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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1309.2173 | E. Ahmed | E.Ahmed and Muntaser Safan | On evolutionary games with periodic payoffs | 3 pages | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two cases of evolutionary stable strategy with periodic payoffs are studied.
The first is a generalization of Uyttendaele et al. The second is prisoner's
dilemma with periodic payoff. It is shown that reducing the defection payoff by
a periodic term is sufficient to introduce cooperation provided that the
initial fraction of cooperators is greater than 0.6.
| [
{
"created": "Thu, 5 Sep 2013 06:53:59 GMT",
"version": "v1"
}
] | 2013-09-10 | [
[
"Ahmed",
"E.",
""
],
[
"Safan",
"Muntaser",
""
]
] | Two cases of evolutionary stable strategy with periodic payoffs are studied. The first is a generalization of Uyttendaele et al. The second is prisoner's dilemma with periodic payoff. It is shown that reducing the defection payoff by a periodic term is sufficient to introduce cooperation provided that the initial fraction of cooperators is greater than 0.6. |
2007.05348 | Fernando Antoneli Jr | Yangyang Wang, Zhengyuan Huang, Fernando Antoneli, Martin Golubitsky | The Structure of Infinitesimal Homeostasis in Input-Output Networks | 45 pages, 7 figures, 2 tables, minor revision | Journal of Mathematical Biology, Volume 82, May 2021, Article
number: 62 | 10.1007/s00285-021-01614-1 | null | q-bio.MN math.CA physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Homeostasis refers to a phenomenon whereby the output $x_o$ of a system is
approximately constant on variation of an input $\mathcal{I}$. Homeostasis
occurs frequently in biochemical networks and in other networks of interacting
elements where mathematical models are based on differential equations
associated to the network. These networks can be abstracted as digraphs
$\mathcal{G}$ with a distinguished input node $\iota$, a different
distinguished output node $o$, and a number of regulatory nodes
$\rho_1,\ldots,\rho_n$. In these models the input-output map $x_o(\mathcal{I})$
is defined by a stable equilibrium $X_0$ at $\mathcal{I}_0$. Stability implies
that there is a stable equilibrium $X(\mathcal{I})$ for each $\mathcal{I}$ near
$\mathcal{I}_0$ and infinitesimal homeostasis occurs at $\mathcal{I}_0$ when
$(dx_o/d\mathcal{I})(\mathcal{I}_0) = 0$. We show that there is an
$(n+1)\times(n+1)$ homeostasis matrix $H(\mathcal{I})$ for which
$dx_o/d\mathcal{I} = 0$ if and only if $\det(H) = 0$. We note that the entries
in $H$ are linearized couplings and $\det(H)$ is a homogeneous polynomial of
degree $n+1$ in these entries. We use combinatorial matrix theory to factor the
polynomial $\det(H)$ and thereby determine a menu of different types of
possible homeostasis associated with each digraph $\mathcal{G}$. Specifically,
we prove that each factor corresponds to a subnetwork of $\mathcal{G}$. The
factors divide into two combinatorially defined classes: structural and
appendage. Structural factors correspond to feedforward motifs and appendage
factors correspond to feedback motifs. Finally, we discover an algorithm for
determining the homeostasis subnetwork motif corresponding to each factor of
$\det(H)$ without performing numerical simulations on model equations. The
algorithm allows us to classify low degree factors of $\det(H)$.
| [
{
"created": "Wed, 8 Jul 2020 18:23:14 GMT",
"version": "v1"
},
{
"created": "Mon, 3 Aug 2020 14:27:35 GMT",
"version": "v2"
},
{
"created": "Wed, 23 Dec 2020 16:34:51 GMT",
"version": "v3"
},
{
"created": "Tue, 25 May 2021 21:11:20 GMT",
"version": "v4"
}
] | 2021-05-27 | [
[
"Wang",
"Yangyang",
""
],
[
"Huang",
"Zhengyuan",
""
],
[
"Antoneli",
"Fernando",
""
],
[
"Golubitsky",
"Martin",
""
]
] | Homeostasis refers to a phenomenon whereby the output $x_o$ of a system is approximately constant on variation of an input $\mathcal{I}$. Homeostasis occurs frequently in biochemical networks and in other networks of interacting elements where mathematical models are based on differential equations associated to the network. These networks can be abstracted as digraphs $\mathcal{G}$ with a distinguished input node $\iota$, a different distinguished output node $o$, and a number of regulatory nodes $\rho_1,\ldots,\rho_n$. In these models the input-output map $x_o(\mathcal{I})$ is defined by a stable equilibrium $X_0$ at $\mathcal{I}_0$. Stability implies that there is a stable equilibrium $X(\mathcal{I})$ for each $\mathcal{I}$ near $\mathcal{I}_0$ and infinitesimal homeostasis occurs at $\mathcal{I}_0$ when $(dx_o/d\mathcal{I})(\mathcal{I}_0) = 0$. We show that there is an $(n+1)\times(n+1)$ homeostasis matrix $H(\mathcal{I})$ for which $dx_o/d\mathcal{I} = 0$ if and only if $\det(H) = 0$. We note that the entries in $H$ are linearized couplings and $\det(H)$ is a homogeneous polynomial of degree $n+1$ in these entries. We use combinatorial matrix theory to factor the polynomial $\det(H)$ and thereby determine a menu of different types of possible homeostasis associated with each digraph $\mathcal{G}$. Specifically, we prove that each factor corresponds to a subnetwork of $\mathcal{G}$. The factors divide into two combinatorially defined classes: structural and appendage. Structural factors correspond to feedforward motifs and appendage factors correspond to feedback motifs. Finally, we discover an algorithm for determining the homeostasis subnetwork motif corresponding to each factor of $\det(H)$ without performing numerical simulations on model equations. The algorithm allows us to classify low degree factors of $\det(H)$. |
1807.10030 | Trinh Xuan Hoang | Phuong Thuy Bui and Trinh Xuan Hoang | Protein escape at the ribosomal exit tunnel: Effects of native
interactions, tunnel length and macromolecular crowding | null | Journal of Chemical Physics 149, 045102 (2018) | 10.1063/1.5033361 | null | q-bio.BM cond-mat.stat-mech q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How fast a post-translational nascent protein escapes from the ribosomal exit
tunnel is relevant to its folding and protection against aggregation. Here, by
using Langevin molecular dynamics, we show that non-local native interactions
help decreasing the escape time, and foldable proteins generally escape much
faster than same-length self-repulsive homopolymers at low temperatures. The
escape process, however, is slowed down by the local interactions that
stabilize the {\alpha}-helices. The escape time is found to increase with both
the tunnel length and the concentration of macromolecular crowders outside the
tunnel. We show that a simple diffusion model described by the Smoluchowski
equation with an effective linear potential can be used to map out the escape
time distribution for various tunnel lengths and various crowder
concentrations. The consistency between the simulation data and the diffusion
model however is found only for the tunnel length smaller than a cross-over
length of 90 {\AA} to 110 {\AA}, above which the escape time increases much
faster with the tunnel length. It is suggested that the length of ribosomal
exit tunnel has been selected by evolution to facilitate both the efficient
folding and efficient escape of single domain proteins. We show that
macromolecular crowders lead to an increase of the escape time, and attractive
crowders are unfavorable for the folding of nascent polypeptide.
| [
{
"created": "Thu, 26 Jul 2018 09:26:49 GMT",
"version": "v1"
}
] | 2018-07-27 | [
[
"Bui",
"Phuong Thuy",
""
],
[
"Hoang",
"Trinh Xuan",
""
]
] | How fast a post-translational nascent protein escapes from the ribosomal exit tunnel is relevant to its folding and protection against aggregation. Here, by using Langevin molecular dynamics, we show that non-local native interactions help decreasing the escape time, and foldable proteins generally escape much faster than same-length self-repulsive homopolymers at low temperatures. The escape process, however, is slowed down by the local interactions that stabilize the {\alpha}-helices. The escape time is found to increase with both the tunnel length and the concentration of macromolecular crowders outside the tunnel. We show that a simple diffusion model described by the Smoluchowski equation with an effective linear potential can be used to map out the escape time distribution for various tunnel lengths and various crowder concentrations. The consistency between the simulation data and the diffusion model however is found only for the tunnel length smaller than a cross-over length of 90 {\AA} to 110 {\AA}, above which the escape time increases much faster with the tunnel length. It is suggested that the length of ribosomal exit tunnel has been selected by evolution to facilitate both the efficient folding and efficient escape of single domain proteins. We show that macromolecular crowders lead to an increase of the escape time, and attractive crowders are unfavorable for the folding of nascent polypeptide. |
1801.10562 | Min Xu | Bo Zhou, Qiang Guo, Xiangrui Zeng, Min Xu | Feature Decomposition Based Saliency Detection in Electron
Cryo-Tomograms | 14 pages | IEEE International Conference on Bioinformatics & Biomedicine,
Workshop on Machine Learning in High Resolution Microscopy (BIBM-MLHRM 2018) | null | null | q-bio.QM cs.CV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Electron Cryo-Tomography (ECT) allows 3D visualization of subcellular
structures at the submolecular resolution in close to the native state.
However, due to the high degree of structural complexity and imaging limits,
the automatic segmentation of cellular components from ECT images is very
difficult. To complement and speed up existing segmentation methods, it is
desirable to develop a generic cell component segmentation method that is 1)
not specific to particular types of cellular components, 2) able to segment
unknown cellular components, 3) fully unsupervised and does not rely on the
availability of training data. As an important step towards this goal, in this
paper, we propose a saliency detection method that computes the likelihood that
a subregion in a tomogram stands out from the background. Our method consists
of four steps: supervoxel over-segmentation, feature extraction, feature matrix
decomposition, and computation of saliency. The method produces a distribution
map that represents the regions' saliency in tomograms. Our experiments show
that our method can successfully label most salient regions detected by a human
observer, and able to filter out regions not containing cellular components.
Therefore, our method can remove the majority of the background region, and
significantly speed up the subsequent processing of segmentation and
recognition of cellular components captured by ECT.
| [
{
"created": "Wed, 31 Jan 2018 17:25:14 GMT",
"version": "v1"
}
] | 2019-08-08 | [
[
"Zhou",
"Bo",
""
],
[
"Guo",
"Qiang",
""
],
[
"Zeng",
"Xiangrui",
""
],
[
"Xu",
"Min",
""
]
] | Electron Cryo-Tomography (ECT) allows 3D visualization of subcellular structures at the submolecular resolution in close to the native state. However, due to the high degree of structural complexity and imaging limits, the automatic segmentation of cellular components from ECT images is very difficult. To complement and speed up existing segmentation methods, it is desirable to develop a generic cell component segmentation method that is 1) not specific to particular types of cellular components, 2) able to segment unknown cellular components, 3) fully unsupervised and does not rely on the availability of training data. As an important step towards this goal, in this paper, we propose a saliency detection method that computes the likelihood that a subregion in a tomogram stands out from the background. Our method consists of four steps: supervoxel over-segmentation, feature extraction, feature matrix decomposition, and computation of saliency. The method produces a distribution map that represents the regions' saliency in tomograms. Our experiments show that our method can successfully label most salient regions detected by a human observer, and able to filter out regions not containing cellular components. Therefore, our method can remove the majority of the background region, and significantly speed up the subsequent processing of segmentation and recognition of cellular components captured by ECT. |
2004.10762 | Ayan Paul | Hyunju Kim and Ayan Paul | Automated Contact Tracing: a game of big numbers in the time of COVID-19 | 10 pages and 2 figures | J. The Royal Soc. Interface 18, 20200954 | 10.1098/rsif.2020.0954 | DESY 20-069, HU-EP-20/10 | q-bio.PE physics.bio-ph physics.med-ph physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the more widely advocated solutions for slowing down the spread of
COVID-19 has been automated contact tracing. Since proximity data can be
collected by personal mobile devices, the natural proposal has been to use this
for automated contact tracing providing a major gain over a manual
implementation. In this work, we study the characteristics of voluntary and
automated contact tracing and its effectiveness for mapping the spread of a
pandemic due to the spread of SARS-CoV-2. We highlight the infrastructure and
social structures required for automated contact tracing to work. We display
the vulnerabilities of the strategy to inadequate sampling of the population,
which results in the inability to sufficiently determine significant contact
with infected individuals. Of crucial importance will be the participation of a
significant fraction of the population for which we derive a minimum threshold.
We conclude that relying largely on automated contact tracing without
population-wide participation to contain the spread of the SARS-CoV-2 pandemic
can be counterproductive and allow the pandemic to spread unchecked. The
simultaneous implementation of various mitigation methods along with automated
contact tracing is necessary for reaching an optimal solution to contain the
pandemic.
| [
{
"created": "Wed, 22 Apr 2020 18:00:03 GMT",
"version": "v1"
},
{
"created": "Mon, 14 Sep 2020 00:03:09 GMT",
"version": "v2"
},
{
"created": "Tue, 9 Feb 2021 07:03:15 GMT",
"version": "v3"
}
] | 2023-10-09 | [
[
"Kim",
"Hyunju",
""
],
[
"Paul",
"Ayan",
""
]
] | One of the more widely advocated solutions for slowing down the spread of COVID-19 has been automated contact tracing. Since proximity data can be collected by personal mobile devices, the natural proposal has been to use this for automated contact tracing providing a major gain over a manual implementation. In this work, we study the characteristics of voluntary and automated contact tracing and its effectiveness for mapping the spread of a pandemic due to the spread of SARS-CoV-2. We highlight the infrastructure and social structures required for automated contact tracing to work. We display the vulnerabilities of the strategy to inadequate sampling of the population, which results in the inability to sufficiently determine significant contact with infected individuals. Of crucial importance will be the participation of a significant fraction of the population for which we derive a minimum threshold. We conclude that relying largely on automated contact tracing without population-wide participation to contain the spread of the SARS-CoV-2 pandemic can be counterproductive and allow the pandemic to spread unchecked. The simultaneous implementation of various mitigation methods along with automated contact tracing is necessary for reaching an optimal solution to contain the pandemic. |
1707.03591 | Sebastiano Stramaglia | Sebastiano Stramaglia, Iege Bassez, Luca Faes, Daniele Marinazzo | Multiscale Granger causality analysis by \`a trous wavelet transform | 4 pages, 3 figures | null | null | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since interactions in neural systems occur across multiple temporal scales,
it is likely that information flow will exhibit a multiscale structure, thus
requiring a multiscale generalization of classical temporal precedence
causality analysis like Granger's approach. However, the computation of
multiscale measures of information dynamics is complicated by theoretical and
practical issues such as filtering and undersampling: to overcome these
problems, we propose a wavelet-based approach for multiscale Granger causality
(GC) analysis, which is characterized by the following properties: (i) only the
candidate driver variable is wavelet transformed (ii) the decomposition is
performed using the \`a trous wavelet transform with cubic B-spline filter. We
measure GC, at a given scale, by including the wavelet coefficients of the
driver times series, at that scale, in the regression model of the target. To
validate our method, we apply it to publicly available scalp EEG signals, and
we find that the condition of closed eyes, at rest, is characterized by an
enhanced GC among channels at slow scales w.r.t. eye open condition, whilst the
standard Granger causality is not significantly different in the two
conditions.
| [
{
"created": "Wed, 12 Jul 2017 08:20:55 GMT",
"version": "v1"
}
] | 2017-07-13 | [
[
"Stramaglia",
"Sebastiano",
""
],
[
"Bassez",
"Iege",
""
],
[
"Faes",
"Luca",
""
],
[
"Marinazzo",
"Daniele",
""
]
] | Since interactions in neural systems occur across multiple temporal scales, it is likely that information flow will exhibit a multiscale structure, thus requiring a multiscale generalization of classical temporal precedence causality analysis like Granger's approach. However, the computation of multiscale measures of information dynamics is complicated by theoretical and practical issues such as filtering and undersampling: to overcome these problems, we propose a wavelet-based approach for multiscale Granger causality (GC) analysis, which is characterized by the following properties: (i) only the candidate driver variable is wavelet transformed (ii) the decomposition is performed using the \`a trous wavelet transform with cubic B-spline filter. We measure GC, at a given scale, by including the wavelet coefficients of the driver times series, at that scale, in the regression model of the target. To validate our method, we apply it to publicly available scalp EEG signals, and we find that the condition of closed eyes, at rest, is characterized by an enhanced GC among channels at slow scales w.r.t. eye open condition, whilst the standard Granger causality is not significantly different in the two conditions. |
1409.5526 | Xi Huo | Xi Huo | A mathematical model about human infections of H7N9 influenza in China
with the intervention of live poultry markets closing | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper develops a deterministic differential equations model that
captures the H7N9 virus transmission from live poultry to human via
poultry-human contacts in live poultry markets (LPMs). The virus circulation
among live poultry, which happens but is hard to be detected (since
contaminated live poultry appear to be asymptomatic), is also incorporated in
the model. The time-dependent contact rate between human and live poultry based
on LPMs closing information can be estimated. From data of LPMs closing news,
the contact rate function can be easily estimated. This model could serve as a
rational basis for public health authorities to evaluate the effectiveness of
LPM closing, as well as other interventions according to simple modifications.
Without data about daily cases, I also provide suggestions for some of the
basic parameters that would be a useful fitness parameter set for future
simulation.
| [
{
"created": "Fri, 19 Sep 2014 06:12:41 GMT",
"version": "v1"
}
] | 2014-09-22 | [
[
"Huo",
"Xi",
""
]
] | This paper develops a deterministic differential equations model that captures the H7N9 virus transmission from live poultry to human via poultry-human contacts in live poultry markets (LPMs). The virus circulation among live poultry, which happens but is hard to be detected (since contaminated live poultry appear to be asymptomatic), is also incorporated in the model. The time-dependent contact rate between human and live poultry based on LPMs closing information can be estimated. From data of LPMs closing news, the contact rate function can be easily estimated. This model could serve as a rational basis for public health authorities to evaluate the effectiveness of LPM closing, as well as other interventions according to simple modifications. Without data about daily cases, I also provide suggestions for some of the basic parameters that would be a useful fitness parameter set for future simulation. |
2311.07135 | Eduardo Henrique Colombo | Eduardo H. Colombo, Ricardo Martinez-Garcia, Justin M. Calabrese,
Crist\'obal L\'opez, Emilio Hern\'andez-Garc\'ia | Pulsed interactions unify reaction-diffusion and spatial nonlocal models
for biological pattern formation | null | Journal of Statistical Mechanics: Theory and Experiment 2024,
034001 (2024) | 10.1088/1742-5468/ad2b57 | null | q-bio.PE cond-mat.soft cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The emergence of a spatially-organized population distribution depends on the
dynamics of the population and mediators of interaction (activators and
inhibitors). Two broad classes of models have been used to investigate when and
how self-organization is triggered, namely, reaction-diffusion and spatially
nonlocal models. Nevertheless, these models implicitly assume smooth
propagation scenarios, neglecting that individuals many times interact by
exchanging short and abrupt pulses of the mediating substance. A recently
proposed framework advances in the direction of properly accounting for these
short-scale fluctuations by applying a coarse-graining procedure on the pulse
dynamics. In this paper, we generalize the coarse-graining procedure and apply
the extended formalism to new scenarios in which mediators influence
individuals' reproductive success or their motility. We show that, in the slow-
and fast-mediator limits, pulsed interactions recover, respectively, the
reaction-diffusion and nonlocal models, providing a mechanistic connection
between them. Furthermore, at each limit, the spatial stability condition is
qualitatively different, leading to a timescale-induced transition where
spatial patterns emerge as mediator dynamics becomes sufficiently fast.
| [
{
"created": "Mon, 13 Nov 2023 08:04:29 GMT",
"version": "v1"
},
{
"created": "Thu, 21 Mar 2024 14:54:46 GMT",
"version": "v2"
}
] | 2024-04-04 | [
[
"Colombo",
"Eduardo H.",
""
],
[
"Martinez-Garcia",
"Ricardo",
""
],
[
"Calabrese",
"Justin M.",
""
],
[
"López",
"Cristóbal",
""
],
[
"Hernández-García",
"Emilio",
""
]
] | The emergence of a spatially-organized population distribution depends on the dynamics of the population and mediators of interaction (activators and inhibitors). Two broad classes of models have been used to investigate when and how self-organization is triggered, namely, reaction-diffusion and spatially nonlocal models. Nevertheless, these models implicitly assume smooth propagation scenarios, neglecting that individuals many times interact by exchanging short and abrupt pulses of the mediating substance. A recently proposed framework advances in the direction of properly accounting for these short-scale fluctuations by applying a coarse-graining procedure on the pulse dynamics. In this paper, we generalize the coarse-graining procedure and apply the extended formalism to new scenarios in which mediators influence individuals' reproductive success or their motility. We show that, in the slow- and fast-mediator limits, pulsed interactions recover, respectively, the reaction-diffusion and nonlocal models, providing a mechanistic connection between them. Furthermore, at each limit, the spatial stability condition is qualitatively different, leading to a timescale-induced transition where spatial patterns emerge as mediator dynamics becomes sufficiently fast. |
2303.00049 | Merat Rezaei | Merat Rezaei, Saad S. Nagi, Chang Xu, Sarah McIntyre, Hakan Olausson,
Gregory J. Gerling | Thin Films on the Skin, but not Frictional Agents, Attenuate the Percept
of Pleasantness to Brushed Stimuli | null | null | 10.1109/WHC49131.2021.9517259 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Brushed stimuli are perceived as pleasant when stroked lightly on the skin
surface of a touch receiver at certain velocities. While the relationship
between brush velocity and pleasantness has been widely replicated, we do not
understand how resultant skin movements - e.g., lateral stretch, stick-slip,
normal indentation - drive us to form such judgments. In a series of
psychophysical experiments, this work modulates skin movements by varying
stimulus stiffness and employing various treatments. The stimuli include
brushes of three levels of stiffness and an ungloved human finger. The skin's
friction is modulated via non-hazardous chemicals and washing protocols, and
the skin's thickness and lateral movement are modulated by thin sheets of
adhesive film. The stimuli are hand-brushed at controlled forces and
velocities. Human participants report perceived pleasantness per trial using
ratio scaling. The results indicate that a brush's stiffness influenced
pleasantness more than any skin treatment. Surprisingly, varying the skin's
friction did not affect pleasantness. However, the application of a thin
elastic film modulated pleasantness. Such barriers, though elastic and only 40
microns thick, inhibit the skin's tangential movement and disperse normal
force. The finding that thin films modulate affective interactions has
implications for wearable sensors and actuation devices.
| [
{
"created": "Tue, 28 Feb 2023 19:46:49 GMT",
"version": "v1"
}
] | 2023-03-02 | [
[
"Rezaei",
"Merat",
""
],
[
"Nagi",
"Saad S.",
""
],
[
"Xu",
"Chang",
""
],
[
"McIntyre",
"Sarah",
""
],
[
"Olausson",
"Hakan",
""
],
[
"Gerling",
"Gregory J.",
""
]
] | Brushed stimuli are perceived as pleasant when stroked lightly on the skin surface of a touch receiver at certain velocities. While the relationship between brush velocity and pleasantness has been widely replicated, we do not understand how resultant skin movements - e.g., lateral stretch, stick-slip, normal indentation - drive us to form such judgments. In a series of psychophysical experiments, this work modulates skin movements by varying stimulus stiffness and employing various treatments. The stimuli include brushes of three levels of stiffness and an ungloved human finger. The skin's friction is modulated via non-hazardous chemicals and washing protocols, and the skin's thickness and lateral movement are modulated by thin sheets of adhesive film. The stimuli are hand-brushed at controlled forces and velocities. Human participants report perceived pleasantness per trial using ratio scaling. The results indicate that a brush's stiffness influenced pleasantness more than any skin treatment. Surprisingly, varying the skin's friction did not affect pleasantness. However, the application of a thin elastic film modulated pleasantness. Such barriers, though elastic and only 40 microns thick, inhibit the skin's tangential movement and disperse normal force. The finding that thin films modulate affective interactions has implications for wearable sensors and actuation devices. |
1410.3344 | Colby Long | Colby Long and Seth Sullivant | Tying Up Loose Strands: Defining Equations of the Strand Symmetric Model | 5 pages, 0 figures | null | null | null | q-bio.PE math.AG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The strand symmetric model is a phylogenetic model designed to reflect the
symmetry inherent in the double-stranded structure of DNA. We show that the set
of known phylogenetic invariants for the general strand symmetric model of the
three leaf claw tree entirely defines the ideal. This knowledge allows one to
determine the vanishing ideal of the general strand symmetric model of any
trivalent tree. Our proof of the main result is computational. We use the fact
that the Zariski closure of the strand symmetric model is the secant variety of
a toric variety to compute the dimension of the variety. We then show that the
known equations generate a prime ideal of the correct dimension using
elimination theory.
| [
{
"created": "Mon, 13 Oct 2014 15:13:45 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Oct 2014 14:36:34 GMT",
"version": "v2"
}
] | 2014-10-21 | [
[
"Long",
"Colby",
""
],
[
"Sullivant",
"Seth",
""
]
] | The strand symmetric model is a phylogenetic model designed to reflect the symmetry inherent in the double-stranded structure of DNA. We show that the set of known phylogenetic invariants for the general strand symmetric model of the three leaf claw tree entirely defines the ideal. This knowledge allows one to determine the vanishing ideal of the general strand symmetric model of any trivalent tree. Our proof of the main result is computational. We use the fact that the Zariski closure of the strand symmetric model is the secant variety of a toric variety to compute the dimension of the variety. We then show that the known equations generate a prime ideal of the correct dimension using elimination theory. |
2310.11563 | Daniel Beller | Jimmy Gonzalez Nu\~nez and Jayson Paulose and Wolfram M\"obius and
Daniel A. Beller | Connecting the Dots: Range Expansions across Landscapes with Quenched
Noise | 20 pages, 13 figures | null | null | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When biological populations expand into new territory, the evolutionary
outcomes can be strongly influenced by genetic drift, the random fluctuations
in allele frequencies. Meanwhile, spatial variability in the environment can
also significantly influence the competition between sub-populations vying for
space. Little is known about the interplay of these intrinsic and extrinsic
sources of noise in population dynamics: When does environmental heterogeneity
dominate over genetic drift or vice versa, and what distinguishes their
population genetics signatures? Here, in the context of neutral evolution, we
examine the interplay between a population's intrinsic, demographic noise and
an extrinsic, quenched random noise provided by a heterogeneous environment.
