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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
q-bio/0608002 | Leor Weinberger | Leor S. Weinberger, John C. Burnett, Jared E. Toettcher, Adam P.
Arkin, and David V. Schaffer | Stochastic Gene Expression in a Lentiviral Positive Feedback Loop: HIV-1
Tat Fluctuations Drive Phenotypic Diversity | Supplemental data available as q-bio.MN/0608003 | Cell. 2005 Jul 29;122(2):169-82 | null | null | q-bio.MN cond-mat.soft physics.bio-ph q-bio.CB | null | Stochastic gene expression has been implicated in a variety of cellular
processes, including cell differentiation and disease. In this issue of Cell,
Weinberger et al. (2005) take an integrated computational-experimental approach
to study the Tat transactivation feedback loop in HIV-1 and show that
fluctuations in a key regulator, Tat, can result in a phenotypic bifurcation.
This phenomenon is observed in an isogenic population where individual cells
display two distinct expression states corresponding to latent and productive
infection by HIV-1. These findings demonstrate the importance of stochastic
gene expression in molecular "decision-making."
| [
{
"created": "Tue, 1 Aug 2006 19:37:32 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Weinberger",
"Leor S.",
""
],
[
"Burnett",
"John C.",
""
],
[
"Toettcher",
"Jared E.",
""
],
[
"Arkin",
"Adam P.",
""
],
[
"Schaffer",
"David V.",
""
]
] | Stochastic gene expression has been implicated in a variety of cellular processes, including cell differentiation and disease. In this issue of Cell, Weinberger et al. (2005) take an integrated computational-experimental approach to study the Tat transactivation feedback loop in HIV-1 and show that fluctuations in a key regulator, Tat, can result in a phenotypic bifurcation. This phenomenon is observed in an isogenic population where individual cells display two distinct expression states corresponding to latent and productive infection by HIV-1. These findings demonstrate the importance of stochastic gene expression in molecular "decision-making." |
2011.12350 | Changchuan Yin Dr. | Changchuan Yin, Stephen S.-T. Yau | Inverted repeats in coronavirus SARS-CoV-2 genome and implications in
evolution | null | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The coronavirus disease (COVID-19) pandemic, caused by the coronavirus
SARS-CoV-2, has caused 60 millions of infections and 1.38 millions of
fatalities. Genomic analysis of SARS-CoV-2 can provide insights on drug design
and vaccine development for controlling the pandemic. Inverted repeats in a
genome greatly impact the stability of the genome structure and regulate gene
expression. Inverted repeats involve cellular evolution and genetic diversity,
genome arrangements, and diseases. Here, we investigate the inverted repeats in
the coronavirus SARS-CoV-2 genome. We found that SARS-CoV-2 genome has an
abundance of inverted repeats. The inverted repeats are mainly located in the
gene of the Spike protein. This result suggests the Spike protein gene
undergoes recombination events, therefore, is essential for fast evolution.
Comparison of the inverted repeat signatures in human and bat coronaviruses
suggest that SARS-CoV-2 is mostly related SARS-related coronavirus,
SARSr-CoV/RaTG13. The study also reveals that the recent SARS-related
coronavirus, SARSr-CoV/RmYN02, has a high amount of inverted repeats in the
spike protein gene. Besides, this study demonstrates that the inverted repeat
distribution in a genome can be considered as the genomic signature. This study
highlights the significance of inverted repeats in the evolution of SARS-CoV-2
and presents the inverted repeats as the genomic signature in genome analysis.
| [
{
"created": "Tue, 24 Nov 2020 20:11:40 GMT",
"version": "v1"
}
] | 2020-11-26 | [
[
"Yin",
"Changchuan",
""
],
[
"Yau",
"Stephen S. -T.",
""
]
] | The coronavirus disease (COVID-19) pandemic, caused by the coronavirus SARS-CoV-2, has caused 60 millions of infections and 1.38 millions of fatalities. Genomic analysis of SARS-CoV-2 can provide insights on drug design and vaccine development for controlling the pandemic. Inverted repeats in a genome greatly impact the stability of the genome structure and regulate gene expression. Inverted repeats involve cellular evolution and genetic diversity, genome arrangements, and diseases. Here, we investigate the inverted repeats in the coronavirus SARS-CoV-2 genome. We found that SARS-CoV-2 genome has an abundance of inverted repeats. The inverted repeats are mainly located in the gene of the Spike protein. This result suggests the Spike protein gene undergoes recombination events, therefore, is essential for fast evolution. Comparison of the inverted repeat signatures in human and bat coronaviruses suggest that SARS-CoV-2 is mostly related SARS-related coronavirus, SARSr-CoV/RaTG13. The study also reveals that the recent SARS-related coronavirus, SARSr-CoV/RmYN02, has a high amount of inverted repeats in the spike protein gene. Besides, this study demonstrates that the inverted repeat distribution in a genome can be considered as the genomic signature. This study highlights the significance of inverted repeats in the evolution of SARS-CoV-2 and presents the inverted repeats as the genomic signature in genome analysis. |
2003.13282 | Tarun Jain | Tarun Jain, Bijendra Nath Jain | Accelerated infection testing at scale: a proposal for inference with
single test on multiple patients | 8 pages | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In pandemics or epidemics, public health authorities need to rapidly test a
large number of individuals, both to determine the line of treatment as well as
to know the spread of infection to plan containment, mitigation and future
responses. However, the lack of adequate testing kits could be a bottleneck,
especially in the case of unanticipated new diseases, such as COVID-19, where
the testing technology, manufacturing capability, distribution, human skills
and laboratories might be unavailable or in short supply. In addition, the cost
of the standard PCR test is approximately USD 48, which is prohibitive for
poorer patients and most governments. We address this bottleneck by proposing a
test methodology that pools the sample from two (or more) patients in a single
test. The key insight is that a single negative result from a pooled sample
likely implies negative infection of all the individual patients. and It
thereby rules out further tests for the patients. This protocol, therefore,
requires significantly fewer tests. This may, however, result in somewhat
increased false negatives. Our simulations show that combining samples from two
patients with 7% underlying likelihood of infection implies that 36% fewer test
kits are required, with 14% additional units of time for testing.
| [
{
"created": "Mon, 30 Mar 2020 09:06:08 GMT",
"version": "v1"
}
] | 2020-03-31 | [
[
"Jain",
"Tarun",
""
],
[
"Jain",
"Bijendra Nath",
""
]
] | In pandemics or epidemics, public health authorities need to rapidly test a large number of individuals, both to determine the line of treatment as well as to know the spread of infection to plan containment, mitigation and future responses. However, the lack of adequate testing kits could be a bottleneck, especially in the case of unanticipated new diseases, such as COVID-19, where the testing technology, manufacturing capability, distribution, human skills and laboratories might be unavailable or in short supply. In addition, the cost of the standard PCR test is approximately USD 48, which is prohibitive for poorer patients and most governments. We address this bottleneck by proposing a test methodology that pools the sample from two (or more) patients in a single test. The key insight is that a single negative result from a pooled sample likely implies negative infection of all the individual patients. and It thereby rules out further tests for the patients. This protocol, therefore, requires significantly fewer tests. This may, however, result in somewhat increased false negatives. Our simulations show that combining samples from two patients with 7% underlying likelihood of infection implies that 36% fewer test kits are required, with 14% additional units of time for testing. |
2003.09564 | Nigel Goldenfeld | Sergei Maslov and Nigel Goldenfeld | Window of Opportunity for Mitigation to Prevent Overflow of ICU capacity
in Chicago by COVID-19 | null | null | null | null | q-bio.PE physics.med-ph physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We estimate the growth in demand for ICU beds in Chicago during the emerging
COVID-19 epidemic, using state-of-the-art computer simulations calibrated for
the SARS-CoV-2 virus. The questions we address are these:
(1) Will the ICU capacity in Chicago be exceeded, and if so by how much?
(2) Can strong mitigation strategies, such as lockdown or shelter in place
order, prevent the overflow of capacity?
(3) When should such strategies be implemented?
Our answers are as follows:
(1) The ICU capacity may be exceeded by a large amount, probably by a factor
of ten.
(2) Strong mitigation can avert this emergency situation potentially, but
even that will not work if implemented too late.
(3) If the strong mitigation precedes April 1st, then the growth of COVID-19
can be controlled and the ICU capacity could be adequate. The earlier the
strong mitigation is implemented, the greater the probability that it will be
successful. After around April 1 2020, any strong mitigation will not avert the
emergency situation. In Italy, the lockdown occurred too late and the number of
deaths is still doubling every 2.3 days. It is difficult to be sure about the
precise dates for this window of opportunity, due to the inherent uncertainties
in computer simulation. But there is high confidence in the main conclusion
that it exists and will soon be closed.
Our conclusion is that, being fully cognizant of the societal trade-offs,
there is a rapidly closing window of opportunity to avert a worst-case scenario
in Chicago, but only with strong mitigation/lockdown implemented in the next
week at the latest. If this window is missed, the epidemic will get worse and
then strong mitigation/lockdown will be required after all, but it will be too
late.
| [
{
"created": "Sat, 21 Mar 2020 02:47:39 GMT",
"version": "v1"
}
] | 2020-03-24 | [
[
"Maslov",
"Sergei",
""
],
[
"Goldenfeld",
"Nigel",
""
]
] | We estimate the growth in demand for ICU beds in Chicago during the emerging COVID-19 epidemic, using state-of-the-art computer simulations calibrated for the SARS-CoV-2 virus. The questions we address are these: (1) Will the ICU capacity in Chicago be exceeded, and if so by how much? (2) Can strong mitigation strategies, such as lockdown or shelter in place order, prevent the overflow of capacity? (3) When should such strategies be implemented? Our answers are as follows: (1) The ICU capacity may be exceeded by a large amount, probably by a factor of ten. (2) Strong mitigation can avert this emergency situation potentially, but even that will not work if implemented too late. (3) If the strong mitigation precedes April 1st, then the growth of COVID-19 can be controlled and the ICU capacity could be adequate. The earlier the strong mitigation is implemented, the greater the probability that it will be successful. After around April 1 2020, any strong mitigation will not avert the emergency situation. In Italy, the lockdown occurred too late and the number of deaths is still doubling every 2.3 days. It is difficult to be sure about the precise dates for this window of opportunity, due to the inherent uncertainties in computer simulation. But there is high confidence in the main conclusion that it exists and will soon be closed. Our conclusion is that, being fully cognizant of the societal trade-offs, there is a rapidly closing window of opportunity to avert a worst-case scenario in Chicago, but only with strong mitigation/lockdown implemented in the next week at the latest. If this window is missed, the epidemic will get worse and then strong mitigation/lockdown will be required after all, but it will be too late. |
1404.0674 | Paulo Bandiera-Paiva | Paulo Bandiera-Paiva and Marcelo R.S. Briones | PGA: A Program for Genome Annotation by Comparative Analysis of Maximum
Likelihood Phylogenies of Genes and Species | arXiv admin note: substantial text overlap with arXiv:1404.0630 | null | null | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Phylogenetic Genome Annotator (PGA) is a computer program that enables
real-time comparison of 'gene trees' versus 'species trees' obtained from
predicted open reading frames of whole genome data. The gene phylogenies are
inferred for each individual genome predicted proteins whereas the species
phylogenies are inferred from rDNA data. The correlated protein domains,
defined by PFAM, are then displayed side-by-side with a phylogeny of the
corresponding species. The statistical support of gene clusters (branches) is
given by the quartet puzzling method. This analysis readily discriminates
paralogs from orthologs, enabling the identification of proteins originated by
gene duplications and the prediction of possible functional divergence in
groups of similar sequences.
| [
{
"created": "Wed, 2 Apr 2014 17:55:42 GMT",
"version": "v1"
}
] | 2014-04-04 | [
[
"Bandiera-Paiva",
"Paulo",
""
],
[
"Briones",
"Marcelo R. S.",
""
]
] | The Phylogenetic Genome Annotator (PGA) is a computer program that enables real-time comparison of 'gene trees' versus 'species trees' obtained from predicted open reading frames of whole genome data. The gene phylogenies are inferred for each individual genome predicted proteins whereas the species phylogenies are inferred from rDNA data. The correlated protein domains, defined by PFAM, are then displayed side-by-side with a phylogeny of the corresponding species. The statistical support of gene clusters (branches) is given by the quartet puzzling method. This analysis readily discriminates paralogs from orthologs, enabling the identification of proteins originated by gene duplications and the prediction of possible functional divergence in groups of similar sequences. |
1703.01999 | Quico Spaen | Quico Spaen, Dorit S. Hochbaum, Roberto As\'in-Ach\'a | HNCcorr: A Novel Combinatorial Approach for Cell Identification in
Calcium-Imaging Movies | null | null | 10.1523/ENEURO.0304-18.2019 | null | q-bio.QM math.OC q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Calcium imaging has emerged as a workhorse method in neuroscience to
investigate patterns of neuronal activity. Instrumentation to acquire calcium
imaging movies has rapidly progressed and has become standard across labs.
Still, algorithms to automatically detect and extract activity signals from
calcium imaging movies are highly variable from~lab~to~lab and more advanced
algorithms are continuously being developed. Here we present HNCcorr, a novel
algorithm for cell identification in calcium imaging movies based on
combinatorial optimization. The algorithm identifies cells by finding distinct
groups of highly similar pixels in correlation space, where a pixel is
represented by the vector of correlations to a set of other pixels. The HNCcorr
algorithm achieves the best known results for the cell identification benchmark
of Neurofinder, and guarantees an optimal solution to the underlying
deterministic optimization model resulting in a transparent mapping from input
data to outcome.
| [
{
"created": "Mon, 6 Mar 2017 17:42:25 GMT",
"version": "v1"
}
] | 2019-06-03 | [
[
"Spaen",
"Quico",
""
],
[
"Hochbaum",
"Dorit S.",
""
],
[
"Asín-Achá",
"Roberto",
""
]
] | Calcium imaging has emerged as a workhorse method in neuroscience to investigate patterns of neuronal activity. Instrumentation to acquire calcium imaging movies has rapidly progressed and has become standard across labs. Still, algorithms to automatically detect and extract activity signals from calcium imaging movies are highly variable from~lab~to~lab and more advanced algorithms are continuously being developed. Here we present HNCcorr, a novel algorithm for cell identification in calcium imaging movies based on combinatorial optimization. The algorithm identifies cells by finding distinct groups of highly similar pixels in correlation space, where a pixel is represented by the vector of correlations to a set of other pixels. The HNCcorr algorithm achieves the best known results for the cell identification benchmark of Neurofinder, and guarantees an optimal solution to the underlying deterministic optimization model resulting in a transparent mapping from input data to outcome. |
1403.5376 | Suman Kumar Banik | Srijeeta Talukder, Shrabani Sen, Prantik Chakraborti, Ralf Metzler,
Suman K Banik, Pinaki Chaudhury | Breathing dynamics based parameter sensitivity analysis of
hetero-polymeric DNA | 10 pages, 3 figures, 4 tables | J. Chem. Phys. 140 (2014) 125101 | 10.1063/1.4869112 | null | q-bio.BM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the parameter sensitivity of hetero-polymeric DNA within the purview
of DNA breathing dynamics. The degree of correlation between the mean bubble
size and the model parameters are estimated for this purpose for three
different DNA sequences. The analysis leads us to a better understanding of the
sequence dependent nature of the breathing dynamics of hetero-polymeric DNA.
Out of the fourteen model parameters for DNA stability in the statistical
Poland-Scheraga approach, the hydrogen bond interaction
$\epsilon_{hb}(\mathtt{AT})$ for an $\mathtt{AT}$ base pair and the ring factor
$\xi$ turn out to be the most sensitive parameters. In addition, the stacking
interaction $\epsilon_{st}(\mathtt{TA}-\mathtt{TA})$ for an
$\mathtt{TA}-\mathtt{TA}$ nearest neighbor pair of base-pairs is found to be
the most sensitive one among all stacking interactions. Moreover, we also
establish that the nature of stacking interaction has a deciding effect on the
DNA breathing dynamics, not the number of times a particular stacking
interaction appears in a sequence. We show that the sensitivity analysis can be
used as an effective measure to guide a stochastic optimization technique to
find the kinetic rate constants related to the dynamics as opposed to the case
where the rate constants are measured using the conventional unbiased way of
optimization.
| [
{
"created": "Fri, 21 Mar 2014 06:19:03 GMT",
"version": "v1"
}
] | 2014-03-27 | [
[
"Talukder",
"Srijeeta",
""
],
[
"Sen",
"Shrabani",
""
],
[
"Chakraborti",
"Prantik",
""
],
[
"Metzler",
"Ralf",
""
],
[
"Banik",
"Suman K",
""
],
[
"Chaudhury",
"Pinaki",
""
]
] | We study the parameter sensitivity of hetero-polymeric DNA within the purview of DNA breathing dynamics. The degree of correlation between the mean bubble size and the model parameters are estimated for this purpose for three different DNA sequences. The analysis leads us to a better understanding of the sequence dependent nature of the breathing dynamics of hetero-polymeric DNA. Out of the fourteen model parameters for DNA stability in the statistical Poland-Scheraga approach, the hydrogen bond interaction $\epsilon_{hb}(\mathtt{AT})$ for an $\mathtt{AT}$ base pair and the ring factor $\xi$ turn out to be the most sensitive parameters. In addition, the stacking interaction $\epsilon_{st}(\mathtt{TA}-\mathtt{TA})$ for an $\mathtt{TA}-\mathtt{TA}$ nearest neighbor pair of base-pairs is found to be the most sensitive one among all stacking interactions. Moreover, we also establish that the nature of stacking interaction has a deciding effect on the DNA breathing dynamics, not the number of times a particular stacking interaction appears in a sequence. We show that the sensitivity analysis can be used as an effective measure to guide a stochastic optimization technique to find the kinetic rate constants related to the dynamics as opposed to the case where the rate constants are measured using the conventional unbiased way of optimization. |
2111.00108 | Muhammad Ammar Malik | Muhammad Ammar Malik, Adriaan-Alexander Ludl, Tom Michoel | High-dimensional multi-trait GWAS by reverse prediction of genotypes | null | null | null | null | q-bio.GN cs.LG q-bio.QM stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-trait genome-wide association studies (GWAS) use multi-variate
statistical methods to identify associations between genetic variants and
multiple correlated traits simultaneously, and have higher statistical power
than independent univariate analyses of traits. Reverse regression, where
genotypes of genetic variants are regressed on multiple traits simultaneously,
has emerged as a promising approach to perform multi-trait GWAS in
high-dimensional settings where the number of traits exceeds the number of
samples. We analyzed different machine learning methods (ridge regression,
naive Bayes/independent univariate, random forests and support vector machines)
for reverse regression in multi-trait GWAS, using genotypes, gene expression
data and ground-truth transcriptional regulatory networks from the DREAM5
SysGen Challenge and from a cross between two yeast strains to evaluate
methods. We found that genotype prediction performance, in terms of root mean
squared error (RMSE), allowed to distinguish between genomic regions with high
and low transcriptional activity. Moreover, model feature coefficients
correlated with the strength of association between variants and individual
traits, and were predictive of true trans-eQTL target genes, with complementary
findings across methods. Code to reproduce the analysis is available at
https://github.com/michoel-lab/Reverse-Pred-GWAS
| [
{
"created": "Fri, 29 Oct 2021 22:34:35 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Feb 2022 14:45:03 GMT",
"version": "v2"
}
] | 2022-02-10 | [
[
"Malik",
"Muhammad Ammar",
""
],
[
"Ludl",
"Adriaan-Alexander",
""
],
[
"Michoel",
"Tom",
""
]
] | Multi-trait genome-wide association studies (GWAS) use multi-variate statistical methods to identify associations between genetic variants and multiple correlated traits simultaneously, and have higher statistical power than independent univariate analyses of traits. Reverse regression, where genotypes of genetic variants are regressed on multiple traits simultaneously, has emerged as a promising approach to perform multi-trait GWAS in high-dimensional settings where the number of traits exceeds the number of samples. We analyzed different machine learning methods (ridge regression, naive Bayes/independent univariate, random forests and support vector machines) for reverse regression in multi-trait GWAS, using genotypes, gene expression data and ground-truth transcriptional regulatory networks from the DREAM5 SysGen Challenge and from a cross between two yeast strains to evaluate methods. We found that genotype prediction performance, in terms of root mean squared error (RMSE), allowed to distinguish between genomic regions with high and low transcriptional activity. Moreover, model feature coefficients correlated with the strength of association between variants and individual traits, and were predictive of true trans-eQTL target genes, with complementary findings across methods. Code to reproduce the analysis is available at https://github.com/michoel-lab/Reverse-Pred-GWAS |
2210.16098 | Yiqiang Yi | Yiqiang Yi, Xu Wan, Kangfei Zhao, Le Ou-Yang, Peilin Zhao | Predicting Protein-Ligand Binding Affinity with Equivariant Line Graph
Network | null | null | null | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Binding affinity prediction of three-dimensional (3D) protein ligand
complexes is critical for drug repositioning and virtual drug screening.
Existing approaches transform a 3D protein-ligand complex to a two-dimensional
(2D) graph, and then use graph neural networks (GNNs) to predict its binding
affinity. However, the node and edge features of the 2D graph are extracted
based on invariant local coordinate systems of the 3D complex. As a result, the
method can not fully learn the global information of the complex, such as, the
physical symmetry and the topological information of bonds. To address these
issues, we propose a novel Equivariant Line Graph Network (ELGN) for affinity
prediction of 3D protein ligand complexes. The proposed ELGN firstly adds a
super node to the 3D complex, and then builds a line graph based on the 3D
complex. After that, ELGN uses a new E(3)-equivariant network layer to pass the
messages between nodes and edges based on the global coordinate system of the
3D complex. Experimental results on two real datasets demonstrate the
effectiveness of ELGN over several state-of-the-art baselines.
| [
{
"created": "Thu, 27 Oct 2022 02:15:52 GMT",
"version": "v1"
}
] | 2022-10-31 | [
[
"Yi",
"Yiqiang",
""
],
[
"Wan",
"Xu",
""
],
[
"Zhao",
"Kangfei",
""
],
[
"Ou-Yang",
"Le",
""
],
[
"Zhao",
"Peilin",
""
]
] | Binding affinity prediction of three-dimensional (3D) protein ligand complexes is critical for drug repositioning and virtual drug screening. Existing approaches transform a 3D protein-ligand complex to a two-dimensional (2D) graph, and then use graph neural networks (GNNs) to predict its binding affinity. However, the node and edge features of the 2D graph are extracted based on invariant local coordinate systems of the 3D complex. As a result, the method can not fully learn the global information of the complex, such as, the physical symmetry and the topological information of bonds. To address these issues, we propose a novel Equivariant Line Graph Network (ELGN) for affinity prediction of 3D protein ligand complexes. The proposed ELGN firstly adds a super node to the 3D complex, and then builds a line graph based on the 3D complex. After that, ELGN uses a new E(3)-equivariant network layer to pass the messages between nodes and edges based on the global coordinate system of the 3D complex. Experimental results on two real datasets demonstrate the effectiveness of ELGN over several state-of-the-art baselines. |
1305.6043 | Bjarki Eldon | Matthias Birkner, Jochen Blath, Bjarki Eldon | Statistical properties of the site-frequency spectrum associated with
Lambda-coalescents | 45 pages, 14 figures, 4 tables, Appendix, supporting information | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Statistical properties of the site frequency spectrum associated with
Lambda-coalescents are our objects of study. In particular, we derive
recursions for the expected value, variance, and covariance of the spectrum,
extending earlier results of Fu (1995) for the classical Kingman coalescent.
Estimating coalescent parameters introduced by certain Lambda-coalescents for
datasets too large for full likelihood methods is our focus. The recursions for
the expected values we obtain can be used to find the parameter values which
give the best fit to the observed frequency spectrum. The expected values are
also used to approximate the probability a (derived) mutation arises on a
branch subtending a given number of leaves (DNA sequences), allowing us to
apply a pseudo-likelihood inference to estimate coalescence parameters
associated with certain subclasses of Lambda coalescents. The properties of the
pseudo-likelihood approach are investigated on simulated as well as real mtDNA
datasets for the high fecundity Atlantic cod (\emph{Gadus morhua}). Our results
for two subclasses of Lambda coalescents show that one can distinguish these
subclasses from the Kingman coalescent, as well as between the
Lambda-subclasses, even for moderate sample sizes.
| [
{
"created": "Sun, 26 May 2013 16:37:54 GMT",
"version": "v1"
},
{
"created": "Sat, 24 Aug 2013 17:15:28 GMT",
"version": "v2"
}
] | 2013-08-27 | [
[
"Birkner",
"Matthias",
""
],
[
"Blath",
"Jochen",
""
],
[
"Eldon",
"Bjarki",
""
]
] | Statistical properties of the site frequency spectrum associated with Lambda-coalescents are our objects of study. In particular, we derive recursions for the expected value, variance, and covariance of the spectrum, extending earlier results of Fu (1995) for the classical Kingman coalescent. Estimating coalescent parameters introduced by certain Lambda-coalescents for datasets too large for full likelihood methods is our focus. The recursions for the expected values we obtain can be used to find the parameter values which give the best fit to the observed frequency spectrum. The expected values are also used to approximate the probability a (derived) mutation arises on a branch subtending a given number of leaves (DNA sequences), allowing us to apply a pseudo-likelihood inference to estimate coalescence parameters associated with certain subclasses of Lambda coalescents. The properties of the pseudo-likelihood approach are investigated on simulated as well as real mtDNA datasets for the high fecundity Atlantic cod (\emph{Gadus morhua}). Our results for two subclasses of Lambda coalescents show that one can distinguish these subclasses from the Kingman coalescent, as well as between the Lambda-subclasses, even for moderate sample sizes. |
1707.00664 | Alessio Franci | Alessio Franci, Guillaume Drion, Rodolphe Sepulchre | Robust and tunable bursting requires slow positive feedback | null | null | null | null | q-bio.NC math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We highlight that the robustness and tunability of a bursting model
critically relies on currents that provide slow positive feedback to the
membrane potential. Such currents have the ability of making the total
conductance of the circuit negative in a time scale that is termed slow because
intermediate between the fast time scale of the spike upstroke and the
ultraslow time scale of even slower adaptation currents. We discuss how such
currents can be assessed either in voltage-clamp experiments or in
computational models. We show that, while frequent in the literature,
mathematical and computational models of bursting that lack the slow negative
conductance are fragile and rigid. Our results suggest that modeling the slow
negative conductance of cellular models is important when studying the
neuromodulation of rhythmic circuits at any broader scale.
| [
{
"created": "Mon, 3 Jul 2017 17:29:04 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Jan 2018 15:56:43 GMT",
"version": "v2"
}
] | 2018-01-29 | [
[
"Franci",
"Alessio",
""
],
[
"Drion",
"Guillaume",
""
],
[
"Sepulchre",
"Rodolphe",
""
]
] | We highlight that the robustness and tunability of a bursting model critically relies on currents that provide slow positive feedback to the membrane potential. Such currents have the ability of making the total conductance of the circuit negative in a time scale that is termed slow because intermediate between the fast time scale of the spike upstroke and the ultraslow time scale of even slower adaptation currents. We discuss how such currents can be assessed either in voltage-clamp experiments or in computational models. We show that, while frequent in the literature, mathematical and computational models of bursting that lack the slow negative conductance are fragile and rigid. Our results suggest that modeling the slow negative conductance of cellular models is important when studying the neuromodulation of rhythmic circuits at any broader scale. |
1509.09192 | Mohammad Soltani | Thierry Platini, Mohammad Soltani, Abhyudai Singh | Stochastic Analysis Of An Incoherent Feedforward Genetic Motif | 8 pages | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene products (RNAs, proteins) often occur at low molecular counts inside
individual cells, and hence are subject to considerable random fluctuations
(noise) in copy number over time. Not surprisingly, cells encode diverse
regulatory mechanisms to buffer noise. One such mechanism is the incoherent
feedforward circuit. We analyze a simplistic version of this circuit, where an
upstream regulator X affects both the production and degradation of a protein
Y. Thus, any random increase in X's copy numbers would increase both production
and degradation, keeping Y levels unchanged. To study its stochastic dynamics,
we formulate this network into a mathematical model using the Chemical Master
Equation formulation. We prove that if the functional dependence of Y's
production and degradation on X is similar, then the steady-distribution of Y's
copy numbers is independent of X. To investigate how fluctuations in Y
propagate downstream, a protein Z whose production rate only depend on Y is
introduced. Intriguingly, results show that the extent of noise in Z increases
with noise in X, in spite of the fact that the magnitude of noise in Y is
invariant of X. Such counter intuitive results arise because X enhances the
time-scale of fluctuations in Y, which amplifies fluctuations in downstream
processes. In summary, while feedforward systems can buffer a protein from
noise in its upstream regulators, noise can propagate downstream due to changes
in the time-scale of fluctuations.
| [
{
"created": "Wed, 30 Sep 2015 14:30:08 GMT",
"version": "v1"
}
] | 2015-10-01 | [
[
"Platini",
"Thierry",
""
],
[
"Soltani",
"Mohammad",
""
],
[
"Singh",
"Abhyudai",
""
]
] | Gene products (RNAs, proteins) often occur at low molecular counts inside individual cells, and hence are subject to considerable random fluctuations (noise) in copy number over time. Not surprisingly, cells encode diverse regulatory mechanisms to buffer noise. One such mechanism is the incoherent feedforward circuit. We analyze a simplistic version of this circuit, where an upstream regulator X affects both the production and degradation of a protein Y. Thus, any random increase in X's copy numbers would increase both production and degradation, keeping Y levels unchanged. To study its stochastic dynamics, we formulate this network into a mathematical model using the Chemical Master Equation formulation. We prove that if the functional dependence of Y's production and degradation on X is similar, then the steady-distribution of Y's copy numbers is independent of X. To investigate how fluctuations in Y propagate downstream, a protein Z whose production rate only depend on Y is introduced. Intriguingly, results show that the extent of noise in Z increases with noise in X, in spite of the fact that the magnitude of noise in Y is invariant of X. Such counter intuitive results arise because X enhances the time-scale of fluctuations in Y, which amplifies fluctuations in downstream processes. In summary, while feedforward systems can buffer a protein from noise in its upstream regulators, noise can propagate downstream due to changes in the time-scale of fluctuations. |
1201.3170 | Pascal Buenzli | Peter Pivonka, Pascal R. Buenzli, Stefan Scheiner, Christian Hellmich,
Colin R. Dunstan | The influence of bone surface availability in bone remodelling - A
mathematical model including coupled geometrical and biomechanical
regulations of bone cells | 17 pages, 9 figures, 3 tables; Changes in v2: New title, C Hellmich
added as author for his contribution to biomechanical part of the model,
rewritten in this version. One figure (Fig 4) added. Some misprints and
errors of v1 corrected. Some sylistic rearrangements | Eng Struct (2013) 47:134-147 | 10.1016/j.engstruct.2012.09.006 | null | q-bio.TO physics.bio-ph physics.med-ph q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bone is a biomaterial undergoing continuous renewal. The renewal process is
known as bone remodelling and is operated by bone-resorbing cells (osteoclasts)
and bone-forming cells (osteoblasts). Both biochemical and biomechanical
regulatory mechanisms have been identified in the interaction between
osteoclasts and osteoblasts. Here we focus on an additional and poorly
understood potential regulatory mechanism of bone cells, that involves the
morphology of the microstructure of bone. Bone cells can only remove and
replace bone at a bone surface. However, the microscopic availability of bone
surface depends in turn on the ever-changing bone microstructure. The
importance of this geometrical dependence is unknown and difficult to quantify
experimentally. Therefore, we develop a sophisticated mathematical model of
bone cell interactions that takes into account biochemical, biomechanical and
geometrical regulations. We then investigate numerically the influence of bone
surface availability in bone remodelling within a representative bone tissue
sample. The interdependence between the bone cells' activity, which modifies
the bone microstructure, and changes in the microscopic bone surface
availability, which in turn influences bone cell development and activity, is
implemented using a remarkable experimental relationship between bone specific
surface and bone porosity. Our model suggests that geometrical regulation of
the activation of new remodelling events could have a significant effect on
bone porosity and bone stiffness. On the other hand, geometrical regulation of
late stages of osteoblast and osteoclast differentiation seems less
significant. We conclude that the development of osteoporosis is probably
accelerated by this geometrical regulation in cortical bone, but probably
slowed down in trabecular bone.
