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q-bio/0410032
|
Paul van der Schoot
|
P. van der Schoot and R. Bruinsma
|
Electrostatics and the Assembly of an RNA Virus
|
41 pages, 4 figures
| null |
10.1103/PhysRevE.71.061928
| null |
q-bio.BM q-bio.SC
| null |
Electrostatic interactions play a central role in the assembly of
single-stranded RNA viruses. Under physiological conditions of salinity and
acidity, virus capsid assembly requires the presence of genomic material that
is oppositely charged to the core proteins. In this paper we apply basic
polymer physics and statistical mechanics methods to the self-assembly of a
synthetic virus encapsidating generic polyelectrolyte molecules. We find that
(i) the mean concentration of the encapsidated polyelectrolyte material depends
on the surface charge density, the radius of the capsid, and the linear charge
density of the polymer but neither on the salt concentration or the Kuhn
length, (ii) the total charge of the capsid interior is equal but opposite to
that of the empty capsid, a form of charge reversal. Unlike natural viruses,
synthetic viruses are predicted not to be under an osmotic swelling pressure.
The design condition that self-assembly only produces filled capsids is shown
to coincide with the condition that the capsid surface charge exceeds the
desorption threshold of polymer surface adsorption. We compare our results with
studies on the self-assembly of both synthetic and natural viruses.
|
[
{
"created": "Wed, 27 Oct 2004 20:02:04 GMT",
"version": "v1"
}
] |
2009-11-10
|
[
[
"van der Schoot",
"P.",
""
],
[
"Bruinsma",
"R.",
""
]
] |
Electrostatic interactions play a central role in the assembly of single-stranded RNA viruses. Under physiological conditions of salinity and acidity, virus capsid assembly requires the presence of genomic material that is oppositely charged to the core proteins. In this paper we apply basic polymer physics and statistical mechanics methods to the self-assembly of a synthetic virus encapsidating generic polyelectrolyte molecules. We find that (i) the mean concentration of the encapsidated polyelectrolyte material depends on the surface charge density, the radius of the capsid, and the linear charge density of the polymer but neither on the salt concentration or the Kuhn length, (ii) the total charge of the capsid interior is equal but opposite to that of the empty capsid, a form of charge reversal. Unlike natural viruses, synthetic viruses are predicted not to be under an osmotic swelling pressure. The design condition that self-assembly only produces filled capsids is shown to coincide with the condition that the capsid surface charge exceeds the desorption threshold of polymer surface adsorption. We compare our results with studies on the self-assembly of both synthetic and natural viruses.
|
2207.00584
|
Siyuan Shan
|
Vishal Athreya Baskaran, Jolene Ranek, Siyuan Shan, Natalie Stanley,
Junier B. Oliva
|
Distribution-based Sketching of Single-Cell Samples
|
Accepted by ACM-BCB 2022
| null |
10.1145/3535508.3545539
| null |
q-bio.QM cs.LG
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Modern high-throughput single-cell immune profiling technologies, such as
flow and mass cytometry and single-cell RNA sequencing can readily measure the
expression of a large number of protein or gene features across the millions of
cells in a multi-patient cohort. While bioinformatics approaches can be used to
link immune cell heterogeneity to external variables of interest, such as,
clinical outcome or experimental label, they often struggle to accommodate such
a large number of profiled cells. To ease this computational burden, a limited
number of cells are typically \emph{sketched} or subsampled from each patient.
However, existing sketching approaches fail to adequately subsample rare cells
from rare cell-populations, or fail to preserve the true frequencies of
particular immune cell-types. Here, we propose a novel sketching approach based
on Kernel Herding that selects a limited subsample of all cells while
preserving the underlying frequencies of immune cell-types. We tested our
approach on three flow and mass cytometry datasets and on one single-cell RNA
sequencing dataset and demonstrate that the sketched cells (1) more accurately
represent the overall cellular landscape and (2) facilitate increased
performance in downstream analysis tasks, such as classifying patients
according to their clinical outcome. An implementation of sketching with Kernel
Herding is publicly available at
\url{https://github.com/vishalathreya/Set-Summarization}.
|
[
{
"created": "Thu, 30 Jun 2022 19:43:06 GMT",
"version": "v1"
}
] |
2022-07-05
|
[
[
"Baskaran",
"Vishal Athreya",
""
],
[
"Ranek",
"Jolene",
""
],
[
"Shan",
"Siyuan",
""
],
[
"Stanley",
"Natalie",
""
],
[
"Oliva",
"Junier B.",
""
]
] |
Modern high-throughput single-cell immune profiling technologies, such as flow and mass cytometry and single-cell RNA sequencing can readily measure the expression of a large number of protein or gene features across the millions of cells in a multi-patient cohort. While bioinformatics approaches can be used to link immune cell heterogeneity to external variables of interest, such as, clinical outcome or experimental label, they often struggle to accommodate such a large number of profiled cells. To ease this computational burden, a limited number of cells are typically \emph{sketched} or subsampled from each patient. However, existing sketching approaches fail to adequately subsample rare cells from rare cell-populations, or fail to preserve the true frequencies of particular immune cell-types. Here, we propose a novel sketching approach based on Kernel Herding that selects a limited subsample of all cells while preserving the underlying frequencies of immune cell-types. We tested our approach on three flow and mass cytometry datasets and on one single-cell RNA sequencing dataset and demonstrate that the sketched cells (1) more accurately represent the overall cellular landscape and (2) facilitate increased performance in downstream analysis tasks, such as classifying patients according to their clinical outcome. An implementation of sketching with Kernel Herding is publicly available at \url{https://github.com/vishalathreya/Set-Summarization}.
|
2403.03234
|
Yair Schiff
|
Yair Schiff, Chia-Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu, and
Volodymyr Kuleshov
|
Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
|
ICML 2024; Code to reproduce our experiments is available at
https://github.com/kuleshov-group/caduceus
| null | null | null |
q-bio.GN cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Large-scale sequence modeling has sparked rapid advances that now extend into
biology and genomics. However, modeling genomic sequences introduces challenges
such as the need to model long-range token interactions, the effects of
upstream and downstream regions of the genome, and the reverse complementarity
(RC) of DNA. Here, we propose an architecture motivated by these challenges
that builds off the long-range Mamba block, and extends it to a BiMamba
component that supports bi-directionality, and to a MambaDNA block that
additionally supports RC equivariance. We use MambaDNA as the basis of
Caduceus, the first family of RC equivariant bi-directional long-range DNA
language models, and we introduce pre-training and fine-tuning strategies that
yield Caduceus DNA foundation models. Caduceus outperforms previous long-range
models on downstream benchmarks; on a challenging long-range variant effect
prediction task, Caduceus exceeds the performance of 10x larger models that do
not leverage bi-directionality or equivariance.
|
[
{
"created": "Tue, 5 Mar 2024 01:42:51 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Jun 2024 21:02:37 GMT",
"version": "v2"
}
] |
2024-06-07
|
[
[
"Schiff",
"Yair",
""
],
[
"Kao",
"Chia-Hsiang",
""
],
[
"Gokaslan",
"Aaron",
""
],
[
"Dao",
"Tri",
""
],
[
"Gu",
"Albert",
""
],
[
"Kuleshov",
"Volodymyr",
""
]
] |
Large-scale sequence modeling has sparked rapid advances that now extend into biology and genomics. However, modeling genomic sequences introduces challenges such as the need to model long-range token interactions, the effects of upstream and downstream regions of the genome, and the reverse complementarity (RC) of DNA. Here, we propose an architecture motivated by these challenges that builds off the long-range Mamba block, and extends it to a BiMamba component that supports bi-directionality, and to a MambaDNA block that additionally supports RC equivariance. We use MambaDNA as the basis of Caduceus, the first family of RC equivariant bi-directional long-range DNA language models, and we introduce pre-training and fine-tuning strategies that yield Caduceus DNA foundation models. Caduceus outperforms previous long-range models on downstream benchmarks; on a challenging long-range variant effect prediction task, Caduceus exceeds the performance of 10x larger models that do not leverage bi-directionality or equivariance.
|
1810.06844
|
Kristina Wicke
|
Mareike Fischer and Michelle Galla and Lina Herbst and Yangjing Long
and Kristina Wicke
|
Classes of treebased networks
|
45 pages, 26 figures
| null | null | null |
q-bio.PE math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, so-called treebased phylogenetic networks have gained considerable
interest in the literature, where a treebased network is a network that can be
constructed from a phylogenetic tree, called the base tree, by adding
additional edges. The main aim of this manuscript is to provide some sufficient
criteria for treebasedness by reducing phylogenetic networks to related graph
structures. While it is generally known that deciding whether a network is
treebased is NP-complete, one of these criteria, namely edgebasedness, can be
verified in linear time. Surprisingly, the class of edgebased networks is
closely related to a well-known family of graphs, namely the class of
generalized series parallel graphs, and we will explore this relationship in
full detail. Additionally, we introduce further classes of treebased networks
and analyze their relationships.
|
[
{
"created": "Tue, 16 Oct 2018 07:22:35 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Oct 2018 10:11:33 GMT",
"version": "v2"
},
{
"created": "Fri, 16 Aug 2019 14:03:08 GMT",
"version": "v3"
},
{
"created": "Wed, 27 Nov 2019 08:32:38 GMT",
"version": "v4"
}
] |
2019-11-28
|
[
[
"Fischer",
"Mareike",
""
],
[
"Galla",
"Michelle",
""
],
[
"Herbst",
"Lina",
""
],
[
"Long",
"Yangjing",
""
],
[
"Wicke",
"Kristina",
""
]
] |
Recently, so-called treebased phylogenetic networks have gained considerable interest in the literature, where a treebased network is a network that can be constructed from a phylogenetic tree, called the base tree, by adding additional edges. The main aim of this manuscript is to provide some sufficient criteria for treebasedness by reducing phylogenetic networks to related graph structures. While it is generally known that deciding whether a network is treebased is NP-complete, one of these criteria, namely edgebasedness, can be verified in linear time. Surprisingly, the class of edgebased networks is closely related to a well-known family of graphs, namely the class of generalized series parallel graphs, and we will explore this relationship in full detail. Additionally, we introduce further classes of treebased networks and analyze their relationships.
|
1703.05755
|
Guillermo Abramson
|
Laila D. Kazimierski, Marcelo N. Kuperman, Horacio S. Wio and
Guillermo Abramson
|
Waves of seed propagation induced by delayed animal dispersion
|
Accepted in Journal of Theoretical Biology
| null |
10.1016/j.jtbi.2017.09.030
| null |
q-bio.PE physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study a model of seed dispersal that considers the inclusion of an animal
disperser moving diffusively, feeding on fruits and transporting the seeds,
which are later deposited and capable of germination. The dynamics depends on
several population parameters of growth, decay, harvesting, transport,
digestion and germination. In particular, the deposition of transported seeds
at places away from their collection sites produces a delay in the dynamics,
whose effects are the focus of this work. Analytical and numerical solutions of
different simplified scenarios show the existence of travelling waves. The
effect of zoochory is apparent in the increase of the velocity of these waves.
The results support the hypothesis of the relevance of animal mediated seed
dispersion when trying to understand the origin of the high rates of vegetable
invasion observed in real systems.
|
[
{
"created": "Thu, 16 Mar 2017 17:56:39 GMT",
"version": "v1"
},
{
"created": "Mon, 2 Oct 2017 12:58:13 GMT",
"version": "v2"
}
] |
2017-10-03
|
[
[
"Kazimierski",
"Laila D.",
""
],
[
"Kuperman",
"Marcelo N.",
""
],
[
"Wio",
"Horacio S.",
""
],
[
"Abramson",
"Guillermo",
""
]
] |
We study a model of seed dispersal that considers the inclusion of an animal disperser moving diffusively, feeding on fruits and transporting the seeds, which are later deposited and capable of germination. The dynamics depends on several population parameters of growth, decay, harvesting, transport, digestion and germination. In particular, the deposition of transported seeds at places away from their collection sites produces a delay in the dynamics, whose effects are the focus of this work. Analytical and numerical solutions of different simplified scenarios show the existence of travelling waves. The effect of zoochory is apparent in the increase of the velocity of these waves. The results support the hypothesis of the relevance of animal mediated seed dispersion when trying to understand the origin of the high rates of vegetable invasion observed in real systems.
|
1912.03934
|
Lionel Gil
|
Dora Matzakos-Karvouniari, Bruno Cessac and L. Gil
|
Noise driven broadening of the neural synchronisation transition in
stage II retinal waves
| null | null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Based on a biophysical model of retinal Starburst Amacrine Cell (SAC)
\cite{karvouniari-gil-etal:19} we analyse here the dynamics of retinal waves,
arising during the visual system development. Waves are induced by spontaneous
bursting of SACs and their coupling via acetycholine. We show that, despite the
acetylcholine coupling intensity has been experimentally observed to change
during development \cite{zheng-lee-etal:04}, SACs retinal waves can
nevertheless stay in a regime with power law distributions, reminiscent of a
critical regime. Thus, this regime occurs on a range of coupling parameters
instead of a single point as in usual phase transitions. We explain this
phenomenon thanks to a coherence-resonance mechanism, where noise is
responsible for the broadening of the critical coupling strength range.
|
[
{
"created": "Mon, 9 Dec 2019 09:58:51 GMT",
"version": "v1"
}
] |
2019-12-10
|
[
[
"Matzakos-Karvouniari",
"Dora",
""
],
[
"Cessac",
"Bruno",
""
],
[
"Gil",
"L.",
""
]
] |
Based on a biophysical model of retinal Starburst Amacrine Cell (SAC) \cite{karvouniari-gil-etal:19} we analyse here the dynamics of retinal waves, arising during the visual system development. Waves are induced by spontaneous bursting of SACs and their coupling via acetycholine. We show that, despite the acetylcholine coupling intensity has been experimentally observed to change during development \cite{zheng-lee-etal:04}, SACs retinal waves can nevertheless stay in a regime with power law distributions, reminiscent of a critical regime. Thus, this regime occurs on a range of coupling parameters instead of a single point as in usual phase transitions. We explain this phenomenon thanks to a coherence-resonance mechanism, where noise is responsible for the broadening of the critical coupling strength range.
|
1301.6931
|
Marco M\"oller
|
Marco M\"oller and Barbara Drossel
|
Scaling laws in critical random Boolean networks with general in- and
out-degree distributions
| null | null |
10.1103/PhysRevE.87.052106
| null |
q-bio.MN cond-mat.stat-mech physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We evaluate analytically and numerically the size of the frozen core and
various scaling laws for critical Boolean networks that have a power-law in-
and/or out-degree distribution. To this purpose, we generalize an efficient
method that has previously been used for conventional random Boolean networks
and for networks with power-law in-degree distributions. With this
generalization, we can also deal with power-law out-degree distributions. When
the power-law exponent is between 2 and 3, the second moment of the
distribution diverges with network size, and the scaling exponent of the
nonfrozen nodes depends on the degree distribution exponent. Furthermore, the
exponent depends also on the dependence of the cutoff of the degree
distribution on the system size. Altogether, we obtain an impressive number of
different scaling laws depending on the type of cutoff as well as on the
exponents of the in- and out-degree distributions. We confirm our scaling
arguments and analytical considerations by numerical investigations.
|
[
{
"created": "Tue, 29 Jan 2013 14:32:55 GMT",
"version": "v1"
}
] |
2015-06-12
|
[
[
"Möller",
"Marco",
""
],
[
"Drossel",
"Barbara",
""
]
] |
We evaluate analytically and numerically the size of the frozen core and various scaling laws for critical Boolean networks that have a power-law in- and/or out-degree distribution. To this purpose, we generalize an efficient method that has previously been used for conventional random Boolean networks and for networks with power-law in-degree distributions. With this generalization, we can also deal with power-law out-degree distributions. When the power-law exponent is between 2 and 3, the second moment of the distribution diverges with network size, and the scaling exponent of the nonfrozen nodes depends on the degree distribution exponent. Furthermore, the exponent depends also on the dependence of the cutoff of the degree distribution on the system size. Altogether, we obtain an impressive number of different scaling laws depending on the type of cutoff as well as on the exponents of the in- and out-degree distributions. We confirm our scaling arguments and analytical considerations by numerical investigations.
|
2310.15194
|
Jie Ruan
|
Shiang Hu, Jie Ruan, Juan Hou, Pedro Antonio Valdes-Sosa, Zhao Lv
|
How do the resting EEG preprocessing states affect the outcomes of
postprocessing?
| null | null | null | null |
q-bio.NC cs.HC eess.SP q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Plenty of artifact removal tools and pipelines have been developed to correct
the EEG recordings and discover the values below the waveforms. Without visual
inspection from the experts, it is susceptible to derive improper preprocessing
states, like the insufficient preprocessed EEG (IPE), and the excessive
preprocessed EEG (EPE). However, little is known about the impacts of IPE or
EPE on the postprocessing in the frequency, spatial and temporal domains,
particularly as to the spectra and the functional connectivity (FC) analysis.
Here, the clean EEG (CE) was synthesized as the ground truth based on the
New-York head model and the multivariate autoregressive model. Later, the IPE
and the EPE were simulated by injecting the Gaussian noise and losing the brain
activities, respectively. Then, the impacts on postprocessing were quantified
by the deviation caused by the IPE or EPE from the CE as to the 4 temporal
statistics, the multichannel power, the cross spectra, the dispersion of source
imaging, and the properties of scalp EEG network. Lastly, the association
analysis was performed between the PaLOSi metric and the varying trends of
postprocessing with the evolution of preprocessing states. This study shed
light on how the postprocessing outcomes are affected by the preprocessing
states and PaLOSi may be a potential effective quality metric.
|
[
{
"created": "Sun, 22 Oct 2023 08:08:46 GMT",
"version": "v1"
},
{
"created": "Tue, 12 Dec 2023 14:53:48 GMT",
"version": "v2"
}
] |
2023-12-13
|
[
[
"Hu",
"Shiang",
""
],
[
"Ruan",
"Jie",
""
],
[
"Hou",
"Juan",
""
],
[
"Valdes-Sosa",
"Pedro Antonio",
""
],
[
"Lv",
"Zhao",
""
]
] |
Plenty of artifact removal tools and pipelines have been developed to correct the EEG recordings and discover the values below the waveforms. Without visual inspection from the experts, it is susceptible to derive improper preprocessing states, like the insufficient preprocessed EEG (IPE), and the excessive preprocessed EEG (EPE). However, little is known about the impacts of IPE or EPE on the postprocessing in the frequency, spatial and temporal domains, particularly as to the spectra and the functional connectivity (FC) analysis. Here, the clean EEG (CE) was synthesized as the ground truth based on the New-York head model and the multivariate autoregressive model. Later, the IPE and the EPE were simulated by injecting the Gaussian noise and losing the brain activities, respectively. Then, the impacts on postprocessing were quantified by the deviation caused by the IPE or EPE from the CE as to the 4 temporal statistics, the multichannel power, the cross spectra, the dispersion of source imaging, and the properties of scalp EEG network. Lastly, the association analysis was performed between the PaLOSi metric and the varying trends of postprocessing with the evolution of preprocessing states. This study shed light on how the postprocessing outcomes are affected by the preprocessing states and PaLOSi may be a potential effective quality metric.
|
1105.2362
|
Liaofu Luo
|
Liaofu Luo
|
Protein Photo-folding and Quantum Folding Theory
|
17 pages, 1 figure
| null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The rates of protein folding with photon absorption or emission and the cross
section of photon -protein inelastic scattering are calculated from the quantum
folding theory by use of standard field-theoretical method. All these protein
photo-folding processes are compared with common protein folding without
interaction of photons (nonradiative folding). It is demonstrated that there
exists a common factor (thermo-averaged overlap integral of vibration wave
function, TAOI) for protein folding and protein photo-folding. Based on this
finding it is predicted that: 1) the stimulated photo-folding rates show the
same temperature dependence as protein folding; 2) the spectral line of
electronic transition is broadened to a band which includes abundant vibration
spectrum without and with conformational transition and the width of the
vibration spectral line is largely reduced; 3) the resonance fluorescence cross
section changes with temperature obeying the same law (Luo-Lu's law). The
particular form of the folding rate - temperature relation and the abundant
spectral structure imply the existence of a set of quantum oscillators in the
transition process and these oscillators are mainly of torsion type of low
frequency, imply the quantum tunneling between protein conformations does exist
in folding and photo-folding processes and the tunneling is rooted deeply in
the coherent motion of the conformational-electronic system.
|
[
{
"created": "Thu, 12 May 2011 03:18:55 GMT",
"version": "v1"
}
] |
2011-05-13
|
[
[
"Luo",
"Liaofu",
""
]
] |
The rates of protein folding with photon absorption or emission and the cross section of photon -protein inelastic scattering are calculated from the quantum folding theory by use of standard field-theoretical method. All these protein photo-folding processes are compared with common protein folding without interaction of photons (nonradiative folding). It is demonstrated that there exists a common factor (thermo-averaged overlap integral of vibration wave function, TAOI) for protein folding and protein photo-folding. Based on this finding it is predicted that: 1) the stimulated photo-folding rates show the same temperature dependence as protein folding; 2) the spectral line of electronic transition is broadened to a band which includes abundant vibration spectrum without and with conformational transition and the width of the vibration spectral line is largely reduced; 3) the resonance fluorescence cross section changes with temperature obeying the same law (Luo-Lu's law). The particular form of the folding rate - temperature relation and the abundant spectral structure imply the existence of a set of quantum oscillators in the transition process and these oscillators are mainly of torsion type of low frequency, imply the quantum tunneling between protein conformations does exist in folding and photo-folding processes and the tunneling is rooted deeply in the coherent motion of the conformational-electronic system.
|
1503.07796
|
Joachim Krug
|
Stefan Nowak and Joachim Krug
|
Analysis of adaptive walks on NK fitness landscapes with different
interaction schemes
|
29 pages, 9 figures
|
J. Stat. Mech. (2015) P06014
|
10.1088/1742-5468/2015/06/P06014
| null |
q-bio.PE cond-mat.dis-nn
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Fitness landscapes are genotype to fitness mappings commonly used in
evolutionary biology and computer science which are closely related to spin
glass models. In this paper, we study the NK model for fitness landscapes where
the interaction scheme between genes can be explicitly defined. The focus is on
how this scheme influences the overall shape of the landscape. Our main tool
for the analysis are adaptive walks, an idealized dynamics by which the
population moves uphill in fitness and terminates at a local fitness maximum.
We use three different types of walks and investigate how their length (the
number of steps required to reach a local peak) and height (the fitness at the
endpoint of the walk) depend on the dimensionality and structure of the
landscape. We find that the distribution of local maxima over the landscape is
particularly sensitive to the choice of interaction pattern. Most quantities
that we measure are simply correlated to the rank of the scheme, which is equal
to the number of nonzero coefficients in the expansion of the fitness landscape
in terms of Walsh functions.
|
[
{
"created": "Thu, 26 Mar 2015 17:24:20 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Jun 2015 10:36:25 GMT",
"version": "v2"
}
] |
2015-06-12
|
[
[
"Nowak",
"Stefan",
""
],
[
"Krug",
"Joachim",
""
]
] |
Fitness landscapes are genotype to fitness mappings commonly used in evolutionary biology and computer science which are closely related to spin glass models. In this paper, we study the NK model for fitness landscapes where the interaction scheme between genes can be explicitly defined. The focus is on how this scheme influences the overall shape of the landscape. Our main tool for the analysis are adaptive walks, an idealized dynamics by which the population moves uphill in fitness and terminates at a local fitness maximum. We use three different types of walks and investigate how their length (the number of steps required to reach a local peak) and height (the fitness at the endpoint of the walk) depend on the dimensionality and structure of the landscape. We find that the distribution of local maxima over the landscape is particularly sensitive to the choice of interaction pattern. Most quantities that we measure are simply correlated to the rank of the scheme, which is equal to the number of nonzero coefficients in the expansion of the fitness landscape in terms of Walsh functions.
|
1207.2242
|
Claus Metzner
|
F. Stadler, C. Metzner, J. Steinwachs, B. Fabry
|
Inhomogeneous ensembles of correlated random walkers
| null | null | null | null |
q-bio.QM cond-mat.stat-mech
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Discrete time random walks, in which a step of random sign but constant
length $\delta x$ is performed after each time interval $\delta t$, are widely
used models for stochastic processes. In the case of a correlated random walk,
the next step has the same sign as the previous one with a probability $q \neq
1/2$. We extend this model to an inhomogeneous ensemble of random walkers with
a given distribution of persistence probabilites $p(q)$ and show that
remarkable statistical properties can result from this inhomogenity: Depending
on the distribution $p(q)$, we find that the probability density $p(\Delta x,
\Delta t)$ for a displacement $\Delta x$ after lagtime $\Delta t$ can have a
leptocurtic shape and that mean squared displacements can increase
approximately like a fractional powerlaw with $\Delta t$. For the special case
of persistence parameters distributed equally in the full range $q \in [0,1]$,
the mean squared displacement is derived analytically. The model is further
extended by allowing different step lengths $\delta x_j$ for each member $j$ of
the ensemble. We show that two ensembles $[\delta t, {(q_j,\delta x_j)}]$ and
$[\delta t^{\prime}, {(q^{\prime}_j,\delta x^{\prime}_j)}]$ defined at
different time intervals $\delta t\neq\delta t^{\prime}$ can have the same
statistical properties at long lagtimes $\Delta t$, if their parameters are
related by a certain scaling transformation. Finally, we argue that similar
statistical properties are expected for homogeneous ensembles, in which the
parameters $(q_j(t),\delta x_j(t))$ of each individual walker fluctuate
temporarily, provided the parameters can be considered constant for time
periods $T\gg\Delta t$ longer than the considered lagtime $\Delta t$.
|
[
{
"created": "Tue, 10 Jul 2012 06:58:58 GMT",
"version": "v1"
}
] |
2012-07-11
|
[
[
"Stadler",
"F.",
""
],
[
"Metzner",
"C.",
""
],
[
"Steinwachs",
"J.",
""
],
[
"Fabry",
"B.",
""
]
] |
Discrete time random walks, in which a step of random sign but constant length $\delta x$ is performed after each time interval $\delta t$, are widely used models for stochastic processes. In the case of a correlated random walk, the next step has the same sign as the previous one with a probability $q \neq 1/2$. We extend this model to an inhomogeneous ensemble of random walkers with a given distribution of persistence probabilites $p(q)$ and show that remarkable statistical properties can result from this inhomogenity: Depending on the distribution $p(q)$, we find that the probability density $p(\Delta x, \Delta t)$ for a displacement $\Delta x$ after lagtime $\Delta t$ can have a leptocurtic shape and that mean squared displacements can increase approximately like a fractional powerlaw with $\Delta t$. For the special case of persistence parameters distributed equally in the full range $q \in [0,1]$, the mean squared displacement is derived analytically. The model is further extended by allowing different step lengths $\delta x_j$ for each member $j$ of the ensemble. We show that two ensembles $[\delta t, {(q_j,\delta x_j)}]$ and $[\delta t^{\prime}, {(q^{\prime}_j,\delta x^{\prime}_j)}]$ defined at different time intervals $\delta t\neq\delta t^{\prime}$ can have the same statistical properties at long lagtimes $\Delta t$, if their parameters are related by a certain scaling transformation. Finally, we argue that similar statistical properties are expected for homogeneous ensembles, in which the parameters $(q_j(t),\delta x_j(t))$ of each individual walker fluctuate temporarily, provided the parameters can be considered constant for time periods $T\gg\Delta t$ longer than the considered lagtime $\Delta t$.
|
1612.07425
|
Petter Holme
|
Petter Holme, Nelly Litvak
|
Cost-efficient vaccination protocols for network epidemiology
| null | null |
10.1371/journal.pcbi.1005696
| null |
q-bio.PE physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We investigate methods to vaccinate contact networks -- i.e. removing nodes
in such a way that disease spreading is hindered as much as possible -- with
respect to their cost-efficiency. Any real implementation of such protocols
would come with costs related both to the vaccination itself, and gathering of
information about the network. Disregarding this, we argue, would lead to
erroneous evaluation of vaccination protocols. We use the
susceptible-infected-recovered model -- the generic model for diseases making
patients immune upon recovery -- as our disease-spreading scenario, and analyze
outbreaks on both empirical and model networks. For different relative costs,
different protocols dominate. For high vaccination costs and low costs of
gathering information, the so-called acquaintance vaccination is the most cost
efficient. For other parameter values, protocols designed for query-efficient
identification of the network's largest degrees are most efficient.
|
[
{
"created": "Thu, 22 Dec 2016 03:07:34 GMT",
"version": "v1"
},
{
"created": "Sat, 20 May 2017 05:10:34 GMT",
"version": "v2"
}
] |
2017-11-01
|
[
[
"Holme",
"Petter",
""
],
[
"Litvak",
"Nelly",
""
]
] |
We investigate methods to vaccinate contact networks -- i.e. removing nodes in such a way that disease spreading is hindered as much as possible -- with respect to their cost-efficiency. Any real implementation of such protocols would come with costs related both to the vaccination itself, and gathering of information about the network. Disregarding this, we argue, would lead to erroneous evaluation of vaccination protocols. We use the susceptible-infected-recovered model -- the generic model for diseases making patients immune upon recovery -- as our disease-spreading scenario, and analyze outbreaks on both empirical and model networks. For different relative costs, different protocols dominate. For high vaccination costs and low costs of gathering information, the so-called acquaintance vaccination is the most cost efficient. For other parameter values, protocols designed for query-efficient identification of the network's largest degrees are most efficient.
|
0708.0426
|
Patricia Faisca
|
Rui D.M. Travasso, M.M. Telo da Gama and P.F.N. Faisca
|
Pathways to folding, nucleation events and native geometry
|
Accepted in J. Chem. Phys
| null |
10.1063/1.2777150
| null |
q-bio.BM
| null |
We perform extensive Monte Carlo simulations of a lattice model and the Go
potential to investigate the existence of folding pathways at the level of
contact cluster formation for two native structures with markedly different
geometries. Our analysis of folding pathways revealed a common underlying
folding mechanism, based on nucleation phenomena, for both protein models.