Using a multi-species Eden model, we simulate a population expanding over a
landscape with random variations in local growth rates and measure how this
variability affects genealogical tree structure, and thus genetic diversity. We
find that, for strong heterogeneity, the genetic makeup of the expansion front
is to a great extent predetermined by the set of fastest paths through the
environment. The landscape-dependent statistics of these optimal paths then
supersede those of the population's intrinsic noise as the main determinant of
evolutionary dynamics. Remarkably, the statistics for coalescence of
genealogical lineages, derived from those deterministic paths, strongly
resemble the statistics emerging from demographic noise alone in uniform
landscapes. This cautions interpretations of coalescence statistics and raises
new challenges for inferring past population dynamics.
| [
{
"created": "Tue, 17 Oct 2023 20:21:02 GMT",
"version": "v1"
},
{
"created": "Tue, 25 Jun 2024 20:40:14 GMT",
"version": "v2"
}
] | 2024-06-27 | [
[
"Nuñez",
"Jimmy Gonzalez",
""
],
[
"Paulose",
"Jayson",
""
],
[
"Möbius",
"Wolfram",
""
],
[
"Beller",
"Daniel A.",
""
]
] | When biological populations expand into new territory, the evolutionary outcomes can be strongly influenced by genetic drift, the random fluctuations in allele frequencies. Meanwhile, spatial variability in the environment can also significantly influence the competition between sub-populations vying for space. Little is known about the interplay of these intrinsic and extrinsic sources of noise in population dynamics: When does environmental heterogeneity dominate over genetic drift or vice versa, and what distinguishes their population genetics signatures? Here, in the context of neutral evolution, we examine the interplay between a population's intrinsic, demographic noise and an extrinsic, quenched random noise provided by a heterogeneous environment. Using a multi-species Eden model, we simulate a population expanding over a landscape with random variations in local growth rates and measure how this variability affects genealogical tree structure, and thus genetic diversity. We find that, for strong heterogeneity, the genetic makeup of the expansion front is to a great extent predetermined by the set of fastest paths through the environment. The landscape-dependent statistics of these optimal paths then supersede those of the population's intrinsic noise as the main determinant of evolutionary dynamics. Remarkably, the statistics for coalescence of genealogical lineages, derived from those deterministic paths, strongly resemble the statistics emerging from demographic noise alone in uniform landscapes. This cautions interpretations of coalescence statistics and raises new challenges for inferring past population dynamics. |
2003.11328 | Alejandro Tabas | Alejandro Tabas, Glad Mihai, Stefan Kiebel, Robert Trampel, and
Katharina von Kriegstein | Predictive coding underlies adaptation in the subcortical sensory
pathway | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The subcortical sensory pathways are the fundamental channels for mapping the
outside world to our minds. Sensory pathways efficiently transmit information
by adapting neural responses to the local statistics of the sensory input. The
longstanding mechanistic explanation for this adaptive behaviour is that
neuronal habituation scales activity to the local statistics of the stimuli. An
alternative account is that neural coding is directly driven by expectations of
the sensory input. Here we used abstract rules to manipulate expectations
independently of local stimulus statistics. The ultra-high-field functional-MRI
data show that expectations, and not habituation, are the main driver of the
response amplitude to tones in the human auditory pathway. These results
provide first unambiguous evidence of predictive coding and abstract processing
in a subcortical sensory pathway, indicating that the brain only holds
subjective representations of the outside world even at initial points of the
processing hierarchy.
| [
{
"created": "Wed, 25 Mar 2020 11:14:56 GMT",
"version": "v1"
}
] | 2020-03-26 | [
[
"Tabas",
"Alejandro",
""
],
[
"Mihai",
"Glad",
""
],
[
"Kiebel",
"Stefan",
""
],
[
"Trampel",
"Robert",
""
],
[
"von Kriegstein",
"Katharina",
""
]
] | The subcortical sensory pathways are the fundamental channels for mapping the outside world to our minds. Sensory pathways efficiently transmit information by adapting neural responses to the local statistics of the sensory input. The longstanding mechanistic explanation for this adaptive behaviour is that neuronal habituation scales activity to the local statistics of the stimuli. An alternative account is that neural coding is directly driven by expectations of the sensory input. Here we used abstract rules to manipulate expectations independently of local stimulus statistics. The ultra-high-field functional-MRI data show that expectations, and not habituation, are the main driver of the response amplitude to tones in the human auditory pathway. These results provide first unambiguous evidence of predictive coding and abstract processing in a subcortical sensory pathway, indicating that the brain only holds subjective representations of the outside world even at initial points of the processing hierarchy. |
1712.06026 | Gergely R\"ost | Zsolt Vizi, Istv\'an Z. Kiss, Joel C. Miller, Gergely R\"ost | A monotonic relationship between the variability of the infectious
period and final size in pairwise epidemic modelling | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For a recently derived pairwise model of network epidemics with non-Markovian
recovery, we prove that under some mild technical conditions on the
distribution of the infectious periods, smaller variance in the recovery time
leads to higher reproduction number, and consequently to a larger epidemic
outbreak, when the mean infectious period is fixed. We discuss how this result
is related to various stochastic orderings of the distributions of infectious
periods. The results are illustrated by a number of explicit stochastic
simulations, suggesting that their validity goes beyond regular networks.
| [
{
"created": "Sat, 16 Dec 2017 22:28:15 GMT",
"version": "v1"
},
{
"created": "Mon, 24 Dec 2018 12:03:54 GMT",
"version": "v2"
}
] | 2018-12-27 | [
[
"Vizi",
"Zsolt",
""
],
[
"Kiss",
"István Z.",
""
],
[
"Miller",
"Joel C.",
""
],
[
"Röst",
"Gergely",
""
]
] | For a recently derived pairwise model of network epidemics with non-Markovian recovery, we prove that under some mild technical conditions on the distribution of the infectious periods, smaller variance in the recovery time leads to higher reproduction number, and consequently to a larger epidemic outbreak, when the mean infectious period is fixed. We discuss how this result is related to various stochastic orderings of the distributions of infectious periods. The results are illustrated by a number of explicit stochastic simulations, suggesting that their validity goes beyond regular networks. |
1905.12554 | Giorgio Mantica | Theophile Caby and Giorgio Mantica | Extreme value theory of evolving phenomena in complex dynamical systems:
firing cascades in a model of neural network | 21 pages, 13 figures | null | 10.1063/1.5120570 | null | q-bio.NC math.DS nlin.CD physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We extend the scope of the dynamical theory of extreme values to cover
phenomena that do not happen instantaneously, but evolve over a finite, albeit
unknown at the onset, time interval. We consider complex dynamical systems,
composed of many individual subsystems linked by a network of interactions. As
a specific example of the general theory, a model of neural network, introduced
to describe the electrical activity of the cerebral cortex, is analyzed in
detail: on the basis of this analysis we propose a novel definition of neuronal
cascade, a physiological phenomenon of primary importance. We derive extreme
value laws for the statistics of these cascades, both from the point of view of
exceedances (that satisfy critical scaling theory) and of block maxima.
| [
{
"created": "Tue, 28 May 2019 13:26:13 GMT",
"version": "v1"
}
] | 2020-05-20 | [
[
"Caby",
"Theophile",
""
],
[
"Mantica",
"Giorgio",
""
]
] | We extend the scope of the dynamical theory of extreme values to cover phenomena that do not happen instantaneously, but evolve over a finite, albeit unknown at the onset, time interval. We consider complex dynamical systems, composed of many individual subsystems linked by a network of interactions. As a specific example of the general theory, a model of neural network, introduced to describe the electrical activity of the cerebral cortex, is analyzed in detail: on the basis of this analysis we propose a novel definition of neuronal cascade, a physiological phenomenon of primary importance. We derive extreme value laws for the statistics of these cascades, both from the point of view of exceedances (that satisfy critical scaling theory) and of block maxima. |
1509.06278 | Mark Transtrum | Mark K. Transtrum and Peng Qiu | Bridging Mechanistic and Phenomenological Models of Complex Biological
Systems | null | PLoS Computational Biology 12(5): e1004915, 2016 | 10.1371/journal.pcbi.1004915 | null | q-bio.QM nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The inherent complexity of biological systems gives rise to complicated
mechanistic models with a large number of parameters. On the other hand, the
collective behavior of these systems can often be characterized by a relatively
small number of phenomenological parameters. We use the Manifold Boundary
Approximation Method (MBAM) as a tool for deriving simple phenomenological
models from complicated mechanistic models. The resulting models are not black
boxes, but remain expressed in terms of the microscopic parameters. In this
way, we explicitly connect the macroscopic and microscopic descriptions,
characterize the equivalence class of distinct systems exhibiting the same
range of collective behavior, and identify the combinations of components that
function as tunable control knobs for the behavior. We demonstrate the
procedure for adaptation behavior exhibited by the EGFR pathway. From a 48
parameter mechanistic model, the system can be effectively described by a
single adaptation parameter $\tau$ characterizing the ratio of time scales for
the initial response and recovery time of the system which can in turn be
expressed as a combination of microscopic reaction rates, Michaelis-Menten
constants, and biochemical concentrations. The situation is not unlike modeling
in physics in which microscopically complex processes can often be renormalized
into simple phenomenological models with only a few effective parameters. The
proposed method additionally provides a mechanistic explanation for
non-universal features of the behavior.
| [
{
"created": "Mon, 21 Sep 2015 15:43:27 GMT",
"version": "v1"
},
{
"created": "Tue, 9 Feb 2016 17:03:11 GMT",
"version": "v2"
}
] | 2016-06-15 | [
[
"Transtrum",
"Mark K.",
""
],
[
"Qiu",
"Peng",
""
]
] | The inherent complexity of biological systems gives rise to complicated mechanistic models with a large number of parameters. On the other hand, the collective behavior of these systems can often be characterized by a relatively small number of phenomenological parameters. We use the Manifold Boundary Approximation Method (MBAM) as a tool for deriving simple phenomenological models from complicated mechanistic models. The resulting models are not black boxes, but remain expressed in terms of the microscopic parameters. In this way, we explicitly connect the macroscopic and microscopic descriptions, characterize the equivalence class of distinct systems exhibiting the same range of collective behavior, and identify the combinations of components that function as tunable control knobs for the behavior. We demonstrate the procedure for adaptation behavior exhibited by the EGFR pathway. From a 48 parameter mechanistic model, the system can be effectively described by a single adaptation parameter $\tau$ characterizing the ratio of time scales for the initial response and recovery time of the system which can in turn be expressed as a combination of microscopic reaction rates, Michaelis-Menten constants, and biochemical concentrations. The situation is not unlike modeling in physics in which microscopically complex processes can often be renormalized into simple phenomenological models with only a few effective parameters. The proposed method additionally provides a mechanistic explanation for non-universal features of the behavior. |
1602.07625 | Gorka Zamora-L\'opez | Gorka Zamora-L\'opez, Yuhan Chen, Gustavo Deco, Morten L. Kringelbach,
and Changsong Zhou | Functional complexity emerging from anatomical constraints in the brain:
the significance of network modularity and rich-clubs | null | Scientific Reports 2016 | 10.1038/srep38424 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The large-scale structural ingredients of the brain and neural connectomes
have been identified in recent years. These are, similar to the features found
in many other real networks: the arrangement of brain regions into modules and
the presence of highly connected regions (hubs) forming rich-clubs. Here, we
examine how modules and hubs shape the collective dynamics on networks and we
find that both ingredients lead to the emergence of complex dynamics. Comparing
the connectomes of C. elegans, cats, macaques and humans to surrogate networks
in which either modules or hubs are destroyed, we find that functional
complexity always decreases in the perturbed networks. A comparison between
simulated and empirically obtained resting-state functional connectivity
indicates that the human brain, at rest, lies in a dynamical state that
reflects the largest complexity its anatomical connectome can host. Last, we
generalise the topology of neural connectomes into a new hierarchical network
model that successfully combines modular organisation with rich-club forming
hubs. This is achieved by centralising the cross-modular connections through a
preferential attachment rule. Our network model hosts more complex dynamics
than other hierarchical models widely used as benchmarks.
| [
{
"created": "Wed, 24 Feb 2016 18:10:43 GMT",
"version": "v1"
},
{
"created": "Mon, 31 Oct 2016 22:36:28 GMT",
"version": "v2"
}
] | 2018-11-01 | [
[
"Zamora-López",
"Gorka",
""
],
[
"Chen",
"Yuhan",
""
],
[
"Deco",
"Gustavo",
""
],
[
"Kringelbach",
"Morten L.",
""
],
[
"Zhou",
"Changsong",
""
]
] | The large-scale structural ingredients of the brain and neural connectomes have been identified in recent years. These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs. Here, we examine how modules and hubs shape the collective dynamics on networks and we find that both ingredients lead to the emergence of complex dynamics. Comparing the connectomes of C. elegans, cats, macaques and humans to surrogate networks in which either modules or hubs are destroyed, we find that functional complexity always decreases in the perturbed networks. A comparison between simulated and empirically obtained resting-state functional connectivity indicates that the human brain, at rest, lies in a dynamical state that reflects the largest complexity its anatomical connectome can host. Last, we generalise the topology of neural connectomes into a new hierarchical network model that successfully combines modular organisation with rich-club forming hubs. This is achieved by centralising the cross-modular connections through a preferential attachment rule. Our network model hosts more complex dynamics than other hierarchical models widely used as benchmarks. |
1203.3073 | Taro Toyoizumi | Taro Toyoizumi | Nearly extensive sequential memory lifetime achieved by coupled
nonlinear neurons | 21 pages, 5 figures, the manuscript has been accepted for publication
in Neural Computation | Neural Computation 24 (2012) 2678-2699 | 10.1162/NECO_a_00324 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many cognitive processes rely on the ability of the brain to hold sequences
of events in short-term memory. Recent studies have revealed that such memory
can be read out from the transient dynamics of a network of neurons. However,
the memory performance of such a network in buffering past information has only
been rigorously estimated in networks of linear neurons. When signal gain is
kept low, so that neurons operate primarily in the linear part of their
response nonlinearity, the memory lifetime is bounded by the square root of the
network size. In this work, I demonstrate that it is possible to achieve a
memory lifetime almost proportional to the network size, "an extensive memory
lifetime", when the nonlinearity of neurons is appropriately utilized. The
analysis of neural activity revealed that nonlinear dynamics prevented the
accumulation of noise by partially removing noise in each time step. With this
error-correcting mechanism, I demonstrate that a memory lifetime of order
$N/\log N$ can be achieved.
| [
{
"created": "Wed, 14 Mar 2012 13:00:51 GMT",
"version": "v1"
}
] | 2012-08-31 | [
[
"Toyoizumi",
"Taro",
""
]
] | Many cognitive processes rely on the ability of the brain to hold sequences of events in short-term memory. Recent studies have revealed that such memory can be read out from the transient dynamics of a network of neurons. However, the memory performance of such a network in buffering past information has only been rigorously estimated in networks of linear neurons. When signal gain is kept low, so that neurons operate primarily in the linear part of their response nonlinearity, the memory lifetime is bounded by the square root of the network size. In this work, I demonstrate that it is possible to achieve a memory lifetime almost proportional to the network size, "an extensive memory lifetime", when the nonlinearity of neurons is appropriately utilized. The analysis of neural activity revealed that nonlinear dynamics prevented the accumulation of noise by partially removing noise in each time step. With this error-correcting mechanism, I demonstrate that a memory lifetime of order $N/\log N$ can be achieved. |
1407.7622 | Adam Kapelner | Adam Kapelner and Matthew Vorsanger | Starvation of Cancer via Induced Ketogenesis and Severe Hypoglycemia | 17 pages, 1 figure, 1 table | null | 10.1016/j.mehy.2014.11.002 | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neoplasms are highly dependent on glucose as their substrate for energy
production and are generally not able to catabolize other fuel sources such as
ketones and fatty acids. Thus, removing access to glucose has the potential to
starve cancer cells and induce apoptosis. Unfortunately, other body tissues are
also dependent on glucose for energy under normal conditions. However, in human
starvation (or in the setting of diet-induced ketogenesis), the body
"keto-adapts" and glucose requirements of most tissues drop to almost nil.
Exceptions include the central nervous system (CNS) and various other tissues
which have a small but obligatory requirement of glucose. Our hypothesized
treatment takes keto-adaptation as a prerequisite. We then propose the
induction of severe hypoglycemia by depressing gluconeogenesis while
administering glucose to the brain. Although severe hypoglycemia normally
produces adverse effects such as seizure and coma, it is relatively safe
following ketoadaptation. We hypothesize that our therapeutic hypoglycemia
treatment has potential to rapidly induce tumor cell necrosis.
| [
{
"created": "Tue, 29 Jul 2014 03:11:55 GMT",
"version": "v1"
},
{
"created": "Mon, 8 Dec 2014 22:50:51 GMT",
"version": "v2"
}
] | 2021-06-01 | [
[
"Kapelner",
"Adam",
""
],
[
"Vorsanger",
"Matthew",
""
]
] | Neoplasms are highly dependent on glucose as their substrate for energy production and are generally not able to catabolize other fuel sources such as ketones and fatty acids. Thus, removing access to glucose has the potential to starve cancer cells and induce apoptosis. Unfortunately, other body tissues are also dependent on glucose for energy under normal conditions. However, in human starvation (or in the setting of diet-induced ketogenesis), the body "keto-adapts" and glucose requirements of most tissues drop to almost nil. Exceptions include the central nervous system (CNS) and various other tissues which have a small but obligatory requirement of glucose. Our hypothesized treatment takes keto-adaptation as a prerequisite. We then propose the induction of severe hypoglycemia by depressing gluconeogenesis while administering glucose to the brain. Although severe hypoglycemia normally produces adverse effects such as seizure and coma, it is relatively safe following ketoadaptation. We hypothesize that our therapeutic hypoglycemia treatment has potential to rapidly induce tumor cell necrosis. |
1505.06670 | Barak Pearlmutter | Andrei Barbu, N. Siddharth, Caiming Xiong, Jason J. Corso, Christiane
D. Fellbaum, Catherine Hanson, Stephen Jos\'e Hanson, S\'ebastien H\'elie,
Evguenia Malaia, Barak A. Pearlmutter, Jeffrey Mark Siskind, Thomas Michael
Talavage, and Ronnie B. Wilbur | The Compositional Nature of Event Representations in the Human Brain | 28 pages; 8 figures; 8 tables | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How does the human brain represent simple compositions of constituents:
actors, verbs, objects, directions, and locations? Subjects viewed videos
during neuroimaging (fMRI) sessions from which sentential descriptions of those
videos were identified by decoding the brain representations based only on
their fMRI activation patterns. Constituents (e.g., "fold" and "shirt") were
independently decoded from a single presentation. Independent constituent
classification was then compared to joint classification of aggregate concepts
(e.g., "fold-shirt"); results were similar as measured by accuracy and
correlation. The brain regions used for independent constituent classification
are largely disjoint and largely cover those used for joint classification.
This allows recovery of sentential descriptions of stimulus videos by composing
the results of the independent constituent classifiers. Furthermore,
classifiers trained on the words one set of subjects think of when watching a
video can recognise sentences a different subject thinks of when watching a
different video.
| [
{
"created": "Mon, 25 May 2015 15:49:03 GMT",
"version": "v1"
}
] | 2015-05-26 | [
[
"Barbu",
"Andrei",
""
],
[
"Siddharth",
"N.",
""
],
[
"Xiong",
"Caiming",
""
],
[
"Corso",
"Jason J.",
""
],
[
"Fellbaum",
"Christiane D.",
""
],
[
"Hanson",
"Catherine",
""
],
[
"Hanson",
"Stephen José",
"... | How does the human brain represent simple compositions of constituents: actors, verbs, objects, directions, and locations? Subjects viewed videos during neuroimaging (fMRI) sessions from which sentential descriptions of those videos were identified by decoding the brain representations based only on their fMRI activation patterns. Constituents (e.g., "fold" and "shirt") were independently decoded from a single presentation. Independent constituent classification was then compared to joint classification of aggregate concepts (e.g., "fold-shirt"); results were similar as measured by accuracy and correlation. The brain regions used for independent constituent classification are largely disjoint and largely cover those used for joint classification. This allows recovery of sentential descriptions of stimulus videos by composing the results of the independent constituent classifiers. Furthermore, classifiers trained on the words one set of subjects think of when watching a video can recognise sentences a different subject thinks of when watching a different video. |
2004.07229 | Deisy Morselli Gysi | Deisy Morselli Gysi and \'Italo Do Valle and Marinka Zitnik and Asher
Ameli and Xiao Gan and Onur Varol and Susan Dina Ghiassian and JJ Patten and
Robert Davey and Joseph Loscalzo and Albert-L\'aszl\'o Barab\'asi | Network Medicine Framework for Identifying Drug Repurposing
Opportunities for COVID-19 | null | null | 10.1073/pnas.2025581118 | null | q-bio.MN cs.LG q-bio.QM stat.ML | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The current pandemic has highlighted the need for methodologies that can
quickly and reliably prioritize clinically approved compounds for their
potential effectiveness for SARS-CoV-2 infections. In the past decade, network
medicine has developed and validated multiple predictive algorithms for drug
repurposing, exploiting the sub-cellular network-based relationship between a
drug's targets and disease genes. Here, we deployed algorithms relying on
artificial intelligence, network diffusion, and network proximity, tasking each
of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To
test the predictions, we used as ground truth 918 drugs that had been
experimentally screened in VeroE6 cells, and the list of drugs under clinical
trial, that capture the medical community's assessment of drugs with potential
COVID-19 efficacy. We find that while most algorithms offer predictive power
for these ground truth data, no single method offers consistently reliable
outcomes across all datasets and metrics. This prompted us to develop a
multimodal approach that fuses the predictions of all algorithms, showing that
a consensus among the different predictive methods consistently exceeds the
performance of the best individual pipelines. We find that 76 of the 77 drugs
that successfully reduced viral infection do not bind the proteins targeted by
SARS-CoV-2, indicating that these drugs rely on network-based actions that
cannot be identified using docking-based strategies. These advances offer a
methodological pathway to identify repurposable drugs for future pathogens and
neglected diseases underserved by the costs and extended timeline of de novo
drug development.
| [
{
"created": "Wed, 15 Apr 2020 17:40:29 GMT",
"version": "v1"
},
{
"created": "Sun, 9 Aug 2020 15:52:14 GMT",
"version": "v2"
}
] | 2022-05-04 | [
[
"Gysi",
"Deisy Morselli",
""
],
[
"Valle",
"Ítalo Do",
""
],
[
"Zitnik",
"Marinka",
""
],
[
"Ameli",
"Asher",
""
],
[
"Gan",
"Xiao",
""
],
[
"Varol",
"Onur",
""
],
[
"Ghiassian",
"Susan Dina",
""
],
[
... | The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development. |
1504.02496 | Miguel Nicolelis | Sankaranarayani Rajangam, Po-He Tseng, Allen Yin, Mikhail A. Lebedev,
Miguel A. L. Nicolelis | Direct Cortical Control of Primate Whole-Body Navigation in a Mobile
Robotic Wheelchair | 15 pages, 4 main figure, 9 supplementary figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We and others have previously developed brain-machine-interfaces (BMIs),
which allowed ensembles of cortical neurons to control artificial limbs (1-4).
However, it is unclear whether cortical ensembles could operate a BMI for
whole-body navigation. Here we show that rhesus monkeys can learn to navigate a
robotic wheelchair while seated on top of it, and using their cortical activity
as the robot control signal. Two monkeys were chronically implanted with
multichannel electrode arrays which simultaneously sampled activity of roughly
150 premotor and sensorimotor cortex neurons per monkey. This neuronal ensemble
activity was transformed by a linear decoder into the robotic wheelchair's
translational and rotational velocities. During several weeks of training,
monkeys significantly improved their ability to navigate the wheelchair toward
the location of a food reward. The navigation was enacted by ensemble
modulations attuned to the whole-body displacements, and also to the distance
to the food location. These results demonstrate that intracranial BMIs could
restore whole-body mobility to severely paralyzed patients in the future.
| [
{
"created": "Thu, 9 Apr 2015 20:57:48 GMT",
"version": "v1"
}
] | 2015-04-13 | [
[
"Rajangam",
"Sankaranarayani",
""
],
[
"Tseng",
"Po-He",
""
],
[
"Yin",
"Allen",
""
],
[
"Lebedev",
"Mikhail A.",
""
],
[
"Nicolelis",
"Miguel A. L.",
""
]
] | We and others have previously developed brain-machine-interfaces (BMIs), which allowed ensembles of cortical neurons to control artificial limbs (1-4). However, it is unclear whether cortical ensembles could operate a BMI for whole-body navigation. Here we show that rhesus monkeys can learn to navigate a robotic wheelchair while seated on top of it, and using their cortical activity as the robot control signal. Two monkeys were chronically implanted with multichannel electrode arrays which simultaneously sampled activity of roughly 150 premotor and sensorimotor cortex neurons per monkey. This neuronal ensemble activity was transformed by a linear decoder into the robotic wheelchair's translational and rotational velocities. During several weeks of training, monkeys significantly improved their ability to navigate the wheelchair toward the location of a food reward. The navigation was enacted by ensemble modulations attuned to the whole-body displacements, and also to the distance to the food location. These results demonstrate that intracranial BMIs could restore whole-body mobility to severely paralyzed patients in the future. |
q-bio/0510014 | Can Ozan Tan Mr. | Can Ozan Tan, Uygar Ozesmi and Bahtiyar Kurt | Predictive Models for Characterization of Ecological Data | 14 pages, 1 figure. Submitted to Ecology as a Statistical Report | null | null | null | q-bio.QM q-bio.PE | null | Although ARTMAP and ART-based models were introduced in early 70's they were
not used in characterizing and classifying ecological observations. ART-based
models have been extensively used for classification models based on satellite
imagery. This report, to our knowledge, is the first application of ART-based
methods and specifically ARTMAP for predicting habitat selection and spatial
distribution of species. We compare the performance of ARTMAP to assess the
breeding success of three bird species (Lanius senator, Hippolais pallida, and
Calandrella brachydactyla) based on multi-spectral satellite imagery and
environmental variables. ARTMAP is superior both in terms of performance
(percent correctly classified - pcc = 1.00) and generalizability (pcc >0.96) to
those of feedforward multilayer backpropogation (>0.87, >0.65), linear and
quadratic discriminant analysis (>0.48, >0.46) and k-nearest neighbor (>0.82,
>0.66) methods. Compared to other methods, ARTMAP is able to incorporate new
observations with far less computational effort and can easily add data to
already trained models.
| [
{
"created": "Thu, 6 Oct 2005 15:35:47 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Tan",
"Can Ozan",
""
],
[
"Ozesmi",
"Uygar",
""
],
[
"Kurt",
"Bahtiyar",
""
]
] | Although ARTMAP and ART-based models were introduced in early 70's they were not used in characterizing and classifying ecological observations. ART-based models have been extensively used for classification models based on satellite imagery. This report, to our knowledge, is the first application of ART-based methods and specifically ARTMAP for predicting habitat selection and spatial distribution of species. We compare the performance of ARTMAP to assess the breeding success of three bird species (Lanius senator, Hippolais pallida, and Calandrella brachydactyla) based on multi-spectral satellite imagery and environmental variables. ARTMAP is superior both in terms of performance (percent correctly classified - pcc = 1.00) and generalizability (pcc >0.96) to those of feedforward multilayer backpropogation (>0.87, >0.65), linear and quadratic discriminant analysis (>0.48, >0.46) and k-nearest neighbor (>0.82, >0.66) methods. Compared to other methods, ARTMAP is able to incorporate new observations with far less computational effort and can easily add data to already trained models. |
1902.00337 | Luiz Pessoa | Luiz Pessoa | Neural dynamics of emotion and cognition: from trajectories to
underlying neural geometry | 12 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This paper describes the outlines of a research program for understanding the
cognitive-emotional brain, with an emphasis on the issue of dynamics: How can
we study, characterize, and understand the neural underpinnings of
cognitive-emotional behaviors as inherently dynamic processes? The framework
embraces many of the central themes developed by Steve Grossberg in his
extensive body of work in the past 50 years. By embracing head on the
leitmotifs of dynamics, decentralized computation, emergence, selection and
competition, and autonomy, it is proposed that a science of the mind-brain can
be developed that is built upon a solid foundation of understanding behavior
while employing computational and mathematical tools in an integral manner. A
key implication of the framework is that standard ways of thinking about
causation are inadequate when unravelling the workings of a complex system such
as the brain. Instead, it is proposed that researchers should focus on
determining the dynamic multivariate structure of brain data. Accordingly,
central problems become to characterize the dimensionality of neural
trajectories, and the geometry of the underlying neural space. At a time when
the development of neurotechniques has reached a fever pitch, neuroscience
needs to redirect its focus and invest comparable energy in the conceptual and
theoretical dimensions of its research endeavor. Otherwise we run the risk of
being able to measure 'every atom' in the brain in a theoretical vacuum.
| [
{
"created": "Fri, 1 Feb 2019 14:03:28 GMT",
"version": "v1"
}
] | 2019-02-04 | [
[
"Pessoa",
"Luiz",
""
]
] | This paper describes the outlines of a research program for understanding the cognitive-emotional brain, with an emphasis on the issue of dynamics: How can we study, characterize, and understand the neural underpinnings of cognitive-emotional behaviors as inherently dynamic processes? The framework embraces many of the central themes developed by Steve Grossberg in his extensive body of work in the past 50 years. By embracing head on the leitmotifs of dynamics, decentralized computation, emergence, selection and competition, and autonomy, it is proposed that a science of the mind-brain can be developed that is built upon a solid foundation of understanding behavior while employing computational and mathematical tools in an integral manner. A key implication of the framework is that standard ways of thinking about causation are inadequate when unravelling the workings of a complex system such as the brain. Instead, it is proposed that researchers should focus on determining the dynamic multivariate structure of brain data. Accordingly, central problems become to characterize the dimensionality of neural trajectories, and the geometry of the underlying neural space. At a time when the development of neurotechniques has reached a fever pitch, neuroscience needs to redirect its focus and invest comparable energy in the conceptual and theoretical dimensions of its research endeavor. Otherwise we run the risk of being able to measure 'every atom' in the brain in a theoretical vacuum. |
2002.02807 | Thomas Passer Jensen | T. P. Jensen, S. Tata, A. J. Ijspeert, S. Tolu | Adaptive control for hindlimb locomotion in a simulated mouse through
temporal cerebellar learning | To be published in NICE '20: Proceedings of the 8th Annual
Neuro-inspired Computational Elements Workshop. 8 pages, 13 figures | null | 10.1145/3381755 | null | q-bio.NC cs.NE | http://creativecommons.org/licenses/by/4.0/ | Human beings and other vertebrates show remarkable performance and efficiency
in locomotion, but the functioning of their biological control systems for
locomotion is still only partially understood. The basic patterns and timing
for locomotion are provided by a central pattern generator (CPG) in the spinal
cord. The cerebellum is known to play an important role in adaptive locomotion.
