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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
q-bio/0512039 | Mauro Mobilia Dr | Mauro Mobilia, Ivan T. Georgiev and Uwe C. Tauber | Phase Transitions and Spatio-Temporal Fluctuations in Stochastic Lattice
Lotka-Volterra Models | 19 pages, 11 figures, 2-column revtex4 format. Minor modifications.
Accepted in the Journal of Statistical Physics. Movies corresponding to
Figures 2 and 3 are available at
http://www.phys.vt.edu/~tauber/PredatorPrey/movies/ | Journal of Statistical Physics, Vol.128 (Nos. 1/2), 447 (2007) | 10.1007/s10955-006-9146-3 | null | q-bio.PE cond-mat.stat-mech nlin.AO q-bio.QM | null | We study the general properties of stochastic two-species models for
predator-prey competition and coexistence with Lotka-Volterra type interactions
defined on a $d$-dimensional lattice. Introducing spatial degrees of freedom
and allowing for stochastic fluctuations generically invalidates the classical,
deterministic mean-field picture. Already within mean-field theory, however,
spatial constraints, modeling locally limited resources, lead to the emergence
of a continuous active-to-absorbing state phase transition. Field-theoretic
arguments, supported by Monte Carlo simulation results, indicate that this
transition, which represents an extinction threshold for the predator
population, is governed by the directed percolation universality class. In the
active state, where predators and prey coexist, the classical center
singularities with associated population cycles are replaced by either nodes or
foci. In the vicinity of the stable nodes, the system is characterized by
essentially stationary localized clusters of predators in a sea of prey. Near
the stable foci, however, the stochastic lattice Lotka-Volterra system displays
complex, correlated spatio-temporal patterns of competing activity fronts.
Correspondingly, the population densities in our numerical simulations turn out
to oscillate irregularly in time, with amplitudes that tend to zero in the
thermodynamic limit. Yet in finite systems these oscillatory fluctuations are
quite persistent, and their features are determined by the intrinsic
interaction rates rather than the initial conditions. We emphasize the
robustness of this scenario with respect to various model perturbations.
| [
{
"created": "Thu, 22 Dec 2005 23:58:59 GMT",
"version": "v1"
},
{
"created": "Wed, 24 May 2006 20:14:37 GMT",
"version": "v2"
}
] | 2007-06-07 | [
[
"Mobilia",
"Mauro",
""
],
[
"Georgiev",
"Ivan T.",
""
],
[
"Tauber",
"Uwe C.",
""
]
] | We study the general properties of stochastic two-species models for predator-prey competition and coexistence with Lotka-Volterra type interactions defined on a $d$-dimensional lattice. Introducing spatial degrees of freedom and allowing for stochastic fluctuations generically invalidates the classical, deterministic mean-field picture. Already within mean-field theory, however, spatial constraints, modeling locally limited resources, lead to the emergence of a continuous active-to-absorbing state phase transition. Field-theoretic arguments, supported by Monte Carlo simulation results, indicate that this transition, which represents an extinction threshold for the predator population, is governed by the directed percolation universality class. In the active state, where predators and prey coexist, the classical center singularities with associated population cycles are replaced by either nodes or foci. In the vicinity of the stable nodes, the system is characterized by essentially stationary localized clusters of predators in a sea of prey. Near the stable foci, however, the stochastic lattice Lotka-Volterra system displays complex, correlated spatio-temporal patterns of competing activity fronts. Correspondingly, the population densities in our numerical simulations turn out to oscillate irregularly in time, with amplitudes that tend to zero in the thermodynamic limit. Yet in finite systems these oscillatory fluctuations are quite persistent, and their features are determined by the intrinsic interaction rates rather than the initial conditions. We emphasize the robustness of this scenario with respect to various model perturbations. |
1510.06008 | Iddo Friedberg | James T Morton, Stefan D Freed, Shaun W Lee and Iddo Friedberg | A large scale prediction of bacteriocin gene blocks suggests a wide
functional spectrum for bacteriocins | Accepted for publication in BMC Bioinformatics | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Bacteriocins are peptide-derived molecules produced by bacteria, whose
recently-discovered functions include virulence factors and signalling
molecules as well as their better known roles as antibiotics. To date, close to
five hundred bacteriocins have been identified and classified. Recent
discoveries have shown that bacteriocins are highly diverse and widely
distributed among bacterial species. Given the heterogeneity of bacteriocin
compounds, many tools struggle with identifying novel bacteriocins due to their
vast sequence and structural diversity. Many bacteriocins undergo
post-translational processing or modifications necessary for the biosynthesis
of the final mature form. Enzymatic modification of bacteriocins as well as
their export is achieved by proteins whose genes are often located in a
discrete gene cluster proximal to the bacteriocin precursor gene, referred to
as \textit{context genes} in this study. Although bacteriocins themselves are
structurally diverse, context genes have been shown to be largely conserved
across unrelated species. Using this knowledge, we set out to identify new
candidates for context genes which may clarify how bacteriocins are
synthesized, and identify new candidates for bacteriocins that bear no sequence
similarity to known toxins. To achieve these goals, we have developed a
software tool, Bacteriocin Operon and gene block Associator (BOA) that can
identify homologous bacteriocin associated gene clusters and predict novel
ones. We discover that several phyla have a strong preference for bactericon
genes, suggesting distinct functions for this group of molecules. Availability:
https://github.com/idoerg/BOA
| [
{
"created": "Tue, 20 Oct 2015 19:00:59 GMT",
"version": "v1"
}
] | 2015-10-21 | [
[
"Morton",
"James T",
""
],
[
"Freed",
"Stefan D",
""
],
[
"Lee",
"Shaun W",
""
],
[
"Friedberg",
"Iddo",
""
]
] | Bacteriocins are peptide-derived molecules produced by bacteria, whose recently-discovered functions include virulence factors and signalling molecules as well as their better known roles as antibiotics. To date, close to five hundred bacteriocins have been identified and classified. Recent discoveries have shown that bacteriocins are highly diverse and widely distributed among bacterial species. Given the heterogeneity of bacteriocin compounds, many tools struggle with identifying novel bacteriocins due to their vast sequence and structural diversity. Many bacteriocins undergo post-translational processing or modifications necessary for the biosynthesis of the final mature form. Enzymatic modification of bacteriocins as well as their export is achieved by proteins whose genes are often located in a discrete gene cluster proximal to the bacteriocin precursor gene, referred to as \textit{context genes} in this study. Although bacteriocins themselves are structurally diverse, context genes have been shown to be largely conserved across unrelated species. Using this knowledge, we set out to identify new candidates for context genes which may clarify how bacteriocins are synthesized, and identify new candidates for bacteriocins that bear no sequence similarity to known toxins. To achieve these goals, we have developed a software tool, Bacteriocin Operon and gene block Associator (BOA) that can identify homologous bacteriocin associated gene clusters and predict novel ones. We discover that several phyla have a strong preference for bactericon genes, suggesting distinct functions for this group of molecules. Availability: https://github.com/idoerg/BOA |
1612.03270 | Tsung-Ren Huang | Tsung-Ren Huang | Hebbian Plasticity for Improving Perceptual Decisions | 7 pages, 2 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Shibata et al. reported that humans could learn to repeatedly evoke a
stimulus-associated functional magnetic resonance imaging (fMRI) activity
pattern in visual areas V1/V2 through which visual perceptual learning was
achieved without stimulus presentation. Contrary to their attribution of visual
improvements to neuroplasticity in adult V1/V2, our Hebbian learning
interpretation of these data explains the attainment of better perceptual
decisions without plastic V1/V2.
| [
{
"created": "Sat, 10 Dec 2016 08:32:35 GMT",
"version": "v1"
}
] | 2016-12-13 | [
[
"Huang",
"Tsung-Ren",
""
]
] | Shibata et al. reported that humans could learn to repeatedly evoke a stimulus-associated functional magnetic resonance imaging (fMRI) activity pattern in visual areas V1/V2 through which visual perceptual learning was achieved without stimulus presentation. Contrary to their attribution of visual improvements to neuroplasticity in adult V1/V2, our Hebbian learning interpretation of these data explains the attainment of better perceptual decisions without plastic V1/V2. |
1203.6319 | Benzi Roberto | Roberto Benzi, Mogens H. Jensen, David R. Nelson, Prasad Perlekar,
Simone Pigolotti, Federico Toschi | Population dynamics in compressible flows | 16 pages, talk delivered at the Geilo Winter School 2011 | null | 10.1140/epjst/e2012-01552-0 | null | q-bio.PE cond-mat.soft nlin.CD | http://creativecommons.org/licenses/publicdomain/ | Organisms often grow, migrate and compete in liquid environments, as well as
on solid surfaces. However, relatively little is known about what happens when
competing species are mixed and compressed by fluid turbulence. In these
lectures we review our recent work on population dynamics and population
genetics in compressible velocity fields of one and two dimensions. We discuss
why compressible turbulence is relevant for population dynamics in the ocean
and we consider cases both where the velocity field is turbulent and when it is
static. Furthermore, we investigate populations in terms of a continuos density
field and when the populations are treated via discrete particles. In the last
case we focus on the competition and fixation of one species compared to
another
| [
{
"created": "Wed, 28 Mar 2012 17:08:13 GMT",
"version": "v1"
}
] | 2015-06-04 | [
[
"Benzi",
"Roberto",
""
],
[
"Jensen",
"Mogens H.",
""
],
[
"Nelson",
"David R.",
""
],
[
"Perlekar",
"Prasad",
""
],
[
"Pigolotti",
"Simone",
""
],
[
"Toschi",
"Federico",
""
]
] | Organisms often grow, migrate and compete in liquid environments, as well as on solid surfaces. However, relatively little is known about what happens when competing species are mixed and compressed by fluid turbulence. In these lectures we review our recent work on population dynamics and population genetics in compressible velocity fields of one and two dimensions. We discuss why compressible turbulence is relevant for population dynamics in the ocean and we consider cases both where the velocity field is turbulent and when it is static. Furthermore, we investigate populations in terms of a continuos density field and when the populations are treated via discrete particles. In the last case we focus on the competition and fixation of one species compared to another |
0909.2521 | Juergen Reingruber | Juergen Reingruber David Holcman | The Gated Narrow Escape Time for molecular signaling | 6 pages, 5 figures | null | null | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The mean time for a diffusing ligand to activate a target protein located on
the surface of a microdomain can regulate cellular signaling. When the ligand
switches between various states induced by chemical interactions or
conformational changes, while target activation occurs in only one state, this
activation time is affected. We investigate this dynamics using new equations
for the sojourn times spent in each state. For two states, we obtain exact
solutions in dimension one, and asymptotic ones confirmed by Brownian
simulations in dimension 3. We find that the activation time is quite sensitive
to changes of the switching rates, which can be used to modulate signaling.
Interestingly, our analysis reveals that activation can be fast although the
ligand spends most of the time 'hidden' in the non-activating state. Finally,
we obtain a new formula for the narrow escape time in the presence of
switching.
| [
{
"created": "Mon, 14 Sep 2009 11:35:20 GMT",
"version": "v1"
}
] | 2009-09-15 | [
[
"Holcman",
"Juergen Reingruber David",
""
]
] | The mean time for a diffusing ligand to activate a target protein located on the surface of a microdomain can regulate cellular signaling. When the ligand switches between various states induced by chemical interactions or conformational changes, while target activation occurs in only one state, this activation time is affected. We investigate this dynamics using new equations for the sojourn times spent in each state. For two states, we obtain exact solutions in dimension one, and asymptotic ones confirmed by Brownian simulations in dimension 3. We find that the activation time is quite sensitive to changes of the switching rates, which can be used to modulate signaling. Interestingly, our analysis reveals that activation can be fast although the ligand spends most of the time 'hidden' in the non-activating state. Finally, we obtain a new formula for the narrow escape time in the presence of switching. |
1002.0299 | Adam Barrett DPhil | Adam B. Barrett, Lionel Barnett, Anil K. Seth | Multivariate Granger Causality and Generalized Variance | added 1 reference, minor change to discussion, typos corrected; 28
pages, 3 figures, 1 table, LaTeX | Physical Rev E, Vol 81, 041907 (2010) | 10.1103/PhysRevE.81.041907 | null | q-bio.NC physics.data-an q-bio.QM stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Granger causality analysis is a popular method for inference on directed
interactions in complex systems of many variables. A shortcoming of the
standard framework for Granger causality is that it only allows for examination
of interactions between single (univariate) variables within a system, perhaps
conditioned on other variables. However, interactions do not necessarily take
place between single variables, but may occur among groups, or "ensembles", of
variables. In this study we establish a principled framework for Granger
causality in the context of causal interactions among two or more multivariate
sets of variables. Building on Geweke's seminal 1982 work, we offer new
justifications for one particular form of multivariate Granger causality based
on the generalized variances of residual errors. Taken together, our results
support a comprehensive and theoretically consistent extension of Granger
causality to the multivariate case. Treated individually, they highlight
several specific advantages of the generalized variance measure, which we
illustrate using applications in neuroscience as an example. We further show
how the measure can be used to define "partial" Granger causality in the
multivariate context and we also motivate reformulations of "causal density"
and "Granger autonomy". Our results are directly applicable to experimental
data and promise to reveal new types of functional relations in complex
systems, neural and otherwise.
| [
{
"created": "Mon, 1 Feb 2010 18:31:12 GMT",
"version": "v1"
},
{
"created": "Tue, 13 Apr 2010 17:43:51 GMT",
"version": "v2"
}
] | 2010-04-14 | [
[
"Barrett",
"Adam B.",
""
],
[
"Barnett",
"Lionel",
""
],
[
"Seth",
"Anil K.",
""
]
] | Granger causality analysis is a popular method for inference on directed interactions in complex systems of many variables. A shortcoming of the standard framework for Granger causality is that it only allows for examination of interactions between single (univariate) variables within a system, perhaps conditioned on other variables. However, interactions do not necessarily take place between single variables, but may occur among groups, or "ensembles", of variables. In this study we establish a principled framework for Granger causality in the context of causal interactions among two or more multivariate sets of variables. Building on Geweke's seminal 1982 work, we offer new justifications for one particular form of multivariate Granger causality based on the generalized variances of residual errors. Taken together, our results support a comprehensive and theoretically consistent extension of Granger causality to the multivariate case. Treated individually, they highlight several specific advantages of the generalized variance measure, which we illustrate using applications in neuroscience as an example. We further show how the measure can be used to define "partial" Granger causality in the multivariate context and we also motivate reformulations of "causal density" and "Granger autonomy". Our results are directly applicable to experimental data and promise to reveal new types of functional relations in complex systems, neural and otherwise. |
2001.06371 | Nicola Pedreschi | Nicola Pedreschi, Christophe Bernard, Wesley Clawson, Pascale
Quilichini, Alain Barrat, Demian Battaglia | Dynamic core-periphery structure of information sharing networks in the
entorhinal cortex and the hippocampus | 33 pages, 13 figures | null | null | null | q-bio.NC physics.app-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Neural computation is associated with the emergence, reconfiguration and
dissolution of cell assemblies in the context of varying oscillatory states.
Here, we describe the complex spatio-temporal dynamics of cell assemblies
through temporal network formalism. We use a sliding window approach to extract
sequences of networks of information sharing among single units in hippocampus
and enthorinal cortex during anesthesia and study how global and node-wise
functional connectivity properties evolve along time. First, we find that
information sharing networks display, at any time, a core-periphery structure
in which an integrated core of more tightly functionally interconnected units
link to more loosely connected network leaves. However the units participating
to the core or to the periphery substantially change across time-windows.
Second, we find that discrete network states can be defined on top of this
continuously ongoing liquid core-periphery reorganization. Switching between
network states results in a more abrupt modification of the units belonging to
the core and is only loosely linked to transitions between global oscillatory
states. Third, we characterize different styles of temporal connectivity that
cells can exhibit within each state of the sharing network. While inhibitory
cells tend to be central, we show that, otherwise, anatomical localization only
poorly influences the patterns of temporal connectivity of the different cells.
Cells can also change temporal connectivity style when the network changes
state. Altogether, these findings reveal that the sharing of information
mediated by the intrinsic dynamics of hippocampal and enthorinal cortex cell
assemblies have a rich spatiotemporal structure, which could not have been
identified by more conventional time- or state-averaged analyses of functional
connectivity.
| [
{
"created": "Fri, 17 Jan 2020 15:26:01 GMT",
"version": "v1"
}
] | 2020-01-20 | [
[
"Pedreschi",
"Nicola",
""
],
[
"Bernard",
"Christophe",
""
],
[
"Clawson",
"Wesley",
""
],
[
"Quilichini",
"Pascale",
""
],
[
"Barrat",
"Alain",
""
],
[
"Battaglia",
"Demian",
""
]
] | Neural computation is associated with the emergence, reconfiguration and dissolution of cell assemblies in the context of varying oscillatory states. Here, we describe the complex spatio-temporal dynamics of cell assemblies through temporal network formalism. We use a sliding window approach to extract sequences of networks of information sharing among single units in hippocampus and enthorinal cortex during anesthesia and study how global and node-wise functional connectivity properties evolve along time. First, we find that information sharing networks display, at any time, a core-periphery structure in which an integrated core of more tightly functionally interconnected units link to more loosely connected network leaves. However the units participating to the core or to the periphery substantially change across time-windows. Second, we find that discrete network states can be defined on top of this continuously ongoing liquid core-periphery reorganization. Switching between network states results in a more abrupt modification of the units belonging to the core and is only loosely linked to transitions between global oscillatory states. Third, we characterize different styles of temporal connectivity that cells can exhibit within each state of the sharing network. While inhibitory cells tend to be central, we show that, otherwise, anatomical localization only poorly influences the patterns of temporal connectivity of the different cells. Cells can also change temporal connectivity style when the network changes state. Altogether, these findings reveal that the sharing of information mediated by the intrinsic dynamics of hippocampal and enthorinal cortex cell assemblies have a rich spatiotemporal structure, which could not have been identified by more conventional time- or state-averaged analyses of functional connectivity. |
1610.03809 | Daniel Moyer | Daniel Moyer, Boris A. Gutman, Joshua Faskowitz, Neda Jahanshad, Paul
M. Thompson | A Continuous Model of Cortical Connectivity | Accepted at MICCAI 2016 | null | null | null | q-bio.NC cs.CE q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a continuous model for structural brain connectivity based on the
Poisson point process. The model treats each streamline curve in a tractography
as an observed event in connectome space, here a product space of cortical
white matter boundaries. We approximate the model parameter via kernel density
estimation. To deal with the heavy computational burden, we develop a fast
parameter estimation method by pre-computing associated Legendre products of
the data, leveraging properties of the spherical heat kernel. We show how our
approach can be used to assess the quality of cortical parcellations with
respect to connectivty. We further present empirical results that suggest the
discrete connectomes derived from our model have substantially higher
test-retest reliability compared to standard methods.
| [
{
"created": "Wed, 12 Oct 2016 18:11:46 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Nov 2018 22:33:37 GMT",
"version": "v2"
}
] | 2018-11-07 | [
[
"Moyer",
"Daniel",
""
],
[
"Gutman",
"Boris A.",
""
],
[
"Faskowitz",
"Joshua",
""
],
[
"Jahanshad",
"Neda",
""
],
[
"Thompson",
"Paul M.",
""
]
] | We present a continuous model for structural brain connectivity based on the Poisson point process. The model treats each streamline curve in a tractography as an observed event in connectome space, here a product space of cortical white matter boundaries. We approximate the model parameter via kernel density estimation. To deal with the heavy computational burden, we develop a fast parameter estimation method by pre-computing associated Legendre products of the data, leveraging properties of the spherical heat kernel. We show how our approach can be used to assess the quality of cortical parcellations with respect to connectivty. We further present empirical results that suggest the discrete connectomes derived from our model have substantially higher test-retest reliability compared to standard methods. |
1005.0776 | Valery Mukhin | V. Mukhin | Frequency Structure of Heart Rate Variability | 6 pages, 4 figures, 2 tables This article contain a half of data from
the [Mobilisation readiness state and the frequency structure of heart rate
variability] article in Russian. | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Factor structure of heart rate periodogram has been detected with factor
analysis. The results showed that there are at least four periodical phenomena
of HRV. Two of them have not been discovered and physiologically explained yet.
Their frequency ranges are 0.21 to 0.31 1/beat with the peak at 0.26 1/beat and
0.25 to 0.5 1/beat with the peak 0.35 1/beat. Despite of differences of the
peak frequencies the frequency rages of the factors are overlapped. Therefore,
power of spectral density within any frequency range could not be a measure of
a modulating physiological mechanism activity.
| [
{
"created": "Wed, 5 May 2010 15:21:09 GMT",
"version": "v1"
},
{
"created": "Thu, 6 May 2010 13:43:06 GMT",
"version": "v2"
}
] | 2010-05-07 | [
[
"Mukhin",
"V.",
""
]
] | Factor structure of heart rate periodogram has been detected with factor analysis. The results showed that there are at least four periodical phenomena of HRV. Two of them have not been discovered and physiologically explained yet. Their frequency ranges are 0.21 to 0.31 1/beat with the peak at 0.26 1/beat and 0.25 to 0.5 1/beat with the peak 0.35 1/beat. Despite of differences of the peak frequencies the frequency rages of the factors are overlapped. Therefore, power of spectral density within any frequency range could not be a measure of a modulating physiological mechanism activity. |
1111.5187 | Pierre Sens | Maria Zeitz, Pierre Sens | Reversibility of Red blood Cell deformation | 4 pages, 3 figures | null | 10.1103/PhysRevE.85.051904 | null | q-bio.CB cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ability of cells to undergo reversible shape changes is often crucial to
their survival. For Red Blood Cells (RBCs), irreversible alteration of the cell
shape and flexibility often causes anemia. Here we show theoretically that RBCs
may react irreversibly to mechanical perturbations because of tensile stress in
their cytoskeleton. The transient polymerization of protein fibers inside the
cell seen in sickle cell anemia or a transient external force can trigger the
formation of a cytoskeleton-free membrane protrusion of micrometer dimensions.
The complex relaxation kinetics of the cell shape is shown to be responsible
for selecting the final state once the perturbation is removed, thereby
controlling the reversibility of the deformation. In some case, tubular
protrusion are expected to relax via a peculiar "pearling instability".
| [
{
"created": "Tue, 22 Nov 2011 13:00:25 GMT",
"version": "v1"
}
] | 2015-06-03 | [
[
"Zeitz",
"Maria",
""
],
[
"Sens",
"Pierre",
""
]
] | The ability of cells to undergo reversible shape changes is often crucial to their survival. For Red Blood Cells (RBCs), irreversible alteration of the cell shape and flexibility often causes anemia. Here we show theoretically that RBCs may react irreversibly to mechanical perturbations because of tensile stress in their cytoskeleton. The transient polymerization of protein fibers inside the cell seen in sickle cell anemia or a transient external force can trigger the formation of a cytoskeleton-free membrane protrusion of micrometer dimensions. The complex relaxation kinetics of the cell shape is shown to be responsible for selecting the final state once the perturbation is removed, thereby controlling the reversibility of the deformation. In some case, tubular protrusion are expected to relax via a peculiar "pearling instability". |
0704.0429 | Ping Ao | P. Ao | Quantitative Resolution to some "Absolute Discrepancies" in Cancer
Theories: a View from Phage lambda Genetic Switch | latex, 7 pages | CellularOncology29:67-69,2007 | null | null | q-bio.SC q-bio.CB | null | Is it possible to understand cancer? Or more specifically, is it possible to
understand cancer from genetic side? There already many answers in literature.
The most optimistic one has claimed that it is mission-possible. Duesberg and
his colleagues reviewed the impressive amount of research results on cancer
accumulated over 100 years. It confirms the a general opinion that considering
all available experimental results and clinical observations there is no cancer
theory without major difficulties, including the prevailing gene-based cancer
theories. They have then listed 9 "absolute discrepancies" for such cancer
theory. In this letter the quantitative evidence against one of their major
reasons for dismissing mutation cancer theory, by both in vivo experiment and a
first principle computation, is explicitly pointed out.
| [
{
"created": "Tue, 3 Apr 2007 16:27:02 GMT",
"version": "v1"
}
] | 2008-11-26 | [
[
"Ao",
"P.",
""
]
] | Is it possible to understand cancer? Or more specifically, is it possible to understand cancer from genetic side? There already many answers in literature. The most optimistic one has claimed that it is mission-possible. Duesberg and his colleagues reviewed the impressive amount of research results on cancer accumulated over 100 years. It confirms the a general opinion that considering all available experimental results and clinical observations there is no cancer theory without major difficulties, including the prevailing gene-based cancer theories. They have then listed 9 "absolute discrepancies" for such cancer theory. In this letter the quantitative evidence against one of their major reasons for dismissing mutation cancer theory, by both in vivo experiment and a first principle computation, is explicitly pointed out. |
2108.08916 | John Stevenson PhD | John C. Stevenson | Agentization of Two Population-Driven Models of Mathematical Biology | 10 pages, 5 figures. Presented at First International Workshop on
Agentization, Rendering Conventional Models with Agent-Based Computing | null | null | null | q-bio.PE cs.MA physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Single species population models and discrete stochastic gene frequency
models are two standards of mathematical biology important for the evolution of
populations. An agent based model is presented which reproduces these models
and then explores where these models agree and disagree under relaxed
specifications. For the population models, the requirement of homogeneous
mixing prevents prediction of extinctions due to local resource depletion.
These models also suggest equilibrium based on attainment of constant
population levels though underlying population characteristics may be nowhere
close to equilibrium. The discrete stochastic gene frequency models assume well
mixed populations at constant levels. The models' predictions for non-constant
populations in strongly oscillating and chaotic regimes are surprisingly good,
only diverging from the ABM at the most chaotic levels.
| [
{
"created": "Mon, 16 Aug 2021 19:22:08 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Sep 2021 20:06:35 GMT",
"version": "v2"
}
] | 2021-09-21 | [
[
"Stevenson",
"John C.",
""
]
] | Single species population models and discrete stochastic gene frequency models are two standards of mathematical biology important for the evolution of populations. An agent based model is presented which reproduces these models and then explores where these models agree and disagree under relaxed specifications. For the population models, the requirement of homogeneous mixing prevents prediction of extinctions due to local resource depletion. These models also suggest equilibrium based on attainment of constant population levels though underlying population characteristics may be nowhere close to equilibrium. The discrete stochastic gene frequency models assume well mixed populations at constant levels. The models' predictions for non-constant populations in strongly oscillating and chaotic regimes are surprisingly good, only diverging from the ABM at the most chaotic levels. |
1107.3410 | Kseniia Kravchuk | Alexander K. Vidybida and Kseniya G. Kravchuk | Firing statistics of inhibitory neuron with delayed feedback. I. Output
ISI probability density | 23 pages, 8 figures | BioSystems 112 (2013) 224-232 | 10.1016/j.biosystems.2012.12.006 | null | q-bio.NC math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Activity of inhibitory neuron with delayed feedback is considered in the
framework of point stochastic processes. The neuron receives excitatory input
impulses from a Poisson stream, and inhibitory impulses from the feedback line
with a delay. We investigate here, how does the presence of inhibitory feedback
affect the output firing statistics. Using binding neuron (BN) as a model, we
derive analytically the exact expressions for the output interspike intervals
(ISI) probability density, mean output ISI and coefficient of variation as
functions of model's parameters for the case of threshold 2. Using the leaky
integrate-and-fire (LIF) model, as well as the BN model with higher thresholds,
these statistical quantities are found numerically. In contrast to the
previously studied situation of no feedback, the ISI probability densities
found here both for BN and LIF neuron become bimodal and have discontinuity of
jump type. Nevertheless, the presence of inhibitory delayed feedback was not
found to affect substantially the output ISI coefficient of variation. The ISI
coefficient of variation found ranges between 0.5 and 1. It is concluded that
introduction of delayed inhibitory feedback can radically change neuronal
output firing statistics. This statistics is as well distinct from what was
found previously (Vidybida & Kravchuk, 2009) by a similar method for excitatory
neuron with delayed feedback.
| [
{
"created": "Mon, 18 Jul 2011 12:05:31 GMT",
"version": "v1"
},
{
"created": "Mon, 9 Sep 2013 11:53:34 GMT",
"version": "v2"
}
] | 2013-09-10 | [
[
"Vidybida",
"Alexander K.",
""
],
[
"Kravchuk",
"Kseniya G.",
""
]
] | Activity of inhibitory neuron with delayed feedback is considered in the framework of point stochastic processes. The neuron receives excitatory input impulses from a Poisson stream, and inhibitory impulses from the feedback line with a delay. We investigate here, how does the presence of inhibitory feedback affect the output firing statistics. Using binding neuron (BN) as a model, we derive analytically the exact expressions for the output interspike intervals (ISI) probability density, mean output ISI and coefficient of variation as functions of model's parameters for the case of threshold 2. Using the leaky integrate-and-fire (LIF) model, as well as the BN model with higher thresholds, these statistical quantities are found numerically. In contrast to the previously studied situation of no feedback, the ISI probability densities found here both for BN and LIF neuron become bimodal and have discontinuity of jump type. Nevertheless, the presence of inhibitory delayed feedback was not found to affect substantially the output ISI coefficient of variation. The ISI coefficient of variation found ranges between 0.5 and 1. It is concluded that introduction of delayed inhibitory feedback can radically change neuronal output firing statistics. This statistics is as well distinct from what was found previously (Vidybida & Kravchuk, 2009) by a similar method for excitatory neuron with delayed feedback. |
0807.1013 | Maria Davidich | Maria Davidich, Stefan Bornholdt | From differential equations to Boolean networks: A Case Study in
modeling regulatory networks | 17 pages, 3 Tables, 3 Figs. Submitted to Journal of Theoretical
Biology | null | null | null | q-bio.MN q-bio.CB q-bio.GN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Methods of modeling cellular regulatory networks as diverse as differential
equations and Boolean networks co-exist, however, without any closer
correspondence to each other. With the example system of the fission yeast cell
cycle control network, we here set the two approaches in relation to each
other. We find that the Boolean network can be formulated as a specific
coarse-grained limit of the more detailed differential network model for this
system. This lays the mathematical foundation on which Boolean networks can be
applied to biological regulatory networks in a controlled way.
| [
{
"created": "Mon, 7 Jul 2008 13:34:13 GMT",
"version": "v1"
},
{
"created": "Mon, 7 Jul 2008 20:38:29 GMT",
"version": "v2"
}
] | 2015-03-13 | [
[
"Davidich",
"Maria",
""
],
[
"Bornholdt",
"Stefan",
""
]
] | Methods of modeling cellular regulatory networks as diverse as differential equations and Boolean networks co-exist, however, without any closer correspondence to each other. With the example system of the fission yeast cell cycle control network, we here set the two approaches in relation to each other. We find that the Boolean network can be formulated as a specific coarse-grained limit of the more detailed differential network model for this system. This lays the mathematical foundation on which Boolean networks can be applied to biological regulatory networks in a controlled way. |
2006.14676 | Clara Shaw | Clara L. Shaw and David A. Kennedy | What the reproductive number R_0 can and cannot tell us about COVID-19
dynamics | 25 pages, 2 figures | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The reproductive number R_0 (and its value after initial disease emergence R)
has long been used to predict the likelihood of pathogen invasion, to gauge the
potential severity of an epidemic, and to set policy around interventions.
