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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1511.09468 | Cengiz Pehlevan | Cengiz Pehlevan, Dmitri B. Chklovskii | Optimization theory of Hebbian/anti-Hebbian networks for PCA and
whitening | Annual Allerton Conference on Communication, Control, and Computing
(Allerton) 2015 | null | 10.1109/ALLERTON.2015.7447180 | null | q-bio.NC cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In analyzing information streamed by sensory organs, our brains face
challenges similar to those solved in statistical signal processing. This
suggests that biologically plausible implementations of online signal
processing algorithms may model neural computation. Here, we focus on such
workhorses of signal processing as Principal Component Analysis (PCA) and
whitening which maximize information transmission in the presence of noise. We
adopt the similarity matching framework, recently developed for principal
subspace extraction, but modify the existing objective functions by adding a
decorrelating term. From the modified objective functions, we derive online PCA
and whitening algorithms which are implementable by neural networks with local
learning rules, i.e. synaptic weight updates that depend on the activity of
only pre- and postsynaptic neurons. Our theory offers a principled model of
neural computations and makes testable predictions such as the dropout of
underutilized neurons.
| [
{
"created": "Mon, 30 Nov 2015 20:52:39 GMT",
"version": "v1"
}
] | 2016-04-27 | [
[
"Pehlevan",
"Cengiz",
""
],
[
"Chklovskii",
"Dmitri B.",
""
]
] | In analyzing information streamed by sensory organs, our brains face challenges similar to those solved in statistical signal processing. This suggests that biologically plausible implementations of online signal processing algorithms may model neural computation. Here, we focus on such workhorses of signal processing as Principal Component Analysis (PCA) and whitening which maximize information transmission in the presence of noise. We adopt the similarity matching framework, recently developed for principal subspace extraction, but modify the existing objective functions by adding a decorrelating term. From the modified objective functions, we derive online PCA and whitening algorithms which are implementable by neural networks with local learning rules, i.e. synaptic weight updates that depend on the activity of only pre- and postsynaptic neurons. Our theory offers a principled model of neural computations and makes testable predictions such as the dropout of underutilized neurons. |
0711.2945 | Vadim N. Biktashev | V. N.Biktashev, A. Arutunyan, N. A. Sarvazyan | Generation and escape of local waves from the boundary of uncoupled
cardiac tissue | 28 pages, 10 figures, submitted to Biophysical Journal | null | 10.1529/biophysj.107.117630 | null | q-bio.TO q-bio.CB | null | We aim to understand the formation of abnormal waves of activity from
myocardial regions with diminished cell-to-cell coupling. In route to this
goal, we studied the behavior of a heterogeneous myocyte network in which a
sharp coupling gradient was placed under conditions of increasing network
automaticity. Experiments were conducted in monolayers of neonatal rat
cardiomyocytes using heptanol and isoproterenol as means of altering
cell-to-cell coupling and automaticity respectively. Experimental findings were
explained and expanded using a modified Beeler-Reuter numerical model. The data
suggests that the combination of a heterogeneous substrate, a gradient of
coupling and an increase in oscillatory activity of individual cells creates a
rich set of behaviors associated with self-generated spiral waves and ectopic
sources. Spiral waves feature a flattened shape and a pin-unpin drift type of
tip motion. These intercellular waves are action-potential based and can be
visualized with either voltage or calcium transient measurements. A source/load
mismatch on the interface between the boundary and well-coupled layers can lock
wavefronts emanating from both ectopic sources and rotating waves within the
inner layers of the coupling gradient. A numerical approach allowed us to
explore how: i) the spatial distribution of cells, ii) the amplitude and
dispersion of cell automaticity, iii) and the speed at which the coupling
gradient moves in space, affects wave behavior, including its escape into
well-coupled tissue.
| [
{
"created": "Mon, 19 Nov 2007 08:06:24 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Biktashev",
"V. N.",
""
],
[
"Arutunyan",
"A.",
""
],
[
"Sarvazyan",
"N. A.",
""
]
] | We aim to understand the formation of abnormal waves of activity from myocardial regions with diminished cell-to-cell coupling. In route to this goal, we studied the behavior of a heterogeneous myocyte network in which a sharp coupling gradient was placed under conditions of increasing network automaticity. Experiments were conducted in monolayers of neonatal rat cardiomyocytes using heptanol and isoproterenol as means of altering cell-to-cell coupling and automaticity respectively. Experimental findings were explained and expanded using a modified Beeler-Reuter numerical model. The data suggests that the combination of a heterogeneous substrate, a gradient of coupling and an increase in oscillatory activity of individual cells creates a rich set of behaviors associated with self-generated spiral waves and ectopic sources. Spiral waves feature a flattened shape and a pin-unpin drift type of tip motion. These intercellular waves are action-potential based and can be visualized with either voltage or calcium transient measurements. A source/load mismatch on the interface between the boundary and well-coupled layers can lock wavefronts emanating from both ectopic sources and rotating waves within the inner layers of the coupling gradient. A numerical approach allowed us to explore how: i) the spatial distribution of cells, ii) the amplitude and dispersion of cell automaticity, iii) and the speed at which the coupling gradient moves in space, affects wave behavior, including its escape into well-coupled tissue. |
1804.08713 | Catherine Reason | Catherine M Reason | A Theoretical Limit to Physicalism: A Non-Technical Explanation of the
Gemini Theorem | null | null | null | null | q-bio.NC physics.hist-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Gemini theorem asserts that, given certain reasonable assumptions, no
physical system can be certainly aware of its own existence. The theorem can be
proved algorithmically, but the proof of this theorem is somewhat obscure, and
there exists very little literature on it. The purpose of this article is to
provide a brief non-technical summary of the theorem and its proof, with a view
to stimulating critical discussion of the proof and its implications. Since the
theorem implies that a violation of the conservation of energy will take place
within the brains of conscious human beings, it has obvious implications for
any physical theory.
| [
{
"created": "Tue, 10 Apr 2018 14:33:31 GMT",
"version": "v1"
}
] | 2018-04-25 | [
[
"Reason",
"Catherine M",
""
]
] | The Gemini theorem asserts that, given certain reasonable assumptions, no physical system can be certainly aware of its own existence. The theorem can be proved algorithmically, but the proof of this theorem is somewhat obscure, and there exists very little literature on it. The purpose of this article is to provide a brief non-technical summary of the theorem and its proof, with a view to stimulating critical discussion of the proof and its implications. Since the theorem implies that a violation of the conservation of energy will take place within the brains of conscious human beings, it has obvious implications for any physical theory. |
1904.08886 | Benjamin M. Friedrich | Andr\'e Scholich, Simon Syga, Hern\'an Morales-Navarrete, Fabi\'an
Segovia-Miranda, Hidenori Nonaka, Kirstin Meyer, Walter de Back, Lutz Brusch,
Yannis Kalaidzidis, Marino Zerial, Frank J\"ulicher, Benjamin M. Friedrich | Quantification of Nematic Cell Polarity in Three-dimensional Tissues | 27 pages, 9 color figures | null | 10.1371/journal.pcbi.1008412 | null | q-bio.TO cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How epithelial cells coordinate their polarity to form functional tissues is
an open question in cell biology. Here, we characterize a unique type of
polarity found in liver tissue, nematic cell polarity, which is different from
vectorial cell polarity in simple, sheet-like epithelia. We propose a
conceptual and algorithmic framework to characterize complex patterns of
polarity proteins on the surface of a cell in terms of a multipole expansion.
To rigorously quantify previously observed tissue-level patterns of nematic
cell polarity (Morales-Navarette et al., eLife 8:e44860, 2019), we introduce
the concept of co-orientational order parameters, which generalize the known
biaxial order parameters of the theory of liquid crystals. Applying these
concepts to three-dimensional reconstructions of single cells from
high-resolution imaging data of mouse liver tissue, we show that the axes of
nematic cell polarity of hepatocytes exhibit local coordination and are aligned
with the biaxially anisotropic sinusoidal network for blood transport. Our
study characterizes liver tissue as a biological example of a biaxial liquid
crystal. The general methodology developed here could be applied to other
tissues or in-vitro organoids.
| [
{
"created": "Thu, 18 Apr 2019 16:52:15 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Apr 2020 14:45:17 GMT",
"version": "v2"
}
] | 2021-01-27 | [
[
"Scholich",
"André",
""
],
[
"Syga",
"Simon",
""
],
[
"Morales-Navarrete",
"Hernán",
""
],
[
"Segovia-Miranda",
"Fabián",
""
],
[
"Nonaka",
"Hidenori",
""
],
[
"Meyer",
"Kirstin",
""
],
[
"de Back",
"Walter",
... | How epithelial cells coordinate their polarity to form functional tissues is an open question in cell biology. Here, we characterize a unique type of polarity found in liver tissue, nematic cell polarity, which is different from vectorial cell polarity in simple, sheet-like epithelia. We propose a conceptual and algorithmic framework to characterize complex patterns of polarity proteins on the surface of a cell in terms of a multipole expansion. To rigorously quantify previously observed tissue-level patterns of nematic cell polarity (Morales-Navarette et al., eLife 8:e44860, 2019), we introduce the concept of co-orientational order parameters, which generalize the known biaxial order parameters of the theory of liquid crystals. Applying these concepts to three-dimensional reconstructions of single cells from high-resolution imaging data of mouse liver tissue, we show that the axes of nematic cell polarity of hepatocytes exhibit local coordination and are aligned with the biaxially anisotropic sinusoidal network for blood transport. Our study characterizes liver tissue as a biological example of a biaxial liquid crystal. The general methodology developed here could be applied to other tissues or in-vitro organoids. |
1501.01620 | Giuseppe Vitiello | Luc Montagnier, Emilio Del Giudice, Jamal A\"issa, Claude Lavallee,
Steven Motschwiller, Antonio Capolupo, Albino Polcari, Paola Romano, Alberto
Tedeschi, Giuseppe Vitiello | Transduction of DNA information through water and electromagnetic waves | 10 pages, 6 figures | Electromagnetic Biology and Medicine 2015.34:106-112 | 10.3109/15368378.2015.1036072 | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The experimental conditions by which electromagnetic signals (EMS) of low
frequency can be emitted by diluted aqueous solutions of some bacterial and
viral DNAs are described. That the recorded EMS and nanostructures induced in
water carry the DNA information (sequence) is shown by retrieval of that same
DNA by classical PCR amplification using the TAQ polymerase, including both
primers and nucleotides. Moreover, such a transduction process has also been
observed in living human cells exposed to EMS irradiation. These experiments
suggest that coherent long range molecular interaction must be at work in water
so to allow the observed features. The quantum field theory analysis of the
phenomenon is presented.
| [
{
"created": "Sat, 27 Dec 2014 10:58:17 GMT",
"version": "v1"
}
] | 2015-07-01 | [
[
"Montagnier",
"Luc",
""
],
[
"Del Giudice",
"Emilio",
""
],
[
"Aïssa",
"Jamal",
""
],
[
"Lavallee",
"Claude",
""
],
[
"Motschwiller",
"Steven",
""
],
[
"Capolupo",
"Antonio",
""
],
[
"Polcari",
"Albino",
""... | The experimental conditions by which electromagnetic signals (EMS) of low frequency can be emitted by diluted aqueous solutions of some bacterial and viral DNAs are described. That the recorded EMS and nanostructures induced in water carry the DNA information (sequence) is shown by retrieval of that same DNA by classical PCR amplification using the TAQ polymerase, including both primers and nucleotides. Moreover, such a transduction process has also been observed in living human cells exposed to EMS irradiation. These experiments suggest that coherent long range molecular interaction must be at work in water so to allow the observed features. The quantum field theory analysis of the phenomenon is presented. |
1812.07642 | Nurdan \c{C}abuko\u{g}lu | Nurdan \c{C}abuko\u{g}lu | Impact of the purposeful kinesis on running waves | 12 pages, 17 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | The basic model of purposeful kinesis was developed recently (Gorban and
Cabukoglu, Ecol. Complex. 2018, 33, 75 83) on the basis of the let well enough
alone idea. According to this model the diffusion drops while the reproduction
coefficient is increasing. That is, species prefer to stay in a good condition
and the population gives birth; otherwise, in the bad situation individuals
want to run away because of the fatal conditions. In this study, we analyse the
impact of the purposeful kinesis model on running waves. The running waves in
the population with kinesis are studied using numerical experiments. Both
monotonic and non monotonic (Allee effect) dependence of the reproduction
coefficient on the population density are studied. The possible benefits of the
purposeful kinesis are demonstrated: with the higher diffusion, while the
population without kinesis ends up with extinction, the population with kinesis
stays alive and has the running wave behavior. While the kinesis of the prey
population is decreasing, the wave amplitude gets smaller. On the other hand,
for the lower kinesis of predators they have a sharp increase.
| [
{
"created": "Thu, 22 Nov 2018 11:44:47 GMT",
"version": "v1"
}
] | 2018-12-20 | [
[
"Çabukoğlu",
"Nurdan",
""
]
] | The basic model of purposeful kinesis was developed recently (Gorban and Cabukoglu, Ecol. Complex. 2018, 33, 75 83) on the basis of the let well enough alone idea. According to this model the diffusion drops while the reproduction coefficient is increasing. That is, species prefer to stay in a good condition and the population gives birth; otherwise, in the bad situation individuals want to run away because of the fatal conditions. In this study, we analyse the impact of the purposeful kinesis model on running waves. The running waves in the population with kinesis are studied using numerical experiments. Both monotonic and non monotonic (Allee effect) dependence of the reproduction coefficient on the population density are studied. The possible benefits of the purposeful kinesis are demonstrated: with the higher diffusion, while the population without kinesis ends up with extinction, the population with kinesis stays alive and has the running wave behavior. While the kinesis of the prey population is decreasing, the wave amplitude gets smaller. On the other hand, for the lower kinesis of predators they have a sharp increase. |
q-bio/0402048 | David R. Bickel | David R. Bickel | Reliably determining which genes have a high posterior probability of
differential expression: A microarray application of decision-theoretic
multiple testing | Submitted for publication on 8/14/03 | null | null | null | q-bio.QM q-bio.MN | null | Microarray data are often used to determine which genes are differentially
expressed between groups, for example, between treatment and control groups.
There are methods of determining which genes have a high probability of
differential expression, but those methods depend on the estimation of
probability densities. Theoretical results have shown such estimation to be
unreliable when high-probability genes are identified.
The genes that are probably differentially expressed can be found using
decision theory instead of density estimation. Simulations show that the
proposed decision-theoretic method is much more reliable than a
density-estimation method. The proposed method is used to determine which genes
to consider differentially expressed between patients with different types of
cancer.
The proposed method determines which genes have a high probability of
differential expression. It can be applied to data sets that have replicate
microarrays in each of two or more groups of patients or experiments.
| [
{
"created": "Sun, 29 Feb 2004 14:56:23 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Bickel",
"David R.",
""
]
] | Microarray data are often used to determine which genes are differentially expressed between groups, for example, between treatment and control groups. There are methods of determining which genes have a high probability of differential expression, but those methods depend on the estimation of probability densities. Theoretical results have shown such estimation to be unreliable when high-probability genes are identified. The genes that are probably differentially expressed can be found using decision theory instead of density estimation. Simulations show that the proposed decision-theoretic method is much more reliable than a density-estimation method. The proposed method is used to determine which genes to consider differentially expressed between patients with different types of cancer. The proposed method determines which genes have a high probability of differential expression. It can be applied to data sets that have replicate microarrays in each of two or more groups of patients or experiments. |
0904.4298 | Shi Chen | Chen Shi and Fang Yuan | A Dynamic Programming Implemented 2x2 non-cooperative Game Theory Model
for ESS Analysis | 9 pages 3 sub models to illustrate how dynamic programming is
implemented to construct payoff matrix of 2x2 symmetric game | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/3.0/ | Game Theory has been frequently applied in biological research since 1970s.
While the key idea of Game Theory is Nash Equilibrium, it is critical to
understand and figure out the payoff matrix in order to calculate Nash
Equilibrium. In this paper we present a dynamic programming implemented method
to compute 2x2 non-cooperative finite resource allocation game's payoff matrix.
