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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2407.18494
|
Yiduo Chen
|
Yi-Duo Chen, Jian-Yue Guan, Zhi-Xi Wu (Lanzhou Center for Theoretical
Physics, Key Laboratory of Theoretical Physics of Gansu Province, and Key
Laboratory of Quantum Theory and Applications of MoE, Lanzhou University,
Lanzhou, China and Institute of Computational Physics and Complex Systems,
Lanzhou University, Lanzhou, China)
|
Coevolutionary game dynamics with localized environmental resource
feedback
| null | null | null | null |
q-bio.PE nlin.AO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Dynamic environments shape diverse dynamics in evolutionary game systems. We
introduce spatial heterogeneity of resources into the Prisoner's Dilemma Game
model to explore the co-evolution of individuals' strategies and environmental
resources. The adequacy of resources significantly affects the survival
competitiveness of surrounding individuals. Feedback between individuals'
strategies and the resources they can use leads to the dynamic of the
"oscillatory tragedy of the commons". Our findings indicate that when the
influence of individuals' strategies on the update rate of resources is
significantly high, individuals can form sustained spatial clustered patterns.
These sustained patterns can directly trigger a transition in the system from
the persistent periodic oscillating state to an equilibrium state. These
findings align with observed phenomena in real ecosystems, where organisms
organize their spatial structures to maintain system stability. We discuss
critical phenomena in detail, demonstrating that the aforementioned phase
transition is robust across various system parameters including: the strength
of cooperators in restoring the environment, the initial distributions of
cooperators, and noise.
|
[
{
"created": "Fri, 26 Jul 2024 03:56:09 GMT",
"version": "v1"
}
] |
2024-07-29
|
[
[
"Chen",
"Yi-Duo",
"",
"Lanzhou Center for Theoretical\n Physics, Key Laboratory of Theoretical Physics of Gansu Province, and Key\n Laboratory of Quantum Theory and Applications of MoE, Lanzhou University,\n Lanzhou, China and Institute of Computational Physics and Complex Systems,\n Lanzhou University, Lanzhou, China"
],
[
"Guan",
"Jian-Yue",
"",
"Lanzhou Center for Theoretical\n Physics, Key Laboratory of Theoretical Physics of Gansu Province, and Key\n Laboratory of Quantum Theory and Applications of MoE, Lanzhou University,\n Lanzhou, China and Institute of Computational Physics and Complex Systems,\n Lanzhou University, Lanzhou, China"
],
[
"Wu",
"Zhi-Xi",
"",
"Lanzhou Center for Theoretical\n Physics, Key Laboratory of Theoretical Physics of Gansu Province, and Key\n Laboratory of Quantum Theory and Applications of MoE, Lanzhou University,\n Lanzhou, China and Institute of Computational Physics and Complex Systems,\n Lanzhou University, Lanzhou, China"
]
] |
Dynamic environments shape diverse dynamics in evolutionary game systems. We introduce spatial heterogeneity of resources into the Prisoner's Dilemma Game model to explore the co-evolution of individuals' strategies and environmental resources. The adequacy of resources significantly affects the survival competitiveness of surrounding individuals. Feedback between individuals' strategies and the resources they can use leads to the dynamic of the "oscillatory tragedy of the commons". Our findings indicate that when the influence of individuals' strategies on the update rate of resources is significantly high, individuals can form sustained spatial clustered patterns. These sustained patterns can directly trigger a transition in the system from the persistent periodic oscillating state to an equilibrium state. These findings align with observed phenomena in real ecosystems, where organisms organize their spatial structures to maintain system stability. We discuss critical phenomena in detail, demonstrating that the aforementioned phase transition is robust across various system parameters including: the strength of cooperators in restoring the environment, the initial distributions of cooperators, and noise.
|
1808.03642
|
Alexandre De Brevern
|
Sneha Vishwanath (Molecular Biophysics Unit), Alexandre De Brevern
(BIGR), Narayanaswamy Srinivasan
|
Same but not alike: Structure, flexibility and energetics of domains in
multi-domain proteins are influenced by the presence of other domains
| null |
PLoS Computational Biology, Public Library of Science, 2018, 14
(2), pp.e1006008
|
10.1371/journal.pcbi.1006008
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The majority of the proteins encoded in the genomes of eukaryotes contain
more than one domain. Reasons for high prevalence of multi-domain proteins in
various organisms have been attributed to higher stability and functional and
folding advantages over single-domain proteins. Despite these advantages, many
proteins are composed of only one domain while their homologous domains are
part of multi-domain proteins. In the study presented here, differences in the
properties of protein domains in single-domain and multi-domain systems and
their influence on functions are discussed. We studied 20 pairs of identical
protein domains, which were crystallized in two forms (a) tethered to other
proteins domains and (b) tethered to fewer protein domains than (a) or not
tethered to any protein domain. Results suggest that tethering of domains in
multi-domain proteins influences the structural, dynamic and energetic
properties of the constituent protein domains. 50% of the protein domain pairs
show significant structural deviations while 90% of the protein domain pairs
show differences in dynamics and 12% of the residues show differences in the
energetics. To gain further insights on the influence of tethering on the
function of the domains, 4 pairs of homologous protein domains, where one of
them is a full-length single-domain protein and the other protein domain is a
part of a multi-domain protein, were studied. Analyses showed that identical
and structurally equivalent functional residues show differential dynamics in
homologous protein domains, though comparable dynamics between in-silico
generated chimera protein and multi-domain proteins were observed. From these
observations, the differences observed in the functions of homologous proteins
could be attributed to the presence of tethered domain. Overall, we conclude
that tethered domains in multi-domain proteins not only provide stability or
folding advantages but also influence pathways resulting in differences in
function or regulatory properties.
|
[
{
"created": "Fri, 10 Aug 2018 07:01:29 GMT",
"version": "v1"
}
] |
2018-08-14
|
[
[
"Vishwanath",
"Sneha",
"",
"Molecular Biophysics Unit"
],
[
"De Brevern",
"Alexandre",
"",
"BIGR"
],
[
"Srinivasan",
"Narayanaswamy",
""
]
] |
The majority of the proteins encoded in the genomes of eukaryotes contain more than one domain. Reasons for high prevalence of multi-domain proteins in various organisms have been attributed to higher stability and functional and folding advantages over single-domain proteins. Despite these advantages, many proteins are composed of only one domain while their homologous domains are part of multi-domain proteins. In the study presented here, differences in the properties of protein domains in single-domain and multi-domain systems and their influence on functions are discussed. We studied 20 pairs of identical protein domains, which were crystallized in two forms (a) tethered to other proteins domains and (b) tethered to fewer protein domains than (a) or not tethered to any protein domain. Results suggest that tethering of domains in multi-domain proteins influences the structural, dynamic and energetic properties of the constituent protein domains. 50% of the protein domain pairs show significant structural deviations while 90% of the protein domain pairs show differences in dynamics and 12% of the residues show differences in the energetics. To gain further insights on the influence of tethering on the function of the domains, 4 pairs of homologous protein domains, where one of them is a full-length single-domain protein and the other protein domain is a part of a multi-domain protein, were studied. Analyses showed that identical and structurally equivalent functional residues show differential dynamics in homologous protein domains, though comparable dynamics between in-silico generated chimera protein and multi-domain proteins were observed. From these observations, the differences observed in the functions of homologous proteins could be attributed to the presence of tethered domain. Overall, we conclude that tethered domains in multi-domain proteins not only provide stability or folding advantages but also influence pathways resulting in differences in function or regulatory properties.
|
1908.10897
|
Andrew Eckford
|
Gregory R. Hessler, Andrew W. Eckford, and Peter J. Thomas
|
Linear Noise Approximation of Intensity-Driven Signal Transduction
Channels
|
Accepted for publication at the 2019 IEEE Global Communications
Conference (GLOBECOM)
| null | null | null |
q-bio.QM cs.IT math.IT q-bio.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Biochemical signal transduction, a form of molecular communication, can be
modeled using graphical Markov channels with input-modulated transition rates.
Such channel models are strongly non-Gaussian. In this paper we use a linear
noise approximation to construct a novel class of Gaussian additive white noise
channels that capture essential features of fully- and partially-observed
intensity-driven signal transduction. When channel state transitions that are
sensitive to the input signal are directly observable, high-frequency
information is transduced more efficiently than low-frequency information, and
the mutual information rate per bandwidth (spectral efficiency) is
significantly greater than when sensitive transitions and observable
transitions are disjoint. When both observable and hidden transitions are
input-sensitive, we observe a superadditive increase in spectral efficiency.
|
[
{
"created": "Wed, 28 Aug 2019 18:24:57 GMT",
"version": "v1"
}
] |
2019-08-30
|
[
[
"Hessler",
"Gregory R.",
""
],
[
"Eckford",
"Andrew W.",
""
],
[
"Thomas",
"Peter J.",
""
]
] |
Biochemical signal transduction, a form of molecular communication, can be modeled using graphical Markov channels with input-modulated transition rates. Such channel models are strongly non-Gaussian. In this paper we use a linear noise approximation to construct a novel class of Gaussian additive white noise channels that capture essential features of fully- and partially-observed intensity-driven signal transduction. When channel state transitions that are sensitive to the input signal are directly observable, high-frequency information is transduced more efficiently than low-frequency information, and the mutual information rate per bandwidth (spectral efficiency) is significantly greater than when sensitive transitions and observable transitions are disjoint. When both observable and hidden transitions are input-sensitive, we observe a superadditive increase in spectral efficiency.
|
1802.01328
|
Marcus Krantz
|
Jesper Romers, Sebastian Thieme, Ulrike M\"unzner and Marcus Krantz
|
Using rxncon to develop rule based models
| null | null | null | null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a protocol for building, validating and simulating models of
signal transduction networks. These networks are challenging modelling targets
due to the combinatorial complexity and sparse data, which have made it a major
challenge even to formalise the current knowledge. To address this, the
community has developed methods to model biomolecular reaction networks based
on site dynamics. The strength of this approach is that reactions and states
can be defined at variable resolution, which makes it possible to adapt the
model resolution to the empirical data. This improves both scalability and
accuracy, making it possible to formalise large models of signal transduction
networks. Here, we present a method to build and validate large models of
signal transduction networks. The workflow is based on rxncon, the
reaction-contingency language. In a five-step process, we create a mechanistic
network model, convert it into an executable Boolean model, use the Boolean
model to evaluate and improve the network, and finally export the rxncon model
into a rule based format. We provide an introduction to the rxncon language and
an annotated, step-by-step protocol for the workflow. Finally, we create a
small model of the insulin signalling pathway to illustrate the protocol,
together with some of the challenges - and some of their solutions - in
modelling signal transduction.
|
[
{
"created": "Mon, 5 Feb 2018 10:18:44 GMT",
"version": "v1"
}
] |
2018-02-06
|
[
[
"Romers",
"Jesper",
""
],
[
"Thieme",
"Sebastian",
""
],
[
"Münzner",
"Ulrike",
""
],
[
"Krantz",
"Marcus",
""
]
] |
We present a protocol for building, validating and simulating models of signal transduction networks. These networks are challenging modelling targets due to the combinatorial complexity and sparse data, which have made it a major challenge even to formalise the current knowledge. To address this, the community has developed methods to model biomolecular reaction networks based on site dynamics. The strength of this approach is that reactions and states can be defined at variable resolution, which makes it possible to adapt the model resolution to the empirical data. This improves both scalability and accuracy, making it possible to formalise large models of signal transduction networks. Here, we present a method to build and validate large models of signal transduction networks. The workflow is based on rxncon, the reaction-contingency language. In a five-step process, we create a mechanistic network model, convert it into an executable Boolean model, use the Boolean model to evaluate and improve the network, and finally export the rxncon model into a rule based format. We provide an introduction to the rxncon language and an annotated, step-by-step protocol for the workflow. Finally, we create a small model of the insulin signalling pathway to illustrate the protocol, together with some of the challenges - and some of their solutions - in modelling signal transduction.
|
2201.11215
|
Olusola Odeyomi
|
Olusola Odeyomi, and Gergely Zaruba
|
Predicting Succinylation Sites in Proteins with Improved Deep Learning
Architecture
| null | null | null | null |
q-bio.BM cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Post-translational modifications (PTMs) in proteins occur after the process
of translation. PTMs account for many cellular processes such as
deoxyribonucleic acid (DNA) repair, cell signaling and cell death. One of the
recent PTMs is succinylation. Succinylation modifies lysine residue from $-1$
to $+1$. Locating succinylation sites using experimental methods, such as mass
spectrometry is very laborious. Hence, computational methods are favored using
machine learning techniques. This paper proposes a deep learning architecture
to predict succinylation sites. The performance of the proposed architecture is
compared to the state-of-the-art deep learning architecture and other
traditional machine learning techniques for succinylation. It is shown from the
performance metrics that the proposed architecture provides a good trade-off
between speed of computation and classification accuracy.
|
[
{
"created": "Mon, 27 Dec 2021 16:15:34 GMT",
"version": "v1"
}
] |
2022-01-28
|
[
[
"Odeyomi",
"Olusola",
""
],
[
"Zaruba",
"Gergely",
""
]
] |
Post-translational modifications (PTMs) in proteins occur after the process of translation. PTMs account for many cellular processes such as deoxyribonucleic acid (DNA) repair, cell signaling and cell death. One of the recent PTMs is succinylation. Succinylation modifies lysine residue from $-1$ to $+1$. Locating succinylation sites using experimental methods, such as mass spectrometry is very laborious. Hence, computational methods are favored using machine learning techniques. This paper proposes a deep learning architecture to predict succinylation sites. The performance of the proposed architecture is compared to the state-of-the-art deep learning architecture and other traditional machine learning techniques for succinylation. It is shown from the performance metrics that the proposed architecture provides a good trade-off between speed of computation and classification accuracy.
|
2106.03276
|
Zhou Fang
|
Zhou Fang, Ankit Gupta, Mustafa Khammash
|
Stochastic filtering for multiscale stochastic reaction networks based
on hybrid approximations
|
8 figures, 33 pages. Accepted to the Journal of Computational Physics
| null |
10.1016/j.jcp.2022.111441
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the past few decades, the development of fluorescent technologies and
microscopic techniques has greatly improved scientists' ability to observe
real-time single-cell activities. In this paper, we consider the filtering
problem associate with these advanced technologies, i.e., how to estimate
latent dynamic states of an intracellular multiscale stochastic reaction
network from time-course measurements of fluorescent reporters. A good solution
to this problem can further improve scientists' ability to extract information
about intracellular systems from time-course experiments.
A straightforward approach to this filtering problem is to use a particle
filter where particles are generated by simulation of the full model and
weighted according to observations. However, the exact simulation of the full
dynamic model usually takes an impractical amount of computational time and
prevents this type of particle filters from being used for real-time
applications, such as transcription regulation networks. Inspired by the recent
development of hybrid approximations to multiscale chemical reaction networks,
we approach the filtering problem in an alternative way. We first prove that
accurate solutions to the filtering problem can be constructed by solving the
filtering problem for a reduced model that represents the dynamics as a hybrid
process. The model reduction is based on exploiting the time-scale separations
in the original network and, therefore, can greatly reduce the computational
effort required to simulate the dynamics. As a result, we are able to develop
efficient particle filters to solve the filtering problem for the original
model by applying particle filters to the reduced model. We illustrate the
accuracy and the computational efficiency of our approach using several
numerical examples.
|
[
{
"created": "Sun, 6 Jun 2021 23:28:44 GMT",
"version": "v1"
},
{
"created": "Sat, 9 Jul 2022 20:38:38 GMT",
"version": "v2"
}
] |
2022-07-27
|
[
[
"Fang",
"Zhou",
""
],
[
"Gupta",
"Ankit",
""
],
[
"Khammash",
"Mustafa",
""
]
] |
In the past few decades, the development of fluorescent technologies and microscopic techniques has greatly improved scientists' ability to observe real-time single-cell activities. In this paper, we consider the filtering problem associate with these advanced technologies, i.e., how to estimate latent dynamic states of an intracellular multiscale stochastic reaction network from time-course measurements of fluorescent reporters. A good solution to this problem can further improve scientists' ability to extract information about intracellular systems from time-course experiments. A straightforward approach to this filtering problem is to use a particle filter where particles are generated by simulation of the full model and weighted according to observations. However, the exact simulation of the full dynamic model usually takes an impractical amount of computational time and prevents this type of particle filters from being used for real-time applications, such as transcription regulation networks. Inspired by the recent development of hybrid approximations to multiscale chemical reaction networks, we approach the filtering problem in an alternative way. We first prove that accurate solutions to the filtering problem can be constructed by solving the filtering problem for a reduced model that represents the dynamics as a hybrid process. The model reduction is based on exploiting the time-scale separations in the original network and, therefore, can greatly reduce the computational effort required to simulate the dynamics. As a result, we are able to develop efficient particle filters to solve the filtering problem for the original model by applying particle filters to the reduced model. We illustrate the accuracy and the computational efficiency of our approach using several numerical examples.
|
1602.02945
|
Adeel Razi
|
Adeel Razi and Karl Friston
|
The connected brain: Causality, models and intrinsic dynamics
|
52 pages, Feature Article, Accepted, IEEE Signal Processing Magazine
| null |
10.1109/MSP.2015.2482121
| null |
q-bio.NC math.DS stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, there have been several concerted international efforts - the BRAIN
initiative, European Human Brain Project and the Human Connectome Project, to
name a few - that hope to revolutionize our understanding of the connected
brain. Over the past two decades, functional neuroimaging has emerged as the
predominant technique in systems neuroscience. This is foreshadowed by an ever
increasing number of publications on functional connectivity, causal modeling,
connectomics, and multivariate analyses of distributed patterns of brain
responses. In this article, we summarize pedagogically the (deep) history of
brain mapping. We will highlight the theoretical advances made in the (dynamic)
causal modelling of brain function - that may have escaped the wider audience
of this article - and provide a brief overview of recent developments and
interesting clinical applications. We hope that this article will engage the
signal processing community by showcasing the inherently multidisciplinary
nature of this important topic and the intriguing questions that are being
addressed.
|
[
{
"created": "Tue, 9 Feb 2016 11:54:38 GMT",
"version": "v1"
}
] |
2016-02-10
|
[
[
"Razi",
"Adeel",
""
],
[
"Friston",
"Karl",
""
]
] |
Recently, there have been several concerted international efforts - the BRAIN initiative, European Human Brain Project and the Human Connectome Project, to name a few - that hope to revolutionize our understanding of the connected brain. Over the past two decades, functional neuroimaging has emerged as the predominant technique in systems neuroscience. This is foreshadowed by an ever increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. In this article, we summarize pedagogically the (deep) history of brain mapping. We will highlight the theoretical advances made in the (dynamic) causal modelling of brain function - that may have escaped the wider audience of this article - and provide a brief overview of recent developments and interesting clinical applications. We hope that this article will engage the signal processing community by showcasing the inherently multidisciplinary nature of this important topic and the intriguing questions that are being addressed.
|
2010.13478
|
Yong-Joon Song
|
Yong Joon Song, Dong Jin Ji, Hye In Seo, Gyu Bum Han, and Dong Ho Cho
|
Pairwise heuristic sequence alignment algorithm based on deep
reinforcement learning
|
20pages, 9figures
| null | null | null |
q-bio.QM q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Various methods have been developed to analyze the association between
organisms and their genomic sequences. Among them, sequence alignment is the
most frequently used for comparative analysis of biological genomes. However,
the traditional sequence alignment method is considerably complicated in
proportion to the sequences' length, and it is significantly challenging to
align long sequences such as a human genome. Currently, several multiple
sequence alignment algorithms are available that can reduce the complexity and
improve the alignment performance of various genomes. However, there have been
relatively fewer attempts to improve the alignment performance of the pairwise
alignment algorithm. After grasping these problems, we intend to propose a new
sequence alignment method using deep reinforcement learning. This research
shows the application method of the deep reinforcement learning to the sequence
alignment system and the way how the deep reinforcement learning can improve
the conventional sequence alignment method.
|
[
{
"created": "Mon, 26 Oct 2020 10:49:12 GMT",
"version": "v1"
}
] |
2020-10-27
|
[
[
"Song",
"Yong Joon",
""
],
[
"Ji",
"Dong Jin",
""
],
[
"Seo",
"Hye In",
""
],
[
"Han",
"Gyu Bum",
""
],
[
"Cho",
"Dong Ho",
""
]
] |
Various methods have been developed to analyze the association between organisms and their genomic sequences. Among them, sequence alignment is the most frequently used for comparative analysis of biological genomes. However, the traditional sequence alignment method is considerably complicated in proportion to the sequences' length, and it is significantly challenging to align long sequences such as a human genome. Currently, several multiple sequence alignment algorithms are available that can reduce the complexity and improve the alignment performance of various genomes. However, there have been relatively fewer attempts to improve the alignment performance of the pairwise alignment algorithm. After grasping these problems, we intend to propose a new sequence alignment method using deep reinforcement learning. This research shows the application method of the deep reinforcement learning to the sequence alignment system and the way how the deep reinforcement learning can improve the conventional sequence alignment method.
|
2002.07327
|
Soha Hassoun
|
Gian Marco Visani, Michael C. Hughes, Soha Hassoun
|
Enzyme promiscuity prediction using hierarchy-informed multi-label
classification
|
Presented as a poster at the 2019 Machine Learning for Computational
Biology Symposium, Vancouver, CA Accepted for publication, Bioinformatics,
Jan 22, 2021
| null | null | null |
q-bio.CB cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
As experimental efforts are costly and time consuming, computational
characterization of enzyme capabilities is an attractive alternative. We
present and evaluate several machine-learning models to predict which of 983
distinct enzymes, as defined via the Enzyme Commission, EC, numbers, are likely
to interact with a given query molecule. Our data consists of enzyme-substrate
interactions from the BRENDA database. Some interactions are attributed to
natural selection and involve the enzyme's natural substrates. The majority of
the interactions however involve non-natural substrates, thus reflecting
promiscuous enzymatic activities. We frame this enzyme promiscuity prediction
problem as a multi-label classification task. We maximally utilize inhibitor
and unlabelled data to train prediction models that can take advantage of known
hierarchical relationships between enzyme classes. We report that a
hierarchical multi-label neural network, EPP-HMCNF, is the best model for
solving this problem, outperforming k-nearest neighbors similarity-based and
other machine learning models. We show that inhibitor information during
training consistently improves predictive power, particularly for EPP-HMCNF. We
also show that all promiscuity prediction models perform worse under a
realistic data split when compared to a random data split, and when evaluating
performance on non-natural substrates compared to natural substrates. We
provide Python code for EPP-HMCNF and other models in a repository termed EPP
(Enzyme Promiscuity Prediction) at https://github.com/hassounlab/EPP.
|
[
{
"created": "Tue, 18 Feb 2020 01:39:24 GMT",
"version": "v1"
},
{
"created": "Tue, 26 Jan 2021 03:01:52 GMT",
"version": "v2"
}
] |
2021-01-27
|
[
[
"Visani",
"Gian Marco",
""
],
[
"Hughes",
"Michael C.",
""
],
[
"Hassoun",
"Soha",
""
]
] |
As experimental efforts are costly and time consuming, computational characterization of enzyme capabilities is an attractive alternative. We present and evaluate several machine-learning models to predict which of 983 distinct enzymes, as defined via the Enzyme Commission, EC, numbers, are likely to interact with a given query molecule. Our data consists of enzyme-substrate interactions from the BRENDA database. Some interactions are attributed to natural selection and involve the enzyme's natural substrates. The majority of the interactions however involve non-natural substrates, thus reflecting promiscuous enzymatic activities. We frame this enzyme promiscuity prediction problem as a multi-label classification task. We maximally utilize inhibitor and unlabelled data to train prediction models that can take advantage of known hierarchical relationships between enzyme classes. We report that a hierarchical multi-label neural network, EPP-HMCNF, is the best model for solving this problem, outperforming k-nearest neighbors similarity-based and other machine learning models. We show that inhibitor information during training consistently improves predictive power, particularly for EPP-HMCNF. We also show that all promiscuity prediction models perform worse under a realistic data split when compared to a random data split, and when evaluating performance on non-natural substrates compared to natural substrates. We provide Python code for EPP-HMCNF and other models in a repository termed EPP (Enzyme Promiscuity Prediction) at https://github.com/hassounlab/EPP.
|
2010.16027
|
Aditi Krishnapriyan
|
Nicolas Swenson, Aditi S. Krishnapriyan, Aydin Buluc, Dmitriy Morozov,
and Katherine Yelick
|
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning
to Structure-Based Protein Function Prediction
|
The first two authors contributed equally to this work
| null | null | null |
q-bio.BM cs.LG math.AT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Understanding protein structure-function relationships is a key challenge in
computational biology, with applications across the biotechnology and
pharmaceutical industries. While it is known that protein structure directly
impacts protein function, many functional prediction tasks use only protein
sequence. In this work, we isolate protein structure to make functional
annotations for proteins in the Protein Data Bank in order to study the
expressiveness of different structure-based prediction schemes. We present
PersGNN - an end-to-end trainable deep learning model that combines graph
representation learning with topological data analysis to capture a complex set
of both local and global structural features. While variations of these
techniques have been successfully applied to proteins before, we demonstrate
that our hybridized approach, PersGNN, outperforms either method on its own as
well as a baseline neural network that learns from the same information.
PersGNN achieves a 9.3% boost in area under the precision recall curve (AUPR)
compared to the best individual model, as well as high F1 scores across
different gene ontology categories, indicating the transferability of this
approach.
|
[
{
"created": "Fri, 30 Oct 2020 02:24:35 GMT",
"version": "v1"
}
] |
2020-11-02
|
[
[
"Swenson",
"Nicolas",
""
],
[
"Krishnapriyan",
"Aditi S.",
""
],
[
"Buluc",
"Aydin",
""
],
[
"Morozov",
"Dmitriy",
""
],
[
"Yelick",
"Katherine",
""
]
] |
Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While it is known that protein structure directly impacts protein function, many functional prediction tasks use only protein sequence. In this work, we isolate protein structure to make functional annotations for proteins in the Protein Data Bank in order to study the expressiveness of different structure-based prediction schemes. We present PersGNN - an end-to-end trainable deep learning model that combines graph representation learning with topological data analysis to capture a complex set of both local and global structural features. While variations of these techniques have been successfully applied to proteins before, we demonstrate that our hybridized approach, PersGNN, outperforms either method on its own as well as a baseline neural network that learns from the same information. PersGNN achieves a 9.3% boost in area under the precision recall curve (AUPR) compared to the best individual model, as well as high F1 scores across different gene ontology categories, indicating the transferability of this approach.
|
1209.0813
|
Matthias Steinruecken
|
Matthias Steinr\"ucken, Matthias Birkner, Jochen Blath
|
Analysis of DNA sequence variation within marine species using
Beta-coalescents
|
15 pages, 16 figures
| null | null | null |
q-bio.PE math.PR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We apply recently developed inference methods based on general coalescent
processes to DNA sequence data obtained from various marine species. Several of
these species are believed to exhibit so-called shallow gene genealogies,
potentially due to extreme reproductive behaviour, e.g. via Hedgecock's
"reproduction sweepstakes". Besides the data analysis, in particular the
inference of mutation rates and the estimation of the (real) time to the most
recent common ancestor, we briefly address the question whether the genealogies
might be adequately described by so-called Beta coalescents (as opposed to
Kingman's coalescent), allowing multiple mergers of genealogies.