However, folding to the more complex geometry (i.e. that with more non-local
contacts) is driven by a folding nucleus whose geometric traits more closely
resemble those of the native fold. For this geometry folding is clearly a more
cooperative process.
|
[
{
"created": "Thu, 2 Aug 2007 21:35:18 GMT",
"version": "v1"
}
] |
2009-11-13
|
[
[
"Travasso",
"Rui D. M.",
""
],
[
"da Gama",
"M. M. Telo",
""
],
[
"Faisca",
"P. F. N.",
""
]
] |
We perform extensive Monte Carlo simulations of a lattice model and the Go potential to investigate the existence of folding pathways at the level of contact cluster formation for two native structures with markedly different geometries. Our analysis of folding pathways revealed a common underlying folding mechanism, based on nucleation phenomena, for both protein models. However, folding to the more complex geometry (i.e. that with more non-local contacts) is driven by a folding nucleus whose geometric traits more closely resemble those of the native fold. For this geometry folding is clearly a more cooperative process.
|
1911.11840
|
Mahsa Yazdani
|
Mahsa Yazdani and Omid Tavakoli
|
The Effect of Salt Shock on Growth and Pigment Accumulation of
Dunaliella Salina
| null |
The 5th International Symposium on Biological Engineering and
Natural Sciences, August 14-16, 2017, Osaka, Japan
| null | null |
q-bio.QM physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Dunaliella Salina is a halotolerant microalga with great pharmaceutical and
industrial potential, which commonly exists in hypersaline environments.
Moreover, it is the best commercial source of beta-carotene (which has high
anti-oxidant properties) in comparison to other microalgae. In this study, we
investigated growth and accumulations of chlorophyll a and b, beta-carotene,
and carotenoid after salt shock in 1, 1.5, 2, 2.5, 3 M concentrations of NaCl.
The highest cell growth rate was observed in 1 M salt shock at 22-25 centigrade
with a light intensity of 2.084 (mW.cm)^(-2), a light period of 12-12, and at
an initial pH of about 7.1. Although the cell growth was enhanced in 1 and
1.5M, further increase in salt content harmed cell growth. The most
considerable beta-carotene quantity was attained after 1M salt shock. According
to the experimental observations, it was seen that the salt shock in some
concentrations is one of the practical approaches to improve the accumulation
of pigments.
Keywords: beta-carotene, Dunaliella salina, salt shock, pigment accumulation.
|
[
{
"created": "Tue, 26 Nov 2019 21:31:09 GMT",
"version": "v1"
}
] |
2019-11-28
|
[
[
"Yazdani",
"Mahsa",
""
],
[
"Tavakoli",
"Omid",
""
]
] |
Dunaliella Salina is a halotolerant microalga with great pharmaceutical and industrial potential, which commonly exists in hypersaline environments. Moreover, it is the best commercial source of beta-carotene (which has high anti-oxidant properties) in comparison to other microalgae. In this study, we investigated growth and accumulations of chlorophyll a and b, beta-carotene, and carotenoid after salt shock in 1, 1.5, 2, 2.5, 3 M concentrations of NaCl. The highest cell growth rate was observed in 1 M salt shock at 22-25 centigrade with a light intensity of 2.084 (mW.cm)^(-2), a light period of 12-12, and at an initial pH of about 7.1. Although the cell growth was enhanced in 1 and 1.5M, further increase in salt content harmed cell growth. The most considerable beta-carotene quantity was attained after 1M salt shock. According to the experimental observations, it was seen that the salt shock in some concentrations is one of the practical approaches to improve the accumulation of pigments. Keywords: beta-carotene, Dunaliella salina, salt shock, pigment accumulation.
|
q-bio/0608010
|
Chunguang Li
|
Chunguang Li, Luonan Chen, Kazuyuki Aihara
|
Transient Resetting: A Novel Mechanism for Synchrony and Its Biological
Examples
|
17 pages, 7 figures
|
PLoS Computational Biology 2 (8): e103, 2006
|
10.1371/journal.pcbi.0020103
| null |
q-bio.MN nlin.CD
| null |
The study of synchronization in biological systems is essential for the
understanding of the rhythmic phenomena of living organisms at both molecular
and cellular levels. In this paper, by using simple dynamical systems theory,
we present a novel mechanism, named transient resetting, for the
synchronization of uncoupled biological oscillators with stimuli. This
mechanism not only can unify and extend many existing results on (deterministic
and stochastic) stimulus-induced synchrony, but also may actually play an
important role in biological rhythms. We argue that transient resetting is a
possible mechanism for the synchronization in many biological organisms, which
might also be further used in medical therapy of rhythmic disorders. Examples
on the synchronization of neural and circadian oscillators are presented to
verify our hypothesis.
|
[
{
"created": "Fri, 4 Aug 2006 03:02:30 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Li",
"Chunguang",
""
],
[
"Chen",
"Luonan",
""
],
[
"Aihara",
"Kazuyuki",
""
]
] |
The study of synchronization in biological systems is essential for the understanding of the rhythmic phenomena of living organisms at both molecular and cellular levels. In this paper, by using simple dynamical systems theory, we present a novel mechanism, named transient resetting, for the synchronization of uncoupled biological oscillators with stimuli. This mechanism not only can unify and extend many existing results on (deterministic and stochastic) stimulus-induced synchrony, but also may actually play an important role in biological rhythms. We argue that transient resetting is a possible mechanism for the synchronization in many biological organisms, which might also be further used in medical therapy of rhythmic disorders. Examples on the synchronization of neural and circadian oscillators are presented to verify our hypothesis.
|
2206.12240
|
Sirui Liu
|
Sirui Liu, Jun Zhang, Haotian Chu, Min Wang, Boxin Xue, Ningxi Ni,
Jialiang Yu, Yuhao Xie, Zhenyu Chen, Mengyun Chen, Yuan Liu, Piya Patra, Fan
Xu, Jie Chen, Zidong Wang, Lijiang Yang, Fan Yu, Lei Chen, Yi Qin Gao
|
PSP: Million-level Protein Sequence Dataset for Protein Structure
Prediction
| null | null | null | null |
q-bio.BM cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Proteins are essential component of human life and their structures are
important for function and mechanism analysis. Recent work has shown the
potential of AI-driven methods for protein structure prediction. However, the
development of new models is restricted by the lack of dataset and benchmark
training procedure. To the best of our knowledge, the existing open source
datasets are far less to satisfy the needs of modern protein sequence-structure
related research. To solve this problem, we present the first million-level
protein structure prediction dataset with high coverage and diversity, named as
PSP. This dataset consists of 570k true structure sequences (10TB) and 745k
complementary distillation sequences (15TB). We provide in addition the
benchmark training procedure for SOTA protein structure prediction model on
this dataset. We validate the utility of this dataset for training by
participating CAMEO contest in which our model won the first place. We hope our
PSP dataset together with the training benchmark can enable a broader community
of AI/biology researchers for AI-driven protein related research.
|
[
{
"created": "Fri, 24 Jun 2022 14:08:44 GMT",
"version": "v1"
}
] |
2022-06-27
|
[
[
"Liu",
"Sirui",
""
],
[
"Zhang",
"Jun",
""
],
[
"Chu",
"Haotian",
""
],
[
"Wang",
"Min",
""
],
[
"Xue",
"Boxin",
""
],
[
"Ni",
"Ningxi",
""
],
[
"Yu",
"Jialiang",
""
],
[
"Xie",
"Yuhao",
""
],
[
"Chen",
"Zhenyu",
""
],
[
"Chen",
"Mengyun",
""
],
[
"Liu",
"Yuan",
""
],
[
"Patra",
"Piya",
""
],
[
"Xu",
"Fan",
""
],
[
"Chen",
"Jie",
""
],
[
"Wang",
"Zidong",
""
],
[
"Yang",
"Lijiang",
""
],
[
"Yu",
"Fan",
""
],
[
"Chen",
"Lei",
""
],
[
"Gao",
"Yi Qin",
""
]
] |
Proteins are essential component of human life and their structures are important for function and mechanism analysis. Recent work has shown the potential of AI-driven methods for protein structure prediction. However, the development of new models is restricted by the lack of dataset and benchmark training procedure. To the best of our knowledge, the existing open source datasets are far less to satisfy the needs of modern protein sequence-structure related research. To solve this problem, we present the first million-level protein structure prediction dataset with high coverage and diversity, named as PSP. This dataset consists of 570k true structure sequences (10TB) and 745k complementary distillation sequences (15TB). We provide in addition the benchmark training procedure for SOTA protein structure prediction model on this dataset. We validate the utility of this dataset for training by participating CAMEO contest in which our model won the first place. We hope our PSP dataset together with the training benchmark can enable a broader community of AI/biology researchers for AI-driven protein related research.
|
1304.0479
|
Wendy Ingram
|
Wendy Marie Ingram, Leeanne M Goodrich, Ellen A Robey, Michael B Eisen
|
Mice Infected with Low-virulence Strains of Toxoplasma gondii Lose their
Innate Aversion to Cat Urine, Even after Extensive Parasite Clearance
|
14 pages, 3 figures
| null |
10.1371/journal.pone.0075246
| null |
q-bio.TO q-bio.NC
|
http://creativecommons.org/licenses/by/3.0/
|
Toxoplasma gondii chronic infection in rodent secondary hosts has been
reported to lead to a loss of innate, hard-wired fear toward cats, its primary
host. However the generality of this response across T. gondii strains and the
underlying mechanism for this pathogen mediated behavioral change remain
unknown. To begin exploring these questions, we evaluated the effects of
infection with two previously uninvestigated isolates from the three major
North American clonal lineages of T. gondii, Type III and an attenuated strain
of Type I. Using an hour-long open field activity assay optimized for this
purpose, we measured mouse aversion toward predator and non-predator urines. We
show that loss of innate aversion of cat urine is a general trait caused by
infection with any of the three major clonal lineages of parasite.
Surprisingly, we found that infection with the attenuated Type I parasite
results in sustained loss of aversion at times post infection when neither
parasite nor ongoing brain inflammation were detectable. This suggests that T.
gondii-mediated interruption of mouse innate aversion toward cat urine may
occur during early acute infection in a permanent manner, not requiring
persistence of parasitecysts or continuing brain inflammation.
|
[
{
"created": "Mon, 1 Apr 2013 20:53:18 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Jul 2013 21:08:20 GMT",
"version": "v2"
}
] |
2014-03-05
|
[
[
"Ingram",
"Wendy Marie",
""
],
[
"Goodrich",
"Leeanne M",
""
],
[
"Robey",
"Ellen A",
""
],
[
"Eisen",
"Michael B",
""
]
] |
Toxoplasma gondii chronic infection in rodent secondary hosts has been reported to lead to a loss of innate, hard-wired fear toward cats, its primary host. However the generality of this response across T. gondii strains and the underlying mechanism for this pathogen mediated behavioral change remain unknown. To begin exploring these questions, we evaluated the effects of infection with two previously uninvestigated isolates from the three major North American clonal lineages of T. gondii, Type III and an attenuated strain of Type I. Using an hour-long open field activity assay optimized for this purpose, we measured mouse aversion toward predator and non-predator urines. We show that loss of innate aversion of cat urine is a general trait caused by infection with any of the three major clonal lineages of parasite. Surprisingly, we found that infection with the attenuated Type I parasite results in sustained loss of aversion at times post infection when neither parasite nor ongoing brain inflammation were detectable. This suggests that T. gondii-mediated interruption of mouse innate aversion toward cat urine may occur during early acute infection in a permanent manner, not requiring persistence of parasitecysts or continuing brain inflammation.
|
1603.02414
|
Christos Skiadas H
|
Christos H Skiadas
|
The Health-Mortality Approach in Estimating the Healthy Life Years Lost
Compared to the Global Burden of Disease Studies and Applications
|
26 pages, 11 figures, 6 tables. arXiv admin note: substantial text
overlap with arXiv:1510.07346
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a series of methods and models in order to explore the Global
Burden of Disease Study and the provided healthy life expectancy HALE estimates
from the World Health Organization WHO based on the mortality mx of a
population provided in a classical life table and a mortality diagram. Our
estimates are compared with the HALE estimates for the World territories and
the WHO regions along with providing comparative results with to findings of
Chang, Molla, Truman et al. (2015) on the Differences in healthy life
expectancy for the US population by sex, race or ethnicity and geographic
region in 2008 and from Yong and Saito (2009) regarding Trends in healthy life
expectancy in Japan. From the mortality point of view we have developed a
simple model for the estimation of a characteristic parameter b related to the
healthy life years lost to disability and providing full application details
along with characteristic parameter selection and stability of the
coefficients. We also provide a direct estimation method of the parameter b
from the life tables. We straighten the importance of our methodology by
proposing and applying estimates of the parameter b by using the Gompertz and
the Weibull models. From the Health State point of view we summarize the main
points of the first exit time theory to life table data and present the basic
models starting from the first related model published by Janssen and Skiadas
(1995). Even more we develop the simpler 2-parameter health state model and an
extension of a model expressing the infant mortality to a 4-parameter model
which is the simpler model providing very good fitting on the logarithm of the
force of mortality. More important is the use of the Health State Function and
the relative impact on mortality to find an estimate for the healthy life years
lost to disability.
|
[
{
"created": "Tue, 8 Mar 2016 08:31:51 GMT",
"version": "v1"
}
] |
2016-03-09
|
[
[
"Skiadas",
"Christos H",
""
]
] |
We propose a series of methods and models in order to explore the Global Burden of Disease Study and the provided healthy life expectancy HALE estimates from the World Health Organization WHO based on the mortality mx of a population provided in a classical life table and a mortality diagram. Our estimates are compared with the HALE estimates for the World territories and the WHO regions along with providing comparative results with to findings of Chang, Molla, Truman et al. (2015) on the Differences in healthy life expectancy for the US population by sex, race or ethnicity and geographic region in 2008 and from Yong and Saito (2009) regarding Trends in healthy life expectancy in Japan. From the mortality point of view we have developed a simple model for the estimation of a characteristic parameter b related to the healthy life years lost to disability and providing full application details along with characteristic parameter selection and stability of the coefficients. We also provide a direct estimation method of the parameter b from the life tables. We straighten the importance of our methodology by proposing and applying estimates of the parameter b by using the Gompertz and the Weibull models. From the Health State point of view we summarize the main points of the first exit time theory to life table data and present the basic models starting from the first related model published by Janssen and Skiadas (1995). Even more we develop the simpler 2-parameter health state model and an extension of a model expressing the infant mortality to a 4-parameter model which is the simpler model providing very good fitting on the logarithm of the force of mortality. More important is the use of the Health State Function and the relative impact on mortality to find an estimate for the healthy life years lost to disability.
|
2306.13429
|
Leonardo Novelli
|
Leonardo Novelli, Karl Friston, Adeel Razi
|
Spectral Dynamic Causal Modelling: A Didactic Introduction and its
Relationship with Functional Connectivity
| null | null | null | null |
q-bio.NC cs.CE
|
http://creativecommons.org/licenses/by/4.0/
|
We present a didactic introduction to spectral Dynamic Causal Modelling
(DCM), a Bayesian state-space modelling approach used to infer effective
connectivity from non-invasive neuroimaging data. Spectral DCM is currently the
most widely applied DCM variant for resting-state functional MRI analysis. Our
aim is to explain its technical foundations to an audience with limited
expertise in state-space modelling and spectral data analysis. Particular
attention will be paid to cross-spectral density, which is the most distinctive
feature of spectral DCM and is closely related to functional connectivity, as
measured by (zero-lag) Pearson correlations. In fact, the model parameters
estimated by spectral DCM are those that best reproduce the cross-correlations
between all measurements--at all time lags--including the zero-lag correlations
that are usually interpreted as functional connectivity. We derive the
functional connectivity matrix from the model equations and show how changing a
single effective connectivity parameter can affect all pairwise correlations.
To complicate matters, the pairs of brain regions showing the largest changes
in functional connectivity do not necessarily coincide with those presenting
the largest changes in effective connectivity. We discuss the implications and
conclude with a comprehensive summary of the assumptions and limitations of
spectral DCM.
|
[
{
"created": "Fri, 23 Jun 2023 10:46:39 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Sep 2023 02:24:23 GMT",
"version": "v2"
}
] |
2023-09-07
|
[
[
"Novelli",
"Leonardo",
""
],
[
"Friston",
"Karl",
""
],
[
"Razi",
"Adeel",
""
]
] |
We present a didactic introduction to spectral Dynamic Causal Modelling (DCM), a Bayesian state-space modelling approach used to infer effective connectivity from non-invasive neuroimaging data. Spectral DCM is currently the most widely applied DCM variant for resting-state functional MRI analysis. Our aim is to explain its technical foundations to an audience with limited expertise in state-space modelling and spectral data analysis. Particular attention will be paid to cross-spectral density, which is the most distinctive feature of spectral DCM and is closely related to functional connectivity, as measured by (zero-lag) Pearson correlations. In fact, the model parameters estimated by spectral DCM are those that best reproduce the cross-correlations between all measurements--at all time lags--including the zero-lag correlations that are usually interpreted as functional connectivity. We derive the functional connectivity matrix from the model equations and show how changing a single effective connectivity parameter can affect all pairwise correlations. To complicate matters, the pairs of brain regions showing the largest changes in functional connectivity do not necessarily coincide with those presenting the largest changes in effective connectivity. We discuss the implications and conclude with a comprehensive summary of the assumptions and limitations of spectral DCM.
|
1711.07258
|
Marie-Constance Corsi
|
Marie-Constance Corsi, Mario Chavez, Denis Schwartz, Laurent
Hugueville, Ankit N. Khambhati, Danielle S. Bassett, Fabrizio De Vico Fallani
|
Integrating EEG and MEG signals to improve motor imagery classification
in brain-computer interfaces
| null | null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a fusion approach that combines features from simultaneously
recorded electroencephalographic (EEG) and magnetoencephalographic (MEG)
signals to improve classification performances in motor imagery-based
brain-computer interfaces (BCIs). We applied our approach to a group of 15
healthy subjects and found a significant classification performance enhancement
as compared to standard single-modality approaches in the alpha and beta bands.
Taken together, our findings demonstrate the advantage of considering
multimodal approaches as complementary tools for improving the impact of
non-invasive BCIs.
|
[
{
"created": "Mon, 20 Nov 2017 11:30:15 GMT",
"version": "v1"
},
{
"created": "Mon, 26 Mar 2018 13:27:13 GMT",
"version": "v2"
}
] |
2018-03-28
|
[
[
"Corsi",
"Marie-Constance",
""
],
[
"Chavez",
"Mario",
""
],
[
"Schwartz",
"Denis",
""
],
[
"Hugueville",
"Laurent",
""
],
[
"Khambhati",
"Ankit N.",
""
],
[
"Bassett",
"Danielle S.",
""
],
[
"Fallani",
"Fabrizio De Vico",
""
]
] |
We propose a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of non-invasive BCIs.
|
0910.4077
|
Marcin Zag\'orski
|
Z. Burda, A. Krzywicki, O. C. Martin, M. Zagorski
|
Sparse essential interactions in model networks of gene regulation
|
9 pages, 5 figures
| null | null | null |
q-bio.MN cond-mat.stat-mech
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Gene regulatory networks typically have low in-degrees, whereby any given
gene is regulated by few of the genes in the network. What mechanisms might be
responsible for these low in-degrees? Starting with an accepted framework of
the binding of transcription factors to DNA, we consider a simple model of gene
regulatory dynamics. In this model, we show that the constraint of having a
given function leads to the emergence of minimum connectivities compatible with
function. We exhibit mathematically this behavior within a limit of our model
and show that it also arises in the full model. As a consequence, functionality
in these gene networks is parsimonious, i.e., is concentrated on a sparse
number of interactions as measured for instance by their essentiality. Our
model thus provides a simple mechanism for the emergence of sparse regulatory
networks, and leads to very heterogeneous effects of mutations.
|
[
{
"created": "Wed, 21 Oct 2009 12:58:49 GMT",
"version": "v1"
}
] |
2009-10-22
|
[
[
"Burda",
"Z.",
""
],
[
"Krzywicki",
"A.",
""
],
[
"Martin",
"O. C.",
""
],
[
"Zagorski",
"M.",
""
]
] |
Gene regulatory networks typically have low in-degrees, whereby any given gene is regulated by few of the genes in the network. What mechanisms might be responsible for these low in-degrees? Starting with an accepted framework of the binding of transcription factors to DNA, we consider a simple model of gene regulatory dynamics. In this model, we show that the constraint of having a given function leads to the emergence of minimum connectivities compatible with function. We exhibit mathematically this behavior within a limit of our model and show that it also arises in the full model. As a consequence, functionality in these gene networks is parsimonious, i.e., is concentrated on a sparse number of interactions as measured for instance by their essentiality. Our model thus provides a simple mechanism for the emergence of sparse regulatory networks, and leads to very heterogeneous effects of mutations.
|
1711.11161
|
Pedro M. F. Pereira
|
Pedro M. F. Pereira
|
Can Complex Collective Behaviour Be Generated Through Randomness, Memory
and a Pinch of Luck?
| null | null | null | null |
q-bio.PE nlin.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Machine Learning techniques have been used to teach computer programs how to
play games as complicated as Chess and Go. These were achieved using powerful
tools such as Neural Networks and Parallel Computing on Supercomputers. In this
paper, we define a model of populational growth and evolution based on the idea
of Reinforcement Learning, but using only the 3 sources stated in the title
processed on a low-tier laptop. The model correctly predicts the development of
a population around food sources and their migration in search of a new one
when the known ones become saturated. Additionally, we compared our model to a
pure random one and the population number was fitted to a logistic function for
two interesting evolutions of the system.
|
[
{
"created": "Wed, 29 Nov 2017 23:56:29 GMT",
"version": "v1"
}
] |
2017-12-01
|
[
[
"Pereira",
"Pedro M. F.",
""
]
] |
Machine Learning techniques have been used to teach computer programs how to play games as complicated as Chess and Go. These were achieved using powerful tools such as Neural Networks and Parallel Computing on Supercomputers. In this paper, we define a model of populational growth and evolution based on the idea of Reinforcement Learning, but using only the 3 sources stated in the title processed on a low-tier laptop. The model correctly predicts the development of a population around food sources and their migration in search of a new one when the known ones become saturated. Additionally, we compared our model to a pure random one and the population number was fitted to a logistic function for two interesting evolutions of the system.
|
1008.1063
|
Attila Szolnoki
|
Gyorgy Szabo, Attila Szolnoki, Melinda Varga, Livia Hanusovszky
|
Ordering in spatial evolutionary games for pairwise collective strategy
updates
|
9 pages, 6 figures; accepted for publication in Physical Review E
|
Physical Review E 82 (2010) 026110
|
10.1103/PhysRevE.82.026110
| null |
q-bio.PE cond-mat.stat-mech physics.bio-ph physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Evolutionary $2 \times 2$ games are studied with players located on a square
lattice. During the evolution the randomly chosen neighboring players try to
maximize their collective income by adopting a random strategy pair with a
probability dependent on the difference of their summed payoffs between the
final and initial state assuming quenched strategies in their neighborhood. In
the case of the anti-coordination game this system behaves alike an
anti-ferromagnetic kinetic Ising model. Within a wide region of social dilemmas
this dynamical rule supports the formation of similar spatial arrangement of
the cooperators and defectors ensuring the optimum total payoff if the
temptation to choose defection exceeds a threshold value dependent on the
sucker's payoff. The comparison of the results with those achieved for pairwise
imitation and myopic strategy updates has indicated the relevant advantage of
pairwise collective strategy update in the maintenance of cooperation.
|
[
{
"created": "Thu, 5 Aug 2010 20:01:31 GMT",
"version": "v1"
}
] |
2010-08-23
|
[
[
"Szabo",
"Gyorgy",
""
],
[
"Szolnoki",
"Attila",
""
],
[
"Varga",
"Melinda",
""
],
[
"Hanusovszky",
"Livia",
""
]
] |
Evolutionary $2 \times 2$ games are studied with players located on a square lattice. During the evolution the randomly chosen neighboring players try to maximize their collective income by adopting a random strategy pair with a probability dependent on the difference of their summed payoffs between the final and initial state assuming quenched strategies in their neighborhood. In the case of the anti-coordination game this system behaves alike an anti-ferromagnetic kinetic Ising model. Within a wide region of social dilemmas this dynamical rule supports the formation of similar spatial arrangement of the cooperators and defectors ensuring the optimum total payoff if the temptation to choose defection exceeds a threshold value dependent on the sucker's payoff. The comparison of the results with those achieved for pairwise imitation and myopic strategy updates has indicated the relevant advantage of pairwise collective strategy update in the maintenance of cooperation.
|
1609.04316
|
Carlo Nicolini
|
Carlo Nicolini, C\'ecile Bordier, Angelo Bifone
|
Community detection in weighted brain connectivity networks beyond the
resolution limit
|
27 pages with 6 figures and 1 table. Conference version for CCS2016
| null | null | null |
q-bio.NC physics.soc-ph q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Graph theory provides a powerful framework to investigate brain functional
connectivity networks and their modular organization. However, most graph-based
methods suffer from a fundamental resolution limit that may have affected
previous studies and prevented detection of modules, or communities, that are
smaller than a specific scale. Surprise, a resolution-limit-free function
rooted in discrete probability theory, has been recently introduced and applied
to brain networks, revealing a wide size-distribution of functional modules, in
contrast with many previous reports. However, the use of Surprise is limited to
binary networks, while brain networks are intrinsically weighted, reflecting a
continuous distribution of connectivity strengths between different brain
regions. Here, we propose Asymptotical Surprise, a continuous version of
Surprise, for the study of weighted brain connectivity networks, and validate
this approach in synthetic networks endowed with a ground-truth modular
structure. We compare Asymptotical Surprise with leading community detection
methods currently in use and show its superior sensitivity in the detection of
small modules even in the presence of noise and intersubject variability such
as those observed in fMRI data. Finally, we apply our novel approach to
functional connectivity networks from resting state fMRI experimenta, and
demonstrate a heterogeneous modular organization, with a wide distribution of
clusters spanning multiple scales.
|
[
{
"created": "Wed, 14 Sep 2016 15:36:49 GMT",
"version": "v1"
}
] |
2016-09-15
|
[
[
"Nicolini",
"Carlo",
""
],
[
"Bordier",
"Cécile",
""
],
[
"Bifone",
"Angelo",
""
]
] |
Graph theory provides a powerful framework to investigate brain functional connectivity networks and their modular organization. However, most graph-based methods suffer from a fundamental resolution limit that may have affected previous studies and prevented detection of modules, or communities, that are smaller than a specific scale. Surprise, a resolution-limit-free function rooted in discrete probability theory, has been recently introduced and applied to brain networks, revealing a wide size-distribution of functional modules, in contrast with many previous reports. However, the use of Surprise is limited to binary networks, while brain networks are intrinsically weighted, reflecting a continuous distribution of connectivity strengths between different brain regions. Here, we propose Asymptotical Surprise, a continuous version of Surprise, for the study of weighted brain connectivity networks, and validate this approach in synthetic networks endowed with a ground-truth modular structure. We compare Asymptotical Surprise with leading community detection methods currently in use and show its superior sensitivity in the detection of small modules even in the presence of noise and intersubject variability such as those observed in fMRI data. Finally, we apply our novel approach to functional connectivity networks from resting state fMRI experimenta, and demonstrate a heterogeneous modular organization, with a wide distribution of clusters spanning multiple scales.
|
1809.09450
|
Alican Ozkan
|
Alican Ozkan, Neda Ghousifam, P. Jack Hoopes, Marissa Nichole Rylander
|
In Vitro Vascularized Liver and Tumor Tissue Microenvironments on a Chip
for Dynamic Determination of Nanoparticle Transport and Toxicity
|
42 pages, 10 figures, 1 table
| null | null | null |
q-bio.TO physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents the development of a vascularized breast tumor and
healthy or tumorigenic liver microenvironments-on-a-chip connected in series.
