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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
q-bio/0410002 | Jan Karbowski | Jan Karbowski, G.B. Ermentrout | Model of the early development of thalamo-cortical connections and area
patterning via signaling molecules | brain, model, neural development, cortical area patterning, signaling
molecules | Journal of Computational Neuroscience 17: 347-363 (2004) | null | null | q-bio.NC q-bio.MN | null | The mammalian cortex is divided into architectonic and functionally distinct
areas. There is growing experimental evidence that their emergence and
development is controlled by both epigenetic and genetic factors. The latter
were recently implicated as dominating the early cortical area specification.
In this paper, we present a theoretical model that explicitly considers the
genetic factors and that is able to explain several sets of experiments on
cortical area regulation involving transcription factors Emx2 and Pax6, and
fibroblast growth factor FGF8. The model consists of the dynamics of thalamo-
cortical connections modulated by signaling molecules that are regulated
genetically, and by axonal competition for neocortical space. The model can
make predictions and provides a basic mathematical framework for the early
development of the thalamo-cortical connections and area patterning that can be
further refined as more experimental facts become known.
| [
{
"created": "Fri, 1 Oct 2004 23:30:46 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Karbowski",
"Jan",
""
],
[
"Ermentrout",
"G. B.",
""
]
] | The mammalian cortex is divided into architectonic and functionally distinct areas. There is growing experimental evidence that their emergence and development is controlled by both epigenetic and genetic factors. The latter were recently implicated as dominating the early cortical area specification. In this paper, we present a theoretical model that explicitly considers the genetic factors and that is able to explain several sets of experiments on cortical area regulation involving transcription factors Emx2 and Pax6, and fibroblast growth factor FGF8. The model consists of the dynamics of thalamo- cortical connections modulated by signaling molecules that are regulated genetically, and by axonal competition for neocortical space. The model can make predictions and provides a basic mathematical framework for the early development of the thalamo-cortical connections and area patterning that can be further refined as more experimental facts become known. |
2010.10614 | Sam Sinai | Sam Sinai and Eric D Kelsic | A primer on model-guided exploration of fitness landscapes for
biological sequence design | null | null | null | null | q-bio.QM cs.LG q-bio.BM q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Machine learning methods are increasingly employed to address challenges
faced by biologists. One area that will greatly benefit from this
cross-pollination is the problem of biological sequence design, which has
massive potential for therapeutic applications. However, significant
inefficiencies remain in communication between these fields which result in
biologists finding the progress in machine learning inaccessible, and hinder
machine learning scientists from contributing to impactful problems in
bioengineering. Sequence design can be seen as a search process on a discrete,
high-dimensional space, where each sequence is associated with a function. This
sequence-to-function map is known as a "Fitness Landscape". Designing a
sequence with a particular function is hence a matter of "discovering" such a
(often rare) sequence within this space. Today we can build predictive models
with good interpolation ability due to impressive progress in the synthesis and
testing of biological sequences in large numbers, which enables model training
and validation. However, it often remains a challenge to find useful sequences
with the properties that we like using these models. In particular, in this
primer we highlight that algorithms for experimental design, what we call
"exploration strategies", are a related, yet distinct problem from building
good models of sequence-to-function maps. We review advances and insights from
current literature -- by no means a complete treatment -- while highlighting
desirable features of optimal model-guided exploration, and cover potential
pitfalls drawn from our own experience. This primer can serve as a starting
point for researchers from different domains that are interested in the problem
of searching a sequence space with a model, but are perhaps unaware of
approaches that originate outside their field.
| [
{
"created": "Sun, 4 Oct 2020 21:32:07 GMT",
"version": "v1"
},
{
"created": "Fri, 23 Oct 2020 14:25:05 GMT",
"version": "v2"
}
] | 2020-10-26 | [
[
"Sinai",
"Sam",
""
],
[
"Kelsic",
"Eric D",
""
]
] | Machine learning methods are increasingly employed to address challenges faced by biologists. One area that will greatly benefit from this cross-pollination is the problem of biological sequence design, which has massive potential for therapeutic applications. However, significant inefficiencies remain in communication between these fields which result in biologists finding the progress in machine learning inaccessible, and hinder machine learning scientists from contributing to impactful problems in bioengineering. Sequence design can be seen as a search process on a discrete, high-dimensional space, where each sequence is associated with a function. This sequence-to-function map is known as a "Fitness Landscape". Designing a sequence with a particular function is hence a matter of "discovering" such a (often rare) sequence within this space. Today we can build predictive models with good interpolation ability due to impressive progress in the synthesis and testing of biological sequences in large numbers, which enables model training and validation. However, it often remains a challenge to find useful sequences with the properties that we like using these models. In particular, in this primer we highlight that algorithms for experimental design, what we call "exploration strategies", are a related, yet distinct problem from building good models of sequence-to-function maps. We review advances and insights from current literature -- by no means a complete treatment -- while highlighting desirable features of optimal model-guided exploration, and cover potential pitfalls drawn from our own experience. This primer can serve as a starting point for researchers from different domains that are interested in the problem of searching a sequence space with a model, but are perhaps unaware of approaches that originate outside their field. |
1304.4928 | Matjaz Perc | Luo-Luo Jiang, Matjaz Perc, Attila Szolnoki | If cooperation is likely punish mildly: Insights from economic
experiments based on the snowdrift game | 15 pages, 6 figures; accepted for publication in PLoS ONE | PLoS ONE 8 (2013) e64677 | 10.1371/journal.pone.0064677 | null | q-bio.PE cs.GT physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Punishment may deter antisocial behavior. Yet to punish is costly, and the
costs often do not offset the gains that are due to elevated levels of
cooperation. However, the effectiveness of punishment depends not only on how
costly it is, but also on the circumstances defining the social dilemma. Using
the snowdrift game as the basis, we have conducted a series of economic
experiments to determine whether severe punishment is more effective than mild
punishment. We have observed that severe punishment is not necessarily more
effective, even if the cost of punishment is identical in both cases. The
benefits of severe punishment become evident only under extremely adverse
conditions, when to cooperate is highly improbable in the absence of sanctions.
If cooperation is likely, mild punishment is not less effective and leads to
higher average payoffs, and is thus the much preferred alternative. Presented
results suggest that the positive effects of punishment stem not only from
imposed fines, but may also have a psychological background. Small fines can do
wonders in motivating us to chose cooperation over defection, but without the
paralyzing effect that may be brought about by large fines. The later should be
utilized only when absolutely necessary.
| [
{
"created": "Wed, 17 Apr 2013 19:55:53 GMT",
"version": "v1"
}
] | 2013-06-04 | [
[
"Jiang",
"Luo-Luo",
""
],
[
"Perc",
"Matjaz",
""
],
[
"Szolnoki",
"Attila",
""
]
] | Punishment may deter antisocial behavior. Yet to punish is costly, and the costs often do not offset the gains that are due to elevated levels of cooperation. However, the effectiveness of punishment depends not only on how costly it is, but also on the circumstances defining the social dilemma. Using the snowdrift game as the basis, we have conducted a series of economic experiments to determine whether severe punishment is more effective than mild punishment. We have observed that severe punishment is not necessarily more effective, even if the cost of punishment is identical in both cases. The benefits of severe punishment become evident only under extremely adverse conditions, when to cooperate is highly improbable in the absence of sanctions. If cooperation is likely, mild punishment is not less effective and leads to higher average payoffs, and is thus the much preferred alternative. Presented results suggest that the positive effects of punishment stem not only from imposed fines, but may also have a psychological background. Small fines can do wonders in motivating us to chose cooperation over defection, but without the paralyzing effect that may be brought about by large fines. The later should be utilized only when absolutely necessary. |
1202.4724 | Subhadip Raychaudhuri | Philippos K. Tsourkas, Somkanya C. Das, Paul Yu-Yang, Wanli Liu, Susan
K. Pierce, and Subhadip Raychaudhuri | Formation of BCR Oligomers Provides a Mechanism for B cell Affinity
Discrimination | 29 pages, 9 figures | null | null | null | q-bio.CB physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | B cells encounter antigen over a wide affinity range. The strength of B cell
signaling in response to antigen increases with affinity, a process known as
"affinity discrimination". In this work, we use a computational simulation of B
cell surface dynamics and signaling to show that affinity discrimination can
arise from the formation of BCR oligomers. It is known that BCRs form oligomers
upon encountering antigen, and that the size and rate of formation of these
oligomers increase with affinity. In our simulation, we have introduced a
requirement that only BCR-antigen complexes that are part of an oligomer can
engage cytoplasmic signaling molecules such as Src-family kinases. Our
simulation shows that as affinity increases, not only does the number of
collected antigen increases, but so does signaling activity. Our results are
also consistent with the existence of an experimentally-observed threshold
affinity of activation (no signaling activity below this affinity value) and
affinity discrimination ceiling (no affinity discrimination above this affinity
value). Comparison with experiments shows that the time scale of dimer
formation predicted by our model (less than 10 s) is well within the time scale
of experimentally observed association of BCR with Src-family kinases (10-20
s).
| [
{
"created": "Tue, 21 Feb 2012 18:41:31 GMT",
"version": "v1"
}
] | 2012-02-22 | [
[
"Tsourkas",
"Philippos K.",
""
],
[
"Das",
"Somkanya C.",
""
],
[
"Yu-Yang",
"Paul",
""
],
[
"Liu",
"Wanli",
""
],
[
"Pierce",
"Susan K.",
""
],
[
"Raychaudhuri",
"Subhadip",
""
]
] | B cells encounter antigen over a wide affinity range. The strength of B cell signaling in response to antigen increases with affinity, a process known as "affinity discrimination". In this work, we use a computational simulation of B cell surface dynamics and signaling to show that affinity discrimination can arise from the formation of BCR oligomers. It is known that BCRs form oligomers upon encountering antigen, and that the size and rate of formation of these oligomers increase with affinity. In our simulation, we have introduced a requirement that only BCR-antigen complexes that are part of an oligomer can engage cytoplasmic signaling molecules such as Src-family kinases. Our simulation shows that as affinity increases, not only does the number of collected antigen increases, but so does signaling activity. Our results are also consistent with the existence of an experimentally-observed threshold affinity of activation (no signaling activity below this affinity value) and affinity discrimination ceiling (no affinity discrimination above this affinity value). Comparison with experiments shows that the time scale of dimer formation predicted by our model (less than 10 s) is well within the time scale of experimentally observed association of BCR with Src-family kinases (10-20 s). |
1003.2427 | Jens Christian Claussen | Markus Sch\"utt and Jens Christian Claussen | Mean extinction times in cyclic coevolutionary rock-paper-scissors
dynamics | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dynamical mechanisms that can stabilize the coexistence or diversity in
biology are generally of fundamental interest. In contrast to many two-strategy
evolutionary games, games with three strategies and cyclic dominance like the
rock-paper-scissors game (RPS) stabilize coexistence and thus preserve
biodiversity in this system. In the limit of infinite populations, resembling
the traditional picture of evolutionary game theory, replicator equations
predict the existence of a fixed point in the interior of the phase space. But
in finite populations, strategy frequencies will run out of the fixed point
because of stochastic fluctuations, and strategies can even go extinct. For
three different processes and for zero-sum and non-zero-sum RPS as well, we
present results of extensive simulations for the mean extinction time (MET),
depending on the number of agents N, and we introduce two analytical approaches
for the derivation of the MET.
| [
{
"created": "Thu, 11 Mar 2010 21:21:25 GMT",
"version": "v1"
}
] | 2010-03-15 | [
[
"Schütt",
"Markus",
""
],
[
"Claussen",
"Jens Christian",
""
]
] | Dynamical mechanisms that can stabilize the coexistence or diversity in biology are generally of fundamental interest. In contrast to many two-strategy evolutionary games, games with three strategies and cyclic dominance like the rock-paper-scissors game (RPS) stabilize coexistence and thus preserve biodiversity in this system. In the limit of infinite populations, resembling the traditional picture of evolutionary game theory, replicator equations predict the existence of a fixed point in the interior of the phase space. But in finite populations, strategy frequencies will run out of the fixed point because of stochastic fluctuations, and strategies can even go extinct. For three different processes and for zero-sum and non-zero-sum RPS as well, we present results of extensive simulations for the mean extinction time (MET), depending on the number of agents N, and we introduce two analytical approaches for the derivation of the MET. |
1606.00261 | Roberto Rivera | Roberto Rivera, Oelisoa M. Andriankaja, Cynthia M. Perez, Kaumudi
Joshipura | Relationship between Periodontal disease and Asthma among
overweight/obese adults | null | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Aim: To assess the relationship between oral health and asthma. Methods: Data
from 1,315 overweight or obese individuals, aged 40-65 years was used. Asthma
was self-reported, while periodontitis, bleeding on probing (BOP), and plaque
index were determined by clinical examinations. Results: Using logistic
regression adjusting for gender, smoking status, age, body mass index, family
history of asthma, and income level, revealed that the odds ratio (OR) of
asthma for a participant with severe periodontitis was 0.44 (95% confidence
interval: 0.27, 0.70) that of a participant with none/mild periodontitis . On
the other hand, proportion of BOP sites, and plaque index were not
statistically significant. For a participant with severe periodontitis, the OR
of taking asthma medication was 0.20 (95% confidence interval: 0.09, 0.43) that
of a participant with none/mild periodontitis. Moreover, proportion of BOP
sites was statistically associated to use of asthma medication while plaque
index still remained non-significant. Conclusion: Participants with severe
periodontitis were less likely to have asthma. Stronger evidence of an inverse
association was found when using asthma medication as outcome. Keywords:
asthma; periodontal disease; asthma medication; periodontitis; hygiene
hypothesis
| [
{
"created": "Sat, 7 May 2016 01:38:27 GMT",
"version": "v1"
}
] | 2016-06-02 | [
[
"Rivera",
"Roberto",
""
],
[
"Andriankaja",
"Oelisoa M.",
""
],
[
"Perez",
"Cynthia M.",
""
],
[
"Joshipura",
"Kaumudi",
""
]
] | Aim: To assess the relationship between oral health and asthma. Methods: Data from 1,315 overweight or obese individuals, aged 40-65 years was used. Asthma was self-reported, while periodontitis, bleeding on probing (BOP), and plaque index were determined by clinical examinations. Results: Using logistic regression adjusting for gender, smoking status, age, body mass index, family history of asthma, and income level, revealed that the odds ratio (OR) of asthma for a participant with severe periodontitis was 0.44 (95% confidence interval: 0.27, 0.70) that of a participant with none/mild periodontitis . On the other hand, proportion of BOP sites, and plaque index were not statistically significant. For a participant with severe periodontitis, the OR of taking asthma medication was 0.20 (95% confidence interval: 0.09, 0.43) that of a participant with none/mild periodontitis. Moreover, proportion of BOP sites was statistically associated to use of asthma medication while plaque index still remained non-significant. Conclusion: Participants with severe periodontitis were less likely to have asthma. Stronger evidence of an inverse association was found when using asthma medication as outcome. Keywords: asthma; periodontal disease; asthma medication; periodontitis; hygiene hypothesis |
1210.3480 | Tomas Bohr | Renaud Bastien, Bruno Moulia, St\'ephane Douady and Tomas Bohr | Analytical Solution of the Proprio-Graviceptive equation for shoot
gravitropism of plants | 4 pages | null | null | null | q-bio.TO math-ph math.MP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We derive the analytical solutions to the second order generalised
gravi-proprioceptive equation given in our recent paper [Bastien et al. 2012].
These equations show how plants adjust to the surrounding gravitation field and
highlight the fact that the plant must be able to not only sense its local
posture with respect to the gravitational field, but also to sense its own
local curvature. In [Bastien et al. 2012] we obtained explicit analytical
solutions of these equations in terms of (sums of) Bessel functions, and in the
present paper we derive these solutions.
| [
{
"created": "Fri, 12 Oct 2012 11:44:05 GMT",
"version": "v1"
}
] | 2012-10-15 | [
[
"Bastien",
"Renaud",
""
],
[
"Moulia",
"Bruno",
""
],
[
"Douady",
"Stéphane",
""
],
[
"Bohr",
"Tomas",
""
]
] | We derive the analytical solutions to the second order generalised gravi-proprioceptive equation given in our recent paper [Bastien et al. 2012]. These equations show how plants adjust to the surrounding gravitation field and highlight the fact that the plant must be able to not only sense its local posture with respect to the gravitational field, but also to sense its own local curvature. In [Bastien et al. 2012] we obtained explicit analytical solutions of these equations in terms of (sums of) Bessel functions, and in the present paper we derive these solutions. |
1112.4768 | Hong Qian | Jia-Zeng Wang and Min Qian and Hong Qian | Circular Stochastic Fluctuations in SIS Epidemics with Heterogeneous
Contacts Among Sub-populations | 29 pages, 5 figures | Theoretical Population Biology, 81, 223-231 (2012) | 10.1016/j.tpb.2012.01.002 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The conceptual difference between equilibrium and non-equilibrium steady
state (NESS) is well established in physics and chemistry. This distinction,
however, is not widely appreciated in dynamical descriptions of biological
populations in terms of differential equations in which fixed point, steady
state, and equilibrium are all synonymous. We study NESS in a stochastic SIS
(susceptible-infectious-susceptible) system with heterogeneous individuals in
their contact behavior represented in terms of subgroups. In the infinite
population limit, the stochastic dynamics yields a system of deterministic
evolution equations for population densities; and for very large but finite
system a diffusion process is obtained. We report the emergence of a circular
dynamics in the diffusion process, with an intrinsic frequency, near the
endemic steady state. The endemic steady state is represented by a stable node
in the deterministic dynamics; As a NESS phenomenon, the circular motion is
caused by the intrinsic heterogeneity within the subgroups, leading to a broken
symmetry and time irreversibility.
| [
{
"created": "Tue, 20 Dec 2011 16:55:51 GMT",
"version": "v1"
}
] | 2012-02-23 | [
[
"Wang",
"Jia-Zeng",
""
],
[
"Qian",
"Min",
""
],
[
"Qian",
"Hong",
""
]
] | The conceptual difference between equilibrium and non-equilibrium steady state (NESS) is well established in physics and chemistry. This distinction, however, is not widely appreciated in dynamical descriptions of biological populations in terms of differential equations in which fixed point, steady state, and equilibrium are all synonymous. We study NESS in a stochastic SIS (susceptible-infectious-susceptible) system with heterogeneous individuals in their contact behavior represented in terms of subgroups. In the infinite population limit, the stochastic dynamics yields a system of deterministic evolution equations for population densities; and for very large but finite system a diffusion process is obtained. We report the emergence of a circular dynamics in the diffusion process, with an intrinsic frequency, near the endemic steady state. The endemic steady state is represented by a stable node in the deterministic dynamics; As a NESS phenomenon, the circular motion is caused by the intrinsic heterogeneity within the subgroups, leading to a broken symmetry and time irreversibility. |
2404.16358 | Chananchida Sang-Aram | Chananchida Sang-aram, Robin Browaeys, Ruth Seurinck, Yvan Saeys | Unraveling cell-cell communication with NicheNet by inferring active
ligands from transcriptomics data | 28 pages, 3 figures | null | null | null | q-bio.CB | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Ligand-receptor interactions constitute a fundamental mechanism of cell-cell
communication and signaling. NicheNet is a well-established computational tool
that infers ligand-receptor interactions that potentially regulate gene
expression changes in receiver cell populations. Whereas the original
publication delves into the algorithm and validation, this paper describes a
best practices workflow cultivated over four years of experience and user
feedback. Starting from the input single-cell expression matrix, we describe a
"sender-agnostic" approach which considers ligands from the entire
microenvironment, and a "sender-focused" approach which only considers ligands
from cell populations of interest. As output, users will obtain a list of
prioritized ligands and their potential target genes, along with multiple
visualizations. In NicheNet v2, we have updated the data sources and
implemented a downstream procedure for prioritizing cell-type-specific
ligand-receptor pairs. Although a standard NicheNet analysis takes less than 10
minutes to run, users often invest additional time in making decisions about
the approach and parameters that best suit their biological question. This
paper serves to aid in this decision-making process by describing the most
appropriate workflow for common experimental designs like case-control and cell
differentiation studies. Finally, in addition to the step-by-step description
of the code, we also provide wrapper functions that enable the analysis to be
run in one line of code, thus tailoring the workflow to users at all levels of
computational proficiency.
| [
{
"created": "Thu, 25 Apr 2024 06:36:56 GMT",
"version": "v1"
}
] | 2024-04-26 | [
[
"Sang-aram",
"Chananchida",
""
],
[
"Browaeys",
"Robin",
""
],
[
"Seurinck",
"Ruth",
""
],
[
"Saeys",
"Yvan",
""
]
] | Ligand-receptor interactions constitute a fundamental mechanism of cell-cell communication and signaling. NicheNet is a well-established computational tool that infers ligand-receptor interactions that potentially regulate gene expression changes in receiver cell populations. Whereas the original publication delves into the algorithm and validation, this paper describes a best practices workflow cultivated over four years of experience and user feedback. Starting from the input single-cell expression matrix, we describe a "sender-agnostic" approach which considers ligands from the entire microenvironment, and a "sender-focused" approach which only considers ligands from cell populations of interest. As output, users will obtain a list of prioritized ligands and their potential target genes, along with multiple visualizations. In NicheNet v2, we have updated the data sources and implemented a downstream procedure for prioritizing cell-type-specific ligand-receptor pairs. Although a standard NicheNet analysis takes less than 10 minutes to run, users often invest additional time in making decisions about the approach and parameters that best suit their biological question. This paper serves to aid in this decision-making process by describing the most appropriate workflow for common experimental designs like case-control and cell differentiation studies. Finally, in addition to the step-by-step description of the code, we also provide wrapper functions that enable the analysis to be run in one line of code, thus tailoring the workflow to users at all levels of computational proficiency. |
2008.03377 | Andrey L. Shilnikov | Aaron Kelley, Andrey L. Shilnikov | 2$\theta$-burster for rhythm-generating circuits | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose and demonstrate the use of a minimal 2$\theta$ model for
endogenous bursters coupled in 3-cell neural circuits. This 2$\theta$ model
offers the benefit of simplicity of designing larger neural networks along with
an acute reduction on the computation cost.
| [
{
"created": "Wed, 29 Jul 2020 21:08:57 GMT",
"version": "v1"
}
] | 2020-08-11 | [
[
"Kelley",
"Aaron",
""
],
[
"Shilnikov",
"Andrey L.",
""
]
] | We propose and demonstrate the use of a minimal 2$\theta$ model for endogenous bursters coupled in 3-cell neural circuits. This 2$\theta$ model offers the benefit of simplicity of designing larger neural networks along with an acute reduction on the computation cost. |
1802.04892 | Sidney Redner | Laurent H\'ebert-Dufresne, Adam F. A. Pellegrini, Uttam Bhat, Sidney
Redner, Stephen W. Pacala, and Andrew M. Berdahl | Edge fires drive the shape and stability of tropical forests | 21 pages, 4 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In tropical regions, fires propagate readily in grasslands but typically
consume only edges of forest patches. Thus forest patches grow due to tree
propagation and shrink by fires in surrounding grasslands. The interplay
between these competing edge effects is unknown, but critical in determining
the shape and stability of individual forest patches, as well the
landscape-level spatial distribution and stability of forests. We analyze
high-resolution remote-sensing data from protected areas of the Brazilian
Cerrado and find that forest shapes obey a robust perimeter-area scaling
relation across climatic zones. We explain this scaling by introducing a
heterogeneous fire propagation model of tropical forest-grassland ecotones.
Deviations from this perimeter-area relation determine the stability of
individual forest patches. At a larger scale, our model predicts that the
relative rates of tree growth due to propagative expansion and long-distance
seed dispersal determine whether collapse of regional-scale tree cover is
continuous or discontinuous as fire frequency changes.
| [
{
"created": "Tue, 13 Feb 2018 23:11:05 GMT",
"version": "v1"
}
] | 2018-02-15 | [
[
"Hébert-Dufresne",
"Laurent",
""
],
[
"Pellegrini",
"Adam F. A.",
""
],
[
"Bhat",
"Uttam",
""
],
[
"Redner",
"Sidney",
""
],
[
"Pacala",
"Stephen W.",
""
],
[
"Berdahl",
"Andrew M.",
""
]
] | In tropical regions, fires propagate readily in grasslands but typically consume only edges of forest patches. Thus forest patches grow due to tree propagation and shrink by fires in surrounding grasslands. The interplay between these competing edge effects is unknown, but critical in determining the shape and stability of individual forest patches, as well the landscape-level spatial distribution and stability of forests. We analyze high-resolution remote-sensing data from protected areas of the Brazilian Cerrado and find that forest shapes obey a robust perimeter-area scaling relation across climatic zones. We explain this scaling by introducing a heterogeneous fire propagation model of tropical forest-grassland ecotones. Deviations from this perimeter-area relation determine the stability of individual forest patches. At a larger scale, our model predicts that the relative rates of tree growth due to propagative expansion and long-distance seed dispersal determine whether collapse of regional-scale tree cover is continuous or discontinuous as fire frequency changes. |
1006.3410 | Oscar Sotolongo | Oscar Sotolongo-Grau, Daniel Rodr\'iguez-P\'erez, Jos\'e Carlos
Antoranz, Oscar Sotolongo-Costa | Non-extensive radiobiology | 8 pages, 1 figure. Sent to MaxEnt 2010. To be submitted for
publication | null | 10.1063/1.3573620 | null | q-bio.QM physics.bio-ph physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The expression of survival factors for radiation damaged cells is based on
probabilistic assumptions and experimentally fitted for each tumor, radiation
and conditions. Here we show how the simplest of these radiobiological models
can be derived from the maximum entropy principle of the classical
Boltzmann-Gibbs expression. We extend this derivation using the Tsallis entropy
and a cutoff hypothesis, motivated by clinical observations. A generalization
of the exponential, the logarithm and the product to a non-extensive framework,
provides a simple formula for the survival fraction corresponding to the
application of several radiation doses on a living tissue. The obtained
expression shows a remarkable agreement with the experimental data found in the
literature, also providing a new interpretation of some of the parameters
introduced anew. It is also shown how the presented formalism may has direct
application in radiotherapy treatment optimization through the definition of
the potential effect difference, simply calculated between the tumour and the
surrounding tissue.
| [
{
"created": "Thu, 17 Jun 2010 09:08:36 GMT",
"version": "v1"
}
] | 2015-05-19 | [
[
"Sotolongo-Grau",
"Oscar",
""
],
[
"Rodríguez-Pérez",
"Daniel",
""
],
[
"Antoranz",
"José Carlos",
""
],
[
"Sotolongo-Costa",
"Oscar",
""
]
] | The expression of survival factors for radiation damaged cells is based on probabilistic assumptions and experimentally fitted for each tumor, radiation and conditions. Here we show how the simplest of these radiobiological models can be derived from the maximum entropy principle of the classical Boltzmann-Gibbs expression. We extend this derivation using the Tsallis entropy and a cutoff hypothesis, motivated by clinical observations. A generalization of the exponential, the logarithm and the product to a non-extensive framework, provides a simple formula for the survival fraction corresponding to the application of several radiation doses on a living tissue. The obtained expression shows a remarkable agreement with the experimental data found in the literature, also providing a new interpretation of some of the parameters introduced anew. It is also shown how the presented formalism may has direct application in radiotherapy treatment optimization through the definition of the potential effect difference, simply calculated between the tumour and the surrounding tissue. |
2303.06945 | Xiaoxi Hu | Jiaxing Guo, Xuening Zhu, Zixin Hu, Xiaoxi Hu | CoGANPPIS: A Coevolution-enhanced Global Attention Neural Network for
Protein-Protein Interaction Site Prediction | null | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Protein-protein interactions are of great importance in biochemical
processes. Accurate prediction of protein-protein interaction sites (PPIs) is
crucial for our understanding of biological mechanism. Although numerous
approaches have been developed recently and achieved gratifying results, there
are still two limitations: (1) Most existing models have excavated a number of
useful input features, but failed to take coevolutionary features into account,
which could provide clues for inter-residue relationships; (2) The
attention-based models only allocate attention weights for neighboring
residues, instead of doing it globally, which may limit the model's prediction
performance since some residues being far away from the target residues might
also matter.
We propose a coevolution-enhanced global attention neural network, a
sequence-based deep learning model for PPIs prediction, called CoGANPPIS.