However, often ignored complexities have generated confusion around use of the
metric. This is particularly apparent with the emergent pandemic virus
SARS-CoV-2, the causative agent of COVID-19. We address some of these
misconceptions, namely, how R changes over time, varies over space, and relates
to epidemic size by referencing the mathematical definition of R and examples
from the current pandemic. We hope that a better appreciation of the uses,
nuances, and limitations of R facilitates a better understanding of epidemic
spread, epidemic severity, and the effects of interventions in the context of
SARS-CoV-2.
| [
{
"created": "Thu, 25 Jun 2020 19:52:34 GMT",
"version": "v1"
}
] | 2020-06-29 | [
[
"Shaw",
"Clara L.",
""
],
[
"Kennedy",
"David A.",
""
]
] | The reproductive number R_0 (and its value after initial disease emergence R) has long been used to predict the likelihood of pathogen invasion, to gauge the potential severity of an epidemic, and to set policy around interventions. However, often ignored complexities have generated confusion around use of the metric. This is particularly apparent with the emergent pandemic virus SARS-CoV-2, the causative agent of COVID-19. We address some of these misconceptions, namely, how R changes over time, varies over space, and relates to epidemic size by referencing the mathematical definition of R and examples from the current pandemic. We hope that a better appreciation of the uses, nuances, and limitations of R facilitates a better understanding of epidemic spread, epidemic severity, and the effects of interventions in the context of SARS-CoV-2. |
0909.5125 | Yan Fu | Yan Fu, Meng Zhu, Jianhua Xing | Resonant activation: a strategy against bacterial persistence | 21 pages, 12 figures, submitted | null | 10.1088/1478-3975/7/1/016013 | null | q-bio.MN q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A bacterial colony may develop a small number of cells genetically identical
to, but phenotypically different from other normally growing bacteria. These
so-called persister cells keep themselves in a dormant state and thus are
insensitive to antibiotic treatment, resulting in serious problems of drug
resistance. In this paper, we proposed a novel strategy to "kill" persister
cells by triggering them to switch, in a fast and synchronized way, into
normally growing cells that are susceptible to antibiotics. The strategy is
based on resonant activation (RA), a well-studied phenomenon in physics where
the internal noise of a system can constructively facilitate fast and
synchronized barrier crossings. Through stochastic Gilliespie simulation with a
generic toggle switch model, we demonstrated that RA exists in the phenotypic
switching of a single bacterium. Further, by coupling single cell level and
population level simulations, we showed that with RA, one can greatly reduce
the time and total amount of antibiotics needed to sterilize a bacterial
population. We suggest that resonant activation is a general phenomenon in
phenotypic transition, and can find other applications such as cancer therapy.
| [
{
"created": "Mon, 28 Sep 2009 15:47:23 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Feb 2010 17:02:36 GMT",
"version": "v2"
}
] | 2010-02-11 | [
[
"Fu",
"Yan",
""
],
[
"Zhu",
"Meng",
""
],
[
"Xing",
"Jianhua",
""
]
] | A bacterial colony may develop a small number of cells genetically identical to, but phenotypically different from other normally growing bacteria. These so-called persister cells keep themselves in a dormant state and thus are insensitive to antibiotic treatment, resulting in serious problems of drug resistance. In this paper, we proposed a novel strategy to "kill" persister cells by triggering them to switch, in a fast and synchronized way, into normally growing cells that are susceptible to antibiotics. The strategy is based on resonant activation (RA), a well-studied phenomenon in physics where the internal noise of a system can constructively facilitate fast and synchronized barrier crossings. Through stochastic Gilliespie simulation with a generic toggle switch model, we demonstrated that RA exists in the phenotypic switching of a single bacterium. Further, by coupling single cell level and population level simulations, we showed that with RA, one can greatly reduce the time and total amount of antibiotics needed to sterilize a bacterial population. We suggest that resonant activation is a general phenomenon in phenotypic transition, and can find other applications such as cancer therapy. |
1409.7272 | Ulrich Stern | Ulrich Stern and Chung-Hui Yang | Ctrax extensions for tracking in difficult lighting conditions | null | Scientific Reports 5 (2015), 10432 | 10.1038/srep10432 | null | q-bio.QM cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The fly tracking software Ctrax by Branson et al. is popular for positional
tracking of animals both within and beyond the fly community. Ctrax was not
designed to handle tracking in difficult lighting conditions with strong
shadows or recurring "on"/"off" changes in lighting - a condition that will
likely become increasingly common due to the advent of red-shifted
channelrhodopsin. We describe Ctrax extensions we developed that address this
problem. The extensions enabled good tracking accuracy in three types of
difficult lighting conditions in our lab. Our technique handling shadows relies
on "single animal tracking"; the other techniques should be widely applicable.
| [
{
"created": "Thu, 25 Sep 2014 14:35:27 GMT",
"version": "v1"
}
] | 2015-08-05 | [
[
"Stern",
"Ulrich",
""
],
[
"Yang",
"Chung-Hui",
""
]
] | The fly tracking software Ctrax by Branson et al. is popular for positional tracking of animals both within and beyond the fly community. Ctrax was not designed to handle tracking in difficult lighting conditions with strong shadows or recurring "on"/"off" changes in lighting - a condition that will likely become increasingly common due to the advent of red-shifted channelrhodopsin. We describe Ctrax extensions we developed that address this problem. The extensions enabled good tracking accuracy in three types of difficult lighting conditions in our lab. Our technique handling shadows relies on "single animal tracking"; the other techniques should be widely applicable. |
1706.06763 | Brodie Lawson | Brodie A. J. Lawson, Christopher C. Drovandi, Nicole Cusimano, Pamela
Burrage, Blanca Rodriguez, Kevin Burrage | Unlocking datasets by calibrating populations of models to data density:
a study in atrial electrophysiology | null | null | null | null | q-bio.TO stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The understanding of complex physical or biological systems nearly always
requires a characterisation of the variability that underpins these processes.
In addition, the data used to calibrate such models may also often exhibit
considerable variability. A recent approach to deal with these issues has been
to calibrate populations of models (POMs), that is multiple copies of a single
mathematical model but with different parameter values. To date this
calibration has been limited to selecting models that produce outputs that fall
within the ranges of the dataset, ignoring any trends that might be present in
the data. We present here a novel and general methodology for calibrating POMs
to the distributions of a set of measured values in a dataset. We demonstrate
the benefits of our technique using a dataset from a cardiac atrial
electrophysiology study based on the differences in atrial action potential
readings between patients exhibiting sinus rhythm (SR) or chronic atrial
fibrillation (cAF) and the Courtemanche--Ramirez--Nattel model for human atrial
action potentials. Our approach accurately captures the variability inherent in
the experimental population, and allows us to identify the differences
underlying stratified data as well as the effects of drug block.
| [
{
"created": "Wed, 21 Jun 2017 07:08:25 GMT",
"version": "v1"
}
] | 2017-06-22 | [
[
"Lawson",
"Brodie A. J.",
""
],
[
"Drovandi",
"Christopher C.",
""
],
[
"Cusimano",
"Nicole",
""
],
[
"Burrage",
"Pamela",
""
],
[
"Rodriguez",
"Blanca",
""
],
[
"Burrage",
"Kevin",
""
]
] | The understanding of complex physical or biological systems nearly always requires a characterisation of the variability that underpins these processes. In addition, the data used to calibrate such models may also often exhibit considerable variability. A recent approach to deal with these issues has been to calibrate populations of models (POMs), that is multiple copies of a single mathematical model but with different parameter values. To date this calibration has been limited to selecting models that produce outputs that fall within the ranges of the dataset, ignoring any trends that might be present in the data. We present here a novel and general methodology for calibrating POMs to the distributions of a set of measured values in a dataset. We demonstrate the benefits of our technique using a dataset from a cardiac atrial electrophysiology study based on the differences in atrial action potential readings between patients exhibiting sinus rhythm (SR) or chronic atrial fibrillation (cAF) and the Courtemanche--Ramirez--Nattel model for human atrial action potentials. Our approach accurately captures the variability inherent in the experimental population, and allows us to identify the differences underlying stratified data as well as the effects of drug block. |
1001.1111 | Elise Filotas | Elise Filotas, Martin Grant, Lael Parrott and Per Arne Rikvold | Positive interactions and the emergence of community structure in
metacommunities | 38 pages, 3 figures | Journal of Theoretical Biology 266 (2010): 419-429 | null | null | q-bio.PE cond-mat.stat-mech physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The significant role of space in maintaining species coexistence and
determining community structure and function is well established. However,
community ecology studies have mainly focused on simple competition and
predation systems, and the relative impact of positive interspecific
interactions in shaping communities in a spatial context is not well
understood. Here we employ a spatially explicit metacommunity model to
investigate the effect of local dispersal on the structure and function of
communities in which species are linked through an interaction web comprising
mutualism, competition and exploitation. Our results show that function,
diversity and interspecific interactions of locally linked communities undergo
a phase transition with changes in the rate of species dispersal. We find that
low spatial interconnectedness favors the spontaneous emergence of strongly
mutualistic communities which are more stable but less productive and diverse.
On the other hand, high spatial interconnectedness promotes local biodiversity
at the expense of local stability and supports communities with a wide range of
interspecific interactions. We argue that investigations of the relationship
between spatial processes and the self-organization of complex interaction webs
are critical to understanding the geographic structure of interactions in real
landscapes.
| [
{
"created": "Thu, 7 Jan 2010 16:59:08 GMT",
"version": "v1"
},
{
"created": "Fri, 6 Aug 2010 18:43:39 GMT",
"version": "v2"
}
] | 2010-08-09 | [
[
"Filotas",
"Elise",
""
],
[
"Grant",
"Martin",
""
],
[
"Parrott",
"Lael",
""
],
[
"Rikvold",
"Per Arne",
""
]
] | The significant role of space in maintaining species coexistence and determining community structure and function is well established. However, community ecology studies have mainly focused on simple competition and predation systems, and the relative impact of positive interspecific interactions in shaping communities in a spatial context is not well understood. Here we employ a spatially explicit metacommunity model to investigate the effect of local dispersal on the structure and function of communities in which species are linked through an interaction web comprising mutualism, competition and exploitation. Our results show that function, diversity and interspecific interactions of locally linked communities undergo a phase transition with changes in the rate of species dispersal. We find that low spatial interconnectedness favors the spontaneous emergence of strongly mutualistic communities which are more stable but less productive and diverse. On the other hand, high spatial interconnectedness promotes local biodiversity at the expense of local stability and supports communities with a wide range of interspecific interactions. We argue that investigations of the relationship between spatial processes and the self-organization of complex interaction webs are critical to understanding the geographic structure of interactions in real landscapes. |
2204.10673 | Cheng Tan | Cheng Tan, Zhangyang Gao, Jun Xia, Bozhen Hu, Stan Z. Li | Generative De Novo Protein Design with Global Context | ICASSP 2023 | null | null | null | q-bio.BM cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The linear sequence of amino acids determines protein structure and function.
Protein design, known as the inverse of protein structure prediction, aims to
obtain a novel protein sequence that will fold into the defined structure.
Recent works on computational protein design have studied designing sequences
for the desired backbone structure with local positional information and
achieved competitive performance. However, similar local environments in
different backbone structures may result in different amino acids, indicating
that protein structure's global context matters. Thus, we propose the
Global-Context Aware generative de novo protein design method (GCA), consisting
of local and global modules. While local modules focus on relationships between
neighbor amino acids, global modules explicitly capture non-local contexts.
Experimental results demonstrate that the proposed GCA method outperforms
state-of-the-arts on de novo protein design. Our code and pretrained model will
be released.
| [
{
"created": "Thu, 21 Apr 2022 02:55:01 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Feb 2023 02:57:41 GMT",
"version": "v2"
}
] | 2023-02-22 | [
[
"Tan",
"Cheng",
""
],
[
"Gao",
"Zhangyang",
""
],
[
"Xia",
"Jun",
""
],
[
"Hu",
"Bozhen",
""
],
[
"Li",
"Stan Z.",
""
]
] | The linear sequence of amino acids determines protein structure and function. Protein design, known as the inverse of protein structure prediction, aims to obtain a novel protein sequence that will fold into the defined structure. Recent works on computational protein design have studied designing sequences for the desired backbone structure with local positional information and achieved competitive performance. However, similar local environments in different backbone structures may result in different amino acids, indicating that protein structure's global context matters. Thus, we propose the Global-Context Aware generative de novo protein design method (GCA), consisting of local and global modules. While local modules focus on relationships between neighbor amino acids, global modules explicitly capture non-local contexts. Experimental results demonstrate that the proposed GCA method outperforms state-of-the-arts on de novo protein design. Our code and pretrained model will be released. |
2006.14414 | Boyan Petkov Ph.D. | Boyan H Petkov | Some considerations on the protection against the health hazards
associated with solar ultraviolet radiation | 8 pages, 4 figures | null | null | null | q-bio.TO q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The present report briefly reviews the basic features of the current strategy
for the protection against the health harms caused by solar ultraviolet (UV,
295 - 400 nm). The emphasis has been made upon the erythema as being the
best-studied UV harm and the ability of UV irradiance to damage the
deoxyribonucleic acid (DNA) molecules, which leads to carcinogenesis. The
erythemally weighted UV irradiance that determines the ultraviolet index (UVI),
represents a common measure of the solar UV radiation level at the Earth's
surface and the current protective messages have been made by using UVI as a
basic parameter. However, such an approach seems insufficiently grounded to be
used also in the messages against the skin cancer, bearing in mind the
different nature of the erythema and DNA lesions. In this context, an
enlargement of the strategy basis by including additional biological effects of
UV radiation studied during the past years has been discussed. For instance,
the weight of the spectral UV-A (315 - 400 nm) band that in practice had been
neglected by UVI definition can be enhanced since it was found to play an
important role in DNA damaging. In addition, features of the contemporaneous
life style can be taken into account together with some people habits.
| [
{
"created": "Thu, 25 Jun 2020 13:57:16 GMT",
"version": "v1"
}
] | 2020-06-26 | [
[
"Petkov",
"Boyan H",
""
]
] | The present report briefly reviews the basic features of the current strategy for the protection against the health harms caused by solar ultraviolet (UV, 295 - 400 nm). The emphasis has been made upon the erythema as being the best-studied UV harm and the ability of UV irradiance to damage the deoxyribonucleic acid (DNA) molecules, which leads to carcinogenesis. The erythemally weighted UV irradiance that determines the ultraviolet index (UVI), represents a common measure of the solar UV radiation level at the Earth's surface and the current protective messages have been made by using UVI as a basic parameter. However, such an approach seems insufficiently grounded to be used also in the messages against the skin cancer, bearing in mind the different nature of the erythema and DNA lesions. In this context, an enlargement of the strategy basis by including additional biological effects of UV radiation studied during the past years has been discussed. For instance, the weight of the spectral UV-A (315 - 400 nm) band that in practice had been neglected by UVI definition can be enhanced since it was found to play an important role in DNA damaging. In addition, features of the contemporaneous life style can be taken into account together with some people habits. |
1107.0261 | Roberto Anglani | P.F. Stifanelli, T.M. Creanza, R. Anglani, V.C. Liuzzi, S. Mukherjee,
N. Ancona | A comparative study of Gaussian Graphical Model approaches for genomic
data | 7 pages, 1 figure, RevTex4, version to appear in the proceedings of
1st International Workshop on Pattern Recognition, Proteomics, Structural
Biology and Bioinformatics: PR PS BB 2011, Ravenna, Italy, 13 September 2011 | null | 10.1393/ncc/i2012-11341-3 | BA-TH/643-11 | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The inference of networks of dependencies by Gaussian Graphical models on
high-throughput data is an open issue in modern molecular biology. In this
paper we provide a comparative study of three methods to obtain small sample
and high dimension estimates of partial correlation coefficients: the
Moore-Penrose pseudoinverse (PINV), residual correlation (RCM) and
covariance-regularized method $(\ell_{2C})$. We first compare them on simulated
datasets and we find that PINV is less stable in terms of AUC performance when
the number of variables changes. The two regularized methods have comparable
performances but $\ell_{2C}$ is much faster than RCM. Finally, we present the
results of an application of $\ell_{2C}$ for the inference of a gene network
for isoprenoid biosynthesis pathways in Arabidopsis thaliana.
| [
{
"created": "Fri, 1 Jul 2011 16:00:29 GMT",
"version": "v1"
}
] | 2022-03-02 | [
[
"Stifanelli",
"P. F.",
""
],
[
"Creanza",
"T. M.",
""
],
[
"Anglani",
"R.",
""
],
[
"Liuzzi",
"V. C.",
""
],
[
"Mukherjee",
"S.",
""
],
[
"Ancona",
"N.",
""
]
] | The inference of networks of dependencies by Gaussian Graphical models on high-throughput data is an open issue in modern molecular biology. In this paper we provide a comparative study of three methods to obtain small sample and high dimension estimates of partial correlation coefficients: the Moore-Penrose pseudoinverse (PINV), residual correlation (RCM) and covariance-regularized method $(\ell_{2C})$. We first compare them on simulated datasets and we find that PINV is less stable in terms of AUC performance when the number of variables changes. The two regularized methods have comparable performances but $\ell_{2C}$ is much faster than RCM. Finally, we present the results of an application of $\ell_{2C}$ for the inference of a gene network for isoprenoid biosynthesis pathways in Arabidopsis thaliana. |
2408.05119 | Daniel Riveline | Karsten Kruse, R\'emi Berthoz, Luca Barberi, Anne-C\'ecile Reymann,
Daniel Riveline | Acto-myosin clusters as active units shaping living matter | null | null | null | null | q-bio.TO q-bio.BM q-bio.CB q-bio.MN q-bio.SC | http://creativecommons.org/licenses/by/4.0/ | Stress generation by the actin cytoskeleton shapes cells and tissues. Despite
impressive progress in live imaging and quantitative physical descriptions of
cytoskeletal network dynamics, the connection between processes at molecular
scales and cell-scale spatio-temporal patterns is still unclear. Here we review
studies reporting acto-myosin clusters of micrometer size and with lifetimes of
several minutes in a large number of organisms ranging from fission yeast to
humans. Such structures have also been found in reconstituted systems in vitro
and in theoretical analysis of cytoskeletal dynamics. We propose that tracking
these clusters can serve as a simple readout for characterising living matter.
Spatio-temporal patterns of clusters could serve as determinants of
morphogenetic processes that play similar roles in diverse organisms.
| [
{
"created": "Fri, 9 Aug 2024 15:17:42 GMT",
"version": "v1"
}
] | 2024-08-12 | [
[
"Kruse",
"Karsten",
""
],
[
"Berthoz",
"Rémi",
""
],
[
"Barberi",
"Luca",
""
],
[
"Reymann",
"Anne-Cécile",
""
],
[
"Riveline",
"Daniel",
""
]
] | Stress generation by the actin cytoskeleton shapes cells and tissues. Despite impressive progress in live imaging and quantitative physical descriptions of cytoskeletal network dynamics, the connection between processes at molecular scales and cell-scale spatio-temporal patterns is still unclear. Here we review studies reporting acto-myosin clusters of micrometer size and with lifetimes of several minutes in a large number of organisms ranging from fission yeast to humans. Such structures have also been found in reconstituted systems in vitro and in theoretical analysis of cytoskeletal dynamics. We propose that tracking these clusters can serve as a simple readout for characterising living matter. Spatio-temporal patterns of clusters could serve as determinants of morphogenetic processes that play similar roles in diverse organisms. |
1704.07565 | George Constable Dr | George W. A. Constable and Alan J. McKane | A mapping of the stochastic Lotka-Volterra model to models of population
genetics and game theory | 21 pages, 5 figures | Phys. Rev. E 96, 022416 (2017) | 10.1103/PhysRevE.96.022416 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The relationship between the M-species stochastic Lotka-Volterra competition
(SLVC) model and the M-allele Moran model of population genetics is explored
via timescale separation arguments. When selection for species is weak and the
population size is large but finite, precise conditions are determined for the
stochastic dynamics of the SLVC model to be mappable to the neutral Moran
model, the Moran model with frequency-independent selection and the Moran model
with frequency-dependent selection (equivalently, a game-theoretic formulation
of the Moran model). We demonstrate how these mappings can be used to calculate
extinction probabilities and the times until a species' extinction in the SLVC
model.
| [
{
"created": "Tue, 25 Apr 2017 07:29:26 GMT",
"version": "v1"
}
] | 2017-08-31 | [
[
"Constable",
"George W. A.",
""
],
[
"McKane",
"Alan J.",
""
]
] | The relationship between the M-species stochastic Lotka-Volterra competition (SLVC) model and the M-allele Moran model of population genetics is explored via timescale separation arguments. When selection for species is weak and the population size is large but finite, precise conditions are determined for the stochastic dynamics of the SLVC model to be mappable to the neutral Moran model, the Moran model with frequency-independent selection and the Moran model with frequency-dependent selection (equivalently, a game-theoretic formulation of the Moran model). We demonstrate how these mappings can be used to calculate extinction probabilities and the times until a species' extinction in the SLVC model. |
1906.08200 | Michele Piana | Francesco Fiz, Helmut Dittmann, Cristina Campi, Matthias Weissinger,
Samine Sahbai, Matthias Reimold, Arnulf Stenzl, Michele Piana, Gianmario
Sambuceti, Christian la Foug\`ere | Automated Definition of Skeletal Disease Burden in Metastatic Prostate
Carcinoma: a 3D analysis of SPECT/CT images | null | null | null | null | q-bio.TO cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To meet the current need for skeletal tumor-load estimation in prostate
cancer (mCRPC), we developed a novel approach, based on adaptive bone
segmentation. In this study, we compared the program output with existing
estimates and with the radiological outcome. Seventy-six whole-body
99mTc-DPD-SPECT/CT from mCRPC patients were analyzed. The software identified
the whole skeletal volume (SVol) and classified it voxels metastases (MVol) or
normal bone (BVol). SVol was compared with the estimation of a commercial
software. MVol was compared with manual assessment and with PSA-level.
Counts/voxel were extracted from MVol and BVol. After six cycles of
223RaCl2-therapy every patient was re-evaluated as progressing (PD), stabilized
(SD) or responsive (PR). SVol correlated with the one of the commercial
software (R=0,99, p<0,001). MVol correlated with manually-counted lesions
(R=0,61, p<0,001) and PSA (R=0,46, p<0.01). PD had a lower counts/voxel in MVol
than PR/SD (715 \pm 190 Vs. 975 \pm 215 and 1058 \pm 255, p<0,05 and p<0,01)
and in BVol (PD 275 \pm 60, PR 515 \pm 188 and SD 528 \pm 162 counts/voxel,
p<0,001). Segmentation-based tumor load correlated with radiological/laboratory
indices. Uptake was linked with the clinical outcome, suggesting that
metastases in PD patients have a lower affinity for bone-seeking radionuclides
and might benefit less from bone-targeted radioisotope therapies.
| [
{
"created": "Wed, 19 Jun 2019 16:23:09 GMT",
"version": "v1"
}
] | 2019-06-20 | [
[
"Fiz",
"Francesco",
""
],
[
"Dittmann",
"Helmut",
""
],
[
"Campi",
"Cristina",
""
],
[
"Weissinger",
"Matthias",
""
],
[
"Sahbai",
"Samine",
""
],
[
"Reimold",
"Matthias",
""
],
[
"Stenzl",
"Arnulf",
""
],
[
"Piana",
"Michele",
""
],
[
"Sambuceti",
"Gianmario",
""
],
[
"la Fougère",
"Christian",
""
]
] | To meet the current need for skeletal tumor-load estimation in prostate cancer (mCRPC), we developed a novel approach, based on adaptive bone segmentation. In this study, we compared the program output with existing estimates and with the radiological outcome. Seventy-six whole-body 99mTc-DPD-SPECT/CT from mCRPC patients were analyzed. The software identified the whole skeletal volume (SVol) and classified it voxels metastases (MVol) or normal bone (BVol). SVol was compared with the estimation of a commercial software. MVol was compared with manual assessment and with PSA-level. Counts/voxel were extracted from MVol and BVol. After six cycles of 223RaCl2-therapy every patient was re-evaluated as progressing (PD), stabilized (SD) or responsive (PR). SVol correlated with the one of the commercial software (R=0,99, p<0,001). MVol correlated with manually-counted lesions (R=0,61, p<0,001) and PSA (R=0,46, p<0.01). PD had a lower counts/voxel in MVol than PR/SD (715 \pm 190 Vs. 975 \pm 215 and 1058 \pm 255, p<0,05 and p<0,01) and in BVol (PD 275 \pm 60, PR 515 \pm 188 and SD 528 \pm 162 counts/voxel, p<0,001). Segmentation-based tumor load correlated with radiological/laboratory indices. Uptake was linked with the clinical outcome, suggesting that metastases in PD patients have a lower affinity for bone-seeking radionuclides and might benefit less from bone-targeted radioisotope therapies. |
0906.1202 | Friedrich Sommer | William K. Coulter, Christopher J. Hillar, Friedrich T. Sommer | Adaptive compressed sensing - a new class of self-organizing coding
models for neuroscience | 4 pages, 4 figures, ICASSP 2010 | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sparse coding networks, which utilize unsupervised learning to maximize
coding efficiency, have successfully reproduced response properties found in
primary visual cortex \cite{AN:OlshausenField96}. However, conventional sparse
coding models require that the coding circuit can fully sample the sensory data
in a one-to-one fashion, a requirement not supported by experimental data from
the thalamo-cortical projection. To relieve these strict wiring requirements,
we propose a sparse coding network constructed by introducing synaptic learning
in the framework of compressed sensing. We demonstrate that the new model
evolves biologically realistic spatially smooth receptive fields despite the
fact that the feedforward connectivity subsamples the input and thus the
learning has to rely on an impoverished and distorted account of the original
visual data. Further, we demonstrate that the model could form a general scheme
of cortical communication: it can form meaningful representations in a
secondary sensory area, which receives input from the primary sensory area
through a "compressing" cortico-cortical projection. Finally, we prove that our
model belongs to a new class of sparse coding algorithms in which recurrent
connections are essential in forming the spatial receptive fields.
| [
{
"created": "Fri, 5 Jun 2009 20:09:58 GMT",
"version": "v1"
},
{
"created": "Tue, 24 May 2011 01:34:17 GMT",
"version": "v2"
}
] | 2011-05-25 | [
[
"Coulter",
"William K.",
""
],
[
"Hillar",
"Christopher J.",
""
],
[
"Sommer",
"Friedrich T.",
""
]
] | Sparse coding networks, which utilize unsupervised learning to maximize coding efficiency, have successfully reproduced response properties found in primary visual cortex \cite{AN:OlshausenField96}. However, conventional sparse coding models require that the coding circuit can fully sample the sensory data in a one-to-one fashion, a requirement not supported by experimental data from the thalamo-cortical projection. To relieve these strict wiring requirements, we propose a sparse coding network constructed by introducing synaptic learning in the framework of compressed sensing. We demonstrate that the new model evolves biologically realistic spatially smooth receptive fields despite the fact that the feedforward connectivity subsamples the input and thus the learning has to rely on an impoverished and distorted account of the original visual data. Further, we demonstrate that the model could form a general scheme of cortical communication: it can form meaningful representations in a secondary sensory area, which receives input from the primary sensory area through a "compressing" cortico-cortical projection. Finally, we prove that our model belongs to a new class of sparse coding algorithms in which recurrent connections are essential in forming the spatial receptive fields. |
2312.10044 | Weihua Geng | Xin Yang, Elyssa Sliheet, Reece Iriye, Daniel Reynolds, Weihua Geng | Optimized Parallelization of Boundary Integral Poisson-Boltzmann Solvers | null | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The Poisson-Boltzmann (PB) model governs the electrostatics of solvated
biomolecules, i.e., potential, field, energy, and force. These quantities can
provide useful information about protein properties, functions, and dynamics.
By considering the advantages of current algorithms and computer hardware, we
focus on the parallelization of the treecode-accelerated boundary integral
(TABI) PB solver using the Message Passing Interface (MPI) on CPUs and the
direct-sum boundary integral (DSBI) PB solver using KOKKOS on GPUs. We provide
optimization guidance for users when the DSBI solver on GPU or the TABI solver
with MPI on CPU should be used depending on the size of the problem.
Specifically, when the number of unknowns is smaller than a predetermined
threshold, the GPU-accelerated DSBI solver converges rapidly thus has the
potential to perform PB model-based molecular dynamics or Monte Carlo
simulation. As practical appliations, our parallelized boundary integral PB
solvers are used to solve electrostatics on selected proteins that play
significant roles in the spread, treatment, and prevention of COVID-19 virus
diseases. For each selected protein, the simulation produces the electrostatic
solvation energy as a global measurement and electrostatic surface potential
for local details.
| [
{
"created": "Thu, 30 Nov 2023 22:18:35 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Dec 2023 14:56:16 GMT",
"version": "v2"
}
] | 2023-12-29 | [
[
"Yang",
"Xin",
""
],
[
"Sliheet",
"Elyssa",
""
],
[
"Iriye",
"Reece",
""
],
[
"Reynolds",
"Daniel",
""
],
[
"Geng",
"Weihua",
""
]
] | The Poisson-Boltzmann (PB) model governs the electrostatics of solvated biomolecules, i.e., potential, field, energy, and force. These quantities can provide useful information about protein properties, functions, and dynamics. By considering the advantages of current algorithms and computer hardware, we focus on the parallelization of the treecode-accelerated boundary integral (TABI) PB solver using the Message Passing Interface (MPI) on CPUs and the direct-sum boundary integral (DSBI) PB solver using KOKKOS on GPUs. We provide optimization guidance for users when the DSBI solver on GPU or the TABI solver with MPI on CPU should be used depending on the size of the problem. Specifically, when the number of unknowns is smaller than a predetermined threshold, the GPU-accelerated DSBI solver converges rapidly thus has the potential to perform PB model-based molecular dynamics or Monte Carlo simulation. As practical appliations, our parallelized boundary integral PB solvers are used to solve electrostatics on selected proteins that play significant roles in the spread, treatment, and prevention of COVID-19 virus diseases. For each selected protein, the simulation produces the electrostatic solvation energy as a global measurement and electrostatic surface potential for local details. |
1709.05546 | Sarah Verner | S. N. Verner, K. Garikipati | A computational study of the mechanisms of growth-driven folding
patterns on shells, with application to the developing brain | 5 figures, 1 supplementary movie | null | 10.1016/j.eml.2017.11.003 | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the mechanisms by which folds, or sulci (troughs) and gyri
(crests), develop in the brain. This feature, common to many gyrencephalic
species including humans, has attracted recent attention from soft matter
physicists. It occurs due to inhomogeneous, and predominantly tangential,
growth of the cortex, which causes circumferential compression, leading to a
bifurcation of the solution path to a folded configuration. The problem can be
framed as one of buckling in the regime of linearized elasticity. However, the
brain is a very soft solid, which is subject to large strains due to
inhomogeneous growth. As a consequence, the morphomechanics of the developing
brain demonstrates an extensive post-bifurcation regime. Nonlinear elasticity
studies of growth-driven brain folding have established the conditions
necessary for the onset of folding, and for its progression to configurations
broadly resembling gyrencephalic brains. The reference, unfolded,
configurations in these treatments have a high degree of symmetry--typically,
ellipsoidal. Depending on the boundary conditions, the folded configurations
have symmetric or anti-symmetric patterns. However, these configurations do not
approximate the actual morphology of, e.g., human brains, which display
unsymmetric folding. More importantly, from a neurodevelopmental standpoint,
many of the unsymmetric sulci and gyri are notably robust in their locations.