Recent studies have given insights into the error signals responsible for
driving the cerebellar adaptation in locomotion. However, the question of how
the cerebellar output influences the gait remains unanswered. We hypothesize
that the cerebellar correction is applied to the pattern formation part of the
CPG. Here, a bio-inspired control system for adaptive locomotion of the
musculoskeletal system of the mouse is presented, where a cerebellar-like
module adapts the step time by using the double support interlimb asymmetry as
a temporal teaching signal. The control system is tested on a simulated mouse
in a split-belt treadmill setup similar to those used in experiments with real
mice. The results show adaptive locomotion behavior in the interlimb parameters
similar to that seen in humans and mice. The control system adaptively
decreases the double support asymmetry that occurs due to environmental
perturbations in the split-belt protocol.
| [
{
"created": "Fri, 7 Feb 2020 14:25:21 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Feb 2020 09:00:27 GMT",
"version": "v2"
}
] | 2020-07-14 | [
[
"Jensen",
"T. P.",
""
],
[
"Tata",
"S.",
""
],
[
"Ijspeert",
"A. J.",
""
],
[
"Tolu",
"S.",
""
]
] | Human beings and other vertebrates show remarkable performance and efficiency in locomotion, but the functioning of their biological control systems for locomotion is still only partially understood. The basic patterns and timing for locomotion are provided by a central pattern generator (CPG) in the spinal cord. The cerebellum is known to play an important role in adaptive locomotion. Recent studies have given insights into the error signals responsible for driving the cerebellar adaptation in locomotion. However, the question of how the cerebellar output influences the gait remains unanswered. We hypothesize that the cerebellar correction is applied to the pattern formation part of the CPG. Here, a bio-inspired control system for adaptive locomotion of the musculoskeletal system of the mouse is presented, where a cerebellar-like module adapts the step time by using the double support interlimb asymmetry as a temporal teaching signal. The control system is tested on a simulated mouse in a split-belt treadmill setup similar to those used in experiments with real mice. The results show adaptive locomotion behavior in the interlimb parameters similar to that seen in humans and mice. The control system adaptively decreases the double support asymmetry that occurs due to environmental perturbations in the split-belt protocol. |
1801.05389 | Veli Shakhmurov | Veli Shakhmurov | On the dynamics of a cancer tumor growth model with multiphase structure | null | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we study a phase-space analysis of a mathematical model of
tumor growth with an immune response. Mathematical analysis of the model
equations with multipoint initial condition, regarding to dissipativity,
boundedness of solutions, invariance of non-negativity, nature of equilibria,
local and global stability will be investigated. We study some features of
behavior of one three-dimensional tumor growth model with dynamics described in
terms of densities of three cells populations: tumor cells, healthy host cells
and effector immune cells. We find the upper and lower bounds for the effector
immune cells population. Further, we derive sufficient conditions under which
trajectories from the positive domain of feasible multipoint initial conditions
tend to one of equilibrium points. Here cases of the small tumor mass
equilibrium point; the healthy equilibrium point; the "death" equilibrium point
are examined. Biological implications of our results are considered
| [
{
"created": "Tue, 9 Jan 2018 13:47:39 GMT",
"version": "v1"
}
] | 2018-01-17 | [
[
"Shakhmurov",
"Veli",
""
]
] | In this paper, we study a phase-space analysis of a mathematical model of tumor growth with an immune response. Mathematical analysis of the model equations with multipoint initial condition, regarding to dissipativity, boundedness of solutions, invariance of non-negativity, nature of equilibria, local and global stability will be investigated. We study some features of behavior of one three-dimensional tumor growth model with dynamics described in terms of densities of three cells populations: tumor cells, healthy host cells and effector immune cells. We find the upper and lower bounds for the effector immune cells population. Further, we derive sufficient conditions under which trajectories from the positive domain of feasible multipoint initial conditions tend to one of equilibrium points. Here cases of the small tumor mass equilibrium point; the healthy equilibrium point; the "death" equilibrium point are examined. Biological implications of our results are considered |
1611.10091 | Michael Bengfort | Michael Bengfort, Ivo Siekmann, Horst Malchow | Invasive competition with Fokker-Planck diffusion and noise | 10 pages | null | 10.1016/j.ecocom.2017.09.001 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Defeat and success of the competitive invasion of a populated area is
described with a standard Lotka-Volterra competition model. The resident is
adapted to the heterogeneous living conditions, i.e., its motion is modelled as
space-dependent, so-called Fokker-Planck diffusion. The invader's diffusion is
taken as neutral Fickian. Furthermore, it is studied how multiplicative
environmental noise fosters or hinders the invasion.
| [
{
"created": "Wed, 30 Nov 2016 11:06:41 GMT",
"version": "v1"
}
] | 2017-10-12 | [
[
"Bengfort",
"Michael",
""
],
[
"Siekmann",
"Ivo",
""
],
[
"Malchow",
"Horst",
""
]
] | Defeat and success of the competitive invasion of a populated area is described with a standard Lotka-Volterra competition model. The resident is adapted to the heterogeneous living conditions, i.e., its motion is modelled as space-dependent, so-called Fokker-Planck diffusion. The invader's diffusion is taken as neutral Fickian. Furthermore, it is studied how multiplicative environmental noise fosters or hinders the invasion. |
2001.09936 | Charlotta Bengtson | Charlotta Bengtson and Annemie Bogaerts | On the anti-cancer effect of cold atmospheric plasma and the possible
role of catalase-dependent apoptotic pathways | null | Cells 2020, 9, 2330 | 10.3390/cells9102330 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cold atmospheric plasma (CAP) is a promising new agent for (selective) cancer
treatment, but the underlying cause of the anti-cancer effect of CAP is not
well understood yet. Among different theories and observations, one theory in
particular has been postulated in great detail and consists of a very complex
network of reactions that are claimed to account for the anti-cancer effect of
CAP. Here, the key concept is a reactivation of two specific apoptotic cell
signaling pathways through catalase inactivation caused by CAP. Thus, it is
postulated that the anti-cancer effect of CAP is due to its ability to
inactivate catalase, either directly or indirectly. A theoretical investigation
of the proposed theory, especially the role of catalase inactivation, can
contribute to the understanding of the underlying cause of the anti-cancer
effect of CAP. In the present study, we develop a mathematical model to analyze
the proposed catalase-dependent anti-cancer effect of CAP. Our results show
that a catalase-dependent reactivation of the two apoptotic pathways of
interest is unlikely to contribute to the observed anti-cancer effect of CAP.
Thus, we believe that other theories of the underlying cause should be
considered and evaluated to gain knowledge about the principles of CAP-induced
cancer cell death.
| [
{
"created": "Mon, 27 Jan 2020 17:55:38 GMT",
"version": "v1"
},
{
"created": "Tue, 27 Oct 2020 15:49:13 GMT",
"version": "v2"
}
] | 2020-10-28 | [
[
"Bengtson",
"Charlotta",
""
],
[
"Bogaerts",
"Annemie",
""
]
] | Cold atmospheric plasma (CAP) is a promising new agent for (selective) cancer treatment, but the underlying cause of the anti-cancer effect of CAP is not well understood yet. Among different theories and observations, one theory in particular has been postulated in great detail and consists of a very complex network of reactions that are claimed to account for the anti-cancer effect of CAP. Here, the key concept is a reactivation of two specific apoptotic cell signaling pathways through catalase inactivation caused by CAP. Thus, it is postulated that the anti-cancer effect of CAP is due to its ability to inactivate catalase, either directly or indirectly. A theoretical investigation of the proposed theory, especially the role of catalase inactivation, can contribute to the understanding of the underlying cause of the anti-cancer effect of CAP. In the present study, we develop a mathematical model to analyze the proposed catalase-dependent anti-cancer effect of CAP. Our results show that a catalase-dependent reactivation of the two apoptotic pathways of interest is unlikely to contribute to the observed anti-cancer effect of CAP. Thus, we believe that other theories of the underlying cause should be considered and evaluated to gain knowledge about the principles of CAP-induced cancer cell death. |
1703.09852 | Eugene Goltsman | Eugene Goltsman, Isaac Ho, Daniel Rokhsar | Meraculous-2D: Haplotype-sensitive Assembly of Highly Heterozygous
genomes | Availability: Meraculous-2D is available under the GNU General Public
License from https://sourceforge.net/projects/meraculous20/ | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While many short read assemblers attempt to simplify the de Brujin graph by
identifying and resolving variant-induced bubbles to produce a haploid mosaic
result, this approach is only viable when variants are relatively rare and the
bubbles are well defined in a graph context. We observed that diploid genomes
with very high levels of heterozygosity fail to display well-resolved bubble
structures in a typical assembly graph and thus result in highly fragmented and
incomplete assemblies. Here we present an enhancement of Meraculous2 algorithm,
called Meraculous-2D, which preserves haplotypes across variant sites and
generates accurate assembly of highly heterozygous diploid genomes. Preserving
and taking advantage of the allelic variation throughout the assembly process
allows reconstructing both haplomes at once, without the need to pick arbitrary
paths through bubble structures. We also enhanced the original diploidy
resolution method of Meraculous2 to maintain and report phased haplotype
variant information.
| [
{
"created": "Wed, 29 Mar 2017 00:58:02 GMT",
"version": "v1"
}
] | 2017-03-30 | [
[
"Goltsman",
"Eugene",
""
],
[
"Ho",
"Isaac",
""
],
[
"Rokhsar",
"Daniel",
""
]
] | While many short read assemblers attempt to simplify the de Brujin graph by identifying and resolving variant-induced bubbles to produce a haploid mosaic result, this approach is only viable when variants are relatively rare and the bubbles are well defined in a graph context. We observed that diploid genomes with very high levels of heterozygosity fail to display well-resolved bubble structures in a typical assembly graph and thus result in highly fragmented and incomplete assemblies. Here we present an enhancement of Meraculous2 algorithm, called Meraculous-2D, which preserves haplotypes across variant sites and generates accurate assembly of highly heterozygous diploid genomes. Preserving and taking advantage of the allelic variation throughout the assembly process allows reconstructing both haplomes at once, without the need to pick arbitrary paths through bubble structures. We also enhanced the original diploidy resolution method of Meraculous2 to maintain and report phased haplotype variant information. |
1901.00280 | Tom Chou | Stephanie M. Lewkiewicz, Yao-Li Chuang, Tom Chou | A mathematical model of the effects of aging on naive T-cell population
and diversity | 29 pages, 6 figures | null | null | null | q-bio.PE q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The human adaptive immune response is known to weaken in advanced age,
resulting in increased severity of pathogen-born illness, poor vaccine
efficacy, and a higher prevalence of cancer in the elderly. Age-related erosion
of the T-cell compartment has been implicated as a likely cause, but the
underlying mechanisms driving this immunosenescence have not been
quantitatively modeled and systematically analyzed. T-cell receptor diversity,
or the extent of pathogen-derived antigen responsiveness of the T-cell pool, is
known to diminish with age, but inherent experimental difficulties preclude
accurate analysis on the full organismal level. In this paper, we formulate a
mechanistic mathematical model of T-cell population dynamics on the
immunoclonal subpopulation level, which provides quantitative estimates of
diversity. We define different estimates for diversity that depend on the
individual number of cells in a specific immunoclone. We show that diversity
decreases with age primarily due to diminished thymic output of new T-cells and
the resulting overall loss of small immunoclones.
| [
{
"created": "Wed, 2 Jan 2019 06:48:00 GMT",
"version": "v1"
}
] | 2019-01-03 | [
[
"Lewkiewicz",
"Stephanie M.",
""
],
[
"Chuang",
"Yao-Li",
""
],
[
"Chou",
"Tom",
""
]
] | The human adaptive immune response is known to weaken in advanced age, resulting in increased severity of pathogen-born illness, poor vaccine efficacy, and a higher prevalence of cancer in the elderly. Age-related erosion of the T-cell compartment has been implicated as a likely cause, but the underlying mechanisms driving this immunosenescence have not been quantitatively modeled and systematically analyzed. T-cell receptor diversity, or the extent of pathogen-derived antigen responsiveness of the T-cell pool, is known to diminish with age, but inherent experimental difficulties preclude accurate analysis on the full organismal level. In this paper, we formulate a mechanistic mathematical model of T-cell population dynamics on the immunoclonal subpopulation level, which provides quantitative estimates of diversity. We define different estimates for diversity that depend on the individual number of cells in a specific immunoclone. We show that diversity decreases with age primarily due to diminished thymic output of new T-cells and the resulting overall loss of small immunoclones. |
2207.04577 | Josinaldo Menezes | J. Menezes, B. Moura, E. Rangel | Adaptive survival movement strategy to local epidemic outbreaks in
cyclic models | 20 pages, 7 figures | Journal of Physics: Complexity 3, 045008 (2022) | 10.1088/2632-072X/aca251 | null | q-bio.PE math.DS nlin.AO nlin.PS physics.app-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the generalised rock-paper-scissors game with five species whose
organisms face local epidemic outbreaks. As an evolutionary behavioural
survival strategy, organisms of one out of the species move in the direction
with more enemies of their enemies to benefit from protection against
selection. We consider that each organism scans the environment, performing
social distancing instead of agglomerating when perceiving that the density of
sick organisms is higher than a tolerable threshold. Running stochastic
simulations, we study the interference of the adaptive movement survival
strategy in spatial pattern formation, calculating the characteristic length
scale of the typical spatial domains inhabited by organisms of each species. We
compute how social distancing trigger impacts the chances of an individual
being killed in the cyclic game and contaminated by the disease. The outcomes
show that the species predominates in the cyclic game because of the organisms'
local adaptation. The territory occupied by the species grows with the
proportion of individuals learning to trigger the social distancing tactic. We
also show that organisms that perceive large distances more properly execute
the adaptive strategy, promptly triggering the social distancing tactic and
choosing the correct direction to move. Our findings may contribute to
understanding the role of adaptive behaviour when environmental changes
threaten biodiversity.
| [
{
"created": "Mon, 11 Jul 2022 01:34:51 GMT",
"version": "v1"
},
{
"created": "Tue, 27 Dec 2022 18:35:03 GMT",
"version": "v2"
}
] | 2022-12-29 | [
[
"Menezes",
"J.",
""
],
[
"Moura",
"B.",
""
],
[
"Rangel",
"E.",
""
]
] | We study the generalised rock-paper-scissors game with five species whose organisms face local epidemic outbreaks. As an evolutionary behavioural survival strategy, organisms of one out of the species move in the direction with more enemies of their enemies to benefit from protection against selection. We consider that each organism scans the environment, performing social distancing instead of agglomerating when perceiving that the density of sick organisms is higher than a tolerable threshold. Running stochastic simulations, we study the interference of the adaptive movement survival strategy in spatial pattern formation, calculating the characteristic length scale of the typical spatial domains inhabited by organisms of each species. We compute how social distancing trigger impacts the chances of an individual being killed in the cyclic game and contaminated by the disease. The outcomes show that the species predominates in the cyclic game because of the organisms' local adaptation. The territory occupied by the species grows with the proportion of individuals learning to trigger the social distancing tactic. We also show that organisms that perceive large distances more properly execute the adaptive strategy, promptly triggering the social distancing tactic and choosing the correct direction to move. Our findings may contribute to understanding the role of adaptive behaviour when environmental changes threaten biodiversity. |
1704.04818 | Liane Gabora | Liane Gabora, Samantha Thomson, and Kirsty Kitto | A Layperson Introduction to the Quantum Approach to Humor | 11 pages; In W. Ruch (Ed.) Humor: Transdisciplinary approaches (pp.
317-322). Bogot\'a Colombia: Universidad Cooperativa de Colombia Press | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite our familiarity with and fondness of humor, until relatively recently
very little was known about the underlying psychology of this complex and
nuanced phenomenon. Recently, however, cognitive psychologists have begun
investigating how people understand humor and why we find certain things funny.
This chapter introduces a new cognitive approach to modeling humor that we
refer to as the 'quantum approach', which will be explained here in intuitive,
non-mathematical terms later (a formal treatment can be found in Gabora &
Kitto, 2017). What makes the quantum approach a promising candidate for a
theory of humor is that it can be useful for representing states of ambiguity,
and it defines states and variables with reference to a context. Contextuality
and ambiguity both play a key role in humor, which often hangs on an ambiguous
word, phrase, or situation that might not make sense, or even be socially
acceptable outside the specific context of the joke. The quantum approach does
not attempt to explain all aspects of humor, such as the contagious quality of
laughter, or why children tease each other, or why people might find it funny
when someone is hit in the face with a pie (and laugh even if they know it will
happen in advance); what it aims to do is to mathematically represent the
underlying cognitive process of "getting" a joke. After briefly overviewing the
relevant historical antecedents of the quantum approach and other related
approaches in cognitive psychology, we present the theoretical basis of our
approach, and outline a recent study that provides empirical support for it.
| [
{
"created": "Sun, 16 Apr 2017 20:52:41 GMT",
"version": "v1"
},
{
"created": "Wed, 13 Mar 2019 22:24:36 GMT",
"version": "v2"
}
] | 2019-03-15 | [
[
"Gabora",
"Liane",
""
],
[
"Thomson",
"Samantha",
""
],
[
"Kitto",
"Kirsty",
""
]
] | Despite our familiarity with and fondness of humor, until relatively recently very little was known about the underlying psychology of this complex and nuanced phenomenon. Recently, however, cognitive psychologists have begun investigating how people understand humor and why we find certain things funny. This chapter introduces a new cognitive approach to modeling humor that we refer to as the 'quantum approach', which will be explained here in intuitive, non-mathematical terms later (a formal treatment can be found in Gabora & Kitto, 2017). What makes the quantum approach a promising candidate for a theory of humor is that it can be useful for representing states of ambiguity, and it defines states and variables with reference to a context. Contextuality and ambiguity both play a key role in humor, which often hangs on an ambiguous word, phrase, or situation that might not make sense, or even be socially acceptable outside the specific context of the joke. The quantum approach does not attempt to explain all aspects of humor, such as the contagious quality of laughter, or why children tease each other, or why people might find it funny when someone is hit in the face with a pie (and laugh even if they know it will happen in advance); what it aims to do is to mathematically represent the underlying cognitive process of "getting" a joke. After briefly overviewing the relevant historical antecedents of the quantum approach and other related approaches in cognitive psychology, we present the theoretical basis of our approach, and outline a recent study that provides empirical support for it. |
2112.01223 | Ryohei Fukuma | Ryohei Fukuma, Takufumi Yanagisawa, Shinji Nishimoto, Hidenori Sugano,
Kentaro Tamura, Shota Yamamoto, Yasushi Iimura, Yuya Fujita, Satoru Oshino,
Naoki Tani, Naoko Koide-Majima, Yukiyasu Kamitani, Haruhiko Kishima | Voluntary control of semantic neural representations by imagery with
conflicting visual stimulation | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural representations of visual perception are affected by mental imagery
and attention. Although attention is known to modulate neural representations,
it is unknown how imagery changes neural representations when imagined and
perceived images semantically conflict. We hypothesized that imagining an image
would activate a neural representation during its perception even while
watching a conflicting image. To test this hypothesis, we developed a
closed-loop system to show images inferred from electrocorticograms using a
visual semantic space. The successful control of the feedback images
demonstrated that the semantic vector inferred from electrocorticograms became
closer to the vector of the imagined category, even while watching images from
different categories. Moreover, modulation of the inferred vectors by mental
imagery depended on both the image category and time from the initiation of
imagery. The closed-loop control of the semantic vectors revealed an
asymmetrical interaction between visual perception and imagery.
| [
{
"created": "Sun, 7 Nov 2021 14:33:28 GMT",
"version": "v1"
}
] | 2021-12-03 | [
[
"Fukuma",
"Ryohei",
""
],
[
"Yanagisawa",
"Takufumi",
""
],
[
"Nishimoto",
"Shinji",
""
],
[
"Sugano",
"Hidenori",
""
],
[
"Tamura",
"Kentaro",
""
],
[
"Yamamoto",
"Shota",
""
],
[
"Iimura",
"Yasushi",
""
... | Neural representations of visual perception are affected by mental imagery and attention. Although attention is known to modulate neural representations, it is unknown how imagery changes neural representations when imagined and perceived images semantically conflict. We hypothesized that imagining an image would activate a neural representation during its perception even while watching a conflicting image. To test this hypothesis, we developed a closed-loop system to show images inferred from electrocorticograms using a visual semantic space. The successful control of the feedback images demonstrated that the semantic vector inferred from electrocorticograms became closer to the vector of the imagined category, even while watching images from different categories. Moreover, modulation of the inferred vectors by mental imagery depended on both the image category and time from the initiation of imagery. The closed-loop control of the semantic vectors revealed an asymmetrical interaction between visual perception and imagery. |
0809.1578 | Artem Novozhilov S | Artem S. Novozhilov | Heterogeneous Susceptibles-Infectives model: Mechanistic derivation of
the power law transmission function | 14 pages, 1 figure. This is an extended version of the manuscript
accepted for the publication in Proceedings of the 6th International
Conference Differential Equations and Dynamical Systems, Baltimore, USA, 2008 | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many epidemiological models a nonlinear transmission function is used in
the form of power law relationship. It is constantly argued that such form
reflects population heterogeneities including differences in the mixing
pattern, susceptibility, and spatial patchiness, although the function itself
is considered phenomenological. Comparison with large-scale simulations show
that models with this transmission function accurately approximate data from
highly heterogeneous sources. In this note we provide a mechanistic derivation
of the power law transmission function, starting with a simple heterogeneous
susceptibles--infectives (SI) model, which is based on a standard mass action
assumption. We also consider the simplest SI model with separable mixing and
compare our results with known results from the literature.
| [
{
"created": "Tue, 9 Sep 2008 19:07:26 GMT",
"version": "v1"
}
] | 2008-09-10 | [
[
"Novozhilov",
"Artem S.",
""
]
] | In many epidemiological models a nonlinear transmission function is used in the form of power law relationship. It is constantly argued that such form reflects population heterogeneities including differences in the mixing pattern, susceptibility, and spatial patchiness, although the function itself is considered phenomenological. Comparison with large-scale simulations show that models with this transmission function accurately approximate data from highly heterogeneous sources. In this note we provide a mechanistic derivation of the power law transmission function, starting with a simple heterogeneous susceptibles--infectives (SI) model, which is based on a standard mass action assumption. We also consider the simplest SI model with separable mixing and compare our results with known results from the literature. |
1706.03375 | Stefan Engblom | Stefan Engblom Daniel B. Wilson and Ruth E. Baker | Scalable population-level modeling of biological cells incorporating
mechanics and kinetics in continuous time | null | Roy. Soc. Open Sci. 5(8), (2018) | 10.1098/rsos.180379 | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The processes taking place inside the living cell are now understood to the
point where predictive computational models can be used to gain detailed
understanding of important biological phenomena. A key challenge is to
extrapolate this detailed knowledge of the individual cell to be able to
explain at the population level how cells interact and respond with each other
and their environment. In particular, the goal is to understand how organisms
develop, maintain and repair functional tissues and organs.
In this paper we propose a novel computational framework for modeling
populations of interacting cells. Our framework incorporates mechanistic,
constitutive descriptions of biomechanical properties of the cell population,
and uses a coarse graining approach to derive individual rate laws that enable
propagation of the population through time. Thanks to its multiscale nature,
the resulting simulation algorithm is extremely scalable and highly efficient.
As highlighted in our computational examples, the framework is also very
flexible and may straightforwardly be coupled with continuous-time descriptions
of biochemical signalling within, and between, individual cells.
| [
{
"created": "Sun, 11 Jun 2017 16:27:06 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Sep 2017 15:16:08 GMT",
"version": "v2"
},
{
"created": "Thu, 8 Mar 2018 14:00:41 GMT",
"version": "v3"
}
] | 2018-10-26 | [
[
"Wilson",
"Stefan Engblom Daniel B.",
""
],
[
"Baker",
"Ruth E.",
""
]
] | The processes taking place inside the living cell are now understood to the point where predictive computational models can be used to gain detailed understanding of important biological phenomena. A key challenge is to extrapolate this detailed knowledge of the individual cell to be able to explain at the population level how cells interact and respond with each other and their environment. In particular, the goal is to understand how organisms develop, maintain and repair functional tissues and organs. In this paper we propose a novel computational framework for modeling populations of interacting cells. Our framework incorporates mechanistic, constitutive descriptions of biomechanical properties of the cell population, and uses a coarse graining approach to derive individual rate laws that enable propagation of the population through time. Thanks to its multiscale nature, the resulting simulation algorithm is extremely scalable and highly efficient. As highlighted in our computational examples, the framework is also very flexible and may straightforwardly be coupled with continuous-time descriptions of biochemical signalling within, and between, individual cells. |
1406.5115 | Michael Paulin | Michael G. Paulin, Andre van Schaik | Bayesian Inference with Spiking Neurons | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans and other animals behave as if we perform fast Bayesian inference
underlying decisions and movement control given uncertain sense data. Here we
show that a biophysically realistic model of the subthreshold membrane
potential of a single neuron can exactly compute the numerator in Bayes rule
for inferring the Poisson parameter of a sensory spike train. A simple network
of spiking neurons can construct and represent the Bayesian posterior density
of a parameter of an external cause that affects the Poisson parameter,
accurately and in real time.
| [
{
"created": "Thu, 19 Jun 2014 17:09:11 GMT",
"version": "v1"
}
] | 2014-06-20 | [
[
"Paulin",
"Michael G.",
""
],
[
"van Schaik",
"Andre",
""
]
] | Humans and other animals behave as if we perform fast Bayesian inference underlying decisions and movement control given uncertain sense data. Here we show that a biophysically realistic model of the subthreshold membrane potential of a single neuron can exactly compute the numerator in Bayes rule for inferring the Poisson parameter of a sensory spike train. A simple network of spiking neurons can construct and represent the Bayesian posterior density of a parameter of an external cause that affects the Poisson parameter, accurately and in real time. |
2205.11828 | Priya Chakraborty | Priya Chakraborty, Sayantari Ghosh | Resource allocation determines alternate cell fate in Bistable Genetic
Switch | 11 pages, 5 figures | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Living cells need a constant availability of certain resources to have a
sustained gene expression process. Limited availability of cellular resources
for gene expression, like ribosomes, along with a variation of resource
affinity, significantly modifies the system dynamics. Factors like the
variation in rate of binding, or variation in efficiency of the recruited
resource have the potential to affect crucial dynamical phenomena like cell
fate determination. In this paper, we have taken a very important motif, a
bistable genetic toggle switch, and explored the effect of resource imbalance
in this circuit in terms of the bifurcations taking place. We show that initial
asymmetric biasing to resource via resource affinity or gene copy number,
significantly modifies the cell fate transition, both in pitchfork and saddle
node type bifurcation. Our study establishes that in a limited resource
environment, controlled resource allocation can be an important factor for
robust functioning of the synthetic or cellular genetic switches.
| [
{
"created": "Tue, 24 May 2022 06:47:12 GMT",
"version": "v1"
}
] | 2022-05-25 | [
[
"Chakraborty",
"Priya",
""
],
[
"Ghosh",
"Sayantari",
""
]
] | Living cells need a constant availability of certain resources to have a sustained gene expression process. Limited availability of cellular resources for gene expression, like ribosomes, along with a variation of resource affinity, significantly modifies the system dynamics. Factors like the variation in rate of binding, or variation in efficiency of the recruited resource have the potential to affect crucial dynamical phenomena like cell fate determination. In this paper, we have taken a very important motif, a bistable genetic toggle switch, and explored the effect of resource imbalance in this circuit in terms of the bifurcations taking place. We show that initial asymmetric biasing to resource via resource affinity or gene copy number, significantly modifies the cell fate transition, both in pitchfork and saddle node type bifurcation. Our study establishes that in a limited resource environment, controlled resource allocation can be an important factor for robust functioning of the synthetic or cellular genetic switches. |
1212.1928 | Diana David-Rus | Diana David-Rus | In the search of molecular signature of sarcopenia in C. elegans | 79 pages | null | null | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Age-related muscle decline, a condition referred to as sarcopenia and defined
as loss in muscle mass and muscle strength over time, is one of the most
pervasive problems of the elderly, such that significant declines in strength
and mobility affects essentially every old person We have found that ageing C.
elegans body wall muscle undergoes a process remarkably reminiscent of human
sarcopenia. Both have mid-life onset and are characterized by progressive loss
of sarcomeres and cytoplasmic volume; both are associated with locomotory
decline. To extend understanding of this fundamental problem, I surveyed
expression of all known muscle related genes to describe a profile of
transcriptional changes in muscle that transpires during adult life and ageing.
Importantly, the intersection of this dataset with that from ageing flies and
some human studies can suggest conserved genes that might impact the process
most strongly. Hypotheses I formulate will be used to drive experiments at the
bench and perhaps to focus attention for human therapies.
| [
{
"created": "Sun, 9 Dec 2012 22:30:12 GMT",
"version": "v1"
}
] | 2012-12-11 | [
[
"David-Rus",
"Diana",
""
]
] | Age-related muscle decline, a condition referred to as sarcopenia and defined as loss in muscle mass and muscle strength over time, is one of the most pervasive problems of the elderly, such that significant declines in strength and mobility affects essentially every old person We have found that ageing C. elegans body wall muscle undergoes a process remarkably reminiscent of human sarcopenia. Both have mid-life onset and are characterized by progressive loss of sarcomeres and cytoplasmic volume; both are associated with locomotory decline. To extend understanding of this fundamental problem, I surveyed expression of all known muscle related genes to describe a profile of transcriptional changes in muscle that transpires during adult life and ageing. Importantly, the intersection of this dataset with that from ageing flies and some human studies can suggest conserved genes that might impact the process most strongly. Hypotheses I formulate will be used to drive experiments at the bench and perhaps to focus attention for human therapies. |
2307.16167 | Peter Carstensen | Jacob Bendsen, Peter Emil Carstensen, Asbj{\o}rn Thode Reenberg,
Tobias K. S. Ritschel, John Bagterp J{\o}rgensen | Quantitative modeling and simulation of biochemical processes in the
human body | 18 pages, 12 figures, 7 tables. arXiv admin note: text overlap with
arXiv:2205.01473 | null | null | null | q-bio.QM cs.SY eess.SY math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a whole-body model of human metabolism that utilizes a system of
organs and blood vessels to simulate the enzymatic reactions. The model focuses
on key organs, including the brain, heart and lungs, liver, gut, and kidney, as
well as muscle and adipose tissue. The model equations are formulated using
stoichiometry and Michaelis-Menten kinetics to describe the enzymatic
reactions. We demonstrate how the model can be used to simulate the effects of
prolonged fasting and intermittent fasting on selected metabolite
concentrations and glucose flux. Furthermore, by simulating intermittent
fasting the effect on the carbohydrate, the protein and the lipid storage is
examined. We propose this method as a simple and intuitive approach for
modeling the human metabolism, which is general, systematic and easy to
incorporate. This could have potential applications in PK/PD drug development
and in understanding metabolic disorders.
| [
{
"created": "Sun, 30 Jul 2023 08:25:53 GMT",
"version": "v1"
}
] | 2023-08-01 | [
[
"Bendsen",
"Jacob",
""
],
[
"Carstensen",
"Peter Emil",
""
],
[
"Reenberg",
"Asbjørn Thode",
""
],
[
"Ritschel",
"Tobias K. S.",
""
],
[
"Jørgensen",
"John Bagterp",
""
]
] | We present a whole-body model of human metabolism that utilizes a system of organs and blood vessels to simulate the enzymatic reactions. The model focuses on key organs, including the brain, heart and lungs, liver, gut, and kidney, as well as muscle and adipose tissue. The model equations are formulated using stoichiometry and Michaelis-Menten kinetics to describe the enzymatic reactions. We demonstrate how the model can be used to simulate the effects of prolonged fasting and intermittent fasting on selected metabolite concentrations and glucose flux. Furthermore, by simulating intermittent fasting the effect on the carbohydrate, the protein and the lipid storage is examined. We propose this method as a simple and intuitive approach for modeling the human metabolism, which is general, systematic and easy to incorporate. This could have potential applications in PK/PD drug development and in understanding metabolic disorders. |
1408.1906 | Randal Olson | Randal S. Olson, Patrick B. Haley, Fred C. Dyer, Christoph Adami | Exploring the evolution of a trade-off between vigilance and foraging in
group-living organisms | 26 pages (double-spaced, single column), 6 figures, 2 SI figures | Royal Society open science 2 (2015) 150135 | 10.1098/rsos.150135 | null | q-bio.PE cs.GT cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the fact that grouping behavior has been actively studied for over a
century, the relative importance of the numerous proposed fitness benefits of
grouping remain unclear. We use a digital model of evolving prey under
simulated predation to directly explore the evolution of gregarious foraging
behavior according to one such benefit, the "many eyes" hypothesis. According
to this hypothesis, collective vigilance allows prey in large groups to detect
predators more efficiently by making alarm signals or behavioral cues to each
other, thereby allowing individuals within the group to spend more time
foraging. Here, we find that collective vigilance is sufficient to select for
gregarious foraging behavior as long there is not a direct cost for grouping
(e.g., competition for limited food resources), even when controlling for
confounding factors such as the dilution effect. Further, we explore the role
of the genetic relatedness and reproductive strategy of the prey, and find that
highly related groups of prey with a semelparous reproductive strategy are the
most likely to evolve gregarious foraging behavior mediated by the benefit of
vigilance. These findings, combined with earlier studies with evolving digital
organisms, further sharpen our understanding of the factors favoring grouping
behavior.
| [
{
"created": "Fri, 8 Aug 2014 16:37:03 GMT",
"version": "v1"
}
] | 2015-11-18 | [
[
"Olson",
"Randal S.",
""
],
[
"Haley",
"Patrick B.",
""
],
[
"Dyer",
"Fred C.",
""
],
[
"Adami",
"Christoph",
""
]
] | Despite the fact that grouping behavior has been actively studied for over a century, the relative importance of the numerous proposed fitness benefits of grouping remain unclear. We use a digital model of evolving prey under simulated predation to directly explore the evolution of gregarious foraging behavior according to one such benefit, the "many eyes" hypothesis. According to this hypothesis, collective vigilance allows prey in large groups to detect predators more efficiently by making alarm signals or behavioral cues to each other, thereby allowing individuals within the group to spend more time foraging. Here, we find that collective vigilance is sufficient to select for gregarious foraging behavior as long there is not a direct cost for grouping (e.g., competition for limited food resources), even when controlling for confounding factors such as the dilution effect. Further, we explore the role of the genetic relatedness and reproductive strategy of the prey, and find that highly related groups of prey with a semelparous reproductive strategy are the most likely to evolve gregarious foraging behavior mediated by the benefit of vigilance. These findings, combined with earlier studies with evolving digital organisms, further sharpen our understanding of the factors favoring grouping behavior. |
1811.01199 | Christoph von der Malsburg | Christoph von der Malsburg | Concerning the Neural Code | 28 pages, 5 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The central problem with understanding brain and mind is the neural code
issue: understanding the matter of our brain as basis for the phenomena of our
mind. The richness with which our mind represents our environment, the
parsimony of genetic data, the tremendous efficiency with which the brain
learns from scant sensory input and the creativity with which our mind
constructs mental worlds all speak in favor of mind as an emergent phenomenon.