Specifically, CoGANPPIS utilizes three layers in parallel for feature
extraction: (1) Local-level representation aggregation layer, which aggregates
the neighboring residues' features as the local feature representation; (2)
Global-level representation learning layer, which employs a novel
coevolution-enhanced global attention mechanism to allocate attention weights
to all residues on the same protein sequences; (3) Coevolutionary information
learning layer, which applies CNN & pooling to coevolutionary information to
obtain the coevolutionary profile representation. Then, the three outputs are
concatenated and passed into several fully connected layers for the final
prediction. Extensive experiments on two benchmark datasets have been
conducted, demonstrating that our proposed model achieves the state-of-the-art
performance.
| [
{
"created": "Mon, 13 Mar 2023 09:27:34 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Mar 2023 15:34:44 GMT",
"version": "v2"
},
{
"created": "Mon, 3 Apr 2023 07:17:02 GMT",
"version": "v3"
},
{
"created": "Sun, 24 Sep 2023 04:09:01 GMT",
"version": "v4"
}
] | 2023-09-26 | [
[
"Guo",
"Jiaxing",
""
],
[
"Zhu",
"Xuening",
""
],
[
"Hu",
"Zixin",
""
],
[
"Hu",
"Xiaoxi",
""
]
] | Protein-protein interactions are of great importance in biochemical processes. Accurate prediction of protein-protein interaction sites (PPIs) is crucial for our understanding of biological mechanism. Although numerous approaches have been developed recently and achieved gratifying results, there are still two limitations: (1) Most existing models have excavated a number of useful input features, but failed to take coevolutionary features into account, which could provide clues for inter-residue relationships; (2) The attention-based models only allocate attention weights for neighboring residues, instead of doing it globally, which may limit the model's prediction performance since some residues being far away from the target residues might also matter. We propose a coevolution-enhanced global attention neural network, a sequence-based deep learning model for PPIs prediction, called CoGANPPIS. Specifically, CoGANPPIS utilizes three layers in parallel for feature extraction: (1) Local-level representation aggregation layer, which aggregates the neighboring residues' features as the local feature representation; (2) Global-level representation learning layer, which employs a novel coevolution-enhanced global attention mechanism to allocate attention weights to all residues on the same protein sequences; (3) Coevolutionary information learning layer, which applies CNN & pooling to coevolutionary information to obtain the coevolutionary profile representation. Then, the three outputs are concatenated and passed into several fully connected layers for the final prediction. Extensive experiments on two benchmark datasets have been conducted, demonstrating that our proposed model achieves the state-of-the-art performance. |
2010.08957 | Farzad Fatehi | Farzad Fatehi, Richard J Bingham, Eric C Dykeman, Peter G Stockley,
Reidun Twarock | Comparing antiviral strategies against COVID-19 via multiscale
within-host modelling | Published version by Royal Society Open Science | R. Soc. Open Sci., 8, 210082 (2021) | 10.1098/rsos.210082 | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Within-host models of COVID-19 infection dynamics enable the merits of
different forms of antiviral therapy to be assessed in individual patients. A
stochastic agent-based model of COVID-19 intracellular dynamics is introduced
here, that incorporates essential steps of the viral life cycle targeted by
treatment options. Integration of model predictions with an intercellular ODE
model of within-host infection dynamics, fitted to patient data, generates a
generic profile of disease progression in patients that have recovered in the
absence of treatment. This is contrasted with the profiles obtained after
variation of model parameters pertinent to the immune response, such as
effector cell and antibody proliferation rates, mimicking disease progression
in immunocompromised patients. These profiles are then compared with disease
progression in the presence of antiviral and convalescent plasma therapy
against COVID-19 infections. The model reveals that using both therapies in
combination can be very effective in reducing the length of infection, but
these synergistic effects decline with a delayed treatment start. Conversely,
early treatment with either therapy alone can actually increase the duration of
infection, with infectious virions still present after the decline of other
markers of infection. This suggests that usage of these treatments should
remain carefully controlled in a clinical environment.
| [
{
"created": "Sun, 18 Oct 2020 10:42:50 GMT",
"version": "v1"
},
{
"created": "Wed, 4 Aug 2021 12:51:37 GMT",
"version": "v2"
},
{
"created": "Tue, 21 Dec 2021 18:15:46 GMT",
"version": "v3"
}
] | 2021-12-22 | [
[
"Fatehi",
"Farzad",
""
],
[
"Bingham",
"Richard J",
""
],
[
"Dykeman",
"Eric C",
""
],
[
"Stockley",
"Peter G",
""
],
[
"Twarock",
"Reidun",
""
]
] | Within-host models of COVID-19 infection dynamics enable the merits of different forms of antiviral therapy to be assessed in individual patients. A stochastic agent-based model of COVID-19 intracellular dynamics is introduced here, that incorporates essential steps of the viral life cycle targeted by treatment options. Integration of model predictions with an intercellular ODE model of within-host infection dynamics, fitted to patient data, generates a generic profile of disease progression in patients that have recovered in the absence of treatment. This is contrasted with the profiles obtained after variation of model parameters pertinent to the immune response, such as effector cell and antibody proliferation rates, mimicking disease progression in immunocompromised patients. These profiles are then compared with disease progression in the presence of antiviral and convalescent plasma therapy against COVID-19 infections. The model reveals that using both therapies in combination can be very effective in reducing the length of infection, but these synergistic effects decline with a delayed treatment start. Conversely, early treatment with either therapy alone can actually increase the duration of infection, with infectious virions still present after the decline of other markers of infection. This suggests that usage of these treatments should remain carefully controlled in a clinical environment. |
1305.7303 | Koh Hashimoto Dr. | Koh Hashimoto | Multigame Effect in Finite Populations Induces Strategy Linkage Between
Two Games | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evolutionary game dynamics with two 2-strategy games in a finite population
has been investigated in this study. Traditionally, frequency-dependent
evolutionary dynamics are modeled by deterministic replicator dynamics under
the assumption that the population size is infinite. However, in reality,
population sizes are finite. Recently, stochastic processes in finite
populations have been introduced into evolutionary games in order to study
finite size effects in evolutionary game dynamics. However, most of these
studies focus on populations playing only single games. In this study, we
investigate a finite population with two games and show that a finite
population playing two games tends to evolve toward a specific direction to
form particular linkages between the strategies of the two games.
| [
{
"created": "Fri, 31 May 2013 05:17:18 GMT",
"version": "v1"
}
] | 2013-06-03 | [
[
"Hashimoto",
"Koh",
""
]
] | Evolutionary game dynamics with two 2-strategy games in a finite population has been investigated in this study. Traditionally, frequency-dependent evolutionary dynamics are modeled by deterministic replicator dynamics under the assumption that the population size is infinite. However, in reality, population sizes are finite. Recently, stochastic processes in finite populations have been introduced into evolutionary games in order to study finite size effects in evolutionary game dynamics. However, most of these studies focus on populations playing only single games. In this study, we investigate a finite population with two games and show that a finite population playing two games tends to evolve toward a specific direction to form particular linkages between the strategies of the two games. |
2308.08829 | Sky Button | Sky Button and Ama\"el Borz\'ee | A new multi-metric approach for quantifying global biodiscovery and
conservation priorities reveals overlooked hotspots for amphibians | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Undocumented species represent one of the largest hurdles for conservation
efforts due to the uncertainty they introduce into conservation planning. Until
the distribution of earth's biodiversity is better understood, substantial
conjecture will continue to be required for protecting species from
anthropogenic extinction. Therefore, we developed a novel approach for
identifying regions with promising biodiscovery prospects, linked to
integrative conservation priorities, which we illustrate using amphibians. Our
approach builds on previous estimates of biodiscovery priorities by
simultaneously (1) considering linkages between spatio-environmental variables
and biodiversity, (2) accounting for the negative relationship between past
sampling intensity and future biodiscovery potential, (3) incorporating a
priori knowledge about global species distribution patterns, (4) addressing
spatial autocorrelation in community composition, and (5) weighting theoretical
undocumented species by their predicted levels of conservation need. Using
boosted regression trees and 50km^2 map pixels spread across the global range
of amphibians, we identified several regions likely to contain many
undocumented amphibian species and conservation needs, including the Southeast
Asian Archipelago, humid portions of sub-Saharan Africa, and undersampled
portions of the Amazon, Andes Mountains, and Central America. We also ranked
top-scoring ecoregions by their mean and maximum biodiscovery potential and
found that the top-20 ranked ecoregions were most concentrated in the Southeast
Asian Archipelago and tropical Africa for undocumented species richness, and in
tropical Africa and tropical South America for integrative undocumented
amphibian conservation needs. However, high-scoring pixels tended to be widely
distributed across different ecoregions for both biodiscovery scoring
approaches.
| [
{
"created": "Thu, 17 Aug 2023 07:42:27 GMT",
"version": "v1"
}
] | 2023-08-21 | [
[
"Button",
"Sky",
""
],
[
"Borzée",
"Amaël",
""
]
] | Undocumented species represent one of the largest hurdles for conservation efforts due to the uncertainty they introduce into conservation planning. Until the distribution of earth's biodiversity is better understood, substantial conjecture will continue to be required for protecting species from anthropogenic extinction. Therefore, we developed a novel approach for identifying regions with promising biodiscovery prospects, linked to integrative conservation priorities, which we illustrate using amphibians. Our approach builds on previous estimates of biodiscovery priorities by simultaneously (1) considering linkages between spatio-environmental variables and biodiversity, (2) accounting for the negative relationship between past sampling intensity and future biodiscovery potential, (3) incorporating a priori knowledge about global species distribution patterns, (4) addressing spatial autocorrelation in community composition, and (5) weighting theoretical undocumented species by their predicted levels of conservation need. Using boosted regression trees and 50km^2 map pixels spread across the global range of amphibians, we identified several regions likely to contain many undocumented amphibian species and conservation needs, including the Southeast Asian Archipelago, humid portions of sub-Saharan Africa, and undersampled portions of the Amazon, Andes Mountains, and Central America. We also ranked top-scoring ecoregions by their mean and maximum biodiscovery potential and found that the top-20 ranked ecoregions were most concentrated in the Southeast Asian Archipelago and tropical Africa for undocumented species richness, and in tropical Africa and tropical South America for integrative undocumented amphibian conservation needs. However, high-scoring pixels tended to be widely distributed across different ecoregions for both biodiscovery scoring approaches. |
2302.02263 | Lorena Bulhosa | Lorena C. Bulhosa, Juliane F. Oliveira | Vaccination in a two-strain model with cross-immunity and
antibody-dependent enhancement | Corrected typos. Revised figures, results unchanged | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Dengue and Zika incidence data and the latest research have raised questions
about how dengue vaccine strategies might be impacted by the emergence of Zika
virus. Existing antibodies to one virus might temporarily protect or promote
infection by the other through antibody-dependent enhancement (ADE). With this
condition, understanding the dynamics of propagation of these two viruses is of
great importance when implementing vaccines. In this work, we analyze the
effect of vaccination against one strain, in a two-strain model that accounts
for cross-immunity and ADE. Using basic and invasion reproductive numbers, we
examined the dynamics of the model and provide conditions to ensure the
stability of the disease-free equilibrium. We provide conditions on
cross-immunity, ADE and vaccination rate under which the vaccination could
ensure the global stability of the disease-free equilibrium. The results
indicate scenarios in which vaccination against one strain may improve or
worsen the control of the other, as well as contribute to the eradication or
persistence of one or both viruses in the population.
| [
{
"created": "Sat, 4 Feb 2023 23:57:16 GMT",
"version": "v1"
},
{
"created": "Sun, 12 Mar 2023 22:17:30 GMT",
"version": "v2"
}
] | 2023-03-14 | [
[
"Bulhosa",
"Lorena C.",
""
],
[
"Oliveira",
"Juliane F.",
""
]
] | Dengue and Zika incidence data and the latest research have raised questions about how dengue vaccine strategies might be impacted by the emergence of Zika virus. Existing antibodies to one virus might temporarily protect or promote infection by the other through antibody-dependent enhancement (ADE). With this condition, understanding the dynamics of propagation of these two viruses is of great importance when implementing vaccines. In this work, we analyze the effect of vaccination against one strain, in a two-strain model that accounts for cross-immunity and ADE. Using basic and invasion reproductive numbers, we examined the dynamics of the model and provide conditions to ensure the stability of the disease-free equilibrium. We provide conditions on cross-immunity, ADE and vaccination rate under which the vaccination could ensure the global stability of the disease-free equilibrium. The results indicate scenarios in which vaccination against one strain may improve or worsen the control of the other, as well as contribute to the eradication or persistence of one or both viruses in the population. |
2402.06005 | Alexander Strang | Christopher Cebra, Alexander Strang | The Almost Sure Evolution of Hierarchy Among Similar Competitors | 14 pages, 4 figures (main text), 10 page supplement, 3 figures | null | null | null | q-bio.PE math.DS | http://creativecommons.org/licenses/by/4.0/ | While generic competitive systems exhibit mixtures of hierarchy and cycles,
real-world systems are predominantly hierarchical. We demonstrate and extend a
mechanism for hierarchy; systems with similar agents approach perfect hierarchy
in expectation. A variety of evolutionary mechanisms plausibly select for
nearly homogeneous populations, however, extant work does not explicitly link
selection dynamics to hierarchy formation via population concentration.
Moreover, previous work lacked numerical demonstration. This paper contributes
in four ways. First, populations that converge to perfect hierarchy in
expectation converge to hierarchy in probability. Second, we analyze hierarchy
formation in populations subject to the continuous replicator dynamic with
diffusive exploration, linking population dynamics to emergent structure.
Third, we show how to predict the degree of cyclicity sustained by concentrated
populations at internal equilibria. This theory can differentiate learning
rules and random payout models. Finally, we provide direct numerical evidence
by simulating finite populations of agents subject to a modified Moran process
with Gaussian exploration. As examples, we consider three bimatrix games and an
ensemble of games with random payouts. Through this analysis, we explicitly
link the temporal dynamics of a population undergoing selection to the
development of hierarchy.
| [
{
"created": "Thu, 8 Feb 2024 19:02:38 GMT",
"version": "v1"
}
] | 2024-02-12 | [
[
"Cebra",
"Christopher",
""
],
[
"Strang",
"Alexander",
""
]
] | While generic competitive systems exhibit mixtures of hierarchy and cycles, real-world systems are predominantly hierarchical. We demonstrate and extend a mechanism for hierarchy; systems with similar agents approach perfect hierarchy in expectation. A variety of evolutionary mechanisms plausibly select for nearly homogeneous populations, however, extant work does not explicitly link selection dynamics to hierarchy formation via population concentration. Moreover, previous work lacked numerical demonstration. This paper contributes in four ways. First, populations that converge to perfect hierarchy in expectation converge to hierarchy in probability. Second, we analyze hierarchy formation in populations subject to the continuous replicator dynamic with diffusive exploration, linking population dynamics to emergent structure. Third, we show how to predict the degree of cyclicity sustained by concentrated populations at internal equilibria. This theory can differentiate learning rules and random payout models. Finally, we provide direct numerical evidence by simulating finite populations of agents subject to a modified Moran process with Gaussian exploration. As examples, we consider three bimatrix games and an ensemble of games with random payouts. Through this analysis, we explicitly link the temporal dynamics of a population undergoing selection to the development of hierarchy. |
1206.6782 | Richard A Neher | Richard A. Neher and Boris I. Shraiman | Fluctuations of fitness distributions and the rate of Muller's ratchet | Genetics 2012 | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The accumulation of deleterious mutations is driven by rare fluctuations
which lead to the loss of all mutation free individuals, a process known as
Muller's ratchet. Even though Muller's ratchet is a paradigmatic process in
population genetics, a quantitative understanding of its rate is still lacking.
The difficulty lies in the nontrivial nature of fluctuations in the fitness
distribution which control the rate of extinction of the fittest genotype. We
address this problem using the simple but classic model of mutation selection
balance with deleterious mutations all having the same effect on fitness. We
show analytically how fluctuations among the fittest individuals propagate to
individuals of lower fitness and have a dramatically amplified effects on the
bulk of the population at a later time. If a reduction in the size of the
fittest class reduces the mean fitness only after a delay, selection opposing
this reduction is also delayed. This delayed restoring force speeds up Muller's
ratchet. We show how the delayed response can be accounted for using a path
integral formulation of the stochastic dynamics and provide an expression for
the rate of the ratchet that is accurate across a broad range of parameters.
| [
{
"created": "Thu, 28 Jun 2012 18:03:21 GMT",
"version": "v1"
}
] | 2012-06-29 | [
[
"Neher",
"Richard A.",
""
],
[
"Shraiman",
"Boris I.",
""
]
] | The accumulation of deleterious mutations is driven by rare fluctuations which lead to the loss of all mutation free individuals, a process known as Muller's ratchet. Even though Muller's ratchet is a paradigmatic process in population genetics, a quantitative understanding of its rate is still lacking. The difficulty lies in the nontrivial nature of fluctuations in the fitness distribution which control the rate of extinction of the fittest genotype. We address this problem using the simple but classic model of mutation selection balance with deleterious mutations all having the same effect on fitness. We show analytically how fluctuations among the fittest individuals propagate to individuals of lower fitness and have a dramatically amplified effects on the bulk of the population at a later time. If a reduction in the size of the fittest class reduces the mean fitness only after a delay, selection opposing this reduction is also delayed. This delayed restoring force speeds up Muller's ratchet. We show how the delayed response can be accounted for using a path integral formulation of the stochastic dynamics and provide an expression for the rate of the ratchet that is accurate across a broad range of parameters. |
q-bio/0510016 | Can Ozan Tan Mr. | Uygar Ozesmi, Can Ozan Tan, Stacy L. Ozesmi and Raleigh J. Robertson | Generalizability of Artificial Neural Network Models in Ecological
Applications: Predicting Nest Occurrence and Breeding Success of the
Red-winged Blackbird Agelaius phoeniceus | 42 pages, 3 figures. Presented in ISEI3 conference (2002). Ecological
Modeling in press | Ecological Modelling, 195:94-104. 2006 | 10.1016/j.ecolmodel.2005.11.013 | null | q-bio.PE q-bio.QM | null | Separate artificial neural network (ANN) models were developed from data in
two geographical regions and years apart for a marsh-nesting bird, the
red-winged blackbird Agelaius phoeniceus. Each model was independently tested
on the spatially and temporally distinct data from the other region to
determine how generalizable it was. The first model was developed to predict
occurrence of nests in two wetlands on Lake Erie, Ohio in 1995 and 1996. The
second model was developed to predict breeding success in two marshes in
Connecticut, USA in 1969 and 1970. Independent variables were vegetation
durability, stem density, stem/nest height, distance to open water, distance to
edge, and water depth. With input variable relevances, sensitivity analyses and
neural interpretation diagrams we were able to understand how the different
models predicted nest occurrence and breeding success and compare their
differences and similarities. Both models also predicted increasing nest
occurrence/breeding success with increasing water depth under the nest and
increasing distance to edge. However, relationships for prediction differed in
the models. Generalizability of the models was poor except when the marshes had
similar values of important variables in the model. ANN models performed better
than generalized linear models (GLM) on marshes with similar structures.
Generalizability of the models did not differ in nest occurrence and breeding
success data. Extensive testing also showed that the GLMs were not necessarily
more generalizable than ANNs, suggesting that ANN models make good definitions
of a study system but are too specific to generalize well to other ecologically
complex systems unless input variable distributions are very similar.
| [
{
"created": "Thu, 6 Oct 2005 21:38:50 GMT",
"version": "v1"
}
] | 2011-07-29 | [
[
"Ozesmi",
"Uygar",
""
],
[
"Tan",
"Can Ozan",
""
],
[
"Ozesmi",
"Stacy L.",
""
],
[
"Robertson",
"Raleigh J.",
""
]
] | Separate artificial neural network (ANN) models were developed from data in two geographical regions and years apart for a marsh-nesting bird, the red-winged blackbird Agelaius phoeniceus. Each model was independently tested on the spatially and temporally distinct data from the other region to determine how generalizable it was. The first model was developed to predict occurrence of nests in two wetlands on Lake Erie, Ohio in 1995 and 1996. The second model was developed to predict breeding success in two marshes in Connecticut, USA in 1969 and 1970. Independent variables were vegetation durability, stem density, stem/nest height, distance to open water, distance to edge, and water depth. With input variable relevances, sensitivity analyses and neural interpretation diagrams we were able to understand how the different models predicted nest occurrence and breeding success and compare their differences and similarities. Both models also predicted increasing nest occurrence/breeding success with increasing water depth under the nest and increasing distance to edge. However, relationships for prediction differed in the models. Generalizability of the models was poor except when the marshes had similar values of important variables in the model. ANN models performed better than generalized linear models (GLM) on marshes with similar structures. Generalizability of the models did not differ in nest occurrence and breeding success data. Extensive testing also showed that the GLMs were not necessarily more generalizable than ANNs, suggesting that ANN models make good definitions of a study system but are too specific to generalize well to other ecologically complex systems unless input variable distributions are very similar. |
1310.2129 | Marc Robinson-Rechavi | Marta Rosikiewicz, Marc Robinson-Rechavi | IQRray, a new method for Affymetrix microarray quality control, and the
homologous organ conservation score, a new benchmark method for quality
control metrics | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Microarray results accumulated in public repositories are widely
re-used in meta-analytical studies and secondary databases. The quality of the
data obtained with this technology varies from experiment to experiment and
efficient method for quality assessment is neces-sary to ensure their
reliability. Results: The lack of a good benchmark has hampered evaluation of
existing methods for quality control. In this study we propose a new
inde-pendent quality metric that is based on evolutionary conservation of
expression profiles. We show, using 11 large organ-specific datasets, that
IQRray, a new quality metrics developed by us, exhibits the highest correlation
with this reference metric, among 14 metrics tested. IQRray outperforms other
methods in identification of poor quality arrays in dataset composed of arrays
from many independent experiments. In con-trast, the performance of methods
designed for detecting outliers in a single experiment like NUSE and RLE was
low because of the inability of these method to detect datasets containing only
low quality arrays, and the fact that the scores cannot be directly compared
between ex-periments. Availability: The R implementation of IQRray is available
at: ftp://lausanne.isb-sib.ch/pub/databases/Bgee/general/IQRray.R
| [
{
"created": "Tue, 8 Oct 2013 13:30:37 GMT",
"version": "v1"
}
] | 2013-10-09 | [
[
"Rosikiewicz",
"Marta",
""
],
[
"Robinson-Rechavi",
"Marc",
""
]
] | Motivation: Microarray results accumulated in public repositories are widely re-used in meta-analytical studies and secondary databases. The quality of the data obtained with this technology varies from experiment to experiment and efficient method for quality assessment is neces-sary to ensure their reliability. Results: The lack of a good benchmark has hampered evaluation of existing methods for quality control. In this study we propose a new inde-pendent quality metric that is based on evolutionary conservation of expression profiles. We show, using 11 large organ-specific datasets, that IQRray, a new quality metrics developed by us, exhibits the highest correlation with this reference metric, among 14 metrics tested. IQRray outperforms other methods in identification of poor quality arrays in dataset composed of arrays from many independent experiments. In con-trast, the performance of methods designed for detecting outliers in a single experiment like NUSE and RLE was low because of the inability of these method to detect datasets containing only low quality arrays, and the fact that the scores cannot be directly compared between ex-periments. Availability: The R implementation of IQRray is available at: ftp://lausanne.isb-sib.ch/pub/databases/Bgee/general/IQRray.R |
2206.08666 | Konstantin Willeke | Konstantin F. Willeke (1 and 2 and 3), Paul G. Fahey (4 and 5),
Mohammad Bashiri (1 and 2 and 3), Laura Pede (3), Max F. Burg (1 and 2 and 3
and 6), Christoph Blessing (3), Santiago A. Cadena (1 and 3 and 6), Zhiwei
Ding (4 and 5), Konstantin-Klemens Lurz (1 and 2 and 3), Kayla Ponder (4 and
5), Taliah Muhammad (4 and 5), Saumil S. Patel (4 and 5), Alexander S. Ecker
(3 and 7), Andreas S. Tolias (4 and 5 and 8), Fabian H. Sinz (2 and 3 and 4
and 5) ((1) International Max Planck Research School for Intelligent Systems,
University of Tuebingen, Germany, (2) Institute for Bioinformatics and
Medical Informatics, University of Tuebingen, Germany (3) Institute of
Computer Science and Campus Institute Data Science, University of Goettingen,
Germany, (4) Department of Neuroscience, Baylor College of Medicine, Houston,
USA, (5) Center for Neuroscience and Artificial Intelligence, Baylor College
of Medicine, Houston, USA, (6) Institute for Theoretical Physics, University
of Tuebingen, Germany, (7) Max Planck Institute for Dynamics and
Self-Organization, Goettingen, Germany, (8) Electrical and Computer
Engineering, Rice University, Houston, USA) | The Sensorium competition on predicting large-scale mouse primary visual
cortex activity | NeurIPS 2022 Competition Track | null | null | null | q-bio.NC cs.AI cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The neural underpinning of the biological visual system is challenging to
study experimentally, in particular as the neuronal activity becomes
increasingly nonlinear with respect to visual input. Artificial neural networks
(ANNs) can serve a variety of goals for improving our understanding of this
complex system, not only serving as predictive digital twins of sensory cortex
for novel hypothesis generation in silico, but also incorporating bio-inspired
architectural motifs to progressively bridge the gap between biological and
machine vision. The mouse has recently emerged as a popular model system to
study visual information processing, but no standardized large-scale benchmark
to identify state-of-the-art models of the mouse visual system has been
established. To fill this gap, we propose the Sensorium benchmark competition.
We collected a large-scale dataset from mouse primary visual cortex containing
the responses of more than 28,000 neurons across seven mice stimulated with
thousands of natural images, together with simultaneous behavioral measurements
that include running speed, pupil dilation, and eye movements. The benchmark
challenge will rank models based on predictive performance for neuronal
responses on a held-out test set, and includes two tracks for model input
limited to either stimulus only (Sensorium) or stimulus plus behavior
(Sensorium+). We provide a starting kit to lower the barrier for entry,
including tutorials, pre-trained baseline models, and APIs with one line
commands for data loading and submission. We would like to see this as a
starting point for regular challenges and data releases, and as a standard tool
for measuring progress in large-scale neural system identification models of
the mouse visual system and beyond.
| [
{
"created": "Fri, 17 Jun 2022 10:09:57 GMT",
"version": "v1"
}
] | 2022-06-20 | [
[
"Willeke",
"Konstantin F.",
"",
"1 and 2 and 3"
],
[
"Fahey",
"Paul G.",
"",
"4 and 5"
],
[
"Bashiri",
"Mohammad",
"",
"1 and 2 and 3"
],
[
"Pede",
"Laura",
"",
"1 and 2 and 3\n and 6"
],
[
"Burg",
"Max F.",
"",
"1 and 2 and 3\n and 6"
],
[
"Blessing",
"Christoph",
"",
"1 and 3 and 6"
],
[
"Cadena",
"Santiago A.",
"",
"1 and 3 and 6"
],
[
"Ding",
"Zhiwei",
"",
"4 and 5"
],
[
"Lurz",
"Konstantin-Klemens",
"",
"1 and 2 and 3"
],
[
"Ponder",
"Kayla",
"",
"4 and\n 5"
],
[
"Muhammad",
"Taliah",
"",
"4 and 5"
],
[
"Patel",
"Saumil S.",
"",
"4 and 5"
],
[
"Ecker",
"Alexander S.",
"",
"3 and 7"
],
[
"Tolias",
"Andreas S.",
"",
"4 and 5 and 8"
],
[
"Sinz",
"Fabian H.",
"",
"2 and 3 and 4\n and 5"
]
] | The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input. Artificial neural networks (ANNs) can serve a variety of goals for improving our understanding of this complex system, not only serving as predictive digital twins of sensory cortex for novel hypothesis generation in silico, but also incorporating bio-inspired architectural motifs to progressively bridge the gap between biological and machine vision. The mouse has recently emerged as a popular model system to study visual information processing, but no standardized large-scale benchmark to identify state-of-the-art models of the mouse visual system has been established. To fill this gap, we propose the Sensorium benchmark competition. We collected a large-scale dataset from mouse primary visual cortex containing the responses of more than 28,000 neurons across seven mice stimulated with thousands of natural images, together with simultaneous behavioral measurements that include running speed, pupil dilation, and eye movements. The benchmark challenge will rank models based on predictive performance for neuronal responses on a held-out test set, and includes two tracks for model input limited to either stimulus only (Sensorium) or stimulus plus behavior (Sensorium+). We provide a starting kit to lower the barrier for entry, including tutorials, pre-trained baseline models, and APIs with one line commands for data loading and submission. We would like to see this as a starting point for regular challenges and data releases, and as a standard tool for measuring progress in large-scale neural system identification models of the mouse visual system and beyond. |
1603.00695 | Mark Leake | Adam J. M. Wollman, Helen Miller, Simon Foster, Mark C. Leake | Automated image segmentation and division plane detection in single live
Staphylococcus aureus cells | null | null | null | null | q-bio.SC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Staphylococcus aureus is a coccal bacterium, which divides by binary fission.
After division the cells remain attached giving rise to small clusters, with a
characteristic 'bunch of grapes' morphology. S. aureus is an important human
pathogen and this, combined with the increasing prevalence of
antibiotic-resistant strains, such as Methicillin Resistant S. aureus (MRSA),
make it an excellent subject for studies of new methods of antimicrobial
action. Many antibiotics, such as penicillin, prevent S. aureus cell division
and so an understanding of this fundamental process may pave the way to the
identification of novel drugs. We present here a novel image analysis framework
for automated detection and segmentation of cells in S. aureus clusters, and
identification of their cell division planes. We demonstrate the technique on
GFP labelled EzrA, a protein that localises to a mid-cell plane during division
and is involved in regulation of cell size and division. The algorithms may
have wider applicability in detecting morphologically complex structures of
fluorescently-labelled proteins within cells in other cell clusters.
| [
{
"created": "Wed, 2 Mar 2016 13:08:19 GMT",
"version": "v1"
}
] | 2016-03-03 | [
[
"Wollman",
"Adam J. M.",
""
],
[
"Miller",
"Helen",
""
],
[
"Foster",
"Simon",
""
],
[
"Leake",
"Mark C.",
""
]
] | Staphylococcus aureus is a coccal bacterium, which divides by binary fission. After division the cells remain attached giving rise to small clusters, with a characteristic 'bunch of grapes' morphology. S. aureus is an important human pathogen and this, combined with the increasing prevalence of antibiotic-resistant strains, such as Methicillin Resistant S. aureus (MRSA), make it an excellent subject for studies of new methods of antimicrobial action. Many antibiotics, such as penicillin, prevent S. aureus cell division and so an understanding of this fundamental process may pave the way to the identification of novel drugs. We present here a novel image analysis framework for automated detection and segmentation of cells in S. aureus clusters, and identification of their cell division planes. We demonstrate the technique on GFP labelled EzrA, a protein that localises to a mid-cell plane during division and is involved in regulation of cell size and division. The algorithms may have wider applicability in detecting morphologically complex structures of fluorescently-labelled proteins within cells in other cell clusters. |
1208.1054 | Gabriele Scheler | Gabriele Scheler | Transfer Functions for Protein Signal Transduction: Application to a
Model of Striatal Neural Plasticity | 13 pages, 5 tables, 15 figures | PLoS ONE 8(2): e55762. (Feb 6th, 2013) | 10.1371/journal.pone.0055762 | null | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel formulation for biochemical reaction networks in the
context of signal transduction. The model consists of input-output transfer
functions, which are derived from differential equations, using stable
equilibria. We select a set of 'source' species, which receive input signals.
