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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1204.6313 | Michael Deem | Jiankui He and Michael W. Deem | Low-dimensional clustering detects incipient dominant influenza strain
clusters | 50 pages, 6 figures, 1 table, supplement | Protein Engineering, Design & Selection 23 (2010) 935-946 | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Influenza has been circulating in the human population and has caused three
pandemics in the last century (1918 H1N1, 1957 H2N2, 1968 H3N2). The 2009
A(H1N1) was classified by the World Health Organization (WHO) as the fourth
pandemic. Influenza has a high evolution rate, which makes vaccine design
challenging. We here consider an approach for early detection of new dominant
strains. By clustering the 2009 A(H1N1) sequence data, we found two main
clusters. We then define a metric to detect the emergence of dominant strains.
We show on historical H3N2 data that this method is able to identify a cluster
around an incipient dominant strain before it becomes dominant. For example,
for H3N2 as of March 30, 2009, the method detects the cluster for the new
A/British Columbia/RV1222/2009 strain. This strain detection tool would appear
to be useful for annual influenza vaccine selection.
| [
{
"created": "Fri, 27 Apr 2012 14:48:15 GMT",
"version": "v1"
}
] | 2012-05-01 | [
[
"He",
"Jiankui",
""
],
[
"Deem",
"Michael W.",
""
]
] | Influenza has been circulating in the human population and has caused three pandemics in the last century (1918 H1N1, 1957 H2N2, 1968 H3N2). The 2009 A(H1N1) was classified by the World Health Organization (WHO) as the fourth pandemic. Influenza has a high evolution rate, which makes vaccine design challenging. We here consider an approach for early detection of new dominant strains. By clustering the 2009 A(H1N1) sequence data, we found two main clusters. We then define a metric to detect the emergence of dominant strains. We show on historical H3N2 data that this method is able to identify a cluster around an incipient dominant strain before it becomes dominant. For example, for H3N2 as of March 30, 2009, the method detects the cluster for the new A/British Columbia/RV1222/2009 strain. This strain detection tool would appear to be useful for annual influenza vaccine selection. |
q-bio/0509040 | German Andres Enciso | Bhaskar DasGupta, German Andres Enciso, Eduardo Sontag and Yi Zhang | Algorithmic and Complexity Results for Decompositions of Biological
Networks into Monotone Subsystems | 34 pages, 5 Postscript figures | null | null | null | q-bio.MN | null | A useful approach to the mathematical analysis of large-scale biological
networks is based upon their decompositions into monotone dynamical systems.
This paper deals with two computational problems associated to finding
decompositions which are optimal in an appropriate sense. In graph-theoretic
language, the problems can be recast in terms of maximal sign-consistent
subgraphs. The theoretical results include polynomial-time approximation
algorithms as well as constant-ratio inapproximability results. One of the
algorithms, which has a worst-case guarantee of 87.9 percent from optimality,
is based on the semidefinite programming relaxation approach of
Goemans-Williamson. The algorithm was implemented and tested on a Drosophila
segmentation network and an Epidermal Growth Factor Receptor pathway model, and
it was found to perform close to optimally.
| [
{
"created": "Tue, 27 Sep 2005 23:03:46 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"DasGupta",
"Bhaskar",
""
],
[
"Enciso",
"German Andres",
""
],
[
"Sontag",
"Eduardo",
""
],
[
"Zhang",
"Yi",
""
]
] | A useful approach to the mathematical analysis of large-scale biological networks is based upon their decompositions into monotone dynamical systems. This paper deals with two computational problems associated to finding decompositions which are optimal in an appropriate sense. In graph-theoretic language, the problems can be recast in terms of maximal sign-consistent subgraphs. The theoretical results include polynomial-time approximation algorithms as well as constant-ratio inapproximability results. One of the algorithms, which has a worst-case guarantee of 87.9 percent from optimality, is based on the semidefinite programming relaxation approach of Goemans-Williamson. The algorithm was implemented and tested on a Drosophila segmentation network and an Epidermal Growth Factor Receptor pathway model, and it was found to perform close to optimally. |
1801.10227 | Song Feng | Ryan Suderman, Eshan D. Mitra, Yen Ting Lin, Keesha E. Erickson, Song
Feng, William S. Hlavacek | Generalizing Gillespie's direct method to enable network-free
simulations | 27 pages, 6 figures | null | null | null | q-bio.QM q-bio.MN q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gillespie's direct method for stochastic simulation of chemical kinetics is a
staple of computational systems biology research. However, the algorithm
requires explicit enumeration of all reactions and all chemical species that
may arise in the system. In many cases, this is not feasible due to the
combinatorial explosion of reactions and species in biological networks.
Rule-based modeling frameworks provide a way to exactly represent networks
containing such combinatorial complexity, and generalizations of Gillespie's
direct method have been developed as simulation engines for rule-based modeling
languages. Here, we provide both a high-level description of the algorithms
underlying the simulation engines, termed network-free simulation algorithms,
and how they have been applied in systems biology research. We also define a
generic rule-based modeling framework and describe a number of technical
details required for adapting Gillespie's direct method for network-free
simulation. Finally, we briefly discuss potential avenues for advancing
network-free simulation and the role they continue to play in modeling
dynamical systems in biology.
| [
{
"created": "Tue, 30 Jan 2018 21:13:04 GMT",
"version": "v1"
}
] | 2018-02-01 | [
[
"Suderman",
"Ryan",
""
],
[
"Mitra",
"Eshan D.",
""
],
[
"Lin",
"Yen Ting",
""
],
[
"Erickson",
"Keesha E.",
""
],
[
"Feng",
"Song",
""
],
[
"Hlavacek",
"William S.",
""
]
] | Gillespie's direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie's direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termed network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie's direct method for network-free simulation. Finally, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology. |
q-bio/0701047 | Hermann Riecke | Hermann Riecke, Alex Roxin, Santiago Madruga, Sara A. Solla | Many Attractors, Long Chaotic Transients, and Failure in Small-World
Networks of Excitable Neurons | 14 pages 23 figures | null | 10.1063/1.2743611 | null | q-bio.NC cond-mat.dis-nn nlin.CD physics.bio-ph | null | We study the dynamical states that emerge in a small-world network of
recurrently coupled excitable neurons through both numerical and analytical
methods. These dynamics depend in large part on the fraction of long-range
connections or `short-cuts' and the delay in the neuronal interactions.
Persistent activity arises for a small fraction of `short-cuts', while a
transition to failure occurs at a critical value of the `short-cut' density.
The persistent activity consists of multi-stable periodic attractors, the
number of which is at least on the order of the number of neurons in the
network. For long enough delays, network activity at high `short-cut' densities
is shown to exhibit exceedingly long chaotic transients whose failure-times
averaged over many network configurations follow a stretched exponential. We
show how this functional form arises in the ensemble-averaged activity if each
network realization has a characteristic failure-time which is exponentially
distributed.
| [
{
"created": "Sun, 28 Jan 2007 21:57:45 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Riecke",
"Hermann",
""
],
[
"Roxin",
"Alex",
""
],
[
"Madruga",
"Santiago",
""
],
[
"Solla",
"Sara A.",
""
]
] | We study the dynamical states that emerge in a small-world network of recurrently coupled excitable neurons through both numerical and analytical methods. These dynamics depend in large part on the fraction of long-range connections or `short-cuts' and the delay in the neuronal interactions. Persistent activity arises for a small fraction of `short-cuts', while a transition to failure occurs at a critical value of the `short-cut' density. The persistent activity consists of multi-stable periodic attractors, the number of which is at least on the order of the number of neurons in the network. For long enough delays, network activity at high `short-cut' densities is shown to exhibit exceedingly long chaotic transients whose failure-times averaged over many network configurations follow a stretched exponential. We show how this functional form arises in the ensemble-averaged activity if each network realization has a characteristic failure-time which is exponentially distributed. |
q-bio/0409033 | Ingrid Hartmann | Ingrid Hartmann | A more comprehensive formulation of evolutionary equations | 12 pages | null | null | null | q-bio.PE | null | As mathematical model for the evolutionary equations of species the
masterequation is choiced. Two formulations will be demonstrated to include the
changes of parameters into the masterequation - that is, on the one hand, the
formation of a second masterequation for the development of parameters, and, on
the other hand, the use of the Wigner-distribution to describe the development
of parameters. Moreover, the Wigner-distribution is used to describe morphic
fields and involved in the theorie of selforganization.
| [
{
"created": "Tue, 28 Sep 2004 20:45:42 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Hartmann",
"Ingrid",
""
]
] | As mathematical model for the evolutionary equations of species the masterequation is choiced. Two formulations will be demonstrated to include the changes of parameters into the masterequation - that is, on the one hand, the formation of a second masterequation for the development of parameters, and, on the other hand, the use of the Wigner-distribution to describe the development of parameters. Moreover, the Wigner-distribution is used to describe morphic fields and involved in the theorie of selforganization. |
1802.07204 | Haiming Tang | Haiming Tang, Paul D Thomas, Huaiyu Mi | Automation of gene function prediction through modeling human curators'
decisions in GO phylogenetic annotation project | null | null | null | null | q-bio.QM q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | The Gene Ontology Consortium launched the GO-PAINT project (Phylogenetic
Annotation and INference Tool) 9 years ago and is currently being used in the
GO Reference Genome Annotation Project to support inference of GO function
terms (molecular function, cellular component and biological process) by
homology. PAINT uses a phylogenetic model to infer gene function by homology, a
process that requires manual curation of experienced biocurators. Tremendous
amount of time and efforts have been spent on the GO-PAINT project yielding
more than 4000 fully annotated phylogenetic families with more than 170,000
annotations. These preliminary data have thus enabled potential algorithmic
representation and automatic solvation of the additional 9000 unannoated
phylogenetic families. Here we present an automated pipeline for phylogenetic
annotation and inference, which simulates the standard annotation procedures of
curators and models the curators' decisions during the manual curation process.
The pipeline has been built into the newest version of PAINT software available
at http://www.pantherdb.org/downloads/index.jsp. The standalone automation
pipeline and datasets are available at
https://github.com/haimingt/GO-PAINT-automation
| [
{
"created": "Tue, 20 Feb 2018 17:07:46 GMT",
"version": "v1"
}
] | 2018-02-21 | [
[
"Tang",
"Haiming",
""
],
[
"Thomas",
"Paul D",
""
],
[
"Mi",
"Huaiyu",
""
]
] | The Gene Ontology Consortium launched the GO-PAINT project (Phylogenetic Annotation and INference Tool) 9 years ago and is currently being used in the GO Reference Genome Annotation Project to support inference of GO function terms (molecular function, cellular component and biological process) by homology. PAINT uses a phylogenetic model to infer gene function by homology, a process that requires manual curation of experienced biocurators. Tremendous amount of time and efforts have been spent on the GO-PAINT project yielding more than 4000 fully annotated phylogenetic families with more than 170,000 annotations. These preliminary data have thus enabled potential algorithmic representation and automatic solvation of the additional 9000 unannoated phylogenetic families. Here we present an automated pipeline for phylogenetic annotation and inference, which simulates the standard annotation procedures of curators and models the curators' decisions during the manual curation process. The pipeline has been built into the newest version of PAINT software available at http://www.pantherdb.org/downloads/index.jsp. The standalone automation pipeline and datasets are available at https://github.com/haimingt/GO-PAINT-automation |
1910.09112 | Aditya Narayan Singh | Aditya N. Singh and Arun Yethiraj | The Driving Force for the Complexation of Charged Polypeptides | null | null | null | null | q-bio.BM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The phase separation of oppositely-charged polyelectrolytes in solution is of
current interest . In this work we study the driving force for polyelectrolyte
complexation using molecular dynamics simulations. We calculate the potential
of mean force between poly(lysine) and poly(glutamate) oligomers using three
different forcefields, an atomistic force field and two coarse-grained force
fields. There is qualitative agreement between all forcefields, i.e., the sign
and magnitude of the free energy and the nature of the driving force are
similar, which suggests that the molecular nature of water does not play a
significant role. For fully charged peptides, we find that the driving force
for association is entropic in all cases when small ions either neutralize the
poly-ions, or are in excess. The removal of all counterions switches the
driving force, making complexation energetic. This suggests that the entropy of
complexation is dominated by the counterions. When only 6 residues of a 11-mer
are charged, however, the driving force is enthalpic in salt-free conditions.
The simulations shed insight into the mechanism of complex coacervation and the
importance of realistic models for the polyions.
| [
{
"created": "Mon, 21 Oct 2019 02:04:36 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Jan 2020 16:26:39 GMT",
"version": "v2"
}
] | 2020-01-08 | [
[
"Singh",
"Aditya N.",
""
],
[
"Yethiraj",
"Arun",
""
]
] | The phase separation of oppositely-charged polyelectrolytes in solution is of current interest . In this work we study the driving force for polyelectrolyte complexation using molecular dynamics simulations. We calculate the potential of mean force between poly(lysine) and poly(glutamate) oligomers using three different forcefields, an atomistic force field and two coarse-grained force fields. There is qualitative agreement between all forcefields, i.e., the sign and magnitude of the free energy and the nature of the driving force are similar, which suggests that the molecular nature of water does not play a significant role. For fully charged peptides, we find that the driving force for association is entropic in all cases when small ions either neutralize the poly-ions, or are in excess. The removal of all counterions switches the driving force, making complexation energetic. This suggests that the entropy of complexation is dominated by the counterions. When only 6 residues of a 11-mer are charged, however, the driving force is enthalpic in salt-free conditions. The simulations shed insight into the mechanism of complex coacervation and the importance of realistic models for the polyions. |
2012.00459 | Cheng Zhang | Cheng Zhang | Improved Variational Bayesian Phylogenetic Inference with Normalizing
Flows | NeurIPS 2020 | null | null | null | q-bio.PE stat.ML | http://creativecommons.org/licenses/by/4.0/ | Variational Bayesian phylogenetic inference (VBPI) provides a promising
general variational framework for efficient estimation of phylogenetic
posteriors. However, the current diagonal Lognormal branch length approximation
would significantly restrict the quality of the approximating distributions. In
this paper, we propose a new type of VBPI, VBPI-NF, as a first step to empower
phylogenetic posterior estimation with deep learning techniques. By handling
the non-Euclidean branch length space of phylogenetic models with carefully
designed permutation equivariant transformations, VBPI-NF uses normalizing
flows to provide a rich family of flexible branch length distributions that
generalize across different tree topologies. We show that VBPI-NF significantly
improves upon the vanilla VBPI on a benchmark of challenging real data Bayesian
phylogenetic inference problems. Further investigation also reveals that the
structured parameterization in those permutation equivariant transformations
can provide additional amortization benefit.
| [
{
"created": "Tue, 1 Dec 2020 13:10:00 GMT",
"version": "v1"
}
] | 2020-12-02 | [
[
"Zhang",
"Cheng",
""
]
] | Variational Bayesian phylogenetic inference (VBPI) provides a promising general variational framework for efficient estimation of phylogenetic posteriors. However, the current diagonal Lognormal branch length approximation would significantly restrict the quality of the approximating distributions. In this paper, we propose a new type of VBPI, VBPI-NF, as a first step to empower phylogenetic posterior estimation with deep learning techniques. By handling the non-Euclidean branch length space of phylogenetic models with carefully designed permutation equivariant transformations, VBPI-NF uses normalizing flows to provide a rich family of flexible branch length distributions that generalize across different tree topologies. We show that VBPI-NF significantly improves upon the vanilla VBPI on a benchmark of challenging real data Bayesian phylogenetic inference problems. Further investigation also reveals that the structured parameterization in those permutation equivariant transformations can provide additional amortization benefit. |
1307.6028 | Daniela Andres Dr | Daniela Sabrina Andres, Florian Gomez, Daniel Cerquetti, Marcelo
Merello and Ruedi Stoop | A hierarchical coding-window model of Parkinson's disease | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Parkinson's disease is an ongoing challenge to theoretical neuroscience and
to medical treatment. During the evolution of the disease, neurodegeneration
leads to physiological and anatomical changes that affect the neuronal
discharge of the Basal Ganglia to an extent that impairs normal behavioral
patterns. To investigate this problem, single Globus Pallidus pars interna
(GPi) neurons of the 6-OHDA rat model of Parkinson's disease were
extracellularly recorded at different degrees of alertness and compared to
non-Parkinson control neurons. A structure function analysis of these data
revealed that the temporal range of rate-coded information in GPi was
substantially reduced in the Parkinson animal-model, suggesting that a
dominance of small neighborhood dynamics could be the hallmark of Parkinson's
disease. A mathematical-model of the GPi circuit, where the small neighborhood
coupling is expressed in terms of a diffusion constant, corroborates this
interpretation.
| [
{
"created": "Tue, 23 Jul 2013 11:45:49 GMT",
"version": "v1"
},
{
"created": "Fri, 6 Dec 2013 10:45:15 GMT",
"version": "v2"
},
{
"created": "Thu, 6 Mar 2014 14:03:37 GMT",
"version": "v3"
}
] | 2014-03-07 | [
[
"Andres",
"Daniela Sabrina",
""
],
[
"Gomez",
"Florian",
""
],
[
"Cerquetti",
"Daniel",
""
],
[
"Merello",
"Marcelo",
""
],
[
"Stoop",
"Ruedi",
""
]
] | Parkinson's disease is an ongoing challenge to theoretical neuroscience and to medical treatment. During the evolution of the disease, neurodegeneration leads to physiological and anatomical changes that affect the neuronal discharge of the Basal Ganglia to an extent that impairs normal behavioral patterns. To investigate this problem, single Globus Pallidus pars interna (GPi) neurons of the 6-OHDA rat model of Parkinson's disease were extracellularly recorded at different degrees of alertness and compared to non-Parkinson control neurons. A structure function analysis of these data revealed that the temporal range of rate-coded information in GPi was substantially reduced in the Parkinson animal-model, suggesting that a dominance of small neighborhood dynamics could be the hallmark of Parkinson's disease. A mathematical-model of the GPi circuit, where the small neighborhood coupling is expressed in terms of a diffusion constant, corroborates this interpretation. |
2003.06584 | Quratul Ain Dr. | Qurat-ul-Ain, Abhijit Basu, Sebistein Iben, M. Iqbal Choudhary, and
Karin Scharffetter-Kochanek | A Novel Bis-Coumarin Targets Multiple Tyrosine Kinases of Key Signaling
Pathways in Melanoma and Inhibits Melanoma Cell Survival, Proliferation, and
Migration | 16 Pages, 6 Figures | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Melanoma is one of the most dangerous skin malignancies due to its high
metastatic tendency and high mortality. Activation of key signaling pathways
enforcing melanoma progression depends on phosphorylation of tyrosine kinases,
and oxidative stress. We here investigated the effect of the new bis-coumarin
derivative (3,5-DCPBC) on human melanoma cell survival, growth, proliferation,
migration, and intracellular redox state, and deciphered associated signal
pathways. This novel derivative was found to be toxic for melanoma cells, and
non-toxic for their benign counterparts, melanocytes and fibroblasts. 3,5-DCPBC
inhibited cell survival, migration and proliferation of different metastatic,
and non-metastatic melanoma cell lines through the profound suppression of
phosphorylation of the Epidermal Growth Factor receptor, and related downstream
pathways. Suppression of phosphorylation of key downstream transcription
factors and different tyrosine kinases comprise JAK/STAT, SRC kinases, ERK and
MAP kinases (p38alpha), all involved in melanoma progression. Simultaneous and
specific targeting of multiple tyrosine kinases and corresponding key genes in
melanoma cells makes 3,5-DCPBC a highly interesting anti-melanoma, and
anti-metastatic drug candidate which may in the long term hold promise in the
therapy of advanced melanoma.
| [
{
"created": "Sat, 14 Mar 2020 09:28:26 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Apr 2020 15:43:51 GMT",
"version": "v2"
}
] | 2020-04-21 | [
[
"Qurat-ul-Ain",
"",
""
],
[
"Basu",
"Abhijit",
""
],
[
"Iben",
"Sebistein",
""
],
[
"Choudhary",
"M. Iqbal",
""
],
[
"Scharffetter-Kochanek",
"Karin",
""
]
] | Melanoma is one of the most dangerous skin malignancies due to its high metastatic tendency and high mortality. Activation of key signaling pathways enforcing melanoma progression depends on phosphorylation of tyrosine kinases, and oxidative stress. We here investigated the effect of the new bis-coumarin derivative (3,5-DCPBC) on human melanoma cell survival, growth, proliferation, migration, and intracellular redox state, and deciphered associated signal pathways. This novel derivative was found to be toxic for melanoma cells, and non-toxic for their benign counterparts, melanocytes and fibroblasts. 3,5-DCPBC inhibited cell survival, migration and proliferation of different metastatic, and non-metastatic melanoma cell lines through the profound suppression of phosphorylation of the Epidermal Growth Factor receptor, and related downstream pathways. Suppression of phosphorylation of key downstream transcription factors and different tyrosine kinases comprise JAK/STAT, SRC kinases, ERK and MAP kinases (p38alpha), all involved in melanoma progression. Simultaneous and specific targeting of multiple tyrosine kinases and corresponding key genes in melanoma cells makes 3,5-DCPBC a highly interesting anti-melanoma, and anti-metastatic drug candidate which may in the long term hold promise in the therapy of advanced melanoma. |
2209.10468 | Stephen Gliske | Abdallah Alsammani, William C. Stacey, Stephen V. Gliske | Estimation of circular statistics in the presence of measurement bias | null | null | 10.1109/JBHI.2023.3334684 | null | q-bio.QM stat.ME | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Background and objective. Circular statistics and Rayleigh tests are
important tools for analyzing the occurrence of cyclic events. However, current
methods fail in the presence of measurement bias, such as incomplete or
otherwise non-uniform sampling. Consider, for example, studying 24-cyclicity
but having data not recorded uniformly over the full 24-hour cycle. The
objective of this paper is to present a method to estimate circular statistics
and their statistical significance even in this circumstance.
Methods. We present our objective as a special case of a more general
problem: estimating probability distributions in the context of imperfect
measurements, a highly studied problem in high energy physics. Our solution
combines 1) existing approaches that estimate the measurement process via
numeric simulation and 2) innovative use of linear parametrizations of the
underlying distributions. We compute the estimation error for several toy
examples as well as a real-world example: analyzing the 24-hour cyclicity of an
electrographic biomarker of epileptic tissue controlled for state of vigilance.
Results. Our method shows low estimation error. In a real-world example, we
observed the corrected moments had a root mean square residual less than 0.007.
We additionally found that, even with unfolding, Rayleigh test statistics still
often underestimate the p-values (and thus overestimate statistical
significance) in the presence of non-uniform sampling. Numerical estimation of
statistical significance, as described herein, is thus preferable.
Conclusions. The presented methods provide a robust solution to addressing
incomplete or otherwise non-uniform sampling. The general method presented is
also applicable to a wider set of analyses involving estimation of the true
probability distribution adjusted for imperfect measurement processes.
| [
{
"created": "Wed, 21 Sep 2022 16:10:55 GMT",
"version": "v1"
}
] | 2023-12-11 | [
[
"Alsammani",
"Abdallah",
""
],
[
"Stacey",
"William C.",
""
],
[
"Gliske",
"Stephen V.",
""
]
] | Background and objective. Circular statistics and Rayleigh tests are important tools for analyzing the occurrence of cyclic events. However, current methods fail in the presence of measurement bias, such as incomplete or otherwise non-uniform sampling. Consider, for example, studying 24-cyclicity but having data not recorded uniformly over the full 24-hour cycle. The objective of this paper is to present a method to estimate circular statistics and their statistical significance even in this circumstance. Methods. We present our objective as a special case of a more general problem: estimating probability distributions in the context of imperfect measurements, a highly studied problem in high energy physics. Our solution combines 1) existing approaches that estimate the measurement process via numeric simulation and 2) innovative use of linear parametrizations of the underlying distributions. We compute the estimation error for several toy examples as well as a real-world example: analyzing the 24-hour cyclicity of an electrographic biomarker of epileptic tissue controlled for state of vigilance. Results. Our method shows low estimation error. In a real-world example, we observed the corrected moments had a root mean square residual less than 0.007. We additionally found that, even with unfolding, Rayleigh test statistics still often underestimate the p-values (and thus overestimate statistical significance) in the presence of non-uniform sampling. Numerical estimation of statistical significance, as described herein, is thus preferable. Conclusions. The presented methods provide a robust solution to addressing incomplete or otherwise non-uniform sampling. The general method presented is also applicable to a wider set of analyses involving estimation of the true probability distribution adjusted for imperfect measurement processes. |
2404.12395 | Peter beim Graben | Peter beim Graben | A neural network account to Kant's philosophical aesthetics | 33 pages, 3 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | According to Kant's (1724 -- 1804) philosophical aesthetics, laid down in his
Critique of the Power of Judgement (1790), beauty is "subjective
purposefulness", reflected by the "harmony of the cognitive faculties", which
are "understanding" and "imagination". On the one hand, understanding refers to
the mental capability to find regularities in sensory manifolds, while
imagination refers to intuition, fantasy, and creativity of the mind, on the
other hand. Inspired by the reinforcement learning theory of Schmidhuber, I
present a neural network analogy for the harmony of the faculties in terms of
generative adversarial networks (GAN) - also often employed for artificial
music composition -- by identifying the generator module with the faculty of
imagination and the discriminator module with the faculty of understanding.
According to the GAN algorithm, both modules are engaged in an adversarial
game, thereby optimizing a particular objective function. In my reconstruction,
the convergence of the GAN algorithm during the reception of art, either music
or fine, entails the harmony of the faculties and thereby a neural network
analogue of subjective purposefulness, i.e., beauty.
| [
{
"created": "Fri, 12 Apr 2024 16:44:57 GMT",
"version": "v1"
}
] | 2024-04-22 | [
[
"Graben",
"Peter beim",
""
]
] | According to Kant's (1724 -- 1804) philosophical aesthetics, laid down in his Critique of the Power of Judgement (1790), beauty is "subjective purposefulness", reflected by the "harmony of the cognitive faculties", which are "understanding" and "imagination". On the one hand, understanding refers to the mental capability to find regularities in sensory manifolds, while imagination refers to intuition, fantasy, and creativity of the mind, on the other hand. Inspired by the reinforcement learning theory of Schmidhuber, I present a neural network analogy for the harmony of the faculties in terms of generative adversarial networks (GAN) - also often employed for artificial music composition -- by identifying the generator module with the faculty of imagination and the discriminator module with the faculty of understanding. According to the GAN algorithm, both modules are engaged in an adversarial game, thereby optimizing a particular objective function. In my reconstruction, the convergence of the GAN algorithm during the reception of art, either music or fine, entails the harmony of the faculties and thereby a neural network analogue of subjective purposefulness, i.e., beauty. |
2307.05519 | Kimmo Kartasalo | Xiaoyi Ji, Richard Salmon, Nita Mulliqi, Umair Khan, Yinxi Wang,
Anders Blilie, Henrik Olsson, Bodil Ginnerup Pedersen, Karina Dalsgaard
S{\o}rensen, Benedicte Parm Ulh{\o}i, Svein R Kjosavik, Emilius AM Janssen,
Mattias Rantalainen, Lars Egevad, Pekka Ruusuvuori, Martin Eklund, Kimmo
Kartasalo | Physical Color Calibration of Digital Pathology Scanners for Robust
Artificial Intelligence Assisted Cancer Diagnosis | null | null | null | null | q-bio.QM cs.AI cs.CV eess.IV | http://creativecommons.org/licenses/by/4.0/ | The potential of artificial intelligence (AI) in digital pathology is limited
by technical inconsistencies in the production of whole slide images (WSIs),
leading to degraded AI performance and posing a challenge for widespread
clinical application as fine-tuning algorithms for each new site is
impractical. Changes in the imaging workflow can also lead to compromised
diagnoses and patient safety risks. We evaluated whether physical color
calibration of scanners can standardize WSI appearance and enable robust AI
performance. We employed a color calibration slide in four different
laboratories and evaluated its impact on the performance of an AI system for
prostate cancer diagnosis on 1,161 WSIs. Color standardization resulted in
consistently improved AI model calibration and significant improvements in
Gleason grading performance. The study demonstrates that physical color
calibration provides a potential solution to the variation introduced by
different scanners, making AI-based cancer diagnostics more reliable and
applicable in clinical settings.
| [
{
"created": "Fri, 7 Jul 2023 12:02:54 GMT",
"version": "v1"
}
] | 2023-07-13 | [
[
"Ji",
"Xiaoyi",
""
],
[
"Salmon",
"Richard",
""
],
[
"Mulliqi",
"Nita",
""
],
[
"Khan",
"Umair",
""
],
[
"Wang",
"Yinxi",
""
],
[
"Blilie",
"Anders",
""
],
[
"Olsson",
"Henrik",
""
],
[
"Pedersen",
... | The potential of artificial intelligence (AI) in digital pathology is limited by technical inconsistencies in the production of whole slide images (WSIs), leading to degraded AI performance and posing a challenge for widespread clinical application as fine-tuning algorithms for each new site is impractical. Changes in the imaging workflow can also lead to compromised diagnoses and patient safety risks. We evaluated whether physical color calibration of scanners can standardize WSI appearance and enable robust AI performance. We employed a color calibration slide in four different laboratories and evaluated its impact on the performance of an AI system for prostate cancer diagnosis on 1,161 WSIs. Color standardization resulted in consistently improved AI model calibration and significant improvements in Gleason grading performance. The study demonstrates that physical color calibration provides a potential solution to the variation introduced by different scanners, making AI-based cancer diagnostics more reliable and applicable in clinical settings. |
q-bio/0702009 | Brigitte Gaillard | Caroline Habold (DEPE-IPHC), Charlotte Foltzer-Jourdainne, Yvon Le
Maho (DEPE-IPHC), Jean-Herv\'e Lignot (DEPE-IPHC), Hugues Oudart (DEPE-IPHC) | Intestinal gluconeogenesis and glucose transport according to body fuel
availability in rats | null | J Physiol 566 (15/07/2005) 575-86 | 10.1113/jphysiol.2005.085217 | null | q-bio.PE | null | Intestinal hexose absorption and gluconeogenesis have been studied in
relation to refeeding after two different fasting phases: a long period of
protein sparing during which energy expenditure is derived from lipid oxidation
(phase II), and a later phase characterized by a rise in plasma corticosterone
triggering protein catabolism (phase III). Such a switch in body fuel uses,
leading to changes in body reserves and gluconeogenic precursors, could
modulate intestinal gluconeogenesis and glucose transport. The gene and protein
levels, and the cellular localization of the sodium-glucose cotransporter
SGLT1, and of GLUT5 and GLUT2, as well as that of the key gluconeogenic enzymes
phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (Glc6Pase)
were measured. PEPCK and Glc6Pase activities were also determined. In phase III
fasted rats, SGLT1 was up-regulated and intestinal glucose uptake rates were
higher than in phase II fasted and fed rats. PEPCK and Glc6Pase mRNA, protein
levels and activities also increased in phase III. GLUT5 and GLUT2 were
down-regulated throughout the fast, but increased after refeeding, with GLUT2
recruited to the apical membrane. The increase in SGLT1 expression during phase
III may allow glucose absorption at low concentrations as soon as food is
available. Furthermore, an increased epithelial permeability due to fasting may
induce a paracellular movement of glucose. In the absence of intestinal GLUT2
during fasting, Glc6Pase could be involved in glucose release to the
bloodstream via membrane trafficking. Finally, refeeding triggered GLUT2 and
GLUT5 synthesis and apical recruitment of GLUT2, to absorb larger amounts of
hexoses.
| [
{
"created": "Tue, 6 Feb 2007 16:57:28 GMT",
"version": "v1"
}
] | 2016-08-14 | [
[
"Habold",
"Caroline",
"",
"DEPE-IPHC"
],
[
"Foltzer-Jourdainne",
"Charlotte",
"",
"DEPE-IPHC"
],
[
"Maho",
"Yvon Le",
"",
"DEPE-IPHC"
],
[
"Lignot",
"Jean-Hervé",
"",
"DEPE-IPHC"
],
[
"Oudart",
"Hugues",
"",
"DEPE-IPHC... | Intestinal hexose absorption and gluconeogenesis have been studied in relation to refeeding after two different fasting phases: a long period of protein sparing during which energy expenditure is derived from lipid oxidation (phase II), and a later phase characterized by a rise in plasma corticosterone triggering protein catabolism (phase III). Such a switch in body fuel uses, leading to changes in body reserves and gluconeogenic precursors, could modulate intestinal gluconeogenesis and glucose transport. The gene and protein levels, and the cellular localization of the sodium-glucose cotransporter SGLT1, and of GLUT5 and GLUT2, as well as that of the key gluconeogenic enzymes phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (Glc6Pase) were measured. PEPCK and Glc6Pase activities were also determined. In phase III fasted rats, SGLT1 was up-regulated and intestinal glucose uptake rates were higher than in phase II fasted and fed rats. PEPCK and Glc6Pase mRNA, protein levels and activities also increased in phase III. GLUT5 and GLUT2 were down-regulated throughout the fast, but increased after refeeding, with GLUT2 recruited to the apical membrane. The increase in SGLT1 expression during phase III may allow glucose absorption at low concentrations as soon as food is available. Furthermore, an increased epithelial permeability due to fasting may induce a paracellular movement of glucose. In the absence of intestinal GLUT2 during fasting, Glc6Pase could be involved in glucose release to the bloodstream via membrane trafficking. Finally, refeeding triggered GLUT2 and GLUT5 synthesis and apical recruitment of GLUT2, to absorb larger amounts of hexoses. |
0908.1037 | Tidjani Negadi | Tidjani Negadi | A taylor-made arithmetic model of the genetic code and applications | Work presented at the Symmetry Festival 2009, 31-Jul.5-Aug. 2009,
Budapest, Hungary | Symmetry: Culture and Science, Vol. 20, Numbers 1-4, 2009 | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a completely new version of our arithmetic model of the standard
genetic code and compute in a straightforward manner the exact numeric
degeneracies of the five multiplets without any trick for the doublets and the
sextets, as we have done previously. We give also some interesting
applications.
| [
{
"created": "Fri, 7 Aug 2009 12:42:01 GMT",
"version": "v1"
}
] | 2009-08-10 | [
[
"Negadi",
"Tidjani",
""
]
] | We present a completely new version of our arithmetic model of the standard genetic code and compute in a straightforward manner the exact numeric degeneracies of the five multiplets without any trick for the doublets and the sextets, as we have done previously. We give also some interesting applications. |
1703.03065 | Elad Schneidman | Roy Harpaz, Ga\v{s}per Tka\v{c}ik, Elad Schneidman | Discrete modes of social information processing predict individual
behavior of fish in a group | null | null | 10.1073/pnas.1703817114 | null | q-bio.NC physics.bio-ph q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Individual computations and social interactions underlying collective
behavior in groups of animals are of great ethological, behavioral, and
theoretical interest. While complex individual behaviors have successfully been
parsed into small dictionaries of stereotyped behavioral modes, studies of
collective behavior largely ignored these findings; instead, their focus was on
inferring single, mode-independent social interaction rules that reproduced
macroscopic and often qualitative features of group behavior. Here we bring
these two approaches together to predict individual swimming patterns of adult
zebrafish in a group. We show that fish alternate between an active mode in
which they are sensitive to the swimming patterns of conspecifics, and a
passive mode where they ignore them. Using a model that accounts for these two
modes explicitly, we predict behaviors of individual fish with high accuracy,
outperforming previous approaches that assumed a single continuous computation
by individuals and simple metric or topological weighing of neighbors behavior.