This raises the further issue of how the neural code supports these processes
of organization. The central point of this communication is that the neural
code has the form of structured net fragments that are formed by network
self-organization, activate and de-activate on the functional time scale, and
spontaneously combine to form larger nets with the same basic structure.
| [
{
"created": "Sat, 3 Nov 2018 12:35:44 GMT",
"version": "v1"
}
] | 2018-11-06 | [
[
"von der Malsburg",
"Christoph",
""
]
] | The central problem with understanding brain and mind is the neural code issue: understanding the matter of our brain as basis for the phenomena of our mind. The richness with which our mind represents our environment, the parsimony of genetic data, the tremendous efficiency with which the brain learns from scant sensory input and the creativity with which our mind constructs mental worlds all speak in favor of mind as an emergent phenomenon. This raises the further issue of how the neural code supports these processes of organization. The central point of this communication is that the neural code has the form of structured net fragments that are formed by network self-organization, activate and de-activate on the functional time scale, and spontaneously combine to form larger nets with the same basic structure. |
0704.3049 | Ryan Gutenkunst | Ryan N. Gutenkunst, Fergal P. Casey, Joshua J. Waterfall, Christopher
R. Myers, James P. Sethna | Extracting falsifiable predictions from sloppy models | 4 pages, 2 figures. Submitted to the Annals of the New York Academy
of Sciences for publication in "Reverse Engineering Biological Networks:
Opportunities and Challenges in Computational Methods for Pathway Inference" | Annals of the New York Academy of Sciences 1115:203-211 (2007) | 10.1196/annals.1407.003 | null | q-bio.QM | null | Successful predictions are among the most compelling validations of any
model. Extracting falsifiable predictions from nonlinear multiparameter models
is complicated by the fact that such models are commonly sloppy, possessing
sensitivities to different parameter combinations that range over many decades.
Here we discuss how sloppiness affects the sorts of data that best constrain
model predictions, makes linear uncertainty approximations dangerous, and
introduces computational difficulties in Monte-Carlo uncertainty analysis. We
also present a useful test problem and suggest refinements to the standards by
which models are communicated.
| [
{
"created": "Mon, 23 Apr 2007 17:00:09 GMT",
"version": "v1"
}
] | 2007-11-24 | [
[
"Gutenkunst",
"Ryan N.",
""
],
[
"Casey",
"Fergal P.",
""
],
[
"Waterfall",
"Joshua J.",
""
],
[
"Myers",
"Christopher R.",
""
],
[
"Sethna",
"James P.",
""
]
] | Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated. |
1202.4550 | Hong Qian | Hong Qian and Hao Ge | Mesoscopic Biochemical Basis of Isogenetic Inheritance and Canalization:
Stochasticity, Nonlinearity, and Emergent Landscape | 24 pages, 6 figures | MCB: Molecular & Cellular Biomechanics, Vol. 9, pp. 1-30 (2012) | 10.3970/mcb.2012.009.001 | null | q-bio.SC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biochemical reaction systems in mesoscopic volume, under sustained
environmental chemical gradient(s), can have multiple stochastic attractors.
Two distinct mechanisms are known for their origins: ($a$) Stochastic
single-molecule events, such as gene expression, with slow gene on-off
dynamics; and ($b$) nonlinear networks with feedbacks. These two mechanisms
yield different volume dependence for the sojourn time of an attractor. As in
the classic Arrhenius theory for temperature dependent transition rates, a
landscape perspective provides a natural framework for the system's behavior.
However, due to the nonequilibrium nature of the open chemical systems, the
landscape, and the attractors it represents, are all themselves {\em emergent
properties} of complex, mesoscopic dynamics. In terms of the landscape, we show
a generalization of Kramers' approach is possible to provide a rate theory. The
emergence of attractors is a form of self-organization in the mesoscopic
system; stochastic attractors in biochemical systems such as gene regulation
and cellular signaling are naturally inheritable via cell division.
Delbr\"{u}ck-Gillespie's mesoscopic reaction system theory, therefore, provides
a biochemical basis for spontaneous isogenetic switching and canalization.
| [
{
"created": "Tue, 21 Feb 2012 08:01:46 GMT",
"version": "v1"
}
] | 2012-05-15 | [
[
"Qian",
"Hong",
""
],
[
"Ge",
"Hao",
""
]
] | Biochemical reaction systems in mesoscopic volume, under sustained environmental chemical gradient(s), can have multiple stochastic attractors. Two distinct mechanisms are known for their origins: ($a$) Stochastic single-molecule events, such as gene expression, with slow gene on-off dynamics; and ($b$) nonlinear networks with feedbacks. These two mechanisms yield different volume dependence for the sojourn time of an attractor. As in the classic Arrhenius theory for temperature dependent transition rates, a landscape perspective provides a natural framework for the system's behavior. However, due to the nonequilibrium nature of the open chemical systems, the landscape, and the attractors it represents, are all themselves {\em emergent properties} of complex, mesoscopic dynamics. In terms of the landscape, we show a generalization of Kramers' approach is possible to provide a rate theory. The emergence of attractors is a form of self-organization in the mesoscopic system; stochastic attractors in biochemical systems such as gene regulation and cellular signaling are naturally inheritable via cell division. Delbr\"{u}ck-Gillespie's mesoscopic reaction system theory, therefore, provides a biochemical basis for spontaneous isogenetic switching and canalization. |
q-bio/0403029 | David Lusseau | David Lusseau, M.E.J. Newman | Identifying the role that individual animals play in their social
network | 9 pages, 4 figures, submitted to Ecology Letters | Proc. R. Soc. London B (Suppl.) 271, S477-S481 (2004) | null | null | q-bio.PE cond-mat.stat-mech physics.bio-ph q-bio.QM | null | Techniques recently developed for the analysis of human social networks are
applied to the social network of bottlenose dolphins living in Doubtful Sound,
New Zealand. We identify communities and subcommunities within the dolphin
population and present evidence that sex- and age-related homophily play a role
in the formation of clusters of preferred companionship. We also identify
brokers who act as links between subcommunities and who appear to be crucial to
the social cohesion of the population as a whole. The network is found to be
similar to human social networks in some respects but different in some others
such as the level of assortative mixing by degree within the population. This
difference elucidates some of the means by which the network formed and
evolves.
| [
{
"created": "Sat, 20 Mar 2004 14:05:53 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Lusseau",
"David",
""
],
[
"Newman",
"M. E. J.",
""
]
] | Techniques recently developed for the analysis of human social networks are applied to the social network of bottlenose dolphins living in Doubtful Sound, New Zealand. We identify communities and subcommunities within the dolphin population and present evidence that sex- and age-related homophily play a role in the formation of clusters of preferred companionship. We also identify brokers who act as links between subcommunities and who appear to be crucial to the social cohesion of the population as a whole. The network is found to be similar to human social networks in some respects but different in some others such as the level of assortative mixing by degree within the population. This difference elucidates some of the means by which the network formed and evolves. |
1703.04176 | Satohiro Tajima | Satohiro Tajima, Takeshi Mita, Douglas J. Bakkum, Hirokazu Takahashi,
Taro Toyoizumi | Locally embedded presages of global network bursts | null | null | 10.1073/pnas.1705981114 | null | q-bio.NC math.DS nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spontaneous, synchronous bursting of neural population is a widely observed
phenomenon in nervous networks, which is considered important for functions and
dysfunctions of the brain. However, how the global synchrony across a large
number of neurons emerges from an initially non-bursting network state is not
fully understood. In this study, we develop a new state-space reconstruction
method combined with high-resolution recordings of cultured neurons. This
method extracts deterministic signatures of upcoming global bursts in "local"
dynamics of individual neurons during non-bursting periods. We find that local
information within a single-cell time series can compare with or even
outperform the global mean field activity for predicting future global bursts.
Moreover, the inter-cell variability in the burst predictability is found to
reflect the network structure realized in the non-bursting periods. These
findings demonstrate the deterministic mechanisms underlying the locally
concentrated early-warnings of the global state transition in self-organized
networks.
| [
{
"created": "Sun, 12 Mar 2017 21:04:39 GMT",
"version": "v1"
}
] | 2022-06-08 | [
[
"Tajima",
"Satohiro",
""
],
[
"Mita",
"Takeshi",
""
],
[
"Bakkum",
"Douglas J.",
""
],
[
"Takahashi",
"Hirokazu",
""
],
[
"Toyoizumi",
"Taro",
""
]
] | Spontaneous, synchronous bursting of neural population is a widely observed phenomenon in nervous networks, which is considered important for functions and dysfunctions of the brain. However, how the global synchrony across a large number of neurons emerges from an initially non-bursting network state is not fully understood. In this study, we develop a new state-space reconstruction method combined with high-resolution recordings of cultured neurons. This method extracts deterministic signatures of upcoming global bursts in "local" dynamics of individual neurons during non-bursting periods. We find that local information within a single-cell time series can compare with or even outperform the global mean field activity for predicting future global bursts. Moreover, the inter-cell variability in the burst predictability is found to reflect the network structure realized in the non-bursting periods. These findings demonstrate the deterministic mechanisms underlying the locally concentrated early-warnings of the global state transition in self-organized networks. |
2408.00057 | Dan Kalifa | Dan Kalifa, Uriel Singer, Kira Radinsky | GOProteinGNN: Leveraging Protein Knowledge Graphs for Protein
Representation Learning | null | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Proteins play a vital role in biological processes and are indispensable for
living organisms. Accurate representation of proteins is crucial, especially in
drug development. Recently, there has been a notable increase in interest in
utilizing machine learning and deep learning techniques for unsupervised
learning of protein representations. However, these approaches often focus
solely on the amino acid sequence of proteins and lack factual knowledge about
proteins and their interactions, thus limiting their performance. In this
study, we present GOProteinGNN, a novel architecture that enhances protein
language models by integrating protein knowledge graph information during the
creation of amino acid level representations. Our approach allows for the
integration of information at both the individual amino acid level and the
entire protein level, enabling a comprehensive and effective learning process
through graph-based learning. By doing so, we can capture complex relationships
and dependencies between proteins and their functional annotations, resulting
in more robust and contextually enriched protein representations. Unlike
previous fusion methods, GOProteinGNN uniquely learns the entire protein
knowledge graph during training, which allows it to capture broader relational
nuances and dependencies beyond mere triplets as done in previous work. We
perform a comprehensive evaluation on several downstream tasks demonstrating
that GOProteinGNN consistently outperforms previous methods, showcasing its
effectiveness and establishing it as a state-of-the-art solution for protein
representation learning.
| [
{
"created": "Wed, 31 Jul 2024 17:54:22 GMT",
"version": "v1"
}
] | 2024-08-02 | [
[
"Kalifa",
"Dan",
""
],
[
"Singer",
"Uriel",
""
],
[
"Radinsky",
"Kira",
""
]
] | Proteins play a vital role in biological processes and are indispensable for living organisms. Accurate representation of proteins is crucial, especially in drug development. Recently, there has been a notable increase in interest in utilizing machine learning and deep learning techniques for unsupervised learning of protein representations. However, these approaches often focus solely on the amino acid sequence of proteins and lack factual knowledge about proteins and their interactions, thus limiting their performance. In this study, we present GOProteinGNN, a novel architecture that enhances protein language models by integrating protein knowledge graph information during the creation of amino acid level representations. Our approach allows for the integration of information at both the individual amino acid level and the entire protein level, enabling a comprehensive and effective learning process through graph-based learning. By doing so, we can capture complex relationships and dependencies between proteins and their functional annotations, resulting in more robust and contextually enriched protein representations. Unlike previous fusion methods, GOProteinGNN uniquely learns the entire protein knowledge graph during training, which allows it to capture broader relational nuances and dependencies beyond mere triplets as done in previous work. We perform a comprehensive evaluation on several downstream tasks demonstrating that GOProteinGNN consistently outperforms previous methods, showcasing its effectiveness and establishing it as a state-of-the-art solution for protein representation learning. |
1009.1786 | Bob Eisenberg | Bob Eisenberg | Crowded Charges in Ion Channels | Paper will appear in Advances in Chemical Physics, Stuart Rice
Editor. Accepted for publication September 8, 2010 | null | null | null | q-bio.BM cond-mat.soft cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ions in water are the liquid of life. Life occurs almost entirely in 'salt
water'. Water itself (without ions) is lethal to animal cells and damaging for
most proteins. Water must contain the right ions in the right amounts if it is
to sustain life. Physical chemistry is the language of electrolyte solutions.
Physical chemistry and biology are intertwined. Physical chemists and
biologists come from different traditions that separated for several decades as
biologists described the molecules of life. Communication is not easy between a
fundamentally descriptive tradition and a fundamentally analytical one.
Biologists have now learned to study well defined systems with physical
techniques, of considerable interest to physical chemists. Physical chemists
are increasingly interested in spatially inhomogeneous systems with structures
on the atomic scale so common in biology. Physical chemists will find it
productive to work on well defined systems built by evolution to be reasonably
robust, with input output relations insensitive to environmental insults. This
article deals with properties of ion channels that in my view can be dealt with
by 'physics as usual', with much the same tools that physical chemists apply to
other systems. Indeed, I introduce and use a tool of physicists-a field theory
(and boundary conditions) based on an energy variational approach developed by
Chun Liu-not too widely used among physical chemists. My goal is to provide the
knowledge base, and identify the assumptions, that biologists use in studying
ion channels, avoiding jargon. Rather simple models of selectivity and
permeation in ion channels work quite well in important cases. Those physical
models and cases are the main focus of this review because they demonstrate the
strong essential link between the traditional treatments of ions in chemical
physics, and the biological function of ion channels.
| [
{
"created": "Thu, 9 Sep 2010 14:08:20 GMT",
"version": "v1"
}
] | 2010-09-10 | [
[
"Eisenberg",
"Bob",
""
]
] | Ions in water are the liquid of life. Life occurs almost entirely in 'salt water'. Water itself (without ions) is lethal to animal cells and damaging for most proteins. Water must contain the right ions in the right amounts if it is to sustain life. Physical chemistry is the language of electrolyte solutions. Physical chemistry and biology are intertwined. Physical chemists and biologists come from different traditions that separated for several decades as biologists described the molecules of life. Communication is not easy between a fundamentally descriptive tradition and a fundamentally analytical one. Biologists have now learned to study well defined systems with physical techniques, of considerable interest to physical chemists. Physical chemists are increasingly interested in spatially inhomogeneous systems with structures on the atomic scale so common in biology. Physical chemists will find it productive to work on well defined systems built by evolution to be reasonably robust, with input output relations insensitive to environmental insults. This article deals with properties of ion channels that in my view can be dealt with by 'physics as usual', with much the same tools that physical chemists apply to other systems. Indeed, I introduce and use a tool of physicists-a field theory (and boundary conditions) based on an energy variational approach developed by Chun Liu-not too widely used among physical chemists. My goal is to provide the knowledge base, and identify the assumptions, that biologists use in studying ion channels, avoiding jargon. Rather simple models of selectivity and permeation in ion channels work quite well in important cases. Those physical models and cases are the main focus of this review because they demonstrate the strong essential link between the traditional treatments of ions in chemical physics, and the biological function of ion channels. |
1708.05554 | Svetlana Poznanovi\'c | Fidel Barrera-Cruz, Christine Heitsch, Svetlana Poznanovi\'c | On the structure of RNA branching polytopes | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The prevalent method for RNA secondary structure prediction for a single
sequence is free energy minimization based on the nearest neighbor
thermodynamic model (NNTM). One of the least well-developed parts of the model
is the energy function assigned to the multibranch loops. Parametric analysis
can be performed to elucidate the dependance of the prediction on the branching
parameters used in the NNTM. Since the objective function is linear, this boils
down to analyzing the normal fans of the branching polytopes. Here we show that
because of the way the multibranch loops are scored under the NNTM, certain
branching patterns are possible for all sequences. We do this by characterizing
the dominant parts of the parameter space obtained by looking at the relevant
section of the normal fan; therefore, we conclude that the structures that are
normally found in nature are obtained for a relatively small set of parameters.
| [
{
"created": "Fri, 18 Aug 2017 10:30:15 GMT",
"version": "v1"
}
] | 2017-08-21 | [
[
"Barrera-Cruz",
"Fidel",
""
],
[
"Heitsch",
"Christine",
""
],
[
"Poznanović",
"Svetlana",
""
]
] | The prevalent method for RNA secondary structure prediction for a single sequence is free energy minimization based on the nearest neighbor thermodynamic model (NNTM). One of the least well-developed parts of the model is the energy function assigned to the multibranch loops. Parametric analysis can be performed to elucidate the dependance of the prediction on the branching parameters used in the NNTM. Since the objective function is linear, this boils down to analyzing the normal fans of the branching polytopes. Here we show that because of the way the multibranch loops are scored under the NNTM, certain branching patterns are possible for all sequences. We do this by characterizing the dominant parts of the parameter space obtained by looking at the relevant section of the normal fan; therefore, we conclude that the structures that are normally found in nature are obtained for a relatively small set of parameters. |
2109.07778 | Sergio Verduzco-Flores | Sergio Verduzco-Flores, Erik De Schutter | Self-configuring feedback loops for sensorimotor control | 32 pages, 9 figures. Appendix, 9 supplementary figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | How dynamic interactions between nervous system regions in mammals performs
online motor control remains an unsolved problem. In this paper we show that
feedback control is a simple, yet powerful way to understand the neural
dynamics of sensorimotor control. We make our case using a minimal model
comprising spinal cord, sensory and motor cortex, coupled by long connections
that are plastic. It succeeds in learning how to perform reaching movements of
a planar arm with 6 muscles in several directions from scratch. The model
satisfies biological plausibility constraints, like neural implementation,
transmission delays, local synaptic learning and continuous online learning.
Using differential Hebbian plasticity the model can go from motor babbling to
reaching arbitrary targets in less than 10 minutes of in silico time. Moreover,
independently of the learning mechanism, properly configured feedback control
has many emergent properties: neural populations in motor cortex show
directional tuning and oscillatory dynamics, the spinal cord creates convergent
force fields that add linearly, and movements are ataxic (as in a motor system
without a cerebellum).
| [
{
"created": "Thu, 16 Sep 2021 07:55:36 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Nov 2021 03:16:54 GMT",
"version": "v2"
},
{
"created": "Wed, 2 Feb 2022 03:13:03 GMT",
"version": "v3"
},
{
"created": "Fri, 19 Aug 2022 05:37:00 GMT",
"version": "v4"
},
{
"cr... | 2022-10-24 | [
[
"Verduzco-Flores",
"Sergio",
""
],
[
"De Schutter",
"Erik",
""
]
] | How dynamic interactions between nervous system regions in mammals performs online motor control remains an unsolved problem. In this paper we show that feedback control is a simple, yet powerful way to understand the neural dynamics of sensorimotor control. We make our case using a minimal model comprising spinal cord, sensory and motor cortex, coupled by long connections that are plastic. It succeeds in learning how to perform reaching movements of a planar arm with 6 muscles in several directions from scratch. The model satisfies biological plausibility constraints, like neural implementation, transmission delays, local synaptic learning and continuous online learning. Using differential Hebbian plasticity the model can go from motor babbling to reaching arbitrary targets in less than 10 minutes of in silico time. Moreover, independently of the learning mechanism, properly configured feedback control has many emergent properties: neural populations in motor cortex show directional tuning and oscillatory dynamics, the spinal cord creates convergent force fields that add linearly, and movements are ataxic (as in a motor system without a cerebellum). |
1501.03258 | Lin Du | Lin Du, Xiaodan Huang, Jian Tan, Yongjun Lu, Shining Zhou | Yeast caspase 1 suppresses the burst of reactive oxygen species and
maintains mitochondrial stability in Saccharomyces cerevisiae | null | null | null | null | q-bio.CB q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Caspases are a family of cysteine proteases that play essential roles during
apoptosis, and we presume some of them may also protect the cell from oxidative
stress. We found that the absence of yeast caspase 1(Yca1)in Saccharomyces
cerevisiae leads to a more intense burst of mitochondrial reactive oxygen
species (ROS) In addition, compared to wild type yeast cells, the ability of
yca1 mutant cells to maintain mitochondrial activity is significantly reduced
after either oxidative stress treatment or aging. During mitochondrial ROS
burst, deletion of the yca1 gene delayed structural damage of a green
fluorescent protein (GFP) reporter bound in the inner mitochondrial membrane.
This work implies that yeast caspase 1 is closely connected to the oxidative
stress response. We speculate that Yca1 can discriminate proteins damaged by
oxidation and accelerate their hydrolysis to attenuate the ROS burst.
| [
{
"created": "Wed, 14 Jan 2015 06:31:24 GMT",
"version": "v1"
}
] | 2015-01-15 | [
[
"Du",
"Lin",
""
],
[
"Huang",
"Xiaodan",
""
],
[
"Tan",
"Jian",
""
],
[
"Lu",
"Yongjun",
""
],
[
"Zhou",
"Shining",
""
]
] | Caspases are a family of cysteine proteases that play essential roles during apoptosis, and we presume some of them may also protect the cell from oxidative stress. We found that the absence of yeast caspase 1(Yca1)in Saccharomyces cerevisiae leads to a more intense burst of mitochondrial reactive oxygen species (ROS) In addition, compared to wild type yeast cells, the ability of yca1 mutant cells to maintain mitochondrial activity is significantly reduced after either oxidative stress treatment or aging. During mitochondrial ROS burst, deletion of the yca1 gene delayed structural damage of a green fluorescent protein (GFP) reporter bound in the inner mitochondrial membrane. This work implies that yeast caspase 1 is closely connected to the oxidative stress response. We speculate that Yca1 can discriminate proteins damaged by oxidation and accelerate their hydrolysis to attenuate the ROS burst. |
1903.09710 | Bryan Daniels | Bryan C. Daniels and Pawel Romanczuk | Quantifying the impact of network structure on speed and accuracy in
collective decision-making | 16 pages, 4 figures | null | null | null | q-bio.NC cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Found in varied contexts from neurons to ants to fish, binary decision-making
is one of the simplest forms of collective computation. In this process,
information collected by individuals about an uncertain environment is
accumulated to guide behavior at the aggregate scale. We study binary
decision-making dynamics in networks responding to inputs with small
signal-to-noise ratios, looking for quantitative measures of collectivity that
control decision-making performance. We find that decision accuracy is
controlled largely by three factors: the leading eigenvalue of the network
adjacency matrix, the corresponding eigenvector's participation ratio, and
distance from the corresponding symmetry-breaking bifurcation. This allows us
to predict how decision-making performance scales in large networks based on
their spectral properties. Specifically, we explore the effects of localization
caused by the hierarchical assortative structure of a "rich club" topology.
This gives insight into the tradeoffs involved in the higher-order structure
found in living networks performing collective computations.
| [
{
"created": "Fri, 22 Mar 2019 21:07:16 GMT",
"version": "v1"
}
] | 2019-03-26 | [
[
"Daniels",
"Bryan C.",
""
],
[
"Romanczuk",
"Pawel",
""
]
] | Found in varied contexts from neurons to ants to fish, binary decision-making is one of the simplest forms of collective computation. In this process, information collected by individuals about an uncertain environment is accumulated to guide behavior at the aggregate scale. We study binary decision-making dynamics in networks responding to inputs with small signal-to-noise ratios, looking for quantitative measures of collectivity that control decision-making performance. We find that decision accuracy is controlled largely by three factors: the leading eigenvalue of the network adjacency matrix, the corresponding eigenvector's participation ratio, and distance from the corresponding symmetry-breaking bifurcation. This allows us to predict how decision-making performance scales in large networks based on their spectral properties. Specifically, we explore the effects of localization caused by the hierarchical assortative structure of a "rich club" topology. This gives insight into the tradeoffs involved in the higher-order structure found in living networks performing collective computations. |
1501.04621 | Rajib Rana | Sajib Saha, Frank de Hoog, Ya.I. Nesterets, Rajib Rana, M. Tahtali and
T.E. Gureyev | Sparse Bayesian Learning for EEG Source Localization | arXiv admin note: substantial text overlap with arXiv:1406.2434 | null | null | null | q-bio.QM cs.LG q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Purpose: Localizing the sources of electrical activity from
electroencephalographic (EEG) data has gained considerable attention over the
last few years. In this paper, we propose an innovative source localization
method for EEG, based on Sparse Bayesian Learning (SBL). Methods: To better
specify the sparsity profile and to ensure efficient source localization, the
proposed approach considers grouping of the electrical current dipoles inside
human brain. SBL is used to solve the localization problem in addition with
imposed constraint that the electric current dipoles associated with the brain
activity are isotropic. Results: Numerical experiments are conducted on a
realistic head model that is obtained by segmentation of MRI images of the head
and includes four major components, namely the scalp, the skull, the
cerebrospinal fluid (CSF) and the brain, with appropriate relative conductivity
values. The results demonstrate that the isotropy constraint significantly
improves the performance of SBL. In a noiseless environment, the proposed
method was 1 found to accurately (with accuracy of >75%) locate up to 6
simultaneously active sources, whereas for SBL without the isotropy constraint,
the accuracy of finding just 3 simultaneously active sources was <75%.
Conclusions: Compared to the state-of-the-art algorithms, the proposed method
is potentially more consistent in specifying the sparsity profile of human
brain activity and is able to produce better source localization for EEG.
| [
{
"created": "Mon, 19 Jan 2015 16:11:03 GMT",
"version": "v1"
}
] | 2015-01-21 | [
[
"Saha",
"Sajib",
""
],
[
"de Hoog",
"Frank",
""
],
[
"Nesterets",
"Ya. I.",
""
],
[
"Rana",
"Rajib",
""
],
[
"Tahtali",
"M.",
""
],
[
"Gureyev",
"T. E.",
""
]
] | Purpose: Localizing the sources of electrical activity from electroencephalographic (EEG) data has gained considerable attention over the last few years. In this paper, we propose an innovative source localization method for EEG, based on Sparse Bayesian Learning (SBL). Methods: To better specify the sparsity profile and to ensure efficient source localization, the proposed approach considers grouping of the electrical current dipoles inside human brain. SBL is used to solve the localization problem in addition with imposed constraint that the electric current dipoles associated with the brain activity are isotropic. Results: Numerical experiments are conducted on a realistic head model that is obtained by segmentation of MRI images of the head and includes four major components, namely the scalp, the skull, the cerebrospinal fluid (CSF) and the brain, with appropriate relative conductivity values. The results demonstrate that the isotropy constraint significantly improves the performance of SBL. In a noiseless environment, the proposed method was 1 found to accurately (with accuracy of >75%) locate up to 6 simultaneously active sources, whereas for SBL without the isotropy constraint, the accuracy of finding just 3 simultaneously active sources was <75%. Conclusions: Compared to the state-of-the-art algorithms, the proposed method is potentially more consistent in specifying the sparsity profile of human brain activity and is able to produce better source localization for EEG. |
0806.2500 | Mike Steel Prof. | Mareike Fischer, Mike Steel | Sequence length bounds for resolving a deep phylogenetic divergence | 13 pages, 1 figure | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In evolutionary biology, genetic sequences carry with them a trace of the
underlying tree that describes their evolution from a common ancestral
sequence. The question of how many sequence sites are required to recover this
evolutionary relationship accurately depends on the model of sequence
evolution, the substitution rate, divergence times and the method used to infer
phylogenetic history. A particularly challenging problem for phylogenetic
methods arises when a rapid divergence event occurred in the distant past. We
analyse an idealised form of this problem in which the terminal edges of a
symmetric four--taxon tree are some factor ($p$) times the length of the
interior edge. We determine an order $p^2$ lower bound on the growth rate for
the sequence length required to resolve the tree (independent of any particular
branch length). We also show that this rate of sequence length growth can be
achieved by existing methods (including the simple `maximum parsimony' method),
and compare these order $p^2$ bounds with an order $p$ growth rate for a model
that describes low-homoplasy evolution. In the final section, we provide a
generic bound on the sequence length requirement for a more general class of
Markov processes.
| [
{
"created": "Mon, 16 Jun 2008 06:10:30 GMT",
"version": "v1"
}
] | 2008-06-17 | [
[
"Fischer",
"Mareike",
""
],
[
"Steel",
"Mike",
""
]
] | In evolutionary biology, genetic sequences carry with them a trace of the underlying tree that describes their evolution from a common ancestral sequence. The question of how many sequence sites are required to recover this evolutionary relationship accurately depends on the model of sequence evolution, the substitution rate, divergence times and the method used to infer phylogenetic history. A particularly challenging problem for phylogenetic methods arises when a rapid divergence event occurred in the distant past. We analyse an idealised form of this problem in which the terminal edges of a symmetric four--taxon tree are some factor ($p$) times the length of the interior edge. We determine an order $p^2$ lower bound on the growth rate for the sequence length required to resolve the tree (independent of any particular branch length). We also show that this rate of sequence length growth can be achieved by existing methods (including the simple `maximum parsimony' method), and compare these order $p^2$ bounds with an order $p$ growth rate for a model that describes low-homoplasy evolution. In the final section, we provide a generic bound on the sequence length requirement for a more general class of Markov processes. |
2408.00892 | Jacob Luber | Thuong Le Hoai Pham, Jillur Rahman Saurav, Aisosa A. Omere, Calvin J.