Signals are transmitted to all other species in the system (the 'target'
species) with a specific delay and transmission strength. The delay is computed
as the maximal reaction time until a stable equilibrium for the target species
is reached, in the context of all other reactions in the system. The
transmission strength is the concentration change of the target species. The
computed input-output transfer functions can be stored in a matrix, fitted with
parameters, and recalled to build discrete dynamical models. By separating
reaction time and concentration we can greatly simplify the model,
circumventing typical problems of complex dynamical systems. The transfer
function transformation can be applied to mass-action kinetic models of signal
transduction. The paper shows that this approach yields significant insight,
while remaining an executable dynamical model for signal transduction. In
particular we can deconstruct the complex system into local transfer functions
between individual species. As an example, we examine modularity and signal
integration using a published model of striatal neural plasticity. The modules
that emerge correspond to a known biological distinction between
calcium-dependent and cAMP-dependent pathways. We also found that overall
interconnectedness depends on the magnitude of input, with high connectivity at
low input and less connectivity at moderate to high input. This general result,
which directly follows from the properties of individual transfer functions,
contradicts notions of ubiquitous complexity by showing input-dependent signal
transmission inactivation.
| [
{
"created": "Sun, 5 Aug 2012 21:42:51 GMT",
"version": "v1"
},
{
"created": "Thu, 9 Aug 2012 14:57:51 GMT",
"version": "v2"
},
{
"created": "Mon, 22 Oct 2012 21:59:25 GMT",
"version": "v3"
},
{
"created": "Sun, 24 Feb 2013 04:58:49 GMT",
"version": "v4"
}
] | 2013-02-26 | [
[
"Scheler",
"Gabriele",
""
]
] | We present a novel formulation for biochemical reaction networks in the context of signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of 'source' species, which receive input signals. Signals are transmitted to all other species in the system (the 'target' species) with a specific delay and transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and recalled to build discrete dynamical models. By separating reaction time and concentration we can greatly simplify the model, circumventing typical problems of complex dynamical systems. The transfer function transformation can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant insight, while remaining an executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modules that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. We also found that overall interconnectedness depends on the magnitude of input, with high connectivity at low input and less connectivity at moderate to high input. This general result, which directly follows from the properties of individual transfer functions, contradicts notions of ubiquitous complexity by showing input-dependent signal transmission inactivation. |
2310.03042 | Martin Frasch | Martin G. Frasch | Brain development dictates energy constraints on neural architecture
search: cross-disciplinary insights on optimization strategies | null | null | null | null | q-bio.NC q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Present day artificial neural architecture search (NAS) strategies are
essentially prediction-error-optimized. That holds true for AI functions in
general. From the developmental neuroscience perspective, I present evidence
for the central role of metabolically, rather than prediction-error-optimized
neural architecture search (NAS). Supporting evidence is drawn from the latest
insights into the glial-neural organization of the human brain and the dynamic
coordination theory which provides a mathematical foundation for the functional
expression of this optimization strategy. This is relevant to devising novel
NAS strategies in AI, especially in AGI. Additional implications arise for
causal reasoning from deep neural nets. Together, the insights from
developmental neuroscience offer a new perspective on NAS and the foundational
assumptions in AI modeling.
| [
{
"created": "Tue, 3 Oct 2023 18:10:43 GMT",
"version": "v1"
}
] | 2023-10-06 | [
[
"Frasch",
"Martin G.",
""
]
] | Present day artificial neural architecture search (NAS) strategies are essentially prediction-error-optimized. That holds true for AI functions in general. From the developmental neuroscience perspective, I present evidence for the central role of metabolically, rather than prediction-error-optimized neural architecture search (NAS). Supporting evidence is drawn from the latest insights into the glial-neural organization of the human brain and the dynamic coordination theory which provides a mathematical foundation for the functional expression of this optimization strategy. This is relevant to devising novel NAS strategies in AI, especially in AGI. Additional implications arise for causal reasoning from deep neural nets. Together, the insights from developmental neuroscience offer a new perspective on NAS and the foundational assumptions in AI modeling. |
1902.04073 | Inbar Seroussi | Inbar Seroussi, Nir Levy, Elad Yom-Tov | Multi-Season Analysis Reveals the Spatial Structure of Disease Spread | null | null | 10.1016/j.physa.2020.124425 | null | q-bio.PE physics.data-an physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the dynamics of infectious disease spread in a heterogeneous
population is an important factor in designing control strategies. Here, we
develop a novel tensor-driven multi-compartment version of the classic
Susceptible-Infected-Recovered (SIR) model and apply it to Internet data to
reveal information about the complex spatial structure of disease spread. The
model is used to analyze state-level Google search data from the US pertaining
to two viruses, Respiratory Syncytial Virus (RSV), and West Nile Virus (WNV).
We fit the data with correlations of $R^2=0.70$, and $0.52$ for RSV and WNV,
respectively. Although no prior assumptions on spatial structure are made,
human movement patterns in the US explain 27-30\% of the estimated inter-state
transmission rates. The transmission rates within states are correlated with
known demographic indicators, such as population density and average age.
Finally, we show that the patterns of disease load for subsequent seasons can
be predicted using the model parameters estimated for previous seasons and as
few as $7$ weeks of data from the current season. Our results are applicable to
other countries and similar viruses, allowing the identification of disease
spread parameters and prediction of disease load for seasonal viruses earlier
in season.
| [
{
"created": "Mon, 11 Feb 2019 14:56:15 GMT",
"version": "v1"
}
] | 2020-04-22 | [
[
"Seroussi",
"Inbar",
""
],
[
"Levy",
"Nir",
""
],
[
"Yom-Tov",
"Elad",
""
]
] | Understanding the dynamics of infectious disease spread in a heterogeneous population is an important factor in designing control strategies. Here, we develop a novel tensor-driven multi-compartment version of the classic Susceptible-Infected-Recovered (SIR) model and apply it to Internet data to reveal information about the complex spatial structure of disease spread. The model is used to analyze state-level Google search data from the US pertaining to two viruses, Respiratory Syncytial Virus (RSV), and West Nile Virus (WNV). We fit the data with correlations of $R^2=0.70$, and $0.52$ for RSV and WNV, respectively. Although no prior assumptions on spatial structure are made, human movement patterns in the US explain 27-30\% of the estimated inter-state transmission rates. The transmission rates within states are correlated with known demographic indicators, such as population density and average age. Finally, we show that the patterns of disease load for subsequent seasons can be predicted using the model parameters estimated for previous seasons and as few as $7$ weeks of data from the current season. Our results are applicable to other countries and similar viruses, allowing the identification of disease spread parameters and prediction of disease load for seasonal viruses earlier in season. |
1411.3507 | Antonio Celani | Antonio Celani, Emmanuel Villermaux and Massimo Vergassola | Odor Landscapes in Turbulent Environments | null | Phys. Rev. X 4, 041015 (2014) | null | null | q-bio.QM physics.flu-dyn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The olfactory system of male moths is exquisitely sensitive to pheromones
emitted by females and transported in the environment by atmospheric
turbulence. Moths respond to minute amounts of pheromones and their behavior is
sensitive to the fine-scale structure of turbulent plumes where pheromone
concentration is detectible. The signal of pheromone whiffs is qualitatively
known to be intermittent, yet quantitative characterization of its statistical
properties is lacking. This challenging fluid dynamics problem is also relevant
for entomology, neurobiology and the technological design of olfactory
stimulators aimed at reproducing physiological odor signals in well-controlled
laboratory conditions. Here, we develop a Lagrangian approach to the transport
of pheromones by turbulent flows and exploit it to predict the statistics of
odor detection during olfactory searches. The theory yields explicit
probability distributions for the intensity and the duration of pheromone
detections, as well as their spacing in time. Predictions are favorably tested
by using numerical simulations, laboratory experiments and field data for the
atmospheric surface layer. The resulting signal of odor detections lends to
implementation with state-of-the-art technologies and quantifies the amount and
the type of information that male moths can exploit during olfactory searches.
| [
{
"created": "Thu, 13 Nov 2014 11:33:41 GMT",
"version": "v1"
}
] | 2014-11-14 | [
[
"Celani",
"Antonio",
""
],
[
"Villermaux",
"Emmanuel",
""
],
[
"Vergassola",
"Massimo",
""
]
] | The olfactory system of male moths is exquisitely sensitive to pheromones emitted by females and transported in the environment by atmospheric turbulence. Moths respond to minute amounts of pheromones and their behavior is sensitive to the fine-scale structure of turbulent plumes where pheromone concentration is detectible. The signal of pheromone whiffs is qualitatively known to be intermittent, yet quantitative characterization of its statistical properties is lacking. This challenging fluid dynamics problem is also relevant for entomology, neurobiology and the technological design of olfactory stimulators aimed at reproducing physiological odor signals in well-controlled laboratory conditions. Here, we develop a Lagrangian approach to the transport of pheromones by turbulent flows and exploit it to predict the statistics of odor detection during olfactory searches. The theory yields explicit probability distributions for the intensity and the duration of pheromone detections, as well as their spacing in time. Predictions are favorably tested by using numerical simulations, laboratory experiments and field data for the atmospheric surface layer. The resulting signal of odor detections lends to implementation with state-of-the-art technologies and quantifies the amount and the type of information that male moths can exploit during olfactory searches. |
1410.4469 | Pieter Trapman | Frank Ball, Lorenzo Pellis and Pieter Trapman | Reproduction numbers for epidemic models with households and other
social structures II: comparisons and implications for vaccination | This paper follows from our earlier paper, entitled "Reproduction
numbers for epidemic models with households and other social structures I:
definition and calculation of $R_0$", previously published in Mathematical
Biosciences (235(1): 85_97, 2012) by the same authors | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we consider epidemic models of directly transmissible SIR
(susceptible $\to$ infective $\to$ recovered) and SEIR (with an additional
latent class) infections in fully-susceptible populations with a social
structure, consisting either of households or of households and workplaces. We
review most reproduction numbers defined in the literature for these models,
including the basic reproduction number $R_0$ introduced in the companion paper
of this, for which we provide a simpler, more elegant derivation. Extending
previous work, we provide a complete overview of the inequalities among these
reproduction numbers and resolve some open questions. Special focus is put on
the exponential-growth-associated reproduction number $R_r$, which is loosely
defined as the estimate of $R_0$ based on the observed exponential growth of an
emerging epidemic obtained when the social structure is ignored. We show that
for the vast majority of the models considered in the literature $R_r \geq R_0$
when $R_0 \ge 1$ and $R_r \leq R_0$ when $R_0 \le 1$. We show that, in contrast
to models without social structure, vaccination of a fraction $1-1/R_0$ of the
population, chosen uniformly at random, with a perfect vaccine is usually
insufficient to prevent large epidemics. In addition, we provide significantly
sharper bounds than the existing ones for bracketing the critical vaccination
coverage between two analytically tractable quantities, which we illustrate by
means of extensive numerical examples.
| [
{
"created": "Thu, 16 Oct 2014 15:37:06 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Dec 2015 09:39:31 GMT",
"version": "v2"
}
] | 2015-12-11 | [
[
"Ball",
"Frank",
""
],
[
"Pellis",
"Lorenzo",
""
],
[
"Trapman",
"Pieter",
""
]
] | In this paper we consider epidemic models of directly transmissible SIR (susceptible $\to$ infective $\to$ recovered) and SEIR (with an additional latent class) infections in fully-susceptible populations with a social structure, consisting either of households or of households and workplaces. We review most reproduction numbers defined in the literature for these models, including the basic reproduction number $R_0$ introduced in the companion paper of this, for which we provide a simpler, more elegant derivation. Extending previous work, we provide a complete overview of the inequalities among these reproduction numbers and resolve some open questions. Special focus is put on the exponential-growth-associated reproduction number $R_r$, which is loosely defined as the estimate of $R_0$ based on the observed exponential growth of an emerging epidemic obtained when the social structure is ignored. We show that for the vast majority of the models considered in the literature $R_r \geq R_0$ when $R_0 \ge 1$ and $R_r \leq R_0$ when $R_0 \le 1$. We show that, in contrast to models without social structure, vaccination of a fraction $1-1/R_0$ of the population, chosen uniformly at random, with a perfect vaccine is usually insufficient to prevent large epidemics. In addition, we provide significantly sharper bounds than the existing ones for bracketing the critical vaccination coverage between two analytically tractable quantities, which we illustrate by means of extensive numerical examples. |
1903.03418 | Marcel Kvassay | Marcel Kvassay | The meta-problem and the transfer of knowledge between theories of
consciousness: a software engineer's take | null | null | null | null | q-bio.NC cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This contribution examines two radically different explanations of our
phenomenal intuitions, one reductive and one strongly non-reductive, and
identifies two germane ideas that could benefit many other theories of
consciousness. Firstly, the ability of sophisticated agent architectures with a
purely physical implementation to support certain functional forms of qualia or
proto-qualia appears to entail the possibility of machine consciousness with
qualia, not only for reductive theories but also for the nonreductive ones that
regard consciousness as ubiquitous in Nature. Secondly, analysis of
introspective psychological material seems to hint that, under the threshold of
our ordinary waking awareness, there exist further 'submerged' or 'subliminal'
layers of consciousness which constitute a hidden foundation and support and
another source of our phenomenal intuitions. These 'submerged' layers might
help explain certain puzzling phenomena concerning subliminal perception, such
as the apparently 'unconscious' multisensory integration and learning of
subliminal stimuli.
| [
{
"created": "Mon, 18 Feb 2019 19:17:44 GMT",
"version": "v1"
}
] | 2019-03-11 | [
[
"Kvassay",
"Marcel",
""
]
] | This contribution examines two radically different explanations of our phenomenal intuitions, one reductive and one strongly non-reductive, and identifies two germane ideas that could benefit many other theories of consciousness. Firstly, the ability of sophisticated agent architectures with a purely physical implementation to support certain functional forms of qualia or proto-qualia appears to entail the possibility of machine consciousness with qualia, not only for reductive theories but also for the nonreductive ones that regard consciousness as ubiquitous in Nature. Secondly, analysis of introspective psychological material seems to hint that, under the threshold of our ordinary waking awareness, there exist further 'submerged' or 'subliminal' layers of consciousness which constitute a hidden foundation and support and another source of our phenomenal intuitions. These 'submerged' layers might help explain certain puzzling phenomena concerning subliminal perception, such as the apparently 'unconscious' multisensory integration and learning of subliminal stimuli. |
1609.08651 | Alessandro Musesti | Giulia Giantesio and Alessandro Musesti | Strain-dependent internal parameters in hyperelastic biological
materials | null | null | 10.1016/j.ijnonlinmec.2017.06.012 | null | q-bio.TO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The behavior of hyperelastic energies depending on an internal parameter,
which is a function of the deformation gradient, is discussed. As an example,
the analysis of two models where the parameter describes the activation of a
tetanized skeletal muscle tissue is presented. In those models, the activation
parameter depends on the strain and it is shown the importance of considering
the derivative of the parameter with respect to the strain in order to capture
the proper stress-strain relations.
| [
{
"created": "Wed, 28 Sep 2016 07:19:39 GMT",
"version": "v1"
},
{
"created": "Wed, 2 May 2018 10:17:52 GMT",
"version": "v2"
}
] | 2018-05-03 | [
[
"Giantesio",
"Giulia",
""
],
[
"Musesti",
"Alessandro",
""
]
] | The behavior of hyperelastic energies depending on an internal parameter, which is a function of the deformation gradient, is discussed. As an example, the analysis of two models where the parameter describes the activation of a tetanized skeletal muscle tissue is presented. In those models, the activation parameter depends on the strain and it is shown the importance of considering the derivative of the parameter with respect to the strain in order to capture the proper stress-strain relations. |
2407.05143 | Carlos Nieto | Carlos M. Nieto, Oscar M. Pimentel, Fabio D. Lora-Clavijo | Novel second-order model for tumor evolution: description of cytostatic
and cytotoxic effects | null | null | null | null | q-bio.QM physics.bio-ph physics.med-ph | http://creativecommons.org/licenses/by/4.0/ | Cancer is a disease that takes millions of lives every year. Then, to propose
treatments, avoid recurrence, and improve the patient's life quality, we need
to analyze this disease from a biophysical perspective with a solid
mathematical formulation. In this paper we introduce a novel deterministic
model for the evolution of tumors under several conditions (untreated tumors
and treated tumors using chemotherapy). Our model is characterized by a
second-order differential equation, whose origin and interpretation are
presented by exploiting our understanding of fluid mechanics (via continuity
equations) and the theory of differential equations. Additionally, we show that
our model can fit various experimental data sets. Thus, we prove that our
nuanced and general model can describe accelerated growth, as well as
cytostatic and cytotoxic effects. All in all, our model opens up a new window
in the understanding of tumor evolution and represents a promising connection
between the macroscopic and microscopic descriptions of cancer.
| [
{
"created": "Sat, 6 Jul 2024 17:50:56 GMT",
"version": "v1"
}
] | 2024-07-09 | [
[
"Nieto",
"Carlos M.",
""
],
[
"Pimentel",
"Oscar M.",
""
],
[
"Lora-Clavijo",
"Fabio D.",
""
]
] | Cancer is a disease that takes millions of lives every year. Then, to propose treatments, avoid recurrence, and improve the patient's life quality, we need to analyze this disease from a biophysical perspective with a solid mathematical formulation. In this paper we introduce a novel deterministic model for the evolution of tumors under several conditions (untreated tumors and treated tumors using chemotherapy). Our model is characterized by a second-order differential equation, whose origin and interpretation are presented by exploiting our understanding of fluid mechanics (via continuity equations) and the theory of differential equations. Additionally, we show that our model can fit various experimental data sets. Thus, we prove that our nuanced and general model can describe accelerated growth, as well as cytostatic and cytotoxic effects. All in all, our model opens up a new window in the understanding of tumor evolution and represents a promising connection between the macroscopic and microscopic descriptions of cancer. |
0803.1082 | Tobias Galla | Yoshimi Yoshino, Tobias Galla, Kei Tokita | Rank abundance relations in evolutionary dynamics of random replicators | 12 pages, 14 figures; text amended, minor corrections/modifications
to figures | null | 10.1103/PhysRevE.78.031924 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a non-equilibrium statistical mechanics description of rank
abundance relations (RAR) in random community models of ecology. Specifically,
we study a multi-species replicator system with quenched random interaction
matrices. We here consider symmetric interactions as well as asymmetric and
anti-symmetric cases. RARs are obtained analytically via a generating
functional analysis, describing fixed-point states of the system in terms of a
small set of order parameters, and in dependence on the symmetry or otherwise
of interactions and on the productivity of the community. Our work is an
extension of Tokita [Phys. Rev. Lett. {\bf 93} 178102 (2004)], where the case
of symmetric interactions was considered within an equilibrium setup. The
species abundance distribution in our model come out as truncated normal
distributions or transformations thereof and, in some case, are similar to
left-skewed distributions observed in ecology. We also discuss the interaction
structure of the resulting food-web of stable species at stationarity, cases of
heterogeneous co-operation pressures as well as effects of finite system size
and of higher-order interactions.
| [
{
"created": "Fri, 7 Mar 2008 13:27:14 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Jul 2008 13:17:22 GMT",
"version": "v2"
}
] | 2009-11-13 | [
[
"Yoshino",
"Yoshimi",
""
],
[
"Galla",
"Tobias",
""
],
[
"Tokita",
"Kei",
""
]
] | We present a non-equilibrium statistical mechanics description of rank abundance relations (RAR) in random community models of ecology. Specifically, we study a multi-species replicator system with quenched random interaction matrices. We here consider symmetric interactions as well as asymmetric and anti-symmetric cases. RARs are obtained analytically via a generating functional analysis, describing fixed-point states of the system in terms of a small set of order parameters, and in dependence on the symmetry or otherwise of interactions and on the productivity of the community. Our work is an extension of Tokita [Phys. Rev. Lett. {\bf 93} 178102 (2004)], where the case of symmetric interactions was considered within an equilibrium setup. The species abundance distribution in our model come out as truncated normal distributions or transformations thereof and, in some case, are similar to left-skewed distributions observed in ecology. We also discuss the interaction structure of the resulting food-web of stable species at stationarity, cases of heterogeneous co-operation pressures as well as effects of finite system size and of higher-order interactions. |
2203.09281 | James Wilsenach | James Wilsenach, Katie Warnaby, Charlotte M. Deane and Gesine Reinert | Ranking of Communities in Multiplex Spatiotemporal Models of Brain
Dynamics | Part of the Special Issue on Community Structure in Networks 2021 (35
Pages, first 22 for main text) | Applied Network Science (2022) 7-15 | 10.1007/S41109-022-00454-2 | null | q-bio.NC cs.LG cs.SI stat.AP stat.ML | http://creativecommons.org/licenses/by/4.0/ | As a relatively new field, network neuroscience has tended to focus on
aggregate behaviours of the brain averaged over many successive experiments or
over long recordings in order to construct robust brain models. These models
are limited in their ability to explain dynamic state changes in the brain
which occurs spontaneously as a result of normal brain function. Hidden Markov
Models (HMMs) trained on neuroimaging time series data have since arisen as a
method to produce dynamical models that are easy to train but can be difficult
to fully parametrise or analyse. We propose an interpretation of these neural
HMMs as multiplex brain state graph models we term Hidden Markov Graph Models
(HMGMs). This interpretation allows for dynamic brain activity to be analysed
using the full repertoire of network analysis techniques. Furthermore, we
propose a general method for selecting HMM hyperparameters in the absence of
external data, based on the principle of maximum entropy, and use this to
select the number of layers in the multiplex model. We produce a new tool for
determining important communities of brain regions using a spatiotemporal
random walk-based procedure that takes advantage of the underlying Markov
structure of the model. Our analysis of real multi-subject fMRI data provides
new results that corroborate the modular processing hypothesis of the brain at
rest as well as contributing new evidence of functional overlap between and
within dynamic brain state communities. Our analysis pipeline provides a way to
characterise dynamic network activity of the brain under novel behaviours or
conditions.
| [
{
"created": "Thu, 17 Mar 2022 12:14:09 GMT",
"version": "v1"
},
{
"created": "Tue, 17 May 2022 22:55:30 GMT",
"version": "v2"
}
] | 2022-05-19 | [
[
"Wilsenach",
"James",
""
],
[
"Warnaby",
"Katie",
""
],
[
"Deane",
"Charlotte M.",
""
],
[
"Reinert",
"Gesine",
""
]
] | As a relatively new field, network neuroscience has tended to focus on aggregate behaviours of the brain averaged over many successive experiments or over long recordings in order to construct robust brain models. These models are limited in their ability to explain dynamic state changes in the brain which occurs spontaneously as a result of normal brain function. Hidden Markov Models (HMMs) trained on neuroimaging time series data have since arisen as a method to produce dynamical models that are easy to train but can be difficult to fully parametrise or analyse. We propose an interpretation of these neural HMMs as multiplex brain state graph models we term Hidden Markov Graph Models (HMGMs). This interpretation allows for dynamic brain activity to be analysed using the full repertoire of network analysis techniques. Furthermore, we propose a general method for selecting HMM hyperparameters in the absence of external data, based on the principle of maximum entropy, and use this to select the number of layers in the multiplex model. We produce a new tool for determining important communities of brain regions using a spatiotemporal random walk-based procedure that takes advantage of the underlying Markov structure of the model. Our analysis of real multi-subject fMRI data provides new results that corroborate the modular processing hypothesis of the brain at rest as well as contributing new evidence of functional overlap between and within dynamic brain state communities. Our analysis pipeline provides a way to characterise dynamic network activity of the brain under novel behaviours or conditions. |
2407.07595 | Shuntaro Sasai | Motoshige Sato, Kenichi Tomeoka, Ilya Horiguchi, Kai Arulkumaran,
Ryota Kanai, Shuntaro Sasai | Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours
of EEG Data | null | null | null | null | q-bio.NC cs.HC cs.SD eess.AS | http://creativecommons.org/licenses/by-sa/4.0/ | Brain-computer interfaces (BCIs) hold great potential for aiding individuals
with speech impairments. Utilizing electroencephalography (EEG) to decode
speech is particularly promising due to its non-invasive nature. However,
recordings are typically short, and the high variability in EEG data has led
researchers to focus on classification tasks with a few dozen classes. To
assess its practical applicability for speech neuroprostheses, we investigate
the relationship between the size of EEG data and decoding accuracy in the open
vocabulary setting. We collected extensive EEG data from a single participant
(175 hours) and conducted zero-shot speech segment classification using
self-supervised representation learning. The model trained on the entire
dataset achieved a top-1 accuracy of 48\% and a top-10 accuracy of 76\%, while
mitigating the effects of myopotential artifacts. Conversely, when the data was
limited to the typical amount used in practice ($\sim$10 hours), the top-1
accuracy dropped to 2.5\%, revealing a significant scaling effect.
Additionally, as the amount of training data increased, the EEG latent
representation progressively exhibited clearer temporal structures of spoken
phrases. This indicates that the decoder can recognize speech segments in a
data-driven manner without explicit measurements of word recognition. This
research marks a significant step towards the practical realization of
EEG-based speech BCIs.
| [
{
"created": "Wed, 10 Jul 2024 12:29:01 GMT",
"version": "v1"
}
] | 2024-07-11 | [
[
"Sato",
"Motoshige",
""
],
[
"Tomeoka",
"Kenichi",
""
],
[
"Horiguchi",
"Ilya",
""
],
[
"Arulkumaran",
"Kai",
""
],
[
"Kanai",
"Ryota",
""
],
[
"Sasai",
"Shuntaro",
""
]
] | Brain-computer interfaces (BCIs) hold great potential for aiding individuals with speech impairments. Utilizing electroencephalography (EEG) to decode speech is particularly promising due to its non-invasive nature. However, recordings are typically short, and the high variability in EEG data has led researchers to focus on classification tasks with a few dozen classes. To assess its practical applicability for speech neuroprostheses, we investigate the relationship between the size of EEG data and decoding accuracy in the open vocabulary setting. We collected extensive EEG data from a single participant (175 hours) and conducted zero-shot speech segment classification using self-supervised representation learning. The model trained on the entire dataset achieved a top-1 accuracy of 48\% and a top-10 accuracy of 76\%, while mitigating the effects of myopotential artifacts. Conversely, when the data was limited to the typical amount used in practice ($\sim$10 hours), the top-1 accuracy dropped to 2.5\%, revealing a significant scaling effect. Additionally, as the amount of training data increased, the EEG latent representation progressively exhibited clearer temporal structures of spoken phrases. This indicates that the decoder can recognize speech segments in a data-driven manner without explicit measurements of word recognition. This research marks a significant step towards the practical realization of EEG-based speech BCIs. |
2203.05806 | Daniele Schon | Neus Ramos-Escobar, Manuel Mercier, Agn\`es Tr\'ebuchon-Fons\'eca,
Antoni Rodriguez-Fornells, Cl\'ement Fran\c{c}ois, Daniele Sch\"on | Hippocampal and auditory contributions to speech segmentation | Cortex, Elsevier, 2022 | null | 10.1016/j.cortex.2022.01.017 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Statistical learning has been proposed as a mechanism to structure and
segment the continuous flow of information in several sensory modalities.
Previous studies proposed that the medial temporal lobe, and in particular the
hippocampus, may be crucial to parse the stream in the visual modality.
