EEG Based Study With Hindustani Classical Music | 6 pages, 12 figures, Presented in 4th International Conference on
Signal Processing and Integrated Networks (SPIN) 2017 | null | null | null | q-bio.NC cs.SD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The term jitter and shimmer has long been used in the domain of speech and
acoustic signal analysis as a parameter for speaker identification and other
prosodic features. In this study, we look forward to use the same parameters in
neural domain to identify and categorize emotional cues in different musical
clips. For this, we chose two ragas of Hindustani music which are
conventionally known to portray contrast emotions and EEG study was conducted
on 5 participants who were made to listen to 3 min clip of these two ragas with
sufficient resting period in between. The neural jitter and shimmer components
were evaluated for each experimental condition. The results reveal interesting
information regarding domain specific arousal of human brain in response to
musical stimuli and also regarding trait characteristics of an individual. This
novel study can have far reaching conclusions when it comes to modeling of
emotional appraisal. The results and implications are discussed in detail.
| [
{
"created": "Sat, 29 Apr 2017 18:58:54 GMT",
"version": "v1"
}
] | 2017-05-11 | [
[
"Nag",
"Sayan",
""
],
[
"Biswas",
"Sayan",
""
],
[
"Sengupta",
"Sourya",
""
],
[
"Sanyal",
"Shankha",
""
],
[
"Banerjee",
"Archi",
""
],
[
"Sengupta",
"Ranjan",
""
],
[
"Ghosh",
"Dipak",
""
]
] | The term jitter and shimmer has long been used in the domain of speech and acoustic signal analysis as a parameter for speaker identification and other prosodic features. In this study, we look forward to use the same parameters in neural domain to identify and categorize emotional cues in different musical clips. For this, we chose two ragas of Hindustani music which are conventionally known to portray contrast emotions and EEG study was conducted on 5 participants who were made to listen to 3 min clip of these two ragas with sufficient resting period in between. The neural jitter and shimmer components were evaluated for each experimental condition. The results reveal interesting information regarding domain specific arousal of human brain in response to musical stimuli and also regarding trait characteristics of an individual. This novel study can have far reaching conclusions when it comes to modeling of emotional appraisal. The results and implications are discussed in detail. |
2010.06072 | Alejandro Cohen | Amit Solomon, Alejandro Cohen, Nir Shlezinger, Yonina C. Eldar, and
Muriel M\'edard | Multi-Level Group Testing with Application to One-Shot Pooled COVID-19
Tests | null | null | null | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A key requirement in containing contagious diseases, such as the Coronavirus
disease 2019 (COVID-19) pandemic, is the ability to efficiently carry out mass
diagnosis over large populations. Some of the leading testing procedures, such
as those utilizing qualitative polymerase chain reaction, involve using
dedicated machinery which can simultaneously process a limited amount of
samples. A candidate method to increase the test throughput is to examine
pooled samples comprised of a mixture of samples from different patients. In
this work we study pooling based tests which operate in a one-shot fashion,
while providing an indication not solely on the presence of infection, but also
on its level, without additional pool tests, as often required in COVID-19
testing. As these requirements limit the application of traditional
group-testing (GT) methods, we propose a multi-level GT scheme, which builds
upon GT principles to enable accurate recovery using much fewer tests than
patients, while operating in a one-shot manner and providing multi-level
indications. We provide a theoretical analysis of the proposed scheme and
characterize conditions under which the algorithm operates reliably and at
affordable computational complexity. Our numerical results demonstrate that
multi level GT accurately and efficiently detects infection levels, while
achieving improved performance over previously proposed one-shot COVID-19
pooled-testing methods.
| [
{
"created": "Mon, 12 Oct 2020 23:18:07 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Aug 2022 05:58:26 GMT",
"version": "v2"
}
] | 2022-08-31 | [
[
"Solomon",
"Amit",
""
],
[
"Cohen",
"Alejandro",
""
],
[
"Shlezinger",
"Nir",
""
],
[
"Eldar",
"Yonina C.",
""
],
[
"Médard",
"Muriel",
""
]
] | A key requirement in containing contagious diseases, such as the Coronavirus disease 2019 (COVID-19) pandemic, is the ability to efficiently carry out mass diagnosis over large populations. Some of the leading testing procedures, such as those utilizing qualitative polymerase chain reaction, involve using dedicated machinery which can simultaneously process a limited amount of samples. A candidate method to increase the test throughput is to examine pooled samples comprised of a mixture of samples from different patients. In this work we study pooling based tests which operate in a one-shot fashion, while providing an indication not solely on the presence of infection, but also on its level, without additional pool tests, as often required in COVID-19 testing. As these requirements limit the application of traditional group-testing (GT) methods, we propose a multi-level GT scheme, which builds upon GT principles to enable accurate recovery using much fewer tests than patients, while operating in a one-shot manner and providing multi-level indications. We provide a theoretical analysis of the proposed scheme and characterize conditions under which the algorithm operates reliably and at affordable computational complexity. Our numerical results demonstrate that multi level GT accurately and efficiently detects infection levels, while achieving improved performance over previously proposed one-shot COVID-19 pooled-testing methods. |
1504.00288 | Amgalnbaatar Baldansuren | Amgalanbaatar Baldansuren | Electron Spin Relaxations in Biological [2Fe-2S] Cluster System | 4 pages, 4 figures, Scientific report | null | null | null | q-bio.BM cond-mat.mes-hall | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The phase coherence relaxation times as long as $T_2\sim830-1030\pm20$ ns
were measured for the [2Fe-2S] cluster in the intrinsic protein environment.
This relaxation corresponds to a relatively long lasting coherence of the
low-spin $S=1/2$ state. For this biological cluster, the phase coherence
relaxation time was significantly affected by the nuclear hyperfine
interactions of $^{14}$N with $I=1$. After labeling the cluster environments
uniformly with the $^{15}$N isotope, $T_2$ exceeds $\sim1.1-1.4\pm0.1~\mu$s at
the canonical orientations. This is already an order of magnitude longer than
the duration of a single-spin qubit manipulation $\sim10-100$ ns. The transient
nutation experiment corresponds to the coherent manipulation of the electron
spin.
| [
{
"created": "Wed, 1 Apr 2015 16:46:46 GMT",
"version": "v1"
}
] | 2015-04-02 | [
[
"Baldansuren",
"Amgalanbaatar",
""
]
] | The phase coherence relaxation times as long as $T_2\sim830-1030\pm20$ ns were measured for the [2Fe-2S] cluster in the intrinsic protein environment. This relaxation corresponds to a relatively long lasting coherence of the low-spin $S=1/2$ state. For this biological cluster, the phase coherence relaxation time was significantly affected by the nuclear hyperfine interactions of $^{14}$N with $I=1$. After labeling the cluster environments uniformly with the $^{15}$N isotope, $T_2$ exceeds $\sim1.1-1.4\pm0.1~\mu$s at the canonical orientations. This is already an order of magnitude longer than the duration of a single-spin qubit manipulation $\sim10-100$ ns. The transient nutation experiment corresponds to the coherent manipulation of the electron spin. |
1407.4706 | Adam Barrett DPhil | Adam B. Barrett | An Integration of Integrated Information Theory with Fundamental Physics | 10 pages, 1 table | Barrett AB (2014) An integration of integrated information theory
with fundamental physics. Front. Psychol. 5:63 | 10.3389/fpsyg.2014.00063 | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To truly eliminate Cartesian ghosts from the science of consciousness, we
must describe consciousness as an aspect of the physical. Integrated
Information Theory states that consciousness arises from intrinsic information
generated by dynamical systems; however existing formulations of this theory
are not applicable to standard models of fundamental physical entities. Modern
physics has shown that fields are fundamental entities, and in particular that
the electromagnetic field is fundamental. Here I hypothesize that consciousness
arises from information intrinsic to fundamental fields. This hypothesis unites
fundamental physics with what we know empirically about the neuroscience
underlying consciousness, and it bypasses the need to consider quantum effects.
| [
{
"created": "Thu, 3 Jul 2014 13:33:45 GMT",
"version": "v1"
}
] | 2014-07-18 | [
[
"Barrett",
"Adam B.",
""
]
] | To truly eliminate Cartesian ghosts from the science of consciousness, we must describe consciousness as an aspect of the physical. Integrated Information Theory states that consciousness arises from intrinsic information generated by dynamical systems; however existing formulations of this theory are not applicable to standard models of fundamental physical entities. Modern physics has shown that fields are fundamental entities, and in particular that the electromagnetic field is fundamental. Here I hypothesize that consciousness arises from information intrinsic to fundamental fields. This hypothesis unites fundamental physics with what we know empirically about the neuroscience underlying consciousness, and it bypasses the need to consider quantum effects. |
2007.10059 | Nadav M. Shnerb | Jayant Pande and Nadav M. Shnerb | Taming the diffusion approximation through a controlling-factor WKB
method | null | Phys. Rev. E 102, 062410 (2020) | 10.1103/PhysRevE.102.062410 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The diffusion approximation (DA) is widely used in the analysis of stochastic
population dynamics, from population genetics to ecology and evolution. DA is
an uncontrolled approximation that assumes the smoothness of the calculated
quantity over the relevant state space and fails when this property is not
satisfied. This failure becomes severe in situations where the direction of
selection switches sign. Here we employ the WKB (large-deviations) method,
which requires only the logarithm of a given quantity to be smooth over its
state space. Combining the WKB scheme with asymptotic matching techniques, we
show how to derive the diffusion approximation in a controlled manner and how
to produce better approximations, applicable for much wider regimes of
parameters. We also introduce a scalable (independent of population size)
WKB-based numerical technique. The method is applied to a central problem in
population genetics and evolution, finding the chance of ultimate fixation in a
zero-sum, two-types competition.
| [
{
"created": "Mon, 20 Jul 2020 12:52:29 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Jul 2020 06:50:09 GMT",
"version": "v2"
},
{
"created": "Fri, 20 Nov 2020 12:07:08 GMT",
"version": "v3"
}
] | 2021-01-04 | [
[
"Pande",
"Jayant",
""
],
[
"Shnerb",
"Nadav M.",
""
]
] | The diffusion approximation (DA) is widely used in the analysis of stochastic population dynamics, from population genetics to ecology and evolution. DA is an uncontrolled approximation that assumes the smoothness of the calculated quantity over the relevant state space and fails when this property is not satisfied. This failure becomes severe in situations where the direction of selection switches sign. Here we employ the WKB (large-deviations) method, which requires only the logarithm of a given quantity to be smooth over its state space. Combining the WKB scheme with asymptotic matching techniques, we show how to derive the diffusion approximation in a controlled manner and how to produce better approximations, applicable for much wider regimes of parameters. We also introduce a scalable (independent of population size) WKB-based numerical technique. The method is applied to a central problem in population genetics and evolution, finding the chance of ultimate fixation in a zero-sum, two-types competition. |
1704.04454 | Jake Taylor-King | Jake P. Taylor-King, Etienne Baratchart, Andrew Dhawan, Elizabeth A.
Coker, Inga Hansine Rye, Hege Russnes, S. Jon Chapman, David Basanta, and
Andriy Marusyk | Simulated Ablation for Detection of Cells Impacting Paracrine Signalling
in Histology Analysis | 17 pages, 5 figures | null | null | null | q-bio.CB q-bio.QM q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intra-tumour phenotypic heterogeneity limits accuracy of clinical diagnostics
and hampers the efficiency of anti-cancer therapies. Dealing with this cellular
heterogeneity requires adequate understanding of its sources, which is
extremely difficult, as phenotypes of tumour cells integrate hardwired
(epi)mutational differences with the dynamic responses to microenvironmental
cues. The later come in form of both direct physical interactions, as well as
inputs from gradients of secreted signalling molecules. Furthermore, tumour
cells can not only receive microenvironmental cues, but also produce them.
Despite high biological and clinical importance of understanding spatial
aspects of paracrine signaling, adequate research tools are largely lacking.
Here, a partial differential equation (PDE) based mathematical model is
developed that mimics the process of cell ablation. This model suggests how
each cell might contribute to the microenvironment by either absorbing or
secreting diffusible factors, and quantifies the extent to which observed
intensities can be explained via diffusion mediated signalling. The model
allows for the separation of phenotypic responses to signalling gradients
within tumour microenvironments from the combined influence of responses
mediated by direct physical contact and hardwired (epi)genetic differences. The
differential equation is solved around cell membrane outlines using a finite
element method (FEM). The method is applied to a multi-channel
immunofluorescence in situ hybridization (iFISH) stained breast cancer
histological specimen and correlations are investigated between: HER2 gene
amplification; HER2 protein expression; and cell interaction with the
diffusible microenvironment. This approach allows partial deconvolution of the
complex inputs...
| [
{
"created": "Fri, 14 Apr 2017 15:27:29 GMT",
"version": "v1"
}
] | 2017-04-17 | [
[
"Taylor-King",
"Jake P.",
""
],
[
"Baratchart",
"Etienne",
""
],
[
"Dhawan",
"Andrew",
""
],
[
"Coker",
"Elizabeth A.",
""
],
[
"Rye",
"Inga Hansine",
""
],
[
"Russnes",
"Hege",
""
],
[
"Chapman",
"S. Jon",
... | Intra-tumour phenotypic heterogeneity limits accuracy of clinical diagnostics and hampers the efficiency of anti-cancer therapies. Dealing with this cellular heterogeneity requires adequate understanding of its sources, which is extremely difficult, as phenotypes of tumour cells integrate hardwired (epi)mutational differences with the dynamic responses to microenvironmental cues. The later come in form of both direct physical interactions, as well as inputs from gradients of secreted signalling molecules. Furthermore, tumour cells can not only receive microenvironmental cues, but also produce them. Despite high biological and clinical importance of understanding spatial aspects of paracrine signaling, adequate research tools are largely lacking. Here, a partial differential equation (PDE) based mathematical model is developed that mimics the process of cell ablation. This model suggests how each cell might contribute to the microenvironment by either absorbing or secreting diffusible factors, and quantifies the extent to which observed intensities can be explained via diffusion mediated signalling. The model allows for the separation of phenotypic responses to signalling gradients within tumour microenvironments from the combined influence of responses mediated by direct physical contact and hardwired (epi)genetic differences. The differential equation is solved around cell membrane outlines using a finite element method (FEM). The method is applied to a multi-channel immunofluorescence in situ hybridization (iFISH) stained breast cancer histological specimen and correlations are investigated between: HER2 gene amplification; HER2 protein expression; and cell interaction with the diffusible microenvironment. This approach allows partial deconvolution of the complex inputs... |
1512.02937 | Nen Saito | Nen Saito, Yuki Sughiyama and Kunihiko Kaneko | Motif Analysis for Small-Number Effects in Chemical Reaction Dynamics | 8 pages, 3 figures | J. Chem. Phys. 145, 094111 (2016) | 10.1063/1.4961675 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The number of molecules involved in a cell or subcellular structure is
sometimes rather small. In this situation, ordinary macroscopic-level
fluctuations can be overwhelmed by non-negligible large fluctuations, which
results in drastic changes in chemical-reaction dynamics and statistics
compared to those observed under a macroscopic system (i.e., with a large
number of molecules). In order to understand how salient changes emerge from
fluctuations in molecular number, we here quantitatively define small-number
effect by focusing on a `mesoscopic' level, in which the concentration
distribution is distinguishable both from micro- and macroscopic ones, and
propose a criterion for determining whether or not such an effect can emerge in
a given chemical reaction network. Using the proposed criterion, we
systematically derive a list of motifs of chemical reaction networks that can
show small-number effects, which includes motifs showing emergence of the power
law and the bimodal distribution observable in a mesoscopic regime with respect
to molecule number. The list of motifs provided herein is helpful in the search
for candidates of biochemical reactions with a small-number effect for possible
biological functions, as well as for designing a reaction system whose behavior
can change drastically depending on molecule number, rather than concentration.
| [
{
"created": "Wed, 9 Dec 2015 16:44:13 GMT",
"version": "v1"
},
{
"created": "Sat, 12 Nov 2016 13:50:09 GMT",
"version": "v2"
}
] | 2016-11-15 | [
[
"Saito",
"Nen",
""
],
[
"Sughiyama",
"Yuki",
""
],
[
"Kaneko",
"Kunihiko",
""
]
] | The number of molecules involved in a cell or subcellular structure is sometimes rather small. In this situation, ordinary macroscopic-level fluctuations can be overwhelmed by non-negligible large fluctuations, which results in drastic changes in chemical-reaction dynamics and statistics compared to those observed under a macroscopic system (i.e., with a large number of molecules). In order to understand how salient changes emerge from fluctuations in molecular number, we here quantitatively define small-number effect by focusing on a `mesoscopic' level, in which the concentration distribution is distinguishable both from micro- and macroscopic ones, and propose a criterion for determining whether or not such an effect can emerge in a given chemical reaction network. Using the proposed criterion, we systematically derive a list of motifs of chemical reaction networks that can show small-number effects, which includes motifs showing emergence of the power law and the bimodal distribution observable in a mesoscopic regime with respect to molecule number. The list of motifs provided herein is helpful in the search for candidates of biochemical reactions with a small-number effect for possible biological functions, as well as for designing a reaction system whose behavior can change drastically depending on molecule number, rather than concentration. |
2101.02403 | Simone Pigolotti | Simone Pigolotti | Generalized Euler-Lotka equation for correlated cell divisions | 6 pages, 5 figures, combined Main Text + SI. Accepted as a Letter in
Physical Review E | Phys. Rev. E 103, 060402 (2021) | 10.1103/PhysRevE.103.L060402 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cell division times in microbial populations display significant
fluctuations, that impact the population growth rate in a non-trivial way. If
fluctuations are uncorrelated among different cells, the population growth rate
is predicted by the Euler-Lotka equation, which is a classic result in
mathematical biology. However, cell division times can be significantly
correlated, due to physical properties of cells that are passed through
generations. In this paper, we derive an equation remarkably similar to the
Euler-Lotka equation which is valid in the presence of correlations. Our exact
result is based on large deviation theory and does not require particularly
strong assumptions on the underlying dynamics. We apply our theory to a
phenomenological model of bacterial cell division in E.coli and to experimental
data. We find that the discrepancy between the growth rate predicted by the
Euler-Lotka equation and our generalized version is relatively small, but large
enough to be measurable by our approach.
| [
{
"created": "Thu, 7 Jan 2021 07:01:37 GMT",
"version": "v1"
},
{
"created": "Wed, 28 Apr 2021 03:54:23 GMT",
"version": "v2"
}
] | 2021-07-07 | [
[
"Pigolotti",
"Simone",
""
]
] | Cell division times in microbial populations display significant fluctuations, that impact the population growth rate in a non-trivial way. If fluctuations are uncorrelated among different cells, the population growth rate is predicted by the Euler-Lotka equation, which is a classic result in mathematical biology. However, cell division times can be significantly correlated, due to physical properties of cells that are passed through generations. In this paper, we derive an equation remarkably similar to the Euler-Lotka equation which is valid in the presence of correlations. Our exact result is based on large deviation theory and does not require particularly strong assumptions on the underlying dynamics. We apply our theory to a phenomenological model of bacterial cell division in E.coli and to experimental data. We find that the discrepancy between the growth rate predicted by the Euler-Lotka equation and our generalized version is relatively small, but large enough to be measurable by our approach. |
2403.03970 | Sukirt Thakur | Sukirt Thakur, Ehsan Esmaili, Sarah Libring, Luis Solorio, and Arezoo
M. Ardekani | Inverse resolution of spatially varying diffusion coefficient using
Physics-Informed neural networks | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Resolving the diffusion coefficient is a key element in many biological and
engineering systems, including pharmacological drug transport and fluid
mechanics analyses. Additionally, these systems often have spatial variation in
the diffusion coefficient which must be determined, such as for injectable
drug-eluting implants into heterogeneous tissues. Unfortunately, obtaining the
diffusion coefficient from images in such cases is an inverse problem with only
discrete data points. The development of a robust method that can work with
such noisy and ill-posed datasets to accurately determine spatially-varying
diffusion coefficients is of great value across a large range of disciplines.
Here, we developed an inverse solver that uses physics informed neural networks
(PINNs) to calculate spatially-varying diffusion coefficients from numerical
and experimental image data in varying biological and engineering applications.
The residual of the transient diffusion equation for a concentration field is
minimized to find the diffusion coefficient. The robustness of the method as an
inverse solver was tested using both numerical and experimental datasets. The
predictions show good agreement with both the numerical and experimental
benchmarks; an error of less than 6.31% was obtained against all numerical
benchmarks, while the diffusion coefficient calculated in experimental datasets
matches the appropriate ranges of other reported literature values. Our work
demonstrates the potential of using PINNs to resolve spatially-varying
diffusion coefficients, which may aid a wide-range of applications, such as
enabling better-designed drug-eluting implants for regenerative medicine or
oncology fields.
| [
{
"created": "Wed, 6 Mar 2024 18:15:47 GMT",
"version": "v1"
}
] | 2024-03-08 | [
[
"Thakur",
"Sukirt",
""
],
[
"Esmaili",
"Ehsan",
""
],
[
"Libring",
"Sarah",
""
],
[
"Solorio",
"Luis",
""
],
[
"Ardekani",
"Arezoo M.",
""
]
] | Resolving the diffusion coefficient is a key element in many biological and engineering systems, including pharmacological drug transport and fluid mechanics analyses. Additionally, these systems often have spatial variation in the diffusion coefficient which must be determined, such as for injectable drug-eluting implants into heterogeneous tissues. Unfortunately, obtaining the diffusion coefficient from images in such cases is an inverse problem with only discrete data points. The development of a robust method that can work with such noisy and ill-posed datasets to accurately determine spatially-varying diffusion coefficients is of great value across a large range of disciplines. Here, we developed an inverse solver that uses physics informed neural networks (PINNs) to calculate spatially-varying diffusion coefficients from numerical and experimental image data in varying biological and engineering applications. The residual of the transient diffusion equation for a concentration field is minimized to find the diffusion coefficient. The robustness of the method as an inverse solver was tested using both numerical and experimental datasets. The predictions show good agreement with both the numerical and experimental benchmarks; an error of less than 6.31% was obtained against all numerical benchmarks, while the diffusion coefficient calculated in experimental datasets matches the appropriate ranges of other reported literature values. Our work demonstrates the potential of using PINNs to resolve spatially-varying diffusion coefficients, which may aid a wide-range of applications, such as enabling better-designed drug-eluting implants for regenerative medicine or oncology fields. |
2301.11965 | Zachary Boyd | Zachary M. Boyd, Nick Callor, Taylor Gledhill, Abigail Jenkins, Robert
Snellman, Benjamin Z. Webb, Raelynn Wonnacott | The persistent homology of genealogical networks | null | Applied Network Science, 2023 | null | null | q-bio.MN cs.DM physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Genealogical networks (i.e. family trees) are of growing interest, with the
largest known data sets now including well over one billion individuals.
Interest in family history also supports an 8.5 billion dollar industry whose
size is projected to double within 7 years (FutureWise report HC1137). Yet
little mathematical attention has been paid to the complex network properties
of genealogical networks, especially at large scales.
