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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
q-bio/0312030 | Francois Coppex | Francois Coppex, Michel Droz, Adam Lipowski | Extinction dynamics of Lotka-Volterra ecosystems on evolving networks | 8 pages, 6 eps figures included | Phys. Rev. E 69, 061901 (2004) | 10.1103/PhysRevE.69.061901 | null | q-bio.PE cond-mat.stat-mech | null | We study a model of a multi-species ecosystem described by
Lotka-Volterra-like equations. Interactions among species form a network whose
evolution is determined by the dynamics of the model. Numerical simulations
show power-law distribution of intervals between extinctions, but only for
ecosystems with sufficient variability of species and with networks of
connectivity above certain threshold that is very close to the percolation
threshold of the network. Effect of slow environmental changes on extinction
dynamics, degree distribution of the network of interspecies interactions, and
some emergent properties of our model are also examined.
| [
{
"created": "Fri, 19 Dec 2003 09:40:19 GMT",
"version": "v1"
},
{
"created": "Wed, 2 Jun 2004 08:18:17 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Coppex",
"Francois",
""
],
[
"Droz",
"Michel",
""
],
[
"Lipowski",
"Adam",
""
]
] | We study a model of a multi-species ecosystem described by Lotka-Volterra-like equations. Interactions among species form a network whose evolution is determined by the dynamics of the model. Numerical simulations show power-law distribution of intervals between extinctions, but only for ecosystems with sufficient variability of species and with networks of connectivity above certain threshold that is very close to the percolation threshold of the network. Effect of slow environmental changes on extinction dynamics, degree distribution of the network of interspecies interactions, and some emergent properties of our model are also examined. |
2306.03399 | Haoyu Cheng | Haoyu Cheng, Mobin Asri, Julian Lucas, Sergey Koren, and Heng Li | Scalable telomere-to-telomere assembly for diploid and polyploid genomes
with double graph | 14 pages, 4 fuhires | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Despite recent advances in the length and the accuracy of long-read data,
building haplotype-resolved genome assemblies from telomere to telomere still
requires considerable computational resources. In this study, we present an
efficient de novo assembly algorithm that combines multiple sequencing
technologies to scale up population-wide telomere-to-telomere assemblies. By
utilizing twenty-two human and two plant genomes, we demonstrate that our
algorithm is around an order of magnitude cheaper than existing methods, while
producing better diploid and haploid assemblies. Notably, our algorithm is the
only feasible solution to the haplotype-resolved assembly of polyploid genomes.
| [
{
"created": "Tue, 6 Jun 2023 04:29:12 GMT",
"version": "v1"
}
] | 2023-06-07 | [
[
"Cheng",
"Haoyu",
""
],
[
"Asri",
"Mobin",
""
],
[
"Lucas",
"Julian",
""
],
[
"Koren",
"Sergey",
""
],
[
"Li",
"Heng",
""
]
] | Despite recent advances in the length and the accuracy of long-read data, building haplotype-resolved genome assemblies from telomere to telomere still requires considerable computational resources. In this study, we present an efficient de novo assembly algorithm that combines multiple sequencing technologies to scale up population-wide telomere-to-telomere assemblies. By utilizing twenty-two human and two plant genomes, we demonstrate that our algorithm is around an order of magnitude cheaper than existing methods, while producing better diploid and haploid assemblies. Notably, our algorithm is the only feasible solution to the haplotype-resolved assembly of polyploid genomes. |
1608.02375 | Dan Wang | Dan Wang, Shuaicheng Li, Fei Guo, Lusheng Wang | Core-genome scaffold comparison reveals the prevalence that inversion
events are associated with pairs of inverted repeats | 8 pages, 11 figures | null | null | null | q-bio.GN cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Genome rearrangement plays an important role in evolutionary
biology and has profound impacts on phenotype in organisms ranging from
microbes to humans. The mechanisms for genome rearrangement events remain
unclear. Lots of comparisons have been conducted among different species. To
reveal the mechanisms for rearrangement events, comparison of different
individuals/strains within the same species or genus (pan-genomes) is more
helpful since they are much closer to each other. Results: We study the
mechanism for inversion events via core-genome scaffold comparison of different
strains within the same species. We focus on two kinds of bacteria, Pseudomonas
aeruginosa and Escherichia coli, and investigate the inversion events among
different strains of the same specie. We find an interesting phenomenon that
long (larger than 10,000 bp) inversion regions are flanked by a pair of
Inverted Repeats (IRs) (with lengths ranging from 385 bp to 27476 bp) which are
often Insertion Sequences (ISs).This mechanism can also explain why the
breakpoint reuses for inversion events happen. We study the prevalence of the
phenomenon and find that it is a major mechanism for inversions. The other
observation is that for different rearrangement events such as transposition
and inverted block interchange, the two ends of the swapped regions are also
associated with repeats so that after the rearrangement operations the two ends
of the swapped regions remain unchanged. To our knowledge, this is the first
time such a phenomenon is reported for transposition event.
| [
{
"created": "Mon, 8 Aug 2016 10:36:36 GMT",
"version": "v1"
}
] | 2016-08-09 | [
[
"Wang",
"Dan",
""
],
[
"Li",
"Shuaicheng",
""
],
[
"Guo",
"Fei",
""
],
[
"Wang",
"Lusheng",
""
]
] | Motivation: Genome rearrangement plays an important role in evolutionary biology and has profound impacts on phenotype in organisms ranging from microbes to humans. The mechanisms for genome rearrangement events remain unclear. Lots of comparisons have been conducted among different species. To reveal the mechanisms for rearrangement events, comparison of different individuals/strains within the same species or genus (pan-genomes) is more helpful since they are much closer to each other. Results: We study the mechanism for inversion events via core-genome scaffold comparison of different strains within the same species. We focus on two kinds of bacteria, Pseudomonas aeruginosa and Escherichia coli, and investigate the inversion events among different strains of the same specie. We find an interesting phenomenon that long (larger than 10,000 bp) inversion regions are flanked by a pair of Inverted Repeats (IRs) (with lengths ranging from 385 bp to 27476 bp) which are often Insertion Sequences (ISs).This mechanism can also explain why the breakpoint reuses for inversion events happen. We study the prevalence of the phenomenon and find that it is a major mechanism for inversions. The other observation is that for different rearrangement events such as transposition and inverted block interchange, the two ends of the swapped regions are also associated with repeats so that after the rearrangement operations the two ends of the swapped regions remain unchanged. To our knowledge, this is the first time such a phenomenon is reported for transposition event. |
1902.03216 | Gaoxiang Zhou | Gaoxiang Zhou, Kai-Wen Liang, Natasa Miskov-Zivanov | Intervention Pathway Discovery via Context-Dependent Dynamic Sensitivity
Analysis | null | null | null | null | q-bio.MN q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | The sensitivity analysis of biological system models can significantly
contribute to identifying and explaining influences of internal or external
changes on model and its elements. We propose here a comprehensive framework to
study sensitivity of intra-cellular networks and to identify key intervention
pathways, by performing both static and dynamic sensitivity analysis. While the
static sensitivity analysis focuses on the impact of network topology and
update functions, the dynamic analysis accounts for context-dependent transient
state distributions. To study sensitivity, we use discrete models, where each
element is represented as a discrete variable and assigned an update rule,
which is a function of element's known direct and indirect regulators. Our
sensitivity analysis framework allows for assessing the effect of context on
individual element sensitivity, as well as on element criticality in reaching
preferred outcomes. The framework also enables discovery of most influential
pathways in the model that are essential for satisfying important system
properties, and thus, could be used for interventions. We discuss the role of
nine different network attributes in identifying key elements and intervention
pathways, and evaluate their performance using model checking method. Finally,
we apply our methods on the model of naive T cell differentiation, and further
demonstrate the importance of context-based sensitivity analysis in identifying
most influential elements and pathways.
| [
{
"created": "Fri, 8 Feb 2019 18:27:24 GMT",
"version": "v1"
}
] | 2019-02-11 | [
[
"Zhou",
"Gaoxiang",
""
],
[
"Liang",
"Kai-Wen",
""
],
[
"Miskov-Zivanov",
"Natasa",
""
]
] | The sensitivity analysis of biological system models can significantly contribute to identifying and explaining influences of internal or external changes on model and its elements. We propose here a comprehensive framework to study sensitivity of intra-cellular networks and to identify key intervention pathways, by performing both static and dynamic sensitivity analysis. While the static sensitivity analysis focuses on the impact of network topology and update functions, the dynamic analysis accounts for context-dependent transient state distributions. To study sensitivity, we use discrete models, where each element is represented as a discrete variable and assigned an update rule, which is a function of element's known direct and indirect regulators. Our sensitivity analysis framework allows for assessing the effect of context on individual element sensitivity, as well as on element criticality in reaching preferred outcomes. The framework also enables discovery of most influential pathways in the model that are essential for satisfying important system properties, and thus, could be used for interventions. We discuss the role of nine different network attributes in identifying key elements and intervention pathways, and evaluate their performance using model checking method. Finally, we apply our methods on the model of naive T cell differentiation, and further demonstrate the importance of context-based sensitivity analysis in identifying most influential elements and pathways. |
q-bio/0401042 | Thomas R. Weikl | Thomas R. Weikl and Reinhard Lipowsky | Mechanisms of pattern formation during T cell adhesion | 12 pages, 8 figures | null | 10.1529/biophysj.104.045609 | null | q-bio.SC cond-mat.stat-mech q-bio.CB | null | T cells form intriguing patterns during adhesion to antigen-presenting cells.
The patterns at the cell-cell contact zone are composed of two types of
domains, which either contain short TCR/MHCp receptor-ligand complexes or the
longer LFA-1/ICAM-1 complexes. The final pattern consists of a central TCR/MHCp
domain surrounded by a ring-shaped LFA-1/ICAM-1 domain, while the
characteristic pattern formed at intermediate times is inverted with TCR/MHCp
complexes at the periphery of the contact zone and LFA-1/ICAM-1 complexes in
the center. In this article, we present a statistical-mechanical model of cell
adhesion and propose a novel mechanism for the T cell pattern formation. Our
mechanism for the formation of the intermediate inverted pattern is based (i)
on the initial nucleation of numerous TCR/MHCp microdomains, and (ii) on the
diffusion of free receptors and ligands into the contact zone. Due to this
inward diffusion, TCR/MHCp microdomains at the rim of the contact zone grow
faster and form an intermediate peripheral ring for sufficiently large TCR/MHCp
concentrations. In agreement with experiments, we find that the formation of
the final pattern with a central TCR/MHCp domain requires active cytoskeletal
transport processes. Without active transport, the intermediate inverted
pattern seems to be metastable in our model, which might explain patterns
observed during natural killer (NK) cell adhesion. At smaller TCR/MHCp complex
concentrations, we observe a different regime of pattern formation with
intermediate multifocal TCR/MHCp patterns which resemble experimental patterns
found during thymozyte adhesion.
| [
{
"created": "Wed, 28 Jan 2004 15:51:43 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Weikl",
"Thomas R.",
""
],
[
"Lipowsky",
"Reinhard",
""
]
] | T cells form intriguing patterns during adhesion to antigen-presenting cells. The patterns at the cell-cell contact zone are composed of two types of domains, which either contain short TCR/MHCp receptor-ligand complexes or the longer LFA-1/ICAM-1 complexes. The final pattern consists of a central TCR/MHCp domain surrounded by a ring-shaped LFA-1/ICAM-1 domain, while the characteristic pattern formed at intermediate times is inverted with TCR/MHCp complexes at the periphery of the contact zone and LFA-1/ICAM-1 complexes in the center. In this article, we present a statistical-mechanical model of cell adhesion and propose a novel mechanism for the T cell pattern formation. Our mechanism for the formation of the intermediate inverted pattern is based (i) on the initial nucleation of numerous TCR/MHCp microdomains, and (ii) on the diffusion of free receptors and ligands into the contact zone. Due to this inward diffusion, TCR/MHCp microdomains at the rim of the contact zone grow faster and form an intermediate peripheral ring for sufficiently large TCR/MHCp concentrations. In agreement with experiments, we find that the formation of the final pattern with a central TCR/MHCp domain requires active cytoskeletal transport processes. Without active transport, the intermediate inverted pattern seems to be metastable in our model, which might explain patterns observed during natural killer (NK) cell adhesion. At smaller TCR/MHCp complex concentrations, we observe a different regime of pattern formation with intermediate multifocal TCR/MHCp patterns which resemble experimental patterns found during thymozyte adhesion. |
1203.0222 | Carsten Lemmen | Carsten Lemmen and Kai W. Wirtz | On the sensitivity of the simulated European Neolithic transition to
climate extremes | Revised version submitted to the Journal of Archaeological Science,
special issue on The World Reshaped: impacts of the Neolithic transition. 10
pages, 4 figures, 1 table + supplementary material | null | 10.1016/j.jas.2012.10.023 | null | q-bio.PE cs.MA math.DS physics.geo-ph | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Was the spread of agropastoralism from the Fertile Crescent throughout Europe
influenced by extreme climate events, or was it independent of climate? We here
generate idealized climate events using palaeoclimate records. In a
mathematical model of regional sociocultural development, these events disturb
the subsistence base of simulated forager and farmer societies. We evaluate the
regional simulated transition timings and durations against a published large
set of radiocarbon dates for western Eurasia; the model is able to
realistically hindcast much of the inhomogeneous space-time evolution of
regional Neolithic transitions. Our study shows that the consideration of
climate events improves the simulation of typical lags between cultural
complexes, but that the overall difference to a model without climate events is
not significant. Climate events may not have been as important for early
sociocultural dynamics as endogenous factors.
| [
{
"created": "Thu, 1 Mar 2012 15:48:59 GMT",
"version": "v1"
},
{
"created": "Sat, 11 Aug 2012 06:02:18 GMT",
"version": "v2"
}
] | 2012-11-01 | [
[
"Lemmen",
"Carsten",
""
],
[
"Wirtz",
"Kai W.",
""
]
] | Was the spread of agropastoralism from the Fertile Crescent throughout Europe influenced by extreme climate events, or was it independent of climate? We here generate idealized climate events using palaeoclimate records. In a mathematical model of regional sociocultural development, these events disturb the subsistence base of simulated forager and farmer societies. We evaluate the regional simulated transition timings and durations against a published large set of radiocarbon dates for western Eurasia; the model is able to realistically hindcast much of the inhomogeneous space-time evolution of regional Neolithic transitions. Our study shows that the consideration of climate events improves the simulation of typical lags between cultural complexes, but that the overall difference to a model without climate events is not significant. Climate events may not have been as important for early sociocultural dynamics as endogenous factors. |
q-bio/0412014 | Tobias Bollenbach | T. Bollenbach, K. Kruse, P. Pantazis, M. Gonz\'alez-Gait\'an, and F.
J\"ulicher | Robust formation of morphogen gradients | null | Physical Review Letters 94, 018103 (2005) | 10.1103/PhysRevLett.94.018103 | null | q-bio.OT physics.bio-ph | null | We discuss the formation of graded morphogen profiles in a cell layer by
nonlinear transport phenomena, important for patterning developing organisms.
We focus on a process termed transcytosis, where morphogen transport results
from binding of ligands to receptors on the cell surface, incorporation into
the cell and subsequent externalization. Starting from a microscopic model, we
derive effective transport equations. We show that, in contrast to morphogen
transport by extracellular diffusion, transcytosis leads to robust ligand
profiles which are insensitive to the rate of ligand production.
| [
{
"created": "Wed, 8 Dec 2004 14:53:16 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Bollenbach",
"T.",
""
],
[
"Kruse",
"K.",
""
],
[
"Pantazis",
"P.",
""
],
[
"González-Gaitán",
"M.",
""
],
[
"Jülicher",
"F.",
""
]
] | We discuss the formation of graded morphogen profiles in a cell layer by nonlinear transport phenomena, important for patterning developing organisms. We focus on a process termed transcytosis, where morphogen transport results from binding of ligands to receptors on the cell surface, incorporation into the cell and subsequent externalization. Starting from a microscopic model, we derive effective transport equations. We show that, in contrast to morphogen transport by extracellular diffusion, transcytosis leads to robust ligand profiles which are insensitive to the rate of ligand production. |
1606.01932 | Warren Lord | Warren M. Lord, Jie Sun, Nicholas T. Ouellette, and Erik M. Bollt | Inference of Causal Information Flow in Collective Animal Behavior | To appear in TMBMC special issue in honor of Claude Shannon's 100th
Birthday | null | 10.1109/TMBMC.2016.2632099 | null | q-bio.QM cs.IT math.DS math.IT q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding and even defining what constitutes animal interactions remains
a challenging problem. Correlational tools may be inappropriate for detecting
communication between a set of many agents exhibiting nonlinear behavior. A
different approach is to define coordinated motions in terms of an information
theoretic channel of direct causal information flow. In this work, we consider
time series data obtained by an experimental protocol of optical tracking of
the insect species Chironomus riparius. The data constitute reconstructed 3-D
spatial trajectories of the insects' flight trajectories and kinematics. We
present an application of the optimal causation entropy (oCSE) principle to
identify direct causal relationships or information channels among the insects.
The collection of channels inferred by oCSE describes a network of information
flow within the swarm. We find that information channels with a long spatial
range are more common than expected under the assumption that causal
information flows should be spatially localized. The tools developed herein are
general and applicable to the inference and study of intercommunication
networks in a wide variety of natural settings.
| [
{
"created": "Fri, 3 Jun 2016 00:53:28 GMT",
"version": "v1"
},
{
"created": "Thu, 29 Dec 2016 21:43:11 GMT",
"version": "v2"
}
] | 2017-01-02 | [
[
"Lord",
"Warren M.",
""
],
[
"Sun",
"Jie",
""
],
[
"Ouellette",
"Nicholas T.",
""
],
[
"Bollt",
"Erik M.",
""
]
] | Understanding and even defining what constitutes animal interactions remains a challenging problem. Correlational tools may be inappropriate for detecting communication between a set of many agents exhibiting nonlinear behavior. A different approach is to define coordinated motions in terms of an information theoretic channel of direct causal information flow. In this work, we consider time series data obtained by an experimental protocol of optical tracking of the insect species Chironomus riparius. The data constitute reconstructed 3-D spatial trajectories of the insects' flight trajectories and kinematics. We present an application of the optimal causation entropy (oCSE) principle to identify direct causal relationships or information channels among the insects. The collection of channels inferred by oCSE describes a network of information flow within the swarm. We find that information channels with a long spatial range are more common than expected under the assumption that causal information flows should be spatially localized. The tools developed herein are general and applicable to the inference and study of intercommunication networks in a wide variety of natural settings. |
q-bio/0611089 | Alain Destexhe | R. Brette, M. Rudolph, T. Carnevale, M. Hines, D. Beeman, J. M. Bower,
M. Diesmann, A. Morrison, P. H. Goodman, F. C. Harris Jr., M. Zirpe, T.
Natschlager, D. Pecevski, B. Ermentrout, M. Djurfeldt, A. Lansner, O. Rochel,
T. Vieville, E. Muller, A. P. Davison, S. El Boustani, A. Destexhe | Simulation of networks of spiking neurons: A review of tools and
strategies | 49 pages, 24 figures, 1 table; review article, Journal of
Computational Neuroscience, in press (2007) | Journal of Computational Neuroscience 2007 Dec;23(3):349-98. Epub
2007 Jul 12 | null | null | q-bio.NC | null | We review different aspects of the simulation of spiking neural networks. We
start by reviewing the different types of simulation strategies and algorithms
that are currently implemented. We next review the precision of those
simulation strategies, in particular in cases where plasticity depends on the
exact timing of the spikes. We overview different simulators and simulation
environments presently available (restricted to those freely available, open
source and documented). For each simulation tool, its advantages and pitfalls
are reviewed, with an aim to allow the reader to identify which simulator is
appropriate for a given task. Finally, we provide a series of benchmark
simulations of different types of networks of spiking neurons, including
Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based
or conductance-based synapses, using clock-driven or event-driven integration
strategies. The same set of models are implemented on the different simulators,
and the codes are made available. The ultimate goal of this review is to
provide a resource to facilitate identifying the appropriate integration
strategy and simulation tool to use for a given modeling problem related to
spiking neural networks.
| [
{
"created": "Tue, 28 Nov 2006 14:35:19 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Apr 2007 21:41:07 GMT",
"version": "v2"
}
] | 2008-01-26 | [
[
"Brette",
"R.",
""
],
[
"Rudolph",
"M.",
""
],
[
"Carnevale",
"T.",
""
],
[
"Hines",
"M.",
""
],
[
"Beeman",
"D.",
""
],
[
"Bower",
"J. M.",
""
],
[
"Diesmann",
"M.",
""
],
[
"Morrison",
"A.",
""
],
[
"Goodman",
"P. H.",
""
],
[
"Harris",
"F. C.",
"Jr."
],
[
"Zirpe",
"M.",
""
],
[
"Natschlager",
"T.",
""
],
[
"Pecevski",
"D.",
""
],
[
"Ermentrout",
"B.",
""
],
[
"Djurfeldt",
"M.",
""
],
[
"Lansner",
"A.",
""
],
[
"Rochel",
"O.",
""
],
[
"Vieville",
"T.",
""
],
[
"Muller",
"E.",
""
],
[
"Davison",
"A. P.",
""
],
[
"Boustani",
"S. El",
""
],
[
"Destexhe",
"A.",
""
]
] | We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks. |
2101.04081 | Apostolos Gkatzionis | Apostolos Gkatzionis, Stephen Burgess and Paul J. Newcombe | Statistical Methods for cis-Mendelian Randomization with Two-sample
Summary-level Data | 39 pages (33 main text + 6 supplement), 4 figures, 7 tables | null | null | null | q-bio.QM q-bio.GN stat.AP | http://creativecommons.org/licenses/by/4.0/ | Mendelian randomization is the use of genetic variants to assess the
existence of a causal relationship between a risk factor and an outcome of
interest. Here, we focus on two-sample summary-data Mendelian randomization
analyses with many correlated variants from a single gene region, and
particularly on cis-Mendelian randomization studies which use protein
expression as a risk factor. Such studies must rely on a small, curated set of
variants from the studied region; using all variants in the region requires
inverting an ill-conditioned genetic correlation matrix and results in
numerically unstable causal effect estimates. We review methods for variable
selection and estimation in cis-Mendelian randomization with summary-level
data, ranging from stepwise pruning and conditional analysis to principal
components analysis, factor analysis and Bayesian variable selection. In a
simulation study, we show that the various methods have a comparable
performance in analyses with large sample sizes and strong genetic instruments.
However, when weak instrument bias is suspected, factor analysis and Bayesian
variable selection produce more reliable inferences than simple pruning
approaches, which are often used in practice. We conclude by examining two case
studies, assessing the effects of LDL-cholesterol and serum testosterone on
coronary heart disease risk using variants in the HMGCR and SHBG gene regions
respectively.
| [
{
"created": "Mon, 11 Jan 2021 18:23:04 GMT",
"version": "v1"
},
{
"created": "Thu, 15 Sep 2022 10:22:10 GMT",
"version": "v2"
}
] | 2022-09-16 | [
[
"Gkatzionis",
"Apostolos",
""
],
[
"Burgess",
"Stephen",
""
],
[
"Newcombe",
"Paul J.",
""
]
] | Mendelian randomization is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data Mendelian randomization analyses with many correlated variants from a single gene region, and particularly on cis-Mendelian randomization studies which use protein expression as a risk factor. Such studies must rely on a small, curated set of variants from the studied region; using all variants in the region requires inverting an ill-conditioned genetic correlation matrix and results in numerically unstable causal effect estimates. We review methods for variable selection and estimation in cis-Mendelian randomization with summary-level data, ranging from stepwise pruning and conditional analysis to principal components analysis, factor analysis and Bayesian variable selection. In a simulation study, we show that the various methods have a comparable performance in analyses with large sample sizes and strong genetic instruments. However, when weak instrument bias is suspected, factor analysis and Bayesian variable selection produce more reliable inferences than simple pruning approaches, which are often used in practice. We conclude by examining two case studies, assessing the effects of LDL-cholesterol and serum testosterone on coronary heart disease risk using variants in the HMGCR and SHBG gene regions respectively. |
2403.02724 | Peng Li | Lingmin Zhan, Yuanyuan Zhang, Yingdong Wang, Aoyi Wang, Caiping Cheng,
Jinzhong Zhao, Wuxia Zhang, Peng Lia, Jianxin Chen | A genome-scale deep learning model to predict gene expression changes of
genetic perturbations from multiplex biological networks | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Systematic characterization of biological effects to genetic perturbation is
essential to the application of molecular biology and biomedicine. However, the
experimental exhaustion of genetic perturbations on the genome-wide scale is
challenging. Here, we show that TranscriptionNet, a deep learning model that
integrates multiple biological networks to systematically predict
transcriptional profiles to three types of genetic perturbations based on
transcriptional profiles induced by genetic perturbations in the L1000 project:
RNA interference (RNAi), clustered regularly interspaced short palindromic
repeat (CRISPR) and overexpression (OE). TranscriptionNet performs better than
existing approaches in predicting inducible gene expression changes for all
three types of genetic perturbations. TranscriptionNet can predict
transcriptional profiles for all genes in existing biological networks and
increases perturbational gene expression changes for each type of genetic
perturbation from a few thousand to 26,945 genes. TranscriptionNet demonstrates
strong generalization ability when comparing predicted and true gene expression
changes on different external tasks. Overall, TranscriptionNet can systemically
predict transcriptional consequences induced by perturbing genes on a
genome-wide scale and thus holds promise to systemically detect gene function
and enhance drug development and target discovery.
| [
{
"created": "Tue, 5 Mar 2024 07:31:46 GMT",
"version": "v1"
}
] | 2024-03-06 | [
[
"Zhan",
"Lingmin",
""
],
[
"Zhang",
"Yuanyuan",
""
],
[
"Wang",
"Yingdong",
""
],
[
"Wang",
"Aoyi",
""
],
[
"Cheng",
"Caiping",
""
],
[
"Zhao",
"Jinzhong",
""
],
[
"Zhang",
"Wuxia",
""
],
[
"Lia",
"Peng",
""
],
[
"Chen",
"Jianxin",
""
]
] | Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Here, we show that TranscriptionNet, a deep learning model that integrates multiple biological networks to systematically predict transcriptional profiles to three types of genetic perturbations based on transcriptional profiles induced by genetic perturbations in the L1000 project: RNA interference (RNAi), clustered regularly interspaced short palindromic repeat (CRISPR) and overexpression (OE). TranscriptionNet performs better than existing approaches in predicting inducible gene expression changes for all three types of genetic perturbations. TranscriptionNet can predict transcriptional profiles for all genes in existing biological networks and increases perturbational gene expression changes for each type of genetic perturbation from a few thousand to 26,945 genes. TranscriptionNet demonstrates strong generalization ability when comparing predicted and true gene expression changes on different external tasks. Overall, TranscriptionNet can systemically predict transcriptional consequences induced by perturbing genes on a genome-wide scale and thus holds promise to systemically detect gene function and enhance drug development and target discovery. |
1804.01342 | Alexander Gorban | Alexander N. Gorban and Nurdan \c{C}abuko\v{g}lu | Mobility cost and degenerated diffusion in kinesis models | The final version submitted to the journal | Ecological Complexity 36 (2018), 16-21 | 10.1016/j.ecocom.2018.06.007 | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | A new critical effect is predicted in population dispersal. It is based on
the fact that a trade-off between the advantages of mobility and the cost of
mobility breaks with a significant deterioration in living conditions. The
recently developed model of purposeful kinesis (Gorban \& \c{C}abuko\v{g}lu,
Ecological Complexity 33, 2018) is based on the "let well enough alone" idea:
mobility decreases for high reproduction coefficient and, therefore, animals
stay longer in good conditions and leave quicker bad conditions. Mobility has a
cost, which should be measured in the changes of the reproduction coefficient.
Introduction of the cost of mobility into the reproduction coefficient leads to
an equation for mobility. It can be solved in a closed form using Lambert
$W$-function.
Surprisingly, the "let well enough alone" models with the simple linear cost
of mobility have an intrinsic phase transition: when conditions worsen then the
mobility increases up to some critical value of the reproduction coefficient.
For worse conditions, there is no solution for mobility. We interpret this
critical effect as the complete loss of mobility that is degeneration of
diffusion. Qualitatively, this means that mobility increases with worsening of
conditions up to some limit, and after that, mobility is nullified.
| [
{
"created": "Wed, 4 Apr 2018 10:59:32 GMT",
"version": "v1"
},
{
"created": "Wed, 16 May 2018 08:52:53 GMT",
"version": "v2"
},
{
"created": "Thu, 28 Feb 2019 08:18:08 GMT",
"version": "v3"
}
] | 2019-03-01 | [
[
"Gorban",
"Alexander N.",
""
],
[
"Çabukoǧlu",
"Nurdan",
""
]
] | A new critical effect is predicted in population dispersal. It is based on the fact that a trade-off between the advantages of mobility and the cost of mobility breaks with a significant deterioration in living conditions. The recently developed model of purposeful kinesis (Gorban \& \c{C}abuko\v{g}lu, Ecological Complexity 33, 2018) is based on the "let well enough alone" idea: mobility decreases for high reproduction coefficient and, therefore, animals stay longer in good conditions and leave quicker bad conditions. Mobility has a cost, which should be measured in the changes of the reproduction coefficient. Introduction of the cost of mobility into the reproduction coefficient leads to an equation for mobility. It can be solved in a closed form using Lambert $W$-function. Surprisingly, the "let well enough alone" models with the simple linear cost of mobility have an intrinsic phase transition: when conditions worsen then the mobility increases up to some critical value of the reproduction coefficient. For worse conditions, there is no solution for mobility. We interpret this critical effect as the complete loss of mobility that is degeneration of diffusion. Qualitatively, this means that mobility increases with worsening of conditions up to some limit, and after that, mobility is nullified. |
1210.4322 | Vladimir Chechetkin R. | V. R. Chechetkin and V.V. Lobzin | Stability of the genetic code and optimal parameters of amino acids | 9 pages, 3 figures | Journal of Theoretical Biology V. 269, Pp. 57-63, 2011 | 10.1016/j.jtbi.2010.10.015 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The standard genetic code is known to be much more efficient in minimizing
adverse effects of misreading errors and one-point mutations in comparison with
a random code having the same structure, i.e. the same number of codons coding
for each particular amino acid. We study the inverse problem, how the code
structure affects the optimal physico-chemical parameters of amino acids
ensuring the highest stability of the genetic code. It is shown that the choice
of two or more amino acids with given properties determines unambiguously all
the others. In this sense the code structure determines strictly the optimal
parameters of amino acids. In the code with the structure of the standard
genetic code the resulting values for hydrophobicity obtained in the scheme
leave one out and in the scheme with fixed maximum and minimum parameters
correlate significantly with the natural scale. This indicates the co-evolution
of the genetic code and physico-chemical properties of amino acids.
| [
{
"created": "Tue, 16 Oct 2012 09:16:48 GMT",
"version": "v1"
}
] | 2012-10-17 | [
[
"Chechetkin",
"V. R.",
""
],
[
"Lobzin",
"V. V.",
""
]
] | The standard genetic code is known to be much more efficient in minimizing adverse effects of misreading errors and one-point mutations in comparison with a random code having the same structure, i.e. the same number of codons coding for each particular amino acid. We study the inverse problem, how the code structure affects the optimal physico-chemical parameters of amino acids ensuring the highest stability of the genetic code. It is shown that the choice of two or more amino acids with given properties determines unambiguously all the others. In this sense the code structure determines strictly the optimal parameters of amino acids. In the code with the structure of the standard genetic code the resulting values for hydrophobicity obtained in the scheme leave one out and in the scheme with fixed maximum and minimum parameters correlate significantly with the natural scale. This indicates the co-evolution of the genetic code and physico-chemical properties of amino acids. |
0803.2085 | Conrad Burden | Sylvain Foret, Susan R. Wilson, Conrad J. Burden | Empirical distribution of k-word matches in biological sequences | 23 pages, 10 figures | Pattern Recognition 42 (2009) 539-548 | 10.1016/j.patcog.2008.06.026 | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This study focuses on an alignment-free sequence comparison method: the
number of words of length k shared between two sequences, also known as the D_2
statistic. The advantages of the use of this statistic over alignment-based
methods are firstly that it does not assume that homologous segments are
contiguous, and secondly that the algorithm is computationally extremely fast,
the runtime being proportional to the size of the sequence under scrutiny.