| [
{
"created": "Mon, 16 Jan 2012 07:45:23 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Feb 2012 07:16:49 GMT",
"version": "v2"
}
] | 2014-05-21 | [
[
"Pivonka",
"Peter",
""
],
[
"Buenzli",
"Pascal R.",
""
],
[
"Scheiner",
"Stefan",
""
],
[
"Hellmich",
"Christian",
""
],
[
"Dunstan",
"Colin R.",
""
]
] | Bone is a biomaterial undergoing continuous renewal. The renewal process is known as bone remodelling and is operated by bone-resorbing cells (osteoclasts) and bone-forming cells (osteoblasts). Both biochemical and biomechanical regulatory mechanisms have been identified in the interaction between osteoclasts and osteoblasts. Here we focus on an additional and poorly understood potential regulatory mechanism of bone cells, that involves the morphology of the microstructure of bone. Bone cells can only remove and replace bone at a bone surface. However, the microscopic availability of bone surface depends in turn on the ever-changing bone microstructure. The importance of this geometrical dependence is unknown and difficult to quantify experimentally. Therefore, we develop a sophisticated mathematical model of bone cell interactions that takes into account biochemical, biomechanical and geometrical regulations. We then investigate numerically the influence of bone surface availability in bone remodelling within a representative bone tissue sample. The interdependence between the bone cells' activity, which modifies the bone microstructure, and changes in the microscopic bone surface availability, which in turn influences bone cell development and activity, is implemented using a remarkable experimental relationship between bone specific surface and bone porosity. Our model suggests that geometrical regulation of the activation of new remodelling events could have a significant effect on bone porosity and bone stiffness. On the other hand, geometrical regulation of late stages of osteoblast and osteoclast differentiation seems less significant. We conclude that the development of osteoporosis is probably accelerated by this geometrical regulation in cortical bone, but probably slowed down in trabecular bone. |
0709.2015 | Henrik Jeldtoft Jensen | Henrik Jeldtoft Jensen and Elsa Arcaute | Complexity, Collective Effects and Modelling of Ecosystems: formation,
function and stability | 11 pages and 1 figure | null | null | null | q-bio.PE q-bio.OT | null | We discuss the relevance of studying ecology within the framework of
Complexity Science from a statistical mechanics approach. Ecology is concerned
with understanding how systems level properties emerge out of the multitude of
interactions amongst large numbers of components, leading to ecosystems that
possess the prototypical characteristics of complex systems. We argue that
statistical mechanics is at present the best methodology available to obtain a
quantitative description of complex systems, and that ecology is in urgent need
of ``integrative'' approaches that are quantitative and non-stationary. We
describe examples where combining statistical mechanics and ecology has led to
improved ecological modelling and, at the same time, broadened the scope of
statistical mechanics.
| [
{
"created": "Thu, 13 Sep 2007 08:13:14 GMT",
"version": "v1"
}
] | 2007-09-14 | [
[
"Jensen",
"Henrik Jeldtoft",
""
],
[
"Arcaute",
"Elsa",
""
]
] | We discuss the relevance of studying ecology within the framework of Complexity Science from a statistical mechanics approach. Ecology is concerned with understanding how systems level properties emerge out of the multitude of interactions amongst large numbers of components, leading to ecosystems that possess the prototypical characteristics of complex systems. We argue that statistical mechanics is at present the best methodology available to obtain a quantitative description of complex systems, and that ecology is in urgent need of ``integrative'' approaches that are quantitative and non-stationary. We describe examples where combining statistical mechanics and ecology has led to improved ecological modelling and, at the same time, broadened the scope of statistical mechanics. |
1606.05912 | Ricardo Martinez-Garcia | Ricardo Martinez-Garcia, Corina E. Tarnita | Seasonality can induce coexistence of multiple bet-hedging strategies in
Dictyostelium discoideum via storage effect | 33 pages, 7 figures | null | 10.1016/j.jtbi.2017.05.019 | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | D. discoideum has been recently suggested as an example of bet-hedging. Upon
starvation a population of unicellular amoebae splits between aggregators,
which form a fruiting body made of a stalk and resistant spores, and
non-aggregators. Spores are favored by long starvation periods, but vegetative
cells can exploit resources in fast-recovering environments. This partition can
be understood as a bet-hedging strategy that evolves in response to stochastic
starvation times. A genotype is defined by a different balance between each
type of cells. In this framework, if the ecological conditions are defined in
terms of the mean starvation time (i.e. time between onset of starvation and
the arrival of a new food pulse), a single genotype dominates each environment,
which is inconsistent with the huge genetic diversity observed in nature. We
investigate whether seasonality, represented by a periodic alternation in the
mean starvation times, allows the coexistence of several strategies. We use a
non-spatial (well-mixed) setting where different strains compete for a pulse of
resources. We find that seasonality, which we model via two seasons, induces a
temporal storage effect that can promote the stable coexistence of multiple
genotypes. Two conditions need to be met. First, the distributions of
starvation times in each season cannot overlap in order to create two well
differentiated habitats within the year. Second, numerous growth-starvation
cycles have to occur during each season to allow well-adapted strains to grow
and survive the subsequent unfavorable period. Additional tradeoffs among
life-history traits can expand the range of coexistence and increase the number
of coexisting strategies, contributing towards explaining the genetic diversity
observed in D. discoideum
| [
{
"created": "Sun, 19 Jun 2016 21:33:51 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Jun 2016 06:23:24 GMT",
"version": "v2"
},
{
"created": "Wed, 17 May 2017 16:37:10 GMT",
"version": "v3"
}
] | 2017-05-18 | [
[
"Martinez-Garcia",
"Ricardo",
""
],
[
"Tarnita",
"Corina E.",
""
]
] | D. discoideum has been recently suggested as an example of bet-hedging. Upon starvation a population of unicellular amoebae splits between aggregators, which form a fruiting body made of a stalk and resistant spores, and non-aggregators. Spores are favored by long starvation periods, but vegetative cells can exploit resources in fast-recovering environments. This partition can be understood as a bet-hedging strategy that evolves in response to stochastic starvation times. A genotype is defined by a different balance between each type of cells. In this framework, if the ecological conditions are defined in terms of the mean starvation time (i.e. time between onset of starvation and the arrival of a new food pulse), a single genotype dominates each environment, which is inconsistent with the huge genetic diversity observed in nature. We investigate whether seasonality, represented by a periodic alternation in the mean starvation times, allows the coexistence of several strategies. We use a non-spatial (well-mixed) setting where different strains compete for a pulse of resources. We find that seasonality, which we model via two seasons, induces a temporal storage effect that can promote the stable coexistence of multiple genotypes. Two conditions need to be met. First, the distributions of starvation times in each season cannot overlap in order to create two well differentiated habitats within the year. Second, numerous growth-starvation cycles have to occur during each season to allow well-adapted strains to grow and survive the subsequent unfavorable period. Additional tradeoffs among life-history traits can expand the range of coexistence and increase the number of coexisting strategies, contributing towards explaining the genetic diversity observed in D. discoideum |
1306.4747 | Ricky Der | Ricky Der, Joshua B. Plotkin | The equilibrium allele frequency distribution for a population with
reproductive skew | Submitted to Genetics | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the population genetics of two neutral alleles under reversible
mutation in the \Lambda-processes, a population model that features a skewed
offspring distribution. We describe the shape of the equilibrium allele
frequency distribution as a function of the model parameters. We show that the
mutation rates can be uniquely identified from the equilibrium distribution,
but that the form of the offspring distribution itself cannot be uniquely
identified. We also introduce an infinite-sites version of the \Lambda-process,
and we use it to study how reproductive skew influences standing genetic
diversity in a population. We derive asymptotic formulae for the expected
number of segregating sizes as a function of sample size. We find that the
Wright-Fisher model minimizes the equilibrium genetic diversity, for a given
mutation rate and variance effective population size, compared to all other
\Lambda-processes.
| [
{
"created": "Thu, 20 Jun 2013 03:29:40 GMT",
"version": "v1"
}
] | 2013-06-21 | [
[
"Der",
"Ricky",
""
],
[
"Plotkin",
"Joshua B.",
""
]
] | We study the population genetics of two neutral alleles under reversible mutation in the \Lambda-processes, a population model that features a skewed offspring distribution. We describe the shape of the equilibrium allele frequency distribution as a function of the model parameters. We show that the mutation rates can be uniquely identified from the equilibrium distribution, but that the form of the offspring distribution itself cannot be uniquely identified. We also introduce an infinite-sites version of the \Lambda-process, and we use it to study how reproductive skew influences standing genetic diversity in a population. We derive asymptotic formulae for the expected number of segregating sizes as a function of sample size. We find that the Wright-Fisher model minimizes the equilibrium genetic diversity, for a given mutation rate and variance effective population size, compared to all other \Lambda-processes. |
1302.0255 | Kieran Sharkey | Robert R. Wilkinson and Kieran J. Sharkey | An Exact Relationship Between Invasion Probability and Endemic
Prevalence for Markovian SIS Dynamics on Networks | 16 pages, 5 figures. Supplementary data available with published
version at http://dx.doi.org/10.1371/journal.pone.0069028 | WPLoS ONE 8(7): e69028 | 10.1371/journal.pone.0069028 | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding models which represent the invasion of network-based systems by
infectious agents can give important insights into many real-world situations,
including the prevention and control of infectious diseases and computer
viruses. Here we consider Markovian susceptible-infectious-susceptible (SIS)
dynamics on finite strongly connected networks, applicable to several sexually
transmitted diseases and computer viruses. In this context, a theoretical
definition of endemic prevalence is easily obtained via the quasi-stationary
distribution (QSD). By representing the model as a percolation process and
utilising the property of duality, we also provide a theoretical definition of
invasion probability. We then show that, for undirected networks, the
probability of invasion from any given individual is equal to the
(probabilistic) endemic prevalence, following successful invasion, at the
individual (we also provide a relationship for the directed case). The total
(fractional) endemic prevalence in the population is thus equal to the average
invasion probability (across all individuals). Consequently, for such systems,
the regions or individuals already supporting a high level of infection are
likely to be the source of a successful invasion by another infectious agent.
This could be used to inform targeted interventions when there is a threat from
an emerging infectious disease.
| [
{
"created": "Fri, 1 Feb 2013 19:16:15 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Aug 2013 15:35:09 GMT",
"version": "v2"
}
] | 2013-08-02 | [
[
"Wilkinson",
"Robert R.",
""
],
[
"Sharkey",
"Kieran J.",
""
]
] | Understanding models which represent the invasion of network-based systems by infectious agents can give important insights into many real-world situations, including the prevention and control of infectious diseases and computer viruses. Here we consider Markovian susceptible-infectious-susceptible (SIS) dynamics on finite strongly connected networks, applicable to several sexually transmitted diseases and computer viruses. In this context, a theoretical definition of endemic prevalence is easily obtained via the quasi-stationary distribution (QSD). By representing the model as a percolation process and utilising the property of duality, we also provide a theoretical definition of invasion probability. We then show that, for undirected networks, the probability of invasion from any given individual is equal to the (probabilistic) endemic prevalence, following successful invasion, at the individual (we also provide a relationship for the directed case). The total (fractional) endemic prevalence in the population is thus equal to the average invasion probability (across all individuals). Consequently, for such systems, the regions or individuals already supporting a high level of infection are likely to be the source of a successful invasion by another infectious agent. This could be used to inform targeted interventions when there is a threat from an emerging infectious disease. |
1605.09070 | Karel B\v{r}inda | Karel B\v{r}inda, Valentina Boeva, Gregory Kucherov | Dynamic read mapping and online consensus calling for better variant
detection | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Variant detection from high-throughput sequencing data is an essential step
in identification of alleles involved in complex diseases and cancer. To deal
with these massive data, elaborated sequence analysis pipelines are employed. A
core component of such pipelines is a read mapping module whose accuracy
strongly affects the quality of resulting variant calls.
We propose a dynamic read mapping approach that significantly improves read
alignment accuracy. The general idea of dynamic mapping is to continuously
update the reference sequence on the basis of previously computed read
alignments. Even though this concept already appeared in the literature, we
believe that our work provides the first comprehensive analysis of this
approach.
To evaluate the benefit of dynamic mapping, we developed a software pipeline
(http://github.com/karel-brinda/dymas) that mimics different dynamic mapping
scenarios. The pipeline was applied to compare dynamic mapping with the
conventional static mapping and, on the other hand, with the so-called
iterative referencing - a computationally expensive procedure computing an
optimal modification of the reference that maximizes the overall quality of all
alignments. We conclude that in all alternatives, dynamic mapping results in a
much better accuracy than static mapping, approaching the accuracy of iterative
referencing.
To correct the reference sequence in the course of dynamic mapping, we
developed an online consensus caller named OCOCO
(http://github.com/karel-brinda/ococo). OCOCO is the first consensus caller
capable to process input reads in the online fashion.
Finally, we provide conclusions about the feasibility of dynamic mapping and
discuss main obstacles that have to be overcome to implement it. We also review
a wide range of possible applications of dynamic mapping with a special
emphasis on variant detection.
| [
{
"created": "Sun, 29 May 2016 22:25:55 GMT",
"version": "v1"
}
] | 2016-05-31 | [
[
"Břinda",
"Karel",
""
],
[
"Boeva",
"Valentina",
""
],
[
"Kucherov",
"Gregory",
""
]
] | Variant detection from high-throughput sequencing data is an essential step in identification of alleles involved in complex diseases and cancer. To deal with these massive data, elaborated sequence analysis pipelines are employed. A core component of such pipelines is a read mapping module whose accuracy strongly affects the quality of resulting variant calls. We propose a dynamic read mapping approach that significantly improves read alignment accuracy. The general idea of dynamic mapping is to continuously update the reference sequence on the basis of previously computed read alignments. Even though this concept already appeared in the literature, we believe that our work provides the first comprehensive analysis of this approach. To evaluate the benefit of dynamic mapping, we developed a software pipeline (http://github.com/karel-brinda/dymas) that mimics different dynamic mapping scenarios. The pipeline was applied to compare dynamic mapping with the conventional static mapping and, on the other hand, with the so-called iterative referencing - a computationally expensive procedure computing an optimal modification of the reference that maximizes the overall quality of all alignments. We conclude that in all alternatives, dynamic mapping results in a much better accuracy than static mapping, approaching the accuracy of iterative referencing. To correct the reference sequence in the course of dynamic mapping, we developed an online consensus caller named OCOCO (http://github.com/karel-brinda/ococo). OCOCO is the first consensus caller capable to process input reads in the online fashion. Finally, we provide conclusions about the feasibility of dynamic mapping and discuss main obstacles that have to be overcome to implement it. We also review a wide range of possible applications of dynamic mapping with a special emphasis on variant detection. |
1311.5696 | Kieran Smallbone | Kieran Smallbone | Striking a balance with Recon 2.1 | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recon 2 is a highly curated reconstruction of the human metabolic network.
Whilst the network is state of the art, it has shortcomings, including the
presence of unbalanced reactions involving generic metabolites. By replacing
these generic molecules with each of their specific instances, we can ensure
full elemental balancing, in turn allowing constraint-based analyses to be
performed. The resultant model, called Recon 2.1, is an order of magnitude
larger than the original.
| [
{
"created": "Fri, 22 Nov 2013 10:19:06 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Nov 2014 00:38:24 GMT",
"version": "v2"
}
] | 2014-11-27 | [
[
"Smallbone",
"Kieran",
""
]
] | Recon 2 is a highly curated reconstruction of the human metabolic network. Whilst the network is state of the art, it has shortcomings, including the presence of unbalanced reactions involving generic metabolites. By replacing these generic molecules with each of their specific instances, we can ensure full elemental balancing, in turn allowing constraint-based analyses to be performed. The resultant model, called Recon 2.1, is an order of magnitude larger than the original. |
1609.04496 | Karina Mazzitello | K. I. Mazzitello, Q. Zhang, M. A. Chrenek, F. Family, H. E.
Grossniklaus, J. M. Nickerson, and Y. Jiang | Druse-Induced Morphology Evolution in Retinal Pigment Epithelium | 10 pages, 9 figures | null | null | null | q-bio.TO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The retinal pigment epithelium (RPE) is a key site of pathogenesis for many
retina diseases. The formation of drusen in the retina is characteristic of
retinal degeneration. We investigate morphological changes in the RPE in the
presence of soft drusen using an integrated experimental and modeling approach.
We collect RPE flat mount images from donated human eyes and develop 1)
statistical tools to quantify the images and 2) a cell-based model to simulate
the morphology evolution. We compare three different mechanisms of RPE repair
evolution, cell apoptosis, cell fusion, and expansion, and Simulations of our
RPE morphogenesis model quantitatively reproduce deformations of human RPE
morphology due to drusen, suggesting that a purse-string mechanism is
sufficient to explain how RPE heals cell loss caused by drusen-damage. We found
that drusen beneath tissue promote cell death in a number that far exceeds the
cell numbers covering the drusen. Tissue deformations are studied using area
distributions, Voronoi domains and a texture tensor.
| [
{
"created": "Thu, 15 Sep 2016 02:46:20 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Mar 2017 17:43:01 GMT",
"version": "v2"
}
] | 2017-03-03 | [
[
"Mazzitello",
"K. I.",
""
],
[
"Zhang",
"Q.",
""
],
[
"Chrenek",
"M. A.",
""
],
[
"Family",
"F.",
""
],
[
"Grossniklaus",
"H. E.",
""
],
[
"Nickerson",
"J. M.",
""
],
[
"Jiang",
"Y.",
""
]
] | The retinal pigment epithelium (RPE) is a key site of pathogenesis for many retina diseases. The formation of drusen in the retina is characteristic of retinal degeneration. We investigate morphological changes in the RPE in the presence of soft drusen using an integrated experimental and modeling approach. We collect RPE flat mount images from donated human eyes and develop 1) statistical tools to quantify the images and 2) a cell-based model to simulate the morphology evolution. We compare three different mechanisms of RPE repair evolution, cell apoptosis, cell fusion, and expansion, and Simulations of our RPE morphogenesis model quantitatively reproduce deformations of human RPE morphology due to drusen, suggesting that a purse-string mechanism is sufficient to explain how RPE heals cell loss caused by drusen-damage. We found that drusen beneath tissue promote cell death in a number that far exceeds the cell numbers covering the drusen. Tissue deformations are studied using area distributions, Voronoi domains and a texture tensor. |
2011.04651 | Wengong Jin | Wengong Jin, Regina Barzilay, Tommi Jaakkola | Discovering Synergistic Drug Combinations for COVID with Biological
Bottleneck Models | Accepted to NeurIPS 2020 Machine Learning for Molecules Workshop | null | null | null | q-bio.BM cs.LG q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Drug combinations play an important role in therapeutics due to its better
efficacy and reduced toxicity. Recent approaches have applied machine learning
to identify synergistic combinations for cancer, but they are not applicable to
new diseases with limited combination data. Given that drug synergy is closely
tied to biological targets, we propose a \emph{biological bottleneck} model
that jointly learns drug-target interaction and synergy. The model consists of
two parts: a drug-target interaction and target-disease association module.
This design enables the model to \emph{explain} how a biological target affects
drug synergy. By utilizing additional biological information, our model
achieves 0.78 test AUC in drug synergy prediction using only 90 COVID drug
combinations for training. We experimentally tested the model predictions in
the U.S. National Center for Advancing Translational Sciences (NCATS)
facilities and discovered two novel drug combinations (Remdesivir + Reserpine
and Remdesivir + IQ-1S) with strong synergy in vitro.
| [
{
"created": "Mon, 9 Nov 2020 03:30:44 GMT",
"version": "v1"
},
{
"created": "Sat, 28 Nov 2020 18:53:07 GMT",
"version": "v2"
}
] | 2020-12-01 | [
[
"Jin",
"Wengong",
""
],
[
"Barzilay",
"Regina",
""
],
[
"Jaakkola",
"Tommi",
""
]
] | Drug combinations play an important role in therapeutics due to its better efficacy and reduced toxicity. Recent approaches have applied machine learning to identify synergistic combinations for cancer, but they are not applicable to new diseases with limited combination data. Given that drug synergy is closely tied to biological targets, we propose a \emph{biological bottleneck} model that jointly learns drug-target interaction and synergy. The model consists of two parts: a drug-target interaction and target-disease association module. This design enables the model to \emph{explain} how a biological target affects drug synergy. By utilizing additional biological information, our model achieves 0.78 test AUC in drug synergy prediction using only 90 COVID drug combinations for training. We experimentally tested the model predictions in the U.S. National Center for Advancing Translational Sciences (NCATS) facilities and discovered two novel drug combinations (Remdesivir + Reserpine and Remdesivir + IQ-1S) with strong synergy in vitro. |
2404.17128 | Xiaoyu Zhang | Xiaoyu Zhang, Pengcheng Yang, Jiawei Feng, Qiang Luo, Wei Lin and Xin
Lu | Network Structure Trumps Neuron Dynamics: Insights from Drosophila
Connectome Simulations | null | null | null | null | q-bio.NC cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the success of artificial neural networks, the necessity of real
network structures in simulating intelligence remains unclear. Utilizing the
largest adult Drosophila connectome data set, we constructed a large-scale
network communication model framework based on simple neuronal activation
mechanisms to simulate the activation behavior observed in the connectome. The
results demonstrate that even with simple propagation rules, models based on
real neural network structures can generate activation patterns similar to
those in the actual brain. Importantly, we found that using different neuronal
dynamics models, all produced similar activation patterns. This consistency
across different models emphasizes the crucial role of network topology in
neural information processing, challenging views that rely solely on neuron
count or complex individual neuron dynamics.Moreover, we test the influence of
network reconnect rate and find that even 1%'s reconnect rate will ruin the
activation patterns appeared before. By comparing network distances and spatial
distances, we found that network distance better explains the information
propagation patterns between neurons, highlighting the importance of
topological structure in neural information processing. To facilitate these
studies, we developed real-time 3D large spatial network visualization
software, bridging a crucial gap in existing tools. Our findings underscore the
importance of network structure in neural activation and provide new insights
into the fundamental principles governing brain functionality.
| [
{
"created": "Fri, 26 Apr 2024 03:07:14 GMT",
"version": "v1"
},
{
"created": "Sun, 9 Jun 2024 03:34:23 GMT",
"version": "v2"
},
{
"created": "Sun, 30 Jun 2024 13:25:32 GMT",
"version": "v3"
}
] | 2024-07-02 | [
[
"Zhang",
"Xiaoyu",
""
],
[
"Yang",
"Pengcheng",
""
],
[
"Feng",
"Jiawei",
""
],
[
"Luo",
"Qiang",
""
],
[
"Lin",
"Wei",
""
],
[
"Lu",
"Xin",
""
]
] | Despite the success of artificial neural networks, the necessity of real network structures in simulating intelligence remains unclear. Utilizing the largest adult Drosophila connectome data set, we constructed a large-scale network communication model framework based on simple neuronal activation mechanisms to simulate the activation behavior observed in the connectome. The results demonstrate that even with simple propagation rules, models based on real neural network structures can generate activation patterns similar to those in the actual brain. Importantly, we found that using different neuronal dynamics models, all produced similar activation patterns. This consistency across different models emphasizes the crucial role of network topology in neural information processing, challenging views that rely solely on neuron count or complex individual neuron dynamics.Moreover, we test the influence of network reconnect rate and find that even 1%'s reconnect rate will ruin the activation patterns appeared before. By comparing network distances and spatial distances, we found that network distance better explains the information propagation patterns between neurons, highlighting the importance of topological structure in neural information processing. To facilitate these studies, we developed real-time 3D large spatial network visualization software, bridging a crucial gap in existing tools. Our findings underscore the importance of network structure in neural activation and provide new insights into the fundamental principles governing brain functionality. |
2208.08896 | Shuqiang Wang | Heng Kong and Shuqiang Wang | Adversarial Learning Based Structural Brain-network Generative Model for
Analyzing Mild Cognitive Impairment | null | null | null | null | q-bio.NC cs.CV eess.IV eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mild cognitive impairment(MCI) is a precursor of Alzheimer's disease(AD), and
the detection of MCI is of great clinical significance. Analyzing the
structural brain networks of patients is vital for the recognition of MCI.
However, the current studies on structural brain networks are totally dependent
on specific toolboxes, which is time-consuming and subjective. Few tools can
obtain the structural brain networks from brain diffusion tensor images. In
this work, an adversarial learning-based structural brain-network generative
model(SBGM) is proposed to directly learn the structural connections from brain
diffusion tensor images. By analyzing the differences in structural brain
networks across subjects, we found that the structural brain networks of
subjects showed a consistent trend from elderly normal controls(NC) to early
mild cognitive impairment(EMCI) to late mild cognitive impairment(LMCI):
structural connectivity progressed in a progressively weaker direction as the
condition worsened. In addition, our proposed model tri-classifies EMCI, LMCI,
and NC subjects, achieving a classification accuracy of 83.33\% on the
Alzheimer's Disease Neuroimaging Initiative(ADNI) database.
| [
{
"created": "Tue, 9 Aug 2022 02:45:53 GMT",
"version": "v1"
}
] | 2022-08-19 | [
[
"Kong",
"Heng",
""
],
[
"Wang",
"Shuqiang",
""
]
] | Mild cognitive impairment(MCI) is a precursor of Alzheimer's disease(AD), and the detection of MCI is of great clinical significance. Analyzing the structural brain networks of patients is vital for the recognition of MCI. However, the current studies on structural brain networks are totally dependent on specific toolboxes, which is time-consuming and subjective. Few tools can obtain the structural brain networks from brain diffusion tensor images. In this work, an adversarial learning-based structural brain-network generative model(SBGM) is proposed to directly learn the structural connections from brain diffusion tensor images. By analyzing the differences in structural brain networks across subjects, we found that the structural brain networks of subjects showed a consistent trend from elderly normal controls(NC) to early mild cognitive impairment(EMCI) to late mild cognitive impairment(LMCI): structural connectivity progressed in a progressively weaker direction as the condition worsened. In addition, our proposed model tri-classifies EMCI, LMCI, and NC subjects, achieving a classification accuracy of 83.33\% on the Alzheimer's Disease Neuroimaging Initiative(ADNI) database. |
1212.1117 | Alexander Stewart | Alexander J. Stewart, Robert M. Seymour, Andrew Pomiankowski, Max
Reuter | Under-dominance constrains the evolution of negative autoregulation in
diploids | null | PLoS Comput Biol, 2013, 9(3): e1002992 | 10.1371/journal.pcbi.1002992 | null | q-bio.PE q-bio.GN q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Regulatory networks have evolved to allow gene expression to rapidly track
changes in the environment as well as to buffer perturbations and maintain
cellular homeostasis in the absence of change. Theoretical work and empirical
investigation in Escherichia coli have shown that negative autoregulation
confers both rapid response times and reduced intrinsic noise, which is
reflected in the fact that almost half of Escherichia coli transcription
factors are negatively autoregulated. However, negative autoregulation is
exceedingly rare amongst the transcription factors of Saccharomyces cerevisiae.