We assume in one population there exists two types of individuals, aggressive
and non-aggressive and each individual has equal and finite resource. The
strength of individual could be described by a function of resource consumption
in discrete development stages. Each individual undergoes logistic growth hence
we divide the development into three stages: initialization, quasilinear growth
and termination. We first discuss the theoretical frame of how to dynamic
programming to calculate payoff matrix then give three numerical examples
representing three different types of aggressive individuals and calculate the
payoff matrix for each of them respectively. Based on the numerical payoff
matrix we further investigate the evolutionary stable strategies (ESS) of the
games.
| [
{
"created": "Tue, 28 Apr 2009 02:52:08 GMT",
"version": "v1"
}
] | 2009-04-29 | [
[
"Shi",
"Chen",
""
],
[
"Yuan",
"Fang",
""
]
] | Game Theory has been frequently applied in biological research since 1970s. While the key idea of Game Theory is Nash Equilibrium, it is critical to understand and figure out the payoff matrix in order to calculate Nash Equilibrium. In this paper we present a dynamic programming implemented method to compute 2x2 non-cooperative finite resource allocation game's payoff matrix. We assume in one population there exists two types of individuals, aggressive and non-aggressive and each individual has equal and finite resource. The strength of individual could be described by a function of resource consumption in discrete development stages. Each individual undergoes logistic growth hence we divide the development into three stages: initialization, quasilinear growth and termination. We first discuss the theoretical frame of how to dynamic programming to calculate payoff matrix then give three numerical examples representing three different types of aggressive individuals and calculate the payoff matrix for each of them respectively. Based on the numerical payoff matrix we further investigate the evolutionary stable strategies (ESS) of the games. |
1711.10547 | Junbai Wang | Kirill Batmanov, Junbai Wang | Predicting variation of DNA shape preferences in protein-DNA interaction
in cancer cells with a new biophysical model | null | Genes 2017, 8(9), 233; | 10.3390/genes8090233 | null | q-bio.BM physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | DNA shape readout is an important mechanism of target site recognition by
transcription factors, in addition to the sequence readout. Several models of
transcription factor-DNA binding which consider DNA shape have been developed
in recent years. We present a new biophysical model of protein-DNA interaction
by considering the DNA shape features, which is based on a neighbour
dinucleotide dependency model BayesPI2. The parameters of the new model are
restricted to a subspace spanned by the 2-mer DNA shape features, which
allowing a biophysical interpretation of the new parameters as
position-dependent preferences towards certain values of the features. Using
the new model, we explore the variation of DNA shape preferences in several
transcription factors across cancer cell lines and cellular conditions. We find
evidence of DNA shape variations at FOXA1 binding sites in MCF7 cells after
treatment with steroids. The new model is useful for elucidating finer details
of transcription factor-DNA interaction. It may be used to improve the
prediction of cancer mutation effects in the future.
| [
{
"created": "Tue, 31 Oct 2017 21:43:35 GMT",
"version": "v1"
}
] | 2017-11-30 | [
[
"Batmanov",
"Kirill",
""
],
[
"Wang",
"Junbai",
""
]
] | DNA shape readout is an important mechanism of target site recognition by transcription factors, in addition to the sequence readout. Several models of transcription factor-DNA binding which consider DNA shape have been developed in recent years. We present a new biophysical model of protein-DNA interaction by considering the DNA shape features, which is based on a neighbour dinucleotide dependency model BayesPI2. The parameters of the new model are restricted to a subspace spanned by the 2-mer DNA shape features, which allowing a biophysical interpretation of the new parameters as position-dependent preferences towards certain values of the features. Using the new model, we explore the variation of DNA shape preferences in several transcription factors across cancer cell lines and cellular conditions. We find evidence of DNA shape variations at FOXA1 binding sites in MCF7 cells after treatment with steroids. The new model is useful for elucidating finer details of transcription factor-DNA interaction. It may be used to improve the prediction of cancer mutation effects in the future. |
2005.09475 | Luiz Goncalves | Igor G. Pereira, Joris M. Guerin, Andouglas G. Silva Junior, Cosimo
Distante, Gabriel S. Garcia, Luiz M. G. Gon\c{c}alves | Forecasting Covid-19 dynamics in Brazil: a data driven approach | First version submitted to Chaos, Solitons and Fractals, 41 pages,
complementary material at http://www.natalnet.br/covid | null | 10.3390/IJERPH17145115 | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | This paper has a twofold contribution. The first is a data driven approach
for predicting the Covid 19 pandemic dynamics, based on data from more advanced
countries. The second is to report and discuss the results obtained with this
approach for Brazilian states, as of May 4th, 2020. We start by presenting
preliminary results obtained by training an LSTM SAE network, which are
somewhat disappointing. Then, our main approach consists in an initial
clustering of the world regions for which data is available and where the
pandemic is at an advanced stage, based on a set of manually engineered
features representing a country response to the early spread of the pandemic. A
Modified Auto-Encoder network is then trained from these clusters and learns to
predict future data for Brazilian states. These predictions are used to
estimate important statistics about the disease, such as peaks. Finally, curve
fitting is carried out on the predictions in order to find the distribution
that best fits the outputs of the MAE, and to refine the estimates of the peaks
of the pandemic. Results indicate that the pandemic is still growing in Brazil,
with most states peaks of infection estimated between the 25th of April and the
19th of May 2020. Predicted numbers reach a total of 240 thousand infected
Brazilians, distributed among the different states, with S\~ao Paulo leading
with almost 65 thousands estimated, confirmed cases. The estimated end of the
pandemics (with 97 percent of cases reaching an outcome) starts as of May 28th
for some states and rests through August 14th, 2020.
| [
{
"created": "Sat, 9 May 2020 11:49:44 GMT",
"version": "v1"
}
] | 2021-06-28 | [
[
"Pereira",
"Igor G.",
""
],
[
"Guerin",
"Joris M.",
""
],
[
"Junior",
"Andouglas G. Silva",
""
],
[
"Distante",
"Cosimo",
""
],
[
"Garcia",
"Gabriel S.",
""
],
[
"Gonçalves",
"Luiz M. G.",
""
]
] | This paper has a twofold contribution. The first is a data driven approach for predicting the Covid 19 pandemic dynamics, based on data from more advanced countries. The second is to report and discuss the results obtained with this approach for Brazilian states, as of May 4th, 2020. We start by presenting preliminary results obtained by training an LSTM SAE network, which are somewhat disappointing. Then, our main approach consists in an initial clustering of the world regions for which data is available and where the pandemic is at an advanced stage, based on a set of manually engineered features representing a country response to the early spread of the pandemic. A Modified Auto-Encoder network is then trained from these clusters and learns to predict future data for Brazilian states. These predictions are used to estimate important statistics about the disease, such as peaks. Finally, curve fitting is carried out on the predictions in order to find the distribution that best fits the outputs of the MAE, and to refine the estimates of the peaks of the pandemic. Results indicate that the pandemic is still growing in Brazil, with most states peaks of infection estimated between the 25th of April and the 19th of May 2020. Predicted numbers reach a total of 240 thousand infected Brazilians, distributed among the different states, with S\~ao Paulo leading with almost 65 thousands estimated, confirmed cases. The estimated end of the pandemics (with 97 percent of cases reaching an outcome) starts as of May 28th for some states and rests through August 14th, 2020. |
2210.13238 | Elena Tutubalina Dr. | Andrey Sakhovskiy and Elena Tutubalina | Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction
Classification | This paper is accepted to Journal of Biomedical Informatics | Journal of Biomedical Informatics, Volume 135, 2022, 104182, ISSN
1532-0464 | 10.1016/j.jbi.2022.104182 | null | q-bio.QM cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | In this paper, we focus on the classification of tweets as sources of
potential signals for adverse drug effects (ADEs) or drug reactions (ADRs).
Following the intuition that text and drug structure representations are
complementary, we introduce a multimodal model with two components. These
components are state-of-the-art BERT-based models for language understanding
and molecular property prediction. Experiments were carried out on multilingual
benchmarks of the Social Media Mining for Health Research and Applications
(#SMM4H) initiative. Our models obtained state-of-the-art results of 0.61 F1
and 0.57 F1 on #SMM4H 2021 Shared Tasks 1a and 2 in English and Russian,
respectively. On the classification of French tweets from SMM4H 2020 Task 1,
our approach pushes the state of the art by an absolute gain of 8% F1. Our
experiments show that the molecular information obtained from neural networks
is more beneficial for ADE classification than traditional molecular
descriptors. The source code for our models is freely available at
https://github.com/Andoree/smm4h_2021_classification.
| [
{
"created": "Fri, 21 Oct 2022 11:41:45 GMT",
"version": "v1"
}
] | 2023-11-21 | [
[
"Sakhovskiy",
"Andrey",
""
],
[
"Tutubalina",
"Elena",
""
]
] | In this paper, we focus on the classification of tweets as sources of potential signals for adverse drug effects (ADEs) or drug reactions (ADRs). Following the intuition that text and drug structure representations are complementary, we introduce a multimodal model with two components. These components are state-of-the-art BERT-based models for language understanding and molecular property prediction. Experiments were carried out on multilingual benchmarks of the Social Media Mining for Health Research and Applications (#SMM4H) initiative. Our models obtained state-of-the-art results of 0.61 F1 and 0.57 F1 on #SMM4H 2021 Shared Tasks 1a and 2 in English and Russian, respectively. On the classification of French tweets from SMM4H 2020 Task 1, our approach pushes the state of the art by an absolute gain of 8% F1. Our experiments show that the molecular information obtained from neural networks is more beneficial for ADE classification than traditional molecular descriptors. The source code for our models is freely available at https://github.com/Andoree/smm4h_2021_classification. |
q-bio/0510033 | Niranjan Joshi Dr. | T. R. Shankar Raman, N. V. Joshi, R. Sukumar | Tropical rainforest bird community structure in relation to altitude,
tree species composition, and null models in the Western Ghats, India | 36 pages, 5 figures, two tables (including one in the appendix)
Submitted to the Journal of the Bombay Natural History Society (JBNHS) | null | null | null | q-bio.PE | null | Studies of species distributions on elevational gradients are essential to
understand principles of community organisation as well as to conserve species
in montane regions. This study examined the patterns of species richness,
abundance, composition, range sizes, and distribution of rainforest birds at 14
sites along an elevational gradient (500-1400 m) in the Kalakad-Mundanthurai
Tiger Reserve (KMTR) of the Western Ghats, India. In contrast to theoretical
expectation, resident bird species richness did not change significantly with
elevation although the species composition changed substantially (<10%
similarity) between the lowest and highest elevation sites. Constancy in
species richness was possibly due to relative constancy in productivity and
lack of elevational trends in vegetation structure. Elevational range size of
birds, expected to increase with elevation according to Rapoport's rule, was
found to show a contrasting inverse U-shaped pattern because species with
narrow elevational distributions, including endemics, occurred at both ends of
the gradient (below 800 m and above 1,200 m). Bird species composition also did
not vary randomly along the gradient as assessed using a hierarchy of null
models of community assembly, from completely unconstrained models to ones with
species richness and range-size distribution restrictions. Instead, bird
community composition was significantly correlated with elevation and tree
species composition of sites, indicating the influence of deterministic factors
on bird community structure. Conservation of low- and high-elevation areas and
maintenance of tree species composition against habitat alteration are
important for bird conservation in the southern Western Ghats rainforests.
| [
{
"created": "Mon, 17 Oct 2005 15:23:00 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Raman",
"T. R. Shankar",
""
],
[
"Joshi",
"N. V.",
""
],
[
"Sukumar",
"R.",
""
]
] | Studies of species distributions on elevational gradients are essential to understand principles of community organisation as well as to conserve species in montane regions. This study examined the patterns of species richness, abundance, composition, range sizes, and distribution of rainforest birds at 14 sites along an elevational gradient (500-1400 m) in the Kalakad-Mundanthurai Tiger Reserve (KMTR) of the Western Ghats, India. In contrast to theoretical expectation, resident bird species richness did not change significantly with elevation although the species composition changed substantially (<10% similarity) between the lowest and highest elevation sites. Constancy in species richness was possibly due to relative constancy in productivity and lack of elevational trends in vegetation structure. Elevational range size of birds, expected to increase with elevation according to Rapoport's rule, was found to show a contrasting inverse U-shaped pattern because species with narrow elevational distributions, including endemics, occurred at both ends of the gradient (below 800 m and above 1,200 m). Bird species composition also did not vary randomly along the gradient as assessed using a hierarchy of null models of community assembly, from completely unconstrained models to ones with species richness and range-size distribution restrictions. Instead, bird community composition was significantly correlated with elevation and tree species composition of sites, indicating the influence of deterministic factors on bird community structure. Conservation of low- and high-elevation areas and maintenance of tree species composition against habitat alteration are important for bird conservation in the southern Western Ghats rainforests. |
2407.18811 | Burak Yelmen | Burak Yelmen, Maris Alver, Estonian Biobank Research Team, Flora Jay,
Lili Milani | Interpreting artificial neural networks to detect genome-wide
association signals for complex traits | null | null | null | null | q-bio.GN cs.LG q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Investigating the genetic architecture of complex diseases is challenging due
to the highly polygenic and interactive landscape of genetic and environmental
factors. Although genome-wide association studies (GWAS) have identified
thousands of variants for multiple complex phenotypes, conventional statistical
approaches can be limited by simplified assumptions such as linearity and lack
of epistasis models. In this work, we trained artificial neural networks for
predicting complex traits using both simulated and real genotype/phenotype
datasets. We extracted feature importance scores via different post hoc
interpretability methods to identify potentially associated loci (PAL) for the
target phenotype. Simulations we performed with various parameters demonstrated
that associated loci can be detected with good precision using strict selection
criteria, but downstream analyses are required for fine-mapping the exact
variants due to linkage disequilibrium, similarly to conventional GWAS. By
applying our approach to the schizophrenia cohort in the Estonian Biobank, we
were able to detect multiple PAL related to this highly polygenic and heritable
disorder. We also performed enrichment analyses with PAL in genic regions,
which predominantly identified terms associated with brain morphology. With
further improvements in model optimization and confidence measures, artificial
neural networks can enhance the identification of genomic loci associated with
complex diseases, providing a more comprehensive approach for GWAS and serving
as initial screening tools for subsequent functional studies.
Keywords: Deep learning, interpretability, genome-wide association studies,
complex diseases
| [
{
"created": "Fri, 26 Jul 2024 15:20:42 GMT",
"version": "v1"
}
] | 2024-07-29 | [
[
"Yelmen",
"Burak",
""
],
[
"Alver",
"Maris",
""
],
[
"Team",
"Estonian Biobank Research",
""
],
[
"Jay",
"Flora",
""
],
[
"Milani",
"Lili",
""
]
] | Investigating the genetic architecture of complex diseases is challenging due to the highly polygenic and interactive landscape of genetic and environmental factors. Although genome-wide association studies (GWAS) have identified thousands of variants for multiple complex phenotypes, conventional statistical approaches can be limited by simplified assumptions such as linearity and lack of epistasis models. In this work, we trained artificial neural networks for predicting complex traits using both simulated and real genotype/phenotype datasets. We extracted feature importance scores via different post hoc interpretability methods to identify potentially associated loci (PAL) for the target phenotype. Simulations we performed with various parameters demonstrated that associated loci can be detected with good precision using strict selection criteria, but downstream analyses are required for fine-mapping the exact variants due to linkage disequilibrium, similarly to conventional GWAS. By applying our approach to the schizophrenia cohort in the Estonian Biobank, we were able to detect multiple PAL related to this highly polygenic and heritable disorder. We also performed enrichment analyses with PAL in genic regions, which predominantly identified terms associated with brain morphology. With further improvements in model optimization and confidence measures, artificial neural networks can enhance the identification of genomic loci associated with complex diseases, providing a more comprehensive approach for GWAS and serving as initial screening tools for subsequent functional studies. Keywords: Deep learning, interpretability, genome-wide association studies, complex diseases |
2009.00664 | Carlos Oliver Dr. | Carlos Oliver, Vincent Mallet, Pericles Philippopoulos, William L.