The choice of the underlying coalescent model for the genealogy has drastic
implications for the estimation of the above quantities, in particular the
real-time embedding of the genealogy.
|
[
{
"created": "Tue, 4 Sep 2012 22:00:08 GMT",
"version": "v1"
},
{
"created": "Sun, 4 Nov 2012 23:23:35 GMT",
"version": "v2"
}
] |
2012-11-06
|
[
[
"Steinrücken",
"Matthias",
""
],
[
"Birkner",
"Matthias",
""
],
[
"Blath",
"Jochen",
""
]
] |
We apply recently developed inference methods based on general coalescent processes to DNA sequence data obtained from various marine species. Several of these species are believed to exhibit so-called shallow gene genealogies, potentially due to extreme reproductive behaviour, e.g. via Hedgecock's "reproduction sweepstakes". Besides the data analysis, in particular the inference of mutation rates and the estimation of the (real) time to the most recent common ancestor, we briefly address the question whether the genealogies might be adequately described by so-called Beta coalescents (as opposed to Kingman's coalescent), allowing multiple mergers of genealogies. The choice of the underlying coalescent model for the genealogy has drastic implications for the estimation of the above quantities, in particular the real-time embedding of the genealogy.
|
1810.04589
|
Thierry Mora
|
Martin Carballo-Pacheco, Jonathan Desponds, Tatyana Gavrilchenko,
Andreas Mayer, Roshan Prizak, Gautam Reddy, Ilya Nemenman, and Thierry Mora
|
Receptor crosstalk improves concentration sensing of multiple ligands
| null |
Phys. Rev. E 99, 022423 (2019)
|
10.1103/PhysRevE.99.022423
| null |
q-bio.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cells need to reliably sense external ligand concentrations to achieve
various biological functions such as chemotaxis or signaling. The molecular
recognition of ligands by surface receptors is degenerate in many systems
leading to crosstalk between different receptors. Crosstalk is often thought of
as a deviation from optimal specific recognition, as the binding of non-cognate
ligands can interfere with the detection of the receptor's cognate ligand,
possibly leading to a false triggering of a downstream signaling pathway. Here
we quantify the optimal precision of sensing the concentrations of multiple
ligands by a collection of promiscuous receptors. We demonstrate that crosstalk
can improve precision in concentration sensing and discrimination tasks. To
achieve superior precision, the additional information about ligand
concentrations contained in short binding events of the non-cognate ligand
should be exploited. We present a proofreading scheme to realize an approximate
estimation of multiple ligand concentrations that reaches a precision close to
the derived optimal bounds. Our results help rationalize the observed ubiquity
of receptor crosstalk in molecular sensing.
|
[
{
"created": "Wed, 10 Oct 2018 15:28:10 GMT",
"version": "v1"
}
] |
2019-03-06
|
[
[
"Carballo-Pacheco",
"Martin",
""
],
[
"Desponds",
"Jonathan",
""
],
[
"Gavrilchenko",
"Tatyana",
""
],
[
"Mayer",
"Andreas",
""
],
[
"Prizak",
"Roshan",
""
],
[
"Reddy",
"Gautam",
""
],
[
"Nemenman",
"Ilya",
""
],
[
"Mora",
"Thierry",
""
]
] |
Cells need to reliably sense external ligand concentrations to achieve various biological functions such as chemotaxis or signaling. The molecular recognition of ligands by surface receptors is degenerate in many systems leading to crosstalk between different receptors. Crosstalk is often thought of as a deviation from optimal specific recognition, as the binding of non-cognate ligands can interfere with the detection of the receptor's cognate ligand, possibly leading to a false triggering of a downstream signaling pathway. Here we quantify the optimal precision of sensing the concentrations of multiple ligands by a collection of promiscuous receptors. We demonstrate that crosstalk can improve precision in concentration sensing and discrimination tasks. To achieve superior precision, the additional information about ligand concentrations contained in short binding events of the non-cognate ligand should be exploited. We present a proofreading scheme to realize an approximate estimation of multiple ligand concentrations that reaches a precision close to the derived optimal bounds. Our results help rationalize the observed ubiquity of receptor crosstalk in molecular sensing.
|
1810.00387
|
Lei Zhou
|
Lei Zhou, Bin Wu, Jinming Du, Long Wang
|
Aspiration dynamics generate robust predictions in structured
populations
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Evolutionary game dynamics in structured populations are strongly affected by
updating rules. Previous studies usually focus on imitation-based rules, which
rely on payoff information of social peers. Recent behavioral experiments
suggest that whether individuals use such social information for strategy
updating may be crucial to the outcomes of social interactions. This hints at
the importance of considering updating rules without dependence on social
peers' payoff information, which, however, is rarely investigated. Here, we
study aspiration-based self-evaluation rules, with which individuals
self-assess the performance of strategies by comparing own payoffs with an
imaginary value they aspire, called the aspiration level. We explore the fate
of strategies on population structures represented by graphs or networks. Under
weak selection, we analytically derive the condition for strategy dominance,
which is found to coincide with the classical condition of risk-dominance. This
condition holds for all networks and all distributions of aspiration levels,
and for individualized ways of self-evaluation. Our condition can be
intuitively interpreted: one strategy prevails over the other if the strategy
brings more satisfaction to individuals than the other does. Our work thus
sheds light on the intrinsic difference between evolutionary dynamics induced
by aspiration-based and imitation-based rules.
|
[
{
"created": "Sun, 30 Sep 2018 14:30:43 GMT",
"version": "v1"
}
] |
2018-10-02
|
[
[
"Zhou",
"Lei",
""
],
[
"Wu",
"Bin",
""
],
[
"Du",
"Jinming",
""
],
[
"Wang",
"Long",
""
]
] |
Evolutionary game dynamics in structured populations are strongly affected by updating rules. Previous studies usually focus on imitation-based rules, which rely on payoff information of social peers. Recent behavioral experiments suggest that whether individuals use such social information for strategy updating may be crucial to the outcomes of social interactions. This hints at the importance of considering updating rules without dependence on social peers' payoff information, which, however, is rarely investigated. Here, we study aspiration-based self-evaluation rules, with which individuals self-assess the performance of strategies by comparing own payoffs with an imaginary value they aspire, called the aspiration level. We explore the fate of strategies on population structures represented by graphs or networks. Under weak selection, we analytically derive the condition for strategy dominance, which is found to coincide with the classical condition of risk-dominance. This condition holds for all networks and all distributions of aspiration levels, and for individualized ways of self-evaluation. Our condition can be intuitively interpreted: one strategy prevails over the other if the strategy brings more satisfaction to individuals than the other does. Our work thus sheds light on the intrinsic difference between evolutionary dynamics induced by aspiration-based and imitation-based rules.
|
2307.15462
|
Guy Ropars
|
Albert Le Floch and Guy Ropars
|
Hebbian control of fixations in a dyslexic reader
|
8 pages, 6 figures
| null | null | null |
q-bio.NC physics.bio-ph
|
http://creativecommons.org/licenses/by/4.0/
|
During reading, dyslexic readers exhibit more and longer fixations than
normal readers. However, there is no significant difference when dyslexic and
control readers perform only visual tasks on a string of letters, showing the
importance of cognitive processes in reading. This linguistic and cognitive
processing demand in reading is often perturbed for dyslexic readers by
perceived additional letter and word mirror-images superposed to the primary
images on the primary cortex, inducing an internal visual crowding. Here we
show that whereas for a normal reader, the number and the duration of fixations
remain invariant whatever the nature of the lighting, the excess of fixations
and total duration of reading can be controlled for a dyslexic reader using the
Hebbian mechanisms to erase the extra images in an optimized pulse-width
lighting. The number of fixations can be reduced by a factor of about 1.8,
recovering the normal reader records.
|
[
{
"created": "Fri, 28 Jul 2023 10:27:54 GMT",
"version": "v1"
}
] |
2023-07-31
|
[
[
"Floch",
"Albert Le",
""
],
[
"Ropars",
"Guy",
""
]
] |
During reading, dyslexic readers exhibit more and longer fixations than normal readers. However, there is no significant difference when dyslexic and control readers perform only visual tasks on a string of letters, showing the importance of cognitive processes in reading. This linguistic and cognitive processing demand in reading is often perturbed for dyslexic readers by perceived additional letter and word mirror-images superposed to the primary images on the primary cortex, inducing an internal visual crowding. Here we show that whereas for a normal reader, the number and the duration of fixations remain invariant whatever the nature of the lighting, the excess of fixations and total duration of reading can be controlled for a dyslexic reader using the Hebbian mechanisms to erase the extra images in an optimized pulse-width lighting. The number of fixations can be reduced by a factor of about 1.8, recovering the normal reader records.
|
2004.11626
|
Yubo Huang
|
Yubo Huang and Weidong Zhang
|
Comprehensive Investigation and Isolation have Effectively Suppressed
the Spread of COVID-19
|
The draft is the first vision
| null |
10.1016/j.chaos.2020.110041
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The outbreak of COVID-19 since Dec. 2019 has caused severe life and economic
damage worldwide, many countries are trapped by medical resource constraints or
absence of targeted therapeutics, and therefore the implement of systemic
policies to block this pandemic should be prioritized. Based on the
transmission mechanisms and physicochemical properties of betacoronaviruses, we
construct a fine-grained transmission dynamics model (ICRD) to forecast the
crucial information of public concern, therein using dynamical coefficients to
quantify the impact of the implement time and intensity of containment policies
on the spread of epidemic. We find that the comprehensive investigation policy
for susceptible population and the quarantine for suspected cases eminently
contribute to reduce casualties during the phase of the dramatic increase of
diagnosed cases. Statistic evidences strongly suggest that society should take
such forceful public health interventions to cut the infection channels in the
initial stage until the pandemic is interrupted.
|
[
{
"created": "Fri, 24 Apr 2020 09:52:58 GMT",
"version": "v1"
}
] |
2020-07-15
|
[
[
"Huang",
"Yubo",
""
],
[
"Zhang",
"Weidong",
""
]
] |
The outbreak of COVID-19 since Dec. 2019 has caused severe life and economic damage worldwide, many countries are trapped by medical resource constraints or absence of targeted therapeutics, and therefore the implement of systemic policies to block this pandemic should be prioritized. Based on the transmission mechanisms and physicochemical properties of betacoronaviruses, we construct a fine-grained transmission dynamics model (ICRD) to forecast the crucial information of public concern, therein using dynamical coefficients to quantify the impact of the implement time and intensity of containment policies on the spread of epidemic. We find that the comprehensive investigation policy for susceptible population and the quarantine for suspected cases eminently contribute to reduce casualties during the phase of the dramatic increase of diagnosed cases. Statistic evidences strongly suggest that society should take such forceful public health interventions to cut the infection channels in the initial stage until the pandemic is interrupted.
|
1408.3028
|
Olivier Marre
|
Olivier Marre, Vicente Botella-Soler, Kristina D. Simmons, Thierry
Mora, Ga\v{s}per Tka\v{c}ik, Michael J. Berry II
|
High accuracy decoding of dynamical motion from a large retinal
population
|
23 pages, 7 figures
| null |
10.1371/journal.pcbi.1004304
| null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Motion tracking is a challenge the visual system has to solve by reading out
the retinal population. Here we recorded a large population of ganglion cells
in a dense patch of salamander and guinea pig retinas while displaying a bar
moving diffusively. We show that the bar position can be reconstructed from
retinal activity with a precision in the hyperacuity regime using a linear
decoder acting on 100+ cells. The classical view would have suggested that the
firing rates of the cells form a moving hill of activity tracking the bar's
position. Instead, we found that ganglion cells fired sparsely over an area
much larger than predicted by their receptive fields, so that the neural image
did not track the bar. This highly redundant organization allows for diverse
collections of ganglion cells to represent high-accuracy motion information in
a form easily read out by downstream neural circuits.
|
[
{
"created": "Wed, 13 Aug 2014 15:25:15 GMT",
"version": "v1"
}
] |
2016-02-17
|
[
[
"Marre",
"Olivier",
""
],
[
"Botella-Soler",
"Vicente",
""
],
[
"Simmons",
"Kristina D.",
""
],
[
"Mora",
"Thierry",
""
],
[
"Tkačik",
"Gašper",
""
],
[
"Berry",
"Michael J.",
"II"
]
] |
Motion tracking is a challenge the visual system has to solve by reading out the retinal population. Here we recorded a large population of ganglion cells in a dense patch of salamander and guinea pig retinas while displaying a bar moving diffusively. We show that the bar position can be reconstructed from retinal activity with a precision in the hyperacuity regime using a linear decoder acting on 100+ cells. The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar's position. Instead, we found that ganglion cells fired sparsely over an area much larger than predicted by their receptive fields, so that the neural image did not track the bar. This highly redundant organization allows for diverse collections of ganglion cells to represent high-accuracy motion information in a form easily read out by downstream neural circuits.
|
1304.4766
|
Yongchao Liu
|
Yongchao Liu, Bernt Popp and Bertil Schmidt
|
High-speed and accurate color-space short-read alignment with CUSHAW2
|
2 pages,1 table
| null | null | null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Summary: We present an extension of CUSHAW2 for fast and accurate alignments
of SOLiD color-space short-reads. Our extension introduces a double-seeding
approach to improve mapping sensitivity, by combining maximal exact match seeds
and variable-length seeds derived from local alignments. We have compared the
performance of CUSHAW2 to SHRiMP2 and BFAST by aligning both simulated and real
color-space mate-paired reads to the human genome. The results show that
CUSHAW2 achieves comparable or better alignment quality compared to SHRiMP2 and
BFAST at an order-of-magnitude faster speed and significantly smaller peak
resident memory size. Availability: CUSHAW2 and all simulated datasets are
available at http://cushaw2.sourceforge.net. Contact: liuy@uni-mainz.de;
bertil.schmidt@uni-mainz.de
|
[
{
"created": "Wed, 17 Apr 2013 11:06:33 GMT",
"version": "v1"
}
] |
2013-04-18
|
[
[
"Liu",
"Yongchao",
""
],
[
"Popp",
"Bernt",
""
],
[
"Schmidt",
"Bertil",
""
]
] |
Summary: We present an extension of CUSHAW2 for fast and accurate alignments of SOLiD color-space short-reads. Our extension introduces a double-seeding approach to improve mapping sensitivity, by combining maximal exact match seeds and variable-length seeds derived from local alignments. We have compared the performance of CUSHAW2 to SHRiMP2 and BFAST by aligning both simulated and real color-space mate-paired reads to the human genome. The results show that CUSHAW2 achieves comparable or better alignment quality compared to SHRiMP2 and BFAST at an order-of-magnitude faster speed and significantly smaller peak resident memory size. Availability: CUSHAW2 and all simulated datasets are available at http://cushaw2.sourceforge.net. Contact: liuy@uni-mainz.de; bertil.schmidt@uni-mainz.de
|
2007.03246
|
Oleksandr Oliynyk
|
Anna Larsson, Soodabeh Majdi, Alexander Oleinick (PASTEUR), Irina Svir
(PASTEUR), Johan Dunevall, Christian Amatore (PASTEUR), Andrew Ewing
|
Intracellular Electrochemical Nanomeasurements Reveal that Exocytosis of
Molecules at Living Neurons is Subquantal and Complex
| null |
Angewandte Chemie International Edition, Wiley-VCH Verlag, 2020,
59 (17), pp.6711-6714
|
10.1002/anie.201914564
| null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Since the early work of Bernard Katz, the process of cellular chemical
communication via exocytosis, quantal release, has been considered to be all or
none. Recent evidence has shown exocytosis to be partial or 'subquantal' at
single-cell model systems, but there is a need to understand this at
communicating nerve cells. Partial release allows nerve cells to control the
signal at the site of release during individual events, where the smaller the
fraction released, the greater the range of regulation. Here we show that the
fraction of the vesicular octopamine content released from a living Drosophila
larval neuromuscular neuron is very small. The percentage of released molecules
was found to be only 4.5% for simple events and 10.7% for complex (i.e.,
oscillating or flickering) events. This large content, combined with partial
release controlled by fluctuations of the fusion pore, offers presynaptic
plasticity that can be widely regulated. Two works published in 2010 suggested
that the Katz principle, [1] was incorrect for all-or-none release and that
only part of the chemical load of vesicles was released during exocytosis, at
least as measured as a full spike during amperometry. [2] The combination of
electrochemical methods to measure both release and vesicle content in 2015
added a wealth of information to support the concept of partial release in
exocytosis. [3] Additionally, this has recently been supported by work with
TIRF microscopy showing 'subquantal' release from vesicles in adrenal
chromaffin cells and using super-resolution STED microscopy. [4] It appears
that the full event generally involves release of only part of the load of
chemical messenger in single-cell model systems like adrenal chromaffin and
PC12 cells. Is this also true at living neurons in a nervous system and to what
extent? To answer this critical question, we quantified the number of
octopamine molecules in the neuromuscular neurons of Drosophila larvae by
adapting an amperometric technique developed in our
|
[
{
"created": "Tue, 7 Jul 2020 07:31:35 GMT",
"version": "v1"
}
] |
2020-07-08
|
[
[
"Larsson",
"Anna",
"",
"PASTEUR"
],
[
"Majdi",
"Soodabeh",
"",
"PASTEUR"
],
[
"Oleinick",
"Alexander",
"",
"PASTEUR"
],
[
"Svir",
"Irina",
"",
"PASTEUR"
],
[
"Dunevall",
"Johan",
"",
"PASTEUR"
],
[
"Amatore",
"Christian",
"",
"PASTEUR"
],
[
"Ewing",
"Andrew",
""
]
] |
Since the early work of Bernard Katz, the process of cellular chemical communication via exocytosis, quantal release, has been considered to be all or none. Recent evidence has shown exocytosis to be partial or 'subquantal' at single-cell model systems, but there is a need to understand this at communicating nerve cells. Partial release allows nerve cells to control the signal at the site of release during individual events, where the smaller the fraction released, the greater the range of regulation. Here we show that the fraction of the vesicular octopamine content released from a living Drosophila larval neuromuscular neuron is very small. The percentage of released molecules was found to be only 4.5% for simple events and 10.7% for complex (i.e., oscillating or flickering) events. This large content, combined with partial release controlled by fluctuations of the fusion pore, offers presynaptic plasticity that can be widely regulated. Two works published in 2010 suggested that the Katz principle, [1] was incorrect for all-or-none release and that only part of the chemical load of vesicles was released during exocytosis, at least as measured as a full spike during amperometry. [2] The combination of electrochemical methods to measure both release and vesicle content in 2015 added a wealth of information to support the concept of partial release in exocytosis. [3] Additionally, this has recently been supported by work with TIRF microscopy showing 'subquantal' release from vesicles in adrenal chromaffin cells and using super-resolution STED microscopy. [4] It appears that the full event generally involves release of only part of the load of chemical messenger in single-cell model systems like adrenal chromaffin and PC12 cells. Is this also true at living neurons in a nervous system and to what extent? To answer this critical question, we quantified the number of octopamine molecules in the neuromuscular neurons of Drosophila larvae by adapting an amperometric technique developed in our
|
0802.0029
|
Ryo Kanada
|
Ryo Kanada, Fumiko Takagi, Macoto Kikuchi
|
Structural Fluctuations of Microtubule Binding Site of KIF1A in
Different Nucleotide States
|
14 pages, 7 figures
| null | null | null |
q-bio.BM
| null |
How molecular motors like Kinesin regulates the affinity to the rail protein
in the process of ATP hydrolysis remains to be uncovered. To understand the
regulation mechanism, we investigate the structural fluctuation of KIF1A in
different nucleotide states that are realized in the ATP hydrolysis process by
molecular dynamics simulations of Go-like model. We found that "alpha4 helix",
which is a part of the microtubule (MT) binding site, changes its fluctuation
systematically according to the nucleotide states. In particular, the frequency
of large fluctuations of alpha4 strongly correlates with the affinity of KIF1A
for microtubule. We also show how the strength of the thermal fluctuation and
the interaction with the nucleotide affect the dynamics of microtubule binding
site. These results suggest that KIF1A regulates the affinity to MT by changing
the flexibility of alpha4 helix according to the nucleotide states.
|
[
{
"created": "Thu, 31 Jan 2008 23:42:57 GMT",
"version": "v1"
}
] |
2008-02-04
|
[
[
"Kanada",
"Ryo",
""
],
[
"Takagi",
"Fumiko",
""
],
[
"Kikuchi",
"Macoto",
""
]
] |
How molecular motors like Kinesin regulates the affinity to the rail protein in the process of ATP hydrolysis remains to be uncovered. To understand the regulation mechanism, we investigate the structural fluctuation of KIF1A in different nucleotide states that are realized in the ATP hydrolysis process by molecular dynamics simulations of Go-like model. We found that "alpha4 helix", which is a part of the microtubule (MT) binding site, changes its fluctuation systematically according to the nucleotide states. In particular, the frequency of large fluctuations of alpha4 strongly correlates with the affinity of KIF1A for microtubule. We also show how the strength of the thermal fluctuation and the interaction with the nucleotide affect the dynamics of microtubule binding site. These results suggest that KIF1A regulates the affinity to MT by changing the flexibility of alpha4 helix according to the nucleotide states.
|
1708.03263
|
Giovanni Petri
|
Giovanni Petri, Sebastian Musslick, Biswadip Dey, Kayhan Ozcimder,
David Turner, Nesreen K. Ahmed, Theodore Willke and Jonathan D. Cohen
|
Topological limits to parallel processing capability of network
architectures
|
version 4. Added SIs, 33 pages total, 4 figures + 14 figures in SI,
major edits to text
| null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The ability to learn new tasks and generalize performance to others is one of
the most remarkable characteristics of the human brain and of recent AI
systems. The ability to perform multiple tasks simultaneously is also a
signature characteristic of large-scale parallel architectures, that is evident
in the human brain, and has been exploited effectively more traditional,
massively parallel computational architectures. Here, we show that these two
characteristics are in tension, reflecting a fundamental tradeoff between
interactive parallelism that supports learning and generalization, and
independent parallelism that supports processing efficiency through concurrent
multitasking. We formally show that, while the maximum number of tasks that can
be performed simultaneously grows linearly with network size, under realistic
scenarios (e.g. in an unpredictable environment), the expected number that can
be performed concurrently grows radically sub-linearly with network size.
Hence, even modest reliance on shared representation strictly constrains the
number of tasks that can be performed simultaneously, implying profound
consequences for the development of artificial intelligence that optimally
manages the tradeoff between learning and processing, and for understanding the
human brains remarkably puzzling mix of sequential and parallel capabilities.
|
[
{
"created": "Thu, 10 Aug 2017 15:37:29 GMT",
"version": "v1"
},
{
"created": "Wed, 16 Aug 2017 09:12:46 GMT",
"version": "v2"
},
{
"created": "Wed, 18 Mar 2020 21:11:16 GMT",
"version": "v3"
},
{
"created": "Tue, 10 Nov 2020 18:03:26 GMT",
"version": "v4"
}
] |
2020-11-11
|
[
[
"Petri",
"Giovanni",
""
],
[
"Musslick",
"Sebastian",
""
],
[
"Dey",
"Biswadip",
""
],
[
"Ozcimder",
"Kayhan",
""
],
[
"Turner",
"David",
""
],
[
"Ahmed",
"Nesreen K.",
""
],
[
"Willke",
"Theodore",
""
],
[
"Cohen",
"Jonathan D.",
""
]
] |
The ability to learn new tasks and generalize performance to others is one of the most remarkable characteristics of the human brain and of recent AI systems. The ability to perform multiple tasks simultaneously is also a signature characteristic of large-scale parallel architectures, that is evident in the human brain, and has been exploited effectively more traditional, massively parallel computational architectures. Here, we show that these two characteristics are in tension, reflecting a fundamental tradeoff between interactive parallelism that supports learning and generalization, and independent parallelism that supports processing efficiency through concurrent multitasking. We formally show that, while the maximum number of tasks that can be performed simultaneously grows linearly with network size, under realistic scenarios (e.g. in an unpredictable environment), the expected number that can be performed concurrently grows radically sub-linearly with network size. Hence, even modest reliance on shared representation strictly constrains the number of tasks that can be performed simultaneously, implying profound consequences for the development of artificial intelligence that optimally manages the tradeoff between learning and processing, and for understanding the human brains remarkably puzzling mix of sequential and parallel capabilities.
|
1102.4570
|
Matjaz Perc
|
Zhen Wang, Aleksandra Murks, Wen-Bo Du, Zhi-Hai Rong, Matjaz Perc
|
Coveting thy neighbors fitness as a means to resolve social dilemmas
|
10 two-column pages, 5 figures; accepted for publication in Journal
of Theoretical Biology
|
J. Theor. Biol. 277 (2011) 19-26
|
10.1016/j.jtbi.2011.02.016
| null |
q-bio.PE cs.SI physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In spatial evolutionary games the fitness of each individual is traditionally
determined by the payoffs it obtains upon playing the game with its neighbors.
Since defection yields the highest individual benefits, the outlook for
cooperators is gloomy. While network reciprocity promotes collaborative
efforts, chances of averting the impending social decline are slim if the
temptation to defect is strong. It is therefore of interest to identify viable
mechanisms that provide additional support for the evolution of cooperation.
Inspired by the fact that the environment may be just as important as
inheritance for individual development, we introduce a simple switch that
allows a player to either keep its original payoff or use the average payoff of
all its neighbors. Depending on which payoff is higher, the influence of either
option can be tuned by means of a single parameter. We show that, in general,
taking into account the environment promotes cooperation. Yet coveting the
fitness of one's neighbors too strongly is not optimal. In fact, cooperation
thrives best only if the influence of payoffs obtained in the traditional way
is equal to that of the average payoff of the neighborhood. We present results
for the prisoner's dilemma and the snowdrift game, for different levels of
uncertainty governing the strategy adoption process, and for different
neighborhood sizes. Our approach outlines a viable route to increased levels of
cooperative behavior in structured populations, but one that requires a
thoughtful implementation.
|
[
{
"created": "Tue, 22 Feb 2011 18:00:55 GMT",
"version": "v1"
}
] |
2011-04-04
|
[
[
"Wang",
"Zhen",
""
],
[
"Murks",
"Aleksandra",
""
],
[
"Du",
"Wen-Bo",
""
],
[
"Rong",
"Zhi-Hai",
""
],
[
"Perc",
"Matjaz",
""
]
] |
In spatial evolutionary games the fitness of each individual is traditionally determined by the payoffs it obtains upon playing the game with its neighbors. Since defection yields the highest individual benefits, the outlook for cooperators is gloomy. While network reciprocity promotes collaborative efforts, chances of averting the impending social decline are slim if the temptation to defect is strong. It is therefore of interest to identify viable mechanisms that provide additional support for the evolution of cooperation. Inspired by the fact that the environment may be just as important as inheritance for individual development, we introduce a simple switch that allows a player to either keep its original payoff or use the average payoff of all its neighbors. Depending on which payoff is higher, the influence of either option can be tuned by means of a single parameter. We show that, in general, taking into account the environment promotes cooperation. Yet coveting the fitness of one's neighbors too strongly is not optimal. In fact, cooperation thrives best only if the influence of payoffs obtained in the traditional way is equal to that of the average payoff of the neighborhood. We present results for the prisoner's dilemma and the snowdrift game, for different levels of uncertainty governing the strategy adoption process, and for different neighborhood sizes. Our approach outlines a viable route to increased levels of cooperative behavior in structured populations, but one that requires a thoughtful implementation.
|
2303.11994
|
Qiang Li
|
Qiang Li, Shujian Yu, Kristoffer H Madsen, Vince D Calhoun, Armin
Iraji
|
Higher-order Organization in the Human Brain from Matrix-Based R\'enyi's
Entropy
|
5 pages, 3 figures; Accepted to Data Science and Learning Workshop:
Unraveling the Brain. A satellite workshop of ICASSP 2023
|
2023 IEEE International Conference on Acoustics, Speech, and
Signal Processing Workshops (ICASSPW)
|
10.1109/ICASSPW59220.2023.10193346
| null |
q-bio.NC cs.IT math.IT math.ST stat.TH
|
http://creativecommons.org/licenses/by/4.0/
|
Pairwise metrics are often employed to estimate statistical dependencies
between brain regions, however they do not capture higher-order information
interactions. It is critical to explore higher-order interactions that go
beyond paired brain areas in order to better understand information processing
in the human brain. To address this problem, we applied multivariate mutual
information, specifically, Total Correlation and Dual Total Correlation to
reveal higher-order information in the brain. In this paper, we estimate these
metrics using matrix-based R\'enyi's entropy, which offers a direct and easily
interpretable approach that is not limited by direct assumptions about
probability distribution functions of multivariate time series. We applied
these metrics to resting-state fMRI data in order to examine higher-order
interactions in the brain. Our results showed that the higher-order information
interactions captured increase gradually as the interaction order increases.
Furthermore, we observed a gradual increase in the correlation between the
Total Correlation and Dual Total Correlation as the interaction order
increased. In addition, the significance of Dual Total Correlation values
compared to Total Correlation values also indicate that the human brain
exhibits synergy dominance during the resting state.
|
[
{
"created": "Tue, 21 Mar 2023 16:23:51 GMT",
"version": "v1"
},
{
"created": "Tue, 25 Apr 2023 15:17:46 GMT",
"version": "v2"
}
] |
2023-08-04
|
[
[
"Li",
"Qiang",
""
],
[
"Yu",
"Shujian",
""
],
[
"Madsen",
"Kristoffer H",
""
],
[
"Calhoun",
"Vince D",
""
],
[
"Iraji",
"Armin",
""
]
] |
Pairwise metrics are often employed to estimate statistical dependencies between brain regions, however they do not capture higher-order information interactions. It is critical to explore higher-order interactions that go beyond paired brain areas in order to better understand information processing in the human brain. To address this problem, we applied multivariate mutual information, specifically, Total Correlation and Dual Total Correlation to reveal higher-order information in the brain. In this paper, we estimate these metrics using matrix-based R\'enyi's entropy, which offers a direct and easily interpretable approach that is not limited by direct assumptions about probability distribution functions of multivariate time series. We applied these metrics to resting-state fMRI data in order to examine higher-order interactions in the brain. Our results showed that the higher-order information interactions captured increase gradually as the interaction order increases. Furthermore, we observed a gradual increase in the correlation between the Total Correlation and Dual Total Correlation as the interaction order increased. In addition, the significance of Dual Total Correlation values compared to Total Correlation values also indicate that the human brain exhibits synergy dominance during the resting state.
|
1910.14490
|
Julien Marlet
|
Jos\'e Bras Cachinho, Maud Fran\c{c}ois, Karl Stefic (MAVIVH - U1259
Inserm - CHRU Tours), Julien Marlet (MAVIVH - U1259 Inserm - CHRU Tours)
|
Positive HBs antigen in the absence of hepatitis B virus infection
|
in French
|
Annales de Biologie Clinique, John Libbey Eurotext, 2019, 77 (5),
pp.543-548
|
10.1684/abc.2019.1483
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
French recommendations for the screening of hepatitis B virus (HBV) infection
were updated in 2019 with the association of three markers: HBs Ag, anti-HBs Ab
and anti-HBc Ab. These three markers allow identification of infected patients,
vaccinated patients and patients who have been in contact with HBV. A positive
HBs Ag is usually associated with HBV infection but this interpretation must
take into account the clinical context. In particular, the absence of anti-HBc
Ab, normal ALAT levels and the absence of jaundice can be associated with
recent HBV vaccination or false-positive HBs Ag. Recent HBV vaccination can
usually be confirmed by patient questioning, while confirmatory tests are
useful to detect false positive HBs Ag. If necessary, a second sample can be
requested to confirm the interpretation.
|
[
{
"created": "Thu, 31 Oct 2019 14:29:24 GMT",
"version": "v1"
}
] |
2019-11-01
|
[
[
"Cachinho",
"José Bras",
"",
"MAVIVH - U1259\n Inserm - CHRU Tours"
],
[
"François",
"Maud",
"",
"MAVIVH - U1259\n Inserm - CHRU Tours"
],
[
"Stefic",
"Karl",
"",
"MAVIVH - U1259\n Inserm - CHRU Tours"
],
[
"Marlet",
"Julien",
"",
"MAVIVH - U1259 Inserm - CHRU Tours"
]
] |
French recommendations for the screening of hepatitis B virus (HBV) infection were updated in 2019 with the association of three markers: HBs Ag, anti-HBs Ab and anti-HBc Ab. These three markers allow identification of infected patients, vaccinated patients and patients who have been in contact with HBV. A positive HBs Ag is usually associated with HBV infection but this interpretation must take into account the clinical context. In particular, the absence of anti-HBc Ab, normal ALAT levels and the absence of jaundice can be associated with recent HBV vaccination or false-positive HBs Ag. Recent HBV vaccination can usually be confirmed by patient questioning, while confirmatory tests are useful to detect false positive HBs Ag. If necessary, a second sample can be requested to confirm the interpretation.
|
1803.10579
|
Alan D. Rendall
|
Pia Brechmann, Alan D. Rendall
|
Dynamics of the Selkov oscillator
| null | null | null | null |
q-bio.SC math.DS physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A classical example of a mathematical model for oscillations in a biological
system is the Selkov oscillator, which is a simple description of glycolysis.