This difference is all the more surprising because E. coli and S. cerevisiae
otherwise have remarkably similar profiles of network motifs. In this study we
first show that regulatory interactions amongst the transcription factors of
Drosophila melanogaster and humans have a similar dearth of negative
autoregulation to that seen in S. cerevisiae. We then present a model
demonstrating that this fundamental difference in the noise reduction
strategies used amongst species can be explained by constraints on the
evolution of negative autoregulation in diploids. We show that regulatory
interactions between pairs of homologous genes within the same cell can lead to
under-dominance - mutations which result in stronger autoregulation, and
decrease noise in homozygotes, paradoxically can cause increased noise in
heterozygotes. This severely limits a diploid's ability to evolve negative
autoregulation as a noise reduction mechanism. Our work offers a simple and
general explanation for a previously unexplained difference between the
regulatory architectures of E. coli and yeast, Drosophila and humans. It also
demonstrates that the effects of diploidy in gene networks can have
counter-intuitive consequences that may profoundly influence the course of
evolution.
| [
{
"created": "Wed, 5 Dec 2012 18:04:15 GMT",
"version": "v1"
}
] | 2013-04-30 | [
[
"Stewart",
"Alexander J.",
""
],
[
"Seymour",
"Robert M.",
""
],
[
"Pomiankowski",
"Andrew",
""
],
[
"Reuter",
"Max",
""
]
] | Regulatory networks have evolved to allow gene expression to rapidly track changes in the environment as well as to buffer perturbations and maintain cellular homeostasis in the absence of change. Theoretical work and empirical investigation in Escherichia coli have shown that negative autoregulation confers both rapid response times and reduced intrinsic noise, which is reflected in the fact that almost half of Escherichia coli transcription factors are negatively autoregulated. However, negative autoregulation is exceedingly rare amongst the transcription factors of Saccharomyces cerevisiae. This difference is all the more surprising because E. coli and S. cerevisiae otherwise have remarkably similar profiles of network motifs. In this study we first show that regulatory interactions amongst the transcription factors of Drosophila melanogaster and humans have a similar dearth of negative autoregulation to that seen in S. cerevisiae. We then present a model demonstrating that this fundamental difference in the noise reduction strategies used amongst species can be explained by constraints on the evolution of negative autoregulation in diploids. We show that regulatory interactions between pairs of homologous genes within the same cell can lead to under-dominance - mutations which result in stronger autoregulation, and decrease noise in homozygotes, paradoxically can cause increased noise in heterozygotes. This severely limits a diploid's ability to evolve negative autoregulation as a noise reduction mechanism. Our work offers a simple and general explanation for a previously unexplained difference between the regulatory architectures of E. coli and yeast, Drosophila and humans. It also demonstrates that the effects of diploidy in gene networks can have counter-intuitive consequences that may profoundly influence the course of evolution. |
1107.5192 | Ingo Lohmar | Ingo Lohmar and Baruch Meerson | Switching between phenotypes and population extinction | 11 pages, 5 figures. Additional discussion paragraph, minor language
improvements; content as published in Phys. Rev. E | Phys. Rev. E 84 (2011) 051901 | 10.1103/PhysRevE.84.051901 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many types of bacteria can survive under stress by switching stochastically
between two different phenotypes: the "normals" who multiply fast, but are
vulnerable to stress, and the "persisters" who hardly multiply, but are
resilient to stress. Previous theoretical studies of such bacterial populations
have focused on the \emph{fitness}: the asymptotic rate of unbounded growth of
the population. Yet for an isolated population of established (and not very
large) size, a more relevant measure may be the population \emph{extinction
risk} due to the interplay of adverse extrinsic variations and intrinsic noise
of birth, death and switching processes. Applying a WKB approximation to the
pertinent master equation of such a two-population system, we quantify the
extinction risk, and find the most likely path to extinction under both
favorable and adverse conditions. Analytical results are obtained both in the
biologically relevant regime when the switching is rare compared with the birth
and death processes, and in the opposite regime of frequent switching. We show
that rare switches are most beneficial in reducing the extinction risk.
| [
{
"created": "Tue, 26 Jul 2011 12:18:08 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Nov 2011 20:21:58 GMT",
"version": "v2"
}
] | 2011-11-10 | [
[
"Lohmar",
"Ingo",
""
],
[
"Meerson",
"Baruch",
""
]
] | Many types of bacteria can survive under stress by switching stochastically between two different phenotypes: the "normals" who multiply fast, but are vulnerable to stress, and the "persisters" who hardly multiply, but are resilient to stress. Previous theoretical studies of such bacterial populations have focused on the \emph{fitness}: the asymptotic rate of unbounded growth of the population. Yet for an isolated population of established (and not very large) size, a more relevant measure may be the population \emph{extinction risk} due to the interplay of adverse extrinsic variations and intrinsic noise of birth, death and switching processes. Applying a WKB approximation to the pertinent master equation of such a two-population system, we quantify the extinction risk, and find the most likely path to extinction under both favorable and adverse conditions. Analytical results are obtained both in the biologically relevant regime when the switching is rare compared with the birth and death processes, and in the opposite regime of frequent switching. We show that rare switches are most beneficial in reducing the extinction risk. |
q-bio/0703033 | Ioana Bena Dr. | Ioana Bena, Michel Droz, Janusz Szwabinski, Andrzej Pekalski | Complex population dynamics as a competition between multiple time-scale
phenomena | 15 pages, 12 figures. Accepted for publication in Phys. Rev. E | Physical Review E 76, 011908 (2007). | 10.1103/PhysRevE.76.011908 | null | q-bio.PE cond-mat.other cond-mat.stat-mech physics.bio-ph | null | The role of the selection pressure and mutation amplitude on the behavior of
a single-species population evolving on a two-dimensional lattice, in a
periodically changing environment, is studied both analytically and
numerically. The mean-field level of description allows to highlight the
delicate interplay between the different time-scale processes in the resulting
complex dynamics of the system. We clarify the influence of the amplitude and
period of the environmental changes on the critical value of the selection
pressure corresponding to a phase-transition "extinct-alive" of the population.
However, the intrinsic stochasticity and the dynamically-built in correlations
among the individuals, as well as the role of the mutation-induced variety in
population's evolution are not appropriately accounted for. A more refined
level of description, which is an individual-based one, has to be considered.
The inherent fluctuations do not destroy the phase transition "extinct-alive",
and the mutation amplitude is strongly influencing the value of the critical
selection pressure. The phase diagram in the plane of the population's
parameters -- selection and mutation is discussed as a function of the
environmental variation characteristics. The differences between a smooth
variation of the environment and an abrupt, catastrophic change are also
addressesd.
| [
{
"created": "Thu, 15 Mar 2007 00:16:27 GMT",
"version": "v1"
},
{
"created": "Thu, 7 Jun 2007 14:29:38 GMT",
"version": "v2"
}
] | 2009-11-13 | [
[
"Bena",
"Ioana",
""
],
[
"Droz",
"Michel",
""
],
[
"Szwabinski",
"Janusz",
""
],
[
"Pekalski",
"Andrzej",
""
]
] | The role of the selection pressure and mutation amplitude on the behavior of a single-species population evolving on a two-dimensional lattice, in a periodically changing environment, is studied both analytically and numerically. The mean-field level of description allows to highlight the delicate interplay between the different time-scale processes in the resulting complex dynamics of the system. We clarify the influence of the amplitude and period of the environmental changes on the critical value of the selection pressure corresponding to a phase-transition "extinct-alive" of the population. However, the intrinsic stochasticity and the dynamically-built in correlations among the individuals, as well as the role of the mutation-induced variety in population's evolution are not appropriately accounted for. A more refined level of description, which is an individual-based one, has to be considered. The inherent fluctuations do not destroy the phase transition "extinct-alive", and the mutation amplitude is strongly influencing the value of the critical selection pressure. The phase diagram in the plane of the population's parameters -- selection and mutation is discussed as a function of the environmental variation characteristics. The differences between a smooth variation of the environment and an abrupt, catastrophic change are also addressesd. |
1711.00250 | Takashi Okada | Takashi Okada, Je-Chiang Tsai, and Atsushi Mochizuki | Structural Bifurcation Analysis in Chemical Reaction Networks | 29 pages, 12 figures. v2: FIG S4 corrected | Phys. Rev. E 98, 012417 (2018) | 10.1103/PhysRevE.98.012417 | RIKEN-iTHEMS-Report-18 | q-bio.MN math.DS physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In living cells, chemical reactions form a complex network. Complicated
dynamics arising from such networks are the origins of biological functions. We
propose a novel mathematical method to analyze bifurcation behaviors of a
reaction system from the network structure alone. The whole network is
decomposed into subnetworks based on "buffering structures". For each
subnetwork, the bifurcation condition is studied independently, and the
parameters that can induce bifurcations and the chemicals that can exhibit
bifurcations are determined. We demonstrate our theory using hypothetical and
real networks.
| [
{
"created": "Wed, 1 Nov 2017 08:37:35 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Nov 2017 06:44:34 GMT",
"version": "v2"
}
] | 2018-08-08 | [
[
"Okada",
"Takashi",
""
],
[
"Tsai",
"Je-Chiang",
""
],
[
"Mochizuki",
"Atsushi",
""
]
] | In living cells, chemical reactions form a complex network. Complicated dynamics arising from such networks are the origins of biological functions. We propose a novel mathematical method to analyze bifurcation behaviors of a reaction system from the network structure alone. The whole network is decomposed into subnetworks based on "buffering structures". For each subnetwork, the bifurcation condition is studied independently, and the parameters that can induce bifurcations and the chemicals that can exhibit bifurcations are determined. We demonstrate our theory using hypothetical and real networks. |
0705.4079 | Alpan Raval | Alpan Raval | Molecular Clock on a Neutral Network | 10 pages | null | 10.1103/PhysRevLett.99.138104 | null | q-bio.PE q-bio.MN | null | The number of fixed mutations accumulated in an evolving population often
displays a variance that is significantly larger than the mean (the
overdispersed molecular clock). By examining a generic evolutionary process on
a neutral network of high-fitness genotypes, we establish a formalism for
computing all cumulants of the full probability distribution of accumulated
mutations in terms of graph properties of the neutral network, and use the
formalism to prove overdispersion of the molecular clock. We further show that
significant overdispersion arises naturally in evolution when the neutral
network is highly sparse, exhibits large global fluctuations in neutrality, and
small local fluctuations in neutrality. The results are also relevant for
elucidating the topological structure of a neutral network from empirical
measurements of the substitution process.
| [
{
"created": "Mon, 28 May 2007 19:01:45 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Raval",
"Alpan",
""
]
] | The number of fixed mutations accumulated in an evolving population often displays a variance that is significantly larger than the mean (the overdispersed molecular clock). By examining a generic evolutionary process on a neutral network of high-fitness genotypes, we establish a formalism for computing all cumulants of the full probability distribution of accumulated mutations in terms of graph properties of the neutral network, and use the formalism to prove overdispersion of the molecular clock. We further show that significant overdispersion arises naturally in evolution when the neutral network is highly sparse, exhibits large global fluctuations in neutrality, and small local fluctuations in neutrality. The results are also relevant for elucidating the topological structure of a neutral network from empirical measurements of the substitution process. |
1305.3902 | Sayak Mukherjee | Sayak Mukherjee, Sang-Cheol Seok, Veronica J. Vieland and Jayajit Das | Data-driven quantification of robustness and sensitivity of cell
signaling networks | 46 pages, 11 figures. Physical Biology, 2013 | null | 10.1088/1478-3975/10/6/066002 | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robustness and sensitivity of responses generated by cell signaling networks
has been associated with survival and evolvability of organisms. However,
existing methods analyzing robustness and sensitivity of signaling networks
ignore the experimentally observed cell-to-cell variations of protein
abundances and cell functions or contain ad hoc assumptions. We propose and
apply a data driven Maximum Entropy (MaxEnt) based method to quantify
robustness and sensitivity of Escherichia coli (E. coli) chemotaxis signaling
network. Our analysis correctly rank orders different models of E. coli
chemotaxis based on their robustness and suggests that parameters regulating
cell signaling are evolutionary selected to vary in individual cells according
to their abilities to perturb cell functions. Furthermore, predictions from our
approach regarding distribution of protein abundances and properties of
chemotactic responses in individual cells based on cell population averaged
data are in excellent agreement with their experimental counterparts. Our
approach is general and can be used to evaluate robustness as well as generate
predictions of single cell properties based on population averaged experimental
data in a wide range of cell signaling systems.
| [
{
"created": "Thu, 16 May 2013 19:46:09 GMT",
"version": "v1"
},
{
"created": "Tue, 29 Oct 2013 20:28:21 GMT",
"version": "v2"
}
] | 2013-10-31 | [
[
"Mukherjee",
"Sayak",
""
],
[
"Seok",
"Sang-Cheol",
""
],
[
"Vieland",
"Veronica J.",
""
],
[
"Das",
"Jayajit",
""
]
] | Robustness and sensitivity of responses generated by cell signaling networks has been associated with survival and evolvability of organisms. However, existing methods analyzing robustness and sensitivity of signaling networks ignore the experimentally observed cell-to-cell variations of protein abundances and cell functions or contain ad hoc assumptions. We propose and apply a data driven Maximum Entropy (MaxEnt) based method to quantify robustness and sensitivity of Escherichia coli (E. coli) chemotaxis signaling network. Our analysis correctly rank orders different models of E. coli chemotaxis based on their robustness and suggests that parameters regulating cell signaling are evolutionary selected to vary in individual cells according to their abilities to perturb cell functions. Furthermore, predictions from our approach regarding distribution of protein abundances and properties of chemotactic responses in individual cells based on cell population averaged data are in excellent agreement with their experimental counterparts. Our approach is general and can be used to evaluate robustness as well as generate predictions of single cell properties based on population averaged experimental data in a wide range of cell signaling systems. |
1203.3954 | Klaus Jaffe Dr | Klaus Jaffe, Guillermo Mascitti and Daniella Seguias | Gender differences in time perception and its relation with academic
performance: non-linear dynamics in the formation of cognitive systems | Politically incorrect paper practically impossible to publish in a
psychology journal | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Non-linear dynamics is probably much more common in the epigenetic dynamics
of living beings than hitherto recognized. Here we report a case of global
bifurcation triggered by gender that affects higher cognitive functions in
humans. We report a cross-cultural study showing deviations in time perception,
as assessed by estimating the duration of brief sounds, according to their
durations and to the gender of the perciver. Results show that the duration of
sounds lasting less than 10 s were on average overestimated, whereas those
lasting longer were underestimated; estimates of sounds shorter than 1 s were
extremely inaccurate. Females consistently gave longer estimates than males.
Accuracy in time estimation was correlated to academic performance in
disciplines requiring mathematical or scientific skills in male, but not in
female students. This difference in correlation however had nothing to do with
overall skills in mathematics. Both sexes scored similarly in scientific and
technical disciplines, but females had higher grades than males in languages
and lower ones in physical education. Our results confirm existing evidence for
gender differences in cognitive processing, hinting to the existence of
different "mathematical intelligences" with different non-linear relationships
between natural or biological mathematical intuition and time perception.
| [
{
"created": "Sun, 18 Mar 2012 14:29:30 GMT",
"version": "v1"
},
{
"created": "Sat, 24 Mar 2012 13:57:49 GMT",
"version": "v2"
}
] | 2012-03-27 | [
[
"Jaffe",
"Klaus",
""
],
[
"Mascitti",
"Guillermo",
""
],
[
"Seguias",
"Daniella",
""
]
] | Non-linear dynamics is probably much more common in the epigenetic dynamics of living beings than hitherto recognized. Here we report a case of global bifurcation triggered by gender that affects higher cognitive functions in humans. We report a cross-cultural study showing deviations in time perception, as assessed by estimating the duration of brief sounds, according to their durations and to the gender of the perciver. Results show that the duration of sounds lasting less than 10 s were on average overestimated, whereas those lasting longer were underestimated; estimates of sounds shorter than 1 s were extremely inaccurate. Females consistently gave longer estimates than males. Accuracy in time estimation was correlated to academic performance in disciplines requiring mathematical or scientific skills in male, but not in female students. This difference in correlation however had nothing to do with overall skills in mathematics. Both sexes scored similarly in scientific and technical disciplines, but females had higher grades than males in languages and lower ones in physical education. Our results confirm existing evidence for gender differences in cognitive processing, hinting to the existence of different "mathematical intelligences" with different non-linear relationships between natural or biological mathematical intuition and time perception. |
1607.00952 | Mahmoud Hassan | Aya Kabbara, Wassim El Falou, Mohamad Khalil, Fabrice Wendling and
Mahmoud Hassan | Graph analysis of spontaneous brain network using EEG source
connectivity | International Conference on Bio-engineering for Smart Technologies
(BioSMART 2016) | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Exploring the human brain networks during rest is a topic of great interest.
Several structural and functional studies have previously been conducted to
study the intrinsic brain networks. In this paper, we focus on investigating
the human brain network topology using dense Electroencephalography (EEG)
source connectivity approach. We applied graph theoretical methods on
functional networks reconstructed from resting state data acquired using EEG in
14 healthy subjects. Our findings confirmed the existence of sets of brain
regions considered as functional hubs. In particular, the isthmus cingulate and
the orbitofrontal regions reveal high levels of integration. Results also
emphasize on the critical role of the default mode network (DMN) in enabling an
efficient communication between brain regions.
| [
{
"created": "Mon, 4 Jul 2016 16:38:16 GMT",
"version": "v1"
}
] | 2016-07-05 | [
[
"Kabbara",
"Aya",
""
],
[
"Falou",
"Wassim El",
""
],
[
"Khalil",
"Mohamad",
""
],
[
"Wendling",
"Fabrice",
""
],
[
"Hassan",
"Mahmoud",
""
]
] | Exploring the human brain networks during rest is a topic of great interest. Several structural and functional studies have previously been conducted to study the intrinsic brain networks. In this paper, we focus on investigating the human brain network topology using dense Electroencephalography (EEG) source connectivity approach. We applied graph theoretical methods on functional networks reconstructed from resting state data acquired using EEG in 14 healthy subjects. Our findings confirmed the existence of sets of brain regions considered as functional hubs. In particular, the isthmus cingulate and the orbitofrontal regions reveal high levels of integration. Results also emphasize on the critical role of the default mode network (DMN) in enabling an efficient communication between brain regions. |
2307.06495 | Benjamin Allen | Benjamin Allen | Symmetry in models of natural selection | 21 pages, 4 figures | null | null | null | q-bio.PE math.GR math.PR | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Symmetry arguments are frequently used -- often implicitly -- in mathematical
modeling of natural selection. Symmetry simplifies the analysis of models and
reduces the number of distinct population states to be considered. Here, I
introduce a formal definition of symmetry in mathematical models of natural
selection. This definition applies to a broad class of models that satisfy a
minimal set of assumptions, using a framework developed in previous works. In
this framework, population structure is represented by a set of sites at which
alleles can live, and transitions occur via replacement of some alleles by
copies of others. A symmetry is defined as a permutation of sites that
preserves probabilities of replacement and mutation. The symmetries of a given
selection process form a group, which acts on population states in a way that
preserves the Markov chain representing selection. Applying classical results
on group actions, I formally characterize the use of symmetry to reduce the
states of this Markov chain, and obtain bounds on the number of states in the
reduced chain.
| [
{
"created": "Thu, 13 Jul 2023 00:07:42 GMT",
"version": "v1"
}
] | 2023-07-14 | [
[
"Allen",
"Benjamin",
""
]
] | Symmetry arguments are frequently used -- often implicitly -- in mathematical modeling of natural selection. Symmetry simplifies the analysis of models and reduces the number of distinct population states to be considered. Here, I introduce a formal definition of symmetry in mathematical models of natural selection. This definition applies to a broad class of models that satisfy a minimal set of assumptions, using a framework developed in previous works. In this framework, population structure is represented by a set of sites at which alleles can live, and transitions occur via replacement of some alleles by copies of others. A symmetry is defined as a permutation of sites that preserves probabilities of replacement and mutation. The symmetries of a given selection process form a group, which acts on population states in a way that preserves the Markov chain representing selection. Applying classical results on group actions, I formally characterize the use of symmetry to reduce the states of this Markov chain, and obtain bounds on the number of states in the reduced chain. |
0706.1504 | George Bass Ph.D. | George E. Bass, Bernd Meibohm, James T. Dalton and Robert Sayre | Free Energy of Activation for the Comorosan Effect | 21 pages, 3 figures, 2 tables | null | null | null | q-bio.SC q-bio.BM | null | Initial reaction rate data for lactic dehydrogenase / pyruvate, lactic
dehydrogenase / lactate and malic dehydrogenase / malate enzyme reactions were
analyzed to obtain activation free energy changes of -329, -195 and -221
cal/mole, respectively, for rate increases associated with time-specific
irradiation of the crystalline substrates prior to dissolution and
incorporation in the reaction solutions. These energies, presumably, correspond
to conformational or vibrational changes in the reactants or the activated
complex. For the lactic dehydrogenase / pyruvate reaction, it is estimated that
on the order of 10% of the irradiation energy (546 nm, 400 footcandles for 5
seconds) would be required to produce the observed reaction rate increase if a
presumed photoproduct is consumed stoichiometrically with the pyruvate
substrate. These findings are consistent with the proposition that the observed
reaction rate enhancement involves photoproducts derived from oscillatory
atmospheric gas reactions at the crystalline enzyme substrate surfaces rather
than photo-excitations of the substrate molecules, per se.
| [
{
"created": "Mon, 11 Jun 2007 16:03:22 GMT",
"version": "v1"
}
] | 2007-06-12 | [
[
"Bass",
"George E.",
""
],
[
"Meibohm",
"Bernd",
""
],
[
"Dalton",
"James T.",
""
],
[
"Sayre",
"Robert",
""
]
] | Initial reaction rate data for lactic dehydrogenase / pyruvate, lactic dehydrogenase / lactate and malic dehydrogenase / malate enzyme reactions were analyzed to obtain activation free energy changes of -329, -195 and -221 cal/mole, respectively, for rate increases associated with time-specific irradiation of the crystalline substrates prior to dissolution and incorporation in the reaction solutions. These energies, presumably, correspond to conformational or vibrational changes in the reactants or the activated complex. For the lactic dehydrogenase / pyruvate reaction, it is estimated that on the order of 10% of the irradiation energy (546 nm, 400 footcandles for 5 seconds) would be required to produce the observed reaction rate increase if a presumed photoproduct is consumed stoichiometrically with the pyruvate substrate. These findings are consistent with the proposition that the observed reaction rate enhancement involves photoproducts derived from oscillatory atmospheric gas reactions at the crystalline enzyme substrate surfaces rather than photo-excitations of the substrate molecules, per se. |
1104.2204 | Juergen Reingruber | Juergen Reingruber and David Holcman | Transcription factor search for a DNA promoter in a three-states model | 4 pages, 3 figures | null | 10.1103/PhysRevE.84.020901 | null | q-bio.SC q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To ensure fast gene activation, Transcription Factors (TF) use a mechanism
known as facilitated diffusion to find their DNA promoter site. Here we analyze
such a process where a TF alternates between 3D and 1D diffusion. In the latter
(TF bound to the DNA), the TF further switches between a fast translocation
state dominated by interaction with the DNA backbone, and a slow examination
state where interaction with DNA base pairs is predominant. We derive a new
formula for the mean search time, and show that it is faster and less sensitive
to the binding energy fluctuations compared to the case of a single sliding
state. We find that for an optimal search, the time spent bound to the DNA is
larger compared to the 3D time in the nucleus, in agreement with recent
experimental data. Our results further suggest that modifying switching via
phosphorylation or methylation of the TF or the DNA can efficiently regulate
transcription.
| [
{
"created": "Tue, 12 Apr 2011 13:16:09 GMT",
"version": "v1"
}
] | 2015-05-27 | [
[
"Reingruber",
"Juergen",
""
],
[
"Holcman",
"David",
""
]
] | To ensure fast gene activation, Transcription Factors (TF) use a mechanism known as facilitated diffusion to find their DNA promoter site. Here we analyze such a process where a TF alternates between 3D and 1D diffusion. In the latter (TF bound to the DNA), the TF further switches between a fast translocation state dominated by interaction with the DNA backbone, and a slow examination state where interaction with DNA base pairs is predominant. We derive a new formula for the mean search time, and show that it is faster and less sensitive to the binding energy fluctuations compared to the case of a single sliding state. We find that for an optimal search, the time spent bound to the DNA is larger compared to the 3D time in the nucleus, in agreement with recent experimental data. Our results further suggest that modifying switching via phosphorylation or methylation of the TF or the DNA can efficiently regulate transcription. |
2001.03019 | Burcu Gungor | Hilal Hacilar, O.Ufuk Nalbantoglu, Oya Aran, Burcu Bakir-Gungor | Inflammatory Bowel Disease Biomarkers of Human Gut Microbiota Selected
via Ensemble Feature Selection Methods | 9 pages, 10 figures | null | null | null | q-bio.QM cs.LG q-bio.GN stat.ML | http://creativecommons.org/licenses/by/4.0/ | The tremendous boost in the next generation sequencing and in the omics
technologies makes it possible to characterize human gut microbiome (the
collective genomes of the microbial community that reside in our
gastrointestinal tract). While some of these microorganisms are considered as
essential regulators of our immune system, some others can cause several
diseases such as Inflammatory Bowel Diseases (IBD), diabetes, and cancer. IBD,
is a gut related disorder where the deviations from the healthy gut microbiome
are considered to be associated with IBD. Although existing studies attempt to
unveal the composition of the gut microbiome in relation to IBD diseases, a
comprehensive picture is far from being complete. Due to the complexity of
metagenomic studies, the applications of the state of the art machine learning
techniques became popular to address a wide range of questions in the field of
metagenomic data analysis. In this regard, using IBD associated metagenomics
dataset, this study utilizes both supervised and unsupervised machine learning
algorithms, i) to generate a classification model that aids IBD diagnosis, ii)
to discover IBD associated biomarkers, iii) to find subgroups of IBD patients
using k means and hierarchical clustering. To deal with the high dimensionality
of features, we applied robust feature selection algorithms such as Conditional
Mutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF),
min redundancy max relevance (mRMR) and Extreme Gradient Boosting (XGBoost). In
our experiments with 10 fold cross validation, XGBoost had a considerable
effect in terms of minimizing the microbiota used for the diagnosis of IBD and
thus reducing the cost and time. We observed that compared to the single
classifiers, ensemble methods such as kNN and logitboost resulted in better
performance measures for the classification of IBD.
| [
{
"created": "Wed, 8 Jan 2020 13:17:26 GMT",
"version": "v1"
}
] | 2020-01-10 | [
[
"Hacilar",
"Hilal",
""
],
[
"Nalbantoglu",
"O. Ufuk",
""
],
[
"Aran",
"Oya",
""
],
[
"Bakir-Gungor",
"Burcu",
""
]
] | The tremendous boost in the next generation sequencing and in the omics technologies makes it possible to characterize human gut microbiome (the collective genomes of the microbial community that reside in our gastrointestinal tract). While some of these microorganisms are considered as essential regulators of our immune system, some others can cause several diseases such as Inflammatory Bowel Diseases (IBD), diabetes, and cancer. IBD, is a gut related disorder where the deviations from the healthy gut microbiome are considered to be associated with IBD. Although existing studies attempt to unveal the composition of the gut microbiome in relation to IBD diseases, a comprehensive picture is far from being complete. Due to the complexity of metagenomic studies, the applications of the state of the art machine learning techniques became popular to address a wide range of questions in the field of metagenomic data analysis. In this regard, using IBD associated metagenomics dataset, this study utilizes both supervised and unsupervised machine learning algorithms, i) to generate a classification model that aids IBD diagnosis, ii) to discover IBD associated biomarkers, iii) to find subgroups of IBD patients using k means and hierarchical clustering. To deal with the high dimensionality of features, we applied robust feature selection algorithms such as Conditional Mutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF), min redundancy max relevance (mRMR) and Extreme Gradient Boosting (XGBoost). In our experiments with 10 fold cross validation, XGBoost had a considerable effect in terms of minimizing the microbiota used for the diagnosis of IBD and thus reducing the cost and time. We observed that compared to the single classifiers, ensemble methods such as kNN and logitboost resulted in better performance measures for the classification of IBD. |
1312.7331 | Ziv Williams | Ziv M Williams | Trans-generational effect of trained aversive and appetitive experiences
in Drosophila | 11 pages, 3 figures | null | null | null | q-bio.NC q-bio.PE | http://creativecommons.org/licenses/by/3.0/ | Associative learning allows animals to rapidly adapt to changes in the
environment. Whether and what aspects of such acquired traits may be
transmittable across generations remains unclear. Using prolonged olfactory
training and subsequent two-forced choice testing in Drosophila melanogaster,
it is observed that certain aspects of learned behavior were transmitted from
parents to offspring. Offspring of parents exposed to distinct odors during
both aversive and appetitive conditioning displayed a heightened sensitivity to
those same odors. The conditioned responses associated with those odors,
however, were not transmitted to the offspring as they displayed a constitutive
preference to the parent-exposed stimuli irrespective of whether they were
associated with aversive or appetitive training. Moreover, the degree to which
the offspring preferred the conditioned stimuli markedly varied from
odor-to-odor. These findings suggest that heightened sensitivities to certain
salient stimuli in the environment, but not their associated conditioned
behaviors, may be transmittable from parents to offspring. Such
trans-generational adaptations may influence animal traits over short
evolutionary time-scales.
| [
{
"created": "Fri, 27 Dec 2013 20:22:23 GMT",
"version": "v1"
}
] | 2013-12-30 | [
[
"Williams",
"Ziv M",
""
]
] | Associative learning allows animals to rapidly adapt to changes in the environment. Whether and what aspects of such acquired traits may be transmittable across generations remains unclear. Using prolonged olfactory training and subsequent two-forced choice testing in Drosophila melanogaster, it is observed that certain aspects of learned behavior were transmitted from parents to offspring. Offspring of parents exposed to distinct odors during both aversive and appetitive conditioning displayed a heightened sensitivity to those same odors. The conditioned responses associated with those odors, however, were not transmitted to the offspring as they displayed a constitutive preference to the parent-exposed stimuli irrespective of whether they were associated with aversive or appetitive training. Moreover, the degree to which the offspring preferred the conditioned stimuli markedly varied from odor-to-odor. These findings suggest that heightened sensitivities to certain salient stimuli in the environment, but not their associated conditioned behaviors, may be transmittable from parents to offspring. Such trans-generational adaptations may influence animal traits over short evolutionary time-scales. |
1404.5441 | Sacha S. J. Laurent | Sacha Laurent and Marc Robinson-Rechavi and Nicolas Salamin | Detecting patterns of species diversification in the presence of both
rate shifts and mass extinctions | 34 pages, 11 figures | BMC Evolutionary Biology 2015 15:157 | 10.1186/s12862-015-0432-z | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Recent methodological advances are enabling better examination of speciation
and extinction processes and patterns. A major open question is the origin of
large discrepancies in species number between groups of the same age. Existing
frameworks to model this diversity either focus on changes between lineages,
neglecting global effects such as mass extinctions, or focus on changes over
time which would affect all lineages. Yet it seems probable that both lineages
differences and mass extinctions affect the same groups. Here we used
simulations to test the performance of two widely used methods, under complex
scenarios. We report good performances, although with a tendency to
over-predict events when increasing the complexity of the scenario. Overall, we
find that lineage shifts are better detected than mass extinctions. This work
has significance for assessing the methods currently used for estimating
changes in diversification using phylogenies and developing new tests.
| [
{
"created": "Tue, 22 Apr 2014 09:55:12 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Nov 2014 17:13:46 GMT",
"version": "v2"
},
{
"created": "Mon, 31 Aug 2015 12:31:30 GMT",
"version": "v3"
}
] | 2015-09-01 | [
[
"Laurent",
"Sacha",
""
],
[
"Robinson-Rechavi",
"Marc",
""
],
[
"Salamin",
"Nicolas",
""
]
] | Recent methodological advances are enabling better examination of speciation and extinction processes and patterns. A major open question is the origin of large discrepancies in species number between groups of the same age. Existing frameworks to model this diversity either focus on changes between lineages, neglecting global effects such as mass extinctions, or focus on changes over time which would affect all lineages. Yet it seems probable that both lineages differences and mass extinctions affect the same groups. Here we used simulations to test the performance of two widely used methods, under complex scenarios. We report good performances, although with a tendency to over-predict events when increasing the complexity of the scenario. Overall, we find that lineage shifts are better detected than mass extinctions. This work has significance for assessing the methods currently used for estimating changes in diversification using phylogenies and developing new tests. |
1804.00969 | Teodoro Dannemann | Teodoro Dannemann, Denis Boyer, Octavio Miramontes | L\'evy flight movements prevent extinctions and maximize population
abundances in fragile Lotka Volterra systems | null | PNAS. 201719889, 2018 | 10.1073/pnas.1719889115 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multiple-scale mobility is ubiquitous in nature and has become instrumental
for understanding and modeling animal foraging behavior. However, the impact of
individual movements on the long-term stability of populations remains largely
unexplored. We analyze deterministic and stochastic Lotka Volterra systems,
where mobile predators consume scarce resources (prey) confined in patches. In
fragile systems (that is, those unfavorable to species coexistence), the
predator species has a maximized abundance and is resilient to degraded prey
conditions when individual mobility is multiple scaled. Within the L\'evy
flight model, highly superdiffusive foragers rarely encounter prey patches and
go extinct, whereas normally diffusing foragers tend to proliferate within
patches, causing extinctions by overexploitation. L\'evy flights of
intermediate index allow a sustainable balance between patch exploitation and
regeneration over wide ranges of demographic rates. Our analytical and
simulated results can explain field observations and suggest that scale-free
random movements are an important mechanism by which entire populations adapt
to scarcity in fragmented ecosystems.
| [
{
"created": "Fri, 30 Mar 2018 03:37:22 GMT",
"version": "v1"
}
] | 2018-04-04 | [
[
"Dannemann",
"Teodoro",
""
],
[
"Boyer",
"Denis",
""
],
[
"Miramontes",
"Octavio",
""
]
] | Multiple-scale mobility is ubiquitous in nature and has become instrumental for understanding and modeling animal foraging behavior. However, the impact of individual movements on the long-term stability of populations remains largely unexplored. We analyze deterministic and stochastic Lotka Volterra systems, where mobile predators consume scarce resources (prey) confined in patches. In fragile systems (that is, those unfavorable to species coexistence), the predator species has a maximized abundance and is resilient to degraded prey conditions when individual mobility is multiple scaled. Within the L\'evy flight model, highly superdiffusive foragers rarely encounter prey patches and go extinct, whereas normally diffusing foragers tend to proliferate within patches, causing extinctions by overexploitation. L\'evy flights of intermediate index allow a sustainable balance between patch exploitation and regeneration over wide ranges of demographic rates. Our analytical and simulated results can explain field observations and suggest that scale-free random movements are an important mechanism by which entire populations adapt to scarcity in fragmented ecosystems. |
2101.11656 | Sayan Ghosal | Sayan Ghosal, Qiang Chen, Giulio Pergola, Aaron L. Goldman, William
Ulrich, Karen F. Berman, Giuseppe Blasi, Leonardo Fazio, Antonio Rampino,
Alessandro Bertolino, Daniel R. Weinberger, Venkata S. Mattay, and Archana
Venkataraman | G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for
Biomarker Identification and Disease Classification | null | null | null | null | q-bio.QM cs.LG eess.IV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We propose a novel deep neural network architecture to integrate imaging and
genetics data, as guided by diagnosis, that provides interpretable biomarkers.
