Base datasets
Collection
Basic datasets from which other combined datasets are formed.
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0707.0026
|
Maria A. Avi\~no-Diaz
|
Maria A. Avino-Diaz
|
Introducing a Probabilistic Structure on Sequential Dynamical Systems,
Simulation and Reduction of Probabilistic Sequential Networks
|
14 pages
| null | null | null |
q-bio.GN math.PR q-bio.MN
| null |
A probabilistic structure on sequential dynamical systems is introduced here,
the new model will be called Probabilistic Sequential Network, PSN. The
morphisms of Probabilistic Sequential Networks are defined using two algebraic
conditions. It is proved here that two homomorphic Probabilistic Sequential
Networks have the same equilibrium or steady state probabilities if the
morphism is either an epimorphism or a monomorphism. Additionally, the proof of
the set of PSN with its morphisms form the category PSN, having the category of
sequential dynamical systems SDS, as a full subcategory is given. Several
examples of morphisms, subsystems and simulations are given.
|
[
{
"created": "Fri, 29 Jun 2007 23:34:16 GMT",
"version": "v1"
},
{
"created": "Wed, 30 Apr 2008 13:44:03 GMT",
"version": "v2"
}
] |
2008-04-30
|
[
[
"Avino-Diaz",
"Maria A.",
""
]
] |
A probabilistic structure on sequential dynamical systems is introduced here, the new model will be called Probabilistic Sequential Network, PSN. The morphisms of Probabilistic Sequential Networks are defined using two algebraic conditions. It is proved here that two homomorphic Probabilistic Sequential Networks have the same equilibrium or steady state probabilities if the morphism is either an epimorphism or a monomorphism. Additionally, the proof of the set of PSN with its morphisms form the category PSN, having the category of sequential dynamical systems SDS, as a full subcategory is given. Several examples of morphisms, subsystems and simulations are given.
|
1810.12954
|
Li Xiao
|
Li Xiao, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, and
Yu-Ping Wang
|
Alternating Diffusion Map Based Fusion of Multimodal Brain Connectivity
Networks for IQ Prediction
| null | null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To explain individual differences in development, behavior, and cognition,
most previous studies focused on projecting resting-state functional MRI (fMRI)
based functional connectivity (FC) data into a low-dimensional space via linear
dimensionality reduction techniques, followed by executing analysis operations.
However, linear dimensionality analysis techniques may fail to capture
nonlinearity of brain neuroactivity. Moreover, besides resting-state FC, FC
based on task fMRI can be expected to provide complementary information.
Motivated by these considerations, we nonlinearly fuse resting-state and
task-based FC networks (FCNs) to seek a better representation in this paper. We
propose a framework based on alternating diffusion map (ADM), which extracts
geometry-preserving low-dimensional embeddings that successfully parameterize
the intrinsic variables driving the phenomenon of interest. Specifically, we
first separately build resting-state and task-based FCNs by symmetric positive
definite matrices using sparse inverse covariance estimation for each subject,
and then utilize the ADM to fuse them in order to extract significant
low-dimensional embeddings, which are used as fingerprints to identify
individuals. The proposed framework is validated on the Philadelphia
Neurodevelopmental Cohort data, where we conduct extensive experimental study
on resting-state and fractal $n$-back task fMRI for the classification of
intelligence quotient (IQ). The fusion of resting-state and $n$-back task fMRI
by the proposed framework achieves better classification accuracy than any
single fMRI, and the proposed framework is shown to outperform several other
data fusion methods. To our knowledge, this paper is the first to demonstrate a
successful extension of the ADM to fuse resting-state and task-based fMRI data
for accurate prediction of IQ.
|
[
{
"created": "Tue, 30 Oct 2018 18:29:50 GMT",
"version": "v1"
}
] |
2018-11-01
|
[
[
"Xiao",
"Li",
""
],
[
"Stephen",
"Julia M.",
""
],
[
"Wilson",
"Tony W.",
""
],
[
"Calhoun",
"Vince D.",
""
],
[
"Wang",
"Yu-Ping",
""
]
] |
To explain individual differences in development, behavior, and cognition, most previous studies focused on projecting resting-state functional MRI (fMRI) based functional connectivity (FC) data into a low-dimensional space via linear dimensionality reduction techniques, followed by executing analysis operations. However, linear dimensionality analysis techniques may fail to capture nonlinearity of brain neuroactivity. Moreover, besides resting-state FC, FC based on task fMRI can be expected to provide complementary information. Motivated by these considerations, we nonlinearly fuse resting-state and task-based FC networks (FCNs) to seek a better representation in this paper. We propose a framework based on alternating diffusion map (ADM), which extracts geometry-preserving low-dimensional embeddings that successfully parameterize the intrinsic variables driving the phenomenon of interest. Specifically, we first separately build resting-state and task-based FCNs by symmetric positive definite matrices using sparse inverse covariance estimation for each subject, and then utilize the ADM to fuse them in order to extract significant low-dimensional embeddings, which are used as fingerprints to identify individuals. The proposed framework is validated on the Philadelphia Neurodevelopmental Cohort data, where we conduct extensive experimental study on resting-state and fractal $n$-back task fMRI for the classification of intelligence quotient (IQ). The fusion of resting-state and $n$-back task fMRI by the proposed framework achieves better classification accuracy than any single fMRI, and the proposed framework is shown to outperform several other data fusion methods. To our knowledge, this paper is the first to demonstrate a successful extension of the ADM to fuse resting-state and task-based fMRI data for accurate prediction of IQ.
|
2111.08507
|
Akankshita Dash
|
Akankshita Dash
|
Machine Learning for Genomic Data
|
Number of pages: 53
| null | null | null |
q-bio.GN cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This report explores the application of machine learning techniques on short
timeseries gene expression data. Although standard machine learning algorithms
work well on longer time-series', they often fail to find meaningful insights
from fewer timepoints. In this report, we explore model-based clustering
techniques. We combine popular unsupervised learning techniques like K-Means,
Gaussian Mixture Models, Bayesian Networks, Hidden Markov Models with the
well-known Expectation Maximization algorithm. K-Means and Gaussian Mixture
Models are fairly standard, while Hidden Markov Model and Bayesian Networks
clustering are more novel ideas that suit time-series gene expression data.
|
[
{
"created": "Mon, 15 Nov 2021 14:34:20 GMT",
"version": "v1"
}
] |
2021-11-17
|
[
[
"Dash",
"Akankshita",
""
]
] |
This report explores the application of machine learning techniques on short timeseries gene expression data. Although standard machine learning algorithms work well on longer time-series', they often fail to find meaningful insights from fewer timepoints. In this report, we explore model-based clustering techniques. We combine popular unsupervised learning techniques like K-Means, Gaussian Mixture Models, Bayesian Networks, Hidden Markov Models with the well-known Expectation Maximization algorithm. K-Means and Gaussian Mixture Models are fairly standard, while Hidden Markov Model and Bayesian Networks clustering are more novel ideas that suit time-series gene expression data.
|
1401.8028
|
Mercedes P\'erez Mill\'an
|
Mercedes P\'erez Mill\'an and Alicia Dickenstein
|
Implicit dose-response curves
|
The final publication is available at Springer via
http://dx.doi.org/10.1007/s00285-014-0809-4
| null |
10.1007/s00285-014-0809-4
| null |
q-bio.QM math.AG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We develop tools from computational algebraic geometry for the study of
steady state features of autonomous polynomial dynamical systems via
elimination of variables. In particular, we obtain nontrivial bounds for the
steady state concentration of a given species in biochemical reaction networks
with mass-action kinetics. This species is understood as the output of the
network and we thus bound the maximal response of the system. The improved
bounds give smaller starting boxes to launch numerical methods. We apply our
results to the sequential enzymatic network studied in Markevich et al.(2004)
to find nontrivial upper bounds for the different substrate concentrations at
steady state.
Our approach does not require any simulation, analytical expression to
describe the output in terms of the input, or the absence of multistationarity.
Instead, we show how to extract information from effectively computable
implicit dose-response curves with the use of resultants and discriminants. We
moreover illustrate in the application to an enzymatic network, the relation
between the exact implicit dose-response curve we obtain symbolically and the
standard hysteresis diagram provided by a numerical solver.
The setting and tools we propose could yield many other results adapted to
any autonomous polynomial dynamical system, beyond those where it is possible
to get explicit expressions.
|
[
{
"created": "Thu, 30 Jan 2014 23:25:46 GMT",
"version": "v1"
},
{
"created": "Fri, 11 Jul 2014 10:25:00 GMT",
"version": "v2"
}
] |
2014-07-14
|
[
[
"Millán",
"Mercedes Pérez",
""
],
[
"Dickenstein",
"Alicia",
""
]
] |
We develop tools from computational algebraic geometry for the study of steady state features of autonomous polynomial dynamical systems via elimination of variables. In particular, we obtain nontrivial bounds for the steady state concentration of a given species in biochemical reaction networks with mass-action kinetics. This species is understood as the output of the network and we thus bound the maximal response of the system. The improved bounds give smaller starting boxes to launch numerical methods. We apply our results to the sequential enzymatic network studied in Markevich et al.(2004) to find nontrivial upper bounds for the different substrate concentrations at steady state. Our approach does not require any simulation, analytical expression to describe the output in terms of the input, or the absence of multistationarity. Instead, we show how to extract information from effectively computable implicit dose-response curves with the use of resultants and discriminants. We moreover illustrate in the application to an enzymatic network, the relation between the exact implicit dose-response curve we obtain symbolically and the standard hysteresis diagram provided by a numerical solver. The setting and tools we propose could yield many other results adapted to any autonomous polynomial dynamical system, beyond those where it is possible to get explicit expressions.
|
2003.13932
|
Samuel Willian Schwertner Costiche
|
Rodrigo A. Schulz, Carlos H. Coimbra-Ara\'ujo and Samuel W. S.
Costiche
|
COVID-19: A model for studying the evolution of contamination in Brazil
|
18 pages, 4 figures; corrected references and parameters
| null | null | null |
q-bio.PE physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the present article we introduce an epidemiological model for the
investigation of the spread of epidemics caused by viruses. The model is
applied specifically to COVID-19, the disease caused by the SARS-Cov-2 virus
(aka "novel coronavirus"). The SIR (Susceptible - Infectious - Recovered) model
is used as a basis for studying the evolution of the epidemic. Nevertheless, we
have modified some of the model hypotheses in order to obtain an estimate of
the contamination free of overestimated predictions. This extended model is
then applied to the case of the recent advance of the epidemic in Brazil. In
this regard, it is possible to obtain the evolution for the number of
infectious significantly close to that provided by current data. Accordingly,
we evaluate possible future scenarios for the disease spread. Regarding the
population susceptibility, we consider different social behaviors in response
to quarantine measures and precautions to avoid contagion. We conclude that the
future scenario of the epidemic depends significantly on the social behavior
adopted to date, as well as on the contagion control measures. The extent of
such measures would be likely to cause thousands, millions or tens of millions
of contaminations in the next few months.
|
[
{
"created": "Tue, 31 Mar 2020 03:12:14 GMT",
"version": "v1"
},
{
"created": "Wed, 1 Apr 2020 05:00:47 GMT",
"version": "v2"
},
{
"created": "Thu, 2 Apr 2020 12:57:16 GMT",
"version": "v3"
},
{
"created": "Wed, 8 Apr 2020 23:07:57 GMT",
"version": "v4"
}
] |
2020-04-10
|
[
[
"Schulz",
"Rodrigo A.",
""
],
[
"Coimbra-Araújo",
"Carlos H.",
""
],
[
"Costiche",
"Samuel W. S.",
""
]
] |
In the present article we introduce an epidemiological model for the investigation of the spread of epidemics caused by viruses. The model is applied specifically to COVID-19, the disease caused by the SARS-Cov-2 virus (aka "novel coronavirus"). The SIR (Susceptible - Infectious - Recovered) model is used as a basis for studying the evolution of the epidemic. Nevertheless, we have modified some of the model hypotheses in order to obtain an estimate of the contamination free of overestimated predictions. This extended model is then applied to the case of the recent advance of the epidemic in Brazil. In this regard, it is possible to obtain the evolution for the number of infectious significantly close to that provided by current data. Accordingly, we evaluate possible future scenarios for the disease spread. Regarding the population susceptibility, we consider different social behaviors in response to quarantine measures and precautions to avoid contagion. We conclude that the future scenario of the epidemic depends significantly on the social behavior adopted to date, as well as on the contagion control measures. The extent of such measures would be likely to cause thousands, millions or tens of millions of contaminations in the next few months.
|
2009.10378
|
Jean-Sebastien Guez
|
Val\'erie Lecl\'ere, Max B\'echet, Akram Adam, Jean-Sebastien Guez
(IP), Bernard Wathelet, Marc Ongena, Philippe Thonart, Fr\'ed\'erique Gancel,
Marl\'ene Chollet-Imbert, Philippe Jacques
|
Mycosubtilin Overproduction by Bacillus subtilis BBG100 Enhances the
Organism's Antagonistic and Biocontrol Activities
| null |
Applied and Environmental Microbiology, American Society for
Microbiology, 2005, 71, pp.4577 - 4584
|
10.1128/AEM.71.8.4577-4584.2005
| null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A Bacillus subtilis derivative was obtained from strain ATCC 6633 by
replacement of the native promoter of the mycosubtilin operon by a constitutive
promoter originating from the replication gene repU of the Staphylococcus
aureus plasmid pUB110. The recombinant strain, designated BBG100, produced up
to 15-fold more mycosubtilin than the wild type produced. The overproducing
phenotype was related to enhancement of the antagonistic activities against
several yeasts and pathogenic fungi. Hemolytic activities were also clearly
increased in the modified strain. Mass spectrometry analyses of enriched
mycosubtilin extracts showed similar patterns of lipopeptides for BBG100 and
the wild type. Interestingly, these analyses also revealed a new form of
mycosubtilin which was more easily detected in the BBG100 sample. When tested
for its biocontrol potential, wild-type strain ATCC 6633 was almost ineffective
for reducing a Pythium infection of tomato seedlings. However, treatment of
seeds with the BBG100 overproducing strain resulted in a marked increase in the
germination rate of seeds. This protective effect afforded by mycosubtilin
overproduction was also visualized by the significantly greater fresh weight of
emerging seedlings treated with BBG100 compared to controls or seedlings
inoculated with the wild-type strain.
|
[
{
"created": "Tue, 22 Sep 2020 08:10:13 GMT",
"version": "v1"
}
] |
2020-09-23
|
[
[
"Leclére",
"Valérie",
"",
"IP"
],
[
"Béchet",
"Max",
"",
"IP"
],
[
"Adam",
"Akram",
"",
"IP"
],
[
"Guez",
"Jean-Sebastien",
"",
"IP"
],
[
"Wathelet",
"Bernard",
""
],
[
"Ongena",
"Marc",
""
],
[
"Thonart",
"Philippe",
""
],
[
"Gancel",
"Frédérique",
""
],
[
"Chollet-Imbert",
"Marléne",
""
],
[
"Jacques",
"Philippe",
""
]
] |
A Bacillus subtilis derivative was obtained from strain ATCC 6633 by replacement of the native promoter of the mycosubtilin operon by a constitutive promoter originating from the replication gene repU of the Staphylococcus aureus plasmid pUB110. The recombinant strain, designated BBG100, produced up to 15-fold more mycosubtilin than the wild type produced. The overproducing phenotype was related to enhancement of the antagonistic activities against several yeasts and pathogenic fungi. Hemolytic activities were also clearly increased in the modified strain. Mass spectrometry analyses of enriched mycosubtilin extracts showed similar patterns of lipopeptides for BBG100 and the wild type. Interestingly, these analyses also revealed a new form of mycosubtilin which was more easily detected in the BBG100 sample. When tested for its biocontrol potential, wild-type strain ATCC 6633 was almost ineffective for reducing a Pythium infection of tomato seedlings. However, treatment of seeds with the BBG100 overproducing strain resulted in a marked increase in the germination rate of seeds. This protective effect afforded by mycosubtilin overproduction was also visualized by the significantly greater fresh weight of emerging seedlings treated with BBG100 compared to controls or seedlings inoculated with the wild-type strain.
|
1503.07552
|
Andrey Shilnikov L
|
D. Alacam and A.L. Shilnikov
|
Making a swim central pattern generator out of latent parabolic bursters
| null | null | null | null |
q-bio.NC nlin.PS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study the rhythmogenesis of oscillatory patterns emerging in network
motifs composed of inhibitory coupled tonic spiking neurons represented by the
Plant model of R15 nerve cells. Such motifs are argued to be used as building
blocks for a larger central pattern generator network controlling swim
locomotion of sea slug Melibe leonina.
|
[
{
"created": "Wed, 25 Mar 2015 20:54:56 GMT",
"version": "v1"
}
] |
2015-03-27
|
[
[
"Alacam",
"D.",
""
],
[
"Shilnikov",
"A. L.",
""
]
] |
We study the rhythmogenesis of oscillatory patterns emerging in network motifs composed of inhibitory coupled tonic spiking neurons represented by the Plant model of R15 nerve cells. Such motifs are argued to be used as building blocks for a larger central pattern generator network controlling swim locomotion of sea slug Melibe leonina.
|
0706.3195
|
George Bass Ph.D.
|
George E. Bass
|
Genetic Transferability of Anomalous Irradiation Alterations of
Antibiotic Activity
|
17 pages, 3 figures
| null | null | null |
q-bio.BM
| null |
It previously has been discovered that visible light irradiation of
crystalline substrates can lead to enhancement of subsequent enzymatic reaction
rates as sharply peaked oscillatory functions of irradiation time. The
particular activating irradiation times can vary with source of a given enzyme
and thus, presumably, its molecular structure. The experiments reported here
demonstrate that the potential for this anomalous enzyme reaction rate
enhancement can be transferred from one bacterial species to another coincident
with transfer of the genetic determinant for the relevant enzyme. In
particular, the effect of crystal-irradiated chloramphenicol on growth of
bacterial strains in which a transferable R-factor DNA plasmid coding for
chloramphenicol resistance was or was not present (S. panama R+, E. coli R+,
and E. coli R-) was determined. Chloramphenicol samples irradiated 10, 35 and
60 sec produced increased growth rates (diminished inhibition) for the
resistant S. panama and E. coli strains, while having no such effect on growth
rate of the sensitive E. coli strain. Consistent with past findings,
chloramphenicol samples irradiated 5, 30 and 55 sec produced decreased growth
rates (increased inhibition) for all three strains.
|
[
{
"created": "Thu, 21 Jun 2007 17:24:15 GMT",
"version": "v1"
}
] |
2007-06-22
|
[
[
"Bass",
"George E.",
""
]
] |
It previously has been discovered that visible light irradiation of crystalline substrates can lead to enhancement of subsequent enzymatic reaction rates as sharply peaked oscillatory functions of irradiation time. The particular activating irradiation times can vary with source of a given enzyme and thus, presumably, its molecular structure. The experiments reported here demonstrate that the potential for this anomalous enzyme reaction rate enhancement can be transferred from one bacterial species to another coincident with transfer of the genetic determinant for the relevant enzyme. In particular, the effect of crystal-irradiated chloramphenicol on growth of bacterial strains in which a transferable R-factor DNA plasmid coding for chloramphenicol resistance was or was not present (S. panama R+, E. coli R+, and E. coli R-) was determined. Chloramphenicol samples irradiated 10, 35 and 60 sec produced increased growth rates (diminished inhibition) for the resistant S. panama and E. coli strains, while having no such effect on growth rate of the sensitive E. coli strain. Consistent with past findings, chloramphenicol samples irradiated 5, 30 and 55 sec produced decreased growth rates (increased inhibition) for all three strains.
|
2106.10041
|
Michael Inouye
|
Ewan Birney, Michael Inouye, Jennifer Raff, Adam Rutherford, Aylwyn
Scally
|
The language of race, ethnicity, and ancestry in human genetic research
| null | null | null | null |
q-bio.PE
|
http://creativecommons.org/licenses/by/4.0/
|
The language commonly used in human genetics can inadvertently pose problems
for multiple reasons. Terms like "ancestry", "ethnicity", and other ways of
grouping people can have complex, often poorly understood, or multiple meanings
within the various fields of genetics, between different domains of biological
sciences and medicine, and between scientists and the general public.
