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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1812.05992 | Soraida Fiol Gonzalez | Soraida Fiol Gonz\'alez | La fauna de mam\'iferos f\'osiles del dep\'osito paleontol\'ogico "El
Abr\'on" (nivel ix), Pinar del R\'io, Cuba | in Spanish. Advisor: Joao G. Mart\'inez L\'opez | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | "El Abr\'on" is a fossil deposit located in Pinar del Rio, Cuba, and whose
age is only reference level VII (17 406 years BP), it is classified as the
largest collection of fossils accumulated for our archipelago, produced by
trophic action of barn owls for thousands of years. The aim of this study was
to determine the living taxonomic composition of the fauna of extinct mammals,
and throughout the paleontological study of the deeper level of said tank
(Level IX). The extracted material which it is currently stored in the
warehouse of paleontological collections of the National Museum of Natural
History in Havana, Cuba (MNHNCu) was analyzed. We proceeded to clean the bones,
to classify and to identify them from the species and also the taphonomic
analysis of the condition of the remains. It was found that the mammal fauna of
the paleontological deposit under study is composed essentially of 3 orders, 7
families and 14 species. The most significative order is Chiroptera (bat
fauna), represented by 4 families, 9 genus and 9 species of the total which
were identified. There were reported four species of bats Erophylla sezecorni,
Monophyllus redmani, Pteronotus parnelli and Tadarida brasiliensis in the
location. The results are the basis of the future paleoecological studies in
order to reconstruct the natural history of these species. Moreover, the
discovery of new species in this area is a contribution to the knowledge about
the distribution of these species in the Cuban archipelago and the age of them.
Finally, the taphonomic analysis of the conservation status of these remains
permitted the understanding of the processes that gave rise to the tank and its
characteristics, and also it contribute to an adequate estimation of the
species present in it and the relationship between spatiotemporal with the
fossil.
| [
{
"created": "Fri, 14 Dec 2018 15:53:01 GMT",
"version": "v1"
}
] | 2018-12-17 | [
[
"González",
"Soraida Fiol",
""
]
] | "El Abr\'on" is a fossil deposit located in Pinar del Rio, Cuba, and whose age is only reference level VII (17 406 years BP), it is classified as the largest collection of fossils accumulated for our archipelago, produced by trophic action of barn owls for thousands of years. The aim of this study was to determine the living taxonomic composition of the fauna of extinct mammals, and throughout the paleontological study of the deeper level of said tank (Level IX). The extracted material which it is currently stored in the warehouse of paleontological collections of the National Museum of Natural History in Havana, Cuba (MNHNCu) was analyzed. We proceeded to clean the bones, to classify and to identify them from the species and also the taphonomic analysis of the condition of the remains. It was found that the mammal fauna of the paleontological deposit under study is composed essentially of 3 orders, 7 families and 14 species. The most significative order is Chiroptera (bat fauna), represented by 4 families, 9 genus and 9 species of the total which were identified. There were reported four species of bats Erophylla sezecorni, Monophyllus redmani, Pteronotus parnelli and Tadarida brasiliensis in the location. The results are the basis of the future paleoecological studies in order to reconstruct the natural history of these species. Moreover, the discovery of new species in this area is a contribution to the knowledge about the distribution of these species in the Cuban archipelago and the age of them. Finally, the taphonomic analysis of the conservation status of these remains permitted the understanding of the processes that gave rise to the tank and its characteristics, and also it contribute to an adequate estimation of the species present in it and the relationship between spatiotemporal with the fossil. |
1308.0551 | Jaeyun Sung | Jaeyun Sung, Pan-Jun Kim, Shuyi Ma, Cory C. Funk, Andrew T. Magis,
Yuliang Wang, Leroy Hood, Donald Geman, and Nathan D. Price | Multi-study Integration of Brain Cancer Transcriptomes Reveals
Organ-Level Molecular Signatures | 27 pages of main text including 4 figures and 4 tables. 32 pages of
supplementary material (Text, Figures, and Tables) | PLoS Comput Biol 9(7): e1003148 (2013) | 10.1371/journal.pcbi.1003148 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We utilized abundant transcriptomic data for the primary classes of brain
cancers to study the feasibility of separating all of these diseases
simultaneously based on molecular data alone. These signatures were based on a
new method reported herein that resulted in a brain cancer marker panel of 44
unique genes. Many of these genes have established relevance to the brain
cancers examined, with others having known roles in cancer biology. Analyses on
large-scale data from multiple sources must deal with significant challenges
associated with heterogeneity between different published studies, for it was
observed that the variation among individual studies often had a larger effect
on the transcriptome than did phenotype differences, as is typical. We found
that learning signatures across multiple datasets greatly enhanced
reproducibility and accuracy in predictive performance on truly independent
validation sets, even when keeping the size of the training set the same. This
was most likely due to the meta-signature encompassing more of the
heterogeneity across different sources and conditions, while amplifying signal
from the repeated global characteristics of the phenotype. When molecular
signatures of brain cancers were constructed from all currently available
microarray data, 90 percent phenotype prediction accuracy, or the accuracy of
identifying a particular brain cancer from the background of all phenotypes,
was found. Looking forward, we discuss our approach in the context of the
eventual development of organ-specific molecular signatures from peripheral
fluids such as the blood.
| [
{
"created": "Fri, 2 Aug 2013 16:53:23 GMT",
"version": "v1"
}
] | 2013-08-05 | [
[
"Sung",
"Jaeyun",
""
],
[
"Kim",
"Pan-Jun",
""
],
[
"Ma",
"Shuyi",
""
],
[
"Funk",
"Cory C.",
""
],
[
"Magis",
"Andrew T.",
""
],
[
"Wang",
"Yuliang",
""
],
[
"Hood",
"Leroy",
""
],
[
"Geman",
"... | We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein that resulted in a brain cancer marker panel of 44 unique genes. Many of these genes have established relevance to the brain cancers examined, with others having known roles in cancer biology. Analyses on large-scale data from multiple sources must deal with significant challenges associated with heterogeneity between different published studies, for it was observed that the variation among individual studies often had a larger effect on the transcriptome than did phenotype differences, as is typical. We found that learning signatures across multiple datasets greatly enhanced reproducibility and accuracy in predictive performance on truly independent validation sets, even when keeping the size of the training set the same. This was most likely due to the meta-signature encompassing more of the heterogeneity across different sources and conditions, while amplifying signal from the repeated global characteristics of the phenotype. When molecular signatures of brain cancers were constructed from all currently available microarray data, 90 percent phenotype prediction accuracy, or the accuracy of identifying a particular brain cancer from the background of all phenotypes, was found. Looking forward, we discuss our approach in the context of the eventual development of organ-specific molecular signatures from peripheral fluids such as the blood. |
1103.0342 | Bin Wu | Jing Wang, Bin Wu, Daniel W. C. Ho, Long Wang | Evolution of cooperation in multilevel public goods games with community
structures | 6 pages, 4 figures, Accepted by EPL | null | 10.1209/0295-5075/93/58001 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a community-structured population, public goods games (PGG) occur both
within and between communities. Such type of PGG is referred as multilevel
public goods games (MPGG). We propose a minimalist evolutionary model of the
MPGG and analytically study the evolution of cooperation. We demonstrate that
in the case of sufficiently large community size and community number, if the
imitation strength within community is weak, i.e., an individual imitates
another one in the same community almost randomly, cooperation as well as
punishment are more abundant than defection in the long run; if the imitation
strength between communities is strong, i.e., the more successful strategy in
two individuals from distinct communities is always imitated, cooperation and
punishment are also more abundant. However, when both of the two imitation
intensities are strong, defection becomes the most abundant strategy in the
population. Our model provides insight into the investigation of the
large-scale cooperation in public social dilemma among contemporary
communities.
| [
{
"created": "Wed, 2 Mar 2011 03:30:50 GMT",
"version": "v1"
}
] | 2015-05-27 | [
[
"Wang",
"Jing",
""
],
[
"Wu",
"Bin",
""
],
[
"Ho",
"Daniel W. C.",
""
],
[
"Wang",
"Long",
""
]
] | In a community-structured population, public goods games (PGG) occur both within and between communities. Such type of PGG is referred as multilevel public goods games (MPGG). We propose a minimalist evolutionary model of the MPGG and analytically study the evolution of cooperation. We demonstrate that in the case of sufficiently large community size and community number, if the imitation strength within community is weak, i.e., an individual imitates another one in the same community almost randomly, cooperation as well as punishment are more abundant than defection in the long run; if the imitation strength between communities is strong, i.e., the more successful strategy in two individuals from distinct communities is always imitated, cooperation and punishment are also more abundant. However, when both of the two imitation intensities are strong, defection becomes the most abundant strategy in the population. Our model provides insight into the investigation of the large-scale cooperation in public social dilemma among contemporary communities. |
2401.14819 | Philip Hartout | Dexiong Chen, Philip Hartout, Paolo Pellizzoni, Carlos Oliver, Karsten
Borgwardt | Endowing Protein Language Models with Structural Knowledge | null | null | null | null | q-bio.QM cs.LG q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the relationships between protein sequence, structure and
function is a long-standing biological challenge with manifold implications
from drug design to our understanding of evolution. Recently, protein language
models have emerged as the preferred method for this challenge, thanks to their
ability to harness large sequence databases. Yet, their reliance on expansive
sequence data and parameter sets limits their flexibility and practicality in
real-world scenarios. Concurrently, the recent surge in computationally
predicted protein structures unlocks new opportunities in protein
representation learning. While promising, the computational burden carried by
such complex data still hinders widely-adopted practical applications. To
address these limitations, we introduce a novel framework that enhances protein
language models by integrating protein structural data. Drawing from recent
advances in graph transformers, our approach refines the self-attention
mechanisms of pretrained language transformers by integrating structural
information with structure extractor modules. This refined model, termed
Protein Structure Transformer (PST), is further pretrained on a small protein
structure database, using the same masked language modeling objective as
traditional protein language models. Empirical evaluations of PST demonstrate
its superior parameter efficiency relative to protein language models, despite
being pretrained on a dataset comprising only 542K structures. Notably, PST
consistently outperforms the state-of-the-art foundation model for protein
sequences, ESM-2, setting a new benchmark in protein function prediction. Our
findings underscore the potential of integrating structural information into
protein language models, paving the way for more effective and efficient
protein modeling Code and pretrained models are available at
https://github.com/BorgwardtLab/PST.
| [
{
"created": "Fri, 26 Jan 2024 12:47:54 GMT",
"version": "v1"
}
] | 2024-01-29 | [
[
"Chen",
"Dexiong",
""
],
[
"Hartout",
"Philip",
""
],
[
"Pellizzoni",
"Paolo",
""
],
[
"Oliver",
"Carlos",
""
],
[
"Borgwardt",
"Karsten",
""
]
] | Understanding the relationships between protein sequence, structure and function is a long-standing biological challenge with manifold implications from drug design to our understanding of evolution. Recently, protein language models have emerged as the preferred method for this challenge, thanks to their ability to harness large sequence databases. Yet, their reliance on expansive sequence data and parameter sets limits their flexibility and practicality in real-world scenarios. Concurrently, the recent surge in computationally predicted protein structures unlocks new opportunities in protein representation learning. While promising, the computational burden carried by such complex data still hinders widely-adopted practical applications. To address these limitations, we introduce a novel framework that enhances protein language models by integrating protein structural data. Drawing from recent advances in graph transformers, our approach refines the self-attention mechanisms of pretrained language transformers by integrating structural information with structure extractor modules. This refined model, termed Protein Structure Transformer (PST), is further pretrained on a small protein structure database, using the same masked language modeling objective as traditional protein language models. Empirical evaluations of PST demonstrate its superior parameter efficiency relative to protein language models, despite being pretrained on a dataset comprising only 542K structures. Notably, PST consistently outperforms the state-of-the-art foundation model for protein sequences, ESM-2, setting a new benchmark in protein function prediction. Our findings underscore the potential of integrating structural information into protein language models, paving the way for more effective and efficient protein modeling Code and pretrained models are available at https://github.com/BorgwardtLab/PST. |
2406.13839 | Chaitanya K. Joshi | Rishabh Anand, Chaitanya K. Joshi, Alex Morehead, Arian R. Jamasb,
Charles Harris, Simon V. Mathis, Kieran Didi, Bryan Hooi, Pietro Li\`o | RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design | To be presented as an Oral at ICML 2024 Structured Probabilistic
Inference & Generative Modeling Workshop, and a Spotlight at ICML 2024
AI4Science Workshop | null | null | null | q-bio.BM cs.LG q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce RNA-FrameFlow, the first generative model for 3D RNA backbone
design. We build upon SE(3) flow matching for protein backbone generation and
establish protocols for data preparation and evaluation to address unique
challenges posed by RNA modeling. We formulate RNA structures as a set of
rigid-body frames and associated loss functions which account for larger, more
conformationally flexible RNA backbones (13 atoms per nucleotide) vs. proteins
(4 atoms per residue). Toward tackling the lack of diversity in 3D RNA
datasets, we explore training with structural clustering and cropping
augmentations. Additionally, we define a suite of evaluation metrics to measure
whether the generated RNA structures are globally self-consistent (via inverse
folding followed by forward folding) and locally recover RNA-specific
structural descriptors. The most performant version of RNA-FrameFlow generates
locally realistic RNA backbones of 40-150 nucleotides, over 40% of which pass
our validity criteria as measured by a self-consistency TM-score >= 0.45, at
which two RNAs have the same global fold. Open-source code:
https://github.com/rish-16/rna-backbone-design
| [
{
"created": "Wed, 19 Jun 2024 21:06:44 GMT",
"version": "v1"
}
] | 2024-06-21 | [
[
"Anand",
"Rishabh",
""
],
[
"Joshi",
"Chaitanya K.",
""
],
[
"Morehead",
"Alex",
""
],
[
"Jamasb",
"Arian R.",
""
],
[
"Harris",
"Charles",
""
],
[
"Mathis",
"Simon V.",
""
],
[
"Didi",
"Kieran",
""
],
... | We introduce RNA-FrameFlow, the first generative model for 3D RNA backbone design. We build upon SE(3) flow matching for protein backbone generation and establish protocols for data preparation and evaluation to address unique challenges posed by RNA modeling. We formulate RNA structures as a set of rigid-body frames and associated loss functions which account for larger, more conformationally flexible RNA backbones (13 atoms per nucleotide) vs. proteins (4 atoms per residue). Toward tackling the lack of diversity in 3D RNA datasets, we explore training with structural clustering and cropping augmentations. Additionally, we define a suite of evaluation metrics to measure whether the generated RNA structures are globally self-consistent (via inverse folding followed by forward folding) and locally recover RNA-specific structural descriptors. The most performant version of RNA-FrameFlow generates locally realistic RNA backbones of 40-150 nucleotides, over 40% of which pass our validity criteria as measured by a self-consistency TM-score >= 0.45, at which two RNAs have the same global fold. Open-source code: https://github.com/rish-16/rna-backbone-design |
1706.09570 | Nithin Nagaraj | Suresh Jois and Nithin Nagaraj | Simulation Study of Two Measures of Integrated Information | 10 pages, 3 figures. The work reported in this paper, in summary
form, was presented as a poster at The Science of Consciousness (TSC)
Conference, June 5-10, held at La Jolla, USA | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Many authors have proposed Quantitative Theories of Consciousness
(QTC) based on theoretical principles like information theory, Granger
causality and complexity. Recently, Virmani and Nagaraj (arXiv:1608.08450v2
[cs.IT]) noted the similarity between Integrated Information and
Compression-Complexity, and on this basis, proposed a novel measure of network
complexity called Phi-Compression Complexity (Phi-C or $\Phi^C$). Their
computer simulations using Boolean networks showed that $\Phi^C$ compares
favorably to Giulio Tononi et al's Integrated Information measure $\Phi$ 3.0
and exhibits desirable mathematical and computational characteristics. Methods:
In the present work, $\Phi^C$ was measured for two types of simulated networks:
(A) Networks representing simple neuronal connectivity motifs (presented in
Fig.9 of Tononi and Sporns, BMC Neuroscience 4(1), 2003); (B) random networks
derived from Erd\"os-R \'enyi G(N, p)graphs. Code for all simulations was
written in Python 3.6, and the library NetworkX was used to simulate the
graphs. Results and discussions summary: In simulations A, for the same set of
networks, $\Phi^C$ values differ from the values of IIT 1.0 $\Phi$ in a
counter-intuitive manner. It appears that $\Phi^C$ captures some invariant
aspects of the interplay between information integration, network topology,
graph composition and node entropy. While Virmani and Nagaraj
(arXiv:1608.08450v2 [cs.IT]) sought to highlight the correlations between
$\Phi^C$ and IIT $\Phi$, the results of simulations A highlight the differences
between the two measures in the way they capture the integrated information. In
simulations B, the results of simulations A are extended to the more general
case of random networks. In the concluding section we outline the novel aspects
of this paper, and our ongoing and future research.
| [
{
"created": "Thu, 29 Jun 2017 04:06:01 GMT",
"version": "v1"
}
] | 2017-06-30 | [
[
"Jois",
"Suresh",
""
],
[
"Nagaraj",
"Nithin",
""
]
] | Background: Many authors have proposed Quantitative Theories of Consciousness (QTC) based on theoretical principles like information theory, Granger causality and complexity. Recently, Virmani and Nagaraj (arXiv:1608.08450v2 [cs.IT]) noted the similarity between Integrated Information and Compression-Complexity, and on this basis, proposed a novel measure of network complexity called Phi-Compression Complexity (Phi-C or $\Phi^C$). Their computer simulations using Boolean networks showed that $\Phi^C$ compares favorably to Giulio Tononi et al's Integrated Information measure $\Phi$ 3.0 and exhibits desirable mathematical and computational characteristics. Methods: In the present work, $\Phi^C$ was measured for two types of simulated networks: (A) Networks representing simple neuronal connectivity motifs (presented in Fig.9 of Tononi and Sporns, BMC Neuroscience 4(1), 2003); (B) random networks derived from Erd\"os-R \'enyi G(N, p)graphs. Code for all simulations was written in Python 3.6, and the library NetworkX was used to simulate the graphs. Results and discussions summary: In simulations A, for the same set of networks, $\Phi^C$ values differ from the values of IIT 1.0 $\Phi$ in a counter-intuitive manner. It appears that $\Phi^C$ captures some invariant aspects of the interplay between information integration, network topology, graph composition and node entropy. While Virmani and Nagaraj (arXiv:1608.08450v2 [cs.IT]) sought to highlight the correlations between $\Phi^C$ and IIT $\Phi$, the results of simulations A highlight the differences between the two measures in the way they capture the integrated information. In simulations B, the results of simulations A are extended to the more general case of random networks. In the concluding section we outline the novel aspects of this paper, and our ongoing and future research. |
2203.02438 | Zachary Kilpatrick PhD | Heather L Cihak, Tahra L Eissa, and Zachary P Kilpatrick | Distinct excitatory and inhibitory bump wandering in a stochastic neural
field | 28 pages; 10 figures | null | null | null | q-bio.NC nlin.PS | http://creativecommons.org/licenses/by/4.0/ | Localized persistent cortical neural activity is a validated neural substrate
of parametric working memory. Such activity `bumps' represent the continuous
location of a cue over several seconds. Pyramidal (excitatory) and
interneuronal (inhibitory) subpopulations exhibit tuned bumps of activity,
linking neural dynamics to behavioral inaccuracies observed in memory recall.
However, many bump attractor models collapse these subpopulations into a single
joint excitatory/inhibitory (lateral inhibitory) population, and do not
consider the role of interpopulation neural architecture and noise
correlations. Both factors have a high potential to impinge upon the stochastic
dynamics of these bumps, ultimately shaping behavioral response variance. In
our study, we consider a neural field model with separate excitatory/inhibitory
(E/I) populations and leverage asymptotic analysis to derive a nonlinear
Langevin system describing E/I bump interactions. While the E bump attracts the
I bump, the I bump stabilizes but can also repel the E bump, which can result
in prolonged relaxation dynamics when both bumps are perturbed. Furthermore,
the structure of noise correlations within and between subpopulations strongly
shapes the variance in bump position. Surprisingly, higher interpopulation
correlations reduce variance.
| [
{
"created": "Fri, 4 Mar 2022 17:14:13 GMT",
"version": "v1"
}
] | 2022-03-07 | [
[
"Cihak",
"Heather L",
""
],
[
"Eissa",
"Tahra L",
""
],
[
"Kilpatrick",
"Zachary P",
""
]
] | Localized persistent cortical neural activity is a validated neural substrate of parametric working memory. Such activity `bumps' represent the continuous location of a cue over several seconds. Pyramidal (excitatory) and interneuronal (inhibitory) subpopulations exhibit tuned bumps of activity, linking neural dynamics to behavioral inaccuracies observed in memory recall. However, many bump attractor models collapse these subpopulations into a single joint excitatory/inhibitory (lateral inhibitory) population, and do not consider the role of interpopulation neural architecture and noise correlations. Both factors have a high potential to impinge upon the stochastic dynamics of these bumps, ultimately shaping behavioral response variance. In our study, we consider a neural field model with separate excitatory/inhibitory (E/I) populations and leverage asymptotic analysis to derive a nonlinear Langevin system describing E/I bump interactions. While the E bump attracts the I bump, the I bump stabilizes but can also repel the E bump, which can result in prolonged relaxation dynamics when both bumps are perturbed. Furthermore, the structure of noise correlations within and between subpopulations strongly shapes the variance in bump position. Surprisingly, higher interpopulation correlations reduce variance. |
1907.00973 | Ivan Ezhov | Ivan Ezhov, Jana Lipkova, Suprosanna Shit, Florian Kofler, Nore
Collomb, Benjamin Lemasson, Emmanuel Barbier, Bjoern Menze | Neural parameters estimation for brain tumor growth modeling | null | null | 10.1007/978-3-030-32245-8_87 | null | q-bio.QM cs.LG eess.IV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the dynamics of brain tumor progression is essential for
optimal treatment planning. Cast in a mathematical formulation, it is typically
viewed as evaluation of a system of partial differential equations, wherein the
physiological processes that govern the growth of the tumor are considered. To
personalize the model, i.e. find a relevant set of parameters, with respect to
the tumor dynamics of a particular patient, the model is informed from
empirical data, e.g., medical images obtained from diagnostic modalities, such
as magnetic-resonance imaging. Existing model-observation coupling schemes
require a large number of forward integrations of the biophysical model and
rely on simplifying assumption on the functional form, linking the output of
the model with the image information. In this work, we propose a learning-based
technique for the estimation of tumor growth model parameters from medical
scans. The technique allows for explicit evaluation of the posterior
distribution of the parameters by sequentially training a mixture-density
network, relaxing the constraint on the functional form and reducing the number
of samples necessary to propagate through the forward model for the estimation.
We test the method on synthetic and real scans of rats injected with brain
tumors to calibrate the model and to predict tumor progression.
| [
{
"created": "Mon, 1 Jul 2019 17:57:14 GMT",
"version": "v1"
},
{
"created": "Thu, 9 Jan 2020 19:04:45 GMT",
"version": "v2"
}
] | 2020-01-13 | [
[
"Ezhov",
"Ivan",
""
],
[
"Lipkova",
"Jana",
""
],
[
"Shit",
"Suprosanna",
""
],
[
"Kofler",
"Florian",
""
],
[
"Collomb",
"Nore",
""
],
[
"Lemasson",
"Benjamin",
""
],
[
"Barbier",
"Emmanuel",
""
],
[
... | Understanding the dynamics of brain tumor progression is essential for optimal treatment planning. Cast in a mathematical formulation, it is typically viewed as evaluation of a system of partial differential equations, wherein the physiological processes that govern the growth of the tumor are considered. To personalize the model, i.e. find a relevant set of parameters, with respect to the tumor dynamics of a particular patient, the model is informed from empirical data, e.g., medical images obtained from diagnostic modalities, such as magnetic-resonance imaging. Existing model-observation coupling schemes require a large number of forward integrations of the biophysical model and rely on simplifying assumption on the functional form, linking the output of the model with the image information. In this work, we propose a learning-based technique for the estimation of tumor growth model parameters from medical scans. The technique allows for explicit evaluation of the posterior distribution of the parameters by sequentially training a mixture-density network, relaxing the constraint on the functional form and reducing the number of samples necessary to propagate through the forward model for the estimation. We test the method on synthetic and real scans of rats injected with brain tumors to calibrate the model and to predict tumor progression. |
2102.04896 | Dalton Sakthivadivel | Dalton A R Sakthivadivel | Formalising the Use of the Activation Function in Neural Inference | 14+2 pages, two figures. TikZ code included in submission | Complex Systems, 31(4), 2022 | 10.25088/ComplexSystems.31.4.433 | null | q-bio.NC cond-mat.dis-nn cond-mat.stat-mech stat.ML | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We investigate how the activation function can be used to describe neural
firing in an abstract way, and in turn, why it works well in artificial neural
networks. We discuss how a spike in a biological neurone belongs to a
particular universality class of phase transitions in statistical physics. We
then show that the artificial neurone is, mathematically, a mean field model of
biological neural membrane dynamics, which arises from modelling spiking as a
phase transition. This allows us to treat selective neural firing in an
abstract way, and formalise the role of the activation function in perceptron
learning. The resultant statistical physical model allows us to recover the
expressions for some known activation functions as various special cases. Along
with deriving this model and specifying the analogous neural case, we analyse
the phase transition to understand the physics of neural network learning.
Together, it is shown that there is not only a biological meaning, but a
physical justification, for the emergence and performance of typical activation
functions; implications for neural learning and inference are also discussed.
| [
{
"created": "Tue, 2 Feb 2021 19:42:21 GMT",
"version": "v1"
},
{
"created": "Tue, 27 Jul 2021 16:55:31 GMT",
"version": "v2"
},
{
"created": "Sun, 25 Dec 2022 04:16:51 GMT",
"version": "v3"
}
] | 2022-12-27 | [
[
"Sakthivadivel",
"Dalton A R",
""
]
] | We investigate how the activation function can be used to describe neural firing in an abstract way, and in turn, why it works well in artificial neural networks. We discuss how a spike in a biological neurone belongs to a particular universality class of phase transitions in statistical physics. We then show that the artificial neurone is, mathematically, a mean field model of biological neural membrane dynamics, which arises from modelling spiking as a phase transition. This allows us to treat selective neural firing in an abstract way, and formalise the role of the activation function in perceptron learning. The resultant statistical physical model allows us to recover the expressions for some known activation functions as various special cases. Along with deriving this model and specifying the analogous neural case, we analyse the phase transition to understand the physics of neural network learning. Together, it is shown that there is not only a biological meaning, but a physical justification, for the emergence and performance of typical activation functions; implications for neural learning and inference are also discussed. |
q-bio/0403013 | Dmitry Tsigankov | Dmitry N. Tsigankov and Alexei A. Koulakov | Can repulsion be induced by attraction: a role of ephrin-B1 in
retinotectal mapping? | 4 pages | null | null | null | q-bio.NC q-bio.QM | null | We study a role of EphB receptors and their ligand ephrin-B1 in
dorsal-ventral retinotopic mapping. Earlier studies suggested that ephrin-B1
acts as an attractant for EphB expressing axons. We address the results of the
recent experiment in chick tectum (McLaughlin et al., 2003b) in which axons of
retinal ganglion cells were shown to be repelled by high ephrin-B1 density.
Thus it was proposed that ephrin-B1 might act as both attractant and repellent.
We show that the same axonal behavior may follow from attraction to ephrin-B1
density and axonal competition for space. Therefore, we show how apparent
repulsive interaction can be induced by a combination of attraction to the
target and competitive interactions between axons. We suggest an experimental
test that may distinguish repulsive interaction with the target from repulsion
induced by attraction and competition.
| [
{
"created": "Thu, 11 Mar 2004 17:10:04 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Tsigankov",
"Dmitry N.",
""
],
[
"Koulakov",
"Alexei A.",
""
]
] | We study a role of EphB receptors and their ligand ephrin-B1 in dorsal-ventral retinotopic mapping. Earlier studies suggested that ephrin-B1 acts as an attractant for EphB expressing axons. We address the results of the recent experiment in chick tectum (McLaughlin et al., 2003b) in which axons of retinal ganglion cells were shown to be repelled by high ephrin-B1 density. Thus it was proposed that ephrin-B1 might act as both attractant and repellent. We show that the same axonal behavior may follow from attraction to ephrin-B1 density and axonal competition for space. Therefore, we show how apparent repulsive interaction can be induced by a combination of attraction to the target and competitive interactions between axons. We suggest an experimental test that may distinguish repulsive interaction with the target from repulsion induced by attraction and competition. |
0810.1752 | Christopher Frenz | Gregory Martyn, Christopher M. Frenz | ESPSim: A JAVA Application for Calculating Electrostatic Potential Map
Similarity Scores | Published in the Proceedings of the 2008 International Conference on
Bioinformatics and Computational Biology (BIOCOMP 2008). Pages 735-737 | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | ESPSim is an open source JAVA program that enables the comparisons of protein
electrostatic potential maps via the computation of an electrostatic similarity
measure. This program has been utilized to demonstrate a high degree of
electrostatic similarity among the potential maps of lysozyme proteins,
suggesting that protein electrostatic states are conserved within lysozyme
proteins. ESPSim is freely available under the AGPL License from
http://www.bioinformatics.org/project/?group_id=830
| [
{
"created": "Thu, 9 Oct 2008 20:40:59 GMT",
"version": "v1"
}
] | 2008-10-13 | [
[
"Martyn",
"Gregory",
""
],
[
"Frenz",
"Christopher M.",
""
]
] | ESPSim is an open source JAVA program that enables the comparisons of protein electrostatic potential maps via the computation of an electrostatic similarity measure. This program has been utilized to demonstrate a high degree of electrostatic similarity among the potential maps of lysozyme proteins, suggesting that protein electrostatic states are conserved within lysozyme proteins. ESPSim is freely available under the AGPL License from http://www.bioinformatics.org/project/?group_id=830 |
2006.16955 | Stanislaw Jastrzebski | Tobiasz Cieplinski, Tomasz Danel, Sabina Podlewska, Stanislaw
Jastrzebski | We Should at Least Be Able to Design Molecules That Dock Well | Published in Journal of Chemical Information and Modeling | null | 10.1021/acs.jcim.2c01355 | null | q-bio.BM cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Designing compounds with desired properties is a key element of the drug
discovery process. However, measuring progress in the field has been
challenging due to the lack of realistic retrospective benchmarks, and the
large cost of prospective validation. To close this gap, we propose a benchmark
based on docking, a popular computational method for assessing molecule binding
to a protein. Concretely, the goal is to generate drug-like molecules that are
scored highly by SMINA, a popular docking software. We observe that popular
graph-based generative models fail to generate molecules with a high docking
score when trained using a realistically sized training set. This suggests a
limitation of the current incarnation of models for de novo drug design.