The structure of genealogical networks is of particular interest due to the
practice of forming unions, e.g. marriages, that are typically well outside
one's immediate family. In most other networks, including other social
networks, no equivalent restriction exists on the distance at which
relationships form. To study the effect this has on genealogical networks we
use persistent homology to identify and compare the structure of 101
genealogical and 31 other social networks. Specifically, we introduce the
notion of a network's persistence curve, which encodes the network's set of
persistence intervals. We find that the persistence curves of genealogical
networks have a distinct structure when compared to other social networks. This
difference in structure also extends to subnetworks of genealogical and social
networks suggesting that, even with incomplete data, persistent homology can be
used to meaningfully analyze genealogical networks. Here we also describe how
concepts from genealogical networks, such as common ancestor cycles, are
represented using persistent homology. We expect that persistent homology tools
will become increasingly important in genealogical exploration as popular
interest in ancestry research continues to expand.
| [
{
"created": "Fri, 27 Jan 2023 19:42:58 GMT",
"version": "v1"
}
] | 2023-01-31 | [
[
"Boyd",
"Zachary M.",
""
],
[
"Callor",
"Nick",
""
],
[
"Gledhill",
"Taylor",
""
],
[
"Jenkins",
"Abigail",
""
],
[
"Snellman",
"Robert",
""
],
[
"Webb",
"Benjamin Z.",
""
],
[
"Wonnacott",
"Raelynn",
""
... | Genealogical networks (i.e. family trees) are of growing interest, with the largest known data sets now including well over one billion individuals. Interest in family history also supports an 8.5 billion dollar industry whose size is projected to double within 7 years (FutureWise report HC1137). Yet little mathematical attention has been paid to the complex network properties of genealogical networks, especially at large scales. The structure of genealogical networks is of particular interest due to the practice of forming unions, e.g. marriages, that are typically well outside one's immediate family. In most other networks, including other social networks, no equivalent restriction exists on the distance at which relationships form. To study the effect this has on genealogical networks we use persistent homology to identify and compare the structure of 101 genealogical and 31 other social networks. Specifically, we introduce the notion of a network's persistence curve, which encodes the network's set of persistence intervals. We find that the persistence curves of genealogical networks have a distinct structure when compared to other social networks. This difference in structure also extends to subnetworks of genealogical and social networks suggesting that, even with incomplete data, persistent homology can be used to meaningfully analyze genealogical networks. Here we also describe how concepts from genealogical networks, such as common ancestor cycles, are represented using persistent homology. We expect that persistent homology tools will become increasingly important in genealogical exploration as popular interest in ancestry research continues to expand. |
2009.14482 | Marko Jusup | Chen Shen, Marko Jusup, Lei Shi, Zhen Wang, Matjaz Perc, Petter Holme | Exit rights open complex pathways to cooperation | null | J. R. Soc. Interface 18, 20200777 (2021) | 10.1098/rsif.2020.0777 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the evolutionary dynamics of the prisoner's dilemma game in which
cooperators and defectors interact with another actor type called exiters.
Rather than being exploited by defectors, exiters exit the game in favour of a
small payoff. We find that this simple extension of the game allows cooperation
to flourish in well-mixed populations when iterations or reputation are added.
In networked populations, however, the exit option is less conducive to
cooperation. Instead, it enables the coexistence of cooperators, defectors, and
exiters through cyclic dominance. Other outcomes are also possible as the exit
payoff increases or the network structure changes, including network-wide
oscillations in actor abundances that may cause the extinction of exiters and
the domination of defectors, although game parameters should favour exiting.
The complex dynamics that emerges in the wake of a simple option to exit the
game implies that nuances matter even if our analyses are restricted to
incentives for rational behaviour.
| [
{
"created": "Wed, 30 Sep 2020 07:35:43 GMT",
"version": "v1"
},
{
"created": "Mon, 7 Dec 2020 03:27:48 GMT",
"version": "v2"
}
] | 2021-01-20 | [
[
"Shen",
"Chen",
""
],
[
"Jusup",
"Marko",
""
],
[
"Shi",
"Lei",
""
],
[
"Wang",
"Zhen",
""
],
[
"Perc",
"Matjaz",
""
],
[
"Holme",
"Petter",
""
]
] | We study the evolutionary dynamics of the prisoner's dilemma game in which cooperators and defectors interact with another actor type called exiters. Rather than being exploited by defectors, exiters exit the game in favour of a small payoff. We find that this simple extension of the game allows cooperation to flourish in well-mixed populations when iterations or reputation are added. In networked populations, however, the exit option is less conducive to cooperation. Instead, it enables the coexistence of cooperators, defectors, and exiters through cyclic dominance. Other outcomes are also possible as the exit payoff increases or the network structure changes, including network-wide oscillations in actor abundances that may cause the extinction of exiters and the domination of defectors, although game parameters should favour exiting. The complex dynamics that emerges in the wake of a simple option to exit the game implies that nuances matter even if our analyses are restricted to incentives for rational behaviour. |
2210.07308 | Juan Marin | M. Ahumada, A. Ledesma-Araujo, Leonardo Gordillo, Juan F. Mar\'in | Mutation and SARS-CoV-2 strain competition under vaccination in a
modified SIR model | 28 pages, 8 figures | Chaos, Solitons & Fractals, Volume 166, January 2023, 112964 | 10.1016/j.chaos.2022.112964 | null | q-bio.PE nlin.CD physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | The crisis caused by the COVID-19 outbreak around the globe raised an
increasing concern about the ongoing emergence of variants of SARS-CoV-2 that
may evade the immune response provided by vaccines. New variants appear due to
mutation, and as the cases accumulate, the probability of the emergence of a
variant of concern increases. In this article, we propose a modified SIR model
with waning immunity that captures the competition of two strain classes of an
infectious disease under the effect of vaccination with a highly contagious and
deadly strain class emerging from a prior strain due to mutation. When these
strains compete for a limited supply of susceptible individuals, changes in the
efficiency of vaccines may affect the behaviour of the disease in a non-trivial
way, resulting in complex outcomes. We characterise the parameter space
including intrinsic parameters of the disease, and using the vaccine
efficiencies as control variables. We find different types of transcritical
bifurcations between endemic fixed points and a disease-free equilibrium and
identify a region of strain competition where the two strain classes coexist
during a transient period. We show that a strain can be extinguished either due
to strain competition or vaccination, and we obtain the critical values of the
efficiency of vaccines to eradicate the disease. Numerical studies using
parameters estimated from publicly reported data agree with our theoretical
results. Our mathematical model could be a tool to assess quantitatively the
vaccination policies of competing and emerging strains using the dynamics in
epidemics of infectious diseases.
| [
{
"created": "Thu, 13 Oct 2022 19:23:35 GMT",
"version": "v1"
}
] | 2022-12-14 | [
[
"Ahumada",
"M.",
""
],
[
"Ledesma-Araujo",
"A.",
""
],
[
"Gordillo",
"Leonardo",
""
],
[
"Marín",
"Juan F.",
""
]
] | The crisis caused by the COVID-19 outbreak around the globe raised an increasing concern about the ongoing emergence of variants of SARS-CoV-2 that may evade the immune response provided by vaccines. New variants appear due to mutation, and as the cases accumulate, the probability of the emergence of a variant of concern increases. In this article, we propose a modified SIR model with waning immunity that captures the competition of two strain classes of an infectious disease under the effect of vaccination with a highly contagious and deadly strain class emerging from a prior strain due to mutation. When these strains compete for a limited supply of susceptible individuals, changes in the efficiency of vaccines may affect the behaviour of the disease in a non-trivial way, resulting in complex outcomes. We characterise the parameter space including intrinsic parameters of the disease, and using the vaccine efficiencies as control variables. We find different types of transcritical bifurcations between endemic fixed points and a disease-free equilibrium and identify a region of strain competition where the two strain classes coexist during a transient period. We show that a strain can be extinguished either due to strain competition or vaccination, and we obtain the critical values of the efficiency of vaccines to eradicate the disease. Numerical studies using parameters estimated from publicly reported data agree with our theoretical results. Our mathematical model could be a tool to assess quantitatively the vaccination policies of competing and emerging strains using the dynamics in epidemics of infectious diseases. |
1508.02906 | Tom Nye | Tom M. W. Nye | Convergence of random walks to Brownian motion on cubical complexes | 14 pages, 2 figures. The results in the original submission have been
changed substantially. In particular, the main theorem has been generalized
to apply to a wide class of cubical complexes rather than
Billera-Holmes-Vogtmann tree space alone. This simplifies some parts of the
proof, although the main ideas are the same. Tree space is now dealt with as
a special example in Section 5 | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cubical complexes are metric spaces constructed by gluing together unit cubes
in an analogous way to the construction of simplicial complexes. We construct
Brownian motion on such spaces, define random walks, and prove that the
transition kernels of the random walks converge to that for Brownian motion.
The proof involves pulling back onto the complex the distribution of Brownian
sample paths on the standard cube, and combining this with a distribution on
walks between cubes in the complex. The main application lies in analysing sets
of evolutionary trees: several tree spaces are cubical complexes and we briefly
describe our results and some applications in this context. Our results extend
readily to a class of polyhedral complex in which every cell of maximal
dimension is isometric to a given fixed polyhedron.
| [
{
"created": "Wed, 12 Aug 2015 13:11:18 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Aug 2016 15:45:43 GMT",
"version": "v2"
},
{
"created": "Mon, 23 Apr 2018 15:49:38 GMT",
"version": "v3"
},
{
"created": "Wed, 22 May 2019 07:43:07 GMT",
"version": "v4"
}
] | 2019-05-23 | [
[
"Nye",
"Tom M. W.",
""
]
] | Cubical complexes are metric spaces constructed by gluing together unit cubes in an analogous way to the construction of simplicial complexes. We construct Brownian motion on such spaces, define random walks, and prove that the transition kernels of the random walks converge to that for Brownian motion. The proof involves pulling back onto the complex the distribution of Brownian sample paths on the standard cube, and combining this with a distribution on walks between cubes in the complex. The main application lies in analysing sets of evolutionary trees: several tree spaces are cubical complexes and we briefly describe our results and some applications in this context. Our results extend readily to a class of polyhedral complex in which every cell of maximal dimension is isometric to a given fixed polyhedron. |
0812.2327 | Valmir Barbosa | Valmir C. Barbosa, Raul Donangelo, Sergio R. Souza | Network growth for enhanced natural selection | null | Physical Review E 80 (2009), 026115 | 10.1103/PhysRevE.80.026115 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Natural selection and random drift are competing phenomena for explaining the
evolution of populations. Combining a highly fit mutant with a population
structure that improves the odds that the mutant spreads through the whole
population tips the balance in favor of natural selection. The probability that
the spread occurs, known as the fixation probability, depends heavily on how
the population is structured. Certain topologies, albeit highly artificially
contrived, have been shown to exist that favor fixation. We introduce a
randomized mechanism for network growth that is loosely inspired in some of
these topologies' key properties and demonstrate, through simulations, that it
is capable of giving rise to structured populations for which the fixation
probability significantly surpasses that of an unstructured population. This
discovery provides important support to the notion that natural selection can
be enhanced over random drift in naturally occurring population structures.
| [
{
"created": "Fri, 12 Dec 2008 09:28:56 GMT",
"version": "v1"
}
] | 2009-08-14 | [
[
"Barbosa",
"Valmir C.",
""
],
[
"Donangelo",
"Raul",
""
],
[
"Souza",
"Sergio R.",
""
]
] | Natural selection and random drift are competing phenomena for explaining the evolution of populations. Combining a highly fit mutant with a population structure that improves the odds that the mutant spreads through the whole population tips the balance in favor of natural selection. The probability that the spread occurs, known as the fixation probability, depends heavily on how the population is structured. Certain topologies, albeit highly artificially contrived, have been shown to exist that favor fixation. We introduce a randomized mechanism for network growth that is loosely inspired in some of these topologies' key properties and demonstrate, through simulations, that it is capable of giving rise to structured populations for which the fixation probability significantly surpasses that of an unstructured population. This discovery provides important support to the notion that natural selection can be enhanced over random drift in naturally occurring population structures. |
2007.12204 | Martina Conte | Martina Conte and Christina Surulescu | Mathematical modeling of glioma invasion: acid- and vasculature mediated
go-or-grow dichotomy and the influence of tissue anisotropy | 30 pages, 10 figures | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Starting from kinetic transport equations and subcellular dynamics we deduce
a multiscale model for glioma invasion relying on the go-or-grow dichotomy and
the influence of vasculature, acidity, and brain tissue anisotropy. Numerical
simulations are performed for this model with multiple taxis, in order to
assess the solution behavior under several scenarios of taxis and growth for
tumor and endothelial cells. An extension of the model to incorporate the
macroscopic evolution of normal tissue and necrotic matter allows us to perform
tumor grading.
| [
{
"created": "Thu, 23 Jul 2020 18:23:56 GMT",
"version": "v1"
}
] | 2020-07-27 | [
[
"Conte",
"Martina",
""
],
[
"Surulescu",
"Christina",
""
]
] | Starting from kinetic transport equations and subcellular dynamics we deduce a multiscale model for glioma invasion relying on the go-or-grow dichotomy and the influence of vasculature, acidity, and brain tissue anisotropy. Numerical simulations are performed for this model with multiple taxis, in order to assess the solution behavior under several scenarios of taxis and growth for tumor and endothelial cells. An extension of the model to incorporate the macroscopic evolution of normal tissue and necrotic matter allows us to perform tumor grading. |
1904.09367 | Robert Marsland III | Robert Marsland III and Wenping Cui and Joshua Goldford and Pankaj
Mehta | The Community Simulator: A Python package for microbial ecology | 14 pages, 6 figures | null | 10.1371/journal.pone.0230430 | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Natural microbial communities contain hundreds to thousands of interacting
species. For this reason, computational simulations are playing an increasingly
important role in microbial ecology. In this manuscript, we present a new
open-source, freely available Python package called Community Simulator for
simulating microbial population dynamics in a reproducible, transparent and
scalable way. The Community Simulator includes five major elements: tools for
preparing the initial states and environmental conditions for a set of samples,
automatic generation of dynamical equations based on a dictionary of modeling
assumptions, random parameter sampling with tunable levels of metabolic and
taxonomic structure, parallel integration of the dynamical equations, and
support for metacommunity dynamics with migration between samples. To
significantly speed up simulations using Community Simulator, our Python
package implements a new Expectation-Maximization (EM) algorithm for finding
equilibrium states of community dynamics that exploits a recently discovered
duality between ecological dynamics and convex optimization. We present data
showing that this EM algorithm improves performance by between one and two
orders compared to direct numerical integration of the corresponding ordinary
differential equations. We conclude by listing several recent applications of
the Community Simulator to problems in microbial ecology, and discussing
possible extensions of the package for directly analyzing microbiome
compositional data.
| [
{
"created": "Fri, 19 Apr 2019 23:16:00 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Jul 2019 16:20:21 GMT",
"version": "v2"
},
{
"created": "Thu, 23 Jan 2020 20:56:47 GMT",
"version": "v3"
},
{
"created": "Wed, 4 Mar 2020 15:05:29 GMT",
"version": "v4"
}
] | 2020-07-01 | [
[
"Marsland",
"Robert",
"III"
],
[
"Cui",
"Wenping",
""
],
[
"Goldford",
"Joshua",
""
],
[
"Mehta",
"Pankaj",
""
]
] | Natural microbial communities contain hundreds to thousands of interacting species. For this reason, computational simulations are playing an increasingly important role in microbial ecology. In this manuscript, we present a new open-source, freely available Python package called Community Simulator for simulating microbial population dynamics in a reproducible, transparent and scalable way. The Community Simulator includes five major elements: tools for preparing the initial states and environmental conditions for a set of samples, automatic generation of dynamical equations based on a dictionary of modeling assumptions, random parameter sampling with tunable levels of metabolic and taxonomic structure, parallel integration of the dynamical equations, and support for metacommunity dynamics with migration between samples. To significantly speed up simulations using Community Simulator, our Python package implements a new Expectation-Maximization (EM) algorithm for finding equilibrium states of community dynamics that exploits a recently discovered duality between ecological dynamics and convex optimization. We present data showing that this EM algorithm improves performance by between one and two orders compared to direct numerical integration of the corresponding ordinary differential equations. We conclude by listing several recent applications of the Community Simulator to problems in microbial ecology, and discussing possible extensions of the package for directly analyzing microbiome compositional data. |
1704.00494 | Jiancheng Zhuang | Jiancheng Zhuang | The eddy current distortion in the multiband diffusion images: diagnosis
and correction | null | null | null | null | q-bio.NC physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The diffusion weighted images acquired with the multiband sequence or the
Lifespan protocols shows a type of slice distortion artifact. We find that this
artifact is caused by the eddy currents, which can be induced by the diffusion
gradient associated with either current DW image or the previous DW images. The
artifact can be corrected by further tuning the compensation circuit in the MR
hardware, or by a correction algorithm which includes the diffusion gradients
from the current and previous DW images.
| [
{
"created": "Mon, 3 Apr 2017 09:37:02 GMT",
"version": "v1"
}
] | 2017-04-04 | [
[
"Zhuang",
"Jiancheng",
""
]
] | The diffusion weighted images acquired with the multiband sequence or the Lifespan protocols shows a type of slice distortion artifact. We find that this artifact is caused by the eddy currents, which can be induced by the diffusion gradient associated with either current DW image or the previous DW images. The artifact can be corrected by further tuning the compensation circuit in the MR hardware, or by a correction algorithm which includes the diffusion gradients from the current and previous DW images. |
1603.03828 | Stuart Hagler | Stuart Hagler, Holly B. Jimison, Misha Pavel | Assessing Executive Function Using a Computer Game: Computational
Modeling of Cognitive Processes | 11 pages, 4 figures | IEEE J Biomed Health Inform. 2014, 18(4): 1442-52 | 10.1109/JBHI.2014.2299793 | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Early and reliable detection of cognitive decline is one of the most
important challenges of current healthcare. In this project we developed an
approach whereby a frequently played computer game can be used to assess a
variety of cognitive processes and estimate the results of the pen-and-paper
Trail-Making Test (TMT) - known to measure executive function, as well as
visual pattern recognition, speed of processing, working memory, and
set-switching ability. We developed a computational model of the TMT based on a
decomposition of the test into several independent processes, each
characterized by a set of parameters that can be estimated from play of a
computer game designed to resemble the TMT. An empirical evaluation of the
model suggests that it is possible to use the game data to estimate the
parameters of the underlying cognitive processes and using the values of the
parameters to estimate the TMT performance. Cognitive measures and trends in
these measures can be used to identify individuals for further assessment, to
provide a mechanism for improving the early detection of neurological problems,
and to provide feedback and monitoring for cognitive interventions in the home.
| [
{
"created": "Sat, 12 Mar 2016 00:03:45 GMT",
"version": "v1"
}
] | 2016-11-15 | [
[
"Hagler",
"Stuart",
""
],
[
"Jimison",
"Holly B.",
""
],
[
"Pavel",
"Misha",
""
]
] | Early and reliable detection of cognitive decline is one of the most important challenges of current healthcare. In this project we developed an approach whereby a frequently played computer game can be used to assess a variety of cognitive processes and estimate the results of the pen-and-paper Trail-Making Test (TMT) - known to measure executive function, as well as visual pattern recognition, speed of processing, working memory, and set-switching ability. We developed a computational model of the TMT based on a decomposition of the test into several independent processes, each characterized by a set of parameters that can be estimated from play of a computer game designed to resemble the TMT. An empirical evaluation of the model suggests that it is possible to use the game data to estimate the parameters of the underlying cognitive processes and using the values of the parameters to estimate the TMT performance. Cognitive measures and trends in these measures can be used to identify individuals for further assessment, to provide a mechanism for improving the early detection of neurological problems, and to provide feedback and monitoring for cognitive interventions in the home. |
1809.08410 | Kuan Tung | Kuan Tung, Po-Kang Liu, Yu-Chuan Chuang, Sheng-Hui Wang, An-Yeu Wu | Entropy-Assisted Multi-Modal Emotion Recognition Framework Based on
Physiological Signals | null | null | null | null | q-bio.QM cs.LG eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As the result of the growing importance of the Human Computer Interface
system, understanding human's emotion states has become a consequential ability
for the computer. This paper aims to improve the performance of emotion
recognition by conducting the complexity analysis of physiological signals.
Based on AMIGOS dataset, we extracted several entropy-domain features such as
Refined Composite Multi-Scale Entropy (RCMSE), Refined Composite Multi-Scale
Permutation Entropy (RCMPE) from ECG and GSR signals, and Multivariate
Multi-Scale Entropy (MMSE), Multivariate Multi-Scale Permutation Entropy (MMPE)
from EEG, respectively. The statistical results show that RCMSE in GSR has a
dominating performance in arousal, while RCMPE in GSR would be the excellent
feature in valence. Furthermore, we selected XGBoost model to predict emotion
and get 68% accuracy in arousal and 84% in valence.
| [
{
"created": "Sat, 22 Sep 2018 08:45:15 GMT",
"version": "v1"
}
] | 2018-09-25 | [
[
"Tung",
"Kuan",
""
],
[
"Liu",
"Po-Kang",
""
],
[
"Chuang",
"Yu-Chuan",
""
],
[
"Wang",
"Sheng-Hui",
""
],
[
"Wu",
"An-Yeu",
""
]
] | As the result of the growing importance of the Human Computer Interface system, understanding human's emotion states has become a consequential ability for the computer. This paper aims to improve the performance of emotion recognition by conducting the complexity analysis of physiological signals. Based on AMIGOS dataset, we extracted several entropy-domain features such as Refined Composite Multi-Scale Entropy (RCMSE), Refined Composite Multi-Scale Permutation Entropy (RCMPE) from ECG and GSR signals, and Multivariate Multi-Scale Entropy (MMSE), Multivariate Multi-Scale Permutation Entropy (MMPE) from EEG, respectively. The statistical results show that RCMSE in GSR has a dominating performance in arousal, while RCMPE in GSR would be the excellent feature in valence. Furthermore, we selected XGBoost model to predict emotion and get 68% accuracy in arousal and 84% in valence. |
q-bio/0407032 | Gavin E. Crooks | Gavin E. Crooks, Steven E. Brenner | An Alternative Model of Amino Acid Replacement | Minor improvements. Added figure and references | Bioinformatics 2005 21(7):975-980 | 10.1093/bioinformatics/bti109 | null | q-bio.BM q-bio.PE | null | The observed correlations between pairs of homologous protein sequences are
typically explained in terms of a Markovian dynamic of amino acid substitution.
This model assumes that every location on the protein sequence has the same
background distribution of amino acids, an assumption that is incompatible with
the observed heterogeneity of protein amino acid profiles and with the success
of profile multiple sequence alignment. We propose an alternative model of
amino acid replacement during protein evolution based upon the assumption that
the variation of the amino acid background distribution from one residue to the
next is sufficient to explain the observed sequence correlations of homologs.
The resulting dynamical model of independent replacements drawn from
heterogeneous backgrounds is simple and consistent, and provides a unified
homology match score for sequence-sequence, sequence-profile and
profile-profile alignment.
| [
{
"created": "Sat, 24 Jul 2004 01:58:20 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Oct 2004 22:03:20 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Crooks",
"Gavin E.",
""
],
[
"Brenner",
"Steven E.",
""
]
] | The observed correlations between pairs of homologous protein sequences are typically explained in terms of a Markovian dynamic of amino acid substitution. This model assumes that every location on the protein sequence has the same background distribution of amino acids, an assumption that is incompatible with the observed heterogeneity of protein amino acid profiles and with the success of profile multiple sequence alignment. We propose an alternative model of amino acid replacement during protein evolution based upon the assumption that the variation of the amino acid background distribution from one residue to the next is sufficient to explain the observed sequence correlations of homologs. The resulting dynamical model of independent replacements drawn from heterogeneous backgrounds is simple and consistent, and provides a unified homology match score for sequence-sequence, sequence-profile and profile-profile alignment. |
1810.13188 | Anna Kraut | Anna Kraut, Anton Bovier | From Adaptive Dynamics to Adaptive Walks | 45 pages, 5 figures; Published version that contains major revisions,
including the formulation of the cut-off model and some results | J. Math. Biol. 79, 1699-1747 (2019) | 10.1007/s00285-019-01408-6 | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider an asexually reproducing population on a finite type space whose
evolution is driven by exponential birth, death and competition rates, as well
as the possibility of mutation at a birth event. On the individual-based level
this population can be modelled as a measure-valued Markov process. Multiple
variations of this system have been studied in the simultaneous limit of large
populations and rare mutations, where the regime is chosen such that mutations
are separated. We consider the deterministic system, resulting from the large
population limit, and then let the mutation probability tend to zero. This
corresponds to a much higher frequency of mutations, where multiple microscopic
types are present at the same time. The limiting process resembles an adaptive
walk or flight and jumps between different equilibria of coexisting types. The
graph structure on the type space, determined by the possibilities to mutate,
plays an important role in defining this jump process. In a variation of the
above model, where the radius in which mutants can be spread is limited, we
study the possibility of crossing valleys in the fitness landscape and derive
different kinds of limiting walks.
| [
{
"created": "Wed, 31 Oct 2018 09:54:36 GMT",
"version": "v1"
},
{
"created": "Fri, 7 Feb 2020 10:21:26 GMT",
"version": "v2"
}
] | 2020-02-10 | [
[
"Kraut",
"Anna",
""
],
[
"Bovier",
"Anton",
""
]
] | We consider an asexually reproducing population on a finite type space whose evolution is driven by exponential birth, death and competition rates, as well as the possibility of mutation at a birth event. On the individual-based level this population can be modelled as a measure-valued Markov process. Multiple variations of this system have been studied in the simultaneous limit of large populations and rare mutations, where the regime is chosen such that mutations are separated. We consider the deterministic system, resulting from the large population limit, and then let the mutation probability tend to zero. This corresponds to a much higher frequency of mutations, where multiple microscopic types are present at the same time. The limiting process resembles an adaptive walk or flight and jumps between different equilibria of coexisting types. The graph structure on the type space, determined by the possibilities to mutate, plays an important role in defining this jump process. In a variation of the above model, where the radius in which mutants can be spread is limited, we study the possibility of crossing valleys in the fitness landscape and derive different kinds of limiting walks. |
2204.11678 | Alexander Partin | Alexander Partin (1), Thomas Brettin (1), Yitan Zhu (1), James M.