At the group level, switching between active and passive modes is uncorrelated
among fish, yet correlated directional swimming behavior still emerges. Our
quantitative approach for studying complex, multi-modal individual behavior
jointly with emergent group behavior is readily extensible to additional
behavioral modes and their neural correlates, as well as to other species.
| [
{
"created": "Wed, 8 Mar 2017 22:55:45 GMT",
"version": "v1"
}
] | 2022-06-08 | [
[
"Harpaz",
"Roy",
""
],
[
"Tkačik",
"Gašper",
""
],
[
"Schneidman",
"Elad",
""
]
] | Individual computations and social interactions underlying collective behavior in groups of animals are of great ethological, behavioral, and theoretical interest. While complex individual behaviors have successfully been parsed into small dictionaries of stereotyped behavioral modes, studies of collective behavior largely ignored these findings; instead, their focus was on inferring single, mode-independent social interaction rules that reproduced macroscopic and often qualitative features of group behavior. Here we bring these two approaches together to predict individual swimming patterns of adult zebrafish in a group. We show that fish alternate between an active mode in which they are sensitive to the swimming patterns of conspecifics, and a passive mode where they ignore them. Using a model that accounts for these two modes explicitly, we predict behaviors of individual fish with high accuracy, outperforming previous approaches that assumed a single continuous computation by individuals and simple metric or topological weighing of neighbors behavior. At the group level, switching between active and passive modes is uncorrelated among fish, yet correlated directional swimming behavior still emerges. Our quantitative approach for studying complex, multi-modal individual behavior jointly with emergent group behavior is readily extensible to additional behavioral modes and their neural correlates, as well as to other species. |
0711.2616 | Ganesh Bagler Dr | Ganesh Bagler (Centre for Cellular and Molecular Biology, Hyderabad,
India) | Modeling Protein Contact Networks | Ph.D. thesis. 118 pages including 12 tables and 37 figures. LaTeX
source with included style files. Presently at National Centre for Biological
Sciences, Tata Institute of Fundamantal Research, Bangalore, India | null | null | null | q-bio.MN q-bio.BM | null | Proteins are an important class of biomolecules that serve as essential
building blocks of the cells. Their three-dimensional structures are
responsible for their functions. In this thesis we have investigated the
protein structures using a network theoretical approach. While doing so we used
a coarse-grained method, viz., complex network analysis. We model protein
structures at two length scales as Protein Contact Networks (PCN) and as
Long-range Interaction Networks (LINs). We found that proteins by virtue of
being characterised by high amount of clustering, are small-world networks.
Apart from the small-world nature, we found that proteins have another general
property, viz., assortativity. This is an interesting and exceptional finding
as all other complex networks (except for social networks) are known to be
disassortative. Importantly, we could identify one of the major topological
determinant of assortativity by building appropriate controls.
| [
{
"created": "Fri, 16 Nov 2007 05:19:25 GMT",
"version": "v1"
}
] | 2007-11-19 | [
[
"Bagler",
"Ganesh",
"",
"Centre for Cellular and Molecular Biology, Hyderabad,\n India"
]
] | Proteins are an important class of biomolecules that serve as essential building blocks of the cells. Their three-dimensional structures are responsible for their functions. In this thesis we have investigated the protein structures using a network theoretical approach. While doing so we used a coarse-grained method, viz., complex network analysis. We model protein structures at two length scales as Protein Contact Networks (PCN) and as Long-range Interaction Networks (LINs). We found that proteins by virtue of being characterised by high amount of clustering, are small-world networks. Apart from the small-world nature, we found that proteins have another general property, viz., assortativity. This is an interesting and exceptional finding as all other complex networks (except for social networks) are known to be disassortative. Importantly, we could identify one of the major topological determinant of assortativity by building appropriate controls. |
2311.09716 | Vittorio Lippi | Vittorio Lippi, Isabelle Daniela Walz, Tobias Heimbach, Simone Meier,
Jochen Brich, Christian Haverkamp, Christoph Maurer | Using the Built-in iPhone Body Tracking System for Neurological Tests:
The Example of Assessing Arm Weakness in Stroke Patients. A Preliminary
Evaluation of Accuracy and Performance | 8 pages. Presented at ICINCO 2023, november 2023 Rome (Italy) | In Proceedings of ICINCO 2023 - Volume 2, pages 181-188 ISBN:
978-989-758-670-5; ISSN: 2184-2809 | 10.5220/0000168300003543 | null | q-bio.NC cs.HC | http://creativecommons.org/licenses/by/4.0/ | Timely treatment of stroke is critical to minimize brain damage. Therefore,
efforts are being made to educate the public on detecting stroke symptoms,
e.g., face, arms, and speech test (FAST). In this position paper, we propose to
perform the arm weakness test using the integrated video tracking from an
iPhone - some general tests to assess the tracking quality and discuss
potential critical points. The test has been performed on 4 stroke patients.
The result is compared with the report of the clinician. Although presenting
some limitations, the system proved to be able to detect arm weakness as a
symptom of stroke. We envisage that introducing a portable body tracking system
in such clinical tests will provide advantages in terms of objectivity,
repeatability, and the possibility to record and compare groups of patients.
| [
{
"created": "Thu, 16 Nov 2023 09:49:28 GMT",
"version": "v1"
}
] | 2023-11-17 | [
[
"Lippi",
"Vittorio",
""
],
[
"Walz",
"Isabelle Daniela",
""
],
[
"Heimbach",
"Tobias",
""
],
[
"Meier",
"Simone",
""
],
[
"Brich",
"Jochen",
""
],
[
"Haverkamp",
"Christian",
""
],
[
"Maurer",
"Christoph",
... | Timely treatment of stroke is critical to minimize brain damage. Therefore, efforts are being made to educate the public on detecting stroke symptoms, e.g., face, arms, and speech test (FAST). In this position paper, we propose to perform the arm weakness test using the integrated video tracking from an iPhone - some general tests to assess the tracking quality and discuss potential critical points. The test has been performed on 4 stroke patients. The result is compared with the report of the clinician. Although presenting some limitations, the system proved to be able to detect arm weakness as a symptom of stroke. We envisage that introducing a portable body tracking system in such clinical tests will provide advantages in terms of objectivity, repeatability, and the possibility to record and compare groups of patients. |
q-bio/0409035 | Lawren Smithline | Lawren Smithline | Probabilistic pairwise sequence alignment | 17 pages, including figures | null | null | null | q-bio.PE q-bio.GN | null | We describe an new algorithm for visualizing an alignment of biological
sequences according to a probabilistic model of evolution. The resulting data
array is readily interpreted by the human eye and amenable to digital image
techniques. We present examples using mRNA sequences from mouse and rat: three
cytochromes, Rattus norvegicus Cyp2a1, Cyp2a2, (Medline: 90212624) and Mus
musculus Cyp2a12 (Medline: 93249380); and two zinc finger proteins, Mus
musculus zfp111 and zfp235 (Medline: 22683274). The underlying evolutionary
model is derived from one proposed by Thorne, Kishino, and Felsenstein and
improved by Hein and others. The demonstration implementation aligns two
sequences using time and memory quadratic in the mean sequence length. The
algorithm is extensible, after Hein, to multiple sequences. We mention a basic
method to reduce time and memory demands.
| [
{
"created": "Wed, 29 Sep 2004 19:27:18 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Smithline",
"Lawren",
""
]
] | We describe an new algorithm for visualizing an alignment of biological sequences according to a probabilistic model of evolution. The resulting data array is readily interpreted by the human eye and amenable to digital image techniques. We present examples using mRNA sequences from mouse and rat: three cytochromes, Rattus norvegicus Cyp2a1, Cyp2a2, (Medline: 90212624) and Mus musculus Cyp2a12 (Medline: 93249380); and two zinc finger proteins, Mus musculus zfp111 and zfp235 (Medline: 22683274). The underlying evolutionary model is derived from one proposed by Thorne, Kishino, and Felsenstein and improved by Hein and others. The demonstration implementation aligns two sequences using time and memory quadratic in the mean sequence length. The algorithm is extensible, after Hein, to multiple sequences. We mention a basic method to reduce time and memory demands. |
2203.13632 | Laura Cifuentes Fontanals | Laura Cifuentes-Fontanals, Elisa Tonello and Heike Siebert | Node and edge control strategy identification via trap spaces in Boolean
networks | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The study of control mechanisms of biological systems allows for interesting
applications in bioengineering and medicine, for instance in cell reprogramming
or drug target identification. A control strategy often consists of a set of
interventions that, by fixing the values of some components, ensure that the
long term dynamics of the controlled system is in a desired state. A common
approach to control in the Boolean framework consists in checking how the fixed
values propagate through the network, to establish whether the effect of
percolating the interventions is sufficient to induce the target state.
Although methods based uniquely on value percolation allow for efficient
computation, they can miss many control strategies. Exhaustive methods for
control strategy identification, on the other hand, often entail high
computational costs. In order to increase the number of control strategies
identified while still benefiting from an efficient implementation, we
introduce a method based on value percolation that uses trap spaces, subspaces
of the state space that are closed with respect to the dynamics, and that can
usually be easily computed in biological networks. The approach allows for node
interventions, which fix the value of certain components, and edge
interventions, which fix the effect that one component has on another. The
method is implemented using Answer Set Programming, extending an existing
efficient implementation of value percolation to allow for the use of trap
spaces and edge control. The applicability of the approach is studied for
different control targets in a biological case study, identifying in all cases
new control strategies that would escape usual percolation-based methods.
| [
{
"created": "Fri, 25 Mar 2022 13:11:14 GMT",
"version": "v1"
}
] | 2022-03-28 | [
[
"Cifuentes-Fontanals",
"Laura",
""
],
[
"Tonello",
"Elisa",
""
],
[
"Siebert",
"Heike",
""
]
] | The study of control mechanisms of biological systems allows for interesting applications in bioengineering and medicine, for instance in cell reprogramming or drug target identification. A control strategy often consists of a set of interventions that, by fixing the values of some components, ensure that the long term dynamics of the controlled system is in a desired state. A common approach to control in the Boolean framework consists in checking how the fixed values propagate through the network, to establish whether the effect of percolating the interventions is sufficient to induce the target state. Although methods based uniquely on value percolation allow for efficient computation, they can miss many control strategies. Exhaustive methods for control strategy identification, on the other hand, often entail high computational costs. In order to increase the number of control strategies identified while still benefiting from an efficient implementation, we introduce a method based on value percolation that uses trap spaces, subspaces of the state space that are closed with respect to the dynamics, and that can usually be easily computed in biological networks. The approach allows for node interventions, which fix the value of certain components, and edge interventions, which fix the effect that one component has on another. The method is implemented using Answer Set Programming, extending an existing efficient implementation of value percolation to allow for the use of trap spaces and edge control. The applicability of the approach is studied for different control targets in a biological case study, identifying in all cases new control strategies that would escape usual percolation-based methods. |
2111.04399 | J\'ozsef Z. Farkas | Jozsef Z. Farkas, Stephen A. Gourley, Rongsong Liu | Dengue transmission dynamics in age-structured human populations in the
presence of Wolbachia | null | null | null | null | q-bio.PE math.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | According to the World Health Organization the global incidence rate of
dengue infections have risen drastically in recent years. It is estimated that
globally the number of new infections is in the range of $100$ to $400$ million
per annum. At the same time a number of recent studies reported the existence
of Wolbachia strains, which inhibit dengue virus replication in mosquito
species that are primary vectors for dengue transmission. In this study we
focus on the impact of Wolbachia on dengue transmission dynamics in an
age-structured human population. We introduce a mathematical model, which takes
into account age-related effects, such as age-dependent human recovery and
mortality rates, as well as age-dependent vector to human dengue transmission
efficacy. We deduce an explicit formula for the basic reproduction number
$\mathcal{R}_0$, which allows us to assess the impact of the above mentioned
age-related effects on the local asymptotic stability of the dengue free
equilibrium. The formula we deduce for $\mathcal{R}_0$ also shows the intricate
relationship between human demography and the presence of a dengue inhibiting
Wolbachia strain.
| [
{
"created": "Mon, 8 Nov 2021 12:05:25 GMT",
"version": "v1"
}
] | 2021-11-09 | [
[
"Farkas",
"Jozsef Z.",
""
],
[
"Gourley",
"Stephen A.",
""
],
[
"Liu",
"Rongsong",
""
]
] | According to the World Health Organization the global incidence rate of dengue infections have risen drastically in recent years. It is estimated that globally the number of new infections is in the range of $100$ to $400$ million per annum. At the same time a number of recent studies reported the existence of Wolbachia strains, which inhibit dengue virus replication in mosquito species that are primary vectors for dengue transmission. In this study we focus on the impact of Wolbachia on dengue transmission dynamics in an age-structured human population. We introduce a mathematical model, which takes into account age-related effects, such as age-dependent human recovery and mortality rates, as well as age-dependent vector to human dengue transmission efficacy. We deduce an explicit formula for the basic reproduction number $\mathcal{R}_0$, which allows us to assess the impact of the above mentioned age-related effects on the local asymptotic stability of the dengue free equilibrium. The formula we deduce for $\mathcal{R}_0$ also shows the intricate relationship between human demography and the presence of a dengue inhibiting Wolbachia strain. |
1506.02987 | Anthony Cousien | Anthony Cousien (IAME), Viet Chi Tran, Sylvie Deuffic-Burban (IAME),
Marie Jauffret-Roustide (CERMES3, INVS), Jean-St\'ephane Dhersin (LAGA),
Yazdan Yazdanpanah (IAME) | Impact of a treatment as prevention strategy on hepatitis C virus
transmission and on morbidity in people who inject drugs | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Highly effective direct-acting antiviral (DAA) regimens (90%
efficacy) are becoming available for hepatitis C virus (HCV) treatment. This
therapeutic revolution leads us to consider possibility of eradicating the
virus. However, for this, an effective cascade of care is required. Methods: In
the context of the incoming DAAs, we used a dynamic individual-based model
including a model of the people who inject drugs (PWID) social network to
simulate the impact of improved testing, linkage to care, and adherence to
treatment, and of modified treatment recommendation on the transmission and on
the morbidity of HCV in PWID in France. Results: Under the current incidence
and cascade of care, with treatment initiated at fibrosis stage $\ge$F2, the
HCV prevalence decreased from 42.8% to 24.9% [95% confidence interval
24.8%--24.9%] after 10 years. Changing treatment initiation criteria to treat
from F0 was the only intervention leading to a substantial additional decrease
in the prevalence, which fell to 11.6% [11.6%--11.7%] at 10 years. Combining
this change with improved testing, linkage to care, and adherence to treatment
decreased HCV prevalence to 7% [7%--7.1%] at 10 years and avoided 15.3%
[14.0%-16.6%] and 29.0% [27.9%--30.1%] of cirrhosis complications over 10 and
40 years respectively. Conclusion: A high decrease in viral transmission occurs
only when treatment is initiated before liver disease progresses to severe
stages, suggesting that systematic treatment in PWID, where incidence remains
high, would be beneficial. However, eradication will be difficult to achieve.
| [
{
"created": "Mon, 8 Jun 2015 08:26:09 GMT",
"version": "v1"
}
] | 2015-06-10 | [
[
"Cousien",
"Anthony",
"",
"IAME"
],
[
"Tran",
"Viet Chi",
"",
"IAME"
],
[
"Deuffic-Burban",
"Sylvie",
"",
"IAME"
],
[
"Jauffret-Roustide",
"Marie",
"",
"CERMES3, INVS"
],
[
"Dhersin",
"Jean-Stéphane",
"",
"LAGA"
],
... | Background: Highly effective direct-acting antiviral (DAA) regimens (90% efficacy) are becoming available for hepatitis C virus (HCV) treatment. This therapeutic revolution leads us to consider possibility of eradicating the virus. However, for this, an effective cascade of care is required. Methods: In the context of the incoming DAAs, we used a dynamic individual-based model including a model of the people who inject drugs (PWID) social network to simulate the impact of improved testing, linkage to care, and adherence to treatment, and of modified treatment recommendation on the transmission and on the morbidity of HCV in PWID in France. Results: Under the current incidence and cascade of care, with treatment initiated at fibrosis stage $\ge$F2, the HCV prevalence decreased from 42.8% to 24.9% [95% confidence interval 24.8%--24.9%] after 10 years. Changing treatment initiation criteria to treat from F0 was the only intervention leading to a substantial additional decrease in the prevalence, which fell to 11.6% [11.6%--11.7%] at 10 years. Combining this change with improved testing, linkage to care, and adherence to treatment decreased HCV prevalence to 7% [7%--7.1%] at 10 years and avoided 15.3% [14.0%-16.6%] and 29.0% [27.9%--30.1%] of cirrhosis complications over 10 and 40 years respectively. Conclusion: A high decrease in viral transmission occurs only when treatment is initiated before liver disease progresses to severe stages, suggesting that systematic treatment in PWID, where incidence remains high, would be beneficial. However, eradication will be difficult to achieve. |
0806.2394 | Randen Patterson | Kyung Dae Ko (1,4), Yoojin Hong (2,4), Gue Su Chang (1,4), Gaurav
Bhardwaj (1), Damian B. van Rossum (1,3), and Randen L. Patterson (1,3), ((1)
Department of Biology, The Pennsylvania State University, (2) Department of
Computer Science and Engineering, The Pennsylvania State University, (3)
Center for Computational Proteomics, The Pennsylvania State University) | Phylogenetic Profiles as a Unified Framework for Measuring Protein
Structure, Function and Evolution | 21 Pages, 10 Figures | null | null | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The sequence of amino acids in a protein is believed to determine its native
state structure, which in turn is related to the functionality of the protein.
In addition, information pertaining to evolutionary relationships is contained
in homologous sequences. One powerful method for inferring these sequence
attributes is through comparison of a query sequence with reference sequences
that contain significant homology and whose structure, function, and/or
evolutionary relationships are already known. In spite of decades of concerted
work, there is no simple framework for deducing structure, function, and
evolutionary (SF&E) relationships directly from sequence information alone,
especially when the pair-wise identity is less than a threshold figure ~25%
[1,2]. However, recent research has shown that sequence identity as low as 8%
is sufficient to yield common structure/function relationships and sequence
identities as large as 88% may yet result in distinct structure and function
[3,4]. Starting with a basic premise that protein sequence encodes information
about SF&E, one might ask how one could tease out these measures in an unbiased
manner. Here we present a unified framework for inferring SF&E from sequence
information using a knowledge-based approach which generates phylogenetic
profiles in an unbiased manner. We illustrate the power of phylogenetic
profiles generated using the Gestalt Domain Detection Algorithm Basic Local
Alignment Tool (GDDA-BLAST) to derive structural domains, functional
annotation, and evolutionary relationships for a host of ion-channels and human
proteins of unknown function. These data are in excellent accord with published
data and new experiments. Our results suggest that there is a wealth of
previously unexplored information in protein sequence.
| [
{
"created": "Sun, 15 Jun 2008 18:27:28 GMT",
"version": "v1"
}
] | 2008-06-17 | [
[
"Ko",
"Kyung Dae",
""
],
[
"Hong",
"Yoojin",
""
],
[
"Chang",
"Gue Su",
""
],
[
"Bhardwaj",
"Gaurav",
""
],
[
"van Rossum",
"Damian B.",
""
],
[
"Patterson",
"Randen L.",
""
]
] | The sequence of amino acids in a protein is believed to determine its native state structure, which in turn is related to the functionality of the protein. In addition, information pertaining to evolutionary relationships is contained in homologous sequences. One powerful method for inferring these sequence attributes is through comparison of a query sequence with reference sequences that contain significant homology and whose structure, function, and/or evolutionary relationships are already known. In spite of decades of concerted work, there is no simple framework for deducing structure, function, and evolutionary (SF&E) relationships directly from sequence information alone, especially when the pair-wise identity is less than a threshold figure ~25% [1,2]. However, recent research has shown that sequence identity as low as 8% is sufficient to yield common structure/function relationships and sequence identities as large as 88% may yet result in distinct structure and function [3,4]. Starting with a basic premise that protein sequence encodes information about SF&E, one might ask how one could tease out these measures in an unbiased manner. Here we present a unified framework for inferring SF&E from sequence information using a knowledge-based approach which generates phylogenetic profiles in an unbiased manner. We illustrate the power of phylogenetic profiles generated using the Gestalt Domain Detection Algorithm Basic Local Alignment Tool (GDDA-BLAST) to derive structural domains, functional annotation, and evolutionary relationships for a host of ion-channels and human proteins of unknown function. These data are in excellent accord with published data and new experiments. Our results suggest that there is a wealth of previously unexplored information in protein sequence. |
1502.03662 | He Wang | He Wang, Kin Lam, C. C. Alan Fung, K. Y. Michael Wong, and Si Wu | A Rich Spectrum of Neural Field Dynamics in the Presence of Short-Term
Synaptic Depression | null | Phys. Rev. E 92, 032908 (2015) | 10.1103/PhysRevE.92.032908 | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In continuous attractor neural networks (CANNs), spatially continuous
information such as orientation, head direction, and spatial location is
represented by Gaussian-like tuning curves that can be displaced continuously
in the space of the preferred stimuli of the neurons. We investigate how
short-term synaptic depression (STD) can reshape the intrinsic dynamics of the
CANN model and its responses to a single static input. In particular, CANNs
with STD can support various complex firing patterns and chaotic behaviors.
These chaotic behaviors have the potential to encode various stimuli in the
neuronal system.
| [
{
"created": "Thu, 12 Feb 2015 14:01:07 GMT",
"version": "v1"
}
] | 2015-09-23 | [
[
"Wang",
"He",
""
],
[
"Lam",
"Kin",
""
],
[
"Fung",
"C. C. Alan",
""
],
[
"Wong",
"K. Y. Michael",
""
],
[
"Wu",
"Si",
""
]
] | In continuous attractor neural networks (CANNs), spatially continuous information such as orientation, head direction, and spatial location is represented by Gaussian-like tuning curves that can be displaced continuously in the space of the preferred stimuli of the neurons. We investigate how short-term synaptic depression (STD) can reshape the intrinsic dynamics of the CANN model and its responses to a single static input. In particular, CANNs with STD can support various complex firing patterns and chaotic behaviors. These chaotic behaviors have the potential to encode various stimuli in the neuronal system. |
1310.1341 | Vijay Singh | Vijay Singh, Martin Tchernookov, Rebecca Butterfield, and Ilya
Nemenman | Director Field Model of the Primary Visual Cortex for Contour Detection | 9 pages, 7 figures | PLoS ONE 9(10): e108991 (2014) | 10.1371/journal.pone.0108991 | null | q-bio.NC cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We aim to build the simplest possible model capable of detecting long, noisy
contours in a cluttered visual scene. For this, we model the neural dynamics in
the primate primary visual cortex in terms of a continuous director field that
describes the average rate and the average orientational preference of active
neurons at a particular point in the cortex. We then use a linear-nonlinear
dynamical model with long range connectivity patterns to enforce long-range
statistical context present in the analyzed images. The resulting model has
substantially fewer degrees of freedom than traditional models, and yet it can
distinguish large contiguous objects from the background clutter by suppressing
the clutter and by filling-in occluded elements of object contours. This
results in high-precision, high-recall detection of large objects in cluttered
scenes. Parenthetically, our model has a direct correspondence with the Landau
- de Gennes theory of nematic liquid crystal in two dimensions.
| [
{
"created": "Fri, 4 Oct 2013 17:27:48 GMT",
"version": "v1"
},
{
"created": "Sat, 18 Oct 2014 23:20:40 GMT",
"version": "v2"
}
] | 2014-10-21 | [
[
"Singh",
"Vijay",
""
],
[
"Tchernookov",
"Martin",
""
],
[
"Butterfield",
"Rebecca",
""
],
[
"Nemenman",
"Ilya",
""
]
] | We aim to build the simplest possible model capable of detecting long, noisy contours in a cluttered visual scene. For this, we model the neural dynamics in the primate primary visual cortex in terms of a continuous director field that describes the average rate and the average orientational preference of active neurons at a particular point in the cortex. We then use a linear-nonlinear dynamical model with long range connectivity patterns to enforce long-range statistical context present in the analyzed images. The resulting model has substantially fewer degrees of freedom than traditional models, and yet it can distinguish large contiguous objects from the background clutter by suppressing the clutter and by filling-in occluded elements of object contours. This results in high-precision, high-recall detection of large objects in cluttered scenes. Parenthetically, our model has a direct correspondence with the Landau - de Gennes theory of nematic liquid crystal in two dimensions. |
1605.09781 | Ivan Tomba | Massimo Ferri, Ivan Tomba, Andrea Visotti, Ignazio Stanganelli | A feasibility study for a persistent homology based k-Nearest Neighbor
search algorithm in melanoma detection | null | null | null | null | q-bio.QM math.AT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Persistent Homology is a fairly new branch of Computational Topology which
combines geometry and topology for an effective shape description of use in
Pattern Recognition. In particular it registers through "Betti Numbers" the
presence of holes and their persistence while a parameter ("filtering
function") is varied. In this paper, some recent developments in this field are
integrated in a k-Nearest Neighbor search algorithm suited for an automatic
retrieval of melanocytic lesions. Since long, dermatologists use five
morphological parameters (A = Asymmetry, B = Boundary, C = Color, D = Diameter,
E = Elevation or Evolution) for assessing the malignancy of a lesion. The
algorithm is based on a qualitative assessment of the segmented images by
computing both 1 and 2-dimensional Persistent Betti Numbers functions related
to the ABCDE parameters and to the internal texture of the lesion. The results
of a feasibility test on a set of 107 melanocytic lesions are reported in the
section dedicated to the numerical experiments.
| [
{
"created": "Mon, 4 Apr 2016 17:16:47 GMT",
"version": "v1"
}
] | 2016-06-01 | [
[
"Ferri",
"Massimo",
""
],
[
"Tomba",
"Ivan",
""
],
[
"Visotti",
"Andrea",
""
],
[
"Stanganelli",
"Ignazio",
""
]
] | Persistent Homology is a fairly new branch of Computational Topology which combines geometry and topology for an effective shape description of use in Pattern Recognition. In particular it registers through "Betti Numbers" the presence of holes and their persistence while a parameter ("filtering function") is varied. In this paper, some recent developments in this field are integrated in a k-Nearest Neighbor search algorithm suited for an automatic retrieval of melanocytic lesions. Since long, dermatologists use five morphological parameters (A = Asymmetry, B = Boundary, C = Color, D = Diameter, E = Elevation or Evolution) for assessing the malignancy of a lesion. The algorithm is based on a qualitative assessment of the segmented images by computing both 1 and 2-dimensional Persistent Betti Numbers functions related to the ABCDE parameters and to the internal texture of the lesion. The results of a feasibility test on a set of 107 melanocytic lesions are reported in the section dedicated to the numerical experiments. |
2005.05437 | Hyeong-Jin Kim | Hyeong-Jin Kim, Minji Lee, Seong-Whan Lee | End-to-End Automatic Sleep Stage Classification Using Spectral-Temporal
Sleep Features | null | null | null | null | q-bio.NC eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sleep disorder is one of many neurological diseases that can affect greatly
the quality of daily life. It is very burdensome to manually classify the sleep
stages to detect sleep disorders. Therefore, the automatic sleep stage
classification techniques are needed. However, the previous automatic sleep
scoring methods using raw signals are still low classification performance. In
this study, we proposed an end-to-end automatic sleep staging framework based
on optimal spectral-temporal sleep features using a sleep-edf dataset. The
input data were modified using a bandpass filter and then applied to a
convolutional neural network model. For five sleep stage classification, the
classification performance 85.6% and 91.1% using the raw input data and the
proposed input, respectively. This result also shows the highest performance
compared to conventional studies using the same dataset. The proposed framework
has shown high performance by using optimal features associated with each sleep
stage, which may help to find new features in the automatic sleep stage method.
| [
{
"created": "Mon, 4 May 2020 08:47:42 GMT",
"version": "v1"
}
] | 2020-05-13 | [
[
"Kim",
"Hyeong-Jin",
""
],
[
"Lee",
"Minji",
""
],
[
"Lee",
"Seong-Whan",
""
]
] | Sleep disorder is one of many neurological diseases that can affect greatly the quality of daily life. It is very burdensome to manually classify the sleep stages to detect sleep disorders. Therefore, the automatic sleep stage classification techniques are needed. However, the previous automatic sleep scoring methods using raw signals are still low classification performance. In this study, we proposed an end-to-end automatic sleep staging framework based on optimal spectral-temporal sleep features using a sleep-edf dataset. The input data were modified using a bandpass filter and then applied to a convolutional neural network model. For five sleep stage classification, the classification performance 85.6% and 91.1% using the raw input data and the proposed input, respectively. This result also shows the highest performance compared to conventional studies using the same dataset. The proposed framework has shown high performance by using optimal features associated with each sleep stage, which may help to find new features in the automatic sleep stage method. |
1512.07603 | James Tan | James P.L. Tan | Symmetric and Asymmetric Tendencies in Stable Complex Systems | 21 pages, 5 figures. v2: Corrected typos in abstract. v3: major
revision. v4: communicated to Sci Rep | Sci. Rep. 6 (2016) 31762 | 10.1038/srep31762 | null | q-bio.PE cond-mat.dis-nn cond-mat.stat-mech nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A commonly used approach to study stability in a complex system is by
analyzing the Jacobian matrix at an equilibrium point of a dynamical system.