Existing applications of the D_2 statistic include the clustering of related
sequences in large EST databases such as the STACK database. Such applications
have typically relied on heuristics without any statistical basis. Rigorous
statistical characterisations of the distribution of D_2 have subsequently been
undertaken, but have focussed on the distribution's asymptotic behaviour,
leaving the distribution of D_2 uncharacterised for most practical cases. The
work presented here bridges these two worlds to give usable approximations of
the distribution of D_2 for ranges of parameters most frequently encountered in
the study of biological sequences.
| [
{
"created": "Fri, 14 Mar 2008 05:14:55 GMT",
"version": "v1"
}
] | 2009-09-08 | [
[
"Foret",
"Sylvain",
""
],
[
"Wilson",
"Susan R.",
""
],
[
"Burden",
"Conrad J.",
""
]
] | This study focuses on an alignment-free sequence comparison method: the number of words of length k shared between two sequences, also known as the D_2 statistic. The advantages of the use of this statistic over alignment-based methods are firstly that it does not assume that homologous segments are contiguous, and secondly that the algorithm is computationally extremely fast, the runtime being proportional to the size of the sequence under scrutiny. Existing applications of the D_2 statistic include the clustering of related sequences in large EST databases such as the STACK database. Such applications have typically relied on heuristics without any statistical basis. Rigorous statistical characterisations of the distribution of D_2 have subsequently been undertaken, but have focussed on the distribution's asymptotic behaviour, leaving the distribution of D_2 uncharacterised for most practical cases. The work presented here bridges these two worlds to give usable approximations of the distribution of D_2 for ranges of parameters most frequently encountered in the study of biological sequences. |
1502.01061 | Robert Noble | Robert Noble, Oliver Kaltz, Michael E Hochberg | Statistical interpretations and new findings on Variation in Cancer Risk
Among Tissues | 17 pages | null | null | null | q-bio.PE q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tomasetti and Vogelstein (2015a) find that the incidence of a set of cancer
types is correlated with the total number of normal stem cell divisions. Here,
we separate the effects of standing stem cell number (i.e., organ or tissue
size) and per stem cell lifetime replication rate. We show that each has a
statistically significant and independent effect on explaining variation in
cancer incidence over the 31 cases considered by Tomasetti and Vogelstein. When
considering the total number of stem cell divisions and when removing cases
associated with disease or carcinogens, we find that cancer incidence attains a
plateau of approximately 0.6% incidence for the cases considered by these
authors. We further demonstrate that grouping by anatomical site explains most
of the remaining variation in risk between cancer types. This new analysis
suggests that cancer risk depends not only on the number of stem cell divisions
but varies enormously ($\sim$10,000 times) depending on the stem cell's
environment. Future research should investigate how tissue characteristics
(anatomical site, type, size, stem cell divisions) explain cancer incidence
over a wider range of cancers, to what extent different tissues express
specific protective mechanisms, and whether any differential protection can be
attributed to natural selection.
| [
{
"created": "Tue, 3 Feb 2015 22:58:02 GMT",
"version": "v1"
}
] | 2015-02-05 | [
[
"Noble",
"Robert",
""
],
[
"Kaltz",
"Oliver",
""
],
[
"Hochberg",
"Michael E",
""
]
] | Tomasetti and Vogelstein (2015a) find that the incidence of a set of cancer types is correlated with the total number of normal stem cell divisions. Here, we separate the effects of standing stem cell number (i.e., organ or tissue size) and per stem cell lifetime replication rate. We show that each has a statistically significant and independent effect on explaining variation in cancer incidence over the 31 cases considered by Tomasetti and Vogelstein. When considering the total number of stem cell divisions and when removing cases associated with disease or carcinogens, we find that cancer incidence attains a plateau of approximately 0.6% incidence for the cases considered by these authors. We further demonstrate that grouping by anatomical site explains most of the remaining variation in risk between cancer types. This new analysis suggests that cancer risk depends not only on the number of stem cell divisions but varies enormously ($\sim$10,000 times) depending on the stem cell's environment. Future research should investigate how tissue characteristics (anatomical site, type, size, stem cell divisions) explain cancer incidence over a wider range of cancers, to what extent different tissues express specific protective mechanisms, and whether any differential protection can be attributed to natural selection. |
q-bio/0309023 | Bijan Pesaran | B. Pesaran and P. P. Mitra | Idl Signal Processing Library 1.0 | 13 IDL .pro files, 1 .html file, 1 .ps file, 1 license file. Download
the source for the IDL files (save as .tar.gz) Read idl_lib.ps for
instructions on use. Originally submitted to the neuro-sys archive which was
never publicly announced (was 9801001) | null | null | CMP-001 | q-bio.QM | null | We make available a library of documented IDL .pro files as well as a
shareable object library that allows IDL to call routines from LAPACK. The
routines are for use in the spectral analysis of time series data. The primary
focus of these routines are David Thomson's multitaper methods but a whole
range of functions will be made available in future revisions of the
submission. At present routines are provided to carry out the following
operations: calculate prolate spheroidal sequences and eigenvalues, project
time-series into frequency bands, calculate spectral estimates with or without
moving windows, and calculate the cross-coherence between two time series as a
function of frequency as well as the coherence between frequencies for a single
time series.
| [
{
"created": "Tue, 20 Jan 1998 18:38:17 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Pesaran",
"B.",
""
],
[
"Mitra",
"P. P.",
""
]
] | We make available a library of documented IDL .pro files as well as a shareable object library that allows IDL to call routines from LAPACK. The routines are for use in the spectral analysis of time series data. The primary focus of these routines are David Thomson's multitaper methods but a whole range of functions will be made available in future revisions of the submission. At present routines are provided to carry out the following operations: calculate prolate spheroidal sequences and eigenvalues, project time-series into frequency bands, calculate spectral estimates with or without moving windows, and calculate the cross-coherence between two time series as a function of frequency as well as the coherence between frequencies for a single time series. |
1605.06925 | Lorenz K. Muller | Lorenz K. Muller and Giacomo Indiveri | Neural Sampling by Irregular Gating Inhibition of Spiking Neurons and
Attractor Networks | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A long tradition in theoretical neuroscience casts sensory processing in the
brain as the process of inferring the maximally consistent interpretations of
imperfect sensory input. Recently it has been shown that Gamma-band inhibition
can enable neural attractor networks to approximately carry out such a sampling
mechanism. In this paper we propose a novel neural network model based on
irregular gating inhibition, show analytically how it implements a Monte-Carlo
Markov Chain (MCMC) sampler, and describe how it can be used to model networks
of both neural attractors as well as of single spiking neurons. Finally we show
how this model applied to spiking neurons gives rise to a new putative
mechanism that could be used to implement stochastic synaptic weights in
biological neural networks and in neuromorphic hardware.
| [
{
"created": "Mon, 23 May 2016 07:54:46 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Sep 2016 07:13:07 GMT",
"version": "v2"
},
{
"created": "Thu, 31 Aug 2017 08:43:49 GMT",
"version": "v3"
},
{
"created": "Fri, 1 Sep 2017 06:39:55 GMT",
"version": "v4"
}
] | 2017-09-04 | [
[
"Muller",
"Lorenz K.",
""
],
[
"Indiveri",
"Giacomo",
""
]
] | A long tradition in theoretical neuroscience casts sensory processing in the brain as the process of inferring the maximally consistent interpretations of imperfect sensory input. Recently it has been shown that Gamma-band inhibition can enable neural attractor networks to approximately carry out such a sampling mechanism. In this paper we propose a novel neural network model based on irregular gating inhibition, show analytically how it implements a Monte-Carlo Markov Chain (MCMC) sampler, and describe how it can be used to model networks of both neural attractors as well as of single spiking neurons. Finally we show how this model applied to spiking neurons gives rise to a new putative mechanism that could be used to implement stochastic synaptic weights in biological neural networks and in neuromorphic hardware. |
1707.01974 | Dervis Vural | Aylin Acun, Dervis Can Vural, Pinar Zorlutuna | A Tissue Engineered Model of Aging: Interdependence and Cooperative
Effects in Failing Tissues | null | null | null | null | q-bio.TO q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Aging remains a fundamental open problem in modern biology. Although there
exist a number of theories on aging on the cellular scale, nearly nothing is
known about how microscopic failures cascade to macroscopic failures of
tissues, organs and ultimately the organism. The goal of this work is to bridge
microscopic cell failure to macroscopic manifestations of aging. We use tissue
engineered constructs to control the cellular-level damage and cell-cell
distance in individual tissues to establish the role of complex interdependence
and interactions between cells in aging tissues. We found that while
microscopic mechanisms drive aging, the interdependency between cells plays a
major role in tissue death, providing evidence on how cellular aging is
connected to its higher systemic consequences.
| [
{
"created": "Thu, 6 Jul 2017 21:38:39 GMT",
"version": "v1"
}
] | 2017-07-10 | [
[
"Acun",
"Aylin",
""
],
[
"Vural",
"Dervis Can",
""
],
[
"Zorlutuna",
"Pinar",
""
]
] | Aging remains a fundamental open problem in modern biology. Although there exist a number of theories on aging on the cellular scale, nearly nothing is known about how microscopic failures cascade to macroscopic failures of tissues, organs and ultimately the organism. The goal of this work is to bridge microscopic cell failure to macroscopic manifestations of aging. We use tissue engineered constructs to control the cellular-level damage and cell-cell distance in individual tissues to establish the role of complex interdependence and interactions between cells in aging tissues. We found that while microscopic mechanisms drive aging, the interdependency between cells plays a major role in tissue death, providing evidence on how cellular aging is connected to its higher systemic consequences. |
1309.0267 | Wolfram Liebermeister | Wolfram Liebermeister | Structural thermokinetic modelling | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Translating metabolic networks into dynamic models is difficult if kinetic
constants are unknown. Structural Kinetic Modelling (SKM) replaces reaction
elasticities by independent random numbers. Here I propose a variant that
accounts for reversible reactions and thermodynamics: in Structural
Thermokinetic Modelling (STM), correlated elasticities are computed from enzyme
saturation values and thermodynamic forces, which are physically independent.
STM relies on a dependency schema in which basic variables can be sampled,
fitted to data, or optimised, while all other variables are computed from them.
Probability distributions in the dependency schema define a model ensemble,
which leads to probabilistic predictions even if data are scarce. STM
highlights the importance of variabilities, dependencies and covariances of
biological variables. By choosing or sampling the basic variables, we can
convert metabolic networks into kinetic models with consistent reversible rate
laws. Metabolic control coefficients obtained from these models can tell us
about metabolic dynamics, including responses and optimal adaptations to
perturbations as well as enzyme synergies, metabolite correlations, and
metabolic fluctuations arising from chemical noise. By comparing model variants
with different network structures, fluxes, thermodynamic forces, regulation, or
types of rate laws, we can quantify the effects of these model features. To
showcase STM, I study metabolic control, metabolic fluctuations, and enzyme
synergies, and how they are shaped by thermodynamic forces. Thermodynamics can
be used to obtain more precise predictions of flux control, enzyme synergies,
correlated flux and metabolite variations, and of the emergence and propagation
of metabolic noise.
| [
{
"created": "Sun, 1 Sep 2013 21:35:06 GMT",
"version": "v1"
},
{
"created": "Mon, 7 Mar 2022 09:51:43 GMT",
"version": "v2"
}
] | 2022-03-08 | [
[
"Liebermeister",
"Wolfram",
""
]
] | Translating metabolic networks into dynamic models is difficult if kinetic constants are unknown. Structural Kinetic Modelling (SKM) replaces reaction elasticities by independent random numbers. Here I propose a variant that accounts for reversible reactions and thermodynamics: in Structural Thermokinetic Modelling (STM), correlated elasticities are computed from enzyme saturation values and thermodynamic forces, which are physically independent. STM relies on a dependency schema in which basic variables can be sampled, fitted to data, or optimised, while all other variables are computed from them. Probability distributions in the dependency schema define a model ensemble, which leads to probabilistic predictions even if data are scarce. STM highlights the importance of variabilities, dependencies and covariances of biological variables. By choosing or sampling the basic variables, we can convert metabolic networks into kinetic models with consistent reversible rate laws. Metabolic control coefficients obtained from these models can tell us about metabolic dynamics, including responses and optimal adaptations to perturbations as well as enzyme synergies, metabolite correlations, and metabolic fluctuations arising from chemical noise. By comparing model variants with different network structures, fluxes, thermodynamic forces, regulation, or types of rate laws, we can quantify the effects of these model features. To showcase STM, I study metabolic control, metabolic fluctuations, and enzyme synergies, and how they are shaped by thermodynamic forces. Thermodynamics can be used to obtain more precise predictions of flux control, enzyme synergies, correlated flux and metabolite variations, and of the emergence and propagation of metabolic noise. |
2401.17328 | Farnoush Shishehbori | Farnoush Shishehbori, Zainab Awan | Enhancing Cardiovascular Disease Risk Prediction with Machine Learning
Models | 46 pages (including references), 5 Figures | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Cardiovascular disease remains a leading global cause of mortality,
necessitating accurate risk prediction tools. Traditional methods, such as
QRISK and the Framingham heart score, exhibit limitations in their ability to
incorporate comprehensive patient data, potentially resulting in incomplete
risk factor consideration. To address these shortcomings, this study conducts a
meticulous review focusing on the application of machine learning models to
enhance predictive accuracy. Machine learning models, such as support vector
machines, and Random Forest, as well as deep learning techniques like
convolutional neural networks and recurrent neural networks, have emerged as
promising alternatives. These models offer superior performance, accommodating
a broader spectrum of variables and providing precise subgroup-specific
predictions. While machine learning integration holds promise for enhancing
risk assessment, it presents challenges such as data requirements and
computational constraints. Additionally, large language models have
revolutionised healthcare applications, augmenting diagnostic precision and
patient care. This study examines the core aspects of cardiovascular disease
event risk and presents a thorough review of traditional and machine learning
models, alongside deep learning techniques, for improved accuracy. It offers a
comprehensive survey of relevant datasets, critically compares ML models with
conventional approaches, and synthesizes key findings, highlighting their
implications for clinical practice. Furthermore, the potential of machine
learning and large language models in cardiovascular medicine is undeniable.
However, rigorous validation and optimisation are imperative before widespread
application in healthcare. This integration promises more accurate and
personalised cardiovascular care.
| [
{
"created": "Mon, 29 Jan 2024 19:08:33 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Feb 2024 10:42:53 GMT",
"version": "v2"
},
{
"created": "Fri, 9 Feb 2024 16:13:09 GMT",
"version": "v3"
}
] | 2024-02-12 | [
[
"Shishehbori",
"Farnoush",
""
],
[
"Awan",
"Zainab",
""
]
] | Cardiovascular disease remains a leading global cause of mortality, necessitating accurate risk prediction tools. Traditional methods, such as QRISK and the Framingham heart score, exhibit limitations in their ability to incorporate comprehensive patient data, potentially resulting in incomplete risk factor consideration. To address these shortcomings, this study conducts a meticulous review focusing on the application of machine learning models to enhance predictive accuracy. Machine learning models, such as support vector machines, and Random Forest, as well as deep learning techniques like convolutional neural networks and recurrent neural networks, have emerged as promising alternatives. These models offer superior performance, accommodating a broader spectrum of variables and providing precise subgroup-specific predictions. While machine learning integration holds promise for enhancing risk assessment, it presents challenges such as data requirements and computational constraints. Additionally, large language models have revolutionised healthcare applications, augmenting diagnostic precision and patient care. This study examines the core aspects of cardiovascular disease event risk and presents a thorough review of traditional and machine learning models, alongside deep learning techniques, for improved accuracy. It offers a comprehensive survey of relevant datasets, critically compares ML models with conventional approaches, and synthesizes key findings, highlighting their implications for clinical practice. Furthermore, the potential of machine learning and large language models in cardiovascular medicine is undeniable. However, rigorous validation and optimisation are imperative before widespread application in healthcare. This integration promises more accurate and personalised cardiovascular care. |
2105.01167 | Qi Su | Qi Su, Joshua. B Plotkin | Evolution of cooperation with asymmetric social interactions | 40 pages, 11 figures | null | 10.1073/pnas.2113468118 | null | q-bio.PE math.DS physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | How cooperation emerges in human societies is both an evolutionary enigma,
and a practical problem with tangible implications for societal health.
Population structure has long been recognized as a catalyst for cooperation
because local interactions enable reciprocity. Analysis of this phenomenon
typically assumes bi-directional social interactions, even though real-world
interactions are often uni-directional. Uni-directional interactions -- where
one individual has the opportunity to contribute altruistically to another, but
not conversely -- arise in real-world populations as the result of
organizational hierarchies, social stratification, popularity effects, and
endogenous mechanisms of network growth. Here we expand the theory of
cooperation in structured populations to account for both uni- and
bi-directional social interactions. Even though directed interactions remove
the opportunity for reciprocity, we find that cooperation can nonetheless be
favored in directed social networks and that cooperation is provably maximized
for networks with an intermediate proportion of directed interactions, as
observed in many empirical settings. We also identify two simple structural
motifs that allow efficient modification of interaction directionality to
promote cooperation by orders of magnitude. We discuss how our results relate
to the concepts of generalized and indirect reciprocity.
| [
{
"created": "Mon, 3 May 2021 20:57:10 GMT",
"version": "v1"
},
{
"created": "Thu, 20 May 2021 19:50:15 GMT",
"version": "v2"
}
] | 2022-01-06 | [
[
"Su",
"Qi",
""
],
[
"Plotkin",
"Joshua. B",
""
]
] | How cooperation emerges in human societies is both an evolutionary enigma, and a practical problem with tangible implications for societal health. Population structure has long been recognized as a catalyst for cooperation because local interactions enable reciprocity. Analysis of this phenomenon typically assumes bi-directional social interactions, even though real-world interactions are often uni-directional. Uni-directional interactions -- where one individual has the opportunity to contribute altruistically to another, but not conversely -- arise in real-world populations as the result of organizational hierarchies, social stratification, popularity effects, and endogenous mechanisms of network growth. Here we expand the theory of cooperation in structured populations to account for both uni- and bi-directional social interactions. Even though directed interactions remove the opportunity for reciprocity, we find that cooperation can nonetheless be favored in directed social networks and that cooperation is provably maximized for networks with an intermediate proportion of directed interactions, as observed in many empirical settings. We also identify two simple structural motifs that allow efficient modification of interaction directionality to promote cooperation by orders of magnitude. We discuss how our results relate to the concepts of generalized and indirect reciprocity. |
1702.06405 | David Caldwell | David J. Caldwell, Jing Wu, Kaitlyn Casimo, Jeffrey G. Ojemann, Rajesh
P.N. Rao | Interactive Web Application for Exploring Matrices of Neural
Connectivity | 4 pages, IEEE NER 2017 | null | 10.1109/NER.2017.8008287 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present here a browser-based application for visualizing patterns of
connectivity in 3D stacked data matrices with large numbers of pairwise
relations. Visualizing a connectivity matrix, looking for trends and patterns,
and dynamically manipulating these values is a challenge for scientists from
diverse fields, including neuroscience and genomics. In particular,
high-dimensional neural data include those acquired via electroencephalography
(EEG), electrocorticography (ECoG), magnetoencephalography (MEG), and
functional MRI. Neural connectivity data contains multivariate attributes for
each edge between different brain regions, which motivated our lightweight,
open source, easy-to-use visualization tool for the exploration of these
connectivity matrices to highlight connections of interest. Here we present a
client-side, mobile-compatible visualization tool written entirely in
HTML5/JavaScript that allows in-browser manipulation of user-defined files for
exploration of brain connectivity. Visualizations can highlight different
aspects of the data simultaneously across different dimensions. Input files are
in JSON format, and custom Python scripts have been written to parse MATLAB or
Python data files into JSON-loadable format. We demonstrate the analysis of
connectivity data acquired via human ECoG recordings as a domain-specific
implementation of our application. We envision applications for this
interactive tool in fields seeking to visualize pairwise connectivity.
| [
{
"created": "Tue, 21 Feb 2017 14:36:17 GMT",
"version": "v1"
}
] | 2018-01-04 | [
[
"Caldwell",
"David J.",
""
],
[
"Wu",
"Jing",
""
],
[
"Casimo",
"Kaitlyn",
""
],
[
"Ojemann",
"Jeffrey G.",
""
],
[
"Rao",
"Rajesh P. N.",
""
]
] | We present here a browser-based application for visualizing patterns of connectivity in 3D stacked data matrices with large numbers of pairwise relations. Visualizing a connectivity matrix, looking for trends and patterns, and dynamically manipulating these values is a challenge for scientists from diverse fields, including neuroscience and genomics. In particular, high-dimensional neural data include those acquired via electroencephalography (EEG), electrocorticography (ECoG), magnetoencephalography (MEG), and functional MRI. Neural connectivity data contains multivariate attributes for each edge between different brain regions, which motivated our lightweight, open source, easy-to-use visualization tool for the exploration of these connectivity matrices to highlight connections of interest. Here we present a client-side, mobile-compatible visualization tool written entirely in HTML5/JavaScript that allows in-browser manipulation of user-defined files for exploration of brain connectivity. Visualizations can highlight different aspects of the data simultaneously across different dimensions. Input files are in JSON format, and custom Python scripts have been written to parse MATLAB or Python data files into JSON-loadable format. We demonstrate the analysis of connectivity data acquired via human ECoG recordings as a domain-specific implementation of our application. We envision applications for this interactive tool in fields seeking to visualize pairwise connectivity. |
q-bio/0509007 | Isaac Hubner | Isaac A. Hubner, Eric J. Deeds, and Eugene I. Shakhnovich | High resolution protein folding with a transferable potential | submitted to PNAS 2005-03-16 | null | 10.1073/pnas.0502181102 | null | q-bio.BM | null | A generalized computational method for folding proteins with a fully
transferable potential and geometrically realistic all-atom model is presented
and tested on seven different helix bundle proteins. The protocol, which
includes graph-theoretical analysis of the ensemble of resulting folded
conformations, was systematically applied and consistently produced structure
predictions of approximately 3 Angstroms without any knowledge of the native
state. To measure and understand the significance of the results, extensive
control simulations were conducted. Graph theoretic analysis provides a means
for systematically identifying the native fold and provides physical insight,
conceptually linking the results to modern theoretical views of protein
folding. In addition to presenting a method for prediction of structure and
folding mechanism, our model suggests that a accurate all-atom amino acid
representation coupled with a physically reasonable atomic interaction
potential (that does not require optimization to the test set) and hydrogen
bonding are essential features for a realistic protein model.
| [
{
"created": "Wed, 7 Sep 2005 14:24:27 GMT",
"version": "v1"
}
] | 2009-11-11 | [
[
"Hubner",
"Isaac A.",
""
],
[
"Deeds",
"Eric J.",
""
],
[
"Shakhnovich",
"Eugene I.",
""
]
] | A generalized computational method for folding proteins with a fully transferable potential and geometrically realistic all-atom model is presented and tested on seven different helix bundle proteins. The protocol, which includes graph-theoretical analysis of the ensemble of resulting folded conformations, was systematically applied and consistently produced structure predictions of approximately 3 Angstroms without any knowledge of the native state. To measure and understand the significance of the results, extensive control simulations were conducted. Graph theoretic analysis provides a means for systematically identifying the native fold and provides physical insight, conceptually linking the results to modern theoretical views of protein folding. In addition to presenting a method for prediction of structure and folding mechanism, our model suggests that a accurate all-atom amino acid representation coupled with a physically reasonable atomic interaction potential (that does not require optimization to the test set) and hydrogen bonding are essential features for a realistic protein model. |
1007.0327 | Michael Assaf | Michael Assaf and Mauro Mobilia | Large Fluctuations and Fixation in Evolutionary Games | 17 pages, 10 figures, to appear in JSTAT | J. Stat. Mech., (2010) P09009 | 10.1088/1742-5468/2010/09/P09009 | null | q-bio.PE cond-mat.stat-mech nlin.AO q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study large fluctuations in evolutionary games belonging to the
coordination and anti-coordination classes. The dynamics of these games,
modeling cooperation dilemmas, is characterized by a coexistence fixed point
separating two absorbing states. We are particularly interested in the problem
of fixation that refers to the possibility that a few mutants take over the
entire population. Here, the fixation phenomenon is induced by large
fluctuations and is investigated by a semi-classical WKB
(Wentzel-Kramers-Brillouin) theory generalized to treat stochastic systems
possessing multiple absorbing states. Importantly, this method allows us to
analyze the combined influence of selection and random fluctuations on the
evolutionary dynamics \textit{beyond} the weak selection limit often considered
in previous works. We accurately compute, including pre-exponential factors,
the probability distribution function in the long-lived coexistence state and
the mean fixation time necessary for a few mutants to take over the entire
population in anti-coordination games, and also the fixation probability in the
coordination class. Our analytical results compare excellently with extensive
numerical simulations. Furthermore, we demonstrate that our treatment is
superior to the Fokker-Planck approximation when the selection intensity is
finite.
| [
{
"created": "Fri, 2 Jul 2010 10:03:34 GMT",
"version": "v1"
},
{
"created": "Wed, 25 Aug 2010 12:42:45 GMT",
"version": "v2"
},
{
"created": "Thu, 26 Aug 2010 10:36:31 GMT",
"version": "v3"
}
] | 2015-05-19 | [
[
"Assaf",
"Michael",
""
],
[
"Mobilia",
"Mauro",
""
]
] | We study large fluctuations in evolutionary games belonging to the coordination and anti-coordination classes. The dynamics of these games, modeling cooperation dilemmas, is characterized by a coexistence fixed point separating two absorbing states. We are particularly interested in the problem of fixation that refers to the possibility that a few mutants take over the entire population. Here, the fixation phenomenon is induced by large fluctuations and is investigated by a semi-classical WKB (Wentzel-Kramers-Brillouin) theory generalized to treat stochastic systems possessing multiple absorbing states. Importantly, this method allows us to analyze the combined influence of selection and random fluctuations on the evolutionary dynamics \textit{beyond} the weak selection limit often considered in previous works. We accurately compute, including pre-exponential factors, the probability distribution function in the long-lived coexistence state and the mean fixation time necessary for a few mutants to take over the entire population in anti-coordination games, and also the fixation probability in the coordination class. Our analytical results compare excellently with extensive numerical simulations. Furthermore, we demonstrate that our treatment is superior to the Fokker-Planck approximation when the selection intensity is finite. |
1907.00230 | Alessandro Torcini Dr | Hongjie Bi, Marco Segneri, Matteo di Volo, Alessandro Torcini | Coexistence of fast and slow gamma oscillations in one population of
inhibitory spiking neurons | 20 pages, 14 figures | Phys. Rev. Research 2, 013042 (2020) | 10.1103/PhysRevResearch.2.013042 | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Oscillations are a hallmark of neural population activity in various brain
regions with a spectrum covering a wide range of frequencies. Within this
spectrum gamma oscillations have received particular attention due to their
ubiquitous nature and to their correlation with higher brain functions.
Recently, it has been reported that gamma oscillations in the hippocampus of
behaving rodents are segregated in two distinct frequency bands: slow and fast.
These two gamma rhythms correspond to dfferent states of the network, but their
origin has been not yet clarified. Here, we show theoretically and numerically
that a single inhibitory population can give rise to coexisting slow and fast
gamma rhythms corresponding to collective oscillations of a balanced spiking
network. The slow and fast gamma rhythms are generated via two different
mechanisms: the fast one being driven by the coordinated tonic neural firing
and the slow one by endogenous fluctuations due to irregular neural activity.
We show that almost instantaneous stimulations can switch the collective gamma
oscillations from slow to fast and vice versa. Furthermore, to make a closer
contact with the experimental observations, we consider the modulation of the
gamma rhythms induced by a slower (theta) rhythm driving the network dynamics.
