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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1709.03031
|
Alex McAvoy
|
Kamran Kaveh, Alex McAvoy, Martin A. Nowak
|
Environmental fitness heterogeneity in the Moran process
|
23 pages, 9 figures; final version
| null |
10.1098/rsos.181661
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many mathematical models of evolution assume that all individuals experience
the same environment. Here, we study the Moran process in heterogeneous
environments. The population is of finite size with two competing types, which
are exposed to a fixed number of environmental conditions. Reproductive rate is
determined by both the type and the environment. We first calculate the
condition for selection to favor the mutant relative to the resident wild type.
In large populations, the mutant is favored if and only if the mutant's spatial
average reproductive rate exceeds that of the resident. But environmental
heterogeneity elucidates an interesting asymmetry between the mutant and the
resident. Specifically, mutant heterogeneity suppresses its fixation
probability; if this heterogeneity is strong enough, it can even completely
offset the effects of selection (including in large populations). In contrast,
resident heterogeneity has no effect on a mutant's fixation probability in
large populations and can amplify it in small populations.
|
[
{
"created": "Sun, 10 Sep 2017 03:20:17 GMT",
"version": "v1"
},
{
"created": "Mon, 14 May 2018 17:00:23 GMT",
"version": "v2"
},
{
"created": "Tue, 18 Dec 2018 15:04:32 GMT",
"version": "v3"
}
] |
2018-12-19
|
[
[
"Kaveh",
"Kamran",
""
],
[
"McAvoy",
"Alex",
""
],
[
"Nowak",
"Martin A.",
""
]
] |
Many mathematical models of evolution assume that all individuals experience the same environment. Here, we study the Moran process in heterogeneous environments. The population is of finite size with two competing types, which are exposed to a fixed number of environmental conditions. Reproductive rate is determined by both the type and the environment. We first calculate the condition for selection to favor the mutant relative to the resident wild type. In large populations, the mutant is favored if and only if the mutant's spatial average reproductive rate exceeds that of the resident. But environmental heterogeneity elucidates an interesting asymmetry between the mutant and the resident. Specifically, mutant heterogeneity suppresses its fixation probability; if this heterogeneity is strong enough, it can even completely offset the effects of selection (including in large populations). In contrast, resident heterogeneity has no effect on a mutant's fixation probability in large populations and can amplify it in small populations.
|
0807.1041
|
Thierry Rabilloud
|
Thierry Rabilloud (BBSI), Mireille Chevallet (BBSI), Sylvie Luche
(BBSI), Emmanuelle Leize-Wagner
|
Oxidative stress response: a proteomic view
| null |
Expert Rev Proteomics 2, 6 (2005) 949-56
|
10.1586/14789450.2.6.949
| null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The oxidative stress response is characterized by various effects on a range
of biologic molecules. When examined at the protein level, both expression
levels and protein modifications are altered by oxidative stress. While these
effects have been studied in the past by classic biochemical methods, the
recent onset of proteomics methods has allowed the oxidative stress response to
be studied on a much wider scale. The input of proteomics in the study of
oxidative stress response and in the evidence of an oxidative stress component
in biologic phenomena is reviewed in this paper.
|
[
{
"created": "Mon, 7 Jul 2008 15:30:32 GMT",
"version": "v1"
}
] |
2008-07-08
|
[
[
"Rabilloud",
"Thierry",
"",
"BBSI"
],
[
"Chevallet",
"Mireille",
"",
"BBSI"
],
[
"Luche",
"Sylvie",
"",
"BBSI"
],
[
"Leize-Wagner",
"Emmanuelle",
""
]
] |
The oxidative stress response is characterized by various effects on a range of biologic molecules. When examined at the protein level, both expression levels and protein modifications are altered by oxidative stress. While these effects have been studied in the past by classic biochemical methods, the recent onset of proteomics methods has allowed the oxidative stress response to be studied on a much wider scale. The input of proteomics in the study of oxidative stress response and in the evidence of an oxidative stress component in biologic phenomena is reviewed in this paper.
|
0805.1085
|
Liaofu Luo
|
Liaofu Luo
|
On the Law of Directionality of Genome Evolution
|
18 pages
| null | null |
Version 2-2011
|
q-bio.GN q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The problem of the directionality of genome evolution is studied from the
information-theoretic view. We propose that the function-coding information
quantity of a genome always grows in the course of evolution through sequence
duplication, expansion of code, and gene transfer between genomes. The
function-coding information quantity of a genome consists of two parts,
p-coding information quantity which encodes functional protein and n-coding
information quantity which encodes other functional elements except amino acid
sequence. The relation of the proposed law to the thermodynamic laws is
indicated. The evolutionary trends of DNA sequences revealed by bioinformatics
are investigated which afford further evidences on the evolutionary law. It is
argued that the directionality of genome evolution comes from species
competition adaptive to environment. An expression on the evolutionary rate of
genome is proposed that the rate is a function of Darwin temperature
(describing species competition) and fitness slope (describing adaptive
landscape). Finally, the problem of directly experimental test on the
evolutionary directionality is discussed briefly.
|
[
{
"created": "Thu, 8 May 2008 01:13:20 GMT",
"version": "v1"
},
{
"created": "Thu, 4 Aug 2011 01:01:54 GMT",
"version": "v2"
}
] |
2011-08-05
|
[
[
"Luo",
"Liaofu",
""
]
] |
The problem of the directionality of genome evolution is studied from the information-theoretic view. We propose that the function-coding information quantity of a genome always grows in the course of evolution through sequence duplication, expansion of code, and gene transfer between genomes. The function-coding information quantity of a genome consists of two parts, p-coding information quantity which encodes functional protein and n-coding information quantity which encodes other functional elements except amino acid sequence. The relation of the proposed law to the thermodynamic laws is indicated. The evolutionary trends of DNA sequences revealed by bioinformatics are investigated which afford further evidences on the evolutionary law. It is argued that the directionality of genome evolution comes from species competition adaptive to environment. An expression on the evolutionary rate of genome is proposed that the rate is a function of Darwin temperature (describing species competition) and fitness slope (describing adaptive landscape). Finally, the problem of directly experimental test on the evolutionary directionality is discussed briefly.
|
2006.16189
|
Dmytro Fishman
|
Ian Walsh, Dmytro Fishman, Dario Garcia-Gasulla, Tiina Titma, Gianluca
Pollastri, The ELIXIR Machine Learning focus group, Jen Harrow, Fotis E.
Psomopoulos and Silvio C.E. Tosatto
|
DOME: Recommendations for supervised machine learning validation in
biology
| null | null | null | null |
q-bio.OT cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Modern biology frequently relies on machine learning to provide predictions
and improve decision processes. There have been recent calls for more scrutiny
on machine learning performance and possible limitations. Here we present a set
of community-wide recommendations aiming to help establish standards of
supervised machine learning validation in biology. Adopting a structured
methods description for machine learning based on data, optimization, model,
evaluation (DOME) will aim to help both reviewers and readers to better
understand and assess the performance and limitations of a method or outcome.
The recommendations are formulated as questions to anyone wishing to pursue
implementation of a machine learning algorithm. Answers to these questions can
be easily included in the supplementary material of published papers.
|
[
{
"created": "Thu, 25 Jun 2020 12:01:39 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Oct 2020 12:54:48 GMT",
"version": "v2"
},
{
"created": "Tue, 6 Oct 2020 07:57:00 GMT",
"version": "v3"
},
{
"created": "Thu, 7 Jan 2021 07:29:24 GMT",
"version": "v4"
}
] |
2021-01-08
|
[
[
"Walsh",
"Ian",
""
],
[
"Fishman",
"Dmytro",
""
],
[
"Garcia-Gasulla",
"Dario",
""
],
[
"Titma",
"Tiina",
""
],
[
"Pollastri",
"Gianluca",
""
],
[
"group",
"The ELIXIR Machine Learning focus",
""
],
[
"Harrow",
"Jen",
""
],
[
"Psomopoulos",
"Fotis E.",
""
],
[
"Tosatto",
"Silvio C. E.",
""
]
] |
Modern biology frequently relies on machine learning to provide predictions and improve decision processes. There have been recent calls for more scrutiny on machine learning performance and possible limitations. Here we present a set of community-wide recommendations aiming to help establish standards of supervised machine learning validation in biology. Adopting a structured methods description for machine learning based on data, optimization, model, evaluation (DOME) will aim to help both reviewers and readers to better understand and assess the performance and limitations of a method or outcome. The recommendations are formulated as questions to anyone wishing to pursue implementation of a machine learning algorithm. Answers to these questions can be easily included in the supplementary material of published papers.
|
1907.03005
|
Japan Patel
|
Japan K. Patel, John J. Kuczek, and Richard Vasques
|
One-Way Coupled Tumor Response Model for
Combined-Hyperthermia-Radiotherapy Treatment with Anisotropic Scattering
|
4 pages, 2 figures, submitted to ANS Winter Meeting and Expo. arXiv
admin note: substantial text overlap with arXiv:1905.10441
|
Transactions of the American Nuclear Society 121 (2019), 65-68
| null | null |
q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Therapies such as combined-hyperthermia-radiotherapy (CHR) take advantage of
excellent radiosensitization properties of hyperthermia and treat of tumors
with both radiation and heat. To appropriately model a CHR treatment, features
like tumor heating (heat transfer), dosimetry (radiation transport), and tumor
dynamics (cell population dynamics) must be considered together. Our first
paper on modeling such treatments introduced a one-way coupled model that could
only account for isotropic scattering of radiation. In the present work, we
extend our transport model to incorporate anisotropic scattering.
|
[
{
"created": "Wed, 3 Jul 2019 18:47:33 GMT",
"version": "v1"
}
] |
2020-05-14
|
[
[
"Patel",
"Japan K.",
""
],
[
"Kuczek",
"John J.",
""
],
[
"Vasques",
"Richard",
""
]
] |
Therapies such as combined-hyperthermia-radiotherapy (CHR) take advantage of excellent radiosensitization properties of hyperthermia and treat of tumors with both radiation and heat. To appropriately model a CHR treatment, features like tumor heating (heat transfer), dosimetry (radiation transport), and tumor dynamics (cell population dynamics) must be considered together. Our first paper on modeling such treatments introduced a one-way coupled model that could only account for isotropic scattering of radiation. In the present work, we extend our transport model to incorporate anisotropic scattering.
|
2012.00252
|
Pankaj Mehta
|
Robert Marsland III, Owen Howell, Andreas Mayer, Pankaj Mehta
|
Tregs self-organize into a "computing ecosystem" and implement a
sophisticated optimization algorithm for mediating immune response
|
8 pages, 4 figures + Appendix; Accepted at PNAS
| null | null | null |
q-bio.PE cond-mat.soft cond-mat.stat-mech nlin.AO q-bio.TO
|
http://creativecommons.org/licenses/by/4.0/
|
Regulatory T cells (Tregs) play a crucial role in mediating immune response.
Yet an algorithmic understanding of the role of Tregs in adaptive immunity
remains lacking. Here, we present a biophysically realistic model of Treg
mediated self-tolerance in which Tregs bind to self-antigens and locally
inhibit the proliferation of nearby activated T cells. By exploiting a duality
between ecological dynamics and constrained optimization, we show that Tregs
tile the potential antigen space while simultaneously minimizing the overlap
between Treg activation profiles. We find that for sufficiently high Treg
diversity, Treg mediated self-tolerance is robust to fluctuations in
self-antigen concentrations but lowering the Treg diversity results in a sharp
transition -- related to the Gardner transition in perceptrons -- to a regime
where changes in self-antigen concentrations can result in an auto-immune
response. We propose a novel experimental test of this transition in
immune-deficient mice and discuss potential implications for autoimmune
diseases.
|
[
{
"created": "Tue, 1 Dec 2020 04:12:40 GMT",
"version": "v1"
}
] |
2020-12-02
|
[
[
"Marsland",
"Robert",
"III"
],
[
"Howell",
"Owen",
""
],
[
"Mayer",
"Andreas",
""
],
[
"Mehta",
"Pankaj",
""
]
] |
Regulatory T cells (Tregs) play a crucial role in mediating immune response. Yet an algorithmic understanding of the role of Tregs in adaptive immunity remains lacking. Here, we present a biophysically realistic model of Treg mediated self-tolerance in which Tregs bind to self-antigens and locally inhibit the proliferation of nearby activated T cells. By exploiting a duality between ecological dynamics and constrained optimization, we show that Tregs tile the potential antigen space while simultaneously minimizing the overlap between Treg activation profiles. We find that for sufficiently high Treg diversity, Treg mediated self-tolerance is robust to fluctuations in self-antigen concentrations but lowering the Treg diversity results in a sharp transition -- related to the Gardner transition in perceptrons -- to a regime where changes in self-antigen concentrations can result in an auto-immune response. We propose a novel experimental test of this transition in immune-deficient mice and discuss potential implications for autoimmune diseases.
|
1605.03060
|
Matthew Ricci
|
Matthew Ricci, Junkyung Kim, Fredrik Johansson
|
A Passage-of-time Model of the Cerebellar Purkinje Cell
|
14 pages, 10 figures; fixed typos on page 6, 7
| null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The cerebellar Purkinje cell controlling eyeblinks can learn, remember and
reproduce the interstimulus interval in a classical conditioning paradigm.
Given temporally separated inputs, the cerebellar Purkinje cell learns to pause
its tonic inhibition of a motor pathway with high temporal precision so that an
overt blink occurs at the right time. Most models relegate the Purkinje cell's
passage-of-time representation to afferent granule cells, a subpopulation of
which is supposedly selected for synaptic depression in order to make the
Purkinje cell pause. However, granule cell models have recently faced two
crucial challenges: 1) bypassing granule cells and directly stimulating the
Purkinje cell's pre-synaptic fibers during training still produces a well-timed
pause, and 2) the Purkinje cell can reproduce the learned pause, invariant to
the temporal structure of probe stimulation. Here, we present a passage-of-time
model which is internal to the Purkinje cell and is invariant to probe
structure. The model accurately simulates the Purkinje cell learning mechanism
and makes testable electrophysiological predictions. Importantly, the model is
a numerical proof-of-principle for a biological learning mechanism which does
not rely on changes of synaptic strength.
|
[
{
"created": "Tue, 10 May 2016 15:32:03 GMT",
"version": "v1"
},
{
"created": "Thu, 12 May 2016 17:48:13 GMT",
"version": "v2"
}
] |
2016-05-13
|
[
[
"Ricci",
"Matthew",
""
],
[
"Kim",
"Junkyung",
""
],
[
"Johansson",
"Fredrik",
""
]
] |
The cerebellar Purkinje cell controlling eyeblinks can learn, remember and reproduce the interstimulus interval in a classical conditioning paradigm. Given temporally separated inputs, the cerebellar Purkinje cell learns to pause its tonic inhibition of a motor pathway with high temporal precision so that an overt blink occurs at the right time. Most models relegate the Purkinje cell's passage-of-time representation to afferent granule cells, a subpopulation of which is supposedly selected for synaptic depression in order to make the Purkinje cell pause. However, granule cell models have recently faced two crucial challenges: 1) bypassing granule cells and directly stimulating the Purkinje cell's pre-synaptic fibers during training still produces a well-timed pause, and 2) the Purkinje cell can reproduce the learned pause, invariant to the temporal structure of probe stimulation. Here, we present a passage-of-time model which is internal to the Purkinje cell and is invariant to probe structure. The model accurately simulates the Purkinje cell learning mechanism and makes testable electrophysiological predictions. Importantly, the model is a numerical proof-of-principle for a biological learning mechanism which does not rely on changes of synaptic strength.
|
q-bio/0601019
|
Jie Liang
|
Yan Y. Tseng and Jie Liang
|
Estimation of Amino Acid Residue Substitution Rates at Local Spatial
Regions and Application in Protein Function Inference: A Bayesian Monte Carlo
Approach
|
27 pages, 7 figures
|
Mol Biol Evol. 2006 Feb;23(2):421-36. Epub 2005 Oct 26
|
10.1093/molbev/msj048
| null |
q-bio.BM
| null |
The amino acid sequences of proteins provide rich information for inferring
distant phylogenetic relationships and for predicting protein functions.
Estimating the rate matrix of residue substitutions from amino acid sequences
is also important because the rate matrix can be used to develop scoring
matrices for sequence alignment. Here we use a continuous time Markov process
to model the substitution rates of residues and develop a Bayesian Markov chain
Monte Carlo method for rate estimation. We validate our method using simulated
artificial protein sequences. Because different local regions such as binding
surfaces and the protein interior core experience different selection pressures
due to functional or stability constraints, we use our method to estimate the
substitution rates of local regions. Our results show that the substitution
rates are very different for residues in the buried core and residues on the
solvent exposed surfaces. In addition, the rest of the proteins on the binding
surfaces also have very different substitution rates from residues. Based on
these findings, we further develop a method for protein function prediction by
surface matching using scoring matrices derived from estimated substitution
rates for residues located on the binding surfaces. We show with examples that
our method is effective in identifying functionally related proteins that have
overall low sequence identity, a task known to be very challenging.
|
[
{
"created": "Fri, 13 Jan 2006 07:39:12 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Tseng",
"Yan Y.",
""
],
[
"Liang",
"Jie",
""
]
] |
The amino acid sequences of proteins provide rich information for inferring distant phylogenetic relationships and for predicting protein functions. Estimating the rate matrix of residue substitutions from amino acid sequences is also important because the rate matrix can be used to develop scoring matrices for sequence alignment. Here we use a continuous time Markov process to model the substitution rates of residues and develop a Bayesian Markov chain Monte Carlo method for rate estimation. We validate our method using simulated artificial protein sequences. Because different local regions such as binding surfaces and the protein interior core experience different selection pressures due to functional or stability constraints, we use our method to estimate the substitution rates of local regions. Our results show that the substitution rates are very different for residues in the buried core and residues on the solvent exposed surfaces. In addition, the rest of the proteins on the binding surfaces also have very different substitution rates from residues. Based on these findings, we further develop a method for protein function prediction by surface matching using scoring matrices derived from estimated substitution rates for residues located on the binding surfaces. We show with examples that our method is effective in identifying functionally related proteins that have overall low sequence identity, a task known to be very challenging.
|
2208.00153
|
Kwadwo Antwi-Fordjour
|
Rana D. Parshad, Sureni Wickramsooriya, Kwadwo Antwi-Fordjour, Aniket
Banerjee
|
Additional food causes predator "explosion" -- unless the predators
compete
|
30 pages
| null |
10.1142/S0218127423500347
| null |
q-bio.PE math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The literature posits that an introduced predator population, is able to
drive it's target pest population extinct, if supplemented with high quality
additional food of quantity $\xi > \xi_{critical}$, \cite{SP11, SPV18, SPD17,
SPM13}. We show this approach leads to infinite time blow-up of the predator
population. We propose an alternate model in which the additional food induces
predator competition. Analysis herein indicates that there are threshold values
$c^{*}_{1} < c^{*}_{2} < c^{*}_{3}$ of the competition parameter $c$, s.t. when
$c < c^{*}_{1}$, the pest free state is globally stable, when $c^{*}_{2} < c <
c^{*}_{3}$, bi-stability is possible, and when $c^{*}_{3} < c$, up to three
interior equilibriums could exist. As $c$ and $\xi$-$c$ are varied, standard
co-dimension one and co-dimension two bifurcations are observed. The recent
dynamical systems literature involving predator competition, report several
non-standard bifurcations such as the saddle-node-transcritical bifurcation
(SNTC) occurring in co-dimension two \cite{KSV10, BS07}, and cusp-transcritical
bifurcation (CPTC) in co-dimension three, \cite{D20, BS07}. We show that in our
model structural symmetries can be exploited to construct a SNTC in
co-dimension two, and a CPTC also in co-dimension two. We further use these
symmetries to construct a novel pitchfork-transcritical bifurcation (PTC) in
co-dimension two, thus explicitly characterizing a new organizing center of the
model. Dynamics such as homoclinic orbits, concurrently occurring limit cycles,
and competition driven Turing patterns are also observed. Our findings indicate
that increasing additional food in predator-pest models, can hinder
bio-control, contrary to some of the literature. However, additional food that
also induces predator competition, leads to novel bio-control scenarios, and
complements the work in \cite{H21, B98, K04, D20, BS07, VH19}.
|
[
{
"created": "Sat, 30 Jul 2022 07:10:35 GMT",
"version": "v1"
}
] |
2023-04-05
|
[
[
"Parshad",
"Rana D.",
""
],
[
"Wickramsooriya",
"Sureni",
""
],
[
"Antwi-Fordjour",
"Kwadwo",
""
],
[
"Banerjee",
"Aniket",
""
]
] |
The literature posits that an introduced predator population, is able to drive it's target pest population extinct, if supplemented with high quality additional food of quantity $\xi > \xi_{critical}$, \cite{SP11, SPV18, SPD17, SPM13}. We show this approach leads to infinite time blow-up of the predator population. We propose an alternate model in which the additional food induces predator competition. Analysis herein indicates that there are threshold values $c^{*}_{1} < c^{*}_{2} < c^{*}_{3}$ of the competition parameter $c$, s.t. when $c < c^{*}_{1}$, the pest free state is globally stable, when $c^{*}_{2} < c < c^{*}_{3}$, bi-stability is possible, and when $c^{*}_{3} < c$, up to three interior equilibriums could exist. As $c$ and $\xi$-$c$ are varied, standard co-dimension one and co-dimension two bifurcations are observed. The recent dynamical systems literature involving predator competition, report several non-standard bifurcations such as the saddle-node-transcritical bifurcation (SNTC) occurring in co-dimension two \cite{KSV10, BS07}, and cusp-transcritical bifurcation (CPTC) in co-dimension three, \cite{D20, BS07}. We show that in our model structural symmetries can be exploited to construct a SNTC in co-dimension two, and a CPTC also in co-dimension two. We further use these symmetries to construct a novel pitchfork-transcritical bifurcation (PTC) in co-dimension two, thus explicitly characterizing a new organizing center of the model. Dynamics such as homoclinic orbits, concurrently occurring limit cycles, and competition driven Turing patterns are also observed. Our findings indicate that increasing additional food in predator-pest models, can hinder bio-control, contrary to some of the literature. However, additional food that also induces predator competition, leads to novel bio-control scenarios, and complements the work in \cite{H21, B98, K04, D20, BS07, VH19}.
|
2006.07882
|
Bastian Rieck
|
Bastian Rieck, Tristan Yates, Christian Bock, Karsten Borgwardt, Guy
Wolf, Nicholas Turk-Browne, Smita Krishnaswamy
|
Uncovering the Topology of Time-Varying fMRI Data using Cubical
Persistence
|
Accepted at the Conference on Neural Information Processing Systems
(NeurIPS) 2020; camera-ready version
| null | null | null |
q-bio.NC cs.LG eess.IV math.AT stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Functional magnetic resonance imaging (fMRI) is a crucial technology for
gaining insights into cognitive processes in humans. Data amassed from fMRI
measurements result in volumetric data sets that vary over time. However,
analysing such data presents a challenge due to the large degree of noise and
person-to-person variation in how information is represented in the brain. To
address this challenge, we present a novel topological approach that encodes
each time point in an fMRI data set as a persistence diagram of topological
features, i.e. high-dimensional voids present in the data. This representation
naturally does not rely on voxel-by-voxel correspondence and is robust to
noise. We show that these time-varying persistence diagrams can be clustered to
find meaningful groupings between participants, and that they are also useful
in studying within-subject brain state trajectories of subjects performing a
particular task. Here, we apply both clustering and trajectory analysis
techniques to a group of participants watching the movie 'Partly Cloudy'. We
observe significant differences in both brain state trajectories and overall
topological activity between adults and children watching the same movie.
|
[
{
"created": "Sun, 14 Jun 2020 12:29:37 GMT",
"version": "v1"
},
{
"created": "Thu, 22 Oct 2020 17:35:21 GMT",
"version": "v2"
}
] |
2020-10-23
|
[
[
"Rieck",
"Bastian",
""
],
[
"Yates",
"Tristan",
""
],
[
"Bock",
"Christian",
""
],
[
"Borgwardt",
"Karsten",
""
],
[
"Wolf",
"Guy",
""
],
[
"Turk-Browne",
"Nicholas",
""
],
[
"Krishnaswamy",
"Smita",
""
]
] |
Functional magnetic resonance imaging (fMRI) is a crucial technology for gaining insights into cognitive processes in humans. Data amassed from fMRI measurements result in volumetric data sets that vary over time. However, analysing such data presents a challenge due to the large degree of noise and person-to-person variation in how information is represented in the brain. To address this challenge, we present a novel topological approach that encodes each time point in an fMRI data set as a persistence diagram of topological features, i.e. high-dimensional voids present in the data. This representation naturally does not rely on voxel-by-voxel correspondence and is robust to noise. We show that these time-varying persistence diagrams can be clustered to find meaningful groupings between participants, and that they are also useful in studying within-subject brain state trajectories of subjects performing a particular task. Here, we apply both clustering and trajectory analysis techniques to a group of participants watching the movie 'Partly Cloudy'. We observe significant differences in both brain state trajectories and overall topological activity between adults and children watching the same movie.
|
0809.1231
|
Franco Bagnoli
|
Franco Bagnoli
|
Evolutionary models for simple biosystems
|
new version
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The concept of evolutionary development of structures constituted a
\emph{real} revolution in biology: it was possible to understand how the very
complex structures of life can arise in an out-of-equilibrium system. The
investigation of such systems has shown that indeed, systems under a flux of
energy or matter can self-organize into complex patterns, think for instance to
Rayleigh-Bernard convection, Liesegang rings, patterns formed by granular
systems under shear. Following this line, one could characterize life as a
state of matter, characterized by the slow, continuous process that we call
evolution. In this paper we try to identify the organizational level of life,
that spans several orders of magnitude from the elementary constituents to
whole ecosystems.
Although similar structures can be found in other contexts like ideas (memes)
in neural systems and self-replicating elements (computer viruses, worms, etc.)
in computer systems, we shall concentrate on biological evolutionary structure,
and try to put into evidence the role and the emergence of network structure in
such systems.
|
[
{
"created": "Sun, 7 Sep 2008 16:31:18 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Aug 2009 13:00:02 GMT",
"version": "v2"
}
] |
2009-08-27
|
[
[
"Bagnoli",
"Franco",
""
]
] |
The concept of evolutionary development of structures constituted a \emph{real} revolution in biology: it was possible to understand how the very complex structures of life can arise in an out-of-equilibrium system. The investigation of such systems has shown that indeed, systems under a flux of energy or matter can self-organize into complex patterns, think for instance to Rayleigh-Bernard convection, Liesegang rings, patterns formed by granular systems under shear. Following this line, one could characterize life as a state of matter, characterized by the slow, continuous process that we call evolution. In this paper we try to identify the organizational level of life, that spans several orders of magnitude from the elementary constituents to whole ecosystems. Although similar structures can be found in other contexts like ideas (memes) in neural systems and self-replicating elements (computer viruses, worms, etc.) in computer systems, we shall concentrate on biological evolutionary structure, and try to put into evidence the role and the emergence of network structure in such systems.
|
1005.1088
|
Ryan Gutenkunst
|
Ryan N. Gutenkunst, Daniel Coombs, Toby Star, Michael L. Dustin and
Byron Goldstein
|
A biophysical model of cell adhesion mediated by immunoadhesin drugs and
antibodies
|
13 pages, 5 figures
| null |
10.1371/journal.pone.0019701
|
LA-UR 10-02105
|
q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A promising direction in drug development is to exploit the ability of
natural killer cells to kill antibody-labeled target cells. Monoclonal
antibodies and drugs designed to elicit this effect typically bind cell-surface
epitopes that are overexpressed on target cells but also present on other
cells. Thus it is important to understand adhesion of cells by antibodies and
similar molecules. We present an equilibrium model of such adhesion,
incorporating heterogeneity in target cell epitope density and epitope
immobility. We compare with experiments on the adhesion of Jurkat T cells to
bilayers containing the relevant natural killer cell receptor, with adhesion
mediated by the drug alefacept. We show that a model in which all target cell
epitopes are mobile and available is inconsistent with the data, suggesting
that more complex mechanisms are at work. We hypothesize that the immobile
epitope fraction may change with cell adhesion, and we find that such a model
is more consistent with the data. We also quantitatively describe the parameter
space in which binding occurs. Our results point toward mechanisms relating
epitope immobility to cell adhesion and offer insight into the activity of an
important class of drugs.
|
[
{
"created": "Thu, 6 May 2010 21:21:31 GMT",
"version": "v1"
}
] |
2015-05-18
|
[
[
"Gutenkunst",
"Ryan N.",
""
],
[
"Coombs",
"Daniel",
""
],
[
"Star",
"Toby",
""
],
[
"Dustin",
"Michael L.",
""
],
[
"Goldstein",
"Byron",
""
]
] |
A promising direction in drug development is to exploit the ability of natural killer cells to kill antibody-labeled target cells. Monoclonal antibodies and drugs designed to elicit this effect typically bind cell-surface epitopes that are overexpressed on target cells but also present on other cells. Thus it is important to understand adhesion of cells by antibodies and similar molecules. We present an equilibrium model of such adhesion, incorporating heterogeneity in target cell epitope density and epitope immobility. We compare with experiments on the adhesion of Jurkat T cells to bilayers containing the relevant natural killer cell receptor, with adhesion mediated by the drug alefacept. We show that a model in which all target cell epitopes are mobile and available is inconsistent with the data, suggesting that more complex mechanisms are at work. We hypothesize that the immobile epitope fraction may change with cell adhesion, and we find that such a model is more consistent with the data. We also quantitatively describe the parameter space in which binding occurs. Our results point toward mechanisms relating epitope immobility to cell adhesion and offer insight into the activity of an important class of drugs.
|
1705.05090
|
Caterina La Porta AM
|
Costanza Giampietro, Maria Chiara Lionetti, Giulio Costantini,
Federico Mutti, Stefano Zapperi, Caterina A.M. La Porta
|
Cholesterol impairment contributes to neuroserpin aggregation
|
7 figures
|
Scientific Reports 7, Article number: 43669 (2017)
|
10.1038/srep43669
| null |
q-bio.CB
|
http://creativecommons.org/licenses/by/4.0/
|
Intraneural accumulation of misfolded proteins is a common feature of several
neurodegenerative pathologies including Alzheimer's and Parkinson's diseases,
and Familial Encephalopathy with Neuroserpin Inclusion Bodies (FENIB). FENIB is
a rare disease due to a point mutation in neuroserpin which accelerates protein
aggregation in the endoplasmic reticulum (ER). Here we show that cholesterol
depletion induced either by prolonged exposure to statins or by inhibiting the
sterol regulatory binding-element protein (SREBP) pathway also enhances
aggregation of neuroserpin proteins. These findings can be explained
considering a computational model of protein aggregation under non-equilibrium
conditions, where a decrease in the rate of protein clearance improves
aggregation. Decreasing cholesterol in cell membranes affects their biophysical
properties, including their ability to form the vesicles needed for protein
clearance, as we illustrate by a simple mathematical model. Taken together,
these results suggest that cholesterol reduction induces neuroserpin
aggregation, even in absence of specific neuroserpin mutations. The new
mechanism we uncover could be relevant also for other neurodegenerative
diseases associated with protein aggregation.
|
[
{
"created": "Mon, 15 May 2017 07:18:47 GMT",
"version": "v1"
}
] |
2017-05-16
|
[
[
"Giampietro",
"Costanza",
""
],
[
"Lionetti",
"Maria Chiara",
""
],
[
"Costantini",
"Giulio",
""
],
[
"Mutti",
"Federico",
""
],
[
"Zapperi",
"Stefano",
""
],
[
"La Porta",
"Caterina A. M.",
""
]
] |
Intraneural accumulation of misfolded proteins is a common feature of several neurodegenerative pathologies including Alzheimer's and Parkinson's diseases, and Familial Encephalopathy with Neuroserpin Inclusion Bodies (FENIB). FENIB is a rare disease due to a point mutation in neuroserpin which accelerates protein aggregation in the endoplasmic reticulum (ER). Here we show that cholesterol depletion induced either by prolonged exposure to statins or by inhibiting the sterol regulatory binding-element protein (SREBP) pathway also enhances aggregation of neuroserpin proteins. These findings can be explained considering a computational model of protein aggregation under non-equilibrium conditions, where a decrease in the rate of protein clearance improves aggregation. Decreasing cholesterol in cell membranes affects their biophysical properties, including their ability to form the vesicles needed for protein clearance, as we illustrate by a simple mathematical model. Taken together, these results suggest that cholesterol reduction induces neuroserpin aggregation, even in absence of specific neuroserpin mutations. The new mechanism we uncover could be relevant also for other neurodegenerative diseases associated with protein aggregation.