Here, we initiate studies on the physical mechanisms and geometry that control
the development of primary sulci and gyri. In this preliminary communication we
carry out computations with idealized geometries, boundary conditions and
parameters, seeking a pattern resembling one of the first folds to form: the
Central Sulcus.
| [
{
"created": "Sat, 16 Sep 2017 18:29:04 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Nov 2017 04:29:28 GMT",
"version": "v2"
}
] | 2017-11-27 | [
[
"Verner",
"S. N.",
""
],
[
"Garikipati",
"K.",
""
]
] | We consider the mechanisms by which folds, or sulci (troughs) and gyri (crests), develop in the brain. This feature, common to many gyrencephalic species including humans, has attracted recent attention from soft matter physicists. It occurs due to inhomogeneous, and predominantly tangential, growth of the cortex, which causes circumferential compression, leading to a bifurcation of the solution path to a folded configuration. The problem can be framed as one of buckling in the regime of linearized elasticity. However, the brain is a very soft solid, which is subject to large strains due to inhomogeneous growth. As a consequence, the morphomechanics of the developing brain demonstrates an extensive post-bifurcation regime. Nonlinear elasticity studies of growth-driven brain folding have established the conditions necessary for the onset of folding, and for its progression to configurations broadly resembling gyrencephalic brains. The reference, unfolded, configurations in these treatments have a high degree of symmetry--typically, ellipsoidal. Depending on the boundary conditions, the folded configurations have symmetric or anti-symmetric patterns. However, these configurations do not approximate the actual morphology of, e.g., human brains, which display unsymmetric folding. More importantly, from a neurodevelopmental standpoint, many of the unsymmetric sulci and gyri are notably robust in their locations. Here, we initiate studies on the physical mechanisms and geometry that control the development of primary sulci and gyri. In this preliminary communication we carry out computations with idealized geometries, boundary conditions and parameters, seeking a pattern resembling one of the first folds to form: the Central Sulcus. |
1311.3211 | Mihai Alexandru Petrovici | Mihai A. Petrovici, Johannes Bill, Ilja Bytschok, Johannes Schemmel,
Karlheinz Meier | Stochastic inference with deterministic spiking neurons | 6 pages, 4 figures | null | null | null | q-bio.NC cond-mat.dis-nn cs.NE physics.bio-ph stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The seemingly stochastic transient dynamics of neocortical circuits observed
in vivo have been hypothesized to represent a signature of ongoing stochastic
inference. In vitro neurons, on the other hand, exhibit a highly deterministic
response to various types of stimulation. We show that an ensemble of
deterministic leaky integrate-and-fire neurons embedded in a spiking noisy
environment can attain the correct firing statistics in order to sample from a
well-defined target distribution. We provide an analytical derivation of the
activation function on the single cell level; for recurrent networks, we
examine convergence towards stationarity in computer simulations and
demonstrate sample-based Bayesian inference in a mixed graphical model. This
establishes a rigorous link between deterministic neuron models and functional
stochastic dynamics on the network level.
| [
{
"created": "Wed, 13 Nov 2013 17:04:41 GMT",
"version": "v1"
}
] | 2017-03-14 | [
[
"Petrovici",
"Mihai A.",
""
],
[
"Bill",
"Johannes",
""
],
[
"Bytschok",
"Ilja",
""
],
[
"Schemmel",
"Johannes",
""
],
[
"Meier",
"Karlheinz",
""
]
] | The seemingly stochastic transient dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference. In vitro neurons, on the other hand, exhibit a highly deterministic response to various types of stimulation. We show that an ensemble of deterministic leaky integrate-and-fire neurons embedded in a spiking noisy environment can attain the correct firing statistics in order to sample from a well-defined target distribution. We provide an analytical derivation of the activation function on the single cell level; for recurrent networks, we examine convergence towards stationarity in computer simulations and demonstrate sample-based Bayesian inference in a mixed graphical model. This establishes a rigorous link between deterministic neuron models and functional stochastic dynamics on the network level. |
1209.1341 | Xiang Zhou | Xiang Zhou and Peter Carbonetto and Matthew Stephens | Polygenic Modeling with Bayesian Sparse Linear Mixed Models | null | null | null | null | q-bio.QM q-bio.GN stat.AP stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Both linear mixed models (LMMs) and sparse regression models are widely used
in genetics applications, including, recently, polygenic modeling in
genome-wide association studies. These two approaches make very different
assumptions, so are expected to perform well in different situations. However,
in practice, for a given data set one typically does not know which assumptions
will be more accurate. Motivated by this, we consider a hybrid of the two,
which we refer to as a "Bayesian sparse linear mixed model" (BSLMM) that
includes both these models as special cases. We address several key
computational and statistical issues that arise when applying BSLMM, including
appropriate prior specification for the hyper-parameters, and a novel Markov
chain Monte Carlo algorithm for posterior inference. We apply BSLMM and compare
it with other methods for two polygenic modeling applications: estimating the
proportion of variance in phenotypes explained (PVE) by available genotypes,
and phenotype (or breeding value) prediction. For PVE estimation, we
demonstrate that BSLMM combines the advantages of both standard LMMs and sparse
regression modeling. For phenotype prediction it considerably outperforms
either of the other two methods, as well as several other large-scale
regression methods previously suggested for this problem. Software implementing
our method is freely available from
http://stephenslab.uchicago.edu/software.html
| [
{
"created": "Thu, 6 Sep 2012 16:48:45 GMT",
"version": "v1"
},
{
"created": "Wed, 14 Nov 2012 22:30:27 GMT",
"version": "v2"
}
] | 2012-11-16 | [
[
"Zhou",
"Xiang",
""
],
[
"Carbonetto",
"Peter",
""
],
[
"Stephens",
"Matthew",
""
]
] | Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies. These two approaches make very different assumptions, so are expected to perform well in different situations. However, in practice, for a given data set one typically does not know which assumptions will be more accurate. Motivated by this, we consider a hybrid of the two, which we refer to as a "Bayesian sparse linear mixed model" (BSLMM) that includes both these models as special cases. We address several key computational and statistical issues that arise when applying BSLMM, including appropriate prior specification for the hyper-parameters, and a novel Markov chain Monte Carlo algorithm for posterior inference. We apply BSLMM and compare it with other methods for two polygenic modeling applications: estimating the proportion of variance in phenotypes explained (PVE) by available genotypes, and phenotype (or breeding value) prediction. For PVE estimation, we demonstrate that BSLMM combines the advantages of both standard LMMs and sparse regression modeling. For phenotype prediction it considerably outperforms either of the other two methods, as well as several other large-scale regression methods previously suggested for this problem. Software implementing our method is freely available from http://stephenslab.uchicago.edu/software.html |
1705.09228 | Kaie Kubjas | Dimitra Kosta and Kaie Kubjas | Geometry of symmetric group-based models | 21 pages; v2: time-reversible group-based models is replaced by
symmetric group-based models | null | null | null | q-bio.PE math.AG math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phylogenetic models have polynomial parametrization maps. For symmetric
group-based models, Matsen studied the polynomial inequalities that
characterize the joint probabilities in the image of these parametrizations. We
employ this description for maximum likelihood estimation via numerical
algebraic geometry. In particular, we explore an example where the maximum
likelihood estimate does not exist, which would be difficult to discover
without using algebraic methods. We also study the embedding problem for
symmetric group-based models, i.e. we identify which mutation matrices are
matrix exponentials of rate matrices that are invariant under a group action.
| [
{
"created": "Thu, 25 May 2017 15:30:02 GMT",
"version": "v1"
},
{
"created": "Thu, 17 Aug 2017 14:51:35 GMT",
"version": "v2"
}
] | 2017-08-18 | [
[
"Kosta",
"Dimitra",
""
],
[
"Kubjas",
"Kaie",
""
]
] | Phylogenetic models have polynomial parametrization maps. For symmetric group-based models, Matsen studied the polynomial inequalities that characterize the joint probabilities in the image of these parametrizations. We employ this description for maximum likelihood estimation via numerical algebraic geometry. In particular, we explore an example where the maximum likelihood estimate does not exist, which would be difficult to discover without using algebraic methods. We also study the embedding problem for symmetric group-based models, i.e. we identify which mutation matrices are matrix exponentials of rate matrices that are invariant under a group action. |
2310.16182 | Vladimir Subbotin | Vladimir M. Subbotin and Michael V. Subotin | The rejection that defies anti-rejection drugs. Chronic vascular
rejection (allograft vasculopathy): The role of terminology and linguistic
relativity | 22 pages, 3 figures | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | Solid organ transplantation has by now become a common medical procedure.
Owing to the introduction of new immunosuppressive drugs, the allograft loss
due to acute rejection has been reduced significantly over time. Tragically,
the number of donor organs lost due to allograft vasculopathy (AV), generally
named chronic vascular rejection or chronic rejection, has remained significant
and unchanged for decades. We argue that designation of AV as chronic
rejection, and its classification as a delayed long-lasting reaction of
recipient immune effectors against donor alloantigens have given us the wrong
impression that we have identified the necessary cause of the disease. However,
whatever treatment options we have in the anti-rejection toolbox, despite their
success in treating acute rejection, do not work for AV. Yet, the scientific
community has continued to conceptualize and approach AV within the
alloimmunity and rejection model. Due to unproductive research from the
alloimmunity perspective, the number of transplanted hearts lost due to this
pathology today is almost the same as it was fifty years ago. We believe that
this phenomenon falls under the rubric of linguistic relativity, and that the
language we chose to name the disease has restricted our cognitive ability to
solve the problem. While the initial perception of the AV as chronic rejection
was logical and scientific, the subsequent experience revealed that such
perception and approach have been fruitless, and likely are incorrect.
Considering our tragic failure to prevent and treat allograft vasculopathy
using all available knowledge on alloimmunity and rejection, we must finally
disassociate the former from the latter. A good way to start this process is to
change the words we are using; particularly, the words we chose to name the
disease. We have to step out of the alloimmunity rejection box
| [
{
"created": "Tue, 24 Oct 2023 21:00:11 GMT",
"version": "v1"
},
{
"created": "Sun, 11 Aug 2024 21:31:40 GMT",
"version": "v2"
}
] | 2024-08-13 | [
[
"Subbotin",
"Vladimir M.",
""
],
[
"Subotin",
"Michael V.",
""
]
] | Solid organ transplantation has by now become a common medical procedure. Owing to the introduction of new immunosuppressive drugs, the allograft loss due to acute rejection has been reduced significantly over time. Tragically, the number of donor organs lost due to allograft vasculopathy (AV), generally named chronic vascular rejection or chronic rejection, has remained significant and unchanged for decades. We argue that designation of AV as chronic rejection, and its classification as a delayed long-lasting reaction of recipient immune effectors against donor alloantigens have given us the wrong impression that we have identified the necessary cause of the disease. However, whatever treatment options we have in the anti-rejection toolbox, despite their success in treating acute rejection, do not work for AV. Yet, the scientific community has continued to conceptualize and approach AV within the alloimmunity and rejection model. Due to unproductive research from the alloimmunity perspective, the number of transplanted hearts lost due to this pathology today is almost the same as it was fifty years ago. We believe that this phenomenon falls under the rubric of linguistic relativity, and that the language we chose to name the disease has restricted our cognitive ability to solve the problem. While the initial perception of the AV as chronic rejection was logical and scientific, the subsequent experience revealed that such perception and approach have been fruitless, and likely are incorrect. Considering our tragic failure to prevent and treat allograft vasculopathy using all available knowledge on alloimmunity and rejection, we must finally disassociate the former from the latter. A good way to start this process is to change the words we are using; particularly, the words we chose to name the disease. We have to step out of the alloimmunity rejection box |
2406.16788 | Lane Yoder | Lane Yoder | Explicit Retinal Networks Produce Center-Surround Opponent Color Cells | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Previous articles proposed an explicit retinal logic circuit that can
generate neural correlates of phenomena central to color vision. Here it is
shown that this network can be divided into its component parts and slightly
modified to produce center-surround phenomena.
| [
{
"created": "Mon, 24 Jun 2024 16:49:36 GMT",
"version": "v1"
}
] | 2024-06-25 | [
[
"Yoder",
"Lane",
""
]
] | Previous articles proposed an explicit retinal logic circuit that can generate neural correlates of phenomena central to color vision. Here it is shown that this network can be divided into its component parts and slightly modified to produce center-surround phenomena. |
2306.17259 | Daniel Edler | Daniel Edler, Anton Holmgren, Alexis Rojas, Joaqu\'in Calatayud,
Martin Rosvall, Alexandre Antonelli | Infomap Bioregions 2 - Exploring the interplay between biogeography and
evolution | null | null | null | null | q-bio.PE q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Identifying and understanding the large-scale biodiversity patterns in time
and space is vital for conservation and addressing fundamental ecological and
evolutionary questions. Network-based methods have proven useful for
simplifying and highlighting important structures in species distribution data.
However, current network-based biogeography approaches cannot exploit the
evolutionary information available in phylogenetic data. We introduce a method
for incorporating evolutionary relationships into species occurrence networks
to produce more biologically informative and robust bioregions. To keep the
bipartite network structure where bioregions are grid cells indirectly
connected through shared species, we incorporate the phylogenetic tree by
connecting ancestral nodes to the grid cells where their descendant species
occur. To incorporate the whole tree without destroying the spatial signal of
narrowly distributed species or ancestral nodes, we weigh tree nodes by the
geographic information they provide. For a more detailed analysis, we enable
integration of the evolutionary relationships at a specific time in the tree.
By sweeping through the phylogenetic tree in time, our method interpolates
between finding bioregions based only on distributional data and finding
spatially segregated clades, uncovering evolutionarily distinct bioregions at
different time slices. We also introduce a way to segregate the connections
between evolutionary branches at a selected time to enable exploration of
overlapping evolutionarily distinct regions. We have implemented these methods
in Infomap Bioregions, an interactive web application that makes it easy to
explore the possibly hierarchical and fuzzy patterns of biodiversity on
different scales in time and space.
| [
{
"created": "Thu, 29 Jun 2023 18:58:06 GMT",
"version": "v1"
}
] | 2023-07-03 | [
[
"Edler",
"Daniel",
""
],
[
"Holmgren",
"Anton",
""
],
[
"Rojas",
"Alexis",
""
],
[
"Calatayud",
"Joaquín",
""
],
[
"Rosvall",
"Martin",
""
],
[
"Antonelli",
"Alexandre",
""
]
] | Identifying and understanding the large-scale biodiversity patterns in time and space is vital for conservation and addressing fundamental ecological and evolutionary questions. Network-based methods have proven useful for simplifying and highlighting important structures in species distribution data. However, current network-based biogeography approaches cannot exploit the evolutionary information available in phylogenetic data. We introduce a method for incorporating evolutionary relationships into species occurrence networks to produce more biologically informative and robust bioregions. To keep the bipartite network structure where bioregions are grid cells indirectly connected through shared species, we incorporate the phylogenetic tree by connecting ancestral nodes to the grid cells where their descendant species occur. To incorporate the whole tree without destroying the spatial signal of narrowly distributed species or ancestral nodes, we weigh tree nodes by the geographic information they provide. For a more detailed analysis, we enable integration of the evolutionary relationships at a specific time in the tree. By sweeping through the phylogenetic tree in time, our method interpolates between finding bioregions based only on distributional data and finding spatially segregated clades, uncovering evolutionarily distinct bioregions at different time slices. We also introduce a way to segregate the connections between evolutionary branches at a selected time to enable exploration of overlapping evolutionarily distinct regions. We have implemented these methods in Infomap Bioregions, an interactive web application that makes it easy to explore the possibly hierarchical and fuzzy patterns of biodiversity on different scales in time and space. |
2003.01268 | Sina Sajjadi | Sina Sajjadi, Mohammad Reza Ejtehadi, Fakhteh Ghanbarnejad | Impact of temporal correlations on high risk outbreaks of independent
and cooperative SIR dynamics | 16 pages, 12 figures | null | 10.1371/journal.pone.0253563 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We first propose a quantitative approach to detect high risk outbreaks of
independent and coinfective SIR dynamics on three empirical networks: a school,
a conference and a hospital contact network. This measurement is based on the
k-means clustering method and identifies proper samples for calculating the
mean outbreak size and the outbreak probability. Then we systematically study
the impact of different temporal correlations on high risk outbreaks over the
original and differently shuffled counterparts of each network. We observe
that, on the one hand, in the coinfection process, randomization of the
sequence of the events increases the mean outbreak size of high risk cases. On
the other hand, these correlations don't have a consistent effect on the
independent infection dynamics, and can either decrease or increase this mean.
While randomization of the daily pattern correlations has no significant effect
on the size of outbreak in either of the coinfection or independent spreading
cases. We also observer that an increase in the mean outbreak size doesn't
always coincide with an increase in the outbreak probability; therefore we
argue that merely considering the mean outbreak size of all realizations may
lead us into misestimating the outbreak risks. Our results suggest that some
sort of randomizing contacts in organization level of schools, events or
hospitals might help to suppress the spreading dynamics while the risk of an
outbreak is high.
| [
{
"created": "Tue, 3 Mar 2020 00:38:52 GMT",
"version": "v1"
}
] | 2021-09-15 | [
[
"Sajjadi",
"Sina",
""
],
[
"Ejtehadi",
"Mohammad Reza",
""
],
[
"Ghanbarnejad",
"Fakhteh",
""
]
] | We first propose a quantitative approach to detect high risk outbreaks of independent and coinfective SIR dynamics on three empirical networks: a school, a conference and a hospital contact network. This measurement is based on the k-means clustering method and identifies proper samples for calculating the mean outbreak size and the outbreak probability. Then we systematically study the impact of different temporal correlations on high risk outbreaks over the original and differently shuffled counterparts of each network. We observe that, on the one hand, in the coinfection process, randomization of the sequence of the events increases the mean outbreak size of high risk cases. On the other hand, these correlations don't have a consistent effect on the independent infection dynamics, and can either decrease or increase this mean. While randomization of the daily pattern correlations has no significant effect on the size of outbreak in either of the coinfection or independent spreading cases. We also observer that an increase in the mean outbreak size doesn't always coincide with an increase in the outbreak probability; therefore we argue that merely considering the mean outbreak size of all realizations may lead us into misestimating the outbreak risks. Our results suggest that some sort of randomizing contacts in organization level of schools, events or hospitals might help to suppress the spreading dynamics while the risk of an outbreak is high. |
q-bio/0311032 | Suckjoon Jun | Suckjoon Jun, John Herrick, Aaron Bensimon, and John Bechhoefer | Persistence length of chromatin determines origin spacing in Xenopus
early-embryo DNA replication: Quantitative comparisons between theory and
experiment | To appear at Cell Cycle (vol 3, no 2, 2004) 14 pages, 4 figures. How
physics can help solve a long-standing mysteries in DNA replication. See also
cond-mat/0306546 (dynamics of polymer looping) | Cell Cycle 3, 223-229 (2004) | 10.4161/cc.3.2.655 | null | q-bio.QM q-bio.BM | null | In Xenopus early embryos, replication origins neither require specific DNA
sequences nor is there an efficient S/M checkpoint, even though the whole
genome (3 billion bases) is completely duplicated within 10-20 minutes. This
leads to the"random-completion problem" of DNA replication in embryos, where
one needs to find a mechanism that ensures complete, faithful, timely
reproduction of the genome without any sequence dependence of replication
origins. We analyze recent DNA replication data in Xenopus laevis egg extracts
and find discrepancies with models where replication origins are distributed
independently of chromatin structure. Motivated by these discrepancies, we have
investigated the role that chromatin looping may play in DNA replication. We
find that the loop-size distribution predicted from a wormlike-chain model of
chromatin can account for the spatial distribution of replication origins in
this system quantitatively. Together with earlier findings of increasing
frequency of origin firings, our results can explain the random-completion
problem. The agreement between experimental data (molecular combing) and
theoretical predictions suggests that the intrinsic stiffness of chromatin
loops plays a fundamental biological role in DNA replication in early-embryo
Xenopus in regulating the origin spacing.
| [
{
"created": "Mon, 24 Nov 2003 06:17:45 GMT",
"version": "v1"
}
] | 2018-11-06 | [
[
"Jun",
"Suckjoon",
""
],
[
"Herrick",
"John",
""
],
[
"Bensimon",
"Aaron",
""
],
[
"Bechhoefer",
"John",
""
]
] | In Xenopus early embryos, replication origins neither require specific DNA sequences nor is there an efficient S/M checkpoint, even though the whole genome (3 billion bases) is completely duplicated within 10-20 minutes. This leads to the"random-completion problem" of DNA replication in embryos, where one needs to find a mechanism that ensures complete, faithful, timely reproduction of the genome without any sequence dependence of replication origins. We analyze recent DNA replication data in Xenopus laevis egg extracts and find discrepancies with models where replication origins are distributed independently of chromatin structure. Motivated by these discrepancies, we have investigated the role that chromatin looping may play in DNA replication. We find that the loop-size distribution predicted from a wormlike-chain model of chromatin can account for the spatial distribution of replication origins in this system quantitatively. Together with earlier findings of increasing frequency of origin firings, our results can explain the random-completion problem. The agreement between experimental data (molecular combing) and theoretical predictions suggests that the intrinsic stiffness of chromatin loops plays a fundamental biological role in DNA replication in early-embryo Xenopus in regulating the origin spacing. |
2102.10471 | Fei He | Bhavya Vasudeva, Runfeng Tian, Dee H. Wu, Shirley A. James, Hazem H.
Refai, Fei He, Yuan Yang | Multi-Phase Locking Value: A Generalized Method for Determining
Instantaneous Multi-frequency Phase Coupling | 9 pages, 8 figures | null | null | null | q-bio.NC eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many physical, biological and neural systems behave as coupled oscillators,
with characteristic phase coupling across different frequencies. Methods such
as $n:m$ phase locking value and bi-phase locking value have previously been
proposed to quantify phase coupling between two resonant frequencies (e.g. $f$,
$2f/3$) and across three frequencies (e.g. $f_1$, $f_2$, $f_1+f_2$),
respectively. However, the existing phase coupling metrics have their
limitations and limited applications. They cannot be used to detect or quantify
phase coupling across multiple frequencies (e.g. $f_1$, $f_2$, $f_3$, $f_4$,
$f_1+f_2+f_3-f_4$), or coupling that involves non-integer multiples of the
frequencies (e.g. $f_1$, $f_2$, $2f_1/3+f_2/3$). To address the gap, this paper
proposes a generalized approach, named multi-phase locking value (M-PLV), for
the quantification of various types of instantaneous multi-frequency phase
coupling. Different from most instantaneous phase coupling metrics that measure
the simultaneous phase coupling, the proposed M-PLV method also allows the
detection of delayed phase coupling and the associated time lag between coupled
oscillators. The M-PLV has been tested on cases where synthetic coupled signals
are generated using white Gaussian signals, and a system comprised of multiple
coupled R\"ossler oscillators. Results indicate that the M-PLV can provide a
reliable estimation of the time window and frequency combination where the
phase coupling is significant, as well as a precise determination of time lag
in the case of delayed coupling. This method has the potential to become a
powerful new tool for exploring phase coupling in complex nonlinear dynamic
systems.
| [
{
"created": "Sat, 20 Feb 2021 23:10:49 GMT",
"version": "v1"
},
{
"created": "Sun, 2 Jan 2022 16:34:56 GMT",
"version": "v2"
}
] | 2022-01-04 | [
[
"Vasudeva",
"Bhavya",
""
],
[
"Tian",
"Runfeng",
""
],
[
"Wu",
"Dee H.",
""
],
[
"James",
"Shirley A.",
""
],
[
"Refai",
"Hazem H.",
""
],
[
"He",
"Fei",
""
],
[
"Yang",
"Yuan",
""
]
] | Many physical, biological and neural systems behave as coupled oscillators, with characteristic phase coupling across different frequencies. Methods such as $n:m$ phase locking value and bi-phase locking value have previously been proposed to quantify phase coupling between two resonant frequencies (e.g. $f$, $2f/3$) and across three frequencies (e.g. $f_1$, $f_2$, $f_1+f_2$), respectively. However, the existing phase coupling metrics have their limitations and limited applications. They cannot be used to detect or quantify phase coupling across multiple frequencies (e.g. $f_1$, $f_2$, $f_3$, $f_4$, $f_1+f_2+f_3-f_4$), or coupling that involves non-integer multiples of the frequencies (e.g. $f_1$, $f_2$, $2f_1/3+f_2/3$). To address the gap, this paper proposes a generalized approach, named multi-phase locking value (M-PLV), for the quantification of various types of instantaneous multi-frequency phase coupling. Different from most instantaneous phase coupling metrics that measure the simultaneous phase coupling, the proposed M-PLV method also allows the detection of delayed phase coupling and the associated time lag between coupled oscillators. The M-PLV has been tested on cases where synthetic coupled signals are generated using white Gaussian signals, and a system comprised of multiple coupled R\"ossler oscillators. Results indicate that the M-PLV can provide a reliable estimation of the time window and frequency combination where the phase coupling is significant, as well as a precise determination of time lag in the case of delayed coupling. This method has the potential to become a powerful new tool for exploring phase coupling in complex nonlinear dynamic systems. |
2307.08452 | Mayalen Etcheverry | Mayalen Etcheverry, Michael Levin, Cl\'ement Moulin-Frier, Pierre-Yves
Oudeyer | SBMLtoODEjax: Efficient Simulation and Optimization of Biological
Network Models in JAX | null | null | null | null | q-bio.BM cs.LG cs.PL | http://creativecommons.org/licenses/by/4.0/ | Advances in bioengineering and biomedicine demand a deep understanding of the
dynamic behavior of biological systems, ranging from protein pathways to
complex cellular processes. Biological networks like gene regulatory networks
and protein pathways are key drivers of embryogenesis and physiological
processes. Comprehending their diverse behaviors is essential for tackling
diseases, including cancer, as well as for engineering novel biological
constructs. Despite the availability of extensive mathematical models
represented in Systems Biology Markup Language (SBML), researchers face
significant challenges in exploring the full spectrum of behaviors and
optimizing interventions to efficiently shape those behaviors. Existing tools
designed for simulation of biological network models are not tailored to
facilitate interventions on network dynamics nor to facilitate automated
discovery. Leveraging recent developments in machine learning (ML), this paper
introduces SBMLtoODEjax, a lightweight library designed to seamlessly integrate
SBML models with ML-supported pipelines, powered by JAX. SBMLtoODEjax
facilitates the reuse and customization of SBML-based models, harnessing JAX's
capabilities for efficient parallel simulations and optimization, with the aim
to accelerate research in biological network analysis.
| [
{
"created": "Mon, 17 Jul 2023 12:47:33 GMT",
"version": "v1"
},
{
"created": "Sun, 29 Oct 2023 06:29:33 GMT",
"version": "v2"
}
] | 2023-10-31 | [
[
"Etcheverry",
"Mayalen",
""
],
[
"Levin",
"Michael",
""
],
[
"Moulin-Frier",
"Clément",
""
],
[
"Oudeyer",
"Pierre-Yves",
""
]
] | Advances in bioengineering and biomedicine demand a deep understanding of the dynamic behavior of biological systems, ranging from protein pathways to complex cellular processes. Biological networks like gene regulatory networks and protein pathways are key drivers of embryogenesis and physiological processes. Comprehending their diverse behaviors is essential for tackling diseases, including cancer, as well as for engineering novel biological constructs. Despite the availability of extensive mathematical models represented in Systems Biology Markup Language (SBML), researchers face significant challenges in exploring the full spectrum of behaviors and optimizing interventions to efficiently shape those behaviors. Existing tools designed for simulation of biological network models are not tailored to facilitate interventions on network dynamics nor to facilitate automated discovery. Leveraging recent developments in machine learning (ML), this paper introduces SBMLtoODEjax, a lightweight library designed to seamlessly integrate SBML models with ML-supported pipelines, powered by JAX. SBMLtoODEjax facilitates the reuse and customization of SBML-based models, harnessing JAX's capabilities for efficient parallel simulations and optimization, with the aim to accelerate research in biological network analysis. |
0906.1186 | Matjaz Perc | Qingyun Wang, Matjaz Perc, Zhisheng Duan, Guanrong Chen | Delay-induced multiple stochastic resonances on scale-free neuronal
networks | 7 two-column pages, 5 figures; accepted for publication in Chaos | Chaos 19 (2009) 023112 | 10.1063/1.3133126 | null | q-bio.NC cond-mat.dis-nn physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the effects of periodic subthreshold pacemaker activity and
time-delayed coupling on stochastic resonance over scale-free neuronal
networks. As the two extreme options, we introduce the pacemaker respectively
to the neuron with the highest degree and to one of the neurons with the lowest
degree within the network, but we also consider the case when all neurons are
exposed to the periodic forcing. In the absence of delay, we show that an
intermediate intensity of noise is able to optimally assist the pacemaker in
imposing its rhythm on the whole ensemble, irrespective to its placing, thus
providing evidences for stochastic resonance on the scale-free neuronal
networks. Interestingly thereby, if the forcing in form of a periodic pulse
train is introduced to all neurons forming the network, the stochastic
resonance decreases as compared to the case when only a single neuron is paced.
Moreover, we show that finite delays in coupling can significantly affect the
stochastic resonance on scale-free neuronal networks. In particular,
appropriately tuned delays can induce multiple stochastic resonances
independently of the placing of the pacemaker, but they can also altogether
destroy stochastic resonance. Delay-induced multiple stochastic resonances
manifest as well-expressed maxima of the correlation measure, appearing at
every multiple of the pacemaker period. We argue that fine-tuned delays and
locally active pacemakers are vital for assuring optimal conditions for
stochastic resonance on complex neuronal networks.
| [
{
"created": "Fri, 5 Jun 2009 19:23:47 GMT",
"version": "v1"
}
] | 2009-06-09 | [
[
"Wang",
"Qingyun",
""
],
[
"Perc",
"Matjaz",
""
],
[
"Duan",
"Zhisheng",
""
],
[
"Chen",
"Guanrong",
""
]
] | We study the effects of periodic subthreshold pacemaker activity and time-delayed coupling on stochastic resonance over scale-free neuronal networks. As the two extreme options, we introduce the pacemaker respectively to the neuron with the highest degree and to one of the neurons with the lowest degree within the network, but we also consider the case when all neurons are exposed to the periodic forcing. In the absence of delay, we show that an intermediate intensity of noise is able to optimally assist the pacemaker in imposing its rhythm on the whole ensemble, irrespective to its placing, thus providing evidences for stochastic resonance on the scale-free neuronal networks. Interestingly thereby, if the forcing in form of a periodic pulse train is introduced to all neurons forming the network, the stochastic resonance decreases as compared to the case when only a single neuron is paced. Moreover, we show that finite delays in coupling can significantly affect the stochastic resonance on scale-free neuronal networks. In particular, appropriately tuned delays can induce multiple stochastic resonances independently of the placing of the pacemaker, but they can also altogether destroy stochastic resonance. Delay-induced multiple stochastic resonances manifest as well-expressed maxima of the correlation measure, appearing at every multiple of the pacemaker period. We argue that fine-tuned delays and locally active pacemakers are vital for assuring optimal conditions for stochastic resonance on complex neuronal networks. |
1707.05317 | Matteo Smerlak | Matteo Smerlak | Natural selection as coarsening | Submitted to J. Stat. Phys. for special issue on evolutionary
dynamics | null | 10.1007/s10955-017-1925-5 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Analogies between evolutionary dynamics and statistical mechanics, such as
Fisher's second-law-like "fundamental theorem of natural selection" and
Wright's "fitness landscapes", have had a deep and fruitful influence on the
development of evolutionary theory. Here I discuss a new conceptual link
between evolution and statistical physics. I argue that natural selection can
be viewed as a coarsening phenomenon, similar to the growth of domain size in
quenched magnets or to Ostwald ripening in alloys and emulsions. In particular,
I show that the most remarkable features of coarsening---scaling and
self-similarity---have strict equivalents in evolutionary dynamics. This
analogy has three main virtues: it brings a set of well-developed mathematical
tools to bear on evolutionary dynamics; it suggests new problems in theoretical
evolution; and it provides coarsening physics with a new exactly soluble model.
| [
{
"created": "Mon, 17 Jul 2017 15:33:42 GMT",
"version": "v1"
},
{
"created": "Fri, 6 Oct 2017 18:44:23 GMT",
"version": "v2"
}
] | 2017-12-06 | [
[
"Smerlak",
"Matteo",
""
]
] | Analogies between evolutionary dynamics and statistical mechanics, such as Fisher's second-law-like "fundamental theorem of natural selection" and Wright's "fitness landscapes", have had a deep and fruitful influence on the development of evolutionary theory. Here I discuss a new conceptual link between evolution and statistical physics. I argue that natural selection can be viewed as a coarsening phenomenon, similar to the growth of domain size in quenched magnets or to Ostwald ripening in alloys and emulsions. In particular, I show that the most remarkable features of coarsening---scaling and self-similarity---have strict equivalents in evolutionary dynamics. This analogy has three main virtues: it brings a set of well-developed mathematical tools to bear on evolutionary dynamics; it suggests new problems in theoretical evolution; and it provides coarsening physics with a new exactly soluble model. |
q-bio/0505032 | Antonio Trovato | Trinh X. Hoang, Antonio Trovato, Flavio Seno, Jayanth R. Banavar, Amos
Maritan | Geometrical model for the native-state folds of proteins | 12 pages, 3 figures | Biophys. Chem. 115 (2005), 289-294 | 10.1016/j.bpc.2004.12.036 | null | q-bio.BM | null | We recently introduced a physical model [Hoang et al., P. Natl. Acad. Sci.
