id stringlengths 9 13 | submitter stringlengths 4 48 | authors stringlengths 4 9.62k | title stringlengths 4 343 | comments stringlengths 2 480 ⌀ | journal-ref stringlengths 9 309 ⌀ | doi stringlengths 12 138 ⌀ | report-no stringclasses 277 values | categories stringlengths 8 87 | license stringclasses 9 values | orig_abstract stringlengths 27 3.76k | versions listlengths 1 15 | update_date stringlengths 10 10 | authors_parsed listlengths 1 147 | abstract stringlengths 24 3.75k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1410.1278 | Vince Grolmusz | Csaba Kerepesi and Vince Grolmusz | Giant Viruses of the Kutch Desert | null | null | null | null | q-bio.GN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Kutch desert (Great Rann of Kutch, Gujarat, India) is a unique ecosystem:
in the larger part of the year it is a hot, salty desert that is flooded
regularly in the Indian monsoon season. In the dry season, the crystallized
salt deposits form the "white desert" in large regions. The first metagenomic
analysis of the soil samples of Kutch was published in 2013, and the data was
deposited in the NCBI Sequence Read Archive. The sequences were analyzed at the
same time phylogenetically for prokaryotes, especially for bacterial taxa.
In the present work, we are searching for the DNA sequences of the recently
discovered giant viruses in the soil samples of the Kutch desert. Since most
giant viruses were discovered in biofilms in industrial cooling towers, ocean
water and freshwater ponds, we were surprised to find their DNA sequences in
the soil samples of a seasonally very hot and arid, salty environment.
| [
{
"created": "Mon, 6 Oct 2014 07:50:22 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Oct 2014 10:46:39 GMT",
"version": "v2"
}
] | 2014-10-08 | [
[
"Kerepesi",
"Csaba",
""
],
[
"Grolmusz",
"Vince",
""
]
] | The Kutch desert (Great Rann of Kutch, Gujarat, India) is a unique ecosystem: in the larger part of the year it is a hot, salty desert that is flooded regularly in the Indian monsoon season. In the dry season, the crystallized salt deposits form the "white desert" in large regions. The first metagenomic analysis of the soil samples of Kutch was published in 2013, and the data was deposited in the NCBI Sequence Read Archive. The sequences were analyzed at the same time phylogenetically for prokaryotes, especially for bacterial taxa. In the present work, we are searching for the DNA sequences of the recently discovered giant viruses in the soil samples of the Kutch desert. Since most giant viruses were discovered in biofilms in industrial cooling towers, ocean water and freshwater ponds, we were surprised to find their DNA sequences in the soil samples of a seasonally very hot and arid, salty environment. |
1912.05502 | Alejandro Abarca-Blanco | Juan F. Yee-de Le\'on, Brenda Soto-Garc\'ia, Diana
Ar\'aiz-Hern\'andez, Jes\'us Rolando Delgado-Balderas, Miguel A. Esparza,
Carlos Aguilar-Avelar, J. D. Wong-Campos, Franco Chac\'on, Jos\'e Y.
L\'opez-Hern\'andez, A. Mauricio Gonz\'alez-Trevi\~no, Jos\'e R. Yee-de
Le\'on, Jorge L. Zamora-Mendoza, Mario M. Alvarez, Grissel Trujillo-de
Santiago, Lauro S. G\'omez-Guerra, Celia N. S\'anchez-Dom\'inguez, Liza P.
Velarde-Calvillo and Alejandro Abarca-Blanco | Characterization of a novel automated microfiltration device for the
efficient isolation and analysis of circulating tumor cells from clinical
blood samples | 13 pages, 6 figures, under review | null | 10.1038/s41598-020-63672-7 | null | q-bio.QM q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The detection and analysis of circulating tumor cells (CTCs) may enable a
broad range of cancer-related applications, including the identification of
acquired drug resistance during treatments. However, the non-scalable
fabrication, prolonged sample processing times, and the lack of automation,
associated with most of the technologies developed to isolate these rare cells,
have impeded their transition into the clinical practice. This work describes a
novel membrane-based microfiltration device comprised of a fully automated
sample processing unit and a machine-vision-enabled imaging system that allows
the efficient isolation and rapid analysis of CTCs from blood. The device
performance was characterized using four prostate cancer cell lines, including
PC-3, VCaP, DU-145, and LNCaP, obtaining high assay reproducibility and capture
efficiencies greater than 93% after processing 7.5 mL blood samples from
healthy donors, spiked with 100 cancer cells. Cancer cells remained viable
after filtration due to the minimal shear stress exerted over cells during the
procedure, while the identification of cancer cells by immunostaining was not
affected by the number of non-specific events captured on the membrane. We were
also able to identify the androgen receptor (AR) point mutation T878A from 7.5
mL blood samples spiked with 50 LNCaP cells using RT-PCR and Sanger sequencing.
Finally, CTCs were detected in 8 of 8 samples from patients diagnosed with
metastatic prostate cancer (mean $\pm$ SEM = 21 $\pm$ 2.957 CTCs/mL, median =
21 CTC/mL), thereby validating the potential clinical utility of the device.
| [
{
"created": "Wed, 11 Dec 2019 18:05:18 GMT",
"version": "v1"
}
] | 2020-05-07 | [
[
"León",
"Juan F. Yee-de",
""
],
[
"Soto-García",
"Brenda",
""
],
[
"Aráiz-Hernández",
"Diana",
""
],
[
"Delgado-Balderas",
"Jesús Rolando",
""
],
[
"Esparza",
"Miguel A.",
""
],
[
"Aguilar-Avelar",
"Carlos",
""
],
[
... | The detection and analysis of circulating tumor cells (CTCs) may enable a broad range of cancer-related applications, including the identification of acquired drug resistance during treatments. However, the non-scalable fabrication, prolonged sample processing times, and the lack of automation, associated with most of the technologies developed to isolate these rare cells, have impeded their transition into the clinical practice. This work describes a novel membrane-based microfiltration device comprised of a fully automated sample processing unit and a machine-vision-enabled imaging system that allows the efficient isolation and rapid analysis of CTCs from blood. The device performance was characterized using four prostate cancer cell lines, including PC-3, VCaP, DU-145, and LNCaP, obtaining high assay reproducibility and capture efficiencies greater than 93% after processing 7.5 mL blood samples from healthy donors, spiked with 100 cancer cells. Cancer cells remained viable after filtration due to the minimal shear stress exerted over cells during the procedure, while the identification of cancer cells by immunostaining was not affected by the number of non-specific events captured on the membrane. We were also able to identify the androgen receptor (AR) point mutation T878A from 7.5 mL blood samples spiked with 50 LNCaP cells using RT-PCR and Sanger sequencing. Finally, CTCs were detected in 8 of 8 samples from patients diagnosed with metastatic prostate cancer (mean $\pm$ SEM = 21 $\pm$ 2.957 CTCs/mL, median = 21 CTC/mL), thereby validating the potential clinical utility of the device. |
2109.01347 | Chiyin Zheng | Chiyin Zheng | Account for Neuronal Representations from the Perspective of Neurons | 49 pages, 6 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Mounting evidence in neuroscience suggests the possibility of neuronal
representations that individual neurons serve as the substrates of different
mental representations in a point-to-point way. Combined with associationism,
it can potentially address a range of theoretical problems and provide a
straightforward explanation for our cognition. However, this idea is merely a
hypothesis with many questions unsolved. In this paper, I will bring up a new
framework to defend the idea of neuronal representations. The strategy is from
micro- to macro-level. Specifically, in the micro-level, I first propose that
our brain' prefers and preserves more active neurons. Yet as total chance of
discharge, neurons must take strategies to fire more strongly and frequently.
Then I describe how they take synaptic plasticity, inhibition, and
synchronization as their strategies and demonstrate how the execution of these
strategies during turn them into specialized neurons that selectively but
strongly respond to familiar entities. In the macro-level, I further discuss
how these specialized neurons underlie various cognitive functions and
phenomena. Significantly, this paper, through defending neuronal
representation, introduces a novel way to understand the relationship between
brain and cognition.
| [
{
"created": "Fri, 3 Sep 2021 07:13:29 GMT",
"version": "v1"
}
] | 2021-09-06 | [
[
"Zheng",
"Chiyin",
""
]
] | Mounting evidence in neuroscience suggests the possibility of neuronal representations that individual neurons serve as the substrates of different mental representations in a point-to-point way. Combined with associationism, it can potentially address a range of theoretical problems and provide a straightforward explanation for our cognition. However, this idea is merely a hypothesis with many questions unsolved. In this paper, I will bring up a new framework to defend the idea of neuronal representations. The strategy is from micro- to macro-level. Specifically, in the micro-level, I first propose that our brain' prefers and preserves more active neurons. Yet as total chance of discharge, neurons must take strategies to fire more strongly and frequently. Then I describe how they take synaptic plasticity, inhibition, and synchronization as their strategies and demonstrate how the execution of these strategies during turn them into specialized neurons that selectively but strongly respond to familiar entities. In the macro-level, I further discuss how these specialized neurons underlie various cognitive functions and phenomena. Significantly, this paper, through defending neuronal representation, introduces a novel way to understand the relationship between brain and cognition. |
2310.01768 | Yikai Liu | Yikai Liu, Ming Chen, Guang Lin | Backdiff: a diffusion model for generalized transferable protein
backmapping | 22 pages, 5 figures | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Coarse-grained (CG) models play a crucial role in the study of protein
structures, protein thermodynamic properties, and protein conformation
dynamics. Due to the information loss in the coarse-graining process,
backmapping from CG to all-atom configurations is essential in many protein
design and drug discovery applications when detailed atomic representations are
needed for in-depth studies. Despite recent progress in data-driven backmapping
approaches, devising a backmapping method that can be universally applied
across various CG models and proteins remains unresolved. In this work, we
propose BackDiff, a new generative model designed to achieve generalization and
reliability in the protein backmapping problem. BackDiff leverages the
conditional score-based diffusion model with geometric representations. Since
different CG models can contain different coarse-grained sites which include
selected atoms (CG atoms) and simple CG auxiliary functions of atomistic
coordinates (CG auxiliary variables), we design a self-supervised training
framework to adapt to different CG atoms, and constrain the diffusion sampling
paths with arbitrary CG auxiliary variables as conditions. Our method
facilitates end-to-end training and allows efficient sampling across different
proteins and diverse CG models without the need for retraining. Comprehensive
experiments over multiple popular CG models demonstrate BackDiff's superior
performance to existing state-of-the-art approaches, and generalization and
flexibility that these approaches cannot achieve. A pretrained BackDiff model
can offer a convenient yet reliable plug-and-play solution for protein
researchers, enabling them to investigate further from their own CG models.
| [
{
"created": "Tue, 3 Oct 2023 03:32:07 GMT",
"version": "v1"
},
{
"created": "Wed, 29 Nov 2023 03:43:56 GMT",
"version": "v2"
}
] | 2023-11-30 | [
[
"Liu",
"Yikai",
""
],
[
"Chen",
"Ming",
""
],
[
"Lin",
"Guang",
""
]
] | Coarse-grained (CG) models play a crucial role in the study of protein structures, protein thermodynamic properties, and protein conformation dynamics. Due to the information loss in the coarse-graining process, backmapping from CG to all-atom configurations is essential in many protein design and drug discovery applications when detailed atomic representations are needed for in-depth studies. Despite recent progress in data-driven backmapping approaches, devising a backmapping method that can be universally applied across various CG models and proteins remains unresolved. In this work, we propose BackDiff, a new generative model designed to achieve generalization and reliability in the protein backmapping problem. BackDiff leverages the conditional score-based diffusion model with geometric representations. Since different CG models can contain different coarse-grained sites which include selected atoms (CG atoms) and simple CG auxiliary functions of atomistic coordinates (CG auxiliary variables), we design a self-supervised training framework to adapt to different CG atoms, and constrain the diffusion sampling paths with arbitrary CG auxiliary variables as conditions. Our method facilitates end-to-end training and allows efficient sampling across different proteins and diverse CG models without the need for retraining. Comprehensive experiments over multiple popular CG models demonstrate BackDiff's superior performance to existing state-of-the-art approaches, and generalization and flexibility that these approaches cannot achieve. A pretrained BackDiff model can offer a convenient yet reliable plug-and-play solution for protein researchers, enabling them to investigate further from their own CG models. |
q-bio/0402017 | Manuel Middendorf | Manuel Middendorf, Etay Ziv, Carter Adams, Jen Hom, Robin Koytcheff,
Chaya Levovitz, Gregory Woods, Linda Chen, Chris Wiggins | Discriminative Topological Features Reveal Biological Network Mechanisms | supplemental website:
http://www.columbia.edu/itc/applied/wiggins/netclass/ | BMC Bioinformatics 2004, 5:181 (22 November 2004) | null | null | q-bio.MN | null | Recent genomic and bioinformatic advances have motivated the development of
numerous random network models purporting to describe graphs of biological,
technological, and sociological origin. The success of a model has been
evaluated by how well it reproduces a few key features of the real-world data,
such as degree distributions, mean geodesic lengths, and clustering
coefficients. Often pairs of models can reproduce these features with
indistinguishable fidelity despite being generated by vastly different
mechanisms. In such cases, these few target features are insufficient to
distinguish which of the different models best describes real world networks of
interest; moreover, it is not clear a priori that any of the presently-existing
algorithms for network generation offers a predictive description of the
networks inspiring them. To derive discriminative classifiers, we construct a
mapping from the set of all graphs to a high-dimensional (in principle
infinite-dimensional) ``word space.'' This map defines an input space for
classification schemes which allow us for the first time to state unambiguously
which models are most descriptive of the networks they purport to describe. Our
training sets include networks generated from 17 models either drawn from the
literature or introduced in this work, source code for which is freely
available. We anticipate that this new approach to network analysis will be of
broad impact to a number of communities.
| [
{
"created": "Mon, 9 Feb 2004 06:56:43 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Middendorf",
"Manuel",
""
],
[
"Ziv",
"Etay",
""
],
[
"Adams",
"Carter",
""
],
[
"Hom",
"Jen",
""
],
[
"Koytcheff",
"Robin",
""
],
[
"Levovitz",
"Chaya",
""
],
[
"Woods",
"Gregory",
""
],
[
"Chen",... | Recent genomic and bioinformatic advances have motivated the development of numerous random network models purporting to describe graphs of biological, technological, and sociological origin. The success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest; moreover, it is not clear a priori that any of the presently-existing algorithms for network generation offers a predictive description of the networks inspiring them. To derive discriminative classifiers, we construct a mapping from the set of all graphs to a high-dimensional (in principle infinite-dimensional) ``word space.'' This map defines an input space for classification schemes which allow us for the first time to state unambiguously which models are most descriptive of the networks they purport to describe. Our training sets include networks generated from 17 models either drawn from the literature or introduced in this work, source code for which is freely available. We anticipate that this new approach to network analysis will be of broad impact to a number of communities. |
1203.4482 | Samir Suweis Dr. | S. Suweis, E. Bertuzzo, L. Mari, I. Rodriguez-Iturbe, A. Maritan and
A. Rinaldo | On Species Persistence-Time Distributions | 30 pages, 5 figures | null | null | null | q-bio.PE cond-mat.stat-mech physics.bio-ph physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present new theoretical and empirical results on the probability
distributions of species persistence times in natural ecosystems. Persistence
times, defined as the timespans occurring between species' colonization and
local extinction in a given geographic region, are empirically estimated from
local observations of species' presence/absence. A connected sampling problem
is presented, generalized and solved analytically. Species persistence is shown
to provide a direct connection with key spatial macroecological patterns like
species-area and endemics-area relationships. Our empirical analysis pertains
to two different ecosystems and taxa: a herbaceous plant community and a
estuarine fish database. Despite the substantial differences in ecological
interactions and spatial scales, we confirm earlier evidence on the general
properties of the scaling of persistence times, including the predicted effects
of the structure of the spatial interaction network. The framework tested here
allows to investigate directly nature and extent of spatial effects in the
context of ecosystem dynamics. The notable coherence between spatial and
temporal macroecological patterns, theoretically derived and empirically
verified, is suggested to underlie general features of the dynamic evolution of
ecosystems.
| [
{
"created": "Mon, 19 Mar 2012 09:46:27 GMT",
"version": "v1"
}
] | 2012-03-21 | [
[
"Suweis",
"S.",
""
],
[
"Bertuzzo",
"E.",
""
],
[
"Mari",
"L.",
""
],
[
"Rodriguez-Iturbe",
"I.",
""
],
[
"Maritan",
"A.",
""
],
[
"Rinaldo",
"A.",
""
]
] | We present new theoretical and empirical results on the probability distributions of species persistence times in natural ecosystems. Persistence times, defined as the timespans occurring between species' colonization and local extinction in a given geographic region, are empirically estimated from local observations of species' presence/absence. A connected sampling problem is presented, generalized and solved analytically. Species persistence is shown to provide a direct connection with key spatial macroecological patterns like species-area and endemics-area relationships. Our empirical analysis pertains to two different ecosystems and taxa: a herbaceous plant community and a estuarine fish database. Despite the substantial differences in ecological interactions and spatial scales, we confirm earlier evidence on the general properties of the scaling of persistence times, including the predicted effects of the structure of the spatial interaction network. The framework tested here allows to investigate directly nature and extent of spatial effects in the context of ecosystem dynamics. The notable coherence between spatial and temporal macroecological patterns, theoretically derived and empirically verified, is suggested to underlie general features of the dynamic evolution of ecosystems. |
1710.02876 | Lucas Daniel Wittwer | Lucas D. Wittwer, Michael Peters, Sebastian Aland, Dagmar Iber | Simulating Organogenesis in COMSOL: Comparison Of Methods For Simulating
Branching Morphogenesis | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | During organogenesis tissue grows and deforms. The growth processes are
controlled by diffusible proteins, so-called morphogens. Many different
patterning mechanisms have been proposed. The stereotypic branching program
during lung development can be recapitulated by a receptor-ligand based Turing
model. Our group has previously used the Arbitrary Lagrangian-Eulerian (ALE)
framework for solving the receptor-ligand Turing model on growing lung domains.
However, complex mesh deformations which occur during lung growth severely
limit the number of branch generations that can be simulated. A new Phase-Field
implementation avoids mesh deformations by considering the surface of the
modelling domains as interfaces between phases, and by coupling the
reaction-diffusion framework to these surfaces. In this paper, we present a
rigorous comparison between the Phase-Field approach and the ALE-based
simulation.
| [
{
"created": "Sun, 8 Oct 2017 19:51:09 GMT",
"version": "v1"
}
] | 2017-10-10 | [
[
"Wittwer",
"Lucas D.",
""
],
[
"Peters",
"Michael",
""
],
[
"Aland",
"Sebastian",
""
],
[
"Iber",
"Dagmar",
""
]
] | During organogenesis tissue grows and deforms. The growth processes are controlled by diffusible proteins, so-called morphogens. Many different patterning mechanisms have been proposed. The stereotypic branching program during lung development can be recapitulated by a receptor-ligand based Turing model. Our group has previously used the Arbitrary Lagrangian-Eulerian (ALE) framework for solving the receptor-ligand Turing model on growing lung domains. However, complex mesh deformations which occur during lung growth severely limit the number of branch generations that can be simulated. A new Phase-Field implementation avoids mesh deformations by considering the surface of the modelling domains as interfaces between phases, and by coupling the reaction-diffusion framework to these surfaces. In this paper, we present a rigorous comparison between the Phase-Field approach and the ALE-based simulation. |
2207.03805 | Ferran Larrroya | Ferran Larroya, Tobias Galla | Demographic noise in complex ecological communities | 20 pages, 10 figures | J. Phys. Complex. 4 025012 (2023) | 10.1088/2632-072X/acd21b | null | q-bio.PE cond-mat.dis-nn | http://creativecommons.org/licenses/by/4.0/ | We introduce an individual-based model of a complex ecological community with
random interactions. The model contains a large number of species, each with a
finite population of individuals, subject to discrete reproduction and death
events. The interaction coefficients determining the rates of these events is
chosen from an ensemble of random matrices, and is kept fixed in time. The
set-up is such that the model reduces to the known generalised Lotka-Volterra
equations with random interaction coefficients in the limit of an infinite
population for each species. Demographic noise in the individual-based model
means that species which would survive in the Lotka-Volterra model can become
extinct. These noise-driven extinctions are the focus of the paper. We find
that, for increasing complexity of interactions, ecological communities
generally become less prone to extinctions induced by demographic noise. An
exception are systems composed entirely of predator-prey pairs. These systems
are known to be stable in deterministic Lotka-Volterra models with random
interactions, but, as we show, they are nevertheless particularly vulnerable to
fluctuations.
| [
{
"created": "Fri, 8 Jul 2022 10:22:05 GMT",
"version": "v1"
}
] | 2023-07-19 | [
[
"Larroya",
"Ferran",
""
],
[
"Galla",
"Tobias",
""
]
] | We introduce an individual-based model of a complex ecological community with random interactions. The model contains a large number of species, each with a finite population of individuals, subject to discrete reproduction and death events. The interaction coefficients determining the rates of these events is chosen from an ensemble of random matrices, and is kept fixed in time. The set-up is such that the model reduces to the known generalised Lotka-Volterra equations with random interaction coefficients in the limit of an infinite population for each species. Demographic noise in the individual-based model means that species which would survive in the Lotka-Volterra model can become extinct. These noise-driven extinctions are the focus of the paper. We find that, for increasing complexity of interactions, ecological communities generally become less prone to extinctions induced by demographic noise. An exception are systems composed entirely of predator-prey pairs. These systems are known to be stable in deterministic Lotka-Volterra models with random interactions, but, as we show, they are nevertheless particularly vulnerable to fluctuations. |
0912.5175 | William Bialek | Thierry Mora, Aleksandra Walczak, William Bialek and Curtis G. Callan
Jr | Maximum entropy models for antibody diversity | null | PNAS 107(12) 5405-5410 (2010) | 10.1073/pnas.1001705107 | null | q-bio.GN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recognition of pathogens relies on families of proteins showing great
diversity. Here we construct maximum entropy models of the sequence repertoire,
building on recent experiments that provide a nearly exhaustive sampling of the
IgM sequences in zebrafish. These models are based solely on pairwise
correlations between residue positions, but correctly capture the higher order
statistical properties of the repertoire. Exploiting the interpretation of
these models as statistical physics problems, we make several predictions for
the collective properties of the sequence ensemble: the distribution of
sequences obeys Zipf's law, the repertoire decomposes into several clusters,
and there is a massive restriction of diversity due to the correlations. These
predictions are completely inconsistent with models in which amino acid
substitutions are made independently at each site, and are in good agreement
with the data. Our results suggest that antibody diversity is not limited by
the sequences encoded in the genome, and may reflect rapid adaptation to
antigenic challenges. This approach should be applicable to the study of the
global properties of other protein families.
| [
{
"created": "Mon, 28 Dec 2009 14:42:08 GMT",
"version": "v1"
}
] | 2011-11-28 | [
[
"Mora",
"Thierry",
""
],
[
"Walczak",
"Aleksandra",
""
],
[
"Bialek",
"William",
""
],
[
"Callan",
"Curtis G.",
"Jr"
]
] | Recognition of pathogens relies on families of proteins showing great diversity. Here we construct maximum entropy models of the sequence repertoire, building on recent experiments that provide a nearly exhaustive sampling of the IgM sequences in zebrafish. These models are based solely on pairwise correlations between residue positions, but correctly capture the higher order statistical properties of the repertoire. Exploiting the interpretation of these models as statistical physics problems, we make several predictions for the collective properties of the sequence ensemble: the distribution of sequences obeys Zipf's law, the repertoire decomposes into several clusters, and there is a massive restriction of diversity due to the correlations. These predictions are completely inconsistent with models in which amino acid substitutions are made independently at each site, and are in good agreement with the data. Our results suggest that antibody diversity is not limited by the sequences encoded in the genome, and may reflect rapid adaptation to antigenic challenges. This approach should be applicable to the study of the global properties of other protein families. |
2006.15336 | Sashikumaar Ganesan Prof. | Sashikumaar Ganesan and Deepak Subramani | Spatio-temporal predictive modeling framework for infectious disease
spread | 9 pages, 4 figures | null | null | null | q-bio.PE math.DS physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A novel predictive modeling framework for the spread of infectious diseases
using high dimensional partial differential equations is developed and
implemented. A scalar function representing the infected population is defined
on a high-dimensional space and its evolution over all directions is described
by a population balance equation (PBE). New infections are introduced among the
susceptible population from non-quarantined infected population based on their
interaction, adherence to distancing norms, hygiene levels and any other
societal interventions. Moreover, recovery, death, immunity and all
aforementioned parameters are modeled on the high-dimensional space. To
epitomize the capabilities and features of the above framework, prognostic
estimates of Covid-19 spread using a six-dimensional (time, 2D space, infection
severity, duration of infection, and population age) PBE is presented. Further,
scenario analysis for different policy interventions and population behavior is
presented, throwing more insights into the spatio-temporal spread of infections
across disease age, intensity and age of population. These insights could be
used for science-informed policy planning.
| [
{
"created": "Sat, 27 Jun 2020 10:36:39 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Jul 2020 01:24:35 GMT",
"version": "v2"
}
] | 2020-07-06 | [
[
"Ganesan",
"Sashikumaar",
""
],
[
"Subramani",
"Deepak",
""
]
] | A novel predictive modeling framework for the spread of infectious diseases using high dimensional partial differential equations is developed and implemented. A scalar function representing the infected population is defined on a high-dimensional space and its evolution over all directions is described by a population balance equation (PBE). New infections are introduced among the susceptible population from non-quarantined infected population based on their interaction, adherence to distancing norms, hygiene levels and any other societal interventions. Moreover, recovery, death, immunity and all aforementioned parameters are modeled on the high-dimensional space. To epitomize the capabilities and features of the above framework, prognostic estimates of Covid-19 spread using a six-dimensional (time, 2D space, infection severity, duration of infection, and population age) PBE is presented. Further, scenario analysis for different policy interventions and population behavior is presented, throwing more insights into the spatio-temporal spread of infections across disease age, intensity and age of population. These insights could be used for science-informed policy planning. |
q-bio/0405024 | Luis Diambra | Luis Diambra | Modeling stochastic Ca$^{2+}$ release from a cluster of IP$_3$-sensitive
receptors | 25 pages 10 figures, revised version | null | null | null | q-bio.SC | null | We focused our attention on Ca$^{2+}$ release from the endoplasmic reticulum
through a cluster of inositol 1,4,5-trisphosphate (IP$_3$) receptor channels.
The random opening and closing of these receptors introduce stochastic effects
that have been observed experimentally. Here, we present a stochastic version
of Othmer-Tang model for IP$_3$ receptor clusters. We address the average
behavior of the channels in response to IP$_3$ stimuli. We found, by stochastic
simulation, that the shape of the receptor response to IP$_3$ (fraction of open
channels versus [IP$_3$]), is affected by the cytosolic Ca$^{2+}$ level. We
also study several aspects of the stochastic properties of Ca${2+}$ release and
we compare with experimental observations.
| [
{
"created": "Mon, 31 May 2004 20:06:35 GMT",
"version": "v1"
},
{
"created": "Thu, 29 Jul 2004 23:28:24 GMT",
"version": "v2"
}
] | 2009-09-29 | [
[
"Diambra",
"Luis",
""
]
] | We focused our attention on Ca$^{2+}$ release from the endoplasmic reticulum through a cluster of inositol 1,4,5-trisphosphate (IP$_3$) receptor channels. The random opening and closing of these receptors introduce stochastic effects that have been observed experimentally. Here, we present a stochastic version of Othmer-Tang model for IP$_3$ receptor clusters. We address the average behavior of the channels in response to IP$_3$ stimuli. We found, by stochastic simulation, that the shape of the receptor response to IP$_3$ (fraction of open channels versus [IP$_3$]), is affected by the cytosolic Ca$^{2+}$ level. We also study several aspects of the stochastic properties of Ca${2+}$ release and we compare with experimental observations. |
q-bio/0412005 | Carl Troein | Stuart Kauffman, Carsten Peterson, Bj\"orn Samuelsson and Carl Troein | Genetic networks with canalyzing Boolean rules are always stable | Final version available through PNAS open access at
http://www.pnas.org/cgi/content/abstract/0407783101v1 | Proc. Natl. Acad. Sci. USA 101 (2004), 17102-17107 | 10.1073/pnas.0407783101 | LU TP 04-10 | q-bio.MN cond-mat.soft | null | We determine stability and attractor properties of random Boolean genetic
network models with canalyzing rules for a variety of architectures. For all
power law, exponential, and flat in-degree distributions, we find that the
networks are dynamically stable. Furthermore, for architectures with few inputs
per node, the dynamics of the networks is close to critical. In addition, the
fraction of genes that are active decreases with the number of inputs per node.
These results are based upon investigating ensembles of networks using
analytical methods. Also, for different in-degree distributions, the numbers of
fixed points and cycles are calculated, with results intuitively consistent
with stability analysis; fewer inputs per node implies more cycles, and vice
versa. There are hints that genetic networks acquire broader degree
distributions with evolution, and hence our results indicate that for single
cells, the dynamics should become more stable with evolution. However, such an
effect is very likely compensated for by multicellular dynamics, because one
expects less stability when interactions among cells are included. We verify
this by simulations of a simple model for interactions among cells.
| [
{
"created": "Thu, 2 Dec 2004 16:35:45 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Kauffman",
"Stuart",
""
],
[
"Peterson",
"Carsten",
""
],
[
"Samuelsson",
"Björn",
""
],
[
"Troein",
"Carl",
""
]
] | We determine stability and attractor properties of random Boolean genetic network models with canalyzing rules for a variety of architectures. For all power law, exponential, and flat in-degree distributions, we find that the networks are dynamically stable. Furthermore, for architectures with few inputs per node, the dynamics of the networks is close to critical. In addition, the fraction of genes that are active decreases with the number of inputs per node. These results are based upon investigating ensembles of networks using analytical methods. Also, for different in-degree distributions, the numbers of fixed points and cycles are calculated, with results intuitively consistent with stability analysis; fewer inputs per node implies more cycles, and vice versa. There are hints that genetic networks acquire broader degree distributions with evolution, and hence our results indicate that for single cells, the dynamics should become more stable with evolution. However, such an effect is very likely compensated for by multicellular dynamics, because one expects less stability when interactions among cells are included. We verify this by simulations of a simple model for interactions among cells. |
2101.02698 | Daniel Han Mr. | Sergei Fedotov, Daniel Han, Andrey Yu. Zubarev, Mark Johnston and
Victoria J Allan | Variable-order fractional master equation and clustering of particles:
non-uniform lysosome distribution | arXiv admin note: text overlap with arXiv:1902.03087 | null | 10.1098/rsta.2020.0317 | null | q-bio.SC cond-mat.stat-mech physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | In this paper, we formulate the space-dependent variable-order fractional
master equation to model clustering of particles, organelles, inside living
cells. We find its solution in the long time limit describing non-uniform
distribution due to a space dependent fractional exponent. In the continuous
space limit, the solution of this fractional master equation is found to be
exactly the same as the space-dependent variable-order fractional diffusion
equation. In addition, we show that the clustering of lysosomes, an essential
organelle for healthy functioning of mammalian cells, exhibit space-dependent
fractional exponents. Furthermore, we demonstrate that the non-uniform
distribution of lysosomes in living cells is accurately described by the
asymptotic solution of the space-dependent variable-order fractional master
equation. Finally, Monte Carlo simulations of the fractional master equation
validate our analytical solution.
| [
{
"created": "Thu, 7 Jan 2021 18:58:52 GMT",
"version": "v1"
}
] | 2021-07-22 | [
[
"Fedotov",
"Sergei",
""
],
[
"Han",
"Daniel",
""
],
[
"Zubarev",
"Andrey Yu.",
""
],
[
"Johnston",
"Mark",
""
],
[
"Allan",
"Victoria J",
""
]
] | In this paper, we formulate the space-dependent variable-order fractional master equation to model clustering of particles, organelles, inside living cells. We find its solution in the long time limit describing non-uniform distribution due to a space dependent fractional exponent. In the continuous space limit, the solution of this fractional master equation is found to be exactly the same as the space-dependent variable-order fractional diffusion equation. In addition, we show that the clustering of lysosomes, an essential organelle for healthy functioning of mammalian cells, exhibit space-dependent fractional exponents. Furthermore, we demonstrate that the non-uniform distribution of lysosomes in living cells is accurately described by the asymptotic solution of the space-dependent variable-order fractional master equation. Finally, Monte Carlo simulations of the fractional master equation validate our analytical solution. |
1304.4515 | Ewan Birney | Mikhail Spivakov, Thomas O. Auer, Ravindra Peravali, Ian Dunham, Dirk
Dolle, Asao Fujiyama, Atsushi Toyoda, Tomoyuki Aizu, Yohei Minakuchi, Felix
Loosli, Kiyoshi Naruse, Ewan Birney, Joachim Wittbrodt | Genomic and phenotypic characterisation of a wild Medaka population:
Establishing an isogenic population genetic resource in fish | 5 figures, 30 pages | null | null | null | q-bio.GN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background Oryzias latipes (Medaka) has been established as a vertebrate
genetic model for over a century, and has recently been rediscovered outside
its native Japan. The power of new sequencing methods now makes it possible to
reinvigorate Medaka genetics, in particular by establishing a near-isogenic
panel derived from a single wild population. Results Here we characterise the
genomes of wild Medaka catches obtained from a single Southern Japanese
population in Kiyosu as a precursor for the establishment of a near isogenic
panel of wild lines. The population is free of significant detrimental
population structure, and has advantageous linkage disequilibrium properties
suitable for establishment of the proposed panel. Analysis of morphometric
traits in five representative inbred strains suggests phenotypic mapping will
be feasible in the panel. In addition high throughput genome sequencing of
these Medaka strains confirms their evolutionary relationships on lines of
geographic separation and provides further evidence that there has been little
significant interbreeding between the Southern and Northern Medaka population
since the Southern/Northern population split. The sequence data suggest that
the Southern Japanese Medaka existed as a larger older population which went
through a relatively recent bottleneck around 10,000 years ago. In addition we
detect patterns of recent positive selection in the Southern population.