|
1903.02795
|
Miquel Palmer
|
Miquel Palmer, Borja Tolosa, Antoni Maria Grau, Maria del Mar Gil,
Clara Obregona, Beatriz Morales-Nin
|
Combining sale records of landings and fishers knowledge for predicting
metiers in a small-scale, multi-gear, multispecies fishery
|
7 figures, 5 tables, http://hdl.handle.net/10261/174686
|
Journal of Fisheries Research, Volume 195, November 2017, Pages
59-70
|
10.1016/j.fishres.2017.07.001
| null |
q-bio.PE
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Stock management should be guided by assessment models that, among others,
need to be fed by reliable data of catch and effort. However, precise data are
difficult to obtain in heterogeneous fisheries. Specifically, small scale,
multi gear, multispecies fisheries are dynamic systems where fishers may lively
change fishing strategy conditioned by multiple drivers. Provided that some
stocks can be shared by several metiers, a precise categorization of metiers
should be the first step toward metier specific estimates of catch and effort,
which in turn would allow a better understanding of the system dynamics. Here
we propose an approach for predicting the metier of any given fishing trip from
its landing records. This approach combines the knowledge of expert fishers
with the existing sales register of landings in Mallorca. It successfully
predicts metiers for all the 162815 small scale fishery fishing trips from
Mallorca between 2004 and 2015. The largest effort is invested in the metiers
Cuttlefish Fish and Spiny lobster, landings peak for Cuttlefish Fish and
Dolphinfish and revenues for Spiny lobster and Dolphinfish. Metier predictions
also allowed us to describe the temporal trends experienced by each metier and
to characterize the species that are specific to each metier. Seasonal
variability is by far more relevant than between year variability, which
confirms that at least some fishers are adopting a rotation cycle of metiers
along the year. Effort, landings and gross revenues decreased in the last 12
years. The approach proposed is also applicable to any other fishery for which
the metier for a fishing trip sample is known, but relying on fishers expertise
points more directly to fishers intention. Thus, metier predictions produced
with the proposed approach are closer to the actual uses of fishers, providing
better grounds for an improved management.
|
[
{
"created": "Thu, 7 Mar 2019 09:51:29 GMT",
"version": "v1"
}
] |
2019-03-08
|
[
[
"Palmer",
"Miquel",
""
],
[
"Tolosa",
"Borja",
""
],
[
"Grau",
"Antoni Maria",
""
],
[
"Gil",
"Maria del Mar",
""
],
[
"Obregona",
"Clara",
""
],
[
"Morales-Nin",
"Beatriz",
""
]
] |
Stock management should be guided by assessment models that, among others, need to be fed by reliable data of catch and effort. However, precise data are difficult to obtain in heterogeneous fisheries. Specifically, small scale, multi gear, multispecies fisheries are dynamic systems where fishers may lively change fishing strategy conditioned by multiple drivers. Provided that some stocks can be shared by several metiers, a precise categorization of metiers should be the first step toward metier specific estimates of catch and effort, which in turn would allow a better understanding of the system dynamics. Here we propose an approach for predicting the metier of any given fishing trip from its landing records. This approach combines the knowledge of expert fishers with the existing sales register of landings in Mallorca. It successfully predicts metiers for all the 162815 small scale fishery fishing trips from Mallorca between 2004 and 2015. The largest effort is invested in the metiers Cuttlefish Fish and Spiny lobster, landings peak for Cuttlefish Fish and Dolphinfish and revenues for Spiny lobster and Dolphinfish. Metier predictions also allowed us to describe the temporal trends experienced by each metier and to characterize the species that are specific to each metier. Seasonal variability is by far more relevant than between year variability, which confirms that at least some fishers are adopting a rotation cycle of metiers along the year. Effort, landings and gross revenues decreased in the last 12 years. The approach proposed is also applicable to any other fishery for which the metier for a fishing trip sample is known, but relying on fishers expertise points more directly to fishers intention. Thus, metier predictions produced with the proposed approach are closer to the actual uses of fishers, providing better grounds for an improved management.
|
1606.00463
|
Nihal Temamogullari
|
N Ezgi Temamogullari, H Frederik Nijhout, Michael C Reed
|
Mathematical Modeling of Perifusion Cell Culture Experiments on GnRH
Signaling
| null | null | null | null |
q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The effects of pulsatile GnRH stimulation on anterior pituitary cells are
studied using perifusion cell cultures, where constantly moving medium over the
immo- bilized cells allows intermittent GnRH delivery. The LH content of the
outgoing medium serves as a readout of the GnRH signaling pathway activation in
the cells. The challenge lies in relating the LH content of the medium leaving
the chamber to the cellular processes producing LH secretion. To investigate
this relation we developed and analyzed a mathematical model consisting of
coupled partial differential equations describing LH secretion in a perifusion
cell culture. We match the mathematical model to three different data sets and
give cellular mechanisms that explain the data. Our model illustrates the
importance of the negative feedback in the signaling pathway and receptor
desensitization. We demonstrate that different LH outcomes in oxytocin and GnRH
stimulations might originate from different receptor dynamics and
concentration. We ana- lyze the model to understand the influence of
parameters, like the velocity of the medium flow or the fraction collection
time, on the LH outcomes. We show that slow velocities lead to high LH
outcomes. Also, we show that fraction collection times, which do not divide the
GnRH pulse period evenly, lead to irregularities in the data. We examine the
influence of the rate of binding and dissociation of GnRH on the GnRH movement
down the chamber. Our model serves as an important tool that can help in the
design of perifusion experiments and the interpretation of results.
|
[
{
"created": "Wed, 9 Dec 2015 21:09:16 GMT",
"version": "v1"
}
] |
2016-06-03
|
[
[
"Temamogullari",
"N Ezgi",
""
],
[
"Nijhout",
"H Frederik",
""
],
[
"Reed",
"Michael C",
""
]
] |
The effects of pulsatile GnRH stimulation on anterior pituitary cells are studied using perifusion cell cultures, where constantly moving medium over the immo- bilized cells allows intermittent GnRH delivery. The LH content of the outgoing medium serves as a readout of the GnRH signaling pathway activation in the cells. The challenge lies in relating the LH content of the medium leaving the chamber to the cellular processes producing LH secretion. To investigate this relation we developed and analyzed a mathematical model consisting of coupled partial differential equations describing LH secretion in a perifusion cell culture. We match the mathematical model to three different data sets and give cellular mechanisms that explain the data. Our model illustrates the importance of the negative feedback in the signaling pathway and receptor desensitization. We demonstrate that different LH outcomes in oxytocin and GnRH stimulations might originate from different receptor dynamics and concentration. We ana- lyze the model to understand the influence of parameters, like the velocity of the medium flow or the fraction collection time, on the LH outcomes. We show that slow velocities lead to high LH outcomes. Also, we show that fraction collection times, which do not divide the GnRH pulse period evenly, lead to irregularities in the data. We examine the influence of the rate of binding and dissociation of GnRH on the GnRH movement down the chamber. Our model serves as an important tool that can help in the design of perifusion experiments and the interpretation of results.
|
1708.07612
|
Akira Kinjo
|
Akira R. Kinjo
|
Cooperative "folding transition" in the sequence space facilitates
function-driven evolution of protein families
|
13 pages, 7 figures, 2 tables (a new subsection added)
|
Journal of Theoretical Biology 443:18-27 (2018)
|
10.1016/j.jtbi.2018.01.019
| null |
q-bio.BM physics.bio-ph
|
http://creativecommons.org/licenses/by/4.0/
|
In the protein sequence space, natural proteins form clusters of families
which are characterized by their unique native folds whereas the great majority
of random polypeptides are neither clustered nor foldable to unique structures.
Since a given polypeptide can be either foldable or unfoldable, a kind of
"folding transition" is expected at the boundary of a protein family in the
sequence space. By Monte Carlo simulations of a statistical mechanical model of
protein sequence alignment that coherently incorporates both short-range and
long-range interactions as well as variable-length insertions to reproduce the
statistics of the multiple sequence alignment of a given protein family, we
demonstrate the existence of such transition between natural-like sequences and
random sequences in the sequence subspaces for 15 domain families of various
folds. The transition was found to be highly cooperative and two-state-like.
Furthermore, enforcing or suppressing consensus residues on a few of the
well-conserved sites enhanced or diminished, respectively, the natural-like
pattern formation over the entire sequence. In most families, the key sites
included ligand binding sites. These results suggest some selective pressure on
the key residues, such as ligand binding activity, may cooperatively facilitate
the emergence of a protein family during evolution. From a more practical
aspect, the present results highlight an essential role of long-range effects
in precisely defining protein families, which are absent in conventional
sequence models.
|
[
{
"created": "Fri, 25 Aug 2017 04:38:47 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Dec 2017 00:16:09 GMT",
"version": "v2"
},
{
"created": "Tue, 16 Jan 2018 05:22:05 GMT",
"version": "v3"
}
] |
2018-02-06
|
[
[
"Kinjo",
"Akira R.",
""
]
] |
In the protein sequence space, natural proteins form clusters of families which are characterized by their unique native folds whereas the great majority of random polypeptides are neither clustered nor foldable to unique structures. Since a given polypeptide can be either foldable or unfoldable, a kind of "folding transition" is expected at the boundary of a protein family in the sequence space. By Monte Carlo simulations of a statistical mechanical model of protein sequence alignment that coherently incorporates both short-range and long-range interactions as well as variable-length insertions to reproduce the statistics of the multiple sequence alignment of a given protein family, we demonstrate the existence of such transition between natural-like sequences and random sequences in the sequence subspaces for 15 domain families of various folds. The transition was found to be highly cooperative and two-state-like. Furthermore, enforcing or suppressing consensus residues on a few of the well-conserved sites enhanced or diminished, respectively, the natural-like pattern formation over the entire sequence. In most families, the key sites included ligand binding sites. These results suggest some selective pressure on the key residues, such as ligand binding activity, may cooperatively facilitate the emergence of a protein family during evolution. From a more practical aspect, the present results highlight an essential role of long-range effects in precisely defining protein families, which are absent in conventional sequence models.
|
1411.4624
|
John Abel
|
John H. Abel, Lukas A. Widmer, Peter C. St. John, J\"org Stelling,
Francis J. Doyle III
|
A Coupled Stochastic Model Explains Differences in Circadian Behavior of
Cry1 and Cry2 Knockouts
|
15 pages, 5 figures update 22-Feb 2015: text revisions, typographical
error fixes
| null | null | null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the mammalian suprachiasmatic nucleus (SCN), a population of noisy
cell-autonomous oscillators synchronizes to generate robust circadian rhythms
at the organism-level. Within these cells two isoforms of Cryptochrome, Cry1
and Cry2, participate in a negative feedback loop driving circadian
rhythmicity. Previous work has shown that single, dissociated SCN neurons
respond differently to Cry1 and Cry2 knockouts: Cry1 knockouts are arrhythmic
while Cry2 knockouts display more regular rhythms. These differences have led
to speculation that CRY1 and CRY2 may play different functional roles in the
oscillator. To address this proposition, we have developed a new coupled,
stochastic model focused on the Period (Per) and Cry feedback loop, and
incorporating intercellular coupling via vasoactive intestinal peptide (VIP).
Due to the stochastic nature of molecular oscillations, we demonstrate that
single-cell Cry1 knockout oscillations display partially rhythmic behavior, and
cannot be classified as simply rhythmic or arrhythmic. Our model demonstrates
that intrinsic molecular noise and differences in relative abundance, rather
than differing functions, are sufficient to explain the range of rhythmicity
encountered in Cry knockouts in the SCN. Our results further highlight the
essential role of stochastic behavior in understanding and accurately modeling
the circadian network and its response to perturbation.
|
[
{
"created": "Mon, 17 Nov 2014 20:24:37 GMT",
"version": "v1"
},
{
"created": "Sun, 22 Feb 2015 19:59:49 GMT",
"version": "v2"
}
] |
2015-02-24
|
[
[
"Abel",
"John H.",
""
],
[
"Widmer",
"Lukas A.",
""
],
[
"John",
"Peter C. St.",
""
],
[
"Stelling",
"Jörg",
""
],
[
"Doyle",
"Francis J.",
"III"
]
] |
In the mammalian suprachiasmatic nucleus (SCN), a population of noisy cell-autonomous oscillators synchronizes to generate robust circadian rhythms at the organism-level. Within these cells two isoforms of Cryptochrome, Cry1 and Cry2, participate in a negative feedback loop driving circadian rhythmicity. Previous work has shown that single, dissociated SCN neurons respond differently to Cry1 and Cry2 knockouts: Cry1 knockouts are arrhythmic while Cry2 knockouts display more regular rhythms. These differences have led to speculation that CRY1 and CRY2 may play different functional roles in the oscillator. To address this proposition, we have developed a new coupled, stochastic model focused on the Period (Per) and Cry feedback loop, and incorporating intercellular coupling via vasoactive intestinal peptide (VIP). Due to the stochastic nature of molecular oscillations, we demonstrate that single-cell Cry1 knockout oscillations display partially rhythmic behavior, and cannot be classified as simply rhythmic or arrhythmic. Our model demonstrates that intrinsic molecular noise and differences in relative abundance, rather than differing functions, are sufficient to explain the range of rhythmicity encountered in Cry knockouts in the SCN. Our results further highlight the essential role of stochastic behavior in understanding and accurately modeling the circadian network and its response to perturbation.
|
1411.1672
|
Rafael Frigori
|
Rafael B. Frigori
|
Breakout character of islet amyloid polypeptide hydrophobic mutations at
the onset of type-2 diabetes
|
8 pages, 6 figures, 1 table; final version to appear in Physical
Review E
|
Physical Review E 90, 052716 (2014)
|
10.1103/PhysRevE.90.052716
| null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Toxic fibrillar aggregates of Islet Amyloid PolyPeptide (IAPP) appear as the
physical outcome of a peptidic phase-transition signaling the onset of type-2
diabetes mellitus in different mammalian species. In particular, experimentally
verified mutations on the amyloidogenic segment 20-29 in humans, cats and rats
are highly correlated with the molecular aggregation propensities. Through a
microcanonical analysis of the aggregation of IAPP_{20-29} isoforms, we show
that a minimalist one-bead hydrophobic-polar continuum model for protein
interactions properly quantifies those propensities from free-energy barriers.
Our results highlight the central role of sequence-dependent hydrophobic
mutations on hot spots for stabilization, and so for the engineering, of such
biological peptides.
|
[
{
"created": "Wed, 5 Nov 2014 11:10:49 GMT",
"version": "v1"
}
] |
2014-11-27
|
[
[
"Frigori",
"Rafael B.",
""
]
] |
Toxic fibrillar aggregates of Islet Amyloid PolyPeptide (IAPP) appear as the physical outcome of a peptidic phase-transition signaling the onset of type-2 diabetes mellitus in different mammalian species. In particular, experimentally verified mutations on the amyloidogenic segment 20-29 in humans, cats and rats are highly correlated with the molecular aggregation propensities. Through a microcanonical analysis of the aggregation of IAPP_{20-29} isoforms, we show that a minimalist one-bead hydrophobic-polar continuum model for protein interactions properly quantifies those propensities from free-energy barriers. Our results highlight the central role of sequence-dependent hydrophobic mutations on hot spots for stabilization, and so for the engineering, of such biological peptides.
|
2204.00583
|
Vincent Painchaud
|
Vincent Painchaud, Nicolas Doyon and Patrick Desrosiers
|
Beyond Wilson-Cowan dynamics: oscillations and chaos without inhibition
|
21 pages, 15 figures
|
Biological Cybernetics (2022)
|
10.1007/s00422-022-00941-w
| null |
q-bio.NC physics.bio-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Fifty years ago, Wilson and Cowan developed a mathematical model to describe
the activity of neural populations. In this seminal work, they divided the
cells in three groups: active, sensitive and refractory, and obtained a
dynamical system to describe the evolution of the average firing rates of the
populations. In the present work, we investigate the impact of the often
neglected refractory state and show that taking it into account can introduce
new dynamics. Starting from a continuous-time Markov chain, we perform a
rigorous derivation of a mean-field model that includes the refractory
fractions of populations as dynamical variables. Then, we perform bifurcation
analysis to explain the occurance of periodic solutions in cases where the
classical Wilson-Cowan does not predict oscillations. We also show that our
mean-field model is able to predict chaotic behavior in the dynamics of
networks with as little as two populations.
|
[
{
"created": "Fri, 1 Apr 2022 17:13:55 GMT",
"version": "v1"
},
{
"created": "Mon, 30 May 2022 19:08:13 GMT",
"version": "v2"
}
] |
2022-09-07
|
[
[
"Painchaud",
"Vincent",
""
],
[
"Doyon",
"Nicolas",
""
],
[
"Desrosiers",
"Patrick",
""
]
] |
Fifty years ago, Wilson and Cowan developed a mathematical model to describe the activity of neural populations. In this seminal work, they divided the cells in three groups: active, sensitive and refractory, and obtained a dynamical system to describe the evolution of the average firing rates of the populations. In the present work, we investigate the impact of the often neglected refractory state and show that taking it into account can introduce new dynamics. Starting from a continuous-time Markov chain, we perform a rigorous derivation of a mean-field model that includes the refractory fractions of populations as dynamical variables. Then, we perform bifurcation analysis to explain the occurance of periodic solutions in cases where the classical Wilson-Cowan does not predict oscillations. We also show that our mean-field model is able to predict chaotic behavior in the dynamics of networks with as little as two populations.
|
1311.2260
|
Chen Jia
|
Chen Jia, Minping Qian, Daquan Jiang
|
Overshoot in biological systems modeled by Markov chains: a
nonequilibrium dynamic phenomenon
|
15 pages, 3 figures
|
IET Systems Biology, 8(4):138-145, 2014
|
10.1049/iet-syb.2013.0050
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A number of biological systems can be modeled by Markov chains. Recently,
there has been an increasing concern about when biological systems modeled by
Markov chains will perform a dynamic phenomenon called overshoot. In this
article, we found that the steady-state behavior of the system will have a
great effect on the occurrence of overshoot. We confirmed that overshoot in
general cannot occur in systems which will finally approach an equilibrium
steady state. We further classified overshoot into two types, named as simple
overshoot and oscillating overshoot. We showed that except for extreme cases,
oscillating overshoot will occur if the system is far from equilibrium. All
these results clearly show that overshoot is a nonequilibrium dynamic
phenomenon with energy consumption. In addition, the main result in this
article is validated with real experimental data.
|
[
{
"created": "Sun, 10 Nov 2013 11:47:15 GMT",
"version": "v1"
},
{
"created": "Sat, 24 May 2014 17:14:37 GMT",
"version": "v2"
}
] |
2014-08-27
|
[
[
"Jia",
"Chen",
""
],
[
"Qian",
"Minping",
""
],
[
"Jiang",
"Daquan",
""
]
] |
A number of biological systems can be modeled by Markov chains. Recently, there has been an increasing concern about when biological systems modeled by Markov chains will perform a dynamic phenomenon called overshoot. In this article, we found that the steady-state behavior of the system will have a great effect on the occurrence of overshoot. We confirmed that overshoot in general cannot occur in systems which will finally approach an equilibrium steady state. We further classified overshoot into two types, named as simple overshoot and oscillating overshoot. We showed that except for extreme cases, oscillating overshoot will occur if the system is far from equilibrium. All these results clearly show that overshoot is a nonequilibrium dynamic phenomenon with energy consumption. In addition, the main result in this article is validated with real experimental data.
|
2002.06616
|
Bernard Offmann
|
Surbhi Dhingra, Ramanathan Sowdhamini, Fr\'ed\'eric Cadet, and Bernard
Offmann
|
A glance into the evolution of template-free protein structure
prediction methodologies
|
17 pages, 1 figure, 1 table
| null |
10.1016/j.biochi.2020.04.026
| null |
q-bio.QM q-bio.BM
|
http://creativecommons.org/licenses/by/4.0/
|
Prediction of protein structures using computational approaches has been
explored for over two decades, paving a way for more focused research and
development of algorithms in comparative modelling, ab intio modelling and
structure refinement protocols. A tremendous success has been witnessed in
template-based modelling protocols, whereas strategies that involve
template-free modelling still lag behind, specifically for larger proteins (>
150 a.a.). Various improvements have been observed in ab initio protein
structure prediction methodologies overtime, with recent ones attributed to the
usage of deep learning approaches to construct protein backbone structure from
its amino acid sequence. This review highlights the major strategies undertaken
for template-free modelling of protein structures while discussing few tools
developed under each strategy. It will also briefly comment on the progress
observed in the field of ab initio modelling of proteins over the course of
time as seen through the evolution of CASP platform.
|
[
{
"created": "Sun, 16 Feb 2020 16:36:58 GMT",
"version": "v1"
},
{
"created": "Fri, 24 Apr 2020 12:50:27 GMT",
"version": "v2"
}
] |
2020-05-19
|
[
[
"Dhingra",
"Surbhi",
""
],
[
"Sowdhamini",
"Ramanathan",
""
],
[
"Cadet",
"Frédéric",
""
],
[
"Offmann",
"Bernard",
""
]
] |
Prediction of protein structures using computational approaches has been explored for over two decades, paving a way for more focused research and development of algorithms in comparative modelling, ab intio modelling and structure refinement protocols. A tremendous success has been witnessed in template-based modelling protocols, whereas strategies that involve template-free modelling still lag behind, specifically for larger proteins (> 150 a.a.). Various improvements have been observed in ab initio protein structure prediction methodologies overtime, with recent ones attributed to the usage of deep learning approaches to construct protein backbone structure from its amino acid sequence. This review highlights the major strategies undertaken for template-free modelling of protein structures while discussing few tools developed under each strategy. It will also briefly comment on the progress observed in the field of ab initio modelling of proteins over the course of time as seen through the evolution of CASP platform.
|
2202.05031
|
Jiahui Chen
|
Jiahui Chen and Guo-Wei Wei
|
Omicron BA.2 (B.1.1.529.2): high potential to becoming the next
dominating variant
| null | null | null | null |
q-bio.PE q-bio.BM
|
http://creativecommons.org/licenses/by/4.0/
|
The Omicron variant of severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) has rapidly replaced the Delta variant as a dominating SARS-CoV-2
variant because of natural selection, which favors the variant with higher
infectivity and stronger vaccine breakthrough ability. Omicron has three
lineages or subvariants, BA.1 (B.1.1.529.1), BA.2 (B.1.1.529.2), and BA.3
(B.1.1.529.3). Among them, BA.1 is the currently prevailing subvariant. BA.2
shares 32 mutations with BA.1 but has 28 distinct ones. BA.3 shares most of its
mutations with BA.1 and BA.2 except for one. BA.2 is found to be able to
alarmingly reinfect patients originally infected by Omicron BA.1. An important
question is whether BA.2 or BA.3 will become a new dominating "variant of
concern". Currently, no experimental data has been reported about BA.2 and
BA.3. We construct a novel algebraic topology-based deep learning model trained
with tens of thousands of mutational and deep mutational data to systematically
evaluate BA.2's and BA.3's infectivity, vaccine breakthrough capability, and
antibody resistance. Our comparative analysis of all main variants namely,
Alpha, Beta, Gamma, Delta, Lambda, Mu, BA.1, BA.2, and BA.3, unveils that BA.2
is about 1.5 and 4.2 times as contagious as BA.1 and Delta, respectively. It is
also 30% and 17-fold more capable than BA.1 and Delta, respectively, to escape
current vaccines. Therefore, we project that Omicron BA.2 is on its path to
becoming the next dominating variant. We forecast that like Omicron BA.1, BA.2
will also seriously compromise most existing mAbs, except for sotrovimab
developed by GlaxoSmithKline.
|
[
{
"created": "Thu, 10 Feb 2022 13:38:44 GMT",
"version": "v1"
}
] |
2022-02-11
|
[
[
"Chen",
"Jiahui",
""
],
[
"Wei",
"Guo-Wei",
""
]
] |
The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly replaced the Delta variant as a dominating SARS-CoV-2 variant because of natural selection, which favors the variant with higher infectivity and stronger vaccine breakthrough ability. Omicron has three lineages or subvariants, BA.1 (B.1.1.529.1), BA.2 (B.1.1.529.2), and BA.3 (B.1.1.529.3). Among them, BA.1 is the currently prevailing subvariant. BA.2 shares 32 mutations with BA.1 but has 28 distinct ones. BA.3 shares most of its mutations with BA.1 and BA.2 except for one. BA.2 is found to be able to alarmingly reinfect patients originally infected by Omicron BA.1. An important question is whether BA.2 or BA.3 will become a new dominating "variant of concern". Currently, no experimental data has been reported about BA.2 and BA.3. We construct a novel algebraic topology-based deep learning model trained with tens of thousands of mutational and deep mutational data to systematically evaluate BA.2's and BA.3's infectivity, vaccine breakthrough capability, and antibody resistance. Our comparative analysis of all main variants namely, Alpha, Beta, Gamma, Delta, Lambda, Mu, BA.1, BA.2, and BA.3, unveils that BA.2 is about 1.5 and 4.2 times as contagious as BA.1 and Delta, respectively. It is also 30% and 17-fold more capable than BA.1 and Delta, respectively, to escape current vaccines. Therefore, we project that Omicron BA.2 is on its path to becoming the next dominating variant. We forecast that like Omicron BA.1, BA.2 will also seriously compromise most existing mAbs, except for sotrovimab developed by GlaxoSmithKline.
|
2101.03323
|
Jingwei Liu
|
Jingwei Liu
|
SARS-Cov-2 RNA Sequence Classification Based on Territory Information
|
7 figures
| null | null | null |
q-bio.QM cs.LG stat.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
CovID-19 genetics analysis is critical to determine virus type,virus variant
and evaluate vaccines. In this paper, SARS-Cov-2 RNA sequence analysis relative
to region or territory is investigated. A uniform framework of sequence SVM
model with various genetics length from short to long and mixed-bases is
developed by projecting SARS-Cov-2 RNA sequence to different dimensional space,
then scoring it according to the output probability of pre-trained SVM models
to explore the territory or origin information of SARS-Cov-2. Different sample
size ratio of training set and test set is also discussed in the data analysis.
Two SARS-Cov-2 RNA classification tasks are constructed based on GISAID
database, one is for mainland, Hongkong and Taiwan of China, and the other is a
6-class classification task (Africa, Asia, Europe, North American, South
American\& Central American, Ocean) of 7 continents. For 3-class classification
of China, the Top-1 accuracy rate can reach 82.45\% (train 60\%, test=40\%);
For 2-class classification of China, the Top-1 accuracy rate can reach 97.35\%
(train 80\%, test 20\%); For 6-class classification task of world, when the
ratio of training set and test set is 20\% : 80\% , the Top-1 accuracy rate can
achieve 30.30\%. And, some Top-N results are also given.
|
[
{
"created": "Sat, 9 Jan 2021 09:12:27 GMT",
"version": "v1"
}
] |
2021-01-12
|
[
[
"Liu",
"Jingwei",
""
]
] |
CovID-19 genetics analysis is critical to determine virus type,virus variant and evaluate vaccines. In this paper, SARS-Cov-2 RNA sequence analysis relative to region or territory is investigated. A uniform framework of sequence SVM model with various genetics length from short to long and mixed-bases is developed by projecting SARS-Cov-2 RNA sequence to different dimensional space, then scoring it according to the output probability of pre-trained SVM models to explore the territory or origin information of SARS-Cov-2. Different sample size ratio of training set and test set is also discussed in the data analysis. Two SARS-Cov-2 RNA classification tasks are constructed based on GISAID database, one is for mainland, Hongkong and Taiwan of China, and the other is a 6-class classification task (Africa, Asia, Europe, North American, South American\& Central American, Ocean) of 7 continents. For 3-class classification of China, the Top-1 accuracy rate can reach 82.45\% (train 60\%, test=40\%); For 2-class classification of China, the Top-1 accuracy rate can reach 97.35\% (train 80\%, test 20\%); For 6-class classification task of world, when the ratio of training set and test set is 20\% : 80\% , the Top-1 accuracy rate can achieve 30.30\%. And, some Top-N results are also given.
|
1412.2818
|
Fabian Chersi
|
Fabian Chersi
|
The hippocampal-striatal circuit for goal-directed and habitual choice
| null | null | null | null |
q-bio.NC
|
http://creativecommons.org/licenses/by-nc-sa/3.0/
|
It is now widely accepted that one of the roles of the hippocampus is to
maintain episodic spatial representations, while parallel striatal pathways
contribute to both declarative and procedural value computations by encoding
different input-specific outcome predictions. In this paper we investigate the
use of these brain mechanisms for action selection, linking them to model-based
and model-free controllers for decision making. To this aim we propose a
biologically inspired computational model that embodies these theories and
explains the functioning of the hippocampal-striatal circuit in a rat
navigation task. Its main characteristic is to allow the cooperation of
habitual and goal-directed behaviors, with the hippocampus primarily involved
in encoding spatial information and simulating possible navigation paths, and
the ventral and dorsal striatum involved in learning stimulus-response
behaviors and evaluating the reward expectancies associated to predicted
locations and sensed stimuli, respectively. The architecture we present employs
an unsupervised reinforcement learning rule for the hippocampal-striatal
network that is able to build a representation of the environment in which
rewarding sites and informative landmarks produce value gradients that are used
for planning and decision making. Additionally, it utilizes an arbitration
mechanism that balances between exploitation, i.e. stimulus-response behaviors,
and mental exploration, i.e. motor imagery processes, based on the intensity
and the variability of the responses of striatal neurons. We interpret these
results in light of recent experimental data that show anticipatory activations
in hippocampal and striatal areas.
|
[
{
"created": "Tue, 9 Dec 2014 00:19:25 GMT",
"version": "v1"
}
] |
2014-12-10
|
[
[
"Chersi",
"Fabian",
""
]
] |
It is now widely accepted that one of the roles of the hippocampus is to maintain episodic spatial representations, while parallel striatal pathways contribute to both declarative and procedural value computations by encoding different input-specific outcome predictions. In this paper we investigate the use of these brain mechanisms for action selection, linking them to model-based and model-free controllers for decision making. To this aim we propose a biologically inspired computational model that embodies these theories and explains the functioning of the hippocampal-striatal circuit in a rat navigation task. Its main characteristic is to allow the cooperation of habitual and goal-directed behaviors, with the hippocampus primarily involved in encoding spatial information and simulating possible navigation paths, and the ventral and dorsal striatum involved in learning stimulus-response behaviors and evaluating the reward expectancies associated to predicted locations and sensed stimuli, respectively. The architecture we present employs an unsupervised reinforcement learning rule for the hippocampal-striatal network that is able to build a representation of the environment in which rewarding sites and informative landmarks produce value gradients that are used for planning and decision making. Additionally, it utilizes an arbitration mechanism that balances between exploitation, i.e. stimulus-response behaviors, and mental exploration, i.e. motor imagery processes, based on the intensity and the variability of the responses of striatal neurons. We interpret these results in light of recent experimental data that show anticipatory activations in hippocampal and striatal areas.
|
1306.6656
|
Gergely J Sz\"oll\H{o}si
|
Murray Patterson and Gergely J Sz\"oll\H{o}si and Vincent Daubin and
Eric Tannier
|
Lateral Gene Transfer, Rearrangement and Reconciliation
|
submitted for RECOMB CG 2013
| null | null | null |
q-bio.PE q-bio.GN
|
http://creativecommons.org/licenses/by-nc-sa/3.0/
|
Background.
Models of ancestral gene order reconstruction have progressively integrated
different evolutionary patterns and processes such as unequal gene content,
gene duplications, and implicitly sequence evolution via reconciled gene trees.
In unicellular organisms, these models have so far ignored lateral gene
transfer, even though it can have an important confounding effect on such
models, as well as a rich source of information on the function of genes
through the detection of transfers of entire clusters of genes.
Result.
We report an algorithm together with its implementation, DeCoLT, that
reconstructs ancestral genome organization based on reconciled gene trees which
summarize information on sequence evolution, gene origination, duplication,
loss, and lateral transfer. DeCoLT finds in polynomial time the minimum number
of rearrangements, computed as the number of gains and breakages of adjacencies
between pairs of genes. We apply DeCoLT to 1099 gene families from 36
cyanobacteria genomes.
Conclusion.