However, the involvement of the hippocampus in auditory statistical learning,
and specifically in speech segmentation is less clear. To explore the role of
the hippocampus in speech segmentation based on statistical learning, we
exposed seven pharmaco-resistant temporal lobe epilepsy patients to a
continuous stream of trisyllabic pseudowords and recorded intracranial
stereotaxic electro-encephalography (sEEG). We used frequency-tagging analysis
to quantify neuronal synchronization of the hippocampus and auditory regions to
the temporal structure of words and syllables of the stream. Results show that
while auditory regions highly respond to syllable frequency, the hippocampus
responds mostly to word frequency. These findings provide direct evidence of
the involvement of the hippocampus in speech segmentation process and suggest a
hierarchical organization of auditory information during speech processing.
| [
{
"created": "Fri, 11 Mar 2022 09:00:33 GMT",
"version": "v1"
}
] | 2022-03-14 | [
[
"Ramos-Escobar",
"Neus",
""
],
[
"Mercier",
"Manuel",
""
],
[
"Trébuchon-Fonséca",
"Agnès",
""
],
[
"Rodriguez-Fornells",
"Antoni",
""
],
[
"François",
"Clément",
""
],
[
"Schön",
"Daniele",
""
]
] | Statistical learning has been proposed as a mechanism to structure and segment the continuous flow of information in several sensory modalities. Previous studies proposed that the medial temporal lobe, and in particular the hippocampus, may be crucial to parse the stream in the visual modality. However, the involvement of the hippocampus in auditory statistical learning, and specifically in speech segmentation is less clear. To explore the role of the hippocampus in speech segmentation based on statistical learning, we exposed seven pharmaco-resistant temporal lobe epilepsy patients to a continuous stream of trisyllabic pseudowords and recorded intracranial stereotaxic electro-encephalography (sEEG). We used frequency-tagging analysis to quantify neuronal synchronization of the hippocampus and auditory regions to the temporal structure of words and syllables of the stream. Results show that while auditory regions highly respond to syllable frequency, the hippocampus responds mostly to word frequency. These findings provide direct evidence of the involvement of the hippocampus in speech segmentation process and suggest a hierarchical organization of auditory information during speech processing. |
1408.6303 | Anatol Wegner | Anatol E. Wegner | Motif Conservation Laws for the Configuration Model | 3 pages, 3 figures | null | null | null | q-bio.MN cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The observation that some subgraphs, called motifs, appear more often in real
networks than in their randomized counterparts has attracted much attention in
the scientific community. In the prevalent approach the detection of motifs is
based on comparing subgraph counts in a network with their counterparts in the
configuration model with the same degree distribution as the network. In this
short note we derive conservation laws that relate motif counts in the
configuration model.
| [
{
"created": "Wed, 27 Aug 2014 03:07:32 GMT",
"version": "v1"
}
] | 2014-08-28 | [
[
"Wegner",
"Anatol E.",
""
]
] | The observation that some subgraphs, called motifs, appear more often in real networks than in their randomized counterparts has attracted much attention in the scientific community. In the prevalent approach the detection of motifs is based on comparing subgraph counts in a network with their counterparts in the configuration model with the same degree distribution as the network. In this short note we derive conservation laws that relate motif counts in the configuration model. |
1005.1699 | Frederick Matsen IV | Steven N. Evans and Frederick A. Matsen | The phylogenetic Kantorovich-Rubinstein metric for environmental
sequence samples | Some new additions and a complete revision of structure | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Using modern technology, it is now common to survey microbial communities by
sequencing DNA or RNA extracted in bulk from a given environment. Comparative
methods are needed that indicate the extent to which two communities differ
given data sets of this type. UniFrac, a method built around a somewhat ad hoc
phylogenetics-based distance between two communities, is one of the most
commonly used tools for these analyses. We provide a foundation for such
methods by establishing that if one equates a metagenomic sample with its
empirical distribution on a reference phylogenetic tree, then the weighted
UniFrac distance between two samples is just the classical
Kantorovich-Rubinstein (KR) distance between the corresponding empirical
distributions. We demonstrate that this KR distance and extensions of it that
arise from incorporating uncertainty in the location of sample points can be
written as a readily computable integral over the tree, we develop $L^p$
Zolotarev-type generalizations of the metric, and we show how the p-value of
the resulting natural permutation test of the null hypothesis "no difference
between the two communities" can be approximated using a functional of a
Gaussian process indexed by the tree. We relate the $L^2$ case to an ANOVA-type
decomposition and find that the distribution of its associated Gaussian
functional is that of a computable linear combination of independent $\chi_1^2$
random variables.
| [
{
"created": "Tue, 11 May 2010 01:00:57 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Sep 2010 23:31:25 GMT",
"version": "v2"
},
{
"created": "Wed, 4 May 2011 22:10:24 GMT",
"version": "v3"
}
] | 2011-05-06 | [
[
"Evans",
"Steven N.",
""
],
[
"Matsen",
"Frederick A.",
""
]
] | Using modern technology, it is now common to survey microbial communities by sequencing DNA or RNA extracted in bulk from a given environment. Comparative methods are needed that indicate the extent to which two communities differ given data sets of this type. UniFrac, a method built around a somewhat ad hoc phylogenetics-based distance between two communities, is one of the most commonly used tools for these analyses. We provide a foundation for such methods by establishing that if one equates a metagenomic sample with its empirical distribution on a reference phylogenetic tree, then the weighted UniFrac distance between two samples is just the classical Kantorovich-Rubinstein (KR) distance between the corresponding empirical distributions. We demonstrate that this KR distance and extensions of it that arise from incorporating uncertainty in the location of sample points can be written as a readily computable integral over the tree, we develop $L^p$ Zolotarev-type generalizations of the metric, and we show how the p-value of the resulting natural permutation test of the null hypothesis "no difference between the two communities" can be approximated using a functional of a Gaussian process indexed by the tree. We relate the $L^2$ case to an ANOVA-type decomposition and find that the distribution of its associated Gaussian functional is that of a computable linear combination of independent $\chi_1^2$ random variables. |
1907.02713 | Milan Sencanski | Milan Sencanski, Neven Sumonja, Vladimir Perovic, Sanja Glisic, Nevena
Veljkovic, and Veljko Veljkovic | Application of Information Spectrum Method on Small Molecules and Target
Recognition | Keywords: ISM method, CIS spectra, small molecules, smiles notation,
target-ligand recognition, protein target regions | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current methods for investigation of receptor - ligand interactions in drug
discovery are based on three-dimensional complementarity of receptor and ligand
surfaces, and they include pharmacophore modelling, QSAR, molecular docking
etc. Those methods only consider short-range molecular interactions (distances
<5A), and not include long-range interactions (distances >5A) which are
essential for kinetic of biochemical reactions because they influence the
number of productive collisions between interacting molecules. Previously was
shown that the electron-ion interaction potential (EIIP) represents the
physical property which determines the long-range properties of biological
molecules. This molecular descriptor served as a base for development of the
informational spectrum method (ISM), a virtual spectroscopy method for
investigation of protein-protein interactions. In this paper, we proposed a new
approach to treat small molecules as linear entities, allowing study of the
small molecule - protein interaction by ISM. We analyzed here 21 sets of KEGG
drug-protein interactions and showed that this new approach allows an efficient
discrimination between biologically active and inactive ligands, and
consistence with AA regions of their binding site on the target protein.
| [
{
"created": "Fri, 5 Jul 2019 08:10:37 GMT",
"version": "v1"
},
{
"created": "Fri, 31 Jan 2020 11:20:42 GMT",
"version": "v2"
},
{
"created": "Wed, 15 Apr 2020 16:35:55 GMT",
"version": "v3"
}
] | 2020-04-16 | [
[
"Sencanski",
"Milan",
""
],
[
"Sumonja",
"Neven",
""
],
[
"Perovic",
"Vladimir",
""
],
[
"Glisic",
"Sanja",
""
],
[
"Veljkovic",
"Nevena",
""
],
[
"Veljkovic",
"Veljko",
""
]
] | Current methods for investigation of receptor - ligand interactions in drug discovery are based on three-dimensional complementarity of receptor and ligand surfaces, and they include pharmacophore modelling, QSAR, molecular docking etc. Those methods only consider short-range molecular interactions (distances <5A), and not include long-range interactions (distances >5A) which are essential for kinetic of biochemical reactions because they influence the number of productive collisions between interacting molecules. Previously was shown that the electron-ion interaction potential (EIIP) represents the physical property which determines the long-range properties of biological molecules. This molecular descriptor served as a base for development of the informational spectrum method (ISM), a virtual spectroscopy method for investigation of protein-protein interactions. In this paper, we proposed a new approach to treat small molecules as linear entities, allowing study of the small molecule - protein interaction by ISM. We analyzed here 21 sets of KEGG drug-protein interactions and showed that this new approach allows an efficient discrimination between biologically active and inactive ligands, and consistence with AA regions of their binding site on the target protein. |
2003.11716 | Wenyuan Liu | Wenyuan Liu, Peter Tsung-Wen Yen and Siew Ann Cheong | Spatial-Temporal Dataset of COVID-19 Outbreak in China | 11 pages, 7 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present Coronavirus disease 2019 (COVID-19) statistics in China dataset:
daily statistics of the COVID-19 outbreak in China at the city/county level.
For each city/country, we include the six most important numbers for epidemic
research: daily new infections, accumulated infections, daily new recoveries,
accumulated recoveries, daily new deaths, and accumulated deaths. We cross
validate the dataset and the estimate error rate is about 0.04%. We then give
several examples to show how to trace the spreading in particular cities or
provinces, and also contrast the development of COVID-19 in all cities in China
at the early, middle and late stages. We hope this dataset can help researchers
around the world better understand the spreading dynamics of COVID-19 at a
regional level, to inform intervention and mitigation strategies for
policymakers.
| [
{
"created": "Thu, 26 Mar 2020 02:59:58 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Apr 2020 03:30:29 GMT",
"version": "v2"
}
] | 2020-04-08 | [
[
"Liu",
"Wenyuan",
""
],
[
"Yen",
"Peter Tsung-Wen",
""
],
[
"Cheong",
"Siew Ann",
""
]
] | We present Coronavirus disease 2019 (COVID-19) statistics in China dataset: daily statistics of the COVID-19 outbreak in China at the city/county level. For each city/country, we include the six most important numbers for epidemic research: daily new infections, accumulated infections, daily new recoveries, accumulated recoveries, daily new deaths, and accumulated deaths. We cross validate the dataset and the estimate error rate is about 0.04%. We then give several examples to show how to trace the spreading in particular cities or provinces, and also contrast the development of COVID-19 in all cities in China at the early, middle and late stages. We hope this dataset can help researchers around the world better understand the spreading dynamics of COVID-19 at a regional level, to inform intervention and mitigation strategies for policymakers. |
1706.00925 | Rodrigo Echeveste | Rodrigo Echeveste, Guillaume Hennequin, M\'at\'e Lengyel | Asymptotic scaling properties of the posterior mean and variance in the
Gaussian scale mixture model | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Gaussian scale mixture model (GSM) is a simple yet powerful probabilistic
generative model of natural image patches. In line with the well-established
idea that sensory processing is adapted to the statistics of the natural
environment, the GSM has also been considered a model of the early visual
system, as a reasonable "first-order" approximation of the internal model that
the primary visual cortex (V1) implements. According to this view, neural
activities in V1 represent the posterior distribution under the GSM given a
particular visual stimulus. Indeed, (approximate) inference under the GSM has
successfully accounted for various nonlinearities in the mean (trial-average)
responses of V1 neurons, as well as the dependence of (across-trial) response
variability with stimulus contrast found in V1 recordings. However, previous
work almost exclusively relied on numerical simulations to obtain these
results. Thus, for a deeper insight into the realm of possible behaviours the
GSM can (and cannot) exhibit and predict, here we present analytical
derivations for the limiting behaviour of the mean and (co)variance of the GSM
posterior at very low and very high contrast levels. These results should guide
future work exploring neural circuit dynamics appropriate for implementing
inference under the GSM.
| [
{
"created": "Sat, 3 Jun 2017 10:39:52 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Nov 2017 10:01:08 GMT",
"version": "v2"
}
] | 2017-11-29 | [
[
"Echeveste",
"Rodrigo",
""
],
[
"Hennequin",
"Guillaume",
""
],
[
"Lengyel",
"Máté",
""
]
] | The Gaussian scale mixture model (GSM) is a simple yet powerful probabilistic generative model of natural image patches. In line with the well-established idea that sensory processing is adapted to the statistics of the natural environment, the GSM has also been considered a model of the early visual system, as a reasonable "first-order" approximation of the internal model that the primary visual cortex (V1) implements. According to this view, neural activities in V1 represent the posterior distribution under the GSM given a particular visual stimulus. Indeed, (approximate) inference under the GSM has successfully accounted for various nonlinearities in the mean (trial-average) responses of V1 neurons, as well as the dependence of (across-trial) response variability with stimulus contrast found in V1 recordings. However, previous work almost exclusively relied on numerical simulations to obtain these results. Thus, for a deeper insight into the realm of possible behaviours the GSM can (and cannot) exhibit and predict, here we present analytical derivations for the limiting behaviour of the mean and (co)variance of the GSM posterior at very low and very high contrast levels. These results should guide future work exploring neural circuit dynamics appropriate for implementing inference under the GSM. |
1210.1060 | Celia Blanco | Celia Blanco and David Hochberg | Induced mirror symmetry breaking via template-controlled
copolymerization: theoretical insights | This article is part of the ChemComm 'Chirality' web themed issue.
Supplementary Information available | Chem. Commun., 2012,48, 3659-3661 | 10.1039/C2CC18045F | null | q-bio.QM physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A chemical equilibrium model of template-controlled copolymerization is
presented for describing the outcome of the experimental induced
desymmetrization scenarios recently proposed by Lahav and coworkers.
| [
{
"created": "Wed, 3 Oct 2012 11:08:02 GMT",
"version": "v1"
}
] | 2012-10-04 | [
[
"Blanco",
"Celia",
""
],
[
"Hochberg",
"David",
""
]
] | A chemical equilibrium model of template-controlled copolymerization is presented for describing the outcome of the experimental induced desymmetrization scenarios recently proposed by Lahav and coworkers. |
2403.12912 | Juannan Zhou | Kristen Van Gelder, Steffen N. Lindner, Andrew D. Hanson, Juannan Zhou | Strangers in a foreign land: 'Yeastizing' plant enzymes | 37 pages, 3 figures | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Expressing plant metabolic pathways in microbial platforms is an efficient,
cost-effective solution for producing many desired plant compounds. As
eukaryotic organisms, yeasts are often the preferred platform. However,
expression of plant enzymes in a yeast frequently leads to failure because the
enzymes are poorly adapted to the foreign yeast cellular environment. Here we
first summarize current engineering approaches for optimizing performance of
plant enzymes in yeast. A critical limitation of these approaches is that they
are labor-intensive and must be customized for each individual enzyme, which
significantly hinders the establishment of plant pathways in cellular
factories. In response to this challenge, we propose the development of a
cost-effective computational pipeline to redesign plant enzymes for better
adaptation to the yeast cellular milieu. This proposition is underpinned by
compelling evidence that plant and yeast enzymes exhibit distinct sequence
features that are generalizable across enzyme families. Consequently, we
introduce a data-driven machine learning framework designed to extract
'yeastizing' rules from natural protein sequence variations, which can be
broadly applied to all enzymes. Additionally, we discuss the potential to
integrate the machine learning model into a full design-build-test-cycle.
| [
{
"created": "Tue, 19 Mar 2024 17:10:52 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Mar 2024 02:23:48 GMT",
"version": "v2"
}
] | 2024-03-21 | [
[
"Van Gelder",
"Kristen",
""
],
[
"Lindner",
"Steffen N.",
""
],
[
"Hanson",
"Andrew D.",
""
],
[
"Zhou",
"Juannan",
""
]
] | Expressing plant metabolic pathways in microbial platforms is an efficient, cost-effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here we first summarize current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labor-intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost-effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data-driven machine learning framework designed to extract 'yeastizing' rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design-build-test-cycle. |
2407.10376 | Yuejiao Wang | Yuejiao Wang, Xianmin Gong, Lingwei Meng, Xixin Wu, Helen Meng | Large Language Model-based FMRI Encoding of Language Functions for
Subjects with Neurocognitive Disorder | 5 pages, accepted by Interspeech 2024 | null | null | null | q-bio.NC cs.CL | http://creativecommons.org/licenses/by/4.0/ | Functional magnetic resonance imaging (fMRI) is essential for developing
encoding models that identify functional changes in language-related brain
areas of individuals with Neurocognitive Disorders (NCD). While large language
model (LLM)-based fMRI encoding has shown promise, existing studies
predominantly focus on healthy, young adults, overlooking older NCD populations
and cognitive level correlations. This paper explores language-related
functional changes in older NCD adults using LLM-based fMRI encoding and brain
scores, addressing current limitations. We analyze the correlation between
brain scores and cognitive scores at both whole-brain and language-related ROI
levels. Our findings reveal that higher cognitive abilities correspond to
better brain scores, with correlations peaking in the middle temporal gyrus.
This study highlights the potential of fMRI encoding models and brain scores
for detecting early functional changes in NCD patients.
| [
{
"created": "Mon, 15 Jul 2024 01:09:08 GMT",
"version": "v1"
}
] | 2024-07-16 | [
[
"Wang",
"Yuejiao",
""
],
[
"Gong",
"Xianmin",
""
],
[
"Meng",
"Lingwei",
""
],
[
"Wu",
"Xixin",
""
],
[
"Meng",
"Helen",
""
]
] | Functional magnetic resonance imaging (fMRI) is essential for developing encoding models that identify functional changes in language-related brain areas of individuals with Neurocognitive Disorders (NCD). While large language model (LLM)-based fMRI encoding has shown promise, existing studies predominantly focus on healthy, young adults, overlooking older NCD populations and cognitive level correlations. This paper explores language-related functional changes in older NCD adults using LLM-based fMRI encoding and brain scores, addressing current limitations. We analyze the correlation between brain scores and cognitive scores at both whole-brain and language-related ROI levels. Our findings reveal that higher cognitive abilities correspond to better brain scores, with correlations peaking in the middle temporal gyrus. This study highlights the potential of fMRI encoding models and brain scores for detecting early functional changes in NCD patients. |
2312.11592 | Cameron Smith | Cameron A. Smith and Ben Ashby | Efficient coupling of within- and between-host infectious disease
dynamics | 34 pages, 5 figures | null | null | null | q-bio.PE q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Mathematical models of infectious disease transmission typically neglect
within-host dynamics. Yet within-host dynamics - including pathogen
replication, host immune responses, and interactions with microbiota - are
crucial not only for determining the progression of disease at the individual
level, but also for driving within-host evolution and onwards transmission, and
therefore shape dynamics at the population level. Various approaches have been
proposed to model both within- and between-host dynamics, but these typically
require considerable simplifying assumptions to couple processes at contrasting
scales (e.g., the within-host dynamics quickly reach a steady state) or are
computationally intensive. Here we propose a novel, readily adaptable and
broadly applicable method for modelling both within- and between-host processes
which can fully couple dynamics across scales and is both realistic and
computationally efficient. By individually tracking the deterministic
within-host dynamics of infected individuals, and stochastically coupling these
to continuous host state variables at the population-level, we take advantage
of fast numerical methods at both scales while still capturing individual
transient within-host dynamics and stochasticity in transmission between hosts.
Our approach closely agrees with full stochastic individual-based simulations
and is especially useful when the within-host dynamics do not rapidly reach a
steady state or over longer timescales to track pathogen evolution. By applying
our method to different pathogen growth scenarios we show how common
simplifying assumptions fundamentally change epidemiological and evolutionary
dynamics.
| [
{
"created": "Mon, 18 Dec 2023 16:45:31 GMT",
"version": "v1"
}
] | 2023-12-20 | [
[
"Smith",
"Cameron A.",
""
],
[
"Ashby",
"Ben",
""
]
] | Mathematical models of infectious disease transmission typically neglect within-host dynamics. Yet within-host dynamics - including pathogen replication, host immune responses, and interactions with microbiota - are crucial not only for determining the progression of disease at the individual level, but also for driving within-host evolution and onwards transmission, and therefore shape dynamics at the population level. Various approaches have been proposed to model both within- and between-host dynamics, but these typically require considerable simplifying assumptions to couple processes at contrasting scales (e.g., the within-host dynamics quickly reach a steady state) or are computationally intensive. Here we propose a novel, readily adaptable and broadly applicable method for modelling both within- and between-host processes which can fully couple dynamics across scales and is both realistic and computationally efficient. By individually tracking the deterministic within-host dynamics of infected individuals, and stochastically coupling these to continuous host state variables at the population-level, we take advantage of fast numerical methods at both scales while still capturing individual transient within-host dynamics and stochasticity in transmission between hosts. Our approach closely agrees with full stochastic individual-based simulations and is especially useful when the within-host dynamics do not rapidly reach a steady state or over longer timescales to track pathogen evolution. By applying our method to different pathogen growth scenarios we show how common simplifying assumptions fundamentally change epidemiological and evolutionary dynamics. |
2008.05377 | Dokyoon Kim | Yonghyun Nam, Jae-Seung Yun, Seung Mi Lee, Ji Won Park, Ziqi Chen,
Brian Lee, Anurag Verma, Xia Ning, Li Shen, Dokyoon Kim | Network reinforcement driven drug repurposing for COVID-19 by exploiting
disease-gene-drug associations | 4 figures | null | null | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Currently, the number of patients with COVID-19 has significantly increased.
Thus, there is an urgent need for developing treatments for COVID-19. Drug
repurposing, which is the process of reusing already-approved drugs for new
medical conditions, can be a good way to solve this problem quickly and
broadly. Many clinical trials for COVID-19 patients using treatments for other
diseases have already been in place or will be performed at clinical sites in
the near future. Additionally, patients with comorbidities such as diabetes
mellitus, obesity, liver cirrhosis, kidney diseases, hypertension, and asthma
are at higher risk for severe illness from COVID-19. Thus, the relationship of
comorbidity disease with COVID-19 may help to find repurposable drugs. To
reduce trial and error in finding treatments for COVID-19, we propose building
a network-based drug repurposing framework to prioritize repurposable drugs.
First, we utilized knowledge of COVID-19 to construct a disease-gene-drug
network (DGDr-Net) representing a COVID-19-centric interactome with components
for diseases, genes, and drugs. DGDr-Net consisted of 592 diseases, 26,681
human genes and 2,173 drugs, and medical information for 18 common
comorbidities. The DGDr-Net recommended candidate repurposable drugs for
COVID-19 through network reinforcement driven scoring algorithms. The scoring
algorithms determined the priority of recommendations by utilizing graph-based
semi-supervised learning. From the predicted scores, we recommended 30 drugs,
including dexamethasone, resveratrol, methotrexate, indomethacin, quercetin,
etc., as repurposable drugs for COVID-19, and the results were verified with
drugs that have been under clinical trials. The list of drugs via a data-driven
computational approach could help reduce trial-and-error in finding treatment
for COVID-19.
| [
{
"created": "Wed, 12 Aug 2020 15:19:11 GMT",
"version": "v1"
}
] | 2020-08-13 | [
[
"Nam",
"Yonghyun",
""
],
[
"Yun",
"Jae-Seung",
""
],
[
"Lee",
"Seung Mi",
""
],
[
"Park",
"Ji Won",
""
],
[
"Chen",
"Ziqi",
""
],
[
"Lee",
"Brian",
""
],
[
"Verma",
"Anurag",
""
],
[
"Ning",
"Xia",
""
],
[
"Shen",
"Li",
""
],
[
"Kim",
"Dokyoon",
""
]
] | Currently, the number of patients with COVID-19 has significantly increased. Thus, there is an urgent need for developing treatments for COVID-19. Drug repurposing, which is the process of reusing already-approved drugs for new medical conditions, can be a good way to solve this problem quickly and broadly. Many clinical trials for COVID-19 patients using treatments for other diseases have already been in place or will be performed at clinical sites in the near future. Additionally, patients with comorbidities such as diabetes mellitus, obesity, liver cirrhosis, kidney diseases, hypertension, and asthma are at higher risk for severe illness from COVID-19. Thus, the relationship of comorbidity disease with COVID-19 may help to find repurposable drugs. To reduce trial and error in finding treatments for COVID-19, we propose building a network-based drug repurposing framework to prioritize repurposable drugs. First, we utilized knowledge of COVID-19 to construct a disease-gene-drug network (DGDr-Net) representing a COVID-19-centric interactome with components for diseases, genes, and drugs. DGDr-Net consisted of 592 diseases, 26,681 human genes and 2,173 drugs, and medical information for 18 common comorbidities. The DGDr-Net recommended candidate repurposable drugs for COVID-19 through network reinforcement driven scoring algorithms. The scoring algorithms determined the priority of recommendations by utilizing graph-based semi-supervised learning. From the predicted scores, we recommended 30 drugs, including dexamethasone, resveratrol, methotrexate, indomethacin, quercetin, etc., as repurposable drugs for COVID-19, and the results were verified with drugs that have been under clinical trials. The list of drugs via a data-driven computational approach could help reduce trial-and-error in finding treatment for COVID-19. |
2011.12537 | Ina Schmidt | Ina Schmidt (1), Areti Papastavrou (1), Paul Steinmann (2) ((1)
Nuremberg Tech, (2) University of Erlangen-Nuremberg) | Concurrent consideration of cortical and cancellous bone within
continuum bone remodelling | 18 pages, 11 figures | null | 10.1080/10255842.2021.1880573 | null | q-bio.TO cs.CE | http://creativecommons.org/licenses/by/4.0/ | Continuum bone remodelling is an important tool for predicting the effects of
mechanical stimuli on bone density evolution. While the modelling of only
cancellous bone is considered in many studies based on continuum bone
remodelling, this work presents an approach of modelling also cortical bone and
the interaction of both bone types. The distinction between bone types is made
by introducing an initial volume fraction. A simple point-wise example is used
to study the behaviour of novel model options, as well as a proximal femur
example, where the interaction of both bone types is demonstrated using initial
density distributions. The results of the proposed model options indicate that
the consideration of cortical bone remarkably changes the density evolution of
cancellous bone, and should therefore not be neglected.
| [
{
"created": "Wed, 25 Nov 2020 06:13:31 GMT",
"version": "v1"
}
] | 2021-02-15 | [
[
"Schmidt",
"Ina",
""
],
[
"Papastavrou",
"Areti",
""
],
[
"Steinmann",
"Paul",
""
]
] | Continuum bone remodelling is an important tool for predicting the effects of mechanical stimuli on bone density evolution. While the modelling of only cancellous bone is considered in many studies based on continuum bone remodelling, this work presents an approach of modelling also cortical bone and the interaction of both bone types. The distinction between bone types is made by introducing an initial volume fraction. A simple point-wise example is used to study the behaviour of novel model options, as well as a proximal femur example, where the interaction of both bone types is demonstrated using initial density distributions. The results of the proposed model options indicate that the consideration of cortical bone remarkably changes the density evolution of cancellous bone, and should therefore not be neglected. |
2109.09424 | Ivana Pajic-Lijakovic Dr. | Ivana Pajic-Lijakovic and Milan Milivojevic | Surface activity of cancer cells: the fusion of two cell aggregates | 18 pages, 6 figures,5535 words | null | null | null | q-bio.CB | http://creativecommons.org/licenses/by/4.0/ | Although a good comprehension of how cancer cells collectively migrate by
following molecular rules which influence the state of cell-cell adhesion
contacts has been generated, the impact of collective migration on cellular
rearrangement from subcellular to supracellular level remains less understood.
Thus, considering collective cell migration (CCM) of cancer mesenchymal cells
on one side and healthy epithelial cells on the other during the fusion of two
cell aggregates could result in a powerful tool in order to address the
contribution of structural changes at subcellular level which influence the
cellular rearrangements and help to understand this important, but still
controversial topic. While healthy epithelial cells undergo volumetric cell
rearrangement driven by the tissue surface tension, which results in a
collision of opposite directed velocity front near the contact point between
two cell aggregates, mesenchymal cells follow quite different scenario. These
cells are capable of reducing the surface tension and undergo surface cell
rearrangement. The main goal of this contribution is to discuss the origin of
surface activity of cancer cells by accounting for the crosstalk between
cell-cell and cell-ECM adhesion contacts influenced by the cell contractility.
| [
{
"created": "Mon, 20 Sep 2021 11:04:43 GMT",
"version": "v1"
}
] | 2021-09-21 | [
[
"Pajic-Lijakovic",
"Ivana",
""
],
[
"Milivojevic",
"Milan",
""
]
] | Although a good comprehension of how cancer cells collectively migrate by following molecular rules which influence the state of cell-cell adhesion contacts has been generated, the impact of collective migration on cellular rearrangement from subcellular to supracellular level remains less understood. Thus, considering collective cell migration (CCM) of cancer mesenchymal cells on one side and healthy epithelial cells on the other during the fusion of two cell aggregates could result in a powerful tool in order to address the contribution of structural changes at subcellular level which influence the cellular rearrangements and help to understand this important, but still controversial topic. While healthy epithelial cells undergo volumetric cell rearrangement driven by the tissue surface tension, which results in a collision of opposite directed velocity front near the contact point between two cell aggregates, mesenchymal cells follow quite different scenario. These cells are capable of reducing the surface tension and undergo surface cell rearrangement. The main goal of this contribution is to discuss the origin of surface activity of cancer cells by accounting for the crosstalk between cell-cell and cell-ECM adhesion contacts influenced by the cell contractility. |
1504.08255 | Takahiro Wada | Takahiro Wada, Hiroyuki Konno, Satoru Fujisawa, Shunichi Doi | Can Passenger's Active Head Tilt Decrease The Severity of Carsickness? -
Effect of Head Tilt on Severity of Motion Sickness in a Lateral Acceleration
Environment | null | Human Factors, 54(2), pp.71-78, 2012 | 10.1177/0018720812436584 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objective: We investigated the effect of the passenger head-tilt strategy on
the severity of carsickness in lateral acceleration situations in automobiles.
Background: It is well known that the driver is generally less susceptible to
carsickness than are the passengers. However, it is also known that the driver
tilts his or her head toward the curve center when negotiating a curve, whereas
the passenger's head moves in the opposite direction. Therefore, we
hypothesized that the head-tilt strategy has the effect of reducing the
severity of carsickness. Method: A passenger car was driven on a quasi-oval
track with a pylon slalom while the participant sat in the navigator seat. The
experiment was terminated when either the participant felt the initial symptoms
of motion sickness or the car finished 20 laps. In the natural head-tilt
condition, the participants were instructed to sit naturally, to relax, and not
to oppose the lateral acceleration intentionally. In the active head-tilt
condition, the participants were asked to tilt their heads against the
centrifugal acceleration, thus imitating the driver's head tilt. Results: The
number of laps achieved in the active condition was significantly greater than
that in the natural condition. In addition, the subjective ratings of motion
sickness and symptoms in the active condition were significantly lower than
those in the natural condition. Conclusion: We suggest that an active head tilt
against centrifugal acceleration reduces the severity of motion sickness.
Application: Potential applications of this study include development of a
methodology to reduce carsickness.
| [
{
"created": "Thu, 30 Apr 2015 14:51:51 GMT",
"version": "v1"
}
] | 2015-05-01 | [
[
"Wada",
"Takahiro",
""
],
[
"Konno",
"Hiroyuki",
""
],
[
"Fujisawa",
"Satoru",
""
],
[
"Doi",
"Shunichi",
""
]
] | Objective: We investigated the effect of the passenger head-tilt strategy on the severity of carsickness in lateral acceleration situations in automobiles. Background: It is well known that the driver is generally less susceptible to carsickness than are the passengers. However, it is also known that the driver tilts his or her head toward the curve center when negotiating a curve, whereas the passenger's head moves in the opposite direction. Therefore, we hypothesized that the head-tilt strategy has the effect of reducing the severity of carsickness. Method: A passenger car was driven on a quasi-oval track with a pylon slalom while the participant sat in the navigator seat. The experiment was terminated when either the participant felt the initial symptoms of motion sickness or the car finished 20 laps. In the natural head-tilt condition, the participants were instructed to sit naturally, to relax, and not to oppose the lateral acceleration intentionally. In the active head-tilt condition, the participants were asked to tilt their heads against the centrifugal acceleration, thus imitating the driver's head tilt. Results: The number of laps achieved in the active condition was significantly greater than that in the natural condition. In addition, the subjective ratings of motion sickness and symptoms in the active condition were significantly lower than those in the natural condition. Conclusion: We suggest that an active head tilt against centrifugal acceleration reduces the severity of motion sickness. Application: Potential applications of this study include development of a methodology to reduce carsickness. |
2204.11857 | Shaohua Jiang | Lyu Zhijian, Jiang Shaohua, Liang Yigao and Gao Min | GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and
Double GRU | pages:13 conferfece:DMML2022 | null | 10.5121/csit.2022.120703 | null | q-bio.QM cs.AI cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The work for predicting drug and target affinity(DTA) is crucial for drug
development and repurposing. In this work, we propose a novel method called
GDGRU-DTA to predict the binding affinity between drugs and targets, which is
based on GraphDTA, but we consider that protein sequences are long sequences,
so simple CNN cannot capture the context dependencies in protein sequences
well. Therefore, we improve it by interpreting the protein sequences as time
series and extracting their features using Gate Recurrent Unit(GRU) and
Bidirectional Gate Recurrent Unit(BiGRU). For the drug, our processing method
is similar to that of GraphDTA, but uses two different graph convolution
methods. Subsequently, the representation of drugs and proteins are
concatenated for final prediction. We evaluate the proposed model on two
benchmark datasets. Our model outperforms some state-of-the-art deep learning
methods, and the results demonstrate the feasibility and excellent feature
capture ability of our model.
| [
{
"created": "Mon, 25 Apr 2022 13:21:37 GMT",
"version": "v1"
}
] | 2022-04-27 | [
[
"Zhijian",
"Lyu",
""
],
[
"Shaohua",
"Jiang",
""
],
[
"Yigao",
"Liang",
""
],
[
"Min",
"Gao",
""
]
] | The work for predicting drug and target affinity(DTA) is crucial for drug development and repurposing. In this work, we propose a novel method called GDGRU-DTA to predict the binding affinity between drugs and targets, which is based on GraphDTA, but we consider that protein sequences are long sequences, so simple CNN cannot capture the context dependencies in protein sequences well. Therefore, we improve it by interpreting the protein sequences as time series and extracting their features using Gate Recurrent Unit(GRU) and Bidirectional Gate Recurrent Unit(BiGRU). For the drug, our processing method is similar to that of GraphDTA, but uses two different graph convolution methods. Subsequently, the representation of drugs and proteins are concatenated for final prediction. We evaluate the proposed model on two benchmark datasets. Our model outperforms some state-of-the-art deep learning methods, and the results demonstrate the feasibility and excellent feature capture ability of our model. |
1707.04171 | Farzaneh Ghasemi Tahrir | Farzaneh Ghasemi Tahrir | Modeling Hormesis Using a Non-Monotonic Copula Method | null | null | null | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a probabilistic method for capturing non-monotonic
behavior under the biphasic dose-response regime observed in many biological
systems experiencing different types of stress. The proposed method is based on
the rolling-pin method introduced earlier to estimate highly nonlinear and
non-monotonic joint probability distributions from continuous domain data. We
show that the proposed method outperforms the conventional parametric methods
in terms of the error (namely RMSE) and it needs fewer parameters to be
estimated a priori, while offering high flexibility. The application and
performance of the proposed method are shown through an example.
| [
{
"created": "Thu, 13 Jul 2017 15:21:43 GMT",
"version": "v1"
}
] | 2017-07-14 | [
[
"Tahrir",
"Farzaneh Ghasemi",
""
]
] | This paper presents a probabilistic method for capturing non-monotonic behavior under the biphasic dose-response regime observed in many biological systems experiencing different types of stress. The proposed method is based on the rolling-pin method introduced earlier to estimate highly nonlinear and non-monotonic joint probability distributions from continuous domain data. We show that the proposed method outperforms the conventional parametric methods in terms of the error (namely RMSE) and it needs fewer parameters to be estimated a priori, while offering high flexibility. The application and performance of the proposed method are shown through an example. |
1010.2479 | Michael Desai | Aleksandra M. Walczak, Lauren E. Nicolaisen, Joshua B. Plotkin,
Michael M. Desai | The Structure of Genealogies in the Presence of Purifying Selection: A
"Fitness-Class Coalescent" | 73 pages, 9 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Compared to a neutral model, purifying selection distorts the structure of
genealogies and hence alters the patterns of sampled genetic variation.