Conclusions These data indicate that the genetic structure of the Kiyosu Medaka
samples are suitable for the establishment of a vertebrate near isogenic panel
and therefore inbreeding of 200 lines based on this population has commenced.
Progress of this project can be tracked at
http://www.ebi.ac.uk/birney-srv/medaka-ref-panel
| [
{
"created": "Tue, 16 Apr 2013 16:40:59 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Nov 2013 15:19:40 GMT",
"version": "v2"
}
] | 2013-11-20 | [
[
"Spivakov",
"Mikhail",
""
],
[
"Auer",
"Thomas O.",
""
],
[
"Peravali",
"Ravindra",
""
],
[
"Dunham",
"Ian",
""
],
[
"Dolle",
"Dirk",
""
],
[
"Fujiyama",
"Asao",
""
],
[
"Toyoda",
"Atsushi",
""
],
[
... | Background Oryzias latipes (Medaka) has been established as a vertebrate genetic model for over a century, and has recently been rediscovered outside its native Japan. The power of new sequencing methods now makes it possible to reinvigorate Medaka genetics, in particular by establishing a near-isogenic panel derived from a single wild population. Results Here we characterise the genomes of wild Medaka catches obtained from a single Southern Japanese population in Kiyosu as a precursor for the establishment of a near isogenic panel of wild lines. The population is free of significant detrimental population structure, and has advantageous linkage disequilibrium properties suitable for establishment of the proposed panel. Analysis of morphometric traits in five representative inbred strains suggests phenotypic mapping will be feasible in the panel. In addition high throughput genome sequencing of these Medaka strains confirms their evolutionary relationships on lines of geographic separation and provides further evidence that there has been little significant interbreeding between the Southern and Northern Medaka population since the Southern/Northern population split. The sequence data suggest that the Southern Japanese Medaka existed as a larger older population which went through a relatively recent bottleneck around 10,000 years ago. In addition we detect patterns of recent positive selection in the Southern population. Conclusions These data indicate that the genetic structure of the Kiyosu Medaka samples are suitable for the establishment of a vertebrate near isogenic panel and therefore inbreeding of 200 lines based on this population has commenced. Progress of this project can be tracked at http://www.ebi.ac.uk/birney-srv/medaka-ref-panel |
1509.06123 | Ricardo Oliveros-Ramos | Ricardo Oliveros-Ramos, Philippe Verley and Yunne-Jai Shin | A sequential approach to calibrate ecosystem models with multiple time
series data | 33 pages, 4 tables, 13 figures, 2 appendices | null | 10.1016/j.pocean.2017.01.002 | null | q-bio.QM q-bio.PE stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ecosystem approach to fisheries requires a thorough understanding of fishing
impacts on ecosystem status and processes as well as predictive tools such as
ecosystem models to provide useful information for management. The credibility
of such models is essential when used as decision making tools, and model
fitting to observed data is one major criterion to assess such credibility.
However, more attention has been given to the exploration of model behavior
than to a rigorous confrontation to observations, as the calibration of
ecosystem models is challenging in many ways. First, ecosystem models can only
be simulated numerically and are generally too complex for mathematical
analysis and explicit parameter estimation; secondly, the complex dynamics
represented in ecosystem models allow species-specific parameters to impact
other species parameters through ecological interactions; thirdly, critical
data about non-commercial species are often poor; lastly, technical aspects can
be impediments to the calibration with regard to the high computational cost
potentially involved and the scarce documentation published on fitting complex
ecosystem models to data. This work highlights some issues related to the
confrontation of complex ecosystem models to data and proposes a methodology
for a sequential multi-phases calibration of ecosystem models. We propose
criteria to classify the parameters of a model: model dependency and time
variability of the parameters. These criteria and the availability of
approximate initial estimates are used as decision rules to determine which
parameters need to be estimated, and their precedence order in the sequential
calibration process. The end-to-end ecosystem model ROMS-PISCES-OSMOSE applied
to the Northern Humboldt Current Ecosystem is used as an illustrative case
study.
| [
{
"created": "Mon, 21 Sep 2015 06:59:23 GMT",
"version": "v1"
}
] | 2024-04-30 | [
[
"Oliveros-Ramos",
"Ricardo",
""
],
[
"Verley",
"Philippe",
""
],
[
"Shin",
"Yunne-Jai",
""
]
] | Ecosystem approach to fisheries requires a thorough understanding of fishing impacts on ecosystem status and processes as well as predictive tools such as ecosystem models to provide useful information for management. The credibility of such models is essential when used as decision making tools, and model fitting to observed data is one major criterion to assess such credibility. However, more attention has been given to the exploration of model behavior than to a rigorous confrontation to observations, as the calibration of ecosystem models is challenging in many ways. First, ecosystem models can only be simulated numerically and are generally too complex for mathematical analysis and explicit parameter estimation; secondly, the complex dynamics represented in ecosystem models allow species-specific parameters to impact other species parameters through ecological interactions; thirdly, critical data about non-commercial species are often poor; lastly, technical aspects can be impediments to the calibration with regard to the high computational cost potentially involved and the scarce documentation published on fitting complex ecosystem models to data. This work highlights some issues related to the confrontation of complex ecosystem models to data and proposes a methodology for a sequential multi-phases calibration of ecosystem models. We propose criteria to classify the parameters of a model: model dependency and time variability of the parameters. These criteria and the availability of approximate initial estimates are used as decision rules to determine which parameters need to be estimated, and their precedence order in the sequential calibration process. The end-to-end ecosystem model ROMS-PISCES-OSMOSE applied to the Northern Humboldt Current Ecosystem is used as an illustrative case study. |
2010.04864 | Nathan Baker | Arun V. Sathanur, Nathan A. Baker | A clustering-based biased Monte Carlo approach to protein titration
curve prediction | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we developed an efficient approach to compute ensemble averages
in systems with pairwise-additive energetic interactions between the entities.
Methods involving full enumeration of the configuration space result in
exponential complexity. Sampling methods such as Markov Chain Monte Carlo
(MCMC) algorithms have been proposed to tackle the exponential complexity of
these problems; however, in certain scenarios where significant energetic
coupling exists between the entities, the efficiency of the such algorithms can
be diminished. We used a strategy to improve the efficiency of MCMC by taking
advantage of the cluster structure in the interaction energy matrix to bias the
sampling. We pursued two different schemes for the biased MCMC runs and show
that they are valid MCMC schemes. We used both synthesized and real-world
systems to show the improved performance of our biased MCMC methods when
compared to the regular MCMC method. In particular, we applied these algorithms
to the problem of estimating protonation ensemble averages and titration curves
of residues in a protein.
| [
{
"created": "Sat, 10 Oct 2020 01:32:52 GMT",
"version": "v1"
}
] | 2020-10-13 | [
[
"Sathanur",
"Arun V.",
""
],
[
"Baker",
"Nathan A.",
""
]
] | In this work, we developed an efficient approach to compute ensemble averages in systems with pairwise-additive energetic interactions between the entities. Methods involving full enumeration of the configuration space result in exponential complexity. Sampling methods such as Markov Chain Monte Carlo (MCMC) algorithms have been proposed to tackle the exponential complexity of these problems; however, in certain scenarios where significant energetic coupling exists between the entities, the efficiency of the such algorithms can be diminished. We used a strategy to improve the efficiency of MCMC by taking advantage of the cluster structure in the interaction energy matrix to bias the sampling. We pursued two different schemes for the biased MCMC runs and show that they are valid MCMC schemes. We used both synthesized and real-world systems to show the improved performance of our biased MCMC methods when compared to the regular MCMC method. In particular, we applied these algorithms to the problem of estimating protonation ensemble averages and titration curves of residues in a protein. |
1807.00844 | Yasser A. Ahmed | Nashwa Araby, Soha Soliman, Eman Abdel Raheem and Yasser Ahmed | Morphogenesis of the Sternum in Quail Embryos | null | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The flat bone develops through intramembranous ossification, in which the
mesenchymal cells are directly driven towards osteogenic lineage without the
formation of cartilage template. While long bone develops through endochondral
ossification, where cartilage template act as an intermediate stage between
mesenchymal and bone tissues. Although the avian sternum is a flat bone, some
studies describe formation of a cartilage template during its development. The
aim of the current study was to observe the mechanism of ossification in quail
sternum during embryonic development. Thirty quail embryos were collected for
the current study (5 embryos/ day) during the period between Day (D) 5 and D10
of embryonic development and processed for light microscopy. The
differentiation of mesenchymal condensation in to the chondrogenic cells was
observed at D5 whereas the secretion of extracellular matrix could be evident
at D6. The cartilage primordia were observed by D7 which were consisted of
chondrocytes, embedded in matrix and surrounded by perichondrium. Later these
primordia were developed in to cartilage template by D8 where the chondrocytes
were present in their lacuna. This template attained the shape of future
sternum by D9, which was more distinct at D10. These preliminary observations
suggested that the quail sternum grows through endochondral ossification. The
future study will further explore the histological changes of quail sternum
during post-hatching development.
| [
{
"created": "Mon, 2 Jul 2018 18:07:05 GMT",
"version": "v1"
}
] | 2018-07-04 | [
[
"Araby",
"Nashwa",
""
],
[
"Soliman",
"Soha",
""
],
[
"Raheem",
"Eman Abdel",
""
],
[
"Ahmed",
"Yasser",
""
]
] | The flat bone develops through intramembranous ossification, in which the mesenchymal cells are directly driven towards osteogenic lineage without the formation of cartilage template. While long bone develops through endochondral ossification, where cartilage template act as an intermediate stage between mesenchymal and bone tissues. Although the avian sternum is a flat bone, some studies describe formation of a cartilage template during its development. The aim of the current study was to observe the mechanism of ossification in quail sternum during embryonic development. Thirty quail embryos were collected for the current study (5 embryos/ day) during the period between Day (D) 5 and D10 of embryonic development and processed for light microscopy. The differentiation of mesenchymal condensation in to the chondrogenic cells was observed at D5 whereas the secretion of extracellular matrix could be evident at D6. The cartilage primordia were observed by D7 which were consisted of chondrocytes, embedded in matrix and surrounded by perichondrium. Later these primordia were developed in to cartilage template by D8 where the chondrocytes were present in their lacuna. This template attained the shape of future sternum by D9, which was more distinct at D10. These preliminary observations suggested that the quail sternum grows through endochondral ossification. The future study will further explore the histological changes of quail sternum during post-hatching development. |
1403.1034 | Sang-Yoon Kim | Sang-Yoon Kim and Woochang Lim | Effect of Small-World Connectivity on Fast Sparsely Synchronized
Cortical Rhythms | null | null | null | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fast cortical rhythms with stochastic and intermittent neural discharges have
been observed in electric recordings of brain activity. Recently, Brunel et al.
developed a framework to describe this kind of fast sparse synchronization in
both random and globally-coupled networks of suprathreshold spiking neurons.
However, in a real cortical circuit, synaptic connections are known to have
complex topology which is neither regular nor random. Hence, in order to extend
the works of Brunel et al. to realistic neural networks, we study the effect of
network architecture on these fast sparsely synchronized rhythms in an
inhibitory population of suprathreshold fast spiking (FS) Izhikevich
interneurons. We first employ the conventional Erd\"{o}s-Renyi random graph of
suprathreshold FS Izhikevich interneurons for modeling the complex connectivity
in neural systems, and study emergence of the population synchronized states by
varying both the synaptic inhibition strength $J$ and the noise intensity $D$.
Thus, fast sparsely synchronized states of relatively high degree are found to
appear for large values of $J$ and $D$. Second, for fixed values of $J$ and $D$
where fast sparse synchronization occurs in the random network, we consider the
Watts-Strogatz small-world network of suprathreshold FS Izhikevich interneurons
which interpolates between regular lattice and random graph via rewiring, and
investigate the effect of small-world synaptic connectivity on emergence of
fast sparsely synchronized rhythms by varying the rewiring probability $p$ from
short-range to long-range connection. When passing a small critical value
$p^*_c$ $(\simeq 0.12)$, fast sparsely synchronized population rhythms are
found to emerge in small-world networks with predominantly local connections
and rare long-range connections.
| [
{
"created": "Wed, 5 Mar 2014 08:11:42 GMT",
"version": "v1"
},
{
"created": "Mon, 16 Jun 2014 08:25:03 GMT",
"version": "v2"
},
{
"created": "Thu, 10 Jul 2014 05:46:20 GMT",
"version": "v3"
},
{
"created": "Tue, 2 Sep 2014 01:56:01 GMT",
"version": "v4"
}
] | 2014-09-03 | [
[
"Kim",
"Sang-Yoon",
""
],
[
"Lim",
"Woochang",
""
]
] | Fast cortical rhythms with stochastic and intermittent neural discharges have been observed in electric recordings of brain activity. Recently, Brunel et al. developed a framework to describe this kind of fast sparse synchronization in both random and globally-coupled networks of suprathreshold spiking neurons. However, in a real cortical circuit, synaptic connections are known to have complex topology which is neither regular nor random. Hence, in order to extend the works of Brunel et al. to realistic neural networks, we study the effect of network architecture on these fast sparsely synchronized rhythms in an inhibitory population of suprathreshold fast spiking (FS) Izhikevich interneurons. We first employ the conventional Erd\"{o}s-Renyi random graph of suprathreshold FS Izhikevich interneurons for modeling the complex connectivity in neural systems, and study emergence of the population synchronized states by varying both the synaptic inhibition strength $J$ and the noise intensity $D$. Thus, fast sparsely synchronized states of relatively high degree are found to appear for large values of $J$ and $D$. Second, for fixed values of $J$ and $D$ where fast sparse synchronization occurs in the random network, we consider the Watts-Strogatz small-world network of suprathreshold FS Izhikevich interneurons which interpolates between regular lattice and random graph via rewiring, and investigate the effect of small-world synaptic connectivity on emergence of fast sparsely synchronized rhythms by varying the rewiring probability $p$ from short-range to long-range connection. When passing a small critical value $p^*_c$ $(\simeq 0.12)$, fast sparsely synchronized population rhythms are found to emerge in small-world networks with predominantly local connections and rare long-range connections. |
1705.03738 | Fengyan Wu | Fengyan Wu, Xiaoli Chen, Yayun Zheng, Jinqiao Duan, J\"urgen Kurths,
Xiaofan Li | L\'{e}vy noise-induced transitions in gene regulatory networks | null | null | null | null | q-bio.MN math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Important effects of noise on a one-dimensional gene expression model
involving a single gene have recently been discussed. However, few works have
been devoted to the transition in two-dimensional models which include the
interaction of genes. Therefore, we investigate here, a quantitative
two-dimensional model (MeKS network) of gene expression dynamics describing the
competence development in the B. subtilis under the influence of L\'evy as well
as Brownian motions, where noises can do the B. subtilis a favor in nutrient
depletion. To analyze the transitions between the vegetative and the competence
regions therein, two deterministic quantities, the mean first exit time (MFET)
and the first escape probability (FEP) from a microscopic perspective, as well
as their averaged versions from a macroscopic perspective, are applied. The
relative contribution factor (RCF), the ratio of non-Gaussian and Gaussian
noise strengths, is adopted to implement optimal control in these transitions.
Schematic representations indicate that there exists an optimum choice that
makes the transition occurring at the highest probability. Additionally, we use
a geometric concept, the stochastic basin of attraction, to exhibit a pictorial
comprehension about the influence of the L\'{e}vy motion on the basin stability
of the competence state.
| [
{
"created": "Wed, 10 May 2017 13:04:57 GMT",
"version": "v1"
}
] | 2017-05-11 | [
[
"Wu",
"Fengyan",
""
],
[
"Chen",
"Xiaoli",
""
],
[
"Zheng",
"Yayun",
""
],
[
"Duan",
"Jinqiao",
""
],
[
"Kurths",
"Jürgen",
""
],
[
"Li",
"Xiaofan",
""
]
] | Important effects of noise on a one-dimensional gene expression model involving a single gene have recently been discussed. However, few works have been devoted to the transition in two-dimensional models which include the interaction of genes. Therefore, we investigate here, a quantitative two-dimensional model (MeKS network) of gene expression dynamics describing the competence development in the B. subtilis under the influence of L\'evy as well as Brownian motions, where noises can do the B. subtilis a favor in nutrient depletion. To analyze the transitions between the vegetative and the competence regions therein, two deterministic quantities, the mean first exit time (MFET) and the first escape probability (FEP) from a microscopic perspective, as well as their averaged versions from a macroscopic perspective, are applied. The relative contribution factor (RCF), the ratio of non-Gaussian and Gaussian noise strengths, is adopted to implement optimal control in these transitions. Schematic representations indicate that there exists an optimum choice that makes the transition occurring at the highest probability. Additionally, we use a geometric concept, the stochastic basin of attraction, to exhibit a pictorial comprehension about the influence of the L\'{e}vy motion on the basin stability of the competence state. |
2404.08711 | Pratham Kankariya | Pratham Kankariya, Rachita Rode, Kevin Mudaliar, Prof. Pranali Hatode | Drug Repurposing for Parkinson's Disease Using Random Walk With Restart
Algorithm and the Parkinson's Disease Ontology Database | 5 pages, Final Year Engineering Project on Machine Learning and
Healthcare Industry | null | null | null | q-bio.QM cs.LG q-bio.BM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Parkinson's disease is a progressive and slowly developing neurodegenerative
disease, characterized by dopaminergic neuron loss in the substantia nigra
region of the brain. Despite extensive research by scientists, there is not yet
a cure to this problem and the available therapies mainly help to reduce some
of the Parkinson's symptoms. Drug repurposing (that is, the process of finding
new uses for existing drugs) receives more appraisals as an efficient way that
allows for reducing the time, resources, and risks associated with the
development of new drugs. In this research, we design a novel computational
platform that integrates gene expression data, biological networks, and the
PDOD database to identify possible drug-repositioning agents for PD therapy. By
using machine learning approaches like the RWR algorithm and PDOD scoring
system we arrange drug-disease conversions and sort our potential sandboxes
according to their possible efficacy. We propose gene expression analysis,
network prioritization, and drug target data analysis to arrive at a
comprehensive evaluation of drug repurposing chances. Our study results
highlight such therapies as promising drug candidates to conduct further
research on PD treatment. We also provide the rationale for promising drug
repurposing ideas by using various sources of data and computational
approaches.
| [
{
"created": "Thu, 11 Apr 2024 20:11:25 GMT",
"version": "v1"
}
] | 2024-04-16 | [
[
"Kankariya",
"Pratham",
""
],
[
"Rode",
"Rachita",
""
],
[
"Mudaliar",
"Kevin",
""
],
[
"Hatode",
"Prof. Pranali",
""
]
] | Parkinson's disease is a progressive and slowly developing neurodegenerative disease, characterized by dopaminergic neuron loss in the substantia nigra region of the brain. Despite extensive research by scientists, there is not yet a cure to this problem and the available therapies mainly help to reduce some of the Parkinson's symptoms. Drug repurposing (that is, the process of finding new uses for existing drugs) receives more appraisals as an efficient way that allows for reducing the time, resources, and risks associated with the development of new drugs. In this research, we design a novel computational platform that integrates gene expression data, biological networks, and the PDOD database to identify possible drug-repositioning agents for PD therapy. By using machine learning approaches like the RWR algorithm and PDOD scoring system we arrange drug-disease conversions and sort our potential sandboxes according to their possible efficacy. We propose gene expression analysis, network prioritization, and drug target data analysis to arrive at a comprehensive evaluation of drug repurposing chances. Our study results highlight such therapies as promising drug candidates to conduct further research on PD treatment. We also provide the rationale for promising drug repurposing ideas by using various sources of data and computational approaches. |
2212.00168 | Vince Grolmusz | Daniel Hegedus and Vince Grolmusz | Robust Circuitry-Based Scores of Structural Importance of Human Brain
Areas | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | We consider the 1015-vertex human consensus connectome computed from the
diffusion MRI data of 1064 subjects. We define seven different orders on these
1015 graph vertices, where the orders depend on parameters derived from the
brain circuitry, that is, from the properties of the edges (or connections)
incident to the vertices ordered. We order the vertices according to their
degree, the sum, the maximum, and the average of the fiber counts on the
incident edges, and the sum, the maximum and the average length of the fibers
in the incident edges. We analyze the similarities of these seven orders by the
Spearman correlation coefficient and by their inversion numbers and have found
that all of these seven orders have great similarities. In other words, if we
interpret the orders as scoring of the importance of the vertices in the
consensus connectome, then the scores of the vertices will be similar in all
seven orderings. That is, important vertices of the human connectome typically
have many neighbors, connected with long and thick axonal fibers (where
thickness is measured by fiber numbers), and their incident edges have high
maximum and average values of length and fiber-number parameters, too.
Therefore, these parameters may yield robust ways of deciding which vertices
are more important in the anatomy of our brain circuitry than the others.
| [
{
"created": "Wed, 30 Nov 2022 23:32:26 GMT",
"version": "v1"
}
] | 2022-12-02 | [
[
"Hegedus",
"Daniel",
""
],
[
"Grolmusz",
"Vince",
""
]
] | We consider the 1015-vertex human consensus connectome computed from the diffusion MRI data of 1064 subjects. We define seven different orders on these 1015 graph vertices, where the orders depend on parameters derived from the brain circuitry, that is, from the properties of the edges (or connections) incident to the vertices ordered. We order the vertices according to their degree, the sum, the maximum, and the average of the fiber counts on the incident edges, and the sum, the maximum and the average length of the fibers in the incident edges. We analyze the similarities of these seven orders by the Spearman correlation coefficient and by their inversion numbers and have found that all of these seven orders have great similarities. In other words, if we interpret the orders as scoring of the importance of the vertices in the consensus connectome, then the scores of the vertices will be similar in all seven orderings. That is, important vertices of the human connectome typically have many neighbors, connected with long and thick axonal fibers (where thickness is measured by fiber numbers), and their incident edges have high maximum and average values of length and fiber-number parameters, too. Therefore, these parameters may yield robust ways of deciding which vertices are more important in the anatomy of our brain circuitry than the others. |
1504.00033 | Guo-Wei Wei | Kelin Xia and Zhixiong Zhao and Guo-Wei Wei | Multiresolution topological simplification | 22 pages and 14 figures | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Persistent homology has been devised as a promising tool for the topological
simplification of complex data. However, it is computationally intractable for
large data sets. In this work, we introduce multiresolution persistent homology
for tackling large data sets. Our basic idea is to match the resolution with
the scale of interest so as to create a topological microscopy for the
underlying data. We utilize flexibility-rigidity index (FRI) to access the
topological connectivity of the data set and define a rigidity density for the
filtration analysis. By appropriately tuning the resolution, we are able to
focus the topological lens on a desirable scale. The proposed multiresolution
topological analysis is validated by a hexagonal fractal image which has three
distinct scales. We further demonstrate the proposed method for extracting
topological fingerprints from DNA and RNA molecules. In particular, the
topological persistence of a virus capsid with 240 protein monomers is
successfully analyzed which would otherwise be inaccessible to the normal point
cloud method and unreliable by using coarse-grained multiscale persistent
homology. The proposed method has also been successfully applied to the protein
domain classification, which is the first time that persistent homology is used
for practical protein domain analysis, to our knowledge. The proposed
multiresolution topological method has potential applications in arbitrary data
sets, such as social networks, biological networks and graphs.
| [
{
"created": "Tue, 31 Mar 2015 20:47:59 GMT",
"version": "v1"
}
] | 2015-04-02 | [
[
"Xia",
"Kelin",
""
],
[
"Zhao",
"Zhixiong",
""
],
[
"Wei",
"Guo-Wei",
""
]
] | Persistent homology has been devised as a promising tool for the topological simplification of complex data. However, it is computationally intractable for large data sets. In this work, we introduce multiresolution persistent homology for tackling large data sets. Our basic idea is to match the resolution with the scale of interest so as to create a topological microscopy for the underlying data. We utilize flexibility-rigidity index (FRI) to access the topological connectivity of the data set and define a rigidity density for the filtration analysis. By appropriately tuning the resolution, we are able to focus the topological lens on a desirable scale. The proposed multiresolution topological analysis is validated by a hexagonal fractal image which has three distinct scales. We further demonstrate the proposed method for extracting topological fingerprints from DNA and RNA molecules. In particular, the topological persistence of a virus capsid with 240 protein monomers is successfully analyzed which would otherwise be inaccessible to the normal point cloud method and unreliable by using coarse-grained multiscale persistent homology. The proposed method has also been successfully applied to the protein domain classification, which is the first time that persistent homology is used for practical protein domain analysis, to our knowledge. The proposed multiresolution topological method has potential applications in arbitrary data sets, such as social networks, biological networks and graphs. |
2310.08345 | Shesha Gopal Marehalli Srinivas | Shesha Gopal Marehalli Srinivas, Francesco Avanzini, Massimiliano
Esposito | Characterizing the Conditions for Indefinite Growth in Open Chemical
Reaction Networks | null | null | null | null | q-bio.MN cond-mat.stat-mech | http://creativecommons.org/licenses/by/4.0/ | The thermodynamic and dynamical conditions necessary to observe indefinite
growth in homogeneous open chemical reaction networks (CRNs) satisfying mass
action kinetics were presented in Srinivas et al. (2023): Unimolecular CRNs can
only accumulate equilibrium concentrations of species while multimolecular CRNs
are needed to produce indefinite growth with nonequilibrium concentrations.
Within multimolecular CRNs, pseudo-unimolecular CRNs produce nonequilibrium
concentrations with zero efficiencies. Nonequilibrium growth with finite
efficiencies requires dynamically nonlinear CRNs. In this paper, we provide a
detailed analysis supporting these results. Mathematical proofs are provided
for growth in unimolecular and pseudo-unimolecular CRNs. For multimolecular
CRNs, four models displaying very distinctive topological properties are
extensively studied, both numerically and partly analytically.
| [
{
"created": "Thu, 12 Oct 2023 14:08:50 GMT",
"version": "v1"
}
] | 2023-10-13 | [
[
"Srinivas",
"Shesha Gopal Marehalli",
""
],
[
"Avanzini",
"Francesco",
""
],
[
"Esposito",
"Massimiliano",
""
]
] | The thermodynamic and dynamical conditions necessary to observe indefinite growth in homogeneous open chemical reaction networks (CRNs) satisfying mass action kinetics were presented in Srinivas et al. (2023): Unimolecular CRNs can only accumulate equilibrium concentrations of species while multimolecular CRNs are needed to produce indefinite growth with nonequilibrium concentrations. Within multimolecular CRNs, pseudo-unimolecular CRNs produce nonequilibrium concentrations with zero efficiencies. Nonequilibrium growth with finite efficiencies requires dynamically nonlinear CRNs. In this paper, we provide a detailed analysis supporting these results. Mathematical proofs are provided for growth in unimolecular and pseudo-unimolecular CRNs. For multimolecular CRNs, four models displaying very distinctive topological properties are extensively studied, both numerically and partly analytically. |
2311.17965 | Manal Helal | Manal Helal, Fanrong Kong, Sharon C. A. Chen, Michael Bain, Richard
Christen, Vitali Sintchenko | Defining Reference Sequences for Nocardia Species by Similarity and
Clustering Analyses of 16S rRNA Gene Sequence Data | null | PLoS ONE June 2011 | Volume 6 | Issue 6 | e19517 | 10.1371/journal.pone.0019517 | null | q-bio.GN cs.LG | http://creativecommons.org/licenses/by/4.0/ | The intra- and inter-species genetic diversity of bacteria and the absence of
'reference', or the most representative, sequences of individual species
present a significant challenge for sequence-based identification. The aims of
this study were to determine the utility, and compare the performance of
several clustering and classification algorithms to identify the species of 364
sequences of 16S rRNA gene with a defined species in GenBank, and 110 sequences
of 16S rRNA gene with no defined species, all within the genus Nocardia. A
total of 364 16S rRNA gene sequences of Nocardia species were studied. In
addition, 110 16S rRNA gene sequences assigned only to the Nocardia genus level
at the time of submission to GenBank were used for machine learning
classification experiments. Different clustering algorithms were compared with
a novel algorithm or the linear mapping (LM) of the distance matrix. Principal
Components Analysis was used for the dimensionality reduction and
visualization. Results: The LM algorithm achieved the highest performance and
classified the set of 364 16S rRNA sequences into 80 clusters, the majority of
which (83.52%) corresponded with the original species. The most representative
16S rRNA sequences for individual Nocardia species have been identified as
'centroids' in respective clusters from which the distances to all other
sequences were minimized; 110 16S rRNA gene sequences with identifications
recorded only at the genus level were classified using machine learning
methods. Simple kNN machine learning demonstrated the highest performance and
classified Nocardia species sequences with an accuracy of 92.7% and a mean
frequency of 0.578.
| [
{
"created": "Wed, 29 Nov 2023 12:09:02 GMT",
"version": "v1"
}
] | 2023-12-01 | [
[
"Helal",
"Manal",
""
],
[
"Kong",
"Fanrong",
""
],
[
"Chen",
"Sharon C. A.",
""
],
[
"Bain",
"Michael",
""
],
[
"Christen",
"Richard",
""
],
[
"Sintchenko",
"Vitali",
""
]
] | The intra- and inter-species genetic diversity of bacteria and the absence of 'reference', or the most representative, sequences of individual species present a significant challenge for sequence-based identification. The aims of this study were to determine the utility, and compare the performance of several clustering and classification algorithms to identify the species of 364 sequences of 16S rRNA gene with a defined species in GenBank, and 110 sequences of 16S rRNA gene with no defined species, all within the genus Nocardia. A total of 364 16S rRNA gene sequences of Nocardia species were studied. In addition, 110 16S rRNA gene sequences assigned only to the Nocardia genus level at the time of submission to GenBank were used for machine learning classification experiments. Different clustering algorithms were compared with a novel algorithm or the linear mapping (LM) of the distance matrix. Principal Components Analysis was used for the dimensionality reduction and visualization. Results: The LM algorithm achieved the highest performance and classified the set of 364 16S rRNA sequences into 80 clusters, the majority of which (83.52%) corresponded with the original species. The most representative 16S rRNA sequences for individual Nocardia species have been identified as 'centroids' in respective clusters from which the distances to all other sequences were minimized; 110 16S rRNA gene sequences with identifications recorded only at the genus level were classified using machine learning methods. Simple kNN machine learning demonstrated the highest performance and classified Nocardia species sequences with an accuracy of 92.7% and a mean frequency of 0.578. |
2103.02048 | Javier Rubio-Herrero | Javier Rubio-Herrero and Yuchen Wang | A Flexible Rolling Regression Framework for Time-Varying SIRD models:
Application to COVID-19 | null | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The present paper introduces a data-driven framework for describing the
time-varying nature of an SIRD model in the context of COVID-19. By embedding a
rolling regression in a mixed integer bilevel nonlinear programming problem,
our aim is to provide the research community with a model that reproduces
accurately the observed changes in the number of infected, recovered, and death
cases, while providing information about the time dependency of the parameters
that govern the SIRD model. We propose this optimization model and a genetic
algorithm to tackle its solution. Moreover, we test this algorithm with 2020
COVID-19 data from the state of Minnesota and found that our results are
consistent both qualitatively and quantitatively, thus proving that the
framework proposed is an effective an flexible tool to describe the dynamics of
a pandemic.
| [
{
"created": "Tue, 2 Mar 2021 21:53:32 GMT",
"version": "v1"
}
] | 2021-03-04 | [
[
"Rubio-Herrero",
"Javier",
""
],
[
"Wang",
"Yuchen",
""
]
] | The present paper introduces a data-driven framework for describing the time-varying nature of an SIRD model in the context of COVID-19. By embedding a rolling regression in a mixed integer bilevel nonlinear programming problem, our aim is to provide the research community with a model that reproduces accurately the observed changes in the number of infected, recovered, and death cases, while providing information about the time dependency of the parameters that govern the SIRD model. We propose this optimization model and a genetic algorithm to tackle its solution. Moreover, we test this algorithm with 2020 COVID-19 data from the state of Minnesota and found that our results are consistent both qualitatively and quantitatively, thus proving that the framework proposed is an effective an flexible tool to describe the dynamics of a pandemic. |
1805.05359 | Laura Ellwein | Laura Ellwein Fix (1), Joseph Khoury (2), Russell Moores (2), Lauren
Linkous (1), Matthew Brandes (3), and Henry J. Rozycki (2) ((1) Department of
Mathematics and Applied Mathematics, Virginia Commonwealth University,
Richmond, VA, (2) Division of Neonatal Medicine, Children's Hospital of
Richmond, Virginia Commonwealth University, Richmond, VA, (3) VCU School of
Medicine, Virginia Commonwealth University, Richmond, VA) | Theoretical open-loop model of respiratory mechanics in the extremely
preterm infant | 22 pages, 5 figures | null | 10.1371/journal.pone.0198425 | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Non-invasive ventilation is increasingly used for respiratory support in
preterm infants, and is associated with a lower risk of chronic lung disease.