DeCoLT is able to reconstruct adjacencies in 35 ancestral bacterial genomes
with a thousand genes families in a few hours, and detects clusters of
co-transferred genes. As there is no constraint on genome organization,
adjacencies can be generalized to any relationship between genes to reconstruct
ancestral interactions, functions or complexes with the same framework.
|
[
{
"created": "Thu, 27 Jun 2013 20:38:34 GMT",
"version": "v1"
}
] |
2013-07-01
|
[
[
"Patterson",
"Murray",
""
],
[
"Szöllősi",
"Gergely J",
""
],
[
"Daubin",
"Vincent",
""
],
[
"Tannier",
"Eric",
""
]
] |
Background. Models of ancestral gene order reconstruction have progressively integrated different evolutionary patterns and processes such as unequal gene content, gene duplications, and implicitly sequence evolution via reconciled gene trees. In unicellular organisms, these models have so far ignored lateral gene transfer, even though it can have an important confounding effect on such models, as well as a rich source of information on the function of genes through the detection of transfers of entire clusters of genes. Result. We report an algorithm together with its implementation, DeCoLT, that reconstructs ancestral genome organization based on reconciled gene trees which summarize information on sequence evolution, gene origination, duplication, loss, and lateral transfer. DeCoLT finds in polynomial time the minimum number of rearrangements, computed as the number of gains and breakages of adjacencies between pairs of genes. We apply DeCoLT to 1099 gene families from 36 cyanobacteria genomes. Conclusion. DeCoLT is able to reconstruct adjacencies in 35 ancestral bacterial genomes with a thousand genes families in a few hours, and detects clusters of co-transferred genes. As there is no constraint on genome organization, adjacencies can be generalized to any relationship between genes to reconstruct ancestral interactions, functions or complexes with the same framework.
|
q-bio/0403008
|
Mark Ya. Azbel'
|
Mark Ya. Azbel'
|
Immortality as a physical problem
|
refined version
| null | null | null |
q-bio.QM q-bio.PE
| null |
Well protected human and laboratory animal populations with abundant
resources are evolutionary unprecedented. Physical approach, which takes
advantage of their extensively quantified mortality, establishes that its
dominant fraction yields the exact law, whose universality from yeast to humans
is unprecedented, and suggests its unusual mechanism. Singularities of the law
demonstrate new kind of stepwise adaptation. The law proves that universal
mortality is an evolutionary byproduct, which at any age is reversible,
independent of previous life history, and may be disposable. Recent experiments
verify these predictions. Life expectancy may be extended, arguably to
immortality, by relatively small and universal biological amendments in the
animals. Indeed, it doubled with improving conditions in humans; increased
2.4-fold with genotype change in Drosophila, and 6-fold (to 430 years in human
terms), with no apparent loss in health and vitality, in nematodes with a small
number of perturbed genes and tissues. The law suggests a physical mechanism of
the universal mortality and its regulation.
|
[
{
"created": "Thu, 4 Mar 2004 14:04:31 GMT",
"version": "v1"
},
{
"created": "Mon, 3 May 2004 15:48:36 GMT",
"version": "v2"
}
] |
2007-05-23
|
[
[
"Azbel'",
"Mark Ya.",
""
]
] |
Well protected human and laboratory animal populations with abundant resources are evolutionary unprecedented. Physical approach, which takes advantage of their extensively quantified mortality, establishes that its dominant fraction yields the exact law, whose universality from yeast to humans is unprecedented, and suggests its unusual mechanism. Singularities of the law demonstrate new kind of stepwise adaptation. The law proves that universal mortality is an evolutionary byproduct, which at any age is reversible, independent of previous life history, and may be disposable. Recent experiments verify these predictions. Life expectancy may be extended, arguably to immortality, by relatively small and universal biological amendments in the animals. Indeed, it doubled with improving conditions in humans; increased 2.4-fold with genotype change in Drosophila, and 6-fold (to 430 years in human terms), with no apparent loss in health and vitality, in nematodes with a small number of perturbed genes and tissues. The law suggests a physical mechanism of the universal mortality and its regulation.
|
2007.10048
|
Nadav M. Shnerb
|
Jayant Pande and Nadav M. Shnerb
|
Population dynamics in stochastic environments
| null | null | null | null |
q-bio.PE cond-mat.stat-mech
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Populations are made up of an integer number of individuals and are subject
to stochastic birth-death processes whose rates may vary in time. Useful
quantities, like the chance of ultimate fixation, satisfy an appropriate
difference (master) equation, but closed-form solutions of these equations are
rare. Analytical insights in fields like population genetics, ecology and
evolution rely, almost exclusively, on an uncontrolled application of the
diffusion approximation (DA) which assumes the smoothness of the relevant
quantities over the set of integers. Here we combine asymptotic matching
techniques with a first-order (controlling-factor) WKB method to obtain a
theory whose range of applicability is much wider. This allows us to rederive
DA from a more general theory, to identify its limitations, and to suggest
alternative analytical solutions and scalable numerical techniques when it
fails. We carry out our analysis for the calculation of the fixation
probability in a fluctuating environment, highlighting the difference between
(on average) deleterious and beneficial mutant invasion and the intricate
distinction between weak and strong selection.
|
[
{
"created": "Mon, 20 Jul 2020 12:41:05 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Jul 2020 06:52:07 GMT",
"version": "v2"
}
] |
2020-07-22
|
[
[
"Pande",
"Jayant",
""
],
[
"Shnerb",
"Nadav M.",
""
]
] |
Populations are made up of an integer number of individuals and are subject to stochastic birth-death processes whose rates may vary in time. Useful quantities, like the chance of ultimate fixation, satisfy an appropriate difference (master) equation, but closed-form solutions of these equations are rare. Analytical insights in fields like population genetics, ecology and evolution rely, almost exclusively, on an uncontrolled application of the diffusion approximation (DA) which assumes the smoothness of the relevant quantities over the set of integers. Here we combine asymptotic matching techniques with a first-order (controlling-factor) WKB method to obtain a theory whose range of applicability is much wider. This allows us to rederive DA from a more general theory, to identify its limitations, and to suggest alternative analytical solutions and scalable numerical techniques when it fails. We carry out our analysis for the calculation of the fixation probability in a fluctuating environment, highlighting the difference between (on average) deleterious and beneficial mutant invasion and the intricate distinction between weak and strong selection.
|
1908.02434
|
Fabio Sanchez PhD
|
Fabio Sanchez and Juan G. Calvo
|
Dengue model with early-life stage of vectors and age-structure within
host
|
16 pages, 11 figures
| null | null | null |
q-bio.PE math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We construct an epidemic model for the transmission of dengue fever with an
early-life stage in the vector dynamics and age-structure within hosts. The
early-life stage of the vector is modeled via a general function that supports
multiple vector densities. The {\it basic reproductive number} and {\it vector
demographic threshold} are computed to study the local and global stability of
the infection-free state. A numerical framework is implemented and simulations
are performed.
|
[
{
"created": "Wed, 7 Aug 2019 04:09:07 GMT",
"version": "v1"
},
{
"created": "Mon, 30 Sep 2019 15:49:24 GMT",
"version": "v2"
}
] |
2019-10-01
|
[
[
"Sanchez",
"Fabio",
""
],
[
"Calvo",
"Juan G.",
""
]
] |
We construct an epidemic model for the transmission of dengue fever with an early-life stage in the vector dynamics and age-structure within hosts. The early-life stage of the vector is modeled via a general function that supports multiple vector densities. The {\it basic reproductive number} and {\it vector demographic threshold} are computed to study the local and global stability of the infection-free state. A numerical framework is implemented and simulations are performed.
|
1806.03872
|
Zachary Kilpatrick PhD
|
Khanh P Nguyen, Kresimir Josic, and Zachary P Kilpatrick
|
Optimizing sequential decisions in the drift-diffusion model
|
20 pages, 6 figures
| null | null | null |
q-bio.NC math.PR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To make decisions organisms often accumulate information across multiple
timescales. However, most experimental and modeling studies of decision-making
focus on sequences of independent trials. On the other hand, natural
environments are characterized by long temporal correlations, and evidence used
to make a present choice is often relevant to future decisions. To understand
decision-making under these conditions we analyze how a model ideal observer
accumulates evidence to freely make choices across a sequence of correlated
trials. We use principles of probabilistic inference to show that an ideal
observer incorporates information obtained on one trial as an initial bias on
the next. This bias decreases the time, but not the accuracy of the next
decision. Furthermore, in finite sequences of trials the rate of reward is
maximized when the observer deliberates longer for early decisions, but
responds more quickly towards the end of the sequence. Our model also explains
experimentally observed patterns in decision times and choices, thus providing
a mathematically principled foundation for evidence-accumulation models of
sequential decisions.
|
[
{
"created": "Mon, 11 Jun 2018 09:24:54 GMT",
"version": "v1"
}
] |
2018-06-12
|
[
[
"Nguyen",
"Khanh P",
""
],
[
"Josic",
"Kresimir",
""
],
[
"Kilpatrick",
"Zachary P",
""
]
] |
To make decisions organisms often accumulate information across multiple timescales. However, most experimental and modeling studies of decision-making focus on sequences of independent trials. On the other hand, natural environments are characterized by long temporal correlations, and evidence used to make a present choice is often relevant to future decisions. To understand decision-making under these conditions we analyze how a model ideal observer accumulates evidence to freely make choices across a sequence of correlated trials. We use principles of probabilistic inference to show that an ideal observer incorporates information obtained on one trial as an initial bias on the next. This bias decreases the time, but not the accuracy of the next decision. Furthermore, in finite sequences of trials the rate of reward is maximized when the observer deliberates longer for early decisions, but responds more quickly towards the end of the sequence. Our model also explains experimentally observed patterns in decision times and choices, thus providing a mathematically principled foundation for evidence-accumulation models of sequential decisions.
|
1710.07365
|
Themistoklis Melissourgos
|
Themistoklis Melissourgos, Sotiris Nikoletseas, Christoforos
Raptopoulos and Paul Spirakis
|
An extension of the Moran process using type-specific connection graphs
| null | null | null | null |
q-bio.PE cs.DM cs.GT cs.SI math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Moran process, as studied by [Lieberman, E., Hauert, C. and Nowak, M.
Evolutionary dynamics on graphs. Nature 433, pp. 312-316 (2005)], is a
stochastic process modeling the spread of genetic mutations in populations. In
this process, agents of a two-type population (i.e. mutants and residents) are
associated with the vertices of a graph. Initially, only one vertex chosen
uniformly at random is a mutant, with fitness $r > 0$, while all other
individuals are residents, with fitness $1$. In every step, an individual is
chosen with probability proportional to its fitness, and its state (mutant or
resident) is passed on to a neighbor which is chosen uniformly at random. In
this paper, we introduce and study a generalization of the model of Lieberman
et al. by assuming that different types of individuals perceive the population
through different graphs, namely $G_R(V,E_R)$ for residents and $G_M(V,E_M)$
for mutants. In this model, we study the fixation probability, i.e. the
probability that eventually only mutants remain in the population, for various
pairs of graphs. First, we transfer known results from the original
single-graph model of Lieberman et al. to our 2-graph model. Among them, we
provide a generalization of the Isothermal Theorem of Lieberman et al., that
gives sufficient conditions for a pair of graphs to have the same fixation
probability as a pair of cliques. Next, we give a 2-player strategic game view
of the process where player payoffs correspond to fixation and/or extinction
probabilities. In this setting, we attempt to identify best responses for each
player and give evidence that the clique is the most beneficial graph for both
players. Finally, we examine the possibility of efficient approximation of the
fixation probability and provide a FPRAS for the special case where the mutant
graph is complete.
|
[
{
"created": "Thu, 19 Oct 2017 22:48:51 GMT",
"version": "v1"
},
{
"created": "Wed, 23 May 2018 11:38:08 GMT",
"version": "v2"
},
{
"created": "Mon, 26 Jul 2021 15:12:29 GMT",
"version": "v3"
}
] |
2021-07-27
|
[
[
"Melissourgos",
"Themistoklis",
""
],
[
"Nikoletseas",
"Sotiris",
""
],
[
"Raptopoulos",
"Christoforos",
""
],
[
"Spirakis",
"Paul",
""
]
] |
The Moran process, as studied by [Lieberman, E., Hauert, C. and Nowak, M. Evolutionary dynamics on graphs. Nature 433, pp. 312-316 (2005)], is a stochastic process modeling the spread of genetic mutations in populations. In this process, agents of a two-type population (i.e. mutants and residents) are associated with the vertices of a graph. Initially, only one vertex chosen uniformly at random is a mutant, with fitness $r > 0$, while all other individuals are residents, with fitness $1$. In every step, an individual is chosen with probability proportional to its fitness, and its state (mutant or resident) is passed on to a neighbor which is chosen uniformly at random. In this paper, we introduce and study a generalization of the model of Lieberman et al. by assuming that different types of individuals perceive the population through different graphs, namely $G_R(V,E_R)$ for residents and $G_M(V,E_M)$ for mutants. In this model, we study the fixation probability, i.e. the probability that eventually only mutants remain in the population, for various pairs of graphs. First, we transfer known results from the original single-graph model of Lieberman et al. to our 2-graph model. Among them, we provide a generalization of the Isothermal Theorem of Lieberman et al., that gives sufficient conditions for a pair of graphs to have the same fixation probability as a pair of cliques. Next, we give a 2-player strategic game view of the process where player payoffs correspond to fixation and/or extinction probabilities. In this setting, we attempt to identify best responses for each player and give evidence that the clique is the most beneficial graph for both players. Finally, we examine the possibility of efficient approximation of the fixation probability and provide a FPRAS for the special case where the mutant graph is complete.
|
1512.03825
|
Maani Beigy
|
Maani Beigy
|
Generalized Resemblance Theory of Evidence: a Proposal for
Precision/Personalized Evidence-Based Medicine
| null | null | null | null |
q-bio.OT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Precision medicine emerges as the most important contemporary paradigm shift
of medical practice but has several challenges in evidence formation and
implementation for clinical practice. Precision/Personalized evidence-based
medicine (pEBM) requires theoretical support for decision making and
information management. This study aims to provide the required methodological
framework. Generalized Resemblance Theory of Evidence mainly rests upon
Generalized Theory of Uncertainty which manages information as generalized
constraints rather than limited statistical data, and also Prototype
Resemblance Theory of Disease which defines diseases/conditions when there is a
similarity relationship with prototypes (best examples of the disease). The
proposed theory explains that precisely-personalized structure of evidence is
formed as a generalized constraint on particular research questions, where the
constraining relation deals with averaged effect sizes of studies and its
comparison to null hypothesis; which might be of either probabilistic or
possibilistic nature. Similarity measures were employed to deal with
comparisons of high-dimensional characteristics. Real examples of a
meta-analysis and its clinical application are provided. This is one of the
first attempts for introducing a framework in medicine, which provides optimal
balance between generalizability of formed evidence and homogeneity of studied
populations.
|
[
{
"created": "Sun, 13 Dec 2015 11:15:30 GMT",
"version": "v1"
}
] |
2015-12-15
|
[
[
"Beigy",
"Maani",
""
]
] |
Precision medicine emerges as the most important contemporary paradigm shift of medical practice but has several challenges in evidence formation and implementation for clinical practice. Precision/Personalized evidence-based medicine (pEBM) requires theoretical support for decision making and information management. This study aims to provide the required methodological framework. Generalized Resemblance Theory of Evidence mainly rests upon Generalized Theory of Uncertainty which manages information as generalized constraints rather than limited statistical data, and also Prototype Resemblance Theory of Disease which defines diseases/conditions when there is a similarity relationship with prototypes (best examples of the disease). The proposed theory explains that precisely-personalized structure of evidence is formed as a generalized constraint on particular research questions, where the constraining relation deals with averaged effect sizes of studies and its comparison to null hypothesis; which might be of either probabilistic or possibilistic nature. Similarity measures were employed to deal with comparisons of high-dimensional characteristics. Real examples of a meta-analysis and its clinical application are provided. This is one of the first attempts for introducing a framework in medicine, which provides optimal balance between generalizability of formed evidence and homogeneity of studied populations.
|
1906.05369
|
Eduardo Mart\'inez-Montes
|
Julio A. Peraza-Goicolea, Eduardo Mart\'inez-Montes, Eduardo Aubert,
Pedro A. Vald\'es-Hern\'andez, Roberto Mulet
|
Modeling functional resting-state brain networks through neural message
passing on the human connectome
| null | null | null | null |
q-bio.NC cond-mat.dis-nn cond-mat.stat-mech nlin.AO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Understanding the relationship between the structure and function of the
human brain is one of the most important open questions in Neurosciences. In
particular, Resting State Networks (RSN) and more specifically the Default Mode
Network (DMN) of the brain, which are defined from the analysis of functional
data lack a definitive justification consistent with the anatomical structure
of the brain. In this work, we show that a possible connection may naturally
rest on the idea that information flows in the brain through a neural
message-passing dynamics between macroscopic structures, like those defined by
the human connectome (HC). In our model, each brain region in the HC is assumed
to have a binary behavior (active or not), the strength of interactions among
them is encoded in the anatomical connectivity matrix defined by the HC, and
the dynamics of the system is defined by a neural message-passing algorithm,
Belief Propagation (BP), working near the critical point of the human
connectome. We show that in the absence of direct external stimuli the BP
algorithm converges to a spatial map of activations that is similar to the DMN.
Moreover, we computed, using Susceptibility Propagation (SP), the matrix of
correlations between the different regions and show that the modules defined by
a clustering of this matrix resemble several Resting States Networks determined
experimentally. Both results suggest that the functional DMN and RSNs can be
seen as simple consequences of the anatomical structure of the brain and a
neural message-passing dynamics between macroscopic regions. We then show
preliminary results indicating our predictions on how functional DMN maps
change when the anatomical brain network suffers structural anomalies, like in
Alzheimers Disease and in lesions of the Corpus Callosum.
|
[
{
"created": "Wed, 12 Jun 2019 20:32:57 GMT",
"version": "v1"
}
] |
2019-06-14
|
[
[
"Peraza-Goicolea",
"Julio A.",
""
],
[
"Martínez-Montes",
"Eduardo",
""
],
[
"Aubert",
"Eduardo",
""
],
[
"Valdés-Hernández",
"Pedro A.",
""
],
[
"Mulet",
"Roberto",
""
]
] |
Understanding the relationship between the structure and function of the human brain is one of the most important open questions in Neurosciences. In particular, Resting State Networks (RSN) and more specifically the Default Mode Network (DMN) of the brain, which are defined from the analysis of functional data lack a definitive justification consistent with the anatomical structure of the brain. In this work, we show that a possible connection may naturally rest on the idea that information flows in the brain through a neural message-passing dynamics between macroscopic structures, like those defined by the human connectome (HC). In our model, each brain region in the HC is assumed to have a binary behavior (active or not), the strength of interactions among them is encoded in the anatomical connectivity matrix defined by the HC, and the dynamics of the system is defined by a neural message-passing algorithm, Belief Propagation (BP), working near the critical point of the human connectome. We show that in the absence of direct external stimuli the BP algorithm converges to a spatial map of activations that is similar to the DMN. Moreover, we computed, using Susceptibility Propagation (SP), the matrix of correlations between the different regions and show that the modules defined by a clustering of this matrix resemble several Resting States Networks determined experimentally. Both results suggest that the functional DMN and RSNs can be seen as simple consequences of the anatomical structure of the brain and a neural message-passing dynamics between macroscopic regions. We then show preliminary results indicating our predictions on how functional DMN maps change when the anatomical brain network suffers structural anomalies, like in Alzheimers Disease and in lesions of the Corpus Callosum.
|
1010.0919
|
Jaewook Joo
|
Luong Nguyen, Dubravka Bodiroga, Reka Kelemen, Jaewook Joo, and
Kimberly D. Gwinn
|
Modeling the effects of cymene on the distribution of germination and
growth of Beauveria bassiana
|
Student draft submitted to NIMBioS REU program, 19 pages, 12 figures
| null | null | null |
q-bio.QM q-bio.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Essential oils have antifungal and antipathogenic effects and therefore are
targets in plant pathology research for their potential uses as natural
substitutes for inorganic plant pesticides. Beauveria bassiana, an
entomopathogenic fungus, can endophytically colonize a vast number of plant
species and trigger induced systemic resistance against plant pathogens. Spore
germination is the most vulnerable in the fungal life cycle and is therefore a
good candidate for monitoring the effect of essential oils on the growth of B.
bassiana. Percentage germination of fungal spores and length of germination
tubes were recorded from experiments. A mathematical model that was able to
capture the effects of cymene, an essential oil produced by Monarda, on the
germination and growth was developed. This is the first report of a model for
the impact of essential oils on B. bassiana spore germination.
|
[
{
"created": "Tue, 5 Oct 2010 15:31:11 GMT",
"version": "v1"
}
] |
2010-10-06
|
[
[
"Nguyen",
"Luong",
""
],
[
"Bodiroga",
"Dubravka",
""
],
[
"Kelemen",
"Reka",
""
],
[
"Joo",
"Jaewook",
""
],
[
"Gwinn",
"Kimberly D.",
""
]
] |
Essential oils have antifungal and antipathogenic effects and therefore are targets in plant pathology research for their potential uses as natural substitutes for inorganic plant pesticides. Beauveria bassiana, an entomopathogenic fungus, can endophytically colonize a vast number of plant species and trigger induced systemic resistance against plant pathogens. Spore germination is the most vulnerable in the fungal life cycle and is therefore a good candidate for monitoring the effect of essential oils on the growth of B. bassiana. Percentage germination of fungal spores and length of germination tubes were recorded from experiments. A mathematical model that was able to capture the effects of cymene, an essential oil produced by Monarda, on the germination and growth was developed. This is the first report of a model for the impact of essential oils on B. bassiana spore germination.
|
1810.06066
|
Thomas House
|
A. Bishop, I. Z. Kiss and T. House
|
Consistent Approximation of Epidemic Dynamics on Degree-heterogeneous
Clustered Networks
|
14 pages, 7 figures
| null | null | null |
q-bio.PE physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Realistic human contact networks capable of spreading infectious disease, for
example studied in social contact surveys, exhibit both significant degree
heterogeneity and clustering, both of which greatly affect epidemic dynamics.
To understand the joint effects of these two network properties on epidemic
dynamics, the effective degree model of Lindquist et al. is reformulated with a
new moment closure to apply to highly clustered networks. A simulation study
comparing alternative ODE models and stochastic simulations is performed for
SIR (Susceptible-Infected-Removed) epidemic dynamics, including a test for the
conjectured error behaviour in Pellis et al., providing evidence that this
novel model can be a more accurate approximation to epidemic dynamics on
complex networks than existing approaches.
|
[
{
"created": "Sun, 14 Oct 2018 17:13:51 GMT",
"version": "v1"
}
] |
2018-10-16
|
[
[
"Bishop",
"A.",
""
],
[
"Kiss",
"I. Z.",
""
],
[
"House",
"T.",
""
]
] |
Realistic human contact networks capable of spreading infectious disease, for example studied in social contact surveys, exhibit both significant degree heterogeneity and clustering, both of which greatly affect epidemic dynamics. To understand the joint effects of these two network properties on epidemic dynamics, the effective degree model of Lindquist et al. is reformulated with a new moment closure to apply to highly clustered networks. A simulation study comparing alternative ODE models and stochastic simulations is performed for SIR (Susceptible-Infected-Removed) epidemic dynamics, including a test for the conjectured error behaviour in Pellis et al., providing evidence that this novel model can be a more accurate approximation to epidemic dynamics on complex networks than existing approaches.
|
1902.05845
|
Michel Kana PhD
|
Michel Kana
|
Assessing the Level of Autonomic Nervous Activity for Effective
Biofeedback Training
| null | null | null | null |
q-bio.NC physics.med-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper proposes a prototype of a new biofeedback training based on
mathematical models of cardiovascular control. For this purpose we develop a
low-cost device that is able to record and process arterial pulse wave via
photoplethysmograph and skin temperature on the peripheral part of the arm. A
benchmark analysis of our device against a registered cardiovascular
measurement system (Biopac MP35 from Biopac Inc., USA) shows that heart rate
and skin temperature values delivered by our device are acceptable with a very
low residual error (+/-1 beat/min, +/-1 oC).
Both measured signals are feed into a mathematical model which estimates the
level of activity of both sympathetic and parasympathetic branches of the
autonomic nervous system. That information is used jointly with other
biological parameters for investigating the stress score of the subject. The
data is processed in real-time and continuously displayed to the user for
effective biofeedback training.
The complete solution was preliminary tested on three volunteers who used the
displayed biofeedback information in order to regulate their emotional state
successfully during Biofeedback training. They exhibited a significant
reduction in stress score compared to three control subjects who did not used
our solution during biofeedback training. This supports the benefits of
biofeedback training with autonomic nervous tone assessment as effective
holistic healing method.
|
[
{
"created": "Fri, 15 Feb 2019 15:30:38 GMT",
"version": "v1"
}
] |
2019-02-18
|
[
[
"Kana",
"Michel",
""
]
] |
This paper proposes a prototype of a new biofeedback training based on mathematical models of cardiovascular control. For this purpose we develop a low-cost device that is able to record and process arterial pulse wave via photoplethysmograph and skin temperature on the peripheral part of the arm. A benchmark analysis of our device against a registered cardiovascular measurement system (Biopac MP35 from Biopac Inc., USA) shows that heart rate and skin temperature values delivered by our device are acceptable with a very low residual error (+/-1 beat/min, +/-1 oC). Both measured signals are feed into a mathematical model which estimates the level of activity of both sympathetic and parasympathetic branches of the autonomic nervous system. That information is used jointly with other biological parameters for investigating the stress score of the subject. The data is processed in real-time and continuously displayed to the user for effective biofeedback training. The complete solution was preliminary tested on three volunteers who used the displayed biofeedback information in order to regulate their emotional state successfully during Biofeedback training. They exhibited a significant reduction in stress score compared to three control subjects who did not used our solution during biofeedback training. This supports the benefits of biofeedback training with autonomic nervous tone assessment as effective holistic healing method.
|
2303.12813
|
Sarah Pungitore
|
Sarah Pungitore, Toluwanimi Olorunnisola, Jarrod Mosier, Vignesh
Subbian
|
Computable Phenotypes for Post-acute sequelae of SARS-CoV-2: A National
COVID Cohort Collaborative Analysis
| null | null | null | null |
q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
Post-acute sequelae of SARS-CoV-2 (PASC) is an increasingly recognized yet
incompletely understood public health concern. Several studies have examined
various ways to phenotype PASC to better characterize this heterogeneous
condition. However, many gaps in PASC phenotyping research exist, including a
lack of the following: 1) standardized definitions for PASC based on
symptomatology; 2) generalizable and reproducible phenotyping heuristics and
meta-heuristics; and 3) phenotypes based on both COVID-19 severity and symptom
duration. In this study, we defined computable phenotypes (or heuristics) and
meta-heuristics for PASC phenotypes based on COVID-19 severity and symptom
duration. We also developed a symptom profile for PASC based on a common data
standard. We identified four phenotypes based on COVID-19 severity (mild vs.
moderate/severe) and duration of PASC symptoms (subacute vs. chronic). The
symptoms groups with the highest frequency among phenotypes were cardiovascular
and neuropsychiatric with each phenotype characterized by a different set of
symptoms.
|
[
{
"created": "Wed, 22 Mar 2023 02:14:31 GMT",
"version": "v1"
},
{
"created": "Tue, 8 Aug 2023 00:04:42 GMT",
"version": "v2"
}
] |
2023-08-09
|
[
[
"Pungitore",
"Sarah",
""
],
[
"Olorunnisola",
"Toluwanimi",
""
],
[
"Mosier",
"Jarrod",
""
],
[
"Subbian",
"Vignesh",
""
]
] |
Post-acute sequelae of SARS-CoV-2 (PASC) is an increasingly recognized yet incompletely understood public health concern. Several studies have examined various ways to phenotype PASC to better characterize this heterogeneous condition. However, many gaps in PASC phenotyping research exist, including a lack of the following: 1) standardized definitions for PASC based on symptomatology; 2) generalizable and reproducible phenotyping heuristics and meta-heuristics; and 3) phenotypes based on both COVID-19 severity and symptom duration. In this study, we defined computable phenotypes (or heuristics) and meta-heuristics for PASC phenotypes based on COVID-19 severity and symptom duration. We also developed a symptom profile for PASC based on a common data standard. We identified four phenotypes based on COVID-19 severity (mild vs. moderate/severe) and duration of PASC symptoms (subacute vs. chronic). The symptoms groups with the highest frequency among phenotypes were cardiovascular and neuropsychiatric with each phenotype characterized by a different set of symptoms.
|
2006.14790
|
Emilio Angelina Angelina
|
Emilio Angelina, Sebastian Andujar, Oscar Parravicini, Daniel Enriz
and Nelida Peruchena
|
Drug Repurposing to find Inhibitors of SARS-CoV-2 Main Protease
| null | null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the strain of
coronavirus that causes coronavirus disease 2019 (COVID-19), the respiratory
illness responsible for the COVID-19 pandemic. Currently there is no known
vaccine or specific antiviral treatment for COVID-19 and so, there is an urgent
need for expedite discovery of new therapeutics to combat the disease until a
vaccine will be available worldwide. Drug repurposing is a strategy for
identifying new uses for approved drugs that has the advantage (over
conventional approaches that attempt to develop a drug from scratch) that time
frame of the overall process can be significantly reduced because of the few
number of clinical trial required. In this work, a virtual screening of
FDA-approved drugs was performed for repositioning as potential inhibitors of
the main protease Mpro of SARS-CoV-2. As a result of this study, 12 drugs are
proposed as candidates for inhibitors of the Mpro enzyme. Some of the selected
compounds are antiviral drugs that are already being tested in COVID-19
clinical trials (i.e. ribavirin) or are used to alleviate symptoms of the
disease (i.e. codeine). Surprisingly, the most promising candidate is the
naturally occurring broad spectrum antibiotic oxytetracycline. This compound
has largely outperformed the remaining selected candidates along all filtering
steps of our virtual screening protocol. If the activity of any of these drugs
is experimentally corroborated, they could be used directly in clinical trials
without the need for pre-clinical testing or safety evaluation since they are
already used as drugs for other diseases.
|
[
{
"created": "Fri, 26 Jun 2020 04:19:28 GMT",
"version": "v1"
}
] |
2020-06-29
|
[
[
"Angelina",
"Emilio",
""
],
[
"Andujar",
"Sebastian",
""
],
[
"Parravicini",
"Oscar",
""
],
[
"Enriz",
"Daniel",
""
],
[
"Peruchena",
"Nelida",
""
]
] |
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the strain of coronavirus that causes coronavirus disease 2019 (COVID-19), the respiratory illness responsible for the COVID-19 pandemic. Currently there is no known vaccine or specific antiviral treatment for COVID-19 and so, there is an urgent need for expedite discovery of new therapeutics to combat the disease until a vaccine will be available worldwide. Drug repurposing is a strategy for identifying new uses for approved drugs that has the advantage (over conventional approaches that attempt to develop a drug from scratch) that time frame of the overall process can be significantly reduced because of the few number of clinical trial required. In this work, a virtual screening of FDA-approved drugs was performed for repositioning as potential inhibitors of the main protease Mpro of SARS-CoV-2. As a result of this study, 12 drugs are proposed as candidates for inhibitors of the Mpro enzyme. Some of the selected compounds are antiviral drugs that are already being tested in COVID-19 clinical trials (i.e. ribavirin) or are used to alleviate symptoms of the disease (i.e. codeine). Surprisingly, the most promising candidate is the naturally occurring broad spectrum antibiotic oxytetracycline. This compound has largely outperformed the remaining selected candidates along all filtering steps of our virtual screening protocol. If the activity of any of these drugs is experimentally corroborated, they could be used directly in clinical trials without the need for pre-clinical testing or safety evaluation since they are already used as drugs for other diseases.
|
2102.06125
|
Dong Si
|
Dong Si, Andrew Nakamura, Runbang Tang, Haowen Guan, Jie Hou, Ammaar
Firozi, Renzhi Cao, Kyle Hippe, Minglei Zhao
|
Artificial Intelligence Advances for De Novo Molecular Structure
Modeling in Cryo-EM
| null |
Wiley Interdisciplinary Reviews: Computational Molecular Science,
e1542 (2021)
|
10.1002/wcms.1542
| null |
q-bio.BM cs.AI physics.bio-ph physics.comp-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Cryo-electron microscopy (cryo-EM) has become a major experimental technique
to determine the structures of large protein complexes and molecular
assemblies, as evidenced by the 2017 Nobel Prize. Although cryo-EM has been
drastically improved to generate high-resolution three-dimensional (3D) maps
that contain detailed structural information about macromolecules, the
computational methods for using the data to automatically build structure
models are lagging far behind. The traditional cryo-EM model building approach
is template-based homology modeling. Manual de novo modeling is very
time-consuming when no template model is found in the database. In recent
years, de novo cryo-EM modeling using machine learning (ML) and deep learning
(DL) has ranked among the top-performing methods in macromolecular structure
modeling. Deep-learning-based de novo cryo-EM modeling is an important
application of artificial intelligence, with impressive results and great
potential for the next generation of molecular biomedicine. Accordingly, we
systematically review the representative ML/DL-based de novo cryo-EM modeling
methods. And their significances are discussed from both practical and
methodological viewpoints. We also briefly describe the background of cryo-EM
data processing workflow. Overall, this review provides an introductory guide
to modern research on artificial intelligence (AI) for de novo molecular
structure modeling and future directions in this emerging field.