USA (2004), Banavar et al., Phys. Rev. E (2004)] for proteins which
incorporates, in an approximate manner, several key features such as the
inherent anisotropy of a chain molecule, the geometrical and energetic
constraints placed by the hydrogen bonds and sterics, and the role played by
hydrophobicity. Within this framework, marginally compact conformations
resembling the native state folds of proteins emerge as broad competing minima
in the free energy landscape even for a homopolymer. Here we show how the
introduction of sequence heterogeneity using a simple scheme of just two types
of amino acids, hydrophobic (H) and polar (P), and sequence design allows a
selected putative native fold to become the free energy minimum at low
temperature. The folding transition exhibits thermodynamic cooperativity, if
one neglects the degeneracy between two different low energy conformations
sharing the same fold topology.
| [
{
"created": "Tue, 17 May 2005 11:37:11 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Hoang",
"Trinh X.",
""
],
[
"Trovato",
"Antonio",
""
],
[
"Seno",
"Flavio",
""
],
[
"Banavar",
"Jayanth R.",
""
],
[
"Maritan",
"Amos",
""
]
] | We recently introduced a physical model [Hoang et al., P. Natl. Acad. Sci. USA (2004), Banavar et al., Phys. Rev. E (2004)] for proteins which incorporates, in an approximate manner, several key features such as the inherent anisotropy of a chain molecule, the geometrical and energetic constraints placed by the hydrogen bonds and sterics, and the role played by hydrophobicity. Within this framework, marginally compact conformations resembling the native state folds of proteins emerge as broad competing minima in the free energy landscape even for a homopolymer. Here we show how the introduction of sequence heterogeneity using a simple scheme of just two types of amino acids, hydrophobic (H) and polar (P), and sequence design allows a selected putative native fold to become the free energy minimum at low temperature. The folding transition exhibits thermodynamic cooperativity, if one neglects the degeneracy between two different low energy conformations sharing the same fold topology. |
1701.07100 | Sebastian Streichan | Sebastian J Streichan and Matthew F Lefebvre and Nicholas Noll and
Eric F Wieschaus and Boris I Shraiman | Quantification of myosin distribution predicts global morphogenetic flow
in the fly embryo | 15 total pages, with 9 total figures (7 pages main text with 4
figures and 8 pages SI text with 5 SI figures) | null | null | null | q-bio.TO q-bio.CB q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | During embryogenesis tissue layers continuously rearrange and fold into
specific shapes. Developmental biology identified patterns of gene expression
and cytoskeletal regulation underlying local tissue dynamics, but how actions
of multiple domains of distinct cell types coordinate to remodel tissues at the
organ scale remains unclear. We use in toto light-sheet microscopy, automated
image analysis, and physical modeling to quantitatively investigate the link
between kinetics of global tissue transformations and force generation patterns
during Drosophila gastrulation. We find embryo-scale shape changes are
represented by a temporal sequence of three simple flow field configurations.
Each phase is accompanied by a characteristic spatial myosin distribution,
quantified in terms of a coarse-grained 'myosin tensor' that captures both
concentration and anisotropy. Our model assumes tissue flow is driven by stress
proportional to the myosin tensor, and is effectively visco-elastic with two
parameters that control 'irrotational' and 'divergence-less' components of the
flow. With just three global parameters, this model achieves up to 90%
agreement between predicted and measured flow. The analysis uncovers importance
of a) spatial modulation of myosin distribution and b) long-range spreading of
its effect due to mechanical interaction of cells. In particular, we find
germband extension phase is associated with the onset of effective areal
incompressibility of the epithelium, which makes the relation of flow and
myosin forcing strongly non-local. Our analysis also revealed a new function
for basal myosin in generating a dorsally directed flow and, combined with
mutant analysis, identified an unconventional control mechanism through twist
dependent reduction of basal myosin levels on the ventral side. We conclude
that understanding morphogenetic flow requires a fundamentally global
perspective.
| [
{
"created": "Tue, 24 Jan 2017 22:56:37 GMT",
"version": "v1"
}
] | 2017-01-26 | [
[
"Streichan",
"Sebastian J",
""
],
[
"Lefebvre",
"Matthew F",
""
],
[
"Noll",
"Nicholas",
""
],
[
"Wieschaus",
"Eric F",
""
],
[
"Shraiman",
"Boris I",
""
]
] | During embryogenesis tissue layers continuously rearrange and fold into specific shapes. Developmental biology identified patterns of gene expression and cytoskeletal regulation underlying local tissue dynamics, but how actions of multiple domains of distinct cell types coordinate to remodel tissues at the organ scale remains unclear. We use in toto light-sheet microscopy, automated image analysis, and physical modeling to quantitatively investigate the link between kinetics of global tissue transformations and force generation patterns during Drosophila gastrulation. We find embryo-scale shape changes are represented by a temporal sequence of three simple flow field configurations. Each phase is accompanied by a characteristic spatial myosin distribution, quantified in terms of a coarse-grained 'myosin tensor' that captures both concentration and anisotropy. Our model assumes tissue flow is driven by stress proportional to the myosin tensor, and is effectively visco-elastic with two parameters that control 'irrotational' and 'divergence-less' components of the flow. With just three global parameters, this model achieves up to 90% agreement between predicted and measured flow. The analysis uncovers importance of a) spatial modulation of myosin distribution and b) long-range spreading of its effect due to mechanical interaction of cells. In particular, we find germband extension phase is associated with the onset of effective areal incompressibility of the epithelium, which makes the relation of flow and myosin forcing strongly non-local. Our analysis also revealed a new function for basal myosin in generating a dorsally directed flow and, combined with mutant analysis, identified an unconventional control mechanism through twist dependent reduction of basal myosin levels on the ventral side. We conclude that understanding morphogenetic flow requires a fundamentally global perspective. |
2201.05353 | Xiaoliang Wang | Xiaoliang Wang and Andrew Harrison | The evolution of cooperation: an evolutionary advantage of individuals
impedes the evolution of the population | 13 pages, 8 figures | null | null | null | q-bio.PE physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Range expansion is a universal process in biological systems, and therefore
plays a part in biological evolution. Using a quantitative individual-based
method based on the stochastic process, we identify that enhancing the inherent
self-proliferation advantage of cooperators relative to defectors is a more
effective channel to promote the evolution of cooperation in range expansion
than weakening the benefit acquisition of defectors from cooperators. With this
self-proliferation advantage, cooperators can rapidly colonize virgin space and
establish spatial segregation more readily, which acts like a protective shield
to further promote the evolution of cooperation in return. We also show that
lower cell density and migration rate have a positive effect on the competition
of cooperators with defectors. Biological evolution is based on competition
between individuals and should therefore favor selfish behaviors. However, we
observe a counterintuitive phenomenon that the evolution of a population is
impeded by the fitness-enhancing chemotactic movement of individuals. This
highlights a conflict between the interests of the individual and the
population. The short-sighted selfish behavior of individuals may not be that
favored in the competition between populations. Such information provides
important implications for the handling of cooperation.
| [
{
"created": "Fri, 14 Jan 2022 09:24:46 GMT",
"version": "v1"
}
] | 2022-01-17 | [
[
"Wang",
"Xiaoliang",
""
],
[
"Harrison",
"Andrew",
""
]
] | Range expansion is a universal process in biological systems, and therefore plays a part in biological evolution. Using a quantitative individual-based method based on the stochastic process, we identify that enhancing the inherent self-proliferation advantage of cooperators relative to defectors is a more effective channel to promote the evolution of cooperation in range expansion than weakening the benefit acquisition of defectors from cooperators. With this self-proliferation advantage, cooperators can rapidly colonize virgin space and establish spatial segregation more readily, which acts like a protective shield to further promote the evolution of cooperation in return. We also show that lower cell density and migration rate have a positive effect on the competition of cooperators with defectors. Biological evolution is based on competition between individuals and should therefore favor selfish behaviors. However, we observe a counterintuitive phenomenon that the evolution of a population is impeded by the fitness-enhancing chemotactic movement of individuals. This highlights a conflict between the interests of the individual and the population. The short-sighted selfish behavior of individuals may not be that favored in the competition between populations. Such information provides important implications for the handling of cooperation. |
2301.02709 | Andrew Leifer | Sandeep Kumar, Anuj K Sharma, Andrew Tran and Andrew M Leifer | Inhibitory feedback from the motor circuit gates mechanosensory
processing in C. elegans | null | PLoS Biol 21(9):e3002280, 2023 | 10.1371/journal.pbio.3002280 | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Animals must integrate sensory cues with their current behavioral context to
generate a suitable response. How this integration occurs is poorly understood.
Previously we developed high throughput methods to probe neural activity in
populations of Caenorhabditis elegans and discovered that the animal's
mechanosensory processing is rapidly modulated by the animal's locomotion.
Specifically we found that when the worm turns it suppresses its
mechanosensory-evoked reversal response. Here we report that C. elegans use
inhibitory feedback from turning-associated neurons to provide this rapid
modulation of mechanosensory processing. By performing high-throughput
optogenetic perturbations triggered on behavior, we show that turning
associated neurons SAA, RIV and/or SMB suppress mechanosensory-evoked reversals
during turns. We find that activation of the gentle-touch mechanosensory
neurons or of any of the interneurons AIZ, RIM, AIB and AVE during a turn is
less likely to evoke a reversal than activation during forward movement.
Inhibiting neurons SAA, RIV and SMB during a turn restores the likelihood with
which mechanosensory activation evokes reversals. Separately, activation of
premotor interneuron AVA evokes reversals regardless of whether the animal is
turning or moving forward. We therefore propose that inhibitory signals from
SAA, RIV and/or SMB gate mechanosensory signals upstream of neuron AVA. We
conclude that C. elegans rely on inhibitory feedback from the motor circuit to
modulate its response to sensory stimuli on fast timescales. This need for
motor signals in sensory processing may explain the ubiquity in many organisms
of motor-related neural activity patterns seen across the brain, including in
sensory processing areas.
| [
{
"created": "Fri, 6 Jan 2023 20:45:09 GMT",
"version": "v1"
},
{
"created": "Wed, 11 Jan 2023 13:24:00 GMT",
"version": "v2"
},
{
"created": "Sat, 20 May 2023 10:37:00 GMT",
"version": "v3"
},
{
"created": "Tue, 27 Jun 2023 21:33:49 GMT",
"version": "v4"
}
] | 2023-09-25 | [
[
"Kumar",
"Sandeep",
""
],
[
"Sharma",
"Anuj K",
""
],
[
"Tran",
"Andrew",
""
],
[
"Leifer",
"Andrew M",
""
]
] | Animals must integrate sensory cues with their current behavioral context to generate a suitable response. How this integration occurs is poorly understood. Previously we developed high throughput methods to probe neural activity in populations of Caenorhabditis elegans and discovered that the animal's mechanosensory processing is rapidly modulated by the animal's locomotion. Specifically we found that when the worm turns it suppresses its mechanosensory-evoked reversal response. Here we report that C. elegans use inhibitory feedback from turning-associated neurons to provide this rapid modulation of mechanosensory processing. By performing high-throughput optogenetic perturbations triggered on behavior, we show that turning associated neurons SAA, RIV and/or SMB suppress mechanosensory-evoked reversals during turns. We find that activation of the gentle-touch mechanosensory neurons or of any of the interneurons AIZ, RIM, AIB and AVE during a turn is less likely to evoke a reversal than activation during forward movement. Inhibiting neurons SAA, RIV and SMB during a turn restores the likelihood with which mechanosensory activation evokes reversals. Separately, activation of premotor interneuron AVA evokes reversals regardless of whether the animal is turning or moving forward. We therefore propose that inhibitory signals from SAA, RIV and/or SMB gate mechanosensory signals upstream of neuron AVA. We conclude that C. elegans rely on inhibitory feedback from the motor circuit to modulate its response to sensory stimuli on fast timescales. This need for motor signals in sensory processing may explain the ubiquity in many organisms of motor-related neural activity patterns seen across the brain, including in sensory processing areas. |
1408.3495 | Daniel Zinder | Daniel Zinder, Trevor Bedford, Edward B. Baskerville, Robert J. Woods,
Manojit Roy, Mercedes Pascual | Seasonality in the migration and establishment of H3N2 Influenza
lineages with epidemic growth and decline | in BMC Evolutionary Biology 2014, 14 | null | 10.1186/s12862-014-0272-2 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Influenza A/H3N2 has been circulating in humans since 1968,
causing considerable morbidity and mortality. Although H3N2 incidence is highly
seasonal, how such seasonality contributes to global phylogeographic migration
dynamics has not yet been established.
Results: Incorporating seasonally varying migration rates improves the
modeling of migration. In our global model, windows of increased immigration
map to the seasonal timing of epidemic spread, while windows of increased
emigration map to epidemic decline. Seasonal patterns also correlate with the
probability that local lineages go extinct and fail to contribute to long term
viral evolution, as measured through the trunk of the phylogeny. However, the
fraction of the trunk in each community was found to be better determined by
its overall human population size
Conclusions: Seasonal migration and rapid turnover within regions is
sustained by the invasion of 'fertile epidemic grounds' at the end of older
epidemics. Thus, the current emphasis on connectivity, including air-travel,
should be complemented with a better understanding of the conditions and timing
required for successful establishment.Models which account for migration
seasonality will improve our understanding of the seasonal drivers of
influenza,enhance epidemiological predictions, and ameliorate vaccine updating
by identifying strains that not only escape immunity but also have the seasonal
opportunity to establish and spread. Further work is also needed on additional
conditions that contribute to the persistence and long term evolution of
influenza within the human population,such as spatial heterogeneity with
respect to climate and seasonality
| [
{
"created": "Fri, 15 Aug 2014 08:37:33 GMT",
"version": "v1"
},
{
"created": "Thu, 5 Feb 2015 20:56:04 GMT",
"version": "v2"
}
] | 2015-02-06 | [
[
"Zinder",
"Daniel",
""
],
[
"Bedford",
"Trevor",
""
],
[
"Baskerville",
"Edward B.",
""
],
[
"Woods",
"Robert J.",
""
],
[
"Roy",
"Manojit",
""
],
[
"Pascual",
"Mercedes",
""
]
] | Background: Influenza A/H3N2 has been circulating in humans since 1968, causing considerable morbidity and mortality. Although H3N2 incidence is highly seasonal, how such seasonality contributes to global phylogeographic migration dynamics has not yet been established. Results: Incorporating seasonally varying migration rates improves the modeling of migration. In our global model, windows of increased immigration map to the seasonal timing of epidemic spread, while windows of increased emigration map to epidemic decline. Seasonal patterns also correlate with the probability that local lineages go extinct and fail to contribute to long term viral evolution, as measured through the trunk of the phylogeny. However, the fraction of the trunk in each community was found to be better determined by its overall human population size Conclusions: Seasonal migration and rapid turnover within regions is sustained by the invasion of 'fertile epidemic grounds' at the end of older epidemics. Thus, the current emphasis on connectivity, including air-travel, should be complemented with a better understanding of the conditions and timing required for successful establishment.Models which account for migration seasonality will improve our understanding of the seasonal drivers of influenza,enhance epidemiological predictions, and ameliorate vaccine updating by identifying strains that not only escape immunity but also have the seasonal opportunity to establish and spread. Further work is also needed on additional conditions that contribute to the persistence and long term evolution of influenza within the human population,such as spatial heterogeneity with respect to climate and seasonality |
1704.05343 | Yijie Wang | Yijie Wang, Dong-Yeon Cho, Hangnoh Lee, Justin Fear, Brian Oliver, and
Teresa M Przytycka | NetREX: Network Rewiring using EXpression - Towards Context Specific
Regulatory Networks | RECOMB 2017 | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding gene regulation is a fundamental step towards understanding of
how cells function and respond to environmental cues and perturbations. An
important step in this direction is to infer the transcription factor-gene
regulatory network (GRN). However gene regulatory networks are typically
constructed disregarding the fact that regulatory programs are conditioned on
tissue type, developmental stage, sex, and other factors. Collecting multitude
of features required for a reliable construction of GRNs such as physical
features and functional features for every context of interest is costly.
Therefore we need methods that is able to use the knowledge of a
context-agnostic network for construction of a context specific regulatory
network.
To address this challenge we developed a computational approach that uses
context specific expression data and a GRN constructed in a different but
related context to construct a context specific GRN. Our method, NetREX, is
inspired by network component analysis that estimates TF activities and their
influences on target genes given predetermined topology of a TF-gene network.
To predict a network under a different condition, NetREX removes the
restriction that the topology of the TF-gene network is fixed and allows for
adding and removing edges to that network. To solve the corresponding
optimization problem, we provide a general mathematical framework allowing use
of the recently proposed PALM technique and develop a convergent algorithm.
We tested our NetREX on simulated data and subsequently applied it to gene
expression data in adult females from Drosophila deletion (DrosDel) panel. The
networks predicted by NetREX showed higher biological consistency than
alternative approaches. In addition, we used the list of recently identified
targets of the Doublesex (DSX) transcription factor to demonstrate the
predictive power of our method.
| [
{
"created": "Tue, 18 Apr 2017 13:58:50 GMT",
"version": "v1"
},
{
"created": "Mon, 24 Apr 2017 14:55:24 GMT",
"version": "v2"
}
] | 2017-04-25 | [
[
"Wang",
"Yijie",
""
],
[
"Cho",
"Dong-Yeon",
""
],
[
"Lee",
"Hangnoh",
""
],
[
"Fear",
"Justin",
""
],
[
"Oliver",
"Brian",
""
],
[
"Przytycka",
"Teresa M",
""
]
] | Understanding gene regulation is a fundamental step towards understanding of how cells function and respond to environmental cues and perturbations. An important step in this direction is to infer the transcription factor-gene regulatory network (GRN). However gene regulatory networks are typically constructed disregarding the fact that regulatory programs are conditioned on tissue type, developmental stage, sex, and other factors. Collecting multitude of features required for a reliable construction of GRNs such as physical features and functional features for every context of interest is costly. Therefore we need methods that is able to use the knowledge of a context-agnostic network for construction of a context specific regulatory network. To address this challenge we developed a computational approach that uses context specific expression data and a GRN constructed in a different but related context to construct a context specific GRN. Our method, NetREX, is inspired by network component analysis that estimates TF activities and their influences on target genes given predetermined topology of a TF-gene network. To predict a network under a different condition, NetREX removes the restriction that the topology of the TF-gene network is fixed and allows for adding and removing edges to that network. To solve the corresponding optimization problem, we provide a general mathematical framework allowing use of the recently proposed PALM technique and develop a convergent algorithm. We tested our NetREX on simulated data and subsequently applied it to gene expression data in adult females from Drosophila deletion (DrosDel) panel. The networks predicted by NetREX showed higher biological consistency than alternative approaches. In addition, we used the list of recently identified targets of the Doublesex (DSX) transcription factor to demonstrate the predictive power of our method. |
q-bio/0312025 | Wei Wang | Wei Wang and Jean-Jacques E. Slotine | Fast Computation with Neural Oscillators | 7 pages, 5 figures | null | null | null | q-bio.NC | null | Artificial spike-based computation, inspired by models of computations in the
central nervous system, may present significant performance advantages over
traditional methods for specific types of large scale problems. In this paper,
we study new models for two common instances of such computation,
winner-take-all and coincidence detection. In both cases, very fast convergence
is achieved independent of initial conditions, and network complexity is linear
in the number of inputs.
| [
{
"created": "Wed, 17 Dec 2003 16:18:34 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Wang",
"Wei",
""
],
[
"Slotine",
"Jean-Jacques E.",
""
]
] | Artificial spike-based computation, inspired by models of computations in the central nervous system, may present significant performance advantages over traditional methods for specific types of large scale problems. In this paper, we study new models for two common instances of such computation, winner-take-all and coincidence detection. In both cases, very fast convergence is achieved independent of initial conditions, and network complexity is linear in the number of inputs. |
2311.14682 | Sophie Moufawad | Rawan H. Madi and Sophie M. Moufawad and Nabil R. Nassif | Data-Driven Models for studying the Dynamics of the COVID-19 Pandemics | 59 pages | null | null | null | q-bio.PE math.DS | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This paper seeks to study the evolution of the COVID-19 pandemic based on
daily published data from Worldometer website, using a time-dependent SIR
model. Our findings indicate that this model fits well such data, for different
chosen periods and different regions. This well-known model, consisting of
three disjoint compartments, susceptible , infected , and removed , depends in
our case on two time dependent parameters, the infection rate $\beta(t)$ and
the removal rate $\rho(t)$. After deriving the model, we prove the local
exponential behavior of the number of infected people, be it growth or decay.
Furthermore, we extract a time dependent replacement factor $\sigma_s(t)
={\beta(t)}s(t)/{\rho(t) }$, where $s(t)$ is the ratio of susceptible people at
time $t$. In addition, $i(t)$ and $r(t)$ are respectively the ratios of
infected and removed people, based on a population of size $N$, usually assumed
to be constant. Besides these theoretical results, the report provides
simulations on the daily data obtained for Germany, Italy, and the entire
World, as collected from Worldometer over the period stretching from April 2020
to June 2022. The computational model consists of the estimation of $\beta(t)$,
$\rho(t)$ and $s(t)$ based on the time-dependent SIR model. The validation of
our approach is demonstrated by comparing the profiles of the collected $i(t),
r(t)$ data and those obtained from the SIR model with the approximated
parameters. We also consider matching the data with a constant-coefficient SIR
model, which seems to be working only for short periods. Thus, such model helps
understanding and predicting the evolution of the pandemics for short periods
of time where no radical change occurs.
| [
{
"created": "Thu, 2 Nov 2023 13:28:21 GMT",
"version": "v1"
}
] | 2023-11-28 | [
[
"Madi",
"Rawan H.",
""
],
[
"Moufawad",
"Sophie M.",
""
],
[
"Nassif",
"Nabil R.",
""
]
] | This paper seeks to study the evolution of the COVID-19 pandemic based on daily published data from Worldometer website, using a time-dependent SIR model. Our findings indicate that this model fits well such data, for different chosen periods and different regions. This well-known model, consisting of three disjoint compartments, susceptible , infected , and removed , depends in our case on two time dependent parameters, the infection rate $\beta(t)$ and the removal rate $\rho(t)$. After deriving the model, we prove the local exponential behavior of the number of infected people, be it growth or decay. Furthermore, we extract a time dependent replacement factor $\sigma_s(t) ={\beta(t)}s(t)/{\rho(t) }$, where $s(t)$ is the ratio of susceptible people at time $t$. In addition, $i(t)$ and $r(t)$ are respectively the ratios of infected and removed people, based on a population of size $N$, usually assumed to be constant. Besides these theoretical results, the report provides simulations on the daily data obtained for Germany, Italy, and the entire World, as collected from Worldometer over the period stretching from April 2020 to June 2022. The computational model consists of the estimation of $\beta(t)$, $\rho(t)$ and $s(t)$ based on the time-dependent SIR model. The validation of our approach is demonstrated by comparing the profiles of the collected $i(t), r(t)$ data and those obtained from the SIR model with the approximated parameters. We also consider matching the data with a constant-coefficient SIR model, which seems to be working only for short periods. Thus, such model helps understanding and predicting the evolution of the pandemics for short periods of time where no radical change occurs. |
1203.1790 | Philippe Desjardins-Proulx | Philippe Desjardins-Proulx and James L. Rosindell and Timoth\'ee
Poisot and Dominique Gravel | A simple model to study phylogeographies and speciation patterns in
space | 4 pages, 1 figure, 34 references | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this working paper, we present a simple theoretical framework based on
network theory to study how speciation, the process by which new species
appear, shapes spatial patterns of diversity. We show that this framework can
be expanded to account for different types of networks and interactions, and
incorporates different modes of speciation.
| [
{
"created": "Thu, 8 Mar 2012 13:21:50 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Sep 2012 02:02:12 GMT",
"version": "v2"
}
] | 2012-09-13 | [
[
"Desjardins-Proulx",
"Philippe",
""
],
[
"Rosindell",
"James L.",
""
],
[
"Poisot",
"Timothée",
""
],
[
"Gravel",
"Dominique",
""
]
] | In this working paper, we present a simple theoretical framework based on network theory to study how speciation, the process by which new species appear, shapes spatial patterns of diversity. We show that this framework can be expanded to account for different types of networks and interactions, and incorporates different modes of speciation. |
1309.7272 | Michele Tizzoni | Michele Tizzoni, Paolo Bajardi, Adeline Decuyper, Guillaume Kon Kam
King, Christian M. Schneider, Vincent Blondel, Zbigniew Smoreda, Marta C.
Gonz\'alez, Vittoria Colizza | On the use of human mobility proxy for the modeling of epidemics | Accepted fro publication in PLOS Computational Biology. Abstract
shortened to fit Arxiv limits. 35 pages, 6 figures | Tizzoni M, Bajardi P, Decuyper A, Kon Kam King G, Schneider CM, et
al. (2014) On the Use of Human Mobility Proxies for Modeling Epidemics. PLoS
Comput Biol 10(7): e1003716 | 10.1371/journal.pcbi.1003716 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human mobility is a key component of large-scale spatial-transmission models
of infectious diseases. Correctly modeling and quantifying human mobility is
critical for improving epidemic control policies, but may be hindered by
incomplete data in some regions of the world. Here we explore the opportunity
of using proxy data or models for individual mobility to describe commuting
movements and predict the diffusion of infectious disease. We consider three
European countries and the corresponding commuting networks at different
resolution scales obtained from official census surveys, from proxy data for
human mobility extracted from mobile phone call records, and from the radiation
model calibrated with census data. Metapopulation models defined on the three
countries and integrating the different mobility layers are compared in terms
of epidemic observables. We show that commuting networks from mobile phone data
well capture the empirical commuting patterns, accounting for more than 87% of
the total fluxes. The distributions of commuting fluxes per link from both
sources of data - mobile phones and census - are similar and highly correlated,
however a systematic overestimation of commuting traffic in the mobile phone
data is observed. This leads to epidemics that spread faster than on census
commuting networks, however preserving the order of infection of newly infected
locations. Match in the epidemic invasion pattern is sensitive to initial
conditions: the radiation model shows higher accuracy with respect to mobile
phone data when the seed is central in the network, while the mobile phone
proxy performs better for epidemics seeded in peripheral locations. Results
suggest that different proxies can be used to approximate commuting patterns
across different resolution scales in spatial epidemic simulations, in light of
the desired accuracy in the epidemic outcome under study.
| [
{
"created": "Fri, 27 Sep 2013 15:21:21 GMT",
"version": "v1"
},
{
"created": "Tue, 27 May 2014 12:36:18 GMT",
"version": "v2"
}
] | 2014-07-14 | [
[
"Tizzoni",
"Michele",
""
],
[
"Bajardi",
"Paolo",
""
],
[
"Decuyper",
"Adeline",
""
],
[
"King",
"Guillaume Kon Kam",
""
],
[
"Schneider",
"Christian M.",
""
],
[
"Blondel",
"Vincent",
""
],
[
"Smoreda",
"Zbigniew",
""
],
[
"González",
"Marta C.",
""
],
[
"Colizza",
"Vittoria",
""
]
] | Human mobility is a key component of large-scale spatial-transmission models of infectious diseases. Correctly modeling and quantifying human mobility is critical for improving epidemic control policies, but may be hindered by incomplete data in some regions of the world. Here we explore the opportunity of using proxy data or models for individual mobility to describe commuting movements and predict the diffusion of infectious disease. We consider three European countries and the corresponding commuting networks at different resolution scales obtained from official census surveys, from proxy data for human mobility extracted from mobile phone call records, and from the radiation model calibrated with census data. Metapopulation models defined on the three countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data well capture the empirical commuting patterns, accounting for more than 87% of the total fluxes. The distributions of commuting fluxes per link from both sources of data - mobile phones and census - are similar and highly correlated, however a systematic overestimation of commuting traffic in the mobile phone data is observed. This leads to epidemics that spread faster than on census commuting networks, however preserving the order of infection of newly infected locations. Match in the epidemic invasion pattern is sensitive to initial conditions: the radiation model shows higher accuracy with respect to mobile phone data when the seed is central in the network, while the mobile phone proxy performs better for epidemics seeded in peripheral locations. Results suggest that different proxies can be used to approximate commuting patterns across different resolution scales in spatial epidemic simulations, in light of the desired accuracy in the epidemic outcome under study. |
2212.00136 | Benson Chen | Kirill Shmilovich, Benson Chen, Theofanis Karaletsos, Mohammad M.