This is the first description of a vascularized multi tissue-on-a-chip
microenvironment for modeling cancerous breast and cancerous/healthy liver
microenvironments, to allow for the study of dynamic and spatial transport of
particles. This device enables the dynamic determination of vessel
permeability, the measurement of drug and nanoparticle transport, and the
assessment of the associated efficacy and toxicity to the liver. The platform
is utilized to determine the effect of particle size on the spatiotemporal
diffusion of particles through each microenvironment, both independently and in
response to the circulation of particles in varying sequences of
microenvironments. The results show that when breast cancer cells were cultured
in the microenvironments they had a 2.62-fold higher vessel porosity relative
to vessels within healthy liver microenvironments. Hence, the permeability of
the tumor microenvironment increased by 2.35- and 2.77-fold compared to a
healthy liver for small and large particles, respectively. The ECM accumulation
rate of larger particles was 2.57-fold lower than smaller particles in a
healthy liver. However, the accumulation rate was 5.57-fold greater in the
breast tumor microenvironment. These results are in agreement with comparable
in vivo studies. Ultimately, the platform could be utilized to determine the
impact of the tissue or tumor microenvironment, or drug and nanoparticle
properties, on transport, efficacy, selectivity, and toxicity in a dynamic, and
high throughput manner for use in treatment optimization.
|
[
{
"created": "Tue, 25 Sep 2018 13:02:54 GMT",
"version": "v1"
},
{
"created": "Wed, 28 Nov 2018 03:05:35 GMT",
"version": "v2"
}
] |
2018-11-29
|
[
[
"Ozkan",
"Alican",
""
],
[
"Ghousifam",
"Neda",
""
],
[
"Hoopes",
"P. Jack",
""
],
[
"Rylander",
"Marissa Nichole",
""
]
] |
This paper presents the development of a vascularized breast tumor and healthy or tumorigenic liver microenvironments-on-a-chip connected in series. This is the first description of a vascularized multi tissue-on-a-chip microenvironment for modeling cancerous breast and cancerous/healthy liver microenvironments, to allow for the study of dynamic and spatial transport of particles. This device enables the dynamic determination of vessel permeability, the measurement of drug and nanoparticle transport, and the assessment of the associated efficacy and toxicity to the liver. The platform is utilized to determine the effect of particle size on the spatiotemporal diffusion of particles through each microenvironment, both independently and in response to the circulation of particles in varying sequences of microenvironments. The results show that when breast cancer cells were cultured in the microenvironments they had a 2.62-fold higher vessel porosity relative to vessels within healthy liver microenvironments. Hence, the permeability of the tumor microenvironment increased by 2.35- and 2.77-fold compared to a healthy liver for small and large particles, respectively. The ECM accumulation rate of larger particles was 2.57-fold lower than smaller particles in a healthy liver. However, the accumulation rate was 5.57-fold greater in the breast tumor microenvironment. These results are in agreement with comparable in vivo studies. Ultimately, the platform could be utilized to determine the impact of the tissue or tumor microenvironment, or drug and nanoparticle properties, on transport, efficacy, selectivity, and toxicity in a dynamic, and high throughput manner for use in treatment optimization.
|
2403.13851
|
Lucas B\"ottcher
|
Lucas B\"ottcher, Luis L. Fonseca, Reinhard C. Laubenbacher
|
Control of Medical Digital Twins with Artificial Neural Networks
|
13 pages, 5 figures
| null | null | null |
q-bio.QM cs.LG cs.SY eess.SY math.DS math.OC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The objective of personalized medicine is to tailor interventions to an
individual patient's unique characteristics. A key technology for this purpose
involves medical digital twins, computational models of human biology that can
be personalized and dynamically updated to incorporate patient-specific data
collected over time. Certain aspects of human biology, such as the immune
system, are not easily captured with physics-based models, such as differential
equations. Instead, they are often multi-scale, stochastic, and hybrid. This
poses a challenge to existing model-based control and optimization approaches
that cannot be readily applied to such models. Recent advances in automatic
differentiation and neural-network control methods hold promise in addressing
complex control problems. However, the application of these approaches to
biomedical systems is still in its early stages. This work introduces
dynamics-informed neural-network controllers as an alternative approach to
control of medical digital twins. As a first use case for this method, the
focus is on agent-based models, a versatile and increasingly common modeling
platform in biomedicine. The effectiveness of the proposed neural-network
control method is illustrated and benchmarked against other methods with two
widely-used agent-based model types. The relevance of the method introduced
here extends beyond medical digital twins to other complex dynamical systems.
|
[
{
"created": "Mon, 18 Mar 2024 19:30:46 GMT",
"version": "v1"
}
] |
2024-03-22
|
[
[
"Böttcher",
"Lucas",
""
],
[
"Fonseca",
"Luis L.",
""
],
[
"Laubenbacher",
"Reinhard C.",
""
]
] |
The objective of personalized medicine is to tailor interventions to an individual patient's unique characteristics. A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dynamically updated to incorporate patient-specific data collected over time. Certain aspects of human biology, such as the immune system, are not easily captured with physics-based models, such as differential equations. Instead, they are often multi-scale, stochastic, and hybrid. This poses a challenge to existing model-based control and optimization approaches that cannot be readily applied to such models. Recent advances in automatic differentiation and neural-network control methods hold promise in addressing complex control problems. However, the application of these approaches to biomedical systems is still in its early stages. This work introduces dynamics-informed neural-network controllers as an alternative approach to control of medical digital twins. As a first use case for this method, the focus is on agent-based models, a versatile and increasingly common modeling platform in biomedicine. The effectiveness of the proposed neural-network control method is illustrated and benchmarked against other methods with two widely-used agent-based model types. The relevance of the method introduced here extends beyond medical digital twins to other complex dynamical systems.
|
1304.8045
|
Buhm Han
|
Buhm Han, Jae Hoon Sul, Eleazar Eskin, Paul I. W. de Bakker, Soumya
Raychaudhuri
|
A general framework for meta-analyzing dependent studies with
overlapping subjects in association mapping
|
1/17/14: Minor text changes
| null | null | null |
q-bio.QM stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Meta-analysis of genome-wide association studies is increasingly popular and
many meta-analytic methods have been recently proposed. A majority of
meta-analytic methods combine information from multiple studies by assuming
that studies are independent since individuals collected in one study are
unlikely to be collected again by another study. However, it has become
increasingly common to utilize the same control individuals among multiple
studies to reduce genotyping or sequencing cost. This causes those studies that
share the same individuals to be dependent, and spurious associations may arise
if overlapping subjects are not taken into account in a meta-analysis. In this
paper, we propose a general framework for meta-analyzing dependent studies with
overlapping subjects. Given dependent studies, our approach "decouples" the
studies into independent studies such that meta-analysis methods assuming
independent studies can be applied. This enables many meta-analysis methods,
such as the random effects model, to account for overlapping subjects. Another
advantage is that one can continue to use preferred software in the analysis
pipeline which may not support overlapping subjects. Using simulations and the
Wellcome Trust Case Control Consortium data, we show that our decoupling
approach allows both the fixed and the random effects models to account for
overlapping subjects while retaining desirable false positive rate and power.
|
[
{
"created": "Tue, 30 Apr 2013 16:01:14 GMT",
"version": "v1"
},
{
"created": "Mon, 7 Oct 2013 21:53:36 GMT",
"version": "v2"
},
{
"created": "Fri, 17 Jan 2014 17:59:19 GMT",
"version": "v3"
}
] |
2014-01-20
|
[
[
"Han",
"Buhm",
""
],
[
"Sul",
"Jae Hoon",
""
],
[
"Eskin",
"Eleazar",
""
],
[
"de Bakker",
"Paul I. W.",
""
],
[
"Raychaudhuri",
"Soumya",
""
]
] |
Meta-analysis of genome-wide association studies is increasingly popular and many meta-analytic methods have been recently proposed. A majority of meta-analytic methods combine information from multiple studies by assuming that studies are independent since individuals collected in one study are unlikely to be collected again by another study. However, it has become increasingly common to utilize the same control individuals among multiple studies to reduce genotyping or sequencing cost. This causes those studies that share the same individuals to be dependent, and spurious associations may arise if overlapping subjects are not taken into account in a meta-analysis. In this paper, we propose a general framework for meta-analyzing dependent studies with overlapping subjects. Given dependent studies, our approach "decouples" the studies into independent studies such that meta-analysis methods assuming independent studies can be applied. This enables many meta-analysis methods, such as the random effects model, to account for overlapping subjects. Another advantage is that one can continue to use preferred software in the analysis pipeline which may not support overlapping subjects. Using simulations and the Wellcome Trust Case Control Consortium data, we show that our decoupling approach allows both the fixed and the random effects models to account for overlapping subjects while retaining desirable false positive rate and power.
|
2002.00245
|
Mandev Gill
|
Mandev S. Gill, Philippe Lemey, Marc A. Suchard, Andrew Rambaut, Guy
Baele
|
Online Bayesian phylodynamic inference in BEAST with application to
epidemic reconstruction
|
20 pages, 3 figures
| null | null | null |
q-bio.PE stat.ME
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Reconstructing pathogen dynamics from genetic data as they become available
during an outbreak or epidemic represents an important statistical scenario in
which observations arrive sequentially in time and one is interested in
performing inference in an 'online' fashion. Widely-used Bayesian phylogenetic
inference packages are not set up for this purpose, generally requiring one to
recompute trees and evolutionary model parameters de novo when new data arrive.
To accommodate increasing data flow in a Bayesian phylogenetic framework, we
introduce a methodology to efficiently update the posterior distribution with
newly available genetic data. Our procedure is implemented in the BEAST 1.10
software package, and relies on a distance-based measure to insert new taxa
into the current estimate of the phylogeny and imputes plausible values for new
model parameters to accommodate growing dimensionality. This augmentation
creates informed starting values and re-uses optimally tuned transition kernels
for posterior exploration of growing data sets, reducing the time necessary to
converge to target posterior distributions. We apply our framework to data from
the recent West African Ebola virus epidemic and demonstrate a considerable
reduction in time required to obtain posterior estimates at different time
points of the outbreak. Beyond epidemic monitoring, this framework easily finds
other applications within the phylogenetics community, where changes in the
data -- in terms of alignment changes, sequence addition or removal -- present
common scenarios that can benefit from online inference.
|
[
{
"created": "Sat, 1 Feb 2020 17:30:59 GMT",
"version": "v1"
}
] |
2020-02-04
|
[
[
"Gill",
"Mandev S.",
""
],
[
"Lemey",
"Philippe",
""
],
[
"Suchard",
"Marc A.",
""
],
[
"Rambaut",
"Andrew",
""
],
[
"Baele",
"Guy",
""
]
] |
Reconstructing pathogen dynamics from genetic data as they become available during an outbreak or epidemic represents an important statistical scenario in which observations arrive sequentially in time and one is interested in performing inference in an 'online' fashion. Widely-used Bayesian phylogenetic inference packages are not set up for this purpose, generally requiring one to recompute trees and evolutionary model parameters de novo when new data arrive. To accommodate increasing data flow in a Bayesian phylogenetic framework, we introduce a methodology to efficiently update the posterior distribution with newly available genetic data. Our procedure is implemented in the BEAST 1.10 software package, and relies on a distance-based measure to insert new taxa into the current estimate of the phylogeny and imputes plausible values for new model parameters to accommodate growing dimensionality. This augmentation creates informed starting values and re-uses optimally tuned transition kernels for posterior exploration of growing data sets, reducing the time necessary to converge to target posterior distributions. We apply our framework to data from the recent West African Ebola virus epidemic and demonstrate a considerable reduction in time required to obtain posterior estimates at different time points of the outbreak. Beyond epidemic monitoring, this framework easily finds other applications within the phylogenetics community, where changes in the data -- in terms of alignment changes, sequence addition or removal -- present common scenarios that can benefit from online inference.
|
1912.10489
|
Tai Sing Lee
|
Siming Yan, Xuyang Fang, Bowen Xiao, Harold Rockwell, Yimeng Zhang,
Tai Sing Lee
|
Recurrent Feedback Improves Feedforward Representations in Deep Neural
Networks
|
10 pages, 5 figures
| null | null | null |
q-bio.NC cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The abundant recurrent horizontal and feedback connections in the primate
visual cortex are thought to play an important role in bringing global and
semantic contextual information to early visual areas during perceptual
inference, helping to resolve local ambiguity and fill in missing details. In
this study, we find that introducing feedback loops and horizontal recurrent
connections to a deep convolution neural network (VGG16) allows the network to
become more robust against noise and occlusion during inference, even in the
initial feedforward pass. This suggests that recurrent feedback and contextual
modulation transform the feedforward representations of the network in a
meaningful and interesting way. We study the population codes of neurons in the
network, before and after learning with feedback, and find that learning with
feedback yielded an increase in discriminability (measured by d-prime) between
the different object classes in the population codes of the neurons in the
feedforward path, even at the earliest layer that receives feedback. We find
that recurrent feedback, by injecting top-down semantic meaning to the
population activities, helps the network learn better feedforward paths to
robustly map noisy image patches to the latent representations corresponding to
important visual concepts of each object class, resulting in greater robustness
of the network against noises and occlusion as well as better fine-grained
recognition.
|
[
{
"created": "Sun, 22 Dec 2019 17:40:19 GMT",
"version": "v1"
}
] |
2019-12-24
|
[
[
"Yan",
"Siming",
""
],
[
"Fang",
"Xuyang",
""
],
[
"Xiao",
"Bowen",
""
],
[
"Rockwell",
"Harold",
""
],
[
"Zhang",
"Yimeng",
""
],
[
"Lee",
"Tai Sing",
""
]
] |
The abundant recurrent horizontal and feedback connections in the primate visual cortex are thought to play an important role in bringing global and semantic contextual information to early visual areas during perceptual inference, helping to resolve local ambiguity and fill in missing details. In this study, we find that introducing feedback loops and horizontal recurrent connections to a deep convolution neural network (VGG16) allows the network to become more robust against noise and occlusion during inference, even in the initial feedforward pass. This suggests that recurrent feedback and contextual modulation transform the feedforward representations of the network in a meaningful and interesting way. We study the population codes of neurons in the network, before and after learning with feedback, and find that learning with feedback yielded an increase in discriminability (measured by d-prime) between the different object classes in the population codes of the neurons in the feedforward path, even at the earliest layer that receives feedback. We find that recurrent feedback, by injecting top-down semantic meaning to the population activities, helps the network learn better feedforward paths to robustly map noisy image patches to the latent representations corresponding to important visual concepts of each object class, resulting in greater robustness of the network against noises and occlusion as well as better fine-grained recognition.
|
2003.00328
|
Gerhard Mayer
|
Gerhard Mayer
|
Mass spectrometry for semi-experimental protein structure determination
and modeling
|
28 pages
| null | null | null |
q-bio.OT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The structure of proteins is essential for its function. The determination of
protein structures is possible by experimental or predicted by computational
methods, but also a combination of both approaches is possible. Here, first an
overview about experimental structure determination methods with their pros and
cons is given. Then we describe how mass spectrometry is useful for
semi-experimental integrative protein structure determination. We review the
methodology and describe software programs supporting such integrated protein
structure prediction approaches, making use of distance constraints got from
mass spectrometry cross-linking experiments
|
[
{
"created": "Sat, 29 Feb 2020 18:43:09 GMT",
"version": "v1"
}
] |
2020-03-03
|
[
[
"Mayer",
"Gerhard",
""
]
] |
The structure of proteins is essential for its function. The determination of protein structures is possible by experimental or predicted by computational methods, but also a combination of both approaches is possible. Here, first an overview about experimental structure determination methods with their pros and cons is given. Then we describe how mass spectrometry is useful for semi-experimental integrative protein structure determination. We review the methodology and describe software programs supporting such integrated protein structure prediction approaches, making use of distance constraints got from mass spectrometry cross-linking experiments
|
2307.11033
|
Roberto Corral L\'opez
|
Roberto Corral L\'opez and Samir Suweis and Sandro Azaele and Miguel
A. Mu\~noz
|
Stochastic trade-offs and the emergence of diversification in E. coli
evolution experiments
| null | null | null | null |
q-bio.PE physics.bio-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Laboratory experiments with bacterial colonies, under well-controlled
conditions often lead to evolutionary diversification, where at least two
ecotypes emerge from an initially monomorphic population. Empirical evidence
suggests that such "evolutionary branching" occurs stochastically, even under
fixed and stable conditions. This stochastic nature is characterized by: (i)
occurrence in a significant fraction, but not all, of experimental settings,
(ii) emergence at widely varying times, and (iii) variable relative abundances
of the resulting subpopulations across experiments. Theoretical approaches to
understanding evolutionary branching under these conditions have been
previously developed within the (deterministic) framework of "adaptive
dynamics." Here, we advance the understanding of the stochastic nature of
evolutionary outcomes by introducing the concept of "stochastic trade-offs" as
opposed to "hard" ones. The key idea is that the stochasticity of mutations
occurs in a high-dimensional trait space and this translates into variability
that is constrained to a flexible tradeoff curve. By incorporating this
additional source of stochasticity, we are able to account for the observed
empirical variability and make predictions regarding the likelihood of
evolutionary branching under different conditions. This approach effectively
bridges the gap between theoretical predictions and experimental observations,
providing insights into when and how evolutionary branching is more likely to
occur in laboratory experiments.
|
[
{
"created": "Thu, 20 Jul 2023 17:08:05 GMT",
"version": "v1"
},
{
"created": "Tue, 23 Jul 2024 06:19:04 GMT",
"version": "v2"
}
] |
2024-07-24
|
[
[
"López",
"Roberto Corral",
""
],
[
"Suweis",
"Samir",
""
],
[
"Azaele",
"Sandro",
""
],
[
"Muñoz",
"Miguel A.",
""
]
] |
Laboratory experiments with bacterial colonies, under well-controlled conditions often lead to evolutionary diversification, where at least two ecotypes emerge from an initially monomorphic population. Empirical evidence suggests that such "evolutionary branching" occurs stochastically, even under fixed and stable conditions. This stochastic nature is characterized by: (i) occurrence in a significant fraction, but not all, of experimental settings, (ii) emergence at widely varying times, and (iii) variable relative abundances of the resulting subpopulations across experiments. Theoretical approaches to understanding evolutionary branching under these conditions have been previously developed within the (deterministic) framework of "adaptive dynamics." Here, we advance the understanding of the stochastic nature of evolutionary outcomes by introducing the concept of "stochastic trade-offs" as opposed to "hard" ones. The key idea is that the stochasticity of mutations occurs in a high-dimensional trait space and this translates into variability that is constrained to a flexible tradeoff curve. By incorporating this additional source of stochasticity, we are able to account for the observed empirical variability and make predictions regarding the likelihood of evolutionary branching under different conditions. This approach effectively bridges the gap between theoretical predictions and experimental observations, providing insights into when and how evolutionary branching is more likely to occur in laboratory experiments.
|
1312.5492
|
Shinya Kuroda
|
Takuya Koumura, Hidetoshi Urakubo, Kaoru Ohashi, Masashi Fujii and
Shinya Kuroda
|
Stochasticity in Ca$^{2+}$ increase in spines enables robust and
sensitive information coding
|
47 pages, 4 figures, 8 supplementary figures
| null |
10.1371/journal.pone.0099040
| null |
q-bio.MN q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A dendritic spine is a very small structure (~0.1 {\mu}m$^3$) of a neuron
that processes input timing information. Why are spines so small? Here, we
provide functional reasons; the size of spines is optimal for information
coding. Spines code input timing information by the probability of Ca$^{2+}$
increases, which makes robust and sensitive information coding possible. We
created a stochastic simulation model of input timing-dependent Ca$^{2+}$
increases in a cerebellar Purkinje cell's spine. Spines used probability coding
of Ca$^{2+}$ increases rather than amplitude coding for input timing detection
via stochastic facilitation by utilizing the small number of molecules in a
spine volume, where information per volume appeared optimal. Probability coding
of Ca$^{2+}$ increases in a spine volume was more robust against input
fluctuation and more sensitive to input numbers than amplitude coding of
Ca$^{2+}$ increases in a cell volume. Thus, stochasticity is a strategy by
which neurons robustly and sensitively code information.
|
[
{
"created": "Thu, 19 Dec 2013 11:49:19 GMT",
"version": "v1"
},
{
"created": "Tue, 25 Feb 2014 11:55:53 GMT",
"version": "v2"
}
] |
2014-06-18
|
[
[
"Koumura",
"Takuya",
""
],
[
"Urakubo",
"Hidetoshi",
""
],
[
"Ohashi",
"Kaoru",
""
],
[
"Fujii",
"Masashi",
""
],
[
"Kuroda",
"Shinya",
""
]
] |
A dendritic spine is a very small structure (~0.1 {\mu}m$^3$) of a neuron that processes input timing information. Why are spines so small? Here, we provide functional reasons; the size of spines is optimal for information coding. Spines code input timing information by the probability of Ca$^{2+}$ increases, which makes robust and sensitive information coding possible. We created a stochastic simulation model of input timing-dependent Ca$^{2+}$ increases in a cerebellar Purkinje cell's spine. Spines used probability coding of Ca$^{2+}$ increases rather than amplitude coding for input timing detection via stochastic facilitation by utilizing the small number of molecules in a spine volume, where information per volume appeared optimal. Probability coding of Ca$^{2+}$ increases in a spine volume was more robust against input fluctuation and more sensitive to input numbers than amplitude coding of Ca$^{2+}$ increases in a cell volume. Thus, stochasticity is a strategy by which neurons robustly and sensitively code information.
|
2305.01580
|
Li Kai
|
Li Kai, Li Ning, Zhang Wei, Gao Ming
|
Molecular design method based on novel molecular representation and
variational auto-encoder
|
13 pages, 7 figures, conference: NIAI
|
4th International Conference on Natural Language Processing,
Information Retrieval and AI (NIAI 2023), Volume 13, Number 03, February
2023, pp. 23-35, 2023. CS & IT - CSCP 2023
|
10.5121/csit.2023.130303
| null |
q-bio.BM cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Based on the traditional VAE, a novel neural network model is presented, with
the latest molecular representation, SELFIES, to improve the effect of
generating new molecules. In this model, multi-layer convolutional network and
Fisher information are added to the original encoding layer to learn the data
characteristics and guide the encoding process, which makes the features of the
data hiding layer more aggregated, and integrates the Long Short Term Memory
neural network (LSTM) into the decoding layer for better data generation, which
effectively solves the degradation phenomenon generated by the encoding layer
and decoding layer of the original VAE model. Through experiments on zinc
molecular data sets, it is found that the similarity in the new VAE is 8.47%
higher than that of the original ones. SELFIES are better at generating a
variety of molecules than the traditional molecular representation, SELFIES.
Experiments have shown that using SELFIES and the new VAE model presented in
this paper can improve the effectiveness of generating new molecules.
|
[
{
"created": "Mon, 20 Feb 2023 05:11:53 GMT",
"version": "v1"
}
] |
2023-05-03
|
[
[
"Kai",
"Li",
""
],
[
"Ning",
"Li",
""
],
[
"Wei",
"Zhang",
""
],
[
"Ming",
"Gao",
""
]
] |
Based on the traditional VAE, a novel neural network model is presented, with the latest molecular representation, SELFIES, to improve the effect of generating new molecules. In this model, multi-layer convolutional network and Fisher information are added to the original encoding layer to learn the data characteristics and guide the encoding process, which makes the features of the data hiding layer more aggregated, and integrates the Long Short Term Memory neural network (LSTM) into the decoding layer for better data generation, which effectively solves the degradation phenomenon generated by the encoding layer and decoding layer of the original VAE model. Through experiments on zinc molecular data sets, it is found that the similarity in the new VAE is 8.47% higher than that of the original ones. SELFIES are better at generating a variety of molecules than the traditional molecular representation, SELFIES. Experiments have shown that using SELFIES and the new VAE model presented in this paper can improve the effectiveness of generating new molecules.
|
1606.03235
|
Pablo Villegas G\'ongora
|
Pablo Villegas, Jos\'e Ruiz-Franco, Jorge Hidalgo, Miguel A. Mu\~noz
|
Intrinsic noise and deviations from criticality in Boolean
gene-regulatory networks
|
14 pages, 6 figures and 1 table. Submitted to Scientific Reports
|
Scientific Reports 6, 34743 (2016)
|
10.1038/srep34743
| null |
q-bio.MN cond-mat.dis-nn nlin.AO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Gene regulatory networks can be successfully modeled as Boolean networks. A
much discussed hypothesis says that such model networks reproduce empirical
findings the best if they are tuned to operate at criticality, i.e. at the
borderline between their ordered and disordered phases. Critical networks have
been argued to lead to a number of functional advantages such as maximal
dynamical range, maximal sensitivity to environmental changes, as well as to an
excellent trade off between stability and flexibility. Here, we study the
effect of noise within the context of Boolean networks trained to learn complex
tasks under supervision. We verify that quasi-critical networks are the ones
learning in the fastest possible way --even for asynchronous updating rules--
and that the larger the task complexity the smaller the distance to
criticality. On the other hand, when additional sources of intrinsic noise in
the network states and/or in its wiring pattern are introduced, the optimally
performing networks become clearly subcritical. These results suggest that in
order to compensate for inherent stochasticity, regulatory and other type of
biological networks might become subcritical rather than being critical, all
the most if the task to be performed has limited complexity.
|
[
{
"created": "Fri, 10 Jun 2016 09:06:11 GMT",
"version": "v1"
}
] |
2016-10-12
|
[
[
"Villegas",
"Pablo",
""
],
[
"Ruiz-Franco",
"José",
""
],
[
"Hidalgo",
"Jorge",
""
],
[
"Muñoz",
"Miguel A.",
""
]
] |
Gene regulatory networks can be successfully modeled as Boolean networks. A much discussed hypothesis says that such model networks reproduce empirical findings the best if they are tuned to operate at criticality, i.e. at the borderline between their ordered and disordered phases. Critical networks have been argued to lead to a number of functional advantages such as maximal dynamical range, maximal sensitivity to environmental changes, as well as to an excellent trade off between stability and flexibility. Here, we study the effect of noise within the context of Boolean networks trained to learn complex tasks under supervision. We verify that quasi-critical networks are the ones learning in the fastest possible way --even for asynchronous updating rules-- and that the larger the task complexity the smaller the distance to criticality. On the other hand, when additional sources of intrinsic noise in the network states and/or in its wiring pattern are introduced, the optimally performing networks become clearly subcritical. These results suggest that in order to compensate for inherent stochasticity, regulatory and other type of biological networks might become subcritical rather than being critical, all the most if the task to be performed has limited complexity.
|
0911.1843
|
Cecile Fauvelot
|
Cecile Fauvelot (COREUS), Francesca Bertozzi (CIRSA), Federica
Costantini (CIRSA), Laura Airoldi (CIRSA), Marco Abbiati (CIRSA)
|
Lower genetic diversity in the limpet Patella caerulea on urban coastal
structures compared to natural rocky habitats
| null |
Marine Biology 156 (2009) 2313
| null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Human-made structures are increasingly found in marine coastal habitats. The
aim of the present study was to explore whether urban coastal structures can
affect the genetic variation of hard-bottom species. We conducted a population
genetic analysis on the limpet Patella caerulea sampled in both natural and
artificial habitats along the Adriatic coast. Five microsatellite loci were
used to test for differences in genetic diversity and structure among samples.
Three microsatellite loci showed strong Hardy-Weinberg disequilibrium likely
linked with the presence of null alleles. Genetic diversity was significantly
higher in natural habitat than in artificial habitat. A weak but significant
differentiation over all limpet samples was observed, but not related to the
type of habitat. While the exact causes of the differences in genetic diversity
deserve further investigation, these results clearly point that the expansion
of urban structures can lead to genetic diversity loss at regional scales.
|
[
{
"created": "Tue, 10 Nov 2009 07:16:54 GMT",
"version": "v1"
}
] |
2009-11-11
|
[
[
"Fauvelot",
"Cecile",
"",
"COREUS"
],
[
"Bertozzi",
"Francesca",
"",
"CIRSA"
],
[
"Costantini",
"Federica",
"",
"CIRSA"
],
[
"Airoldi",
"Laura",
"",
"CIRSA"
],
[
"Abbiati",
"Marco",
"",
"CIRSA"
]
] |
Human-made structures are increasingly found in marine coastal habitats. The aim of the present study was to explore whether urban coastal structures can affect the genetic variation of hard-bottom species. We conducted a population genetic analysis on the limpet Patella caerulea sampled in both natural and artificial habitats along the Adriatic coast. Five microsatellite loci were used to test for differences in genetic diversity and structure among samples. Three microsatellite loci showed strong Hardy-Weinberg disequilibrium likely linked with the presence of null alleles. Genetic diversity was significantly higher in natural habitat than in artificial habitat. A weak but significant differentiation over all limpet samples was observed, but not related to the type of habitat. While the exact causes of the differences in genetic diversity deserve further investigation, these results clearly point that the expansion of urban structures can lead to genetic diversity loss at regional scales.
|
2109.11985
|
Matt Holzer
|
Ashley Armbruster, Matt Holzer, Noah Roselli, Lena Underwood
|
Epidemic spreading on complex networks as front propagation into an
unstable state
| null | null | null | null |
q-bio.PE math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study epidemic arrival times in meta-population disease models through the
lens of front propagation into unstable states. We demonstrate that several
features of invasion fronts in the PDE context are also relevant to the network
case. We show that the susceptible-infected-recovered model on a network is
linearly determined in the sense that the arrival times in the nonlinear system
are approximated by the arrival times of the instability in the system
linearized near the disease free state. Arrival time predictions are extended
to an susceptible-exposed-infected-recovered model. We then study a recent
model of social epidemics where high order interactions of individuals lead to
faster invasion speeds. For these pushed fronts we compute corrections to the
estimated arrival time in this case. Finally, we show how inhomogeneities in
local infection rates lead to faster average arrival times.
|
[
{
"created": "Fri, 24 Sep 2021 14:17:44 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Oct 2022 17:41:02 GMT",
"version": "v2"
}
] |
2022-10-19
|
[
[
"Armbruster",
"Ashley",
""
],
[
"Holzer",
"Matt",
""
],
[
"Roselli",
"Noah",
""
],
[
"Underwood",
"Lena",
""
]
] |
We study epidemic arrival times in meta-population disease models through the lens of front propagation into unstable states. We demonstrate that several features of invasion fronts in the PDE context are also relevant to the network case. We show that the susceptible-infected-recovered model on a network is linearly determined in the sense that the arrival times in the nonlinear system are approximated by the arrival times of the instability in the system linearized near the disease free state. Arrival time predictions are extended to an susceptible-exposed-infected-recovered model. We then study a recent model of social epidemics where high order interactions of individuals lead to faster invasion speeds. For these pushed fronts we compute corrections to the estimated arrival time in this case. Finally, we show how inhomogeneities in local infection rates lead to faster average arrival times.
|
1704.08851
|
Sebastian Weichwald
|
Sebastian Weichwald and Moritz Grosse-Wentrup
|
The right tool for the right question --- beyond the encoding versus
decoding dichotomy
|
preprint
| null | null | null |
q-bio.NC stat.AP stat.ME
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
There are two major questions that neuroimaging studies attempt to answer:
First, how are sensory stimuli represented in the brain (which we term the
stimulus-based setting)? And, second, how does the brain generate cognition
(termed the response-based setting)? There has been a lively debate in the
neuroimaging community whether encoding and decoding models can provide
insights into these questions. In this commentary, we construct two simple and
analytically tractable examples to demonstrate that while an encoding model
analysis helps with the former, neither model is appropriate to satisfactorily
answer the latter question. Consequently, we argue that if we want to
understand how the brain generates cognition, we need to move beyond the
encoding versus decoding dichotomy and instead discuss and develop tools that
are specifically tailored to our endeavour.
|
[
{
"created": "Fri, 28 Apr 2017 08:56:47 GMT",
"version": "v1"
}
] |
2017-05-01
|
[
[
"Weichwald",
"Sebastian",
""
],
[
"Grosse-Wentrup",
"Moritz",
""
]
] |
There are two major questions that neuroimaging studies attempt to answer: First, how are sensory stimuli represented in the brain (which we term the stimulus-based setting)? And, second, how does the brain generate cognition (termed the response-based setting)? There has been a lively debate in the neuroimaging community whether encoding and decoding models can provide insights into these questions. In this commentary, we construct two simple and analytically tractable examples to demonstrate that while an encoding model analysis helps with the former, neither model is appropriate to satisfactorily answer the latter question. Consequently, we argue that if we want to understand how the brain generates cognition, we need to move beyond the encoding versus decoding dichotomy and instead discuss and develop tools that are specifically tailored to our endeavour.
|
1808.04458
|
Mohammadsadegh Ghiasi
|
Mohammad S. Ghiasi, Jason E. Chen, Edward K. Rodriguez, Ashkan Vaziri,
Ara Nazarian
|
Computational Modeling of the Effects of Inflammatory Response and
Granulation Tissue Properties on Human Bone Fracture Healing
|
25 Pages, 7 Figures
| null | null | null |
q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Bone healing process includes four phases: inflammatory response, soft callus
formation, hard callus development, and remodeling. Mechanobiological models
have been used to investigate the role of various mechanical and biological
factors on the bone healing. However, the initial phase of healing, which
includes the inflammatory response, the granulation tissue formation and the
initial callus formation during the first few days post-fracture, are generally
neglected in such studies. In this study, we developed a finite-element-based
model to simulate different levels of diffusion coefficient for mesenchymal
stem cell (MSC) migration, Young's modulus of granulation tissue, callus
thickness and interfragmentary gap size to understand the modulatory effects of
these initial phase parameters on bone healing. The results showed that faster
MSC migration, stiffer granulation tissue, thicker callus and smaller
interfragmentary gap enhanced healing to some extent. After a certain
threshold, a state of saturation was reached for MSC migration rate,
granulation tissue stiffness and callus thickness. Therefore, a parametric
study was performed to verify that the callus formed at the initial phase, in
agreement with experimental observations, has an ideal range of geometry and
material properties to have the most efficient healing time. Findings from this
paper quantified the effects of the healing initial phase on healing outcome to
better understand the biological and mechanobiological mechanisms and their
utilization in the design and optimization of treatment strategies. Simulation
outcomes also demonstrated that for fractures, where bone segments are in close
proximity, callus development is not required. This finding is consistent with
the concepts of primary and secondary bone healing.
|
[
{
"created": "Mon, 13 Aug 2018 20:37:01 GMT",
"version": "v1"
}
] |
2018-08-15
|
[
[
"Ghiasi",
"Mohammad S.",
""
],
[
"Chen",
"Jason E.",
""
],
[
"Rodriguez",
"Edward K.",
""
],
[
"Vaziri",
"Ashkan",
""
],
[
"Nazarian",
"Ara",
""
]
] |
Bone healing process includes four phases: inflammatory response, soft callus formation, hard callus development, and remodeling. Mechanobiological models have been used to investigate the role of various mechanical and biological factors on the bone healing. However, the initial phase of healing, which includes the inflammatory response, the granulation tissue formation and the initial callus formation during the first few days post-fracture, are generally neglected in such studies. In this study, we developed a finite-element-based model to simulate different levels of diffusion coefficient for mesenchymal stem cell (MSC) migration, Young's modulus of granulation tissue, callus thickness and interfragmentary gap size to understand the modulatory effects of these initial phase parameters on bone healing. The results showed that faster MSC migration, stiffer granulation tissue, thicker callus and smaller interfragmentary gap enhanced healing to some extent. After a certain threshold, a state of saturation was reached for MSC migration rate, granulation tissue stiffness and callus thickness. Therefore, a parametric study was performed to verify that the callus formed at the initial phase, in agreement with experimental observations, has an ideal range of geometry and material properties to have the most efficient healing time. Findings from this paper quantified the effects of the healing initial phase on healing outcome to better understand the biological and mechanobiological mechanisms and their utilization in the design and optimization of treatment strategies. Simulation outcomes also demonstrated that for fractures, where bone segments are in close proximity, callus development is not required. This finding is consistent with the concepts of primary and secondary bone healing.
|
1803.10044
|
Roland L. Knorr
|
Roland L. Knorr, Jan Steinkuehler and Rumiana Dimova
|
Micron-sized domains in quasi single-component giant vesicles
| null | null | null | null |
q-bio.BM cond-mat.soft physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Giant unilamellar vesicles (GUVs), are a convenient tool to study
membrane-bound processes using optical microscopy. An increasing number of
studies highlights the potential of these model membranes when addressing
questions in membrane biophysics and cell biology. Among them, phase
transitions and domain formation, dynamics and stability in raft-like mixtures
are probably some of the most intensively investigated. In doing so, many
research teams rely on standard protocols for GUV preparation and handling
involving the use of sugar solutions. Here, we demonstrate that following such
a standard approach can lead to abnormal formation of micron-sized domains in
GUVs grown from only a single phospholipid. The membrane heterogeneity is
visualized by means of a small fraction (0.1 mol%) of a fluorescent lipid dye.