In this context, depending on the strength of the forcing, we observe
phase-amplitude and phase-phase coupling between the fast and slow gamma
oscillations and the theta forcing. Phase-phase coupling reveals different
theta-phases preferences for the two coexisting gamma rhythms.
| [
{
"created": "Sat, 29 Jun 2019 16:09:48 GMT",
"version": "v1"
}
] | 2020-01-22 | [
[
"Bi",
"Hongjie",
""
],
[
"Segneri",
"Marco",
""
],
[
"di Volo",
"Matteo",
""
],
[
"Torcini",
"Alessandro",
""
]
] | Oscillations are a hallmark of neural population activity in various brain regions with a spectrum covering a wide range of frequencies. Within this spectrum gamma oscillations have received particular attention due to their ubiquitous nature and to their correlation with higher brain functions. Recently, it has been reported that gamma oscillations in the hippocampus of behaving rodents are segregated in two distinct frequency bands: slow and fast. These two gamma rhythms correspond to dfferent states of the network, but their origin has been not yet clarified. Here, we show theoretically and numerically that a single inhibitory population can give rise to coexisting slow and fast gamma rhythms corresponding to collective oscillations of a balanced spiking network. The slow and fast gamma rhythms are generated via two different mechanisms: the fast one being driven by the coordinated tonic neural firing and the slow one by endogenous fluctuations due to irregular neural activity. We show that almost instantaneous stimulations can switch the collective gamma oscillations from slow to fast and vice versa. Furthermore, to make a closer contact with the experimental observations, we consider the modulation of the gamma rhythms induced by a slower (theta) rhythm driving the network dynamics. In this context, depending on the strength of the forcing, we observe phase-amplitude and phase-phase coupling between the fast and slow gamma oscillations and the theta forcing. Phase-phase coupling reveals different theta-phases preferences for the two coexisting gamma rhythms. |
1301.7734 | Pablo Cordero | Pablo Cordero, Wipapat Kladwang, Christopher C. VanLang and Rhiju Das | A mutate-and-map protocol for inferring base pairs in structured RNA | 22 pages, 5 figures | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Chemical mapping is a widespread technique for structural analysis of nucleic
acids in which a molecule's reactivity to different probes is quantified at
single-nucleotide resolution and used to constrain structural modeling. This
experimental framework has been extensively revisited in the past decade with
new strategies for high-throughput read-outs, chemical modification, and rapid
data analysis. Recently, we have coupled the technique to high-throughput
mutagenesis. Point mutations of a base-paired nucleotide can lead to exposure
of not only that nucleotide but also its interaction partner. Carrying out the
mutation and mapping for the entire system gives an experimental approximation
of the molecules contact map. Here, we give our in-house protocol for this
mutate-and-map strategy, based on 96-well capillary electrophoresis, and we
provide practical tips on interpreting the data to infer nucleic acid
structure.
| [
{
"created": "Thu, 31 Jan 2013 19:58:16 GMT",
"version": "v1"
}
] | 2013-02-01 | [
[
"Cordero",
"Pablo",
""
],
[
"Kladwang",
"Wipapat",
""
],
[
"VanLang",
"Christopher C.",
""
],
[
"Das",
"Rhiju",
""
]
] | Chemical mapping is a widespread technique for structural analysis of nucleic acids in which a molecule's reactivity to different probes is quantified at single-nucleotide resolution and used to constrain structural modeling. This experimental framework has been extensively revisited in the past decade with new strategies for high-throughput read-outs, chemical modification, and rapid data analysis. Recently, we have coupled the technique to high-throughput mutagenesis. Point mutations of a base-paired nucleotide can lead to exposure of not only that nucleotide but also its interaction partner. Carrying out the mutation and mapping for the entire system gives an experimental approximation of the molecules contact map. Here, we give our in-house protocol for this mutate-and-map strategy, based on 96-well capillary electrophoresis, and we provide practical tips on interpreting the data to infer nucleic acid structure. |
2204.07186 | Naoki Hiratani | Naoki Hiratani, Haim Sompolinsky | Optimal quadratic binding for relational reasoning in vector symbolic
neural architectures | 32 pages, 9 figures | null | null | null | q-bio.NC cs.AI | http://creativecommons.org/licenses/by/4.0/ | Binding operation is fundamental to many cognitive processes, such as
cognitive map formation, relational reasoning, and language comprehension. In
these processes, two different modalities, such as location and objects, events
and their contextual cues, and words and their roles, need to be bound
together, but little is known about the underlying neural mechanisms. Previous
works introduced a binding model based on quadratic functions of bound pairs,
followed by vector summation of multiple pairs. Based on this framework, we
address following questions: Which classes of quadratic matrices are optimal
for decoding relational structures? And what is the resultant accuracy? We
introduce a new class of binding matrices based on a matrix representation of
octonion algebra, an eight-dimensional extension of complex numbers. We show
that these matrices enable a more accurate unbinding than previously known
methods when a small number of pairs are present. Moreover, numerical
optimization of a binding operator converges to this octonion binding. We also
show that when there are a large number of bound pairs, however, a random
quadratic binding performs as well as the octonion and previously-proposed
binding methods. This study thus provides new insight into potential neural
mechanisms of binding operations in the brain.
| [
{
"created": "Thu, 14 Apr 2022 18:41:27 GMT",
"version": "v1"
}
] | 2022-04-18 | [
[
"Hiratani",
"Naoki",
""
],
[
"Sompolinsky",
"Haim",
""
]
] | Binding operation is fundamental to many cognitive processes, such as cognitive map formation, relational reasoning, and language comprehension. In these processes, two different modalities, such as location and objects, events and their contextual cues, and words and their roles, need to be bound together, but little is known about the underlying neural mechanisms. Previous works introduced a binding model based on quadratic functions of bound pairs, followed by vector summation of multiple pairs. Based on this framework, we address following questions: Which classes of quadratic matrices are optimal for decoding relational structures? And what is the resultant accuracy? We introduce a new class of binding matrices based on a matrix representation of octonion algebra, an eight-dimensional extension of complex numbers. We show that these matrices enable a more accurate unbinding than previously known methods when a small number of pairs are present. Moreover, numerical optimization of a binding operator converges to this octonion binding. We also show that when there are a large number of bound pairs, however, a random quadratic binding performs as well as the octonion and previously-proposed binding methods. This study thus provides new insight into potential neural mechanisms of binding operations in the brain. |
1409.1933 | Alessandro Torcini Dr | Stefano Luccioli, Eshel Ben-Jacob, Ari Barzilai, Paolo Bonifazi,
Alessandro Torcini | Clique of functional hubs orchestrates population bursts in
developmentally regulated neural networks | 39 pages, 15 figures, to appear in PLOS Computational Biology | PLoS Comput Biol 10(9) (2014) e1003823 | 10.1371/journal.pcbi.1003823 | null | q-bio.NC cond-mat.dis-nn physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It has recently been discovered that single neuron stimulation can impact
network dynamics in immature and adult neuronal circuits. Here we report a
novel mechanism which can explain in neuronal circuits, at an early stage of
development, the peculiar role played by a few specific neurons in
promoting/arresting the population activity. For this purpose, we consider a
standard neuronal network model, with short-term synaptic plasticity, whose
population activity is characterized by bursting behavior. The addition of
developmentally inspired constraints and correlations in the distribution of
the neuronal connectivities and excitabilities leads to the emergence of
functional hub neurons, whose stimulation/deletion is critical for the network
activity. Functional hubs form a clique, where a precise sequential activation
of the neurons is essential to ignite collective events without any need for a
specific topological architecture. Unsupervised time-lagged firings of
supra-threshold cells, in connection with coordinated entrainments of
near-threshold neurons, are the key ingredients to orchestrate
| [
{
"created": "Fri, 5 Sep 2014 20:11:32 GMT",
"version": "v1"
}
] | 2015-04-14 | [
[
"Luccioli",
"Stefano",
""
],
[
"Ben-Jacob",
"Eshel",
""
],
[
"Barzilai",
"Ari",
""
],
[
"Bonifazi",
"Paolo",
""
],
[
"Torcini",
"Alessandro",
""
]
] | It has recently been discovered that single neuron stimulation can impact network dynamics in immature and adult neuronal circuits. Here we report a novel mechanism which can explain in neuronal circuits, at an early stage of development, the peculiar role played by a few specific neurons in promoting/arresting the population activity. For this purpose, we consider a standard neuronal network model, with short-term synaptic plasticity, whose population activity is characterized by bursting behavior. The addition of developmentally inspired constraints and correlations in the distribution of the neuronal connectivities and excitabilities leads to the emergence of functional hub neurons, whose stimulation/deletion is critical for the network activity. Functional hubs form a clique, where a precise sequential activation of the neurons is essential to ignite collective events without any need for a specific topological architecture. Unsupervised time-lagged firings of supra-threshold cells, in connection with coordinated entrainments of near-threshold neurons, are the key ingredients to orchestrate |
2003.14284 | Alfonso M. Ganan-Calvo | Alfonso M. Ganan-Calvo and Juan A. Hernandez Ramos | The fractal time growth of COVID-19 pandemic: an accurate self-similar
model, and urgent conclusions | null | null | null | null | q-bio.PE nlin.AO physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current available data of the worldwide impact of the COVID-19 pandemic has
been analyzed using dimensional analysis and self-similarity hypotheses. We
show that the time series of infected population and deaths of the most
impacted and unprepared countries exhibits an asymptotic power law behavior,
compatible with the propagation of a signal in a fractal network. We propose a
model which predicts an asymptotically self-similar expansion of deaths in time
before containment, and the final death toll under total containment measures,
as a function of the delay in taking those measures after the expansion is
observed. The physics of the model resembles the expansion of a flame in a
homogeneous domain with a fractal dimension 3.75. After containment measures
are taken, the natural fractal structure of the network is drastically altered
and a secondary evolution is observed. This evolution, akin to the homogeneous
combustion in a static isolated enclosure with a final quenching, has a
characteristic time of 20.1 days, according to available data of the pandemic
behavior in China. The proposed model is remarkably consistent with available
data, which supports the simplifying hypotheses made in the model. A universal
formulation for a quarantine as a function of that delay is also proposed.
| [
{
"created": "Tue, 31 Mar 2020 15:17:54 GMT",
"version": "v1"
}
] | 2020-04-01 | [
[
"Ganan-Calvo",
"Alfonso M.",
""
],
[
"Ramos",
"Juan A. Hernandez",
""
]
] | Current available data of the worldwide impact of the COVID-19 pandemic has been analyzed using dimensional analysis and self-similarity hypotheses. We show that the time series of infected population and deaths of the most impacted and unprepared countries exhibits an asymptotic power law behavior, compatible with the propagation of a signal in a fractal network. We propose a model which predicts an asymptotically self-similar expansion of deaths in time before containment, and the final death toll under total containment measures, as a function of the delay in taking those measures after the expansion is observed. The physics of the model resembles the expansion of a flame in a homogeneous domain with a fractal dimension 3.75. After containment measures are taken, the natural fractal structure of the network is drastically altered and a secondary evolution is observed. This evolution, akin to the homogeneous combustion in a static isolated enclosure with a final quenching, has a characteristic time of 20.1 days, according to available data of the pandemic behavior in China. The proposed model is remarkably consistent with available data, which supports the simplifying hypotheses made in the model. A universal formulation for a quarantine as a function of that delay is also proposed. |
2407.03441 | Simon Coetzee | Simon G. Coetzee and Dennis J. Hazelett | MotifbreakR v2: extended capability and database integration | 4 pages of text, 1 figure with 2 panels, 12 total pages. Source code,
documentation, and tutorials are available on Bioconductor at
https://bioconductor.org/packages/release/bioc/html/motifbreakR.html and
GitHub at https://github.com/Simon-Coetzee/motifBreakR | null | null | null | q-bio.GN q-bio.QM | http://creativecommons.org/licenses/by-sa/4.0/ | MotifbreakR is a software tool that scans genetic variants against position
weight matrices of transcription factors (TF) to determine the potential for
the disruption of TF binding at the site of the variant. It leverages the
Bioconductor suite of software packages and annotations to operate across a
diverse array of genomes and motif databases. Initially developed to
interrogate the effect of single nucleotide variants (common and rare SNVs) on
potential TF binding sites, in motifbreakR v2, we have updated the
functionality. New features include the ability to query other types of more
complex genetic variants, such as short insertions and deletions (indels). This
function allows modeling a more extensive array of variants that may have more
significant effects on TF binding. Additionally, while TF binding is based
partly on sequence preference, predictions of TF binding based on sequence
preference alone can indicate many more potential binding events than observed.
Adding information from DNA-binding sequencing datasets lends confidence to
motif disruption prediction by demonstrating TF binding in cell lines and
tissue types. Therefore, motifbreakR implements querying the ReMap2022 database
for evidence that a TF matching the disrupted motif binds over the disrupting
variant. Finally, in motifbreakR, in addition to the existing interface, we
have implemented an R/Shiny graphical user interface to simplify and enhance
access to researchers with different skill sets.
| [
{
"created": "Wed, 3 Jul 2024 18:34:03 GMT",
"version": "v1"
}
] | 2024-07-08 | [
[
"Coetzee",
"Simon G.",
""
],
[
"Hazelett",
"Dennis J.",
""
]
] | MotifbreakR is a software tool that scans genetic variants against position weight matrices of transcription factors (TF) to determine the potential for the disruption of TF binding at the site of the variant. It leverages the Bioconductor suite of software packages and annotations to operate across a diverse array of genomes and motif databases. Initially developed to interrogate the effect of single nucleotide variants (common and rare SNVs) on potential TF binding sites, in motifbreakR v2, we have updated the functionality. New features include the ability to query other types of more complex genetic variants, such as short insertions and deletions (indels). This function allows modeling a more extensive array of variants that may have more significant effects on TF binding. Additionally, while TF binding is based partly on sequence preference, predictions of TF binding based on sequence preference alone can indicate many more potential binding events than observed. Adding information from DNA-binding sequencing datasets lends confidence to motif disruption prediction by demonstrating TF binding in cell lines and tissue types. Therefore, motifbreakR implements querying the ReMap2022 database for evidence that a TF matching the disrupted motif binds over the disrupting variant. Finally, in motifbreakR, in addition to the existing interface, we have implemented an R/Shiny graphical user interface to simplify and enhance access to researchers with different skill sets. |
2008.11491 | Sam Blakeman | Sam Blakeman, Denis Mareschal | Selective Particle Attention: Visual Feature-Based Attention in Deep
Reinforcement Learning | null | null | null | null | q-bio.NC cs.CV cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The human brain uses selective attention to filter perceptual input so that
only the components that are useful for behaviour are processed using its
limited computational resources. We focus on one particular form of visual
attention known as feature-based attention, which is concerned with identifying
features of the visual input that are important for the current task regardless
of their spatial location. Visual feature-based attention has been proposed to
improve the efficiency of Reinforcement Learning (RL) by reducing the
dimensionality of state representations and guiding learning towards relevant
features. Despite achieving human level performance in complex perceptual-motor
tasks, Deep RL algorithms have been consistently criticised for their poor
efficiency and lack of flexibility. Visual feature-based attention therefore
represents one option for addressing these criticisms. Nevertheless, it is
still an open question how the brain is able to learn which features to attend
to during RL. To help answer this question we propose a novel algorithm, termed
Selective Particle Attention (SPA), which imbues a Deep RL agent with the
ability to perform selective feature-based attention. SPA learns which
combinations of features to attend to based on their bottom-up saliency and how
accurately they predict future reward. We evaluate SPA on a multiple choice
task and a 2D video game that both involve raw pixel input and dynamic changes
to the task structure. We show various benefits of SPA over approaches that
naively attend to either all or random subsets of features. Our results
demonstrate (1) how visual feature-based attention in Deep RL models can
improve their learning efficiency and ability to deal with sudden changes in
task structure and (2) that particle filters may represent a viable
computational account of how visual feature-based attention occurs in the
brain.
| [
{
"created": "Wed, 26 Aug 2020 11:07:50 GMT",
"version": "v1"
}
] | 2020-08-31 | [
[
"Blakeman",
"Sam",
""
],
[
"Mareschal",
"Denis",
""
]
] | The human brain uses selective attention to filter perceptual input so that only the components that are useful for behaviour are processed using its limited computational resources. We focus on one particular form of visual attention known as feature-based attention, which is concerned with identifying features of the visual input that are important for the current task regardless of their spatial location. Visual feature-based attention has been proposed to improve the efficiency of Reinforcement Learning (RL) by reducing the dimensionality of state representations and guiding learning towards relevant features. Despite achieving human level performance in complex perceptual-motor tasks, Deep RL algorithms have been consistently criticised for their poor efficiency and lack of flexibility. Visual feature-based attention therefore represents one option for addressing these criticisms. Nevertheless, it is still an open question how the brain is able to learn which features to attend to during RL. To help answer this question we propose a novel algorithm, termed Selective Particle Attention (SPA), which imbues a Deep RL agent with the ability to perform selective feature-based attention. SPA learns which combinations of features to attend to based on their bottom-up saliency and how accurately they predict future reward. We evaluate SPA on a multiple choice task and a 2D video game that both involve raw pixel input and dynamic changes to the task structure. We show various benefits of SPA over approaches that naively attend to either all or random subsets of features. Our results demonstrate (1) how visual feature-based attention in Deep RL models can improve their learning efficiency and ability to deal with sudden changes in task structure and (2) that particle filters may represent a viable computational account of how visual feature-based attention occurs in the brain. |
1902.08511 | David Papo | David Papo | Gauging functional brain activity: from distinguishability to
accessibility | 7 pages, 0 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Standard neuroimaging techniques provide non-invasive access not only to
human brain anatomy but also to its physiology. The activity recorded with
these techniques is generally called functional imaging, but what is observed
per se is an instance of dynamics, from which functional brain activity should
be extracted. Distinguishing between bare dynamics and genuine function is a
highly non-trivial task, but a crucially important one when comparing
experimental observations and interpreting their significance. Here we
illustrate how the ability of neuroimaging to extract genuine functional brain
activity is bounded by the structure of functional representations. To do so,
we first provide a simple definition of functional brain activity from a
system-level brain imaging perspective. We then review how the properties of
the space on which brain activity is represented allow defining relations
ranging from distinguishability to accessibility of observed imaging data. We
show how these properties result from the structure defined on dynamical data
and dynamics-to-function projections, and consider some implications that the
way and extent to which these are defined have for the interpretation of
experimental data from standard system-level brain recording techniques.
| [
{
"created": "Fri, 22 Feb 2019 14:39:40 GMT",
"version": "v1"
}
] | 2019-02-25 | [
[
"Papo",
"David",
""
]
] | Standard neuroimaging techniques provide non-invasive access not only to human brain anatomy but also to its physiology. The activity recorded with these techniques is generally called functional imaging, but what is observed per se is an instance of dynamics, from which functional brain activity should be extracted. Distinguishing between bare dynamics and genuine function is a highly non-trivial task, but a crucially important one when comparing experimental observations and interpreting their significance. Here we illustrate how the ability of neuroimaging to extract genuine functional brain activity is bounded by the structure of functional representations. To do so, we first provide a simple definition of functional brain activity from a system-level brain imaging perspective. We then review how the properties of the space on which brain activity is represented allow defining relations ranging from distinguishability to accessibility of observed imaging data. We show how these properties result from the structure defined on dynamical data and dynamics-to-function projections, and consider some implications that the way and extent to which these are defined have for the interpretation of experimental data from standard system-level brain recording techniques. |
2004.11841 | Felix Sattler | Felix Sattler, Jackie Ma, Patrick Wagner, David Neumann, Markus
Wenzel, Ralf Sch\"afer, Wojciech Samek, Klaus-Robert M\"uller, Thomas Wiegand | Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy
Measurements | null | null | null | null | q-bio.QM cs.LG q-bio.PE stat.AP stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Digital contact tracing approaches based on Bluetooth low energy (BLE) have
the potential to efficiently contain and delay outbreaks of infectious diseases
such as the ongoing SARS-CoV-2 pandemic. In this work we propose a novel
machine learning based approach to reliably detect subjects that have spent
enough time in close proximity to be at risk of being infected. Our study is an
important proof of concept that will aid the battery of epidemiological
policies aiming to slow down the rapid spread of COVID-19.
| [
{
"created": "Wed, 22 Apr 2020 20:10:35 GMT",
"version": "v1"
}
] | 2020-04-27 | [
[
"Sattler",
"Felix",
""
],
[
"Ma",
"Jackie",
""
],
[
"Wagner",
"Patrick",
""
],
[
"Neumann",
"David",
""
],
[
"Wenzel",
"Markus",
""
],
[
"Schäfer",
"Ralf",
""
],
[
"Samek",
"Wojciech",
""
],
[
"Müller",
"Klaus-Robert",
""
],
[
"Wiegand",
"Thomas",
""
]
] | Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic. In this work we propose a novel machine learning based approach to reliably detect subjects that have spent enough time in close proximity to be at risk of being infected. Our study is an important proof of concept that will aid the battery of epidemiological policies aiming to slow down the rapid spread of COVID-19. |
1406.6424 | Corey Bradshaw | Richard Frankham, Corey J. A. Bradshaw, Barry W. Brook | 50/500 or 100/1000 debate is not about the time frame - Reply to
Rosenfeld | 5 pages, 0 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Letter from Rosenfeld (2014, Biological Conservation) in response to
Jamieson and Allendorf (2012, Trends in Ecology and Evolution) and Frankham et
al. (2014, Biological Conservation) and related papers is misleading in places
and requires clarification and correction. We provide those here.
| [
{
"created": "Wed, 25 Jun 2014 00:41:47 GMT",
"version": "v1"
},
{
"created": "Tue, 1 Jul 2014 01:10:01 GMT",
"version": "v2"
}
] | 2014-07-02 | [
[
"Frankham",
"Richard",
""
],
[
"Bradshaw",
"Corey J. A.",
""
],
[
"Brook",
"Barry W.",
""
]
] | The Letter from Rosenfeld (2014, Biological Conservation) in response to Jamieson and Allendorf (2012, Trends in Ecology and Evolution) and Frankham et al. (2014, Biological Conservation) and related papers is misleading in places and requires clarification and correction. We provide those here. |
1511.00255 | Carina Curto | Carina Curto and Nora Youngs | Neural ring homomorphisms and maps between neural codes | 15 pages, 2 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural codes are binary codes that are used for information processing and
representation in the brain. In previous work, we have shown how an algebraic
structure, called the {\it neural ring}, can be used to efficiently encode
geometric and combinatorial properties of a neural code [1]. In this work, we
consider maps between neural codes and the associated homomorphisms of their
neural rings. In order to ensure that these maps are meaningful and preserve
relevant structure, we find that we need additional constraints on the ring
homomorphisms. This motivates us to define {\it neural ring homomorphisms}. Our
main results characterize all code maps corresponding to neural ring
homomorphisms as compositions of 5 elementary code maps. As an application, we
find that neural ring homomorphisms behave nicely with respect to convexity. In
particular, if $\mathcal{C}$ and $\mathcal{D}$ are convex codes, the existence
of a surjective code map $\mathcal{C}\rightarrow \mathcal{D}$ with a
corresponding neural ring homomorphism implies that the minimal embedding
dimensions satisfy $d(\mathcal{D}) \leq d(\mathcal{C})$.
| [
{
"created": "Sun, 1 Nov 2015 14:29:03 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Nov 2018 16:40:08 GMT",
"version": "v2"
},
{
"created": "Wed, 13 Feb 2019 18:33:31 GMT",
"version": "v3"
}
] | 2019-02-14 | [
[
"Curto",
"Carina",
""
],
[
"Youngs",
"Nora",
""
]
] | Neural codes are binary codes that are used for information processing and representation in the brain. In previous work, we have shown how an algebraic structure, called the {\it neural ring}, can be used to efficiently encode geometric and combinatorial properties of a neural code [1]. In this work, we consider maps between neural codes and the associated homomorphisms of their neural rings. In order to ensure that these maps are meaningful and preserve relevant structure, we find that we need additional constraints on the ring homomorphisms. This motivates us to define {\it neural ring homomorphisms}. Our main results characterize all code maps corresponding to neural ring homomorphisms as compositions of 5 elementary code maps. As an application, we find that neural ring homomorphisms behave nicely with respect to convexity. In particular, if $\mathcal{C}$ and $\mathcal{D}$ are convex codes, the existence of a surjective code map $\mathcal{C}\rightarrow \mathcal{D}$ with a corresponding neural ring homomorphism implies that the minimal embedding dimensions satisfy $d(\mathcal{D}) \leq d(\mathcal{C})$. |
1412.3893 | Ian Ochs | Ian E. Ochs and Michael M. Desai | The competition between simple and complex evolutionary trajectories in
asexual populations | 8 pages, 3 figures | BMC Evolutionary Biology 2015, 15:55 | 10.1186/s12862-015-0334-0 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | On rugged fitness landscapes where sign epistasis is common, adaptation can
often involve either individually beneficial "uphill" mutations or more complex
mutational trajectories involving fitness valleys or plateaus. The dynamics of
the evolutionary process determine the probability that evolution will take any
specific path among a variety of competing possible trajectories. Understanding
this evolutionary choice is essential if we are to understand the outcomes and
predictability of adaptation on rugged landscapes. We present a simple model to
analyze the probability that evolution will eschew immediately uphill paths in
favor of crossing fitness valleys or plateaus that lead to higher fitness but
less accessible genotypes. We calculate how this probability depends on the
population size, mutation rates, and relevant selection pressures, and compare
our analytical results to Wright-Fisher simulations. We find that the
probability of valley crossing depends nonmonotonically on population size:
intermediate size populations are most likely to follow a "greedy" strategy of
acquiring immediately beneficial mutations even if they lead to evolutionary
dead ends, while larger and smaller populations are more likely to cross
fitness valleys to reach distant advantageous genotypes. We explicitly identify
the boundaries between these different regimes in terms of the relevant
evolutionary parameters. Above a certain threshold population size, we show
that the degree of evolutionary "foresight" depends only on a single simple
combination of the relevant parameters.
| [
{
"created": "Fri, 12 Dec 2014 05:35:40 GMT",
"version": "v1"
}
] | 2015-10-06 | [
[
"Ochs",
"Ian E.",
""
],
[
"Desai",
"Michael M.",
""
]
] | On rugged fitness landscapes where sign epistasis is common, adaptation can often involve either individually beneficial "uphill" mutations or more complex mutational trajectories involving fitness valleys or plateaus. The dynamics of the evolutionary process determine the probability that evolution will take any specific path among a variety of competing possible trajectories. Understanding this evolutionary choice is essential if we are to understand the outcomes and predictability of adaptation on rugged landscapes. We present a simple model to analyze the probability that evolution will eschew immediately uphill paths in favor of crossing fitness valleys or plateaus that lead to higher fitness but less accessible genotypes. We calculate how this probability depends on the population size, mutation rates, and relevant selection pressures, and compare our analytical results to Wright-Fisher simulations. We find that the probability of valley crossing depends nonmonotonically on population size: intermediate size populations are most likely to follow a "greedy" strategy of acquiring immediately beneficial mutations even if they lead to evolutionary dead ends, while larger and smaller populations are more likely to cross fitness valleys to reach distant advantageous genotypes. We explicitly identify the boundaries between these different regimes in terms of the relevant evolutionary parameters. Above a certain threshold population size, we show that the degree of evolutionary "foresight" depends only on a single simple combination of the relevant parameters. |
1404.5252 | Wolfram Liebermeister | Wolfram Liebermeister | Enzyme economy and metabolic control | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The metabolic state of a cell, comprising fluxes, metabolite concentrations
and enzyme levels, is shaped by a compromise between metabolic benefit and
enzyme cost. This hypothesis and its consequences can be studied by
computational models and using a theory of metabolic value. In optimal
metabolic states, any increase of an enzyme level must improve the metabolic
performance to justify its own cost, so each active enzyme must contribute to
the cell's benefit by producing valuable products. This principle of value
production leads to variation rules that relate metabolic fluxes and reaction
elasticities to enzyme costs. Metabolic value theory provides a language to
describe this. It postulates a balance of local values, which I derive here
from concepts of metabolic control theory. Economic state variables, called
economic potentials and loads, describe how metabolites, reactions, and enzymes
contribute to metabolic performance. Economic potentials describe the indirect
value of metabolite production, while economic loads describe the indirect
value of metabolite concentrations. These economic variables, and others, are
linked by local balance equations. These laws for optimal metabolic states
define conditions for metabolic fluxes that hold for a wide range of rate laws.
To produce metabolic value, fluxes run from lower to higher economic
potentials, must be free of futile cycles, and satisfy a principle of minimal
weighted fluxes. Given an economical flux mode, one can systematically
construct kinetic models in which all enzymes have positive effects on
metabolic performance.
| [
{
"created": "Mon, 21 Apr 2014 17:45:13 GMT",
"version": "v1"
},
{
"created": "Tue, 4 Oct 2022 08:00:18 GMT",
"version": "v2"
}
] | 2022-10-05 | [
[
"Liebermeister",
"Wolfram",
""
]
] | The metabolic state of a cell, comprising fluxes, metabolite concentrations and enzyme levels, is shaped by a compromise between metabolic benefit and enzyme cost. This hypothesis and its consequences can be studied by computational models and using a theory of metabolic value. In optimal metabolic states, any increase of an enzyme level must improve the metabolic performance to justify its own cost, so each active enzyme must contribute to the cell's benefit by producing valuable products. This principle of value production leads to variation rules that relate metabolic fluxes and reaction elasticities to enzyme costs. Metabolic value theory provides a language to describe this. It postulates a balance of local values, which I derive here from concepts of metabolic control theory. Economic state variables, called economic potentials and loads, describe how metabolites, reactions, and enzymes contribute to metabolic performance. Economic potentials describe the indirect value of metabolite production, while economic loads describe the indirect value of metabolite concentrations. These economic variables, and others, are linked by local balance equations. These laws for optimal metabolic states define conditions for metabolic fluxes that hold for a wide range of rate laws. To produce metabolic value, fluxes run from lower to higher economic potentials, must be free of futile cycles, and satisfy a principle of minimal weighted fluxes. Given an economical flux mode, one can systematically construct kinetic models in which all enzymes have positive effects on metabolic performance. |
2211.09705 | Divyanshu Aggarwal | Divyanshu Aggarwal and Yasha Hasija | A Review of Deep Learning Techniques for Protein Function Prediction | null | 2021 2nd International Conference for Emerging Technology (INCET)
Belgaum, India. May 21-23, 2021 | null | null | q-bio.BM cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Deep Learning and big data have shown tremendous success in bioinformatics
and computational biology in recent years; artificial intelligence methods have
also significantly contributed in the task of protein function classification.
This review paper analyzes the recent developments in approaches for the task
of predicting protein function using deep learning. We explain the importance
of determining the protein function and why automating the following task is
crucial. Then, after reviewing the widely used deep learning techniques for
this task, we continue our review and highlight the emergence of the modern
State of The Art (SOTA) deep learning models which have achieved groundbreaking
results in the field of computer vision, natural language processing and
multi-modal learning in the last few years. We hope that this review will
provide a broad view of the current role and advances of deep learning in
biological sciences, especially in predicting protein function tasks and
encourage new researchers to contribute to this area.
| [
{
"created": "Thu, 27 Oct 2022 20:30:25 GMT",
"version": "v1"
}
] | 2022-11-18 | [
[
"Aggarwal",
"Divyanshu",
""
],
[
"Hasija",
"Yasha",
""
]
] | Deep Learning and big data have shown tremendous success in bioinformatics and computational biology in recent years; artificial intelligence methods have also significantly contributed in the task of protein function classification. This review paper analyzes the recent developments in approaches for the task of predicting protein function using deep learning. We explain the importance of determining the protein function and why automating the following task is crucial. Then, after reviewing the widely used deep learning techniques for this task, we continue our review and highlight the emergence of the modern State of The Art (SOTA) deep learning models which have achieved groundbreaking results in the field of computer vision, natural language processing and multi-modal learning in the last few years. We hope that this review will provide a broad view of the current role and advances of deep learning in biological sciences, especially in predicting protein function tasks and encourage new researchers to contribute to this area. |
0907.4386 | Leonard M. Sander | David A. Kessler and Leonard M. Sander | Fluctuations and Dispersal Rates in Population Dyanmics | 4 pages, 3 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dispersal of species to find a more favorable habitat is important in
population dynamics. Dispersal rates evolve in response to the relative success
of different dispersal strategies. In a simplified deterministic treatment (J.
Dockery, V. Hutson, K. Mischaikow, et al., J. Math. Bio. 37, 61 (1998)) of two
species which differ only in their dispersal rates the slow species always
dominates. We demonstrate that fluctuations can change this conclusion and can
lead to dominance by the fast species or to coexistence, depending on
parameters. We discuss two different effects of fluctuations, and show that our
results are consistent with more complex treatments that find that selected
dispersal rates are not monotonic with the cost of migration.
| [
{
"created": "Fri, 24 Jul 2009 21:50:33 GMT",
"version": "v1"
}
] | 2009-07-28 | [
[
"Kessler",
"David A.",
""
],
[
"Sander",
"Leonard M.",
""
]
] | Dispersal of species to find a more favorable habitat is important in population dynamics. Dispersal rates evolve in response to the relative success of different dispersal strategies. In a simplified deterministic treatment (J. Dockery, V. Hutson, K. Mischaikow, et al., J. Math. Bio. 37, 61 (1998)) of two species which differ only in their dispersal rates the slow species always dominates. We demonstrate that fluctuations can change this conclusion and can lead to dominance by the fast species or to coexistence, depending on parameters. We discuss two different effects of fluctuations, and show that our results are consistent with more complex treatments that find that selected dispersal rates are not monotonic with the cost of migration. |
1910.08784 | James McIntosh | J. R. McIntosh, P. Sajda | Estimation of phase in EEG rhythms for real-time applications | null | null | 10.1088/1741-2552/ab8683 | null | q-bio.QM eess.SP q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objective. We identify two linked problems related to estimating the phase of
the alpha rhythm when the signal after a specific event is unknown (real-time
case), or corrupted (offline analysis). We propose methods to estimate the
phase prior to such events. Approach. Machine learning is used to mimic a
non-causal signal-processing chain with a purely causal one. Main results. We
demonstrate the ability of these methods to estimate instantaneous phase from
an electroencephalography signal subjected to very minor pre-processing with
higher accuracy than more standard signal-processing methods. Significance.
Phase estimation of EEG-rhythms is a challenge due to non-stationarity and low
signal to noise ratio. The methods presented enable scientists and engineers to
achieve relatively low error by optimizing causal phase estimation on a
non-causally processed signal for a real-time experiments and offline analysis.
| [
{
"created": "Sat, 19 Oct 2019 14:56:26 GMT",
"version": "v1"
}
] | 2020-04-07 | [
[
"McIntosh",
"J. R.",
""
],
[
"Sajda",
"P.",
""
]
] | Objective. We identify two linked problems related to estimating the phase of the alpha rhythm when the signal after a specific event is unknown (real-time case), or corrupted (offline analysis). We propose methods to estimate the phase prior to such events. Approach. Machine learning is used to mimic a non-causal signal-processing chain with a purely causal one. Main results. We demonstrate the ability of these methods to estimate instantaneous phase from an electroencephalography signal subjected to very minor pre-processing with higher accuracy than more standard signal-processing methods. Significance. Phase estimation of EEG-rhythms is a challenge due to non-stationarity and low signal to noise ratio. The methods presented enable scientists and engineers to achieve relatively low error by optimizing causal phase estimation on a non-causally processed signal for a real-time experiments and offline analysis. |
1903.05590 | Jennifer Creaser | Jennifer Creaser, Peter Ashwin, Claire Postlethwaite, and Juliane
Britz | Noisy network attractor models for transitions between EEG microstates | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The brain is intrinsically organized into large-scale networks that
constantly re-organize on multiple timescales, even when the brain is at rest.