Heyl, Mohammad Sadegh Nasr, Cody Tyler Reynolds, Jai Prakash Yadav Veerla,
Helen H Shang, Justyn Jaworski, Alison Ravenscraft, Joseph Anthony Buonomo,
Jacob M. Luber | Peptide Sequencing Via Protein Language Models | null | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We introduce a protein language model for determining the complete sequence
of a peptide based on measurement of a limited set of amino acids. To date,
protein sequencing relies on mass spectrometry, with some novel edman
degregation based platforms able to sequence non-native peptides. Current
protein sequencing techniques face limitations in accurately identifying all
amino acids, hindering comprehensive proteome analysis. Our method simulates
partial sequencing data by selectively masking amino acids that are
experimentally difficult to identify in protein sequences from the UniRef
database. This targeted masking mimics real-world sequencing limitations. We
then modify and finetune a ProtBert derived transformer-based model, for a new
downstream task predicting these masked residues, providing an approximation of
the complete sequence. Evaluating on three bacterial Escherichia species, we
achieve per-amino-acid accuracy up to 90.5% when only four amino acids ([KCYM])
are known. Structural assessment using AlphaFold and TM-score validates the
biological relevance of our predictions. The model also demonstrates potential
for evolutionary analysis through cross-species performance. This integration
of simulated experimental constraints with computational predictions offers a
promising avenue for enhancing protein sequence analysis, potentially
accelerating advancements in proteomics and structural biology by providing a
probabilistic reconstruction of the complete protein sequence from limited
experimental data.
| [
{
"created": "Thu, 1 Aug 2024 20:12:49 GMT",
"version": "v1"
}
] | 2024-08-05 | [
[
"Pham",
"Thuong Le Hoai",
""
],
[
"Saurav",
"Jillur Rahman",
""
],
[
"Omere",
"Aisosa A.",
""
],
[
"Heyl",
"Calvin J.",
""
],
[
"Nasr",
"Mohammad Sadegh",
""
],
[
"Reynolds",
"Cody Tyler",
""
],
[
"Veerla",
"Ja... | We introduce a protein language model for determining the complete sequence of a peptide based on measurement of a limited set of amino acids. To date, protein sequencing relies on mass spectrometry, with some novel edman degregation based platforms able to sequence non-native peptides. Current protein sequencing techniques face limitations in accurately identifying all amino acids, hindering comprehensive proteome analysis. Our method simulates partial sequencing data by selectively masking amino acids that are experimentally difficult to identify in protein sequences from the UniRef database. This targeted masking mimics real-world sequencing limitations. We then modify and finetune a ProtBert derived transformer-based model, for a new downstream task predicting these masked residues, providing an approximation of the complete sequence. Evaluating on three bacterial Escherichia species, we achieve per-amino-acid accuracy up to 90.5% when only four amino acids ([KCYM]) are known. Structural assessment using AlphaFold and TM-score validates the biological relevance of our predictions. The model also demonstrates potential for evolutionary analysis through cross-species performance. This integration of simulated experimental constraints with computational predictions offers a promising avenue for enhancing protein sequence analysis, potentially accelerating advancements in proteomics and structural biology by providing a probabilistic reconstruction of the complete protein sequence from limited experimental data. |
2008.05909 | Yan Chu | Tongtong Huang, Yan Chu, Shayan Shams, Yejin Kim, Genevera Allen,
Ananth V Annapragada, Devika Subramanian, Ioannis Kakadiaris, Assaf Gottlieb,
Xiaoqian Jiang | Population stratification enables modeling effects of reopening policies
on mortality and hospitalization rates | null | null | null | null | q-bio.PE stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objective: We study the influence of local reopening policies on the
composition of the infectious population and their impact on future
hospitalization and mortality rates. Materials and Methods: We collected
datasets of daily reported hospitalization and cumulative morality of COVID 19
in Houston, Texas, from May 1, 2020 until June 29, 2020. These datasets are
from multiple sources (USA FACTS, Southeast Texas Regional Advisory Council
COVID 19 report, TMC daily news, and New York Times county level mortality
reporting). Our model, risk stratified SIR HCD uses separate variables to model
the dynamics of local contact (e.g., work from home) and high contact (e.g.,
work on site) subpopulations while sharing parameters to control their
respective $R_0(t)$ over time. Results: We evaluated our models forecasting
performance in Harris County, TX (the most populated county in the Greater
Houston area) during the Phase I and Phase II reopening. Not only did our model
outperform other competing models, it also supports counterfactual analysis to
simulate the impact of future policies in a local setting, which is unique
among existing approaches. Discussion: Local mortality and hospitalization are
significantly impacted by quarantine and reopening policies. No existing model
has directly accounted for the effect of these policies on local trends in
infections, hospitalizations, and deaths in an explicit and explainable manner.
Our work is an attempt to close this important technical gap to support
decision making. Conclusion: Despite several limitations, we think it is a
timely effort to rethink about how to best model the dynamics of pandemics
under the influence of reopening policies.
| [
{
"created": "Mon, 10 Aug 2020 04:39:59 GMT",
"version": "v1"
}
] | 2020-08-14 | [
[
"Huang",
"Tongtong",
""
],
[
"Chu",
"Yan",
""
],
[
"Shams",
"Shayan",
""
],
[
"Kim",
"Yejin",
""
],
[
"Allen",
"Genevera",
""
],
[
"Annapragada",
"Ananth V",
""
],
[
"Subramanian",
"Devika",
""
],
[
... | Objective: We study the influence of local reopening policies on the composition of the infectious population and their impact on future hospitalization and mortality rates. Materials and Methods: We collected datasets of daily reported hospitalization and cumulative morality of COVID 19 in Houston, Texas, from May 1, 2020 until June 29, 2020. These datasets are from multiple sources (USA FACTS, Southeast Texas Regional Advisory Council COVID 19 report, TMC daily news, and New York Times county level mortality reporting). Our model, risk stratified SIR HCD uses separate variables to model the dynamics of local contact (e.g., work from home) and high contact (e.g., work on site) subpopulations while sharing parameters to control their respective $R_0(t)$ over time. Results: We evaluated our models forecasting performance in Harris County, TX (the most populated county in the Greater Houston area) during the Phase I and Phase II reopening. Not only did our model outperform other competing models, it also supports counterfactual analysis to simulate the impact of future policies in a local setting, which is unique among existing approaches. Discussion: Local mortality and hospitalization are significantly impacted by quarantine and reopening policies. No existing model has directly accounted for the effect of these policies on local trends in infections, hospitalizations, and deaths in an explicit and explainable manner. Our work is an attempt to close this important technical gap to support decision making. Conclusion: Despite several limitations, we think it is a timely effort to rethink about how to best model the dynamics of pandemics under the influence of reopening policies. |
0911.0652 | Randen Patterson | Kyung Dae Ko, Yoojin Hong, Gaurav Bhardwaj, Teresa M. Killick, Damian
B. van Rossum, Randen L. Patterson | Brainstorming through the Sequence Universe: Theories on the Protein
Problem | 18 pages, 8 figures | null | null | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Just as physicists strive to develop a TOE (theory of everything), which
explains and unifies the physical laws of the universe, the life-scientist
wishes to uncover the TOE as it relates to cellular systems. This can only be
achieved with a quantitative platform that can comprehensively deduce and
relate protein structure, functional, and evolution of genomes and proteomes in
a comparative fashion. Were this perfected, proper analyses would start to
uncover the underlying physical laws governing the emergent behavior of
biological systems and the evolutionary pressures responsible for functional
innovation. In the near term, such methodology would allow the vast quantities
of uncharacterized (e.g. metagenomic samples) primary amino acid sequences to
be rapidly decoded. Analogous to natural products found in the Amazon, genomes
of living organisms contain large numbers of proteins that would prove useful
as new therapeutics for human health, energy sources, and/or waste management
solutions if they could be identified and characterized. We previously
theorized that phylogenetic profiles could provide a quantitative platform for
obtaining unified measures of structure, function, and evolution (SF&E)(1). In
the present manuscript, we present data that support this theory and
demonstrates how refinements of our analysis algorithms improve the performance
of phylogenetic profiles for deriving structural/functional relationships.
| [
{
"created": "Tue, 3 Nov 2009 19:05:51 GMT",
"version": "v1"
}
] | 2009-11-04 | [
[
"Ko",
"Kyung Dae",
""
],
[
"Hong",
"Yoojin",
""
],
[
"Bhardwaj",
"Gaurav",
""
],
[
"Killick",
"Teresa M.",
""
],
[
"van Rossum",
"Damian B.",
""
],
[
"Patterson",
"Randen L.",
""
]
] | Just as physicists strive to develop a TOE (theory of everything), which explains and unifies the physical laws of the universe, the life-scientist wishes to uncover the TOE as it relates to cellular systems. This can only be achieved with a quantitative platform that can comprehensively deduce and relate protein structure, functional, and evolution of genomes and proteomes in a comparative fashion. Were this perfected, proper analyses would start to uncover the underlying physical laws governing the emergent behavior of biological systems and the evolutionary pressures responsible for functional innovation. In the near term, such methodology would allow the vast quantities of uncharacterized (e.g. metagenomic samples) primary amino acid sequences to be rapidly decoded. Analogous to natural products found in the Amazon, genomes of living organisms contain large numbers of proteins that would prove useful as new therapeutics for human health, energy sources, and/or waste management solutions if they could be identified and characterized. We previously theorized that phylogenetic profiles could provide a quantitative platform for obtaining unified measures of structure, function, and evolution (SF&E)(1). In the present manuscript, we present data that support this theory and demonstrates how refinements of our analysis algorithms improve the performance of phylogenetic profiles for deriving structural/functional relationships. |
2208.06649 | Delfim F. M. Torres | Mohamed A. Zaitri, Cristiana J. Silva, Delfim F. M. Torres | Stability Analysis of Delayed COVID-19 Models | This is a preprint of a paper whose final and definite form is
published Open Access in 'Axioms' at [https://doi.org/10.3390/axioms11080400] | Axioms 11 (2022), no. 8, Art. 400, 21 pp | 10.3390/axioms11080400 | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We analyze mathematical models for COVID-19 with discrete time delays and
vaccination. Sufficient conditions for the local stability of the endemic and
disease-free equilibrium points are proved for any positive time delay. The
stability results are illustrated through numerical simulations performed in
MATLAB.
| [
{
"created": "Sat, 13 Aug 2022 13:34:14 GMT",
"version": "v1"
}
] | 2022-08-16 | [
[
"Zaitri",
"Mohamed A.",
""
],
[
"Silva",
"Cristiana J.",
""
],
[
"Torres",
"Delfim F. M.",
""
]
] | We analyze mathematical models for COVID-19 with discrete time delays and vaccination. Sufficient conditions for the local stability of the endemic and disease-free equilibrium points are proved for any positive time delay. The stability results are illustrated through numerical simulations performed in MATLAB. |
2007.00524 | Petr Marsalek | Petr Marsalek, Pavel Sanda, Zbynek Bures | On the precision of neural computation with interaural time differences
in the medial superior olive | A pre-print. In preparation to be submitted into a refereed journal | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Incoming sound is in cochlea and auditory nerve encoded into spike trains. At
the third neuron of the auditory pathway, spike trains of the left and right
sides are processed in brainstem nuclei to yield sound localization
information. Two different localization encoding mechanisms are employed in two
centers for low and high sound frequencies in the brainstem. The centers are
superior olivary nuclei, medial and lateral. This paper contains analytical
estimates of parameters needed in description of auditory coding in sound
localization neural circuit. Our model spike trains are based on
electro-physiological recordings. We arrive to best estimates for neuronal
signaling with the use of just noticeable difference of the ideal observer. We
describe spike timing jitter and its role in the spike train processing. All
parameters are accompanied with detailed estimates of their values and
variability. Intervals bounding all the parameter from lower and higher values
are discussed.
| [
{
"created": "Wed, 1 Jul 2020 14:38:40 GMT",
"version": "v1"
}
] | 2020-07-02 | [
[
"Marsalek",
"Petr",
""
],
[
"Sanda",
"Pavel",
""
],
[
"Bures",
"Zbynek",
""
]
] | Incoming sound is in cochlea and auditory nerve encoded into spike trains. At the third neuron of the auditory pathway, spike trains of the left and right sides are processed in brainstem nuclei to yield sound localization information. Two different localization encoding mechanisms are employed in two centers for low and high sound frequencies in the brainstem. The centers are superior olivary nuclei, medial and lateral. This paper contains analytical estimates of parameters needed in description of auditory coding in sound localization neural circuit. Our model spike trains are based on electro-physiological recordings. We arrive to best estimates for neuronal signaling with the use of just noticeable difference of the ideal observer. We describe spike timing jitter and its role in the spike train processing. All parameters are accompanied with detailed estimates of their values and variability. Intervals bounding all the parameter from lower and higher values are discussed. |
1605.09023 | Caroline Holmes | Kyle H. Srivastava, Caroline M. Holmes, Michiel Vellema, Andrea Pack,
Coen P. H. Elemans, Ilya Nemenman, and Samuel J. Sober | Motor control by precisely timed spike patterns | 48 pages, 16 figures | null | 10.1073/pnas.1611734114 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A fundamental problem in neuroscience is to understand how sequences of
action potentials ("spikes") encode information about sensory signals and motor
outputs. Although traditional theories of neural coding assume that information
is conveyed by the total number of spikes fired (spike rate), recent studies of
sensory and motor activity have shown that far more information is carried by
the millisecond-scale timing patterns of action potentials (spike timing).
However, it is unknown whether or how subtle differences in spike timing drive
differences in perception or behavior, leaving it unclear whether the
information carried by spike timing actually plays a causal role in brain
function. Here we demonstrate how a precise spike timing code is read out
downstream by the muscles to control behavior. We provide both correlative and
causal evidence to show that the nervous system uses millisecond-scale
variations in the timing of spikes within multi-spike patterns to regulate a
relatively simple behavior - respiration in the Bengalese finch, a songbird.
These findings suggest that a fundamental assumption of current theories of
motor coding requires revision, and that significant improvements in
applications, such as neural prosthetic devices, can be achieved by using
precise spike timing information.
| [
{
"created": "Sun, 29 May 2016 16:21:03 GMT",
"version": "v1"
},
{
"created": "Tue, 31 May 2016 01:40:35 GMT",
"version": "v2"
}
] | 2022-10-12 | [
[
"Srivastava",
"Kyle H.",
""
],
[
"Holmes",
"Caroline M.",
""
],
[
"Vellema",
"Michiel",
""
],
[
"Pack",
"Andrea",
""
],
[
"Elemans",
"Coen P. H.",
""
],
[
"Nemenman",
"Ilya",
""
],
[
"Sober",
"Samuel J.",
"... | A fundamental problem in neuroscience is to understand how sequences of action potentials ("spikes") encode information about sensory signals and motor outputs. Although traditional theories of neural coding assume that information is conveyed by the total number of spikes fired (spike rate), recent studies of sensory and motor activity have shown that far more information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear whether the information carried by spike timing actually plays a causal role in brain function. Here we demonstrate how a precise spike timing code is read out downstream by the muscles to control behavior. We provide both correlative and causal evidence to show that the nervous system uses millisecond-scale variations in the timing of spikes within multi-spike patterns to regulate a relatively simple behavior - respiration in the Bengalese finch, a songbird. These findings suggest that a fundamental assumption of current theories of motor coding requires revision, and that significant improvements in applications, such as neural prosthetic devices, can be achieved by using precise spike timing information. |
2209.07453 | Zeb Kurth-Nelson | Zeb Kurth-Nelson, Timothy Behrens, Greg Wayne, Kevin Miller, Lennart
Luettgau, Ray Dolan, Yunzhe Liu, Philipp Schwartenbeck | Replay and compositional computation | null | null | null | null | q-bio.NC | http://creativecommons.org/publicdomain/zero/1.0/ | Replay in the brain has been viewed as rehearsal, or, more recently, as
sampling from a transition model. Here, we propose a new hypothesis: that
replay is able to implement a form of compositional computation where entities
are assembled into relationally-bound structures to derive qualitatively new
knowledge. This idea builds on recent advances in neuroscience which indicate
that the hippocampus flexibly binds objects to generalizable roles and that
replay strings these role-bound objects into compound statements. We suggest
experiments to test our hypothesis, and we end by noting the implications for
AI systems which lack the human ability to radically generalize past experience
to solve new problems.
| [
{
"created": "Thu, 15 Sep 2022 16:59:05 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Dec 2022 11:03:45 GMT",
"version": "v2"
}
] | 2022-12-21 | [
[
"Kurth-Nelson",
"Zeb",
""
],
[
"Behrens",
"Timothy",
""
],
[
"Wayne",
"Greg",
""
],
[
"Miller",
"Kevin",
""
],
[
"Luettgau",
"Lennart",
""
],
[
"Dolan",
"Ray",
""
],
[
"Liu",
"Yunzhe",
""
],
[
"Schw... | Replay in the brain has been viewed as rehearsal, or, more recently, as sampling from a transition model. Here, we propose a new hypothesis: that replay is able to implement a form of compositional computation where entities are assembled into relationally-bound structures to derive qualitatively new knowledge. This idea builds on recent advances in neuroscience which indicate that the hippocampus flexibly binds objects to generalizable roles and that replay strings these role-bound objects into compound statements. We suggest experiments to test our hypothesis, and we end by noting the implications for AI systems which lack the human ability to radically generalize past experience to solve new problems. |
2012.15854 | Sitabhra Sinha | Anand Pathak, Shakti N. Menon and Sitabhra Sinha | Uncovering the invariant structural organization of the human connectome | 16 pages, 7 figures + 5 pages Supplementary Information | null | null | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In order to understand the complex cognitive functions of the human brain, it
is essential to study the structural connectome, i.e., the wiring of different
brain regions to each other through axonal pathways. However, the high degree
of plasticity and cross-population variability in human brains makes it
difficult to relate structure to function, motivating a search for invariant
patterns in the connectivity. At the same time, variability within a population
can provide information about generative mechanisms. In this paper we analyze
the connection topology and link-weight distribution of human structural
connectomes obtained from a database comprising 196 subjects. By demonstrating
a correspondence between the occurrence frequency of individual links and their
average weight across the population, we show that the process by which the
brain is wired is not independent of the process by which the link weights of
the connectome are determined. Furthermore, using the specific distribution of
the weights associated with each link over the entire population, we show that
a single parameter that is specific to a link can account for its frequency of
occurrence, as well as, the variation in its weight across different subjects.
This parameter provides a basis for ``rescaling'' the link weights in each
connectome, allowing us to obtain a generic network representative of the human
brain, distinct from a simple average over the connectomes. We obtain
functional connectomes by implementing a neural mass model on each of the
vertices of the corresponding structural connectomes. By comparing with the
empirical functional brain networks, we demonstrate that the rescaling
procedure yields a closer structure-function correspondence. Finally, we show
that the representative network can be decomposed into a basal component that
is stable across the population and a highly variable superstructure.
| [
{
"created": "Thu, 31 Dec 2020 18:58:33 GMT",
"version": "v1"
}
] | 2021-01-25 | [
[
"Pathak",
"Anand",
""
],
[
"Menon",
"Shakti N.",
""
],
[
"Sinha",
"Sitabhra",
""
]
] | In order to understand the complex cognitive functions of the human brain, it is essential to study the structural connectome, i.e., the wiring of different brain regions to each other through axonal pathways. However, the high degree of plasticity and cross-population variability in human brains makes it difficult to relate structure to function, motivating a search for invariant patterns in the connectivity. At the same time, variability within a population can provide information about generative mechanisms. In this paper we analyze the connection topology and link-weight distribution of human structural connectomes obtained from a database comprising 196 subjects. By demonstrating a correspondence between the occurrence frequency of individual links and their average weight across the population, we show that the process by which the brain is wired is not independent of the process by which the link weights of the connectome are determined. Furthermore, using the specific distribution of the weights associated with each link over the entire population, we show that a single parameter that is specific to a link can account for its frequency of occurrence, as well as, the variation in its weight across different subjects. This parameter provides a basis for ``rescaling'' the link weights in each connectome, allowing us to obtain a generic network representative of the human brain, distinct from a simple average over the connectomes. We obtain functional connectomes by implementing a neural mass model on each of the vertices of the corresponding structural connectomes. By comparing with the empirical functional brain networks, we demonstrate that the rescaling procedure yields a closer structure-function correspondence. Finally, we show that the representative network can be decomposed into a basal component that is stable across the population and a highly variable superstructure. |
0906.0297 | Ibrahim Inanc | Osman Burak Okan, Ali Rana Atilgan, Canan Atilgan | Nanosecond motions in proteins impose bounds on the timescale
distributions of local dynamics | 26 pages, 9 figures; suplementary materials added | null | null | null | q-bio.QM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We elucidate the physics of the dynamical transition via 10-100ns molecular
dynamics simulations at temperatures spanning 160-300K. By tracking the energy
fluctuations, we show that the protein dynamical transition is marked by a
cross-over from piecewise stationary to stationary processes that underlie the
dynamics of protein motions. A two-time-scale function captures the
non-exponential character of backbone structural relaxations. One is attributed
to the collective segmental motions and the other to local relaxations. The
former is well-defined by a single-exponential, nanosecond decay, operative at
all temperatures. The latter is described by a set of processes that display a
distribution of time-scales. Though their average remains on the picosecond
time-scale, the distribution is markedly contracted at the onset of the
transition. The collective motions are shown to impose bounds on time-scales
spanned by local dynamical processes. The piecewise stationary character below
the transition implicates the presence of a collection of sub-states whose
interactions are restricted. At these temperatures, a wide distribution of
local motion time-scales, extending beyond that of nanoseconds is observed. At
physiological temperatures, local motions are confined to time-scales faster
than nanoseconds. This relatively narrow window makes possible the appearance
of multiple channels for the backbone dynamics to operate.
| [
{
"created": "Mon, 1 Jun 2009 14:46:40 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Jun 2009 07:01:54 GMT",
"version": "v2"
}
] | 2009-06-17 | [
[
"Okan",
"Osman Burak",
""
],
[
"Atilgan",
"Ali Rana",
""
],
[
"Atilgan",
"Canan",
""
]
] | We elucidate the physics of the dynamical transition via 10-100ns molecular dynamics simulations at temperatures spanning 160-300K. By tracking the energy fluctuations, we show that the protein dynamical transition is marked by a cross-over from piecewise stationary to stationary processes that underlie the dynamics of protein motions. A two-time-scale function captures the non-exponential character of backbone structural relaxations. One is attributed to the collective segmental motions and the other to local relaxations. The former is well-defined by a single-exponential, nanosecond decay, operative at all temperatures. The latter is described by a set of processes that display a distribution of time-scales. Though their average remains on the picosecond time-scale, the distribution is markedly contracted at the onset of the transition. The collective motions are shown to impose bounds on time-scales spanned by local dynamical processes. The piecewise stationary character below the transition implicates the presence of a collection of sub-states whose interactions are restricted. At these temperatures, a wide distribution of local motion time-scales, extending beyond that of nanoseconds is observed. At physiological temperatures, local motions are confined to time-scales faster than nanoseconds. This relatively narrow window makes possible the appearance of multiple channels for the backbone dynamics to operate. |
2003.13747 | Jose Amaro E | J. E. Amaro | The D model for deaths by COVID-19 | 7 pages, 4 figures | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a simple analytical model to describe the fast increase of deaths
produced by the corona virus (COVID-19) infections. The 'D' (deaths) model
comes from a simplified version of the SIR (susceptible-infected-recovered)
model known as SI model. It assumes that there is no recovery. In that case the
dynamical equations can be solved analytically and the result is extended to
describe the D-function that depends on three parameters that we can fit to the
data. Results for the data from Spain, Italy and China are presented. The model
is validated by comparing with the data of deaths in China, which are well
described. This allows to make predictions for the development of the disease
in Spain and Italy.
| [
{
"created": "Mon, 30 Mar 2020 18:52:54 GMT",
"version": "v1"
}
] | 2020-04-01 | [
[
"Amaro",
"J. E.",
""
]
] | We present a simple analytical model to describe the fast increase of deaths produced by the corona virus (COVID-19) infections. The 'D' (deaths) model comes from a simplified version of the SIR (susceptible-infected-recovered) model known as SI model. It assumes that there is no recovery. In that case the dynamical equations can be solved analytically and the result is extended to describe the D-function that depends on three parameters that we can fit to the data. Results for the data from Spain, Italy and China are presented. The model is validated by comparing with the data of deaths in China, which are well described. This allows to make predictions for the development of the disease in Spain and Italy. |
2403.03089 | Dianbo Liu | Jiawei Wu, Mingyuan Yan, Dianbo Liu | VQSynery: Robust Drug Synergy Prediction With Vector Quantization
Mechanism | null | null | null | null | q-bio.QM cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | The pursuit of optimizing cancer therapies is significantly advanced by the
accurate prediction of drug synergy. Traditional methods, such as clinical
trials, are reliable yet encumbered by extensive time and financial demands.
The emergence of high-throughput screening and computational innovations has
heralded a shift towards more efficient methodologies for exploring drug
interactions. In this study, we present VQSynergy, a novel framework that
employs the Vector Quantization (VQ) mechanism, integrated with gated residuals
and a tailored attention mechanism, to enhance the precision and
generalizability of drug synergy predictions. Our findings demonstrate that
VQSynergy surpasses existing models in terms of robustness, particularly under
Gaussian noise conditions, highlighting its superior performance and utility in
the complex and often noisy domain of drug synergy research. This study
underscores the potential of VQSynergy in revolutionizing the field through its
advanced predictive capabilities, thereby contributing to the optimization of
cancer treatment strategies.
| [
{
"created": "Tue, 5 Mar 2024 16:21:53 GMT",
"version": "v1"
}
] | 2024-03-06 | [
[
"Wu",
"Jiawei",
""
],
[
"Yan",
"Mingyuan",
""
],
[
"Liu",
"Dianbo",
""
]
] | The pursuit of optimizing cancer therapies is significantly advanced by the accurate prediction of drug synergy. Traditional methods, such as clinical trials, are reliable yet encumbered by extensive time and financial demands. The emergence of high-throughput screening and computational innovations has heralded a shift towards more efficient methodologies for exploring drug interactions. In this study, we present VQSynergy, a novel framework that employs the Vector Quantization (VQ) mechanism, integrated with gated residuals and a tailored attention mechanism, to enhance the precision and generalizability of drug synergy predictions. Our findings demonstrate that VQSynergy surpasses existing models in terms of robustness, particularly under Gaussian noise conditions, highlighting its superior performance and utility in the complex and often noisy domain of drug synergy research. This study underscores the potential of VQSynergy in revolutionizing the field through its advanced predictive capabilities, thereby contributing to the optimization of cancer treatment strategies. |
1604.03834 | Yaneer Bar-Yam | Dan Evans, Fred Nijhout, Raphael Parens, Alfredo J. Morales and Yaneer
Bar-Yam | A Possible Link Between Pyriproxyfen and Microcephaly | 6 pages | null | null | New England Complex Systems Institute Report 2016-04-02 | q-bio.QM q-bio.BM q-bio.NC q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Zika virus is the primary suspect in the large increase in microcephaly
cases in 2015-6 in Brazil, however its role is unconfirmed despite individual
cases of viral infections found in neural tissue. Here we consider the
alternative that the insecticide pyriproxyfen, used in Brazilian drinking water
for mosquito control, may actually be the cause. Pyriproxifen is an analog of
juvenile hormone, which corresponds in mammals to regulatory molecules
including retinoic acid, a vitamin A metabolite, with which it has
cross-reactivity and whose application during development causes microcephaly.
Methoprene, another juvenile hormone analog approved as an insecticide has
metabolites that bind to the retinoid X receptor, and causes developmental
disorders in mammals. Isotretinoin is another example of a retinoid causing
microcephaly in human babies via activation of the retinoid X receptor.
Moreover, tests of pyriproxyfen by the manufacturer, Sumitomo, widely quoted as
giving no evidence for developmental toxicity, actually found some evidence for
such an effect, including low brain mass and arhinencephaly--incomplete
formation of the anterior cerebral hemispheres--in rat pups. Finally, the
pyriproxyfen use in Brazil is unprecedented--it has never before been applied
to a water supply on such a scale. Claims that it is not being used in Recife,
the epicenter of microcephaly cases, do not distinguish the metropolitan area
of Recife, where it is widely used, and the municipality, where it is not.