|
[
{
"created": "Thu, 11 Feb 2021 17:06:20 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Feb 2021 02:03:01 GMT",
"version": "v2"
}
] |
2021-06-01
|
[
[
"Si",
"Dong",
""
],
[
"Nakamura",
"Andrew",
""
],
[
"Tang",
"Runbang",
""
],
[
"Guan",
"Haowen",
""
],
[
"Hou",
"Jie",
""
],
[
"Firozi",
"Ammaar",
""
],
[
"Cao",
"Renzhi",
""
],
[
"Hippe",
"Kyle",
""
],
[
"Zhao",
"Minglei",
""
]
] |
Cryo-electron microscopy (cryo-EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize. Although cryo-EM has been drastically improved to generate high-resolution three-dimensional (3D) maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo-EM model building approach is template-based homology modeling. Manual de novo modeling is very time-consuming when no template model is found in the database. In recent years, de novo cryo-EM modeling using machine learning (ML) and deep learning (DL) has ranked among the top-performing methods in macromolecular structure modeling. Deep-learning-based de novo cryo-EM modeling is an important application of artificial intelligence, with impressive results and great potential for the next generation of molecular biomedicine. Accordingly, we systematically review the representative ML/DL-based de novo cryo-EM modeling methods. And their significances are discussed from both practical and methodological viewpoints. We also briefly describe the background of cryo-EM data processing workflow. Overall, this review provides an introductory guide to modern research on artificial intelligence (AI) for de novo molecular structure modeling and future directions in this emerging field.
|
2201.00287
|
Jatin Kashyap
|
Jatin Kashyap, Dibakar Datta
|
Drug repurposing for SARS-COV-2: A high-throughput molecular docking,
molecular dynamics, machine learning, & ab-initio study
| null | null |
10.1007/s10853-022-07195-8
| null |
q-bio.BM physics.bio-ph
|
http://creativecommons.org/licenses/by/4.0/
|
A molecule of dimension 125nm has caused around 479 Million human infections
(80M for the USA) & 6.1 Million human deaths (977,000 for the USA) worldwide
and slashed the global economy by US$ 8.5 Trillion over two years. The only
other events in recent history that caused comparative human life loss through
direct usage (either by (wo)man or nature, respectively) of structure-property
relations of 'nano-structures' (either (wo)man-made or nature, respectively)
were nuclear bomb attacks of Japanese cities by the USA during World War II and
1918 Flu Pandemic. This molecule is SARS-CoV-2, which causes a disease known as
COVID-19. The high liability cost of the pandemic had incentivized various
private, government, and academic entities to work towards finding a cure for
these & emerging diseases. As result, multiple vaccine candidates are
discovered to avoid the infection in first place. But so far, there has been no
success in finding fully effective therapeutics candidates. In this paper, we
attempted to provide multiple therapy candidates based upon a sophisticated
multi-scale in-silico framework. We have used the following robust framework to
screen the ligands; Step-I: high throughput docking, Step-II: molecular
dynamics, Step-III: density functional theory analysis. In total, we have
analyzed 2.2 Million unique protein binding site/ligand combinations. The
proteins were selected based on recent experimental studies. Step-I had
filtered that number down to 10 ligands/protein based on molecular docking
binding energy, further screening down to 2 ligands/protein based on
drug-likeness analysis. Additionally, these two ligands/proteins were
investigated in Step-II with a molecular dynamic based RMSD analysis. It
finally suggested three ligands (ZINC1176619532, ZINC517580540, ZINC952855827)
attacking different binding sites of the protein(7BV2), which were further
analyzed in Step III.
|
[
{
"created": "Sun, 2 Jan 2022 04:22:50 GMT",
"version": "v1"
},
{
"created": "Sat, 2 Apr 2022 03:17:50 GMT",
"version": "v2"
},
{
"created": "Thu, 7 Apr 2022 03:52:06 GMT",
"version": "v3"
}
] |
2022-06-29
|
[
[
"Kashyap",
"Jatin",
""
],
[
"Datta",
"Dibakar",
""
]
] |
A molecule of dimension 125nm has caused around 479 Million human infections (80M for the USA) & 6.1 Million human deaths (977,000 for the USA) worldwide and slashed the global economy by US$ 8.5 Trillion over two years. The only other events in recent history that caused comparative human life loss through direct usage (either by (wo)man or nature, respectively) of structure-property relations of 'nano-structures' (either (wo)man-made or nature, respectively) were nuclear bomb attacks of Japanese cities by the USA during World War II and 1918 Flu Pandemic. This molecule is SARS-CoV-2, which causes a disease known as COVID-19. The high liability cost of the pandemic had incentivized various private, government, and academic entities to work towards finding a cure for these & emerging diseases. As result, multiple vaccine candidates are discovered to avoid the infection in first place. But so far, there has been no success in finding fully effective therapeutics candidates. In this paper, we attempted to provide multiple therapy candidates based upon a sophisticated multi-scale in-silico framework. We have used the following robust framework to screen the ligands; Step-I: high throughput docking, Step-II: molecular dynamics, Step-III: density functional theory analysis. In total, we have analyzed 2.2 Million unique protein binding site/ligand combinations. The proteins were selected based on recent experimental studies. Step-I had filtered that number down to 10 ligands/protein based on molecular docking binding energy, further screening down to 2 ligands/protein based on drug-likeness analysis. Additionally, these two ligands/proteins were investigated in Step-II with a molecular dynamic based RMSD analysis. It finally suggested three ligands (ZINC1176619532, ZINC517580540, ZINC952855827) attacking different binding sites of the protein(7BV2), which were further analyzed in Step III.
|
0808.3870
|
Utz-Uwe Haus
|
Utz-Uwe Haus (1), Kathrin Niermann (1), Klaus Truemper (2), Robert
Weismantel (1) ((1) Magdeburg, Germany, (2) Dallas, Texas)
|
Logic Integer Programming Models for Signaling Networks
| null |
Journal of Computational Biology. May 2009, 16(5): 725-743
|
10.1089/cmb.2008.0163
| null |
q-bio.QM q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a static and a dynamic approach to model biological signaling
networks, and show how each can be used to answer relevant biological
questions. For this we use the two different mathematical tools of
Propositional Logic and Integer Programming. The power of discrete mathematics
for handling qualitative as well as quantitative data has so far not been
exploited in Molecular Biology, which is mostly driven by experimental
research, relying on first-order or statistical models. The arising logic
statements and integer programs are analyzed and can be solved with standard
software. For a restricted class of problems the logic models reduce to a
polynomial-time solvable satisfiability algorithm. Additionally, a more dynamic
model enables enumeration of possible time resolutions in poly-logarithmic
time. Computational experiments are included.
|
[
{
"created": "Thu, 28 Aug 2008 10:11:18 GMT",
"version": "v1"
}
] |
2009-05-13
|
[
[
"Haus",
"Utz-Uwe",
"",
"Magdeburg, Germany"
],
[
"Niermann",
"Kathrin",
"",
"Magdeburg, Germany"
],
[
"Truemper",
"Klaus",
"",
"Dallas, Texas"
],
[
"Weismantel",
"Robert",
"",
"Magdeburg, Germany"
]
] |
We propose a static and a dynamic approach to model biological signaling networks, and show how each can be used to answer relevant biological questions. For this we use the two different mathematical tools of Propositional Logic and Integer Programming. The power of discrete mathematics for handling qualitative as well as quantitative data has so far not been exploited in Molecular Biology, which is mostly driven by experimental research, relying on first-order or statistical models. The arising logic statements and integer programs are analyzed and can be solved with standard software. For a restricted class of problems the logic models reduce to a polynomial-time solvable satisfiability algorithm. Additionally, a more dynamic model enables enumeration of possible time resolutions in poly-logarithmic time. Computational experiments are included.
|
2212.00692
|
Denise Grappein
|
Stefano Berrone, Chiara Giverso, Denise Grappein, Luigi Preziosi,
Stefano Scial\`o
|
An optimization based 3D-1D coupling strategy for tissue perfusion and
chemical transport during tumor-induced angiogenesis
| null | null | null | null |
q-bio.TO cs.NA math.NA
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
A new mathematical model and numerical approach are proposed for the
simulation of fluid and chemical exchanges between a growing capillary network
and the surrounding tissue, in the context of tumor-induced angiogenesis.
Thanks to proper modeling assumptions the capillaries are reduced to their
centerline: a well posed mathematical model is hence worked out, based on the
coupling between a three-dimensional and a one-dimensional equation (3D-1D
coupled problem). Also the application of a PDE-constrained optimization
formulation is here proposed for the first time for angiogenesis simulations.
Under this approach no mesh conformity is required, thus making the method
particularly suitable for this kind of application, since no remeshing is
required as the capillary network grows. In order to handle both the evolution
of the quantities of interest and the changes in the geometry, a
discrete-hybrid strategy is adopted, combining a continuous modeling of the
tissue and of the chemicals with a discrete tip-tracking model to account for
the vascular network growth. The tip-tracking strategy, together with some
proper rules for branching and anastomosis, is able to provide a realistic
representation of the capillary network.
|
[
{
"created": "Thu, 17 Nov 2022 11:07:51 GMT",
"version": "v1"
}
] |
2022-12-02
|
[
[
"Berrone",
"Stefano",
""
],
[
"Giverso",
"Chiara",
""
],
[
"Grappein",
"Denise",
""
],
[
"Preziosi",
"Luigi",
""
],
[
"Scialò",
"Stefano",
""
]
] |
A new mathematical model and numerical approach are proposed for the simulation of fluid and chemical exchanges between a growing capillary network and the surrounding tissue, in the context of tumor-induced angiogenesis. Thanks to proper modeling assumptions the capillaries are reduced to their centerline: a well posed mathematical model is hence worked out, based on the coupling between a three-dimensional and a one-dimensional equation (3D-1D coupled problem). Also the application of a PDE-constrained optimization formulation is here proposed for the first time for angiogenesis simulations. Under this approach no mesh conformity is required, thus making the method particularly suitable for this kind of application, since no remeshing is required as the capillary network grows. In order to handle both the evolution of the quantities of interest and the changes in the geometry, a discrete-hybrid strategy is adopted, combining a continuous modeling of the tissue and of the chemicals with a discrete tip-tracking model to account for the vascular network growth. The tip-tracking strategy, together with some proper rules for branching and anastomosis, is able to provide a realistic representation of the capillary network.
|
2209.13038
|
Ankush Aggarwal
|
Ankush Aggarwal, Luke T. Hudson, Devin W. Laurence, Chung-Hao Lee,
Sanjay Pant
|
A Bayesian constitutive model selection framework for biaxial mechanical
testing of planar soft tissues: application to porcine aortic valves
| null | null |
10.1016/j.jmbbm.2023.105657
| null |
q-bio.TO physics.bio-ph physics.med-ph
|
http://creativecommons.org/licenses/by/4.0/
|
A variety of constitutive models have been developed for soft tissue
mechanics. However, there is no established criterion to select a suitable
model for a specific application. Although the model that best fits the
experimental data can be deemed the most suitable model, this practice often
can be insufficient given the inter-sample variability of experimental
observations. Herein, we present a Bayesian approach to calculate the relative
probabilities of constitutive models based on biaxial mechanical testing of
tissue samples. 46 samples of porcine aortic valve tissue were tested using a
biaxial stretching setup. For each sample, seven ratios of stresses along and
perpendicular to the fiber direction were applied. The probabilities of eight
invariant-based constitutive models were calculated based on the experimental
data using the proposed model selection framework. The calculated probabilities
showed that, out of the considered models and based on the information
available through the utilized experimental dataset, the May--Newman model was
the most probable model for the porcine aortic valve data. When the samples
were grouped into different cusp types, the May--Newman model remained the most
probable for the left- and right-coronary cusps, whereas for non-coronary cusps
two models were found to be equally probable: the Lee--Sacks model and the
May--Newman model. This difference between cusp types was found to be
associated with the first principal component analysis (PCA) mode, where this
mode's amplitudes of the non-coronary and right-coronary cusps were found to be
significantly different. Our results show that a PCA-based statistical model
can capture significant variations in the mechanical properties of soft
tissues. The presented framework is applicable to any tissue type, and has the
potential to provide a structured and rational way of making simulations
population-based.
|
[
{
"created": "Mon, 26 Sep 2022 21:39:44 GMT",
"version": "v1"
},
{
"created": "Tue, 3 Jan 2023 10:19:09 GMT",
"version": "v2"
}
] |
2023-01-06
|
[
[
"Aggarwal",
"Ankush",
""
],
[
"Hudson",
"Luke T.",
""
],
[
"Laurence",
"Devin W.",
""
],
[
"Lee",
"Chung-Hao",
""
],
[
"Pant",
"Sanjay",
""
]
] |
A variety of constitutive models have been developed for soft tissue mechanics. However, there is no established criterion to select a suitable model for a specific application. Although the model that best fits the experimental data can be deemed the most suitable model, this practice often can be insufficient given the inter-sample variability of experimental observations. Herein, we present a Bayesian approach to calculate the relative probabilities of constitutive models based on biaxial mechanical testing of tissue samples. 46 samples of porcine aortic valve tissue were tested using a biaxial stretching setup. For each sample, seven ratios of stresses along and perpendicular to the fiber direction were applied. The probabilities of eight invariant-based constitutive models were calculated based on the experimental data using the proposed model selection framework. The calculated probabilities showed that, out of the considered models and based on the information available through the utilized experimental dataset, the May--Newman model was the most probable model for the porcine aortic valve data. When the samples were grouped into different cusp types, the May--Newman model remained the most probable for the left- and right-coronary cusps, whereas for non-coronary cusps two models were found to be equally probable: the Lee--Sacks model and the May--Newman model. This difference between cusp types was found to be associated with the first principal component analysis (PCA) mode, where this mode's amplitudes of the non-coronary and right-coronary cusps were found to be significantly different. Our results show that a PCA-based statistical model can capture significant variations in the mechanical properties of soft tissues. The presented framework is applicable to any tissue type, and has the potential to provide a structured and rational way of making simulations population-based.
|
2203.14194
|
Chang Sub Kim
|
Chang Sub Kim
|
Free energy and inference in living systems
|
31 pages, 5 figures
| null | null | null |
q-bio.NC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Organisms are nonequilibrium, stationary systems self-organized via
spontaneous symmetry breaking and undergoing metabolic cycles with broken
detailed balance in the environment. The thermodynamic free-energy principle
describes an organism's homeostasis as the regulation of biochemical work
constrained by the physical free-energy cost. In contrast, recent research in
neuroscience and theoretical biology explains a higher organism's homeostasis
and allostasis as Bayesian inference facilitated by the informational free
energy. As an integrated approach to living systems, this study presents a
free-energy minimization theory overarching the essential features of both the
thermodynamic and neuroscientific free-energy principles. Our results reveal
that the perception and action of animals result from active inference entailed
by free-energy minimization in the brain, and the brain operates as
Schr{\"o}dinger's machine conducting the neural mechanics of minimizing sensory
uncertainty. A parsimonious model suggests that the Bayesian brain develops the
optimal trajectories in neural manifolds and induces a dynamic bifurcation
between neural attractors in the process of active inference.
|
[
{
"created": "Sun, 27 Mar 2022 03:16:06 GMT",
"version": "v1"
},
{
"created": "Wed, 23 Nov 2022 10:28:39 GMT",
"version": "v2"
}
] |
2022-11-24
|
[
[
"Kim",
"Chang Sub",
""
]
] |
Organisms are nonequilibrium, stationary systems self-organized via spontaneous symmetry breaking and undergoing metabolic cycles with broken detailed balance in the environment. The thermodynamic free-energy principle describes an organism's homeostasis as the regulation of biochemical work constrained by the physical free-energy cost. In contrast, recent research in neuroscience and theoretical biology explains a higher organism's homeostasis and allostasis as Bayesian inference facilitated by the informational free energy. As an integrated approach to living systems, this study presents a free-energy minimization theory overarching the essential features of both the thermodynamic and neuroscientific free-energy principles. Our results reveal that the perception and action of animals result from active inference entailed by free-energy minimization in the brain, and the brain operates as Schr{\"o}dinger's machine conducting the neural mechanics of minimizing sensory uncertainty. A parsimonious model suggests that the Bayesian brain develops the optimal trajectories in neural manifolds and induces a dynamic bifurcation between neural attractors in the process of active inference.
|
2101.05012
|
Hasindu Gamaarachchi
|
Hasindu Gamaarachchi
|
Computer Architecture-Aware Optimisation of DNA Analysis Systems
|
Supervisors: Parameswaran, Sri , Computer Science & Engineering,
Faculty of Engineering, UNSW; Ignjatovic, Aleksandar , Computer Science &
Engineering, Faculty of Engineering, UNSW; Smith, Martin A., Garvan Institute
of Medical Research, Faculty of Medicine, UNSW unsworks:
http://handle.unsw.edu.au/1959.4/70488
| null | null | null |
q-bio.GN cs.CE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
DNA sequencing is revolutionising the field of medicine. DNA sequencers, the
machines which perform DNA sequencing, have evolved from the size of a fridge
to that of a mobile phone over the last two decades. The cost of sequencing a
human genome also has reduced from billions of dollars to hundreds of dollars.
Despite these improvements, DNA sequencers output hundreds or thousands of
gigabytes of data that must be analysed on computers to discover meaningful
information with biological implications. Unfortunately, the analysis
techniques have not kept the pace with rapidly improving sequencing
technologies. Consequently, even today, the process of DNA analysis is
performed on high-performance computers, just as it was a couple of decades
ago. Such high-performance computers are not portable. Consequently, the full
utility of an ultra-portable sequencer for sequencing in-the-field or at the
point-of-care is limited by the lack of portable lightweight analytic
techniques. This thesis proposes computer architecture-aware optimisation of
DNA analysis software. DNA analysis software is inevitably convoluted due to
the complexity associated with biological data. Modern computer architectures
are also complex. Performing architecture-aware optimisations requires the
synergistic use of knowledge from both domains, (i.e, DNA sequence analysis and
computer architecture). This thesis aims to draw the two domains together. In
this thesis, gold-standard DNA sequence analysis workflows are systematically
examined for algorithmic components that cause performance bottlenecks.
Identified bottlenecks are resolved through architecture-aware optimisations at
different levels, i.e., memory, cache, register and processor. The optimised
software tools are used in complete end-to-end analysis workflows and their
efficacy is demonstrated by running on prototypical embedded systems.
|
[
{
"created": "Wed, 13 Jan 2021 11:29:12 GMT",
"version": "v1"
}
] |
2021-01-14
|
[
[
"Gamaarachchi",
"Hasindu",
""
]
] |
DNA sequencing is revolutionising the field of medicine. DNA sequencers, the machines which perform DNA sequencing, have evolved from the size of a fridge to that of a mobile phone over the last two decades. The cost of sequencing a human genome also has reduced from billions of dollars to hundreds of dollars. Despite these improvements, DNA sequencers output hundreds or thousands of gigabytes of data that must be analysed on computers to discover meaningful information with biological implications. Unfortunately, the analysis techniques have not kept the pace with rapidly improving sequencing technologies. Consequently, even today, the process of DNA analysis is performed on high-performance computers, just as it was a couple of decades ago. Such high-performance computers are not portable. Consequently, the full utility of an ultra-portable sequencer for sequencing in-the-field or at the point-of-care is limited by the lack of portable lightweight analytic techniques. This thesis proposes computer architecture-aware optimisation of DNA analysis software. DNA analysis software is inevitably convoluted due to the complexity associated with biological data. Modern computer architectures are also complex. Performing architecture-aware optimisations requires the synergistic use of knowledge from both domains, (i.e, DNA sequence analysis and computer architecture). This thesis aims to draw the two domains together. In this thesis, gold-standard DNA sequence analysis workflows are systematically examined for algorithmic components that cause performance bottlenecks. Identified bottlenecks are resolved through architecture-aware optimisations at different levels, i.e., memory, cache, register and processor. The optimised software tools are used in complete end-to-end analysis workflows and their efficacy is demonstrated by running on prototypical embedded systems.
|
1310.7568
|
Atsushi Tero
|
Atsushi Tero, Masakazu Akiyama, Dai Owaki, Takeshi Kano, Akio
Ishiguro, and Ryo Kobayashi
|
Interlimb neural connection is not required for gait transition in
quadruped locomotion
|
6 pages, 2figures
| null | null | null |
q-bio.QM cs.RO cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Quadrupeds transition spontaneously to various gait patterns (e.g., walk,
trot, pace, gallop) in response to the locomotion speed. The generation of
these gait patterns has been the subject of debate for a long time. We propose
a coupled oscillator model that is coupled with the physical interactions of
the body. The results of this study showed that the gait pattern transitions
spontaneously to walking/trotting/pacing/bounding in manner similar to that of
real quadruped animals when the resonating portion of the body is changed
according to the speed of leg movement. We also observed that pacing is
expressed exclusively instead of trotting by changing the physical
characteristics. In addition to leading to understanding of the principles of
locomotion in living things, the coupled oscillator model proposed in this
study is expected to lead to the creation of a legged robot that can select an
energy-efficient gait and transition to it spontaneously.
|
[
{
"created": "Mon, 28 Oct 2013 08:59:30 GMT",
"version": "v1"
}
] |
2013-10-30
|
[
[
"Tero",
"Atsushi",
""
],
[
"Akiyama",
"Masakazu",
""
],
[
"Owaki",
"Dai",
""
],
[
"Kano",
"Takeshi",
""
],
[
"Ishiguro",
"Akio",
""
],
[
"Kobayashi",
"Ryo",
""
]
] |
Quadrupeds transition spontaneously to various gait patterns (e.g., walk, trot, pace, gallop) in response to the locomotion speed. The generation of these gait patterns has been the subject of debate for a long time. We propose a coupled oscillator model that is coupled with the physical interactions of the body. The results of this study showed that the gait pattern transitions spontaneously to walking/trotting/pacing/bounding in manner similar to that of real quadruped animals when the resonating portion of the body is changed according to the speed of leg movement. We also observed that pacing is expressed exclusively instead of trotting by changing the physical characteristics. In addition to leading to understanding of the principles of locomotion in living things, the coupled oscillator model proposed in this study is expected to lead to the creation of a legged robot that can select an energy-efficient gait and transition to it spontaneously.
|
1202.5092
|
Masamichi Sato
|
Masamichi Sato and Kenji Fukumizu
|
Deformed Toric Ideal Constraints for Stoichiometric Networks
| null | null | null | null |
q-bio.MN q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We discuss chemical reaction networks and metabolic pathways based on
stoichiometric network analysis, and introduce deformed toric ideal constraints
by the algebraic geometrical approach. This paper concerns steady state flux of
chemical reaction networks and metabolic pathways. With the deformed toric
ideal constraints, the linear combination parameters of extreme pathways are
automatically constrained without introducing ad hoc constraints. To illustrate
the effectiveness of such constraints, we discuss two examples of chemical
reaction network and metabolic pathway; in the former the flux and the
concentrations are constrained completely by deformed toric ideal constraints,
and in the latter, it is shown the deformed toric ideal constrains the linear
combination parameters of flux at least partially. Even in the latter case, the
flux and the concentrations are constrained completely with the additional
constraint that the total amount of enzyme is constant.
|
[
{
"created": "Thu, 23 Feb 2012 05:42:41 GMT",
"version": "v1"
},
{
"created": "Tue, 23 Apr 2013 09:20:36 GMT",
"version": "v2"
}
] |
2013-04-24
|
[
[
"Sato",
"Masamichi",
""
],
[
"Fukumizu",
"Kenji",
""
]
] |
We discuss chemical reaction networks and metabolic pathways based on stoichiometric network analysis, and introduce deformed toric ideal constraints by the algebraic geometrical approach. This paper concerns steady state flux of chemical reaction networks and metabolic pathways. With the deformed toric ideal constraints, the linear combination parameters of extreme pathways are automatically constrained without introducing ad hoc constraints. To illustrate the effectiveness of such constraints, we discuss two examples of chemical reaction network and metabolic pathway; in the former the flux and the concentrations are constrained completely by deformed toric ideal constraints, and in the latter, it is shown the deformed toric ideal constrains the linear combination parameters of flux at least partially. Even in the latter case, the flux and the concentrations are constrained completely with the additional constraint that the total amount of enzyme is constant.
|
2010.12054
|
Christopher Eddy
|
Christopher Z. Eddy, Helena Raposo, Ryan Wong, Bo Sun
|
Extracellular Matrix regulates the morphodynamics of 3D migrating cancer
cells
| null | null |
10.1038/s41598-021-99902-9
| null |
q-bio.CB physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cell shape is linked to cell function. The significance of cell
morphodynamics, namely the temporal fluctuation of cell shape, is much less
understood. Here we study the morphodynamics of MDA-MB-231 cells in type I
collagen extracellular matrix (ECM). We systematically vary ECM physical
properties by tuning collagen concentrations, alignment, and gelation
temperatures. We find that morphodynamics of 3D migrating cells are externally
controlled by ECM mechanics and internally modulated by Rho-signaling. We
employ machine learning to classify cell shape into four different
morphological phenotypes, each corresponding to a distinct migration mode. As a
result, we map cell morphodynamics at mesoscale into the temporal evolution of
morphological phenotypes. We characterize the mesoscale dynamics including
occurrence probability, dwell time and transition matrix at varying ECM
conditions, which demonstrate the complex phenotype landscape and optimal
pathways for phenotype transitions. In light of the mesoscale dynamics, we show
that 3D cancer cell motility is a hidden Markov process whereby the step size
distributions of cell migration are coupled with simultaneous cell
morphodynamics. We also show that morphological phenotype transitions
facilitate cancer cells to navigate non-uniform ECM such as traversing the
interface between matrices of two distinct microstructures. In conclusion, we
demonstrate that 3D migrating cancer cells exhibit rich morphodynamics that is
regulated by ECM mechanics, Rho-signaling, and is closely related with cell
motility. Our results pave the way to the functional understanding and
mechanical programming of cell morphodynamics for normal and malignant cells.
|
[
{
"created": "Thu, 22 Oct 2020 20:48:15 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Oct 2021 18:05:22 GMT",
"version": "v2"
}
] |
2021-10-12
|
[
[
"Eddy",
"Christopher Z.",
""
],
[
"Raposo",
"Helena",
""
],
[
"Wong",
"Ryan",
""
],
[
"Sun",
"Bo",
""
]
] |
Cell shape is linked to cell function. The significance of cell morphodynamics, namely the temporal fluctuation of cell shape, is much less understood. Here we study the morphodynamics of MDA-MB-231 cells in type I collagen extracellular matrix (ECM). We systematically vary ECM physical properties by tuning collagen concentrations, alignment, and gelation temperatures. We find that morphodynamics of 3D migrating cells are externally controlled by ECM mechanics and internally modulated by Rho-signaling. We employ machine learning to classify cell shape into four different morphological phenotypes, each corresponding to a distinct migration mode. As a result, we map cell morphodynamics at mesoscale into the temporal evolution of morphological phenotypes. We characterize the mesoscale dynamics including occurrence probability, dwell time and transition matrix at varying ECM conditions, which demonstrate the complex phenotype landscape and optimal pathways for phenotype transitions. In light of the mesoscale dynamics, we show that 3D cancer cell motility is a hidden Markov process whereby the step size distributions of cell migration are coupled with simultaneous cell morphodynamics. We also show that morphological phenotype transitions facilitate cancer cells to navigate non-uniform ECM such as traversing the interface between matrices of two distinct microstructures. In conclusion, we demonstrate that 3D migrating cancer cells exhibit rich morphodynamics that is regulated by ECM mechanics, Rho-signaling, and is closely related with cell motility. Our results pave the way to the functional understanding and mechanical programming of cell morphodynamics for normal and malignant cells.
|
0708.0181
|
Eduardo Candelario-Jalil
|
E Candelario-Jalil, A Gonzalez-Falcon, M Garcia-Cabrera, OS Leon, BL
Fiebich
|
Post-ischaemic treatment with the cyclooxygenase-2 inhibitor nimesulide
reduces blood-brain barrier disruption and leukocyte infiltration following
transient focal cerebral ischaemia in rats
| null |
Journal of Neurochemistry 100(4): 1108-1120 (2007)
| null | null |
q-bio.NC q-bio.TO
| null |
Several studies suggest that cyclooxygenase (COX)-2 plays a pivotal role in
the progression of ischaemic brain damage. In the present study, we
investigated the effects of selective inhibition of COX-2 with nimesulide (12
mg/kg) and selective inhibition of COX-1 with valeryl salicylate (VAS, 12-120
mg/kg) on prostaglandin E2 (PGE2) levels, myeloperoxidase (MPO) activity, Evans
blue (EB) extravasation and infarct volume in a standardized model of transient
focal cerebral ischaemia in the rat. Post-ischaemic treatment with nimesulide
markedly reduced the increase in PGE2 levels in the ischaemic cerebral cortex
24 h after stroke and diminished infarct size by 48% with respect to
vehicle-treated animals after 3 days of reperfusion. Furthermore, nimesulide
significantly attenuated the blood-brain barrier (BBB) damage and leukocyte
infiltration (as measured by EB leakage and MPO activity, respectively) seen at
48 h after the initial ischaemic episode. These studies provide the first
experimental evidence that COX-2 inhibition with nimesulide is able to limit
BBB disruption and leukocyte infiltration following transient focal cerebral
ischaemia. Neuroprotection afforded by nimesulide is observed even when the
treatment is delayed until 6 h after the onset of ischaemia, confirming a wide
therapeutic window of COX-2 inhibitors in experimental stroke. On the contrary,
selective inhibition of COX-1 with VAS had no significant effect on the
evaluated parameters. These data suggest that COX-2 activity, but not COX-1
activity, contributes to the progression of focal ischaemic brain injury, and
that the beneficial effects observed with non-selective COX inhibitors are
probably associated to COX-2 rather than to COX-1 inhibition.
|
[
{
"created": "Wed, 1 Aug 2007 15:47:39 GMT",
"version": "v1"
}
] |
2007-08-02
|
[
[
"Candelario-Jalil",
"E",
""
],
[
"Gonzalez-Falcon",
"A",
""
],
[
"Garcia-Cabrera",
"M",
""
],
[
"Leon",
"OS",
""
],
[
"Fiebich",
"BL",
""
]
] |
Several studies suggest that cyclooxygenase (COX)-2 plays a pivotal role in the progression of ischaemic brain damage. In the present study, we investigated the effects of selective inhibition of COX-2 with nimesulide (12 mg/kg) and selective inhibition of COX-1 with valeryl salicylate (VAS, 12-120 mg/kg) on prostaglandin E2 (PGE2) levels, myeloperoxidase (MPO) activity, Evans blue (EB) extravasation and infarct volume in a standardized model of transient focal cerebral ischaemia in the rat. Post-ischaemic treatment with nimesulide markedly reduced the increase in PGE2 levels in the ischaemic cerebral cortex 24 h after stroke and diminished infarct size by 48% with respect to vehicle-treated animals after 3 days of reperfusion. Furthermore, nimesulide significantly attenuated the blood-brain barrier (BBB) damage and leukocyte infiltration (as measured by EB leakage and MPO activity, respectively) seen at 48 h after the initial ischaemic episode. These studies provide the first experimental evidence that COX-2 inhibition with nimesulide is able to limit BBB disruption and leukocyte infiltration following transient focal cerebral ischaemia. Neuroprotection afforded by nimesulide is observed even when the treatment is delayed until 6 h after the onset of ischaemia, confirming a wide therapeutic window of COX-2 inhibitors in experimental stroke. On the contrary, selective inhibition of COX-1 with VAS had no significant effect on the evaluated parameters. These data suggest that COX-2 activity, but not COX-1 activity, contributes to the progression of focal ischaemic brain injury, and that the beneficial effects observed with non-selective COX inhibitors are probably associated to COX-2 rather than to COX-1 inhibition.
|
q-bio/0412018
|
Wannapong Triampo
|
Waipot Ngamsaad, Wannapong Triampo, Paisan Kanthang, I-Ming Tang,
Narin Nuttawut and Charin Modjung
|
A one-dimensional Lattice Boltzmann method for modeling the dynamic
pole-to-pole oscillations of Min proteins for determining the position of the
midcell division plane
|
13 pages, 2 figures
| null | null | null |
q-bio.QM q-bio.SC
| null |
Determining the middle of the bacteria cell and the proper placement of the
septum is essential to the division of the bacterial cell. In E. coli, this
process depends on the proteins MinC, MinD, and MinE. Here, the Lattice
Boltzmann method (LBM) is used to study the dynamics of the oscillations of the
min proteins from pole to pole. This determines the midcell division plane at
the cellular level. The LBM is applied to the set of the deterministic reaction
diffusion equations proposed by Howard et. al. [1] to describe the dynamics of
the Min proteins. The LBM results are in good agreement with those of Howard et
al, and agree qualitatively with the experimental results. Our good results
indicate that the LBM can be an alternative computational tool for simulating
problems dealing with complex biological system which are described by the
reaction-diffusion equations.
|
[
{
"created": "Thu, 9 Dec 2004 21:38:41 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Ngamsaad",
"Waipot",
""
],
[
"Triampo",
"Wannapong",
""
],
[
"Kanthang",
"Paisan",
""
],
[
"Tang",
"I-Ming",
""
],
[
"Nuttawut",
"Narin",
""
],
[
"Modjung",
"Charin",
""
]
] |
Determining the middle of the bacteria cell and the proper placement of the septum is essential to the division of the bacterial cell. In E. coli, this process depends on the proteins MinC, MinD, and MinE. Here, the Lattice Boltzmann method (LBM) is used to study the dynamics of the oscillations of the min proteins from pole to pole. This determines the midcell division plane at the cellular level. The LBM is applied to the set of the deterministic reaction diffusion equations proposed by Howard et. al. [1] to describe the dynamics of the Min proteins. The LBM results are in good agreement with those of Howard et al, and agree qualitatively with the experimental results. Our good results indicate that the LBM can be an alternative computational tool for simulating problems dealing with complex biological system which are described by the reaction-diffusion equations.
|
2402.16652
|
John Helliwell R
|
John R. Helliwell, James R. Hester, Loes Kroon-Batenburg, Brian
McMahon and Selina L. S. Storm
|
The Evolution of Raw Data Archiving and the Growth of Its Importance in
Crystallography
|
34 pages, 2 figures, 2 tables
| null | null | null |
q-bio.OT
|
http://creativecommons.org/licenses/by/4.0/
|
The hardware for data archiving has expanded capacities for digital storage
enormously in the past decade or more. This article charts the efforts of IUCr
to facilitate discussions and plans relating to raw data archiving and reuse
within the various communities of crystallography, diffraction, and scattering.
|
[
{
"created": "Fri, 23 Feb 2024 15:03:53 GMT",
"version": "v1"
}
] |
2024-02-27
|
[
[
"Helliwell",
"John R.",
""
],
[
"Hester",
"James R.",
""
],
[
"Kroon-Batenburg",
"Loes",
""
],
[
"McMahon",
"Brian",
""
],
[
"Storm",
"Selina L. S.",
""
]
] |
The hardware for data archiving has expanded capacities for digital storage enormously in the past decade or more. This article charts the efforts of IUCr to facilitate discussions and plans relating to raw data archiving and reuse within the various communities of crystallography, diffraction, and scattering.
|
2405.06110
|
Anastasiya Salova
|
Anastasiya Salova, Istv\'an A. Kov\'acs
|
Combined topological and spatial constraints are required to capture the
structure of neural connectomes
|
24 pages, 20 figures
| null | null | null |
q-bio.NC physics.bio-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Volumetric brain reconstructions provide an unprecedented opportunity to gain
insights into the complex connectivity patterns of neurons in an increasing
number of organisms. Here, we model and quantify the complexity of the
resulting neural connectomes in the fruit fly, mouse, and human and unveil a
simple set of shared organizing principles across these organisms. To put the
connectomes in a physical context, we also construct contactomes, the network
of neurons in physical contact in each organism. With these, we establish that
physical constraints -- either given by pairwise distances or the contactome --
play a crucial role in shaping the network structure. For example, neuron
positions are highly optimal in terms of distance from their neighbors.