Dolezal (2), Sara Kochanny (2), Alexander T. Pearson (2), Maulik Shukla (1),
Yvonne A. Evrard (3), James H. Doroshow (4), Rick L. Stevens (1 and 5) ((1)
Computing, Environment and Life Sciences, Argonne National Laboratory,
Lemont, IL, USA, (2) Department of Medicine, Section of Hematology/Oncology,
University of Chicago Medical Center, Chicago, IL, USA, (3) Frederick
National Laboratory for Cancer Research, Leidos Biomedical Research, Inc.
Frederick, MD, USA, (4) Division of Cancer Therapeutics and Diagnosis,
National Cancer Institute, Bethesda, MD, USA, (5) Department of Computer
Science, The University of Chicago, Chicago, IL, USA) | Data augmentation and multimodal learning for predicting drug response
in patient-derived xenografts from gene expressions and histology images | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Patient-derived xenografts (PDXs) are an appealing platform for preclinical
drug studies because the in vivo environment of PDXs helps preserve tumor
heterogeneity and usually better mimics drug response of patients with cancer
compared to CCLs. We investigate multimodal neural network (MM-Net) and data
augmentation for drug response prediction in PDXs. The MM-Net learns to predict
response using drug descriptors, gene expressions (GE), and histology
whole-slide images (WSIs) where the multi-modality refers to the tumor
features. We explore whether the integration of WSIs with GE improves
predictions as compared with models that use GE alone. We use two methods to
address the limited number of response values: 1) homogenize drug
representations which allows to combine single-drug and drug-pairs treatments
into a single dataset, 2) augment drug-pair samples by switching the order of
drug features which doubles the sample size of all drug-pair samples. These
methods enable us to combine single-drug and drug-pair treatments, allowing us
to train multimodal and unimodal neural networks (NNs) without changing
architectures or the dataset. Prediction performance of three unimodal NNs
which use GE are compared to assess the contribution of data augmentation
methods. NN that uses the full dataset which includes the original and the
augmented drug-pair treatments as well as single-drug treatments significantly
outperforms NNs that ignore either the augmented drug-pairs or the single-drug
treatments. In assessing the contribution of multimodal learning based on the
MCC metric, MM-Net statistically significantly outperforms all the baselines.
Our results show that data augmentation and integration of histology images
with GE can improve prediction performance of drug response in PDXs.
| [
{
"created": "Mon, 25 Apr 2022 14:14:09 GMT",
"version": "v1"
}
] | 2022-04-26 | [
[
"Partin",
"Alexander",
"",
"1 and 5"
],
[
"Brettin",
"Thomas",
"",
"1 and 5"
],
[
"Zhu",
"Yitan",
"",
"1 and 5"
],
[
"Dolezal",
"James M.",
"",
"1 and 5"
],
[
"Kochanny",
"Sara",
"",
"1 and 5"
],
[
"Pearson",
... | Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies because the in vivo environment of PDXs helps preserve tumor heterogeneity and usually better mimics drug response of patients with cancer compared to CCLs. We investigate multimodal neural network (MM-Net) and data augmentation for drug response prediction in PDXs. The MM-Net learns to predict response using drug descriptors, gene expressions (GE), and histology whole-slide images (WSIs) where the multi-modality refers to the tumor features. We explore whether the integration of WSIs with GE improves predictions as compared with models that use GE alone. We use two methods to address the limited number of response values: 1) homogenize drug representations which allows to combine single-drug and drug-pairs treatments into a single dataset, 2) augment drug-pair samples by switching the order of drug features which doubles the sample size of all drug-pair samples. These methods enable us to combine single-drug and drug-pair treatments, allowing us to train multimodal and unimodal neural networks (NNs) without changing architectures or the dataset. Prediction performance of three unimodal NNs which use GE are compared to assess the contribution of data augmentation methods. NN that uses the full dataset which includes the original and the augmented drug-pair treatments as well as single-drug treatments significantly outperforms NNs that ignore either the augmented drug-pairs or the single-drug treatments. In assessing the contribution of multimodal learning based on the MCC metric, MM-Net statistically significantly outperforms all the baselines. Our results show that data augmentation and integration of histology images with GE can improve prediction performance of drug response in PDXs. |
1401.7055 | Nicola Fameli | Nicola Fameli, Oluseye A. Ogunbayo, Cornelis van Breemen, A. Mark
Evans | Cytoplasmic nanojunctions between lysosomes and sarcoplasmic reticulum
are required for specific calcium signaling | 25 pages, 7 figures, 2 tables; a slightly expanded version of this
manuscript was submitted for publication in PLoS One | null | 10.12688/f1000research.3720.1 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We demonstrate how nanojunctions between lysosomes and sarcoplasmic reticulum
(L-SR junctions) serve to couple lysosomal activation to regenerative,
ryanodine receptor-mediated cellular calcium (Ca2+) waves. In pulmonary artery
smooth muscle cells (PASMCs) nicotinic acid adenine dinucleotide phosphate
(NAADP) may trigger increases in cytoplasmic Ca2+ via L-SR junctions, in a
manner that requires initial Ca2+ release from lysosomes and subsequent
Ca2+-induced Ca2+ release (CICR) via ryanodine receptor (RyR) subtype 3 on the
SR membrane proximal to lysosomes. L-SR junction membrane separation has been
estimated to be <400 nm and thus beyond the resolution of light microscopy.
This study utilizes transmission electron microscopy to provide a thorough
ultrastructural characterization of the L-SR junctions in PASMCs. These
junctions are prominent features in these cells and we estimate that the
membrane separation and extension are about 15 nm and 300 nm, respectively. We
also develop a quantitative model of the L-SR junction using these
measurements, prior kinetic and specific Ca2+ signal information as input data.
Simulations of NAADP-dependent junctional Ca2+ transients show that the
magnitude of these signals can breach the threshold for CICR via RyR3. By
correlation analysis of live cell Ca2+ signals and simulated L-SR junctional
Ca2+ transients, we estimate that "trigger zones" with a 60-100 junctions are
required to confer a signal of similar magnitude. This is compatible with the
130 lysosomes/cell estimated from our ultrastructural observations. Most
importantly, our model shows that increasing the L-SR junctional width above 50
nm lowers the magnitude of junctional [Ca2+] such that there is a failure to
breach the threshold for CICR via RyR3. L-SR junctions are therefore a
pre-requisite for efficient Ca2+ signal coupling and may contribute to cellular
function in health and disease.
| [
{
"created": "Tue, 28 Jan 2014 00:25:52 GMT",
"version": "v1"
}
] | 2014-11-03 | [
[
"Fameli",
"Nicola",
""
],
[
"Ogunbayo",
"Oluseye A.",
""
],
[
"van Breemen",
"Cornelis",
""
],
[
"Evans",
"A. Mark",
""
]
] | We demonstrate how nanojunctions between lysosomes and sarcoplasmic reticulum (L-SR junctions) serve to couple lysosomal activation to regenerative, ryanodine receptor-mediated cellular calcium (Ca2+) waves. In pulmonary artery smooth muscle cells (PASMCs) nicotinic acid adenine dinucleotide phosphate (NAADP) may trigger increases in cytoplasmic Ca2+ via L-SR junctions, in a manner that requires initial Ca2+ release from lysosomes and subsequent Ca2+-induced Ca2+ release (CICR) via ryanodine receptor (RyR) subtype 3 on the SR membrane proximal to lysosomes. L-SR junction membrane separation has been estimated to be <400 nm and thus beyond the resolution of light microscopy. This study utilizes transmission electron microscopy to provide a thorough ultrastructural characterization of the L-SR junctions in PASMCs. These junctions are prominent features in these cells and we estimate that the membrane separation and extension are about 15 nm and 300 nm, respectively. We also develop a quantitative model of the L-SR junction using these measurements, prior kinetic and specific Ca2+ signal information as input data. Simulations of NAADP-dependent junctional Ca2+ transients show that the magnitude of these signals can breach the threshold for CICR via RyR3. By correlation analysis of live cell Ca2+ signals and simulated L-SR junctional Ca2+ transients, we estimate that "trigger zones" with a 60-100 junctions are required to confer a signal of similar magnitude. This is compatible with the 130 lysosomes/cell estimated from our ultrastructural observations. Most importantly, our model shows that increasing the L-SR junctional width above 50 nm lowers the magnitude of junctional [Ca2+] such that there is a failure to breach the threshold for CICR via RyR3. L-SR junctions are therefore a pre-requisite for efficient Ca2+ signal coupling and may contribute to cellular function in health and disease. |
2309.05351 | Danny Raj Masila | Divakar Badal, Aloke Kumar, Varsha Singh, Danny Raj M | A dynamic fluid landscape mediates the spread of bacteria | 14 pages of main text, 5 figures, 4 pages of SI added | null | null | null | q-bio.CB | http://creativecommons.org/licenses/by/4.0/ | Microbial interactions regulate their spread and survival in competitive
environments. It is not clear if the physical parameters of the environment
regulate the outcome of these interactions. In this work, we show that the
opportunistic pathogen Pseudomonas aeruginosa occupies a larger area on the
substratum in the presence of yeast such as Cryptococcus neoformans , than
without it. At the microscopic level, bacterial cells show an enhanced activity
in the vicinity of yeast cells. We observe this behaviour even when the live
yeast cells are replaced with heat-killed cells or with spherical glass beads
of similar morphology, which suggests that the observed behaviour is not
specific to the biology of microbes. Upon careful investigation, we find that a
fluid pool is formed around yeast cells which facilitates the swimming of the
flagellated P. aeruginosa , causing their enhanced motility. Using mathematical
modeling we demonstrate how this local enhancement of bacterial motility leads
to the enhanced spread observed at the level of the plate. We find that the
dynamics of the fluid landscape around the bacteria, mediated by the growing
yeast lawn, affects the spreading. For instance, when the yeast lawn grows
faster, a bacterial colony prefers a lower initial loading of yeast cells for
optimum enhancement in the spread. We confirm our predictions using Candida
albicans and C. neoformans, at different initial compositions. In summary, our
work shows the importance of considering the dynamically changing physical
environment while studying bacterial motility in complex environments.
| [
{
"created": "Mon, 11 Sep 2023 09:51:02 GMT",
"version": "v1"
}
] | 2023-09-12 | [
[
"Badal",
"Divakar",
""
],
[
"Kumar",
"Aloke",
""
],
[
"Singh",
"Varsha",
""
],
[
"M",
"Danny Raj",
""
]
] | Microbial interactions regulate their spread and survival in competitive environments. It is not clear if the physical parameters of the environment regulate the outcome of these interactions. In this work, we show that the opportunistic pathogen Pseudomonas aeruginosa occupies a larger area on the substratum in the presence of yeast such as Cryptococcus neoformans , than without it. At the microscopic level, bacterial cells show an enhanced activity in the vicinity of yeast cells. We observe this behaviour even when the live yeast cells are replaced with heat-killed cells or with spherical glass beads of similar morphology, which suggests that the observed behaviour is not specific to the biology of microbes. Upon careful investigation, we find that a fluid pool is formed around yeast cells which facilitates the swimming of the flagellated P. aeruginosa , causing their enhanced motility. Using mathematical modeling we demonstrate how this local enhancement of bacterial motility leads to the enhanced spread observed at the level of the plate. We find that the dynamics of the fluid landscape around the bacteria, mediated by the growing yeast lawn, affects the spreading. For instance, when the yeast lawn grows faster, a bacterial colony prefers a lower initial loading of yeast cells for optimum enhancement in the spread. We confirm our predictions using Candida albicans and C. neoformans, at different initial compositions. In summary, our work shows the importance of considering the dynamically changing physical environment while studying bacterial motility in complex environments. |
q-bio/0612042 | Ashok Palaniappan | Ashok Palaniappan | A robust methodology for inferring physiology of a protein family:
application to K+-ion channel family | 10 pages, 2 figures, 1 table | null | null | null | q-bio.GN q-bio.BM q-bio.QM | null | We are interested in the subtle variations of function among the members of a
protein family. A protein family is usually subdivided into subfamilies based
on functional differences. Existence of this functional diversity is essential
for the successful performance of physiological roles expected of the family.
This presents a unique problem: there must be preservation of the active site;
simultaneously there should be specificity of protein action according to
subfamily. Though the classification into subfamilies is by no means a
formalized one, it is most times based on the character of regulation of the
primary function. The function of a subfamily is a modification of when the
protein performs its function, for example, by changing the protein's
sensitivity to regulatory factors. Rarely, a subfamily possesses a function
completely different for its family. A study of these details is necessary for
understanding the fine-tuning of protein function. I describe a theory for
studying subfamily-based functional specificity and then validate it with an
example application to deciphering the residue-level basis of fine functional
variations in the diverse set of K+-channel subfamilies. I provide specific
results that will be useful to channel physiologists, whereas the strategy
developed will be widely applicable to problems in comparative and functional
genomics.
| [
{
"created": "Fri, 22 Dec 2006 08:43:43 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Palaniappan",
"Ashok",
""
]
] | We are interested in the subtle variations of function among the members of a protein family. A protein family is usually subdivided into subfamilies based on functional differences. Existence of this functional diversity is essential for the successful performance of physiological roles expected of the family. This presents a unique problem: there must be preservation of the active site; simultaneously there should be specificity of protein action according to subfamily. Though the classification into subfamilies is by no means a formalized one, it is most times based on the character of regulation of the primary function. The function of a subfamily is a modification of when the protein performs its function, for example, by changing the protein's sensitivity to regulatory factors. Rarely, a subfamily possesses a function completely different for its family. A study of these details is necessary for understanding the fine-tuning of protein function. I describe a theory for studying subfamily-based functional specificity and then validate it with an example application to deciphering the residue-level basis of fine functional variations in the diverse set of K+-channel subfamilies. I provide specific results that will be useful to channel physiologists, whereas the strategy developed will be widely applicable to problems in comparative and functional genomics. |
2103.00621 | Elad Noor | Moritz E. Beber, Mattia G. Gollub, Dana Mozaffari, Kevin M. Shebek,
Elad Noor | eQuilibrator 3.0 -- a platform for the estimation of thermodynamic
constants | 14 pages, 2 figures | null | null | null | q-bio.MN q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | eQuilibrator (equilibrator.weizmann.ac.il) is a calculator for biochemical
equilibrium constants and Gibbs free energies, originally designed as a
web-based interface. While the website now counts ${\sim}1000$ distinct monthly
users, its design could not accommodate larger compound databases and it lacked
an application programming interface (API) for integration in other tools
developed by the systems biology community. Here, we report a new python-based
package for eQuilibrator, that comes with many new features such as a 50-fold
larger compound database, the ability to add novel compound structures,
improvements in speed and memory use, and correction for Mg2+ ion
concentrations. Moreover, it adds the ability to compute the covariance matrix
of the uncertainty between estimates, for which we show the advantages and
describe the application in metabolic modeling. We foresee that these
improvements will make thermodynamic modeling more accessible and facilitate
the integration of eQuilibrator into other software platforms.
| [
{
"created": "Sun, 28 Feb 2021 21:23:54 GMT",
"version": "v1"
}
] | 2021-03-02 | [
[
"Beber",
"Moritz E.",
""
],
[
"Gollub",
"Mattia G.",
""
],
[
"Mozaffari",
"Dana",
""
],
[
"Shebek",
"Kevin M.",
""
],
[
"Noor",
"Elad",
""
]
] | eQuilibrator (equilibrator.weizmann.ac.il) is a calculator for biochemical equilibrium constants and Gibbs free energies, originally designed as a web-based interface. While the website now counts ${\sim}1000$ distinct monthly users, its design could not accommodate larger compound databases and it lacked an application programming interface (API) for integration in other tools developed by the systems biology community. Here, we report a new python-based package for eQuilibrator, that comes with many new features such as a 50-fold larger compound database, the ability to add novel compound structures, improvements in speed and memory use, and correction for Mg2+ ion concentrations. Moreover, it adds the ability to compute the covariance matrix of the uncertainty between estimates, for which we show the advantages and describe the application in metabolic modeling. We foresee that these improvements will make thermodynamic modeling more accessible and facilitate the integration of eQuilibrator into other software platforms. |
1601.05113 | Pedro Antonio Vald\'es-Hern\'andez | Pedro A. Valdes-Hernandez, Jihye Bae, Yinchen Song, Akira Sumiyoshi,
Eduardo Aubert-Vazquez, Jorge J. Riera | Validating non-invasive EEG source imaging using optimal electrode
configurations on a representative rat head model | 27 pages, 2 tables and 14 figures | null | null | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The curtain of technical limitations impeding rat multichannel non-invasive
electroencephalography (EEG) has risen. Given the importance of this
preclinical model, development and validation of EEG source imaging (ESI) is
essential. We investigate the validity of well-known human ESI methodologies in
rats which individual tissue geometries have been approximated by those
extracted from an MRI template, leading also to imprecision in electrode
localizations. With the half and fifth sensitivity volumes we determine both
the theoretical minimum electrode separation for non-redundant scalp EEG
measurements and the electrode sensitivity resolution, which vary over the
scalp because of the head geometry. According to our results, electrodes should
be at least ~3-3.5 mm apart for an optimal configuration. The sensitivity
resolution is generally worse for electrodes at the boundaries of the scalp
measured region, though, by analogy with human montages, concentrates the
sensitivity enough to localize sources. Cram\'er-Rao lower bounds of source
localization errors indicate it is theoretically possible to achieve ESI
accuracy at the level of anatomical structures, such as the stimulus-specific
somatosensory areas, using the template. More validation for this approximation
is provided through the comparison between the template and the individual lead
field matrices, for several rats. Finally, using well-accepted inverse methods,
we demonstrate that somatosensory ESI is not only expected but also allows
exploring unknown phenomena related to global sensory integration. Inheriting
the advantages and pitfalls of human ESI, rat ESI will boost the understanding
of brain pathophysiological mechanisms and the evaluation of ESI methodologies,
new pharmacological treatments and ESI-based biomarkers.
| [
{
"created": "Tue, 19 Jan 2016 22:03:07 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Jan 2016 18:56:41 GMT",
"version": "v2"
}
] | 2016-01-26 | [
[
"Valdes-Hernandez",
"Pedro A.",
""
],
[
"Bae",
"Jihye",
""
],
[
"Song",
"Yinchen",
""
],
[
"Sumiyoshi",
"Akira",
""
],
[
"Aubert-Vazquez",
"Eduardo",
""
],
[
"Riera",
"Jorge J.",
""
]
] | The curtain of technical limitations impeding rat multichannel non-invasive electroencephalography (EEG) has risen. Given the importance of this preclinical model, development and validation of EEG source imaging (ESI) is essential. We investigate the validity of well-known human ESI methodologies in rats which individual tissue geometries have been approximated by those extracted from an MRI template, leading also to imprecision in electrode localizations. With the half and fifth sensitivity volumes we determine both the theoretical minimum electrode separation for non-redundant scalp EEG measurements and the electrode sensitivity resolution, which vary over the scalp because of the head geometry. According to our results, electrodes should be at least ~3-3.5 mm apart for an optimal configuration. The sensitivity resolution is generally worse for electrodes at the boundaries of the scalp measured region, though, by analogy with human montages, concentrates the sensitivity enough to localize sources. Cram\'er-Rao lower bounds of source localization errors indicate it is theoretically possible to achieve ESI accuracy at the level of anatomical structures, such as the stimulus-specific somatosensory areas, using the template. More validation for this approximation is provided through the comparison between the template and the individual lead field matrices, for several rats. Finally, using well-accepted inverse methods, we demonstrate that somatosensory ESI is not only expected but also allows exploring unknown phenomena related to global sensory integration. Inheriting the advantages and pitfalls of human ESI, rat ESI will boost the understanding of brain pathophysiological mechanisms and the evaluation of ESI methodologies, new pharmacological treatments and ESI-based biomarkers. |
2104.14636 | Ulrich S. Schwarz | Oliver M. Drozdowski, Falko Ziebert, Ulrich S. Schwarz (Heidelberg
University) | Optogenetic control of intracellular flows and cell migration: a
comprehensive mathematical analysis with a minimal active gel model | 16 pages, 11 figures. Revisions compared to first version mainly
concern Eqs. 1, 3, 6, 8, 14 and Fig. 3 (effect of objective derivative) | Phys. Rev. E 104, 024406 (2021) | 10.1103/PhysRevE.104.024406 | null | q-bio.SC cond-mat.soft | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The actin cytoskeleton of cells is in continuous motion due to both
polymerization of new filaments and their contraction by myosin II molecular
motors. Through adhesion to the substrate, such intracellular flow can be
converted into cell migration. Recently, optogenetics has emerged as a new
powerful experimental method to control both actin polymerization and myosin II
contraction. While optogenetic control of polymerization can initiate cell
migration by generating protrusion, it is less clear if and how optogenetic
control of contraction can also affect cell migration. Here we analyze the
latter situation using a minimal variant of active gel theory into which we
include optogenetic activation as a spatiotemporally constrained perturbation.