The timing of these dynamics is crucial for sensation, perception, cognition
and ultimately consciousness, but the underlying dynamics governing the
constant reorganization and switching between networks are not yet well
understood. Functional magnetic resonance imaging (fMRI) and
electroencephalography (EEG) provide anatomical and temporal information about
the resting-state networks (RSNs), respectively. EEG microstates are brief
periods of stable scalp topography, and four distinct configurations with
characteristic switching patterns between them are reliably identified at rest.
Microstates have been identified as the electrophysiological correlate of
fMRI-defined RSNs, this link could be established because EEG microstate
sequences are scale-free and have long-range temporal correlations. This
property is crucial for any approach to model EEG microstates. This paper
proposes a novel modeling approach for microstates: we consider nonlinear
stochastic differential equations (SDEs) that exhibit a noisy network attractor
between nodes that represent the microstates. Using a single layer network
between four states, we can reproduce the transition probabilities between
microstates but not the heavy tailed residence time distributions. Introducing
a two layer network with a hidden layer gives the flexibility to capture these
heavy tails and their long-range temporal correlations. We fit these models to
capture the statistical properties of microstate sequences from EEG data
recorded inside and outside the MRI scanner and show that the processing
required to separate the EEG signal from the fMRI machine noise results in a
loss of information which is reflected in differences in the long tail of the
dwell-time distributions.
| [
{
"created": "Wed, 13 Mar 2019 16:40:59 GMT",
"version": "v1"
}
] | 2019-03-14 | [
[
"Creaser",
"Jennifer",
""
],
[
"Ashwin",
"Peter",
""
],
[
"Postlethwaite",
"Claire",
""
],
[
"Britz",
"Juliane",
""
]
] | The brain is intrinsically organized into large-scale networks that constantly re-organize on multiple timescales, even when the brain is at rest. The timing of these dynamics is crucial for sensation, perception, cognition and ultimately consciousness, but the underlying dynamics governing the constant reorganization and switching between networks are not yet well understood. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide anatomical and temporal information about the resting-state networks (RSNs), respectively. EEG microstates are brief periods of stable scalp topography, and four distinct configurations with characteristic switching patterns between them are reliably identified at rest. Microstates have been identified as the electrophysiological correlate of fMRI-defined RSNs, this link could be established because EEG microstate sequences are scale-free and have long-range temporal correlations. This property is crucial for any approach to model EEG microstates. This paper proposes a novel modeling approach for microstates: we consider nonlinear stochastic differential equations (SDEs) that exhibit a noisy network attractor between nodes that represent the microstates. Using a single layer network between four states, we can reproduce the transition probabilities between microstates but not the heavy tailed residence time distributions. Introducing a two layer network with a hidden layer gives the flexibility to capture these heavy tails and their long-range temporal correlations. We fit these models to capture the statistical properties of microstate sequences from EEG data recorded inside and outside the MRI scanner and show that the processing required to separate the EEG signal from the fMRI machine noise results in a loss of information which is reflected in differences in the long tail of the dwell-time distributions. |
1404.6684 | Domenico Gatti | Greg W. Clark, Sharon H. Ackerman, Elisabeth R. Tillier, Domenico L.
Gatti | Multidimensional mutual information methods for the analysis of
covariation in multiple sequence alignments | 21 pages, 4 figures, 1 table, supporting information containing 2
additional figures is included at the end of the manuscript | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several methods are available for the detection of covarying positions from a
multiple sequence alignment (MSA). If the MSA contains a large number of
sequences, information about the proximities between residues derived from
covariation maps can be sufficient to predict a protein fold. If the structure
is already known, information on the covarying positions can be valuable to
understand the protein mechanism.
In this study we have sought to determine whether a multivariate extension of
traditional mutual information (MI) can be an additional tool to study
covariation. The performance of two multidimensional MI (mdMI) methods,
designed to remove the effect of ternary/quaternary interdependencies, was
tested with a set of 9 MSAs each containing <400 sequences, and was shown to be
comparable to that of methods based on maximum entropy/pseudolikelyhood
statistical models of protein sequences. However, while all the methods tested
detected a similar number of covarying pairs among the residues separated by <
8 {\AA} in the reference X-ray structures, there was on average less than 65%
overlap between the top scoring pairs detected by methods that are based on
different principles.
We have also attempted to identify whether the difference in performance
among methods is due to different efficiency in removing covariation
originating from chains of structural contacts. We found that the reason why
methods that derive partial correlation between the columns of a MSA provide a
better recognition of close contacts is not because they remove chaining
effects, but because they filter out the correlation between distant residues
that originates from general fitness constraints. In contrast we found that
true chaining effects are expression of real physical perturbations that
propagate inside proteins, and therefore are not removed by the derivation of
partial correlation between variables.
| [
{
"created": "Sat, 26 Apr 2014 21:10:57 GMT",
"version": "v1"
}
] | 2014-04-29 | [
[
"Clark",
"Greg W.",
""
],
[
"Ackerman",
"Sharon H.",
""
],
[
"Tillier",
"Elisabeth R.",
""
],
[
"Gatti",
"Domenico L.",
""
]
] | Several methods are available for the detection of covarying positions from a multiple sequence alignment (MSA). If the MSA contains a large number of sequences, information about the proximities between residues derived from covariation maps can be sufficient to predict a protein fold. If the structure is already known, information on the covarying positions can be valuable to understand the protein mechanism. In this study we have sought to determine whether a multivariate extension of traditional mutual information (MI) can be an additional tool to study covariation. The performance of two multidimensional MI (mdMI) methods, designed to remove the effect of ternary/quaternary interdependencies, was tested with a set of 9 MSAs each containing <400 sequences, and was shown to be comparable to that of methods based on maximum entropy/pseudolikelyhood statistical models of protein sequences. However, while all the methods tested detected a similar number of covarying pairs among the residues separated by < 8 {\AA} in the reference X-ray structures, there was on average less than 65% overlap between the top scoring pairs detected by methods that are based on different principles. We have also attempted to identify whether the difference in performance among methods is due to different efficiency in removing covariation originating from chains of structural contacts. We found that the reason why methods that derive partial correlation between the columns of a MSA provide a better recognition of close contacts is not because they remove chaining effects, but because they filter out the correlation between distant residues that originates from general fitness constraints. In contrast we found that true chaining effects are expression of real physical perturbations that propagate inside proteins, and therefore are not removed by the derivation of partial correlation between variables. |
1407.5328 | Dean Wyatte | Dean Wyatte | What happens next and when "next" happens: Mechanisms of spatial and
temporal prediction | Doctoral thesis (May 2014) | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The physics of the environment provide a rich spatiotemporal structure for
our experience. Objects move in predictable ways and their features and
identity remain stable across time and space. How does the brain leverage this
structure to make predictions about and learn from the environment? This thesis
describes research centered around a mechanistic description of sensory
prediction called LeabraTI (TI: Temporal Integration) that explains precisely
how predictive processing is accomplished in neocortical microcircuits. The
fundamental prediction of LeabraTI is that predictions and sensations are
interleaved across the same neural tissue at an overall rate of 10 Hz,
corresponding to the widely studied alpha rhythm of posterior cortex.
Experiments described herein tested this prediction by manipulating the
spatiotemporal properties of three-dimensional object stimuli in a laboratory
setting. EEG results indicated that predictions were subserved by ~10 Hz
oscillations that reliably tracked the onset of stimuli and differentiated
between spatially predictable and unpredictable object sequences. There was a
behavioral advantage for combined spatial and temporal predictability for
discrimination of unlearned objects, but prolonged study of objects under this
combined predictability context impaired discriminability relative to other
learning contexts. This counterintuitive pattern of results was accounted for
by a neural network model that learned three-dimensional viewpoint invariance
with LeabraTI's spatiotemporal prediction rule. Synaptic weight scaling from
prolonged learning built viewpoint invariance, but led to confusion between
ambiguous views of objects, producing slightly lower performance on average.
Overall, this work advances a biological architecture for sensory prediction
accompanied by empirical evidence that supports learning of realistic time- and
space-varying inputs.
| [
{
"created": "Sun, 20 Jul 2014 19:11:51 GMT",
"version": "v1"
}
] | 2014-07-22 | [
[
"Wyatte",
"Dean",
""
]
] | The physics of the environment provide a rich spatiotemporal structure for our experience. Objects move in predictable ways and their features and identity remain stable across time and space. How does the brain leverage this structure to make predictions about and learn from the environment? This thesis describes research centered around a mechanistic description of sensory prediction called LeabraTI (TI: Temporal Integration) that explains precisely how predictive processing is accomplished in neocortical microcircuits. The fundamental prediction of LeabraTI is that predictions and sensations are interleaved across the same neural tissue at an overall rate of 10 Hz, corresponding to the widely studied alpha rhythm of posterior cortex. Experiments described herein tested this prediction by manipulating the spatiotemporal properties of three-dimensional object stimuli in a laboratory setting. EEG results indicated that predictions were subserved by ~10 Hz oscillations that reliably tracked the onset of stimuli and differentiated between spatially predictable and unpredictable object sequences. There was a behavioral advantage for combined spatial and temporal predictability for discrimination of unlearned objects, but prolonged study of objects under this combined predictability context impaired discriminability relative to other learning contexts. This counterintuitive pattern of results was accounted for by a neural network model that learned three-dimensional viewpoint invariance with LeabraTI's spatiotemporal prediction rule. Synaptic weight scaling from prolonged learning built viewpoint invariance, but led to confusion between ambiguous views of objects, producing slightly lower performance on average. Overall, this work advances a biological architecture for sensory prediction accompanied by empirical evidence that supports learning of realistic time- and space-varying inputs. |
2009.07479 | Anisleidy Gonz\'alez-Mitjans | A. Gonz\'alez-Mitjans, D. Paz-Linares, A. Areces-Gonzalez, M. Li, Y.
Wang, ML. Bringas-Vega, and P.A Vald\'es-Sosa | Accurate and efficient Simulation of very high-dimensional Neural Mass
Models with distributed-delay Connectome Tensors | 12 pages, 6 figures, 2 tables | null | null | null | q-bio.NC cs.CE cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces methods and a novel toolbox that efficiently integrates
any high-dimensional Neural Mass Models (NMMs) specified by two essential
components. The first is the set of nonlinear Random Differential Equations of
the dynamics of each neural mass. The second is the highly sparse
three-dimensional Connectome Tensor (CT) that encodes the strength of the
connections and the delays of information transfer along the axons of each
connection. Semi-analytical integration of the RDE is done with the Local
Linearization scheme for each neural mass model, which is the only scheme
guaranteeing dynamical fidelity to the original continuous-time nonlinear
dynamic. It also seamlessly allows modeling distributed delays CT with any
level of complexity or realism, as shown by the Moore-Penrose diagram of the
algorithm. This ease of implementation includes models with distributed-delay
CTs. We achieve high computational efficiency by using a tensor representation
of the model that leverages semi-analytic expressions to integrate the Random
Differential Equations (RDEs) underlying the NMM. We discretized the state
equation with Local Linearization via an algebraic formulation. This approach
increases numerical integration speed and efficiency, a crucial aspect of
large-scale NMM simulations. To illustrate the usefulness of the toolbox, we
simulate both a single Zetterberg-Jansen-Rit (ZJR) cortical column and an
interconnected population of such columns. These examples illustrate the
consequence of modifying the CT in these models, especially by introducing
distributed delays. We provide an open-source Matlab live script for the
toolbox.
| [
{
"created": "Wed, 16 Sep 2020 05:55:17 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Sep 2020 02:17:54 GMT",
"version": "v2"
},
{
"created": "Tue, 23 Feb 2021 07:02:09 GMT",
"version": "v3"
},
{
"created": "Fri, 14 May 2021 05:54:11 GMT",
"version": "v4"
},
{
"created": "Fri, 17 Dec 2021 07:28:09 GMT",
"version": "v5"
},
{
"created": "Fri, 10 Jun 2022 03:05:22 GMT",
"version": "v6"
}
] | 2022-06-13 | [
[
"González-Mitjans",
"A.",
""
],
[
"Paz-Linares",
"D.",
""
],
[
"Areces-Gonzalez",
"A.",
""
],
[
"Li",
"M.",
""
],
[
"Wang",
"Y.",
""
],
[
"Bringas-Vega",
"ML.",
""
],
[
"Valdés-Sosa",
"P. A",
""
]
] | This paper introduces methods and a novel toolbox that efficiently integrates any high-dimensional Neural Mass Models (NMMs) specified by two essential components. The first is the set of nonlinear Random Differential Equations of the dynamics of each neural mass. The second is the highly sparse three-dimensional Connectome Tensor (CT) that encodes the strength of the connections and the delays of information transfer along the axons of each connection. Semi-analytical integration of the RDE is done with the Local Linearization scheme for each neural mass model, which is the only scheme guaranteeing dynamical fidelity to the original continuous-time nonlinear dynamic. It also seamlessly allows modeling distributed delays CT with any level of complexity or realism, as shown by the Moore-Penrose diagram of the algorithm. This ease of implementation includes models with distributed-delay CTs. We achieve high computational efficiency by using a tensor representation of the model that leverages semi-analytic expressions to integrate the Random Differential Equations (RDEs) underlying the NMM. We discretized the state equation with Local Linearization via an algebraic formulation. This approach increases numerical integration speed and efficiency, a crucial aspect of large-scale NMM simulations. To illustrate the usefulness of the toolbox, we simulate both a single Zetterberg-Jansen-Rit (ZJR) cortical column and an interconnected population of such columns. These examples illustrate the consequence of modifying the CT in these models, especially by introducing distributed delays. We provide an open-source Matlab live script for the toolbox. |
1812.03783 | Noemi Kurt | Jochen Blath, Adri\'an Gonz\'alez Casanova, Noemi Kurt, Maite
Wilke-Berenguer | The seed bank coalescent with simultaneous switching | null | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new Wright-Fisher type model for seed banks incorporating
"simultaneous switching", which is motivated by recent work on microbial
dormancy. We show that the simultaneous switching mechanism leads to a new
jump-diffusion limit for the scaled frequency processes, extending the
classical Wright-Fisher and seed bank diffusion limits. We further establish a
new dual coalescent structure with multiple activation and deactivation events
of lineages. While this seems reminiscent of multiple merger events in general
exchangeable coalescents, it actually leads to an entirely new class of
coalescent processes with unique qualitative and quantitative behaviour. To
illustrate this, we provide a novel kind of condition for coming down from
infinity for these coalescents using recent results of Griffiths.
| [
{
"created": "Mon, 10 Dec 2018 13:36:30 GMT",
"version": "v1"
},
{
"created": "Fri, 21 Dec 2018 10:24:57 GMT",
"version": "v2"
}
] | 2018-12-24 | [
[
"Blath",
"Jochen",
""
],
[
"Casanova",
"Adrián González",
""
],
[
"Kurt",
"Noemi",
""
],
[
"Wilke-Berenguer",
"Maite",
""
]
] | We introduce a new Wright-Fisher type model for seed banks incorporating "simultaneous switching", which is motivated by recent work on microbial dormancy. We show that the simultaneous switching mechanism leads to a new jump-diffusion limit for the scaled frequency processes, extending the classical Wright-Fisher and seed bank diffusion limits. We further establish a new dual coalescent structure with multiple activation and deactivation events of lineages. While this seems reminiscent of multiple merger events in general exchangeable coalescents, it actually leads to an entirely new class of coalescent processes with unique qualitative and quantitative behaviour. To illustrate this, we provide a novel kind of condition for coming down from infinity for these coalescents using recent results of Griffiths. |
2204.11747 | Chottiwatt Jittprasong | Chottiwatt Jittprasong (Biomedical Robotics Laboratory, Department of
Biomedical Engineering, City University of Hong Kong) | A feasibility study proposal of the predictive model to enable the
prediction of population susceptibility to COVID-19 by analysis of vaccine
utilization for advising deployment of a booster dose | 4 pages with 7 figures, pdfLaTeX | null | null | null | q-bio.PE cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | With the present highly infectious dominant SARS-CoV-2 strain of B1.1.529 or
Omicron spreading around the globe, there is concern that the COVID-19 pandemic
will not end soon and that it will be a race against time until a more
contagious and virulent variant emerges. One of the most promising approaches
for preventing virus propagation is to maintain continuous high vaccination
efficacy among the population, thereby strengthening the population protective
effect and preventing the majority of infection in the vaccinated population,
as is known to occur with the Omicron variant frequently. Countries must
structure vaccination programs in accordance with their populations'
susceptibility to infection, optimizing vaccination efforts by delivering
vaccines progressively enough to protect the majority of the population. We
present a feasibility study proposal for maintaining optimal continuous
vaccination by assessing the susceptible population, the decline of vaccine
efficacy in the population, and advising booster dosage deployment to maintain
the population's protective efficacy through the use of a predictive model.
Numerous studies have been conducted in the direction of analyzing vaccine
utilization; however, very little study has been conducted to substantiate the
optimal deployment of booster dosage vaccination with the help of a predictive
model based on machine learning algorithms.
| [
{
"created": "Mon, 25 Apr 2022 16:05:59 GMT",
"version": "v1"
}
] | 2022-04-26 | [
[
"Jittprasong",
"Chottiwatt",
"",
"Biomedical Robotics Laboratory, Department of\n Biomedical Engineering, City University of Hong Kong"
]
] | With the present highly infectious dominant SARS-CoV-2 strain of B1.1.529 or Omicron spreading around the globe, there is concern that the COVID-19 pandemic will not end soon and that it will be a race against time until a more contagious and virulent variant emerges. One of the most promising approaches for preventing virus propagation is to maintain continuous high vaccination efficacy among the population, thereby strengthening the population protective effect and preventing the majority of infection in the vaccinated population, as is known to occur with the Omicron variant frequently. Countries must structure vaccination programs in accordance with their populations' susceptibility to infection, optimizing vaccination efforts by delivering vaccines progressively enough to protect the majority of the population. We present a feasibility study proposal for maintaining optimal continuous vaccination by assessing the susceptible population, the decline of vaccine efficacy in the population, and advising booster dosage deployment to maintain the population's protective efficacy through the use of a predictive model. Numerous studies have been conducted in the direction of analyzing vaccine utilization; however, very little study has been conducted to substantiate the optimal deployment of booster dosage vaccination with the help of a predictive model based on machine learning algorithms. |
2004.07937 | Syed Muhammad Usman | Syed Muhammad Usman, Shahzad Latif, Arshad Beg | Principle components analysis for seizures prediction using wavelet
transform | null | null | null | null | q-bio.NC cs.LG eess.SP stat.ML | http://creativecommons.org/licenses/by/4.0/ | Epilepsy is a disease in which frequent seizures occur due to abnormal
activity of neurons. Patients affected by this disease can be treated with the
help of medicines or surgical procedures. However, both of these methods are
not quite useful. The only method to treat epilepsy patients effectively is to
predict the seizure before its onset. It has been observed that abnormal
activity in the brain signals starts before the occurrence of seizure known as
the preictal state. Many researchers have proposed machine learning models for
prediction of epileptic seizures by detecting the start of preictal state.
However, pre-processing, feature extraction and classification remains a great
challenge in the prediction of preictal state. Therefore, we propose a model
that uses common spatial pattern filtering and wavelet transform for
preprocessing, principal component analysis for feature extraction and support
vector machines for detecting preictal state. We have applied our model on 23
subjects and an average sensitivity of 93.1% has been observed for 84 seizures.
| [
{
"created": "Mon, 9 Mar 2020 04:32:57 GMT",
"version": "v1"
}
] | 2020-04-20 | [
[
"Usman",
"Syed Muhammad",
""
],
[
"Latif",
"Shahzad",
""
],
[
"Beg",
"Arshad",
""
]
] | Epilepsy is a disease in which frequent seizures occur due to abnormal activity of neurons. Patients affected by this disease can be treated with the help of medicines or surgical procedures. However, both of these methods are not quite useful. The only method to treat epilepsy patients effectively is to predict the seizure before its onset. It has been observed that abnormal activity in the brain signals starts before the occurrence of seizure known as the preictal state. Many researchers have proposed machine learning models for prediction of epileptic seizures by detecting the start of preictal state. However, pre-processing, feature extraction and classification remains a great challenge in the prediction of preictal state. Therefore, we propose a model that uses common spatial pattern filtering and wavelet transform for preprocessing, principal component analysis for feature extraction and support vector machines for detecting preictal state. We have applied our model on 23 subjects and an average sensitivity of 93.1% has been observed for 84 seizures. |
1907.10790 | Katsuyoshi Matsushita | Katsuyoshi Matsushita and Kazuya Horibe and Naoya Kamamoto and Koichi
Fujimoto | Cell Motion Alignment as Polarity Memory Effect | 6 pages, 3 figures | null | 10.7566/JPSJ.88.103801 | null | q-bio.CB cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The clarification of the motion alignment mechanism in collective cell
migration is an important issue commonly in physics and biology. In analogy
with the self-propelled disk, the polarity memory effect of eukaryotic cell is
a fundamental candidate for this alignment mechanism. In the present paper, we
theoretically examine the polarity memory effect for the motion alignment of
cells on the basis of the cellular Potts model. We show that the polarity
memory effect can align motion of cells. We also find that the polarity memory
effect emerges for the persistent length of cell trajectories longer than
average cell-cell distance.
| [
{
"created": "Thu, 25 Jul 2019 01:44:15 GMT",
"version": "v1"
}
] | 2019-10-02 | [
[
"Matsushita",
"Katsuyoshi",
""
],
[
"Horibe",
"Kazuya",
""
],
[
"Kamamoto",
"Naoya",
""
],
[
"Fujimoto",
"Koichi",
""
]
] | The clarification of the motion alignment mechanism in collective cell migration is an important issue commonly in physics and biology. In analogy with the self-propelled disk, the polarity memory effect of eukaryotic cell is a fundamental candidate for this alignment mechanism. In the present paper, we theoretically examine the polarity memory effect for the motion alignment of cells on the basis of the cellular Potts model. We show that the polarity memory effect can align motion of cells. We also find that the polarity memory effect emerges for the persistent length of cell trajectories longer than average cell-cell distance. |
q-bio/0611044 | Nils Becker | Nils B. Becker and Ralf Everaers | DNA: From rigid base-pairs to semiflexible polymers | 13 pages, 6 figures, 6 tables | null | 10.1103/PhysRevE.76.021923 | null | q-bio.BM | null | The sequence-dependent elasticity of double-helical DNA on a nm length scale
can be captured by the rigid base-pair model, whose strains are the relative
position and orientation of adjacent base-pairs. Corresponding elastic
potentials have been obtained from all-atom MD simulation and from
high-resolution structural data. On the scale of a hundred nm, DNA is
successfully described by a continuous worm-like chain model with homogeneous
elastic properties characterized by a set of four elastic constants, which have
been directly measured in single-molecule experiments. We present here a theory
that links these experiments on different scales, by systematically
coarse-graining the rigid base-pair model for random sequence DNA to an
effective worm-like chain description. The average helical geometry of the
molecule is exactly taken into account in our approach. We find that the
available microscopic parameters sets predict qualitatively similar mesoscopic
parameters. The thermal bending and twisting persistence lengths computed from
MD data are 42 and 48 nm, respectively. The static persistence lengths are
generally much higher, in agreement with cyclization experiments. All
microscopic parameter sets predict negative twist-stretch coupling. The
variability and anisotropy of bending stiffness in short random chains lead to
non-Gaussian bend angle distributions, but become unimportant after two helical
turns.
| [
{
"created": "Wed, 15 Nov 2006 09:13:40 GMT",
"version": "v1"
}
] | 2013-05-29 | [
[
"Becker",
"Nils B.",
""
],
[
"Everaers",
"Ralf",
""
]
] | The sequence-dependent elasticity of double-helical DNA on a nm length scale can be captured by the rigid base-pair model, whose strains are the relative position and orientation of adjacent base-pairs. Corresponding elastic potentials have been obtained from all-atom MD simulation and from high-resolution structural data. On the scale of a hundred nm, DNA is successfully described by a continuous worm-like chain model with homogeneous elastic properties characterized by a set of four elastic constants, which have been directly measured in single-molecule experiments. We present here a theory that links these experiments on different scales, by systematically coarse-graining the rigid base-pair model for random sequence DNA to an effective worm-like chain description. The average helical geometry of the molecule is exactly taken into account in our approach. We find that the available microscopic parameters sets predict qualitatively similar mesoscopic parameters. The thermal bending and twisting persistence lengths computed from MD data are 42 and 48 nm, respectively. The static persistence lengths are generally much higher, in agreement with cyclization experiments. All microscopic parameter sets predict negative twist-stretch coupling. The variability and anisotropy of bending stiffness in short random chains lead to non-Gaussian bend angle distributions, but become unimportant after two helical turns. |
1704.08994 | Jian-Jun Shu | Jian-Jun Shu, Kian-Yan Yong | Fourier-based classification of protein secondary structures | null | Biochemical and Biophysical Research Communications, Vol. 485, No.
4, pp. 731-735, 2017 | 10.1016/j.bbrc.2017.02.117 | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The correct prediction of protein secondary structures is one of the key
issues in predicting the correct protein folded shape, which is used for
determining gene function. Existing methods make use of amino acids properties
as indices to classify protein secondary structures, but are faced with a
significant number of misclassifications. The paper presents a technique for
the classification of protein secondary structures based on protein
"signal-plotting" and the use of the Fourier technique for digital signal
processing. New indices are proposed to classify protein secondary structures
by analyzing hydrophobicity profiles. The approach is simple and
straightforward. Results show that the more types of protein secondary
structures can be classified by means of these newly-proposed indices.
| [
{
"created": "Fri, 28 Apr 2017 16:20:50 GMT",
"version": "v1"
}
] | 2017-05-01 | [
[
"Shu",
"Jian-Jun",
""
],
[
"Yong",
"Kian-Yan",
""
]
] | The correct prediction of protein secondary structures is one of the key issues in predicting the correct protein folded shape, which is used for determining gene function. Existing methods make use of amino acids properties as indices to classify protein secondary structures, but are faced with a significant number of misclassifications. The paper presents a technique for the classification of protein secondary structures based on protein "signal-plotting" and the use of the Fourier technique for digital signal processing. New indices are proposed to classify protein secondary structures by analyzing hydrophobicity profiles. The approach is simple and straightforward. Results show that the more types of protein secondary structures can be classified by means of these newly-proposed indices. |
1611.04193 | Stuart Hagler | Stuart Hagler | Patterns of Selection of Human Movements III: Energy Efficiency,
Mechanical Advantage, and Walking Gait | 21 pages, 5 figures | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human movements are physical processes combining the classical mechanics of
the human body moving in space and the biomechanics of the muscles generating
the forces acting on the body under sophisticated sensory-motor control. One
way to characterize movement performance is through measures of energy
efficiency that relate the mechanical energy of the body and metabolic energy
expended by the muscles. We expect the practical utility of such measures to be
greater when human subjects execute movements that maximize energy efficiency.
We therefore seek to understand if and when subjects select movements with that
maximizing energy efficiency. We proceed using a model-based approach to
describe movements which perform a task requiring the body to add or remove
external mechanical work to or from an object. We use the specific example of
walking gaits doing external mechanical work by pulling a cart, and estimate
the relationship between the avg. walking speed and avg. step length. In the
limit where no external work is done, we find that the estimated maximum energy
efficiency walking gait is much slower than the walking gaits healthy adults
typically select. We then modify the situation of the walking gait by
introducing an idealized mechanical device that creates an adjustable
mechanical advantage. The walking gaits that maximize the energy efficiency
using the optimal mechanical advantage are again much slower than the walking
gaits healthy adults typically select. We finally modify the situation so that
the avg. walking speed is fixed and derive the pattern of the avg. step length
and mechanical advantage that maximize energy efficiency.
| [
{
"created": "Sun, 13 Nov 2016 21:16:15 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Nov 2016 22:50:17 GMT",
"version": "v2"
},
{
"created": "Sun, 9 Dec 2018 19:52:31 GMT",
"version": "v3"
}
] | 2018-12-11 | [
[
"Hagler",
"Stuart",
""
]
] | Human movements are physical processes combining the classical mechanics of the human body moving in space and the biomechanics of the muscles generating the forces acting on the body under sophisticated sensory-motor control. One way to characterize movement performance is through measures of energy efficiency that relate the mechanical energy of the body and metabolic energy expended by the muscles. We expect the practical utility of such measures to be greater when human subjects execute movements that maximize energy efficiency. We therefore seek to understand if and when subjects select movements with that maximizing energy efficiency. We proceed using a model-based approach to describe movements which perform a task requiring the body to add or remove external mechanical work to or from an object. We use the specific example of walking gaits doing external mechanical work by pulling a cart, and estimate the relationship between the avg. walking speed and avg. step length. In the limit where no external work is done, we find that the estimated maximum energy efficiency walking gait is much slower than the walking gaits healthy adults typically select. We then modify the situation of the walking gait by introducing an idealized mechanical device that creates an adjustable mechanical advantage. The walking gaits that maximize the energy efficiency using the optimal mechanical advantage are again much slower than the walking gaits healthy adults typically select. We finally modify the situation so that the avg. walking speed is fixed and derive the pattern of the avg. step length and mechanical advantage that maximize energy efficiency. |
2303.16209 | Kyoungmin Min | Myeonghun Lee and Kyoungmin Min | AmorProt: Amino Acid Molecular Fingerprints Repurposing based Protein
Fingerprint | null | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | As protein therapeutics play an important role in almost all medical fields,
numerous studies have been conducted on proteins using artificial intelligence.