The equilibrium point is stable if all eigenvalues have negative real parts.
Here, by obtaining eigenvalue bounds of the Jacobian, we show that stable
complex systems will favor mutualistic and competitive relationships that are
asymmetrical (non-reciprocative) and trophic relationships that are symmetrical
(reciprocative). Additionally, we define a measure called the interdependence
diversity that quantifies how distributed the dependencies are between the
dynamical variables in the system. We find that increasing interdependence
diversity has a destabilizing effect on the equilibrium point, and the effect
is greater for trophic relationships than for mutualistic and competitive
relationships. These predictions are consistent with empirical observations in
ecology. More importantly, our findings suggest stabilization algorithms that
can apply very generally to a variety of complex systems.
| [
{
"created": "Wed, 23 Dec 2015 19:56:19 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Dec 2015 12:28:50 GMT",
"version": "v2"
},
{
"created": "Mon, 25 Apr 2016 15:40:38 GMT",
"version": "v3"
},
{
"created": "Tue, 23 Aug 2016 01:43:20 GMT",
"version": "v4"
}
] | 2016-09-02 | [
[
"Tan",
"James P. L.",
""
]
] | A commonly used approach to study stability in a complex system is by analyzing the Jacobian matrix at an equilibrium point of a dynamical system. The equilibrium point is stable if all eigenvalues have negative real parts. Here, by obtaining eigenvalue bounds of the Jacobian, we show that stable complex systems will favor mutualistic and competitive relationships that are asymmetrical (non-reciprocative) and trophic relationships that are symmetrical (reciprocative). Additionally, we define a measure called the interdependence diversity that quantifies how distributed the dependencies are between the dynamical variables in the system. We find that increasing interdependence diversity has a destabilizing effect on the equilibrium point, and the effect is greater for trophic relationships than for mutualistic and competitive relationships. These predictions are consistent with empirical observations in ecology. More importantly, our findings suggest stabilization algorithms that can apply very generally to a variety of complex systems. |
q-bio/0401035 | Dalius Balciunas | D. Balciunas | The Waterwheel in the Waterfall | 3 pages | null | null | null | q-bio.PE | null | A fundamental problem in evolutionary ecology research is to explain how
different species coexist in natural ecosystems. This question is directly
related with species trophic competition. However, competition theory, based on
the classical logistic Lotka-Volterra equations, leads to erroneous conclusions
about species coexistence. The reason for this is incorrectly interpreted
interspecific interactions, expressed in the form of the competition
coefficients. Here I use the logistic Lotka-Volterra type competition equations
derived from the so called resource competition models to obtain the necessary
conditions for species coexistence. These models show that only species with
identical competitive abilities may coexist. Due to such relations between
competing species ecosystems biodiversity decreases in the course of evolution.
| [
{
"created": "Mon, 26 Jan 2004 23:29:21 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Balciunas",
"D.",
""
]
] | A fundamental problem in evolutionary ecology research is to explain how different species coexist in natural ecosystems. This question is directly related with species trophic competition. However, competition theory, based on the classical logistic Lotka-Volterra equations, leads to erroneous conclusions about species coexistence. The reason for this is incorrectly interpreted interspecific interactions, expressed in the form of the competition coefficients. Here I use the logistic Lotka-Volterra type competition equations derived from the so called resource competition models to obtain the necessary conditions for species coexistence. These models show that only species with identical competitive abilities may coexist. Due to such relations between competing species ecosystems biodiversity decreases in the course of evolution. |
1606.06113 | Yasushi Saka | Yasushi Saka, Murray MacPherson, Claudiu V. Giuraniuc | Generation and precise control of dynamic biochemical gradients for
cellular assays | Main text 18 pages (7 figures), Supplementary information 10 pages (7
supplementary figures) | Physica A 470 (2017) 132-145 | 10.1016/j.physa.2016.11.134 | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spatial gradients of diffusible signalling molecules play crucial roles in
controlling diverse cellular behaviour such as cell differentiation, tissue
patterning and chemotaxis. In this paper, we report the design and testing of a
microfluidic device for diffusion-based gradient generation for cellular
assays. A unique channel design of the device eliminates cross-flow between the
source and sink channels, thereby stabilising gradients by passive diffusion.
The platform also enables quick and flexible control of chemical concentration
that makes highly dynamic gradients in diffusion chambers. A model with the
first approximation of diffusion and surface adsorption of molecules
recapitulates the experimentally observed gradients. Budding yeast cells
cultured in a gradient of a chemical inducer expressed a reporter fluorescence
protein in a concentration-dependent manner. This microfluidic platform serves
as a versatile prototype applicable to a broad range of biomedical
investigations.
| [
{
"created": "Mon, 20 Jun 2016 13:40:26 GMT",
"version": "v1"
},
{
"created": "Mon, 11 Jul 2016 15:54:52 GMT",
"version": "v2"
},
{
"created": "Sat, 17 Sep 2016 16:09:48 GMT",
"version": "v3"
},
{
"created": "Thu, 10 Nov 2016 17:34:11 GMT",
"version": "v4"
}
] | 2016-12-15 | [
[
"Saka",
"Yasushi",
""
],
[
"MacPherson",
"Murray",
""
],
[
"Giuraniuc",
"Claudiu V.",
""
]
] | Spatial gradients of diffusible signalling molecules play crucial roles in controlling diverse cellular behaviour such as cell differentiation, tissue patterning and chemotaxis. In this paper, we report the design and testing of a microfluidic device for diffusion-based gradient generation for cellular assays. A unique channel design of the device eliminates cross-flow between the source and sink channels, thereby stabilising gradients by passive diffusion. The platform also enables quick and flexible control of chemical concentration that makes highly dynamic gradients in diffusion chambers. A model with the first approximation of diffusion and surface adsorption of molecules recapitulates the experimentally observed gradients. Budding yeast cells cultured in a gradient of a chemical inducer expressed a reporter fluorescence protein in a concentration-dependent manner. This microfluidic platform serves as a versatile prototype applicable to a broad range of biomedical investigations. |
1406.6092 | Tidjani Negadi | Tidjani Negadi | The genetic code invariance: when Euler and Fibonacci meet | Work presented at Symmetry Festival 2013, Delft, The Netherlands,
August 2-7, 2013 | Symmetry: Culture and Science, Vol.25, No.3, 145-288, 2014 | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The number of atoms in the four ribonucleotides uridine monophosphate,
cytidine monophosphate, adenine monophosphate and guanine monophosphate is
taken as a key parameter. A mathematical relation describing the condensation
of the three basic subunits a nucleobase, a ribose and a phosphate group, to
form a ribonucleotide, is first obtained from this parameter. Next, the use of
the latter and Euler totient function is shown to lead to the atom number
content of the 64 codons and also to Rakocevic pattern. Finally, selected
finite sums of Fibonacci numbers are shown to lead to the nucleon number
content of the amino acids in various degeneracy patterns, and also to the
multiplet structure of the 20 amino acids as well as to the degeneracy.
| [
{
"created": "Mon, 23 Jun 2014 20:59:58 GMT",
"version": "v1"
}
] | 2014-06-25 | [
[
"Negadi",
"Tidjani",
""
]
] | The number of atoms in the four ribonucleotides uridine monophosphate, cytidine monophosphate, adenine monophosphate and guanine monophosphate is taken as a key parameter. A mathematical relation describing the condensation of the three basic subunits a nucleobase, a ribose and a phosphate group, to form a ribonucleotide, is first obtained from this parameter. Next, the use of the latter and Euler totient function is shown to lead to the atom number content of the 64 codons and also to Rakocevic pattern. Finally, selected finite sums of Fibonacci numbers are shown to lead to the nucleon number content of the amino acids in various degeneracy patterns, and also to the multiplet structure of the 20 amino acids as well as to the degeneracy. |
2211.06889 | Chiara De Luca | Chiara De Luca, Leonardo Tonielli, Elena Pastorelli, Cristiano Capone,
Francesco Simula, Cosimo Lupo, Irene Bernava, Giulia De Bonis, Gianmarco
Tiddia, Bruno Golosio and Pier Stanislao Paolucci | NREM and REM: cognitive and energetic gains in thalamo-cortical sleeping
and awake spiking model | 22 pages, 9 figures | null | null | null | q-bio.NC cs.DC | http://creativecommons.org/licenses/by/4.0/ | Sleep is essential for learning and cognition, but the mechanisms by which it
stabilizes learning, supports creativity, and manages the energy consumption of
networks engaged in post-sleep task have not been yet modelled. During sleep,
the brain cycles between non-rapid eye movement (NREM), a mainly unconscious
state characterized by collective oscillations, and rapid eye movement (REM),
associated with the integrated experience of dreaming. We propose a
biologically grounded two-area thalamo-cortical plastic spiking neural network
model and investigate the role of NREM - REM cycles on its awake performance.
We demonstrate that sleep has a positive effect on energy consumption and
cognitive performance during the post-sleep awake classification task of
handwritten digits. NREM and REM simulated dynamics modify the synaptic
structure into a sharper representation of training experiences. Sleep-induced
synaptic modifications reduce firing rates and synaptic activity without
reducing cognitive performance. Also, it creates novel multi-area associations.
The model leverages the apical amplification, isolation and drive
experimentally grounded principles and the combination of contextual and
perceptual information. In summary, the main novelty is the proposal of a
multi-area plastic model that also expresses REM and integrates information
during a plastic dream-like state, with cognitive and energetic benefits during
post-sleep awake classification.
| [
{
"created": "Sun, 13 Nov 2022 12:30:47 GMT",
"version": "v1"
},
{
"created": "Tue, 3 Jan 2023 10:33:16 GMT",
"version": "v2"
}
] | 2023-01-04 | [
[
"De Luca",
"Chiara",
""
],
[
"Tonielli",
"Leonardo",
""
],
[
"Pastorelli",
"Elena",
""
],
[
"Capone",
"Cristiano",
""
],
[
"Simula",
"Francesco",
""
],
[
"Lupo",
"Cosimo",
""
],
[
"Bernava",
"Irene",
""
]... | Sleep is essential for learning and cognition, but the mechanisms by which it stabilizes learning, supports creativity, and manages the energy consumption of networks engaged in post-sleep task have not been yet modelled. During sleep, the brain cycles between non-rapid eye movement (NREM), a mainly unconscious state characterized by collective oscillations, and rapid eye movement (REM), associated with the integrated experience of dreaming. We propose a biologically grounded two-area thalamo-cortical plastic spiking neural network model and investigate the role of NREM - REM cycles on its awake performance. We demonstrate that sleep has a positive effect on energy consumption and cognitive performance during the post-sleep awake classification task of handwritten digits. NREM and REM simulated dynamics modify the synaptic structure into a sharper representation of training experiences. Sleep-induced synaptic modifications reduce firing rates and synaptic activity without reducing cognitive performance. Also, it creates novel multi-area associations. The model leverages the apical amplification, isolation and drive experimentally grounded principles and the combination of contextual and perceptual information. In summary, the main novelty is the proposal of a multi-area plastic model that also expresses REM and integrates information during a plastic dream-like state, with cognitive and energetic benefits during post-sleep awake classification. |
1404.4515 | Peter Csermely | Kristof Z. Szalay, Ruth Nussinov and Peter Csermely | Attractor structures of signaling networks: Consequences of different
conformational barcode dynamics and their relations to network-based drug
design | 8 pages, 2 figures | Molecular Informatics (2014) 33: 463-468 | 10.1002/minf.201400029 | null | q-bio.MN nlin.AO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conformational barcodes tag functional sites of proteins, and are decoded by
interacting molecules transmitting the incoming signal. Conformational barcodes
are modified by all co-occurring allosteric events induced by
post-translational modifications, pathogen, drug binding, etc. We argue that
fuzziness (plasticity) of conformational barcodes may be increased by
disordered protein structures, by integrative plasticity of
multi-phosphorylation events, by increased intracellular water content
(decreased molecular crowding) and by increased action of molecular chaperones.
This leads to increased plasticity of signaling and cellular networks.
Increased plasticity is both substantiated by and inducing an increased noise
level. Using the versatile network dynamics tool, Turbine
(www.turbine.linkgroup.hu), here we show that the 10% noise level expected in
cellular systems shifts a cancer-related signaling network of human cells from
its proliferative attractors to its largest, apoptotic attractor representing
their health-preserving response in the carcinogen containing and tumor
suppressor deficient environment modeled in our study. Thus, fuzzy
conformational barcodes may not only make the cellular system more plastic, and
therefore more adaptable, but may also stabilize the complex system allowing
better access to its largest attractor.
| [
{
"created": "Thu, 17 Apr 2014 13:01:55 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Jun 2014 15:38:39 GMT",
"version": "v2"
}
] | 2014-06-27 | [
[
"Szalay",
"Kristof Z.",
""
],
[
"Nussinov",
"Ruth",
""
],
[
"Csermely",
"Peter",
""
]
] | Conformational barcodes tag functional sites of proteins, and are decoded by interacting molecules transmitting the incoming signal. Conformational barcodes are modified by all co-occurring allosteric events induced by post-translational modifications, pathogen, drug binding, etc. We argue that fuzziness (plasticity) of conformational barcodes may be increased by disordered protein structures, by integrative plasticity of multi-phosphorylation events, by increased intracellular water content (decreased molecular crowding) and by increased action of molecular chaperones. This leads to increased plasticity of signaling and cellular networks. Increased plasticity is both substantiated by and inducing an increased noise level. Using the versatile network dynamics tool, Turbine (www.turbine.linkgroup.hu), here we show that the 10% noise level expected in cellular systems shifts a cancer-related signaling network of human cells from its proliferative attractors to its largest, apoptotic attractor representing their health-preserving response in the carcinogen containing and tumor suppressor deficient environment modeled in our study. Thus, fuzzy conformational barcodes may not only make the cellular system more plastic, and therefore more adaptable, but may also stabilize the complex system allowing better access to its largest attractor. |
1407.1884 | Marcelo de Oliveira M. | Marcelo M. de Oliveira and Ronald Dickman | Phase diagram of the symbiotic two-species contact process | to appear in Physical Review E | null | 10.1103/PhysRevE.90.032120 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the two-species symbiotic contact process (2SCP), recently proposed
in [de Oliveira, Santos and Dickman, Phys. Rev. E {\bf 86}, 011121 (2012)] . In
this model, each site of a lattice may be vacant or host single individuals of
species A and/or B. Individuals at sites with both species present interact in
a symbiotic manner, having a reduced death rate, $\mu < 1$. Otherwise, the
dynamics follows the rules of the basic CP, with individuals reproducing to
vacant neighbor sites at rate $\lambda$ and dying at a rate of unity. We
determine the full phase diagram in the $\lambda-\mu$ plane in one and two
dimensions by means of exact numerical quasistationary distributions, cluster
approximations, and Monte Carlo simulations. We also study the effects of
asymmetric creation rates and diffusion of individuals. In two dimensions, for
sufficiently strong symbiosis (i.e., small $\mu$), the absorbing-state phase
transition becomes discontinuous for diffusion rates $D$ within a certain
range. We report preliminary results on the critical surface and tricritical
line in the $\lambda-\mu-D$ space. Our results raise the possibility that
strongly symbiotic associations of mobile species may be vulnerable to sudden
extinction under increasingly adverse conditions.
| [
{
"created": "Mon, 7 Jul 2014 20:56:22 GMT",
"version": "v1"
},
{
"created": "Mon, 1 Sep 2014 00:47:25 GMT",
"version": "v2"
}
] | 2015-06-22 | [
[
"de Oliveira",
"Marcelo M.",
""
],
[
"Dickman",
"Ronald",
""
]
] | We study the two-species symbiotic contact process (2SCP), recently proposed in [de Oliveira, Santos and Dickman, Phys. Rev. E {\bf 86}, 011121 (2012)] . In this model, each site of a lattice may be vacant or host single individuals of species A and/or B. Individuals at sites with both species present interact in a symbiotic manner, having a reduced death rate, $\mu < 1$. Otherwise, the dynamics follows the rules of the basic CP, with individuals reproducing to vacant neighbor sites at rate $\lambda$ and dying at a rate of unity. We determine the full phase diagram in the $\lambda-\mu$ plane in one and two dimensions by means of exact numerical quasistationary distributions, cluster approximations, and Monte Carlo simulations. We also study the effects of asymmetric creation rates and diffusion of individuals. In two dimensions, for sufficiently strong symbiosis (i.e., small $\mu$), the absorbing-state phase transition becomes discontinuous for diffusion rates $D$ within a certain range. We report preliminary results on the critical surface and tricritical line in the $\lambda-\mu-D$ space. Our results raise the possibility that strongly symbiotic associations of mobile species may be vulnerable to sudden extinction under increasingly adverse conditions. |
2005.08894 | Marie Roch | Pu Li, Xiaobai Liua, K. J. Palmer, Erica Fleishman, Douglas Gillespie,
Eva-Marie Nosal, Yu Shiu, Holger Klinck, Danielle Cholewiak, Tyler Helble,
and Marie A. Roch | Learning Deep Models from Synthetic Data for Extracting Dolphin Whistle
Contours | Invited paper for International Joint Conference on Neural Networks | in Intl. Joint Conf. Neural Net. (Glasgow, Scotland, July 19-24),
pp. 10 (2020) | null | IJCNN paper 6435539 | q-bio.QM cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a learning-based method for extracting whistles of toothed whales
(Odontoceti) in hydrophone recordings. Our method represents audio signals as
time-frequency spectrograms and decomposes each spectrogram into a set of
time-frequency patches. A deep neural network learns archetypical patterns
(e.g., crossings, frequency modulated sweeps) from the spectrogram patches and
predicts time-frequency peaks that are associated with whistles. We also
developed a comprehensive method to synthesize training samples from background
environments and train the network with minimal human annotation effort. We
applied the proposed learn-from-synthesis method to a subset of the public
Detection, Classification, Localization, and Density Estimation (DCLDE) 2011
workshop data to extract whistle confidence maps, which we then processed with
an existing contour extractor to produce whistle annotations. The F1-score of
our best synthesis method was 0.158 greater than our baseline whistle
extraction algorithm (~25% improvement) when applied to common dolphin
(Delphinus spp.) and bottlenose dolphin (Tursiops truncatus) whistles.
| [
{
"created": "Mon, 18 May 2020 17:09:34 GMT",
"version": "v1"
}
] | 2020-05-19 | [
[
"Li",
"Pu",
""
],
[
"Liua",
"Xiaobai",
""
],
[
"Palmer",
"K. J.",
""
],
[
"Fleishman",
"Erica",
""
],
[
"Gillespie",
"Douglas",
""
],
[
"Nosal",
"Eva-Marie",
""
],
[
"Shiu",
"Yu",
""
],
[
"Klinck",
... | We present a learning-based method for extracting whistles of toothed whales (Odontoceti) in hydrophone recordings. Our method represents audio signals as time-frequency spectrograms and decomposes each spectrogram into a set of time-frequency patches. A deep neural network learns archetypical patterns (e.g., crossings, frequency modulated sweeps) from the spectrogram patches and predicts time-frequency peaks that are associated with whistles. We also developed a comprehensive method to synthesize training samples from background environments and train the network with minimal human annotation effort. We applied the proposed learn-from-synthesis method to a subset of the public Detection, Classification, Localization, and Density Estimation (DCLDE) 2011 workshop data to extract whistle confidence maps, which we then processed with an existing contour extractor to produce whistle annotations. The F1-score of our best synthesis method was 0.158 greater than our baseline whistle extraction algorithm (~25% improvement) when applied to common dolphin (Delphinus spp.) and bottlenose dolphin (Tursiops truncatus) whistles. |
1805.09884 | Rajaram Gana | Rajaram Gana and Sona Vasudevan | Ridge Regression Estimated Linear Probability Model Predictions of
O-glycosylation in Proteins with Structural and Sequence Data | 40 pages | BMC Molecular and Cell Biology 2019 | 10.1186/s12860-019-0200-9 | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The likelihood of O-GlcNAc glycosylation in human proteins is predicted using
the ridge regression estimated linear probability model (LPM). To achieve this,
sequences from three similar post-translational modifications (PTMs) of
proteins occurring at, or very near, the S or T site are analyzed:
N-glycosylation, O-mucin type (O-GalNAc) glycosylation, and phosphorylation.
Results found include: 1) The consensus composite sequon for O-glycosylation
does NOT have W on either side of the glycosylation site. 2) The same holds for
the consensus sequon for phosphorylation. 3) For LPM estimation, N-glycosylated
sequences are found to be good approximations to non-O-glycosylatable
sequences. 4) The selective positioning of an amino acid along the sequence,
differentiates the PTMs of proteins. 5) Some N-glycosylated sequences are also
phosphorylated at the S or T site. 6) ASA values for N-glycosylated sequences
are stochastically larger than those for O-GlcNAc glycosylated sequences. 7)
Structural attributes (beta turn II, II', helix, beta bridges, beta hairpin,
and the phi angle) are significant LPM predictors of O-GlcNAc glycosylation.
The LPM with sequence and structural data as explanatory variables yields a
Kolmogorov-Smirnov (KS) statistic value of 99%. 8) With only sequence data, the
KS statistic erodes to 80%, underscoring the germaneness of structural
information, which is sparse on O-glycosylated sequences. With 50% as the
cutoff probability for predicting O-GlcNAc glycosylation, this LPM mispredicts
21% of out-of-sample O-GlcNAc glycosylated sequences as not being glycosylated.
The 95% confidence interval around this mispredictions rate is 16% to 26%
| [
{
"created": "Thu, 24 May 2018 20:14:53 GMT",
"version": "v1"
},
{
"created": "Sat, 16 Feb 2019 17:12:41 GMT",
"version": "v2"
}
] | 2019-07-09 | [
[
"Gana",
"Rajaram",
""
],
[
"Vasudevan",
"Sona",
""
]
] | The likelihood of O-GlcNAc glycosylation in human proteins is predicted using the ridge regression estimated linear probability model (LPM). To achieve this, sequences from three similar post-translational modifications (PTMs) of proteins occurring at, or very near, the S or T site are analyzed: N-glycosylation, O-mucin type (O-GalNAc) glycosylation, and phosphorylation. Results found include: 1) The consensus composite sequon for O-glycosylation does NOT have W on either side of the glycosylation site. 2) The same holds for the consensus sequon for phosphorylation. 3) For LPM estimation, N-glycosylated sequences are found to be good approximations to non-O-glycosylatable sequences. 4) The selective positioning of an amino acid along the sequence, differentiates the PTMs of proteins. 5) Some N-glycosylated sequences are also phosphorylated at the S or T site. 6) ASA values for N-glycosylated sequences are stochastically larger than those for O-GlcNAc glycosylated sequences. 7) Structural attributes (beta turn II, II', helix, beta bridges, beta hairpin, and the phi angle) are significant LPM predictors of O-GlcNAc glycosylation. The LPM with sequence and structural data as explanatory variables yields a Kolmogorov-Smirnov (KS) statistic value of 99%. 8) With only sequence data, the KS statistic erodes to 80%, underscoring the germaneness of structural information, which is sparse on O-glycosylated sequences. With 50% as the cutoff probability for predicting O-GlcNAc glycosylation, this LPM mispredicts 21% of out-of-sample O-GlcNAc glycosylated sequences as not being glycosylated. The 95% confidence interval around this mispredictions rate is 16% to 26% |
0906.3106 | Louxin Zhang | Louxin Zhang, Jian Shen, Jialiang Yang, Guoliang Li | Analyzing the Accuracy of the Fitch Method for Reconstructing Ancestral
States on Ultrametric Phylogenies | 14pages | null | null | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recurrence formulas are presented for studying the accuracy of the Fitch
method for reconstructing the ancestral states in a given phylogenetic tree. As
their applications, we analyze the convergence of the accuracy of
reconstructing the root state in a complete binary tree of $2^n$ as $n$ goes to
infinity and also give a lower bound on the accuracy of reconstructing the root
state in an ultrametric tree.
| [
{
"created": "Wed, 17 Jun 2009 08:32:43 GMT",
"version": "v1"
}
] | 2009-06-18 | [
[
"Zhang",
"Louxin",
""
],
[
"Shen",
"Jian",
""
],
[
"Yang",
"Jialiang",
""
],
[
"Li",
"Guoliang",
""
]
] | Recurrence formulas are presented for studying the accuracy of the Fitch method for reconstructing the ancestral states in a given phylogenetic tree. As their applications, we analyze the convergence of the accuracy of reconstructing the root state in a complete binary tree of $2^n$ as $n$ goes to infinity and also give a lower bound on the accuracy of reconstructing the root state in an ultrametric tree. |
2002.07874 | Matthew Leming | Matthew Leming, Juan Manuel G\'orriz, John Suckling | Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and
Other Tasks | null | null | 10.1142/S0129065720500124 | null | q-bio.QM cs.LG eess.IV q-bio.NC stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep learning models for MRI classification face two recurring problems: they
are typically limited by low sample size, and are abstracted by their own
complexity (the "black box problem"). In this paper, we train a convolutional
neural network (CNN) with the largest multi-source, functional MRI (fMRI)
connectomic dataset ever compiled, consisting of 43,858 datapoints. We apply
this model to a cross-sectional comparison of autism (ASD) vs typically
developing (TD) controls that has proved difficult to characterise with
inferential statistics. To contextualise these findings, we additionally
perform classifications of gender and task vs rest. Employing class-balancing
to build a training set, we trained 3$\times$300 modified CNNs in an ensemble
model to classify fMRI connectivity matrices with overall AUROCs of 0.6774,
0.7680, and 0.9222 for ASD vs TD, gender, and task vs rest, respectively.
Additionally, we aim to address the black box problem in this context using two
visualization methods. First, class activation maps show which functional
connections of the brain our models focus on when performing classification.
Second, by analyzing maximal activations of the hidden layers, we were also
able to explore how the model organizes a large and mixed-centre dataset,
finding that it dedicates specific areas of its hidden layers to processing
different covariates of data (depending on the independent variable analyzed),
and other areas to mix data from different sources. Our study finds that deep
learning models that distinguish ASD from TD controls focus broadly on temporal
and cerebellar connections, with a particularly high focus on the right caudate
nucleus and paracentral sulcus.
| [
{
"created": "Fri, 14 Feb 2020 17:28:16 GMT",
"version": "v1"
},
{
"created": "Wed, 27 May 2020 16:31:37 GMT",
"version": "v2"
}
] | 2020-05-28 | [
[
"Leming",
"Matthew",
""
],
[
"Górriz",
"Juan Manuel",
""
],
[
"Suckling",
"John",
""
]
] | Deep learning models for MRI classification face two recurring problems: they are typically limited by low sample size, and are abstracted by their own complexity (the "black box problem"). In this paper, we train a convolutional neural network (CNN) with the largest multi-source, functional MRI (fMRI) connectomic dataset ever compiled, consisting of 43,858 datapoints. We apply this model to a cross-sectional comparison of autism (ASD) vs typically developing (TD) controls that has proved difficult to characterise with inferential statistics. To contextualise these findings, we additionally perform classifications of gender and task vs rest. Employing class-balancing to build a training set, we trained 3$\times$300 modified CNNs in an ensemble model to classify fMRI connectivity matrices with overall AUROCs of 0.6774, 0.7680, and 0.9222 for ASD vs TD, gender, and task vs rest, respectively. Additionally, we aim to address the black box problem in this context using two visualization methods. First, class activation maps show which functional connections of the brain our models focus on when performing classification. Second, by analyzing maximal activations of the hidden layers, we were also able to explore how the model organizes a large and mixed-centre dataset, finding that it dedicates specific areas of its hidden layers to processing different covariates of data (depending on the independent variable analyzed), and other areas to mix data from different sources. Our study finds that deep learning models that distinguish ASD from TD controls focus broadly on temporal and cerebellar connections, with a particularly high focus on the right caudate nucleus and paracentral sulcus. |
1901.05958 | Albee Ling | Albee Y. Ling, Allison W. Kurian, Jennifer L. Caswell-Jin, George W.
Sledge Jr., Nigam H. Shah, Suzanne R. Tamang | A Semi-Supervised Machine Learning Approach to Detecting Recurrent
Metastatic Breast Cancer Cases Using Linked Cancer Registry and Electronic
Medical Record Data | null | JAMIA open 2.4 (2019): 528-537 | 10.1093/jamiaopen/ooz040 | null | q-bio.QM cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objectives: Most cancer data sources lack information on metastatic
recurrence. Electronic medical records (EMRs) and population-based cancer
registries contain complementary information on cancer treatment and outcomes,
yet are rarely used synergistically. To enable detection of metastatic breast
cancer (MBC), we applied a semi-supervised machine learning framework to linked
EMR-California Cancer Registry (CCR) data. Materials and Methods: We studied
11,459 female patients treated at Stanford Health Care who received an incident
breast cancer diagnosis from 2000-2014. The dataset consisted of structured
data and unstructured free-text clinical notes from EMR, linked to CCR, a
component of the Surveillance, Epidemiology and End Results (SEER) database. We
extracted information on metastatic disease from patient notes to infer a class
label and then trained a regularized logistic regression model for MBC
classification. We evaluated model performance on a gold standard set of set of
146 patients. Results: There are 495 patients with de novo stage IV MBC, 1,374
patients initially diagnosed with Stage 0-III disease had recurrent MBC, and
9,590 had no evidence of metastatis. The median follow-up time is 96.3 months
(mean 97.8, standard deviation 46.7). The best-performing model incorporated
both EMR and CCR features. The area under the receiver-operating characteristic
curve=0.925 [95% confidence interval: 0.880-0.969], sensitivity=0.861,
specificity=0.878 and overall accuracy=0.870. Discussion and Conclusion: A
framework for MBC case detection combining EMR and CCR data achieved good
sensitivity, specificity and discrimination without requiring expert-labeled
examples. This approach enables population-based research on how patients die
from cancer and may identify novel predictors of cancer recurrence.
| [
{
"created": "Thu, 17 Jan 2019 04:22:52 GMT",
"version": "v1"
}
] | 2021-07-22 | [
[
"Ling",
"Albee Y.",
""
],
[
"Kurian",
"Allison W.",
""
],
[
"Caswell-Jin",
"Jennifer L.",
""
],
[
"Sledge",
"George W.",
"Jr."