The model can describe the symmetrical flow of the actomyosin system observed
in optogenetic experiments, but not the long-lasting polarization required for
cell migration. Motile solutions become possible if cytoskeletal polymerization
is included through the boundary conditions. Optogenetic activation of
contraction can then initiate locomotion in a symmetrically spreading cell and
strengthen motility in an asymmetrically polymerizing one. If designed
appropriately, it can also arrest motility even for protrusive boundaries.
| [
{
"created": "Thu, 29 Apr 2021 20:12:29 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Jul 2021 05:06:04 GMT",
"version": "v2"
}
] | 2021-08-11 | [
[
"Drozdowski",
"Oliver M.",
"",
"Heidelberg\n University"
],
[
"Ziebert",
"Falko",
"",
"Heidelberg\n University"
],
[
"Schwarz",
"Ulrich S.",
"",
"Heidelberg\n University"
]
] | The actin cytoskeleton of cells is in continuous motion due to both polymerization of new filaments and their contraction by myosin II molecular motors. Through adhesion to the substrate, such intracellular flow can be converted into cell migration. Recently, optogenetics has emerged as a new powerful experimental method to control both actin polymerization and myosin II contraction. While optogenetic control of polymerization can initiate cell migration by generating protrusion, it is less clear if and how optogenetic control of contraction can also affect cell migration. Here we analyze the latter situation using a minimal variant of active gel theory into which we include optogenetic activation as a spatiotemporally constrained perturbation. The model can describe the symmetrical flow of the actomyosin system observed in optogenetic experiments, but not the long-lasting polarization required for cell migration. Motile solutions become possible if cytoskeletal polymerization is included through the boundary conditions. Optogenetic activation of contraction can then initiate locomotion in a symmetrically spreading cell and strengthen motility in an asymmetrically polymerizing one. If designed appropriately, it can also arrest motility even for protrusive boundaries. |
1101.5861 | Tao Jia | Charles Baker, Tao Jia, Rahul V. Kulkarni | Stochastic modeling of regulation of gene expression by multiple small
RNAs | null | null | 10.1103/PhysRevE.85.061915 | null | q-bio.MN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A wealth of new research has highlighted the critical roles of small RNAs
(sRNAs) in diverse processes such as quorum sensing and cellular responses to
stress. The pathways controlling these processes often have a central motif
comprising of a master regulator protein whose expression is controlled by
multiple sRNAs. However, the regulation of stochastic gene expression of a
single target gene by multiple sRNAs is currently not well understood. To
address this issue, we analyze a stochastic model of regulation of gene
expression by multiple sRNAs. For this model, we derive exact analytic results
for the regulated protein distribution including compact expressions for its
mean and variance. The derived results provide novel insights into the roles of
multiple sRNAs in fine-tuning the noise in gene expression. In particular, we
show that, in contrast to regulation by a single sRNA, multiple sRNAs provide a
mechanism for independently controlling the mean and variance of the regulated
protein distribution.
| [
{
"created": "Mon, 31 Jan 2011 07:31:08 GMT",
"version": "v1"
},
{
"created": "Fri, 11 Feb 2011 06:46:40 GMT",
"version": "v2"
}
] | 2015-05-27 | [
[
"Baker",
"Charles",
""
],
[
"Jia",
"Tao",
""
],
[
"Kulkarni",
"Rahul V.",
""
]
] | A wealth of new research has highlighted the critical roles of small RNAs (sRNAs) in diverse processes such as quorum sensing and cellular responses to stress. The pathways controlling these processes often have a central motif comprising of a master regulator protein whose expression is controlled by multiple sRNAs. However, the regulation of stochastic gene expression of a single target gene by multiple sRNAs is currently not well understood. To address this issue, we analyze a stochastic model of regulation of gene expression by multiple sRNAs. For this model, we derive exact analytic results for the regulated protein distribution including compact expressions for its mean and variance. The derived results provide novel insights into the roles of multiple sRNAs in fine-tuning the noise in gene expression. In particular, we show that, in contrast to regulation by a single sRNA, multiple sRNAs provide a mechanism for independently controlling the mean and variance of the regulated protein distribution. |
1807.00123 | Marinka Zitnik | Marinka Zitnik, Francis Nguyen, Bo Wang, Jure Leskovec, Anna
Goldenberg, Michael M. Hoffman | Machine Learning for Integrating Data in Biology and Medicine:
Principles, Practice, and Opportunities | null | Information Fusion 50 (2019) 71-91 | 10.1016/j.inffus.2018.09.012 | null | q-bio.QM cs.CE cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | New technologies have enabled the investigation of biology and human health
at an unprecedented scale and in multiple dimensions. These dimensions include
a myriad of properties describing genome, epigenome, transcriptome, microbiome,
phenotype, and lifestyle. No single data type, however, can capture the
complexity of all the factors relevant to understanding a phenomenon such as a
disease. Integrative methods that combine data from multiple technologies have
thus emerged as critical statistical and computational approaches. The key
challenge in developing such approaches is the identification of effective
models to provide a comprehensive and relevant systems view. An ideal method
can answer a biological or medical question, identifying important features and
predicting outcomes, by harnessing heterogeneous data across several dimensions
of biological variation. In this Review, we describe the principles of data
integration and discuss current methods and available implementations. We
provide examples of successful data integration in biology and medicine.
Finally, we discuss current challenges in biomedical integrative methods and
our perspective on the future development of the field.
| [
{
"created": "Sat, 30 Jun 2018 04:31:59 GMT",
"version": "v1"
},
{
"created": "Wed, 10 Oct 2018 18:35:23 GMT",
"version": "v2"
}
] | 2018-10-22 | [
[
"Zitnik",
"Marinka",
""
],
[
"Nguyen",
"Francis",
""
],
[
"Wang",
"Bo",
""
],
[
"Leskovec",
"Jure",
""
],
[
"Goldenberg",
"Anna",
""
],
[
"Hoffman",
"Michael M.",
""
]
] | New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include a myriad of properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field. |
1907.00703 | Benjamin Bleier | B.S. Bleier | Information Flow Theory (IFT) of Biologic and Machine Consciousness:
Implications for Artificial General Intelligence and the Technological
Singularity | 23 Pages, 2 Figures, 1 Table, 1 Appendix | null | null | null | q-bio.NC cs.AI cs.ET cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The subjective experience of consciousness is at once familiar and yet deeply
mysterious. Strategies exploring the top-down mechanisms of conscious thought
within the human brain have been unable to produce a generalized explanatory
theory that scales through evolution and can be applied to artificial systems.
Information Flow Theory (IFT) provides a novel framework for understanding both
the development and nature of consciousness in any system capable of processing
information. In prioritizing the direction of information flow over information
computation, IFT produces a range of unexpected predictions. The purpose of
this manuscript is to introduce the basic concepts of IFT and explore the
manifold implications regarding artificial intelligence, superhuman
consciousness, and our basic perception of reality.
| [
{
"created": "Fri, 21 Jun 2019 15:01:25 GMT",
"version": "v1"
}
] | 2019-07-02 | [
[
"Bleier",
"B. S.",
""
]
] | The subjective experience of consciousness is at once familiar and yet deeply mysterious. Strategies exploring the top-down mechanisms of conscious thought within the human brain have been unable to produce a generalized explanatory theory that scales through evolution and can be applied to artificial systems. Information Flow Theory (IFT) provides a novel framework for understanding both the development and nature of consciousness in any system capable of processing information. In prioritizing the direction of information flow over information computation, IFT produces a range of unexpected predictions. The purpose of this manuscript is to introduce the basic concepts of IFT and explore the manifold implications regarding artificial intelligence, superhuman consciousness, and our basic perception of reality. |
1312.1395 | Kelley Harris | Kelley Harris and Rasmus Nielsen | Error-prone polymerase activity causes multinucleotide mutations in
humans | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | About 2% of human genetic polymorphisms have been hypothesized to arise via
multinucleotide mutations (MNMs), complex events that generate SNPs at multiple
sites in a single generation. MNMs have the potential to accelerate the pace at
which single genes evolve and to confound studies of demography and selection
that assume all SNPs arise independently. In this paper, we examine clustered
mutations that are segregating in a set of 1,092 human genomes, demonstrating
that MNMs become enriched as large numbers of individuals are sampled. We
leverage the size of the dataset to deduce new information about the allelic
spectrum of MNMs, estimating the percentage of linked SNP pairs that were
generated by simultaneous mutation as a function of the distance between the
affected sites and showing that MNMs exhibit a high percentage of transversions
relative to transitions. These findings are reproducible in data from multiple
sequencing platforms. Among tandem mutations that occur simultaneously at
adjacent sites, we find an especially skewed distribution of ancestral and
derived dinucleotides, with $\textrm{GC}\to \textrm{AA}$, $\textrm{GA}\to
\textrm{TT}$ and their reverse complements making up 36% of the total. These
same mutations dominate the spectrum of tandem mutations produced by the
upregulation of low-fidelity Polymerase $\zeta$ in mutator strains of S.
cerevisiae that have impaired DNA excision repair machinery. This suggests that
low-fidelity DNA replication by Pol $\zeta$ is at least partly responsible for
the MNMs that are segregating in the human population, and that useful
information about the biochemistry of MNM can be extracted from ordinary
population genomic data. We incorporate our findings into a mathematical model
of the multinucleotide mutation process that can be used to correct
phylogenetic and population genetic methods for the presence of MNMs.
| [
{
"created": "Thu, 5 Dec 2013 00:36:20 GMT",
"version": "v1"
},
{
"created": "Tue, 29 Apr 2014 18:56:09 GMT",
"version": "v2"
}
] | 2014-04-30 | [
[
"Harris",
"Kelley",
""
],
[
"Nielsen",
"Rasmus",
""
]
] | About 2% of human genetic polymorphisms have been hypothesized to arise via multinucleotide mutations (MNMs), complex events that generate SNPs at multiple sites in a single generation. MNMs have the potential to accelerate the pace at which single genes evolve and to confound studies of demography and selection that assume all SNPs arise independently. In this paper, we examine clustered mutations that are segregating in a set of 1,092 human genomes, demonstrating that MNMs become enriched as large numbers of individuals are sampled. We leverage the size of the dataset to deduce new information about the allelic spectrum of MNMs, estimating the percentage of linked SNP pairs that were generated by simultaneous mutation as a function of the distance between the affected sites and showing that MNMs exhibit a high percentage of transversions relative to transitions. These findings are reproducible in data from multiple sequencing platforms. Among tandem mutations that occur simultaneously at adjacent sites, we find an especially skewed distribution of ancestral and derived dinucleotides, with $\textrm{GC}\to \textrm{AA}$, $\textrm{GA}\to \textrm{TT}$ and their reverse complements making up 36% of the total. These same mutations dominate the spectrum of tandem mutations produced by the upregulation of low-fidelity Polymerase $\zeta$ in mutator strains of S. cerevisiae that have impaired DNA excision repair machinery. This suggests that low-fidelity DNA replication by Pol $\zeta$ is at least partly responsible for the MNMs that are segregating in the human population, and that useful information about the biochemistry of MNM can be extracted from ordinary population genomic data. We incorporate our findings into a mathematical model of the multinucleotide mutation process that can be used to correct phylogenetic and population genetic methods for the presence of MNMs. |
1305.3655 | Michiaki Hamada | Michiaki Hamada | Fighting against uncertainty: An essential issue in bioinformatics | This manuscript was accepted in Briefings in Bioinformatics for
publication | null | null | null | q-bio.QM q-bio.BM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many bioinformatics problems, such as sequence alignment, gene prediction,
phylogenetic tree estimation and RNA secondary structure prediction, are often
affected by the "uncertainty" of a solution; that is, the probability of the
solution is extremely small. This situation arises for estimation problems on
high-dimensional discrete spaces in which the number of possible discrete
solutions is immense. In the analysis of biological data or the development of
prediction algorithms, this uncertainty should be handled carefully and
appropriately. In this review, I will explain several methods to combat this
uncertainty, presenting a number of examples in bioinformatics. The methods
include (i) avoiding point estimation, (ii) maximum expected accuracy (MEA)
estimations, and (iii) several strategies to design a pipeline involving
several prediction methods. I believe that the basic concepts and ideas
described in this review will be generally useful for estimation problems in
various areas of bioinformatics.
| [
{
"created": "Wed, 15 May 2013 23:49:59 GMT",
"version": "v1"
}
] | 2013-05-17 | [
[
"Hamada",
"Michiaki",
""
]
] | Many bioinformatics problems, such as sequence alignment, gene prediction, phylogenetic tree estimation and RNA secondary structure prediction, are often affected by the "uncertainty" of a solution; that is, the probability of the solution is extremely small. This situation arises for estimation problems on high-dimensional discrete spaces in which the number of possible discrete solutions is immense. In the analysis of biological data or the development of prediction algorithms, this uncertainty should be handled carefully and appropriately. In this review, I will explain several methods to combat this uncertainty, presenting a number of examples in bioinformatics. The methods include (i) avoiding point estimation, (ii) maximum expected accuracy (MEA) estimations, and (iii) several strategies to design a pipeline involving several prediction methods. I believe that the basic concepts and ideas described in this review will be generally useful for estimation problems in various areas of bioinformatics. |
1909.09451 | Corinna Maier | Corinna Maier, Niklas Hartung, Jana de Wiljes, Charlotte Kloft and
Wilhelm Huisinga | Bayesian data assimilation to support informed decision-making in
individualised chemotherapy | null | null | null | null | q-bio.QM physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An essential component of therapeutic drug/biomarker monitoring (TDM) is to
combine patient data with prior knowledge for model-based predictions of
therapy outcomes. Current Bayesian forecasting tools typically rely only on the
most probable model parameters (maximum a-posteriori (MAP) estimate). This
MAP-based approach, however, does neither necessarily predict the most probable
outcome nor does it quantify the risks of treatment inefficacy or toxicity.
Bayesian data assimilation (DA) methods overcome these limitations by providing
a comprehensive uncertainty quantification. We compare DA methods with
MAP-based approaches and show how probabilistic statements about key markers
related to chemotherapy-induced neutropenia can be leveraged for more
informative decision support in individualised chemotherapy. Sequential
Bayesian DA proved to be most computational efficient for handling
interoccasion variability and integrating TDM data. For new digital monitoring
devices enabling more frequent data collection, these features will be of
critical importance to improve patient care decisions in various therapeutic
areas.
| [
{
"created": "Fri, 20 Sep 2019 12:25:53 GMT",
"version": "v1"
}
] | 2019-09-23 | [
[
"Maier",
"Corinna",
""
],
[
"Hartung",
"Niklas",
""
],
[
"de Wiljes",
"Jana",
""
],
[
"Kloft",
"Charlotte",
""
],
[
"Huisinga",
"Wilhelm",
""
]
] | An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a-posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP-based approaches and show how probabilistic statements about key markers related to chemotherapy-induced neutropenia can be leveraged for more informative decision support in individualised chemotherapy. Sequential Bayesian DA proved to be most computational efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas. |
1404.0290 | William Pearse | William D. Pearse, Helen K. Green, and David Aldridge | Catching crabs: a case study in local-scale English conservation | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wells-next-the-Sea and Cromer in Norfolk (England) both rely upon their local
crab populations, since crabbing (gillying) is a major part of their tourist
industry. Compared to a control site with no crabbing, crabs from Wells harbour
and Cromer pier were found to have nearly six times the amount of limb damage.
Crabs caught by the general public had more injuries than crabs caught in
controlled conditions, suggesting the buckets in which the crabs were kept were
to blame. Since there is much evidence that such injuries have negative impacts
on the survival and reproductive success of the shore crab, this is taken as
evidence of non-lethal injury from humans having a population-level effect on
these animals. Questionnaire data demonstrated a public lack of awareness and
want for information, which was then used to obtain funding to produce a
leaflet campaign informing the public of how to crab responsibly. All data
collected is available online at http://dx.doi.org/10.6084/m9.figshare.979288.
| [
{
"created": "Tue, 1 Apr 2014 16:01:37 GMT",
"version": "v1"
}
] | 2014-04-02 | [
[
"Pearse",
"William D.",
""
],
[
"Green",
"Helen K.",
""
],
[
"Aldridge",
"David",
""
]
] | Wells-next-the-Sea and Cromer in Norfolk (England) both rely upon their local crab populations, since crabbing (gillying) is a major part of their tourist industry. Compared to a control site with no crabbing, crabs from Wells harbour and Cromer pier were found to have nearly six times the amount of limb damage. Crabs caught by the general public had more injuries than crabs caught in controlled conditions, suggesting the buckets in which the crabs were kept were to blame. Since there is much evidence that such injuries have negative impacts on the survival and reproductive success of the shore crab, this is taken as evidence of non-lethal injury from humans having a population-level effect on these animals. Questionnaire data demonstrated a public lack of awareness and want for information, which was then used to obtain funding to produce a leaflet campaign informing the public of how to crab responsibly. All data collected is available online at http://dx.doi.org/10.6084/m9.figshare.979288. |
1005.2588 | Deepak Chandran | Deepak Chandran and Herbert M. Sauro | C Library for Simulated Evolution of Biological Networks | 10 pages, 3 figures | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Simulated evolution of biological networks can be used to generate functional
networks as well as investigate hypotheses regarding natural evolution. A
handful of studies have shown how simulated evolution can be used for studying
the functional space spanned by biochemical networks, studying natural
evolution, or designing new synthetic networks. If there was a method for
easily performing such studies, it can allow the community to further
experiment with simulated evolution and explore all of its uses. As a result,
we have developed a library written in the C language that performs all the
basic functions needed to carry out simulated evolution of biological networks.
The library comes with a generic genetic algorithm as well as genetic
algorithms for specifically evolving genetic networks, protein networks, or
mass-action networks. The library also comes with functions for simulating
these networks. A user needs to specify a desired function. A GUI is provided
for users to become oriented with all the options available in the library. The
library is free and open source under the BSD lisence and can be obtained at
evolvenetworks.sourceforge.net. It can be built on all major platforms. The
code can be most conveniently compiled using cross-platform make (CMake).
| [
{
"created": "Fri, 14 May 2010 18:04:09 GMT",
"version": "v1"
}
] | 2010-05-17 | [
[
"Chandran",
"Deepak",
""
],
[
"Sauro",
"Herbert M.",
""
]
] | Simulated evolution of biological networks can be used to generate functional networks as well as investigate hypotheses regarding natural evolution. A handful of studies have shown how simulated evolution can be used for studying the functional space spanned by biochemical networks, studying natural evolution, or designing new synthetic networks. If there was a method for easily performing such studies, it can allow the community to further experiment with simulated evolution and explore all of its uses. As a result, we have developed a library written in the C language that performs all the basic functions needed to carry out simulated evolution of biological networks. The library comes with a generic genetic algorithm as well as genetic algorithms for specifically evolving genetic networks, protein networks, or mass-action networks. The library also comes with functions for simulating these networks. A user needs to specify a desired function. A GUI is provided for users to become oriented with all the options available in the library. The library is free and open source under the BSD lisence and can be obtained at evolvenetworks.sourceforge.net. It can be built on all major platforms. The code can be most conveniently compiled using cross-platform make (CMake). |
2204.05110 | Islem Rekik | Nada Chaari and Hatice Camgoz Akdag and Islem Rekik | Comparative Survey of Multigraph Integration Methods for Holistic Brain
Connectivity Mapping | null | null | null | null | q-bio.NC cs.AI cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | One of the greatest scientific challenges in network neuroscience is to
create a representative map of a population of heterogeneous brain networks,
which acts as a connectional fingerprint. The connectional brain template
(CBT), also named network atlas, presents a powerful tool for capturing the
most representative and discriminative traits of a given population while
preserving its topological patterns. The idea of a CBT is to integrate a
population of heterogeneous brain connectivity networks, derived from different
neuroimaging modalities or brain views (e.g., structural and functional), into
a unified holistic representation. Here we review current state-of-the-art
methods designed to estimate well-centered and representative CBT for
populations of single-view and multi-view brain networks. We start by reviewing
each CBT learning method, then we introduce the evaluation measures to compare
CBT representativeness of populations generated by single-view and multigraph
integration methods, separately, based on the following criteria: centeredness,
biomarker-reproducibility, node-level similarity, global-level similarity, and
distance-based similarity. We demonstrate that the deep graph normalizer (DGN)
method significantly outperforms other multi-graph and all single-view
integration methods for estimating CBTs using a variety of healthy and
disordered datasets in terms of centeredness, reproducibility (i.e.,
graph-derived biomarkers reproducibility that disentangle the typical from the
atypical connectivity variability), and preserving the topological traits at
both local and global graph-levels.
| [
{
"created": "Tue, 5 Apr 2022 13:34:34 GMT",
"version": "v1"
}
] | 2022-04-12 | [
[
"Chaari",
"Nada",
""
],
[
"Akdag",
"Hatice Camgoz",
""
],
[
"Rekik",
"Islem",
""
]
] | One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint. The connectional brain template (CBT), also named network atlas, presents a powerful tool for capturing the most representative and discriminative traits of a given population while preserving its topological patterns. The idea of a CBT is to integrate a population of heterogeneous brain connectivity networks, derived from different neuroimaging modalities or brain views (e.g., structural and functional), into a unified holistic representation. Here we review current state-of-the-art methods designed to estimate well-centered and representative CBT for populations of single-view and multi-view brain networks. We start by reviewing each CBT learning method, then we introduce the evaluation measures to compare CBT representativeness of populations generated by single-view and multigraph integration methods, separately, based on the following criteria: centeredness, biomarker-reproducibility, node-level similarity, global-level similarity, and distance-based similarity. We demonstrate that the deep graph normalizer (DGN) method significantly outperforms other multi-graph and all single-view integration methods for estimating CBTs using a variety of healthy and disordered datasets in terms of centeredness, reproducibility (i.e., graph-derived biomarkers reproducibility that disentangle the typical from the atypical connectivity variability), and preserving the topological traits at both local and global graph-levels. |
2004.07580 | Stephanie Nelli | Andrew Saxe, Stephanie Nelli and Christopher Summerfield | If deep learning is the answer, then what is the question? | 4 Figures, 17 Pages | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Neuroscience research is undergoing a minor revolution. Recent advances in
machine learning and artificial intelligence (AI) research have opened up new
ways of thinking about neural computation. Many researchers are excited by the
possibility that deep neural networks may offer theories of perception,
cognition and action for biological brains. This perspective has the potential
to radically reshape our approach to understanding neural systems, because the
computations performed by deep networks are learned from experience, not
endowed by the researcher. If so, how can neuroscientists use deep networks to
model and understand biological brains? What is the outlook for neuroscientists
who seek to characterise computations or neural codes, or who wish to
understand perception, attention, memory, and executive functions? In this
Perspective, our goal is to offer a roadmap for systems neuroscience research
in the age of deep learning. We discuss the conceptual and methodological
challenges of comparing behaviour, learning dynamics, and neural representation
in artificial and biological systems. We highlight new research questions that
have emerged for neuroscience as a direct consequence of recent advances in
machine learning.
| [
{
"created": "Thu, 16 Apr 2020 10:42:44 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Apr 2020 13:24:03 GMT",
"version": "v2"
}
] | 2020-04-20 | [
[
"Saxe",
"Andrew",
""
],
[
"Nelli",
"Stephanie",
""
],
[
"Summerfield",
"Christopher",
""
]
] | Neuroscience research is undergoing a minor revolution. Recent advances in machine learning and artificial intelligence (AI) research have opened up new ways of thinking about neural computation. Many researchers are excited by the possibility that deep neural networks may offer theories of perception, cognition and action for biological brains. This perspective has the potential to radically reshape our approach to understanding neural systems, because the computations performed by deep networks are learned from experience, not endowed by the researcher. If so, how can neuroscientists use deep networks to model and understand biological brains? What is the outlook for neuroscientists who seek to characterise computations or neural codes, or who wish to understand perception, attention, memory, and executive functions? In this Perspective, our goal is to offer a roadmap for systems neuroscience research in the age of deep learning. We discuss the conceptual and methodological challenges of comparing behaviour, learning dynamics, and neural representation in artificial and biological systems. We highlight new research questions that have emerged for neuroscience as a direct consequence of recent advances in machine learning. |
0706.1748 | George Bass Ph.D. | George E. Bass | Crystal Irradiation Stimulation of Enzyme Reactivity: An Explanation | 41 pages, 4 figures, 9 tables | null | null | null | q-bio.SC q-bio.BM | null | In 1968, Sorin Comorosan first reported a phenomenon wherein irradiation of
the substrate of an enzyme reaction, in the crystalline state, for a specific
number of seconds could lead to an enhanced aqueous solution reaction rate for
the enzyme(up to 30%). Dependence on crystal irradiation time was found to be
oscillatory with a fixed period. The basis for this unusual phenomenon has
remained a mystery. Previously unreported experimental results are presented
which demonstrate, for the LDH / pyruvate reaction, that the identity of the
crystalline material irradiated is, largely, inconsequential. It is proposed
here that the irradiation procedure drives oscillatory reactions involving
atmospheric gases adsorbed on the crystals and that these photoproducts, or
related dark-reaction species, when dissolved, function as enzyme cofactors.
| [
{
"created": "Tue, 12 Jun 2007 17:22:29 GMT",
"version": "v1"
}
] | 2007-06-13 | [
[
"Bass",
"George E.",
""
]
] | In 1968, Sorin Comorosan first reported a phenomenon wherein irradiation of the substrate of an enzyme reaction, in the crystalline state, for a specific number of seconds could lead to an enhanced aqueous solution reaction rate for the enzyme(up to 30%). Dependence on crystal irradiation time was found to be oscillatory with a fixed period. The basis for this unusual phenomenon has remained a mystery. Previously unreported experimental results are presented which demonstrate, for the LDH / pyruvate reaction, that the identity of the crystalline material irradiated is, largely, inconsequential. It is proposed here that the irradiation procedure drives oscillatory reactions involving atmospheric gases adsorbed on the crystals and that these photoproducts, or related dark-reaction species, when dissolved, function as enzyme cofactors. |
q-bio/0608022 | Ram Ramaswamy | Amitabha Nandi, G. Santhosh, R. K. Brojen Singh, Ram Ramaswamy | The synchronization of stochastic oscillators | 4 figures, 4 pages | null | null | null | q-bio.QM cond-mat.stat-mech nlin.CD q-bio.MN | null | We examine microscopic mechanisms for coupling stochastic oscillators so that
they display similar and correlated temporal variations. Unlike oscillatory
motion in deterministic dynamical systems, complete synchronization of
stochastic oscillators does not occur, but appropriately defined oscillator
phase variables coincide. This is illustrated in model chemical systems and
genetic networks that produce oscillations in the dynamical variables, and we
show that suitable coupling of different networks can result in their phase
synchronization.