Artificial intelligence has enabled data driven predictions without the need
for expensive experiments. Nevertheless, unlike the various molecular
fingerprint algorithms that have been developed, protein fingerprint algorithms
have rarely been studied. In this study, we proposed the amino acid molecular
fingerprints repurposing based protein (AmorProt) fingerprint, a protein
sequence representation method that effectively uses the molecular fingerprints
corresponding to 20 amino acids. Subsequently, the performances of the tree
based machine learning and artificial neural network models were compared using
(1) amyloid classification and (2) isoelectric point regression. Finally, the
applicability and advantages of the developed platform were demonstrated
through a case study and the following experiments: (3) comparison of dataset
dependence with feature based methods; (4) feature importance analysis; and (5)
protein space analysis. Consequently, the significantly improved model
performance and data set independent versatility of the AmorProt fingerprint
were verified. The results revealed that the current protein representation
method can be applied to various fields related to proteins, such as predicting
their fundamental properties or interaction with ligands.
| [
{
"created": "Mon, 27 Mar 2023 23:57:47 GMT",
"version": "v1"
}
] | 2023-03-30 | [
[
"Lee",
"Myeonghun",
""
],
[
"Min",
"Kyoungmin",
""
]
] | As protein therapeutics play an important role in almost all medical fields, numerous studies have been conducted on proteins using artificial intelligence. Artificial intelligence has enabled data driven predictions without the need for expensive experiments. Nevertheless, unlike the various molecular fingerprint algorithms that have been developed, protein fingerprint algorithms have rarely been studied. In this study, we proposed the amino acid molecular fingerprints repurposing based protein (AmorProt) fingerprint, a protein sequence representation method that effectively uses the molecular fingerprints corresponding to 20 amino acids. Subsequently, the performances of the tree based machine learning and artificial neural network models were compared using (1) amyloid classification and (2) isoelectric point regression. Finally, the applicability and advantages of the developed platform were demonstrated through a case study and the following experiments: (3) comparison of dataset dependence with feature based methods; (4) feature importance analysis; and (5) protein space analysis. Consequently, the significantly improved model performance and data set independent versatility of the AmorProt fingerprint were verified. The results revealed that the current protein representation method can be applied to various fields related to proteins, such as predicting their fundamental properties or interaction with ligands. |
q-bio/0312040 | Peng-Ye Wang | Ping Xie, Shuo-Xing Dou, Peng-Ye Wang | A model for processivity of molecular motors | 10 pages, 7 figures | Chinese Physics, 13, 1569 (2004) | 10.1088/1009-1963/13/9/036 | null | q-bio.BM | null | We propose a two-dimensional model for a complete description of the dynamics
of molecular motors, including both the processive movement along track
filaments and the dissociation from the filaments. The theoretical results on
the distributions of the run length and dwell time at a given ATP
concentration, the dependences of mean run length, mean dwell time and mean
velocity on ATP concentration and load are in good agreement with the previous
experimental results.
| [
{
"created": "Thu, 25 Dec 2003 09:58:52 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Xie",
"Ping",
""
],
[
"Dou",
"Shuo-Xing",
""
],
[
"Wang",
"Peng-Ye",
""
]
] | We propose a two-dimensional model for a complete description of the dynamics of molecular motors, including both the processive movement along track filaments and the dissociation from the filaments. The theoretical results on the distributions of the run length and dwell time at a given ATP concentration, the dependences of mean run length, mean dwell time and mean velocity on ATP concentration and load are in good agreement with the previous experimental results. |
1402.0511 | Charles Fisher | Charles K. Fisher, Pankaj Mehta | Identifying Keystone Species in the Human Gut Microbiome from
Metagenomic Timeseries using Sparse Linear Regression | null | null | 10.1371/journal.pone.0102451 | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human associated microbial communities exert tremendous influence over human
health and disease. With modern metagenomic sequencing methods it is possible
to follow the relative abundance of microbes in a community over time. These
microbial communities exhibit rich ecological dynamics and an important goal of
microbial ecology is to infer the interactions between species from sequence
data. Any algorithm for inferring species interactions must overcome three
obstacles: 1) a correlation between the abundances of two species does not
imply that those species are interacting, 2) the sum constraint on the relative
abundances obtained from metagenomic studies makes it difficult to infer the
parameters in timeseries models, and 3) errors due to experimental uncertainty,
or mis-assignment of sequencing reads into operational taxonomic units, bias
inferences of species interactions. Here we introduce an approach, Learning
Interactions from MIcrobial Time Series (LIMITS), that overcomes these
obstacles. LIMITS uses sparse linear regression with boostrap aggregation to
infer a discrete-time Lotka-Volterra model for microbial dynamics. We tested
LIMITS on synthetic data and showed that it could reliably infer the topology
of the inter-species ecological interactions. We then used LIMITS to
characterize the species interactions in the gut microbiomes of two individuals
and found that the interaction networks varied significantly between
individuals. Furthermore, we found that the interaction networks of the two
individuals are dominated by distinct "keystone species", Bacteroides fragilis
and Bacteroided stercosis, that have a disproportionate influence on the
structure of the gut microbiome even though they are only found in moderate
abundance. Based on our results, we hypothesize that the abundances of certain
keystone species may be responsible for individuality in the human gut
microbiome.
| [
{
"created": "Mon, 3 Feb 2014 21:00:29 GMT",
"version": "v1"
}
] | 2015-06-18 | [
[
"Fisher",
"Charles K.",
""
],
[
"Mehta",
"Pankaj",
""
]
] | Human associated microbial communities exert tremendous influence over human health and disease. With modern metagenomic sequencing methods it is possible to follow the relative abundance of microbes in a community over time. These microbial communities exhibit rich ecological dynamics and an important goal of microbial ecology is to infer the interactions between species from sequence data. Any algorithm for inferring species interactions must overcome three obstacles: 1) a correlation between the abundances of two species does not imply that those species are interacting, 2) the sum constraint on the relative abundances obtained from metagenomic studies makes it difficult to infer the parameters in timeseries models, and 3) errors due to experimental uncertainty, or mis-assignment of sequencing reads into operational taxonomic units, bias inferences of species interactions. Here we introduce an approach, Learning Interactions from MIcrobial Time Series (LIMITS), that overcomes these obstacles. LIMITS uses sparse linear regression with boostrap aggregation to infer a discrete-time Lotka-Volterra model for microbial dynamics. We tested LIMITS on synthetic data and showed that it could reliably infer the topology of the inter-species ecological interactions. We then used LIMITS to characterize the species interactions in the gut microbiomes of two individuals and found that the interaction networks varied significantly between individuals. Furthermore, we found that the interaction networks of the two individuals are dominated by distinct "keystone species", Bacteroides fragilis and Bacteroided stercosis, that have a disproportionate influence on the structure of the gut microbiome even though they are only found in moderate abundance. Based on our results, we hypothesize that the abundances of certain keystone species may be responsible for individuality in the human gut microbiome. |
1810.01452 | Arni S.R. Srinivasa Rao | Arni S.R. Srinivasa Rao | A Partition Theorem for a Randomly Selected Large Population | 12 pages, 4 figures. A new result in population dynamics | Acta Biotheoretica (Springer) 2021 | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We state and prove a theorem on the partitioning of a randomly selected large
population into stationary and non-stationary components by using a property of
stationary population identity. Applications of this theorem for practical
purposes is summarized at the end.
| [
{
"created": "Tue, 2 Oct 2018 18:47:37 GMT",
"version": "v1"
},
{
"created": "Mon, 22 Oct 2018 02:07:38 GMT",
"version": "v2"
},
{
"created": "Thu, 1 Jul 2021 17:16:42 GMT",
"version": "v3"
}
] | 2021-10-20 | [
[
"Rao",
"Arni S. R. Srinivasa",
""
]
] | We state and prove a theorem on the partitioning of a randomly selected large population into stationary and non-stationary components by using a property of stationary population identity. Applications of this theorem for practical purposes is summarized at the end. |
1201.2900 | Mikhail Peslyak | Mikhail Peslyak | Model of pathogenesis of psoriasis. Part 2. Local processes | English edition e1.3, Russia, Moscow, MYPE, 2012, 110 pages, 30
figures, ISBN 9785905504044 | null | null | null | q-bio.CB q-bio.TO | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Analytical research of results of experimental and theoretical studies on
pathogenesis of psoriatic disease is carried out. The new model of pathogenesis
- skin reaction to systemic psoriatic process SPP is formulated. ... Psoriatic
inflammation is regarded as a reaction of the skin immune system to activity of
Mo-R and DC-R involved in derma from blood flow. They contain Y-antigen and,
getting to derma, can be transformed in mature maDC-Y and present this antigen
to Y-specific T-lymphocytes as well as activate them. Y-antigen is a part of
the interpeptide bridge IB-Y. Therefore, the skin immune system can incorrectly
interpret Y-antigen presentation as a sign of external PsB-infection and switch
one of mechanisms of protection against bacterial infection - epidermal
hyperproliferation. Psoriatic plaque can be initiated only during action of
local inflammatory process LP2 in derma causing not only innate, but also
adaptive response. In particular, it is possible at LP2(IN) - open trauma of
derma or at LP2(HPV) - HPV-carriage of keratinocytes. The level of Y-priming
(presence and concentration of Y-specific T-lymphocytes in prepsoriatic derma
and in lymph nodes) also determines possibility of psoriatic plaque initiation.
Existence and severity of psoriatic plaque is determined by intensity of
Y-antigen income into derma (inside Mo-R and DC-R). ... Severity of plaque is
aggravated by LP2-inflammation if it persists after this plaque initiation. New
Mo-T, DC-T (incl. Mo-R, DC-R) and Y-specific T-lymphocytes are constantly
attracted into plaques from blood flow, and so support vicious cycles. Only at
decrease of SPP severity, these vicious cycles weaken and natural remission of
plaques takes place, up to their complete disappearance. The detailed analysis
comparing the new model of pathogenesis with five other previously published
models is carried out. Part 1. arXiv:1110.0584
| [
{
"created": "Fri, 13 Jan 2012 17:31:10 GMT",
"version": "v1"
},
{
"created": "Sat, 14 Apr 2012 16:47:02 GMT",
"version": "v2"
}
] | 2012-04-20 | [
[
"Peslyak",
"Mikhail",
""
]
] | Analytical research of results of experimental and theoretical studies on pathogenesis of psoriatic disease is carried out. The new model of pathogenesis - skin reaction to systemic psoriatic process SPP is formulated. ... Psoriatic inflammation is regarded as a reaction of the skin immune system to activity of Mo-R and DC-R involved in derma from blood flow. They contain Y-antigen and, getting to derma, can be transformed in mature maDC-Y and present this antigen to Y-specific T-lymphocytes as well as activate them. Y-antigen is a part of the interpeptide bridge IB-Y. Therefore, the skin immune system can incorrectly interpret Y-antigen presentation as a sign of external PsB-infection and switch one of mechanisms of protection against bacterial infection - epidermal hyperproliferation. Psoriatic plaque can be initiated only during action of local inflammatory process LP2 in derma causing not only innate, but also adaptive response. In particular, it is possible at LP2(IN) - open trauma of derma or at LP2(HPV) - HPV-carriage of keratinocytes. The level of Y-priming (presence and concentration of Y-specific T-lymphocytes in prepsoriatic derma and in lymph nodes) also determines possibility of psoriatic plaque initiation. Existence and severity of psoriatic plaque is determined by intensity of Y-antigen income into derma (inside Mo-R and DC-R). ... Severity of plaque is aggravated by LP2-inflammation if it persists after this plaque initiation. New Mo-T, DC-T (incl. Mo-R, DC-R) and Y-specific T-lymphocytes are constantly attracted into plaques from blood flow, and so support vicious cycles. Only at decrease of SPP severity, these vicious cycles weaken and natural remission of plaques takes place, up to their complete disappearance. The detailed analysis comparing the new model of pathogenesis with five other previously published models is carried out. Part 1. arXiv:1110.0584 |
0911.1844 | Anne Feltz | Norbert Weiss, Christophe Arnoult, Anne Feltz (NEURO), Michel De Waard | Contribution of the kinetics of G protein dissociation to the
characteristic modifications of N-type calcium channel activity | null | Neuroscience Research 56, 3 (2006) 332-43 | 10.1016/j.neures.2006.08.002 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Direct G protein inhibition of N-type calcium channels is recognized by
characteristic biophysical modifications. In this study, we quantify and
simulate the importance of G protein dissociation on the phenotype of G
protein-regulated whole-cell currents. Based on the observation that the
voltage-dependence of the time constant of recovery from G protein inhibition
is correlated with the voltage-dependence of channel opening, we depict all G
protein effects by a simple kinetic scheme. All landmark modifications in
calcium currents, except inhibition, can be successfully described using three
simple biophysical parameters (extent of block, extent of recovery, and time
constant of recovery). Modifications of these parameters by auxiliary beta
subunits are at the origin of differences in N-type channel regulation by G
proteins. The simulation data illustrate that channel reluctance can occur as
the result of an experimental bias linked to the variable extent of G protein
dissociation when peak currents are measured at various membrane potentials. To
produce alterations in channel kinetics, the two most important parameters are
the extents of initial block and recovery. These data emphasize the
contribution of the degree and kinetics of G protein dissociation in the
modification of N-type currents.
| [
{
"created": "Tue, 10 Nov 2009 07:28:18 GMT",
"version": "v1"
}
] | 2009-11-11 | [
[
"Weiss",
"Norbert",
"",
"NEURO"
],
[
"Arnoult",
"Christophe",
"",
"NEURO"
],
[
"Feltz",
"Anne",
"",
"NEURO"
],
[
"De Waard",
"Michel",
""
]
] | Direct G protein inhibition of N-type calcium channels is recognized by characteristic biophysical modifications. In this study, we quantify and simulate the importance of G protein dissociation on the phenotype of G protein-regulated whole-cell currents. Based on the observation that the voltage-dependence of the time constant of recovery from G protein inhibition is correlated with the voltage-dependence of channel opening, we depict all G protein effects by a simple kinetic scheme. All landmark modifications in calcium currents, except inhibition, can be successfully described using three simple biophysical parameters (extent of block, extent of recovery, and time constant of recovery). Modifications of these parameters by auxiliary beta subunits are at the origin of differences in N-type channel regulation by G proteins. The simulation data illustrate that channel reluctance can occur as the result of an experimental bias linked to the variable extent of G protein dissociation when peak currents are measured at various membrane potentials. To produce alterations in channel kinetics, the two most important parameters are the extents of initial block and recovery. These data emphasize the contribution of the degree and kinetics of G protein dissociation in the modification of N-type currents. |
2407.19328 | Nicholas Dimonaco | Nicholas J. Dimonaco | PyamilySeq: A Python Tool for Interpretable Gene (Re)Clustering and
Pangenomic Inference Across Species and Genera | See here for installation and use:
https://pypi.org/project/PyamilySeq/ | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | PyamilySeq is a Python-based tool designed for interpretable gene clustering
and pangenomic inference, supporting analyses at both species and genus levels.
It facilitates the clustering of gene sequences into families based on sequence
similarity using CD-HIT, and can take the output of tried-and-tested sequence
clustering tools such as CD-HIT, BLAST, DIAMOND, and MMseqs2. PyamilySeq is
distinctive in its ability to integrate new sequences into existing clusters,
providing a robust framework for iterative analysis while preserving the
original clusters, useful when reannotating genomes. In addition to the
standard Species mode which as with other tools performs core-gene analysis
across a species range, PyamilySeq can be run in Genus mode where it detects
the presence of gene families shared across multiple genera. These features
enhance the tools applicability for ongoing and past genomic studies and
comparative analyses. PyamilySeq generates comprehensive outputs, including
gene presence-absence matrices and aligned sequence data, enabling downstream
analysis and interpretation of the identified gene groups and pangenomic data.
| [
{
"created": "Sat, 27 Jul 2024 19:32:35 GMT",
"version": "v1"
}
] | 2024-07-30 | [
[
"Dimonaco",
"Nicholas J.",
""
]
] | PyamilySeq is a Python-based tool designed for interpretable gene clustering and pangenomic inference, supporting analyses at both species and genus levels. It facilitates the clustering of gene sequences into families based on sequence similarity using CD-HIT, and can take the output of tried-and-tested sequence clustering tools such as CD-HIT, BLAST, DIAMOND, and MMseqs2. PyamilySeq is distinctive in its ability to integrate new sequences into existing clusters, providing a robust framework for iterative analysis while preserving the original clusters, useful when reannotating genomes. In addition to the standard Species mode which as with other tools performs core-gene analysis across a species range, PyamilySeq can be run in Genus mode where it detects the presence of gene families shared across multiple genera. These features enhance the tools applicability for ongoing and past genomic studies and comparative analyses. PyamilySeq generates comprehensive outputs, including gene presence-absence matrices and aligned sequence data, enabling downstream analysis and interpretation of the identified gene groups and pangenomic data. |
1810.03282 | Suman Kumar Banik | Mintu Nandi, Ayan Biswas, Suman K Banik and Pinaki Chaudhury | Information processing in a simple one-step cascade | 26 pages, 5 figures | null | 10.1103/PhysRevE.98.042310 | null | q-bio.MN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Using the formalism of information theory, we analyze the mechanism of
information transduction in a simple one-step signaling cascade S$\rightarrow$X
representing the gene regulatory network. Approximating the signaling channel
to be Gaussian, we describe the dynamics using Langevin equations. Upon
discretization, we calculate the associated second moments for linear and
nonlinear regulation of the output by the input, which follows the birth-death
process. While mutual information between the input and the output
characterizes the channel capacity, the Fano factor of the output gives a clear
idea of how internal and external fluctuations assemble at the output level. To
quantify the contribution of the present state of the input to predict the
future output, transfer entropy is computed. We find that higher amount of
transfer entropy is accompanied by the greater magnitude of external
fluctuations (quantified by the Fano factor of the output) propagation from the
input to the output. We notice that low input population characterized by the
number of signaling molecules S, which fluctuates in a relatively slower
fashion compared to its downstream (target) species X, is maximally able to
predict (as quantified by transfer entropy) the future state of the output. Our
computations also reveal that with increased linear nature of the input-output
interaction, all three metrics of mutual information, Fano factor and, transfer
entropy achieve relatively larger magnitudes.
| [
{
"created": "Mon, 8 Oct 2018 06:44:40 GMT",
"version": "v1"
}
] | 2018-11-14 | [
[
"Nandi",
"Mintu",
""
],
[
"Biswas",
"Ayan",
""
],
[
"Banik",
"Suman K",
""
],
[
"Chaudhury",
"Pinaki",
""
]
] | Using the formalism of information theory, we analyze the mechanism of information transduction in a simple one-step signaling cascade S$\rightarrow$X representing the gene regulatory network. Approximating the signaling channel to be Gaussian, we describe the dynamics using Langevin equations. Upon discretization, we calculate the associated second moments for linear and nonlinear regulation of the output by the input, which follows the birth-death process. While mutual information between the input and the output characterizes the channel capacity, the Fano factor of the output gives a clear idea of how internal and external fluctuations assemble at the output level. To quantify the contribution of the present state of the input to predict the future output, transfer entropy is computed. We find that higher amount of transfer entropy is accompanied by the greater magnitude of external fluctuations (quantified by the Fano factor of the output) propagation from the input to the output. We notice that low input population characterized by the number of signaling molecules S, which fluctuates in a relatively slower fashion compared to its downstream (target) species X, is maximally able to predict (as quantified by transfer entropy) the future state of the output. Our computations also reveal that with increased linear nature of the input-output interaction, all three metrics of mutual information, Fano factor and, transfer entropy achieve relatively larger magnitudes. |
2107.13709 | Tom Chou | Mingtao Xia, Lucas B\"ottcher, Tom Chou | Controlling epidemics through optimal allocation of test kits and
vaccine doses across networks | 13 pages, 8 figures, Submitted to IEEE Transactions on Network
Science and Engineering | IEEE Trans. Netw. Sci. Eng. 9, 1422-1436 (2022) | 10.1109/TNSE.2022.3144624 | null | q-bio.PE cs.SI math.OC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Efficient testing and vaccination protocols are critical aspects of epidemic
management. To study the optimal allocation of limited testing and vaccination
resources in a heterogeneous contact network of interacting susceptible,
recovered, and infected individuals, we present a degree-based testing and
vaccination model for which we use control-theoretic methods to derive optimal
testing and vaccination policies. Within our framework, we find that optimal
intervention policies first target high-degree nodes before shifting to
lower-degree nodes in a time-dependent manner. Using such optimal policies, it
is possible to delay outbreaks and reduce incidence rates to a greater extent
than uniform and reinforcement-learning-based interventions, particularly on
certain scale-free networks.
| [
{
"created": "Thu, 29 Jul 2021 02:23:45 GMT",
"version": "v1"
},
{
"created": "Sat, 31 Jul 2021 01:57:19 GMT",
"version": "v2"
}
] | 2022-06-22 | [
[
"Xia",
"Mingtao",
""
],
[
"Böttcher",
"Lucas",
""
],
[
"Chou",
"Tom",
""
]
] | Efficient testing and vaccination protocols are critical aspects of epidemic management. To study the optimal allocation of limited testing and vaccination resources in a heterogeneous contact network of interacting susceptible, recovered, and infected individuals, we present a degree-based testing and vaccination model for which we use control-theoretic methods to derive optimal testing and vaccination policies. Within our framework, we find that optimal intervention policies first target high-degree nodes before shifting to lower-degree nodes in a time-dependent manner. Using such optimal policies, it is possible to delay outbreaks and reduce incidence rates to a greater extent than uniform and reinforcement-learning-based interventions, particularly on certain scale-free networks. |
1712.04223 | Andrew Francis | Andrew Francis and Vincent Moulton | Identifiability of tree-child phylogenetic networks under a
probabilistic recombination-mutation model of evolution | 18 pages, 4 figures | Journal of Theoretical Biology, Volume 446, 7 June 2018, Pages
160-167 | 10.1016/j.jtbi.2018.03.011 | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phylogenetic networks are an extension of phylogenetic trees which are used
to represent evolutionary histories in which reticulation events (such as
recombination and hybridization) have occurred. A central question for such
networks is that of identifiability, which essentially asks under what
circumstances can we reliably identify the phylogenetic network that gave rise
to the observed data? Recently, identifiability results have appeared for
networks relative to a model of sequence evolution that generalizes the
standard Markov models used for phylogenetic trees. However, these results are
quite limited in terms of the complexity of the networks that are considered.
In this paper, by introducing an alternative probabilistic model for evolution
along a network that is based on some ground-breaking work by Thatte for
pedigrees, we are able to obtain an identifiability result for a much larger
class of phylogenetic networks (essentially the class of so-called tree-child
networks). To prove our main theorem, we derive some new results for
identifying tree-child networks combinatorially, and then adapt some techniques
developed by Thatte for pedigrees to show that our combinatorial results imply
identifiability in the probabilistic setting. We hope that the introduction of
our new model for networks could lead to new approaches to reliably construct
phylogenetic networks.
| [
{
"created": "Tue, 12 Dec 2017 10:51:45 GMT",
"version": "v1"
}
] | 2018-03-28 | [
[
"Francis",
"Andrew",
""
],
[
"Moulton",
"Vincent",
""
]
] | Phylogenetic networks are an extension of phylogenetic trees which are used to represent evolutionary histories in which reticulation events (such as recombination and hybridization) have occurred. A central question for such networks is that of identifiability, which essentially asks under what circumstances can we reliably identify the phylogenetic network that gave rise to the observed data? Recently, identifiability results have appeared for networks relative to a model of sequence evolution that generalizes the standard Markov models used for phylogenetic trees. However, these results are quite limited in terms of the complexity of the networks that are considered. In this paper, by introducing an alternative probabilistic model for evolution along a network that is based on some ground-breaking work by Thatte for pedigrees, we are able to obtain an identifiability result for a much larger class of phylogenetic networks (essentially the class of so-called tree-child networks). To prove our main theorem, we derive some new results for identifying tree-child networks combinatorially, and then adapt some techniques developed by Thatte for pedigrees to show that our combinatorial results imply identifiability in the probabilistic setting. We hope that the introduction of our new model for networks could lead to new approaches to reliably construct phylogenetic networks. |
1801.08366 | Stephen Coombes | S Coombes, Y-M Lai, M Sayli and R Thul | Networks of piecewise linear neural mass models | null | null | null | null | q-bio.NC math.DS nlin.AO | http://creativecommons.org/licenses/by/4.0/ | Neural mass models are ubiquitous in large scale brain modelling. At the node
level they are written in terms of a set of ODEs with a nonlinearity that is
typically a sigmoidal shape. Using structural data from brain atlases they may
be connected into a network to investigate the emergence of functional dynamic
states, such as synchrony. With the simple restriction of the classic sigmoidal
nonlinearity to a piecewise linear caricature we show that the famous
Wilson-Cowan neural mass model can be analysed at both the node and network
level. The construction of periodic orbits at the node level is achieved by
patching together matrix exponential solutions, and stability is determined
using Floquet theory. For networks with interactions described by circulant
matrices, we show that the stability of the synchronous state can be determined
in terms of a low-dimensional Floquet problem parameterised by the eigenvalues
of the interaction matrix. This network Floquet problem is readily solved using
linear algebra, to predict the onset of spatio-temporal network patterns
arising from a synchronous instability. We consider the case of a discontinuous
choice for the node nonlinearity, namely the replacement of the sigmoid by a
Heaviside nonlinearity. This gives rise to a continuous-time switching network.
At the node level this allows for the existence of unstable sliding periodic
orbits, which we construct. The stability of a periodic orbit is now treated
with a modification of Floquet theory to treat the evolution of small
perturbations through switching manifolds via saltation matrices. At the
network level the stability analysis of the synchronous state is considerably
more challenging. Here we report on the use of ideas originally developed for
the study of Glass networks to treat the stability of periodic network states
in neural mass models with discontinuous interactions.
| [
{
"created": "Thu, 25 Jan 2018 11:55:24 GMT",
"version": "v1"
}
] | 2018-01-26 | [
[
"Coombes",
"S",
""
],
[
"Lai",
"Y-M",
""
],
[
"Sayli",
"M",
""
],
[
"Thul",
"R",
""
]
] | Neural mass models are ubiquitous in large scale brain modelling. At the node level they are written in terms of a set of ODEs with a nonlinearity that is typically a sigmoidal shape. Using structural data from brain atlases they may be connected into a network to investigate the emergence of functional dynamic states, such as synchrony. With the simple restriction of the classic sigmoidal nonlinearity to a piecewise linear caricature we show that the famous Wilson-Cowan neural mass model can be analysed at both the node and network level. The construction of periodic orbits at the node level is achieved by patching together matrix exponential solutions, and stability is determined using Floquet theory. For networks with interactions described by circulant matrices, we show that the stability of the synchronous state can be determined in terms of a low-dimensional Floquet problem parameterised by the eigenvalues of the interaction matrix. This network Floquet problem is readily solved using linear algebra, to predict the onset of spatio-temporal network patterns arising from a synchronous instability. We consider the case of a discontinuous choice for the node nonlinearity, namely the replacement of the sigmoid by a Heaviside nonlinearity. This gives rise to a continuous-time switching network. At the node level this allows for the existence of unstable sliding periodic orbits, which we construct. The stability of a periodic orbit is now treated with a modification of Floquet theory to treat the evolution of small perturbations through switching manifolds via saltation matrices. At the network level the stability analysis of the synchronous state is considerably more challenging. Here we report on the use of ideas originally developed for the study of Glass networks to treat the stability of periodic network states in neural mass models with discontinuous interactions. |
1112.1733 | Petter Holme | Sungmin Lee, Petter Holme, Zhi-Xi Wu | Cooperation, structure and hierarchy in multiadaptive games | null | Phys. Rev. E. 84, 061148 (2011) | 10.1103/PhysRevE.84.061148 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Game-theoretical models where the rules of the game and the interaction
structure both coevolves with the game dynamics -- multiadaptive games --
capture very flexible situations where cooperation among selfish agents can
emerge. In this work, we will discuss a multiadaptive model presented in a
recent Letter [Phys. Rev. Lett. 106, 028702 (2011)], and generalizations of it.
The model captures a non-equilibrium situation where social unrest increases
the incentive to cooperate and, simultaneously, agents are partly free to
influence with whom they interact. First, we investigate the details of how the
feedback from the behavior of agents determines the emergence of cooperation
and hierarchical contact structures. We also study the stability of the system
to different types of noise, and find that different regions of parameter space
show very different response. Some types of noise can destroy an all-cooperator
state. If, on the other hand, hubs are stable, then so is the all-C state.
Finally, we investigate the dependence of the ratio between the timescales of
strategy updates and the evolution of the interaction structure. We find that a
comparatively fast strategy dynamics is a prerequisite for the emergence of
cooperation.
| [
{
"created": "Wed, 7 Dec 2011 23:30:13 GMT",
"version": "v1"
}
] | 2012-10-10 | [
[
"Lee",
"Sungmin",
""
],
[
"Holme",
"Petter",
""
],
[
"Wu",
"Zhi-Xi",
""
]
] | Game-theoretical models where the rules of the game and the interaction structure both coevolves with the game dynamics -- multiadaptive games -- capture very flexible situations where cooperation among selfish agents can emerge. In this work, we will discuss a multiadaptive model presented in a recent Letter [Phys. Rev. Lett. 106, 028702 (2011)], and generalizations of it. The model captures a non-equilibrium situation where social unrest increases the incentive to cooperate and, simultaneously, agents are partly free to influence with whom they interact. First, we investigate the details of how the feedback from the behavior of agents determines the emergence of cooperation and hierarchical contact structures. We also study the stability of the system to different types of noise, and find that different regions of parameter space show very different response. Some types of noise can destroy an all-cooperator state. If, on the other hand, hubs are stable, then so is the all-C state. Finally, we investigate the dependence of the ratio between the timescales of strategy updates and the evolution of the interaction structure. We find that a comparatively fast strategy dynamics is a prerequisite for the emergence of cooperation. |
2112.13273 | Seyedeh Sajedeh Mousavi Dr | S. Sajedeh Mousavi, Mohammad Jalil Zorriehzahra | High Expression of CDK1 and NDC80 Predicts Poor Prognosis of Bladder
Cancer | Talk in 4th International biotechnology congress of Islamic Republic
of Iran (2021) | null | null | null | q-bio.GN q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | Background: Bladder cancer is the 10th most common cancer worldwide, and its
prevalence is increasing, especially in developing countries. Objective: In the
present study, we employed gene expression profiles from the GSE163209 data set
in the GEO database to identify potential molecular and genetic markers in BC
patients. Methods: The data set comprised 217 samples, with 113 stage Ta tumor
tissue samples and 104 stage T1 tumor tissue samples. The top 766 genes were
chosen. P.value<0.0001 and |logFC|=1 was used to change the cutoff criteria for
defining DEGs. Moreover, the MCODE plugin and cytoHubba plugin were employed to
produce a module and detect 20 hub genes in these DEGs. We used GO and KEGG
pathway enrichment analyses to get a better understanding of these DEGs.