Furthermore, some categories in frequently used datasets carry scientifically
misleading, outmoded or even racist perspectives derived from the history of
science. Here, we discuss examples of problematic lexicon in genetics, and how
commonly used statistical practices to control for the non-genetic environment
may exacerbate difficulties in our terminology, and therefore understanding.
Our intention is to stimulate a much-needed discussion about the language of
genetics, to begin a process to clarify existing terminology, and in some cases
adopt a new lexicon that both serves scientific insight, and cuts us loose from
various aspects of a pernicious past.
|
[
{
"created": "Fri, 18 Jun 2021 10:24:15 GMT",
"version": "v1"
}
] |
2021-06-21
|
[
[
"Birney",
"Ewan",
""
],
[
"Inouye",
"Michael",
""
],
[
"Raff",
"Jennifer",
""
],
[
"Rutherford",
"Adam",
""
],
[
"Scally",
"Aylwyn",
""
]
] |
The language commonly used in human genetics can inadvertently pose problems for multiple reasons. Terms like "ancestry", "ethnicity", and other ways of grouping people can have complex, often poorly understood, or multiple meanings within the various fields of genetics, between different domains of biological sciences and medicine, and between scientists and the general public. Furthermore, some categories in frequently used datasets carry scientifically misleading, outmoded or even racist perspectives derived from the history of science. Here, we discuss examples of problematic lexicon in genetics, and how commonly used statistical practices to control for the non-genetic environment may exacerbate difficulties in our terminology, and therefore understanding. Our intention is to stimulate a much-needed discussion about the language of genetics, to begin a process to clarify existing terminology, and in some cases adopt a new lexicon that both serves scientific insight, and cuts us loose from various aspects of a pernicious past.
|
1812.05780
|
Silke Bergeler
|
Matthias Kober, Silke Bergeler, Erwin Frey
|
Can a flux-based mechanism explain positioning of protein clusters in a
three-dimensional cell geometry?
|
9 pages, 4 figures, 10 pages of supplemental information (including 4
figures and 1 table)
| null |
10.1016/j.bpj.2019.06.031
| null |
q-bio.SC physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The plane of bacterial cell division must be precisely positioned. In the
bacterium Myxococcus xanthus, the proteins PomX and PomY form a large cluster,
which is tethered to the nucleoid by the ATPase PomZ and moves in a stochastic,
but biased manner towards midcell, where it initiates cell division.
Previously, a positioning mechanism based on the fluxes of PomZ on the nucleoid
was proposed. However, the cluster dynamics was analyzed in a reduced,
one-dimensional geometry. Here we introduce a mathematical model that accounts
for the three-dimensional shape of the nucleoid, such that nucleoid-bound PomZ
dimers can diffuse past the cluster without interacting with it. Using
stochastic simulations, we find that the cluster still moves to and localizes
at midcell. Redistribution of PomZ by diffusion in the cytosol is essential for
this cluster dynamics. Our mechanism also positions two clusters equidistantly
on the nucleoid. We conclude that a flux-based mechanism allows for cluster
positioning in a biologically realistic three-dimensional cell geometry.
|
[
{
"created": "Fri, 14 Dec 2018 05:01:04 GMT",
"version": "v1"
}
] |
2019-09-04
|
[
[
"Kober",
"Matthias",
""
],
[
"Bergeler",
"Silke",
""
],
[
"Frey",
"Erwin",
""
]
] |
The plane of bacterial cell division must be precisely positioned. In the bacterium Myxococcus xanthus, the proteins PomX and PomY form a large cluster, which is tethered to the nucleoid by the ATPase PomZ and moves in a stochastic, but biased manner towards midcell, where it initiates cell division. Previously, a positioning mechanism based on the fluxes of PomZ on the nucleoid was proposed. However, the cluster dynamics was analyzed in a reduced, one-dimensional geometry. Here we introduce a mathematical model that accounts for the three-dimensional shape of the nucleoid, such that nucleoid-bound PomZ dimers can diffuse past the cluster without interacting with it. Using stochastic simulations, we find that the cluster still moves to and localizes at midcell. Redistribution of PomZ by diffusion in the cytosol is essential for this cluster dynamics. Our mechanism also positions two clusters equidistantly on the nucleoid. We conclude that a flux-based mechanism allows for cluster positioning in a biologically realistic three-dimensional cell geometry.
|
2206.07542
|
Abdulah Fawaz
|
Abdulah Fawaz, Logan Z. Williams, A. David Edwards, Emma Robinson
|
A Deep Generative Model of Neonatal Cortical Surface Development
| null | null | null | null |
q-bio.NC cs.CV cs.LG eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
The neonatal cortical surface is known to be affected by preterm birth, and
the subsequent changes to cortical organisation have been associated with
poorer neurodevelopmental outcomes. Deep Generative models have the potential
to lead to clinically interpretable models of disease, but developing these on
the cortical surface is challenging since established techniques for learning
convolutional filters are inappropriate on non-flat topologies. To close this
gap, we implement a surface-based CycleGAN using mixture model CNNs (MoNet) to
translate sphericalised neonatal cortical surface features (curvature and
T1w/T2w cortical myelin) between different stages of cortical maturity. Results
show our method is able to reliably predict changes in individual patterns of
cortical organisation at later stages of gestation, validated by comparison to
longitudinal data; and translate appearance between preterm and term gestation
(> 37 weeks gestation), validated through comparison with a trained
term/preterm classifier. Simulated differences in cortical maturation are
consistent with observations in the literature.
|
[
{
"created": "Wed, 15 Jun 2022 13:59:43 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Jun 2022 12:16:33 GMT",
"version": "v2"
}
] |
2022-06-23
|
[
[
"Fawaz",
"Abdulah",
""
],
[
"Williams",
"Logan Z.",
""
],
[
"Edwards",
"A. David",
""
],
[
"Robinson",
"Emma",
""
]
] |
The neonatal cortical surface is known to be affected by preterm birth, and the subsequent changes to cortical organisation have been associated with poorer neurodevelopmental outcomes. Deep Generative models have the potential to lead to clinically interpretable models of disease, but developing these on the cortical surface is challenging since established techniques for learning convolutional filters are inappropriate on non-flat topologies. To close this gap, we implement a surface-based CycleGAN using mixture model CNNs (MoNet) to translate sphericalised neonatal cortical surface features (curvature and T1w/T2w cortical myelin) between different stages of cortical maturity. Results show our method is able to reliably predict changes in individual patterns of cortical organisation at later stages of gestation, validated by comparison to longitudinal data; and translate appearance between preterm and term gestation (> 37 weeks gestation), validated through comparison with a trained term/preterm classifier. Simulated differences in cortical maturation are consistent with observations in the literature.
|
1912.09929
|
Benjamin M. Friedrich
|
Jens Karschau, Andre Scholich, Jonathan Wise, Hernan
Morales-Navarette, Yannis Kalaidzidis, Marino Zerial, Benjamin M Friedrich
|
Resilience of three-dimensional sinusoidal networks in liver tissue
|
20 pages, 7 figures
| null |
10.1371/journal.pcbi.1007965
| null |
q-bio.TO physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Can three-dimensional, microvasculature networks still ensure blood supply if
individual links fail? We address this question in the sinusoidal network, a
plexus-like microvasculature network, which transports nutrient-rich blood to
every hepatocyte in liver tissue, by building on recent advances in
high-resolution imaging and digital reconstruction of adult mice liver tissue.
We find that the topology of the three-dimensional sinusoidal network reflects
its two design requirements of a space-filling network that connects all
hepatocytes, while using shortest transport routes: sinusoidal networks are
sub-graphs of the Delaunay graph of their set of branching points, and also
contain the corresponding minimum spanning tree, both to good approximation. To
overcome the spatial limitations of experimental samples and generate
arbitrarily-sized networks, we developed a network generation algorithm that
reproduces the statistical features of 0.3-mm-sized samples of sinusoidal
networks, using multi-objective optimization for node degree and edge length
distribution. Nematic order in these simulated networks implies anisotropic
transport properties, characterized by an empirical linear relation between a
nematic order parameter and the anisotropy of the permeability tensor. Under
the assumption that all sinusoid tubes have a constant and equal flow
resistance, we predict that the distribution of currents in the network is very
inhomogeneous, with a small number of edges carrying a substantial part of the
flow. We quantify network resilience in terms of a permeability-at-risk, i.e.\
permeability as function of the fraction of removed edges. We find that
sinusoidal networks are resilient to random removal of edges, but vulnerable to
the removal of high-current edges. Our findings suggest the existence of a
mechanism counteracting flow inhomogeneity to balance metabolic load on the
liver.
|
[
{
"created": "Fri, 20 Dec 2019 16:41:54 GMT",
"version": "v1"
}
] |
2020-09-09
|
[
[
"Karschau",
"Jens",
""
],
[
"Scholich",
"Andre",
""
],
[
"Wise",
"Jonathan",
""
],
[
"Morales-Navarette",
"Hernan",
""
],
[
"Kalaidzidis",
"Yannis",
""
],
[
"Zerial",
"Marino",
""
],
[
"Friedrich",
"Benjamin M",
""
]
] |
Can three-dimensional, microvasculature networks still ensure blood supply if individual links fail? We address this question in the sinusoidal network, a plexus-like microvasculature network, which transports nutrient-rich blood to every hepatocyte in liver tissue, by building on recent advances in high-resolution imaging and digital reconstruction of adult mice liver tissue. We find that the topology of the three-dimensional sinusoidal network reflects its two design requirements of a space-filling network that connects all hepatocytes, while using shortest transport routes: sinusoidal networks are sub-graphs of the Delaunay graph of their set of branching points, and also contain the corresponding minimum spanning tree, both to good approximation. To overcome the spatial limitations of experimental samples and generate arbitrarily-sized networks, we developed a network generation algorithm that reproduces the statistical features of 0.3-mm-sized samples of sinusoidal networks, using multi-objective optimization for node degree and edge length distribution. Nematic order in these simulated networks implies anisotropic transport properties, characterized by an empirical linear relation between a nematic order parameter and the anisotropy of the permeability tensor. Under the assumption that all sinusoid tubes have a constant and equal flow resistance, we predict that the distribution of currents in the network is very inhomogeneous, with a small number of edges carrying a substantial part of the flow. We quantify network resilience in terms of a permeability-at-risk, i.e.\ permeability as function of the fraction of removed edges. We find that sinusoidal networks are resilient to random removal of edges, but vulnerable to the removal of high-current edges. Our findings suggest the existence of a mechanism counteracting flow inhomogeneity to balance metabolic load on the liver.
|
0809.1127
|
Naoki Masuda Dr.
|
Yuko K. Takahashi, Hiroshi Kori, Naoki Masuda
|
Self-organization of feedforward structure and entrainment in excitatory
neural networks with spike-timing-dependent plasticity
|
11 figures, 1 table
|
Physical Review E, 79, 051904 (2009)
|
10.1103/PhysRevE.79.051904
| null |
q-bio.NC cond-mat.dis-nn
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Spike-timing dependent plasticity (STDP) is an organizing principle of
biological neural networks. While synchronous firing of neurons is considered
to be an important functional block in the brain, how STDP shapes neural
networks possibly toward synchrony is not entirely clear. We examine relations
between STDP and synchronous firing in spontaneously firing neural populations.
Using coupled heterogeneous phase oscillators placed on initial networks, we
show numerically that STDP prunes some synapses and promotes formation of a
feedforward network. Eventually a pacemaker, which is the neuron with the
fastest inherent frequency in our numerical simulations, emerges at the root of
the feedforward network. In each oscillatory cycle, a packet of neural activity
is propagated from the pacemaker to downstream neurons along layers of the
feedforward network. This event occurs above a clear-cut threshold value of the
initial synaptic weight. Below the threshold, neurons are self-organized into
separate clusters each of which is a feedforward network.
|
[
{
"created": "Sat, 6 Sep 2008 03:44:09 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Sep 2008 11:41:50 GMT",
"version": "v2"
},
{
"created": "Wed, 20 May 2009 11:23:05 GMT",
"version": "v3"
}
] |
2009-05-20
|
[
[
"Takahashi",
"Yuko K.",
""
],
[
"Kori",
"Hiroshi",
""
],
[
"Masuda",
"Naoki",
""
]
] |
Spike-timing dependent plasticity (STDP) is an organizing principle of biological neural networks. While synchronous firing of neurons is considered to be an important functional block in the brain, how STDP shapes neural networks possibly toward synchrony is not entirely clear. We examine relations between STDP and synchronous firing in spontaneously firing neural populations. Using coupled heterogeneous phase oscillators placed on initial networks, we show numerically that STDP prunes some synapses and promotes formation of a feedforward network. Eventually a pacemaker, which is the neuron with the fastest inherent frequency in our numerical simulations, emerges at the root of the feedforward network. In each oscillatory cycle, a packet of neural activity is propagated from the pacemaker to downstream neurons along layers of the feedforward network. This event occurs above a clear-cut threshold value of the initial synaptic weight. Below the threshold, neurons are self-organized into separate clusters each of which is a feedforward network.
|
2305.14369
|
Eloy Philip Theo Geenjaar
|
Eloy Geenjaar, Donghyun Kim, Riyasat Ohib, Marlena Duda, Amrit
Kashyap, Sergey Plis, Vince Calhoun
|
Learning low-dimensional dynamics from whole-brain data improves task
capture
|
9 pages, 4 figures
| null | null | null |
q-bio.NC cs.CE cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The neural dynamics underlying brain activity are critical to understanding
cognitive processes and mental disorders. However, current voxel-based
whole-brain dimensionality reduction techniques fall short of capturing these
dynamics, producing latent timeseries that inadequately relate to behavioral
tasks. To address this issue, we introduce a novel approach to learning
low-dimensional approximations of neural dynamics by using a sequential
variational autoencoder (SVAE) that represents the latent dynamical system via
a neural ordinary differential equation (NODE). Importantly, our method finds
smooth dynamics that can predict cognitive processes with accuracy higher than
classical methods. Our method also shows improved spatial localization to
task-relevant brain regions and identifies well-known structures such as the
motor homunculus from fMRI motor task recordings. We also find that non-linear
projections to the latent space enhance performance for specific tasks,
offering a promising direction for future research. We evaluate our approach on
various task-fMRI datasets, including motor, working memory, and relational
processing tasks, and demonstrate that it outperforms widely used
dimensionality reduction techniques in how well the latent timeseries relates
to behavioral sub-tasks, such as left-hand or right-hand tapping. Additionally,
we replace the NODE with a recurrent neural network (RNN) and compare the two
approaches to understand the importance of explicitly learning a dynamical
system. Lastly, we analyze the robustness of the learned dynamical systems
themselves and find that their fixed points are robust across seeds,
highlighting our method's potential for the analysis of cognitive processes as
dynamical systems.
|
[
{
"created": "Thu, 18 May 2023 18:43:13 GMT",
"version": "v1"
}
] |
2023-05-25
|
[
[
"Geenjaar",
"Eloy",
""
],
[
"Kim",
"Donghyun",
""
],
[
"Ohib",
"Riyasat",
""
],
[
"Duda",
"Marlena",
""
],
[
"Kashyap",
"Amrit",
""
],
[
"Plis",
"Sergey",
""
],
[
"Calhoun",
"Vince",
""
]
] |
The neural dynamics underlying brain activity are critical to understanding cognitive processes and mental disorders. However, current voxel-based whole-brain dimensionality reduction techniques fall short of capturing these dynamics, producing latent timeseries that inadequately relate to behavioral tasks. To address this issue, we introduce a novel approach to learning low-dimensional approximations of neural dynamics by using a sequential variational autoencoder (SVAE) that represents the latent dynamical system via a neural ordinary differential equation (NODE). Importantly, our method finds smooth dynamics that can predict cognitive processes with accuracy higher than classical methods. Our method also shows improved spatial localization to task-relevant brain regions and identifies well-known structures such as the motor homunculus from fMRI motor task recordings. We also find that non-linear projections to the latent space enhance performance for specific tasks, offering a promising direction for future research. We evaluate our approach on various task-fMRI datasets, including motor, working memory, and relational processing tasks, and demonstrate that it outperforms widely used dimensionality reduction techniques in how well the latent timeseries relates to behavioral sub-tasks, such as left-hand or right-hand tapping. Additionally, we replace the NODE with a recurrent neural network (RNN) and compare the two approaches to understand the importance of explicitly learning a dynamical system. Lastly, we analyze the robustness of the learned dynamical systems themselves and find that their fixed points are robust across seeds, highlighting our method's potential for the analysis of cognitive processes as dynamical systems.
|
q-bio/0607028
|
Garegin Papoian
|
Yueheng Lan, Garegin A. Papoian
|
The interplay between discrete noise and nonlinear chemical kinetics in
a signal amplification cascade
|
16 pages, 9 figures
| null |
10.1063/1.2358342
| null |
q-bio.MN q-bio.QM
| null |
We used various analytical and numerical techniques to elucidate signal
propagation in a small enzymatic cascade which is subjected to external and
internal noise. The nonlinear character of catalytic reactions, which underlie
protein signal transduction cascades, renders stochastic signaling dynamics in
cytosol biochemical networks distinct from the usual description of stochastic
dynamics in gene regulatory networks. For a simple 2-step enzymatic cascade
which underlies many important protein signaling pathways, we demonstrated that
the commonly used techniques such as the linear noise approximation and the
Langevin equation become inadequate when the number of proteins becomes too
low. Consequently, we developed a new analytical approximation, based on mixing
the generating function and distribution function approaches, to the solution
of the master equation that describes nonlinear chemical signaling kinetics for
this important class of biochemical reactions. Our techniques work in a much
wider range of protein number fluctuations than the methods used previously. We
found that under certain conditions the burst-phase noise may be injected into
the downstream signaling network dynamics, resulting possibly in unusually
large macroscopic fluctuations. In addition to computing first and second
moments, which is the goal of commonly used analytical techniques, our new
approach provides the full time-dependent probability distributions of the
colored non-Gaussian processes in a nonlinear signal transduction cascade.
|
[
{
"created": "Wed, 19 Jul 2006 06:41:28 GMT",
"version": "v1"
}
] |
2009-11-13
|
[
[
"Lan",
"Yueheng",
""
],
[
"Papoian",
"Garegin A.",
""
]
] |
We used various analytical and numerical techniques to elucidate signal propagation in a small enzymatic cascade which is subjected to external and internal noise. The nonlinear character of catalytic reactions, which underlie protein signal transduction cascades, renders stochastic signaling dynamics in cytosol biochemical networks distinct from the usual description of stochastic dynamics in gene regulatory networks. For a simple 2-step enzymatic cascade which underlies many important protein signaling pathways, we demonstrated that the commonly used techniques such as the linear noise approximation and the Langevin equation become inadequate when the number of proteins becomes too low. Consequently, we developed a new analytical approximation, based on mixing the generating function and distribution function approaches, to the solution of the master equation that describes nonlinear chemical signaling kinetics for this important class of biochemical reactions. Our techniques work in a much wider range of protein number fluctuations than the methods used previously. We found that under certain conditions the burst-phase noise may be injected into the downstream signaling network dynamics, resulting possibly in unusually large macroscopic fluctuations. In addition to computing first and second moments, which is the goal of commonly used analytical techniques, our new approach provides the full time-dependent probability distributions of the colored non-Gaussian processes in a nonlinear signal transduction cascade.
|
1611.00285
|
Till Frank
|
J.M. Gordon, S. Kim, T.D. Frank
|
Linear non-equilibrium thermodynamics of human voluntary behavior: a
canonical-dissipative Fokker-Planck equation approach involving potentials
beyond the harmonic oscillator case
|
6 pages, 0 figure
|
Condens. Matter Phys., vol. 19, No. 3, 34001 (2016)
|
10.5488/CMP.19.34001
| null |
q-bio.NC cond-mat.soft cond-mat.stat-mech
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A novel experimental paradigm and a novel modelling approach are presented to
investigate oscillatory human motor performance by means of a key concept from
condensed matter physics, namely, thermodynamic state variables. To this end,
in the novel experimental paradigm participants performed pendulum swinging
movements at self-selected oscillation frequencies in contrast to earlier
studies in which pacing signals were used. Moreover, in the novel modelling
approach, a canonical-dissipative limit cycle oscillator model was used with a
conservative part that accounts for nonharmonic oscillator components in
contrast to earlier studies in which only harmonic components were considered.