However, spatial constraints alone cannot capture the network topology,
including the broad degree distribution. Conversely, the degree sequence alone
is insufficient to recover the spatial structure. We resolve this apparent
conflict by formulating scalable maximum entropy models, incorporating both
types of constraints. The resulting generative models have predictive power
beyond the input data, as they capture several additional biological and
network characteristics, like synaptic weights and graphlet statistics.
|
[
{
"created": "Thu, 9 May 2024 21:31:50 GMT",
"version": "v1"
}
] |
2024-05-13
|
[
[
"Salova",
"Anastasiya",
""
],
[
"Kovács",
"István A.",
""
]
] |
Volumetric brain reconstructions provide an unprecedented opportunity to gain insights into the complex connectivity patterns of neurons in an increasing number of organisms. Here, we model and quantify the complexity of the resulting neural connectomes in the fruit fly, mouse, and human and unveil a simple set of shared organizing principles across these organisms. To put the connectomes in a physical context, we also construct contactomes, the network of neurons in physical contact in each organism. With these, we establish that physical constraints -- either given by pairwise distances or the contactome -- play a crucial role in shaping the network structure. For example, neuron positions are highly optimal in terms of distance from their neighbors. However, spatial constraints alone cannot capture the network topology, including the broad degree distribution. Conversely, the degree sequence alone is insufficient to recover the spatial structure. We resolve this apparent conflict by formulating scalable maximum entropy models, incorporating both types of constraints. The resulting generative models have predictive power beyond the input data, as they capture several additional biological and network characteristics, like synaptic weights and graphlet statistics.
|
1401.3262
|
Jianmin Sun
|
Jianmin Sun and Michael Grabe
|
Cooperativity Can Enhance Cellular Signal Detection
| null | null | null | null |
q-bio.MN physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Most sensory cells use surface receptors to detect environmental stimuli and
initiate downstream signaling. Cooperative interactions among sensory receptors
is known to play a crucial role in enhancing the sensitivity of biochemical
processes such as oxygen sensing by hemoglobin, but whether cooperativity
enhances the fidelity with which a system with multiple receptors can
accurately and quickly detect a signal is poorly understood. We model the
kinetics of small clusters of receptors in the presence of ligand, where the
receptors act independently or cooperatively. We show that the interaction
strength and how it is coupled to the dynamics influences the macroscopic
observables. Contrary to recent reports, our analysis shows that receptor
cooperativity can increase the signal-to-noise ratio, but this increase depends
on the underlying dynamics of the signaling receptor cluster.
|
[
{
"created": "Tue, 14 Jan 2014 17:15:19 GMT",
"version": "v1"
}
] |
2014-01-15
|
[
[
"Sun",
"Jianmin",
""
],
[
"Grabe",
"Michael",
""
]
] |
Most sensory cells use surface receptors to detect environmental stimuli and initiate downstream signaling. Cooperative interactions among sensory receptors is known to play a crucial role in enhancing the sensitivity of biochemical processes such as oxygen sensing by hemoglobin, but whether cooperativity enhances the fidelity with which a system with multiple receptors can accurately and quickly detect a signal is poorly understood. We model the kinetics of small clusters of receptors in the presence of ligand, where the receptors act independently or cooperatively. We show that the interaction strength and how it is coupled to the dynamics influences the macroscopic observables. Contrary to recent reports, our analysis shows that receptor cooperativity can increase the signal-to-noise ratio, but this increase depends on the underlying dynamics of the signaling receptor cluster.
|
2108.02678
|
Kevin Esvelt
|
The Nucleic Acid Observatory Consortium
|
A Global Nucleic Acid Observatory for Biodefense and Planetary Health
| null | null | null | null |
q-bio.GN q-bio.PE q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
The spread of pandemic viruses and invasive species can be catastrophic for
human societies and natural ecosystems. SARS-CoV-2 demonstrated that the speed
of our response is critical, as each day of delay permitted exponential growth
and dispersion of the virus. Here we propose a global Nucleic Acid Observatory
(NAO) to monitor the relative frequency of everything biological through
comprehensive metagenomic sequencing of waterways and wastewater. By searching
for divergences from historical baseline frequencies at sites throughout the
world, NAO could detect any virus or invasive organism undergoing exponential
growth whose nucleic acids end up in the water, even those previously unknown
to science. Continuously monitoring nucleic acid diversity would provide us
with universal early warning, obviate subtle bioweapons, and generate a wealth
of sequence data sufficient to transform ecology, microbiology, and
conservation. We call for the immediate construction of a global NAO to defend
and illuminate planetary health.
|
[
{
"created": "Thu, 5 Aug 2021 15:19:05 GMT",
"version": "v1"
}
] |
2021-08-06
|
[
[
"Consortium",
"The Nucleic Acid Observatory",
""
]
] |
The spread of pandemic viruses and invasive species can be catastrophic for human societies and natural ecosystems. SARS-CoV-2 demonstrated that the speed of our response is critical, as each day of delay permitted exponential growth and dispersion of the virus. Here we propose a global Nucleic Acid Observatory (NAO) to monitor the relative frequency of everything biological through comprehensive metagenomic sequencing of waterways and wastewater. By searching for divergences from historical baseline frequencies at sites throughout the world, NAO could detect any virus or invasive organism undergoing exponential growth whose nucleic acids end up in the water, even those previously unknown to science. Continuously monitoring nucleic acid diversity would provide us with universal early warning, obviate subtle bioweapons, and generate a wealth of sequence data sufficient to transform ecology, microbiology, and conservation. We call for the immediate construction of a global NAO to defend and illuminate planetary health.
|
q-bio/0607018
|
Branko Dragovich
|
Branko Dragovich and Alexandra Dragovich
|
A p-Adic Model of DNA Sequence and Genetic Code
|
13 pages, 2 tables
|
p-Adic Numbers, Ultrametric Analysis and Applications 1 (2009)
34-41
|
10.1134/S2070046609010038
| null |
q-bio.GN cs.IT math-ph math.IT math.MP physics.bio-ph
| null |
Using basic properties of p-adic numbers, we consider a simple new approach
to describe main aspects of DNA sequence and genetic code. Central role in our
investigation plays an ultrametric p-adic information space which basic
elements are nucleotides, codons and genes. We show that a 5-adic model is
appropriate for DNA sequence. This 5-adic model, combined with 2-adic distance,
is also suitable for genetic code and for a more advanced employment in
genomics. We find that genetic code degeneracy is related to the p-adic
distance between codons.
|
[
{
"created": "Thu, 13 Jul 2006 16:41:35 GMT",
"version": "v1"
}
] |
2010-12-01
|
[
[
"Dragovich",
"Branko",
""
],
[
"Dragovich",
"Alexandra",
""
]
] |
Using basic properties of p-adic numbers, we consider a simple new approach to describe main aspects of DNA sequence and genetic code. Central role in our investigation plays an ultrametric p-adic information space which basic elements are nucleotides, codons and genes. We show that a 5-adic model is appropriate for DNA sequence. This 5-adic model, combined with 2-adic distance, is also suitable for genetic code and for a more advanced employment in genomics. We find that genetic code degeneracy is related to the p-adic distance between codons.
|
q-bio/0702046
|
Erel Levine
|
Erel Levine, Peter McHale and Herbert Levine
|
microRNAs may sharpen spatial expression patterns
| null | null |
10.1371/journal.pcbi.0030233
| null |
q-bio.MN
| null |
The precise layout of gene expression patterns is a crucial step in
development. Formation of a sharp boundary between high and low expression
domains requires a genetic mechanism which is both sensitive and robust to
fluctuations, a demand that may not be easily achieved by morphogens alone.
Recently it has been demonstrated that small RNAs (and, in particular,
microRNAs) play many roles in embryonic development. While some RNAs are
essential for embryogenesis, others are limited to fine-tuning a predetermined
gene expression pattern. Here we explore the possibility that small RNAs
participate in sharpening a gene expression profile that was crudely
established by a morphogen. To this end we study a model where small RNAs
interact with a target gene and diffusively move from cell to cell. Though
diffusion generally smears spatial expression patterns, we find that
intercellular mobility of small RNAs is actually critical in sharpening the
interface between target expression domains in a robust manner. We discuss the
applicability of our results, as examples, to the case of leaf polarity
establishment in maize and Hox patterning in the early {\it Drosophila} embryo.
Our findings point out the functional significance of some mechanistic
properties, such as mobility of small RNAs and the irreversibility of their
interactions. These properties are yet to be established directly for most
classes of small RNAs. An indirect yet simple experimental test of the proposed
mechanism is suggested in some detail.
|
[
{
"created": "Thu, 22 Feb 2007 19:31:09 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Jun 2007 00:07:29 GMT",
"version": "v2"
}
] |
2015-06-26
|
[
[
"Levine",
"Erel",
""
],
[
"McHale",
"Peter",
""
],
[
"Levine",
"Herbert",
""
]
] |
The precise layout of gene expression patterns is a crucial step in development. Formation of a sharp boundary between high and low expression domains requires a genetic mechanism which is both sensitive and robust to fluctuations, a demand that may not be easily achieved by morphogens alone. Recently it has been demonstrated that small RNAs (and, in particular, microRNAs) play many roles in embryonic development. While some RNAs are essential for embryogenesis, others are limited to fine-tuning a predetermined gene expression pattern. Here we explore the possibility that small RNAs participate in sharpening a gene expression profile that was crudely established by a morphogen. To this end we study a model where small RNAs interact with a target gene and diffusively move from cell to cell. Though diffusion generally smears spatial expression patterns, we find that intercellular mobility of small RNAs is actually critical in sharpening the interface between target expression domains in a robust manner. We discuss the applicability of our results, as examples, to the case of leaf polarity establishment in maize and Hox patterning in the early {\it Drosophila} embryo. Our findings point out the functional significance of some mechanistic properties, such as mobility of small RNAs and the irreversibility of their interactions. These properties are yet to be established directly for most classes of small RNAs. An indirect yet simple experimental test of the proposed mechanism is suggested in some detail.
|
1707.00320
|
Daniele De Martino
|
Daniele De Martino
|
A Van-Der-Waals picture for metabolic networks from MaxEnt modeling:
inherent bistability and elusive coexistence
|
9 pages, 4 figures
|
Phys. Rev. E 96, 060401 (2017)
|
10.1103/PhysRevE.96.060401
| null |
q-bio.MN cond-mat.stat-mech physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work maximum entropy distributions in the space of steady states of
metabolic networks are defined upon constraining the first and second moment of
the growth rate. Inherent bistability of fast and slow phenotypes, akin to a
Van-Der Waals picture, emerges upon considering control on the average growth
(optimization/repression) and its fluctuations (heterogeneity). This is applied
to the carbon catabolic core of E.coli where it agrees with some stylized facts
on the persisters phenotype and it provides a quantitative map with metabolic
fluxes, opening for the possibility to detect coexistence from flux data.
Preliminary analysis on data for E.Coli cultures in standard conditions shows,
on the other hand, degeneracy for the inferred parameters that extend in the
coexistence region.
|
[
{
"created": "Sun, 2 Jul 2017 16:51:36 GMT",
"version": "v1"
}
] |
2017-12-27
|
[
[
"De Martino",
"Daniele",
""
]
] |
In this work maximum entropy distributions in the space of steady states of metabolic networks are defined upon constraining the first and second moment of the growth rate. Inherent bistability of fast and slow phenotypes, akin to a Van-Der Waals picture, emerges upon considering control on the average growth (optimization/repression) and its fluctuations (heterogeneity). This is applied to the carbon catabolic core of E.coli where it agrees with some stylized facts on the persisters phenotype and it provides a quantitative map with metabolic fluxes, opening for the possibility to detect coexistence from flux data. Preliminary analysis on data for E.Coli cultures in standard conditions shows, on the other hand, degeneracy for the inferred parameters that extend in the coexistence region.
|
1510.08045
|
Richard Betzel
|
Richard F. Betzel, Bratislav Mi\v{s}i\'c, Ye He, Jeffrey Rumschlag,
Xi-Nian Zuo, Olaf Sporns
|
Functional brain modules reconfigure at multiple scales across the human
lifespan
|
56 pages, 7 figures, 6 supplemental figures
| null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The human brain is a complex network of interconnected brain regions
organized into functional modules with distinct roles in cognition and
behavior. An important question concerns the persistence and stability of these
modules over the human lifespan. Here we use graph-theoretic analysis to
algorithmically uncover the brain's intrinsic modular organization across
multiple spatial scales ranging from small communities comprised of only a few
brain regions to large communities made up of many regions. We find that at
coarse scales modules become progressively more segregated, while at finer
scales segregation decreases. Module composition also exhibits scale-specific
and age-dependent changes. At coarse scales, the module assignments of regions
normally associated with control, default mode, attention, and visual networks
are highly flexible. At fine scales the most flexible regions are associated
with the default mode network. Finally, we show that, with age, some regions in
the default mode network, specifically retrosplenial cortex, maintain a greater
proportion of functional connections to their own module, while regions
associated with somatomotor and saliency/ventral attention networks distribute
their links more evenly across modules.
|
[
{
"created": "Tue, 27 Oct 2015 19:53:24 GMT",
"version": "v1"
}
] |
2015-10-28
|
[
[
"Betzel",
"Richard F.",
""
],
[
"Mišić",
"Bratislav",
""
],
[
"He",
"Ye",
""
],
[
"Rumschlag",
"Jeffrey",
""
],
[
"Zuo",
"Xi-Nian",
""
],
[
"Sporns",
"Olaf",
""
]
] |
The human brain is a complex network of interconnected brain regions organized into functional modules with distinct roles in cognition and behavior. An important question concerns the persistence and stability of these modules over the human lifespan. Here we use graph-theoretic analysis to algorithmically uncover the brain's intrinsic modular organization across multiple spatial scales ranging from small communities comprised of only a few brain regions to large communities made up of many regions. We find that at coarse scales modules become progressively more segregated, while at finer scales segregation decreases. Module composition also exhibits scale-specific and age-dependent changes. At coarse scales, the module assignments of regions normally associated with control, default mode, attention, and visual networks are highly flexible. At fine scales the most flexible regions are associated with the default mode network. Finally, we show that, with age, some regions in the default mode network, specifically retrosplenial cortex, maintain a greater proportion of functional connections to their own module, while regions associated with somatomotor and saliency/ventral attention networks distribute their links more evenly across modules.
|
1403.7660
|
Ion Udroiu
|
Ion Udroiu
|
Estimation of erythrocyte surface area in mammals
| null |
Methods Protoc. 2024, 7(1), 11
|
10.3390/mps7010011
| null |
q-bio.QM q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Measures of erythrocytes volume and surface are helpful in several
physiological studies, both for zoologists and veterinarians. Whilst diameter
and volume are assessed with ease from observations of blood smears and
complete blood count, respectively, thickness and surface area, instead, are
much more difficult to be obtained. The accurate description of the erythrocyte
geometry is given by the equation of the oval of Cassini, but the formulas
deriving from it are very complex, comprising elliptic integrals. In this
article three solids are proposed as models approximating the erythrocyte:
sphere, cylinder and a spheroid with concave caps. Volumes and Surface Areas
obtained with these models are compared to those effectively measured.
|
[
{
"created": "Sat, 29 Mar 2014 18:40:43 GMT",
"version": "v1"
}
] |
2024-06-10
|
[
[
"Udroiu",
"Ion",
""
]
] |
Measures of erythrocytes volume and surface are helpful in several physiological studies, both for zoologists and veterinarians. Whilst diameter and volume are assessed with ease from observations of blood smears and complete blood count, respectively, thickness and surface area, instead, are much more difficult to be obtained. The accurate description of the erythrocyte geometry is given by the equation of the oval of Cassini, but the formulas deriving from it are very complex, comprising elliptic integrals. In this article three solids are proposed as models approximating the erythrocyte: sphere, cylinder and a spheroid with concave caps. Volumes and Surface Areas obtained with these models are compared to those effectively measured.
|
2104.12249
|
Hong-Gyu Yoon
|
Hong-Gyu Yoon and Pilwon Kim
|
A STDP-based Encoding Algorithm for Associative and Composite Data
|
12 pages of main text. Source for simplified MATLAB programs
performing two numerical tests presented in this article can be found in the
following link: https://github.com/hkyoon94/NRSTDP.git
| null | null | null |
q-bio.NC cs.NE math.DS nlin.AO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Spike-timing-dependent plasticity(STDP) is a biological process of synaptic
modification caused by the difference of firing order and timing between
neurons. One of the neurodynamical roles of STDP is to form a macroscopic
geometrical structure in the neuronal state space in response to a periodic
input. This work proposes a practical memory model based on STDP that can store
and retrieve high-dimensional associative data. The model combines STDP
dynamics with an encoding scheme for distributed representations and can handle
multiple composite data in a continuous manner. In the auto-associative memory
task where a group of images is continuously streamed to the model, the images
are successfully retrieved from an oscillating neural state whenever a proper
cue is given. In the second task that deals with semantic memories embedded
from sentences, the results show that words can recall multiple sentences
simultaneously or one exclusively, depending on their grammatical relations.
|
[
{
"created": "Sun, 25 Apr 2021 20:26:52 GMT",
"version": "v1"
},
{
"created": "Fri, 16 Jul 2021 00:56:36 GMT",
"version": "v2"
},
{
"created": "Mon, 9 Aug 2021 01:52:05 GMT",
"version": "v3"
}
] |
2021-08-10
|
[
[
"Yoon",
"Hong-Gyu",
""
],
[
"Kim",
"Pilwon",
""
]
] |
Spike-timing-dependent plasticity(STDP) is a biological process of synaptic modification caused by the difference of firing order and timing between neurons. One of the neurodynamical roles of STDP is to form a macroscopic geometrical structure in the neuronal state space in response to a periodic input. This work proposes a practical memory model based on STDP that can store and retrieve high-dimensional associative data. The model combines STDP dynamics with an encoding scheme for distributed representations and can handle multiple composite data in a continuous manner. In the auto-associative memory task where a group of images is continuously streamed to the model, the images are successfully retrieved from an oscillating neural state whenever a proper cue is given. In the second task that deals with semantic memories embedded from sentences, the results show that words can recall multiple sentences simultaneously or one exclusively, depending on their grammatical relations.
|
2302.00146
|
Xiaowei Yu
|
Xiaowei Yu, Lu Zhang, Haixing Dai, Lin Zhao, Yanjun Lyu, Zihao Wu,
Tianming Liu, Dajiang Zhu
|
Gyri vs. Sulci: Disentangling Brain Core-Periphery Functional Networks
via Twin-Transformer
|
13 pages, 4 figures
| null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The human cerebral cortex is highly convoluted into convex gyri and concave
sulci. It has been demonstrated that gyri and sulci are significantly different
in their anatomy, connectivity, and function, besides exhibiting opposite shape
patterns, long-distance axonal fibers connected to gyri are much denser than
those connected to sulci, and neural signals on gyri are more complex in
low-frequency while sulci are more complex in high-frequency. Although
accumulating evidence shows significant differences between gyri and sulci,
their primary roles in brain function have not been elucidated yet. To solve
this fundamental problem, we design a novel Twin-Transformer framework to
unveil the unique functional roles of gyri and sulci as well as their
relationship in the whole brain function. Our Twin-Transformer framework adopts
two structure-identical (twin) Transformers to disentangle spatial-temporal
patterns of gyri and sulci, one focuses on the information of gyri and the
other is on sulci. The Gyro-Sulcal interactions, along with the tremendous but
widely existing variability across subjects, are characterized in the loss
design. We validated our Twin-Transformer on the HCP task-fMRI dataset, for the
first time, to elucidate the different roles of gyri and sulci in brain
function. Our results suggest that gyri and sulci could work together in a
core-periphery network manner, that is, gyri could serve as core networks for
information gathering and distributing, while sulci could serve as periphery
networks for specific local information processing. These findings have shed
new light on our fundamental understanding of the brain's basic structural and
functional mechanisms.
|
[
{
"created": "Tue, 31 Jan 2023 23:45:40 GMT",
"version": "v1"
}
] |
2023-02-02
|
[
[
"Yu",
"Xiaowei",
""
],
[
"Zhang",
"Lu",
""
],
[
"Dai",
"Haixing",
""
],
[
"Zhao",
"Lin",
""
],
[
"Lyu",
"Yanjun",
""
],
[
"Wu",
"Zihao",
""
],
[
"Liu",
"Tianming",
""
],
[
"Zhu",
"Dajiang",
""
]
] |
The human cerebral cortex is highly convoluted into convex gyri and concave sulci. It has been demonstrated that gyri and sulci are significantly different in their anatomy, connectivity, and function, besides exhibiting opposite shape patterns, long-distance axonal fibers connected to gyri are much denser than those connected to sulci, and neural signals on gyri are more complex in low-frequency while sulci are more complex in high-frequency. Although accumulating evidence shows significant differences between gyri and sulci, their primary roles in brain function have not been elucidated yet. To solve this fundamental problem, we design a novel Twin-Transformer framework to unveil the unique functional roles of gyri and sulci as well as their relationship in the whole brain function. Our Twin-Transformer framework adopts two structure-identical (twin) Transformers to disentangle spatial-temporal patterns of gyri and sulci, one focuses on the information of gyri and the other is on sulci. The Gyro-Sulcal interactions, along with the tremendous but widely existing variability across subjects, are characterized in the loss design. We validated our Twin-Transformer on the HCP task-fMRI dataset, for the first time, to elucidate the different roles of gyri and sulci in brain function. Our results suggest that gyri and sulci could work together in a core-periphery network manner, that is, gyri could serve as core networks for information gathering and distributing, while sulci could serve as periphery networks for specific local information processing. These findings have shed new light on our fundamental understanding of the brain's basic structural and functional mechanisms.
|
0705.1974
|
Franco Bagnoli
|
Franco Bagnoli, Pietro Lio, Luca Sguanci
|
Risk perception in epidemic modeling
|
6 pages, 6 figures, completely new version
|
Phys. Rev. E 76, 061904 (2007)
|
10.1103/PhysRevE.76.061904
| null |
q-bio.PE q-bio.OT
| null |
We investigate the effects of risk perception in a simple model of epidemic
spreading. We assume that the perception of the risk of being infected depends
on the fraction of neighbors that are ill. The effect of this factor is to
decrease the infectivity, that therefore becomes a dynamical component of the
model. We study the problem in the mean-field approximation and by numerical
simulations for regular, random and scale-free networks.
We show that for homogeneous and random networks, there is always a value of
perception that stops the epidemics. In the ``worst-case'' scenario of a
scale-free network with diverging input connectivity, a linear perception
cannot stop the epidemics; however we show that a non-linear increase of the
perception risk may lead to the extinction of the disease. This transition is
discontinuous, and is not predicted by the mean-field analysis.
|
[
{
"created": "Mon, 14 May 2007 16:16:34 GMT",
"version": "v1"
},
{
"created": "Fri, 18 May 2007 12:54:36 GMT",
"version": "v2"
},
{
"created": "Thu, 23 Aug 2007 15:56:01 GMT",
"version": "v3"
}
] |
2007-12-06
|
[
[
"Bagnoli",
"Franco",
""
],
[
"Lio",
"Pietro",
""
],
[
"Sguanci",
"Luca",
""
]
] |
We investigate the effects of risk perception in a simple model of epidemic spreading. We assume that the perception of the risk of being infected depends on the fraction of neighbors that are ill. The effect of this factor is to decrease the infectivity, that therefore becomes a dynamical component of the model. We study the problem in the mean-field approximation and by numerical simulations for regular, random and scale-free networks. We show that for homogeneous and random networks, there is always a value of perception that stops the epidemics. In the ``worst-case'' scenario of a scale-free network with diverging input connectivity, a linear perception cannot stop the epidemics; however we show that a non-linear increase of the perception risk may lead to the extinction of the disease. This transition is discontinuous, and is not predicted by the mean-field analysis.
|
2203.01175
|
Herbert Sauro Dr
|
Ciaran Welsh, Jin Xu, Lucian Smith, Matthias K\"onig, Kiri Choi,
Herbert M. Sauro
|
libRoadRunner 2.0: A High-Performance SBML Simulation and Analysis
Library
| null | null | null | null |
q-bio.QM cs.CE q-bio.MN q-bio.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Motivation: This paper presents libRoadRunner 2.0, an extensible,
high-performance, cross-platform, open-source software library for the
simulation and analysis of models expressed using Systems Biology Markup
Language SBML).
Results: libRoadRunner is a self-contained library, able to run both as a
component inside other tools via its C++ and C bindings, and interactively
through its Python or Julia interface. libRoadRunner uses a custom Just-In-Time
JIT compiler built on the widely-used LLVM JIT compiler framework. It compiles
SBML-specified models directly into native machine code for a large variety of
processors, making it appropriate for solving extremely large models or
repeated runs. libRoadRunner is flexible, supporting the bulk of the SBML
specification (except for delay and nonlinear algebraic equations) and
including several SBML extensions such as composition and distributions. It
offers multiple deterministic and stochastic integrators, as well as tools for
steady-state, sensitivity, stability analysis, and structural analysis of the
stoichiometric matrix.