Our model consists of an encoder, a decoder and a classifier. The encoder
learns a non-linear subspace shared between the input data modalities. The
classifier and the decoder act as regularizers to ensure that the
low-dimensional encoding captures predictive differences between patients and
controls. We use a learnable dropout layer to extract interpretable biomarkers
from the data, and our unique training strategy can easily accommodate missing
data modalities across subjects. We have evaluated our model on a population
study of schizophrenia that includes two functional MRI (fMRI) paradigms and
Single Nucleotide Polymorphism (SNP) data. Using 10-fold cross validation, we
demonstrate that our model achieves better classification accuracy than
baseline methods, and that this performance generalizes to a second dataset
collected at a different site. In an exploratory analysis we further show that
the biomarkers identified by our model are closely associated with the
well-documented deficits in schizophrenia.
| [
{
"created": "Wed, 27 Jan 2021 19:28:04 GMT",
"version": "v1"
}
] | 2021-01-29 | [
[
"Ghosal",
"Sayan",
""
],
[
"Chen",
"Qiang",
""
],
[
"Pergola",
"Giulio",
""
],
[
"Goldman",
"Aaron L.",
""
],
[
"Ulrich",
"William",
""
],
[
"Berman",
"Karen F.",
""
],
[
"Blasi",
"Giuseppe",
""
],
[
"Fazio",
"Leonardo",
""
],
[
"Rampino",
"Antonio",
""
],
[
"Bertolino",
"Alessandro",
""
],
[
"Weinberger",
"Daniel R.",
""
],
[
"Mattay",
"Venkata S.",
""
],
[
"Venkataraman",
"Archana",
""
]
] | We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. Our model consists of an encoder, a decoder and a classifier. The encoder learns a non-linear subspace shared between the input data modalities. The classifier and the decoder act as regularizers to ensure that the low-dimensional encoding captures predictive differences between patients and controls. We use a learnable dropout layer to extract interpretable biomarkers from the data, and our unique training strategy can easily accommodate missing data modalities across subjects. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data. Using 10-fold cross validation, we demonstrate that our model achieves better classification accuracy than baseline methods, and that this performance generalizes to a second dataset collected at a different site. In an exploratory analysis we further show that the biomarkers identified by our model are closely associated with the well-documented deficits in schizophrenia. |
2312.11700 | Seongwon Kim | Seongwon Kim, Parisa Mollaei, Amir Barati Farimani and Anne Skaja
Robinson | Characterization of Phosphorylated Tau-Microtubule complex with
Molecular Dynamics (MD) simulation | 27pages, 12 figure | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Alzheimer's Disease (AD), a neurodegenerative disorder, is reported as one of
the most severe health and socioeconomic problems in current public health. Tau
proteins are assumed to be a crucial driving factor of AD that detach from
microtubules (MT) and accumulate as neurotoxic aggregates in the brains of AD
patients. Extensive experimental and computational research has observed that
phosphorylation at specific tau residues enhances aggregation, but the exact
mechanisms underlying this phenomenon remain unclear. In this study, we
employed molecular dynamics (MD) simulations on pseudo-phosphorylated tau-MT
complex (residue 199 ~ 312), incorporating structural data from recent
cryo-electron microscopy studies. Simulation results have revealed altered tau
conformations after applying pseudo-phosphorylation. Additionally,
root-mean-square deviation (RMSD) analyses and dimensionality reduction of
dihedral angles revealed key residues responsible for these conformational
shifts
| [
{
"created": "Mon, 18 Dec 2023 20:56:13 GMT",
"version": "v1"
}
] | 2023-12-20 | [
[
"Kim",
"Seongwon",
""
],
[
"Mollaei",
"Parisa",
""
],
[
"Farimani",
"Amir Barati",
""
],
[
"Robinson",
"Anne Skaja",
""
]
] | Alzheimer's Disease (AD), a neurodegenerative disorder, is reported as one of the most severe health and socioeconomic problems in current public health. Tau proteins are assumed to be a crucial driving factor of AD that detach from microtubules (MT) and accumulate as neurotoxic aggregates in the brains of AD patients. Extensive experimental and computational research has observed that phosphorylation at specific tau residues enhances aggregation, but the exact mechanisms underlying this phenomenon remain unclear. In this study, we employed molecular dynamics (MD) simulations on pseudo-phosphorylated tau-MT complex (residue 199 ~ 312), incorporating structural data from recent cryo-electron microscopy studies. Simulation results have revealed altered tau conformations after applying pseudo-phosphorylation. Additionally, root-mean-square deviation (RMSD) analyses and dimensionality reduction of dihedral angles revealed key residues responsible for these conformational shifts |
2311.10403 | Junbo Jia | Junbo Jia and Luonan Chen | Velde: constructing cell potential landscapes by RNA velocity vector
field decomposition | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Waddington landscape serves as a metaphor illustrating the developmental
process of cells, likening it to a small ball rolling down various trajectories
into valleys. Constructing an epigenetic landscape of this nature aids in
visualizing and gaining insights into cell differentiation. Development
encompasses intricate processes involving both cell differentiation and cell
cycles. However, current landscape methods solely focus on constructing a
potential landscape for cell differentiation, neglecting the accompanying cell
cycle. This paper introduces a novel method that simultaneously constructs two
types of potential landscapes using single-cell RNA sequencing data.
Specifically, it presents the natural Helmholtz-Hodge decomposition (nHHD) of a
continuous vector field within a bounded domain in n-dimensional Euclidean
space. This decomposition uniquely breaks down the vector field into a gradient
field, a rotation field, and a harmonic field. Utilizing this approach, the RNA
velocity vector field is separated into a curl-free component representing cell
differentiation and a curl component representing the cell cycle. By
calculating the corresponding potential functions, potential landscapes for
both cell differentiation and the cell cycle are obtained. Finally, the
efficacy of this method is demonstrated through its application to synthetic
and real datasets.
| [
{
"created": "Fri, 17 Nov 2023 09:08:54 GMT",
"version": "v1"
}
] | 2023-11-20 | [
[
"Jia",
"Junbo",
""
],
[
"Chen",
"Luonan",
""
]
] | The Waddington landscape serves as a metaphor illustrating the developmental process of cells, likening it to a small ball rolling down various trajectories into valleys. Constructing an epigenetic landscape of this nature aids in visualizing and gaining insights into cell differentiation. Development encompasses intricate processes involving both cell differentiation and cell cycles. However, current landscape methods solely focus on constructing a potential landscape for cell differentiation, neglecting the accompanying cell cycle. This paper introduces a novel method that simultaneously constructs two types of potential landscapes using single-cell RNA sequencing data. Specifically, it presents the natural Helmholtz-Hodge decomposition (nHHD) of a continuous vector field within a bounded domain in n-dimensional Euclidean space. This decomposition uniquely breaks down the vector field into a gradient field, a rotation field, and a harmonic field. Utilizing this approach, the RNA velocity vector field is separated into a curl-free component representing cell differentiation and a curl component representing the cell cycle. By calculating the corresponding potential functions, potential landscapes for both cell differentiation and the cell cycle are obtained. Finally, the efficacy of this method is demonstrated through its application to synthetic and real datasets. |
2308.07465 | Nikolai Slavov | Andrew Leduc, Hannah Harens, and Nikolai Slavov | Modeling and interpretation of single-cell proteogenomic data | null | null | null | null | q-bio.GN q-bio.BM q-bio.TO | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Biological functions stem from coordinated interactions among proteins,
nucleic acids and small molecules. Mass spectrometry technologies for reliable,
high throughput single-cell proteomics will add a new modality to genomics and
enable data-driven modeling of the molecular mechanisms coordinating proteins
and nucleic acids at single-cell resolution. This promising potential requires
estimating the reliability of measurements and computational analysis so that
models can distinguish biological regulation from technical artifacts. We
highlight different measurement modes that can support single-cell
proteogenomic analysis and how to estimate their reliability. We then discuss
approaches for developing both abstract and mechanistic models that aim to
biologically interpret the measured differences across modalities, including
specific applications to directed stem cell differentiation and to inferring
protein interactions in cancer cells from the buffing of DNA copy-number
variations. Single-cell proteogenomic data will support mechanistic models of
direct molecular interactions that will provide generalizable and predictive
representations of biological systems.
| [
{
"created": "Mon, 14 Aug 2023 21:25:56 GMT",
"version": "v1"
},
{
"created": "Sat, 4 Nov 2023 20:55:46 GMT",
"version": "v2"
}
] | 2023-11-07 | [
[
"Leduc",
"Andrew",
""
],
[
"Harens",
"Hannah",
""
],
[
"Slavov",
"Nikolai",
""
]
] | Biological functions stem from coordinated interactions among proteins, nucleic acids and small molecules. Mass spectrometry technologies for reliable, high throughput single-cell proteomics will add a new modality to genomics and enable data-driven modeling of the molecular mechanisms coordinating proteins and nucleic acids at single-cell resolution. This promising potential requires estimating the reliability of measurements and computational analysis so that models can distinguish biological regulation from technical artifacts. We highlight different measurement modes that can support single-cell proteogenomic analysis and how to estimate their reliability. We then discuss approaches for developing both abstract and mechanistic models that aim to biologically interpret the measured differences across modalities, including specific applications to directed stem cell differentiation and to inferring protein interactions in cancer cells from the buffing of DNA copy-number variations. Single-cell proteogenomic data will support mechanistic models of direct molecular interactions that will provide generalizable and predictive representations of biological systems. |
2004.12836 | Christina Bohk-Ewald | Christina Bohk-Ewald and Christian Dudel and Mikko Myrskyl\"a | A demographic scaling model for estimating the total number of COVID-19
infections | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding how widely COVID-19 has spread is critical for examining the
pandemic's progression. Despite efforts to carefully monitor the pandemic, the
number of confirmed cases may underestimate the total number of infections. We
introduce a demographic scaling model to estimate COVID-19 infections using an
broadly applicable approach that is based on minimal data requirements:
COVID-19 related deaths, infection fatality rates (IFRs), and life tables. As
many countries lack reliable estimates of age-specific IFRs, we scale IFRs
between countries using remaining life expectancy as a marker to account for
differences in age structures, health conditions, and medical services. Across
10 countries with most COVID-19 deaths as of May 13, 2020, the number of
infections is estimated to be four [95% prediction interval: 2-11] times higher
than the number of confirmed cases. Cross-country variation is high. The
estimated number of infections is 1.4 million (six times the number of
confirmed cases) for Italy; 3.1 million (2.2 times the number of confirmed
cases) for the U.S.; and 1.8 times the number of confirmed cases for Germany,
where testing has been comparatively extensive. Our prevalence estimates,
however, are markedly lower than most others based on local seroprevalence
studies. We introduce formulas for quantifying the bias that is required in our
data on deaths in order to reproduce estimates published elsewhere. This bias
analysis shows that either COVID-19 deaths are severely underestimated, by a
factor of two or more; or alternatively, the seroprevalence based results are
overestimates and not representative for the total population.
| [
{
"created": "Fri, 24 Apr 2020 17:26:50 GMT",
"version": "v1"
},
{
"created": "Tue, 26 May 2020 08:46:18 GMT",
"version": "v2"
}
] | 2020-05-27 | [
[
"Bohk-Ewald",
"Christina",
""
],
[
"Dudel",
"Christian",
""
],
[
"Myrskylä",
"Mikko",
""
]
] | Understanding how widely COVID-19 has spread is critical for examining the pandemic's progression. Despite efforts to carefully monitor the pandemic, the number of confirmed cases may underestimate the total number of infections. We introduce a demographic scaling model to estimate COVID-19 infections using an broadly applicable approach that is based on minimal data requirements: COVID-19 related deaths, infection fatality rates (IFRs), and life tables. As many countries lack reliable estimates of age-specific IFRs, we scale IFRs between countries using remaining life expectancy as a marker to account for differences in age structures, health conditions, and medical services. Across 10 countries with most COVID-19 deaths as of May 13, 2020, the number of infections is estimated to be four [95% prediction interval: 2-11] times higher than the number of confirmed cases. Cross-country variation is high. The estimated number of infections is 1.4 million (six times the number of confirmed cases) for Italy; 3.1 million (2.2 times the number of confirmed cases) for the U.S.; and 1.8 times the number of confirmed cases for Germany, where testing has been comparatively extensive. Our prevalence estimates, however, are markedly lower than most others based on local seroprevalence studies. We introduce formulas for quantifying the bias that is required in our data on deaths in order to reproduce estimates published elsewhere. This bias analysis shows that either COVID-19 deaths are severely underestimated, by a factor of two or more; or alternatively, the seroprevalence based results are overestimates and not representative for the total population. |
1910.06113 | Thomas Bolton | Thomas A. W. Bolton, Constantin Tuleasca, Gwladys Rey, Diana Wotruba,
Julian Gaviria, Herberto Dhanis, Eva Blondiaux, Baptise Gauthier, Lukasz
Smigielski, Dimitri Van De Ville | TbCAPs: A ToolBox for Co-Activation Pattern Analysis | 15 pages, 4 figures, 1 table | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Functional magnetic resonance imaging provides rich spatio-temporal data of
human brain activity during task and rest. Many recent efforts have focussed on
characterising dynamics of brain activity. One notable instance is
co-activation pattern (CAP) analysis, a frame-wise analytical approach that
disentangles the different functional brain networks interacting with a
user-defined seed region. While promising applications in various clinical
settings have been demonstrated, there is not yet any centralised, publicly
accessible resource to facilitate the deployment of the technique.
Here, we release a working version of TbCAPs, a new toolbox for CAP analysis,
which includes all steps of the analytical pipeline, introduces new
methodological developments that build on already existing concepts, and
enables a facilitated inspection of CAPs and resulting metrics of brain
dynamics. The toolbox is available on a public academic repository
(https://c4science.ch/source/CAP_Toolbox.git).
In addition, to illustrate the feasibility and usefulness of our pipeline, we
describe an application to the study of human cognition. CAPs are constructed
from resting-state fMRI using as seed the right dorsolateral prefrontal cortex,
and, in a separate sample, we successfully predict a behavioural measure of
continuous attentional performance from the metrics of CAP dynamics (R=0.59).
| [
{
"created": "Mon, 14 Oct 2019 12:53:52 GMT",
"version": "v1"
}
] | 2019-10-15 | [
[
"Bolton",
"Thomas A. W.",
""
],
[
"Tuleasca",
"Constantin",
""
],
[
"Rey",
"Gwladys",
""
],
[
"Wotruba",
"Diana",
""
],
[
"Gaviria",
"Julian",
""
],
[
"Dhanis",
"Herberto",
""
],
[
"Blondiaux",
"Eva",
""
],
[
"Gauthier",
"Baptise",
""
],
[
"Smigielski",
"Lukasz",
""
],
[
"Van De Ville",
"Dimitri",
""
]
] | Functional magnetic resonance imaging provides rich spatio-temporal data of human brain activity during task and rest. Many recent efforts have focussed on characterising dynamics of brain activity. One notable instance is co-activation pattern (CAP) analysis, a frame-wise analytical approach that disentangles the different functional brain networks interacting with a user-defined seed region. While promising applications in various clinical settings have been demonstrated, there is not yet any centralised, publicly accessible resource to facilitate the deployment of the technique. Here, we release a working version of TbCAPs, a new toolbox for CAP analysis, which includes all steps of the analytical pipeline, introduces new methodological developments that build on already existing concepts, and enables a facilitated inspection of CAPs and resulting metrics of brain dynamics. The toolbox is available on a public academic repository (https://c4science.ch/source/CAP_Toolbox.git). In addition, to illustrate the feasibility and usefulness of our pipeline, we describe an application to the study of human cognition. CAPs are constructed from resting-state fMRI using as seed the right dorsolateral prefrontal cortex, and, in a separate sample, we successfully predict a behavioural measure of continuous attentional performance from the metrics of CAP dynamics (R=0.59). |
2004.05895 | Audrey B\"urki | A. B\"urki, S. Elbuy, S. Madec, S. Vasishth | What did we learn from forty years of research on semantic interference?
A Bayesian metaanalysis | null | null | null | null | q-bio.NC stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When participants in an experiment have to name pictures while ignoring
distractor words superimposed on the picture or presented auditorily (i.e.,
picture-word interference paradigm), they take more time when the word to be
named (or target) and distractor words are from the same semantic category
(e.g., cat-dog). This experimental effect is known as the semantic interference
effect, and is probably one of the most studied in the language production
literature. The functional origin of the effect and the exact conditions in
which it occurs are however still debated. Since Lupker reported the effect in
the first response time experiment about 40 years ago, more than 300 similar
experiments have been conducted. The semantic interference effect was
replicated in many experiments, but several studies also reported the absence
of an effect in a subset of experimental conditions. The aim of the present
study is to provide a comprehensive theoretical review of the existing evidence
to date and several Bayesian meta-analyses and meta-regressions to determine
the size of the effect and explore the experimental conditions in which the
effect surfaces. The results are discussed in the light of current debates
about the functional origin of the semantic interference effect and its
implications for our understanding of the language production system.
| [
{
"created": "Thu, 2 Apr 2020 15:07:26 GMT",
"version": "v1"
},
{
"created": "Sun, 26 Apr 2020 09:41:47 GMT",
"version": "v2"
}
] | 2020-04-28 | [
[
"Bürki",
"A.",
""
],
[
"Elbuy",
"S.",
""
],
[
"Madec",
"S.",
""
],
[
"Vasishth",
"S.",
""
]
] | When participants in an experiment have to name pictures while ignoring distractor words superimposed on the picture or presented auditorily (i.e., picture-word interference paradigm), they take more time when the word to be named (or target) and distractor words are from the same semantic category (e.g., cat-dog). This experimental effect is known as the semantic interference effect, and is probably one of the most studied in the language production literature. The functional origin of the effect and the exact conditions in which it occurs are however still debated. Since Lupker reported the effect in the first response time experiment about 40 years ago, more than 300 similar experiments have been conducted. The semantic interference effect was replicated in many experiments, but several studies also reported the absence of an effect in a subset of experimental conditions. The aim of the present study is to provide a comprehensive theoretical review of the existing evidence to date and several Bayesian meta-analyses and meta-regressions to determine the size of the effect and explore the experimental conditions in which the effect surfaces. The results are discussed in the light of current debates about the functional origin of the semantic interference effect and its implications for our understanding of the language production system. |
1209.5760 | Suzanne Bowen Dr | Suzanne Bowen | Protein function influences frequency of encoded regions containing
VNTRs and number of unique interactions | 21 pages, 4 figures | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proteins encoded by genes containing regions of variable number tandem
repeats (VNTRs) are known to be polymorphic within species but the influence of
their instability in molecular interactions remains unclear. VNTRs are
overrepresented in encoding sequence of particular functional groups where
their presence could influence protein interactions. Using human consensus
coding sequence, this work examines if genomic instability, determined by
regions of VNTRs, influences the number of protein interactions. Findings
reveal that, in relation to protein function, the frequency of unique
interactions in human proteins increase with the number of repeated regions.
This supports experimental evidence that repeat expansion may lead to an
increase in molecular interactions. Genetic diversity, estimated by Ka/Ks,
appeared to decrease as the number of protein-protein interactions increased.
Additionally, G+C and CpG content were negatively correlated with increasing
occurrence of VNTRs. This may indicate that nucleotide composition along with
selective processes can increase genomic stability and thereby restrict the
expansion of repeated regions. Proteins involved in acetylation are associated
with a high number of repeated regions and interactions but a low G+C and CpG
content. While in contrast, less interactive membrane proteins contain a lower
number of repeated regions but higher levels of C+G and CpGs. This work
provides further evidence that VNTRs may provide the genetic variability to
generate unique interactions between proteins.
| [
{
"created": "Tue, 25 Sep 2012 20:32:43 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Sep 2012 14:05:17 GMT",
"version": "v2"
}
] | 2012-09-28 | [
[
"Bowen",
"Suzanne",
""
]
] | Proteins encoded by genes containing regions of variable number tandem repeats (VNTRs) are known to be polymorphic within species but the influence of their instability in molecular interactions remains unclear. VNTRs are overrepresented in encoding sequence of particular functional groups where their presence could influence protein interactions. Using human consensus coding sequence, this work examines if genomic instability, determined by regions of VNTRs, influences the number of protein interactions. Findings reveal that, in relation to protein function, the frequency of unique interactions in human proteins increase with the number of repeated regions. This supports experimental evidence that repeat expansion may lead to an increase in molecular interactions. Genetic diversity, estimated by Ka/Ks, appeared to decrease as the number of protein-protein interactions increased. Additionally, G+C and CpG content were negatively correlated with increasing occurrence of VNTRs. This may indicate that nucleotide composition along with selective processes can increase genomic stability and thereby restrict the expansion of repeated regions. Proteins involved in acetylation are associated with a high number of repeated regions and interactions but a low G+C and CpG content. While in contrast, less interactive membrane proteins contain a lower number of repeated regions but higher levels of C+G and CpGs. This work provides further evidence that VNTRs may provide the genetic variability to generate unique interactions between proteins. |
2306.01935 | Vivek N. Prakash | Setareh Gooshvar, Gopika Madhu, Melissa Ruszczyk, and Vivek N. Prakash | Non-bilaterians as Model Systems for Tissue Mechanics | Review paper, Comments/suggestions are welcome | Integrative and Comparative Biology, 2023 | 10.1093/icb/icad074 | null | q-bio.TO physics.bio-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In animals, epithelial tissues are barriers against the external environment,
providing protection against biological, chemical, and physical damage.
Depending on the animal's physiology and behavior, these tissues encounter
different types of mechanical forces and need to provide a suitable adaptive
response to ensure success. Therefore, understanding tissue mechanics in
different contexts is an important research area. Here, we review recent tissue
mechanics discoveries in a few early-divergent non-bilaterian animals --
Trichoplax adhaerens, Hydra vulgaris, and Aurelia aurita. We highlight each
animal's simple body plan and biology, and unique, rapid tissue remodeling
phenomena that play a crucial role in its physiology. We also discuss the
emergent large-scale mechanics that arise from small-scale phenomena. Finally,
we emphasize the enormous potential of these non-bilaterian animals to be model
systems for further investigation in tissue mechanics.
| [
{
"created": "Fri, 2 Jun 2023 22:28:16 GMT",
"version": "v1"
}
] | 2023-12-29 | [
[
"Gooshvar",
"Setareh",
""
],
[
"Madhu",
"Gopika",
""
],
[
"Ruszczyk",
"Melissa",
""
],
[
"Prakash",
"Vivek N.",
""
]
] | In animals, epithelial tissues are barriers against the external environment, providing protection against biological, chemical, and physical damage. Depending on the animal's physiology and behavior, these tissues encounter different types of mechanical forces and need to provide a suitable adaptive response to ensure success. Therefore, understanding tissue mechanics in different contexts is an important research area. Here, we review recent tissue mechanics discoveries in a few early-divergent non-bilaterian animals -- Trichoplax adhaerens, Hydra vulgaris, and Aurelia aurita. We highlight each animal's simple body plan and biology, and unique, rapid tissue remodeling phenomena that play a crucial role in its physiology. We also discuss the emergent large-scale mechanics that arise from small-scale phenomena. Finally, we emphasize the enormous potential of these non-bilaterian animals to be model systems for further investigation in tissue mechanics. |
2407.08224 | Wenwen Min | Shuailin Xue, Fangfang Zhu, Changmiao Wang and Wenwen Min | stEnTrans: Transformer-based deep learning for spatial transcriptomics
enhancement | ISBRA2024, Code: https://github.com/shuailinxue/stEnTrans | null | null | null | q-bio.QM cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The spatial location of cells within tissues and organs is crucial for the
manifestation of their specific functions.Spatial transcriptomics technology
enables comprehensive measurement of the gene expression patterns in tissues
while retaining spatial information. However, current popular spatial
transcriptomics techniques either have shallow sequencing depth or low
resolution. We present stEnTrans, a deep learning method based on Transformer
architecture that provides comprehensive predictions for gene expression in
unmeasured areas or unexpectedly lost areas and enhances gene expression in
original and inputed spots. Utilizing a self-supervised learning approach,
stEnTrans establishes proxy tasks on gene expression profile without requiring
additional data, mining intrinsic features of the tissues as supervisory
information. We evaluate stEnTrans on six datasets and the results indicate
superior performance in enhancing spots resolution and predicting gene
expression in unmeasured areas compared to other deep learning and traditional
interpolation methods. Additionally, Our method also can help the discovery of
spatial patterns in Spatial Transcriptomics and enrich to more biologically
significant pathways. Our source code is available at
https://github.com/shuailinxue/stEnTrans.
| [
{
"created": "Thu, 11 Jul 2024 06:50:34 GMT",
"version": "v1"
}
] | 2024-07-12 | [
[
"Xue",
"Shuailin",
""
],
[
"Zhu",
"Fangfang",
""
],
[
"Wang",
"Changmiao",
""
],
[
"Min",
"Wenwen",
""
]
] | The spatial location of cells within tissues and organs is crucial for the manifestation of their specific functions.Spatial transcriptomics technology enables comprehensive measurement of the gene expression patterns in tissues while retaining spatial information. However, current popular spatial transcriptomics techniques either have shallow sequencing depth or low resolution. We present stEnTrans, a deep learning method based on Transformer architecture that provides comprehensive predictions for gene expression in unmeasured areas or unexpectedly lost areas and enhances gene expression in original and inputed spots. Utilizing a self-supervised learning approach, stEnTrans establishes proxy tasks on gene expression profile without requiring additional data, mining intrinsic features of the tissues as supervisory information. We evaluate stEnTrans on six datasets and the results indicate superior performance in enhancing spots resolution and predicting gene expression in unmeasured areas compared to other deep learning and traditional interpolation methods. Additionally, Our method also can help the discovery of spatial patterns in Spatial Transcriptomics and enrich to more biologically significant pathways. Our source code is available at https://github.com/shuailinxue/stEnTrans. |
1611.08259 | Antti Niemi | Alexandr Nasedkin, Jan Davidsson, Antti J. Niemi, Xubiao Peng | Solution X-ray scattering (S/WAXS) and structure formation in protein
dynamics | 10 figures | Phys. Rev. E 96, 062405 (2017) | 10.1103/PhysRevE.96.062405 | null | q-bio.BM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose to develop mean field theory in combination with Glauber
algorithm, to model and interpret protein dynamics and structure formation in
small to wide angle x-ray scattering (S/WAXS) experiments. We develop the
methodology by analysing the Engrailed homeodomain protein as an example. We
demonstrate how to interpret S/WAXS data with a good precision and over an
extended temperature range. We explain experimentally observed phenomena in
terms of protein phase structure, and we make predictions for future
experiments how the scattering data behaves at different ambient temperature
values. We conclude that a combination of mean field theory with Glauber
algorithm has the potential to develop into a highly accurate, computationally
effective and predictive tool for analysing S/WAXS data. Finally, we compare
our results with those obtained previously in an all-atom molecular dynamics
simulation.
| [
{
"created": "Thu, 24 Nov 2016 17:09:22 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Jun 2017 11:22:17 GMT",
"version": "v2"
}
] | 2017-12-20 | [
[
"Nasedkin",
"Alexandr",
""
],
[
"Davidsson",
"Jan",
""
],
[
"Niemi",
"Antti J.",
""
],
[
"Peng",
"Xubiao",
""
]
] | We propose to develop mean field theory in combination with Glauber algorithm, to model and interpret protein dynamics and structure formation in small to wide angle x-ray scattering (S/WAXS) experiments. We develop the methodology by analysing the Engrailed homeodomain protein as an example. We demonstrate how to interpret S/WAXS data with a good precision and over an extended temperature range. We explain experimentally observed phenomena in terms of protein phase structure, and we make predictions for future experiments how the scattering data behaves at different ambient temperature values. We conclude that a combination of mean field theory with Glauber algorithm has the potential to develop into a highly accurate, computationally effective and predictive tool for analysing S/WAXS data. Finally, we compare our results with those obtained previously in an all-atom molecular dynamics simulation. |
1206.0889 | Gilles Guillot | Gilles Guillot | Detection of correlation between genotypes and environmental variables.