],
[
"Shah",
"Nigam H.",
""
],
[
"Tamang",
"Suzanne R.",
""
]
] | Objectives: Most cancer data sources lack information on metastatic recurrence. Electronic medical records (EMRs) and population-based cancer registries contain complementary information on cancer treatment and outcomes, yet are rarely used synergistically. To enable detection of metastatic breast cancer (MBC), we applied a semi-supervised machine learning framework to linked EMR-California Cancer Registry (CCR) data. Materials and Methods: We studied 11,459 female patients treated at Stanford Health Care who received an incident breast cancer diagnosis from 2000-2014. The dataset consisted of structured data and unstructured free-text clinical notes from EMR, linked to CCR, a component of the Surveillance, Epidemiology and End Results (SEER) database. We extracted information on metastatic disease from patient notes to infer a class label and then trained a regularized logistic regression model for MBC classification. We evaluated model performance on a gold standard set of set of 146 patients. Results: There are 495 patients with de novo stage IV MBC, 1,374 patients initially diagnosed with Stage 0-III disease had recurrent MBC, and 9,590 had no evidence of metastatis. The median follow-up time is 96.3 months (mean 97.8, standard deviation 46.7). The best-performing model incorporated both EMR and CCR features. The area under the receiver-operating characteristic curve=0.925 [95% confidence interval: 0.880-0.969], sensitivity=0.861, specificity=0.878 and overall accuracy=0.870. Discussion and Conclusion: A framework for MBC case detection combining EMR and CCR data achieved good sensitivity, specificity and discrimination without requiring expert-labeled examples. This approach enables population-based research on how patients die from cancer and may identify novel predictors of cancer recurrence. |
1704.07207 | Jinbo Xu | Zhen Li, Sheng Wang, Yizhou Yu and Jinbo Xu | Predicting membrane protein contacts from non-membrane proteins by deep
transfer learning | null | null | null | null | q-bio.BM cs.LG cs.NE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computational prediction of membrane protein (MP) structures is very
challenging partially due to lack of sufficient solved structures for homology
modeling. Recently direct evolutionary coupling analysis (DCA) sheds some light
on protein contact prediction and accordingly, contact-assisted folding, but
DCA is effective only on some very large-sized families since it uses
information only in a single protein family. This paper presents a deep
transfer learning method that can significantly improve MP contact prediction
by learning contact patterns and complex sequence-contact relationship from
thousands of non-membrane proteins (non-MPs). Tested on 510 non-redundant MPs,
our deep model (learned from only non-MPs) has top L/10 long-range contact
prediction accuracy 0.69, better than our deep model trained by only MPs (0.63)
and much better than a representative DCA method CCMpred (0.47) and the CASP11
winner MetaPSICOV (0.55). The accuracy of our deep model can be further
improved to 0.72 when trained by a mix of non-MPs and MPs. When only contacts
in transmembrane regions are evaluated, our method has top L/10 long-range
accuracy 0.62, 0.57, and 0.53 when trained by a mix of non-MPs and MPs, by
non-MPs only, and by MPs only, respectively, still much better than MetaPSICOV
(0.45) and CCMpred (0.40). All these results suggest that sequence-structure
relationship learned by our deep model from non-MPs generalizes well to MP
contact prediction. Improved contact prediction also leads to better
contact-assisted folding. Using only top predicted contacts as restraints, our
deep learning method can fold 160 and 200 of 510 MPs with TMscore>0.6 when
trained by non-MPs only and by a mix of non-MPs and MPs, respectively, while
CCMpred and MetaPSICOV can do so for only 56 and 77 MPs, respectively. Our
contact-assisted folding also greatly outperforms homology modeling.
| [
{
"created": "Mon, 24 Apr 2017 13:27:22 GMT",
"version": "v1"
}
] | 2017-05-11 | [
[
"Li",
"Zhen",
""
],
[
"Wang",
"Sheng",
""
],
[
"Yu",
"Yizhou",
""
],
[
"Xu",
"Jinbo",
""
]
] | Computational prediction of membrane protein (MP) structures is very challenging partially due to lack of sufficient solved structures for homology modeling. Recently direct evolutionary coupling analysis (DCA) sheds some light on protein contact prediction and accordingly, contact-assisted folding, but DCA is effective only on some very large-sized families since it uses information only in a single protein family. This paper presents a deep transfer learning method that can significantly improve MP contact prediction by learning contact patterns and complex sequence-contact relationship from thousands of non-membrane proteins (non-MPs). Tested on 510 non-redundant MPs, our deep model (learned from only non-MPs) has top L/10 long-range contact prediction accuracy 0.69, better than our deep model trained by only MPs (0.63) and much better than a representative DCA method CCMpred (0.47) and the CASP11 winner MetaPSICOV (0.55). The accuracy of our deep model can be further improved to 0.72 when trained by a mix of non-MPs and MPs. When only contacts in transmembrane regions are evaluated, our method has top L/10 long-range accuracy 0.62, 0.57, and 0.53 when trained by a mix of non-MPs and MPs, by non-MPs only, and by MPs only, respectively, still much better than MetaPSICOV (0.45) and CCMpred (0.40). All these results suggest that sequence-structure relationship learned by our deep model from non-MPs generalizes well to MP contact prediction. Improved contact prediction also leads to better contact-assisted folding. Using only top predicted contacts as restraints, our deep learning method can fold 160 and 200 of 510 MPs with TMscore>0.6 when trained by non-MPs only and by a mix of non-MPs and MPs, respectively, while CCMpred and MetaPSICOV can do so for only 56 and 77 MPs, respectively. Our contact-assisted folding also greatly outperforms homology modeling. |
1503.05103 | Cyrielle Dumont | Sol\'ene Desm\'ee (IAME), France Mentr\'e (IAME), Christine
Veyrat-Follet, J\'er\'emie Guedj (IAME) | Nonlinear Mixed-Effect Models for Prostate-Specific Antigen Kinetics and
Link with Survival in the Context of Metastatic Prostate Cancer: a Comparison
by Simulation of Two-Stage and Joint Approaches | The AAPS Journal, 2015, pp.1550-7416 | null | 10.1208/s12248-015-9745-5 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In metastatic castration-resistant prostate cancer (mCRPC) clinical trials,
the assessment of treatment efficacy essentially relies on the time-to-death
and the kinetics of prostate-specific antigen (PSA). Joint modelling has been
increasingly used to characterize the relationship between a time-to-event and
a biomarker kinetics but numerical difficulties often limit this approach to
linear models. Here we evaluated by simulation the capability of a new feature
of the Stochastic Approximation Expectation-Maximization algorithm in Monolix
to estimate the parameters of a joint model where PSA kinetics was defined by a
mechanistic nonlinear mixed-effect model. The design of the study and the
parameter values were inspired from one arm of a clinical trial. Increasingly
high levels of association between PSA and survival were considered and results
were compared with those found using two simplified alternatives to joint
model, a two-stage and a joint sequential model. We found that joint model
allowed for a precise estimation of all longitudinal and survival parameters.
In particular the effect of PSA kinetics on survival could be precisely
estimated, regardless of the strength of the association. In contrast, both
simplified approaches led to bias on longitudinal parameters and two-stage
model systematically underestimated the effect of PSA kinetics on survival. In
summary we showed that joint model can be used to characterize the relationship
between a nonlinear kinetics and survival. This opens the way for the use of
more complex and physiological models to improve treatment evaluation and
prediction in oncology.
| [
{
"created": "Tue, 17 Mar 2015 15:53:52 GMT",
"version": "v1"
}
] | 2015-03-18 | [
[
"Desmée",
"Soléne",
"",
"IAME"
],
[
"Mentré",
"France",
"",
"IAME"
],
[
"Veyrat-Follet",
"Christine",
"",
"IAME"
],
[
"Guedj",
"Jérémie",
"",
"IAME"
]
] | In metastatic castration-resistant prostate cancer (mCRPC) clinical trials, the assessment of treatment efficacy essentially relies on the time-to-death and the kinetics of prostate-specific antigen (PSA). Joint modelling has been increasingly used to characterize the relationship between a time-to-event and a biomarker kinetics but numerical difficulties often limit this approach to linear models. Here we evaluated by simulation the capability of a new feature of the Stochastic Approximation Expectation-Maximization algorithm in Monolix to estimate the parameters of a joint model where PSA kinetics was defined by a mechanistic nonlinear mixed-effect model. The design of the study and the parameter values were inspired from one arm of a clinical trial. Increasingly high levels of association between PSA and survival were considered and results were compared with those found using two simplified alternatives to joint model, a two-stage and a joint sequential model. We found that joint model allowed for a precise estimation of all longitudinal and survival parameters. In particular the effect of PSA kinetics on survival could be precisely estimated, regardless of the strength of the association. In contrast, both simplified approaches led to bias on longitudinal parameters and two-stage model systematically underestimated the effect of PSA kinetics on survival. In summary we showed that joint model can be used to characterize the relationship between a nonlinear kinetics and survival. This opens the way for the use of more complex and physiological models to improve treatment evaluation and prediction in oncology. |
1208.2778 | Bao-quan Ai | Bao-quan Ai and Shi-Liang Zhu | Complex quantum network model of energy transfer in photosynthetic
complexes | 8 pages, 9 figures | Phys. Rev. E 86, 061917 (2012) | 10.1103/PhysRevE.86.061917 | null | q-bio.MN physics.bio-ph quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The quantum network model with real variables is usually used to describe the
excitation energy transfer (EET) in the Fenna-Matthews-Olson(FMO) complexes. In
this paper we add the quantum phase factors to the hopping terms and find that
the quantum phase factors play an important role in the EET. The quantum phase
factors allow us to consider the space structure of the pigments. It is found
that phase coherence within the complexes would allow quantum interference to
affect the dynamics of the EET. There exist some optimal phase regions where
the transfer efficiency takes its maxima, which indicates that when the
pigments are optimally spaced, the exciton can pass through the FMO with
perfect efficiency. Moreover, the optimal phase regions almost do not change
with the environments. In addition, we find that the phase factors are useful
in the EET just in the case of multiple-pathway. Therefore, we demonstrate
that, the quantum phases may bring the other two factors, the optimal space of
the pigments and multiple-pathway, together to contribute the EET in
photosynthetic complexes with perfect efficiency.
| [
{
"created": "Tue, 14 Aug 2012 03:41:03 GMT",
"version": "v1"
},
{
"created": "Sun, 30 Dec 2012 03:41:00 GMT",
"version": "v2"
}
] | 2015-03-20 | [
[
"Ai",
"Bao-quan",
""
],
[
"Zhu",
"Shi-Liang",
""
]
] | The quantum network model with real variables is usually used to describe the excitation energy transfer (EET) in the Fenna-Matthews-Olson(FMO) complexes. In this paper we add the quantum phase factors to the hopping terms and find that the quantum phase factors play an important role in the EET. The quantum phase factors allow us to consider the space structure of the pigments. It is found that phase coherence within the complexes would allow quantum interference to affect the dynamics of the EET. There exist some optimal phase regions where the transfer efficiency takes its maxima, which indicates that when the pigments are optimally spaced, the exciton can pass through the FMO with perfect efficiency. Moreover, the optimal phase regions almost do not change with the environments. In addition, we find that the phase factors are useful in the EET just in the case of multiple-pathway. Therefore, we demonstrate that, the quantum phases may bring the other two factors, the optimal space of the pigments and multiple-pathway, together to contribute the EET in photosynthetic complexes with perfect efficiency. |
q-bio/0402027 | Ka-Lok Ng | Ka-Lok Ng, Chien-Hung Huang | Global Topological Study of the Protein-protein Interaction Networks | 13 pages, 9 figures, 4 tables | null | null | null | q-bio.MN | null | We employed the random graph theory approach to analyze the protein-protein
interaction database DIP (Feb. 2004), for seven species (S. cerevisiae, H.
pylori, E. coli, C. elegans, H. sapiens, M. musculus and D. melanogaster).
Several global topological parameters (such as node connectivity, average
diameter, node connectivity correlation) were used to characterize these
protein-protein interaction networks (PINs). The logarithm of the connectivity
distribution vs. the logarithm of connectivity study indicated that PINs follow
a power law (P(k) ~ k-\gamma) behavior. Using the regression analysis method we
determined that \gamma lies between 1.5 and 2.4, for the seven species.
Correlation analysis provides good evidence supporting the fact that the seven
PINs form a scale-free network. The average diameters of the networks and their
randomized version are found to have large difference. We also demonstrated
that the interaction networks are quite robust when subject to random
perturbation. Average node connectivity correlation study supports the earlier
results that nodes of low connectivity are correlated, whereas nodes of high
connectivity are not directly linked. These results provided some evidence
suggesting such correlation relations might be a general feature of the PINs
across different species.
| [
{
"created": "Wed, 11 Feb 2004 22:24:56 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Apr 2004 06:39:43 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Ng",
"Ka-Lok",
""
],
[
"Huang",
"Chien-Hung",
""
]
] | We employed the random graph theory approach to analyze the protein-protein interaction database DIP (Feb. 2004), for seven species (S. cerevisiae, H. pylori, E. coli, C. elegans, H. sapiens, M. musculus and D. melanogaster). Several global topological parameters (such as node connectivity, average diameter, node connectivity correlation) were used to characterize these protein-protein interaction networks (PINs). The logarithm of the connectivity distribution vs. the logarithm of connectivity study indicated that PINs follow a power law (P(k) ~ k-\gamma) behavior. Using the regression analysis method we determined that \gamma lies between 1.5 and 2.4, for the seven species. Correlation analysis provides good evidence supporting the fact that the seven PINs form a scale-free network. The average diameters of the networks and their randomized version are found to have large difference. We also demonstrated that the interaction networks are quite robust when subject to random perturbation. Average node connectivity correlation study supports the earlier results that nodes of low connectivity are correlated, whereas nodes of high connectivity are not directly linked. These results provided some evidence suggesting such correlation relations might be a general feature of the PINs across different species. |
1509.07304 | Daniele Ramazzotti | Luca De Sano, Giulio Caravagna, Daniele Ramazzotti, Alex Graudenzi,
Giancarlo Mauri, Bud Mishra, Marco Antoniotti | TRONCO: an R package for the inference of cancer progression models from
heterogeneous genomic data | null | null | 10.1093/bioinformatics/btw035 | null | q-bio.QM q-bio.GN stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: We introduce TRONCO (TRanslational ONCOlogy), an open-source R
package that implements the state-of-the-art algorithms for the inference of
cancer progression models from (epi)genomic mutational profiles. TRONCO can be
used to extract population-level models describing the trends of accumulation
of alterations in a cohort of cross-sectional samples, e.g., retrieved from
publicly available databases, and individual-level models that reveal the
clonal evolutionary history in single cancer patients, when multiple samples,
e.g., multiple biopsies or single-cell sequencing data, are available. The
resulting models can provide key hints in uncovering the evolutionary
trajectories of cancer, especially for precision medicine or personalized
therapy.
Availability: TRONCO is released under the GPL license, it is hosted in the
Software section at http://bimib.disco.unimib.it/ and archived also at
bioconductor.org.
Contact: tronco@disco.unimib.it
| [
{
"created": "Thu, 24 Sep 2015 10:34:30 GMT",
"version": "v1"
},
{
"created": "Tue, 13 Oct 2015 15:04:33 GMT",
"version": "v2"
},
{
"created": "Tue, 22 Dec 2015 16:16:20 GMT",
"version": "v3"
},
{
"created": "Wed, 23 Dec 2015 09:33:47 GMT",
"version": "v4"
},
{
"c... | 2016-02-11 | [
[
"De Sano",
"Luca",
""
],
[
"Caravagna",
"Giulio",
""
],
[
"Ramazzotti",
"Daniele",
""
],
[
"Graudenzi",
"Alex",
""
],
[
"Mauri",
"Giancarlo",
""
],
[
"Mishra",
"Bud",
""
],
[
"Antoniotti",
"Marco",
""
]
] | Motivation: We introduce TRONCO (TRanslational ONCOlogy), an open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models from (epi)genomic mutational profiles. TRONCO can be used to extract population-level models describing the trends of accumulation of alterations in a cohort of cross-sectional samples, e.g., retrieved from publicly available databases, and individual-level models that reveal the clonal evolutionary history in single cancer patients, when multiple samples, e.g., multiple biopsies or single-cell sequencing data, are available. The resulting models can provide key hints in uncovering the evolutionary trajectories of cancer, especially for precision medicine or personalized therapy. Availability: TRONCO is released under the GPL license, it is hosted in the Software section at http://bimib.disco.unimib.it/ and archived also at bioconductor.org. Contact: tronco@disco.unimib.it |
1008.2714 | Michael Deem | Jiankui He and Michael W. Deem | Heterogeneous diversity of spacers within CRISPR | 5 pages, 5 figures, to appear in Phys. Rev. Lett | null | null | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Clustered regularly interspaced short palindromic repeats (CRISPR) in
bacterial and archaeal DNA have recently been shown to be a new type of
anti-viral immune system in these organisms. We here study the diversity of
spacers in CRISPR under selective pressure. We propose a population dynamics
model that explains the biological observation that the leader-proximal end of
CRISPR is more diversified and the leader-distal end of CRISPR is more
conserved. This result is shown to be in agreement with recent experiments. Our
results show thatthe CRISPR spacer structure is influenced by and provides a
record of the viral challenges that bacteria face.
| [
{
"created": "Mon, 16 Aug 2010 17:20:13 GMT",
"version": "v1"
}
] | 2010-08-17 | [
[
"He",
"Jiankui",
""
],
[
"Deem",
"Michael W.",
""
]
] | Clustered regularly interspaced short palindromic repeats (CRISPR) in bacterial and archaeal DNA have recently been shown to be a new type of anti-viral immune system in these organisms. We here study the diversity of spacers in CRISPR under selective pressure. We propose a population dynamics model that explains the biological observation that the leader-proximal end of CRISPR is more diversified and the leader-distal end of CRISPR is more conserved. This result is shown to be in agreement with recent experiments. Our results show thatthe CRISPR spacer structure is influenced by and provides a record of the viral challenges that bacteria face. |
0801.3651 | Matthias Kaschube | Matthias Kaschube, Michael Schnabel and Fred Wolf | Self-organization and the selection of pinwheel density in visual
cortical development | 22 pages, 3 figures | null | 10.1088/1367-2630/10/1/015009 | null | q-bio.NC | null | Self-organization of neural circuitry is an appealing framework for
understanding cortical development, yet its applicability remains unconfirmed.
Models for the self-organization of neural circuits have been proposed, but
experimentally testable predictions of these models have been less clear. The
visual cortex contains a large number of topological point defects, called
pinwheels, which are detectable in experiments and therefore in principle well
suited for testing predictions of self-organization empirically. Here, we
analytically calculate the density of pinwheels predicted by a pattern
formation model of visual cortical development. An important factor controlling
the density of pinwheels in this model appears to be the presence of non-local
long-range interactions, a property which distinguishes cortical circuits from
many nonliving systems in which self-organization has been studied. We show
that in the limit where the range of these interactions is infinite, the
average pinwheel density converges to $\pi$. Moreover, an average pinwheel
density close to this value is robustly selected even for intermediate
interaction ranges, a regime arguably covering interaction-ranges in a wide
range of different species. In conclusion, our paper provides the first direct
theoretical demonstration and analysis of pinwheel density selection in models
of cortical self-organization and suggests to quantitatively probe this type of
prediction in future high-precision experiments.
| [
{
"created": "Wed, 23 Jan 2008 19:42:06 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Kaschube",
"Matthias",
""
],
[
"Schnabel",
"Michael",
""
],
[
"Wolf",
"Fred",
""
]
] | Self-organization of neural circuitry is an appealing framework for understanding cortical development, yet its applicability remains unconfirmed. Models for the self-organization of neural circuits have been proposed, but experimentally testable predictions of these models have been less clear. The visual cortex contains a large number of topological point defects, called pinwheels, which are detectable in experiments and therefore in principle well suited for testing predictions of self-organization empirically. Here, we analytically calculate the density of pinwheels predicted by a pattern formation model of visual cortical development. An important factor controlling the density of pinwheels in this model appears to be the presence of non-local long-range interactions, a property which distinguishes cortical circuits from many nonliving systems in which self-organization has been studied. We show that in the limit where the range of these interactions is infinite, the average pinwheel density converges to $\pi$. Moreover, an average pinwheel density close to this value is robustly selected even for intermediate interaction ranges, a regime arguably covering interaction-ranges in a wide range of different species. In conclusion, our paper provides the first direct theoretical demonstration and analysis of pinwheel density selection in models of cortical self-organization and suggests to quantitatively probe this type of prediction in future high-precision experiments. |
0903.2004 | Michael Deem | Enrique Munoz, Jeong-Man Park, and Michael W. Deem | Solution of the Crow-Kimura and Eigen models for alphabets of arbitrary
size by Schwinger spin coherent states | 50 pages, 8 figures, to appear in J. Stat. Phys; some typos fixed | null | 10.1007/s10955-009-9732-2 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To represent the evolution of nucleic acid and protein sequence, we express
the parallel and Eigen models for molecular evolution in terms of a functional
integral representation with an $h$-letter alphabet, lifting the two-state,
purine/pyrimidine assumption often made in quasi-species theory. For arbitrary
$h$ and a general mutation scheme, we obtain the solution of this model in
terms of a maximum principle. Euler's theorem for homogeneous functions is used
to derive this `thermodynamic' formulation of evolution. The general result for
the parallel model reduces to known results for the purine/pyrimidine $h=2$
alphabet and the nucleic acid $h=4$ alphabet for the Kimura 3 ST mutation
scheme. Examples are presented for the $h=4$ and $h=20$ cases. We derive the
maximum principle for the Eigen model for general $h$. The general result for
the Eigen model reduces to a known result for $h=2$. Examples are presented for
the nucleic acid $h=4$ and the amino acid $h=20$ alphabet. An error catastrophe
phase transition occurs in these models, and the order of the phase transition
changes from second to first order for smooth fitness functions when the
alphabet size is increased beyond two letters to the generic case. As examples,
we analyze the general analytic solution for sharp peak, linear, quadratic, and
quartic fitness functions.
| [
{
"created": "Wed, 11 Mar 2009 16:45:16 GMT",
"version": "v1"
},
{
"created": "Mon, 16 Mar 2009 15:47:57 GMT",
"version": "v2"
}
] | 2015-05-13 | [
[
"Munoz",
"Enrique",
""
],
[
"Park",
"Jeong-Man",
""
],
[
"Deem",
"Michael W.",
""
]
] | To represent the evolution of nucleic acid and protein sequence, we express the parallel and Eigen models for molecular evolution in terms of a functional integral representation with an $h$-letter alphabet, lifting the two-state, purine/pyrimidine assumption often made in quasi-species theory. For arbitrary $h$ and a general mutation scheme, we obtain the solution of this model in terms of a maximum principle. Euler's theorem for homogeneous functions is used to derive this `thermodynamic' formulation of evolution. The general result for the parallel model reduces to known results for the purine/pyrimidine $h=2$ alphabet and the nucleic acid $h=4$ alphabet for the Kimura 3 ST mutation scheme. Examples are presented for the $h=4$ and $h=20$ cases. We derive the maximum principle for the Eigen model for general $h$. The general result for the Eigen model reduces to a known result for $h=2$. Examples are presented for the nucleic acid $h=4$ and the amino acid $h=20$ alphabet. An error catastrophe phase transition occurs in these models, and the order of the phase transition changes from second to first order for smooth fitness functions when the alphabet size is increased beyond two letters to the generic case. As examples, we analyze the general analytic solution for sharp peak, linear, quadratic, and quartic fitness functions. |
2405.17530 | Ramon Grima | Zhixing Cao, Yiling Wang, Ramon Grima | Universal deterministic patterns in stochastic count data | 13 pages, 5 figures | null | null | null | q-bio.QM physics.data-an physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | We report the existence of deterministic patterns in plots showing the
relationship between the mean and the Fano factor (ratio of variance and mean)
of stochastic count data. These patterns are found in a wide variety of
datasets, including those from genomics, paper citations, commerce, ecology,
disease outbreaks, and employment statistics. We develop a theory showing that
the patterns naturally emerge when data sampled from discrete probability
distributions is organised in matrix form. The theory precisely predicts the
patterns and shows that they are a function of only one variable - the sample
size.
| [
{
"created": "Mon, 27 May 2024 17:38:48 GMT",
"version": "v1"
}
] | 2024-05-29 | [
[
"Cao",
"Zhixing",
""
],
[
"Wang",
"Yiling",
""
],
[
"Grima",
"Ramon",
""
]
] | We report the existence of deterministic patterns in plots showing the relationship between the mean and the Fano factor (ratio of variance and mean) of stochastic count data. These patterns are found in a wide variety of datasets, including those from genomics, paper citations, commerce, ecology, disease outbreaks, and employment statistics. We develop a theory showing that the patterns naturally emerge when data sampled from discrete probability distributions is organised in matrix form. The theory precisely predicts the patterns and shows that they are a function of only one variable - the sample size. |
2405.09664 | Niket Thakkar | Niket Thakkar, Sonia Jindal, and Katherine Rosenfeld | Seasonality and susceptibility from measles time series | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | This paper develops mathematical tools to estimate seasonal changes in
measles transmission rates and corresponding variation in population
susceptibility. The tools are designed to leverage times series of cases in the
absence of demographic data. In particular, we focus on publicly available
suspected case reports from the World Health Organization (WHO), which
routinely publishes country-level, monthly aggregated time series. With that as
input, we show that measles epidemiologies can be characterized efficiently at
global-scale, and we use our estimates to recommend context-specific, future
supplementary immunization times. Throughout the paper, comparisons with more
data-informed models illustrate that the approach captures the essential
dynamics, and broadly speaking, the tools we describe represent a scalable
intermediate between conventional empirical approaches and more intricate
disease models.
| [
{
"created": "Wed, 15 May 2024 19:08:39 GMT",
"version": "v1"
}
] | 2024-05-17 | [
[
"Thakkar",
"Niket",
""
],
[
"Jindal",
"Sonia",
""
],
[
"Rosenfeld",
"Katherine",
""
]
] | This paper develops mathematical tools to estimate seasonal changes in measles transmission rates and corresponding variation in population susceptibility. The tools are designed to leverage times series of cases in the absence of demographic data. In particular, we focus on publicly available suspected case reports from the World Health Organization (WHO), which routinely publishes country-level, monthly aggregated time series. With that as input, we show that measles epidemiologies can be characterized efficiently at global-scale, and we use our estimates to recommend context-specific, future supplementary immunization times. Throughout the paper, comparisons with more data-informed models illustrate that the approach captures the essential dynamics, and broadly speaking, the tools we describe represent a scalable intermediate between conventional empirical approaches and more intricate disease models. |
1803.03792 | Nitin Agarwal | Daniel Rangel Rojas, Irmgard Tegeder, Rohini Kuner, Nitin Agarwal | Hypoxia-inducible factor 1a protects peripheral sensory neurons from
diabetic peripheral neuropathy by suppressing accumulation of reactive oxygen
species | null | null | null | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Diabetic peripheral neuropathy (DPN) is one of the most common diabetic
complications. Mechanisms underlying nerve damage and sensory loss following
metabolic dysfunction remain large unclear. Recently, hyperglycemia-induced
mitochondrial dysfunction and the generation of ROS have gained attention as
possible mechanisms of organ damage in diabetes. Hypoxia-inducible factor
1(HIF1a) is a key transcription factor activated by hypoxia, hyperglycemia,
nitric oxide as well as ROS, suggesting a fundamental role in DPN
susceptibility. Genetically-modified mutant mice, which conditionally lack
HIF1a in peripheral sensory neurons (SNS-HIF1a-/-), were analyzed
longitudinally up to 6 months in the streptozotocin (STZ) model of type1
diabetes. Behavioral measurements of sensitivity to thermal and mechanical
stimuli, quantitative morphological analyses of intraepidermal nerve fiber
density and measurements of reactive oxygen species (ROS) in sensory neurons in
vivo were undertaken over several months post-STZ injections to delineate the
role of HIF1a in DPN. Longitudinal behavioral and morphological analyses at 5,
13 and 24 wks post-STZ treatment revealed that SNS-HIF1a-/- developed stronger
hyperglycemia-evoked losses of peripheral nociceptive sensory axons associated
with stronger losses of mechano- and heat sensation with a faster onset than
HIF1afl/fl mice. Mechanistically, these histomorphologic and behavioral
differences were associated with significantly higher level of STZ-induced
production of ROS in sensory neurons of SNS-HIF1a-/- mice as compared with
HIF1afl/fl. Our results indicate that HIF1a is as an upstream modulator of ROS
in peripheral sensory neurons and exerts a protective function in suppressing
hyperglycemia-induced nerve damage by limiting ROS levels. HIF1a stabilization
may be thus a new strategy target for limiting sensory loss, a debilitating
late complication of diabetes.
| [
{
"created": "Sat, 10 Mar 2018 11:29:56 GMT",
"version": "v1"
}
] | 2018-03-13 | [
[
"Rojas",
"Daniel Rangel",
""
],
[
"Tegeder",
"Irmgard",
""
],
[
"Kuner",
"Rohini",
""
],
[
"Agarwal",
"Nitin",
""
]
] | Diabetic peripheral neuropathy (DPN) is one of the most common diabetic complications. Mechanisms underlying nerve damage and sensory loss following metabolic dysfunction remain large unclear. Recently, hyperglycemia-induced mitochondrial dysfunction and the generation of ROS have gained attention as possible mechanisms of organ damage in diabetes. Hypoxia-inducible factor 1(HIF1a) is a key transcription factor activated by hypoxia, hyperglycemia, nitric oxide as well as ROS, suggesting a fundamental role in DPN susceptibility. Genetically-modified mutant mice, which conditionally lack HIF1a in peripheral sensory neurons (SNS-HIF1a-/-), were analyzed longitudinally up to 6 months in the streptozotocin (STZ) model of type1 diabetes. Behavioral measurements of sensitivity to thermal and mechanical stimuli, quantitative morphological analyses of intraepidermal nerve fiber density and measurements of reactive oxygen species (ROS) in sensory neurons in vivo were undertaken over several months post-STZ injections to delineate the role of HIF1a in DPN. Longitudinal behavioral and morphological analyses at 5, 13 and 24 wks post-STZ treatment revealed that SNS-HIF1a-/- developed stronger hyperglycemia-evoked losses of peripheral nociceptive sensory axons associated with stronger losses of mechano- and heat sensation with a faster onset than HIF1afl/fl mice. Mechanistically, these histomorphologic and behavioral differences were associated with significantly higher level of STZ-induced production of ROS in sensory neurons of SNS-HIF1a-/- mice as compared with HIF1afl/fl. Our results indicate that HIF1a is as an upstream modulator of ROS in peripheral sensory neurons and exerts a protective function in suppressing hyperglycemia-induced nerve damage by limiting ROS levels. HIF1a stabilization may be thus a new strategy target for limiting sensory loss, a debilitating late complication of diabetes. |
2405.06732 | Maedeh Sadeghi | Maedeh Sadeghi (1), Mahdi Aliyari Shoorehdeli (1), Shole jamali (2),
Abbas Haghparast (2) ((1) Fault Detection and Identification (FDI)
Laboratory, Faculty of Electrical Engineering, K. N. Toosi University of
Technology, Tehran, Iran, (2) Neuroscience Research Center, School of
Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran) | A Global Data-Driven Model for The Hippocampus and Nucleus Accumbens of
Rat From The Local Field Potential Recordings (LFP) | null | null | null | null | q-bio.NC cs.LG | http://creativecommons.org/licenses/by/4.0/ | In brain neural networks, Local Field Potential (LFP) signals represent the
dynamic flow of information. Analyzing LFP clinical data plays a critical role
in improving our understanding of brain mechanisms. One way to enhance our
understanding of these mechanisms is to identify a global model to predict
brain signals in different situations. This paper identifies a global
data-driven based on LFP recordings of the Nucleus Accumbens and Hippocampus
regions in freely moving rats. The LFP is recorded from each rat in two
different situations: before and after the process of getting a reward which
can be either a drug (Morphine) or natural food (like popcorn or biscuit). A
comparison of five machine learning methods including Long Short Term Memory
(LSTM), Echo State Network (ESN), Deep Echo State Network (DeepESN), Radial
Basis Function (RBF), and Local Linear Model Tree (LLM) is conducted to develop
this model. LoLiMoT was chosen with the best performance among all methods.