However, this mode is often not successful in the extremely preterm infant in
part due to their markedly increased chest wall compliance that does not
provide enough structure against which the forces of inhalation can generate
sufficient pressure. To address the continued challenge of studying treatments
in this fragile population, we developed a nonlinear lumped-parameter model of
respiratory system mechanics of the extremely preterm infant that incorporates
nonlinear lung and chest wall compliances and lung volume parameters tuned to
this population. In particular we developed a novel empirical representation of
progressive volume loss based on compensatory alveolar pressure increase
resulting from collapsed alveoli. The model demonstrates increased rate of
volume loss related to high chest wall compliance, and simulates laryngeal
braking for elevation of end-expiratory lung volume and constant positive
airway pressure (CPAP). The model predicts that low chest wall compliance
(chest stiffening) in addition to laryngeal braking and CPAP enhance breathing
and delay lung volume loss. These results motivate future data collection
strategies and investigation into treatments for chest wall stiffening.
| [
{
"created": "Mon, 14 May 2018 18:03:42 GMT",
"version": "v1"
}
] | 2018-07-04 | [
[
"Fix",
"Laura Ellwein",
""
],
[
"Khoury",
"Joseph",
""
],
[
"Moores",
"Russell",
""
],
[
"Linkous",
"Lauren",
""
],
[
"Brandes",
"Matthew",
""
],
[
"Rozycki",
"Henry J.",
""
]
] | Non-invasive ventilation is increasingly used for respiratory support in preterm infants, and is associated with a lower risk of chronic lung disease. However, this mode is often not successful in the extremely preterm infant in part due to their markedly increased chest wall compliance that does not provide enough structure against which the forces of inhalation can generate sufficient pressure. To address the continued challenge of studying treatments in this fragile population, we developed a nonlinear lumped-parameter model of respiratory system mechanics of the extremely preterm infant that incorporates nonlinear lung and chest wall compliances and lung volume parameters tuned to this population. In particular we developed a novel empirical representation of progressive volume loss based on compensatory alveolar pressure increase resulting from collapsed alveoli. The model demonstrates increased rate of volume loss related to high chest wall compliance, and simulates laryngeal braking for elevation of end-expiratory lung volume and constant positive airway pressure (CPAP). The model predicts that low chest wall compliance (chest stiffening) in addition to laryngeal braking and CPAP enhance breathing and delay lung volume loss. These results motivate future data collection strategies and investigation into treatments for chest wall stiffening. |
1909.08553 | Asohan Amarasingham | Jonathan Platkiewicz, Zachary Saccomano, Sam McKenzie, Daniel English
and Asohan Amarasingham | Monosynaptic inference via finely-timed spikes | 45 pages, 11 figures | J Comput Neurosci (2021) | 10.1007/s10827-020-00770-5 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Observations of finely-timed spike relationships in population recordings
have been used to support partial reconstruction of neural microcircuit
diagrams. In this approach, fine-timescale components of paired spike train
interactions are isolated and subsequently attributed to synaptic parameters.
Recent perturbation studies strengthen the case for such an inference, yet the
complete set of measurements needed to calibrate statistical models are
unavailable. To address this gap, we study features of pairwise spiking in a
large-scale in vivo dataset where presynaptic neurons were explicitly decoupled
from network activity by juxtacellular stimulation. We then construct
biophysical models of paired spike trains to reproduce the observed
phenomenology of in vivo monosynaptic interactions, including both
fine-timescale spike-spike correlations and firing irregularity. A key
characteristic of these models is that the paired neurons are coupled by
rapidly-fluctuating background inputs. We quantify a monosynapse's causal
effect by comparing the postsynaptic train with its counterfactual, when the
monosynapse is removed. Subsequently, we develop statistical techniques for
estimating this causal effect from the pre- and post-synaptic spike trains. A
particular focus is the justification and application of a nonparametric
separation of timescale principle to implement synaptic inference. Using
simulated data generated from the biophysical models, we characterize the
regimes in which the estimators accurately identify the monosynaptic effect. A
secondary goal is to initiate a critical exploration of neurostatistical
assumptions in terms of biophysical mechanisms, particularly with regards to
the challenging but arguably fundamental issue of fast, unobservable
nonstationarities in background dynamics.
| [
{
"created": "Wed, 18 Sep 2019 16:22:38 GMT",
"version": "v1"
},
{
"created": "Sat, 5 Sep 2020 23:55:40 GMT",
"version": "v2"
}
] | 2021-02-15 | [
[
"Platkiewicz",
"Jonathan",
""
],
[
"Saccomano",
"Zachary",
""
],
[
"McKenzie",
"Sam",
""
],
[
"English",
"Daniel",
""
],
[
"Amarasingham",
"Asohan",
""
]
] | Observations of finely-timed spike relationships in population recordings have been used to support partial reconstruction of neural microcircuit diagrams. In this approach, fine-timescale components of paired spike train interactions are isolated and subsequently attributed to synaptic parameters. Recent perturbation studies strengthen the case for such an inference, yet the complete set of measurements needed to calibrate statistical models are unavailable. To address this gap, we study features of pairwise spiking in a large-scale in vivo dataset where presynaptic neurons were explicitly decoupled from network activity by juxtacellular stimulation. We then construct biophysical models of paired spike trains to reproduce the observed phenomenology of in vivo monosynaptic interactions, including both fine-timescale spike-spike correlations and firing irregularity. A key characteristic of these models is that the paired neurons are coupled by rapidly-fluctuating background inputs. We quantify a monosynapse's causal effect by comparing the postsynaptic train with its counterfactual, when the monosynapse is removed. Subsequently, we develop statistical techniques for estimating this causal effect from the pre- and post-synaptic spike trains. A particular focus is the justification and application of a nonparametric separation of timescale principle to implement synaptic inference. Using simulated data generated from the biophysical models, we characterize the regimes in which the estimators accurately identify the monosynaptic effect. A secondary goal is to initiate a critical exploration of neurostatistical assumptions in terms of biophysical mechanisms, particularly with regards to the challenging but arguably fundamental issue of fast, unobservable nonstationarities in background dynamics. |
2202.11182 | Matthew Macauley | Isadora Deal, Matthew Macauley, and Robin Davies | Boolean models of the transport, synthesis, and metabolism of tryptophan
in Escherichia Coli | 33 pages, 12 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The tryptophan (trp) operon in E. coli codes for the proteins responsible for
the synthesis of the amino acid tryptophan from chorismic acid, and has been
one of the most well-studied gene networks since its discovery in the 1960s.
The tryptophanase (tna) operon codes for proteins needed to transport and
metabolize it. Both of these have been modeled individually with delay
differential equations under the assumption of mass-action kinetics. Recent
work has provided strong evidence for bistable behavior of the tna operon. The
authors of (Orozco, 2019) identified a medium range of tryptophan in which the
system has two stable steady-states, and they reproduced these experimentally.
In this paper, we will show how a Boolean model can capture this bistability.
We will also develop and analyze a Boolean model of the trp operon. Finally, we
will combine these two to create a single Boolean model of the transport,
synthesis, and metabolism of tryptophan. In this amalgamated model, the
bistability disappears, presumably reflecting the ability of the trp operon to
produce tryptophan and drive the system toward homeostasis. All of these models
have longer attractors that we call "artifacts of synchrony", which disappear
in the asynchronous automata. This curiously matches the behavior of a recent
Boolean model of the arabinose operon in E. coli, and we discuss some
open-ended questions that arise along these lines.
| [
{
"created": "Tue, 22 Feb 2022 21:22:29 GMT",
"version": "v1"
}
] | 2022-02-24 | [
[
"Deal",
"Isadora",
""
],
[
"Macauley",
"Matthew",
""
],
[
"Davies",
"Robin",
""
]
] | The tryptophan (trp) operon in E. coli codes for the proteins responsible for the synthesis of the amino acid tryptophan from chorismic acid, and has been one of the most well-studied gene networks since its discovery in the 1960s. The tryptophanase (tna) operon codes for proteins needed to transport and metabolize it. Both of these have been modeled individually with delay differential equations under the assumption of mass-action kinetics. Recent work has provided strong evidence for bistable behavior of the tna operon. The authors of (Orozco, 2019) identified a medium range of tryptophan in which the system has two stable steady-states, and they reproduced these experimentally. In this paper, we will show how a Boolean model can capture this bistability. We will also develop and analyze a Boolean model of the trp operon. Finally, we will combine these two to create a single Boolean model of the transport, synthesis, and metabolism of tryptophan. In this amalgamated model, the bistability disappears, presumably reflecting the ability of the trp operon to produce tryptophan and drive the system toward homeostasis. All of these models have longer attractors that we call "artifacts of synchrony", which disappear in the asynchronous automata. This curiously matches the behavior of a recent Boolean model of the arabinose operon in E. coli, and we discuss some open-ended questions that arise along these lines. |
2405.04011 | Wei Xie | Keilung Choy and Wei Xie | Adjoint Sensitivity Analysis on Multi-Scale Bioprocess Stochastic
Reaction Network | 11 pages, 2 figures | null | null | null | q-bio.MN stat.ML | http://creativecommons.org/licenses/by/4.0/ | Motivated by the pressing challenges in the digital twin development for
biomanufacturing systems, we introduce an adjoint sensitivity analysis (SA)
approach to expedite the learning of mechanistic model parameters. In this
paper, we consider enzymatic stochastic reaction networks representing a
multi-scale bioprocess mechanistic model that allows us to integrate disparate
data from diverse production processes and leverage the information from
existing macro-kinetic and genome-scale models. To support forward prediction
and backward reasoning, we develop a convergent adjoint SA algorithm studying
how the perturbations of model parameters and inputs (e.g., initial state)
propagate through enzymatic reaction networks and impact on output trajectory
predictions. This SA can provide a sample efficient and interpretable way to
assess the sensitivities between inputs and outputs accounting for their causal
dependencies. Our empirical study underscores the resilience of these
sensitivities and illuminates a deeper comprehension of the regulatory
mechanisms behind bioprocess through sensitivities.
| [
{
"created": "Tue, 7 May 2024 05:06:45 GMT",
"version": "v1"
},
{
"created": "Fri, 28 Jun 2024 21:50:16 GMT",
"version": "v2"
}
] | 2024-07-02 | [
[
"Choy",
"Keilung",
""
],
[
"Xie",
"Wei",
""
]
] | Motivated by the pressing challenges in the digital twin development for biomanufacturing systems, we introduce an adjoint sensitivity analysis (SA) approach to expedite the learning of mechanistic model parameters. In this paper, we consider enzymatic stochastic reaction networks representing a multi-scale bioprocess mechanistic model that allows us to integrate disparate data from diverse production processes and leverage the information from existing macro-kinetic and genome-scale models. To support forward prediction and backward reasoning, we develop a convergent adjoint SA algorithm studying how the perturbations of model parameters and inputs (e.g., initial state) propagate through enzymatic reaction networks and impact on output trajectory predictions. This SA can provide a sample efficient and interpretable way to assess the sensitivities between inputs and outputs accounting for their causal dependencies. Our empirical study underscores the resilience of these sensitivities and illuminates a deeper comprehension of the regulatory mechanisms behind bioprocess through sensitivities. |
1706.03085 | Jacqueline Wentz | J. M. Wentz (University of Colorado, Boulder), A. Mendenhall
(University of Washington, Seattle), D. M. Bortz (University of Colorado,
Boulder) | Pattern Formation in the Longevity-Related Expression of Heat Shock
Protein-16.2 in Caenorhabditis elegans | 32 pages with appendix, 10 figures, 1 table | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Aging in Caenorhabditis elegans is controlled, in part, by the insulin-like
signaling and heat shock response pathways. Following thermal stress,
expression levels of small heat shock protein 16.2 show a spatial patterning
across the 20 intestinal cells that reside along the length of the worm. Here,
we present a hypothesized mechanism that could lead to this patterned response
and develop a mathematical model of this system to test our hypothesis. We
propose that the patterned expression of heat shock protein is caused by a
diffusion-driven instability within the pseudocoelom, or fluid-filled cavity,
that borders the intestinal cells in C. elegans. This instability is due to the
interactions between two classes of insulin like peptides that serve
antagonistic roles. We examine output from the developed model and compare it
to experimental data on heat shock protein expression. Furthermore, we use the
model to gain insight on possible biological parameters in the system. The
model presented is capable of producing patterns similar to what is observed
experimentally and provides a first step in mathematically modeling
aging-related mechanisms in C. elegans.
| [
{
"created": "Fri, 9 Jun 2017 18:29:30 GMT",
"version": "v1"
}
] | 2017-06-13 | [
[
"Wentz",
"J. M.",
"",
"University of Colorado, Boulder"
],
[
"Mendenhall",
"A.",
"",
"University of Washington, Seattle"
],
[
"Bortz",
"D. M.",
"",
"University of Colorado,\n Boulder"
]
] | Aging in Caenorhabditis elegans is controlled, in part, by the insulin-like signaling and heat shock response pathways. Following thermal stress, expression levels of small heat shock protein 16.2 show a spatial patterning across the 20 intestinal cells that reside along the length of the worm. Here, we present a hypothesized mechanism that could lead to this patterned response and develop a mathematical model of this system to test our hypothesis. We propose that the patterned expression of heat shock protein is caused by a diffusion-driven instability within the pseudocoelom, or fluid-filled cavity, that borders the intestinal cells in C. elegans. This instability is due to the interactions between two classes of insulin like peptides that serve antagonistic roles. We examine output from the developed model and compare it to experimental data on heat shock protein expression. Furthermore, we use the model to gain insight on possible biological parameters in the system. The model presented is capable of producing patterns similar to what is observed experimentally and provides a first step in mathematically modeling aging-related mechanisms in C. elegans. |
q-bio/0606030 | Herculano Martinho | Sergio Godoy Penteado, Claudio S. Meneses, Anderson de Oliveira Lobo,
Airton Abrahao Martin, Herculano da Silva Martinho | Diagnosis of rotator cuff lesions by FT-Raman spectroscopy: a
biochemical study | 17 pages, presented on SPEC 2006-Heidelberg, Germany | null | null | null | q-bio.TO q-bio.BM | null | The biochemical changes on normal and degenerated tissues of rotator cuff
supraspinatus tendons were probed by FT-Raman spectroscopy. The Raman spectra
showed differences on the spectral regions of cysteine, amino acids, nucleic
acids, carbohydrates, and lipids. These spectral differences were assigned to
pathological biochemical alterations due to the degenerative process of the
tendon. Principal Components Analysis was performed on the spectral data and
enabled the correct classification of the spectra as normal (grade 1) and
degenerated (grades 2 and 3). These findings indicate that Raman spectroscopy
could be a very promising tool for the rotator cuff supraspinatus tendon
diagnosis and for quantification of their degenerative degree.
| [
{
"created": "Thu, 22 Jun 2006 12:16:43 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Penteado",
"Sergio Godoy",
""
],
[
"Meneses",
"Claudio S.",
""
],
[
"Lobo",
"Anderson de Oliveira",
""
],
[
"Martin",
"Airton Abrahao",
""
],
[
"Martinho",
"Herculano da Silva",
""
]
] | The biochemical changes on normal and degenerated tissues of rotator cuff supraspinatus tendons were probed by FT-Raman spectroscopy. The Raman spectra showed differences on the spectral regions of cysteine, amino acids, nucleic acids, carbohydrates, and lipids. These spectral differences were assigned to pathological biochemical alterations due to the degenerative process of the tendon. Principal Components Analysis was performed on the spectral data and enabled the correct classification of the spectra as normal (grade 1) and degenerated (grades 2 and 3). These findings indicate that Raman spectroscopy could be a very promising tool for the rotator cuff supraspinatus tendon diagnosis and for quantification of their degenerative degree. |
1810.11522 | Yoel Shkolnisky | Ido Greenberg and Yoel Shkolnisky | Common lines modeling for reference free ab-initio reconstruction in
cryo-EM | null | Journal of Structural Biology, 200(2): 106-117, 2017 | 10.1016/j.jsb.2017.09.007 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of estimating an unbiased and reference-free ab-inito
model for non-symmetric molecules from images generated by single-particle
cryo-electron microscopy. The proposed algorithm finds the globally optimal
assignment of orientations that simultaneously respects all common lines
between all images. The contribution of each common line to the estimated
orientations is weighted according to a statistical model for common lines'
detection errors. The key property of the proposed algorithm is that it finds
the global optimum for the orientations given the common lines. In particular,
any local optima in the common lines energy landscape do not affect the
proposed algorithm. As a result, it is applicable to thousands of images at
once, very robust to noise, completely reference free, and not biased towards
any initial model. A byproduct of the algorithm is a set of measures that allow
to asses the reliability of the obtained ab-initio model. We demonstrate the
algorithm using class averages from two experimental data sets, resulting in
ab-initio models with resolutions of 20A or better, even from class averages
consisting of as few as three raw images per class.
| [
{
"created": "Fri, 26 Oct 2018 20:35:15 GMT",
"version": "v1"
}
] | 2018-10-30 | [
[
"Greenberg",
"Ido",
""
],
[
"Shkolnisky",
"Yoel",
""
]
] | We consider the problem of estimating an unbiased and reference-free ab-inito model for non-symmetric molecules from images generated by single-particle cryo-electron microscopy. The proposed algorithm finds the globally optimal assignment of orientations that simultaneously respects all common lines between all images. The contribution of each common line to the estimated orientations is weighted according to a statistical model for common lines' detection errors. The key property of the proposed algorithm is that it finds the global optimum for the orientations given the common lines. In particular, any local optima in the common lines energy landscape do not affect the proposed algorithm. As a result, it is applicable to thousands of images at once, very robust to noise, completely reference free, and not biased towards any initial model. A byproduct of the algorithm is a set of measures that allow to asses the reliability of the obtained ab-initio model. We demonstrate the algorithm using class averages from two experimental data sets, resulting in ab-initio models with resolutions of 20A or better, even from class averages consisting of as few as three raw images per class. |
1903.07585 | Marianna Colasuonno | Marianna Colasuonno, Anna Lisa Palange, Rachida Aid, Miguel Ferreira,
Hilaria Mollica, Roberto Palomba, Michele Emdin, Massimo Del Sette, C\'edric
Chauvierre, Didier Letourneur, Paolo Decuzzi | Erythrocyte-Inspired Discoidal Polymeric Nanoconstructs carrying Tissue
Plasminogen Activator for the Enhanced Lysis of Blood Clots | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tissue plasminogen activator (tPA) is the sole approved therapeutic molecule
for the treatment of acute ischemic stroke. Yet, only a small percentage of
patients could benefit from this life-saving treatment because of medical
contraindications and severe side effects, including brain hemorrhage,
associated with delayed administration. Here, a nano therapeutic agent is
realized by directly associating the clinical formulation of tPA to the porous
structure of soft discoidal polymeric nanoconstructs (tPA-DPNs). The porous
matrix of DPNs protects tPA from rapid degradation, allowing tPA-DPNs to
preserve over 70 % of the tPA original activity after 3 h of exposure to serum
proteins. Under dynamic conditions, tPA-DPNs dissolve clots more efficiently
than free tPA, as demonstrated in a microfluidic chip where clots are formed
mimicking in vivo conditions. At 60 min post treatment initiation, the clot
area reduces by half (57 + 8 %) with tPA-DPNs, whereas a similar result (56 +
21 %) is obtained only after 90 min for free tPA. In murine mesentery venules,
the intravenous administration of 2.5 mg/kg of tPA-DPNs resolves almost 90 % of
the blood clots, whereas a similar dose of free tPA successfully recanalize
only about 40 % of the treated vessels. At about 1/10 of the clinical dose (1.0
mg/kg), tPA-DPNs still effectively dissolve 70 % of the clots, whereas free tPA
works efficiently only on 16 % of the vessels. In vivo, discoidal tPA-DPNs
outperform the lytic activity of 200 nm spherical tPA-coated nanoconstructs in
terms of both percentage of successful recanalization events and clot area
reduction. The conjugation of tPA with preserved lytic activity, the
deformability and blood circulating time of DPNs together with the faster blood
clot dissolution would make tPA-DPNs a promising nanotool for enhancing both
potency and safety of thrombolytic therapies.
| [
{
"created": "Fri, 8 Mar 2019 11:17:10 GMT",
"version": "v1"
}
] | 2019-03-19 | [
[
"Colasuonno",
"Marianna",
""
],
[
"Palange",
"Anna Lisa",
""
],
[
"Aid",
"Rachida",
""
],
[
"Ferreira",
"Miguel",
""
],
[
"Mollica",
"Hilaria",
""
],
[
"Palomba",
"Roberto",
""
],
[
"Emdin",
"Michele",
""
... | Tissue plasminogen activator (tPA) is the sole approved therapeutic molecule for the treatment of acute ischemic stroke. Yet, only a small percentage of patients could benefit from this life-saving treatment because of medical contraindications and severe side effects, including brain hemorrhage, associated with delayed administration. Here, a nano therapeutic agent is realized by directly associating the clinical formulation of tPA to the porous structure of soft discoidal polymeric nanoconstructs (tPA-DPNs). The porous matrix of DPNs protects tPA from rapid degradation, allowing tPA-DPNs to preserve over 70 % of the tPA original activity after 3 h of exposure to serum proteins. Under dynamic conditions, tPA-DPNs dissolve clots more efficiently than free tPA, as demonstrated in a microfluidic chip where clots are formed mimicking in vivo conditions. At 60 min post treatment initiation, the clot area reduces by half (57 + 8 %) with tPA-DPNs, whereas a similar result (56 + 21 %) is obtained only after 90 min for free tPA. In murine mesentery venules, the intravenous administration of 2.5 mg/kg of tPA-DPNs resolves almost 90 % of the blood clots, whereas a similar dose of free tPA successfully recanalize only about 40 % of the treated vessels. At about 1/10 of the clinical dose (1.0 mg/kg), tPA-DPNs still effectively dissolve 70 % of the clots, whereas free tPA works efficiently only on 16 % of the vessels. In vivo, discoidal tPA-DPNs outperform the lytic activity of 200 nm spherical tPA-coated nanoconstructs in terms of both percentage of successful recanalization events and clot area reduction. The conjugation of tPA with preserved lytic activity, the deformability and blood circulating time of DPNs together with the faster blood clot dissolution would make tPA-DPNs a promising nanotool for enhancing both potency and safety of thrombolytic therapies. |
q-bio/0412030 | Wan Ahmad Tajuddin Wan Abdullah | Wan Ahmad Tajuddin Wan Abdullah (Universiti Malaya, Kuala Lumpur) | Love before Sex | Paper presented at PERFIK 2004, Oct. 2004, Kuala Lumpur. 8 pages. pdf
only | null | null | null | q-bio.PE | null | Much has been debated about the benefit of sexual over asexual reproduction
in terms of evolutionary fitness. Here we focus on the advantage that may be
brought about by the process of mating, where the choosing of mates contributes
to the increase in fitness in a constructive way. We carry out computer
simulations of such mating systems and investigate, on one hand, how mate
phenotypes contribute to offspring fitness, and, on the other hand, how
selection affects mate phenotypes. We discuss how helpful such a mechanism may
be in determining trajectories on rugged energy landscapes leading to global
optimum.
| [
{
"created": "Thu, 16 Dec 2004 05:26:05 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Abdullah",
"Wan Ahmad Tajuddin Wan",
"",
"Universiti Malaya, Kuala Lumpur"
]
] | Much has been debated about the benefit of sexual over asexual reproduction in terms of evolutionary fitness. Here we focus on the advantage that may be brought about by the process of mating, where the choosing of mates contributes to the increase in fitness in a constructive way. We carry out computer simulations of such mating systems and investigate, on one hand, how mate phenotypes contribute to offspring fitness, and, on the other hand, how selection affects mate phenotypes. We discuss how helpful such a mechanism may be in determining trajectories on rugged energy landscapes leading to global optimum. |
1505.01138 | Sebastian Kmiecik | Maciej Blaszczyk, Mateusz Kurcinski, Maksim Kouza, Lukasz Wieteska,
Aleksander Debinski, Andrzej Kolinski, Sebastian Kmiecik | Modeling of protein-peptide interactions using the CABS-dock web server
for binding site search and flexible docking | Published in Methods journal, available online 10 July 2015 | Methods, 93:72-83, 2016 | 10.1016/j.ymeth.2015.07.004 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Protein-peptide interactions play essential functional roles in living
organisms and their structural characterization is a hot subject of current
experimental and theoretical research. Computational modeling of the structure
of protein-peptide interactions is usually divided into two stages: prediction
of the binding site at a protein receptor surface, and then docking (and
modeling) the peptide structure into the known binding site. This paper
presents a comprehensive CABS-dock method for the simultaneous search of
binding sites and flexible protein-peptide docking, available as a users
friendly web server. We present example CABS-dock results obtained in the
default CABS-dock mode and using its advanced options that enable the user to
increase the range of flexibility for chosen receptor fragments or to exclude
user-selected binding modes from docking search. Furthermore, we demonstrate a
strategy to improve CABS-dock performance by assessing the quality of models
with classical molecular dynamics. Finally, we discuss the promising extensions
and applications of the CABS-dock method and provide a tutorial appendix for
the convenient analysis and visualization of CABS-dock results. The CABS-dock
web server is freely available at http://biocomp.chem.uw.edu.pl/CABSdock/
| [
{
"created": "Tue, 5 May 2015 19:34:52 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Jul 2015 14:27:05 GMT",
"version": "v2"
}
] | 2016-01-12 | [
[
"Blaszczyk",
"Maciej",
""
],
[
"Kurcinski",
"Mateusz",
""
],
[
"Kouza",
"Maksim",
""
],
[
"Wieteska",
"Lukasz",
""
],
[
"Debinski",
"Aleksander",
""
],
[
"Kolinski",
"Andrzej",
""
],
[
"Kmiecik",
"Sebastian",
... | Protein-peptide interactions play essential functional roles in living organisms and their structural characterization is a hot subject of current experimental and theoretical research. Computational modeling of the structure of protein-peptide interactions is usually divided into two stages: prediction of the binding site at a protein receptor surface, and then docking (and modeling) the peptide structure into the known binding site. This paper presents a comprehensive CABS-dock method for the simultaneous search of binding sites and flexible protein-peptide docking, available as a users friendly web server. We present example CABS-dock results obtained in the default CABS-dock mode and using its advanced options that enable the user to increase the range of flexibility for chosen receptor fragments or to exclude user-selected binding modes from docking search. Furthermore, we demonstrate a strategy to improve CABS-dock performance by assessing the quality of models with classical molecular dynamics. Finally, we discuss the promising extensions and applications of the CABS-dock method and provide a tutorial appendix for the convenient analysis and visualization of CABS-dock results. The CABS-dock web server is freely available at http://biocomp.chem.uw.edu.pl/CABSdock/ |
2104.05991 | Nicol\'as Gallego | Nicol\'as Gallego-Molina, Marco Formoso, Andr\'es Ortiz, Francisco J.
Mart\'inez-Murcia, Juan L. Luque | Temporal EigenPAC for dyslexia diagnosis | null | null | 10.1007/978-3-030-85099-9_4 | null | q-bio.NC cs.LG eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Electroencephalography signals allow to explore the functional activity of
the brain cortex in a non-invasive way. However, the analysis of these signals
is not straightforward due to the presence of different artifacts and the very
low signal-to-noise ratio. Cross-Frequency Coupling (CFC) methods provide a way
to extract information from EEG, related to the synchronization among frequency
bands. However, CFC methods are usually applied in a local way, computing the
interaction between phase and amplitude at the same electrode. In this work we
show a method to compute PAC features among electrodes to study the functional
connectivity. Moreover, this has been applied jointly with Principal Component
Analysis to explore patterns related to Dyslexia in 7-years-old children. The
developed methodology reveals the temporal evolution of PAC-based connectivity.