Hamilton, Jerome Waldispuhl | VeRNAl: Mining RNA Structures for Fuzzy Base Pairing Network Motifs | null | null | 10.1093/bioinformatics/btab844 | null | q-bio.MN cs.LG cs.SI | http://creativecommons.org/licenses/by/4.0/ | RNA 3D motifs are recurrent substructures, modelled as networks of base pair
interactions, which are crucial for understanding structure-function
relationships. The task of automatically identifying such motifs is
computationally hard, and remains a key challenge in the field of RNA
structural biology and network analysis. State of the art methods solve special
cases of the motif problem by constraining the structural variability in
occurrences of a motif, and narrowing the substructure search space. Here, we
relax these constraints by posing the motif finding problem as a graph
representation learning and clustering task. This framing takes advantage of
the continuous nature of graph representations to model the flexibility and
variability of RNA motifs in an efficient manner. We propose a set of node
similarity functions, clustering methods, and motif construction algorithms to
recover flexible RNA motifs. Our tool, VeRNAl can be easily customized by users
to desired levels of motif flexibility, abundance and size. We show that VeRNAl
is able to retrieve and expand known classes of motifs, as well as to propose
novel motifs.
| [
{
"created": "Tue, 1 Sep 2020 19:03:06 GMT",
"version": "v1"
},
{
"created": "Sat, 19 Dec 2020 20:34:37 GMT",
"version": "v2"
},
{
"created": "Mon, 18 Oct 2021 14:45:41 GMT",
"version": "v3"
}
] | 2022-06-03 | [
[
"Oliver",
"Carlos",
""
],
[
"Mallet",
"Vincent",
""
],
[
"Philippopoulos",
"Pericles",
""
],
[
"Hamilton",
"William L.",
""
],
[
"Waldispuhl",
"Jerome",
""
]
] | RNA 3D motifs are recurrent substructures, modelled as networks of base pair interactions, which are crucial for understanding structure-function relationships. The task of automatically identifying such motifs is computationally hard, and remains a key challenge in the field of RNA structural biology and network analysis. State of the art methods solve special cases of the motif problem by constraining the structural variability in occurrences of a motif, and narrowing the substructure search space. Here, we relax these constraints by posing the motif finding problem as a graph representation learning and clustering task. This framing takes advantage of the continuous nature of graph representations to model the flexibility and variability of RNA motifs in an efficient manner. We propose a set of node similarity functions, clustering methods, and motif construction algorithms to recover flexible RNA motifs. Our tool, VeRNAl can be easily customized by users to desired levels of motif flexibility, abundance and size. We show that VeRNAl is able to retrieve and expand known classes of motifs, as well as to propose novel motifs. |
1808.01612 | Birgitta Dresp-Langley | Birgitta Dresp-Langley and Adam Reeves | Colour for behavioural success | null | 2018, i-Perception, 9(2), 2041669518767171 | 10.1177/2041669518767171 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Colour information not only helps sustain the survival of animal species by
guiding sexual selection and foraging behaviour, but also is an important
factor in the cultural and technological development of our own species. This
is illustrated by examples from the visual arts and from state-of-the-art
imaging technology, where the strategic use of colour has become a powerful
tool for guiding the planning and execution of interventional procedures. The
functional role of colour information in terms of its potential benefits to
behavioural success across the species is addressed in the introduction here to
clarify why colour perception may have evolved to generate behavioural success.
It is argued that evolutionary and environmental pressures influence not only
colour trait production in the different species, but also their ability to
process and exploit colour information for goal-specific purposes. We then leap
straight to the human primate with insight from current research on the
facilitating role of colour cues on performance training with precision
technology for image-guided surgical planning and intervention. It is shown
that local colour cues in 2D images generated by a surgical fisheye camera help
individuals become more precise rapidly across a limited number of trial sets
in simulator training for specific manual gestures with a tool. The
progressively facilitating effect of a local colour cue on performance
evolution in a video controlled simulator task can be explained in terms of
colour-based figure-ground segregation guiding attention towards local image
parts, with an immediate cost on performance time, and a lasting long-term
benefit in terms of greater task precision.
| [
{
"created": "Sun, 5 Aug 2018 13:20:57 GMT",
"version": "v1"
}
] | 2022-06-03 | [
[
"Dresp-Langley",
"Birgitta",
""
],
[
"Reeves",
"Adam",
""
]
] | Colour information not only helps sustain the survival of animal species by guiding sexual selection and foraging behaviour, but also is an important factor in the cultural and technological development of our own species. This is illustrated by examples from the visual arts and from state-of-the-art imaging technology, where the strategic use of colour has become a powerful tool for guiding the planning and execution of interventional procedures. The functional role of colour information in terms of its potential benefits to behavioural success across the species is addressed in the introduction here to clarify why colour perception may have evolved to generate behavioural success. It is argued that evolutionary and environmental pressures influence not only colour trait production in the different species, but also their ability to process and exploit colour information for goal-specific purposes. We then leap straight to the human primate with insight from current research on the facilitating role of colour cues on performance training with precision technology for image-guided surgical planning and intervention. It is shown that local colour cues in 2D images generated by a surgical fisheye camera help individuals become more precise rapidly across a limited number of trial sets in simulator training for specific manual gestures with a tool. The progressively facilitating effect of a local colour cue on performance evolution in a video controlled simulator task can be explained in terms of colour-based figure-ground segregation guiding attention towards local image parts, with an immediate cost on performance time, and a lasting long-term benefit in terms of greater task precision. |
q-bio/0605030 | Peter Csermely | Csaba Soti and Peter Csermely | Aging cellular networks: chaperones as major participants | 7 pages, 4 figures | Experimental Gerontology 42, 113-119 (2007) | 10.1016/j.exger.2006.05.017 | null | q-bio.MN | null | We increasingly rely on the network approach to understand the complexity of
cellular functions. Chaperones (heat shock proteins) are key "networkers",
which have among their functions to sequester and repair damaged protein. In
order to link the network approach and chaperones with the aging process, we
first summarize the properties of aging networks suggesting a "weak link theory
of aging". This theory suggests that age-related random damage primarily
affects the overwhelming majority of the low affinity, transient interactions
(weak links) in cellular networks leading to increased noise, destabilization
and diversity. These processes may be further amplified by age-specific network
remodelling and by the sequestration of weakly linked cellular proteins to
protein aggregates of aging cells. Chaperones are weakly linked hubs [i.e.,
network elements with a large number of connections] and inter-modular bridge
elements of protein-protein interaction, signalling and mitochondrial networks.
As aging proceeds, the increased overload of damaged proteins is an especially
important element contributing to cellular disintegration and destabilization.
Additionally, chaperone overload may contribute to the increase of "noise" in
aging cells, which leads to an increased stochastic resonance resulting in a
deficient discrimination between signals and noise. Chaperone- and other
multi-target therapies, which restore the missing weak links in aging cellular
networks, may emerge as important anti-aging interventions.
| [
{
"created": "Thu, 18 May 2006 19:16:03 GMT",
"version": "v1"
},
{
"created": "Fri, 22 Dec 2006 08:13:46 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Soti",
"Csaba",
""
],
[
"Csermely",
"Peter",
""
]
] | We increasingly rely on the network approach to understand the complexity of cellular functions. Chaperones (heat shock proteins) are key "networkers", which have among their functions to sequester and repair damaged protein. In order to link the network approach and chaperones with the aging process, we first summarize the properties of aging networks suggesting a "weak link theory of aging". This theory suggests that age-related random damage primarily affects the overwhelming majority of the low affinity, transient interactions (weak links) in cellular networks leading to increased noise, destabilization and diversity. These processes may be further amplified by age-specific network remodelling and by the sequestration of weakly linked cellular proteins to protein aggregates of aging cells. Chaperones are weakly linked hubs [i.e., network elements with a large number of connections] and inter-modular bridge elements of protein-protein interaction, signalling and mitochondrial networks. As aging proceeds, the increased overload of damaged proteins is an especially important element contributing to cellular disintegration and destabilization. Additionally, chaperone overload may contribute to the increase of "noise" in aging cells, which leads to an increased stochastic resonance resulting in a deficient discrimination between signals and noise. Chaperone- and other multi-target therapies, which restore the missing weak links in aging cellular networks, may emerge as important anti-aging interventions. |
2101.01884 | Tong Wang | Yao Li, Tong Wang, Juanrong Zhang, Bin Shao, Haipeng Gong, Yusong
Wang, Siyuan Liu and Tie-Yan Liu | Exploring the Regulatory Function of the N-terminal Domain of SARS-CoV-2
Spike Protein Through Molecular Dynamics Simulation | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | SARS-CoV-2 is what has caused the COVID-19 pandemic. Early viral infection is
mediated by the SARS-CoV-2 homo-trimeric Spike (S) protein with its receptor
binding domains (RBDs) in the receptor-accessible state. We performed molecular
dynamics simulation on the S protein with a focus on the function of its
N-terminal domains (NTDs). Our study reveals that the NTD acts as a "wedge" and
plays a crucial regulatory role in the conformational changes of the S protein.
The complete RBD structural transition is allowed only when the neighboring NTD
that typically prohibits the RBD's movements as a wedge detaches and swings
away. Based on this NTD "wedge" model, we propose that the NTD-RBD interface
should be a potential drug target.
| [
{
"created": "Wed, 6 Jan 2021 05:53:23 GMT",
"version": "v1"
}
] | 2021-01-07 | [
[
"Li",
"Yao",
""
],
[
"Wang",
"Tong",
""
],
[
"Zhang",
"Juanrong",
""
],
[
"Shao",
"Bin",
""
],
[
"Gong",
"Haipeng",
""
],
[
"Wang",
"Yusong",
""
],
[
"Liu",
"Siyuan",
""
],
[
"Liu",
"Tie-Yan",
... | SARS-CoV-2 is what has caused the COVID-19 pandemic. Early viral infection is mediated by the SARS-CoV-2 homo-trimeric Spike (S) protein with its receptor binding domains (RBDs) in the receptor-accessible state. We performed molecular dynamics simulation on the S protein with a focus on the function of its N-terminal domains (NTDs). Our study reveals that the NTD acts as a "wedge" and plays a crucial regulatory role in the conformational changes of the S protein. The complete RBD structural transition is allowed only when the neighboring NTD that typically prohibits the RBD's movements as a wedge detaches and swings away. Based on this NTD "wedge" model, we propose that the NTD-RBD interface should be a potential drug target. |
2004.04567 | Pavel Golovinski | P.A. Golovinski | The pandemic of viruses with a long incubation phase in the small world | null | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A model of the spread of viruses in selected city and in a network of cities
is considered, taking into account the delay caused by the long incubation
period of the virus. The effect of delay effects is shown in comparison with
pandemics without such delay. A temporary asymmetry of the spread of infection
has been identified, which means that the time for a pandemic to develop
significantly exceeds the time for its completion. Model calculations of the
spread of viruses in a network of interconnected large and small cities were
carried out, and dynamics features were revealed in comparison with the spread
of viruses in a single city, including the possibility of reinfection of
megalopolis.
| [
{
"created": "Wed, 8 Apr 2020 08:02:53 GMT",
"version": "v1"
},
{
"created": "Sun, 3 May 2020 09:18:51 GMT",
"version": "v2"
}
] | 2020-05-05 | [
[
"Golovinski",
"P. A.",
""
]
] | A model of the spread of viruses in selected city and in a network of cities is considered, taking into account the delay caused by the long incubation period of the virus. The effect of delay effects is shown in comparison with pandemics without such delay. A temporary asymmetry of the spread of infection has been identified, which means that the time for a pandemic to develop significantly exceeds the time for its completion. Model calculations of the spread of viruses in a network of interconnected large and small cities were carried out, and dynamics features were revealed in comparison with the spread of viruses in a single city, including the possibility of reinfection of megalopolis. |
2003.13634 | Thorsten Pr\"ustel | Thorsten Pr\"ustel and Martin Meier-Schellersheim | Stochastic single-particle based simulations of cellular signaling
embedded into computational models of cellular morphology | 26 pages, 7 figures | null | null | null | q-bio.QM | http://creativecommons.org/publicdomain/zero/1.0/ | Cells exhibit a wide variety of different shapes. This diversity poses a
challenge for computational approaches that attempt to shed light on the role
cell geometry plays in regulating cell physiology and behavior. The simulation
platform Simmune is capable of embedding the computational representation of
signaling pathways into realistic models of cellular morphology. However,
Simmune's current approach to account for the cell geometry is limited to
deterministic models of reaction-diffusion processes, thus providing a
coarse-grained description that ignores stochastic local fluctuations. Here we
present an extension of Simmune that removes these limitations by employing an
alternative computational representation of cellular geometry that is smooth
and grid-free. These features make it possible to incorporate a fully
stochastic, spatially resolved description of the cellular biochemistry. The
alternative computational representation is compatible with Simmune's current
approach for specifying molecular interactions. This means that a modeler using
the approach needs to create a model of cellular biochemistry and morphology
only once to be able to use it for both, deterministic and stochastic
simulations.
| [
{
"created": "Mon, 30 Mar 2020 17:10:04 GMT",
"version": "v1"
}
] | 2020-03-31 | [
[
"Prüstel",
"Thorsten",
""
],
[
"Meier-Schellersheim",
"Martin",
""
]
] | Cells exhibit a wide variety of different shapes. This diversity poses a challenge for computational approaches that attempt to shed light on the role cell geometry plays in regulating cell physiology and behavior. The simulation platform Simmune is capable of embedding the computational representation of signaling pathways into realistic models of cellular morphology. However, Simmune's current approach to account for the cell geometry is limited to deterministic models of reaction-diffusion processes, thus providing a coarse-grained description that ignores stochastic local fluctuations. Here we present an extension of Simmune that removes these limitations by employing an alternative computational representation of cellular geometry that is smooth and grid-free. These features make it possible to incorporate a fully stochastic, spatially resolved description of the cellular biochemistry. The alternative computational representation is compatible with Simmune's current approach for specifying molecular interactions. This means that a modeler using the approach needs to create a model of cellular biochemistry and morphology only once to be able to use it for both, deterministic and stochastic simulations. |
q-bio/0605029 | Fabio De Blasio | Fabio Vittorio De Blasio | Thriving at high hydrostatic pressure: the example of ammonoids (extinct
cephalopods) | 18 pages, 3 figures, 1 table | null | null | null | q-bio.OT | null | Ammonoids are a group of extinct mollusks belonging to the same class of the
living genus Nautilus (Cephalopoda). In both Nautili and ammonoids, the
(usually planospiral) shell is divided into chambers separated by septa that
during the lifetime were filled with gas at atmospheric pressure. The
intersection of septa with the external shell generates a curve called the
suture line, which in living and most fossil Nautili is fairly uncomplicated.
In contrast, suture lines of ancient ammonoid were gently curved and during the
evolution of the group became highly complex, in some cases so extensively
frilled to be considerable as fractal curves. Numerous theories have been put
forward to explain the complexity of suture ammonoid lines. Calculations
presented here lend support to the hypothesis that complex suture lines aided
in counteracting the effect of the external water pressure. Additionally, it is
found that complex suture lines diminished shell shrinkage caused by water
pressure, and thus aided improve buoyancy. Understanding the reason for complex
sutures in ammonoids does not only represent an important issue in
paleobiology, but is also a challenging problem in the resistance of complex
mechanical structures subjected to high pressure.
| [
{
"created": "Thu, 18 May 2006 09:42:01 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"De Blasio",
"Fabio Vittorio",
""
]
] | Ammonoids are a group of extinct mollusks belonging to the same class of the living genus Nautilus (Cephalopoda). In both Nautili and ammonoids, the (usually planospiral) shell is divided into chambers separated by septa that during the lifetime were filled with gas at atmospheric pressure. The intersection of septa with the external shell generates a curve called the suture line, which in living and most fossil Nautili is fairly uncomplicated. In contrast, suture lines of ancient ammonoid were gently curved and during the evolution of the group became highly complex, in some cases so extensively frilled to be considerable as fractal curves. Numerous theories have been put forward to explain the complexity of suture ammonoid lines. Calculations presented here lend support to the hypothesis that complex suture lines aided in counteracting the effect of the external water pressure. Additionally, it is found that complex suture lines diminished shell shrinkage caused by water pressure, and thus aided improve buoyancy. Understanding the reason for complex sutures in ammonoids does not only represent an important issue in paleobiology, but is also a challenging problem in the resistance of complex mechanical structures subjected to high pressure. |
2303.17502 | Louisa Smieska | Louisa Smieska, Mary Lou Guerinot, Karin Olson Hoal, Matthew Reid,
Olena Vatamaniuk | Synchrotron Science for Sustainability: Life Cycle of Metals in the
Environment | 25 pages with references, 8 figures, submitted to Metallomics | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The movement of metals through the environment links together a wide range of
scientific fields: from earth sciences and geology as weathering releases
minerals; to environmental sciences as metals are mobilized and transformed,
cycling through soil and water; to biology as living things take up metals from
their surroundings. Studies of these fundamental processes all require
quantitative analysis of metal concentrations, locations, and chemical states.