It is a system of two ordinary differential equations which, when expressed in
dimensionless variables, depends on two parameters. Surprisingly it appears
that no complete rigorous analysis of the dynamics of this model has ever been
given. In this paper several properties of the dynamics of solutions of the
model are established. With a view to studying unbounded solutions a thorough
analysis of the Poincar\'e compactification of the system is given. It is
proved that for any values of the parameters there are solutions which tend to
infinity at late times and are eventually monotone. It is shown that when the
unique steady state is stable any bounded solution converges to the steady
state at late times. When the steady state is unstable it is shown that for
given values of the parameters either there is a unique periodic solution to
which all bounded solutions other than the steady state converge at late times
or there is no periodic solution and all solutions other than the steady state
are unbounded. In the latter case each unbounded solution which tends to
infinity is eventually monotone and each unbounded solution which does not tend
to infinity has the property that each variable takes on arbitrarily large and
small values at arbitrarily late times.
|
[
{
"created": "Wed, 28 Mar 2018 13:17:09 GMT",
"version": "v1"
}
] |
2018-03-29
|
[
[
"Brechmann",
"Pia",
""
],
[
"Rendall",
"Alan D.",
""
]
] |
A classical example of a mathematical model for oscillations in a biological system is the Selkov oscillator, which is a simple description of glycolysis. It is a system of two ordinary differential equations which, when expressed in dimensionless variables, depends on two parameters. Surprisingly it appears that no complete rigorous analysis of the dynamics of this model has ever been given. In this paper several properties of the dynamics of solutions of the model are established. With a view to studying unbounded solutions a thorough analysis of the Poincar\'e compactification of the system is given. It is proved that for any values of the parameters there are solutions which tend to infinity at late times and are eventually monotone. It is shown that when the unique steady state is stable any bounded solution converges to the steady state at late times. When the steady state is unstable it is shown that for given values of the parameters either there is a unique periodic solution to which all bounded solutions other than the steady state converge at late times or there is no periodic solution and all solutions other than the steady state are unbounded. In the latter case each unbounded solution which tends to infinity is eventually monotone and each unbounded solution which does not tend to infinity has the property that each variable takes on arbitrarily large and small values at arbitrarily late times.
|
q-bio/0608020
|
Lei-Han Tang
|
Sheng Hui and Lei-Han Tang
|
Ground state and glass transition of the RNA secondary structure
| null | null |
10.1140/epjb/e2006-00347-x
| null |
q-bio.BM cond-mat.dis-nn
| null |
RNA molecules form a sequence-specific self-pairing pattern at low
temperatures. We analyze this problem using a random pairing energy model as
well as a random sequence model that includes a base stacking energy in favor
of helix propagation. The free energy cost for separating a chain into two
equal halves offers a quantitative measure of sequence specific pairing. In the
low temperature glass phase, this quantity grows quadratically with the
logarithm of the chain length, but it switches to a linear behavior of entropic
origin in the high temperature molten phase. Transition between the two phases
is continuous, with characteristics that resemble those of a disordered elastic
manifold in two dimensions. For designed sequences, however, a power-law
distribution of pairing energies on a coarse-grained level may be more
appropriate. Extreme value statistics arguments then predict a power-law growth
of the free energy cost to break a chain, in agreement with numerical
simulations. Interestingly, the distribution of pairing distances in the ground
state secondary structure follows a remarkable power-law with an exponent -4/3,
independent of the specific assumptions for the base pairing energies.
|
[
{
"created": "Wed, 9 Aug 2006 22:47:35 GMT",
"version": "v1"
}
] |
2009-11-13
|
[
[
"Hui",
"Sheng",
""
],
[
"Tang",
"Lei-Han",
""
]
] |
RNA molecules form a sequence-specific self-pairing pattern at low temperatures. We analyze this problem using a random pairing energy model as well as a random sequence model that includes a base stacking energy in favor of helix propagation. The free energy cost for separating a chain into two equal halves offers a quantitative measure of sequence specific pairing. In the low temperature glass phase, this quantity grows quadratically with the logarithm of the chain length, but it switches to a linear behavior of entropic origin in the high temperature molten phase. Transition between the two phases is continuous, with characteristics that resemble those of a disordered elastic manifold in two dimensions. For designed sequences, however, a power-law distribution of pairing energies on a coarse-grained level may be more appropriate. Extreme value statistics arguments then predict a power-law growth of the free energy cost to break a chain, in agreement with numerical simulations. Interestingly, the distribution of pairing distances in the ground state secondary structure follows a remarkable power-law with an exponent -4/3, independent of the specific assumptions for the base pairing energies.
|
1509.03734
|
Adam Kleczkowski
|
Adam Kleczkowski, Ciaran Ellis, Dave Goulson, Nick Hanley
|
Ecological-economic modelling of interactions between wild and
commercial bees and pesticide use
|
Working paper
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The decline in extent of wild pollinators in recent years has been partly
associated with changing farm practices and in particular with increasing
pesticide use. In this paper we combine ecological modelling with economic
analysis of a single farm output under the as- sumption that both pollination
and pest control are essential inputs. We show that the drive to increase farm
output can lead to a local decline in the wild bee population. Commercial bees
are often considered an alternative to wild pollinators, but we show that their
intro- duction can lead to further decline and finally local extinction of wild
bees. The transitions between different outcomes are characterised by threshold
behaviour and are potentially difficult to predict and detect in advance. Small
changes in economic parameters (input prices) and ecological parameters (wild
bees carrying capacity and effect of pesticides on bees) can move the
economic-ecological system beyond the extinction threshold. We also show that
increasing the pesticide price or decreasing the commercial bee price might
lead to re-establishment of wild bees following their local extinction. Thus,
we demonstrate the importance of combining ecological modelling with economics
to study the provision of ecosystem services and to inform sustainable
management of ecosystem service providers.
|
[
{
"created": "Sat, 12 Sep 2015 12:17:49 GMT",
"version": "v1"
}
] |
2015-09-15
|
[
[
"Kleczkowski",
"Adam",
""
],
[
"Ellis",
"Ciaran",
""
],
[
"Goulson",
"Dave",
""
],
[
"Hanley",
"Nick",
""
]
] |
The decline in extent of wild pollinators in recent years has been partly associated with changing farm practices and in particular with increasing pesticide use. In this paper we combine ecological modelling with economic analysis of a single farm output under the as- sumption that both pollination and pest control are essential inputs. We show that the drive to increase farm output can lead to a local decline in the wild bee population. Commercial bees are often considered an alternative to wild pollinators, but we show that their intro- duction can lead to further decline and finally local extinction of wild bees. The transitions between different outcomes are characterised by threshold behaviour and are potentially difficult to predict and detect in advance. Small changes in economic parameters (input prices) and ecological parameters (wild bees carrying capacity and effect of pesticides on bees) can move the economic-ecological system beyond the extinction threshold. We also show that increasing the pesticide price or decreasing the commercial bee price might lead to re-establishment of wild bees following their local extinction. Thus, we demonstrate the importance of combining ecological modelling with economics to study the provision of ecosystem services and to inform sustainable management of ecosystem service providers.
|
2401.08805
|
Carles Falc\'o
|
Carles Falc\'o, Daniel J. Cohen, Jos\'e A. Carrillo, Ruth E. Baker
|
Quantifying cell cycle regulation by tissue crowding
| null | null | null | null |
q-bio.QM physics.bio-ph
|
http://creativecommons.org/licenses/by/4.0/
|
The spatiotemporal coordination and regulation of cell proliferation is
fundamental in many aspects of development and tissue maintenance. Cells have
the ability to adapt their division rates in response to mechanical
constraints, yet we do not fully understand how cell proliferation regulation
impacts cell migration phenomena. Here, we present a minimal continuum model of
cell migration with cell cycle dynamics, which includes density-dependent
effects and hence can account for cell proliferation regulation. By combining
minimal mathematical modelling, Bayesian inference, and recent experimental
data, we quantify the impact of tissue crowding across different cell cycle
stages in epithelial tissue expansion experiments. Our model suggests that
cells sense local density and adapt cell cycle progression in response, during
G1 and the combined S/G2/M phases, providing an explicit relationship between
each cell cycle stage duration and local tissue density, which is consistent
with several experimental observations. Finally, we compare our mathematical
model predictions to different experiments studying cell cycle regulation and
present a quantitative analysis on the impact of density-dependent regulation
on cell migration patterns. Our work presents a systematic approach for
investigating and analysing cell cycle data, providing mechanistic insights
into how individual cells regulate proliferation, based on population-based
experimental measurements.
|
[
{
"created": "Tue, 16 Jan 2024 20:01:59 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Feb 2024 15:51:26 GMT",
"version": "v2"
},
{
"created": "Wed, 24 Apr 2024 10:50:44 GMT",
"version": "v3"
}
] |
2024-04-25
|
[
[
"Falcó",
"Carles",
""
],
[
"Cohen",
"Daniel J.",
""
],
[
"Carrillo",
"José A.",
""
],
[
"Baker",
"Ruth E.",
""
]
] |
The spatiotemporal coordination and regulation of cell proliferation is fundamental in many aspects of development and tissue maintenance. Cells have the ability to adapt their division rates in response to mechanical constraints, yet we do not fully understand how cell proliferation regulation impacts cell migration phenomena. Here, we present a minimal continuum model of cell migration with cell cycle dynamics, which includes density-dependent effects and hence can account for cell proliferation regulation. By combining minimal mathematical modelling, Bayesian inference, and recent experimental data, we quantify the impact of tissue crowding across different cell cycle stages in epithelial tissue expansion experiments. Our model suggests that cells sense local density and adapt cell cycle progression in response, during G1 and the combined S/G2/M phases, providing an explicit relationship between each cell cycle stage duration and local tissue density, which is consistent with several experimental observations. Finally, we compare our mathematical model predictions to different experiments studying cell cycle regulation and present a quantitative analysis on the impact of density-dependent regulation on cell migration patterns. Our work presents a systematic approach for investigating and analysing cell cycle data, providing mechanistic insights into how individual cells regulate proliferation, based on population-based experimental measurements.
|
2202.05468
|
Shigehiro Yasui
|
Shigehiro Yasui, Yutaka Hatakeyama, Yoshiyasu Okuhara
|
Criticality in stochastic SIR model for infectious diseases
|
15 pages, 1 figure
| null | null | null |
q-bio.PE cond-mat.stat-mech physics.bio-ph
|
http://creativecommons.org/licenses/by/4.0/
|
We discuss the criticality in the stochastic SIR model for infectious
diseases. We adopt the path-integral formalism for the propagation of
infections among susceptible, infectious, and removed individuals, and perform
the perturbative and nonperturbative analyses to evaluate the critical value of
the basic reproduction number ${\cal R}$. In the perturbation theory, we
calculate the mean values and the variances of the number of infectious
individuals near the initial time, and find that the critical value ${\cal
R}_{\text{c}}=1/3$-$2/3$ should be adopted in order to suppress the stochastic
spread of infections sufficiently. In the nonperturbative approach, we derive
the effective potential by integrating out the stochastic fluctuations, and
obtain the effective Euler-Lagrange equations for the time-evolution of the
numbers of susceptible, infectious, and removed individuals. From the
asymptotic behaviors for a long time, we find that the critical value ${\cal
R}_{\text{c}}=2/3$ should be adopted for the sufficient convergence of
infections. We also find that the endemic state can be generated dynamically by
the stochastic fluctuation which is absent in the conventional SIR model. Those
analyses show that the critical value of the basic reproduction number should
be less than one, against the usually known critical value ${\cal
R}_{\text{c}}=1$, when the stochastic fluctuations are taken into account in
the SIR model.
|
[
{
"created": "Fri, 11 Feb 2022 06:03:40 GMT",
"version": "v1"
}
] |
2022-02-14
|
[
[
"Yasui",
"Shigehiro",
""
],
[
"Hatakeyama",
"Yutaka",
""
],
[
"Okuhara",
"Yoshiyasu",
""
]
] |
We discuss the criticality in the stochastic SIR model for infectious diseases. We adopt the path-integral formalism for the propagation of infections among susceptible, infectious, and removed individuals, and perform the perturbative and nonperturbative analyses to evaluate the critical value of the basic reproduction number ${\cal R}$. In the perturbation theory, we calculate the mean values and the variances of the number of infectious individuals near the initial time, and find that the critical value ${\cal R}_{\text{c}}=1/3$-$2/3$ should be adopted in order to suppress the stochastic spread of infections sufficiently. In the nonperturbative approach, we derive the effective potential by integrating out the stochastic fluctuations, and obtain the effective Euler-Lagrange equations for the time-evolution of the numbers of susceptible, infectious, and removed individuals. From the asymptotic behaviors for a long time, we find that the critical value ${\cal R}_{\text{c}}=2/3$ should be adopted for the sufficient convergence of infections. We also find that the endemic state can be generated dynamically by the stochastic fluctuation which is absent in the conventional SIR model. Those analyses show that the critical value of the basic reproduction number should be less than one, against the usually known critical value ${\cal R}_{\text{c}}=1$, when the stochastic fluctuations are taken into account in the SIR model.
|
1011.6048
|
Jose Vilar
|
Jose M. G. Vilar
|
Noisy-threshold control of cell death
|
Supplementary information available at
http://www.biomedcentral.com/1752-0509/4/152
|
BMC Systems Biology 4, 152 (2010)
| null | null |
q-bio.CB physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cellular responses to death-promoting stimuli typically proceed through a
differentiated multistage process, involving a lag phase, extensive death, and
potential adaptation. Deregulation of this chain of events is at the root of
many diseases. Improper adaptation is particularly important because it allows
cell sub-populations to survive even in the continuous presence of death
conditions, which results, among others, in the eventual failure of many
targeted anticancer therapies. Here, I show that these typical responses arise
naturally from the interplay of intracellular variability with a
threshold-based control mechanism that detects cellular changes in addition to
just the cellular state itself. Implementation of this mechanism in a
quantitative model for T-cell apoptosis, a prototypical example of programmed
cell death, captures with exceptional accuracy experimental observations for
different expression levels of the oncogene Bcl-xL and directly links
adaptation with noise in an ATP threshold below which cells die. These results
indicate that oncogenes like Bcl-xL, besides regulating absolute death values,
can have a novel role as active controllers of cell-cell variability and the
extent of adaptation.
|
[
{
"created": "Sun, 28 Nov 2010 15:00:06 GMT",
"version": "v1"
}
] |
2010-12-03
|
[
[
"Vilar",
"Jose M. G.",
""
]
] |
Cellular responses to death-promoting stimuli typically proceed through a differentiated multistage process, involving a lag phase, extensive death, and potential adaptation. Deregulation of this chain of events is at the root of many diseases. Improper adaptation is particularly important because it allows cell sub-populations to survive even in the continuous presence of death conditions, which results, among others, in the eventual failure of many targeted anticancer therapies. Here, I show that these typical responses arise naturally from the interplay of intracellular variability with a threshold-based control mechanism that detects cellular changes in addition to just the cellular state itself. Implementation of this mechanism in a quantitative model for T-cell apoptosis, a prototypical example of programmed cell death, captures with exceptional accuracy experimental observations for different expression levels of the oncogene Bcl-xL and directly links adaptation with noise in an ATP threshold below which cells die. These results indicate that oncogenes like Bcl-xL, besides regulating absolute death values, can have a novel role as active controllers of cell-cell variability and the extent of adaptation.
|
2307.10246
|
Subba Reddy Oota
|
Subba Reddy Oota, Zijiao Chen, Manish Gupta, Raju S. Bapi, Gael
Jobard, Frederic Alexandre, Xavier Hinaut
|
Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding
(Survey)
|
47 pages, 23 figures
| null | null | null |
q-bio.NC cs.AI cs.CL cs.CV cs.HC cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Can we obtain insights about the brain using AI models? How is the
information in deep learning models related to brain recordings? Can we improve
AI models with the help of brain recordings? Such questions can be tackled by
studying brain recordings like functional magnetic resonance imaging (fMRI). As
a first step, the neuroscience community has contributed several large
cognitive neuroscience datasets related to passive reading/listening/viewing of
concept words, narratives, pictures, and movies. Encoding and decoding models
using these datasets have also been proposed in the past two decades. These
models serve as additional tools for basic cognitive science and neuroscience
research. Encoding models aim at generating fMRI brain representations given a
stimulus automatically. They have several practical applications in evaluating
and diagnosing neurological conditions and thus may also help design therapies
for brain damage. Decoding models solve the inverse problem of reconstructing
the stimuli given the fMRI. They are useful for designing brain-machine or
brain-computer interfaces. Inspired by the effectiveness of deep learning
models for natural language processing, computer vision, and speech, several
neural encoding and decoding models have been recently proposed. In this
survey, we will first discuss popular representations of language, vision and
speech stimuli, and present a summary of neuroscience datasets. Further, we
will review popular deep learning based encoding and decoding architectures and
note their benefits and limitations. Finally, we will conclude with a summary
and discussion about future trends. Given the large amount of recently
published work in the computational cognitive neuroscience (CCN) community, we
believe that this survey enables an entry point for DNN researchers to
diversify into CCN research.
|
[
{
"created": "Mon, 17 Jul 2023 06:54:36 GMT",
"version": "v1"
},
{
"created": "Mon, 8 Jul 2024 13:44:56 GMT",
"version": "v2"
}
] |
2024-07-09
|
[
[
"Oota",
"Subba Reddy",
""
],
[
"Chen",
"Zijiao",
""
],
[
"Gupta",
"Manish",
""
],
[
"Bapi",
"Raju S.",
""
],
[
"Jobard",
"Gael",
""
],
[
"Alexandre",
"Frederic",
""
],
[
"Hinaut",
"Xavier",
""
]
] |
Can we obtain insights about the brain using AI models? How is the information in deep learning models related to brain recordings? Can we improve AI models with the help of brain recordings? Such questions can be tackled by studying brain recordings like functional magnetic resonance imaging (fMRI). As a first step, the neuroscience community has contributed several large cognitive neuroscience datasets related to passive reading/listening/viewing of concept words, narratives, pictures, and movies. Encoding and decoding models using these datasets have also been proposed in the past two decades. These models serve as additional tools for basic cognitive science and neuroscience research. Encoding models aim at generating fMRI brain representations given a stimulus automatically. They have several practical applications in evaluating and diagnosing neurological conditions and thus may also help design therapies for brain damage. Decoding models solve the inverse problem of reconstructing the stimuli given the fMRI. They are useful for designing brain-machine or brain-computer interfaces. Inspired by the effectiveness of deep learning models for natural language processing, computer vision, and speech, several neural encoding and decoding models have been recently proposed. In this survey, we will first discuss popular representations of language, vision and speech stimuli, and present a summary of neuroscience datasets. Further, we will review popular deep learning based encoding and decoding architectures and note their benefits and limitations. Finally, we will conclude with a summary and discussion about future trends. Given the large amount of recently published work in the computational cognitive neuroscience (CCN) community, we believe that this survey enables an entry point for DNN researchers to diversify into CCN research.
|
1908.07209
|
Jianxing Hu Mr
|
Yibo Li, Jianxing Hu, Yanxing Wang, Jielong Zhou, Liangren Zhang and
Zhenming Liu
|
DeepScaffold: a comprehensive tool for scaffold-based de novo drug
discovery using deep learning
|
Updates to this version 1. Add supporting information (aux.pdf) 2.
Improvements to Section 2.2 3. Resolve grammar issues
| null | null | null |
q-bio.QM cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The ultimate goal of drug design is to find novel compounds with desirable
pharmacological properties. Designing molecules retaining particular scaffolds
as the core structures of the molecules is one of the efficient ways to obtain
potential drug candidates with desirable properties. We proposed a
scaffold-based molecular generative model for scaffold-based drug discovery,
which performs molecule generation based on a wide spectrum of scaffold
definitions, including BM-scaffolds, cyclic skeletons, as well as scaffolds
with specifications on side-chain properties. The model can generalize the
learned chemical rules of adding atoms and bonds to a given scaffold.
Furthermore, the generated compounds were evaluated by molecular docking in
DRD2 targets and the results demonstrated that this approach can be effectively
applied to solve several drug design problems, including the generation of
compounds containing a given scaffold and de novo drug design of potential drug
candidates with specific docking scores. Finally, a command line interface is
created.
|
[
{
"created": "Tue, 20 Aug 2019 08:04:00 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Aug 2019 14:25:05 GMT",
"version": "v2"
},
{
"created": "Fri, 23 Aug 2019 01:35:27 GMT",
"version": "v3"
},
{
"created": "Thu, 5 Sep 2019 00:47:25 GMT",
"version": "v4"
}
] |
2019-09-06
|
[
[
"Li",
"Yibo",
""
],
[
"Hu",
"Jianxing",
""
],
[
"Wang",
"Yanxing",
""
],
[
"Zhou",
"Jielong",
""
],
[
"Zhang",
"Liangren",
""
],
[
"Liu",
"Zhenming",
""
]
] |
The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as the core structures of the molecules is one of the efficient ways to obtain potential drug candidates with desirable properties. We proposed a scaffold-based molecular generative model for scaffold-based drug discovery, which performs molecule generation based on a wide spectrum of scaffold definitions, including BM-scaffolds, cyclic skeletons, as well as scaffolds with specifications on side-chain properties. The model can generalize the learned chemical rules of adding atoms and bonds to a given scaffold. Furthermore, the generated compounds were evaluated by molecular docking in DRD2 targets and the results demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold and de novo drug design of potential drug candidates with specific docking scores. Finally, a command line interface is created.
|
2109.01172
|
Fernando da Costa
|
Pedro R.S. Antunes, Fernando P. da Costa, Jo\~ao T. Pinto, Rafael
Sasportes
|
Modelling silicosis: dynamics of a model with piecewise constant rate
coefficients
|
28 pages, 7 figures
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study the dynamics about equilibria of an infinite dimension
coagulation-fragmentation-death model for the silicosis disease mechanism
introduced recently by da Costa, Drmota, and Grinfeld [Modelling silicosis:
structure of equilibria, Euro. J. Appl. Math., 31 (6), (2020) 950-967] in the
case where the rate coefficients are piecewise constant.
|
[
{
"created": "Thu, 2 Sep 2021 18:39:25 GMT",
"version": "v1"
}
] |
2021-09-06
|
[
[
"Antunes",
"Pedro R. S.",
""
],
[
"da Costa",
"Fernando P.",
""
],
[
"Pinto",
"João T.",
""
],
[
"Sasportes",
"Rafael",
""
]
] |
We study the dynamics about equilibria of an infinite dimension coagulation-fragmentation-death model for the silicosis disease mechanism introduced recently by da Costa, Drmota, and Grinfeld [Modelling silicosis: structure of equilibria, Euro. J. Appl. Math., 31 (6), (2020) 950-967] in the case where the rate coefficients are piecewise constant.
|
2003.12493
|
Sitabhra Sinha
|
Chandrashekar Kuyyamudi, Shakti N. Menon and Sitabhra Sinha
|
Morphogen-regulated contact-mediated signaling between cells can drive
the transitions underlying body segmentation in vertebrates
|
10 pages, 5 figures + 13 pages Supplementary Information
|
Phys. Biol. 19, 016001 (2021)
|
10.1088/1478-3975/ac31a3
| null |
q-bio.TO nlin.PS physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a unified mechanism that reproduces the sequence of dynamical
transitions observed during somitogenesis, the process of body segmentation
during embryonic development, that is invariant across all vertebrate species.
This is achieved by combining inter-cellular interactions mediated via
receptor-ligand coupling with global spatial heterogeneity introduced through a
morphogen gradient known to occur along the anteroposterior axis. Our model
reproduces synchronized oscillations in the gene expression in cells at the
anterior of the presomitic mesoderm (PSM) as it grows by adding new cells at
its posterior, followed by traveling waves and subsequent arrest of activity,
with the eventual appearance of somite-like patterns. This framework integrates
a boundary-organized pattern formation mechanism, which uses positional
information provided by a morphogen gradient, with the coupling-mediated
self-organized emergence of collective dynamics, to explain the processes that
lead to segmentation.
|
[
{
"created": "Fri, 27 Mar 2020 15:55:30 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Sep 2021 18:39:53 GMT",
"version": "v2"
}
] |
2024-06-11
|
[
[
"Kuyyamudi",
"Chandrashekar",
""
],
[
"Menon",
"Shakti N.",
""
],
[
"Sinha",
"Sitabhra",
""
]
] |
We propose a unified mechanism that reproduces the sequence of dynamical transitions observed during somitogenesis, the process of body segmentation during embryonic development, that is invariant across all vertebrate species. This is achieved by combining inter-cellular interactions mediated via receptor-ligand coupling with global spatial heterogeneity introduced through a morphogen gradient known to occur along the anteroposterior axis. Our model reproduces synchronized oscillations in the gene expression in cells at the anterior of the presomitic mesoderm (PSM) as it grows by adding new cells at its posterior, followed by traveling waves and subsequent arrest of activity, with the eventual appearance of somite-like patterns. This framework integrates a boundary-organized pattern formation mechanism, which uses positional information provided by a morphogen gradient, with the coupling-mediated self-organized emergence of collective dynamics, to explain the processes that lead to segmentation.
|
2310.08613
|
Teddy Lazebnik Dr.
|
Teddy Lazebnik, Orr Spiegel
|
Individual Variation Affects Outbreak Magnitude and Predictability in an
Extended Multi-Pathogen SIR Model of Pigeons Vising Dairy Farms
| null | null | null | null |
q-bio.PE cs.CE cs.IR cs.MA
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Zoonotic disease transmission between animals and humans is a growing risk
and the agricultural context acts as a likely point of transition, with
individual heterogeneity acting as an important contributor. Thus,
understanding the dynamics of disease spread in the wildlife-livestock
interface is crucial for mitigating these risks of transmission. Specifically,
the interactions between pigeons and in-door cows at dairy farms can lead to
significant disease transmission and economic losses for farmers; putting
livestock, adjacent human populations, and other wildlife species at risk. In
this paper, we propose a novel spatio-temporal multi-pathogen model with
continuous spatial movement. The model expands on the
Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) framework and accounts for
both within-species and cross-species transmission of pathogens, as well as the
exploration-exploitation movement dynamics of pigeons, which play a critical
role in the spread of infection agents. In addition to model formulation, we
also implement it as an agent-based simulation approach and use empirical field
data to investigate different biologically realistic scenarios, evaluating the
effect of various parameters on the epidemic spread. Namely, in agreement with
theoretical expectations, the model predicts that the heterogeneity of the
pigeons' movement dynamics can drastically affect both the magnitude and
stability of outbreaks. In addition, joint infection by multiple pathogens can
have an interactive effect unobservable in single-pathogen SIR models,
reflecting a non-intuitive inhibition of the outbreak. Our findings highlight
the impact of heterogeneity in host behavior on their pathogens and allow
realistic predictions of outbreak dynamics in the multi-pathogen
wildlife-livestock interface with consequences to zoonotic diseases in various
systems.
|
[
{
"created": "Thu, 12 Oct 2023 06:26:20 GMT",
"version": "v1"
}
] |
2023-10-16
|
[
[
"Lazebnik",
"Teddy",
""
],
[
"Spiegel",
"Orr",
""
]
] |
Zoonotic disease transmission between animals and humans is a growing risk and the agricultural context acts as a likely point of transition, with individual heterogeneity acting as an important contributor. Thus, understanding the dynamics of disease spread in the wildlife-livestock interface is crucial for mitigating these risks of transmission. Specifically, the interactions between pigeons and in-door cows at dairy farms can lead to significant disease transmission and economic losses for farmers; putting livestock, adjacent human populations, and other wildlife species at risk. In this paper, we propose a novel spatio-temporal multi-pathogen model with continuous spatial movement. The model expands on the Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) framework and accounts for both within-species and cross-species transmission of pathogens, as well as the exploration-exploitation movement dynamics of pigeons, which play a critical role in the spread of infection agents. In addition to model formulation, we also implement it as an agent-based simulation approach and use empirical field data to investigate different biologically realistic scenarios, evaluating the effect of various parameters on the epidemic spread. Namely, in agreement with theoretical expectations, the model predicts that the heterogeneity of the pigeons' movement dynamics can drastically affect both the magnitude and stability of outbreaks. In addition, joint infection by multiple pathogens can have an interactive effect unobservable in single-pathogen SIR models, reflecting a non-intuitive inhibition of the outbreak. Our findings highlight the impact of heterogeneity in host behavior on their pathogens and allow realistic predictions of outbreak dynamics in the multi-pathogen wildlife-livestock interface with consequences to zoonotic diseases in various systems.
|
2405.12645
|
Yuanhong Tang
|
Yuanhong Tang, Shanshan Jia, Tiejun Huang, Zhaofei Yu, Jian K. Liu
|
Implementing feature binding through dendritic networks of a single
neuron
| null | null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A single neuron receives an extensive array of synaptic inputs through its
dendrites, raising the fundamental question of how these inputs undergo
integration and summation, culminating in the initiation of spikes in the soma.
Experimental and computational investigations have revealed various modes of
integration operations that include linear, superlinear, and sublinear
summation. Interestingly, distinct neuron types exhibit diverse patterns of
dendritic integration contingent upon the spatial distribution of dendrites.
The functional implications of these specific integration modalities remain
largely unexplored. In this study, we employ the Purkinje cell as a model
system to investigate these intricate questions. Our findings reveal that
Purkinje cells (PCs) generally exhibit sublinear summation across their
expansive dendrites. The degree of sublinearity is dynamically modulated by
both spatial and temporal input. Strong sublinearity necessitates that the
synaptic distribution in PCs be globally scattered sensitive, whereas weak
sublinearity facilitates the generation of complex firing patterns in PCs.
Leveraging dendritic branches characterized by strong sublinearity as
computational units, we demonstrate that a neuron can adeptly address the
feature-binding problem. Collectively, these results offer a systematic
perspective on the functional role of dendritic sublinearity, providing
inspiration for a broader understanding of dendritic integration across various
neuronal types.
|
[
{
"created": "Tue, 21 May 2024 09:55:51 GMT",
"version": "v1"
}
] |
2024-05-22
|
[
[
"Tang",
"Yuanhong",
""
],
[
"Jia",
"Shanshan",
""
],
[
"Huang",
"Tiejun",
""
],
[
"Yu",
"Zhaofei",
""
],
[
"Liu",
"Jian K.",
""
]
] |
A single neuron receives an extensive array of synaptic inputs through its dendrites, raising the fundamental question of how these inputs undergo integration and summation, culminating in the initiation of spikes in the soma. Experimental and computational investigations have revealed various modes of integration operations that include linear, superlinear, and sublinear summation. Interestingly, distinct neuron types exhibit diverse patterns of dendritic integration contingent upon the spatial distribution of dendrites. The functional implications of these specific integration modalities remain largely unexplored. In this study, we employ the Purkinje cell as a model system to investigate these intricate questions. Our findings reveal that Purkinje cells (PCs) generally exhibit sublinear summation across their expansive dendrites. The degree of sublinearity is dynamically modulated by both spatial and temporal input. Strong sublinearity necessitates that the synaptic distribution in PCs be globally scattered sensitive, whereas weak sublinearity facilitates the generation of complex firing patterns in PCs. Leveraging dendritic branches characterized by strong sublinearity as computational units, we demonstrate that a neuron can adeptly address the feature-binding problem. Collectively, these results offer a systematic perspective on the functional role of dendritic sublinearity, providing inspiration for a broader understanding of dendritic integration across various neuronal types.
|
q-bio/0402001
|
Eli Ben-Naim
|
E. Ben-Naim, P.L. Krapivsky
|
Size of Outbreaks Near the Epidemic Threshold
|
4 pages, 5 figures
|
Phys. Rev. E 69, 050901R (2004)
|
10.1103/PhysRevE.69.050901
| null |
q-bio.PE cond-mat.stat-mech math.PR
| null |
The spread of infectious diseases near the epidemic threshold is
investigated. Scaling laws for the size and the duration of outbreaks
originating from a single infected individual in a large susceptible population
are obtained. The maximal size of an outbreak n_* scales as N^{2/3} with N the
population size. This scaling law implies that the average outbreak size <n>
scales as N^{1/3}. Moreover, the maximal and the average duration of an
outbreak grow as t_* ~ N^{1/3} and <t> ~ ln N, respectively.
|
[
{
"created": "Sun, 1 Feb 2004 00:10:53 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Ben-Naim",
"E.",
""
],
[
"Krapivsky",
"P. L.",
""
]
] |
The spread of infectious diseases near the epidemic threshold is investigated. Scaling laws for the size and the duration of outbreaks originating from a single infected individual in a large susceptible population are obtained. The maximal size of an outbreak n_* scales as N^{2/3} with N the population size. This scaling law implies that the average outbreak size <n> scales as N^{1/3}. Moreover, the maximal and the average duration of an outbreak grow as t_* ~ N^{1/3} and <t> ~ ln N, respectively.
|
1811.00948
|
Casper Beentjes
|
Casper Beentjes and Ruth Baker
|
Uniformisation techniques for stochastic simulation of chemical reaction
networks
| null |
J. Chem. Phys. 150, 154107 (2019)
|
10.1063/1.5081043
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This work considers the method of uniformisation for continuous-time Markov
chains in the context of chemical reaction networks. Previous work in the
literature has shown that uniformisation can be beneficial in the context of
time-inhomogeneous models, such as chemical reaction networks incorporating
extrinsic noise. This paper lays focus on the understanding of uniformisation
from the viewpoint of sample paths of chemical reaction networks. In
particular, an efficient pathwise stochastic simulation algorithm for
time-homogeneous models is presented which is complexity-wise equal to
Gillespie's direct method. This new approach therefore enlarges the class of
problems for which the uniformisation approach forms a computationally
attractive choice. Furthermore, as a new application of the uniformisation
method, we provide a novel variance reduction method for (raw) moment
estimators of chemical reaction networks based upon the combination of
stratification and uniformisation.
|
[
{
"created": "Fri, 2 Nov 2018 15:57:03 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Apr 2019 07:49:18 GMT",
"version": "v2"
}
] |
2019-04-18
|
[
[
"Beentjes",
"Casper",
""
],
[
"Baker",
"Ruth",
""
]
] |
This work considers the method of uniformisation for continuous-time Markov chains in the context of chemical reaction networks. Previous work in the literature has shown that uniformisation can be beneficial in the context of time-inhomogeneous models, such as chemical reaction networks incorporating extrinsic noise. This paper lays focus on the understanding of uniformisation from the viewpoint of sample paths of chemical reaction networks. In particular, an efficient pathwise stochastic simulation algorithm for time-homogeneous models is presented which is complexity-wise equal to Gillespie's direct method. This new approach therefore enlarges the class of problems for which the uniformisation approach forms a computationally attractive choice. Furthermore, as a new application of the uniformisation method, we provide a novel variance reduction method for (raw) moment estimators of chemical reaction networks based upon the combination of stratification and uniformisation.
|
1305.3507
|
Sungroh Yoon
|
Hanjoo Kim, Pablo Cordero, Rhiju Das, Sungroh Yoon
|
HiTRACE-Web: an online tool for robust analysis of high-throughput
capillary electrophoresis
| null | null |
10.1093/nar/gkt501
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To facilitate the analysis of large-scale high-throughput capillary
electrophoresis data, we previously proposed a suite of efficient analysis
software named HiTRACE (High Throughput Robust Analysis of Capillary
Electrophoresis). HiTRACE has been used extensively for quantitating data from
RNA and DNA structure mapping experiments, including mutate-and-map contact
inference, chromatin footprinting, the EteRNA RNA design project and other
high-throughput applications. However, HiTRACE is based on a suite of
command-line MATLAB scripts that requires nontrivial efforts to learn, use, and
extend. Here we present HiTRACE-Web, an online version of HiTRACE that includes
standard features previously available in the command-line version as well as
additional features such as automated band annotation and flexible adjustment
of annotations, all via a user-friendly environment. By making use of
parallelization, the on-line workflow is also faster than software
implementations available to most users on their local computers. Free access:
http://hitrace.org
|
[
{
"created": "Wed, 15 May 2013 14:51:59 GMT",
"version": "v1"
},
{
"created": "Tue, 21 May 2013 10:39:12 GMT",
"version": "v2"
}
] |
2015-02-10
|
[
[
"Kim",
"Hanjoo",
""
],
[
"Cordero",
"Pablo",
""
],
[
"Das",
"Rhiju",
""
],
[
"Yoon",
"Sungroh",
""
]
] |
To facilitate the analysis of large-scale high-throughput capillary electrophoresis data, we previously proposed a suite of efficient analysis software named HiTRACE (High Throughput Robust Analysis of Capillary Electrophoresis). HiTRACE has been used extensively for quantitating data from RNA and DNA structure mapping experiments, including mutate-and-map contact inference, chromatin footprinting, the EteRNA RNA design project and other high-throughput applications. However, HiTRACE is based on a suite of command-line MATLAB scripts that requires nontrivial efforts to learn, use, and extend. Here we present HiTRACE-Web, an online version of HiTRACE that includes standard features previously available in the command-line version as well as additional features such as automated band annotation and flexible adjustment of annotations, all via a user-friendly environment. By making use of parallelization, the on-line workflow is also faster than software implementations available to most users on their local computers. Free access: http://hitrace.org
|
2011.04252
|
Michael Stumpf
|
Anissa Guillemin and Michael P.H. Stumpf
|
Non-equilibrium statistical physics, transitory epigenetic landscapes,
and cell fate decision dynamics
|
14 pages, 3 Figures; Review (for Mathematical Biosciences and
Bioengineering)
| null | null | null |
q-bio.CB
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Statistical physics provides a useful perspective for the analysis of many
complex systems; it allows us to relate microscopic fluctuations to macroscopic
observations. Developmental biology, but also cell biology more generally, are
examples where apparently robust behaviour emerges from highly complex and
stochastic sub-cellular processes. Here we attempt to make connections between
different theoretical perspectives to gain qualitative insights into the types
of cell-fate decision making processes that are at the heart of stem cell and
developmental biology. We discuss both dynamical systems as well as statistical
mechanics perspectives on the classical Waddington or epigenetic landscape. We
find that non-equilibrium approaches are required to overcome some of the
shortcomings of classical equilibrium statistical thermodynamics or statistical
mechanics in order to shed light on biological processes, which, almost by
definition, are typically far from equilibrium.
|
[
{
"created": "Mon, 9 Nov 2020 08:56:33 GMT",
"version": "v1"
}
] |
2020-11-10
|
[
[
"Guillemin",
"Anissa",
""
],
[
"Stumpf",
"Michael P. H.",
""
]
] |
Statistical physics provides a useful perspective for the analysis of many complex systems; it allows us to relate microscopic fluctuations to macroscopic observations. Developmental biology, but also cell biology more generally, are examples where apparently robust behaviour emerges from highly complex and stochastic sub-cellular processes. Here we attempt to make connections between different theoretical perspectives to gain qualitative insights into the types of cell-fate decision making processes that are at the heart of stem cell and developmental biology. We discuss both dynamical systems as well as statistical mechanics perspectives on the classical Waddington or epigenetic landscape. We find that non-equilibrium approaches are required to overcome some of the shortcomings of classical equilibrium statistical thermodynamics or statistical mechanics in order to shed light on biological processes, which, almost by definition, are typically far from equilibrium.
|
2003.13655
|
Gian Tartaglia
|
Andrea Vandelli, Michele Monti, Edoardo Milanetti, Riccardo Delli
Ponti and Gian Gaetano Tartaglia
|
Structural analysis of SARS-CoV-2 and prediction of the human
interactome
|
30 pages, 4 figures
| null | null | null |
q-bio.BM q-bio.GN q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Specific elements of viral genomes regulate interactions within host cells.