This model can predict the future states of these regions with one pre-trained
model. Identifying this model showed that Morphine and natural rewards do not
change the dynamic features of neurons in these regions.
| [
{
"created": "Fri, 10 May 2024 15:58:39 GMT",
"version": "v1"
}
] | 2024-05-14 | [
[
"Sadeghi",
"Maedeh",
""
],
[
"Shoorehdeli",
"Mahdi Aliyari",
""
],
[
"jamali",
"Shole",
""
],
[
"Haghparast",
"Abbas",
""
]
] | In brain neural networks, Local Field Potential (LFP) signals represent the dynamic flow of information. Analyzing LFP clinical data plays a critical role in improving our understanding of brain mechanisms. One way to enhance our understanding of these mechanisms is to identify a global model to predict brain signals in different situations. This paper identifies a global data-driven based on LFP recordings of the Nucleus Accumbens and Hippocampus regions in freely moving rats. The LFP is recorded from each rat in two different situations: before and after the process of getting a reward which can be either a drug (Morphine) or natural food (like popcorn or biscuit). A comparison of five machine learning methods including Long Short Term Memory (LSTM), Echo State Network (ESN), Deep Echo State Network (DeepESN), Radial Basis Function (RBF), and Local Linear Model Tree (LLM) is conducted to develop this model. LoLiMoT was chosen with the best performance among all methods. This model can predict the future states of these regions with one pre-trained model. Identifying this model showed that Morphine and natural rewards do not change the dynamic features of neurons in these regions. |
2108.13611 | Aleksandra Miljevic | Aleksandra Miljevic, Neil W. Bailey, Fidel Vila-Rodriguez, Sally E.
Herring, Paul B. Fitzgerald | EEG-connectivity: A fundamental guide and checklist for optimal study
design and evaluation | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Brain connectivity can be estimated through a wide number of analyses applied
to electroencephalographic (EEG) data. However, substantial heterogeneity in
the implementation of connectivity methods exist. Heterogeneity in
conceptualization of connectivity measures, data collection, or data
pre-processing may be associated with variability in robustness of measurement.
While it is difficult to compare the results of studies using different EEG
connectivity measures, standardization of processing and reporting may
facilitate the task. We discuss how factors such as referencing, epoch length
and number, controls for volume conduction, artefact removal, and statistical
control of multiple comparisons influence the EEG connectivity estimate for
connectivity measures, and what can be done to control for potential confounds
associated with these factors. Based on the results reported in previous
literature, this article presents recommendations and a novel checklist
developed for quality assessment of EEG connectivity studies. This checklist
and its recommendations are made in an effort to draw attention to factors that
may influence connectivity estimates and factors that need to be improved in
future research. Standardization of procedures and reporting in EEG
connectivity may lead to EEG connectivity studies to be made more synthesisable
and comparable despite variations in the methodology underlying connectivity
estimates.
| [
{
"created": "Tue, 31 Aug 2021 04:28:17 GMT",
"version": "v1"
}
] | 2021-09-01 | [
[
"Miljevic",
"Aleksandra",
""
],
[
"Bailey",
"Neil W.",
""
],
[
"Vila-Rodriguez",
"Fidel",
""
],
[
"Herring",
"Sally E.",
""
],
[
"Fitzgerald",
"Paul B.",
""
]
] | Brain connectivity can be estimated through a wide number of analyses applied to electroencephalographic (EEG) data. However, substantial heterogeneity in the implementation of connectivity methods exist. Heterogeneity in conceptualization of connectivity measures, data collection, or data pre-processing may be associated with variability in robustness of measurement. While it is difficult to compare the results of studies using different EEG connectivity measures, standardization of processing and reporting may facilitate the task. We discuss how factors such as referencing, epoch length and number, controls for volume conduction, artefact removal, and statistical control of multiple comparisons influence the EEG connectivity estimate for connectivity measures, and what can be done to control for potential confounds associated with these factors. Based on the results reported in previous literature, this article presents recommendations and a novel checklist developed for quality assessment of EEG connectivity studies. This checklist and its recommendations are made in an effort to draw attention to factors that may influence connectivity estimates and factors that need to be improved in future research. Standardization of procedures and reporting in EEG connectivity may lead to EEG connectivity studies to be made more synthesisable and comparable despite variations in the methodology underlying connectivity estimates. |
1906.01678 | Todd Hylton | Todd Hylton | Thermodynamic Neural Network | 26 pages, 16 figures | null | 10.3390/e22030256 | null | q-bio.NC physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A thermodynamically motivated neural network model is described that
self-organizes to transport charge associated with internal and external
potentials while in contact with a thermal reservoir. The model integrates
techniques for rapid, large-scale, reversible, conservative equilibration of
node states and slow, small-scale, irreversible, dissipative adaptation of the
edge states as a means to create multiscale order. All interactions in the
network are local and the network structures can be generic and recurrent.
Isolated networks show multiscale dynamics, and externally driven networks
evolve to efficiently connect external positive and negative potentials. The
model integrates concepts of conservation, potentiation, fluctuation,
dissipation, adaptation, equilibration and causation to illustrate the
thermodynamic evolution of organization in open systems. A key conclusion of
the work is that the transport and dissipation of conserved physical quantities
drives the self-organization of open thermodynamic systems.
| [
{
"created": "Fri, 31 May 2019 01:18:30 GMT",
"version": "v1"
},
{
"created": "Sun, 13 Oct 2019 20:32:47 GMT",
"version": "v2"
},
{
"created": "Tue, 5 Nov 2019 22:49:05 GMT",
"version": "v3"
},
{
"created": "Thu, 30 Jan 2020 21:09:27 GMT",
"version": "v4"
}
] | 2020-04-22 | [
[
"Hylton",
"Todd",
""
]
] | A thermodynamically motivated neural network model is described that self-organizes to transport charge associated with internal and external potentials while in contact with a thermal reservoir. The model integrates techniques for rapid, large-scale, reversible, conservative equilibration of node states and slow, small-scale, irreversible, dissipative adaptation of the edge states as a means to create multiscale order. All interactions in the network are local and the network structures can be generic and recurrent. Isolated networks show multiscale dynamics, and externally driven networks evolve to efficiently connect external positive and negative potentials. The model integrates concepts of conservation, potentiation, fluctuation, dissipation, adaptation, equilibration and causation to illustrate the thermodynamic evolution of organization in open systems. A key conclusion of the work is that the transport and dissipation of conserved physical quantities drives the self-organization of open thermodynamic systems. |
2405.09395 | Xiaoliang Luo | Xiaoliang Luo, Guangzhi Sun, Bradley C. Love | Matching domain experts by training from scratch on domain knowledge | ICML 2024 (Large Language Models and Cognition) | null | null | null | q-bio.NC cs.AI cs.CL | http://creativecommons.org/licenses/by/4.0/ | Recently, large language models (LLMs) have outperformed human experts in
predicting the results of neuroscience experiments (Luo et al., 2024). What is
the basis for this performance? One possibility is that statistical patterns in
that specific scientific literature, as opposed to emergent reasoning abilities
arising from broader training, underlie LLMs' performance. To evaluate this
possibility, we trained (next word prediction) a relatively small
124M-parameter GPT-2 model on 1.3 billion tokens of domain-specific knowledge.
Despite being orders of magnitude smaller than larger LLMs trained on trillions
of tokens, small models achieved expert-level performance in predicting
neuroscience results. Small models trained on the neuroscience literature
succeeded when they were trained from scratch using a tokenizer specifically
trained on neuroscience text or when the neuroscience literature was used to
finetune a pretrained GPT-2. Our results indicate that expert-level performance
may be attained by even small LLMs through domain-specific, auto-regressive
training approaches.
| [
{
"created": "Wed, 15 May 2024 14:50:51 GMT",
"version": "v1"
},
{
"created": "Tue, 2 Jul 2024 16:42:48 GMT",
"version": "v2"
}
] | 2024-07-03 | [
[
"Luo",
"Xiaoliang",
""
],
[
"Sun",
"Guangzhi",
""
],
[
"Love",
"Bradley C.",
""
]
] | Recently, large language models (LLMs) have outperformed human experts in predicting the results of neuroscience experiments (Luo et al., 2024). What is the basis for this performance? One possibility is that statistical patterns in that specific scientific literature, as opposed to emergent reasoning abilities arising from broader training, underlie LLMs' performance. To evaluate this possibility, we trained (next word prediction) a relatively small 124M-parameter GPT-2 model on 1.3 billion tokens of domain-specific knowledge. Despite being orders of magnitude smaller than larger LLMs trained on trillions of tokens, small models achieved expert-level performance in predicting neuroscience results. Small models trained on the neuroscience literature succeeded when they were trained from scratch using a tokenizer specifically trained on neuroscience text or when the neuroscience literature was used to finetune a pretrained GPT-2. Our results indicate that expert-level performance may be attained by even small LLMs through domain-specific, auto-regressive training approaches. |
0905.3154 | Patrick Crotty | Jeffrey Seely, Patrick Crotty | Optimization of the leak conductance in the squid giant axon | 9 pages; 9 figures; accepted for publication in Physical Review E | null | 10.1103/PhysRevE.82.021906 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We report on a theoretical study showing that the leak conductance density,
$\GL$, in the squid giant axon appears to be optimal for the action potential
firing frequency. More precisely, the standard assumption that the leak current
is composed of chloride ions leads to the result that the experimental value
for $\GL$ is very close to the optimal value in the Hodgkin-Huxley model which
minimizes the absolute refractory period of the action potential, thereby
maximizing the maximum firing frequency under stimulation by sharp, brief input
current spikes to one end of the axon. The measured value of $\GL$ also appears
to be close to optimal for the frequency of repetitive firing caused by a
constant current input to one end of the axon, especially when temperature
variations are taken into account. If, by contrast, the leak current is assumed
to be composed of separate voltage-independent sodium and potassium currents,
then these optimizations are not observed.
| [
{
"created": "Tue, 19 May 2009 17:50:29 GMT",
"version": "v1"
},
{
"created": "Mon, 26 Jul 2010 14:14:32 GMT",
"version": "v2"
}
] | 2015-05-13 | [
[
"Seely",
"Jeffrey",
""
],
[
"Crotty",
"Patrick",
""
]
] | We report on a theoretical study showing that the leak conductance density, $\GL$, in the squid giant axon appears to be optimal for the action potential firing frequency. More precisely, the standard assumption that the leak current is composed of chloride ions leads to the result that the experimental value for $\GL$ is very close to the optimal value in the Hodgkin-Huxley model which minimizes the absolute refractory period of the action potential, thereby maximizing the maximum firing frequency under stimulation by sharp, brief input current spikes to one end of the axon. The measured value of $\GL$ also appears to be close to optimal for the frequency of repetitive firing caused by a constant current input to one end of the axon, especially when temperature variations are taken into account. If, by contrast, the leak current is assumed to be composed of separate voltage-independent sodium and potassium currents, then these optimizations are not observed. |
q-bio/0404009 | Matthew Berryman | Matthew J. Berryman, Sabrina L. Spencer, Andrew Allison and Derek
Abbott | Fluctuations and noise in cancer development | 11 pages, 6 figures | null | 10.1117/12.546641 | null | q-bio.CB q-bio.MN | null | This paper explores fluctuations and noise in various facets of cancer
development. The three areas of particular focus are the stochastic progression
of cells to cancer, fluctuations of the tumor size during treatment, and noise
in cancer cell signalling. We explore the stochastic dynamics of tumor growth
and response to treatment using a Markov model, and fluctutions in tumor size
in response to treatment using partial differential equations. We also explore
noise within gene networks in cancer cells, and noise in inter-cell signalling.
| [
{
"created": "Wed, 7 Apr 2004 06:45:46 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Berryman",
"Matthew J.",
""
],
[
"Spencer",
"Sabrina L.",
""
],
[
"Allison",
"Andrew",
""
],
[
"Abbott",
"Derek",
""
]
] | This paper explores fluctuations and noise in various facets of cancer development. The three areas of particular focus are the stochastic progression of cells to cancer, fluctuations of the tumor size during treatment, and noise in cancer cell signalling. We explore the stochastic dynamics of tumor growth and response to treatment using a Markov model, and fluctutions in tumor size in response to treatment using partial differential equations. We also explore noise within gene networks in cancer cells, and noise in inter-cell signalling. |
1606.08607 | Takashi Okada | Takashi Okada, Atsushi Mochizuki | Law of localization in chemical reaction networks | 18 pages 5 figures; v2: minor corrections (accepted for publication
in Physical Review Letters) | null | 10.1103/PhysRevLett.117.048101 | null | q-bio.MN math.DS physics.bio-ph q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In living cells, chemical reactions are connected by sharing their products
and substrates, and form complex networks, e.g. metabolic pathways. Here we
developed a theory to predict the sensitivity, i.e. the responses of
concentrations and fluxes to perturbations of enzymes, from network structure
alone. Responses turn out to exhibit two characteristic patterns,
$localization$ and $hierarchy$. We present a general theorem connecting
sensitivity with network topology that explains these characteristic patterns.
Our results imply that network topology is an origin of biological robustness.
Finally, we suggest a strategy to determine real networks from experimental
measurements.
| [
{
"created": "Tue, 28 Jun 2016 08:34:20 GMT",
"version": "v1"
},
{
"created": "Mon, 4 Jul 2016 09:25:34 GMT",
"version": "v2"
}
] | 2016-08-24 | [
[
"Okada",
"Takashi",
""
],
[
"Mochizuki",
"Atsushi",
""
]
] | In living cells, chemical reactions are connected by sharing their products and substrates, and form complex networks, e.g. metabolic pathways. Here we developed a theory to predict the sensitivity, i.e. the responses of concentrations and fluxes to perturbations of enzymes, from network structure alone. Responses turn out to exhibit two characteristic patterns, $localization$ and $hierarchy$. We present a general theorem connecting sensitivity with network topology that explains these characteristic patterns. Our results imply that network topology is an origin of biological robustness. Finally, we suggest a strategy to determine real networks from experimental measurements. |
1609.08310 | Denis Michel | Denis Michel, Benjamin Boutin, Philippe Ruelle | The accuracy of biochemical interactions is ensured by endothermic
stepwise kinetics | 14 pages, 8 figures | Prog. Biophys. Mol. Biol. 121, 35-44 (2016) | 10.1016/j.pbiomolbio.2016.02.001 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The discerning behavior of living systems relies on accurate interactions
selected from the lot of molecular collisions occurring in the cell. To ensure
the reliability of interactions, binding partners are classically envisioned as
finely preadapted molecules, evolutionarily selected on the basis of their
affinity in one-step associations. But the counterselection of inappropriate
interactions can in fact be much more efficiently obtained through difficult
multi-step adjustment, whose final high energy state is locked by a fluctuation
ratchet. The progressive addition of molecular bonds during stereo-adjustment
can be modeled as a predominantly backward random walk whose first arrival is
frozen by a micro-irreversible transition. A new criterion of ligand
specificity is presented, that is based on the ratio rejection/incorporation.
In addition to its role in the selectivity of interactions, this generic recipe
can underlie other important biological phenomena such as the regular synthesis
at low level of supramolecular complexes, monostable kinetic bimodality,
substrate concentration thresholds or the preparation of rapidly
depolymerizable structureswith stored energy, like microtubules.
| [
{
"created": "Tue, 27 Sep 2016 08:25:58 GMT",
"version": "v1"
}
] | 2016-09-28 | [
[
"Michel",
"Denis",
""
],
[
"Boutin",
"Benjamin",
""
],
[
"Ruelle",
"Philippe",
""
]
] | The discerning behavior of living systems relies on accurate interactions selected from the lot of molecular collisions occurring in the cell. To ensure the reliability of interactions, binding partners are classically envisioned as finely preadapted molecules, evolutionarily selected on the basis of their affinity in one-step associations. But the counterselection of inappropriate interactions can in fact be much more efficiently obtained through difficult multi-step adjustment, whose final high energy state is locked by a fluctuation ratchet. The progressive addition of molecular bonds during stereo-adjustment can be modeled as a predominantly backward random walk whose first arrival is frozen by a micro-irreversible transition. A new criterion of ligand specificity is presented, that is based on the ratio rejection/incorporation. In addition to its role in the selectivity of interactions, this generic recipe can underlie other important biological phenomena such as the regular synthesis at low level of supramolecular complexes, monostable kinetic bimodality, substrate concentration thresholds or the preparation of rapidly depolymerizable structureswith stored energy, like microtubules. |
1812.11878 | William Bialek | Shiva R. Sinha, William Bialek, and Rob R. de Ruyter van Steveninck | Optimal local estimates of visual motion in a natural environment | null | Phys. Rev. Lett. 126, 018101 (2021) | 10.1103/PhysRevLett.126.018101 | null | q-bio.NC cond-mat.dis-nn physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many organisms, from flies to humans, use visual signals to estimate their
motion through the world. To explore the motion estimation problem, we have
constructed a camera/gyroscope system that allows us to sample, at high
temporal resolution, the joint distribution of input images and rotational
motions during a long walk in the woods. From these data we construct the
optimal estimator of velocity based on spatial and temporal derivatives of
image intensity in small patches of the visual world. Over the bulk of the
naturally occurring dynamic range, the optimal estimator exhibits the same
systematic errors seen in neural and behavioral responses, including the
confounding of velocity and contrast. These results suggest that apparent
errors of sensory processing may reflect an optimal response to the physical
signals in the environment.
| [
{
"created": "Mon, 31 Dec 2018 16:20:12 GMT",
"version": "v1"
}
] | 2021-01-13 | [
[
"Sinha",
"Shiva R.",
""
],
[
"Bialek",
"William",
""
],
[
"van Steveninck",
"Rob R. de Ruyter",
""
]
] | Many organisms, from flies to humans, use visual signals to estimate their motion through the world. To explore the motion estimation problem, we have constructed a camera/gyroscope system that allows us to sample, at high temporal resolution, the joint distribution of input images and rotational motions during a long walk in the woods. From these data we construct the optimal estimator of velocity based on spatial and temporal derivatives of image intensity in small patches of the visual world. Over the bulk of the naturally occurring dynamic range, the optimal estimator exhibits the same systematic errors seen in neural and behavioral responses, including the confounding of velocity and contrast. These results suggest that apparent errors of sensory processing may reflect an optimal response to the physical signals in the environment. |
0711.1568 | Jose Nacher | Jose C Nacher and Jean-Marc Schwartz | A global view of drug-therapy interactions | 16 pages, 4 figures. It was submitted to peer review on August 15,
2007 | null | null | null | q-bio.BM | null | Network science is already making an impact on the study of complex systems
and offers a promising variety of tools to understand their formation and
evolution (1-4) in many disparate fields from large communication networks
(5,6), transportation infrastructures (7) and social communities (8,9) to
biological systems (1,10,11). Even though new highthroughput technologies have
rapidly been generating large amounts of genomic data, drug design has not
followed the same development, and it is still complicated and expensive to
develop new single-target drugs. Nevertheless, recent approaches suggest that
multi-target drug design combined with a network-dependent approach and
large-scale systems-oriented strategies (12-14) create a promising framework to
combat complex multigenetic disorders like cancer or diabetes. Here, we
investigate the human network corresponding to the interactions between all US
approved drugs and human therapies, defined by known drug-therapy
relationships. Our results show that the key paths in this network are shorter
than three steps, indicating that distant therapies are separated by a
surprisingly low number of chemical compounds. We also identify a sub-network
composed by drugs with high centrality measures (15), which represent the
structural back-bone of the drug-therapy system and act as hubs routing
information between distant parts of the network. These findings provide for
the first time a global map of the largescale organization of all known drugs
and associated therapies, bringing new insights on possible strategies for
future drug development. Special attention should be given to drugs which
combine the two properties of (a) having a high centrality value and (b) acting
on multiple targets.
| [
{
"created": "Sat, 10 Nov 2007 03:01:41 GMT",
"version": "v1"
}
] | 2007-11-13 | [
[
"Nacher",
"Jose C",
""
],
[
"Schwartz",
"Jean-Marc",
""
]
] | Network science is already making an impact on the study of complex systems and offers a promising variety of tools to understand their formation and evolution (1-4) in many disparate fields from large communication networks (5,6), transportation infrastructures (7) and social communities (8,9) to biological systems (1,10,11). Even though new highthroughput technologies have rapidly been generating large amounts of genomic data, drug design has not followed the same development, and it is still complicated and expensive to develop new single-target drugs. Nevertheless, recent approaches suggest that multi-target drug design combined with a network-dependent approach and large-scale systems-oriented strategies (12-14) create a promising framework to combat complex multigenetic disorders like cancer or diabetes. Here, we investigate the human network corresponding to the interactions between all US approved drugs and human therapies, defined by known drug-therapy relationships. Our results show that the key paths in this network are shorter than three steps, indicating that distant therapies are separated by a surprisingly low number of chemical compounds. We also identify a sub-network composed by drugs with high centrality measures (15), which represent the structural back-bone of the drug-therapy system and act as hubs routing information between distant parts of the network. These findings provide for the first time a global map of the largescale organization of all known drugs and associated therapies, bringing new insights on possible strategies for future drug development. Special attention should be given to drugs which combine the two properties of (a) having a high centrality value and (b) acting on multiple targets. |
1605.00899 | Ruiqi Li | Ruiqi Li, Wenxu Wang, Zengru Di | Effects of human dynamics on epidemic spreading in C\^{o}te d'Ivoire | null | Physica A: Statistical Mechanics and its Applications, 2017 | 10.1016/j.physa.2016.09.059 | 467:30-40 | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding and predicting outbreaks of contagious diseases are crucial to
the development of society and public health, especially for underdeveloped
countries. However, challenging problems are encountered because of complex
epidemic spreading dynamics influenced by spatial structure and human dynamics
(including both human mobility and human interaction intensity). We propose a
systematical model to depict nationwide epidemic spreading in C\^{o}te
d'Ivoire, which integrates multiple factors, such as human mobility, human
interaction intensity, and demographic features. We provide insights to aid in
modeling and predicting the epidemic spreading process by data-driven
simulation and theoretical analysis, which is otherwise beyond the scope of
local evaluation and geometrical views. We show that the requirement that the
average local basic reproductive number to be greater than unity is not
necessary for outbreaks of epidemics. The observed spreading phenomenon can be
roughly explained as a heterogeneous diffusion-reaction process by redefining
mobility distance according to the human mobility volume between nodes, which
is beyond the geometrical viewpoint. However, the heterogeneity of human
dynamics still poses challenges to precise prediction.
| [
{
"created": "Sat, 30 Apr 2016 05:20:35 GMT",
"version": "v1"
}
] | 2022-02-15 | [
[
"Li",
"Ruiqi",
""
],
[
"Wang",
"Wenxu",
""
],
[
"Di",
"Zengru",
""
]
] | Understanding and predicting outbreaks of contagious diseases are crucial to the development of society and public health, especially for underdeveloped countries. However, challenging problems are encountered because of complex epidemic spreading dynamics influenced by spatial structure and human dynamics (including both human mobility and human interaction intensity). We propose a systematical model to depict nationwide epidemic spreading in C\^{o}te d'Ivoire, which integrates multiple factors, such as human mobility, human interaction intensity, and demographic features. We provide insights to aid in modeling and predicting the epidemic spreading process by data-driven simulation and theoretical analysis, which is otherwise beyond the scope of local evaluation and geometrical views. We show that the requirement that the average local basic reproductive number to be greater than unity is not necessary for outbreaks of epidemics. The observed spreading phenomenon can be roughly explained as a heterogeneous diffusion-reaction process by redefining mobility distance according to the human mobility volume between nodes, which is beyond the geometrical viewpoint. However, the heterogeneity of human dynamics still poses challenges to precise prediction. |
q-bio/0410019 | Paul Tiesinga | Paul H. Tiesinga | Stimulus competition by inhibitory interference | 20 pages, 7 figures, 1 table | null | null | null | q-bio.NC | null | When two stimuli are present in the receptive field of a V4 neuron, the
firing rate response is between the weakest and strongest response elicited by
each of the stimuli alone (Reynolds et al, 1999, Journal of Neuroscience
19:1736-1753). When attention is directed towards the stimulus eliciting the
strongest response (the preferred stimulus), the response to the pair is
increased, whereas the response decreases when attention is directed to the
other stimulus (the poor stimulus). These experimental results were reproduced
in a model of a V4 neuron under the assumption that attention modulates the
activity of local interneuron networks. The V4 model neuron received
stimulus-specific asynchronous excitation from V2 and synchronous inhibitory
inputs from two local interneuron networks in V4. Each interneuron network was
driven by stimulus-specific excitatory inputs from V2 and was modulated by a
projection from the frontal eye fields. Stimulus competition was present
because of a delay in arrival time of synchronous volleys from each interneuron
network. For small delays, the firing rate was close to the rate elicited by
the preferred stimulus alone, whereas for larger delays it approached the
firing rate of the poor stimulus. When either stimulus was presented alone the
neuron's response was not altered by the change in delay. The model suggests
that top-down attention biases the competition between V2 columns for control
of V4 neurons by changing the relative timing of inhibition rather than by
changes in the degree of synchrony of interneuron networks. The mechanism
proposed here for attentional modulation of firing rate - gain modulation by
inhibitory interference - is likely to have more general applicability to
cortical information processing.
| [
{
"created": "Mon, 18 Oct 2004 15:43:13 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Tiesinga",
"Paul H.",
""
]
] | When two stimuli are present in the receptive field of a V4 neuron, the firing rate response is between the weakest and strongest response elicited by each of the stimuli alone (Reynolds et al, 1999, Journal of Neuroscience 19:1736-1753). When attention is directed towards the stimulus eliciting the strongest response (the preferred stimulus), the response to the pair is increased, whereas the response decreases when attention is directed to the other stimulus (the poor stimulus). These experimental results were reproduced in a model of a V4 neuron under the assumption that attention modulates the activity of local interneuron networks. The V4 model neuron received stimulus-specific asynchronous excitation from V2 and synchronous inhibitory inputs from two local interneuron networks in V4. Each interneuron network was driven by stimulus-specific excitatory inputs from V2 and was modulated by a projection from the frontal eye fields. Stimulus competition was present because of a delay in arrival time of synchronous volleys from each interneuron network. For small delays, the firing rate was close to the rate elicited by the preferred stimulus alone, whereas for larger delays it approached the firing rate of the poor stimulus. When either stimulus was presented alone the neuron's response was not altered by the change in delay. The model suggests that top-down attention biases the competition between V2 columns for control of V4 neurons by changing the relative timing of inhibition rather than by changes in the degree of synchrony of interneuron networks. The mechanism proposed here for attentional modulation of firing rate - gain modulation by inhibitory interference - is likely to have more general applicability to cortical information processing. |
2207.14294 | Tiffany Callahan | Tiffany J. Callahan, Adrianne L. Stefanski, Jin-Dong Kim, William A.
Baumgartner Jr., Jordan M. Wyrwa, Lawrence E. Hunter | Knowledge-Driven Mechanistic Enrichment of the Preeclampsia Ignorome | Preprint of an article submitted for consideration in Pacific
Symposium on Biocomputing \copyright 2022 copyright World Scientific
Publishing Company https://psb.stanford.edu/ | null | 10.5281/zenodo.5716389 | null | q-bio.GN cs.AI | http://creativecommons.org/licenses/by/4.0/ | Preeclampsia is a leading cause of maternal and fetal morbidity and
mortality. Currently, the only definitive treatment of preeclampsia is delivery
of the placenta, which is central to the pathogenesis of the disease.
Transcriptional profiling of human placenta from pregnancies complicated by
preeclampsia has been extensively performed to identify differentially
expressed genes (DEGs). The decisions to investigate DEGs experimentally are
biased by many factors, causing many DEGs to remain uninvestigated. A set of
DEGs which are associated with a disease experimentally, but which have no
known association to the disease in the literature are known as the ignorome.
Preeclampsia has an extensive body of scientific literature, a large pool of
DEG data, and only one definitive treatment. Tools facilitating knowledge-based
analyses, which are capable of combining disparate data from many sources in
order to suggest underlying mechanisms of action, may be a valuable resource to
support discovery and improve our understanding of this disease. In this work
we demonstrate how a biomedical knowledge graph (KG) can be used to identify
novel preeclampsia molecular mechanisms. Existing open source biomedical
resources and publicly available high-throughput transcriptional profiling data
were used to identify and annotate the function of currently uninvestigated
preeclampsia-associated DEGs. Experimentally investigated genes associated with
preeclampsia were identified from PubMed abstracts using text-mining
methodologies. The relative complement of the text-mined- and
meta-analysis-derived lists were identified as the uninvestigated
preeclampsia-associated DEGs (n=445), i.e., the preeclampsia ignorome. Using
the KG to investigate relevant DEGs revealed 53 novel clinically relevant and
biologically actionable mechanistic associations.
| [
{
"created": "Thu, 28 Jul 2022 03:29:09 GMT",
"version": "v1"
},
{
"created": "Sun, 2 Oct 2022 21:18:58 GMT",
"version": "v2"
}
] | 2022-10-04 | [
[
"Callahan",
"Tiffany J.",
""
],
[
"Stefanski",
"Adrianne L.",
""
],
[
"Kim",
"Jin-Dong",
""
],
[
"Baumgartner",
"William A.",
"Jr."
],
[
"Wyrwa",
"Jordan M.",
""
],
[
"Hunter",
"Lawrence E.",
""
]
] | Preeclampsia is a leading cause of maternal and fetal morbidity and mortality. Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease. Transcriptional profiling of human placenta from pregnancies complicated by preeclampsia has been extensively performed to identify differentially expressed genes (DEGs). The decisions to investigate DEGs experimentally are biased by many factors, causing many DEGs to remain uninvestigated. A set of DEGs which are associated with a disease experimentally, but which have no known association to the disease in the literature are known as the ignorome. Preeclampsia has an extensive body of scientific literature, a large pool of DEG data, and only one definitive treatment. Tools facilitating knowledge-based analyses, which are capable of combining disparate data from many sources in order to suggest underlying mechanisms of action, may be a valuable resource to support discovery and improve our understanding of this disease. In this work we demonstrate how a biomedical knowledge graph (KG) can be used to identify novel preeclampsia molecular mechanisms. Existing open source biomedical resources and publicly available high-throughput transcriptional profiling data were used to identify and annotate the function of currently uninvestigated preeclampsia-associated DEGs. Experimentally investigated genes associated with preeclampsia were identified from PubMed abstracts using text-mining methodologies. The relative complement of the text-mined- and meta-analysis-derived lists were identified as the uninvestigated preeclampsia-associated DEGs (n=445), i.e., the preeclampsia ignorome. Using the KG to investigate relevant DEGs revealed 53 novel clinically relevant and biologically actionable mechanistic associations. |
0704.2793 | Matthew Scott | Matthew Scott, Terence Hwa and Brian Ingalls | Deterministic characterization of stochastic genetic circuits | 6 pages (Supplementary Information is appended) | Proceedings of the National Academy of Sciences USA (2007), vol.