Synchrotron x-ray tools can address these requirements with high sensitivity,
high spatial resolution, and minimal sample preparation. This perspective
describes the state of fundamental scientific questions in the lifecycle of
metals, from rocks to ecosystems, from soils to plants, and from environment to
animals. Key x-ray capabilities and facility infrastructure for future
synchrotron-based analytical resources serving these areas are summarized, and
potential opportunities for future experiments are explored.
| [
{
"created": "Thu, 30 Mar 2023 16:13:50 GMT",
"version": "v1"
}
] | 2023-03-31 | [
[
"Smieska",
"Louisa",
""
],
[
"Guerinot",
"Mary Lou",
""
],
[
"Hoal",
"Karin Olson",
""
],
[
"Reid",
"Matthew",
""
],
[
"Vatamaniuk",
"Olena",
""
]
] | The movement of metals through the environment links together a wide range of scientific fields: from earth sciences and geology as weathering releases minerals; to environmental sciences as metals are mobilized and transformed, cycling through soil and water; to biology as living things take up metals from their surroundings. Studies of these fundamental processes all require quantitative analysis of metal concentrations, locations, and chemical states. Synchrotron x-ray tools can address these requirements with high sensitivity, high spatial resolution, and minimal sample preparation. This perspective describes the state of fundamental scientific questions in the lifecycle of metals, from rocks to ecosystems, from soils to plants, and from environment to animals. Key x-ray capabilities and facility infrastructure for future synchrotron-based analytical resources serving these areas are summarized, and potential opportunities for future experiments are explored. |
2006.01731 | Daniel Guti\'errez Reina Dr | Teodoro Alamo, D. G. Reina, Pablo Mill\'an | Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19
Pandemic: Leveraging Data Science, Epidemiology and Control Theory | null | null | null | null | q-bio.PE cs.LG physics.data-an physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This document analyzes the role of data-driven methodologies in Covid-19
pandemic. We provide a SWOT analysis and a roadmap that goes from the access to
data sources to the final decision-making step. We aim to review the available
methodologies while anticipating the difficulties and challenges in the
development of data-driven strategies to combat the Covid-19 pandemic. A
3M-analysis is presented: Monitoring, Modelling and Making decisions. The focus
is on the potential of well-known datadriven schemes to address different
challenges raised by the pandemic: i) monitoring and forecasting the spread of
the epidemic; (ii) assessing the effectiveness of government decisions; (iii)
making timely decisions. Each step of the roadmap is detailed through a review
of consolidated theoretical results and their potential application in the
Covid-19 context. When possible, we provide examples of their applications on
past or present epidemics. We do not provide an exhaustive enumeration of
methodologies, algorithms and applications. We do try to serve as a bridge
between different disciplines required to provide a holistic approach to the
epidemic: data science, epidemiology, controltheory, etc. That is, we highlight
effective data-driven methodologies that have been shown to be successful in
other contexts and that have potential application in the different steps of
the proposed roadmap. To make this document more functional and adapted to the
specifics of each discipline, we encourage researchers and practitioners to
provide feedback. We will update this document regularly.
| [
{
"created": "Mon, 1 Jun 2020 12:56:43 GMT",
"version": "v1"
},
{
"created": "Wed, 10 Jun 2020 15:25:14 GMT",
"version": "v2"
}
] | 2020-06-11 | [
[
"Alamo",
"Teodoro",
""
],
[
"Reina",
"D. G.",
""
],
[
"Millán",
"Pablo",
""
]
] | This document analyzes the role of data-driven methodologies in Covid-19 pandemic. We provide a SWOT analysis and a roadmap that goes from the access to data sources to the final decision-making step. We aim to review the available methodologies while anticipating the difficulties and challenges in the development of data-driven strategies to combat the Covid-19 pandemic. A 3M-analysis is presented: Monitoring, Modelling and Making decisions. The focus is on the potential of well-known datadriven schemes to address different challenges raised by the pandemic: i) monitoring and forecasting the spread of the epidemic; (ii) assessing the effectiveness of government decisions; (iii) making timely decisions. Each step of the roadmap is detailed through a review of consolidated theoretical results and their potential application in the Covid-19 context. When possible, we provide examples of their applications on past or present epidemics. We do not provide an exhaustive enumeration of methodologies, algorithms and applications. We do try to serve as a bridge between different disciplines required to provide a holistic approach to the epidemic: data science, epidemiology, controltheory, etc. That is, we highlight effective data-driven methodologies that have been shown to be successful in other contexts and that have potential application in the different steps of the proposed roadmap. To make this document more functional and adapted to the specifics of each discipline, we encourage researchers and practitioners to provide feedback. We will update this document regularly. |
1101.1094 | Alain Destexhe | Claude Bedard and Alain Destexhe | A generalized theory for current-source density analysis in brain tissue | Physical Review E, in press, 2011 | Physical Review E 84: 041909 (2011) | 10.1103/PhysRevE.84.041909 | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The current-source density (CSD) analysis is a widely used method in brain
electrophysiology, but this method rests on a series of assumptions, namely
that the surrounding extracellular medium is resistive and uniform, and in some
versions of the theory, that the current sources are exclusively made by
dipoles. Because of these assumptions, this standard model does not correctly
describe the contributions of monopolar sources or of non-resistive aspects of
the extracellular medium. We propose here a general framework to model electric
fields and potentials resulting from current source densities, without relying
on the above assumptions. We develop a mean-field formalism which is a
generalization of the standard model, and which can directly incorporate
non-resistive (non-ohmic) properties of the extracellular medium, such as ionic
diffusion effects. This formalism recovers the classic results of the standard
model such as the CSD analysis, but in addition, we provide expressions to
generalize the CSD approach to situations with non-resistive media and
arbitrarily complex multipolar configurations of current sources. We found that
the power spectrum of the signal contains the signature of the nature of
current sources and extracellular medium, which provides a direct way to
estimate those properties from experimental data, and in particular, estimate
the possible contribution of electric monopoles.
| [
{
"created": "Wed, 5 Jan 2011 21:04:11 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Jan 2011 17:08:24 GMT",
"version": "v2"
},
{
"created": "Tue, 13 Sep 2011 21:01:19 GMT",
"version": "v3"
}
] | 2015-05-20 | [
[
"Bedard",
"Claude",
""
],
[
"Destexhe",
"Alain",
""
]
] | The current-source density (CSD) analysis is a widely used method in brain electrophysiology, but this method rests on a series of assumptions, namely that the surrounding extracellular medium is resistive and uniform, and in some versions of the theory, that the current sources are exclusively made by dipoles. Because of these assumptions, this standard model does not correctly describe the contributions of monopolar sources or of non-resistive aspects of the extracellular medium. We propose here a general framework to model electric fields and potentials resulting from current source densities, without relying on the above assumptions. We develop a mean-field formalism which is a generalization of the standard model, and which can directly incorporate non-resistive (non-ohmic) properties of the extracellular medium, such as ionic diffusion effects. This formalism recovers the classic results of the standard model such as the CSD analysis, but in addition, we provide expressions to generalize the CSD approach to situations with non-resistive media and arbitrarily complex multipolar configurations of current sources. We found that the power spectrum of the signal contains the signature of the nature of current sources and extracellular medium, which provides a direct way to estimate those properties from experimental data, and in particular, estimate the possible contribution of electric monopoles. |
1803.06002 | Rajaram Gana | Rajaram Gana, Swagata Naha, Raja Mazumder, Radoslav Goldman, and Sona
Vasudevan | Ridge Regression Estimated Linear Probability Model Predictions of
N-glycosylation in Proteins with Structural and Sequence Data | 20 pages | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Absent experimental evidence, a robust methodology to predict the likelihood
of N-glycosylation in human proteins is essential for guiding experimental
work. Based on the distribution of amino acids in the neighborhood of the NxS/T
sequon (N-site); the structural attributes of the N-site that include
Accessible Surface Area, secondary structural elements, main-chain phi-psi,
turn types; the relative location of the N-site in the primary sequence; and
the nature of the glycan bound, the ridge regression estimated linear
probability model is used to predict this likelihood. This model yields a
Kolmogorov-Smirnov (Gini coefficient) statistic value of about 74% (89%), which
is reasonable.
| [
{
"created": "Thu, 15 Mar 2018 20:56:11 GMT",
"version": "v1"
}
] | 2018-03-26 | [
[
"Gana",
"Rajaram",
""
],
[
"Naha",
"Swagata",
""
],
[
"Mazumder",
"Raja",
""
],
[
"Goldman",
"Radoslav",
""
],
[
"Vasudevan",
"Sona",
""
]
] | Absent experimental evidence, a robust methodology to predict the likelihood of N-glycosylation in human proteins is essential for guiding experimental work. Based on the distribution of amino acids in the neighborhood of the NxS/T sequon (N-site); the structural attributes of the N-site that include Accessible Surface Area, secondary structural elements, main-chain phi-psi, turn types; the relative location of the N-site in the primary sequence; and the nature of the glycan bound, the ridge regression estimated linear probability model is used to predict this likelihood. This model yields a Kolmogorov-Smirnov (Gini coefficient) statistic value of about 74% (89%), which is reasonable. |
1208.5156 | Aleksandr Kivenson | Aleksandr Kivenson and Michael F. Hagan | Diffusion-limited rates on low-dimensional manifolds with extreme aspect
ratios | 6 pages, 4 figures, submitted to Phys. Rev. E | null | null | null | q-bio.BM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a single-species diffusion-limited annihilation reaction with
reactants confined to a two-dimensional surface with one arbitrarily large
dimension and the other comparable in size to interparticle distances. This
situation could describe reactants which undergo both longitudinal and
transverse diffusion on long filamentous molecules (such as microtubules), or
molecules that undergo truly one-dimensional translational diffusion (e.g. a
transcription factor on DNA) but simultaneously exhibit diffusive behavior in a
second dimension corresponding to a rotational or conformational degree of
freedom. We combine simple analytical arguments and Monte Carlo simulations to
show that the reaction rate law exhibits a crossover from one-dimensional to
two-dimensional diffusion as a function of particle concentration and the size
of the smaller dimension. In the case of a reversible binding reaction, the
diffusion-limited reaction rate is given by the Smoluchowski expression, but
the crossover is revealed in the statistics of particle collision histories.
The results can also be applied to a particle-antiparticle annihilation
reaction.
| [
{
"created": "Sat, 25 Aug 2012 18:30:15 GMT",
"version": "v1"
}
] | 2015-03-20 | [
[
"Kivenson",
"Aleksandr",
""
],
[
"Hagan",
"Michael F.",
""
]
] | We consider a single-species diffusion-limited annihilation reaction with reactants confined to a two-dimensional surface with one arbitrarily large dimension and the other comparable in size to interparticle distances. This situation could describe reactants which undergo both longitudinal and transverse diffusion on long filamentous molecules (such as microtubules), or molecules that undergo truly one-dimensional translational diffusion (e.g. a transcription factor on DNA) but simultaneously exhibit diffusive behavior in a second dimension corresponding to a rotational or conformational degree of freedom. We combine simple analytical arguments and Monte Carlo simulations to show that the reaction rate law exhibits a crossover from one-dimensional to two-dimensional diffusion as a function of particle concentration and the size of the smaller dimension. In the case of a reversible binding reaction, the diffusion-limited reaction rate is given by the Smoluchowski expression, but the crossover is revealed in the statistics of particle collision histories. The results can also be applied to a particle-antiparticle annihilation reaction. |
2209.05548 | Johannes Zierenberg | Anna Levina, Viola Priesemann, Johannes Zierenberg | Tackling the subsampling problem to infer collective properties from
limited data | 20 pages, 6 figures, review article | Nat. Rev. Phys. (2022) | 10.1038/s42254-022-00532-5 | null | q-bio.NC cond-mat.dis-nn physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Complex systems are fascinating because their rich macroscopic properties
emerge from the interaction of many simple parts. Understanding the building
principles of these emergent phenomena in nature requires assessing natural
complex systems experimentally. However, despite the development of large-scale
data-acquisition techniques, experimental observations are often limited to a
tiny fraction of the system. This spatial subsampling is particularly severe in
neuroscience, where only a tiny fraction of millions or even billions of
neurons can be individually recorded. Spatial subsampling may lead to
significant systematic biases when inferring the collective properties of the
entire system naively from a subsampled part. To overcome such biases, powerful
mathematical tools have been developed in the past. In this perspective, we
overview some issues arising from subsampling and review recently developed
approaches to tackle the subsampling problem. These approaches enable one to
assess, e.g., graph structures, collective dynamics of animals, neural network
activity, or the spread of disease correctly from observing only a tiny
fraction of the system. However, our current approaches are still far from
having solved the subsampling problem in general, and hence we conclude by
outlining what we believe are the main open challenges. Solving these
challenges alongside the development of large-scale recording techniques will
enable further fundamental insights into the working of complex and living
systems.
| [
{
"created": "Mon, 12 Sep 2022 18:55:48 GMT",
"version": "v1"
},
{
"created": "Sat, 17 Sep 2022 18:37:33 GMT",
"version": "v2"
}
] | 2022-11-17 | [
[
"Levina",
"Anna",
""
],
[
"Priesemann",
"Viola",
""
],
[
"Zierenberg",
"Johannes",
""
]
] | Complex systems are fascinating because their rich macroscopic properties emerge from the interaction of many simple parts. Understanding the building principles of these emergent phenomena in nature requires assessing natural complex systems experimentally. However, despite the development of large-scale data-acquisition techniques, experimental observations are often limited to a tiny fraction of the system. This spatial subsampling is particularly severe in neuroscience, where only a tiny fraction of millions or even billions of neurons can be individually recorded. Spatial subsampling may lead to significant systematic biases when inferring the collective properties of the entire system naively from a subsampled part. To overcome such biases, powerful mathematical tools have been developed in the past. In this perspective, we overview some issues arising from subsampling and review recently developed approaches to tackle the subsampling problem. These approaches enable one to assess, e.g., graph structures, collective dynamics of animals, neural network activity, or the spread of disease correctly from observing only a tiny fraction of the system. However, our current approaches are still far from having solved the subsampling problem in general, and hence we conclude by outlining what we believe are the main open challenges. Solving these challenges alongside the development of large-scale recording techniques will enable further fundamental insights into the working of complex and living systems. |
2007.12523 | Yen Ting Lin | Yen Ting Lin, Jacob Neumann, Ely Miller, Richard G. Posner, Abhishek
Mallela, Cosmin Safta, Jaideep Ray, Gautam Thakur, Supriya Chinthavali, and
William S. Hlavacek | Daily Forecasting of New Cases for Regional Epidemics of Coronavirus
Disease 2019 with Bayesian Uncertainty Quantification | 48 pages, 10 figures, 4 Appendix figures, 3 tables, 1 Appendix
figure, 1 Appendix text | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To increase situational awareness and support evidence-based policy-making,
we formulated two types of mathematical models for COVID-19 transmission within
a regional population. One is a fitting function that can be calibrated to
reproduce an epidemic curve with two timescales (e.g., fast growth and slow
decay). The other is a compartmental model that accounts for quarantine,
self-isolation, social distancing, a non-exponentially distributed incubation
period, asymptomatic individuals, and mild and severe forms of symptomatic
disease. Using Bayesian inference, we have been calibrating our models daily
for consistency with new reports of confirmed cases from the 15 most populous
metropolitan statistical areas in the United States and quantifying uncertainty
in parameter estimates and predictions of future case reports. This online
learning approach allows for early identification of new trends despite
considerable variability in case reporting. We infer new significant upward
trends for five of the metropolitan areas starting between 19-April-2020 and
12-June-2020.