Here, we calculated the secondary structure content of >2500 coronaviruses and
computed >100000 human protein interactions with severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2). We found that the 3 and 5 prime ends are
the most structured elements in the viral genome and the 5 prime end has the
strongest propensity to associate with human proteins. The domain encompassing
nucleotides 23000-24000 is highly conserved both at the sequence and structural
level, while the region upstream varies significantly. These two sequences code
for a domain of the viral protein Spike S that interacts with the human
receptor angiotensin-converting enzyme 2 (ACE2) and has the potential to bind
sialic acids. Our predictions indicate that the first 1000 nucleotides in the 5
prime end can interact with proteins involved in viral RNA processing such as
double-stranded RNA specific editases and ATP-dependent RNA-helicases, in
addition to other high-confidence candidate partners. These interactions,
previously reported to be also implicated in HIV, reveal important information
on host-virus interactions. The list of transcriptional and
post-transcriptional elements recruited by SARS-CoV-2 genome provides clues on
the biological pathways associated with gene expression changes in human cells.
|
[
{
"created": "Mon, 30 Mar 2020 17:41:26 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Apr 2020 20:34:53 GMT",
"version": "v2"
},
{
"created": "Sun, 12 Apr 2020 14:30:28 GMT",
"version": "v3"
},
{
"created": "Wed, 29 Apr 2020 07:35:01 GMT",
"version": "v4"
}
] |
2020-04-30
|
[
[
"Vandelli",
"Andrea",
""
],
[
"Monti",
"Michele",
""
],
[
"Milanetti",
"Edoardo",
""
],
[
"Ponti",
"Riccardo Delli",
""
],
[
"Tartaglia",
"Gian Gaetano",
""
]
] |
Specific elements of viral genomes regulate interactions within host cells. Here, we calculated the secondary structure content of >2500 coronaviruses and computed >100000 human protein interactions with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We found that the 3 and 5 prime ends are the most structured elements in the viral genome and the 5 prime end has the strongest propensity to associate with human proteins. The domain encompassing nucleotides 23000-24000 is highly conserved both at the sequence and structural level, while the region upstream varies significantly. These two sequences code for a domain of the viral protein Spike S that interacts with the human receptor angiotensin-converting enzyme 2 (ACE2) and has the potential to bind sialic acids. Our predictions indicate that the first 1000 nucleotides in the 5 prime end can interact with proteins involved in viral RNA processing such as double-stranded RNA specific editases and ATP-dependent RNA-helicases, in addition to other high-confidence candidate partners. These interactions, previously reported to be also implicated in HIV, reveal important information on host-virus interactions. The list of transcriptional and post-transcriptional elements recruited by SARS-CoV-2 genome provides clues on the biological pathways associated with gene expression changes in human cells.
|
1604.02099
|
Yoo Ah Kim
|
Yoo-Ah Kim, Sanna Madan, and Teresa M. Przytycka
|
WeSME: Uncovering Mutual Exclusivity of Cancer Drivers and Beyond
|
Paper accepted at RECOMB-CCB 2016
| null | null | null |
q-bio.QM cs.CE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mutual exclusivity is a widely recognized property of many cancer drivers.
Knowledge about these relationships can provide important insights into cancer
drivers, cancer-driving pathways, and cancer subtypes. It can also be used to
predict new functional interactions between cancer driving genes and uncover
novel cancer drivers. Currently, most of mutual exclusivity analyses are
preformed focusing on a limited set of genes in part due to the computational
cost required to rigorously compute p-values. To reduce the computing cost and
perform less restricted mutual exclusivity analysis, we developed an efficient
method to estimate p-values while controlling the mutation rates of individual
patients and genes similar to the permutation test. A comprehensive mutual
exclusivity analysis allowed us to uncover mutually exclusive pairs, some of
which may have relatively low mutation rates. These pairs often included likely
cancer drivers that have been missed in previous analyses. More importantly,
our results demonstrated that mutual exclusivity can also provide information
that goes beyond the interactions between cancer drivers and can, for example,
elucidate different mutagenic processes in different cancer groups. In
particular, including frequently mutated, long genes such as TTN in our
analysis allowed us to observe interesting patterns of APOBEC activity in
breast cancer and identify a set of related driver genes that are highly
predictive of patient survival. In addition, we utilized our mutual exclusivity
analysis in support of a previously proposed model where APOBEC activity is the
underlying process that causes TP53 mutations in a subset of breast cancer
cases.
|
[
{
"created": "Thu, 7 Apr 2016 18:18:21 GMT",
"version": "v1"
}
] |
2016-04-08
|
[
[
"Kim",
"Yoo-Ah",
""
],
[
"Madan",
"Sanna",
""
],
[
"Przytycka",
"Teresa M.",
""
]
] |
Mutual exclusivity is a widely recognized property of many cancer drivers. Knowledge about these relationships can provide important insights into cancer drivers, cancer-driving pathways, and cancer subtypes. It can also be used to predict new functional interactions between cancer driving genes and uncover novel cancer drivers. Currently, most of mutual exclusivity analyses are preformed focusing on a limited set of genes in part due to the computational cost required to rigorously compute p-values. To reduce the computing cost and perform less restricted mutual exclusivity analysis, we developed an efficient method to estimate p-values while controlling the mutation rates of individual patients and genes similar to the permutation test. A comprehensive mutual exclusivity analysis allowed us to uncover mutually exclusive pairs, some of which may have relatively low mutation rates. These pairs often included likely cancer drivers that have been missed in previous analyses. More importantly, our results demonstrated that mutual exclusivity can also provide information that goes beyond the interactions between cancer drivers and can, for example, elucidate different mutagenic processes in different cancer groups. In particular, including frequently mutated, long genes such as TTN in our analysis allowed us to observe interesting patterns of APOBEC activity in breast cancer and identify a set of related driver genes that are highly predictive of patient survival. In addition, we utilized our mutual exclusivity analysis in support of a previously proposed model where APOBEC activity is the underlying process that causes TP53 mutations in a subset of breast cancer cases.
|
1509.04559
|
Mohammad Soltani
|
Mohammad Soltani, Cesar Augusto Vargas-Garcia, Duarte Antunes,
Abhyudai Singh
|
Decomposing variability in protein levels from noisy expression, genome
duplication and partitioning errors during cell-divisions
|
40 pages, 10 figures
| null | null | null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Inside individual cells, expression of genes is inherently stochastic and
manifests as cell-to-cell variability or noise in protein copy numbers. Since
proteins half-lives can be comparable to the cell-cycle length, randomness in
cell-division times generates additional intercellular variability in protein
levels. Moreover, as many mRNA/protein species are expressed at low-copy
numbers, errors incurred in partitioning of molecules between the mother and
daughter cells are significant. We derive analytical formulas for the total
noise in protein levels for a general class of cell-division time and
partitioning error distributions. Using a novel hybrid approach the total noise
is decomposed into components arising from i) stochastic expression; ii)
partitioning errors at the time of cell-division and iii) random cell-division
events. These formulas reveal that random cell-division times not only generate
additional extrinsic noise but also critically affect the mean protein copy
numbers and intrinsic noise components. Counter intuitively, in some parameter
regimes noise in protein levels can decrease as cell-division times become more
stochastic. Computations are extended to consider genome duplication, where the
gene dosage is increased by two-fold at a random point in the cell-cycle. We
systematically investigate how the timing of genome duplication influences
different protein noise components. Intriguingly, results show that noise
contribution from stochastic expression is minimized at an optimal genome
duplication time. Our theoretical results motivate new experimental methods for
decomposing protein noise levels from single-cell expression data.
Characterizing the contributions of individual noise mechanisms will lead to
precise estimates of gene expression parameters and techniques for altering
stochasticity to change phenotype of individual cells.
|
[
{
"created": "Sat, 5 Sep 2015 22:15:36 GMT",
"version": "v1"
},
{
"created": "Fri, 2 Oct 2015 20:26:35 GMT",
"version": "v2"
}
] |
2015-10-06
|
[
[
"Soltani",
"Mohammad",
""
],
[
"Vargas-Garcia",
"Cesar Augusto",
""
],
[
"Antunes",
"Duarte",
""
],
[
"Singh",
"Abhyudai",
""
]
] |
Inside individual cells, expression of genes is inherently stochastic and manifests as cell-to-cell variability or noise in protein copy numbers. Since proteins half-lives can be comparable to the cell-cycle length, randomness in cell-division times generates additional intercellular variability in protein levels. Moreover, as many mRNA/protein species are expressed at low-copy numbers, errors incurred in partitioning of molecules between the mother and daughter cells are significant. We derive analytical formulas for the total noise in protein levels for a general class of cell-division time and partitioning error distributions. Using a novel hybrid approach the total noise is decomposed into components arising from i) stochastic expression; ii) partitioning errors at the time of cell-division and iii) random cell-division events. These formulas reveal that random cell-division times not only generate additional extrinsic noise but also critically affect the mean protein copy numbers and intrinsic noise components. Counter intuitively, in some parameter regimes noise in protein levels can decrease as cell-division times become more stochastic. Computations are extended to consider genome duplication, where the gene dosage is increased by two-fold at a random point in the cell-cycle. We systematically investigate how the timing of genome duplication influences different protein noise components. Intriguingly, results show that noise contribution from stochastic expression is minimized at an optimal genome duplication time. Our theoretical results motivate new experimental methods for decomposing protein noise levels from single-cell expression data. Characterizing the contributions of individual noise mechanisms will lead to precise estimates of gene expression parameters and techniques for altering stochasticity to change phenotype of individual cells.
|
1912.10251
|
Chowdhury Rahman
|
Ruhul Amin, Chowdhury Rafeed Rahman, Md. Habibur Rahman Sifat, Md
Nazmul Khan Liton, Md. Moshiur Rahman, Swakkhar Shatabda and Sajid Ahmed
|
iPromoter-BnCNN: a Novel Branched CNN Based Predictor for Identifying
and Classifying Sigma Promoters
| null | null |
10.1093/bioinformatics/btaa609
| null |
q-bio.QM cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Promoter is a short region of DNA which is responsible for initiating
transcription of specific genes. Development of computational tools for
automatic identification of promoters is in high demand. According to the
difference of functions, promoters can be of different types. Promoters may
have both intra and inter class variation and similarity in terms of consensus
sequences. Accurate classification of various types of sigma promoters still
remains a challenge. We present iPromoter-BnCNN for identification and accurate
classification of six types of promoters - sigma24, sigma28, sigma32, sigma38,
sigma54, sigma70. It is a Convolutional Neural Network (CNN) based classifier
which combines local features related to monomer nucleotide sequence, trimer
nucleotide sequence, dimer structural properties and trimer structural
properties through the use of parallel branching. We conducted experiments on a
benchmark dataset and compared with two state-of-the-art tools to show our
supremacy on 5-fold cross-validation. Moreover, we tested our classifier on an
independent test dataset. Our proposed tool iPromoter-BnCNN web server is
freely available at http://103.109.52.8/iPromoter-BnCNN. The runnable source
code can be found at
https://colab.research.google.com/drive/1yWWh7BXhsm8U4PODgPqlQRy23QGjF2DZ.
|
[
{
"created": "Sat, 21 Dec 2019 11:59:38 GMT",
"version": "v1"
},
{
"created": "Wed, 25 Dec 2019 06:51:47 GMT",
"version": "v2"
},
{
"created": "Fri, 10 Jan 2020 01:20:09 GMT",
"version": "v3"
},
{
"created": "Tue, 16 Jun 2020 20:44:32 GMT",
"version": "v4"
}
] |
2020-07-06
|
[
[
"Amin",
"Ruhul",
""
],
[
"Rahman",
"Chowdhury Rafeed",
""
],
[
"Sifat",
"Md. Habibur Rahman",
""
],
[
"Liton",
"Md Nazmul Khan",
""
],
[
"Rahman",
"Md. Moshiur",
""
],
[
"Shatabda",
"Swakkhar",
""
],
[
"Ahmed",
"Sajid",
""
]
] |
Promoter is a short region of DNA which is responsible for initiating transcription of specific genes. Development of computational tools for automatic identification of promoters is in high demand. According to the difference of functions, promoters can be of different types. Promoters may have both intra and inter class variation and similarity in terms of consensus sequences. Accurate classification of various types of sigma promoters still remains a challenge. We present iPromoter-BnCNN for identification and accurate classification of six types of promoters - sigma24, sigma28, sigma32, sigma38, sigma54, sigma70. It is a Convolutional Neural Network (CNN) based classifier which combines local features related to monomer nucleotide sequence, trimer nucleotide sequence, dimer structural properties and trimer structural properties through the use of parallel branching. We conducted experiments on a benchmark dataset and compared with two state-of-the-art tools to show our supremacy on 5-fold cross-validation. Moreover, we tested our classifier on an independent test dataset. Our proposed tool iPromoter-BnCNN web server is freely available at http://103.109.52.8/iPromoter-BnCNN. The runnable source code can be found at https://colab.research.google.com/drive/1yWWh7BXhsm8U4PODgPqlQRy23QGjF2DZ.
|
1512.01389
|
Gerardo Chowell
|
C\'ecile Viboud, Lone Simonsen, Gerardo Chowell
|
A generalized-growth model to characterize the early ascending phase of
infectious disease outbreaks
|
31 pages, 9 Figures, 1 Supp. Figure, 1 Table, final accepted version
(in press), Epidemics - The Journal on Infectious Disease Dynamics, 2016
| null | null | null |
q-bio.QM q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A better characterization of the early growth dynamics of an epidemic is
needed to dissect the important drivers of disease transmission. We introduce a
2-parameter generalized-growth model to characterize the ascending phase of an
outbreak and capture epidemic profiles ranging from sub-exponential to
exponential growth. We test the model against empirical outbreak data
representing a variety of viral pathogens and provide simulations highlighting
the importance of sub-exponential growth for forecasting purposes. We applied
the generalized-growth model to 20 infectious disease outbreaks representing a
range of transmission routes. We uncovered epidemic profiles ranging from very
slow growth (p=0.14 for the Ebola outbreak in Bomi, Liberia (2014)) to near
exponential (p>0.9 for the smallpox outbreak in Khulna (1972), and the 1918
pandemic influenza in San Francisco). The foot-and-mouth disease outbreak in
Uruguay displayed a profile of slower growth while the growth pattern of the
HIV/AIDS epidemic in Japan was approximately linear. The West African Ebola
epidemic provided a unique opportunity to explore how growth profiles vary by
geography; analysis of the largest district-level outbreaks revealed
substantial growth variations (mean p=0.59, range: 0.14-0.97). Our findings
reveal significant variation in epidemic growth patterns across different
infectious disease outbreaks and highlights that sub-exponential growth is a
common phenomenon. Sub-exponential growth profiles may result from
heterogeneity in contact structures or risk groups, reactive behavior changes,
or the early onset of interventions strategies, and consideration of
"deceleration parameters" may be useful to refine existing mathematical
transmission models and improve disease forecasts.
|
[
{
"created": "Fri, 4 Dec 2015 12:36:10 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Jan 2016 16:46:56 GMT",
"version": "v2"
}
] |
2016-01-18
|
[
[
"Viboud",
"Cécile",
""
],
[
"Simonsen",
"Lone",
""
],
[
"Chowell",
"Gerardo",
""
]
] |
A better characterization of the early growth dynamics of an epidemic is needed to dissect the important drivers of disease transmission. We introduce a 2-parameter generalized-growth model to characterize the ascending phase of an outbreak and capture epidemic profiles ranging from sub-exponential to exponential growth. We test the model against empirical outbreak data representing a variety of viral pathogens and provide simulations highlighting the importance of sub-exponential growth for forecasting purposes. We applied the generalized-growth model to 20 infectious disease outbreaks representing a range of transmission routes. We uncovered epidemic profiles ranging from very slow growth (p=0.14 for the Ebola outbreak in Bomi, Liberia (2014)) to near exponential (p>0.9 for the smallpox outbreak in Khulna (1972), and the 1918 pandemic influenza in San Francisco). The foot-and-mouth disease outbreak in Uruguay displayed a profile of slower growth while the growth pattern of the HIV/AIDS epidemic in Japan was approximately linear. The West African Ebola epidemic provided a unique opportunity to explore how growth profiles vary by geography; analysis of the largest district-level outbreaks revealed substantial growth variations (mean p=0.59, range: 0.14-0.97). Our findings reveal significant variation in epidemic growth patterns across different infectious disease outbreaks and highlights that sub-exponential growth is a common phenomenon. Sub-exponential growth profiles may result from heterogeneity in contact structures or risk groups, reactive behavior changes, or the early onset of interventions strategies, and consideration of "deceleration parameters" may be useful to refine existing mathematical transmission models and improve disease forecasts.
|
2112.09806
|
Jeremi K. Ochab
|
Dante R. Chialvo and Ignacio Cifre and Jeremi K. Ochab
|
Untangling the brain web: from the early days of complex functional
networks to the non-linear dynamical directed functional connectivity
measures
| null | null | null | null |
q-bio.NC nlin.AO q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Already two decades passed since the first applications of graph theory to
brain neuroimaging. Since that early description, the characterization of the
brain as a very large interacting complex network has evolved in several
directions. In this brief review we discuss our contributions to this topic and
discuss some perspective for future work.
|
[
{
"created": "Fri, 17 Dec 2021 23:42:54 GMT",
"version": "v1"
}
] |
2021-12-21
|
[
[
"Chialvo",
"Dante R.",
""
],
[
"Cifre",
"Ignacio",
""
],
[
"Ochab",
"Jeremi K.",
""
]
] |
Already two decades passed since the first applications of graph theory to brain neuroimaging. Since that early description, the characterization of the brain as a very large interacting complex network has evolved in several directions. In this brief review we discuss our contributions to this topic and discuss some perspective for future work.
|
1101.1836
|
Ahmad Khoureich Ka
|
Ahmad Khoureich Ka (IRMAR)
|
ECG beats classification using waveform similarity and RR interval
|
4 pages
| null | null | null |
q-bio.QM physics.med-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper present an electrocardiogram (ECG) beat classification method
based on waveform similarity and RR interval. The purpose of the method is to
classify six types of heart beats (normal beat, atrial premature beat, paced
beat, premature ventricular beat, left bundle branch block beat and right
bundle branch block beat). The electrocardiogram signal is first denoised using
wavelet transform based techniques. Heart beats of 128 samples data centered on
the R peak are extracted from the ECG signal and thence reduced to 16 samples
data to constitute a feature. RR intervals surrounding the beat are also
exploited as feature. A database of annotated beats is built for the classifier
for waveform comparison to unknown beats. Tested on 46 records in the MIT/BIH
arrhythmia database, the method shows classification rate of 97.52%.
|
[
{
"created": "Mon, 10 Jan 2011 15:03:05 GMT",
"version": "v1"
}
] |
2011-01-11
|
[
[
"Ka",
"Ahmad Khoureich",
"",
"IRMAR"
]
] |
This paper present an electrocardiogram (ECG) beat classification method based on waveform similarity and RR interval. The purpose of the method is to classify six types of heart beats (normal beat, atrial premature beat, paced beat, premature ventricular beat, left bundle branch block beat and right bundle branch block beat). The electrocardiogram signal is first denoised using wavelet transform based techniques. Heart beats of 128 samples data centered on the R peak are extracted from the ECG signal and thence reduced to 16 samples data to constitute a feature. RR intervals surrounding the beat are also exploited as feature. A database of annotated beats is built for the classifier for waveform comparison to unknown beats. Tested on 46 records in the MIT/BIH arrhythmia database, the method shows classification rate of 97.52%.
|
1909.02664
|
Babak Ravandi
|
Babak Ravandi and Arash Ravandi
|
Network-Based Approach for Modeling and Analyzing Coronary Angiography
| null | null |
10.1007/978-3-030-40943-2_15
| null |
q-bio.QM physics.data-an q-bio.TO stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Significant intra-observer and inter-observer variability in the
interpretation of coronary angiograms are reported. This variability is in part
due to the common practices that rely on performing visual inspections by
specialists (e.g., the thickness of coronaries). Quantitative Coronary
Angiography (QCA) approaches are emerging to minimize observer's error and
furthermore perform predictions and analysis on angiography images. However,
QCA approaches suffer from the same problem as they mainly rely on performing
visual inspections by utilizing image processing techniques.
In this work, we propose an approach to model and analyze the entire
cardiovascular tree as a complex network derived from coronary angiography
images. This approach enables to analyze the graph structure of coronary
arteries. We conduct the assessments of network integration, degree
distribution, and controllability on a healthy and a diseased coronary
angiogram. Through our discussion and assessments, we propose modeling the
cardiovascular system as a complex network is an essential phase to fully
automate the interpretation of coronary angiographic images. We show how
network science can provide a new perspective to look at coronary angiograms.
|
[
{
"created": "Thu, 5 Sep 2019 22:50:35 GMT",
"version": "v1"
}
] |
2020-02-27
|
[
[
"Ravandi",
"Babak",
""
],
[
"Ravandi",
"Arash",
""
]
] |
Significant intra-observer and inter-observer variability in the interpretation of coronary angiograms are reported. This variability is in part due to the common practices that rely on performing visual inspections by specialists (e.g., the thickness of coronaries). Quantitative Coronary Angiography (QCA) approaches are emerging to minimize observer's error and furthermore perform predictions and analysis on angiography images. However, QCA approaches suffer from the same problem as they mainly rely on performing visual inspections by utilizing image processing techniques. In this work, we propose an approach to model and analyze the entire cardiovascular tree as a complex network derived from coronary angiography images. This approach enables to analyze the graph structure of coronary arteries. We conduct the assessments of network integration, degree distribution, and controllability on a healthy and a diseased coronary angiogram. Through our discussion and assessments, we propose modeling the cardiovascular system as a complex network is an essential phase to fully automate the interpretation of coronary angiographic images. We show how network science can provide a new perspective to look at coronary angiograms.
|
2005.01579
|
Donald Forsdyke Dr.
|
Donald R. Forsdyke
|
SARS-CoV-2 mortality in blacks and temperature-sensitivity to an
angiotensin-2 receptor blocker
|
Pages: 20. Words: 5739. Figures: 3. References: 72. Updated
references and text
| null | null | null |
q-bio.TO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Tropical climates provoke adaptations in skin pigmentation and in mechanisms
controlling the volume, salt-content and pressure of body fluids. For many
whose distant ancestors moved to temperate climes, these adaptations proved
harmful: pigmentation decreased by natural selection and susceptibility to
hypertension emerged. Now an added risk is lung inflammation from coronavirus
that may be furthered by innate immune differences. Hypertension and
coronavirus have in common angiotensin converting enzyme 2 (ACE2), which
decreases blood pressure and mediates virus entry. In keeping with less
detailed studies, a long-term case-report shows that decreased blood pressure
induced by blocking a primary angiotensin receptor is supplemented, above
critical blocker dosage, by a further temperature-dependent fall, likely
mediated by ACE2 and secondary angiotensin receptors. Temperature-dependence
suggests a linkage with tropical heritage and an influence of blockers on the
progress of coronavirus infections. Positive therapeutic results should result
from negation of host pro-inflammatory effects mediated by the primary
angiotensin receptor and concomitant promotion of countervailing
anti-inflammatory effects mediated by ACE2 through other receptors. These
effects may involve innate immune system components (lectin complement pathway,
NAD metabolome). Black vulnerability - more likely based on physiological than
on socioeconomic differences - provides an important clue that may guide
treatments.
|
[
{
"created": "Thu, 30 Apr 2020 16:53:26 GMT",
"version": "v1"
},
{
"created": "Wed, 6 May 2020 15:58:25 GMT",
"version": "v2"
},
{
"created": "Sun, 24 May 2020 14:46:09 GMT",
"version": "v3"
},
{
"created": "Sat, 13 Jun 2020 15:10:49 GMT",
"version": "v4"
},
{
"created": "Wed, 5 Aug 2020 20:59:38 GMT",
"version": "v5"
}
] |
2020-08-07
|
[
[
"Forsdyke",
"Donald R.",
""
]
] |
Tropical climates provoke adaptations in skin pigmentation and in mechanisms controlling the volume, salt-content and pressure of body fluids. For many whose distant ancestors moved to temperate climes, these adaptations proved harmful: pigmentation decreased by natural selection and susceptibility to hypertension emerged. Now an added risk is lung inflammation from coronavirus that may be furthered by innate immune differences. Hypertension and coronavirus have in common angiotensin converting enzyme 2 (ACE2), which decreases blood pressure and mediates virus entry. In keeping with less detailed studies, a long-term case-report shows that decreased blood pressure induced by blocking a primary angiotensin receptor is supplemented, above critical blocker dosage, by a further temperature-dependent fall, likely mediated by ACE2 and secondary angiotensin receptors. Temperature-dependence suggests a linkage with tropical heritage and an influence of blockers on the progress of coronavirus infections. Positive therapeutic results should result from negation of host pro-inflammatory effects mediated by the primary angiotensin receptor and concomitant promotion of countervailing anti-inflammatory effects mediated by ACE2 through other receptors. These effects may involve innate immune system components (lectin complement pathway, NAD metabolome). Black vulnerability - more likely based on physiological than on socioeconomic differences - provides an important clue that may guide treatments.
|
0810.5434
|
Laurent Noe
|
Mihkail Roytberg (IMPB-RAS), Anna Gambin, Laurent No\'e (LIFL, INRIA
Lille - Nord Europe), Slawomir Lasota, Eugenia Furletova (IMPB-RAS), Ewa
Szczurek (MPI), Gregory Kucherov (LIFL, INRIA Lille - Nord Europe)
|
Efficient seeding techniques for protein similarity search
| null |
BIRD - ALBIO 13 (2008)
|
10.1007/978-3-540-70600-7
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We apply the concept of subset seeds proposed in [1] to similarity search in
protein sequences. The main question studied is the design of efficient seed
alphabets to construct seeds with optimal sensitivity/selectivity trade-offs.