Sultan | DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded Libraries | null | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | DNA-Encoded Library (DEL) technology has enabled significant advances in hit
identification by enabling efficient testing of combinatorially-generated
molecular libraries. DEL screens measure protein binding affinity though
sequencing reads of molecules tagged with unique DNA-barcodes that survive a
series of selection experiments. Computational models have been deployed to
learn the latent binding affinities that are correlated to the sequenced count
data; however, this correlation is often obfuscated by various sources of noise
introduced in its complicated data-generation process. In order to denoise DEL
count data and screen for molecules with good binding affinity, computational
models require the correct assumptions in their modeling structure to capture
the correct signals underlying the data. Recent advances in DEL models have
focused on probabilistic formulations of count data, but existing approaches
have thus far been limited to only utilizing 2-D molecule-level
representations. We introduce a new paradigm, DEL-Dock, that combines
ligand-based descriptors with 3-D spatial information from docked
protein-ligand complexes. 3-D spatial information allows our model to learn
over the actual binding modality rather than using only structured-based
information of the ligand. We show that our model is capable of effectively
denoising DEL count data to predict molecule enrichment scores that are better
correlated with experimental binding affinity measurements compared to prior
works. Moreover, by learning over a collection of docked poses we demonstrate
that our model, trained only on DEL data, implicitly learns to perform good
docking pose selection without requiring external supervision from
expensive-to-source protein crystal structures.
| [
{
"created": "Wed, 30 Nov 2022 22:00:24 GMT",
"version": "v1"
},
{
"created": "Wed, 14 Dec 2022 19:39:24 GMT",
"version": "v2"
}
] | 2022-12-16 | [
[
"Shmilovich",
"Kirill",
""
],
[
"Chen",
"Benson",
""
],
[
"Karaletsos",
"Theofanis",
""
],
[
"Sultan",
"Mohammad M.",
""
]
] | DNA-Encoded Library (DEL) technology has enabled significant advances in hit identification by enabling efficient testing of combinatorially-generated molecular libraries. DEL screens measure protein binding affinity though sequencing reads of molecules tagged with unique DNA-barcodes that survive a series of selection experiments. Computational models have been deployed to learn the latent binding affinities that are correlated to the sequenced count data; however, this correlation is often obfuscated by various sources of noise introduced in its complicated data-generation process. In order to denoise DEL count data and screen for molecules with good binding affinity, computational models require the correct assumptions in their modeling structure to capture the correct signals underlying the data. Recent advances in DEL models have focused on probabilistic formulations of count data, but existing approaches have thus far been limited to only utilizing 2-D molecule-level representations. We introduce a new paradigm, DEL-Dock, that combines ligand-based descriptors with 3-D spatial information from docked protein-ligand complexes. 3-D spatial information allows our model to learn over the actual binding modality rather than using only structured-based information of the ligand. We show that our model is capable of effectively denoising DEL count data to predict molecule enrichment scores that are better correlated with experimental binding affinity measurements compared to prior works. Moreover, by learning over a collection of docked poses we demonstrate that our model, trained only on DEL data, implicitly learns to perform good docking pose selection without requiring external supervision from expensive-to-source protein crystal structures. |
2401.09840 | Artem Tsypin | Alexander Telepov, Artem Tsypin, Kuzma Khrabrov, Sergey Yakukhnov,
Pavel Strashnov, Petr Zhilyaev, Egor Rumiantsev, Daniel Ezhov, Manvel
Avetisian, Olga Popova, Artur Kadurin | FREED++: Improving RL Agents for Fragment-Based Molecule Generation by
Thorough Reproduction | 37 pages, 10 figures, to be published in TMLR journal
(https://www.jmlr.org/tmlr/) | null | null | null | q-bio.BM cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | A rational design of new therapeutic drugs aims to find a molecular structure
with desired biological functionality, e.g., an ability to activate or suppress
a specific protein via binding to it. Molecular docking is a common technique
for evaluating protein-molecule interactions. Recently, Reinforcement Learning
(RL) has emerged as a promising approach to generating molecules with the
docking score (DS) as a reward. In this work, we reproduce, scrutinize and
improve the recent RL model for molecule generation called FREED
(arXiv:2110.01219). Extensive evaluation of the proposed method reveals several
limitations and challenges despite the outstanding results reported for three
target proteins. Our contributions include fixing numerous implementation bugs
and simplifying the model while increasing its quality, significantly extending
experiments, and conducting an accurate comparison with current
state-of-the-art methods for protein-conditioned molecule generation. We show
that the resulting fixed model is capable of producing molecules with superior
docking scores compared to alternative approaches.
| [
{
"created": "Thu, 18 Jan 2024 09:54:19 GMT",
"version": "v1"
}
] | 2024-01-19 | [
[
"Telepov",
"Alexander",
""
],
[
"Tsypin",
"Artem",
""
],
[
"Khrabrov",
"Kuzma",
""
],
[
"Yakukhnov",
"Sergey",
""
],
[
"Strashnov",
"Pavel",
""
],
[
"Zhilyaev",
"Petr",
""
],
[
"Rumiantsev",
"Egor",
""
],
[
"Ezhov",
"Daniel",
""
],
[
"Avetisian",
"Manvel",
""
],
[
"Popova",
"Olga",
""
],
[
"Kadurin",
"Artur",
""
]
] | A rational design of new therapeutic drugs aims to find a molecular structure with desired biological functionality, e.g., an ability to activate or suppress a specific protein via binding to it. Molecular docking is a common technique for evaluating protein-molecule interactions. Recently, Reinforcement Learning (RL) has emerged as a promising approach to generating molecules with the docking score (DS) as a reward. In this work, we reproduce, scrutinize and improve the recent RL model for molecule generation called FREED (arXiv:2110.01219). Extensive evaluation of the proposed method reveals several limitations and challenges despite the outstanding results reported for three target proteins. Our contributions include fixing numerous implementation bugs and simplifying the model while increasing its quality, significantly extending experiments, and conducting an accurate comparison with current state-of-the-art methods for protein-conditioned molecule generation. We show that the resulting fixed model is capable of producing molecules with superior docking scores compared to alternative approaches. |
2005.11182 | Armando G. M. Neves | Armando G. M. Neves and Gustavo Guerrero | Predicting the evolution of the COVID-19 epidemic with the A-SIR model:
Lombardy, Italy and S\~ao Paulo state, Brazil | 33 pages, 14 figures | null | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The presence of a large number of infected individuals with few or no
symptoms is an important epidemiological difficulty and the main mathematical
feature of COVID-19. The A-SIR model, i.e. a SIR (Susceptible-Infected-Removed)
model with a compartment for infected individuals with no symptoms or few
symptoms was proposed by Giuseppe Gaeta, arXiv:2003.08720 [q-bio.PE] (2020). In
this paper we investigate a slightly generalized version of the same model and
propose a scheme for fitting the parameters of the model to real data using the
time series only of the deceased individuals. The scheme is applied to the
concrete cases of Lombardy, Italy and S\~ao Paulo state, Brazil, showing
different aspects of the epidemics. For each case we show that we may have good
fits to the data up to the present, but with very large differences in the
future behavior. The reasons behind such disparate outcomes are the uncertainty
on the value of a key parameter, the probability that an infected individual is
fully symptomatic, and on the intensity of the social distancing measures
adopted. This conclusion enforces the necessity of trying to determine the real
number of infected individuals in a population, symptomatic or asymptomatic.
| [
{
"created": "Fri, 22 May 2020 13:37:41 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Aug 2020 19:51:48 GMT",
"version": "v2"
}
] | 2020-08-19 | [
[
"Neves",
"Armando G. M.",
""
],
[
"Guerrero",
"Gustavo",
""
]
] | The presence of a large number of infected individuals with few or no symptoms is an important epidemiological difficulty and the main mathematical feature of COVID-19. The A-SIR model, i.e. a SIR (Susceptible-Infected-Removed) model with a compartment for infected individuals with no symptoms or few symptoms was proposed by Giuseppe Gaeta, arXiv:2003.08720 [q-bio.PE] (2020). In this paper we investigate a slightly generalized version of the same model and propose a scheme for fitting the parameters of the model to real data using the time series only of the deceased individuals. The scheme is applied to the concrete cases of Lombardy, Italy and S\~ao Paulo state, Brazil, showing different aspects of the epidemics. For each case we show that we may have good fits to the data up to the present, but with very large differences in the future behavior. The reasons behind such disparate outcomes are the uncertainty on the value of a key parameter, the probability that an infected individual is fully symptomatic, and on the intensity of the social distancing measures adopted. This conclusion enforces the necessity of trying to determine the real number of infected individuals in a population, symptomatic or asymptomatic. |
1309.3772 | Vera Pancaldi | Peter M. F. Emmrich, Hannah E. Roberts, Vera Pancaldi | A Boolean Gene Regulatory Model of heterosis and speciation | See online version for supplementary material | BMC Evolutionary Biology 2015, 15:24 | 10.1186/s12862-015-0298-0 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modelling genetic phenomena affecting biological traits is important for the
development of agriculture as it allows breeders to predict the potential of
breeding for certain traits. One such phenomenon is heterosis or hybrid vigor:
crossing individuals from genetically distinct populations often results in
improvements in quantitative traits, such as growth rate, biomass production
and stress resistance. Heterosis has become a very useful tool in global
agriculture, but its genetic basis remains controversial and its effects hard
to predict. We have taken a computational approach to studying heterosis,
developing a simulation of evolution, independent reassortment of alleles and
hybridization of Gene Regulatory Networks (GRNs) in a Boolean framework.
Fitness is measured as the ability of a network to respond to external inputs
in a pre-defined way. Our model reproduced common experimental observations on
heterosis using only biologically justified parameters. Hybrid vigor was
observed and its extent was seen to increase as parental populations diverged,
up until a point of sudden collapse of hybrid fitness. We also reproduce, for
the first time in a model, the fact that hybrid vigor cannot easily be fixed by
within a breeding line, currently an important limitation of the use of hybrid
crops. The simulation allowed us to study the effects of three standard models
for the genetic basis of heterosis and the level of detail in our model allows
us to suggest possible warning signs of the impending collapse of hybrid vigor
in breeding. In addition, the simulation provides a framework that can be
extended to study other aspects of heterosis and alternative evolutionary
scenarios.
| [
{
"created": "Sun, 15 Sep 2013 15:56:14 GMT",
"version": "v1"
},
{
"created": "Wed, 18 Dec 2013 15:55:42 GMT",
"version": "v2"
},
{
"created": "Mon, 23 Mar 2015 08:56:32 GMT",
"version": "v3"
}
] | 2015-03-24 | [
[
"Emmrich",
"Peter M. F.",
""
],
[
"Roberts",
"Hannah E.",
""
],
[
"Pancaldi",
"Vera",
""
]
] | Modelling genetic phenomena affecting biological traits is important for the development of agriculture as it allows breeders to predict the potential of breeding for certain traits. One such phenomenon is heterosis or hybrid vigor: crossing individuals from genetically distinct populations often results in improvements in quantitative traits, such as growth rate, biomass production and stress resistance. Heterosis has become a very useful tool in global agriculture, but its genetic basis remains controversial and its effects hard to predict. We have taken a computational approach to studying heterosis, developing a simulation of evolution, independent reassortment of alleles and hybridization of Gene Regulatory Networks (GRNs) in a Boolean framework. Fitness is measured as the ability of a network to respond to external inputs in a pre-defined way. Our model reproduced common experimental observations on heterosis using only biologically justified parameters. Hybrid vigor was observed and its extent was seen to increase as parental populations diverged, up until a point of sudden collapse of hybrid fitness. We also reproduce, for the first time in a model, the fact that hybrid vigor cannot easily be fixed by within a breeding line, currently an important limitation of the use of hybrid crops. The simulation allowed us to study the effects of three standard models for the genetic basis of heterosis and the level of detail in our model allows us to suggest possible warning signs of the impending collapse of hybrid vigor in breeding. In addition, the simulation provides a framework that can be extended to study other aspects of heterosis and alternative evolutionary scenarios. |
1311.0651 | Bastien Boussau | Gergely J. Sz\"ollosi, Eric Tannier, Vincent Daubin, Bastien Boussau | The inference of gene trees with species trees | Review article in relation to the "Mathematical and Computational
Evolutionary Biology" conference, Montpellier, 2013 | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Molecular phylogeny has focused mainly on improving models for the
reconstruction of gene trees based on sequence alignments. Yet, most
phylogeneticists seek to reveal the history of species. Although the histories
of genes and species are tightly linked, they are seldom identical, because
genes duplicate, are lost or horizontally transferred, and because alleles can
co-exist in populations for periods that may span several speciation events.
Building models describing the relationship between gene and species trees can
thus improve the reconstruction of gene trees when a species tree is known, and
vice-versa. Several approaches have been proposed to solve the problem in one
direction or the other, but in general neither gene trees nor species trees are
known. Only a few studies have attempted to jointly infer gene trees and
species trees. In this article we review the various models that have been used
to describe the relationship between gene trees and species trees. These models
account for gene duplication and loss, transfer or incomplete lineage sorting.
Some of them consider several types of events together, but none exists
currently that considers the full repertoire of processes that generate gene
trees along the species tree. Simulations as well as empirical studies on
genomic data show that combining gene tree-species tree models with models of
sequence evolution improves gene tree reconstruction. In turn, these better
gene trees provide a better basis for studying genome evolution or
reconstructing ancestral chromosomes and ancestral gene sequences. We predict
that gene tree-species tree methods that can deal with genomic data sets will
be instrumental to advancing our understanding of genomic evolution.
| [
{
"created": "Mon, 4 Nov 2013 11:34:52 GMT",
"version": "v1"
}
] | 2013-11-05 | [
[
"Szöllosi",
"Gergely J.",
""
],
[
"Tannier",
"Eric",
""
],
[
"Daubin",
"Vincent",
""
],
[
"Boussau",
"Bastien",
""
]
] | Molecular phylogeny has focused mainly on improving models for the reconstruction of gene trees based on sequence alignments. Yet, most phylogeneticists seek to reveal the history of species. Although the histories of genes and species are tightly linked, they are seldom identical, because genes duplicate, are lost or horizontally transferred, and because alleles can co-exist in populations for periods that may span several speciation events. Building models describing the relationship between gene and species trees can thus improve the reconstruction of gene trees when a species tree is known, and vice-versa. Several approaches have been proposed to solve the problem in one direction or the other, but in general neither gene trees nor species trees are known. Only a few studies have attempted to jointly infer gene trees and species trees. In this article we review the various models that have been used to describe the relationship between gene trees and species trees. These models account for gene duplication and loss, transfer or incomplete lineage sorting. Some of them consider several types of events together, but none exists currently that considers the full repertoire of processes that generate gene trees along the species tree. Simulations as well as empirical studies on genomic data show that combining gene tree-species tree models with models of sequence evolution improves gene tree reconstruction. In turn, these better gene trees provide a better basis for studying genome evolution or reconstructing ancestral chromosomes and ancestral gene sequences. We predict that gene tree-species tree methods that can deal with genomic data sets will be instrumental to advancing our understanding of genomic evolution. |
0907.3504 | Andrew Mugler | Andrew Mugler, Aleksandra M. Walczak, Chris H. Wiggins | Spectral solutions to stochastic models of gene expression with bursts
and regulation | 20 pages, 5 figures | PRE 80, 041921 (2009) | 10.1103/PhysRevE.80.041921 | null | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Signal-processing molecules inside cells are often present at low copy
number, which necessitates probabilistic models to account for intrinsic noise.
Probability distributions have traditionally been found using simulation-based
approaches which then require estimating the distributions from many samples.
Here we present in detail an alternative method for directly calculating a
probability distribution by expanding in the natural eigenfunctions of the
governing equation, which is linear. We apply the resulting spectral method to
three general models of stochastic gene expression: a single gene with multiple
expression states (often used as a model of bursting in the limit of two
states), a gene regulatory cascade, and a combined model of bursting and
regulation. In all cases we find either analytic results or numerical
prescriptions that greatly outperform simulations in efficiency and accuracy.
In the last case, we show that bimodal response in the limit of slow switching
is not only possible but optimal in terms of information transmission.
| [
{
"created": "Mon, 20 Jul 2009 22:25:03 GMT",
"version": "v1"
}
] | 2009-11-09 | [
[
"Mugler",
"Andrew",
""
],
[
"Walczak",
"Aleksandra M.",
""
],
[
"Wiggins",
"Chris H.",
""
]
] | Signal-processing molecules inside cells are often present at low copy number, which necessitates probabilistic models to account for intrinsic noise. Probability distributions have traditionally been found using simulation-based approaches which then require estimating the distributions from many samples. Here we present in detail an alternative method for directly calculating a probability distribution by expanding in the natural eigenfunctions of the governing equation, which is linear. We apply the resulting spectral method to three general models of stochastic gene expression: a single gene with multiple expression states (often used as a model of bursting in the limit of two states), a gene regulatory cascade, and a combined model of bursting and regulation. In all cases we find either analytic results or numerical prescriptions that greatly outperform simulations in efficiency and accuracy. In the last case, we show that bimodal response in the limit of slow switching is not only possible but optimal in terms of information transmission. |
2109.09119 | Ling-Yun Wu | Jiating Yu, Jiacheng Leng, Ling-Yun Wu | Network Refinement: A unified framework for enhancing signal or removing
noise of networks | 20 pages, 7 figures, 1 table, 44 references, and 2 appendices.
Submitted to IEEE Transactions on Network Science and Engineering | null | null | null | q-bio.MN cs.SI physics.bio-ph physics.soc-ph q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Networks are widely used in many fields for their powerful ability to provide
vivid representations of relationships between variables. However, many of them
may be corrupted by experimental noise or inappropriate network inference
methods that inherently hamper the efficacy of network-based downstream
analysis. Consequently, it's necessary to develop systematic methods for
denoising networks, namely, improve the Signal-to-Noise Ratio (SNR) of noisy
networks. In this paper, we have explored the properties of network signal and
noise and proposed a novel network denoising framework called Network
Refinement (NR) that adjusts the edge weights by applying a nonlinear graph
operator based on a diffusion process defined by random walk on the graph.
Specifically, this unified framework consists of two closely linked approaches
named NR-F and NR-B, which improve the SNR of noisy input networks from two
different perspectives: NR-F aims at enhancing signal strength, while NR-B aims
at weakening noise strength. Users can choose from which angle to improve the
SNR of the network according to the characteristics of the network itself. We
show that NR can significantly refine the quality of many networks by several
applications on simulated networks and typical real-world biological and social
networks.
| [
{
"created": "Sun, 19 Sep 2021 13:25:16 GMT",
"version": "v1"
}
] | 2021-09-21 | [
[
"Yu",
"Jiating",
""
],
[
"Leng",
"Jiacheng",
""
],
[
"Wu",
"Ling-Yun",
""
]
] | Networks are widely used in many fields for their powerful ability to provide vivid representations of relationships between variables. However, many of them may be corrupted by experimental noise or inappropriate network inference methods that inherently hamper the efficacy of network-based downstream analysis. Consequently, it's necessary to develop systematic methods for denoising networks, namely, improve the Signal-to-Noise Ratio (SNR) of noisy networks. In this paper, we have explored the properties of network signal and noise and proposed a novel network denoising framework called Network Refinement (NR) that adjusts the edge weights by applying a nonlinear graph operator based on a diffusion process defined by random walk on the graph. Specifically, this unified framework consists of two closely linked approaches named NR-F and NR-B, which improve the SNR of noisy input networks from two different perspectives: NR-F aims at enhancing signal strength, while NR-B aims at weakening noise strength. Users can choose from which angle to improve the SNR of the network according to the characteristics of the network itself. We show that NR can significantly refine the quality of many networks by several applications on simulated networks and typical real-world biological and social networks. |
1512.06979 | Daniel Gautheret | Daniel Gautheret (IGM) | Screening Genome Sequences for Known RNA Genes and Motifs | Handbook of RNA Biochemistry, 2014 | null | 10.1002/9783527647064.ch28 | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This methods paper presents computational protocols for the identification of
non-coding RNA genes or RNA motifs within genomic sequences. An application to
bacterial small RNA is proposed.
| [
{
"created": "Tue, 22 Dec 2015 08:21:18 GMT",
"version": "v1"
}
] | 2015-12-23 | [
[
"Gautheret",
"Daniel",
"",
"IGM"
]
] | This methods paper presents computational protocols for the identification of non-coding RNA genes or RNA motifs within genomic sequences. An application to bacterial small RNA is proposed. |
2210.04453 | Hamid Alinejad Rokny | Roxana Zahedi Nasab, Mohammad Reza Eftekhariyan Ghamsari, Ahmadreza
Argha, Callum Macphillamy, Amin Beheshti, Roohallah Alizadehsani, Nigel H.
Lovell, Mohammad Lotfollahi, Hamid Alinejad-Rokny | Deep Learning in Spatially Resolved Transcriptomics: A Comprehensive
Technical View | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Spatially resolved transcriptomics (SRT) has evolved rapidly through various
technologies, enabling scientists to investigate both morphological contexts
and gene expression profiling at single-cell resolution in parallel. SRT data
are complex and multi-modal, comprising gene expression matrices, spatial
information, and often high-resolution histology images. Because of this
complexity and multi-modality, sophisticated computational algorithms are
required to accurately analyze SRT data. Most efforts in this domain have been
made to utilize conventional machine learning and statistical approaches,
exhibiting sub-optimal results due to the complicated nature of SRT datasets.
To address these shortcomings, researchers have recently employed deep learning
algorithms including various state-of-the-art methods mainly in spatial
clustering, spatially variable gene identification, and alignment. While great
progress has been made in developing deep learning-based models for SRT data
analysis, further improvement is still needed to create more biologically aware
models that consider aspects such as phylogeny-aware clustering or the analysis
of small histology image patches. Additionally, strategies for batch effect
removal, normalization, and handling overdispersion and zero inflation patterns
of gene expression are still needed in the analysis of SRT data using deep
learning methods. In this paper, we provide a comprehensive overview of these
deep learning methods, including their strengths and limitations. We also
highlight new frontiers, current challenges, limitations, and open questions in
this field. Also, we provide a comprehensive list of all available SRT
databases that can be used as an extensive resource for future studies.
| [
{
"created": "Mon, 10 Oct 2022 06:33:13 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Oct 2022 17:18:43 GMT",
"version": "v2"
},
{
"created": "Mon, 8 May 2023 16:49:02 GMT",
"version": "v3"
}
] | 2023-05-09 | [
[
"Nasab",
"Roxana Zahedi",
""
],
[
"Ghamsari",
"Mohammad Reza Eftekhariyan",
""
],
[
"Argha",
"Ahmadreza",
""
],
[
"Macphillamy",
"Callum",
""
],
[
"Beheshti",
"Amin",
""
],
[
"Alizadehsani",
"Roohallah",
""
],
[
"Lovell",
"Nigel H.",
""
],
[
"Lotfollahi",
"Mohammad",
""
],
[
"Alinejad-Rokny",
"Hamid",
""
]
] | Spatially resolved transcriptomics (SRT) has evolved rapidly through various technologies, enabling scientists to investigate both morphological contexts and gene expression profiling at single-cell resolution in parallel. SRT data are complex and multi-modal, comprising gene expression matrices, spatial information, and often high-resolution histology images. Because of this complexity and multi-modality, sophisticated computational algorithms are required to accurately analyze SRT data. Most efforts in this domain have been made to utilize conventional machine learning and statistical approaches, exhibiting sub-optimal results due to the complicated nature of SRT datasets. To address these shortcomings, researchers have recently employed deep learning algorithms including various state-of-the-art methods mainly in spatial clustering, spatially variable gene identification, and alignment. While great progress has been made in developing deep learning-based models for SRT data analysis, further improvement is still needed to create more biologically aware models that consider aspects such as phylogeny-aware clustering or the analysis of small histology image patches. Additionally, strategies for batch effect removal, normalization, and handling overdispersion and zero inflation patterns of gene expression are still needed in the analysis of SRT data using deep learning methods. In this paper, we provide a comprehensive overview of these deep learning methods, including their strengths and limitations. We also highlight new frontiers, current challenges, limitations, and open questions in this field. Also, we provide a comprehensive list of all available SRT databases that can be used as an extensive resource for future studies. |
2104.10191 | Vu Anh Truong Nguyen | Vu AT Nguyen and Dervis C Vural | Extinction in complex communities as driven by adaptive dynamics | 10 pages, 5 figures. Journal of Evolutionary Biology (2021) | null | 10.1111/jeb.13796 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a complex community, species continuously adapt to each other. On rare
occasions, the adaptation of a species can lead to the extinction of others,
and even its own. "Adaptive dynamics" is the standard mathematical framework to
describe evolutionary changes in community interactions, and in particular,
predict adaptation driven extinction. Unfortunately, most authors implement the
equations of adaptive dynamics through computer simulations, that require
assuming a large number of questionable parameters and fitness functions. In
this study we present analytical solutions to adaptive dynamics equations,
thereby clarifying how outcomes depend on any computational input. We develop
general formulas that predict equilibrium abundances over evolutionary time
scales. Additionally, we predict which species will go extinct next, and when
this will happen.
| [
{
"created": "Tue, 20 Apr 2021 18:20:07 GMT",
"version": "v1"
},
{
"created": "Fri, 9 Jul 2021 20:10:28 GMT",
"version": "v2"
}
] | 2021-07-13 | [
[
"Nguyen",
"Vu AT",
""
],
[
"Vural",
"Dervis C",
""
]
] | In a complex community, species continuously adapt to each other. On rare occasions, the adaptation of a species can lead to the extinction of others, and even its own. "Adaptive dynamics" is the standard mathematical framework to describe evolutionary changes in community interactions, and in particular, predict adaptation driven extinction. Unfortunately, most authors implement the equations of adaptive dynamics through computer simulations, that require assuming a large number of questionable parameters and fitness functions. In this study we present analytical solutions to adaptive dynamics equations, thereby clarifying how outcomes depend on any computational input. We develop general formulas that predict equilibrium abundances over evolutionary time scales. Additionally, we predict which species will go extinct next, and when this will happen. |
1308.1975 | Zhiyong Wang | Zhiyong Wang and Jinbo Xu | Predicting protein contact map using evolutionary and physical
constraints by integer programming (extended version) | 14 pages, 13 figures, 10 tables | Bioinformatics (2013) 29 (13): i266-i273 | 10.1093/bioinformatics/btt211 | null | q-bio.QM cs.CE cs.LG math.OC q-bio.BM stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation. Protein contact map describes the pairwise spatial and functional
relationship of residues in a protein and contains key information for protein
3D structure prediction. Although studied extensively, it remains very
challenging to predict contact map using only sequence information. Most
existing methods predict the contact map matrix element-by-element, ignoring
correlation among contacts and physical feasibility of the whole contact map. A
couple of recent methods predict contact map based upon residue co-evolution,
taking into consideration contact correlation and enforcing a sparsity
restraint, but these methods require a very large number of sequence homologs
for the protein under consideration and the resultant contact map may be still
physically unfavorable.
Results. This paper presents a novel method PhyCMAP for contact map
prediction, integrating both evolutionary and physical restraints by machine
learning and integer linear programming (ILP). The evolutionary restraints
include sequence profile, residue co-evolution and context-specific statistical
potential. The physical restraints specify more concrete relationship among
contacts than the sparsity restraint. As such, our method greatly reduces the
solution space of the contact map matrix and thus, significantly improves
prediction accuracy. Experimental results confirm that PhyCMAP outperforms
currently popular methods no matter how many sequence homologs are available
for the protein under consideration. PhyCMAP can predict contacts within
minutes after PSIBLAST search for sequence homologs is done, much faster than
the two recent methods PSICOV and EvFold.
See http://raptorx.uchicago.edu for the web server.
| [
{
"created": "Thu, 8 Aug 2013 20:44:01 GMT",
"version": "v1"
},
{
"created": "Mon, 19 Aug 2013 16:24:06 GMT",
"version": "v2"
}
] | 2014-01-21 | [
[
"Wang",
"Zhiyong",
""
],
[
"Xu",
"Jinbo",
""
]
] | Motivation. Protein contact map describes the pairwise spatial and functional relationship of residues in a protein and contains key information for protein 3D structure prediction. Although studied extensively, it remains very challenging to predict contact map using only sequence information. Most existing methods predict the contact map matrix element-by-element, ignoring correlation among contacts and physical feasibility of the whole contact map. A couple of recent methods predict contact map based upon residue co-evolution, taking into consideration contact correlation and enforcing a sparsity restraint, but these methods require a very large number of sequence homologs for the protein under consideration and the resultant contact map may be still physically unfavorable. Results. This paper presents a novel method PhyCMAP for contact map prediction, integrating both evolutionary and physical restraints by machine learning and integer linear programming (ILP). The evolutionary restraints include sequence profile, residue co-evolution and context-specific statistical potential. The physical restraints specify more concrete relationship among contacts than the sparsity restraint. As such, our method greatly reduces the solution space of the contact map matrix and thus, significantly improves prediction accuracy. Experimental results confirm that PhyCMAP outperforms currently popular methods no matter how many sequence homologs are available for the protein under consideration. PhyCMAP can predict contacts within minutes after PSIBLAST search for sequence homologs is done, much faster than the two recent methods PSICOV and EvFold. See http://raptorx.uchicago.edu for the web server. |
0812.4736 | Thierry Rabilloud | Thierry Rabilloud (BBSI), Mireille Chevallet (BBSI), Sylvie Luche
(BBSI), C\'ecile Lelong (BBSI) | Fully denaturing two-dimensional electrophoresis of membrane proteins: a
critical update | null | Proteomics 8, 19 (2008) 3965-73 | 10.1002/pmic.200800043 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The quality and ease of proteomics analysis depends on the performance of the
analytical tools used, and thus of the performances of the protein separation
tools used to deconvolute complex protein samples. Among protein samples,
membrane proteins are one of the most difficult sample classes, because of
their hydrophobicity and embedment in the lipid bilayers. This review deals
with the recent progresses and advances made in the separation of membrane
proteins by 2-DE separating only denatured proteins. Traditional 2-D methods,
i.e., methods using IEF in the first dimension are compared to methods using
only zone electrophoresis in both dimensions, i.e., electrophoresis in the
presence of cationic or anionic detergents. The overall performances and fields
of application of both types of method is critically examined, as are future
prospects for this field.
| [
{
"created": "Sat, 27 Dec 2008 11:12:00 GMT",
"version": "v1"
}
] | 2008-12-31 | [
[
"Rabilloud",
"Thierry",
"",
"BBSI"
],
[
"Chevallet",
"Mireille",
"",
"BBSI"
],
[
"Luche",
"Sylvie",
"",
"BBSI"
],
[
"Lelong",
"Cécile",
"",
"BBSI"
]
] | The quality and ease of proteomics analysis depends on the performance of the analytical tools used, and thus of the performances of the protein separation tools used to deconvolute complex protein samples. Among protein samples, membrane proteins are one of the most difficult sample classes, because of their hydrophobicity and embedment in the lipid bilayers. This review deals with the recent progresses and advances made in the separation of membrane proteins by 2-DE separating only denatured proteins. Traditional 2-D methods, i.e., methods using IEF in the first dimension are compared to methods using only zone electrophoresis in both dimensions, i.e., electrophoresis in the presence of cationic or anionic detergents. The overall performances and fields of application of both types of method is critically examined, as are future prospects for this field. |
2008.03248 | Aycil Cesmelioglu | Aycil Cesmelioglu, Kenneth L. Kuttler, Meir Shillor and Anna M.
Spagnuolo | A mathematical model of the COVID-19 pandemic dynamics with dependent
variable infection rate: Application to the Republic of Korea | 37 pages, 9 figures, 2 tables | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work constructs, analyzes, and simulates a new compartmental SEIR-type
model for the dynamics and potential control of the current COVID-19 pandemic.
The novelty in this work is two-fold. First, the population is divided
according to its compliance with disease control directives (lockdown,
shelter-in-place, masks/face coverings, physical distancing, etc.) into those
who fully comply and those who follow the directives partially, or are
necessarily mobile (such as medical staff). This split, indirectly, reflects on
the quality and consistency of these measures. This allows the assessment of
the overall effectiveness of the control measures and the impact of their
relaxing or tightening on the disease spread. Second, the adequate contact
rate, which directly affects the infection rate, is one of the model unknowns,
as it keeps track of the changes in the population behavior and the
effectiveness of various disease treatment modalities via a differential
inclusion. Existence, uniqueness and positivity results are proved using a
nonstandard convex analysis-based approach. As a case study, the pandemic
outbreak in the Republic of Korea (South Korea) is simulated. The model
parameters were found by minimizing the deviation of the model prediction from
the reported data over the first 100 days of the pandemic in South Korea.The
simulations show that the model captures accurately the pandemic dynamics in
the subsequent 75 days, which provides confidence in the model predictions and
its future use. In particular, the model predicts that about 40% of the
infections were not documented, which implies that asymptomatic infections
contribute silently but substantially to the spread of the disease indicating
that more widespread asymptomatic testing is necessary.
| [
{
"created": "Mon, 27 Jul 2020 15:56:14 GMT",
"version": "v1"
},
{
"created": "Thu, 13 May 2021 15:26:47 GMT",
"version": "v2"
}
] | 2021-05-14 | [
[
"Cesmelioglu",
"Aycil",
""
],
[
"Kuttler",
"Kenneth L.",
""
],
[
"Shillor",
"Meir",
""
],
[
"Spagnuolo",
"Anna M.",
""
]
] | This work constructs, analyzes, and simulates a new compartmental SEIR-type model for the dynamics and potential control of the current COVID-19 pandemic. The novelty in this work is two-fold. First, the population is divided according to its compliance with disease control directives (lockdown, shelter-in-place, masks/face coverings, physical distancing, etc.) into those who fully comply and those who follow the directives partially, or are necessarily mobile (such as medical staff). This split, indirectly, reflects on the quality and consistency of these measures. This allows the assessment of the overall effectiveness of the control measures and the impact of their relaxing or tightening on the disease spread. Second, the adequate contact rate, which directly affects the infection rate, is one of the model unknowns, as it keeps track of the changes in the population behavior and the effectiveness of various disease treatment modalities via a differential inclusion. Existence, uniqueness and positivity results are proved using a nonstandard convex analysis-based approach. As a case study, the pandemic outbreak in the Republic of Korea (South Korea) is simulated. The model parameters were found by minimizing the deviation of the model prediction from the reported data over the first 100 days of the pandemic in South Korea.The simulations show that the model captures accurately the pandemic dynamics in the subsequent 75 days, which provides confidence in the model predictions and its future use. In particular, the model predicts that about 40% of the infections were not documented, which implies that asymptomatic infections contribute silently but substantially to the spread of the disease indicating that more widespread asymptomatic testing is necessary. |
2204.10182 | Ekaterina Moreva Dr. | G. Petrini, G. Tomagra, E. Bernardi, E. Moreva, P. Traina, A.