| [
{
"created": "Mon, 20 Jul 2020 16:24:33 GMT",
"version": "v1"
}
] | 2020-07-27 | [
[
"Lin",
"Yen Ting",
""
],
[
"Neumann",
"Jacob",
""
],
[
"Miller",
"Ely",
""
],
[
"Posner",
"Richard G.",
""
],
[
"Mallela",
"Abhishek",
""
],
[
"Safta",
"Cosmin",
""
],
[
"Ray",
"Jaideep",
""
],
[
"T... | To increase situational awareness and support evidence-based policy-making, we formulated two types of mathematical models for COVID-19 transmission within a regional population. One is a fitting function that can be calibrated to reproduce an epidemic curve with two timescales (e.g., fast growth and slow decay). The other is a compartmental model that accounts for quarantine, self-isolation, social distancing, a non-exponentially distributed incubation period, asymptomatic individuals, and mild and severe forms of symptomatic disease. Using Bayesian inference, we have been calibrating our models daily for consistency with new reports of confirmed cases from the 15 most populous metropolitan statistical areas in the United States and quantifying uncertainty in parameter estimates and predictions of future case reports. This online learning approach allows for early identification of new trends despite considerable variability in case reporting. We infer new significant upward trends for five of the metropolitan areas starting between 19-April-2020 and 12-June-2020. |
1706.02764 | Ruth Rosenholtz | Ruth Rosenholtz | What modern vision science reveals about the awareness puzzle:
Summary-statistic encoding plus decision limits underlie the richness of
visual perception and its quirky failures | 12 pages, 8 figures. This is the "extended abstract" for a
presentation at the Vision Sciences Society symposium on summary statistics
and awareness, 2017 | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is a fundamental puzzle in understanding our awareness of the visual
world. On one hand, our subjective experience is one of a rich visual world,
which we perceive effortlessly. However, when we actually test perception,
observers know surprisingly little. A number of tasks, from search, through
inattentional blindness, to change blindness, suggest that there is
surprisingly little awareness or perception without attention. Meanwhile,
another set of tasks, such as multiple object tracking, dual-task performance,
and visual working memory tasks suggest that both attention and working memory
have low capacity. These two components together - poor perception without
attention, and greatly limited capacity for attention and memory - imply that
perception is impoverished.
How can we make sense of this awareness puzzle, of the riddle of our rich
subjective experience coupled with poor performance on experimental tasks? I
suggest that, looked at in the right way, there is in fact no awareness puzzle.
In particular, I will argue that the tasks that show limits are inherently
difficult tasks, and that there exists a unified explanation for both the rich
subjective experience and the apparent limits.
| [
{
"created": "Thu, 8 Jun 2017 20:41:50 GMT",
"version": "v1"
}
] | 2017-06-12 | [
[
"Rosenholtz",
"Ruth",
""
]
] | There is a fundamental puzzle in understanding our awareness of the visual world. On one hand, our subjective experience is one of a rich visual world, which we perceive effortlessly. However, when we actually test perception, observers know surprisingly little. A number of tasks, from search, through inattentional blindness, to change blindness, suggest that there is surprisingly little awareness or perception without attention. Meanwhile, another set of tasks, such as multiple object tracking, dual-task performance, and visual working memory tasks suggest that both attention and working memory have low capacity. These two components together - poor perception without attention, and greatly limited capacity for attention and memory - imply that perception is impoverished. How can we make sense of this awareness puzzle, of the riddle of our rich subjective experience coupled with poor performance on experimental tasks? I suggest that, looked at in the right way, there is in fact no awareness puzzle. In particular, I will argue that the tasks that show limits are inherently difficult tasks, and that there exists a unified explanation for both the rich subjective experience and the apparent limits. |
2309.00660 | Roberto Beneduci | Roberto Beneduci, Giovanni Mascali | Forest fire spreading: a nonlinear stochastic model continuous in space
and time | 25 pages, 27 figures | null | null | null | q-bio.PE math-ph math.AP math.MP math.PR | http://creativecommons.org/licenses/by/4.0/ | Forest fire spreading is a complex phenomenon characterized by a stochastic
behavior. Nowadays, the enormous quantity of georeferenced data and the
availability of powerful techniques for their analysis can provide a very
careful picture of forest fires opening the way to more realistic models. We
propose a stochastic spreading model continuous in space and time that is able
to use such data in their full power. The state of the forest fire is described
by the subprobability densities of the green trees and of the trees on fire
that can be estimated thanks to data coming from satellites and earth
detectors. The fire dynamics is encoded into a density probability kernel which
can take into account wind conditions, land slope, spotting phenomena and so
on, bringing to a system of integro-differential equations for the probability
densities. Existence and uniqueness of the solutions is proved by using
Banach's fixed point theorem. The asymptotic behavior of the model is analyzed
as well. Stochastic models based on cellular automata can be considered as
particular cases of the present model from which they can be derived by space
and/or time discretization. Suggesting a particular structure for the kernel,
we obtain numerical simulations of the fire spreading under different
conditions. For example, in the case of a forest fire evolving towards a river,
the simulations show that the probability density of the trees on fire is
different from zero beyond the river due to the spotting phenomenon.
Firefighters interventions and weather changes can be easily introduced into
the model.
| [
{
"created": "Fri, 1 Sep 2023 12:34:12 GMT",
"version": "v1"
}
] | 2023-09-06 | [
[
"Beneduci",
"Roberto",
""
],
[
"Mascali",
"Giovanni",
""
]
] | Forest fire spreading is a complex phenomenon characterized by a stochastic behavior. Nowadays, the enormous quantity of georeferenced data and the availability of powerful techniques for their analysis can provide a very careful picture of forest fires opening the way to more realistic models. We propose a stochastic spreading model continuous in space and time that is able to use such data in their full power. The state of the forest fire is described by the subprobability densities of the green trees and of the trees on fire that can be estimated thanks to data coming from satellites and earth detectors. The fire dynamics is encoded into a density probability kernel which can take into account wind conditions, land slope, spotting phenomena and so on, bringing to a system of integro-differential equations for the probability densities. Existence and uniqueness of the solutions is proved by using Banach's fixed point theorem. The asymptotic behavior of the model is analyzed as well. Stochastic models based on cellular automata can be considered as particular cases of the present model from which they can be derived by space and/or time discretization. Suggesting a particular structure for the kernel, we obtain numerical simulations of the fire spreading under different conditions. For example, in the case of a forest fire evolving towards a river, the simulations show that the probability density of the trees on fire is different from zero beyond the river due to the spotting phenomenon. Firefighters interventions and weather changes can be easily introduced into the model. |
1401.4181 | Iain Mathieson | Iain Mathieson and Gil McVean | Demography and the age of rare variants | Revised version | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/3.0/ | Large whole-genome sequencing projects have provided access to much of the
rare variation in human populations, which is highly informative about
population structure and recent demography. Here, we show how the age of rare
variants can be estimated from patterns of haplotype sharing and how these ages
can be related to historical relationships between populations. We investigate
the distribution of the age of variants occurring exactly twice (f2 variants)
in a worldwide sample sequenced by the 1000 Genomes Project, revealing enormous
variation across populations. The median age of haplotypes carrying f2 variants
is 50 to 160 generations across populations within Europe or Asia, and 170 to
320 generations within Africa. Haplotypes shared between continents are much
older with median ages for haplotypes shared between Europe and Asia ranging
from 320 to 670 generations. The distribution of the ages of f2 haplotypes is
informative about their demography, revealing recent bottlenecks, ancient
splits, and more modern connections between populations. We see the signature
of selection in the observation that functional variants are significantly
younger than nonfunctional variants of the same frequency. This approach is
relatively insensitive to mutation rate and complements other nonparametric
methods for demographic inference.
| [
{
"created": "Thu, 16 Jan 2014 21:07:50 GMT",
"version": "v1"
},
{
"created": "Sat, 5 Apr 2014 00:07:24 GMT",
"version": "v2"
},
{
"created": "Fri, 6 Jun 2014 18:56:20 GMT",
"version": "v3"
}
] | 2014-06-09 | [
[
"Mathieson",
"Iain",
""
],
[
"McVean",
"Gil",
""
]
] | Large whole-genome sequencing projects have provided access to much of the rare variation in human populations, which is highly informative about population structure and recent demography. Here, we show how the age of rare variants can be estimated from patterns of haplotype sharing and how these ages can be related to historical relationships between populations. We investigate the distribution of the age of variants occurring exactly twice (f2 variants) in a worldwide sample sequenced by the 1000 Genomes Project, revealing enormous variation across populations. The median age of haplotypes carrying f2 variants is 50 to 160 generations across populations within Europe or Asia, and 170 to 320 generations within Africa. Haplotypes shared between continents are much older with median ages for haplotypes shared between Europe and Asia ranging from 320 to 670 generations. The distribution of the ages of f2 haplotypes is informative about their demography, revealing recent bottlenecks, ancient splits, and more modern connections between populations. We see the signature of selection in the observation that functional variants are significantly younger than nonfunctional variants of the same frequency. This approach is relatively insensitive to mutation rate and complements other nonparametric methods for demographic inference. |
2407.00984 | Chengyi Li | Chengyi Li, Shan Yu, Yue Cui | Individual brain parcellation: Review of methods, validations and
applications | 15 pages, 2 figures | null | null | null | q-bio.NC cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Individual brains vary greatly in morphology, connectivity and organization.
The applicability of group-level parcellations is limited by the rapid
development of precision medicine today because they do not take into account
the variation of parcels at the individual level. Accurate mapping of brain
functional regions at the individual level is pivotal for a comprehensive
understanding of the variations in brain function and behaviors, early and
precise identification of brain abnormalities, as well as personalized
treatments for neuropsychiatric disorders. With the development of neuroimaging
and machine learning techniques, studies on individual brain parcellation are
booming. In this paper, we offer an overview of recent advances in the
methodologies of individual brain parcellation, including optimization- and
learning-based methods. Comprehensive evaluation metrics to validate individual
brain mapping have been introduced. We also review the studies of how
individual brain mapping promotes neuroscience research and clinical medicine.
Finally, we summarize the major challenges and important future directions of
individualized brain parcellation. Collectively, we intend to offer a thorough
overview of individual brain parcellation methods, validations, and
applications, along with highlighting the current challenges that call for an
urgent demand for integrated platforms that integrate datasets, methods, and
validations.
| [
{
"created": "Mon, 1 Jul 2024 05:48:05 GMT",
"version": "v1"
}
] | 2024-07-02 | [
[
"Li",
"Chengyi",
""
],
[
"Yu",
"Shan",
""
],
[
"Cui",
"Yue",
""
]
] | Individual brains vary greatly in morphology, connectivity and organization. The applicability of group-level parcellations is limited by the rapid development of precision medicine today because they do not take into account the variation of parcels at the individual level. Accurate mapping of brain functional regions at the individual level is pivotal for a comprehensive understanding of the variations in brain function and behaviors, early and precise identification of brain abnormalities, as well as personalized treatments for neuropsychiatric disorders. With the development of neuroimaging and machine learning techniques, studies on individual brain parcellation are booming. In this paper, we offer an overview of recent advances in the methodologies of individual brain parcellation, including optimization- and learning-based methods. Comprehensive evaluation metrics to validate individual brain mapping have been introduced. We also review the studies of how individual brain mapping promotes neuroscience research and clinical medicine. Finally, we summarize the major challenges and important future directions of individualized brain parcellation. Collectively, we intend to offer a thorough overview of individual brain parcellation methods, validations, and applications, along with highlighting the current challenges that call for an urgent demand for integrated platforms that integrate datasets, methods, and validations. |
2107.04084 | Srdjan Ostojic | Mehrdad Jazayeri, Srdjan Ostojic | Interpreting neural computations by examining intrinsic and embedding
dimensionality of neural activity | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ongoing exponential rise in recording capacity calls for new approaches
for analysing and interpreting neural data. Effective dimensionality has
emerged as an important property of neural activity across populations of
neurons, yet different studies rely on different definitions and
interpretations of this quantity. Here we focus on intrinsic and embedding
dimensionality, and discuss how they might reveal computational principles from
data. Reviewing recent works, we propose that the intrinsic dimensionality
reflects information about the latent variables encoded in collective activity,
while embedding dimensionality reveals the manner in which this information is
processed. We conclude by highlighting the role of network models as an ideal
substrate for testing more specifically various hypotheses on the computational
principles reflected through intrinsic and embedding dimensionality.
| [
{
"created": "Thu, 8 Jul 2021 19:38:29 GMT",
"version": "v1"
},
{
"created": "Fri, 27 Aug 2021 11:20:16 GMT",
"version": "v2"
}
] | 2021-08-30 | [
[
"Jazayeri",
"Mehrdad",
""
],
[
"Ostojic",
"Srdjan",
""
]
] | The ongoing exponential rise in recording capacity calls for new approaches for analysing and interpreting neural data. Effective dimensionality has emerged as an important property of neural activity across populations of neurons, yet different studies rely on different definitions and interpretations of this quantity. Here we focus on intrinsic and embedding dimensionality, and discuss how they might reveal computational principles from data. Reviewing recent works, we propose that the intrinsic dimensionality reflects information about the latent variables encoded in collective activity, while embedding dimensionality reveals the manner in which this information is processed. We conclude by highlighting the role of network models as an ideal substrate for testing more specifically various hypotheses on the computational principles reflected through intrinsic and embedding dimensionality. |
1904.01166 | Mikhail Papisov | Beata Durcanova, Janine Appleton, Nyshidha Gurijala, Vasily Belov,
Pilar Giffenig, Elisabeth Moeller, Matthew Hogan, Fredella Lee, and Mikhail
Papisov | The Configuration of the Perivascular System Transporting Macromolecules
in the CNS (PREPRINT) | 16 pages, 6 figures, 61 references | null | 10.3389/fnins.2019.00511 | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large blood vessels entering the CNS are surrounded by perivascular spaces
that communicate with the cerebrospinal fluid and, at their termini, with the
interstitial space. Solutes and particles can translocate along these
perivascular conduits, reportedly in both directions. Recently, this prompted a
renewed interest in the intrathecal therapy delivery route for CNS-targeted
therapeutics. However, the extent of the CNS coverage by the perivascular
system is unknown, making the outcome of drug administration to the CSF
uncertain. We traced the translocation of model macromolecules from the CSF
into the CNS of rats and non-human primates. Conduits transporting
macromolecules were found to extend throughout the parenchyma from both
external and internal (fissures) CNS boundaries, excluding ventricles, in large
numbers, on average ca. 40 channels per mm2 in rats and non-human primates. The
high density and depth of extension of the perivascular channels suggest that
the perivascular route can be suitable for delivery of therapeutics to
parenchymal targets throughout the CNS.