We propose several different design methods and use them to construct several
alphabets.We then perform an analysis of seeds built over those alphabet and
compare them with the standard Blastp seeding method [2,3], as well as with the
family of vector seeds proposed in [4]. While the formalism of subset seed is
less expressive (but less costly to implement) than the accumulative principle
used in Blastp and vector seeds, our seeds show a similar or even better
performance than Blastp on Bernoulli models of proteins compatible with the
common BLOSUM62 matrix.
|
[
{
"created": "Thu, 30 Oct 2008 07:41:00 GMT",
"version": "v1"
}
] |
2008-10-31
|
[
[
"Roytberg",
"Mihkail",
"",
"IMPB-RAS"
],
[
"Gambin",
"Anna",
"",
"LIFL, INRIA\n Lille - Nord Europe"
],
[
"Noé",
"Laurent",
"",
"LIFL, INRIA\n Lille - Nord Europe"
],
[
"Lasota",
"Slawomir",
"",
"IMPB-RAS"
],
[
"Furletova",
"Eugenia",
"",
"IMPB-RAS"
],
[
"Szczurek",
"Ewa",
"",
"MPI"
],
[
"Kucherov",
"Gregory",
"",
"LIFL, INRIA Lille - Nord Europe"
]
] |
We apply the concept of subset seeds proposed in [1] to similarity search in protein sequences. The main question studied is the design of efficient seed alphabets to construct seeds with optimal sensitivity/selectivity trade-offs. We propose several different design methods and use them to construct several alphabets.We then perform an analysis of seeds built over those alphabet and compare them with the standard Blastp seeding method [2,3], as well as with the family of vector seeds proposed in [4]. While the formalism of subset seed is less expressive (but less costly to implement) than the accumulative principle used in Blastp and vector seeds, our seeds show a similar or even better performance than Blastp on Bernoulli models of proteins compatible with the common BLOSUM62 matrix.
|
2109.12100
|
Jiahui Chen
|
Jiahui Chen, Weihua Geng, Guo-Wei Wei
|
MLIMC: Machine learning-based implicit-solvent Monte Carlo
| null | null |
10.1063/1674-0068/cjcp2109150
| null |
q-bio.BM cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Monte Carlo (MC) methods are important computational tools for molecular
structure optimizations and predictions. When solvent effects are explicitly
considered, MC methods become very expensive due to the large degree of freedom
associated with the water molecules and mobile ions. Alternatively
implicit-solvent MC can largely reduce the computational cost by applying a
mean field approximation to solvent effects and meanwhile maintains the atomic
detail of the target molecule. The two most popular implicit-solvent models are
the Poisson-Boltzmann (PB) model and the Generalized Born (GB) model in a way
such that the GB model is an approximation to the PB model but is much faster
in simulation time. In this work, we develop a machine learning-based
implicit-solvent Monte Carlo (MLIMC) method by combining the advantages of both
implicit solvent models in accuracy and efficiency. Specifically, the MLIMC
method uses a fast and accurate PB-based machine learning (PBML) scheme to
compute the electrostatic solvation free energy at each step. We validate our
MLIMC method by using a benzene-water system and a protein-water system. We
show that the proposed MLIMC method has great advantages in speed and accuracy
for molecular structure optimization and prediction.
|
[
{
"created": "Fri, 24 Sep 2021 17:47:07 GMT",
"version": "v1"
}
] |
2022-02-09
|
[
[
"Chen",
"Jiahui",
""
],
[
"Geng",
"Weihua",
""
],
[
"Wei",
"Guo-Wei",
""
]
] |
Monte Carlo (MC) methods are important computational tools for molecular structure optimizations and predictions. When solvent effects are explicitly considered, MC methods become very expensive due to the large degree of freedom associated with the water molecules and mobile ions. Alternatively implicit-solvent MC can largely reduce the computational cost by applying a mean field approximation to solvent effects and meanwhile maintains the atomic detail of the target molecule. The two most popular implicit-solvent models are the Poisson-Boltzmann (PB) model and the Generalized Born (GB) model in a way such that the GB model is an approximation to the PB model but is much faster in simulation time. In this work, we develop a machine learning-based implicit-solvent Monte Carlo (MLIMC) method by combining the advantages of both implicit solvent models in accuracy and efficiency. Specifically, the MLIMC method uses a fast and accurate PB-based machine learning (PBML) scheme to compute the electrostatic solvation free energy at each step. We validate our MLIMC method by using a benzene-water system and a protein-water system. We show that the proposed MLIMC method has great advantages in speed and accuracy for molecular structure optimization and prediction.
|
1403.7072
|
Jack Dekker
|
Milton J. Haar and Jack Dekker
|
Weedy Adaptation in Setaria spp.: VII. Seed Germination Heteroblasty in
Setaria faberi
|
19 pages, 5 figures, 4 tables
| null | null | null |
q-bio.QM q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The dormancy status of S. faberi seed at abscission was assessed with
reference to tiller and panicle development. Seed from a single genetic line
were grown under field, greenhouse and controlled environment growth chamber
conditions. At abscission, a small fraction (<10%) of S. faberi seed germinated
under favorable conditions. Seed were dissected and germination of caryopses
and embryos also tested. Removal of seed structures exterior to the embryo
increased percentage germination. As the seed rain progressed mean percentage
germination and variation among samples increased. Changes in germination were
correlated with tiller development and relative time of seed maturity within a
panicle. Seed produced on tillers that developed earlier were more likely to be
dormant than seed from later-developing tillers. Seed that matured later on a
panicle were more likely to germinate than seed that matured earlier on the
same panicle. A consistent trend toward later maturing seed having less
dormancy was found for seed grown under different environments which implies an
inherent or parental source for variation in giant foxtail seed dormancy. The
variation in percentage germination at abscission and following stratification
treatments indicates that the S. faberi seed rain consists of individual seeds,
possibly each with a different degree of dormancy.
|
[
{
"created": "Thu, 27 Mar 2014 15:13:21 GMT",
"version": "v1"
}
] |
2014-03-28
|
[
[
"Haar",
"Milton J.",
""
],
[
"Dekker",
"Jack",
""
]
] |
The dormancy status of S. faberi seed at abscission was assessed with reference to tiller and panicle development. Seed from a single genetic line were grown under field, greenhouse and controlled environment growth chamber conditions. At abscission, a small fraction (<10%) of S. faberi seed germinated under favorable conditions. Seed were dissected and germination of caryopses and embryos also tested. Removal of seed structures exterior to the embryo increased percentage germination. As the seed rain progressed mean percentage germination and variation among samples increased. Changes in germination were correlated with tiller development and relative time of seed maturity within a panicle. Seed produced on tillers that developed earlier were more likely to be dormant than seed from later-developing tillers. Seed that matured later on a panicle were more likely to germinate than seed that matured earlier on the same panicle. A consistent trend toward later maturing seed having less dormancy was found for seed grown under different environments which implies an inherent or parental source for variation in giant foxtail seed dormancy. The variation in percentage germination at abscission and following stratification treatments indicates that the S. faberi seed rain consists of individual seeds, possibly each with a different degree of dormancy.
|
1407.5590
|
Petko Bogdanov
|
Petko Bogdanov, Nazli Dereli, Danielle S. Bassett, Scott T. Grafton,
Ambuj K. Singh
|
Learning about Learning: Human Brain Sub-Network Biomarkers in fMRI Data
| null | null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
It has become increasingly popular to study the brain as a network due to the
realization that functionality cannot be explained exclusively by independent
activation of specialized regions. Instead, across a large spectrum of
behaviors, function arises due to the dynamic interactions between brain
regions. The existing literature on functional brain networks focuses mainly on
a battery of network properties characterizing the "resting state" using for
example the modularity, clustering, or path length among regions. In contrast,
we seek to uncover subgraphs of functional connectivity that predict or drive
individual differences in sensorimotor learning across subjects. We employ a
principled approach for the discovery of significant subgraphs of functional
connectivity, induced by brain activity (measured via fMRI imaging) while
subjects perform a motor learning task. Our aim is to uncover patterns of
functional connectivity that discriminate between high and low rates of
learning among subjects. The discovery of such significant discriminative
subgraphs promises a better data-driven understanding of the dynamic brain
processes associated with brain plasticity.
|
[
{
"created": "Mon, 21 Jul 2014 18:23:58 GMT",
"version": "v1"
}
] |
2014-07-22
|
[
[
"Bogdanov",
"Petko",
""
],
[
"Dereli",
"Nazli",
""
],
[
"Bassett",
"Danielle S.",
""
],
[
"Grafton",
"Scott T.",
""
],
[
"Singh",
"Ambuj K.",
""
]
] |
It has become increasingly popular to study the brain as a network due to the realization that functionality cannot be explained exclusively by independent activation of specialized regions. Instead, across a large spectrum of behaviors, function arises due to the dynamic interactions between brain regions. The existing literature on functional brain networks focuses mainly on a battery of network properties characterizing the "resting state" using for example the modularity, clustering, or path length among regions. In contrast, we seek to uncover subgraphs of functional connectivity that predict or drive individual differences in sensorimotor learning across subjects. We employ a principled approach for the discovery of significant subgraphs of functional connectivity, induced by brain activity (measured via fMRI imaging) while subjects perform a motor learning task. Our aim is to uncover patterns of functional connectivity that discriminate between high and low rates of learning among subjects. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with brain plasticity.
|
1811.03910
|
Niladri Sarkar
|
Niladri Sarkar, Jacques Prost, Frank J\"ulicher
|
Field induced cell proliferation and death in a thick epithelium
|
18 pages, 12 figures
| null | null | null |
q-bio.CB cond-mat.soft physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study the dynamics of a thick polar epithelium subjected to the action of
both an electric and a flow field in a planar geometry. We develop a
generalized continuum hydrodynamic description and describe the tissue as a two
component fluid system. The cells and the interstitial fluid are the two
components and we keep all terms allowed by symmetry. In particular we keep
track of the cell pumping activity for both solvent flow and electric current
and discuss the corresponding orders of magnitude. We study the growth dynamics
of tissue slabs, their steady states and obtain the dependence of the cell
velocity, net cell division rate, and cell stress on the flow strength and the
applied electric field. We find that finite thickness tissue slabs exist only
in a restricted region of phase space and that relatively modest electric
fields or imposed external flows can induce either proliferation or death.
|
[
{
"created": "Fri, 9 Nov 2018 14:12:02 GMT",
"version": "v1"
}
] |
2018-11-12
|
[
[
"Sarkar",
"Niladri",
""
],
[
"Prost",
"Jacques",
""
],
[
"Jülicher",
"Frank",
""
]
] |
We study the dynamics of a thick polar epithelium subjected to the action of both an electric and a flow field in a planar geometry. We develop a generalized continuum hydrodynamic description and describe the tissue as a two component fluid system. The cells and the interstitial fluid are the two components and we keep all terms allowed by symmetry. In particular we keep track of the cell pumping activity for both solvent flow and electric current and discuss the corresponding orders of magnitude. We study the growth dynamics of tissue slabs, their steady states and obtain the dependence of the cell velocity, net cell division rate, and cell stress on the flow strength and the applied electric field. We find that finite thickness tissue slabs exist only in a restricted region of phase space and that relatively modest electric fields or imposed external flows can induce either proliferation or death.
|
1506.02394
|
Ulrich S. Schwarz
|
Ulrich S. Schwarz and Jerome R.D. Soine (Heidelberg University)
|
Traction force microscopy on soft elastic substrates: a guide to recent
computational advances
|
Revtex, 29 pages, 3 PDF figures, 2 tables. BBA - Molecular Cell
Research, online since 27 May 2015, special issue on mechanobiology
| null |
10.1016/j.bbamcr.2015.05.028
| null |
q-bio.QM cond-mat.soft q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The measurement of cellular traction forces on soft elastic substrates has
become a standard tool for many labs working on mechanobiology. Here we review
the basic principles and different variants of this approach. In general, the
extraction of the substrate displacement field from image data and the
reconstruction procedure for the forces are closely linked to each other and
limited by the presence of experimental noise. We discuss different strategies
to reconstruct cellular forces as they follow from the foundations of
elasticity theory, including two- versus three-dimensional, inverse versus
direct and linear versus non-linear approaches. We also discuss how biophysical
models can improve force reconstruction and comment on practical issues like
substrate preparation, image processing and the availability of software for
traction force microscopy.
|
[
{
"created": "Mon, 8 Jun 2015 08:24:24 GMT",
"version": "v1"
}
] |
2015-06-09
|
[
[
"Schwarz",
"Ulrich S.",
"",
"Heidelberg University"
],
[
"Soine",
"Jerome R. D.",
"",
"Heidelberg University"
]
] |
The measurement of cellular traction forces on soft elastic substrates has become a standard tool for many labs working on mechanobiology. Here we review the basic principles and different variants of this approach. In general, the extraction of the substrate displacement field from image data and the reconstruction procedure for the forces are closely linked to each other and limited by the presence of experimental noise. We discuss different strategies to reconstruct cellular forces as they follow from the foundations of elasticity theory, including two- versus three-dimensional, inverse versus direct and linear versus non-linear approaches. We also discuss how biophysical models can improve force reconstruction and comment on practical issues like substrate preparation, image processing and the availability of software for traction force microscopy.
|
q-bio/0310027
|
Ayse Erzan
|
Duygu Balcan, Ayse Erzan
|
Random model for RNA interference yields scale free network
|
9 pages, 13 figures, submitted to Midterm Conference COSIN on
``Growing Networks and Graphs in Statistical Physics, Finance, Biology and
Social Systems'', Rome, 1-5 September 2003
| null |
10.1140/epjb/e2004-00055-7
| null |
q-bio.GN
| null |
We introduce a random bit-string model of post-transcriptional genetic
regulation based on sequence matching. The model spontaneously yields a scale
free network with power law scaling with $ \gamma=-1$ and also exhibits
log-periodic behaviour. The in-degree distribution is much narrower, and
exhibits a pronounced peak followed by a Gaussian distribution. The network is
of the smallest world type, with the average minimum path length independent of
the size of the network, as long as the network consists of one giant cluster.
The percolation threshold depends on the system size.
|
[
{
"created": "Tue, 21 Oct 2003 14:39:58 GMT",
"version": "v1"
}
] |
2009-11-10
|
[
[
"Balcan",
"Duygu",
""
],
[
"Erzan",
"Ayse",
""
]
] |
We introduce a random bit-string model of post-transcriptional genetic regulation based on sequence matching. The model spontaneously yields a scale free network with power law scaling with $ \gamma=-1$ and also exhibits log-periodic behaviour. The in-degree distribution is much narrower, and exhibits a pronounced peak followed by a Gaussian distribution. The network is of the smallest world type, with the average minimum path length independent of the size of the network, as long as the network consists of one giant cluster. The percolation threshold depends on the system size.
|
2212.05617
|
Michael Grinfeld
|
Bingzhang Chen and Michael Grinfeld
|
Decomposition of the Leinster-Cobbold Diversity Index
|
10 pages, 1 figure
| null | null | null |
q-bio.PE
|
http://creativecommons.org/licenses/by/4.0/
|
The Leinster and Cobbold diversity index possesses a number of merits; in
particular, it generalises many existing indices and defines an effective
number. We present a scheme to quantify the contribution of richness, evenness,
and taxonomic similarity to this index. Compared to the work of van Dam (2019),
our approach gives unbiased estimates of both evenness and similarity in a
non-homogeneous community. We also introduce a notion of taxonomic tree
equilibration which should be of use in the description of community structure.
|
[
{
"created": "Sun, 11 Dec 2022 22:20:46 GMT",
"version": "v1"
}
] |
2022-12-13
|
[
[
"Chen",
"Bingzhang",
""
],
[
"Grinfeld",
"Michael",
""
]
] |
The Leinster and Cobbold diversity index possesses a number of merits; in particular, it generalises many existing indices and defines an effective number. We present a scheme to quantify the contribution of richness, evenness, and taxonomic similarity to this index. Compared to the work of van Dam (2019), our approach gives unbiased estimates of both evenness and similarity in a non-homogeneous community. We also introduce a notion of taxonomic tree equilibration which should be of use in the description of community structure.
|
2312.11743
|
Gurdip Uppal
|
Gurdip Uppal, Dervis Can Vural
|
On the possibility of engineering social evolution in microfluidic
environments
|
16 pages, 5 figures
| null | null | null |
q-bio.PE cond-mat.soft
|
http://creativecommons.org/licenses/by/4.0/
|
Many species of microbes cooperate by producing public goods from which they
collectively benefit. However, these populations are under the risk of being
taken over by cheating mutants that do not contribute to the pool of public
goods. Here we present theoretical findings that address how the social
evolution of microbes can be manipulated by external perturbations, to inhibit
or promote the fixation of cheaters. To control social evolution, we determine
the effects of fluid-dynamical properties such as flow rate or boundary
geometry. We also study the social evolutionary consequences of introducing
beneficial or harmful chemicals at steady state and in a time dependent
fashion. We show that by modulating the flow rate and by applying pulsed
chemical signals, we can modulate the spatial structure and dynamics of the
population, in a way that can select for more or less cooperative microbial
populations.
|
[
{
"created": "Mon, 18 Dec 2023 22:57:07 GMT",
"version": "v1"
}
] |
2023-12-20
|
[
[
"Uppal",
"Gurdip",
""
],
[
"Vural",
"Dervis Can",
""
]
] |
Many species of microbes cooperate by producing public goods from which they collectively benefit. However, these populations are under the risk of being taken over by cheating mutants that do not contribute to the pool of public goods. Here we present theoretical findings that address how the social evolution of microbes can be manipulated by external perturbations, to inhibit or promote the fixation of cheaters. To control social evolution, we determine the effects of fluid-dynamical properties such as flow rate or boundary geometry. We also study the social evolutionary consequences of introducing beneficial or harmful chemicals at steady state and in a time dependent fashion. We show that by modulating the flow rate and by applying pulsed chemical signals, we can modulate the spatial structure and dynamics of the population, in a way that can select for more or less cooperative microbial populations.
|
2111.04740
|
Nadia Brancati
|
Nadia Brancati, Anna Maria Anniciello, Pushpak Pati, Daniel Riccio,
Giosu\`e Scognamiglio, Guillaume Jaume, Giuseppe De Pietro, Maurizio Di
Bonito, Antonio Foncubierta, Gerardo Botti, Maria Gabrani, Florinda Feroce,
and Maria Frucci
|
BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images
|
10 pages, 3 figures, 8 tables, 30 references
| null | null | null |
q-bio.QM cs.AI cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Breast cancer is the most commonly diagnosed cancer and registers the highest
number of deaths for women with cancer. Recent advancements in diagnostic
activities combined with large-scale screening policies have significantly
lowered the mortality rates for breast cancer patients. However, the manual
inspection of tissue slides by the pathologists is cumbersome, time-consuming,
and is subject to significant inter- and intra-observer variability. Recently,
the advent of whole-slide scanning systems have empowered the rapid
digitization of pathology slides, and enabled to develop digital workflows.
These advances further enable to leverage Artificial Intelligence (AI) to
assist, automate, and augment pathological diagnosis. But the AI techniques,
especially Deep Learning (DL), require a large amount of high-quality annotated
data to learn from. Constructing such task-specific datasets poses several
challenges, such as, data-acquisition level constrains, time-consuming and
expensive annotations, and anonymization of private information. In this paper,
we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of
annotated Hematoxylin & Eosin (H&E)-stained images to facilitate the
characterization of breast lesions. BRACS contains 547 Whole-Slide Images
(WSIs), and 4539 Regions of Interest (ROIs) extracted from the WSIs. Each WSI,
and respective ROIs, are annotated by the consensus of three board-certified
pathologists into different lesion categories. Specifically, BRACS includes
three lesion types, i.e., benign, malignant and atypical, which are further
subtyped into seven categories. It is, to the best of our knowledge, the
largest annotated dataset for breast cancer subtyping both at WSI- and
ROI-level. Further, by including the understudied atypical lesions, BRACS
offers an unique opportunity for leveraging AI to better understand their
characteristics.
|
[
{
"created": "Mon, 8 Nov 2021 15:04:16 GMT",
"version": "v1"
}
] |
2021-11-10
|
[
[
"Brancati",
"Nadia",
""
],
[
"Anniciello",
"Anna Maria",
""
],
[
"Pati",
"Pushpak",
""
],
[
"Riccio",
"Daniel",
""
],
[
"Scognamiglio",
"Giosuè",
""
],
[
"Jaume",
"Guillaume",
""
],
[
"De Pietro",
"Giuseppe",
""
],
[
"Di Bonito",
"Maurizio",
""
],
[
"Foncubierta",
"Antonio",
""
],
[
"Botti",
"Gerardo",
""
],
[
"Gabrani",
"Maria",
""
],
[
"Feroce",
"Florinda",
""
],
[
"Frucci",
"Maria",
""
]
] |
Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women with cancer. Recent advancements in diagnostic activities combined with large-scale screening policies have significantly lowered the mortality rates for breast cancer patients. However, the manual inspection of tissue slides by the pathologists is cumbersome, time-consuming, and is subject to significant inter- and intra-observer variability. Recently, the advent of whole-slide scanning systems have empowered the rapid digitization of pathology slides, and enabled to develop digital workflows. These advances further enable to leverage Artificial Intelligence (AI) to assist, automate, and augment pathological diagnosis. But the AI techniques, especially Deep Learning (DL), require a large amount of high-quality annotated data to learn from. Constructing such task-specific datasets poses several challenges, such as, data-acquisition level constrains, time-consuming and expensive annotations, and anonymization of private information. In this paper, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of annotated Hematoxylin & Eosin (H&E)-stained images to facilitate the characterization of breast lesions. BRACS contains 547 Whole-Slide Images (WSIs), and 4539 Regions of Interest (ROIs) extracted from the WSIs. Each WSI, and respective ROIs, are annotated by the consensus of three board-certified pathologists into different lesion categories. Specifically, BRACS includes three lesion types, i.e., benign, malignant and atypical, which are further subtyped into seven categories. It is, to the best of our knowledge, the largest annotated dataset for breast cancer subtyping both at WSI- and ROI-level. Further, by including the understudied atypical lesions, BRACS offers an unique opportunity for leveraging AI to better understand their characteristics.
|
q-bio/0504034
|
Jayprokas Chakrabarti
|
Bibekanand Mallick, Jayprokas Chakrabarti, Zhumur Ghosh, Smarajit Das
and Satyabrata Sahoo
|
tRNA-alike in Nanoarchaeum equitans ?
| null | null | null | null |
q-bio.GN q-bio.QM
| null |
The recent algorithm for five split tRNA-genes in N.equitans is new . It
locates missing tRNA-trp, tRNA-imet, tRNA-glu and tRNA-his . But the split
tRNA-trp(CCA) solution is anomalous ; the tRNA-imet lacks cognition elements
for aminoacylation . In view therefore we present here alternate non-split
composite solutions for tRNA-trp, tRNA-imet, tRNA-glu and tRNA-his .
|
[
{
"created": "Fri, 29 Apr 2005 06:45:47 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Mallick",
"Bibekanand",
""
],
[
"Chakrabarti",
"Jayprokas",
""
],
[
"Ghosh",
"Zhumur",
""
],
[
"Das",
"Smarajit",
""
],
[
"Sahoo",
"Satyabrata",
""
]
] |
The recent algorithm for five split tRNA-genes in N.equitans is new . It locates missing tRNA-trp, tRNA-imet, tRNA-glu and tRNA-his . But the split tRNA-trp(CCA) solution is anomalous ; the tRNA-imet lacks cognition elements for aminoacylation . In view therefore we present here alternate non-split composite solutions for tRNA-trp, tRNA-imet, tRNA-glu and tRNA-his .
|
1609.08880
|
Alexey Miroshnikov
|
Alexey Miroshnikov, Matthias Steinr\"ucken
|
Computing the joint distribution of the total tree length across loci in
populations with variable size
|
11 figures, 2 tables
|
Theoretical Population Biology, Volume 118, December 2017, Pages
1-19
|
10.1016/j.tpb.2017.09.002
| null |
q-bio.PE math.AP math.PR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years, a number of methods have been developed to infer complex
demographic histories, especially historical population size changes, from
genomic sequence data. Coalescent Hidden Markov Models have proven to be
particularly useful for this type of inference. Due to the Markovian structure
of these models, an essential building block is the joint distribution of local
genealogical trees, or statistics of these genealogies, at two neighboring loci
in populations of variable size. Here, we present a novel method to compute the
marginal and the joint distribution of the total length of the genealogical
trees at two loci separated by at most one recombination event for samples of
arbitrary size. To our knowledge, no method to compute these distributions has
been presented in the literature to date. We show that they can be obtained
from the solution of certain hyperbolic systems of partial differential
equations. We present a numerical algorithm, based on the method of
characteristics, that can be used to efficiently and accurately solve these
systems and compute the marginal and the joint distributions. We demonstrate
its utility to study the properties of the joint distribution. Our flexible
method can be straightforwardly extended to handle an arbitrary fixed number of
recombination events, to include the distributions of other statistics of the
genealogies as well, and can also be applied in structured populations.
|
[
{
"created": "Wed, 28 Sep 2016 12:21:52 GMT",
"version": "v1"
},
{
"created": "Fri, 7 Oct 2016 07:44:55 GMT",
"version": "v2"
},
{
"created": "Wed, 23 Nov 2016 07:47:31 GMT",
"version": "v3"
},
{
"created": "Fri, 8 Sep 2017 05:25:39 GMT",
"version": "v4"
},
{
"created": "Sat, 7 Oct 2017 23:24:57 GMT",
"version": "v5"
}
] |
2017-10-10
|
[
[
"Miroshnikov",
"Alexey",
""
],
[
"Steinrücken",
"Matthias",
""
]
] |
In recent years, a number of methods have been developed to infer complex demographic histories, especially historical population size changes, from genomic sequence data. Coalescent Hidden Markov Models have proven to be particularly useful for this type of inference. Due to the Markovian structure of these models, an essential building block is the joint distribution of local genealogical trees, or statistics of these genealogies, at two neighboring loci in populations of variable size. Here, we present a novel method to compute the marginal and the joint distribution of the total length of the genealogical trees at two loci separated by at most one recombination event for samples of arbitrary size. To our knowledge, no method to compute these distributions has been presented in the literature to date. We show that they can be obtained from the solution of certain hyperbolic systems of partial differential equations. We present a numerical algorithm, based on the method of characteristics, that can be used to efficiently and accurately solve these systems and compute the marginal and the joint distributions. We demonstrate its utility to study the properties of the joint distribution. Our flexible method can be straightforwardly extended to handle an arbitrary fixed number of recombination events, to include the distributions of other statistics of the genealogies as well, and can also be applied in structured populations.
|
2001.02132
|
Xin Zhang
|
Xin Zhang, Zhao Zhang, Yanan Wei, Muhan Li, Pengxiang Zhao, Yao
Mawulikplimi Adzavon, Mengyu Liu, Xiaokang Zhang, Fei Xie, Andong Wang,
Jihong Sun, Yunlong Shao, Xiayan Wang, Xuejun Sun, Xuemei Ma (Corresponding
author)
|
Mitochondria in higher plants possess H2 evolving activity which is
closely related to complex I
| null | null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Hydrogenase occupy a central place in the energy metabolism of anaerobic
bacteria. Although the structure of mitochondrial complex I is similar to that
of hydrogenase, whether it has hydrogen metabolic activity remain unclear.
Here, we show that a H2 evolving activity exists in higher plants mitochondria
and is closely related to complex I, especially around ubiquinone binding site.
The H2 production could be inhibited by rotenone and ubiquinone. Hypoxia could
simultaneously promote H2 evolution and succinate accumulation. Redox
properties of quinone pool, adjusted by NADH or succinate according to oxygen
concentration, acts as a valve to control the flow of protons and electrons and
the production of H2. The coupling of H2 evolving activity of mitochondrial
complex I with metabolic regulation reveals a more effective redox homeostasis
regulation mechanism. Considering the ubiquity of mitochondria in eukaryotes,
H2 metabolism might be the innate function of higher organisms. This may serve
to explain, at least in part, the broad physiological effects of H2.
|
[
{
"created": "Tue, 7 Jan 2020 15:47:08 GMT",
"version": "v1"
}
] |
2020-01-08
|
[
[
"Zhang",
"Xin",
"",
"Corresponding\n author"
],
[
"Zhang",
"Zhao",
"",
"Corresponding\n author"
],
[
"Wei",
"Yanan",
"",
"Corresponding\n author"
],
[
"Li",
"Muhan",
"",
"Corresponding\n author"
],
[
"Zhao",
"Pengxiang",
"",
"Corresponding\n author"
],
[
"Adzavon",
"Yao Mawulikplimi",
"",
"Corresponding\n author"
],
[
"Liu",
"Mengyu",
"",
"Corresponding\n author"
],
[
"Zhang",
"Xiaokang",
"",
"Corresponding\n author"
],
[
"Xie",
"Fei",
"",
"Corresponding\n author"
],
[
"Wang",
"Andong",
"",
"Corresponding\n author"
],
[
"Sun",
"Jihong",
"",
"Corresponding\n author"
],
[
"Shao",
"Yunlong",
"",
"Corresponding\n author"
],
[
"Wang",
"Xiayan",
"",
"Corresponding\n author"
],
[
"Sun",
"Xuejun",
"",
"Corresponding\n author"
],
[
"Ma",
"Xuemei",
"",
"Corresponding\n author"
]
] |
Hydrogenase occupy a central place in the energy metabolism of anaerobic bacteria. Although the structure of mitochondrial complex I is similar to that of hydrogenase, whether it has hydrogen metabolic activity remain unclear. Here, we show that a H2 evolving activity exists in higher plants mitochondria and is closely related to complex I, especially around ubiquinone binding site. The H2 production could be inhibited by rotenone and ubiquinone. Hypoxia could simultaneously promote H2 evolution and succinate accumulation. Redox properties of quinone pool, adjusted by NADH or succinate according to oxygen concentration, acts as a valve to control the flow of protons and electrons and the production of H2. The coupling of H2 evolving activity of mitochondrial complex I with metabolic regulation reveals a more effective redox homeostasis regulation mechanism. Considering the ubiquity of mitochondria in eukaryotes, H2 metabolism might be the innate function of higher organisms. This may serve to explain, at least in part, the broad physiological effects of H2.
|
1902.05915
|
Jamila Andoh
|
Mario Rosero Pahi, Juliana Cavalli, Frauke Nees, Herta Flor and Jamila
Andoh
|
Disruption of the prefrontal cortex improves implicit contextual
memory-guided attention: combined behavioural and electrophysiological
evidence
|
24 pages
| null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many studies have shown that the dorsolateral prefrontal cortex (DLPFC) plays
an important role in top-down cognitive control over intentional and deliberate
behavior. However, recent studies have reported that DLPFC-mediated top-down
control interferes with implicit forms of learning. Here we used continuous
theta burst stimulation (cTBS) combined with electroencephalography (EEG) to
investigate the causal role of DLPFC in implicit contextual memory-guided
attention. We aimed to test whether transient disruption of the DLPFC would
interfere with implicit learning performance and related electrical brain
activity. We applied neuronavigation-guided cTBS to the DLPFC or to the vertex
as a control region prior to the performance of an implicit contextual learning
task. We found that cTBS applied over the DLPFC significantly improved
performance during implicit contextual learning. We also noted that beta-band
(13-19 Hz) oscillatory power was reduced at fronto-central channels about 140
to 370 ms after visual stimulus onset in cTBS DLPFC compared with cTBS vertex.
Taken together, our results provide evidence that DLPFC-mediated top-down
control interferes with contextual memory-guided attention and beta-band
oscillatory activity.
|
[
{
"created": "Fri, 15 Feb 2019 17:56:25 GMT",
"version": "v1"
}
] |
2019-02-18
|
[
[
"Pahi",
"Mario Rosero",
""
],
[
"Cavalli",
"Juliana",
""
],
[
"Nees",
"Frauke",
""
],
[
"Flor",
"Herta",
""
],
[
"Andoh",
"Jamila",
""
]
] |
Many studies have shown that the dorsolateral prefrontal cortex (DLPFC) plays an important role in top-down cognitive control over intentional and deliberate behavior. However, recent studies have reported that DLPFC-mediated top-down control interferes with implicit forms of learning. Here we used continuous theta burst stimulation (cTBS) combined with electroencephalography (EEG) to investigate the causal role of DLPFC in implicit contextual memory-guided attention. We aimed to test whether transient disruption of the DLPFC would interfere with implicit learning performance and related electrical brain activity. We applied neuronavigation-guided cTBS to the DLPFC or to the vertex as a control region prior to the performance of an implicit contextual learning task. We found that cTBS applied over the DLPFC significantly improved performance during implicit contextual learning. We also noted that beta-band (13-19 Hz) oscillatory power was reduced at fronto-central channels about 140 to 370 ms after visual stimulus onset in cTBS DLPFC compared with cTBS vertex. Taken together, our results provide evidence that DLPFC-mediated top-down control interferes with contextual memory-guided attention and beta-band oscillatory activity.
|
1405.2596
|
Mohammad Soltani
|
Mohammad Soltani, Pavol Bokes, Zachary Fox, Abhyudai Singh
|
Nonspecific transcription factor binding reduces variability in
transcription factor and target protein expression
|
10 pages, 5 figures, Physical Biology
| null | null | null |
q-bio.SC q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Transcription factors (TFs) interact with a multitude of binding sites on DNA
and partner proteins inside cells. We investigate how nonspecific
binding/unbinding to such decoy binding sites affects the magnitude and
time-scale of random fluctuations in TF copy numbers arising from stochastic
gene expression. A stochastic model of TF gene expression, together with decoy
site interactions is formulated. Distributions for the total (bound and
unbound) and free (unbound) TF levels are derived by analytically solving the
chemical master equation under physiologically relevant assumptions. Our
results show that increasing the number of decoy binding sides considerably
reduces stochasticity in free TF copy numbers. The TF autocorrelation function
reveals that decoy sites can either enhance or shorten the time-scale of TF
fluctuations depending on model parameters. To understand how noise in TF
abundances propagates downstream, a TF target gene is included in the model.