Results: The KEGG pathway enrichment results indicated that the top genes were
mainly involved: Systemic lupus erythematosus, Alcoholism, and Viral
carcinogenesis. SLE activation in the renal glomeruli could explain the
connection between this disease's route and bladder cancer, and according to
our results and previous researches, heavy alcohol intake can increase the risk
of BC in males and particular populations. Conclusion: According to our hub
genes, we can consider CDK1 and NDC80 as bladder cancer biomarkers. Not much
research has been done on the effect of this gene on bladder cancer.
| [
{
"created": "Sat, 25 Dec 2021 19:25:47 GMT",
"version": "v1"
}
] | 2021-12-28 | [
[
"Mousavi",
"S. Sajedeh",
""
],
[
"Zorriehzahra",
"Mohammad Jalil",
""
]
] | Background: Bladder cancer is the 10th most common cancer worldwide, and its prevalence is increasing, especially in developing countries. Objective: In the present study, we employed gene expression profiles from the GSE163209 data set in the GEO database to identify potential molecular and genetic markers in BC patients. Methods: The data set comprised 217 samples, with 113 stage Ta tumor tissue samples and 104 stage T1 tumor tissue samples. The top 766 genes were chosen. P.value<0.0001 and |logFC|=1 was used to change the cutoff criteria for defining DEGs. Moreover, the MCODE plugin and cytoHubba plugin were employed to produce a module and detect 20 hub genes in these DEGs. We used GO and KEGG pathway enrichment analyses to get a better understanding of these DEGs. Results: The KEGG pathway enrichment results indicated that the top genes were mainly involved: Systemic lupus erythematosus, Alcoholism, and Viral carcinogenesis. SLE activation in the renal glomeruli could explain the connection between this disease's route and bladder cancer, and according to our results and previous researches, heavy alcohol intake can increase the risk of BC in males and particular populations. Conclusion: According to our hub genes, we can consider CDK1 and NDC80 as bladder cancer biomarkers. Not much research has been done on the effect of this gene on bladder cancer. |
1310.5202 | Omur Arslan | Omur Arslan, Dan P. Guralnik and Daniel E. Koditschek | Discriminative Measures for Comparison of Phylogenetic Trees | 24 pages, 7 figures, 1 table, a new graph-theoretic formulation of
the NNI navigation dissimilarity | null | null | null | q-bio.PE cs.CE cs.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we introduce and study three new measures for efficient
discriminative comparison of phylogenetic trees. The NNI navigation
dissimilarity $d_{nav}$ counts the steps along a "combing" of the Nearest
Neighbor Interchange (NNI) graph of binary hierarchies, providing an efficient
approximation to the (NP-hard) NNI distance in terms of "edit length". At the
same time, a closed form formula for $d_{nav}$ presents it as a weighted count
of pairwise incompatibilities between clusters, lending it the character of an
edge dissimilarity measure as well. A relaxation of this formula to a simple
count yields another measure on all trees --- the crossing dissimilarity
$d_{CM}$. Both dissimilarities are symmetric and positive definite (vanish only
between identical trees) on binary hierarchies but they fail to satisfy the
triangle inequality. Nevertheless, both are bounded below by the widely used
Robinson-Foulds metric and bounded above by a closely related true metric, the
cluster-cardinality metric $d_{CC}$. We show that each of the three proposed
new dissimilarities is computable in time $O(n^2)$ in the number of leaves $n$,
and conclude the paper with a brief numerical exploration of the distribution
over tree space of these dissimilarities in comparison with the Robinson-Foulds
metric and the more recently introduced matching-split distance.
| [
{
"created": "Sat, 19 Oct 2013 05:03:23 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Oct 2015 14:10:43 GMT",
"version": "v2"
}
] | 2015-10-21 | [
[
"Arslan",
"Omur",
""
],
[
"Guralnik",
"Dan P.",
""
],
[
"Koditschek",
"Daniel E.",
""
]
] | In this paper we introduce and study three new measures for efficient discriminative comparison of phylogenetic trees. The NNI navigation dissimilarity $d_{nav}$ counts the steps along a "combing" of the Nearest Neighbor Interchange (NNI) graph of binary hierarchies, providing an efficient approximation to the (NP-hard) NNI distance in terms of "edit length". At the same time, a closed form formula for $d_{nav}$ presents it as a weighted count of pairwise incompatibilities between clusters, lending it the character of an edge dissimilarity measure as well. A relaxation of this formula to a simple count yields another measure on all trees --- the crossing dissimilarity $d_{CM}$. Both dissimilarities are symmetric and positive definite (vanish only between identical trees) on binary hierarchies but they fail to satisfy the triangle inequality. Nevertheless, both are bounded below by the widely used Robinson-Foulds metric and bounded above by a closely related true metric, the cluster-cardinality metric $d_{CC}$. We show that each of the three proposed new dissimilarities is computable in time $O(n^2)$ in the number of leaves $n$, and conclude the paper with a brief numerical exploration of the distribution over tree space of these dissimilarities in comparison with the Robinson-Foulds metric and the more recently introduced matching-split distance. |
2307.03415 | Oliver Eales | Oliver Eales, Steven Riley | Differences between the true reproduction number and the apparent
reproduction number of an epidemic time series | null | null | null | null | q-bio.PE q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The time-varying reproduction number $R(t)$ measures the number of new
infections per infectious individual and is closely correlated with the time
series of infection incidence by definition. The timings of actual infections
are rarely known, and analysis of epidemics usually relies on time series data
for other outcomes such as symptom onset. A common implicit assumption, when
estimating $R(t)$ from an epidemic time series, is that $R(t)$ has the same
relationship with these downstream outcomes as it does with the time series of
incidence. However, this assumption is unlikely to be valid given that most
epidemic time series are not perfect proxies of incidence. Rather they
represent convolutions of incidence with uncertain delay distributions. Here we
define the apparent time-varying reproduction number, $R_A(t)$, the
reproduction number calculated from a downstream epidemic time series and
demonstrate how differences between $R_A(t)$ and $R(t)$ depend on the
convolution function. The mean of the convolution function sets a time offset
between the two signals, whilst the variance of the convolution function
introduces a relative distortion between them. We present the convolution
functions of epidemic time series that were available during the SARS-CoV-2
pandemic. Infection prevalence, measured by random sampling studies, presents
fewer biases than other epidemic time series. Here we show that additionally
the mean and variance of its convolution function were similar to that obtained
from traditional surveillance based on mass-testing and could be reduced using
more frequent testing, or by using stricter thresholds for positivity.
Infection prevalence studies continue to be a versatile tool for tracking the
temporal trends of $R(t)$, and with additional refinements to their study
protocol, will be of even greater utility during any future epidemics or
pandemics.
| [
{
"created": "Fri, 7 Jul 2023 06:53:25 GMT",
"version": "v1"
}
] | 2023-07-10 | [
[
"Eales",
"Oliver",
""
],
[
"Riley",
"Steven",
""
]
] | The time-varying reproduction number $R(t)$ measures the number of new infections per infectious individual and is closely correlated with the time series of infection incidence by definition. The timings of actual infections are rarely known, and analysis of epidemics usually relies on time series data for other outcomes such as symptom onset. A common implicit assumption, when estimating $R(t)$ from an epidemic time series, is that $R(t)$ has the same relationship with these downstream outcomes as it does with the time series of incidence. However, this assumption is unlikely to be valid given that most epidemic time series are not perfect proxies of incidence. Rather they represent convolutions of incidence with uncertain delay distributions. Here we define the apparent time-varying reproduction number, $R_A(t)$, the reproduction number calculated from a downstream epidemic time series and demonstrate how differences between $R_A(t)$ and $R(t)$ depend on the convolution function. The mean of the convolution function sets a time offset between the two signals, whilst the variance of the convolution function introduces a relative distortion between them. We present the convolution functions of epidemic time series that were available during the SARS-CoV-2 pandemic. Infection prevalence, measured by random sampling studies, presents fewer biases than other epidemic time series. Here we show that additionally the mean and variance of its convolution function were similar to that obtained from traditional surveillance based on mass-testing and could be reduced using more frequent testing, or by using stricter thresholds for positivity. Infection prevalence studies continue to be a versatile tool for tracking the temporal trends of $R(t)$, and with additional refinements to their study protocol, will be of even greater utility during any future epidemics or pandemics. |
0806.3823 | Francois Bonneton | Fran\c{c}ois Bonneton (IGFL), Arnaud Chaumot (UR BELY), Vincent Laudet
(IGFL) | Annotation of Tribolium nuclear receptors reveals an evolutionary
overacceleration of a network controlling the ecdysone cascade | null | Insect Biochemistry and Molecular Biology 4, 38 (2008) 416-429 | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Tribolium genome contains 21 nuclear receptors, representing all of the
six known subfamilies. When compared to other species, this first complete set
for a Coleoptera reveals a strong conservation of the number and identity of
nuclear receptors in holometabolous insects. Two novelties are observed: the
atypical NR0 gene knirps is present only in brachyceran flies, while the NR2E6
gene is found only in Tribolium and in Apis. Using a quantitative analysis of
the evolutionary rate, we discovered that nuclear receptors could be divided
into two groups. In one group of 13 proteins, the rates follow the trend of the
Mecopterida genome-wide acceleration. In a second group of five nuclear
receptors, all acting together at the top of the ecdysone cascade, we observed
an overacceleration of the evolutionary rate during the early divergence of
Mecopterida. We thus extended our analysis to the twelve classic ecdysone
transcriptional regulators and found that six of them (ECR, USP, HR3, E75, HR4
and Kr-h1) underwent an overacceleration at the base of the Mecopterida
lineage. By contrast, E74, E93, BR, HR39, FTZ-F1 and E78 do not show this
divergence. We suggest that coevolution occurred within a network of regulators
that control the ecdysone cascade. The advent of Tribolium as a powerful model
should allow a better understanding of this evolution.
| [
{
"created": "Tue, 24 Jun 2008 06:20:29 GMT",
"version": "v1"
}
] | 2008-12-18 | [
[
"Bonneton",
"François",
"",
"IGFL"
],
[
"Chaumot",
"Arnaud",
"",
"UR BELY"
],
[
"Laudet",
"Vincent",
"",
"IGFL"
]
] | The Tribolium genome contains 21 nuclear receptors, representing all of the six known subfamilies. When compared to other species, this first complete set for a Coleoptera reveals a strong conservation of the number and identity of nuclear receptors in holometabolous insects. Two novelties are observed: the atypical NR0 gene knirps is present only in brachyceran flies, while the NR2E6 gene is found only in Tribolium and in Apis. Using a quantitative analysis of the evolutionary rate, we discovered that nuclear receptors could be divided into two groups. In one group of 13 proteins, the rates follow the trend of the Mecopterida genome-wide acceleration. In a second group of five nuclear receptors, all acting together at the top of the ecdysone cascade, we observed an overacceleration of the evolutionary rate during the early divergence of Mecopterida. We thus extended our analysis to the twelve classic ecdysone transcriptional regulators and found that six of them (ECR, USP, HR3, E75, HR4 and Kr-h1) underwent an overacceleration at the base of the Mecopterida lineage. By contrast, E74, E93, BR, HR39, FTZ-F1 and E78 do not show this divergence. We suggest that coevolution occurred within a network of regulators that control the ecdysone cascade. The advent of Tribolium as a powerful model should allow a better understanding of this evolution. |
1907.01681 | Fabio Chalub | Fabio A. C. C. Chalub and L\'eonard Monsaingeon and Ana Margarida
Ribeiro and Max O. Souza | Gradient flow formulations of discrete and continuous evolutionary
models: a unifying perspective | null | null | null | null | q-bio.PE math.AP math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider three classical models of biological evolution: (i) the Moran
process, an example of a reducible Markov Chain; (ii) the Kimura Equation, a
particular case of a degenerated Fokker-Planck Diffusion; (iii) the Replicator
Equation, a paradigm in Evolutionary Game Theory. While these approaches are
not completely equivalent, they are intimately connected, since (ii) is the
diffusion approximation of (i), and (iii) is obtained from (ii) in an
appropriate limit. It is well known that the Replicator Dynamics for two
strategies is a gradient flow with respect to the celebrated Shahshahani
distance. We reformulate the Moran process and the Kimura Equation as gradient
flows and in the sequel we discuss conditions such that the associated gradient
structures converge: (i) to (ii) and (ii) to (iii). This provides a geometric
characterisation of these evolutionary processes and provides a reformulation
of the above examples as time minimization of free energy functionals.
| [
{
"created": "Tue, 2 Jul 2019 23:37:47 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Oct 2020 15:53:44 GMT",
"version": "v2"
}
] | 2020-10-09 | [
[
"Chalub",
"Fabio A. C. C.",
""
],
[
"Monsaingeon",
"Léonard",
""
],
[
"Ribeiro",
"Ana Margarida",
""
],
[
"Souza",
"Max O.",
""
]
] | We consider three classical models of biological evolution: (i) the Moran process, an example of a reducible Markov Chain; (ii) the Kimura Equation, a particular case of a degenerated Fokker-Planck Diffusion; (iii) the Replicator Equation, a paradigm in Evolutionary Game Theory. While these approaches are not completely equivalent, they are intimately connected, since (ii) is the diffusion approximation of (i), and (iii) is obtained from (ii) in an appropriate limit. It is well known that the Replicator Dynamics for two strategies is a gradient flow with respect to the celebrated Shahshahani distance. We reformulate the Moran process and the Kimura Equation as gradient flows and in the sequel we discuss conditions such that the associated gradient structures converge: (i) to (ii) and (ii) to (iii). This provides a geometric characterisation of these evolutionary processes and provides a reformulation of the above examples as time minimization of free energy functionals. |
1809.03866 | Divine Wanduku (Dr. ) | Divine Wanduku | The stochastic permanence of malaria, and the existence of a stationary
distribution for a class of malaria models | International Journal of Biomathematics, 2020. arXiv admin note: text
overlap with arXiv:1808.09842 | null | 10.1142/S1793524520500242 | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This paper investigates the stochastic permanence of malaria and the
existence of a stationary distribution for the stochastic process describing
the disease dynamics over sufficiently longtime. The malaria system is highly
random with fluctuations from the disease transmission and natural deathrates,
which are expressed as independent white noise processes in a family of
stochastic differential equation epidemic models. Other sources of variability
in the malaria dynamics are the random incubation and naturally acquired
immunity periods of malaria. Improved analytical techniques and local
martingale characterizations are applied to describe the character of the
sample paths of the solution process of the system in the neighborhood of an
endemic equilibrium. Emphasis of this study is laid on examination of the
impacts of (1) the sources of variability- disease transmission and natural
death rates, and (2) the intensities of the white noise processes in the system
on the stochastic permanence of malaria, and also on the existence of the
stationary distribution for the solution process over sufficiently long time.
Numerical simulation examples are presented to illuminate the persistence and
stochastic permanence of malaria, and also to numerically approximate the
stationary distribution of the states of the solution process.
| [
{
"created": "Sat, 8 Sep 2018 18:15:41 GMT",
"version": "v1"
}
] | 2020-05-05 | [
[
"Wanduku",
"Divine",
""
]
] | This paper investigates the stochastic permanence of malaria and the existence of a stationary distribution for the stochastic process describing the disease dynamics over sufficiently longtime. The malaria system is highly random with fluctuations from the disease transmission and natural deathrates, which are expressed as independent white noise processes in a family of stochastic differential equation epidemic models. Other sources of variability in the malaria dynamics are the random incubation and naturally acquired immunity periods of malaria. Improved analytical techniques and local martingale characterizations are applied to describe the character of the sample paths of the solution process of the system in the neighborhood of an endemic equilibrium. Emphasis of this study is laid on examination of the impacts of (1) the sources of variability- disease transmission and natural death rates, and (2) the intensities of the white noise processes in the system on the stochastic permanence of malaria, and also on the existence of the stationary distribution for the solution process over sufficiently long time. Numerical simulation examples are presented to illuminate the persistence and stochastic permanence of malaria, and also to numerically approximate the stationary distribution of the states of the solution process. |
0710.5625 | Daniel Rockmore | Gregory Leibon, Daniel Rockmore, Martin Pollak | A simple computational method for the identification of
disease-associated loci in complex, incomplete pedigrees | 20 pages, 9 figures | null | null | null | q-bio.GN q-bio.QM | null | We present an approach, called the "Shadow Method," for the identification of
disease loci from dense genetic marker maps in complex, potentially incomplete
pedigrees. "Shadow" is a simple method based on an analysis of the patterns of
obligate meiotic recombination events in genotypic data. This method can be
applied to any high density marker map and was specifically designed to exploit
the fact that extremely dense marker maps are becoming more readily available.
We also describe how to interpret and associate meaningful P-Values to the
results. Shadow has significant advantages over traditional parametric linkage
analysis methods in that it can be readily applied even in cases in which the
topology of a pedigree or pedigrees can only be partially determined. In
addition, Shadow is robust to variability in a range of parameters and in
particular does not require prior knowledge of mode of inheritance, penetrance
or clinical misdiagnosis rate. Shadow can be used for any SNP data, but is
especially effective when applied to dense samplings. Our primary example uses
data from Affymetrix 100k SNPChip samples in which we illustrate our approach
by analyzing simulated data as well as genome-wide SNP data from two pedigrees
with inherited forms of kidney failure, one of which is compared with a typical
LOD score analysis.
| [
{
"created": "Tue, 30 Oct 2007 02:27:11 GMT",
"version": "v1"
}
] | 2007-10-31 | [
[
"Leibon",
"Gregory",
""
],
[
"Rockmore",
"Daniel",
""
],
[
"Pollak",
"Martin",
""
]
] | We present an approach, called the "Shadow Method," for the identification of disease loci from dense genetic marker maps in complex, potentially incomplete pedigrees. "Shadow" is a simple method based on an analysis of the patterns of obligate meiotic recombination events in genotypic data. This method can be applied to any high density marker map and was specifically designed to exploit the fact that extremely dense marker maps are becoming more readily available. We also describe how to interpret and associate meaningful P-Values to the results. Shadow has significant advantages over traditional parametric linkage analysis methods in that it can be readily applied even in cases in which the topology of a pedigree or pedigrees can only be partially determined. In addition, Shadow is robust to variability in a range of parameters and in particular does not require prior knowledge of mode of inheritance, penetrance or clinical misdiagnosis rate. Shadow can be used for any SNP data, but is especially effective when applied to dense samplings. Our primary example uses data from Affymetrix 100k SNPChip samples in which we illustrate our approach by analyzing simulated data as well as genome-wide SNP data from two pedigrees with inherited forms of kidney failure, one of which is compared with a typical LOD score analysis. |
2211.03051 | Christopher Fusco | Christopher Fusco, Angel Allen | Multilayer Perceptron Network Discriminates Larval Zebrafish Genotype
using Behaviour | Preprint | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by-sa/4.0/ | Zebrafish are a common model organism used to identify new disease
therapeutics. High-throughput drug screens can be performed on larval zebrafish
in multi-well plates by observing changes in behaviour following a treatment.
Analysis of this behaviour can be difficult, however, due to the high
dimensionality of the data obtained. Statistical analysis of individual
statistics (such as the distance travelled) is generally not powerful enough to
detect meaningful differences between treatment groups. Here, we propose a
method for classifying zebrafish models of Parkinson's disease by genotype at 5
days old. Using a set of 2D behavioural features, we train a multi-layer
perceptron neural network. We further show that the use of integrated gradients
can give insight into the impact of each behaviour feature on genotype
classifications by the model. In this way, we provide a novel pipeline for
classifying zebrafish larvae, beginning with feature preparation and ending
with an impact analysis of said features.
| [
{
"created": "Sun, 6 Nov 2022 07:36:31 GMT",
"version": "v1"
},
{
"created": "Tue, 8 Nov 2022 01:48:45 GMT",
"version": "v2"
}
] | 2022-11-09 | [
[
"Fusco",
"Christopher",
""
],
[
"Allen",
"Angel",
""
]
] | Zebrafish are a common model organism used to identify new disease therapeutics. High-throughput drug screens can be performed on larval zebrafish in multi-well plates by observing changes in behaviour following a treatment. Analysis of this behaviour can be difficult, however, due to the high dimensionality of the data obtained. Statistical analysis of individual statistics (such as the distance travelled) is generally not powerful enough to detect meaningful differences between treatment groups. Here, we propose a method for classifying zebrafish models of Parkinson's disease by genotype at 5 days old. Using a set of 2D behavioural features, we train a multi-layer perceptron neural network. We further show that the use of integrated gradients can give insight into the impact of each behaviour feature on genotype classifications by the model. In this way, we provide a novel pipeline for classifying zebrafish larvae, beginning with feature preparation and ending with an impact analysis of said features. |
2005.12513 | Fernanda Ribeiro | Fernanda L. Ribeiro, Steffen Bollmann, Alexander M. Puckett | DeepRetinotopy: Predicting the Functional Organization of Human Visual
Cortex from Structural MRI Data using Geometric Deep Learning | null | null | 10.1101/2020.02.11.934471 | MIDL/2020/ExtendedAbstract/Nw_trRFjPE | q-bio.NC cs.CV cs.LG q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Whether it be in a man-made machine or a biological system, form and function
are often directly related. In the latter, however, this particular
relationship is often unclear due to the intricate nature of biology. Here we
developed a geometric deep learning model capable of exploiting the actual
structure of the cortex to learn the complex relationship between brain
function and anatomy from structural and functional MRI data. Our model was not
only able to predict the functional organization of human visual cortex from
anatomical properties alone, but it was also able to predict nuanced variations
across individuals.
| [
{
"created": "Tue, 26 May 2020 04:54:31 GMT",
"version": "v1"
}
] | 2020-05-27 | [
[
"Ribeiro",
"Fernanda L.",
""
],
[
"Bollmann",
"Steffen",
""
],
[
"Puckett",
"Alexander M.",
""
]
] | Whether it be in a man-made machine or a biological system, form and function are often directly related. In the latter, however, this particular relationship is often unclear due to the intricate nature of biology. Here we developed a geometric deep learning model capable of exploiting the actual structure of the cortex to learn the complex relationship between brain function and anatomy from structural and functional MRI data. Our model was not only able to predict the functional organization of human visual cortex from anatomical properties alone, but it was also able to predict nuanced variations across individuals. |
2307.09169 | Zihan Liu | Zihan Liu, Jiaqi Wang, Yun Luo, Shuang Zhao, Wenbin Li, Stan Z. Li | Efficient Prediction of Peptide Self-assembly through Sequential and
Graphical Encoding | null | null | null | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, there has been an explosion of research on the application
of deep learning to the prediction of various peptide properties, due to the
significant development and market potential of peptides. Molecular dynamics
has enabled the efficient collection of large peptide datasets, providing
reliable training data for deep learning. However, the lack of systematic
analysis of the peptide encoding, which is essential for AI-assisted
peptide-related tasks, makes it an urgent problem to be solved for the
improvement of prediction accuracy. To address this issue, we first collect a
high-quality, colossal simulation dataset of peptide self-assembly containing
over 62,000 samples generated by coarse-grained molecular dynamics (CGMD).
Then, we systematically investigate the effect of peptide encoding of amino
acids into sequences and molecular graphs using state-of-the-art sequential
(i.e., RNN, LSTM, and Transformer) and structural deep learning models (i.e.,
GCN, GAT, and GraphSAGE), on the accuracy of peptide self-assembly prediction,
an essential physiochemical process prior to any peptide-related applications.
Extensive benchmarking studies have proven Transformer to be the most powerful
sequence-encoding-based deep learning model, pushing the limit of peptide
self-assembly prediction to decapeptides. In summary, this work provides a
comprehensive benchmark analysis of peptide encoding with advanced deep
learning models, serving as a guide for a wide range of peptide-related
predictions such as isoelectric points, hydration free energy, etc.
| [
{
"created": "Mon, 17 Jul 2023 00:43:33 GMT",
"version": "v1"
}
] | 2023-07-19 | [
[
"Liu",
"Zihan",
""
],
[
"Wang",
"Jiaqi",
""
],
[
"Luo",
"Yun",
""
],
[
"Zhao",
"Shuang",
""
],
[
"Li",
"Wenbin",
""
],
[
"Li",
"Stan Z.",
""
]
] | In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the efficient collection of large peptide datasets, providing reliable training data for deep learning. However, the lack of systematic analysis of the peptide encoding, which is essential for AI-assisted peptide-related tasks, makes it an urgent problem to be solved for the improvement of prediction accuracy. To address this issue, we first collect a high-quality, colossal simulation dataset of peptide self-assembly containing over 62,000 samples generated by coarse-grained molecular dynamics (CGMD). Then, we systematically investigate the effect of peptide encoding of amino acids into sequences and molecular graphs using state-of-the-art sequential (i.e., RNN, LSTM, and Transformer) and structural deep learning models (i.e., GCN, GAT, and GraphSAGE), on the accuracy of peptide self-assembly prediction, an essential physiochemical process prior to any peptide-related applications. Extensive benchmarking studies have proven Transformer to be the most powerful sequence-encoding-based deep learning model, pushing the limit of peptide self-assembly prediction to decapeptides. In summary, this work provides a comprehensive benchmark analysis of peptide encoding with advanced deep learning models, serving as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc. |
2010.12857 | William McCorkindale Mr. | William McCorkindale, Carl Poelking, Alpha A. Lee | Investigating 3D Atomic Environments for Enhanced QSAR | null | null | null | null | q-bio.QM cs.LG physics.comp-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Predicting bioactivity and physical properties of molecules is a longstanding
challenge in drug design. Most approaches use molecular descriptors based on a
2D representation of molecules as a graph of atoms and bonds, abstracting away
the molecular shape. A difficulty in accounting for 3D shape is in designing
molecular descriptors can precisely capture molecular shape while remaining
invariant to rotations/translations. We describe a novel alignment-free 3D QSAR
method using Smooth Overlap of Atomic Positions (SOAP), a well-established
formalism developed for interpolating potential energy surfaces. We show that
this approach rigorously describes local 3D atomic environments to compare
molecular shapes in a principled manner. This method performs competitively
with traditional fingerprint-based approaches as well as state-of-the-art graph
neural networks on pIC$_{50}$ ligand-binding prediction in both random and
scaffold split scenarios. We illustrate the utility of SOAP descriptors by
showing that its inclusion in ensembling diverse representations statistically
improves performance, demonstrating that incorporating 3D atomic environments
could lead to enhanced QSAR for cheminformatics.
| [
{
"created": "Sat, 24 Oct 2020 10:04:48 GMT",
"version": "v1"
}
] | 2020-10-27 | [
[
"McCorkindale",
"William",
""
],
[
"Poelking",
"Carl",
""
],
[
"Lee",
"Alpha A.",
""
]
] | Predicting bioactivity and physical properties of molecules is a longstanding challenge in drug design. Most approaches use molecular descriptors based on a 2D representation of molecules as a graph of atoms and bonds, abstracting away the molecular shape. A difficulty in accounting for 3D shape is in designing molecular descriptors can precisely capture molecular shape while remaining invariant to rotations/translations. We describe a novel alignment-free 3D QSAR method using Smooth Overlap of Atomic Positions (SOAP), a well-established formalism developed for interpolating potential energy surfaces. We show that this approach rigorously describes local 3D atomic environments to compare molecular shapes in a principled manner. This method performs competitively with traditional fingerprint-based approaches as well as state-of-the-art graph neural networks on pIC$_{50}$ ligand-binding prediction in both random and scaffold split scenarios. We illustrate the utility of SOAP descriptors by showing that its inclusion in ensembling diverse representations statistically improves performance, demonstrating that incorporating 3D atomic environments could lead to enhanced QSAR for cheminformatics. |
1412.0291 | Viola Priesemann | Michael Wibral, Joseph T. Lizier, Viola Priesemann | Bits from Biology for Computational Intelligence | null | Frontiers in Robotics and AI, 2:5 (2015) | 10.3389/frobt.2015.00005 | null | q-bio.NC cs.IT math.IT physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computational intelligence is broadly defined as biologically-inspired
computing. Usually, inspiration is drawn from neural systems. This article
shows how to analyze neural systems using information theory to obtain
constraints that help identify the algorithms run by such systems and the
information they represent. Algorithms and representations identified
information-theoretically may then guide the design of biologically inspired
computing systems (BICS). The material covered includes the necessary
introduction to information theory and the estimation of information theoretic
quantities from neural data. We then show how to analyze the information
encoded in a system about its environment, and also discuss recent
methodological developments on the question of how much information each agent
carries about the environment either uniquely, or redundantly or
synergistically together with others. Last, we introduce the framework of local
information dynamics, where information processing is decomposed into component
processes of information storage, transfer, and modification -- locally in
space and time. We close by discussing example applications of these measures
to neural data and other complex systems.
| [
{
"created": "Sun, 30 Nov 2014 21:47:15 GMT",
"version": "v1"
}
] | 2018-05-11 | [
[
"Wibral",
"Michael",
""
],
[
"Lizier",
"Joseph T.",
""
],
[
"Priesemann",
"Viola",
""
]
] | Computational intelligence is broadly defined as biologically-inspired computing. Usually, inspiration is drawn from neural systems. This article shows how to analyze neural systems using information theory to obtain constraints that help identify the algorithms run by such systems and the information they represent. Algorithms and representations identified information-theoretically may then guide the design of biologically inspired computing systems (BICS). The material covered includes the necessary introduction to information theory and the estimation of information theoretic quantities from neural data. We then show how to analyze the information encoded in a system about its environment, and also discuss recent methodological developments on the question of how much information each agent carries about the environment either uniquely, or redundantly or synergistically together with others. Last, we introduce the framework of local information dynamics, where information processing is decomposed into component processes of information storage, transfer, and modification -- locally in space and time. We close by discussing example applications of these measures to neural data and other complex systems. |
1408.0463 | Peter O. Fedichev | Valeria Kogan, Ivan Molodtcov, Leonid I. Menshikov, Robert J.
Shmookler Reis and Peter Fedichev | Stability analysis of a model gene network links aging, stress
resistance, and negligible senescence | 8 pages, 2 figures | Scientific Reports 5, Article number: 13589 (2015) | 10.1038/srep13589 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several animal species are considered to exhibit what is called negligible
senescence, i.e. they do not show signs of functional decline or any increase
of mortality with age, and do not have measurable reductions in reproductive
capacity with age. Recent studies in Naked Mole Rat (NMR) and long- lived sea
urchin showed that the level of gene expression changes with age is lower than
in other organisms. These phenotypic observations correlate well with
exceptional endurance of NMR tissues to various genotoxic stresses. Therefore,
the lifelong transcriptional stability of an organism may be a key determinant
of longevity. However, the exact relation between genetic network stability,
stress-resistance and aging has not been defined. We analyze the stability of a
simple genetic- network model of a living organism under the influence of
external and endogenous factors. We demonstrate that under most common
circumstances a gene network is inherently unstable and suffers from
exponential accumulation of gene-regulation deviations leading to death.
However, should the repair systems be sufficiently effective, the gene network
can stabilize so that gene damage remains constrained along with mortality of
the organism, which may then enjoy a remarkable degree of stability over very
long times. We clarify the relation between stress-resistance and aging and
suggest that stabilization of the genetic network may provide a mathematical
explanation of the Gompertz equation describing the relationship between age
and mortality in many species, and of the apparently negligible senescence
observed in exceptionally long-lived animals. The model may support a range of
applications, such as systematic searches for therapeutics to extend lifespan
and healthspan.
| [
{
"created": "Sun, 3 Aug 2014 07:14:09 GMT",
"version": "v1"
}
] | 2015-10-15 | [
[
"Kogan",
"Valeria",
""
],
[
"Molodtcov",
"Ivan",
""
],
[
"Menshikov",
"Leonid I.",
""
],
[
"Reis",
"Robert J. Shmookler",
""
],
[
"Fedichev",
"Peter",
""
]
] | Several animal species are considered to exhibit what is called negligible senescence, i.e. they do not show signs of functional decline or any increase of mortality with age, and do not have measurable reductions in reproductive capacity with age. Recent studies in Naked Mole Rat (NMR) and long- lived sea urchin showed that the level of gene expression changes with age is lower than in other organisms. These phenotypic observations correlate well with exceptional endurance of NMR tissues to various genotoxic stresses. Therefore, the lifelong transcriptional stability of an organism may be a key determinant of longevity. However, the exact relation between genetic network stability, stress-resistance and aging has not been defined. We analyze the stability of a simple genetic- network model of a living organism under the influence of external and endogenous factors. We demonstrate that under most common circumstances a gene network is inherently unstable and suffers from exponential accumulation of gene-regulation deviations leading to death. However, should the repair systems be sufficiently effective, the gene network can stabilize so that gene damage remains constrained along with mortality of the organism, which may then enjoy a remarkable degree of stability over very long times. We clarify the relation between stress-resistance and aging and suggest that stabilization of the genetic network may provide a mathematical explanation of the Gompertz equation describing the relationship between age and mortality in many species, and of the apparently negligible senescence observed in exceptionally long-lived animals. The model may support a range of applications, such as systematic searches for therapeutics to extend lifespan and healthspan. |
1611.03952 | Christoph Adami | A. Gupta and C. Adami | Shared information between residues is sufficient to detect pair-wise
epistasis in a protein | 4 pages, 1 figure. To appear in PLoS Genetics | PLoS Genetics 12 (2016) e1006471 | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a comment on our manuscript "Strong selection significantly increases
epistatic interactions in the long-term evolution of a protein", Dr. Crona
challenges our assertion that shared entropy (that is, information) between two
residues implies epistasis between those residues, by constructing an explicit
example of three loci (say A, B, and C), where A and B are epistatically linked
(leading to shared entropy between A and B), and A and C also depend
epistatically (leading to shared entropy between A and C), so that loci B and C
are correlated (share entropy).
| [
{
"created": "Sat, 12 Nov 2016 04:18:18 GMT",
"version": "v1"
}
] | 2016-12-07 | [
[
"Gupta",
"A.",
""
],
[
"Adami",
"C.",
""
]
] | In a comment on our manuscript "Strong selection significantly increases epistatic interactions in the long-term evolution of a protein", Dr. Crona challenges our assertion that shared entropy (that is, information) between two residues implies epistasis between those residues, by constructing an explicit example of three loci (say A, B, and C), where A and B are epistatically linked (leading to shared entropy between A and B), and A and C also depend epistatically (leading to shared entropy between A and C), so that loci B and C are correlated (share entropy). |
1406.0399 | Nikolai Slavov | Nikolai Slavov, Sefan Semrau, Edoardo Airoldi, Bogdan Budnik,
Alexander van Oudenaarden | Differential stoichiometry among core ribosomal proteins | 31 pages, 8 figures | Cell Reports 13: 865 - 873, 2015 | 10.1016/j.celrep.2015.09.056 | null | q-bio.GN q-bio.BM q-bio.MN q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the regulation and structure of ribosomes is essential to
understanding protein synthesis and its deregulation in disease. While
ribosomes are believed to have a fixed stoichiometry among their core ribosomal
proteins (RPs), some experiments suggest a more variable composition. Testing
such variability requires direct and precise quantification of RPs. We used
mass-spectrometry to directly quantify RPs across monosomes and polysomes of
mouse embryonic stem cells (ESC) and budding yeast. Our data show that the
stoichiometry among core RPs in wild-type yeast cells and ESC depends both on
the growth conditions and on the number of ribosomes bound per mRNA.