Directions of greatest variance computed by PCA are called eigenPACs here,
since they resemble the classical \textit{eigenfaces} representation. The
projection of PAC data onto the eigenPACs provide a set of features that has
demonstrates their discriminative capability, specifically in the Beta-Gamma
bands.
| [
{
"created": "Tue, 13 Apr 2021 07:51:07 GMT",
"version": "v1"
}
] | 2022-01-24 | [
[
"Gallego-Molina",
"Nicolás",
""
],
[
"Formoso",
"Marco",
""
],
[
"Ortiz",
"Andrés",
""
],
[
"Martínez-Murcia",
"Francisco J.",
""
],
[
"Luque",
"Juan L.",
""
]
] | Electroencephalography signals allow to explore the functional activity of the brain cortex in a non-invasive way. However, the analysis of these signals is not straightforward due to the presence of different artifacts and the very low signal-to-noise ratio. Cross-Frequency Coupling (CFC) methods provide a way to extract information from EEG, related to the synchronization among frequency bands. However, CFC methods are usually applied in a local way, computing the interaction between phase and amplitude at the same electrode. In this work we show a method to compute PAC features among electrodes to study the functional connectivity. Moreover, this has been applied jointly with Principal Component Analysis to explore patterns related to Dyslexia in 7-years-old children. The developed methodology reveals the temporal evolution of PAC-based connectivity. Directions of greatest variance computed by PCA are called eigenPACs here, since they resemble the classical \textit{eigenfaces} representation. The projection of PAC data onto the eigenPACs provide a set of features that has demonstrates their discriminative capability, specifically in the Beta-Gamma bands. |
2001.09411 | Farshid Mohammad-Rafiee | Fatemeh Khodabandeh, Hashem Fatemi, and Farshid Mohammad-Rafiee1 | Insight into the Unwrapping of the Dinucleosome | null | null | null | null | q-bio.BM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dynamics of nucleosomes, the building blocks of the chromatin, has crucial
effects on expression, replication and repair of genomes in eukaryotes. Beside
constant movements of nucleosomes by thermal fluctuations, ATP-dependent
chromatin remodelling complexes cause their active displacements. Here we
propose a theoretical analysis of dinucleosome wrapping and unwrapping dynamics
in the presence of an external force. We explore the energy landscape and
configurations of dinucleosome in different unwrapped states. Moreover, using a
dynamical Monte-Carlo simulation algorithm, we demonstrate the dynamical
features of the system such as the unwrapping force for partial and full
wrapping processes. Furthermore, we show that in the short length of linker DNA
($\sim 10 - 90$ bp), the asymmetric unwrapping occurs. These findings could
shed some light on chromatin dynamics and gene accessibility.
| [
{
"created": "Sun, 26 Jan 2020 06:30:19 GMT",
"version": "v1"
}
] | 2020-01-28 | [
[
"Khodabandeh",
"Fatemeh",
""
],
[
"Fatemi",
"Hashem",
""
],
[
"Mohammad-Rafiee1",
"Farshid",
""
]
] | Dynamics of nucleosomes, the building blocks of the chromatin, has crucial effects on expression, replication and repair of genomes in eukaryotes. Beside constant movements of nucleosomes by thermal fluctuations, ATP-dependent chromatin remodelling complexes cause their active displacements. Here we propose a theoretical analysis of dinucleosome wrapping and unwrapping dynamics in the presence of an external force. We explore the energy landscape and configurations of dinucleosome in different unwrapped states. Moreover, using a dynamical Monte-Carlo simulation algorithm, we demonstrate the dynamical features of the system such as the unwrapping force for partial and full wrapping processes. Furthermore, we show that in the short length of linker DNA ($\sim 10 - 90$ bp), the asymmetric unwrapping occurs. These findings could shed some light on chromatin dynamics and gene accessibility. |
2205.02118 | Antonio Batista | Matheus Hansen, Paulo R. Protachevicz, Kelly C. Iarosz, Ibere L.
Caldas, Antonio M. Batista, Elbert E. N. Macau | The effect of time delay for synchronisation suppression in neuronal
networks | null | null | 10.1016/j.chaos.2022.112690 | null | q-bio.NC nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the time delay in the synaptic conductance for suppression of spike
synchronisation in a random network of Hodgkin Huxley neurons coupled by means
of chemical synapses. In the first part, we examine in detail how the time
delay acts over the network during the synchronised and desynchronised neuronal
activities. We observe a relation between the neuronal dynamics and the syaptic
conductance distributions. We find parameter values in which the time delay has
high effectiveness in promoting the suppression of spike synchronisation. In
the second part, we analyse how the delayed neuronal networks react when pulsed
inputs with different profiles (periodic, random, and mixed) are applied on the
neurons. We show the main parameters responsible for inducing or not
synchronous neuronal oscillations in delayed networks.
| [
{
"created": "Sat, 30 Apr 2022 14:10:04 GMT",
"version": "v1"
}
] | 2022-10-19 | [
[
"Hansen",
"Matheus",
""
],
[
"Protachevicz",
"Paulo R.",
""
],
[
"Iarosz",
"Kelly C.",
""
],
[
"Caldas",
"Ibere L.",
""
],
[
"Batista",
"Antonio M.",
""
],
[
"Macau",
"Elbert E. N.",
""
]
] | We study the time delay in the synaptic conductance for suppression of spike synchronisation in a random network of Hodgkin Huxley neurons coupled by means of chemical synapses. In the first part, we examine in detail how the time delay acts over the network during the synchronised and desynchronised neuronal activities. We observe a relation between the neuronal dynamics and the syaptic conductance distributions. We find parameter values in which the time delay has high effectiveness in promoting the suppression of spike synchronisation. In the second part, we analyse how the delayed neuronal networks react when pulsed inputs with different profiles (periodic, random, and mixed) are applied on the neurons. We show the main parameters responsible for inducing or not synchronous neuronal oscillations in delayed networks. |
1802.02523 | Zg Ma | John Z. G. Ma | Plasma Brain Dynamics (PBD): A Mechanism for EEG Waves Under Human
Consciousness | null | Cosmos and History: The Journal of Natural and Social Philosophy,
Vol 13, No 2 (2017) | null | null | q-bio.NC physics.med-ph | http://creativecommons.org/publicdomain/zero/1.0/ | EEG signals are records of nonlinear solitary waves in human brains. The
waves have several types (e.g., a, b, g, q, d) in response to different levels
of consciousness. They are classified into two groups: Group-1 consists of
complex storm-like waves (a, b, and g); Group-2 is composed of simple
quasilinear waves (q and d). In order to elucidate the mechanism of EEG wave
formation and propagation, this paper extends the Vlasov-Maxwell equations of
Plasma Brain Dynamics (PBD) to a set of two-fluid, self-similar, nonlinear
solitary wave equations. Numerical simulations are performed for different EEG
signals. Main results include: (1) The excitation and propagation of the EEG
wave packets are dependent of electric and magnetic fields, brain aqua-ions,
electron and ion temperatures, masses, and their initial fluid speeds; (2)
Group-1 complex waves contain three ingredients: the high-frequency
ion-acoustic (IA) mode, the intermediate-frequency lower-hybrid (LH) mode, and
the low-frequency ion-cyclotron (IC) mode; (3) Group-2 simple waves fall within
the IA band, featured by one or a combination of the three envelopes:
sinusoidal, sawtooth, and spiky/bipolar. The study proposes an alternative
model to Quantum Brain Dynamics (QBD) by suggesting that the formation and
propagation of the nonlinear solitary EEG waves in the brain have the same
mechanism as that of the waves in space plasmas
| [
{
"created": "Tue, 16 Jan 2018 01:51:18 GMT",
"version": "v1"
}
] | 2019-07-22 | [
[
"Ma",
"John Z. G.",
""
]
] | EEG signals are records of nonlinear solitary waves in human brains. The waves have several types (e.g., a, b, g, q, d) in response to different levels of consciousness. They are classified into two groups: Group-1 consists of complex storm-like waves (a, b, and g); Group-2 is composed of simple quasilinear waves (q and d). In order to elucidate the mechanism of EEG wave formation and propagation, this paper extends the Vlasov-Maxwell equations of Plasma Brain Dynamics (PBD) to a set of two-fluid, self-similar, nonlinear solitary wave equations. Numerical simulations are performed for different EEG signals. Main results include: (1) The excitation and propagation of the EEG wave packets are dependent of electric and magnetic fields, brain aqua-ions, electron and ion temperatures, masses, and their initial fluid speeds; (2) Group-1 complex waves contain three ingredients: the high-frequency ion-acoustic (IA) mode, the intermediate-frequency lower-hybrid (LH) mode, and the low-frequency ion-cyclotron (IC) mode; (3) Group-2 simple waves fall within the IA band, featured by one or a combination of the three envelopes: sinusoidal, sawtooth, and spiky/bipolar. The study proposes an alternative model to Quantum Brain Dynamics (QBD) by suggesting that the formation and propagation of the nonlinear solitary EEG waves in the brain have the same mechanism as that of the waves in space plasmas |
2303.16743 | Florian Jug | Damian Edward Dalle Nogare, Matthew Hartley, Joran Deschamps, Jan
Ellenberg, Florian Jug | bAIoimage analysis: elevating the rate of scientific discovery -- as a
community | 5 pages, 1 figure, opinion | null | 10.1038/s41592-023-01929-5 | null | q-bio.OT eess.IV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The future of bioimage analysis is increasingly defined by the development
and use of tools that rely on deep learning and artificial intelligence (AI).
For this trend to continue in a way most useful for stimulating scientific
progress, it will require our multidisciplinary community to work together,
establish FAIR data sharing and deliver usable, reproducible analytical tools.
| [
{
"created": "Wed, 29 Mar 2023 14:49:40 GMT",
"version": "v1"
}
] | 2023-08-31 | [
[
"Nogare",
"Damian Edward Dalle",
""
],
[
"Hartley",
"Matthew",
""
],
[
"Deschamps",
"Joran",
""
],
[
"Ellenberg",
"Jan",
""
],
[
"Jug",
"Florian",
""
]
] | The future of bioimage analysis is increasingly defined by the development and use of tools that rely on deep learning and artificial intelligence (AI). For this trend to continue in a way most useful for stimulating scientific progress, it will require our multidisciplinary community to work together, establish FAIR data sharing and deliver usable, reproducible analytical tools. |
1012.2025 | Adriano Barra Dr. | Adriano Barra, Silvio Franz, Thiago Sabetta | Some thoughts on the ontogenesis in B-cell immune networks | null | null | null | null | q-bio.CB physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We are interested in modeling theoretical immunology within a statistical
mechanics flavor: focusing on the antigen-independent maturation process of
B-cells, in this paper we try to revise the problem of self vs non-self
discrimination by mature B lymphocytes. We consider only B lymphocytes: despite
this is of course an oversimplification, however such a toy model may help to
highlight features of their interactions otherwise shadowed by main driven
mechanisms due to i.e. helper T-cell signalling. By analyzing possible
influences of the ontogenesis of the immune system on the final behavior of B
lymphocytes, we try to merge over the purely negative selection mechanism at
their birth with the adult self-regulation process. The final goal is a
"thermodynamical picture" by which both the scenarios can exist and, actually,
be synergically complementary: Trough numerical simulations we impose on a
recent scheme for B-cell interactions, that part of self-reactive lymphocytes
are killed during the ontogenesis by which two observations stem: At first the
so built system is able to show anergy with respect to the previously
encountered self even in its mature life, then this naturally leads to an
increasing variance (and average) in the connectivity distribution of the
resulting idiotypic network. As a consequence, following Varela perspective,
this shift may contribute to push to anergy those self-directed cells which are
free to explore the body: identifying the latter as the highly connected ones,
anergy is imposed even via the B-network regulation, and its strength is
influenced by the negative selection.
| [
{
"created": "Thu, 9 Dec 2010 15:05:03 GMT",
"version": "v1"
}
] | 2010-12-10 | [
[
"Barra",
"Adriano",
""
],
[
"Franz",
"Silvio",
""
],
[
"Sabetta",
"Thiago",
""
]
] | We are interested in modeling theoretical immunology within a statistical mechanics flavor: focusing on the antigen-independent maturation process of B-cells, in this paper we try to revise the problem of self vs non-self discrimination by mature B lymphocytes. We consider only B lymphocytes: despite this is of course an oversimplification, however such a toy model may help to highlight features of their interactions otherwise shadowed by main driven mechanisms due to i.e. helper T-cell signalling. By analyzing possible influences of the ontogenesis of the immune system on the final behavior of B lymphocytes, we try to merge over the purely negative selection mechanism at their birth with the adult self-regulation process. The final goal is a "thermodynamical picture" by which both the scenarios can exist and, actually, be synergically complementary: Trough numerical simulations we impose on a recent scheme for B-cell interactions, that part of self-reactive lymphocytes are killed during the ontogenesis by which two observations stem: At first the so built system is able to show anergy with respect to the previously encountered self even in its mature life, then this naturally leads to an increasing variance (and average) in the connectivity distribution of the resulting idiotypic network. As a consequence, following Varela perspective, this shift may contribute to push to anergy those self-directed cells which are free to explore the body: identifying the latter as the highly connected ones, anergy is imposed even via the B-network regulation, and its strength is influenced by the negative selection. |
1208.6497 | Christel Kamp | Christel Kamp, Mathieu Moslonka-Lefebvre, Samuel Alizon | Predicting epidemics on weighted networks | 20 pages, 3 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The contact structure between hosts has a critical influence on disease
spread. However, most networkbased models used in epidemiology tend to ignore
heterogeneity in the weighting of contacts. This assumption is known to be at
odds with the data for many contact networks (e.g. sexual contact networks) and
to have a strong effect on the predictions of epidemiological models. One of
the reasons why models usually ignore heterogeneity in transmission is that we
currently lack tools to analyze weighted networks, such that most studies rely
on numerical simulations. Here, we present a novel framework to estimate key
epidemiological variables, such as the rate of early epidemic expansion and the
basic reproductive ratio, from joint probability distributions of number of
partners (contacts) and number of interaction events through which contacts are
weighted. This framework also allows for a derivation of the full time course
of epidemic prevalence and contact behaviour which is validated using numerical
simulations. Our framework allows for the incorporation of more realistic
contact networks into epidemiological models, thus improving predictions on the
spread of emerging infectious diseases.
| [
{
"created": "Fri, 31 Aug 2012 13:47:29 GMT",
"version": "v1"
}
] | 2012-09-03 | [
[
"Kamp",
"Christel",
""
],
[
"Moslonka-Lefebvre",
"Mathieu",
""
],
[
"Alizon",
"Samuel",
""
]
] | The contact structure between hosts has a critical influence on disease spread. However, most networkbased models used in epidemiology tend to ignore heterogeneity in the weighting of contacts. This assumption is known to be at odds with the data for many contact networks (e.g. sexual contact networks) and to have a strong effect on the predictions of epidemiological models. One of the reasons why models usually ignore heterogeneity in transmission is that we currently lack tools to analyze weighted networks, such that most studies rely on numerical simulations. Here, we present a novel framework to estimate key epidemiological variables, such as the rate of early epidemic expansion and the basic reproductive ratio, from joint probability distributions of number of partners (contacts) and number of interaction events through which contacts are weighted. This framework also allows for a derivation of the full time course of epidemic prevalence and contact behaviour which is validated using numerical simulations. Our framework allows for the incorporation of more realistic contact networks into epidemiological models, thus improving predictions on the spread of emerging infectious diseases. |
2010.03951 | Kexin Huang | Kexin Huang, Tianfan Fu, Dawood Khan, Ali Abid, Ali Abdalla, Abubakar
Abid, Lucas M. Glass, Marinka Zitnik, Cao Xiao, Jimeng Sun | MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning | NeurIPS 2020 Demonstration Track | null | null | null | q-bio.QM cs.HC cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The efficacy of a drug depends on its binding affinity to the therapeutic
target and pharmacokinetics. Deep learning (DL) has demonstrated remarkable
progress in predicting drug efficacy. We develop MolDesigner, a
human-in-the-loop web user-interface (UI), to assist drug developers leverage
DL predictions to design more effective drugs. A developer can draw a drug
molecule in the interface. In the backend, more than 17 state-of-the-art DL
models generate predictions on important indices that are crucial for a drug's
efficacy. Based on these predictions, drug developers can edit the drug
molecule and reiterate until satisfaction. MolDesigner can make predictions in
real-time with a latency of less than a second.
| [
{
"created": "Mon, 5 Oct 2020 21:25:25 GMT",
"version": "v1"
}
] | 2020-10-09 | [
[
"Huang",
"Kexin",
""
],
[
"Fu",
"Tianfan",
""
],
[
"Khan",
"Dawood",
""
],
[
"Abid",
"Ali",
""
],
[
"Abdalla",
"Ali",
""
],
[
"Abid",
"Abubakar",
""
],
[
"Glass",
"Lucas M.",
""
],
[
"Zitnik",
"... | The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics. Deep learning (DL) has demonstrated remarkable progress in predicting drug efficacy. We develop MolDesigner, a human-in-the-loop web user-interface (UI), to assist drug developers leverage DL predictions to design more effective drugs. A developer can draw a drug molecule in the interface. In the backend, more than 17 state-of-the-art DL models generate predictions on important indices that are crucial for a drug's efficacy. Based on these predictions, drug developers can edit the drug molecule and reiterate until satisfaction. MolDesigner can make predictions in real-time with a latency of less than a second. |
1909.11070 | R. Mulet | Jorge Fernandez-de-Cossio-Diaz and Roberto Mulet | Spin Glass Theory of Interacting Metabolic Networks | 4 Figures | Phys. Rev. E 101, 042401 (2020) | 10.1103/PhysRevE.101.042401 | null | q-bio.MN cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We cast the metabolism of interacting cells within a statistical mechanics
framework considering both, the actual phenotypic capacities of each cell and
its interaction with its neighbors. Reaction fluxes will be the components of
high-dimensional spin vectors, whose values will be constrained by the
stochiometry and the energy requirements of the metabolism. Within this
picture, finding the phenotypic states of the population turns out to be
equivalent to searching for the equilibrium states of a disordered spin model.
We provide a general solution of this problem for arbitrary metabolic networks
and interactions. We apply this solution to a simplified model of metabolism
and to a complex metabolic network, the central core of the \emph{E. coli}, and
demonstrate that the combination of selective pressure and interactions define
a complex phenotypic space. Cells may specialize in producing or consuming
metabolites complementing each other at the population level and this is
described by an equilibrium phase space with multiple minima, like in a
spin-glass model.
| [
{
"created": "Tue, 24 Sep 2019 17:47:40 GMT",
"version": "v1"
}
] | 2020-04-08 | [
[
"Fernandez-de-Cossio-Diaz",
"Jorge",
""
],
[
"Mulet",
"Roberto",
""
]
] | We cast the metabolism of interacting cells within a statistical mechanics framework considering both, the actual phenotypic capacities of each cell and its interaction with its neighbors. Reaction fluxes will be the components of high-dimensional spin vectors, whose values will be constrained by the stochiometry and the energy requirements of the metabolism. Within this picture, finding the phenotypic states of the population turns out to be equivalent to searching for the equilibrium states of a disordered spin model. We provide a general solution of this problem for arbitrary metabolic networks and interactions. We apply this solution to a simplified model of metabolism and to a complex metabolic network, the central core of the \emph{E. coli}, and demonstrate that the combination of selective pressure and interactions define a complex phenotypic space. Cells may specialize in producing or consuming metabolites complementing each other at the population level and this is described by an equilibrium phase space with multiple minima, like in a spin-glass model. |
2109.10224 | Gregory Rehm | Gregory Rehm, Jimmy Nguyen, Chelsea Gilbeau, Marc T Bomactao, Chen-Nee
Chuah, Jason Adams | Clinical Validation of Single-Chamber Model-Based Algorithms Used to
Estimate Respiratory Compliance | null | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Non-invasive estimation of respiratory physiology using computational
algorithms promises to be a valuable technique for future clinicians to detect
detrimental changes in patient pathophysiology. However, few clinical
algorithms used to non-invasively analyze lung physiology have undergone
rigorous validation in a clinical setting, and are often validated either using
mechanical devices, or with small clinical validation datasets using 2-8
patients. This work aims to improve this situation by first, establishing an
open, and clinically validated dataset comprising data from both mechanical
lungs and nearly 40,000 breaths from 18 intubated patients. Next, we use this
data to evaluate 15 different algorithms that use the "single chamber" model of
estimating respiratory compliance. We evaluate these algorithms under varying
clinical scenarios patients typically experience during hospitalization. In
particular, we explore algorithm performance under four different types of
patient ventilator asynchrony. We also analyze algorithms under varying
ventilation modes to benchmark algorithm performance and to determine if
ventilation mode has any impact on the algorithm. Our approach yields several
advances by 1) showing which specific algorithms work best clinically under
varying mode and asynchrony scenarios, 2) developing a simple mathematical
method to reduce variance in algorithmic results, and 3) presenting additional
insights about single-chamber model algorithms. We hope that our paper,
approach, dataset, and software framework can thus be used by future
researchers to improve their work and allow future integration of "single
chamber" algorithms into clinical practice.
| [
{
"created": "Sun, 19 Sep 2021 07:34:15 GMT",
"version": "v1"
}
] | 2021-09-22 | [
[
"Rehm",
"Gregory",
""
],
[
"Nguyen",
"Jimmy",
""
],
[
"Gilbeau",
"Chelsea",
""
],
[
"Bomactao",
"Marc T",
""
],
[
"Chuah",
"Chen-Nee",
""
],
[
"Adams",
"Jason",
""
]
] | Non-invasive estimation of respiratory physiology using computational algorithms promises to be a valuable technique for future clinicians to detect detrimental changes in patient pathophysiology. However, few clinical algorithms used to non-invasively analyze lung physiology have undergone rigorous validation in a clinical setting, and are often validated either using mechanical devices, or with small clinical validation datasets using 2-8 patients. This work aims to improve this situation by first, establishing an open, and clinically validated dataset comprising data from both mechanical lungs and nearly 40,000 breaths from 18 intubated patients. Next, we use this data to evaluate 15 different algorithms that use the "single chamber" model of estimating respiratory compliance. We evaluate these algorithms under varying clinical scenarios patients typically experience during hospitalization. In particular, we explore algorithm performance under four different types of patient ventilator asynchrony. We also analyze algorithms under varying ventilation modes to benchmark algorithm performance and to determine if ventilation mode has any impact on the algorithm. Our approach yields several advances by 1) showing which specific algorithms work best clinically under varying mode and asynchrony scenarios, 2) developing a simple mathematical method to reduce variance in algorithmic results, and 3) presenting additional insights about single-chamber model algorithms. We hope that our paper, approach, dataset, and software framework can thus be used by future researchers to improve their work and allow future integration of "single chamber" algorithms into clinical practice. |
1705.03457 | Omer Faruk Gulban | Omer Faruk Gulban | The relation between color spaces and compositional data analysis
demonstrated with magnetic resonance image processing applications | 13 pages, 3 figures, short paper, submitted to Austrian Journal of
Statistics compositional data analysis special issue, first revision, fix
rendering error in fig2 | null | null | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a novel application of compositional data analysis
methods in the context of color image processing. A vector decomposition method
is proposed to reveal compositional components of any vector with positive
components followed by compositional data analysis to demonstrate the relation
between color space concepts such as hue and saturation to their compositional
counterparts. The proposed methods are applied to a magnetic resonance imaging
dataset acquired from a living human brain and a digital color photograph to
perform image fusion. Potential future applications in magnetic resonance
imaging are mentioned and the benefits/disadvantages of the proposed methods
are discussed in terms of color image processing.
| [
{
"created": "Tue, 9 May 2017 07:14:26 GMT",
"version": "v1"
},
{
"created": "Mon, 16 Oct 2017 17:50:56 GMT",
"version": "v2"
},
{
"created": "Tue, 27 Mar 2018 16:35:10 GMT",
"version": "v3"
},
{
"created": "Mon, 11 Jun 2018 10:19:36 GMT",
"version": "v4"
}
] | 2018-06-12 | [
[
"Gulban",
"Omer Faruk",
""
]
] | This paper presents a novel application of compositional data analysis methods in the context of color image processing. A vector decomposition method is proposed to reveal compositional components of any vector with positive components followed by compositional data analysis to demonstrate the relation between color space concepts such as hue and saturation to their compositional counterparts. The proposed methods are applied to a magnetic resonance imaging dataset acquired from a living human brain and a digital color photograph to perform image fusion. Potential future applications in magnetic resonance imaging are mentioned and the benefits/disadvantages of the proposed methods are discussed in terms of color image processing. |
1306.6129 | Taichi Haruna | Taichi Haruna | Robustness and Directed Structures in Ecological Flow Networks | 7 pages | null | null | null | q-bio.PE nlin.AO physics.soc-ph q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robustness of ecological flow networks under random failure of arcs is
considered with respect to two different functionalities: coherence and
circulation. In our previous work, we showed that each functionality is
associated with a natural path notion: lateral path for the former and directed
path for the latter. Robustness of a network is measured in terms of the size
of the giant laterally connected arc component and that of the giant strongly
connected arc component, respectively. We study how realistic structures of
ecological flow networks affect the robustness with respect to each
functionality. To quantify the impact of realistic network structures, two null
models are considered for a given real ecological flow network: one is random
networks with the same degree distribution and the other is those with the same
average degree. Robustness of the null models is calculated by theoretically
solving the size of giant components for the configuration model. We show that
realistic network structures have positive effect on robustness for coherence,
whereas they have negative effect on robustness for circulation.
| [
{
"created": "Wed, 26 Jun 2013 05:00:52 GMT",
"version": "v1"
}
] | 2013-06-27 | [
[
"Haruna",
"Taichi",
""
]
] | Robustness of ecological flow networks under random failure of arcs is considered with respect to two different functionalities: coherence and circulation. In our previous work, we showed that each functionality is associated with a natural path notion: lateral path for the former and directed path for the latter. Robustness of a network is measured in terms of the size of the giant laterally connected arc component and that of the giant strongly connected arc component, respectively. We study how realistic structures of ecological flow networks affect the robustness with respect to each functionality. To quantify the impact of realistic network structures, two null models are considered for a given real ecological flow network: one is random networks with the same degree distribution and the other is those with the same average degree. Robustness of the null models is calculated by theoretically solving the size of giant components for the configuration model. We show that realistic network structures have positive effect on robustness for coherence, whereas they have negative effect on robustness for circulation. |
2008.05888 | Murali Padmanabha | Murali Padmanabha, Alexander Kobelski, Arne-Jens Hempel, Stefan Streif | A comprehensive dynamic growth and development model of Hermetia
illucens larvae | null | null | 10.1371/journal.pone.0239084 | null | q-bio.QM cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Larvae of Hermetia illucens, also commonly known as black soldier fly (BSF)
have gained significant importance in the feed industry, primarily used as feed
for aquaculture and other livestock farming. Mathematical model such as Von
Bertalanffy growth model and dynamic energy budget models are available for
modelling the growth of various organisms but have their demerits for their
application to the growth and development of BSF. Also, such dynamic models
were not yet applied to the growth of the BSF larvae despite models proven to
be useful for automation of industrial production process (e.g. feeding,
heating/cooling, ventilation, harvesting, etc.). This work primarily focuses on
developing a model based on the principles of the afore mentioned models from
literature that can provide accurate mathematical description of the dry mass
changes throughout the life cycle and the transition of development phases of
the larvae. To further improve the accuracy of these models, various factors
affecting the growth and development such as temperature, feed quality, feeding
rate, moisture content in feed, and airflow rate are developed and integrated
into the dynamic growth model. An extensive set of data were aggregated from
various literature and used for the model development, parameter estimation and
validation. Models describing the environmental factors were individually
validated based on the data sets collected. In addition, the dynamic growth
model was also validated for dry mass evolution and development stage
transition of larvae reared on different substrate feeding rates. The developed
models with the estimated parameters performed well highlighting its
application in decision-support systems and automation for large scale
production.
| [
{
"created": "Thu, 13 Aug 2020 13:21:38 GMT",
"version": "v1"
}
] | 2021-01-27 | [
[
"Padmanabha",
"Murali",
""
],
[
"Kobelski",
"Alexander",
""
],
[
"Hempel",
"Arne-Jens",
""
],
[
"Streif",
"Stefan",
""
]
] | Larvae of Hermetia illucens, also commonly known as black soldier fly (BSF) have gained significant importance in the feed industry, primarily used as feed for aquaculture and other livestock farming. Mathematical model such as Von Bertalanffy growth model and dynamic energy budget models are available for modelling the growth of various organisms but have their demerits for their application to the growth and development of BSF. Also, such dynamic models were not yet applied to the growth of the BSF larvae despite models proven to be useful for automation of industrial production process (e.g. feeding, heating/cooling, ventilation, harvesting, etc.). This work primarily focuses on developing a model based on the principles of the afore mentioned models from literature that can provide accurate mathematical description of the dry mass changes throughout the life cycle and the transition of development phases of the larvae. To further improve the accuracy of these models, various factors affecting the growth and development such as temperature, feed quality, feeding rate, moisture content in feed, and airflow rate are developed and integrated into the dynamic growth model. An extensive set of data were aggregated from various literature and used for the model development, parameter estimation and validation. Models describing the environmental factors were individually validated based on the data sets collected. In addition, the dynamic growth model was also validated for dry mass evolution and development stage transition of larvae reared on different substrate feeding rates. The developed models with the estimated parameters performed well highlighting its application in decision-support systems and automation for large scale production. |
1305.0727 | Daniel Beard | Klas H. Pettersen, Scott M. Bugenhagen, Javaid Nauman, Daniel A.
Beard, and Stig W. Omholt | Arterial stiffening provides sufficient explanation for primary
hypertension | 19 pages, 4 figures | null | 10.1371/journal.pcbi.1003634 | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hypertension is one of the most common age-related chronic diseases and by
predisposing individuals for heart failure, stroke and kidney disease, it is a
major source of morbidity and mortality. Its etiology remains enigmatic despite
intense research efforts over many decades. By use of empirically
well-constrained computer models describing the coupled function of the
baroreceptor reflex and mechanics of the circulatory system, we demonstrate
quantitatively that arterial stiffening seems sufficient to explain age-related
emergence of hypertension. Specifically, the empirically observed chronic
changes in pulse pressure with age, and the impaired capacity of hypertensive
individuals to regulate short-term changes in blood pressure, arise as emergent
properties of the integrated system. Results are consistent with available
experimental data from chemical and surgical manipulation of the
cardio-vascular system. In contrast to widely held opinions, the results
suggest that primary hypertension can be attributed to a mechanogenic etiology
without challenging current conceptions of renal and sympathetic nervous system
function. The results support the view that a major target for treating chronic
hypertension in the elderly is the reestablishment of a proper baroreflex
response.
| [
{
"created": "Fri, 3 May 2013 14:43:46 GMT",
"version": "v1"
},
{
"created": "Mon, 6 May 2013 15:11:44 GMT",
"version": "v2"
}
] | 2015-06-15 | [
[
"Pettersen",
"Klas H.",
""
],
[
"Bugenhagen",
"Scott M.",
""
],
[
"Nauman",
"Javaid",
""
],
[
"Beard",
"Daniel A.",
""
],
[
"Omholt",
"Stig W.",
""
]
] | Hypertension is one of the most common age-related chronic diseases and by predisposing individuals for heart failure, stroke and kidney disease, it is a major source of morbidity and mortality. Its etiology remains enigmatic despite intense research efforts over many decades. By use of empirically well-constrained computer models describing the coupled function of the baroreceptor reflex and mechanics of the circulatory system, we demonstrate quantitatively that arterial stiffening seems sufficient to explain age-related emergence of hypertension. Specifically, the empirically observed chronic changes in pulse pressure with age, and the impaired capacity of hypertensive individuals to regulate short-term changes in blood pressure, arise as emergent properties of the integrated system. Results are consistent with available experimental data from chemical and surgical manipulation of the cardio-vascular system. In contrast to widely held opinions, the results suggest that primary hypertension can be attributed to a mechanogenic etiology without challenging current conceptions of renal and sympathetic nervous system function. The results support the view that a major target for treating chronic hypertension in the elderly is the reestablishment of a proper baroreflex response. |
1411.0733 | Silvia Grigolon | Silvia Grigolon, Peter Sollich, Olivier C. Martin | Modeling the emergence of polarity patterns for the intercellular
transport of auxin in plants | 17 pages and 9 figures (Main Text), 9 pages and 4 figures
(Supplementary Material), revised version with some rearrangements | J. R. Soc. Interface, 2015, 12, 20141223 | 10.1098/rsif.2014.1223 | null | q-bio.TO cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The hormone auxin is actively transported throughout plants via protein
machineries including the dedicated transporter known as PIN. The associated
transport is ordered with nearby cells driving auxin flux in similar
directions. Here we provide a model of both the auxin transport and of the
dynamics of cellular polarisation based on flux sensing. Our main findings are:
(i) spontaneous intracellular PIN polarisation arises if PIN recycling dynamics
are sufficiently non-linear, (ii) there is no need for an auxin concentration
gradient, and (iii) ordered multi-cellular patterns of PIN polarisation are
favored by molecular noise.
| [
{
"created": "Mon, 3 Nov 2014 23:22:22 GMT",
"version": "v1"
},
{
"created": "Fri, 13 Mar 2015 11:05:33 GMT",
"version": "v2"
}
] | 2016-04-12 | [
[
"Grigolon",
"Silvia",
""
],
[
"Sollich",
"Peter",
""
],
[
"Martin",
"Olivier C.",
""
]
] | The hormone auxin is actively transported throughout plants via protein machineries including the dedicated transporter known as PIN. The associated transport is ordered with nearby cells driving auxin flux in similar directions. Here we provide a model of both the auxin transport and of the dynamics of cellular polarisation based on flux sensing. Our main findings are: (i) spontaneous intracellular PIN polarisation arises if PIN recycling dynamics are sufficiently non-linear, (ii) there is no need for an auxin concentration gradient, and (iii) ordered multi-cellular patterns of PIN polarisation are favored by molecular noise. |
2406.05108 | Dinh Viet Cuong | Dinh Viet Cuong, Branislava Lali\'c, Mina Petri\'c, Binh Nguyen, Mark
Roantree | Adapting Physics-Informed Neural Networks To Optimize ODEs in Mosquito
Population Dynamics | null | null | null | null | q-bio.PE cs.LG | http://creativecommons.org/licenses/by/4.0/ | Physics informed neural networks have been gaining popularity due to their
unique ability to incorporate physics laws into data-driven models, ensuring
that the predictions are not only consistent with empirical data but also align
with domain-specific knowledge in the form of physics equations. The
integration of physics principles enables the method to require less data while
maintaining the robustness of deep learning in modeling complex dynamical
systems. However, current PINN frameworks are not sufficiently mature for
real-world ODE systems, especially those with extreme multi-scale behavior such
as mosquito population dynamical modelling. In this research, we propose a PINN
framework with several improvements for forward and inverse problems for ODE
systems with a case study application in modelling the dynamics of mosquito
populations. The framework tackles the gradient imbalance and stiff problems
posed by mosquito ordinary differential equations. The method offers a simple
but effective way to resolve the time causality issue in PINNs by gradually
expanding the training time domain until it covers entire domain of interest.