Given this combination of information we strongly recommend that the use of
pyriproxyfen in Brazil be suspended pending further investigation.
| [
{
"created": "Wed, 13 Apr 2016 15:35:21 GMT",
"version": "v1"
}
] | 2016-04-14 | [
[
"Evans",
"Dan",
""
],
[
"Nijhout",
"Fred",
""
],
[
"Parens",
"Raphael",
""
],
[
"Morales",
"Alfredo J.",
""
],
[
"Bar-Yam",
"Yaneer",
""
]
] | The Zika virus is the primary suspect in the large increase in microcephaly cases in 2015-6 in Brazil, however its role is unconfirmed despite individual cases of viral infections found in neural tissue. Here we consider the alternative that the insecticide pyriproxyfen, used in Brazilian drinking water for mosquito control, may actually be the cause. Pyriproxifen is an analog of juvenile hormone, which corresponds in mammals to regulatory molecules including retinoic acid, a vitamin A metabolite, with which it has cross-reactivity and whose application during development causes microcephaly. Methoprene, another juvenile hormone analog approved as an insecticide has metabolites that bind to the retinoid X receptor, and causes developmental disorders in mammals. Isotretinoin is another example of a retinoid causing microcephaly in human babies via activation of the retinoid X receptor. Moreover, tests of pyriproxyfen by the manufacturer, Sumitomo, widely quoted as giving no evidence for developmental toxicity, actually found some evidence for such an effect, including low brain mass and arhinencephaly--incomplete formation of the anterior cerebral hemispheres--in rat pups. Finally, the pyriproxyfen use in Brazil is unprecedented--it has never before been applied to a water supply on such a scale. Claims that it is not being used in Recife, the epicenter of microcephaly cases, do not distinguish the metropolitan area of Recife, where it is widely used, and the municipality, where it is not. Given this combination of information we strongly recommend that the use of pyriproxyfen in Brazil be suspended pending further investigation. |
q-bio/0507024 | Jose Luis Sebastian | S. Munoz, J.L. Sebastian, M. Sancho and G. Alvarez | Modeling Human Erythrocyte Shape and Size Abnormalities | submitted to Bioelectromagnetics | null | null | null | q-bio.QM q-bio.CB | null | We present simple parametric equations in terms of Jacobi elliptic functions
that provide a realistic model of the shape of human normal erythrocytes as
well as of variations in size (anisocytosis) and shape (poikilocytosis)
thereof. We illustrate our results with parameterizations of microcytes,
macrocytes and stomatocytes, and show the applicability of these
parameterizations to the numerical calculation of the induced transmembrane
voltage in microcytes, macrocytes and stomatocytes exposed to an external RF
field of 1800 MHz.
| [
{
"created": "Thu, 14 Jul 2005 09:54:51 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Munoz",
"S.",
""
],
[
"Sebastian",
"J. L.",
""
],
[
"Sancho",
"M.",
""
],
[
"Alvarez",
"G.",
""
]
] | We present simple parametric equations in terms of Jacobi elliptic functions that provide a realistic model of the shape of human normal erythrocytes as well as of variations in size (anisocytosis) and shape (poikilocytosis) thereof. We illustrate our results with parameterizations of microcytes, macrocytes and stomatocytes, and show the applicability of these parameterizations to the numerical calculation of the induced transmembrane voltage in microcytes, macrocytes and stomatocytes exposed to an external RF field of 1800 MHz. |
0706.2602 | Hugues Berry | Benoit Siri (INRIA Futurs), Mathias Quoy (ETIS), Bruno Delord (ANIM),
Bruno Cessac (INLN, INRIA Sophia Antipolis), Hugues Berry (INRIA Futurs) | Effects of Hebbian learning on the dynamics and structure of random
networks with inhibitory and excitatory neurons | null | null | null | null | q-bio.NC | null | The aim of the present paper is to study the effects of Hebbian learning in
random recurrent neural networks with biological connectivity, i.e. sparse
connections and separate populations of excitatory and inhibitory neurons. We
furthermore consider that the neuron dynamics may occur at a (shorter) time
scale than synaptic plasticity and consider the possibility of learning rules
with passive forgetting. We show that the application of such Hebbian learning
leads to drastic changes in the network dynamics and structure. In particular,
the learning rule contracts the norm of the weight matrix and yields a rapid
decay of the dynamics complexity and entropy. In other words, the network is
rewired by Hebbian learning into a new synaptic structure that emerges with
learning on the basis of the correlations that progressively build up between
neurons. We also observe that, within this emerging structure, the strongest
synapses organize as a small-world network. The second effect of the decay of
the weight matrix spectral radius consists in a rapid contraction of the
spectral radius of the Jacobian matrix. This drives the system through the
``edge of chaos'' where sensitivity to the input pattern is maximal. Taken
together, this scenario is remarkably predicted by theoretical arguments
derived from dynamical systems and graph theory.
| [
{
"created": "Mon, 18 Jun 2007 13:42:16 GMT",
"version": "v1"
}
] | 2007-06-19 | [
[
"Siri",
"Benoit",
"",
"INRIA Futurs"
],
[
"Quoy",
"Mathias",
"",
"ETIS"
],
[
"Delord",
"Bruno",
"",
"ANIM"
],
[
"Cessac",
"Bruno",
"",
"INLN, INRIA Sophia Antipolis"
],
[
"Berry",
"Hugues",
"",
"INRIA Futurs"
]
] | The aim of the present paper is to study the effects of Hebbian learning in random recurrent neural networks with biological connectivity, i.e. sparse connections and separate populations of excitatory and inhibitory neurons. We furthermore consider that the neuron dynamics may occur at a (shorter) time scale than synaptic plasticity and consider the possibility of learning rules with passive forgetting. We show that the application of such Hebbian learning leads to drastic changes in the network dynamics and structure. In particular, the learning rule contracts the norm of the weight matrix and yields a rapid decay of the dynamics complexity and entropy. In other words, the network is rewired by Hebbian learning into a new synaptic structure that emerges with learning on the basis of the correlations that progressively build up between neurons. We also observe that, within this emerging structure, the strongest synapses organize as a small-world network. The second effect of the decay of the weight matrix spectral radius consists in a rapid contraction of the spectral radius of the Jacobian matrix. This drives the system through the ``edge of chaos'' where sensitivity to the input pattern is maximal. Taken together, this scenario is remarkably predicted by theoretical arguments derived from dynamical systems and graph theory. |
2204.03348 | David Beers | David Beers, Despoina Goniotaki, Diane P. Hanger, Alain Goriely,
Heather A. Harrington | Barcodes distinguish morphology of neuronal tauopathy | 25 pages, 10 figures | null | null | null | q-bio.NC math.AT | http://creativecommons.org/licenses/by/4.0/ | The geometry of neurons is known to be important for their functions. Hence,
neurons are often classified by their morphology. Two recent methods,
persistent homology and the topological morphology descriptor, assign a
morphology descriptor called a barcode to a neuron equipped with a given
function, such as the Euclidean distance from the root of the neuron. These
barcodes can be converted into matrices called persistence images, which can
then be averaged across groups. We show that when the defining function is the
path length from the root, both the topological morphology descriptor and
persistent homology are equivalent. We further show that persistence images
arising from the path length procedure provide an interpretable summary of
neuronal morphology. We introduce {topological morphology functions}, a class
of functions similar to Sholl functions, that can be recovered from the
associated topological morphology descriptor. To demonstrate this topological
approach, we compare healthy cortical and hippocampal mouse neurons to those
affected by progressive tauopathy. We find a significant difference in the
morphology of healthy neurons and those with a tauopathy at a postsymptomatic
age. We use persistence images to conclude that the diseased group tends to
have neurons with shorter branches as well as fewer branches far from the soma.
| [
{
"created": "Thu, 7 Apr 2022 10:40:14 GMT",
"version": "v1"
}
] | 2022-04-08 | [
[
"Beers",
"David",
""
],
[
"Goniotaki",
"Despoina",
""
],
[
"Hanger",
"Diane P.",
""
],
[
"Goriely",
"Alain",
""
],
[
"Harrington",
"Heather A.",
""
]
] | The geometry of neurons is known to be important for their functions. Hence, neurons are often classified by their morphology. Two recent methods, persistent homology and the topological morphology descriptor, assign a morphology descriptor called a barcode to a neuron equipped with a given function, such as the Euclidean distance from the root of the neuron. These barcodes can be converted into matrices called persistence images, which can then be averaged across groups. We show that when the defining function is the path length from the root, both the topological morphology descriptor and persistent homology are equivalent. We further show that persistence images arising from the path length procedure provide an interpretable summary of neuronal morphology. We introduce {topological morphology functions}, a class of functions similar to Sholl functions, that can be recovered from the associated topological morphology descriptor. To demonstrate this topological approach, we compare healthy cortical and hippocampal mouse neurons to those affected by progressive tauopathy. We find a significant difference in the morphology of healthy neurons and those with a tauopathy at a postsymptomatic age. We use persistence images to conclude that the diseased group tends to have neurons with shorter branches as well as fewer branches far from the soma. |
2112.13280 | Vladimir Makarenkov | Boc Alix, Diallo Alpha Boubacar, Makarenkov Vladimir | Un nouvel algorithme pour la detection des transferts horizontaux de
genes partiels entre les especes et pour la classification des transferts
inferes | in French | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | In this article we are describing a new algorithm for detecting and
validating partial horizontal gene transfers (HGT). The presented algorithm is
based on a sliding window procedure which analyzes fragments of the given
multiple sequence alignment. A bootstrap procedure incorporated in our method
can be used to estimate the support of each inferred partial HGT. The new
algorithm can be also applied to confirm or discard complete (i.e.,
traditional) horizontal gene transfers detected by any HGT inferring algorithm.
While working on a full-genome scale, the introduced algorithm can be used to
assess the level of mosaicism of the whole species genomes as well as the rates
of complete and partial HGT underlying the evolution of the considered set of
species.
| [
{
"created": "Sat, 25 Dec 2021 20:11:23 GMT",
"version": "v1"
}
] | 2021-12-28 | [
[
"Alix",
"Boc",
""
],
[
"Boubacar",
"Diallo Alpha",
""
],
[
"Vladimir",
"Makarenkov",
""
]
] | In this article we are describing a new algorithm for detecting and validating partial horizontal gene transfers (HGT). The presented algorithm is based on a sliding window procedure which analyzes fragments of the given multiple sequence alignment. A bootstrap procedure incorporated in our method can be used to estimate the support of each inferred partial HGT. The new algorithm can be also applied to confirm or discard complete (i.e., traditional) horizontal gene transfers detected by any HGT inferring algorithm. While working on a full-genome scale, the introduced algorithm can be used to assess the level of mosaicism of the whole species genomes as well as the rates of complete and partial HGT underlying the evolution of the considered set of species. |
2407.16715 | Yufeng Li | Yufeng Li, Wenchao Zhao, Bo Dang, Xu Yan, Weimin Wang, Min Gao,
Mingxuan Xiao | Research on Adverse Drug Reaction Prediction Model Combining Knowledge
Graph Embedding and Deep Learning | 12 pages, 4 figures, 9 tables | null | null | null | q-bio.QM cs.AI cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In clinical treatment, identifying potential adverse reactions of drugs can
help assist doctors in making medication decisions. In response to the problems
in previous studies that features are high-dimensional and sparse, independent
prediction models need to be constructed for each adverse reaction of drugs,
and the prediction accuracy is low, this paper develops an adverse drug
reaction prediction model based on knowledge graph embedding and deep learning,
which can predict experimental results. Unified prediction of adverse drug
reactions covered. Knowledge graph embedding technology can fuse the associated
information between drugs and alleviate the shortcomings of high-dimensional
sparsity in feature matrices, and the efficient training capabilities of deep
learning can improve the prediction accuracy of the model. This article builds
an adverse drug reaction knowledge graph based on drug feature data; by
analyzing the embedding effect of the knowledge graph under different embedding
strategies, the best embedding strategy is selected to obtain sample vectors;
and then a convolutional neural network model is constructed to predict adverse
reactions. The results show that under the DistMult embedding model and
400-dimensional embedding strategy, the convolutional neural network model has
the best prediction effect; the average accuracy, F_1 score, recall rate and
area under the curve of repeated experiments are better than the methods
reported in the literature. The obtained prediction model has good prediction
accuracy and stability, and can provide an effective reference for later safe
medication guidance.
| [
{
"created": "Tue, 23 Jul 2024 03:25:55 GMT",
"version": "v1"
},
{
"created": "Sat, 27 Jul 2024 15:09:51 GMT",
"version": "v2"
}
] | 2024-07-30 | [
[
"Li",
"Yufeng",
""
],
[
"Zhao",
"Wenchao",
""
],
[
"Dang",
"Bo",
""
],
[
"Yan",
"Xu",
""
],
[
"Wang",
"Weimin",
""
],
[
"Gao",
"Min",
""
],
[
"Xiao",
"Mingxuan",
""
]
] | In clinical treatment, identifying potential adverse reactions of drugs can help assist doctors in making medication decisions. In response to the problems in previous studies that features are high-dimensional and sparse, independent prediction models need to be constructed for each adverse reaction of drugs, and the prediction accuracy is low, this paper develops an adverse drug reaction prediction model based on knowledge graph embedding and deep learning, which can predict experimental results. Unified prediction of adverse drug reactions covered. Knowledge graph embedding technology can fuse the associated information between drugs and alleviate the shortcomings of high-dimensional sparsity in feature matrices, and the efficient training capabilities of deep learning can improve the prediction accuracy of the model. This article builds an adverse drug reaction knowledge graph based on drug feature data; by analyzing the embedding effect of the knowledge graph under different embedding strategies, the best embedding strategy is selected to obtain sample vectors; and then a convolutional neural network model is constructed to predict adverse reactions. The results show that under the DistMult embedding model and 400-dimensional embedding strategy, the convolutional neural network model has the best prediction effect; the average accuracy, F_1 score, recall rate and area under the curve of repeated experiments are better than the methods reported in the literature. The obtained prediction model has good prediction accuracy and stability, and can provide an effective reference for later safe medication guidance. |
q-bio/0609040 | Sidney Redner | T. Antal, K.B. Blagoev, S.A. Trugman, and S. Redner | Aging and Immortality in a Cell Proliferation Model | 6 pages, 1 figure, 2-column revtex4 format; version 2: final
published form; contains various improvements in response to referee comments | Journal of Theoretical Biology 248, 411-417 (2007) | 10.1016/j.jtbi.2007.06.009 | LA-UR-06-6713 | q-bio.CB cond-mat.stat-mech physics.bio-ph | null | We investigate a model of cell division in which the length of telomeres
within the cell regulate their proliferative potential. At each cell division
the ends of linear chromosomes change and a cell becomes senescent when one or
more of its telomeres become shorter than a critical length. In addition to
this systematic shortening, exchange of telomere DNA between the two daughter
cells can occur at each cell division. We map this telomere dynamics onto a
biased branching diffusion process with an absorbing boundary condition
whenever any telomere reaches the critical length. As the relative effects of
telomere shortening and cell division are varied, there is a phase transition
between finite lifetime and infinite proliferation of the cell population.
Using simple first-passage ideas, we quantify the nature of this transition.
| [
{
"created": "Tue, 26 Sep 2006 01:46:38 GMT",
"version": "v1"
},
{
"created": "Wed, 23 Apr 2008 00:06:34 GMT",
"version": "v2"
}
] | 2008-04-23 | [
[
"Antal",
"T.",
""
],
[
"Blagoev",
"K. B.",
""
],
[
"Trugman",
"S. A.",
""
],
[
"Redner",
"S.",
""
]
] | We investigate a model of cell division in which the length of telomeres within the cell regulate their proliferative potential. At each cell division the ends of linear chromosomes change and a cell becomes senescent when one or more of its telomeres become shorter than a critical length. In addition to this systematic shortening, exchange of telomere DNA between the two daughter cells can occur at each cell division. We map this telomere dynamics onto a biased branching diffusion process with an absorbing boundary condition whenever any telomere reaches the critical length. As the relative effects of telomere shortening and cell division are varied, there is a phase transition between finite lifetime and infinite proliferation of the cell population. Using simple first-passage ideas, we quantify the nature of this transition. |
1003.4217 | Sebastiano Stramaglia | Mario Pellicoro and Sebastiano Stramaglia | Granger causality and the inverse Ising problem | 6 pages and 8 figures. Revised version in press on Physica A | null | 10.1016/j.physa.2010.06.028 | null | q-bio.NC cond-mat.dis-nn physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study Ising models for describing data and show that autoregressive
methods may be used to learn their connections, also in the case of asymmetric
connections and for multi-spin interactions. For each link the linear Granger
causality is two times the corresponding transfer entropy (i.e. the information
flow on that link) in the weak coupling limit. For sparse connections and a low
number of samples, the L1 regularized least squares method is used to detect
the interacting pairs of spins. Nonlinear Granger causality is related to
multispin interactions.
| [
{
"created": "Mon, 22 Mar 2010 17:10:58 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Jun 2010 14:21:49 GMT",
"version": "v2"
}
] | 2015-05-18 | [
[
"Pellicoro",
"Mario",
""
],
[
"Stramaglia",
"Sebastiano",
""
]
] | We study Ising models for describing data and show that autoregressive methods may be used to learn their connections, also in the case of asymmetric connections and for multi-spin interactions. For each link the linear Granger causality is two times the corresponding transfer entropy (i.e. the information flow on that link) in the weak coupling limit. For sparse connections and a low number of samples, the L1 regularized least squares method is used to detect the interacting pairs of spins. Nonlinear Granger causality is related to multispin interactions. |
1603.08646 | Thiparat Chotibut | Thiparat Chotibut, David R. Nelson, Sauro Succi | Striated Populations in Disordered Environments with Advection | 30 pages, 8 figures | null | 10.1016/j.physa.2016.08.059 | null | q-bio.PE cond-mat.dis-nn cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Growth in static and controlled environments such as a Petri dish can be used
to study the spatial population dynamics of microorganisms. However, natural
populations such as marine microbes experience fluid advection and often grow
up in heterogeneous environments. We investigate a generalized
Fisher-Kolmogorov-Petrovsky-Piscounov (FKPP) equation describing single species
population subject to a constant flow field and quenched random spatially
inhomogeneous growth rates with a fertile overall growth condition. We
analytically and numerically demonstrate that the non-equilibrium steady-state
population density develops a flow-driven striation pattern. The striations are
highly asymmetric with a longitudinal correlation length that diverges linearly
with the flow speed and a transverse correlation length that approaches a
finite velocity-independent value. Linear response theory is developed to study
the statistics of the steady states. Theoretical predictions show excellent
agreement with the numerical steady states of the generalized FKPP equation
obtained from Lattice Boltzmann simulations. These findings suggest that,
although the growth disorder can be spatially uncorrelated, correlated
population structures with striations emerge naturally at sufficiently strong
advection.
| [
{
"created": "Tue, 29 Mar 2016 05:21:51 GMT",
"version": "v1"
}
] | 2016-09-05 | [
[
"Chotibut",
"Thiparat",
""
],
[
"Nelson",
"David R.",
""
],
[
"Succi",
"Sauro",
""
]
] | Growth in static and controlled environments such as a Petri dish can be used to study the spatial population dynamics of microorganisms. However, natural populations such as marine microbes experience fluid advection and often grow up in heterogeneous environments. We investigate a generalized Fisher-Kolmogorov-Petrovsky-Piscounov (FKPP) equation describing single species population subject to a constant flow field and quenched random spatially inhomogeneous growth rates with a fertile overall growth condition. We analytically and numerically demonstrate that the non-equilibrium steady-state population density develops a flow-driven striation pattern. The striations are highly asymmetric with a longitudinal correlation length that diverges linearly with the flow speed and a transverse correlation length that approaches a finite velocity-independent value. Linear response theory is developed to study the statistics of the steady states. Theoretical predictions show excellent agreement with the numerical steady states of the generalized FKPP equation obtained from Lattice Boltzmann simulations. These findings suggest that, although the growth disorder can be spatially uncorrelated, correlated population structures with striations emerge naturally at sufficiently strong advection. |
1606.00042 | Ronan M.T. Fleming Dr | Alberto Noronha, Anna Dr\"ofn Danielsd\'ottir, Freyr J\'ohannsson,
Soffia J\'onsd\'ottir, Sindri Jarlsson, J\'on P\'etur Gunnarsson,
Sigur{\dh}ur Brynj\'olfsson, Piotr Gawron, Reinhard Schneider, Ines Thiele,
and Ronan M. T. Fleming | ReconMap: An interactive visualisation of human metabolism | 3 pages, 1 figure, submitted | null | null | null | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A genome-scale reconstruction of human metabolism, Recon 2, is available but
no interface exists to interactively visualise its content integrated with
omics data and simulation results. We manually drew a comprehensive map,
ReconMap 2.0, that is consistent with the content of Recon 2. We present it
within a web interface that allows content query, visualization of custom
datasets and submission of feedback to manual curators. ReconMap can be
accessed via http://vmh.uni.lu, with network export in a Systems Biology
Graphical Notation compliant format. A Constraint-Based Reconstruction and
Analysis (COBRA) Toolbox extension to interact with ReconMap is available via
https://github.com/opencobra/cobratoolbox.
| [
{
"created": "Tue, 31 May 2016 20:57:05 GMT",
"version": "v1"
}
] | 2016-06-02 | [
[
"Noronha",
"Alberto",
""
],
[
"Danielsdóttir",
"Anna Dröfn",
""
],
[
"Jóhannsson",
"Freyr",
""
],
[
"Jónsdóttir",
"Soffia",
""
],
[
"Jarlsson",
"Sindri",
""
],
[
"Gunnarsson",
"Jón Pétur",
""
],
[
"Brynjólfsson",
... | A genome-scale reconstruction of human metabolism, Recon 2, is available but no interface exists to interactively visualise its content integrated with omics data and simulation results. We manually drew a comprehensive map, ReconMap 2.0, that is consistent with the content of Recon 2. We present it within a web interface that allows content query, visualization of custom datasets and submission of feedback to manual curators. ReconMap can be accessed via http://vmh.uni.lu, with network export in a Systems Biology Graphical Notation compliant format. A Constraint-Based Reconstruction and Analysis (COBRA) Toolbox extension to interact with ReconMap is available via https://github.com/opencobra/cobratoolbox. |
1810.11306 | Massimiliano Bonomi | Massimiliano Bonomi and Michele Vendruscolo | Determination of protein structural ensembles using cryo-electron
microscopy | 22 pages, 3 figures | null | 10.1016/j.bpj.2018.11.1794 | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Achieving a comprehensive understanding of the behaviour of proteins is
greatly facilitated by the knowledge of their structures, thermodynamics and
dynamics. All this information can be provided in an effective manner in terms
of structural ensembles. A structural ensemble can be obtained by determining
the structures, populations and interconversion rates for all the main states
that a protein can occupy. To achieve this goal, integrative methods that
combine experimental and computational approaches provide powerful tools. Here
we focus on cryo-electron microscopy, which has become over recent years an
invaluable resource to bridge the gap from order to disorder in structural
biology. In this review, we provide a perspective of the current challenges and
opportunities in determining protein structural ensembles using integrative
approaches that can combine cryo-electron microscopy data with other available
sources of information, along with an overview of the tools available to the
community.
| [
{
"created": "Fri, 26 Oct 2018 13:09:26 GMT",
"version": "v1"
}
] | 2019-03-27 | [
[
"Bonomi",
"Massimiliano",
""
],
[
"Vendruscolo",
"Michele",
""
]
] | Achieving a comprehensive understanding of the behaviour of proteins is greatly facilitated by the knowledge of their structures, thermodynamics and dynamics. All this information can be provided in an effective manner in terms of structural ensembles. A structural ensemble can be obtained by determining the structures, populations and interconversion rates for all the main states that a protein can occupy. To achieve this goal, integrative methods that combine experimental and computational approaches provide powerful tools. Here we focus on cryo-electron microscopy, which has become over recent years an invaluable resource to bridge the gap from order to disorder in structural biology. In this review, we provide a perspective of the current challenges and opportunities in determining protein structural ensembles using integrative approaches that can combine cryo-electron microscopy data with other available sources of information, along with an overview of the tools available to the community. |
1603.06986 | Tahir Yusufaly | Tahir I. Yusufaly and James Q. Boedicker | Spatial Dispersal of Bacterial Colonies Induces a Dynamical Transition
From Local to Global Quorum Sensing | Revised version, accepted to Phys. Rev. E | Phys. Rev. E 94, 062410 (2016) | 10.1103/PhysRevE.94.062410 | null | q-bio.MN q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bacteria communicate using external chemical signals called autoinducers (AI)
in a process known as quorum sensing (QS). QS efficiency is reduced by both
limitations of AI diffusion and potential interference from neighboring
strains. There is thus a need for predictive theories of how spatial community
structure shapes information processing in complex microbial ecosystems. As a
step in this direction, we apply a reaction-diffusion model to study
autoinducer signaling dynamics in a single-species community as a function of
the spatial distribution of colonies in the system. We predict a dynamical
transition between a local quorum sensing (LQS) regime, with the AI signaling
dynamics primarily controlled by the local population densities of individual
colonies, and a global quorum sensing (GQS) regime, with the dynamics being
dependent on collective inter-colony diffusive interactions. The crossover
between LQS to GQS is intimately connected to a tradeoff between the signaling
network's latency, or speed of activation, and its throughput, or the total
spatial range over which all the components of the system communicate.
| [
{
"created": "Tue, 22 Mar 2016 21:18:24 GMT",
"version": "v1"
},
{
"created": "Fri, 2 Dec 2016 20:34:14 GMT",
"version": "v2"
}
] | 2016-12-28 | [
[
"Yusufaly",
"Tahir I.",
""
],
[
"Boedicker",
"James Q.",
""
]
] | Bacteria communicate using external chemical signals called autoinducers (AI) in a process known as quorum sensing (QS). QS efficiency is reduced by both limitations of AI diffusion and potential interference from neighboring strains. There is thus a need for predictive theories of how spatial community structure shapes information processing in complex microbial ecosystems. As a step in this direction, we apply a reaction-diffusion model to study autoinducer signaling dynamics in a single-species community as a function of the spatial distribution of colonies in the system. We predict a dynamical transition between a local quorum sensing (LQS) regime, with the AI signaling dynamics primarily controlled by the local population densities of individual colonies, and a global quorum sensing (GQS) regime, with the dynamics being dependent on collective inter-colony diffusive interactions. The crossover between LQS to GQS is intimately connected to a tradeoff between the signaling network's latency, or speed of activation, and its throughput, or the total spatial range over which all the components of the system communicate. |
2010.16154 | Musa Mhlanga | Partha P. Majumder, Musa M. Mhlanga, and Alex K. Shalek | The Human Cell Atlas & Equity: Lessons Learned | null | Nature Medicine 26 (2020) 1509-1511; | 10.1038/s41591-020-1100-4 | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | The Human Cell Atlas has been undergoing a massive effort to support global
scientific equity. The co-leaders of its Equity Working Group share some
lessons learned in the process.
| [
{
"created": "Fri, 30 Oct 2020 09:50:29 GMT",
"version": "v1"
}
] | 2020-11-02 | [
[
"Majumder",
"Partha P.",
""
],
[
"Mhlanga",
"Musa M.",
""
],
[
"Shalek",
"Alex K.",
""
]
] | The Human Cell Atlas has been undergoing a massive effort to support global scientific equity. The co-leaders of its Equity Working Group share some lessons learned in the process. |
2009.04060 | William Ristenpart | Santiago Barreda, Sima Asadi, Christopher D. Cappa, Anthony S. Wexler,
Nicole M. Bouvier, and William D. Ristenpart | The Impact of Vocalization Loudness on COVID-19 Transmission in Indoor
Spaces | 15 pages, 2 figures; supplementary included with 4 pages, 1 figure | null | null | null | q-bio.QM physics.soc-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | There have been several documented outbreaks of COVID-19 associated with
vocalization, either by speech or by singing, in indoor confined spaces. Here,
we model the risk of in-room airborne disease transmission via expiratory
particle emission versus the average loudness of vocalization and for variable
room ventilation rates. The model indicates that a 6-decibel reduction in
average vocalization intensity yields a reduction in aerosol transmission
probability equivalent to doubling the room ventilation rate. The results
suggest that public health authorities should consider implementing "quiet
zones" in high-risk indoor environments, such as hospital waiting rooms or
dining facilities, to mitigate transmission of COVID-19 and other airborne
respiratory diseases.
| [
{
"created": "Wed, 9 Sep 2020 01:30:35 GMT",
"version": "v1"
}
] | 2020-09-10 | [
[
"Barreda",
"Santiago",
""
],
[
"Asadi",
"Sima",
""
],
[
"Cappa",
"Christopher D.",
""
],
[
"Wexler",
"Anthony S.",
""
],
[
"Bouvier",
"Nicole M.",
""
],
[
"Ristenpart",
"William D.",
""
]
] | There have been several documented outbreaks of COVID-19 associated with vocalization, either by speech or by singing, in indoor confined spaces. Here, we model the risk of in-room airborne disease transmission via expiratory particle emission versus the average loudness of vocalization and for variable room ventilation rates. The model indicates that a 6-decibel reduction in average vocalization intensity yields a reduction in aerosol transmission probability equivalent to doubling the room ventilation rate. The results suggest that public health authorities should consider implementing "quiet zones" in high-risk indoor environments, such as hospital waiting rooms or dining facilities, to mitigate transmission of COVID-19 and other airborne respiratory diseases. |
0804.0221 | Guillermo Raul Zemba | Matias dell'Erba, Guillermo R. Zemba | Thermodynamics of a model for RNA folding | 11 pages, 4 figures | null | 10.1103/PhysRevE.79.011913 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We analyze the thermodynamic properties of a simplified model for folded RNA
molecules recently studied by G. Vernizzi, H. Orland, A. Zee (in {\it Phys.