For dipalmitoylphosphatidylcholine GUVs, different types of membrane
heterogeneities were detected. First, an unexpected formation of micron-sized
dye-depleted domains was observed upon cooling. These domains nucleated about
10 K above the lipid main phase transition temperature, TM. In addition, upon
further cooling of the GUVs down to the immediate vicinity of TM, stripe-like
dye-enriched structures around the domains are detected. The micron-sized
domains in quasi single-component GUVs were observed also when using two other
lipids. Whereas the stripe structures are related to the phase transition of
the lipid, the dye-excluding domains seem to be caused by traces of impurities
present in the glucose. Supplementing glucose solutions with nm-sized liposomes
at millimolar lipid concentration suppresses the formation of the micron-sized
domains, presumably by providing competitive binding of the impurities to the
liposome membrane in excess. It is likely that such traces of impurities can
significantly alter lipid phase diagrams and cause differences among reported
ones.
|
[
{
"created": "Tue, 27 Mar 2018 12:38:46 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Jun 2018 19:17:10 GMT",
"version": "v2"
}
] |
2018-06-27
|
[
[
"Knorr",
"Roland L.",
""
],
[
"Steinkuehler",
"Jan",
""
],
[
"Dimova",
"Rumiana",
""
]
] |
Giant unilamellar vesicles (GUVs), are a convenient tool to study membrane-bound processes using optical microscopy. An increasing number of studies highlights the potential of these model membranes when addressing questions in membrane biophysics and cell biology. Among them, phase transitions and domain formation, dynamics and stability in raft-like mixtures are probably some of the most intensively investigated. In doing so, many research teams rely on standard protocols for GUV preparation and handling involving the use of sugar solutions. Here, we demonstrate that following such a standard approach can lead to abnormal formation of micron-sized domains in GUVs grown from only a single phospholipid. The membrane heterogeneity is visualized by means of a small fraction (0.1 mol%) of a fluorescent lipid dye. For dipalmitoylphosphatidylcholine GUVs, different types of membrane heterogeneities were detected. First, an unexpected formation of micron-sized dye-depleted domains was observed upon cooling. These domains nucleated about 10 K above the lipid main phase transition temperature, TM. In addition, upon further cooling of the GUVs down to the immediate vicinity of TM, stripe-like dye-enriched structures around the domains are detected. The micron-sized domains in quasi single-component GUVs were observed also when using two other lipids. Whereas the stripe structures are related to the phase transition of the lipid, the dye-excluding domains seem to be caused by traces of impurities present in the glucose. Supplementing glucose solutions with nm-sized liposomes at millimolar lipid concentration suppresses the formation of the micron-sized domains, presumably by providing competitive binding of the impurities to the liposome membrane in excess. It is likely that such traces of impurities can significantly alter lipid phase diagrams and cause differences among reported ones.
|
1307.7861
|
Aaron Darling
|
Michal N\'an\'asi, Tom\'a\v{s} Vina\v{r}, and Bro\v{n}a Brejov\'a
|
Probabilistic Approaches to Alignment with Tandem Repeats
|
Peer-reviewed and presented as part of the 13th Workshop on
Algorithms in Bioinformatics (WABI2013)
| null | null | null |
q-bio.QM q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a simple tractable pair hidden Markov model for pairwise sequence
alignment that accounts for the presence of short tandem repeats. Using the
framework of gain functions, we design several optimization criteria for
decoding this model and describe the resulting decoding algorithms, ranging
from the traditional Viterbi and posterior decoding to block-based decoding
algorithms specialized for our model. We compare the accuracy of individual
decoding algorithms on simulated data and find our approach superior to the
classical three-state pair HMM in simulations.
|
[
{
"created": "Tue, 30 Jul 2013 08:02:34 GMT",
"version": "v1"
}
] |
2013-07-31
|
[
[
"Nánási",
"Michal",
""
],
[
"Vinař",
"Tomáš",
""
],
[
"Brejová",
"Broňa",
""
]
] |
We propose a simple tractable pair hidden Markov model for pairwise sequence alignment that accounts for the presence of short tandem repeats. Using the framework of gain functions, we design several optimization criteria for decoding this model and describe the resulting decoding algorithms, ranging from the traditional Viterbi and posterior decoding to block-based decoding algorithms specialized for our model. We compare the accuracy of individual decoding algorithms on simulated data and find our approach superior to the classical three-state pair HMM in simulations.
|
2212.07638
|
Muhammad Anwari Leksono
|
Muhammad Anwari Leksono and Ayu Purwarianti
|
Sequential Labelling and DNABERT For Splice Site Prediction in Homo
Sapiens DNA
|
revision 1, 5 figures, 3 tables
| null | null | null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Genome sequencing technology has improved significantly in few last years and
resulted in abundance genetic data. Artificial intelligence has been employed
to analyze genetic data in response to its sheer size and variability. Gene
prediction on single DNA has been conducted using various deep learning
architectures to discover splice sites and therefore discover intron and exon
region. Recent predictions are carried out with models trained on sequence with
fixed splice site location which eliminates possibility of multiple splice
sites existence in single sequence. This paper proposes sequential labelling to
predict splice sites regardless their position in sequence. Sequential
labelling is carried out on DNA to determine intron and exon region and thus
discover splice sites. Sequential labelling models used are based on pretrained
DNABERT-3 which has been trained on human genome. Both fine-tuning and
feature-based approach are tested. Proposed model is benchmarked against latest
sequential labelling model designed for mutation type and location prediction.
While achieving high F1 scores on validation data, both baseline and proposed
model perform poorly on test data. Error and test results analysis reveal that
model experience overfitting and therefore, model is deemed not suitable for
splice site prediction.
|
[
{
"created": "Thu, 15 Dec 2022 07:18:36 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Mar 2023 11:41:59 GMT",
"version": "v2"
}
] |
2023-03-17
|
[
[
"Leksono",
"Muhammad Anwari",
""
],
[
"Purwarianti",
"Ayu",
""
]
] |
Genome sequencing technology has improved significantly in few last years and resulted in abundance genetic data. Artificial intelligence has been employed to analyze genetic data in response to its sheer size and variability. Gene prediction on single DNA has been conducted using various deep learning architectures to discover splice sites and therefore discover intron and exon region. Recent predictions are carried out with models trained on sequence with fixed splice site location which eliminates possibility of multiple splice sites existence in single sequence. This paper proposes sequential labelling to predict splice sites regardless their position in sequence. Sequential labelling is carried out on DNA to determine intron and exon region and thus discover splice sites. Sequential labelling models used are based on pretrained DNABERT-3 which has been trained on human genome. Both fine-tuning and feature-based approach are tested. Proposed model is benchmarked against latest sequential labelling model designed for mutation type and location prediction. While achieving high F1 scores on validation data, both baseline and proposed model perform poorly on test data. Error and test results analysis reveal that model experience overfitting and therefore, model is deemed not suitable for splice site prediction.
|
1403.5686
|
Haris Vikalo
|
Xiaohu Shen, Manohar Shamaiah, and Haris Vikalo
|
Iterative Learning for Reference-Guided DNA Sequence Assembly from Short
Reads: Algorithms and Limits of Performance
|
Submitted to IEEE Transactions on Signal Processing
| null |
10.1109/TSP.2014.2333564
| null |
q-bio.GN cs.CE cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent emergence of next-generation DNA sequencing technology has enabled
acquisition of genetic information at unprecedented scales. In order to
determine the genetic blueprint of an organism, sequencing platforms typically
employ so-called shotgun sequencing strategy to oversample the target genome
with a library of relatively short overlapping reads. The order of nucleotides
in the reads is determined by processing the acquired noisy signals generated
by the sequencing instrument. Assembly of a genome from potentially erroneous
short reads is a computationally daunting task even in the scenario where a
reference genome exists. Errors and gaps in the reference, and perfect repeat
regions in the target, further render the assembly challenging and cause
inaccuracies. In this paper, we formulate the reference-guided sequence
assembly problem as the inference of the genome sequence on a bipartite graph
and solve it using a message-passing algorithm. The proposed algorithm can be
interpreted as the well-known classical belief propagation scheme under a
certain prior. Unlike existing state-of-the-art methods, the proposed algorithm
combines the information provided by the reads without needing to know
reliability of the short reads (so-called quality scores). Relation of the
message-passing algorithm to a provably convergent power iteration scheme is
discussed. To evaluate and benchmark the performance of the proposed technique,
we find an analytical expression for the probability of error of a genie-aided
maximum a posteriori (MAP) decision scheme. Results on both simulated and
experimental data demonstrate that the proposed message-passing algorithm
outperforms commonly used state-of-the-art tools, and it nearly achieves the
performance of the aforementioned MAP decision scheme.
|
[
{
"created": "Sat, 22 Mar 2014 16:59:53 GMT",
"version": "v1"
}
] |
2015-06-19
|
[
[
"Shen",
"Xiaohu",
""
],
[
"Shamaiah",
"Manohar",
""
],
[
"Vikalo",
"Haris",
""
]
] |
Recent emergence of next-generation DNA sequencing technology has enabled acquisition of genetic information at unprecedented scales. In order to determine the genetic blueprint of an organism, sequencing platforms typically employ so-called shotgun sequencing strategy to oversample the target genome with a library of relatively short overlapping reads. The order of nucleotides in the reads is determined by processing the acquired noisy signals generated by the sequencing instrument. Assembly of a genome from potentially erroneous short reads is a computationally daunting task even in the scenario where a reference genome exists. Errors and gaps in the reference, and perfect repeat regions in the target, further render the assembly challenging and cause inaccuracies. In this paper, we formulate the reference-guided sequence assembly problem as the inference of the genome sequence on a bipartite graph and solve it using a message-passing algorithm. The proposed algorithm can be interpreted as the well-known classical belief propagation scheme under a certain prior. Unlike existing state-of-the-art methods, the proposed algorithm combines the information provided by the reads without needing to know reliability of the short reads (so-called quality scores). Relation of the message-passing algorithm to a provably convergent power iteration scheme is discussed. To evaluate and benchmark the performance of the proposed technique, we find an analytical expression for the probability of error of a genie-aided maximum a posteriori (MAP) decision scheme. Results on both simulated and experimental data demonstrate that the proposed message-passing algorithm outperforms commonly used state-of-the-art tools, and it nearly achieves the performance of the aforementioned MAP decision scheme.
|
2001.11972
|
Shawn Gu
|
Shawn Gu and Tijana Milenkovic
|
Data-driven biological network alignment that uses topological,
sequence, and functional information
| null | null | null | null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many proteins remain functionally unannotated. Sequence alignment (SA)
uncovers missing annotations by transferring functional knowledge between
species' sequence-conserved regions. Because SA is imperfect, network alignment
(NA) complements SA by transferring functional knowledge between conserved
biological network, rather than just sequence, regions of different species.
Existing NA assumes that it is topological similarity (isomorphic-like
matching) between network regions that corresponds to the regions' functional
relatedness. However, we recently found that functionally unrelated proteins
are almost as topologically similar as functionally related proteins. So, we
redefined NA as a data-driven framework, TARA, which learns from network and
protein functional data what kind of topological relatedness (rather than
similarity) between proteins corresponds to the proteins' functional
relatedness. TARA used topological information (within each network) but not
sequence information (between proteins across networks). Yet, its alignments
yielded higher protein functional prediction accuracy than alignments of
existing NA methods, even those that used both topological and sequence
information. Here, we propose TARA++ that is also data-driven, like TARA and
unlike other existing methods, but that uses across-network sequence
information on top of within-network topological information, unlike TARA. To
deal with the within-and-across-network analysis, we adapt social network
embedding to the problem of biological NA. TARA++ outperforms protein
functional prediction accuracy of existing methods.
|
[
{
"created": "Fri, 31 Jan 2020 17:43:13 GMT",
"version": "v1"
},
{
"created": "Fri, 12 Jun 2020 19:36:28 GMT",
"version": "v2"
}
] |
2020-06-16
|
[
[
"Gu",
"Shawn",
""
],
[
"Milenkovic",
"Tijana",
""
]
] |
Many proteins remain functionally unannotated. Sequence alignment (SA) uncovers missing annotations by transferring functional knowledge between species' sequence-conserved regions. Because SA is imperfect, network alignment (NA) complements SA by transferring functional knowledge between conserved biological network, rather than just sequence, regions of different species. Existing NA assumes that it is topological similarity (isomorphic-like matching) between network regions that corresponds to the regions' functional relatedness. However, we recently found that functionally unrelated proteins are almost as topologically similar as functionally related proteins. So, we redefined NA as a data-driven framework, TARA, which learns from network and protein functional data what kind of topological relatedness (rather than similarity) between proteins corresponds to the proteins' functional relatedness. TARA used topological information (within each network) but not sequence information (between proteins across networks). Yet, its alignments yielded higher protein functional prediction accuracy than alignments of existing NA methods, even those that used both topological and sequence information. Here, we propose TARA++ that is also data-driven, like TARA and unlike other existing methods, but that uses across-network sequence information on top of within-network topological information, unlike TARA. To deal with the within-and-across-network analysis, we adapt social network embedding to the problem of biological NA. TARA++ outperforms protein functional prediction accuracy of existing methods.
|
2311.17755
|
Franck Andre
|
Juan Gonz\'alez-Cuevas, Ricardo Arg\"uello, Marcos Florentin, Franck
M. Andr\'e (METSY), Lluis Mir (IGR, METSY)
|
Experimental and Theoretical Brownian Dynamics Analysis of Ion Transport
During Cellular Electroporation of E. coli Bacteria
|
Annals of Biomedical Engineering, 2023
| null |
10.1007/s10439-023-03353-4
| null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Escherichia coli bacterium is a rod-shaped organism composed of a complex
double membrane structure. Knowledge of electric field driven ion transport
through both membranes and the evolution of their induced permeabilization has
important applications in biomedical engineering, delivery of genes and
antibacterial agents. However, few studies have been conducted on Gram-negative
bacteria in this regard considering the contribution of all ion types. To
address this gap in knowledge, we have developed a deterministic and stochastic
Brownian dynamics model to simulate in 3D space the motion of ions through
pores formed in the plasma membranes of E. coli cells during electroporation.
The diffusion coefficient, mobility, and translation time of Ca$^{2+}$,
Mg$^{2+}$, Na$^+$, K$^+$, and Cl$^-$ ions within the pore region are estimated
from the numerical model. Calculations of pore's conductance have been
validated with experiments conducted at Gustave Roussy. From the simulations,
it was found that the main driving force of ionic uptake during the pulse is
the one due to the externally applied electric field. The results from this
work provide a better understanding of ion transport during electroporation,
aiding in the design of electrical pulses for maximizing ion throughput,
primarily for application in cancer treatment.
|
[
{
"created": "Wed, 29 Nov 2023 15:57:32 GMT",
"version": "v1"
}
] |
2023-11-30
|
[
[
"González-Cuevas",
"Juan",
"",
"METSY"
],
[
"Argüello",
"Ricardo",
"",
"METSY"
],
[
"Florentin",
"Marcos",
"",
"METSY"
],
[
"André",
"Franck M.",
"",
"METSY"
],
[
"Mir",
"Lluis",
"",
"IGR, METSY"
]
] |
Escherichia coli bacterium is a rod-shaped organism composed of a complex double membrane structure. Knowledge of electric field driven ion transport through both membranes and the evolution of their induced permeabilization has important applications in biomedical engineering, delivery of genes and antibacterial agents. However, few studies have been conducted on Gram-negative bacteria in this regard considering the contribution of all ion types. To address this gap in knowledge, we have developed a deterministic and stochastic Brownian dynamics model to simulate in 3D space the motion of ions through pores formed in the plasma membranes of E. coli cells during electroporation. The diffusion coefficient, mobility, and translation time of Ca$^{2+}$, Mg$^{2+}$, Na$^+$, K$^+$, and Cl$^-$ ions within the pore region are estimated from the numerical model. Calculations of pore's conductance have been validated with experiments conducted at Gustave Roussy. From the simulations, it was found that the main driving force of ionic uptake during the pulse is the one due to the externally applied electric field. The results from this work provide a better understanding of ion transport during electroporation, aiding in the design of electrical pulses for maximizing ion throughput, primarily for application in cancer treatment.
|
1407.5503
|
Ellen Baake
|
Corinna Ernst and Ellen Baake
|
Rare event simulation in immune biology: Models of negative selection in
T-cell maturation
|
13 pages, 5 figures; accepted for 10th International Workshop on Rare
Event Simulation, Amsterdam, Aug. 27-29, 2014
| null | null | null |
q-bio.CB math.PR q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a probabilistic T-cell model that includes negative selection and
takes contrasting models of tissue-restricted antigen (TRA) expression in the
thymus into account. We start from the basic model of van den Berg, Rand, and
Burroughs (2001) and include negative selection via individual-based T-cell
modelling, in which each T-cell is defined by its stimulation rates to all
relevant self antigens. We present a simulation approach based on partial
tilting of the stimulation rates recognized by a single T-cell. We investigate
the effects of negative selection for diverging modes of thymic antigen
presentation, namely arbitrary TRA presentation, and more or less strict
emulation of tissue-specific cell lines. We observe that negative selection
leads to truncation of the tail of the distribution of the stimulation rates
mature T-cells receive from self antigens, i.e., the self background is
reduced. This increases the activation probabilities of single T-cells in the
presence of non-self antigens.
|
[
{
"created": "Mon, 21 Jul 2014 14:16:06 GMT",
"version": "v1"
}
] |
2014-07-22
|
[
[
"Ernst",
"Corinna",
""
],
[
"Baake",
"Ellen",
""
]
] |
We present a probabilistic T-cell model that includes negative selection and takes contrasting models of tissue-restricted antigen (TRA) expression in the thymus into account. We start from the basic model of van den Berg, Rand, and Burroughs (2001) and include negative selection via individual-based T-cell modelling, in which each T-cell is defined by its stimulation rates to all relevant self antigens. We present a simulation approach based on partial tilting of the stimulation rates recognized by a single T-cell. We investigate the effects of negative selection for diverging modes of thymic antigen presentation, namely arbitrary TRA presentation, and more or less strict emulation of tissue-specific cell lines. We observe that negative selection leads to truncation of the tail of the distribution of the stimulation rates mature T-cells receive from self antigens, i.e., the self background is reduced. This increases the activation probabilities of single T-cells in the presence of non-self antigens.
|
1607.00483
|
Vaibhav Madhok
|
Vaibhav Madhok
|
Quasi-Species in High Dimensional Spaces
|
Ideas on high dimensionality geometry, concentration of measure and
quasi-species evolution. Work in progress
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We show that, under certain assumptions, the fitness of almost all
quasi-species becomes independent of mutational probabilities and the initial
frequency distributions of the sequences in high dimensional sequence spaces.
This result is the consequence of the concentration of measure on a high
dimensional hypersphere and its extension to Lipschitz functions knows as the
Levy's Lemma. Therefore, evolutionary dynamics almost always yields the same
value for fitness of the quasi-species, independent of the mutational process
and initial conditions, and is quite robust to mutational changes and
fluctuations in initial conditions. Our results naturally extend to any
Lipschitz function whose input parameters are the frequencies of individual
constituents of the quasi-species. This suggests that the functional
capabilities of high dimensional quasi-species are robust to fluctuations in
the mutational probabilities and initial conditions.
|
[
{
"created": "Sat, 2 Jul 2016 09:32:50 GMT",
"version": "v1"
}
] |
2016-07-05
|
[
[
"Madhok",
"Vaibhav",
""
]
] |
We show that, under certain assumptions, the fitness of almost all quasi-species becomes independent of mutational probabilities and the initial frequency distributions of the sequences in high dimensional sequence spaces. This result is the consequence of the concentration of measure on a high dimensional hypersphere and its extension to Lipschitz functions knows as the Levy's Lemma. Therefore, evolutionary dynamics almost always yields the same value for fitness of the quasi-species, independent of the mutational process and initial conditions, and is quite robust to mutational changes and fluctuations in initial conditions. Our results naturally extend to any Lipschitz function whose input parameters are the frequencies of individual constituents of the quasi-species. This suggests that the functional capabilities of high dimensional quasi-species are robust to fluctuations in the mutational probabilities and initial conditions.
|
1811.03177
|
Younhun Kim
|
Younhun Kim, Frederic Koehler, Ankur Moitra, Elchanan Mossel and
Govind Ramnarayan
|
How Many Subpopulations is Too Many? Exponential Lower Bounds for
Inferring Population Histories
|
38 pages, Appeared in RECOMB 2019
| null | null | null |
q-bio.PE math.ST q-bio.QM stat.TH
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Reconstruction of population histories is a central problem in population
genetics. Existing coalescent-based methods, like the seminal work of Li and
Durbin (Nature, 2011), attempt to solve this problem using sequence data but
have no rigorous guarantees. Determining the amount of data needed to correctly
reconstruct population histories is a major challenge. Using a variety of tools
from information theory, the theory of extremal polynomials, and approximation
theory, we prove new sharp information-theoretic lower bounds on the problem of
reconstructing population structure -- the history of multiple subpopulations
that merge, split and change sizes over time. Our lower bounds are exponential
in the number of subpopulations, even when reconstructing recent histories. We
demonstrate the sharpness of our lower bounds by providing algorithms for
distinguishing and learning population histories with matching dependence on
the number of subpopulations. Along the way and of independent interest, we
essentially determine the optimal number of samples needed to learn an
exponential mixture distribution information-theoretically, proving the upper
bound by analyzing natural (and efficient) algorithms for this problem.
|
[
{
"created": "Wed, 7 Nov 2018 23:00:15 GMT",
"version": "v1"
},
{
"created": "Wed, 8 May 2019 15:24:19 GMT",
"version": "v2"
}
] |
2020-05-11
|
[
[
"Kim",
"Younhun",
""
],
[
"Koehler",
"Frederic",
""
],
[
"Moitra",
"Ankur",
""
],
[
"Mossel",
"Elchanan",
""
],
[
"Ramnarayan",
"Govind",
""
]
] |
Reconstruction of population histories is a central problem in population genetics. Existing coalescent-based methods, like the seminal work of Li and Durbin (Nature, 2011), attempt to solve this problem using sequence data but have no rigorous guarantees. Determining the amount of data needed to correctly reconstruct population histories is a major challenge. Using a variety of tools from information theory, the theory of extremal polynomials, and approximation theory, we prove new sharp information-theoretic lower bounds on the problem of reconstructing population structure -- the history of multiple subpopulations that merge, split and change sizes over time. Our lower bounds are exponential in the number of subpopulations, even when reconstructing recent histories. We demonstrate the sharpness of our lower bounds by providing algorithms for distinguishing and learning population histories with matching dependence on the number of subpopulations. Along the way and of independent interest, we essentially determine the optimal number of samples needed to learn an exponential mixture distribution information-theoretically, proving the upper bound by analyzing natural (and efficient) algorithms for this problem.
|
1204.5999
|
Michael Deem
|
Dirk M. Lorenz, Alice Jeng, and Michael W. Deem
|
The Emergence of Modularity in Biological Systems
|
54 pages, 25 figures
|
Physics of Life Reviews, 8 (2011) 129-160
|
10.1016/j.plrev.2011.02.003
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this review, we discuss modularity and hierarchy in biological systems. We
review examples from protein structure, genetics, and biological networks of
modular partitioning of the geometry of biological space. We review theories to
explain modular organization of biology, with a focus on explaining how biology
may spontaneously organize to a structured form. That is, we seek to explain
how biology nucleated from among the many possibilities in chemistry. The
emergence of modular organization of biological structure will be described as
a symmetry-breaking phase transition, with modularity as the order parameter.