| [
{
"created": "Thu, 10 Aug 2006 09:06:20 GMT",
"version": "v1"
}
] | 2009-09-29 | [
[
"Nandi",
"Amitabha",
""
],
[
"Santhosh",
"G.",
""
],
[
"Singh",
"R. K. Brojen",
""
],
[
"Ramaswamy",
"Ram",
""
]
] | We examine microscopic mechanisms for coupling stochastic oscillators so that they display similar and correlated temporal variations. Unlike oscillatory motion in deterministic dynamical systems, complete synchronization of stochastic oscillators does not occur, but appropriately defined oscillator phase variables coincide. This is illustrated in model chemical systems and genetic networks that produce oscillations in the dynamical variables, and we show that suitable coupling of different networks can result in their phase synchronization. |
1302.3349 | Sergei Nechaev | O.V. Valba, S.K. Nechaev and O. Vasieva | On prediction of regulatory genes by analysis of C.elegans functional
networks | 14 pages, 2 figures | null | null | null | q-bio.GN q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Connectivity networks have recently become widely used in biology due to
increasing amounts of information on the physical and functional links between
individual proteins. This connectivity data provides valuable material for
expanding our knowledge far beyond the experimentally validated via
mathematical analysis and theoretical predictions of new functional
interactions. In this paper we demonstrate an application of several algorithms
developed for the ranking of potential gene-expression regulators within the
context of an associated network. We analyze how different types of
connectivity between genes and proteins affect the topology of the integral
C.elegans functional network and thereby validate algorithmic performance. We
demonstrate the possible definition of co-expression gene clusters within a
network context from their specific motif distribution signatures. We also show
that the method based on the shortest path function (SPF) applied to gene
interactions sub-network of the co-expression gene cluster, efficiently
predicts novel regulatory transcription factors (TFs). Simultaneous application
of other methods, including only interactions with neighborhood genes, allows
rapid ranking of potential regulators that could be functionally linked with
the group of co-expressed genes. Predicting functions of regulators for a
cluster of ribosomal/mRNA metabolic genes we highlight a role of mRNA
translation and decay in a longevity of organisms.
| [
{
"created": "Thu, 14 Feb 2013 09:57:30 GMT",
"version": "v1"
}
] | 2013-02-15 | [
[
"Valba",
"O. V.",
""
],
[
"Nechaev",
"S. K.",
""
],
[
"Vasieva",
"O.",
""
]
] | Connectivity networks have recently become widely used in biology due to increasing amounts of information on the physical and functional links between individual proteins. This connectivity data provides valuable material for expanding our knowledge far beyond the experimentally validated via mathematical analysis and theoretical predictions of new functional interactions. In this paper we demonstrate an application of several algorithms developed for the ranking of potential gene-expression regulators within the context of an associated network. We analyze how different types of connectivity between genes and proteins affect the topology of the integral C.elegans functional network and thereby validate algorithmic performance. We demonstrate the possible definition of co-expression gene clusters within a network context from their specific motif distribution signatures. We also show that the method based on the shortest path function (SPF) applied to gene interactions sub-network of the co-expression gene cluster, efficiently predicts novel regulatory transcription factors (TFs). Simultaneous application of other methods, including only interactions with neighborhood genes, allows rapid ranking of potential regulators that could be functionally linked with the group of co-expressed genes. Predicting functions of regulators for a cluster of ribosomal/mRNA metabolic genes we highlight a role of mRNA translation and decay in a longevity of organisms. |
0709.0443 | Diana Marco | D. E. Marco, S. A. Cannas, M. A. Montemurro, B. Hu and S. Cheng | Similar self-organizing scale-invariant properties characterize early
cancer invasion and long range species spread | 21 pages, 2 figures | Journal of Theoretical Biology 256: 65-75 (2008) | 10.1016/j.jtbi.2008.09.011 | null | q-bio.PE q-bio.CB | null | Occupancy of new habitats through dispersion is a central process in nature.
In particular, long range dispersal is involved in the spread of species and
epidemics, although it has not been previously related with cancer invasion, a
process that involves spread to new tissues. We show that the early spread of
cancer cells is similar to the species individuals spread and that both
processes are represented by a common spatio-temporal signature, characterized
by a particular fractal geometry of the boundaries of patches generated, and a
power law-scaled, disrupted patch size distribution. We show that both
properties are a direct result of long-distance dispersal, and that they
reflect homologous ecological processes of population self-organization. Our
results are significant for processes involving long-range dispersal like
biological invasions, epidemics and cancer metastasis.
| [
{
"created": "Tue, 4 Sep 2007 14:32:01 GMT",
"version": "v1"
}
] | 2009-02-16 | [
[
"Marco",
"D. E.",
""
],
[
"Cannas",
"S. A.",
""
],
[
"Montemurro",
"M. A.",
""
],
[
"Hu",
"B.",
""
],
[
"Cheng",
"S.",
""
]
] | Occupancy of new habitats through dispersion is a central process in nature. In particular, long range dispersal is involved in the spread of species and epidemics, although it has not been previously related with cancer invasion, a process that involves spread to new tissues. We show that the early spread of cancer cells is similar to the species individuals spread and that both processes are represented by a common spatio-temporal signature, characterized by a particular fractal geometry of the boundaries of patches generated, and a power law-scaled, disrupted patch size distribution. We show that both properties are a direct result of long-distance dispersal, and that they reflect homologous ecological processes of population self-organization. Our results are significant for processes involving long-range dispersal like biological invasions, epidemics and cancer metastasis. |
1905.07050 | Elizabeth Allman | Elizabeth Allman and Hector Banos and John Rhodes | NANUQ: A method for inferring species networks from gene trees under the
coalescent model | null | null | null | null | q-bio.PE math.ST stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Species networks generalize the notion of species trees to allow for
hybridization or other lateral gene transfer. Under the Network Multispecies
Coalescent Model, individual gene trees arising from a network can have any
topology, but arise with frequencies dependent on the network structure and
numerical parameters. We propose a new algorithm for statistical inference of a
level-1 species network under this model, from data consisting of gene tree
topologies, and provide the theoretical justification for it. The algorithm is
based in an analysis of quartets displayed on gene trees, combining several
statistical hypothesis tests with combinatorial ideas such as a quartet-based
intertaxon distance appropriate to networks, the NeighborNet algorithm for
circular split systems, and the Circular Network algorithm for constructing a
splits graph.
| [
{
"created": "Thu, 16 May 2019 22:11:52 GMT",
"version": "v1"
}
] | 2019-05-20 | [
[
"Allman",
"Elizabeth",
""
],
[
"Banos",
"Hector",
""
],
[
"Rhodes",
"John",
""
]
] | Species networks generalize the notion of species trees to allow for hybridization or other lateral gene transfer. Under the Network Multispecies Coalescent Model, individual gene trees arising from a network can have any topology, but arise with frequencies dependent on the network structure and numerical parameters. We propose a new algorithm for statistical inference of a level-1 species network under this model, from data consisting of gene tree topologies, and provide the theoretical justification for it. The algorithm is based in an analysis of quartets displayed on gene trees, combining several statistical hypothesis tests with combinatorial ideas such as a quartet-based intertaxon distance appropriate to networks, the NeighborNet algorithm for circular split systems, and the Circular Network algorithm for constructing a splits graph. |
1706.00877 | Phan Nguyen | Phan Nguyen and Rosemary Braun | Semi-supervised network inference using simulated gene expression
dynamics | null | null | 10.1093/bioinformatics/btx748 | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Inferring the structure of gene regulatory networks from
high--throughput datasets remains an important and unsolved problem. Current
methods are hampered by problems such as noise, low sample size, and incomplete
characterizations of regulatory dynamics, leading to networks with missing and
anomalous links. Integration of prior network information (e.g., from pathway
databases) has the potential to improve reconstructions.
Results: We developed a semi--supervised network reconstruction algorithm
that enables the synthesis of information from partially known networks with
time course gene expression data. We adapted PLS-VIP for time course data and
used reference networks to simulate expression data from which null
distributions of VIP scores are generated and used to estimate edge
probabilities for input expression data. By using simulated dynamics to
generate reference distributions, this approach incorporates previously known
regulatory relationships and links the network to the dynamics to form a
semi-supervised approach that discovers novel and anomalous connections. We
applied this approach to data from a sleep deprivation study with KEGG pathways
treated as prior networks, as well as to synthetic data from several DREAM
challenges, and find that it is able to recover many of the true edges and
identify errors in these networks, suggesting its ability to derive posterior
networks that accurately reflect gene expression dynamics.
| [
{
"created": "Sat, 3 Jun 2017 00:06:53 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Jun 2017 20:44:45 GMT",
"version": "v2"
},
{
"created": "Fri, 1 Dec 2017 08:14:58 GMT",
"version": "v3"
}
] | 2017-12-04 | [
[
"Nguyen",
"Phan",
""
],
[
"Braun",
"Rosemary",
""
]
] | Motivation: Inferring the structure of gene regulatory networks from high--throughput datasets remains an important and unsolved problem. Current methods are hampered by problems such as noise, low sample size, and incomplete characterizations of regulatory dynamics, leading to networks with missing and anomalous links. Integration of prior network information (e.g., from pathway databases) has the potential to improve reconstructions. Results: We developed a semi--supervised network reconstruction algorithm that enables the synthesis of information from partially known networks with time course gene expression data. We adapted PLS-VIP for time course data and used reference networks to simulate expression data from which null distributions of VIP scores are generated and used to estimate edge probabilities for input expression data. By using simulated dynamics to generate reference distributions, this approach incorporates previously known regulatory relationships and links the network to the dynamics to form a semi-supervised approach that discovers novel and anomalous connections. We applied this approach to data from a sleep deprivation study with KEGG pathways treated as prior networks, as well as to synthetic data from several DREAM challenges, and find that it is able to recover many of the true edges and identify errors in these networks, suggesting its ability to derive posterior networks that accurately reflect gene expression dynamics. |
2307.03057 | Manuel Eduardo Hern\'andez-Garc\'ia | Manuel Eduardo Hern\'andez-Garc\'ia, Jorge Vel\'azquez-Castro | Corrected Hill Function in Stochastic Gene Regulatory Networks | 23 pages, 14 figures | null | null | null | q-bio.MN q-bio.BM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Describing reaction rates in stochastic bio-circuits is commonly done by
directly introducing the deterministically deduced Hill function into the
master equation. However, when fluctuations in enzymatic reaction rates are not
neglectable, the Hill function must be derived, considering all the involved
stochastic reactions. In this work, we derived the stochastic version of the
Hill function from the master equation of the complete set of reactions that,
in the macroscopic limit, lead to the Hill function reaction rate. We performed
a series expansion around the average values of the concentrations, which
allowed us to find corrections for the deterministic Hill function. This
process allowed us to quantify the fluctuations of enzymatic reactions. We
found that the underlying variability in propensity rates of gene regulatory
networks has an important non-linear effect that reduces the intrinsic
fluctuations of the mRNA and protein concentrations.
| [
{
"created": "Thu, 6 Jul 2023 15:23:54 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Nov 2023 22:49:41 GMT",
"version": "v2"
}
] | 2023-11-07 | [
[
"Hernández-García",
"Manuel Eduardo",
""
],
[
"Velázquez-Castro",
"Jorge",
""
]
] | Describing reaction rates in stochastic bio-circuits is commonly done by directly introducing the deterministically deduced Hill function into the master equation. However, when fluctuations in enzymatic reaction rates are not neglectable, the Hill function must be derived, considering all the involved stochastic reactions. In this work, we derived the stochastic version of the Hill function from the master equation of the complete set of reactions that, in the macroscopic limit, lead to the Hill function reaction rate. We performed a series expansion around the average values of the concentrations, which allowed us to find corrections for the deterministic Hill function. This process allowed us to quantify the fluctuations of enzymatic reactions. We found that the underlying variability in propensity rates of gene regulatory networks has an important non-linear effect that reduces the intrinsic fluctuations of the mRNA and protein concentrations. |
0911.5518 | Dong-Hee Kim | Dong-Hee Kim, Adilson E. Motter | Slave nodes and the controllability of metabolic networks | null | New Journal of Physics 11, 113047 (2009) | 10.1088/1367-2630/11/11/113047 | null | q-bio.MN cond-mat.dis-nn physics.bio-ph q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work on synthetic rescues has shown that the targeted deletion of
specific metabolic genes can often be used to rescue otherwise non-viable
mutants. This raises a fundamental biophysical question: to what extent can the
whole-cell behavior of a large metabolic network be controlled by constraining
the flux of one or more reactions in the network? This touches upon the issue
of the number of degrees of freedom contained by one such network. Using the
metabolic network of Escherichia coli as a model system, here we address this
question theoretically by exploring not only reaction deletions, but also a
continuum of all possible reaction expression levels. We show that the behavior
of the metabolic network can be largely manipulated by the pinned expression of
a single reaction. In particular, a relevant fraction of the metabolic
reactions exhibits canalizing interactions, in that the specification of one
reaction flux determines cellular growth as well as the fluxes of most other
reactions in optimal steady states. The activity of individual reactions can
thus be used as surrogates to monitor and possibly control cellular growth and
other whole-cell behaviors. In addition to its implications for the study of
control processes, our methodology provides a new approach to study how the
integrated dynamics of the entire metabolic network emerges from the
coordinated behavior of its component parts.
| [
{
"created": "Sun, 29 Nov 2009 21:27:54 GMT",
"version": "v1"
}
] | 2009-12-01 | [
[
"Kim",
"Dong-Hee",
""
],
[
"Motter",
"Adilson E.",
""
]
] | Recent work on synthetic rescues has shown that the targeted deletion of specific metabolic genes can often be used to rescue otherwise non-viable mutants. This raises a fundamental biophysical question: to what extent can the whole-cell behavior of a large metabolic network be controlled by constraining the flux of one or more reactions in the network? This touches upon the issue of the number of degrees of freedom contained by one such network. Using the metabolic network of Escherichia coli as a model system, here we address this question theoretically by exploring not only reaction deletions, but also a continuum of all possible reaction expression levels. We show that the behavior of the metabolic network can be largely manipulated by the pinned expression of a single reaction. In particular, a relevant fraction of the metabolic reactions exhibits canalizing interactions, in that the specification of one reaction flux determines cellular growth as well as the fluxes of most other reactions in optimal steady states. The activity of individual reactions can thus be used as surrogates to monitor and possibly control cellular growth and other whole-cell behaviors. In addition to its implications for the study of control processes, our methodology provides a new approach to study how the integrated dynamics of the entire metabolic network emerges from the coordinated behavior of its component parts. |
1906.01767 | Stevan Harnad | Stevan Harnad | Codes, communication and cognition | 4 pages, no figures, 7 references | Behav Brain Sci 42 (2019) e231 | 10.1017/S0140525X19001481 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Brette (2019) criticizes the notion of neural coding because it seems to
entail that neural signals need to be decoded by or for some receiver in the
head. If that were so, then neural coding would indeed be homuncular (Brette
calls it dualistic), requiring an entity to decipher the code. But I think the
plea of Brett to think instead in terms of complex, interactive causal
throughput is preaching to the converted. Turing (not Shannon) has already
shown the way. In any case, the metaphor of neural coding has little to do with
the symbol grounding problem.
| [
{
"created": "Wed, 5 Jun 2019 00:41:06 GMT",
"version": "v1"
}
] | 2019-12-04 | [
[
"Harnad",
"Stevan",
""
]
] | Brette (2019) criticizes the notion of neural coding because it seems to entail that neural signals need to be decoded by or for some receiver in the head. If that were so, then neural coding would indeed be homuncular (Brette calls it dualistic), requiring an entity to decipher the code. But I think the plea of Brett to think instead in terms of complex, interactive causal throughput is preaching to the converted. Turing (not Shannon) has already shown the way. In any case, the metaphor of neural coding has little to do with the symbol grounding problem. |
1710.04897 | Alessandro Treves | Michelangelo Naim, Vezha Boboeva, Chol Jun Kang, Alessandro Treves | Reducing a cortical network to a Potts model yields storage capacity
estimates | 51 pages, 7 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An autoassociative network of Potts units, coupled via tensor connections,
has been proposed and analysed as an effective model of an extensive cortical
network with distinct short- and long-range synaptic connections, but it has
not been clarified in what sense it can be regarded as an effective model. We
draw here the correspondence between the two, which indicates the need to
introduce a local feedback term in the reduced model, i.e., in the Potts
network. An effective model allows the study of phase transitions. As an
example, we study the storage capacity of the Potts network with this
additional term, the local feedback $w$, which contributes to drive the
activity of the network towards one of the stored patterns. The storage
capacity calculation, performed using replica tools, is limited to fully
connected networks, for which a Hamiltonian can be defined. To extend the
results to the case of intermediate partial connectivity, we also derive the
self-consistent signal-to-noise analysis for the Potts network; and finally we
discuss implications for semantic memory in humans.
| [
{
"created": "Fri, 13 Oct 2017 12:54:38 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Oct 2017 14:46:24 GMT",
"version": "v2"
},
{
"created": "Fri, 2 Feb 2018 14:25:56 GMT",
"version": "v3"
}
] | 2018-02-05 | [
[
"Naim",
"Michelangelo",
""
],
[
"Boboeva",
"Vezha",
""
],
[
"Kang",
"Chol Jun",
""
],
[
"Treves",
"Alessandro",
""
]
] | An autoassociative network of Potts units, coupled via tensor connections, has been proposed and analysed as an effective model of an extensive cortical network with distinct short- and long-range synaptic connections, but it has not been clarified in what sense it can be regarded as an effective model. We draw here the correspondence between the two, which indicates the need to introduce a local feedback term in the reduced model, i.e., in the Potts network. An effective model allows the study of phase transitions. As an example, we study the storage capacity of the Potts network with this additional term, the local feedback $w$, which contributes to drive the activity of the network towards one of the stored patterns. The storage capacity calculation, performed using replica tools, is limited to fully connected networks, for which a Hamiltonian can be defined. To extend the results to the case of intermediate partial connectivity, we also derive the self-consistent signal-to-noise analysis for the Potts network; and finally we discuss implications for semantic memory in humans. |
1901.09944 | Michael Baker Ph.D. | Michael E. Baker | Steroid Receptors and Vertebrate Evolution | 18 pages, 5 figures | null | null | null | q-bio.MN q-bio.TO | http://creativecommons.org/publicdomain/zero/1.0/ | Considering that life on earth evolved about 3.7 billion years ago,
vertebrates are young, appearing in the fossil record during the Cambrian
explosion about 542 to 515 million years ago. Results from sequence analyses of
genomes from bacteria, yeast, plants, invertebrates and vertebrates indicate
that receptors for adrenal steroids (aldosterone, cortisol), and sex steroids
(estrogen, progesterone, testosterone) also are young, with receptors for
estrogens and 3-ketosteroids first appearing in basal chordates
(cephalochordates: amphioxus), which are close ancestors of vertebrates. An
ancestral progesterone receptor and an ancestral corticoid receptor, the common
ancestor of the glucocorticoid and mineralocorticoid receptors, evolved in
jawless vertebrates (cyclostomes: lampreys, hagfish). This was followed by
evolution of an androgen receptor and distinct glucocorticoid and
mineralocorticoid receptors in cartilaginous fishes (gnathostomes: sharks).
Adrenal and sex steroid receptors are not found in echinoderms: and
hemichordates, which are ancestors in the lineage of cephalochordates and
vertebrates. The presence of steroid receptors in vertebrates, in which these
steroid receptors act as master switches to regulate differentiation,
development, reproduction, immune responses, electrolyte homeostasis and stress
responses, argues for an important role for steroid receptors in the
evolutionary success of vertebrates, considering that the human genome contains
about 22,000 genes, which is not much larger than genomes of invertebrates,
such as Caenorhabditis elegans (~18,000 genes) and Drosophila (~14,000 genes).
| [
{
"created": "Mon, 28 Jan 2019 19:04:43 GMT",
"version": "v1"
},
{
"created": "Fri, 19 Apr 2019 17:14:10 GMT",
"version": "v2"
}
] | 2019-04-22 | [
[
"Baker",
"Michael E.",
""
]
] | Considering that life on earth evolved about 3.7 billion years ago, vertebrates are young, appearing in the fossil record during the Cambrian explosion about 542 to 515 million years ago. Results from sequence analyses of genomes from bacteria, yeast, plants, invertebrates and vertebrates indicate that receptors for adrenal steroids (aldosterone, cortisol), and sex steroids (estrogen, progesterone, testosterone) also are young, with receptors for estrogens and 3-ketosteroids first appearing in basal chordates (cephalochordates: amphioxus), which are close ancestors of vertebrates. An ancestral progesterone receptor and an ancestral corticoid receptor, the common ancestor of the glucocorticoid and mineralocorticoid receptors, evolved in jawless vertebrates (cyclostomes: lampreys, hagfish). This was followed by evolution of an androgen receptor and distinct glucocorticoid and mineralocorticoid receptors in cartilaginous fishes (gnathostomes: sharks). Adrenal and sex steroid receptors are not found in echinoderms: and hemichordates, which are ancestors in the lineage of cephalochordates and vertebrates. The presence of steroid receptors in vertebrates, in which these steroid receptors act as master switches to regulate differentiation, development, reproduction, immune responses, electrolyte homeostasis and stress responses, argues for an important role for steroid receptors in the evolutionary success of vertebrates, considering that the human genome contains about 22,000 genes, which is not much larger than genomes of invertebrates, such as Caenorhabditis elegans (~18,000 genes) and Drosophila (~14,000 genes). |
2003.00657 | Ryan Kaveh | Ryan Kaveh, Justin Doong, Andy Zhou, Carolyn Schwendeman, Karthik
Gopalan, Fred Burghardt, Ana C. Arias, Michel Maharbiz, Rikky Muller | Wireless User-Generic Ear EEG | null | null | null | null | q-bio.QM eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the past few years it has been demonstrated that electroencephalography
(EEG) can be recorded from inside the ear (in-ear EEG). To open the door to
low-profile earpieces as wearable brain-computer interfaces (BCIs), this work
presents a practical in-ear EEG device based on multiple dry electrodes, a
user-generic design, and a lightweight wireless interface for streaming data
and device programming. The earpiece is designed for improved ear canal contact
across a wide population of users and is fabricated in a low-cost and scalable
manufacturing process based on standard techniques such as vacuum
forming,plasma-treatment, and spray coating. A 2.5x2.5 cm^2 wireless recording
module is designed to record and stream data wirelessly to a host computer.