Intriguingly, we find that noise in the expression of the target gene decreases
with increasing decoy sites for linear TF-target protein dose-responses, even
in regimes where decoy sites enhance TF autocorrelation times. Moreover,
counterintuitive noise transmissions arise for nonlinear dose-responses. In
summary, our study highlights the critical role of molecular sequestration by
decoy binding sites in regulating the stochastic dynamics of TFs and target
proteins at the single-cell level.
|
[
{
"created": "Sun, 11 May 2014 22:48:34 GMT",
"version": "v1"
},
{
"created": "Tue, 14 Apr 2015 19:07:27 GMT",
"version": "v2"
}
] |
2015-04-15
|
[
[
"Soltani",
"Mohammad",
""
],
[
"Bokes",
"Pavol",
""
],
[
"Fox",
"Zachary",
""
],
[
"Singh",
"Abhyudai",
""
]
] |
Transcription factors (TFs) interact with a multitude of binding sites on DNA and partner proteins inside cells. We investigate how nonspecific binding/unbinding to such decoy binding sites affects the magnitude and time-scale of random fluctuations in TF copy numbers arising from stochastic gene expression. A stochastic model of TF gene expression, together with decoy site interactions is formulated. Distributions for the total (bound and unbound) and free (unbound) TF levels are derived by analytically solving the chemical master equation under physiologically relevant assumptions. Our results show that increasing the number of decoy binding sides considerably reduces stochasticity in free TF copy numbers. The TF autocorrelation function reveals that decoy sites can either enhance or shorten the time-scale of TF fluctuations depending on model parameters. To understand how noise in TF abundances propagates downstream, a TF target gene is included in the model. Intriguingly, we find that noise in the expression of the target gene decreases with increasing decoy sites for linear TF-target protein dose-responses, even in regimes where decoy sites enhance TF autocorrelation times. Moreover, counterintuitive noise transmissions arise for nonlinear dose-responses. In summary, our study highlights the critical role of molecular sequestration by decoy binding sites in regulating the stochastic dynamics of TFs and target proteins at the single-cell level.
|
2007.04897
|
Sungsoo Ahn
|
Sungsoo Ahn, Junsu Kim, Hankook Lee, Jinwoo Shin
|
Guiding Deep Molecular Optimization with Genetic Exploration
| null | null | null | null |
q-bio.QM cs.NE stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
De novo molecular design attempts to search over the chemical space for
molecules with the desired property. Recently, deep learning has gained
considerable attention as a promising approach to solve the problem. In this
paper, we propose genetic expert-guided learning (GEGL), a simple yet novel
framework for training a deep neural network (DNN) to generate highly-rewarding
molecules. Our main idea is to design a "genetic expert improvement" procedure,
which generates high-quality targets for imitation learning of the DNN.
Extensive experiments show that GEGL significantly improves over
state-of-the-art methods. For example, GEGL manages to solve the penalized
octanol-water partition coefficient optimization with a score of 31.40, while
the best-known score in the literature is 27.22. Besides, for the GuacaMol
benchmark with 20 tasks, our method achieves the highest score for 19 tasks, in
comparison with state-of-the-art methods, and newly obtains the perfect score
for three tasks.
|
[
{
"created": "Sat, 4 Jul 2020 05:01:26 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Jul 2020 11:23:07 GMT",
"version": "v2"
},
{
"created": "Tue, 27 Oct 2020 10:49:47 GMT",
"version": "v3"
}
] |
2020-10-28
|
[
[
"Ahn",
"Sungsoo",
""
],
[
"Kim",
"Junsu",
""
],
[
"Lee",
"Hankook",
""
],
[
"Shin",
"Jinwoo",
""
]
] |
De novo molecular design attempts to search over the chemical space for molecules with the desired property. Recently, deep learning has gained considerable attention as a promising approach to solve the problem. In this paper, we propose genetic expert-guided learning (GEGL), a simple yet novel framework for training a deep neural network (DNN) to generate highly-rewarding molecules. Our main idea is to design a "genetic expert improvement" procedure, which generates high-quality targets for imitation learning of the DNN. Extensive experiments show that GEGL significantly improves over state-of-the-art methods. For example, GEGL manages to solve the penalized octanol-water partition coefficient optimization with a score of 31.40, while the best-known score in the literature is 27.22. Besides, for the GuacaMol benchmark with 20 tasks, our method achieves the highest score for 19 tasks, in comparison with state-of-the-art methods, and newly obtains the perfect score for three tasks.
|
q-bio/0506014
|
Patricia Faisca
|
P. F. N. Faisca and M. M. Telo da Gama
|
Folding of small proteins: A matter of geometry?
|
To appear in Molecular Physics
|
Molec. Phys. 103, 2903-2910 (2005)
|
10.1080/00268970500221891
| null |
q-bio.BM
| null |
We review some of our recent results obtained within the scope of simple
lattice models and Monte Carlo simulations that illustrate the role of native
geometry in the folding kinetics of two state folders.
|
[
{
"created": "Fri, 10 Jun 2005 14:53:02 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Faisca",
"P. F. N.",
""
],
[
"da Gama",
"M. M. Telo",
""
]
] |
We review some of our recent results obtained within the scope of simple lattice models and Monte Carlo simulations that illustrate the role of native geometry in the folding kinetics of two state folders.
|
0804.0838
|
Dmitry Tsigankov
|
Dmitry Tsigankov and Alexei Koulakov
|
Optimal axonal and dendritic branching strategies during the development
of neural circuitry
|
13 pages
| null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In developing brain, axons and dendrites are capable of connecting to each
other with high precision. Recent advances in imaging have allowed for the
monitoring of axonal, dendritic, and synapse dynamics in vivo. It is observed
that the majority of axonal and dendritic branches are formed 'in error', only
to be retracted later. The functional significance of the overproduction of
branches is not clear. In this study, we use a computational model to
investigate the speed and efficiency of different branching strategies. We show
that branching itself allows for substantial acceleration in the identification
of appropriate targets through the use of a parallel search. We also show that
the formation of new branches in the vicinity of existing synapses leads to the
formation of target connectivity with a decreased number of erroneous branches.
This finding allows us to explain the high correlation between the branch
points and synapses observed in the Xenopus laevis retinotectal system. We also
suggest that the most efficient branching rule is different for axons and
dendrites. The optimal axonal strategy is to form new branches in the vicinity
of existing synapses, whereas the optimal rule for dendrites is to form new
branches preferentially in the vicinity of synapses with correlated pre- and
postsynaptic electric activity. Thus, our studies suggest that the developing
neural system employs a set of sophisticated computational strategies that
facilitate the formation of required circuitry, so that it may proceed in the
fastest and most frugal way.
|
[
{
"created": "Sat, 5 Apr 2008 05:02:29 GMT",
"version": "v1"
}
] |
2008-04-08
|
[
[
"Tsigankov",
"Dmitry",
""
],
[
"Koulakov",
"Alexei",
""
]
] |
In developing brain, axons and dendrites are capable of connecting to each other with high precision. Recent advances in imaging have allowed for the monitoring of axonal, dendritic, and synapse dynamics in vivo. It is observed that the majority of axonal and dendritic branches are formed 'in error', only to be retracted later. The functional significance of the overproduction of branches is not clear. In this study, we use a computational model to investigate the speed and efficiency of different branching strategies. We show that branching itself allows for substantial acceleration in the identification of appropriate targets through the use of a parallel search. We also show that the formation of new branches in the vicinity of existing synapses leads to the formation of target connectivity with a decreased number of erroneous branches. This finding allows us to explain the high correlation between the branch points and synapses observed in the Xenopus laevis retinotectal system. We also suggest that the most efficient branching rule is different for axons and dendrites. The optimal axonal strategy is to form new branches in the vicinity of existing synapses, whereas the optimal rule for dendrites is to form new branches preferentially in the vicinity of synapses with correlated pre- and postsynaptic electric activity. Thus, our studies suggest that the developing neural system employs a set of sophisticated computational strategies that facilitate the formation of required circuitry, so that it may proceed in the fastest and most frugal way.
|
2104.12479
|
Muhammad Ardiyansyah
|
Muhammad Ardiyansyah
|
Distinguishing Level-2 Phylogenetic Networks Using Phylogenetic
Invariants
|
35 pages, 3 tables, and 20 figures
| null | null | null |
q-bio.PE math.AG math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In phylogenetics, it is important for the phylogenetic network model
parameters to be identifiable so that the evolutionary histories of a group of
species can be consistently inferred. However, as the complexity of the
phylogenetic network models grows, the identifiability of network models
becomes increasingly difficult to analyze. As an attempt to analyze the
identifiability of network models, we check whether two networks are
distinguishable. In this paper, we specifically study the distinguishability of
phylogenetic network models associated with level-2 networks. Using an
algebraic approach, namely using discrete Fourier transformation, we present
some results on the distinguishability of some level-2 networks, which
generalize earlier work on the distinguishability of level-1 networks. In
particular, we study simple and semisimple level-2 networks. Simple and
semisimple level-2 networks can be thought as generalizations of level-1 sunlet
and cycle networks, respectively. Moreover, we also compare the varieties
associated with semisimple level-2 and cycle networks.
|
[
{
"created": "Mon, 26 Apr 2021 11:25:36 GMT",
"version": "v1"
}
] |
2021-04-27
|
[
[
"Ardiyansyah",
"Muhammad",
""
]
] |
In phylogenetics, it is important for the phylogenetic network model parameters to be identifiable so that the evolutionary histories of a group of species can be consistently inferred. However, as the complexity of the phylogenetic network models grows, the identifiability of network models becomes increasingly difficult to analyze. As an attempt to analyze the identifiability of network models, we check whether two networks are distinguishable. In this paper, we specifically study the distinguishability of phylogenetic network models associated with level-2 networks. Using an algebraic approach, namely using discrete Fourier transformation, we present some results on the distinguishability of some level-2 networks, which generalize earlier work on the distinguishability of level-1 networks. In particular, we study simple and semisimple level-2 networks. Simple and semisimple level-2 networks can be thought as generalizations of level-1 sunlet and cycle networks, respectively. Moreover, we also compare the varieties associated with semisimple level-2 and cycle networks.
|
2105.07337
|
Xiaoliang Wang
|
Xiaoliang Wang and Dongyun Bai
|
Self-organization principles of cell cycles and gene expressions in the
development of cell populations
|
28 pages, 18 figures
| null | null | null |
q-bio.CB
|
http://creativecommons.org/licenses/by/4.0/
|
A big challenge in current biology is to understand the exact
self-organization mechanism underlying complex multi-physics coupling
developmental processes. With multiscale computations of from subcellular gene
expressions to cell population dynamics that is based on first principles, we
show that cell cycles can self-organize into periodic stripes in the
development of E. coli populations from one single cell, relying on the moving
graded nutrient concentration profile, which provides directing positional
information for cells to keep their cycle phases in place. Resultantly, the
statistical cell cycle distribution within the population is observed to
collapse to a universal function and shows a scale invariance. Depending on the
radial distribution mode of genetic oscillations in cell populations, a
transition between gene patterns is achieved. When an inhibitor-inhibitor gene
network is subsequently activated by a gene-oscillatory network, cell
populations with zebra stripes can be established, with the positioning
precision of cell-fate-specific domains influenced by cells' speed of free
motions. Such information may provide important implications for understanding
relevant dynamic processes of multicellular systems, such as biological
development.
|
[
{
"created": "Sun, 16 May 2021 03:20:13 GMT",
"version": "v1"
}
] |
2021-05-18
|
[
[
"Wang",
"Xiaoliang",
""
],
[
"Bai",
"Dongyun",
""
]
] |
A big challenge in current biology is to understand the exact self-organization mechanism underlying complex multi-physics coupling developmental processes. With multiscale computations of from subcellular gene expressions to cell population dynamics that is based on first principles, we show that cell cycles can self-organize into periodic stripes in the development of E. coli populations from one single cell, relying on the moving graded nutrient concentration profile, which provides directing positional information for cells to keep their cycle phases in place. Resultantly, the statistical cell cycle distribution within the population is observed to collapse to a universal function and shows a scale invariance. Depending on the radial distribution mode of genetic oscillations in cell populations, a transition between gene patterns is achieved. When an inhibitor-inhibitor gene network is subsequently activated by a gene-oscillatory network, cell populations with zebra stripes can be established, with the positioning precision of cell-fate-specific domains influenced by cells' speed of free motions. Such information may provide important implications for understanding relevant dynamic processes of multicellular systems, such as biological development.
|
2306.15280
|
Etienne Moullet
|
Etienne Moullet (CAMIN, WILLOW), Fran\c{c}ois Bailly (CAMIN), Justin
Carpentier (WILLOW, DI-ENS), Christine Azevedo Coste (CAMIN)
|
Vision-based interface for grasping intention detection and grip
selection : towards intuitive upper-limb assistive devices
| null |
Congr{\`e}s annuel de la Soci{\'e}t{\'e} de Biom{\'e}canique, Oct
2023, Grenoble, France
| null | null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Assistive devices for indivuals with upper-limb movement often lack
controllability and intuitiveness, in particular for grasping function. In this
work, we introduce a novel user interface for grasping movement control in
which the user delegates the grasping task decisions to the device, only moving
their (potentially prosthetic) hand toward the targeted object.
|
[
{
"created": "Tue, 27 Jun 2023 08:12:05 GMT",
"version": "v1"
}
] |
2023-06-28
|
[
[
"Moullet",
"Etienne",
"",
"CAMIN, WILLOW"
],
[
"Bailly",
"François",
"",
"CAMIN"
],
[
"Carpentier",
"Justin",
"",
"WILLOW, DI-ENS"
],
[
"Coste",
"Christine Azevedo",
"",
"CAMIN"
]
] |
Assistive devices for indivuals with upper-limb movement often lack controllability and intuitiveness, in particular for grasping function. In this work, we introduce a novel user interface for grasping movement control in which the user delegates the grasping task decisions to the device, only moving their (potentially prosthetic) hand toward the targeted object.
|
1803.05855
|
Andrew Belmonte
|
Russ deForest and Andrew Belmonte
|
A game-theoretic mechanism for aggregation and dispersal of interacting
populations
|
47 pages, 12 figures
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We adapt a fitness function from evolutionary game theory as a mechanism for
aggregation and dispersal in a partial differential equation (PDE) model of two
interacting populations, described by density functions $u$ and $v$. We
consider a spatial model where individuals migrate up local fitness gradients,
seeking out locations where their given traits are more advantageous. The
resulting system of fitness gradient equations is a degenerate system having
spatially structured, smooth, steady state solutions characterized by constant
fitness throughout the domain. When populations are viewed as predator and
prey, our model captures prey aggregation behavior consistent with Hamilton's
selfish herd hypothesis. We also present weak steady state solutions in 1d that
are continuous but in general not smooth everywhere, with an associated fitness
that is discontinuous, piecewise constant. We give numerical examples of
solutions that evolve toward such weak steady states. We also give an example
of a spatial Lotka--Volterra model, where a fitness gradient flux creates
instabilities that lead to spatially structured steady states. Our results also
suggest that when fitness has some dependence on local interactions, a
fitness-based dispersal mechanism may act to create spatial variation across a
habitat.
|
[
{
"created": "Thu, 15 Mar 2018 16:48:47 GMT",
"version": "v1"
}
] |
2018-03-16
|
[
[
"deForest",
"Russ",
""
],
[
"Belmonte",
"Andrew",
""
]
] |
We adapt a fitness function from evolutionary game theory as a mechanism for aggregation and dispersal in a partial differential equation (PDE) model of two interacting populations, described by density functions $u$ and $v$. We consider a spatial model where individuals migrate up local fitness gradients, seeking out locations where their given traits are more advantageous. The resulting system of fitness gradient equations is a degenerate system having spatially structured, smooth, steady state solutions characterized by constant fitness throughout the domain. When populations are viewed as predator and prey, our model captures prey aggregation behavior consistent with Hamilton's selfish herd hypothesis. We also present weak steady state solutions in 1d that are continuous but in general not smooth everywhere, with an associated fitness that is discontinuous, piecewise constant. We give numerical examples of solutions that evolve toward such weak steady states. We also give an example of a spatial Lotka--Volterra model, where a fitness gradient flux creates instabilities that lead to spatially structured steady states. Our results also suggest that when fitness has some dependence on local interactions, a fitness-based dispersal mechanism may act to create spatial variation across a habitat.
|
1605.09489
|
Rajamanickam Murugan
|
G. Niranjani and R. Murugan
|
Theory on the mechanism of site-specific DNA-protein interactions in the
presence of traps
|
23 pages, 5 figures, 1 table
| null |
10.1088/1478-3975/13/4/046003
| null |
q-bio.SC q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The speed of site-specific binding of transcription factor (TFs) proteins
with genomic DNA seems to be strongly retarded by the randomly occurring
sequence traps. Traps are those DNA sequences sharing significant similarity
with the original specific binding sites. It is an intriguing question how the
naturally occurring TFs and their specific binding sites are designed to manage
the retarding effects of such randomly occurring traps. We develop a simple
random walk model on the site-specific binding of TFs with genomic DNA in the
presence of sequence traps. Our dynamical model predicts that (a) the retarding
effects of traps will be minimum when the traps are arranged around the
specific binding site such that there is a negative correlation between the
binding strength of TFs with traps and the distance of traps from the specific
binding site and (b) the retarding effects of sequence traps can be appeased by
the condensed conformational state of DNA. Our computational analysis results
on the distribution of sequence traps around the putative binding sites of
various TFs in mouse and human genome clearly agree well the theoretical
predictions. We propose that the distribution of traps can be used as an
additional metric to efficiently identify the specific binding sites of TFs on
genomic DNA.
|
[
{
"created": "Tue, 31 May 2016 04:11:53 GMT",
"version": "v1"
}
] |
2016-07-22
|
[
[
"Niranjani",
"G.",
""
],
[
"Murugan",
"R.",
""
]
] |
The speed of site-specific binding of transcription factor (TFs) proteins with genomic DNA seems to be strongly retarded by the randomly occurring sequence traps. Traps are those DNA sequences sharing significant similarity with the original specific binding sites. It is an intriguing question how the naturally occurring TFs and their specific binding sites are designed to manage the retarding effects of such randomly occurring traps. We develop a simple random walk model on the site-specific binding of TFs with genomic DNA in the presence of sequence traps. Our dynamical model predicts that (a) the retarding effects of traps will be minimum when the traps are arranged around the specific binding site such that there is a negative correlation between the binding strength of TFs with traps and the distance of traps from the specific binding site and (b) the retarding effects of sequence traps can be appeased by the condensed conformational state of DNA. Our computational analysis results on the distribution of sequence traps around the putative binding sites of various TFs in mouse and human genome clearly agree well the theoretical predictions. We propose that the distribution of traps can be used as an additional metric to efficiently identify the specific binding sites of TFs on genomic DNA.
|
1808.10045
|
Juan Daniel Sebastia-Saez
|
Daniel Sebastia-Saez, Adam Burbidge, Jan Engmann, Marco Ramaioli
|
New trends in mechanistic transdermal drug delivery modelling: Towards
an accurate description of skin microstructure
| null | null |
10.1016/j.compchemeng.2020.106976
| null |
q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Interest for in silico modelling of the absorption of xenobiotics into the
skin has been growing in the last years, owing to their lower cost compared to
experimental alternatives, and the desire to avoid animal experimentation. This
review presents an overview of Physiologically-Based Pharmacokinetic (PBPK)
models and focuses on recent, modelling approaches, such as Finite Element and
Lattice Boltzmann. These methods allow for a detailed geometric representation
of the skin microstructure, in contrast to classic QSPR and compartmental
models. Morphological features of the skin such as the bricks and mortar
description of the stratum corneum, hair follicles, and the pilosebaceous unit
can therefore be represented more accurately, allowing a better description of
the interaction of cosmetics with the skin. This review also highlights several
perspectives to further develop these models in directions relevant to
industry.
|
[
{
"created": "Wed, 29 Aug 2018 21:22:05 GMT",
"version": "v1"
},
{
"created": "Mon, 3 Sep 2018 15:31:51 GMT",
"version": "v2"
},
{
"created": "Sun, 9 Sep 2018 21:28:13 GMT",
"version": "v3"
},
{
"created": "Fri, 22 Nov 2019 14:11:45 GMT",
"version": "v4"
}
] |
2020-12-08
|
[
[
"Sebastia-Saez",
"Daniel",
""
],
[
"Burbidge",
"Adam",
""
],
[
"Engmann",
"Jan",
""
],
[
"Ramaioli",
"Marco",
""
]
] |
Interest for in silico modelling of the absorption of xenobiotics into the skin has been growing in the last years, owing to their lower cost compared to experimental alternatives, and the desire to avoid animal experimentation. This review presents an overview of Physiologically-Based Pharmacokinetic (PBPK) models and focuses on recent, modelling approaches, such as Finite Element and Lattice Boltzmann. These methods allow for a detailed geometric representation of the skin microstructure, in contrast to classic QSPR and compartmental models. Morphological features of the skin such as the bricks and mortar description of the stratum corneum, hair follicles, and the pilosebaceous unit can therefore be represented more accurately, allowing a better description of the interaction of cosmetics with the skin. This review also highlights several perspectives to further develop these models in directions relevant to industry.
|
2005.05572
|
Michael Kummer
|
Michael Kummer, Arunava Banerjee
|
Spike-Triggered Descent
| null | null | null | null |
q-bio.NC cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The characterization of neural responses to sensory stimuli is a central
problem in neuroscience. Spike-triggered average (STA), an influential
technique, has been used to extract optimal linear kernels in a variety of
animal subjects. However, when the model assumptions are not met, it can lead
to misleading and imprecise results. We introduce a technique, called
spike-triggered descent (STD), which can be used alone or in conjunction with
STA to increase precision and yield success in scenarios where STA fails. STD
works by simulating a model neuron that learns to reproduce the observed spike
train. Learning is achieved via parameter optimization that relies on a metric
induced on the space of spike trains modeled as a novel inner product space.
This technique can precisely learn higher order kernels using limited data.
Kernels extracted from a Locusta migratoria tympanal nerve dataset demonstrate
the strength of this approach.
|
[
{
"created": "Tue, 12 May 2020 06:48:04 GMT",
"version": "v1"
}
] |
2020-05-13
|
[
[
"Kummer",
"Michael",
""
],
[
"Banerjee",
"Arunava",
""
]
] |
The characterization of neural responses to sensory stimuli is a central problem in neuroscience. Spike-triggered average (STA), an influential technique, has been used to extract optimal linear kernels in a variety of animal subjects. However, when the model assumptions are not met, it can lead to misleading and imprecise results. We introduce a technique, called spike-triggered descent (STD), which can be used alone or in conjunction with STA to increase precision and yield success in scenarios where STA fails. STD works by simulating a model neuron that learns to reproduce the observed spike train. Learning is achieved via parameter optimization that relies on a metric induced on the space of spike trains modeled as a novel inner product space. This technique can precisely learn higher order kernels using limited data. Kernels extracted from a Locusta migratoria tympanal nerve dataset demonstrate the strength of this approach.
|
1904.12960
|
Andrew Durden
|
Andrew Durden
|
Modelling Diffuse Subcellular Protein Structures as Dynamic Social
Networks
|
Master's Thesis Submitted to the Graduate Faculty of The University
of Georgia in Partial Fulfillment of the Requirements for the Degree. Under
Direction of: Shannon Quinn
| null | null | null |
q-bio.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Fluorescence microscopy has led to impressive quantitative models and new
insights gained from richer sets of biomedical imagery. However, there is a
dearth of rigorous and established bioimaging strategies for modeling
spatiotemporal behavior of diffuse, subcellular components such as mitochondria
or actin. In many cases, these structures are assessed by hand or with other
semi-quantitative measures. We propose to build descriptive and dynamic models
of diffuse subcellular morphologies, using the mitochondrial protein patterns
of cervical epithelial (HeLa) cells. We develop a parametric representation of
the patterns as a mixture of probability masses. This mixture is iteratively
perturbed over time to fit the evolving spatiotemporal behavior of the
subcellular structures. We convert the resulting trajectory into a series of
graph Laplacians to formally define a dynamic network. Finally, we demonstrate
how graph theoretic analyses of the trajectories yield biologically-meaningful
quantifications of the structures.
|
[
{
"created": "Wed, 17 Apr 2019 01:32:31 GMT",
"version": "v1"
}
] |
2019-05-01
|
[
[
"Durden",
"Andrew",
""
]
] |
Fluorescence microscopy has led to impressive quantitative models and new insights gained from richer sets of biomedical imagery. However, there is a dearth of rigorous and established bioimaging strategies for modeling spatiotemporal behavior of diffuse, subcellular components such as mitochondria or actin. In many cases, these structures are assessed by hand or with other semi-quantitative measures. We propose to build descriptive and dynamic models of diffuse subcellular morphologies, using the mitochondrial protein patterns of cervical epithelial (HeLa) cells. We develop a parametric representation of the patterns as a mixture of probability masses. This mixture is iteratively perturbed over time to fit the evolving spatiotemporal behavior of the subcellular structures. We convert the resulting trajectory into a series of graph Laplacians to formally define a dynamic network. Finally, we demonstrate how graph theoretic analyses of the trajectories yield biologically-meaningful quantifications of the structures.
|
0708.3171
|
Bhaswar Ghosh
|
Indrani Bose and Bhaswar Ghosh
|
The p53-MDM2 network: from oscillations to apoptosis
| null | null | null | null |
q-bio.MN
| null |
The p53 protein is well-known for its tumour suppressor function. The
p53-MDM2 negative feedback loop constitutes the core module of a network of
regulatory interactions activated under cellular stress. In normal cells, the
level of p53 proteins is kept low by MDM2, i.e. MDM2 negatively regulates the
activity of p53. In the case of DNA damage,the p53-mediated pathways are
activated leading to cell cycle arrest and repair of the DNA. If repair is not
possible due to excessive damage, the p53-mediated apoptotic pathway is
activated bringing about cell death. In this paper, we give an overview of our
studies on the p53-MDM2 module and the associated pathways from a systems
biology perspective. We discuss a number of key predictions, related to some
specific aspects of cell cycle arrest and cell death, which could be tested in
experiments.
|
[
{
"created": "Thu, 23 Aug 2007 14:36:43 GMT",
"version": "v1"
}
] |
2007-08-24
|
[
[
"Bose",
"Indrani",
""
],
[
"Ghosh",
"Bhaswar",
""
]
] |
The p53 protein is well-known for its tumour suppressor function. The p53-MDM2 negative feedback loop constitutes the core module of a network of regulatory interactions activated under cellular stress. In normal cells, the level of p53 proteins is kept low by MDM2, i.e. MDM2 negatively regulates the activity of p53. In the case of DNA damage,the p53-mediated pathways are activated leading to cell cycle arrest and repair of the DNA. If repair is not possible due to excessive damage, the p53-mediated apoptotic pathway is activated bringing about cell death. In this paper, we give an overview of our studies on the p53-MDM2 module and the associated pathways from a systems biology perspective. We discuss a number of key predictions, related to some specific aspects of cell cycle arrest and cell death, which could be tested in experiments.
|
2207.10794
|
C\'edric Beaulac
|
C\'edric Beaulac, Sidi Wu, Erin Gibson, Michelle F. Miranda, Jiguo
Cao, Leno Rocha, Mirza Faisal Beg, Farouk S. Nathoo
|
Neuroimaging Feature Extraction using a Neural Network Classifier for
Imaging Genetics
|
Under review
|
BMC Bioinformatics 24, 271 (2023)
|
10.1186/s12859-023-05394-x
| null |
q-bio.QM cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
A major issue in the association of genes to neuroimaging phenotypes is the
high dimension of both genetic data and neuroimaging data. In this article, we
tackle the latter problem with an eye toward developing solutions that are
relevant for disease prediction. Supported by a vast literature on the
predictive power of neural networks, our proposed solution uses neural networks
to extract from neuroimaging data features that are relevant for predicting
Alzheimer's Disease (AD) for subsequent relation to genetics. Our
neuroimaging-genetic pipeline is comprised of image processing, neuroimaging
feature extraction and genetic association steps. We propose a neural network
classifier for extracting neuroimaging features that are related with disease
and a multivariate Bayesian group sparse regression model for genetic
association. We compare the predictive power of these features to expert
selected features and take a closer look at the SNPs identified with the new
neuroimaging features.
|
[
{
"created": "Fri, 8 Jul 2022 19:03:00 GMT",
"version": "v1"
}
] |
2023-07-03
|
[
[
"Beaulac",
"Cédric",
""
],
[
"Wu",
"Sidi",
""
],
[
"Gibson",
"Erin",
""
],
[
"Miranda",
"Michelle F.",
""
],
[
"Cao",
"Jiguo",
""
],
[
"Rocha",
"Leno",
""
],
[
"Beg",
"Mirza Faisal",
""
],
[
"Nathoo",
"Farouk S.",
""
]
] |
A major issue in the association of genes to neuroimaging phenotypes is the high dimension of both genetic data and neuroimaging data. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. Our neuroimaging-genetic pipeline is comprised of image processing, neuroimaging feature extraction and genetic association steps. We propose a neural network classifier for extracting neuroimaging features that are related with disease and a multivariate Bayesian group sparse regression model for genetic association. We compare the predictive power of these features to expert selected features and take a closer look at the SNPs identified with the new neuroimaging features.
|
2202.10921
|
Hua Jiang
|
Zhijun Zeng, Zhen Hou, Ting Li, Lei Deng, Jianguo Hou, Xinran Huang,
Jun Li, Meirou Sun, Yunhan Wang, Qiyu Wu, Wenhao Zheng, Hua Jiang, and Qi
Wang
|
A Deep Learning Approach to Predicting Ventilator Parameters for
Mechanically Ventilated Septic Patients
| null | null | null | null |
q-bio.QM cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We develop a deep learning approach to predicting a set of ventilator
parameters for a mechanically ventilated septic patient using a long and short
term memory (LSTM) recurrent neural network (RNN) model. We focus on short-term
predictions of a set of ventilator parameters for the septic patient in
emergency intensive care unit (EICU). The short-term predictability of the
model provides attending physicians with early warnings to make timely
adjustment to the treatment of the patient in the EICU. The patient specific
deep learning model can be trained on any given critically ill patient, making
it an intelligent aide for physicians to use in emergent medical situations.
|
[
{
"created": "Mon, 21 Feb 2022 04:17:22 GMT",
"version": "v1"
}
] |
2022-02-23
|
[
[
"Zeng",
"Zhijun",
""
],
[
"Hou",
"Zhen",
""
],
[
"Li",
"Ting",
""
],
[
"Deng",
"Lei",
""
],
[
"Hou",
"Jianguo",
""
],
[
"Huang",
"Xinran",
""
],
[
"Li",
"Jun",
""
],
[
"Sun",
"Meirou",
""
],
[
"Wang",
"Yunhan",
""
],
[
"Wu",
"Qiyu",
""
],
[
"Zheng",
"Wenhao",
""
],
[
"Jiang",
"Hua",
""
],
[
"Wang",
"Qi",
""
]
] |
We develop a deep learning approach to predicting a set of ventilator parameters for a mechanically ventilated septic patient using a long and short term memory (LSTM) recurrent neural network (RNN) model. We focus on short-term predictions of a set of ventilator parameters for the septic patient in emergency intensive care unit (EICU). The short-term predictability of the model provides attending physicians with early warnings to make timely adjustment to the treatment of the patient in the EICU. The patient specific deep learning model can be trained on any given critically ill patient, making it an intelligent aide for physicians to use in emergent medical situations.