Experimental support for this description will be reviewed. Examples will be
presented from pathogen structure, metabolic networks, gene networks, and
protein-protein interaction networks. Additional examples will be presented
from ecological food networks, developmental pathways, physiology, and social
networks.
|
[
{
"created": "Thu, 26 Apr 2012 18:45:00 GMT",
"version": "v1"
}
] |
2015-06-04
|
[
[
"Lorenz",
"Dirk M.",
""
],
[
"Jeng",
"Alice",
""
],
[
"Deem",
"Michael W.",
""
]
] |
In this review, we discuss modularity and hierarchy in biological systems. We review examples from protein structure, genetics, and biological networks of modular partitioning of the geometry of biological space. We review theories to explain modular organization of biology, with a focus on explaining how biology may spontaneously organize to a structured form. That is, we seek to explain how biology nucleated from among the many possibilities in chemistry. The emergence of modular organization of biological structure will be described as a symmetry-breaking phase transition, with modularity as the order parameter. Experimental support for this description will be reviewed. Examples will be presented from pathogen structure, metabolic networks, gene networks, and protein-protein interaction networks. Additional examples will be presented from ecological food networks, developmental pathways, physiology, and social networks.
|
2401.10211
|
Zhengyi Li
|
Zhengyi Li, Menglu Li, Lida Zhu, Wen Zhang
|
Improving PTM Site Prediction by Coupling of Multi-Granularity Structure
and Multi-Scale Sequence Representation
| null | null | null | null |
q-bio.QM cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Protein post-translational modification (PTM) site prediction is a
fundamental task in bioinformatics. Several computational methods have been
developed to predict PTM sites. However, existing methods ignore the structure
information and merely utilize protein sequences. Furthermore, designing a more
fine-grained structure representation learning method is urgently needed as PTM
is a biological event that occurs at the atom granularity. In this paper, we
propose a PTM site prediction method by Coupling of Multi-Granularity structure
and Multi-Scale sequence representation, PTM-CMGMS for brevity. Specifically,
multigranularity structure-aware representation learning is designed to learn
neighborhood structure representations at the amino acid, atom, and whole
protein granularity from AlphaFold predicted structures, followed by utilizing
contrastive learning to optimize the structure representations.Additionally,
multi-scale sequence representation learning is used to extract context
sequence information, and motif generated by aligning all context sequences of
PTM sites assists the prediction. Extensive experiments on three datasets show
that PTM-CMGMS outperforms the state-of-the-art methods.
|
[
{
"created": "Thu, 4 Jan 2024 20:49:32 GMT",
"version": "v1"
}
] |
2024-01-19
|
[
[
"Li",
"Zhengyi",
""
],
[
"Li",
"Menglu",
""
],
[
"Zhu",
"Lida",
""
],
[
"Zhang",
"Wen",
""
]
] |
Protein post-translational modification (PTM) site prediction is a fundamental task in bioinformatics. Several computational methods have been developed to predict PTM sites. However, existing methods ignore the structure information and merely utilize protein sequences. Furthermore, designing a more fine-grained structure representation learning method is urgently needed as PTM is a biological event that occurs at the atom granularity. In this paper, we propose a PTM site prediction method by Coupling of Multi-Granularity structure and Multi-Scale sequence representation, PTM-CMGMS for brevity. Specifically, multigranularity structure-aware representation learning is designed to learn neighborhood structure representations at the amino acid, atom, and whole protein granularity from AlphaFold predicted structures, followed by utilizing contrastive learning to optimize the structure representations.Additionally, multi-scale sequence representation learning is used to extract context sequence information, and motif generated by aligning all context sequences of PTM sites assists the prediction. Extensive experiments on three datasets show that PTM-CMGMS outperforms the state-of-the-art methods.
|
2109.00364
|
Florian Franke
|
Florian Franke, Sebatian Aland, Hans-Joachim B\"ohme, Anja
Voss-B\"ohme, Steffen Lange
|
Is cell segregation like oil and water: asymptotic versus transitory
regime
|
41 pages, 11+11 figures, 1+1 table
|
PLoS Computational Biology, September 2022
|
10.1371/journal.pcbi.1010460
| null |
q-bio.CB
|
http://creativecommons.org/licenses/by/4.0/
|
Segregation of different cell types is a crucial process for the pattern
formation in tissues, in particular during embryogenesis. Since the involved
cell interactions are complex and difficult to measure individually in
experiments, mathematical modelling plays an increasingly important role to
unravel the mechanisms governing segregation. The analysis of these theoretical
models focuses mainly on the asymptotic behavior at large times, in a steady
regime and for large numbers of cells. Most famously, cell-segregation models
based on the minimization of the total surface energy, a mechanism also driving
the demixing of immiscible fluids, are known to exhibit asymptotically a
particular algebraic scaling behavior. However, it is not clear, whether the
asymptotic regime of the numerical models is relevant at the spatio-temporal
scales of actual biological processes and in-vitro experiments. By developing a
mapping between cell-based models and experimental settings, we are able to
directly compare previous experimental data to numerical simulations of cell
segregation quantitatively. We demonstrate that the experiments are reproduced
by the transitory regime of the models rather than the asymptotic one. Our work
puts a new perspective on previous model-driven conclusions on cell segregation
mechanisms.
|
[
{
"created": "Wed, 1 Sep 2021 12:59:52 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Oct 2021 06:04:19 GMT",
"version": "v2"
},
{
"created": "Wed, 13 Apr 2022 09:31:25 GMT",
"version": "v3"
},
{
"created": "Fri, 22 Jul 2022 08:25:09 GMT",
"version": "v4"
}
] |
2022-09-21
|
[
[
"Franke",
"Florian",
""
],
[
"Aland",
"Sebatian",
""
],
[
"Böhme",
"Hans-Joachim",
""
],
[
"Voss-Böhme",
"Anja",
""
],
[
"Lange",
"Steffen",
""
]
] |
Segregation of different cell types is a crucial process for the pattern formation in tissues, in particular during embryogenesis. Since the involved cell interactions are complex and difficult to measure individually in experiments, mathematical modelling plays an increasingly important role to unravel the mechanisms governing segregation. The analysis of these theoretical models focuses mainly on the asymptotic behavior at large times, in a steady regime and for large numbers of cells. Most famously, cell-segregation models based on the minimization of the total surface energy, a mechanism also driving the demixing of immiscible fluids, are known to exhibit asymptotically a particular algebraic scaling behavior. However, it is not clear, whether the asymptotic regime of the numerical models is relevant at the spatio-temporal scales of actual biological processes and in-vitro experiments. By developing a mapping between cell-based models and experimental settings, we are able to directly compare previous experimental data to numerical simulations of cell segregation quantitatively. We demonstrate that the experiments are reproduced by the transitory regime of the models rather than the asymptotic one. Our work puts a new perspective on previous model-driven conclusions on cell segregation mechanisms.
|
2403.11517
|
Haibao Wang
|
Haibao Wang, Jun Kai Ho, Fan L. Cheng, Shuntaro C. Aoki, Yusuke
Muraki, Misato Tanaka and Yukiyasu Kamitani
|
Inter-individual and inter-site neural code conversion without shared
stimuli
| null | null | null | null |
q-bio.NC cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Inter-individual variability in fine-grained functional brain organization
poses challenges for scalable data analysis and modeling. Functional alignment
techniques can help mitigate these individual differences but typically require
paired brain data with the same stimuli between individuals, which is often
unavailable. We present a neural code conversion method that overcomes this
constraint by optimizing conversion parameters based on the discrepancy between
the stimulus contents represented by original and converted brain activity
patterns. This approach, combined with hierarchical features of deep neural
networks (DNNs) as latent content representations, achieves conversion accuracy
comparable to methods using shared stimuli. The converted brain activity from a
source subject can be accurately decoded using the target's pre-trained
decoders, producing high-quality visual image reconstructions that rival
within-individual decoding, even with data across different sites and limited
training samples. Our approach offers a promising framework for scalable neural
data analysis and modeling and a foundation for brain-to-brain communication.
|
[
{
"created": "Mon, 18 Mar 2024 07:10:52 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Aug 2024 11:16:16 GMT",
"version": "v2"
}
] |
2024-08-02
|
[
[
"Wang",
"Haibao",
""
],
[
"Ho",
"Jun Kai",
""
],
[
"Cheng",
"Fan L.",
""
],
[
"Aoki",
"Shuntaro C.",
""
],
[
"Muraki",
"Yusuke",
""
],
[
"Tanaka",
"Misato",
""
],
[
"Kamitani",
"Yukiyasu",
""
]
] |
Inter-individual variability in fine-grained functional brain organization poses challenges for scalable data analysis and modeling. Functional alignment techniques can help mitigate these individual differences but typically require paired brain data with the same stimuli between individuals, which is often unavailable. We present a neural code conversion method that overcomes this constraint by optimizing conversion parameters based on the discrepancy between the stimulus contents represented by original and converted brain activity patterns. This approach, combined with hierarchical features of deep neural networks (DNNs) as latent content representations, achieves conversion accuracy comparable to methods using shared stimuli. The converted brain activity from a source subject can be accurately decoded using the target's pre-trained decoders, producing high-quality visual image reconstructions that rival within-individual decoding, even with data across different sites and limited training samples. Our approach offers a promising framework for scalable neural data analysis and modeling and a foundation for brain-to-brain communication.
|
1908.04875
|
Casey Fleeter
|
Casey M. Fleeter, Gianluca Geraci, Daniele E. Schiavazzi, Andrew M.
Kahn, Alison L. Marsden
|
Multilevel and multifidelity uncertainty quantification for
cardiovascular hemodynamics
| null | null |
10.1016/j.cma.2020.113030
| null |
q-bio.QM physics.comp-ph stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Standard approaches for uncertainty quantification in cardiovascular modeling
pose challenges due to the large number of uncertain inputs and the significant
computational cost of realistic three-dimensional simulations. We propose an
efficient uncertainty quantification framework utilizing a multilevel
multifidelity Monte Carlo estimator to improve the accuracy of hemodynamic
quantities of interest while maintaining reasonable computational cost. This is
achieved by leveraging three cardiovascular model fidelities, each with varying
spatial resolution to rigorously quantify the variability in hemodynamic
outputs. We employ two low-fidelity models to construct several different
estimators. Our goal is to investigate and compare the efficiency of estimators
built from combinations of these low-fidelity and high-fidelity models. We
demonstrate this framework on healthy and diseased models of aortic and
coronary anatomy, including uncertainties in material property and boundary
condition parameters. We seek to demonstrate that for this application it is
possible to accelerate the convergence of the estimators by utilizing a MLMF
paradigm. Therefore, we compare our approach to Monte Carlo and multilevel
Monte Carlo estimators based only on three-dimensional simulations. We
demonstrate significant reduction in total computational cost with the MLMF
estimators. We also examine the differing properties of the MLMF estimators in
healthy versus diseased models, as well as global versus local quantities of
interest. As expected, global quantities and healthy models show larger
reductions than local quantities and diseased model, as the latter rely more
heavily on the highest fidelity model evaluations. In all cases, our workflow
coupling Dakota's MLMF estimators with the SimVascular cardiovascular modeling
framework makes uncertainty quantification feasible for constrained
computational budgets.
|
[
{
"created": "Tue, 13 Aug 2019 22:10:47 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Apr 2020 23:44:26 GMT",
"version": "v2"
}
] |
2020-04-20
|
[
[
"Fleeter",
"Casey M.",
""
],
[
"Geraci",
"Gianluca",
""
],
[
"Schiavazzi",
"Daniele E.",
""
],
[
"Kahn",
"Andrew M.",
""
],
[
"Marsden",
"Alison L.",
""
]
] |
Standard approaches for uncertainty quantification in cardiovascular modeling pose challenges due to the large number of uncertain inputs and the significant computational cost of realistic three-dimensional simulations. We propose an efficient uncertainty quantification framework utilizing a multilevel multifidelity Monte Carlo estimator to improve the accuracy of hemodynamic quantities of interest while maintaining reasonable computational cost. This is achieved by leveraging three cardiovascular model fidelities, each with varying spatial resolution to rigorously quantify the variability in hemodynamic outputs. We employ two low-fidelity models to construct several different estimators. Our goal is to investigate and compare the efficiency of estimators built from combinations of these low-fidelity and high-fidelity models. We demonstrate this framework on healthy and diseased models of aortic and coronary anatomy, including uncertainties in material property and boundary condition parameters. We seek to demonstrate that for this application it is possible to accelerate the convergence of the estimators by utilizing a MLMF paradigm. Therefore, we compare our approach to Monte Carlo and multilevel Monte Carlo estimators based only on three-dimensional simulations. We demonstrate significant reduction in total computational cost with the MLMF estimators. We also examine the differing properties of the MLMF estimators in healthy versus diseased models, as well as global versus local quantities of interest. As expected, global quantities and healthy models show larger reductions than local quantities and diseased model, as the latter rely more heavily on the highest fidelity model evaluations. In all cases, our workflow coupling Dakota's MLMF estimators with the SimVascular cardiovascular modeling framework makes uncertainty quantification feasible for constrained computational budgets.
|
q-bio/0502037
|
Jean-Pascal Pfister
|
Jean-Pascal Pfister, Taro Toyoizumi, David Barber, Wulfram Gerstner
|
Optimal Spike-Timing Dependent Plasticity for Precise Action Potential
Firing
|
27 pages, 10 figures
| null | null | null |
q-bio.NC
| null |
In timing-based neural codes, neurons have to emit action potentials at
precise moments in time. We use a supervised learning paradigm to derive a
synaptic update rule that optimizes via gradient ascent the likelihood of
postsynaptic firing at one or several desired firing times. We find that the
optimal strategy of up- and downregulating synaptic efficacies can be described
by a two-phase learning window similar to that of Spike-Timing Dependent
Plasticity (STDP). If the presynaptic spike arrives before the desired
postsynaptic spike timing, our optimal learning rule predicts that the synapse
should become potentiated. The dependence of the potentiation on spike timing
directly reflects the time course of an excitatory postsynaptic potential. The
presence and amplitude of depression of synaptic efficacies for reversed spike
timing depends on how constraints are implemented in the optimization problem.
Two different constraints, i.e., control of postsynaptic rates or control of
temporal locality,are discussed.
|
[
{
"created": "Thu, 24 Feb 2005 16:28:38 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Pfister",
"Jean-Pascal",
""
],
[
"Toyoizumi",
"Taro",
""
],
[
"Barber",
"David",
""
],
[
"Gerstner",
"Wulfram",
""
]
] |
In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes via gradient ascent the likelihood of postsynaptic firing at one or several desired firing times. We find that the optimal strategy of up- and downregulating synaptic efficacies can be described by a two-phase learning window similar to that of Spike-Timing Dependent Plasticity (STDP). If the presynaptic spike arrives before the desired postsynaptic spike timing, our optimal learning rule predicts that the synapse should become potentiated. The dependence of the potentiation on spike timing directly reflects the time course of an excitatory postsynaptic potential. The presence and amplitude of depression of synaptic efficacies for reversed spike timing depends on how constraints are implemented in the optimization problem. Two different constraints, i.e., control of postsynaptic rates or control of temporal locality,are discussed.
|
2208.00935
|
Jia Qi Yip
|
Jia Qi Yip, Dianwen Ng, Bin Ma, Konstantin Pervushin, Eng Siong Chng
|
Amino Acid Classification in 2D NMR Spectra via Acoustic Signal
Embeddings
| null | null | null | null |
q-bio.QM eess.AS
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Nuclear Magnetic Resonance (NMR) is used in structural biology to
experimentally determine the structure of proteins, which is used in many areas
of biology and is an important part of drug development. Unfortunately, NMR
data can cost thousands of dollars per sample to collect and it can take a
specialist weeks to assign the observed resonances to specific chemical groups.
There has thus been growing interest in the NMR community to use deep learning
to automate NMR data annotation. Due to similarities between NMR and audio
data, we propose that methods used in acoustic signal processing can be applied
to NMR as well. Using a simulated amino acid dataset, we show that by swapping
out filter banks with a trainable convolutional encoder, acoustic signal
embeddings from speaker verification models can be used for amino acid
classification in 2D NMR spectra by treating each amino acid as a unique
speaker. On an NMR dataset comparable in size with of 46 hours of audio, we
achieve a classification performance of 97.7% on a 20-class problem. We also
achieve a 23% relative improvement by using an acoustic embedding model
compared to an existing NMR-based model.
|
[
{
"created": "Mon, 1 Aug 2022 15:36:22 GMT",
"version": "v1"
}
] |
2022-08-03
|
[
[
"Yip",
"Jia Qi",
""
],
[
"Ng",
"Dianwen",
""
],
[
"Ma",
"Bin",
""
],
[
"Pervushin",
"Konstantin",
""
],
[
"Chng",
"Eng Siong",
""
]
] |
Nuclear Magnetic Resonance (NMR) is used in structural biology to experimentally determine the structure of proteins, which is used in many areas of biology and is an important part of drug development. Unfortunately, NMR data can cost thousands of dollars per sample to collect and it can take a specialist weeks to assign the observed resonances to specific chemical groups. There has thus been growing interest in the NMR community to use deep learning to automate NMR data annotation. Due to similarities between NMR and audio data, we propose that methods used in acoustic signal processing can be applied to NMR as well. Using a simulated amino acid dataset, we show that by swapping out filter banks with a trainable convolutional encoder, acoustic signal embeddings from speaker verification models can be used for amino acid classification in 2D NMR spectra by treating each amino acid as a unique speaker. On an NMR dataset comparable in size with of 46 hours of audio, we achieve a classification performance of 97.7% on a 20-class problem. We also achieve a 23% relative improvement by using an acoustic embedding model compared to an existing NMR-based model.
|
q-bio/0507041
|
Jim Bashford
|
J.D. Bashford and P.D. Jarvis
|
A base pairing model of duplex formation I: Watson-Crick pairing
geometries
|
Latex file, 13 pages, no figures. Refereed draft of manuscript
submitted to Biopolymers
|
Biopolymers 78: 287-297, 2005
|
10.1002/bip.20282
|
UTAS-PHYS-2004-05
|
q-bio.BM
| null |
We present a base-pairing model of oligonuleotide duplex formation and show
in detail its equivalence to the Nearest-Neighbour dimer methods from fits to
free energy of duplex formation data for short DNA-DNA and DNA-RNA hybrids
containing only Watson Crick pairs. In this approach the connection between
rank-deficient polymer and rank-determinant oligonucleotide parameter, sets for
DNA duplexes is transparent. The method is generalised to include RNA/DNA
hybrids where the rank-deficient model with 11 dimer parameters in fact
provides marginally improved predictions relative to the standard method with
16 independent dimer parameters ($\Delta G$ mean errors of 4.5 and 5.4 %
respectively).
|
[
{
"created": "Thu, 28 Jul 2005 04:37:03 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Bashford",
"J. D.",
""
],
[
"Jarvis",
"P. D.",
""
]
] |
We present a base-pairing model of oligonuleotide duplex formation and show in detail its equivalence to the Nearest-Neighbour dimer methods from fits to free energy of duplex formation data for short DNA-DNA and DNA-RNA hybrids containing only Watson Crick pairs. In this approach the connection between rank-deficient polymer and rank-determinant oligonucleotide parameter, sets for DNA duplexes is transparent. The method is generalised to include RNA/DNA hybrids where the rank-deficient model with 11 dimer parameters in fact provides marginally improved predictions relative to the standard method with 16 independent dimer parameters ($\Delta G$ mean errors of 4.5 and 5.4 % respectively).
|
2005.02388
|
Endre Cs\'oka
|
Endre Cs\'oka
|
Application-oriented mathematical algorithms for group testing
| null | null | null | null |
q-bio.QM cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We have a large number of samples and we want to find the infected ones using
as few number of tests as possible. We can use group testing which tells about
a small group of people whether at least one of them is infected. Group testing
is particularly efficient if the infection rate is low. The goal of this
article is to summarize and extend the mathematical knowledge about the most
efficient group testing algorithms, focusing on real-life applications instead
of pure mathematical motivations and approaches.
|
[
{
"created": "Tue, 5 May 2020 14:40:46 GMT",
"version": "v1"
}
] |
2020-05-07
|
[
[
"Csóka",
"Endre",
""
]
] |
We have a large number of samples and we want to find the infected ones using as few number of tests as possible. We can use group testing which tells about a small group of people whether at least one of them is infected. Group testing is particularly efficient if the infection rate is low. The goal of this article is to summarize and extend the mathematical knowledge about the most efficient group testing algorithms, focusing on real-life applications instead of pure mathematical motivations and approaches.
|
2402.10308
|
Wouter-Jan Rappel
|
Timothy J Tyree, Patrick Murphy, Wouter-Jan Rappel
|
Annihilation dynamics during spiral defect chaos revealed by particle
models
|
11 pages, 11 figures
| null | null | null |
q-bio.TO
|
http://creativecommons.org/licenses/by/4.0/
|
Pair-annihilation events are ubiquitous in a variety of spatially extended
systems and are often studied using computationally expensive simulations. Here
we develop an approach in which we simulate the pair-annihilation of spiral
wave tips in cardiac models using a computationally efficient particle model.
Spiral wave tips are represented as particles with dynamics governed by
diffusive behavior and short-ranged attraction. The parameters for diffusion
and attraction are obtained by comparing particle motion to the trajectories of
spiral wave tips in cardiac models during spiral defect chaos. The particle
model reproduces the annihilation rates of the cardiac models and can determine
the statistics of spiral wave dynamics, including its mean termination time. We
show that increasing the attraction coefficient sharply decreases the mean
termination time, making it a possible target for pharmaceutical intervention
|
[
{
"created": "Thu, 15 Feb 2024 20:20:29 GMT",
"version": "v1"
}
] |
2024-02-19
|
[
[
"Tyree",
"Timothy J",
""
],
[
"Murphy",
"Patrick",
""
],
[
"Rappel",
"Wouter-Jan",
""
]
] |
Pair-annihilation events are ubiquitous in a variety of spatially extended systems and are often studied using computationally expensive simulations. Here we develop an approach in which we simulate the pair-annihilation of spiral wave tips in cardiac models using a computationally efficient particle model. Spiral wave tips are represented as particles with dynamics governed by diffusive behavior and short-ranged attraction. The parameters for diffusion and attraction are obtained by comparing particle motion to the trajectories of spiral wave tips in cardiac models during spiral defect chaos. The particle model reproduces the annihilation rates of the cardiac models and can determine the statistics of spiral wave dynamics, including its mean termination time. We show that increasing the attraction coefficient sharply decreases the mean termination time, making it a possible target for pharmaceutical intervention
|
1710.00718
|
Yannis Pantazis
|
Yannis Pantazis and Ioannis Tsamardinos
|
A Unified Approach for Sparse Dynamical System Inference from Temporal
Measurements
|
13 pages, 3 figures
| null | null | null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Temporal variations in biological systems and more generally in natural
sciences are typically modelled as a set of Ordinary, Partial, or Stochastic
Differential or Difference Equations. Algorithms for learning the structure and
the parameters of a dynamical system are distinguished based on whether time is
discrete or continuous, observations are time-series or time-course, and
whether the system is deterministic or stochastic, however, there is no
approach able to handle the various types of dynamical systems simultaneously.
In this paper, we present a unified approach to infer both the structure and
the parameters of nonlinear dynamical systems of any type under the restriction
of being linear with respect to the unknown parameters. Our approach, which is
named Unified Sparse Dynamics Learning (USDL), constitutes of two steps. First,
an atemporal system of equations is derived through the application of the weak
formulation. Then, assuming a sparse representation for the dynamical system,
we show that the inference problem can be expressed as a sparse signal recovery
problem, allowing the application of an extensive body of algorithms and
theoretical results. Results on simulated data demonstrate the efficacy and
superiority of the USDL algorithm as a function of the experimental setup
(sample size, number of time measurements, number of interventions, noise
level). Additionally, USDL's accuracy significantly correlates with theoretical
metrics such as the exact recovery coefficient. On real single-cell data, the
proposed approach is able to induce high-confidence subgraphs of the signaling
pathway. USDL algorithm has been integrated in SCENERY
(\url{http://scenery.csd.uoc.gr/}); an online tool for single-cell mass
cytometry analytics.
|
[
{
"created": "Mon, 2 Oct 2017 15:16:55 GMT",
"version": "v1"
},
{
"created": "Wed, 4 Oct 2017 18:48:45 GMT",
"version": "v2"
},
{
"created": "Sat, 19 Jan 2019 21:25:01 GMT",
"version": "v3"
}
] |
2019-01-23
|
[
[
"Pantazis",
"Yannis",
""
],
[
"Tsamardinos",
"Ioannis",
""
]
] |
Temporal variations in biological systems and more generally in natural sciences are typically modelled as a set of Ordinary, Partial, or Stochastic Differential or Difference Equations. Algorithms for learning the structure and the parameters of a dynamical system are distinguished based on whether time is discrete or continuous, observations are time-series or time-course, and whether the system is deterministic or stochastic, however, there is no approach able to handle the various types of dynamical systems simultaneously. In this paper, we present a unified approach to infer both the structure and the parameters of nonlinear dynamical systems of any type under the restriction of being linear with respect to the unknown parameters. Our approach, which is named Unified Sparse Dynamics Learning (USDL), constitutes of two steps. First, an atemporal system of equations is derived through the application of the weak formulation. Then, assuming a sparse representation for the dynamical system, we show that the inference problem can be expressed as a sparse signal recovery problem, allowing the application of an extensive body of algorithms and theoretical results. Results on simulated data demonstrate the efficacy and superiority of the USDL algorithm as a function of the experimental setup (sample size, number of time measurements, number of interventions, noise level). Additionally, USDL's accuracy significantly correlates with theoretical metrics such as the exact recovery coefficient. On real single-cell data, the proposed approach is able to induce high-confidence subgraphs of the signaling pathway. USDL algorithm has been integrated in SCENERY (\url{http://scenery.csd.uoc.gr/}); an online tool for single-cell mass cytometry analytics.
|
0711.0715
|
Swarnendu Tripathi
|
Swarnendu Tripathi and John J. Portman
|
Inherent flexibility and protein function: the open/closed
conformational transition of the N-terminal domain of calmodulin
|
21 pages, 8 figures
|
J. Chem. Phys. 128, 205104 (2008)
|
10.1063/1.2928634
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The key to understanding a protein's function often lies in its
conformational dynamics. We develop a coarse-grained variational model to
investigate the interplay between structural transitions, conformational
flexibility and function of N-terminal calmodulin (nCaM) domain. In this model,
two energy basins corresponding to the ``closed'' apo conformation and ``open''
holo conformation of nCaM domain are connected by a uniform interpolation
parameter. The resulting detailed transition route from our model is largely
consistent with the recently proposed EF$\beta$-scaffold mechanism in EF-hand
family proteins. We find that the N-terminal part in calcium binding loops I
and II shows higher flexibility than the C-terminal part which form this
EF$\beta$-scaffold structure. The structural transition of binding loops I and
II are compared in detail. Our model predicts that binding loop II, with higher
flexibility and early structural change than binding loop I, dominates the
conformational transition in nCaM domain.
|
[
{
"created": "Mon, 5 Nov 2007 18:29:14 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Jul 2008 17:13:30 GMT",
"version": "v2"
}
] |
2008-07-10
|
[
[
"Tripathi",
"Swarnendu",
""
],
[
"Portman",
"John J.",
""
]
] |
The key to understanding a protein's function often lies in its conformational dynamics. We develop a coarse-grained variational model to investigate the interplay between structural transitions, conformational flexibility and function of N-terminal calmodulin (nCaM) domain. In this model, two energy basins corresponding to the ``closed'' apo conformation and ``open'' holo conformation of nCaM domain are connected by a uniform interpolation parameter. The resulting detailed transition route from our model is largely consistent with the recently proposed EF$\beta$-scaffold mechanism in EF-hand family proteins. We find that the N-terminal part in calcium binding loops I and II shows higher flexibility than the C-terminal part which form this EF$\beta$-scaffold structure. The structural transition of binding loops I and II are compared in detail. Our model predicts that binding loop II, with higher flexibility and early structural change than binding loop I, dominates the conformational transition in nCaM domain.
|
1107.4104
|
Jiapu Zhang
|
Jiapu Zhang, David Y. Gao, and Johh Yearwood
|
A novel canonical dual computational approach for prion AGAAAAGA amyloid
fibril molecular modeling
| null |
J Theor Biol 284 (1) 149-157 (2011); selected by Protein
Crystallography Newsletter Volume 3, No. 9, September 2011, Crystallography
Times; Prions Research Today Volume 7 Issue 7, July 2011, p.14; the 18th of
the Top 25 Hottest Articles (picked up from papers of Jul 2011 to Sept 2011
of J Theor Biol)
|
10.1016/j.jtbi.2011.06.024
| null |
q-bio.BM cs.CE math-ph math.MP math.OC
|
http://creativecommons.org/licenses/by-nc-sa/3.0/
|
Many experimental studies have shown that the prion AGAAAAGA palindrome
hydrophobic region (113-120) has amyloid fibril forming properties and plays an
important role in prion diseases. However, due to the unstable, noncrystalline
and insoluble nature of the amyloid fibril, to date structural information on
AGAAAAGA region (113-120) has been very limited. This region falls just within
the N-terminal unstructured region PrP (1-123) of prion proteins. Traditional
X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy
experimental methods cannot be used to get its structural information. Under
this background, this paper introduces a novel approach of the canonical dual
theory to address the 3D atomic-resolution structure of prion AGAAAAGA amyloid
fibrils. The novel and powerful canonical dual computational approach
introduced in this paper is for the molecular modeling of prion AGAAAAGA
amyloid fibrils, and that the optimal atomic-resolution structures of prion
AGAAAAGA amyloid fibils presented in this paper are useful for the drive to
find treatments for prion diseases in the field of medicinal chemistry.
Overall, this paper presents an important method and provides useful
information for treatments of prion diseases. Overall, this paper could be of
interest to the general readership of Theoretical Biology.
|
[
{
"created": "Mon, 18 Jul 2011 23:20:34 GMT",
"version": "v1"
}
] |
2013-12-10
|
[
[
"Zhang",
"Jiapu",
""
],
[
"Gao",
"David Y.",
""
],
[
"Yearwood",
"Johh",
""
]
] |
Many experimental studies have shown that the prion AGAAAAGA palindrome hydrophobic region (113-120) has amyloid fibril forming properties and plays an important role in prion diseases. However, due to the unstable, noncrystalline and insoluble nature of the amyloid fibril, to date structural information on AGAAAAGA region (113-120) has been very limited. This region falls just within the N-terminal unstructured region PrP (1-123) of prion proteins. Traditional X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy experimental methods cannot be used to get its structural information. Under this background, this paper introduces a novel approach of the canonical dual theory to address the 3D atomic-resolution structure of prion AGAAAAGA amyloid fibrils. The novel and powerful canonical dual computational approach introduced in this paper is for the molecular modeling of prion AGAAAAGA amyloid fibrils, and that the optimal atomic-resolution structures of prion AGAAAAGA amyloid fibils presented in this paper are useful for the drive to find treatments for prion diseases in the field of medicinal chemistry. Overall, this paper presents an important method and provides useful information for treatments of prion diseases. Overall, this paper could be of interest to the general readership of Theoretical Biology.
|
1912.00985
|
Joana Fradinho
|
Joana Fradinho, Adrian Oehmen and Maria Reis
|
Improving polyhydroxyalkanoates production in phototrophic mixed
cultures by optimizing accumulator reactor operating conditions
|
29 pages, 4 figures, 4 tables
| null |
10.1016/j.ijbiomac.2018.12.270
| null |
q-bio.OT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Polyhydroxyalkanoates (PHAs) production with phototrophic mixed cultures
(PMCs) has been recently proposed. These cultures can be selected under the
permanent presence of carbon and the PHA production can be enhanced in
subsequent accumulation steps. To optimize the PHA production in accumulator
reactors, this work evaluated the impact of 1) initial acetate concentration,
2) light intensity, 3) removal of residual nitrogen on the culture performance.