Performance was evaluated on three human subjects over three months and
compared with clinical-grade wet scalp EEG recordings. Recordings of
spontaneous and evoked physiological signals, eye-blinks, alpha rhythm, and the
auditory steady-state response (ASSR), are presented. This is the first
wireless in-ear EEG to our knowledge to incorporate a dry multielectrode,
user-generic design. The user-generic ear EEG recorded a mean alpha modulation
of 2.17, outperforming the state-of-the-art in dry electrode in-ear EEG
systems.
| [
{
"created": "Mon, 2 Mar 2020 04:52:51 GMT",
"version": "v1"
},
{
"created": "Wed, 29 Apr 2020 17:27:15 GMT",
"version": "v2"
}
] | 2020-04-30 | [
[
"Kaveh",
"Ryan",
""
],
[
"Doong",
"Justin",
""
],
[
"Zhou",
"Andy",
""
],
[
"Schwendeman",
"Carolyn",
""
],
[
"Gopalan",
"Karthik",
""
],
[
"Burghardt",
"Fred",
""
],
[
"Arias",
"Ana C.",
""
],
[
"M... | In the past few years it has been demonstrated that electroencephalography (EEG) can be recorded from inside the ear (in-ear EEG). To open the door to low-profile earpieces as wearable brain-computer interfaces (BCIs), this work presents a practical in-ear EEG device based on multiple dry electrodes, a user-generic design, and a lightweight wireless interface for streaming data and device programming. The earpiece is designed for improved ear canal contact across a wide population of users and is fabricated in a low-cost and scalable manufacturing process based on standard techniques such as vacuum forming,plasma-treatment, and spray coating. A 2.5x2.5 cm^2 wireless recording module is designed to record and stream data wirelessly to a host computer. Performance was evaluated on three human subjects over three months and compared with clinical-grade wet scalp EEG recordings. Recordings of spontaneous and evoked physiological signals, eye-blinks, alpha rhythm, and the auditory steady-state response (ASSR), are presented. This is the first wireless in-ear EEG to our knowledge to incorporate a dry multielectrode, user-generic design. The user-generic ear EEG recorded a mean alpha modulation of 2.17, outperforming the state-of-the-art in dry electrode in-ear EEG systems. |
0904.4625 | Kirill Korolev S | K.S. Korolev, Mikkel Avlund, Oskar Hallatschek, David R. Nelson | Genetic Demixing and Evolutionary Forces in the One-Dimensional Stepping
Stone Model | 29 pages, 20 figures; Reviews of Modern Physics, Volume 82,
April-June 2010 | null | 10.1103/RevModPhys.82.1691 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We review and extend results for mutation, selection, genetic drift, and
migration in a one-dimensional continuous population. The population is
described by a continuous limit of the stepping stone model, which leads to the
stochastic Fisher-Kolmogorov-Petrovsky-Piscounov equation with additional terms
describing mutations. Although the stepping stone model was first proposed for
population genetics, it is closely related to "voter models" of interest in
nonequilibrium statistical mechanics. The stepping stone model can also be
regarded as an approximation to the dynamics of a thin layer of actively
growing pioneers at the frontier of a colony of microorganisms undergoing a
range expansion on a Petri dish. We find that the population tends to segregate
into monoallelic domains. This segregation slows down genetic drift and
selection because these two evolutionary forces can only act at the boundaries
between the domains; the effects of mutation, however, are not significantly
affected by the segregation. Although fixation in the neutral well-mixed (or
"zero dimensional") model occurs exponentially in time, it occurs only
algebraically fast in the one-dimensional model. If selection is weak,
selective sweeps occur exponentially fast in both well-mixed and
one-dimensional populations, but the time constants are different. We also find
an unusual sublinear increase in the variance of the spatially averaged allele
frequency with time. Although we focus on two alleles or variants, q-allele
Potts-like models of gene segregation are considered as well. We also
investigate the effects of geometry at the frontier by considering growth of
circular colonies. Our analytical results are checked with simulations, and
could be tested against recent spatial experiments on range expansions off
linear inoculations of Escherichia coli and Saccharomyces cerevisiae.
| [
{
"created": "Wed, 29 Apr 2009 14:27:12 GMT",
"version": "v1"
},
{
"created": "Wed, 13 Apr 2011 15:47:32 GMT",
"version": "v2"
}
] | 2011-04-14 | [
[
"Korolev",
"K. S.",
""
],
[
"Avlund",
"Mikkel",
""
],
[
"Hallatschek",
"Oskar",
""
],
[
"Nelson",
"David R.",
""
]
] | We review and extend results for mutation, selection, genetic drift, and migration in a one-dimensional continuous population. The population is described by a continuous limit of the stepping stone model, which leads to the stochastic Fisher-Kolmogorov-Petrovsky-Piscounov equation with additional terms describing mutations. Although the stepping stone model was first proposed for population genetics, it is closely related to "voter models" of interest in nonequilibrium statistical mechanics. The stepping stone model can also be regarded as an approximation to the dynamics of a thin layer of actively growing pioneers at the frontier of a colony of microorganisms undergoing a range expansion on a Petri dish. We find that the population tends to segregate into monoallelic domains. This segregation slows down genetic drift and selection because these two evolutionary forces can only act at the boundaries between the domains; the effects of mutation, however, are not significantly affected by the segregation. Although fixation in the neutral well-mixed (or "zero dimensional") model occurs exponentially in time, it occurs only algebraically fast in the one-dimensional model. If selection is weak, selective sweeps occur exponentially fast in both well-mixed and one-dimensional populations, but the time constants are different. We also find an unusual sublinear increase in the variance of the spatially averaged allele frequency with time. Although we focus on two alleles or variants, q-allele Potts-like models of gene segregation are considered as well. We also investigate the effects of geometry at the frontier by considering growth of circular colonies. Our analytical results are checked with simulations, and could be tested against recent spatial experiments on range expansions off linear inoculations of Escherichia coli and Saccharomyces cerevisiae. |
2101.01538 | Stuart Kauffman | Stuart Kauffman, Dean Radin | Is Brain-Mind Quantum? A theory and supporting evidence | null | null | null | null | q-bio.NC quant-ph | http://creativecommons.org/licenses/by/4.0/ | We propose a non-substance dualism theory, following Heisenberg: The world
consists of both ontologically real possibilities that do not obey Aristotle's
Law of the Excluded Middle, and ontologically real Actuals that do o0bey the
Law of the Excluded Middle. This quantum approach solves five issues in quantum
mechanics and numerous puzzles about the mind-brain relationship. It raises the
possibility that some aspects of mind are non-local, and that mind plays an
active role in the physical world. We present supporting evidence.
| [
{
"created": "Sun, 3 Jan 2021 01:40:07 GMT",
"version": "v1"
}
] | 2021-01-06 | [
[
"Kauffman",
"Stuart",
""
],
[
"Radin",
"Dean",
""
]
] | We propose a non-substance dualism theory, following Heisenberg: The world consists of both ontologically real possibilities that do not obey Aristotle's Law of the Excluded Middle, and ontologically real Actuals that do o0bey the Law of the Excluded Middle. This quantum approach solves five issues in quantum mechanics and numerous puzzles about the mind-brain relationship. It raises the possibility that some aspects of mind are non-local, and that mind plays an active role in the physical world. We present supporting evidence. |
1805.03592 | Wojciech Tarnowski | Ewa Gudowska-Nowak, Maciej A. Nowak, Dante R. Chialvo, Jeremi K.
Ochab, Wojciech Tarnowski | From synaptic interactions to collective dynamics in random neuronal
networks models: critical role of eigenvectors and transient behavior | 25 pages + 5 pages of refs, 9 figures | Neural Computation 32(2), 395-423 (2020) | 10.1162/neco_a_01253 | null | q-bio.NC cond-mat.dis-nn cond-mat.stat-mech math-ph math.MP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The study of neuronal interactions is currently at the center of several big
collaborative neuroscience projects (including the Human Connectome Project,
the Blue Brain Project, the Brainome, etc.) which attempt to obtain a detailed
map of the entire brain. Under certain constraints, mathematical theory can
advance predictions of the expected neural dynamics based solely on the
statistical properties of the synaptic interaction matrix. This work explores
the application of free random variables to the study of large synaptic
interaction matrices. Besides recovering in a straightforward way known results
on eigenspectra in types of models of neural networks proposed by Rajan and
Abbott, we extend them to heavy-tailed distributions of interactions. More
importantly, we derive analytically the behavior of eigenvector overlaps, which
determine the stability of the spectra. We observe that upon imposing the
neuronal excitation/inhibition balance, despite the eigenvalues remaining
unchanged, their stability dramatically decreases due to the strong
non-orthogonality of associated eigenvectors. It leads us to the conclusion
that the understanding of the temporal evolution of asymmetric neural networks
requires considering the entangled dynamics of both eigenvectors and
eigenvalues, which might bear consequences for learning and memory processes in
these models. Considering the success of free random variables theory in a wide
variety of disciplines, we hope that the results presented here foster the
additional application of these ideas in the area of brain sciences.
| [
{
"created": "Wed, 9 May 2018 15:40:45 GMT",
"version": "v1"
},
{
"created": "Sun, 15 Nov 2020 17:44:18 GMT",
"version": "v2"
}
] | 2020-11-17 | [
[
"Gudowska-Nowak",
"Ewa",
""
],
[
"Nowak",
"Maciej A.",
""
],
[
"Chialvo",
"Dante R.",
""
],
[
"Ochab",
"Jeremi K.",
""
],
[
"Tarnowski",
"Wojciech",
""
]
] | The study of neuronal interactions is currently at the center of several big collaborative neuroscience projects (including the Human Connectome Project, the Blue Brain Project, the Brainome, etc.) which attempt to obtain a detailed map of the entire brain. Under certain constraints, mathematical theory can advance predictions of the expected neural dynamics based solely on the statistical properties of the synaptic interaction matrix. This work explores the application of free random variables to the study of large synaptic interaction matrices. Besides recovering in a straightforward way known results on eigenspectra in types of models of neural networks proposed by Rajan and Abbott, we extend them to heavy-tailed distributions of interactions. More importantly, we derive analytically the behavior of eigenvector overlaps, which determine the stability of the spectra. We observe that upon imposing the neuronal excitation/inhibition balance, despite the eigenvalues remaining unchanged, their stability dramatically decreases due to the strong non-orthogonality of associated eigenvectors. It leads us to the conclusion that the understanding of the temporal evolution of asymmetric neural networks requires considering the entangled dynamics of both eigenvectors and eigenvalues, which might bear consequences for learning and memory processes in these models. Considering the success of free random variables theory in a wide variety of disciplines, we hope that the results presented here foster the additional application of these ideas in the area of brain sciences. |
1202.5997 | \'Eric Brunet | \'Eric Brunet and Bernard Derrida | Genealogies in simple models of evolution | null | null | 10.1088/1742-5468/2013/01/P01006 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We review the statistical properties of the genealogies of a few models of
evolution. In the asexual case, selection leads to coalescence times which grow
logarithmically with the size of the population in contrast with the linear
growth of the neutral case. Moreover for a whole class of models, the
statistics of the genealogies are those of the Bolthausen-Sznitman coalescent
rather than the Kingman coalescent in the neutral case. For sexual
reproduction, the time to reach the first common ancestors to the whole
population and the time for all individuals to have all their ancestors in
common are also logarithmic in the neutral case, as predicted by Chang []. We
discuss how these times are modified in a simple way of introducing selection.
| [
{
"created": "Mon, 27 Feb 2012 16:55:57 GMT",
"version": "v1"
}
] | 2015-06-04 | [
[
"Brunet",
"Éric",
""
],
[
"Derrida",
"Bernard",
""
]
] | We review the statistical properties of the genealogies of a few models of evolution. In the asexual case, selection leads to coalescence times which grow logarithmically with the size of the population in contrast with the linear growth of the neutral case. Moreover for a whole class of models, the statistics of the genealogies are those of the Bolthausen-Sznitman coalescent rather than the Kingman coalescent in the neutral case. For sexual reproduction, the time to reach the first common ancestors to the whole population and the time for all individuals to have all their ancestors in common are also logarithmic in the neutral case, as predicted by Chang []. We discuss how these times are modified in a simple way of introducing selection. |
1001.5258 | Marc Lefranc | Quentin Thommen (PhLAM), Benjamin Pfeuty (PhLAM), Pierre-Emmanuel
Morant (PhLAM), Florence Corellou, Fran\c{c}ois-Yves Bouget, Marc Lefranc
(PhLAM) | Robustness of circadian clocks to daylight fluctuations: hints from the
picoeucaryote Ostreococcus tauri | null | Plos Computational Biology 6, 11 (2010) e1000990 | 10.1371/journal.pcbi.1000990 | null | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The development of systemic approaches in biology has put emphasis on
identifying genetic modules whose behavior can be modeled accurately so as to
gain insight into their structure and function. However most gene circuits in a
cell are under control of external signals and thus quantitative agreement
between experimental data and a mathematical model is difficult. Circadian
biology has been one notable exception: quantitative models of the internal
clock that orchestrates biological processes over the 24-hour diurnal cycle
have been constructed for a few organisms, from cyanobacteria to plants and
mammals. In most cases, a complex architecture with interlocked feedback loops
has been evidenced. Here we present first modeling results for the circadian
clock of the green unicellular alga Ostreococcus tauri. Two plant-like clock
genes have been shown to play a central role in Ostreococcus clock. We find
that their expression time profiles can be accurately reproduced by a minimal
model of a two-gene transcriptional feedback loop. Remarkably, best adjustment
of data recorded under light/dark alternation is obtained when assuming that
the oscillator is not coupled to the diurnal cycle. This suggests that coupling
to light is confined to specific time intervals and has no dynamical effect
when the oscillator is entrained by the diurnal cycle. This intringuing
property may reflect a strategy to minimize the impact of fluctuations in
daylight intensity on the core circadian oscillator, a type of perturbation
that has been rarely considered when assessing the robustness of circadian
clocks.
| [
{
"created": "Thu, 28 Jan 2010 20:07:24 GMT",
"version": "v1"
},
{
"created": "Fri, 29 Jan 2010 06:31:09 GMT",
"version": "v2"
},
{
"created": "Fri, 16 Jul 2010 07:20:28 GMT",
"version": "v3"
}
] | 2010-12-10 | [
[
"Thommen",
"Quentin",
"",
"PhLAM"
],
[
"Pfeuty",
"Benjamin",
"",
"PhLAM"
],
[
"Morant",
"Pierre-Emmanuel",
"",
"PhLAM"
],
[
"Corellou",
"Florence",
"",
"PhLAM"
],
[
"Bouget",
"François-Yves",
"",
"PhLAM"
],
[
"... | The development of systemic approaches in biology has put emphasis on identifying genetic modules whose behavior can be modeled accurately so as to gain insight into their structure and function. However most gene circuits in a cell are under control of external signals and thus quantitative agreement between experimental data and a mathematical model is difficult. Circadian biology has been one notable exception: quantitative models of the internal clock that orchestrates biological processes over the 24-hour diurnal cycle have been constructed for a few organisms, from cyanobacteria to plants and mammals. In most cases, a complex architecture with interlocked feedback loops has been evidenced. Here we present first modeling results for the circadian clock of the green unicellular alga Ostreococcus tauri. Two plant-like clock genes have been shown to play a central role in Ostreococcus clock. We find that their expression time profiles can be accurately reproduced by a minimal model of a two-gene transcriptional feedback loop. Remarkably, best adjustment of data recorded under light/dark alternation is obtained when assuming that the oscillator is not coupled to the diurnal cycle. This suggests that coupling to light is confined to specific time intervals and has no dynamical effect when the oscillator is entrained by the diurnal cycle. This intringuing property may reflect a strategy to minimize the impact of fluctuations in daylight intensity on the core circadian oscillator, a type of perturbation that has been rarely considered when assessing the robustness of circadian clocks. |
1805.08151 | Marie Charpentier | Marie JE Charpentier, Peter M Kappeler | A reply to 'Ranging Behavior Drives Parasite Richness: A More
Parsimonious Hypothesis' | 9 pages, no figure, no table | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This preprint has been reviewed and recommended by Peer Community In Ecology
(https://dx.doi.org/10.24072/pci.ecology.100001). In a recent article,
Bicca-Marques and Calegaro-Marques [2016] discussed the putative assumptions
related to an interpretation we provided regarding an observed positive
relationship between weekly averaged parasite richness of a group of mandrills
(Mandrillus sphinx) and their daily path lengths (DPL), published earlier in
the same journal (Brockmeyer et al., 2015). In our article, we proposed, inter
alia, that "the daily travels of mandrills could be seen as a way to escape
contaminated habitats on a local scale". In their article, Bicca-Marques and
Calegaro-Marques [2016] proposed an alternative mechanism that they considered
to be more parsimonious. In their view, increased DPL also increases exposure
to novel parasites from the environment. In other words, while we proposed that
elevated DPL may be a consequence of elevated parasite richness, they viewed it
as a cause. We are happy to see that our study attracted so much interest that
it evoked a public comment. We are also grateful to Bicca-Marques and
Calegaro-Marques [2016] for pointing out an obvious alternative scenario that
we failed to discuss and for laying out several key factors and assumptions
that should be addressed by future studies examining the links between parasite
risk and group ranging. We use this opportunity to advance this discourse by
responding to some of the criticisms raised in their discussion of our article.
In this reply, we briefly contextualize the main object of criticism. We then
discuss the putative parsimony of the two competing scenarios.
| [
{
"created": "Mon, 21 May 2018 16:09:37 GMT",
"version": "v1"
},
{
"created": "Tue, 5 Jun 2018 19:00:59 GMT",
"version": "v2"
},
{
"created": "Thu, 14 Jun 2018 09:32:33 GMT",
"version": "v3"
}
] | 2018-06-15 | [
[
"Charpentier",
"Marie JE",
""
],
[
"Kappeler",
"Peter M",
""
]
] | This preprint has been reviewed and recommended by Peer Community In Ecology (https://dx.doi.org/10.24072/pci.ecology.100001). In a recent article, Bicca-Marques and Calegaro-Marques [2016] discussed the putative assumptions related to an interpretation we provided regarding an observed positive relationship between weekly averaged parasite richness of a group of mandrills (Mandrillus sphinx) and their daily path lengths (DPL), published earlier in the same journal (Brockmeyer et al., 2015). In our article, we proposed, inter alia, that "the daily travels of mandrills could be seen as a way to escape contaminated habitats on a local scale". In their article, Bicca-Marques and Calegaro-Marques [2016] proposed an alternative mechanism that they considered to be more parsimonious. In their view, increased DPL also increases exposure to novel parasites from the environment. In other words, while we proposed that elevated DPL may be a consequence of elevated parasite richness, they viewed it as a cause. We are happy to see that our study attracted so much interest that it evoked a public comment. We are also grateful to Bicca-Marques and Calegaro-Marques [2016] for pointing out an obvious alternative scenario that we failed to discuss and for laying out several key factors and assumptions that should be addressed by future studies examining the links between parasite risk and group ranging. We use this opportunity to advance this discourse by responding to some of the criticisms raised in their discussion of our article. In this reply, we briefly contextualize the main object of criticism. We then discuss the putative parsimony of the two competing scenarios. |
2407.02771 | Jessica Conrad | Jessica R Conrad, Marisa C Eisenberg | Examining the impact of forcing function inputs on structural
identifiability | null | null | null | null | q-bio.QM math.DS | http://creativecommons.org/licenses/by/4.0/ | For mathematical and experimental ease, models with time varying parameters
are often simplified to assume constant parameters. However, this
simplification can potentially lead to identifiability issues (lack of
uniqueness of parameter estimates). Methods have been developed to
algebraically and numerically determine the identifiability of a model, as well
as resolve identifiability issues. This specific type of simplification
presents an alternate opportunity to instead use this information to resolve
the unidentifiability. Given that re-parameterizing, collecting more data, and
adding inputs can be potentially costly or impractical, this could present new
alternatives.
We present a method for resolving unidentifiability in a system by
introducing a new data stream correlated with a parameter of interest. First,
we demonstrate how and when non-constant input data can be introduced into any
rational function ODE system without worsening the model identifiability. Then,
we prove when these input functions improve structural and potentially also
practical identifiability for a given model and relevant data.
By utilizing pre-existing data streams, these methods can potentially reduce
experimental costs, while still answering key questions. By connecting
mathematical proofs to application, our framework removes guesswork from when,
where, and how researchers can best introduce new data to improve model
outcomes.
| [
{
"created": "Wed, 3 Jul 2024 02:59:40 GMT",
"version": "v1"
}
] | 2024-07-04 | [
[
"Conrad",
"Jessica R",
""
],
[
"Eisenberg",
"Marisa C",
""
]
] | For mathematical and experimental ease, models with time varying parameters are often simplified to assume constant parameters. However, this simplification can potentially lead to identifiability issues (lack of uniqueness of parameter estimates). Methods have been developed to algebraically and numerically determine the identifiability of a model, as well as resolve identifiability issues. This specific type of simplification presents an alternate opportunity to instead use this information to resolve the unidentifiability. Given that re-parameterizing, collecting more data, and adding inputs can be potentially costly or impractical, this could present new alternatives. We present a method for resolving unidentifiability in a system by introducing a new data stream correlated with a parameter of interest. First, we demonstrate how and when non-constant input data can be introduced into any rational function ODE system without worsening the model identifiability. Then, we prove when these input functions improve structural and potentially also practical identifiability for a given model and relevant data. By utilizing pre-existing data streams, these methods can potentially reduce experimental costs, while still answering key questions. By connecting mathematical proofs to application, our framework removes guesswork from when, where, and how researchers can best introduce new data to improve model outcomes. |
1402.1802 | Jonathan Potts | Jonathan R. Potts, Karl Mokross, Mark A. Lewis | A unifying framework for quantifying the nature of animal interactions | null | J Roy Soc Interface (2014) 11:20140333 | 10.1098/rsif.2014.0333 | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Collective phenomena, whereby agent-agent interactions determine spatial
patterns, are ubiquitous in the animal kingdom. On the other hand, movement and
space use are also greatly influenced by the interactions between animals and
their environment. Despite both types of interaction fundamentally influencing
animal behaviour, there has hitherto been no unifying framework for the models
proposed in both areas. Here, we construct a general method for inferring
population-level spatial patterns from underlying individual movement and
interaction processes, a key ingredient in building a statistical mechanics for
ecological systems. We show that resource selection functions, as well as
several examples of collective motion models, arise as special cases of our
framework, thus bringing together resource selection analysis and collective
animal behaviour into a single theory. In particular, we focus on combining the
various mechanistic models of territorial interactions in the literature with
step selection functions, by incorporate interactions into the step selection
framework and demonstrating how to derive territorial patterns from the
resulting models. We demonstrate the efficacy of our model by application to a
population of insectivore birds in the Amazon rainforest.
| [
{
"created": "Sat, 8 Feb 2014 00:20:10 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Mar 2016 10:58:44 GMT",
"version": "v2"
}
] | 2016-03-21 | [
[
"Potts",
"Jonathan R.",
""
],
[
"Mokross",
"Karl",
""
],
[
"Lewis",
"Mark A.",
""
]
] | Collective phenomena, whereby agent-agent interactions determine spatial patterns, are ubiquitous in the animal kingdom. On the other hand, movement and space use are also greatly influenced by the interactions between animals and their environment. Despite both types of interaction fundamentally influencing animal behaviour, there has hitherto been no unifying framework for the models proposed in both areas. Here, we construct a general method for inferring population-level spatial patterns from underlying individual movement and interaction processes, a key ingredient in building a statistical mechanics for ecological systems. We show that resource selection functions, as well as several examples of collective motion models, arise as special cases of our framework, thus bringing together resource selection analysis and collective animal behaviour into a single theory. In particular, we focus on combining the various mechanistic models of territorial interactions in the literature with step selection functions, by incorporate interactions into the step selection framework and demonstrating how to derive territorial patterns from the resulting models. We demonstrate the efficacy of our model by application to a population of insectivore birds in the Amazon rainforest. |
2002.02367 | Olga Krivorotko | Sergey Kabanikhin and Olga Krivorotko and Aliya Takuadina and Darya
Andornaya and Shuhua Zhang | Geo-information system of spread of tuberculosis based on inversion and
prediction | null | null | null | null | q-bio.PE math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The monitoring, analysis and prediction of epidemic spread in the region
require the construction of mathematical model, big data processing and
visualization because the amount of population and the size of the region could
be huge. One of the important steps is refinement of mathematical model, i.e.
determination of initial data and coefficients of system of differential
equations which describe the epidemiology processes. We analyze numerical
method for solving inverse problem of epidemiology based on genetic algorithm
and traditional optimization ideas. Numerical results are applied to analysis
and prediction of epidemic situation in regions of Russian Federation, Republic
of Kazakhstan and People's Republic of China. Due to a great amount of data we
use a special Geo-information system for visualization of epidemic process,
i.e. a special software named Digital Earth.
| [
{
"created": "Mon, 3 Feb 2020 01:26:38 GMT",
"version": "v1"
}
] | 2020-02-07 | [
[
"Kabanikhin",
"Sergey",
""
],
[
"Krivorotko",
"Olga",
""
],
[
"Takuadina",
"Aliya",
""
],
[
"Andornaya",
"Darya",
""
],
[
"Zhang",
"Shuhua",
""
]
] | The monitoring, analysis and prediction of epidemic spread in the region require the construction of mathematical model, big data processing and visualization because the amount of population and the size of the region could be huge. One of the important steps is refinement of mathematical model, i.e. determination of initial data and coefficients of system of differential equations which describe the epidemiology processes. We analyze numerical method for solving inverse problem of epidemiology based on genetic algorithm and traditional optimization ideas. Numerical results are applied to analysis and prediction of epidemic situation in regions of Russian Federation, Republic of Kazakhstan and People's Republic of China. Due to a great amount of data we use a special Geo-information system for visualization of epidemic process, i.e. a special software named Digital Earth. |
2011.01548 | Thomas Caraco | Thomas Caraco | Antibiotic treatment, duration of infectiousness, and disease
transmission | 25 pages of text, 5 figures, 1 table. arXiv admin note: substantial
text overlap with arXiv:2001.06948 | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans, domestic animals, orchard crops, and ornamental plants are commonly
treated with antibiotics in response to bacterial infection. By curing
infectious individuals, antibiotic therapy might limit the spread of contagious
disease among hosts. But an antibiotic`s suppression of within-host pathogen
density might also reduce the probability that the host is otherwise removed
from infectious status prior to therapeutic recovery. When rates of both
recovery via treatment and other removal events (e.g., isolation or mortality)
depend directly on within-host pathogen density, antibiotic treatment can relax
the overall removal rate sufficiently to increase between-host disease
transmission. To explore this dependence, a deterministic within-host dynamics
drives the infectious host's time-dependent probability of disease
transmission, as well as the probabilistic duration of the infectious period.