Finally, we propose a simplified version of the benchmark based on a simpler
scoring function, and show that the tested models are able to partially solve
it. We release the benchmark as an easy to use package available at
https://github.com/cieplinski-tobiasz/smina-docking-benchmark. We hope that our
benchmark will serve as a stepping stone towards the goal of automatically
generating promising drug candidates.
| [
{
"created": "Sat, 20 Jun 2020 16:40:56 GMT",
"version": "v1"
},
{
"created": "Wed, 1 Jul 2020 00:30:07 GMT",
"version": "v2"
},
{
"created": "Mon, 28 Dec 2020 08:10:50 GMT",
"version": "v3"
},
{
"created": "Mon, 28 Jun 2021 08:21:45 GMT",
"version": "v4"
},
{
"cr... | 2023-06-16 | [
[
"Cieplinski",
"Tobiasz",
""
],
[
"Danel",
"Tomasz",
""
],
[
"Podlewska",
"Sabina",
""
],
[
"Jastrzebski",
"Stanislaw",
""
]
] | Designing compounds with desired properties is a key element of the drug discovery process. However, measuring progress in the field has been challenging due to the lack of realistic retrospective benchmarks, and the large cost of prospective validation. To close this gap, we propose a benchmark based on docking, a popular computational method for assessing molecule binding to a protein. Concretely, the goal is to generate drug-like molecules that are scored highly by SMINA, a popular docking software. We observe that popular graph-based generative models fail to generate molecules with a high docking score when trained using a realistically sized training set. This suggests a limitation of the current incarnation of models for de novo drug design. Finally, we propose a simplified version of the benchmark based on a simpler scoring function, and show that the tested models are able to partially solve it. We release the benchmark as an easy to use package available at https://github.com/cieplinski-tobiasz/smina-docking-benchmark. We hope that our benchmark will serve as a stepping stone towards the goal of automatically generating promising drug candidates. |
1710.05067 | Thierry Mora | Christophe Gardella, Olivier Marre, Thierry Mora | Blindfold learning of an accurate neural metric | null | Proc Natl Acad Sci USA (2018) | 10.1073/pnas.1718710115 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The brain has no direct access to physical stimuli, but only to the spiking
activity evoked in sensory organs. It is unclear how the brain can structure
its representation of the world based on differences between those noisy,
correlated responses alone. Here we show how to build a distance map of
responses from the structure of the population activity of retinal ganglion
cells, allowing for the accurate discrimination of distinct visual stimuli from
the retinal response. We introduce the Temporal Restricted Boltzmann Machine to
learn the spatiotemporal structure of the population activity, and use this
model to define a distance between spike trains. We show that this metric
outperforms existing neural distances at discriminating pairs of stimuli that
are barely distinguishable. The proposed method provides a generic and
biologically plausible way to learn to associate similar stimuli based on their
spiking responses, without any other knowledge of these stimuli.
| [
{
"created": "Fri, 13 Oct 2017 20:08:43 GMT",
"version": "v1"
}
] | 2018-04-16 | [
[
"Gardella",
"Christophe",
""
],
[
"Marre",
"Olivier",
""
],
[
"Mora",
"Thierry",
""
]
] | The brain has no direct access to physical stimuli, but only to the spiking activity evoked in sensory organs. It is unclear how the brain can structure its representation of the world based on differences between those noisy, correlated responses alone. Here we show how to build a distance map of responses from the structure of the population activity of retinal ganglion cells, allowing for the accurate discrimination of distinct visual stimuli from the retinal response. We introduce the Temporal Restricted Boltzmann Machine to learn the spatiotemporal structure of the population activity, and use this model to define a distance between spike trains. We show that this metric outperforms existing neural distances at discriminating pairs of stimuli that are barely distinguishable. The proposed method provides a generic and biologically plausible way to learn to associate similar stimuli based on their spiking responses, without any other knowledge of these stimuli. |
0910.1418 | Angelo Troina | Mario Coppo (Dipartimento di Informatica, Universit\'a di Torino),
Ferruccio Damiani (Dipartimento di Informatica, Universit\'a di Torino),
Elena Grassi (Molecular Biotechnology Center, Dipartimento di Genetica,
Biologia e Biochimica and Dipartimento di Informatica, Universit\'a di
Torino), Mike Guether (Dipartimento di Biologia Vegetale, Universit\`a di
Torino), Angelo Troina (Dipartimento di Informatica, Universit\'a di Torino) | Modelling an Ammonium Transporter with SCLS | null | EPTCS 6, 2009, pp. 77-92 | 10.4204/EPTCS.6.6 | null | q-bio.QM cs.CE q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Stochastic Calculus of Looping Sequences (SCLS) is a recently proposed
modelling language for the representation and simulation of biological systems
behaviour. It has been designed with the aim of combining the simplicity of
notation of rewrite systems with the advantage of compositionality. It also
allows a rather simple and accurate description of biological membranes and
their interactions with the environment.
In this work we apply SCLS to model a newly discovered ammonium transporter.
This transporter is believed to play a fundamental role for plant mineral
acquisition, which takes place in the arbuscular mycorrhiza, the most
wide-spread plant-fungus symbiosis on earth. Due to its potential application
in agriculture this kind of symbiosis is one of the main focuses of the BioBITs
project.
In our experiments the passage of NH3 / NH4+ from the fungus to the plant has
been dissected in known and hypothetical mechanisms; with the model so far we
have been able to simulate the behaviour of the system under different
conditions. Our simulations confirmed some of the latest experimental results
about the LjAMT2;2 transporter. The initial simulation results of the modelling
of the symbiosis process are promising and indicate new directions for
biological investigations.
| [
{
"created": "Thu, 8 Oct 2009 19:48:20 GMT",
"version": "v1"
},
{
"created": "Fri, 14 May 2010 11:30:46 GMT",
"version": "v2"
}
] | 2015-03-13 | [
[
"Coppo",
"Mario",
"",
"Dipartimento di Informatica, Universitá di Torino"
],
[
"Damiani",
"Ferruccio",
"",
"Dipartimento di Informatica, Universitá di Torino"
],
[
"Grassi",
"Elena",
"",
"Molecular Biotechnology Center, Dipartimento di Genetica,\n Biologia ... | The Stochastic Calculus of Looping Sequences (SCLS) is a recently proposed modelling language for the representation and simulation of biological systems behaviour. It has been designed with the aim of combining the simplicity of notation of rewrite systems with the advantage of compositionality. It also allows a rather simple and accurate description of biological membranes and their interactions with the environment. In this work we apply SCLS to model a newly discovered ammonium transporter. This transporter is believed to play a fundamental role for plant mineral acquisition, which takes place in the arbuscular mycorrhiza, the most wide-spread plant-fungus symbiosis on earth. Due to its potential application in agriculture this kind of symbiosis is one of the main focuses of the BioBITs project. In our experiments the passage of NH3 / NH4+ from the fungus to the plant has been dissected in known and hypothetical mechanisms; with the model so far we have been able to simulate the behaviour of the system under different conditions. Our simulations confirmed some of the latest experimental results about the LjAMT2;2 transporter. The initial simulation results of the modelling of the symbiosis process are promising and indicate new directions for biological investigations. |
1304.0342 | Dixita Limbachiya PhD candidate | Dixita Limbachiya | Synthetic Biology in Leishmaniasis: Design,simulation and validation of
constructed Genetic circuit | This is Master of Science thesis from Sardar Patel university. Part
of the thesis has been published as the following paper: "Mandlik, Vineetha,
Dixita Limbachiya, Sonali Shinde, Milsee Mol, and Shailza Singh. "Synthetic
circuit of inositol phosphorylceramide synthase in Leishmania: a chemical
biology approach." Journal of Chemical Biology (2012): 1-12" in the Journal
of Chemical Biology | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Building circuits and studying their behavior in cells is a major goal of
systems and synthetic biology. Synthetic biology enables the precise control of
cellular states for systems studies, the discovery of novel parts, control
strategies, and interactions for the design of robust synthetic systems. To the
best of our knowledge,there are no literature reports for the synthetic circuit
construction for protozoan parasites. This paper describes the construction of
genetic circuit for the targeted enzyme inositol phosphorylceramide synthase
belonging to the protozoan parasite Leishmania. To explore the dynamic nature
of the circuit designed, simulation was done followed by circuit validation by
qualitative and quantitative approaches. The genetic circuit designed for
inositol phosphorylceramide synthase shows responsiveness, oscillatory and
bistable behavior, together with intrinsic robustness.
| [
{
"created": "Mon, 1 Apr 2013 12:32:52 GMT",
"version": "v1"
}
] | 2013-04-02 | [
[
"Limbachiya",
"Dixita",
""
]
] | Building circuits and studying their behavior in cells is a major goal of systems and synthetic biology. Synthetic biology enables the precise control of cellular states for systems studies, the discovery of novel parts, control strategies, and interactions for the design of robust synthetic systems. To the best of our knowledge,there are no literature reports for the synthetic circuit construction for protozoan parasites. This paper describes the construction of genetic circuit for the targeted enzyme inositol phosphorylceramide synthase belonging to the protozoan parasite Leishmania. To explore the dynamic nature of the circuit designed, simulation was done followed by circuit validation by qualitative and quantitative approaches. The genetic circuit designed for inositol phosphorylceramide synthase shows responsiveness, oscillatory and bistable behavior, together with intrinsic robustness. |
0806.4449 | Renaud Jolivet | Renaud Jolivet, Michel Antoniazza, Catherine Strehler-Perrin and
Antoine Gander | Impact of road mitigation measures on amphibian populations: A
stage-class population mathematical model | 18 pages, 3 figures, 2 tables and 4 supplementary figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/3.0/ | It is now well established that amphibians are suffering widespread decline
and extinctions. Among other causes, urbanization is responsible for habitat
reduction, habitat fragmentation and massive road kills. In this context, it is
urgent to develop and assess appropriate conservation measures. Using yearly
censuses of migrating adults of two anuran species at one location in
Switzerland, we examined the impact of a road mitigation measure - permanent
under-road tunnels with guiding trenches - along a road separating wintering
forests from breeding wetlands. We observe that the adult migrating populations
do not exhibit any long-term trend but undergo a transient increase a few years
after the installation of the road mitigation measure. Using additional
datasets like climatic data and censuses obtained in a control area, we show
that the observed pattern of migrating populations cannot be explained by any
other data at our disposal. We then checked as a working hypothesis whether the
installation of under-road tunnels could explain the observed transient or not.
To this end, we use a simple population model and show that the road mitigation
measure together with competition for resources can successfully explain the
experimental observations. We conclude by discussing the requirements for
further assessment of this hypothesis as well as consequences for conservation
planners.
| [
{
"created": "Fri, 27 Jun 2008 08:15:59 GMT",
"version": "v1"
}
] | 2009-09-29 | [
[
"Jolivet",
"Renaud",
""
],
[
"Antoniazza",
"Michel",
""
],
[
"Strehler-Perrin",
"Catherine",
""
],
[
"Gander",
"Antoine",
""
]
] | It is now well established that amphibians are suffering widespread decline and extinctions. Among other causes, urbanization is responsible for habitat reduction, habitat fragmentation and massive road kills. In this context, it is urgent to develop and assess appropriate conservation measures. Using yearly censuses of migrating adults of two anuran species at one location in Switzerland, we examined the impact of a road mitigation measure - permanent under-road tunnels with guiding trenches - along a road separating wintering forests from breeding wetlands. We observe that the adult migrating populations do not exhibit any long-term trend but undergo a transient increase a few years after the installation of the road mitigation measure. Using additional datasets like climatic data and censuses obtained in a control area, we show that the observed pattern of migrating populations cannot be explained by any other data at our disposal. We then checked as a working hypothesis whether the installation of under-road tunnels could explain the observed transient or not. To this end, we use a simple population model and show that the road mitigation measure together with competition for resources can successfully explain the experimental observations. We conclude by discussing the requirements for further assessment of this hypothesis as well as consequences for conservation planners. |
1811.04489 | Milena \v{C}uki\'c Dr | \v{C}uki\'c Milena, Stoki\'c Miodrag, Radenkovi\'c Slavoljub,
Ljubisavljevi\'c Milo\v{s}, Simi\'c Slobodan, Danka Savi\'c | Nonlinear analysis of EEG complexity in episode and remission phase of
recurrent depression | 23 pages, 6 figures | 09 December 2019. IJMPR
https://onlinelibrary.wiley.com/doi/full/10.1002/mpr.1816 | 10.1002/MPR.1816 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biomarkers of Major Depressive Disorder(MDD), its phases and forms have long
been sought. Research indicates that the complexity measures of the cortical
electrical activity (EEG) might be candidates for this role. To examine whether
the complexity of EEG activity, measured by Higuchi fractal dimension (HFD) and
sample entropy (SampEn), differs between healthy subjects, patients in
remission and episode phase of the recurrent depression and whether the changes
are differentially distributed between hemispheres and cortical regions.
Resting state EEG with eyes closed was recorded from 26 patients suffering from
recurrent depression and 20 age and sex-matched healthy control subjects.
Artefact-free EEG epochs were analyzed by in-house developed programs running
HFD and SampEn algorithms. Depressed patients had higher HFD and SampEn
complexity compared to healthy subjects. Surprisingly, the complexity was even
higher in patients who were in remission than in those in the episode. Altered
complexity was present in the frontal and centro-parietal regions when compared
to the control group. The complexity in frontal and parietal regions differed
between the two phases of depressive disorder. SampEn manifested higher
sensitivity than HFD in some cortical areas. Complexity measures of EEG
distinguish between the three groups. Further studies are needed to establish
whether these measures carry the potential to aid clinically relevant decisions
about depression.
| [
{
"created": "Sun, 11 Nov 2018 21:55:23 GMT",
"version": "v1"
}
] | 2019-12-19 | [
[
"Milena",
"Čukić",
""
],
[
"Miodrag",
"Stokić",
""
],
[
"Slavoljub",
"Radenković",
""
],
[
"Miloš",
"Ljubisavljević",
""
],
[
"Slobodan",
"Simić",
""
],
[
"Savić",
"Danka",
""
]
] | Biomarkers of Major Depressive Disorder(MDD), its phases and forms have long been sought. Research indicates that the complexity measures of the cortical electrical activity (EEG) might be candidates for this role. To examine whether the complexity of EEG activity, measured by Higuchi fractal dimension (HFD) and sample entropy (SampEn), differs between healthy subjects, patients in remission and episode phase of the recurrent depression and whether the changes are differentially distributed between hemispheres and cortical regions. Resting state EEG with eyes closed was recorded from 26 patients suffering from recurrent depression and 20 age and sex-matched healthy control subjects. Artefact-free EEG epochs were analyzed by in-house developed programs running HFD and SampEn algorithms. Depressed patients had higher HFD and SampEn complexity compared to healthy subjects. Surprisingly, the complexity was even higher in patients who were in remission than in those in the episode. Altered complexity was present in the frontal and centro-parietal regions when compared to the control group. The complexity in frontal and parietal regions differed between the two phases of depressive disorder. SampEn manifested higher sensitivity than HFD in some cortical areas. Complexity measures of EEG distinguish between the three groups. Further studies are needed to establish whether these measures carry the potential to aid clinically relevant decisions about depression. |
1012.3679 | Jose A Capitan | Jose A. Cuesta, Jacobo Aguirre, Jose A. Capitan and Susanna C.
Manrubia | The struggle for space: Viral extinction through competition for cells | 4 pages, 3 figures Accepted for publication in Physical Review
Letters | Physical Review Letters 106, 028104 (2011) | 10.1103/PhysRevLett.106.028104 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The design of protocols to suppress the propagation of viral infections is an
enduring enterprise, especially hindered by limited knowledge of the mechanisms
through which extinction of infection propagation comes about. We here report
on a mechanism causing extinction of a propagating infection due to
intraspecific competition to infect susceptible hosts. Beneficial mutations
allow the pathogen to increase the production of progeny, while the host cell
is allowed to develop defenses against infection. When the number of
susceptible cells is unlimited, a feedback runaway co-evolution between host
resistance and progeny production occurs. However, physical space limits the
advantage that the virus can obtain from increasing offspring numbers, thus
infection clearance may result from an increase in host defenses beyond a
finite threshold. Our results might be relevant to better understand
propagation of viral infections in tissues with mobility constraints, and the
implications that environments with different geometrical properties might have
in devising control strategies.
| [
{
"created": "Thu, 16 Dec 2010 17:09:42 GMT",
"version": "v1"
}
] | 2015-02-18 | [
[
"Cuesta",
"Jose A.",
""
],
[
"Aguirre",
"Jacobo",
""
],
[
"Capitan",
"Jose A.",
""
],
[
"Manrubia",
"Susanna C.",
""
]
] | The design of protocols to suppress the propagation of viral infections is an enduring enterprise, especially hindered by limited knowledge of the mechanisms through which extinction of infection propagation comes about. We here report on a mechanism causing extinction of a propagating infection due to intraspecific competition to infect susceptible hosts. Beneficial mutations allow the pathogen to increase the production of progeny, while the host cell is allowed to develop defenses against infection. When the number of susceptible cells is unlimited, a feedback runaway co-evolution between host resistance and progeny production occurs. However, physical space limits the advantage that the virus can obtain from increasing offspring numbers, thus infection clearance may result from an increase in host defenses beyond a finite threshold. Our results might be relevant to better understand propagation of viral infections in tissues with mobility constraints, and the implications that environments with different geometrical properties might have in devising control strategies. |
1708.06305 | Adam Noel | Adam Noel, Yuting Fang, Nan Yang, Dimitrios Makrakis, Andrew W.
Eckford | Effect of Local Population Uncertainty on Cooperation in Bacteria | 5 pages, 6 figures. Will be presented as an invited paper at the 2017
IEEE Information Theory Workshop in November 2017 in Kaohsiung, Taiwan | null | 10.1109/ITW.2017.8278046 | null | q-bio.CB physics.bio-ph q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bacteria populations rely on mechanisms such as quorum sensing to coordinate
complex tasks that cannot be achieved by a single bacterium. Quorum sensing is
used to measure the local bacteria population density, and it controls
cooperation by ensuring that a bacterium only commits the resources for
cooperation when it expects its neighbors to reciprocate. This paper proposes a
simple model for sharing a resource in a bacterial environment, where knowledge
of the population influences each bacterium's behavior. Game theory is used to
model the behavioral dynamics, where the net payoff (i.e., utility) for each
bacterium is a function of its current behavior and that of the other bacteria.
The game is first evaluated with perfect knowledge of the population. Then, the
unreliability of diffusion introduces uncertainty in the local population
estimate and changes the perceived payoffs. The results demonstrate the
sensitivity to the system parameters and how population uncertainty can
overcome a lack of explicit coordination.
| [
{
"created": "Mon, 21 Aug 2017 16:05:55 GMT",
"version": "v1"
}
] | 2018-02-14 | [
[
"Noel",
"Adam",
""
],
[
"Fang",
"Yuting",
""
],
[
"Yang",
"Nan",
""
],
[
"Makrakis",
"Dimitrios",
""
],
[
"Eckford",
"Andrew W.",
""
]
] | Bacteria populations rely on mechanisms such as quorum sensing to coordinate complex tasks that cannot be achieved by a single bacterium. Quorum sensing is used to measure the local bacteria population density, and it controls cooperation by ensuring that a bacterium only commits the resources for cooperation when it expects its neighbors to reciprocate. This paper proposes a simple model for sharing a resource in a bacterial environment, where knowledge of the population influences each bacterium's behavior. Game theory is used to model the behavioral dynamics, where the net payoff (i.e., utility) for each bacterium is a function of its current behavior and that of the other bacteria. The game is first evaluated with perfect knowledge of the population. Then, the unreliability of diffusion introduces uncertainty in the local population estimate and changes the perceived payoffs. The results demonstrate the sensitivity to the system parameters and how population uncertainty can overcome a lack of explicit coordination. |
1805.03527 | Chengyi Tu | Chengyi Tu, Samir Suweis, Jacopo Grillib, Marco Formentin, Amos
Maritan | Reconciling cooperation, biodiversity and stability in complex
ecological communities | Incorrectdly posted twice. Current version arXiv:1708.03154 | null | null | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Empirical observations show that ecological communities can have a huge
number of coexisting species, also with few or limited number of resources.
These ecosystems are characterized by multiple type of interactions, in
particular displaying cooperative behaviors. However, standard modeling of
population dynamics based on Lotka-Volterra type of equations predicts that
ecosystem stability should decrease as the number of species in the community
increases and that cooperative systems are less stable than communities with
only competitive and/or exploitative interactions. Here we propose a stochastic
model of population dynamics, which includes exploitative interactions as well
as cooperative interactions induced by cross-feeding. The model is exactly
solved and we obtain results for relevant macro-ecological patterns, such as
species abundance distributions and correlation functions. In the large system
size limit, any number of species can coexist for a very general class of
interaction networks and stability increases as the number of species grows.
For pure mutualistic/commensalistic interactions we determine the topological
properties of the network that guarantee species coexistence. We also show that
the stationary state is globally stable and that inferring species interactions
through species abundance correlation analysis may be misleading. Our
theoretical approach thus show that appropriate models of cooperation naturally
leads to a solution of the long-standing question about complexity-stability
paradox and on how highly biodiverse communities can coexist.
| [
{
"created": "Tue, 8 May 2018 07:50:21 GMT",
"version": "v1"
},
{
"created": "Wed, 16 May 2018 18:08:38 GMT",
"version": "v2"
}
] | 2018-05-18 | [
[
"Tu",
"Chengyi",
""
],
[
"Suweis",
"Samir",
""
],
[
"Grillib",
"Jacopo",
""
],
[
"Formentin",
"Marco",
""
],
[
"Maritan",
"Amos",
""
]
] | Empirical observations show that ecological communities can have a huge number of coexisting species, also with few or limited number of resources. These ecosystems are characterized by multiple type of interactions, in particular displaying cooperative behaviors. However, standard modeling of population dynamics based on Lotka-Volterra type of equations predicts that ecosystem stability should decrease as the number of species in the community increases and that cooperative systems are less stable than communities with only competitive and/or exploitative interactions. Here we propose a stochastic model of population dynamics, which includes exploitative interactions as well as cooperative interactions induced by cross-feeding. The model is exactly solved and we obtain results for relevant macro-ecological patterns, such as species abundance distributions and correlation functions. In the large system size limit, any number of species can coexist for a very general class of interaction networks and stability increases as the number of species grows. For pure mutualistic/commensalistic interactions we determine the topological properties of the network that guarantee species coexistence. We also show that the stationary state is globally stable and that inferring species interactions through species abundance correlation analysis may be misleading. Our theoretical approach thus show that appropriate models of cooperation naturally leads to a solution of the long-standing question about complexity-stability paradox and on how highly biodiverse communities can coexist. |
2202.03132 | Hirokuni Miyamoto | Hirokuni Miyamoto, Katsumi Shigeta, Wataru Suda, Yasunori Ichihashi,
Naoto Nihei, Makiko Matsuura, Arisa Tsuboi, Naoki Tominaga, Masahiko Aono,
Muneo Sato, Shunya Taguchi, Teruno Nakaguma, Naoko Tsuji, Chitose Ishii,
Teruo Matsushita, Chie Shindo, Toshiaki Ito, Tamotsu Kato, Hiroshi Ohno,
Atsushi Kurotani, Hideaki Shima, Shigeharu Moriya, Sankichi Horiuchi, Takashi
Satoh, Kenichi Mori, Takumi Nishiuchi, Hisashi Miyamoto, Masahira Hattori,
Hiroaki Kodama, Jun Kikuchi, Yumi Hirai | Agricultural quality matrix-based multiomics structural analysis of
carrots in soils fertilized with thermophile-fermented compost | 6 figures, 1 Table, and support information | null | 10.1038/s43705-023-00233-9 | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Compost is used worldwide as a soil conditioner for crops, but its functions
have still been explored. Here, the omics profiles of carrots were
investigated, as a root vegetable plant model, in a field amended with compost
fermented with thermophilic Bacillaceae for growth and quality indices.
Exposure to compost significantly increased the productivity, antioxidant
activity, red color, and taste of the carrot root and altered the soil
bacterial composition with the levels of characteristic metabolites of the
leaf, root, and soil. Based on the data, structural equation modeling (SEM)
estimated that L-2-aminoadipate, phenylalanine, flavonoids and / or carotenoids
in plants were optimally linked by exposure to compost. The SEM of the soil
estimated that the genus Paenibacillus, L-2-aminoadipate and nicotinamide, and
S-methyl L-cysteine were optimally involved during exposure. These estimates
did not show a contradiction between the whole genomic analysis of
compost-derived Paenibacillus isolates and the bioactivity data, inferring the
presence of a complex cascade of plant growth-promoting effects and modulation
of the nitrogen cycle by compost itself. These observations have provided
information on the qualitative indicators of compost in complex soil-plant
interactions and offer a new perspective for chemically independent sustainable
agriculture through the efficient use of natural nitrogen.
| [
{
"created": "Mon, 7 Feb 2022 13:18:25 GMT",
"version": "v1"
},
{
"created": "Sun, 27 Feb 2022 03:07:20 GMT",
"version": "v2"
},
{
"created": "Mon, 14 Mar 2022 01:55:29 GMT",
"version": "v3"
},
{
"created": "Sat, 15 Oct 2022 22:51:31 GMT",
"version": "v4"
},
{
"cr... | 2023-04-24 | [
[
"Miyamoto",
"Hirokuni",
""
],
[
"Shigeta",
"Katsumi",
""
],
[
"Suda",
"Wataru",
""
],
[
"Ichihashi",
"Yasunori",
""
],
[
"Nihei",
"Naoto",
""
],
[
"Matsuura",
"Makiko",
""
],
[
"Tsuboi",
"Arisa",
""
],
... | Compost is used worldwide as a soil conditioner for crops, but its functions have still been explored. Here, the omics profiles of carrots were investigated, as a root vegetable plant model, in a field amended with compost fermented with thermophilic Bacillaceae for growth and quality indices. Exposure to compost significantly increased the productivity, antioxidant activity, red color, and taste of the carrot root and altered the soil bacterial composition with the levels of characteristic metabolites of the leaf, root, and soil. Based on the data, structural equation modeling (SEM) estimated that L-2-aminoadipate, phenylalanine, flavonoids and / or carotenoids in plants were optimally linked by exposure to compost. The SEM of the soil estimated that the genus Paenibacillus, L-2-aminoadipate and nicotinamide, and S-methyl L-cysteine were optimally involved during exposure. These estimates did not show a contradiction between the whole genomic analysis of compost-derived Paenibacillus isolates and the bioactivity data, inferring the presence of a complex cascade of plant growth-promoting effects and modulation of the nitrogen cycle by compost itself. These observations have provided information on the qualitative indicators of compost in complex soil-plant interactions and offer a new perspective for chemically independent sustainable agriculture through the efficient use of natural nitrogen. |
2312.04607 | Lea Schuh | Lea Schuh, Peter V. Markov, Vladimir M. Veliov, Nikolaos I.
Stilianakis | A mathematical model for the within-host (re)infection dynamics of
SARS-CoV-2 | 18 pages, 6 figures | null | null | null | q-bio.QM physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | Interactions between SARS-CoV-2 and the immune system during infection are
complex. However, understanding the within-host SARS-CoV-2 dynamics is of
enormous importance, especially when it comes to assessing treatment options.
Mathematical models have been developed to describe the within-host SARS-CoV-2
dynamics and to dissect the mechanisms underlying COVID-19 pathogenesis.
Current mathematical models focus on the acute infection phase, thereby
ignoring important post-acute infection effects. We present a mathematical
model, which not only describes the SARS-CoV-2 infection dynamics during the
acute infection phase, but also reflects the recovery of the number of
susceptible epithelial cells to an initial pre-infection homeostatic level,
shows clearance of the infection within the individual, immune waning, and the
formation of long-term immune response levels after infection. Moreover, the
model accommodates reinfection events assuming a new virus variant with either
increased infectivity or immune escape. Together, the model provides an
improved reflection of the SARS-CoV-2 infection dynamics within humans,
particularly important when using mathematical models to develop or optimize
treatment options.
| [
{
"created": "Thu, 7 Dec 2023 12:18:58 GMT",
"version": "v1"
}
] | 2023-12-11 | [
[
"Schuh",
"Lea",
""
],
[
"Markov",
"Peter V.",
""
],
[
"Veliov",
"Vladimir M.",
""
],
[
"Stilianakis",
"Nikolaos I.",
""
]
] | Interactions between SARS-CoV-2 and the immune system during infection are complex. However, understanding the within-host SARS-CoV-2 dynamics is of enormous importance, especially when it comes to assessing treatment options. Mathematical models have been developed to describe the within-host SARS-CoV-2 dynamics and to dissect the mechanisms underlying COVID-19 pathogenesis. Current mathematical models focus on the acute infection phase, thereby ignoring important post-acute infection effects. We present a mathematical model, which not only describes the SARS-CoV-2 infection dynamics during the acute infection phase, but also reflects the recovery of the number of susceptible epithelial cells to an initial pre-infection homeostatic level, shows clearance of the infection within the individual, immune waning, and the formation of long-term immune response levels after infection. Moreover, the model accommodates reinfection events assuming a new virus variant with either increased infectivity or immune escape. Together, the model provides an improved reflection of the SARS-CoV-2 infection dynamics within humans, particularly important when using mathematical models to develop or optimize treatment options. |
1807.05740 | Ivan Lazarevich | Ivan Lazarevich and Sergey Stasenko and Maia Rozhnova and Evgeniya
Pankratova and Alexander Dityatev and Victor Kazantsev | Dynamics of the brain extracellular matrix governed by interactions with
neural cells | null | null | null | null | q-bio.NC q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neuronal and glial cells release diverse proteoglycans and glycoproteins,
which aggregate in the extracellular space and form the extracellular matrix
(ECM) that may in turn regulate major cellular functions. Brain cells also
release extracellular proteases that may degrade the ECM, and both synthesis
and degradation of ECM are activity-dependent. In this study we introduce a
mathematical model describing population dynamics of neurons interacting with
ECM molecules over extended timescales. It is demonstrated that depending on
the prevalent biophysical mechanism of ECM-neuronal interactions, different
dynamical regimes of ECM activity can be observed, including bistable states
with stable stationary levels of ECM molecule concentration, spontaneous ECM
oscillations, and coexistence of ECM oscillations and a stationary state,
allowing dynamical switches between activity regimes.
| [
{
"created": "Mon, 16 Jul 2018 09:06:42 GMT",
"version": "v1"
}
] | 2018-07-18 | [
[
"Lazarevich",
"Ivan",
""
],
[
"Stasenko",
"Sergey",
""
],
[
"Rozhnova",
"Maia",
""
],
[
"Pankratova",
"Evgeniya",
""
],
[
"Dityatev",
"Alexander",
""
],
[
"Kazantsev",
"Victor",
""
]
] | Neuronal and glial cells release diverse proteoglycans and glycoproteins, which aggregate in the extracellular space and form the extracellular matrix (ECM) that may in turn regulate major cellular functions. Brain cells also release extracellular proteases that may degrade the ECM, and both synthesis and degradation of ECM are activity-dependent. In this study we introduce a mathematical model describing population dynamics of neurons interacting with ECM molecules over extended timescales. It is demonstrated that depending on the prevalent biophysical mechanism of ECM-neuronal interactions, different dynamical regimes of ECM activity can be observed, including bistable states with stable stationary levels of ECM molecule concentration, spontaneous ECM oscillations, and coexistence of ECM oscillations and a stationary state, allowing dynamical switches between activity regimes. |
2202.03953 | Rebecca Walters Ms | Rebecca K. Walters (1), Ella M. Gale (1), Jonathan Barnoud (1), David
R. Glowacki (2) and Adrian J. Mulholland (1) | Interactivity: the missing link between virtual reality technology and
drug discovery pipelines | 19 pages, 3 figures | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | The potential of virtual reality (VR) to contribute to drug design and
development has been recognised for many years. Hardware and software
developments now mean that this potential is beginning to be realised, and VR
methods are being actively used in this sphere. A recent advance is to use VR
not only to visualise and interact with molecular structures, but also to
interact with molecular dynamics simulations of 'on the fly' (interactive
molecular dynamics in VR, IMD-VR), which is useful not only for flexible
docking but also to examine binding processes and conformational changes.
iMD-VR has been shown to be useful for creating complexes of ligands bound to
target proteins, e.g., recently applied to peptide inhibitors of the SARS-CoV-2
main protease. In this review, we use the term 'interactive VR' to refer to
software where interactivity is an inherent part of the user VR experience
e.g., in making structural modifications or interacting with a physically
rigorous molecular dynamics (MD) simulation, as opposed to simply using VR
controllers to rotate and translate the molecule for enhanced visualisation.