A fast computational approach for genomewide studies | To appear in Spatial Statistics | null | null | null | q-bio.PE stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Genomic regions (or loci) displaying outstanding correlation with some
environmental variables are likely to be under selection and this is the
rationale of recent methods of identifying selected loci and retrieving
functional information about them. To be efficient, such methods need to be
able to disentangle the potential effect of environmental variables from the
confounding effect of population history. For the routine analysis of
genome-wide datasets, one also needs fast inference and model selection
algorithms. We propose a method based on an explicit spatial model which is an
instance of spatial generalized linear mixed model (SGLMM). For inference, we
make use of the INLA-SPDE theoretical and computational framework developed by
Rue et al. (2009) and Lindgren et al (2011). The method we propose allows one
to quantify the correlation between genotypes and environmental variables. It
works for the most common types of genetic markers, obtained either at the
individual or at the population level. Analyzing simulated data produced under
a geostatistical model then under an explicit model of selection, we show that
the method is efficient. We also re-analyze a dataset relative to nineteen pine
weevils (Hylobius abietis}) populations across Europe. The method proposed
appears also as a statistically sound alternative to the Mantel tests for
testing the association between genetic and environmental variables.
| [
{
"created": "Tue, 5 Jun 2012 11:55:13 GMT",
"version": "v1"
},
{
"created": "Mon, 12 Aug 2013 08:11:53 GMT",
"version": "v2"
}
] | 2013-08-13 | [
[
"Guillot",
"Gilles",
""
]
] | Genomic regions (or loci) displaying outstanding correlation with some environmental variables are likely to be under selection and this is the rationale of recent methods of identifying selected loci and retrieving functional information about them. To be efficient, such methods need to be able to disentangle the potential effect of environmental variables from the confounding effect of population history. For the routine analysis of genome-wide datasets, one also needs fast inference and model selection algorithms. We propose a method based on an explicit spatial model which is an instance of spatial generalized linear mixed model (SGLMM). For inference, we make use of the INLA-SPDE theoretical and computational framework developed by Rue et al. (2009) and Lindgren et al (2011). The method we propose allows one to quantify the correlation between genotypes and environmental variables. It works for the most common types of genetic markers, obtained either at the individual or at the population level. Analyzing simulated data produced under a geostatistical model then under an explicit model of selection, we show that the method is efficient. We also re-analyze a dataset relative to nineteen pine weevils (Hylobius abietis}) populations across Europe. The method proposed appears also as a statistically sound alternative to the Mantel tests for testing the association between genetic and environmental variables. |
2103.16606 | Jia Li | Jia Li, Ilias Rentzeperis, Cees van Leeuwen | Functional and spatial rewiring jointly generate convergent-divergent
units in self-organizing networks | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Self-organization through adaptive rewiring of random neural networks
generates brain-like topologies comprising modular small-world structures with
rich club effects, merely as the product of optimizing the network topology. In
the nervous system, spatial organization is optimized no less by rewiring,
through minimizing wiring distance and maximizing spatially aligned wiring
layouts. We show that such spatial organization principles interact
constructively with adaptive rewiring, contributing to establish the networks'
connectedness and modular structures. We use an evolving neural network model
with weighted and directed connections, in which neural traffic flow is based
on consensus and advection dynamics, to show that wiring cost minimization
supports adaptive rewiring in creating convergent-divergent unit structures.
Convergent-divergent units consist of a convergent input-hub, connected to a
divergent output-hub via subnetworks of intermediate nodes, which may function
as the computational core of the unit. The prominence of minimizing wiring
distance in the dynamic evolution of the network determines the extent to which
the core is encapsulated from the rest of the network, i.e., the
context-sensitivity of its computations. This corresponds to the central role
convergent-divergent units play in establishing context-sensitivity in neuronal
information processing.
| [
{
"created": "Tue, 30 Mar 2021 18:26:34 GMT",
"version": "v1"
},
{
"created": "Thu, 3 Nov 2022 13:37:25 GMT",
"version": "v2"
}
] | 2022-11-04 | [
[
"Li",
"Jia",
""
],
[
"Rentzeperis",
"Ilias",
""
],
[
"van Leeuwen",
"Cees",
""
]
] | Self-organization through adaptive rewiring of random neural networks generates brain-like topologies comprising modular small-world structures with rich club effects, merely as the product of optimizing the network topology. In the nervous system, spatial organization is optimized no less by rewiring, through minimizing wiring distance and maximizing spatially aligned wiring layouts. We show that such spatial organization principles interact constructively with adaptive rewiring, contributing to establish the networks' connectedness and modular structures. We use an evolving neural network model with weighted and directed connections, in which neural traffic flow is based on consensus and advection dynamics, to show that wiring cost minimization supports adaptive rewiring in creating convergent-divergent unit structures. Convergent-divergent units consist of a convergent input-hub, connected to a divergent output-hub via subnetworks of intermediate nodes, which may function as the computational core of the unit. The prominence of minimizing wiring distance in the dynamic evolution of the network determines the extent to which the core is encapsulated from the rest of the network, i.e., the context-sensitivity of its computations. This corresponds to the central role convergent-divergent units play in establishing context-sensitivity in neuronal information processing. |
0805.3675 | Michael Yampolsky | Carolyn M. Salafia, Dawn P. Misra, Michael Yampolsky, Adrian K.
Charles, Richard K. Miller | Allometric metabolic scaling and fetal and placental weight | null | null | null | null | q-bio.TO q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We tested the hypothesis that the fetal-placental relationship scales
allometrically and identified modifying factors.
Among women delivering after 34 weeks but prior to 43 weeks gestation, 24,601
participants in the Collaborative Perinatal Project (CPP) had complete data for
placental gross proportion measures, specifically, disk shape, larger and
smaller disk diameters and thickness, and umbilical cord length. The allometric
metabolic equation was solved for alpha and beta by rewriting PW=
alpha(BW)^beta as Log (PW) = Log(alpha) + beta*Log(BW).
Mean beta was 0.78+ 0.02 (range 0.66, 0.89), 104% of that predicted by a
supply-limited fractal system (0.75). Gestational age, maternal age, maternal
BMI, parity, smoking, socioeconomic status, infant sex, and changes in
placental proportions each had independent and significant effects on alpha.
Conclusions: In the CPP cohort, the placental - birth weight relationship
scales to approximately 3/4 power.
| [
{
"created": "Fri, 23 May 2008 18:03:08 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Jul 2008 16:31:57 GMT",
"version": "v2"
},
{
"created": "Sun, 22 Mar 2009 22:02:32 GMT",
"version": "v3"
}
] | 2009-03-23 | [
[
"Salafia",
"Carolyn M.",
""
],
[
"Misra",
"Dawn P.",
""
],
[
"Yampolsky",
"Michael",
""
],
[
"Charles",
"Adrian K.",
""
],
[
"Miller",
"Richard K.",
""
]
] | We tested the hypothesis that the fetal-placental relationship scales allometrically and identified modifying factors. Among women delivering after 34 weeks but prior to 43 weeks gestation, 24,601 participants in the Collaborative Perinatal Project (CPP) had complete data for placental gross proportion measures, specifically, disk shape, larger and smaller disk diameters and thickness, and umbilical cord length. The allometric metabolic equation was solved for alpha and beta by rewriting PW= alpha(BW)^beta as Log (PW) = Log(alpha) + beta*Log(BW). Mean beta was 0.78+ 0.02 (range 0.66, 0.89), 104% of that predicted by a supply-limited fractal system (0.75). Gestational age, maternal age, maternal BMI, parity, smoking, socioeconomic status, infant sex, and changes in placental proportions each had independent and significant effects on alpha. Conclusions: In the CPP cohort, the placental - birth weight relationship scales to approximately 3/4 power. |
0709.3237 | Matthias Keil | Matthias S. Keil | Gradient Representations and the Perception of Luminosity | This is the longer version of an article which is under review for
publication in Vision Research | null | null | null | q-bio.NC | null | The neuronal mechanisms that serve to distinguish between light-emitting and
light reflecting objects are largely unknown. It has been suggested that
luminosity perception implements a separate pathway in the visual system, such
that luminosity constitutes an independent perceptual feature. Recently, a
psychophysical study was conducted to address the question whether luminosity
has a feature status or not. However, the results of this study lend support to
the hypothesis that luminance gradients are instead a perceptual feature. Here,
I show how the perception of luminosity can emerge from a previously proposed
neuronal architecture for generating representations of luminance gradients.
| [
{
"created": "Thu, 20 Sep 2007 14:06:43 GMT",
"version": "v1"
}
] | 2007-09-21 | [
[
"Keil",
"Matthias S.",
""
]
] | The neuronal mechanisms that serve to distinguish between light-emitting and light reflecting objects are largely unknown. It has been suggested that luminosity perception implements a separate pathway in the visual system, such that luminosity constitutes an independent perceptual feature. Recently, a psychophysical study was conducted to address the question whether luminosity has a feature status or not. However, the results of this study lend support to the hypothesis that luminance gradients are instead a perceptual feature. Here, I show how the perception of luminosity can emerge from a previously proposed neuronal architecture for generating representations of luminance gradients. |
2005.01200 | Gurdip Uppal | Gurdip Uppal, Weiyi Hu, Dervis Can Vural | Evolution of chemotactic hitchhiking | 10 pages, 5 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bacteria typically reside in heterogeneous environments with various
chemogradients where motile cells can gain an advantage over non-motile cells.
Since motility is energetically costly, cells must optimize their swimming
speed and behavior to maximize their fitness. Here we investigate how cheating
strategies might evolve where slow or non-motile microbes exploit faster ones
by sticking together and hitching a ride. Starting with physical and biological
first-principles we computationally study the effects of sticking on the
evolution of motility in a controlled chemostat environment. We find stickiness
allows slow cheaters to dominate when nutrients are dispersed at intermediate
distances. Here, slow microbes exploit faster ones until they consume the
population, leading to a tragedy of commons. For long races, slow microbes do
gain an initial advantage from sticking, but eventually fall behind. Here, fast
microbes are more likely to stick to other fast microbes, and cooperate to
increase their own population. We therefore find the nature of the hitchhiking
interaction, parasitic or mutualistic, depends on the nutrient distribution.
| [
{
"created": "Sun, 3 May 2020 22:34:18 GMT",
"version": "v1"
}
] | 2020-05-05 | [
[
"Uppal",
"Gurdip",
""
],
[
"Hu",
"Weiyi",
""
],
[
"Vural",
"Dervis Can",
""
]
] | Bacteria typically reside in heterogeneous environments with various chemogradients where motile cells can gain an advantage over non-motile cells. Since motility is energetically costly, cells must optimize their swimming speed and behavior to maximize their fitness. Here we investigate how cheating strategies might evolve where slow or non-motile microbes exploit faster ones by sticking together and hitching a ride. Starting with physical and biological first-principles we computationally study the effects of sticking on the evolution of motility in a controlled chemostat environment. We find stickiness allows slow cheaters to dominate when nutrients are dispersed at intermediate distances. Here, slow microbes exploit faster ones until they consume the population, leading to a tragedy of commons. For long races, slow microbes do gain an initial advantage from sticking, but eventually fall behind. Here, fast microbes are more likely to stick to other fast microbes, and cooperate to increase their own population. We therefore find the nature of the hitchhiking interaction, parasitic or mutualistic, depends on the nutrient distribution. |
1503.04059 | Frederic Bartumeus | Joan Garriga, John R. Palmer, Aitana Oltra, Frederic Bartumeus | Expectation-Maximization Binary Clustering for Behavioural Annotation | 34 pages main text including 11 (full page) figures | null | 10.1371/journal.pone.0151984 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a variant of the well sounded Expectation-Maximization Clustering
algorithm that is constrained to generate partitions of the input space into
high and low values. The motivation of splitting input variables into high and
low values is to favour the semantic interpretation of the final clustering.
The Expectation-Maximization binary Clustering is specially useful when a
bimodal conditional distribution of the variables is expected or at least when
a binary discretization of the input space is deemed meaningful. Furthermore,
the algorithm deals with the reliability of the input data such that the larger
their uncertainty the less their role in the final clustering. We show here its
suitability for behavioural annotation of movement trajectories. However, it
can be considered as a general purpose algorithm for the clustering or
segmentation of multivariate data or temporal series.
| [
{
"created": "Fri, 13 Mar 2015 13:30:36 GMT",
"version": "v1"
},
{
"created": "Fri, 4 Dec 2015 16:51:01 GMT",
"version": "v2"
}
] | 2016-04-27 | [
[
"Garriga",
"Joan",
""
],
[
"Palmer",
"John R.",
""
],
[
"Oltra",
"Aitana",
""
],
[
"Bartumeus",
"Frederic",
""
]
] | We present a variant of the well sounded Expectation-Maximization Clustering algorithm that is constrained to generate partitions of the input space into high and low values. The motivation of splitting input variables into high and low values is to favour the semantic interpretation of the final clustering. The Expectation-Maximization binary Clustering is specially useful when a bimodal conditional distribution of the variables is expected or at least when a binary discretization of the input space is deemed meaningful. Furthermore, the algorithm deals with the reliability of the input data such that the larger their uncertainty the less their role in the final clustering. We show here its suitability for behavioural annotation of movement trajectories. However, it can be considered as a general purpose algorithm for the clustering or segmentation of multivariate data or temporal series. |
2401.01811 | Andrij Rovenchak | Andrij Rovenchak and Maksym Druchok | Machine learning-assisted search for novel coagulants: when machine
learning can be efficient even if data availability is low | null | J. Comput. Chem. 45, No. 13, 937-952 (2024) | 10.1002/jcc.27292 | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Design of new drugs is a challenging process: a candidate molecule should
satisfy multiple conditions to act properly and make the least side-effect --
perfect candidates selectively attach to and influence only targets, leaving
off-targets intact. The amount of experimental data about various properties of
molecules constantly grows, promoting data-driven approaches. However, the
applicability of typical predictive machine learning techniques can be
substantially limited by a lack of experimental data about a particular target.
For example, there are many known Thrombin inhibitors (acting as
anticoagulants), but a very limited number of known Protein C inhibitors
(coagulants). In this study, we present our approach to suggest new inhibitor
candidates by building an effective representation of chemical space. For this
aim, we developed a deep learning model -- autoencoder, trained on a large set
of molecules in the SMILES format to map the chemical space. Further, we
applied different sampling strategies to generate novel coagulant candidates.
Symmetrically, we tested our approach on anticoagulant candidates, where we
were able to predict their inhibition towards Thrombin. We also compare our
approach with MegaMolBART -- another deep learning generative model, but
exploiting similar principles of navigation in a chemical space.
| [
{
"created": "Wed, 3 Jan 2024 16:14:37 GMT",
"version": "v1"
}
] | 2024-05-07 | [
[
"Rovenchak",
"Andrij",
""
],
[
"Druchok",
"Maksym",
""
]
] | Design of new drugs is a challenging process: a candidate molecule should satisfy multiple conditions to act properly and make the least side-effect -- perfect candidates selectively attach to and influence only targets, leaving off-targets intact. The amount of experimental data about various properties of molecules constantly grows, promoting data-driven approaches. However, the applicability of typical predictive machine learning techniques can be substantially limited by a lack of experimental data about a particular target. For example, there are many known Thrombin inhibitors (acting as anticoagulants), but a very limited number of known Protein C inhibitors (coagulants). In this study, we present our approach to suggest new inhibitor candidates by building an effective representation of chemical space. For this aim, we developed a deep learning model -- autoencoder, trained on a large set of molecules in the SMILES format to map the chemical space. Further, we applied different sampling strategies to generate novel coagulant candidates. Symmetrically, we tested our approach on anticoagulant candidates, where we were able to predict their inhibition towards Thrombin. We also compare our approach with MegaMolBART -- another deep learning generative model, but exploiting similar principles of navigation in a chemical space. |
2403.11516 | Ya Li | Yue Ding, Hongqiao Shi, Shuang Song, Yonghui Wang and Ya Li | Perceptual learning in contour detection transfer across changes in
contour path and orientation | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The integration of local elements into shape contours is critical for target
detection and identification in cluttered scenes. Previous studies have shown
that observers can learn to use image regularities for contour integration and
target identification. However, we still know little about the generalization
of perceptual learning in contour integration. Specifically, whether training
in contour detection task could transfer to untrained contour type, path or
orientation is still unclear. In a series of four experiments, human perceptual
learning in contour detection was studied using psychophysical methods. We
trained participants to detect contours in cluttered scenes over several days,
which resulted in a significant improvement in sensitivity to trained contour
type. This improved sensitivity was highly specific to contour type, but
transfer across changes in contour path and contour orientation. These results
suggest that short-term training improves the ability to integrate specific
types of contours by optimizing the ability of the visual system to extract
specific image regularities. The differential specificity and generalization
across different stimulus features may support the involvement of both
low-level and higher-level visual areas in perceptual learning in contour
detection. These findings provide further insights into understanding the
nature and the brain plasticity mechanism of contour integration learning.
| [
{
"created": "Mon, 18 Mar 2024 07:06:03 GMT",
"version": "v1"
}
] | 2024-03-19 | [
[
"Ding",
"Yue",
""
],
[
"Shi",
"Hongqiao",
""
],
[
"Song",
"Shuang",
""
],
[
"Wang",
"Yonghui",
""
],
[
"Li",
"Ya",
""
]
] | The integration of local elements into shape contours is critical for target detection and identification in cluttered scenes. Previous studies have shown that observers can learn to use image regularities for contour integration and target identification. However, we still know little about the generalization of perceptual learning in contour integration. Specifically, whether training in contour detection task could transfer to untrained contour type, path or orientation is still unclear. In a series of four experiments, human perceptual learning in contour detection was studied using psychophysical methods. We trained participants to detect contours in cluttered scenes over several days, which resulted in a significant improvement in sensitivity to trained contour type. This improved sensitivity was highly specific to contour type, but transfer across changes in contour path and contour orientation. These results suggest that short-term training improves the ability to integrate specific types of contours by optimizing the ability of the visual system to extract specific image regularities. The differential specificity and generalization across different stimulus features may support the involvement of both low-level and higher-level visual areas in perceptual learning in contour detection. These findings provide further insights into understanding the nature and the brain plasticity mechanism of contour integration learning. |
0801.3382 | Jose Luis Toca-Herrera | Veronica Saravia | Hepatocyte Aggregates: Methods of Preparation in the Microgravity
Simulating Bioreactor Use in Tissue Engineering | MSc Thesis (Chemical Engineering Department, Rovira i Virgili
University, Spain) Supervisors: Dr. Petros Lenas and Dr. Jose L. Toca-Herrera
Pages:32, Figures:15 | null | null | null | q-bio.TO | null | Tissue Engineering concerns the three-dimensional cell growth so that
bio-artificial tissues could be created and used for transplantation. The
recently expressed concerns from the Tissue Engineering research community for
a re-direction of the research activities necessitate the proposition of new
methodologies. We propose a methodology that has to do with the simulation in
bioreactor systems of liver structures as are described in liver anatomy. I
this way the hepatocyte microenvironments that determine their function could
be re-created in vitro. The approach needs the use of hepatocyte aggregates as
entities to load the bioreactor systems. A new bioreactor, the microgravity
simulating rotation bioreactor, has been used for the preparation of cell
aggregates. Microcontact printing has been used to produce a patterned
surfaces. They were tested adsorbing BSA proteins, and will be used in future
for the mmobilization of cell aggregates in order to gain further understanding
of the role of cell heterogeneity in the cooperative behaviour of cells in
vitro.
| [
{
"created": "Tue, 22 Jan 2008 14:40:33 GMT",
"version": "v1"
}
] | 2008-01-23 | [
[
"Saravia",
"Veronica",
""
]
] | Tissue Engineering concerns the three-dimensional cell growth so that bio-artificial tissues could be created and used for transplantation. The recently expressed concerns from the Tissue Engineering research community for a re-direction of the research activities necessitate the proposition of new methodologies. We propose a methodology that has to do with the simulation in bioreactor systems of liver structures as are described in liver anatomy. I this way the hepatocyte microenvironments that determine their function could be re-created in vitro. The approach needs the use of hepatocyte aggregates as entities to load the bioreactor systems. A new bioreactor, the microgravity simulating rotation bioreactor, has been used for the preparation of cell aggregates. Microcontact printing has been used to produce a patterned surfaces. They were tested adsorbing BSA proteins, and will be used in future for the mmobilization of cell aggregates in order to gain further understanding of the role of cell heterogeneity in the cooperative behaviour of cells in vitro. |
1011.5108 | Fabien Campillo | Fabien Campillo (INRIA Sophia Antipolis - INRA/SupAgro UMR 0729 MISTEA
- Montpellier), Marc Joannides (INRIA Sophia Antipolis - INRA/SupAgro UMR
0729 MISTEA - Montpellier, I3M), Ir\`ene Larramendy (I3M) | Stochastic models of the chemostat | null | N° RR-7458 (2010) | null | RR-7458 | q-bio.QM math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the modeling of the dynamics of the chemostat at its very source.
The chemostat is classically represented as a system of ordinary differential
equations. Our goal is to establish a stochastic model that is valid at the
scale immediately preceding the one corresponding to the deterministic model.
At a microscopic scale we present a pure jump stochastic model that gives rise,
at the macroscopic scale, to the ordinary differential equation model. At an
intermediate scale, an approximation diffusion allows us to propose a model in
the form of a system of stochastic differential equations. We expound the
mechanism to switch from one model to another, together with the associated
simulation procedures. We also describe the domain of validity of the different
models.
| [
{
"created": "Mon, 22 Nov 2010 10:30:20 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Jul 2011 05:47:22 GMT",
"version": "v2"
}
] | 2011-07-07 | [
[
"Campillo",
"Fabien",
"",
"INRIA Sophia Antipolis - INRA/SupAgro UMR 0729 MISTEA\n - Montpellier"
],
[
"Joannides",
"Marc",
"",
"INRIA Sophia Antipolis - INRA/SupAgro UMR\n 0729 MISTEA - Montpellier, I3M"
],
[
"Larramendy",
"Irène",
"",
"I3M"
]
] | We consider the modeling of the dynamics of the chemostat at its very source. The chemostat is classically represented as a system of ordinary differential equations. Our goal is to establish a stochastic model that is valid at the scale immediately preceding the one corresponding to the deterministic model. At a microscopic scale we present a pure jump stochastic model that gives rise, at the macroscopic scale, to the ordinary differential equation model. At an intermediate scale, an approximation diffusion allows us to propose a model in the form of a system of stochastic differential equations. We expound the mechanism to switch from one model to another, together with the associated simulation procedures. We also describe the domain of validity of the different models. |
2408.05224 | Liu Hong | Mengshou Wang, Liangrong Pengb, Baoguo Jia, Liu Hong | Optimal Strategy for Stabilizing Protein Folding Intermediates | 19 pages, 5 figures, 2 tables | null | null | null | q-bio.BM math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To manipulate the protein population at certain functional state through
chemical stabilizers is crucial for protein-related studies. It not only plays
a key role in protein structure analysis and protein folding kinetics, but also
affects protein functionality to a large extent and thus has wide applications
in medicine, food industry, etc. However, due to concerns about side effects or
financial costs of stabilizers, identifying optimal strategies for enhancing
protein stability with a minimal amount of stabilizers is of great importance.
Here we prove that either for the fixed terminal time (including both finite
and infinite cases) or the free one, the optimal control strategy for
stabilizing the folding intermediates with a linear strategy for stabilizer
addition belongs to the class of Bang-Bang controls. The corresponding optimal
switching time is derived analytically, whose phase diagram with respect to
several key parameters is explored in detail. The Bang-Bang control will be
broken when nonlinear strategies for stabilizer addition are adopted. Our
current study on optimal strategies for protein stabilizers not only offers
deep insights into the general picture of protein folding kinetics, but also
provides valuable theoretical guidance on treatments for protein-related
diseases in medicine.
| [
{
"created": "Sun, 28 Jul 2024 11:36:29 GMT",
"version": "v1"
}
] | 2024-08-13 | [
[
"Wang",
"Mengshou",
""
],
[
"Pengb",
"Liangrong",
""
],
[
"Jia",
"Baoguo",
""
],
[
"Hong",
"Liu",
""
]
] | To manipulate the protein population at certain functional state through chemical stabilizers is crucial for protein-related studies. It not only plays a key role in protein structure analysis and protein folding kinetics, but also affects protein functionality to a large extent and thus has wide applications in medicine, food industry, etc. However, due to concerns about side effects or financial costs of stabilizers, identifying optimal strategies for enhancing protein stability with a minimal amount of stabilizers is of great importance. Here we prove that either for the fixed terminal time (including both finite and infinite cases) or the free one, the optimal control strategy for stabilizing the folding intermediates with a linear strategy for stabilizer addition belongs to the class of Bang-Bang controls. The corresponding optimal switching time is derived analytically, whose phase diagram with respect to several key parameters is explored in detail. The Bang-Bang control will be broken when nonlinear strategies for stabilizer addition are adopted. Our current study on optimal strategies for protein stabilizers not only offers deep insights into the general picture of protein folding kinetics, but also provides valuable theoretical guidance on treatments for protein-related diseases in medicine. |
1210.0120 | Brant Faircloth | Michael E. Alfaro and Brant C. Faircloth and Laurie Sorenson and
Francesco Santini | A phylogenomic perspective on the radiation of ray-finned fishes based
upon targeted sequencing of ultraconserved elements | null | (2013) PLoS ONE 8(6): e65923 | 10.1371/journal.pone.0065923 | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ray-finned fishes constitute the dominant radiation of vertebrates with over
30,000 species. Although molecular phylogenetics has begun to disentangle major
evolutionary relationships within this vast section of the Tree of Life, there
is no widely available approach for efficiently collecting phylogenomic data
within fishes, leaving much of the enormous potential of massively parallel
sequencing technologies for resolving major radiations in ray-finned fishes
unrealized. Here, we provide a genomic perspective on longstanding questions
regarding the diversification of major groups of ray-finned fishes through
targeted enrichment of ultraconserved nuclear DNA elements (UCEs) and their
flanking sequence. Our workflow efficiently and economically generates data
sets that are orders of magnitude larger than those produced by traditional
approaches and is well-suited to working with museum specimens. Analysis of the
UCE data set recovers a well-supported phylogeny at both shallow and deep
time-scales that supports a monophyletic relationship between Amia and
Lepisosteus (Holostei) and reveals elopomorphs and then osteoglossomorphs to be
the earliest diverging teleost lineages. Divergence time estimation based upon
14 fossil calibrations reveals that crown teleosts appeared ~270 Ma at the end
of the Permian and that elopomorphs, osteoglossomorphs, ostarioclupeomorphs,
and euteleosts diverged from one another by 205 Ma during the Triassic. Our
approach additionally reveals that sequence capture of UCE regions and their
flanking sequence offers enormous potential for resolving phylogenetic
relationships within ray-finned fishes.
| [
{
"created": "Sat, 29 Sep 2012 16:00:44 GMT",
"version": "v1"
}
] | 2013-06-20 | [
[
"Alfaro",
"Michael E.",
""
],
[
"Faircloth",
"Brant C.",
""
],
[
"Sorenson",
"Laurie",
""
],
[
"Santini",
"Francesco",
""
]
] | Ray-finned fishes constitute the dominant radiation of vertebrates with over 30,000 species. Although molecular phylogenetics has begun to disentangle major evolutionary relationships within this vast section of the Tree of Life, there is no widely available approach for efficiently collecting phylogenomic data within fishes, leaving much of the enormous potential of massively parallel sequencing technologies for resolving major radiations in ray-finned fishes unrealized. Here, we provide a genomic perspective on longstanding questions regarding the diversification of major groups of ray-finned fishes through targeted enrichment of ultraconserved nuclear DNA elements (UCEs) and their flanking sequence. Our workflow efficiently and economically generates data sets that are orders of magnitude larger than those produced by traditional approaches and is well-suited to working with museum specimens. Analysis of the UCE data set recovers a well-supported phylogeny at both shallow and deep time-scales that supports a monophyletic relationship between Amia and Lepisosteus (Holostei) and reveals elopomorphs and then osteoglossomorphs to be the earliest diverging teleost lineages. Divergence time estimation based upon 14 fossil calibrations reveals that crown teleosts appeared ~270 Ma at the end of the Permian and that elopomorphs, osteoglossomorphs, ostarioclupeomorphs, and euteleosts diverged from one another by 205 Ma during the Triassic. Our approach additionally reveals that sequence capture of UCE regions and their flanking sequence offers enormous potential for resolving phylogenetic relationships within ray-finned fishes. |
1410.3972 | Eran Elhaik | Eran Elhaik, Tatiana V. Tatarinova, Anatole A. Klyosov, and Dan Graur | An extended reply to Mendez et al.: The 'extremely ancient' chromosome
that still isn't | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Earlier this year, we published a scathing critique of a paper by Mendez et
al. (2013) in which the claim was made that a Y chromosome was 237,000-581,000
years old. Elhaik et al. (2014) also attacked a popular article in Scientific
American by the senior author of Mendez et al. (2013), whose title was "Sex
with other human species might have been the secret of Homo sapiens's [sic]
success" (Hammer 2013). Five of the 11 authors of Mendez et al. (2013) have now
written a "rebuttal," and we were allowed to reply. Unfortunately, our reply
was censored for being "too sarcastic and inflamed." References were removed,
meanings were castrated, and a dedication in the Acknowledgments was deleted.