Consistent with the Landau theory of magnetic phase transitions, we found that
the oscillator model free energy decayed when participants performed
oscillations further and further away from the Hopf bifurcation point of the
canonical-dissipative limit cycle oscillator.
|
[
{
"created": "Thu, 15 Sep 2016 16:10:43 GMT",
"version": "v1"
}
] |
2017-02-13
|
[
[
"Gordon",
"J. M.",
""
],
[
"Kim",
"S.",
""
],
[
"Frank",
"T. D.",
""
]
] |
A novel experimental paradigm and a novel modelling approach are presented to investigate oscillatory human motor performance by means of a key concept from condensed matter physics, namely, thermodynamic state variables. To this end, in the novel experimental paradigm participants performed pendulum swinging movements at self-selected oscillation frequencies in contrast to earlier studies in which pacing signals were used. Moreover, in the novel modelling approach, a canonical-dissipative limit cycle oscillator model was used with a conservative part that accounts for nonharmonic oscillator components in contrast to earlier studies in which only harmonic components were considered. Consistent with the Landau theory of magnetic phase transitions, we found that the oscillator model free energy decayed when participants performed oscillations further and further away from the Hopf bifurcation point of the canonical-dissipative limit cycle oscillator.
|
1909.06442
|
Devin Taylor
|
Devin Taylor, Simeon Spasov and Pietro Li\`o
|
Co-Attentive Cross-Modal Deep Learning for Medical Evidence Synthesis
and Decision Making
|
7 pages, 2 figures, Machine Learning for Health (ML4H) at NeurIPS
2019 - Extended Abstract, clarified graph and math notation, typos corrected
| null | null | null |
q-bio.QM cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Modern medicine requires generalised approaches to the synthesis and
integration of multimodal data, often at different biological scales, that can
be applied to a variety of evidence structures, such as complex disease
analyses and epidemiological models. However, current methods are either slow
and expensive, or ineffective due to the inability to model the complex
relationships between data modes which differ in scale and format. We address
these issues by proposing a cross-modal deep learning architecture and
co-attention mechanism to accurately model the relationships between the
different data modes, while further reducing patient diagnosis time.
Differentiating Parkinson's Disease (PD) patients from healthy patients forms
the basis of the evaluation. The model outperforms the previous
state-of-the-art unimodal analysis by 2.35%, while also being 53% more
parameter efficient than the industry standard cross-modal model. Furthermore,
the evaluation of the attention coefficients allows for qualitative insights to
be obtained. Through the coupling with bioinformatics, a novel link between the
interferon-gamma-mediated pathway, DNA methylation and PD was identified. We
believe that our approach is general and could optimise the process of medical
evidence synthesis and decision making in an actionable way.
|
[
{
"created": "Fri, 13 Sep 2019 20:49:55 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Nov 2019 06:58:53 GMT",
"version": "v2"
}
] |
2019-11-11
|
[
[
"Taylor",
"Devin",
""
],
[
"Spasov",
"Simeon",
""
],
[
"Liò",
"Pietro",
""
]
] |
Modern medicine requires generalised approaches to the synthesis and integration of multimodal data, often at different biological scales, that can be applied to a variety of evidence structures, such as complex disease analyses and epidemiological models. However, current methods are either slow and expensive, or ineffective due to the inability to model the complex relationships between data modes which differ in scale and format. We address these issues by proposing a cross-modal deep learning architecture and co-attention mechanism to accurately model the relationships between the different data modes, while further reducing patient diagnosis time. Differentiating Parkinson's Disease (PD) patients from healthy patients forms the basis of the evaluation. The model outperforms the previous state-of-the-art unimodal analysis by 2.35%, while also being 53% more parameter efficient than the industry standard cross-modal model. Furthermore, the evaluation of the attention coefficients allows for qualitative insights to be obtained. Through the coupling with bioinformatics, a novel link between the interferon-gamma-mediated pathway, DNA methylation and PD was identified. We believe that our approach is general and could optimise the process of medical evidence synthesis and decision making in an actionable way.
|
1101.3570
|
Lev Soyfer
|
Lev Isaakovich Soyfer
|
Processes of the correlation of space (lengths) and time (duration)in
human perception
|
The text of this work, which totals of 183 pages, consists of seven
chapters, references, and four appendices. Major part of the work includes 30
tables and 3 figures. Appendices consist of 37 tables and 4 figures
| null | null | null |
q-bio.NC
|
http://creativecommons.org/licenses/by/3.0/
|
To study the processes and mechanisms of the correlation between space and
time, particularly between lengths and durations in human perception, a special
method (device and procedure) to conduct this experiment was designed and
called LDR (Length Duration Relation) In the present study a pilot and three
series of the primary experiment were conducted under conditions of different
levels of uncertainty. In all types of experiments, signals of a certain
duration and modality were presented twice in random order to the subjects.
Subjects had to respond to time signals of different durations by choosing a
corresponding space interval. The data which were obtained during the 1st and
the 2nd time signal presentations were examined separately. The comparative
data analysis of the experiment showed significant differences between the 1st
and 2nd presentation of signals in the quantity of correct responses, the
responses distribution along the scale of stimuli, the phenomena which occurred
during the experiment. The higher level of uncertainty condition under which a
certain type of the experiment was conducted, the more clearly this difference
was manifested.
Based on results of the experiments comparative data analysis, one can
suppose that the perceptive mechanism, named by us as an innate mechanism of
proportionality, performed the correlation of these intervals into two stages:
adaptation and activation
|
[
{
"created": "Mon, 17 Jan 2011 20:38:38 GMT",
"version": "v1"
}
] |
2011-01-20
|
[
[
"Soyfer",
"Lev Isaakovich",
""
]
] |
To study the processes and mechanisms of the correlation between space and time, particularly between lengths and durations in human perception, a special method (device and procedure) to conduct this experiment was designed and called LDR (Length Duration Relation) In the present study a pilot and three series of the primary experiment were conducted under conditions of different levels of uncertainty. In all types of experiments, signals of a certain duration and modality were presented twice in random order to the subjects. Subjects had to respond to time signals of different durations by choosing a corresponding space interval. The data which were obtained during the 1st and the 2nd time signal presentations were examined separately. The comparative data analysis of the experiment showed significant differences between the 1st and 2nd presentation of signals in the quantity of correct responses, the responses distribution along the scale of stimuli, the phenomena which occurred during the experiment. The higher level of uncertainty condition under which a certain type of the experiment was conducted, the more clearly this difference was manifested. Based on results of the experiments comparative data analysis, one can suppose that the perceptive mechanism, named by us as an innate mechanism of proportionality, performed the correlation of these intervals into two stages: adaptation and activation
|
1304.4274
|
Natalia Denesyuk
|
Natalia A. Denesyuk and D. Thirumalai
|
Entropic stabilization of the folded states of RNA due to macromolecular
crowding
| null | null |
10.1007/s12551-013-0119-x
| null |
q-bio.BM cond-mat.soft physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We review the effects of macromolecular crowding on the folding of RNA by
considering the simplest scenario when excluded volume interactions between
crowding particles and RNA dominate. Using human telomerase enzyme as an
example, we discuss how crowding can alter the equilibrium between pseudoknot
and hairpin states of the same RNA molecule - a key aspect of crowder-RNA
interactions. We summarize data showing that the crowding effect is significant
only if the size of the spherical crowding particle is smaller than the radius
of gyration of the RNA in the absence of crowding particles. The implication
for function of the wild type and mutants of human telomerase is outlined by
using a relationship between enzyme activity and its conformational
equilibrium. In addition, we discuss the interplay between macromolecular
crowding and ionic strength of the RNA buffer. Finally, we briefly review
recent experiments which illustrate the connection between excluded volume due
to macromolecular crowding and the thermodynamics of RNA folding.
|
[
{
"created": "Mon, 15 Apr 2013 21:37:49 GMT",
"version": "v1"
}
] |
2013-04-17
|
[
[
"Denesyuk",
"Natalia A.",
""
],
[
"Thirumalai",
"D.",
""
]
] |
We review the effects of macromolecular crowding on the folding of RNA by considering the simplest scenario when excluded volume interactions between crowding particles and RNA dominate. Using human telomerase enzyme as an example, we discuss how crowding can alter the equilibrium between pseudoknot and hairpin states of the same RNA molecule - a key aspect of crowder-RNA interactions. We summarize data showing that the crowding effect is significant only if the size of the spherical crowding particle is smaller than the radius of gyration of the RNA in the absence of crowding particles. The implication for function of the wild type and mutants of human telomerase is outlined by using a relationship between enzyme activity and its conformational equilibrium. In addition, we discuss the interplay between macromolecular crowding and ionic strength of the RNA buffer. Finally, we briefly review recent experiments which illustrate the connection between excluded volume due to macromolecular crowding and the thermodynamics of RNA folding.
|
2402.03675
|
Chenqing Hua
|
Chenqing Hua, Connor Coley, Guy Wolf, Doina Precup, Shuangjia Zheng
|
Effective Protein-Protein Interaction Exploration with PPIretrieval
| null | null | null | null |
q-bio.BM cs.AI cs.CE cs.LG
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Protein-protein interactions (PPIs) are crucial in regulating numerous
cellular functions, including signal transduction, transportation, and immune
defense. As the accuracy of multi-chain protein complex structure prediction
improves, the challenge has shifted towards effectively navigating the vast
complex universe to identify potential PPIs. Herein, we propose PPIretrieval,
the first deep learning-based model for protein-protein interaction
exploration, which leverages existing PPI data to effectively search for
potential PPIs in an embedding space, capturing rich geometric and chemical
information of protein surfaces. When provided with an unseen query protein
with its associated binding site, PPIretrieval effectively identifies a
potential binding partner along with its corresponding binding site in an
embedding space, facilitating the formation of protein-protein complexes.
|
[
{
"created": "Tue, 6 Feb 2024 03:57:06 GMT",
"version": "v1"
}
] |
2024-02-07
|
[
[
"Hua",
"Chenqing",
""
],
[
"Coley",
"Connor",
""
],
[
"Wolf",
"Guy",
""
],
[
"Precup",
"Doina",
""
],
[
"Zheng",
"Shuangjia",
""
]
] |
Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, and immune defense. As the accuracy of multi-chain protein complex structure prediction improves, the challenge has shifted towards effectively navigating the vast complex universe to identify potential PPIs. Herein, we propose PPIretrieval, the first deep learning-based model for protein-protein interaction exploration, which leverages existing PPI data to effectively search for potential PPIs in an embedding space, capturing rich geometric and chemical information of protein surfaces. When provided with an unseen query protein with its associated binding site, PPIretrieval effectively identifies a potential binding partner along with its corresponding binding site in an embedding space, facilitating the formation of protein-protein complexes.
|
1407.4854
|
Kazuhiro Takemoto
|
Kazuhiro Takemoto
|
Metabolic networks are almost nonfractal: A comprehensive evaluation
|
7 pages, 5 figures
|
Phys. Rev. E 90, 022802 (2014)
|
10.1103/PhysRevE.90.022802
| null |
q-bio.MN physics.data-an physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Network self-similarity or fractality are widely accepted as an important
topological property of metabolic networks; however, recent studies cast doubt
on the reality of self-similarity in the networks. Therefore, we perform a
comprehensive evaluation of metabolic network fractality using a box-covering
method with an earlier version and the latest version of metabolic networks,
and demonstrate that the latest metabolic networks are almost self-dissimilar,
while the earlier ones are fractal, as reported in a number of previous
studies. This result may be because the networks were randomized because of an
increase in network density due to database updates, suggesting that the
previously observed network fractality was due to a lack of available data on
metabolic reactions. This finding may not entirely discount the importance of
self-similarity of metabolic networks. Rather, it highlights the need for a
more suitable definition of network fractality and a more careful examination
of self-similarity of metabolic networks.
|
[
{
"created": "Thu, 17 Jul 2014 22:39:23 GMT",
"version": "v1"
},
{
"created": "Wed, 30 Jul 2014 03:18:32 GMT",
"version": "v2"
}
] |
2014-08-05
|
[
[
"Takemoto",
"Kazuhiro",
""
]
] |
Network self-similarity or fractality are widely accepted as an important topological property of metabolic networks; however, recent studies cast doubt on the reality of self-similarity in the networks. Therefore, we perform a comprehensive evaluation of metabolic network fractality using a box-covering method with an earlier version and the latest version of metabolic networks, and demonstrate that the latest metabolic networks are almost self-dissimilar, while the earlier ones are fractal, as reported in a number of previous studies. This result may be because the networks were randomized because of an increase in network density due to database updates, suggesting that the previously observed network fractality was due to a lack of available data on metabolic reactions. This finding may not entirely discount the importance of self-similarity of metabolic networks. Rather, it highlights the need for a more suitable definition of network fractality and a more careful examination of self-similarity of metabolic networks.
|
2112.10230
|
Johannes M\"uller
|
Johannes M\"uller and Aurelien Tellier and Michael Kurschilgen
|
A model of opinion dynamics with echo chambers explains the spatial
distribution of vaccine hesitancy
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vaccination hesitancy is a major obstacle to achieving and maintaining herd
immunity. It is therefore of prime importance for public health authorities to
understand the dynamics of an anti-vaccine opinion in the population. We
introduce a novel mathematical model of opinion dynamics with spatial
reinforcement, which can generate echo chambers, i.e. opinion bubbles in which
information that is incompatible with one's entrenched worldview, is likely
disregarded. In a first mathematical part, we scale the model both to a
deterministic limit and to a weak-effects limit, and obtain bifurcations, phase
transitions, and the invariant measure. In a second part, we fit our model to
measles and meningococci vaccination coverage across 413 districts in Germany.
We reveal that strong echo chambers explain the occurrence and persistence of
the anti-vaccination opinion. We predict and compare the effectiveness of
different policies aimed at influencing opinion dynamics in order to increase
vaccination uptake.
|
[
{
"created": "Sun, 19 Dec 2021 19:02:22 GMT",
"version": "v1"
}
] |
2021-12-21
|
[
[
"Müller",
"Johannes",
""
],
[
"Tellier",
"Aurelien",
""
],
[
"Kurschilgen",
"Michael",
""
]
] |
Vaccination hesitancy is a major obstacle to achieving and maintaining herd immunity. It is therefore of prime importance for public health authorities to understand the dynamics of an anti-vaccine opinion in the population. We introduce a novel mathematical model of opinion dynamics with spatial reinforcement, which can generate echo chambers, i.e. opinion bubbles in which information that is incompatible with one's entrenched worldview, is likely disregarded. In a first mathematical part, we scale the model both to a deterministic limit and to a weak-effects limit, and obtain bifurcations, phase transitions, and the invariant measure. In a second part, we fit our model to measles and meningococci vaccination coverage across 413 districts in Germany. We reveal that strong echo chambers explain the occurrence and persistence of the anti-vaccination opinion. We predict and compare the effectiveness of different policies aimed at influencing opinion dynamics in order to increase vaccination uptake.
|
2008.01781
|
Wentian Li
|
Wentian Li, Yannis Almirantis, Astero Provata
|
Revisiting the Neutral Dynamics Derived Limiting Guanine-Cytosine
Content Using the Human De Novo Point Mutation Data
|
4 figures
|
Meta Gene 31: 100994 (2022)
|
10.1016/j.mgene.2021.100994
| null |
q-bio.GN q-bio.PE
|
http://creativecommons.org/licenses/by/4.0/
|
We revisit the topic of human genome guanine-cytosine content under neutral
evolution. For this study, the de novo mutation data within human is used to
estimate mutational rate instead of using base substitution data between
related species. We then define a new measure of mutation bias which separate
the de novo mutation counts from the background guanine-cytosine content
itself, making comparison between different datasets easier. We derive a new
formula for calculating limiting guanine-cytosine content by separating
CpG-involved mutational events as an independent variable. Using the formula
when CpG-involved mutations are considered, the guanine-cytosine content drops
less severely in the limit of neutral dynamics. We provide evidence, under
certain assumptions, that an isochore-like structure might remain as a limiting
configuration of the neutral mutational dynamics.
|
[
{
"created": "Tue, 4 Aug 2020 19:31:21 GMT",
"version": "v1"
}
] |
2022-03-15
|
[
[
"Li",
"Wentian",
""
],
[
"Almirantis",
"Yannis",
""
],
[
"Provata",
"Astero",
""
]
] |
We revisit the topic of human genome guanine-cytosine content under neutral evolution. For this study, the de novo mutation data within human is used to estimate mutational rate instead of using base substitution data between related species. We then define a new measure of mutation bias which separate the de novo mutation counts from the background guanine-cytosine content itself, making comparison between different datasets easier. We derive a new formula for calculating limiting guanine-cytosine content by separating CpG-involved mutational events as an independent variable. Using the formula when CpG-involved mutations are considered, the guanine-cytosine content drops less severely in the limit of neutral dynamics. We provide evidence, under certain assumptions, that an isochore-like structure might remain as a limiting configuration of the neutral mutational dynamics.
|
0810.4547
|
Bradly Alicea
|
Bradly Alicea
|
Hierarchies of Biocomplexity: modeling lifes energetic complexity
|
11 pages, 9 figures, 1 table
| null | null | null |
q-bio.PE q-bio.OT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, a model for understanding the effects of selection using
systems- level computational approaches is introduced. A number of concepts and
principles essential for understanding the motivation for constructing the
model will be introduced first. This will be followed by a description of
parameters, measurements, and graphical representations used in the model. Four
possible outcomes for this model are then introduced and described. In
addition, the relationship of relative fitness to selection is described.