104(18): 7402-7407 | 10.1073/pnas.0610468104 | null | q-bio.MN q-bio.QM | null | For cellular biochemical reaction systems where the numbers of molecules is
small, significant noise is associated with chemical reaction events. This
molecular noise can give rise to behavior that is very different from the
predictions of deterministic rate equation models. Unfortunately, there are few
analytic methods for examining the qualitative behavior of stochastic systems.
Here we describe such a method that extends deterministic analysis to include
leading-order corrections due to the molecular noise. The method allows the
steady-state behavior of the stochastic model to be easily computed,
facilitates the mapping of stability phase diagrams that include stochastic
effects and reveals how model parameters affect noise susceptibility, in a
manner not accessible to numerical simulation. By way of illustration we
consider two genetic circuits: a bistable positive-feedback loop and a
negative-feedback oscillator. We find in the positive feedback circuit that
translational activation leads to a far more stable system than transcriptional
control. Conversely, in a negative-feedback loop triggered by a
positive-feedback switch, the stochasticity of transcriptional control is
harnessed to generate reproducible oscillations.
| [
{
"created": "Fri, 20 Apr 2007 21:18:14 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Scott",
"Matthew",
""
],
[
"Hwa",
"Terence",
""
],
[
"Ingalls",
"Brian",
""
]
] | For cellular biochemical reaction systems where the numbers of molecules is small, significant noise is associated with chemical reaction events. This molecular noise can give rise to behavior that is very different from the predictions of deterministic rate equation models. Unfortunately, there are few analytic methods for examining the qualitative behavior of stochastic systems. Here we describe such a method that extends deterministic analysis to include leading-order corrections due to the molecular noise. The method allows the steady-state behavior of the stochastic model to be easily computed, facilitates the mapping of stability phase diagrams that include stochastic effects and reveals how model parameters affect noise susceptibility, in a manner not accessible to numerical simulation. By way of illustration we consider two genetic circuits: a bistable positive-feedback loop and a negative-feedback oscillator. We find in the positive feedback circuit that translational activation leads to a far more stable system than transcriptional control. Conversely, in a negative-feedback loop triggered by a positive-feedback switch, the stochasticity of transcriptional control is harnessed to generate reproducible oscillations. |
1309.4246 | Thomas Stiehl | Thomas Stiehl, Natalia Baran, Anthony D. Ho and Anna Marciniak-Czochra | Clonal selection and therapy resistance in acute leukemias: Mathematical
modelling explains different proliferation patterns at diagnosis and relapse | 10 Figures | J. R. Soc Interface 11(94):20140079, 2014 | 10.1098/rsif.2014.0079 | null | q-bio.TO math.DS q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent experimental evidence suggests that acute myeloid leukemias may
originate from multiple clones of malignant cells. Nevertheless it is not known
how the observed clones may differ with respect to cell properties such as
proliferation and self-renewal. There are scarcely any data on how these cell
properties change due to chemotherapy and relapse. We propose a new
mathematical model to investigate the impact of cell properties on multi-clonal
composition of leukemias. Model results imply that enhanced self-renewal may be
a key mechanism in the clonal selection process. Simulations suggest that fast
proliferating and highly self-renewing cells dominate at primary diagnosis
while relapse following therapy-induced remission is triggered mostly by highly
self-renewing but slowly proliferating cells. Comparison of simulation results
to patient data demonstrates that the proposed model is consistent with
clinically observed dynamics based on a clonal selection process.
| [
{
"created": "Tue, 17 Sep 2013 10:10:16 GMT",
"version": "v1"
},
{
"created": "Sun, 5 Oct 2014 19:51:15 GMT",
"version": "v2"
}
] | 2014-10-07 | [
[
"Stiehl",
"Thomas",
""
],
[
"Baran",
"Natalia",
""
],
[
"Ho",
"Anthony D.",
""
],
[
"Marciniak-Czochra",
"Anna",
""
]
] | Recent experimental evidence suggests that acute myeloid leukemias may originate from multiple clones of malignant cells. Nevertheless it is not known how the observed clones may differ with respect to cell properties such as proliferation and self-renewal. There are scarcely any data on how these cell properties change due to chemotherapy and relapse. We propose a new mathematical model to investigate the impact of cell properties on multi-clonal composition of leukemias. Model results imply that enhanced self-renewal may be a key mechanism in the clonal selection process. Simulations suggest that fast proliferating and highly self-renewing cells dominate at primary diagnosis while relapse following therapy-induced remission is triggered mostly by highly self-renewing but slowly proliferating cells. Comparison of simulation results to patient data demonstrates that the proposed model is consistent with clinically observed dynamics based on a clonal selection process. |
1810.07760 | Horacio Castellini | Horacio Castellini and Bibiana Riquelme | Study of non-linear viscoelastic behavior of the human red blood cell | null | null | null | null | q-bio.CB cond-mat.soft physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | The non-linear behavior of human erythrocytes subjected to shear stress was
analyzed using data series from the Erythrocyte Rheometer and a theoretical
model was developed. Linear behavior was eliminated by means of a slot filter
and a sixth order Savisky-Golay filter was applied to the resulting time series
that allows the elimination of any possible white noise in the data. A fast
Fourier transform was performed on the processed data, which resulted in a
series of frequency dominant peaks. Results suggest the presence of a
non-linear quadratic term in the Kelvin-Voigt phenomenological model. The
correlation dimension studied through recurrence quantification analysis gave
C2=2.58. Results suggest that the underlying dynamics is the same in each RBC
sample corresponding to healthy donors.
| [
{
"created": "Wed, 17 Oct 2018 20:04:47 GMT",
"version": "v1"
}
] | 2018-10-19 | [
[
"Castellini",
"Horacio",
""
],
[
"Riquelme",
"Bibiana",
""
]
] | The non-linear behavior of human erythrocytes subjected to shear stress was analyzed using data series from the Erythrocyte Rheometer and a theoretical model was developed. Linear behavior was eliminated by means of a slot filter and a sixth order Savisky-Golay filter was applied to the resulting time series that allows the elimination of any possible white noise in the data. A fast Fourier transform was performed on the processed data, which resulted in a series of frequency dominant peaks. Results suggest the presence of a non-linear quadratic term in the Kelvin-Voigt phenomenological model. The correlation dimension studied through recurrence quantification analysis gave C2=2.58. Results suggest that the underlying dynamics is the same in each RBC sample corresponding to healthy donors. |
2204.12586 | Xuhua Wang | Binjie Guo, Hanyu Zheng, Haohan Jiang, Xiaodan Li, Naiyu Guan, Yanming
Zuo, Yicheng Zhang, Hengfu Yang, Xuhua Wang | Enhanced compound-protein binding affinity prediction by representing
protein multimodal information via a coevolutionary strategy | 53 pages, 14 figures, 3 tables | null | null | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to the lack of a method to efficiently represent the multimodal
information of a protein, including its structure and sequence information,
predicting compound-protein binding affinity (CPA) still suffers from low
accuracy when applying machine learning methods. To overcome this limitation,
in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary
strategy to jointly represent the structure and sequence features of proteins
and ultimately optimize the mathematical models for predicting CPA.
Furthermore, from the perspective of data-driven approach, we proposed a
rational method that can utilize both high- and low-quality databases to
optimize the accuracy and generalization ability of FeatNN in CPA prediction
tasks. Notably, we visually interpret the feature interaction process between
sequence and structure in the rationally designed architecture. As a result,
FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual
drug screening tasks, indicating the feasibility of this approach for practical
use. FeatNN provides an outstanding method for higher CPA prediction accuracy
and better generalization ability by efficiently representing multimodal
information of proteins via a coevolutionary strategy.
| [
{
"created": "Wed, 30 Mar 2022 00:44:15 GMT",
"version": "v1"
},
{
"created": "Thu, 14 Jul 2022 01:10:09 GMT",
"version": "v2"
},
{
"created": "Thu, 24 Nov 2022 00:37:22 GMT",
"version": "v3"
}
] | 2022-11-28 | [
[
"Guo",
"Binjie",
""
],
[
"Zheng",
"Hanyu",
""
],
[
"Jiang",
"Haohan",
""
],
[
"Li",
"Xiaodan",
""
],
[
"Guan",
"Naiyu",
""
],
[
"Zuo",
"Yanming",
""
],
[
"Zhang",
"Yicheng",
""
],
[
"Yang",
"Hen... | Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug screening tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy. |
2005.11511 | Abhishek Dhar | Arghya Das, Abhishek Dhar, Srashti Goyal, Anupam Kundu, Saurav Pandey | COVID-19: Analytic results for a modified SEIR model and comparison of
different intervention strategies | 15 pages, 10 figures | Chaos, Solitons & Fractals Volume 144,110595 (2021) | 10.1016/j.chaos.2020.110595 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological model is
one of the standard models of disease spreading. Here we analyse an extended
SEIR model that accounts for asymptomatic carriers, believed to play an
important role in COVID-19 transmission. For this model we derive a number of
analytic results for important quantities such as the peak number of
infections, the time taken to reach the peak and the size of the final affected
population. We also propose an accurate way of specifying initial conditions
for the numerics (from insufficient data) using the fact that the early time
exponential growth is well-described by the dominant eigenvector of the
linearized equations. Secondly we explore the effect of different intervention
strategies such as social distancing (SD) and testing-quarantining (TQ). The
two intervention strategies (SD and TQ) try to reduce the disease reproductive
number, $R_0$, to a target value $R^{\rm target}_0 < 1$, but in distinct ways,
which we implement in our model equations. We find that for the same $R^{\rm
target}_0 < 1$, TQ is more efficient in controlling the pandemic than SD.
However, for TQ to be effective, it has to be based on contact tracing and our
study quantifies the required ratio of tests-per-day to the number of new
cases-per-day. Our analysis shows that the largest eigenvalue of the linearised
dynamics provides a simple understanding of the disease progression, both pre-
and post- intervention, and explains observed data for many countries. We apply
our results to the COVID data for India to obtain heuristic projections for the
course of the pandemic, and note that the predictions strongly depend on the
assumed fraction of asymptomatic carriers.
| [
{
"created": "Sat, 23 May 2020 10:38:34 GMT",
"version": "v1"
},
{
"created": "Mon, 8 Jun 2020 04:38:42 GMT",
"version": "v2"
},
{
"created": "Mon, 25 Jan 2021 06:01:57 GMT",
"version": "v3"
}
] | 2021-01-26 | [
[
"Das",
"Arghya",
""
],
[
"Dhar",
"Abhishek",
""
],
[
"Goyal",
"Srashti",
""
],
[
"Kundu",
"Anupam",
""
],
[
"Pandey",
"Saurav",
""
]
] | The Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological model is one of the standard models of disease spreading. Here we analyse an extended SEIR model that accounts for asymptomatic carriers, believed to play an important role in COVID-19 transmission. For this model we derive a number of analytic results for important quantities such as the peak number of infections, the time taken to reach the peak and the size of the final affected population. We also propose an accurate way of specifying initial conditions for the numerics (from insufficient data) using the fact that the early time exponential growth is well-described by the dominant eigenvector of the linearized equations. Secondly we explore the effect of different intervention strategies such as social distancing (SD) and testing-quarantining (TQ). The two intervention strategies (SD and TQ) try to reduce the disease reproductive number, $R_0$, to a target value $R^{\rm target}_0 < 1$, but in distinct ways, which we implement in our model equations. We find that for the same $R^{\rm target}_0 < 1$, TQ is more efficient in controlling the pandemic than SD. However, for TQ to be effective, it has to be based on contact tracing and our study quantifies the required ratio of tests-per-day to the number of new cases-per-day. Our analysis shows that the largest eigenvalue of the linearised dynamics provides a simple understanding of the disease progression, both pre- and post- intervention, and explains observed data for many countries. We apply our results to the COVID data for India to obtain heuristic projections for the course of the pandemic, and note that the predictions strongly depend on the assumed fraction of asymptomatic carriers. |
1605.07052 | Dmitri Parkhomchuk | Dmitri Parkhomchuk, Alice C. McHardy, Alexey Shadrin | Genetic recombination as DNA repair | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Maintenance of sexual reproduction and genetic recombination imposes
physiological costs when compared to parthenogenic reproduction, most
prominently: for maintaining the corresponding (molecular) machinery, for
finding a mating partner, and through the decreased fraction of females in a
population, which decreases the reproductive capacity. Based on principles from
information theory, we have previously developed a new population genetic
model, and applying it in simulations, we have recently hypothesized that all
species maintain the maximum genomic complexity that is required by their niche
and allowed by their mutation rate and selection intensity. Applying this idea
to the complexity overhead of recombination maintenance, its costs must be more
than compensated by an additional capacity for complexity in recombining
populations. Here, we show a simple mechanism, where recombination helps to
maintain larger biases of alleles frequencies in a population, so the
advantageous alleles can have increased frequency. This allows recombining
populations to maintain higher fitness and phenotypic efficiency in comparison
with asexual populations with the same parameters. Random mating alone already
significantly increases the ability to maintain genomic and phenotypic
complexity. Sexual selection provides additional capacity for this complexity.
The model can be considered as a unifying synthesis of previous hypotheses
about the roles of recombination in Muller's ratchet, mutation purging and Red
Queen dynamics, because the introduction of recombination both increases
population frequencies of beneficial alleles and decreases detrimental ones. In
addition, we suggest simple explanations for niche-dependent prevalence of
transient asexuality and the exceptional asexual lineage of Bdelloid rotifers.
| [
{
"created": "Mon, 23 May 2016 15:14:20 GMT",
"version": "v1"
}
] | 2016-05-24 | [
[
"Parkhomchuk",
"Dmitri",
""
],
[
"McHardy",
"Alice C.",
""
],
[
"Shadrin",
"Alexey",
""
]
] | Maintenance of sexual reproduction and genetic recombination imposes physiological costs when compared to parthenogenic reproduction, most prominently: for maintaining the corresponding (molecular) machinery, for finding a mating partner, and through the decreased fraction of females in a population, which decreases the reproductive capacity. Based on principles from information theory, we have previously developed a new population genetic model, and applying it in simulations, we have recently hypothesized that all species maintain the maximum genomic complexity that is required by their niche and allowed by their mutation rate and selection intensity. Applying this idea to the complexity overhead of recombination maintenance, its costs must be more than compensated by an additional capacity for complexity in recombining populations. Here, we show a simple mechanism, where recombination helps to maintain larger biases of alleles frequencies in a population, so the advantageous alleles can have increased frequency. This allows recombining populations to maintain higher fitness and phenotypic efficiency in comparison with asexual populations with the same parameters. Random mating alone already significantly increases the ability to maintain genomic and phenotypic complexity. Sexual selection provides additional capacity for this complexity. The model can be considered as a unifying synthesis of previous hypotheses about the roles of recombination in Muller's ratchet, mutation purging and Red Queen dynamics, because the introduction of recombination both increases population frequencies of beneficial alleles and decreases detrimental ones. In addition, we suggest simple explanations for niche-dependent prevalence of transient asexuality and the exceptional asexual lineage of Bdelloid rotifers. |
1906.07272 | Age Smilde | Age K. Smilde and Thomas Hankemeier | Numerical Representations of Metabolic Systems | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Metabolomics is becoming a mature part of analytical chemistry as evidenced
by the growing number of publications and attendees of international
conferences dedicated to this topic. Yet, a systematic treatment of the
fundamental structure and properties of metabolomics data is lagging behind. We
want to fill this gap by introducing two fundamental theories concerning
metabolomics data: data theory and measurement theory. Our approach is to ask
simple questions, the answers of which require applying these theories to
metabolomics. We show that we can distinguish at least four different levels of
metabolomics data with different properties and warn against confusing data
with numbers.
| [
{
"created": "Fri, 14 Jun 2019 09:48:32 GMT",
"version": "v1"
}
] | 2019-06-19 | [
[
"Smilde",
"Age K.",
""
],
[
"Hankemeier",
"Thomas",
""
]
] | Metabolomics is becoming a mature part of analytical chemistry as evidenced by the growing number of publications and attendees of international conferences dedicated to this topic. Yet, a systematic treatment of the fundamental structure and properties of metabolomics data is lagging behind. We want to fill this gap by introducing two fundamental theories concerning metabolomics data: data theory and measurement theory. Our approach is to ask simple questions, the answers of which require applying these theories to metabolomics. We show that we can distinguish at least four different levels of metabolomics data with different properties and warn against confusing data with numbers. |
1404.3521 | Guillaume Hennequin | Guillaume Hennequin, Laurence Aitchison, M\'at\'e Lengyel | Fast sampling for Bayesian inference in neural circuits | This is a preliminary written version of our Cosyne poster | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Time is at a premium for recurrent network dynamics, and particularly so when
they are stochastic and correlated: the quality of inference from such dynamics
fundamentally depends on how fast the neural circuit generates new samples from
its stationary distribution. Indeed, behavioral decisions can occur on fast
time scales (~100 ms), but it is unclear what neural circuit dynamics afford
sampling at such high rates.
We analyzed a stochastic form of rate-based linear neuronal network dynamics
with synaptic weight matrix $W$, and the dependence on $W$ of the covariance of
the stationary distribution of joint firing rates. This covariance $\Sigma$ can
be actively used to represent posterior uncertainty via sampling under a
linear-Gaussian latent variable model. The key insight is that the mapping
between $W$ and $\Sigma$ is degenerate: there are infinitely many $W$'s that
lead to sampling from the same $\Sigma$ but differ greatly in the speed at
which they sample. We were able to explicitly separate these extra degrees of
freedom in a parametric form and thus study their effects on sampling speed.
We show that previous proposals for probabilistic sampling in neural circuits
correspond to using a symmetric $W$ which violates Dale's law and results in
critically slow sampling, even for moderate stationary correlations. In
contrast, optimizing network dynamics for speed consistently yielded asymmetric
$W$'s and dynamics characterized by fast transients, such that samples of
network activity became fully decorrelated over ~10 ms. Importantly, networks
with separate excitatory/inhibitory populations proved to be particularly
efficient samplers, and were in the balanced regime. Thus, plausible neural
circuit dynamics can perform fast sampling for efficient decoding and
inference.
| [
{
"created": "Mon, 14 Apr 2014 10:06:01 GMT",
"version": "v1"
},
{
"created": "Wed, 23 Apr 2014 16:38:17 GMT",
"version": "v2"
}
] | 2014-04-24 | [
[
"Hennequin",
"Guillaume",
""
],
[
"Aitchison",
"Laurence",
""
],
[
"Lengyel",
"Máté",
""
]
] | Time is at a premium for recurrent network dynamics, and particularly so when they are stochastic and correlated: the quality of inference from such dynamics fundamentally depends on how fast the neural circuit generates new samples from its stationary distribution. Indeed, behavioral decisions can occur on fast time scales (~100 ms), but it is unclear what neural circuit dynamics afford sampling at such high rates. We analyzed a stochastic form of rate-based linear neuronal network dynamics with synaptic weight matrix $W$, and the dependence on $W$ of the covariance of the stationary distribution of joint firing rates. This covariance $\Sigma$ can be actively used to represent posterior uncertainty via sampling under a linear-Gaussian latent variable model. The key insight is that the mapping between $W$ and $\Sigma$ is degenerate: there are infinitely many $W$'s that lead to sampling from the same $\Sigma$ but differ greatly in the speed at which they sample. We were able to explicitly separate these extra degrees of freedom in a parametric form and thus study their effects on sampling speed. We show that previous proposals for probabilistic sampling in neural circuits correspond to using a symmetric $W$ which violates Dale's law and results in critically slow sampling, even for moderate stationary correlations. In contrast, optimizing network dynamics for speed consistently yielded asymmetric $W$'s and dynamics characterized by fast transients, such that samples of network activity became fully decorrelated over ~10 ms. Importantly, networks with separate excitatory/inhibitory populations proved to be particularly efficient samplers, and were in the balanced regime. Thus, plausible neural circuit dynamics can perform fast sampling for efficient decoding and inference. |
1908.05507 | Anna Vanselow AnVan | Anna Vanselow and Sebastian Wieczorek and Ulrike Feudel | When very slow is too fast -- collapse of a predator-prey system | null | Journal of Theoretical Biology, 479, 64-72 (2019) | 10.1016/j.jtbi.2019.07.008 | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Critical transitions or regime shifts are sudden and unexpected changes in
the state of an ecosystem, that are usually associated with dangerous levels of
environmental change. However, recent studies show that critical transitions
can also be triggered by dangerous rates of environmental change. In contrast
to classical regime shifts, such rate-induced critical transitions do not
involve any obvious loss of stability, or a bifurcation, and thus cannot be
explained by the linear stability analysis. In this work, we demonstrate that
the well-known Rosenzweig-MacArthur predator-prey model can undergo a
rate-induced critical transition in response to a continuous decline in the
habitat quality, resulting in a collapse of the predator and prey populations.
Rather surprisingly, the collapse occurs even if the environmental change is
slower than the slowest process in the model. To explain this counterintuitive
phenomenon, we combine methods from geometric singular perturbation theory with
the concept of a moving equilibrium, and study critical rates of environmental
change with dependence on the initial state and the system parameters.
Moreover, for a fixed rate of environmental change, we determine the set of
initial states that undergo a rate-induced population collapse. Our results
suggest that ecosystems may be more sensitive to how fast environmental
conditions change than previously assumed. In particular, unexpected critical
transitions with dramatic ecological consequences can be triggered by
environmental changes that (i) do not exceed any dangerous levels, and (ii) are
slower than the natural timescales of the ecosystem. This poses an interesting
research question whether regime shifts observed in the natural world are
predominantly rate-induced or bifurcation-induced.
| [
{
"created": "Thu, 15 Aug 2019 12:18:46 GMT",
"version": "v1"
}
] | 2019-08-16 | [
[
"Vanselow",
"Anna",
""
],
[
"Wieczorek",
"Sebastian",
""
],
[
"Feudel",
"Ulrike",
""
]
] | Critical transitions or regime shifts are sudden and unexpected changes in the state of an ecosystem, that are usually associated with dangerous levels of environmental change. However, recent studies show that critical transitions can also be triggered by dangerous rates of environmental change. In contrast to classical regime shifts, such rate-induced critical transitions do not involve any obvious loss of stability, or a bifurcation, and thus cannot be explained by the linear stability analysis. In this work, we demonstrate that the well-known Rosenzweig-MacArthur predator-prey model can undergo a rate-induced critical transition in response to a continuous decline in the habitat quality, resulting in a collapse of the predator and prey populations. Rather surprisingly, the collapse occurs even if the environmental change is slower than the slowest process in the model. To explain this counterintuitive phenomenon, we combine methods from geometric singular perturbation theory with the concept of a moving equilibrium, and study critical rates of environmental change with dependence on the initial state and the system parameters. Moreover, for a fixed rate of environmental change, we determine the set of initial states that undergo a rate-induced population collapse. Our results suggest that ecosystems may be more sensitive to how fast environmental conditions change than previously assumed. In particular, unexpected critical transitions with dramatic ecological consequences can be triggered by environmental changes that (i) do not exceed any dangerous levels, and (ii) are slower than the natural timescales of the ecosystem. This poses an interesting research question whether regime shifts observed in the natural world are predominantly rate-induced or bifurcation-induced. |
q-bio/0403020 | Ping Ao | P. Ao | Mathematical Structure of Evolutionary Theory | 10 pages | null | null | null | q-bio.QM cond-mat.stat-mech math.DS nlin.AO q-bio.PE | null | Here we postulate three laws which form a mathematical framework to capture
the essence of Darwinian evolutionary dynamics. The second law is most
quantitative and is explicitly expressed by a unique form of stochastic
differential equation. A precise definition of Wright's adaptive landscape is
given and a new and consistent interpretation of Fisher's fundamental theorem
of natural selection is provided. Based on a recently discovered theorem the
generality of the proposed laws is illustrated by an explicit demonstration of
their equivalence to a general conventional non-equilibrium dynamics
formulation. The proposed laws provide a coherence framework to discuss several
current evolutionary problems, such as speciation and stability, and gives a
firm base for the application of statistical physics tools in Darwinian
dynamics.
| [
{
"created": "Mon, 15 Mar 2004 22:36:21 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Ao",
"P.",
""
]
] | Here we postulate three laws which form a mathematical framework to capture the essence of Darwinian evolutionary dynamics. The second law is most quantitative and is explicitly expressed by a unique form of stochastic differential equation. A precise definition of Wright's adaptive landscape is given and a new and consistent interpretation of Fisher's fundamental theorem of natural selection is provided. Based on a recently discovered theorem the generality of the proposed laws is illustrated by an explicit demonstration of their equivalence to a general conventional non-equilibrium dynamics formulation. The proposed laws provide a coherence framework to discuss several current evolutionary problems, such as speciation and stability, and gives a firm base for the application of statistical physics tools in Darwinian dynamics. |
1804.01840 | Syed Ahmed Aamir | Syed Ahmed Aamir, Paul M\"uller, Gerd Kiene, Laura Kriener, Yannik
Stradmann, Andreas Gr\"ubl, Johannes Schemmel, Karlheinz Meier | A Mixed-Signal Structured AdEx Neuron for Accelerated Neuromorphic Cores | 11 pages, 17 figures (including author photographs) | null | 10.1109/TBCAS.2018.2848203 | null | q-bio.NC cs.ET physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Here we describe a multi-compartment neuron circuit based on the
Adaptive-Exponential I&F (AdEx) model, developed for the second-generation
BrainScaleS hardware. Based on an existing modular Leaky Integrate-and-Fire
(LIF) architecture designed in 65 nm CMOS, the circuit features exponential
spike generation, neuronal adaptation, inter-compartmental connections as well
as a conductance-based reset. The design reproduces a diverse set of firing
patterns observed in cortical pyramidal neurons. Further, it enables the
emulation of sodium and calcium spikes, as well as N-Methyl-D-Aspartate (NMDA)
plateau potentials known from apical and thin dendrites. We characterize the
AdEx circuit extensions and exemplify how the interplay between passive and
non-linear active signal processing enhances the computational capabilities of
single (but structured) on-chip neurons.
| [
{
"created": "Thu, 5 Apr 2018 13:32:10 GMT",
"version": "v1"
},
{
"created": "Mon, 14 May 2018 15:09:44 GMT",
"version": "v2"
},
{
"created": "Mon, 28 May 2018 16:57:30 GMT",
"version": "v3"
},
{
"created": "Tue, 29 May 2018 18:23:02 GMT",
"version": "v4"
}
] | 2019-03-28 | [
[
"Aamir",
"Syed Ahmed",
""
],
[
"Müller",
"Paul",
""
],
[
"Kiene",
"Gerd",
""
],
[
"Kriener",
"Laura",
""
],
[
"Stradmann",
"Yannik",
""
],
[
"Grübl",
"Andreas",
""
],
[
"Schemmel",
"Johannes",
""
],
[
... | Here we describe a multi-compartment neuron circuit based on the Adaptive-Exponential I&F (AdEx) model, developed for the second-generation BrainScaleS hardware. Based on an existing modular Leaky Integrate-and-Fire (LIF) architecture designed in 65 nm CMOS, the circuit features exponential spike generation, neuronal adaptation, inter-compartmental connections as well as a conductance-based reset. The design reproduces a diverse set of firing patterns observed in cortical pyramidal neurons. Further, it enables the emulation of sodium and calcium spikes, as well as N-Methyl-D-Aspartate (NMDA) plateau potentials known from apical and thin dendrites. We characterize the AdEx circuit extensions and exemplify how the interplay between passive and non-linear active signal processing enhances the computational capabilities of single (but structured) on-chip neurons. |
q-bio/0501031 | Miroslaw Kozlowski | J. Marciak-Kozlowska, M. Kozlowski, M. Pelc | Heisenberg uncertainty principle for thermal response of the
microtubules excited by ultra-short laser pulses | 20 pages, 5 figures | null | null | null | q-bio.BM | null | In this paper the heat signaling in microtubules (MT) is investigated. It is
argued that for the description of the heat signaling phenomena in MT, the
hyperbolic heat transport (HHT) equation must be used. It is shown that HHT is
the Klein-Gordon (K-G) equation. The general solution for the K-G equation for
MT is obtained. For the undistorted signal propagation in MT the Heisenberg
uncertainty principle is formulated and discussed.
Key words: Microtubules; Heat signaling; Klein-Gordon equation; Heisenberg
principle.
| [
{
"created": "Mon, 24 Jan 2005 11:42:19 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Jan 2005 10:54:33 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Marciak-Kozlowska",
"J.",
""
],
[
"Kozlowski",
"M.",
""
],
[
"Pelc",
"M.",
""
]
] | In this paper the heat signaling in microtubules (MT) is investigated. It is argued that for the description of the heat signaling phenomena in MT, the hyperbolic heat transport (HHT) equation must be used. It is shown that HHT is the Klein-Gordon (K-G) equation. The general solution for the K-G equation for MT is obtained. For the undistorted signal propagation in MT the Heisenberg uncertainty principle is formulated and discussed. Key words: Microtubules; Heat signaling; Klein-Gordon equation; Heisenberg principle. |
2004.02366 | Anca Radulescu | Anca Radulescu | Course of the first month of the COVID 19 outbreak in the New York State
counties | 8 pages, 7 figures, 4 tables | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We illustrate and study the evolution of reported infections over the month
from March 1st to April 1st in the New York State as a whole, as well as in
each individual county. We search for exponential trends, and try to understand
whether there is any correlation of the timing and dynamics of these trends
with state mandated measures on social distancing and testing. We conclude that
the reports on April 1st may be dramatically under-representing the actual
number of state-wide infections, and we propose reassessment of the data over
the coming weeks, to monitor for effects of the PAUSE directive, and for the
increasing number of casualties as a validating measure.
| [
{
"created": "Mon, 6 Apr 2020 01:22:18 GMT",
"version": "v1"
},
{
"created": "Fri, 10 Apr 2020 10:50:36 GMT",
"version": "v2"
}
] | 2020-04-13 | [
[
"Radulescu",
"Anca",
""
]
] | We illustrate and study the evolution of reported infections over the month from March 1st to April 1st in the New York State as a whole, as well as in each individual county. We search for exponential trends, and try to understand whether there is any correlation of the timing and dynamics of these trends with state mandated measures on social distancing and testing. We conclude that the reports on April 1st may be dramatically under-representing the actual number of state-wide infections, and we propose reassessment of the data over the coming weeks, to monitor for effects of the PAUSE directive, and for the increasing number of casualties as a validating measure. |
2203.10116 | Claudio Maccone | Claudio Maccone | SETI, evolution and human history merged into a mathematical model | null | null | null | null | q-bio.PE astro-ph.EP math.PR physics.soc-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper we propose a new mathematical model capable of merging
Darwinian Evolution, Human History and SETI into a single mathematical scheme:
1) Darwinian Evolution over the last 3.5 billion years is defined as one
particular realization of a certain stochastic process called Geometric
Brownian Motion (GBM). This GBM yields the fluctuations in time of the number
of species living on Earth. Its mean value curve is an increasing exponential
curve, i.e. the exponential growth of Evolution. 2) In 2008 this author
provided the statistical generalization of the Drake equation yielding the
number N of communicating ET civilizations in the Galaxy. N was shown to follow
the lognormal probability distribution. 3) We call "b-lognormals" those
lognormals starting at any positive time b ("birth") larger than zero. Then the
exponential growth curve becomes the geometric locus of the peaks of a
one-parameter family of b-lognormals: this is our way to re-define Cladistics.