At the within-host scale, the model varies (1) inoculum size, (2) bacterial
self-regulation, (3) the time between infection and initiation of therapy, and
(4) antibiotic efficacy. At the between-host scale the model varies (5) the
size/susceptibility of groups randomly encountered by an infectious host.
Results identify conditions where antibiotic treatment can increase duration of
a host`s infectiousness, and consequently increase the expected number of new
infections. At lower antibiotic efficacy, treatment might convert a rare,
serious bacterial disease into a common, but treatable infection.
| [
{
"created": "Mon, 2 Nov 2020 17:38:34 GMT",
"version": "v1"
}
] | 2020-11-04 | [
[
"Caraco",
"Thomas",
""
]
] | Humans, domestic animals, orchard crops, and ornamental plants are commonly treated with antibiotics in response to bacterial infection. By curing infectious individuals, antibiotic therapy might limit the spread of contagious disease among hosts. But an antibiotic`s suppression of within-host pathogen density might also reduce the probability that the host is otherwise removed from infectious status prior to therapeutic recovery. When rates of both recovery via treatment and other removal events (e.g., isolation or mortality) depend directly on within-host pathogen density, antibiotic treatment can relax the overall removal rate sufficiently to increase between-host disease transmission. To explore this dependence, a deterministic within-host dynamics drives the infectious host's time-dependent probability of disease transmission, as well as the probabilistic duration of the infectious period. At the within-host scale, the model varies (1) inoculum size, (2) bacterial self-regulation, (3) the time between infection and initiation of therapy, and (4) antibiotic efficacy. At the between-host scale the model varies (5) the size/susceptibility of groups randomly encountered by an infectious host. Results identify conditions where antibiotic treatment can increase duration of a host`s infectiousness, and consequently increase the expected number of new infections. At lower antibiotic efficacy, treatment might convert a rare, serious bacterial disease into a common, but treatable infection. |
2110.12964 | Xinlei Mi | Xinlei Mi, William Bekerman, Peter A. Sims, Peter D. Canoll, Jianhua
Hu | RZiMM-scRNA: A regularized zero-inflated mixture model framework for
single-cell RNA-seq data | 22 pages, 8 figures | null | null | null | q-bio.GN q-bio.QM stat.ME | http://creativecommons.org/licenses/by/4.0/ | Applications of single-cell RNA sequencing in various biomedical research
areas have been blooming. This new technology provides unprecedented
opportunities to study disease heterogeneity at the cellular level. However,
unique characteristics of scRNA-seq data, including large dimensionality, high
dropout rates, and possibly batch effects, bring great difficulty into the
analysis of such data. Not appropriately addressing these issues obstructs true
scientific discovery. Herein, we propose a unified Regularized Zero-inflated
Mixture Model framework designed for scRNA-seq data (RZiMM-scRNA) to
simultaneously detect cell subgroups and identify gene differential expression
based on a developed importance score, accounting for both dropouts and batch
effects. We conduct extensive simulation studies in which we evaluate the
performance of RZiMM-scRNA and compare it with several popular methods,
including Seurat, SC3, K-Means, and Hierarchical Clustering. Simulation results
show that RZiMM-scRNA demonstrates superior clustering performance and enhanced
biomarker detection accuracy compared to alternative methods, especially when
cell subgroups are less distinct, verifying the robustness of our method. Our
empirical investigations focus on two brain tumor studies dealing with
astrocytoma of various grades, including the most malignant of all brain
tumors, glioblastoma multiforme (GBM). Our goal is to delineate cell
heterogeneity and identify driving biomarkers associated with these tumors.
Notably, RZiMM-scNRA successfully identifies a small group of oligodendrocyte
cells which has drawn much attention in biomedical literature on brain cancers.
| [
{
"created": "Mon, 25 Oct 2021 13:58:31 GMT",
"version": "v1"
}
] | 2021-10-26 | [
[
"Mi",
"Xinlei",
""
],
[
"Bekerman",
"William",
""
],
[
"Sims",
"Peter A.",
""
],
[
"Canoll",
"Peter D.",
""
],
[
"Hu",
"Jianhua",
""
]
] | Applications of single-cell RNA sequencing in various biomedical research areas have been blooming. This new technology provides unprecedented opportunities to study disease heterogeneity at the cellular level. However, unique characteristics of scRNA-seq data, including large dimensionality, high dropout rates, and possibly batch effects, bring great difficulty into the analysis of such data. Not appropriately addressing these issues obstructs true scientific discovery. Herein, we propose a unified Regularized Zero-inflated Mixture Model framework designed for scRNA-seq data (RZiMM-scRNA) to simultaneously detect cell subgroups and identify gene differential expression based on a developed importance score, accounting for both dropouts and batch effects. We conduct extensive simulation studies in which we evaluate the performance of RZiMM-scRNA and compare it with several popular methods, including Seurat, SC3, K-Means, and Hierarchical Clustering. Simulation results show that RZiMM-scRNA demonstrates superior clustering performance and enhanced biomarker detection accuracy compared to alternative methods, especially when cell subgroups are less distinct, verifying the robustness of our method. Our empirical investigations focus on two brain tumor studies dealing with astrocytoma of various grades, including the most malignant of all brain tumors, glioblastoma multiforme (GBM). Our goal is to delineate cell heterogeneity and identify driving biomarkers associated with these tumors. Notably, RZiMM-scNRA successfully identifies a small group of oligodendrocyte cells which has drawn much attention in biomedical literature on brain cancers. |
1801.06363 | Carla Fernandez Espinoza | Carla E. Fern\'andez, Melina Campero, Cintia Uvo and Lars-Anders
Hansson | Disentangling population strategies of two cladocerans adapted to
different ultraviolet regimes | 11 pages | null | 10.1002/ece3.3792 | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Zooplankton have evolved several mechanisms to deal with environmental
threats, such as ultraviolet radiation (UVR), and in order to identify
strategies inherent to organisms exposed to different UVR environments, we here
examine life-history traits of two lineages of Daphnia pulex. The lineages
differed in the UVR dose they had received at their place of origin from
extremely high UVR stress at high-altitude Bolivian lakes to low UVR stress
near the sea level in temperate Sweden. Nine life-history variables of each
lineage were analyzed in laboratory experiments in the presence and the absence
of sub-lethal doses of UVR (UV-A band), and we identified trade-offs among
variables through structural equation modeling (SEM). The UVR treatment was
detrimental to almost all life-history variables of both lineages; however, the
Daphnia historically exposed to higher doses of UVR (HighUV) showed a higher
overall fecundity than those historically exposed to lower doses of UVR
(LowUV). The total offspring and ephippia production, as well as the number of
clutches and number of offspring at first reproduction, was directly affected
by UVR in both lineages. Main differences between lineages involved indirect
effects that affected offspring production as the age at first reproduction. We
here show that organisms within the same species have developed different
strategies as responses to UVR, although no increased physiological tolerance
or plasticity was shown by the HighUV lineage. In addition to known tolerance
strategies to UVR, including avoidance, prevention, or repairing of damages, we
here propose a population strategy that includes early reproduction and high
fertility, which we show compensated for the fitness loss imposed by UVR
stress.
| [
{
"created": "Fri, 19 Jan 2018 11:04:33 GMT",
"version": "v1"
}
] | 2018-01-22 | [
[
"Fernández",
"Carla E.",
""
],
[
"Campero",
"Melina",
""
],
[
"Uvo",
"Cintia",
""
],
[
"Hansson",
"Lars-Anders",
""
]
] | Zooplankton have evolved several mechanisms to deal with environmental threats, such as ultraviolet radiation (UVR), and in order to identify strategies inherent to organisms exposed to different UVR environments, we here examine life-history traits of two lineages of Daphnia pulex. The lineages differed in the UVR dose they had received at their place of origin from extremely high UVR stress at high-altitude Bolivian lakes to low UVR stress near the sea level in temperate Sweden. Nine life-history variables of each lineage were analyzed in laboratory experiments in the presence and the absence of sub-lethal doses of UVR (UV-A band), and we identified trade-offs among variables through structural equation modeling (SEM). The UVR treatment was detrimental to almost all life-history variables of both lineages; however, the Daphnia historically exposed to higher doses of UVR (HighUV) showed a higher overall fecundity than those historically exposed to lower doses of UVR (LowUV). The total offspring and ephippia production, as well as the number of clutches and number of offspring at first reproduction, was directly affected by UVR in both lineages. Main differences between lineages involved indirect effects that affected offspring production as the age at first reproduction. We here show that organisms within the same species have developed different strategies as responses to UVR, although no increased physiological tolerance or plasticity was shown by the HighUV lineage. In addition to known tolerance strategies to UVR, including avoidance, prevention, or repairing of damages, we here propose a population strategy that includes early reproduction and high fertility, which we show compensated for the fitness loss imposed by UVR stress. |
0711.2346 | Emma Jin | Emma Y. Jin and Christian M. Reidys | $k$-noncrossing RNA structures with arc-length $\ge 3$ | 17 pages, 4 figures | null | null | null | q-bio.BM | null | In this paper we enumerate $k$-noncrossing RNA pseudoknot structures with
given minimum arc- and stack-length. That is, we study the numbers of RNA
pseudoknot structures with arc-length $\ge 3$, stack-length $\ge \sigma$ and in
which there are at most $k-1$ mutually crossing bonds, denoted by ${\sf
T}_{k,\sigma}^{[3]}(n)$. In particular we prove that the numbers of 3, 4 and
5-noncrossing RNA structures with arc-length $\ge 3$ and stack-length $\ge 2$
satisfy ${\sf T}_{3,2}^{[3]}(n)^{}\sim K_3 n^{-5} 2.5723^n$, ${\sf
T}^{[3]}_{4,2}(n)\sim K_4 n^{-{21/2}} 3.0306^n$, and ${\sf
T}^{[3]}_{5,2}(n)\sim K_5 n^{-18} 3.4092^n$, respectively, where $K_3,K_4,K_5$
are constants. Our results are of importance for prediction algorithms for RNA
pseudoknot structures.
| [
{
"created": "Thu, 15 Nov 2007 06:34:09 GMT",
"version": "v1"
},
{
"created": "Tue, 4 Dec 2007 09:37:37 GMT",
"version": "v2"
}
] | 2007-12-04 | [
[
"Jin",
"Emma Y.",
""
],
[
"Reidys",
"Christian M.",
""
]
] | In this paper we enumerate $k$-noncrossing RNA pseudoknot structures with given minimum arc- and stack-length. That is, we study the numbers of RNA pseudoknot structures with arc-length $\ge 3$, stack-length $\ge \sigma$ and in which there are at most $k-1$ mutually crossing bonds, denoted by ${\sf T}_{k,\sigma}^{[3]}(n)$. In particular we prove that the numbers of 3, 4 and 5-noncrossing RNA structures with arc-length $\ge 3$ and stack-length $\ge 2$ satisfy ${\sf T}_{3,2}^{[3]}(n)^{}\sim K_3 n^{-5} 2.5723^n$, ${\sf T}^{[3]}_{4,2}(n)\sim K_4 n^{-{21/2}} 3.0306^n$, and ${\sf T}^{[3]}_{5,2}(n)\sim K_5 n^{-18} 3.4092^n$, respectively, where $K_3,K_4,K_5$ are constants. Our results are of importance for prediction algorithms for RNA pseudoknot structures. |
1910.01618 | Alexandre Ren\'e | Alexandre Ren\'e, Andr\'e Longtin and Jakob H. Macke | Inference of a mesoscopic population model from population spike trains | 1st revision: 48 pages, 13 figures Improved statistical validation of
results. Rewrite of Section 4.2 to clarify the link between the mesoscopic
model and a transport equation. Multiple small improvements to the
presentation Original: 46 pages, 12 figures | null | null | null | q-bio.NC cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To understand how rich dynamics emerge in neural populations, we require
models exhibiting a wide range of activity patterns while remaining
interpretable in terms of connectivity and single-neuron dynamics. However, it
has been challenging to fit such mechanistic spiking networks at the single
neuron scale to empirical population data. To close this gap, we propose to fit
such data at a meso scale, using a mechanistic but low-dimensional and hence
statistically tractable model. The mesoscopic representation is obtained by
approximating a population of neurons as multiple homogeneous `pools' of
neurons, and modelling the dynamics of the aggregate population activity within
each pool. We derive the likelihood of both single-neuron and connectivity
parameters given this activity, which can then be used to either optimize
parameters by gradient ascent on the log-likelihood, or to perform Bayesian
inference using Markov Chain Monte Carlo (MCMC) sampling. We illustrate this
approach using a model of generalized integrate-and-fire neurons for which
mesoscopic dynamics have been previously derived, and show that both
single-neuron and connectivity parameters can be recovered from simulated data.
In particular, our inference method extracts posterior correlations between
model parameters, which define parameter subsets able to reproduce the data. We
compute the Bayesian posterior for combinations of parameters using MCMC
sampling and investigate how the approximations inherent to a mesoscopic
population model impact the accuracy of the inferred single-neuron parameters.
| [
{
"created": "Thu, 3 Oct 2019 17:37:42 GMT",
"version": "v1"
},
{
"created": "Sun, 8 Mar 2020 22:40:14 GMT",
"version": "v2"
}
] | 2020-03-10 | [
[
"René",
"Alexandre",
""
],
[
"Longtin",
"André",
""
],
[
"Macke",
"Jakob H.",
""
]
] | To understand how rich dynamics emerge in neural populations, we require models exhibiting a wide range of activity patterns while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single neuron scale to empirical population data. To close this gap, we propose to fit such data at a meso scale, using a mechanistic but low-dimensional and hence statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous `pools' of neurons, and modelling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to either optimize parameters by gradient ascent on the log-likelihood, or to perform Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived, and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent to a mesoscopic population model impact the accuracy of the inferred single-neuron parameters. |
2110.00364 | Eduard Campillo-Funollet | Eduard Campillo-Funollet, Hayley Wragg, James Van Yperen, Duc-Lam
Duong, Anotida Madzvamuse | Reformulating the SIR model in terms of the number of COVID-19 detected
cases: well-posedness of the observational model | null | null | 10.1098/rsta.2021.0306 | null | q-bio.PE math.AP | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Compartmental models are popular in the mathematics of epidemiology for their
simplicity and wide range of applications. Although they are typically solved
as initial value problems for a system of ordinary differential equations, the
observed data is typically akin of a boundary value type problem: we observe
some of the dependent variables at given times, but we do not know the initial
conditions. In this paper, we reformulate the classical
Susceptible-Infectious-Recovered system in terms of the number of detected
positive infected cases at different times, we then prove the existence and
uniqueness of a solution to the derived boundary value problem and then present
a numerical algorithm to approximate the solution.
| [
{
"created": "Fri, 1 Oct 2021 12:52:58 GMT",
"version": "v1"
}
] | 2022-10-12 | [
[
"Campillo-Funollet",
"Eduard",
""
],
[
"Wragg",
"Hayley",
""
],
[
"Van Yperen",
"James",
""
],
[
"Duong",
"Duc-Lam",
""
],
[
"Madzvamuse",
"Anotida",
""
]
] | Compartmental models are popular in the mathematics of epidemiology for their simplicity and wide range of applications. Although they are typically solved as initial value problems for a system of ordinary differential equations, the observed data is typically akin of a boundary value type problem: we observe some of the dependent variables at given times, but we do not know the initial conditions. In this paper, we reformulate the classical Susceptible-Infectious-Recovered system in terms of the number of detected positive infected cases at different times, we then prove the existence and uniqueness of a solution to the derived boundary value problem and then present a numerical algorithm to approximate the solution. |
2202.13685 | Petr Ture\v{c}ek | Petr Ture\v{c}ek, Michal Koz\'ak, Jakub Slav\'ik | Assortative pairing alone can lead to a structured biota in organisms
with cultural transmission | Manuscript (13 pages) and Supplementary material (19 pages) | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Spatial separation is often included in models of ethnic divergence but it
has also been realised that urban subcultures can, and frequently do, emerge in
sympatry. Previous research tended to attribute this phenomenon to the human
tendency to imitate self-similar individuals and actively differentiate oneself
from individuals recognized as members of an outgroup. Application of such a
model to non-human animals has been, however, viewed as problematic. We present
a parsimonious model of subculture emergence where the algorithm of social
learning does not require the assumption of an 'imitation threshold'. All it
takes is a slight modification of Galton-Pearson's biometric model previously
used to approximate cultural inheritance. The new model includes
proportionality between the variance of inputs (cultural 'parents') and the
variance of outputs (cultural 'offspring'). In this model, assortment alone can
lead to the formation of distinct cohesive clusters of individuals
(subcultures) with a low within-group and large between-group variability even
in absence of a spatial separation or disruptive natural selection. Sympatric
emergence of arbitrary behavioural varieties preceding ecological divergence
may thus represent the norm, not the exception, in all cultural animals.
| [
{
"created": "Mon, 28 Feb 2022 11:09:17 GMT",
"version": "v1"
}
] | 2022-03-01 | [
[
"Tureček",
"Petr",
""
],
[
"Kozák",
"Michal",
""
],
[
"Slavík",
"Jakub",
""
]
] | Spatial separation is often included in models of ethnic divergence but it has also been realised that urban subcultures can, and frequently do, emerge in sympatry. Previous research tended to attribute this phenomenon to the human tendency to imitate self-similar individuals and actively differentiate oneself from individuals recognized as members of an outgroup. Application of such a model to non-human animals has been, however, viewed as problematic. We present a parsimonious model of subculture emergence where the algorithm of social learning does not require the assumption of an 'imitation threshold'. All it takes is a slight modification of Galton-Pearson's biometric model previously used to approximate cultural inheritance. The new model includes proportionality between the variance of inputs (cultural 'parents') and the variance of outputs (cultural 'offspring'). In this model, assortment alone can lead to the formation of distinct cohesive clusters of individuals (subcultures) with a low within-group and large between-group variability even in absence of a spatial separation or disruptive natural selection. Sympatric emergence of arbitrary behavioural varieties preceding ecological divergence may thus represent the norm, not the exception, in all cultural animals. |
q-bio/0409032 | Mauro Copelli | Mauro Copelli and Osame Kinouchi | Intensity Coding in Two-Dimensional Excitable Neural Networks | 17 pages, 5 figures | Physica A 349 (2005) 431-442 | 10.1016/j.physa.2004.10.043 | null | q-bio.NC cond-mat.dis-nn cond-mat.stat-mech nlin.CG nlin.PS physics.bio-ph q-bio.TO | null | In the light of recent experimental findings that gap junctions are essential
for low level intensity detection in the sensory periphery, the
Greenberg-Hastings cellular automaton is employed to model the response of a
two-dimensional sensory network to external stimuli. We show that excitable
elements (sensory neurons) that have a small dynamical range are shown to give
rise to a collective large dynamical range. Therefore the network transfer
(gain) function (which is Hill or Stevens law-like) is an emergent property
generated from a pool of small dynamical range cells, providing a basis for a
"neural psychophysics". The growth of the dynamical range with the system size
is approximately logarithmic, suggesting a functional role for electrical
coupling. For a fixed number of neurons, the dynamical range displays a maximum
as a function of the refractory period, which suggests experimental tests for
the model. A biological application to ephaptic interactions in olfactory nerve
fascicles is proposed.
| [
{
"created": "Tue, 28 Sep 2004 15:32:39 GMT",
"version": "v1"
}
] | 2016-09-08 | [
[
"Copelli",
"Mauro",
""
],
[
"Kinouchi",
"Osame",
""
]
] | In the light of recent experimental findings that gap junctions are essential for low level intensity detection in the sensory periphery, the Greenberg-Hastings cellular automaton is employed to model the response of a two-dimensional sensory network to external stimuli. We show that excitable elements (sensory neurons) that have a small dynamical range are shown to give rise to a collective large dynamical range. Therefore the network transfer (gain) function (which is Hill or Stevens law-like) is an emergent property generated from a pool of small dynamical range cells, providing a basis for a "neural psychophysics". The growth of the dynamical range with the system size is approximately logarithmic, suggesting a functional role for electrical coupling. For a fixed number of neurons, the dynamical range displays a maximum as a function of the refractory period, which suggests experimental tests for the model. A biological application to ephaptic interactions in olfactory nerve fascicles is proposed. |
q-bio/0509018 | Oliver Beckstein | Oliver Beckstein and Mark S. P. Sansom | A Hydrophobic Gate in an Ion Channel: The Closed State of the Nicotinic
Acetylcholine Receptor | Accepted by Physical Biology; includes a supplement and a
supplementary mpeg movie can be found at
http://sbcb.bioch.ox.ac.uk/oliver/download/Movies/watergate.mpg | Physical Biology, 3(2):147-159, 2006 | 10.1088/1478-3975/3/2/007 | null | q-bio.BM q-bio.SC | null | The nicotinic acetylcholine receptor (nAChR) is the prototypic member of the
`Cys-loop' superfamily of ligand-gated ion channels which mediate synaptic
neurotransmission, and whose other members include receptors for glycine,
gamma-aminobutyric acid, and serotonin. Cryo-electron microscopy has yielded a
three dimensional structure of the nAChR in its closed state. However, the
exact nature and location of the channel gate remains uncertain. Although the
transmembrane pore is constricted close to its center, it is not completely
occluded. Rather, the pore has a central hydrophobic zone of radius about 3 A.
Model calculations suggest that such a constriction may form a hydrophobic
gate, preventing movement of ions through a channel. We present a detailed and
quantitative simulation study of the hydrophobic gating model of the nicotinic
receptor, in order to fully evaluate this hypothesis. We demonstrate that the
hydrophobic constriction of the nAChR pore indeed forms a closed gate.