Although these distortions may be common in nature, our understanding of how we
expect purifying selection to affect patterns of molecular variation remains
incomplete. Genealogical approaches such as coalescent theory have proven
difficult to generalize to situations involving selection at many linked sites,
unless selection pressures are extremely strong. Here, we introduce an
effective coalescent theory (a "fitness-class coalescent") to describe the
structure of genealogies in the presence of purifying selection at many linked
sites. We use this effective theory to calculate several simple statistics
describing the expected patterns of variation in sequence data, both at the
sites under selection and at linked neutral sites. Our analysis combines our
earlier description of the allele frequency spectrum in the presence of
purifying selection (Desai et al. 2010) with the structured coalescent approach
of Nordborg (1997), to trace the ancestry of individuals through the
distribution of fitnesses within the population. Alternatively, we can derive
our results using an extension of the coalescent approach of Hudson and Kaplan
(1994). We find that purifying selection leads to patterns of genetic variation
that are related but not identical to a neutrally evolving population in which
population size has varied in a specific way in the past.
| [
{
"created": "Tue, 12 Oct 2010 19:38:35 GMT",
"version": "v1"
},
{
"created": "Thu, 26 May 2011 22:32:18 GMT",
"version": "v2"
}
] | 2011-05-30 | [
[
"Walczak",
"Aleksandra M.",
""
],
[
"Nicolaisen",
"Lauren E.",
""
],
[
"Plotkin",
"Joshua B.",
""
],
[
"Desai",
"Michael M.",
""
]
] | Compared to a neutral model, purifying selection distorts the structure of genealogies and hence alters the patterns of sampled genetic variation. Although these distortions may be common in nature, our understanding of how we expect purifying selection to affect patterns of molecular variation remains incomplete. Genealogical approaches such as coalescent theory have proven difficult to generalize to situations involving selection at many linked sites, unless selection pressures are extremely strong. Here, we introduce an effective coalescent theory (a "fitness-class coalescent") to describe the structure of genealogies in the presence of purifying selection at many linked sites. We use this effective theory to calculate several simple statistics describing the expected patterns of variation in sequence data, both at the sites under selection and at linked neutral sites. Our analysis combines our earlier description of the allele frequency spectrum in the presence of purifying selection (Desai et al. 2010) with the structured coalescent approach of Nordborg (1997), to trace the ancestry of individuals through the distribution of fitnesses within the population. Alternatively, we can derive our results using an extension of the coalescent approach of Hudson and Kaplan (1994). We find that purifying selection leads to patterns of genetic variation that are related but not identical to a neutrally evolving population in which population size has varied in a specific way in the past. |
1312.4038 | Christopher Quince | Johannes Alneberg, Brynjar Smari Bjarnason, Ino de Bruijn, Melanie
Schirmer, Joshua Quick, Umer Z. Ijaz, Nicholas J. Loman, Anders F. Andersson,
Christopher Quince | CONCOCT: Clustering cONtigs on COverage and ComposiTion | 28 pages, 14 figures | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Metagenomics enables the reconstruction of microbial genomes in complex
microbial communities without the need for culturing. Since assembly typically
results in fragmented genomes the grouping of genome fragments (contigs)
belonging to the same genome, a process referred to as binning, remains a major
informatics challenge. Here we present CONCOCT, a computer program that
combines three types of information - sequence composition, coverage across
multiple sample, and read-pair linkage - to automatically bin contigs into
genomes. We demonstrate high recall and precision rates of the program on
artificial as well as real human gut metagenome datasets.
| [
{
"created": "Sat, 14 Dec 2013 12:21:38 GMT",
"version": "v1"
}
] | 2013-12-17 | [
[
"Alneberg",
"Johannes",
""
],
[
"Bjarnason",
"Brynjar Smari",
""
],
[
"de Bruijn",
"Ino",
""
],
[
"Schirmer",
"Melanie",
""
],
[
"Quick",
"Joshua",
""
],
[
"Ijaz",
"Umer Z.",
""
],
[
"Loman",
"Nicholas J.",
""
],
[
"Andersson",
"Anders F.",
""
],
[
"Quince",
"Christopher",
""
]
] | Metagenomics enables the reconstruction of microbial genomes in complex microbial communities without the need for culturing. Since assembly typically results in fragmented genomes the grouping of genome fragments (contigs) belonging to the same genome, a process referred to as binning, remains a major informatics challenge. Here we present CONCOCT, a computer program that combines three types of information - sequence composition, coverage across multiple sample, and read-pair linkage - to automatically bin contigs into genomes. We demonstrate high recall and precision rates of the program on artificial as well as real human gut metagenome datasets. |
q-bio/0310008 | Michael Slutsky | Michael Slutsky, Mehran Kardar and Leonid A. Mirny | The long reach of DNA sequence heterogeneity in diffusive processes | null | Phys. Rev. E 69, 061903 (2004) | 10.1103/PhysRevE.69.061903 | null | q-bio.BM cond-mat.dis-nn cond-mat.soft physics.bio-ph | null | Many biological processes involve one dimensional diffusion over a correlated
inhomogeneous energy landscape with a correlation length $\xi_c$. Typical
examples are specific protein target location on DNA, nucleosome repositioning,
or DNA translocation through a nanopore, in all cases with $\xi_c\approx$ 10
nm. We investigate such transport processes by the mean first passage time
(MFPT) formalism, and find diffusion times which exhibit strong sample to
sample fluctuations. For a a displacement $N$, the average MFPT is diffusive,
while its standard deviation over the ensemble of energy profiles scales as
$N^{3/2}$ with a large prefactor. Fluctuations are thus dominant for
displacements smaller than a characteristic $N_c \gg \xi_c$: typical values are
much less than the mean, and governed by an anomalous diffusion rule. Potential
biological consequences of such random walks, composed of rapid scans in the
vicinity of favorable energy valleys and occasional jumps to further valleys,
is discussed.
| [
{
"created": "Thu, 9 Oct 2003 15:38:00 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Oct 2003 21:29:01 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Slutsky",
"Michael",
""
],
[
"Kardar",
"Mehran",
""
],
[
"Mirny",
"Leonid A.",
""
]
] | Many biological processes involve one dimensional diffusion over a correlated inhomogeneous energy landscape with a correlation length $\xi_c$. Typical examples are specific protein target location on DNA, nucleosome repositioning, or DNA translocation through a nanopore, in all cases with $\xi_c\approx$ 10 nm. We investigate such transport processes by the mean first passage time (MFPT) formalism, and find diffusion times which exhibit strong sample to sample fluctuations. For a a displacement $N$, the average MFPT is diffusive, while its standard deviation over the ensemble of energy profiles scales as $N^{3/2}$ with a large prefactor. Fluctuations are thus dominant for displacements smaller than a characteristic $N_c \gg \xi_c$: typical values are much less than the mean, and governed by an anomalous diffusion rule. Potential biological consequences of such random walks, composed of rapid scans in the vicinity of favorable energy valleys and occasional jumps to further valleys, is discussed. |
1606.08889 | Thierry Mora | Christophe Gardella, Olivier Marre, Thierry Mora | A tractable method for describing complex couplings between neurons and
population rate | null | eNeuro 3(4) e0160-15.2016 (2016) | 10.1523/ENEURO.0160-15.2016 | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neurons within a population are strongly correlated, but how to simply
capture these correlations is still a matter of debate. Recent studies have
shown that the activity of each cell is influenced by the population rate,
defined as the summed activity of all neurons in the population. However, an
explicit, tractable model for these interactions is still lacking. Here we
build a probabilistic model of population activity that reproduces the firing
rate of each cell, the distribution of the population rate, and the linear
coupling between them. This model is tractable, meaning that its parameters can
be learned in a few seconds on a standard computer even for large population
recordings. We inferred our model for a population of 160 neurons in the
salamander retina. In this population, single-cell firing rates depended in
unexpected ways on the population rate. In particular, some cells had a
preferred population rate at which they were most likely to fire. These complex
dependencies could not be explained by a linear coupling between the cell and
the population rate. We designed a more general, still tractable model that
could fully account for these non-linear dependencies. We thus provide a simple
and computationally tractable way to learn models that reproduce the dependence
of each neuron on the population rate.
| [
{
"created": "Tue, 28 Jun 2016 21:03:10 GMT",
"version": "v1"
}
] | 2016-12-26 | [
[
"Gardella",
"Christophe",
""
],
[
"Marre",
"Olivier",
""
],
[
"Mora",
"Thierry",
""
]
] | Neurons within a population are strongly correlated, but how to simply capture these correlations is still a matter of debate. Recent studies have shown that the activity of each cell is influenced by the population rate, defined as the summed activity of all neurons in the population. However, an explicit, tractable model for these interactions is still lacking. Here we build a probabilistic model of population activity that reproduces the firing rate of each cell, the distribution of the population rate, and the linear coupling between them. This model is tractable, meaning that its parameters can be learned in a few seconds on a standard computer even for large population recordings. We inferred our model for a population of 160 neurons in the salamander retina. In this population, single-cell firing rates depended in unexpected ways on the population rate. In particular, some cells had a preferred population rate at which they were most likely to fire. These complex dependencies could not be explained by a linear coupling between the cell and the population rate. We designed a more general, still tractable model that could fully account for these non-linear dependencies. We thus provide a simple and computationally tractable way to learn models that reproduce the dependence of each neuron on the population rate. |
1404.0329 | Samuel Kaski | Ali Faisal, Jaakko Peltonen, Elisabeth Georgii, Johan Rung and Samuel
Kaski | Toward computational cumulative biology by combining models of
biological datasets | null | null | 10.1371/journal.pone.0113053 | null | q-bio.QM q-bio.GN stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A main challenge of data-driven sciences is how to make maximal use of the
progressively expanding databases of experimental datasets in order to keep
research cumulative. We introduce the idea of a modeling-based dataset
retrieval engine designed for relating a researcher's experimental dataset to
earlier work in the field. The search is (i) data-driven to enable new
findings, going beyond the state of the art of keyword searches in annotations,
(ii) modeling-driven, to both include biological knowledge and insights learned
from data, and (iii) scalable, as it is accomplished without building one
unified grand model of all data. Assuming each dataset has been modeled
beforehand, by the researchers or by database managers, we apply a rapidly
computable and optimizable combination model to decompose a new dataset into
contributions from earlier relevant models. By using the data-driven
decomposition we identify a network of interrelated datasets from a large
annotated human gene expression atlas. While tissue type and disease were major
driving forces for determining relevant datasets, the found relationships were
richer and the model-based search was more accurate than keyword search; it
moreover recovered biologically meaningful relationships that are not
straightforwardly visible from annotations, for instance, between cells in
different developmental stages such as thymocytes and T-cells. Data-driven
links and citations matched to a large extent; the data-driven links even
uncovered corrections to the publication data, as two of the most linked
datasets were not highly cited and turned out to have wrong publication entries
in the database.
| [
{
"created": "Tue, 1 Apr 2014 17:55:57 GMT",
"version": "v1"
}
] | 2015-06-19 | [
[
"Faisal",
"Ali",
""
],
[
"Peltonen",
"Jaakko",
""
],
[
"Georgii",
"Elisabeth",
""
],
[
"Rung",
"Johan",
""
],
[
"Kaski",
"Samuel",
""
]
] | A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher's experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to both include biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer and the model-based search was more accurate than keyword search; it moreover recovered biologically meaningful relationships that are not straightforwardly visible from annotations, for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database. |
q-bio/0502044 | Jose Vilar | Jose M. G. Vilar and Leonor Saiz | DNA looping in gene regulation: from the assembly of macromolecular
complexes to the control of transcriptional noise | To appear in Current Opinion in Genetics & Development | Current Opinion in Genetics & Development, 15, 136-144 (2005) | 10.1016/j.gde.2005.02.005 | null | q-bio.MN cond-mat.soft physics.bio-ph q-bio.BM q-bio.QM | null | The formation of DNA loops by proteins and protein complexes that bind at
distal DNA sites plays a central role in many cellular processes, such as
transcription, recombination, and replication. Here we review the basic
thermodynamic concepts underlying the assembly of macromolecular complexes on
looped DNA and the effects that this process has in the properties of gene
regulation. Beyond the traditional view of DNA looping as a mechanism to
increase the affinity of regulatory molecules for their cognate sites, recent
developments indicate that DNA looping can also lead to the suppression of
cell-to-cell variability, the control of transcriptional noise, and the
activation of cooperative interactions on demand.
| [
{
"created": "Mon, 28 Feb 2005 02:47:09 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Vilar",
"Jose M. G.",
""
],
[
"Saiz",
"Leonor",
""
]
] | The formation of DNA loops by proteins and protein complexes that bind at distal DNA sites plays a central role in many cellular processes, such as transcription, recombination, and replication. Here we review the basic thermodynamic concepts underlying the assembly of macromolecular complexes on looped DNA and the effects that this process has in the properties of gene regulation. Beyond the traditional view of DNA looping as a mechanism to increase the affinity of regulatory molecules for their cognate sites, recent developments indicate that DNA looping can also lead to the suppression of cell-to-cell variability, the control of transcriptional noise, and the activation of cooperative interactions on demand. |
2007.00469 | Jose Gomez-Tames | Jose Gomez-Tames, Ilkka Laakso, and Akimasa Hirata | Review on Biophysical Modelling and Simulation Studies for Transcranial
Magnetic Stimulation | null | null | 10.1088/1361-6560/aba40d | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transcranial magnetic stimulation (TMS) is a technique for noninvasively
stimulating a brain area for therapeutic, rehabilitation treatments and
neuroscience research. Despite our understanding of the physical principles and
experimental developments pertaining to TMS, it is difficult to identify the
exact brain target as the generated dosage exhibits a non-uniform distribution
owing to the complicated and subject-dependent brain anatomy and the lack of
biomarkers that can quantify the effects of TMS in most cortical areas.
Computational dosimetry has progressed significantly and enables TMS assessment
by computation of the induced electric field (the primary physical agent known
to activate the brain neurons) in a digital representation of the human head.
In this review, TMS dosimetry studies are summarised, clarifying the importance
of the anatomical and human biophysical parameters and computational methods.
This review shows that there is a high consensus on the importance of a
detailed cortical folding representation and an accurate modelling of the
surrounding cerebrospinal fluid. Recent studies have also enabled the
prediction of individually optimised stimulation based on magnetic resonance
imaging of the patient/subject and have attempted to understand the temporal
effects of TMS at the cellular level by incorporating neural modelling. These
efforts, together with the fast deployment of personalised TMS computations,
will permit the adoption of TMS dosimetry as a standard procedure in clinical
procedures.
| [
{
"created": "Mon, 29 Jun 2020 22:31:35 GMT",
"version": "v1"
}
] | 2020-12-30 | [
[
"Gomez-Tames",
"Jose",
""
],
[
"Laakso",
"Ilkka",
""
],
[
"Hirata",
"Akimasa",
""
]
] | Transcranial magnetic stimulation (TMS) is a technique for noninvasively stimulating a brain area for therapeutic, rehabilitation treatments and neuroscience research. Despite our understanding of the physical principles and experimental developments pertaining to TMS, it is difficult to identify the exact brain target as the generated dosage exhibits a non-uniform distribution owing to the complicated and subject-dependent brain anatomy and the lack of biomarkers that can quantify the effects of TMS in most cortical areas. Computational dosimetry has progressed significantly and enables TMS assessment by computation of the induced electric field (the primary physical agent known to activate the brain neurons) in a digital representation of the human head. In this review, TMS dosimetry studies are summarised, clarifying the importance of the anatomical and human biophysical parameters and computational methods. This review shows that there is a high consensus on the importance of a detailed cortical folding representation and an accurate modelling of the surrounding cerebrospinal fluid. Recent studies have also enabled the prediction of individually optimised stimulation based on magnetic resonance imaging of the patient/subject and have attempted to understand the temporal effects of TMS at the cellular level by incorporating neural modelling. These efforts, together with the fast deployment of personalised TMS computations, will permit the adoption of TMS dosimetry as a standard procedure in clinical procedures. |
2110.06339 | Gordana Dodig-Crnkovic | Gordana Dodig-Crnkovic | Natural Computational Architectures for Cognitive Info-Communication | null | null | null | null | q-bio.NC cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recent comprehensive overview of 40 years of research in cognitive
architectures, (Kotseruba and Tsotsos 2020), evaluates modelling of the core
cognitive abilities in humans, but only marginally addresses biologically
plausible approaches based on natural computation. This mini review presents a
set of perspectives and approaches which have shaped the development of
biologically inspired computational models in the recent past that can lead to
the development of biologically more realistic cognitive architectures. For
describing continuum of natural cognitive architectures, from basal cellular to
human-level cognition, we use evolutionary info-computational framework, where
natural/ physical/ morphological computation leads to evolution of increasingly
complex cognitive systems. Forty years ago, when the first cognitive
architectures have been proposed, understanding of cognition, embodiment and
evolution was different. So was the state of the art of information physics,
bioinformatics, information chemistry, computational neuroscience, complexity
theory, self-organization, theory of evolution, information and computation.
Novel developments support a constructive interdisciplinary framework for
cognitive architectures in the context of computing nature, where interactions
between constituents at different levels of organization lead to
complexification of agency and increased cognitive capacities. We identify
several important research questions for further investigation that can
increase understanding of cognition in nature and inspire new developments of
cognitive technologies. Recently, basal cell cognition attracted a lot of
interest for its possible applications in medicine, new computing technologies,
as well as micro- and nanorobotics.
| [
{
"created": "Fri, 1 Oct 2021 18:01:16 GMT",
"version": "v1"
}
] | 2021-10-14 | [
[
"Dodig-Crnkovic",
"Gordana",
""
]
] | Recent comprehensive overview of 40 years of research in cognitive architectures, (Kotseruba and Tsotsos 2020), evaluates modelling of the core cognitive abilities in humans, but only marginally addresses biologically plausible approaches based on natural computation. This mini review presents a set of perspectives and approaches which have shaped the development of biologically inspired computational models in the recent past that can lead to the development of biologically more realistic cognitive architectures. For describing continuum of natural cognitive architectures, from basal cellular to human-level cognition, we use evolutionary info-computational framework, where natural/ physical/ morphological computation leads to evolution of increasingly complex cognitive systems. Forty years ago, when the first cognitive architectures have been proposed, understanding of cognition, embodiment and evolution was different. So was the state of the art of information physics, bioinformatics, information chemistry, computational neuroscience, complexity theory, self-organization, theory of evolution, information and computation. Novel developments support a constructive interdisciplinary framework for cognitive architectures in the context of computing nature, where interactions between constituents at different levels of organization lead to complexification of agency and increased cognitive capacities. We identify several important research questions for further investigation that can increase understanding of cognition in nature and inspire new developments of cognitive technologies. Recently, basal cell cognition attracted a lot of interest for its possible applications in medicine, new computing technologies, as well as micro- and nanorobotics. |
1809.10339 | Marcelo Amanajas Pires Marcelo A. Pires | Marcelo A. Pires, S\'ilvio M. Duarte Queir\'os | Optimal diffusion in ecological dynamics with Allee effect in a
metapopulation | 16 pages; 6 figures | PLoS One, 2019 | 10.1371/journal.pone.0218087 | null | q-bio.PE cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How diffusion impacts on ecological dynamics under the Allee effect and
spatial constraints? That is the question we address. Employing a microscopic
minimal model in a metapopulation (without imposing nonlinear birth and death
rates) we evince --- both numerically and analitically --- the emergence of an
optimal diffusion that maximises the survival probability. Even though, at
first such result seems counter-intuitive, it has empirical support from recent
experiments with engineered bacteria. Moreover, we show that this optimal
diffusion disappears for loose spatial constraints.
| [
{
"created": "Thu, 27 Sep 2018 04:41:27 GMT",
"version": "v1"
}
] | 2019-11-27 | [
[
"Pires",
"Marcelo A.",
""
],
[
"Queirós",
"Sílvio M. Duarte",
""
]
] | How diffusion impacts on ecological dynamics under the Allee effect and spatial constraints? That is the question we address. Employing a microscopic minimal model in a metapopulation (without imposing nonlinear birth and death rates) we evince --- both numerically and analitically --- the emergence of an optimal diffusion that maximises the survival probability. Even though, at first such result seems counter-intuitive, it has empirical support from recent experiments with engineered bacteria. Moreover, we show that this optimal diffusion disappears for loose spatial constraints. |
1511.05500 | Catherine Patterson | Catherine E. Patterson, Bruce P. Ayati, and Sarah A. Holstein | Modeling the Multiple Myeloma Vicious Cycle: Signaling Across the Bone
Marrow Microenvironment | null | null | null | null | q-bio.CB q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multiple myeloma is a plasma cell cancer that leads to a dysregulated bone
remodeling process. We present a partial differential equation model describing
the dynamics of bone remodeling with the presence of myeloma tumor cells. The
model explicitly takes into account the roles of osteoclasts, osteoblasts,
precursor cells, stromal cells, osteocytes, and tumor cells. Previous models
based on ordinary differential equations make the simplifying assumption that
the bone and tumor cells are adjacent to each other. However, in actuality,
these cell populations are separated by the bone marrow. Our model takes this
separation into account by including the diffusion of chemical factors across
the marrow, which can be viewed as communication between the tumor and bone.
Additionally, this model incorporates the growth of the tumor and the
diminishing bone mass by utilizing a ``moving boundary.'' We present numerical
simulations that qualitatively validate our model's description of the cell
population dynamics.
| [
{
"created": "Tue, 17 Nov 2015 18:31:50 GMT",
"version": "v1"
}
] | 2015-11-18 | [
[
"Patterson",
"Catherine E.",
""
],
[
"Ayati",
"Bruce P.",
""
],
[
"Holstein",
"Sarah A.",
""
]
] | Multiple myeloma is a plasma cell cancer that leads to a dysregulated bone remodeling process. We present a partial differential equation model describing the dynamics of bone remodeling with the presence of myeloma tumor cells. The model explicitly takes into account the roles of osteoclasts, osteoblasts, precursor cells, stromal cells, osteocytes, and tumor cells. Previous models based on ordinary differential equations make the simplifying assumption that the bone and tumor cells are adjacent to each other. However, in actuality, these cell populations are separated by the bone marrow. Our model takes this separation into account by including the diffusion of chemical factors across the marrow, which can be viewed as communication between the tumor and bone. Additionally, this model incorporates the growth of the tumor and the diminishing bone mass by utilizing a ``moving boundary.'' We present numerical simulations that qualitatively validate our model's description of the cell population dynamics. |
1004.4233 | Carl Boettiger | Carl Boettiger, Jonathan Dushoff, Joshua S. Weitz | Fluctuation Domains in Adaptive Evolution | null | Theoretical population biology, 77(1), 6-13 2010 | 10.1016/j.tpb.2009.10.003 | null | q-bio.PE | http://creativecommons.org/licenses/by/3.0/ | We derive an expression for the variation between parallel trajectories in
phenotypic evolution, extending the well known result that predicts the mean
evolutionary path in adaptive dynamics or quantitative genetics. We show how
this expression gives rise to the notion of fluctuation domains - parts of the
fitness landscape where the rate of evolution is very predictable (due to
fluctuation dissipation) and parts where it is highly variable (due to
fluctuation enhancement). These fluctuation domains are determined by the
curvature of the fitness landscape. Regions of the fitness landscape with
positive curvature, such as adaptive valleys or branching points, experience
enhancement. Regions with negative curvature, such as adaptive peaks,
experience dissipation. We explore these dynamics in the ecological scenarios
of implicit and explicit competition for a limiting resource.
| [
{
"created": "Fri, 23 Apr 2010 22:20:21 GMT",
"version": "v1"
}
] | 2010-04-27 | [
[
"Boettiger",
"Carl",
""
],
[
"Dushoff",
"Jonathan",
""
],
[
"Weitz",
"Joshua S.",
""
]
] | We derive an expression for the variation between parallel trajectories in phenotypic evolution, extending the well known result that predicts the mean evolutionary path in adaptive dynamics or quantitative genetics. We show how this expression gives rise to the notion of fluctuation domains - parts of the fitness landscape where the rate of evolution is very predictable (due to fluctuation dissipation) and parts where it is highly variable (due to fluctuation enhancement). These fluctuation domains are determined by the curvature of the fitness landscape. Regions of the fitness landscape with positive curvature, such as adaptive valleys or branching points, experience enhancement. Regions with negative curvature, such as adaptive peaks, experience dissipation. We explore these dynamics in the ecological scenarios of implicit and explicit competition for a limiting resource. |
1804.05175 | Dengming Ming | Rui Chen, Dengming Ming, He Huang | Amino-acid network clique analysis of protein mutation correlation
effects: a case study of lysozme | 12 pages, 3 figures, 5 tables | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Optimizing amino-acid mutations has been a most challenging task in modern
bio- industrial enzyme designing. It is well known that many successful designs
often hinge on extensive correlations among mutations at different sites within
the enzyme, however, the underpinning mechanism for these correlations is far
from clear. Here, we present a topology-based model to quantitively
characterize correlation effects between mutations. The method is based on the
molecular dynamic simulations and the amino-acid network clique analysis that
simply examines if two single mutation sites belong to some 3-clique. We
analyzed 13 dual mutations of T4 phage lysozyme and found that the clique-based
model successfully distinguishes highly correlated or non-additive double-site
mutations from those with less correlation or additive mutations. We also
applied the model to the protein Eglin c whose topology is significantly
distinct from that of T4 phage lysozyme, and found that the model can, to some
extension, still identify non-additive mutations from additive ones. Our
calculations showed that mutation correlation effects may heavily depend on
topology relationship among mutation sites, which can be quantitatively
characterized using amino-acid network k-cliques. We also showed that
double-site mutation correlations can be significantly altered by exerting a
third mutation, indicating that more detailed physico-chemistry interactions
might be considered with the network model for better understanding of the
elusive mutation-correlation principle.
| [
{
"created": "Sat, 14 Apr 2018 06:33:36 GMT",
"version": "v1"
}
] | 2018-04-17 | [
[
"Chen",
"Rui",
""
],
[
"Ming",
"Dengming",
""
],
[
"Huang",
"He",
""
]
] | Optimizing amino-acid mutations has been a most challenging task in modern bio- industrial enzyme designing. It is well known that many successful designs often hinge on extensive correlations among mutations at different sites within the enzyme, however, the underpinning mechanism for these correlations is far from clear. Here, we present a topology-based model to quantitively characterize correlation effects between mutations. The method is based on the molecular dynamic simulations and the amino-acid network clique analysis that simply examines if two single mutation sites belong to some 3-clique. We analyzed 13 dual mutations of T4 phage lysozyme and found that the clique-based model successfully distinguishes highly correlated or non-additive double-site mutations from those with less correlation or additive mutations. We also applied the model to the protein Eglin c whose topology is significantly distinct from that of T4 phage lysozyme, and found that the model can, to some extension, still identify non-additive mutations from additive ones. Our calculations showed that mutation correlation effects may heavily depend on topology relationship among mutation sites, which can be quantitatively characterized using amino-acid network k-cliques. We also showed that double-site mutation correlations can be significantly altered by exerting a third mutation, indicating that more detailed physico-chemistry interactions might be considered with the network model for better understanding of the elusive mutation-correlation principle. |
1312.5212 | Steve N'Guyen | Steve N'Guyen, Charles Thurat, Beno\^it Girard | Saccade learning with concurrent cortical and subcortical basal ganglia
loops | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Basal Ganglia is a central structure involved in multiple cortical and
subcortical loops. Some of these loops are believed to be responsible for
saccade target selection. We study here how the very specific structural
relationships of these saccadic loops can affect the ability of learning
spatial and feature-based tasks.
We propose a model of saccade generation with reinforcement learning
capabilities based on our previous basal ganglia and superior colliculus
models. It is structured around the interactions of two parallel cortico-basal
loops and one tecto-basal loop. The two cortical loops separately deal with
spatial and non-spatial information to select targets in a concurrent way. The
subcortical loop is used to make the final target selection leading to the
production of the saccade. These different loops may work in concert or disturb
each other regarding reward maximization. Interactions between these loops and
their learning capabilities are tested on different saccade tasks.
The results show the ability of this model to correctly learn basic target
selection based on different criteria (spatial or not). Moreover the model
reproduces and explains training dependent express saccades toward targets
based on a spatial criterion.