Results indicate that low acetate concentration (<30CmM) and specific light
intensities around 20W/gX are optimal operating conditions that lead to high
polyhydroxybutyrate (PHB) storage yields (0.83+-0.07 Cmol-PHB/Cmol-Acet) and
specific PHB production rates of 2.21+-0.07 Cmol-PHB/Cmol X d. This rate is
three times higher than previously registered in non-optimized accumulation
tests and enabled a PHA content increase from 15 to 30% in less than 4h. Also,
it was shown for the first time, the capability of a PMC to use a real waste,
fermented cheese whey, to produce PHA with a hydroxyvalerate (HV) content of
12%. These results confirm that fermented wastes can be used as substrates for
PHA production with PMCs and that the energy levels in sunlight that lead to
specific light intensities from 10 to 20W/gX are sufficient to drive
phototrophic PHA production processes.
|
[
{
"created": "Mon, 2 Dec 2019 18:23:42 GMT",
"version": "v1"
}
] |
2019-12-03
|
[
[
"Fradinho",
"Joana",
""
],
[
"Oehmen",
"Adrian",
""
],
[
"Reis",
"Maria",
""
]
] |
Polyhydroxyalkanoates (PHAs) production with phototrophic mixed cultures (PMCs) has been recently proposed. These cultures can be selected under the permanent presence of carbon and the PHA production can be enhanced in subsequent accumulation steps. To optimize the PHA production in accumulator reactors, this work evaluated the impact of 1) initial acetate concentration, 2) light intensity, 3) removal of residual nitrogen on the culture performance. Results indicate that low acetate concentration (<30CmM) and specific light intensities around 20W/gX are optimal operating conditions that lead to high polyhydroxybutyrate (PHB) storage yields (0.83+-0.07 Cmol-PHB/Cmol-Acet) and specific PHB production rates of 2.21+-0.07 Cmol-PHB/Cmol X d. This rate is three times higher than previously registered in non-optimized accumulation tests and enabled a PHA content increase from 15 to 30% in less than 4h. Also, it was shown for the first time, the capability of a PMC to use a real waste, fermented cheese whey, to produce PHA with a hydroxyvalerate (HV) content of 12%. These results confirm that fermented wastes can be used as substrates for PHA production with PMCs and that the energy levels in sunlight that lead to specific light intensities from 10 to 20W/gX are sufficient to drive phototrophic PHA production processes.
|
2209.05688
|
M. Ali Al-Radhawi
|
M. Ali Al-Radhawi, Shubham Tripathi, Yun Zhang, Eduardo D. Sontag, and
Herbert Levine
|
Epigenetic factor competition reshapes the EMT landscape
| null |
Proc Natl Acad Sci USA, 119:e2210844119, 2022
|
10.1073/pnas.2210844119
| null |
q-bio.MN q-bio.QM
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The emergence of and transitions between distinct phenotypes in isogenic
cells can be attributed to the intricate interplay of epigenetic marks,
external signals, and gene regulatory elements. These elements include
chromatin remodelers, histone modifiers, transcription factors, and regulatory
RNAs. Mathematical models known as Gene Regulatory Networks (GRNs) are an
increasingly important tool to unravel the workings of such complex networks.
In such models, epigenetic factors are usually proposed to act on the chromatin
regions directly involved in the expression of relevant genes. However, it has
been well-established that these factors operate globally and compete with each
other for targets genome-wide. Therefore, a perturbation of the activity of a
regulator can redistribute epigenetic marks across the genome and modulate the
levels of competing regulators. In this paper, we propose a conceptual and
mathematical modeling framework that incorporates both local and global
competition effects between antagonistic epigenetic regulators in addition to
local transcription factors, and show the counter-intuitive consequences of
such interactions. We apply our approach to recent experimental findings on the
Epithelial-Mesenchymal Transition (EMT). We show that it can explain the
puzzling experimental data as well provide new verifiable predictions.
|
[
{
"created": "Tue, 13 Sep 2022 01:57:49 GMT",
"version": "v1"
}
] |
2022-10-19
|
[
[
"Al-Radhawi",
"M. Ali",
""
],
[
"Tripathi",
"Shubham",
""
],
[
"Zhang",
"Yun",
""
],
[
"Sontag",
"Eduardo D.",
""
],
[
"Levine",
"Herbert",
""
]
] |
The emergence of and transitions between distinct phenotypes in isogenic cells can be attributed to the intricate interplay of epigenetic marks, external signals, and gene regulatory elements. These elements include chromatin remodelers, histone modifiers, transcription factors, and regulatory RNAs. Mathematical models known as Gene Regulatory Networks (GRNs) are an increasingly important tool to unravel the workings of such complex networks. In such models, epigenetic factors are usually proposed to act on the chromatin regions directly involved in the expression of relevant genes. However, it has been well-established that these factors operate globally and compete with each other for targets genome-wide. Therefore, a perturbation of the activity of a regulator can redistribute epigenetic marks across the genome and modulate the levels of competing regulators. In this paper, we propose a conceptual and mathematical modeling framework that incorporates both local and global competition effects between antagonistic epigenetic regulators in addition to local transcription factors, and show the counter-intuitive consequences of such interactions. We apply our approach to recent experimental findings on the Epithelial-Mesenchymal Transition (EMT). We show that it can explain the puzzling experimental data as well provide new verifiable predictions.
|
1210.5348
|
Erich Schmid
|
Erich W. Schmid and Wolfgang Fink
|
Operational Design Considerations for Retinal Prostheses
| null | null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Three critical improvements for present day and future retinal vision
implants are proposed and discussed: (1) A time profile for the stimulation
current that leads predominantly to transverse stimulation of nerve cells; (2)
auxiliary electric currents for electric field shaping with a time profile
chosen such that these currents have small probability to cause stimulation;
and (3) a local area scanning procedure that results in high pixel density for
image/percept formation (except for losses at the boundary of an electrode
array).
|
[
{
"created": "Fri, 19 Oct 2012 09:07:33 GMT",
"version": "v1"
}
] |
2012-10-22
|
[
[
"Schmid",
"Erich W.",
""
],
[
"Fink",
"Wolfgang",
""
]
] |
Three critical improvements for present day and future retinal vision implants are proposed and discussed: (1) A time profile for the stimulation current that leads predominantly to transverse stimulation of nerve cells; (2) auxiliary electric currents for electric field shaping with a time profile chosen such that these currents have small probability to cause stimulation; and (3) a local area scanning procedure that results in high pixel density for image/percept formation (except for losses at the boundary of an electrode array).
|
2401.00102
|
Phuc Nguyen
|
Phuc Nguyen, Rohit Arora, Elliot D. Hill, Jasper Braun, Alexandra
Morgan, Liza M. Quintana, Gabrielle Mazzoni, Ghee Rye Lee, Rima Arnaout, Ramy
Arnaout
|
$\textit{greylock}$: A Python Package for Measuring The Composition of
Complex Datasets
|
42 pages, many figures. Many thanks to Ralf Bundschuh for help with
the submission process
| null | null | null |
q-bio.QM
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Machine-learning datasets are typically characterized by measuring their size
and class balance. However, there exists a richer and potentially more useful
set of measures, termed diversity measures, that incorporate elements'
frequencies and between-element similarities. Although these have been
available in the R and Julia programming languages for other applications, they
have not been as readily available in Python, which is widely used for machine
learning, and are not easily applied to machine-learning-sized datasets without
special coding considerations. To address these issues, we developed
$\textit{greylock}$, a Python package that calculates diversity measures and is
tailored to large datasets. $\textit{greylock}$ can calculate any of the
frequency-sensitive measures of Hill's D-number framework, and going beyond
Hill, their similarity-sensitive counterparts (Greylock is a mountain).
$\textit{greylock}$ also outputs measures that compare datasets (beta
diversities). We first briefly review the D-number framework, illustrating how
it incorporates elements' frequencies and between-element similarities. We then
describe $\textit{greylock}$'s key features and usage. We end with several
examples - immunomics, metagenomics, computational pathology, and medical
imaging - illustrating $\textit{greylock}$'s applicability across a range of
dataset types and fields.
|
[
{
"created": "Fri, 29 Dec 2023 23:51:48 GMT",
"version": "v1"
}
] |
2024-01-02
|
[
[
"Nguyen",
"Phuc",
""
],
[
"Arora",
"Rohit",
""
],
[
"Hill",
"Elliot D.",
""
],
[
"Braun",
"Jasper",
""
],
[
"Morgan",
"Alexandra",
""
],
[
"Quintana",
"Liza M.",
""
],
[
"Mazzoni",
"Gabrielle",
""
],
[
"Lee",
"Ghee Rye",
""
],
[
"Arnaout",
"Rima",
""
],
[
"Arnaout",
"Ramy",
""
]
] |
Machine-learning datasets are typically characterized by measuring their size and class balance. However, there exists a richer and potentially more useful set of measures, termed diversity measures, that incorporate elements' frequencies and between-element similarities. Although these have been available in the R and Julia programming languages for other applications, they have not been as readily available in Python, which is widely used for machine learning, and are not easily applied to machine-learning-sized datasets without special coding considerations. To address these issues, we developed $\textit{greylock}$, a Python package that calculates diversity measures and is tailored to large datasets. $\textit{greylock}$ can calculate any of the frequency-sensitive measures of Hill's D-number framework, and going beyond Hill, their similarity-sensitive counterparts (Greylock is a mountain). $\textit{greylock}$ also outputs measures that compare datasets (beta diversities). We first briefly review the D-number framework, illustrating how it incorporates elements' frequencies and between-element similarities. We then describe $\textit{greylock}$'s key features and usage. We end with several examples - immunomics, metagenomics, computational pathology, and medical imaging - illustrating $\textit{greylock}$'s applicability across a range of dataset types and fields.
|
2103.09563
|
Werner M\"uller
|
Elham Yousefi and Werner G. M\"uller
|
Impact of the error structure on the design and analysis of enzyme
kinetic models
| null | null | null | null |
q-bio.MN stat.ME
|
http://creativecommons.org/licenses/by/4.0/
|
The statistical analysis of enzyme kinetic reactions usually involves models
of the response functions which are well defined on the basis of
Michaelis-Menten type equations. The error structure however is often without
good reason assumed as additive Gaussian noise. This simple assumption may lead
to undesired properties of the analysis, particularly when simulations are
involved and consequently negative simulated reaction rates may occur. In this
study we investigate the effect of assuming multiplicative lognormal errors
instead. While there is typically little impact on the estimates, the
experimental designs and their efficiencies are decisively affected,
particularly when it comes to model discrimination problems.
|
[
{
"created": "Wed, 17 Mar 2021 10:59:23 GMT",
"version": "v1"
}
] |
2021-03-19
|
[
[
"Yousefi",
"Elham",
""
],
[
"Müller",
"Werner G.",
""
]
] |
The statistical analysis of enzyme kinetic reactions usually involves models of the response functions which are well defined on the basis of Michaelis-Menten type equations. The error structure however is often without good reason assumed as additive Gaussian noise. This simple assumption may lead to undesired properties of the analysis, particularly when simulations are involved and consequently negative simulated reaction rates may occur. In this study we investigate the effect of assuming multiplicative lognormal errors instead. While there is typically little impact on the estimates, the experimental designs and their efficiencies are decisively affected, particularly when it comes to model discrimination problems.
|
1712.00843
|
Marinka Zitnik
|
Monica Agrawal, Marinka Zitnik, Jure Leskovec
|
Large-scale analysis of disease pathways in the human interactome
| null |
Pacific Symposium on Biocomputing 23:111-122(2018)
| null | null |
q-bio.MN cs.LG cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Discovering disease pathways, which can be defined as sets of proteins
associated with a given disease, is an important problem that has the potential
to provide clinically actionable insights for disease diagnosis, prognosis, and
treatment. Computational methods aid the discovery by relying on
protein-protein interaction (PPI) networks. They start with a few known
disease-associated proteins and aim to find the rest of the pathway by
exploring the PPI network around the known disease proteins. However, the
success of such methods has been limited, and failure cases have not been well
understood. Here we study the PPI network structure of 519 disease pathways. We
find that 90% of pathways do not correspond to single well-connected components
in the PPI network. Instead, proteins associated with a single disease tend to
form many separate connected components/regions in the network. We then
evaluate state-of-the-art disease pathway discovery methods and show that their
performance is especially poor on diseases with disconnected pathways. Thus, we
conclude that network connectivity structure alone may not be sufficient for
disease pathway discovery. However, we show that higher-order network
structures, such as small subgraphs of the pathway, provide a promising
direction for the development of new methods.
|
[
{
"created": "Sun, 3 Dec 2017 21:51:07 GMT",
"version": "v1"
}
] |
2017-12-05
|
[
[
"Agrawal",
"Monica",
""
],
[
"Zitnik",
"Marinka",
""
],
[
"Leskovec",
"Jure",
""
]
] |
Discovering disease pathways, which can be defined as sets of proteins associated with a given disease, is an important problem that has the potential to provide clinically actionable insights for disease diagnosis, prognosis, and treatment. Computational methods aid the discovery by relying on protein-protein interaction (PPI) networks. They start with a few known disease-associated proteins and aim to find the rest of the pathway by exploring the PPI network around the known disease proteins. However, the success of such methods has been limited, and failure cases have not been well understood. Here we study the PPI network structure of 519 disease pathways. We find that 90% of pathways do not correspond to single well-connected components in the PPI network. Instead, proteins associated with a single disease tend to form many separate connected components/regions in the network. We then evaluate state-of-the-art disease pathway discovery methods and show that their performance is especially poor on diseases with disconnected pathways. Thus, we conclude that network connectivity structure alone may not be sufficient for disease pathway discovery. However, we show that higher-order network structures, such as small subgraphs of the pathway, provide a promising direction for the development of new methods.
|
1611.08929
|
Md Jahoor Alam
|
Md. Jahoor Alam
|
GnRH induced Phase Synchrony of Coupled Neurons
|
9 pages, 3 figures
| null | null | null |
q-bio.MN q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Gonadotropin-releasing hormone (GnRH) is reported to control mammalian
reproductive processes. GnRH a neurohormone which is pulsatile released into
the pituitary portal blood by hypothalamic GnRH neurons. In the present study,
the phase synchronization among a population of identical neurons subjected to
a pool of coupling molecules GnRH in extracellular medium via mean-field
coupling mechanism is investigated. In the model of populated neurons, GnRH is
considered to be autocrine signaling molecule and is taken to be common to all
neurons to act as synchronizing agent. The rate of synchrony is estimated
qualitatively and quantitatively by measuring phase locking values, time
evolution of the phase differences and recurrence plots. Our numerical results
show a phase transition like behavior separating the synchronized and
desynchronized regimes. We also investigated long range communication or relay
information transfer for one dimensional array of such neurons.
|
[
{
"created": "Sun, 27 Nov 2016 22:43:24 GMT",
"version": "v1"
}
] |
2016-12-06
|
[
[
"Alam",
"Md. Jahoor",
""
]
] |
Gonadotropin-releasing hormone (GnRH) is reported to control mammalian reproductive processes. GnRH a neurohormone which is pulsatile released into the pituitary portal blood by hypothalamic GnRH neurons. In the present study, the phase synchronization among a population of identical neurons subjected to a pool of coupling molecules GnRH in extracellular medium via mean-field coupling mechanism is investigated. In the model of populated neurons, GnRH is considered to be autocrine signaling molecule and is taken to be common to all neurons to act as synchronizing agent. The rate of synchrony is estimated qualitatively and quantitatively by measuring phase locking values, time evolution of the phase differences and recurrence plots. Our numerical results show a phase transition like behavior separating the synchronized and desynchronized regimes. We also investigated long range communication or relay information transfer for one dimensional array of such neurons.
|
2108.05848
|
Ilan Gronau
|
Zehavit Leibovich and Ilan Gronau
|
Eliminating unwanted patterns with minimal interference
|
This research was done as part of Zehavit Leibovich's dissertation
for an M.Sc degree in Computer Science. Relevant code available at
https://github.com/zehavitc/EliminatingDNAPatterns.git
| null | null | null |
q-bio.BM cs.CE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Artificial synthesis of DNA molecules is an essential part of the study of
biological mechanisms. The design of a synthetic DNA molecule usually involves
many objectives. One of the important objectives is to eliminate short sequence
patterns that correspond to binding sites of restriction enzymes or
transcription factors. While many design tools address this problem, no
adequate formal solution exists for the pattern elimination problem. In this
work, we present a formal description of the elimination problem and suggest
efficient algorithms that eliminate unwanted patterns and allow optimization of
other objectives with minimal interference to the desired DNA functionality.
Our approach is flexible, efficient, and straightforward, and therefore can be
easily incorporated in existing DNA design tools, making them considerably more
powerful.
|
[
{
"created": "Tue, 3 Aug 2021 19:51:43 GMT",
"version": "v1"
}
] |
2021-08-13
|
[
[
"Leibovich",
"Zehavit",
""
],
[
"Gronau",
"Ilan",
""
]
] |
Artificial synthesis of DNA molecules is an essential part of the study of biological mechanisms. The design of a synthetic DNA molecule usually involves many objectives. One of the important objectives is to eliminate short sequence patterns that correspond to binding sites of restriction enzymes or transcription factors. While many design tools address this problem, no adequate formal solution exists for the pattern elimination problem. In this work, we present a formal description of the elimination problem and suggest efficient algorithms that eliminate unwanted patterns and allow optimization of other objectives with minimal interference to the desired DNA functionality. Our approach is flexible, efficient, and straightforward, and therefore can be easily incorporated in existing DNA design tools, making them considerably more powerful.
|
2206.13345
|
Weifeng Li
|
Zechen Wang, Liangzhen Zheng, Sheng Wang, Mingzhi Lin, Zhihao Wang,
Adams Wai-Kin Kong, Yuguang Mu, Yanjie Wei, Weifeng Li
|
A fully differentiable ligand pose optimization framework guided by deep
learning and traditional scoring functions
| null |
Brief Bioinform . 2023 Jan 19;24(1):bbac520
|
10.1093/bib/bbac520
| null |
q-bio.QM q-bio.BM
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The machine learning (ML) and deep learning (DL) techniques are widely
recognized to be powerful tools for virtual drug screening. The recently
reported ML- or DL-based scoring functions have shown exciting performance in
predicting protein-ligand binding affinities with fruitful application
prospects. However, the differentiation between highly similar ligand
conformations, including the native binding pose (the global energy minimum
state), remains challenging which could greatly enhance the docking. In this
work, we propose a fully differentiable framework for ligand pose optimization
based on a hybrid scoring function (SF) combined with a multi-layer perceptron
(DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which
combines (1) the root mean square deviation (RMSD) of the docking pose with
respect to the native pose and (2) the AutoDock Vina score, is fully
differentiable thus is capable of optimizing the ligand binding pose to the
energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset,
the DeepRMSD+Vina reaches a success rate of 95.4%, which is by far the best
reported SF to date. Based on this SF, an end-to-end ligand pose optimization
framework was implemented to improve the docking pose quality. We demonstrated
that this method significantly improves the docking success rate (by 15%) in
redocking and crossdocking tasks, revealing the high potentialities of this
framework in drug design and discovery.
|
[
{
"created": "Mon, 27 Jun 2022 14:49:40 GMT",
"version": "v1"
}
] |
2023-07-07
|
[
[
"Wang",
"Zechen",
""
],
[
"Zheng",
"Liangzhen",
""
],
[
"Wang",
"Sheng",
""
],
[
"Lin",
"Mingzhi",
""
],
[
"Wang",
"Zhihao",
""
],
[
"Kong",
"Adams Wai-Kin",
""
],
[
"Mu",
"Yuguang",
""
],
[
"Wei",
"Yanjie",
""
],
[
"Li",
"Weifeng",
""
]
] |
The machine learning (ML) and deep learning (DL) techniques are widely recognized to be powerful tools for virtual drug screening. The recently reported ML- or DL-based scoring functions have shown exciting performance in predicting protein-ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging which could greatly enhance the docking. In this work, we propose a fully differentiable framework for ligand pose optimization based on a hybrid scoring function (SF) combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 95.4%, which is by far the best reported SF to date. Based on this SF, an end-to-end ligand pose optimization framework was implemented to improve the docking pose quality. We demonstrated that this method significantly improves the docking success rate (by 15%) in redocking and crossdocking tasks, revealing the high potentialities of this framework in drug design and discovery.
|
2311.16946
|
Jacob Durrant
|
Mayar Ahmed, Alex M. Maldonado, Jacob D. Durrant
|
From Byte to Bench to Bedside: Molecular Dynamics Simulations and Drug
Discovery
|
15 pages including references, 0 figures
| null | null | null |
q-bio.QM q-bio.BM
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Molecular dynamics (MD) simulations and computer-aided drug design (CADD)
have advanced substantially over the past two decades, thanks to continuous
computer hardware and software improvements. Given these advancements, MD
simulations are poised to become even more powerful tools for investigating the
dynamic interactions between potential small-molecule drugs and their target
proteins, with significant implications for pharmacological research.
|
[
{
"created": "Tue, 28 Nov 2023 16:49:04 GMT",
"version": "v1"
}
] |
2023-11-29
|
[
[
"Ahmed",
"Mayar",
""
],
[
"Maldonado",
"Alex M.",
""
],
[
"Durrant",
"Jacob D.",
""
]
] |
Molecular dynamics (MD) simulations and computer-aided drug design (CADD) have advanced substantially over the past two decades, thanks to continuous computer hardware and software improvements. Given these advancements, MD simulations are poised to become even more powerful tools for investigating the dynamic interactions between potential small-molecule drugs and their target proteins, with significant implications for pharmacological research.
|
1806.08454
|
Dalit Engelhardt
|
Dalit Engelhardt and Eugene I. Shakhnovich
|
Mutation rate variability as a driving force in adaptive evolution
| null |
Phys. Rev. E 99, 022424 (2019)
|
10.1103/PhysRevE.99.022424
| null |
q-bio.PE physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mutation rate is a key determinant of the pace as well as outcome of
evolution, and variability in this rate has been shown in different scenarios
to play a key role in evolutionary adaptation and resistance evolution under
stress caused by selective pressure. Here we investigate the dynamics of
resistance fixation in a bacterial population with variable mutation rates and
show that evolutionary outcomes are most sensitive to mutation rate variations
when the population is subject to environmental and demographic conditions that
suppress the evolutionary advantage of high-fitness subpopulations. By directly
mapping a biophysical fitness function to the system-level dynamics of the
population we show that both low and very high, but not intermediate, levels of
stress in the form of an antibiotic result in a disproportionate effect of
hypermutation on resistance fixation. We demonstrate how this behavior is
directly tied to the extent of genetic hitchhiking in the system, the
propagation of high-mutation rate cells through association with high-fitness
mutations. Our results indicate a substantial role for mutation rate
flexibility in the evolution of antibiotic resistance under conditions that
present a weak advantage over wildtype to resistant cells.
|
[
{
"created": "Thu, 21 Jun 2018 23:30:17 GMT",
"version": "v1"
},
{
"created": "Sun, 3 Feb 2019 17:30:17 GMT",
"version": "v2"
}
] |
2019-03-01
|
[
[
"Engelhardt",
"Dalit",
""
],
[
"Shakhnovich",
"Eugene I.",
""
]
] |
Mutation rate is a key determinant of the pace as well as outcome of evolution, and variability in this rate has been shown in different scenarios to play a key role in evolutionary adaptation and resistance evolution under stress caused by selective pressure. Here we investigate the dynamics of resistance fixation in a bacterial population with variable mutation rates and show that evolutionary outcomes are most sensitive to mutation rate variations when the population is subject to environmental and demographic conditions that suppress the evolutionary advantage of high-fitness subpopulations. By directly mapping a biophysical fitness function to the system-level dynamics of the population we show that both low and very high, but not intermediate, levels of stress in the form of an antibiotic result in a disproportionate effect of hypermutation on resistance fixation. We demonstrate how this behavior is directly tied to the extent of genetic hitchhiking in the system, the propagation of high-mutation rate cells through association with high-fitness mutations. Our results indicate a substantial role for mutation rate flexibility in the evolution of antibiotic resistance under conditions that present a weak advantage over wildtype to resistant cells.
|
2203.10867
|
Emilio N.M. Cirillo
|
Claudio Durastanti and Emilio N.M. Cirillo and Ilaria De Benedictis
and Mario Ledda and Antonio Sciortino and Antonella Lisi and Annalisa
Convertino and Valentina Mussi
|
Statistical classification for Raman spectra of tumoral genomic DNA
| null | null | null | null |
q-bio.QM stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We exploit Surface-Enhanced Raman Scattering (SERS) to investigate aqueous
droplets of genomic DNA deposited onto silver-coated silicon nanowires and we
show that it is possible to efficiently discriminate between spectra of tumoral
and healthy cells. To assess the robustness of the proposed technique, we
develop two different statistical approaches, one based on the Principal
Component Analysis of spectral data and one based on the computation of the
$\ell^2$ distance between spectra. Both methods prove to be highly efficient
and we test their accuracy via the so-called Cohen's $\kappa$ statistics. We
show that the synergistic combination of the SERS spectroscopy and the
statistical analysis methods leads to efficient and fast cancer diagnostic
applications allowing a rapid and unexpansive discrimination between healthy
and tumoral genomic DNA alternative to the more complex and expensive DNA
sequencing.
|
[
{
"created": "Mon, 21 Mar 2022 10:41:07 GMT",
"version": "v1"
}
] |
2022-03-22
|
[
[
"Durastanti",
"Claudio",
""
],
[
"Cirillo",
"Emilio N. M.",
""
],
[
"De Benedictis",
"Ilaria",
""
],
[
"Ledda",
"Mario",
""
],
[
"Sciortino",
"Antonio",
""
],
[
"Lisi",
"Antonella",
""
],
[
"Convertino",
"Annalisa",
""
],
[
"Mussi",
"Valentina",
""
]
] |
We exploit Surface-Enhanced Raman Scattering (SERS) to investigate aqueous droplets of genomic DNA deposited onto silver-coated silicon nanowires and we show that it is possible to efficiently discriminate between spectra of tumoral and healthy cells. To assess the robustness of the proposed technique, we develop two different statistical approaches, one based on the Principal Component Analysis of spectral data and one based on the computation of the $\ell^2$ distance between spectra. Both methods prove to be highly efficient and we test their accuracy via the so-called Cohen's $\kappa$ statistics. We show that the synergistic combination of the SERS spectroscopy and the statistical analysis methods leads to efficient and fast cancer diagnostic applications allowing a rapid and unexpansive discrimination between healthy and tumoral genomic DNA alternative to the more complex and expensive DNA sequencing.
|
2301.03408
|
Christine Ahrends
|
Christine Ahrends (1), Diego Vidaurre (1 and 2) ((1) Center of
Functionally Integrative Neuroscience, Department of Clinical Medicine,
Aarhus University, Denmark, (2) Department of Psychiatry, University of
Oxford, United Kingdom)
|
Dynamic Functional Connectivity
| null | null | null | null |
q-bio.NC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Most generally, dynamic functional connectivity (FC) refers to the
non-instantaneous couplings across timeseries from a set of brain areas, here
as measured by fMRI. This is in contrast to static FC, which is defined as
purely instantaneous relations. In this chapter, we provide a hands-on
description of a non-exhaustive selection of different methods used to estimate
dynamic FC (such as sliding windows, clustering approaches, Hidden Markov
Models, and multivariate autoregressive models), and we explain, using
practical examples, how data should be prepared for dynamic FC analyses and how
models of dynamic FC can be evaluated. We also discuss current developments in
the dynamic FC research field, including challenges of reliability and
reproducibility, and perspectives of using dynamic FC for prediction.
|
[
{
"created": "Mon, 9 Jan 2023 15:04:12 GMT",
"version": "v1"
}
] |
2023-01-10
|
[
[
"Ahrends",
"Christine",
"",
"1 and 2"
],
[
"Vidaurre",
"Diego",
"",
"1 and 2"
]
] |
Most generally, dynamic functional connectivity (FC) refers to the non-instantaneous couplings across timeseries from a set of brain areas, here as measured by fMRI. This is in contrast to static FC, which is defined as purely instantaneous relations. In this chapter, we provide a hands-on description of a non-exhaustive selection of different methods used to estimate dynamic FC (such as sliding windows, clustering approaches, Hidden Markov Models, and multivariate autoregressive models), and we explain, using practical examples, how data should be prepared for dynamic FC analyses and how models of dynamic FC can be evaluated. We also discuss current developments in the dynamic FC research field, including challenges of reliability and reproducibility, and perspectives of using dynamic FC for prediction.
|
1806.06412
|
Yuri A. Dabaghian
|
Luca Perotti, Justin DeVito, Daniel Bessis, Yuri Dabaghian
|
Discrete structure of the brain rhythms
|
17 pages, 9 figures
| null | null | null |
q-bio.QM q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Neuronal activity in the brain generates synchronous oscillations of the
Local Field Potential (LFP). The traditional analyses of the LFPs are based on
decomposing the signal into simpler components, such as sinusoidal harmonics.
However, a common drawback of such methods is that the decomposition primitives
are usually presumed from the onset, which may bias our understanding of the
signal's structure. Here, we introduce an alternative approach that allows an
impartial, high resolution, hands-off decomposition of the brain waves into a
small number of discrete, frequency-modulated oscillatory processes, which we
call oscillons. In particular, we demonstrate that mouse hippocampal LFP
contain a single oscillon that occupies the $\theta$-frequency band and a
couple of $\gamma$-oscillons that correspond, respectively, to slow and fast
$\gamma$-waves. Since the oscillons were identified empirically, they may
represent the actual, physical structure of synchronous oscillations in
neuronal ensembles, whereas Fourier-defined "brain waves" are nothing but
poorly resolved oscillons.
|
[
{
"created": "Sun, 17 Jun 2018 16:42:50 GMT",
"version": "v1"
}
] |
2018-06-19
|
[
[
"Perotti",
"Luca",
""
],
[
"DeVito",
"Justin",
""
],
[
"Bessis",
"Daniel",
""
],
[
"Dabaghian",
"Yuri",
""
]
] |
Neuronal activity in the brain generates synchronous oscillations of the Local Field Potential (LFP). The traditional analyses of the LFPs are based on decomposing the signal into simpler components, such as sinusoidal harmonics. However, a common drawback of such methods is that the decomposition primitives are usually presumed from the onset, which may bias our understanding of the signal's structure. Here, we introduce an alternative approach that allows an impartial, high resolution, hands-off decomposition of the brain waves into a small number of discrete, frequency-modulated oscillatory processes, which we call oscillons. In particular, we demonstrate that mouse hippocampal LFP contain a single oscillon that occupies the $\theta$-frequency band and a couple of $\gamma$-oscillons that correspond, respectively, to slow and fast $\gamma$-waves. Since the oscillons were identified empirically, they may represent the actual, physical structure of synchronous oscillations in neuronal ensembles, whereas Fourier-defined "brain waves" are nothing but poorly resolved oscillons.
|
1503.05440
|
Lionel Roques
|
L. Roques, E. Walker, P. Franck, S. Soubeyrand, E. K. Klein
|
Using genetic data to estimate diffusion rates in heterogeneous
landscapes
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Having a precise knowledge of the dispersal ability of a population in a
heterogeneous environment is of critical importance in agroecology and
conservation biology as it can provide management tools to limit the effects of
pests or to increase the survival of endangered species. In this paper, we
propose a mechanistic-statistical method to estimate space-dependent diffusion
parameters of spatially-explicit models based on stochastic differential
equations, using genetic data. Dividing the total population into
subpopulations corresponding to different habitat patches with known allele
frequencies, the expected proportions of individuals from each subpopulation at
each position is computed by solving a system of reaction-diffusion equations.