Availability: libRoadRunner binary distributions are available for Mac OS X,
Linux, and Windows. The library is licensed under the Apache License Version
2.0. libRoadRunner is also available for ARM-based computers such as the
Raspberry Pi and can in principle be compiled on any system supported by
LLVM-13. http://sys-bio.github.io/roadrunner/index.html provides online
documentation, full build instructions, binaries, and a git source repository.
|
[
{
"created": "Sat, 26 Feb 2022 00:59:08 GMT",
"version": "v1"
}
] |
2022-03-03
|
[
[
"Welsh",
"Ciaran",
""
],
[
"Xu",
"Jin",
""
],
[
"Smith",
"Lucian",
""
],
[
"König",
"Matthias",
""
],
[
"Choi",
"Kiri",
""
],
[
"Sauro",
"Herbert M.",
""
]
] |
Motivation: This paper presents libRoadRunner 2.0, an extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models expressed using Systems Biology Markup Language SBML). Results: libRoadRunner is a self-contained library, able to run both as a component inside other tools via its C++ and C bindings, and interactively through its Python or Julia interface. libRoadRunner uses a custom Just-In-Time JIT compiler built on the widely-used LLVM JIT compiler framework. It compiles SBML-specified models directly into native machine code for a large variety of processors, making it appropriate for solving extremely large models or repeated runs. libRoadRunner is flexible, supporting the bulk of the SBML specification (except for delay and nonlinear algebraic equations) and including several SBML extensions such as composition and distributions. It offers multiple deterministic and stochastic integrators, as well as tools for steady-state, sensitivity, stability analysis, and structural analysis of the stoichiometric matrix. Availability: libRoadRunner binary distributions are available for Mac OS X, Linux, and Windows. The library is licensed under the Apache License Version 2.0. libRoadRunner is also available for ARM-based computers such as the Raspberry Pi and can in principle be compiled on any system supported by LLVM-13. http://sys-bio.github.io/roadrunner/index.html provides online documentation, full build instructions, binaries, and a git source repository.
|
1103.5685
|
Jakub Otwinowski
|
Jakub Otwinowski, Stefan Boettcher
|
Accumulation of beneficial mutations in one dimension
| null |
Physical Review E, 84(1), 011925, 2011
|
10.1103/PhysRevE.84.011925
| null |
q-bio.PE cond-mat.dis-nn
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
When beneficial mutations are relatively common, competition between multiple
unfixed mutations can reduce the rate of fixation in well-mixed asexual
populations. We introduce a one dimensional model with a steady accumulation of
beneficial mutations. We find a transition between periodic selection and
multiple-mutation regimes. In the multiple-mutation regime, the increase of
fitness along the lattice bears a striking similarity to surface growth
phenomena, with power law growth and saturation of the interface width. We also
find significant differences compared to the well-mixed model. In our lattice
model, the transition between regimes happens at a much lower mutation rate due
to slower fixation times in one dimension. Also the rate of fixation is reduced
with increasing mutation rate due to the more intense competition, and it
saturates with large population size.
|
[
{
"created": "Tue, 29 Mar 2011 15:38:36 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Feb 2013 21:06:20 GMT",
"version": "v2"
}
] |
2013-02-19
|
[
[
"Otwinowski",
"Jakub",
""
],
[
"Boettcher",
"Stefan",
""
]
] |
When beneficial mutations are relatively common, competition between multiple unfixed mutations can reduce the rate of fixation in well-mixed asexual populations. We introduce a one dimensional model with a steady accumulation of beneficial mutations. We find a transition between periodic selection and multiple-mutation regimes. In the multiple-mutation regime, the increase of fitness along the lattice bears a striking similarity to surface growth phenomena, with power law growth and saturation of the interface width. We also find significant differences compared to the well-mixed model. In our lattice model, the transition between regimes happens at a much lower mutation rate due to slower fixation times in one dimension. Also the rate of fixation is reduced with increasing mutation rate due to the more intense competition, and it saturates with large population size.
|
1202.0012
|
Miguel Navascues
|
Miguel Navascu\'es, Solenn Stoeckel, Stephanie Mariette
|
Genetic diversity and fitness in small populations of partially asexual,
self-incompatible plants
| null |
Heredity 104, 5 (2010) 482-92
|
10.1038/hdy.2009.159
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
How self-incompatibility systems are maintained in plant populations is still
a debated issue. Theoretical models predict that self-incompatibility systems
break down according to the intensity of inbreeding depression and number of
S-alleles. Other studies have explored the function of asexual reproduction in
the maintenance of self-incompatibility. However, the population genetics of
partially asexual, self-incompatible populations are poorly understood and
previous studies have failed to consider all possible effects of asexual
reproduction or could only speculate on those effects. In this study, we
investigated how partial asexuality may affect genetic diversity at the S-locus
and fitness in small self-incompatible populations. A genetic model including
an S-locus and a viability locus was developed to perform forward simulations
of the evolution of populations of various sizes. Drift combined with partial
asexuality produced a decrease in the number of alleles at the S-locus. In
addition, an excess of heterozygotes was present in the population, causing an
increase in mutation load. This heterozygote excess was enhanced by the
self-incompatibility system in small populations. In addition, in highly
asexual populations, individuals produced asexually had some fitness advantages
over individuals produced sexually, because sexual reproduction produces
homozygotes of the deleterious allele, contrary to asexual reproduction. Our
results suggest that future research on the function of asexuality for the
maintenance of self-incompatibility will need to (1) account for whole-genome
fitness (mutation load generated by asexuality, self-incompatibility and drift)
and (2) acknowledge that the maintenance of self-incompatibility may not be
independent of the maintenance of sex itself.
|
[
{
"created": "Tue, 31 Jan 2012 21:00:56 GMT",
"version": "v1"
}
] |
2012-02-02
|
[
[
"Navascués",
"Miguel",
""
],
[
"Stoeckel",
"Solenn",
""
],
[
"Mariette",
"Stephanie",
""
]
] |
How self-incompatibility systems are maintained in plant populations is still a debated issue. Theoretical models predict that self-incompatibility systems break down according to the intensity of inbreeding depression and number of S-alleles. Other studies have explored the function of asexual reproduction in the maintenance of self-incompatibility. However, the population genetics of partially asexual, self-incompatible populations are poorly understood and previous studies have failed to consider all possible effects of asexual reproduction or could only speculate on those effects. In this study, we investigated how partial asexuality may affect genetic diversity at the S-locus and fitness in small self-incompatible populations. A genetic model including an S-locus and a viability locus was developed to perform forward simulations of the evolution of populations of various sizes. Drift combined with partial asexuality produced a decrease in the number of alleles at the S-locus. In addition, an excess of heterozygotes was present in the population, causing an increase in mutation load. This heterozygote excess was enhanced by the self-incompatibility system in small populations. In addition, in highly asexual populations, individuals produced asexually had some fitness advantages over individuals produced sexually, because sexual reproduction produces homozygotes of the deleterious allele, contrary to asexual reproduction. Our results suggest that future research on the function of asexuality for the maintenance of self-incompatibility will need to (1) account for whole-genome fitness (mutation load generated by asexuality, self-incompatibility and drift) and (2) acknowledge that the maintenance of self-incompatibility may not be independent of the maintenance of sex itself.
|
1912.02474
|
Sergei Shedko
|
S.V. Shedko
|
Assembly ASM291031v2 (Genbank: GCA_002910315.2) identified as assembly
of the Northern Dolly Varden (Salvelinus malma malma) genome, and not the
Arctic char (S. alpinus) genome
|
15 pages, in Russian; typos corrected
| null | null | null |
q-bio.GN q-bio.PE
|
http://creativecommons.org/licenses/by/4.0/
|
To date, twelve complete genomes representing eleven species belonging to six
genera have been sequenced in salmonids. For the genus Salvelinus, it was
supposed to sequence the genome of Arctic char, one of the most variable
species of vertebrate animals. Sequencing was carried out (Christensen et al.,
2018) using the tissues of the female IW2-2015 obtained from the company
engaged in industrial aquaculture of chars - Icy Waters Ltd. The company
exploits two of its own broodstocks - NL and TR, originating from the chars
from the Nauyuk Lake and the Tree River (Nunavut, Canada). Since the complete
mitochondrial genome of the female IW2-2015 was absent in the published
assembly ASM291031v2, we determined its type and complete sequence from the
sequence read archives taken from Genbank. It was found that the female's
mitogenome belongs to the BERING haplogroup, which is characteristic of
Northern Dolly Varden S. malma malma. Analysis of other unlinked diagnostic
loci encoded by nuclear DNA (ITS1, RAG1, SFO-12, SFO-18, SMM-21) also revealed
distinctive characters of Northern Dolly Varden in female IW2-2015. It was
concluded that the genomic assembly ASM291031v2 was obtained not from an
individual of Arctic char S. alpinus, but from an individual of a related
species - Northern Dolly Varden S. malma malma. The identical to the IW2-2015
female characteristics of diagnostic loci were found in other individuals from
the broodstock TR. Apparently, the broodstock TR is entirely a strain derived
from Northern Dolly Varden. Since assembly ASM291031v2 was obtained from a
specimen originated from the marginal population of Northern Dolly Varden (Tree
R.) isolated from the main range of the species and with some traces of
introgressive hybridization, this assembly can hardly be considered as a
description of a typical genome of S. malma malma.
|
[
{
"created": "Thu, 5 Dec 2019 10:13:07 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Dec 2019 11:23:02 GMT",
"version": "v2"
}
] |
2019-12-24
|
[
[
"Shedko",
"S. V.",
""
]
] |
To date, twelve complete genomes representing eleven species belonging to six genera have been sequenced in salmonids. For the genus Salvelinus, it was supposed to sequence the genome of Arctic char, one of the most variable species of vertebrate animals. Sequencing was carried out (Christensen et al., 2018) using the tissues of the female IW2-2015 obtained from the company engaged in industrial aquaculture of chars - Icy Waters Ltd. The company exploits two of its own broodstocks - NL and TR, originating from the chars from the Nauyuk Lake and the Tree River (Nunavut, Canada). Since the complete mitochondrial genome of the female IW2-2015 was absent in the published assembly ASM291031v2, we determined its type and complete sequence from the sequence read archives taken from Genbank. It was found that the female's mitogenome belongs to the BERING haplogroup, which is characteristic of Northern Dolly Varden S. malma malma. Analysis of other unlinked diagnostic loci encoded by nuclear DNA (ITS1, RAG1, SFO-12, SFO-18, SMM-21) also revealed distinctive characters of Northern Dolly Varden in female IW2-2015. It was concluded that the genomic assembly ASM291031v2 was obtained not from an individual of Arctic char S. alpinus, but from an individual of a related species - Northern Dolly Varden S. malma malma. The identical to the IW2-2015 female characteristics of diagnostic loci were found in other individuals from the broodstock TR. Apparently, the broodstock TR is entirely a strain derived from Northern Dolly Varden. Since assembly ASM291031v2 was obtained from a specimen originated from the marginal population of Northern Dolly Varden (Tree R.) isolated from the main range of the species and with some traces of introgressive hybridization, this assembly can hardly be considered as a description of a typical genome of S. malma malma.
|
1703.09780
|
Thierry Mora
|
Andreas Mayer, Thierry Mora, Olivier Rivoire, Aleksandra M. Walczak
|
Transitions in optimal adaptive strategies for populations in
fluctuating environments
| null |
Phys. Rev. E 96, 032412 (2017)
|
10.1103/PhysRevE.96.032412
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Biological populations are subject to fluctuating environmental conditions.
Different adaptive strategies can allow them to cope with these fluctuations:
specialization to one particular environmental condition, adoption of a
generalist phenotype that compromise between conditions, or population-wise
diversification (bet-hedging). Which strategy provides the largest selective
advantage in the long run depends on the range of accessible phenotypes and the
statistics of the environmental fluctuations. Here, we analyze this problem in
a simple mathematical model of population growth. First, we review and extend a
graphical method to identify the nature of the optimal strategy when the
environmental fluctuations are uncorrelated. Temporal correlations in
environmental fluctuations open up new strategies that rely on memory but are
mathematically challenging to study: we present here new analytical results to
address this challenge. We illustrate our general approach by analyzing optimal
adaptive strategies in the presence of trade-offs that constrain the range of
accessible phenotypes. Our results extend several previous studies and have
applications to a variety of biological phenomena, from antibiotic resistance
in bacteria to immune responses in vertebrates.
|
[
{
"created": "Tue, 28 Mar 2017 20:14:54 GMT",
"version": "v1"
}
] |
2017-09-27
|
[
[
"Mayer",
"Andreas",
""
],
[
"Mora",
"Thierry",
""
],
[
"Rivoire",
"Olivier",
""
],
[
"Walczak",
"Aleksandra M.",
""
]
] |
Biological populations are subject to fluctuating environmental conditions. Different adaptive strategies can allow them to cope with these fluctuations: specialization to one particular environmental condition, adoption of a generalist phenotype that compromise between conditions, or population-wise diversification (bet-hedging). Which strategy provides the largest selective advantage in the long run depends on the range of accessible phenotypes and the statistics of the environmental fluctuations. Here, we analyze this problem in a simple mathematical model of population growth. First, we review and extend a graphical method to identify the nature of the optimal strategy when the environmental fluctuations are uncorrelated. Temporal correlations in environmental fluctuations open up new strategies that rely on memory but are mathematically challenging to study: we present here new analytical results to address this challenge. We illustrate our general approach by analyzing optimal adaptive strategies in the presence of trade-offs that constrain the range of accessible phenotypes. Our results extend several previous studies and have applications to a variety of biological phenomena, from antibiotic resistance in bacteria to immune responses in vertebrates.
|
1802.07875
|
Robert Griffiths Professor
|
Conrad J. Burden and Robert C. Griffiths
|
The stationary distribution of a sample from the Wright-Fisher diffusion
model with general small mutation rates
|
14 pages, 0 figures
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The stationary distribution of a sample taken from a Wright-Fisher diffusion
with general small mutation rates is found using a coalescent approach. The
approximation is equivalent to having at most one mutation in the coalescent
tree to the first order in the rates. The sample probabilities characterize an
approximation for the stationary distribution from the Wright-Fisher diffusion.
The approach is different from Burden and Tang (2016,2017) who use a
probability flux argument to obtain the same results from a forward diffusion
generator equation. The solution has interest because the solution is not known
when rates are not small. An analogous solution is found for the configuration
of alleles in a general exchangeable binary coalescent tree. In particular an
explicit solution is found for a pure birth process tree when individuals
reproduce at rate lambda.
|
[
{
"created": "Thu, 22 Feb 2018 02:26:57 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Mar 2018 10:25:08 GMT",
"version": "v2"
},
{
"created": "Fri, 1 Jun 2018 07:09:20 GMT",
"version": "v3"
},
{
"created": "Tue, 25 Sep 2018 23:27:55 GMT",
"version": "v4"
},
{
"created": "Tue, 30 Oct 2018 00:17:05 GMT",
"version": "v5"
}
] |
2018-10-31
|
[
[
"Burden",
"Conrad J.",
""
],
[
"Griffiths",
"Robert C.",
""
]
] |
The stationary distribution of a sample taken from a Wright-Fisher diffusion with general small mutation rates is found using a coalescent approach. The approximation is equivalent to having at most one mutation in the coalescent tree to the first order in the rates. The sample probabilities characterize an approximation for the stationary distribution from the Wright-Fisher diffusion. The approach is different from Burden and Tang (2016,2017) who use a probability flux argument to obtain the same results from a forward diffusion generator equation. The solution has interest because the solution is not known when rates are not small. An analogous solution is found for the configuration of alleles in a general exchangeable binary coalescent tree. In particular an explicit solution is found for a pure birth process tree when individuals reproduce at rate lambda.
|
2208.13283
|
Samuel Neuenschwander
|
Samuel Neuenschwander (1 and 2), Diana I. Cruz D\'avalos (1 and 3),
Lucas Anchieri (1 and 3), B\'arbara Sousa da Mota (1 and 3), Davide Bozzi (1
and 3), Simone Rubinacci (1 and 3), Olivier Delaneau (1 and 3), Simon
Rasmussen (4), and Anna-Sapfo Malaspinas (1 and 3) ((1) Department of
Computational Biology, University of Lausanne, Switzerland, (2) Vital-IT,
Swiss Institute of Bioinformatics, Lausanne, Switzerland, (3) Swiss Institute
of Bioinformatics, Lausanne, Switzerland, (4) Novo Nordisk Foundation Center
for Protein Research, University of Copenhagen, Denmark)
|
Mapache: a flexible pipeline to map ancient DNA
|
Availability: Mapache is freely available on GitHub
(https://github.com/sneuensc/mapache). Supplementary information: An
extensive manual is provided at https://github.com/sneuensc/mapache/wiki
| null |
10.1093/bioinformatics/btad028
| null |
q-bio.GN
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Summary: Mapping ancient DNA to a reference genome is challenging as it
involves numerous steps, is time-consuming and has to be repeated within a
study to assess the quality of extracts and libraries; as a result, the mapping
needs to be automatized to handle large amounts of data in a reproducible way.
We present mapache, a flexible, robust, and scalable pipeline to map, quantify
and impute ancient and present-day DNA in a reproducible way. Mapache is
implemented in the workflow manager Snakemake and is optimized for low-space
consumption, allowing to efficiently (re)map large data sets such as reference
panels and multiple extracts and libraries.
|
[
{
"created": "Sun, 28 Aug 2022 20:24:44 GMT",
"version": "v1"
}
] |
2023-03-09
|
[
[
"Neuenschwander",
"Samuel",
"",
"1 and 2"
],
[
"Dávalos",
"Diana I. Cruz",
"",
"1 and 3"
],
[
"Anchieri",
"Lucas",
"",
"1 and 3"
],
[
"da Mota",
"Bárbara Sousa",
"",
"1 and 3"
],
[
"Bozzi",
"Davide",
"",
"1\n and 3"
],
[
"Rubinacci",
"Simone",
"",
"1 and 3"
],
[
"Delaneau",
"Olivier",
"",
"1 and 3"
],
[
"Rasmussen",
"Simon",
"",
"1 and 3"
],
[
"Malaspinas",
"Anna-Sapfo",
"",
"1 and 3"
]
] |
Summary: Mapping ancient DNA to a reference genome is challenging as it involves numerous steps, is time-consuming and has to be repeated within a study to assess the quality of extracts and libraries; as a result, the mapping needs to be automatized to handle large amounts of data in a reproducible way. We present mapache, a flexible, robust, and scalable pipeline to map, quantify and impute ancient and present-day DNA in a reproducible way. Mapache is implemented in the workflow manager Snakemake and is optimized for low-space consumption, allowing to efficiently (re)map large data sets such as reference panels and multiple extracts and libraries.
|
1410.8499
|
Brian Williams Dr
|
Brian G. Williams and Christopher Dye
|
Dynamics and Control of Infections on Social Networks
|
9 pages
| null | null | null |
q-bio.QM q-bio.PE stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Random mixing in host populations has been a convenient simplifying
assumption in the study of epidemics, but neglects important differences in
contact rates within and between population groups. For HIV/AIDS, the
assumption of random mixing is inappropriate for epidemics that are
concentrated in groups of people at high risk, including female sex workers
(FSW) and their male clients (MCF), injection drug users (IDU) and men who have
sex with men (MSM). To find out who transmits infection to whom and how that
affects the spread and containment of infection remains a major empirical
challenge in the epidemiology of HIV/AIDS. Here we develop a technique, based
on the routine sampling of infection in linked population groups, which shows
how an Asian HIV/AIDS epidemic began in FSW, was propagated mainly by IDU, and
ultimately generated most cases among the female partners of MCF (FPM).
Calculation of the case reproduction numbers within and between groups, and for
the whole network, provides insights into control that cannot be deduced simply
from observations on the prevalence of infection. Specifically, the per capita
rate of HIV transmission was highest from FSW to MCF, and most HIV infections
occurred in FPM, but the number of infections in the whole network is best
reduced by interrupting transmission to and from IDU. This network analysis can
be used to guide HIV/AIDS interventions based on needle exchange, condom
distribution and antiretroviral therapy. The method requires only routine data
and could be applied to infections in other populations.
|
[
{
"created": "Tue, 21 Oct 2014 12:14:29 GMT",
"version": "v1"
},
{
"created": "Tue, 23 Feb 2016 13:47:34 GMT",
"version": "v2"
}
] |
2016-02-24
|
[
[
"Williams",
"Brian G.",
""
],
[
"Dye",
"Christopher",
""
]
] |
Random mixing in host populations has been a convenient simplifying assumption in the study of epidemics, but neglects important differences in contact rates within and between population groups. For HIV/AIDS, the assumption of random mixing is inappropriate for epidemics that are concentrated in groups of people at high risk, including female sex workers (FSW) and their male clients (MCF), injection drug users (IDU) and men who have sex with men (MSM). To find out who transmits infection to whom and how that affects the spread and containment of infection remains a major empirical challenge in the epidemiology of HIV/AIDS. Here we develop a technique, based on the routine sampling of infection in linked population groups, which shows how an Asian HIV/AIDS epidemic began in FSW, was propagated mainly by IDU, and ultimately generated most cases among the female partners of MCF (FPM). Calculation of the case reproduction numbers within and between groups, and for the whole network, provides insights into control that cannot be deduced simply from observations on the prevalence of infection. Specifically, the per capita rate of HIV transmission was highest from FSW to MCF, and most HIV infections occurred in FPM, but the number of infections in the whole network is best reduced by interrupting transmission to and from IDU. This network analysis can be used to guide HIV/AIDS interventions based on needle exchange, condom distribution and antiretroviral therapy. The method requires only routine data and could be applied to infections in other populations.
|
1211.7022
|
Preetish K L
|
Milind M. Rao and K.L. Preetish
|
Stability and Hopf Bifurcation Analysis of the Delay Logistic Equation
|
12 pages
| null | null | null |
q-bio.PE math.CA nlin.CD
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Logistic functions are good models of biological population growth. They are
also popular in marketing in modelling demand-supply curves and in a different
context, to chart the sales of new products over time. Delays being inherent in
any biological system, we seek to analyse the effect of delays on the growth of
populations governed by the logistic equation. In this paper, the local
stability analysis, rate of convergence and local bifurcation analysis of the
logistic equation with one and two delays is carried out and it can be extended
to a system with multiple delays. Since fluctuating populations are susceptible
to extinction due to sudden and unforeseen environmental disturbances, a
knowledge of the conditions in which the population density is fluctuating or
stable is of great interest in planning and designing control as well as
management strategies.
|
[
{
"created": "Thu, 29 Nov 2012 19:03:18 GMT",
"version": "v1"
}
] |
2012-11-30
|
[
[
"Rao",
"Milind M.",
""
],
[
"Preetish",
"K. L.",
""
]
] |
Logistic functions are good models of biological population growth. They are also popular in marketing in modelling demand-supply curves and in a different context, to chart the sales of new products over time. Delays being inherent in any biological system, we seek to analyse the effect of delays on the growth of populations governed by the logistic equation. In this paper, the local stability analysis, rate of convergence and local bifurcation analysis of the logistic equation with one and two delays is carried out and it can be extended to a system with multiple delays. Since fluctuating populations are susceptible to extinction due to sudden and unforeseen environmental disturbances, a knowledge of the conditions in which the population density is fluctuating or stable is of great interest in planning and designing control as well as management strategies.
|
2011.10494
|
Neil Sheeley Jr.
|
Neil R. Sheeley Jr
|
A Mathematical Model For the Spread of a Virus
|
86 pages, 44 figures
| null | null | null |
q-bio.PE
|
http://creativecommons.org/licenses/by/4.0/
|
This paper describes a mathematical model for the spread of a virus through
an isolated population of a given size. The model uses three, color-coded
components, called molecules (red for infected and still contagious; green for
infected, but no longer contagious; and blue for uninfected). In retrospect,
the model turns out to be a digital analogue for the well-known SIR model of
Kermac and McKendrick (1927). In our RGB model, the number of accumulated
infections goes through three phases, beginning at a very low level, then
changing to a transition ramp of rapid growth, and ending in a plateau of final
values. Consequently, the differential change or growth rate begins at 0, rises
to a peak corresponding to the maximum slope of the transition ramp, and then
falls back to 0. The properties of these time variations, including the slope,
duration, and height of the transition ramp, and the width and height of the
infection rate, depend on a single parameter - the time that a red molecule is
contagious divided by the average time between collisions of the molecules.
Various temporal milestones, including the starting time of the transition
ramp, the time that the accumulating number of infections obtains its maximum
slope, and the location of the peak of the infection rate depend on the size of
the population in addition to the contagious lifetime ratio. Explicit formulas
for these quantities are derived and summarized. Finally, Appendix E has been
added to describe the effect of vaccinations.
|
[
{
"created": "Fri, 20 Nov 2020 16:39:49 GMT",
"version": "v1"
},
{
"created": "Fri, 28 May 2021 21:10:31 GMT",
"version": "v2"
}
] |
2021-06-01
|
[
[
"Sheeley",
"Neil R.",
"Jr"
]
] |
This paper describes a mathematical model for the spread of a virus through an isolated population of a given size. The model uses three, color-coded components, called molecules (red for infected and still contagious; green for infected, but no longer contagious; and blue for uninfected). In retrospect, the model turns out to be a digital analogue for the well-known SIR model of Kermac and McKendrick (1927). In our RGB model, the number of accumulated infections goes through three phases, beginning at a very low level, then changing to a transition ramp of rapid growth, and ending in a plateau of final values. Consequently, the differential change or growth rate begins at 0, rises to a peak corresponding to the maximum slope of the transition ramp, and then falls back to 0. The properties of these time variations, including the slope, duration, and height of the transition ramp, and the width and height of the infection rate, depend on a single parameter - the time that a red molecule is contagious divided by the average time between collisions of the molecules. Various temporal milestones, including the starting time of the transition ramp, the time that the accumulating number of infections obtains its maximum slope, and the location of the peak of the infection rate depend on the size of the population in addition to the contagious lifetime ratio. Explicit formulas for these quantities are derived and summarized. Finally, Appendix E has been added to describe the effect of vaccinations.
|
2101.10215
|
Yakup Kutlu
|
Enver Kaan Alpturk, Yakup Kutlu
|
Analysis of Relation between Motor Activity and Imaginary EEG Records
|
6 pages, 4 figures, Journal of Artificial Intellicence with
Application
|
Journal of Artificial Intellicence with Application, 2020
| null | null |
q-bio.NC cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Electroencephalography (EEG) signals signals are often used to learn about
brain structure and to learn what thinking. EEG signals can be easily affected
by external factors. For this reason, they should be applied various
pre-process during their analysis. In this study, it is used the EEG signals
received from 109 subjects when opening and closing their right or left fists
and performing hand and foot movements and imagining the same movements. The
relationship between motor activities and imaginary of that motor activities
were investigated. Algorithms with high performance rates have been used for
feature extraction , selection and classification using the nearest neighbour
algorithm.
|
[
{
"created": "Thu, 21 Jan 2021 05:02:05 GMT",
"version": "v1"
}
] |
2021-01-26
|
[
[
"Alpturk",
"Enver Kaan",
""
],
[
"Kutlu",
"Yakup",
""
]
] |
Electroencephalography (EEG) signals signals are often used to learn about brain structure and to learn what thinking. EEG signals can be easily affected by external factors. For this reason, they should be applied various pre-process during their analysis. In this study, it is used the EEG signals received from 109 subjects when opening and closing their right or left fists and performing hand and foot movements and imagining the same movements. The relationship between motor activities and imaginary of that motor activities were investigated. Algorithms with high performance rates have been used for feature extraction , selection and classification using the nearest neighbour algorithm.
|
2402.06992
|
Raja Marjieh
|
Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Harin Lee, Thomas L.
Griffiths, Nori Jacoby
|
A Rational Analysis of the Speech-to-Song Illusion
|
7 pages, 5 figures
| null | null | null |
q-bio.NC cs.AI cs.CL stat.AP
|
http://creativecommons.org/licenses/by/4.0/
|
The speech-to-song illusion is a robust psychological phenomenon whereby a
spoken sentence sounds increasingly more musical as it is repeated. Despite
decades of research, a complete formal account of this transformation is still
lacking, and some of its nuanced characteristics, namely, that certain phrases
appear to transform while others do not, is not well understood. Here we
provide a formal account of this phenomenon, by recasting it as a statistical
inference whereby a rational agent attempts to decide whether a sequence of
utterances is more likely to have been produced in a song or speech. Using this
approach and analyzing song and speech corpora, we further introduce a novel
prose-to-lyrics illusion that is purely text-based. In this illusion, simply
duplicating written sentences makes them appear more like song lyrics. We
provide robust evidence for this new illusion in both human participants and
large language models.
|
[
{
"created": "Sat, 10 Feb 2024 16:54:28 GMT",
"version": "v1"
}
] |
2024-02-13
|
[
[
"Marjieh",
"Raja",
""
],
[
"van Rijn",
"Pol",
""
],
[
"Sucholutsky",
"Ilia",
""
],
[
"Lee",
"Harin",
""
],
[
"Griffiths",
"Thomas L.",
""
],
[
"Jacoby",
"Nori",
""
]
] |
The speech-to-song illusion is a robust psychological phenomenon whereby a spoken sentence sounds increasingly more musical as it is repeated. Despite decades of research, a complete formal account of this transformation is still lacking, and some of its nuanced characteristics, namely, that certain phrases appear to transform while others do not, is not well understood. Here we provide a formal account of this phenomenon, by recasting it as a statistical inference whereby a rational agent attempts to decide whether a sequence of utterances is more likely to have been produced in a song or speech. Using this approach and analyzing song and speech corpora, we further introduce a novel prose-to-lyrics illusion that is purely text-based. In this illusion, simply duplicating written sentences makes them appear more like song lyrics. We provide robust evidence for this new illusion in both human participants and large language models.
|
2009.01894
|
Nitin Kamra
|
Nitin Kamra, Yizhou Zhang, Sirisha Rambhatla, Chuizheng Meng, Yan Liu
|
PolSIRD: Modeling Epidemic Spread under Intervention Policies
|
Modeling the spread of the first wave of Covid-19 in the United
States
|
Journal of Healthcare Informatics Research (2021)
|
10.1007/s41666-021-00099-3
| null |
q-bio.PE physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Epidemic spread in a population is traditionally modeled via
compartmentalized models which represent the free evolution of disease in
absence of any intervention policies. In addition, these models assume full
observability of disease cases and do not account for under-reporting. We
present a mathematical model, namely PolSIRD, which accounts for the
under-reporting by introducing an observation mechanism. It also captures the
effects of intervention policies on the disease spread parameters by leveraging
intervention policy data along with the reported disease cases. Furthermore, we
allow our recurrent model to learn the initial hidden state of all compartments
end-to-end along with other parameters via gradient-based training. We apply
our model to the spread of the recent global outbreak of COVID-19 in the United
States, where our model outperforms the methods employed by the CDC in
predicting the spread. We also provide counterfactual simulations from our
model to analyze the effect of lifting the intervention policies prematurely
and our model correctly predicts the second wave of the epidemic.
|
[
{
"created": "Thu, 3 Sep 2020 19:26:02 GMT",
"version": "v1"
},
{
"created": "Wed, 12 May 2021 08:13:43 GMT",
"version": "v2"
}
] |
2021-06-16
|
[
[
"Kamra",
"Nitin",
""
],
[
"Zhang",
"Yizhou",
""
],
[
"Rambhatla",
"Sirisha",
""
],
[
"Meng",
"Chuizheng",
""
],
[
"Liu",
"Yan",
""
]
] |
Epidemic spread in a population is traditionally modeled via compartmentalized models which represent the free evolution of disease in absence of any intervention policies. In addition, these models assume full observability of disease cases and do not account for under-reporting. We present a mathematical model, namely PolSIRD, which accounts for the under-reporting by introducing an observation mechanism. It also captures the effects of intervention policies on the disease spread parameters by leveraging intervention policy data along with the reported disease cases. Furthermore, we allow our recurrent model to learn the initial hidden state of all compartments end-to-end along with other parameters via gradient-based training. We apply our model to the spread of the recent global outbreak of COVID-19 in the United States, where our model outperforms the methods employed by the CDC in predicting the spread. We also provide counterfactual simulations from our model to analyze the effect of lifting the intervention policies prematurely and our model correctly predicts the second wave of the epidemic.
|
2402.12188
|
David Clark
|
David G. Clark, Manuel Beiran
|
Structure of activity in multiregion recurrent neural networks
|
18 pages, 10 figures; updated author info
| null | null | null |
q-bio.NC cond-mat.dis-nn cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Neural circuits are composed of multiple regions, each with rich dynamics and
engaging in communication with other regions. The combination of local,
within-region dynamics and global, network-level dynamics is thought to provide
computational flexibility. However, the nature of such multiregion dynamics and
the underlying synaptic connectivity patterns remain poorly understood. Here,
we study the dynamics of recurrent neural networks with multiple interconnected
regions. Within each region, neurons have a combination of random and
structured recurrent connections. Motivated by experimental evidence of
communication subspaces between cortical areas, these networks have low-rank
connectivity between regions, enabling selective routing of activity. These
networks exhibit two interacting forms of dynamics: high-dimensional
fluctuations within regions and low-dimensional signal transmission between
regions. To characterize this interaction, we develop a dynamical mean-field
theory to analyze such networks in the limit where each region contains
infinitely many neurons, with cross-region currents as key order parameters.