Potential of mean force (PMF) calculations reveal that the constriction
presents a barrier of height ca. 10 kT to the permeation of sodium ions,
placing an upper bound on the closed channel conductance of 0.3 pS. Thus, a 3 A
radius hydrophobic pore can form a functional barrier to the permeation of a 1
A radius Na+ ion. Using a united atom force field for the protein instead of an
all atom one retains the qualitative features but results in differing
conductances, showing that the PMF is sensitive to the detailed molecular
interactions.
| [
{
"created": "Thu, 15 Sep 2005 00:17:46 GMT",
"version": "v1"
},
{
"created": "Thu, 15 Jun 2006 05:27:05 GMT",
"version": "v2"
}
] | 2019-07-02 | [
[
"Beckstein",
"Oliver",
""
],
[
"Sansom",
"Mark S. P.",
""
]
] | The nicotinic acetylcholine receptor (nAChR) is the prototypic member of the `Cys-loop' superfamily of ligand-gated ion channels which mediate synaptic neurotransmission, and whose other members include receptors for glycine, gamma-aminobutyric acid, and serotonin. Cryo-electron microscopy has yielded a three dimensional structure of the nAChR in its closed state. However, the exact nature and location of the channel gate remains uncertain. Although the transmembrane pore is constricted close to its center, it is not completely occluded. Rather, the pore has a central hydrophobic zone of radius about 3 A. Model calculations suggest that such a constriction may form a hydrophobic gate, preventing movement of ions through a channel. We present a detailed and quantitative simulation study of the hydrophobic gating model of the nicotinic receptor, in order to fully evaluate this hypothesis. We demonstrate that the hydrophobic constriction of the nAChR pore indeed forms a closed gate. Potential of mean force (PMF) calculations reveal that the constriction presents a barrier of height ca. 10 kT to the permeation of sodium ions, placing an upper bound on the closed channel conductance of 0.3 pS. Thus, a 3 A radius hydrophobic pore can form a functional barrier to the permeation of a 1 A radius Na+ ion. Using a united atom force field for the protein instead of an all atom one retains the qualitative features but results in differing conductances, showing that the PMF is sensitive to the detailed molecular interactions. |
2311.07313 | Sridhar Seshan | S.Sridhar and Richard.H.Clayton | Fibroblast mediated dynamics in diffusively uncoupled myocytes -- a
simulation study using 2-cell motifs | null | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | In healthy hearts myocytes are typically coupled to nearest neighbours
through gap junctions. Under pathological conditions such as fibrosis, or in
scar tissue, or across ablation lines myocytes can uncouple from their
neighbours. Electrical conduction may still occur via fibroblasts that not only
couple proximal myocytes but can also couple otherwise unconnected regions. We
hypothesise that such coupling can alter conduction between myocytes via
introduction of delays or by initiation of premature stimuli that can
potentially result in reentry or conduction blocks. To test this hypothesis we
have developed several $2$-cell motifs and investigated the effect of
fibroblast mediated electrical coupling between uncoupled myocytes. We have
identified various regimes of myocyte behaviour that depend on the strength of
gap-junctional conductance, connection topology, and parameters of the myocyte
and fibroblast models. These motifs are useful in developing a mechanistic
understanding of long-distance coupling on myocyte dynamics and enable the
characterisation of interaction between different features such as myocyte and
fibroblast properties, coupling strengths and pacing period. They are
computationally inexpensive and allow for incorporation of spatial effects such
as conduction velocity. They provide a framework for constructing scar tissue
boundaries and enable linking of cellular level interactions with scar induced
arrhythmia.
| [
{
"created": "Mon, 13 Nov 2023 13:08:24 GMT",
"version": "v1"
}
] | 2023-11-14 | [
[
"Sridhar",
"S.",
""
],
[
"Clayton",
"Richard. H.",
""
]
] | In healthy hearts myocytes are typically coupled to nearest neighbours through gap junctions. Under pathological conditions such as fibrosis, or in scar tissue, or across ablation lines myocytes can uncouple from their neighbours. Electrical conduction may still occur via fibroblasts that not only couple proximal myocytes but can also couple otherwise unconnected regions. We hypothesise that such coupling can alter conduction between myocytes via introduction of delays or by initiation of premature stimuli that can potentially result in reentry or conduction blocks. To test this hypothesis we have developed several $2$-cell motifs and investigated the effect of fibroblast mediated electrical coupling between uncoupled myocytes. We have identified various regimes of myocyte behaviour that depend on the strength of gap-junctional conductance, connection topology, and parameters of the myocyte and fibroblast models. These motifs are useful in developing a mechanistic understanding of long-distance coupling on myocyte dynamics and enable the characterisation of interaction between different features such as myocyte and fibroblast properties, coupling strengths and pacing period. They are computationally inexpensive and allow for incorporation of spatial effects such as conduction velocity. They provide a framework for constructing scar tissue boundaries and enable linking of cellular level interactions with scar induced arrhythmia. |
1204.1558 | Donald Cooper Ph.D. | Peter Dobelis, Andrew L. Varnell, Kevin J. Staley, and Donald C.
Cooper | Nicotinic {\alpha}7 acetylcholine receptor-mediated currents are not
modulated by the tryptophan metabolite kynurenic acid in adult hippocampal
interneurons | 2 pages, 2 figures, Nature Precedings
http://dx.doi.org/10.1038/npre.2011.6277.1 | null | 10.1038/npre.2011.6277.1 | null | q-bio.NC q-bio.BM q-bio.CB | http://creativecommons.org/licenses/by-nc-sa/3.0/ | The tryptophan metabolite, kynurenic acid (KYNA), is classically known to be
an antagonist of ionotropic glutamate receptors. Within the last decade several
reports have been published suggesting that KYNA also blocks nicotinic
acetylcholine receptors (nAChRs) containing the \alpha7 subunit (\alpha7*).
Most of these reports involve either indirect measurements of KYNA effects on
\alpha7 nAChR function, or are reports of KYNA effects in complicated in vivo
systems. However, a recent report investigating KYNA interactions with \alpha7
nAChRs failed to detect an interaction using direct measurements of \alpha7
nAChRs function. Further, it showed that a KYNA blockade of \alpha7 nAChR
stimulated GABA release (an indirect measure of \alpha7 nAChR function) was not
due to KYNA blockade of the \alpha7 nAChRs. The current study measured the
direct effects of KYNA on \alpha7-containing nAChRs expressed on interneurons
in the hilar and CA1 stratum radiatum regions of the mouse hippocampus and on
interneurons in the CA1 region of the rat hippocampus. Here we show that KYNA
does not block \alpha7* nACHRs using direct patch-clamprecording of \alpha7
currents in adult brain slices.
| [
{
"created": "Fri, 6 Apr 2012 20:33:47 GMT",
"version": "v1"
}
] | 2012-04-10 | [
[
"Dobelis",
"Peter",
""
],
[
"Varnell",
"Andrew L.",
""
],
[
"Staley",
"Kevin J.",
""
],
[
"Cooper",
"Donald C.",
""
]
] | The tryptophan metabolite, kynurenic acid (KYNA), is classically known to be an antagonist of ionotropic glutamate receptors. Within the last decade several reports have been published suggesting that KYNA also blocks nicotinic acetylcholine receptors (nAChRs) containing the \alpha7 subunit (\alpha7*). Most of these reports involve either indirect measurements of KYNA effects on \alpha7 nAChR function, or are reports of KYNA effects in complicated in vivo systems. However, a recent report investigating KYNA interactions with \alpha7 nAChRs failed to detect an interaction using direct measurements of \alpha7 nAChRs function. Further, it showed that a KYNA blockade of \alpha7 nAChR stimulated GABA release (an indirect measure of \alpha7 nAChR function) was not due to KYNA blockade of the \alpha7 nAChRs. The current study measured the direct effects of KYNA on \alpha7-containing nAChRs expressed on interneurons in the hilar and CA1 stratum radiatum regions of the mouse hippocampus and on interneurons in the CA1 region of the rat hippocampus. Here we show that KYNA does not block \alpha7* nACHRs using direct patch-clamprecording of \alpha7 currents in adult brain slices. |
2210.16647 | J. C. Phillips | J. C. Phillips | Evolution and Function of SMC Proteins | 1 pages, 12 figures, 1 table | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Structural Maintenance of Chromosomes, SMCs, proteins have long rod like
structures immersed in water. Here we use our hydroanalytic methods based on
amino acid sequences to discuss their dynamics at multiple length scales
identified by evolution. The length scales are 10 to 100 times longer than used
in normal studies of sequence evolution. Their hydropathic profiles exhibit
many features unique to their structure and function.
| [
{
"created": "Sat, 29 Oct 2022 16:30:48 GMT",
"version": "v1"
}
] | 2022-11-01 | [
[
"Phillips",
"J. C.",
""
]
] | Structural Maintenance of Chromosomes, SMCs, proteins have long rod like structures immersed in water. Here we use our hydroanalytic methods based on amino acid sequences to discuss their dynamics at multiple length scales identified by evolution. The length scales are 10 to 100 times longer than used in normal studies of sequence evolution. Their hydropathic profiles exhibit many features unique to their structure and function. |
2401.11746 | Sonja Billerbeck | Alicia Maci\'a Valero, Rianne C. Prins, Thijs de Vroet, Sonja
Billerbeck | Combining oligo pools and Golden Gate cloning to create protein variant
libraries or guide RNA libraries for CRISPR applications | null | null | null | null | q-bio.QM q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Oligo pools are array-synthesized, user-defined mixtures of single-stranded
oligonucleotides that can be used as a source of synthetic DNA for library
cloning. While currently offering the most affordable source of synthetic DNA,
oligo pools also come with limitations such as a maximum synthesis length
(approximately 350 bases), a higher error rate compared to alternative
synthesis methods, and the presence of truncated molecules in the pool due to
incomplete synthesis. Here, we provide users with a comprehensive protocol that
details how oligo pools can be used in combination with Golden Gate cloning to
create user-defined protein mutant libraries, as well as single guide RNA
libraries for CRISPR applications. Our methods are optimized to work within the
Yeast Toolkit Golden Gate scheme, but are in principle compatible with any
other Golden Gate-based modular cloning toolkit and extendable to other
restriction enzyme-based cloning methods beyond Golden Gate. Our methods yield
high-quality, affordable, in-house variant libraries.
| [
{
"created": "Mon, 22 Jan 2024 08:13:23 GMT",
"version": "v1"
}
] | 2024-01-23 | [
[
"Valero",
"Alicia Maciá",
""
],
[
"Prins",
"Rianne C.",
""
],
[
"de Vroet",
"Thijs",
""
],
[
"Billerbeck",
"Sonja",
""
]
] | Oligo pools are array-synthesized, user-defined mixtures of single-stranded oligonucleotides that can be used as a source of synthetic DNA for library cloning. While currently offering the most affordable source of synthetic DNA, oligo pools also come with limitations such as a maximum synthesis length (approximately 350 bases), a higher error rate compared to alternative synthesis methods, and the presence of truncated molecules in the pool due to incomplete synthesis. Here, we provide users with a comprehensive protocol that details how oligo pools can be used in combination with Golden Gate cloning to create user-defined protein mutant libraries, as well as single guide RNA libraries for CRISPR applications. Our methods are optimized to work within the Yeast Toolkit Golden Gate scheme, but are in principle compatible with any other Golden Gate-based modular cloning toolkit and extendable to other restriction enzyme-based cloning methods beyond Golden Gate. Our methods yield high-quality, affordable, in-house variant libraries. |
1602.09054 | Valerey Grytsay Dr | V.I. Grytsay | Lyapunov Indices and the Poincar\'e Mapping in a Study of the Stability
of the Krebs Cycle | null | Ukr. J. Phys. 2015, Vol. 60, N 6, p.561-574 | 10.15407/ujpe60.06.0561 | null | q-bio.MN nlin.AO nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | On the basis of a mathematical model, we continue the study of the metabolic
Krebs cycle (or the tricarboxilic acid cycle). For the first time, we consider
its consistency and stability, which depend on the dissipation of a
transmembrane potential formed by the respiratory chain in the plasmatic
membrane of a cell. The phase-parametric characteristic of the dynamics of the
ATP level depending on a given parameter is constructed. The scenario of
formation of multiple autoperiodic and chaotic modes is presented. Poincar\'{e}
sections and mappings are constructed. The stability of modes and the
fractality of the obtained bifurcations are studied. The full spectra of
Lyapunov indices, divergences, KS-entropies, horizons of predictability, and
Lyapunov dimensionalities of strange attractors are calculated. Some
conclusions about the structural-functional connections determining the
dependence of the cell respiration cyclicity on the synchronization of the
functioning of the tricarboxilic acid cycle and the electron transport chain
are presented.
| [
{
"created": "Thu, 28 Jan 2016 14:52:30 GMT",
"version": "v1"
}
] | 2016-03-01 | [
[
"Grytsay",
"V. I.",
""
]
] | On the basis of a mathematical model, we continue the study of the metabolic Krebs cycle (or the tricarboxilic acid cycle). For the first time, we consider its consistency and stability, which depend on the dissipation of a transmembrane potential formed by the respiratory chain in the plasmatic membrane of a cell. The phase-parametric characteristic of the dynamics of the ATP level depending on a given parameter is constructed. The scenario of formation of multiple autoperiodic and chaotic modes is presented. Poincar\'{e} sections and mappings are constructed. The stability of modes and the fractality of the obtained bifurcations are studied. The full spectra of Lyapunov indices, divergences, KS-entropies, horizons of predictability, and Lyapunov dimensionalities of strange attractors are calculated. Some conclusions about the structural-functional connections determining the dependence of the cell respiration cyclicity on the synchronization of the functioning of the tricarboxilic acid cycle and the electron transport chain are presented. |
1401.4913 | Krishanu Deyasi | Krishanu Deyasi, Shashankaditya Upadhyay, Anirban Banerjee | Communication on structure of biological networks | null | Pramana, Volume 86, Issue 3 , pp 617-635 (2016) | 10.1007/s12043-015-1035-3 | null | q-bio.QM physics.soc-ph q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Networks are widely used to represent interaction pattern among the
components in complex systems. Structures of real networks from differ- ent
domains may vary quite significantly. Since there is an interplay be- tween
network architecture and dynamics, structure plays an important role in
communication and information spreading on a network. Here we investigate the
underlying undirected topology of different biological networks which support
faster spreading of information and are better in communication. We analyze the
good expansion property by using the spectral gap and communicability between
nodes. Different epidemic models are also used to study the transmission of
information in terms of disease spreading through individuals (nodes) in those
networks. More- over, we explore the structural conformation and properties
which may be responsible for better communication. Among all biological
networks studied here, the undirected structure of neuronal networks not only
pos- sesses the small-world property but the same is expressed remarkably to a
higher degree than any randomly generated network which possesses the same
degree sequence. A relatively high percentage of nodes, in neuronal networks,
form a higher core in their structure. Our study shows that the underlying
undirected topology in neuronal networks is significantly qualitatively
different than the same from other biological networks and that they may have
evolved in such a way that they inherit a (undirected) structure which is
excellent and robust in communication.
| [
{
"created": "Mon, 20 Jan 2014 14:19:44 GMT",
"version": "v1"
},
{
"created": "Sun, 28 Dec 2014 09:21:38 GMT",
"version": "v2"
}
] | 2016-05-10 | [
[
"Deyasi",
"Krishanu",
""
],
[
"Upadhyay",
"Shashankaditya",
""
],
[
"Banerjee",
"Anirban",
""
]
] | Networks are widely used to represent interaction pattern among the components in complex systems. Structures of real networks from differ- ent domains may vary quite significantly. Since there is an interplay be- tween network architecture and dynamics, structure plays an important role in communication and information spreading on a network. Here we investigate the underlying undirected topology of different biological networks which support faster spreading of information and are better in communication. We analyze the good expansion property by using the spectral gap and communicability between nodes. Different epidemic models are also used to study the transmission of information in terms of disease spreading through individuals (nodes) in those networks. More- over, we explore the structural conformation and properties which may be responsible for better communication. Among all biological networks studied here, the undirected structure of neuronal networks not only pos- sesses the small-world property but the same is expressed remarkably to a higher degree than any randomly generated network which possesses the same degree sequence. A relatively high percentage of nodes, in neuronal networks, form a higher core in their structure. Our study shows that the underlying undirected topology in neuronal networks is significantly qualitatively different than the same from other biological networks and that they may have evolved in such a way that they inherit a (undirected) structure which is excellent and robust in communication. |
2103.00661 | Josinaldo Menezes | J. Menezes | Antipredator behavior in the rock-paper-scissors model | 6 pages, 7 figures | Phys. Rev. E 103, 052216 (2021) | 10.1103/PhysRevE.103.052216 | null | q-bio.PE nlin.AO nlin.PS physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When faced with an imminent risk of predation, many animals react to escape
consumption. Antipredator strategies are performed by individuals acting as a
group to intimidate predators and minimize the damage when attacked. We study
the antipredator prey response in spatial tritrophic systems with cyclic
species dominance using the rock-paper-scissors game. The impact of the
antipredator behavior is local, with the predation probability reducing
exponentially with the number of preys in the predator's neighborhood. In
contrast to the standard Lotka-Volterra implementation of the
rock-paper-scissors model, where no spiral waves appear, our outcomes show that
the antipredator behavior leads to spiral patterns from random initial
conditions. The results show that the predation risk decreases exponentially
with the level of antipredator strength. Finally, we investigate the
coexistence probability and verify that antipredator behavior may jeopardize
biodiversity for high mobility. Our findings may help biologists to understand
ecosystems formed by species whose individuals behave strategically to resist
predation.
| [
{
"created": "Sun, 28 Feb 2021 23:57:42 GMT",
"version": "v1"
},
{
"created": "Tue, 25 May 2021 17:00:48 GMT",
"version": "v2"
}
] | 2021-06-02 | [
[
"Menezes",
"J.",
""
]
] | When faced with an imminent risk of predation, many animals react to escape consumption. Antipredator strategies are performed by individuals acting as a group to intimidate predators and minimize the damage when attacked. We study the antipredator prey response in spatial tritrophic systems with cyclic species dominance using the rock-paper-scissors game. The impact of the antipredator behavior is local, with the predation probability reducing exponentially with the number of preys in the predator's neighborhood. In contrast to the standard Lotka-Volterra implementation of the rock-paper-scissors model, where no spiral waves appear, our outcomes show that the antipredator behavior leads to spiral patterns from random initial conditions. The results show that the predation risk decreases exponentially with the level of antipredator strength. Finally, we investigate the coexistence probability and verify that antipredator behavior may jeopardize biodiversity for high mobility. Our findings may help biologists to understand ecosystems formed by species whose individuals behave strategically to resist predation. |
2001.00492 | H Gilbert Welch | H. Gilbert Welch, Michael J. Barry, William C Black, Yunjie Song,
Elliott S. Fisher | The Effect of Treatment-Related Deaths and "Sticky" Diagnoses on
Recorded Prostate Cancer Mortality | 20 pages, 5 figures | null | null | null | q-bio.PE q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Background: Although recorded cancer mortality should include both deaths
from cancer and deaths from cancer treatment, there is evidence suggesting that
the measure may be incomplete. To investigate the completeness of recorded
prostate cancer mortality, we compared other-cause (non-prostate cancer)
mortality in men found and not found to have prostate cancer following a needle
biopsy.
Methods: We linked Medicare claims data to SEER data to analyze survival in
the population of men aged 65+ enrolled in Medicare who resided in a SEER area
and received a needle biopsy in 1993-2001. We compared other-cause mortality in
men found to have prostate cancer (n=53,462) to that in men not found to have
prostate cancer (n=103,659).
Results: The age-race adjusted other-cause mortality rate was 471 per 10,000
person-years in men found to have prostate cancer vs. 468 per 10,000 in men not
found to have prostate cancer (RR = 1.01;95% CI:0.98-1.03). The effect was
modified, however, by age. The RR declined in a stepwise fashion from 1.08 (95%
CI:1.03-1.14) in men age 65-69 to 0.89 (95% CI:0.83-0.95) in men age 85 and
older. If the excess (or deficit) in other-cause mortality were added to the
recorded prostate cancer mortality, prostate cancer mortality would rise 23% in
the youngest age group (from 90 to 111 per 10,000) and would fall 30% in the
oldest age group (from 551 to 388 per 10,000).
Conclusion: Although recorded prostate cancer mortality appears to be an
accurate measure overall, it systematically underestimates the mortality
associated with prostate cancer diagnosis and treatment in younger men and
overestimates it in the very old. We surmise that in younger men
treatment-related deaths are incompletely captured in recorded prostate cancer
mortality, while in older men the diagnosis "sticks"-- once diagnosed, they are
more likely to be said to have died from the disease.
| [
{
"created": "Thu, 2 Jan 2020 15:37:55 GMT",
"version": "v1"
}
] | 2020-01-03 | [
[
"Welch",
"H. Gilbert",
""
],
[
"Barry",
"Michael J.",
""
],
[
"Black",
"William C",
""
],
[
"Song",
"Yunjie",
""
],
[
"Fisher",
"Elliott S.",
""
]
] | Background: Although recorded cancer mortality should include both deaths from cancer and deaths from cancer treatment, there is evidence suggesting that the measure may be incomplete. To investigate the completeness of recorded prostate cancer mortality, we compared other-cause (non-prostate cancer) mortality in men found and not found to have prostate cancer following a needle biopsy. Methods: We linked Medicare claims data to SEER data to analyze survival in the population of men aged 65+ enrolled in Medicare who resided in a SEER area and received a needle biopsy in 1993-2001. We compared other-cause mortality in men found to have prostate cancer (n=53,462) to that in men not found to have prostate cancer (n=103,659). Results: The age-race adjusted other-cause mortality rate was 471 per 10,000 person-years in men found to have prostate cancer vs. 468 per 10,000 in men not found to have prostate cancer (RR = 1.01;95% CI:0.98-1.03). The effect was modified, however, by age. The RR declined in a stepwise fashion from 1.08 (95% CI:1.03-1.14) in men age 65-69 to 0.89 (95% CI:0.83-0.95) in men age 85 and older. If the excess (or deficit) in other-cause mortality were added to the recorded prostate cancer mortality, prostate cancer mortality would rise 23% in the youngest age group (from 90 to 111 per 10,000) and would fall 30% in the oldest age group (from 551 to 388 per 10,000). Conclusion: Although recorded prostate cancer mortality appears to be an accurate measure overall, it systematically underestimates the mortality associated with prostate cancer diagnosis and treatment in younger men and overestimates it in the very old. We surmise that in younger men treatment-related deaths are incompletely captured in recorded prostate cancer mortality, while in older men the diagnosis "sticks"-- once diagnosed, they are more likely to be said to have died from the disease. |
2008.09704 | Marcos Edel Martinez-Montero Dr. | M.E. Martinez-Montero, M.T. Gonzalez-Arnao, C. Borroto-Nordelo, C.
Puentes-Diaz, F. Engelmann | Cryopreservation of sugarcane embryogenic callus using a simplified
freezing process | 6 pages, 3 tables | CRYO-LETTERS, Volume: 19, Issue: 3, Pages: 171-176, Published:
MAY-JUN 1998, Document Type:Article Published by Cryo-Letters, 7, Wootton
Way, Cambridge CB3 9LX, U.K | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A simplified freezing process was developed and successfully applied to
embryogenic calluses of three sugarcane commercial hybrids (Saccharum sp. cv.
CP 5243, C 91-301 and C 1051-73). To obtain optimal survival, the calluses were
pretreated with a cryoprotective solution containing 10% DMSO and 0.3 to 0.75M
sucrose. For freezing, the samples were immersed in an alcohol bath placed in a
-40 degrees C freezer, thus allowing a freezing rate comprised between 0.4 and
0.6 degrees C/min. Samples were held at that temperature for 2 h before
immersion in liquid nitrogen. The highest survival rates of cryopreserved
calluses ranged between 20 and 94% depending on the variety, and fully
developed plantlets could be obtained from regenerating calluses. Embryogenic
calluses of one variety were stored for 14 months in liquid nitrogen without
any effect on their survival rate and plantlet production.
| [
{
"created": "Fri, 21 Aug 2020 22:39:24 GMT",
"version": "v1"
}
] | 2020-08-25 | [
[
"Martinez-Montero",
"M. E.",
""
],
[
"Gonzalez-Arnao",
"M. T.",
""
],
[
"Borroto-Nordelo",
"C.",
""
],
[
"Puentes-Diaz",
"C.",
""
],
[
"Engelmann",
"F.",
""
]
] | A simplified freezing process was developed and successfully applied to embryogenic calluses of three sugarcane commercial hybrids (Saccharum sp. cv. CP 5243, C 91-301 and C 1051-73). To obtain optimal survival, the calluses were pretreated with a cryoprotective solution containing 10% DMSO and 0.3 to 0.75M sucrose. For freezing, the samples were immersed in an alcohol bath placed in a -40 degrees C freezer, thus allowing a freezing rate comprised between 0.4 and 0.6 degrees C/min. Samples were held at that temperature for 2 h before immersion in liquid nitrogen. The highest survival rates of cryopreserved calluses ranged between 20 and 94% depending on the variety, and fully developed plantlets could be obtained from regenerating calluses. Embryogenic calluses of one variety were stored for 14 months in liquid nitrogen without any effect on their survival rate and plantlet production. |
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