Modelling the capture and genotyping of the individuals with a statistical
approach, we derive a numerically tractable formula for the likelihood function
associated with the diffusion parameters.
In a simulated environment made of three types of regions, each associated
with a different diffusion coefficient, we successfully estimate the diffusion
parameters with a maximum-likelihood approach. Although higher genetic
differentiation among subpopulations leads to more accurate estimations, once a
certain level of differentiation has been reached, the finite size of the
genotyped population becomes the limiting factor for accurate estimation.
|
[
{
"created": "Wed, 18 Mar 2015 15:03:16 GMT",
"version": "v1"
}
] |
2015-03-19
|
[
[
"Roques",
"L.",
""
],
[
"Walker",
"E.",
""
],
[
"Franck",
"P.",
""
],
[
"Soubeyrand",
"S.",
""
],
[
"Klein",
"E. K.",
""
]
] |
Having a precise knowledge of the dispersal ability of a population in a heterogeneous environment is of critical importance in agroecology and conservation biology as it can provide management tools to limit the effects of pests or to increase the survival of endangered species. In this paper, we propose a mechanistic-statistical method to estimate space-dependent diffusion parameters of spatially-explicit models based on stochastic differential equations, using genetic data. Dividing the total population into subpopulations corresponding to different habitat patches with known allele frequencies, the expected proportions of individuals from each subpopulation at each position is computed by solving a system of reaction-diffusion equations. Modelling the capture and genotyping of the individuals with a statistical approach, we derive a numerically tractable formula for the likelihood function associated with the diffusion parameters. In a simulated environment made of three types of regions, each associated with a different diffusion coefficient, we successfully estimate the diffusion parameters with a maximum-likelihood approach. Although higher genetic differentiation among subpopulations leads to more accurate estimations, once a certain level of differentiation has been reached, the finite size of the genotyped population becomes the limiting factor for accurate estimation.
|
1705.10854
|
Larissa Albantakis
|
Larissa Albantakis
|
A Tale of Two Animats: What does it take to have goals?
|
This article is a contribution to the FQXi 2016-2017 essay contest
"Wandering Towards a Goal"
| null | null | null |
q-bio.NC cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
What does it take for a system, biological or not, to have goals? Here, this
question is approached in the context of in silico artificial evolution. By
examining the informational and causal properties of artificial organisms
('animats') controlled by small, adaptive neural networks (Markov Brains), this
essay discusses necessary requirements for intrinsic information, autonomy, and
meaning. The focus lies on comparing two types of Markov Brains that evolved in
the same simple environment: one with purely feedforward connections between
its elements, the other with an integrated set of elements that causally
constrain each other. While both types of brains 'process' information about
their environment and are equally fit, only the integrated one forms a causally
autonomous entity above a background of external influences. This suggests that
to assess whether goals are meaningful for a system itself, it is important to
understand what the system is, rather than what it does.
|
[
{
"created": "Tue, 30 May 2017 20:19:17 GMT",
"version": "v1"
}
] |
2017-06-01
|
[
[
"Albantakis",
"Larissa",
""
]
] |
What does it take for a system, biological or not, to have goals? Here, this question is approached in the context of in silico artificial evolution. By examining the informational and causal properties of artificial organisms ('animats') controlled by small, adaptive neural networks (Markov Brains), this essay discusses necessary requirements for intrinsic information, autonomy, and meaning. The focus lies on comparing two types of Markov Brains that evolved in the same simple environment: one with purely feedforward connections between its elements, the other with an integrated set of elements that causally constrain each other. While both types of brains 'process' information about their environment and are equally fit, only the integrated one forms a causally autonomous entity above a background of external influences. This suggests that to assess whether goals are meaningful for a system itself, it is important to understand what the system is, rather than what it does.
|
1611.01037
|
Valery Kirzhner
|
Valery Kirzhner, Zeev Volkovich, Renata Avros and Katerina Korenblat
|
Analysis of Metagenome Composition by the Method of Random Primers
|
18 pages, 4 figures
| null | null | null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Metagenome, a mixture of different genomes (as a rule, bacterial), represents
a pattern, and the analysis of its composition is, currently, one of the
challenging problems of bioinformatics. In the present study, the possibility
of evaluating metagenome composition by DNA-marker methods is investigated.
These methods are based on using primers, short nucleic acid fragments. Each
primer picks out of the tested genome the fragment set specific just for this
genome, which is called its spectrum (for the given primer) and is used for
identifying the genome. The DNA-marker method, applied to a metagenome, also
gives its spectrum, which, obviously, represents the union of the spectra of
all genomes belonging to the metagenome. Thus each primer provides a projection
of the genomes and of the metagenome onto the corresponding spectra set. Here
we propose to apply the random projection (random primer) approach for
analyzing metagenome composition and present some estimates of the method
effectiveness for the case of Random Amplified Polymorphic DNA (RAPD)
technology.
|
[
{
"created": "Wed, 2 Nov 2016 18:43:14 GMT",
"version": "v1"
}
] |
2016-11-04
|
[
[
"Kirzhner",
"Valery",
""
],
[
"Volkovich",
"Zeev",
""
],
[
"Avros",
"Renata",
""
],
[
"Korenblat",
"Katerina",
""
]
] |
Metagenome, a mixture of different genomes (as a rule, bacterial), represents a pattern, and the analysis of its composition is, currently, one of the challenging problems of bioinformatics. In the present study, the possibility of evaluating metagenome composition by DNA-marker methods is investigated. These methods are based on using primers, short nucleic acid fragments. Each primer picks out of the tested genome the fragment set specific just for this genome, which is called its spectrum (for the given primer) and is used for identifying the genome. The DNA-marker method, applied to a metagenome, also gives its spectrum, which, obviously, represents the union of the spectra of all genomes belonging to the metagenome. Thus each primer provides a projection of the genomes and of the metagenome onto the corresponding spectra set. Here we propose to apply the random projection (random primer) approach for analyzing metagenome composition and present some estimates of the method effectiveness for the case of Random Amplified Polymorphic DNA (RAPD) technology.
|
1603.01789
|
Pu Tian
|
Shiyang Long and Pu Tian
|
Nonlinear backbone torsional pair correlations in proteins
|
25 pages, 8 figures
|
Scientific Report, 6:34481, 2016
|
10.1038/srep34481
| null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Protein allostery requires dynamical structural correlations. Physical origin
of which, however, remain elusive despite intensive studies during last two
decades. Based on analysis of molecular dynamics (MD) simulation trajectories
for ten proteins with different sizes and folds, we found that nonlinear
backbone torsional pair (BTP) correlations, which are spatially more
long-ranged and are mainly executed by loop residues, exist extensively in most
analyzed proteins. Examination of torsional motion for correlated BTPs
suggested that aharmonic torsional state transitions are essential for such
non-linear correlations, which correspondingly occur on widely different and
relatively longer time scales. In contrast, BTP correlations between backbone
torsions in stable $\alpha$ helices and $\beta$ strands are mainly linear and
spatially more short-ranged, and are more likely to associate with intra-well
torsional dynamics. Further analysis revealed that the direct cause of
non-linear contributions are heterogeneous, and in extreme cases canceling,
linear correlations associated with different torsional states of participating
torsions. Therefore, torsional state transitions of participating torsions for
a correlated BTP are only necessary but not sufficient condition for
significant non-linear contributions. These findings implicate a general search
strategy for novel allosteric modulation of protein activities. Meanwhile, it
was suggested that ensemble averaged correlation calculation and static contact
network analysis, while insightful, are not sufficient to elucidate mechanisms
underlying allosteric signal transmission in general, dynamical and time scale
resolved analysis are essential.
|
[
{
"created": "Sun, 6 Mar 2016 05:41:23 GMT",
"version": "v1"
},
{
"created": "Fri, 6 May 2016 02:55:23 GMT",
"version": "v2"
}
] |
2017-02-23
|
[
[
"Long",
"Shiyang",
""
],
[
"Tian",
"Pu",
""
]
] |
Protein allostery requires dynamical structural correlations. Physical origin of which, however, remain elusive despite intensive studies during last two decades. Based on analysis of molecular dynamics (MD) simulation trajectories for ten proteins with different sizes and folds, we found that nonlinear backbone torsional pair (BTP) correlations, which are spatially more long-ranged and are mainly executed by loop residues, exist extensively in most analyzed proteins. Examination of torsional motion for correlated BTPs suggested that aharmonic torsional state transitions are essential for such non-linear correlations, which correspondingly occur on widely different and relatively longer time scales. In contrast, BTP correlations between backbone torsions in stable $\alpha$ helices and $\beta$ strands are mainly linear and spatially more short-ranged, and are more likely to associate with intra-well torsional dynamics. Further analysis revealed that the direct cause of non-linear contributions are heterogeneous, and in extreme cases canceling, linear correlations associated with different torsional states of participating torsions. Therefore, torsional state transitions of participating torsions for a correlated BTP are only necessary but not sufficient condition for significant non-linear contributions. These findings implicate a general search strategy for novel allosteric modulation of protein activities. Meanwhile, it was suggested that ensemble averaged correlation calculation and static contact network analysis, while insightful, are not sufficient to elucidate mechanisms underlying allosteric signal transmission in general, dynamical and time scale resolved analysis are essential.
|
1803.02136
|
Krzysztof Bartoszek
|
Krzysztof Bartoszek
|
Limit distribution of the quartet balance index for Aldous's b>=0-model
| null |
Applicationes Mathematicae 47:29-44, 2020
|
10.4064/am2385-6-2019
| null |
q-bio.PE math.PR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper builds up on T. Martinez-Coronado, A. Mir, F. Rossello and G.
Valiente's work "A balance index for phylogenetic trees based on quartets",
introducing a new balance index for trees. We show here that this balance
index, in the case of Aldous's b>=0-model, convergences weakly to a
distribution that can be characterized as the fixed point of a contraction
operator on a class of distributions.
|
[
{
"created": "Tue, 6 Mar 2018 12:17:53 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Nov 2018 19:00:11 GMT",
"version": "v2"
},
{
"created": "Fri, 30 Aug 2019 06:26:37 GMT",
"version": "v3"
}
] |
2020-11-23
|
[
[
"Bartoszek",
"Krzysztof",
""
]
] |
This paper builds up on T. Martinez-Coronado, A. Mir, F. Rossello and G. Valiente's work "A balance index for phylogenetic trees based on quartets", introducing a new balance index for trees. We show here that this balance index, in the case of Aldous's b>=0-model, convergences weakly to a distribution that can be characterized as the fixed point of a contraction operator on a class of distributions.
|
2405.06851
|
Francesca Mignacco
|
Francesca Mignacco, Chi-Ning Chou, SueYeon Chung
|
Nonlinear classification of neural manifolds with contextual information
|
5 pages, 5 figures
| null | null | null |
q-bio.NC cond-mat.dis-nn cond-mat.stat-mech cs.NE stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Understanding how neural systems efficiently process information through
distributed representations is a fundamental challenge at the interface of
neuroscience and machine learning. Recent approaches analyze the statistical
and geometrical attributes of neural representations as population-level
mechanistic descriptors of task implementation. In particular, manifold
capacity has emerged as a promising framework linking population geometry to
the separability of neural manifolds. However, this metric has been limited to
linear readouts. Here, we propose a theoretical framework that overcomes this
limitation by leveraging contextual input information. We derive an exact
formula for the context-dependent capacity that depends on manifold geometry
and context correlations, and validate it on synthetic and real data. Our
framework's increased expressivity captures representation untanglement in deep
networks at early stages of the layer hierarchy, previously inaccessible to
analysis. As context-dependent nonlinearity is ubiquitous in neural systems,
our data-driven and theoretically grounded approach promises to elucidate
context-dependent computation across scales, datasets, and models.
|
[
{
"created": "Fri, 10 May 2024 23:37:31 GMT",
"version": "v1"
}
] |
2024-05-14
|
[
[
"Mignacco",
"Francesca",
""
],
[
"Chou",
"Chi-Ning",
""
],
[
"Chung",
"SueYeon",
""
]
] |
Understanding how neural systems efficiently process information through distributed representations is a fundamental challenge at the interface of neuroscience and machine learning. Recent approaches analyze the statistical and geometrical attributes of neural representations as population-level mechanistic descriptors of task implementation. In particular, manifold capacity has emerged as a promising framework linking population geometry to the separability of neural manifolds. However, this metric has been limited to linear readouts. Here, we propose a theoretical framework that overcomes this limitation by leveraging contextual input information. We derive an exact formula for the context-dependent capacity that depends on manifold geometry and context correlations, and validate it on synthetic and real data. Our framework's increased expressivity captures representation untanglement in deep networks at early stages of the layer hierarchy, previously inaccessible to analysis. As context-dependent nonlinearity is ubiquitous in neural systems, our data-driven and theoretically grounded approach promises to elucidate context-dependent computation across scales, datasets, and models.
|
1509.06863
|
Youdong Mao
|
Zhou Yu, Wei Li Wang, Luis R. Castillo-Menendez, Joseph Sodroski,
Youdong Mao
|
On the parameters affecting dual-target-function evaluation of
single-particle selection from cryo-electron micrographs
|
62 pages, 11 figures. arXiv admin note: text overlap with
arXiv:1309.2618
|
BMC Bioinformatics 2019; 20:169
|
10.1186/s12859-019-2714-8
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the analysis of frozen hydrated biomolecules by single-particle
cryo-electron microscopy, template-based particle picking by a target function
called fast local correlation (FLC) allows a large number of particle images to
be automatically picked from micrographs. A second, independent target function
based on maximum likelihood (ML) can be used to align the images and verify the
presence of signal in the picked particles. Although the paradigm of this
dual-target-function (DTF) evaluation of single-particle selection has been
practiced in recent years, it remains unclear how the performance of this DTF
approach is affected by the signal-to-noise ratio of the images and by the
choice of references for FLC and ML. Here we examine this problem through a
systematic study of simulated data, followed by experimental substantiation. We
quantitatively pinpoint the critical signal-to-noise ratio (SNR), at which the
DTF approach starts losing its ability to select and verify particles from
cryo-EM micrographs. A Gaussian model is shown to be as effective in picking
particles as a single projection view of the imaged molecule in the tested
cases. For both simulated micrographs and real cryo-EM data of the 173-kDa
glucose isomerase complex, we found that the use of a Gaussian model to
initialize the target functions suppressed the detrimental effect of reference
bias in template-based particle selection. Given a sufficient signal-to-noise
ratio in the images and the appropriate choice of references, the DTF approach
can expedite the automated assembly of single-particle data sets.
|
[
{
"created": "Wed, 23 Sep 2015 07:13:28 GMT",
"version": "v1"
}
] |
2019-04-16
|
[
[
"Yu",
"Zhou",
""
],
[
"Wang",
"Wei Li",
""
],
[
"Castillo-Menendez",
"Luis R.",
""
],
[
"Sodroski",
"Joseph",
""
],
[
"Mao",
"Youdong",
""
]
] |
In the analysis of frozen hydrated biomolecules by single-particle cryo-electron microscopy, template-based particle picking by a target function called fast local correlation (FLC) allows a large number of particle images to be automatically picked from micrographs. A second, independent target function based on maximum likelihood (ML) can be used to align the images and verify the presence of signal in the picked particles. Although the paradigm of this dual-target-function (DTF) evaluation of single-particle selection has been practiced in recent years, it remains unclear how the performance of this DTF approach is affected by the signal-to-noise ratio of the images and by the choice of references for FLC and ML. Here we examine this problem through a systematic study of simulated data, followed by experimental substantiation. We quantitatively pinpoint the critical signal-to-noise ratio (SNR), at which the DTF approach starts losing its ability to select and verify particles from cryo-EM micrographs. A Gaussian model is shown to be as effective in picking particles as a single projection view of the imaged molecule in the tested cases. For both simulated micrographs and real cryo-EM data of the 173-kDa glucose isomerase complex, we found that the use of a Gaussian model to initialize the target functions suppressed the detrimental effect of reference bias in template-based particle selection. Given a sufficient signal-to-noise ratio in the images and the appropriate choice of references, the DTF approach can expedite the automated assembly of single-particle data sets.
|
1206.0973
|
Uwe C. T\"auber
|
Ulrich Dobramysl and Uwe C. Tauber (Virginia Tech)
|
Environmental vs. demographic variability in two-species predator-prey
models
|
5 pages, 4 figures included; to appear in Phys. Rev. Lett. (2013)
|
Phys. Rev. Lett. 110 (2013) 048105
|
10.1103/PhysRevLett.110.048105
| null |
q-bio.PE cond-mat.stat-mech
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We investigate the competing effects and relative importance of intrinsic
demographic and environmental variability on the evolutionary dynamics of a
stochastic two-species Lotka-Volterra model by means of Monte Carlo simulations
on a two-dimensional lattice. Individuals are assigned inheritable predation
efficiencies; quenched randomness in the spatially varying reaction rates
serves as environmental noise. We find that environmental variability enhances
the population densities of both predators and prey while demographic
variability leads to essentially neutral optimization.
|
[
{
"created": "Tue, 5 Jun 2012 15:59:50 GMT",
"version": "v1"
},
{
"created": "Fri, 28 Dec 2012 17:43:28 GMT",
"version": "v2"
}
] |
2013-01-28
|
[
[
"Dobramysl",
"Ulrich",
"",
"Virginia Tech"
],
[
"Tauber",
"Uwe C.",
"",
"Virginia Tech"
]
] |
We investigate the competing effects and relative importance of intrinsic demographic and environmental variability on the evolutionary dynamics of a stochastic two-species Lotka-Volterra model by means of Monte Carlo simulations on a two-dimensional lattice. Individuals are assigned inheritable predation efficiencies; quenched randomness in the spatially varying reaction rates serves as environmental noise. We find that environmental variability enhances the population densities of both predators and prey while demographic variability leads to essentially neutral optimization.
|
1605.03553
|
Kieran Fox
|
Kieran C.R. Fox, Yoona Kang, Michael Lifshitz, Kalina Christoff
|
Increasing cognitive-emotional flexibility with meditation and hypnosis:
The cognitive neuroscience of de-automatization
| null | null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Meditation and hypnosis both aim to facilitate cognitive-emotional
flexibility, i.e., the "de-automatization" of thought and behavior. However,
little research or theory has addressed how internal thought patterns might
change after such interventions, even though alterations in the internal flow
of consciousness may precede externally observable changes in behavior. This
chapter outlines three mechanisms by which meditation or hypnosis might alter
or reduce automatic associations and elaborations of spontaneous thought: by an
overall reduction of the chaining of thoughts into an associative stream; by
de-automatizing and diversifying the content of thought chains (i.e.,
increasing thought flexibility or variety); and, finally, by re-automatizing
chains of thought along desired or valued paths (i.e., forming new, voluntarily
chosen mental habits). The authors discuss behavioral and cognitive
neuroscientific evidence demonstrating the influence of hypnosis and meditation
on internal cognition and highlight the putative neurobiological basis, as well
as potential benefits, of these forms of de-automatization.
|
[
{
"created": "Wed, 11 May 2016 19:06:35 GMT",
"version": "v1"
}
] |
2016-05-12
|
[
[
"Fox",
"Kieran C. R.",
""
],
[
"Kang",
"Yoona",
""
],
[
"Lifshitz",
"Michael",
""
],
[
"Christoff",
"Kalina",
""
]
] |
Meditation and hypnosis both aim to facilitate cognitive-emotional flexibility, i.e., the "de-automatization" of thought and behavior. However, little research or theory has addressed how internal thought patterns might change after such interventions, even though alterations in the internal flow of consciousness may precede externally observable changes in behavior. This chapter outlines three mechanisms by which meditation or hypnosis might alter or reduce automatic associations and elaborations of spontaneous thought: by an overall reduction of the chaining of thoughts into an associative stream; by de-automatizing and diversifying the content of thought chains (i.e., increasing thought flexibility or variety); and, finally, by re-automatizing chains of thought along desired or valued paths (i.e., forming new, voluntarily chosen mental habits). The authors discuss behavioral and cognitive neuroscientific evidence demonstrating the influence of hypnosis and meditation on internal cognition and highlight the putative neurobiological basis, as well as potential benefits, of these forms of de-automatization.
|
2210.02183
|
Fabiano L. Ribeiro
|
William Roberto Luiz S. Pereira and Fabiano L. Ribeiro
|
The metabolic origins of big size in aquatic mammals
| null | null | null | null |
q-bio.PE q-bio.QM
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The group of large aquatic mammals has representatives being the largest
living beings on earth, surpassing the weight and size of dinosaurs. In this
paper, we present some empirical evidence and a mathematical model to argue
that fat accumulation in marine mammals triggers a series of metabolic events
that result in these animals' increased size. Our study starts by analysing 43
ontogenetic trajectories of species of different types and sizes. For instance,
the analyses include organisms with asymptotic mass from 27g (Taiwan field
mouse) to $2.10^{7}$g (grey whale). The available data allows us to determine
all available species' ontogenetic parameters (catabolism and anabolism
constant, scaling exponent and asymptotic mass). The analyses of those data
show a minimisation of catabolism and scaling exponent in marine mammals
compared to other species analysed. We present a possible explanation for this,
arguing that the large proportion of adipose tissue in these animals can cause
this minimisation. That is because adipocytes have different scaling properties
in comparison to non-adipose (typical) cells, expressed in reduced energetic
demand and lower metabolism. The conclusion is that when we have an animal with
a relatively large amount of adipose tissue, as is the case of aquatic mammals,
the cellular metabolic rate decreases compared to other animals with the same
mass but with proportionally smaller fat tissue. A final consequence of this
cause-effect process is the increase of the asymptotic mass of these mammals.
|
[
{
"created": "Tue, 4 Oct 2022 17:32:33 GMT",
"version": "v1"
}
] |
2022-10-06
|
[
[
"Pereira",
"William Roberto Luiz S.",
""
],
[
"Ribeiro",
"Fabiano L.",
""
]
] |
The group of large aquatic mammals has representatives being the largest living beings on earth, surpassing the weight and size of dinosaurs. In this paper, we present some empirical evidence and a mathematical model to argue that fat accumulation in marine mammals triggers a series of metabolic events that result in these animals' increased size. Our study starts by analysing 43 ontogenetic trajectories of species of different types and sizes. For instance, the analyses include organisms with asymptotic mass from 27g (Taiwan field mouse) to $2.10^{7}$g (grey whale). The available data allows us to determine all available species' ontogenetic parameters (catabolism and anabolism constant, scaling exponent and asymptotic mass). The analyses of those data show a minimisation of catabolism and scaling exponent in marine mammals compared to other species analysed. We present a possible explanation for this, arguing that the large proportion of adipose tissue in these animals can cause this minimisation. That is because adipocytes have different scaling properties in comparison to non-adipose (typical) cells, expressed in reduced energetic demand and lower metabolism. The conclusion is that when we have an animal with a relatively large amount of adipose tissue, as is the case of aquatic mammals, the cellular metabolic rate decreases compared to other animals with the same mass but with proportionally smaller fat tissue. A final consequence of this cause-effect process is the increase of the asymptotic mass of these mammals.
|
0710.1622
|
Peter Csermely
|
Robin Palotai, Mate S. Szalay, Peter Csermely
|
Chaperones as integrators of cellular networks: Changes of cellular
integrity in stress and diseases
|
13 pages, 3 figures, 1 glossary
|
IUBMB Life (2008) 60, 10-15
|
10.1002/iub.8
| null |
q-bio.MN
| null |
Cellular networks undergo rearrangements during stress and diseases. In
un-stressed state the yeast protein-protein interaction network (interactome)
is highly compact, and the centrally organized modules have a large overlap.
During stress several original modules became more separated, and a number of
novel modules also appear. A few basic functions, such as the proteasome
preserve their central position. However, several functions with high energy
demand, such the cell-cycle regulation loose their original centrality during
stress. A number of key stress-dependent protein complexes, such as the
disaggregation-specific chaperone, Hsp104, gain centrality in the stressed
yeast interactome. Molecular chaperones, heat shock, or stress proteins form
complex interaction networks (the chaperome) with each other and their
partners. Here we show that the human chaperome recovers the segregation of
protein synthesis-coupled and stress-related chaperones observed in yeast
recently. Examination of yeast and human interactomes shows that (1) chaperones
are inter-modular integrators of protein-protein interaction networks, which
(2) often bridge hubs and (3) are favorite candidates for extensive
phosphorylation. Moreover, chaperones (4) become more central in the
organization of the isolated modules of the stressed yeast protein-protein
interaction network, which highlights their importance in the de-coupling and
re-coupling of network modules during and after stress. Chaperone-mediated
evolvability of cellular networks may play a key role in cellular adaptation
during stress and various polygenic and chronic diseases, such as cancer,
diabetes or neurodegeneration.
|
[
{
"created": "Mon, 8 Oct 2007 19:32:35 GMT",
"version": "v1"
},
{
"created": "Sat, 23 Feb 2008 20:16:18 GMT",
"version": "v2"
}
] |
2008-02-23
|
[
[
"Palotai",
"Robin",
""
],
[
"Szalay",
"Mate S.",
""
],
[
"Csermely",
"Peter",
""
]
] |
Cellular networks undergo rearrangements during stress and diseases. In un-stressed state the yeast protein-protein interaction network (interactome) is highly compact, and the centrally organized modules have a large overlap. During stress several original modules became more separated, and a number of novel modules also appear. A few basic functions, such as the proteasome preserve their central position. However, several functions with high energy demand, such the cell-cycle regulation loose their original centrality during stress. A number of key stress-dependent protein complexes, such as the disaggregation-specific chaperone, Hsp104, gain centrality in the stressed yeast interactome. Molecular chaperones, heat shock, or stress proteins form complex interaction networks (the chaperome) with each other and their partners. Here we show that the human chaperome recovers the segregation of protein synthesis-coupled and stress-related chaperones observed in yeast recently. Examination of yeast and human interactomes shows that (1) chaperones are inter-modular integrators of protein-protein interaction networks, which (2) often bridge hubs and (3) are favorite candidates for extensive phosphorylation. Moreover, chaperones (4) become more central in the organization of the isolated modules of the stressed yeast protein-protein interaction network, which highlights their importance in the de-coupling and re-coupling of network modules during and after stress. Chaperone-mediated evolvability of cellular networks may play a key role in cellular adaptation during stress and various polygenic and chronic diseases, such as cancer, diabetes or neurodegeneration.
|
1607.05398
|
Ross McVinish
|
R.J.G. Lester and R. McVinish
|
What causes the increase in aggregation as a parasite moves up a food
chain?
|
This is a preprint. The definitive version has been published under
the title "Does moving up a food chain increase aggregation in parasites?"
|
Journal of the Royal Society Interface, 13 (2016) 20160102
|
10.1098/rsif.2016.0102
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
General laws in ecological parasitology are scarce. Here we evaluate data
published by over 100 authors to determine whether the number of hosts in a
life cycle is associated with the degree of aggregation of fish parasites at
different stages. Parasite species were grouped taxonomically to produce 20 or
more data points per group as far as possible. Most parasites that remained at
one trophic level were less aggregated than those that had passed up a food
chain. We use a stochastic model to show that high parasite overdispersion in
predators can be solely the result of the accumulation of parasites in their
prey. The model is further developed to show that a change in the predators
feeding behaviour with age may further increase parasite aggregation.
|
[
{
"created": "Tue, 19 Jul 2016 04:24:26 GMT",
"version": "v1"
}
] |
2016-07-20
|
[
[
"Lester",
"R. J. G.",
""
],
[
"McVinish",
"R.",
""
]
] |
General laws in ecological parasitology are scarce. Here we evaluate data published by over 100 authors to determine whether the number of hosts in a life cycle is associated with the degree of aggregation of fish parasites at different stages. Parasite species were grouped taxonomically to produce 20 or more data points per group as far as possible. Most parasites that remained at one trophic level were less aggregated than those that had passed up a food chain. We use a stochastic model to show that high parasite overdispersion in predators can be solely the result of the accumulation of parasites in their prey. The model is further developed to show that a change in the predators feeding behaviour with age may further increase parasite aggregation.
|
1007.4461
|
Tsvi Tlusty
|
Yonatan Savir, Elad Noor, Ron Milo and Tsvi Tlusty
|
Cross-species analysis traces adaptation of Rubisco towards optimality
in a low dimensional landscape
|
http://www.pnas.org/content/107/8/3475.short
http://www.ncbi.nlm.nih.gov/pubmed/20142476
http://www.weizmann.ac.il/complex/tlusty/papers/PNAS2010.pdf
|
PNAS February 23, 2010 vol. 107 no. 8 3475-3480
|
10.1073/pnas.0911663107
| null |
q-bio.BM physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Rubisco, probably the most abundant protein in the biosphere, performs an
essential part in the process of carbon fixation through photosynthesis thus
facilitating life on earth. Despite the significant effect that Rubisco has on
the fitness of plants and other photosynthetic organisms, this enzyme is known
to have a remarkably low catalytic rate and a tendency to confuse its
substrate, carbon dioxide, with oxygen. This apparent inefficiency is puzzling
and raises questions regarding the roles of evolution versus biochemical
constraints in shaping Rubisco. Here we examine these questions by analyzing
the measured kinetic parameters of Rubisco from various organisms in various
environments. The analysis presented here suggests that the evolution of
Rubisco is confined to an effectively one-dimensional landscape, which is
manifested in simple power law correlations between its kinetic parameters.