Finally, the consequences and potential lessons learned from the model are
discussed.
|
[
{
"created": "Fri, 24 Oct 2008 20:32:11 GMT",
"version": "v1"
},
{
"created": "Fri, 7 Nov 2008 19:19:15 GMT",
"version": "v2"
},
{
"created": "Wed, 4 Mar 2009 15:53:36 GMT",
"version": "v3"
}
] |
2009-03-04
|
[
[
"Alicea",
"Bradly",
""
]
] |
In this paper, a model for understanding the effects of selection using systems- level computational approaches is introduced. A number of concepts and principles essential for understanding the motivation for constructing the model will be introduced first. This will be followed by a description of parameters, measurements, and graphical representations used in the model. Four possible outcomes for this model are then introduced and described. In addition, the relationship of relative fitness to selection is described. Finally, the consequences and potential lessons learned from the model are discussed.
|
1410.6763
|
Denis Semenov A.
|
Denis A. Semenov
|
Epigenetic effects of cytosine derivatives are caused by their tautomers
in Hoogsteen base pairs
|
6 pages, 5 figures
| null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deoxycitidine in solution exists as two tautomers one of which is an
uncanonical imino one. The latter can dominate with such derivatives as
5-methyl, 5-hydroxymethyl- and 5-formylcytosine. The imino tautomer potentially
is able to form a hoosteen GC base pair. To detect such pair, it is suggested
to use 1H15N NMR. Formation of GC-Hoogsteen base pair with imino tautomer of
cytosine can be a reason for epigenetic effects of 5-methyl- and
5-hydroxymethylcytosine.
|
[
{
"created": "Sat, 30 Aug 2014 18:15:05 GMT",
"version": "v1"
}
] |
2014-10-27
|
[
[
"Semenov",
"Denis A.",
""
]
] |
Deoxycitidine in solution exists as two tautomers one of which is an uncanonical imino one. The latter can dominate with such derivatives as 5-methyl, 5-hydroxymethyl- and 5-formylcytosine. The imino tautomer potentially is able to form a hoosteen GC base pair. To detect such pair, it is suggested to use 1H15N NMR. Formation of GC-Hoogsteen base pair with imino tautomer of cytosine can be a reason for epigenetic effects of 5-methyl- and 5-hydroxymethylcytosine.
|
1606.03630
|
Cameron Mura
|
Cameron Mura
|
The Structures, Functions, and Evolution of Sm-like Archaeal Proteins
(SmAPs)
|
215 pages, distributed across an Abstract, Synopsis, five Chapters
(the main body) and an Appendix; the work in this dissertation was performed
in the Eisenberg lab at UCLA from ca. 1999 to 2002
| null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Sm proteins were discovered nearly 20 years ago as a group of small antigenic
proteins ($\approx$ 90-120 residues). Since then, an extensive amount of
biochemical and genetic data have illuminated the crucial roles of these
proteins in forming ribonucleoprotein (RNP) complexes that are used in RNA
processing, e.g., spliceosomal removal of introns from pre-mRNAs. Spliceosomes
are large macromolecular machines that are comparable to ribosomes in size and
complexity, and are composed of uridine-rich small nuclear RNPs (U snRNPs).
Various sets of seven different Sm proteins form the cores of most snRNPs.
Despite their importance, very little is known about the atomic-resolution
structure of snRNPs or their Sm cores. As a first step towards a
high-resolution image of snRNPs and their hierarchic assembly, we have
determined the crystal structures of archaeal homologs of Sm proteins, which we
term Sm-like archaeal proteins (SmAPs).
|
[
{
"created": "Sat, 11 Jun 2016 21:20:30 GMT",
"version": "v1"
}
] |
2016-06-14
|
[
[
"Mura",
"Cameron",
""
]
] |
Sm proteins were discovered nearly 20 years ago as a group of small antigenic proteins ($\approx$ 90-120 residues). Since then, an extensive amount of biochemical and genetic data have illuminated the crucial roles of these proteins in forming ribonucleoprotein (RNP) complexes that are used in RNA processing, e.g., spliceosomal removal of introns from pre-mRNAs. Spliceosomes are large macromolecular machines that are comparable to ribosomes in size and complexity, and are composed of uridine-rich small nuclear RNPs (U snRNPs). Various sets of seven different Sm proteins form the cores of most snRNPs. Despite their importance, very little is known about the atomic-resolution structure of snRNPs or their Sm cores. As a first step towards a high-resolution image of snRNPs and their hierarchic assembly, we have determined the crystal structures of archaeal homologs of Sm proteins, which we term Sm-like archaeal proteins (SmAPs).
|
1905.00555
|
Abhishek Deshpande
|
Abhishek Deshpande, Thomas E. Ouldridge
|
Optimizing enzymatic catalysts for rapid turnover of substrates with low
enzyme sequestration
|
16 pages, 9 figures
|
Biological Cybernetics 2020
| null | null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We analyse the mechanism of enzyme-substrate catalysis from the perspective
of minimizing the load on the enzymes through sequestration, whilst maintaining
at least a minimum reaction flux. In particular, we ask: which binding free
energies of the enzyme-substrate and enzyme-product reaction intermediates
minimize the fraction of enzymes sequestered in complexes, while sustaining at
a certain minimal flux? Under reasonable biophysical assumptions, we find that
the optimal design will saturate the bound on the minimal flux, and reflects a
basic trade-off in catalytic operation. If both binding free energies are too
high, there is low sequestration, but the effective progress of the reaction is
hampered. If both binding free energies are too low, there is high
sequestration, and the reaction flux may also be suppressed in extreme cases.
The optimal binding free energies are therefore neither too high nor too low,
but in fact moderate. Moreover, the optimal difference in substrate and product
binding free energies, which contributes to the thermodynamic driving force of
the reaction, is in general strongly constrained by the intrinsic free-energy
difference between products and reactants. Both the strategies of using a
negative binding free-energy difference to drive the catalyst-bound reaction
forward, and of using a positive binding free-energy difference to enhance
detachment of the product, are limited in their efficacy.
|
[
{
"created": "Thu, 2 May 2019 02:41:46 GMT",
"version": "v1"
},
{
"created": "Thu, 17 Sep 2020 19:31:15 GMT",
"version": "v2"
}
] |
2020-09-21
|
[
[
"Deshpande",
"Abhishek",
""
],
[
"Ouldridge",
"Thomas E.",
""
]
] |
We analyse the mechanism of enzyme-substrate catalysis from the perspective of minimizing the load on the enzymes through sequestration, whilst maintaining at least a minimum reaction flux. In particular, we ask: which binding free energies of the enzyme-substrate and enzyme-product reaction intermediates minimize the fraction of enzymes sequestered in complexes, while sustaining at a certain minimal flux? Under reasonable biophysical assumptions, we find that the optimal design will saturate the bound on the minimal flux, and reflects a basic trade-off in catalytic operation. If both binding free energies are too high, there is low sequestration, but the effective progress of the reaction is hampered. If both binding free energies are too low, there is high sequestration, and the reaction flux may also be suppressed in extreme cases. The optimal binding free energies are therefore neither too high nor too low, but in fact moderate. Moreover, the optimal difference in substrate and product binding free energies, which contributes to the thermodynamic driving force of the reaction, is in general strongly constrained by the intrinsic free-energy difference between products and reactants. Both the strategies of using a negative binding free-energy difference to drive the catalyst-bound reaction forward, and of using a positive binding free-energy difference to enhance detachment of the product, are limited in their efficacy.
|
1502.07793
|
James Herbert-Read
|
James E. Herbert-Read, Ashley J.W. Ward, David J.T. Sumpter, Richard
P. Mann
|
Escape path complexity and its context dependency in Pacific blue-eyes
(Pseudomugil signifer)
|
9 pages
| null |
10.1242/jeb.154534
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The escape trajectories animals take following a predatory attack appear to
show high degrees of apparent 'randomness' - a property that has been described
as 'protean behaviour'. Here we present a method of quantifying the escape
trajectories of individual animals using a path complexity approach. When fish
(Pseudomugil signifer) were attacked either on their own or in groups, we find
that an individual's path rapidly increases in entropy (our measure of
complexity) following the attack. For individuals on their own, this entropy
remains elevated (indicating a more random path) for a sustained period (10
seconds) after the attack, whilst it falls more quickly for individuals in
groups. The entropy of the path is context dependent. When attacks towards
single fish come from greater distances, a fish's path shows less complexity
compared to attacks that come from short range. This context dependency effect
did not exist, however, when individuals were in groups. Nor did the path
complexity of individuals in groups depend on a fish's local density of
neighbours. We separate out the components of speed and direction changes to
determine which of these components contributes to the overall increase in path
complexity following an attack. We found that both speed and direction measures
contribute similarly to an individual's path's complexity in absolute terms.
Our work highlights the adaptive behavioural tactics that animals use to avoid
predators and also provides a novel method for quantifying the escape
trajectories of animals.
|
[
{
"created": "Thu, 26 Feb 2015 23:47:14 GMT",
"version": "v1"
}
] |
2022-09-13
|
[
[
"Herbert-Read",
"James E.",
""
],
[
"Ward",
"Ashley J. W.",
""
],
[
"Sumpter",
"David J. T.",
""
],
[
"Mann",
"Richard P.",
""
]
] |
The escape trajectories animals take following a predatory attack appear to show high degrees of apparent 'randomness' - a property that has been described as 'protean behaviour'. Here we present a method of quantifying the escape trajectories of individual animals using a path complexity approach. When fish (Pseudomugil signifer) were attacked either on their own or in groups, we find that an individual's path rapidly increases in entropy (our measure of complexity) following the attack. For individuals on their own, this entropy remains elevated (indicating a more random path) for a sustained period (10 seconds) after the attack, whilst it falls more quickly for individuals in groups. The entropy of the path is context dependent. When attacks towards single fish come from greater distances, a fish's path shows less complexity compared to attacks that come from short range. This context dependency effect did not exist, however, when individuals were in groups. Nor did the path complexity of individuals in groups depend on a fish's local density of neighbours. We separate out the components of speed and direction changes to determine which of these components contributes to the overall increase in path complexity following an attack. We found that both speed and direction measures contribute similarly to an individual's path's complexity in absolute terms. Our work highlights the adaptive behavioural tactics that animals use to avoid predators and also provides a novel method for quantifying the escape trajectories of animals.
|
1607.02642
|
Wayne Hayes
|
Nil Mamano and Wayne Hayes
|
SANA: Simulated Annealing Network Alignment Applied to Biological
Networks
| null | null | null | null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The alignment of biological networks has the potential to teach us as much
about biology and disease as has sequence alignment. Sequence alignment can be
optimally solved in polynomial time. In contrast, network alignment is
$NP$-hard, meaning optimal solutions are impossible to find, and the quality of
found alignments depend strongly upon the algorithm used to create them. Every
network alignment algorithm consists of two orthogonal components: first, an
objective function or measure $M$ that is used to evaluate the quality of any
proposed alignment, and second, a search algorithm used to explore the
exponentially large set of possible alignments in an effort to find "good" ones
according to $M$. Objective functions fall into many categories, including
biological measures such as sequence similarity, as well as topological
measures like graphlet similarity and edge coverage (possibly weighted).
Algorithms to search the space of all possible alignments can be deterministic
or stochastic, and many possibilities have been offered over the past decade.
In this paper we introduce a new stochastic search algorithm called SANA:
Simulated Annealing Network Aligner. We test it on several popular objective
functions and demonstrate that it almost universally optimizes each one
significantly better than existing search algorithms. Finally, we compare
several topological objective functions using SANA. Software available at
http://sana.ics.uci.edu.
|
[
{
"created": "Sat, 9 Jul 2016 18:13:30 GMT",
"version": "v1"
}
] |
2016-07-12
|
[
[
"Mamano",
"Nil",
""
],
[
"Hayes",
"Wayne",
""
]
] |
The alignment of biological networks has the potential to teach us as much about biology and disease as has sequence alignment. Sequence alignment can be optimally solved in polynomial time. In contrast, network alignment is $NP$-hard, meaning optimal solutions are impossible to find, and the quality of found alignments depend strongly upon the algorithm used to create them. Every network alignment algorithm consists of two orthogonal components: first, an objective function or measure $M$ that is used to evaluate the quality of any proposed alignment, and second, a search algorithm used to explore the exponentially large set of possible alignments in an effort to find "good" ones according to $M$. Objective functions fall into many categories, including biological measures such as sequence similarity, as well as topological measures like graphlet similarity and edge coverage (possibly weighted). Algorithms to search the space of all possible alignments can be deterministic or stochastic, and many possibilities have been offered over the past decade. In this paper we introduce a new stochastic search algorithm called SANA: Simulated Annealing Network Aligner. We test it on several popular objective functions and demonstrate that it almost universally optimizes each one significantly better than existing search algorithms. Finally, we compare several topological objective functions using SANA. Software available at http://sana.ics.uci.edu.
|
2310.10598
|
Jenna Fromer
|
Jenna C. Fromer, David E. Graff, Connor W. Coley
|
Pareto Optimization to Accelerate Multi-Objective Virtual Screening
| null | null | null | null |
q-bio.QM cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The discovery of therapeutic molecules is fundamentally a multi-objective
optimization problem. One formulation of the problem is to identify molecules
that simultaneously exhibit strong binding affinity for a target protein,
minimal off-target interactions, and suitable pharmacokinetic properties.
Inspired by prior work that uses active learning to accelerate the
identification of strong binders, we implement multi-objective Bayesian
optimization to reduce the computational cost of multi-property virtual
screening and apply it to the identification of ligands predicted to be
selective based on docking scores to on- and off-targets. We demonstrate the
superiority of Pareto optimization over scalarization across three case
studies. Further, we use the developed optimization tool to search a virtual
library of over 4M molecules for those predicted to be selective dual
inhibitors of EGFR and IGF1R, acquiring 100% of the molecules that form the
library's Pareto front after exploring only 8% of the library. This workflow
and associated open source software can reduce the screening burden of
molecular design projects and is complementary to research aiming to improve
the accuracy of binding predictions and other molecular properties.
|
[
{
"created": "Mon, 16 Oct 2023 17:19:46 GMT",
"version": "v1"
}
] |
2023-10-17
|
[
[
"Fromer",
"Jenna C.",
""
],
[
"Graff",
"David E.",
""
],
[
"Coley",
"Connor W.",
""
]
] |
The discovery of therapeutic molecules is fundamentally a multi-objective optimization problem. One formulation of the problem is to identify molecules that simultaneously exhibit strong binding affinity for a target protein, minimal off-target interactions, and suitable pharmacokinetic properties. Inspired by prior work that uses active learning to accelerate the identification of strong binders, we implement multi-objective Bayesian optimization to reduce the computational cost of multi-property virtual screening and apply it to the identification of ligands predicted to be selective based on docking scores to on- and off-targets. We demonstrate the superiority of Pareto optimization over scalarization across three case studies. Further, we use the developed optimization tool to search a virtual library of over 4M molecules for those predicted to be selective dual inhibitors of EGFR and IGF1R, acquiring 100% of the molecules that form the library's Pareto front after exploring only 8% of the library. This workflow and associated open source software can reduce the screening burden of molecular design projects and is complementary to research aiming to improve the accuracy of binding predictions and other molecular properties.
|
1611.05918
|
Sujoy Ganguly
|
Sujoy Ganguly and Olivier Trottier and Xin Liang and Hugo
Bowne-Anderson and Jonathon Howard
|
Morphology of Fly Larval Class IV Dendrites Accords with a Random
Branching and Contact Based Branch Deletion Model
|
12 pages, 4 figures. Supplementary Information: 3 Figures
| null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Dendrites are branched neuronal processes that receive input signals from
other neurons or the outside world [1]. To maintain connectivity as the
organism grows, dendrites must also continue to grow. For example, the
dendrites in the peripheral nervous system continue to grow and branch to
maintain proper coverage of their receptor fields [2, 3, 4, 5]. One such neuron
is the Drosophila melanogaster class IV dendritic arborization neuron [6]. The
dendritic arbors of these neurons tile the larval surface [7], where they
detect localized noxious stimuli, such as jabs from parasitic wasps [8]. In the
present study, we used a novel measure, the hitting probability, to show that
the class IV neuron forms a tight mesh that covers the larval surface.
Furthermore, we found that the mesh size remains largely unchanged during the
larval stages, despite a dramatic increase in overall size of the neuron and
the larva. We also found that the class IV dendrites are dense (assayed with
the fractal dimension) and uniform (assayed with the lacunarity) throughout the
larval stages. To understand how the class IV neuron maintains its morphology
during larval development, we constructed a mathematical model based on random
branching and self-avoidance. We found that if the branching rate is uniform in
space and time and that if all contacting branches are deleted, we can
reproduce the branch length distribution, mesh size and density of the class IV
dendrites throughout the larval stages. Thus, a simple set of statistical rules
can generate and maintain a complex branching morphology during growth.
|
[
{
"created": "Thu, 17 Nov 2016 22:08:20 GMT",
"version": "v1"
}
] |
2016-11-21
|
[
[
"Ganguly",
"Sujoy",
""
],
[
"Trottier",
"Olivier",
""
],
[
"Liang",
"Xin",
""
],
[
"Bowne-Anderson",
"Hugo",
""
],
[
"Howard",
"Jonathon",
""
]
] |
Dendrites are branched neuronal processes that receive input signals from other neurons or the outside world [1]. To maintain connectivity as the organism grows, dendrites must also continue to grow. For example, the dendrites in the peripheral nervous system continue to grow and branch to maintain proper coverage of their receptor fields [2, 3, 4, 5]. One such neuron is the Drosophila melanogaster class IV dendritic arborization neuron [6]. The dendritic arbors of these neurons tile the larval surface [7], where they detect localized noxious stimuli, such as jabs from parasitic wasps [8]. In the present study, we used a novel measure, the hitting probability, to show that the class IV neuron forms a tight mesh that covers the larval surface. Furthermore, we found that the mesh size remains largely unchanged during the larval stages, despite a dramatic increase in overall size of the neuron and the larva. We also found that the class IV dendrites are dense (assayed with the fractal dimension) and uniform (assayed with the lacunarity) throughout the larval stages. To understand how the class IV neuron maintains its morphology during larval development, we constructed a mathematical model based on random branching and self-avoidance. We found that if the branching rate is uniform in space and time and that if all contacting branches are deleted, we can reproduce the branch length distribution, mesh size and density of the class IV dendrites throughout the larval stages. Thus, a simple set of statistical rules can generate and maintain a complex branching morphology during growth.
|
1708.02603
|
Tae Seung Kang
|
Tae Seung Kang and Arunava Banerjee
|
Learning Feedforward and Recurrent Deterministic Spiking Neuron Network
Feedback Controllers
| null | null | null | null |
q-bio.NC cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We address the problem of learning feedback control where the controller is a
network constructed solely of deterministic spiking neurons. In contrast to
previous investigations that were based on a spike rate model of the neuron,
the control signal here is determined by the precise temporal positions of
spikes generated by the output neurons of the network. We model the problem
formally as a hybrid dynamical system comprised of a closed loop between a
plant and a spiking neuron network. We derive a novel synaptic weight update
rule via which the spiking neuron network controller learns to hold process
variables at desired set points. The controller achieves its learning objective
based solely on access to the plant's process variables and their derivatives
with respect to changing control signals; in particular, it requires no
internal model of the plant. We demonstrate the efficacy of the rule by
applying it to the classical control problem of the cart-pole (inverted
pendulum) and a model of fish locomotion. Experiments show that the proposed
controller has a stability region comparable to a traditional PID controller,
its trajectories differ qualitatively from those of a PID controller, and in
many instances the controller achieves its objective using very sparse spike
train outputs.
|
[
{
"created": "Tue, 8 Aug 2017 18:42:17 GMT",
"version": "v1"
},
{
"created": "Tue, 25 Sep 2018 22:54:29 GMT",
"version": "v2"
}
] |
2018-09-27
|
[
[
"Kang",
"Tae Seung",
""
],
[
"Banerjee",
"Arunava",
""
]
] |
We address the problem of learning feedback control where the controller is a network constructed solely of deterministic spiking neurons. In contrast to previous investigations that were based on a spike rate model of the neuron, the control signal here is determined by the precise temporal positions of spikes generated by the output neurons of the network. We model the problem formally as a hybrid dynamical system comprised of a closed loop between a plant and a spiking neuron network. We derive a novel synaptic weight update rule via which the spiking neuron network controller learns to hold process variables at desired set points. The controller achieves its learning objective based solely on access to the plant's process variables and their derivatives with respect to changing control signals; in particular, it requires no internal model of the plant. We demonstrate the efficacy of the rule by applying it to the classical control problem of the cart-pole (inverted pendulum) and a model of fish locomotion. Experiments show that the proposed controller has a stability region comparable to a traditional PID controller, its trajectories differ qualitatively from those of a PID controller, and in many instances the controller achieves its objective using very sparse spike train outputs.
|
1307.5468
|
Changbong Hyeon
|
Jeseong Yoon, D. Thirumalai, Changbong Hyeon
|
Urea-induced denaturation of PreQ1-riboswitch
|
41 pages, 18 figures
|
J. Am. Chem. Soc. 2013
|
10.1016/j.bpj.2012.11.1853
| null |
q-bio.BM cond-mat.soft
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Urea, a polar molecule with a large dipole moment, not only destabilizes the
folded RNA structures, but can also enhance the folding rates of large
ribozymes. Unlike the mechanism of urea-induced unfolding of proteins, which is
well understood, the action of urea on RNA has barely been explored. We
performed extensive all atom molecular dynamics (MD) simulations to determine
the molecular underpinnings of urea-induced RNA denaturation. Urea displays its
denaturing power in both secondary and tertiary motifs of the riboswitch (RS)
structure. Our simulations reveal that the denaturation of RNA structures is
mainly driven by the hydrogen bonds and stacking interactions of urea with the
bases. Through detailed studies of the simulation trajectories, we found that
geminate pairs between urea and bases due to hydrogen bonds and stacks persist
only ~ (0.1-1) ns, which suggests that urea-base interaction is highly dynamic.