Marcantoni, F. Picollo, K. Kvakova, P. Cigler, I.P. Degiovanni, V. Carabelli,
M. Genovese | Nanodiamond quantum sensors reveal temperature variation associated to
hippocampal neurons firing | 27 pages, 5 figures, 3 tables | Advanced Science, 2022 | 10.1002/advs.202202014 | 2202014 | q-bio.NC quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Temperature is one of the most relevant parameters for the regulation of
intracellular processes. Measuring localized subcellular temperature gradients
is fundamental for a deeper understanding of cell function, such as the genesis
of action potentials, and cell metabolism. Here, we detect for the first time
temperature variations (1{\deg}C) associated with potentiation and depletion of
neuronal firing, exploiting a nanoscale thermometer based on optically detected
magnetic resonance in nanodiamonds. Our results provide a tool for assessing
neuronal spiking activity under physiological and pathological conditions and,
conjugated with the high sensitivity of this technique (in perspective
sensitive to < 0.1{\deg}C variations), pave the way to a systematic study of
the generation of localized temperature gradients. Furthermore, they prompt
further studies explaining in detail the physiological mechanism originating
this effect.
| [
{
"created": "Thu, 31 Mar 2022 09:39:57 GMT",
"version": "v1"
}
] | 2022-08-04 | [
[
"Petrini",
"G.",
""
],
[
"Tomagra",
"G.",
""
],
[
"Bernardi",
"E.",
""
],
[
"Moreva",
"E.",
""
],
[
"Traina",
"P.",
""
],
[
"Marcantoni",
"A.",
""
],
[
"Picollo",
"F.",
""
],
[
"Kvakova",
"K.",
""
],
[
"Cigler",
"P.",
""
],
[
"Degiovanni",
"I. P.",
""
],
[
"Carabelli",
"V.",
""
],
[
"Genovese",
"M.",
""
]
] | Temperature is one of the most relevant parameters for the regulation of intracellular processes. Measuring localized subcellular temperature gradients is fundamental for a deeper understanding of cell function, such as the genesis of action potentials, and cell metabolism. Here, we detect for the first time temperature variations (1{\deg}C) associated with potentiation and depletion of neuronal firing, exploiting a nanoscale thermometer based on optically detected magnetic resonance in nanodiamonds. Our results provide a tool for assessing neuronal spiking activity under physiological and pathological conditions and, conjugated with the high sensitivity of this technique (in perspective sensitive to < 0.1{\deg}C variations), pave the way to a systematic study of the generation of localized temperature gradients. Furthermore, they prompt further studies explaining in detail the physiological mechanism originating this effect. |
2110.14549 | Paul Haider | Paul Haider, Benjamin Ellenberger, Laura Kriener, Jakob Jordan, Walter
Senn, Mihai A. Petrovici | Latent Equilibrium: A unified learning theory for arbitrarily fast
computation with arbitrarily slow neurons | Accepted for publication in Advances in Neural Information Processing
Systems 34 (NeurIPS 2021); 13 pages, 4 figures; 10 pages of supplementary
material, 1 supplementary figure | null | null | null | q-bio.NC cs.AI cs.LG cs.NE eess.SP | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The response time of physical computational elements is finite, and neurons
are no exception. In hierarchical models of cortical networks each layer thus
introduces a response lag. This inherent property of physical dynamical systems
results in delayed processing of stimuli and causes a timing mismatch between
network output and instructive signals, thus afflicting not only inference, but
also learning. We introduce Latent Equilibrium, a new framework for inference
and learning in networks of slow components which avoids these issues by
harnessing the ability of biological neurons to phase-advance their output with
respect to their membrane potential. This principle enables quasi-instantaneous
inference independent of network depth and avoids the need for phased
plasticity or computationally expensive network relaxation phases. We jointly
derive disentangled neuron and synapse dynamics from a prospective energy
function that depends on a network's generalized position and momentum. The
resulting model can be interpreted as a biologically plausible approximation of
error backpropagation in deep cortical networks with continuous-time, leaky
neuronal dynamics and continuously active, local plasticity. We demonstrate
successful learning of standard benchmark datasets, achieving competitive
performance using both fully-connected and convolutional architectures, and
show how our principle can be applied to detailed models of cortical
microcircuitry. Furthermore, we study the robustness of our model to
spatio-temporal substrate imperfections to demonstrate its feasibility for
physical realization, be it in vivo or in silico.
| [
{
"created": "Wed, 27 Oct 2021 16:15:55 GMT",
"version": "v1"
}
] | 2021-10-28 | [
[
"Haider",
"Paul",
""
],
[
"Ellenberger",
"Benjamin",
""
],
[
"Kriener",
"Laura",
""
],
[
"Jordan",
"Jakob",
""
],
[
"Senn",
"Walter",
""
],
[
"Petrovici",
"Mihai A.",
""
]
] | The response time of physical computational elements is finite, and neurons are no exception. In hierarchical models of cortical networks each layer thus introduces a response lag. This inherent property of physical dynamical systems results in delayed processing of stimuli and causes a timing mismatch between network output and instructive signals, thus afflicting not only inference, but also learning. We introduce Latent Equilibrium, a new framework for inference and learning in networks of slow components which avoids these issues by harnessing the ability of biological neurons to phase-advance their output with respect to their membrane potential. This principle enables quasi-instantaneous inference independent of network depth and avoids the need for phased plasticity or computationally expensive network relaxation phases. We jointly derive disentangled neuron and synapse dynamics from a prospective energy function that depends on a network's generalized position and momentum. The resulting model can be interpreted as a biologically plausible approximation of error backpropagation in deep cortical networks with continuous-time, leaky neuronal dynamics and continuously active, local plasticity. We demonstrate successful learning of standard benchmark datasets, achieving competitive performance using both fully-connected and convolutional architectures, and show how our principle can be applied to detailed models of cortical microcircuitry. Furthermore, we study the robustness of our model to spatio-temporal substrate imperfections to demonstrate its feasibility for physical realization, be it in vivo or in silico. |
1302.5104 | Michael Elliot | Michael G. Elliot, Arne O. Mooers | Inferring ancestral states without assuming neutrality or gradualism
using a stable model of continuous character evolution | 39 pages, 5 figures, 4 tables | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The value of a continuous character evolving on a phylogenetic tree is
commonly modelled as the location of a particle moving under one-dimensional
Brownian motion with constant rate. The Brownian motion model is best suited to
characters evolving under neutral drift or tracking an optimum that drifts
neutrally. We present a generalization of the Brownian motion model which
relaxes assumptions of neutrality and gradualism by considering increments to
evolving characters to be drawn from a heavy-tailed stable distribution (of
which the normal distribution is a specialized form). We describe Markov chain
Monte Carlo methods for fitting the model to biological data paying special
attention to ancestral state reconstruction, and study the performance of the
model in comparison with a selection of existing comparative methods, using
both simulated data and a database of body mass in 1,679 mammalian species. We
discuss hypothesis testing and model selection. The new model is well suited to
a stochastic process with a volatile rate of change in which biological
characters undergo a mixture of neutral drift and occasional evolutionary
events of large magnitude.
| [
{
"created": "Wed, 20 Feb 2013 20:56:28 GMT",
"version": "v1"
}
] | 2013-02-21 | [
[
"Elliot",
"Michael G.",
""
],
[
"Mooers",
"Arne O.",
""
]
] | The value of a continuous character evolving on a phylogenetic tree is commonly modelled as the location of a particle moving under one-dimensional Brownian motion with constant rate. The Brownian motion model is best suited to characters evolving under neutral drift or tracking an optimum that drifts neutrally. We present a generalization of the Brownian motion model which relaxes assumptions of neutrality and gradualism by considering increments to evolving characters to be drawn from a heavy-tailed stable distribution (of which the normal distribution is a specialized form). We describe Markov chain Monte Carlo methods for fitting the model to biological data paying special attention to ancestral state reconstruction, and study the performance of the model in comparison with a selection of existing comparative methods, using both simulated data and a database of body mass in 1,679 mammalian species. We discuss hypothesis testing and model selection. The new model is well suited to a stochastic process with a volatile rate of change in which biological characters undergo a mixture of neutral drift and occasional evolutionary events of large magnitude. |
1909.03083 | Ed Lein | Rafael Yuste, Michael Hawrylycz, Nadia Aalling, Detlev Arendt, Ruben
Armananzas, Giorgio Ascoli, Concha Bielza, Vahid Bokharaie, Tobias Bergmann,
Irina Bystron, Marco Capogna, Yoonjeung Chang, Ann Clemens, Christiaan de
Kock, Javier DeFelipe, Sandra Dos Santos, Keagan Dunville, Dirk Feldmeyer,
Richard Fiath, Gordon Fishell, Angelica Foggetti, Xuefan Gao, Parviz Ghaderi,
Onur Gunturkun, Vanessa Jane Hall, Moritz Helmstaedter, Suzana
Herculano-Houzel, Markus Hilscher, Hajime Hirase, Jens Hjerling-Leffler,
Rebecca Hodge, Z. Josh Huang, Rafiq Huda, Yuan Juan, Konstantin Khodosevich,
Ole Kiehn, Henner Koch, Eric Kuebler, Malte Kuhnemund, Pedro Larranaga,
Boudewijn Lelieveldt, Emma Louise Louth, Jan Lui, Huibert Mansvelder, Oscar
Marin, Julio Mart\'inez-Trujillo, Homeira Moradi, Natalia Goriounova, Alok
Mohapatra, Maiken Nedergaard, Pavel N\v{e}mec, Netanel Ofer, Ulrich
Pfisterer, Samuel Pontes, William Redmond, Jean Rossier, Joshua Sanes,
Richard Scheuermann, Esther Serrano Saiz, Peter Somogyi, G\'abor Tam\'as,
Andreas Tolias, Maria Tosches, Miguel Turrero Garcia, Argel Aguilar-Valles,
Hermany Munguba, Christian Wozny, Thomas Wuttke, Liu Yong, Hongkui Zeng, Ed
S. Lein | A community-based transcriptomics classification and nomenclature of
neocortical cell types | 21 pages, 3 figures | null | null | null | q-bio.GN q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | To understand the function of cortical circuits it is necessary to classify
their underlying cellular diversity. Traditional attempts based on comparing
anatomical or physiological features of neurons and glia, while productive,
have not resulted in a unified taxonomy of neural cell types. The recent
development of single-cell transcriptomics has enabled, for the first time,
systematic high-throughput profiling of large numbers of cortical cells and the
generation of datasets that hold the promise of being complete, accurate and
permanent. Statistical analyses of these data have revealed the existence of
clear clusters, many of which correspond to cell types defined by traditional
criteria, and which are conserved across cortical areas and species. To
capitalize on these innovations and advance the field, we, the Copenhagen
Convention Group, propose the community adopts a transcriptome-based taxonomy
of the cell types in the adult mammalian neocortex. This core classification
should be ontological, hierarchical and use a standardized nomenclature. It
should be configured to flexibly incorporate new data from multiple approaches,
developmental stages and a growing number of species, enabling improvement and
revision of the classification. This community-based strategy could serve as a
common foundation for future detailed analysis and reverse engineering of
cortical circuits and serve as an example for cell type classification in other
parts of the nervous system and other organs.
| [
{
"created": "Fri, 6 Sep 2019 18:12:50 GMT",
"version": "v1"
}
] | 2019-09-10 | [
[
"Yuste",
"Rafael",
""
],
[
"Hawrylycz",
"Michael",
""
],
[
"Aalling",
"Nadia",
""
],
[
"Arendt",
"Detlev",
""
],
[
"Armananzas",
"Ruben",
""
],
[
"Ascoli",
"Giorgio",
""
],
[
"Bielza",
"Concha",
""
],
[
"Bokharaie",
"Vahid",
""
],
[
"Bergmann",
"Tobias",
""
],
[
"Bystron",
"Irina",
""
],
[
"Capogna",
"Marco",
""
],
[
"Chang",
"Yoonjeung",
""
],
[
"Clemens",
"Ann",
""
],
[
"de Kock",
"Christiaan",
""
],
[
"DeFelipe",
"Javier",
""
],
[
"Santos",
"Sandra Dos",
""
],
[
"Dunville",
"Keagan",
""
],
[
"Feldmeyer",
"Dirk",
""
],
[
"Fiath",
"Richard",
""
],
[
"Fishell",
"Gordon",
""
],
[
"Foggetti",
"Angelica",
""
],
[
"Gao",
"Xuefan",
""
],
[
"Ghaderi",
"Parviz",
""
],
[
"Gunturkun",
"Onur",
""
],
[
"Hall",
"Vanessa Jane",
""
],
[
"Helmstaedter",
"Moritz",
""
],
[
"Herculano-Houzel",
"Suzana",
""
],
[
"Hilscher",
"Markus",
""
],
[
"Hirase",
"Hajime",
""
],
[
"Hjerling-Leffler",
"Jens",
""
],
[
"Hodge",
"Rebecca",
""
],
[
"Huang",
"Z. Josh",
""
],
[
"Huda",
"Rafiq",
""
],
[
"Juan",
"Yuan",
""
],
[
"Khodosevich",
"Konstantin",
""
],
[
"Kiehn",
"Ole",
""
],
[
"Koch",
"Henner",
""
],
[
"Kuebler",
"Eric",
""
],
[
"Kuhnemund",
"Malte",
""
],
[
"Larranaga",
"Pedro",
""
],
[
"Lelieveldt",
"Boudewijn",
""
],
[
"Louth",
"Emma Louise",
""
],
[
"Lui",
"Jan",
""
],
[
"Mansvelder",
"Huibert",
""
],
[
"Marin",
"Oscar",
""
],
[
"Martínez-Trujillo",
"Julio",
""
],
[
"Moradi",
"Homeira",
""
],
[
"Goriounova",
"Natalia",
""
],
[
"Mohapatra",
"Alok",
""
],
[
"Nedergaard",
"Maiken",
""
],
[
"Němec",
"Pavel",
""
],
[
"Ofer",
"Netanel",
""
],
[
"Pfisterer",
"Ulrich",
""
],
[
"Pontes",
"Samuel",
""
],
[
"Redmond",
"William",
""
],
[
"Rossier",
"Jean",
""
],
[
"Sanes",
"Joshua",
""
],
[
"Scheuermann",
"Richard",
""
],
[
"Saiz",
"Esther Serrano",
""
],
[
"Somogyi",
"Peter",
""
],
[
"Tamás",
"Gábor",
""
],
[
"Tolias",
"Andreas",
""
],
[
"Tosches",
"Maria",
""
],
[
"Garcia",
"Miguel Turrero",
""
],
[
"Aguilar-Valles",
"Argel",
""
],
[
"Munguba",
"Hermany",
""
],
[
"Wozny",
"Christian",
""
],
[
"Wuttke",
"Thomas",
""
],
[
"Yong",
"Liu",
""
],
[
"Zeng",
"Hongkui",
""
],
[
"Lein",
"Ed S.",
""
]
] | To understand the function of cortical circuits it is necessary to classify their underlying cellular diversity. Traditional attempts based on comparing anatomical or physiological features of neurons and glia, while productive, have not resulted in a unified taxonomy of neural cell types. The recent development of single-cell transcriptomics has enabled, for the first time, systematic high-throughput profiling of large numbers of cortical cells and the generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data have revealed the existence of clear clusters, many of which correspond to cell types defined by traditional criteria, and which are conserved across cortical areas and species. To capitalize on these innovations and advance the field, we, the Copenhagen Convention Group, propose the community adopts a transcriptome-based taxonomy of the cell types in the adult mammalian neocortex. This core classification should be ontological, hierarchical and use a standardized nomenclature. It should be configured to flexibly incorporate new data from multiple approaches, developmental stages and a growing number of species, enabling improvement and revision of the classification. This community-based strategy could serve as a common foundation for future detailed analysis and reverse engineering of cortical circuits and serve as an example for cell type classification in other parts of the nervous system and other organs. |
2312.16875 | Peter Csermely | Tamas Veres, Mark Kerestely, Borbala M. Kovacs, David Keresztes, Klara
Schulc, Erik Seitz, Zsolt Vassy, Daniel V. Veres, Peter Csermely | Cellular forgetting, desensitisation, stress and aging in signalling
networks. When do cells refuse to learn more? | 19 pages, 4 figures | Cellular and Molecular Life Sciences (2024) 81,97 | 10.1007/s00018-024-05112-7 | null | q-bio.MN q-bio.CB | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Recent findings show that single, non-neuronal cells are also able to learn
signalling responses developing cellular memory. In cellular learning nodes of
signalling networks strengthen their interactions e.g. by the conformational
memory of intrinsically disordered proteins, protein translocation, miRNAs,
lncRNAs, chromatin memory and signalling cascades. This can be described by a
generalized, unicellular Hebbian learning process, where those signalling
connections, which participate in learning, become stronger. Here we review
those scenarios, where cellular signalling is not only repeated in a few times
(when learning occurs), but becomes too frequent, too large, or too complex and
overloads the cell. This leads to desensitisation of signalling networks by
decoupling signalling components, receptor internalization, and consequent
downregulation. These molecular processes are examples of anti-Hebbian learning
and forgetting of signalling networks. Stress can be perceived as signalling
overload inducing the desensitisation of signalling pathways. Aging occurs by
the summative effects of cumulative stress downregulating signalling. We
propose that cellular learning desensitisation, stress and aging may be placed
along the same axis of more and more intensive (prolonged or repeated)
signalling. We discuss how cells might discriminate between repeated and
unexpected signals, and highlight the Hebbian and anti-Hebbian mechanisms
behind the fold-change detection in the NF-\k{appa}B signalling pathway. We
list drug design methods using Hebbian learning (such as chemically-induced
proximity) and clinical treatment modalities inducing (cancer, drug allergies)
desensitisation or avoiding drug-induced desensitisation. A better
discrimination between cellular learning, desensitisation and stress may open
novel directions in drug design, e.g., helping to overcome drug-resistance.
| [
{
"created": "Thu, 28 Dec 2023 08:04:05 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Feb 2024 07:27:00 GMT",
"version": "v2"
}
] | 2024-02-21 | [
[
"Veres",
"Tamas",
""
],
[
"Kerestely",
"Mark",
""
],
[
"Kovacs",
"Borbala M.",
""
],
[
"Keresztes",
"David",
""
],
[
"Schulc",
"Klara",
""
],
[
"Seitz",
"Erik",
""
],
[
"Vassy",
"Zsolt",
""
],
[
"Veres",
"Daniel V.",
""
],
[
"Csermely",
"Peter",
""
]
] | Recent findings show that single, non-neuronal cells are also able to learn signalling responses developing cellular memory. In cellular learning nodes of signalling networks strengthen their interactions e.g. by the conformational memory of intrinsically disordered proteins, protein translocation, miRNAs, lncRNAs, chromatin memory and signalling cascades. This can be described by a generalized, unicellular Hebbian learning process, where those signalling connections, which participate in learning, become stronger. Here we review those scenarios, where cellular signalling is not only repeated in a few times (when learning occurs), but becomes too frequent, too large, or too complex and overloads the cell. This leads to desensitisation of signalling networks by decoupling signalling components, receptor internalization, and consequent downregulation. These molecular processes are examples of anti-Hebbian learning and forgetting of signalling networks. Stress can be perceived as signalling overload inducing the desensitisation of signalling pathways. Aging occurs by the summative effects of cumulative stress downregulating signalling. We propose that cellular learning desensitisation, stress and aging may be placed along the same axis of more and more intensive (prolonged or repeated) signalling. We discuss how cells might discriminate between repeated and unexpected signals, and highlight the Hebbian and anti-Hebbian mechanisms behind the fold-change detection in the NF-\k{appa}B signalling pathway. We list drug design methods using Hebbian learning (such as chemically-induced proximity) and clinical treatment modalities inducing (cancer, drug allergies) desensitisation or avoiding drug-induced desensitisation. A better discrimination between cellular learning, desensitisation and stress may open novel directions in drug design, e.g., helping to overcome drug-resistance. |
q-bio/0511012 | Juergen Jost | Juergen Jost | Temporal correlation based learning in neuron models | null | null | null | null | q-bio.NC | null | We study a learning rule based upon the temporal correlation (weighted by a
learning kernel) between incoming spikes and the internal state of the
postsynaptic neuron, building upon previous studies of spike timing dependent
synaptic plasticity (\cite{KGvHW,KGvH1,vH}). Our learning rule for the synaptic
weight $w_{ij}$ is $$ \dot w_{ij}(t)= \epsilon \int_{-\infty}^\infty
\frac{1}{T_l} \int_{t-T_l}^t \sum_\mu \delta(\tau+s-t_{j,\mu}) u(\tau) d\tau\
\Gamma(s)ds $$ where the $t_{j,\mu}$ are the arrival times of spikes from the
presynaptic neuron $j$ and the function $u(t)$ describes the state of the
postsynaptic neuron $i$. Thus, the spike-triggered average contained in the
inner integral is weighted by a kernel $\Gamma(s)$, the learning window,
positive for negative, negative for positive values of the time diffence $s$
between post- and presynaptic activity. An antisymmetry assumption for the
learning window enables us to derive analytical expressions for a general class
of neuron models and to study the changes in input-output relationships
following from synaptic weight changes. This is a genuinely non-linear effect
(\cite{SMA}).
| [
{
"created": "Fri, 11 Nov 2005 08:23:45 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Jost",
"Juergen",
""
]
] | We study a learning rule based upon the temporal correlation (weighted by a learning kernel) between incoming spikes and the internal state of the postsynaptic neuron, building upon previous studies of spike timing dependent synaptic plasticity (\cite{KGvHW,KGvH1,vH}). Our learning rule for the synaptic weight $w_{ij}$ is $$ \dot w_{ij}(t)= \epsilon \int_{-\infty}^\infty \frac{1}{T_l} \int_{t-T_l}^t \sum_\mu \delta(\tau+s-t_{j,\mu}) u(\tau) d\tau\ \Gamma(s)ds $$ where the $t_{j,\mu}$ are the arrival times of spikes from the presynaptic neuron $j$ and the function $u(t)$ describes the state of the postsynaptic neuron $i$. Thus, the spike-triggered average contained in the inner integral is weighted by a kernel $\Gamma(s)$, the learning window, positive for negative, negative for positive values of the time diffence $s$ between post- and presynaptic activity. An antisymmetry assumption for the learning window enables us to derive analytical expressions for a general class of neuron models and to study the changes in input-output relationships following from synaptic weight changes. This is a genuinely non-linear effect (\cite{SMA}). |
2406.09470 | Etienne Joubert | Etienne Joubert, Charlotte S\`eve, St\'ephanie Mah\'evas, Adrian Bach,
Marc Bouchoucha | Nursery function rehabilitation projects in port areas can support fish
populations but they remain less effective than ensuring compliance to
fisheries management | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conservation measures are implemented to support biodiversity in areas that
are degraded or under anthropogenic pressure. Over the past decade, numerous
projects aimed at rehabilitating a fish nursery function in ports, through the
installation of artificial structures, have emerged. While studies conducted on
these solutions seem promising on a very local scale (e.g., higher densities of
juvenile fish on artificial fish nurseries compared to bare port
infrastructures), no evaluation has been undertaken yet to establish their
contribution to the renewal of coastal fish populations or their performance
compared to other conservation measures such as fishing regulation. Here, we
used a coupled model of fish population dynamics and fisheries management,
ISIS-fish, to describe the coastal commercial fish population, the white
seabream (Diplodus sargus) in the highly artificialized Bay of Toulon. Using
ISIS-Fish, we simulated rehabilitation and fisheries management scenarios. We
provided the first quantitative assessment of the implementation of artificial
structures in ports covering 10% and 100% of the available port area and
compared, at population level and fishing fleets level, the quantitative
consequences of these rehabilitation measures with fishing control measures
leading to strict compliance with minimum catch sizes. The rehabilitation of
the nursery function in ports demonstrated a potential to enhance the renewal
of fish populations and catches. When the size of projects is small the
outcomes they provide remain relatively modest in contrast to the impact of
regulatory fishing measures. However, we have demonstrated that combining
fishing reduction measures and rehabilitation projects has a synergistic effect
on fish populations, resulting in increased populations and catches. This study
is the first quantitative assessment of fish nursery rehabilitation projects in
port areas, by evaluating their effectiveness in renewing coastal fish
populations and fisheries and comparing their outcomes with fishing control
measures. Small-scale port-area nursery rehabilitation projects can support
fish populations, but are less effective than controlling fisheries.
| [
{
"created": "Thu, 13 Jun 2024 06:40:24 GMT",
"version": "v1"
}
] | 2024-06-17 | [
[
"Joubert",
"Etienne",
""
],
[
"Sève",
"Charlotte",
""
],
[
"Mahévas",
"Stéphanie",
""
],
[
"Bach",
"Adrian",
""
],
[
"Bouchoucha",
"Marc",
""
]
] | Conservation measures are implemented to support biodiversity in areas that are degraded or under anthropogenic pressure. Over the past decade, numerous projects aimed at rehabilitating a fish nursery function in ports, through the installation of artificial structures, have emerged. While studies conducted on these solutions seem promising on a very local scale (e.g., higher densities of juvenile fish on artificial fish nurseries compared to bare port infrastructures), no evaluation has been undertaken yet to establish their contribution to the renewal of coastal fish populations or their performance compared to other conservation measures such as fishing regulation. Here, we used a coupled model of fish population dynamics and fisheries management, ISIS-fish, to describe the coastal commercial fish population, the white seabream (Diplodus sargus) in the highly artificialized Bay of Toulon. Using ISIS-Fish, we simulated rehabilitation and fisheries management scenarios. We provided the first quantitative assessment of the implementation of artificial structures in ports covering 10% and 100% of the available port area and compared, at population level and fishing fleets level, the quantitative consequences of these rehabilitation measures with fishing control measures leading to strict compliance with minimum catch sizes. The rehabilitation of the nursery function in ports demonstrated a potential to enhance the renewal of fish populations and catches. When the size of projects is small the outcomes they provide remain relatively modest in contrast to the impact of regulatory fishing measures. However, we have demonstrated that combining fishing reduction measures and rehabilitation projects has a synergistic effect on fish populations, resulting in increased populations and catches. This study is the first quantitative assessment of fish nursery rehabilitation projects in port areas, by evaluating their effectiveness in renewing coastal fish populations and fisheries and comparing their outcomes with fishing control measures. Small-scale port-area nursery rehabilitation projects can support fish populations, but are less effective than controlling fisheries. |
0810.4839 | Matti Peltom\"aki | Matti Peltomaki, Martin Rost, and Mikko Alava | Oscillations and patterns in interacting populations of two species | 4 pages, 4 figures, accepted for publication in Phys. Rev. E as a
Rapid Communication | Physical Review E 78, 050903(R) (2008) | 10.1103/PhysRevE.78.050903 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Interacting populations often create complicated spatiotemporal behavior, and
understanding it is a basic problem in the dynamics of spatial systems. We
study the two-species case by simulations of a host--parasitoid model. In the
case of co-existence, there are spatial patterns leading to noise-sustained
oscillations. We introduce a new measure for the patterns, and explain the
oscillations as a consequence of a timescale separation and noise. They are
linked together with the patterns by letting the spreading rates depend on
instantaneous population densities. Applications are discussed.
| [
{
"created": "Mon, 27 Oct 2008 15:26:40 GMT",
"version": "v1"
}
] | 2009-01-16 | [
[
"Peltomaki",
"Matti",
""
],
[
"Rost",
"Martin",
""
],
[
"Alava",
"Mikko",
""
]
] | Interacting populations often create complicated spatiotemporal behavior, and understanding it is a basic problem in the dynamics of spatial systems. We study the two-species case by simulations of a host--parasitoid model. In the case of co-existence, there are spatial patterns leading to noise-sustained oscillations. We introduce a new measure for the patterns, and explain the oscillations as a consequence of a timescale separation and noise. They are linked together with the patterns by letting the spreading rates depend on instantaneous population densities. Applications are discussed. |
2301.09548 | William Sulis | William Sulis | Collective Intelligence and Neurodynamics: Functional Homologies | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-sa/4.0/ | A deep understanding of the dynamics of the human nervous system requires the
simultaneous study of multiple spatiotemporal scales from the level of
neurotransmitters up to the level of human cultures. This is likely impossible
for technical and ethical reasons. Piecemeal analysis provides some
understanding of the dynamics at single levels, but this does not illuminate
the interactions between levels which are, at the very least, of great
importance clinically. It would be useful to have an accessible biological
system which could serve as a proxy for the nervous system and from which
useful insights might be obtained. Functional homologies between the nervous
system and collective intelligence systems, in particular social insect
colonies, are described. It is proposed that social insects colonies could
serve as functional proxies for nervous systems. Thus a multiscale study of
social insect colonies may provide insights into the dynamics of nervous
systems.
| [
{
"created": "Mon, 23 Jan 2023 17:06:06 GMT",
"version": "v1"
}
] | 2023-01-24 | [
[
"Sulis",
"William",
""
]
] | A deep understanding of the dynamics of the human nervous system requires the simultaneous study of multiple spatiotemporal scales from the level of neurotransmitters up to the level of human cultures. This is likely impossible for technical and ethical reasons. Piecemeal analysis provides some understanding of the dynamics at single levels, but this does not illuminate the interactions between levels which are, at the very least, of great importance clinically. It would be useful to have an accessible biological system which could serve as a proxy for the nervous system and from which useful insights might be obtained. Functional homologies between the nervous system and collective intelligence systems, in particular social insect colonies, are described. It is proposed that social insects colonies could serve as functional proxies for nervous systems. Thus a multiscale study of social insect colonies may provide insights into the dynamics of nervous systems. |
1102.0566 | Vadas Gintautas | Vadas Gintautas, Michael I. Ham, Benjamin Kunsberg, Shawn Barr, Steven
P. Brumby, Craig Rasmussen, John S. George, Ilya Nemenman, Luis M. A.
Bettencourt, Garrett T. Kenyon | Model Cortical Association Fields Account for the Time Course and
Dependence on Target Complexity of Human Contour Perception | 18 pages, 8 figures | PLoS Comput Biol 7(10): e1002162, 2011 | 10.1371/journal.pcbi.1002162 | LA-UR 11-00499 | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Can lateral connectivity in the primary visual cortex account for the time
dependence and intrinsic task difficulty of human contour detection? To answer
this question, we created a synthetic image set that prevents sole reliance on
either low-level visual features or high-level context for the detection of
target objects. Rendered images consist of smoothly varying, globally aligned
contour fragments (amoebas) distributed among groups of randomly rotated
fragments (clutter). The time course and accuracy of amoeba detection by humans
was measured using a two-alternative forced choice protocol with self-reported
confidence and variable image presentation time (20-200 ms), followed by an
image mask optimized so as to interrupt visual processing. Measured
psychometric functions were well fit by sigmoidal functions with exponential
time constants of 30-91 ms, depending on amoeba complexity. Key aspects of the
psychophysical experiments were accounted for by a computational network model,
in which simulated responses across retinotopic arrays of orientation-selective
elements were modulated by cortical association fields, represented as
multiplicative kernels computed from the differences in pairwise edge
statistics between target and distractor images. Comparing the experimental and
the computational results suggests that each iteration of the lateral
interactions takes at least 37.5 ms of cortical processing time. Our results
provide evidence that cortical association fields between orientation selective
elements in early visual areas can account for important temporal and
task-dependent aspects of the psychometric curves characterizing human contour
perception, with the remaining discrepancies postulated to arise from the
influence of higher cortical areas.
| [
{
"created": "Wed, 2 Feb 2011 21:10:02 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Jul 2011 15:57:22 GMT",
"version": "v2"
}
] | 2014-05-06 | [
[
"Gintautas",
"Vadas",
""
],
[
"Ham",
"Michael I.",
""
],
[
"Kunsberg",
"Benjamin",
""
],
[
"Barr",
"Shawn",
""
],
[
"Brumby",
"Steven P.",
""
],
[
"Rasmussen",
"Craig",
""
],
[
"George",
"John S.",
""
],
[
"Nemenman",
"Ilya",
""
],
[
"Bettencourt",
"Luis M. A.",
""
],
[
"Kenyon",
"Garrett T.",
""
]
] | Can lateral connectivity in the primary visual cortex account for the time dependence and intrinsic task difficulty of human contour detection? To answer this question, we created a synthetic image set that prevents sole reliance on either low-level visual features or high-level context for the detection of target objects. Rendered images consist of smoothly varying, globally aligned contour fragments (amoebas) distributed among groups of randomly rotated fragments (clutter). The time course and accuracy of amoeba detection by humans was measured using a two-alternative forced choice protocol with self-reported confidence and variable image presentation time (20-200 ms), followed by an image mask optimized so as to interrupt visual processing. Measured psychometric functions were well fit by sigmoidal functions with exponential time constants of 30-91 ms, depending on amoeba complexity. Key aspects of the psychophysical experiments were accounted for by a computational network model, in which simulated responses across retinotopic arrays of orientation-selective elements were modulated by cortical association fields, represented as multiplicative kernels computed from the differences in pairwise edge statistics between target and distractor images. Comparing the experimental and the computational results suggests that each iteration of the lateral interactions takes at least 37.5 ms of cortical processing time. Our results provide evidence that cortical association fields between orientation selective elements in early visual areas can account for important temporal and task-dependent aspects of the psychometric curves characterizing human contour perception, with the remaining discrepancies postulated to arise from the influence of higher cortical areas. |
1403.2753 | Rhea Kimpo | Rhea R. Kimpo, Jacob M. Rinaldi, Christina K. Kim, Hannah L. Payne and
Jennifer L. Raymond | Gating of neural error signals during motor learning | To be published in eLife | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cerebellar climbing fiber activity encodes performance errors during many
motor learning tasks, but the role of these error signals in learning has been
controversial. We compared two motor learning paradigms that elicited equally
robust putative error signals in the same climbing fibers: learned increases
and decreases in the gain of the vestibulo-ocular reflex (VOR). During
VOR-increase training, climbing fiber activity on one trial predicted changes
in cerebellar output on the next trial, and optogenetic activation of climbing
fibers to mimic their encoding of performance errors was sufficient to implant
a motor memory. In contrast, during VOR-decrease training, there was no
trial-by-trial correlation between climbing fiber activity and changes in
cerebellar output, and climbing fiber activation did not induce VOR-decrease
learning. Comparison of the two training paradigms suggests that the ability of
climbing fibers to induce plasticity can be dynamically gated in vivo by the
state of the cerebellar circuit, even under conditions where the climbing
fibers are robustly activated by performance errors.