Finally, the model predicts that in absence of prefrontal control, the
spatial loop should dominate.
| [
{
"created": "Wed, 18 Dec 2013 16:38:51 GMT",
"version": "v1"
},
{
"created": "Mon, 30 Dec 2013 10:43:17 GMT",
"version": "v2"
}
] | 2013-12-31 | [
[
"N'Guyen",
"Steve",
""
],
[
"Thurat",
"Charles",
""
],
[
"Girard",
"Benoît",
""
]
] | The Basal Ganglia is a central structure involved in multiple cortical and subcortical loops. Some of these loops are believed to be responsible for saccade target selection. We study here how the very specific structural relationships of these saccadic loops can affect the ability of learning spatial and feature-based tasks. We propose a model of saccade generation with reinforcement learning capabilities based on our previous basal ganglia and superior colliculus models. It is structured around the interactions of two parallel cortico-basal loops and one tecto-basal loop. The two cortical loops separately deal with spatial and non-spatial information to select targets in a concurrent way. The subcortical loop is used to make the final target selection leading to the production of the saccade. These different loops may work in concert or disturb each other regarding reward maximization. Interactions between these loops and their learning capabilities are tested on different saccade tasks. The results show the ability of this model to correctly learn basic target selection based on different criteria (spatial or not). Moreover the model reproduces and explains training dependent express saccades toward targets based on a spatial criterion. Finally, the model predicts that in absence of prefrontal control, the spatial loop should dominate. |
2204.10476 | Xin Li | Xin Li, Hsinchun Chen, Zan Huang, Hua Su, Jesse D. Martinez | Global Mapping of Gene/Protein Interactions in PubMed Abstracts: A
Framework and an Experiment with P53 Interactions | null | Journal of biomedical informatics, 2007 | 10.1016/j.jbi.2007.01.001 | null | q-bio.MN cs.LG cs.SI stat.AP | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Gene/protein interactions provide critical information for a thorough
understanding of cellular processes. Recently, considerable interest and effort
has been focused on the construction and analysis of genome-wide gene networks.
The large body of biomedical literature is an important source of gene/protein
interaction information. Recent advances in text mining tools have made it
possible to automatically extract such documented interactions from free-text
literature. In this paper, we propose a comprehensive framework for
constructing and analyzing large-scale gene functional networks based on the
gene/protein interactions extracted from biomedical literature repositories
using text mining tools. Our proposed framework consists of analyses of the
network topology, network topology-gene function relationship, and temporal
network evolution to distill valuable information embedded in the gene
functional interactions in literature. We demonstrate the application of the
proposed framework using a testbed of P53-related PubMed abstracts, which shows
that literature-based P53 networks exhibit small-world and scale-free
properties. We also found that high degree genes in the literature-based
networks have a high probability of appearing in the manually curated database
and genes in the same pathway tend to form local clusters in our
literature-based networks. Temporal analysis showed that genes interacting with
many other genes tend to be involved in a large number of newly discovered
interactions.
| [
{
"created": "Fri, 22 Apr 2022 03:04:19 GMT",
"version": "v1"
}
] | 2022-04-25 | [
[
"Li",
"Xin",
""
],
[
"Chen",
"Hsinchun",
""
],
[
"Huang",
"Zan",
""
],
[
"Su",
"Hua",
""
],
[
"Martinez",
"Jesse D.",
""
]
] | Gene/protein interactions provide critical information for a thorough understanding of cellular processes. Recently, considerable interest and effort has been focused on the construction and analysis of genome-wide gene networks. The large body of biomedical literature is an important source of gene/protein interaction information. Recent advances in text mining tools have made it possible to automatically extract such documented interactions from free-text literature. In this paper, we propose a comprehensive framework for constructing and analyzing large-scale gene functional networks based on the gene/protein interactions extracted from biomedical literature repositories using text mining tools. Our proposed framework consists of analyses of the network topology, network topology-gene function relationship, and temporal network evolution to distill valuable information embedded in the gene functional interactions in literature. We demonstrate the application of the proposed framework using a testbed of P53-related PubMed abstracts, which shows that literature-based P53 networks exhibit small-world and scale-free properties. We also found that high degree genes in the literature-based networks have a high probability of appearing in the manually curated database and genes in the same pathway tend to form local clusters in our literature-based networks. Temporal analysis showed that genes interacting with many other genes tend to be involved in a large number of newly discovered interactions. |
2007.15559 | Muhammad E. H. Chowdhury | Muhammad E. H. Chowdhury, Tawsifur Rahman, Amith Khandakar, Somaya
Al-Madeed, Susu M. Zughaier, Suhail A. R. Doi, Hanadi Hassen, Mohammad T.
Islam | An early warning tool for predicting mortality risk of COVID-19 patients
using machine learning | 23 pages, 8 Figure, 6 Tables | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | COVID-19 pandemic has created an extreme pressure on the global healthcare
services. Fast, reliable and early clinical assessment of the severity of the
disease can help in allocating and prioritizing resources to reduce mortality.
In order to study the important blood biomarkers for predicting disease
mortality, a retrospective study was conducted on 375 COVID-19 positive
patients admitted to Tongji Hospital (China) from January 10 to February 18,
2020. Demographic and clinical characteristics, and patient outcomes were
investigated using machine learning tools to identify key biomarkers to predict
the mortality of individual patient. A nomogram was developed for predicting
the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils
(%), lymphocyte (%), high sensitive C-reactive protein, and age - acquired at
hospital admission were identified as key predictors of death by multi-tree
XGBoost model. The area under curve (AUC) of the nomogram for the derivation
and validation cohort were 0.961 and 0.991, respectively. An integrated score
(LNLCA) was calculated with the corresponding death probability. COVID-19
patients were divided into three subgroups: low-, moderate- and high-risk
groups using LNLCA cut-off values of 10.4 and 12.65 with the death probability
less than 5%, 5% to 50%, and above 50%, respectively. The prognostic model,
nomogram and LNLCA score can help in early detection of high mortality risk of
COVID-19 patients, which will help doctors to improve the management of patient
stratification.
| [
{
"created": "Wed, 29 Jul 2020 15:16:09 GMT",
"version": "v1"
}
] | 2020-07-31 | [
[
"Chowdhury",
"Muhammad E. H.",
""
],
[
"Rahman",
"Tawsifur",
""
],
[
"Khandakar",
"Amith",
""
],
[
"Al-Madeed",
"Somaya",
""
],
[
"Zughaier",
"Susu M.",
""
],
[
"Doi",
"Suhail A. R.",
""
],
[
"Hassen",
"Hanadi",
""
],
[
"Islam",
"Mohammad T.",
""
]
] | COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics, and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high sensitive C-reactive protein, and age - acquired at hospital admission were identified as key predictors of death by multi-tree XGBoost model. The area under curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate- and high-risk groups using LNLCA cut-off values of 10.4 and 12.65 with the death probability less than 5%, 5% to 50%, and above 50%, respectively. The prognostic model, nomogram and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification. |
1802.04340 | Jose Vanterler Da Costa Sousa | J. Vanterler da C. Sousa, Magun N. N. dos Santos, L. A. Magna, E.
Capelas de Oliveira | Validation of a fractional model for erythrocyte sedimentation rate | 18 pages; 8 figures; 2 tables | null | null | null | q-bio.TO math.CA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present the validation of a recent fractional mathematical model for
erythrocyte sedimentation proposed by Sharma et al. \cite{GMR}. The model uses
a Caputo fractional derivative to build a time fractional diffusion equation
suitable to predict blood sedimentation rates. This validation was carried out
by means of erythrocyte sedimentation tests in laboratory. Data on
sedimentation rates (percentages) were analyzed and compared with the
analytical solution of the time fractional diffusion equation. The behavior of
the analytical solution related to each blood sample sedimentation data was
described and analyzed.
| [
{
"created": "Fri, 9 Feb 2018 17:08:56 GMT",
"version": "v1"
}
] | 2018-02-14 | [
[
"Sousa",
"J. Vanterler da C.",
""
],
[
"Santos",
"Magun N. N. dos",
""
],
[
"Magna",
"L. A.",
""
],
[
"de Oliveira",
"E. Capelas",
""
]
] | We present the validation of a recent fractional mathematical model for erythrocyte sedimentation proposed by Sharma et al. \cite{GMR}. The model uses a Caputo fractional derivative to build a time fractional diffusion equation suitable to predict blood sedimentation rates. This validation was carried out by means of erythrocyte sedimentation tests in laboratory. Data on sedimentation rates (percentages) were analyzed and compared with the analytical solution of the time fractional diffusion equation. The behavior of the analytical solution related to each blood sample sedimentation data was described and analyzed. |
1307.4375 | Mauro Mobilia | Mauro Mobilia | Evolutionary games with facilitators: When does selection favor
cooperation? | 12 pages, 5 figures. Version to be published (special issue on
"collective behavior and evolutionary games") | Chaos, Solitons & Fractals 56, 113 (2013) | 10.1016/j.chaos.2013.07.011 | null | q-bio.PE cond-mat.stat-mech nlin.AO physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the combined influence of selection and random fluctuations on the
evolutionary dynamics of two-strategy ("cooperation" and "defection") games in
populations comprising cooperation facilitators. The latter are individuals
that support cooperation by enhancing the reproductive potential of cooperators
relative to the fitness of defectors. By computing the fixation probability of
a single cooperator in finite and well-mixed populations that include a fixed
number of facilitators, and by using mean field analysis, we determine when
selection promotes cooperation in the important classes of prisoner's dilemma,
snowdrift and stag-hunt games. In particular, we identify the circumstances
under which selection favors the replacement and invasion of defection by
cooperation. Our findings, corroborated by stochastic simulations, show that
the spread of cooperation can be promoted through various scenarios when the
density of facilitators exceeds a critical value whose dependence on the
population size and selection strength is analyzed. We also determine under
which conditions cooperation is more likely to replace defection than vice
versa.
Keywords: Evolutionary games; dynamics of cooperation; social dilemmas;
fixation; population dynamics.
| [
{
"created": "Tue, 16 Jul 2013 18:50:31 GMT",
"version": "v1"
},
{
"created": "Sat, 17 Aug 2013 15:46:32 GMT",
"version": "v2"
}
] | 2013-11-13 | [
[
"Mobilia",
"Mauro",
""
]
] | We study the combined influence of selection and random fluctuations on the evolutionary dynamics of two-strategy ("cooperation" and "defection") games in populations comprising cooperation facilitators. The latter are individuals that support cooperation by enhancing the reproductive potential of cooperators relative to the fitness of defectors. By computing the fixation probability of a single cooperator in finite and well-mixed populations that include a fixed number of facilitators, and by using mean field analysis, we determine when selection promotes cooperation in the important classes of prisoner's dilemma, snowdrift and stag-hunt games. In particular, we identify the circumstances under which selection favors the replacement and invasion of defection by cooperation. Our findings, corroborated by stochastic simulations, show that the spread of cooperation can be promoted through various scenarios when the density of facilitators exceeds a critical value whose dependence on the population size and selection strength is analyzed. We also determine under which conditions cooperation is more likely to replace defection than vice versa. Keywords: Evolutionary games; dynamics of cooperation; social dilemmas; fixation; population dynamics. |
1301.6137 | Daniel Smith | Daniel Smith and Jian Liu | Nonlocal actin orientation models select for a unique orientation
pattern | Submitted to SIAM Journal on Applied Mathematics | null | null | null | q-bio.SC math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many models have been developed to study the role of branching actin networks
in motility. One important component of those models is the distribution of
filament orientations relative to the cell membrane. Two mean-field models
previously proposed are generalized and analyzed. In particular, we find that
both models uniquely select for a dominant orientation pattern. In the linear
case, the pattern is the eigenfunction associated with the principal
eigenvalue. In the nonlinear case, we show there exists a unique equilibrium
and that the equilibrium is locally stable. Approximate techniques are then
used to provide evidence for global stability.
| [
{
"created": "Fri, 25 Jan 2013 19:36:10 GMT",
"version": "v1"
},
{
"created": "Tue, 29 Jan 2013 17:45:24 GMT",
"version": "v2"
},
{
"created": "Tue, 29 Oct 2013 23:14:13 GMT",
"version": "v3"
},
{
"created": "Thu, 31 Oct 2013 21:31:09 GMT",
"version": "v4"
}
] | 2013-11-04 | [
[
"Smith",
"Daniel",
""
],
[
"Liu",
"Jian",
""
]
] | Many models have been developed to study the role of branching actin networks in motility. One important component of those models is the distribution of filament orientations relative to the cell membrane. Two mean-field models previously proposed are generalized and analyzed. In particular, we find that both models uniquely select for a dominant orientation pattern. In the linear case, the pattern is the eigenfunction associated with the principal eigenvalue. In the nonlinear case, we show there exists a unique equilibrium and that the equilibrium is locally stable. Approximate techniques are then used to provide evidence for global stability. |
2105.05475 | Ramon Grima | Augustinas Sukys and Ramon Grima | MomentClosure.jl: automated moment closure approximations in Julia | 2 pages, 1 figure | null | null | null | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | MomentClosure.jl is a Julia package providing automated derivation of the
time-evolution equations of the moments of molecule numbers for virtually any
chemical reaction network using a wide range of moment closure approximations.
It extends the capabilities of modelling stochastic biochemical systems in
Julia and can be particularly useful when exact analytic solutions of the
chemical master equation are unavailable and when Monte Carlo simulations are
computationally expensive.
MomentClosure.jl is freely accessible under the MIT license. Source code and
documentation are available at https://github.com/augustinas1/MomentClosure.jl
| [
{
"created": "Wed, 12 May 2021 07:22:32 GMT",
"version": "v1"
}
] | 2021-05-13 | [
[
"Sukys",
"Augustinas",
""
],
[
"Grima",
"Ramon",
""
]
] | MomentClosure.jl is a Julia package providing automated derivation of the time-evolution equations of the moments of molecule numbers for virtually any chemical reaction network using a wide range of moment closure approximations. It extends the capabilities of modelling stochastic biochemical systems in Julia and can be particularly useful when exact analytic solutions of the chemical master equation are unavailable and when Monte Carlo simulations are computationally expensive. MomentClosure.jl is freely accessible under the MIT license. Source code and documentation are available at https://github.com/augustinas1/MomentClosure.jl |
q-bio/0312032 | Pau Fern\'andez | Ricard V. Sole, and Pau Fernandez | Modularity "for free" in genome architecture? | Submitted to BMC Evolutionary Biology | null | null | null | q-bio.GN q-bio.MN | null | Background: Recent models of genome-proteome evolution have shown that some
of the key traits displayed by the global structure of cellular networks might
be a natural result of a duplication-diversification (DD) process. One of the
consequences of such evolution is the emergence of a small world architecture
together with a scale-free distribution of interactions. Here we show that the
domain of parameter space were such structure emerges is related to a phase
transition phenomenon. At this transition point, modular architecture
spontaneously emerges as a byproduct of the DD process.
Results: Although the DD models lack any functionality and are thus free from
meeting functional constraints, they show the observed features displayed by
the real proteome maps when tuned close to a sharp transition point separating
a highly connected graph from a disconnected system. Close to such boundary,
the maps are shown to display scale-free hierarchical organization, behave as
small worlds and exhibit modularity.
Conclusions: It is conjectured that natural selection tuned the average
connectivity in such a way that the network reaches a sparse graph of
connections. One consequence of such scenario is that the scaling laws and the
essential ingredients for building a modular net emerge for free close to such
transition.
| [
{
"created": "Fri, 19 Dec 2003 15:40:07 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Sole",
"Ricard V.",
""
],
[
"Fernandez",
"Pau",
""
]
] | Background: Recent models of genome-proteome evolution have shown that some of the key traits displayed by the global structure of cellular networks might be a natural result of a duplication-diversification (DD) process. One of the consequences of such evolution is the emergence of a small world architecture together with a scale-free distribution of interactions. Here we show that the domain of parameter space were such structure emerges is related to a phase transition phenomenon. At this transition point, modular architecture spontaneously emerges as a byproduct of the DD process. Results: Although the DD models lack any functionality and are thus free from meeting functional constraints, they show the observed features displayed by the real proteome maps when tuned close to a sharp transition point separating a highly connected graph from a disconnected system. Close to such boundary, the maps are shown to display scale-free hierarchical organization, behave as small worlds and exhibit modularity. Conclusions: It is conjectured that natural selection tuned the average connectivity in such a way that the network reaches a sparse graph of connections. One consequence of such scenario is that the scaling laws and the essential ingredients for building a modular net emerge for free close to such transition. |
0810.5676 | Miguel Navascues | Miguel Navascues (BIO), Brent C. Emerson (BIO) | Natural recovery of genetic diversity by gene flow in reforested areas
of the endemic Canary Island pine, Pinus canariensis | null | Forest Ecology and Management 244, 1-3 (2007) 122-128 | 10.1016/j.foreco.2007.04.009 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The endemic pine, Pinus canariensis, forms one of the main forest ecosystems
in the Canary Islands. In this archipelago, pine forest is a mosaic of natural
stands (remnants of past forest overexploitation) and artificial stands planted
from the 1940's. The genetic makeup of the artificially regenerated forest is
of some concern. The use of reproductive material with uncontrolled origin or
from a reduced number of parental trees may produce stands ill adapted to local
conditions or unable to adapt in response to environmental change. The genetic
diversity within a transect of reforested stands connecting two natural forest
fragments has been studied with nuclear and chloroplast microsatellites. Little
genetic differentiation and similar levels of genetic diversity to the
surrounding natural stands were found for nuclear markers. However, chloroplast
microsatellites presented lower haplotype diversity in reforested stands, and
this may be a consequence of the lower effective population size of the
chloroplast genome, meaning chloroplast markers have a higher sensitivity to
bottlenecks. Understory natural regeneration within the reforestation was also
analysed to study gene flow from natural forest into artificial stands.
Estimates of immigration rate into artificially regenerated forest were high
(0.68-0.75), producing a significant increase of genetic diversity (both in
chloroplast and nuclear microsatellites), which indicates the capacity for
genetic recovery for P. canariensis reforestations surrounded by larger natural
stands.
| [
{
"created": "Fri, 31 Oct 2008 13:05:05 GMT",
"version": "v1"
}
] | 2008-11-03 | [
[
"Navascues",
"Miguel",
"",
"BIO"
],
[
"Emerson",
"Brent C.",
"",
"BIO"
]
] | The endemic pine, Pinus canariensis, forms one of the main forest ecosystems in the Canary Islands. In this archipelago, pine forest is a mosaic of natural stands (remnants of past forest overexploitation) and artificial stands planted from the 1940's. The genetic makeup of the artificially regenerated forest is of some concern. The use of reproductive material with uncontrolled origin or from a reduced number of parental trees may produce stands ill adapted to local conditions or unable to adapt in response to environmental change. The genetic diversity within a transect of reforested stands connecting two natural forest fragments has been studied with nuclear and chloroplast microsatellites. Little genetic differentiation and similar levels of genetic diversity to the surrounding natural stands were found for nuclear markers. However, chloroplast microsatellites presented lower haplotype diversity in reforested stands, and this may be a consequence of the lower effective population size of the chloroplast genome, meaning chloroplast markers have a higher sensitivity to bottlenecks. Understory natural regeneration within the reforestation was also analysed to study gene flow from natural forest into artificial stands. Estimates of immigration rate into artificially regenerated forest were high (0.68-0.75), producing a significant increase of genetic diversity (both in chloroplast and nuclear microsatellites), which indicates the capacity for genetic recovery for P. canariensis reforestations surrounded by larger natural stands. |
2003.02985 | Siyu Liu | Jiwei Jia, Jian Ding, Siyu Liu, Guidong Liao, Jingzhi Li, Ben Duan,
Guoqing Wang, Ran Zhang | Modeling the Control of COVID-19: Impact of Policy Interventions and
Meteorological Factors | null | null | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a dynamical model to describe the transmission of
COVID-19, which is spreading in China and many other countries. To avoid a
larger outbreak in the worldwide, Chinese government carried out a series of
strong strategies to prevent the situation from deteriorating. Home quarantine
is the most important one to prevent the spread of COVID-19. In order to
estimate the effect of population quarantine, we divide the population into
seven categories for simulation. Based on a Least-Squares procedure and
officially published data, the estimation of parameters for the proposed model
is given. Numerical simulations show that the proposed model can describe the
transmission of COVID-19 accurately, the corresponding prediction of the trend
of the disease is given. The home quarantine strategy plays an important role
in controlling the disease spread and speeding up the decline of COVID-19. The
control reproduction number of most provinces in China are analyzed and
discussed adequately. We should pay attention to that, though the epidemic is
in decline in China, the disease still has high risk of human-to-human
transmission continuously. Once the control strategy is removed, COVID-19 may
become a normal epidemic disease just like flu. Further control for the disease
is still necessary, we focus on the relationship between the spread rate of the
virus and the meteorological conditions. A comprehensive meteorological index
is introduced to represent the impact of meteorological factors on both high
and low migration groups. As the progress on the new vaccine, we design detail
vaccination strategies for COVID-19 in different control phases and show the
effectiveness of efficient vaccination. Once the vaccine comes into use, the
numerical simulation provide a promptly prospective research.
| [
{
"created": "Fri, 6 Mar 2020 01:06:54 GMT",
"version": "v1"
}
] | 2020-03-09 | [
[
"Jia",
"Jiwei",
""
],
[
"Ding",
"Jian",
""
],
[
"Liu",
"Siyu",
""
],
[
"Liao",
"Guidong",
""
],
[
"Li",
"Jingzhi",
""
],
[
"Duan",
"Ben",
""
],
[
"Wang",
"Guoqing",
""
],
[
"Zhang",
"Ran",
""
]
] | In this paper, we propose a dynamical model to describe the transmission of COVID-19, which is spreading in China and many other countries. To avoid a larger outbreak in the worldwide, Chinese government carried out a series of strong strategies to prevent the situation from deteriorating. Home quarantine is the most important one to prevent the spread of COVID-19. In order to estimate the effect of population quarantine, we divide the population into seven categories for simulation. Based on a Least-Squares procedure and officially published data, the estimation of parameters for the proposed model is given. Numerical simulations show that the proposed model can describe the transmission of COVID-19 accurately, the corresponding prediction of the trend of the disease is given. The home quarantine strategy plays an important role in controlling the disease spread and speeding up the decline of COVID-19. The control reproduction number of most provinces in China are analyzed and discussed adequately. We should pay attention to that, though the epidemic is in decline in China, the disease still has high risk of human-to-human transmission continuously. Once the control strategy is removed, COVID-19 may become a normal epidemic disease just like flu. Further control for the disease is still necessary, we focus on the relationship between the spread rate of the virus and the meteorological conditions. A comprehensive meteorological index is introduced to represent the impact of meteorological factors on both high and low migration groups. As the progress on the new vaccine, we design detail vaccination strategies for COVID-19 in different control phases and show the effectiveness of efficient vaccination. Once the vaccine comes into use, the numerical simulation provide a promptly prospective research. |
1109.6524 | Yasser Roudi | Peter E. Latham and Yasser Roudi | Role of correlations in population coding | To appear in "Principles of Neural Coding", edited by Stefano Panzeri
and Rodrigo Quian Quiroga | null | null | null | q-bio.NC q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Correlations among spikes, both on the same neuron and across neurons, are
ubiquitous in the brain. For example cross-correlograms can have large peaks,
at least in the periphery, and smaller -- but still non-negligible -- ones in
cortex, and auto-correlograms almost always exhibit non-trivial temporal
structure at a range of timescales. Although this has been known for over forty
years, it's still not clear what role these correlations play in the brain --
and, indeed, whether they play any role at all. The goal of this chapter is to
shed light on this issue by reviewing some of the work on this subject.
| [
{
"created": "Thu, 29 Sep 2011 13:37:34 GMT",
"version": "v1"
}
] | 2011-09-30 | [
[
"Latham",
"Peter E.",
""
],
[
"Roudi",
"Yasser",
""
]
] | Correlations among spikes, both on the same neuron and across neurons, are ubiquitous in the brain. For example cross-correlograms can have large peaks, at least in the periphery, and smaller -- but still non-negligible -- ones in cortex, and auto-correlograms almost always exhibit non-trivial temporal structure at a range of timescales. Although this has been known for over forty years, it's still not clear what role these correlations play in the brain -- and, indeed, whether they play any role at all. The goal of this chapter is to shed light on this issue by reviewing some of the work on this subject. |
0708.0527 | Ralf Bundschuh | Ralf Bundschuh and Robijn Bruinsma | Melting of Branched RNA Molecules | 4 pages, 3 figures | null | 10.1103/PhysRevLett.100.148101 | null | q-bio.BM cond-mat.stat-mech | null | Stability of the branching structure of an RNA molecule is an important
condition for its function. In this letter we show that the melting
thermodynamics of RNA molecules is very sensitive to their branching geometry
for the case of a molecule whose groundstate has the branching geometry of a
Cayley Tree and whose pairing interactions are described by the Go model.
Whereas RNA molecules with a linear geometry melt via a conventional continuous
phase transition with classical exponents, molecules with a Cayley Tree
geometry are found to have a free energy that seems smooth, at least within our
precision. Yet, we show analytically that this free energy in fact has a
mathematical singularity at the stability limit of the ordered structure. The
correlation length appears to diverge on the high-temperature side of this
singularity.
| [
{
"created": "Fri, 3 Aug 2007 14:44:50 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Bundschuh",
"Ralf",
""
],
[
"Bruinsma",
"Robijn",
""
]
] | Stability of the branching structure of an RNA molecule is an important condition for its function. In this letter we show that the melting thermodynamics of RNA molecules is very sensitive to their branching geometry for the case of a molecule whose groundstate has the branching geometry of a Cayley Tree and whose pairing interactions are described by the Go model. Whereas RNA molecules with a linear geometry melt via a conventional continuous phase transition with classical exponents, molecules with a Cayley Tree geometry are found to have a free energy that seems smooth, at least within our precision. Yet, we show analytically that this free energy in fact has a mathematical singularity at the stability limit of the ordered structure. The correlation length appears to diverge on the high-temperature side of this singularity. |
1304.6158 | Qixin Wang | Qixin Wang, Menghui Li, Li Charlie Xia, Ge Wen, Hualong Zu, Mingyi Gao | Genetic analysis of differentiation of T-helper lymphocytes | null | Genetics and Molecular Research 2 (2012) 972-987 | 10.4238/2013.April.2.13 | null | q-bio.CB math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the human immune system, T-helper cells are able to differentiate into two
lymphocyte subsets: Th1 and Th2. The intracellular signaling pathways of
differentiation form a dynamic regulation network by secreting distinctive
types of cytokines, while differentiation is regulated by two major gene loci:
T-bet and GATA-3. We developed a system dynamics model to simulate the
differentiation and re-differentiation process of T-helper cells, based on gene
expression levels of T-bet and GATA-3 during differentiation of these cells. We
arrived at three ultimate states of the model and came to the conclusion that
cell differentiation potential exists as long as the system dynamics is at an
unstable equilibrium point; the T-helper cells will no longer have the
potential of differentiation when the model reaches a stable equilibrium point.
In addition, the time lag caused by expression of transcription factors can
lead to oscillations in the secretion of cytokines during differentiation.
| [
{
"created": "Tue, 23 Apr 2013 03:21:21 GMT",
"version": "v1"
}
] | 2013-04-24 | [
[
"Wang",
"Qixin",
""
],
[
"Li",
"Menghui",
""
],
[
"Xia",
"Li Charlie",
""
],
[
"Wen",
"Ge",
""
],
[
"Zu",
"Hualong",
""
],
[
"Gao",
"Mingyi",
""
]
] | In the human immune system, T-helper cells are able to differentiate into two lymphocyte subsets: Th1 and Th2. The intracellular signaling pathways of differentiation form a dynamic regulation network by secreting distinctive types of cytokines, while differentiation is regulated by two major gene loci: T-bet and GATA-3. We developed a system dynamics model to simulate the differentiation and re-differentiation process of T-helper cells, based on gene expression levels of T-bet and GATA-3 during differentiation of these cells. We arrived at three ultimate states of the model and came to the conclusion that cell differentiation potential exists as long as the system dynamics is at an unstable equilibrium point; the T-helper cells will no longer have the potential of differentiation when the model reaches a stable equilibrium point. In addition, the time lag caused by expression of transcription factors can lead to oscillations in the secretion of cytokines during differentiation. |
2407.20538 | Zi Chen | Xing Guo, Lin Wang, Kayla Duval, Jing Fan, Shaobing Zhou, and Zi Chen | Dimeric Drug Polymeric Micelles with Acid-Active Tumor Targeting and
FRET-indicated Drug Release | null | null | null | null | q-bio.TO q-bio.BM q-bio.CB | http://creativecommons.org/licenses/by/4.0/ | Trans-activating transcriptional activator (TAT), a cell-penetrating peptide,
has been extensively used for facilitating cellular uptake and nuclear
targeting of drug delivery systems. However, the positively charged TAT peptide
usually strongly interacts with serum components and undergoes substantial
phagocytosis by the reticuloendothelial system, causing a short blood
circulation in vivo. In this work, an acid-active tumor targeting nanoplatform
DA-TAT-PECL was developed to effectively inhibit the nonspecific interactions
of TAT in the bloodstream. 2,3-dimethylmaleic anhydride (DA) was first used to
convert the TAT amines to carboxylic acid, the resulting DA-TAT was further
conjugated to get DA-TAT-PECL. After self-assembly into polymeric micelles,
they were capable of circulating in the physiological condition for a long time
and promoting cell penetration upon accumulation at the tumor site and
de-shielding the DA group. Moreover, camptothecin (CPT) was used as the
anticancer drug and modified into a dimer (CPT)2-ss-Mal, in which two CPT
molecules were connected by a reduction-labile maleimide thioether bond. The
FRET signal between CPT and maleimide thioether bond was monitored to visualize
the drug release process and effective targeted delivery of antitumor drugs was
demonstrated. This pH/reduction dual-responsive micelle system provides a new
platform for high fidelity cancer therapy.
| [
{
"created": "Tue, 30 Jul 2024 04:43:58 GMT",
"version": "v1"
}
] | 2024-07-31 | [
[
"Guo",
"Xing",
""
],
[
"Wang",
"Lin",
""
],
[
"Duval",
"Kayla",
""
],
[
"Fan",
"Jing",
""
],
[
"Zhou",
"Shaobing",
""
],
[
"Chen",
"Zi",
""
]
] | Trans-activating transcriptional activator (TAT), a cell-penetrating peptide, has been extensively used for facilitating cellular uptake and nuclear targeting of drug delivery systems. However, the positively charged TAT peptide usually strongly interacts with serum components and undergoes substantial phagocytosis by the reticuloendothelial system, causing a short blood circulation in vivo. In this work, an acid-active tumor targeting nanoplatform DA-TAT-PECL was developed to effectively inhibit the nonspecific interactions of TAT in the bloodstream. 2,3-dimethylmaleic anhydride (DA) was first used to convert the TAT amines to carboxylic acid, the resulting DA-TAT was further conjugated to get DA-TAT-PECL. After self-assembly into polymeric micelles, they were capable of circulating in the physiological condition for a long time and promoting cell penetration upon accumulation at the tumor site and de-shielding the DA group. Moreover, camptothecin (CPT) was used as the anticancer drug and modified into a dimer (CPT)2-ss-Mal, in which two CPT molecules were connected by a reduction-labile maleimide thioether bond. The FRET signal between CPT and maleimide thioether bond was monitored to visualize the drug release process and effective targeted delivery of antitumor drugs was demonstrated. This pH/reduction dual-responsive micelle system provides a new platform for high fidelity cancer therapy. |
2208.11509 | Carlos Hernandez-Suarez M | Carlos Hernandez-Suarez and Osval Montesinos Lopez | A simple and intuitive method to calculate $R_0$ in complex epidemic
models | PDF has 26 pages and contains 13 figures | null | null | null | q-bio.PE math.PR | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Epidemic models are a valuable tool in the decision making process. Once a
mathematical model for an epidemics has been established, the very next step is
calculating a mathematical expression for the basic reproductive number, $R_0$,
which is the average number of infections caused by an individual that is
introduced in a population of susceptibles. Finding a mathematical expression
for $R_0$ is important because it allows to analyze the effect of the different
parameters in the model on $R_0$ so that we can act on them to keep $R_0 < 1$,
so that the epidemic fades out. In this work we show how to calculate $R_0$ in
complicated epidemic models by using only basic concepts of Markov chains.
| [
{
"created": "Mon, 22 Aug 2022 20:27:49 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Sep 2022 06:09:28 GMT",
"version": "v2"
}
] | 2022-09-07 | [
[
"Hernandez-Suarez",
"Carlos",
""
],
[
"Lopez",
"Osval Montesinos",
""
]
] | Epidemic models are a valuable tool in the decision making process. Once a mathematical model for an epidemics has been established, the very next step is calculating a mathematical expression for the basic reproductive number, $R_0$, which is the average number of infections caused by an individual that is introduced in a population of susceptibles. Finding a mathematical expression for $R_0$ is important because it allows to analyze the effect of the different parameters in the model on $R_0$ so that we can act on them to keep $R_0 < 1$, so that the epidemic fades out. In this work we show how to calculate $R_0$ in complicated epidemic models by using only basic concepts of Markov chains. |
0812.0191 | Jeffrey Dick | Jeffrey M. Dick | Calculation of the relative metastabilities of proteins in subcellular
compartments of Saccharomyces cerevisiae | 32 pages, 7 figures; supporting information is available at
http://www.chnosz.net/yeast | null | null | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | [abridged] Background: The distribution of chemical species in an open system
at metastable equilibrium can be expressed as a function of environmental
variables which can include temperature, oxidation-reduction potential and
others. Calculations of metastable equilibrium for various model systems were
used to characterize chemical transformations among proteins and groups of
proteins found in different compartments of yeast cells.