Regions can act as both generators and transmitters of activity, roles that we
show are in conflict. Specifically, taming the complexity of activity within a
region is necessary for it to route signals to and from other regions. Unlike
previous models of routing in neural circuits, which suppressed the activities
of neuronal groups to control signal flow, routing in our model is achieved by
exciting different high-dimensional activity patterns through a combination of
connectivity structure and nonlinear recurrent dynamics. This theory provides
insight into the interpretation of both multiregion neural data and trained
neural networks.
|
[
{
"created": "Mon, 19 Feb 2024 14:51:55 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Feb 2024 17:32:32 GMT",
"version": "v2"
}
] |
2024-02-21
|
[
[
"Clark",
"David G.",
""
],
[
"Beiran",
"Manuel",
""
]
] |
Neural circuits are composed of multiple regions, each with rich dynamics and engaging in communication with other regions. The combination of local, within-region dynamics and global, network-level dynamics is thought to provide computational flexibility. However, the nature of such multiregion dynamics and the underlying synaptic connectivity patterns remain poorly understood. Here, we study the dynamics of recurrent neural networks with multiple interconnected regions. Within each region, neurons have a combination of random and structured recurrent connections. Motivated by experimental evidence of communication subspaces between cortical areas, these networks have low-rank connectivity between regions, enabling selective routing of activity. These networks exhibit two interacting forms of dynamics: high-dimensional fluctuations within regions and low-dimensional signal transmission between regions. To characterize this interaction, we develop a dynamical mean-field theory to analyze such networks in the limit where each region contains infinitely many neurons, with cross-region currents as key order parameters. Regions can act as both generators and transmitters of activity, roles that we show are in conflict. Specifically, taming the complexity of activity within a region is necessary for it to route signals to and from other regions. Unlike previous models of routing in neural circuits, which suppressed the activities of neuronal groups to control signal flow, routing in our model is achieved by exciting different high-dimensional activity patterns through a combination of connectivity structure and nonlinear recurrent dynamics. This theory provides insight into the interpretation of both multiregion neural data and trained neural networks.
|
1601.04110
|
I-Lin Ho
|
I Lin Ho, Arash Moshkforoush, Kwangseok Hong, Gerald A. Meininger,
Michael A. Hill, Nikolaos M. Tsoukias, Watson Kuo
|
Inherent rhythm of smooth muscle cells in rat mesenteric arterioles: an
eigensystem formulation
|
56 pages, 18 figures
|
Phys. Rev. E 93, 042415 (2016)
|
10.1103/PhysRevE.93.042415
| null |
q-bio.CB physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
On the basis of experimental data and mathematical equations in the
literature, we remodel the ionic dynamics of smooth muscle cells (SMCs) as an
eigensystem formulation, which is valid for investigating finite variations of
variables from the equilibrium like in common experimental operations. This
algorithm provides an alternate viewpoint from frequency-domain analysis and
enables one to probe functionalities of SMC's rhythm by means of a
resonance-related mechanism. Numerical results show three types of calcium
oscillations of SMCs in mesenteric arterioles: spontaneous calcium oscillation,
agonist-dependent calcium oscillation, and agonist-dependent calcium spike. For
simple single and double SMCs, we demonstrate properties of synchronization
among complex signals related to calcium oscillations, and show different
correlation relations between calcium and voltage signals for various
synchronization and resonance conditions. For practical cell clusters, our
analyses indicate that the rhythm of SMCs could (1) benefit enhancements of
signal communications among remote cells, (2) respond to a significant calcium
peaking against transient stimulations for triggering globally-oscillating
modes, and (3) characterize the globally-oscillating modes via frog-leap
(non-molecular-diffusion) calcium waves across inhomogeneous SMCs.
|
[
{
"created": "Sat, 16 Jan 2016 01:36:53 GMT",
"version": "v1"
},
{
"created": "Sat, 19 Mar 2016 20:24:08 GMT",
"version": "v2"
}
] |
2021-06-08
|
[
[
"Ho",
"I Lin",
""
],
[
"Moshkforoush",
"Arash",
""
],
[
"Hong",
"Kwangseok",
""
],
[
"Meininger",
"Gerald A.",
""
],
[
"Hill",
"Michael A.",
""
],
[
"Tsoukias",
"Nikolaos M.",
""
],
[
"Kuo",
"Watson",
""
]
] |
On the basis of experimental data and mathematical equations in the literature, we remodel the ionic dynamics of smooth muscle cells (SMCs) as an eigensystem formulation, which is valid for investigating finite variations of variables from the equilibrium like in common experimental operations. This algorithm provides an alternate viewpoint from frequency-domain analysis and enables one to probe functionalities of SMC's rhythm by means of a resonance-related mechanism. Numerical results show three types of calcium oscillations of SMCs in mesenteric arterioles: spontaneous calcium oscillation, agonist-dependent calcium oscillation, and agonist-dependent calcium spike. For simple single and double SMCs, we demonstrate properties of synchronization among complex signals related to calcium oscillations, and show different correlation relations between calcium and voltage signals for various synchronization and resonance conditions. For practical cell clusters, our analyses indicate that the rhythm of SMCs could (1) benefit enhancements of signal communications among remote cells, (2) respond to a significant calcium peaking against transient stimulations for triggering globally-oscillating modes, and (3) characterize the globally-oscillating modes via frog-leap (non-molecular-diffusion) calcium waves across inhomogeneous SMCs.
|
1801.03953
|
Mike Steel Prof.
|
Mike Steel and Wim Hordijk
|
Tractable models of self-sustaining autocatalytic networks
|
19 pages, 7 figures
| null | null | null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Self-sustaining autocatalytic networks play a central role in living systems,
from metabolism at the origin of life, simple RNA networks, and the modern
cell, to ecology and cognition. A collectively autocatalytic network that can
be sustained from an ambient food set is also referred to more formally as a
`Reflexively Autocatalytic F-generated' (RAF) set. In this paper, we first
investigate a simplified setting for studying RAFs, which are nevertheless
relevant to real biochemistry and allows for a more exact mathematical analysis
based on graph-theoretic concepts. This, in turn, allows for the development of
efficient (polynomial-time) algorithms for questions that are computationally
NP-hard in the general RAF setting. We then show how this simplified setting
for RAF systems leads naturally to a more general notion of RAFs that are
`generative' (they can be built up from simpler RAFs) and for which efficient
algorithms carry over to this more general setting. Finally, we show how
classical RAF theory can be extended to deal with ensembles of catalysts as
well as the assignment of rates to reactions according to which catalysts (or
combinations of catalysts) are available.
|
[
{
"created": "Thu, 11 Jan 2018 19:02:09 GMT",
"version": "v1"
},
{
"created": "Tue, 27 Mar 2018 22:55:52 GMT",
"version": "v2"
}
] |
2018-03-29
|
[
[
"Steel",
"Mike",
""
],
[
"Hordijk",
"Wim",
""
]
] |
Self-sustaining autocatalytic networks play a central role in living systems, from metabolism at the origin of life, simple RNA networks, and the modern cell, to ecology and cognition. A collectively autocatalytic network that can be sustained from an ambient food set is also referred to more formally as a `Reflexively Autocatalytic F-generated' (RAF) set. In this paper, we first investigate a simplified setting for studying RAFs, which are nevertheless relevant to real biochemistry and allows for a more exact mathematical analysis based on graph-theoretic concepts. This, in turn, allows for the development of efficient (polynomial-time) algorithms for questions that are computationally NP-hard in the general RAF setting. We then show how this simplified setting for RAF systems leads naturally to a more general notion of RAFs that are `generative' (they can be built up from simpler RAFs) and for which efficient algorithms carry over to this more general setting. Finally, we show how classical RAF theory can be extended to deal with ensembles of catalysts as well as the assignment of rates to reactions according to which catalysts (or combinations of catalysts) are available.
|
0902.4640
|
Gabriel Cardona
|
Gabriel Cardona, Merce Llabres, Francesc Rossello, Gabriel Valiente
|
The comparison of tree-sibling time consistent phylogenetic networks is
graph isomorphism-complete
|
10 pages, 3 figures
| null | null | null |
q-bio.PE cs.DM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In a previous work, we gave a metric on the class of semibinary tree-sibling
time consistent phylogenetic networks that is computable in polynomial time; in
particular, the problem of deciding if two networks of this kind are isomorphic
is in P. In this paper, we show that if we remove the semibinarity condition
above, then the problem becomes much harder. More precisely, we proof that the
isomorphism problem for generic tree-sibling time consistent phylogenetic
networks is polynomially equivalent to the graph isomorphism problem. Since the
latter is believed to be neither in P nor NP-complete, the chances are that it
is impossible to define a metric on the class of all tree-sibling time
consistent phylogenetic networks that can be computed in polynomial time.
|
[
{
"created": "Thu, 26 Feb 2009 17:30:07 GMT",
"version": "v1"
}
] |
2009-02-27
|
[
[
"Cardona",
"Gabriel",
""
],
[
"Llabres",
"Merce",
""
],
[
"Rossello",
"Francesc",
""
],
[
"Valiente",
"Gabriel",
""
]
] |
In a previous work, we gave a metric on the class of semibinary tree-sibling time consistent phylogenetic networks that is computable in polynomial time; in particular, the problem of deciding if two networks of this kind are isomorphic is in P. In this paper, we show that if we remove the semibinarity condition above, then the problem becomes much harder. More precisely, we proof that the isomorphism problem for generic tree-sibling time consistent phylogenetic networks is polynomially equivalent to the graph isomorphism problem. Since the latter is believed to be neither in P nor NP-complete, the chances are that it is impossible to define a metric on the class of all tree-sibling time consistent phylogenetic networks that can be computed in polynomial time.
|
1908.05214
|
Michael Holst
|
L.M. Stolerman, M. Getz, S.G. Llewellyn Smith, M. Holst, P. Rangamani
|
Stability Analysis of a Bulk-Surface Reaction Model for Membrane-Protein
Clustering
|
30 pages, 13 figures
| null | null | null |
q-bio.SC math.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Protein aggregation on the plasma membrane (PM) is of critical importance to
many cellular processes such as cell adhesion, endocytosis, fibrillar
conformation, and vesicle transport. Lateral diffusion of protein aggregates or
clusters on the surface of the PM plays an important role in governing their
heterogeneous surface distribution. However, the stability behavior of the
surface distribution of protein aggregates remains poorly understood.
Therefore, understanding the spatial patterns that can emerge on the PM solely
through protein-protein interaction, lateral diffusion, and feedback is an
important step towards a complete description of the mechanisms behind protein
clustering on the cell surface. In this work, we investigate the pattern
formation of a reaction-diffusion model that describes the dynamics of a system
of ligand-receptor complexes. The purely diffusive ligand in the cytosol can
bind receptors in the PM, and the resultant ligand-receptor complexes not only
diffuse laterally but can also form clusters resulting in different oligomers.
Finally, the largest oligomers recruit ligands from the cytosol in a positive
feedback. From a methodological viewpoint, we provide theoretical estimates for
diffusion-driven instabilities of the protein aggregates based on the Turing
mechanism. Our main result is a threshold phenomenon, in which a sufficiently
high recruitment of ligands promotes the input of new monomeric components and
consequently drives the formation of a single-patch spatially heterogeneous
steady-state.
|
[
{
"created": "Wed, 14 Aug 2019 16:41:41 GMT",
"version": "v1"
}
] |
2019-08-15
|
[
[
"Stolerman",
"L. M.",
""
],
[
"Getz",
"M.",
""
],
[
"Smith",
"S. G. Llewellyn",
""
],
[
"Holst",
"M.",
""
],
[
"Rangamani",
"P.",
""
]
] |
Protein aggregation on the plasma membrane (PM) is of critical importance to many cellular processes such as cell adhesion, endocytosis, fibrillar conformation, and vesicle transport. Lateral diffusion of protein aggregates or clusters on the surface of the PM plays an important role in governing their heterogeneous surface distribution. However, the stability behavior of the surface distribution of protein aggregates remains poorly understood. Therefore, understanding the spatial patterns that can emerge on the PM solely through protein-protein interaction, lateral diffusion, and feedback is an important step towards a complete description of the mechanisms behind protein clustering on the cell surface. In this work, we investigate the pattern formation of a reaction-diffusion model that describes the dynamics of a system of ligand-receptor complexes. The purely diffusive ligand in the cytosol can bind receptors in the PM, and the resultant ligand-receptor complexes not only diffuse laterally but can also form clusters resulting in different oligomers. Finally, the largest oligomers recruit ligands from the cytosol in a positive feedback. From a methodological viewpoint, we provide theoretical estimates for diffusion-driven instabilities of the protein aggregates based on the Turing mechanism. Our main result is a threshold phenomenon, in which a sufficiently high recruitment of ligands promotes the input of new monomeric components and consequently drives the formation of a single-patch spatially heterogeneous steady-state.
|
q-bio/0401037
|
Alan McKane
|
Christopher Quince, Paul Higgs and Alan McKane
|
Deleting species from model food webs
|
30 pages, 9 figures
| null | null | null |
q-bio.PE cond-mat.stat-mech
| null |
We use food webs generated by a model to investigate the effects of deleting
species on other species in the web and on the web as a whole. The model
incorporates a realistic population dynamics, adaptive foragers and other
features which allow for the construction of model webs which resemble
empirical food webs. A large number of simulations were carried out to produce
a substantial number of model webs on which deletion experiments could be
performed. We deleted each species in four hundred distinct model webs and
determined, on average, how many species were eliminated from the web as a
result. Typically only a small number of species became extinct; in no instance
was the web close to collapse. Next, we examined how the the probability of
extinction of a species depended on its relationship with the deleted species.
This involved the exploration of the concept of indirect predator and prey
species and the extent that the probability of extinction depended on the
trophic level of the two species. The effect of deletions on the web itself was
studied by searching for keystone species, whose removal caused a major
restructuring of the community, and also by looking at the correlation between
a number of food web properties (number of species, linkage density, fraction
of omnivores, degree of cycling and redundancy) and the stability of the web to
deletions. With the exception of redundancy, we found little or no correlation.
In particular, we found no evidence that complexity in terms of increased
species number or links per species is destabilising.
|
[
{
"created": "Tue, 27 Jan 2004 12:22:21 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Quince",
"Christopher",
""
],
[
"Higgs",
"Paul",
""
],
[
"McKane",
"Alan",
""
]
] |
We use food webs generated by a model to investigate the effects of deleting species on other species in the web and on the web as a whole. The model incorporates a realistic population dynamics, adaptive foragers and other features which allow for the construction of model webs which resemble empirical food webs. A large number of simulations were carried out to produce a substantial number of model webs on which deletion experiments could be performed. We deleted each species in four hundred distinct model webs and determined, on average, how many species were eliminated from the web as a result. Typically only a small number of species became extinct; in no instance was the web close to collapse. Next, we examined how the the probability of extinction of a species depended on its relationship with the deleted species. This involved the exploration of the concept of indirect predator and prey species and the extent that the probability of extinction depended on the trophic level of the two species. The effect of deletions on the web itself was studied by searching for keystone species, whose removal caused a major restructuring of the community, and also by looking at the correlation between a number of food web properties (number of species, linkage density, fraction of omnivores, degree of cycling and redundancy) and the stability of the web to deletions. With the exception of redundancy, we found little or no correlation. In particular, we found no evidence that complexity in terms of increased species number or links per species is destabilising.
|
1405.0044
|
Benjamin Dunn
|
Benjamin Dunn, Maria M{\o}rreaunet, Yasser Roudi
|
Correlations and functional connections in a population of grid cells
|
Accepted for publication in PLoS Computational Biology
| null |
10.1371/journal.pcbi.1004052
| null |
q-bio.NC q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study the statistics of spike trains of simultaneously recorded grid cells
in freely behaving rats. We evaluate pairwise correlations between these cells
and, using a generalized linear model (kinetic Ising model), study their
functional connectivity. Even when we account for the covariations in firing
rates due to overlapping fields, both the pairwise correlations and functional
connections decay as a function of the shortest distance between the vertices
of the spatial firing pattern of pairs of grid cells, i.e. their phase
difference. The functional connectivity takes positive values between cells
with nearby phases and approaches zero or negative values for larger phase
differences. We also find similar results when, in addition to correlations due
to overlapping fields, we account for correlations due to theta oscillations
and head directional inputs. The inferred connections between neurons can be
both negative and positive regardless of whether the cells share common spatial
firing characteristics, that is, whether they belong to the same modules, or
not. The mean strength of these inferred connections is close to zero, but the
strongest inferred connections are found between cells of the same module.
Taken together, our results suggest that grid cells in the same module do
indeed form a local network of interconnected neurons with a functional
connectivity that supports a role for attractor dynamics in the generation of
the grid pattern.
|
[
{
"created": "Wed, 30 Apr 2014 22:05:47 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Oct 2014 11:27:25 GMT",
"version": "v2"
}
] |
2015-06-19
|
[
[
"Dunn",
"Benjamin",
""
],
[
"Mørreaunet",
"Maria",
""
],
[
"Roudi",
"Yasser",
""
]
] |
We study the statistics of spike trains of simultaneously recorded grid cells in freely behaving rats. We evaluate pairwise correlations between these cells and, using a generalized linear model (kinetic Ising model), study their functional connectivity. Even when we account for the covariations in firing rates due to overlapping fields, both the pairwise correlations and functional connections decay as a function of the shortest distance between the vertices of the spatial firing pattern of pairs of grid cells, i.e. their phase difference. The functional connectivity takes positive values between cells with nearby phases and approaches zero or negative values for larger phase differences. We also find similar results when, in addition to correlations due to overlapping fields, we account for correlations due to theta oscillations and head directional inputs. The inferred connections between neurons can be both negative and positive regardless of whether the cells share common spatial firing characteristics, that is, whether they belong to the same modules, or not. The mean strength of these inferred connections is close to zero, but the strongest inferred connections are found between cells of the same module. Taken together, our results suggest that grid cells in the same module do indeed form a local network of interconnected neurons with a functional connectivity that supports a role for attractor dynamics in the generation of the grid pattern.
|
1611.03191
|
Hon-Cheong So
|
Hon-Cheong So, Carlos Kwan-Long Chau, Fu-Kiu Ao, Cheuk-Hei Mo,
Pak-Chung Sham
|
Exploring shared genetic bases and causal relationships of schizophrenia
and bipolar disorder with 28 cardiovascular and metabolic traits
| null | null | null | null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cardiovascular diseases (CVD) represent a major health issue in patients with
schizophrneia (SCZ) and bipolar disorder (BD), but the exact nature of
cardiometabolic (CM) abnormalities involved and the underlying mechanisms
remain unclear. Using polygenic risk scores (PRS) and LD score regression, we
investigated the shared genetic bases of SCZ and BD with a panel of 28
cardiometabolic traits. We performed Mendelian randomization (MR) to elucidate
casual relationships between the two groups of disorders. The analysis was
based on large-scale meta-analyses of genome-wide association studies (GWAS).
We also identified the potential shared genetic variants by a statistical
approach based on local true discovery rates, and inferred the pathways
involved. We found polygenic associations of SCZ with glucose metabolism
abnormalities, adverse adipokine profiles, increased wait-hip ratio and raised
visceral adiposity. However, BMI showed inverse genetic correlation and
polygenic link with SCZ. On the other hand, we observed polygenic associations
with an overall favorable CM profile in BD. MR analysis showed that SCZ may be
causally linked to raised triglyceride and that lower fasting glucose may be
linked to BD; otherwise MR did not reveal other significant causal
relationships in general. We also identified numerous SNPs and pathways shared
between SCZ/BD with cardiometabolic traits, some of which are related to
inflammation or the immune system. In conclusion, SCZ patients may be
genetically associated with several CM abnormalities independent of medication
side-effects, and proper surveillance and management of CV risk factors may be
required from the onset of the disease. On the other hand, CM abnormalities in
BD are more likely to be secondary.
|
[
{
"created": "Thu, 10 Nov 2016 05:47:23 GMT",
"version": "v1"
},
{
"created": "Sat, 7 Jan 2017 22:56:21 GMT",
"version": "v2"
},
{
"created": "Wed, 14 Jun 2017 04:01:15 GMT",
"version": "v3"
},
{
"created": "Fri, 24 Nov 2017 13:38:37 GMT",
"version": "v4"
},
{
"created": "Tue, 5 Dec 2017 13:41:51 GMT",
"version": "v5"
}
] |
2017-12-06
|
[
[
"So",
"Hon-Cheong",
""
],
[
"Chau",
"Carlos Kwan-Long",
""
],
[
"Ao",
"Fu-Kiu",
""
],
[
"Mo",
"Cheuk-Hei",
""
],
[
"Sham",
"Pak-Chung",
""
]
] |
Cardiovascular diseases (CVD) represent a major health issue in patients with schizophrneia (SCZ) and bipolar disorder (BD), but the exact nature of cardiometabolic (CM) abnormalities involved and the underlying mechanisms remain unclear. Using polygenic risk scores (PRS) and LD score regression, we investigated the shared genetic bases of SCZ and BD with a panel of 28 cardiometabolic traits. We performed Mendelian randomization (MR) to elucidate casual relationships between the two groups of disorders. The analysis was based on large-scale meta-analyses of genome-wide association studies (GWAS). We also identified the potential shared genetic variants by a statistical approach based on local true discovery rates, and inferred the pathways involved. We found polygenic associations of SCZ with glucose metabolism abnormalities, adverse adipokine profiles, increased wait-hip ratio and raised visceral adiposity. However, BMI showed inverse genetic correlation and polygenic link with SCZ. On the other hand, we observed polygenic associations with an overall favorable CM profile in BD. MR analysis showed that SCZ may be causally linked to raised triglyceride and that lower fasting glucose may be linked to BD; otherwise MR did not reveal other significant causal relationships in general. We also identified numerous SNPs and pathways shared between SCZ/BD with cardiometabolic traits, some of which are related to inflammation or the immune system. In conclusion, SCZ patients may be genetically associated with several CM abnormalities independent of medication side-effects, and proper surveillance and management of CV risk factors may be required from the onset of the disease. On the other hand, CM abnormalities in BD are more likely to be secondary.
|
1111.2992
|
Adnan Ali
|
Adnan Ali, Ell\'ak Somfai, Stefan Grosskinsky
|
Reproduction time statistics and segregation patterns in growing
populations
| null |
Phys. Rev. E 85(2), 021923 (2012)
|
10.1103/PhysRevE.85.021923
| null |
q-bio.PE cond-mat.stat-mech
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Pattern formation in microbial colonies of competing strains under purely
space-limited population growth has recently attracted considerable research
interest. We show that the reproduction time statistics of individuals has a
significant impact on the sectoring patterns. Generalizing the standard Eden
growth model, we introduce a simple one-parameter family of reproduction time
distributions indexed by the variation coefficient {\delta} \in [0, 1], which
includes deterministic ({\delta} = 0) and memoryless exponential distribution
({\delta} = 1) as extreme cases. We present convincing numerical evidence and
heuristic arguments that the generalized model is still in the KPZ universality
class, and the changes in patterns are due to changing prefactors in the
scaling relations, which we are able to predict quantitatively. At the example
of Saccharomyces cerevisiae, we show that our approach using the variation
coefficient also works for more realistic reproduction time distributions.
|
[
{
"created": "Sun, 13 Nov 2011 07:07:44 GMT",
"version": "v1"
},
{
"created": "Sun, 15 Apr 2012 21:14:49 GMT",
"version": "v2"
}
] |
2015-06-30
|
[
[
"Ali",
"Adnan",
""
],
[
"Somfai",
"Ellák",
""
],
[
"Grosskinsky",
"Stefan",
""
]
] |
Pattern formation in microbial colonies of competing strains under purely space-limited population growth has recently attracted considerable research interest. We show that the reproduction time statistics of individuals has a significant impact on the sectoring patterns. Generalizing the standard Eden growth model, we introduce a simple one-parameter family of reproduction time distributions indexed by the variation coefficient {\delta} \in [0, 1], which includes deterministic ({\delta} = 0) and memoryless exponential distribution ({\delta} = 1) as extreme cases. We present convincing numerical evidence and heuristic arguments that the generalized model is still in the KPZ universality class, and the changes in patterns are due to changing prefactors in the scaling relations, which we are able to predict quantitatively. At the example of Saccharomyces cerevisiae, we show that our approach using the variation coefficient also works for more realistic reproduction time distributions.
|
1212.2832
|
C. Titus Brown
|
Adina Chuang Howe, Janet Jansson, Stephanie A. Malfatti, Susannah G.
Tringe, James M. Tiedje, C. Titus Brown
|
Assembling large, complex environmental metagenomes
|
Includes supporting information
| null | null | null |
q-bio.GN
|
http://creativecommons.org/licenses/publicdomain/
|
The large volumes of sequencing data required to sample complex environments
deeply pose new challenges to sequence analysis approaches. De novo metagenomic
assembly effectively reduces the total amount of data to be analyzed but
requires significant computational resources. We apply two pre-assembly
filtering approaches, digital normalization and partitioning, to make large
metagenome assemblies more comput\ ationaly tractable. Using a human gut mock
community dataset, we demonstrate that these methods result in assemblies
nearly identical to assemblies from unprocessed data. We then assemble two
large soil metagenomes from matched Iowa corn and native prairie soils. The
predicted functional content and phylogenetic origin of the assembled contigs
indicate significant taxonomic differences despite similar function. The
assembly strategies presented are generic and can be extended to any
metagenome; full source code is freely available under a BSD license.
|
[
{
"created": "Wed, 12 Dec 2012 14:56:18 GMT",
"version": "v1"
},
{
"created": "Fri, 28 Dec 2012 20:34:13 GMT",
"version": "v2"
}
] |
2013-01-01
|
[
[
"Howe",
"Adina Chuang",
""
],
[
"Jansson",
"Janet",
""
],
[
"Malfatti",
"Stephanie A.",
""
],
[
"Tringe",
"Susannah G.",
""
],
[
"Tiedje",
"James M.",
""
],
[
"Brown",
"C. Titus",
""
]
] |
The large volumes of sequencing data required to sample complex environments deeply pose new challenges to sequence analysis approaches. De novo metagenomic assembly effectively reduces the total amount of data to be analyzed but requires significant computational resources. We apply two pre-assembly filtering approaches, digital normalization and partitioning, to make large metagenome assemblies more comput\ ationaly tractable. Using a human gut mock community dataset, we demonstrate that these methods result in assemblies nearly identical to assemblies from unprocessed data. We then assemble two large soil metagenomes from matched Iowa corn and native prairie soils. The predicted functional content and phylogenetic origin of the assembled contigs indicate significant taxonomic differences despite similar function. The assembly strategies presented are generic and can be extended to any metagenome; full source code is freely available under a BSD license.
|
2307.02502
|
Renato P. Dos Santos
|
Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos
|
Math Agents: Computational Infrastructure, Mathematical Embedding, and
Genomics
| null | null | null | null |
q-bio.OT cs.AI cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh entries to the "Moore's Law of Mathematics", using a
GPT-based workflow to convert equations from literature into LaTeX and Python
formats. While many digital equation representations exist, there's a lack of
automated large-scale evaluation tools. LLMs are pivotal as linguistic user
interfaces, providing natural language access for human-AI chat and formal
languages for large-scale AI-assisted computational infrastructure. Given the
infinite formal possibility spaces, Math Agents, which interact with math,
could potentially shift us from "big data" to "big math". Math, unlike the more
flexible natural language, has properties subject to proof, enabling its use
beyond traditional applications like high-validation math-certified icons for
AI alignment aims. This project aims to use Math Agents and mathematical
embeddings to address the ageing issue in information systems biology by
applying multiscalar physics mathematics to disease models and genomic data.
Generative AI with episodic memory could help analyse causal relations in
longitudinal health records, using SIR Precision Health models. Genomic data is
suggested for addressing the unsolved Alzheimer's disease problem.
|
[
{
"created": "Tue, 4 Jul 2023 20:16:32 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Swan",
"Melanie",
""
],
[
"Kido",
"Takashi",
""
],
[
"Roland",
"Eric",
""
],
[
"Santos",
"Renato P. dos",
""
]
] |
The advancement in generative AI could be boosted with more accessible mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in programming, algorithm discovery, and theorem proving, yet their genomics application is limited. This project introduces Math Agents and mathematical embedding as fresh entries to the "Moore's Law of Mathematics", using a GPT-based workflow to convert equations from literature into LaTeX and Python formats. While many digital equation representations exist, there's a lack of automated large-scale evaluation tools. LLMs are pivotal as linguistic user interfaces, providing natural language access for human-AI chat and formal languages for large-scale AI-assisted computational infrastructure. Given the infinite formal possibility spaces, Math Agents, which interact with math, could potentially shift us from "big data" to "big math". Math, unlike the more flexible natural language, has properties subject to proof, enabling its use beyond traditional applications like high-validation math-certified icons for AI alignment aims. This project aims to use Math Agents and mathematical embeddings to address the ageing issue in information systems biology by applying multiscalar physics mathematics to disease models and genomic data. Generative AI with episodic memory could help analyse causal relations in longitudinal health records, using SIR Precision Health models. Genomic data is suggested for addressing the unsolved Alzheimer's disease problem.
|
2105.08856
|
Rohan Williams
|
Elizabeth A. McDaniel, Sebastian Aljoscha Wahl, Shun'ichi Ishii, Ameet
Pinto, Ryan Ziels, Per H. Nielsen, Katherine D. McMahon, Rohan B.H. Williams
|
Prospects for Multi-omics in the Microbial Ecology of Water Engineering
|
Review article
| null | null | null |
q-bio.GN
|
http://creativecommons.org/licenses/by/4.0/
|
Advances in high-throughput sequencing technologies and bioinformatics
approaches over almost the last three decades have substantially increased our
ability to explore microorganisms and their functions-including those that have
yet to be cultivated in pure isolation. Genome-resolved metagenomic approaches
have enabled linking powerful functional predictions to specific taxonomical
groups with increasing fidelity. Additionally, whole community gene expression
surveys and metabolite profiling have permitted direct surveys of
community-scale functions in specific environmental settings. These advances
have allowed for a shift in microbiome science away from descriptive studies
and towards mechanistic and predictive frameworks for designing and harnessing
microbial communities for desired beneficial outcomes. Here, we review how
modern genome-resolved metagenomic approaches have been applied to a variety of
water engineering applications from lab-scale bioreactors to full-scale
systems. We describe integrated omics analysis across engineered water systems
and the foundations for pairing these insights with modeling approaches.