Most importantly, the early stage of base pair disruption is triggered by
penetration of water molecules into the hydrophobic domain between the RNA
bases. The infiltration of water into the narrow space between base pairs is
critical in increasing the accessibility of urea to transiently disrupted
bases, thus allowing urea to displace inter base hydrogen bonds. This
mechanism, water-induced disruption of base-pairs resulting in the formation of
a "wet" destabilized RNA followed by solvation by urea, is the exact opposite
of the two-stage denaturation of proteins by urea. In the latter case, initial
urea penetration creates a dry-globule, which is subsequently solvated by water
penetration leading to global protein unfolding. Our work shows that the
ability to interact with both water and polar, non-polar components of
nucleotides makes urea a powerful chemical denaturant for nucleic acids.
|
[
{
"created": "Sat, 20 Jul 2013 22:02:38 GMT",
"version": "v1"
}
] |
2017-08-23
|
[
[
"Yoon",
"Jeseong",
""
],
[
"Thirumalai",
"D.",
""
],
[
"Hyeon",
"Changbong",
""
]
] |
Urea, a polar molecule with a large dipole moment, not only destabilizes the folded RNA structures, but can also enhance the folding rates of large ribozymes. Unlike the mechanism of urea-induced unfolding of proteins, which is well understood, the action of urea on RNA has barely been explored. We performed extensive all atom molecular dynamics (MD) simulations to determine the molecular underpinnings of urea-induced RNA denaturation. Urea displays its denaturing power in both secondary and tertiary motifs of the riboswitch (RS) structure. Our simulations reveal that the denaturation of RNA structures is mainly driven by the hydrogen bonds and stacking interactions of urea with the bases. Through detailed studies of the simulation trajectories, we found that geminate pairs between urea and bases due to hydrogen bonds and stacks persist only ~ (0.1-1) ns, which suggests that urea-base interaction is highly dynamic. Most importantly, the early stage of base pair disruption is triggered by penetration of water molecules into the hydrophobic domain between the RNA bases. The infiltration of water into the narrow space between base pairs is critical in increasing the accessibility of urea to transiently disrupted bases, thus allowing urea to displace inter base hydrogen bonds. This mechanism, water-induced disruption of base-pairs resulting in the formation of a "wet" destabilized RNA followed by solvation by urea, is the exact opposite of the two-stage denaturation of proteins by urea. In the latter case, initial urea penetration creates a dry-globule, which is subsequently solvated by water penetration leading to global protein unfolding. Our work shows that the ability to interact with both water and polar, non-polar components of nucleotides makes urea a powerful chemical denaturant for nucleic acids.
|
2010.12127
|
Dongqi Han
|
Dongqi Han, Erik De Schutter, Sungho Hong
|
Lamina-specific neuronal properties promote robust, stable signal
propagation in feedforward networks
|
NeurIPS 2020
| null | null | null |
q-bio.NC cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Feedforward networks (FFN) are ubiquitous structures in neural systems and
have been studied to understand mechanisms of reliable signal and information
transmission. In many FFNs, neurons in one layer have intrinsic properties that
are distinct from those in their pre-/postsynaptic layers, but how this affects
network-level information processing remains unexplored. Here we show that
layer-to-layer heterogeneity arising from lamina-specific cellular properties
facilitates signal and information transmission in FFNs. Specifically, we found
that signal transformations, made by each layer of neurons on an input-driven
spike signal, demodulate signal distortions introduced by preceding layers.
This mechanism boosts information transfer carried by a propagating spike
signal and thereby supports reliable spike signal and information transmission
in a deep FFN. Our study suggests that distinct cell types in neural circuits,
performing different computational functions, facilitate information processing
on the whole.
|
[
{
"created": "Fri, 23 Oct 2020 01:57:46 GMT",
"version": "v1"
}
] |
2020-10-26
|
[
[
"Han",
"Dongqi",
""
],
[
"De Schutter",
"Erik",
""
],
[
"Hong",
"Sungho",
""
]
] |
Feedforward networks (FFN) are ubiquitous structures in neural systems and have been studied to understand mechanisms of reliable signal and information transmission. In many FFNs, neurons in one layer have intrinsic properties that are distinct from those in their pre-/postsynaptic layers, but how this affects network-level information processing remains unexplored. Here we show that layer-to-layer heterogeneity arising from lamina-specific cellular properties facilitates signal and information transmission in FFNs. Specifically, we found that signal transformations, made by each layer of neurons on an input-driven spike signal, demodulate signal distortions introduced by preceding layers. This mechanism boosts information transfer carried by a propagating spike signal and thereby supports reliable spike signal and information transmission in a deep FFN. Our study suggests that distinct cell types in neural circuits, performing different computational functions, facilitate information processing on the whole.
|
2201.07338
|
Noelia Ferruz
|
Noelia Ferruz and Birte H\"ocker
|
Controllable Protein Design with Language Models
|
This is a version before peer-review. A view-only, peer-reviewed,
published version can be found here: https://rdcu.be/cQbmH. The peer-reviewed
version is under embargo at Nat Mach Intell until 12/2022
|
Controllable protein design with language models. Nat Mach Intell
4, 521-532, 2022
|
10.1038/s42256-022-00499-z
| null |
q-bio.BM
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The 21st century is presenting humankind with unprecedented environmental and
medical challenges. The ability to design novel proteins tailored for specific
purposes could transform our ability to respond timely to these issues. Recent
advances in the field of artificial intelligence are now setting the stage to
make this goal achievable. Protein sequences are inherently similar to natural
languages: Amino acids arrange in a multitude of combinations to form
structures that carry function, the same way as letters form words and
sentences that carry meaning. Therefore, it is not surprising that throughout
the history of Natural Language Processing (NLP), many of its techniques have
been applied to protein research problems. In the last few years, we have
witnessed revolutionary breakthroughs in the field of NLP. The implementation
of Transformer pre-trained models has enabled text generation with human-like
capabilities, including texts with specific properties such as style or
subject. Motivated by its considerable success in NLP tasks, we expect
dedicated Transformers to dominate custom protein sequence generation in the
near future. Finetuning pre-trained models on protein families will enable the
extension of their repertoires with novel sequences that could be highly
divergent but still potentially functional. The combination of control tags
such as cellular compartment or function will further enable the controllable
design of novel protein functions. Moreover, recent model interpretability
methods will allow us to open the 'black box' and thus enhance our
understanding of folding principles. While early initiatives show the enormous
potential of generative language models to design functional sequences, the
field is still in its infancy. We believe that protein language models are a
promising and largely unexplored field and discuss their foreseeable impact on
protein design.
|
[
{
"created": "Tue, 18 Jan 2022 22:23:03 GMT",
"version": "v1"
},
{
"created": "Mon, 22 Aug 2022 18:44:27 GMT",
"version": "v2"
}
] |
2022-08-24
|
[
[
"Ferruz",
"Noelia",
""
],
[
"Höcker",
"Birte",
""
]
] |
The 21st century is presenting humankind with unprecedented environmental and medical challenges. The ability to design novel proteins tailored for specific purposes could transform our ability to respond timely to these issues. Recent advances in the field of artificial intelligence are now setting the stage to make this goal achievable. Protein sequences are inherently similar to natural languages: Amino acids arrange in a multitude of combinations to form structures that carry function, the same way as letters form words and sentences that carry meaning. Therefore, it is not surprising that throughout the history of Natural Language Processing (NLP), many of its techniques have been applied to protein research problems. In the last few years, we have witnessed revolutionary breakthroughs in the field of NLP. The implementation of Transformer pre-trained models has enabled text generation with human-like capabilities, including texts with specific properties such as style or subject. Motivated by its considerable success in NLP tasks, we expect dedicated Transformers to dominate custom protein sequence generation in the near future. Finetuning pre-trained models on protein families will enable the extension of their repertoires with novel sequences that could be highly divergent but still potentially functional. The combination of control tags such as cellular compartment or function will further enable the controllable design of novel protein functions. Moreover, recent model interpretability methods will allow us to open the 'black box' and thus enhance our understanding of folding principles. While early initiatives show the enormous potential of generative language models to design functional sequences, the field is still in its infancy. We believe that protein language models are a promising and largely unexplored field and discuss their foreseeable impact on protein design.
|
1602.08314
|
Pramod Shinde
|
Pramod Shinde and Sarika Jalan
|
A multilayer PPI network analysis of different life stages in C. elegans
| null |
EPL (Europhysics Letters). 2015 Dec 17;112(5):58001
|
10.1209/0295-5075/112/58001
| null |
q-bio.MN q-bio.PE q-bio.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Molecular networks act as the backbone of cellular activities, providing an
{excellent} opportunity to understand the developmental changes in an organism.
While network data usually constitute only stationary network graphs,
constructing multilayer PPI network may provide clues to the particular
developmental role at each {stage of life} and may unravel the importance of
these developmental changes. The developmental biology model of {Caenorhabditis
elegans} {analyzed} here provides a ripe platform to understand the patterns of
evolution during life stages of an organism. In the present study, the widely
studied network properties exhibit overall similar statistics for all the PPI
layers. Further, the analysis of the degree-degree correlation and spectral
properties not only reveals crucial differences in each PPI layer but also
indicates the presence of the varying complexity among them. The PPI layer of
Nematode life stage exhibits various network properties different to rest of
the PPI layers, indicating the specific role of cellular diversity and
developmental transitions at this stage. The framework presented here provides
a direction to explore and understand developmental changes occurring in
different life stages of an organism.
|
[
{
"created": "Fri, 26 Feb 2016 13:26:52 GMT",
"version": "v1"
}
] |
2016-02-29
|
[
[
"Shinde",
"Pramod",
""
],
[
"Jalan",
"Sarika",
""
]
] |
Molecular networks act as the backbone of cellular activities, providing an {excellent} opportunity to understand the developmental changes in an organism. While network data usually constitute only stationary network graphs, constructing multilayer PPI network may provide clues to the particular developmental role at each {stage of life} and may unravel the importance of these developmental changes. The developmental biology model of {Caenorhabditis elegans} {analyzed} here provides a ripe platform to understand the patterns of evolution during life stages of an organism. In the present study, the widely studied network properties exhibit overall similar statistics for all the PPI layers. Further, the analysis of the degree-degree correlation and spectral properties not only reveals crucial differences in each PPI layer but also indicates the presence of the varying complexity among them. The PPI layer of Nematode life stage exhibits various network properties different to rest of the PPI layers, indicating the specific role of cellular diversity and developmental transitions at this stage. The framework presented here provides a direction to explore and understand developmental changes occurring in different life stages of an organism.
|
q-bio/0501013
|
Anthonie Muller
|
Anthonie W. J. Muller
|
Thermosynthesis as energy source for the RNA World: a new model for the
origin of life
|
12 pages, 6 figures; changes in text (minor) and 2 figures
| null | null | null |
q-bio.PE
| null |
The thermosynthesis concept, biological free energy gain from thermal
cycling, is combined with the concept of the RNA World. The resulting overall
origin of life model gives new explanations for the emergence of the genetic
code and the ribosome. The first protein named pF1 obtains the energy to
support the RNA world by a thermal variation of F1 ATP synthase's binding
change mechanism. This pF1 is the single translation product during the
emergence of the genetic machinery. During thermal cycling pF1 condenses many
substrates with broad specificity, yielding NTPs and randomly constituted
protein and RNA libraries that contain (self)-replicating RNA. The smallness of
pF1 permits the emergence of the genetic machinery by selection of RNA that
increases the fraction of pF1s in the protein library: (1) a progenitor of rRNA
that concatenates amino acids bound to (2) a chain of 'positional tRNAs' linked
by mutual recognition, yielding a pF1 (or its main motif); this positional tRNA
set gradually evolves to a set of regular tRNAs functioning according to the
genetic code, with concomitant emergence of (3) an mRNA coding for pF1.
|
[
{
"created": "Tue, 11 Jan 2005 19:06:12 GMT",
"version": "v1"
},
{
"created": "Wed, 30 Mar 2005 21:48:04 GMT",
"version": "v2"
}
] |
2007-05-23
|
[
[
"Muller",
"Anthonie W. J.",
""
]
] |
The thermosynthesis concept, biological free energy gain from thermal cycling, is combined with the concept of the RNA World. The resulting overall origin of life model gives new explanations for the emergence of the genetic code and the ribosome. The first protein named pF1 obtains the energy to support the RNA world by a thermal variation of F1 ATP synthase's binding change mechanism. This pF1 is the single translation product during the emergence of the genetic machinery. During thermal cycling pF1 condenses many substrates with broad specificity, yielding NTPs and randomly constituted protein and RNA libraries that contain (self)-replicating RNA. The smallness of pF1 permits the emergence of the genetic machinery by selection of RNA that increases the fraction of pF1s in the protein library: (1) a progenitor of rRNA that concatenates amino acids bound to (2) a chain of 'positional tRNAs' linked by mutual recognition, yielding a pF1 (or its main motif); this positional tRNA set gradually evolves to a set of regular tRNAs functioning according to the genetic code, with concomitant emergence of (3) an mRNA coding for pF1.
|
1702.02510
|
Juan Abdon Miranda Correa
|
Juan Abdon Miranda-Correa and Mojtaba Khomami Abadi and Nicu Sebe and
Ioannis Patras
|
AMIGOS: A Dataset for Affect, Personality and Mood Research on
Individuals and Groups
|
14 pages, Transaction on Affective Computing
| null | null | null |
q-bio.NC cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present AMIGOS-- A dataset for Multimodal research of affect, personality
traits and mood on Individuals and GrOupS. Different to other databases, we
elicited affect using both short and long videos in two social contexts, one
with individual viewers and one with groups of viewers. The database allows the
multimodal study of the affective responses, by means of neuro-physiological
signals of individuals in relation to their personality and mood, and with
respect to the social context and videos' duration. The data is collected in
two experimental settings. In the first one, 40 participants watched 16 short
emotional videos. In the second one, the participants watched 4 long videos,
some of them alone and the rest in groups. The participants' signals, namely,
Electroencephalogram (EEG), Electrocardiogram (ECG) and Galvanic Skin Response
(GSR), were recorded using wearable sensors. Participants' frontal HD video and
both RGB and depth full body videos were also recorded. Participants emotions
have been annotated with both self-assessment of affective levels (valence,
arousal, control, familiarity, liking and basic emotions) felt during the
videos as well as external-assessment of levels of valence and arousal. We
present a detailed correlation analysis of the different dimensions as well as
baseline methods and results for single-trial classification of valence and
arousal, personality traits, mood and social context. The database is made
publicly available.
|
[
{
"created": "Thu, 2 Feb 2017 08:04:47 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Mar 2017 14:27:10 GMT",
"version": "v2"
},
{
"created": "Thu, 13 Apr 2017 16:10:00 GMT",
"version": "v3"
}
] |
2017-04-14
|
[
[
"Miranda-Correa",
"Juan Abdon",
""
],
[
"Abadi",
"Mojtaba Khomami",
""
],
[
"Sebe",
"Nicu",
""
],
[
"Patras",
"Ioannis",
""
]
] |
We present AMIGOS-- A dataset for Multimodal research of affect, personality traits and mood on Individuals and GrOupS. Different to other databases, we elicited affect using both short and long videos in two social contexts, one with individual viewers and one with groups of viewers. The database allows the multimodal study of the affective responses, by means of neuro-physiological signals of individuals in relation to their personality and mood, and with respect to the social context and videos' duration. The data is collected in two experimental settings. In the first one, 40 participants watched 16 short emotional videos. In the second one, the participants watched 4 long videos, some of them alone and the rest in groups. The participants' signals, namely, Electroencephalogram (EEG), Electrocardiogram (ECG) and Galvanic Skin Response (GSR), were recorded using wearable sensors. Participants' frontal HD video and both RGB and depth full body videos were also recorded. Participants emotions have been annotated with both self-assessment of affective levels (valence, arousal, control, familiarity, liking and basic emotions) felt during the videos as well as external-assessment of levels of valence and arousal. We present a detailed correlation analysis of the different dimensions as well as baseline methods and results for single-trial classification of valence and arousal, personality traits, mood and social context. The database is made publicly available.
|
2008.08758
|
Affan Affan
|
Affan Affan and Tamer Inanc
|
Semi-Blind and l1 Robust System Identification for Anemia Management
|
Under-review at The Fifth IEEE/ACM conference on Connected Health:
Applications, Systems and Engineering Technologies (CHASE) 2020
| null | null | null |
q-bio.QM cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Chronic diseases such as cancer, diabetes, heart diseases, chronic kidney
disease (CKD) require a drug management system that ensures a stable and robust
output of the patient's condition in response to drug dosage. In the case of
CKD, the patients suffer from the deficiency of red blood cell count and
external human recombinant erythropoietin (EPO) is required to maintain healthy
levels of hemoglobin (Hb). Anemia is a common comorbidity in patients with CKD.
For an efficient and robust anemia management system for CKD patients instead
of traditional population-based approaches, individualized patient-specific
approaches are needed. Hence, individualized system (patient) models for
patient-specific drug-dose responses are required. In this research, system
identification for CKD is performed for individual patients. For
control-oriented system identification, two robust identification techniques
are applied: (1) l1 robust identification considering zero initial conditions
and (2) semi-blind robust system identification considering non-zero initial
conditions. The EPO data of patients are used as the input and Hb data is used
as the output of the system. For this study, individualized patient models are
developed by using patient-specific data. The ARX one-step-ahead prediction
technique is used for model validation at real patient data. The performance of
these two techniques is compared by calculating minimum means square error
(MMSE). By comparison, we show that the semi-blind robust identification
technique gives better results as compared to l1 robust identification.
|
[
{
"created": "Thu, 20 Aug 2020 03:50:10 GMT",
"version": "v1"
}
] |
2020-08-21
|
[
[
"Affan",
"Affan",
""
],
[
"Inanc",
"Tamer",
""
]
] |
Chronic diseases such as cancer, diabetes, heart diseases, chronic kidney disease (CKD) require a drug management system that ensures a stable and robust output of the patient's condition in response to drug dosage. In the case of CKD, the patients suffer from the deficiency of red blood cell count and external human recombinant erythropoietin (EPO) is required to maintain healthy levels of hemoglobin (Hb). Anemia is a common comorbidity in patients with CKD. For an efficient and robust anemia management system for CKD patients instead of traditional population-based approaches, individualized patient-specific approaches are needed. Hence, individualized system (patient) models for patient-specific drug-dose responses are required. In this research, system identification for CKD is performed for individual patients. For control-oriented system identification, two robust identification techniques are applied: (1) l1 robust identification considering zero initial conditions and (2) semi-blind robust system identification considering non-zero initial conditions. The EPO data of patients are used as the input and Hb data is used as the output of the system. For this study, individualized patient models are developed by using patient-specific data. The ARX one-step-ahead prediction technique is used for model validation at real patient data. The performance of these two techniques is compared by calculating minimum means square error (MMSE). By comparison, we show that the semi-blind robust identification technique gives better results as compared to l1 robust identification.
|
1111.6916
|
Nilima Nigam
|
Marc Ryser, Svetlana V. Komarova, Nilima Nigam
|
The cellular dynamics of bone remodeling: a mathematical model
| null |
SIAM J. Appl. Math. 70, pp. 1899-1921
| null | null |
q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The mechanical properties of vertebrate bone are largely determined by a
process which involves the complex interplay of three different cell types.