As part of a robust evaluation, we conduct experiments using simulated data to
evaluate the effectiveness of the approach. Preliminary results indicate that
physics-informed machine learning holds significant potential for advancing the
study of ecological systems.
| [
{
"created": "Fri, 7 Jun 2024 17:40:38 GMT",
"version": "v1"
}
] | 2024-06-10 | [
[
"Cuong",
"Dinh Viet",
""
],
[
"Lalić",
"Branislava",
""
],
[
"Petrić",
"Mina",
""
],
[
"Nguyen",
"Binh",
""
],
[
"Roantree",
"Mark",
""
]
] | Physics informed neural networks have been gaining popularity due to their unique ability to incorporate physics laws into data-driven models, ensuring that the predictions are not only consistent with empirical data but also align with domain-specific knowledge in the form of physics equations. The integration of physics principles enables the method to require less data while maintaining the robustness of deep learning in modeling complex dynamical systems. However, current PINN frameworks are not sufficiently mature for real-world ODE systems, especially those with extreme multi-scale behavior such as mosquito population dynamical modelling. In this research, we propose a PINN framework with several improvements for forward and inverse problems for ODE systems with a case study application in modelling the dynamics of mosquito populations. The framework tackles the gradient imbalance and stiff problems posed by mosquito ordinary differential equations. The method offers a simple but effective way to resolve the time causality issue in PINNs by gradually expanding the training time domain until it covers entire domain of interest. As part of a robust evaluation, we conduct experiments using simulated data to evaluate the effectiveness of the approach. Preliminary results indicate that physics-informed machine learning holds significant potential for advancing the study of ecological systems. |
2008.12027 | Markus Lill | Amr H. Mahmoud, Jonas F. Lill, Markus A. Lill | Graph-convolution neural network-based flexible docking utilizing
coarse-grained distance matrix | null | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Prediction of protein-ligand complexes for flexible proteins remains still a
challenging problem in computational structural biology and drug design. Here
we present two novel deep neural network approaches with significant
improvement in efficiency and accuracy of binding mode prediction on a large
and diverse set of protein systems compared to standard docking. Whereas the
first graph convolutional network is used for re-ranking poses the second
approach aims to generate and rank poses independent of standard docking
approaches. This novel approach relies on the prediction of distance matrices
between ligand atoms and protein C_alpha atoms thus incorporating side-chain
flexibility implicitly.
| [
{
"created": "Thu, 27 Aug 2020 10:04:51 GMT",
"version": "v1"
}
] | 2020-08-28 | [
[
"Mahmoud",
"Amr H.",
""
],
[
"Lill",
"Jonas F.",
""
],
[
"Lill",
"Markus A.",
""
]
] | Prediction of protein-ligand complexes for flexible proteins remains still a challenging problem in computational structural biology and drug design. Here we present two novel deep neural network approaches with significant improvement in efficiency and accuracy of binding mode prediction on a large and diverse set of protein systems compared to standard docking. Whereas the first graph convolutional network is used for re-ranking poses the second approach aims to generate and rank poses independent of standard docking approaches. This novel approach relies on the prediction of distance matrices between ligand atoms and protein C_alpha atoms thus incorporating side-chain flexibility implicitly. |
2402.03529 | Carlos Calvo Tapia | Carlos Calvo Tapia, Valeriy A. Makarov Slizneva, and Cees van Leeuwen | Basic principles drive self-organization of brain-like connectivity
structure | null | Communications in Nonlinear Science and Numerical Simulation 82
105065, 2020 | 10.1016/j.cnsns.2019.105065 | null | q-bio.NC math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The brain can be considered as a system that dynamically optimizes the
structure of anatomical connections based on the efficiency requirements of
functional connectivity. To illustrate the power of this principle in
organizing the complexity of brain architecture, we portray the functional
connectivity as diffusion on the current network structure. The diffusion
drives adaptive rewiring, resulting in changes to the network to enhance its
efficiency. This dynamic evolution of the network structure generates, and thus
explains, modular small-worlds with rich club effects, f eatures commonly
observed in neural anatomy. Taking wiring length and propagating waves into
account leads to the morphogenesis of more specific neural structures that are
stalwarts of the detailed brain functional anatomy, such as parallelism,
divergence, convergence, super-rings, and super-chains. By showing how such
structures emerge, largely independently of their specific biological
realization, we offer a new conjecture on how natural and artificial brain-like
structures can be physically implemented.
| [
{
"created": "Mon, 5 Feb 2024 21:37:03 GMT",
"version": "v1"
}
] | 2024-02-07 | [
[
"Tapia",
"Carlos Calvo",
""
],
[
"Slizneva",
"Valeriy A. Makarov",
""
],
[
"van Leeuwen",
"Cees",
""
]
] | The brain can be considered as a system that dynamically optimizes the structure of anatomical connections based on the efficiency requirements of functional connectivity. To illustrate the power of this principle in organizing the complexity of brain architecture, we portray the functional connectivity as diffusion on the current network structure. The diffusion drives adaptive rewiring, resulting in changes to the network to enhance its efficiency. This dynamic evolution of the network structure generates, and thus explains, modular small-worlds with rich club effects, f eatures commonly observed in neural anatomy. Taking wiring length and propagating waves into account leads to the morphogenesis of more specific neural structures that are stalwarts of the detailed brain functional anatomy, such as parallelism, divergence, convergence, super-rings, and super-chains. By showing how such structures emerge, largely independently of their specific biological realization, we offer a new conjecture on how natural and artificial brain-like structures can be physically implemented. |
2212.00146 | Lorenzo Pareschi | Lorenzo Pareschi, Giuseppe Toscani | The kinetic theory of mutation rates | null | null | null | null | q-bio.PE math-ph math.MP math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Luria--Delbr\"uck mutation model is a cornerstone of evolution theory and
has been mathematically formulated in a number of ways. In this paper we
illustrate how this model of mutation rates can be derived by means of
classical statistical mechanics tools, in particular by modeling the phenomenon
resorting to methodologies borrowed from classical kinetic theory of rarefied
gases. The aim is to construct a linear kinetic model that can reproduce the
Luria--Delbr\"uck distribution starting from the elementary interactions that
qualitatively and quantitatively describe the variation of mutated cells. The
kinetic description is easily adaptable to different situations and makes it
possible to clearly identify the differences between the elementary variations
leading to the formulations of Luria--Delbr\"uck, Lea--Coulson, and Kendall,
respectively. The kinetic approach additionally emphasizes basic principles
which not only help to unify existing results but also allow for useful
extensions.
| [
{
"created": "Wed, 30 Nov 2022 22:46:17 GMT",
"version": "v1"
}
] | 2022-12-02 | [
[
"Pareschi",
"Lorenzo",
""
],
[
"Toscani",
"Giuseppe",
""
]
] | The Luria--Delbr\"uck mutation model is a cornerstone of evolution theory and has been mathematically formulated in a number of ways. In this paper we illustrate how this model of mutation rates can be derived by means of classical statistical mechanics tools, in particular by modeling the phenomenon resorting to methodologies borrowed from classical kinetic theory of rarefied gases. The aim is to construct a linear kinetic model that can reproduce the Luria--Delbr\"uck distribution starting from the elementary interactions that qualitatively and quantitatively describe the variation of mutated cells. The kinetic description is easily adaptable to different situations and makes it possible to clearly identify the differences between the elementary variations leading to the formulations of Luria--Delbr\"uck, Lea--Coulson, and Kendall, respectively. The kinetic approach additionally emphasizes basic principles which not only help to unify existing results but also allow for useful extensions. |
2407.09488 | Xin Li | Xin Li | Manifold Learning via Memory and Context | null | null | null | null | q-bio.NC cs.LG cs.NE | http://creativecommons.org/publicdomain/zero/1.0/ | Given a memory with infinite capacity, can we solve the learning problem?
Apparently, nature has solved this problem as evidenced by the evolution of
mammalian brains. Inspired by the organizational principles underlying
hippocampal-neocortical systems, we present a navigation-based approach to
manifold learning using memory and context. The key insight is to navigate on
the manifold and memorize the positions of each route as inductive/design bias
of direct-fit-to-nature. We name it navigation-based because our approach can
be interpreted as navigating in the latent space of sensorimotor learning via
memory (local maps) and context (global indexing). The indexing to the library
of local maps within global coordinates is collected by an associative memory
serving as the librarian, which mimics the coupling between the hippocampus and
the neocortex. In addition to breaking from the notorious bias-variance dilemma
and the curse of dimensionality, we discuss the biological implementation of
our navigation-based learning by episodic and semantic memories in neural
systems. The energy efficiency of navigation-based learning makes it suitable
for hardware implementation on non-von Neumann architectures, such as the
emerging in-memory computing paradigm, including spiking neural networks and
memristor neural networks.
| [
{
"created": "Fri, 17 May 2024 17:06:19 GMT",
"version": "v1"
}
] | 2024-07-16 | [
[
"Li",
"Xin",
""
]
] | Given a memory with infinite capacity, can we solve the learning problem? Apparently, nature has solved this problem as evidenced by the evolution of mammalian brains. Inspired by the organizational principles underlying hippocampal-neocortical systems, we present a navigation-based approach to manifold learning using memory and context. The key insight is to navigate on the manifold and memorize the positions of each route as inductive/design bias of direct-fit-to-nature. We name it navigation-based because our approach can be interpreted as navigating in the latent space of sensorimotor learning via memory (local maps) and context (global indexing). The indexing to the library of local maps within global coordinates is collected by an associative memory serving as the librarian, which mimics the coupling between the hippocampus and the neocortex. In addition to breaking from the notorious bias-variance dilemma and the curse of dimensionality, we discuss the biological implementation of our navigation-based learning by episodic and semantic memories in neural systems. The energy efficiency of navigation-based learning makes it suitable for hardware implementation on non-von Neumann architectures, such as the emerging in-memory computing paradigm, including spiking neural networks and memristor neural networks. |
1910.11194 | Yi-Hsuan Lin | Alan N. Amin, Yi-Hsuan Lin, Suman Das, Hue Sun Chan | Analytical Theory for Sequence-Specific Binary Fuzzy Complexes of
Charged Intrinsically Disordered Proteins | 51 pages, 11 figures. Accepted for Publication in J. Phys. Chem. B | J. Phys. Chem. B 124, 6709--6720 (2020) | 10.1021/acs.jpcb.0c04575 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intrinsically disordered proteins (IDPs) are important for biological
functions. In contrast to folded proteins, molecular recognition among certain
IDPs is "fuzzy" in that their binding and/or phase separation are
stochastically governed by the interacting IDPs' amino acid sequences while
their assembled conformations remain largely disordered. To help elucidate a
basic aspect of this fascinating yet poorly understood phenomenon, the binding
of a homo- or hetero-dimeric pair of polyampholytic IDPs is modeled statistical
mechanically using cluster expansion. We find that the binding affinities of
binary fuzzy complexes in the model correlate strongly with a newly derived
simple "jSCD" parameter readily calculable from the pair of IDPs' sequence
charge patterns. Predictions by our analytical theory are in essential
agreement with coarse-grained explicit-chain simulations. This computationally
efficient theoretical framework is expected to be broadly applicable to
rationalizing and predicting sequence-specific IDP-IDP polyelectrostatic
interactions.
| [
{
"created": "Thu, 24 Oct 2019 14:48:14 GMT",
"version": "v1"
},
{
"created": "Fri, 15 May 2020 18:50:48 GMT",
"version": "v2"
},
{
"created": "Tue, 7 Jul 2020 18:10:31 GMT",
"version": "v3"
}
] | 2020-08-10 | [
[
"Amin",
"Alan N.",
""
],
[
"Lin",
"Yi-Hsuan",
""
],
[
"Das",
"Suman",
""
],
[
"Chan",
"Hue Sun",
""
]
] | Intrinsically disordered proteins (IDPs) are important for biological functions. In contrast to folded proteins, molecular recognition among certain IDPs is "fuzzy" in that their binding and/or phase separation are stochastically governed by the interacting IDPs' amino acid sequences while their assembled conformations remain largely disordered. To help elucidate a basic aspect of this fascinating yet poorly understood phenomenon, the binding of a homo- or hetero-dimeric pair of polyampholytic IDPs is modeled statistical mechanically using cluster expansion. We find that the binding affinities of binary fuzzy complexes in the model correlate strongly with a newly derived simple "jSCD" parameter readily calculable from the pair of IDPs' sequence charge patterns. Predictions by our analytical theory are in essential agreement with coarse-grained explicit-chain simulations. This computationally efficient theoretical framework is expected to be broadly applicable to rationalizing and predicting sequence-specific IDP-IDP polyelectrostatic interactions. |
2210.06303 | Pakorn Uttayopas | Pakorn Uttayopas, Xiaoxiao Cheng, Udaya Bhaskar Rongala, Henrik
J\"orntell, Etienne Burdet | Dynamic neuronal networks efficiently achieve classification in robotic
interactions with real-world objects | 9 pages, 8 figures.aim to use it for ph-coding reporting and further | null | null | null | q-bio.NC cs.RO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Biological cortical networks are potentially fully recurrent networks without
any distinct output layer, where recognition may instead rely on the
distribution of activity across its neurons. Because such biological networks
can have rich dynamics, they are well-designed to cope with dynamical
interactions of the types that occur in nature, while traditional machine
learning networks may struggle to make sense of such data. Here we connected a
simple model neuronal network (based on the 'linear summation neuron model'
featuring biologically realistic dynamics (LSM), consisting of 10 of excitatory
and 10 inhibitory neurons, randomly connected) to a robot finger with multiple
types of force sensors when interacting with materials of different levels of
compliance. Scope: to explore the performance of the network on classification
accuracy. Therefore, we compared the performance of the network output with
principal component analysis of statistical features of the sensory data as
well as its mechanical properties. Remarkably, even though the LSM was a very
small and untrained network, and merely designed to provide rich internal
network dynamics while the neuron model itself was highly simplified, we found
that the LSM outperformed these other statistical approaches in terms of
accuracy.
| [
{
"created": "Wed, 12 Oct 2022 15:09:59 GMT",
"version": "v1"
},
{
"created": "Fri, 11 Nov 2022 13:14:54 GMT",
"version": "v2"
}
] | 2022-11-14 | [
[
"Uttayopas",
"Pakorn",
""
],
[
"Cheng",
"Xiaoxiao",
""
],
[
"Rongala",
"Udaya Bhaskar",
""
],
[
"Jörntell",
"Henrik",
""
],
[
"Burdet",
"Etienne",
""
]
] | Biological cortical networks are potentially fully recurrent networks without any distinct output layer, where recognition may instead rely on the distribution of activity across its neurons. Because such biological networks can have rich dynamics, they are well-designed to cope with dynamical interactions of the types that occur in nature, while traditional machine learning networks may struggle to make sense of such data. Here we connected a simple model neuronal network (based on the 'linear summation neuron model' featuring biologically realistic dynamics (LSM), consisting of 10 of excitatory and 10 inhibitory neurons, randomly connected) to a robot finger with multiple types of force sensors when interacting with materials of different levels of compliance. Scope: to explore the performance of the network on classification accuracy. Therefore, we compared the performance of the network output with principal component analysis of statistical features of the sensory data as well as its mechanical properties. Remarkably, even though the LSM was a very small and untrained network, and merely designed to provide rich internal network dynamics while the neuron model itself was highly simplified, we found that the LSM outperformed these other statistical approaches in terms of accuracy. |
q-bio/0610033 | Raffaele Vardavas | Raffaele Vardavas, Romulus Breban, Sally Blower | The Vaccinee's Dilemma: Individual-level Decisions, Self- Organization &
Influenza Epidemics | null | null | null | null | q-bio.PE | null | Inspired by Minority Games, we constructed a novel individual-level game of
adaptive decision-making based on the dilemma of deciding whether to
participate in voluntary influenza vaccination programs. The proportion of the
population vaccinated (i.e., the vaccination coverage) determines epidemic
severity. Above a critical vaccination coverage, epidemics are prevented; hence
individuals find it unnecessary to vaccinate. The adaptive dynamics of the
decisions directly affect influenza epidemiology and, conversely, influenza
epidemiology strongly influences decision-making. This feedback mechanism
creates a unique self-organized state where epidemics are prevented. This state
is attracting, but unstable; thus epidemics are rarely prevented. This result
implies that vaccination will have to be mandatory if the public health
objective is to prevent influenza epidemics. We investigated how collective
behavior changes when public health programs are implemented. Surprisingly,
programs requiring advance payment for several years of vaccination prevents
severe epidemics, even with voluntary vaccination. Prevention is determined by
the individuals' adaptability, memory, and number of pre-paid vaccinations.
Notably, vaccinating families exacerbates and increases the frequency of severe
epidemics.
| [
{
"created": "Tue, 17 Oct 2006 21:10:58 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Vardavas",
"Raffaele",
""
],
[
"Breban",
"Romulus",
""
],
[
"Blower",
"Sally",
""
]
] | Inspired by Minority Games, we constructed a novel individual-level game of adaptive decision-making based on the dilemma of deciding whether to participate in voluntary influenza vaccination programs. The proportion of the population vaccinated (i.e., the vaccination coverage) determines epidemic severity. Above a critical vaccination coverage, epidemics are prevented; hence individuals find it unnecessary to vaccinate. The adaptive dynamics of the decisions directly affect influenza epidemiology and, conversely, influenza epidemiology strongly influences decision-making. This feedback mechanism creates a unique self-organized state where epidemics are prevented. This state is attracting, but unstable; thus epidemics are rarely prevented. This result implies that vaccination will have to be mandatory if the public health objective is to prevent influenza epidemics. We investigated how collective behavior changes when public health programs are implemented. Surprisingly, programs requiring advance payment for several years of vaccination prevents severe epidemics, even with voluntary vaccination. Prevention is determined by the individuals' adaptability, memory, and number of pre-paid vaccinations. Notably, vaccinating families exacerbates and increases the frequency of severe epidemics. |
2012.14319 | Pablo Moisset de Espan\'es | Patricio Cumsille, Oscar Rojas-D\'iaz, Pablo Moisset de Espan\'es | Forecasting COVID-19 Chile's second outbreak by a generalized SIR model
with constant time delays and a fitted positivity rate | 23 pages, 7 figures | null | null | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The COVID-19 disease has forced countries to make a considerable
collaborative effort between scientists and governments to provide indicators
to suitable follow-up the pandemic's consequences. Mathematical modeling plays
a crucial role in quantifying indicators describing diverse aspects of the
pandemic. Consequently, this work aims to develop a clear, efficient, and
reproducible methodology for parameter optimization, whose implementation is
illustrated using data from three representative regions from Chile and a
suitable generalized SIR model together with a fitted positivity rate. Our
results reproduce the general trend of the infected's curve, distinguishing the
reported and real cases. Finally, our methodology is robust, and it allows us
to forecast a second outbreak of COVID-19 and the infection fatality rate of
COVID-19 qualitatively according to the reported dead cases.
| [
{
"created": "Mon, 28 Dec 2020 16:02:06 GMT",
"version": "v1"
}
] | 2020-12-29 | [
[
"Cumsille",
"Patricio",
""
],
[
"Rojas-Díaz",
"Oscar",
""
],
[
"de Espanés",
"Pablo Moisset",
""
]
] | The COVID-19 disease has forced countries to make a considerable collaborative effort between scientists and governments to provide indicators to suitable follow-up the pandemic's consequences. Mathematical modeling plays a crucial role in quantifying indicators describing diverse aspects of the pandemic. Consequently, this work aims to develop a clear, efficient, and reproducible methodology for parameter optimization, whose implementation is illustrated using data from three representative regions from Chile and a suitable generalized SIR model together with a fitted positivity rate. Our results reproduce the general trend of the infected's curve, distinguishing the reported and real cases. Finally, our methodology is robust, and it allows us to forecast a second outbreak of COVID-19 and the infection fatality rate of COVID-19 qualitatively according to the reported dead cases. |
1305.7147 | Yucheng Hu | Yucheng Hu and Tianqi Zhu | Cell Growth and Size Homeostasis in Silico | null | null | 10.1016/j.bpj.2014.01.038 | null | q-bio.CB | http://creativecommons.org/licenses/by/3.0/ | Cell growth in size is a complex process coordinated by intrinsic and
environmental signals. In a recent work [Tzur et al., Science, 2009,
325:167-171], size distributions in an exponentially growing population of
mammalian cells were used to infer the growth rate in size. The results suggest
that cell growth is neither linear nor exponential, but subject to
size-dependent regulation. To explain their data, we build a model in which the
cell growth rate is controlled by the relative amount of mRNA and ribosomes in
a cell. Plus a stochastic division rule, the evolutionary process of a
population of cells can be simulated and the statistics of the in-silico
population agree well with the experimental data. To further explore the model
space, alternative growth models and division rules are studied. This work may
serve as a starting point for us to understand the rational behind cell growth
and size regulation using predictive models.
| [
{
"created": "Thu, 30 May 2013 15:52:42 GMT",
"version": "v1"
},
{
"created": "Fri, 7 Jun 2013 16:21:05 GMT",
"version": "v2"
},
{
"created": "Mon, 4 Nov 2013 06:12:43 GMT",
"version": "v3"
}
] | 2015-06-16 | [
[
"Hu",
"Yucheng",
""
],
[
"Zhu",
"Tianqi",
""
]
] | Cell growth in size is a complex process coordinated by intrinsic and environmental signals. In a recent work [Tzur et al., Science, 2009, 325:167-171], size distributions in an exponentially growing population of mammalian cells were used to infer the growth rate in size. The results suggest that cell growth is neither linear nor exponential, but subject to size-dependent regulation. To explain their data, we build a model in which the cell growth rate is controlled by the relative amount of mRNA and ribosomes in a cell. Plus a stochastic division rule, the evolutionary process of a population of cells can be simulated and the statistics of the in-silico population agree well with the experimental data. To further explore the model space, alternative growth models and division rules are studied. This work may serve as a starting point for us to understand the rational behind cell growth and size regulation using predictive models. |
1804.10925 | Valmir C. Barbosa | Valmir C. Barbosa, Raul Donangelo, Sergio R. Souza | Co-evolution of the mitotic and meiotic modes of eukaryotic cellular
division | null | Physical Review E 98 (2018), 032409 | 10.1103/PhysRevE.98.032409 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The genetic material of a eukaryotic cell comprises both nuclear DNA (ncDNA)
and mitochondrial DNA (mtDNA). These differ markedly in several aspects but
nevertheless must encode proteins that are compatible with one another. Here we
introduce a network model of the hypothetical co-evolution of the two most
common modes of cellular division for reproduction: by mitosis (supporting
asexual reproduction) and by meiosis (supporting sexual reproduction). Our
model is based on a random hypergraph, with two nodes for each possible
genotype, each encompassing both ncDNA and mtDNA. One of the nodes is
necessarily generated by mitosis occurring at a parent genotype, the other by
meiosis occurring at two parent genotypes. A genotype's fitness depends on the
compatibility of its ncDNA and mtDNA. The model has two probability parameters,
$p$ and $r$, the former accounting for the diversification of ncDNA during
meiosis, the latter for the diversification of mtDNA accompanying both meiosis
and mitosis. Another parameter, $\lambda$, is used to regulate the relative
rate at which mitosis- and meiosis-generated genotypes are produced. We have
found that, even though $p$ and $r$ do affect the existence of evolutionary
pathways in the network, the crucial parameter regulating the coexistence of
the two modes of cellular division is $\lambda$. Depending on genotype size,
$\lambda$ can be valued so that either mode of cellular division prevails. Our
study is closely related to a recent hypothesis that views the appearance of
cellular division by meiosis, as opposed to division by mitosis, as an
evolutionary strategy for boosting ncDNA diversification to keep up with that
of mtDNA. Our results indicate that this may well have been the case, thus
lending support to the first hypothesis in the field to take into account the
role of such ubiquitous and essential organelles as mitochondria.
| [
{
"created": "Sun, 29 Apr 2018 13:33:27 GMT",
"version": "v1"
}
] | 2020-09-04 | [
[
"Barbosa",
"Valmir C.",
""
],
[
"Donangelo",
"Raul",
""
],
[
"Souza",
"Sergio R.",
""
]
] | The genetic material of a eukaryotic cell comprises both nuclear DNA (ncDNA) and mitochondrial DNA (mtDNA). These differ markedly in several aspects but nevertheless must encode proteins that are compatible with one another. Here we introduce a network model of the hypothetical co-evolution of the two most common modes of cellular division for reproduction: by mitosis (supporting asexual reproduction) and by meiosis (supporting sexual reproduction). Our model is based on a random hypergraph, with two nodes for each possible genotype, each encompassing both ncDNA and mtDNA. One of the nodes is necessarily generated by mitosis occurring at a parent genotype, the other by meiosis occurring at two parent genotypes. A genotype's fitness depends on the compatibility of its ncDNA and mtDNA. The model has two probability parameters, $p$ and $r$, the former accounting for the diversification of ncDNA during meiosis, the latter for the diversification of mtDNA accompanying both meiosis and mitosis. Another parameter, $\lambda$, is used to regulate the relative rate at which mitosis- and meiosis-generated genotypes are produced. We have found that, even though $p$ and $r$ do affect the existence of evolutionary pathways in the network, the crucial parameter regulating the coexistence of the two modes of cellular division is $\lambda$. Depending on genotype size, $\lambda$ can be valued so that either mode of cellular division prevails. Our study is closely related to a recent hypothesis that views the appearance of cellular division by meiosis, as opposed to division by mitosis, as an evolutionary strategy for boosting ncDNA diversification to keep up with that of mtDNA. Our results indicate that this may well have been the case, thus lending support to the first hypothesis in the field to take into account the role of such ubiquitous and essential organelles as mitochondria. |
2101.10902 | Brandon Carter | Ge Liu, Alexander Dimitrakakis, Brandon Carter, David Gifford | Maximum n-times Coverage for Vaccine Design | ICLR 2022 | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce the maximum $n$-times coverage problem that selects $k$ overlays
to maximize the summed coverage of weighted elements, where each element must
be covered at least $n$ times. We also define the min-cost $n$-times coverage
problem where the objective is to select the minimum set of overlays such that
the sum of the weights of elements that are covered at least $n$ times is at
least $\tau$. Maximum $n$-times coverage is a generalization of the multi-set
multi-cover problem, is NP-complete, and is not submodular. We introduce two
new practical solutions for $n$-times coverage based on integer linear
programming and sequential greedy optimization. We show that maximum $n$-times
coverage is a natural way to frame peptide vaccine design, and find that it
produces a pan-strain COVID-19 vaccine design that is superior to 29 other
published designs in predicted population coverage and the expected number of
peptides displayed by each individual's HLA molecules.
| [
{
"created": "Sun, 24 Jan 2021 22:20:24 GMT",
"version": "v1"
},
{
"created": "Sat, 12 Jun 2021 00:46:04 GMT",
"version": "v2"
},
{
"created": "Tue, 15 Jun 2021 15:07:02 GMT",
"version": "v3"
},
{
"created": "Mon, 21 Feb 2022 18:12:51 GMT",
"version": "v4"
},
{
"c... | 2022-05-06 | [
[
"Liu",
"Ge",
""
],
[
"Dimitrakakis",
"Alexander",
""
],
[
"Carter",
"Brandon",
""
],
[
"Gifford",
"David",
""
]
] | We introduce the maximum $n$-times coverage problem that selects $k$ overlays to maximize the summed coverage of weighted elements, where each element must be covered at least $n$ times. We also define the min-cost $n$-times coverage problem where the objective is to select the minimum set of overlays such that the sum of the weights of elements that are covered at least $n$ times is at least $\tau$. Maximum $n$-times coverage is a generalization of the multi-set multi-cover problem, is NP-complete, and is not submodular. We introduce two new practical solutions for $n$-times coverage based on integer linear programming and sequential greedy optimization. We show that maximum $n$-times coverage is a natural way to frame peptide vaccine design, and find that it produces a pan-strain COVID-19 vaccine design that is superior to 29 other published designs in predicted population coverage and the expected number of peptides displayed by each individual's HLA molecules. |
1504.07266 | Sonya Ridden Miss | Sonya J. Ridden and Hannah H. Chang and Konstantinos C. Zygalakis and
Ben D. MacArthur | Entropy, Ergodicity and Stem Cell Multipotency | 6 pages, 3 figures | null | null | null | q-bio.CB physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Populations of mammalian stem cells commonly exhibit considerable cell-cell
variability. However, the functional role of this diversity is unclear. Here,
we analyze expression fluctuations of the stem cell surface marker Sca1 in
mouse hematopoietic progenitor cells using a simple stochastic model and find
that the observed dynamics naturally lie close to a critical state, thereby
producing a diverse population that is able to respond rapidly to environmental
changes. We propose an information-theoretic interpretation of these results
that views cellular multipotency as an instance of maximum entropy statistical
inference.
| [
{
"created": "Mon, 27 Apr 2015 20:22:12 GMT",
"version": "v1"
},
{
"created": "Sat, 19 Sep 2015 16:24:19 GMT",
"version": "v2"
},
{
"created": "Fri, 16 Oct 2015 21:56:09 GMT",
"version": "v3"
}
] | 2015-10-20 | [
[
"Ridden",
"Sonya J.",
""
],
[
"Chang",
"Hannah H.",
""
],
[
"Zygalakis",
"Konstantinos C.",
""
],
[
"MacArthur",
"Ben D.",
""
]
] | Populations of mammalian stem cells commonly exhibit considerable cell-cell variability. However, the functional role of this diversity is unclear. Here, we analyze expression fluctuations of the stem cell surface marker Sca1 in mouse hematopoietic progenitor cells using a simple stochastic model and find that the observed dynamics naturally lie close to a critical state, thereby producing a diverse population that is able to respond rapidly to environmental changes. We propose an information-theoretic interpretation of these results that views cellular multipotency as an instance of maximum entropy statistical inference. |
0812.4708 | Christophe Deroulers | Christophe Deroulers (IMNC), Marine Aubert (IMNC), Mathilde Badoual
(IMNC), Basil Grammaticos (IMNC) | Modeling tumor cell migration: from microscopic to macroscopic | Final published version; 14 pages, 7 figures | Physical Review E: Statistical, Nonlinear, and Soft Matter Physics
79, 3 (2009) 031917 | 10.1103/PhysRevE.79.031917 | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It has been shown experimentally that contact interactions may influence the
migration of cancer cells. Previous works have modelized this thanks to
stochastic, discrete models (cellular automata) at the cell level. However, for
the study of the growth of real-size tumors with several millions of cells, it
is best to use a macroscopic model having the form of a partial differential
equation (PDE) for the density of cells. The difficulty is to predict the
effect, at the macroscopic scale, of contact interactions that take place at
the microscopic scale. To address this we use a multiscale approach: starting
from a very simple, yet experimentally validated, microscopic model of
migration with contact interactions, we derive a macroscopic model. We show
that a diffusion equation arises, as is often postulated in the field of glioma
modeling, but it is nonlinear because of the interactions. We give the explicit
dependence of diffusivity on the cell density and on a parameter governing
cell-cell interactions. We discuss in details the conditions of validity of the
approximations used in the derivation and we compare analytic results from our
PDE to numerical simulations and to some in vitro experiments. We notice that
the family of microscopic models we started from includes as special cases some
kinetically constrained models that were introduced for the study of the
physics of glasses, supercooled liquids and jamming systems.
| [
{
"created": "Fri, 26 Dec 2008 21:01:07 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Mar 2009 15:50:27 GMT",
"version": "v2"
}
] | 2009-03-26 | [
[
"Deroulers",
"Christophe",
"",
"IMNC"
],
[
"Aubert",
"Marine",
"",
"IMNC"
],
[
"Badoual",
"Mathilde",
"",
"IMNC"
],
[
"Grammaticos",
"Basil",
"",
"IMNC"
]
] | It has been shown experimentally that contact interactions may influence the migration of cancer cells. Previous works have modelized this thanks to stochastic, discrete models (cellular automata) at the cell level. However, for the study of the growth of real-size tumors with several millions of cells, it is best to use a macroscopic model having the form of a partial differential equation (PDE) for the density of cells. The difficulty is to predict the effect, at the macroscopic scale, of contact interactions that take place at the microscopic scale. To address this we use a multiscale approach: starting from a very simple, yet experimentally validated, microscopic model of migration with contact interactions, we derive a macroscopic model. We show that a diffusion equation arises, as is often postulated in the field of glioma modeling, but it is nonlinear because of the interactions. We give the explicit dependence of diffusivity on the cell density and on a parameter governing cell-cell interactions. We discuss in details the conditions of validity of the approximations used in the derivation and we compare analytic results from our PDE to numerical simulations and to some in vitro experiments. We notice that the family of microscopic models we started from includes as special cases some kinetically constrained models that were introduced for the study of the physics of glasses, supercooled liquids and jamming systems. |
q-bio/0505026 | Sheng Bao | Shi Chen, Sheng Bao, Ling Yan, Cheng Huang | A Predation Behavior Model Based on Game Theory | 5 pages,1 figure,1 table | null | null | null | q-bio.PE | null | This article adopts game theory to build a model for explaining the predation
behavior of animals.We assume that both the prey and the preydator have two
stratigies in this game,the active one and the passive one.By calculating the
outcome and the income of energy in different stratigies, we find the solution
to analyze the different evolution path of both the prey and the predator.A
simulation result approximately represents the correctness of our model.
| [
{
"created": "Sat, 14 May 2005 08:45:38 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Chen",
"Shi",
""
],
[
"Bao",
"Sheng",
""
],
[
"Yan",
"Ling",
""
],
[
"Huang",
"Cheng",
""
]
] | This article adopts game theory to build a model for explaining the predation behavior of animals.We assume that both the prey and the preydator have two stratigies in this game,the active one and the passive one.By calculating the outcome and the income of energy in different stratigies, we find the solution to analyze the different evolution path of both the prey and the predator.A simulation result approximately represents the correctness of our model. |
1609.07462 | Toni Valles-Catala | Borja Esteve-Altava, Toni Valles-Catala, Roger Guimera, Marta
Sales-Pardo and Diego Rasskin-Gutman | Bone fusion in normal and pathological development is constrained by the
network architecture of the human skull | 15 pages, 2 figures | null | null | null | q-bio.TO q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The premature fusion of cranial bones, craniosynostosis, affects the correct
development of the skull producing morphological malformations in newborns. To
assess the susceptibility of each craniofacial articulation to close
prematurely, we used a network model of the skull to quantify the link
reliability (an index based on stochastic block modeling and Bayesian
inference) of each articulation. We show that, of the 93 human skull
articulations at birth, the few articulations that are associated with
nonsyndromic craniosynostosis conditions have statistically significant lower
reliability scores than the others. In a similar way, articulations that close
during the normal postnatal development of the skull have also lower
reliability scores than those articulations that persist through adult live.