Furthermore, we find that the fitness of cells with a deleted RP-gene is
inversely proportional to the enrichment of the corresponding RP in polysomes.
Together, our findings support the existence of ribosomes with distinct protein
composition and physiological function.
| [
{
"created": "Mon, 2 Jun 2014 14:57:33 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Apr 2015 12:17:10 GMT",
"version": "v2"
}
] | 2015-11-24 | [
[
"Slavov",
"Nikolai",
""
],
[
"Semrau",
"Sefan",
""
],
[
"Airoldi",
"Edoardo",
""
],
[
"Budnik",
"Bogdan",
""
],
[
"van Oudenaarden",
"Alexander",
""
]
] | Understanding the regulation and structure of ribosomes is essential to understanding protein synthesis and its deregulation in disease. While ribosomes are believed to have a fixed stoichiometry among their core ribosomal proteins (RPs), some experiments suggest a more variable composition. Testing such variability requires direct and precise quantification of RPs. We used mass-spectrometry to directly quantify RPs across monosomes and polysomes of mouse embryonic stem cells (ESC) and budding yeast. Our data show that the stoichiometry among core RPs in wild-type yeast cells and ESC depends both on the growth conditions and on the number of ribosomes bound per mRNA. Furthermore, we find that the fitness of cells with a deleted RP-gene is inversely proportional to the enrichment of the corresponding RP in polysomes. Together, our findings support the existence of ribosomes with distinct protein composition and physiological function. |
1702.06977 | Carsten Lemmen | Carsten Lemmen and Detlef Gronenborn | The Diffusion of Humans and Cultures in the Course of the Spread of
Farming | 20 pages, 5 figures, submitted to Diffusive Spreading in Nature,
Technology and Society, edited by Armin Bunde, J\"urgen Caro, J\"org
K\"arger, Gero Vogl, Chapter 17 | In: Bunde A., Caro J., K\"arger J., Vogl G. (eds) Diffusive
Spreading in Nature, Technology and Society. Springer, Cham | 10.1007/978-3-319-67798-9_17 | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The most profound change in the relationship between humans and their
environment was the introduction of agriculture and pastoralism. [....] For an
understanding of the expansion process, it appears appropriate to apply a
diffusive model. Broadly, these numerical modeling approaches can be catego-
rized in correlative, continuous and discrete. Common to all approaches is the
comparison to collections of radiocarbon data that show the apparent wave of
advance of the transition to farming. However, these data sets differ in entry
density and data quality. Often they disregard local and regional specifics and
research gaps, or dating uncertainties. Thus, most of these data bases may only
be used on a very general, broad scale. One of the pitfalls of using
irregularly spaced or irregularly documented radiocarbon data becomes evident
from the map generated by Fort (this volume, Chapter 16): while the general
east-west and south-north trends become evident, some areas appear as having
undergone anomalously early transitions to farming. This may be due to faulty
entries into the data base or regional problems with radiocarbon dating, if not
unnoticed or undocumented laboratory mistakes.
| [
{
"created": "Wed, 22 Feb 2017 19:28:18 GMT",
"version": "v1"
}
] | 2018-05-10 | [
[
"Lemmen",
"Carsten",
""
],
[
"Gronenborn",
"Detlef",
""
]
] | The most profound change in the relationship between humans and their environment was the introduction of agriculture and pastoralism. [....] For an understanding of the expansion process, it appears appropriate to apply a diffusive model. Broadly, these numerical modeling approaches can be catego- rized in correlative, continuous and discrete. Common to all approaches is the comparison to collections of radiocarbon data that show the apparent wave of advance of the transition to farming. However, these data sets differ in entry density and data quality. Often they disregard local and regional specifics and research gaps, or dating uncertainties. Thus, most of these data bases may only be used on a very general, broad scale. One of the pitfalls of using irregularly spaced or irregularly documented radiocarbon data becomes evident from the map generated by Fort (this volume, Chapter 16): while the general east-west and south-north trends become evident, some areas appear as having undergone anomalously early transitions to farming. This may be due to faulty entries into the data base or regional problems with radiocarbon dating, if not unnoticed or undocumented laboratory mistakes. |
2109.11616 | K.S. Joseph | D.W. Rurak, M.Y. Shen and K.S. Joseph | Fetal oxygen delivery and consumption and blood gases in relation to
gestational age | 88 pages, 26 Figures 233 references and | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Fetal oxygen delivery and consumption and blood gases in relation to
gestational age. Oxygen crosses the placenta by diffusion and placental
permeability to O$_2$ is high. Thus, the fetus receives adequate amounts, but
vascular Po$_2$ is much lower than after birth. Studies of sustained fetal
hypoxemia and acute 40-45% hemorrhage show that hypoxemia is not tolerated
whereas hemorrhage is. This suggests that if fetal Po$_2$ falls markedly, O$_2$
diffusion from blood to tissue is impaired. Uterine blood and umbilical blood
flows/fetal weight fall progressively with advancing gestation. This results in
fetal hypoxemia, an increase in Pco$_2$, and decrease in pH. This decreases
fetal O$_2$ delivery, and in fetal lambs and horses there is a decrease in
fetal O$_2$ consumption. The decrease in O$_2$ demands is linked to a decrease
in fetal breathing and body movements and growth rate. The decrease in fetal
motility is due to an increase in fetal plasma PGE2 concentration, which begins
at ~120 days GA in sheep and is due to the prepartum rise in fetal cortisol.
Also, adenosine administration to fetal lambs decreases fetal breathing and REM
sleep and the plasma adenosine concentration increases in late gestation. The
fetal plasma levels of neurosteroids, which suppress fetal motility, increase
with advancing gestation. The prepartum cortisol rise also inhibits fetal
growth. In normal pregnancies, these mechanisms operate effectively to maintain
an appropriate balance between fetal oxygen consumption and delivery. However,
in pregnancies with either further reduce O$_2$ delivery or increase fetal
O$_2$ demands, the mismatch between O$_2$ delivery and consumption may worsen
leading to IUGR, hypoxic organ damage or stillbirth.
| [
{
"created": "Thu, 23 Sep 2021 19:41:14 GMT",
"version": "v1"
}
] | 2021-09-27 | [
[
"Rurak",
"D. W.",
""
],
[
"Shen",
"M. Y.",
""
],
[
"Joseph",
"K. S.",
""
]
] | Fetal oxygen delivery and consumption and blood gases in relation to gestational age. Oxygen crosses the placenta by diffusion and placental permeability to O$_2$ is high. Thus, the fetus receives adequate amounts, but vascular Po$_2$ is much lower than after birth. Studies of sustained fetal hypoxemia and acute 40-45% hemorrhage show that hypoxemia is not tolerated whereas hemorrhage is. This suggests that if fetal Po$_2$ falls markedly, O$_2$ diffusion from blood to tissue is impaired. Uterine blood and umbilical blood flows/fetal weight fall progressively with advancing gestation. This results in fetal hypoxemia, an increase in Pco$_2$, and decrease in pH. This decreases fetal O$_2$ delivery, and in fetal lambs and horses there is a decrease in fetal O$_2$ consumption. The decrease in O$_2$ demands is linked to a decrease in fetal breathing and body movements and growth rate. The decrease in fetal motility is due to an increase in fetal plasma PGE2 concentration, which begins at ~120 days GA in sheep and is due to the prepartum rise in fetal cortisol. Also, adenosine administration to fetal lambs decreases fetal breathing and REM sleep and the plasma adenosine concentration increases in late gestation. The fetal plasma levels of neurosteroids, which suppress fetal motility, increase with advancing gestation. The prepartum cortisol rise also inhibits fetal growth. In normal pregnancies, these mechanisms operate effectively to maintain an appropriate balance between fetal oxygen consumption and delivery. However, in pregnancies with either further reduce O$_2$ delivery or increase fetal O$_2$ demands, the mismatch between O$_2$ delivery and consumption may worsen leading to IUGR, hypoxic organ damage or stillbirth. |
1110.1739 | Graciano Dieck Kattas | Graciano Dieck Kattas, Xiao-Ke Xu and Michael Small | Dynamical modeling of collective behavior from pigeon flight data: flock
cohesion and dispersion | null | null | 10.1371/journal.pcbi.1002449 | null | q-bio.OT physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several models of flocking have been promoted based on simulations with
qualitatively naturalistic behavior. In this paper we provide the first direct
application of computational modeling methods to infer flocking behavior from
experimental field data. We show that this approach is able to infer general
rules for interaction, or lack of interaction, among members of a flock or,
more generally, any community. Using experimental field measurements of homing
pigeons in flight we demonstrate the existence of a basic distance dependent
attraction/repulsion relationship and show that this rule is sufficient to
explain collective behavior observed in nature. Positional data of individuals
over time are used as input data to a computational algorithm capable of
building complex nonlinear functions that can represent the system behavior.
Topological nearest neighbor interactions are considered to characterize the
components within this model. The efficacy of this method is demonstrated with
simulated noisy data generated from the classical (two dimensional) Vicsek
model. When applied to experimental data from homing pigeon flights we show
that the more complex three dimensional models are capable of predicting and
simulating trajectories, as well as exhibiting realistic collective dynamics.
The simulations of the reconstructed models are used to extract properties of
the collective behavior in pigeons, and how it is affected by changing the
initial conditions of the system. Our results demonstrate that this approach
may be applied to construct models capable of simulating trajectories and
collective dynamics using experimental field measurements of herd movement.
From these models, the behavior of the individual agents (animals) may be
inferred.
| [
{
"created": "Sat, 8 Oct 2011 14:08:23 GMT",
"version": "v1"
}
] | 2015-05-30 | [
[
"Kattas",
"Graciano Dieck",
""
],
[
"Xu",
"Xiao-Ke",
""
],
[
"Small",
"Michael",
""
]
] | Several models of flocking have been promoted based on simulations with qualitatively naturalistic behavior. In this paper we provide the first direct application of computational modeling methods to infer flocking behavior from experimental field data. We show that this approach is able to infer general rules for interaction, or lack of interaction, among members of a flock or, more generally, any community. Using experimental field measurements of homing pigeons in flight we demonstrate the existence of a basic distance dependent attraction/repulsion relationship and show that this rule is sufficient to explain collective behavior observed in nature. Positional data of individuals over time are used as input data to a computational algorithm capable of building complex nonlinear functions that can represent the system behavior. Topological nearest neighbor interactions are considered to characterize the components within this model. The efficacy of this method is demonstrated with simulated noisy data generated from the classical (two dimensional) Vicsek model. When applied to experimental data from homing pigeon flights we show that the more complex three dimensional models are capable of predicting and simulating trajectories, as well as exhibiting realistic collective dynamics. The simulations of the reconstructed models are used to extract properties of the collective behavior in pigeons, and how it is affected by changing the initial conditions of the system. Our results demonstrate that this approach may be applied to construct models capable of simulating trajectories and collective dynamics using experimental field measurements of herd movement. From these models, the behavior of the individual agents (animals) may be inferred. |
2110.11339 | Li Liu | Hengyang Wang, Xianghao Zhan, Li Liu, Asif Ullah, Huiyan Li, Han Gao,
You Wang, Guang Li | Unsupervised cross-user adaptation in taste sensation recognition based
on surface electromyography with conformal prediction and domain regularized
component analysis | null | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human taste sensation can be qualitatively described with surface
electromyography. However, the pattern recognition models trained on one
subject (the source domain) do not generalize well on other subjects (the
target domain). To improve the generalizability and transferability of taste
sensation models developed with sEMG data, two methods were innovatively
applied in this study: domain regularized component analysis (DRCA) and
conformal prediction with shrunken centroids (CPSC). The effectiveness of these
two methods was investigated independently in an unlabeled data augmentation
process with the unlabeled data from the target domain, and the same cross-user
adaptation pipeline were conducted on six subjects. The results show that DRCA
improved the classification accuracy on six subjects (p < 0.05), compared with
the baseline models trained only with the source domain data;, while CPSC did
not guarantee the accuracy improvement. Furthermore, the combination of DRCA
and CPSC presented statistically significant improvement (p < 0.05) in
classification accuracy on six subjects. The proposed strategy combining DRCA
and CPSC showed its effectiveness in addressing the cross-user data
distribution drift in sEMG-based taste sensation recognition application. It
also shows the potential in more cross-user adaptation applications.
| [
{
"created": "Wed, 20 Oct 2021 09:11:14 GMT",
"version": "v1"
},
{
"created": "Sat, 11 Dec 2021 12:40:39 GMT",
"version": "v2"
}
] | 2021-12-14 | [
[
"Wang",
"Hengyang",
""
],
[
"Zhan",
"Xianghao",
""
],
[
"Liu",
"Li",
""
],
[
"Ullah",
"Asif",
""
],
[
"Li",
"Huiyan",
""
],
[
"Gao",
"Han",
""
],
[
"Wang",
"You",
""
],
[
"Li",
"Guang",
""
]
] | Human taste sensation can be qualitatively described with surface electromyography. However, the pattern recognition models trained on one subject (the source domain) do not generalize well on other subjects (the target domain). To improve the generalizability and transferability of taste sensation models developed with sEMG data, two methods were innovatively applied in this study: domain regularized component analysis (DRCA) and conformal prediction with shrunken centroids (CPSC). The effectiveness of these two methods was investigated independently in an unlabeled data augmentation process with the unlabeled data from the target domain, and the same cross-user adaptation pipeline were conducted on six subjects. The results show that DRCA improved the classification accuracy on six subjects (p < 0.05), compared with the baseline models trained only with the source domain data;, while CPSC did not guarantee the accuracy improvement. Furthermore, the combination of DRCA and CPSC presented statistically significant improvement (p < 0.05) in classification accuracy on six subjects. The proposed strategy combining DRCA and CPSC showed its effectiveness in addressing the cross-user data distribution drift in sEMG-based taste sensation recognition application. It also shows the potential in more cross-user adaptation applications. |
2404.10854 | Matthew Andres Moreno | Matthew Andres Moreno | Methods to Estimate Cryptic Sequence Complexity | null | null | null | null | q-bio.PE cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Complexity is a signature quality of interest in artificial life systems.
Alongside other dimensions of assessment, it is common to quantify genome sites
that contribute to fitness as a complexity measure. However, limitations to the
sensitivity of fitness assays in models with implicit replication criteria
involving rich biotic interactions introduce the possibility of
difficult-to-detect ``cryptic'' adaptive sites, which contribute small fitness
effects below the threshold of individual detectability or involve epistatic
redundancies. Here, we propose three knockout-based assay procedures designed
to quantify cryptic adaptive sites within digital genomes. We report initial
tests of these methods on a simple genome model with explicitly configured site
fitness effects. In these limited tests, estimation results reflect ground
truth cryptic sequence complexities well. Presented work provides initial steps
toward development of new methods and software tools that improve the
resolution, rigor, and tractability of complexity analyses across alife
systems, particularly those requiring expensive in situ assessments of organism
fitness.
| [
{
"created": "Tue, 16 Apr 2024 19:04:03 GMT",
"version": "v1"
},
{
"created": "Fri, 31 May 2024 13:59:27 GMT",
"version": "v2"
}
] | 2024-06-03 | [
[
"Moreno",
"Matthew Andres",
""
]
] | Complexity is a signature quality of interest in artificial life systems. Alongside other dimensions of assessment, it is common to quantify genome sites that contribute to fitness as a complexity measure. However, limitations to the sensitivity of fitness assays in models with implicit replication criteria involving rich biotic interactions introduce the possibility of difficult-to-detect ``cryptic'' adaptive sites, which contribute small fitness effects below the threshold of individual detectability or involve epistatic redundancies. Here, we propose three knockout-based assay procedures designed to quantify cryptic adaptive sites within digital genomes. We report initial tests of these methods on a simple genome model with explicitly configured site fitness effects. In these limited tests, estimation results reflect ground truth cryptic sequence complexities well. Presented work provides initial steps toward development of new methods and software tools that improve the resolution, rigor, and tractability of complexity analyses across alife systems, particularly those requiring expensive in situ assessments of organism fitness. |
1307.2461 | Martin Kapun PhD | Martin Kapun, Hester van Schalkwyk, Bryant McAllister, Thomas Flatt
and Christian Schl\"otterer | Inference of chromosomal inversion dynamics from Pool-Seq data in
natural and laboratory populations of Drosophila melanogaster | 31 pages, 4 main figures, 1 main table, 7 supporting figures, 11
supporting tables | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sequencing of pools of individuals (Pool-Seq) represents a reliable and cost-
effective approach for estimating genome-wide SNP and transposable element
insertion frequencies. However, Pool-Seq does not provide direct information on
haplotypes so that for example obtaining inversion frequencies has not been
possible until now. Here, we have developed a new set of diagnostic marker SNPs
for 7 cosmopolitan inversions in Drosophila melanogaster that can be used to
infer inversion frequencies from Pool-Seq data. We applied our novel marker set
to Pool-Seq data from an experimental evolution study and from North American
and Australian latitudinal clines. In the experimental evolution data, we find
evidence that positive selection has driven the frequencies of In(3R)C and
In(3R)Mo to increase over time. In the clinal data, we confirm the existence of
frequency clines for In(2L)t, In(3L)P and In(3R)Payne in both North America and
Australia and detect a previously unknown latitudinal cline for In(3R)Mo in
North America. The inversion markers developed here provide a versatile and
robust tool for characterizing inversion frequencies and their dynamics in
Pool- Seq data from diverse D. melanogaster populations.
| [
{
"created": "Tue, 9 Jul 2013 14:04:06 GMT",
"version": "v1"
}
] | 2013-07-10 | [
[
"Kapun",
"Martin",
""
],
[
"van Schalkwyk",
"Hester",
""
],
[
"McAllister",
"Bryant",
""
],
[
"Flatt",
"Thomas",
""
],
[
"Schlötterer",
"Christian",
""
]
] | Sequencing of pools of individuals (Pool-Seq) represents a reliable and cost- effective approach for estimating genome-wide SNP and transposable element insertion frequencies. However, Pool-Seq does not provide direct information on haplotypes so that for example obtaining inversion frequencies has not been possible until now. Here, we have developed a new set of diagnostic marker SNPs for 7 cosmopolitan inversions in Drosophila melanogaster that can be used to infer inversion frequencies from Pool-Seq data. We applied our novel marker set to Pool-Seq data from an experimental evolution study and from North American and Australian latitudinal clines. In the experimental evolution data, we find evidence that positive selection has driven the frequencies of In(3R)C and In(3R)Mo to increase over time. In the clinal data, we confirm the existence of frequency clines for In(2L)t, In(3L)P and In(3R)Payne in both North America and Australia and detect a previously unknown latitudinal cline for In(3R)Mo in North America. The inversion markers developed here provide a versatile and robust tool for characterizing inversion frequencies and their dynamics in Pool- Seq data from diverse D. melanogaster populations. |
1707.03360 | Haozhe Shan | Haozhe Shan, Peggy Mason | Unsupervised identification of rat behavioral motifs across timescales | 9 pages, 6 figures | NeurIPS 2020 Learning Meaningful Representations of Life (LMRL)
workshop | null | null | q-bio.QM stat.ML | http://creativecommons.org/licenses/by/4.0/ | Behaviors of several laboratory animals can be modeled as sequences of
stereotyped behaviors, or behavioral motifs. However, identifying such motifs
is a challenging problem. Behaviors have a multi-scale structure: the animal
can be simultaneously performing a small-scale motif and a large-scale one
(e.g. \textit{chewing} and \textit{feeding}). Motifs are compositional: a
large-scale motif is a chain of smaller-scale ones, folded in (some behavioral)
space in a specific manner. We demonstrate an approach which captures these
structures, using rat locomotor data as an example. From the same dataset, we
used a preprocessing procedure to create different versions, each describing
motifs of a different scale. We then trained several Hidden Markov Models
(HMMs) in parallel, one for each dataset version. This approach essentially
forced each HMM to learn motifs on a different scale, allowing us to capture
behavioral structures lost in previous approaches. By comparing HMMs with
models representing different null hypotheses, we found that rat locomotion was
composed of distinct motifs from second scale to minute scale. We found that
transitions between motifs were modulated by rats' location in the environment,
leading to non-Markovian transitions. To test the ethological relevance of
motifs we discovered, we compared their usage between rats with differences in
a high-level trait, prosociality. We found that these rats had distinct motif
repertoires, suggesting that motif usage statistics can be used to infer
internal states of rats. Our method is therefore an efficient way to discover
multi-scale, compositional structures in animal behaviors. It may also be
applied as a sensitive assay for internal states.
| [
{
"created": "Tue, 11 Jul 2017 16:55:48 GMT",
"version": "v1"
},
{
"created": "Wed, 23 Aug 2017 16:13:48 GMT",
"version": "v2"
},
{
"created": "Wed, 1 Jul 2020 14:51:26 GMT",
"version": "v3"
}
] | 2021-05-12 | [
[
"Shan",
"Haozhe",
""
],
[
"Mason",
"Peggy",
""
]
] | Behaviors of several laboratory animals can be modeled as sequences of stereotyped behaviors, or behavioral motifs. However, identifying such motifs is a challenging problem. Behaviors have a multi-scale structure: the animal can be simultaneously performing a small-scale motif and a large-scale one (e.g. \textit{chewing} and \textit{feeding}). Motifs are compositional: a large-scale motif is a chain of smaller-scale ones, folded in (some behavioral) space in a specific manner. We demonstrate an approach which captures these structures, using rat locomotor data as an example. From the same dataset, we used a preprocessing procedure to create different versions, each describing motifs of a different scale. We then trained several Hidden Markov Models (HMMs) in parallel, one for each dataset version. This approach essentially forced each HMM to learn motifs on a different scale, allowing us to capture behavioral structures lost in previous approaches. By comparing HMMs with models representing different null hypotheses, we found that rat locomotion was composed of distinct motifs from second scale to minute scale. We found that transitions between motifs were modulated by rats' location in the environment, leading to non-Markovian transitions. To test the ethological relevance of motifs we discovered, we compared their usage between rats with differences in a high-level trait, prosociality. We found that these rats had distinct motif repertoires, suggesting that motif usage statistics can be used to infer internal states of rats. Our method is therefore an efficient way to discover multi-scale, compositional structures in animal behaviors. It may also be applied as a sensitive assay for internal states. |
2004.02069 | Nikolai Slavov | Nikolai Slavov | Single-cell protein analysis by mass-spectrometry | keywords: single-cell analysis; single-cell proteomics;
mass-spectrometry; isobaric carrier; sample preparation; systems biology | Current Opinion in Chemical Biology (2020) | 10.1016/j.cbpa.2020.04.018 | null | q-bio.QM q-bio.BM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Human physiology and pathology arise from the coordinated interactions of
diverse single cells. However, analyzing single cells has been limited by the
low sensitivity and throughput of analytical methods. DNA sequencing has
recently made such analysis feasible for nucleic acids, but single-cell protein
analysis remains limited. Mass-spectrometry is the most powerful method for
protein analysis, but its application to single cells faces three major
challenges: Efficiently delivering proteins/peptides to MS detectors,
identifying their sequences, and scaling the analysis to many thousands of
single cells. These challenges have motivated corresponding solutions,
including SCoPE-design multiplexing and clean, automated, and miniaturized
sample preparation. Synergistically applied, these solutions enable quantifying
thousands of proteins across many single cells and establish a solid foundation
for further advances. Building upon this foundation, the SCoPE concept will
enable analyzing subcellular organelles and post-translational modifications
while increases in multiplexing capabilities will increase the throughput and
decrease cost.
| [
{
"created": "Sun, 5 Apr 2020 02:05:20 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Apr 2020 17:18:09 GMT",
"version": "v2"
},
{
"created": "Sat, 20 Jun 2020 19:51:36 GMT",
"version": "v3"
}
] | 2020-06-23 | [
[
"Slavov",
"Nikolai",
""
]
] | Human physiology and pathology arise from the coordinated interactions of diverse single cells. However, analyzing single cells has been limited by the low sensitivity and throughput of analytical methods. DNA sequencing has recently made such analysis feasible for nucleic acids, but single-cell protein analysis remains limited. Mass-spectrometry is the most powerful method for protein analysis, but its application to single cells faces three major challenges: Efficiently delivering proteins/peptides to MS detectors, identifying their sequences, and scaling the analysis to many thousands of single cells. These challenges have motivated corresponding solutions, including SCoPE-design multiplexing and clean, automated, and miniaturized sample preparation. Synergistically applied, these solutions enable quantifying thousands of proteins across many single cells and establish a solid foundation for further advances. Building upon this foundation, the SCoPE concept will enable analyzing subcellular organelles and post-translational modifications while increases in multiplexing capabilities will increase the throughput and decrease cost. |
1502.05667 | Giovanni Bussi | Sandro Bottaro, Francesco Di Palma and Giovanni Bussi | Towards de novo RNA 3D structure prediction | Accepted for publication on RNA & Disease | RNA & Disease 2, e544 (2015) | 10.14800/rd.544 | null | q-bio.BM physics.bio-ph physics.chem-ph physics.comp-ph | http://creativecommons.org/licenses/by/3.0/ | RNA is a fundamental class of biomolecules that mediate a large variety of
molecular processes within the cell. Computational algorithms can be of great
help in the understanding of RNA structure-function relationship. One of the
main challenges in this field is the development of structure-prediction
algorithms, which aim at the prediction of the three-dimensional (3D) native
fold from the sole knowledge of the sequence. In a recent paper, we have
introduced a scoring function for RNA structure prediction. Here, we analyze in
detail the performance of the method, we underline strengths and shortcomings,
and we discuss the results with respect to state-of-the-art techniques. These
observations provide a starting point for improving current methodologies, thus
paving the way to the advances of more accurate approaches for RNA 3D structure
prediction.
| [
{
"created": "Thu, 19 Feb 2015 18:42:46 GMT",
"version": "v1"
}
] | 2015-02-20 | [
[
"Bottaro",
"Sandro",
""
],
[
"Di Palma",
"Francesco",
""
],
[
"Bussi",
"Giovanni",
""
]
] | RNA is a fundamental class of biomolecules that mediate a large variety of molecular processes within the cell. Computational algorithms can be of great help in the understanding of RNA structure-function relationship. One of the main challenges in this field is the development of structure-prediction algorithms, which aim at the prediction of the three-dimensional (3D) native fold from the sole knowledge of the sequence. In a recent paper, we have introduced a scoring function for RNA structure prediction. Here, we analyze in detail the performance of the method, we underline strengths and shortcomings, and we discuss the results with respect to state-of-the-art techniques. These observations provide a starting point for improving current methodologies, thus paving the way to the advances of more accurate approaches for RNA 3D structure prediction. |
1409.1542 | Norman Poh | Norman Poh, Andrew McGovern and Simon de Lusignan | Towards automated identification of changes in laboratory measurement of
renal function: implications for longitudinal research and observing trends
in glomerular filtration rate (GFR) | null | null | null | TR-14-03 | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Introduction: Kidney function is reported using estimates of glomerular
filtration rate (eGFR). However, eGFR values are recorded without reference to
the creatinine (SCr) assays used to derive them, and newer assays were
introduced at different time points across laboratories in UK. These changes
may cause systematic bias in eGFR reported in routinely collected data; even
though laboratory reported eGFR values have a correction factor applied.
Design: An algorithm to detect changes in SCr which affect eGFR calculation
method by comparing the mapping of SCr values on to eGFR values across a
time-series of paired eGFR and SCr measurements.
Setting: Routinely collected primary care data from 20,000 people with the
richest renal function data from the Quality Improvement in Chronic Kidney
Disease (QICKD) trial.
Results: The algorithm identified a change in eGFR calculation method in 80
(63%) of the 127 included practices. This change was identified in 4,736
(23.7%) patient time series analysed. This change in calibration method was
found to cause a significant step change in reported eGFR values producing a
systematic bias. eGFR values could not be recalibrated by applying the
Modification of Diet in Renal Disease (MDRD) equation to the laboratory
reported SCr values.
Conclusions: This algorithm can identify laboratory changes in eGFR
calculation methods and changes in SCr assay. Failure to account for these
changes may misconstrue renal function changes over time. Researchers using
routine eGFR data should account for these effects.
| [
{
"created": "Wed, 3 Sep 2014 09:53:20 GMT",
"version": "v1"
}
] | 2014-09-05 | [
[
"Poh",
"Norman",
""
],
[
"McGovern",
"Andrew",
""
],
[
"de Lusignan",
"Simon",
""
]
] | Introduction: Kidney function is reported using estimates of glomerular filtration rate (eGFR). However, eGFR values are recorded without reference to the creatinine (SCr) assays used to derive them, and newer assays were introduced at different time points across laboratories in UK. These changes may cause systematic bias in eGFR reported in routinely collected data; even though laboratory reported eGFR values have a correction factor applied. Design: An algorithm to detect changes in SCr which affect eGFR calculation method by comparing the mapping of SCr values on to eGFR values across a time-series of paired eGFR and SCr measurements. Setting: Routinely collected primary care data from 20,000 people with the richest renal function data from the Quality Improvement in Chronic Kidney Disease (QICKD) trial. Results: The algorithm identified a change in eGFR calculation method in 80 (63%) of the 127 included practices. This change was identified in 4,736 (23.7%) patient time series analysed. This change in calibration method was found to cause a significant step change in reported eGFR values producing a systematic bias. eGFR values could not be recalibrated by applying the Modification of Diet in Renal Disease (MDRD) equation to the laboratory reported SCr values. Conclusions: This algorithm can identify laboratory changes in eGFR calculation methods and changes in SCr assay. Failure to account for these changes may misconstrue renal function changes over time. Researchers using routine eGFR data should account for these effects. |
1911.05663 | Rohan Gala | Rohan Gala, Nathan Gouwens, Zizhen Yao, Agata Budzillo, Osnat Penn,
Bosiljka Tasic, Gabe Murphy, Hongkui Zeng, Uygar S\"umb\"ul | A coupled autoencoder approach for multi-modal analysis of cell types | Main text : 10 pages, 5 figures. Supp text : 6 pages, 3 figures | null | null | null | q-bio.NC cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent developments in high throughput profiling of individual neurons have
spurred data driven exploration of the idea that there exist natural groupings
of neurons referred to as cell types. The promise of this idea is that the
immense complexity of brain circuits can be reduced, and effectively studied by
means of interactions between cell types. While clustering of neuron
populations based on a particular data modality can be used to define cell
types, such definitions are often inconsistent across different
characterization modalities. We pose this issue of cross-modal alignment as an
optimization problem and develop an approach based on coupled training of
autoencoders as a framework for such analyses. We apply this framework to a
Patch-seq dataset consisting of transcriptomic and electrophysiological
profiles for the same set of neurons to study consistency of representations
across modalities, and evaluate cross-modal data prediction ability. We explore
the problem where only a subset of neurons is characterized with more than one
modality, and demonstrate that representations learned by coupled autoencoders
can be used to identify types sampled only by a single modality.
| [
{
"created": "Wed, 6 Nov 2019 00:58:02 GMT",
"version": "v1"
}
] | 2019-11-14 | [
[
"Gala",
"Rohan",
""
],
[
"Gouwens",
"Nathan",
""
],
[
"Yao",
"Zizhen",
""
],
[
"Budzillo",
"Agata",
""
],
[
"Penn",
"Osnat",
""
],
[
"Tasic",
"Bosiljka",
""
],
[
"Murphy",
"Gabe",
""
],
[
"Zeng",
"Hongkui",
""
],
[
"Sümbül",
"Uygar",
""
]
] | Recent developments in high throughput profiling of individual neurons have spurred data driven exploration of the idea that there exist natural groupings of neurons referred to as cell types. The promise of this idea is that the immense complexity of brain circuits can be reduced, and effectively studied by means of interactions between cell types. While clustering of neuron populations based on a particular data modality can be used to define cell types, such definitions are often inconsistent across different characterization modalities. We pose this issue of cross-modal alignment as an optimization problem and develop an approach based on coupled training of autoencoders as a framework for such analyses. We apply this framework to a Patch-seq dataset consisting of transcriptomic and electrophysiological profiles for the same set of neurons to study consistency of representations across modalities, and evaluate cross-modal data prediction ability. We explore the problem where only a subset of neurons is characterized with more than one modality, and demonstrate that representations learned by coupled autoencoders can be used to identify types sampled only by a single modality. |
2208.09871 | Mattia Miotto | Greta Grassmann, Lorenzo Di Rienzo, Giorgio Gosti, Marco Leonetti,
Giancarlo Ruocco, Mattia Miotto, Edoardo Milanetti | Electrostatic complementarity at the interface drives transient
protein-protein interactions | 16 pages, 5 figures, 3 tables | Sci Rep 13, 10207 (2023) | 10.1038/s41598-023-37130-z | null | q-bio.BM physics.bio-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Understanding the molecular mechanisms driving the binding between
bio-molecules is a crucial challenge in molecular biology. In this respect,
characteristics like the preferentially hydrophobic composition of the binding
interfaces, the role of van der Waals interactions (short range forces), and
the consequent shape complementarity between the interacting molecular surfaces
are well established. However, no consensus has yet been reached on how and how
much electrostatic participates in the various stages of protein-protein
interactions. Here, we perform extensive analyses on a large dataset of protein
complexes for which both experimental binding affinity and pH data were
available. We found that (i) although different classes of dimers do not
present marked differences in the amino acid composition and charges
disposition in the binding region, (ii) homodimers with identical binding
region show higher electrostatic compatibility with respect to both homodimers
with non-identical binding region and heterodimers. The level of electrostatic
compatibility also varies with the pH of the complex, reaching the lowest
values for low pH. Interestingly, (iii) shape and electrostatic complementarity
behave oppositely when one stratifies the complexes by their binding affinity.