Here, we describe these methods and their application to problems relevant to
drug discovery, highlighting the possibilities that they offer in this arena.
We suggest that the ease of viewing and manipulating molecular structures and
dynamics, and the ability to modify structures on the fly (e.g., adding or
deleting atoms) makes modern interactive VR a valuable tool to add to the
armoury of drug development methods.
| [
{
"created": "Tue, 8 Feb 2022 16:03:32 GMT",
"version": "v1"
}
] | 2022-02-09 | [
[
"Walters",
"Rebecca K.",
""
],
[
"Gale",
"Ella M.",
""
],
[
"Barnoud",
"Jonathan",
""
],
[
"Glowacki",
"David R.",
""
],
[
"Mulholland",
"Adrian J.",
""
]
] | The potential of virtual reality (VR) to contribute to drug design and development has been recognised for many years. Hardware and software developments now mean that this potential is beginning to be realised, and VR methods are being actively used in this sphere. A recent advance is to use VR not only to visualise and interact with molecular structures, but also to interact with molecular dynamics simulations of 'on the fly' (interactive molecular dynamics in VR, IMD-VR), which is useful not only for flexible docking but also to examine binding processes and conformational changes. iMD-VR has been shown to be useful for creating complexes of ligands bound to target proteins, e.g., recently applied to peptide inhibitors of the SARS-CoV-2 main protease. In this review, we use the term 'interactive VR' to refer to software where interactivity is an inherent part of the user VR experience e.g., in making structural modifications or interacting with a physically rigorous molecular dynamics (MD) simulation, as opposed to simply using VR controllers to rotate and translate the molecule for enhanced visualisation. Here, we describe these methods and their application to problems relevant to drug discovery, highlighting the possibilities that they offer in this arena. We suggest that the ease of viewing and manipulating molecular structures and dynamics, and the ability to modify structures on the fly (e.g., adding or deleting atoms) makes modern interactive VR a valuable tool to add to the armoury of drug development methods. |
1412.7519 | Helene Montanie | H\'el\`ene Montani\'e (LIENSs), Pascaline Ory (LIENSs), Francis
Orvain, Daniel Delmas, Christine Dupuy (LIENSs) | Microbial interactions in marine water amended by eroded benthic
biofilm: A case study from an intertidal mudflat | null | Journal of Sea Research, Elsevier, 2014, 92, pp.74 - 85 | 10.1016/j.seares.2013.11.011 | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In shallow macrotidal ecosystems with large intertidal mudflats, the
sediment-water coupling plays a crucial role in structuring the pelagic
microbial food web functioning, since inorganic and organic matter and
microbial components (viruses and microbes) of the microphytobenthic biofilm
can be suspended toward the water column. Two experimental bioassays were
conducted in March and July 2008 to investigate the importance of biofilm input
for the pelagic Microbial and Viral Loops. Pelagic inocula (<0.6$\mu$ and
<10$\mu$ filtrates) were diluted either with \textless{}30kDa-ultrafiltered
seawater or with this ultrafiltrate enriched with the respective
size-fractionated benthic biofilm or with \textless{}30kDa-benthic compounds
(BC). The kinetics of heterotrophic nanoflagellates (HNF), bacteria and viruses
were assessed together with bacterial and viral genomic fingerprints, bacterial
enzymatic activities and viral life strategies. The experimental design allowed
us to evaluate the effect of BC modulated by those of benthic size-fractionated
microorganisms (virus+bacteria, +HNF). BC presented (1) in March, a positive
effect on viruses and bacteria weakened by pelagic HNF. Benthic microorganisms
consolidated this negative effect and sustained the viral production together
with a relatively diverse and uneven bacterial assemblage structure; (2) in
July, no direct impact on viruses but a positive effect on bacteria modulated
by HNF, which indirectly enhanced viral multiplication. Both effects were
intensified by benthic microorganisms and bacterial assemblage structure became
more even. HNF indirectly profited from BC more in March than in July. The
Microbial Loop would be stimulated by biofilm during periods of high resources
(March) and the Viral Loop during periods of depleted resources (July).
| [
{
"created": "Tue, 23 Dec 2014 20:50:46 GMT",
"version": "v1"
}
] | 2014-12-24 | [
[
"Montanié",
"Hélène",
"",
"LIENSs"
],
[
"Ory",
"Pascaline",
"",
"LIENSs"
],
[
"Orvain",
"Francis",
"",
"LIENSs"
],
[
"Delmas",
"Daniel",
"",
"LIENSs"
],
[
"Dupuy",
"Christine",
"",
"LIENSs"
]
] | In shallow macrotidal ecosystems with large intertidal mudflats, the sediment-water coupling plays a crucial role in structuring the pelagic microbial food web functioning, since inorganic and organic matter and microbial components (viruses and microbes) of the microphytobenthic biofilm can be suspended toward the water column. Two experimental bioassays were conducted in March and July 2008 to investigate the importance of biofilm input for the pelagic Microbial and Viral Loops. Pelagic inocula (<0.6$\mu$ and <10$\mu$ filtrates) were diluted either with \textless{}30kDa-ultrafiltered seawater or with this ultrafiltrate enriched with the respective size-fractionated benthic biofilm or with \textless{}30kDa-benthic compounds (BC). The kinetics of heterotrophic nanoflagellates (HNF), bacteria and viruses were assessed together with bacterial and viral genomic fingerprints, bacterial enzymatic activities and viral life strategies. The experimental design allowed us to evaluate the effect of BC modulated by those of benthic size-fractionated microorganisms (virus+bacteria, +HNF). BC presented (1) in March, a positive effect on viruses and bacteria weakened by pelagic HNF. Benthic microorganisms consolidated this negative effect and sustained the viral production together with a relatively diverse and uneven bacterial assemblage structure; (2) in July, no direct impact on viruses but a positive effect on bacteria modulated by HNF, which indirectly enhanced viral multiplication. Both effects were intensified by benthic microorganisms and bacterial assemblage structure became more even. HNF indirectly profited from BC more in March than in July. The Microbial Loop would be stimulated by biofilm during periods of high resources (March) and the Viral Loop during periods of depleted resources (July). |
1506.06138 | Simon DeDeo | Sarah E. Marzen, Simon DeDeo | The evolution of lossy compression | 14 pages, 4 figures | Journal of the Royal Society Interface 14: 20170166 (2017) | 10.1098/rsif.2017.0166 | null | q-bio.NC cs.IT math.IT nlin.AO physics.soc-ph q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In complex environments, there are costs to both ignorance and perception. An
organism needs to track fitness-relevant information about its world, but the
more information it tracks, the more resources it must devote to memory and
processing. Rate-distortion theory shows that, when errors are allowed,
remarkably efficient internal representations can be found by
biologically-plausible hill-climbing mechanisms. We identify two regimes: a
high-fidelity regime where perceptual costs scale logarithmically with
environmental complexity, and a low-fidelity regime where perceptual costs are,
remarkably, independent of the environment. When environmental complexity is
rising, Darwinian evolution should drive organisms to the threshold between the
high- and low-fidelity regimes. Organisms that code efficiently will find
themselves able to make, just barely, the most subtle distinctions in their
environment.
| [
{
"created": "Fri, 19 Jun 2015 20:00:31 GMT",
"version": "v1"
}
] | 2018-10-17 | [
[
"Marzen",
"Sarah E.",
""
],
[
"DeDeo",
"Simon",
""
]
] | In complex environments, there are costs to both ignorance and perception. An organism needs to track fitness-relevant information about its world, but the more information it tracks, the more resources it must devote to memory and processing. Rate-distortion theory shows that, when errors are allowed, remarkably efficient internal representations can be found by biologically-plausible hill-climbing mechanisms. We identify two regimes: a high-fidelity regime where perceptual costs scale logarithmically with environmental complexity, and a low-fidelity regime where perceptual costs are, remarkably, independent of the environment. When environmental complexity is rising, Darwinian evolution should drive organisms to the threshold between the high- and low-fidelity regimes. Organisms that code efficiently will find themselves able to make, just barely, the most subtle distinctions in their environment. |
2004.00261 | Seung Ki Baek | Yohsuke Murase and Seung Ki Baek | Five rules for friendly rivalry in direct reciprocity | 21 pages, 8 figures | Sci. Rep. 10, 16904 (2020) | 10.1038/s41598-020-73855-x | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Direct reciprocity is one of the key mechanisms accounting for cooperation in
our social life. According to recent understanding, most of classical
strategies for direct reciprocity fall into one of two classes, `partners' or
`rivals'. A `partner' is a generous strategy achieving mutual cooperation, and
a `rival' never lets the co-player become better off. They have different
working conditions: For example, partners show good performance in a large
population, whereas rivals do in head-to-head matches. By means of exhaustive
enumeration, we demonstrate the existence of strategies that act as both
partners and rivals. Among them, we focus on a human-interpretable strategy,
named `CAPRI' after its five characteristic ingredients, i.e., cooperate,
accept, punish, recover, and defect otherwise. Our evolutionary simulation
shows excellent performance of CAPRI in a broad range of environmental
conditions.
| [
{
"created": "Wed, 1 Apr 2020 07:22:04 GMT",
"version": "v1"
},
{
"created": "Fri, 9 Oct 2020 14:03:39 GMT",
"version": "v2"
}
] | 2020-10-12 | [
[
"Murase",
"Yohsuke",
""
],
[
"Baek",
"Seung Ki",
""
]
] | Direct reciprocity is one of the key mechanisms accounting for cooperation in our social life. According to recent understanding, most of classical strategies for direct reciprocity fall into one of two classes, `partners' or `rivals'. A `partner' is a generous strategy achieving mutual cooperation, and a `rival' never lets the co-player become better off. They have different working conditions: For example, partners show good performance in a large population, whereas rivals do in head-to-head matches. By means of exhaustive enumeration, we demonstrate the existence of strategies that act as both partners and rivals. Among them, we focus on a human-interpretable strategy, named `CAPRI' after its five characteristic ingredients, i.e., cooperate, accept, punish, recover, and defect otherwise. Our evolutionary simulation shows excellent performance of CAPRI in a broad range of environmental conditions. |
1708.08837 | Jacopo Grilli | Theo Gibbs, Jacopo Grilli, Tim Rogers, Stefano Allesina | The effect of population abundances on the stability of large random
ecosystems | 23 pages, 12 figures | Phys. Rev. E 98, 022410 (2018) | 10.1103/PhysRevE.98.022410 | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Random matrix theory successfully connects the structure of interactions of
large ecological communities to their ability to respond to perturbations. One
of the most debated aspects of this approach is the missing role of population
abundances. Despite being one of the most studied patterns in ecology, and one
of the most empirically accessible quantities, population abundances are always
neglected in random matrix approaches and their role in determining stability
is still not understood. Here, we tackle this question by explicitly including
population abundances in a random matrix framework. We obtain an analytical
formula that describes the spectrum of a large community matrix for arbitrary
feasible species abundance distributions. The emerging picture is remarkably
simple: while population abundances affect the rate to return to equilibrium
after a perturbation, the stability of large ecosystems is uniquely determined
by the interaction matrix. We confirm this result by showing that the
likelihood of having a feasible and unstable solution in the Lotka-Volterra
system of equations decreases exponentially with the number of species for
stable interaction matrices.
| [
{
"created": "Tue, 29 Aug 2017 15:52:53 GMT",
"version": "v1"
}
] | 2018-09-12 | [
[
"Gibbs",
"Theo",
""
],
[
"Grilli",
"Jacopo",
""
],
[
"Rogers",
"Tim",
""
],
[
"Allesina",
"Stefano",
""
]
] | Random matrix theory successfully connects the structure of interactions of large ecological communities to their ability to respond to perturbations. One of the most debated aspects of this approach is the missing role of population abundances. Despite being one of the most studied patterns in ecology, and one of the most empirically accessible quantities, population abundances are always neglected in random matrix approaches and their role in determining stability is still not understood. Here, we tackle this question by explicitly including population abundances in a random matrix framework. We obtain an analytical formula that describes the spectrum of a large community matrix for arbitrary feasible species abundance distributions. The emerging picture is remarkably simple: while population abundances affect the rate to return to equilibrium after a perturbation, the stability of large ecosystems is uniquely determined by the interaction matrix. We confirm this result by showing that the likelihood of having a feasible and unstable solution in the Lotka-Volterra system of equations decreases exponentially with the number of species for stable interaction matrices. |
2303.06047 | Xin Li | Xin Li, Bin Liu, and Shuo Wang | Toward NeuroDM: Where Computational Neuroscience Meets Data Mining | null | null | null | null | q-bio.NC cs.NE | http://creativecommons.org/publicdomain/zero/1.0/ | At the intersection of computational neuroscience (CN) and data mining (DM),
we advocate a holistic view toward their rich connections. On the one hand,
fundamental concepts in neuroscience such as saliency, memory, and emotion can
find novel applications in data mining. On the other hand, multimodal imaging
has opened the door for data mining to facilitate the extraction of important
cognitive and behavioral information from multimodal neural data. By NeuroDM,
we advocate for more collaboration between CN and DM to expedite the advances
in two well-established fields. The analogy between the over-parameterization
of biological and artificial neural networks might suggest a unifying
perspective of advancing both fields.
| [
{
"created": "Tue, 7 Mar 2023 19:48:13 GMT",
"version": "v1"
}
] | 2023-03-13 | [
[
"Li",
"Xin",
""
],
[
"Liu",
"Bin",
""
],
[
"Wang",
"Shuo",
""
]
] | At the intersection of computational neuroscience (CN) and data mining (DM), we advocate a holistic view toward their rich connections. On the one hand, fundamental concepts in neuroscience such as saliency, memory, and emotion can find novel applications in data mining. On the other hand, multimodal imaging has opened the door for data mining to facilitate the extraction of important cognitive and behavioral information from multimodal neural data. By NeuroDM, we advocate for more collaboration between CN and DM to expedite the advances in two well-established fields. The analogy between the over-parameterization of biological and artificial neural networks might suggest a unifying perspective of advancing both fields. |
1501.01455 | Institut Pasteur Tunis | Oussema Souiai (TAGCTAGC), Fatma Guerfali, Slimane Ben Miled,
Christine Brun (TAGCTAGC), Alia Benkahla | In silico prediction of protein-protein interactions in human
macrophages | null | BMC Research Notes, BioMed Central, 2014, 7, pp.157 | 10.1073/pnas.091062498 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Protein-protein interaction (PPI) network analyses are highly
valuable in deciphering and understanding the intricate organisation of
cellular functions. Nevertheless, the majority of available protein-protein
interaction networks are context-less, i.e. without any reference to the
spatial, temporal or physiological conditions in which the interactions may
occur. In this work, we are proposing a protocol to infer the most likely
protein-protein interaction (PPI) network in human macrophages. Results: We
integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer
(APID) with different meta-data to infer a contextualized macrophage-specific
interactome using a combination of statistical methods. The obtained
interactome is enriched in experimentally verified interactions and in proteins
involved in macrophage-related biological processes (i.e. immune response
activation, regulation of apoptosis). As a case study, we used the
contextualized interactome to highlight the cellular processes induced upon
Mycobacterium tuberculosis infection. Conclusion: Our work confirms that
contextualizing interactomes improves the biological significance of
bioinformatic analyses. More specifically, studying such inferred network
rather than focusing at the gene expression level only, is informative on the
processes involved in the host response. Indeed, important immune features such
as apoptosis are solely highlighted when the spotlight is on the protein
interaction level.
| [
{
"created": "Wed, 7 Jan 2015 12:06:51 GMT",
"version": "v1"
}
] | 2015-01-08 | [
[
"Souiai",
"Oussema",
"",
"TAGCTAGC"
],
[
"Guerfali",
"Fatma",
"",
"TAGCTAGC"
],
[
"Miled",
"Slimane Ben",
"",
"TAGCTAGC"
],
[
"Brun",
"Christine",
"",
"TAGCTAGC"
],
[
"Benkahla",
"Alia",
""
]
] | Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level. |
1709.02963 | Leo Bronstein | Leo Bronstein, Heinz Koeppl | A variational approach to moment-closure approximations for the kinetics
of biomolecular reaction networks | Minor changes and clarifications; corrected some typos | The Journal of Chemical Physics 148, 014105 (2018) | 10.1063/1.5003892 | null | q-bio.QM physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Approximate solutions of the chemical master equation and the chemical
Fokker-Planck equation are an important tool in the analysis of biomolecular
reaction networks. Previous studies have highlighted a number of problems with
the moment-closure approach used to obtain such approximations, calling it an
ad-hoc method. In this article, we give a new variational derivation of
moment-closure equations which provides us with an intuitive understanding of
their properties and failure modes and allows us to correct some of these
problems. We use mixtures of product-Poisson distributions to obtain a flexible
parametric family which solves the commonly observed problem of divergences at
low system sizes. We also extend the recently introduced entropic matching
approach to arbitrary ansatz distributions and Markov processes, demonstrating
that it is a special case of variational moment closure. This provides us with
a particularly principled approximation method. Finally, we extend the above
approaches to cover the approximation of multi-time joint distributions,
resulting in a viable alternative to process-level approximations which are
often intractable.
| [
{
"created": "Sat, 9 Sep 2017 15:17:24 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Nov 2017 15:12:25 GMT",
"version": "v2"
}
] | 2018-05-22 | [
[
"Bronstein",
"Leo",
""
],
[
"Koeppl",
"Heinz",
""
]
] | Approximate solutions of the chemical master equation and the chemical Fokker-Planck equation are an important tool in the analysis of biomolecular reaction networks. Previous studies have highlighted a number of problems with the moment-closure approach used to obtain such approximations, calling it an ad-hoc method. In this article, we give a new variational derivation of moment-closure equations which provides us with an intuitive understanding of their properties and failure modes and allows us to correct some of these problems. We use mixtures of product-Poisson distributions to obtain a flexible parametric family which solves the commonly observed problem of divergences at low system sizes. We also extend the recently introduced entropic matching approach to arbitrary ansatz distributions and Markov processes, demonstrating that it is a special case of variational moment closure. This provides us with a particularly principled approximation method. Finally, we extend the above approaches to cover the approximation of multi-time joint distributions, resulting in a viable alternative to process-level approximations which are often intractable. |
1108.4790 | Hiroshi Kori | Hiroshi Kori, Yoji Kawamura, Naoki Masuda | Structure of Cell Networks Critically Determines Oscillation Regularity | null | Journal of Theoretical Biology 297, 61-72 (2012) | 10.1016/j.jtbi.2011.12.007 | null | q-bio.CB nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biological rhythms are generated by pacemaker organs, such as the heart
pacemaker organ (the sinoatrial node) and the master clock of the circadian
rhythms (the suprachiasmatic nucleus), which are composed of a network of
autonomously oscillatory cells. Such biological rhythms have notable
periodicity despite the internal and external noise present in each cell.
Previous experimental studies indicate that the regularity of oscillatory
dynamics is enhanced when noisy oscillators interact and become synchronized.
This effect, called the collective enhancement of temporal precision, has been
studied theoretically using particular assumptions. In this study, we propose a
general theoretical framework that enables us to understand the dependence of
temporal precision on network parameters including size, connectivity, and
coupling intensity; this effect has been poorly understood to date. Our
framework is based on a phase oscillator model that is applicable to general
oscillator networks with any coupling mechanism if coupling and noise are
sufficiently weak. In particular, we can manage general directed and weighted
networks. We quantify the precision of the activity of a single cell and the
mean activity of an arbitrary subset of cells. We find that, in general
undirected networks, the standard deviation of cycle-to-cycle periods scales
with the system size $N$ as $1/\sqrt{N}$, but only up to a certain system size
$N^*$ that depends on network parameters. Enhancement of temporal precision is
ineffective when $N>N^*$. We also reveal the advantage of long-range
interactions among cells to temporal precision.
| [
{
"created": "Wed, 24 Aug 2011 09:02:38 GMT",
"version": "v1"
}
] | 2016-08-23 | [
[
"Kori",
"Hiroshi",
""
],
[
"Kawamura",
"Yoji",
""
],
[
"Masuda",
"Naoki",
""
]
] | Biological rhythms are generated by pacemaker organs, such as the heart pacemaker organ (the sinoatrial node) and the master clock of the circadian rhythms (the suprachiasmatic nucleus), which are composed of a network of autonomously oscillatory cells. Such biological rhythms have notable periodicity despite the internal and external noise present in each cell. Previous experimental studies indicate that the regularity of oscillatory dynamics is enhanced when noisy oscillators interact and become synchronized. This effect, called the collective enhancement of temporal precision, has been studied theoretically using particular assumptions. In this study, we propose a general theoretical framework that enables us to understand the dependence of temporal precision on network parameters including size, connectivity, and coupling intensity; this effect has been poorly understood to date. Our framework is based on a phase oscillator model that is applicable to general oscillator networks with any coupling mechanism if coupling and noise are sufficiently weak. In particular, we can manage general directed and weighted networks. We quantify the precision of the activity of a single cell and the mean activity of an arbitrary subset of cells. We find that, in general undirected networks, the standard deviation of cycle-to-cycle periods scales with the system size $N$ as $1/\sqrt{N}$, but only up to a certain system size $N^*$ that depends on network parameters. Enhancement of temporal precision is ineffective when $N>N^*$. We also reveal the advantage of long-range interactions among cells to temporal precision. |
1704.02780 | Maurizio Mattia | Matteo Biggio, Marco Storace, Maurizio Mattia | Equivalence between synaptic current dynamics and heterogeneous
propagation delays in spiking neuron networks | 14 pages, 5 figures, submitted for publication | null | 10.1371/journal.pcbi.1007404 | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Message passing between components of a distributed physical system is
non-instantaneous and contributes to determine the time scales of the emerging
collective dynamics like an effective inertia. In biological neuron networks
this inertia is due in part to local synaptic filtering of exchanged spikes,
and in part to the distribution of the axonal transmission delays. How
differently these two kinds of inertia affect the network dynamics is an open
issue not yet addressed due to the difficulties in dealing with the
non-Markovian nature of synaptic transmission. Here, we develop a mean-field
dimensional reduction yielding to an effective Markovian dynamics of the
population density of the neuronal membrane potential, valid under the
hypothesis of small fluctuations of the synaptic current. The resulting theory
allows us to prove the formal equivalence between local and distributed
inertia, holding for any synaptic time scale, integrate-and-fire neuron model,
spike emission regimes and for different network states even when the neuron
number is finite.
| [
{
"created": "Mon, 10 Apr 2017 09:37:38 GMT",
"version": "v1"
}
] | 2019-10-15 | [
[
"Biggio",
"Matteo",
""
],
[
"Storace",
"Marco",
""
],
[
"Mattia",
"Maurizio",
""
]
] | Message passing between components of a distributed physical system is non-instantaneous and contributes to determine the time scales of the emerging collective dynamics like an effective inertia. In biological neuron networks this inertia is due in part to local synaptic filtering of exchanged spikes, and in part to the distribution of the axonal transmission delays. How differently these two kinds of inertia affect the network dynamics is an open issue not yet addressed due to the difficulties in dealing with the non-Markovian nature of synaptic transmission. Here, we develop a mean-field dimensional reduction yielding to an effective Markovian dynamics of the population density of the neuronal membrane potential, valid under the hypothesis of small fluctuations of the synaptic current. The resulting theory allows us to prove the formal equivalence between local and distributed inertia, holding for any synaptic time scale, integrate-and-fire neuron model, spike emission regimes and for different network states even when the neuron number is finite. |
1905.02335 | Nan Xu | Nan Xu | Deep phenotyping in C. elegans | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep phenotyping study has become an emerging field to understand the gene
function and the structure of biological networks. For the living animal C.
elegans, recent advances in genome-editing tools, microfluidic devices and
phenotypic analyses allow for a deeper understanding of the
genotype-to-phenotype pathway. In this article, I reviewed the evolution of
deep phenotyping study in cell development, neuron activity, and the behaviors
of intact animals.
| [
{
"created": "Tue, 7 May 2019 02:46:50 GMT",
"version": "v1"
}
] | 2019-05-08 | [
[
"Xu",
"Nan",
""
]
] | Deep phenotyping study has become an emerging field to understand the gene function and the structure of biological networks. For the living animal C. elegans, recent advances in genome-editing tools, microfluidic devices and phenotypic analyses allow for a deeper understanding of the genotype-to-phenotype pathway. In this article, I reviewed the evolution of deep phenotyping study in cell development, neuron activity, and the behaviors of intact animals. |
1502.00726 | Frank Poelwijk | Frank J. Poelwijk, Vinod Krishna, Rama Ranganathan | The context-dependence of mutations: a linkage of formalisms | 6 pages, 3 figures, supplementary information | null | 10.1371/journal.pcbi.1004771 | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Defining the extent of epistasis - the non-independence of the effects of
mutations - is essential for understanding the relationship of genotype,
phenotype, and fitness in biological systems. The applications cover many areas
of biological research, including biochemistry, genomics, protein and systems
engineering, medicine, and evolutionary biology. However, the quantitative
definitions of epistasis vary among fields, and its analysis beyond just
pairwise effects remains obscure in general. Here, we show that different
definitions of epistasis are versions of a single mathematical formalism - the
weighted Walsh-Hadamard transform. We discuss that one of the definitions, the
backgound-averaged epistasis, is the most informative when the goal is to
uncover the general epistatic structure of a biological system, a description
that can be rather different from the local epistatic structure of specific
model systems. Key issues are the choice of effective ensembles for averaging
and to practically contend with the vast combinatorial complexity of mutations.
In this regard, we discuss possible approaches for optimally learning the
epistatic structure of biological systems.
| [
{
"created": "Tue, 3 Feb 2015 03:49:32 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Apr 2015 18:43:39 GMT",
"version": "v2"
}
] | 2016-07-06 | [
[
"Poelwijk",
"Frank J.",
""
],
[
"Krishna",
"Vinod",
""
],
[
"Ranganathan",
"Rama",
""
]
] | Defining the extent of epistasis - the non-independence of the effects of mutations - is essential for understanding the relationship of genotype, phenotype, and fitness in biological systems. The applications cover many areas of biological research, including biochemistry, genomics, protein and systems engineering, medicine, and evolutionary biology. However, the quantitative definitions of epistasis vary among fields, and its analysis beyond just pairwise effects remains obscure in general. Here, we show that different definitions of epistasis are versions of a single mathematical formalism - the weighted Walsh-Hadamard transform. We discuss that one of the definitions, the backgound-averaged epistasis, is the most informative when the goal is to uncover the general epistatic structure of a biological system, a description that can be rather different from the local epistatic structure of specific model systems. Key issues are the choice of effective ensembles for averaging and to practically contend with the vast combinatorial complexity of mutations. In this regard, we discuss possible approaches for optimally learning the epistatic structure of biological systems. |
2001.09400 | Jie Wen | Jie Wen, Feiyan Zeng, Dmitriy Yablonskiy, Alexander Sukstansky, Ying
Liu, Bin Cai, Yong Zhang, Weifu Lv | Fast library-driven approach for implementation of the voxel spread
function technique for correcting magnetic field inhomogeneity artifacts | 14 pages, 5 figures | null | null | null | q-bio.QM eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Purpose: Previously-developed Voxel Spread Function (VSF) method (Yablonskiy,
et al, MRM, 2013;70:1283) provides means to correct artifacts induced by
macroscopic magnetic field inhomogeneities in the images obtained by
multi-Gradient-Recalled-Echo (mGRE) techniques. The goal of this study is to
develop a library-driven approach for fast VSF implementation. Methods: The VSF
approach describes the contribution of the magnetic field inhomogeneity effects
on the mGRE signal decay in terms of the F-function calculated from mGRE phase
and magnitude images. A pre-calculated library accounting for a variety of
background field gradients caused by magnetic field inhomogeneities was used
herein to speed up calculation of the F-function and to generate quantitative
R2* maps from the mGRE data collected from two healthy volunteers. Results: As
compared with direct calculation of the F-function based on a voxel-wise
approach, the new library-driven method substantially reduces computational
time from several hours to few minutes, while, at the same time, providing
similar accuracy of R2* mapping. Conclusion: The new procedure proposed in this
study provides a fast post-processing algorithm that can be incorporated in the
quantitative analysis of mGRE data to account for background field
inhomogeneity artifacts, thus can facilitate the applications of mGRE-based
quantitative techniques in clinical practices.
| [
{
"created": "Sun, 26 Jan 2020 05:09:38 GMT",
"version": "v1"
}
] | 2020-01-28 | [
[
"Wen",
"Jie",
""
],
[
"Zeng",
"Feiyan",
""
],
[
"Yablonskiy",
"Dmitriy",
""
],
[
"Sukstansky",
"Alexander",
""
],
[
"Liu",
"Ying",
""
],
[
"Cai",
"Bin",
""
],
[
"Zhang",
"Yong",
""
],
[
"Lv",
"W... | Purpose: Previously-developed Voxel Spread Function (VSF) method (Yablonskiy, et al, MRM, 2013;70:1283) provides means to correct artifacts induced by macroscopic magnetic field inhomogeneities in the images obtained by multi-Gradient-Recalled-Echo (mGRE) techniques. The goal of this study is to develop a library-driven approach for fast VSF implementation. Methods: The VSF approach describes the contribution of the magnetic field inhomogeneity effects on the mGRE signal decay in terms of the F-function calculated from mGRE phase and magnitude images. A pre-calculated library accounting for a variety of background field gradients caused by magnetic field inhomogeneities was used herein to speed up calculation of the F-function and to generate quantitative R2* maps from the mGRE data collected from two healthy volunteers. Results: As compared with direct calculation of the F-function based on a voxel-wise approach, the new library-driven method substantially reduces computational time from several hours to few minutes, while, at the same time, providing similar accuracy of R2* mapping. Conclusion: The new procedure proposed in this study provides a fast post-processing algorithm that can be incorporated in the quantitative analysis of mGRE data to account for background field inhomogeneity artifacts, thus can facilitate the applications of mGRE-based quantitative techniques in clinical practices. |
1304.2616 | Jonathan Tennant | Jonathan Tennant | Osteology of a Near-Complete Skeleton of Tenontosaurus tilletti
(Dinosauria: Ornithopoda) from the Cloverly Formation, Montana, USA | Also uploaded to Figshare
(http://dx.doi.org/10.6084/m9.figshare.638693); Masters Thesis | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/3.0/ | The character diagnosis of Tenontosaurus tilletti has been revised and
redefined into a more robust and quantifiable state. Significant emphasis is
placed on constructing phylogenetic definition in such a method, as it prevents
occlusion of true character states by alleviating potential individual
interpretational bias. Previous placement within the Iguanodontia is refuted
based on the lack of character affinity with the defining synapomorphies of the
clade. The clade Hypsilophodontidae (=Hypsilophodontia), along with
Iguanodontia, however is deemed to be in critical need of refinement to account
for recent discoveries and re-classifications of certain euornithopods. Several
of the synapomorphies are out-dated and deemed redundant in favour of a more
quantifiable approach. Re- definition of these clades is critical if the
current state of basal euornithopodan relationships is to be resolved.