This process is called {\it bone remodeling}, and occurs asynchronously at
multiple sites in the mature skeleton. The cells involved are bone resorbing
osteoclasts, bone matrix producing osteoblasts and mechanosensing osteocytes.
These cells communicate with each other by means of autocrine and paracrine
signaling factors and operate in complex entities, the so-called bone
multicellular units (BMU). To investigate the BMU dynamics in silico, we
develop a novel mathematical model resulting in a system of nonlinear partial
differential equations with time delays. The model describes the osteoblast and
osteoclast populations together with the dynamics of the key messenger molecule
RANKL and its decoy receptor OPG. Scaling theory is used to address parameter
sensitivity and predict the emergence of pathological remodeling regimes. The
model is studied numerically in one and two space dimensions using finite
difference schemes in space and explicit delay equation solvers in time. The
computational results are in agreement with in vivo observations and provide
new insights into the role of the RANKL/OPG pathway in the spatial regulation
of bone remodeling.
|
[
{
"created": "Sun, 27 Nov 2011 21:43:03 GMT",
"version": "v1"
}
] |
2011-11-30
|
[
[
"Ryser",
"Marc",
""
],
[
"Komarova",
"Svetlana V.",
""
],
[
"Nigam",
"Nilima",
""
]
] |
The mechanical properties of vertebrate bone are largely determined by a process which involves the complex interplay of three different cell types. This process is called {\it bone remodeling}, and occurs asynchronously at multiple sites in the mature skeleton. The cells involved are bone resorbing osteoclasts, bone matrix producing osteoblasts and mechanosensing osteocytes. These cells communicate with each other by means of autocrine and paracrine signaling factors and operate in complex entities, the so-called bone multicellular units (BMU). To investigate the BMU dynamics in silico, we develop a novel mathematical model resulting in a system of nonlinear partial differential equations with time delays. The model describes the osteoblast and osteoclast populations together with the dynamics of the key messenger molecule RANKL and its decoy receptor OPG. Scaling theory is used to address parameter sensitivity and predict the emergence of pathological remodeling regimes. The model is studied numerically in one and two space dimensions using finite difference schemes in space and explicit delay equation solvers in time. The computational results are in agreement with in vivo observations and provide new insights into the role of the RANKL/OPG pathway in the spatial regulation of bone remodeling.
|
1111.6631
|
Arash Sangari Mr.
|
Arash Sangari, Adel Ardalan, Larry Lambe, Hamid Eghbalnia and Amir H.
Assadi
|
Mathematical Analysis and Computational Integration of Massive
Heterogeneous Data from the Human Retina
|
9 pages, 3 figures, submitted and accepted in Damor2012 conference:
http://www.uninova.pt/damor2012/index.php?page=authors
| null | null | null |
q-bio.QM cs.IR math.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Modern epidemiology integrates knowledge from heterogeneous collections of
data consisting of numerical, descriptive and imaging. Large-scale
epidemiological studies use sophisticated statistical analysis, mathematical
models using differential equations and versatile analytic tools that handle
numerical data. In contrast, knowledge extraction from images and descriptive
information in the form of text and diagrams remain a challenge for most
fields, in particular, for diseases of the eye. In this article we provide a
roadmap towards extraction of knowledge from text and images with focus on
forthcoming applications to epidemiological investigation of retinal diseases,
especially from existing massive heterogeneous collections of data distributed
around the globe.
|
[
{
"created": "Mon, 28 Nov 2011 22:01:19 GMT",
"version": "v1"
},
{
"created": "Wed, 10 Oct 2012 19:52:29 GMT",
"version": "v2"
}
] |
2012-10-11
|
[
[
"Sangari",
"Arash",
""
],
[
"Ardalan",
"Adel",
""
],
[
"Lambe",
"Larry",
""
],
[
"Eghbalnia",
"Hamid",
""
],
[
"Assadi",
"Amir H.",
""
]
] |
Modern epidemiology integrates knowledge from heterogeneous collections of data consisting of numerical, descriptive and imaging. Large-scale epidemiological studies use sophisticated statistical analysis, mathematical models using differential equations and versatile analytic tools that handle numerical data. In contrast, knowledge extraction from images and descriptive information in the form of text and diagrams remain a challenge for most fields, in particular, for diseases of the eye. In this article we provide a roadmap towards extraction of knowledge from text and images with focus on forthcoming applications to epidemiological investigation of retinal diseases, especially from existing massive heterogeneous collections of data distributed around the globe.
|
1411.2103
|
Michael Schaub
|
Yazan N. Billeh, Michael T. Schaub, Costas A. Anastassiou, Mauricio
Barahona, Christof Koch
|
Revealing cell assemblies at multiple levels of granularity
|
18 pages; 13 Figures; published as open access in J Neuro Methods
|
Journal of Neuroscience Methods, Volume 236, 30 October 2014,
Pages 92-106, ISSN 0165-0270
|
10.1016/j.jneumeth.2014.08.011
| null |
q-bio.NC physics.data-an
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Background: Current neuronal monitoring techniques, such as calcium imaging
and multi-electrode arrays, enable recordings of spiking activity from hundreds
of neurons simultaneously. Of primary importance in systems neuroscience is the
identification of cell assemblies: groups of neurons that cooperate in some
form within the recorded population.
New Method: We introduce a simple, integrated framework for the detection of
cell-assemblies from spiking data without a priori assumptions about the size
or number of groups present. We define a biophysically-inspired measure to
extract a directed functional connectivity matrix between both excitatory and
inhibitory neurons based on their spiking history. The resulting network
representation is analyzed using the Markov Stability framework, a graph
theoretical method for community detection across scales, to reveal groups of
neurons that are significantly related in the recorded time-series at different
levels of granularity.
Results and comparison with existing methods: Using synthetic spike-trains,
including simulated data from leaky-integrate-and-fire networks, our method is
able to identify important patterns in the data such as hierarchical structure
that are missed by other standard methods. We further apply the method to
experimental data from retinal ganglion cells of mouse and salamander, in which
we identify cell-groups that correspond to known functional types, and to
hippocampal recordings from rats exploring a linear track, where we detect
place cells with high fidelity.
Conclusions: We present a versatile method to detect neural assemblies in
spiking data applicable across a spectrum of relevant scales that contributes
to understanding spatio-temporal information gathered from systems neuroscience
experiments.
|
[
{
"created": "Sat, 8 Nov 2014 10:02:33 GMT",
"version": "v1"
}
] |
2014-11-11
|
[
[
"Billeh",
"Yazan N.",
""
],
[
"Schaub",
"Michael T.",
""
],
[
"Anastassiou",
"Costas A.",
""
],
[
"Barahona",
"Mauricio",
""
],
[
"Koch",
"Christof",
""
]
] |
Background: Current neuronal monitoring techniques, such as calcium imaging and multi-electrode arrays, enable recordings of spiking activity from hundreds of neurons simultaneously. Of primary importance in systems neuroscience is the identification of cell assemblies: groups of neurons that cooperate in some form within the recorded population. New Method: We introduce a simple, integrated framework for the detection of cell-assemblies from spiking data without a priori assumptions about the size or number of groups present. We define a biophysically-inspired measure to extract a directed functional connectivity matrix between both excitatory and inhibitory neurons based on their spiking history. The resulting network representation is analyzed using the Markov Stability framework, a graph theoretical method for community detection across scales, to reveal groups of neurons that are significantly related in the recorded time-series at different levels of granularity. Results and comparison with existing methods: Using synthetic spike-trains, including simulated data from leaky-integrate-and-fire networks, our method is able to identify important patterns in the data such as hierarchical structure that are missed by other standard methods. We further apply the method to experimental data from retinal ganglion cells of mouse and salamander, in which we identify cell-groups that correspond to known functional types, and to hippocampal recordings from rats exploring a linear track, where we detect place cells with high fidelity. Conclusions: We present a versatile method to detect neural assemblies in spiking data applicable across a spectrum of relevant scales that contributes to understanding spatio-temporal information gathered from systems neuroscience experiments.
|
1510.05917
|
Gestionnaire Hal-Upmc
|
Genevi\'eve Rodier (IGMM, IRCM), Olivier Kirsh (IGMM), Mart\'in
Baraibar (B2A), Thibault Houl\'es (IGMM, IPBS, IRCM), Matthieu Lacroix (UTA,
IRCM), H\'el\'ene Delpech (IGMM, IRCM), Elodie Hatchi (IGMM), St\'ephanie
Arnould (IGMM, IRCM), Dany Severac, Emeric Dubois, Julie Caramel (IGMM), Eric
Julien (IGMM, IRCM), Bertrand Friguet (B2A), Laurent Le Cam (IRCM), Claude
Sardet (IGMM, IRCM)
|
The Transcription Factor E4F1 Coordinates CHK1-Dependent Checkpoint and
Mitochondrial Functions
| null |
Cell Reports, Elsevier, 2015, 11 (2), pp.220-233.
\<10.1016/j.celrep.2015.03.024\>
|
10.1016/j.celrep.2015.03.024
| null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent data support the notion that a group of key transcriptional regulators
involved in tumorigenesis, including MYC, p53, E2F1, and BMI1, share an
intriguing capacity to simultaneously regulate metabolism and cell cycle. Here,
we show that another factor, the multifunctional protein E4F1, directly
controls genes involved in mitochondria functions and cell-cycle checkpoints,
including Chek1, a major component of the DNA damage response. Coordination of
these cellular functions by E4F1 appears essential for the survival of
p53-deficient transformed cells. Acute inactivation of E4F1 in these cells
results in CHK1-dependent checkpoint deficiency and multiple mitochondrial
dysfunctions that lead to increased ROS production, energy stress, and
inhibition of de novo pyrimidine synthesis. This deadly cocktail leads to the
accumulation of uncompensated oxidative damage to proteins and extensive DNA
damage, ending in cell death. This supports the rationale of therapeutic
strategies simultaneously targeting mitochondria and CHK1 for selective killing
of p53-deficient cancer cells.
|
[
{
"created": "Wed, 14 Oct 2015 13:28:27 GMT",
"version": "v1"
}
] |
2015-10-21
|
[
[
"Rodier",
"Geneviéve",
"",
"IGMM, IRCM"
],
[
"Kirsh",
"Olivier",
"",
"IGMM"
],
[
"Baraibar",
"Martín",
"",
"B2A"
],
[
"Houlés",
"Thibault",
"",
"IGMM, IPBS, IRCM"
],
[
"Lacroix",
"Matthieu",
"",
"UTA,\n IRCM"
],
[
"Delpech",
"Héléne",
"",
"IGMM, IRCM"
],
[
"Hatchi",
"Elodie",
"",
"IGMM"
],
[
"Arnould",
"Stéphanie",
"",
"IGMM, IRCM"
],
[
"Severac",
"Dany",
"",
"IGMM"
],
[
"Dubois",
"Emeric",
"",
"IGMM"
],
[
"Caramel",
"Julie",
"",
"IGMM"
],
[
"Julien",
"Eric",
"",
"IGMM, IRCM"
],
[
"Friguet",
"Bertrand",
"",
"B2A"
],
[
"Cam",
"Laurent Le",
"",
"IRCM"
],
[
"Sardet",
"Claude",
"",
"IGMM, IRCM"
]
] |
Recent data support the notion that a group of key transcriptional regulators involved in tumorigenesis, including MYC, p53, E2F1, and BMI1, share an intriguing capacity to simultaneously regulate metabolism and cell cycle. Here, we show that another factor, the multifunctional protein E4F1, directly controls genes involved in mitochondria functions and cell-cycle checkpoints, including Chek1, a major component of the DNA damage response. Coordination of these cellular functions by E4F1 appears essential for the survival of p53-deficient transformed cells. Acute inactivation of E4F1 in these cells results in CHK1-dependent checkpoint deficiency and multiple mitochondrial dysfunctions that lead to increased ROS production, energy stress, and inhibition of de novo pyrimidine synthesis. This deadly cocktail leads to the accumulation of uncompensated oxidative damage to proteins and extensive DNA damage, ending in cell death. This supports the rationale of therapeutic strategies simultaneously targeting mitochondria and CHK1 for selective killing of p53-deficient cancer cells.
|
2310.09529
|
Katherine Ge
|
Katherine Ge, Dayna Olson, and Michel F. Sanner
|
Docking Peptides into HIV/FIV Protease with Deep Learning and Focused
Peptide Docking Methods
|
9 Pages, 5 Figures, 2 Tables
| null | null | null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Molecular docking is a structure-based computational drug design technique
for predicting the interaction between a small molecule (ligand) and a
macromolecule (receptor). Over the past three decades various docking software
programs have been developed, mostly for drug-like molecules. With the recent
interest in peptides as therapeutic molecules, several peptide docking methods
have also been developed. AutoDock CrankPep (ADCP), is a state-of-the-art
peptide docking tool that predicts the interaction of peptide with up to 20
amino acids in a user defined region of a macromolecule, i.e.focused docking.
Recent advances in deep learning (DL) approaches have shown remarkable success
in docking linear peptides composed of natural amino acids only. Unlike ADCP,
these methods provide a confidence level in their predictions. Here we explore
whether ADCP and various DL methods (AlphaFold2 Monomer, AlphaFold2 Multimer,
and OmegaFold) and their prediction confidence metric can be used to
discriminate native and non-native substrates for HIV and FIV proteases. We
found that ADCP successfully predicts the interactions of native peptides but
fails to discriminate non-native ones. This was expected as conventional
docking methods report solutions maximizing ligand receptor interactions for
any ligand. Surprisingly, DL methods underperform when docking native peptides
into these particular docking targets but achieve high success rates with
non-native peptides. While AlphaFold managed to successfully dock a few of the
native peptides, OmegaFold failed to successfully dock any of them. Overall,
none of these methods is currently able to distinguish between native and
non-native peptides, warranting further exploration of specialized
methodologies.
|
[
{
"created": "Sat, 14 Oct 2023 08:14:54 GMT",
"version": "v1"
}
] |
2023-10-18
|
[
[
"Ge",
"Katherine",
""
],
[
"Olson",
"Dayna",
""
],
[
"Sanner",
"Michel F.",
""
]
] |
Molecular docking is a structure-based computational drug design technique for predicting the interaction between a small molecule (ligand) and a macromolecule (receptor). Over the past three decades various docking software programs have been developed, mostly for drug-like molecules. With the recent interest in peptides as therapeutic molecules, several peptide docking methods have also been developed. AutoDock CrankPep (ADCP), is a state-of-the-art peptide docking tool that predicts the interaction of peptide with up to 20 amino acids in a user defined region of a macromolecule, i.e.focused docking. Recent advances in deep learning (DL) approaches have shown remarkable success in docking linear peptides composed of natural amino acids only. Unlike ADCP, these methods provide a confidence level in their predictions. Here we explore whether ADCP and various DL methods (AlphaFold2 Monomer, AlphaFold2 Multimer, and OmegaFold) and their prediction confidence metric can be used to discriminate native and non-native substrates for HIV and FIV proteases. We found that ADCP successfully predicts the interactions of native peptides but fails to discriminate non-native ones. This was expected as conventional docking methods report solutions maximizing ligand receptor interactions for any ligand. Surprisingly, DL methods underperform when docking native peptides into these particular docking targets but achieve high success rates with non-native peptides. While AlphaFold managed to successfully dock a few of the native peptides, OmegaFold failed to successfully dock any of them. Overall, none of these methods is currently able to distinguish between native and non-native peptides, warranting further exploration of specialized methodologies.
|
2311.09261
|
Yongqi Zhang
|
Yongqi Zhang, Quanming Yao, Ling Yue, Xian Wu, Ziheng Zhang, Zhenxi
Lin, Yefeng Zheng
|
Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural
Network with Biomedical Network
|
Accepted by Nature Computational Science
| null | null | null |
q-bio.QM cs.AI cs.CE cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Accurately predicting drug-drug interactions (DDI) for emerging drugs, which
offer possibilities for treating and alleviating diseases, with computational
methods can improve patient care and contribute to efficient drug development.
However, many existing computational methods require large amounts of known DDI
information, which is scarce for emerging drugs. In this paper, we propose
EmerGNN, a graph neural network (GNN) that can effectively predict interactions
for emerging drugs by leveraging the rich information in biomedical networks.
EmerGNN learns pairwise representations of drugs by extracting the paths
between drug pairs, propagating information from one drug to the other, and
incorporating the relevant biomedical concepts on the paths. The different
edges on the biomedical network are weighted to indicate the relevance for the
target DDI prediction. Overall, EmerGNN has higher accuracy than existing
approaches in predicting interactions for emerging drugs and can identify the
most relevant information on the biomedical network.
|
[
{
"created": "Wed, 15 Nov 2023 06:34:00 GMT",
"version": "v1"
}
] |
2023-11-17
|
[
[
"Zhang",
"Yongqi",
""
],
[
"Yao",
"Quanming",
""
],
[
"Yue",
"Ling",
""
],
[
"Wu",
"Xian",
""
],
[
"Zhang",
"Ziheng",
""
],
[
"Lin",
"Zhenxi",
""
],
[
"Zheng",
"Yefeng",
""
]
] |
Accurately predicting drug-drug interactions (DDI) for emerging drugs, which offer possibilities for treating and alleviating diseases, with computational methods can improve patient care and contribute to efficient drug development. However, many existing computational methods require large amounts of known DDI information, which is scarce for emerging drugs. In this paper, we propose EmerGNN, a graph neural network (GNN) that can effectively predict interactions for emerging drugs by leveraging the rich information in biomedical networks. EmerGNN learns pairwise representations of drugs by extracting the paths between drug pairs, propagating information from one drug to the other, and incorporating the relevant biomedical concepts on the paths. The different edges on the biomedical network are weighted to indicate the relevance for the target DDI prediction. Overall, EmerGNN has higher accuracy than existing approaches in predicting interactions for emerging drugs and can identify the most relevant information on the biomedical network.
|
1403.2160
|
Tiberiu Harko
|
Tiberiu Harko, Francisco S. N. Lobo, M. K. Mak
|
Exact analytical solutions of the Susceptible-Infected-Recovered (SIR)
epidemic model and of the SIR model with equal death and birth rates
|
13 pages, 4 figures, accepted for publication in Applied Mathematics
and Computation
|
Applied Mathematics and Computation, 236, 2014, 184-194
|
10.1016/j.amc.2014.03.030
| null |
q-bio.PE nlin.CD
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, the exact analytical solution of the
Susceptible-Infected-Recovered (SIR) epidemic model is obtained in a parametric
form. By using the exact solution we investigate some explicit models
corresponding to fixed values of the parameters, and show that the numerical
solution reproduces exactly the analytical solution. We also show that the
generalization of the SIR model, including births and deaths, described by a
nonlinear system of differential equations, can be reduced to an Abel type
equation. The reduction of the complex SIR model with vital dynamics to an Abel
type equation can greatly simplify the analysis of its properties. The general
solution of the Abel equation is obtained by using a perturbative approach, in
a power series form, and it is shown that the general solution of the SIR model
with vital dynamics can be represented in an exact parametric form.
|
[
{
"created": "Mon, 10 Mar 2014 08:21:07 GMT",
"version": "v1"
}
] |
2014-04-24
|
[
[
"Harko",
"Tiberiu",
""
],
[
"Lobo",
"Francisco S. N.",
""
],
[
"Mak",
"M. K.",
""
]
] |
In this paper, the exact analytical solution of the Susceptible-Infected-Recovered (SIR) epidemic model is obtained in a parametric form. By using the exact solution we investigate some explicit models corresponding to fixed values of the parameters, and show that the numerical solution reproduces exactly the analytical solution. We also show that the generalization of the SIR model, including births and deaths, described by a nonlinear system of differential equations, can be reduced to an Abel type equation. The reduction of the complex SIR model with vital dynamics to an Abel type equation can greatly simplify the analysis of its properties. The general solution of the Abel equation is obtained by using a perturbative approach, in a power series form, and it is shown that the general solution of the SIR model with vital dynamics can be represented in an exact parametric form.
|
1703.00226
|
Thierry Mora
|
Jonathan Desponds, Andreas Mayer, Thierry Mora, Aleksandra M. Walczak
|
Population dynamics of immune repertoires
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The evolution of the adaptive immune system is characterized by changes in
the relative abundances of the B- and T-cell clones that make up its
repertoires. To fully capture this evolution, we need to describe the complex
dynamics of the response to pathogenic and self-antigenic stimulations, as well
as the statistics of novel lymphocyte receptors introduced throughout life.