| [
{
"created": "Tue, 2 Apr 2019 01:47:00 GMT",
"version": "v1"
}
] | 2019-05-08 | [
[
"Durcanova",
"Beata",
""
],
[
"Appleton",
"Janine",
""
],
[
"Gurijala",
"Nyshidha",
""
],
[
"Belov",
"Vasily",
""
],
[
"Giffenig",
"Pilar",
""
],
[
"Moeller",
"Elisabeth",
""
],
[
"Hogan",
"Matthew",
""
]... | Large blood vessels entering the CNS are surrounded by perivascular spaces that communicate with the cerebrospinal fluid and, at their termini, with the interstitial space. Solutes and particles can translocate along these perivascular conduits, reportedly in both directions. Recently, this prompted a renewed interest in the intrathecal therapy delivery route for CNS-targeted therapeutics. However, the extent of the CNS coverage by the perivascular system is unknown, making the outcome of drug administration to the CSF uncertain. We traced the translocation of model macromolecules from the CSF into the CNS of rats and non-human primates. Conduits transporting macromolecules were found to extend throughout the parenchyma from both external and internal (fissures) CNS boundaries, excluding ventricles, in large numbers, on average ca. 40 channels per mm2 in rats and non-human primates. The high density and depth of extension of the perivascular channels suggest that the perivascular route can be suitable for delivery of therapeutics to parenchymal targets throughout the CNS. |
1805.00393 | Miguel Aguilera | Miguel Aguilera, Ezequiel Di Paolo | Integrated Information and Autonomy in the Thermodynamic Limit | This paper was published for a conference and it's quite similar to a
journal version of the manuscript, also published arXiv:1806.07879 | null | null | null | q-bio.NC cond-mat.dis-nn cond-mat.stat-mech nlin.AO physics.bio-ph q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | The concept of autonomy is fundamental for understanding biological
organization and the evolutionary transitions of living systems. Understanding
how a system constitutes itself as an individual, cohesive, self-organized
entity is a fundamental challenge for the understanding of life. However, it is
generally a difficult task to determine whether the system or its environment
has generated the correlations that allow an observer to trace the boundary of
a living system as a coherent unit. Inspired by the framework of integrated
information theory, we propose a measure of the level of integration of a
system as the response of a system to partitions that introduce perturbations
in the interaction between subsystems, without assuming the existence of a
stationary distribution. With the goal of characterizing transitions in
integrated information in the thermodynamic limit, we apply this measure to
kinetic Ising models of infinite size using mean field techniques. Our findings
suggest that, in order to preserve the integration of causal influences of a
system as it grows in size, a living entity must be poised near critical points
maximizing its sensitivity to perturbations in the interaction between
subsystems. Moreover, we observe how such a measure is able to delimit an agent
and its environment, being able to characterize simple instances of
agent-environment asymmetries in which the agent has the ability to modulate
its coupling with the environment.
| [
{
"created": "Mon, 2 Apr 2018 11:19:14 GMT",
"version": "v1"
},
{
"created": "Tue, 22 May 2018 13:01:53 GMT",
"version": "v2"
},
{
"created": "Tue, 19 Jun 2018 12:43:43 GMT",
"version": "v3"
},
{
"created": "Tue, 5 Feb 2019 23:14:51 GMT",
"version": "v4"
}
] | 2019-02-07 | [
[
"Aguilera",
"Miguel",
""
],
[
"Di Paolo",
"Ezequiel",
""
]
] | The concept of autonomy is fundamental for understanding biological organization and the evolutionary transitions of living systems. Understanding how a system constitutes itself as an individual, cohesive, self-organized entity is a fundamental challenge for the understanding of life. However, it is generally a difficult task to determine whether the system or its environment has generated the correlations that allow an observer to trace the boundary of a living system as a coherent unit. Inspired by the framework of integrated information theory, we propose a measure of the level of integration of a system as the response of a system to partitions that introduce perturbations in the interaction between subsystems, without assuming the existence of a stationary distribution. With the goal of characterizing transitions in integrated information in the thermodynamic limit, we apply this measure to kinetic Ising models of infinite size using mean field techniques. Our findings suggest that, in order to preserve the integration of causal influences of a system as it grows in size, a living entity must be poised near critical points maximizing its sensitivity to perturbations in the interaction between subsystems. Moreover, we observe how such a measure is able to delimit an agent and its environment, being able to characterize simple instances of agent-environment asymmetries in which the agent has the ability to modulate its coupling with the environment. |
1203.6560 | Guy Shinar PhD | Guy Shinar and Martin Feinberg | Concordant Chemical Reaction Networks and the Species-Reaction Graph | 71 pages, 9 figures | null | null | null | q-bio.MN math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a recent paper it was shown that, for chemical reaction networks
possessing a subtle structural property called concordance, dynamical behavior
of a very circumscribed (and largely stable) kind is enforced, so long as the
kinetics lies within the very broad and natural weakly monotonic class. In
particular, multiple equilibria are precluded, as are degenerate positive
equilibria. Moreover, under certain circumstances, also related to concordance,
all real eigenvalues associated with a positive equilibrium are negative.
Although concordance of a reaction network can be decided by readily available
computational means, we show here that, when a nondegenerate network's
Species-Reaction Graph satisfies certain mild conditions, concordance and its
dynamical consequences are ensured. These conditions are weaker than earlier
ones invoked to establish kinetic system injectivity, which, in turn, is just
one ramification of network concordance. Because the Species-Reaction Graph
resembles pathway depictions often drawn by biochemists, results here expand
the possibility of inferring signicant dynamical information directly from
standard biochemical reaction diagrams.
| [
{
"created": "Thu, 29 Mar 2012 15:40:12 GMT",
"version": "v1"
},
{
"created": "Thu, 5 Apr 2012 12:04:43 GMT",
"version": "v2"
},
{
"created": "Wed, 25 Apr 2012 15:01:04 GMT",
"version": "v3"
}
] | 2012-04-26 | [
[
"Shinar",
"Guy",
""
],
[
"Feinberg",
"Martin",
""
]
] | In a recent paper it was shown that, for chemical reaction networks possessing a subtle structural property called concordance, dynamical behavior of a very circumscribed (and largely stable) kind is enforced, so long as the kinetics lies within the very broad and natural weakly monotonic class. In particular, multiple equilibria are precluded, as are degenerate positive equilibria. Moreover, under certain circumstances, also related to concordance, all real eigenvalues associated with a positive equilibrium are negative. Although concordance of a reaction network can be decided by readily available computational means, we show here that, when a nondegenerate network's Species-Reaction Graph satisfies certain mild conditions, concordance and its dynamical consequences are ensured. These conditions are weaker than earlier ones invoked to establish kinetic system injectivity, which, in turn, is just one ramification of network concordance. Because the Species-Reaction Graph resembles pathway depictions often drawn by biochemists, results here expand the possibility of inferring signicant dynamical information directly from standard biochemical reaction diagrams. |
2302.13162 | Michael Baker Ph.D. | Yoshinao Katsu, Jiawen Zhang and Michael E. Baker | Reduced steroid activation of elephant shark glucocorticoid and
mineralocorticoid receptors after inserting four amino acids from the
DNA-binding domain of lamprey corticoid receptor-1 | 18 pages, 3 figures. arXiv admin note: text overlap with
arXiv:2210.04111 | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Atlantic sea lamprey contains two corticoid receptors (CRs), CR1 and CR2,
that are identical except for a four amino acid insert (Thr-Arg-Gln-Gly) in the
CR1 DNA-binding domain (DBD). Steroids are stronger transcriptional activators
of CR2 than of CR1 suggesting that the insert reduces the transcriptional
response of lamprey CR1 to steroids. The DBD in elephant shark
mineralocorticoid receptor (MR) and glucocorticoid receptor (GR), which are
descended from a CR, lack these four amino acids, suggesting that a CR2 is
their common ancestor. To determine if, similar to lamprey CR1, the presence of
this insert in elephant shark MR and GR decreases transcriptional activation by
corticosteroids, we inserted these four CR1-specific residues into the DBD of
elephant shark MR and GR. Compared to steroid activation of wild-type elephant
shark MR and GR, cortisol, corticosterone, aldosterone, 11-deoxycorticosterone
and 11-deoxycortisol had lower transcriptional activation of these mutant MR
and GR receptors, indicating that the absence of this four-residue segment in
the DBD in wild-type elephant shark MR and GR increases transcriptional
activation by corticosteroids.
| [
{
"created": "Sat, 25 Feb 2023 21:11:53 GMT",
"version": "v1"
}
] | 2023-02-28 | [
[
"Katsu",
"Yoshinao",
""
],
[
"Zhang",
"Jiawen",
""
],
[
"Baker",
"Michael E.",
""
]
] | Atlantic sea lamprey contains two corticoid receptors (CRs), CR1 and CR2, that are identical except for a four amino acid insert (Thr-Arg-Gln-Gly) in the CR1 DNA-binding domain (DBD). Steroids are stronger transcriptional activators of CR2 than of CR1 suggesting that the insert reduces the transcriptional response of lamprey CR1 to steroids. The DBD in elephant shark mineralocorticoid receptor (MR) and glucocorticoid receptor (GR), which are descended from a CR, lack these four amino acids, suggesting that a CR2 is their common ancestor. To determine if, similar to lamprey CR1, the presence of this insert in elephant shark MR and GR decreases transcriptional activation by corticosteroids, we inserted these four CR1-specific residues into the DBD of elephant shark MR and GR. Compared to steroid activation of wild-type elephant shark MR and GR, cortisol, corticosterone, aldosterone, 11-deoxycorticosterone and 11-deoxycortisol had lower transcriptional activation of these mutant MR and GR receptors, indicating that the absence of this four-residue segment in the DBD in wild-type elephant shark MR and GR increases transcriptional activation by corticosteroids. |
1911.01304 | Nathan Gold | Nathan Gold, Christophe L. Herry, Xiaogang Wang, Martin G. Frasch | Fetal cardiovascular decompensation during labor predicted from the
individual heart rate: a prospective study in fetal sheep near term and the
impact of low sampling rate | null | Front. Pediatr. 2021 | 10.3389/fped.2021.593889 | null | q-bio.QM stat.AP | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We present a novel computerized fetal heart rate intrapartum algorithm for
early and individualized prediction of fetal cardiovascular decompensation, a
key event in the causal chain leading to brain injury. This real-time machine
learning algorithm performs well on noisy fetal heart rate data and requires ~2
hours to train on the individual fetal heart rate tracings in the first stage
of labor; once trained, the algorithm predicts the event of fetal
cardiovascular decompensation with 92% sensitivity. We show that the
algorithm's performance suffers reducing sensitivity to 67% when the fetal
heart rate is acquired at the sampling rate of 4 Hz used in ultrasound
cardiotocographic monitors compared to the electrocardiogram(ECG)-derived
signals as can be acquired from maternal abdominal ECG.
| [
{
"created": "Mon, 4 Nov 2019 16:12:57 GMT",
"version": "v1"
},
{
"created": "Tue, 5 Nov 2019 21:28:33 GMT",
"version": "v2"
}
] | 2021-04-21 | [
[
"Gold",
"Nathan",
""
],
[
"Herry",
"Christophe L.",
""
],
[
"Wang",
"Xiaogang",
""
],
[
"Frasch",
"Martin G.",
""
]
] | We present a novel computerized fetal heart rate intrapartum algorithm for early and individualized prediction of fetal cardiovascular decompensation, a key event in the causal chain leading to brain injury. This real-time machine learning algorithm performs well on noisy fetal heart rate data and requires ~2 hours to train on the individual fetal heart rate tracings in the first stage of labor; once trained, the algorithm predicts the event of fetal cardiovascular decompensation with 92% sensitivity. We show that the algorithm's performance suffers reducing sensitivity to 67% when the fetal heart rate is acquired at the sampling rate of 4 Hz used in ultrasound cardiotocographic monitors compared to the electrocardiogram(ECG)-derived signals as can be acquired from maternal abdominal ECG. |
q-bio/0702060 | Damien Eveillard | J\'er\'emie Bourdon (LINA), Damien Eveillard (LINA) | Toll Based Measures for Dynamical Graphs | 11 pages | null | null | null | q-bio.QM math.PR | null | Biological networks are one of the most studied object in computational
biology. Several methods have been developed for studying qualitative
properties of biological networks. Last decade had seen the improvement of
molecular techniques that make quantitative analyses reachable. One of the
major biological modelling goals is therefore to deal with the quantitative
aspect of biological graphs. We propose a probabilistic model that suits with
this quantitative aspects. Our model combines graph with several dynamical
sources. It emphazises various asymptotic statistical properties that might be
useful for giving biological insights
| [
{
"created": "Wed, 28 Feb 2007 16:12:18 GMT",
"version": "v1"
}
] | 2016-08-14 | [
[
"Bourdon",
"Jérémie",
"",
"LINA"
],
[
"Eveillard",
"Damien",
"",
"LINA"
]
] | Biological networks are one of the most studied object in computational biology. Several methods have been developed for studying qualitative properties of biological networks. Last decade had seen the improvement of molecular techniques that make quantitative analyses reachable. One of the major biological modelling goals is therefore to deal with the quantitative aspect of biological graphs. We propose a probabilistic model that suits with this quantitative aspects. Our model combines graph with several dynamical sources. It emphazises various asymptotic statistical properties that might be useful for giving biological insights |
2110.12736 | Iain Johnston | Konstantinos Giannakis, Joanna M. Chustecki, Iain G. Johnston | Encounter networks from collective mitochondrial dynamics support the
emergence of effective mtDNA genomes in plant cells | null | null | null | null | q-bio.SC | http://creativecommons.org/licenses/by/4.0/ | Mitochondria in plant cells form strikingly dynamic populations of largely
individual organelles. Each mitochondrion contains on average less than a full
copy of the mitochondrial DNA (mtDNA) genome. Here, we asked whether
mitochondrial dynamics may allow individual mitochondria to `collect' a full
copy of the mtDNA genome over time, by facilitating exchange between
individuals. Akin to trade on a social network, exchange of mtDNA fragments
across organelles may lead to the emergence of full `effective' genomes in
individuals over time. We characterise the collective dynamics of mitochondria
in \emph{Arabidopsis thaliana} hypocotyl cells using a recent approach
combining single-cell timelapse microscopy, video analysis, and network
science. We then use a quantitative model to predict the capacity for the
sharing and accumulation of genetic information through the networks of
encounters between mitochondria. We find that biological encounter networks are
strikingly well predisposed to support the collection of full genomes over
time, outperforming a range of other networks generated from theory and
simulation. Using results from the coupon collector's problem, we show that the
upper tail of the degree distribution is a key determinant of an encounter
network's performance at this task and discuss how features of mitochondrial
dynamics observed in biology facilitate the emergence of full effective
genomes.
| [
{
"created": "Mon, 25 Oct 2021 08:49:53 GMT",
"version": "v1"
}
] | 2021-10-26 | [
[
"Giannakis",
"Konstantinos",
""
],
[
"Chustecki",
"Joanna M.",
""
],
[
"Johnston",
"Iain G.",
""
]
] | Mitochondria in plant cells form strikingly dynamic populations of largely individual organelles. Each mitochondrion contains on average less than a full copy of the mitochondrial DNA (mtDNA) genome. Here, we asked whether mitochondrial dynamics may allow individual mitochondria to `collect' a full copy of the mtDNA genome over time, by facilitating exchange between individuals. Akin to trade on a social network, exchange of mtDNA fragments across organelles may lead to the emergence of full `effective' genomes in individuals over time. We characterise the collective dynamics of mitochondria in \emph{Arabidopsis thaliana} hypocotyl cells using a recent approach combining single-cell timelapse microscopy, video analysis, and network science. We then use a quantitative model to predict the capacity for the sharing and accumulation of genetic information through the networks of encounters between mitochondria. We find that biological encounter networks are strikingly well predisposed to support the collection of full genomes over time, outperforming a range of other networks generated from theory and simulation. Using results from the coupon collector's problem, we show that the upper tail of the degree distribution is a key determinant of an encounter network's performance at this task and discuss how features of mitochondrial dynamics observed in biology facilitate the emergence of full effective genomes. |
1907.07986 | Kaan \"Ocal | Kaan \"Ocal, Ramon Grima, Guido Sanguinetti | Parameter estimation for biochemical reaction networks using Wasserstein
distances | 22 pages, 8 figures. Slight modifications/additions to the text;
added new section (Section 4.4) and Appendix | null | 10.1088/1751-8121/ab5877 | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a method for estimating parameters in stochastic models of
biochemical reaction networks by fitting steady-state distributions using
Wasserstein distances. We simulate a reaction network at different parameter
settings and train a Gaussian process to learn the Wasserstein distance between
observations and the simulator output for all parameters. We then use Bayesian
optimization to find parameters minimizing this distance based on the trained
Gaussian process. The effectiveness of our method is demonstrated on the
three-stage model of gene expression and a genetic feedback loop for which
moment-based methods are known to perform poorly. Our method is applicable to
any simulator model of stochastic reaction networks, including Brownian
Dynamics.