Lastly, we summarize emerging omics-based technologies that we believe will be
powerful tools for water engineering applications. Overall, we provide a
framework for microbial ecologists specializing in water engineering to apply
cutting-edge omics approaches to their research questions to achieve novel
functional insights. Successful adoption of predictive frameworks in engineered
water systems could enable more economically and environmentally sustainable
bioprocesses as demand for water and energy resources increases.
|
[
{
"created": "Tue, 18 May 2021 23:49:31 GMT",
"version": "v1"
}
] |
2021-05-20
|
[
[
"McDaniel",
"Elizabeth A.",
""
],
[
"Wahl",
"Sebastian Aljoscha",
""
],
[
"Ishii",
"Shun'ichi",
""
],
[
"Pinto",
"Ameet",
""
],
[
"Ziels",
"Ryan",
""
],
[
"Nielsen",
"Per H.",
""
],
[
"McMahon",
"Katherine D.",
""
],
[
"Williams",
"Rohan B. H.",
""
]
] |
Advances in high-throughput sequencing technologies and bioinformatics approaches over almost the last three decades have substantially increased our ability to explore microorganisms and their functions-including those that have yet to be cultivated in pure isolation. Genome-resolved metagenomic approaches have enabled linking powerful functional predictions to specific taxonomical groups with increasing fidelity. Additionally, whole community gene expression surveys and metabolite profiling have permitted direct surveys of community-scale functions in specific environmental settings. These advances have allowed for a shift in microbiome science away from descriptive studies and towards mechanistic and predictive frameworks for designing and harnessing microbial communities for desired beneficial outcomes. Here, we review how modern genome-resolved metagenomic approaches have been applied to a variety of water engineering applications from lab-scale bioreactors to full-scale systems. We describe integrated omics analysis across engineered water systems and the foundations for pairing these insights with modeling approaches. Lastly, we summarize emerging omics-based technologies that we believe will be powerful tools for water engineering applications. Overall, we provide a framework for microbial ecologists specializing in water engineering to apply cutting-edge omics approaches to their research questions to achieve novel functional insights. Successful adoption of predictive frameworks in engineered water systems could enable more economically and environmentally sustainable bioprocesses as demand for water and energy resources increases.
|
1202.6670
|
Yashar Ahmadian
|
Yashar Ahmadian, Daniel B. Rubin and Kenneth D. Miller
|
Analysis of the stabilized supralinear network
|
45 pages, 4 figures
|
Neural Computation 25, 1994-2037 (2013)
| null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study a rate-model neural network composed of excitatory and inhibitory
neurons in which neuronal input-output functions are power laws with a power
greater than 1, as observed in primary visual cortex. This supralinear
input-output function leads to supralinear summation of network responses to
multiple inputs for weak inputs. We show that for stronger inputs, which would
drive the excitatory subnetwork to instability, the network will dynamically
stabilize provided feedback inhibition is sufficiently strong. For a wide range
of network and stimulus parameters, this dynamic stabilization yields a
transition from supralinear to sublinear summation of network responses to
multiple inputs. We compare this to the dynamic stabilization in the "balanced
network", which yields only linear behavior. We more exhaustively analyze the
2-dimensional case of 1 excitatory and 1 inhibitory population. We show that in
this case dynamic stabilization will occur whenever the determinant of the
weight matrix is positive and the inhibitory time constant is sufficiently
small, and analyze the conditions for "supersaturation", or decrease of firing
rates with increasing stimulus contrast (which represents increasing input
firing rates). In work to be presented elsewhere, we have found that this
transition from supralinear to sublinear summation can explain a wide variety
of nonlinearities in cerebral cortical processing.
|
[
{
"created": "Wed, 29 Feb 2012 20:14:17 GMT",
"version": "v1"
},
{
"created": "Tue, 5 Jun 2012 23:13:47 GMT",
"version": "v2"
},
{
"created": "Thu, 6 Dec 2012 22:23:31 GMT",
"version": "v3"
},
{
"created": "Sun, 5 May 2013 19:02:44 GMT",
"version": "v4"
},
{
"created": "Tue, 28 May 2013 22:27:52 GMT",
"version": "v5"
},
{
"created": "Mon, 1 Jul 2013 20:48:29 GMT",
"version": "v6"
}
] |
2015-03-20
|
[
[
"Ahmadian",
"Yashar",
""
],
[
"Rubin",
"Daniel B.",
""
],
[
"Miller",
"Kenneth D.",
""
]
] |
We study a rate-model neural network composed of excitatory and inhibitory neurons in which neuronal input-output functions are power laws with a power greater than 1, as observed in primary visual cortex. This supralinear input-output function leads to supralinear summation of network responses to multiple inputs for weak inputs. We show that for stronger inputs, which would drive the excitatory subnetwork to instability, the network will dynamically stabilize provided feedback inhibition is sufficiently strong. For a wide range of network and stimulus parameters, this dynamic stabilization yields a transition from supralinear to sublinear summation of network responses to multiple inputs. We compare this to the dynamic stabilization in the "balanced network", which yields only linear behavior. We more exhaustively analyze the 2-dimensional case of 1 excitatory and 1 inhibitory population. We show that in this case dynamic stabilization will occur whenever the determinant of the weight matrix is positive and the inhibitory time constant is sufficiently small, and analyze the conditions for "supersaturation", or decrease of firing rates with increasing stimulus contrast (which represents increasing input firing rates). In work to be presented elsewhere, we have found that this transition from supralinear to sublinear summation can explain a wide variety of nonlinearities in cerebral cortical processing.
|
2402.16271
|
Ming-Jia Fu
|
Ming-Jia Fu
|
Rebuildable biochronometer: inferences and hypothesis on eukaryotic
timing system
|
29 pages, 9 figures
| null | null | null |
q-bio.BM q-bio.CB
|
http://creativecommons.org/licenses/by/4.0/
|
The biochronometers used to keep time in eukaryotes include short-period
biochronometer (SPB) and long-period biochronometer (LPB). Because the
circadian clock reflects the biological time rhythm of a day, it is considered
as SPB. Telomere shortening, which reflects the decreasing of telomere DNA
length of chromosomes with the increase of cell division times, can be used to
time the lifespan of organisms, so it is regarded as LPB. It is confirmed that
SPB and LPB exist in most eukaryotes, and it is speculated that SPB and LPB are
closely related. In this paper, based on existing studies, it is speculated
that SPB and LPB of most eukaryotes can be co-attenuated with cell division in
the process of aging. Due to the attenuated phenomenon of key components in the
biochronometers during the growth and development of organisms, the
biochronometers attenuate with the aging. Based on existing research results,
it is preliminarily determined that the biochronometers can be rebuilt in the
co-attenuated process. When the key components of biochronometers are reversed
and increased in the organism, it can lead to the reversal of biochronometers,
which further leads to the phenomenon of biological rejuvenation and makes the
organism younger. In addition, the rebuilding of biochronometers can also lead
to the acceleration of biochronometers and the shortening of the original
timing time of biochronometers, thus shortening the life span of organisms. The
rebuilding of biochronometers includes the reversal of biochronometers, the
truncation of biochronometers timing and Uncoordinated co-attenuation of
biochronometer and so on. The reversal of the biochronometers, which leads to
rejuvenation, can give us a whole new understanding of life expectancy to be
different from anti-aging.
|
[
{
"created": "Mon, 26 Feb 2024 03:15:13 GMT",
"version": "v1"
}
] |
2024-02-27
|
[
[
"Fu",
"Ming-Jia",
""
]
] |
The biochronometers used to keep time in eukaryotes include short-period biochronometer (SPB) and long-period biochronometer (LPB). Because the circadian clock reflects the biological time rhythm of a day, it is considered as SPB. Telomere shortening, which reflects the decreasing of telomere DNA length of chromosomes with the increase of cell division times, can be used to time the lifespan of organisms, so it is regarded as LPB. It is confirmed that SPB and LPB exist in most eukaryotes, and it is speculated that SPB and LPB are closely related. In this paper, based on existing studies, it is speculated that SPB and LPB of most eukaryotes can be co-attenuated with cell division in the process of aging. Due to the attenuated phenomenon of key components in the biochronometers during the growth and development of organisms, the biochronometers attenuate with the aging. Based on existing research results, it is preliminarily determined that the biochronometers can be rebuilt in the co-attenuated process. When the key components of biochronometers are reversed and increased in the organism, it can lead to the reversal of biochronometers, which further leads to the phenomenon of biological rejuvenation and makes the organism younger. In addition, the rebuilding of biochronometers can also lead to the acceleration of biochronometers and the shortening of the original timing time of biochronometers, thus shortening the life span of organisms. The rebuilding of biochronometers includes the reversal of biochronometers, the truncation of biochronometers timing and Uncoordinated co-attenuation of biochronometer and so on. The reversal of the biochronometers, which leads to rejuvenation, can give us a whole new understanding of life expectancy to be different from anti-aging.
|
2004.09478
|
Luca Magri
|
Luca Magri, Nguyen Anh Khoa Doan
|
First-principles machine learning modelling of COVID-19
|
39 pages, 53 figures
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The coronavirus disease 2019 (COVID-19) has changed the world since the World
Health Organization declared its outbreak on 30th January 2020, recognizing the
outbreak as a pandemic on 11th March 2020. As often said by politicians and
scientific advisors, the objective is "to flatten the curve", or "push the peak
down", or similar wording, of the virus spreading. Central to the official
advice are mathematical models and data, which provide estimates on the
evolution of the number of infected, recovered and deaths. The accuracy of the
models is improved day by day by inferring the contact, recovery, and death
rates from data (confirmed cases). A data-driven model trained with {\it both}
data {\it and} first principles is proposed. The model can quickly be
re-trained any time that new data becomes available. The method can be applied
to more detailed epidemic models with virtually no conceptual modification.
|
[
{
"created": "Mon, 20 Apr 2020 17:54:02 GMT",
"version": "v1"
}
] |
2020-04-21
|
[
[
"Magri",
"Luca",
""
],
[
"Doan",
"Nguyen Anh Khoa",
""
]
] |
The coronavirus disease 2019 (COVID-19) has changed the world since the World Health Organization declared its outbreak on 30th January 2020, recognizing the outbreak as a pandemic on 11th March 2020. As often said by politicians and scientific advisors, the objective is "to flatten the curve", or "push the peak down", or similar wording, of the virus spreading. Central to the official advice are mathematical models and data, which provide estimates on the evolution of the number of infected, recovered and deaths. The accuracy of the models is improved day by day by inferring the contact, recovery, and death rates from data (confirmed cases). A data-driven model trained with {\it both} data {\it and} first principles is proposed. The model can quickly be re-trained any time that new data becomes available. The method can be applied to more detailed epidemic models with virtually no conceptual modification.
|
2006.02100
|
Ju Lynn Ong
|
J.L. Ong, T.Y. Lau, S.A.A. Massar, Z.T. Chong, B.K.L. Ng, D. Koek, W.
Zhao, B.T.T. Yeo, K. Cheong and M.W.L. Chee
|
COVID-19 Related Mobility Reduction: Heterogenous Effects on Sleep and
Physical Activity Rhythms
|
30 pages, 3 main figures, 3 tables, 4 supplementary figures
| null | null | null |
q-bio.QM
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Mobility restrictions imposed to suppress coronavirus transmission can alter
physical activity (PA) and sleep patterns. Characterization of response
heterogeneity and their underlying reasons may assist in tailoring customized
interventions. We obtained wearable data covering baseline, incremental
movement restriction and lockdown periods from 1824 city-dwelling, working
adults aged 21 to 40 years, incorporating 206,381 nights of sleep and 334,038
days of PA. Four distinct rest activity rhythms (RARs) were identified using
k-means clustering of participants' temporally distributed step counts.
Hierarchical clustering of the proportion of time spent in each of these RAR
revealed 4 groups who expressed different mixtures of RAR profiles before and
during the lockdown. Substantial but asymmetric delays in bedtime and waketime
resulted in a 24 min increase in weekday sleep duration with no loss in sleep
efficiency. Resting heart rate declined 2 bpm. PA dropped an average of 38%. 4
groups with different compositions of RAR profiles were found. Three were
better able to maintain PA and weekday/weekend differentiation during lockdown.
The least active group comprising 51 percent of the sample, were younger and
predominantly singles. Habitually less active already, this group showed the
greatest reduction in PA during lockdown with little weekday/weekend
differences. Among different mobility restrictions, removal of habitual social
cues by lockdown had the largest effect on PA and sleep. Sleep and resting
heart rate unexpectedly improved. RAR evaluation uncovered heterogeneity of
responses to lockdown and can identify characteristics of persons at risk of
decline in health and wellbeing.
|
[
{
"created": "Wed, 3 Jun 2020 08:31:50 GMT",
"version": "v1"
},
{
"created": "Tue, 14 Jul 2020 04:50:32 GMT",
"version": "v2"
}
] |
2020-07-15
|
[
[
"Ong",
"J. L.",
""
],
[
"Lau",
"T. Y.",
""
],
[
"Massar",
"S. A. A.",
""
],
[
"Chong",
"Z. T.",
""
],
[
"Ng",
"B. K. L.",
""
],
[
"Koek",
"D.",
""
],
[
"Zhao",
"W.",
""
],
[
"Yeo",
"B. T. T.",
""
],
[
"Cheong",
"K.",
""
],
[
"Chee",
"M. W. L.",
""
]
] |
Mobility restrictions imposed to suppress coronavirus transmission can alter physical activity (PA) and sleep patterns. Characterization of response heterogeneity and their underlying reasons may assist in tailoring customized interventions. We obtained wearable data covering baseline, incremental movement restriction and lockdown periods from 1824 city-dwelling, working adults aged 21 to 40 years, incorporating 206,381 nights of sleep and 334,038 days of PA. Four distinct rest activity rhythms (RARs) were identified using k-means clustering of participants' temporally distributed step counts. Hierarchical clustering of the proportion of time spent in each of these RAR revealed 4 groups who expressed different mixtures of RAR profiles before and during the lockdown. Substantial but asymmetric delays in bedtime and waketime resulted in a 24 min increase in weekday sleep duration with no loss in sleep efficiency. Resting heart rate declined 2 bpm. PA dropped an average of 38%. 4 groups with different compositions of RAR profiles were found. Three were better able to maintain PA and weekday/weekend differentiation during lockdown. The least active group comprising 51 percent of the sample, were younger and predominantly singles. Habitually less active already, this group showed the greatest reduction in PA during lockdown with little weekday/weekend differences. Among different mobility restrictions, removal of habitual social cues by lockdown had the largest effect on PA and sleep. Sleep and resting heart rate unexpectedly improved. RAR evaluation uncovered heterogeneity of responses to lockdown and can identify characteristics of persons at risk of decline in health and wellbeing.
|
2211.08085
|
Tatiana Levanova
|
Nikita Barabash, Tatiana Levanova and Sergey Stasenko
|
Rhythmogenesis in the mean field model of the neuron-glial network
|
9 pages, 3 figures
| null |
10.1140/epjs/s11734-023-00778-9
| null |
q-bio.NC
|
http://creativecommons.org/licenses/by/4.0/
|
Despite the fact that the phenomenon of bursting activity is important for
functioning of living neural networks, the mechanisms of its origin are still
not clear. In this paper, we propose a new phenomenological model that can
explain the mechanisms of the formation of bursting activity based on
short-term synaptic plasticity, recurrent connections, and neuron-glial
interactions. We show that neuron-glial interactions can induce bursting
activity. The bifurcation scenarios of emergence of bursting activity are in
the focus of the paper. Proposed study is important for understanding of the
complex dynamics in neural networks.
|
[
{
"created": "Tue, 15 Nov 2022 12:11:43 GMT",
"version": "v1"
}
] |
2023-03-01
|
[
[
"Barabash",
"Nikita",
""
],
[
"Levanova",
"Tatiana",
""
],
[
"Stasenko",
"Sergey",
""
]
] |
Despite the fact that the phenomenon of bursting activity is important for functioning of living neural networks, the mechanisms of its origin are still not clear. In this paper, we propose a new phenomenological model that can explain the mechanisms of the formation of bursting activity based on short-term synaptic plasticity, recurrent connections, and neuron-glial interactions. We show that neuron-glial interactions can induce bursting activity. The bifurcation scenarios of emergence of bursting activity are in the focus of the paper. Proposed study is important for understanding of the complex dynamics in neural networks.
|
1003.2922
|
Jens Christian Claussen
|
Markus Sch\"utt and Jens Christian Claussen
|
Stabilization of biodiversity in the coevolutionary rock-paper-scissors
game on complex networks
| null | null | null | null |
q-bio.PE physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The dynamical mechanisms that can stabilize the coexistence of species (or
strategies) are of substantial interest for the maintenance of biodiversity and
in sociobehavioural dynamics. We investigate the mean extinction time in the
coevolutionary dynamics of three cyclically invading strategies for different
evolutionary processes on various classes of complex networks, including random
graphs, scale-free and small world networks. We find that scale-free and random
graphs lead to a strong stabilization of coexistence both for the Moran process
and the Local Update process. The stabilization is of an order of magnitude
stronger compared to a lattice topology, and is mainly caused by the degree
heterogeneity of the graph. However, evolutionary processes on graphs can be
defined in many variants, and we show that in a process using effective payoffs
the effect of the network topology can be completely reversed. Thus,
stabilization of coexistence depends on both network geometry and underlying
evolutionary process.
|
[
{
"created": "Mon, 15 Mar 2010 13:53:56 GMT",
"version": "v1"
}
] |
2010-03-16
|
[
[
"Schütt",
"Markus",
""
],
[
"Claussen",
"Jens Christian",
""
]
] |
The dynamical mechanisms that can stabilize the coexistence of species (or strategies) are of substantial interest for the maintenance of biodiversity and in sociobehavioural dynamics. We investigate the mean extinction time in the coevolutionary dynamics of three cyclically invading strategies for different evolutionary processes on various classes of complex networks, including random graphs, scale-free and small world networks. We find that scale-free and random graphs lead to a strong stabilization of coexistence both for the Moran process and the Local Update process. The stabilization is of an order of magnitude stronger compared to a lattice topology, and is mainly caused by the degree heterogeneity of the graph. However, evolutionary processes on graphs can be defined in many variants, and we show that in a process using effective payoffs the effect of the network topology can be completely reversed. Thus, stabilization of coexistence depends on both network geometry and underlying evolutionary process.
|
1908.02055
|
Ina Humpert
|
Ina Humpert, Danila Di Meo, Andreas W. P\"uschel, Jan-Frederik
Pietschmann
|
On the Role of Vesicle Transport in Neurite Growth: Modelling and
Experiments
| null | null | null | null |
q-bio.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The processes that determine the establishment of the complex morphology of
neurons during development are still poorly understood. We present experiments
that use live imaging to examine the role of vesicle transport and propose a
lattice-based model that shows symmetry breaking features similar to a neuron
during its polarization. In a otherwise symmetric situation our model predicts
that a difference in neurite length increases the growth potential of the
longer neurite indicating that vesicle transport can be regarded as a major
factor in neurite growth.
|
[
{
"created": "Tue, 6 Aug 2019 10:27:01 GMT",
"version": "v1"
}
] |
2019-08-07
|
[
[
"Humpert",
"Ina",
""
],
[
"Di Meo",
"Danila",
""
],
[
"Püschel",
"Andreas W.",
""
],
[
"Pietschmann",
"Jan-Frederik",
""
]
] |
The processes that determine the establishment of the complex morphology of neurons during development are still poorly understood. We present experiments that use live imaging to examine the role of vesicle transport and propose a lattice-based model that shows symmetry breaking features similar to a neuron during its polarization. In a otherwise symmetric situation our model predicts that a difference in neurite length increases the growth potential of the longer neurite indicating that vesicle transport can be regarded as a major factor in neurite growth.
|
2009.02513
|
Knut Heidemann
|
Prakhar Godara, Stephan Herminghaus, Knut M. Heidemann
|
A control theory approach to optimal pandemic mitigation
|
Error in Fig. 6 corrected (red curve for infinite immune response was
flawed)
| null |
10.1371/journal.pone.0247445
| null |
q-bio.PE math.OC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the framework of homogeneous susceptible-infected-recovered (SIR) models,
we use a control theory approach to identify optimal pandemic mitigation
strategies. We derive rather general conditions for reaching herd immunity
while minimizing the costs incurred by the introduction of societal control
measures (such as closing schools, social distancing, lockdowns, etc.), under
the constraint that the infected fraction of the population does never exceed a
certain maximum corresponding to public health system capacity. Optimality is
derived and verified by variational and numerical methods for a number of model
cost functions. The effects of immune response decay after recovery are taken
into account and discussed in terms of the feasibility of strategies based on
herd immunity.
|
[
{
"created": "Sat, 5 Sep 2020 10:45:04 GMT",
"version": "v1"
},
{
"created": "Fri, 16 Oct 2020 13:29:08 GMT",
"version": "v2"
},
{
"created": "Thu, 18 Feb 2021 10:31:31 GMT",
"version": "v3"
},
{
"created": "Fri, 2 Jul 2021 12:10:00 GMT",
"version": "v4"
}
] |
2021-07-05
|
[
[
"Godara",
"Prakhar",
""
],
[
"Herminghaus",
"Stephan",
""
],
[
"Heidemann",
"Knut M.",
""
]
] |
In the framework of homogeneous susceptible-infected-recovered (SIR) models, we use a control theory approach to identify optimal pandemic mitigation strategies. We derive rather general conditions for reaching herd immunity while minimizing the costs incurred by the introduction of societal control measures (such as closing schools, social distancing, lockdowns, etc.), under the constraint that the infected fraction of the population does never exceed a certain maximum corresponding to public health system capacity. Optimality is derived and verified by variational and numerical methods for a number of model cost functions. The effects of immune response decay after recovery are taken into account and discussed in terms of the feasibility of strategies based on herd immunity.
|
q-bio/0601017
|
Fei Liu
|
Fei Liu and Zhong-can Ou-Yang
|
Force Modulating Dynamic Disorder: Physical Theory of Catch-slip bond
Transitions in Receptor-Ligand Forced Dissociation Experiments
|
8 pages, 3 figures, submitted
| null |
10.1103/PhysRevE.74.051904
| null |
q-bio.CB q-bio.BM
| null |
Recently experiments showed that some adhesive receptor-ligand complexes
increase their lifetimes when they are stretched by mechanical force, while the
force increase beyond some thresholds their lifetimes decrease. Several
specific chemical kinetic models have been developed to explain the intriguing
transitions from the "catch-bonds" to the "slip-bonds". In this work we suggest
that the counterintuitive forced dissociation of the complexes is a typical
rate process with dynamic disorder. An uniform one-dimension force modulating
Agmon-Hopfield model is used to quantitatively describe the transitions
observed in the single bond P-selctin glycoprotein ligand
1(PSGL-1)$-$P-selectin forced dissociation experiments, which were respectively
carried out on the constant force [Marshall, {\it et al.}, (2003) Nature {\bf
423}, 190-193] and the force steady- or jump-ramp [Evans {\it et al.}, (2004)
Proc. Natl. Acad. Sci. USA {\bf 98}, 11281-11286] modes. Our calculation shows
that the novel catch-slip bond transition arises from a competition of the two
components of external applied force along the dissociation reaction coordinate
and the complex conformational coordinate: the former accelerates the
dissociation by lowering the height of the energy barrier between the bound and
free states (slip), while the later stabilizes the complex by dragging the
system to the higher barrier height (catch).
|
[
{
"created": "Thu, 12 Jan 2006 03:06:34 GMT",
"version": "v1"
}
] |
2009-11-13
|
[
[
"Liu",
"Fei",
""
],
[
"Ou-Yang",
"Zhong-can",
""
]
] |
Recently experiments showed that some adhesive receptor-ligand complexes increase their lifetimes when they are stretched by mechanical force, while the force increase beyond some thresholds their lifetimes decrease. Several specific chemical kinetic models have been developed to explain the intriguing transitions from the "catch-bonds" to the "slip-bonds". In this work we suggest that the counterintuitive forced dissociation of the complexes is a typical rate process with dynamic disorder. An uniform one-dimension force modulating Agmon-Hopfield model is used to quantitatively describe the transitions observed in the single bond P-selctin glycoprotein ligand 1(PSGL-1)$-$P-selectin forced dissociation experiments, which were respectively carried out on the constant force [Marshall, {\it et al.}, (2003) Nature {\bf 423}, 190-193] and the force steady- or jump-ramp [Evans {\it et al.}, (2004) Proc. Natl. Acad. Sci. USA {\bf 98}, 11281-11286] modes. Our calculation shows that the novel catch-slip bond transition arises from a competition of the two components of external applied force along the dissociation reaction coordinate and the complex conformational coordinate: the former accelerates the dissociation by lowering the height of the energy barrier between the bound and free states (slip), while the later stabilizes the complex by dragging the system to the higher barrier height (catch).
|
2209.02590
|
Joshua Plotkin
|
Guocheng Wang, Qi Su, Long Wang, Joshua B. Plotkin
|
The arrow of evolution when the offspring variance is large
| null | null | null | null |
q-bio.PE
|
http://creativecommons.org/licenses/by/4.0/
|
The concept of fitness is central to evolution, but it quantifies only the
expected number of offspring an individual will produce. The actual number of
offspring is also subject to noise, arising from environmental or demographic
stochasticity. In nature, individuals who are more fecund tend to have greater
variance in their offspring number -- sometimes far greater than the Poisson
variance assumed in classical models of population genetics. Here, we develop a
model for the evolution of two types reproducing in a population of
non-constant size. The frequency-dependent fitness of each type is determined
by pairwise interactions in a prisoner's dilemma game, but the offspring number
is subject to an exogenously controlled variance that may depend upon the mean.
Whereas defectors are preferred by natural selection in classical well-mixed
populations, since they always have greater fitness than cooperators, we show
that large offspring variance can reverse the direction of evolution and favor
cooperation. Reproductive over-dispersion produces qualitatively new dynamics
for other types of social interactions, as well, which cannot arise in
populations with a fixed size or Poisson offspring variance.
|
[
{
"created": "Tue, 6 Sep 2022 15:46:24 GMT",
"version": "v1"
}
] |
2022-09-07
|
[
[
"Wang",
"Guocheng",
""
],
[
"Su",
"Qi",
""
],
[
"Wang",
"Long",
""
],
[
"Plotkin",
"Joshua B.",
""
]
] |
The concept of fitness is central to evolution, but it quantifies only the expected number of offspring an individual will produce. The actual number of offspring is also subject to noise, arising from environmental or demographic stochasticity. In nature, individuals who are more fecund tend to have greater variance in their offspring number -- sometimes far greater than the Poisson variance assumed in classical models of population genetics. Here, we develop a model for the evolution of two types reproducing in a population of non-constant size. The frequency-dependent fitness of each type is determined by pairwise interactions in a prisoner's dilemma game, but the offspring number is subject to an exogenously controlled variance that may depend upon the mean. Whereas defectors are preferred by natural selection in classical well-mixed populations, since they always have greater fitness than cooperators, we show that large offspring variance can reverse the direction of evolution and favor cooperation. Reproductive over-dispersion produces qualitatively new dynamics for other types of social interactions, as well, which cannot arise in populations with a fixed size or Poisson offspring variance.
|
1910.03447
|
Eric Lock
|
Sarah Samorodnitsky, Katherine A. Hoadley, and Eric F. Lock
|
A Pan-Cancer and Polygenic Bayesian Hierarchical Model for the Effect of
Somatic Mutations on Survival
|
20 pages, 4 figures
| null | null | null |
q-bio.QM stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We built a novel Bayesian hierarchical survival model based on the somatic
mutation profile of patients across 50 genes and 27 cancer types. The
pan-cancer quality allows for the model to "borrow" information across cancer
types, motivated by the assumption that similar mutation profiles may have
similar (but not necessarily identical) effects on survival across different
tissues-of-origin or tumor types. The effect of a mutation at each gene was
allowed to vary by cancer type while the mean effect of each gene was shared
across cancers. Within this framework we considered four parametric survival
models (normal, log-normal, exponential, and Weibull), and we compared their
performance via a cross-validation approach in which we fit each model on
training data and estimate the log-posterior predictive likelihood on test
data. The log-normal model gave the best fit, and we investigated the partial
effect of each gene on survival via a forward selection procedure. Through this
we determined that mutations at TP53 and FAT4 were together the most useful for
predicting patient survival. We validated the model via simulation to ensure
that our algorithm for posterior computation gave nominal coverage rates. The
code used for this analysis can be found at
http://github.com/sarahsamorodnitsky/Pan-Cancer-Survival-Modeling , and the
results are at http://ericfrazerlock.com/surv_figs/SurvivalDisplay.html .
|
[
{
"created": "Tue, 8 Oct 2019 15:11:29 GMT",
"version": "v1"
}
] |
2019-10-09
|
[
[
"Samorodnitsky",
"Sarah",
""
],
[
"Hoadley",
"Katherine A.",
""
],
[
"Lock",
"Eric F.",
""
]
] |
We built a novel Bayesian hierarchical survival model based on the somatic mutation profile of patients across 50 genes and 27 cancer types. The pan-cancer quality allows for the model to "borrow" information across cancer types, motivated by the assumption that similar mutation profiles may have similar (but not necessarily identical) effects on survival across different tissues-of-origin or tumor types. The effect of a mutation at each gene was allowed to vary by cancer type while the mean effect of each gene was shared across cancers. Within this framework we considered four parametric survival models (normal, log-normal, exponential, and Weibull), and we compared their performance via a cross-validation approach in which we fit each model on training data and estimate the log-posterior predictive likelihood on test data. The log-normal model gave the best fit, and we investigated the partial effect of each gene on survival via a forward selection procedure. Through this we determined that mutations at TP53 and FAT4 were together the most useful for predicting patient survival. We validated the model via simulation to ensure that our algorithm for posterior computation gave nominal coverage rates. The code used for this analysis can be found at http://github.com/sarahsamorodnitsky/Pan-Cancer-Survival-Modeling , and the results are at http://ericfrazerlock.com/surv_figs/SurvivalDisplay.html .
|
2406.01622
|
Trevor Norton
|
Trevor Norton and Debswapna Bhattacharya
|
Sifting through the Noise: A Survey of Diffusion Probabilistic Models
and Their Applications to Biomolecules
|
31 pages, 6 figures
| null | null | null |
q-bio.BM cs.AI cs.LG q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
Diffusion probabilistic models have made their way into a number of
high-profile applications since their inception. In particular, there has been
a wave of research into using diffusion models in the prediction and design of
biomolecular structures and sequences. Their growing ubiquity makes it
imperative for researchers in these fields to understand them. This paper
serves as a general overview for the theory behind these models and the current
state of research. We first introduce diffusion models and discuss common
motifs used when applying them to biomolecules. We then present the significant
outcomes achieved through the application of these models in generative and
predictive tasks. This survey aims to provide readers with a comprehensive
understanding of the increasingly critical role of diffusion models.
|
[
{
"created": "Fri, 31 May 2024 21:39:51 GMT",
"version": "v1"
}
] |
2024-06-05
|
[
[
"Norton",
"Trevor",
""
],
[
"Bhattacharya",
"Debswapna",
""
]
] |
Diffusion probabilistic models have made their way into a number of high-profile applications since their inception. In particular, there has been a wave of research into using diffusion models in the prediction and design of biomolecular structures and sequences. Their growing ubiquity makes it imperative for researchers in these fields to understand them. This paper serves as a general overview for the theory behind these models and the current state of research. We first introduce diffusion models and discuss common motifs used when applying them to biomolecules. We then present the significant outcomes achieved through the application of these models in generative and predictive tasks. This survey aims to provide readers with a comprehensive understanding of the increasingly critical role of diffusion models.
|
1910.09904
|
Anke Cajar
|
Anke Cajar, Ralf Engbert, Jochen Laubrock
|
How spatial frequencies and color drive object search in real-world
scenes: A new eye-movement corpus
|
29 pages, 6 figures
| null |
10.1167/jov.20.7.8
| null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
When studying how people search for objects in scenes, the inhomogeneity of
the visual field is often ignored. Due to physiological limitations peripheral
vision is blurred and mainly uses coarse-grained information (i.e., low spatial
frequencies) for selecting saccade targets, whereas high-acuity central vision
uses fine-grained information (i.e., high spatial frequencies) for analysis of
details. Here we investigated how spatial frequencies and color affect object
search in real-world scenes. Using gaze-contingent filters we attenuated high
or low frequencies in central or peripheral vision while viewers searched color
or grayscale scenes. Results showed that peripheral filters and central
high-pass filters hardly affected search accuracy, whereas accuracy dropped
drastically with central low-pass filters. Peripheral filtering increased the
time to localize the target by decreasing saccade amplitudes and increasing
number and duration of fixations. The use of coarse-grained information in the
periphery was limited to color scenes. Central filtering increased the time to
verify target identity instead, especially with low-pass filters. We conclude
that peripheral vision is critical for object localization and central vision
is critical for object identification. Visual guidance during peripheral object
localization is dominated by low-frequency color information, whereas
high-frequency information, relatively independent of color, is most important
for object identification in central vision.
|
[
{
"created": "Tue, 22 Oct 2019 11:46:12 GMT",
"version": "v1"
},
{
"created": "Fri, 14 Feb 2020 10:05:18 GMT",
"version": "v2"
},
{
"created": "Fri, 20 Mar 2020 13:24:56 GMT",
"version": "v3"
}
] |
2021-01-05
|
[
[
"Cajar",
"Anke",
""
],
[
"Engbert",
"Ralf",
""
],
[
"Laubrock",
"Jochen",
""
]
] |
When studying how people search for objects in scenes, the inhomogeneity of the visual field is often ignored. Due to physiological limitations peripheral vision is blurred and mainly uses coarse-grained information (i.e., low spatial frequencies) for selecting saccade targets, whereas high-acuity central vision uses fine-grained information (i.e., high spatial frequencies) for analysis of details. Here we investigated how spatial frequencies and color affect object search in real-world scenes. Using gaze-contingent filters we attenuated high or low frequencies in central or peripheral vision while viewers searched color or grayscale scenes. Results showed that peripheral filters and central high-pass filters hardly affected search accuracy, whereas accuracy dropped drastically with central low-pass filters. Peripheral filtering increased the time to localize the target by decreasing saccade amplitudes and increasing number and duration of fixations. The use of coarse-grained information in the periphery was limited to color scenes. Central filtering increased the time to verify target identity instead, especially with low-pass filters. We conclude that peripheral vision is critical for object localization and central vision is critical for object identification. Visual guidance during peripheral object localization is dominated by low-frequency color information, whereas high-frequency information, relatively independent of color, is most important for object identification in central vision.
|
1906.02603
|
Kevin Parker PhD
|
Kevin J. Parker
|
The first order statistics of backscatter from the fractal branching
vasculature
|
26 pages, 14 figures. This article has been submitted to The Journal
of the Acoustical Society of America. After it is published, it will be found
at http://asa.scitation.org/journal/jas
|
J Acoust Soc Am 146(5) p.3318-3326, 2019
|
10.1121/1.5132934
| null |
q-bio.QM eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The issue of speckle statistics from ultrasound images of soft tissues such
as the liver has a long and rich history. A number of theoretical
distributions, some related to random scatterers or fades in optics and radar,
have been formulated for pulse-echo interference patterns. This work proposes
an alternative framework in which the dominant echoes are presumed to result
from Born scattering from fluid filled vessels that permeate the tissue
parenchyma. These are modeled as a branching, fractal, self-similar, multi
scale collection of cylindrical scatterers governed by a power law distribution
relating the number of branches at each radius. A deterministic accounting of
the echo envelopes across the scales from small to large is undertaken, leading
to a closed form theoretical formula for the histogram of the envelope of the
echoes. The normalized histogram is found to be related to the classical Burr
distribution, with the key power law parameter directly related to that of the
number density of vessels vs. diameter, frequently reported in the range of 2
to 4. Examples are given from liver scans to demonstrate the applicability of
the theory.
|
[
{
"created": "Thu, 6 Jun 2019 14:15:12 GMT",
"version": "v1"
},
{
"created": "Fri, 7 Jun 2019 13:33:35 GMT",
"version": "v2"
},
{
"created": "Thu, 19 Sep 2019 18:17:57 GMT",
"version": "v3"
}
] |
2020-02-14
|
[
[
"Parker",
"Kevin J.",
""
]
] |
The issue of speckle statistics from ultrasound images of soft tissues such as the liver has a long and rich history. A number of theoretical distributions, some related to random scatterers or fades in optics and radar, have been formulated for pulse-echo interference patterns. This work proposes an alternative framework in which the dominant echoes are presumed to result from Born scattering from fluid filled vessels that permeate the tissue parenchyma. These are modeled as a branching, fractal, self-similar, multi scale collection of cylindrical scatterers governed by a power law distribution relating the number of branches at each radius. A deterministic accounting of the echo envelopes across the scales from small to large is undertaken, leading to a closed form theoretical formula for the histogram of the envelope of the echoes. The normalized histogram is found to be related to the classical Burr distribution, with the key power law parameter directly related to that of the number density of vessels vs. diameter, frequently reported in the range of 2 to 4. Examples are given from liver scans to demonstrate the applicability of the theory.
|
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