Now, that the so-called rebuttal by 45% of the authors of Mendez et al. (2013)
has been published together with our vasectomized reply, we decided to make
public our entire reply to the so called "rebuttal." In fact, we go one step
further, and publish a version of the reply that has not even been
self-censored.
| [
{
"created": "Wed, 15 Oct 2014 08:45:15 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Oct 2014 21:22:26 GMT",
"version": "v2"
}
] | 2014-10-22 | [
[
"Elhaik",
"Eran",
""
],
[
"Tatarinova",
"Tatiana V.",
""
],
[
"Klyosov",
"Anatole A.",
""
],
[
"Graur",
"Dan",
""
]
] | Earlier this year, we published a scathing critique of a paper by Mendez et al. (2013) in which the claim was made that a Y chromosome was 237,000-581,000 years old. Elhaik et al. (2014) also attacked a popular article in Scientific American by the senior author of Mendez et al. (2013), whose title was "Sex with other human species might have been the secret of Homo sapiens's [sic] success" (Hammer 2013). Five of the 11 authors of Mendez et al. (2013) have now written a "rebuttal," and we were allowed to reply. Unfortunately, our reply was censored for being "too sarcastic and inflamed." References were removed, meanings were castrated, and a dedication in the Acknowledgments was deleted. Now, that the so-called rebuttal by 45% of the authors of Mendez et al. (2013) has been published together with our vasectomized reply, we decided to make public our entire reply to the so called "rebuttal." In fact, we go one step further, and publish a version of the reply that has not even been self-censored. |
1707.04192 | Marco Lehmann | Marco Lehmann, He Xu, Vasiliki Liakoni, Michael Herzog, Wulfram
Gerstner, Kerstin Preuschoff | One-shot learning and behavioral eligibility traces in sequential
decision making | null | eLife 2019; 8:e47463 | 10.7554/eLife.47463 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many daily tasks we make multiple decisions before reaching a goal. In
order to learn such sequences of decisions, a mechanism to link earlier actions
to later reward is necessary. Reinforcement learning theory suggests two
classes of algorithms solving this credit assignment problem: In classic
temporal-difference learning, earlier actions receive reward information only
after multiple repetitions of the task, whereas models with eligibility traces
reinforce entire sequences of actions from a single experience (one-shot). Here
we asked whether humans use eligibility traces. We developed a novel paradigm
to directly observe which actions and states along a multi-step sequence are
reinforced after a single reward. By focusing our analysis on those states for
which RL with and without eligibility trace make qualitatively distinct
predictions, we find direct behavioral (choice probability) and physiological
(pupil dilation) signatures of reinforcement learning with eligibility trace
across multiple sensory modalities.
| [
{
"created": "Thu, 13 Jul 2017 16:04:34 GMT",
"version": "v1"
},
{
"created": "Fri, 22 Feb 2019 15:22:49 GMT",
"version": "v2"
},
{
"created": "Tue, 12 Nov 2019 10:00:22 GMT",
"version": "v3"
}
] | 2019-11-13 | [
[
"Lehmann",
"Marco",
""
],
[
"Xu",
"He",
""
],
[
"Liakoni",
"Vasiliki",
""
],
[
"Herzog",
"Michael",
""
],
[
"Gerstner",
"Wulfram",
""
],
[
"Preuschoff",
"Kerstin",
""
]
] | In many daily tasks we make multiple decisions before reaching a goal. In order to learn such sequences of decisions, a mechanism to link earlier actions to later reward is necessary. Reinforcement learning theory suggests two classes of algorithms solving this credit assignment problem: In classic temporal-difference learning, earlier actions receive reward information only after multiple repetitions of the task, whereas models with eligibility traces reinforce entire sequences of actions from a single experience (one-shot). Here we asked whether humans use eligibility traces. We developed a novel paradigm to directly observe which actions and states along a multi-step sequence are reinforced after a single reward. By focusing our analysis on those states for which RL with and without eligibility trace make qualitatively distinct predictions, we find direct behavioral (choice probability) and physiological (pupil dilation) signatures of reinforcement learning with eligibility trace across multiple sensory modalities. |
1805.05433 | Joshua M. Deutsch | J. M. Deutsch | Computational mechanisms in genetic regulation by RNA | 18 pages, 10 figures | null | null | null | q-bio.MN q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evolution of the genome has led to very sophisticated and complex
regulation. Because of the abundance of non-coding RNA (ncRNA) in the cell,
different species will promiscuously associate with each other, suggesting
collective dynamics similar to artificial neural networks. Here we present a
simple mechanism allowing ncRNA to perform computations equivalent to neural
network algorithms such as Boltzmann machines and the Hopfield model. The
quantities analogous to the neural couplings are the equilibrium constants
between different RNA species. The relatively rapid equilibration of RNA
binding and unbinding is regulated by a slower process that degrades and
creates new RNA. The model requires that the creation rate for each species be
an increasing function of the ratio of total to unbound RNA. Similar mechanisms
have already been found to exist experimentally for ncRNA regulation. With the
overall concentration of RNA regulated, equilibrium constants can be chosen to
store many different patterns, or many different input-output relations. The
network is also quite insensitive to random mutations in equilibrium constants.
Therefore one expects that this kind of mechanism will have a much higher
mutation rate than ones typically regarded as being under evolutionary
constraint.
| [
{
"created": "Mon, 14 May 2018 20:39:02 GMT",
"version": "v1"
}
] | 2018-05-16 | [
[
"Deutsch",
"J. M.",
""
]
] | The evolution of the genome has led to very sophisticated and complex regulation. Because of the abundance of non-coding RNA (ncRNA) in the cell, different species will promiscuously associate with each other, suggesting collective dynamics similar to artificial neural networks. Here we present a simple mechanism allowing ncRNA to perform computations equivalent to neural network algorithms such as Boltzmann machines and the Hopfield model. The quantities analogous to the neural couplings are the equilibrium constants between different RNA species. The relatively rapid equilibration of RNA binding and unbinding is regulated by a slower process that degrades and creates new RNA. The model requires that the creation rate for each species be an increasing function of the ratio of total to unbound RNA. Similar mechanisms have already been found to exist experimentally for ncRNA regulation. With the overall concentration of RNA regulated, equilibrium constants can be chosen to store many different patterns, or many different input-output relations. The network is also quite insensitive to random mutations in equilibrium constants. Therefore one expects that this kind of mechanism will have a much higher mutation rate than ones typically regarded as being under evolutionary constraint. |
0711.2531 | Hendrik Blok | Michael Doebeli, Hendrik J. Blok, Olof Leimar, Ulf Dieckmann | Multimodal pattern formation in phenotype distributions of sexual
populations | null | Proc. R. Soc. B (2007) 274, 347-357 | 10.1098/rspb.2006.3725 | null | q-bio.PE | null | During bouts of evolutionary diversification, such as adaptive radiations,
the emerging species cluster around different locations in phenotype space, How
such multimodal patterns in phenotype space can emerge from a single ancestral
species is a fundamental question in biology. Frequency-dependent competition
is one potential mechanism for such pattern formation, as has previously been
shown in models based on the theory of adaptive dynamics. Here we demonstrate
that also in models similar to those used in quantitative genetics, phenotype
distributions can split into multiple modes under the force of
frequency-dependent competition. In sexual populations, this requires
assortative mating, and we show that the multimodal splitting of initially
unimodal distributions occurs over a range of assortment parameters. In
addition, assortative mating can be favoured evolutionarily even if it incurs
costs, because it provides a means of alleviating the effects of frequency
dependence. Our results reveal that models at both ends of the spectrum between
essentially monomorphic (adaptive dynamics) and fully polymorphic (quantitative
genetics) yield similar results. This underscores that frequency-dependent
selection is a strong agent of pattern formation in phenotype distributions,
potentially resulting in adaptive speciation.
| [
{
"created": "Thu, 15 Nov 2007 23:25:00 GMT",
"version": "v1"
}
] | 2007-11-19 | [
[
"Doebeli",
"Michael",
""
],
[
"Blok",
"Hendrik J.",
""
],
[
"Leimar",
"Olof",
""
],
[
"Dieckmann",
"Ulf",
""
]
] | During bouts of evolutionary diversification, such as adaptive radiations, the emerging species cluster around different locations in phenotype space, How such multimodal patterns in phenotype space can emerge from a single ancestral species is a fundamental question in biology. Frequency-dependent competition is one potential mechanism for such pattern formation, as has previously been shown in models based on the theory of adaptive dynamics. Here we demonstrate that also in models similar to those used in quantitative genetics, phenotype distributions can split into multiple modes under the force of frequency-dependent competition. In sexual populations, this requires assortative mating, and we show that the multimodal splitting of initially unimodal distributions occurs over a range of assortment parameters. In addition, assortative mating can be favoured evolutionarily even if it incurs costs, because it provides a means of alleviating the effects of frequency dependence. Our results reveal that models at both ends of the spectrum between essentially monomorphic (adaptive dynamics) and fully polymorphic (quantitative genetics) yield similar results. This underscores that frequency-dependent selection is a strong agent of pattern formation in phenotype distributions, potentially resulting in adaptive speciation. |
2306.07812 | Xu Wang | Xu Wang and Huan Zhao and Weiwei Tu and Quanming Yao | Automated 3D Pre-Training for Molecular Property Prediction | null | null | 10.1145/3580305.3599252 | null | q-bio.QM cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Molecular property prediction is an important problem in drug discovery and
materials science. As geometric structures have been demonstrated necessary for
molecular property prediction, 3D information has been combined with various
graph learning methods to boost prediction performance. However, obtaining the
geometric structure of molecules is not feasible in many real-world
applications due to the high computational cost. In this work, we propose a
novel 3D pre-training framework (dubbed 3D PGT), which pre-trains a model on 3D
molecular graphs, and then fine-tunes it on molecular graphs without 3D
structures. Based on fact that bond length, bond angle, and dihedral angle are
three basic geometric descriptors corresponding to a complete molecular 3D
conformer, we first develop a multi-task generative pre-train framework based
on these three attributes. Next, to automatically fuse these three generative
tasks, we design a surrogate metric using the \textit{total energy} to search
for weight distribution of the three pretext task since total energy
corresponding to the quality of 3D conformer.Extensive experiments on 2D
molecular graphs are conducted to demonstrate the accuracy, efficiency and
generalization ability of the proposed 3D PGT compared to various pre-training
baselines.
| [
{
"created": "Tue, 13 Jun 2023 14:43:13 GMT",
"version": "v1"
},
{
"created": "Sun, 2 Jul 2023 13:03:27 GMT",
"version": "v2"
}
] | 2023-07-04 | [
[
"Wang",
"Xu",
""
],
[
"Zhao",
"Huan",
""
],
[
"Tu",
"Weiwei",
""
],
[
"Yao",
"Quanming",
""
]
] | Molecular property prediction is an important problem in drug discovery and materials science. As geometric structures have been demonstrated necessary for molecular property prediction, 3D information has been combined with various graph learning methods to boost prediction performance. However, obtaining the geometric structure of molecules is not feasible in many real-world applications due to the high computational cost. In this work, we propose a novel 3D pre-training framework (dubbed 3D PGT), which pre-trains a model on 3D molecular graphs, and then fine-tunes it on molecular graphs without 3D structures. Based on fact that bond length, bond angle, and dihedral angle are three basic geometric descriptors corresponding to a complete molecular 3D conformer, we first develop a multi-task generative pre-train framework based on these three attributes. Next, to automatically fuse these three generative tasks, we design a surrogate metric using the \textit{total energy} to search for weight distribution of the three pretext task since total energy corresponding to the quality of 3D conformer.Extensive experiments on 2D molecular graphs are conducted to demonstrate the accuracy, efficiency and generalization ability of the proposed 3D PGT compared to various pre-training baselines. |
1806.07477 | Heyrim Cho | Heyrim Cho and Doron Levy | The Impact of Competition Between Cancer Cells and Healthy Cells on
Optimal Drug Delivery | 18 pages | null | 10.1051/mmnp/2019043 | null | q-bio.PE q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cell competition is recognized to be instrumental to the dynamics and
structure of the tumor-host interface in invasive cancers. In mild competition
scenarios, the healthy tissue and cancer cells can coexist. When the
competition is aggressive, competitive cells, the so called super-competitors,
expand by killing other cells. Novel cytotoxic drugs and molecularly targeted
drugs are commonly administered as part of cancer therapy. Both types of drugs
are susceptible to various mechanisms of drug resistance, obstructing or
preventing a successful outcome. In this paper, we develop a cancer growth
model that accounts for the competition between cancer cells and healthy cells.
The model incorporates resistance to both cytotoxic and targeted drugs. In both
cases, the level of drug resistance is assumed to be a continuous variable
ranging from fully-sensitive to fully-resistant. Using our model we demonstrate
that when the competition is moderate, therapies using both drugs are more
effective compared with single drug therapies. However, when cancer cells are
highly competitive, targeted drugs become more effective. In this case,
therapies that are initiated with a targeted drug and are exposed to it for a
sufficiently long time are shown to have better outcomes. The results of the
study stress the importance of adjusting the therapy to the pre-treatment
resistance levels. We conclude with a study of the spatiotemporal propagation
of drug resistance in a competitive setting, verifying that the same
conclusions hold in the spatially heterogeneous case.
| [
{
"created": "Tue, 19 Jun 2018 21:26:02 GMT",
"version": "v1"
}
] | 2022-04-19 | [
[
"Cho",
"Heyrim",
""
],
[
"Levy",
"Doron",
""
]
] | Cell competition is recognized to be instrumental to the dynamics and structure of the tumor-host interface in invasive cancers. In mild competition scenarios, the healthy tissue and cancer cells can coexist. When the competition is aggressive, competitive cells, the so called super-competitors, expand by killing other cells. Novel cytotoxic drugs and molecularly targeted drugs are commonly administered as part of cancer therapy. Both types of drugs are susceptible to various mechanisms of drug resistance, obstructing or preventing a successful outcome. In this paper, we develop a cancer growth model that accounts for the competition between cancer cells and healthy cells. The model incorporates resistance to both cytotoxic and targeted drugs. In both cases, the level of drug resistance is assumed to be a continuous variable ranging from fully-sensitive to fully-resistant. Using our model we demonstrate that when the competition is moderate, therapies using both drugs are more effective compared with single drug therapies. However, when cancer cells are highly competitive, targeted drugs become more effective. In this case, therapies that are initiated with a targeted drug and are exposed to it for a sufficiently long time are shown to have better outcomes. The results of the study stress the importance of adjusting the therapy to the pre-treatment resistance levels. We conclude with a study of the spatiotemporal propagation of drug resistance in a competitive setting, verifying that the same conclusions hold in the spatially heterogeneous case. |
2308.09725 | Ziwei Yang | Ziwei Yang, Zheng Chen, Yasuko Matsubara, Yasushi Sakurai | MoCLIM: Towards Accurate Cancer Subtyping via Multi-Omics Contrastive
Learning with Omics-Inference Modeling | CIKM'23 Long/Full Papers | null | 10.1145/3583780.3614970 | null | q-bio.GN cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Precision medicine fundamentally aims to establish causality between
dysregulated biochemical mechanisms and cancer subtypes. Omics-based cancer
subtyping has emerged as a revolutionary approach, as different level of omics
records the biochemical products of multistep processes in cancers. This paper
focuses on fully exploiting the potential of multi-omics data to improve cancer
subtyping outcomes, and hence developed MoCLIM, a representation learning
framework. MoCLIM independently extracts the informative features from distinct
omics modalities. Using a unified representation informed by contrastive
learning of different omics modalities, we can well-cluster the subtypes, given
cancer, into a lower latent space. This contrast can be interpreted as a
projection of inter-omics inference observed in biological networks.
Experimental results on six cancer datasets demonstrate that our approach
significantly improves data fit and subtyping performance in fewer
high-dimensional cancer instances. Moreover, our framework incorporates various
medical evaluations as the final component, providing high interpretability in
medical analysis.
| [
{
"created": "Thu, 17 Aug 2023 10:49:48 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Aug 2023 04:38:45 GMT",
"version": "v2"
}
] | 2023-08-25 | [
[
"Yang",
"Ziwei",
""
],
[
"Chen",
"Zheng",
""
],
[
"Matsubara",
"Yasuko",
""
],
[
"Sakurai",
"Yasushi",
""
]
] | Precision medicine fundamentally aims to establish causality between dysregulated biochemical mechanisms and cancer subtypes. Omics-based cancer subtyping has emerged as a revolutionary approach, as different level of omics records the biochemical products of multistep processes in cancers. This paper focuses on fully exploiting the potential of multi-omics data to improve cancer subtyping outcomes, and hence developed MoCLIM, a representation learning framework. MoCLIM independently extracts the informative features from distinct omics modalities. Using a unified representation informed by contrastive learning of different omics modalities, we can well-cluster the subtypes, given cancer, into a lower latent space. This contrast can be interpreted as a projection of inter-omics inference observed in biological networks. Experimental results on six cancer datasets demonstrate that our approach significantly improves data fit and subtyping performance in fewer high-dimensional cancer instances. Moreover, our framework incorporates various medical evaluations as the final component, providing high interpretability in medical analysis. |
2003.03580 | Pengli Lu | Pengli Lu and JingJuan Yu | Two new methods for identifying proteins based on the domain protein
complexes and topological properties | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The recognition of essential proteins not only can help to understand the
mechanism of cell operation, but also help to study the mechanism of biological
evolution. At present, many scholars have been discovering essential proteins
according to the topological structure of protein network and complexes. While
some proteins still can not be recognized. In this paper, we proposed two new
methods complex degree centrality (CDC) and complex in-degree and betweenness
definition (CIBD) which integrate the local character of protein complexes and
topological properties to determine the essentiality of proteins. First, we
give the definitions of complex average centrality (CAC) and complex hybrid
centrality (CHC) which both describe the properties of protein complexes. Then
we propose these new methods CDC and CIBD based on CAC and CHC definitions. In
order to access these two methods, different Protein-Protein Interaction (PPI)
networks of Saccharomyces cerevisiae, DIP, MIPS and YMBD are used as
experimental materials. Experimental results in networks show that the methods
of CDC and CIBD can help to improve the precision of predicting essential
proteins.
| [
{
"created": "Sat, 7 Mar 2020 13:56:35 GMT",
"version": "v1"
}
] | 2020-03-10 | [
[
"Lu",
"Pengli",
""
],
[
"Yu",
"JingJuan",
""
]
] | The recognition of essential proteins not only can help to understand the mechanism of cell operation, but also help to study the mechanism of biological evolution. At present, many scholars have been discovering essential proteins according to the topological structure of protein network and complexes. While some proteins still can not be recognized. In this paper, we proposed two new methods complex degree centrality (CDC) and complex in-degree and betweenness definition (CIBD) which integrate the local character of protein complexes and topological properties to determine the essentiality of proteins. First, we give the definitions of complex average centrality (CAC) and complex hybrid centrality (CHC) which both describe the properties of protein complexes. Then we propose these new methods CDC and CIBD based on CAC and CHC definitions. In order to access these two methods, different Protein-Protein Interaction (PPI) networks of Saccharomyces cerevisiae, DIP, MIPS and YMBD are used as experimental materials. Experimental results in networks show that the methods of CDC and CIBD can help to improve the precision of predicting essential proteins. |
2305.05086 | Brian Sun | Braden Barnett, Yiqi Lyu, Kyle Pichney, Brian Sun, Jixiao Wu | Mechanical Evidence for the Phylogenetic Origin of the Red Panda's False
Thumb as an Adaptation to Arboreal Locomotion | 14 pages, 10 figures | null | null | null | q-bio.PE cs.RO | http://creativecommons.org/licenses/by/4.0/ | We constructed a modular, biomimetic red panda paw with which to
experimentally investigate the evolutionary reason for the existence of the
false thumbs of red pandas. These thumbs were once believed to have shared a
common origin with the similar false thumbs of giant pandas; however, the
discovery of a carnivorous fossil ancestor of the red panda that had false
thumbs implies that the red panda did not evolve its thumbs to assist in eating
bamboo, as the giant panda did, but rather evolved its thumbs for some other
purpose. The leading proposal for this purpose is that the thumbs developed to
aid arboreal locomotion. To test this hypothesis, we conducted grasp tests on
rods 5-15 mm in diameter using a biomimetic paw with 0-16 mm interchangeable
thumb lengths. The results of these tests demonstrated an optimal thumb length
of 7 mm, which is just above that of the red panda's true thumb length of 5.5
mm. Given trends in the data that suggest that smaller thumbs are better suited
to grasping larger diameter rods, we conclude that the red panda's thumb being
sized below the optimum length suggests an adaptation toward grasping branches
as opposed to relatively thinner food items, supporting the new proposal that
the red panda's thumbs are an adaptation primary to climbing rather than food
manipulation.
| [
{
"created": "Mon, 8 May 2023 23:05:39 GMT",
"version": "v1"
}
] | 2023-05-10 | [
[
"Barnett",
"Braden",
""
],
[
"Lyu",
"Yiqi",
""
],
[
"Pichney",
"Kyle",
""
],
[
"Sun",
"Brian",
""
],
[
"Wu",
"Jixiao",
""
]
] | We constructed a modular, biomimetic red panda paw with which to experimentally investigate the evolutionary reason for the existence of the false thumbs of red pandas. These thumbs were once believed to have shared a common origin with the similar false thumbs of giant pandas; however, the discovery of a carnivorous fossil ancestor of the red panda that had false thumbs implies that the red panda did not evolve its thumbs to assist in eating bamboo, as the giant panda did, but rather evolved its thumbs for some other purpose. The leading proposal for this purpose is that the thumbs developed to aid arboreal locomotion. To test this hypothesis, we conducted grasp tests on rods 5-15 mm in diameter using a biomimetic paw with 0-16 mm interchangeable thumb lengths. The results of these tests demonstrated an optimal thumb length of 7 mm, which is just above that of the red panda's true thumb length of 5.5 mm. Given trends in the data that suggest that smaller thumbs are better suited to grasping larger diameter rods, we conclude that the red panda's thumb being sized below the optimum length suggests an adaptation toward grasping branches as opposed to relatively thinner food items, supporting the new proposal that the red panda's thumbs are an adaptation primary to climbing rather than food manipulation. |
q-bio/0507018 | Toby Johnson | Toby Johnson | Bayesian Method for Disease QTL Detection and Mapping, using a Case and
Control Design and DNA Pooling | null | Biostatistics (2007) 8:546--565 | 10.1093/biostatistics/kxl028 | null | q-bio.GN q-bio.PE q-bio.QM | null | This paper describes a Bayesian statistical method for determining the
genetic basis of a complex genetic trait. The method uses a sample of unrelated
individuals classified into two groups, for example cases and controls. Each
group is assumed to have been genotyped at a battery of marker loci using a
laboratory effort efficient technique called DNA pooling. The aim is to detect
and map a quantitative trait locus (QTL) that is not one of the typed markers.
The method works by conducting an exact Bayesian analysis under a number of
simplifying population genetic assumptions that are somewhat unrealistic.
Despite this, the method is shown to perform acceptably on datasets simulated
under a more realistic model, and furthermore is shown to outperform classical
single point methods.
| [
{
"created": "Wed, 13 Jul 2005 11:59:52 GMT",
"version": "v1"
}
] | 2008-02-21 | [
[
"Johnson",
"Toby",
""
]
] | This paper describes a Bayesian statistical method for determining the genetic basis of a complex genetic trait. The method uses a sample of unrelated individuals classified into two groups, for example cases and controls. Each group is assumed to have been genotyped at a battery of marker loci using a laboratory effort efficient technique called DNA pooling. The aim is to detect and map a quantitative trait locus (QTL) that is not one of the typed markers. The method works by conducting an exact Bayesian analysis under a number of simplifying population genetic assumptions that are somewhat unrealistic. Despite this, the method is shown to perform acceptably on datasets simulated under a more realistic model, and furthermore is shown to outperform classical single point methods. |
2405.03707 | Lixin Lin | Lixin Lin, Homayoun Hamedmoghadam, Robert Shorten, Lewi Stone | Quantifying indirect and direct vaccination effects arising in the SIR
model | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Vaccination campaigns have both direct and indirect effects that act to
control an infectious disease as it spreads through a population. Indirect
effects arise when vaccinated individuals block disease transmission in any
infection chains they are part of, and this in turn can benefit both vaccinated
and unvaccinated individuals. Indirect effects are difficult to quantify in
practice, but here, working with the Susceptible-Infected-Recovered (SIR)
model, they are analytically calculated in important cases, through pivoting on
the Final Size formula for epidemics. Their relationship to herd immunity is
also clarified. Furthermore, we identify the important distinction between
quantifying indirect effects of vaccination at the "population level" versus
the "per capita" individual level, which often results in radically different
conclusions. As an important example, the analysis unpacks why population-level
indirect effect can appear significantly larger than its per capita analogue.
In addition, we consider a recently proposed epidemiological
non-pharamaceutical intervention used over COVID-19, referred to as
"shielding", and study its impact in our mathematical analysis. The shielding
scheme is extended by inclusion of limited vaccination.
| [
{
"created": "Fri, 3 May 2024 20:57:57 GMT",
"version": "v1"
}
] | 2024-05-08 | [
[
"Lin",
"Lixin",
""
],
[
"Hamedmoghadam",
"Homayoun",
""
],
[
"Shorten",
"Robert",
""
],
[
"Stone",
"Lewi",
""
]
] | Vaccination campaigns have both direct and indirect effects that act to control an infectious disease as it spreads through a population. Indirect effects arise when vaccinated individuals block disease transmission in any infection chains they are part of, and this in turn can benefit both vaccinated and unvaccinated individuals. Indirect effects are difficult to quantify in practice, but here, working with the Susceptible-Infected-Recovered (SIR) model, they are analytically calculated in important cases, through pivoting on the Final Size formula for epidemics. Their relationship to herd immunity is also clarified. Furthermore, we identify the important distinction between quantifying indirect effects of vaccination at the "population level" versus the "per capita" individual level, which often results in radically different conclusions. As an important example, the analysis unpacks why population-level indirect effect can appear significantly larger than its per capita analogue. In addition, we consider a recently proposed epidemiological non-pharamaceutical intervention used over COVID-19, referred to as "shielding", and study its impact in our mathematical analysis. The shielding scheme is extended by inclusion of limited vaccination. |
1612.02035 | Donald Forsdyke Dr. | Donald R. Forsdyke | Elusive preferred hosts or nucleic acid level selection? A commentary
on: Evolutionary interpretations of mycobacteriophage biodiversity and
host-range through the analysis of codon usage bias (Esposito et al. 2016) | Submitted (less the reference to Meyer et al. 2016) to Microbial
Genomics on 8th November 2016 | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While confirming the long held view that viruses do not closely imitate the
use of their host's codon catalogue, Esposito and coworkers nevertheless
consider it surprising that, despite having the ability to infect the same
host, many mycobacteriophages share little or no genetic similarity (i.e.
similarity in their GC contents and codon utilization patterns). Arguing
correctly that efficient translation of a phage's proteins within a host is
likely to be optimized by the phage's ability to match the host's codon usage
pattern, it is concluded that the preferred host of many mycobacteriophages is
not Mycobacterium smegmatis, despite their having been isolated on that
organism. Thus, a virus and its elusive preferred hosts would have had similar
GC percentages and codon usages, but the same virus could still infect a
less-preferred host (Mycobacterium smegmatis), where the virus-host similarity
would be less evident. However, there is another evolutionary interpretation.
| [
{
"created": "Tue, 6 Dec 2016 21:29:26 GMT",
"version": "v1"
}
] | 2016-12-08 | [
[
"Forsdyke",
"Donald R.",
""
]
] | While confirming the long held view that viruses do not closely imitate the use of their host's codon catalogue, Esposito and coworkers nevertheless consider it surprising that, despite having the ability to infect the same host, many mycobacteriophages share little or no genetic similarity (i.e. similarity in their GC contents and codon utilization patterns). Arguing correctly that efficient translation of a phage's proteins within a host is likely to be optimized by the phage's ability to match the host's codon usage pattern, it is concluded that the preferred host of many mycobacteriophages is not Mycobacterium smegmatis, despite their having been isolated on that organism. Thus, a virus and its elusive preferred hosts would have had similar GC percentages and codon usages, but the same virus could still infect a less-preferred host (Mycobacterium smegmatis), where the virus-host similarity would be less evident. However, there is another evolutionary interpretation. |
2303.06423 | Herv\'e Isambert | Marcel da C\^amara Ribeiro-Dantas, Honghao Li, Vincent Cabeli, Louise
Dupuis, Franck Simon, Liza Hettal, Anne-Sophie Hamy, and Herv\'e Isambert | Learning interpretable causal networks from very large datasets,
application to 400,000 medical records of breast cancer patients | 19 pages, 6 figures, 8 supplementary figures and 5 pages supporting
information | null | null | null | q-bio.QM cs.LG physics.data-an q-bio.MN stat.ME | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Discovering causal effects is at the core of scientific investigation but
remains challenging when only observational data is available. In practice,
causal networks are difficult to learn and interpret, and limited to relatively
small datasets. We report a more reliable and scalable causal discovery method
(iMIIC), based on a general mutual information supremum principle, which
greatly improves the precision of inferred causal relations while
distinguishing genuine causes from putative and latent causal effects. We
showcase iMIIC on synthetic and real-life healthcare data from 396,179 breast
cancer patients from the US Surveillance, Epidemiology, and End Results
program. More than 90\% of predicted causal effects appear correct, while the
remaining unexpected direct and indirect causal effects can be interpreted in
terms of diagnostic procedures, therapeutic timing, patient preference or
socio-economic disparity. iMIIC's unique capabilities open up new avenues to
discover reliable and interpretable causal networks across a range of research
fields.
| [
{
"created": "Sat, 11 Mar 2023 15:18:19 GMT",
"version": "v1"
}
] | 2023-03-14 | [
[
"Ribeiro-Dantas",
"Marcel da Câmara",
""
],
[
"Li",
"Honghao",
""
],
[
"Cabeli",
"Vincent",
""
],
[
"Dupuis",
"Louise",
""
],
[
"Simon",
"Franck",
""
],
[
"Hettal",
"Liza",
""
],
[
"Hamy",
"Anne-Sophie",
""
],
[
"Isambert",
"Hervé",
""
]
] | Discovering causal effects is at the core of scientific investigation but remains challenging when only observational data is available. In practice, causal networks are difficult to learn and interpret, and limited to relatively small datasets. We report a more reliable and scalable causal discovery method (iMIIC), based on a general mutual information supremum principle, which greatly improves the precision of inferred causal relations while distinguishing genuine causes from putative and latent causal effects. We showcase iMIIC on synthetic and real-life healthcare data from 396,179 breast cancer patients from the US Surveillance, Epidemiology, and End Results program. More than 90\% of predicted causal effects appear correct, while the remaining unexpected direct and indirect causal effects can be interpreted in terms of diagnostic procedures, therapeutic timing, patient preference or socio-economic disparity. iMIIC's unique capabilities open up new avenues to discover reliable and interpretable causal networks across a range of research fields. |
1905.08129 | Carsten Conradi | Carsten Conradi and Elisenda Feliu and Maya Mincheva | On the existence of Hopf bifurcations in the sequential and distributive
double phosphorylation cycle | null | null | 10.3934/mbe.2020027 | null | q-bio.MN math.AG math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Protein phosphorylation cycles are important mechanisms of the post
translational modification of a protein and as such an integral part of
intracellular signaling and control. We consider the sequential phosphorylation
and dephosphorylation of a protein at two binding sites. While it is known that
proteins where phosphorylation is processive and dephosphorylation is
distributive admit oscillations (for some value of the rate constants and total
concentrations) it is not known whether or not this is the case if both
phosphorylation and dephosphorylation are distributive. We study four
simplified mass action models of sequential and distributive phosphorylation
and show that for each of those there do not exist rate constants and total
concentrations where a Hopf bifurcation occurs. To arrive at this result we use
convex parameters to parameterize the steady state and Hurwitz matrices.