Within this one dimensional landscape, which may represent biochemical and
structural constraints, Rubisco appears to be tuned to the intracellular
environment in which it resides such that the net photosynthesis rate is nearly
optimal. Our analysis indicates that the specificity of Rubisco is not the main
determinant of its efficiency but rather the tradeoff between the carboxylation
velocity and CO2 affinity. As a result, the presence of oxygen has only
moderate effect on the optimal performance of Rubisco, which is determined
mostly by the local CO2 concentration. Rubisco appears as an experimentally
testable example for the evolution of proteins subject both to strong selection
pressure and to biochemical constraints which strongly confine the evolutionary
plasticity to a low dimensional landscape.
|
[
{
"created": "Mon, 26 Jul 2010 13:51:15 GMT",
"version": "v1"
}
] |
2010-07-27
|
[
[
"Savir",
"Yonatan",
""
],
[
"Noor",
"Elad",
""
],
[
"Milo",
"Ron",
""
],
[
"Tlusty",
"Tsvi",
""
]
] |
Rubisco, probably the most abundant protein in the biosphere, performs an essential part in the process of carbon fixation through photosynthesis thus facilitating life on earth. Despite the significant effect that Rubisco has on the fitness of plants and other photosynthetic organisms, this enzyme is known to have a remarkably low catalytic rate and a tendency to confuse its substrate, carbon dioxide, with oxygen. This apparent inefficiency is puzzling and raises questions regarding the roles of evolution versus biochemical constraints in shaping Rubisco. Here we examine these questions by analyzing the measured kinetic parameters of Rubisco from various organisms in various environments. The analysis presented here suggests that the evolution of Rubisco is confined to an effectively one-dimensional landscape, which is manifested in simple power law correlations between its kinetic parameters. Within this one dimensional landscape, which may represent biochemical and structural constraints, Rubisco appears to be tuned to the intracellular environment in which it resides such that the net photosynthesis rate is nearly optimal. Our analysis indicates that the specificity of Rubisco is not the main determinant of its efficiency but rather the tradeoff between the carboxylation velocity and CO2 affinity. As a result, the presence of oxygen has only moderate effect on the optimal performance of Rubisco, which is determined mostly by the local CO2 concentration. Rubisco appears as an experimentally testable example for the evolution of proteins subject both to strong selection pressure and to biochemical constraints which strongly confine the evolutionary plasticity to a low dimensional landscape.
|
1409.0675
|
Felix Polyakov
|
Felix Polyakov
|
Affine differential geometry and smoothness maximization as tools for
identifying geometric movement primitives
|
The current version of the manuscript is result of significant
revision. It contains novel solutions, some formulations and explanations
have been corrected and in many parts of the text improved. The manuscript
now contains discussion about performance of the compromised motor control
system in the framework of the theory under consideration
| null | null | null |
q-bio.NC math.DG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Neuroscientific studies of drawing-like movements usually analyze neural
representation of either geometric (eg. direction, shape) or temporal (eg.
speed) features of trajectories rather than trajectory's representation as a
whole. This work is about empirically supported mathematical ideas behind
splitting and merging geometric and temporal features which characterize
biological movements. Movement primitives supposedly facilitate the efficiency
of movements' representation in the brain and comply with different criteria
for biological movements, among them kinematic smoothness and geometric
constraint. Criterion for trajectories' maximal smoothness of arbitrary order
$n$ is employed, $n = 3$ is the case of the minimum-jerk model. I derive a
class of differential equations obeyed by movement paths for which $n$-th order
maximally smooth trajectories have constant rate of accumulating geometric
measurement along the drawn path. Constant rate of accumulating equi-affine arc
corresponds to compliance with the two-thirds power-law model. Geometric
measurement is invariant under a class of geometric transformations and may be
chosen to be an arc in certain geometry. Equations' solutions presumably serve
as candidates for geometric movement primitives. The derived class of
differential equations consists of two parts. The first part is identical for
all geometric parameterizations of the path. The second part enforces
consistency with desired (geometric) parametrization of curves on solutions of
the first part. Equations in different geometries in plane and in space and
their known solutions are presented. Connection between geometric invariance,
motion smoothness, compositionality and performance of the compromised motor
control system is discussed. The derived class of differential equations is a
novel tool for discovering candidates for geometric movement primitives.
|
[
{
"created": "Tue, 2 Sep 2014 11:50:34 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Oct 2014 18:51:10 GMT",
"version": "v2"
},
{
"created": "Mon, 29 Dec 2014 20:53:43 GMT",
"version": "v3"
},
{
"created": "Wed, 27 Jan 2016 20:47:11 GMT",
"version": "v4"
}
] |
2016-01-28
|
[
[
"Polyakov",
"Felix",
""
]
] |
Neuroscientific studies of drawing-like movements usually analyze neural representation of either geometric (eg. direction, shape) or temporal (eg. speed) features of trajectories rather than trajectory's representation as a whole. This work is about empirically supported mathematical ideas behind splitting and merging geometric and temporal features which characterize biological movements. Movement primitives supposedly facilitate the efficiency of movements' representation in the brain and comply with different criteria for biological movements, among them kinematic smoothness and geometric constraint. Criterion for trajectories' maximal smoothness of arbitrary order $n$ is employed, $n = 3$ is the case of the minimum-jerk model. I derive a class of differential equations obeyed by movement paths for which $n$-th order maximally smooth trajectories have constant rate of accumulating geometric measurement along the drawn path. Constant rate of accumulating equi-affine arc corresponds to compliance with the two-thirds power-law model. Geometric measurement is invariant under a class of geometric transformations and may be chosen to be an arc in certain geometry. Equations' solutions presumably serve as candidates for geometric movement primitives. The derived class of differential equations consists of two parts. The first part is identical for all geometric parameterizations of the path. The second part enforces consistency with desired (geometric) parametrization of curves on solutions of the first part. Equations in different geometries in plane and in space and their known solutions are presented. Connection between geometric invariance, motion smoothness, compositionality and performance of the compromised motor control system is discussed. The derived class of differential equations is a novel tool for discovering candidates for geometric movement primitives.
|
1109.6231
|
Jeremy Gunawardena
|
Jeremy Gunawardena
|
A linear elimination framework
|
27 pages, 8 figures
| null | null | null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Key insights in molecular biology, such as enzyme kinetics, protein allostery
and gene regulation emerged from quantitative analysis based on time-scale
separation, allowing internal complexity to be eliminated and resulting in the
well-known formulas of Michaelis-Menten, Monod-Wyman-Changeux and
Ackers-Johnson-Shea. In systems biology, steady-state analysis has yielded
eliminations that reveal emergent properties of multi-component networks. Here
we show that these analyses of nonlinear biochemical systems are consequences
of the same linear framework, consisting of a labelled, directed graph on which
a Laplacian dynamics is defined, whose steady states can be algorithmically
calculated. Analyses previously considered distinct are revealed as identical,
while new methods of analysis become feasible.
|
[
{
"created": "Wed, 28 Sep 2011 15:04:51 GMT",
"version": "v1"
}
] |
2011-09-29
|
[
[
"Gunawardena",
"Jeremy",
""
]
] |
Key insights in molecular biology, such as enzyme kinetics, protein allostery and gene regulation emerged from quantitative analysis based on time-scale separation, allowing internal complexity to be eliminated and resulting in the well-known formulas of Michaelis-Menten, Monod-Wyman-Changeux and Ackers-Johnson-Shea. In systems biology, steady-state analysis has yielded eliminations that reveal emergent properties of multi-component networks. Here we show that these analyses of nonlinear biochemical systems are consequences of the same linear framework, consisting of a labelled, directed graph on which a Laplacian dynamics is defined, whose steady states can be algorithmically calculated. Analyses previously considered distinct are revealed as identical, while new methods of analysis become feasible.
|
2406.17086
|
Yifan Yang
|
Yifan Yang, Yutong Mao, Xufu Liu, Xiao Liu
|
BrainMAE: A Region-aware Self-supervised Learning Framework for Brain
Signals
|
27 pages, 16 figures
| null | null | null |
q-bio.QM cs.LG q-bio.NC
|
http://creativecommons.org/licenses/by/4.0/
|
The human brain is a complex, dynamic network, which is commonly studied
using functional magnetic resonance imaging (fMRI) and modeled as network of
Regions of interest (ROIs) for understanding various brain functions. Recent
studies utilize deep learning approaches to learn the brain network
representation based on functional connectivity (FC) profile, broadly falling
into two main categories. The Fixed-FC approaches, utilizing the FC profile
which represents the linear temporal relation within the brain network, are
limited by failing to capture informative brain temporal dynamics. On the other
hand, the Dynamic-FC approaches, modeling the evolving FC profile over time,
often exhibit less satisfactory performance due to challenges in handling the
inherent noisy nature of fMRI data.
To address these challenges, we propose Brain Masked Auto-Encoder (BrainMAE)
for learning representations directly from fMRI time-series data. Our approach
incorporates two essential components: a region-aware graph attention mechanism
designed to capture the relationships between different brain ROIs, and a novel
self-supervised masked autoencoding framework for effective model pre-training.
These components enable the model to capture rich temporal dynamics of brain
activity while maintaining resilience to inherent noise in fMRI data. Our
experiments demonstrate that BrainMAE consistently outperforms established
baseline methods by significant margins in four distinct downstream tasks.
Finally, leveraging the model's inherent interpretability, our analysis of
model-generated representations reveals findings that resonate with ongoing
research in the field of neuroscience.
|
[
{
"created": "Mon, 24 Jun 2024 19:16:24 GMT",
"version": "v1"
}
] |
2024-06-26
|
[
[
"Yang",
"Yifan",
""
],
[
"Mao",
"Yutong",
""
],
[
"Liu",
"Xufu",
""
],
[
"Liu",
"Xiao",
""
]
] |
The human brain is a complex, dynamic network, which is commonly studied using functional magnetic resonance imaging (fMRI) and modeled as network of Regions of interest (ROIs) for understanding various brain functions. Recent studies utilize deep learning approaches to learn the brain network representation based on functional connectivity (FC) profile, broadly falling into two main categories. The Fixed-FC approaches, utilizing the FC profile which represents the linear temporal relation within the brain network, are limited by failing to capture informative brain temporal dynamics. On the other hand, the Dynamic-FC approaches, modeling the evolving FC profile over time, often exhibit less satisfactory performance due to challenges in handling the inherent noisy nature of fMRI data. To address these challenges, we propose Brain Masked Auto-Encoder (BrainMAE) for learning representations directly from fMRI time-series data. Our approach incorporates two essential components: a region-aware graph attention mechanism designed to capture the relationships between different brain ROIs, and a novel self-supervised masked autoencoding framework for effective model pre-training. These components enable the model to capture rich temporal dynamics of brain activity while maintaining resilience to inherent noise in fMRI data. Our experiments demonstrate that BrainMAE consistently outperforms established baseline methods by significant margins in four distinct downstream tasks. Finally, leveraging the model's inherent interpretability, our analysis of model-generated representations reveals findings that resonate with ongoing research in the field of neuroscience.
|
2402.16854
|
Divahar Sivanesan
|
Divahar Sivanesan
|
Attention Based Molecule Generation via Hierarchical Variational
Autoencoder
| null | null | null | null |
q-bio.BM cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Molecule generation is a task made very difficult by the complex ways in
which we represent molecules computationally. A common technique used in
molecular generative modeling is to use SMILES strings with recurrent neural
networks built into variational autoencoders - but these suffer from a myriad
of issues: vanishing gradients, long-range forgetting, and invalid molecules.
In this work, we show that by combining recurrent neural networks with
convolutional networks in a hierarchical manner, we are able to both extract
autoregressive information from SMILES strings while maintaining signal and
long-range dependencies. This allows for generations with very high validity
rates on the order of 95% when reconstructing known molecules. We also observe
an average Tanimoto similarity of .6 between test set and reconstructed
molecules, which suggests our method is able to map between SMILES strings and
their learned representations in a more effective way than prior works using
similar methods.
|
[
{
"created": "Thu, 18 Jan 2024 21:45:12 GMT",
"version": "v1"
}
] |
2024-02-28
|
[
[
"Sivanesan",
"Divahar",
""
]
] |
Molecule generation is a task made very difficult by the complex ways in which we represent molecules computationally. A common technique used in molecular generative modeling is to use SMILES strings with recurrent neural networks built into variational autoencoders - but these suffer from a myriad of issues: vanishing gradients, long-range forgetting, and invalid molecules. In this work, we show that by combining recurrent neural networks with convolutional networks in a hierarchical manner, we are able to both extract autoregressive information from SMILES strings while maintaining signal and long-range dependencies. This allows for generations with very high validity rates on the order of 95% when reconstructing known molecules. We also observe an average Tanimoto similarity of .6 between test set and reconstructed molecules, which suggests our method is able to map between SMILES strings and their learned representations in a more effective way than prior works using similar methods.
|
1201.5211
|
Alexei Ryabov
|
Alexei B. Ryabov
|
Phytoplankton competition in deep biomass maximum
|
13 pages, 7 figures; Theoretical Ecology 2012
| null |
10.1007/s12080-012-0158-0
| null |
q-bio.PE nlin.AO nlin.PS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Resource competition in heterogeneous environments is still an unresolved
problem of theoretical ecology. In this article I analyze competition between
two phytoplankton species in a deep water column, where the distributions of
main resources (light and a limiting nutrient) have opposing gradients and
co-limitation by both resources causes a deep biomass maximum. Assuming that
the species have a trade-off in resource requirements and the water column is
weakly mixed, I apply the invasion threshold analysis (Ryabov and Blasius 2011)
to determine relations between environmental conditions and phytoplankton
composition. Although species deplete resources in the interior of the water
column, the resource levels at the bottom and surface remain high. As a result,
the slope of resources gradients becomes a new crucial factor which, rather
than the local resource values, determines the outcome of competition. The
value of resource gradients nonlinearly depend on the density of consumers.
This leads to complex relationships between environmental parameters and
species composition. In particular, it is shown that an increase of both the
incident light intensity or bottom nutrient concentrations favors the best
light competitors, while an increase of the turbulent mixing or background
turbidity favors the best nutrient competitors. These results might be
important for prediction of species composition in deep ocean.
|
[
{
"created": "Wed, 25 Jan 2012 09:22:13 GMT",
"version": "v1"
}
] |
2012-01-26
|
[
[
"Ryabov",
"Alexei B.",
""
]
] |
Resource competition in heterogeneous environments is still an unresolved problem of theoretical ecology. In this article I analyze competition between two phytoplankton species in a deep water column, where the distributions of main resources (light and a limiting nutrient) have opposing gradients and co-limitation by both resources causes a deep biomass maximum. Assuming that the species have a trade-off in resource requirements and the water column is weakly mixed, I apply the invasion threshold analysis (Ryabov and Blasius 2011) to determine relations between environmental conditions and phytoplankton composition. Although species deplete resources in the interior of the water column, the resource levels at the bottom and surface remain high. As a result, the slope of resources gradients becomes a new crucial factor which, rather than the local resource values, determines the outcome of competition. The value of resource gradients nonlinearly depend on the density of consumers. This leads to complex relationships between environmental parameters and species composition. In particular, it is shown that an increase of both the incident light intensity or bottom nutrient concentrations favors the best light competitors, while an increase of the turbulent mixing or background turbidity favors the best nutrient competitors. These results might be important for prediction of species composition in deep ocean.
|
1004.4387
|
Areejit Samal
|
Pierre-Yves Bourguignon, Areejit Samal, Fran\c{c}ois K\'ep\`es,
J\"urgen Jost, Olivier C. Martin
|
Challenges in experimental data integration within genome-scale
metabolic models
|
5 pages
|
Algorithms for Molecular Biology, 5:20 (2010)
http://www.almob.org/content/5/1/20
| null | null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A report of the meeting "Challenges in experimental data integration within
genome-scale metabolic models", Institut Henri Poincar\'e, Paris, October 10-11
2009, organized by the CNRS-MPG joint program in Systems Biology.
|
[
{
"created": "Sun, 25 Apr 2010 22:41:35 GMT",
"version": "v1"
}
] |
2010-04-27
|
[
[
"Bourguignon",
"Pierre-Yves",
""
],
[
"Samal",
"Areejit",
""
],
[
"Képès",
"François",
""
],
[
"Jost",
"Jürgen",
""
],
[
"Martin",
"Olivier C.",
""
]
] |
A report of the meeting "Challenges in experimental data integration within genome-scale metabolic models", Institut Henri Poincar\'e, Paris, October 10-11 2009, organized by the CNRS-MPG joint program in Systems Biology.
|
1304.1565
|
Muhammad Asim Mubeen
|
Asim M. Mubeen, Kevin H. Knuth
|
Bayesian Odds-Ratio Filters: A Template-Based Method for Online
Detection of P300 Evoked Responses
|
9 pages, 3 figures
| null | null | null |
q-bio.NC physics.med-ph stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Template-based signal detection most often relies on computing a correlation,
or a dot product, between an incoming data stream and a signal template. While
such a correlation results in an ongoing estimate of the magnitude of the
signal in the data stream, it does not directly indicate the presence or
absence of a signal. Instead, the problem of signal detection is one of
model-selection. Here we explore the use of the Bayesian odds-ratio (OR), which
is the ratio of posterior probabilities of a signal-plus-noise model over a
noise-only model. We demonstrate this method by applying it to simulated
electroencephalographic (EEG) signals based on the P300 response, which is
widely used in both Brain Computer Interface (BCI) and Brain Machine Interface
(BMI) systems. The efficacy of this algorithm is demonstrated by comparing the
receiver operating characteristic (ROC) curves of the OR-based (logOR) filter
to the usual correlation method where we find a significant improvement in P300
detection. The logOR filter promises to improve the accuracy and speed of the
detection of evoked brain responses in BCI/BMI applications as well the
detection of template signals in general.
|
[
{
"created": "Thu, 4 Apr 2013 21:27:40 GMT",
"version": "v1"
}
] |
2013-04-08
|
[
[
"Mubeen",
"Asim M.",
""
],
[
"Knuth",
"Kevin H.",
""
]
] |
Template-based signal detection most often relies on computing a correlation, or a dot product, between an incoming data stream and a signal template. While such a correlation results in an ongoing estimate of the magnitude of the signal in the data stream, it does not directly indicate the presence or absence of a signal. Instead, the problem of signal detection is one of model-selection. Here we explore the use of the Bayesian odds-ratio (OR), which is the ratio of posterior probabilities of a signal-plus-noise model over a noise-only model. We demonstrate this method by applying it to simulated electroencephalographic (EEG) signals based on the P300 response, which is widely used in both Brain Computer Interface (BCI) and Brain Machine Interface (BMI) systems. The efficacy of this algorithm is demonstrated by comparing the receiver operating characteristic (ROC) curves of the OR-based (logOR) filter to the usual correlation method where we find a significant improvement in P300 detection. The logOR filter promises to improve the accuracy and speed of the detection of evoked brain responses in BCI/BMI applications as well the detection of template signals in general.
|
2006.00115
|
Daniel Moyer
|
Daniel Moyer, Greg Ver Steeg, Paul M. Thompson
|
Overview of Scanner Invariant Representations
|
Accepted as a short paper in MIDL 2020. In accordance with the MIDL
2020 Call for Papers, this short paper is an overview of an already published
work arXiv:1904.05375, and was submitted to MIDL in order to allow
presentation and discussion at the meeting
| null | null |
MIDL/2020/ExtendedAbstract/yqm9RD_XHT
|
q-bio.QM cs.CV cs.LG eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Pooled imaging data from multiple sources is subject to bias from each
source. Studies that do not correct for these scanner/site biases at best lose
statistical power, and at worst leave spurious correlations in their data.
Estimation of the bias effects is non-trivial due to the paucity of data with
correspondence across sites, so called "traveling phantom" data, which is
expensive to collect. Nevertheless, numerous solutions leveraging direct
correspondence have been proposed. In contrast to this, Moyer et al. (2019)
proposes an unsupervised solution using invariant representations, one which
does not require correspondence and thus does not require paired images. By
leveraging the data processing inequality, an invariant representation can then
be used to create an image reconstruction that is uninformative of its original
source, yet still faithful to the underlying structure. In the present abstract
we provide an overview of this method.
|
[
{
"created": "Fri, 29 May 2020 22:56:47 GMT",
"version": "v1"
}
] |
2020-06-02
|
[
[
"Moyer",
"Daniel",
""
],
[
"Steeg",
"Greg Ver",
""
],
[
"Thompson",
"Paul M.",
""
]
] |
Pooled imaging data from multiple sources is subject to bias from each source. Studies that do not correct for these scanner/site biases at best lose statistical power, and at worst leave spurious correlations in their data. Estimation of the bias effects is non-trivial due to the paucity of data with correspondence across sites, so called "traveling phantom" data, which is expensive to collect. Nevertheless, numerous solutions leveraging direct correspondence have been proposed. In contrast to this, Moyer et al. (2019) proposes an unsupervised solution using invariant representations, one which does not require correspondence and thus does not require paired images. By leveraging the data processing inequality, an invariant representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to the underlying structure. In the present abstract we provide an overview of this method.
|
1612.07106
|
Xerxes D. Arsiwalla
|
Xerxes D. Arsiwalla and Paul Verschure
|
The Global Dynamical Complexity of the Human Brain Network
|
16 pages, 6 figures
| null |
10.1007/s41109-016-0018-8
| null |
q-bio.NC cs.IT math.DS math.IT physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
How much information do large brain networks integrate as a whole over the
sum of their parts? Can the dynamical complexity of such networks be globally
quantified in an information-theoretic way and be meaningfully coupled to brain
function? Recently, measures of dynamical complexity such as integrated
information have been proposed. However, problems related to the normalization
and Bell number of partitions associated to these measures make these
approaches computationally infeasible for large-scale brain networks. Our goal
in this work is to address this problem. Our formulation of network integrated
information is based on the Kullback-Leibler divergence between the
multivariate distribution on the set of network states versus the corresponding
factorized distribution over its parts. We find that implementing the maximum
information partition optimizes computations. These methods are well-suited for
large networks with linear stochastic dynamics. We compute the integrated
information for both, the system's attractor states, as well as non-stationary
dynamical states of the network. We then apply this formalism to brain networks
to compute the integrated information for the human brain's connectome.
Compared to a randomly re-wired network, we find that the specific topology of
the brain generates greater information complexity.
|
[
{
"created": "Wed, 21 Dec 2016 13:44:31 GMT",
"version": "v1"
}
] |
2016-12-22
|
[
[
"Arsiwalla",
"Xerxes D.",
""
],
[
"Verschure",
"Paul",
""
]
] |
How much information do large brain networks integrate as a whole over the sum of their parts? Can the dynamical complexity of such networks be globally quantified in an information-theoretic way and be meaningfully coupled to brain function? Recently, measures of dynamical complexity such as integrated information have been proposed. However, problems related to the normalization and Bell number of partitions associated to these measures make these approaches computationally infeasible for large-scale brain networks. Our goal in this work is to address this problem. Our formulation of network integrated information is based on the Kullback-Leibler divergence between the multivariate distribution on the set of network states versus the corresponding factorized distribution over its parts. We find that implementing the maximum information partition optimizes computations. These methods are well-suited for large networks with linear stochastic dynamics. We compute the integrated information for both, the system's attractor states, as well as non-stationary dynamical states of the network. We then apply this formalism to brain networks to compute the integrated information for the human brain's connectome. Compared to a randomly re-wired network, we find that the specific topology of the brain generates greater information complexity.
|
1701.07061
|
Diego Mateos
|
D. M. Mateos, R. Guevara Erra, R. Wennberg, J.L. Perez Velazquez
|
Measures of Entropy and Complexity in altered states of consciousness
|
2 figures
| null | null | null |
q-bio.NC cond-mat.stat-mech
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Quantification of complexity in neurophysiological signals has been studied
using different methods, especially those from information or dynamical system
theory. These studies revealed the dependence on different states of
consciousness, particularly that wakefulness is characterized by larger
complexity of brain signals perhaps due to the necessity of the brain to handle
varied sensorimotor information. Thus these frameworks are very useful in
attempts at quantifying cognitive states. We set out to analyze different types
of signals including scalp and intracerebral electroencephalography (EEG), and
magnetoencephalography (MEG) in subjects during different states of
consciousness: awake, sleep stages and epileptic seizures. The signals were
analyzed using a statistical (Permutation Entropy) and a deterministic
(Permutation Lempel Ziv Complexity) analytical method. The results are
presented in a complexity vs entropy graph, showing that the values of entropy
and complexity of the signals tend to be greatest when the subjects are in
fully alert states, falling in states with loss of awareness or consciousness.
These results are robust for all three types of recordings. We propose that the
investigation of the structure of cognition using the frameworks of complexity
will reveal mechanistic aspects of brain dynamics associated not only with
altered states of consciousness but also with normal and pathological
conditions.
|
[
{
"created": "Mon, 9 Jan 2017 20:10:15 GMT",
"version": "v1"
}
] |
2017-01-26
|
[
[
"Mateos",
"D. M.",
""
],
[
"Erra",
"R. Guevara",
""
],
[
"Wennberg",
"R.",
""
],
[
"Velazquez",
"J. L. Perez",
""
]
] |
Quantification of complexity in neurophysiological signals has been studied using different methods, especially those from information or dynamical system theory. These studies revealed the dependence on different states of consciousness, particularly that wakefulness is characterized by larger complexity of brain signals perhaps due to the necessity of the brain to handle varied sensorimotor information. Thus these frameworks are very useful in attempts at quantifying cognitive states. We set out to analyze different types of signals including scalp and intracerebral electroencephalography (EEG), and magnetoencephalography (MEG) in subjects during different states of consciousness: awake, sleep stages and epileptic seizures. The signals were analyzed using a statistical (Permutation Entropy) and a deterministic (Permutation Lempel Ziv Complexity) analytical method. The results are presented in a complexity vs entropy graph, showing that the values of entropy and complexity of the signals tend to be greatest when the subjects are in fully alert states, falling in states with loss of awareness or consciousness. These results are robust for all three types of recordings. We propose that the investigation of the structure of cognition using the frameworks of complexity will reveal mechanistic aspects of brain dynamics associated not only with altered states of consciousness but also with normal and pathological conditions.
|
1707.00180
|
Melanie Weber
|
Melanie Weber, Johannes Stelzer, Emil Saucan, Alexander Naitsat,
Gabriele Lohmann and J\"urgen Jost
|
Curvature-based Methods for Brain Network Analysis
|
Under Review
| null | null | null |
q-bio.NC cs.DM cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The human brain forms functional networks on all spatial scales. Modern fMRI
scanners allow to resolve functional brain data in high resolutions, allowing
to study large-scale networks that relate to cognitive processes. The analysis
of such networks forms a cornerstone of experimental neuroscience. Due to the
immense size and complexity of the underlying data sets, efficient evaluation
and visualization remain a challenge for data analysis. In this study, we
combine recent advances in experimental neuroscience and applied mathematics to
perform a mathematical characterization of complex networks constructed from
fMRI data. We use task-related edge densities [Lohmann et al., 2016] for
constructing networks of task-related changes in synchronization. This
construction captures the dynamic formation of patterns of neuronal activity
and therefore represents efficiently the connectivity structure between brain
regions. Using geometric methods that utilize Forman-Ricci curvature as an
edge-based network characteristic [Weber et al., 2017], we perform a
mathematical analysis of the resulting complex networks. We motivate the use of
edge-based characteristics to evaluate the network structure with geometric
methods. The geometric features could aid in understanding the connectivity and
interplay of brain regions in cognitive processes.
|
[
{
"created": "Sat, 1 Jul 2017 17:55:28 GMT",
"version": "v1"
},
{
"created": "Mon, 13 May 2019 16:03:12 GMT",
"version": "v2"
}
] |
2019-05-14
|
[
[
"Weber",
"Melanie",
""
],
[
"Stelzer",
"Johannes",
""
],
[
"Saucan",
"Emil",
""
],
[
"Naitsat",
"Alexander",
""
],
[
"Lohmann",
"Gabriele",
""
],
[
"Jost",
"Jürgen",
""
]
] |
The human brain forms functional networks on all spatial scales. Modern fMRI scanners allow to resolve functional brain data in high resolutions, allowing to study large-scale networks that relate to cognitive processes. The analysis of such networks forms a cornerstone of experimental neuroscience. Due to the immense size and complexity of the underlying data sets, efficient evaluation and visualization remain a challenge for data analysis. In this study, we combine recent advances in experimental neuroscience and applied mathematics to perform a mathematical characterization of complex networks constructed from fMRI data. We use task-related edge densities [Lohmann et al., 2016] for constructing networks of task-related changes in synchronization. This construction captures the dynamic formation of patterns of neuronal activity and therefore represents efficiently the connectivity structure between brain regions. Using geometric methods that utilize Forman-Ricci curvature as an edge-based network characteristic [Weber et al., 2017], we perform a mathematical analysis of the resulting complex networks. We motivate the use of edge-based characteristics to evaluate the network structure with geometric methods. The geometric features could aid in understanding the connectivity and interplay of brain regions in cognitive processes.
|
2004.14767
|
Helmut Hlavacs
|
Helmut Hlavacs
|
How Often Should People be Tested for Corona to Avoid a Shutdown?
|
Please comment
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Based on the well known SIR model, this paper develops a model for predicting
the number of necessary testings of asymptomatic persons in order to push Reff
below 1, thus suppressing an outbreak. The model considers R0, time for
obtaining a test result, and effect of population discipline. The outcome are
closed form expressions for the number of daily tests.
|
[
{
"created": "Mon, 27 Apr 2020 17:59:47 GMT",
"version": "v1"
}
] |
2020-05-01
|
[
[
"Hlavacs",
"Helmut",
""
]
] |
Based on the well known SIR model, this paper develops a model for predicting the number of necessary testings of asymptomatic persons in order to push Reff below 1, thus suppressing an outbreak. The model considers R0, time for obtaining a test result, and effect of population discipline. The outcome are closed form expressions for the number of daily tests.
|
1504.06574
|
Heng Li
|
Heng Li
|
FermiKit: assembly-based variant calling for Illumina resequencing data
| null | null | null | null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Summary: FermiKit is a variant calling pipeline for Illumina data. It de novo
assembles short reads and then maps the assembly against a reference genome to
call SNPs, short insertions/deletions (INDELs) and structural variations (SVs).
FermiKit takes about one day to assemble 30-fold human whole-genome data on a
modern 16-core server with 85GB RAM at the peak, and calls variants in half an
hour to an accuracy comparable to the current practice. FermiKit assembly is a
reduced representation of raw data while retaining most of the original
information.
Availability and implementation: https://github.com/lh3/fermikit
Contact: hengli@broadinstitute.org
|
[
{
"created": "Fri, 24 Apr 2015 17:27:42 GMT",
"version": "v1"
}
] |
2015-04-27
|
[
[
"Li",
"Heng",
""
]
] |
Summary: FermiKit is a variant calling pipeline for Illumina data. It de novo assembles short reads and then maps the assembly against a reference genome to call SNPs, short insertions/deletions (INDELs) and structural variations (SVs). FermiKit takes about one day to assemble 30-fold human whole-genome data on a modern 16-core server with 85GB RAM at the peak, and calls variants in half an hour to an accuracy comparable to the current practice. FermiKit assembly is a reduced representation of raw data while retaining most of the original information. Availability and implementation: https://github.com/lh3/fermikit Contact: hengli@broadinstitute.org
|
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