4) b-lognormals may be also be interpreted as the lifespan of any living being
(a cell, or an animal, a plant, a human, or even the historic lifetime of any
civilization). Applying this new mathematical apparatus to Human History, leads
to the discovery of the exponential progress between Ancient Greece and the
current USA as the envelope of all b-lognormals of Western Civilizations over a
period of 2500 years. 5) We then invoke Shannon's Information Theory. The
b-lognormals' entropy turns out to be the index of "development level" reached
by each historic civilization. We thus get a numerical estimate of the entropy
difference between any two civilizations, like the Aztec-Spaniard difference in
1519. 6) In conclusion, we have derived a mathematical scheme capable of
estimating how much more advanced than Humans an Alien Civilization will be
when the SETI scientists will detect the first hints about ETs.
| [
{
"created": "Mon, 14 Mar 2022 10:43:20 GMT",
"version": "v1"
}
] | 2022-03-22 | [
[
"Maccone",
"Claudio",
""
]
] | In this paper we propose a new mathematical model capable of merging Darwinian Evolution, Human History and SETI into a single mathematical scheme: 1) Darwinian Evolution over the last 3.5 billion years is defined as one particular realization of a certain stochastic process called Geometric Brownian Motion (GBM). This GBM yields the fluctuations in time of the number of species living on Earth. Its mean value curve is an increasing exponential curve, i.e. the exponential growth of Evolution. 2) In 2008 this author provided the statistical generalization of the Drake equation yielding the number N of communicating ET civilizations in the Galaxy. N was shown to follow the lognormal probability distribution. 3) We call "b-lognormals" those lognormals starting at any positive time b ("birth") larger than zero. Then the exponential growth curve becomes the geometric locus of the peaks of a one-parameter family of b-lognormals: this is our way to re-define Cladistics. 4) b-lognormals may be also be interpreted as the lifespan of any living being (a cell, or an animal, a plant, a human, or even the historic lifetime of any civilization). Applying this new mathematical apparatus to Human History, leads to the discovery of the exponential progress between Ancient Greece and the current USA as the envelope of all b-lognormals of Western Civilizations over a period of 2500 years. 5) We then invoke Shannon's Information Theory. The b-lognormals' entropy turns out to be the index of "development level" reached by each historic civilization. We thus get a numerical estimate of the entropy difference between any two civilizations, like the Aztec-Spaniard difference in 1519. 6) In conclusion, we have derived a mathematical scheme capable of estimating how much more advanced than Humans an Alien Civilization will be when the SETI scientists will detect the first hints about ETs. |
1612.00098 | Yuri Shestopaloff | Yuri K. Shestopaloff | Metabolic allometric scaling of multicellular organisms as a product of
evolutionary development and optimization of food chains | 45 pages, 12 figures, 11 tables | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Production of energy is a foundation of life. Metabolic rate of organisms
(amount of energy produced per unit time) generally increases slower than
organisms' mass. This phenomenon, when considered across different taxa, is
called interspecific allometric scaling. Its origin puzzles scientists for
ninety two years. Here, we introduce a general biomechanical model, and apply
it to modelling dynamic and kinematic properties of motion of mammals,
reptiles, fish and birds. We consider these models from the perspective of food
chain composition, and use them for finding metabolic properties. Food chains
are viewed as entities developing in the process of evolution under the forces
of natural selection, and optimized from the perspective of sharing common
resources of a food chain's habitat among species composing food chains. This
optimized sharing of resources occurs in a way that gives all species
sufficient quantity of resources to reproduce in adequate quantities, thus
preserving the continuity of a food chain, while also serving as a consumed
resource for the rest of a food chain. In this selection and optimization
process, a metabolic allometric exponent represents a quantitative measure of
thus evolutionary found balanced state of sharing of common resources. This
optimized sharing provides an optimal, albeit dynamic, stability of a food
chain in time and space for a given habitat. Certainly, such selection is
framed by physical, physiological, environmental constraints. The comparison of
theoretically calculated values of metabolic allometric exponents with
available experimental data for the maximal and basal metabolic rates showed:
(a) high adequacy of proposed model to real phenomenon; (b) strong indication
that the discovered mechanism of formation of food chains on principles of
natural selection and optimization is a real cause of metabolic allometric
scaling.
| [
{
"created": "Thu, 1 Dec 2016 01:07:54 GMT",
"version": "v1"
},
{
"created": "Thu, 29 Dec 2016 16:44:48 GMT",
"version": "v2"
},
{
"created": "Thu, 2 Nov 2017 01:43:35 GMT",
"version": "v3"
},
{
"created": "Sat, 15 Jun 2024 23:00:50 GMT",
"version": "v4"
}
] | 2024-06-18 | [
[
"Shestopaloff",
"Yuri K.",
""
]
] | Production of energy is a foundation of life. Metabolic rate of organisms (amount of energy produced per unit time) generally increases slower than organisms' mass. This phenomenon, when considered across different taxa, is called interspecific allometric scaling. Its origin puzzles scientists for ninety two years. Here, we introduce a general biomechanical model, and apply it to modelling dynamic and kinematic properties of motion of mammals, reptiles, fish and birds. We consider these models from the perspective of food chain composition, and use them for finding metabolic properties. Food chains are viewed as entities developing in the process of evolution under the forces of natural selection, and optimized from the perspective of sharing common resources of a food chain's habitat among species composing food chains. This optimized sharing of resources occurs in a way that gives all species sufficient quantity of resources to reproduce in adequate quantities, thus preserving the continuity of a food chain, while also serving as a consumed resource for the rest of a food chain. In this selection and optimization process, a metabolic allometric exponent represents a quantitative measure of thus evolutionary found balanced state of sharing of common resources. This optimized sharing provides an optimal, albeit dynamic, stability of a food chain in time and space for a given habitat. Certainly, such selection is framed by physical, physiological, environmental constraints. The comparison of theoretically calculated values of metabolic allometric exponents with available experimental data for the maximal and basal metabolic rates showed: (a) high adequacy of proposed model to real phenomenon; (b) strong indication that the discovered mechanism of formation of food chains on principles of natural selection and optimization is a real cause of metabolic allometric scaling. |
1807.04425 | Thierry Mora | Zachary Sethna, Yuval Elhanati, Curtis G. Callan Jr., Aleksandra M.
Walczak, Thierry Mora | OLGA: fast computation of generation probabilities of B- and T-cell
receptor amino acid sequences and motifs | null | Bioinformatics 35 (17) 2974--2981 (2019) | 10.1093/bioinformatics/btz035 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: High-throughput sequencing of large immune repertoires has
enabled the development of methods to predict the probability of generation by
V(D)J recombination of T- and B-cell receptors of any specific nucleotide
sequence. These generation probabilities are very non-homogeneous, ranging over
20 orders of magnitude in real repertoires. Since the function of a receptor
really depends on its protein sequence, it is important to be able to predict
this probability of generation at the amino acid level. However, brute-force
summation over all the nucleotide sequences with the correct amino acid
translation is computationally intractable. The purpose of this paper is to
present a solution to this problem.
Results: We use dynamic programming to construct an efficient and flexible
algorithm, called OLGA (Optimized Likelihood estimate of immunoGlobulin
Amino-acid sequences), for calculating the probability of generating a given
CDR3 amino acid sequence or motif, with or without V/J restriction, as a result
of V(D)J recombination in B or T cells. We apply it to databases of
epitope-specific T-cell receptors to evaluate the probability that a typical
human subject will possess T cells responsive to specific disease-associated
epitopes. The model prediction shows an excellent agreement with published
data. We suggest that OLGA may be a useful tool to guide vaccine design.
Availability: Source code is available at https://github.com/zsethna/OLGA
| [
{
"created": "Thu, 12 Jul 2018 05:03:31 GMT",
"version": "v1"
},
{
"created": "Tue, 13 Nov 2018 11:18:08 GMT",
"version": "v2"
}
] | 2020-11-20 | [
[
"Sethna",
"Zachary",
""
],
[
"Elhanati",
"Yuval",
""
],
[
"Callan",
"Curtis G.",
"Jr."
],
[
"Walczak",
"Aleksandra M.",
""
],
[
"Mora",
"Thierry",
""
]
] | Motivation: High-throughput sequencing of large immune repertoires has enabled the development of methods to predict the probability of generation by V(D)J recombination of T- and B-cell receptors of any specific nucleotide sequence. These generation probabilities are very non-homogeneous, ranging over 20 orders of magnitude in real repertoires. Since the function of a receptor really depends on its protein sequence, it is important to be able to predict this probability of generation at the amino acid level. However, brute-force summation over all the nucleotide sequences with the correct amino acid translation is computationally intractable. The purpose of this paper is to present a solution to this problem. Results: We use dynamic programming to construct an efficient and flexible algorithm, called OLGA (Optimized Likelihood estimate of immunoGlobulin Amino-acid sequences), for calculating the probability of generating a given CDR3 amino acid sequence or motif, with or without V/J restriction, as a result of V(D)J recombination in B or T cells. We apply it to databases of epitope-specific T-cell receptors to evaluate the probability that a typical human subject will possess T cells responsive to specific disease-associated epitopes. The model prediction shows an excellent agreement with published data. We suggest that OLGA may be a useful tool to guide vaccine design. Availability: Source code is available at https://github.com/zsethna/OLGA |
0711.1012 | Akira Kinjo | Akira R. Kinjo and Haruki Nakamura | Similarity search for local protein structures at atomic resolution by
exploiting a database management system | 29 pages, 8 figures, 3 tables | BIOPHYSICS Vol. 3, pp. 75-84 (2007) | 10.2142/biophysics.3.75 | null | q-bio.BM | null | A method to search for local structural similarities in proteins at atomic
resolution is presented. It is demonstrated that a huge amount of structural
data can be handled within a reasonable CPU time by using a conventional
relational database management system with appropriate indexing of geometric
data. This method, which we call geometric indexing, can enumerate ligand
binding sites that are structurally similar to sub-structures of a query
protein among more than 160,000 possible candidates within a few hours of CPU
time on an ordinary desktop computer. After detecting a set of high scoring
ligand binding sites by the geometric indexing search, structural alignments at
atomic resolution are constructed by iteratively applying the Hungarian
algorithm, and the statistical significance of the final score is estimated
from an empirical model based on a gamma distribution. Applications of this
method to several protein structures clearly shows that significant
similarities can be detected between local structures of non-homologous as well
as homologous proteins.
| [
{
"created": "Wed, 7 Nov 2007 05:38:58 GMT",
"version": "v1"
}
] | 2007-12-28 | [
[
"Kinjo",
"Akira R.",
""
],
[
"Nakamura",
"Haruki",
""
]
] | A method to search for local structural similarities in proteins at atomic resolution is presented. It is demonstrated that a huge amount of structural data can be handled within a reasonable CPU time by using a conventional relational database management system with appropriate indexing of geometric data. This method, which we call geometric indexing, can enumerate ligand binding sites that are structurally similar to sub-structures of a query protein among more than 160,000 possible candidates within a few hours of CPU time on an ordinary desktop computer. After detecting a set of high scoring ligand binding sites by the geometric indexing search, structural alignments at atomic resolution are constructed by iteratively applying the Hungarian algorithm, and the statistical significance of the final score is estimated from an empirical model based on a gamma distribution. Applications of this method to several protein structures clearly shows that significant similarities can be detected between local structures of non-homologous as well as homologous proteins. |
1401.2749 | Daniel Jost | Daniel Jost | Bifurcation in epigenetics: implications in development, proliferation
and diseases | accepted in Physical Review E as a Rapid Communication | null | 10.1103/PhysRevE.89.010701 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cells often exhibit different and stable phenotypes from the same DNA
sequence. Robustness and plasticity of such cellular states are controlled by
diverse transcriptional and epigenetic mechanisms, among them the modification
of biochemical marks on chromatin. Here, we develop a stochastic model that
describes the dynamics of epigenetic marks along a given DNA region. Through
mathematical analysis, we show the emergence of bistable and persistent
epigenetic states from the cooperative recruitment of modifying enzymes. We
also find that the dynamical system exhibits a critical point and displays, in
presence of asymmetries in recruitment, a bifurcation diagram with hysteresis.
These results have deep implications for our understanding of epigenetic
regulation. In particular, our study allows to reconcile within the same
formalism the robust maintenance of epigenetic identity observed in
differentiated cells, the epigenetic plasticity of pluripotent cells during
differentiation and the effects of epigenetic misregulation in diseases.
Moreover, it suggests a possible mechanism for developmental transitions where
the system is shifted close to the critical point to benefit from high
susceptibility to developmental cues.
| [
{
"created": "Mon, 13 Jan 2014 08:35:27 GMT",
"version": "v1"
}
] | 2015-06-18 | [
[
"Jost",
"Daniel",
""
]
] | Cells often exhibit different and stable phenotypes from the same DNA sequence. Robustness and plasticity of such cellular states are controlled by diverse transcriptional and epigenetic mechanisms, among them the modification of biochemical marks on chromatin. Here, we develop a stochastic model that describes the dynamics of epigenetic marks along a given DNA region. Through mathematical analysis, we show the emergence of bistable and persistent epigenetic states from the cooperative recruitment of modifying enzymes. We also find that the dynamical system exhibits a critical point and displays, in presence of asymmetries in recruitment, a bifurcation diagram with hysteresis. These results have deep implications for our understanding of epigenetic regulation. In particular, our study allows to reconcile within the same formalism the robust maintenance of epigenetic identity observed in differentiated cells, the epigenetic plasticity of pluripotent cells during differentiation and the effects of epigenetic misregulation in diseases. Moreover, it suggests a possible mechanism for developmental transitions where the system is shifted close to the critical point to benefit from high susceptibility to developmental cues. |
2310.13910 | Stuart Newman | Stuart A. Newman | Form, function, mind: what doesn't compute (and what might) | null | null | null | null | q-bio.NC q-bio.SC q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | The applicability of computational and dynamical systems models to organisms
is scrutinized, using examples from developmental biology and cognition.
Developmental morphogenesis is dependent on the inherent material properties of
developing tissues, a non-computational modality, but cell differentiation,
which utilizes chromatin-based revisable memory banks and program-like
function-calling, via the developmental gene co-expression system unique to
metazoans, has a quasi-computational basis. Multi-attractor dynamical models
are argued to be misapplied to global properties of development, and it is
suggested that along with computationalism, dynamicism is similarly unsuitable
to accounting for cognitive phenomena. Proposals are made for treating brains
and other nervous tissues as novel forms of excitable matter with inherent
properties which enable the intensification of cell-based basal cognition
capabilities present throughout the tree of life.
| [
{
"created": "Sat, 21 Oct 2023 04:50:50 GMT",
"version": "v1"
}
] | 2023-10-24 | [
[
"Newman",
"Stuart A.",
""
]
] | The applicability of computational and dynamical systems models to organisms is scrutinized, using examples from developmental biology and cognition. Developmental morphogenesis is dependent on the inherent material properties of developing tissues, a non-computational modality, but cell differentiation, which utilizes chromatin-based revisable memory banks and program-like function-calling, via the developmental gene co-expression system unique to metazoans, has a quasi-computational basis. Multi-attractor dynamical models are argued to be misapplied to global properties of development, and it is suggested that along with computationalism, dynamicism is similarly unsuitable to accounting for cognitive phenomena. Proposals are made for treating brains and other nervous tissues as novel forms of excitable matter with inherent properties which enable the intensification of cell-based basal cognition capabilities present throughout the tree of life. |
2109.11938 | Bradly Alicea | Bradly Alicea, Jesse Parent | Meta-brain Models: biologically-inspired cognitive agents | 20 pages, 3 figures | null | 10.1088/1757-899X/1261/1/012019 | null | q-bio.NC cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence (AI) systems based solely on neural networks or
symbolic computation present a representational complexity challenge. While
minimal representations can produce behavioral outputs like locomotion or
simple decision-making, more elaborate internal representations might offer a
richer variety of behaviors. We propose that these issues can be addressed with
a computational approach we call meta-brain models. Meta-brain models are
embodied hybrid models that include layered components featuring varying
degrees of representational complexity. We will propose combinations of layers
composed using specialized types of models. Rather than using a generic black
box approach to unify each component, this relationship mimics systems like the
neocortical-thalamic system relationship of the mammalian brain, which utilizes
both feedforward and feedback connectivity to facilitate functional
communication. Importantly, the relationship between layers can be made
anatomically explicit. This allows for structural specificity that can be
incorporated into the model's function in interesting ways. We will propose
several types of layers that might be functionally integrated into agents that
perform unique types of tasks, from agents that simultaneously perform
morphogenesis and perception, to agents that undergo morphogenesis and the
acquisition of conceptual representations simultaneously. Our approach to
meta-brain models involves creating models with different degrees of
representational complexity, creating a layered meta-architecture that mimics
the structural and functional heterogeneity of biological brains, and an
input/output methodology flexible enough to accommodate cognitive functions,
social interactions, and adaptive behaviors more generally. We will conclude by
proposing next steps in the development of this flexible and open-source
approach.
| [
{
"created": "Tue, 31 Aug 2021 05:20:53 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Feb 2022 06:38:47 GMT",
"version": "v2"
},
{
"created": "Thu, 16 Jun 2022 21:17:32 GMT",
"version": "v3"
}
] | 2022-10-19 | [
[
"Alicea",
"Bradly",
""
],
[
"Parent",
"Jesse",
""
]
] | Artificial Intelligence (AI) systems based solely on neural networks or symbolic computation present a representational complexity challenge. While minimal representations can produce behavioral outputs like locomotion or simple decision-making, more elaborate internal representations might offer a richer variety of behaviors. We propose that these issues can be addressed with a computational approach we call meta-brain models. Meta-brain models are embodied hybrid models that include layered components featuring varying degrees of representational complexity. We will propose combinations of layers composed using specialized types of models. Rather than using a generic black box approach to unify each component, this relationship mimics systems like the neocortical-thalamic system relationship of the mammalian brain, which utilizes both feedforward and feedback connectivity to facilitate functional communication. Importantly, the relationship between layers can be made anatomically explicit. This allows for structural specificity that can be incorporated into the model's function in interesting ways. We will propose several types of layers that might be functionally integrated into agents that perform unique types of tasks, from agents that simultaneously perform morphogenesis and perception, to agents that undergo morphogenesis and the acquisition of conceptual representations simultaneously. Our approach to meta-brain models involves creating models with different degrees of representational complexity, creating a layered meta-architecture that mimics the structural and functional heterogeneity of biological brains, and an input/output methodology flexible enough to accommodate cognitive functions, social interactions, and adaptive behaviors more generally. We will conclude by proposing next steps in the development of this flexible and open-source approach. |
q-bio/0501006 | Piero Fariselli | Piero Fariselli, Pier Luigi Martelli, and Rita Casadio | The posterior-Viterbi: a new decoding algorithm for hidden Markov models | 23 pages, 3 figures | null | null | null | q-bio.BM q-bio.GN | null | Background: Hidden Markov models (HMM) are powerful machine learning tools
successfully applied to problems of computational Molecular Biology. In a
predictive task, the HMM is endowed with a decoding algorithm in order to
assign the most probable state path, and in turn the class labeling, to an
unknown sequence. The Viterbi and the posterior decoding algorithms are the
most common. The former is very efficient when one path dominates, while the
latter, even though does not guarantee to preserve the automaton grammar, is
more effective when several concurring paths have similar probabilities. A
third good alternative is 1-best, which was shown to perform equal or better
than Viterbi. Results: In this paper we introduce the posterior-Viterbi (PV) a
new decoding which combines the posterior and Viterbi algorithms. PV is a two
step process: first the posterior probability of each state is computed and
then the best posterior allowed path through the model is evaluated by a
Viterbi algorithm.
Conclusions: We show that PV decoding performs better than other algorithms
first on toy models and then on the computational biological problem of the
prediction of the topology of beta-barrel membrane proteins.
| [
{
"created": "Tue, 4 Jan 2005 08:06:09 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Fariselli",
"Piero",
""
],
[
"Martelli",
"Pier Luigi",
""
],
[
"Casadio",
"Rita",
""
]
] | Background: Hidden Markov models (HMM) are powerful machine learning tools successfully applied to problems of computational Molecular Biology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the class labeling, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the automaton grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. Results: In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. Conclusions: We show that PV decoding performs better than other algorithms first on toy models and then on the computational biological problem of the prediction of the topology of beta-barrel membrane proteins. |
2207.05757 | Justin Engelmann | Justin Engelmann, Ana Villaplana-Velasco, Amos Storkey, Miguel O.
Bernabeu | Robust and efficient computation of retinal fractal dimension through
deep approximation | null | null | null | null | q-bio.QM cs.AI cs.CV eess.IV | http://creativecommons.org/licenses/by/4.0/ | A retinal trait, or phenotype, summarises a specific aspect of a retinal
image in a single number. This can then be used for further analyses, e.g. with
statistical methods. However, reducing an aspect of a complex image to a
single, meaningful number is challenging. Thus, methods for calculating retinal
traits tend to be complex, multi-step pipelines that can only be applied to
high quality images. This means that researchers often have to discard
substantial portions of the available data. We hypothesise that such pipelines
can be approximated with a single, simpler step that can be made robust to
common quality issues. We propose Deep Approximation of Retinal Traits (DART)
where a deep neural network is used predict the output of an existing pipeline
on high quality images from synthetically degraded versions of these images. We
demonstrate DART on retinal Fractal Dimension (FD) calculated by VAMPIRE, using
retinal images from UK Biobank that previous work identified as high quality.
Our method shows very high agreement with FD VAMPIRE on unseen test images
(Pearson r=0.9572). Even when those images are severely degraded, DART can
still recover an FD estimate that shows good agreement with FD VAMPIRE obtained
from the original images (Pearson r=0.8817). This suggests that our method
could enable researchers to discard fewer images in the future. Our method can
compute FD for over 1,000img/s using a single GPU. We consider these to be very
encouraging initial results and hope to develop this approach into a useful
tool for retinal analysis.
| [
{
"created": "Tue, 12 Jul 2022 15:34:35 GMT",
"version": "v1"
}
] | 2022-07-14 | [
[
"Engelmann",
"Justin",
""
],
[
"Villaplana-Velasco",
"Ana",
""
],
[
"Storkey",
"Amos",
""
],
[
"Bernabeu",
"Miguel O.",
""
]
] | A retinal trait, or phenotype, summarises a specific aspect of a retinal image in a single number. This can then be used for further analyses, e.g. with statistical methods. However, reducing an aspect of a complex image to a single, meaningful number is challenging. Thus, methods for calculating retinal traits tend to be complex, multi-step pipelines that can only be applied to high quality images. This means that researchers often have to discard substantial portions of the available data. We hypothesise that such pipelines can be approximated with a single, simpler step that can be made robust to common quality issues. We propose Deep Approximation of Retinal Traits (DART) where a deep neural network is used predict the output of an existing pipeline on high quality images from synthetically degraded versions of these images. We demonstrate DART on retinal Fractal Dimension (FD) calculated by VAMPIRE, using retinal images from UK Biobank that previous work identified as high quality. Our method shows very high agreement with FD VAMPIRE on unseen test images (Pearson r=0.9572). Even when those images are severely degraded, DART can still recover an FD estimate that shows good agreement with FD VAMPIRE obtained from the original images (Pearson r=0.8817). This suggests that our method could enable researchers to discard fewer images in the future. Our method can compute FD for over 1,000img/s using a single GPU. We consider these to be very encouraging initial results and hope to develop this approach into a useful tool for retinal analysis. |
1908.02843 | Irena Papst | Irena Papst, David J.D. Earn | Invariant predictions of epidemic patterns from radically different
forms of seasonal forcing | 17 pages, 5 figures | J. R. Soc. Interface 16: 20190202 (2019) | 10.1098/rsif.2019.0202 | null | q-bio.PE math.DS nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Seasonal variation in environmental variables, and in rates of contact among
individuals, are fundamental drivers of infectious disease dynamics. Unlike
most periodically-forced physical systems, for which the precise pattern of
forcing is typically known, underlying patterns of seasonal variation in
transmission rates can be estimated approximately at best, and only the period
of forcing is accurately known. Yet solutions of epidemic models depend
strongly on the forcing function, so dynamical predictions---such as changes in
epidemic patterns that can be induced by demographic transitions or mass
vaccination---are always subject to the objection that the underlying patterns
of seasonality are poorly specified. Here, we demonstrate that the key
bifurcations of the standard epidemic model are invariant to the shape of
seasonal forcing if the amplitude of forcing is appropriately adjusted.
Consequently, analyses applicable to real disease dynamics can be conducted
with a smooth, idealized sinusoidal forcing function, and qualitative changes
in epidemic patterns can be predicted without precise knowledge of the
underlying forcing pattern. We find similar invariance in a seasonally forced
predator-prey model, and conjecture that this phenomenon---and the associated
robustness of predictions---might be a feature of many other periodically
forced dynamical systems.
| [
{
"created": "Wed, 7 Aug 2019 21:16:34 GMT",
"version": "v1"
}
] | 2019-08-09 | [
[
"Papst",
"Irena",
""
],
[
"Earn",
"David J. D.",
""
]
] | Seasonal variation in environmental variables, and in rates of contact among individuals, are fundamental drivers of infectious disease dynamics. Unlike most periodically-forced physical systems, for which the precise pattern of forcing is typically known, underlying patterns of seasonal variation in transmission rates can be estimated approximately at best, and only the period of forcing is accurately known. Yet solutions of epidemic models depend strongly on the forcing function, so dynamical predictions---such as changes in epidemic patterns that can be induced by demographic transitions or mass vaccination---are always subject to the objection that the underlying patterns of seasonality are poorly specified. Here, we demonstrate that the key bifurcations of the standard epidemic model are invariant to the shape of seasonal forcing if the amplitude of forcing is appropriately adjusted. Consequently, analyses applicable to real disease dynamics can be conducted with a smooth, idealized sinusoidal forcing function, and qualitative changes in epidemic patterns can be predicted without precise knowledge of the underlying forcing pattern. We find similar invariance in a seasonally forced predator-prey model, and conjecture that this phenomenon---and the associated robustness of predictions---might be a feature of many other periodically forced dynamical systems. |
1611.02317 | Prakash Narayan PhD | Jake A. Nieto, Janice Zhu, Bin Duan, Jingsong Li, Ping Zhou, Latha
Paka, Michael A. Yamin, Itzhak D. Goldberg and Prakash Narayan | Renal Parenchymal Area and Kidney Collagen Content | 17 pages, 6 figures, 3 equations | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The extent of renal scarring in chronic kidney disease (CKD) can only be
ascertained by highly invasive, painful and sometimes risky tissue biopsy.
Interestingly, CKD-related abnormalities in kidney size can often be visualized
using ultrasound. Nevertheless, not only does the ellipsoid formula used today
underestimate true renal size but also the relation governing renal size and
collagen content remains unclear. We used coronal kidney sections from healthy
mice and mice with renal disease to develop a new technique for estimating the
renal parenchymal area. While treating the kidney as an ellipse with the major
axis the polar distance, this technique involves extending the minor axis into
the renal pelvis. The calculated renal parenchymal area is remarkably similar
to the measured area. Biochemically determined kidney collagen content revealed
a strong and positive correlation with the calculated renal parenchymal area.
The extent of renal scarring, i.e. kidney collagen content, can now be computed
by making just two renal axial measurements which can easily be accomplished
via noninvasive imaging of this organ.
| [
{
"created": "Mon, 7 Nov 2016 21:56:51 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Nov 2016 20:44:43 GMT",
"version": "v2"
}
] | 2016-11-11 | [
[
"Nieto",
"Jake A.",
""
],
[
"Zhu",
"Janice",
""
],
[
"Duan",
"Bin",
""
],
[
"Li",
"Jingsong",
""
],
[
"Zhou",
"Ping",
""
],
[
"Paka",
"Latha",
""
],
[
"Yamin",
"Michael A.",
""
],
[
"Goldberg",
... | The extent of renal scarring in chronic kidney disease (CKD) can only be ascertained by highly invasive, painful and sometimes risky tissue biopsy. Interestingly, CKD-related abnormalities in kidney size can often be visualized using ultrasound. Nevertheless, not only does the ellipsoid formula used today underestimate true renal size but also the relation governing renal size and collagen content remains unclear. We used coronal kidney sections from healthy mice and mice with renal disease to develop a new technique for estimating the renal parenchymal area. While treating the kidney as an ellipse with the major axis the polar distance, this technique involves extending the minor axis into the renal pelvis. The calculated renal parenchymal area is remarkably similar to the measured area. Biochemically determined kidney collagen content revealed a strong and positive correlation with the calculated renal parenchymal area. The extent of renal scarring, i.e. kidney collagen content, can now be computed by making just two renal axial measurements which can easily be accomplished via noninvasive imaging of this organ. |
1506.03893 | Justin Werfel | Justin Werfel, Donald E. Ingber, Yaneer Bar-Yam | Theory and associated phenomenology for intrinsic mortality arising from
natural selection | 24 pages, 6 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Standard evolutionary theories of aging and mortality, implicitly based on
assumptions of spatial averaging, hold that natural selection cannot favor
shorter lifespan without direct compensating benefit to individual reproductive
success. Here we show that both theory and phenomenology are consistent with
programmed death. Spatial evolutionary models show that self-limited lifespan
robustly results in long-term benefit to a lineage; longer-lived variants may
have a reproductive advantage for many generations, but shorter lifespan
ultimately confers long-term reproductive advantage through environmental
feedback acting on much longer time scales. Numerous model variations produce
the same qualitative result, demonstrating insensitivity to detailed
assumptions; the key conditions under which self-limited lifespan is favored
are spatial extent and locally exhaustible resources. Numerous empirical
observations can parsimoniously be explained in terms of long-term selective
advantage for intrinsic mortality. Classically anomalous empirical data on
natural lifespans and intrinsic mortality, including observations of longer
lifespan associated with increased predation, and evidence of programmed death
in both unicellular and multicellular organisms, are consistent with specific
model predictions. The generic nature of the spatial model conditions under
which intrinsic mortality is favored suggests a firm theoretical basis for the
idea that evolution can quite generally select for shorter lifespan directly.
| [
{
"created": "Fri, 12 Jun 2015 03:26:24 GMT",
"version": "v1"
}
] | 2015-06-15 | [
[
"Werfel",
"Justin",
""
],
[
"Ingber",
"Donald E.",
""
],
[
"Bar-Yam",
"Yaneer",
""
]
] | Standard evolutionary theories of aging and mortality, implicitly based on assumptions of spatial averaging, hold that natural selection cannot favor shorter lifespan without direct compensating benefit to individual reproductive success. Here we show that both theory and phenomenology are consistent with programmed death. Spatial evolutionary models show that self-limited lifespan robustly results in long-term benefit to a lineage; longer-lived variants may have a reproductive advantage for many generations, but shorter lifespan ultimately confers long-term reproductive advantage through environmental feedback acting on much longer time scales. Numerous model variations produce the same qualitative result, demonstrating insensitivity to detailed assumptions; the key conditions under which self-limited lifespan is favored are spatial extent and locally exhaustible resources. Numerous empirical observations can parsimoniously be explained in terms of long-term selective advantage for intrinsic mortality. Classically anomalous empirical data on natural lifespans and intrinsic mortality, including observations of longer lifespan associated with increased predation, and evidence of programmed death in both unicellular and multicellular organisms, are consistent with specific model predictions. The generic nature of the spatial model conditions under which intrinsic mortality is favored suggests a firm theoretical basis for the idea that evolution can quite generally select for shorter lifespan directly. |
2111.09803 | Jacques Hermes | Jacques Hermes, Marcus Rosenblatt, Christian T\"onsing, Jens Timmer | Non-parametric model-based estimation of the effective reproduction
number for SARS-CoV-2 | null | null | null | null | q-bio.PE physics.data-an | http://creativecommons.org/licenses/by/4.0/ | Viral outbreaks, such as the current COVID-19 pandemic, are commonly
described by compartmental models by means of ordinary differential equation
(ODE) systems. The parameter values of these ODE models are typically unknown
and need to be estimated based on accessible data. In order to describe
realistic pandemic scenarios with strongly varying situations, these model
parameters need to be assumed as time-dependent. While parameter estimation for
the typical case of time-constant parameters does not pose larger issues, the
determination of time-dependent parameters, e.g.~the transition rates of
compartmental models, remains notoriously difficult, in particular since the
function class of these time-dependent parameters is unknown.