| [
{
"created": "Tue, 11 Mar 2014 20:57:03 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Mar 2014 15:55:33 GMT",
"version": "v2"
}
] | 2014-03-18 | [
[
"Kimpo",
"Rhea R.",
""
],
[
"Rinaldi",
"Jacob M.",
""
],
[
"Kim",
"Christina K.",
""
],
[
"Payne",
"Hannah L.",
""
],
[
"Raymond",
"Jennifer L.",
""
]
] | Cerebellar climbing fiber activity encodes performance errors during many motor learning tasks, but the role of these error signals in learning has been controversial. We compared two motor learning paradigms that elicited equally robust putative error signals in the same climbing fibers: learned increases and decreases in the gain of the vestibulo-ocular reflex (VOR). During VOR-increase training, climbing fiber activity on one trial predicted changes in cerebellar output on the next trial, and optogenetic activation of climbing fibers to mimic their encoding of performance errors was sufficient to implant a motor memory. In contrast, during VOR-decrease training, there was no trial-by-trial correlation between climbing fiber activity and changes in cerebellar output, and climbing fiber activation did not induce VOR-decrease learning. Comparison of the two training paradigms suggests that the ability of climbing fibers to induce plasticity can be dynamically gated in vivo by the state of the cerebellar circuit, even under conditions where the climbing fibers are robustly activated by performance errors. |
1312.0688 | Michael Manhart | Allan Haldane, Michael Manhart, and Alexandre V. Morozov | Biophysical Fitness Landscapes for Transcription Factor Binding Sites | null | PLoS Comput Biol 10:e1003683, 2014 | 10.1371/journal.pcbi.1003683 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evolutionary trajectories and phenotypic states available to cell populations
are ultimately dictated by intermolecular interactions between DNA, RNA,
proteins, and other molecular species. Here we study how evolution of gene
regulation in a single-cell eukaryote S. cerevisiae is affected by the
interactions between transcription factors (TFs) and their cognate genomic
sites. Our study is informed by high-throughput in vitro measurements of TF-DNA
binding interactions and by a comprehensive collection of genomic binding
sites. Using an evolutionary model for monomorphic populations evolving on a
fitness landscape, we infer fitness as a function of TF-DNA binding energy for
a collection of 12 yeast TFs, and show that the shape of the predicted fitness
functions is in broad agreement with a simple thermodynamic model of two-state
TF-DNA binding. However, the effective temperature of the model is not always
equal to the physical temperature, indicating selection pressures in addition
to biophysical constraints caused by TF-DNA interactions. We find little
statistical support for the fitness landscape in which each position in the
binding site evolves independently, showing that epistasis is common in
evolution of gene regulation. Finally, by correlating TF-DNA binding energies
with biological properties of the sites or the genes they regulate, we are able
to rule out several scenarios of site-specific selection, under which binding
sites of the same TF would experience a spectrum of selection pressures
depending on their position in the genome. These findings argue for the
existence of universal fitness landscapes which shape evolution of all sites
for a given TF, and whose properties are determined in part by the physics of
protein-DNA interactions.
| [
{
"created": "Tue, 3 Dec 2013 03:23:25 GMT",
"version": "v1"
}
] | 2014-08-19 | [
[
"Haldane",
"Allan",
""
],
[
"Manhart",
"Michael",
""
],
[
"Morozov",
"Alexandre V.",
""
]
] | Evolutionary trajectories and phenotypic states available to cell populations are ultimately dictated by intermolecular interactions between DNA, RNA, proteins, and other molecular species. Here we study how evolution of gene regulation in a single-cell eukaryote S. cerevisiae is affected by the interactions between transcription factors (TFs) and their cognate genomic sites. Our study is informed by high-throughput in vitro measurements of TF-DNA binding interactions and by a comprehensive collection of genomic binding sites. Using an evolutionary model for monomorphic populations evolving on a fitness landscape, we infer fitness as a function of TF-DNA binding energy for a collection of 12 yeast TFs, and show that the shape of the predicted fitness functions is in broad agreement with a simple thermodynamic model of two-state TF-DNA binding. However, the effective temperature of the model is not always equal to the physical temperature, indicating selection pressures in addition to biophysical constraints caused by TF-DNA interactions. We find little statistical support for the fitness landscape in which each position in the binding site evolves independently, showing that epistasis is common in evolution of gene regulation. Finally, by correlating TF-DNA binding energies with biological properties of the sites or the genes they regulate, we are able to rule out several scenarios of site-specific selection, under which binding sites of the same TF would experience a spectrum of selection pressures depending on their position in the genome. These findings argue for the existence of universal fitness landscapes which shape evolution of all sites for a given TF, and whose properties are determined in part by the physics of protein-DNA interactions. |
1012.1684 | Ling Xue Ms | Ling Xue, H. Morgan Scott, Lee. Cohnstaedt, Caterina Scoglio | A Network-Based Meta-Population Approach to Model Rift Valley Fever
Epidemics | published on Journal of Theoretical biology | J Theor Biol. 2012, 306:129-44 | 10.1016/j.jtbi.2012.04.029 | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rift Valley fever virus (RVFV) has been expanding its geographical
distribution with important implications for both human and animal health. The
emergence of Rift Valley fever (RVF) in the Middle East, and its continuing
presence in many areas of Africa, has negatively impacted both medical and
veterinary infrastructures and human health. Furthermore, worldwide attention
should be directed towards the broader infection dynamics of RVFV. We propose a
new compartmentalized model of RVF and the related ordinary differential
equations to assess disease spread in both time and space; with the latter
driven as a function of contact networks. The model is based on weighted
contact networks, where nodes of the networks represent geographical regions
and the weights represent the level of contact between regional pairings for
each set of species. The inclusion of human, animal, and vector movements among
regions is new to RVF modeling. The movement of the infected individuals is not
only treated as a possibility, but also an actuality that can be incorporated
into the model. We have tested, calibrated, and evaluated the model using data
from the recent 2010 RVF outbreak in South Africa as a case study; mapping the
epidemic spread within and among three South African provinces. An extensive
set of simulation results shows the potential of the proposed approach for
accurately modeling the RVF spreading process in additional regions of the
world. The benefits of the proposed model are twofold: not only can the model
differentiate the maximum number of infected individuals among different
provinces, but also it can reproduce the different starting times of the
outbreak in multiple locations. Finally, the exact value of the reproduction
number is numerically computed and upper and lower bounds for the reproduction
number are analytically derived in the case of homogeneous populations.
| [
{
"created": "Wed, 8 Dec 2010 04:38:58 GMT",
"version": "v1"
},
{
"created": "Sun, 25 Sep 2011 03:16:38 GMT",
"version": "v2"
},
{
"created": "Mon, 10 Sep 2012 15:29:24 GMT",
"version": "v3"
}
] | 2015-03-17 | [
[
"Xue",
"Ling",
""
],
[
"Scott",
"H. Morgan",
""
],
[
"Cohnstaedt",
"Lee.",
""
],
[
"Scoglio",
"Caterina",
""
]
] | Rift Valley fever virus (RVFV) has been expanding its geographical distribution with important implications for both human and animal health. The emergence of Rift Valley fever (RVF) in the Middle East, and its continuing presence in many areas of Africa, has negatively impacted both medical and veterinary infrastructures and human health. Furthermore, worldwide attention should be directed towards the broader infection dynamics of RVFV. We propose a new compartmentalized model of RVF and the related ordinary differential equations to assess disease spread in both time and space; with the latter driven as a function of contact networks. The model is based on weighted contact networks, where nodes of the networks represent geographical regions and the weights represent the level of contact between regional pairings for each set of species. The inclusion of human, animal, and vector movements among regions is new to RVF modeling. The movement of the infected individuals is not only treated as a possibility, but also an actuality that can be incorporated into the model. We have tested, calibrated, and evaluated the model using data from the recent 2010 RVF outbreak in South Africa as a case study; mapping the epidemic spread within and among three South African provinces. An extensive set of simulation results shows the potential of the proposed approach for accurately modeling the RVF spreading process in additional regions of the world. The benefits of the proposed model are twofold: not only can the model differentiate the maximum number of infected individuals among different provinces, but also it can reproduce the different starting times of the outbreak in multiple locations. Finally, the exact value of the reproduction number is numerically computed and upper and lower bounds for the reproduction number are analytically derived in the case of homogeneous populations. |
1710.07880 | Anastasios Matzavinos | Karen Larson, Clark Bowman, Zhizhong Chen, Panagiotis Hadjidoukas,
Costas Papadimitriou, Petros Koumoutsakos, Anastasios Matzavinos | Data-driven prediction and origin identification of epidemics in
population networks | null | null | null | null | q-bio.PE q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Effective intervention strategies for epidemics rely on the identification of
their origin and on the robustness of the predictions made by network disease
models. We introduce a Bayesian uncertainty quantification framework to infer
model parameters for a disease spreading on a network of communities from
limited, noisy observations; the state-of-the-art computational framework
compensates for the model complexity by exploiting massively parallel computing
architectures. Using noisy, synthetic data, we show the potential of the
approach to perform robust model fitting and additionally demonstrate that we
can effectively identify the disease origin via Bayesian model selection. As
disease-related data are increasingly available, the proposed framework has
broad practical relevance for the prediction and management of epidemics.
| [
{
"created": "Sun, 22 Oct 2017 03:24:20 GMT",
"version": "v1"
},
{
"created": "Wed, 1 Apr 2020 04:53:21 GMT",
"version": "v2"
}
] | 2020-04-02 | [
[
"Larson",
"Karen",
""
],
[
"Bowman",
"Clark",
""
],
[
"Chen",
"Zhizhong",
""
],
[
"Hadjidoukas",
"Panagiotis",
""
],
[
"Papadimitriou",
"Costas",
""
],
[
"Koumoutsakos",
"Petros",
""
],
[
"Matzavinos",
"Anastasios",
""
]
] | Effective intervention strategies for epidemics rely on the identification of their origin and on the robustness of the predictions made by network disease models. We introduce a Bayesian uncertainty quantification framework to infer model parameters for a disease spreading on a network of communities from limited, noisy observations; the state-of-the-art computational framework compensates for the model complexity by exploiting massively parallel computing architectures. Using noisy, synthetic data, we show the potential of the approach to perform robust model fitting and additionally demonstrate that we can effectively identify the disease origin via Bayesian model selection. As disease-related data are increasingly available, the proposed framework has broad practical relevance for the prediction and management of epidemics. |
2111.03988 | ChangHwan Lee | Nimmy S. John, Michelle A. Urman and ChangHwan Lee | 4Dia: A tool for automated 4D microscope image alignment | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Recent advances in microscopy enable three-dimensional live imaging at a high
resolution. Long-term live imaging of a multicellular organism requires
immobilization of the organism under stable physiological conditions. Despite
proper immobilization, challenges remain within live imaging data analysis due
to other intrinsic and extrinsic dynamics, which can result in misalignments of
an image series over time. 4Dia, an ImageJ/Fiji macro script, aligns 3D
timelapse images through Z-stacks as well as over time using any user selected
channel. 4Dia can be used for essentially any tissue sample with no limit on
the size of Z-stack or the number of timepoints.
| [
{
"created": "Sun, 7 Nov 2021 02:39:48 GMT",
"version": "v1"
}
] | 2021-11-09 | [
[
"John",
"Nimmy S.",
""
],
[
"Urman",
"Michelle A.",
""
],
[
"Lee",
"ChangHwan",
""
]
] | Recent advances in microscopy enable three-dimensional live imaging at a high resolution. Long-term live imaging of a multicellular organism requires immobilization of the organism under stable physiological conditions. Despite proper immobilization, challenges remain within live imaging data analysis due to other intrinsic and extrinsic dynamics, which can result in misalignments of an image series over time. 4Dia, an ImageJ/Fiji macro script, aligns 3D timelapse images through Z-stacks as well as over time using any user selected channel. 4Dia can be used for essentially any tissue sample with no limit on the size of Z-stack or the number of timepoints. |
1202.2448 | Artem Novozhilov S | Artem S. Novozhilov | Epidemiological models with parametric heterogeneity: Deterministic
theory for closed populations | 26 pages, 6 figures, submitted to Mathematical Modelling of Natural
Phenomena | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a unified mathematical approach to epidemiological models with
parametric heterogeneity, i.e., to the models that describe individuals in the
population as having specific parameter (trait) values that vary from one
individuals to another. This is a natural framework to model, e.g.,
heterogeneity in susceptibility or infectivity of individuals. We review, along
with the necessary theory, the results obtained using the discussed approach.
In particular, we formulate and analyze an SIR model with distributed
susceptibility and infectivity, showing that the epidemiological models for
closed populations are well suited to the suggested framework. A number of
known results from the literature is derived, including the final epidemic size
equation for an SIR model with distributed susceptibility. It is proved that
the bottom up approach of the theory of heterogeneous populations with
parametric heterogeneity allows to infer the population level description,
which was previously used without a firm mechanistic basis; in particular, the
power law transmission function is shown to be a consequence of the initial
gamma distributed susceptibility and infectivity. We discuss how the general
theory can be applied to the modeling goals to include the heterogeneous
contact population structure and provide analysis of an SI model with
heterogeneous contacts. We conclude with a number of open questions and
promising directions, where the theory of heterogeneous populations can lead to
important simplifications and generalizations.
| [
{
"created": "Sat, 11 Feb 2012 15:01:42 GMT",
"version": "v1"
}
] | 2012-02-14 | [
[
"Novozhilov",
"Artem S.",
""
]
] | We present a unified mathematical approach to epidemiological models with parametric heterogeneity, i.e., to the models that describe individuals in the population as having specific parameter (trait) values that vary from one individuals to another. This is a natural framework to model, e.g., heterogeneity in susceptibility or infectivity of individuals. We review, along with the necessary theory, the results obtained using the discussed approach. In particular, we formulate and analyze an SIR model with distributed susceptibility and infectivity, showing that the epidemiological models for closed populations are well suited to the suggested framework. A number of known results from the literature is derived, including the final epidemic size equation for an SIR model with distributed susceptibility. It is proved that the bottom up approach of the theory of heterogeneous populations with parametric heterogeneity allows to infer the population level description, which was previously used without a firm mechanistic basis; in particular, the power law transmission function is shown to be a consequence of the initial gamma distributed susceptibility and infectivity. We discuss how the general theory can be applied to the modeling goals to include the heterogeneous contact population structure and provide analysis of an SI model with heterogeneous contacts. We conclude with a number of open questions and promising directions, where the theory of heterogeneous populations can lead to important simplifications and generalizations. |
1410.8596 | Tatiana T. Marquez-Lago | Atiyo Ghosh and Tatiana T. Marquez-Lago | Simulating Stochastic Reaction-Diffusion Systems on and within Moving
Boundaries | 22 pages, 7 figures | null | 10.1371/journal.pone.0133401 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Chemical reactions inside cells are generally considered to happen within
fixed-size compartments. Needless to say, cells and their compartments are
highly dynamic. Thus, such stringent assumptions may not reflect biochemical
reality, and can highly bias conclusions from simulation studies. In this work,
we present an intuitive algorithm for particle-based diffusion in and on moving
boundaries, for both point particles and spherical particles. We first
benchmark in appropriate scenarios our proposed stochastic method against
solutions of partial differential equations, and further demonstrate that
moving boundaries can give rise to super diffusive motion as well as
time-inhomogeneous reaction rates. Finally, we conduct a numerical experiment
representing photobleaching of diffusing fluorescent proteins in dividing
Saccharomyces cerevisiae cells to demonstrate that moving boundaries might
cause important effects neglected in previously published studies.
| [
{
"created": "Fri, 31 Oct 2014 00:18:02 GMT",
"version": "v1"
}
] | 2016-02-17 | [
[
"Ghosh",
"Atiyo",
""
],
[
"Marquez-Lago",
"Tatiana T.",
""
]
] | Chemical reactions inside cells are generally considered to happen within fixed-size compartments. Needless to say, cells and their compartments are highly dynamic. Thus, such stringent assumptions may not reflect biochemical reality, and can highly bias conclusions from simulation studies. In this work, we present an intuitive algorithm for particle-based diffusion in and on moving boundaries, for both point particles and spherical particles. We first benchmark in appropriate scenarios our proposed stochastic method against solutions of partial differential equations, and further demonstrate that moving boundaries can give rise to super diffusive motion as well as time-inhomogeneous reaction rates. Finally, we conduct a numerical experiment representing photobleaching of diffusing fluorescent proteins in dividing Saccharomyces cerevisiae cells to demonstrate that moving boundaries might cause important effects neglected in previously published studies. |
1010.0888 | Jaewook Joo | Jaewook Joo, Steven J. Plimpton, Jean-Loup Faulon | Noise-induced oscillatory shuttling of NF-{\kappa}B in a two compartment
IKK-NF-{\kappa}B-I{\kappa}B-A20 signaling model | 49 pages, 12 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | NF-{\kappa}B is a pleiotropic protein whose nucleo-cytoplasmic trafficking is
tightly regulated by multiple negative feedback loops embedded in the
NF-{\kappa}B signaling network and contributes to diverse gene expression
profiles important in immune cell differentiation, cell apoptosis, and innate
immunity. The intracellular signaling processes and their control mechanisms,
however, are susceptible to both extrinsic and intrinsic noise. In this
article, we present numerical evidence for a universal dynamic behavior of
NF-{\kappa}B, namely oscillatory nucleo-cytoplasmic shuttling, due to the
fundamentally stochastic nature of the NF-{\kappa}B signaling network. We
simulated the effect of extrinsic noise with a deterministic ODE model, using a
statistical ensemble approach, generating many copies of the signaling network
with different kinetic rates sampled from a biologically feasible parameter
space. We modeled the effect of intrinsic noise by simulating the same networks
stochastically using the Gillespie algorithm. The results demonstrate that
extrinsic noise diversifies the shuttling patterns of NF-{\kappa}B response,
whereas intrinsic noise induces oscillatory behavior in many of the otherwise
non-oscillatory patterns. We identify two key model parameters which
significantly affect the NF-{\kappa}B dynamic response and deduce a
two-dimensional phase-diagram of the NF-{\kappa}B response as a function of
these parameters. We conclude that if single-cell experiments are performed, a
rich variety of NF-{\kappa}B response will be observed, even if
population-level experiments, which average response over large numbers of
cells, do not evidence oscillatory behavior.
| [
{
"created": "Tue, 5 Oct 2010 14:01:43 GMT",
"version": "v1"
}
] | 2010-10-06 | [
[
"Joo",
"Jaewook",
""
],
[
"Plimpton",
"Steven J.",
""
],
[
"Faulon",
"Jean-Loup",
""
]
] | NF-{\kappa}B is a pleiotropic protein whose nucleo-cytoplasmic trafficking is tightly regulated by multiple negative feedback loops embedded in the NF-{\kappa}B signaling network and contributes to diverse gene expression profiles important in immune cell differentiation, cell apoptosis, and innate immunity. The intracellular signaling processes and their control mechanisms, however, are susceptible to both extrinsic and intrinsic noise. In this article, we present numerical evidence for a universal dynamic behavior of NF-{\kappa}B, namely oscillatory nucleo-cytoplasmic shuttling, due to the fundamentally stochastic nature of the NF-{\kappa}B signaling network. We simulated the effect of extrinsic noise with a deterministic ODE model, using a statistical ensemble approach, generating many copies of the signaling network with different kinetic rates sampled from a biologically feasible parameter space. We modeled the effect of intrinsic noise by simulating the same networks stochastically using the Gillespie algorithm. The results demonstrate that extrinsic noise diversifies the shuttling patterns of NF-{\kappa}B response, whereas intrinsic noise induces oscillatory behavior in many of the otherwise non-oscillatory patterns. We identify two key model parameters which significantly affect the NF-{\kappa}B dynamic response and deduce a two-dimensional phase-diagram of the NF-{\kappa}B response as a function of these parameters. We conclude that if single-cell experiments are performed, a rich variety of NF-{\kappa}B response will be observed, even if population-level experiments, which average response over large numbers of cells, do not evidence oscillatory behavior. |
1808.07852 | Misha Katsnelson | Mikhail I. Katsnelson, Yuri I. Wolf, Eugene V. Koonin | On the feasibility of saltational evolution | Extended version, in particular, the section is added on
non-equilibrium model of stress-induced mutagenesis | null | 10.1073/pnas.1909031116 | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Is evolution always gradual or can it make leaps? We examine a mathematical
model of an evolutionary process on a fitness landscape and obtain analytic
solutions for the probability of multi-mutation leaps, that is, several
mutations occurring simultaneously, within a single generation in one genome,
and being fixed all together in the evolving population. The results indicate
that, for typical, empirically observed combinations of the parameters of the
evolutionary process, namely, effective population size, mutation rate, and
distribution of selection coefficients of mutations, the probability of a
multi-mutation leap is low, and accordingly, the contribution of such leaps is
minor at best. However, we show that, taking sign epistasis into account, leaps
could become an important factor of evolution in cases of substantially
elevated mutation rates, such as stress-induced mutagenesis in microbes. We
hypothesize that stress-induced mutagenesis is an evolvable adaptive strategy.
| [
{
"created": "Thu, 23 Aug 2018 17:24:45 GMT",
"version": "v1"
},
{
"created": "Mon, 20 May 2019 09:47:00 GMT",
"version": "v2"
}
] | 2022-10-12 | [
[
"Katsnelson",
"Mikhail I.",
""
],
[
"Wolf",
"Yuri I.",
""
],
[
"Koonin",
"Eugene V.",
""
]
] | Is evolution always gradual or can it make leaps? We examine a mathematical model of an evolutionary process on a fitness landscape and obtain analytic solutions for the probability of multi-mutation leaps, that is, several mutations occurring simultaneously, within a single generation in one genome, and being fixed all together in the evolving population. The results indicate that, for typical, empirically observed combinations of the parameters of the evolutionary process, namely, effective population size, mutation rate, and distribution of selection coefficients of mutations, the probability of a multi-mutation leap is low, and accordingly, the contribution of such leaps is minor at best. However, we show that, taking sign epistasis into account, leaps could become an important factor of evolution in cases of substantially elevated mutation rates, such as stress-induced mutagenesis in microbes. We hypothesize that stress-induced mutagenesis is an evolvable adaptive strategy. |
1310.7619 | Junghyo Jo | Hyunsuk Hong and Junghyo Jo and Sang-Jin Sin | Stable and flexible system for glucose homeostasis | 6 pages, 3 figures, accepted in PRE | null | 10.1103/PhysRevE.88.032711 | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pancreatic islets, controlling glucose homeostasis, consist of \alpha, \beta,
and \delta\ cells. It has been observed that \alpha\ and \beta\ cells generate
out-of-phase synchronization in the release of glucagon and insulin,
counter-regulatory hormones for increasing and decreasing glucose levels, while
\beta\ and \delta\ cells produce in-phase synchronization in the release of the
insulin and somatostatin. Pieces of interactions between the islet cells have
been observed for a long time, although their physiological role as a whole has
not been explored yet. We model the synchronized hormone pulses of islets with
coupled phase oscillators that incorporate the observed cellular interactions.
The integrated model shows that the interaction from \beta\ to \delta\ cells,
of which sign has controversial reports, should be positive to reproduce the
in-phase synchronization between \beta\ and \delta\ cells. The model also
suggests that \delta\ cells help the islet system flexibly respond to changes
of glucose environment.
| [
{
"created": "Fri, 6 Sep 2013 01:30:12 GMT",
"version": "v1"
}
] | 2015-06-17 | [
[
"Hong",
"Hyunsuk",
""
],
[
"Jo",
"Junghyo",
""
],
[
"Sin",
"Sang-Jin",
""
]
] | Pancreatic islets, controlling glucose homeostasis, consist of \alpha, \beta, and \delta\ cells. It has been observed that \alpha\ and \beta\ cells generate out-of-phase synchronization in the release of glucagon and insulin, counter-regulatory hormones for increasing and decreasing glucose levels, while \beta\ and \delta\ cells produce in-phase synchronization in the release of the insulin and somatostatin. Pieces of interactions between the islet cells have been observed for a long time, although their physiological role as a whole has not been explored yet. We model the synchronized hormone pulses of islets with coupled phase oscillators that incorporate the observed cellular interactions. The integrated model shows that the interaction from \beta\ to \delta\ cells, of which sign has controversial reports, should be positive to reproduce the in-phase synchronization between \beta\ and \delta\ cells. The model also suggests that \delta\ cells help the islet system flexibly respond to changes of glucose environment. |
2105.10074 | Alessio D'Alessandro | Alessio D'Alessandro | High rate of SARS-CoV2 nonsense spike genomes coding for prematurely
truncated proteins | A systematic sequencing error in the original long-read data
(recurrent insertions/deletions in T or A homopolymer regions) appeared to
survive the consensus-based error-cleaning procedure and to significantly
affect results. Moreover, at page 20, the theoretical model of prematurely
truncated genomes needs an integration to account for the host's innate
immune response besides antibody response | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by-sa/4.0/ | Replication of SARS-CoV2 virions is an error-prone process which may
eventually generate a percentage of impaired protein copies with complete lack
of functionality. For instance, after RNA mis-replication, a very premature
stop codon (UAG, UAA, UGA) coding for a prematurely truncated
(nonsense-mutated) spike protein may occur.
In the natural virus replication process via cell infection, the nonsense
genomes are corrected by the proofreading enzymes of the virus, strongly
penalized by natural selection and condemned to a very short life by the host
cell's mRNA watching mechanisms. However, for the very long spike genome of
1273 codons, a truncated non-functional spike protein may potentially still
occur with a high frequency, even in presence of a low mutation rate per single
nucleotide.
With this paper, a hi-fidelity post-processing of SARS-CoV2 spike sequences
is provided: in ex-vivo samples from patients, an impressively high rate of
26\% of prematurely-stopped (nonsense-mutated) spike genomes sequences due to
insertions/deletions is found, compared with a 9.7\% obtained from in-vitro
cell culture.
A general warning on the possible high rate of prematurely-stopped spike
protein sequences is also raised for "artificial" de novo DNA synthesis
processes of SARS-CoV2 spike genomes with no associated natural
proofreading/selection, possibly including vaccine preparations.
Finally, a metric based on the ratio between prematurely stopped and "normal"
genomes is proposed as a potential host-independent variant-watching tool, able
to classify the infectivity of new spike mutations.
| [
{
"created": "Fri, 21 May 2021 00:45:16 GMT",
"version": "v1"
},
{
"created": "Mon, 7 Jun 2021 07:49:42 GMT",
"version": "v2"
}
] | 2021-06-08 | [
[
"D'Alessandro",
"Alessio",
""
]
] | Replication of SARS-CoV2 virions is an error-prone process which may eventually generate a percentage of impaired protein copies with complete lack of functionality. For instance, after RNA mis-replication, a very premature stop codon (UAG, UAA, UGA) coding for a prematurely truncated (nonsense-mutated) spike protein may occur. In the natural virus replication process via cell infection, the nonsense genomes are corrected by the proofreading enzymes of the virus, strongly penalized by natural selection and condemned to a very short life by the host cell's mRNA watching mechanisms. However, for the very long spike genome of 1273 codons, a truncated non-functional spike protein may potentially still occur with a high frequency, even in presence of a low mutation rate per single nucleotide. With this paper, a hi-fidelity post-processing of SARS-CoV2 spike sequences is provided: in ex-vivo samples from patients, an impressively high rate of 26\% of prematurely-stopped (nonsense-mutated) spike genomes sequences due to insertions/deletions is found, compared with a 9.7\% obtained from in-vitro cell culture. A general warning on the possible high rate of prematurely-stopped spike protein sequences is also raised for "artificial" de novo DNA synthesis processes of SARS-CoV2 spike genomes with no associated natural proofreading/selection, possibly including vaccine preparations. Finally, a metric based on the ratio between prematurely stopped and "normal" genomes is proposed as a potential host-independent variant-watching tool, able to classify the infectivity of new spike mutations. |
1601.01048 | Iddo Friedberg | Iddo Friedberg and Predrag Radivojac | Community-Wide Evaluation of Computational Function Prediction | Accepted as a book chapter in Methods in Molecular Biology Series.
Publisher: Springer | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | A biological experiment is the most reliable way of assigning function to a
protein. However, in the era of high-throughput sequencing, scientists are
unable to carry out experiments to determine the function of every single gene
product. Therefore, to gain insights into the activity of these molecules and
guide experiments, we must rely on computational means to functionally annotate
the majority of sequence data. To understand how well these algorithms perform,
we have established a challenge involving a broad scientific community in which
we evaluate different annotation methods according to their ability to predict
the associations between previously unannotated protein sequences and Gene
Ontology terms. Here we discuss the rationale, benefits and issues associated
with evaluating computational methods in an ongoing community-wide challenge.
| [
{
"created": "Wed, 6 Jan 2016 02:01:15 GMT",
"version": "v1"
}
] | 2016-01-07 | [
[
"Friedberg",
"Iddo",
""
],
[
"Radivojac",
"Predrag",
""
]
] | A biological experiment is the most reliable way of assigning function to a protein. However, in the era of high-throughput sequencing, scientists are unable to carry out experiments to determine the function of every single gene product. Therefore, to gain insights into the activity of these molecules and guide experiments, we must rely on computational means to functionally annotate the majority of sequence data. To understand how well these algorithms perform, we have established a challenge involving a broad scientific community in which we evaluate different annotation methods according to their ability to predict the associations between previously unannotated protein sequences and Gene Ontology terms. Here we discuss the rationale, benefits and issues associated with evaluating computational methods in an ongoing community-wide challenge. |
1402.3375 | Thaddeus Cybulski R | Thaddeus R. Cybulski, Joshua I. Glaser, Adam H. Marblestone, Bradley
M. Zamft, Edward S. Boyden, George M. Church, Konrad P. Kording | Spatial Information in Large-Scale Neural Recordings | 38 pages, 7 figures | null | 10.3389/fncom.2014.00172 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A central issue in neural recording is that of distinguishing the activities
of many neurons. Here, we develop a framework, based on Fisher information, to
quantify how separable a neuron's activity is from the activities of nearby
neurons. We (1) apply this framework to model information flow and spatial
distinguishability for several electrical and optical neural recording methods,
(2) provide analytic expressions for information content, and (3) demonstrate
potential applications of the approach. This method generalizes to many
recording devices that resolve objects in space and thus may be useful in the
design of next-generation scalable neural recording systems.
| [
{
"created": "Fri, 14 Feb 2014 06:28:06 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Oct 2014 14:41:10 GMT",
"version": "v2"
}
] | 2020-02-04 | [
[
"Cybulski",
"Thaddeus R.",
""
],
[
"Glaser",
"Joshua I.",
""
],
[
"Marblestone",
"Adam H.",
""
],
[
"Zamft",
"Bradley M.",
""
],
[
"Boyden",
"Edward S.",
""
],
[
"Church",
"George M.",
""
],
[
"Kording",
"Konrad P.",
""
]
] | A central issue in neural recording is that of distinguishing the activities of many neurons. Here, we develop a framework, based on Fisher information, to quantify how separable a neuron's activity is from the activities of nearby neurons. We (1) apply this framework to model information flow and spatial distinguishability for several electrical and optical neural recording methods, (2) provide analytic expressions for information content, and (3) demonstrate potential applications of the approach. This method generalizes to many recording devices that resolve objects in space and thus may be useful in the design of next-generation scalable neural recording systems. |
1811.09922 | Takuma Tanaka | Takuma Tanaka | The most probable neural circuit exhibits low-dimensional sustained
activity | 22 pages, 7 figures | null | null | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cortical neurons whose activity is recorded in behavioral experiments has
been classified into several types such as stimulus-related neurons,
delay-period neurons, and reward-related neurons. Moreover, the population
activity of neurons during a reaching task can be described by dynamics in
low-dimensional space. These results suggest that the low-dimensional dynamics
of a few types of neurons emerge in the cerebral cortex that is trained to
perform a task. This study investigates a simple neural circuit emerges with a
few types of neurons. Assuming an infinite number of neurons in the circuit
with connection weights drawn from a Gaussian distribution, we model the
dynamics of neurons by using a kernel function. Given that the system is
infinitely large, almost all capable circuits approximate the most probable
circuit for the task. We simulate two delayed response tasks and a
motor-pattern generation task. The model network exhibits low-dimensional
dynamics in all tasks. Considering that the connection weights are drawn from a
Gaussian distribution, the most probable circuit is the circuit with the
smallest connection weight; that is, the simplest circuit with the simplest
dynamics. Finally, we relate the dynamics to algorithmic information theory.
| [
{
"created": "Sun, 25 Nov 2018 01:42:12 GMT",
"version": "v1"
}
] | 2018-11-27 | [
[
"Tanaka",
"Takuma",
""
]
] | Cortical neurons whose activity is recorded in behavioral experiments has been classified into several types such as stimulus-related neurons, delay-period neurons, and reward-related neurons. Moreover, the population activity of neurons during a reaching task can be described by dynamics in low-dimensional space. These results suggest that the low-dimensional dynamics of a few types of neurons emerge in the cerebral cortex that is trained to perform a task. This study investigates a simple neural circuit emerges with a few types of neurons. Assuming an infinite number of neurons in the circuit with connection weights drawn from a Gaussian distribution, we model the dynamics of neurons by using a kernel function. Given that the system is infinitely large, almost all capable circuits approximate the most probable circuit for the task. We simulate two delayed response tasks and a motor-pattern generation task. The model network exhibits low-dimensional dynamics in all tasks. Considering that the connection weights are drawn from a Gaussian distribution, the most probable circuit is the circuit with the smallest connection weight; that is, the simplest circuit with the simplest dynamics. Finally, we relate the dynamics to algorithmic information theory. |
1905.07606 | Maxwell Bertolero Dr | Maxwell A. Bertolero, Ann Sizemore Blevins, Graham L. Baum, Ruben C.