| [
{
"created": "Thu, 18 Jul 2019 10:57:58 GMT",
"version": "v1"
},
{
"created": "Mon, 21 Oct 2019 18:07:06 GMT",
"version": "v2"
}
] | 2020-01-29 | [
[
"Öcal",
"Kaan",
""
],
[
"Grima",
"Ramon",
""
],
[
"Sanguinetti",
"Guido",
""
]
] | We present a method for estimating parameters in stochastic models of biochemical reaction networks by fitting steady-state distributions using Wasserstein distances. We simulate a reaction network at different parameter settings and train a Gaussian process to learn the Wasserstein distance between observations and the simulator output for all parameters. We then use Bayesian optimization to find parameters minimizing this distance based on the trained Gaussian process. The effectiveness of our method is demonstrated on the three-stage model of gene expression and a genetic feedback loop for which moment-based methods are known to perform poorly. Our method is applicable to any simulator model of stochastic reaction networks, including Brownian Dynamics. |
2105.12976 | Ohad Felsenstein | Ohad Felsenstein and Moshe Abeles | Spatio-Temporal Investigation of Brain-Wide Sequences | 40 pages, 10 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-sa/4.0/ | In "The Organization of Behavior" (Hebb, 1949), Hebb suggested that the
propagation of activity between transiently grouped neurons plays an important
role in behavior. Since then, multiple studies have provided evidence
supporting Hebb's claim; however, most findings have been found locally in
confined brain regions during unimodal tasks. Here we report on brain-wide
behavioral-specific sequences in humans performing a multimodal task. To
investigate the structure of these sequences, we used MEG to record brain
activity in multiple brain regions simultaneously in participants performing a
sensory-motor synchronization task. We detected local transient events
corresponding to synchronously activating populations of pyramidal neurons and
searched for their global organization as spatio-temporal patterns of
activation sequences between distant neural populations. We focused our
analysis on two types of spatio-temporal patterns: the most frequently
repeating patterns and the most discriminative patterns, to concentrate on
patterns with high relevancy to behavior. The findings revealed that global
temporally precise sequences can be found and that these sequences have
partially stereotypical characteristics, both temporally and spatially, with
consistent properties across subjects. By implementing a simplistic
single-trial decoding approach, we found that brain-wide sequences have a
temporal precision of 17-31 milliseconds, which resembles the temporal
precision found locally in neural assemblies.
| [
{
"created": "Thu, 27 May 2021 07:40:30 GMT",
"version": "v1"
}
] | 2021-05-28 | [
[
"Felsenstein",
"Ohad",
""
],
[
"Abeles",
"Moshe",
""
]
] | In "The Organization of Behavior" (Hebb, 1949), Hebb suggested that the propagation of activity between transiently grouped neurons plays an important role in behavior. Since then, multiple studies have provided evidence supporting Hebb's claim; however, most findings have been found locally in confined brain regions during unimodal tasks. Here we report on brain-wide behavioral-specific sequences in humans performing a multimodal task. To investigate the structure of these sequences, we used MEG to record brain activity in multiple brain regions simultaneously in participants performing a sensory-motor synchronization task. We detected local transient events corresponding to synchronously activating populations of pyramidal neurons and searched for their global organization as spatio-temporal patterns of activation sequences between distant neural populations. We focused our analysis on two types of spatio-temporal patterns: the most frequently repeating patterns and the most discriminative patterns, to concentrate on patterns with high relevancy to behavior. The findings revealed that global temporally precise sequences can be found and that these sequences have partially stereotypical characteristics, both temporally and spatially, with consistent properties across subjects. By implementing a simplistic single-trial decoding approach, we found that brain-wide sequences have a temporal precision of 17-31 milliseconds, which resembles the temporal precision found locally in neural assemblies. |
0910.4469 | Ramon Grima | Ramon Grima | Investigating the robustness of the classical enzyme kinetic equations
in small intracellular compartments | null | BMC Systems Biology 2009, 3:101 | null | null | q-bio.SC q-bio.MN | http://creativecommons.org/licenses/by/3.0/ | Classical descriptions of enzyme kinetics ignore the physical nature of the
intracellular environment. Main implicit assumptions behind such approaches are
that reactions occur in compartment volumes which are large enough so that
molecular discreteness can be ignored and that molecular transport occurs via
diffusion. Starting from a master equation description of enzyme reaction
kinetics and assuming metabolic steady-state conditions, we derive novel
mesoscopic rate equations which take into account (i) the intrinsic molecular
noise due to the low copy number of molecules in intracellular compartments
(ii) the physical nature of the substrate transport process, i.e. diffusion or
vesicle-mediated transport. These equations replace the conventional
macroscopic and deterministic equations in the context of intracellular
kinetics. The latter are recovered in the limit of infinite compartment
volumes. We find that deviations from the predictions of classical kinetics are
pronounced (hundreds of percent in the estimate for the reaction velocity) for
enzyme reactions occurring in compartments which are smaller than approximately
200nm, for the case of substrate transport to the compartment being mediated
principally by vesicle or granule transport and in the presence of competitive
enzyme inhibitors. This has implications for the common approach of modelling
large intracellular reaction networks using ordinary differential equations and
also for the calculation of the effective dosage of competitive inhibitor
drugs.
| [
{
"created": "Fri, 23 Oct 2009 13:42:20 GMT",
"version": "v1"
}
] | 2009-10-26 | [
[
"Grima",
"Ramon",
""
]
] | Classical descriptions of enzyme kinetics ignore the physical nature of the intracellular environment. Main implicit assumptions behind such approaches are that reactions occur in compartment volumes which are large enough so that molecular discreteness can be ignored and that molecular transport occurs via diffusion. Starting from a master equation description of enzyme reaction kinetics and assuming metabolic steady-state conditions, we derive novel mesoscopic rate equations which take into account (i) the intrinsic molecular noise due to the low copy number of molecules in intracellular compartments (ii) the physical nature of the substrate transport process, i.e. diffusion or vesicle-mediated transport. These equations replace the conventional macroscopic and deterministic equations in the context of intracellular kinetics. The latter are recovered in the limit of infinite compartment volumes. We find that deviations from the predictions of classical kinetics are pronounced (hundreds of percent in the estimate for the reaction velocity) for enzyme reactions occurring in compartments which are smaller than approximately 200nm, for the case of substrate transport to the compartment being mediated principally by vesicle or granule transport and in the presence of competitive enzyme inhibitors. This has implications for the common approach of modelling large intracellular reaction networks using ordinary differential equations and also for the calculation of the effective dosage of competitive inhibitor drugs. |
1307.7941 | Aaron Darling | Ilya Minkin, Anand Patel, Mikhail Kolmogorov, Nikolay Vyahhi, and Son
Pham | Sibelia: A scalable and comprehensive synteny block generation tool for
closely related microbial genomes | 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/ | Comparing strains within the same microbial species has proven effective in
the identification of genes and genomic regions responsible for virulence, as
well as in the diagnosis and treatment of infectious diseases. In this paper,
we present Sibelia, a tool for finding synteny blocks in multiple closely
related microbial genomes using iterative de Bruijn graphs. Unlike most other
tools, Sibelia can find synteny blocks that are repeated within genomes as well
as blocks shared by multiple genomes. It represents synteny blocks in a
hierarchy structure with multiple layers, each of which representing a
different granularity level. Sibelia has been designed to work efficiently with
a large number of microbial genomes; it finds synteny blocks in 31 S. aureus
genomes within 31 minutes and in 59 E.coli genomes within 107 minutes on a
standard desktop. Sibelia software is distributed under the GNU GPL v2 license
and is available at: https://github.com/bioinf/Sibelia Sibelia's web-server is
available at: http://etool.me/software/sibelia
| [
{
"created": "Tue, 30 Jul 2013 12:30:35 GMT",
"version": "v1"
}
] | 2013-07-31 | [
[
"Minkin",
"Ilya",
""
],
[
"Patel",
"Anand",
""
],
[
"Kolmogorov",
"Mikhail",
""
],
[
"Vyahhi",
"Nikolay",
""
],
[
"Pham",
"Son",
""
]
] | Comparing strains within the same microbial species has proven effective in the identification of genes and genomic regions responsible for virulence, as well as in the diagnosis and treatment of infectious diseases. In this paper, we present Sibelia, a tool for finding synteny blocks in multiple closely related microbial genomes using iterative de Bruijn graphs. Unlike most other tools, Sibelia can find synteny blocks that are repeated within genomes as well as blocks shared by multiple genomes. It represents synteny blocks in a hierarchy structure with multiple layers, each of which representing a different granularity level. Sibelia has been designed to work efficiently with a large number of microbial genomes; it finds synteny blocks in 31 S. aureus genomes within 31 minutes and in 59 E.coli genomes within 107 minutes on a standard desktop. Sibelia software is distributed under the GNU GPL v2 license and is available at: https://github.com/bioinf/Sibelia Sibelia's web-server is available at: http://etool.me/software/sibelia |
q-bio/0403045 | Jose Nacher Dr. | J.C. Nacher, N. Ueda, T. Yamada, M. Kanehisa, T. Akutsu | Clustering under the line graph transformation: Application to reaction
network | 20 pages, 12 figures, REVTeX 4 style | BMC Bioinformatics 5, 207 (2004) | 10.1186/1471-2105-5-207 | null | q-bio.MN | null | Many real networks can be understood as two complementary networks with two
kind of nodes. This is the case of metabolic networks where the first network
has chemical compounds as nodes and the second one has nodes as reactions. The
second network can be related to the first one by a technique called line graph
transformation (i.e., edges in an initial network are transformed into nodes).
Recently, the main topological properties of the metabolic networks have been
properly described by means of a hierarchical model. In our work, we apply the
line graph transformation to a hierarchical network and the clustering
coefficient $C(k)$ is calculated for the transformed network, where $k$ is the
node degree. While $C(k)$ follows the scaling law $C(k)\sim k^{-1.1}$ for the
initial hierarchical network, $C(k)$ scales weakly as $k^{0.08}$ for the
transformed network. These results indicate that the reaction network can be
identified as a degree-independent clustering network.
| [
{
"created": "Thu, 1 Apr 2004 00:54:40 GMT",
"version": "v1"
},
{
"created": "Wed, 18 Aug 2004 04:59:56 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Nacher",
"J. C.",
""
],
[
"Ueda",
"N.",
""
],
[
"Yamada",
"T.",
""
],
[
"Kanehisa",
"M.",
""
],
[
"Akutsu",
"T.",
""
]
] | Many real networks can be understood as two complementary networks with two kind of nodes. This is the case of metabolic networks where the first network has chemical compounds as nodes and the second one has nodes as reactions. The second network can be related to the first one by a technique called line graph transformation (i.e., edges in an initial network are transformed into nodes). Recently, the main topological properties of the metabolic networks have been properly described by means of a hierarchical model. In our work, we apply the line graph transformation to a hierarchical network and the clustering coefficient $C(k)$ is calculated for the transformed network, where $k$ is the node degree. While $C(k)$ follows the scaling law $C(k)\sim k^{-1.1}$ for the initial hierarchical network, $C(k)$ scales weakly as $k^{0.08}$ for the transformed network. These results indicate that the reaction network can be identified as a degree-independent clustering network. |
1406.3051 | Ronald Rousseau | Xiaojun Hu and Ronald Rousseau | Synthetic biology: From a word to a world | 8 pages | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Synthetic biology is one of the battlefields where the main countries fight
for the supremacy in science. The word synthetic biology hides a big world,
ready to be explored by interdisciplinary research collaborations. The purpose
of this investigation is to reveal the what, where, when of the current
situation in this emerging field. A keyword search string for the field was
constructed and applied in the Web of Science and in the Derwent Innovations
Index. In particular, we calculated year based h-type indices for high-frequent
keywords.
| [
{
"created": "Tue, 10 Jun 2014 14:53:39 GMT",
"version": "v1"
}
] | 2014-06-13 | [
[
"Hu",
"Xiaojun",
""
],
[
"Rousseau",
"Ronald",
""
]
] | Synthetic biology is one of the battlefields where the main countries fight for the supremacy in science. The word synthetic biology hides a big world, ready to be explored by interdisciplinary research collaborations. The purpose of this investigation is to reveal the what, where, when of the current situation in this emerging field. A keyword search string for the field was constructed and applied in the Web of Science and in the Derwent Innovations Index. In particular, we calculated year based h-type indices for high-frequent keywords. |
2211.13657 | Giovanni Bussi | Valerio Piomponi and Mattia Bernetti and Giovanni Bussi | Molecular dynamics simulations of chemically modified ribonucleotides | Submitted as a chapter for the book "RNA Structure and Function",
series "RNA Technologies", published by Springer | In: Barciszewski, J. (eds) RNA Structure and Function. RNA
Technologies, vol 14 (2023). Springer, Cham | 10.1007/978-3-031-36390-0_26 | null | q-bio.BM physics.bio-ph physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Post-transcriptional modifications are crucial for RNA function, with roles
ranging from the stabilization of functional RNA structures to modulation of
RNA--protein interactions. Additionally, artificially modified RNAs have been
suggested as optimal oligonucleotides for therapeutic purposes. The impact of
chemical modifications on secondary structure has been rationalized for some of
the most common modifications. However, the characterization of how the
modifications affect the three-dimensional RNA structure and dynamics and its
capability to bind proteins is still highly challenging. Molecular dynamics
simulations, coupled with enhanced sampling methods and integration of
experimental data, provide a direct access to RNA structural dynamics. In the
context of RNA chemical modifications, alchemical simulations where a wild type
nucleotide is converted to a modified one are particularly common. In this
Chapter, we review recent molecular dynamics studies of modified
ribonucleotides. We discuss the technical aspects of the reviewed works,
including the employed force fields, enhanced sampling methods, and alchemical
methods, in a way that is accessible to experimentalists. Finally, we provide
our perspective on this quickly growing field of research. The goal of this
Chapter is to provide a guide for experimentalists to understand molecular
dynamics works and, at the same time, give to molecular dynamics experts a
solid review of published articles that will be a useful starting point for new
research.
| [
{
"created": "Thu, 24 Nov 2022 15:16:53 GMT",
"version": "v1"
}
] | 2023-11-23 | [
[
"Piomponi",
"Valerio",
""
],
[
"Bernetti",
"Mattia",
""
],
[
"Bussi",
"Giovanni",
""
]
] | Post-transcriptional modifications are crucial for RNA function, with roles ranging from the stabilization of functional RNA structures to modulation of RNA--protein interactions. Additionally, artificially modified RNAs have been suggested as optimal oligonucleotides for therapeutic purposes. The impact of chemical modifications on secondary structure has been rationalized for some of the most common modifications. However, the characterization of how the modifications affect the three-dimensional RNA structure and dynamics and its capability to bind proteins is still highly challenging. Molecular dynamics simulations, coupled with enhanced sampling methods and integration of experimental data, provide a direct access to RNA structural dynamics. In the context of RNA chemical modifications, alchemical simulations where a wild type nucleotide is converted to a modified one are particularly common. In this Chapter, we review recent molecular dynamics studies of modified ribonucleotides. We discuss the technical aspects of the reviewed works, including the employed force fields, enhanced sampling methods, and alchemical methods, in a way that is accessible to experimentalists. Finally, we provide our perspective on this quickly growing field of research. The goal of this Chapter is to provide a guide for experimentalists to understand molecular dynamics works and, at the same time, give to molecular dynamics experts a solid review of published articles that will be a useful starting point for new research. |
1906.07698 | Abbas Saberi Abbas Ali Saberi | Youness Azimzade, Abbas Ali Saberi, and Muhammad Sahimi | Regulation of Migration of Chemotactic Tumor Cells by the Spatial
Distribution of the Collagen Fibers' Orientation | 5 figures (accepted, Phys. Rev. E (2019)) | Phys. Rev. E 99, 062414 (2019) | 10.1103/PhysRevE.99.062414 | null | q-bio.CB cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Collagen fibers, an important component of the extracellular matrix (ECM),
can both inhibit and promote cellular migration. {\it In-vitro} studies have
revealed that the fibers' orientations are crucial to cellular invasion, while
{\it in-vivo} investigations have led to the development of tumor-associated
collagen signatures (TACS) as an important prognostic factor. Studying
biophysical regulation of cell invasion and the effect of the fibers'
oritentation not only deepens our understanding of the phenomenon, but also
helps classifying the TACSs precisely, which is currently lacking. We present a
stochastic model for random/chemotactic migration of cells in fibrous ECM, and
study the role of the various factors in it. The model provides a framework,
for the first time to our knowledge, for quantitative classification of the
TACSs, and reproduces quantitatively recent experimental data for cell
motility. It also indicates that the spatial distribution of the fibers'
orientations and extended correlations between them, hitherto ignored, as well
as dynamics of cellular motion all contribute to regulation of the cells'
invasion length, which represents a measure of metastatic risk. Although the
fibers' orientations trivially affect randomly moving cells, their effect on
chemotactic cells is completely nontrivial and unexplored, which we study in
this paper.