Phylogenetic studies must be approached from a multidisciplinary perspective;
integration of tectonostratigraphical, ontogenetic, palaeoecological, and
biomechanical data with sets of well-defined primary homologies are essential
in increasing phylogenetic resolution and generating stratigraphically feasible
ancestor-descendant relationships. Material attributed to Tenontosaurus
tilletti is in need of strict re-analysis; the significant quantity of
specimens attributed to this species is potentially the result of poor
stratigraphic constraints and the vast spatiotemporal span occupied. Future
revision of this material is expected to reveal temporal variations on the
species -level inherently linked to environmental evolution, as well as
possibly provide clues to sexual dimorphism in contemporaneous, yet
morphologically distinct tenontosaurs.
| [
{
"created": "Tue, 9 Apr 2013 14:40:33 GMT",
"version": "v1"
}
] | 2013-04-10 | [
[
"Tennant",
"Jonathan",
""
]
] | The character diagnosis of Tenontosaurus tilletti has been revised and redefined into a more robust and quantifiable state. Significant emphasis is placed on constructing phylogenetic definition in such a method, as it prevents occlusion of true character states by alleviating potential individual interpretational bias. Previous placement within the Iguanodontia is refuted based on the lack of character affinity with the defining synapomorphies of the clade. The clade Hypsilophodontidae (=Hypsilophodontia), along with Iguanodontia, however is deemed to be in critical need of refinement to account for recent discoveries and re-classifications of certain euornithopods. Several of the synapomorphies are out-dated and deemed redundant in favour of a more quantifiable approach. Re- definition of these clades is critical if the current state of basal euornithopodan relationships is to be resolved. Phylogenetic studies must be approached from a multidisciplinary perspective; integration of tectonostratigraphical, ontogenetic, palaeoecological, and biomechanical data with sets of well-defined primary homologies are essential in increasing phylogenetic resolution and generating stratigraphically feasible ancestor-descendant relationships. Material attributed to Tenontosaurus tilletti is in need of strict re-analysis; the significant quantity of specimens attributed to this species is potentially the result of poor stratigraphic constraints and the vast spatiotemporal span occupied. Future revision of this material is expected to reveal temporal variations on the species -level inherently linked to environmental evolution, as well as possibly provide clues to sexual dimorphism in contemporaneous, yet morphologically distinct tenontosaurs. |
1512.08339 | Dongmei Shi | Dongmei Shi, Chitin Shih, Yenjen Lin, Chungchuan Lo, and Annshyn
Chiang | Detecting local processing unit in drosophila brain by using network
theory | null | null | null | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Community detection method in network theory was applied to the neuron
network constructed from the image overlapping between neuron pairs to detect
the Local Processing Unit (LPU) automatically in Drosophila brain. 26
communities consistent with the known LPUs, and 13 subdivisions were found.
Besides, 45 tracts were detected and could be discriminated from the LPUs by
analyzing the distribution of participation coefficient P. Furthermore, layer
structures in fan-shaped body (FB) were observed which coincided with the
images shot by the optical devices, and a total of 13 communities were proven
closely related to FB. The method proposed in this work was proven effective to
identify the LPU structure in Drosophila brain irrespectively of any subjective
aspect, and could be applied to the relevant areas extensively.
| [
{
"created": "Mon, 28 Dec 2015 08:00:27 GMT",
"version": "v1"
}
] | 2015-12-29 | [
[
"Shi",
"Dongmei",
""
],
[
"Shih",
"Chitin",
""
],
[
"Lin",
"Yenjen",
""
],
[
"Lo",
"Chungchuan",
""
],
[
"Chiang",
"Annshyn",
""
]
] | Community detection method in network theory was applied to the neuron network constructed from the image overlapping between neuron pairs to detect the Local Processing Unit (LPU) automatically in Drosophila brain. 26 communities consistent with the known LPUs, and 13 subdivisions were found. Besides, 45 tracts were detected and could be discriminated from the LPUs by analyzing the distribution of participation coefficient P. Furthermore, layer structures in fan-shaped body (FB) were observed which coincided with the images shot by the optical devices, and a total of 13 communities were proven closely related to FB. The method proposed in this work was proven effective to identify the LPU structure in Drosophila brain irrespectively of any subjective aspect, and could be applied to the relevant areas extensively. |
2310.10966 | Federica Ferretti | Federica Ferretti and Mehran Kardar | Universal characterization of epitope immunodominance from a multi-scale
model of clonal competition in germinal centers | 10 pages + 3 pages (appendix), 6 figures | null | null | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | We introduce a novel, multi-scale model for affinity maturation, which aims
to capture the intra-clonal, inter-clonal and epitope-specific organization of
the B cell population in a germinal center. We describe the evolution of the B
cell population via a quasispecies dynamics, with species corresponding to
unique B cell receptors (BCRs), where the desired multi-scale structure is
reflected on the mutational connectivity of the accessible BCR space, and on
the statistical properties of its fitness landscape. Within this mathematical
framework, we study the competition among classes of BCRs targeting different
antigen epitopes, and construct an effective \emph{immunogenic space} where
epitope immunodominance relations can be universally characterized. We finally
study how varying the relative composition of a mixture of antigens with
variable and conserved domains allows for a parametric exploration of this
space, and identify general principles for the rational design of two-antigen
cocktails.
| [
{
"created": "Tue, 17 Oct 2023 03:28:05 GMT",
"version": "v1"
},
{
"created": "Wed, 31 Jan 2024 20:12:29 GMT",
"version": "v2"
}
] | 2024-02-02 | [
[
"Ferretti",
"Federica",
""
],
[
"Kardar",
"Mehran",
""
]
] | We introduce a novel, multi-scale model for affinity maturation, which aims to capture the intra-clonal, inter-clonal and epitope-specific organization of the B cell population in a germinal center. We describe the evolution of the B cell population via a quasispecies dynamics, with species corresponding to unique B cell receptors (BCRs), where the desired multi-scale structure is reflected on the mutational connectivity of the accessible BCR space, and on the statistical properties of its fitness landscape. Within this mathematical framework, we study the competition among classes of BCRs targeting different antigen epitopes, and construct an effective \emph{immunogenic space} where epitope immunodominance relations can be universally characterized. We finally study how varying the relative composition of a mixture of antigens with variable and conserved domains allows for a parametric exploration of this space, and identify general principles for the rational design of two-antigen cocktails. |
2009.04935 | Ayan Paul | Ayan Paul, Philipp Englert and Melinda Varga | Socio-economic disparities and COVID-19 in the USA | 10 pages, 5 figures and 1 table | null | 10.1088/2632-072X/ac0fc7 | DESY 20-134, HU-EP-20/20 | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | COVID-19 is not a universal killer. We study the spread of COVID-19 at the
county level for the United States up until the 15$^{th}$ of August, 2020. We
show that the prevalence of the disease and the death rate are correlated with
the local socio-economic conditions often going beyond local population density
distributions, especially in rural areas. We correlate the COVID-19 prevalence
and death rate with data from the US Census Bureau and point out how the
spreading patterns of the disease show asymmetries in urban and rural areas
separately and are preferentially affecting the counties where a large fraction
of the population is non-white. Our findings can be used for more targeted
policy building and deployment of resources for future occurrence of a pandemic
due to SARS-CoV-2. Our methodology, based on interpretable machine learning and
game theory, can be extended to study the spread of other diseases.
| [
{
"created": "Thu, 10 Sep 2020 15:24:45 GMT",
"version": "v1"
},
{
"created": "Wed, 30 Jun 2021 00:01:14 GMT",
"version": "v2"
}
] | 2021-07-01 | [
[
"Paul",
"Ayan",
""
],
[
"Englert",
"Philipp",
""
],
[
"Varga",
"Melinda",
""
]
] | COVID-19 is not a universal killer. We study the spread of COVID-19 at the county level for the United States up until the 15$^{th}$ of August, 2020. We show that the prevalence of the disease and the death rate are correlated with the local socio-economic conditions often going beyond local population density distributions, especially in rural areas. We correlate the COVID-19 prevalence and death rate with data from the US Census Bureau and point out how the spreading patterns of the disease show asymmetries in urban and rural areas separately and are preferentially affecting the counties where a large fraction of the population is non-white. Our findings can be used for more targeted policy building and deployment of resources for future occurrence of a pandemic due to SARS-CoV-2. Our methodology, based on interpretable machine learning and game theory, can be extended to study the spread of other diseases. |
1509.07038 | Ying-Cheng Lai | Le-Zhi Wang, Ri-Qi Su, Zi-Gang Huang, Xiao Wang, Wenxu Wang, Celso
Grebogi, and Ying-Cheng Lai | Control and controllability of nonlinear dynamical networks: a
geometrical approach | 22 pages, 8 figures | null | null | null | q-bio.MN cs.SY nlin.CD physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In spite of the recent interest and advances in linear controllability of
complex networks, controlling nonlinear network dynamics remains to be an
outstanding problem. We develop an experimentally feasible control framework
for nonlinear dynamical networks that exhibit multistability (multiple
coexisting final states or attractors), which are representative of, e.g., gene
regulatory networks (GRNs). The control objective is to apply parameter
perturbation to drive the system from one attractor to another, assuming that
the former is undesired and the latter is desired. To make our framework
practically useful, we consider RESTRICTED parameter perturbation by imposing
the following two constraints: (a) it must be experimentally realizable and (b)
it is applied only temporarily. We introduce the concept of ATTRACTOR NETWORK,
in which the nodes are the distinct attractors of the system, and there is a
directional link from one attractor to another if the system can be driven from
the former to the latter using restricted control perturbation. Introduction of
the attractor network allows us to formulate a controllability framework for
nonlinear dynamical networks: a network is more controllable if the underlying
attractor network is more strongly connected, which can be quantified. We
demonstrate our control framework using examples from various models of
experimental GRNs. A finding is that, due to nonlinearity, noise can
counter-intuitively facilitate control of the network dynamics.
| [
{
"created": "Wed, 23 Sep 2015 15:48:58 GMT",
"version": "v1"
}
] | 2015-09-24 | [
[
"Wang",
"Le-Zhi",
""
],
[
"Su",
"Ri-Qi",
""
],
[
"Huang",
"Zi-Gang",
""
],
[
"Wang",
"Xiao",
""
],
[
"Wang",
"Wenxu",
""
],
[
"Grebogi",
"Celso",
""
],
[
"Lai",
"Ying-Cheng",
""
]
] | In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains to be an outstanding problem. We develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability (multiple coexisting final states or attractors), which are representative of, e.g., gene regulatory networks (GRNs). The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically useful, we consider RESTRICTED parameter perturbation by imposing the following two constraints: (a) it must be experimentally realizable and (b) it is applied only temporarily. We introduce the concept of ATTRACTOR NETWORK, in which the nodes are the distinct attractors of the system, and there is a directional link from one attractor to another if the system can be driven from the former to the latter using restricted control perturbation. Introduction of the attractor network allows us to formulate a controllability framework for nonlinear dynamical networks: a network is more controllable if the underlying attractor network is more strongly connected, which can be quantified. We demonstrate our control framework using examples from various models of experimental GRNs. A finding is that, due to nonlinearity, noise can counter-intuitively facilitate control of the network dynamics. |
1911.09376 | Jitesh Jhawar | Jitesh Jhawar (1) and Vishwesha Guttal (1) ((1) Center for Ecological
Sciences, Indian Institute of Science, Bangalore, India) | Noise-induced Effects in Collective Dynamics and Inferring Local
Interactions from Data | The article has 24 pages containing 5 main figures, 5 supplementary
figures, 3 boxes and 1 table insides one of the box | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In animal groups, individual decisions are best characterised by
probabilistic rules. Furthermore, animals of many species live in small groups.
Probabilistic interactions among small numbers of individuals lead to a so
called intrinsic noise at the group level. Theory predicts that the strength of
intrinsic noise is not a constant but often depends on the collective state of
the group; hence, it is also called a state-dependent noise or a multiplicative
noise. Surprisingly, such noise may produce collective order. However, only a
few empirical studies on collective behaviour have paid attention to such
effects due to the lack of methods that enable us to connect data with theory.
Here, we demonstrate a method to characterise the role of stochasticity
directly from high-resolution time-series data of collective dynamics. We do
this by employing two well-studied individual-based toy models of collective
behaviour. We argue that the group-level noise may encode important information
about the underlying processes at the individual scale. In summary, we describe
a method that enables us to establish connections between empirical data of
animal (or cellular) collectives with the phenomenon of noise-induced states, a
field that is otherwise largely limited to the theoretical literature.
| [
{
"created": "Thu, 21 Nov 2019 10:05:38 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Apr 2020 18:11:26 GMT",
"version": "v2"
}
] | 2020-04-23 | [
[
"Jhawar",
"Jitesh",
""
],
[
"Guttal",
"Vishwesha",
""
]
] | In animal groups, individual decisions are best characterised by probabilistic rules. Furthermore, animals of many species live in small groups. Probabilistic interactions among small numbers of individuals lead to a so called intrinsic noise at the group level. Theory predicts that the strength of intrinsic noise is not a constant but often depends on the collective state of the group; hence, it is also called a state-dependent noise or a multiplicative noise. Surprisingly, such noise may produce collective order. However, only a few empirical studies on collective behaviour have paid attention to such effects due to the lack of methods that enable us to connect data with theory. Here, we demonstrate a method to characterise the role of stochasticity directly from high-resolution time-series data of collective dynamics. We do this by employing two well-studied individual-based toy models of collective behaviour. We argue that the group-level noise may encode important information about the underlying processes at the individual scale. In summary, we describe a method that enables us to establish connections between empirical data of animal (or cellular) collectives with the phenomenon of noise-induced states, a field that is otherwise largely limited to the theoretical literature. |
q-bio/0702026 | Robert Finkel | Robert W. Finkel | Effective Potential Energy Expression for Membrane Transport | 3 pages in pdf format | null | null | null | q-bio.SC q-bio.QM | null | All living cells transport molecules and ions across membranes, often against
concentration gradients. This active transport requires continual energy
expenditure and is clearly a nonequilibrium process for which standard
equilibrium thermodynamics is not rigorously applicable. Here we derive a
nonequilibrium effective potential that evaluates the per particle transport
energy invested by the membrane. A novel method is used whereby a Hamiltonian
function is constructed using particle concentrations as generalized
coordinates. The associated generalized momenta are simply related to the
individual particle energy from which we identify the effective potential.
Examples are given and the formalism is compared with the equilibrium Gibb's
free energy.
| [
{
"created": "Sun, 11 Feb 2007 21:12:08 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Finkel",
"Robert W.",
""
]
] | All living cells transport molecules and ions across membranes, often against concentration gradients. This active transport requires continual energy expenditure and is clearly a nonequilibrium process for which standard equilibrium thermodynamics is not rigorously applicable. Here we derive a nonequilibrium effective potential that evaluates the per particle transport energy invested by the membrane. A novel method is used whereby a Hamiltonian function is constructed using particle concentrations as generalized coordinates. The associated generalized momenta are simply related to the individual particle energy from which we identify the effective potential. Examples are given and the formalism is compared with the equilibrium Gibb's free energy. |
1409.0528 | R.K. Brojen Singh | Gurumayum Reenaroy Devi, Md. Jahoor Alam and R.K. Brojen Singh | Nitric Oxide as stress inducer and synchronizer of p53 dynamics | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study how the temporal behaviours of p53 and MDM2 are affected by stress
inducing bioactive molecules NO (Nitric Oxide) in the p53-MDM2-NO regulatory
network. We also study synchronization among a group of identical stress
systems arranged in a three dimensional array with nearest neighbour diffusive
coupling. The role of NO and effect of noise are investigated. In the single
system study, we have found three distinct types of temporal behaviour of p53,
namely, oscillation death, damped oscillation and sustain oscillation,
depending on the amount of stress induced by the NO concentration, indicating
how p53 responds to the incoming stress. The correlation among the coupled
systems increases as the value of coupling constant (\epsilon) is increased
(\gamma increases) and becomes constant after certain value of \epsilon. The
permutation entropy spectra H(\epsilon) for p53 and MDM2 as a function of
\epsilon are found to be different due to direct and indirect interaction of NO
with the respective proteins. \gamma versus \epsilon for p53 and MDM2 are found
to be similar in deterministic approach, but different in stochastic approach
and the separation between \gamma of the respective proteins as a function of
\epsilon decreases as system size increases. The role of NO is found to be
twofold: stress induced by it is prominent at small and large values of
\epsilon but synchrony inducing by it dominates in moderate range of \epsilon.
Excess stress induce apoptosis to the system.
| [
{
"created": "Mon, 1 Sep 2014 13:07:01 GMT",
"version": "v1"
}
] | 2014-09-03 | [
[
"Devi",
"Gurumayum Reenaroy",
""
],
[
"Alam",
"Md. Jahoor",
""
],
[
"Singh",
"R. K. Brojen",
""
]
] | We study how the temporal behaviours of p53 and MDM2 are affected by stress inducing bioactive molecules NO (Nitric Oxide) in the p53-MDM2-NO regulatory network. We also study synchronization among a group of identical stress systems arranged in a three dimensional array with nearest neighbour diffusive coupling. The role of NO and effect of noise are investigated. In the single system study, we have found three distinct types of temporal behaviour of p53, namely, oscillation death, damped oscillation and sustain oscillation, depending on the amount of stress induced by the NO concentration, indicating how p53 responds to the incoming stress. The correlation among the coupled systems increases as the value of coupling constant (\epsilon) is increased (\gamma increases) and becomes constant after certain value of \epsilon. The permutation entropy spectra H(\epsilon) for p53 and MDM2 as a function of \epsilon are found to be different due to direct and indirect interaction of NO with the respective proteins. \gamma versus \epsilon for p53 and MDM2 are found to be similar in deterministic approach, but different in stochastic approach and the separation between \gamma of the respective proteins as a function of \epsilon decreases as system size increases. The role of NO is found to be twofold: stress induced by it is prominent at small and large values of \epsilon but synchrony inducing by it dominates in moderate range of \epsilon. Excess stress induce apoptosis to the system. |
1606.03910 | Kishan Manani | Kishan A. Manani, Kim Christensen, Nicholas S. Peters | Myocardial Architecture and Patient Variability in Clinical Patterns of
Atrial Fibrillation | 5 pages, 4 figures. For supplementary materials please contact Kishan
A. Manani at kishan.a.manani@gmail.com | Phys. Rev. E 94, 042401 (2016) | 10.1103/PhysRevE.94.042401 | null | q-bio.TO physics.bio-ph physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Atrial fibrillation (AF) increases the risk of stroke by a factor of four to
five and is the most common abnormal heart rhythm. The progression of AF with
age, from short self-terminating episodes to persistence, varies between
individuals and is poorly understood. An inability to understand and predict
variation in AF progression has resulted in less patient-specific therapy.
Likewise, it has been a challenge to relate the microstructural features of
heart muscle tissue (myocardial architecture) with the emergent temporal
clinical patterns of AF. We use a simple model of activation wavefront
propagation on an anisotropic structure, mimicking heart muscle tissue, to show
how variation in AF behaviour arises naturally from microstructural differences
between individuals. We show that the stochastic nature of progressive
transversal uncoupling of muscle strands (e.g., due to fibrosis or gap
junctional remodelling), as occurs with age, results in variability in AF
episode onset time, frequency, duration, burden and progression between
individuals. This is consistent with clinical observations. The uncoupling of
muscle strands can cause critical architectural patterns in the myocardium.
These critical patterns anchor micro-re-entrant wavefronts and thereby trigger
AF. It is the number of local critical patterns of uncoupling as opposed to
global uncoupling that determines AF progression. This insight may eventually
lead to patient specific therapy when it becomes possible to observe the
cellular structure of a patient's heart.
| [
{
"created": "Mon, 13 Jun 2016 12:01:23 GMT",
"version": "v1"
}
] | 2016-10-12 | [
[
"Manani",
"Kishan A.",
""
],
[
"Christensen",
"Kim",
""
],
[
"Peters",
"Nicholas S.",
""
]
] | Atrial fibrillation (AF) increases the risk of stroke by a factor of four to five and is the most common abnormal heart rhythm. The progression of AF with age, from short self-terminating episodes to persistence, varies between individuals and is poorly understood. An inability to understand and predict variation in AF progression has resulted in less patient-specific therapy. Likewise, it has been a challenge to relate the microstructural features of heart muscle tissue (myocardial architecture) with the emergent temporal clinical patterns of AF. We use a simple model of activation wavefront propagation on an anisotropic structure, mimicking heart muscle tissue, to show how variation in AF behaviour arises naturally from microstructural differences between individuals. We show that the stochastic nature of progressive transversal uncoupling of muscle strands (e.g., due to fibrosis or gap junctional remodelling), as occurs with age, results in variability in AF episode onset time, frequency, duration, burden and progression between individuals. This is consistent with clinical observations. The uncoupling of muscle strands can cause critical architectural patterns in the myocardium. These critical patterns anchor micro-re-entrant wavefronts and thereby trigger AF. It is the number of local critical patterns of uncoupling as opposed to global uncoupling that determines AF progression. This insight may eventually lead to patient specific therapy when it becomes possible to observe the cellular structure of a patient's heart. |
2209.13886 | Priya Chakraborty | Priya Chakraborty and Ushasi Roy and Sayantari Ghosh | Resource competition in Three-gene-motif & Emergence of Feed-forward
response: A Spatiotemporal Study | 19 pages, 6 figures | null | null | null | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Feed-forward dynamics, which is well-known to have several important
implications in nonlinear dynamical systems, frequently occurs in gene
expression motifs, and has been well explored experimentally and
mathematically. However, dependency of the components of a genetic circuit upon
its host, due to the requirement for resources like ribosome, ATP,
transcription factors, tRNA, etc., and related effects are of utmost
importance, which is commonly ignored in mathematical models. In a
resource-limited environment, two apparently unconnected genes can compete for
resources for their respective expression and may exhibit indirect regulatory
connection; an emergent response thus arises in the system completely because
of resource competition. In this work, we have shown how the responses of the
feed-forward loop (FFL), a well-studied regulatory genetic motif, can be
recreated considering the resource competition in a three-gene pathway.
Exploring the genetic system with temporal as well as spatiotemporal stability
analysis, interesting transient and steady-state responses have been observed.
The genetic motifs explored in this paper show many of the characteristic
features of the conventional FFL structure, like response delay and pulse
generation. Most interestingly, in a two-dimensional cellular arrangement,
characteristic pattern formation under a concentration gradient of input
signals have also been observed. This study pinpoints a larger area of research
and exploration in synthetic and cellular systems, which will reveal novel
controlling ideas and unique behavioral changes in the system for its context
dependencies.
| [
{
"created": "Wed, 28 Sep 2022 07:38:25 GMT",
"version": "v1"
},
{
"created": "Sun, 7 Jan 2024 05:41:56 GMT",
"version": "v2"
}
] | 2024-01-09 | [
[
"Chakraborty",
"Priya",
""
],
[
"Roy",
"Ushasi",
""
],
[
"Ghosh",
"Sayantari",
""
]
] | Feed-forward dynamics, which is well-known to have several important implications in nonlinear dynamical systems, frequently occurs in gene expression motifs, and has been well explored experimentally and mathematically. However, dependency of the components of a genetic circuit upon its host, due to the requirement for resources like ribosome, ATP, transcription factors, tRNA, etc., and related effects are of utmost importance, which is commonly ignored in mathematical models. In a resource-limited environment, two apparently unconnected genes can compete for resources for their respective expression and may exhibit indirect regulatory connection; an emergent response thus arises in the system completely because of resource competition. In this work, we have shown how the responses of the feed-forward loop (FFL), a well-studied regulatory genetic motif, can be recreated considering the resource competition in a three-gene pathway. Exploring the genetic system with temporal as well as spatiotemporal stability analysis, interesting transient and steady-state responses have been observed. The genetic motifs explored in this paper show many of the characteristic features of the conventional FFL structure, like response delay and pulse generation. Most interestingly, in a two-dimensional cellular arrangement, characteristic pattern formation under a concentration gradient of input signals have also been observed. This study pinpoints a larger area of research and exploration in synthetic and cellular systems, which will reveal novel controlling ideas and unique behavioral changes in the system for its context dependencies. |
2202.01521 | Robert Rosenbaum | Vicky Zhu and Robert Rosenbaum | Evaluating the extent to which homeostatic plasticity learns to compute
prediction errors in unstructured neuronal networks | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | The brain is believed to operate in part by making predictions about sensory
stimuli and encoding deviations from these predictions in the activity of
"prediction error neurons." This principle defines the widely influential
theory of predictive coding. The precise circuitry and plasticity mechanisms
through which animals learn to compute and update their predictions are
unknown. Homeostatic inhibitory synaptic plasticity is a promising mechanism
for training neuronal networks to perform predictive coding. Homeostatic
plasticity causes neurons to maintain a steady, baseline firing rate in
response to inputs that closely match the inputs on which a network was
trained, but firing rates can deviate away from this baseline in response to
stimuli that are mismatched from training. We combine computer simulations and
mathematical analysis systematically to test the extent to which randomly
connected, unstructured networks compute prediction errors after training with
homeostatic inhibitory synaptic plasticity. We find that homeostatic plasticity
alone is sufficient for computing prediction errors for trivial time-constant
stimuli, but not for more realistic time-varying stimuli. We use a mean-field
theory of plastic networks to explain our findings and characterize the
assumptions under which they apply.
| [
{
"created": "Thu, 3 Feb 2022 11:01:53 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Feb 2022 13:01:11 GMT",
"version": "v2"
}
] | 2022-02-21 | [
[
"Zhu",
"Vicky",
""
],
[
"Rosenbaum",
"Robert",
""
]
] | The brain is believed to operate in part by making predictions about sensory stimuli and encoding deviations from these predictions in the activity of "prediction error neurons." This principle defines the widely influential theory of predictive coding. The precise circuitry and plasticity mechanisms through which animals learn to compute and update their predictions are unknown. Homeostatic inhibitory synaptic plasticity is a promising mechanism for training neuronal networks to perform predictive coding. Homeostatic plasticity causes neurons to maintain a steady, baseline firing rate in response to inputs that closely match the inputs on which a network was trained, but firing rates can deviate away from this baseline in response to stimuli that are mismatched from training. We combine computer simulations and mathematical analysis systematically to test the extent to which randomly connected, unstructured networks compute prediction errors after training with homeostatic inhibitory synaptic plasticity. We find that homeostatic plasticity alone is sufficient for computing prediction errors for trivial time-constant stimuli, but not for more realistic time-varying stimuli. We use a mean-field theory of plastic networks to explain our findings and characterize the assumptions under which they apply. |
1610.06360 | Keith Smith | Keith Smith, Daniel Abasalo and Javier Escudero | Accounting for the Complex Hierarchical Topology of EEG Phase-Based
Functional Connectivity in Network Binarisation | Accepted for publication in PLOS One, 27th September 2017 | PLoS ONE12(10): e0186164 (2017) | 10.1371/journal.pone.0186164 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Research into binary network analysis of brain function faces a
methodological challenge in selecting an appropriate threshold to binarise edge
weights. For EEG phase-based functional connectivity, we test the hypothesis
that such binarisation should take into account the complex hierarchical
structure found in functional connectivity. We explore the density range
suitable for such structure and provide a comparison of state-of-the-art
binarisation techniques, the recently proposed Cluster-Span Threshold (CST),
minimum spanning trees, efficiency-cost optimisation and union of shortest path
graphs, with arbitrary proportional thresholds and weighted networks. We test
these techniques on weighted complex hierarchy models by contrasting model
realisations with small parametric differences. We also test the robustness of
these techniques to random and targeted topological attacks.We find that the
CST performs consistenty well in state-of-the-art modelling of EEG network
topology, robustness to topological network attacks, and in three real
datasets, agreeing with our hypothesis of hierarchical complexity. This
provides interesting new evidence into the relevance of considering a large
number of edges in EEG functional connectivity research to provide
informational density in the topology.
| [
{
"created": "Thu, 20 Oct 2016 11:19:02 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Mar 2017 12:01:19 GMT",
"version": "v2"
},
{
"created": "Fri, 29 Sep 2017 13:42:05 GMT",
"version": "v3"
}
] | 2017-10-24 | [
[
"Smith",
"Keith",
""
],
[
"Abasalo",
"Daniel",
""
],
[
"Escudero",
"Javier",
""
]
] | Research into binary network analysis of brain function faces a methodological challenge in selecting an appropriate threshold to binarise edge weights. For EEG phase-based functional connectivity, we test the hypothesis that such binarisation should take into account the complex hierarchical structure found in functional connectivity. We explore the density range suitable for such structure and provide a comparison of state-of-the-art binarisation techniques, the recently proposed Cluster-Span Threshold (CST), minimum spanning trees, efficiency-cost optimisation and union of shortest path graphs, with arbitrary proportional thresholds and weighted networks. We test these techniques on weighted complex hierarchy models by contrasting model realisations with small parametric differences. We also test the robustness of these techniques to random and targeted topological attacks.We find that the CST performs consistenty well in state-of-the-art modelling of EEG network topology, robustness to topological network attacks, and in three real datasets, agreeing with our hypothesis of hierarchical complexity. This provides interesting new evidence into the relevance of considering a large number of edges in EEG functional connectivity research to provide informational density in the topology. |
2102.10041 | Shubhadeep Sadhukhan | Shubhadeep Sadhukhan, Rohitashwa Chattopadhyay, Sagar Chakraborty | Cooperators overcome migration dilemma through synchronization | 12 pages, 8 figures | Phys. Rev. Research 3, 013009 (2021) | 10.1103/PhysRevResearch.3.013009 | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Synchronization, cooperation, and chaos are ubiquitous phenomena in nature.