Results: With increasing oxygen fugacity, the relative metastability fields
of model proteins for major subcellular compartments go as mitochondrion,
endoplasmic reticulum, cytoplasm, nucleus. In a metastable equilibrium setting
at relatively high oxygen fugacity, proteins making up actin are predominant,
but those constituting the microtubule occur with a low chemical activity. A
reaction sequence involving the microtubule and spindle pole proteins was
predicted by combining the known intercompartmental interactions with a
hypothetical program of oxygen fugacity changes in the local environment. In
further calculations, the most-abundant proteins within compartments generally
occur in relative abundances that only weakly correspond to a metastable
equilibrium distribution. However, physiological populations of proteins that
form complexes often show an overall positive or negative correlation with the
relative abundances of proteins in metastable assemblages.
Conclusions: This study explored the outlines of a thermodynamic description
of chemical transformations among interacting proteins in yeast cells. The
results suggest that these methods can be used to measure the degree of
departure of a natural biochemical process or population from a local minimum
in Gibbs energy.
| [
{
"created": "Mon, 1 Dec 2008 20:10:42 GMT",
"version": "v1"
}
] | 2008-12-02 | [
[
"Dick",
"Jeffrey M.",
""
]
] | [abridged] Background: The distribution of chemical species in an open system at metastable equilibrium can be expressed as a function of environmental variables which can include temperature, oxidation-reduction potential and others. Calculations of metastable equilibrium for various model systems were used to characterize chemical transformations among proteins and groups of proteins found in different compartments of yeast cells. Results: With increasing oxygen fugacity, the relative metastability fields of model proteins for major subcellular compartments go as mitochondrion, endoplasmic reticulum, cytoplasm, nucleus. In a metastable equilibrium setting at relatively high oxygen fugacity, proteins making up actin are predominant, but those constituting the microtubule occur with a low chemical activity. A reaction sequence involving the microtubule and spindle pole proteins was predicted by combining the known intercompartmental interactions with a hypothetical program of oxygen fugacity changes in the local environment. In further calculations, the most-abundant proteins within compartments generally occur in relative abundances that only weakly correspond to a metastable equilibrium distribution. However, physiological populations of proteins that form complexes often show an overall positive or negative correlation with the relative abundances of proteins in metastable assemblages. Conclusions: This study explored the outlines of a thermodynamic description of chemical transformations among interacting proteins in yeast cells. The results suggest that these methods can be used to measure the degree of departure of a natural biochemical process or population from a local minimum in Gibbs energy. |
2111.01275 | Christopher J. Cueva | Christopher J. Cueva, Adel Ardalan, Misha Tsodyks, Ning Qian | Recurrent neural network models for working memory of continuous
variables: activity manifolds, connectivity patterns, and dynamic codes | null | null | null | null | q-bio.NC cs.NE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many daily activities and psychophysical experiments involve keeping multiple
items in working memory. When items take continuous values (e.g., orientation,
contrast, length, loudness) they must be stored in a continuous structure of
appropriate dimensions. We investigate how this structure is represented in
neural circuits by training recurrent networks to report two previously shown
stimulus orientations. We find the activity manifold for the two orientations
resembles a Clifford torus. Although a Clifford and standard torus (the surface
of a donut) are topologically equivalent, they have important functional
differences. A Clifford torus treats the two orientations equally and keeps
them in orthogonal subspaces, as demanded by the task, whereas a standard torus
does not. We find and characterize the connectivity patterns that support the
Clifford torus. Moreover, in addition to attractors that store information via
persistent activity, our networks also use a dynamic code where units change
their tuning to prevent new sensory input from overwriting the previously
stored one. We argue that such dynamic codes are generally required whenever
multiple inputs enter a memory system via shared connections. Finally, we apply
our framework to a human psychophysics experiment in which subjects reported
two remembered orientations. By varying the training conditions of the RNNs, we
test and support the hypothesis that human behavior is a product of both neural
noise and reliance on the more stable and behaviorally relevant memory of the
ordinal relationship between the two orientations. This suggests that suitable
inductive biases in RNNs are important for uncovering how the human brain
implements working memory. Together, these results offer an understanding of
the neural computations underlying a class of visual decoding tasks, bridging
the scales from human behavior to synaptic connectivity.
| [
{
"created": "Mon, 1 Nov 2021 21:52:48 GMT",
"version": "v1"
},
{
"created": "Sat, 18 Dec 2021 07:52:16 GMT",
"version": "v2"
}
] | 2021-12-21 | [
[
"Cueva",
"Christopher J.",
""
],
[
"Ardalan",
"Adel",
""
],
[
"Tsodyks",
"Misha",
""
],
[
"Qian",
"Ning",
""
]
] | Many daily activities and psychophysical experiments involve keeping multiple items in working memory. When items take continuous values (e.g., orientation, contrast, length, loudness) they must be stored in a continuous structure of appropriate dimensions. We investigate how this structure is represented in neural circuits by training recurrent networks to report two previously shown stimulus orientations. We find the activity manifold for the two orientations resembles a Clifford torus. Although a Clifford and standard torus (the surface of a donut) are topologically equivalent, they have important functional differences. A Clifford torus treats the two orientations equally and keeps them in orthogonal subspaces, as demanded by the task, whereas a standard torus does not. We find and characterize the connectivity patterns that support the Clifford torus. Moreover, in addition to attractors that store information via persistent activity, our networks also use a dynamic code where units change their tuning to prevent new sensory input from overwriting the previously stored one. We argue that such dynamic codes are generally required whenever multiple inputs enter a memory system via shared connections. Finally, we apply our framework to a human psychophysics experiment in which subjects reported two remembered orientations. By varying the training conditions of the RNNs, we test and support the hypothesis that human behavior is a product of both neural noise and reliance on the more stable and behaviorally relevant memory of the ordinal relationship between the two orientations. This suggests that suitable inductive biases in RNNs are important for uncovering how the human brain implements working memory. Together, these results offer an understanding of the neural computations underlying a class of visual decoding tasks, bridging the scales from human behavior to synaptic connectivity. |
1505.04471 | Anna Melbinger | Anna Melbinger and Massimo Vergassola | Evolutionary Fitness in Variable Environments | main: 5 pages, 4 figures; supplement: 7 pages, 7 figues | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One essential ingredient of evolutionary theory is the concept of fitness as
a measure for a species' success in its living conditions. Here, we quantify
the effect of environmental fluctuations onto fitness by analytical
calculations on a general evolutionary model and by studying corresponding
individual-based microscopic models. We demonstrate that not only larger growth
rates and viabilities, but also reduced sensitivity to environmental
variability substantially increases the fitness. Even for neutral evolution,
variability in the growth rates plays the crucial role of strongly reducing the
expected fixation times. Thereby, environmental fluctuations constitute a
mechanism to account for the effective population sizes inferred from genetic
data that often are much smaller than the census population size.
| [
{
"created": "Sun, 17 May 2015 22:56:36 GMT",
"version": "v1"
}
] | 2015-05-19 | [
[
"Melbinger",
"Anna",
""
],
[
"Vergassola",
"Massimo",
""
]
] | One essential ingredient of evolutionary theory is the concept of fitness as a measure for a species' success in its living conditions. Here, we quantify the effect of environmental fluctuations onto fitness by analytical calculations on a general evolutionary model and by studying corresponding individual-based microscopic models. We demonstrate that not only larger growth rates and viabilities, but also reduced sensitivity to environmental variability substantially increases the fitness. Even for neutral evolution, variability in the growth rates plays the crucial role of strongly reducing the expected fixation times. Thereby, environmental fluctuations constitute a mechanism to account for the effective population sizes inferred from genetic data that often are much smaller than the census population size. |
1107.5212 | Dilano Saldin | Dilano Saldin, Hin-Cheuck Poon, Peter Schwander, Miraj Uddin, and
Marius Schmidt | Reconstructing an Icosahedral Virus from Single-Particle Diffraction
Experiments | 18 pages, 10 figures | null | 10.1364/OE.19.017318 | null | q-bio.BM physics.bio-ph physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The first experimental data from single-particle scattering experiments from
free electron lasers (FELs) are now becoming available. The first such
experiments are being performed on relatively large objects such as viruses,
which produce relatively low-resolution, low-noise diffraction patterns in
so-called "diffract-and-destroy" experiments. We describe a very simple test on
the angular correlations of measured diffraction data to determine if the
scattering is from an icosahedral particle. If this is confirmed, the efficient
algorithm proposed can then combine diffraction data from multiple shots of
particles in random unknown orientations to generate a full 3D image of the
icosahedral particle. We demonstrate this with a simulation for the satellite
tobacco necrosis virus (STNV), the atomic coordinates of whose asymmetric unit
is given in Protein Data Bank entry 2BUK.
| [
{
"created": "Tue, 26 Jul 2011 13:40:13 GMT",
"version": "v1"
}
] | 2015-05-28 | [
[
"Saldin",
"Dilano",
""
],
[
"Poon",
"Hin-Cheuck",
""
],
[
"Schwander",
"Peter",
""
],
[
"Uddin",
"Miraj",
""
],
[
"Schmidt",
"Marius",
""
]
] | The first experimental data from single-particle scattering experiments from free electron lasers (FELs) are now becoming available. The first such experiments are being performed on relatively large objects such as viruses, which produce relatively low-resolution, low-noise diffraction patterns in so-called "diffract-and-destroy" experiments. We describe a very simple test on the angular correlations of measured diffraction data to determine if the scattering is from an icosahedral particle. If this is confirmed, the efficient algorithm proposed can then combine diffraction data from multiple shots of particles in random unknown orientations to generate a full 3D image of the icosahedral particle. We demonstrate this with a simulation for the satellite tobacco necrosis virus (STNV), the atomic coordinates of whose asymmetric unit is given in Protein Data Bank entry 2BUK. |
1501.03179 | Julia Mossbridge | Julia Mossbridge, Patrizio Tressoldi, Jessica Utts, John A. Ives, Dean
Radin, Wayne B. Jonas | We Did See This Coming: Response to, We Should Have Seen This Coming, by
D. Sam Schwarzkopf | 1 figure | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We appreciate the effort by Schwarzkopf to examine alternative explanations
for predictive anticipatory activity (PAA) or presentiment (for first response,
see: Schwarzkopf 2014a; for additional response, see: Schwarzkopf 2014b, for
original article, see: Mossbridge et al. 2014). These commentaries are a
laudable effort to promote collegial discussion of the controversial claim of
presentiment, whereby physiological measures preceding unpredictable emotional
events differ from physiological measures preceding calm or neutral events
(Mossbridge et al., 2012; Mossbridge et al., 2014). What is called truth at any
given time in science has achieved that status through a continuous process of
measurement and interpretation based on the current knowledge at hand. Here we
address six points in his original commentary (Schwarzkopf 2014a), though our
responses are informed by the points he made in his his supplementary
commentary (Schwarzkopf 2014b). We hope our responses will help Schwarzkopf and
others understand our interpretation of these data.
| [
{
"created": "Tue, 13 Jan 2015 21:26:56 GMT",
"version": "v1"
},
{
"created": "Sun, 18 Jan 2015 16:33:18 GMT",
"version": "v2"
}
] | 2015-01-20 | [
[
"Mossbridge",
"Julia",
""
],
[
"Tressoldi",
"Patrizio",
""
],
[
"Utts",
"Jessica",
""
],
[
"Ives",
"John A.",
""
],
[
"Radin",
"Dean",
""
],
[
"Jonas",
"Wayne B.",
""
]
] | We appreciate the effort by Schwarzkopf to examine alternative explanations for predictive anticipatory activity (PAA) or presentiment (for first response, see: Schwarzkopf 2014a; for additional response, see: Schwarzkopf 2014b, for original article, see: Mossbridge et al. 2014). These commentaries are a laudable effort to promote collegial discussion of the controversial claim of presentiment, whereby physiological measures preceding unpredictable emotional events differ from physiological measures preceding calm or neutral events (Mossbridge et al., 2012; Mossbridge et al., 2014). What is called truth at any given time in science has achieved that status through a continuous process of measurement and interpretation based on the current knowledge at hand. Here we address six points in his original commentary (Schwarzkopf 2014a), though our responses are informed by the points he made in his his supplementary commentary (Schwarzkopf 2014b). We hope our responses will help Schwarzkopf and others understand our interpretation of these data. |
1105.0448 | Christopher Wylie | C Scott Wylie and Eugene I Shakhnovich | A biophysical protein folding model accounts for most mutational fitness
effects in viruses | Main text: 12 pages, 5 figures Supplementary Information: 10 pages, 5
figures | null | null | null | q-bio.PE q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fitness effects of mutations fall on a continuum ranging from lethal to
deleterious to beneficial. The distribution of fitness effects (DFE) among
random mutations is an essential component of every evolutionary model and a
mathematical portrait of robustness. Recent experiments on five viral species
all revealed a characteristic bimodal shaped DFE, featuring peaks at neutrality
and lethality. However, the phenotypic causes underlying observed fitness
effects are still unknown, and presumably thought to vary unpredictably from
one mutation to another. By combining population genetics simulations with a
simple biophysical protein folding model, we show that protein thermodynamic
stability accounts for a large fraction of observed mutational effects. We
assume that moderately destabilizing mutations inflict a fitness penalty
proportional to the reduction in folded protein, which depends continuously on
folding free energy (\Delta G). Most mutations in our model affect fitness by
altering \Delta G, while, based on simple estimates, \approx10% abolish
activity and are unconditionally lethal. Mutations pushing \Delta G>0 are also
considered lethal. Contrary to neutral network theory, we find that, in
mutation/selection/drift steady-state, high mutation rates (m) lead to less
stable proteins and a more dispersed DFE, i.e. less mutational robustness.
Small population size (N) also decreases stability and robustness. In our
model, a continuum of non-lethal mutations reduces fitness by \approx2% on
average, while \approx10-35% of mutations are lethal, depending on N and m.
Compensatory mutations are common in small populations with high mutation
rates. More broadly, we conclude that interplay between biophysical and
population genetic forces shapes the DFE.
| [
{
"created": "Mon, 2 May 2011 22:20:01 GMT",
"version": "v1"
}
] | 2011-05-04 | [
[
"Wylie",
"C Scott",
""
],
[
"Shakhnovich",
"Eugene I",
""
]
] | Fitness effects of mutations fall on a continuum ranging from lethal to deleterious to beneficial. The distribution of fitness effects (DFE) among random mutations is an essential component of every evolutionary model and a mathematical portrait of robustness. Recent experiments on five viral species all revealed a characteristic bimodal shaped DFE, featuring peaks at neutrality and lethality. However, the phenotypic causes underlying observed fitness effects are still unknown, and presumably thought to vary unpredictably from one mutation to another. By combining population genetics simulations with a simple biophysical protein folding model, we show that protein thermodynamic stability accounts for a large fraction of observed mutational effects. We assume that moderately destabilizing mutations inflict a fitness penalty proportional to the reduction in folded protein, which depends continuously on folding free energy (\Delta G). Most mutations in our model affect fitness by altering \Delta G, while, based on simple estimates, \approx10% abolish activity and are unconditionally lethal. Mutations pushing \Delta G>0 are also considered lethal. Contrary to neutral network theory, we find that, in mutation/selection/drift steady-state, high mutation rates (m) lead to less stable proteins and a more dispersed DFE, i.e. less mutational robustness. Small population size (N) also decreases stability and robustness. In our model, a continuum of non-lethal mutations reduces fitness by \approx2% on average, while \approx10-35% of mutations are lethal, depending on N and m. Compensatory mutations are common in small populations with high mutation rates. More broadly, we conclude that interplay between biophysical and population genetic forces shapes the DFE. |
1503.08070 | Richard Varro | Richard Varro | Gonosomal Algebra | null | null | null | null | q-bio.QM math.RA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce the gonosomal algebra. Gonosomal algebra extend the evolution
algebra of the bisexual population (EABP) defined by Ladra and Rozikov. We show
that gonosomal algebras can represent algebraically a wide variety of sex
determination systems observed in bisexual populations. We illustrate this by
about twenty genetic examples, most of these examples cannot be represented by
an EABP. We give seven algebraic constructions of gonosomal algebras, each is
illustrated by genetic examples. We show that unlike the EABP gonosomal
algebras are not dibaric. We approach the existence of dibaric function and
idempotent in gonosomal algebras.
| [
{
"created": "Sun, 22 Mar 2015 11:29:29 GMT",
"version": "v1"
}
] | 2015-03-30 | [
[
"Varro",
"Richard",
""
]
] | We introduce the gonosomal algebra. Gonosomal algebra extend the evolution algebra of the bisexual population (EABP) defined by Ladra and Rozikov. We show that gonosomal algebras can represent algebraically a wide variety of sex determination systems observed in bisexual populations. We illustrate this by about twenty genetic examples, most of these examples cannot be represented by an EABP. We give seven algebraic constructions of gonosomal algebras, each is illustrated by genetic examples. We show that unlike the EABP gonosomal algebras are not dibaric. We approach the existence of dibaric function and idempotent in gonosomal algebras. |
2401.02124 | Zeynep Hilal Kilimci | Zeynep Hilal Kilimci, Mustafa Yalcin | ACP-ESM: A novel framework for classification of anticancer peptides
using protein-oriented transformer approach | null | null | null | null | q-bio.BM cs.AI cs.CE cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Anticancer peptides (ACPs) are a class of molecules that have gained
significant attention in the field of cancer research and therapy. ACPs are
short chains of amino acids, the building blocks of proteins, and they possess
the ability to selectively target and kill cancer cells. One of the key
advantages of ACPs is their ability to selectively target cancer cells while
sparing healthy cells to a greater extent. This selectivity is often attributed
to differences in the surface properties of cancer cells compared to normal
cells. That is why ACPs are being investigated as potential candidates for
cancer therapy. ACPs may be used alone or in combination with other treatment
modalities like chemotherapy and radiation therapy. While ACPs hold promise as
a novel approach to cancer treatment, there are challenges to overcome,
including optimizing their stability, improving selectivity, and enhancing
their delivery to cancer cells, continuous increasing in number of peptide
sequences, developing a reliable and precise prediction model. In this work, we
propose an efficient transformer-based framework to identify anticancer
peptides for by performing accurate a reliable and precise prediction model.
For this purpose, four different transformer models, namely ESM, ProtBert,
BioBERT, and SciBERT are employed to detect anticancer peptides from amino acid
sequences. To demonstrate the contribution of the proposed framework, extensive
experiments are carried on widely-used datasets in the literature, two versions
of AntiCp2, cACP-DeepGram, ACP-740. Experiment results show the usage of
proposed model enhances classification accuracy when compared to the
state-of-the-art studies. The proposed framework, ESM, exhibits 96.45 of
accuracy for AntiCp2 dataset, 97.66 of accuracy for cACP-DeepGram dataset, and
88.51 of accuracy for ACP-740 dataset, thence determining new state-of-the-art.
| [
{
"created": "Thu, 4 Jan 2024 08:19:27 GMT",
"version": "v1"
}
] | 2024-01-05 | [
[
"Kilimci",
"Zeynep Hilal",
""
],
[
"Yalcin",
"Mustafa",
""
]
] | Anticancer peptides (ACPs) are a class of molecules that have gained significant attention in the field of cancer research and therapy. ACPs are short chains of amino acids, the building blocks of proteins, and they possess the ability to selectively target and kill cancer cells. One of the key advantages of ACPs is their ability to selectively target cancer cells while sparing healthy cells to a greater extent. This selectivity is often attributed to differences in the surface properties of cancer cells compared to normal cells. That is why ACPs are being investigated as potential candidates for cancer therapy. ACPs may be used alone or in combination with other treatment modalities like chemotherapy and radiation therapy. While ACPs hold promise as a novel approach to cancer treatment, there are challenges to overcome, including optimizing their stability, improving selectivity, and enhancing their delivery to cancer cells, continuous increasing in number of peptide sequences, developing a reliable and precise prediction model. In this work, we propose an efficient transformer-based framework to identify anticancer peptides for by performing accurate a reliable and precise prediction model. For this purpose, four different transformer models, namely ESM, ProtBert, BioBERT, and SciBERT are employed to detect anticancer peptides from amino acid sequences. To demonstrate the contribution of the proposed framework, extensive experiments are carried on widely-used datasets in the literature, two versions of AntiCp2, cACP-DeepGram, ACP-740. Experiment results show the usage of proposed model enhances classification accuracy when compared to the state-of-the-art studies. The proposed framework, ESM, exhibits 96.45 of accuracy for AntiCp2 dataset, 97.66 of accuracy for cACP-DeepGram dataset, and 88.51 of accuracy for ACP-740 dataset, thence determining new state-of-the-art. |
2306.09186 | Christoph Zechner | Tommaso Bianucci and Christoph Zechner | A local polynomial moment approximation for compartmentalised
biochemical systems | null | null | null | null | q-bio.MN q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Compartmentalised biochemical reactions are a ubiquitous building block of
biological systems. The interplay between chemical and compartmental dynamics
can drive rich and complex dynamical behaviors that are difficult to analyse
mathematically -- especially in the presence of stochasticity. We have recently
proposed an effective moment equation approach to study the statistical
properties of compartmentalised biochemical systems. So far, however, this
approach is limited to polynomial rate laws and moreover, it relies on suitable
moment closure approximations, which can be difficult to find in practice. In
this work we propose a systematic method to derive closed moment dynamics for
compartmentalised biochemical systems. We show that for the considered class of
systems, the moment equations involve expectations over functions that
factorize into two parts, one depending on the molecular content of the
compartments and one depending on the compartment number distribution. Our
method exploits this structure and approximates each function with suitable
polynomial expansions, leading to a closed system of moment equations. We
demonstrate the method using three systems inspired by cell populations and
organelle networks and study its accuracy across different dynamical regimes.
| [
{
"created": "Thu, 15 Jun 2023 15:07:54 GMT",
"version": "v1"
}
] | 2023-06-16 | [
[
"Bianucci",
"Tommaso",
""
],
[
"Zechner",
"Christoph",
""
]
] | Compartmentalised biochemical reactions are a ubiquitous building block of biological systems. The interplay between chemical and compartmental dynamics can drive rich and complex dynamical behaviors that are difficult to analyse mathematically -- especially in the presence of stochasticity. We have recently proposed an effective moment equation approach to study the statistical properties of compartmentalised biochemical systems. So far, however, this approach is limited to polynomial rate laws and moreover, it relies on suitable moment closure approximations, which can be difficult to find in practice. In this work we propose a systematic method to derive closed moment dynamics for compartmentalised biochemical systems. We show that for the considered class of systems, the moment equations involve expectations over functions that factorize into two parts, one depending on the molecular content of the compartments and one depending on the compartment number distribution. Our method exploits this structure and approximates each function with suitable polynomial expansions, leading to a closed system of moment equations. We demonstrate the method using three systems inspired by cell populations and organelle networks and study its accuracy across different dynamical regimes. |
2205.09514 | Hamed Nili | Hamed Nili, Alexander Walther, Arjen Alink and Nikolaus Kriegeskorte | Inferring exemplar discriminability in brain representations | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Representational distinctions within categories are important in all
perceptual modalities and also in cognitive and motor representations. Recent
pattern-information studies of brain activity have used condition-rich designs
to sample the stimulus space more densely. To test whether brain response
patterns discriminate among a set of stimuli (e.g. exemplars within a category)
with good sensitivity, we can pool statistical evidence over all pairwise
comparisons. Here we describe a wide range of statistical tests of exemplar
discriminability and assess the validity (specificity) and power (sensitivity)
of each test. The tests include previously used and novel, parametric and
nonparametric tests, which treat subject as a random or fixed effect, and are
based on different dissimilarity measures, different test statistics, and
different inference procedures. We use simulated and real data to determine
which tests are valid and which are most sensitive. A popular test statistic
reflecting exemplar information is the exemplar discriminability index (EDI),
which is defined as the average of the pattern dissimilarity estimates between
different exemplars minus the average of the pattern dissimilarity estimates
between repetitions of identical exemplars. The popular across-subject t test
of the EDI (typically using correlation distance as the pattern dissimilarity
measure) requires the assumption that the EDI is 0-mean normal under H0.
Although this assumption is not strictly true, our simulations suggest that the
test controls the false-positives rate at the nominal level, and is thus valid,
in practice. However, test statistics based on average Mahalanobis distances or
average linear-discriminant t values (both accounting for the multivariate
error covariance among responses) are substantially more powerful for both
random- and fixed-effects inference.
| [
{
"created": "Fri, 13 May 2022 15:13:13 GMT",
"version": "v1"
}
] | 2022-05-20 | [
[
"Nili",
"Hamed",
""
],
[
"Walther",
"Alexander",
""
],
[
"Alink",
"Arjen",
""
],
[
"Kriegeskorte",
"Nikolaus",
""
]
] | Representational distinctions within categories are important in all perceptual modalities and also in cognitive and motor representations. Recent pattern-information studies of brain activity have used condition-rich designs to sample the stimulus space more densely. To test whether brain response patterns discriminate among a set of stimuli (e.g. exemplars within a category) with good sensitivity, we can pool statistical evidence over all pairwise comparisons. Here we describe a wide range of statistical tests of exemplar discriminability and assess the validity (specificity) and power (sensitivity) of each test. The tests include previously used and novel, parametric and nonparametric tests, which treat subject as a random or fixed effect, and are based on different dissimilarity measures, different test statistics, and different inference procedures. We use simulated and real data to determine which tests are valid and which are most sensitive. A popular test statistic reflecting exemplar information is the exemplar discriminability index (EDI), which is defined as the average of the pattern dissimilarity estimates between different exemplars minus the average of the pattern dissimilarity estimates between repetitions of identical exemplars. The popular across-subject t test of the EDI (typically using correlation distance as the pattern dissimilarity measure) requires the assumption that the EDI is 0-mean normal under H0. Although this assumption is not strictly true, our simulations suggest that the test controls the false-positives rate at the nominal level, and is thus valid, in practice. However, test statistics based on average Mahalanobis distances or average linear-discriminant t values (both accounting for the multivariate error covariance among responses) are substantially more powerful for both random- and fixed-effects inference. |
2104.09105 | Ander Movilla Miangolarra | Ander Movilla Miangolarra, Sophia Hsin-Jung Li, Jean-Fran\c{c}ois
Joanny, Ned S. Wingreen, and Michele Castellana | Steric interactions and out-of-equilibrium processes control the
internal organization of bacteria | 21 pages, 11 figures | null | 10.1073/pnas.2106014118 | null | q-bio.CB cond-mat.stat-mech | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Despite the absence of a membrane-enclosed nucleus, the bacterial DNA is
typically condensed into a compact body - the nucleoid. This compaction
influences the localization and dynamics of many cellular processes including
transcription, translation, and cell division. Here, we develop a model that
takes into account steric interactions among the components of the Escherichia
coli transcriptional-translational machinery (TTM) and out-of-equilibrium
effects of mRNA transcription, translation, and degradation, in order to
explain many observed features of the nucleoid. We show that steric effects,
due to the different molecular shapes of the TTM components, are sufficient to
drive equilibrium phase separation of the DNA, explaining the formation and
size of the nucleoid. In addition, we show that the observed positioning of the
nucleoid at midcell is due to the out-of-equilibrium process of messenger RNA
(mRNA) synthesis and degradation: mRNAs apply a pressure on both sides of the
nucleoid, localizing it to midcell. We demonstrate that, as the cell grows, the
production of these mRNAs is responsible for the nucleoid splitting into two
lobes, and for their well-known positioning to 1/4 and 3/4 positions on the
long cell axis. Finally, our model quantitatively accounts for the observed
expansion of the nucleoid when the pool of cytoplasmic mRNAs is depleted.