Rev. Lett.} {\bf 94} (2005) 168103). The model consists of a chain of
one-flavor base molecules with a flexible backbone and all possible pairing
interactions equally allowed. The spatial pseudoknot structure of the model can
be efficiently studied by introducing a $N \times N$ hermitian random matrix
model at each chain site, and associating Feynman diagrams of these models to
spatial configurations of the molecules. We obtain an exact expression for the
topological expansion of the partition function of the system. We calculate
exact and asymptotic expressions for the free energy, specific heat,
entanglement and chemical potential and study their behavior as a function of
temperature. Our results are consistent with the interpretation of $1/N$ as
being a measure of the concentration of $\rm{Mg}^{++}$ in solution.
| [
{
"created": "Tue, 1 Apr 2008 19:00:38 GMT",
"version": "v1"
},
{
"created": "Fri, 19 Sep 2008 12:19:21 GMT",
"version": "v2"
},
{
"created": "Fri, 26 Sep 2008 13:21:01 GMT",
"version": "v3"
},
{
"created": "Mon, 1 Dec 2008 15:06:05 GMT",
"version": "v4"
}
] | 2009-11-13 | [
[
"dell'Erba",
"Matias",
""
],
[
"Zemba",
"Guillermo R.",
""
]
] | We analyze the thermodynamic properties of a simplified model for folded RNA molecules recently studied by G. Vernizzi, H. Orland, A. Zee (in {\it Phys. Rev. Lett.} {\bf 94} (2005) 168103). The model consists of a chain of one-flavor base molecules with a flexible backbone and all possible pairing interactions equally allowed. The spatial pseudoknot structure of the model can be efficiently studied by introducing a $N \times N$ hermitian random matrix model at each chain site, and associating Feynman diagrams of these models to spatial configurations of the molecules. We obtain an exact expression for the topological expansion of the partition function of the system. We calculate exact and asymptotic expressions for the free energy, specific heat, entanglement and chemical potential and study their behavior as a function of temperature. Our results are consistent with the interpretation of $1/N$ as being a measure of the concentration of $\rm{Mg}^{++}$ in solution. |
1907.11174 | Mohit Kumar Jolly | Shubham Tripathi, Jianhua Xing, Herbert Levine and Mohit Kumar Jolly | Mathematical Modeling of Plasticity and Heterogeneity in EMT | null | null | null | null | q-bio.MN q-bio.CB | http://creativecommons.org/licenses/by/4.0/ | Epithelial-Mesenchymal Transition (EMT), and the corresponding reverse
process, Mesenchymal-Epithelial Transition (MET), are dynamic and reversible
cellular programs orchestrated by many changes at biochemical and morphological
levels. A recent surge in identifying the molecular mechanisms underlying
EMT/MET has led to the development of various mathematical models that have
contributed to our improved understanding of dynamics at single-cell and
population levels: a) multi-stability (how many phenotypes can cells attain en
route EMT/MET?), b) reversibility/irreversibility (what time and/or
concentration of an EMT inducer marks the 'tipping point' when cells induced to
undergo EMT cannot revert?), c) symmetry in EMT/MET (do cells take the same
path while reverting as they took during the induction of EMT?), and d)
non-cell autonomous mechanisms (how does a cell undergoing EMT alter the
tendency of its neighbors to undergo EMT?). These dynamical traits may
facilitate a heterogeneous response within a cell population undergoing
EMT/MET. Here, we present a few examples of designing different mathematical
models that can contribute to decoding EMT/MET dynamics.
| [
{
"created": "Thu, 25 Jul 2019 16:30:05 GMT",
"version": "v1"
}
] | 2019-07-26 | [
[
"Tripathi",
"Shubham",
""
],
[
"Xing",
"Jianhua",
""
],
[
"Levine",
"Herbert",
""
],
[
"Jolly",
"Mohit Kumar",
""
]
] | Epithelial-Mesenchymal Transition (EMT), and the corresponding reverse process, Mesenchymal-Epithelial Transition (MET), are dynamic and reversible cellular programs orchestrated by many changes at biochemical and morphological levels. A recent surge in identifying the molecular mechanisms underlying EMT/MET has led to the development of various mathematical models that have contributed to our improved understanding of dynamics at single-cell and population levels: a) multi-stability (how many phenotypes can cells attain en route EMT/MET?), b) reversibility/irreversibility (what time and/or concentration of an EMT inducer marks the 'tipping point' when cells induced to undergo EMT cannot revert?), c) symmetry in EMT/MET (do cells take the same path while reverting as they took during the induction of EMT?), and d) non-cell autonomous mechanisms (how does a cell undergoing EMT alter the tendency of its neighbors to undergo EMT?). These dynamical traits may facilitate a heterogeneous response within a cell population undergoing EMT/MET. Here, we present a few examples of designing different mathematical models that can contribute to decoding EMT/MET dynamics. |
1109.1934 | M. Angeles Serrano | M. Angeles Serrano, Marian Boguna, Francesc Sagues | Uncovering the hidden geometry behind metabolic networks | null | null | null | null | q-bio.MN cond-mat.dis-nn physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Metabolism is a fascinating cell machinery underlying life and disease and
genome-scale reconstructions provide us with a captivating view of its
complexity. However, deciphering the relationship between metabolic structure
and function remains a major challenge. In particular, turning observed
structural regularities into organizing principles underlying systemic
functions is a crucial task that can be significantly addressed after endowing
complex network representations of metabolism with the notion of geometric
distance. Here, we design a cartographic map of metabolic networks by embedding
them into a simple geometry that provides a natural explanation for their
observed network topology and that codifies node proximity as a measure of
hidden structural similarities. We assume a simple and general connectivity law
that gives more probability of interaction to metabolite/reaction pairs which
are closer in the hidden space. Remarkably, we find an astonishing congruency
between the architecture of E. coli and human cell metabolisms and the
underlying geometry. In addition, the formalism unveils a backbone-like
structure of connected biochemical pathways on the basis of a quantitative
cross-talk. Pathways thus acquire a new perspective which challenges their
classical view as self-contained functional units.
| [
{
"created": "Fri, 9 Sep 2011 08:21:49 GMT",
"version": "v1"
}
] | 2011-09-12 | [
[
"Serrano",
"M. Angeles",
""
],
[
"Boguna",
"Marian",
""
],
[
"Sagues",
"Francesc",
""
]
] | Metabolism is a fascinating cell machinery underlying life and disease and genome-scale reconstructions provide us with a captivating view of its complexity. However, deciphering the relationship between metabolic structure and function remains a major challenge. In particular, turning observed structural regularities into organizing principles underlying systemic functions is a crucial task that can be significantly addressed after endowing complex network representations of metabolism with the notion of geometric distance. Here, we design a cartographic map of metabolic networks by embedding them into a simple geometry that provides a natural explanation for their observed network topology and that codifies node proximity as a measure of hidden structural similarities. We assume a simple and general connectivity law that gives more probability of interaction to metabolite/reaction pairs which are closer in the hidden space. Remarkably, we find an astonishing congruency between the architecture of E. coli and human cell metabolisms and the underlying geometry. In addition, the formalism unveils a backbone-like structure of connected biochemical pathways on the basis of a quantitative cross-talk. Pathways thus acquire a new perspective which challenges their classical view as self-contained functional units. |
1302.3685 | Mark Robinson | Simon Anders, Davis J. McCarthy, Yunshen Chen, Michal Okoniewski,
Gordon K. Smyth, Wolfgang Huber, Mark D. Robinson | Count-based differential expression analysis of RNA sequencing data
using R and Bioconductor | null | null | 10.1038/nprot.2013.099 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of
transcriptomes in many areas of biology, including studies into gene
regulation, development and disease. Of particular interest is the discovery of
differentially expressed genes across different conditions (e.g., tissues,
perturbations), while optionally adjusting for other systematic factors that
affect the data collection process. There are a number of subtle yet critical
aspects of these analyses, such as read counting, appropriate treatment of
biological variability, quality control checks and appropriate setup of
statistical modeling. Several variations have been presented in the literature,
and there is a need for guidance on current best practices. This protocol
presents a "state-of-the-art" computational and statistical RNA-seq
differential expression analysis workflow largely based on the free open-source
R language and Bioconductor software and in particular, two widely-used tools
DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10
samples) can be <1 hour, with computation time <1 day using a standard desktop
PC.
| [
{
"created": "Fri, 15 Feb 2013 06:32:57 GMT",
"version": "v1"
},
{
"created": "Thu, 4 Apr 2013 16:24:29 GMT",
"version": "v2"
},
{
"created": "Thu, 20 Jun 2013 22:36:49 GMT",
"version": "v3"
}
] | 2016-07-26 | [
[
"Anders",
"Simon",
""
],
[
"McCarthy",
"Davis J.",
""
],
[
"Chen",
"Yunshen",
""
],
[
"Okoniewski",
"Michal",
""
],
[
"Smyth",
"Gordon K.",
""
],
[
"Huber",
"Wolfgang",
""
],
[
"Robinson",
"Mark D.",
""
]... | RNA sequencing (RNA-seq) has been rapidly adopted for the profiling of transcriptomes in many areas of biology, including studies into gene regulation, development and disease. Of particular interest is the discovery of differentially expressed genes across different conditions (e.g., tissues, perturbations), while optionally adjusting for other systematic factors that affect the data collection process. There are a number of subtle yet critical aspects of these analyses, such as read counting, appropriate treatment of biological variability, quality control checks and appropriate setup of statistical modeling. Several variations have been presented in the literature, and there is a need for guidance on current best practices. This protocol presents a "state-of-the-art" computational and statistical RNA-seq differential expression analysis workflow largely based on the free open-source R language and Bioconductor software and in particular, two widely-used tools DESeq and edgeR. Hands-on time for typical small experiments (e.g., 4-10 samples) can be <1 hour, with computation time <1 day using a standard desktop PC. |
1009.3155 | Georg Fritz | Georg Fritz, Christiane Koller, Korinna Burdack, Larissa Tetsch, Ina
Haneburger, Kirsten Jung, Ulrich Gerland | Induction kinetics of a conditional pH stress response system in
Escherichia coli | 13 pages, 8 figures, supplementary material available upon request
from the authors | J. Mol. Biol. (2009) 393, 272-286 | 10.1016/j.jmb.2009.08.037 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The analysis of stress response systems in microorganisms can reveal
molecular strategies for regulatory control and adaptation. Here, we focus on
the Cad module, a subsystem of E. coli's response to acidic stress, which is
conditionally activated at low pH only when lysine is available. When
expressed, the Cad system counteracts the elevated H+ concentration by
converting lysine to cadaverine under the consumption of H+, and exporting
cadaverine in exchange for external lysine. Surprisingly, the cad operon
displays a transient response, even when the conditions for its induction
persist. To quantitatively characterize the regulation of the Cad module, we
have experimentally recorded and theoretically modeled the dynamics of
important system variables. We establish a quantitative model that adequately
describes and predicts the transient expression behavior for various initial
conditions. Our quantitative analysis of the Cad system supports a negative
feedback by external cadaverine as the origin of the transient response.
Furthermore, the analysis puts causal constraints on the precise mechanism of
signal transduction via the regulatory protein CadC.
| [
{
"created": "Thu, 16 Sep 2010 12:15:50 GMT",
"version": "v1"
}
] | 2010-09-17 | [
[
"Fritz",
"Georg",
""
],
[
"Koller",
"Christiane",
""
],
[
"Burdack",
"Korinna",
""
],
[
"Tetsch",
"Larissa",
""
],
[
"Haneburger",
"Ina",
""
],
[
"Jung",
"Kirsten",
""
],
[
"Gerland",
"Ulrich",
""
]
] | The analysis of stress response systems in microorganisms can reveal molecular strategies for regulatory control and adaptation. Here, we focus on the Cad module, a subsystem of E. coli's response to acidic stress, which is conditionally activated at low pH only when lysine is available. When expressed, the Cad system counteracts the elevated H+ concentration by converting lysine to cadaverine under the consumption of H+, and exporting cadaverine in exchange for external lysine. Surprisingly, the cad operon displays a transient response, even when the conditions for its induction persist. To quantitatively characterize the regulation of the Cad module, we have experimentally recorded and theoretically modeled the dynamics of important system variables. We establish a quantitative model that adequately describes and predicts the transient expression behavior for various initial conditions. Our quantitative analysis of the Cad system supports a negative feedback by external cadaverine as the origin of the transient response. Furthermore, the analysis puts causal constraints on the precise mechanism of signal transduction via the regulatory protein CadC. |
1608.07232 | Marouen Ben Guebila | Marouen Ben Guebila, Johan Thunberg | Toward a closed-loop subcutaneous delivery of L-DOPA | null | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | L-DOPA has been the gold standard treatment for Parkinson's disease since 50
years. Being the direct biochemical precursor of dopamine, L-DOPA is
effectively converted in the brain, but two major phenomena reduce its
therapeutic action: i) competition with amino acids in the gut wall and in the
blood brain barrier and ii) its fast kinetics (absorption, distribution,
metabolism, and elimination). Continuous administration of L-DOPA, such as
jejunal pumps, have addressed the issue of fast absorption. Considering a
subcutaneous delivery of L-DOPA allows to bypass the gastrointestinal tract and
avoid competition with dietary amino acids. Remains the competition at the
blood barrier between amino acids and L-DOPA, which we address by proposing a
closed-loop controlled, continuous subcutaneous delivery pump. In the
proof-of-concept format, the delivery strategy evaluated on comprehensive model
of L-DOPA kinetics, holds the promise of improving the treatment of late-stage
Parkinson's disease patients.
| [
{
"created": "Wed, 24 Aug 2016 15:41:20 GMT",
"version": "v1"
}
] | 2016-08-26 | [
[
"Guebila",
"Marouen Ben",
""
],
[
"Thunberg",
"Johan",
""
]
] | L-DOPA has been the gold standard treatment for Parkinson's disease since 50 years. Being the direct biochemical precursor of dopamine, L-DOPA is effectively converted in the brain, but two major phenomena reduce its therapeutic action: i) competition with amino acids in the gut wall and in the blood brain barrier and ii) its fast kinetics (absorption, distribution, metabolism, and elimination). Continuous administration of L-DOPA, such as jejunal pumps, have addressed the issue of fast absorption. Considering a subcutaneous delivery of L-DOPA allows to bypass the gastrointestinal tract and avoid competition with dietary amino acids. Remains the competition at the blood barrier between amino acids and L-DOPA, which we address by proposing a closed-loop controlled, continuous subcutaneous delivery pump. In the proof-of-concept format, the delivery strategy evaluated on comprehensive model of L-DOPA kinetics, holds the promise of improving the treatment of late-stage Parkinson's disease patients. |
2111.03621 | Maurizio Mattia | Gianni V. Vinci and Maurizio Mattia | A `Rosetta stone' for the population dynamics of spiking neuron networks | 11 pages, no figures | null | null | null | q-bio.NC cond-mat.dis-nn | http://creativecommons.org/licenses/by/4.0/ | Populations of spiking neuron models have densities of their microscopic
variables (e.g., single-cell membrane potentials) whose evolution fully capture
the collective dynamics of biological networks, even outside equilibrium.
Despite its general applicability, the Fokker-Planck equation governing such
evolution is mainly studied within the borders of the linear response theory,
although alternative spectral expansion approaches offer some advantages in the
study of the out-of-equilibrium dynamics. This is mainly due to the difficulty
in computing the state-dependent coefficients of the expanded system of
differential equations. Here, we address this issue by deriving analytic
expressions for such coefficients by pairing perturbative solutions of the
Fokker-Planck approach with their counterparts from the spectral expansion. A
tight relationship emerges between several of these coefficients and the
Laplace transform of the inter-spike interval density (i.e., the distribution
of first-passage times). `Coefficients' like the current-to-rate gain function,
the eigenvalues of the Fokker-Planck operator and its eigenfunctions at the
boundaries are derived without resorting to integral expressions. For the leaky
integrate-and-fire neurons, the coupling terms between stationary and
nonstationary modes are also worked out paving the way to accurately
characterize the critical points and the relaxation timescales in networks of
interacting populations.
| [
{
"created": "Fri, 5 Nov 2021 17:14:09 GMT",
"version": "v1"
}
] | 2021-11-08 | [
[
"Vinci",
"Gianni V.",
""
],
[
"Mattia",
"Maurizio",
""
]
] | Populations of spiking neuron models have densities of their microscopic variables (e.g., single-cell membrane potentials) whose evolution fully capture the collective dynamics of biological networks, even outside equilibrium. Despite its general applicability, the Fokker-Planck equation governing such evolution is mainly studied within the borders of the linear response theory, although alternative spectral expansion approaches offer some advantages in the study of the out-of-equilibrium dynamics. This is mainly due to the difficulty in computing the state-dependent coefficients of the expanded system of differential equations. Here, we address this issue by deriving analytic expressions for such coefficients by pairing perturbative solutions of the Fokker-Planck approach with their counterparts from the spectral expansion. A tight relationship emerges between several of these coefficients and the Laplace transform of the inter-spike interval density (i.e., the distribution of first-passage times). `Coefficients' like the current-to-rate gain function, the eigenvalues of the Fokker-Planck operator and its eigenfunctions at the boundaries are derived without resorting to integral expressions. For the leaky integrate-and-fire neurons, the coupling terms between stationary and nonstationary modes are also worked out paving the way to accurately characterize the critical points and the relaxation timescales in networks of interacting populations. |
2404.10946 | Trym Lindell | Trym A. E. Lindell, Ola H. Ramstad, Ionna Sandvig, Axel Sandvig,
Stefano Nichele | Information encoding and decoding in in-vitro neural networks on micro
electrode arrays through stimulation timing | 50 pages, 23 figures | null | null | null | q-bio.NC cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A primary challenge in utilizing in-vitro biological neural networks for
computations is finding good encoding and decoding schemes for inputting and
decoding data to and from the networks. Furthermore, identifying the optimal
parameter settings for a given combination of encoding and decoding schemes
adds additional complexity to this challenge. In this study we explore
stimulation timing as an encoding method, i.e. we encode information as the
delay between stimulation pulses and identify the bounds and acuity of
stimulation timings which produce linearly separable spike responses. We also
examine the optimal readout parameters for a linear decoder in the form of
epoch length, time bin size and epoch offset. Our results suggest that
stimulation timings between 36 and 436ms may be optimal for encoding and that
different combinations of readout parameters may be optimal at different parts
of the evoked spike response.
| [
{
"created": "Tue, 16 Apr 2024 22:59:40 GMT",
"version": "v1"
}
] | 2024-04-18 | [
[
"Lindell",
"Trym A. E.",
""
],
[
"Ramstad",
"Ola H.",
""
],
[
"Sandvig",
"Ionna",
""
],
[
"Sandvig",
"Axel",
""
],
[
"Nichele",
"Stefano",
""
]
] | A primary challenge in utilizing in-vitro biological neural networks for computations is finding good encoding and decoding schemes for inputting and decoding data to and from the networks. Furthermore, identifying the optimal parameter settings for a given combination of encoding and decoding schemes adds additional complexity to this challenge. In this study we explore stimulation timing as an encoding method, i.e. we encode information as the delay between stimulation pulses and identify the bounds and acuity of stimulation timings which produce linearly separable spike responses. We also examine the optimal readout parameters for a linear decoder in the form of epoch length, time bin size and epoch offset. Our results suggest that stimulation timings between 36 and 436ms may be optimal for encoding and that different combinations of readout parameters may be optimal at different parts of the evoked spike response. |
2009.08667 | Jonas Stapmanns | Jonas Stapmanns, Jan Hahne, Moritz Helias, Matthias Bolten, Markus
Diesmann and David Dahmen | Event-based update of synapses in voltage-based learning rules | 45 pages, 13 figures, 7 tables | null | 10.3389/fninf.2021.609147 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to the point-like nature of neuronal spiking, efficient neural network
simulators often employ event-based simulation schemes for synapses. Yet many
types of synaptic plasticity rely on the membrane potential of the postsynaptic
cell as a third factor in addition to pre- and postsynaptic spike times.
Synapses therefore require continuous information to update their strength
which a priori necessitates a continuous update in a time-driven manner. The
latter hinders scaling of simulations to realistic cortical network sizes and
relevant time scales for learning. Here, we derive two efficient algorithms for
archiving postsynaptic membrane potentials, both compatible with modern
simulation engines based on event-based synapse updates. We theoretically
contrast the two algorithms with a time-driven synapse update scheme to analyze
advantages in terms of memory and computations. We further present a reference
implementation in the spiking neural network simulator NEST for two
prototypical voltage-based plasticity rules: the Clopath rule and the
Urbanczik-Senn rule. For both rules, the two event-based algorithms
significantly outperform the time-driven scheme. Depending on the amount of
data to be stored for plasticity, which heavily differs between the rules, a
strong performance increase can be achieved by compressing or sampling of
information on membrane potentials. Our results on computational efficiency
related to archiving of information provide guidelines for the design of
learning rules in order to make them practically usable in large-scale
networks.
| [
{
"created": "Fri, 18 Sep 2020 07:37:50 GMT",
"version": "v1"
},
{
"created": "Wed, 10 Mar 2021 15:00:03 GMT",
"version": "v2"
}
] | 2022-04-05 | [
[
"Stapmanns",
"Jonas",
""
],
[
"Hahne",
"Jan",
""
],
[
"Helias",
"Moritz",
""
],
[
"Bolten",
"Matthias",
""
],
[
"Diesmann",
"Markus",
""
],
[
"Dahmen",
"David",
""
]
] | Due to the point-like nature of neuronal spiking, efficient neural network simulators often employ event-based simulation schemes for synapses. Yet many types of synaptic plasticity rely on the membrane potential of the postsynaptic cell as a third factor in addition to pre- and postsynaptic spike times. Synapses therefore require continuous information to update their strength which a priori necessitates a continuous update in a time-driven manner. The latter hinders scaling of simulations to realistic cortical network sizes and relevant time scales for learning. Here, we derive two efficient algorithms for archiving postsynaptic membrane potentials, both compatible with modern simulation engines based on event-based synapse updates. We theoretically contrast the two algorithms with a time-driven synapse update scheme to analyze advantages in terms of memory and computations. We further present a reference implementation in the spiking neural network simulator NEST for two prototypical voltage-based plasticity rules: the Clopath rule and the Urbanczik-Senn rule. For both rules, the two event-based algorithms significantly outperform the time-driven scheme. Depending on the amount of data to be stored for plasticity, which heavily differs between the rules, a strong performance increase can be achieved by compressing or sampling of information on membrane potentials. Our results on computational efficiency related to archiving of information provide guidelines for the design of learning rules in order to make them practically usable in large-scale networks. |
1911.12549 | Liane Gabora | Victoria S. Scotney, Jasmine Schwartz, Nicole Carbert, Adam Saab, and
Liane Gabora | The Form of a Half-baked Creative Idea: Empirical Explorations into the
Structure of Ill-defined Mental Representations | Forthcoming in Acta Psychologica. Note: this draft may not be
identical to the version that was accepted by Elsevier Press for publication;
51 pages | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Creative thought is conventionally believed to involve searching memory and
generating multiple independent candidate ideas followed by selection and
refinement of the most promising. Honing theory, which grew out of the quantum
approach to describing how concepts interact, posits that what appears to be
discrete, separate ideas are actually different projections of the same
underlying mental representation, which can be described as a superposition
state, and which may take different outward forms when reflected upon from
different perspectives. As creative thought proceeds, this representation loses
potentiality to be viewed from different perspectives and manifest as different
outcomes. Honing theory yields different predictions from conventional theories
about the mental representation of an idea midway through the creative process.
These predictions were pitted against one another in two studies: one
closed-ended and one open-ended. In the first study, participants were
interrupted midway through solving an analogy problem and wrote down what they
were thinking in terms of a solution. In the second, participants were
instructed to create a painting that expressed their true essence and describe
how they conceived of the painting. For both studies, na\"ive judges
categorized these responses as supportive of either the conventional view or
the honing theory view. The results of both studies were significantly more
consistent with the predictions of honing theory. Some implications for
creative cognition, and cognition in general, are discussed.
| [
{
"created": "Thu, 28 Nov 2019 06:16:37 GMT",
"version": "v1"
}
] | 2019-12-02 | [
[
"Scotney",
"Victoria S.",
""
],
[
"Schwartz",
"Jasmine",
""
],
[
"Carbert",
"Nicole",
""
],
[
"Saab",
"Adam",
""
],
[
"Gabora",
"Liane",
""
]
] | Creative thought is conventionally believed to involve searching memory and generating multiple independent candidate ideas followed by selection and refinement of the most promising. Honing theory, which grew out of the quantum approach to describing how concepts interact, posits that what appears to be discrete, separate ideas are actually different projections of the same underlying mental representation, which can be described as a superposition state, and which may take different outward forms when reflected upon from different perspectives. As creative thought proceeds, this representation loses potentiality to be viewed from different perspectives and manifest as different outcomes. Honing theory yields different predictions from conventional theories about the mental representation of an idea midway through the creative process. These predictions were pitted against one another in two studies: one closed-ended and one open-ended. In the first study, participants were interrupted midway through solving an analogy problem and wrote down what they were thinking in terms of a solution. In the second, participants were instructed to create a painting that expressed their true essence and describe how they conceived of the painting. For both studies, na\"ive judges categorized these responses as supportive of either the conventional view or the honing theory view. The results of both studies were significantly more consistent with the predictions of honing theory. Some implications for creative cognition, and cognition in general, are discussed. |
1507.06064 | Ivo Siekmann | Ivo Siekmann, Pengxing Cao, James Sneyd and Edmund J. Crampin | Data-driven modelling of the inositol trisphosphate receptor (IPR) and
its role in calcium induced calcium release (CICR) | 23 pages, 7 figures | null | null | null | q-bio.QM q-bio.BM q-bio.SC stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We give a review of the current state of the art of data-driven modelling of
the inositol trisphosphate receptor (IPR). After explaining that the IPR plays
a crucial role as a central regulator in calcium dynamics, several sources of
relevant experimental data are introduced. Single ion channels are best studied
by recording single-channel currents under different ligand concentrations via
the patch-clamp technique. The particular relevance of modal gating, the
spontaneous switching between different levels of channel activity that occur
even at constant ligand concentrations, is highlighted. In order to investigate
the interactions of IPRs, calcium release from small clusters of channels,
so-called calcium puffs, can be used. We then present the mathematical
framework common to all models based on single-channel data, aggregated
continuous-time Markov models, and give a short review of statistical
approaches for parameterising these models with experimental data. The process
of building a Markov model that integrates various sources of experimental data
is illustrated using two recent examples, the model by Ullah et al. and the
"Park-Drive" model by Siekmann et al., the only models that account for all
sources of data currently available. Finally, it is demonstrated that the
essential features of the Park-Drive model in different models of calcium
dynamics are preserved after reducing it to a two-state model that only
accounts for the switching between the inactive "park" and the active "drive"
mode. This highlights the fact that modal gating is the most important
mechanism of ligand regulation in the IPR. It also emphasises that data-driven
models of ion channels do not necessarily have to lead to detailed models but
can be constructed so that relevant data is selected to represent ion channels
at the appropriate level of complexity for a given application.
| [
{
"created": "Wed, 22 Jul 2015 05:13:57 GMT",
"version": "v1"
}
] | 2015-07-23 | [
[
"Siekmann",
"Ivo",
""
],
[
"Cao",
"Pengxing",
""
],
[
"Sneyd",
"James",
""
],
[
"Crampin",
"Edmund J.",
""
]
] | We give a review of the current state of the art of data-driven modelling of the inositol trisphosphate receptor (IPR). After explaining that the IPR plays a crucial role as a central regulator in calcium dynamics, several sources of relevant experimental data are introduced. Single ion channels are best studied by recording single-channel currents under different ligand concentrations via the patch-clamp technique. The particular relevance of modal gating, the spontaneous switching between different levels of channel activity that occur even at constant ligand concentrations, is highlighted. In order to investigate the interactions of IPRs, calcium release from small clusters of channels, so-called calcium puffs, can be used. We then present the mathematical framework common to all models based on single-channel data, aggregated continuous-time Markov models, and give a short review of statistical approaches for parameterising these models with experimental data. The process of building a Markov model that integrates various sources of experimental data is illustrated using two recent examples, the model by Ullah et al. and the "Park-Drive" model by Siekmann et al., the only models that account for all sources of data currently available. Finally, it is demonstrated that the essential features of the Park-Drive model in different models of calcium dynamics are preserved after reducing it to a two-state model that only accounts for the switching between the inactive "park" and the active "drive" mode. This highlights the fact that modal gating is the most important mechanism of ligand regulation in the IPR. It also emphasises that data-driven models of ion channels do not necessarily have to lead to detailed models but can be constructed so that relevant data is selected to represent ion channels at the appropriate level of complexity for a given application. |
1404.3299 | Norshuhaila Mohamed Sunar N.M.Sunar | N.M. Sunar, D.I. Stewart, E.I. Stentiford, L.A. Flecther | A rapid molecular approach to determining the occurrence of pathogen
indicators in compost | Proceeding of Twelfth International Waste Management and Landfill.
Sardinia 2009 Symposium | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An accurate method for enumerating pathogen indicators, such as Escherichia
coli (E. coli), and Salmonella spp. is important for assessing the safety of
compost samples. This study aimed to determine the occurrence of pathogen
indicators in compost samples by using a molecular approach known as Polymerase
Chain Reaction (PCR). The DNA sample was extracted from sewage sludge compost.
The specificity of the probes and primers at the species level were verified by
performing NCBI-BLAST2 (Basic Local Alignment Search Tool). Primers that target
the gadAB gene for E.coli and invA gene for Salmonella spp. were selected which
produce fragment lengths around 670bp and 285bp respectively. The primers were
tested against bacterial cultures of both species and produced a strong signal
band of the expected fragment length. It provided results within 6 hours which
is relatively rapid compared to conventional culturing techniques. The other
advantages of PCR are shown to be its high sensitivity, and high specificity.
| [
{
"created": "Sat, 12 Apr 2014 16:00:34 GMT",
"version": "v1"
}
] | 2014-04-15 | [
[
"Sunar",
"N. M.",
""
],
[
"Stewart",
"D. I.",
""
],
[
"Stentiford",
"E. I.",
""
],
[
"Flecther",
"L. A.",
""
]
] | An accurate method for enumerating pathogen indicators, such as Escherichia coli (E. coli), and Salmonella spp. is important for assessing the safety of compost samples. This study aimed to determine the occurrence of pathogen indicators in compost samples by using a molecular approach known as Polymerase Chain Reaction (PCR). The DNA sample was extracted from sewage sludge compost. The specificity of the probes and primers at the species level were verified by performing NCBI-BLAST2 (Basic Local Alignment Search Tool). Primers that target the gadAB gene for E.coli and invA gene for Salmonella spp. were selected which produce fragment lengths around 670bp and 285bp respectively. The primers were tested against bacterial cultures of both species and produced a strong signal band of the expected fragment length. It provided results within 6 hours which is relatively rapid compared to conventional culturing techniques. The other advantages of PCR are shown to be its high sensitivity, and high specificity. |
0711.3088 | Hiroshi Nishiura | Gerardo Chowell and Hiroshi Nishiura | Quantifying the transmission potential of pandemic influenza | 79 pages (revised version), 3 figures; added 1 table and minor
revisions were made in the main text; to appear in Physics of Life Reviews;
Gerardo's website (http://www.public.asu.edu/~gchowel/), Hiroshi's website
(http://plaza.umin.ac.jp/~infepi/hnishiura.htm) | null | 10.1016/j.plrev.2007.12.001 | null | q-bio.PE | null | This article reviews quantitative methods to estimate the basic reproduction
number of pandemic influenza, a key threshold quantity to help determine the
intensity of interventions required to control the disease. Although it is
difficult to assess the transmission potential of a probable future pandemic,
historical epidemiologic data is readily available from previous pandemics, and
as a reference quantity for future pandemic planning, mathematical and
statistical analyses of historical data are crucial. In particular, because
many historical records tend to document only the temporal distribution of
cases or deaths (i.e. epidemic curve), our review focuses on methods to
maximize the utility of time-evolution data and to clarify the detailed
mechanisms of the spread of influenza. First, we highlight structured epidemic
models and their parameter estimation method which can quantify the detailed
disease dynamics including those we cannot observe directly.