In a population composed of many distinct groups of individuals playing the
prisoner's dilemma game, there exists a migration dilemma: No cooperator would
migrate to a group playing the prisoner's dilemma game lest it should be
exploited by a defector; but unless the migration takes place, there is no
chance of the entire population's cooperator-fraction to increase. Employing a
randomly rewired coupled map lattice of chaotic replicator maps, modelling
replication-selection evolutionary game dynamics, we demonstrate that the
cooperators -- evolving in synchrony -- overcome the migration dilemma to
proliferate across the population when altruism is mildly incentivized making
few of the demes play the leader game.
| [
{
"created": "Wed, 17 Feb 2021 06:32:42 GMT",
"version": "v1"
}
] | 2021-02-22 | [
[
"Sadhukhan",
"Shubhadeep",
""
],
[
"Chattopadhyay",
"Rohitashwa",
""
],
[
"Chakraborty",
"Sagar",
""
]
] | Synchronization, cooperation, and chaos are ubiquitous phenomena in nature. In a population composed of many distinct groups of individuals playing the prisoner's dilemma game, there exists a migration dilemma: No cooperator would migrate to a group playing the prisoner's dilemma game lest it should be exploited by a defector; but unless the migration takes place, there is no chance of the entire population's cooperator-fraction to increase. Employing a randomly rewired coupled map lattice of chaotic replicator maps, modelling replication-selection evolutionary game dynamics, we demonstrate that the cooperators -- evolving in synchrony -- overcome the migration dilemma to proliferate across the population when altruism is mildly incentivized making few of the demes play the leader game. |
1006.2761 | Yuliang Jin | Yuliang Jin, Dmitrij Turaev, Thomas Weinmaier, Thomas Rattei, Hernan
A. Makse | The evolutionary dynamics of protein-protein interaction networks
inferred from the reconstruction of ancient networks | null | PLoS ONE 2013, Volume 8, Issue 3, e58134 | 10.1371/journal.pone.0058134 | null | q-bio.MN cond-mat.dis-nn physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cellular functions are based on the complex interplay of proteins, therefore
the structure and dynamics of these protein-protein interaction (PPI) networks
are the key to the functional understanding of cells. In the last years,
large-scale PPI networks of several model organisms were investigated.
Methodological improvements now allow the analysis of PPI networks of multiple
organisms simultaneously as well as the direct modeling of ancestral networks.
This provides the opportunity to challenge existing assumptions on network
evolution. We utilized present-day PPI networks from integrated datasets of
seven model organisms and developed a theoretical and bioinformatic framework
for studying the evolutionary dynamics of PPI networks. A novel filtering
approach using percolation analysis was developed to remove low confidence
interactions based on topological constraints. We then reconstructed the
ancient PPI networks of different ancestors, for which the ancestral proteomes,
as well as the ancestral interactions, were inferred. Ancestral proteins were
reconstructed using orthologous groups on different evolutionary levels. A
stochastic approach, using the duplication-divergence model, was developed for
estimating the probabilities of ancient interactions from today's PPI networks.
The growth rates for nodes, edges, sizes and modularities of the networks
indicate multiplicative growth and are consistent with the results from
independent static analysis. Our results support the duplication-divergence
model of evolution and indicate fractality and multiplicative growth as general
properties of the PPI network structure and dynamics.
| [
{
"created": "Mon, 14 Jun 2010 16:40:39 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Feb 2013 15:36:49 GMT",
"version": "v2"
}
] | 2013-03-27 | [
[
"Jin",
"Yuliang",
""
],
[
"Turaev",
"Dmitrij",
""
],
[
"Weinmaier",
"Thomas",
""
],
[
"Rattei",
"Thomas",
""
],
[
"Makse",
"Hernan A.",
""
]
] | Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today's PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI network structure and dynamics. |
2403.04142 | Karina Silina | Karina Silina, Francesco Ciompi | Hitchhiker's guide to cancer-associated lymphoid aggregates in histology
images: manual and deep learning-based quantification approaches | 14 pages, 3 figures, 1 table, 3 boxes, protocol/guideline | null | null | null | q-bio.TO cs.CV q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Quantification of lymphoid aggregates including tertiary lymphoid structures
with germinal centers in histology images of cancer is a promising approach for
developing prognostic and predictive tissue biomarkers. In this article, we
provide recommendations for identifying lymphoid aggregates in tissue sections
from routine pathology workflows such as hematoxylin and eosin staining. To
overcome the intrinsic variability associated with manual image analysis (such
as subjective decision making, attention span), we recently developed a deep
learning-based algorithm called HookNet-TLS to detect lymphoid aggregates and
germinal centers in various tissues. Here, we additionally provide a guideline
for using manually annotated images for training and implementing HookNet-TLS
for automated and objective quantification of lymphoid aggregates in various
cancer types.
| [
{
"created": "Wed, 6 Mar 2024 15:32:05 GMT",
"version": "v1"
}
] | 2024-03-08 | [
[
"Silina",
"Karina",
""
],
[
"Ciompi",
"Francesco",
""
]
] | Quantification of lymphoid aggregates including tertiary lymphoid structures with germinal centers in histology images of cancer is a promising approach for developing prognostic and predictive tissue biomarkers. In this article, we provide recommendations for identifying lymphoid aggregates in tissue sections from routine pathology workflows such as hematoxylin and eosin staining. To overcome the intrinsic variability associated with manual image analysis (such as subjective decision making, attention span), we recently developed a deep learning-based algorithm called HookNet-TLS to detect lymphoid aggregates and germinal centers in various tissues. Here, we additionally provide a guideline for using manually annotated images for training and implementing HookNet-TLS for automated and objective quantification of lymphoid aggregates in various cancer types. |
2109.09143 | Giuseppe Gaeta | Giuseppe Gaeta | Mass vaccination in a roaring pandemic | 13 pages; to appear in "Chaos, Solitons and Fractals" | null | 10.1016/j.chaos.2021.111786 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mass vaccination produces a reduction in virus circulation, but also
evolutive pressure towards the appearance of virus-resistant strains. We
discuss the balance between these two effects, in particular when the mass
vaccination takes place in the middle of an epidemic period.
| [
{
"created": "Sun, 19 Sep 2021 15:17:17 GMT",
"version": "v1"
},
{
"created": "Fri, 31 Dec 2021 18:23:05 GMT",
"version": "v2"
},
{
"created": "Wed, 26 Jan 2022 22:12:12 GMT",
"version": "v3"
}
] | 2022-03-02 | [
[
"Gaeta",
"Giuseppe",
""
]
] | Mass vaccination produces a reduction in virus circulation, but also evolutive pressure towards the appearance of virus-resistant strains. We discuss the balance between these two effects, in particular when the mass vaccination takes place in the middle of an epidemic period. |
1810.01485 | Patrick Schwab | Patrick Schwab, Walter Karlen | PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data | AAAI Conference on Artificial Intelligence 2019 | null | null | null | q-bio.NC cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Parkinson's disease is a neurodegenerative disease that can affect a person's
movement, speech, dexterity, and cognition. Clinicians primarily diagnose
Parkinson's disease by performing a clinical assessment of symptoms. However,
misdiagnoses are common. One factor that contributes to misdiagnoses is that
the symptoms of Parkinson's disease may not be prominent at the time the
clinical assessment is performed. Here, we present a machine-learning approach
towards distinguishing between people with and without Parkinson's disease
using long-term data from smartphone-based walking, voice, tapping and memory
tests. We demonstrate that our attentive deep-learning models achieve
significant improvements in predictive performance over strong baselines (area
under the receiver operating characteristic curve = 0.85) in data from a cohort
of 1853 participants. We also show that our models identify meaningful features
in the input data. Our results confirm that smartphone data collected over
extended periods of time could in the future potentially be used as a digital
biomarker for the diagnosis of Parkinson's disease.
| [
{
"created": "Mon, 1 Oct 2018 11:38:18 GMT",
"version": "v1"
},
{
"created": "Wed, 14 Nov 2018 23:53:32 GMT",
"version": "v2"
}
] | 2018-11-16 | [
[
"Schwab",
"Patrick",
""
],
[
"Karlen",
"Walter",
""
]
] | Parkinson's disease is a neurodegenerative disease that can affect a person's movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson's disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson's disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson's disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson's disease. |
2105.07140 | Zijin Gu | Zijin Gu, Keith W. Jamison, Meenakshi Khosla, Emily J. Allen, Yihan
Wu, Thomas Naselaris, Kendrick Kay, Mert R. Sabuncu, Amy Kuceyeski | NeuroGen: activation optimized image synthesis for discovery
neuroscience | null | null | null | null | q-bio.NC cs.CV q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Functional MRI (fMRI) is a powerful technique that has allowed us to
characterize visual cortex responses to stimuli, yet such experiments are by
nature constructed based on a priori hypotheses, limited to the set of images
presented to the individual while they are in the scanner, are subject to noise
in the observed brain responses, and may vary widely across individuals. In
this work, we propose a novel computational strategy, which we call NeuroGen,
to overcome these limitations and develop a powerful tool for human vision
neuroscience discovery. NeuroGen combines an fMRI-trained neural encoding model
of human vision with a deep generative network to synthesize images predicted
to achieve a target pattern of macro-scale brain activation. We demonstrate
that the reduction of noise that the encoding model provides, coupled with the
generative network's ability to produce images of high fidelity, results in a
robust discovery architecture for visual neuroscience. By using only a small
number of synthetic images created by NeuroGen, we demonstrate that we can
detect and amplify differences in regional and individual human brain response
patterns to visual stimuli. We then verify that these discoveries are reflected
in the several thousand observed image responses measured with fMRI. We further
demonstrate that NeuroGen can create synthetic images predicted to achieve
regional response patterns not achievable by the best-matching natural images.
The NeuroGen framework extends the utility of brain encoding models and opens
up a new avenue for exploring, and possibly precisely controlling, the human
visual system.
| [
{
"created": "Sat, 15 May 2021 04:36:39 GMT",
"version": "v1"
}
] | 2021-05-18 | [
[
"Gu",
"Zijin",
""
],
[
"Jamison",
"Keith W.",
""
],
[
"Khosla",
"Meenakshi",
""
],
[
"Allen",
"Emily J.",
""
],
[
"Wu",
"Yihan",
""
],
[
"Naselaris",
"Thomas",
""
],
[
"Kay",
"Kendrick",
""
],
[
"Sa... | Functional MRI (fMRI) is a powerful technique that has allowed us to characterize visual cortex responses to stimuli, yet such experiments are by nature constructed based on a priori hypotheses, limited to the set of images presented to the individual while they are in the scanner, are subject to noise in the observed brain responses, and may vary widely across individuals. In this work, we propose a novel computational strategy, which we call NeuroGen, to overcome these limitations and develop a powerful tool for human vision neuroscience discovery. NeuroGen combines an fMRI-trained neural encoding model of human vision with a deep generative network to synthesize images predicted to achieve a target pattern of macro-scale brain activation. We demonstrate that the reduction of noise that the encoding model provides, coupled with the generative network's ability to produce images of high fidelity, results in a robust discovery architecture for visual neuroscience. By using only a small number of synthetic images created by NeuroGen, we demonstrate that we can detect and amplify differences in regional and individual human brain response patterns to visual stimuli. We then verify that these discoveries are reflected in the several thousand observed image responses measured with fMRI. We further demonstrate that NeuroGen can create synthetic images predicted to achieve regional response patterns not achievable by the best-matching natural images. The NeuroGen framework extends the utility of brain encoding models and opens up a new avenue for exploring, and possibly precisely controlling, the human visual system. |
2312.11179 | Clara Horvath | Clara Horvath, Andreas K\"orner, Corinna Modiz | Data-based Model Identification of the Hypothalamus-Pituitary-Thyroid
Complex | 15 pages, 6 figures, included in abstract volume of the 11th EUROSIM
Congress on Modelling and Simulation | null | null | null | q-bio.TO math.DS | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The thyroid gland, in conjunction with the pituitary and the hypothalamus,
forms a regulated system due to their mutual influence through released
hormones. The equilibrium point of this system, commonly referred to as the
"set point", is individually determined. This means that determining the
correct amount of medication to be administered to patients with hypothyroidism
requires several treatment appointments creating an extended treatment process.
Because the dynamics of the system have not yet been fully explored,
mathematical models are needed to simulate the mutual influence of the
respective hormones as well as their course over time. These models enable a
deeper understanding of the functionality in the context of data measurements.
Therefore, two existing time-dependent mathematical models are used and further
analyzed to replicate this overall influence of disparate systems. Both are
based on a system of two differential equations modelling the interacting
hormones. The parameters of the two models are identified according to
different calibration approaches by means of patient data collected in a
retrospective study in collaboration with the Medical University of Vienna. The
hormonal course in the time domain as well as equilibrium curves including the
set-point are then simulated and analyzed with respect to the normalized mean
squared error. These calibrated systems allow a more profound insight into the
functionality of the formed complex.
| [
{
"created": "Mon, 18 Dec 2023 13:22:47 GMT",
"version": "v1"
}
] | 2023-12-19 | [
[
"Horvath",
"Clara",
""
],
[
"Körner",
"Andreas",
""
],
[
"Modiz",
"Corinna",
""
]
] | The thyroid gland, in conjunction with the pituitary and the hypothalamus, forms a regulated system due to their mutual influence through released hormones. The equilibrium point of this system, commonly referred to as the "set point", is individually determined. This means that determining the correct amount of medication to be administered to patients with hypothyroidism requires several treatment appointments creating an extended treatment process. Because the dynamics of the system have not yet been fully explored, mathematical models are needed to simulate the mutual influence of the respective hormones as well as their course over time. These models enable a deeper understanding of the functionality in the context of data measurements. Therefore, two existing time-dependent mathematical models are used and further analyzed to replicate this overall influence of disparate systems. Both are based on a system of two differential equations modelling the interacting hormones. The parameters of the two models are identified according to different calibration approaches by means of patient data collected in a retrospective study in collaboration with the Medical University of Vienna. The hormonal course in the time domain as well as equilibrium curves including the set-point are then simulated and analyzed with respect to the normalized mean squared error. These calibrated systems allow a more profound insight into the functionality of the formed complex. |
1602.00668 | Ehsan Kazemi | Ehsan Kazemi and Matthias Grossglauser | On the Structure and Efficient Computation of IsoRank Node Similarities | 8 pages and 1 figure | null | null | null | q-bio.MN cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The alignment of protein-protein interaction (PPI) networks has many
applications, such as the detection of conserved biological network motifs, the
prediction of protein interactions, and the reconstruction of phylogenetic
trees [1, 2, 3]. IsoRank is one of the first global network alignment
algorithms [4, 5, 6], where the goal is to match all (or most) of the nodes of
two PPI networks. The IsoRank algorithm first computes a pairwise node
similarity metric, and then generates a matching between the two node sets
based on this metric. The metric is a convex combination of a structural
similarity score (with weight $ \alpha $) and an extraneous amino-acid sequence
similarity score for two proteins (with weight $ 1 - \alpha $). In this short
paper, we make two contributions. First, we show that when IsoRank similarity
depends only on network structure ($\alpha = 1$), the similarity of two nodes
is only a function of their degrees. In other words, IsoRank similarity is
invariant to any network rewiring that does not affect the node degrees. This
result suggests a reason for the poor performance of IsoRank in structure-only
($ \alpha = 1 $) alignment. Second, using ideas from [7, 8], we develop an
approximation algorithm that outperforms IsoRank (including recent versions
with better scaling, e.g., [9]) by several orders of magnitude in time and
memory complexity, despite only a negligible loss in precision.
| [
{
"created": "Mon, 1 Feb 2016 20:28:40 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Feb 2016 10:10:23 GMT",
"version": "v2"
}
] | 2018-02-22 | [
[
"Kazemi",
"Ehsan",
""
],
[
"Grossglauser",
"Matthias",
""
]
] | The alignment of protein-protein interaction (PPI) networks has many applications, such as the detection of conserved biological network motifs, the prediction of protein interactions, and the reconstruction of phylogenetic trees [1, 2, 3]. IsoRank is one of the first global network alignment algorithms [4, 5, 6], where the goal is to match all (or most) of the nodes of two PPI networks. The IsoRank algorithm first computes a pairwise node similarity metric, and then generates a matching between the two node sets based on this metric. The metric is a convex combination of a structural similarity score (with weight $ \alpha $) and an extraneous amino-acid sequence similarity score for two proteins (with weight $ 1 - \alpha $). In this short paper, we make two contributions. First, we show that when IsoRank similarity depends only on network structure ($\alpha = 1$), the similarity of two nodes is only a function of their degrees. In other words, IsoRank similarity is invariant to any network rewiring that does not affect the node degrees. This result suggests a reason for the poor performance of IsoRank in structure-only ($ \alpha = 1 $) alignment. Second, using ideas from [7, 8], we develop an approximation algorithm that outperforms IsoRank (including recent versions with better scaling, e.g., [9]) by several orders of magnitude in time and memory complexity, despite only a negligible loss in precision. |
1512.04562 | Andrei Zakharov | D.A. Bratsun, D.V. Merkuriev, A.P. Zakharov, L.M. Pismen | Multiscale modelling of tumour growth induced by circadian rhythm
disruption in epithelial tissue | Accepted to the Journal of Biological Physics | null | null | null | q-bio.CB nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a multiscale chemo-mechanical model of cancer tumour development
in an epithelial tissue. The model is based on transformation of normal cells
into the cancerous state triggered by a local failure of spatial
synchronisation of the circadian rhythm. The model includes mechanical
interactions and chemical signal exchange between neighbouring cells, as well
as division of cells and intercalation, and allows for modification of the
respective parameters following transformation into the cancerous state. The
numerical simulations reproduce different dephasing patterns - spiral waves and
quasistationary clustering, with the latter being conducive to cancer
formation. Modification of mechanical properties reproduces distinct behaviour
of invasive and localised carcinoma.
| [
{
"created": "Thu, 9 Jul 2015 22:36:31 GMT",
"version": "v1"
}
] | 2015-12-16 | [
[
"Bratsun",
"D. A.",
""
],
[
"Merkuriev",
"D. V.",
""
],
[
"Zakharov",
"A. P.",
""
],
[
"Pismen",
"L. M.",
""
]
] | We propose a multiscale chemo-mechanical model of cancer tumour development in an epithelial tissue. The model is based on transformation of normal cells into the cancerous state triggered by a local failure of spatial synchronisation of the circadian rhythm. The model includes mechanical interactions and chemical signal exchange between neighbouring cells, as well as division of cells and intercalation, and allows for modification of the respective parameters following transformation into the cancerous state. The numerical simulations reproduce different dephasing patterns - spiral waves and quasistationary clustering, with the latter being conducive to cancer formation. Modification of mechanical properties reproduces distinct behaviour of invasive and localised carcinoma. |
0802.1570 | Mark McDonnell | Mark D. McDonnell and Nigel G. Stocks | Maximally Informative Stimuli and Tuning Curves for Sigmoidal
Rate-Coding Neurons and Populations | Accepted by Physical Review Letters. This revision updates figures
and text | Physical Review Letters 101, 058103, 2008 | 10.1103/PhysRevLett.101.058103 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A general method for deriving maximally informative sigmoidal tuning curves
for neural systems with small normalized variability is presented. The optimal
tuning curve is a nonlinear function of the cumulative distribution function of
the stimulus and depends on the mean-variance relationship of the neural
system. The derivation is based on a known relationship between Shannon's
mutual information and Fisher information, and the optimality of Jeffrey's
prior. It relies on the existence of closed-form solutions to the converse
problem of optimizing the stimulus distribution for a given tuning curve. It is
shown that maximum mutual information corresponds to constant Fisher
information only if the stimulus is uniformly distributed. As an example, the
case of sub-Poisson binomial firing statistics is analyzed in detail.
| [
{
"created": "Tue, 12 Feb 2008 06:32:48 GMT",
"version": "v1"
},
{
"created": "Thu, 15 May 2008 00:47:03 GMT",
"version": "v2"
},
{
"created": "Fri, 4 Jul 2008 01:13:59 GMT",
"version": "v3"
}
] | 2008-08-02 | [
[
"McDonnell",
"Mark D.",
""
],
[
"Stocks",
"Nigel G.",
""
]
] | A general method for deriving maximally informative sigmoidal tuning curves for neural systems with small normalized variability is presented. The optimal tuning curve is a nonlinear function of the cumulative distribution function of the stimulus and depends on the mean-variance relationship of the neural system. The derivation is based on a known relationship between Shannon's mutual information and Fisher information, and the optimality of Jeffrey's prior. It relies on the existence of closed-form solutions to the converse problem of optimizing the stimulus distribution for a given tuning curve. It is shown that maximum mutual information corresponds to constant Fisher information only if the stimulus is uniformly distributed. As an example, the case of sub-Poisson binomial firing statistics is analyzed in detail. |
2005.10227 | Marcio Dorn | Eduardo Avila, Marcio Dorn, Clarice Sampaio Alho, Alessandro Kahmann | Hemogram Data as a Tool for Decision-making in COVID-19 Management:
Applications to Resource Scarcity Scenarios | 14 pages, 5 figures, 2 tables, Tool Available at:
http://sbcb.inf.ufrgs.br/covid | null | null | null | q-bio.OT cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | COVID-19 pandemics has challenged emergency response systems worldwide, with
widespread reports of essential services breakdown and collapse of health care
structure. A critical element involves essential workforce management since
current protocols recommend release from duty for symptomatic individuals,
including essential personnel. Testing capacity is also problematic in several
countries, where diagnosis demand outnumbers available local testing capacity.
This work describes a machine learning model derived from hemogram exam data
performed in symptomatic patients and how they can be used to predict qRT-PCR
test results. Methods: A Naive-Bayes model for machine learning is proposed for
handling different scarcity scenarios, including managing symptomatic essential
workforce and absence of diagnostic tests. Hemogram result data was used to
predict qRT-PCR results in situations where the latter was not performed, or
results are not yet available. Adjusts in assumed prior probabilities allow
fine-tuning of the model, according to actual prediction context. Proposed
models can predict COVID-19 qRT-PCR results in symptomatic individuals with
high accuracy, sensitivity and specificity. Data assessment can be performed in
an individual or simultaneous basis, according to desired outcome. Based on
hemogram data and background scarcity context, resource distribution is
significantly optimized when model-based patient selection is observed,
compared to random choice. The model can help manage testing deficiency and
other critical circumstances. Machine learning models can be derived from
widely available, quick, and inexpensive exam data in order to predict qRT-PCR
results used in COVID-19 diagnosis. These models can be used to assist
strategic decision-making in resource scarcity scenarios, including personnel
shortage, lack of medical resources, and testing insufficiency.
| [
{
"created": "Sun, 10 May 2020 01:45:03 GMT",
"version": "v1"
}
] | 2020-05-21 | [
[
"Avila",
"Eduardo",
""
],
[
"Dorn",
"Marcio",
""
],
[
"Alho",
"Clarice Sampaio",
""
],
[
"Kahmann",
"Alessandro",
""
]
] | COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. Methods: A Naive-Bayes model for machine learning is proposed for handling different scarcity scenarios, including managing symptomatic essential workforce and absence of diagnostic tests. Hemogram result data was used to predict qRT-PCR results in situations where the latter was not performed, or results are not yet available. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity. Data assessment can be performed in an individual or simultaneous basis, according to desired outcome. Based on hemogram data and background scarcity context, resource distribution is significantly optimized when model-based patient selection is observed, compared to random choice. The model can help manage testing deficiency and other critical circumstances. Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency. |
2403.02706 | Hyeongwoo Kim | Hyeongwoo Kim, Seokhyun Moon, Wonho Zhung, Jaechang Lim, and Woo Youn
Kim | DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Fine
Control of Multiple Molecular Properties | 32 pages, 7 figures, and 2 tables for main text | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Optimizing molecules to improve their properties is a fundamental challenge
in drug design. For a fine-tuning of molecular properties without losing
bio-activity validated in advance, the concept of bioisosterism has emerged.
Many in silico methods have been proposed for discovering bioisosteres, but
they require expert knowledge for their applications or are restricted to known
databases. Here, we introduce DeepBioisostere, a deep generative model to
design suitable bioisosteric replacements. Our model allows an end-to-end
chemical replacement by intelligently selecting fragments for removal and
insertion along with their attachment orientation. Through various scenarios of
multiple property control, we showcase the model's capability to modulate
specific properties, addressing the challenge in molecular optimization. Our
model's innovation lies in its capacity to design a bioisosteric replacement
reflecting the compatibility with the surroundings of the modification site,
facilitating the control of sophisticated properties like drug-likeness.
DeepBioisostere can also provide previously unseen bioisosteric replacements,
highlighting its capability for exploring diverse chemical modifications rather
than just mining them from known databases. Lastly, we employed DeepBioisostere
to improve the sensitivity of a known SARS-CoV-2 main protease inhibitor to the
E166V mutant that exhibits drug resistance to the inhibitor, demonstrating its
potential application in lead optimization.
| [
{
"created": "Tue, 5 Mar 2024 06:55:43 GMT",
"version": "v1"
}
] | 2024-03-06 | [
[
"Kim",
"Hyeongwoo",
""
],
[
"Moon",
"Seokhyun",
""
],
[
"Zhung",
"Wonho",
""
],
[
"Lim",
"Jaechang",
""
],
[
"Kim",
"Woo Youn",
""
]
] | Optimizing molecules to improve their properties is a fundamental challenge in drug design. For a fine-tuning of molecular properties without losing bio-activity validated in advance, the concept of bioisosterism has emerged. Many in silico methods have been proposed for discovering bioisosteres, but they require expert knowledge for their applications or are restricted to known databases. Here, we introduce DeepBioisostere, a deep generative model to design suitable bioisosteric replacements. Our model allows an end-to-end chemical replacement by intelligently selecting fragments for removal and insertion along with their attachment orientation. Through various scenarios of multiple property control, we showcase the model's capability to modulate specific properties, addressing the challenge in molecular optimization. Our model's innovation lies in its capacity to design a bioisosteric replacement reflecting the compatibility with the surroundings of the modification site, facilitating the control of sophisticated properties like drug-likeness. DeepBioisostere can also provide previously unseen bioisosteric replacements, highlighting its capability for exploring diverse chemical modifications rather than just mining them from known databases. Lastly, we employed DeepBioisostere to improve the sensitivity of a known SARS-CoV-2 main protease inhibitor to the E166V mutant that exhibits drug resistance to the inhibitor, demonstrating its potential application in lead optimization. |
1002.4386 | Narayanan Viswanath Chulliparambil | Viswanath C Narayanan | About the Number of Base Substitutions Between Humans and Common
Chimpanzees | 7 pages | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/3.0/ | Humans and chimpanzees are believed to have shared a common ancestor about 6
million years ago. Here using a new distance measure called the Jump distance,
we calculate the number of base substitutions that might have occurred in the
mitochondrial DNA during these 6 million years.
| [
{
"created": "Tue, 23 Feb 2010 18:19:32 GMT",
"version": "v1"
}
] | 2010-02-24 | [
[
"Narayanan",
"Viswanath C",
""
]
] | Humans and chimpanzees are believed to have shared a common ancestor about 6 million years ago. Here using a new distance measure called the Jump distance, we calculate the number of base substitutions that might have occurred in the mitochondrial DNA during these 6 million years. |
1901.07212 | Oksana Gorobets Prof. | Svitlana Gorobets, Oksana Gorobets, Alona Magerman, Yuri Gorobets,
Iryna Sharay | Biogenic magnetic nanoparticles in plants | 16 pages, 4 figures, 5 tables | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The genetic programming of biosynthesis of biogenic magnetic nanoparticles
(BMNs) in plants was revealed by methods of comparative genomics. The samples
of leaves and the root of Nicotiana tabacum, the stems and tubers of Solanum
tuberosum and the stems of pea Pisum sativum were examined by scanning probe
microscopy (in atomic force and magnetic power modes), and it was found that
the BMNs are located in the form of chains in the wall of the phloem sieve
tubes (ie, the vascular tissue of plants). Such a localization of BMNs supports
the idea that the chains of BMNs in different organs of plants have common
metabolic functions. Stray gradient magnetic fields about several thousand Oe,
which are created by chains of BMNs, can significantly affect the processes of
mass transfer near the membrane of vesicles, granules, organelles, structural
elements of the membrane, and others. This process is enhanced in plants when
artificial magnetite is added to the soil.
| [
{
"created": "Tue, 22 Jan 2019 08:56:19 GMT",
"version": "v1"
}
] | 2019-01-23 | [
[
"Gorobets",
"Svitlana",
""
],
[
"Gorobets",
"Oksana",
""
],
[
"Magerman",
"Alona",
""
],
[
"Gorobets",
"Yuri",
""
],
[
"Sharay",
"Iryna",
""
]
] | The genetic programming of biosynthesis of biogenic magnetic nanoparticles (BMNs) in plants was revealed by methods of comparative genomics. The samples of leaves and the root of Nicotiana tabacum, the stems and tubers of Solanum tuberosum and the stems of pea Pisum sativum were examined by scanning probe microscopy (in atomic force and magnetic power modes), and it was found that the BMNs are located in the form of chains in the wall of the phloem sieve tubes (ie, the vascular tissue of plants). Such a localization of BMNs supports the idea that the chains of BMNs in different organs of plants have common metabolic functions. Stray gradient magnetic fields about several thousand Oe, which are created by chains of BMNs, can significantly affect the processes of mass transfer near the membrane of vesicles, granules, organelles, structural elements of the membrane, and others. This process is enhanced in plants when artificial magnetite is added to the soil. |
0706.3137 | Emma Jin | Emma Y. Jin and Christian M. Reidys | Asymptotic Enumeration of RNA Structures with Pseudoknots | 22 pages, 7 figures | null | null | null | q-bio.BM math.CO | null | In this paper we present the asymptotic enumeration of RNA structures with
pseudoknots. We develop a general framework for the computation of exponential
growth rate and the sub exponential factors for $k$-noncrossing RNA structures.