These results indicate a relationship between the architecture of the skull
network and the specific articulations that close during normal development and
in pathological conditions. Our findings suggest that the topological
arrangement of skull bones might act as an epigenetic factor, predisposing some
articulations to closure, both in normal and pathological development, and also
affecting the long-term evolution of the skull.
| [
{
"created": "Wed, 27 Jul 2016 10:17:55 GMT",
"version": "v1"
},
{
"created": "Tue, 27 Dec 2016 20:16:32 GMT",
"version": "v2"
}
] | 2016-12-28 | [
[
"Esteve-Altava",
"Borja",
""
],
[
"Valles-Catala",
"Toni",
""
],
[
"Guimera",
"Roger",
""
],
[
"Sales-Pardo",
"Marta",
""
],
[
"Rasskin-Gutman",
"Diego",
""
]
] | The premature fusion of cranial bones, craniosynostosis, affects the correct development of the skull producing morphological malformations in newborns. To assess the susceptibility of each craniofacial articulation to close prematurely, we used a network model of the skull to quantify the link reliability (an index based on stochastic block modeling and Bayesian inference) of each articulation. We show that, of the 93 human skull articulations at birth, the few articulations that are associated with nonsyndromic craniosynostosis conditions have statistically significant lower reliability scores than the others. In a similar way, articulations that close during the normal postnatal development of the skull have also lower reliability scores than those articulations that persist through adult live. These results indicate a relationship between the architecture of the skull network and the specific articulations that close during normal development and in pathological conditions. Our findings suggest that the topological arrangement of skull bones might act as an epigenetic factor, predisposing some articulations to closure, both in normal and pathological development, and also affecting the long-term evolution of the skull. |
2305.13821 | Chaoran Chen | Chaoran Chen, Tanja Stadler | GenSpectrum Chat: Data Exploration in Public Health Using Large Language
Models | null | null | null | null | q-bio.GN cs.AI cs.IR | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Introduction: The COVID-19 pandemic highlighted the importance of making
epidemiological data and scientific insights easily accessible and explorable
for public health agencies, the general public, and researchers.
State-of-the-art approaches for sharing data and insights included regularly
updated reports and web dashboards. However, they face a trade-off between the
simplicity and flexibility of data exploration. With the capabilities of recent
large language models (LLMs) such as GPT-4, this trade-off can be overcome.
Results: We developed the chatbot "GenSpectrum Chat"
(https://cov-spectrum.org/chat) which uses GPT-4 as the underlying large
language model (LLM) to explore SARS-CoV-2 genomic sequencing data. Out of 500
inputs from real-world users, the chatbot provided a correct answer for 453
prompts; an incorrect answer for 13 prompts, and no answer although the
question was within scope for 34 prompts. We also tested the chatbot with
inputs from 10 different languages, and despite being provided solely with
English instructions and examples, it successfully processed prompts in all
tested languages.
Conclusion: LLMs enable new ways of interacting with information systems. In
the field of public health, GenSpectrum Chat can facilitate the analysis of
real-time pathogen genomic data. With our chatbot supporting interactive
exploration in different languages, we envision quick and direct access to the
latest evidence for policymakers around the world.
| [
{
"created": "Tue, 23 May 2023 08:43:43 GMT",
"version": "v1"
}
] | 2023-05-24 | [
[
"Chen",
"Chaoran",
""
],
[
"Stadler",
"Tanja",
""
]
] | Introduction: The COVID-19 pandemic highlighted the importance of making epidemiological data and scientific insights easily accessible and explorable for public health agencies, the general public, and researchers. State-of-the-art approaches for sharing data and insights included regularly updated reports and web dashboards. However, they face a trade-off between the simplicity and flexibility of data exploration. With the capabilities of recent large language models (LLMs) such as GPT-4, this trade-off can be overcome. Results: We developed the chatbot "GenSpectrum Chat" (https://cov-spectrum.org/chat) which uses GPT-4 as the underlying large language model (LLM) to explore SARS-CoV-2 genomic sequencing data. Out of 500 inputs from real-world users, the chatbot provided a correct answer for 453 prompts; an incorrect answer for 13 prompts, and no answer although the question was within scope for 34 prompts. We also tested the chatbot with inputs from 10 different languages, and despite being provided solely with English instructions and examples, it successfully processed prompts in all tested languages. Conclusion: LLMs enable new ways of interacting with information systems. In the field of public health, GenSpectrum Chat can facilitate the analysis of real-time pathogen genomic data. With our chatbot supporting interactive exploration in different languages, we envision quick and direct access to the latest evidence for policymakers around the world. |
q-bio/0407010 | Renaud Jolivet | Renaud Jolivet and Wulfram Gerstner | Predicting spike times of a detailed conductance-based neuron model
driven by stochastic spike arrival | 20 pages, 5 figures | Journal of Physiology - Paris 98 (2004) 442--451 | 10.1016/j.jphysparis.2005.09.010 | null | q-bio.NC | null | Reduced models of neuronal activity such as Integrate-and-Fire models allow a
description of neuronal dynamics in simple, intuitive terms and are easy to
simulate numerically. We present a method to fit an Integrate-and-Fire-type
model of neuronal activity, namely a modified version of the Spike Response
Model, to a detailed Hodgkin-Huxley-type neuron model driven by stochastic
spike arrival. In the Hogkin-Huxley model, spike arrival at the synapse is
modeled by a change of synaptic conductance. For such conductance spike input,
more than 70% of the postsynaptic action potentials can be predicted with the
correct timing by the Integrate-and-Fire-type model. The modified Spike
Response Model is based upon a linearized theory of conductance-driven
Integrate-and-Fire neuron.
| [
{
"created": "Tue, 6 Jul 2004 10:16:38 GMT",
"version": "v1"
}
] | 2020-04-03 | [
[
"Jolivet",
"Renaud",
""
],
[
"Gerstner",
"Wulfram",
""
]
] | Reduced models of neuronal activity such as Integrate-and-Fire models allow a description of neuronal dynamics in simple, intuitive terms and are easy to simulate numerically. We present a method to fit an Integrate-and-Fire-type model of neuronal activity, namely a modified version of the Spike Response Model, to a detailed Hodgkin-Huxley-type neuron model driven by stochastic spike arrival. In the Hogkin-Huxley model, spike arrival at the synapse is modeled by a change of synaptic conductance. For such conductance spike input, more than 70% of the postsynaptic action potentials can be predicted with the correct timing by the Integrate-and-Fire-type model. The modified Spike Response Model is based upon a linearized theory of conductance-driven Integrate-and-Fire neuron. |
2004.04470 | Arthur Genthon | Arthur Genthon, David Lacoste | Fluctuation relations and fitness landscapes of growing cell populations | null | Sci Rep 10, 11889 (2020) | 10.1038/s41598-020-68444-x | null | q-bio.PE physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | We construct a pathwise formulation of a growing population of cells, based
on two different samplings of lineages within the population, namely the
forward and backward samplings. We show that a general symmetry relation,
called fluctuation relation relates these two samplings, independently of the
model used to generate divisions and growth in the cell population. These
relations lead to estimators of the population growth rate, which can be very
efficient as we demonstrate by an analysis of a set of mother machine data.
These fluctuation relations lead to general and important inequalities between
the mean number of divisions and the doubling time of the population. We also
study the fitness landscape, a concept based on the two samplings mentioned
above, which quantifies the correlations between a phenotypic trait of interest
and the number of divisions. We obtain explicit results when the trait is the
age or the size, for age and size-controlled models.
| [
{
"created": "Thu, 9 Apr 2020 10:41:46 GMT",
"version": "v1"
},
{
"created": "Tue, 2 Nov 2021 16:14:05 GMT",
"version": "v2"
}
] | 2021-11-03 | [
[
"Genthon",
"Arthur",
""
],
[
"Lacoste",
"David",
""
]
] | We construct a pathwise formulation of a growing population of cells, based on two different samplings of lineages within the population, namely the forward and backward samplings. We show that a general symmetry relation, called fluctuation relation relates these two samplings, independently of the model used to generate divisions and growth in the cell population. These relations lead to estimators of the population growth rate, which can be very efficient as we demonstrate by an analysis of a set of mother machine data. These fluctuation relations lead to general and important inequalities between the mean number of divisions and the doubling time of the population. We also study the fitness landscape, a concept based on the two samplings mentioned above, which quantifies the correlations between a phenotypic trait of interest and the number of divisions. We obtain explicit results when the trait is the age or the size, for age and size-controlled models. |
2102.03025 | Chikara Furusawa | Kunihiko Kaneko and Chikara Furusawa | Direction and Constraint in Phenotypic Evolution: Dimension Reduction
and Global Proportionality in Phenotype Fluctuation and Responses | 25 pages, 10 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | A macroscopic theory for describing cellular states during steady-growth is
presented, which is based on the consistency between cellular growth and
molecular replication, as well as the robustness of phenotypes against
perturbations. Adaptive changes in high-dimensional phenotypes were shown to be
restricted within a low-dimensional slow manifold, from which a macroscopic law
for cellular states was derived, which was confirmed by adaptation experiments
on bacteria under stress. Next, the theory was extended to phenotypic
evolution, leading to proportionality between phenotypic responses against
genetic evolution and environmental adaptation. The link between robustness to
noise and mutation, as a result of robustness in developmental dynamics to
perturbations, showed proportionality between phenotypic plasticity by genetic
changes and by environmental noise. Accordingly, directionality and constraint
in phenotypic evolution was quantitatively formulated in terms of phenotypic
fluctuation and the response against environmental change. The evolutionary
relevance of slow modes in controlling high-dimensional phenotypes is
discussed.
| [
{
"created": "Fri, 5 Feb 2021 06:56:23 GMT",
"version": "v1"
}
] | 2021-02-08 | [
[
"Kaneko",
"Kunihiko",
""
],
[
"Furusawa",
"Chikara",
""
]
] | A macroscopic theory for describing cellular states during steady-growth is presented, which is based on the consistency between cellular growth and molecular replication, as well as the robustness of phenotypes against perturbations. Adaptive changes in high-dimensional phenotypes were shown to be restricted within a low-dimensional slow manifold, from which a macroscopic law for cellular states was derived, which was confirmed by adaptation experiments on bacteria under stress. Next, the theory was extended to phenotypic evolution, leading to proportionality between phenotypic responses against genetic evolution and environmental adaptation. The link between robustness to noise and mutation, as a result of robustness in developmental dynamics to perturbations, showed proportionality between phenotypic plasticity by genetic changes and by environmental noise. Accordingly, directionality and constraint in phenotypic evolution was quantitatively formulated in terms of phenotypic fluctuation and the response against environmental change. The evolutionary relevance of slow modes in controlling high-dimensional phenotypes is discussed. |
2401.13022 | Caterina Strambio-De-Castillia Ph.D. | Nikki Bialy, Frank Alber, Brenda Andrews, Michael Angelo, Brian
Beliveau, Lacramioara Bintu, Alistair Boettiger, Ulrike Boehm, Claire M.
Brown, Mahmoud Bukar Maina, James J. Chambers, Beth A. Cimini, Kevin
Eliceiri, Rachel Errington, Orestis Faklaris, Nathalie Gaudreault, Ronald N.
Germain, Wojtek Goscinski, David Grunwald, Michael Halter, Dorit Hanein, John
W. Hickey, Judith Lacoste, Alex Laude, Emma Lundberg, Jian Ma, Leonel
Malacrida, Josh Moore, Glyn Nelson, Elizabeth Kathleen Neumann, Roland
Nitschke, Shuichi Onami, Jaime A. Pimentel, Anne L. Plant, Andrea J. Radtke,
Bikash Sabata, Denis Schapiro, Johannes Sch\"oneberg, Jeffrey M. Spraggins,
Damir Sudar, Wouter-Michiel Adrien Maria Vierdag, Niels Volkmann, Carolina
W\"ahlby, Siyuan (Steven) Wang, Ziv Yaniv and Caterina Strambio-De-Castillia | Harmonizing the Generation and Pre-publication Stewardship of FAIR Image
Data | This manuscript is published with a closely related companion
entitled, Enabling Global Image Data Sharing in the Life Sciences, which can
be found at the following link, arXiv:2401.13023 [q-bio.OT] | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Together with the molecular knowledge of genes and proteins, biological
images promise to significantly enhance the scientific understanding of complex
cellular systems and to advance predictive and personalized therapeutic
products for human health. For this potential to be realized, quality-assured
image data must be shared among labs at a global scale to be compared, pooled,
and reanalyzed, thus unleashing untold potential beyond the original purpose
for which the data was generated. There are two broad sets of requirements to
enable image data sharing in the life sciences. One set of requirements is
articulated in the companion White Paper entitled Enabling Global Image Data
Sharing in the Life Sciences, which is published in parallel and addresses the
need to build the cyberinfrastructure for sharing the digital array data. In
this White Paper, we detail a broad set of requirements, which involves
collecting, managing, presenting, and propagating contextual information
essential to assess the quality, understand the content, interpret the
scientific implications, and reuse image data in the context of the
experimental details. We start by providing an overview of the main lessons
learned to date through international community activities, which have recently
made considerable progress toward generating community standard practices for
imaging Quality Control (QC) and metadata. We then provide a clear set of
recommendations for amplifying this work. The driving goal is to address
remaining challenges and democratize access to everyday practices and tools for
a spectrum of biomedical researchers, regardless of their expertise, access to
resources, and geographical location.
| [
{
"created": "Tue, 23 Jan 2024 18:47:50 GMT",
"version": "v1"
},
{
"created": "Mon, 29 Jan 2024 14:06:25 GMT",
"version": "v2"
},
{
"created": "Tue, 30 Jan 2024 17:50:28 GMT",
"version": "v3"
},
{
"created": "Thu, 8 Feb 2024 23:06:17 GMT",
"version": "v4"
}
] | 2024-02-12 | [
[
"Bialy",
"Nikki",
"",
"Steven"
],
[
"Alber",
"Frank",
"",
"Steven"
],
[
"Andrews",
"Brenda",
"",
"Steven"
],
[
"Angelo",
"Michael",
"",
"Steven"
],
[
"Beliveau",
"Brian",
"",
"Steven"
],
[
"Bintu",
"Lacrami... | Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled Enabling Global Image Data Sharing in the Life Sciences, which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data. In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location. |
2309.15844 | Tomoko Matsui | Nourddine Azzaoui, Tomoko Matsui, and Daisuke Murakami | Data-Driven Framework for Uncovering Hidden Control Strategies in
Evolutionary Analysis | 18 pages | null | null | null | q-bio.PE stat.ME | http://creativecommons.org/licenses/by/4.0/ | We have devised a data-driven framework for uncovering hidden control
strategies used by an evolutionary system described by an evolutionary
probability distribution. This innovative framework enables deciphering of the
concealed mechanisms that contribute to the progression or mitigation of such
situations as the spread of COVID-19. Novel algorithms are used to estimate the
optimal control in tandem with the parameters for evolution in general
dynamical systems, thereby extending the concept of model predictive control.
This is a significant departure from conventional control methods, which
require knowledge of the system to manipulate its evolution and of the
controller's strategy or parameters. We used a generalized additive model,
supplemented by extensive statistical testing, to identify a set of predictor
covariates closely linked to the control. Using real-world COVID-19 data, we
successfully delineated the descriptive behaviors of the COVID-19 epidemics in
five prefectures in Japan and nine countries. We compared these nine countries
and grouped them on the basis of shared profiles, providing valuable insights
into their pandemic responses. Our findings underscore the potential of our
framework as a powerful tool for understanding and managing complex
evolutionary processes.
| [
{
"created": "Tue, 26 Sep 2023 13:58:54 GMT",
"version": "v1"
}
] | 2023-09-28 | [
[
"Azzaoui",
"Nourddine",
""
],
[
"Matsui",
"Tomoko",
""
],
[
"Murakami",
"Daisuke",
""
]
] | We have devised a data-driven framework for uncovering hidden control strategies used by an evolutionary system described by an evolutionary probability distribution. This innovative framework enables deciphering of the concealed mechanisms that contribute to the progression or mitigation of such situations as the spread of COVID-19. Novel algorithms are used to estimate the optimal control in tandem with the parameters for evolution in general dynamical systems, thereby extending the concept of model predictive control. This is a significant departure from conventional control methods, which require knowledge of the system to manipulate its evolution and of the controller's strategy or parameters. We used a generalized additive model, supplemented by extensive statistical testing, to identify a set of predictor covariates closely linked to the control. Using real-world COVID-19 data, we successfully delineated the descriptive behaviors of the COVID-19 epidemics in five prefectures in Japan and nine countries. We compared these nine countries and grouped them on the basis of shared profiles, providing valuable insights into their pandemic responses. Our findings underscore the potential of our framework as a powerful tool for understanding and managing complex evolutionary processes. |
0805.3368 | Michael Stiber | M. Stiber, F. Kawasaki and D. Xu | A model of dissociated cortical tissue | 4 pages, 8 PDF figure files, uses graphicx, mathptmx, helvet,
courier, amsmath, and 1 custom style file | Proc. 7th Int. Workshop on Neural Coding, Montevideo, Uruguay,
Nov. 7-12, 2007, pp. 24-27 | null | null | q-bio.NC q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A powerful experimental approach for investigating computation in networks of
biological neurons is the use of cultured dissociated cortical cells grown into
networks on a multi-electrode array. Such preparations allow investigation of
network development, activity, plasticity, responses to stimuli, and the
effects of pharmacological agents. They also exhibit whole-culture pathological
bursting; understanding the mechanisms that underlie this could allow creation
of more useful cell cultures and possibly have medical applications.
| [
{
"created": "Wed, 21 May 2008 23:36:04 GMT",
"version": "v1"
}
] | 2008-05-23 | [
[
"Stiber",
"M.",
""
],
[
"Kawasaki",
"F.",
""
],
[
"Xu",
"D.",
""
]
] | A powerful experimental approach for investigating computation in networks of biological neurons is the use of cultured dissociated cortical cells grown into networks on a multi-electrode array. Such preparations allow investigation of network development, activity, plasticity, responses to stimuli, and the effects of pharmacological agents. They also exhibit whole-culture pathological bursting; understanding the mechanisms that underlie this could allow creation of more useful cell cultures and possibly have medical applications. |
2310.15729 | Macoto Kikuchi | Macoto Kikuchi | Phenotype selection due to mutational robustness | 6 pages, 3 figures | null | null | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Darwinian evolution gives rise not only to the adaptation to the environment
but also to the enhancement of the robustness against mutation. Suppose more
than one phenotypes share the same fitness value. We expect that some
phenotypes are hardly selected as a consequence of the selection bias for
mutational robustness. We investigated this phenotype selection for a model of
gene regulatory networks (GRNs). We constructed the randomly generated set of
GRNs using the multicanonical Monte Carlo method and compared it to the
outcomes of evolutionary simulations. The results suggest that the mutationally
least robust phenotype is suppressed in evolution.
| [
{
"created": "Tue, 24 Oct 2023 11:05:14 GMT",
"version": "v1"
},
{
"created": "Mon, 11 Dec 2023 10:10:24 GMT",
"version": "v2"
}
] | 2023-12-12 | [
[
"Kikuchi",
"Macoto",
""
]
] | Darwinian evolution gives rise not only to the adaptation to the environment but also to the enhancement of the robustness against mutation. Suppose more than one phenotypes share the same fitness value. We expect that some phenotypes are hardly selected as a consequence of the selection bias for mutational robustness. We investigated this phenotype selection for a model of gene regulatory networks (GRNs). We constructed the randomly generated set of GRNs using the multicanonical Monte Carlo method and compared it to the outcomes of evolutionary simulations. The results suggest that the mutationally least robust phenotype is suppressed in evolution. |
2212.06052 | Rafael De Andrade Moral | Rafael A. Moral, Rishabh Vishwakarma, John Connolly, Laura Byrne,
Catherine Hurley, John A. Finn, Caroline Brophy | Going beyond richness: Modelling the BEF relationship using species
identity, evenness, richness and species interactions via the DImodels R
package | null | null | null | null | q-bio.PE stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | BEF studies aim to understand how ecosystems respond to a gradient of species
diversity. Diversity-Interactions (DI) models are suitable for analysing the
BEF relationship. These models relate an ecosystem function response of a
community to the identity of the species in the community, their evenness
(proportions) and interactions. The number of species in the community
(richness) is also implicitly modelled through this approach. It is common in
BEF studies to model an ecosystem function as a function of richness; while
this can uncover trends in the BEF relationship, by definition, species
diversity is much broader than richness alone, and important patterns in the
BEF relationship may remain hidden. In this paper, we introduce the DImodels R
package for implementing DI models. We also compare DI models to traditional
modelling approaches to highlight the advantages of using a multi-dimensional
definition of species diversity. We show that using DI models can lead to
considerably improved model fit over other methods; it does this by
incorporating variation due to the multiple facets of species diversity.
Predicting from a DI model is not limited to the study design points, the model
can extrapolate to predict for any species composition and proportions
(assuming there is sufficient coverage of this space in the study design).
Expressing the BEF relationship as a function of richness alone can be useful
to capture overall trends. However, collapsing the multiple dimensions of
species diversity to a single dimension (such as richness) can result in
valuable ecological information being lost. DI modelling provides a framework
to test the multiple components of species diversity in the BEF relationship.
It facilitates uncovering a deeper ecological understanding of the BEF
relationship and can lead to enhanced inference.
| [
{
"created": "Fri, 9 Dec 2022 16:22:15 GMT",
"version": "v1"
},
{
"created": "Sat, 25 Feb 2023 12:54:07 GMT",
"version": "v2"
},
{
"created": "Fri, 5 May 2023 08:20:43 GMT",
"version": "v3"
}
] | 2023-05-08 | [
[
"Moral",
"Rafael A.",
""
],
[
"Vishwakarma",
"Rishabh",
""
],
[
"Connolly",
"John",
""
],
[
"Byrne",
"Laura",
""
],
[
"Hurley",
"Catherine",
""
],
[
"Finn",
"John A.",
""
],
[
"Brophy",
"Caroline",
""
]
] | BEF studies aim to understand how ecosystems respond to a gradient of species diversity. Diversity-Interactions (DI) models are suitable for analysing the BEF relationship. These models relate an ecosystem function response of a community to the identity of the species in the community, their evenness (proportions) and interactions. The number of species in the community (richness) is also implicitly modelled through this approach. It is common in BEF studies to model an ecosystem function as a function of richness; while this can uncover trends in the BEF relationship, by definition, species diversity is much broader than richness alone, and important patterns in the BEF relationship may remain hidden. In this paper, we introduce the DImodels R package for implementing DI models. We also compare DI models to traditional modelling approaches to highlight the advantages of using a multi-dimensional definition of species diversity. We show that using DI models can lead to considerably improved model fit over other methods; it does this by incorporating variation due to the multiple facets of species diversity. Predicting from a DI model is not limited to the study design points, the model can extrapolate to predict for any species composition and proportions (assuming there is sufficient coverage of this space in the study design). Expressing the BEF relationship as a function of richness alone can be useful to capture overall trends. However, collapsing the multiple dimensions of species diversity to a single dimension (such as richness) can result in valuable ecological information being lost. DI modelling provides a framework to test the multiple components of species diversity in the BEF relationship. It facilitates uncovering a deeper ecological understanding of the BEF relationship and can lead to enhanced inference. |
2310.13723 | Yaroslav Balytskyi | Yaroslav Balytskyi, Nataliia Kalashnyk, Inna Hubenko, Alina Balytska,
Kelly McNear | Enhancing Open-World Bacterial Raman Spectra Identification by Feature
Regularization for Improved Resilience against Unknown Classes | null | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The combination of Deep Learning techniques and Raman spectroscopy shows
great potential offering precise and prompt identification of pathogenic
bacteria in clinical settings. However, the traditional closed-set
classification approaches assume that all test samples belong to one of the
known pathogens, and their applicability is limited since the clinical
environment is inherently unpredictable and dynamic, unknown or emerging
pathogens may not be included in the available catalogs. We demonstrate that
the current state-of-the-art Neural Networks identifying pathogens through
Raman spectra are vulnerable to unknown inputs, resulting in an uncontrollable
false positive rate. To address this issue, first, we developed a novel
ensemble of ResNet architectures combined with the attention mechanism which
outperforms existing closed-world methods, achieving an accuracy of $87.8 \pm
0.1\%$ compared to the best available model's accuracy of $86.7 \pm 0.4\%$.
Second, through the integration of feature regularization by the Objectosphere
loss function, our model achieves both high accuracy in identifying known
pathogens from the catalog and effectively separates unknown samples
drastically reducing the false positive rate. Finally, the proposed feature
regularization method during training significantly enhances the performance of
out-of-distribution detectors during the inference phase improving the
reliability of the detection of unknown classes. Our novel algorithm for Raman
spectroscopy enables the detection of unknown, uncatalogued, and emerging
pathogens providing the flexibility to adapt to future pathogens that may
emerge, and has the potential to improve the reliability of Raman-based
solutions in dynamic operating environments where accuracy is critical, such as
public safety applications.
| [
{
"created": "Thu, 19 Oct 2023 17:19:47 GMT",
"version": "v1"
}
] | 2023-10-24 | [
[
"Balytskyi",
"Yaroslav",
""
],
[
"Kalashnyk",
"Nataliia",
""
],
[
"Hubenko",
"Inna",
""
],
[
"Balytska",
"Alina",
""
],
[
"McNear",
"Kelly",
""
]
] | The combination of Deep Learning techniques and Raman spectroscopy shows great potential offering precise and prompt identification of pathogenic bacteria in clinical settings. However, the traditional closed-set classification approaches assume that all test samples belong to one of the known pathogens, and their applicability is limited since the clinical environment is inherently unpredictable and dynamic, unknown or emerging pathogens may not be included in the available catalogs. We demonstrate that the current state-of-the-art Neural Networks identifying pathogens through Raman spectra are vulnerable to unknown inputs, resulting in an uncontrollable false positive rate. To address this issue, first, we developed a novel ensemble of ResNet architectures combined with the attention mechanism which outperforms existing closed-world methods, achieving an accuracy of $87.8 \pm 0.1\%$ compared to the best available model's accuracy of $86.7 \pm 0.4\%$. Second, through the integration of feature regularization by the Objectosphere loss function, our model achieves both high accuracy in identifying known pathogens from the catalog and effectively separates unknown samples drastically reducing the false positive rate. Finally, the proposed feature regularization method during training significantly enhances the performance of out-of-distribution detectors during the inference phase improving the reliability of the detection of unknown classes. Our novel algorithm for Raman spectroscopy enables the detection of unknown, uncatalogued, and emerging pathogens providing the flexibility to adapt to future pathogens that may emerge, and has the potential to improve the reliability of Raman-based solutions in dynamic operating environments where accuracy is critical, such as public safety applications. |
2301.13015 | Fatih Gulec | Fatih Gulec and Andrew W. Eckford | A Stochastic Biofilm Disruption Model based on Quorum Sensing Mimickers | Accepted for publication in IEEE Transactions on Molecular,
Biological, and Multi-Scale Communications | in IEEE Transactions on Molecular, Biological and Multi-Scale
Communications, vol. 9, no. 3, pp. 346-350, Sept. 2023 | 10.1109/tmbmc.2023.3292321 | null | q-bio.QM eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Quorum sensing (QS) mimickers can be used as an effective tool to disrupt
biofilms which consist of communicating bacteria and extracellular polymeric
substances. In this paper, a stochastic biofilm disruption model based on the
usage of QS mimickers is proposed. A chemical reaction network (CRN) involving
four different states is employed to model the biological processes during the
biofilm formation and its disruption via QS mimickers. In addition, a
state-based stochastic simulation algorithm is proposed to simulate this CRN.