Conversely, complexes with low values of binding affinity exploit Coulombic
complementarity to acquire specificity, suggesting that electrostatic
complementarity may play a greater role in transient (or less stable)
complexes. In light of these results, (iv) we provide a fast and efficient
method to measure electrostatic complementarity without the need of knowing the
complex structure. Expanding the electrostatic potential on a basis of 2D
orthogonal polynomials, we can discriminate between transient and permanent
protein complexes with an AUC of the ROC of 0.8.
| [
{
"created": "Sun, 21 Aug 2022 11:55:07 GMT",
"version": "v1"
}
] | 2023-10-23 | [
[
"Grassmann",
"Greta",
""
],
[
"Di Rienzo",
"Lorenzo",
""
],
[
"Gosti",
"Giorgio",
""
],
[
"Leonetti",
"Marco",
""
],
[
"Ruocco",
"Giancarlo",
""
],
[
"Miotto",
"Mattia",
""
],
[
"Milanetti",
"Edoardo",
""
]
] | Understanding the molecular mechanisms driving the binding between bio-molecules is a crucial challenge in molecular biology. In this respect, characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions (short range forces), and the consequent shape complementarity between the interacting molecular surfaces are well established. However, no consensus has yet been reached on how and how much electrostatic participates in the various stages of protein-protein interactions. Here, we perform extensive analyses on a large dataset of protein complexes for which both experimental binding affinity and pH data were available. We found that (i) although different classes of dimers do not present marked differences in the amino acid composition and charges disposition in the binding region, (ii) homodimers with identical binding region show higher electrostatic compatibility with respect to both homodimers with non-identical binding region and heterodimers. The level of electrostatic compatibility also varies with the pH of the complex, reaching the lowest values for low pH. Interestingly, (iii) shape and electrostatic complementarity behave oppositely when one stratifies the complexes by their binding affinity. Conversely, complexes with low values of binding affinity exploit Coulombic complementarity to acquire specificity, suggesting that electrostatic complementarity may play a greater role in transient (or less stable) complexes. In light of these results, (iv) we provide a fast and efficient method to measure electrostatic complementarity without the need of knowing the complex structure. Expanding the electrostatic potential on a basis of 2D orthogonal polynomials, we can discriminate between transient and permanent protein complexes with an AUC of the ROC of 0.8. |
2207.07215 | Mohsen Annabestani | Ali Olyanasab, Zahra Meskar, Mohsen Annabestani, Ali Mousavi Shaegh,
and Mehdi Fardmanesh | Warp and Weft Wiring method for rapid, modifiable, self-aligned, and
bonding-free fabrication of multi electrodes microfluidic sensors | null | null | null | null | q-bio.QM physics.app-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The need for rapid fabrication of microfluidic devices has become
increasingly critical as microfluidics become part of biomedical sensors. Using
Warp and Weft Wiring (WWW) of copper wires, this paper presents a novel
low-cost method for rapid, self-aligned, bonding-free, and modifiable
fabrication of multi-electrodes microfluidic sensors. All the proposed features
are promising and highly recommended for the development of Point-of-Care Tests
(POCTs), while most of the conventional methods have low chances of coming out
of the research labs and play no role in POCTs development. To have an
experimental proof of concept, the proposed chip was fabricated and then tested
with two sets of experiments that showed the potential applications of water
quality management, hygiene, biomedical impedance measurement, cell analysis,
flow cytometry, etc.
| [
{
"created": "Thu, 14 Jul 2022 21:42:49 GMT",
"version": "v1"
}
] | 2022-07-18 | [
[
"Olyanasab",
"Ali",
""
],
[
"Meskar",
"Zahra",
""
],
[
"Annabestani",
"Mohsen",
""
],
[
"Shaegh",
"Ali Mousavi",
""
],
[
"Fardmanesh",
"Mehdi",
""
]
] | The need for rapid fabrication of microfluidic devices has become increasingly critical as microfluidics become part of biomedical sensors. Using Warp and Weft Wiring (WWW) of copper wires, this paper presents a novel low-cost method for rapid, self-aligned, bonding-free, and modifiable fabrication of multi-electrodes microfluidic sensors. All the proposed features are promising and highly recommended for the development of Point-of-Care Tests (POCTs), while most of the conventional methods have low chances of coming out of the research labs and play no role in POCTs development. To have an experimental proof of concept, the proposed chip was fabricated and then tested with two sets of experiments that showed the potential applications of water quality management, hygiene, biomedical impedance measurement, cell analysis, flow cytometry, etc. |
2101.11929 | Shiva Rudraraju | Debabrata Auddya, Xiaoxuan Zhang, Rahul Gulati, Ritvik Vasan, Krishna
Garikipati, Padmini Rangamani, Shiva Rudraraju | Biomembranes undergo complex, non-axisymmetric deformations governed by
Kirchhoff-Love kinematics and revealed by a three dimensional computational
framework | null | null | 10.1098/rspa.2021.0246 | null | q-bio.QM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biomembranes play a central role in various phenomena like locomotion of
cells, cell-cell interactions, packaging of nutrients, and in maintaining
organelle morphology and functionality. During these processes, the membranes
undergo significant morphological changes through deformation, scission, and
fusion. Modeling the underlying mechanics of such morphological changes has
traditionally relied on reduced order axisymmetric representations of membrane
geometry and deformation. Axisymmetric representations, while robust and
extensively deployed, suffer from their inability to model symmetry breaking
deformations and structural bifurcations. To address this limitation, a 3D
computational mechanics framework for high fidelity modeling of biomembrane
deformation is presented. The proposed framework brings together Kirchhoff-Love
thin-shell kinematics, Helfrich-energy based mechanics, and state-of-the-art
numerical techniques for modeling deformation of surface geometries. Lipid
bilayers are represented as spline-based surfaces immersed in a 3D space; this
enables modeling of a wide spectrum of membrane geometries, boundary
conditions, and deformations that are physically admissible in a 3D space. The
mathematical basis of the framework and its numerical machinery are presented,
and their utility is demonstrated by modeling 3 classical, yet non-trivial,
membrane problems: formation of tubular shapes and their lateral constriction,
Piezo1-induced membrane footprint generation and gating response, and the
budding of membranes by protein coats during endocytosis. For each problem, the
full 3D membrane deformation is captured, potential symmetry-breaking
deformation paths identified, and various case studies of boundary and load
conditions are presented. Using the endocytic vesicle budding as a case study,
we also present a "phase diagram" for its symmetric and broken-symmetry states.
| [
{
"created": "Thu, 28 Jan 2021 11:04:49 GMT",
"version": "v1"
}
] | 2021-12-01 | [
[
"Auddya",
"Debabrata",
""
],
[
"Zhang",
"Xiaoxuan",
""
],
[
"Gulati",
"Rahul",
""
],
[
"Vasan",
"Ritvik",
""
],
[
"Garikipati",
"Krishna",
""
],
[
"Rangamani",
"Padmini",
""
],
[
"Rudraraju",
"Shiva",
""
]
] | Biomembranes play a central role in various phenomena like locomotion of cells, cell-cell interactions, packaging of nutrients, and in maintaining organelle morphology and functionality. During these processes, the membranes undergo significant morphological changes through deformation, scission, and fusion. Modeling the underlying mechanics of such morphological changes has traditionally relied on reduced order axisymmetric representations of membrane geometry and deformation. Axisymmetric representations, while robust and extensively deployed, suffer from their inability to model symmetry breaking deformations and structural bifurcations. To address this limitation, a 3D computational mechanics framework for high fidelity modeling of biomembrane deformation is presented. The proposed framework brings together Kirchhoff-Love thin-shell kinematics, Helfrich-energy based mechanics, and state-of-the-art numerical techniques for modeling deformation of surface geometries. Lipid bilayers are represented as spline-based surfaces immersed in a 3D space; this enables modeling of a wide spectrum of membrane geometries, boundary conditions, and deformations that are physically admissible in a 3D space. The mathematical basis of the framework and its numerical machinery are presented, and their utility is demonstrated by modeling 3 classical, yet non-trivial, membrane problems: formation of tubular shapes and their lateral constriction, Piezo1-induced membrane footprint generation and gating response, and the budding of membranes by protein coats during endocytosis. For each problem, the full 3D membrane deformation is captured, potential symmetry-breaking deformation paths identified, and various case studies of boundary and load conditions are presented. Using the endocytic vesicle budding as a case study, we also present a "phase diagram" for its symmetric and broken-symmetry states. |
2311.12040 | Xiaoqiong Xia | Xiaoqiong Xia, Chaoyu Zhu, Yuqi Shan, Fan Zhong, and Lei Liu | TransCDR: a deep learning model for enhancing the generalizability of
cancer drug response prediction through transfer learning and multimodal data
fusion for drug representation | 8 figures | null | null | null | q-bio.QM cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate and robust drug response prediction is of utmost importance in
precision medicine. Although many models have been developed to utilize the
representations of drugs and cancer cell lines for predicting cancer drug
responses (CDR), their performances can be improved by addressing issues such
as insufficient data modality, suboptimal fusion algorithms, and poor
generalizability for novel drugs or cell lines. We introduce TransCDR, which
uses transfer learning to learn drug representations and fuses multi-modality
features of drugs and cell lines by a self-attention mechanism, to predict the
IC50 values or sensitive states of drugs on cell lines. We are the first to
systematically evaluate the generalization of the CDR prediction model to novel
(i.e., never-before-seen) compound scaffolds and cell line clusters. TransCDR
shows better generalizability than 8 state-of-the-art models. TransCDR
outperforms its 5 variants that train drug encoders (i.e., RNN and AttentiveFP)
from scratch under various scenarios. The most critical contributors among
multiple drug notations and omics profiles are Extended Connectivity
Fingerprint and genetic mutation. Additionally, the attention-based fusion
module further enhances the predictive performance of TransCDR. TransCDR,
trained on the GDSC dataset, demonstrates strong predictive performance on the
external testing set CCLE. It is also utilized to predict missing CDRs on GDSC.
Moreover, we investigate the biological mechanisms underlying drug response by
classifying 7,675 patients from TCGA into drug-sensitive or drug-resistant
groups, followed by a Gene Set Enrichment Analysis. TransCDR emerges as a
potent tool with significant potential in drug response prediction. The source
code and data can be accessed at https://github.com/XiaoqiongXia/TransCDR.
| [
{
"created": "Fri, 17 Nov 2023 14:55:12 GMT",
"version": "v1"
}
] | 2023-11-22 | [
[
"Xia",
"Xiaoqiong",
""
],
[
"Zhu",
"Chaoyu",
""
],
[
"Shan",
"Yuqi",
""
],
[
"Zhong",
"Fan",
""
],
[
"Liu",
"Lei",
""
]
] | Accurate and robust drug response prediction is of utmost importance in precision medicine. Although many models have been developed to utilize the representations of drugs and cancer cell lines for predicting cancer drug responses (CDR), their performances can be improved by addressing issues such as insufficient data modality, suboptimal fusion algorithms, and poor generalizability for novel drugs or cell lines. We introduce TransCDR, which uses transfer learning to learn drug representations and fuses multi-modality features of drugs and cell lines by a self-attention mechanism, to predict the IC50 values or sensitive states of drugs on cell lines. We are the first to systematically evaluate the generalization of the CDR prediction model to novel (i.e., never-before-seen) compound scaffolds and cell line clusters. TransCDR shows better generalizability than 8 state-of-the-art models. TransCDR outperforms its 5 variants that train drug encoders (i.e., RNN and AttentiveFP) from scratch under various scenarios. The most critical contributors among multiple drug notations and omics profiles are Extended Connectivity Fingerprint and genetic mutation. Additionally, the attention-based fusion module further enhances the predictive performance of TransCDR. TransCDR, trained on the GDSC dataset, demonstrates strong predictive performance on the external testing set CCLE. It is also utilized to predict missing CDRs on GDSC. Moreover, we investigate the biological mechanisms underlying drug response by classifying 7,675 patients from TCGA into drug-sensitive or drug-resistant groups, followed by a Gene Set Enrichment Analysis. TransCDR emerges as a potent tool with significant potential in drug response prediction. The source code and data can be accessed at https://github.com/XiaoqiongXia/TransCDR. |
1702.05288 | Roland Kr\"amer | Ulrich Warttinger, Roland Kr\"amer | Instant determination of the potential biomarker heparan sulfate in
human plasma by a mix-and-read fluorescence assay | 18 pages, 5 figures, 2 schemes, 5 tables | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Heparan sulfate (HS) is a linear, polydisperse sulfated polysaccharide
belonging to the glycosaminoglycan family. HS proteoglycans are ubiquitously
found at the cell surface and extracellular matrix in animal species. HS is
involved in the interaction with a wide variety of proteins and the regulation
of many biological activities. In certain pathologic conditions, expression and
shedding of HS proteoglycans is overregulated, or enzymatic degradation of HS
in lysosomes is deficient, both leading to excess circulating free HS chains in
blood plasma. HS has therefore been suggested as a biomarker for various severe
disease states. The structural heterogeneity makes the quantification of
heparan sulfate in complex matrices such as human plasma challenging. HS plasma
levels are usually quantified by either disaccharide analysis or enzyme linked
immunosorbent assay(ELISA). Both methods require time-consuming
multistep-protocols. We describe here the instant detection of heparan sulfate
in spiked plasma samples by the Heparin Red Kit, a commercial mix-and-read
fluorescence microplate assay. The method enables HS quantification in the low
microgram per mL range without sample pretreatment. Heparin Red appears to be
sufficiently sensitive for the detection of highly elevated HS levels as
reported for mucopolysaccharidosis, graft versus host disease after
transplantation, dengue infection or septic shock. This study is a significant
step toward the development of a convenient and fast method for the
quantification of HS in human plasma, with the potential to simplify the
detection and advance the acceptance of HS as a biomarker.
| [
{
"created": "Fri, 17 Feb 2017 10:19:36 GMT",
"version": "v1"
}
] | 2017-02-20 | [
[
"Warttinger",
"Ulrich",
""
],
[
"Krämer",
"Roland",
""
]
] | Heparan sulfate (HS) is a linear, polydisperse sulfated polysaccharide belonging to the glycosaminoglycan family. HS proteoglycans are ubiquitously found at the cell surface and extracellular matrix in animal species. HS is involved in the interaction with a wide variety of proteins and the regulation of many biological activities. In certain pathologic conditions, expression and shedding of HS proteoglycans is overregulated, or enzymatic degradation of HS in lysosomes is deficient, both leading to excess circulating free HS chains in blood plasma. HS has therefore been suggested as a biomarker for various severe disease states. The structural heterogeneity makes the quantification of heparan sulfate in complex matrices such as human plasma challenging. HS plasma levels are usually quantified by either disaccharide analysis or enzyme linked immunosorbent assay(ELISA). Both methods require time-consuming multistep-protocols. We describe here the instant detection of heparan sulfate in spiked plasma samples by the Heparin Red Kit, a commercial mix-and-read fluorescence microplate assay. The method enables HS quantification in the low microgram per mL range without sample pretreatment. Heparin Red appears to be sufficiently sensitive for the detection of highly elevated HS levels as reported for mucopolysaccharidosis, graft versus host disease after transplantation, dengue infection or septic shock. This study is a significant step toward the development of a convenient and fast method for the quantification of HS in human plasma, with the potential to simplify the detection and advance the acceptance of HS as a biomarker. |
1810.12663 | Michael Adamer | Michael F Adamer, Heather A Harrington, Eamonn A Gaffney, Thomas E
Woolley | Coloured Noise from Stochastic Inflows in Reaction-Diffusion Systems | 31 pages, 8 figures | null | null | null | q-bio.QM physics.bio-ph q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present a framework for investigating coloured noise in
reaction-diffusion systems. We start by considering a deterministic
reaction-diffusion equation and show how external forcing can cause temporally
correlated or coloured noise. Here, the main source of external noise is
considered to be fluctuations in the parameter values representing the inflow
of particles to the system. First, we determine which reaction systems, driven
by extrinsic noise, can admit only one steady state, so that effects, such as
stochastic switching, are precluded from our analysis. To analyse the steady
state behaviour of reaction systems, even if the parameter values are changing,
necessitates a parameter-free approach, which has been central to algebraic
analysis in chemical reaction network theory. To identify suitable models we
use tools from real algebraic geometry that link the network structure to its
dynamical properties. We then make a connection to internal noise models and
show how power spectral methods can be used to predict stochastically driven
patterns in systems with coloured noise. In simple cases we show that the power
spectrum of the coloured noise process and the power spectrum of the
reaction-diffusion system modelled with white noise multiply to give the power
spectrum of the coloured noise reaction-diffusion system.
| [
{
"created": "Tue, 30 Oct 2018 11:19:58 GMT",
"version": "v1"
},
{
"created": "Fri, 30 Nov 2018 13:14:15 GMT",
"version": "v2"
}
] | 2018-12-03 | [
[
"Adamer",
"Michael F",
""
],
[
"Harrington",
"Heather A",
""
],
[
"Gaffney",
"Eamonn A",
""
],
[
"Woolley",
"Thomas E",
""
]
] | In this paper we present a framework for investigating coloured noise in reaction-diffusion systems. We start by considering a deterministic reaction-diffusion equation and show how external forcing can cause temporally correlated or coloured noise. Here, the main source of external noise is considered to be fluctuations in the parameter values representing the inflow of particles to the system. First, we determine which reaction systems, driven by extrinsic noise, can admit only one steady state, so that effects, such as stochastic switching, are precluded from our analysis. To analyse the steady state behaviour of reaction systems, even if the parameter values are changing, necessitates a parameter-free approach, which has been central to algebraic analysis in chemical reaction network theory. To identify suitable models we use tools from real algebraic geometry that link the network structure to its dynamical properties. We then make a connection to internal noise models and show how power spectral methods can be used to predict stochastically driven patterns in systems with coloured noise. In simple cases we show that the power spectrum of the coloured noise process and the power spectrum of the reaction-diffusion system modelled with white noise multiply to give the power spectrum of the coloured noise reaction-diffusion system. |
1704.00940 | Antonino Sciarrino | A. Sciarrino and P.Sorba | Symmetry and Minimum Principle at the Basis of the Genetic Code | To appear in BIOMAT 2016, 326 - 362, 2017 | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The importance of the notion of symmetry in physics is well established:
could it also be the case for the genetic code? In this spirit, a model for the
Genetic Code based on continuous symmetries and entitled the "Crystal Basis
Model" has been proposed a few years ago. The present paper is a review of the
model, of some of its first applications as well as of its recent developments.
Indeed, after a motivated presentation of our mathematical model, we illustrate
its pertinence by applying it for the elaboration and verification of sum rules
for codon usage probabilities, as well as for establishing relations and some
predictions between physical-chemical properties of amino-acids. Then, defining
in this context a "bio-spin" structure for the nucleotides and codons, the
interaction between a couple of codon-anticodon can simply be represented by a
(bio) spin-spin potential. This approach will constitute the second part of the
paper where, imposing the minimum energy principle, an analysis of the
evolution of the genetic code can be performed with good agreement with the
generally accepted scheme. A more precise study of this interaction model
provides informations on codon bias, consistent with data.
| [
{
"created": "Tue, 4 Apr 2017 10:17:00 GMT",
"version": "v1"
}
] | 2017-04-05 | [
[
"Sciarrino",
"A.",
""
],
[
"Sorba",
"P.",
""
]
] | The importance of the notion of symmetry in physics is well established: could it also be the case for the genetic code? In this spirit, a model for the Genetic Code based on continuous symmetries and entitled the "Crystal Basis Model" has been proposed a few years ago. The present paper is a review of the model, of some of its first applications as well as of its recent developments. Indeed, after a motivated presentation of our mathematical model, we illustrate its pertinence by applying it for the elaboration and verification of sum rules for codon usage probabilities, as well as for establishing relations and some predictions between physical-chemical properties of amino-acids. Then, defining in this context a "bio-spin" structure for the nucleotides and codons, the interaction between a couple of codon-anticodon can simply be represented by a (bio) spin-spin potential. This approach will constitute the second part of the paper where, imposing the minimum energy principle, an analysis of the evolution of the genetic code can be performed with good agreement with the generally accepted scheme. A more precise study of this interaction model provides informations on codon bias, consistent with data. |
1504.00698 | Richard McMurtrey | Richard J. McMurtrey | Novel Advancements in Three-Dimensional Neural Tissue Engineering and
Regenerative Medicine | null | Neural Regen. Res. 2015;10(3):352-4 | 10.4103/1673-5374.153674 | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neurological diseases and injuries present some of the greatest challenges in
modern medicine, often causing irreversible and lifelong burdens in the people
whom they afflict. These diagnoses have devastating consequences on millions of
people each year, and yet there are currently no therapies or interventions
that can repair the structure of neural circuits and restore neural tissue
function in the brain and spinal cord. Despite the challenges of overcoming
these limitations, there are many new approaches under development that hold
much promise. Neural tissue engineering aims to restore and influence the
function of damaged or diseased neural tissue generally through the use of stem
cells and biomaterials. In this paper, several new 3D tissue constructs and
designs are described for functional reconstruction of neural architecture.
With the use of induced pluripotent stem cells or induced neuronal cells, these
3D constructs could then be studied as regional models of the central nervous
system or could one day be implemented as autologous grafts into damaged sites
of the nervous system in order to restore neural function, particularly for
damaged sites of spinal cord, areas of stroke infarction, tumor resection
sites, peripheral nerve injuries, or areas of neurodegeneration.
| [
{
"created": "Thu, 2 Apr 2015 21:59:26 GMT",
"version": "v1"
}
] | 2015-04-06 | [
[
"McMurtrey",
"Richard J.",
""
]
] | Neurological diseases and injuries present some of the greatest challenges in modern medicine, often causing irreversible and lifelong burdens in the people whom they afflict. These diagnoses have devastating consequences on millions of people each year, and yet there are currently no therapies or interventions that can repair the structure of neural circuits and restore neural tissue function in the brain and spinal cord. Despite the challenges of overcoming these limitations, there are many new approaches under development that hold much promise. Neural tissue engineering aims to restore and influence the function of damaged or diseased neural tissue generally through the use of stem cells and biomaterials. In this paper, several new 3D tissue constructs and designs are described for functional reconstruction of neural architecture. With the use of induced pluripotent stem cells or induced neuronal cells, these 3D constructs could then be studied as regional models of the central nervous system or could one day be implemented as autologous grafts into damaged sites of the nervous system in order to restore neural function, particularly for damaged sites of spinal cord, areas of stroke infarction, tumor resection sites, peripheral nerve injuries, or areas of neurodegeneration. |
1412.6325 | Robert Leech | Peter J. Hellyer, Barbara Jachs, Robert Leech, Claudia Clopath | Local inhibitory plasticity tunes global brain dynamics and allows the
emergence of functional brain networks | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rich, spontaneous brain activity has been observed across a range of
different temporal and spatial scales. These dynamics are thought to be
important t for efficient neural functioning. Experimental evidence suggests
that these neural dynamics are maintained across a variety of different
cognitive states, in response to alterations of the environment and to changes
in brain configuration (e.g., across individuals, development and in many
neurological disorders). This suggests that the brain has evolved mechanisms to
stabilize dynamics and maintain them across a range of situations. Here, we
employ a local homeostatic inhibitory plasticity mechanism, balancing
inhibitory and excitatory activity in a model of macroscopic brain activity
based on white-matter structural connectivity. We demonstrate that the addition
of homeostatic plasticity regulates network activity and allows for the
emergence of rich, spontaneous dynamics across a range of brain configurations.
Furthermore, the presence of homeostatic plasticity maximises the overlap
between empirical and simulated patterns of functional connectivity. Therefore,
this work presents a simple, local, biologically plausible inhibitory mechanism
that allows stable dynamics to emerge in the brain and which facilitates the
formation of functional connectivity networks.
| [
{
"created": "Fri, 19 Dec 2014 13:08:04 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Jan 2015 00:02:22 GMT",
"version": "v2"
}
] | 2015-01-21 | [
[
"Hellyer",
"Peter J.",
""
],
[
"Jachs",
"Barbara",
""
],
[
"Leech",
"Robert",
""
],
[
"Clopath",
"Claudia",
""
]
] | Rich, spontaneous brain activity has been observed across a range of different temporal and spatial scales. These dynamics are thought to be important t for efficient neural functioning. Experimental evidence suggests that these neural dynamics are maintained across a variety of different cognitive states, in response to alterations of the environment and to changes in brain configuration (e.g., across individuals, development and in many neurological disorders). This suggests that the brain has evolved mechanisms to stabilize dynamics and maintain them across a range of situations. Here, we employ a local homeostatic inhibitory plasticity mechanism, balancing inhibitory and excitatory activity in a model of macroscopic brain activity based on white-matter structural connectivity. We demonstrate that the addition of homeostatic plasticity regulates network activity and allows for the emergence of rich, spontaneous dynamics across a range of brain configurations. Furthermore, the presence of homeostatic plasticity maximises the overlap between empirical and simulated patterns of functional connectivity. Therefore, this work presents a simple, local, biologically plausible inhibitory mechanism that allows stable dynamics to emerge in the brain and which facilitates the formation of functional connectivity networks. |
1907.05064 | Francesc Rossell\'o | Tom\'as M. Coronado, Mareike Fischer, Lina Herbst, Francesc
Rossell\'o, Kristina Wicke | On the minimum value of the Colless index and the bifurcating trees that
achieve it | 61 pages. This paper is the result of merging our previous preprints
arXiv:1903.11670 [q-bio.PE] and arXiv:1904.09771 [math.CO] into a single
joint manuscript. Several proofs are new | null | null | null | q-bio.PE cs.DM math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Measures of tree balance play an important role in the analysis of
phylogenetic trees. One of the oldest and most popular indices in this regard
is the Colless index for rooted bifurcating trees, introduced by Colless
(1982). While many of its statistical properties under different probabilistic
models for phylogenetic trees have already been established, little is known
about its minimum value and the trees that achieve it. In this manuscript, we
fill this gap in the literature. To begin with, we derive both recursive and
closed expressions for the minimum Colless index of a tree with $n$ leaves.
Surprisingly, these expressions show a connection between the minimum Colless
index and the so-called Blancmange curve, a fractal curve. We then fully
characterize the tree shapes that achieve this minimum value and we introduce
both an algorithm to generate them and a recurrence to count them. After
focusing on two extremal classes of trees with minimum Colless index (the
maximally balanced trees and the greedy from the bottom trees), we conclude by
showing that all trees with minimum Colless index also have minimum Sackin
index, another popular balance index.
| [
{
"created": "Thu, 11 Jul 2019 09:07:29 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Feb 2020 17:31:14 GMT",
"version": "v2"
}
] | 2020-02-18 | [
[
"Coronado",
"Tomás M.",
""
],
[
"Fischer",
"Mareike",
""
],
[
"Herbst",
"Lina",
""
],
[
"Rosselló",
"Francesc",
""
],
[
"Wicke",
"Kristina",
""
]
] | Measures of tree balance play an important role in the analysis of phylogenetic trees. One of the oldest and most popular indices in this regard is the Colless index for rooted bifurcating trees, introduced by Colless (1982). While many of its statistical properties under different probabilistic models for phylogenetic trees have already been established, little is known about its minimum value and the trees that achieve it. In this manuscript, we fill this gap in the literature. To begin with, we derive both recursive and closed expressions for the minimum Colless index of a tree with $n$ leaves. Surprisingly, these expressions show a connection between the minimum Colless index and the so-called Blancmange curve, a fractal curve. We then fully characterize the tree shapes that achieve this minimum value and we introduce both an algorithm to generate them and a recurrence to count them. After focusing on two extremal classes of trees with minimum Colless index (the maximally balanced trees and the greedy from the bottom trees), we conclude by showing that all trees with minimum Colless index also have minimum Sackin index, another popular balance index. |
1603.04023 | Greg Stephens | Onno D Broekmans, Jarlath B Rodgers, William S Ryu and Greg J Stephens | Resolving coiled shapes reveals new reorientation behaviors in C.
elegans | 14 pages and 8 figures, including supplementary information | eLife 2016;5:e17227 | 10.7554/eLife.17227 | null | q-bio.QM physics.bio-ph q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We exploit the reduced space of C. elegans postures to develop a novel
tracking algorithm which captures both simple shapes and also self-occluding
coils, an important, yet unexplored, component of worm behavior. We apply our
algorithm to show that visually complex, coiled sequences are a superposition
of two simpler patterns: the body wave dynamics and a head-curvature pulse. We
demonstrate the precise coiled dynamics of an escape response and uncover new
behaviors in spontaneous, large amplitude coils; deep reorientations occur
through classical Omega-shaped postures and also through larger, new postural
excitations which we label here as delta-turns. We find that omega and delta
turns occur independently, the serpentine analog of a random left-right step,
suggesting a distinct triggering mechanism. We also show that omega and delta
turns display approximately equal rates and adapt to food-free conditions on a
similar timescale, a simple strategy to avoid navigational bias.
| [
{
"created": "Sun, 13 Mar 2016 12:38:01 GMT",
"version": "v1"
}
] | 2016-11-01 | [
[
"Broekmans",
"Onno D",
""
],
[
"Rodgers",
"Jarlath B",
""
],
[
"Ryu",
"William S",
""
],
[
"Stephens",
"Greg J",
""
]
] | We exploit the reduced space of C. elegans postures to develop a novel tracking algorithm which captures both simple shapes and also self-occluding coils, an important, yet unexplored, component of worm behavior. We apply our algorithm to show that visually complex, coiled sequences are a superposition of two simpler patterns: the body wave dynamics and a head-curvature pulse. We demonstrate the precise coiled dynamics of an escape response and uncover new behaviors in spontaneous, large amplitude coils; deep reorientations occur through classical Omega-shaped postures and also through larger, new postural excitations which we label here as delta-turns. We find that omega and delta turns occur independently, the serpentine analog of a random left-right step, suggesting a distinct triggering mechanism. We also show that omega and delta turns display approximately equal rates and adapt to food-free conditions on a similar timescale, a simple strategy to avoid navigational bias. |
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