Duration-structured epidemic systems are subsequently presented, offering firm
understanding of the definition of the basic and effective reproduction
numbers. When the initial growth phase of an epidemic is investigated, the
distribution of the generation time is key statistical information to
appropriately estimate the transmission potential using the intrinsic growth
rate. Applications of stochastic processes are also highlighted to estimate the
transmission potential using the similar data. Critically important
characteristics of influenza data are subsequently summarized, followed by our
conclusions to suggest potential future methodological improvements.
| [
{
"created": "Tue, 20 Nov 2007 20:51:40 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Dec 2007 12:18:11 GMT",
"version": "v2"
}
] | 2009-11-13 | [
[
"Chowell",
"Gerardo",
""
],
[
"Nishiura",
"Hiroshi",
""
]
] | This article reviews quantitative methods to estimate the basic reproduction number of pandemic influenza, a key threshold quantity to help determine the intensity of interventions required to control the disease. Although it is difficult to assess the transmission potential of a probable future pandemic, historical epidemiologic data is readily available from previous pandemics, and as a reference quantity for future pandemic planning, mathematical and statistical analyses of historical data are crucial. In particular, because many historical records tend to document only the temporal distribution of cases or deaths (i.e. epidemic curve), our review focuses on methods to maximize the utility of time-evolution data and to clarify the detailed mechanisms of the spread of influenza. First, we highlight structured epidemic models and their parameter estimation method which can quantify the detailed disease dynamics including those we cannot observe directly. Duration-structured epidemic systems are subsequently presented, offering firm understanding of the definition of the basic and effective reproduction numbers. When the initial growth phase of an epidemic is investigated, the distribution of the generation time is key statistical information to appropriately estimate the transmission potential using the intrinsic growth rate. Applications of stochastic processes are also highlighted to estimate the transmission potential using the similar data. Critically important characteristics of influenza data are subsequently summarized, followed by our conclusions to suggest potential future methodological improvements. |
1910.09256 | Andrea De Martino | Mattia Miotto, Enzo Marinari, Andrea De Martino | Competing endogenous RNA crosstalk at system level | 25 pages, includes Supporting Information; to appear in PLoS Comp
Biol | null | 10.1371/journal.pcbi.1007474 | null | q-bio.MN cond-mat.dis-nn physics.bio-ph q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | microRNAs (miRNAs) regulate gene expression at post-transcriptional level by
repressing target RNA molecules. Competition to bind miRNAs tends in turn to
correlate their targets, establishing effective RNA-RNA interactions that can
influence expression levels, buffer fluctuations and promote signal
propagation. Such a potential has been characterized mathematically for small
motifs both at steady state and \red{away from stationarity}. Experimental
evidence, on the other hand, suggests that competing endogenous RNA (ceRNA)
crosstalk is rather weak. Extended miRNA-RNA networks could however favour the
integration of many crosstalk interactions, leading to significant large-scale
effects in spite of the weakness of individual links. To clarify the extent to
which crosstalk is sustained by the miRNA interactome, we have studied its
emergent systemic features in silico in large-scale miRNA-RNA network
reconstructions. We show that, although generically weak, system-level
crosstalk patterns (i) are enhanced by transcriptional heterogeneities, (ii)
can achieve high-intensity even for RNAs that are not co-regulated, (iii) are
robust to variability in transcription rates, and (iv) are significantly
non-local, i.e. correlate weakly with miRNA-RNA interaction parameters.
Furthermore, RNA levels are generically more stable when crosstalk is
strongest. As some of these features appear to be encoded in the network's
topology, crosstalk may functionally be favoured by natural selection. These
results suggest that, besides their repressive role, miRNAs mediate a weak but
resilient and context-independent network of cross-regulatory interactions that
interconnect the transcriptome, stabilize expression levels and support
system-level responses.
| [
{
"created": "Mon, 21 Oct 2019 10:20:37 GMT",
"version": "v1"
}
] | 2020-07-01 | [
[
"Miotto",
"Mattia",
""
],
[
"Marinari",
"Enzo",
""
],
[
"De Martino",
"Andrea",
""
]
] | microRNAs (miRNAs) regulate gene expression at post-transcriptional level by repressing target RNA molecules. Competition to bind miRNAs tends in turn to correlate their targets, establishing effective RNA-RNA interactions that can influence expression levels, buffer fluctuations and promote signal propagation. Such a potential has been characterized mathematically for small motifs both at steady state and \red{away from stationarity}. Experimental evidence, on the other hand, suggests that competing endogenous RNA (ceRNA) crosstalk is rather weak. Extended miRNA-RNA networks could however favour the integration of many crosstalk interactions, leading to significant large-scale effects in spite of the weakness of individual links. To clarify the extent to which crosstalk is sustained by the miRNA interactome, we have studied its emergent systemic features in silico in large-scale miRNA-RNA network reconstructions. We show that, although generically weak, system-level crosstalk patterns (i) are enhanced by transcriptional heterogeneities, (ii) can achieve high-intensity even for RNAs that are not co-regulated, (iii) are robust to variability in transcription rates, and (iv) are significantly non-local, i.e. correlate weakly with miRNA-RNA interaction parameters. Furthermore, RNA levels are generically more stable when crosstalk is strongest. As some of these features appear to be encoded in the network's topology, crosstalk may functionally be favoured by natural selection. These results suggest that, besides their repressive role, miRNAs mediate a weak but resilient and context-independent network of cross-regulatory interactions that interconnect the transcriptome, stabilize expression levels and support system-level responses. |
1807.01776 | Joseph Hackman | Joseph V. Hackman (1), Daniel J. Hruschka (2) ((1) University of Utah,
Department of Anthropology (2) Arizona State University, School of Human
Evolution and Social Change) | Disentangling basal and accrued height-for-age for cross-population
comparisons | null | null | null | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objectives: Current standards for comparing stunting across human populations
assume a universal model of child growth. Such comparisons ignore population
differences that are independent of deprivation and health outcomes. This paper
partitions variation in height-for-age that is specifically associated with
deprivation and health outcomes to provide a basis for cross-population
comparisons.
Materials & Methods: Using a multi-level model with a sigmoid relationship of
resources and growth, we partition variation in height-for-age z-scores (HAZ)
from 1,522,564 children across 70 countries into two components: 1) "accrued
HAZ" shaped by environmental inputs (e.g., undernutrition, infectious disease,
inadequate sanitation, poverty), and 2) a country-specific "basal HAZ"
independent of such inputs. We validate these components against
population-level infant mortality rates, and assess how these basal differences
may affect cross-population comparisons of stunting.
Results: Basal HAZ differs reliably across countries (range of 1.5 SD) and is
independent of measures of infant mortality. By contrast, accrued HAZ captures
stunting as impaired growth due to deprivation and is more closely associated
with infant mortality than observed HAZ. Ranking populations by accrued HAZ
suggest that populations in West Africa and the Caribbean suffer much greater
levels of stunting than suggested by observed HAZ.
Discussion: Current universal standards may dramatically underestimate
stunting in populations with taller basal HAZ. Relying on observed HAZ rather
than accrued HAZ may also lead to inappropriate cross-population comparisons,
such as concluding that Haitian children enjoy better conditions for growth
than do Indian or Guatemalan children.
| [
{
"created": "Mon, 2 Jul 2018 20:13:15 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Aug 2018 20:42:35 GMT",
"version": "v2"
},
{
"created": "Tue, 7 May 2019 20:12:11 GMT",
"version": "v3"
},
{
"created": "Sat, 17 Aug 2019 00:31:08 GMT",
"version": "v4"
}
] | 2019-08-20 | [
[
"Hackman",
"Joseph V.",
""
],
[
"Hruschka",
"Daniel J.",
""
]
] | Objectives: Current standards for comparing stunting across human populations assume a universal model of child growth. Such comparisons ignore population differences that are independent of deprivation and health outcomes. This paper partitions variation in height-for-age that is specifically associated with deprivation and health outcomes to provide a basis for cross-population comparisons. Materials & Methods: Using a multi-level model with a sigmoid relationship of resources and growth, we partition variation in height-for-age z-scores (HAZ) from 1,522,564 children across 70 countries into two components: 1) "accrued HAZ" shaped by environmental inputs (e.g., undernutrition, infectious disease, inadequate sanitation, poverty), and 2) a country-specific "basal HAZ" independent of such inputs. We validate these components against population-level infant mortality rates, and assess how these basal differences may affect cross-population comparisons of stunting. Results: Basal HAZ differs reliably across countries (range of 1.5 SD) and is independent of measures of infant mortality. By contrast, accrued HAZ captures stunting as impaired growth due to deprivation and is more closely associated with infant mortality than observed HAZ. Ranking populations by accrued HAZ suggest that populations in West Africa and the Caribbean suffer much greater levels of stunting than suggested by observed HAZ. Discussion: Current universal standards may dramatically underestimate stunting in populations with taller basal HAZ. Relying on observed HAZ rather than accrued HAZ may also lead to inappropriate cross-population comparisons, such as concluding that Haitian children enjoy better conditions for growth than do Indian or Guatemalan children. |
2001.07121 | David Wallis | David Wallis and Morten Elmeros | Tracking European bat species with passive acoustic directional
monitoring | Submitted to Bioacoustics (Taylor and Francis) 15 Nov. 2019 | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We have developed a method of animal localisation that detects the angle from
a sensor towards the direction of an animal call. The method is as simple to
use as deploying a conventional static sound recorder, but provides tracking
information as well as sound recordings. The principal of operation is to
detect the phase difference between microphones positioned closely together.
The phase is detected by converting the signals to their analytic form with a
Hilbert transform. The angle is then calculated from the phase difference,
frequency and microphone separation. Angular measurements provide flight paths
above the sensor, and can give details of activity and behaviour that are not
possible with a single channel static recorder. We recorded flight paths for 5
bat species on a single night at a site in Denmark (Pipistrellus nathusii,
Pipistrellus pygmaeus, Eptesicus serotinus, Myotis daubentonii and Nyctalus
noctula). The median error in angular measurement for the species was between 3
and 7 degrees. Calls at high angles from normal, corresponding with a poor
signal-to-noise ratio, had larger errors compared to calls recorded in the
centre of the field of view. Locations in space could be estimated by combining
angular measurements from two or more sensors.
| [
{
"created": "Mon, 20 Jan 2020 14:37:10 GMT",
"version": "v1"
}
] | 2020-01-22 | [
[
"Wallis",
"David",
""
],
[
"Elmeros",
"Morten",
""
]
] | We have developed a method of animal localisation that detects the angle from a sensor towards the direction of an animal call. The method is as simple to use as deploying a conventional static sound recorder, but provides tracking information as well as sound recordings. The principal of operation is to detect the phase difference between microphones positioned closely together. The phase is detected by converting the signals to their analytic form with a Hilbert transform. The angle is then calculated from the phase difference, frequency and microphone separation. Angular measurements provide flight paths above the sensor, and can give details of activity and behaviour that are not possible with a single channel static recorder. We recorded flight paths for 5 bat species on a single night at a site in Denmark (Pipistrellus nathusii, Pipistrellus pygmaeus, Eptesicus serotinus, Myotis daubentonii and Nyctalus noctula). The median error in angular measurement for the species was between 3 and 7 degrees. Calls at high angles from normal, corresponding with a poor signal-to-noise ratio, had larger errors compared to calls recorded in the centre of the field of view. Locations in space could be estimated by combining angular measurements from two or more sensors. |
2403.00020 | Yan Zhang | Yan Zhang, Ming Jia, Meng Li, Jianyu Wang, Xiangmin Hu, Zhihui Xu, and
Tao Chen | Operators' cognitive performance under extreme hot-humid exposure and
its physiological-psychological mechanism based on ECG, fNIRS, and Eye
Tracking | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Operators' cognitive functions are impaired significantly under extreme heat
stress, potentially resulting in more severe secondary disasters. This research
investigated the impact of elevated temperature and humidity (25 60%RH, 30
70%RH, 35 80%RH, 40 90%RH) on the cognitive functions and performance of
operators. Meanwhile, we explored the psychological-physiological mechanism
underlying the change in performance by electrocardiogram (ECG), functional
near-infrared spectroscopy (fNIRS), and eye tracking physiologically.
Psychological aspects such as situation awareness, workload, and working memory
were assessed. Eventually, we verified and extended the maximal adaptability
model to the extreme condition. Unexpectedly, a temporary improvement in simple
reaction tasks but rapid impairment in advanced cognitive functions (i.e.
situation awareness, communication, working memory) was obtained above 35 WBGT.
The best performance in a suitable environment was due to more effective
activation in the prefrontal cortex (PFC). With temperature increasing, more
mistakes occurred and comprehension was impaired due to drowsiness and lower
arousal levels, according to evidence of compensatory effect in fNIRS. In the
extreme environment, the enhanced PFC cooperation with higher functional
connectivity resulted in a temporary improvement, while depressed activation in
PFC, heavy physical load, and poor regulation of the cardiovascular system
restricted it. Our results provide a detailed study of the process of
operators' performance and cognitive functions when encountering increasing
heat stress, as well as its underlying mechanisms from a neuroergonomics
perspective. This can contribute to a better understanding of the interaction
between operators' performance and workplace conditions, and help to achieve a
more reliable human-centered production system in the promising era of Industry
5.0.
| [
{
"created": "Wed, 28 Feb 2024 07:07:39 GMT",
"version": "v1"
},
{
"created": "Mon, 27 May 2024 10:59:34 GMT",
"version": "v2"
}
] | 2024-05-28 | [
[
"Zhang",
"Yan",
""
],
[
"Jia",
"Ming",
""
],
[
"Li",
"Meng",
""
],
[
"Wang",
"Jianyu",
""
],
[
"Hu",
"Xiangmin",
""
],
[
"Xu",
"Zhihui",
""
],
[
"Chen",
"Tao",
""
]
] | Operators' cognitive functions are impaired significantly under extreme heat stress, potentially resulting in more severe secondary disasters. This research investigated the impact of elevated temperature and humidity (25 60%RH, 30 70%RH, 35 80%RH, 40 90%RH) on the cognitive functions and performance of operators. Meanwhile, we explored the psychological-physiological mechanism underlying the change in performance by electrocardiogram (ECG), functional near-infrared spectroscopy (fNIRS), and eye tracking physiologically. Psychological aspects such as situation awareness, workload, and working memory were assessed. Eventually, we verified and extended the maximal adaptability model to the extreme condition. Unexpectedly, a temporary improvement in simple reaction tasks but rapid impairment in advanced cognitive functions (i.e. situation awareness, communication, working memory) was obtained above 35 WBGT. The best performance in a suitable environment was due to more effective activation in the prefrontal cortex (PFC). With temperature increasing, more mistakes occurred and comprehension was impaired due to drowsiness and lower arousal levels, according to evidence of compensatory effect in fNIRS. In the extreme environment, the enhanced PFC cooperation with higher functional connectivity resulted in a temporary improvement, while depressed activation in PFC, heavy physical load, and poor regulation of the cardiovascular system restricted it. Our results provide a detailed study of the process of operators' performance and cognitive functions when encountering increasing heat stress, as well as its underlying mechanisms from a neuroergonomics perspective. This can contribute to a better understanding of the interaction between operators' performance and workplace conditions, and help to achieve a more reliable human-centered production system in the promising era of Industry 5.0. |
1208.4217 | Miroslaw Rewekant PhD MD | Piekarski S. and M. Rewekant | On the Way to More Convenient Description of Drug-Plasma Protein Binding | 6 pages | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The theoretical case is considered where the total amount of plasma protein
is conserved, but the drug is eliminated after its single application. After a
single drug application at time t = 0, the total drug concentration is measured
at times ti,...,tk and the total drug concentration at time ti is denoted by
{\phi}i. Our discussion is limited to one protein binding site. The quantity of
plasma protein ({\Lambda}), the association constant (Ka) and the total
concentration of the drug {\phi}i at time ti are considered as independent
variables. Free drug concentration, plasma protein bound concentration and free
drug fraction are given as functions of these "new" variables. The aim of this
communication is to derive the formula that allows to calculate the free drug
concentration at any time after the drug application, based on 3 parameters:
the association constant of the drug, the total plasma concentration of the
drug and the concentration of the protein. If the plasma protein quantity
({\Lambda}) and the association constant (Ka) are known, then from the
knowledge of the total drug concentration {\phi}i at time ti it is possible to
determine the free drug concentration at time ti.
| [
{
"created": "Tue, 21 Aug 2012 09:03:50 GMT",
"version": "v1"
}
] | 2012-08-22 | [
[
"S.",
"Piekarski",
""
],
[
"Rewekant",
"M.",
""
]
] | The theoretical case is considered where the total amount of plasma protein is conserved, but the drug is eliminated after its single application. After a single drug application at time t = 0, the total drug concentration is measured at times ti,...,tk and the total drug concentration at time ti is denoted by {\phi}i. Our discussion is limited to one protein binding site. The quantity of plasma protein ({\Lambda}), the association constant (Ka) and the total concentration of the drug {\phi}i at time ti are considered as independent variables. Free drug concentration, plasma protein bound concentration and free drug fraction are given as functions of these "new" variables. The aim of this communication is to derive the formula that allows to calculate the free drug concentration at any time after the drug application, based on 3 parameters: the association constant of the drug, the total plasma concentration of the drug and the concentration of the protein. If the plasma protein quantity ({\Lambda}) and the association constant (Ka) are known, then from the knowledge of the total drug concentration {\phi}i at time ti it is possible to determine the free drug concentration at time ti. |
0906.3991 | Karen Alim | Fabian Drube, Karen Alim, Guillaume Witz, Giovanni Dietler, Erwin Frey | Excluded volume effects on semiflexible ring polymers | 5 pages, 4 figures, version as published in Nano Letters | Nano Lett. 10, 1445 (2010) | 10.1021/nl1003575 | LMU-ASC 28/09 | q-bio.BM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two-dimensional semiflexible polymer rings are studied both by imaging
circular DNA adsorbed on a mica surface and by Monte Carlo simulations of
phantom polymers as well as of polymers with finite thickness. Comparison of
size and shape of the different models over the full range of flexibilities
shows that excluded volume caused by finite thickness induces an anisotropic
increase of the main axes of the conformations, a change of shape, accomplished
by an enhanced correlation along the contour.
| [
{
"created": "Mon, 22 Jun 2009 12:52:18 GMT",
"version": "v1"
},
{
"created": "Wed, 14 Apr 2010 18:44:31 GMT",
"version": "v2"
}
] | 2010-04-15 | [
[
"Drube",
"Fabian",
""
],
[
"Alim",
"Karen",
""
],
[
"Witz",
"Guillaume",
""
],
[
"Dietler",
"Giovanni",
""
],
[
"Frey",
"Erwin",
""
]
] | Two-dimensional semiflexible polymer rings are studied both by imaging circular DNA adsorbed on a mica surface and by Monte Carlo simulations of phantom polymers as well as of polymers with finite thickness. Comparison of size and shape of the different models over the full range of flexibilities shows that excluded volume caused by finite thickness induces an anisotropic increase of the main axes of the conformations, a change of shape, accomplished by an enhanced correlation along the contour. |
1808.09113 | Federico Bocci | Federico Bocci, Herbert Levine, Jos\'e Nelson Onuchic, Mohit Kumar
Jolly | Deciphering the dynamics of Epithelial-Mesenchymal Transition and Cancer
Stem Cells in tumor progression | null | null | null | null | q-bio.MN q-bio.CB | http://creativecommons.org/licenses/by/4.0/ | Purpose of review: The epithelial-Mesenchymal Transition (EMT) and the
generation of Cancer Stem Cells (CSC) are two fundamental aspects contributing
to tumor growth, acquisition of resistance to therapy, formation of metastases,
and tumor relapse. Recent experimental data identifying the circuits regulating
EMT and CSCs have driven the development of computational models capturing the
dynamics of these circuits and consequently various aspects of tumor
progression. Recent findings: We review the contribution made by these models
in a) recapitulating experimentally observed behavior, b) making experimentally
testable predictions, and c) driving emerging notions in the field, including
the emphasis on the aggressive potential of hybrid epithelial/mesenchymal (E/M)
phenotype(s). We discuss dynamical and statistical models at intracellular and
population level relating to dynamics of EMT and CSCs, and those focusing on
interconnections between these two processes. Summary: These models highlight
the insights gained via mathematical modeling approaches, and emphasizes that
the connections between hybrid E/M phenotype(s) and stemness can be explained
by analyzing underlying regulatory circuits. Such experimentally curated models
have the potential of serving as platforms for better therapeutic design
strategies.
| [
{
"created": "Tue, 28 Aug 2018 04:46:07 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Nov 2018 13:28:42 GMT",
"version": "v2"
}
] | 2018-11-07 | [
[
"Bocci",
"Federico",
""
],
[
"Levine",
"Herbert",
""
],
[
"Onuchic",
"José Nelson",
""
],
[
"Jolly",
"Mohit Kumar",
""
]
] | Purpose of review: The epithelial-Mesenchymal Transition (EMT) and the generation of Cancer Stem Cells (CSC) are two fundamental aspects contributing to tumor growth, acquisition of resistance to therapy, formation of metastases, and tumor relapse. Recent experimental data identifying the circuits regulating EMT and CSCs have driven the development of computational models capturing the dynamics of these circuits and consequently various aspects of tumor progression. Recent findings: We review the contribution made by these models in a) recapitulating experimentally observed behavior, b) making experimentally testable predictions, and c) driving emerging notions in the field, including the emphasis on the aggressive potential of hybrid epithelial/mesenchymal (E/M) phenotype(s). We discuss dynamical and statistical models at intracellular and population level relating to dynamics of EMT and CSCs, and those focusing on interconnections between these two processes. Summary: These models highlight the insights gained via mathematical modeling approaches, and emphasizes that the connections between hybrid E/M phenotype(s) and stemness can be explained by analyzing underlying regulatory circuits. Such experimentally curated models have the potential of serving as platforms for better therapeutic design strategies. |
1708.02554 | Matjaz Perc | Daqing Guo, Matjaz Perc, Yangsong Zhang, Peng Xu, Dezhong Yao | Frequency-difference-dependent stochastic resonance in neural systems | 6 two-column pages, 7 figures; accepted for publication in Physical
Review E | Phys. Rev. E 96 (2017) 022415 | 10.1103/PhysRevE.96.022415 | null | q-bio.NC nlin.AO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biological neurons receive multiple noisy oscillatory signals, and their
dynamical response to the superposition of these signals is of fundamental
importance for information processing in the brain. Here we study the response
of neural systems to the weak envelope modulation signal, which is superimposed
by two periodic signals with different frequencies. We show that stochastic
resonance occurs at the beat frequency in neural systems at the single-neuron
as well as the population level. The performance of this
frequency-difference-dependent stochastic resonance is influenced by both the
beat frequency and the two forcing frequencies. Compared to a single neuron, a
population of neurons is more efficient in detecting the information carried by
the weak envelope modulation signal at the beat frequency. Furthermore, an
appropriate fine-tuning of the excitation-inhibition balance can further
optimize the response of a neural ensemble to the superimposed signal. Our
results thus introduce and provide insights into the generation and modulation
mechanism of the frequency-difference-dependent stochastic resonance in neural
systems.
| [
{
"created": "Tue, 8 Aug 2017 16:42:52 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Aug 2017 14:52:02 GMT",
"version": "v2"
}
] | 2017-08-28 | [
[
"Guo",
"Daqing",
""
],
[
"Perc",
"Matjaz",
""
],
[
"Zhang",
"Yangsong",
""
],
[
"Xu",
"Peng",
""
],
[
"Yao",
"Dezhong",
""
]
] | Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems. |
1505.05895 | Fernando Antoneli Jr | Fernando Antoneli, Fernando Marcon, Luciano R. Lopes and Marcelo R. S.
Briones | A Kolmogorov-Smirnov test for the molecular clock on Bayesian ensembles
of phylogenies | 14 pages, 9 figures, 8 tables. Minor revision, additin of a new
example and new title. Software:
https://github.com/FernandoMarcon/PKS_Test.git | PLoS ONE 13 (1) (2018): e0190826 | 10.1371/journal.pone.0190826 | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Divergence date estimates are central to understand evolutionary processes
and depend, in the case of molecular phylogenies, on tests of molecular clocks.
Here we propose two non-parametric tests of strict and relaxed molecular clocks
built upon a framework that uses the empirical cumulative distribution (ECD) of
branch lengths obtained from an ensemble of Bayesian trees and well known
non-parametric (one-sample and two-sample) Kolmogorov-Smirnov (KS)
goodness-of-fit test. In the strict clock case, the method consists in using
the one-sample Kolmogorov-Smirnov (KS) test to directly test if the phylogeny
is clock-like, in other words, if it follows a Poisson law. The ECD is computed
from the discretized branch lengths and the parameter $\lambda$ of the expected
Poisson distribution is calculated as the average branch length over the
ensemble of trees. To compensate for the auto-correlation in the ensemble of
trees and pseudo-replication we take advantage of thinning and effective sample
size, two features provided by Bayesian inference MCMC samplers. Finally, it is
observed that tree topologies with very long or very short branches lead to
Poisson mixtures and in this case we propose the use of the two-sample KS test
with samples from two continuous branch length distributions, one obtained from
an ensemble of clock-constrained trees and the other from an ensemble of
unconstrained trees. Moreover, in this second form the test can also be applied
to test for relaxed clock models. The use of a statistically equivalent
ensemble of phylogenies to obtain the branch lengths ECD, instead of one
consensus tree, yields considerable reduction of the effects of small sample
size and provides again of power.
| [
{
"created": "Thu, 21 May 2015 20:36:26 GMT",
"version": "v1"
},
{
"created": "Mon, 26 Dec 2016 12:43:53 GMT",
"version": "v2"
},
{
"created": "Tue, 7 Nov 2017 16:20:55 GMT",
"version": "v3"
},
{
"created": "Tue, 21 Nov 2017 19:32:33 GMT",
"version": "v4"
}
] | 2018-01-09 | [
[
"Antoneli",
"Fernando",
""
],
[
"Marcon",
"Fernando",
""
],
[
"Lopes",
"Luciano R.",
""
],
[
"Briones",
"Marcelo R. S.",
""
]
] | Divergence date estimates are central to understand evolutionary processes and depend, in the case of molecular phylogenies, on tests of molecular clocks. Here we propose two non-parametric tests of strict and relaxed molecular clocks built upon a framework that uses the empirical cumulative distribution (ECD) of branch lengths obtained from an ensemble of Bayesian trees and well known non-parametric (one-sample and two-sample) Kolmogorov-Smirnov (KS) goodness-of-fit test. In the strict clock case, the method consists in using the one-sample Kolmogorov-Smirnov (KS) test to directly test if the phylogeny is clock-like, in other words, if it follows a Poisson law. The ECD is computed from the discretized branch lengths and the parameter $\lambda$ of the expected Poisson distribution is calculated as the average branch length over the ensemble of trees. To compensate for the auto-correlation in the ensemble of trees and pseudo-replication we take advantage of thinning and effective sample size, two features provided by Bayesian inference MCMC samplers. Finally, it is observed that tree topologies with very long or very short branches lead to Poisson mixtures and in this case we propose the use of the two-sample KS test with samples from two continuous branch length distributions, one obtained from an ensemble of clock-constrained trees and the other from an ensemble of unconstrained trees. Moreover, in this second form the test can also be applied to test for relaxed clock models. The use of a statistically equivalent ensemble of phylogenies to obtain the branch lengths ECD, instead of one consensus tree, yields considerable reduction of the effects of small sample size and provides again of power. |
2004.02144 | Stam Nicolis | Ph. Blanchard and S. Nicolis | Dynamics of contact epidemic models for finite populations | 17 pages, LaTeX, 6 PNG figures. Uses utphys.bst for the references.
LaTeX version of the paper produced in 1992. Original scanned version
available here: https://cds.cern.ch/record/1057211/files/CM-P00066955.pdf | null | null | BiBOS 506/92 | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study contact epidemic models for the spread of infective diseases in
finite populations. The size dependence enters in the infection rate. The
dynamics of such models is then analyzed within the deterministic
approximation, as well as in terms of a stochastic formulation. At the level of
the deterministic equations, we deduce relations between the parameters of the
model for the disease to become endemic. Within the stochastic formulation, it
is possible to write recursion relations for the probability distribution, that
lead to exact expression for some of its moments and check the validity of the
stochastic threshold theorems.
| [
{
"created": "Sun, 5 Apr 2020 10:19:52 GMT",
"version": "v1"
}
] | 2020-04-07 | [
[
"Blanchard",
"Ph.",
""
],
[
"Nicolis",
"S.",
""
]
] | We study contact epidemic models for the spread of infective diseases in finite populations. The size dependence enters in the infection rate. The dynamics of such models is then analyzed within the deterministic approximation, as well as in terms of a stochastic formulation. At the level of the deterministic equations, we deduce relations between the parameters of the model for the disease to become endemic. Within the stochastic formulation, it is possible to write recursion relations for the probability distribution, that lead to exact expression for some of its moments and check the validity of the stochastic threshold theorems. |
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