The proposed model is validated by the in vitro experimental results of
Pseudomonas aeruginosa biofilm and its disruption by rosmarinic acid as the QS
mimicker. Our results show that there is an uncertainty in state transitions
due to the effect of the randomness in the CRN. In addition to the QS
activation threshold, the presented work demonstrates that there are underlying
two more thresholds for the disruption of EPS and bacteria, which provides a
realistic modeling for biofilm disruption with QS mimickers.
| [
{
"created": "Mon, 30 Jan 2023 15:51:23 GMT",
"version": "v1"
},
{
"created": "Tue, 27 Jun 2023 14:52:38 GMT",
"version": "v2"
}
] | 2023-11-27 | [
[
"Gulec",
"Fatih",
""
],
[
"Eckford",
"Andrew W.",
""
]
] | Quorum sensing (QS) mimickers can be used as an effective tool to disrupt biofilms which consist of communicating bacteria and extracellular polymeric substances. In this paper, a stochastic biofilm disruption model based on the usage of QS mimickers is proposed. A chemical reaction network (CRN) involving four different states is employed to model the biological processes during the biofilm formation and its disruption via QS mimickers. In addition, a state-based stochastic simulation algorithm is proposed to simulate this CRN. The proposed model is validated by the in vitro experimental results of Pseudomonas aeruginosa biofilm and its disruption by rosmarinic acid as the QS mimicker. Our results show that there is an uncertainty in state transitions due to the effect of the randomness in the CRN. In addition to the QS activation threshold, the presented work demonstrates that there are underlying two more thresholds for the disruption of EPS and bacteria, which provides a realistic modeling for biofilm disruption with QS mimickers. |
2011.07639 | Margaret Cheung | Pengzhi Zhang, Jaebeom Han, Piotr Cieplak, Margaret. S. Cheung | Determining the atomic charge of calcium ion requires the information of
its coordination geometry in an EF-hand motif | The following article has been accepted by Journal of Chemical
Physics | J. Chem. Phys. 154, 124104 (2021) | 10.1063/5.0037517 | null | q-bio.BM q-bio.SC | http://creativecommons.org/licenses/by/4.0/ | It is challenging to parameterize the force field for calcium ions (Ca2+) in
calcium-binding proteins because of their unique coordination chemistry that
involves the surrounding atoms required for stability. In this work, we
observed wide variation in Ca2+ binding loop conformations of the Ca2+-binding
protein calmodulin (CaM), which adopts the most populated ternary structures
determined from the MD simulations, followed by ab initio quantum mechanical
(QM) calculations on all twelve amino acids in the loop that coordinate Ca2+ in
aqueous solution. Ca2+ charges were derived by fitting to the electrostatic
potential (ESP) in the context of a classical or polarizable force field (PFF).
We discovered that the atomic radius of Ca2+ in conventional force fields is
too large for the QM calculation to capture the variation in the coordination
geometry of Ca2+ in its ionic form, leading to unphysical charges.
Specifically, we found that the fitted atomic charges of Ca2+ in the context of
PFF depend on the coordinating geometry of electronegative atoms from the amino
acids in the loop. Although nearby water molecules do not influence the atomic
charge of Ca2+, they are crucial for compensating for the coordination of Ca2+
due to the conformational flexibility in the EF-hand loop. Our method advances
the development of force fields for metal ions and protein binding sites in
dynamic environments.
| [
{
"created": "Sun, 15 Nov 2020 22:28:12 GMT",
"version": "v1"
},
{
"created": "Mon, 22 Mar 2021 13:56:36 GMT",
"version": "v2"
}
] | 2021-08-03 | [
[
"Zhang",
"Pengzhi",
""
],
[
"Han",
"Jaebeom",
""
],
[
"Cieplak",
"Piotr",
""
],
[
"Cheung",
"Margaret. S.",
""
]
] | It is challenging to parameterize the force field for calcium ions (Ca2+) in calcium-binding proteins because of their unique coordination chemistry that involves the surrounding atoms required for stability. In this work, we observed wide variation in Ca2+ binding loop conformations of the Ca2+-binding protein calmodulin (CaM), which adopts the most populated ternary structures determined from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on all twelve amino acids in the loop that coordinate Ca2+ in aqueous solution. Ca2+ charges were derived by fitting to the electrostatic potential (ESP) in the context of a classical or polarizable force field (PFF). We discovered that the atomic radius of Ca2+ in conventional force fields is too large for the QM calculation to capture the variation in the coordination geometry of Ca2+ in its ionic form, leading to unphysical charges. Specifically, we found that the fitted atomic charges of Ca2+ in the context of PFF depend on the coordinating geometry of electronegative atoms from the amino acids in the loop. Although nearby water molecules do not influence the atomic charge of Ca2+, they are crucial for compensating for the coordination of Ca2+ due to the conformational flexibility in the EF-hand loop. Our method advances the development of force fields for metal ions and protein binding sites in dynamic environments. |
2004.12338 | Giorgio Guzzetta | Giorgio Guzzetta, Flavia Riccardo, Valentina Marziano, Piero Poletti,
Filippo Trentini, Antonino Bella, Xanthi Andrianou, Martina Del Manso,
Massimo Fabiani, Stefania Bellino, Stefano Boros, Alberto Mateo Urdiales,
Maria Fenicia Vescio, Andrea Piccioli, COVID-19 working group, Silvio
Brusaferro, Giovanni Rezza, Patrizio Pezzotti, Marco Ajelli, Stefano Merler | The impact of a nation-wide lockdown on COVID-19 transmissibility in
Italy | 6 pages, 3 figures; submitted | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | On March 10, 2020, Italy imposed a national lockdown to curtail the spread of
COVID-19. Here we estimate that, fourteen days after the implementation of the
strategy, the net reproduction number has dropped below the epidemic threshold
- estimated range 0.4-0.7. Our findings provide a timeline of the effectiveness
of the implemented lockdown, which is relevant for a large number of countries
that followed Italy in enforcing similar measures.
| [
{
"created": "Sun, 26 Apr 2020 10:04:31 GMT",
"version": "v1"
}
] | 2020-04-28 | [
[
"Guzzetta",
"Giorgio",
""
],
[
"Riccardo",
"Flavia",
""
],
[
"Marziano",
"Valentina",
""
],
[
"Poletti",
"Piero",
""
],
[
"Trentini",
"Filippo",
""
],
[
"Bella",
"Antonino",
""
],
[
"Andrianou",
"Xanthi",
"... | On March 10, 2020, Italy imposed a national lockdown to curtail the spread of COVID-19. Here we estimate that, fourteen days after the implementation of the strategy, the net reproduction number has dropped below the epidemic threshold - estimated range 0.4-0.7. Our findings provide a timeline of the effectiveness of the implemented lockdown, which is relevant for a large number of countries that followed Italy in enforcing similar measures. |
2208.04162 | Christoph Zechner | Anne-Lena Moor and Christoph Zechner | Dynamic Information Transfer in Stochastic Biochemical Networks | null | null | null | null | q-bio.MN q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We develop numerical and analytical approaches to calculate mutual
information between complete paths of two molecular components embedded into a
larger reaction network. In particular, we focus on a continuous-time Markov
chain formalism, frequently used to describe intracellular processes involving
lowly abundant molecular species. Previously, we have shown how the path mutual
information can be calculated for such systems when two molecular components
interact directly with one another with no intermediate molecular components
being present. In this work, we generalize this approach to biochemical
networks involving an arbitrary number of molecular components. We present an
efficient Monte Carlo method as well as an analytical approximation to
calculate the path mutual information and show how it can be decomposed into a
pair of transfer entropies that capture the causal flow of information between
two network components. We apply our methodology to study information transfer
in a simple three-node feedforward network, as well as a more complex positive
feedback system that switches stochastically between two metastable modes.
| [
{
"created": "Mon, 8 Aug 2022 14:02:57 GMT",
"version": "v1"
}
] | 2022-08-09 | [
[
"Moor",
"Anne-Lena",
""
],
[
"Zechner",
"Christoph",
""
]
] | We develop numerical and analytical approaches to calculate mutual information between complete paths of two molecular components embedded into a larger reaction network. In particular, we focus on a continuous-time Markov chain formalism, frequently used to describe intracellular processes involving lowly abundant molecular species. Previously, we have shown how the path mutual information can be calculated for such systems when two molecular components interact directly with one another with no intermediate molecular components being present. In this work, we generalize this approach to biochemical networks involving an arbitrary number of molecular components. We present an efficient Monte Carlo method as well as an analytical approximation to calculate the path mutual information and show how it can be decomposed into a pair of transfer entropies that capture the causal flow of information between two network components. We apply our methodology to study information transfer in a simple three-node feedforward network, as well as a more complex positive feedback system that switches stochastically between two metastable modes. |
q-bio/0609022 | Mikl\'os Cs\H{u}r\"os | Mikl\'os Cs\H{u}r\"os, Laurent No\'e and Gregory Kucherov | Reconsidering the significance of genomic word frequency | null | null | null | null | q-bio.GN | null | We propose that the distribution of DNA words in genomic sequences can be
primarily characterized by a double Pareto-lognormal distribution, which
explains lognormal and power-law features found across all known genomes. Such
a distribution may be the result of completely random sequence evolution by
duplication processes. The parametrization of genomic word frequencies allows
for an assessment of significance for frequent or rare sequence motifs.
| [
{
"created": "Thu, 14 Sep 2006 17:18:30 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Csűrös",
"Miklós",
""
],
[
"Noé",
"Laurent",
""
],
[
"Kucherov",
"Gregory",
""
]
] | We propose that the distribution of DNA words in genomic sequences can be primarily characterized by a double Pareto-lognormal distribution, which explains lognormal and power-law features found across all known genomes. Such a distribution may be the result of completely random sequence evolution by duplication processes. The parametrization of genomic word frequencies allows for an assessment of significance for frequent or rare sequence motifs. |
2201.13406 | Wensi Wu | Wensi Wu, Stephen Ching, Steve A. Maas, Andras Lasso, Patricia Sabin,
Jeffrey A. Weiss, Matthew A. Jolley | A Computational Framework for Atrioventricular Valve Modeling using
Open-Source Software | null | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | Atrioventricular valve regurgitation is a significant cause of morbidity and
mortality in patients with acquired and congenital cardiac valve disease.
Image-derived computational modeling of atrioventricular valves has advanced
substantially over the last decade and holds particular promise to inform valve
repair in small and heterogeneous populations which are less likely to be
optimized through empiric clinical application. While an abundance of
computational biomechanics studies have investigated mitral and tricuspid valve
disease in adults, few studies have investigated application to vulnerable
pediatric and congenital heart populations. Further, to date, investigators
have primarily relied upon a series of commercial applications that are neither
designed for image-derived modeling of cardiac valves, nor freely available to
facilitate transparent and reproducible valve science. To address this
deficiency, we aimed to build an open-source computational framework for the
image-derived biomechanical analysis of atrioventricular valves. In the present
work, we integrated an open-source valve modeling platform, SlicerHeart, and an
open-source biomechanics finite element modeling software, FEBio, to facilitate
image-derived atrioventricular valve model creation and finite element
analysis. We present a detailed verification and sensitivity analysis to
demonstrate the fidelity of this modeling in application to 3D
echocardiography-derived pediatric mitral and tricuspid valve models. Our
analyses achieved excellent agreement with those reported in the literature. As
such, this evolving computational framework offers a promising initial
foundation for future development and investigation of valve mechanics, in
particular collaborative efforts targeting the development of improved repairs
for children with congenital heart disease.
| [
{
"created": "Mon, 31 Jan 2022 18:11:25 GMT",
"version": "v1"
}
] | 2022-02-01 | [
[
"Wu",
"Wensi",
""
],
[
"Ching",
"Stephen",
""
],
[
"Maas",
"Steve A.",
""
],
[
"Lasso",
"Andras",
""
],
[
"Sabin",
"Patricia",
""
],
[
"Weiss",
"Jeffrey A.",
""
],
[
"Jolley",
"Matthew A.",
""
]
] | Atrioventricular valve regurgitation is a significant cause of morbidity and mortality in patients with acquired and congenital cardiac valve disease. Image-derived computational modeling of atrioventricular valves has advanced substantially over the last decade and holds particular promise to inform valve repair in small and heterogeneous populations which are less likely to be optimized through empiric clinical application. While an abundance of computational biomechanics studies have investigated mitral and tricuspid valve disease in adults, few studies have investigated application to vulnerable pediatric and congenital heart populations. Further, to date, investigators have primarily relied upon a series of commercial applications that are neither designed for image-derived modeling of cardiac valves, nor freely available to facilitate transparent and reproducible valve science. To address this deficiency, we aimed to build an open-source computational framework for the image-derived biomechanical analysis of atrioventricular valves. In the present work, we integrated an open-source valve modeling platform, SlicerHeart, and an open-source biomechanics finite element modeling software, FEBio, to facilitate image-derived atrioventricular valve model creation and finite element analysis. We present a detailed verification and sensitivity analysis to demonstrate the fidelity of this modeling in application to 3D echocardiography-derived pediatric mitral and tricuspid valve models. Our analyses achieved excellent agreement with those reported in the literature. As such, this evolving computational framework offers a promising initial foundation for future development and investigation of valve mechanics, in particular collaborative efforts targeting the development of improved repairs for children with congenital heart disease. |
2102.03431 | Benjamin Hollering | Joseph Cummings, Benjamin Hollering, Christopher Manon | Invariants for level-1 phylogenetic networks under the
Cavendar-Farris-Neyman Model | 29 pages, 6 figures | null | null | null | q-bio.PE math.AG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phylogenetic networks can model more complicated evolutionary phenomena that
trees fail to capture such as horizontal gene transfer and hybridization. The
same Markov models that are used to model evolution on trees can also be
extended to networks and similar questions, such as the identifiability of the
network parameter or the invariants of the model, can be asked. In this paper
we focus on finding the invariants of the Cavendar-Farris-Neyman (CFN) model on
level-1 phylogenetic networks. We do this by reducing the problem to finding
invariants of sunlet networks, which are level-1 networks consisting of a
single cycle with leaves at each vertex. We then determine all quadratic
invariants in the sunlet network ideal which we conjecture generate the full
ideal.
| [
{
"created": "Fri, 5 Feb 2021 22:00:44 GMT",
"version": "v1"
}
] | 2021-02-09 | [
[
"Cummings",
"Joseph",
""
],
[
"Hollering",
"Benjamin",
""
],
[
"Manon",
"Christopher",
""
]
] | Phylogenetic networks can model more complicated evolutionary phenomena that trees fail to capture such as horizontal gene transfer and hybridization. The same Markov models that are used to model evolution on trees can also be extended to networks and similar questions, such as the identifiability of the network parameter or the invariants of the model, can be asked. In this paper we focus on finding the invariants of the Cavendar-Farris-Neyman (CFN) model on level-1 phylogenetic networks. We do this by reducing the problem to finding invariants of sunlet networks, which are level-1 networks consisting of a single cycle with leaves at each vertex. We then determine all quadratic invariants in the sunlet network ideal which we conjecture generate the full ideal. |
1902.10589 | Claus Metzner | Claus Metzner | Principles of efficient chemotactic pursuit | null | null | null | null | q-bio.CB physics.bio-ph q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In chemotaxis, cells are modulating their migration patterns in response to
concentration gradients of a guiding substance. Immune cells are believed to
use such chemotactic sensing for remotely detecting and homing in on pathogens.
Considering that an immune cells may encounter a multitude of targets with
vastly different migration properties, ranging from immobile to highly mobile,
it is not clear which strategies of chemotactic pursuit are simultaneously
efficient and versatile. We takle this problem theoretically and define a
tunable response function that maps temporal or spatial concentration gradients
to migration behavior. The seven free parameters of this response function are
optimized numerically with the objective of maximizing search efficiency
against a wide spectrum of target cell properties. Finally, we reverse-engineer
the best-performing parameter sets to uncover the principles of efficient
chemotactic pursuit under different biologically realistic boundary conditions.
Remarkably, the numerical optimization rediscovers chemotactic strategies that
are well-known in biological systems, such as the gradient-dependent swimming
and tumbling modes of E.coli. Some of our results may also be useful for the
design of chemotaxis experiments and for the development of algorithms that
automatically detect and quantify goal oriented behavior in measured immune
cell trajectories.
| [
{
"created": "Wed, 27 Feb 2019 15:29:30 GMT",
"version": "v1"
}
] | 2019-02-28 | [
[
"Metzner",
"Claus",
""
]
] | In chemotaxis, cells are modulating their migration patterns in response to concentration gradients of a guiding substance. Immune cells are believed to use such chemotactic sensing for remotely detecting and homing in on pathogens. Considering that an immune cells may encounter a multitude of targets with vastly different migration properties, ranging from immobile to highly mobile, it is not clear which strategies of chemotactic pursuit are simultaneously efficient and versatile. We takle this problem theoretically and define a tunable response function that maps temporal or spatial concentration gradients to migration behavior. The seven free parameters of this response function are optimized numerically with the objective of maximizing search efficiency against a wide spectrum of target cell properties. Finally, we reverse-engineer the best-performing parameter sets to uncover the principles of efficient chemotactic pursuit under different biologically realistic boundary conditions. Remarkably, the numerical optimization rediscovers chemotactic strategies that are well-known in biological systems, such as the gradient-dependent swimming and tumbling modes of E.coli. Some of our results may also be useful for the design of chemotaxis experiments and for the development of algorithms that automatically detect and quantify goal oriented behavior in measured immune cell trajectories. |
1602.04971 | Sophie L\`ebre | Sophie Lebre and Olivier Gascuel | The combinatorics of overlapping genes | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Overlapping genes exist in all domains of life and are much more abundant
than expected at their first discovery in the late 1970s. Assuming that the
reference gene is read in frame +0, an overlapping gene can be encoded in two
reading frames in the sense strand, denoted by +1 and +2, and in three reading
frames in the opposite strand, denoted by -0, -1 and -2. This motivated
numerous researchers to study the constraints induced by the genetic code on
the various overlapping frames, mostly based on information theory. Our focus
in this paper is on the constraints induced on two overlapping genes in terms
of amino acids, as well as polypeptides. We show that simple linear constraints
bind the amino acid composition of two proteins encoded by overlapping genes.
Novel constraints are revealed when polypeptides are considered, and not just
single amino acids. For example, in double-coding sequences with an overlapping
reading frame -2, each Tyrosine (denoted as Tyr or Y) in the overlapping frame
overlaps a Tyrosine in the reference frame +0 (and reciprocally), whereas
specific words (e.g. YY) never occur. We thus distinguish between null
constraints (YY = 0 in frame -2) and non-null constraints (Y in frame +0 <=> Y
in frame -2). Our equivalence-based constraints are symmetrical and thus enable
the characterization of the joint composition of overlapping proteins. We
describe several formal frameworks and a graph algorithm to characterize and
compute these constraints. These results yield support for understanding the
mechanisms and evolution of overlapping genes, and for developing novel
overlapping gene detection methods.
| [
{
"created": "Tue, 16 Feb 2016 10:18:04 GMT",
"version": "v1"
},
{
"created": "Mon, 3 Oct 2016 15:33:49 GMT",
"version": "v2"
},
{
"created": "Tue, 8 Nov 2016 08:45:53 GMT",
"version": "v3"
},
{
"created": "Thu, 19 Jan 2017 09:15:19 GMT",
"version": "v4"
}
] | 2017-01-20 | [
[
"Lebre",
"Sophie",
""
],
[
"Gascuel",
"Olivier",
""
]
] | Overlapping genes exist in all domains of life and are much more abundant than expected at their first discovery in the late 1970s. Assuming that the reference gene is read in frame +0, an overlapping gene can be encoded in two reading frames in the sense strand, denoted by +1 and +2, and in three reading frames in the opposite strand, denoted by -0, -1 and -2. This motivated numerous researchers to study the constraints induced by the genetic code on the various overlapping frames, mostly based on information theory. Our focus in this paper is on the constraints induced on two overlapping genes in terms of amino acids, as well as polypeptides. We show that simple linear constraints bind the amino acid composition of two proteins encoded by overlapping genes. Novel constraints are revealed when polypeptides are considered, and not just single amino acids. For example, in double-coding sequences with an overlapping reading frame -2, each Tyrosine (denoted as Tyr or Y) in the overlapping frame overlaps a Tyrosine in the reference frame +0 (and reciprocally), whereas specific words (e.g. YY) never occur. We thus distinguish between null constraints (YY = 0 in frame -2) and non-null constraints (Y in frame +0 <=> Y in frame -2). Our equivalence-based constraints are symmetrical and thus enable the characterization of the joint composition of overlapping proteins. We describe several formal frameworks and a graph algorithm to characterize and compute these constraints. These results yield support for understanding the mechanisms and evolution of overlapping genes, and for developing novel overlapping gene detection methods. |
2011.05521 | Antoine Nzeyimana | Antoine Nzeyimana, Kate EA Saunders, John R Geddes, Patrick E McSharry | Lamotrigine Therapy for Bipolar Depression: Analysis of Self-Reported
Patient Data | null | JMIR mental health. 2018;5(4):e63 | 10.2196/mental.9026 | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Background: Depression in people with bipolar disorder is a major cause of
long-term disability, possibly leading to early mortality and currently,
limited safe and effective therapies exist. A double-blinded randomized
placebo-controlled trial (CEQUEL study) was conducted to evaluate the efficacy
of Lamotrigine plus Quetiapine versus Quetiapine monotherapy in patients with
bipolar type I or type II disorders.
Objective: The objective of our study was to reanalyze CEQUEL data and
determine an unbiased classification accuracy for active lamotrigine versus
placebo. We also wanted to establish the time it took for the drug to provide
statistically significant outcomes.
Methods: Between October 21, 2008 and April 27, 2012, 202 participants from
27 sites in United Kingdom were randomly assigned to two treatments; 101:
lamotrigine, 101: placebo. The primary variable used for estimating depressive
symptoms was based on the Quick Inventory of Depressive Symptomatology-self
report version 16 (QIDS-SR16). We analyze the data using feature engineering
and simple classifiers.
Results: From weeks 10 to 14, the mean difference in QIDS-SR16 ratings
between the groups was -1.6317 (P=.09; sample size=81, 77; 95% CI -0.2403 to
3.5036). From weeks 48 to 52, the mean difference was -2.0032 (P=.09; sample
size=54, 48; 95% CI -0.3433 to 4.3497). The coefficient of variation and
detrended fluctuation analysis (DFA) exponent alpha had the greatest
explanatory power. The out-of-sample classification accuracy for the 138
participants who reported more than 10 times after week 12 was 62%. A
consistent classification accuracy higher than the no-information benchmark was
obtained in week 44.
Conclusions: Lamotrigine plus Quetiapine treatment decreased depressive
symptoms in patients, but with substantial temporal instability. A trial of at
least 44 weeks was required to achieve consistent results.
| [
{
"created": "Wed, 11 Nov 2020 02:52:40 GMT",
"version": "v1"
}
] | 2020-11-12 | [
[
"Nzeyimana",
"Antoine",
""
],
[
"Saunders",
"Kate EA",
""
],
[
"Geddes",
"John R",
""
],
[
"McSharry",
"Patrick E",
""
]
] | Background: Depression in people with bipolar disorder is a major cause of long-term disability, possibly leading to early mortality and currently, limited safe and effective therapies exist. A double-blinded randomized placebo-controlled trial (CEQUEL study) was conducted to evaluate the efficacy of Lamotrigine plus Quetiapine versus Quetiapine monotherapy in patients with bipolar type I or type II disorders. Objective: The objective of our study was to reanalyze CEQUEL data and determine an unbiased classification accuracy for active lamotrigine versus placebo. We also wanted to establish the time it took for the drug to provide statistically significant outcomes. Methods: Between October 21, 2008 and April 27, 2012, 202 participants from 27 sites in United Kingdom were randomly assigned to two treatments; 101: lamotrigine, 101: placebo. The primary variable used for estimating depressive symptoms was based on the Quick Inventory of Depressive Symptomatology-self report version 16 (QIDS-SR16). We analyze the data using feature engineering and simple classifiers. Results: From weeks 10 to 14, the mean difference in QIDS-SR16 ratings between the groups was -1.6317 (P=.09; sample size=81, 77; 95% CI -0.2403 to 3.5036). From weeks 48 to 52, the mean difference was -2.0032 (P=.09; sample size=54, 48; 95% CI -0.3433 to 4.3497). The coefficient of variation and detrended fluctuation analysis (DFA) exponent alpha had the greatest explanatory power. The out-of-sample classification accuracy for the 138 participants who reported more than 10 times after week 12 was 62%. A consistent classification accuracy higher than the no-information benchmark was obtained in week 44. Conclusions: Lamotrigine plus Quetiapine treatment decreased depressive symptoms in patients, but with substantial temporal instability. A trial of at least 44 weeks was required to achieve consistent results. |
1107.2504 | Thierry Huillet | Thierry Huillet (LPTM) | A branching diffusion model of selection: from the neutral Wright-Fisher
case to the one including mutations | To appear in: Intern. Math. Forum | null | null | null | q-bio.QM cond-mat.stat-mech math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider diffusion processes x_{t} on the unit interval.
Doob-transformation techniques consist of a selection of x_{t}-paths procedure.
The law of the transformed process is the one of a branching diffusion system
of particles, each diffusing like a new process tilde{x}_{t}, superposing an
additional drift to the one of x_{t}. Killing and/or branching of
tilde{x}_{t}-particles occur at some space-dependent rate lambda. For this
transformed process, so in the class of branching diffusions, the question
arises as to whether the particle system is sub-critical, critical or
super-critical. In the first two cases, extinction occurs with probability one.
We apply this circle of ideas to diffusion processes arising in population
genetics. In this setup, the process x_{t} is a Wright-Fisher (WF) diffusion,
either neutral or with mutations. We study a particular Doob transform which is
based on the exponential function in the usual fitness parameter sigma. We have
in mind that this is an alternative way to introduce selection or fitness in
both WF-like diffusions, leading to branching diffusion models ideas. For this
Doob-transform model of fitness, the usual selection drift sigma x(1-x) should
be superposed to the one of x_{t} to form tilde{x}_{t} which is the process
that can branch, binarily. In the first neutral case, there is a trade-off
between branching events giving birth to new particles and absorption at the
boundaries, killing the particles. Under our assumptions, the branching
diffusion process gets eventually globally extinct in finite time with
exponential tails. In the second case with mutations, there is a trade-off
between killing events removing some particles from the system and reflection
at the boundaries where the particles survive. This branching diffusion process
also gets eventually globally extinct but in very long finite time with
power-law tails. Our approach relies on the spectral expansion of the
transition probability kernels of both x_{t} and tilde{x}_{t}.
| [
{
"created": "Wed, 13 Jul 2011 09:49:52 GMT",
"version": "v1"
}
] | 2011-07-15 | [
[
"Huillet",
"Thierry",
"",
"LPTM"
]
] | We consider diffusion processes x_{t} on the unit interval. Doob-transformation techniques consist of a selection of x_{t}-paths procedure. The law of the transformed process is the one of a branching diffusion system of particles, each diffusing like a new process tilde{x}_{t}, superposing an additional drift to the one of x_{t}. Killing and/or branching of tilde{x}_{t}-particles occur at some space-dependent rate lambda. For this transformed process, so in the class of branching diffusions, the question arises as to whether the particle system is sub-critical, critical or super-critical. In the first two cases, extinction occurs with probability one. We apply this circle of ideas to diffusion processes arising in population genetics. In this setup, the process x_{t} is a Wright-Fisher (WF) diffusion, either neutral or with mutations. We study a particular Doob transform which is based on the exponential function in the usual fitness parameter sigma. We have in mind that this is an alternative way to introduce selection or fitness in both WF-like diffusions, leading to branching diffusion models ideas. For this Doob-transform model of fitness, the usual selection drift sigma x(1-x) should be superposed to the one of x_{t} to form tilde{x}_{t} which is the process that can branch, binarily. In the first neutral case, there is a trade-off between branching events giving birth to new particles and absorption at the boundaries, killing the particles. Under our assumptions, the branching diffusion process gets eventually globally extinct in finite time with exponential tails. In the second case with mutations, there is a trade-off between killing events removing some particles from the system and reflection at the boundaries where the particles survive. This branching diffusion process also gets eventually globally extinct but in very long finite time with power-law tails. Our approach relies on the spectral expansion of the transition probability kernels of both x_{t} and tilde{x}_{t}. |
1706.00247 | Hossam Haick | Inbar Nardi Agmon, Manal Abud, Ori Liran, Naomi Gai-Mo, Maya Ilouze,
Amir Onn, Jair Bar, Rossie Navon, Dekel Shlomi, Hossam Haick and Nir Peled | Exhaled Breath Analysis for Monitoring Response to Treatment in Advanced
Lung Cancer | null | null | 10.1016/j.jtho.2016.02.017 | null | q-bio.TO physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | INTRODUCTION: The Response Evaluation Criteria in Solid Tumors (RECIST) serve
as the accepted standard to monitor treatment efficacy in lung cancer. However,
the time intervals between consecutive computerized tomography scans might be
too long to allow early identification of treatment failure. This study
examines the use of breath sampling to monitor responses to anticancer
treatments in patients with advanced lung cancer. METHODS: A total of 143
breath samples were collected from 39 patients with advanced lung cancer. The
exhaled breath signature, determined by gas chromatography/mass spectrometry
and a nanomaterial-based array of sensors, was correlated with the response to
therapy assessed by RECIST: complete response, partial response, stable
disease, or progressive disease. RESULTS: Gas chromatography/mass spectrometry
analysis identified three volatile organic compounds as significantly
indicating disease control (PR/stable disease), with one of them also
significantly discriminating PR/stable disease from progressive disease. The
nanoarray had the ability to monitor changes in tumor response across therapy,
also indicating any lack of further response to therapy. When one-sensor
analysis was used, 59% of the follow-up samples were identified correctly.
There was 85% success in monitoring disease control (stable disease/partial
response). CONCLUSION: Breath analysis, using mainly the nanoarray, may serve
as a surrogate marker for the response to systemic therapy in lung cancer. As a
monitoring tool, it can provide the oncologist with a quick bedside method of
identifying a lack of response to an anticancer treatment. This may allow
quicker recognition than does the current RECIST analysis. Early recognition of
treatment failure could improve patient care.
| [
{
"created": "Thu, 1 Jun 2017 10:35:03 GMT",
"version": "v1"
}
] | 2017-06-02 | [
[
"Agmon",
"Inbar Nardi",
""
],
[
"Abud",
"Manal",
""
],
[
"Liran",
"Ori",
""
],
[
"Gai-Mo",
"Naomi",
""
],
[
"Ilouze",
"Maya",
""
],
[
"Onn",
"Amir",
""
],
[
"Bar",
"Jair",
""
],
[
"Navon",
"Ross... | INTRODUCTION: The Response Evaluation Criteria in Solid Tumors (RECIST) serve as the accepted standard to monitor treatment efficacy in lung cancer. However, the time intervals between consecutive computerized tomography scans might be too long to allow early identification of treatment failure. This study examines the use of breath sampling to monitor responses to anticancer treatments in patients with advanced lung cancer. METHODS: A total of 143 breath samples were collected from 39 patients with advanced lung cancer. The exhaled breath signature, determined by gas chromatography/mass spectrometry and a nanomaterial-based array of sensors, was correlated with the response to therapy assessed by RECIST: complete response, partial response, stable disease, or progressive disease. RESULTS: Gas chromatography/mass spectrometry analysis identified three volatile organic compounds as significantly indicating disease control (PR/stable disease), with one of them also significantly discriminating PR/stable disease from progressive disease. The nanoarray had the ability to monitor changes in tumor response across therapy, also indicating any lack of further response to therapy. When one-sensor analysis was used, 59% of the follow-up samples were identified correctly. There was 85% success in monitoring disease control (stable disease/partial response). CONCLUSION: Breath analysis, using mainly the nanoarray, may serve as a surrogate marker for the response to systemic therapy in lung cancer. As a monitoring tool, it can provide the oncologist with a quick bedside method of identifying a lack of response to an anticancer treatment. This may allow quicker recognition than does the current RECIST analysis. Early recognition of treatment failure could improve patient care. |
2111.09981 | Tom R\"oschinger | Tom R\"oschinger, Roberto Mor\'an Tovar, Simone Pompei, Michael
L\"assig | Adaptive ratchets and the evolution of molecular complexity | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Biological systems have evolved to amazingly complex states, yet we do not
understand in general how evolution operates to generate increasing genetic and
functional complexity. Molecular recognition sites are short genome segments or
peptides binding a cognate recognition target of sufficient sequence
similarity. Such sites are simple, ubiquitous modules of sequence information,
cellular function, and evolution. Here we show that recognition sites, if
coupled to a time-dependent target, can rapidly evolve to complex states with
larger code length and smaller coding density than sites recognising a static
target. The underlying fitness model contains selection for recognition, which
depends on the sequence similarity between site and target, and a uniform cost
per unit of code length. Site sequences are shown to evolve in a specific
adaptive ratchet, which produces selection of different strength for code
extensions and compressions. Ratchet evolution increases the adaptive width of
evolved sites, accelerating the adaptation to moving targets and facilitating
refinement and innovation of recognition functions. We apply these results to
the recognition of fast-evolving antigens by the human immune system. Our
analysis shows how molecular complexity can evolve as a collateral to selection
for function in a dynamic environment.
| [
{
"created": "Thu, 18 Nov 2021 23:51:14 GMT",
"version": "v1"
}
] | 2021-11-22 | [
[
"Röschinger",
"Tom",
""
],
[
"Tovar",
"Roberto Morán",
""
],
[
"Pompei",
"Simone",
""
],
[
"Lässig",
"Michael",
""
]
] | Biological systems have evolved to amazingly complex states, yet we do not understand in general how evolution operates to generate increasing genetic and functional complexity. Molecular recognition sites are short genome segments or peptides binding a cognate recognition target of sufficient sequence similarity. Such sites are simple, ubiquitous modules of sequence information, cellular function, and evolution. Here we show that recognition sites, if coupled to a time-dependent target, can rapidly evolve to complex states with larger code length and smaller coding density than sites recognising a static target. The underlying fitness model contains selection for recognition, which depends on the sequence similarity between site and target, and a uniform cost per unit of code length. Site sequences are shown to evolve in a specific adaptive ratchet, which produces selection of different strength for code extensions and compressions. Ratchet evolution increases the adaptive width of evolved sites, accelerating the adaptation to moving targets and facilitating refinement and innovation of recognition functions. We apply these results to the recognition of fast-evolving antigens by the human immune system. Our analysis shows how molecular complexity can evolve as a collateral to selection for function in a dynamic environment. |
1208.3407 | Aaron Quinlan Ph.D. | Ryan M. Layer, Kevin Skadron, Gabriel Robins, Ira M. Hall, and Aaron
R. Quinlan | Binary Interval Search (BITS): A Scalable Algorithm for Counting
Interval Intersections | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: The comparison of diverse genomic datasets is fundamental to
understanding genome biology. Researchers must explore many large datasets of
genome intervals (e.g., genes, sequence alignments) to place their experimental
results in a broader context and to make new discoveries. Relationships between
genomic datasets are typically measured by identifying intervals that
intersect: that is, they overlap and thus share a common genome interval. Given
the continued advances in DNA sequencing technologies, efficient methods for
measuring statistically significant relationships between many sets of genomic
features is crucial for future discovery.