|
1912.06306
|
Wenze Ding
|
Wenze Ding and Haipeng Gong
|
Predicting the real-valued distances between residue pairs for proteins
| null | null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Predicting protein structure from the amino acid sequence has been a
challenge with theoretical and practical significance in biophysics. Despite
the recent progresses elicited by improved residue-residue contact prediction,
contact-based structure prediction has gradually reached the performance
ceiling. New methods have been proposed to predict the residue-residue
distance, but unanimously by simplifying the real-valued distance prediction
into a multiclass classification problem. Here we show a regression-based
distance prediction method, which adopts the generative adversarial network to
capture the delicate geometric relationship between residue pairs and thus
could predict the continuous, real-valued residue-residue distance
satisfactorily. The predicted residue distance map allows rapid structure
modeling by the CNS suite, and the constructed models approach at least the
same level of quality as the other state-of-the-art protein structure
prediction methods when tested on available CASP13 targets. Moreover, this
method can be used directly for the structure prediction of membrane proteins
without transfer learning.
|
[
{
"created": "Fri, 13 Dec 2019 03:15:28 GMT",
"version": "v1"
},
{
"created": "Wed, 18 Dec 2019 15:25:17 GMT",
"version": "v2"
}
] |
2019-12-19
|
[
[
"Ding",
"Wenze",
""
],
[
"Gong",
"Haipeng",
""
]
] |
Predicting protein structure from the amino acid sequence has been a challenge with theoretical and practical significance in biophysics. Despite the recent progresses elicited by improved residue-residue contact prediction, contact-based structure prediction has gradually reached the performance ceiling. New methods have been proposed to predict the residue-residue distance, but unanimously by simplifying the real-valued distance prediction into a multiclass classification problem. Here we show a regression-based distance prediction method, which adopts the generative adversarial network to capture the delicate geometric relationship between residue pairs and thus could predict the continuous, real-valued residue-residue distance satisfactorily. The predicted residue distance map allows rapid structure modeling by the CNS suite, and the constructed models approach at least the same level of quality as the other state-of-the-art protein structure prediction methods when tested on available CASP13 targets. Moreover, this method can be used directly for the structure prediction of membrane proteins without transfer learning.
|
1501.04032
|
Ken Kreutz-Delgado
|
Ken Kreutz-Delgado
|
Mean Time-to-Fire for the Noisy LIF Neuron - A Detailed Derivation of
the Siegert Formula
| null | null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
When stimulated by a very large number of Poisson-like presynaptic current
input spikes, the temporal dynamics of the soma membrane potential $V(t)$ of a
leaky integrate-and-fire (LIF) neuron is typically modeled in the diffusion
limit and treated as a Ornstein-Uhlenbeck process (OUP). When the potential
reaches a threshold value $\theta$, $V(t) = \theta$, the LIF neuron fires and
the membrane potential is reset to a resting value, $V_0 < \theta$, and clamped
to this value for a specified (non-stochastic) absolute refractory period $T_r
\ge 0$, after which the cycle is repeated. The time between firings is given by
the random variable $T_f = T_r+ T$ where $T$ is the random time which elapses
between the "unpinning" of the membrane potential clamp and the next,
subsequent firing of the neuron. The mean time-to-fire, $\widehat{T}_f =
\text{E}(T_f) = T_r + \text{E}(T) = T_r + \widehat{T}$, provides a measure
$\rho$ of the average firing rate of the neuron, \[ \rho = \widehat{T}_f^{-1} =
\frac{1}{T_r + \widehat{T}} . \] This note briefly discusses some aspects of
the OUP model and derives the Siegert formula giving the firing rate, $\rho =
\rho(I_0)$ as a function of an injected current, $I_0$. This is a well-known
classical result and no claim to originality is made. The derivation of the
firing rate given in this report, which closely follows the derivation outlined
in the textbook by Gardiner, minimizes the required mathematical background and
is done in some pedagogic detail to facilitate study by graduate students and
others who are new to the subject. Knowledge of the material presented in the
first five chapters of Gardiner should provide an adequate background for
following the derivation given in this note.
|
[
{
"created": "Fri, 16 Jan 2015 16:21:45 GMT",
"version": "v1"
}
] |
2015-01-19
|
[
[
"Kreutz-Delgado",
"Ken",
""
]
] |
When stimulated by a very large number of Poisson-like presynaptic current input spikes, the temporal dynamics of the soma membrane potential $V(t)$ of a leaky integrate-and-fire (LIF) neuron is typically modeled in the diffusion limit and treated as a Ornstein-Uhlenbeck process (OUP). When the potential reaches a threshold value $\theta$, $V(t) = \theta$, the LIF neuron fires and the membrane potential is reset to a resting value, $V_0 < \theta$, and clamped to this value for a specified (non-stochastic) absolute refractory period $T_r \ge 0$, after which the cycle is repeated. The time between firings is given by the random variable $T_f = T_r+ T$ where $T$ is the random time which elapses between the "unpinning" of the membrane potential clamp and the next, subsequent firing of the neuron. The mean time-to-fire, $\widehat{T}_f = \text{E}(T_f) = T_r + \text{E}(T) = T_r + \widehat{T}$, provides a measure $\rho$ of the average firing rate of the neuron, \[ \rho = \widehat{T}_f^{-1} = \frac{1}{T_r + \widehat{T}} . \] This note briefly discusses some aspects of the OUP model and derives the Siegert formula giving the firing rate, $\rho = \rho(I_0)$ as a function of an injected current, $I_0$. This is a well-known classical result and no claim to originality is made. The derivation of the firing rate given in this report, which closely follows the derivation outlined in the textbook by Gardiner, minimizes the required mathematical background and is done in some pedagogic detail to facilitate study by graduate students and others who are new to the subject. Knowledge of the material presented in the first five chapters of Gardiner should provide an adequate background for following the derivation given in this note.
|
2010.06066
|
Ben Frederick Intoy
|
Qianyi Sheng and Ben Intoy and J.W. Halley
|
Quenching to fix metastable states in models of prebiotic chemistry
|
13 pages, 13 figures
|
Phys. Rev. E 102, 062412 (2020)
|
10.1103/PhysRevE.102.062412
| null |
q-bio.PE q-bio.MN q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For prebiotic chemistry to succeed in producing a starting metastable,
autocatalytic and reproducing system subject to evolutionary selection it must
satisfy at least two apparently contradictory requirements: Because such
systems are rare, a search among vast numbers of molecular combinations must
take place naturally, requiring rapid rearrangement and breaking of covalent
bonds. But once a relevant system is found, such rapid disruption and
rearrangement would be very likely to destroy the system before much evolution
could take place. In this paper we explore the possibility, using a model
developed previously, that the search process could occur under different
environmental conditions than the subsequent fixation and growth of a lifelike
chemical system. We use the example of a rapid change in temperature to
illustrate the effect and refer to the rapid change as a `quench' borrowing
terminology from study of the physics and chemistry of glass formation. The
model study shows that interrupting a high temperature nonequilibrium state
with a rapid quench to lower temperatures can substantially increase the
probability of producing a chemical state with lifelike characteristics of
nonequilibrium metastability, internal dynamics and exponential population
growth in time. Previously published data on the length distributions of
proteomes of prokaryotes may be consistent with such an idea and suggest a
prebiotic high temperature `search' phase near the boiling point of water. A
rapid change in pH could have a similar effect. We discuss possible scenarios
on early earth which might have allowed frequent quenches of the sort
considered here to have occurred. The models show a strong dependence of the
effect on the number of chemical monomers available for bond formation.
|
[
{
"created": "Mon, 12 Oct 2020 23:00:34 GMT",
"version": "v1"
}
] |
2021-01-04
|
[
[
"Sheng",
"Qianyi",
""
],
[
"Intoy",
"Ben",
""
],
[
"Halley",
"J. W.",
""
]
] |
For prebiotic chemistry to succeed in producing a starting metastable, autocatalytic and reproducing system subject to evolutionary selection it must satisfy at least two apparently contradictory requirements: Because such systems are rare, a search among vast numbers of molecular combinations must take place naturally, requiring rapid rearrangement and breaking of covalent bonds. But once a relevant system is found, such rapid disruption and rearrangement would be very likely to destroy the system before much evolution could take place. In this paper we explore the possibility, using a model developed previously, that the search process could occur under different environmental conditions than the subsequent fixation and growth of a lifelike chemical system. We use the example of a rapid change in temperature to illustrate the effect and refer to the rapid change as a `quench' borrowing terminology from study of the physics and chemistry of glass formation. The model study shows that interrupting a high temperature nonequilibrium state with a rapid quench to lower temperatures can substantially increase the probability of producing a chemical state with lifelike characteristics of nonequilibrium metastability, internal dynamics and exponential population growth in time. Previously published data on the length distributions of proteomes of prokaryotes may be consistent with such an idea and suggest a prebiotic high temperature `search' phase near the boiling point of water. A rapid change in pH could have a similar effect. We discuss possible scenarios on early earth which might have allowed frequent quenches of the sort considered here to have occurred. The models show a strong dependence of the effect on the number of chemical monomers available for bond formation.
|
1310.1668
|
Liane Gabora
|
Liane Gabora
|
Why the Creative Process is Not Darwinian
|
8 pages
|
Creativity Research Journal, 19(4), 361-365 (2007)
| null | null |
q-bio.PE q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Simonton (2006) makes the unwarranted assumption that nonmonotonicity
supports a Darwinian view of creativity. Darwin's theory of natural selection
was motivated by a paradox that has no equivalent in creative thought: the
paradox of how change accumulates when acquired traits are not inherited. To
describe a process of cumulative change in which acquired traits are retained
is outside of the scope of the theory of natural selection. Even the early
evolution of life itself (prior to genetically mediated template replication)
cannot be described by natural selection. Specifically, natural selection
cannot describe change of state that involves horizontal (Lamarckian) exchange,
or occurs through interaction with an incompletely specified context. It cannot
describe change wherein variants are evaluated sequentially, and wherein this
evaluation can itself change the state space and/or fitness function, because
no two variants are ever evaluated according to the same selection criterion.
Concerns are also raised as to the methodology used in Simonton's study.
|
[
{
"created": "Mon, 7 Oct 2013 04:13:53 GMT",
"version": "v1"
},
{
"created": "Fri, 5 Jul 2019 20:33:55 GMT",
"version": "v2"
}
] |
2019-07-09
|
[
[
"Gabora",
"Liane",
""
]
] |
Simonton (2006) makes the unwarranted assumption that nonmonotonicity supports a Darwinian view of creativity. Darwin's theory of natural selection was motivated by a paradox that has no equivalent in creative thought: the paradox of how change accumulates when acquired traits are not inherited. To describe a process of cumulative change in which acquired traits are retained is outside of the scope of the theory of natural selection. Even the early evolution of life itself (prior to genetically mediated template replication) cannot be described by natural selection. Specifically, natural selection cannot describe change of state that involves horizontal (Lamarckian) exchange, or occurs through interaction with an incompletely specified context. It cannot describe change wherein variants are evaluated sequentially, and wherein this evaluation can itself change the state space and/or fitness function, because no two variants are ever evaluated according to the same selection criterion. Concerns are also raised as to the methodology used in Simonton's study.
|
1912.06275
|
Oliver Beckstein
|
Oliver Beckstein and Fiona Naughton
|
General Principles of Secondary Active Transporter Function
|
accepted for publication in Biophysics Reviews
| null |
10.1063/5.0047967
| null |
q-bio.BM
|
http://creativecommons.org/licenses/by/4.0/
|
Transport of ions and small molecules across the cell membrane against
electrochemical gradients is catalyzed by integral membrane proteins that use a
source of free energy to drive the energetically uphill flux of the transported
substrate. Secondary active transporters couple the spontaneous influx of a
"driving" ion such as Na$^+$ or H$^+$ to the flux of the substrate. The
thermodynamics of such cyclical non-equilibrium systems are well understood and
recent work has focused on the molecular mechanism of secondary active
transport. The fact that these transporters change their conformation between
an inward-facing and outward-facing conformation in a cyclical fashion, called
the alternating access model, is broadly recognized as the molecular framework
in which to describe transporter function. High resolution structures and
detailed computer simulations lead to the recognition of common molecular-level
principles between disparate transporter families. Inverted repeat symmetry in
secondary active transporters has shed light on how protein structures can
encode a bi-stable two-state system. Three broad classes of alternating access
transitions have been described as rocker-switch, rocking-bundle, and elevator
mechanisms. Transporters can be understood as gated pores with at least two
coupled gates that map to distinct parts of the transporter protein.
Enumerating all distinct gate states naturally includes occluded states in the
alternating access picture and suggests observable protein conformations. By
connecting the possible conformational states and ion/substrate bound states in
a kinetic model, a unified picture emerges in which symporter, antiporter, and
uniporter function are extremes in a continuum of functionality. We briefly
discuss how biological complexity may be integrated in quantitative kinetic
models to provide a bridge from structure to function.
|
[
{
"created": "Fri, 13 Dec 2019 00:52:41 GMT",
"version": "v1"
},
{
"created": "Thu, 18 Feb 2021 04:46:05 GMT",
"version": "v2"
},
{
"created": "Fri, 17 Dec 2021 19:14:37 GMT",
"version": "v3"
},
{
"created": "Wed, 23 Feb 2022 22:28:57 GMT",
"version": "v4"
},
{
"created": "Wed, 23 Mar 2022 23:12:41 GMT",
"version": "v5"
}
] |
2022-03-25
|
[
[
"Beckstein",
"Oliver",
""
],
[
"Naughton",
"Fiona",
""
]
] |
Transport of ions and small molecules across the cell membrane against electrochemical gradients is catalyzed by integral membrane proteins that use a source of free energy to drive the energetically uphill flux of the transported substrate. Secondary active transporters couple the spontaneous influx of a "driving" ion such as Na$^+$ or H$^+$ to the flux of the substrate. The thermodynamics of such cyclical non-equilibrium systems are well understood and recent work has focused on the molecular mechanism of secondary active transport. The fact that these transporters change their conformation between an inward-facing and outward-facing conformation in a cyclical fashion, called the alternating access model, is broadly recognized as the molecular framework in which to describe transporter function. High resolution structures and detailed computer simulations lead to the recognition of common molecular-level principles between disparate transporter families. Inverted repeat symmetry in secondary active transporters has shed light on how protein structures can encode a bi-stable two-state system. Three broad classes of alternating access transitions have been described as rocker-switch, rocking-bundle, and elevator mechanisms. Transporters can be understood as gated pores with at least two coupled gates that map to distinct parts of the transporter protein. Enumerating all distinct gate states naturally includes occluded states in the alternating access picture and suggests observable protein conformations. By connecting the possible conformational states and ion/substrate bound states in a kinetic model, a unified picture emerges in which symporter, antiporter, and uniporter function are extremes in a continuum of functionality. We briefly discuss how biological complexity may be integrated in quantitative kinetic models to provide a bridge from structure to function.
|
0811.0541
|
Gerhard Werner MD
|
Gerhard Werner
|
On critical State Transitions between different levels in Neural Systems
|
11
| null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The framework of Modern Theory of Critical State Transitions considers the
relation between different levels of organization in complex systems in terms
of Critical State Transitions. A State Transition between levels entails
changes of scale of observables and, concurrently, new formats of description
at reduced dimensionality. It is here suggested that this principle can be
applied to the hierarchic structure of the Nervous system, whereby the
relations between different levels of its functional organization can be viewed
as successions of State Transitions. Upon State Transition, the lower level
presents to the higher level an abstraction of itself, at reduced
dimensionality and at a coarser scale. The re-scaling in the State Transitions
is associated with new objects of description, displays new properties and
obeys new laws, commensurate to the new scale. To illustrate this process, some
aspects of the neural events thought to be associated with Cognition and
Consciousness are discussed. However, the intent is here also more general in
that State Transitions between all levels of organization are proposed as the
mechanisms by which successively higher levels of organization emerge from
lower levels.
|
[
{
"created": "Tue, 4 Nov 2008 15:44:46 GMT",
"version": "v1"
}
] |
2008-11-05
|
[
[
"Werner",
"Gerhard",
""
]
] |
The framework of Modern Theory of Critical State Transitions considers the relation between different levels of organization in complex systems in terms of Critical State Transitions. A State Transition between levels entails changes of scale of observables and, concurrently, new formats of description at reduced dimensionality. It is here suggested that this principle can be applied to the hierarchic structure of the Nervous system, whereby the relations between different levels of its functional organization can be viewed as successions of State Transitions. Upon State Transition, the lower level presents to the higher level an abstraction of itself, at reduced dimensionality and at a coarser scale. The re-scaling in the State Transitions is associated with new objects of description, displays new properties and obeys new laws, commensurate to the new scale. To illustrate this process, some aspects of the neural events thought to be associated with Cognition and Consciousness are discussed. However, the intent is here also more general in that State Transitions between all levels of organization are proposed as the mechanisms by which successively higher levels of organization emerge from lower levels.
|
1809.02886
|
Anne Shiu
|
Carsten Conradi, Maya Mincheva, Anne Shiu
|
Emergence of oscillations in a mixed-mechanism phosphorylation system
|
24 pages
| null | null | null |
q-bio.MN math.AG math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This work investigates the emergence of oscillations in one of the simplest
cellular signaling networks exhibiting oscillations, namely, the dual-site
phosphorylation and dephosphorylation network (futile cycle), in which the
mechanism for phosphorylation is processive while the one for dephosphorylation
is distributive (or vice-versa). The fact that this network yields oscillations
was shown recently by Suwanmajo and Krishnan. Our results, which significantly
extend their analyses, are as follows. First, in the three-dimensional space of
total amounts, the border between systems with a stable versus unstable steady
state is a surface defined by the vanishing of a single Hurwitz determinant.
Second, this surface consists generically of simple Hopf bifurcations. Next,
simulations suggest that when the steady state is unstable, oscillations are
the norm. Finally, the emergence of oscillations via a Hopf bifurcation is
enabled by the catalytic and association constants of the distributive part of
the mechanism: if these rate constants satisfy two inequalities, then the
system generically admits a Hopf bifurcation. Our proofs are enabled by the
Routh-Hurwitz criterion, a Hopf-bifurcation criterion due to Yang, and a
monomial parametrization of steady states.
|
[
{
"created": "Sat, 8 Sep 2018 22:10:58 GMT",
"version": "v1"
},
{
"created": "Fri, 1 Feb 2019 20:44:38 GMT",
"version": "v2"
}
] |
2019-02-05
|
[
[
"Conradi",
"Carsten",
""
],
[
"Mincheva",
"Maya",
""
],
[
"Shiu",
"Anne",
""
]
] |
This work investigates the emergence of oscillations in one of the simplest cellular signaling networks exhibiting oscillations, namely, the dual-site phosphorylation and dephosphorylation network (futile cycle), in which the mechanism for phosphorylation is processive while the one for dephosphorylation is distributive (or vice-versa). The fact that this network yields oscillations was shown recently by Suwanmajo and Krishnan. Our results, which significantly extend their analyses, are as follows. First, in the three-dimensional space of total amounts, the border between systems with a stable versus unstable steady state is a surface defined by the vanishing of a single Hurwitz determinant. Second, this surface consists generically of simple Hopf bifurcations. Next, simulations suggest that when the steady state is unstable, oscillations are the norm. Finally, the emergence of oscillations via a Hopf bifurcation is enabled by the catalytic and association constants of the distributive part of the mechanism: if these rate constants satisfy two inequalities, then the system generically admits a Hopf bifurcation. Our proofs are enabled by the Routh-Hurwitz criterion, a Hopf-bifurcation criterion due to Yang, and a monomial parametrization of steady states.
|
1909.02148
|
Claus Seidel
|
Mykola Dimura, Thomas O. Peulen, Hugo Sanabria, Dmitro Rodnin,
Katherina Hemmen, Claus A.M. Seidel and Holger Gohlke
|
Automated and optimally FRET-assisted structural modeling
|
18 pages, 2 figures, 1 table
| null |
10.1016/j.bpj.2018.11.1815
| null |
q-bio.QM physics.bio-ph q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
FRET experiments can yield state-specific structural information on complex
dynamic biomolecular assemblies. However, FRET experiments need to be combined
with computer simulations to overcome their sparsity. We introduce (i) an
automated FRET experiment design tool determining optimal FRET pairs for
structural modeling, (ii) a protocol for efficient FRET-assisted computational
structural modeling at multiple scales, and (iii) a quantitative quality
estimate for judging the accuracy of determined structures. We tested against
simulated and experimental data.
|
[
{
"created": "Wed, 4 Sep 2019 23:12:26 GMT",
"version": "v1"
}
] |
2019-09-11
|
[
[
"Dimura",
"Mykola",
""
],
[
"Peulen",
"Thomas O.",
""
],
[
"Sanabria",
"Hugo",
""
],
[
"Rodnin",
"Dmitro",
""
],
[
"Hemmen",
"Katherina",
""
],
[
"Seidel",
"Claus A. M.",
""
],
[
"Gohlke",
"Holger",
""
]
] |
FRET experiments can yield state-specific structural information on complex dynamic biomolecular assemblies. However, FRET experiments need to be combined with computer simulations to overcome their sparsity. We introduce (i) an automated FRET experiment design tool determining optimal FRET pairs for structural modeling, (ii) a protocol for efficient FRET-assisted computational structural modeling at multiple scales, and (iii) a quantitative quality estimate for judging the accuracy of determined structures. We tested against simulated and experimental data.
|
q-bio/0503020
|
Adam Lipowski
|
Adam Lipowski and Dorota Lipowska
|
Long-term evolution of an ecosystem with spontaneous periodicity of mass
extinctions
|
10 pages, Theory in Biosciences (in press). For associated Java
applet see http://spin.amu.edu.pl/~lipowski/prey_pred.html
|
Theory in Biosciences vol.125, pp. 67-77 (2006)
|
10.1016/j.thbio.2006.01.001
| null |
q-bio.PE cond-mat.other q-bio.OT
| null |
Twenty years ago, after analysing palaeontological data, Raup and Sepkoski
suggested that mass extinctions on Earth appear cyclically in time with a
period of approximately 26 million years (My). To explain the 26My period, a
number of proposals were made involving, e.g., astronomical effects, increased
volcanic activity, or the Earth's magnetic field reversal, none of which,
however, has been confirmed. Here we study a spatially extended discrete model
of an ecosystem and show that the periodicity of mass extinctions might be a
natural feature of the ecosystem's dynamics and not the result of a periodic
external perturbation. In our model, periodic changes of the diversity of an
ecosystem and some of its other characteristics are induced by the coevolution
of species. In agreement with some palaeontological data, our results show that
the longevity of a species depends on the evolutionary stage at which the
species is created. Possible further tests of our model are also discussed.
|
[
{
"created": "Sun, 13 Mar 2005 17:52:39 GMT",
"version": "v1"
},
{
"created": "Fri, 10 Jun 2005 20:17:36 GMT",
"version": "v2"
},
{
"created": "Sun, 26 Jun 2005 13:03:55 GMT",
"version": "v3"
},
{
"created": "Fri, 6 Jan 2006 20:14:37 GMT",
"version": "v4"
}
] |
2007-05-23
|
[
[
"Lipowski",
"Adam",
""
],
[
"Lipowska",
"Dorota",
""
]
] |
Twenty years ago, after analysing palaeontological data, Raup and Sepkoski suggested that mass extinctions on Earth appear cyclically in time with a period of approximately 26 million years (My). To explain the 26My period, a number of proposals were made involving, e.g., astronomical effects, increased volcanic activity, or the Earth's magnetic field reversal, none of which, however, has been confirmed. Here we study a spatially extended discrete model of an ecosystem and show that the periodicity of mass extinctions might be a natural feature of the ecosystem's dynamics and not the result of a periodic external perturbation. In our model, periodic changes of the diversity of an ecosystem and some of its other characteristics are induced by the coevolution of species. In agreement with some palaeontological data, our results show that the longevity of a species depends on the evolutionary stage at which the species is created. Possible further tests of our model are also discussed.
|
1412.2110
|
Amit Chattopadhyay
|
Amit K. Chattopadhyay and Subhasish Bandyopadhyay
|
Seasonal variations of EPG Levels in gastro-intestinal parasitic
infection in a southeast asian controlled locale: a statistical analysis
|
9 pages, 12 figures
|
SpringerPlus 2013, 2:205
| null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a data based statistical study on the effects of seasonal
variations in the growth rates of the gastro-intestinal (GI) parasitic
infection in livestock. The alluded growth rate is estimated through the
variation in the number of eggs per gram (EPG) of faeces in animals. In
accordance with earlier studies, our analysis too shows that rainfall is the
dominant variable in determining EPG infection rates compared to other
macro-parameters like temperature and humidity. Our statistical analysis
clearly indicates an oscillatory dependence of EPG levels on rainfall
fluctuations. Monsoon recorded the highest infection with a comparative
increase of at least 2.5 times compared to the next most infected period
(summer). A least square fit of the EPG versus rainfall data indicates an
approach towards a super diffusive (i. e. root mean square displacement growing
faster than the square root of the elapsed time as obtained for simple
diffusion) infection growth pattern regime for low rainfall regimes
(technically defined as zeroth level dependence) that gets remarkably augmented
for large rainfall zones. Our analysis further indicates that for low
fluctuations in temperature (true on the bulk data), EPG level saturates beyond
a critical value of the rainfall, a threshold that is expected to indicate the
onset of the nonlinear regime. The probability density functions (PDFs) of the
EPG data show oscillatory behavior in the large rainfall regime (greater than
500 mm), the frequency of oscillation, once again, being determined by the
ambient wetness (rainfall, and humidity). Data recorded over three pilot
projects spanning three measures of rainfall and humidity bear testimony to the
universality of this statistical argument.
|
[
{
"created": "Wed, 3 Dec 2014 23:15:52 GMT",
"version": "v1"
}
] |
2014-12-08
|
[
[
"Chattopadhyay",
"Amit K.",
""
],
[
"Bandyopadhyay",
"Subhasish",
""
]
] |
We present a data based statistical study on the effects of seasonal variations in the growth rates of the gastro-intestinal (GI) parasitic infection in livestock. The alluded growth rate is estimated through the variation in the number of eggs per gram (EPG) of faeces in animals. In accordance with earlier studies, our analysis too shows that rainfall is the dominant variable in determining EPG infection rates compared to other macro-parameters like temperature and humidity. Our statistical analysis clearly indicates an oscillatory dependence of EPG levels on rainfall fluctuations. Monsoon recorded the highest infection with a comparative increase of at least 2.5 times compared to the next most infected period (summer). A least square fit of the EPG versus rainfall data indicates an approach towards a super diffusive (i. e. root mean square displacement growing faster than the square root of the elapsed time as obtained for simple diffusion) infection growth pattern regime for low rainfall regimes (technically defined as zeroth level dependence) that gets remarkably augmented for large rainfall zones. Our analysis further indicates that for low fluctuations in temperature (true on the bulk data), EPG level saturates beyond a critical value of the rainfall, a threshold that is expected to indicate the onset of the nonlinear regime. The probability density functions (PDFs) of the EPG data show oscillatory behavior in the large rainfall regime (greater than 500 mm), the frequency of oscillation, once again, being determined by the ambient wetness (rainfall, and humidity). Data recorded over three pilot projects spanning three measures of rainfall and humidity bear testimony to the universality of this statistical argument.
|
1907.09270
|
Afshin Montakhab
|
Mahsa Khoshkhou and Afshin Montakhab
|
Spike-timing-dependent plasticity with axonal delay tunes networks of
Izhikevich neurons to the edge of synchronization transition with scale-free
avalanches
|
9 pages, 7 figures, 42 references
|
Front. Syst. Neurosci., 04 December 2019
|
10.3389/fnsys.2019.00073
| null |
q-bio.NC nlin.AO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Critical brain hypothesis has been intensively studied both in experimental
and theoretical neuroscience over the past two decades. However, some important
questions still remain: (i) What is the critical point the brain operates at?
(ii) What is the regulatory mechanism that brings about and maintains such a
critical state? (iii) The critical state is characterized by scale-invariant
behavior which is seemingly at odds with definitive brain oscillations? In this
work we consider a biologically motivated model of Izhikevich neuronal network
with chemical synapses interacting via spike-timingdependent plasticity (STDP)
as well as axonal time delay. Under generic and physiologically relevant
conditions we show that the system is organized and maintained around a
synchronization transition point as opposed to an activity transition point
associated with an absorbing state phase transition. However, such a state
exhibits experimentally relevant signs of critical dynamics including
scale-free avalanches with finite-size scaling as well as branching ratios.
While the system displays stochastic oscillations with highly correlated
fluctuations, it also displays dominant frequency modes seen as sharp peaks in
the power spectrum. The role of STDP as well as time delay is crucial in
achieving and maintaining such critical dynamics, while the role of inhibition
is not as crucial. In this way we provide definitive answers to all three
questions posed above. We also show that one can achieve supercritical or
subcritical dynamics if one changes the average time delay associated with
axonal conduction.
|
[
{
"created": "Mon, 22 Jul 2019 12:28:45 GMT",
"version": "v1"
}
] |
2019-12-19
|
[
[
"Khoshkhou",
"Mahsa",
""
],
[
"Montakhab",
"Afshin",
""
]
] |
Critical brain hypothesis has been intensively studied both in experimental and theoretical neuroscience over the past two decades. However, some important questions still remain: (i) What is the critical point the brain operates at? (ii) What is the regulatory mechanism that brings about and maintains such a critical state? (iii) The critical state is characterized by scale-invariant behavior which is seemingly at odds with definitive brain oscillations? In this work we consider a biologically motivated model of Izhikevich neuronal network with chemical synapses interacting via spike-timingdependent plasticity (STDP) as well as axonal time delay. Under generic and physiologically relevant conditions we show that the system is organized and maintained around a synchronization transition point as opposed to an activity transition point associated with an absorbing state phase transition. However, such a state exhibits experimentally relevant signs of critical dynamics including scale-free avalanches with finite-size scaling as well as branching ratios. While the system displays stochastic oscillations with highly correlated fluctuations, it also displays dominant frequency modes seen as sharp peaks in the power spectrum. The role of STDP as well as time delay is crucial in achieving and maintaining such critical dynamics, while the role of inhibition is not as crucial. In this way we provide definitive answers to all three questions posed above. We also show that one can achieve supercritical or subcritical dynamics if one changes the average time delay associated with axonal conduction.
|
2103.15544
|
Claudius Gros
|
Claudius Gros, Thomas Czypionka, Daniel Gros
|
When to end a lock down? How fast must vaccination campaigns proceed in
order to keep health costs in check?
| null |
Royal Society Open Science 9, 211055 (2022)
|
10.1098/rsos.211055
| null |
q-bio.PE econ.TH physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a simple rule of thumb for countries which have embarked on a
vaccination campaign while still facing the need to keep non-pharmaceutical
interventions (NPI) in place because of the ongoing spread of SARS-CoV-2. If
the aim is to keep the death rate from increasing, NPIs can be loosened when it
is possible to vaccinate more than twice the growth rate of new cases. If the
aim is to keep the pressure on hospitals under control, the vaccination rate
has to be about four times higher. These simple rules can be derived from the
observation that the risk of death or a severe course requiring hospitalization
from a COVID-19 infection increases exponentially with age and that the sizes
of age cohorts decrease linearly at the top of the population pyramid.