| [
{
"created": "Mon, 20 May 2019 14:11:54 GMT",
"version": "v1"
},
{
"created": "Wed, 4 Sep 2019 10:37:29 GMT",
"version": "v2"
}
] | 2019-11-06 | [
[
"Conradi",
"Carsten",
""
],
[
"Feliu",
"Elisenda",
""
],
[
"Mincheva",
"Maya",
""
]
] | Protein phosphorylation cycles are important mechanisms of the post translational modification of a protein and as such an integral part of intracellular signaling and control. We consider the sequential phosphorylation and dephosphorylation of a protein at two binding sites. While it is known that proteins where phosphorylation is processive and dephosphorylation is distributive admit oscillations (for some value of the rate constants and total concentrations) it is not known whether or not this is the case if both phosphorylation and dephosphorylation are distributive. We study four simplified mass action models of sequential and distributive phosphorylation and show that for each of those there do not exist rate constants and total concentrations where a Hopf bifurcation occurs. To arrive at this result we use convex parameters to parameterize the steady state and Hurwitz matrices. |
2310.10978 | Ka My Dang Dr | Ka My Dang, Yi Jia Zhang, Tianchen Zhang, Chao Wang, Anton Sinner,
Piero Coronica, and Joyce K. S. Poon | NeuroQuantify -- An Image Analysis Software for Detection and
Quantification of Neurons and Neurites using Deep Learning | null | null | null | null | q-bio.QM eess.IV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The segmentation of cells and neurites in microscopy images of neuronal
networks provides valuable quantitative information about neuron growth and
neuronal differentiation, including the number of cells, neurites, neurite
length and neurite orientation. This information is essential for assessing the
development of neuronal networks in response to extracellular stimuli, which is
useful for studying neuronal structures, for example, the study of
neurodegenerative diseases and pharmaceuticals. However, automatic and accurate
analysis of neuronal structures from phase contrast images has remained
challenging. To address this, we have developed NeuroQuantify, an open-source
software that uses deep learning to efficiently and quickly segment cells and
neurites in phase contrast microscopy images. NeuroQuantify offers several key
features: (i) automatic detection of cells and neurites; (ii) post-processing
of the images for the quantitative neurite length measurement based on
segmentation of phase contrast microscopy images, and (iii) identification of
neurite orientations. The user-friendly NeuroQuantify software can be installed
and freely downloaded from GitHub
https://github.com/StanleyZ0528/neural-image-segmentation.
| [
{
"created": "Mon, 16 Oct 2023 13:11:59 GMT",
"version": "v1"
},
{
"created": "Thu, 19 Oct 2023 08:33:52 GMT",
"version": "v2"
}
] | 2023-10-20 | [
[
"Dang",
"Ka My",
""
],
[
"Zhang",
"Yi Jia",
""
],
[
"Zhang",
"Tianchen",
""
],
[
"Wang",
"Chao",
""
],
[
"Sinner",
"Anton",
""
],
[
"Coronica",
"Piero",
""
],
[
"Poon",
"Joyce K. S.",
""
]
] | The segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation. This information is essential for assessing the development of neuronal networks in response to extracellular stimuli, which is useful for studying neuronal structures, for example, the study of neurodegenerative diseases and pharmaceuticals. However, automatic and accurate analysis of neuronal structures from phase contrast images has remained challenging. To address this, we have developed NeuroQuantify, an open-source software that uses deep learning to efficiently and quickly segment cells and neurites in phase contrast microscopy images. NeuroQuantify offers several key features: (i) automatic detection of cells and neurites; (ii) post-processing of the images for the quantitative neurite length measurement based on segmentation of phase contrast microscopy images, and (iii) identification of neurite orientations. The user-friendly NeuroQuantify software can be installed and freely downloaded from GitHub https://github.com/StanleyZ0528/neural-image-segmentation. |
2011.04354 | Fedor Garbuzov | F. E. Garbuzov, V. V. Gursky | Nonequilibrium model of short-range repression in gene transcription
regulation | null | Phys. Rev. E 104, 014407 (2021) | 10.1103/PhysRevE.104.014407 | null | q-bio.MN q-bio.GN | http://creativecommons.org/licenses/by-sa/4.0/ | Transcription factors are proteins that regulate gene activity by activating
or repressing gene transcription. A special class of transcriptional repressors
operates via a short-range mechanism, making local DNA regions inaccessible to
binding by activators, and thus providing an indirect repressive action on the
target gene. This mechanism is commonly modeled assuming that repressors
interact with DNA under thermodynamic equilibrium and neglecting some
configurations of the gene regulatory region. We elaborate on a more general
nonequilibrium model of short-range repression using the graph formalism for
transitions between gene states, and we apply analytical calculations to
compare it with the equilibrium model in terms of the repression strength and
expression noise. In contrast to the equilibrium approach, the new model allows
us to separate two basic mechanisms of short-range repression. The first
mechanism is associated with the recruiting of factors that mediate chromatin
condensation, and the second one concerns the blocking of factors that mediate
chromatin loosening. The nonequilibrium model demonstrates better performance
on previously published gene expression data obtained for transcription factors
controlling Drosophila development, and furthermore it predicts that the first
repression mechanism is the most favorable in this system. The presented
approach can be scaled to larger gene networks and can be used to infer
specific modes and parameters of transcriptional regulation from gene
expression data.
| [
{
"created": "Mon, 9 Nov 2020 11:38:13 GMT",
"version": "v1"
},
{
"created": "Mon, 11 Apr 2022 20:01:05 GMT",
"version": "v2"
}
] | 2022-04-13 | [
[
"Garbuzov",
"F. E.",
""
],
[
"Gursky",
"V. V.",
""
]
] | Transcription factors are proteins that regulate gene activity by activating or repressing gene transcription. A special class of transcriptional repressors operates via a short-range mechanism, making local DNA regions inaccessible to binding by activators, and thus providing an indirect repressive action on the target gene. This mechanism is commonly modeled assuming that repressors interact with DNA under thermodynamic equilibrium and neglecting some configurations of the gene regulatory region. We elaborate on a more general nonequilibrium model of short-range repression using the graph formalism for transitions between gene states, and we apply analytical calculations to compare it with the equilibrium model in terms of the repression strength and expression noise. In contrast to the equilibrium approach, the new model allows us to separate two basic mechanisms of short-range repression. The first mechanism is associated with the recruiting of factors that mediate chromatin condensation, and the second one concerns the blocking of factors that mediate chromatin loosening. The nonequilibrium model demonstrates better performance on previously published gene expression data obtained for transcription factors controlling Drosophila development, and furthermore it predicts that the first repression mechanism is the most favorable in this system. The presented approach can be scaled to larger gene networks and can be used to infer specific modes and parameters of transcriptional regulation from gene expression data. |
2001.03207 | Sally Ellingson | Brian Davis, Kevin Mcloughlin, Jonathan Allen, and Sally Ellingson | Split Optimization for Protein/Ligand Binding Models | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we investigate potential biases in datasets used to make drug
binding predictions using machine learning. We investigate a recently published
metric called the Asymmetric Validation Embedding (AVE) bias which is used to
quantify this bias and detect overfitting. We compare it to a slightly revised
version and introduce a new weighted metric. We find that the new metrics allow
to quantify overfitting while not overly limiting training data and produce
models with greater predictive value.
| [
{
"created": "Thu, 9 Jan 2020 20:07:57 GMT",
"version": "v1"
}
] | 2020-01-13 | [
[
"Davis",
"Brian",
""
],
[
"Mcloughlin",
"Kevin",
""
],
[
"Allen",
"Jonathan",
""
],
[
"Ellingson",
"Sally",
""
]
] | In this paper, we investigate potential biases in datasets used to make drug binding predictions using machine learning. We investigate a recently published metric called the Asymmetric Validation Embedding (AVE) bias which is used to quantify this bias and detect overfitting. We compare it to a slightly revised version and introduce a new weighted metric. We find that the new metrics allow to quantify overfitting while not overly limiting training data and produce models with greater predictive value. |
q-bio/0604020 | Jesus M. Cortes | J. Marro, J.J. Torres, J.M. Cortes | Chaotic hopping between attractors in neural networks | 12 pages, 5 figures | null | null | null | q-bio.NC | null | We present a neurobiologically--inspired stochastic cellular automaton whose
state jumps with time between the attractors corresponding to a series of
stored patterns. The jumping varies from regular to chaotic as the model
parameters are modified. The resulting irregular behavior, which mimics the
state of attention in which a systems shows a great adaptability to changing
stimulus, is a consequence in the model of short--time presynaptic noise which
induces synaptic depression. We discuss results from both a mean--field
analysis and Monte Carlo simulations.
| [
{
"created": "Sun, 16 Apr 2006 21:26:40 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Marro",
"J.",
""
],
[
"Torres",
"J. J.",
""
],
[
"Cortes",
"J. M.",
""
]
] | We present a neurobiologically--inspired stochastic cellular automaton whose state jumps with time between the attractors corresponding to a series of stored patterns. The jumping varies from regular to chaotic as the model parameters are modified. The resulting irregular behavior, which mimics the state of attention in which a systems shows a great adaptability to changing stimulus, is a consequence in the model of short--time presynaptic noise which induces synaptic depression. We discuss results from both a mean--field analysis and Monte Carlo simulations. |
2006.09454 | Wenxing Hu | Wenxing Hu, Xianghe Meng, Yuntong Bai, Aiying Zhang, Biao Cai, Gemeng
Zhang, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang | Interpretable multimodal fusion networks reveal mechanisms of brain
cognition | null | null | null | null | q-bio.NC cs.CV cs.LG eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multimodal fusion benefits disease diagnosis by providing a more
comprehensive perspective. Developing algorithms is challenging due to data
heterogeneity and the complex within- and between-modality associations.
Deep-network-based data-fusion models have been developed to capture the
complex associations and the performance in diagnosis has been improved
accordingly. Moving beyond diagnosis prediction, evaluation of disease
mechanisms is critically important for biomedical research. Deep-network-based
data-fusion models, however, are difficult to interpret, bringing about
difficulties for studying biological mechanisms. In this work, we develop an
interpretable multimodal fusion model, namely gCAM-CCL, which can perform
automated diagnosis and result interpretation simultaneously. The gCAM-CCL
model can generate interpretable activation maps, which quantify pixel-level
contributions of the input features. This is achieved by combining intermediate
feature maps using gradient-based weights. Moreover, the estimated activation
maps are class-specific, and the captured cross-data associations are
interest/label related, which further facilitates class-specific analysis and
biological mechanism analysis. We validate the gCAM-CCL model on a brain
imaging-genetic study, and show gCAM-CCL's performed well for both
classification and mechanism analysis. Mechanism analysis suggests that during
task-fMRI scans, several object recognition related regions of interests (ROIs)
are first activated and then several downstream encoding ROIs get involved.
Results also suggest that the higher cognition performing group may have
stronger neurotransmission signaling while the lower cognition performing group
may have problem in brain/neuron development, resulting from genetic
variations.
| [
{
"created": "Tue, 16 Jun 2020 18:52:50 GMT",
"version": "v1"
}
] | 2020-06-18 | [
[
"Hu",
"Wenxing",
""
],
[
"Meng",
"Xianghe",
""
],
[
"Bai",
"Yuntong",
""
],
[
"Zhang",
"Aiying",
""
],
[
"Cai",
"Biao",
""
],
[
"Zhang",
"Gemeng",
""
],
[
"Wilson",
"Tony W.",
""
],
[
"Stephen",
"Julia M.",
""
],
[
"Calhoun",
"Vince D.",
""
],
[
"Wang",
"Yu-Ping",
""
]
] | Multimodal fusion benefits disease diagnosis by providing a more comprehensive perspective. Developing algorithms is challenging due to data heterogeneity and the complex within- and between-modality associations. Deep-network-based data-fusion models have been developed to capture the complex associations and the performance in diagnosis has been improved accordingly. Moving beyond diagnosis prediction, evaluation of disease mechanisms is critically important for biomedical research. Deep-network-based data-fusion models, however, are difficult to interpret, bringing about difficulties for studying biological mechanisms. In this work, we develop an interpretable multimodal fusion model, namely gCAM-CCL, which can perform automated diagnosis and result interpretation simultaneously. The gCAM-CCL model can generate interpretable activation maps, which quantify pixel-level contributions of the input features. This is achieved by combining intermediate feature maps using gradient-based weights. Moreover, the estimated activation maps are class-specific, and the captured cross-data associations are interest/label related, which further facilitates class-specific analysis and biological mechanism analysis. We validate the gCAM-CCL model on a brain imaging-genetic study, and show gCAM-CCL's performed well for both classification and mechanism analysis. Mechanism analysis suggests that during task-fMRI scans, several object recognition related regions of interests (ROIs) are first activated and then several downstream encoding ROIs get involved. Results also suggest that the higher cognition performing group may have stronger neurotransmission signaling while the lower cognition performing group may have problem in brain/neuron development, resulting from genetic variations. |
0905.2875 | Guido Tiana | C. Camilloni, G. Tiana and R. A. Broglia | Atomic-detailed milestones along the folding trajectory of protein G | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The high computational cost of carrying out molecular dynamics simulations of
even small-size proteins is a major obstacle in the study, at atomic detail and
in explicit solvent, of the physical mechanism which is at the basis of the
folding of proteins. Making use of a biasing algorithm, based on the principle
of the ratchet-and-pawl, we have been able to calculate eight folding
trajectories (to an RMSD between 1.2A and 2.5A) of the B1 domain of protein G
in explicit solvent without the need of high-performance computing. The
simulations show that in the denatured state there is a complex network of
cause-effect relationships among contacts, which results in a rather
hierarchical folding mechanism. The network displays few local and nonlocal
native contacts which are cause of most of the others, in agreement with the
NOE signals obtained in mildly-denatured conditions. Also nonnative contacts
play an active role in the folding kinetics. The set of conformations
corresponding to the transition state display phi-values with a correlation
coefficient of 0.69 with the experimental ones. They are structurally quite
homogeneous and topologically native-like, although some of the side chains and
most of the hydrogen bonds are not in place.
| [
{
"created": "Mon, 18 May 2009 12:35:51 GMT",
"version": "v1"
}
] | 2009-05-19 | [
[
"Camilloni",
"C.",
""
],
[
"Tiana",
"G.",
""
],
[
"Broglia",
"R. A.",
""
]
] | The high computational cost of carrying out molecular dynamics simulations of even small-size proteins is a major obstacle in the study, at atomic detail and in explicit solvent, of the physical mechanism which is at the basis of the folding of proteins. Making use of a biasing algorithm, based on the principle of the ratchet-and-pawl, we have been able to calculate eight folding trajectories (to an RMSD between 1.2A and 2.5A) of the B1 domain of protein G in explicit solvent without the need of high-performance computing. The simulations show that in the denatured state there is a complex network of cause-effect relationships among contacts, which results in a rather hierarchical folding mechanism. The network displays few local and nonlocal native contacts which are cause of most of the others, in agreement with the NOE signals obtained in mildly-denatured conditions. Also nonnative contacts play an active role in the folding kinetics. The set of conformations corresponding to the transition state display phi-values with a correlation coefficient of 0.69 with the experimental ones. They are structurally quite homogeneous and topologically native-like, although some of the side chains and most of the hydrogen bonds are not in place. |
1902.07942 | Janusz Szwabi\'nski | Patrycja Kowalek and Hanna Loch-Olszewska and Janusz Szwabi\'nski | Classification of diffusion modes in single-particle tracking data:
Feature-based versus deep-learning approach | null | Phys. Rev. E 100, 032410 (2019) | 10.1103/PhysRevE.100.032410 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Single-particle trajectories measured in microscopy experiments contain
important information about dynamic processes undergoing in a range of
materials including living cells and tissues. However, extracting that
information is not a trivial task due to the stochastic nature of particles'
movement and the sampling noise. In this paper, we adopt a deep-learning method
known as a convolutional neural network (CNN) to classify modes of diffusion
from given trajectories. We compare this fully automated approach working with
raw data to classical machine learning techniques that require data
preprocessing and extraction of human-engineered features from the trajectories
to feed classifiers like random forest or gradient boosting. All methods are
tested using simulated trajectories for which the underlying physical model is
known. From the results it follows that CNN is usually slightly better than the
feature-based methods, but at the costs of much longer processing times.
Moreover, there are still some borderline cases, in which the classical methods
perform better than CNN.
| [
{
"created": "Thu, 21 Feb 2019 10:05:48 GMT",
"version": "v1"
},
{
"created": "Tue, 26 Feb 2019 08:59:01 GMT",
"version": "v2"
},
{
"created": "Thu, 4 Jul 2019 07:56:37 GMT",
"version": "v3"
},
{
"created": "Mon, 2 Sep 2019 08:25:19 GMT",
"version": "v4"
}
] | 2019-09-25 | [
[
"Kowalek",
"Patrycja",
""
],
[
"Loch-Olszewska",
"Hanna",
""
],
[
"Szwabiński",
"Janusz",
""
]
] | Single-particle trajectories measured in microscopy experiments contain important information about dynamic processes undergoing in a range of materials including living cells and tissues. However, extracting that information is not a trivial task due to the stochastic nature of particles' movement and the sampling noise. In this paper, we adopt a deep-learning method known as a convolutional neural network (CNN) to classify modes of diffusion from given trajectories. We compare this fully automated approach working with raw data to classical machine learning techniques that require data preprocessing and extraction of human-engineered features from the trajectories to feed classifiers like random forest or gradient boosting. All methods are tested using simulated trajectories for which the underlying physical model is known. From the results it follows that CNN is usually slightly better than the feature-based methods, but at the costs of much longer processing times. Moreover, there are still some borderline cases, in which the classical methods perform better than CNN. |
1910.08724 | Zhongqi Tian | Zhong-Qi Kyle Tian and Douglas Zhou | Design Efficient Exponential Time Differencing method For Hodgkin-Huxley
Neural Networks | null | null | 10.3389/fncom.2020.00040 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The exponential time differencing (ETD) method allows using a large time step
to efficiently evolve the stiff system such as Hodgkin-Huxley (HH) neural
networks.
For pulse-coupled HH networks, the synaptic spike times cannot be
predetermined and are convoluted with neuron's trajectory itself.
This presents a challenging issue for the design of an efficient numerical
simulation algorithm.
The stiffness in the HH equations are quite different between the spike and
non-spike regions. Here, we design a second-order adaptive exponential time
differencing algorithm (AETD2) for the numerical evolution of HH neural
networks.
Compared with the regular second-order Runge-Kutta method (RK2), our AETD2
method can use time steps one order of magnitude larger and improve
computational efficiency more than ten times while excellently capturing
accurate traces of membrane potentials of HH neurons. This high accuracy and
efficiency can be robustly obtained and do not depend on the dynamical regimes,
connectivity structure or the network size.
| [
{
"created": "Sat, 19 Oct 2019 08:41:15 GMT",
"version": "v1"
},
{
"created": "Sun, 17 Nov 2019 05:33:00 GMT",
"version": "v2"
},
{
"created": "Thu, 21 Nov 2019 13:25:31 GMT",
"version": "v3"
},
{
"created": "Wed, 27 Nov 2019 14:49:11 GMT",
"version": "v4"
}
] | 2020-06-29 | [
[
"Tian",
"Zhong-Qi Kyle",
""
],
[
"Zhou",
"Douglas",
""
]
] | The exponential time differencing (ETD) method allows using a large time step to efficiently evolve the stiff system such as Hodgkin-Huxley (HH) neural networks. For pulse-coupled HH networks, the synaptic spike times cannot be predetermined and are convoluted with neuron's trajectory itself. This presents a challenging issue for the design of an efficient numerical simulation algorithm. The stiffness in the HH equations are quite different between the spike and non-spike regions. Here, we design a second-order adaptive exponential time differencing algorithm (AETD2) for the numerical evolution of HH neural networks. Compared with the regular second-order Runge-Kutta method (RK2), our AETD2 method can use time steps one order of magnitude larger and improve computational efficiency more than ten times while excellently capturing accurate traces of membrane potentials of HH neurons. This high accuracy and efficiency can be robustly obtained and do not depend on the dynamical regimes, connectivity structure or the network size. |
1403.5519 | Manuel Jim\'enez-Mart\'in | Manuel Jim\'enez-Mart\'in and Juan Manuel Pastor and Juan Carlos
Losada and Javier Galeano | Link aggregation process for modelling weighted mutualistic networks | 6 Figures, 2 Tables | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mutualism is a biological interaction mutually beneficial for both species
involved, such as the interaction between plants and their pollinators. Real
mutualistic communities can be understood as weighted bipartite networks and
they present a nested structure and truncated power law degree and strength
distributions. We present a novel link aggregation model that works on a
strength-preferential attachment rule based on the Individual Neutrality
hypothesis. The model generates mutualistic networks with emergent nestedness
and truncated distributions. We provide some analytical results and compare the
simulated and empirical network topology. Upon further improving the shape of
the distributions, we have also studied the role of forbidden interactions on
the model and found that the inclusion of forbidden links does not prevent for
the appearance of super-generalist species. A Python script with the model
algorithms is available.
| [
{
"created": "Fri, 21 Mar 2014 16:58:13 GMT",
"version": "v1"
}
] | 2014-03-24 | [
[
"Jiménez-Martín",
"Manuel",
""
],
[
"Pastor",
"Juan Manuel",
""
],
[
"Losada",
"Juan Carlos",
""
],
[
"Galeano",
"Javier",
""
]
] | Mutualism is a biological interaction mutually beneficial for both species involved, such as the interaction between plants and their pollinators. Real mutualistic communities can be understood as weighted bipartite networks and they present a nested structure and truncated power law degree and strength distributions. We present a novel link aggregation model that works on a strength-preferential attachment rule based on the Individual Neutrality hypothesis. The model generates mutualistic networks with emergent nestedness and truncated distributions. We provide some analytical results and compare the simulated and empirical network topology. Upon further improving the shape of the distributions, we have also studied the role of forbidden interactions on the model and found that the inclusion of forbidden links does not prevent for the appearance of super-generalist species. A Python script with the model algorithms is available. |
2212.08211 | Carina Curto | Caitlyn Parmelee, Juliana Londono Alvarez, Carina Curto, Katherine
Morrison | Sequence generation in inhibition-dominated neural networks | 6 pages, 4 figures, appeared in SIAM DSWeb, 2022 | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This is a brief overview of results from [arXiv:2107.10244, ref 11], on
network architectures that produce sequential dynamics in a special family of
inhibition-dominated neural networks. It was written for SIAM DSWeb.
| [
{
"created": "Fri, 16 Dec 2022 00:51:05 GMT",
"version": "v1"
}
] | 2022-12-19 | [
[
"Parmelee",
"Caitlyn",
""
],
[
"Alvarez",
"Juliana Londono",
""
],
[
"Curto",
"Carina",
""
],
[
"Morrison",
"Katherine",
""
]
] | This is a brief overview of results from [arXiv:2107.10244, ref 11], on network architectures that produce sequential dynamics in a special family of inhibition-dominated neural networks. It was written for SIAM DSWeb. |
1805.09107 | Viktor Stojkoski MSc | Viktor Stojkoski, Zoran Utkovski, Elisabeth Andre, Ljupco Kocarev | Multiplex Network Structure Enhances the Role of Generalized Reciprocity
in Promoting Cooperation | Extended abstract of "The Role of Multiplex Network Structure in
Cooperation through Generalized Reciprocity" | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In multi-agent systems, cooperative behavior is largely determined by the
network structure which dictates the interactions among neighboring agents.
These interactions often exhibit multidimensional features, either as
relationships of different types or temporal dynamics, both of which may be
modeled as a "multiplex" network. Against this background, here we advance the
research on cooperation models inspired by generalized reciprocity, a simple
pay-it-forward behavioral mechanism, by considering a multidimensional
networked society. Our results reveal that a multiplex network structure can
act as an enhancer of the role of generalized reciprocity in promoting
cooperation by acting as a latent support, even when the parameters in some of
the separate network dimensions suggest otherwise (i.e. favor defection). As a
result, generalized reciprocity forces the cooperative contributions of the
individual agents to concentrate in the dimension which is most favorable for
the existence of cooperation.
| [
{
"created": "Wed, 23 May 2018 13:02:34 GMT",
"version": "v1"
}
] | 2018-05-24 | [
[
"Stojkoski",
"Viktor",
""
],
[
"Utkovski",
"Zoran",
""
],
[
"Andre",
"Elisabeth",
""
],
[
"Kocarev",
"Ljupco",
""
]
] | In multi-agent systems, cooperative behavior is largely determined by the network structure which dictates the interactions among neighboring agents. These interactions often exhibit multidimensional features, either as relationships of different types or temporal dynamics, both of which may be modeled as a "multiplex" network. Against this background, here we advance the research on cooperation models inspired by generalized reciprocity, a simple pay-it-forward behavioral mechanism, by considering a multidimensional networked society. Our results reveal that a multiplex network structure can act as an enhancer of the role of generalized reciprocity in promoting cooperation by acting as a latent support, even when the parameters in some of the separate network dimensions suggest otherwise (i.e. favor defection). As a result, generalized reciprocity forces the cooperative contributions of the individual agents to concentrate in the dimension which is most favorable for the existence of cooperation. |
1907.02730 | Koh Onimaru | Koh Onimaru, Luciano Marcon | Systems biology approach to the origin of the tetrapod limb | 22 pages, 5 figures | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | It is still not understood how similar genomic sequences have generated
diverse and spectacular forms during evolution. The difficulty to bridge
phenotypes and genotypes stems from the complexity of multicellular systems,
where thousands of genes and cells interact with each other providing
developmental non-linearity. To understand how diverse morphologies have
evolved, it is essential to find ways to handle such complex systems. Here, we
review the fin-to-limb transition as a case study for the evolution of
multicellular systems. We first describe the historical perspective of
comparative studies between fins and limbs. Second, we introduce our approach
that combines mechanistic theory, computational modeling, and in vivo
experiments to provide a mechanical explanation for the morphological
difference between fish fins and tetrapod limbs. This approach helps resolve a
long-standing debate about anatomical homology between the skeletal elements of
fins and limbs. We will conclude by proposing that due to the counter-intuitive
dynamics of gene interactions, integrative approaches that combine computer
modeling, theory and experiments are essential to understand the evolution of
multicellular organisms.
| [
{
"created": "Fri, 5 Jul 2019 09:03:15 GMT",
"version": "v1"
}
] | 2019-07-08 | [
[
"Onimaru",
"Koh",
""
],
[
"Marcon",
"Luciano",
""
]
] | It is still not understood how similar genomic sequences have generated diverse and spectacular forms during evolution. The difficulty to bridge phenotypes and genotypes stems from the complexity of multicellular systems, where thousands of genes and cells interact with each other providing developmental non-linearity. To understand how diverse morphologies have evolved, it is essential to find ways to handle such complex systems. Here, we review the fin-to-limb transition as a case study for the evolution of multicellular systems. We first describe the historical perspective of comparative studies between fins and limbs. Second, we introduce our approach that combines mechanistic theory, computational modeling, and in vivo experiments to provide a mechanical explanation for the morphological difference between fish fins and tetrapod limbs. This approach helps resolve a long-standing debate about anatomical homology between the skeletal elements of fins and limbs. We will conclude by proposing that due to the counter-intuitive dynamics of gene interactions, integrative approaches that combine computer modeling, theory and experiments are essential to understand the evolution of multicellular organisms. |
2305.00338 | Jorge Arroyo-Esquivel | Jorge Arroyo-Esquivel and Christopher A Klausmeier and Elena Litchman | Using neural ordinary differential equations to predict complex
ecological dynamics from population density data | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Simple models have been used to describe ecological processes for over a
century. However, the complexity of ecological systems makes simple models
subject to modeling bias due to simplifying assumptions or unaccounted factors,
limiting their predictive power. Neural Ordinary Differential Equations (NODEs)
have surged as a machine-learning algorithm that preserves the dynamic nature
of the data \cite{chen_neural_2018}. Although preserving the dynamics in the
data is an advantage, the question of how NODEs perform as a forecasting tool
of ecological communities is unanswered. Here we explore this question using
simulated time series of competing species in a time-varying environment. We
find that NODEs provide more precise forecasts than ARIMA models. {We also find
that untuned NODEs have a similar forecasting accuracy as untuned Long-Short
Term Memory neural networks (LSTMs) and both are outperformed in accuracy and
precision by EDM models. However, we also find NODEs generally outperform all
other methods when evaluating with the interval score, which evaluates
precision and accuracy in terms of prediction intervals rather than pointwise
accuracy.} We also discuss ways to improve the forecasting performance {of
NODEs}. The power of a forecasting tool such as NODEs is that it can provide
insights into population dynamics and should thus broaden the approaches to
studying time series of ecological communities.
| [
{
"created": "Sat, 29 Apr 2023 20:26:42 GMT",
"version": "v1"
},
{
"created": "Mon, 28 Aug 2023 14:25:56 GMT",
"version": "v2"
},
{
"created": "Tue, 23 Jan 2024 18:35:46 GMT",
"version": "v3"
}
] | 2024-01-24 | [
[
"Arroyo-Esquivel",
"Jorge",
""
],
[
"Klausmeier",
"Christopher A",
""
],
[
"Litchman",
"Elena",
""
]
] | Simple models have been used to describe ecological processes for over a century. However, the complexity of ecological systems makes simple models subject to modeling bias due to simplifying assumptions or unaccounted factors, limiting their predictive power. Neural Ordinary Differential Equations (NODEs) have surged as a machine-learning algorithm that preserves the dynamic nature of the data \cite{chen_neural_2018}. Although preserving the dynamics in the data is an advantage, the question of how NODEs perform as a forecasting tool of ecological communities is unanswered. Here we explore this question using simulated time series of competing species in a time-varying environment. We find that NODEs provide more precise forecasts than ARIMA models. {We also find that untuned NODEs have a similar forecasting accuracy as untuned Long-Short Term Memory neural networks (LSTMs) and both are outperformed in accuracy and precision by EDM models. However, we also find NODEs generally outperform all other methods when evaluating with the interval score, which evaluates precision and accuracy in terms of prediction intervals rather than pointwise accuracy.} We also discuss ways to improve the forecasting performance {of NODEs}. The power of a forecasting tool such as NODEs is that it can provide insights into population dynamics and should thus broaden the approaches to studying time series of ecological communities. |
2312.07899 | Yanjun Li | Qiaosi Tang, Ranjala Ratnayake, Gustavo Seabra, Zhe Jiang, Ruogu Fang,
Lina Cui, Yousong Ding, Tamer Kahveci, Jiang Bian, Chenglong Li, Hendrik
Luesch, Yanjun Li | Morphological Profiling for Drug Discovery in the Era of Deep Learning | 44 pages, 5 figure, 5 tables | null | null | null | q-bio.QM cs.AI cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Morphological profiling is a valuable tool in phenotypic drug discovery. The
advent of high-throughput automated imaging has enabled the capturing of a wide
range of morphological features of cells or organisms in response to
perturbations at the single-cell resolution. Concurrently, significant advances
in machine learning and deep learning, especially in computer vision, have led
to substantial improvements in analyzing large-scale high-content images at
high-throughput. These efforts have facilitated understanding of compound
mechanism-of-action (MOA), drug repurposing, characterization of cell
morphodynamics under perturbation, and ultimately contributing to the
development of novel therapeutics. In this review, we provide a comprehensive
overview of the recent advances in the field of morphological profiling. We
summarize the image profiling analysis workflow, survey a broad spectrum of
analysis strategies encompassing feature engineering- and deep learning-based
approaches, and introduce publicly available benchmark datasets. We place a
particular emphasis on the application of deep learning in this pipeline,
covering cell segmentation, image representation learning, and multimodal
learning. Additionally, we illuminate the application of morphological
profiling in phenotypic drug discovery and highlight potential challenges and
opportunities in this field.