Results: We introduce the Binary Interval Search (BITS) algorithm, a novel
and scalable approach to interval set intersection. We demonstrate that BITS
outperforms existing methods at counting interval intersections. Moreover, we
show that BITS is intrinsically suited to parallel computing architectures such
as Graphics Processing Units (GPUs) by illustrating its utility for efficient
Monte-Carlo simulations measuring the significance of relationships between
sets of genomic intervals.
| [
{
"created": "Thu, 16 Aug 2012 16:12:48 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Aug 2012 12:31:24 GMT",
"version": "v2"
}
] | 2012-08-20 | [
[
"Layer",
"Ryan M.",
""
],
[
"Skadron",
"Kevin",
""
],
[
"Robins",
"Gabriel",
""
],
[
"Hall",
"Ira M.",
""
],
[
"Quinlan",
"Aaron R.",
""
]
] | Motivation: The comparison of diverse genomic datasets is fundamental to understanding genome biology. Researchers must explore many large datasets of genome intervals (e.g., genes, sequence alignments) to place their experimental results in a broader context and to make new discoveries. Relationships between genomic datasets are typically measured by identifying intervals that intersect: that is, they overlap and thus share a common genome interval. Given the continued advances in DNA sequencing technologies, efficient methods for measuring statistically significant relationships between many sets of genomic features is crucial for future discovery. Results: We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection. We demonstrate that BITS outperforms existing methods at counting interval intersections. Moreover, we show that BITS is intrinsically suited to parallel computing architectures such as Graphics Processing Units (GPUs) by illustrating its utility for efficient Monte-Carlo simulations measuring the significance of relationships between sets of genomic intervals. |
1412.1597 | Iain Johnston | Iain G. Johnston, Benjamin C. Rickett, Nick S. Jones | Explicit tracking of uncertainty increases the power of quantitative
rule-of-thumb reasoning in cell biology | 8 pages, 3 figures | Biophys. J. 107 2612 (2014) | 10.1016/j.bpj.2014.08.040 | null | q-bio.QM stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | "Back-of-the-envelope" or "rule-of-thumb" calculations involving rough
estimates of quantities play a central scientific role in developing intuition
about the structure and behaviour of physical systems, for example in so-called
`Fermi problems' in the physical sciences. Such calculations can be used to
powerfully and quantitatively reason about biological systems, particularly at
the interface between physics and biology. However, substantial uncertainties
are often associated with values in cell biology, and performing calculations
without taking this uncertainty into account may limit the extent to which
results can be interpreted for a given problem. We present a means to
facilitate such calculations where uncertainties are explicitly tracked through
the line of reasoning, and introduce a `probabilistic calculator' called
Caladis, a web tool freely available at www.caladis.org, designed to perform
this tracking. This approach allows users to perform more statistically robust
calculations in cell biology despite having uncertain values, and to identify
which quantities need to be measured more precisely in order to make confident
statements, facilitating efficient experimental design. We illustrate the use
of our tool for tracking uncertainty in several example biological
calculations, showing that the results yield powerful and interpretable
statistics on the quantities of interest. We also demonstrate that the outcomes
of calculations may differ from point estimates when uncertainty is accurately
tracked. An integral link between Caladis and the Bionumbers repository of
biological quantities further facilitates the straightforward location,
selection, and use of a wealth of experimental data in cell biological
calculations.
| [
{
"created": "Thu, 4 Dec 2014 09:29:48 GMT",
"version": "v1"
}
] | 2014-12-05 | [
[
"Johnston",
"Iain G.",
""
],
[
"Rickett",
"Benjamin C.",
""
],
[
"Jones",
"Nick S.",
""
]
] | "Back-of-the-envelope" or "rule-of-thumb" calculations involving rough estimates of quantities play a central scientific role in developing intuition about the structure and behaviour of physical systems, for example in so-called `Fermi problems' in the physical sciences. Such calculations can be used to powerfully and quantitatively reason about biological systems, particularly at the interface between physics and biology. However, substantial uncertainties are often associated with values in cell biology, and performing calculations without taking this uncertainty into account may limit the extent to which results can be interpreted for a given problem. We present a means to facilitate such calculations where uncertainties are explicitly tracked through the line of reasoning, and introduce a `probabilistic calculator' called Caladis, a web tool freely available at www.caladis.org, designed to perform this tracking. This approach allows users to perform more statistically robust calculations in cell biology despite having uncertain values, and to identify which quantities need to be measured more precisely in order to make confident statements, facilitating efficient experimental design. We illustrate the use of our tool for tracking uncertainty in several example biological calculations, showing that the results yield powerful and interpretable statistics on the quantities of interest. We also demonstrate that the outcomes of calculations may differ from point estimates when uncertainty is accurately tracked. An integral link between Caladis and the Bionumbers repository of biological quantities further facilitates the straightforward location, selection, and use of a wealth of experimental data in cell biological calculations. |
q-bio/0610027 | Zhihui Wang | Thomas S. Deisboeck and Zhihui Wang | Cancer Dissemination: A Consequence of limited Carrying Capacity? | 10 pages | null | null | null | q-bio.TO | null | Assuming that there is feedback between an expanding cancer system and its
organ-typical microenvironment, we argue here that such local tumor growth is
guided by co-existence rather than competition with the surrounding tissue. We
then present a novel concept that understands cancer dissemination as a
biological mechanism to evade the specific carrying capacity limit of its host
organ. This conceptual framework allows us to relate the tumor system's
volumetric growth rate to the host organ's functionality-conveying composite
infrastructure, and, intriguingly, already provides useful insights into
several clinical findings.
| [
{
"created": "Mon, 16 Oct 2006 16:32:11 GMT",
"version": "v1"
},
{
"created": "Tue, 31 Oct 2006 15:10:08 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Deisboeck",
"Thomas S.",
""
],
[
"Wang",
"Zhihui",
""
]
] | Assuming that there is feedback between an expanding cancer system and its organ-typical microenvironment, we argue here that such local tumor growth is guided by co-existence rather than competition with the surrounding tissue. We then present a novel concept that understands cancer dissemination as a biological mechanism to evade the specific carrying capacity limit of its host organ. This conceptual framework allows us to relate the tumor system's volumetric growth rate to the host organ's functionality-conveying composite infrastructure, and, intriguingly, already provides useful insights into several clinical findings. |
q-bio/0702058 | Gregory Batt | Gr\'egory Batt (INRIA Rh\^one-Alpes), Delphine Ropers (INRIA
Rh\^one-Alpes), Hidde De Jong (INRIA Rh\^one-Alpes), Michel Page (INRIA
Rh\^one-Alpes), Johannes Geiselmann | Symbolic Reachability Analysis of Genetic Regulatory Networks using
Qualitative Abstractions | null | null | null | null | q-bio.QM | null | The switch-like character of gene regulation has motivated the use of hybrid,
discrete-continuous models of genetic regulatory networks. While powerful
techniques for the analysis, verification, and control of hybrid systems have
been developed, the specificities of the biological application domain pose a
number of challenges, notably the absence of quantitative information on
parameter values and the size and complexity of networks of biological
interest. We introduce a method for the analysis of reachability properties of
genetic regulatory networks that is based on a class of discontinuous
piecewise-affine (PA) differential equations well-adapted to the above
constraints. More specifically, we introduce a hyperrectangular partition of
the state space that forms the basis for a discrete abstraction preserving the
sign of the derivatives of the state variables. The resulting discrete
transition system provides a conservative approximation of the qualitative
dynamics of the network and can be efficiently computed in a symbolic manner
from inequality constraints on the parameters. The method has been implemented
in the computer tool Genetic Network Analyzer (GNA), which has been applied to
the analysis of a regulatory system whose functioning is not well-understood by
biologists, the nutritional stress response in the bacterium Escherichia coli.
| [
{
"created": "Wed, 28 Feb 2007 13:39:13 GMT",
"version": "v1"
}
] | 2016-08-14 | [
[
"Batt",
"Grégory",
"",
"INRIA Rhône-Alpes"
],
[
"Ropers",
"Delphine",
"",
"INRIA\n Rhône-Alpes"
],
[
"De Jong",
"Hidde",
"",
"INRIA Rhône-Alpes"
],
[
"Page",
"Michel",
"",
"INRIA\n Rhône-Alpes"
],
[
"Geiselmann",
"Johannes",... | The switch-like character of gene regulation has motivated the use of hybrid, discrete-continuous models of genetic regulatory networks. While powerful techniques for the analysis, verification, and control of hybrid systems have been developed, the specificities of the biological application domain pose a number of challenges, notably the absence of quantitative information on parameter values and the size and complexity of networks of biological interest. We introduce a method for the analysis of reachability properties of genetic regulatory networks that is based on a class of discontinuous piecewise-affine (PA) differential equations well-adapted to the above constraints. More specifically, we introduce a hyperrectangular partition of the state space that forms the basis for a discrete abstraction preserving the sign of the derivatives of the state variables. The resulting discrete transition system provides a conservative approximation of the qualitative dynamics of the network and can be efficiently computed in a symbolic manner from inequality constraints on the parameters. The method has been implemented in the computer tool Genetic Network Analyzer (GNA), which has been applied to the analysis of a regulatory system whose functioning is not well-understood by biologists, the nutritional stress response in the bacterium Escherichia coli. |
2206.01092 | Cameron Mura | Nikita Sivakumar, Cameron Mura, Shayn M. Peirce | Innovations in Integrating Machine Learning and Agent-Based Modeling of
Biomedical Systems | 32 pages, 1 table, 8 figures | null | 10.3389/fsysb.2022.959665 | null | q-bio.QM cs.LG cs.MA q-bio.CB | http://creativecommons.org/licenses/by-sa/4.0/ | Agent-based modeling (ABM) is a well-established paradigm for simulating
complex systems via interactions between constituent entities. Machine learning
(ML) refers to approaches whereby statistical algorithms 'learn' from data on
their own, without imposing a priori theories of system behavior. Biological
systems -- from molecules, to cells, to entire organisms -- consist of vast
numbers of entities, governed by complex webs of interactions that span many
spatiotemporal scales and exhibit nonlinearity, stochasticity and intricate
coupling between entities. The macroscopic properties and collective dynamics
of such systems are difficult to capture via continuum modelling and mean-field
formalisms. ABM takes a 'bottom-up' approach that obviates these difficulties
by enabling one to easily propose and test a set of well-defined 'rules' to be
applied to the individual entities (agents) in a system. Evaluating a system
and propagating its state over discrete time-steps effectively simulates the
system, allowing observables to be computed and system properties to be
analyzed. Because the rules that govern an ABM can be difficult to abstract and
formulate from experimental data, there is an opportunity to use ML to help
infer optimal, system-specific ABM rules. Once such rule-sets are devised, ABM
calculations can generate a wealth of data, and ML can be applied there too --
e.g., to probe statistical measures that meaningfully describe a system's
stochastic properties. As an example of synergy in the other direction (from
ABM to ML), ABM simulations can generate realistic datasets for training ML
algorithms (e.g., for regularization, to mitigate overfitting). In these ways,
one can envision various synergistic ABM$\rightleftharpoons$ML loops. This
review summarizes how ABM and ML have been integrated in contexts that span
spatiotemporal scales, from cellular to population-level epidemiology.
| [
{
"created": "Thu, 2 Jun 2022 15:19:09 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Nov 2022 18:36:48 GMT",
"version": "v2"
}
] | 2022-11-10 | [
[
"Sivakumar",
"Nikita",
""
],
[
"Mura",
"Cameron",
""
],
[
"Peirce",
"Shayn M.",
""
]
] | Agent-based modeling (ABM) is a well-established paradigm for simulating complex systems via interactions between constituent entities. Machine learning (ML) refers to approaches whereby statistical algorithms 'learn' from data on their own, without imposing a priori theories of system behavior. Biological systems -- from molecules, to cells, to entire organisms -- consist of vast numbers of entities, governed by complex webs of interactions that span many spatiotemporal scales and exhibit nonlinearity, stochasticity and intricate coupling between entities. The macroscopic properties and collective dynamics of such systems are difficult to capture via continuum modelling and mean-field formalisms. ABM takes a 'bottom-up' approach that obviates these difficulties by enabling one to easily propose and test a set of well-defined 'rules' to be applied to the individual entities (agents) in a system. Evaluating a system and propagating its state over discrete time-steps effectively simulates the system, allowing observables to be computed and system properties to be analyzed. Because the rules that govern an ABM can be difficult to abstract and formulate from experimental data, there is an opportunity to use ML to help infer optimal, system-specific ABM rules. Once such rule-sets are devised, ABM calculations can generate a wealth of data, and ML can be applied there too -- e.g., to probe statistical measures that meaningfully describe a system's stochastic properties. As an example of synergy in the other direction (from ABM to ML), ABM simulations can generate realistic datasets for training ML algorithms (e.g., for regularization, to mitigate overfitting). In these ways, one can envision various synergistic ABM$\rightleftharpoons$ML loops. This review summarizes how ABM and ML have been integrated in contexts that span spatiotemporal scales, from cellular to population-level epidemiology. |
1507.06433 | Magali San Cristobal | Maria-Ines Fariello, Simon Boitard, Sabine Mercier, David Robelin,
Thomas Faraut, C\'ecile Arnould, Julien Recoquillay, Olivier Bouchez,
G\'erald Salin, Patrice Dehais, David Gourichon, Sophie Leroux,
Fr\'ed\'erique Pitel, Christine Leterrier, Magali San Cristobal | A New Local Score Based Method Applied to Behavior-divergent Quail Lines
Sequenced in Pools Precisely Detects Selection Signatures on Genes Related to
Autism | 32 pages, 4 figures | null | null | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Detecting genomic footprints of selection is an important step in the
understanding of evolution. Accounting for linkage disequilibrium in genome
scans allows increasing the detection power, but haplotype-based methods
require individual genotypes and are not applicable on pool-sequenced samples.
We propose to take advantage of the local score approach to account for linkage
disequilibrium, accumulating (possibly small) signals from single markers over
a genomic segment, to clearly pinpoint a selection signal, avoiding windowing
methods. This method provided results similar to haplotype-based methods on two
benchmark data sets with individual genotypes. Results obtained for a divergent
selection experiment on behavior in quail, where two lines were sequenced in
pools, are precise and biologically coherent, while competing methods failed:
our approach led to the detection of signals involving genes known to act on
social responsiveness or autistic traits. This local score approach is general
and can be applied to other genome-wide analyzes such as GWAS or genome scans
for selection.
| [
{
"created": "Thu, 23 Jul 2015 10:14:35 GMT",
"version": "v1"
}
] | 2015-07-24 | [
[
"Fariello",
"Maria-Ines",
""
],
[
"Boitard",
"Simon",
""
],
[
"Mercier",
"Sabine",
""
],
[
"Robelin",
"David",
""
],
[
"Faraut",
"Thomas",
""
],
[
"Arnould",
"Cécile",
""
],
[
"Recoquillay",
"Julien",
""
... | Detecting genomic footprints of selection is an important step in the understanding of evolution. Accounting for linkage disequilibrium in genome scans allows increasing the detection power, but haplotype-based methods require individual genotypes and are not applicable on pool-sequenced samples. We propose to take advantage of the local score approach to account for linkage disequilibrium, accumulating (possibly small) signals from single markers over a genomic segment, to clearly pinpoint a selection signal, avoiding windowing methods. This method provided results similar to haplotype-based methods on two benchmark data sets with individual genotypes. Results obtained for a divergent selection experiment on behavior in quail, where two lines were sequenced in pools, are precise and biologically coherent, while competing methods failed: our approach led to the detection of signals involving genes known to act on social responsiveness or autistic traits. This local score approach is general and can be applied to other genome-wide analyzes such as GWAS or genome scans for selection. |
2203.00743 | Qianqian Song | Minghan Chen, Chunrui Xu, Ziang Xu, Wei He, Haorui Zhang, Jing Su, and
Qianqian Song | Uncovering the dynamic effects of DEX treatment on lung cancer by
integrating bioinformatic inference and multiscale modeling of scRNA-seq and
proteomics data | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Lung cancer is one of the leading causes for cancer-related
death, with a five-year survival rate of 18%. It is a priority for us to
understand the underlying mechanisms that affect the implementation and
effectiveness of lung cancer therapeutics. In this study, we combine the power
of Bioinformatics and Systems Biology to comprehensively uncover functional and
signaling pathways of drug treatment using bioinformatics inference and
multiscale modeling of both scRNA-seq data and proteomics data. The innovative
and cross-disciplinary approach can be further applied to other computational
studies in tumorigenesis and oncotherapy. Results: A time series of lung
adenocarcinoma-derived A549 cells after DEX treatment were analysed. (1) We
first discovered the differentially expressed genes in those lung cancer cells.
Then through the interrogation of their regulatory network, we identified key
hub genes including TGF-\b{eta}, MYC, and SMAD3 varied underlie DEX treatment.
Further enrichment analysis revealed the TGF-\b{eta} signaling pathway as the
top enriched term. Those genes involved in the TGF-\b{eta} pathway and their
crosstalk with the ERBB pathway presented a strong survival prognosis in
clinical lung cancer samples. (2) Based on biological validation and further
curation, a multiscale model of tumor regulation centered on both
TGF-\b{eta}-induced and ERBB-amplified signaling pathways was developed to
characterize the dynamics effects of DEX therapy on lung cancer cells. Our
simulation results were well matched to available data of SMAD2, FOXO3,
TGF\b{eta}1, and TGF\b{eta}R1 over the time course. Moreover, we provided
predictions of different doses to illustrate the trend and therapeutic
potential of DEX treatment.
| [
{
"created": "Tue, 1 Mar 2022 21:00:46 GMT",
"version": "v1"
}
] | 2022-03-03 | [
[
"Chen",
"Minghan",
""
],
[
"Xu",
"Chunrui",
""
],
[
"Xu",
"Ziang",
""
],
[
"He",
"Wei",
""
],
[
"Zhang",
"Haorui",
""
],
[
"Su",
"Jing",
""
],
[
"Song",
"Qianqian",
""
]
] | Motivation: Lung cancer is one of the leading causes for cancer-related death, with a five-year survival rate of 18%. It is a priority for us to understand the underlying mechanisms that affect the implementation and effectiveness of lung cancer therapeutics. In this study, we combine the power of Bioinformatics and Systems Biology to comprehensively uncover functional and signaling pathways of drug treatment using bioinformatics inference and multiscale modeling of both scRNA-seq data and proteomics data. The innovative and cross-disciplinary approach can be further applied to other computational studies in tumorigenesis and oncotherapy. Results: A time series of lung adenocarcinoma-derived A549 cells after DEX treatment were analysed. (1) We first discovered the differentially expressed genes in those lung cancer cells. Then through the interrogation of their regulatory network, we identified key hub genes including TGF-\b{eta}, MYC, and SMAD3 varied underlie DEX treatment. Further enrichment analysis revealed the TGF-\b{eta} signaling pathway as the top enriched term. Those genes involved in the TGF-\b{eta} pathway and their crosstalk with the ERBB pathway presented a strong survival prognosis in clinical lung cancer samples. (2) Based on biological validation and further curation, a multiscale model of tumor regulation centered on both TGF-\b{eta}-induced and ERBB-amplified signaling pathways was developed to characterize the dynamics effects of DEX therapy on lung cancer cells. Our simulation results were well matched to available data of SMAD2, FOXO3, TGF\b{eta}1, and TGF\b{eta}R1 over the time course. Moreover, we provided predictions of different doses to illustrate the trend and therapeutic potential of DEX treatment. |
2407.07226 | Hue Sun Chan | Tanmoy Pal, Jonas Wess\'en, Suman Das, and Hue Sun Chan | Differential Effects of Sequence-Local versus Nonlocal Charge Patterns
on Phase Separation and Conformational Dimensions of Polyampholytes as Model
Intrinsically Disordered Proteins | 56 pages, 4 main-text figures, Supporting Information (containing
supporting text, 1 supporting table, and 9 supporting figures),
Table-of-Contents graphics, and 94 references. Accepted for publication The
Journal of Physical Chemistry Letters | The Journal of Physical Chemistry Letters 15:8248-8256 (2024) | 10.1021/acs.jpclett.4c01973 | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Conformational properties of intrinsically disordered proteins (IDPs) are
governed by a sequence-ensemble relationship. To differentiate the impact of
sequence-local versus sequence-nonlocal features of an IDP's charge pattern on
its conformational dimensions and its phase-separation propensity, the charge
"blockiness'' $\kappa$ and the nonlocality-weighted sequence charge decoration
(SCD) parameters are compared for their correlations with isolated-chain radii
of gyration ($R_{\rm g}$s) and upper critical solution temperatures (UCSTs) of
polyampholytes modeled by random phase approximation, field-theoretic
simulation, and coarse-grained molecular dynamics. SCD is superior to $\kappa$
in predicting $R_{\rm g}$ because SCD accounts for effects of contact order,
i.e., nonlocality, on dimensions of isolated chains. In contrast, $\kappa$ and
SCD are comparably good, though nonideal, predictors of UCST because
frequencies of interchain contacts in the multiple-chain condensed phase are
less sensitive to sequence positions than frequencies of intrachain contacts of
an isolated chain, as reflected by $\kappa$ correlating better with
condensed-phase interaction energy than SCD.
| [
{
"created": "Tue, 9 Jul 2024 20:46:49 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Jul 2024 21:56:53 GMT",
"version": "v2"
}
] | 2024-08-13 | [
[
"Pal",
"Tanmoy",
""
],
[
"Wessén",
"Jonas",
""
],
[
"Das",
"Suman",
""
],
[
"Chan",
"Hue Sun",
""
]
] | Conformational properties of intrinsically disordered proteins (IDPs) are governed by a sequence-ensemble relationship. To differentiate the impact of sequence-local versus sequence-nonlocal features of an IDP's charge pattern on its conformational dimensions and its phase-separation propensity, the charge "blockiness'' $\kappa$ and the nonlocality-weighted sequence charge decoration (SCD) parameters are compared for their correlations with isolated-chain radii of gyration ($R_{\rm g}$s) and upper critical solution temperatures (UCSTs) of polyampholytes modeled by random phase approximation, field-theoretic simulation, and coarse-grained molecular dynamics. SCD is superior to $\kappa$ in predicting $R_{\rm g}$ because SCD accounts for effects of contact order, i.e., nonlocality, on dimensions of isolated chains. In contrast, $\kappa$ and SCD are comparably good, though nonideal, predictors of UCST because frequencies of interchain contacts in the multiple-chain condensed phase are less sensitive to sequence positions than frequencies of intrachain contacts of an isolated chain, as reflected by $\kappa$ correlating better with condensed-phase interaction energy than SCD. |
2402.16390 | Francesco Sannino | Baptiste Filoche, Stefan Hohenegger and Francesco Sannino | Information Theory Unification of Epidemiological and Population
Dynamics | 33 pages, 17 figures | null | null | null | q-bio.PE hep-th stat.AP | http://creativecommons.org/licenses/by/4.0/ | We reformulate models in epidemiology and population dynamics in terms of
probability distributions. This allows us to construct the Fisher information,
which we interpret as the metric of a one-dimensional differentiable manifold.
For systems that can be effectively described by a single degree of freedom, we
show that their time evolution is fully captured by this metric. In this way,
we discover universal features across seemingly very different models. This
further motivates a reorganisation of the dynamics around zeroes of the Fisher
metric, corresponding to extrema of the probability distribution. Concretely,
we propose a simple form of the metric for which we can analytically solve the
dynamics of the system that well approximates the time evolution of various
established models in epidemiology and population dynamics, thus providing a
unifying framework.
| [
{
"created": "Mon, 26 Feb 2024 08:28:51 GMT",
"version": "v1"
}
] | 2024-02-27 | [
[
"Filoche",
"Baptiste",
""
],
[
"Hohenegger",
"Stefan",
""
],
[
"Sannino",
"Francesco",
""
]
] | We reformulate models in epidemiology and population dynamics in terms of probability distributions. This allows us to construct the Fisher information, which we interpret as the metric of a one-dimensional differentiable manifold. For systems that can be effectively described by a single degree of freedom, we show that their time evolution is fully captured by this metric. In this way, we discover universal features across seemingly very different models. This further motivates a reorganisation of the dynamics around zeroes of the Fisher metric, corresponding to extrema of the probability distribution. Concretely, we propose a simple form of the metric for which we can analytically solve the dynamics of the system that well approximates the time evolution of various established models in epidemiology and population dynamics, thus providing a unifying framework. |
q-bio/0703008 | Tom Chou | Tom Chou | The stochastic entry of enveloped viruses: Fusion vs. endocytosis | 7 pages, 6 figures | Biophys. J., 93, 1116-1123, (2007) | 10.1529/biophysj.107.106708 | null | q-bio.SC | null | Viral infection requires the binding of receptors on the target cell membrane
to glycoproteins, or ``spikes,'' on the viral membrane. The initial entry is
usually classified as fusogenic or endocytotic. However, binding of viral
spikes to cell surface receptors not only initiates the viral adhesion and the
wrapping process necessary for internalization, but can simultaneously initiate
direct fusion with the cell membrane. Both fusion and internalization have been
observed to be viable pathways for many viruses. We develop a stochastic model
for viral entry that incorporates a competition between receptor mediated
fusion and endocytosis. The relative probabilities of fusion and endocytosis of
a virus particle initially nonspecifically adsorbed on the host cell membrane
are computed as functions of receptor concentration, binding strength, and
number of spikes. We find different parameter regimes where the entry pathway
probabilities can be analytically expressed. Experimental tests of our
mechanistic hypotheses are proposed and discussed.
| [
{
"created": "Fri, 2 Mar 2007 19:29:08 GMT",
"version": "v1"
},
{
"created": "Fri, 13 Apr 2007 02:14:35 GMT",
"version": "v2"
}
] | 2015-06-26 | [
[
"Chou",
"Tom",
""
]
] | Viral infection requires the binding of receptors on the target cell membrane to glycoproteins, or ``spikes,'' on the viral membrane. The initial entry is usually classified as fusogenic or endocytotic. However, binding of viral spikes to cell surface receptors not only initiates the viral adhesion and the wrapping process necessary for internalization, but can simultaneously initiate direct fusion with the cell membrane. Both fusion and internalization have been observed to be viable pathways for many viruses. We develop a stochastic model for viral entry that incorporates a competition between receptor mediated fusion and endocytosis. The relative probabilities of fusion and endocytosis of a virus particle initially nonspecifically adsorbed on the host cell membrane are computed as functions of receptor concentration, binding strength, and number of spikes. We find different parameter regimes where the entry pathway probabilities can be analytically expressed. Experimental tests of our mechanistic hypotheses are proposed and discussed. |
2006.12006 | Siddhartha Chakrabarty | Dipankar Mondal, Siddhartha P. Chakrabarty | Did the lockdown curb the spread of COVID-19 infection rate in India: A
data-driven analysis | null | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In order to analyze the effectiveness of three successive nationwide lockdown
enforced in India, we present a data-driven analysis of four key parameters,
reducing the transmission rate, restraining the growth rate, flattening the
epidemic curve and improving the health care system. These were quantified by
the consideration of four different metrics, namely, reproduction rate, growth
rate, doubling time and death to recovery ratio. The incidence data of the
COVID-19 (during the period of 2nd March 2020 to 31st May 2020) outbreak in
India was analyzed for the best fit to the epidemic curve, making use of the
exponential growth, the maximum likelihood estimation, sequential Bayesian
method and estimation of time-dependent reproduction. The best fit (based on
the data considered) was for the time-dependent approach. Accordingly, this
approach was used to assess the impact on the effective reproduction rate. The
period of pre-lockdown to the end of lockdown 3, saw a $45\%$ reduction in the
rate of effective reproduction rate. During the same period the growth rate
reduced from $393\%$ during the pre-lockdown to $33\%$ after lockdown 3,
accompanied by the average doubling time increasing form $4$-$6$ days to
$12$-$14$ days. Finally, the death-to-recovery ratio dropped from $0.28$
(pre-lockdown) to $0.08$ after lockdown 3. In conclusion, all the four metrics
considered to assess the effectiveness of the lockdown, exhibited significant
favourable changes, from the pre-lockdown period to the end of lockdown 3.
Analysis of the data in the post-lockdown period with these metrics will
provide greater clarity with regards to the extent of the success of the
lockdown.
| [
{
"created": "Mon, 22 Jun 2020 04:50:29 GMT",
"version": "v1"
}
] | 2020-06-23 | [
[
"Mondal",
"Dipankar",
""
],
[
"Chakrabarty",
"Siddhartha P.",
""
]
] | In order to analyze the effectiveness of three successive nationwide lockdown enforced in India, we present a data-driven analysis of four key parameters, reducing the transmission rate, restraining the growth rate, flattening the epidemic curve and improving the health care system. These were quantified by the consideration of four different metrics, namely, reproduction rate, growth rate, doubling time and death to recovery ratio. The incidence data of the COVID-19 (during the period of 2nd March 2020 to 31st May 2020) outbreak in India was analyzed for the best fit to the epidemic curve, making use of the exponential growth, the maximum likelihood estimation, sequential Bayesian method and estimation of time-dependent reproduction. The best fit (based on the data considered) was for the time-dependent approach. Accordingly, this approach was used to assess the impact on the effective reproduction rate. The period of pre-lockdown to the end of lockdown 3, saw a $45\%$ reduction in the rate of effective reproduction rate. During the same period the growth rate reduced from $393\%$ during the pre-lockdown to $33\%$ after lockdown 3, accompanied by the average doubling time increasing form $4$-$6$ days to $12$-$14$ days. Finally, the death-to-recovery ratio dropped from $0.28$ (pre-lockdown) to $0.08$ after lockdown 3. In conclusion, all the four metrics considered to assess the effectiveness of the lockdown, exhibited significant favourable changes, from the pre-lockdown period to the end of lockdown 3. Analysis of the data in the post-lockdown period with these metrics will provide greater clarity with regards to the extent of the success of the lockdown. |
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