Gur, Raquel E. Gur, David R. Roalf, Theodore D. Satterthwaite, Danielle S.
Bassett | The human brain's network architecture is genetically encoded by modular
pleiotropy | null | null | null | null | q-bio.NC q-bio.GN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For much of biology, the manner in which genotype maps to phenotype remains a
fundamental mystery. The few maps that are known tend to show modular
pleiotropy: sets of phenotypes are determined by distinct sets of genes. One
key map that has evaded discovery is that of the human brain's network
architecture. Here, we determine the form of this map for gene coexpression and
single nucleotide polymorphisms. We discover that mostly non-overlapping sets
of genes encode the connectivity of brain network modules (or so-called
communities), suggesting that brain network communities demarcate genetic
transitions. We find that these clean boundaries break down at connector hubs,
whose integrative connectivity is encoded by pleiotropic genes from mostly
non-overlapping sets. Broadly, this study opens fundamentally new directions in
the study of genetic encoding of brain development, evolution, and disease.
| [
{
"created": "Sat, 18 May 2019 15:56:50 GMT",
"version": "v1"
},
{
"created": "Sun, 3 Nov 2019 16:09:11 GMT",
"version": "v2"
}
] | 2019-11-05 | [
[
"Bertolero",
"Maxwell A.",
""
],
[
"Blevins",
"Ann Sizemore",
""
],
[
"Baum",
"Graham L.",
""
],
[
"Gur",
"Ruben C.",
""
],
[
"Gur",
"Raquel E.",
""
],
[
"Roalf",
"David R.",
""
],
[
"Satterthwaite",
"Theodore D.",
""
],
[
"Bassett",
"Danielle S.",
""
]
] | For much of biology, the manner in which genotype maps to phenotype remains a fundamental mystery. The few maps that are known tend to show modular pleiotropy: sets of phenotypes are determined by distinct sets of genes. One key map that has evaded discovery is that of the human brain's network architecture. Here, we determine the form of this map for gene coexpression and single nucleotide polymorphisms. We discover that mostly non-overlapping sets of genes encode the connectivity of brain network modules (or so-called communities), suggesting that brain network communities demarcate genetic transitions. We find that these clean boundaries break down at connector hubs, whose integrative connectivity is encoded by pleiotropic genes from mostly non-overlapping sets. Broadly, this study opens fundamentally new directions in the study of genetic encoding of brain development, evolution, and disease. |
2306.05654 | Jianhao Ding | Jianhao Ding, Zhaofei Yu, Tiejun Huang and Jian K. Liu | Spike timing reshapes robustness against attacks in spiking neural
networks | null | null | null | null | q-bio.NC cs.NE | http://creativecommons.org/licenses/by/4.0/ | The success of deep learning in the past decade is partially shrouded in the
shadow of adversarial attacks. In contrast, the brain is far more robust at
complex cognitive tasks. Utilizing the advantage that neurons in the brain
communicate via spikes, spiking neural networks (SNNs) are emerging as a new
type of neural network model, boosting the frontier of theoretical
investigation and empirical application of artificial neural networks and deep
learning. Neuroscience research proposes that the precise timing of neural
spikes plays an important role in the information coding and sensory processing
of the biological brain. However, the role of spike timing in SNNs is less
considered and far from understood. Here we systematically explored the timing
mechanism of spike coding in SNNs, focusing on the robustness of the system
against various types of attacks. We found that SNNs can achieve higher
robustness improvement using the coding principle of precise spike timing in
neural encoding and decoding, facilitated by different learning rules. Our
results suggest that the utility of spike timing coding in SNNs could improve
the robustness against attacks, providing a new approach to reliable coding
principles for developing next-generation brain-inspired deep learning.
| [
{
"created": "Fri, 9 Jun 2023 03:48:57 GMT",
"version": "v1"
}
] | 2023-06-12 | [
[
"Ding",
"Jianhao",
""
],
[
"Yu",
"Zhaofei",
""
],
[
"Huang",
"Tiejun",
""
],
[
"Liu",
"Jian K.",
""
]
] | The success of deep learning in the past decade is partially shrouded in the shadow of adversarial attacks. In contrast, the brain is far more robust at complex cognitive tasks. Utilizing the advantage that neurons in the brain communicate via spikes, spiking neural networks (SNNs) are emerging as a new type of neural network model, boosting the frontier of theoretical investigation and empirical application of artificial neural networks and deep learning. Neuroscience research proposes that the precise timing of neural spikes plays an important role in the information coding and sensory processing of the biological brain. However, the role of spike timing in SNNs is less considered and far from understood. Here we systematically explored the timing mechanism of spike coding in SNNs, focusing on the robustness of the system against various types of attacks. We found that SNNs can achieve higher robustness improvement using the coding principle of precise spike timing in neural encoding and decoding, facilitated by different learning rules. Our results suggest that the utility of spike timing coding in SNNs could improve the robustness against attacks, providing a new approach to reliable coding principles for developing next-generation brain-inspired deep learning. |
1705.10693 | Askery A Canabarro | D. Messias, Iram Gleria, S.S. Albuquerque, Askery Canabarro, H.E.
Stanley | A nonlinear delayed model for the immune response in the presence of
viral mutation | null | null | 10.1016/j.physa.2017.09.088 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we consider a delayed nonlinear model of the dynamics of the
immune system against a viral infection that contains wild-type virus and one
mutant. A finite response time of the immune system was considered in order
which leads to sustained oscillatory behavior as well as chaotic behavior,
triggered by the presence of delays. We present a thoroughly numeric analysis
and some analytical results.
| [
{
"created": "Fri, 26 May 2017 21:44:17 GMT",
"version": "v1"
}
] | 2017-11-22 | [
[
"Messias",
"D.",
""
],
[
"Gleria",
"Iram",
""
],
[
"Albuquerque",
"S. S.",
""
],
[
"Canabarro",
"Askery",
""
],
[
"Stanley",
"H. E.",
""
]
] | In this paper we consider a delayed nonlinear model of the dynamics of the immune system against a viral infection that contains wild-type virus and one mutant. A finite response time of the immune system was considered in order which leads to sustained oscillatory behavior as well as chaotic behavior, triggered by the presence of delays. We present a thoroughly numeric analysis and some analytical results. |
1601.03909 | Todd Parsons | Emma J. Bowen, Todd L. Parsons, Thomas P. Curtis, Joshua B. Plotkin,
and Christopher Quince | Integrating Theory and Experiment to Explain the Breakdown of Population
Synchrony in a Complex Microbial Community | 35 pages, 11 figures, 2 tables | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the extension of the `Moran effect', where correlated noise
generates synchrony between isolated single species populations, to the study
of synchrony between populations embedded in multi-species communities. In
laboratory experiments on complex microbial communities, comprising both
predators (protozoa) and prey (bacteria), we observe synchrony in abundances
between isolated replicates. A breakdown in synchrony occurs for both predator
and prey as the reactor dilution rate increases, which corresponds to both an
increased rate of input of external resources and an increased effective
mortality though washout. The breakdown is more rapid, however, for the lower
trophic level. We can explain this phenomenon using a mathematical framework
for determining synchrony between populations in multi-species communities at
equilibrium. We assume that there are multiple sources of environmental noise
with different degrees of correlation that affect the individual species
population dynamics differently. The deterministic dynamics can then influence
the degree of synchrony between species in different communities. In the case
of a stable equilibrium community synchrony is controlled by the eigenvalue
with smallest negative real part. Intuitively fluctuations are minimally damped
in this direction. We show that the experimental observations are consistent
with this framework but only for multiplicative noise.
| [
{
"created": "Fri, 15 Jan 2016 13:27:41 GMT",
"version": "v1"
}
] | 2016-01-18 | [
[
"Bowen",
"Emma J.",
""
],
[
"Parsons",
"Todd L.",
""
],
[
"Curtis",
"Thomas P.",
""
],
[
"Plotkin",
"Joshua B.",
""
],
[
"Quince",
"Christopher",
""
]
] | We consider the extension of the `Moran effect', where correlated noise generates synchrony between isolated single species populations, to the study of synchrony between populations embedded in multi-species communities. In laboratory experiments on complex microbial communities, comprising both predators (protozoa) and prey (bacteria), we observe synchrony in abundances between isolated replicates. A breakdown in synchrony occurs for both predator and prey as the reactor dilution rate increases, which corresponds to both an increased rate of input of external resources and an increased effective mortality though washout. The breakdown is more rapid, however, for the lower trophic level. We can explain this phenomenon using a mathematical framework for determining synchrony between populations in multi-species communities at equilibrium. We assume that there are multiple sources of environmental noise with different degrees of correlation that affect the individual species population dynamics differently. The deterministic dynamics can then influence the degree of synchrony between species in different communities. In the case of a stable equilibrium community synchrony is controlled by the eigenvalue with smallest negative real part. Intuitively fluctuations are minimally damped in this direction. We show that the experimental observations are consistent with this framework but only for multiplicative noise. |
1501.04700 | Christopher Chatham H | Christopher H. Chatham, Nicole M. Long, David Badre | The paradoxical relationship of difficulty and lateral frontal cortex
activity | 20 page main text with figures in-line; 5 page supplement | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Task difficulty is widely cited in current theory regarding cognitive control
and fronto-parietal function. Ongoing debate surrounds the extent to which
global difficulty across multiple cognitive demands is the main driver of
lateral frontal activity. Here, we examine a commonly cited behavioral marker
of difficulty in these accounts: time-on-task (ToT), as assessed by response
time. Specifically, we investigate the task-dependent scaling of frontal BOLD
responses with ToT during hierarchical cognitive control. We observe a
paradoxical relationship, whereby rostral regions show greater scaling with ToT
on a first-order task, despite showing greater recruitment on a second-order
task; caudal regions show the converse relationships. Together, these results
demonstrate that ToT does not reflect a single dimension of difficulty that
uniformly drives lateral frontal activity. Rather, this discrepancy in the mean
and scaling of BOLD requires that multiple, distinct processes are instantiated
across these fronto-parietal regions in the service of cognitive control
function.
| [
{
"created": "Tue, 20 Jan 2015 02:43:54 GMT",
"version": "v1"
}
] | 2015-01-21 | [
[
"Chatham",
"Christopher H.",
""
],
[
"Long",
"Nicole M.",
""
],
[
"Badre",
"David",
""
]
] | Task difficulty is widely cited in current theory regarding cognitive control and fronto-parietal function. Ongoing debate surrounds the extent to which global difficulty across multiple cognitive demands is the main driver of lateral frontal activity. Here, we examine a commonly cited behavioral marker of difficulty in these accounts: time-on-task (ToT), as assessed by response time. Specifically, we investigate the task-dependent scaling of frontal BOLD responses with ToT during hierarchical cognitive control. We observe a paradoxical relationship, whereby rostral regions show greater scaling with ToT on a first-order task, despite showing greater recruitment on a second-order task; caudal regions show the converse relationships. Together, these results demonstrate that ToT does not reflect a single dimension of difficulty that uniformly drives lateral frontal activity. Rather, this discrepancy in the mean and scaling of BOLD requires that multiple, distinct processes are instantiated across these fronto-parietal regions in the service of cognitive control function. |
2009.00805 | Jingtao Wang | Jingtao Wang, Xi Li, and Hua Zhang | GNN-PT: Enhanced Prediction of Compound-protein Interactions by
Integrating Protein Transformer | v2 | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The prediction of protein interactions (CPIs) is crucial for the in-silico
screening step in drug discovery. Recently, many end-to-end representation
learning methods using deep neural networks have achieved significantly better
performance than traditional machine learning algorithms. Much effort has
focused on the compound representation or the information extraction from the
compound-protein interaction to improve the model capability by taking the
advantage of the neural attention mechanism. However, previous studies have
paid little attention to representing the protein sequences, in which the
long-range interactions of residue pairs are essential for characterizing the
structural properties arising from the protein folding. We incorporate the
self-attention mechanism into the protein representation module for CPI
modeling, which aims at capturing the long-range interaction information within
proteins. The proposed module concerning protein representation, called Protein
Transformer, with an integration with an existing CPI model, has shown a
significant improvement in the prediction performance when compared with
several existing CPI models.
| [
{
"created": "Wed, 2 Sep 2020 03:49:43 GMT",
"version": "v1"
},
{
"created": "Fri, 27 Nov 2020 06:55:48 GMT",
"version": "v2"
}
] | 2020-11-30 | [
[
"Wang",
"Jingtao",
""
],
[
"Li",
"Xi",
""
],
[
"Zhang",
"Hua",
""
]
] | The prediction of protein interactions (CPIs) is crucial for the in-silico screening step in drug discovery. Recently, many end-to-end representation learning methods using deep neural networks have achieved significantly better performance than traditional machine learning algorithms. Much effort has focused on the compound representation or the information extraction from the compound-protein interaction to improve the model capability by taking the advantage of the neural attention mechanism. However, previous studies have paid little attention to representing the protein sequences, in which the long-range interactions of residue pairs are essential for characterizing the structural properties arising from the protein folding. We incorporate the self-attention mechanism into the protein representation module for CPI modeling, which aims at capturing the long-range interaction information within proteins. The proposed module concerning protein representation, called Protein Transformer, with an integration with an existing CPI model, has shown a significant improvement in the prediction performance when compared with several existing CPI models. |
1707.03097 | Genki Ichinose | Genki Ichinose and Naoki Masuda | Zero-determinant strategies in finitely repeated games | 24 pages, 2 figures | Journal of Theoretical Biology 438, 61-77 (2018) | 10.1016/j.jtbi.2017.11.002 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Direct reciprocity is a mechanism for sustaining mutual cooperation in
repeated social dilemma games, where a player would keep cooperation to avoid
being retaliated by a co-player in the future. So-called zero-determinant (ZD)
strategies enable a player to unilaterally set a linear relationship between
the player's own payoff and the co-player's payoff regardless of the strategy
of the co-player. In the present study, we analytically study zero-determinant
strategies in finitely repeated (two-person) prisoner's dilemma games with a
general payoff matrix. Our results are as follows. First, we present the forms
of solutions that extend the known results for infinitely repeated games (with
a discount factor w of unity) to the case of finitely repeated games (0 < w <
1). Second, for the three most prominent ZD strategies, the equalizers,
extortioners, and generous strategies, we derive the threshold value of w above
which the ZD strategies exist. Third, we show that the only strategies that
enforce a linear relationship between the two players' payoffs are either the
ZD strategies or unconditional strategies, where the latter independently
cooperates with a fixed probability in each round of the game, proving a
conjecture previously made for infinitely repeated games.
| [
{
"created": "Tue, 11 Jul 2017 01:37:29 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Nov 2017 12:14:52 GMT",
"version": "v2"
}
] | 2017-11-27 | [
[
"Ichinose",
"Genki",
""
],
[
"Masuda",
"Naoki",
""
]
] | Direct reciprocity is a mechanism for sustaining mutual cooperation in repeated social dilemma games, where a player would keep cooperation to avoid being retaliated by a co-player in the future. So-called zero-determinant (ZD) strategies enable a player to unilaterally set a linear relationship between the player's own payoff and the co-player's payoff regardless of the strategy of the co-player. In the present study, we analytically study zero-determinant strategies in finitely repeated (two-person) prisoner's dilemma games with a general payoff matrix. Our results are as follows. First, we present the forms of solutions that extend the known results for infinitely repeated games (with a discount factor w of unity) to the case of finitely repeated games (0 < w < 1). Second, for the three most prominent ZD strategies, the equalizers, extortioners, and generous strategies, we derive the threshold value of w above which the ZD strategies exist. Third, we show that the only strategies that enforce a linear relationship between the two players' payoffs are either the ZD strategies or unconditional strategies, where the latter independently cooperates with a fixed probability in each round of the game, proving a conjecture previously made for infinitely repeated games. |
1912.04166 | Annalisa Iuorio | Addolorata Marasco and Francesco Giannino and Annalisa Iuorio | Modeling competitive interactions and plant-soil feedback in vegetation
dynamics | null | null | null | null | q-bio.PE math.AP nlin.PS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Plant-soil feedback is recognized as a causal mechanism for the emergence of
vegetation patterns of the same species especially when water is not a limiting
resource (e.g. humid environments). Nevertheless, in the field, plants rarely
grow in monoculture but compete with other plant species. In these cases,
plant-soil feedback was shown to play a key role in plant-species coexistence.
Using a mathematical model consisting of four PDEs, we investigate mechanisms
of inter- and intra-specific plant-soil feedback on the coexistence of two
competing plant species. In particular, the model takes into account both
negative and positive feedback influencing the growth of the same and the other
plant species. Both the coexistence of the plant species and the dominance of a
particular plant species is examined with respect to all model parameters
together with the emergence of spatial vegetation patterns.
| [
{
"created": "Mon, 9 Dec 2019 16:43:57 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Dec 2019 12:43:11 GMT",
"version": "v2"
}
] | 2019-12-13 | [
[
"Marasco",
"Addolorata",
""
],
[
"Giannino",
"Francesco",
""
],
[
"Iuorio",
"Annalisa",
""
]
] | Plant-soil feedback is recognized as a causal mechanism for the emergence of vegetation patterns of the same species especially when water is not a limiting resource (e.g. humid environments). Nevertheless, in the field, plants rarely grow in monoculture but compete with other plant species. In these cases, plant-soil feedback was shown to play a key role in plant-species coexistence. Using a mathematical model consisting of four PDEs, we investigate mechanisms of inter- and intra-specific plant-soil feedback on the coexistence of two competing plant species. In particular, the model takes into account both negative and positive feedback influencing the growth of the same and the other plant species. Both the coexistence of the plant species and the dominance of a particular plant species is examined with respect to all model parameters together with the emergence of spatial vegetation patterns. |
1705.02527 | David Holcman | Jerome Cartailler, Zeev Schuss and David Holcman | Geometrical effects on nonlinear electrodiffusion in cell physiology | To appear in Journal of Nonlinear Science | null | 10.1007/s00332-017-9393-2 | null | q-bio.SC math.AP physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We report here new electrical laws, derived from nonlinear electro-diffusion
theory, about the effect of the local geometrical structure, such as curvature,
on the electrical properties of a cell. We adopt the Poisson-Nernst-Planck
(PNP) equations for charge concentration and electric potential as a model of
electro-diffusion. In the case at hand, the entire boundary is impermeable to
ions and the electric field satisfies the compatibility condition of Poisson's
equation. We construct an asymptotic approximation for certain singular limits
to the steady-state solution in a ball with an attached cusp-shaped funnel on
its surface. As the number of charge increases, they concentrate at the end of
cusp-shaped funnel. These results can be used in the design of nano-pipettes
and help to understand the local voltage changes inside dendrites and axons
with heterogenous local geometry.
| [
{
"created": "Sat, 6 May 2017 20:47:08 GMT",
"version": "v1"
}
] | 2017-11-22 | [
[
"Cartailler",
"Jerome",
""
],
[
"Schuss",
"Zeev",
""
],
[
"Holcman",
"David",
""
]
] | We report here new electrical laws, derived from nonlinear electro-diffusion theory, about the effect of the local geometrical structure, such as curvature, on the electrical properties of a cell. We adopt the Poisson-Nernst-Planck (PNP) equations for charge concentration and electric potential as a model of electro-diffusion. In the case at hand, the entire boundary is impermeable to ions and the electric field satisfies the compatibility condition of Poisson's equation. We construct an asymptotic approximation for certain singular limits to the steady-state solution in a ball with an attached cusp-shaped funnel on its surface. As the number of charge increases, they concentrate at the end of cusp-shaped funnel. These results can be used in the design of nano-pipettes and help to understand the local voltage changes inside dendrites and axons with heterogenous local geometry. |
1502.02223 | Sungroh Yoon | Sungmin Lee, Hyeyoung Min, and Sungroh Yoon | Will solid-state drives accelerate your bioinformatics? In-depth
profiling, performance analysis, and beyond | Availability: http://best.snu.ac.kr/pub/biossd; to be published in
Briefings in Bioinformatics | null | null | null | q-bio.GN cs.CE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A wide variety of large-scale data has been produced in bioinformatics. In
response, the need for efficient handling of biomedical big data has been
partly met by parallel computing. However, the time demand of many
bioinformatics programs still remains high for large-scale practical uses due
to factors that hinder acceleration by parallelization. Recently, new
generations of storage devices have emerged, such as NAND flash-based
solid-state drives (SSDs), and with the renewed interest in near-data
processing, they are increasingly becoming acceleration methods that can
accompany parallel processing. In certain cases, a simple drop-in replacement
of hard disk drives (HDDs) by SSDs results in dramatic speedup. Despite the
various advantages and continuous cost reduction of SSDs, there has been little
review of SSD-based profiling and performance exploration of important but
time-consuming bioinformatics programs. For an informative review, we perform
in-depth profiling and analysis of 23 key bioinformatics programs using
multiple types of devices. Based on the insight we obtain from this research,
we further discuss issues related to design and optimize bioinformatics
algorithms and pipelines to fully exploit SSDs. The programs we profile cover
traditional and emerging areas of importance, such as alignment, assembly,
mapping, expression analysis, variant calling, and metagenomics. We explain how
acceleration by parallelization can be combined with SSDs for improved
performance and also how using SSDs can expedite important bioinformatics
pipelines, such as variant calling by the Genome Analysis Toolkit (GATK) and
transcriptome analysis using RNA sequencing (RNA-seq). We hope that this review
can provide useful directions and tips to accompany future bioinformatics
algorithm design procedures that properly consider new generations of powerful
storage devices.
| [
{
"created": "Sun, 8 Feb 2015 07:32:21 GMT",
"version": "v1"
},
{
"created": "Sat, 8 Aug 2015 06:30:54 GMT",
"version": "v2"
}
] | 2015-08-11 | [
[
"Lee",
"Sungmin",
""
],
[
"Min",
"Hyeyoung",
""
],
[
"Yoon",
"Sungroh",
""
]
] | A wide variety of large-scale data has been produced in bioinformatics. In response, the need for efficient handling of biomedical big data has been partly met by parallel computing. However, the time demand of many bioinformatics programs still remains high for large-scale practical uses due to factors that hinder acceleration by parallelization. Recently, new generations of storage devices have emerged, such as NAND flash-based solid-state drives (SSDs), and with the renewed interest in near-data processing, they are increasingly becoming acceleration methods that can accompany parallel processing. In certain cases, a simple drop-in replacement of hard disk drives (HDDs) by SSDs results in dramatic speedup. Despite the various advantages and continuous cost reduction of SSDs, there has been little review of SSD-based profiling and performance exploration of important but time-consuming bioinformatics programs. For an informative review, we perform in-depth profiling and analysis of 23 key bioinformatics programs using multiple types of devices. Based on the insight we obtain from this research, we further discuss issues related to design and optimize bioinformatics algorithms and pipelines to fully exploit SSDs. The programs we profile cover traditional and emerging areas of importance, such as alignment, assembly, mapping, expression analysis, variant calling, and metagenomics. We explain how acceleration by parallelization can be combined with SSDs for improved performance and also how using SSDs can expedite important bioinformatics pipelines, such as variant calling by the Genome Analysis Toolkit (GATK) and transcriptome analysis using RNA sequencing (RNA-seq). We hope that this review can provide useful directions and tips to accompany future bioinformatics algorithm design procedures that properly consider new generations of powerful storage devices. |
1605.03482 | Roee Gilron | Roee Gilron, Jonathan Rosenblatt, Oluwasanmi Koyejo, Russell A.
Poldrack, Roy Mukamel | What's in a pattern? Examining the Type of Signal Multivariate Analysis
Uncovers At the Group Level | Revised version | null | null | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multivoxel pattern analysis (MVPA) has gained enormous popularity in the
neuroimaging community over the past few years. At the group level, most MVPA
studies adopt an "information based" approach in which the sign of the effect
of individual subjects is discarded and a non-directional summary statistic is
carried over to the second level. This is in contrast to a directional
"activation based" approach typical in univariate group level analysis, in
which both signal magnitude and sign are taken into account. The transition
from examining effects in one voxel at a time vs. several voxels (univariate
vs. multivariate) has thus tacitly entailed a transition from directional to
non-directional signal definition at the group level. While a directional
group-level MVPA approach implies that individuals have similar multivariate
spatial patterns of activity, in a non-directional approach each individual may
have a distinct spatial pattern. Using an experimental dataset, we show that
directional and non-directional group-level MVPA approaches uncover distinct
brain regions with only partial overlap. We propose a method to quantify the
degree of spatial similarity in activation patterns over subjects. Applied to
an auditory task, we find higher values in auditory regions compared to control
regions.
| [
{
"created": "Wed, 11 May 2016 15:30:51 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Sep 2016 12:49:29 GMT",
"version": "v2"
}
] | 2016-09-07 | [
[
"Gilron",
"Roee",
""
],
[
"Rosenblatt",
"Jonathan",
""
],
[
"Koyejo",
"Oluwasanmi",
""
],
[
"Poldrack",
"Russell A.",
""
],
[
"Mukamel",
"Roy",
""
]
] | Multivoxel pattern analysis (MVPA) has gained enormous popularity in the neuroimaging community over the past few years. At the group level, most MVPA studies adopt an "information based" approach in which the sign of the effect of individual subjects is discarded and a non-directional summary statistic is carried over to the second level. This is in contrast to a directional "activation based" approach typical in univariate group level analysis, in which both signal magnitude and sign are taken into account. The transition from examining effects in one voxel at a time vs. several voxels (univariate vs. multivariate) has thus tacitly entailed a transition from directional to non-directional signal definition at the group level. While a directional group-level MVPA approach implies that individuals have similar multivariate spatial patterns of activity, in a non-directional approach each individual may have a distinct spatial pattern. Using an experimental dataset, we show that directional and non-directional group-level MVPA approaches uncover distinct brain regions with only partial overlap. We propose a method to quantify the degree of spatial similarity in activation patterns over subjects. Applied to an auditory task, we find higher values in auditory regions compared to control regions. |
2310.11635 | Sang-Yoon Kim | Sang-Yoon Kim and Woochang Lim | Break-up and Recovery of Harmony between Direct and Indirect Pathways in
The Basal Ganglia; Huntington's Disease and Treatment | arXiv admin note: substantial text overlap with arXiv:2309.12540 | null | null | null | q-bio.NC physics.bio-ph | http://creativecommons.org/publicdomain/zero/1.0/ | The basal ganglia (BG) in the brain exhibit diverse functions for motor,
cognition, and emotion. Such BG functions could be made via competitive harmony
between the two competing pathways, direct pathway (DP) (facilitating movement)
and indirect pathway (IP) (suppressing movement). As a result of break-up of
harmony between DP and IP, there appear pathological states with disorder for
movement, cognition, and psychiatry. In this paper, we are concerned about the
Huntington's disease (HD), which is a genetic neurodegenerative disorder
causing involuntary movement and severe cognitive and psychiatric symptoms. For
the HD, the number of D2 SPNs ($N_{\rm D2}$) is decreased due to degenerative
loss, and hence, by decreasing $x_{\rm D2}$ (fraction of $N_{\rm D2}$), we
investigate break-up of harmony between DP and IP in terms of their competition
degree ${\cal C}_d$, given by the ratio of strength of DP (${\cal S}_{DP}$) to
strength of IP (${\cal S}_{IP}$) (i.e., ${\cal C}_d = {\cal S}_{DP} / {\cal
S}_{IP}$). In the case of HD, the IP is under-active, in contrast to the case
of Parkinson's disease with over-active IP, which results in increase in ${\cal
C}_d$ (from the normal value). Thus, hyperkinetic dyskinesia such as chorea
(involuntary jerky movement) occurs. We also investigate treatment of HD, based
on optogenetics and GP ablation, by increasing strength of IP, resulting in
recovery of harmony between DP and IP. Finally, we study effect of loss of
healthy synapses of all the BG cells on HD. Due to loss of healthy synapses,
disharmony between DP and IP increases, leading to worsen symptoms of the HD.
| [
{
"created": "Wed, 18 Oct 2023 00:03:30 GMT",
"version": "v1"
},
{
"created": "Fri, 19 Apr 2024 01:05:40 GMT",
"version": "v2"
}
] | 2024-04-22 | [
[
"Kim",
"Sang-Yoon",
""
],
[
"Lim",
"Woochang",
""
]
] | The basal ganglia (BG) in the brain exhibit diverse functions for motor, cognition, and emotion. Such BG functions could be made via competitive harmony between the two competing pathways, direct pathway (DP) (facilitating movement) and indirect pathway (IP) (suppressing movement). As a result of break-up of harmony between DP and IP, there appear pathological states with disorder for movement, cognition, and psychiatry. In this paper, we are concerned about the Huntington's disease (HD), which is a genetic neurodegenerative disorder causing involuntary movement and severe cognitive and psychiatric symptoms. For the HD, the number of D2 SPNs ($N_{\rm D2}$) is decreased due to degenerative loss, and hence, by decreasing $x_{\rm D2}$ (fraction of $N_{\rm D2}$), we investigate break-up of harmony between DP and IP in terms of their competition degree ${\cal C}_d$, given by the ratio of strength of DP (${\cal S}_{DP}$) to strength of IP (${\cal S}_{IP}$) (i.e., ${\cal C}_d = {\cal S}_{DP} / {\cal S}_{IP}$). In the case of HD, the IP is under-active, in contrast to the case of Parkinson's disease with over-active IP, which results in increase in ${\cal C}_d$ (from the normal value). Thus, hyperkinetic dyskinesia such as chorea (involuntary jerky movement) occurs. We also investigate treatment of HD, based on optogenetics and GP ablation, by increasing strength of IP, resulting in recovery of harmony between DP and IP. Finally, we study effect of loss of healthy synapses of all the BG cells on HD. Due to loss of healthy synapses, disharmony between DP and IP increases, leading to worsen symptoms of the HD. |
2011.06478 | Andreas Kraemer | Andreas Kr\"amer | Master regulators as order parameters of gene expression states | 11 pages, 3 figures | Phys. Rev. E 103, 012409 (2021) | 10.1103/PhysRevE.103.012409 | null | q-bio.MN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cell type-specific gene expression patterns are represented as memory states
of a Hopfield neural network model. It is shown that order parameters of this
model can be interpreted as concentrations of master transcription regulators
that form concurrent positive feedback loops with a large number of downstream
regulated genes. The order parameter free energy then defines an epigenetic
landscape in which local minima correspond to stable cell states. The model is
applied to gene expression data in the context of hematopoiesis.
| [
{
"created": "Thu, 12 Nov 2020 16:30:43 GMT",
"version": "v1"
}
] | 2021-01-27 | [
[
"Krämer",
"Andreas",
""
]
] | Cell type-specific gene expression patterns are represented as memory states of a Hopfield neural network model. It is shown that order parameters of this model can be interpreted as concentrations of master transcription regulators that form concurrent positive feedback loops with a large number of downstream regulated genes. The order parameter free energy then defines an epigenetic landscape in which local minima correspond to stable cell states. The model is applied to gene expression data in the context of hematopoiesis. |
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