Overall, our study suggests that steric interactions and out-of-equilibrium
effects of the TTM are key drivers of the internal spatial organization of
bacterial cells.
| [
{
"created": "Mon, 19 Apr 2021 07:52:04 GMT",
"version": "v1"
}
] | 2021-11-01 | [
[
"Miangolarra",
"Ander Movilla",
""
],
[
"Li",
"Sophia Hsin-Jung",
""
],
[
"Joanny",
"Jean-François",
""
],
[
"Wingreen",
"Ned S.",
""
],
[
"Castellana",
"Michele",
""
]
] | Despite the absence of a membrane-enclosed nucleus, the bacterial DNA is typically condensed into a compact body - the nucleoid. This compaction influences the localization and dynamics of many cellular processes including transcription, translation, and cell division. Here, we develop a model that takes into account steric interactions among the components of the Escherichia coli transcriptional-translational machinery (TTM) and out-of-equilibrium effects of mRNA transcription, translation, and degradation, in order to explain many observed features of the nucleoid. We show that steric effects, due to the different molecular shapes of the TTM components, are sufficient to drive equilibrium phase separation of the DNA, explaining the formation and size of the nucleoid. In addition, we show that the observed positioning of the nucleoid at midcell is due to the out-of-equilibrium process of messenger RNA (mRNA) synthesis and degradation: mRNAs apply a pressure on both sides of the nucleoid, localizing it to midcell. We demonstrate that, as the cell grows, the production of these mRNAs is responsible for the nucleoid splitting into two lobes, and for their well-known positioning to 1/4 and 3/4 positions on the long cell axis. Finally, our model quantitatively accounts for the observed expansion of the nucleoid when the pool of cytoplasmic mRNAs is depleted. Overall, our study suggests that steric interactions and out-of-equilibrium effects of the TTM are key drivers of the internal spatial organization of bacterial cells. |
1504.07089 | Alessandro Farini | Tito Arecchi, Alessandro Farini, Nicola Megna | A test of multiple correlation temporal window characteristic of
non-Markov processes | arXiv admin note: substantial text overlap with arXiv:1204.4559 | null | null | null | q-bio.NC physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a sensitive test of memory effects in successive events. The
test consists of a combination K of binary correlations at successive times. K
decays monotonically from K = 1 for uncorrelated events as a Markov process;
whereas memory effects provide a temporal window with K > 1. For a monotonic
memory fading, K < 1 always. Here we report evidence of a K > 1 temporal window
in cognitive tasks consisting of the visual identification of the front face of
the Necker cube after a previous presentation of the same. The K > 1 behaviour
is maximal at an inter-measurement time {\tau} around 2 sec with inter-subject
differences. The K > 1 persists over a time window of 1 sec around {\tau};
outside this window the K < 1 behaviour is recovered. The universal occurrence
of a K > 1 window in pairs of successive perceptions suggests that, at variance
with single visual stimuli eliciting a suitable response, a pair of stimuli
shortly separated in time displays mutual correlations.
| [
{
"created": "Mon, 27 Apr 2015 13:52:52 GMT",
"version": "v1"
}
] | 2015-04-28 | [
[
"Arecchi",
"Tito",
""
],
[
"Farini",
"Alessandro",
""
],
[
"Megna",
"Nicola",
""
]
] | We introduce a sensitive test of memory effects in successive events. The test consists of a combination K of binary correlations at successive times. K decays monotonically from K = 1 for uncorrelated events as a Markov process; whereas memory effects provide a temporal window with K > 1. For a monotonic memory fading, K < 1 always. Here we report evidence of a K > 1 temporal window in cognitive tasks consisting of the visual identification of the front face of the Necker cube after a previous presentation of the same. The K > 1 behaviour is maximal at an inter-measurement time {\tau} around 2 sec with inter-subject differences. The K > 1 persists over a time window of 1 sec around {\tau}; outside this window the K < 1 behaviour is recovered. The universal occurrence of a K > 1 window in pairs of successive perceptions suggests that, at variance with single visual stimuli eliciting a suitable response, a pair of stimuli shortly separated in time displays mutual correlations. |
2112.11917 | Rim Adenane | Florin Avram, Rim Adenane, Gianluca Bianchin and Andrei Halanay | Stability analysis of an eight parameter SIR-type model including loss
of immunity, and disease and vaccination fatalities | null | null | null | null | q-bio.PE math.CA | http://creativecommons.org/licenses/by/4.0/ | We revisit here a landmark five parameter SIR-type model of [DvdD93, Sec. 4],
which is maybe the simplest example where a complete picture of all cases,
including non-trivial bistability behavior, may be obtained using simple tools.
We also generalize it by adding essential vaccination and vaccination-induced
death parameters, with the aim of revealing the role of vaccination and its
possible failure. The main result is Theorem 5, which describes the stability
behavior of our model in all possible cases.
| [
{
"created": "Thu, 16 Dec 2021 18:32:43 GMT",
"version": "v1"
},
{
"created": "Mon, 3 Jan 2022 12:58:44 GMT",
"version": "v2"
},
{
"created": "Sat, 8 Jan 2022 12:53:26 GMT",
"version": "v3"
}
] | 2022-01-11 | [
[
"Avram",
"Florin",
""
],
[
"Adenane",
"Rim",
""
],
[
"Bianchin",
"Gianluca",
""
],
[
"Halanay",
"Andrei",
""
]
] | We revisit here a landmark five parameter SIR-type model of [DvdD93, Sec. 4], which is maybe the simplest example where a complete picture of all cases, including non-trivial bistability behavior, may be obtained using simple tools. We also generalize it by adding essential vaccination and vaccination-induced death parameters, with the aim of revealing the role of vaccination and its possible failure. The main result is Theorem 5, which describes the stability behavior of our model in all possible cases. |
1801.06046 | Johanna Senk | Johanna Senk, Karol\'ina Korvasov\'a, Jannis Schuecker, Espen Hagen,
Tom Tetzlaff, Markus Diesmann, Moritz Helias | Conditions for wave trains in spiking neural networks | 36 pages, 8 figures, 4 tables | Phys. Rev. Research 2, 023174 (2020) | 10.1103/PhysRevResearch.2.023174 | null | q-bio.NC math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spatiotemporal patterns such as traveling waves are frequently observed in
recordings of neural activity. The mechanisms underlying the generation of such
patterns are largely unknown. Previous studies have investigated the existence
and uniqueness of different types of waves or bumps of activity using
neural-field models, phenomenological coarse-grained descriptions of
neural-network dynamics. But it remains unclear how these insights can be
transferred to more biologically realistic networks of spiking neurons, where
individual neurons fire irregularly. Here, we employ mean-field theory to
reduce a microscopic model of leaky integrate-and-fire (LIF) neurons with
distance-dependent connectivity to an effective neural-field model. In contrast
to existing phenomenological descriptions, the dynamics in this neural-field
model depends on the mean and the variance in the synaptic input, both
determining the amplitude and the temporal structure of the resulting effective
coupling kernel. For the neural-field model we employ liner stability analysis
to derive conditions for the existence of spatial and temporal oscillations and
wave trains, that is, temporally and spatially periodic traveling waves. We
first prove that wave trains cannot occur in a single homogeneous population of
neurons, irrespective of the form of distance dependence of the connection
probability. Compatible with the architecture of cortical neural networks, wave
trains emerge in two-population networks of excitatory and inhibitory neurons
as a combination of delay-induced temporal oscillations and spatial
oscillations due to distance-dependent connectivity profiles. Finally, we
demonstrate quantitative agreement between predictions of the analytically
tractable neural-field model and numerical simulations of both networks of
nonlinear rate-based units and networks of LIF neurons.
| [
{
"created": "Thu, 18 Jan 2018 14:40:27 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Sep 2019 14:44:17 GMT",
"version": "v2"
}
] | 2022-09-16 | [
[
"Senk",
"Johanna",
""
],
[
"Korvasová",
"Karolína",
""
],
[
"Schuecker",
"Jannis",
""
],
[
"Hagen",
"Espen",
""
],
[
"Tetzlaff",
"Tom",
""
],
[
"Diesmann",
"Markus",
""
],
[
"Helias",
"Moritz",
""
]
] | Spatiotemporal patterns such as traveling waves are frequently observed in recordings of neural activity. The mechanisms underlying the generation of such patterns are largely unknown. Previous studies have investigated the existence and uniqueness of different types of waves or bumps of activity using neural-field models, phenomenological coarse-grained descriptions of neural-network dynamics. But it remains unclear how these insights can be transferred to more biologically realistic networks of spiking neurons, where individual neurons fire irregularly. Here, we employ mean-field theory to reduce a microscopic model of leaky integrate-and-fire (LIF) neurons with distance-dependent connectivity to an effective neural-field model. In contrast to existing phenomenological descriptions, the dynamics in this neural-field model depends on the mean and the variance in the synaptic input, both determining the amplitude and the temporal structure of the resulting effective coupling kernel. For the neural-field model we employ liner stability analysis to derive conditions for the existence of spatial and temporal oscillations and wave trains, that is, temporally and spatially periodic traveling waves. We first prove that wave trains cannot occur in a single homogeneous population of neurons, irrespective of the form of distance dependence of the connection probability. Compatible with the architecture of cortical neural networks, wave trains emerge in two-population networks of excitatory and inhibitory neurons as a combination of delay-induced temporal oscillations and spatial oscillations due to distance-dependent connectivity profiles. Finally, we demonstrate quantitative agreement between predictions of the analytically tractable neural-field model and numerical simulations of both networks of nonlinear rate-based units and networks of LIF neurons. |
1901.01885 | Han Peters | Meike T. Wortel, Han Peters, Nils Chr. Stenseth | Coupled fast and slow feedbacks lead to continual evolution: A general
modeling approach | 25 pages, 10 figures | null | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Red Queen Hypothesis, which suggests that continual evolution can result
from solely biotic interactions, has been studied in macroevolutionary and
microevolutionary contexts. While the latter has been effective in describing
examples in which evolution does not cease, describing which properties lead to
continual evolution or to stasis remains a major challenge. In many contexts it
is unclear which assumptions are necessary for continual evolution, and whether
described behavior is robust under perturbations. Our aim here is to prove
continual evolution under minimal conditions and in a general framework, thus
automatically obtaining robustness. We show that the combination of a fast
positive and a slow negative feedback causes continual evolution with a single
evolving trait, provided the ecological timescale is sufficiently separated
from the timescales of mutations and negative feedback. Our approach and
results form a next step towards a deeper understanding of the evolutionary
dynamics resulting from biotic interactions.
| [
{
"created": "Mon, 7 Jan 2019 15:37:10 GMT",
"version": "v1"
}
] | 2019-01-08 | [
[
"Wortel",
"Meike T.",
""
],
[
"Peters",
"Han",
""
],
[
"Stenseth",
"Nils Chr.",
""
]
] | The Red Queen Hypothesis, which suggests that continual evolution can result from solely biotic interactions, has been studied in macroevolutionary and microevolutionary contexts. While the latter has been effective in describing examples in which evolution does not cease, describing which properties lead to continual evolution or to stasis remains a major challenge. In many contexts it is unclear which assumptions are necessary for continual evolution, and whether described behavior is robust under perturbations. Our aim here is to prove continual evolution under minimal conditions and in a general framework, thus automatically obtaining robustness. We show that the combination of a fast positive and a slow negative feedback causes continual evolution with a single evolving trait, provided the ecological timescale is sufficiently separated from the timescales of mutations and negative feedback. Our approach and results form a next step towards a deeper understanding of the evolutionary dynamics resulting from biotic interactions. |
2210.11046 | Isaure CHAUVOT DE BEAUCHENE | Dominique Mias-Lucquin (LORIA), Isaure Chauvot de Beauchene (LORIA) | Conformational variability in proteins bound to single-stranded DNA: a
new benchmark for new docking perspectives | null | Proteins - Structure, Function and Bioinformatics, Wiley, 2022 | null | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explored the Protein DataBank (PDB) to collect protein-ssDNA structures
and create a multiconformational docking benchmark including both bound and
unbound protein structures. Due to ssDNA high flexibility when not bound, no
ssDNA unbound structure is included in the benchmark. For the 91
sequence-identity groups identified as bound-unbound structures of the same
protein, we studied the conformational changes in the protein induced by the
ssDNA binding. Moreover, based on several bound or unbound protein structures
in some groups, we also assessed the intrinsic conformational variability in
either bound or unbound conditions, and compared it to the supposedly
binding-induced modifications. To illustrate a use case of this benchmark, we
performed docking experiments using ATTRACT docking software. This benchmark
is, to our knowledge, the first one made to peruse available structures of
ssDNA-protein interactions to such an extent, aiming to improve computational
docking tools dedicated to this kind of molecular interactions.
| [
{
"created": "Thu, 20 Oct 2022 06:41:24 GMT",
"version": "v1"
}
] | 2022-10-21 | [
[
"Mias-Lucquin",
"Dominique",
"",
"LORIA"
],
[
"de Beauchene",
"Isaure Chauvot",
"",
"LORIA"
]
] | We explored the Protein DataBank (PDB) to collect protein-ssDNA structures and create a multiconformational docking benchmark including both bound and unbound protein structures. Due to ssDNA high flexibility when not bound, no ssDNA unbound structure is included in the benchmark. For the 91 sequence-identity groups identified as bound-unbound structures of the same protein, we studied the conformational changes in the protein induced by the ssDNA binding. Moreover, based on several bound or unbound protein structures in some groups, we also assessed the intrinsic conformational variability in either bound or unbound conditions, and compared it to the supposedly binding-induced modifications. To illustrate a use case of this benchmark, we performed docking experiments using ATTRACT docking software. This benchmark is, to our knowledge, the first one made to peruse available structures of ssDNA-protein interactions to such an extent, aiming to improve computational docking tools dedicated to this kind of molecular interactions. |
1902.00352 | Maritza Hernandez | Maritza Hernandez, Guo Liang Gan, Kirby Linvill, Carl Dukatz, Jun
Feng, and Govinda Bhisetti | A Quantum-Inspired Method for Three-Dimensional Ligand-Based Virtual
Screening | 45 pages, 20 figures. It includes Supporting Information material | Journal of Chemical Information and Modeling, 2019, 59, 10,
4475-4485 | 10.1021/acs.jcim.9b00195 | null | q-bio.QM quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Measuring similarity between molecules is an important part of virtual
screening (VS) experiments deployed during the early stages of drug discovery.
Most widely used methods for evaluating the similarity of molecules use
molecular fingerprints to encode structural information. While similarity
methods using fingerprint encodings are efficient, they do not consider all the
relevant aspects of molecular structure. In this paper, we describe a
quantum-inspired graph-based molecular similarity (GMS) method for ligand-based
VS. The GMS method is formulated as a quadratic unconstrained binary
optimization problem that can be solved using a quantum annealer, providing the
opportunity to take advantage of this nascent and potentially groundbreaking
technology. In this study, we consider various features relevant to
ligand-based VS, such as pharmacophore features and three-dimensional atomic
coordinates, and include them in the GMS method. We evaluate this approach on
various datasets from the DUD_LIB_VS_1.0 library. Our results show that using
three-dimensional atomic coordinates as features for comparison yields higher
early enrichment values. In addition, we evaluate the performance of the GMS
method against conventional fingerprint approaches. The results demonstrate
that the GMS method outperforms fingerprint methods for most of the datasets,
presenting a new alternative in ligand-based VS with the potential for future
enhancement.
| [
{
"created": "Mon, 28 Jan 2019 20:51:43 GMT",
"version": "v1"
}
] | 2019-11-04 | [
[
"Hernandez",
"Maritza",
""
],
[
"Gan",
"Guo Liang",
""
],
[
"Linvill",
"Kirby",
""
],
[
"Dukatz",
"Carl",
""
],
[
"Feng",
"Jun",
""
],
[
"Bhisetti",
"Govinda",
""
]
] | Measuring similarity between molecules is an important part of virtual screening (VS) experiments deployed during the early stages of drug discovery. Most widely used methods for evaluating the similarity of molecules use molecular fingerprints to encode structural information. While similarity methods using fingerprint encodings are efficient, they do not consider all the relevant aspects of molecular structure. In this paper, we describe a quantum-inspired graph-based molecular similarity (GMS) method for ligand-based VS. The GMS method is formulated as a quadratic unconstrained binary optimization problem that can be solved using a quantum annealer, providing the opportunity to take advantage of this nascent and potentially groundbreaking technology. In this study, we consider various features relevant to ligand-based VS, such as pharmacophore features and three-dimensional atomic coordinates, and include them in the GMS method. We evaluate this approach on various datasets from the DUD_LIB_VS_1.0 library. Our results show that using three-dimensional atomic coordinates as features for comparison yields higher early enrichment values. In addition, we evaluate the performance of the GMS method against conventional fingerprint approaches. The results demonstrate that the GMS method outperforms fingerprint methods for most of the datasets, presenting a new alternative in ligand-based VS with the potential for future enhancement. |
1811.11804 | Vladimir Minin | Mathieu Fourment, Andrew F. Magee, Chris Whidden, Arman Bilge,
Frederick A. Matsen IV, Vladimir N. Minin | 19 dubious ways to compute the marginal likelihood of a phylogenetic
tree topology | 37 pages, 5 figures and 1 table in main text, plus supplementary
materials | null | null | null | q-bio.PE stat.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The marginal likelihood of a model is a key quantity for assessing the
evidence provided by the data in support of a model. The marginal likelihood is
the normalizing constant for the posterior density, obtained by integrating the
product of the likelihood and the prior with respect to model parameters. Thus,
the computational burden of computing the marginal likelihood scales with the
dimension of the parameter space. In phylogenetics, where we work with tree
topologies that are high-dimensional models, standard approaches to computing
marginal likelihoods are very slow. Here we study methods to quickly compute
the marginal likelihood of a single fixed tree topology. We benchmark the speed
and accuracy of 19 different methods to compute the marginal likelihood of
phylogenetic topologies on a suite of real datasets. These methods include
several new ones that we develop explicitly to solve this problem, as well as
existing algorithms that we apply to phylogenetic models for the first time.
Altogether, our results show that the accuracy of these methods varies widely,
and that accuracy does not necessarily correlate with computational burden. Our
newly developed methods are orders of magnitude faster than standard
approaches, and in some cases, their accuracy rivals the best established
estimators.
| [
{
"created": "Wed, 28 Nov 2018 19:59:03 GMT",
"version": "v1"
}
] | 2018-11-30 | [
[
"Fourment",
"Mathieu",
""
],
[
"Magee",
"Andrew F.",
""
],
[
"Whidden",
"Chris",
""
],
[
"Bilge",
"Arman",
""
],
[
"Matsen",
"Frederick A.",
"IV"
],
[
"Minin",
"Vladimir N.",
""
]
] | The marginal likelihood of a model is a key quantity for assessing the evidence provided by the data in support of a model. The marginal likelihood is the normalizing constant for the posterior density, obtained by integrating the product of the likelihood and the prior with respect to model parameters. Thus, the computational burden of computing the marginal likelihood scales with the dimension of the parameter space. In phylogenetics, where we work with tree topologies that are high-dimensional models, standard approaches to computing marginal likelihoods are very slow. Here we study methods to quickly compute the marginal likelihood of a single fixed tree topology. We benchmark the speed and accuracy of 19 different methods to compute the marginal likelihood of phylogenetic topologies on a suite of real datasets. These methods include several new ones that we develop explicitly to solve this problem, as well as existing algorithms that we apply to phylogenetic models for the first time. Altogether, our results show that the accuracy of these methods varies widely, and that accuracy does not necessarily correlate with computational burden. Our newly developed methods are orders of magnitude faster than standard approaches, and in some cases, their accuracy rivals the best established estimators. |
1706.01182 | Hitoshi Koyano | Hitoshi Koyano and Kouji Yano | Evolutionary model of a population of DNA sequences through the
interaction with an environment and its application to speciation analysis | null | null | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this study, we construct an evolutionary model of a population of DNA
sequences interacting with the surrounding environment on the topological
monoid A* of strings on the alphabet A = { a, c, g, t }. A partial differential
equation governing the evolution of the DNA population is derived as a kind of
diffusion equation on A*. Analyzing the constructed model in a theoretical
manner, we present conditions for sympatric speciation, the possibility of
which continues to be discussed. It is shown that under other same conditions
one condition determines whether sympatric speciation occurs or the DNA
population continues to move around randomly in a subset of A*. We next
demonstrate that the population maintains a kind of equlibrium state under
certain conditions. In this situation, the population remains nearly unchanged
and does not differentiate even if it can differentiate into others.
Furthermore, we calculate the probability of sympatric speciation and the time
expected to elapse before it.
| [
{
"created": "Mon, 5 Jun 2017 03:39:47 GMT",
"version": "v1"
}
] | 2017-06-06 | [
[
"Koyano",
"Hitoshi",
""
],
[
"Yano",
"Kouji",
""
]
] | In this study, we construct an evolutionary model of a population of DNA sequences interacting with the surrounding environment on the topological monoid A* of strings on the alphabet A = { a, c, g, t }. A partial differential equation governing the evolution of the DNA population is derived as a kind of diffusion equation on A*. Analyzing the constructed model in a theoretical manner, we present conditions for sympatric speciation, the possibility of which continues to be discussed. It is shown that under other same conditions one condition determines whether sympatric speciation occurs or the DNA population continues to move around randomly in a subset of A*. We next demonstrate that the population maintains a kind of equlibrium state under certain conditions. In this situation, the population remains nearly unchanged and does not differentiate even if it can differentiate into others. Furthermore, we calculate the probability of sympatric speciation and the time expected to elapse before it. |
0812.2057 | Stefan Klumpp | Stefan Klumpp and Terence Hwa | Growth-rate dependent partitioning of RNA polymerases in bacteria | includes supporting information | Proc. Natl. Acad. Sci. USA 105, 20245-20250 (2008) | 10.1073/pnas.0804953105 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Physiological changes which result in changes in bacterial gene expression
are often accompanied by changes in the growth rate for fast adapting enteric
bacteria. Since the availability of RNA polymerase (RNAP) in cells is dependent
on the growth rate, transcriptional control involves not only the regulation of
promoters, but also depends on the available (or free) RNAP concentration which
is difficult to quantify directly. Here we develop a simple physical model
describing the partitioning of cellular RNAP into different classes: RNAPs
transcribing mRNA and ribosomal RNA (rRNA), RNAPs non-specifically bound to
DNA, free RNAP, and immature RNAP. Available experimental data for E. coli
allow us to determine the two unknown parameters of the model and hence deduce
the free RNAP concentration at different growth rates. The results allow us to
predict the growth-rate dependence of the activities of constitutive
(unregulated) promoters, and to disentangle the growth-rate dependent
regulation of promoters (e.g., the promoters of rRNA operons) from changes in
transcription due to changes in the free RNAP concentration at different growth
rates. Our model can quantitatively account for the observed changes in gene
expression patterns in mutant E. coli strains with altered levels of RNAP
expression without invoking additional parameters. Applying our model to the
case of the stringent response following amino acid starvation, we can evaluate
the plausibility of various scenarios of passive transcriptional control
proposed to account for the observed changes in the expression of rRNA and
biosynthetic operons.
| [
{
"created": "Thu, 11 Dec 2008 00:12:32 GMT",
"version": "v1"
}
] | 2008-12-12 | [
[
"Klumpp",
"Stefan",
""
],
[
"Hwa",
"Terence",
""
]
] | Physiological changes which result in changes in bacterial gene expression are often accompanied by changes in the growth rate for fast adapting enteric bacteria. Since the availability of RNA polymerase (RNAP) in cells is dependent on the growth rate, transcriptional control involves not only the regulation of promoters, but also depends on the available (or free) RNAP concentration which is difficult to quantify directly. Here we develop a simple physical model describing the partitioning of cellular RNAP into different classes: RNAPs transcribing mRNA and ribosomal RNA (rRNA), RNAPs non-specifically bound to DNA, free RNAP, and immature RNAP. Available experimental data for E. coli allow us to determine the two unknown parameters of the model and hence deduce the free RNAP concentration at different growth rates. The results allow us to predict the growth-rate dependence of the activities of constitutive (unregulated) promoters, and to disentangle the growth-rate dependent regulation of promoters (e.g., the promoters of rRNA operons) from changes in transcription due to changes in the free RNAP concentration at different growth rates. Our model can quantitatively account for the observed changes in gene expression patterns in mutant E. coli strains with altered levels of RNAP expression without invoking additional parameters. Applying our model to the case of the stringent response following amino acid starvation, we can evaluate the plausibility of various scenarios of passive transcriptional control proposed to account for the observed changes in the expression of rRNA and biosynthetic operons. |
q-bio/0502005 | Iaroslav Ispolatov | I. Ispolatov, P. L. Krapivsky, I. Mazo, and A. Yuryev | Cliques and duplication-divergence network growth | 7 pages, 6 figures | New J. Phys. v. 7 (2005) 145 | 10.1088/1367-2630/7/1/145 | null | q-bio.MN cond-mat.dis-nn q-bio.GN | null | A population of complete subgraphs or cliques in a network evolving via
duplication-divergence is considered. We find that a number of cliques of each
size scales linearly with the size of the network. We also derive a clique
population distribution that is in perfect agreement with both the simulation
results and the clique statistic of the protein-protein binding network of the
fruit fly. In addition, we show that such features as fat-tail degree
distribution, various rates of average degree growth and non-averaging,
revealed recently for only the particular case of a completely asymmetric
divergence, are present in a general case of arbitrary divergence.
| [
{
"created": "Mon, 7 Feb 2005 17:50:59 GMT",
"version": "v1"
}
] | 2009-11-11 | [
[
"Ispolatov",
"I.",
""
],
[
"Krapivsky",
"P. L.",
""
],
[
"Mazo",
"I.",
""
],
[
"Yuryev",
"A.",
""
]
] | A population of complete subgraphs or cliques in a network evolving via duplication-divergence is considered. We find that a number of cliques of each size scales linearly with the size of the network. We also derive a clique population distribution that is in perfect agreement with both the simulation results and the clique statistic of the protein-protein binding network of the fruit fly. In addition, we show that such features as fat-tail degree distribution, various rates of average degree growth and non-averaging, revealed recently for only the particular case of a completely asymmetric divergence, are present in a general case of arbitrary divergence. |
2308.10302 | Qianqian Wang | Junhao Zhang, Qianqian Wang, Xiaochuan Wang, Lishan Qiao, Mingxia Liu | Preserving Specificity in Federated Graph Learning for fMRI-based
Neurological Disorder Identification | null | null | null | null | q-bio.QM cs.LG eess.SP | http://creativecommons.org/licenses/by/4.0/ | Resting-state functional magnetic resonance imaging (rs-fMRI) offers a
non-invasive approach to examining abnormal brain connectivity associated with
brain disorders. Graph neural network (GNN) gains popularity in fMRI
representation learning and brain disorder analysis with powerful graph
representation capabilities. Training a general GNN often necessitates a
large-scale dataset from multiple imaging centers/sites, but centralizing
multi-site data generally faces inherent challenges related to data privacy,
security, and storage burden. Federated Learning (FL) enables collaborative
model training without centralized multi-site fMRI data. Unfortunately,
previous FL approaches for fMRI analysis often ignore site-specificity,
including demographic factors such as age, gender, and education level. To this
end, we propose a specificity-aware federated graph learning (SFGL) framework
for rs-fMRI analysis and automated brain disorder identification, with a server
and multiple clients/sites for federated model aggregation and prediction. At
each client, our model consists of a shared and a personalized branch, where
parameters of the shared branch are sent to the server while those of the
personalized branch remain local. This can facilitate knowledge sharing among
sites and also helps preserve site specificity. In the shared branch, we employ
a spatio-temporal attention graph isomorphism network to learn dynamic fMRI
representations. In the personalized branch, we integrate vectorized
demographic information (i.e., age, gender, and education years) and functional
connectivity networks to preserve site-specific characteristics.
Representations generated by the two branches are then fused for
classification. Experimental results on two fMRI datasets with a total of 1,218
subjects suggest that SFGL outperforms several state-of-the-art approaches.
| [
{
"created": "Sun, 20 Aug 2023 15:55:45 GMT",
"version": "v1"
}
] | 2023-08-22 | [
[
"Zhang",
"Junhao",
""
],
[
"Wang",
"Qianqian",
""
],
[
"Wang",
"Xiaochuan",
""
],
[
"Qiao",
"Lishan",
""
],
[
"Liu",
"Mingxia",
""
]
] | Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive approach to examining abnormal brain connectivity associated with brain disorders. Graph neural network (GNN) gains popularity in fMRI representation learning and brain disorder analysis with powerful graph representation capabilities. Training a general GNN often necessitates a large-scale dataset from multiple imaging centers/sites, but centralizing multi-site data generally faces inherent challenges related to data privacy, security, and storage burden. Federated Learning (FL) enables collaborative model training without centralized multi-site fMRI data. Unfortunately, previous FL approaches for fMRI analysis often ignore site-specificity, including demographic factors such as age, gender, and education level. To this end, we propose a specificity-aware federated graph learning (SFGL) framework for rs-fMRI analysis and automated brain disorder identification, with a server and multiple clients/sites for federated model aggregation and prediction. At each client, our model consists of a shared and a personalized branch, where parameters of the shared branch are sent to the server while those of the personalized branch remain local. This can facilitate knowledge sharing among sites and also helps preserve site specificity. In the shared branch, we employ a spatio-temporal attention graph isomorphism network to learn dynamic fMRI representations. In the personalized branch, we integrate vectorized demographic information (i.e., age, gender, and education years) and functional connectivity networks to preserve site-specific characteristics. Representations generated by the two branches are then fused for classification. Experimental results on two fMRI datasets with a total of 1,218 subjects suggest that SFGL outperforms several state-of-the-art approaches. |
2002.08470 | Kevin Scharp | Alison Duncan Kerr and Kevin Scharp | The Information in Emotion Communication | null | null | null | null | q-bio.NC cs.LG q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How much information is transmitted when animals use emotions to communicate?
It is clear that emotions are used as communication systems in humans and other
species. The quantitative theory of emotion information presented here is based
on Shannon's mathematical theory of information in communication systems. The
theory explains myriad aspects of emotion communication and offers dozens of
new directions for research. It is superior to the "contagion" theory of
emotion spreading, which is currently dominant. One important application of
the information theory of emotion communication is that it permits the
development of emotion security systems for social networks to guard against
the widespread emotion manipulation we see online today.
| [
{
"created": "Fri, 14 Feb 2020 22:42:26 GMT",
"version": "v1"
}
] | 2020-02-21 | [
[
"Kerr",
"Alison Duncan",
""
],
[
"Scharp",
"Kevin",
""
]
] | How much information is transmitted when animals use emotions to communicate? It is clear that emotions are used as communication systems in humans and other species. The quantitative theory of emotion information presented here is based on Shannon's mathematical theory of information in communication systems. The theory explains myriad aspects of emotion communication and offers dozens of new directions for research. It is superior to the "contagion" theory of emotion spreading, which is currently dominant. One important application of the information theory of emotion communication is that it permits the development of emotion security systems for social networks to guard against the widespread emotion manipulation we see online today. |
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