Protecting the over 60-year-olds, which constitute approximately one-quarter of
the population in Europe (and most OECD countries), reduces the potential loss
of life by 95 percent.
|
[
{
"created": "Mon, 29 Mar 2021 12:20:34 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Mar 2021 13:47:37 GMT",
"version": "v2"
},
{
"created": "Wed, 31 Mar 2021 14:27:51 GMT",
"version": "v3"
},
{
"created": "Wed, 26 Jan 2022 08:45:53 GMT",
"version": "v4"
}
] |
2022-01-27
|
[
[
"Gros",
"Claudius",
""
],
[
"Czypionka",
"Thomas",
""
],
[
"Gros",
"Daniel",
""
]
] |
We propose a simple rule of thumb for countries which have embarked on a vaccination campaign while still facing the need to keep non-pharmaceutical interventions (NPI) in place because of the ongoing spread of SARS-CoV-2. If the aim is to keep the death rate from increasing, NPIs can be loosened when it is possible to vaccinate more than twice the growth rate of new cases. If the aim is to keep the pressure on hospitals under control, the vaccination rate has to be about four times higher. These simple rules can be derived from the observation that the risk of death or a severe course requiring hospitalization from a COVID-19 infection increases exponentially with age and that the sizes of age cohorts decrease linearly at the top of the population pyramid. Protecting the over 60-year-olds, which constitute approximately one-quarter of the population in Europe (and most OECD countries), reduces the potential loss of life by 95 percent.
|
q-bio/0504031
|
Romulus Breban
|
Romulus Breban and Sally Blower
|
Parametric Resonance May Explain Virologic Failure to HIV Treatment
Interruptions
|
15 pages, 2 figures, 1 table
| null | null | null |
q-bio.PE
| null |
Pilot studies of structured treatment interruptions (STI) in HIV therapy have
shown that patients can maintain low viral loads whilst benefiting from reduced
treatment toxicity. However, a recent STI clinical trial reported a high degree
of virologic failure. Here we present a novel hypothesis that could explain
virologic failure to STI and provides new insights of great clinical relevance.
We analyze a classic mathematical model of HIV within-host viral dynamics and
find that nonlinear parametric resonance occurs when STI are added to the
model; resonance is observed as virologic failure. We use the model to simulate
clinical trial data and to calculate patient-specific resonant spectra. We gain
two important insights. Firstly, within an STI trial, we determine that
patients who begin with similar viral loads can be expected to show extremely
different virologic responses as a result of resonance. Thus, high
heterogeneity of patient response within a STI clinical trial is to be
expected. Secondly and more importantly, we determine that virologic failure is
not simply due to STI or patient characteristics; rather it is the result of a
complex dynamic interaction between STI and patient viral dynamics. Hence, our
analyses demonstrate that no universal regimen with periodic interruptions will
be effective for all patients. On the basis of our results, we suggest that
immunologic and virologic parameters should be used to design patient-specific
STI regimens.
|
[
{
"created": "Wed, 27 Apr 2005 21:57:53 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Breban",
"Romulus",
""
],
[
"Blower",
"Sally",
""
]
] |
Pilot studies of structured treatment interruptions (STI) in HIV therapy have shown that patients can maintain low viral loads whilst benefiting from reduced treatment toxicity. However, a recent STI clinical trial reported a high degree of virologic failure. Here we present a novel hypothesis that could explain virologic failure to STI and provides new insights of great clinical relevance. We analyze a classic mathematical model of HIV within-host viral dynamics and find that nonlinear parametric resonance occurs when STI are added to the model; resonance is observed as virologic failure. We use the model to simulate clinical trial data and to calculate patient-specific resonant spectra. We gain two important insights. Firstly, within an STI trial, we determine that patients who begin with similar viral loads can be expected to show extremely different virologic responses as a result of resonance. Thus, high heterogeneity of patient response within a STI clinical trial is to be expected. Secondly and more importantly, we determine that virologic failure is not simply due to STI or patient characteristics; rather it is the result of a complex dynamic interaction between STI and patient viral dynamics. Hence, our analyses demonstrate that no universal regimen with periodic interruptions will be effective for all patients. On the basis of our results, we suggest that immunologic and virologic parameters should be used to design patient-specific STI regimens.
|
2010.02952
|
Chinchin Wang
|
Chinchin Wang, Tyrel Stokes, jorge Trejo Vargas, Russell Steele, Niels
Wedderkopp, Ian Shrier
|
Injury risk increases minimally over a large range of changes in
activity level in children
| null | null | null | null |
q-bio.QM
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Background: Limited research exists on the association between changes in
physical activity levels and injury in children. Objective: To assess how well
different variations of the acute:chronic workload ratio (ACWR), a measure of
change in activity, predict injury in children. Methods: We conducted a
prospective cohort study using data from 1670 Danish schoolchildren measured
over 5.5 years (2008 to 2014). Coupled 4-week, uncoupled 4-week, and uncoupled
5-week ACWRs were calculated using activity frequency in the past week as the
acute load (numerator), and average weekly activity frequency in the past 4 or
5 weeks as the chronic load (denominator). We modelled the relationship between
different ACWR variations and injury using generalized linear and generalized
additive models, with and without accounting for repeated measures. Results:
The prognostic relationship between the ACWR and injury risk was best
represented using a generalized additive mixed model for the uncoupled 5-week
ACWR. It predicted an injury risk of ~3% for ACWRs between 0.8 (activity level
decreased by 20%) and 1.5 (activity level increased by 50%). When activity
decreased by more than 20% (ACWR< 0.8), injury risk was lower (minimum of 1.5%
at ACWR=0). When activity increased by more than 50% (ACWR > 1.5), injury risk
was higher (maximum of 6% at ACWR = 5). Girls were at significantly higher risk
of injury than boys. Conclusion: Increases in physical activity in children are
associated with much lower injury risks compared to previous results in adults.
|
[
{
"created": "Tue, 6 Oct 2020 18:03:50 GMT",
"version": "v1"
},
{
"created": "Fri, 22 Jan 2021 01:25:50 GMT",
"version": "v2"
}
] |
2021-01-25
|
[
[
"Wang",
"Chinchin",
""
],
[
"Stokes",
"Tyrel",
""
],
[
"Vargas",
"jorge Trejo",
""
],
[
"Steele",
"Russell",
""
],
[
"Wedderkopp",
"Niels",
""
],
[
"Shrier",
"Ian",
""
]
] |
Background: Limited research exists on the association between changes in physical activity levels and injury in children. Objective: To assess how well different variations of the acute:chronic workload ratio (ACWR), a measure of change in activity, predict injury in children. Methods: We conducted a prospective cohort study using data from 1670 Danish schoolchildren measured over 5.5 years (2008 to 2014). Coupled 4-week, uncoupled 4-week, and uncoupled 5-week ACWRs were calculated using activity frequency in the past week as the acute load (numerator), and average weekly activity frequency in the past 4 or 5 weeks as the chronic load (denominator). We modelled the relationship between different ACWR variations and injury using generalized linear and generalized additive models, with and without accounting for repeated measures. Results: The prognostic relationship between the ACWR and injury risk was best represented using a generalized additive mixed model for the uncoupled 5-week ACWR. It predicted an injury risk of ~3% for ACWRs between 0.8 (activity level decreased by 20%) and 1.5 (activity level increased by 50%). When activity decreased by more than 20% (ACWR< 0.8), injury risk was lower (minimum of 1.5% at ACWR=0). When activity increased by more than 50% (ACWR > 1.5), injury risk was higher (maximum of 6% at ACWR = 5). Girls were at significantly higher risk of injury than boys. Conclusion: Increases in physical activity in children are associated with much lower injury risks compared to previous results in adults.
|
2006.14930
|
Sumra Bari
|
Sumra Bari, Nicole L. Vike, Khrystyna Stetsiv, Linda Papa, Eric A.
Nauman, Thomas M. Talavage, Semyon Slobounov, Hans C. Breiter
|
A metabolomic measure of energy metabolism moderates how an inflammatory
miRNA relates to rs-fMRI network and motor control in football athletes
|
64 pages, 4 figures, 1 table. arXiv admin note: text overlap with
arXiv:2006.13264
| null | null | null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Collision sports athletes experience many head acceleration events (HAEs) per
season. The effects of these subconcussive events are largely understudied
since HAEs may produce no overt symptoms, and are likely to diffusely manifest
across multiple scales of study (e.g., molecular, cellular network, and
behavior). This study integrated resting-state fMRI with metabolome,
transcriptome and computational virtual reality (VR) behavior measures to
assess the effects of exposure to HAEs on players in a collegiate American
football team. Permutation-based mediation and moderation analysis was used to
investigate relationships between network fingerprint, changes in omic measures
and VR metrics over the season. Change in an energy cycle fatty acid,
tridecenedioate, moderated the relationship between 1) miR-505 and DMN
fingerprint and 2) the relationship between DMN fingerprint and worsening VR
Balance measures (all p less than or equal to 0.05). In addition, the
similarity in DMN over the season was negatively related to cumulative number
of HAEs above 80G, and DMN fingerprint was less similar across the season in
athletes relative to age-matched non-athletes. miR-505 was also positively
related to average number of HAEs above 25G per session. It is important to
note that tridecenedioate has a double bond making it a candidate for ROS
scavenging. These findings between a candidate ROS-related metabolite,
inflammatory miRNA, altered brain imaging and diminished behavioral performance
suggests that impact athletes may experience chronic neuroinflammation. The
rigorous permutation-based mediation/moderation may provide a methodology for
investigating complex multi-scale biological data within humans alone and thus
assist study of other functional brain problems.
|
[
{
"created": "Tue, 23 Jun 2020 18:44:02 GMT",
"version": "v1"
}
] |
2020-06-29
|
[
[
"Bari",
"Sumra",
""
],
[
"Vike",
"Nicole L.",
""
],
[
"Stetsiv",
"Khrystyna",
""
],
[
"Papa",
"Linda",
""
],
[
"Nauman",
"Eric A.",
""
],
[
"Talavage",
"Thomas M.",
""
],
[
"Slobounov",
"Semyon",
""
],
[
"Breiter",
"Hans C.",
""
]
] |
Collision sports athletes experience many head acceleration events (HAEs) per season. The effects of these subconcussive events are largely understudied since HAEs may produce no overt symptoms, and are likely to diffusely manifest across multiple scales of study (e.g., molecular, cellular network, and behavior). This study integrated resting-state fMRI with metabolome, transcriptome and computational virtual reality (VR) behavior measures to assess the effects of exposure to HAEs on players in a collegiate American football team. Permutation-based mediation and moderation analysis was used to investigate relationships between network fingerprint, changes in omic measures and VR metrics over the season. Change in an energy cycle fatty acid, tridecenedioate, moderated the relationship between 1) miR-505 and DMN fingerprint and 2) the relationship between DMN fingerprint and worsening VR Balance measures (all p less than or equal to 0.05). In addition, the similarity in DMN over the season was negatively related to cumulative number of HAEs above 80G, and DMN fingerprint was less similar across the season in athletes relative to age-matched non-athletes. miR-505 was also positively related to average number of HAEs above 25G per session. It is important to note that tridecenedioate has a double bond making it a candidate for ROS scavenging. These findings between a candidate ROS-related metabolite, inflammatory miRNA, altered brain imaging and diminished behavioral performance suggests that impact athletes may experience chronic neuroinflammation. The rigorous permutation-based mediation/moderation may provide a methodology for investigating complex multi-scale biological data within humans alone and thus assist study of other functional brain problems.
|
2003.00329
|
Gerhard Mayer
|
Gerhard Mayer
|
XLMOD: Cross-linking and chromatography derivatization reagents ontology
|
30 pages
| null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mass spectrometry has experienced a rapid development since its first
application for protein analysis in the 1980s. While the most common use of
mass spectrometry for protein analysis is identification and quantification
workflows on peptides (digested from their parent protein), there is also a
rapidly growing use of mass spectrometry for structural proteomics. One example
is the analysis of cross-linked proteins that can give valuable structural
information, complementing the information gained by classical protein
structure determination methods, useful for integrated methods of structure
determination and modeling. For a broad and reproducible application of
cross-linking mass spectrometry a standardized representation of cross-linking
experimental results is necessary. This paper describes the developing and
release of the xlmod ontology from the HUPO-PSI. xlmod contains terms for the
description of reagents used in cross-linking experiments and of cross-linker
related chemical modifications together with their main properties relevant for
planning and performing cross-linking experiments. We also describe how xlmod
is used within the new release of HUPO-PSI-s mzIdentML data standard, for
reporting the used cross-linking reagents and results in a consistent manner.
In addition xlmod contains terms for GC-MS and LC-MS derivatization reagents
for specifying them in the upcoming mzTab-M and mzTab-L formats.
|
[
{
"created": "Sat, 29 Feb 2020 18:45:43 GMT",
"version": "v1"
}
] |
2020-03-03
|
[
[
"Mayer",
"Gerhard",
""
]
] |
Mass spectrometry has experienced a rapid development since its first application for protein analysis in the 1980s. While the most common use of mass spectrometry for protein analysis is identification and quantification workflows on peptides (digested from their parent protein), there is also a rapidly growing use of mass spectrometry for structural proteomics. One example is the analysis of cross-linked proteins that can give valuable structural information, complementing the information gained by classical protein structure determination methods, useful for integrated methods of structure determination and modeling. For a broad and reproducible application of cross-linking mass spectrometry a standardized representation of cross-linking experimental results is necessary. This paper describes the developing and release of the xlmod ontology from the HUPO-PSI. xlmod contains terms for the description of reagents used in cross-linking experiments and of cross-linker related chemical modifications together with their main properties relevant for planning and performing cross-linking experiments. We also describe how xlmod is used within the new release of HUPO-PSI-s mzIdentML data standard, for reporting the used cross-linking reagents and results in a consistent manner. In addition xlmod contains terms for GC-MS and LC-MS derivatization reagents for specifying them in the upcoming mzTab-M and mzTab-L formats.
|
1304.6570
|
Thierry Platini
|
Hodjat Pendar, Thierry Platini, Rahul V. Kulkarni
|
Exact protein distributions for stochastic models of gene expression
using partitioning of Poisson processes
|
10 pages, 5 figures
| null |
10.1103/PhysRevE.87.042720
| null |
q-bio.MN physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Stochasticity in gene expression gives rise to fluctuations in protein levels
across a population of genetically identical cells. Such fluctuations can lead
to phenotypic variation in clonal populations, hence there is considerable
interest in quantifying noise in gene expression using stochastic models.
However, obtaining exact analytical results for protein distributions has been
an intractable task for all but the simplest models. Here, we invoke the
partitioning property of Poisson processes to develop a mapping that
significantly simplifies the analysis of stochastic models of gene expression.
The mapping leads to exact protein distributions using results for mRNA
distributions in models with promoter-based regulation. Using this approach, we
derive exact analytical results for steady-state and time-dependent
distributions for the basic 2-stage model of gene expression. Furthermore, we
show how the mapping leads to exact protein distributions for extensions of the
basic model that include the effects of post-transcriptional and
post-translational regulation. The approach developed in this work is widely
applicable and can contribute to a quantitative understanding of stochasticity
in gene expression and its regulation.
|
[
{
"created": "Wed, 24 Apr 2013 13:11:17 GMT",
"version": "v1"
}
] |
2015-06-15
|
[
[
"Pendar",
"Hodjat",
""
],
[
"Platini",
"Thierry",
""
],
[
"Kulkarni",
"Rahul V.",
""
]
] |
Stochasticity in gene expression gives rise to fluctuations in protein levels across a population of genetically identical cells. Such fluctuations can lead to phenotypic variation in clonal populations, hence there is considerable interest in quantifying noise in gene expression using stochastic models. However, obtaining exact analytical results for protein distributions has been an intractable task for all but the simplest models. Here, we invoke the partitioning property of Poisson processes to develop a mapping that significantly simplifies the analysis of stochastic models of gene expression. The mapping leads to exact protein distributions using results for mRNA distributions in models with promoter-based regulation. Using this approach, we derive exact analytical results for steady-state and time-dependent distributions for the basic 2-stage model of gene expression. Furthermore, we show how the mapping leads to exact protein distributions for extensions of the basic model that include the effects of post-transcriptional and post-translational regulation. The approach developed in this work is widely applicable and can contribute to a quantitative understanding of stochasticity in gene expression and its regulation.
|
1002.3837
|
Michael Deem
|
Jeong-Man Park, Enrique Munoz, Michael W. Deem
|
Quasispecies theory for finite populations
|
13 pages, 8 figures
|
Phys. Rev. E 81 (2010) 011902
|
10.1103/PhysRevE.81.011902
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present stochastic, finite-population formulations of the Crow-Kimura and
Eigen models of quasispecies theory, for fitness functions that depend in an
arbitrary way on the number of mutations from the wild type. We include back
mutations in our description. We show that the fluctuation of the population
numbers about the average values are exceedingly large in these physical models
of evolution. We further show that horizontal gene transfer reduces by orders
of magnitude the fluctuations in the population numbers and reduces the
accumulation of deleterious mutations in the finite population due to Muller's
ratchet. Indeed the population sizes needed to converge to the infinite
population limit are often larger than those found in nature for smooth fitness
functions in the absence of horizontal gene transfer. These analytical results
are derived for the steady-state by means of a field-theoretic representation.
Numerical results are presented that indicate horizontal gene transfer speeds
up the dynamics of evolution as well.
|
[
{
"created": "Fri, 19 Feb 2010 22:52:21 GMT",
"version": "v1"
}
] |
2010-02-23
|
[
[
"Park",
"Jeong-Man",
""
],
[
"Munoz",
"Enrique",
""
],
[
"Deem",
"Michael W.",
""
]
] |
We present stochastic, finite-population formulations of the Crow-Kimura and Eigen models of quasispecies theory, for fitness functions that depend in an arbitrary way on the number of mutations from the wild type. We include back mutations in our description. We show that the fluctuation of the population numbers about the average values are exceedingly large in these physical models of evolution. We further show that horizontal gene transfer reduces by orders of magnitude the fluctuations in the population numbers and reduces the accumulation of deleterious mutations in the finite population due to Muller's ratchet. Indeed the population sizes needed to converge to the infinite population limit are often larger than those found in nature for smooth fitness functions in the absence of horizontal gene transfer. These analytical results are derived for the steady-state by means of a field-theoretic representation. Numerical results are presented that indicate horizontal gene transfer speeds up the dynamics of evolution as well.
|
0902.1579
|
Yue Chan
|
Yue Chan, Grant M. Cox, Richard G. Haverkamp and James M. Hill
|
Mechanical model for a collagen fibril pair in extracellular matrix
| null | null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we model the mechanics of a collagen pair in the connective
tissue extracellular matrix that exists in abundance throughout animals,
including the human body. This connective tissue comprises repeated units of
two main structures, namely collagens as well as axial, parallel and regular
anionic glycosaminoglycan between collagens. The collagen fibril can be modeled
by Hooke's law whereas anionic glycosaminoglycan behaves more like a
rubber-band rod and as such can be better modeled by the worm-like chain model.
While both computer simulations and continuum mechanics models have been
investigated the behavior of this connective tissue typically, authors either
assume a simple form of the molecular potential energy or entirely ignore the
microscopic structure of the connective tissue. Here, we apply basic physical
methodologies and simple applied mathematical modeling techniques to describe
the collagen pair quantitatively. We find that the growth of fibrils is
intimately related to the maximum length of the anionic glycosaminoglycan and
the relative displacement of two adjacent fibrils, which in return is closely
related to the effectiveness of anionic glycosaminoglycan in transmitting
forces between fibrils. These reveal the importance of the anionic
glycosaminoglycan in maintaining the structural shape of the connective tissue
extracellular matrix and eventually the shape modulus of human tissues. We also
find that some macroscopic properties, like the maximum molecular energy and
the breaking fraction of the collagen, are also related to the microscopic
characteristics of the anionic glycosaminoglycan.
|
[
{
"created": "Tue, 10 Feb 2009 04:21:33 GMT",
"version": "v1"
}
] |
2009-02-11
|
[
[
"Chan",
"Yue",
""
],
[
"Cox",
"Grant M.",
""
],
[
"Haverkamp",
"Richard G.",
""
],
[
"Hill",
"James M.",
""
]
] |
In this paper, we model the mechanics of a collagen pair in the connective tissue extracellular matrix that exists in abundance throughout animals, including the human body. This connective tissue comprises repeated units of two main structures, namely collagens as well as axial, parallel and regular anionic glycosaminoglycan between collagens. The collagen fibril can be modeled by Hooke's law whereas anionic glycosaminoglycan behaves more like a rubber-band rod and as such can be better modeled by the worm-like chain model. While both computer simulations and continuum mechanics models have been investigated the behavior of this connective tissue typically, authors either assume a simple form of the molecular potential energy or entirely ignore the microscopic structure of the connective tissue. Here, we apply basic physical methodologies and simple applied mathematical modeling techniques to describe the collagen pair quantitatively. We find that the growth of fibrils is intimately related to the maximum length of the anionic glycosaminoglycan and the relative displacement of two adjacent fibrils, which in return is closely related to the effectiveness of anionic glycosaminoglycan in transmitting forces between fibrils. These reveal the importance of the anionic glycosaminoglycan in maintaining the structural shape of the connective tissue extracellular matrix and eventually the shape modulus of human tissues. We also find that some macroscopic properties, like the maximum molecular energy and the breaking fraction of the collagen, are also related to the microscopic characteristics of the anionic glycosaminoglycan.
|
1003.1066
|
Grzegorz A Rempala
|
Grzegorz A. Rempala, Michal Seweryn, Leszek Ignatowicz
|
Model for Diversity Analysis of Antigen Receptor Repertoires
|
5 figures
| null | null | null |
q-bio.BM q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In most of the recent immunological literature the differences across antigen
receptor populations are examined via non-parametric statistical measures of
species overlap and diversity borrowed from ecological studies. While this
approach is robust in a wide range of situations, it seems to provide little
insight into the underlying clonal size distribution and the overall mechanism
differentiating the receptor populations. As a possible alternative, the
current paper presents a parametric method which adjusts for the data
under-sampling as well as provides a unifying approach to simultaneous
comparison of multiple receptor groups by means of the modern statistical tools
of unsupervised learning. The parametric model is based on a flexible
multivariate Poisson-lognormal distribution and is seen to be a natural
generalization of the univariate Poisson-lognormal models used in ecological
studies of biodiversity patterns. The procedure for evaluating model's fit is
described along with the public domain software developed to perform the
necessary diagnostics. The model-driven analysis is seen to compare favorably
vis a vis traditional methods when applied to the data from T-cell receptors in
transgenic mice populations.
|
[
{
"created": "Thu, 4 Mar 2010 15:36:11 GMT",
"version": "v1"
}
] |
2010-03-05
|
[
[
"Rempala",
"Grzegorz A.",
""
],
[
"Seweryn",
"Michal",
""
],
[
"Ignatowicz",
"Leszek",
""
]
] |
In most of the recent immunological literature the differences across antigen receptor populations are examined via non-parametric statistical measures of species overlap and diversity borrowed from ecological studies. While this approach is robust in a wide range of situations, it seems to provide little insight into the underlying clonal size distribution and the overall mechanism differentiating the receptor populations. As a possible alternative, the current paper presents a parametric method which adjusts for the data under-sampling as well as provides a unifying approach to simultaneous comparison of multiple receptor groups by means of the modern statistical tools of unsupervised learning. The parametric model is based on a flexible multivariate Poisson-lognormal distribution and is seen to be a natural generalization of the univariate Poisson-lognormal models used in ecological studies of biodiversity patterns. The procedure for evaluating model's fit is described along with the public domain software developed to perform the necessary diagnostics. The model-driven analysis is seen to compare favorably vis a vis traditional methods when applied to the data from T-cell receptors in transgenic mice populations.
|
2304.03293
|
Kyoungmin Min
|
Seungpyo Kang, Minseon Kim, Jiwon Sun, Myeonghun Lee, and Kyoungmin
Min
|
Prediction of Protein Aggregation Propensity via Data-driven Approaches
| null | null | null | null |
q-bio.QM physics.bio-ph physics.data-an
|
http://creativecommons.org/licenses/by/4.0/
|
Protein aggregation occurs when misfolded or unfolded proteins physically
bind together, and can promote the development of various amyloid diseases.
This study aimed to construct surrogate models for predicting protein
aggregation via data-driven methods using two types of databases. First, an
aggregation propensity score database was constructed by calculating the scores
for protein structures in Protein Data Bank using Aggrescan3D 2.0. Moreover,
feature- and graph-based models for predicting protein aggregation have been
developed using this database. The graph-based regression model outperformed
the feature-based model, resulting in R2 of 0.95, although it intrinsically
required protein structures. Second, for the experimental data, a feature-based
model was built using Curated Protein Aggregation Database 2.0, to predict the
aggregated intensity curves. In summary, this study suggests the approaches
that are more effective in predicting protein aggregation, depending on the
type of descriptor and the database.
|
[
{
"created": "Thu, 6 Apr 2023 06:19:37 GMT",
"version": "v1"
}
] |
2023-04-10
|
[
[
"Kang",
"Seungpyo",
""
],
[
"Kim",
"Minseon",
""
],
[
"Sun",
"Jiwon",
""
],
[
"Lee",
"Myeonghun",
""
],
[
"Min",
"Kyoungmin",
""
]
] |
Protein aggregation occurs when misfolded or unfolded proteins physically bind together, and can promote the development of various amyloid diseases. This study aimed to construct surrogate models for predicting protein aggregation via data-driven methods using two types of databases. First, an aggregation propensity score database was constructed by calculating the scores for protein structures in Protein Data Bank using Aggrescan3D 2.0. Moreover, feature- and graph-based models for predicting protein aggregation have been developed using this database. The graph-based regression model outperformed the feature-based model, resulting in R2 of 0.95, although it intrinsically required protein structures. Second, for the experimental data, a feature-based model was built using Curated Protein Aggregation Database 2.0, to predict the aggregated intensity curves. In summary, this study suggests the approaches that are more effective in predicting protein aggregation, depending on the type of descriptor and the database.
|
1810.01414
|
Ramzan Umarov
|
Ramzan Umarov, Hiroyuki Kuwahara, Yu Li, Xin Gao, Victor Solovyev
|
PromID: human promoter prediction by deep learning
|
18 pages, 8 figures, 2 tables
| null | null | null |
q-bio.GN cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Computational identification of promoters is notoriously difficult as human
genes often have unique promoter sequences that provide regulation of
transcription and interaction with transcription initiation complex. While
there are many attempts to develop computational promoter identification
methods, we have no reliable tool to analyze long genomic sequences. In this
work we further develop our deep learning approach that was relatively
successful to discriminate short promoter and non-promoter sequences. Instead
of focusing on the classification accuracy, in this work we predict the exact
positions of the TSS inside the genomic sequences testing every possible
location. We studied human promoters to find effective regions for
discrimination and built corresponding deep learning models. These models use
adaptively constructed negative set which iteratively improves the models
discriminative ability. The developed promoter identification models
significantly outperform the previously developed promoter prediction programs
by considerably reducing the number of false positive predictions. The best
model we have built has recall 0.76, precision 0.77 and MCC 0.76, while the
next best tool FPROM achieved precision 0.48 and MCC 0.60 for the recall of
0.75. Our method is available at http://www.cbrc.kaust.edu.sa/PromID/.
|
[
{
"created": "Tue, 2 Oct 2018 17:35:46 GMT",
"version": "v1"
}
] |
2018-10-04
|
[
[
"Umarov",
"Ramzan",
""
],
[
"Kuwahara",
"Hiroyuki",
""
],
[
"Li",
"Yu",
""
],
[
"Gao",
"Xin",
""
],
[
"Solovyev",
"Victor",
""
]
] |
Computational identification of promoters is notoriously difficult as human genes often have unique promoter sequences that provide regulation of transcription and interaction with transcription initiation complex. While there are many attempts to develop computational promoter identification methods, we have no reliable tool to analyze long genomic sequences. In this work we further develop our deep learning approach that was relatively successful to discriminate short promoter and non-promoter sequences. Instead of focusing on the classification accuracy, in this work we predict the exact positions of the TSS inside the genomic sequences testing every possible location. We studied human promoters to find effective regions for discrimination and built corresponding deep learning models. These models use adaptively constructed negative set which iteratively improves the models discriminative ability. The developed promoter identification models significantly outperform the previously developed promoter prediction programs by considerably reducing the number of false positive predictions. The best model we have built has recall 0.76, precision 0.77 and MCC 0.76, while the next best tool FPROM achieved precision 0.48 and MCC 0.60 for the recall of 0.75. Our method is available at http://www.cbrc.kaust.edu.sa/PromID/.
|
0708.0548
|
Eduardo Candelario-Jalil
|
E. Candelario-Jalil, R. S. Akundi, H. S. Bhatia, K. Lieb, K. Appel, E.
Munoz, M. Hull, B. L. Fiebich
|
Ascorbic acid enhances the inhibitory effect of aspirin on neuronal
cyclooxygenase-2-mediated prostaglandin E2 production
| null |
Journal of Neuroimmunology 174(1-2): 39-51 (2006)
| null | null |
q-bio.SC q-bio.MN
| null |
In the present study, we show that ascorbic acid dose-dependently inhibited
interleukin-1beta (IL-1beta)-mediated PGE2 synthesis in the human neuronal cell
line, SK-N-SH. Furthermore, in combination with aspirin, ascorbic acid
augmented the inhibitory effect of aspirin on PGE2 synthesis. However, ascorbic
acid had no synergistic effect along with other COX inhibitors (SC-58125 and
indomethacin). The inhibition of IL-1beta-mediated PGE2 synthesis by ascorbic
acid was not due to the inhibition of the expression of COX-2 or microsomal
prostaglandin E synthase (mPGES-1). Rather, ascorbic acid dose-dependently
(0.1-100 microM) produced a significant reduction in IL-1beta-mediated
production of 8-iso-prostaglandin F2alpha (8-iso-PGF2alpha), a reliable
indicator of free radical formation, suggesting that the effects of ascorbic
acid on COX-2-mediated PGE2 biosynthesis may be the result of the maintenance
of the neuronal redox status since COX activity is known to be enhanced by
oxidative stress. Our results provide in vitro evidence that the
neuroprotective effects of ascorbic acid may depend, at least in part, on its
ability to reduce neuronal COX-2 activity and PGE2 synthesis, owing to its
antioxidant properties. Further, these experiments suggest that a combination
of aspirin with ascorbic acid constitutes a novel approach to render COX-2 more
sensitive to inhibition by aspirin, allowing an anti-inflammatory therapy with
lower doses of aspirin, thereby avoiding the side effects of the usually high
dose aspirin treatment.
|
[
{
"created": "Fri, 3 Aug 2007 16:46:53 GMT",
"version": "v1"
}
] |
2007-08-06
|
[
[
"Candelario-Jalil",
"E.",
""
],
[
"Akundi",
"R. S.",
""
],
[
"Bhatia",
"H. S.",
""
],
[
"Lieb",
"K.",
""
],
[
"Appel",
"K.",
""
],
[
"Munoz",
"E.",
""
],
[
"Hull",
"M.",
""
],
[
"Fiebich",
"B. L.",
""
]
] |
In the present study, we show that ascorbic acid dose-dependently inhibited interleukin-1beta (IL-1beta)-mediated PGE2 synthesis in the human neuronal cell line, SK-N-SH. Furthermore, in combination with aspirin, ascorbic acid augmented the inhibitory effect of aspirin on PGE2 synthesis. However, ascorbic acid had no synergistic effect along with other COX inhibitors (SC-58125 and indomethacin). The inhibition of IL-1beta-mediated PGE2 synthesis by ascorbic acid was not due to the inhibition of the expression of COX-2 or microsomal prostaglandin E synthase (mPGES-1). Rather, ascorbic acid dose-dependently (0.1-100 microM) produced a significant reduction in IL-1beta-mediated production of 8-iso-prostaglandin F2alpha (8-iso-PGF2alpha), a reliable indicator of free radical formation, suggesting that the effects of ascorbic acid on COX-2-mediated PGE2 biosynthesis may be the result of the maintenance of the neuronal redox status since COX activity is known to be enhanced by oxidative stress. Our results provide in vitro evidence that the neuroprotective effects of ascorbic acid may depend, at least in part, on its ability to reduce neuronal COX-2 activity and PGE2 synthesis, owing to its antioxidant properties. Further, these experiments suggest that a combination of aspirin with ascorbic acid constitutes a novel approach to render COX-2 more sensitive to inhibition by aspirin, allowing an anti-inflammatory therapy with lower doses of aspirin, thereby avoiding the side effects of the usually high dose aspirin treatment.
|
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