Our results are based on the generating function for the number of
$k$-noncrossing RNA pseudoknot structures, ${\sf S}_k(n)$, derived in
\cite{Reidys:07pseu}, where $k-1$ denotes the maximal size of sets of mutually
intersecting bonds. We prove a functional equation for the generating function
$\sum_{n\ge 0}{\sf S}_k(n)z^n$ and obtain for $k=2$ and $k=3$ the analytic
continuation and singular expansions, respectively. It is implicit in our
results that for arbitrary $k$ singular expansions exist and via transfer
theorems of analytic combinatorics we obtain asymptotic expression for the
coefficients. We explicitly derive the asymptotic expressions for 2- and
3-noncrossing RNA structures. Our main result is the derivation of the formula
${\sf S}_3(n) \sim \frac{10.4724\cdot 4!}{n(n-1)...(n-4)}
(\frac{5+\sqrt{21}}{2})^n$.
| [
{
"created": "Thu, 21 Jun 2007 12:31:34 GMT",
"version": "v1"
}
] | 2009-09-29 | [
[
"Jin",
"Emma Y.",
""
],
[
"Reidys",
"Christian M.",
""
]
] | In this paper we present the asymptotic enumeration of RNA structures with pseudoknots. We develop a general framework for the computation of exponential growth rate and the sub exponential factors for $k$-noncrossing RNA structures. Our results are based on the generating function for the number of $k$-noncrossing RNA pseudoknot structures, ${\sf S}_k(n)$, derived in \cite{Reidys:07pseu}, where $k-1$ denotes the maximal size of sets of mutually intersecting bonds. We prove a functional equation for the generating function $\sum_{n\ge 0}{\sf S}_k(n)z^n$ and obtain for $k=2$ and $k=3$ the analytic continuation and singular expansions, respectively. It is implicit in our results that for arbitrary $k$ singular expansions exist and via transfer theorems of analytic combinatorics we obtain asymptotic expression for the coefficients. We explicitly derive the asymptotic expressions for 2- and 3-noncrossing RNA structures. Our main result is the derivation of the formula ${\sf S}_3(n) \sim \frac{10.4724\cdot 4!}{n(n-1)...(n-4)} (\frac{5+\sqrt{21}}{2})^n$. |
q-bio/0604035 | Rui Dilao | Rui Dilao and Abdelkader Lakmeche | On the weak solutions of the McKendrick equation: Existence of
demography cycles | 26 pages, 6 figures | null | null | null | q-bio.PE | null | We develop the qualitative theory of the solutions of the McKendrick partial
differential equation of population dynamics. We calculate explicitly the weak
solutions of the McKendrick equation and of the Lotka renewal integral equation
with time and age dependent birth rate. Mortality modulus is considered age
dependent. We show the existence of demography cycles. For a population with
only one reproductive age class, independently of the stability of the weak
solutions and after a transient time, the temporal evolution of the number of
individuals of a population is always modulated by a time periodic function.
The periodicity of the cycles is equal to the age of the reproductive age
class, and a population retains the memory from the initial data through the
amplitude of oscillations. For a population with a continuous distribution of
reproductive age classes, the amplitude of oscillation is damped. The
periodicity of the damped cycles is associated with the age of the first
reproductive age class. Damping increases as the dispersion of the fertility
function around the age class with maximal fertility increases. In general, the
period of the demography cycles is associated with the time that a species
takes to reach the reproductive maturity.
| [
{
"created": "Thu, 27 Apr 2006 18:50:30 GMT",
"version": "v1"
},
{
"created": "Thu, 7 Sep 2006 12:46:26 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Dilao",
"Rui",
""
],
[
"Lakmeche",
"Abdelkader",
""
]
] | We develop the qualitative theory of the solutions of the McKendrick partial differential equation of population dynamics. We calculate explicitly the weak solutions of the McKendrick equation and of the Lotka renewal integral equation with time and age dependent birth rate. Mortality modulus is considered age dependent. We show the existence of demography cycles. For a population with only one reproductive age class, independently of the stability of the weak solutions and after a transient time, the temporal evolution of the number of individuals of a population is always modulated by a time periodic function. The periodicity of the cycles is equal to the age of the reproductive age class, and a population retains the memory from the initial data through the amplitude of oscillations. For a population with a continuous distribution of reproductive age classes, the amplitude of oscillation is damped. The periodicity of the damped cycles is associated with the age of the first reproductive age class. Damping increases as the dispersion of the fertility function around the age class with maximal fertility increases. In general, the period of the demography cycles is associated with the time that a species takes to reach the reproductive maturity. |
1111.1152 | Tim Rogers | Tim Rogers, Alan J. McKane and Axel G. Rossberg | Demographic noise can lead to the spontaneous formation of species | null | Europhys. Lett. (2012) 97, 40008 | 10.1209/0295-5075/97/40008 | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When a collection of phenotypically diverse organisms compete with each other
for limited resources, with competition being strongest amongst the most
similar, the population can evolve into tightly localised clusters. This
process can be thought of as a simple model of the emergence of species. Past
studies have neglected the effects of demographic noise and studied the
population on a macroscopic scale, where species formation is found to depend
upon the shape of the curve describing the decline of competition strength with
phenotypic distance. In the following, we will show how including the effects
of demographic noise leads to a radically different conclusion. Two situations
are identified: a weak-noise regime in which the population exhibits patterns
of fluctuation around the macroscopic description, and a strong-noise regime
where species appear spontaneously even in the case that all organisms have
equal fitness.
| [
{
"created": "Fri, 4 Nov 2011 15:24:35 GMT",
"version": "v1"
},
{
"created": "Tue, 15 May 2012 09:26:29 GMT",
"version": "v2"
}
] | 2012-05-16 | [
[
"Rogers",
"Tim",
""
],
[
"McKane",
"Alan J.",
""
],
[
"Rossberg",
"Axel G.",
""
]
] | When a collection of phenotypically diverse organisms compete with each other for limited resources, with competition being strongest amongst the most similar, the population can evolve into tightly localised clusters. This process can be thought of as a simple model of the emergence of species. Past studies have neglected the effects of demographic noise and studied the population on a macroscopic scale, where species formation is found to depend upon the shape of the curve describing the decline of competition strength with phenotypic distance. In the following, we will show how including the effects of demographic noise leads to a radically different conclusion. Two situations are identified: a weak-noise regime in which the population exhibits patterns of fluctuation around the macroscopic description, and a strong-noise regime where species appear spontaneously even in the case that all organisms have equal fitness. |
2208.04720 | Richard Tj\"ornhammar | Richard Tj\"ornhammar | Clustering Optimisation Method for Highly Connected Biological Data | null | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Currently, data-driven discovery in biological sciences resides in finding
segmentation strategies in multivariate data that produce sensible descriptions
of the data. Clustering is but one of several approaches and sometimes falls
short because of difficulties in assessing reasonable cutoffs, the number of
clusters that need to be formed or that an approach fails to preserve
topological properties of the original system in its clustered form. In this
work, we show how a simple metric for connectivity clustering evaluation leads
to an optimised segmentation of biological data.
The novelty of the work resides in the creation of a simple optimisation
method for clustering crowded data. The resulting clustering approach only
relies on metrics derived from the inherent properties of the clustering. The
new method facilitates knowledge for optimised clustering, which is easy to
implement.
We discuss how the clustering optimisation strategy corresponds to the viable
information content yielded by the final segmentation. We further elaborate on
how the clustering results, in the optimal solution, corresponds to prior
knowledge of three different data sets.
| [
{
"created": "Mon, 8 Aug 2022 17:33:32 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Aug 2022 17:41:20 GMT",
"version": "v2"
}
] | 2022-08-12 | [
[
"Tjörnhammar",
"Richard",
""
]
] | Currently, data-driven discovery in biological sciences resides in finding segmentation strategies in multivariate data that produce sensible descriptions of the data. Clustering is but one of several approaches and sometimes falls short because of difficulties in assessing reasonable cutoffs, the number of clusters that need to be formed or that an approach fails to preserve topological properties of the original system in its clustered form. In this work, we show how a simple metric for connectivity clustering evaluation leads to an optimised segmentation of biological data. The novelty of the work resides in the creation of a simple optimisation method for clustering crowded data. The resulting clustering approach only relies on metrics derived from the inherent properties of the clustering. The new method facilitates knowledge for optimised clustering, which is easy to implement. We discuss how the clustering optimisation strategy corresponds to the viable information content yielded by the final segmentation. We further elaborate on how the clustering results, in the optimal solution, corresponds to prior knowledge of three different data sets. |
q-bio/0502031 | Julien Mayor | Julien Mayor and Wulfram Gerstner | Signal buffering in random networks of spiking neurons: microscopic vs.
macroscopic phenomena | 5 pages, 3 figures | null | 10.1103/PhysRevE.72.051906 | null | q-bio.NC | null | In randomly connected networks of pulse-coupled elements a time-dependent
input signal can be buffered over a short time. We studied the signal buffering
properties in simulated networks as a function of the networks state,
characterized by both the Lyapunov exponent of the microscopic dynamics and the
macroscopic activity derived from mean-field theory. If all network elements
receive the same signal, signal buffering over delays comparable to the
intrinsic time constant of the network elements can be explained by macroscopic
properties and works best at the phase transition to chaos. However, if only 20
percent of the network units receive a common time-dependent signal, signal
buffering properties improve and can no longer be attributed to the macroscopic
dynamics.
| [
{
"created": "Wed, 23 Feb 2005 10:23:04 GMT",
"version": "v1"
}
] | 2009-11-11 | [
[
"Mayor",
"Julien",
""
],
[
"Gerstner",
"Wulfram",
""
]
] | In randomly connected networks of pulse-coupled elements a time-dependent input signal can be buffered over a short time. We studied the signal buffering properties in simulated networks as a function of the networks state, characterized by both the Lyapunov exponent of the microscopic dynamics and the macroscopic activity derived from mean-field theory. If all network elements receive the same signal, signal buffering over delays comparable to the intrinsic time constant of the network elements can be explained by macroscopic properties and works best at the phase transition to chaos. However, if only 20 percent of the network units receive a common time-dependent signal, signal buffering properties improve and can no longer be attributed to the macroscopic dynamics. |
1406.1537 | Sungwoo Ahn | Leonid L Rubchinsky, Sungwoo Ahn, and Choongseok Park | Dynamics of desynchronized episodes in intermittent synchronization | 12 pages, 2 figures. Accepted to Frontiers in Physics | Front. Phys. 2:38, 2014 | 10.3389/fphy.2014.00038 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intermittent synchronization is observed in a variety of different
experimental settings in physics and beyond and is an established research
topic in nonlinear dynamics. When coupled oscillators exhibit relatively weak,
intermittent synchrony, the trajectory in the phase space spends a substantial
fraction of time away from a vicinity of a synchronized state. Thus to describe
and understand the observed dynamics one may consider both synchronized
episodes and desynchronized episodes (the episodes when oscillators are not
synchronous). This mini-review discusses recent developments in this area. We
explain how one can consider variation in synchrony on the very short
time-scales, provided that there is some degree of overall synchrony. We show
how to implement this approach in the case of intermittent phase locking,
review several recent examples of the application of these ideas to
experimental data and modeling systems, and discuss when and why these methods
may be useful.
| [
{
"created": "Thu, 5 Jun 2014 22:20:25 GMT",
"version": "v1"
}
] | 2021-04-26 | [
[
"Rubchinsky",
"Leonid L",
""
],
[
"Ahn",
"Sungwoo",
""
],
[
"Park",
"Choongseok",
""
]
] | Intermittent synchronization is observed in a variety of different experimental settings in physics and beyond and is an established research topic in nonlinear dynamics. When coupled oscillators exhibit relatively weak, intermittent synchrony, the trajectory in the phase space spends a substantial fraction of time away from a vicinity of a synchronized state. Thus to describe and understand the observed dynamics one may consider both synchronized episodes and desynchronized episodes (the episodes when oscillators are not synchronous). This mini-review discusses recent developments in this area. We explain how one can consider variation in synchrony on the very short time-scales, provided that there is some degree of overall synchrony. We show how to implement this approach in the case of intermittent phase locking, review several recent examples of the application of these ideas to experimental data and modeling systems, and discuss when and why these methods may be useful. |
0902.1477 | Mehmet Erbudak | Mehmet Erbudak and Ayse Erzan | Tracking tumor evolution via the prostate marker PSA: An individual
post-operative study | 9 pages, two figures | null | null | null | q-bio.QM q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The progress of the prostate-specific antigen after radical prostatectomy is
observed for a patient in order to extract information on the growth mode of
the tumor cells. An initial fast-growth mode goes over to a slower power-law
regime within two years of surgery. We argue that such studies may help
determine the appropriate time window for subsequent therapies in order to
increase the life expectancy of the patient.
| [
{
"created": "Mon, 9 Feb 2009 17:04:10 GMT",
"version": "v1"
}
] | 2009-02-10 | [
[
"Erbudak",
"Mehmet",
""
],
[
"Erzan",
"Ayse",
""
]
] | The progress of the prostate-specific antigen after radical prostatectomy is observed for a patient in order to extract information on the growth mode of the tumor cells. An initial fast-growth mode goes over to a slower power-law regime within two years of surgery. We argue that such studies may help determine the appropriate time window for subsequent therapies in order to increase the life expectancy of the patient. |
q-bio/0609037 | Y.-H. Taguchi | Y-h. Taguchi, M. Michael Gromiha | Comparison of amino acid occurrence and composition for predicting
protein folds | null | null | null | IPSJ SIG Technical Report 2007-BIO-008 (2007) 9-16 | q-bio.BM cond-mat.soft nlin.AO q-bio.QM | null | Background:Prediction of protein three-dimensional structures from amino acid
sequences is a long-standing goal in computational/molecular biology. The
successful discrimination of protein folds would help to improve the accuracy
of protein 3D structure prediction. Results: In this work, we propose a method
based on linear discriminant analysis (LDA) for recognizing proteins belonging
to 30 different folds using the occurrence of amino acid residues in a set of
1612 proteins. The present method could discriminate the globular proteins from
30 major folding types with the sensitivity of 37%, which is comparable to or
better than other methods in the literature. A web server has been developed
for predicting the folding type of the protein from amino acid sequence and it
is available at http://granular.com/PROLDA/. Conclusions:Linear discriminant
analysis based on amino acid occurrence could successfully recognize protein
folds. The present method has several advantages such as, (i) it directly
predicts the folding type of a protein without performing pair-wise
comparisons, (ii) it can discriminate folds among large number of proteins and
(iii) it is very fast to obtain the results. This is a simple method, which can
be easily incorporated in any other structure prediction algorithms.
| [
{
"created": "Mon, 25 Sep 2006 02:14:44 GMT",
"version": "v1"
},
{
"created": "Fri, 19 Jan 2007 09:05:13 GMT",
"version": "v2"
},
{
"created": "Thu, 10 May 2007 08:00:04 GMT",
"version": "v3"
}
] | 2007-05-23 | [
[
"Taguchi",
"Y-h.",
""
],
[
"Gromiha",
"M. Michael",
""
]
] | Background:Prediction of protein three-dimensional structures from amino acid sequences is a long-standing goal in computational/molecular biology. The successful discrimination of protein folds would help to improve the accuracy of protein 3D structure prediction. Results: In this work, we propose a method based on linear discriminant analysis (LDA) for recognizing proteins belonging to 30 different folds using the occurrence of amino acid residues in a set of 1612 proteins. The present method could discriminate the globular proteins from 30 major folding types with the sensitivity of 37%, which is comparable to or better than other methods in the literature. A web server has been developed for predicting the folding type of the protein from amino acid sequence and it is available at http://granular.com/PROLDA/. Conclusions:Linear discriminant analysis based on amino acid occurrence could successfully recognize protein folds. The present method has several advantages such as, (i) it directly predicts the folding type of a protein without performing pair-wise comparisons, (ii) it can discriminate folds among large number of proteins and (iii) it is very fast to obtain the results. This is a simple method, which can be easily incorporated in any other structure prediction algorithms. |
2205.08734 | Chika Koyama | Chika Koyama | Brain waves are a repetition of a pause and an activity | 26 pages, 6 figures, 6 supplementary figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-sa/4.0/ | Brain waves still cannot reliably distinguish between awake and asleep
states. Here, I present new original indices, voltage subthreshold wave {\tau}
and abovethreshold wave burst, for advanced LFP/EEG readings. Assuming that
{\tau} is a microwave that fluctuates every sample such as the equipotential,
the total number of {\tau} (N{\tau}) is inferred to be the maximum, and the
amplitude of burst (Abst) is inferred to be the minimum. In fact, they
invariably had a mean {\tau} duration (M{\tau}) of 2-3 sample intervals in any
case. In addition, {\tau} and burst exhibited self-similarity for sample
frequency while occupying approximately 30% and 70% of LFP in the natural
state, respectively. Its threshold and Abst were correlated with the vigilance
state and decreased to 70% by doubling the sample frequency. The dose of
sevoflurane, which inhibits and synchronizes neural activity, was linearly
correlated with decreases in the threshold and N{\tau}. Thus, {\tau} could
reflect the uncertainty of the membrane potential. I propose that {\tau} and
burst represent a pause and an activity such as the rhythm of the brain.
| [
{
"created": "Wed, 18 May 2022 05:54:15 GMT",
"version": "v1"
},
{
"created": "Wed, 29 Jun 2022 12:09:10 GMT",
"version": "v2"
},
{
"created": "Wed, 3 Jul 2024 12:04:52 GMT",
"version": "v3"
}
] | 2024-07-04 | [
[
"Koyama",
"Chika",
""
]
] | Brain waves still cannot reliably distinguish between awake and asleep states. Here, I present new original indices, voltage subthreshold wave {\tau} and abovethreshold wave burst, for advanced LFP/EEG readings. Assuming that {\tau} is a microwave that fluctuates every sample such as the equipotential, the total number of {\tau} (N{\tau}) is inferred to be the maximum, and the amplitude of burst (Abst) is inferred to be the minimum. In fact, they invariably had a mean {\tau} duration (M{\tau}) of 2-3 sample intervals in any case. In addition, {\tau} and burst exhibited self-similarity for sample frequency while occupying approximately 30% and 70% of LFP in the natural state, respectively. Its threshold and Abst were correlated with the vigilance state and decreased to 70% by doubling the sample frequency. The dose of sevoflurane, which inhibits and synchronizes neural activity, was linearly correlated with decreases in the threshold and N{\tau}. Thus, {\tau} could reflect the uncertainty of the membrane potential. I propose that {\tau} and burst represent a pause and an activity such as the rhythm of the brain. |
1312.4875 | William Gray Roncal | William Gray Roncal, Zachary H. Koterba, Disa Mhembere, Dean M.
Kleissas, Joshua T. Vogelstein, Randal Burns, Anita R. Bowles, Dimitrios K.
Donavos, Sephira Ryman, Rex E. Jung, Lei Wu, Vince Calhoun, and R. Jacob
Vogelstein | MIGRAINE: MRI Graph Reliability Analysis and Inference for Connectomics | Published as part of 2013 IEEE GlobalSIP conference | null | 10.1109/GlobalSIP.2013.6736878 | null | q-bio.QM cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Currently, connectomes (e.g., functional or structural brain graphs) can be
estimated in humans at $\approx 1~mm^3$ scale using a combination of diffusion
weighted magnetic resonance imaging, functional magnetic resonance imaging and
structural magnetic resonance imaging scans. This manuscript summarizes a
novel, scalable implementation of open-source algorithms to rapidly estimate
magnetic resonance connectomes, using both anatomical regions of interest
(ROIs) and voxel-size vertices. To assess the reliability of our pipeline, we
develop a novel nonparametric non-Euclidean reliability metric. Here we provide
an overview of the methods used, demonstrate our implementation, and discuss
available user extensions. We conclude with results showing the efficacy and
reliability of the pipeline over previous state-of-the-art.
| [
{
"created": "Tue, 17 Dec 2013 17:39:45 GMT",
"version": "v1"
}
] | 2016-11-17 | [
[
"Roncal",
"William Gray",
""
],
[
"Koterba",
"Zachary H.",
""
],
[
"Mhembere",
"Disa",
""
],
[
"Kleissas",
"Dean M.",
""
],
[
"Vogelstein",
"Joshua T.",
""
],
[
"Burns",
"Randal",
""
],
[
"Bowles",
"Anita R.",
... | Currently, connectomes (e.g., functional or structural brain graphs) can be estimated in humans at $\approx 1~mm^3$ scale using a combination of diffusion weighted magnetic resonance imaging, functional magnetic resonance imaging and structural magnetic resonance imaging scans. This manuscript summarizes a novel, scalable implementation of open-source algorithms to rapidly estimate magnetic resonance connectomes, using both anatomical regions of interest (ROIs) and voxel-size vertices. To assess the reliability of our pipeline, we develop a novel nonparametric non-Euclidean reliability metric. Here we provide an overview of the methods used, demonstrate our implementation, and discuss available user extensions. We conclude with results showing the efficacy and reliability of the pipeline over previous state-of-the-art. |
2002.01299 | Val\'erie Gabelica | Steven Daly, Frederic Rosu, Valerie Gabelica | Mass-Resolved Electronic Circular Dichroism Ion Spectroscopy | main text (7 pages, 2 figures) + supporting information (20 pages, 11
figures) | null | 10.1126/science.abb1822 | null | q-bio.BM physics.bio-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | DNA and proteins are chiral: their three-dimensional structure cannot be
superimposed with its mirror image. Circular dichroism spectroscopy is widely
used to characterize chiral compounds, but data interpretation is difficult in
the case of mixtures. We recorded for the first time the electronic circular
dichroism spectra of DNA helices separated in a mass spectrometer. We
electrosprayed guanine-rich strands having various secondary structures as
negative ions, irradiated them with a laser, and measured the difference in
electron photodetachment efficiency between left and right circularly polarized
light. The reconstructed circular dichroism ion spectra resemble the solution
ones, thereby allowing us to assign the DNA helical topology. The ability to
measure circular dichroism directly on biomolecular ions expands the
capabilities of mass spectrometry for structural analysis.
| [
{
"created": "Tue, 4 Feb 2020 14:26:34 GMT",
"version": "v1"
}
] | 2020-09-09 | [
[
"Daly",
"Steven",
""
],
[
"Rosu",
"Frederic",
""
],
[
"Gabelica",
"Valerie",
""
]
] | DNA and proteins are chiral: their three-dimensional structure cannot be superimposed with its mirror image. Circular dichroism spectroscopy is widely used to characterize chiral compounds, but data interpretation is difficult in the case of mixtures. We recorded for the first time the electronic circular dichroism spectra of DNA helices separated in a mass spectrometer. We electrosprayed guanine-rich strands having various secondary structures as negative ions, irradiated them with a laser, and measured the difference in electron photodetachment efficiency between left and right circularly polarized light. The reconstructed circular dichroism ion spectra resemble the solution ones, thereby allowing us to assign the DNA helical topology. The ability to measure circular dichroism directly on biomolecular ions expands the capabilities of mass spectrometry for structural analysis. |
1711.07383 | Ramon Grima | Abhyudai Singh and Ramon Grima | The Linear-Noise Approximation and moment-closure approximations for
stochastic chemical kinetics | 24 pages, 4 figures. To be published as a chapter in the book
"Quantitative Biology: Theory, Computational Methods and Examples of Models"
edited by Brian Munsky, Lev Tsimring and Bill Hlavack (MIT Press) | null | null | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is a short review of two common approximations in stochastic chemical
and biochemical kinetics. It will appear as Chapter 6 in the book "Quantitative
Biology: Theory, Computational Methods and Examples of Models" edited by Brian
Munsky, Lev Tsimring and Bill Hlavacek (to be published in late 2017/2018 by
MIT Press). All chapter references in this article refer to chapters in the
aforementioned book.
| [
{
"created": "Mon, 20 Nov 2017 15:41:51 GMT",
"version": "v1"
},
{
"created": "Fri, 24 Nov 2017 15:10:54 GMT",
"version": "v2"
}
] | 2017-11-27 | [
[
"Singh",
"Abhyudai",
""
],
[
"Grima",
"Ramon",
""
]
] | This is a short review of two common approximations in stochastic chemical and biochemical kinetics. It will appear as Chapter 6 in the book "Quantitative Biology: Theory, Computational Methods and Examples of Models" edited by Brian Munsky, Lev Tsimring and Bill Hlavacek (to be published in late 2017/2018 by MIT Press). All chapter references in this article refer to chapters in the aforementioned book. |
1308.2150 | Cameron Palmer | Cameron Palmer and Itsik Pe'er | GeneZip: A software package for storage-efficient processing of genotype
data | 6 pages, 1 figure Stylistic edits, added references, added author who
joined project between versions; conclusions unchanged | null | null | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Genome wide association studies directly assay 10^6 single nucleotide
polymorphisms (SNPs) across a study cohort. Probabilistic estimation of
additional sites by genotype imputation can increase this set of variants by
10- to 40-fold. Even with modest sample sizes (10^3-10^4), these resulting
imputed datasets, containing 10^10-10^11 double-precision values, are
incompatible with simultaneous lossless storage in RAM using standard methods.