In this work, we present a novel method which utilizes the Augmented Kalman
Smoother in combination with an Expectation-Maximization algorithm to
simultaneously estimate all time-dependent parameters in an SIRD compartmental
model. This approach only requires incidence data, but no prior knowledge on
model parameters or any further assumptions on the function class of the
time-dependencies. In contrast to other approaches for the estimation of the
time-dependent reproduction number, no assumptions on the parameterization of
the serial interval distribution are required. With this method, we are able to
adequately describe COVID-19 data in Germany and to give non-parametric
model-based time course estimates for the effective reproduction number.
| [
{
"created": "Thu, 18 Nov 2021 17:11:31 GMT",
"version": "v1"
}
] | 2021-11-19 | [
[
"Hermes",
"Jacques",
""
],
[
"Rosenblatt",
"Marcus",
""
],
[
"Tönsing",
"Christian",
""
],
[
"Timmer",
"Jens",
""
]
] | Viral outbreaks, such as the current COVID-19 pandemic, are commonly described by compartmental models by means of ordinary differential equation (ODE) systems. The parameter values of these ODE models are typically unknown and need to be estimated based on accessible data. In order to describe realistic pandemic scenarios with strongly varying situations, these model parameters need to be assumed as time-dependent. While parameter estimation for the typical case of time-constant parameters does not pose larger issues, the determination of time-dependent parameters, e.g.~the transition rates of compartmental models, remains notoriously difficult, in particular since the function class of these time-dependent parameters is unknown. In this work, we present a novel method which utilizes the Augmented Kalman Smoother in combination with an Expectation-Maximization algorithm to simultaneously estimate all time-dependent parameters in an SIRD compartmental model. This approach only requires incidence data, but no prior knowledge on model parameters or any further assumptions on the function class of the time-dependencies. In contrast to other approaches for the estimation of the time-dependent reproduction number, no assumptions on the parameterization of the serial interval distribution are required. With this method, we are able to adequately describe COVID-19 data in Germany and to give non-parametric model-based time course estimates for the effective reproduction number. |
1012.4422 | Christoph Haselwandter | Christoph A. Haselwandter, Martino Calamai, Mehran Kardar, Antoine
Triller, and Rava Azeredo da Silveira | Formation and Stability of Synaptic Receptor Domains | 5 pages, 3 figures, Supplementary Material | Phys. Rev. Lett. 106, 238104 (2011) | 10.1103/PhysRevLett.106.238104 | null | q-bio.NC cond-mat.soft cond-mat.stat-mech q-bio.BM q-bio.MN q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neurotransmitter receptor molecules, concentrated in postsynaptic domains
along with scaffold and a number of other molecules, are key regulators of
signal transmission across synapses. Employing experiment and theory, we
develop a quantitative description of synaptic receptor domains in terms of a
reaction-diffusion model. We show that interactions between only receptor and
scaffold molecules, together with the rapid diffusion of receptors on the cell
membrane, are sufficient for the formation and stable characteristic size of
synaptic receptor domains. Our work reconciles long-term stability of synaptic
receptor domains with rapid turnover and diffusion of individual receptors.
| [
{
"created": "Mon, 20 Dec 2010 18:08:22 GMT",
"version": "v1"
}
] | 2015-05-20 | [
[
"Haselwandter",
"Christoph A.",
""
],
[
"Calamai",
"Martino",
""
],
[
"Kardar",
"Mehran",
""
],
[
"Triller",
"Antoine",
""
],
[
"da Silveira",
"Rava Azeredo",
""
]
] | Neurotransmitter receptor molecules, concentrated in postsynaptic domains along with scaffold and a number of other molecules, are key regulators of signal transmission across synapses. Employing experiment and theory, we develop a quantitative description of synaptic receptor domains in terms of a reaction-diffusion model. We show that interactions between only receptor and scaffold molecules, together with the rapid diffusion of receptors on the cell membrane, are sufficient for the formation and stable characteristic size of synaptic receptor domains. Our work reconciles long-term stability of synaptic receptor domains with rapid turnover and diffusion of individual receptors. |
q-bio/0506041 | Buford Price | H. C. Tung, N. E. Bramall, and P. B. Price | Microbial origin of excess greenhouse gases in glacial ice | 14 pages, 4 figures, submitted to Science | null | null | null | q-bio.PE q-bio.CB | null | We report the discovery of methanogenic archaea that account for abrupt
factor 10 increases in methane concentration found by E. Brook at depths of
2954 and 3036 m in the GISP2 (Greenland Ice Sheet Project 2) ice core. The
total microbial concentration we measured with direct cell counts tracks the
excesses of methanogens that we identified by their F420 fluorescence. The
highly localized (<1 m thick) layers of methanogens suggest flow induced mixing
of layers of microbe laden anaerobic basal ice with glacial ice. The metabolic
rate we found for microbes at 2954 and 3036 m lies roughly on the Arrhenius
line for microbes imprisoned in rock, sediment, and basal ice. Equating the
loss rate of methane recently discovered in the Martian atmosphere to the
production rate by possible methanogens, we estimate that their Martian habitat
would be at a temperature of ~0 degrees C and that the concentration, if
uniformly distributed in a 100 m thick layer, would be 0.04 cell per cubic
centimeter.
| [
{
"created": "Wed, 29 Jun 2005 14:59:31 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Tung",
"H. C.",
""
],
[
"Bramall",
"N. E.",
""
],
[
"Price",
"P. B.",
""
]
] | We report the discovery of methanogenic archaea that account for abrupt factor 10 increases in methane concentration found by E. Brook at depths of 2954 and 3036 m in the GISP2 (Greenland Ice Sheet Project 2) ice core. The total microbial concentration we measured with direct cell counts tracks the excesses of methanogens that we identified by their F420 fluorescence. The highly localized (<1 m thick) layers of methanogens suggest flow induced mixing of layers of microbe laden anaerobic basal ice with glacial ice. The metabolic rate we found for microbes at 2954 and 3036 m lies roughly on the Arrhenius line for microbes imprisoned in rock, sediment, and basal ice. Equating the loss rate of methane recently discovered in the Martian atmosphere to the production rate by possible methanogens, we estimate that their Martian habitat would be at a temperature of ~0 degrees C and that the concentration, if uniformly distributed in a 100 m thick layer, would be 0.04 cell per cubic centimeter. |
2006.07170 | Tiberiu Harko | Tiberiu Harko, Man Kwong Mak | A simple computational approach to the Susceptible-Infected-Recovered
(SIR) epidemic model via the Laplace-Adomian Decomposition Method | 9 pages, 2 figures | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Susceptible-Infected-Recovered (SIR) epidemic model is extensively used
for the study of the spread of infectious diseases. Even that the exact
solution of the model can be obtained in an exact parametric form, in order to
perform the comparison with the epidemiological data a simple but highly
accurate representation of the time evolution of the SIR compartments would be
very useful. In the present paper we obtain a series representation of the
solution of the SIR model by using the Laplace-Adomian Decomposition Method to
solve the basic evolution equation of the model. The solutions are expressed in
the form of infinite series. The series representations of the time evolution
of the SIR compartments are compared with the exact numerical solutions of the
model. We find that there is a good agreement between the Laplace-Adomian
semianalytical solutions containing only three terms, and the numerical
results.
| [
{
"created": "Fri, 12 Jun 2020 13:26:11 GMT",
"version": "v1"
},
{
"created": "Tue, 16 Jun 2020 06:08:20 GMT",
"version": "v2"
}
] | 2020-06-17 | [
[
"Harko",
"Tiberiu",
""
],
[
"Mak",
"Man Kwong",
""
]
] | The Susceptible-Infected-Recovered (SIR) epidemic model is extensively used for the study of the spread of infectious diseases. Even that the exact solution of the model can be obtained in an exact parametric form, in order to perform the comparison with the epidemiological data a simple but highly accurate representation of the time evolution of the SIR compartments would be very useful. In the present paper we obtain a series representation of the solution of the SIR model by using the Laplace-Adomian Decomposition Method to solve the basic evolution equation of the model. The solutions are expressed in the form of infinite series. The series representations of the time evolution of the SIR compartments are compared with the exact numerical solutions of the model. We find that there is a good agreement between the Laplace-Adomian semianalytical solutions containing only three terms, and the numerical results. |
2404.00081 | Huidong Tang Mr | Huidong Tang, Chen Li, Sayaka Kamei, Yoshihiro Yamanishi, Yasuhiko
Morimoto | Molecular Generative Adversarial Network with Multi-Property
Optimization | null | null | null | null | q-bio.BM cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep generative models, such as generative adversarial networks (GANs), have
been employed for $de~novo$ molecular generation in drug discovery. Most prior
studies have utilized reinforcement learning (RL) algorithms, particularly
Monte Carlo tree search (MCTS), to handle the discrete nature of molecular
representations in GANs. However, due to the inherent instability in training
GANs and RL models, along with the high computational cost associated with MCTS
sampling, MCTS RL-based GANs struggle to scale to large chemical databases. To
tackle these challenges, this study introduces a novel GAN based on
actor-critic RL with instant and global rewards, called InstGAN, to generate
molecules at the token-level with multi-property optimization. Furthermore,
maximized information entropy is leveraged to alleviate the mode collapse. The
experimental results demonstrate that InstGAN outperforms other baselines,
achieves comparable performance to state-of-the-art models, and efficiently
generates molecules with multi-property optimization. The source code will be
released upon acceptance of the paper.
| [
{
"created": "Fri, 29 Mar 2024 08:55:39 GMT",
"version": "v1"
}
] | 2024-04-02 | [
[
"Tang",
"Huidong",
""
],
[
"Li",
"Chen",
""
],
[
"Kamei",
"Sayaka",
""
],
[
"Yamanishi",
"Yoshihiro",
""
],
[
"Morimoto",
"Yasuhiko",
""
]
] | Deep generative models, such as generative adversarial networks (GANs), have been employed for $de~novo$ molecular generation in drug discovery. Most prior studies have utilized reinforcement learning (RL) algorithms, particularly Monte Carlo tree search (MCTS), to handle the discrete nature of molecular representations in GANs. However, due to the inherent instability in training GANs and RL models, along with the high computational cost associated with MCTS sampling, MCTS RL-based GANs struggle to scale to large chemical databases. To tackle these challenges, this study introduces a novel GAN based on actor-critic RL with instant and global rewards, called InstGAN, to generate molecules at the token-level with multi-property optimization. Furthermore, maximized information entropy is leveraged to alleviate the mode collapse. The experimental results demonstrate that InstGAN outperforms other baselines, achieves comparable performance to state-of-the-art models, and efficiently generates molecules with multi-property optimization. The source code will be released upon acceptance of the paper. |
2306.17331 | Dylan Morris | Dylan Morris, John Maclean, Andrew J. Black | Computation of random time-shift distributions for stochastic population
models | 46 pages, 10 figures | J. Math. Biol. 89, 33 (2024) | 10.1007/s00285-024-02132-6 | null | q-bio.PE math.PR | http://creativecommons.org/licenses/by/4.0/ | Even in large systems, the effect of noise arising from when populations are
initially small can persist to be measurable on the macroscale. A deterministic
approximation to a stochastic model will fail to capture this effect, but it
can be accurately approximated by including an additional random time-shift to
the initial conditions. We present a efficient numerical method to compute this
time-shift distribution for a large class of stochastic models. The method
relies on differentiation of certain functional equations, which we show can be
effectively automated by deriving rules for different types of model rates that
arise commonly when mass-action mixing is assumed. Explicit computation of the
time-shift distribution can be used to build a practical tool for the efficient
generation of macroscopic trajectories of stochastic population models, without
the need for costly stochastic simulations. Full code is provided to implement
this and we demonstrate our method on an epidemic model and a model of
within-host viral dynamics.
| [
{
"created": "Thu, 29 Jun 2023 22:58:04 GMT",
"version": "v1"
},
{
"created": "Mon, 20 May 2024 11:15:06 GMT",
"version": "v2"
},
{
"created": "Mon, 12 Aug 2024 11:03:58 GMT",
"version": "v3"
}
] | 2024-08-13 | [
[
"Morris",
"Dylan",
""
],
[
"Maclean",
"John",
""
],
[
"Black",
"Andrew J.",
""
]
] | Even in large systems, the effect of noise arising from when populations are initially small can persist to be measurable on the macroscale. A deterministic approximation to a stochastic model will fail to capture this effect, but it can be accurately approximated by including an additional random time-shift to the initial conditions. We present a efficient numerical method to compute this time-shift distribution for a large class of stochastic models. The method relies on differentiation of certain functional equations, which we show can be effectively automated by deriving rules for different types of model rates that arise commonly when mass-action mixing is assumed. Explicit computation of the time-shift distribution can be used to build a practical tool for the efficient generation of macroscopic trajectories of stochastic population models, without the need for costly stochastic simulations. Full code is provided to implement this and we demonstrate our method on an epidemic model and a model of within-host viral dynamics. |
2408.03117 | Bibaswan Dey Dr. | Ruchira Ray and Bibaswan Dey | The Role of Biomarkers on Haemodynamics in Atherosclerotic Artery | null | null | null | null | q-bio.TO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Atherosclerosis, a chronic inflammatory cardiovascular disease, leads to
arterial constriction caused by the accumulation of lipids, cholesterol, and
various substances within artery walls. Such plaque can rupture, resulting in a
blood clot that obstructs major arteries and may initiate myocardial
infarction, ischemic stroke, etc. Atherosclerotic plaque formation begins with
the accumulation of foam cells and macrophages within the intima layer of the
arterial wall. At the latter stage, the smooth muscle cells migrated from
deeper artery wall layers, contributing to the fibrous cap formation and plaque
stabilizing. A developed plaque gradually enters the lumen and narrows down the
lumen to impede blood flow. We introduce a two-phase and macroscopic model to
investigate the progression of plaque growth in its advanced stage and analyze
the minimum gap (Lumen Clearance) within an atherosclerotic artery so that
blood cells can pass through. Cardiac troponin, a high specificity and
sensitivity biomarker, facilitates early detection of elevated myocardial
infarction, Ischemic stroke, etc. risks. This study aims to establish a
relationship between the troponin concentration in atherosclerotic arteries and
their internal clearance, which could significantly improve our understanding
of disease progression. Our observations show that the plaque undergoes rapid
evolution in its initial stages, gradually slowing down over time to reach a
steady state. At the same time, the lumen clearance exhibits an opposite
behavior, decreasing slowly over time. Our study finds a positive correlation
between plaque depth and troponin concentration in the blood and a negative
relationship between troponin concentrations and lumen clearance in
atherosclerotic arteries.
| [
{
"created": "Tue, 6 Aug 2024 11:47:28 GMT",
"version": "v1"
}
] | 2024-08-07 | [
[
"Ray",
"Ruchira",
""
],
[
"Dey",
"Bibaswan",
""
]
] | Atherosclerosis, a chronic inflammatory cardiovascular disease, leads to arterial constriction caused by the accumulation of lipids, cholesterol, and various substances within artery walls. Such plaque can rupture, resulting in a blood clot that obstructs major arteries and may initiate myocardial infarction, ischemic stroke, etc. Atherosclerotic plaque formation begins with the accumulation of foam cells and macrophages within the intima layer of the arterial wall. At the latter stage, the smooth muscle cells migrated from deeper artery wall layers, contributing to the fibrous cap formation and plaque stabilizing. A developed plaque gradually enters the lumen and narrows down the lumen to impede blood flow. We introduce a two-phase and macroscopic model to investigate the progression of plaque growth in its advanced stage and analyze the minimum gap (Lumen Clearance) within an atherosclerotic artery so that blood cells can pass through. Cardiac troponin, a high specificity and sensitivity biomarker, facilitates early detection of elevated myocardial infarction, Ischemic stroke, etc. risks. This study aims to establish a relationship between the troponin concentration in atherosclerotic arteries and their internal clearance, which could significantly improve our understanding of disease progression. Our observations show that the plaque undergoes rapid evolution in its initial stages, gradually slowing down over time to reach a steady state. At the same time, the lumen clearance exhibits an opposite behavior, decreasing slowly over time. Our study finds a positive correlation between plaque depth and troponin concentration in the blood and a negative relationship between troponin concentrations and lumen clearance in atherosclerotic arteries. |
2308.14549 | Sangita Swapnasrita | Sangita Swapnasrita, Joost C de Vries, Carl M \"Oberg, Aur\'elie MF
Carlier, Karin GF Gerritsen | Computational modelling of peritoneal dialysis: an overview | 58 pages, 5 figures | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Peritoneal dialysis (PD) is becoming more popular as a result of a rising
interest in home dialysis, lower intrusion in social life and longer
preservation of residual kidney function. However, PD has several important
drawbacks: small solute clearance is relatively low compared to hemodialysis
and technique survival is limited. Application of continuous flow,
sorbent-based dialysate regeneration and novel glucose-sparing PD solutions are
some solutions proposed to address the limitations of PD. To optimize and
personalize current and novel PD therapies, patient peritoneal characteristics
interacting with PD techniques need to be studied together and separately as
they interplay. However, considering the multitude of parameters, it would be
difficult, expensive, and time consuming to optimize all parameter settings
only with the help of clinical trials. Mathematical modelling is an exciting
tool to dissect these interacting processes and comprehend PD techniques better
at a patient specific level. In this review, we look at the history of
computational PD models, explore the many ways a computational PD model can be
constructed and review the various existing PD models that can be used to
optimize and personalize PD treatment.
| [
{
"created": "Mon, 28 Aug 2023 13:08:35 GMT",
"version": "v1"
}
] | 2023-08-29 | [
[
"Swapnasrita",
"Sangita",
""
],
[
"de Vries",
"Joost C",
""
],
[
"Öberg",
"Carl M",
""
],
[
"Carlier",
"Aurélie MF",
""
],
[
"Gerritsen",
"Karin GF",
""
]
] | Peritoneal dialysis (PD) is becoming more popular as a result of a rising interest in home dialysis, lower intrusion in social life and longer preservation of residual kidney function. However, PD has several important drawbacks: small solute clearance is relatively low compared to hemodialysis and technique survival is limited. Application of continuous flow, sorbent-based dialysate regeneration and novel glucose-sparing PD solutions are some solutions proposed to address the limitations of PD. To optimize and personalize current and novel PD therapies, patient peritoneal characteristics interacting with PD techniques need to be studied together and separately as they interplay. However, considering the multitude of parameters, it would be difficult, expensive, and time consuming to optimize all parameter settings only with the help of clinical trials. Mathematical modelling is an exciting tool to dissect these interacting processes and comprehend PD techniques better at a patient specific level. In this review, we look at the history of computational PD models, explore the many ways a computational PD model can be constructed and review the various existing PD models that can be used to optimize and personalize PD treatment. |
1403.1255 | Sang-Yoon Kim | Sang-Yoon Kim and Woochang Lim | Realistic Thermodynamic and Statistical-Mechanical Measures for Neural
Synchronization | arXiv admin note: substantial text overlap with arXiv:1110.6927,
arXiv:1403.1034 | Journal of Neuroscience Methods 226, 161-170 (2014) | 10.1016/j.jneumeth.2013.12.013 | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Synchronized brain rhythms, associated with diverse cognitive functions, have
been observed in electrical recordings of brain activity. Neural
synchronization may be well described by using the population-averaged global
potential $V_G$ in computational neuroscience. The time-averaged fluctuation of
$V_G$ plays the role of a "thermodynamic" order parameter $\cal {O}$ used for
describing the synchrony-asynchrony transition in neural systems. Population
spike synchronization may be well visualized in the raster plot of neural
spikes. The degree of neural synchronization seen in the raster plot is well
measured in terms of a "statistical-mechanical" spike-based measure $M_s$
introduced by considering the occupation and the pacing patterns of spikes. The
global potential $V_G$ is also used to give a reference global cycle for the
calculation of $M_s$. Hence, $V_G$ becomes an important collective quantity
because it is associated with calculation of both $\cal {O}$ and $M_s$.
However, it is practically difficult to directly get $V_G$ in real experiments.
To overcome this difficulty, instead of $V_G$, we employ the instantaneous
population spike rate (IPSR) which can be obtained in experiments, and develop
realistic thermodynamic and statistical-mechanical measures, based on IPSR, to
make practical characterization of the neural synchronization in both
computational and experimental neuroscience. Particularly, more accurate
characterization of weak sparse spike synchronization can be achieved in terms
of realistic statistical-mechanical IPSR-based measure, in comparison with the
conventional measure based on $V_G$.
| [
{
"created": "Wed, 5 Mar 2014 08:32:05 GMT",
"version": "v1"
}
] | 2014-03-07 | [
[
"Kim",
"Sang-Yoon",
""
],
[
"Lim",
"Woochang",
""
]
] | Synchronized brain rhythms, associated with diverse cognitive functions, have been observed in electrical recordings of brain activity. Neural synchronization may be well described by using the population-averaged global potential $V_G$ in computational neuroscience. The time-averaged fluctuation of $V_G$ plays the role of a "thermodynamic" order parameter $\cal {O}$ used for describing the synchrony-asynchrony transition in neural systems. Population spike synchronization may be well visualized in the raster plot of neural spikes. The degree of neural synchronization seen in the raster plot is well measured in terms of a "statistical-mechanical" spike-based measure $M_s$ introduced by considering the occupation and the pacing patterns of spikes. The global potential $V_G$ is also used to give a reference global cycle for the calculation of $M_s$. Hence, $V_G$ becomes an important collective quantity because it is associated with calculation of both $\cal {O}$ and $M_s$. However, it is practically difficult to directly get $V_G$ in real experiments. To overcome this difficulty, instead of $V_G$, we employ the instantaneous population spike rate (IPSR) which can be obtained in experiments, and develop realistic thermodynamic and statistical-mechanical measures, based on IPSR, to make practical characterization of the neural synchronization in both computational and experimental neuroscience. Particularly, more accurate characterization of weak sparse spike synchronization can be achieved in terms of realistic statistical-mechanical IPSR-based measure, in comparison with the conventional measure based on $V_G$. |
0901.0138 | Amr Ahmed | Amr Ahmed, Le Song and Eric P. Xing | Time-Varying Networks: Recovering Temporally Rewiring Genetic Networks
During the Life Cycle of Drosophila melanogaster | Correcting some figure formatting errors | null | null | Amr Ahmed, Le Song, Eric Xing (2008). Time-Varying Networks:
Reconstructing Temporally Rewiring Genetic Interactions During the Life Cycle
of Drosophila melanogaster. CMU-MLD Technical Report CMU-ML-08-118 | q-bio.MN q-bio.QM stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to the dynamic nature of biological systems, biological networks
underlying temporal process such as the development of {\it Drosophila
melanogaster} can exhibit significant topological changes to facilitate dynamic
regulatory functions. Thus it is essential to develop methodologies that
capture the temporal evolution of networks, which make it possible to study the
driving forces underlying dynamic rewiring of gene regulation circuity, and to
predict future network structures. Using a new machine learning method called
Tesla, which builds on a novel temporal logistic regression technique, we
report the first successful genome-wide reverse-engineering of the latent
sequence of temporally rewiring gene networks over more than 4000 genes during
the life cycle of \textit{Drosophila melanogaster}, given longitudinal gene
expression measurements and even when a single snapshot of such measurement
resulted from each (time-specific) network is available. Our methods offer the
first glimpse of time-specific snapshots and temporal evolution patterns of
gene networks in a living organism during its full developmental course. The
recovered networks with this unprecedented resolution chart the onset and
duration of many gene interactions which are missed by typical static network
analysis, and are suggestive of a wide array of other temporal behaviors of the
gene network over time not noticed before.
| [
{
"created": "Wed, 31 Dec 2008 20:42:52 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Jan 2009 23:28:12 GMT",
"version": "v2"
}
] | 2009-01-07 | [
[
"Ahmed",
"Amr",
""
],
[
"Song",
"Le",
""
],
[
"Xing",
"Eric P.",
""
]
] | Due to the dynamic nature of biological systems, biological networks underlying temporal process such as the development of {\it Drosophila melanogaster} can exhibit significant topological changes to facilitate dynamic regulatory functions. Thus it is essential to develop methodologies that capture the temporal evolution of networks, which make it possible to study the driving forces underlying dynamic rewiring of gene regulation circuity, and to predict future network structures. Using a new machine learning method called Tesla, which builds on a novel temporal logistic regression technique, we report the first successful genome-wide reverse-engineering of the latent sequence of temporally rewiring gene networks over more than 4000 genes during the life cycle of \textit{Drosophila melanogaster}, given longitudinal gene expression measurements and even when a single snapshot of such measurement resulted from each (time-specific) network is available. Our methods offer the first glimpse of time-specific snapshots and temporal evolution patterns of gene networks in a living organism during its full developmental course. The recovered networks with this unprecedented resolution chart the onset and duration of many gene interactions which are missed by typical static network analysis, and are suggestive of a wide array of other temporal behaviors of the gene network over time not noticed before. |
2309.13518 | Jingyi Jessica Li | Qingyang Wang, Zhiqian Zhai, Dongyuan Song, Jingyi Jessica Li | Review of computational methods for estimating cell potency from
single-cell RNA-seq data, with a detailed analysis of discrepancies between
method description and code implementation | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In single-cell RNA sequencing (scRNA-seq) data analysis, a critical challenge
is to infer hidden dynamic cellular processes from measured static cell
snapshots. To tackle this challenge, many computational methods have been
developed from distinct perspectives. Besides the common perspectives of
inferring trajectories (or pseudotime) and RNA velocity, another important
perspective is to estimate the differentiation potential of cells, which is
commonly referred to as "cell potency." In this review, we provide a
comprehensive summary of 11 computational methods that estimate cell potency
from scRNA-seq data under different assumptions, some of which are even
conceptually contradictory. We divide these methods into three categories:
mean-based, entropy-based, and correlation-based methods, depending on how a
method summarizes gene expression levels of a cell or cell type into a potency
measure. Our review focuses on the key similarities and differences of the
methods within each category and between the categories, providing a high-level
intuition of each method. Moreover, we use a unified set of mathematical
notations to detail the 11 methods' methodologies and summarize their usage
complexities, including the number of ad-hoc parameters, the number of required
inputs, and the existence of discrepancies between the method description in
publications and the method implementation in software packages. Realizing the
conceptual contradictions of existing methods and the difficulty of fair
benchmarking without single-cell-level ground truths, we conclude that accurate
estimation of cell potency from scRNA-seq data remains an open challenge.
| [
{
"created": "Sun, 24 Sep 2023 01:00:37 GMT",
"version": "v1"
}
] | 2023-09-26 | [
[
"Wang",
"Qingyang",
""
],
[
"Zhai",
"Zhiqian",
""
],
[
"Song",
"Dongyuan",
""
],
[
"Li",
"Jingyi Jessica",
""
]
] | In single-cell RNA sequencing (scRNA-seq) data analysis, a critical challenge is to infer hidden dynamic cellular processes from measured static cell snapshots. To tackle this challenge, many computational methods have been developed from distinct perspectives. Besides the common perspectives of inferring trajectories (or pseudotime) and RNA velocity, another important perspective is to estimate the differentiation potential of cells, which is commonly referred to as "cell potency." In this review, we provide a comprehensive summary of 11 computational methods that estimate cell potency from scRNA-seq data under different assumptions, some of which are even conceptually contradictory. We divide these methods into three categories: mean-based, entropy-based, and correlation-based methods, depending on how a method summarizes gene expression levels of a cell or cell type into a potency measure. Our review focuses on the key similarities and differences of the methods within each category and between the categories, providing a high-level intuition of each method. Moreover, we use a unified set of mathematical notations to detail the 11 methods' methodologies and summarize their usage complexities, including the number of ad-hoc parameters, the number of required inputs, and the existence of discrepancies between the method description in publications and the method implementation in software packages. Realizing the conceptual contradictions of existing methods and the difficulty of fair benchmarking without single-cell-level ground truths, we conclude that accurate estimation of cell potency from scRNA-seq data remains an open challenge. |
q-bio/0312037 | Kazumi Suematsu | Kazumi Suematsu | Aging Concept in Population Dynamics | 19 Pages, 7 Figures | null | null | null | q-bio.PE q-bio.QM | null | Author's early work on aging is developed to yield a relationship between
life spans and the velocity of aging. The mathematical analysis shows that the
mean extent of the advancement of aging throughout one's life is conserved, or
equivalently, the product of the mean life span, and the mean rate of aging is
constant. The result is in harmony with our experiences: It accounts for the
unlimited replicability of tumor cells, and predicts the prolonged life spans
of hibernating hamsters, in accordance with the Lyman and coworkers experiment.
Comparing the present result and the empirical relationship between life spans
of various mammals and basal metabolic rates, it is suggested that the mean
rate of aging is intimately connected with the mean basal metabolic rate. With
the help of this information, we inquire the reason of the difference in mean
life spans between women and men, the result showing that the relative mean
life span of women to men is 1 08, for various nations, which is close to the
corresponding relative value of the basal metabolic rate. The present theory
suggests, however, that this relationship between life spans and basal
metabolic rates must be treated with caution.
| [
{
"created": "Tue, 23 Dec 2003 04:13:52 GMT",
"version": "v1"
},
{
"created": "Thu, 20 Oct 2005 07:05:22 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Suematsu",
"Kazumi",
""
]
] | Author's early work on aging is developed to yield a relationship between life spans and the velocity of aging. The mathematical analysis shows that the mean extent of the advancement of aging throughout one's life is conserved, or equivalently, the product of the mean life span, and the mean rate of aging is constant. The result is in harmony with our experiences: It accounts for the unlimited replicability of tumor cells, and predicts the prolonged life spans of hibernating hamsters, in accordance with the Lyman and coworkers experiment. Comparing the present result and the empirical relationship between life spans of various mammals and basal metabolic rates, it is suggested that the mean rate of aging is intimately connected with the mean basal metabolic rate. With the help of this information, we inquire the reason of the difference in mean life spans between women and men, the result showing that the relative mean life span of women to men is 1 08, for various nations, which is close to the corresponding relative value of the basal metabolic rate. The present theory suggests, however, that this relationship between life spans and basal metabolic rates must be treated with caution. |
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