Recent experiments, ranging from high-throughput immune repertoire sequencing
to quantification of the response to specific antigens, can help us
characterize the effective dynamics of the immune response. Here we describe
mathematical models informed by experiments that lead to a picture of clonal
competition in a highly stochastic context. We discuss how different types of
competition, noise and selection shape the observed clone-size distributions,
and contrast them with predictions of a neutral theory of clonal evolution.
These mathematical models show that memory and effector immune repertoire
evolution is far from neutral, and is driven by the history of the pathogenic
environment, while naive repertoire dynamics are consistent with neutral theory
and competition in a fixed antigenic environment. Lastly, we investigate the
effect of long-term clonal selection on repertoire aging.
|
[
{
"created": "Wed, 1 Mar 2017 10:54:59 GMT",
"version": "v1"
}
] |
2017-03-02
|
[
[
"Desponds",
"Jonathan",
""
],
[
"Mayer",
"Andreas",
""
],
[
"Mora",
"Thierry",
""
],
[
"Walczak",
"Aleksandra M.",
""
]
] |
The evolution of the adaptive immune system is characterized by changes in the relative abundances of the B- and T-cell clones that make up its repertoires. To fully capture this evolution, we need to describe the complex dynamics of the response to pathogenic and self-antigenic stimulations, as well as the statistics of novel lymphocyte receptors introduced throughout life. Recent experiments, ranging from high-throughput immune repertoire sequencing to quantification of the response to specific antigens, can help us characterize the effective dynamics of the immune response. Here we describe mathematical models informed by experiments that lead to a picture of clonal competition in a highly stochastic context. We discuss how different types of competition, noise and selection shape the observed clone-size distributions, and contrast them with predictions of a neutral theory of clonal evolution. These mathematical models show that memory and effector immune repertoire evolution is far from neutral, and is driven by the history of the pathogenic environment, while naive repertoire dynamics are consistent with neutral theory and competition in a fixed antigenic environment. Lastly, we investigate the effect of long-term clonal selection on repertoire aging.
|
2006.13752
|
Paulo Zingano
|
Paulo R. Zingano, Janaina P. Zingano, Alessandra M. Silva and Carolina
P. Zingano
|
A matlab code to compute reproduction numbers with applications to the
Covid-19 outbreak
|
A complete matlab program (with about 500 lines) implementing the
algorithm described in this work can be downloaded for free at the following
URL address:
https://drive.google.com/drive/folders/16kLxlZyqH-QATOLQI6QWTx7qZnL3IoCP
| null | null | null |
q-bio.PE physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We discuss the generation of various reproduction ratios or numbers that are
very useful to monitor an ongoing epidemic like Covid-19 and examine the
effects of intervention measures. A detailed SEIR algorithm is described for
their computation, with applications given to the current Covid-19 outbreaks in
a number of countries (Argentina, Brazil, France, Italy, Mexico, Spain, UK and
USA). The corresponding matlab script, complete and ready to use, is provided
for free downloading.
|
[
{
"created": "Tue, 23 Jun 2020 17:57:28 GMT",
"version": "v1"
}
] |
2020-06-25
|
[
[
"Zingano",
"Paulo R.",
""
],
[
"Zingano",
"Janaina P.",
""
],
[
"Silva",
"Alessandra M.",
""
],
[
"Zingano",
"Carolina P.",
""
]
] |
We discuss the generation of various reproduction ratios or numbers that are very useful to monitor an ongoing epidemic like Covid-19 and examine the effects of intervention measures. A detailed SEIR algorithm is described for their computation, with applications given to the current Covid-19 outbreaks in a number of countries (Argentina, Brazil, France, Italy, Mexico, Spain, UK and USA). The corresponding matlab script, complete and ready to use, is provided for free downloading.
|
1902.01574
|
Tzvetomir Tzvetanov
|
Tzvetomir Tzvetanov
|
Suppression and facilitation of motion perception in humans: a reply to
Schallmo & Murray (2018)
|
9 pages
| null | null | null |
q-bio.NC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In a recent publication (Tzvetanov (2018), bioRxiv 465807), I made an
extensive analysis with computational modelling and psychophysics of the simple
experimental design of Dr. D.Tadin (Tadin, Lappin, Gilroy and Blake (2003),
Nature, 424:312-315) about motion perception changes in humans due to size and
contrast of the stimulus. This publication sparked from strong claims made in
Schallmo et al. (2018) (eLife, 7:e30334) about two important points: (1)
"divisive normalization", not inhibitory and excitatory mechanisms, creates the
observed psychophysical results and (2) drug-enhanced inhibition showed
perceptual outcomes that hint to "weaker suppression" (i.e. inhibition) not
stronger "suppression". Schallmo & Murray (2018, bioRxiv, 495291) presented
concerns about my extensive publication, specifically about the parts where I
directly analysed some of their methods, results and claims. Here, I show that
their concerns do not provide clear answers to my specific points and further
do not mention other major critiques of data interpretation and modelling of
this experimental design. Therefore, I maintain all my claims that were
elaborated in details in my first publication (Tzvetanov, 2018, bioRxiv
465807): the specific ones that analyse the results of their and other studies,
but also the more broad modelling that is applicable to any study using the
simple experimental design of Dr. D.Tadin.
|
[
{
"created": "Tue, 5 Feb 2019 07:34:35 GMT",
"version": "v1"
}
] |
2019-02-06
|
[
[
"Tzvetanov",
"Tzvetomir",
""
]
] |
In a recent publication (Tzvetanov (2018), bioRxiv 465807), I made an extensive analysis with computational modelling and psychophysics of the simple experimental design of Dr. D.Tadin (Tadin, Lappin, Gilroy and Blake (2003), Nature, 424:312-315) about motion perception changes in humans due to size and contrast of the stimulus. This publication sparked from strong claims made in Schallmo et al. (2018) (eLife, 7:e30334) about two important points: (1) "divisive normalization", not inhibitory and excitatory mechanisms, creates the observed psychophysical results and (2) drug-enhanced inhibition showed perceptual outcomes that hint to "weaker suppression" (i.e. inhibition) not stronger "suppression". Schallmo & Murray (2018, bioRxiv, 495291) presented concerns about my extensive publication, specifically about the parts where I directly analysed some of their methods, results and claims. Here, I show that their concerns do not provide clear answers to my specific points and further do not mention other major critiques of data interpretation and modelling of this experimental design. Therefore, I maintain all my claims that were elaborated in details in my first publication (Tzvetanov, 2018, bioRxiv 465807): the specific ones that analyse the results of their and other studies, but also the more broad modelling that is applicable to any study using the simple experimental design of Dr. D.Tadin.
|
2103.06256
|
Perrine Paul-Gilloteaux
|
Guillaume Potier, Fr\'ed\'eric Lavancier, Stephan Kunne and Perrine
Paul-Gilloteaux
|
A registration error estimation framework for correlative imaging
|
10 pages 2 figures (made of 10 panels in total)
| null |
10.1109/ICIP42928.2021.9506474
| null |
q-bio.QM cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Correlative imaging workflows are now widely used in bioimaging and aims to
image the same sample using at least two different and complementary imaging
modalities. Part of the workflow relies on finding the transformation linking a
source image to a target image. We are specifically interested in the
estimation of registration error in point-based registration. We propose an
application of multivariate linear regression to solve the registration problem
allowing us to propose a framework for the estimation of the associated error
in the case of rigid and affine transformations and with anisotropic noise.
These developments can be used as a decision-support tool for the biologist to
analyze multimodal correlative images and are available under Ec-CLEM, an
open-source plugin under ICY.
|
[
{
"created": "Wed, 10 Mar 2021 18:43:18 GMT",
"version": "v1"
}
] |
2021-08-30
|
[
[
"Potier",
"Guillaume",
""
],
[
"Lavancier",
"Frédéric",
""
],
[
"Kunne",
"Stephan",
""
],
[
"Paul-Gilloteaux",
"Perrine",
""
]
] |
Correlative imaging workflows are now widely used in bioimaging and aims to image the same sample using at least two different and complementary imaging modalities. Part of the workflow relies on finding the transformation linking a source image to a target image. We are specifically interested in the estimation of registration error in point-based registration. We propose an application of multivariate linear regression to solve the registration problem allowing us to propose a framework for the estimation of the associated error in the case of rigid and affine transformations and with anisotropic noise. These developments can be used as a decision-support tool for the biologist to analyze multimodal correlative images and are available under Ec-CLEM, an open-source plugin under ICY.
|
2012.05045
|
Anindita Bhadra
|
Debottam Bhattacharjee and Anindita Bhadra
|
Response to sudden surge in human movement by an urban-adapted animal
|
1 figure
|
Behav Ecol Sociobiol 75, 111 (2021)
|
10.1007/s00265-021-03052-x
| null |
q-bio.PE
|
http://creativecommons.org/licenses/by/4.0/
|
Interaction with its immediate environment determines the ecology of an
organism. Species present in any habitat, wild or urban, may face extreme
pressure due to sudden perturbations. When such disturbances are unpredictable,
it becomes more challenging to tackle. Implementation of specific strategies is
therefore essential for different species to overcome adverse situations.
Numerous biotic and abiotic factors can alter the dynamics of a species.
Anthropogenic disturbance is one such factor that has considerable implications
and also the potential to impact species living in the proximity of human
habitats. We investigated the response of an urban adapted species to a sudden
surge in human footfall or overcrowding. Dogs (Canis lupus familiaris) living
freely in the streets of developing countries experience tremendous
anthropogenic pressure. It is known that human movement in an area can predict
the behaviour of these dogs by largely influencing their personalities. In the
current study, we observed a strong effect of high and sudden human footfall on
the abundance and behavioural activity of dogs. A decline in both the abundance
of dogs and behavioural activities was seen with the increase in human
movement. Further investigation over a restricted temporal scale revealed
reinstated behavioural activity but non-restoration of population abundance.
This provides important evidence on the extent to which humans influence the
behaviour of free-ranging dogs in urban environments.
|
[
{
"created": "Mon, 7 Dec 2020 12:27:34 GMT",
"version": "v1"
}
] |
2022-08-12
|
[
[
"Bhattacharjee",
"Debottam",
""
],
[
"Bhadra",
"Anindita",
""
]
] |
Interaction with its immediate environment determines the ecology of an organism. Species present in any habitat, wild or urban, may face extreme pressure due to sudden perturbations. When such disturbances are unpredictable, it becomes more challenging to tackle. Implementation of specific strategies is therefore essential for different species to overcome adverse situations. Numerous biotic and abiotic factors can alter the dynamics of a species. Anthropogenic disturbance is one such factor that has considerable implications and also the potential to impact species living in the proximity of human habitats. We investigated the response of an urban adapted species to a sudden surge in human footfall or overcrowding. Dogs (Canis lupus familiaris) living freely in the streets of developing countries experience tremendous anthropogenic pressure. It is known that human movement in an area can predict the behaviour of these dogs by largely influencing their personalities. In the current study, we observed a strong effect of high and sudden human footfall on the abundance and behavioural activity of dogs. A decline in both the abundance of dogs and behavioural activities was seen with the increase in human movement. Further investigation over a restricted temporal scale revealed reinstated behavioural activity but non-restoration of population abundance. This provides important evidence on the extent to which humans influence the behaviour of free-ranging dogs in urban environments.
|
2407.07114
|
Josh Morgan
|
Josh L. Morgan
|
Alternatives to the statistical mass confusion of testing for no-effect
|
13 pages, 1 figure
| null | null | null |
q-bio.OT
|
http://creativecommons.org/licenses/by/4.0/
|
Statisticians and researchers have argued about the merits of effect size
estimation relative to hypothesis testing for decades. Cell biology has largely
avoided this debate and is now in a quantitation crisis. In experimental cell
biology, statistical analysis has grown to mean testing the null hypothesis
that there was no experimental effect. This weak form of hypothesis testing
neglects effect size, is universally misinterpreted, and is disastrously prone
to error when combined with high-throughput cell biology. The first part of the
solution proposed here is to limit statistical hypothesis testing to the small
subset of experiments where a biologically meaningful null hypotheses can be
defined prior to the experiment. The second part of the solution is to make
confidence intervals the default statistic in cell biology.
|
[
{
"created": "Fri, 28 Jun 2024 00:08:38 GMT",
"version": "v1"
}
] |
2024-07-11
|
[
[
"Morgan",
"Josh L.",
""
]
] |
Statisticians and researchers have argued about the merits of effect size estimation relative to hypothesis testing for decades. Cell biology has largely avoided this debate and is now in a quantitation crisis. In experimental cell biology, statistical analysis has grown to mean testing the null hypothesis that there was no experimental effect. This weak form of hypothesis testing neglects effect size, is universally misinterpreted, and is disastrously prone to error when combined with high-throughput cell biology. The first part of the solution proposed here is to limit statistical hypothesis testing to the small subset of experiments where a biologically meaningful null hypotheses can be defined prior to the experiment. The second part of the solution is to make confidence intervals the default statistic in cell biology.
|
2009.12023
|
Alessandro Sanzeni
|
Alessandro Sanzeni and Mark H Histed and Nicolas Brunel
|
Emergence of irregular activity in networks of strongly coupled
conductance-based neurons
| null | null | null | null |
q-bio.NC physics.bio-ph
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Cortical neurons are characterized by irregular firing and a broad
distribution of rates. The balanced state model explains these observations
with a cancellation of mean excitatory and inhibitory currents, which makes
fluctuations drive firing. In networks of neurons with current-based synapses,
the balanced state emerges dynamically if coupling is strong, i.e. if the mean
number of synapses per neuron $K$ is large and synaptic efficacy is of order
$1/\sqrt{K}$. When synapses are conductance-based, current fluctuations are
suppressed when coupling is strong, questioning the applicability of the
balanced state idea to biological neural networks. We analyze networks of
strongly coupled conductance-based neurons and show that asynchronous irregular
activity and broad distributions of rates emerge if synapses are of order
$1/\log(K)$. In such networks, unlike in the standard balanced state model,
current fluctuations are small and firing is maintained by a drift-diffusion
balance. This balance emerges dynamically, without fine tuning, if inputs are
smaller than a critical value, which depends on synaptic time constants and
coupling strength, and is significantly more robust to connection
heterogeneities than the classical balanced state model. Our analysis makes
experimentally testable predictions of how the network response properties
should evolve as input increases.
|
[
{
"created": "Fri, 25 Sep 2020 04:05:22 GMT",
"version": "v1"
},
{
"created": "Tue, 13 Oct 2020 22:02:23 GMT",
"version": "v2"
}
] |
2020-10-15
|
[
[
"Sanzeni",
"Alessandro",
""
],
[
"Histed",
"Mark H",
""
],
[
"Brunel",
"Nicolas",
""
]
] |
Cortical neurons are characterized by irregular firing and a broad distribution of rates. The balanced state model explains these observations with a cancellation of mean excitatory and inhibitory currents, which makes fluctuations drive firing. In networks of neurons with current-based synapses, the balanced state emerges dynamically if coupling is strong, i.e. if the mean number of synapses per neuron $K$ is large and synaptic efficacy is of order $1/\sqrt{K}$. When synapses are conductance-based, current fluctuations are suppressed when coupling is strong, questioning the applicability of the balanced state idea to biological neural networks. We analyze networks of strongly coupled conductance-based neurons and show that asynchronous irregular activity and broad distributions of rates emerge if synapses are of order $1/\log(K)$. In such networks, unlike in the standard balanced state model, current fluctuations are small and firing is maintained by a drift-diffusion balance. This balance emerges dynamically, without fine tuning, if inputs are smaller than a critical value, which depends on synaptic time constants and coupling strength, and is significantly more robust to connection heterogeneities than the classical balanced state model. Our analysis makes experimentally testable predictions of how the network response properties should evolve as input increases.
|
2003.09796
|
Jun Chen
|
Jun Chen, Komi Messan, Marisabel Rodriguez Messan, Gloria
DeGrandi-Hoffman, Dingyong Bai, Yun Kang
|
How to model honeybee population dynamics: stage structure and
seasonality
| null | null | null | null |
q-bio.PE math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Western honeybees (Apis Mellifera) serve extremely important roles in our
ecosystem and economics as they are responsible for pollinating $ 215 billion
dollars annually over the world. Unfortunately, honeybee population and their
colonies have been declined dramatically. The purpose of this article is to
explore how we should model honeybee population with age structure and validate
the model using empirical data so that we can identify different factors that
lead to the survival and healthy of the honeybee colony. Our theoretical study
combined with simulations and data validation suggests that the proper age
structure incorporated in the model and seasonality are important for modeling
honeybee population. Specifically, our work implies that the model assuming
that (1) the adult bees are survived from the {egg population} rather than the
brood population; and (2) seasonality in the queen egg laying rate, give the
better fit than other honeybee models. The related theoretical and numerical
analysis of the most fit model indicate that (a) the survival of honeybee
colonies requires a large queen egg-laying rate and smaller values of the other
life history parameter values in addition to proper initial condition; (b) both
brood and adult bee populations are increasing with respect to the increase in
the {egg-laying rate} and the decreasing in other parameter values; and (c)
seasonality may promote/suppress the survival of the honeybee colony.
|
[
{
"created": "Sun, 22 Mar 2020 03:34:49 GMT",
"version": "v1"
}
] |
2020-03-24
|
[
[
"Chen",
"Jun",
""
],
[
"Messan",
"Komi",
""
],
[
"Messan",
"Marisabel Rodriguez",
""
],
[
"DeGrandi-Hoffman",
"Gloria",
""
],
[
"Bai",
"Dingyong",
""
],
[
"Kang",
"Yun",
""
]
] |
Western honeybees (Apis Mellifera) serve extremely important roles in our ecosystem and economics as they are responsible for pollinating $ 215 billion dollars annually over the world. Unfortunately, honeybee population and their colonies have been declined dramatically. The purpose of this article is to explore how we should model honeybee population with age structure and validate the model using empirical data so that we can identify different factors that lead to the survival and healthy of the honeybee colony. Our theoretical study combined with simulations and data validation suggests that the proper age structure incorporated in the model and seasonality are important for modeling honeybee population. Specifically, our work implies that the model assuming that (1) the adult bees are survived from the {egg population} rather than the brood population; and (2) seasonality in the queen egg laying rate, give the better fit than other honeybee models. The related theoretical and numerical analysis of the most fit model indicate that (a) the survival of honeybee colonies requires a large queen egg-laying rate and smaller values of the other life history parameter values in addition to proper initial condition; (b) both brood and adult bee populations are increasing with respect to the increase in the {egg-laying rate} and the decreasing in other parameter values; and (c) seasonality may promote/suppress the survival of the honeybee colony.