Existing solutions for this problem require compromises in either genotype
accuracy or complexity of permissible statistical methods. Here, we present a
C/C++ library that dynamically compresses probabilistic genotype data as they
are loaded into memory. This method uses a customization of the DEFLATE (gzip)
algorithm, and maintains constant-time access to any SNP. Average compression
ratios of more than 9-fold are observed in test data.
| [
{
"created": "Fri, 9 Aug 2013 15:13:28 GMT",
"version": "v1"
},
{
"created": "Sun, 17 Nov 2013 21:27:44 GMT",
"version": "v2"
}
] | 2013-11-19 | [
[
"Palmer",
"Cameron",
""
],
[
"Pe'er",
"Itsik",
""
]
] | Genome wide association studies directly assay 10^6 single nucleotide polymorphisms (SNPs) across a study cohort. Probabilistic estimation of additional sites by genotype imputation can increase this set of variants by 10- to 40-fold. Even with modest sample sizes (10^3-10^4), these resulting imputed datasets, containing 10^10-10^11 double-precision values, are incompatible with simultaneous lossless storage in RAM using standard methods. Existing solutions for this problem require compromises in either genotype accuracy or complexity of permissible statistical methods. Here, we present a C/C++ library that dynamically compresses probabilistic genotype data as they are loaded into memory. This method uses a customization of the DEFLATE (gzip) algorithm, and maintains constant-time access to any SNP. Average compression ratios of more than 9-fold are observed in test data. |
1903.03964 | J.K. Chen | Jiao-Kai Chen | Identical ideal individual hypothesis | 7 pages. Part of the manuscript is rewritten and part is added. arXiv
admin note: substantial text overlap with arXiv:1903.02633 | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | The identical ideal individual hypothesis is proposed. According to this
hypothesis, the identical ideal individuals should be classified into two
classes: the bosonic individuals and the fermionic individuals. The bosonic
individuals can occupy the same behavior state while the fermionic individuals
can not be in the same behavior state. We propose that human beings and many
species of animals are fermionic, which can not occupy the same behavior state
according to the Pauli exclusion principle. An unified theoretical explanation
is given for the natures of two important and seemingly irrelated phenomena in
psychology: the existence of the personal space and the behavior
differentiation under high population density condition.
| [
{
"created": "Sun, 10 Mar 2019 10:06:53 GMT",
"version": "v1"
},
{
"created": "Sun, 9 Jun 2019 12:30:38 GMT",
"version": "v2"
}
] | 2019-06-12 | [
[
"Chen",
"Jiao-Kai",
""
]
] | The identical ideal individual hypothesis is proposed. According to this hypothesis, the identical ideal individuals should be classified into two classes: the bosonic individuals and the fermionic individuals. The bosonic individuals can occupy the same behavior state while the fermionic individuals can not be in the same behavior state. We propose that human beings and many species of animals are fermionic, which can not occupy the same behavior state according to the Pauli exclusion principle. An unified theoretical explanation is given for the natures of two important and seemingly irrelated phenomena in psychology: the existence of the personal space and the behavior differentiation under high population density condition. |
1108.0289 | Wojciech Waga | Stanislaw Cebrat, Dietrich Stauffer | Marry your Sister: Outbreeding Depression in Penna Ageing Model | 7 pages, 7 figures | null | null | SMORF-02 | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | If in the sexual Penna ageing model conditions are applied leading to
complementary bit-strings, then marriages between brothers and sisters, or
between close cousins, may lead to more offspring than for unrelated couples.
| [
{
"created": "Mon, 1 Aug 2011 11:50:48 GMT",
"version": "v1"
}
] | 2011-08-02 | [
[
"Cebrat",
"Stanislaw",
""
],
[
"Stauffer",
"Dietrich",
""
]
] | If in the sexual Penna ageing model conditions are applied leading to complementary bit-strings, then marriages between brothers and sisters, or between close cousins, may lead to more offspring than for unrelated couples. |
2207.11547 | Song Li | Song Li, Song Ke, Chenxing Yang, Jun Chen, Yi Xiong, Lirong Zheng, Hao
Liu, and Liang Hong | A Ligand-and-structure Dual-driven Deep Learning Method for the
Discovery of Highly Potent GnRH1R Antagonist to treat Uterine Diseases | null | null | null | null | q-bio.BM cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gonadotrophin-releasing hormone receptor (GnRH1R) is a promising therapeutic
target for the treatment of uterine diseases. To date, several GnRH1R
antagonists are available in clinical investigation without satisfying multiple
property constraints. To fill this gap, we aim to develop a deep learning-based
framework to facilitate the effective and efficient discovery of a new orally
active small-molecule drug targeting GnRH1R with desirable properties. In the
present work, a ligand-and-structure combined model, namely LS-MolGen, was
firstly proposed for molecular generation by fully utilizing the information on
the known active compounds and the structure of the target protein, which was
demonstrated by its superior performance than ligand- or structure-based
methods separately. Then, a in silico screening including activity prediction,
ADMET evaluation, molecular docking and FEP calculation was conducted, where
~30,000 generated novel molecules were narrowed down to 8 for experimental
synthesis and validation. In vitro and in vivo experiments showed that three of
them exhibited potent inhibition activities (compound 5 IC50 = 0.856 nM,
compound 6 IC50 = 0.901 nM, compound 7 IC50 = 2.54 nM) against GnRH1R, and
compound 5 performed well in fundamental PK properties, such as half-life, oral
bioavailability, and PPB, etc. We believed that the proposed
ligand-and-structure combined molecular generative model and the whole
computer-aided workflow can potentially be extended to similar tasks for de
novo drug design or lead optimization.
| [
{
"created": "Sat, 23 Jul 2022 16:04:54 GMT",
"version": "v1"
}
] | 2022-07-26 | [
[
"Li",
"Song",
""
],
[
"Ke",
"Song",
""
],
[
"Yang",
"Chenxing",
""
],
[
"Chen",
"Jun",
""
],
[
"Xiong",
"Yi",
""
],
[
"Zheng",
"Lirong",
""
],
[
"Liu",
"Hao",
""
],
[
"Hong",
"Liang",
""
]... | Gonadotrophin-releasing hormone receptor (GnRH1R) is a promising therapeutic target for the treatment of uterine diseases. To date, several GnRH1R antagonists are available in clinical investigation without satisfying multiple property constraints. To fill this gap, we aim to develop a deep learning-based framework to facilitate the effective and efficient discovery of a new orally active small-molecule drug targeting GnRH1R with desirable properties. In the present work, a ligand-and-structure combined model, namely LS-MolGen, was firstly proposed for molecular generation by fully utilizing the information on the known active compounds and the structure of the target protein, which was demonstrated by its superior performance than ligand- or structure-based methods separately. Then, a in silico screening including activity prediction, ADMET evaluation, molecular docking and FEP calculation was conducted, where ~30,000 generated novel molecules were narrowed down to 8 for experimental synthesis and validation. In vitro and in vivo experiments showed that three of them exhibited potent inhibition activities (compound 5 IC50 = 0.856 nM, compound 6 IC50 = 0.901 nM, compound 7 IC50 = 2.54 nM) against GnRH1R, and compound 5 performed well in fundamental PK properties, such as half-life, oral bioavailability, and PPB, etc. We believed that the proposed ligand-and-structure combined molecular generative model and the whole computer-aided workflow can potentially be extended to similar tasks for de novo drug design or lead optimization. |
1309.2817 | Ulrich S. Schwarz | Ulrich S. Schwarz (Heidelberg University) and Samuel S. Safran
(Weizmann Institute) | Physics of adherent cells | review, 60 pages, 25 figures | Reviews of Modern Physics 85: 1327-1381 (2013) | 10.1103/RevModPhys.85.1327 | null | q-bio.CB cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the most unique physical features of cell adhesion to external
surfaces is the active generation of mechanical force at the cell-material
interface. This includes pulling forces generated by contractile polymer
bundles and networks, and pushing forces generated by the polymerization of
polymer networks. These forces are transmitted to the substrate mainly by focal
adhesions, which are large, yet highly dynamic adhesion clusters. Tissue cells
use these forces to sense the physical properties of their environment and to
communicate with each other. The effect of forces is intricately linked to the
material properties of cells and their physical environment. Here a review is
given of recent progress in our understanding of the role of forces in cell
adhesion from the viewpoint of theoretical soft matter physics and in close
relation to the relevant experiments.
| [
{
"created": "Mon, 9 Sep 2013 16:19:12 GMT",
"version": "v1"
}
] | 2013-09-12 | [
[
"Schwarz",
"Ulrich S.",
"",
"Heidelberg University"
],
[
"Safran",
"Samuel S.",
"",
"Weizmann Institute"
]
] | One of the most unique physical features of cell adhesion to external surfaces is the active generation of mechanical force at the cell-material interface. This includes pulling forces generated by contractile polymer bundles and networks, and pushing forces generated by the polymerization of polymer networks. These forces are transmitted to the substrate mainly by focal adhesions, which are large, yet highly dynamic adhesion clusters. Tissue cells use these forces to sense the physical properties of their environment and to communicate with each other. The effect of forces is intricately linked to the material properties of cells and their physical environment. Here a review is given of recent progress in our understanding of the role of forces in cell adhesion from the viewpoint of theoretical soft matter physics and in close relation to the relevant experiments. |
0807.4729 | Adrian Melott | Adrian L. Melott (University of Kansas) | Long-term cycles in the history of life: Periodic biodiversity in the
Paleobiology Database | Published in PLoS ONE. 5 pages, 3 figures. Version with live links,
discussion available at
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004044#top | PLoS ONE 3(12): e4044. (2008) | 10.1371/journal.pone.0004044 | null | q-bio.PE astro-ph physics.bio-ph physics.data-an physics.geo-ph q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Time series analysis of fossil biodiversity of marine invertebrates in the
Paleobiology Database (PBDB) shows a significant periodicity at approximately
63 My, in agreement with previous analyses based on the Sepkoski database. I
discuss how this result did not appear in a previous analysis of the PBDB. The
existence of the 63 My periodicity, despite very different treatment of
systematic error in both PBDB and Sepkoski databases strongly argues for
consideration of its reality in the fossil record. Cross-spectral analysis of
the two datasets finds that a 62 My periodicity coincides in phase by 1.6 My,
equivalent to better than the errors in either measurement. Consequently, the
two data sets not only contain the same strong periodicity, but its peaks and
valleys closely correspond in time. Two other spectral peaks appear in the PBDB
analysis, but appear to be artifacts associated with detrending and with the
increased interval length. Sampling-standardization procedures implemented by
the PBDB collaboration suggest that the signal is not an artifact of sampling
bias. Further work should focus on finding the cause of the 62 My periodicity.
| [
{
"created": "Tue, 29 Jul 2008 20:01:51 GMT",
"version": "v1"
},
{
"created": "Fri, 1 Aug 2008 14:23:30 GMT",
"version": "v2"
},
{
"created": "Fri, 26 Sep 2008 19:56:34 GMT",
"version": "v3"
},
{
"created": "Tue, 25 Nov 2008 13:36:31 GMT",
"version": "v4"
},
{
"cr... | 2016-09-08 | [
[
"Melott",
"Adrian L.",
"",
"University of Kansas"
]
] | Time series analysis of fossil biodiversity of marine invertebrates in the Paleobiology Database (PBDB) shows a significant periodicity at approximately 63 My, in agreement with previous analyses based on the Sepkoski database. I discuss how this result did not appear in a previous analysis of the PBDB. The existence of the 63 My periodicity, despite very different treatment of systematic error in both PBDB and Sepkoski databases strongly argues for consideration of its reality in the fossil record. Cross-spectral analysis of the two datasets finds that a 62 My periodicity coincides in phase by 1.6 My, equivalent to better than the errors in either measurement. Consequently, the two data sets not only contain the same strong periodicity, but its peaks and valleys closely correspond in time. Two other spectral peaks appear in the PBDB analysis, but appear to be artifacts associated with detrending and with the increased interval length. Sampling-standardization procedures implemented by the PBDB collaboration suggest that the signal is not an artifact of sampling bias. Further work should focus on finding the cause of the 62 My periodicity. |
0907.4114 | Ulrich S. Schwarz | Ilka B. Bischofs (1,2), Sebastian S. Schmidt (1,3) and Ulrich S.
Schwarz (1,4) ((1) University of Heidelberg, Bioquant, (2) Lawrence Berkeley
National Lab, (3) Helmholtz Center Berlin, (4) University of Karlsruhe,
Theoretical Biophysics Group) | Effect of adhesion geometry and rigidity on cellular force distributions | 4 pages, Revtex with 4 figures | Phys Rev Lett 103, 048101 (2009) | 10.1103/PhysRevLett.103.048101 | null | q-bio.CB cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The behaviour and fate of tissue cells is controlled by the rigidity and
geometry of their adhesive environment, possibly through forces localized to
sites of adhesion. We introduce a mechanical model that predicts cellular force
distributions for cells adhering to adhesive patterns with different geometries
and rigidities. For continuous adhesion along a closed contour, forces are
predicted to be localized to the corners. For discrete sites of adhesion, the
model predicts the forces to be mainly determined by the lateral pull of the
cell contour. With increasing distance between two neighboring sites of
adhesion, the adhesion force increases because cell shape results in steeper
pulling directions. Softer substrates result in smaller forces. Our predictions
agree well with experimental force patterns measured on pillar assays.
| [
{
"created": "Thu, 23 Jul 2009 16:26:35 GMT",
"version": "v1"
}
] | 2009-07-24 | [
[
"Bischofs",
"Ilka B.",
""
],
[
"Schmidt",
"Sebastian S.",
""
],
[
"Schwarz",
"Ulrich S.",
""
]
] | The behaviour and fate of tissue cells is controlled by the rigidity and geometry of their adhesive environment, possibly through forces localized to sites of adhesion. We introduce a mechanical model that predicts cellular force distributions for cells adhering to adhesive patterns with different geometries and rigidities. For continuous adhesion along a closed contour, forces are predicted to be localized to the corners. For discrete sites of adhesion, the model predicts the forces to be mainly determined by the lateral pull of the cell contour. With increasing distance between two neighboring sites of adhesion, the adhesion force increases because cell shape results in steeper pulling directions. Softer substrates result in smaller forces. Our predictions agree well with experimental force patterns measured on pillar assays. |
0904.3308 | Iaroslav Ispolatov | I. Ispolatov and Michael Doebeli | Speciation due to hybrid necrosis in plant-pathogen models | 21 page, 3 figures | Evolution v. 63, December 2009, pp.: 3076-3084 | 10.1111/j.1558-5646.2009.00800.x | null | q-bio.PE q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop a model for speciation due to postzygotic incompatibility
generated by autoimmune reactions. The model is based on predator-prey
interactions between a host plants and their pathogens. Such interactions are
often frequency-dependent, so that pathogen attack is focused on the most
abundant plant phenotype, while rare plant types may escape pathogen attack.
Thus, frequency dependence can generate disruptive selection, which can give
rise to speciation if distant phenotypes become reproductively isolated. Based
on recent experimental evidence from {\it Arabidopsis}, we assume that at the
molecular level, incompatibility between strains is caused by epistatic
interactions between two proteins in the plant immune system, the guard and the
guardee. Within each plant strain, immune reactions occur when the guardee
protein is modified by a pathogen effector, and the guard subsequently binds to
the guardee, thus precipitating an immune response. However, when guard and
guardee proteins come from phenotypically distant parents, a hybrid's immune
system can be triggered by erroneous interactions between these proteins even
in the absence of pathogen attack, leading to severe autoimmune reactions in
hybrids. Our model shows how phenotypic variation generated by
frequency-dependent host-pathogen interactions can lead to postzygotic
incompatibility between extremal types, and hence to speciation.
| [
{
"created": "Tue, 21 Apr 2009 18:25:56 GMT",
"version": "v1"
}
] | 2010-11-02 | [
[
"Ispolatov",
"I.",
""
],
[
"Doebeli",
"Michael",
""
]
] | We develop a model for speciation due to postzygotic incompatibility generated by autoimmune reactions. The model is based on predator-prey interactions between a host plants and their pathogens. Such interactions are often frequency-dependent, so that pathogen attack is focused on the most abundant plant phenotype, while rare plant types may escape pathogen attack. Thus, frequency dependence can generate disruptive selection, which can give rise to speciation if distant phenotypes become reproductively isolated. Based on recent experimental evidence from {\it Arabidopsis}, we assume that at the molecular level, incompatibility between strains is caused by epistatic interactions between two proteins in the plant immune system, the guard and the guardee. Within each plant strain, immune reactions occur when the guardee protein is modified by a pathogen effector, and the guard subsequently binds to the guardee, thus precipitating an immune response. However, when guard and guardee proteins come from phenotypically distant parents, a hybrid's immune system can be triggered by erroneous interactions between these proteins even in the absence of pathogen attack, leading to severe autoimmune reactions in hybrids. Our model shows how phenotypic variation generated by frequency-dependent host-pathogen interactions can lead to postzygotic incompatibility between extremal types, and hence to speciation. |
2402.05815 | Konstantin Kalitin | Konstantin Y. Kalitin, Alexander A. Spasov, Olga Y. Mukha | Effects of kappa-opioid agonist U-50488 and p38 MAPK inhibitor SB203580
on the spike activity of pyramidal neurons in the basolateral amygdala | null | Research Results in Pharmacology 10(1): 1-6 (2024) | 10.18413/rrpharmacology.10.400 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Introduction: Kappa-opioid receptor (KOR) signaling in the basolateral
amygdala (BLA) underlies KOR agonist-induced aversion. In this study, we aimed
to understand the individual and combined effects of KOR agonist U-50488 and
p38 MAPK inhibitor SB203580 on the spiking activity of pyramidal neurons in the
BLA to shed light on the complex interplay between KORs, the p38 MAPK, and
neuronal excitability. Materials and Methods: Electrophysiological experiments
were performed using the patch-clamp technique in the whole-cell configuration.
Rat brain slices containing the amygdala were prepared, and pyramidal neurons
within the BLA were visually patched and recorded in the current clamp mode.
The neurons were identified by their accommodation properties and neural
activity signals were amplified and analyzed. Using local perfusion, we
obtained three dose-response curves for: a) U-50488 (0.001-10 {\mu}M); b)
U-50488 (0.001-10 {\mu}M) in the presence of SB203580 (1 {\mu}M); and c)
U-50488 (0.01-10 {\mu}M) in the presence of SB203580 (5 {\mu}M). Results: After
the application of U-50488, pyramidal neurons had a higher action potential
firing rate in response to a current injection than control neurons (p<0.001).
The dose-dependent curves we obtained indicate that the combination of U-50488
and SB203580 results in non-competitive antagonism. This conclusion is
supported by the observed change in the curve`s slope with reduction in the
maximum effect of U-50488. Thus, it can be assumed that the increase in spike
activity of pyramidal neurons of the amygdala is mediated through the
beta-arrestin pathway. When this pathway is blocked, the spike activity reverts
to its baseline level. Conclusion: Our study found that the KOR agonist-induced
spiking activity of the BLA pyramidal neurons is mediated by the beta-arrestin
pathway and can be suppressed by the application of the p38 MAPK inhibitor
SB203580.
| [
{
"created": "Thu, 8 Feb 2024 16:52:31 GMT",
"version": "v1"
},
{
"created": "Sat, 17 Feb 2024 15:08:12 GMT",
"version": "v2"
}
] | 2024-02-20 | [
[
"Kalitin",
"Konstantin Y.",
""
],
[
"Spasov",
"Alexander A.",
""
],
[
"Mukha",
"Olga Y.",
""
]
] | Introduction: Kappa-opioid receptor (KOR) signaling in the basolateral amygdala (BLA) underlies KOR agonist-induced aversion. In this study, we aimed to understand the individual and combined effects of KOR agonist U-50488 and p38 MAPK inhibitor SB203580 on the spiking activity of pyramidal neurons in the BLA to shed light on the complex interplay between KORs, the p38 MAPK, and neuronal excitability. Materials and Methods: Electrophysiological experiments were performed using the patch-clamp technique in the whole-cell configuration. Rat brain slices containing the amygdala were prepared, and pyramidal neurons within the BLA were visually patched and recorded in the current clamp mode. The neurons were identified by their accommodation properties and neural activity signals were amplified and analyzed. Using local perfusion, we obtained three dose-response curves for: a) U-50488 (0.001-10 {\mu}M); b) U-50488 (0.001-10 {\mu}M) in the presence of SB203580 (1 {\mu}M); and c) U-50488 (0.01-10 {\mu}M) in the presence of SB203580 (5 {\mu}M). Results: After the application of U-50488, pyramidal neurons had a higher action potential firing rate in response to a current injection than control neurons (p<0.001). The dose-dependent curves we obtained indicate that the combination of U-50488 and SB203580 results in non-competitive antagonism. This conclusion is supported by the observed change in the curve`s slope with reduction in the maximum effect of U-50488. Thus, it can be assumed that the increase in spike activity of pyramidal neurons of the amygdala is mediated through the beta-arrestin pathway. When this pathway is blocked, the spike activity reverts to its baseline level. Conclusion: Our study found that the KOR agonist-induced spiking activity of the BLA pyramidal neurons is mediated by the beta-arrestin pathway and can be suppressed by the application of the p38 MAPK inhibitor SB203580. |
0705.1490 | Emidio Capriotti | Emidio Capriotti, Piero Fariselli, Ivan Rossi and Rita Casadio | A three-state prediction of single point mutations on protein stability
changes | Text: 9 pages, Figures: 9 pages, Tables: 1 page, Supplemetary
Material: 1 page | null | null | null | q-bio.BM q-bio.QM | null | A basic question of protein structural studies is to which extent mutations
affect the stability. This question may be addressed starting from sequence
and/or from structure. In proteomics and genomics studies prediction of protein
stability free energy change (DDG) upon single point mutation may also help the
annotation process. The experimental SSG values are affected by uncertainty as
measured by standard deviations. Most of the DDG values are nearly zero (about
32% of the DDG data set ranges from -0.5 to 0.5 Kcal/mol) and both the value
and sign of DDG may be either positive or negative for the same mutation
blurring the relationship among mutations and expected DDG value. In order to
overcome this problem we describe a new predictor that discriminates between 3
mutation classes: destabilizing mutations (DDG<-0.5 Kcal/mol), stabilizing
mutations (DDG>0.5 Kcal/mol) and neutral mutations (-0.5<=DDG<=0.5 Kcal/mol).
In this paper a support vector machine starting from the protein sequence or
structure discriminates between stabilizing, destabilizing and neutral
mutations. We rank all the possible substitutions according to a three state
classification system and show that the overall accuracy of our predictor is as
high as 52% when performed starting from sequence information and 58% when the
protein structure is available, with a mean value correlation coefficient of
0.30 and 0.39, respectively. These values are about 20 points per cent higher
than those of a random predictor.
| [
{
"created": "Thu, 10 May 2007 14:37:34 GMT",
"version": "v1"
},
{
"created": "Wed, 23 May 2007 08:49:48 GMT",
"version": "v2"
}
] | 2007-06-13 | [
[
"Capriotti",
"Emidio",
""
],
[
"Fariselli",
"Piero",
""
],
[
"Rossi",
"Ivan",
""
],
[
"Casadio",
"Rita",
""
]
] | A basic question of protein structural studies is to which extent mutations affect the stability. This question may be addressed starting from sequence and/or from structure. In proteomics and genomics studies prediction of protein stability free energy change (DDG) upon single point mutation may also help the annotation process. The experimental SSG values are affected by uncertainty as measured by standard deviations. Most of the DDG values are nearly zero (about 32% of the DDG data set ranges from -0.5 to 0.5 Kcal/mol) and both the value and sign of DDG may be either positive or negative for the same mutation blurring the relationship among mutations and expected DDG value. In order to overcome this problem we describe a new predictor that discriminates between 3 mutation classes: destabilizing mutations (DDG<-0.5 Kcal/mol), stabilizing mutations (DDG>0.5 Kcal/mol) and neutral mutations (-0.5<=DDG<=0.5 Kcal/mol). In this paper a support vector machine starting from the protein sequence or structure discriminates between stabilizing, destabilizing and neutral mutations. We rank all the possible substitutions according to a three state classification system and show that the overall accuracy of our predictor is as high as 52% when performed starting from sequence information and 58% when the protein structure is available, with a mean value correlation coefficient of 0.30 and 0.39, respectively. These values are about 20 points per cent higher than those of a random predictor. |
q-bio/0605047 | Emmanuel Tannenbaum | Emmanuel Tannenbaum | When does division of labor lead to increased system output? | 10 pages, submitted to the Journal of Theoretical Biology (figures
are included with the journal version) | null | null | null | q-bio.CB q-bio.PE | null | This paper develops a set of simplified dynamical models with which to
explore the conditions under which division of labor leads to optimized system
output, as measured by the rate of production of a given product. We consider
two models: In the first model, we consider the flow of some resource into a
compartment, and the conversion of this resource into some product. In the
second model, we consider the resource-limited growth of autoreplicating
systems. In this case, we divide the replication and metabolic tasks among
different agents. The general features that emerge from our models is that
division of labor is favored when the resource to agent ratio is at
intermediate values, and when the time cost associated with transporting
intermediate products is small compared to characteristic process times. We
discuss the results of this paper in the context of simulations with digital
life. We also argue that division of labor in the context of our replication
model suggests an evolutionary basis for the emergence of the stem-cell-based
tissue architecture in complex organisms.
| [
{
"created": "Mon, 29 May 2006 22:49:31 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Tannenbaum",
"Emmanuel",
""
]
] | This paper develops a set of simplified dynamical models with which to explore the conditions under which division of labor leads to optimized system output, as measured by the rate of production of a given product. We consider two models: In the first model, we consider the flow of some resource into a compartment, and the conversion of this resource into some product. In the second model, we consider the resource-limited growth of autoreplicating systems. In this case, we divide the replication and metabolic tasks among different agents. The general features that emerge from our models is that division of labor is favored when the resource to agent ratio is at intermediate values, and when the time cost associated with transporting intermediate products is small compared to characteristic process times. We discuss the results of this paper in the context of simulations with digital life. We also argue that division of labor in the context of our replication model suggests an evolutionary basis for the emergence of the stem-cell-based tissue architecture in complex organisms. |
2102.03469 | Anna Ritz | Heyuan Zeng, Jinbiao Zhang, Gabriel A. Preising, Tobias Rubel, Pramesh
Singh, Anna Ritz | Graphery: Interactive Tutorials for Biological Network Algorithms | Added reference for pySnooper software | null | 10.1093/nar/gkab420 | null | q-bio.MN | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Networks provide a meaningful way to represent and analyze complex biological
information, but the methodological details of network-based tools are often
described for a technical audience. Graphery is a hands-on tutorial webserver
designed to help biological researchers understand the fundamental concepts
behind commonly-used graph algorithms. Each tutorial describes a graph concept
along with executable Python code that visualizes the concept in a code view
and a graph view. Graphery tutorials help researchers understand graph
statistics (such as degree distribution and network modularity) and classic
graph algorithms (such as shortest paths and random walks). Users navigate each
tutorial using their choice of real-world biological networks, ranging in scale
from molecular interaction graphs to ecological networks. Graphery also allows
users to modify the code within each tutorial or write new programs, which all
can be executed without requiring an account. Discipline-focused tutorials will
be essential to help researchers interpret their biological data. Graphery
accepts ideas for new tutorials and datasets that will be shaped by both
computational and biological researchers, growing into a community-contributed
learning platform. Availability: Graphery is available at
https://graphery.reedcompbio.org/.
| [
{
"created": "Sat, 6 Feb 2021 01:27:17 GMT",
"version": "v1"
},
{
"created": "Thu, 22 Apr 2021 23:47:54 GMT",
"version": "v2"
},
{
"created": "Thu, 15 Feb 2024 21:14:51 GMT",
"version": "v3"
}
] | 2024-02-19 | [
[
"Zeng",
"Heyuan",
""
],
[
"Zhang",
"Jinbiao",
""
],
[
"Preising",
"Gabriel A.",
""
],
[
"Rubel",
"Tobias",
""
],
[
"Singh",
"Pramesh",
""
],
[
"Ritz",
"Anna",
""
]
] | Networks provide a meaningful way to represent and analyze complex biological information, but the methodological details of network-based tools are often described for a technical audience. Graphery is a hands-on tutorial webserver designed to help biological researchers understand the fundamental concepts behind commonly-used graph algorithms. Each tutorial describes a graph concept along with executable Python code that visualizes the concept in a code view and a graph view. Graphery tutorials help researchers understand graph statistics (such as degree distribution and network modularity) and classic graph algorithms (such as shortest paths and random walks). Users navigate each tutorial using their choice of real-world biological networks, ranging in scale from molecular interaction graphs to ecological networks. Graphery also allows users to modify the code within each tutorial or write new programs, which all can be executed without requiring an account. Discipline-focused tutorials will be essential to help researchers interpret their biological data. Graphery accepts ideas for new tutorials and datasets that will be shaped by both computational and biological researchers, growing into a community-contributed learning platform. Availability: Graphery is available at https://graphery.reedcompbio.org/. |
1503.02793 | Nao Takashina | Nao Takashina and Marissa L. Baskett | Exploring the effect of the spatial scale of fishery management | 21 pages, 5 figures | Journal of Theoretical Biology, 390:14-22, 2016 | 10.1016/j.jtbi.2015.11.005 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For any spatially explicit management, determining the appropriate spatial
scale of management decisions is critical to success at achieving a given
management goal. Specifically, managers must decide how much to subdivide a
given managed region: from implementing a uniform approach across the region to
considering a unique approach in each of one hundred patches and everything in
between. Spatially explicit approaches, such as the implementation of marine
spatial planning and marine reserves, are increasingly used in fishery
management. Using a spatially explicit bioeconomic model, we quantify how the
management scale affects optimal fishery profit, biomass, fishery effort, and
the fraction of habitat in marine reserves. We find that, if habitats are
randomly distributed, the fishery profit increases almost linearly with the
number of segments. However, if habitats are positively autocorrelated, then
the fishery profit increases with diminishing returns. Therefore, the true
optimum in management scale given cost to subdivision depends on the habitat
distribution pattern.
| [
{
"created": "Tue, 10 Mar 2015 07:29:05 GMT",
"version": "v1"
},
{
"created": "Sun, 6 Dec 2015 04:01:22 GMT",
"version": "v2"
}
] | 2015-12-08 | [
[
"Takashina",
"Nao",
""
],
[
"Baskett",
"Marissa L.",
""
]
] | For any spatially explicit management, determining the appropriate spatial scale of management decisions is critical to success at achieving a given management goal. Specifically, managers must decide how much to subdivide a given managed region: from implementing a uniform approach across the region to considering a unique approach in each of one hundred patches and everything in between. Spatially explicit approaches, such as the implementation of marine spatial planning and marine reserves, are increasingly used in fishery management. Using a spatially explicit bioeconomic model, we quantify how the management scale affects optimal fishery profit, biomass, fishery effort, and the fraction of habitat in marine reserves. We find that, if habitats are randomly distributed, the fishery profit increases almost linearly with the number of segments. However, if habitats are positively autocorrelated, then the fishery profit increases with diminishing returns. Therefore, the true optimum in management scale given cost to subdivision depends on the habitat distribution pattern. |
2211.03978 | Don Krieger | Don Krieger, Paul Shepard, David O. Okonkwo | Robust Functional Magnetoencephalographic Brain Measures with 1.0
Millimeter Spatial Separation | 14 pages, 4 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Neuroelectric currents were extracted from free-running
magnetoencephalographic (MEG) rest and task recordings from 617 normative
subjects (ages: 18-87). State-dependent neuroelectric differential activation
(DA) with spatial resolution comparable to that of local field potentials was
detected in the majority of this cohort. Rest-high (rest greater than task) or
task-high DA was found in the majority of individual subjects in more than
13,000 1 mm^3 voxels per subject. On average, 6% of the DA voxels bordered a
second voxel whose DA was opposite, i.e., one was rest-high and the other was
task-high. 516 subjects showed more than 100 such opposite voxel pairs 1 mm
apart; 226 subjects showed more than 1000. The number of bordering voxel pairs
with the same DA was consistently higher for almost all subjects and averaged
20%, ruling out the possibility that opposite bordering voxels occur simply by
chance. For 65 brain regions, more than 10% of the cohort showed significantly
more same than opposite pairs. These findings taken together support the
conclusion that neuroelectric DA is consistently distinguishable at single 1
mm^3 brain voxels with 1-mm spatial separation. When restricted to voxels with
near-zero rest or task counts, significantly more rest-high than task-high
voxels were found in 35 regions for at least 10 percent of the subjects. This
inequality was not found when all DA-voxels were included. This supports the
conclusion that the DA found in many rest-high voxels with near-zero task
counts is due in part to task-dependent inhibition.
| [
{
"created": "Tue, 8 Nov 2022 03:16:07 GMT",
"version": "v1"
},
{
"created": "Wed, 16 Nov 2022 17:41:28 GMT",
"version": "v2"
},
{
"created": "Sun, 18 Dec 2022 18:11:29 GMT",
"version": "v3"
},
{
"created": "Sat, 31 Dec 2022 13:55:46 GMT",
"version": "v4"
}
] | 2023-01-03 | [
[
"Krieger",
"Don",
""
],
[
"Shepard",
"Paul",
""
],
[
"Okonkwo",
"David O.",
""
]
] | Neuroelectric currents were extracted from free-running magnetoencephalographic (MEG) rest and task recordings from 617 normative subjects (ages: 18-87). State-dependent neuroelectric differential activation (DA) with spatial resolution comparable to that of local field potentials was detected in the majority of this cohort. Rest-high (rest greater than task) or task-high DA was found in the majority of individual subjects in more than 13,000 1 mm^3 voxels per subject. On average, 6% of the DA voxels bordered a second voxel whose DA was opposite, i.e., one was rest-high and the other was task-high. 516 subjects showed more than 100 such opposite voxel pairs 1 mm apart; 226 subjects showed more than 1000. The number of bordering voxel pairs with the same DA was consistently higher for almost all subjects and averaged 20%, ruling out the possibility that opposite bordering voxels occur simply by chance. For 65 brain regions, more than 10% of the cohort showed significantly more same than opposite pairs. These findings taken together support the conclusion that neuroelectric DA is consistently distinguishable at single 1 mm^3 brain voxels with 1-mm spatial separation. When restricted to voxels with near-zero rest or task counts, significantly more rest-high than task-high voxels were found in 35 regions for at least 10 percent of the subjects. This inequality was not found when all DA-voxels were included. This supports the conclusion that the DA found in many rest-high voxels with near-zero task counts is due in part to task-dependent inhibition. |
1801.03011 | Matteo Manica | Matteo Manica, Roland Mathis, Mar\'ia Rodr\'iguez Mart\'inez | INtERAcT: Interaction Network Inference from Vector Representations of
Words | null | Nature Machine Intelligence (2019) | 10.1038/s42256-019-0036-1 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, the number of biomedical publications has steadfastly grown,
resulting in a rich source of untapped new knowledge. Most biomedical facts are
however not readily available, but buried in the form of unstructured text, and
hence their exploitation requires the time-consuming manual curation of
published articles. Here we present INtERAcT, a novel approach to extract
protein-protein interactions from a corpus of biomedical articles related to a
broad range of scientific domains in a completely unsupervised way. INtERAcT
exploits vector representation of words, computed on a corpus of domain
specific knowledge, and implements a new metric that estimates an interaction
score between two molecules in the space where the corresponding words are
embedded. We demonstrate the power of INtERAcT by reconstructing the molecular
pathways associated to 10 different cancer types using a corpus of
disease-specific articles for each cancer type. We evaluate INtERAcT using
STRING database as a benchmark, and show that our metric outperforms currently
adopted approaches for similarity computation at the task of identifying known
molecular interactions in all studied cancer types. Furthermore, our approach
does not require text annotation, manual curation or the definition of semantic
rules based on expert knowledge, and hence it can be easily and efficiently
applied to different scientific domains. Our findings suggest that INtERAcT may
increase our capability to summarize the understanding of a specific disease
using the published literature in an automated and completely unsupervised
fashion.
| [
{
"created": "Tue, 9 Jan 2018 15:43:37 GMT",
"version": "v1"
},
{
"created": "Mon, 12 Mar 2018 13:31:51 GMT",
"version": "v2"
},
{
"created": "Mon, 16 Apr 2018 09:55:21 GMT",
"version": "v3"
}
] | 2019-11-07 | [
[
"Manica",
"Matteo",
""
],
[
"Mathis",
"Roland",
""
],
[
"Martínez",
"María Rodríguez",
""
]
] | In recent years, the number of biomedical publications has steadfastly grown, resulting in a rich source of untapped new knowledge. Most biomedical facts are however not readily available, but buried in the form of unstructured text, and hence their exploitation requires the time-consuming manual curation of published articles. Here we present INtERAcT, a novel approach to extract protein-protein interactions from a corpus of biomedical articles related to a broad range of scientific domains in a completely unsupervised way. INtERAcT exploits vector representation of words, computed on a corpus of domain specific knowledge, and implements a new metric that estimates an interaction score between two molecules in the space where the corresponding words are embedded. We demonstrate the power of INtERAcT by reconstructing the molecular pathways associated to 10 different cancer types using a corpus of disease-specific articles for each cancer type. We evaluate INtERAcT using STRING database as a benchmark, and show that our metric outperforms currently adopted approaches for similarity computation at the task of identifying known molecular interactions in all studied cancer types. Furthermore, our approach does not require text annotation, manual curation or the definition of semantic rules based on expert knowledge, and hence it can be easily and efficiently applied to different scientific domains. Our findings suggest that INtERAcT may increase our capability to summarize the understanding of a specific disease using the published literature in an automated and completely unsupervised fashion. |
1901.07454 | Alexander Peyser | Nora Abi Akar, Ben Cumming, Vasileios Karakasis, Anne K\"usters,
Wouter Klijn, Alexander Peyser, Stuart Yates | Arbor -- a morphologically-detailed neural network simulation library
for contemporary high-performance computing architectures | PDP 2019 27th Euromicro International Conference on Parallel,
Distributed and Network-based Processing | 2019 27th Euromicro International Conference on Parallel,
Distributed and Network-Based Processing (PDP), Pavia, Italy, 2019, pp.