| [
{
"created": "Wed, 13 Dec 2023 05:08:32 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Jan 2024 21:22:46 GMT",
"version": "v2"
}
] | 2024-01-17 | [
[
"Tang",
"Qiaosi",
""
],
[
"Ratnayake",
"Ranjala",
""
],
[
"Seabra",
"Gustavo",
""
],
[
"Jiang",
"Zhe",
""
],
[
"Fang",
"Ruogu",
""
],
[
"Cui",
"Lina",
""
],
[
"Ding",
"Yousong",
""
],
[
"Kahveci",
"Tamer",
""
],
[
"Bian",
"Jiang",
""
],
[
"Li",
"Chenglong",
""
],
[
"Luesch",
"Hendrik",
""
],
[
"Li",
"Yanjun",
""
]
] | Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high-throughput. These efforts have facilitated understanding of compound mechanism-of-action (MOA), drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field. |
2405.13182 | Zachary Kilpatrick PhD | Heather L Cihak and Zachary P Kilpatrick | Robustly encoding certainty in a metastable neural circuit model | 15 pages, 10 figures | null | null | null | q-bio.NC nlin.PS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Localized persistent neural activity can encode delayed estimates of
continuous variables. Common experiments require that subjects store and report
the feature value (e.g., orientation) of a particular cue (e.g., oriented bar
on a screen) after a delay. Visualizing recorded activity of neurons along
their feature tuning reveals activity bumps whose centers wander
stochastically, degrading the estimate over time. Bump position therefore
represents the remembered estimate. Recent work suggests bump amplitude may
represent estimate certainty reflecting a probabilistic population code for a
Bayesian posterior. Idealized models of this type are fragile due to the fine
tuning common to constructed continuum attractors in dynamical systems. Here we
propose an alternative metastable model for robustly supporting multiple bump
amplitudes by extending neural circuit models to include quantized
nonlinearities. Asymptotic projections of circuit activity produce
low-dimensional evolution equations for the amplitude and position of bump
solutions in response to external stimuli and noise perturbations. Analysis of
reduced equations accurately characterizes phase variance and the dynamics of
amplitude transitions between stable discrete values. More salient cues
generate bumps of higher amplitude which wander less, consistent with the
experimental finding that greater certainty correlates with more accurate
memories.
| [
{
"created": "Tue, 21 May 2024 20:13:35 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Jul 2024 19:15:50 GMT",
"version": "v2"
}
] | 2024-08-01 | [
[
"Cihak",
"Heather L",
""
],
[
"Kilpatrick",
"Zachary P",
""
]
] | Localized persistent neural activity can encode delayed estimates of continuous variables. Common experiments require that subjects store and report the feature value (e.g., orientation) of a particular cue (e.g., oriented bar on a screen) after a delay. Visualizing recorded activity of neurons along their feature tuning reveals activity bumps whose centers wander stochastically, degrading the estimate over time. Bump position therefore represents the remembered estimate. Recent work suggests bump amplitude may represent estimate certainty reflecting a probabilistic population code for a Bayesian posterior. Idealized models of this type are fragile due to the fine tuning common to constructed continuum attractors in dynamical systems. Here we propose an alternative metastable model for robustly supporting multiple bump amplitudes by extending neural circuit models to include quantized nonlinearities. Asymptotic projections of circuit activity produce low-dimensional evolution equations for the amplitude and position of bump solutions in response to external stimuli and noise perturbations. Analysis of reduced equations accurately characterizes phase variance and the dynamics of amplitude transitions between stable discrete values. More salient cues generate bumps of higher amplitude which wander less, consistent with the experimental finding that greater certainty correlates with more accurate memories. |
q-bio/0608029 | Michael Deem | D. B. Saakian, E. Munoz, Chin-Kun Hu, and M. W. Deem | Quasispecies Theory for Multiple-Peak Fitness Landscapes | 10 pages, 3 figures, 2 tables | Phys. Rev. E 73 (2006) 041913 | 10.1103/PhysRevE.73.041913 | null | q-bio.PE cond-mat.stat-mech | null | We use a path integral representation to solve the Eigen and Crow-Kimura
molecular evolution models for the case of multiple fitness peaks with
arbitrary fitness and degradation functions. In the general case, we find that
the solution to these molecular evolution models can be written as the optimum
of a fitness function, with constraints enforced by Lagrange multipliers and
with a term accounting for the entropy of the spreading population in sequence
space. The results for the Eigen model are applied to consider virus or cancer
proliferation under the control of drugs or the immune system.
| [
{
"created": "Tue, 15 Aug 2006 19:39:45 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Saakian",
"D. B.",
""
],
[
"Munoz",
"E.",
""
],
[
"Hu",
"Chin-Kun",
""
],
[
"Deem",
"M. W.",
""
]
] | We use a path integral representation to solve the Eigen and Crow-Kimura molecular evolution models for the case of multiple fitness peaks with arbitrary fitness and degradation functions. In the general case, we find that the solution to these molecular evolution models can be written as the optimum of a fitness function, with constraints enforced by Lagrange multipliers and with a term accounting for the entropy of the spreading population in sequence space. The results for the Eigen model are applied to consider virus or cancer proliferation under the control of drugs or the immune system. |
1707.08984 | Amit Chattopadhyay | Jason Laurie, Amit K Chattopadhyay and Darren R Flower | Protein Lipograms | 8 pages, 2 columns, 5 figures | Journal of Theoretical Biology, vol 430, pg 109, 2017 | 10.1016/j.jtbi.2017.07.009 | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Linguistic analysis of protein sequences is an underexploited technique.
Here, we capitalize on the concept of the lipogram to characterize sequences at
the proteome levels. A lipogram is a literary composition which omits one or
more letters. A protein lipogram likewise omits one or more types of amino
acid. In this article, we establish a usable terminology for the decomposition
of a sequence collection in terms of the lipogram. Next, we characterize
Uniref50 using a lipogram decomposition. At the global level, protein lipograms
exhibit power-law properties. A clear correlation with metabolic cost is seen.
Finally, we use the lipogram construction to differentiate proteomes between
the four branches of the tree-of-life: archaea, bacteria, eukaryotes and
viruses. We conclude from this pilot study that the lipogram demonstrates
considerable potential as an additional tool for sequence analysis and proteome
classification.
| [
{
"created": "Tue, 25 Jul 2017 11:44:23 GMT",
"version": "v1"
}
] | 2017-07-31 | [
[
"Laurie",
"Jason",
""
],
[
"Chattopadhyay",
"Amit K",
""
],
[
"Flower",
"Darren R",
""
]
] | Linguistic analysis of protein sequences is an underexploited technique. Here, we capitalize on the concept of the lipogram to characterize sequences at the proteome levels. A lipogram is a literary composition which omits one or more letters. A protein lipogram likewise omits one or more types of amino acid. In this article, we establish a usable terminology for the decomposition of a sequence collection in terms of the lipogram. Next, we characterize Uniref50 using a lipogram decomposition. At the global level, protein lipograms exhibit power-law properties. A clear correlation with metabolic cost is seen. Finally, we use the lipogram construction to differentiate proteomes between the four branches of the tree-of-life: archaea, bacteria, eukaryotes and viruses. We conclude from this pilot study that the lipogram demonstrates considerable potential as an additional tool for sequence analysis and proteome classification. |
1009.0118 | Amaury Lambert | Amaury Lambert | Species abundance distributions in neutral models with immigration or
mutation and general lifetimes | 16 pages, 4 figures. To appear in Journal of Mathematical Biology.
The final publication is available at http://www.springerlink.com | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a general, neutral, dynamical model of biodiversity. Individuals
have i.i.d. lifetime durations, which are not necessarily exponentially
distributed, and each individual gives birth independently at constant rate
\lambda. We assume that types are clonally inherited. We consider two classes
of speciation models in this setting. In the immigration model, new individuals
of an entirely new species singly enter the population at constant rate \mu
(e.g., from the mainland into the island). In the mutation model, each
individual independently experiences point mutations in its germ line, at
constant rate \theta. We are interested in the species abundance distribution,
i.e., in the numbers, denoted I_n(k) in the immigration model and A_n(k) in the
mutation model, of species represented by k individuals, k=1,2,...,n, when
there are n individuals in the total population. In the immigration model, we
prove that the numbers (I_t(k);k\ge 1) of species represented by k individuals
at time t, are independent Poisson variables with parameters as in Fisher's
log-series. When conditioning on the total size of the population to equal n,
this results in species abundance distributions given by Ewens' sampling
formula. In particular, I_n(k) converges as n\to\infty to a Poisson r.v. with
mean \gamma /k, where \gamma:=\mu/\lambda. In the mutation model, as
n\to\infty, we obtain the almost sure convergence of n^{-1}A_n(k) to a
nonrandom explicit constant. In the case of a critical, linear birth--death
process, this constant is given by Fisher's log-series, namely n^{-1}A_n(k)
converges to \alpha^{k}/k, where \alpha :=\lambda/(\lambda+\theta). In both
models, the abundances of the most abundant species are briefly discussed.
| [
{
"created": "Wed, 1 Sep 2010 08:32:05 GMT",
"version": "v1"
}
] | 2010-09-02 | [
[
"Lambert",
"Amaury",
""
]
] | We consider a general, neutral, dynamical model of biodiversity. Individuals have i.i.d. lifetime durations, which are not necessarily exponentially distributed, and each individual gives birth independently at constant rate \lambda. We assume that types are clonally inherited. We consider two classes of speciation models in this setting. In the immigration model, new individuals of an entirely new species singly enter the population at constant rate \mu (e.g., from the mainland into the island). In the mutation model, each individual independently experiences point mutations in its germ line, at constant rate \theta. We are interested in the species abundance distribution, i.e., in the numbers, denoted I_n(k) in the immigration model and A_n(k) in the mutation model, of species represented by k individuals, k=1,2,...,n, when there are n individuals in the total population. In the immigration model, we prove that the numbers (I_t(k);k\ge 1) of species represented by k individuals at time t, are independent Poisson variables with parameters as in Fisher's log-series. When conditioning on the total size of the population to equal n, this results in species abundance distributions given by Ewens' sampling formula. In particular, I_n(k) converges as n\to\infty to a Poisson r.v. with mean \gamma /k, where \gamma:=\mu/\lambda. In the mutation model, as n\to\infty, we obtain the almost sure convergence of n^{-1}A_n(k) to a nonrandom explicit constant. In the case of a critical, linear birth--death process, this constant is given by Fisher's log-series, namely n^{-1}A_n(k) converges to \alpha^{k}/k, where \alpha :=\lambda/(\lambda+\theta). In both models, the abundances of the most abundant species are briefly discussed. |
2101.10056 | Vitor Manuel Dinis Pereira | Vitor Manuel Dinis Pereira | Occipital and left temporal instantaneous amplitude and frequency
oscillations correlated with access and phenomenal consciousness | 31 pages, 23 figures, according to the Philpapers.org, my manuscript
"Occipital and left temporal instantaneous amplitude and frequency
oscillations correlated with access and phenomenal consciousness" has been
downloaded 161 times until today (since 2017-11-30) without any substantial
critic, at least any substantial critic that I'am aware | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Given the hard problem of consciousness (Chalmers, 1995) there are no brain
electrophysiological correlates of the subjective experience (the felt quality
of redness or the redness of red, the experience of dark and light, the quality
of depth in a visual field, the sound of a clarinet, the smell of mothball,
bodily sensations from pains to orgasms, mental images that are conjured up
internally, the felt quality of emotion, the experience of a stream of
conscious thought or the phenomenology of thought). However, there are brain
occipital and left temporal electrophysiological correlates of the subjective
experience (Pereira, 2015). Notwithstanding, as evoked signal, the change in
event-related brain potentials phase (frequency is the change in phase over
time) is instantaneous, that is, the frequency will transiently be infinite: a
transient peak in frequency (positive or negative), if any, is instantaneous in
electroencephalogram averaging or filtering that the event-related brain
potentials required and the underlying structure of the event-related brain
potentials in the frequency domain cannot be accounted, for example, by the
Wavelet Transform (WT) or the Fast Fourier Transform (FFT) analysis, because
they require that frequency is derived by convolution rather than by
differentiation. However, as I show in the current original research report,
one suitable method for analyse the instantaneous change in event-related brain
potentials phase and accounted for a transient peak in frequency (positive or
negative), if any, in the underlying structure of the event-related brain
potentials is the Empirical Mode Decomposition with post processing (Xie et
al., 2014) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang
Transform (HHT).
| [
{
"created": "Sat, 26 Dec 2020 16:30:40 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Feb 2021 18:23:30 GMT",
"version": "v2"
},
{
"created": "Fri, 5 Mar 2021 18:36:30 GMT",
"version": "v3"
}
] | 2021-03-08 | [
[
"Pereira",
"Vitor Manuel Dinis",
""
]
] | Given the hard problem of consciousness (Chalmers, 1995) there are no brain electrophysiological correlates of the subjective experience (the felt quality of redness or the redness of red, the experience of dark and light, the quality of depth in a visual field, the sound of a clarinet, the smell of mothball, bodily sensations from pains to orgasms, mental images that are conjured up internally, the felt quality of emotion, the experience of a stream of conscious thought or the phenomenology of thought). However, there are brain occipital and left temporal electrophysiological correlates of the subjective experience (Pereira, 2015). Notwithstanding, as evoked signal, the change in event-related brain potentials phase (frequency is the change in phase over time) is instantaneous, that is, the frequency will transiently be infinite: a transient peak in frequency (positive or negative), if any, is instantaneous in electroencephalogram averaging or filtering that the event-related brain potentials required and the underlying structure of the event-related brain potentials in the frequency domain cannot be accounted, for example, by the Wavelet Transform (WT) or the Fast Fourier Transform (FFT) analysis, because they require that frequency is derived by convolution rather than by differentiation. However, as I show in the current original research report, one suitable method for analyse the instantaneous change in event-related brain potentials phase and accounted for a transient peak in frequency (positive or negative), if any, in the underlying structure of the event-related brain potentials is the Empirical Mode Decomposition with post processing (Xie et al., 2014) Ensemble Empirical Mode Decomposition (postEEMD) and Hilbert-Huang Transform (HHT). |
1208.3570 | Darya Novopashina S | D. S. Novopashina, E. K. Apartsin, A. G. Venyaminova | Fluorecently labeled bionanotransporters of nucleic acid based on carbon
nanotubes | http://www.ujp.bitp.kiev.ua | Ukrainian Journal of Physics, 2009, Vol. 54, no. 1-2, pp. 207-215 | null | null | q-bio.BM cond-mat.mtrl-sci physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Here we propose the approach to design of the new type of hybrids of
oligonucleotides with fluorescein-functionalized single-walled carbon
nanotubes. The approach is based on stacking interactions of functionalized
nanotubes with pyrene residues in conjugates of oligonucleotides. The amino-
and fluorescein-modified single-walled carbon nanotubes were obtained, and
their physico-chemical properties were investigated. The effect of carbon
nanotubes functionalization type on the efficacy of sorption of pyrene
conjugates of oligonucleotides was examined. Proposed non-covalent hybrids of
fluorescein-labeled carbon nanotubes with oligonucleotides may be used for
intracellular transport of functional nucleic acids.
| [
{
"created": "Fri, 17 Aug 2012 10:32:53 GMT",
"version": "v1"
}
] | 2015-03-13 | [
[
"Novopashina",
"D. S.",
""
],
[
"Apartsin",
"E. K.",
""
],
[
"Venyaminova",
"A. G.",
""
]
] | Here we propose the approach to design of the new type of hybrids of oligonucleotides with fluorescein-functionalized single-walled carbon nanotubes. The approach is based on stacking interactions of functionalized nanotubes with pyrene residues in conjugates of oligonucleotides. The amino- and fluorescein-modified single-walled carbon nanotubes were obtained, and their physico-chemical properties were investigated. The effect of carbon nanotubes functionalization type on the efficacy of sorption of pyrene conjugates of oligonucleotides was examined. Proposed non-covalent hybrids of fluorescein-labeled carbon nanotubes with oligonucleotides may be used for intracellular transport of functional nucleic acids. |
1604.02193 | Yana Safonova | Alexander Shlemov, Sergey Bankevich, Andrey Bzikadze, Yana Safonova | New algorithmic challenges of adaptive immune repertoire construction | Paper accepted at the RECOMB-Seq 2016 | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: The analysis of antibodies and T-cell receptors (TCRs)
concentrations in serum is a fundamental problem in immunoinformatics.
Repertoire construction is a preliminary step of analysis of clonal lineages,
understanding of immune response dynamics, population analysis of
immunoglobulin and TCR loci. Emergence of MiSeq Illumina sequencing machine in
2013 opened horizons of investigation of adaptive immune repertoires using
highly accurate reads. Reads produced by MiSeq are able to cover repertoires of
moderate size. At the same time, throughput of sequencing machines increases
from year to year. This will enable ultra deep scanning of adaptive immune
repertoires and analysis of their diversity. Such data requires both efficient
and highly accurate repertoire construction tools. In 2015 Safonova et al.
presented IgRepertoireConstructor, a tool for accurate construction of antibody
repertoire and immunoproteogenomics analysis. Unfortunately, proposed algorithm
was very time and memory consuming and could be a bottleneck of processing
large immunosequencing libraries. In this paper we overcome this challenge and
present IgReC, a novel algorithm for adaptive repertoire construction problem.
IgReC reconstructs a repertoire with high precision even if each input read
contains sequencing errors and performs well on contemporary datasets. Results
of computational experiments show that IgReC improves state-of-the-art in the
field. Availability: IgReC is an open source and freely available program
running on Linux platforms. The source code is available at GitHub:
yana-safonova.github.io/ig_repertoire_constructor. Contact:
safonova.yana@gmail.com
| [
{
"created": "Thu, 7 Apr 2016 23:04:37 GMT",
"version": "v1"
}
] | 2016-04-11 | [
[
"Shlemov",
"Alexander",
""
],
[
"Bankevich",
"Sergey",
""
],
[
"Bzikadze",
"Andrey",
""
],
[
"Safonova",
"Yana",
""
]
] | Motivation: The analysis of antibodies and T-cell receptors (TCRs) concentrations in serum is a fundamental problem in immunoinformatics. Repertoire construction is a preliminary step of analysis of clonal lineages, understanding of immune response dynamics, population analysis of immunoglobulin and TCR loci. Emergence of MiSeq Illumina sequencing machine in 2013 opened horizons of investigation of adaptive immune repertoires using highly accurate reads. Reads produced by MiSeq are able to cover repertoires of moderate size. At the same time, throughput of sequencing machines increases from year to year. This will enable ultra deep scanning of adaptive immune repertoires and analysis of their diversity. Such data requires both efficient and highly accurate repertoire construction tools. In 2015 Safonova et al. presented IgRepertoireConstructor, a tool for accurate construction of antibody repertoire and immunoproteogenomics analysis. Unfortunately, proposed algorithm was very time and memory consuming and could be a bottleneck of processing large immunosequencing libraries. In this paper we overcome this challenge and present IgReC, a novel algorithm for adaptive repertoire construction problem. IgReC reconstructs a repertoire with high precision even if each input read contains sequencing errors and performs well on contemporary datasets. Results of computational experiments show that IgReC improves state-of-the-art in the field. Availability: IgReC is an open source and freely available program running on Linux platforms. The source code is available at GitHub: yana-safonova.github.io/ig_repertoire_constructor. Contact: safonova.yana@gmail.com |
0912.4472 | John Rhodes | Elizabeth S. Allman, James H. Degnan, John A. Rhodes | Identifying the Rooted Species Tree from the Distribution of Unrooted
Gene Trees under the Coalescent | Additional material extends results to polytomous species trees | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene trees are evolutionary trees representing the ancestry of genes sampled
from multiple populations. Species trees represent populations of individuals
-- each with many genes -- splitting into new populations or species. The
coalescent process, which models ancestry of gene copies within populations, is
often used to model the probability distribution of gene trees given a fixed
species tree. This multispecies coalescent model provides a framework for
phylogeneticists to infer species trees from gene trees using maximum
likelihood or Bayesian approaches. Because the coalescent models a branching
process over time, all trees are typically assumed to be rooted in this
setting. Often, however, gene trees inferred by traditional phylogenetic
methods are unrooted.
We investigate probabilities of unrooted gene trees under the multispecies
coalescent model. We show that when there are 4 species with one gene sampled
per species, the distribution of unrooted gene tree topologies identifies the
unrooted species tree topology and some, but not all, information in the
species tree edges (branch lengths). The location of the root on the species
tree is not identifiable in this situation. However, for 5 or more species with
one gene sampled per species, we show that the distribution of unrooted gene
tree topologies identifies the rooted species tree topology and all its
internal branch lengths. The length of any pendent branch leading to a leaf of
the species tree is also identifiable for any species from which more than one
gene is sampled.
| [
{
"created": "Tue, 22 Dec 2009 18:00:39 GMT",
"version": "v1"
},
{
"created": "Thu, 29 Jul 2010 18:07:14 GMT",
"version": "v2"
}
] | 2010-07-30 | [
[
"Allman",
"Elizabeth S.",
""
],
[
"Degnan",
"James H.",
""
],
[
"Rhodes",
"John A.",
""
]
] | Gene trees are evolutionary trees representing the ancestry of genes sampled from multiple populations. Species trees represent populations of individuals -- each with many genes -- splitting into new populations or species. The coalescent process, which models ancestry of gene copies within populations, is often used to model the probability distribution of gene trees given a fixed species tree. This multispecies coalescent model provides a framework for phylogeneticists to infer species trees from gene trees using maximum likelihood or Bayesian approaches. Because the coalescent models a branching process over time, all trees are typically assumed to be rooted in this setting. Often, however, gene trees inferred by traditional phylogenetic methods are unrooted. We investigate probabilities of unrooted gene trees under the multispecies coalescent model. We show that when there are 4 species with one gene sampled per species, the distribution of unrooted gene tree topologies identifies the unrooted species tree topology and some, but not all, information in the species tree edges (branch lengths). The location of the root on the species tree is not identifiable in this situation. However, for 5 or more species with one gene sampled per species, we show that the distribution of unrooted gene tree topologies identifies the rooted species tree topology and all its internal branch lengths. The length of any pendent branch leading to a leaf of the species tree is also identifiable for any species from which more than one gene is sampled. |
1006.0020 | Teruhiko Yoneyama | Teruhiko Yoneyama and Mukkai S. Krishnamoorthy | Influence of the Cold War upon Influenza Pandemic of 1957-1958 | null | null | null | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by/3.0/ | Influenza Pandemic of 1957-1958, also called Asian Flu Pandemic, was one of
the most widespread pandemics in history. In this paper, we model the pandemic,
considering the effect of the Cold War. There were some restrictions between
Western and Eastern nations due to the Cold War during the pandemic. We expect
that such restrictions influenced the spread of the pandemic. We propose a
hybrid model to determine how the pandemic spread through the world. The model
combines the SEIR-based model for local areas and the network model for global
connection between countries. First, we reproduce the situation in 19
countries. Then, we run another experiment to find the influence of the war in
the spread of the pandemic; simulation considering international relationships
in different years. The simulation results show that the impact of the pandemic
in each country was much influenced by international relationships. This study
indicates that if there was less effect of the Cold War, Western nations would
have larger number of death cases, Eastern nations would have smaller number of
death cases, and the world impact would be increased somewhat.
| [
{
"created": "Tue, 11 May 2010 22:07:38 GMT",
"version": "v1"
}
] | 2010-06-02 | [
[
"Yoneyama",
"Teruhiko",
""
],
[
"Krishnamoorthy",
"Mukkai S.",
""
]
] | Influenza Pandemic of 1957-1958, also called Asian Flu Pandemic, was one of the most widespread pandemics in history. In this paper, we model the pandemic, considering the effect of the Cold War. There were some restrictions between Western and Eastern nations due to the Cold War during the pandemic. We expect that such restrictions influenced the spread of the pandemic. We propose a hybrid model to determine how the pandemic spread through the world. The model combines the SEIR-based model for local areas and the network model for global connection between countries. First, we reproduce the situation in 19 countries. Then, we run another experiment to find the influence of the war in the spread of the pandemic; simulation considering international relationships in different years. The simulation results show that the impact of the pandemic in each country was much influenced by international relationships. This study indicates that if there was less effect of the Cold War, Western nations would have larger number of death cases, Eastern nations would have smaller number of death cases, and the world impact would be increased somewhat. |
2311.04567 | Robert Petryszak | Kevin Troul\'e, Robert Petryszak, Martin Prete, James Cranley, Alicia
Harasty, Zewen Kelvin Tuong, Sarah A Teichmann, Luz Garcia-Alonso, Roser
Vento-Tormo | CellPhoneDB v5: inferring cell-cell communication from single-cell
multiomics data | 30 pages, 3 figures and 2 tables. Added previously missing figures
and tables; Updated the reference for 'An integrated single-cell reference
atlas of the human endometrium' paper | null | null | null | q-bio.CB q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Cell-cell communication is essential for tissue development, regeneration and
function, and its disruption can lead to diseases and developmental
abnormalities. The revolution of single-cell genomics technologies offers
unprecedented insights into cellular identities, opening new avenues to resolve
the intricate cellular interactions present in tissue niches. CellPhoneDB is a
bioinformatics toolkit designed to infer cell-cell communication by combining a
curated repository of bona fide ligand-receptor interactions with a set of
computational and statistical methods to integrate them with single-cell
genomics data. Importantly, CellPhoneDB captures the multimeric nature of
molecular complexes, thus representing cell-cell communication biology
faithfully. Here we present CellPhoneDB v5, an updated version of the tool,
which offers several new features. Firstly, the repository has been expanded by
one-third with the addition of new interactions. These encompass interactions
mediated by non-protein ligands such as endocrine hormones and GPCR ligands.
Secondly, it includes a differentially expression-based methodology for more
tailored interaction queries. Thirdly, it incorporates novel computational
methods to prioritise specific cell-cell interactions, leveraging other
single-cell modalities, such as spatial information or TF activities (i.e.
CellSign module). Finally, we provide CellPhoneDBViz, a module to interactively
visualise and share results amongst users. Altogether, CellPhoneDB v5 elevates
the precision of cell-cell communication inference, ushering in new
perspectives to comprehend tissue biology in both healthy and pathological
states.
| [
{
"created": "Wed, 8 Nov 2023 09:59:03 GMT",
"version": "v1"
},
{
"created": "Mon, 13 Nov 2023 13:41:51 GMT",
"version": "v2"
}
] | 2023-11-14 | [
[
"Troulé",
"Kevin",
""
],
[
"Petryszak",
"Robert",
""
],
[
"Prete",
"Martin",
""
],
[
"Cranley",
"James",
""
],
[
"Harasty",
"Alicia",
""
],
[
"Tuong",
"Zewen Kelvin",
""
],
[
"Teichmann",
"Sarah A",
""
],
[
"Garcia-Alonso",
"Luz",
""
],
[
"Vento-Tormo",
"Roser",
""
]
] | Cell-cell communication is essential for tissue development, regeneration and function, and its disruption can lead to diseases and developmental abnormalities. The revolution of single-cell genomics technologies offers unprecedented insights into cellular identities, opening new avenues to resolve the intricate cellular interactions present in tissue niches. CellPhoneDB is a bioinformatics toolkit designed to infer cell-cell communication by combining a curated repository of bona fide ligand-receptor interactions with a set of computational and statistical methods to integrate them with single-cell genomics data. Importantly, CellPhoneDB captures the multimeric nature of molecular complexes, thus representing cell-cell communication biology faithfully. Here we present CellPhoneDB v5, an updated version of the tool, which offers several new features. Firstly, the repository has been expanded by one-third with the addition of new interactions. These encompass interactions mediated by non-protein ligands such as endocrine hormones and GPCR ligands. Secondly, it includes a differentially expression-based methodology for more tailored interaction queries. Thirdly, it incorporates novel computational methods to prioritise specific cell-cell interactions, leveraging other single-cell modalities, such as spatial information or TF activities (i.e. CellSign module). Finally, we provide CellPhoneDBViz, a module to interactively visualise and share results amongst users. Altogether, CellPhoneDB v5 elevates the precision of cell-cell communication inference, ushering in new perspectives to comprehend tissue biology in both healthy and pathological states. |
q-bio/0402003 | Hiro-Sato Niwa | Hiro-Sato Niwa | Space-irrelevant scaling law for fish school sizes | 23 pages, 12 figures, to appear in J. Theor. Biol | J. Theor. Biol. 228 (2004) 347-357 | 10.1016/j.jtbi.2004.01.011 | null | q-bio.PE cond-mat.stat-mech | null | Universal scaling in the power-law size distribution of pelagic fish schools
is established. The power-law exponent of size distributions is extracted
through the data collapse. The distribution depends on the school size only
through the ratio of the size to the expected size of the schools an arbitrary
individual engages in. This expected size is linear in the ratio of the spatial
population density of fish to the breakup rate of school. By means of extensive
numerical simulations, it is verified that the law is completely independent of
the dimension of the space in which the fish move. Besides the scaling analysis
on school size distributions, the integrity of schools over extended periods of
time is discussed.
| [
{
"created": "Sun, 1 Feb 2004 21:11:39 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Niwa",
"Hiro-Sato",
""
]
] | Universal scaling in the power-law size distribution of pelagic fish schools is established. The power-law exponent of size distributions is extracted through the data collapse. The distribution depends on the school size only through the ratio of the size to the expected size of the schools an arbitrary individual engages in. This expected size is linear in the ratio of the spatial population density of fish to the breakup rate of school. By means of extensive numerical simulations, it is verified that the law is completely independent of the dimension of the space in which the fish move. Besides the scaling analysis on school size distributions, the integrity of schools over extended periods of time is discussed. |
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