|
1301.6561
|
Alexey Mikaberidze
|
Alexey Mikaberidze, Bruce A. McDonald, Sebastian Bonhoeffer
|
Can high risk fungicides be used in mixtures without selecting for
fungicide resistance?
|
51 pages, 6 figures, accepted for publication in Phytopathology
| null |
10.1094/PHYTO-07-13-0204-R
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Fungicide mixtures produced by the agrochemical industry often contain
low-risk fungicides, to which fungal pathogens are fully sensitive, together
with high-risk fungicides known to be prone to fungicide resistance. Can these
mixtures provide adequate disease control while minimizing the risk for the
development of resistance? We present a population dynamics model to address
this question. We found that the fitness cost of resistance is a crucial
parameter to determine the outcome of competition between the sensitive and
resistant pathogen strains and to assess the usefulness of a mixture. If
fitness costs are absent, then the use of the high-risk fungicide in a mixture
selects for resistance and the fungicide eventually becomes nonfunctional. If
there is a cost of resistance, then an optimal ratio of fungicides in the
mixture can be found, at which selection for resistance is expected to vanish
and the level of disease control can be optimized.
|
[
{
"created": "Mon, 28 Jan 2013 14:42:46 GMT",
"version": "v1"
},
{
"created": "Fri, 20 Sep 2013 08:59:21 GMT",
"version": "v2"
}
] |
2013-09-23
|
[
[
"Mikaberidze",
"Alexey",
""
],
[
"McDonald",
"Bruce A.",
""
],
[
"Bonhoeffer",
"Sebastian",
""
]
] |
Fungicide mixtures produced by the agrochemical industry often contain low-risk fungicides, to which fungal pathogens are fully sensitive, together with high-risk fungicides known to be prone to fungicide resistance. Can these mixtures provide adequate disease control while minimizing the risk for the development of resistance? We present a population dynamics model to address this question. We found that the fitness cost of resistance is a crucial parameter to determine the outcome of competition between the sensitive and resistant pathogen strains and to assess the usefulness of a mixture. If fitness costs are absent, then the use of the high-risk fungicide in a mixture selects for resistance and the fungicide eventually becomes nonfunctional. If there is a cost of resistance, then an optimal ratio of fungicides in the mixture can be found, at which selection for resistance is expected to vanish and the level of disease control can be optimized.
|
2009.03629
|
Jozef Skakala
|
Jozef Skakala and Paolo Lazzari
|
Low complexity model to study scale dependence of phytoplankton dynamics
in the tropical Pacific
|
21 pages, 12 figures
|
Phys. Rev. E 103, 012401 (2021)
|
10.1103/PhysRevE.103.012401
| null |
q-bio.PE physics.ao-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We demonstrate that a simple model based on reaction-diffusion-advection
(RDA) equation forced by realistic surface velocities and nutrients is skilled
in reproducing the distributions of the surface phytoplankton chlorophyll in
the tropical Pacific. We use the low-complexity RDA model to investigate the
scale-relationships in the impact of different drivers (turbulent diffusion,
mean and eddy advection, primary productivity) on the phytoplankton chlorophyll
concentrations. We find that in the 1/4{\deg} (~25km) model, advection has a
substantial impact on the rate of primary productivity, whilst the turbulent
diffusion term has a fairly negligible impact. Turbulent diffusion has an
impact on the phytoplankton variability, with the impact being scale-propagated
and amplified by the larger scale surface currents. We investigate the impact
of a surface nutrient decline and some changes to mesoscale eddy kinetic energy
(climate change projections) on the surface phytoplankton concentrations. The
RDA model suggests that unless mesoscale eddies radically change, phytoplankton
chlorophyll scales sub-linearly with the nutrients, and it is relatively stable
with respect to the nutrient concentrations. Furthermore we explore how a white
multiplicative Gaussian noise introduced into the RDA model on its resolution
scale propagates across spatial scales through the non-linear model dynamics
under different sets of phytoplankton drivers. The unifying message of this
work is that the low complexity (e.g. RDA) models can be successfully used to
realistically model some specific aspects of marine ecosystem dynamics and by
using those models one can explore many questions that would be beyond
computational affordability of the higher-complexity ecosystem models.
|
[
{
"created": "Tue, 8 Sep 2020 10:21:50 GMT",
"version": "v1"
},
{
"created": "Mon, 11 Jan 2021 22:02:03 GMT",
"version": "v2"
}
] |
2021-01-13
|
[
[
"Skakala",
"Jozef",
""
],
[
"Lazzari",
"Paolo",
""
]
] |
We demonstrate that a simple model based on reaction-diffusion-advection (RDA) equation forced by realistic surface velocities and nutrients is skilled in reproducing the distributions of the surface phytoplankton chlorophyll in the tropical Pacific. We use the low-complexity RDA model to investigate the scale-relationships in the impact of different drivers (turbulent diffusion, mean and eddy advection, primary productivity) on the phytoplankton chlorophyll concentrations. We find that in the 1/4{\deg} (~25km) model, advection has a substantial impact on the rate of primary productivity, whilst the turbulent diffusion term has a fairly negligible impact. Turbulent diffusion has an impact on the phytoplankton variability, with the impact being scale-propagated and amplified by the larger scale surface currents. We investigate the impact of a surface nutrient decline and some changes to mesoscale eddy kinetic energy (climate change projections) on the surface phytoplankton concentrations. The RDA model suggests that unless mesoscale eddies radically change, phytoplankton chlorophyll scales sub-linearly with the nutrients, and it is relatively stable with respect to the nutrient concentrations. Furthermore we explore how a white multiplicative Gaussian noise introduced into the RDA model on its resolution scale propagates across spatial scales through the non-linear model dynamics under different sets of phytoplankton drivers. The unifying message of this work is that the low complexity (e.g. RDA) models can be successfully used to realistically model some specific aspects of marine ecosystem dynamics and by using those models one can explore many questions that would be beyond computational affordability of the higher-complexity ecosystem models.
|
2010.00504
|
Sarah Marzen
|
Alexander Hsu and Sarah Marzen
|
Time cells might be optimized for predictive capacity, not redundancy
reduction or memory capacity
| null | null |
10.1103/PhysRevE.102.062404
| null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, researchers have found time cells in the hippocampus that appear to
contain information about the timing of past events. Some researchers have
argued that time cells are taking a Laplace transform of their input in order
to reconstruct the past stimulus. We argue that stimulus prediction, not
stimulus reconstruction or redundancy reduction, is in better agreement with
observed responses of time cells. In the process, we introduce new analyses of
nonlinear, continuous-time reservoirs that model these time cells.
|
[
{
"created": "Thu, 1 Oct 2020 15:53:43 GMT",
"version": "v1"
}
] |
2020-12-30
|
[
[
"Hsu",
"Alexander",
""
],
[
"Marzen",
"Sarah",
""
]
] |
Recently, researchers have found time cells in the hippocampus that appear to contain information about the timing of past events. Some researchers have argued that time cells are taking a Laplace transform of their input in order to reconstruct the past stimulus. We argue that stimulus prediction, not stimulus reconstruction or redundancy reduction, is in better agreement with observed responses of time cells. In the process, we introduce new analyses of nonlinear, continuous-time reservoirs that model these time cells.
|
2311.09354
|
Kylie Trettner
|
Kylie J. Trettner, Jeremy Hsieh, Weikun Xiao, Jerry S.H. Lee, Andrea
M. Armani
|
Nondestructive, quantitative viability analysis of 3D tissue cultures
using machine learning image segmentation
|
52 total pages, Main text and SI included, 35 figures (5 main text,
30 supplemental), 9 tables, 6 datasets (provided on linked GitHub), linked
image files on Zenodo
| null |
10.1063/5.0189222
| null |
q-bio.QM cs.LG eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Ascertaining the collective viability of cells in different cell culture
conditions has typically relied on averaging colorimetric indicators and is
often reported out in simple binary readouts. Recent research has combined
viability assessment techniques with image-based deep-learning models to
automate the characterization of cellular properties. However, further
development of viability measurements to assess the continuity of possible
cellular states and responses to perturbation across cell culture conditions is
needed. In this work, we demonstrate an image processing algorithm for
quantifying cellular viability in 3D cultures without the need for assay-based
indicators. We show that our algorithm performs similarly to a pair of human
experts in whole-well images over a range of days and culture matrix
compositions. To demonstrate potential utility, we perform a longitudinal study
investigating the impact of a known therapeutic on pancreatic cancer spheroids.
Using images taken with a high content imaging system, the algorithm
successfully tracks viability at the individual spheroid and whole-well level.
The method we propose reduces analysis time by 97% in comparison to the
experts. Because the method is independent of the microscope or imaging system
used, this approach lays the foundation for accelerating progress in and for
improving the robustness and reproducibility of 3D culture analysis across
biological and clinical research.
|
[
{
"created": "Wed, 15 Nov 2023 20:28:31 GMT",
"version": "v1"
},
{
"created": "Mon, 4 Mar 2024 21:43:02 GMT",
"version": "v2"
},
{
"created": "Mon, 11 Mar 2024 22:12:25 GMT",
"version": "v3"
}
] |
2024-04-03
|
[
[
"Trettner",
"Kylie J.",
""
],
[
"Hsieh",
"Jeremy",
""
],
[
"Xiao",
"Weikun",
""
],
[
"Lee",
"Jerry S. H.",
""
],
[
"Armani",
"Andrea M.",
""
]
] |
Ascertaining the collective viability of cells in different cell culture conditions has typically relied on averaging colorimetric indicators and is often reported out in simple binary readouts. Recent research has combined viability assessment techniques with image-based deep-learning models to automate the characterization of cellular properties. However, further development of viability measurements to assess the continuity of possible cellular states and responses to perturbation across cell culture conditions is needed. In this work, we demonstrate an image processing algorithm for quantifying cellular viability in 3D cultures without the need for assay-based indicators. We show that our algorithm performs similarly to a pair of human experts in whole-well images over a range of days and culture matrix compositions. To demonstrate potential utility, we perform a longitudinal study investigating the impact of a known therapeutic on pancreatic cancer spheroids. Using images taken with a high content imaging system, the algorithm successfully tracks viability at the individual spheroid and whole-well level. The method we propose reduces analysis time by 97% in comparison to the experts. Because the method is independent of the microscope or imaging system used, this approach lays the foundation for accelerating progress in and for improving the robustness and reproducibility of 3D culture analysis across biological and clinical research.
|
1310.1593
|
Tomasz Rutkowski
|
Shota Kono, Daiki Aminaka, Shoji Makino, and Tomasz M. Rutkowski
|
EEG Signal Processing and Classification for the Novel Tactile-Force
Brain-Computer Interface Paradigm
|
6 pages (in conference proceedings original version); 6 figures,
submitted to The 9th International Conference on Signal Image Technology &
Internet Based Systems, December 2-5, 2013, Kyoto, Japan; to be available at
IEEE Xplore; IEEE Copyright 2013
|
Proceedings of the 9th International Conference on Signal Image
Technology and Internet Based Systems, (Kyoto, Japan), pp. 812-817, IEEE
Computer Society, December 3-5, 2013
|
10.1109/SITIS.2013.132
| null |
q-bio.NC cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The presented study explores the extent to which tactile-force stimulus
delivered to a hand holding a joystick can serve as a platform for a brain
computer interface (BCI). The four pressure directions are used to evoke
tactile brain potential responses, thus defining a tactile-force brain computer
interface (tfBCI). We present brain signal processing and classification
procedures leading to successful interfacing results. Experimental results with
seven subjects performing online BCI experiments provide a validation of the
hand location tfBCI paradigm, while the feasibility of the concept is
illuminated through remarkable information-transfer rates.
|
[
{
"created": "Sun, 6 Oct 2013 15:18:34 GMT",
"version": "v1"
},
{
"created": "Thu, 17 Oct 2013 07:52:01 GMT",
"version": "v2"
}
] |
2013-12-17
|
[
[
"Kono",
"Shota",
""
],
[
"Aminaka",
"Daiki",
""
],
[
"Makino",
"Shoji",
""
],
[
"Rutkowski",
"Tomasz M.",
""
]
] |
The presented study explores the extent to which tactile-force stimulus delivered to a hand holding a joystick can serve as a platform for a brain computer interface (BCI). The four pressure directions are used to evoke tactile brain potential responses, thus defining a tactile-force brain computer interface (tfBCI). We present brain signal processing and classification procedures leading to successful interfacing results. Experimental results with seven subjects performing online BCI experiments provide a validation of the hand location tfBCI paradigm, while the feasibility of the concept is illuminated through remarkable information-transfer rates.
|
1608.06146
|
Sophie Kay
|
Sophie K. Kay, Heather A. Harrington, Sarah Shepherd, Keith Brennan,
Trevor Dale, James M. Osborne, David J. Gavaghan and Helen M. Byrne
|
The Role of the Hes1 Crosstalk Hub in Notch-Wnt Interactions of the
Intestinal Crypt
| null | null |
10.1371/journal.pcbi.1005400
| null |
q-bio.SC q-bio.CB q-bio.MN q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Notch pathway plays a vital role in determining whether cells in the
intestinal epithelium adopt a secretory or an absorptive phenotype. Cell fate
specification is coordinated via Notch's interaction with the canonical Wnt
pathway. Here, we propose a new mathematical model of the Notch and Wnt
pathways, in which the Hes1 promoter acts as a hub for pathway crosstalk.
Computational simulations of the model can assist in understanding how healthy
intestinal tissue is maintained, and predict the likely consequences of
biochemical knockouts upon cell fate selection processes. Chemical reaction
network theory (CRNT) is a powerful, generalised framework which assesses the
capacity of our model for monostability or multistability, by analysing
properties of the underlying network structure without recourse to specific
parameter values or functional forms for reaction rates. CRNT highlights the
role of beta-catenin in stabilising the Notch pathway and damping oscillations,
demonstrating that Wnt-mediated actions on the Hes1 promoter can induce
dynamical transitions in the Notch system, from multistability to
monostability. Time-dependent model simulations of cell pairs reveal the
stabilising influence of Wnt upon the Notch pathway, in which beta-catenin- and
Dsh-mediated action on the Hes1 promoter are key in shaping the subcellular
dynamics. Where Notch-mediated transcription of Hes1 dominates, there is Notch
oscillation and maintenance of fate flexibility; Wnt-mediated transcription of
Hes1 favours bistability akin to cell fate selection. Cells could therefore
regulate the proportion of Wnt- and Notch-mediated control of the Hes1 promoter
to coordinate the timing of cell fate selection as they migrate through the
intestinal epithelium and are subject to reduced Wnt stimuli.
|
[
{
"created": "Mon, 22 Aug 2016 12:32:08 GMT",
"version": "v1"
}
] |
2017-04-12
|
[
[
"Kay",
"Sophie K.",
""
],
[
"Harrington",
"Heather A.",
""
],
[
"Shepherd",
"Sarah",
""
],
[
"Brennan",
"Keith",
""
],
[
"Dale",
"Trevor",
""
],
[
"Osborne",
"James M.",
""
],
[
"Gavaghan",
"David J.",
""
],
[
"Byrne",
"Helen M.",
""
]
] |
The Notch pathway plays a vital role in determining whether cells in the intestinal epithelium adopt a secretory or an absorptive phenotype. Cell fate specification is coordinated via Notch's interaction with the canonical Wnt pathway. Here, we propose a new mathematical model of the Notch and Wnt pathways, in which the Hes1 promoter acts as a hub for pathway crosstalk. Computational simulations of the model can assist in understanding how healthy intestinal tissue is maintained, and predict the likely consequences of biochemical knockouts upon cell fate selection processes. Chemical reaction network theory (CRNT) is a powerful, generalised framework which assesses the capacity of our model for monostability or multistability, by analysing properties of the underlying network structure without recourse to specific parameter values or functional forms for reaction rates. CRNT highlights the role of beta-catenin in stabilising the Notch pathway and damping oscillations, demonstrating that Wnt-mediated actions on the Hes1 promoter can induce dynamical transitions in the Notch system, from multistability to monostability. Time-dependent model simulations of cell pairs reveal the stabilising influence of Wnt upon the Notch pathway, in which beta-catenin- and Dsh-mediated action on the Hes1 promoter are key in shaping the subcellular dynamics. Where Notch-mediated transcription of Hes1 dominates, there is Notch oscillation and maintenance of fate flexibility; Wnt-mediated transcription of Hes1 favours bistability akin to cell fate selection. Cells could therefore regulate the proportion of Wnt- and Notch-mediated control of the Hes1 promoter to coordinate the timing of cell fate selection as they migrate through the intestinal epithelium and are subject to reduced Wnt stimuli.
|
1310.2968
|
Tracy Heath
|
Tracy A. Heath, John P. Huelsenbeck, Tanja Stadler
|
The Fossilized Birth-Death Process: A Coherent Model of Fossil
Calibration for Divergence Time Estimation
|
42 total pages including: 29 text pages, 5 tables, and 12 figures.
Work presented at Evolution 2013
(http://www.slideshare.net/trayc7/heath-evolution-2013)
| null |
10.1073/pnas.1319091111
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Time-calibrated species phylogenies are critical for addressing a wide range
of questions in evolutionary biology, such as those that elucidate historical
biogeography or uncover patterns of coevolution and diversification. Because
molecular sequence data are not informative on absolute time, external data,
most commonly fossil age estimates, are required to calibrate estimates of
species divergence dates. For Bayesian divergence-time methods, the common
practice for calibration using fossil information involves placing arbitrarily
chosen parametric distributions on internal nodes, often disregarding most of
the information in the fossil record. We introduce the 'fossilized birth-death'
(FBD) process, a model for calibrating divergence-time estimates in a Bayesian
framework, explicitly acknowledging that extant species and fossils are part of
the same macroevolutionary process. Under this model, absolute node age
estimates are calibrated by a single diversification model and arbitrary
calibration densities are not necessary. Moreover, the FBD model allows for
inclusion of all available fossils. We performed analyses of simulated data and
show that node-age estimation under the FBD model results in robust and
accurate estimates of species divergence times with realistic measures of
statistical uncertainty, overcoming major limitations of standard divergence
time estimation methods. We then used this model to estimate the speciation
times for a dataset composed of all living bears, indicating that the genus
Ursus diversified in the late Miocene to mid Pliocene.
|
[
{
"created": "Thu, 10 Oct 2013 22:04:03 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Oct 2013 20:48:23 GMT",
"version": "v2"
}
] |
2014-07-11
|
[
[
"Heath",
"Tracy A.",
""
],
[
"Huelsenbeck",
"John P.",
""
],
[
"Stadler",
"Tanja",
""
]
] |
Time-calibrated species phylogenies are critical for addressing a wide range of questions in evolutionary biology, such as those that elucidate historical biogeography or uncover patterns of coevolution and diversification. Because molecular sequence data are not informative on absolute time, external data, most commonly fossil age estimates, are required to calibrate estimates of species divergence dates. For Bayesian divergence-time methods, the common practice for calibration using fossil information involves placing arbitrarily chosen parametric distributions on internal nodes, often disregarding most of the information in the fossil record. We introduce the 'fossilized birth-death' (FBD) process, a model for calibrating divergence-time estimates in a Bayesian framework, explicitly acknowledging that extant species and fossils are part of the same macroevolutionary process. Under this model, absolute node age estimates are calibrated by a single diversification model and arbitrary calibration densities are not necessary. Moreover, the FBD model allows for inclusion of all available fossils. We performed analyses of simulated data and show that node-age estimation under the FBD model results in robust and accurate estimates of species divergence times with realistic measures of statistical uncertainty, overcoming major limitations of standard divergence time estimation methods. We then used this model to estimate the speciation times for a dataset composed of all living bears, indicating that the genus Ursus diversified in the late Miocene to mid Pliocene.
|
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