274-282 | 10.1109/EMPDP.2019.8671560 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce Arbor, a performance portable library for simulation of large
networks of multi-compartment neurons on HPC systems. Arbor is open source
software, developed under the auspices of the HBP. The performance portability
is by virtue of back-end specific optimizations for x86 multicore, Intel KNL,
and NVIDIA GPUs. When coupled with low memory overheads, these optimizations
make Arbor an order of magnitude faster than the most widely-used comparable
simulation software. The single-node performance can be scaled out to run very
large models at extreme scale with efficient weak scaling.
HPC, GPU, neuroscience, neuron, software
| [
{
"created": "Thu, 17 Jan 2019 07:44:39 GMT",
"version": "v1"
}
] | 2019-04-12 | [
[
"Akar",
"Nora Abi",
""
],
[
"Cumming",
"Ben",
""
],
[
"Karakasis",
"Vasileios",
""
],
[
"Küsters",
"Anne",
""
],
[
"Klijn",
"Wouter",
""
],
[
"Peyser",
"Alexander",
""
],
[
"Yates",
"Stuart",
""
]
] | We introduce Arbor, a performance portable library for simulation of large networks of multi-compartment neurons on HPC systems. Arbor is open source software, developed under the auspices of the HBP. The performance portability is by virtue of back-end specific optimizations for x86 multicore, Intel KNL, and NVIDIA GPUs. When coupled with low memory overheads, these optimizations make Arbor an order of magnitude faster than the most widely-used comparable simulation software. The single-node performance can be scaled out to run very large models at extreme scale with efficient weak scaling. HPC, GPU, neuroscience, neuron, software |
2301.11262 | Madhur Mangalam | Aaron D. Likens, Madhur Mangalam, Aaron Y. Wong, Anaelle C. Charles,
Caitlin Mills | Better than DFA? A Bayesian Method for Estimating the Hurst Exponent in
Behavioral Sciences | 50 pages, 14 figures, 6 tables | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Detrended Fluctuation Analysis (DFA) is the most popular fractal analytical
technique used to evaluate the strength of long-range correlations in empirical
time series in terms of the Hurst exponent, $H$. Specifically, DFA quantifies
the linear regression slope in log-log coordinates representing the
relationship between the time series' variability and the number of timescales
over which this variability is computed. We compared the performance of two
methods of fractal analysis -- the current gold standard, DFA, and a Bayesian
method that is not currently well-known in behavioral sciences: the
Hurst-Kolmogorov (HK) method -- in estimating the Hurst exponent of synthetic
and empirical time series. Simulations demonstrate that the HK method
consistently outperforms DFA in three important ways. The HK method: (i)
accurately assesses long-range correlations when the measurement time series is
short, (ii) shows minimal dispersion about the central tendency, and (iii)
yields a point estimate that does not depend on the length of the measurement
time series or its underlying Hurst exponent. Comparing the two methods using
empirical time series from multiple settings further supports these findings.
We conclude that applying DFA to synthetic time series and empirical time
series during brief trials is unreliable and encourage the systematic
application of the HK method to assess the Hurst exponent of empirical time
series in behavioral sciences.
| [
{
"created": "Thu, 26 Jan 2023 18:00:44 GMT",
"version": "v1"
}
] | 2023-01-27 | [
[
"Likens",
"Aaron D.",
""
],
[
"Mangalam",
"Madhur",
""
],
[
"Wong",
"Aaron Y.",
""
],
[
"Charles",
"Anaelle C.",
""
],
[
"Mills",
"Caitlin",
""
]
] | Detrended Fluctuation Analysis (DFA) is the most popular fractal analytical technique used to evaluate the strength of long-range correlations in empirical time series in terms of the Hurst exponent, $H$. Specifically, DFA quantifies the linear regression slope in log-log coordinates representing the relationship between the time series' variability and the number of timescales over which this variability is computed. We compared the performance of two methods of fractal analysis -- the current gold standard, DFA, and a Bayesian method that is not currently well-known in behavioral sciences: the Hurst-Kolmogorov (HK) method -- in estimating the Hurst exponent of synthetic and empirical time series. Simulations demonstrate that the HK method consistently outperforms DFA in three important ways. The HK method: (i) accurately assesses long-range correlations when the measurement time series is short, (ii) shows minimal dispersion about the central tendency, and (iii) yields a point estimate that does not depend on the length of the measurement time series or its underlying Hurst exponent. Comparing the two methods using empirical time series from multiple settings further supports these findings. We conclude that applying DFA to synthetic time series and empirical time series during brief trials is unreliable and encourage the systematic application of the HK method to assess the Hurst exponent of empirical time series in behavioral sciences. |
1906.10184 | Karl Friston | Karl Friston | A free energy principle for a particular physics | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | This monograph attempts a theory of every 'thing' that can be distinguished
from other things in a statistical sense. The ensuing statistical
independencies, mediated by Markov blankets, speak to a recursive composition
of ensembles (of things) at increasingly higher spatiotemporal scales. This
decomposition provides a description of small things; e.g., quantum mechanics -
via the Schrodinger equation, ensembles of small things - via statistical
mechanics and related fluctuation theorems, through to big things - via
classical mechanics. These descriptions are complemented with a Bayesian
mechanics for autonomous or active things. Although this work provides a
formulation of every thing, its main contribution is to examine the
implications of Markov blankets for self-organisation to nonequilibrium
steady-state. In brief, we recover an information geometry and accompanying
free energy principle that allows one to interpret the internal states of
something as representing or making inferences about its external states. The
ensuing Bayesian mechanics is compatible with quantum, statistical and
classical mechanics and may offer a formal description of lifelike particles.
| [
{
"created": "Mon, 24 Jun 2019 19:18:37 GMT",
"version": "v1"
}
] | 2019-06-26 | [
[
"Friston",
"Karl",
""
]
] | This monograph attempts a theory of every 'thing' that can be distinguished from other things in a statistical sense. The ensuing statistical independencies, mediated by Markov blankets, speak to a recursive composition of ensembles (of things) at increasingly higher spatiotemporal scales. This decomposition provides a description of small things; e.g., quantum mechanics - via the Schrodinger equation, ensembles of small things - via statistical mechanics and related fluctuation theorems, through to big things - via classical mechanics. These descriptions are complemented with a Bayesian mechanics for autonomous or active things. Although this work provides a formulation of every thing, its main contribution is to examine the implications of Markov blankets for self-organisation to nonequilibrium steady-state. In brief, we recover an information geometry and accompanying free energy principle that allows one to interpret the internal states of something as representing or making inferences about its external states. The ensuing Bayesian mechanics is compatible with quantum, statistical and classical mechanics and may offer a formal description of lifelike particles. |
2305.14360 | Alberto Lovison Dr. | Franco Cardin, Alberto Lovison, Amos Maritan and Aram Megighian | Brain memory working. Optimal control behavior for improved
Hopfield-like models | 8 pages, 3 figure | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Several authors have recently highlighted the need for a new dynamical
paradigm in the modelling of brain working and evolution. In particular, the
models should include the possibility of non constant and non symmetric
synaptic weights $T_{ij}$ in the neuron-neuron interaction matrix, radically
overcoming the classical Hopfield setting. Krotov and Hopfield have proposed a
non constant, still symmetric model, leading to a vector field describing a
gradient type dynamics then including a Lyapunov-like energy function. In this
note, we first will detail the general condition to produce a Hopfield like
vector field of gradient type obtaining, as a particular case, the
Krotov-Hopfield condition. Secondly, we abandon the symmetry because of two
relevant physiological facts: (1) the actual neural connections have a marked
directional character and (2) the gradient structure deriving from the symmetry
forces the dynamics always towards stationary points, prescribing every pattern
to be recognized. We propose a novel model including a set of limited but
varying controls $|\xi_{ij}|\leq K$ used for correcting a starting constant
interaction matrix, $T_{ij}=A_{ij}+\xi_{ij}$. Besides, we introduce a
reasonable controlled variational functional to be optimized. This allows us to
reproduce the following three possible outcomes when submitting a pattern to
the learning system. If (1) the dynamics leads to an already existing
stationary point without activating the controls, the system has
\emph{recognized} an existing pattern. If (2) a new stationary point is reached
by the activation of controls, then the system has \emph{learned} a new
pattern. If (3) the dynamics is \emph{wandering} without reaching neither
existing or new stationary points, then the system is unable to recognize or
learn the pattern submitted. A further feature (4), appears to model
\emph{forgetting and restoring} memory.
| [
{
"created": "Thu, 11 May 2023 09:59:12 GMT",
"version": "v1"
},
{
"created": "Sat, 13 Jan 2024 14:11:54 GMT",
"version": "v2"
},
{
"created": "Sun, 28 Apr 2024 12:59:59 GMT",
"version": "v3"
}
] | 2024-04-30 | [
[
"Cardin",
"Franco",
""
],
[
"Lovison",
"Alberto",
""
],
[
"Maritan",
"Amos",
""
],
[
"Megighian",
"Aram",
""
]
] | Several authors have recently highlighted the need for a new dynamical paradigm in the modelling of brain working and evolution. In particular, the models should include the possibility of non constant and non symmetric synaptic weights $T_{ij}$ in the neuron-neuron interaction matrix, radically overcoming the classical Hopfield setting. Krotov and Hopfield have proposed a non constant, still symmetric model, leading to a vector field describing a gradient type dynamics then including a Lyapunov-like energy function. In this note, we first will detail the general condition to produce a Hopfield like vector field of gradient type obtaining, as a particular case, the Krotov-Hopfield condition. Secondly, we abandon the symmetry because of two relevant physiological facts: (1) the actual neural connections have a marked directional character and (2) the gradient structure deriving from the symmetry forces the dynamics always towards stationary points, prescribing every pattern to be recognized. We propose a novel model including a set of limited but varying controls $|\xi_{ij}|\leq K$ used for correcting a starting constant interaction matrix, $T_{ij}=A_{ij}+\xi_{ij}$. Besides, we introduce a reasonable controlled variational functional to be optimized. This allows us to reproduce the following three possible outcomes when submitting a pattern to the learning system. If (1) the dynamics leads to an already existing stationary point without activating the controls, the system has \emph{recognized} an existing pattern. If (2) a new stationary point is reached by the activation of controls, then the system has \emph{learned} a new pattern. If (3) the dynamics is \emph{wandering} without reaching neither existing or new stationary points, then the system is unable to recognize or learn the pattern submitted. A further feature (4), appears to model \emph{forgetting and restoring} memory. |
1512.08970 | Valmir Barbosa | Valmir C. Barbosa, Raul Donangelo, Sergio R. Souza | Quasispecies dynamics on a network of interacting genotypes and
idiotypes: Applications to autoimmunity and immunodeficiency | null | Journal of Statistical Mechanics (2016), 063501 | 10.1088/1742-5468/2016/06/063501 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In spite of their many facets, the phenomena of autoimmunity and
immunodeficiency seem to be related to each other through the subtle links
connecting retroviral mutation and action to immune response and adaptation. In
a previous work, we introduced a network model of how a set of interrelated
genotypes (called a quasispecies, in the stationary state) and a set of
interrelated idiotypes (an idiotypic network) interact. That model, which does
not cover the case of a retroviral quasispecies, was instrumental for the study
of quasispecies survival when confronting the immune system and led to the
conclusion that, unlike what happens when a quasispecies is left to evolve by
itself, letting genotypes mutate too infrequently leads to the destruction of
the quasispecies. Here we extend that genotype-idiotype interaction model by
the addition of a further parameter ($\nu$) to account for the action of
retroviruses (i.e., the destruction of idiotypes by genotypes). We give
simulation results within a suitable parameter niche, highlighting the issues
of quasispecies survival and of the onset of autoimmunity through the
appearance of the so-called pathogenic idiotypes. Our main findings refer to
how $\nu$ and $\lambda$, a parameter describing the rate at which idiotypes get
stimulated, relate to each other. While for $\nu>\lambda$ the quasispecies
survives at the expense of weakening the immune system significantly or even
destroying it, for $\nu<\lambda$ the fittest genotypes of the quasispecies
become mimicked inside the immune system as pathogenic idiotypes. The latter is
in agreement with the current understanding of the HIV quasispecies.
| [
{
"created": "Wed, 30 Dec 2015 15:12:17 GMT",
"version": "v1"
}
] | 2016-07-01 | [
[
"Barbosa",
"Valmir C.",
""
],
[
"Donangelo",
"Raul",
""
],
[
"Souza",
"Sergio R.",
""
]
] | In spite of their many facets, the phenomena of autoimmunity and immunodeficiency seem to be related to each other through the subtle links connecting retroviral mutation and action to immune response and adaptation. In a previous work, we introduced a network model of how a set of interrelated genotypes (called a quasispecies, in the stationary state) and a set of interrelated idiotypes (an idiotypic network) interact. That model, which does not cover the case of a retroviral quasispecies, was instrumental for the study of quasispecies survival when confronting the immune system and led to the conclusion that, unlike what happens when a quasispecies is left to evolve by itself, letting genotypes mutate too infrequently leads to the destruction of the quasispecies. Here we extend that genotype-idiotype interaction model by the addition of a further parameter ($\nu$) to account for the action of retroviruses (i.e., the destruction of idiotypes by genotypes). We give simulation results within a suitable parameter niche, highlighting the issues of quasispecies survival and of the onset of autoimmunity through the appearance of the so-called pathogenic idiotypes. Our main findings refer to how $\nu$ and $\lambda$, a parameter describing the rate at which idiotypes get stimulated, relate to each other. While for $\nu>\lambda$ the quasispecies survives at the expense of weakening the immune system significantly or even destroying it, for $\nu<\lambda$ the fittest genotypes of the quasispecies become mimicked inside the immune system as pathogenic idiotypes. The latter is in agreement with the current understanding of the HIV quasispecies. |
0709.3049 | David A. Kessler | David A. Kessler | Epidemic Size in the Sis Model of Endemic Infec- Tions | null | null | null | null | q-bio.PE | null | We study the Susceptible-Infected-Susceptible model of the spread of an
endemic infection. We calculate an exact expression for the mean number of
transmissions for all values of the population and the infectivity. We derive
the large-N asymptotic behavior for the infectivitiy below, above, and in the
critical region. We obtain an analytical expression for the probability
distribution of the number of transmissions, n, in the critical region. We show
that this distribution has a $n^3/2$ singularity for small n and decays
exponentially for large n. The exponent decreases with the distance from
threshold, diverging to infinity far below and approaching zero far above.
| [
{
"created": "Wed, 19 Sep 2007 15:45:01 GMT",
"version": "v1"
}
] | 2007-09-20 | [
[
"Kessler",
"David A.",
""
]
] | We study the Susceptible-Infected-Susceptible model of the spread of an endemic infection. We calculate an exact expression for the mean number of transmissions for all values of the population and the infectivity. We derive the large-N asymptotic behavior for the infectivitiy below, above, and in the critical region. We obtain an analytical expression for the probability distribution of the number of transmissions, n, in the critical region. We show that this distribution has a $n^3/2$ singularity for small n and decays exponentially for large n. The exponent decreases with the distance from threshold, diverging to infinity far below and approaching zero far above. |
1410.5362 | Saptarshi Das | Wasifa Jamal, Saptarshi Das, Ioana-Anastasia Oprescu, Koushik
Maharatna | Prediction of Synchrostate Transitions in EEG Signals Using Markov Chain
Models | 5 pages, 5 figures | Signal Processing Letters, IEEE, Volume 22, Issue 2, Pages 149 -
152, Feb. 2015 | 10.1109/LSP.2014.2352251 | null | q-bio.NC physics.med-ph stat.AP stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a stochastic model using the concept of Markov chains for
the inter-state transitions of the millisecond order quasi-stable phase
synchronized patterns or synchrostates, found in multi-channel
Electroencephalogram (EEG) signals. First and second order transition
probability matrices are estimated for Markov chain modelling from 100 trials
of 128-channel EEG signals during two different face perception tasks.
Prediction accuracies with such finite Markov chain models for synchrostate
transition are also compared, under a data-partitioning based cross-validation
scheme.
| [
{
"created": "Mon, 20 Oct 2014 17:28:13 GMT",
"version": "v1"
}
] | 2014-10-21 | [
[
"Jamal",
"Wasifa",
""
],
[
"Das",
"Saptarshi",
""
],
[
"Oprescu",
"Ioana-Anastasia",
""
],
[
"Maharatna",
"Koushik",
""
]
] | This paper proposes a stochastic model using the concept of Markov chains for the inter-state transitions of the millisecond order quasi-stable phase synchronized patterns or synchrostates, found in multi-channel Electroencephalogram (EEG) signals. First and second order transition probability matrices are estimated for Markov chain modelling from 100 trials of 128-channel EEG signals during two different face perception tasks. Prediction accuracies with such finite Markov chain models for synchrostate transition are also compared, under a data-partitioning based cross-validation scheme. |
1807.05684 | Ehtibar Dzhafarov | Irina Basieva, V\'ictor H. Cervantes, Ehtibar N. Dzhafarov, Andrei
Khrennikov | True Contextuality Beats Direct Influences in Human Decision Making | Journal of Experimental Psychology: General 148, 1925-1937 | Journal of Experimental Psychology: General 148, 1925-1937 | 10.1037/xge0000585 | null | q-bio.NC quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In quantum physics there are well-known situations when measurements of the
same property in different contexts (under different conditions) have the same
probability distribution, but cannot be represented by one and the same random
variable. Such systems of random variables are called contextual. More
generally, true contextuality is observed when different contexts force
measurements of the same property (in psychology, responses to the same
question) to be more dissimilar random variables than warranted by the
difference of their distributions. The difference in distributions is itself a
form of context-dependence, but of another nature: it is attributable to direct
causal influences exerted by contexts upon the random variables. The
Contextuality-by-Default (CbD) theory allows one to separate true contextuality
from direct influences in the overall context-dependence. The CbD analysis of
numerous previous attempts to demonstrate contextuality in human judgments
shows that all context-dependence in them can be accounted for by direct
influences, with no true contextuality present. However, contextual systems in
human behavior can be found. In this paper we present a series of crowdsourcing
experiments that exhibit true contextuality in simple decision making.}{The
design of these experiments is an elaboration of one introduced in the "Snow
Queen" experiment (Decision 5, 193-204, 2018), where contextuality was for the
first time demonstrated unequivocally.
| [
{
"created": "Mon, 16 Jul 2018 05:31:04 GMT",
"version": "v1"
},
{
"created": "Fri, 30 Nov 2018 23:05:06 GMT",
"version": "v2"
},
{
"created": "Wed, 23 Jan 2019 14:50:14 GMT",
"version": "v3"
},
{
"created": "Sat, 30 May 2020 16:47:08 GMT",
"version": "v4"
}
] | 2020-06-02 | [
[
"Basieva",
"Irina",
""
],
[
"Cervantes",
"Víctor H.",
""
],
[
"Dzhafarov",
"Ehtibar N.",
""
],
[
"Khrennikov",
"Andrei",
""
]
] | In quantum physics there are well-known situations when measurements of the same property in different contexts (under different conditions) have the same probability distribution, but cannot be represented by one and the same random variable. Such systems of random variables are called contextual. More generally, true contextuality is observed when different contexts force measurements of the same property (in psychology, responses to the same question) to be more dissimilar random variables than warranted by the difference of their distributions. The difference in distributions is itself a form of context-dependence, but of another nature: it is attributable to direct causal influences exerted by contexts upon the random variables. The Contextuality-by-Default (CbD) theory allows one to separate true contextuality from direct influences in the overall context-dependence. The CbD analysis of numerous previous attempts to demonstrate contextuality in human judgments shows that all context-dependence in them can be accounted for by direct influences, with no true contextuality present. However, contextual systems in human behavior can be found. In this paper we present a series of crowdsourcing experiments that exhibit true contextuality in simple decision making.}{The design of these experiments is an elaboration of one introduced in the "Snow Queen" experiment (Decision 5, 193-204, 2018), where contextuality was for the first time demonstrated unequivocally. |
q-bio/0703054 | Concetta Miccio | C. Destri, C. Miccio | A simple stochastic model for the evolution of protein lengths | 12 pages, 4 figures | null | 10.1103/PhysRevE.76.011924 | null | q-bio.PE q-bio.QM | null | We analyse a simple discrete-time stochastic process for the theoretical
modeling of the evolution of protein lengths. At every step of the process a
new protein is produced as a modification of one of the proteins already
existing and its length is assumed to be random variable which depends only on
the length of the originating protein. Thus a Random Recursive Trees (RRT) is
produced over the natural integers. If (quasi) scale invariance is assumed, the
length distribution in a single history tends to a lognormal form with a
specific signature of the deviations from exact gaussianity. Comparison with
the very large SIMAP protein database shows good agreement.
| [
{
"created": "Mon, 26 Mar 2007 14:20:02 GMT",
"version": "v1"
},
{
"created": "Mon, 26 Mar 2007 22:08:31 GMT",
"version": "v2"
}
] | 2009-11-13 | [
[
"Destri",
"C.",
""
],
[
"Miccio",
"C.",
""
]
] | We analyse a simple discrete-time stochastic process for the theoretical modeling of the evolution of protein lengths. At every step of the process a new protein is produced as a modification of one of the proteins already existing and its length is assumed to be random variable which depends only on the length of the originating protein. Thus a Random Recursive Trees (RRT) is produced over the natural integers. If (quasi) scale invariance is assumed, the length distribution in a single history tends to a lognormal form with a specific signature of the deviations from exact gaussianity. Comparison with the very large SIMAP protein database shows good agreement. |
1103.2834 | Swagatam Mukhopadhyay | Swagatam Mukhopadhyay, Paul D. Schedl, Vasily M. Studitsky and Anirvan
M. Sengupta | Theoretical analysis of the role of chromatin interactions in long-range
action of enhancers and insulators | 10 pages, originally submitted to an (undisclosed) journal in May
2010 | null | 10.1073/pnas.1103845108 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Long-distance regulatory interactions between enhancers and their target
genes are commonplace in higher eukaryotes. Interposed boundaries or insulators
are able to block these long distance regulatory interactions. The mechanistic
basis for insulator activity and how it relates to enhancer
action-at-a-distance remains unclear. Here we explore the idea that topological
loops could simultaneously account for regulatory interactions of distal
enhancers and the insulating activity of boundary elements. We show that while
loop formation is not in itself sufficient to explain action at a distance,
incorporating transient non-specific and moderate attractive interactions
between the chromatin fibers strongly enhances long-distance regulatory
interactions and is sufficient to generate a euchromatin-like state. Under
these same conditions, the subdivision of the loop into two topologically
independent loops by insulators inhibits inter-domain interactions. The
underlying cause of this effect is a suppression of crossings in the contact
map at intermediate distances. Thus our model simultaneously accounts for
regulatory interactions at a distance and the insulator activity of boundary
elements. This unified model of the regulatory roles of chromatin loops makes
several testable predictions that could be confronted with \emph{in vitro}
experiments, as well as genomic chromatin conformation capture and fluorescent
microscopic approaches.
| [
{
"created": "Tue, 15 Mar 2011 03:09:12 GMT",
"version": "v1"
}
] | 2015-05-27 | [
[
"Mukhopadhyay",
"Swagatam",
""
],
[
"Schedl",
"Paul D.",
""
],
[
"Studitsky",
"Vasily M.",
""
],
[
"Sengupta",
"Anirvan M.",
""
]
] | Long-distance regulatory interactions between enhancers and their target genes are commonplace in higher eukaryotes. Interposed boundaries or insulators are able to block these long distance regulatory interactions. The mechanistic basis for insulator activity and how it relates to enhancer action-at-a-distance remains unclear. Here we explore the idea that topological loops could simultaneously account for regulatory interactions of distal enhancers and the insulating activity of boundary elements. We show that while loop formation is not in itself sufficient to explain action at a distance, incorporating transient non-specific and moderate attractive interactions between the chromatin fibers strongly enhances long-distance regulatory interactions and is sufficient to generate a euchromatin-like state. Under these same conditions, the subdivision of the loop into two topologically independent loops by insulators inhibits inter-domain interactions. The underlying cause of this effect is a suppression of crossings in the contact map at intermediate distances. Thus our model simultaneously accounts for regulatory interactions at a distance and the insulator activity of boundary elements. This unified model of the regulatory roles of chromatin loops makes several testable predictions that could be confronted with \emph{in vitro} experiments, as well as genomic chromatin conformation capture and fluorescent microscopic approaches. |
1709.07211 | Mahmoud Hassan | Ahmad Mheich (LTSI), Mahmoud Hassan (LTSI), Mohamad Khalil, Vincent
Gripon (ELEC), Olivier Dufor, Fabrice Wendling (LTSI) | SimiNet: a Novel Method for Quantifying Brain Network Similarity | null | IEEE Transactions on Pattern Analysis and Machine Intelligence,
Institute of Electrical and Electronics Engineers, 2017, pp.1 - 1 | 10.1109/TPAMI.2017.2750160 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Quantifying the similarity between two networks is critical in many
applications. A number of algorithms have been proposed to compute graph
similarity, mainly based on the properties of nodes and edges. Interestingly,
most of these algorithms ignore the physical location of the nodes, which is a
key factor in the context of brain networks involving spatially defined
functional areas. In this paper, we present a novel algorithm called "SimiNet"
for measuring similarity between two graphs whose nodes are defined a priori
within a 3D coordinate system. SimiNet provides a quantified index (ranging
from 0 to 1) that accounts for node, edge and spatiality features. Complex
graphs were simulated to evaluate the performance of SimiNet that is compared
with eight state-of-art methods. Results show that SimiNet is able to detect
weak spatial variations in compared graphs in addition to computing similarity
using both nodes and edges. SimiNet was also applied to real brain networks
obtained during a visual recognition task. The algorithm shows high performance
to detect spatial variation of brain networks obtained during a naming task of
two categories of visual stimuli: animals and tools. A perspective to this work
is a better understanding of object categorization in the human brain.
| [
{
"created": "Thu, 21 Sep 2017 08:38:42 GMT",
"version": "v1"
}
] | 2017-09-22 | [
[
"Mheich",
"Ahmad",
"",
"LTSI"
],
[
"Hassan",
"Mahmoud",
"",
"LTSI"
],
[
"Khalil",
"Mohamad",
"",
"ELEC"
],
[
"Gripon",
"Vincent",
"",
"ELEC"
],
[
"Dufor",
"Olivier",
"",
"LTSI"
],
[
"Wendling",
"Fabrice",
... | Quantifying the similarity between two networks is critical in many applications. A number of algorithms have been proposed to compute graph similarity, mainly based on the properties of nodes and edges. Interestingly, most of these algorithms ignore the physical location of the nodes, which is a key factor in the context of brain networks involving spatially defined functional areas. In this paper, we present a novel algorithm called "SimiNet" for measuring similarity between two graphs whose nodes are defined a priori within a 3D coordinate system. SimiNet provides a quantified index (ranging from 0 to 1) that accounts for node, edge and spatiality features. Complex graphs were simulated to evaluate the performance of SimiNet that is compared with eight state-of-art methods. Results show that SimiNet is able to detect weak spatial variations in compared graphs in addition to computing similarity using both nodes and edges. SimiNet was also applied to real brain networks obtained during a visual recognition task. The algorithm shows high performance to detect spatial variation of brain networks obtained during a naming task of two categories of visual stimuli: animals and tools. A perspective to this work is a better understanding of object categorization in the human brain. |
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