| [
{
"created": "Tue, 18 Jun 2019 17:18:40 GMT",
"version": "v1"
}
] | 2019-07-01 | [
[
"Azimzade",
"Youness",
""
],
[
"Saberi",
"Abbas Ali",
""
],
[
"Sahimi",
"Muhammad",
""
]
] | Collagen fibers, an important component of the extracellular matrix (ECM), can both inhibit and promote cellular migration. {\it In-vitro} studies have revealed that the fibers' orientations are crucial to cellular invasion, while {\it in-vivo} investigations have led to the development of tumor-associated collagen signatures (TACS) as an important prognostic factor. Studying biophysical regulation of cell invasion and the effect of the fibers' oritentation not only deepens our understanding of the phenomenon, but also helps classifying the TACSs precisely, which is currently lacking. We present a stochastic model for random/chemotactic migration of cells in fibrous ECM, and study the role of the various factors in it. The model provides a framework, for the first time to our knowledge, for quantitative classification of the TACSs, and reproduces quantitatively recent experimental data for cell motility. It also indicates that the spatial distribution of the fibers' orientations and extended correlations between them, hitherto ignored, as well as dynamics of cellular motion all contribute to regulation of the cells' invasion length, which represents a measure of metastatic risk. Although the fibers' orientations trivially affect randomly moving cells, their effect on chemotactic cells is completely nontrivial and unexplored, which we study in this paper. |
1501.04824 | Lorenzo Pellis | Lorenzo Pellis, Simon E.F. Spencer and Thomas House | Real-time growth rate for general stochastic SIR epidemics on
unclustered networks | 45 pages, 8 figures, submitted to Mathematical Biosciences on
29/11/2014; Version 2: resubmitted on 15/04/2015; accepted on 17/04/2015.
Changes: better explanations in introduction; restructured section 3.3 (3.3.3
added); section 6.3.1 added; more precise terminology; typos corrected | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Networks have become an important tool for infectious disease epidemiology.
Most previous theoretical studies of transmission network models have either
considered simple Markovian dynamics at the individual level, or have focused
on the invasion threshold and final outcome of the epidemic. Here, we provide a
general theory for early real-time behaviour of epidemics on large
configuration model networks (i.e. static and locally unclustered), in
particular focusing on the computation of the Malthusian parameter that
describes the early exponential epidemic growth. Analytical, numerical and
Monte-Carlo methods under a wide variety of Markovian and non-Markovian
assumptions about the infectivity profile are presented. Numerous examples
provide explicit quantification of the impact of the network structure on the
temporal dynamics of the spread of infection and provide a benchmark for
validating results of large scale simulations.
| [
{
"created": "Tue, 20 Jan 2015 14:39:05 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Apr 2015 12:26:40 GMT",
"version": "v2"
}
] | 2015-04-21 | [
[
"Pellis",
"Lorenzo",
""
],
[
"Spencer",
"Simon E. F.",
""
],
[
"House",
"Thomas",
""
]
] | Networks have become an important tool for infectious disease epidemiology. Most previous theoretical studies of transmission network models have either considered simple Markovian dynamics at the individual level, or have focused on the invasion threshold and final outcome of the epidemic. Here, we provide a general theory for early real-time behaviour of epidemics on large configuration model networks (i.e. static and locally unclustered), in particular focusing on the computation of the Malthusian parameter that describes the early exponential epidemic growth. Analytical, numerical and Monte-Carlo methods under a wide variety of Markovian and non-Markovian assumptions about the infectivity profile are presented. Numerous examples provide explicit quantification of the impact of the network structure on the temporal dynamics of the spread of infection and provide a benchmark for validating results of large scale simulations. |
2005.11278 | Martin Huber | Martin Huber and Henrika Langen | The Impact of Response Measures on COVID-19-Related Hospitalization and
Death Rates in Germany and Switzerland | Evaluation of lockdown measures in Germany and Switzerland aimed at
containing the COVID-19 epidemic | null | null | null | q-bio.PE stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We assess the impact of COVID-19 response measures implemented in Germany and
Switzerland on cumulative COVID-19-related hospitalization and death rates. Our
analysis exploits the fact that the epidemic was more advanced in some regions
than in others when certain lockdown measures came into force, based on
measuring health outcomes relative to the region-specific start of the epidemic
and comparing outcomes across regions with earlier and later start dates. When
estimating the effect of the relative timing of measures, we control for
regional characteristics and initial epidemic trends by linear regression
(Germany and Switzerland), doubly robust estimation (Germany), or synthetic
controls (Switzerland). We find for both countries that a relatively later
exposure to the measures entails higher cumulative hospitalization and death
rates on region-specific days after the outbreak of the epidemic, suggesting
that an earlier imposition of measures is more effective than a later one. For
Germany, we also evaluate curfews (as introduced in a subset of states) based
on cross-regional variation. We do not find any effects of curfews on top of
the federally imposed contact restriction that banned groups of more than 2
individuals. Finally, an analysis of mobility patterns in Switzerland shows an
immediate behavioral effect of the lockdown in terms of reduced mobility.
| [
{
"created": "Wed, 20 May 2020 13:02:43 GMT",
"version": "v1"
},
{
"created": "Thu, 28 May 2020 18:19:45 GMT",
"version": "v2"
},
{
"created": "Fri, 19 Jun 2020 06:24:54 GMT",
"version": "v3"
}
] | 2020-06-22 | [
[
"Huber",
"Martin",
""
],
[
"Langen",
"Henrika",
""
]
] | We assess the impact of COVID-19 response measures implemented in Germany and Switzerland on cumulative COVID-19-related hospitalization and death rates. Our analysis exploits the fact that the epidemic was more advanced in some regions than in others when certain lockdown measures came into force, based on measuring health outcomes relative to the region-specific start of the epidemic and comparing outcomes across regions with earlier and later start dates. When estimating the effect of the relative timing of measures, we control for regional characteristics and initial epidemic trends by linear regression (Germany and Switzerland), doubly robust estimation (Germany), or synthetic controls (Switzerland). We find for both countries that a relatively later exposure to the measures entails higher cumulative hospitalization and death rates on region-specific days after the outbreak of the epidemic, suggesting that an earlier imposition of measures is more effective than a later one. For Germany, we also evaluate curfews (as introduced in a subset of states) based on cross-regional variation. We do not find any effects of curfews on top of the federally imposed contact restriction that banned groups of more than 2 individuals. Finally, an analysis of mobility patterns in Switzerland shows an immediate behavioral effect of the lockdown in terms of reduced mobility. |
1206.2070 | Petr Pancoska | Petr Pancoska, Zdenek Moravek, Uday Kiran Para, Jaroslav Nesetril | Entromics -- thermodynamics of sequence dependent base incorporation
into DNA reveals novel long-distance genome organization | null | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Zero mode waveguide technology of next generation sequencing demonstrated
sequence-dependence of the enzymatic reaction, incorporating a base into the
genomic DNA. We show that these experimental results indicate existence of a
previously uncharacterized physical property of DNA, the incorporation reaction
chemical potential {\Delta}{\mu}. We use the combination of graph theory and
statistical thermodynamics to derive entromics - a series of results providing
the thermodynamic model of {\Delta}{\mu}. We also show that {\Delta}{\mu}i is
quantitatively characterized as incorporation entropy. We present formulae for
computing {\Delta}{\mu} from the genome DNA sequence. We then derive important
restrictions on DNA properties and genome assembly that follow from
thermodynamic properties of {\Delta}{\mu}. Finally, we show how these genome
assembly restrictions lead directly to the evolution of detectable coherences
in incorporation entropy along the entire genome. Examples of entromic
applications, demonstrating functional and biological importance are shown.
| [
{
"created": "Tue, 6 Mar 2012 14:22:32 GMT",
"version": "v1"
}
] | 2012-06-12 | [
[
"Pancoska",
"Petr",
""
],
[
"Moravek",
"Zdenek",
""
],
[
"Para",
"Uday Kiran",
""
],
[
"Nesetril",
"Jaroslav",
""
]
] | Zero mode waveguide technology of next generation sequencing demonstrated sequence-dependence of the enzymatic reaction, incorporating a base into the genomic DNA. We show that these experimental results indicate existence of a previously uncharacterized physical property of DNA, the incorporation reaction chemical potential {\Delta}{\mu}. We use the combination of graph theory and statistical thermodynamics to derive entromics - a series of results providing the thermodynamic model of {\Delta}{\mu}. We also show that {\Delta}{\mu}i is quantitatively characterized as incorporation entropy. We present formulae for computing {\Delta}{\mu} from the genome DNA sequence. We then derive important restrictions on DNA properties and genome assembly that follow from thermodynamic properties of {\Delta}{\mu}. Finally, we show how these genome assembly restrictions lead directly to the evolution of detectable coherences in incorporation entropy along the entire genome. Examples of entromic applications, demonstrating functional and biological importance are shown. |
1810.04056 | Aristides Moustakas | Aristides Moustakas, Ioannis N. Daliakopoulos, and Tim. G. Benton | Data-driven competitive facilitative tree interactions and their
implications on nature-based solutions | null | null | 10.1016/j.scitotenv.2018.09.349 | null | q-bio.PE q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spatio temporal data are more ubiquitous and richer than even before and the
availability of such data poses great challenges in data analytics. Ecological
facilitation, the positive effect of density of individuals on the individual's
survival across a stress gradient, is a complex phenomenon. A large number of
tree individuals coupled with soil moisture, temperature, and water stress data
across a long temporal period were followed. Data driven analysis in the
absence of hypothesis was performed. Information theoretic analysis of multiple
statistical models was employed in order to quantify the best data-driven index
of vegetation density and spatial scale of interactions. Sequentially, tree
survival was quantified as a function of the size of the individual, vegetation
density, and time at the optimal spatial interaction scale. Land surface
temperature and soil moisture were also statistically explained by tree size,
density, and time. Results indicated that in space both facilitation and
competition coexist in the same ecosystem and the sign and magnitude of this
depend on the spatial scale. Overall, within the optimal data driven spatial
scale, tree survival was best explained by the interaction between density and
year, sifting overall from facilitation to competition through time. However,
small sized trees were always facilitated by increased densities, while large
sized trees had either negative or no density effects. Tree size was more
important predictor than density in survival and this has implications for
nature based solutions: maintaining large tree individuals or planting species
that can become large-sized can safeguard against tree less areas by promoting
survival at long time periods through harsh environmental conditions. Large
trees had also a significant effect in moderating land surface temperature.
| [
{
"created": "Tue, 9 Oct 2018 14:53:52 GMT",
"version": "v1"
}
] | 2018-10-10 | [
[
"Moustakas",
"Aristides",
""
],
[
"Daliakopoulos",
"Ioannis N.",
""
],
[
"Benton",
"Tim. G.",
""
]
] | Spatio temporal data are more ubiquitous and richer than even before and the availability of such data poses great challenges in data analytics. Ecological facilitation, the positive effect of density of individuals on the individual's survival across a stress gradient, is a complex phenomenon. A large number of tree individuals coupled with soil moisture, temperature, and water stress data across a long temporal period were followed. Data driven analysis in the absence of hypothesis was performed. Information theoretic analysis of multiple statistical models was employed in order to quantify the best data-driven index of vegetation density and spatial scale of interactions. Sequentially, tree survival was quantified as a function of the size of the individual, vegetation density, and time at the optimal spatial interaction scale. Land surface temperature and soil moisture were also statistically explained by tree size, density, and time. Results indicated that in space both facilitation and competition coexist in the same ecosystem and the sign and magnitude of this depend on the spatial scale. Overall, within the optimal data driven spatial scale, tree survival was best explained by the interaction between density and year, sifting overall from facilitation to competition through time. However, small sized trees were always facilitated by increased densities, while large sized trees had either negative or no density effects. Tree size was more important predictor than density in survival and this has implications for nature based solutions: maintaining large tree individuals or planting species that can become large-sized can safeguard against tree less areas by promoting survival at long time periods through harsh environmental conditions. Large trees had also a significant effect in moderating land surface temperature. |
1108.3245 | Eduardo D. Sontag | Eduardo D. Sontag | Remarks on invariance of population distributions for systems with
equivariant internal dynamics | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For velocity-jump Markov processes with equivariant internal dynamics, we
remark that population distributions are invariant. This provides a
formalization of the fact that FCD (scale) and other symmetry invariant systems
perform identical spatial searches under input transformations.
| [
{
"created": "Tue, 16 Aug 2011 14:17:34 GMT",
"version": "v1"
}
] | 2011-08-17 | [
[
"Sontag",
"Eduardo D.",
""
]
] | For velocity-jump Markov processes with equivariant internal dynamics, we remark that population distributions are invariant. This provides a formalization of the fact that FCD (scale) and other symmetry invariant systems perform identical spatial searches under input transformations. |
1707.05649 | Konstantinos Michmizos | Leo Kozachkov and Konstantinos P. Michmizos | Sequence learning in Associative Neuronal-Astrocytic Network | 8 pages, 5 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The neuronal paradigm of studying the brain has left us with limitations in
both our understanding of how neurons process information to achieve biological
intelligence and how such knowledge may be translated into artificial
intelligence and its most brain-derived branch, neuromorphic computing.
Overturning our fundamental assumptions of how the brain works, the recent
exploration of astrocytes is revealing that these long-neglected brain cells
dynamically regulate learning by interacting with neuronal activity at the
synaptic level. Following recent experimental evidence, we designed an
associative, Hopfield-type, neuronal-astrocytic network and analyzed the
dynamics of the interaction between neurons and astrocytes. We show that
astrocytes were sufficient to trigger transitions between learned memories in
the neuronal component of the network. Further, we mathematically derived the
timing of the transitions that was governed by the dynamics of the
calcium-dependent slow-currents in the astrocytic processes. Overall, we
provide a brain-morphic mechanism for sequence learning that is inspired by,
and aligns with, recent experimental findings. To evaluate our model, we
emulated astrocytic atrophy and showed that memory recall becomes significantly
impaired after a critical point of affected astrocytes was reached. This
brain-inspired and brain-validated approach supports our ongoing efforts to
incorporate non-neuronal computing elements in neuromorphic information
processing.
| [
{
"created": "Sun, 16 Jul 2017 18:16:27 GMT",
"version": "v1"
},
{
"created": "Sun, 10 May 2020 18:18:45 GMT",
"version": "v2"
}
] | 2020-05-12 | [
[
"Kozachkov",
"Leo",
""
],
[
"Michmizos",
"Konstantinos P.",
""
]
] | The neuronal paradigm of studying the brain has left us with limitations in both our understanding of how neurons process information to achieve biological intelligence and how such knowledge may be translated into artificial intelligence and its most brain-derived branch, neuromorphic computing. Overturning our fundamental assumptions of how the brain works, the recent exploration of astrocytes is revealing that these long-neglected brain cells dynamically regulate learning by interacting with neuronal activity at the synaptic level. Following recent experimental evidence, we designed an associative, Hopfield-type, neuronal-astrocytic network and analyzed the dynamics of the interaction between neurons and astrocytes. We show that astrocytes were sufficient to trigger transitions between learned memories in the neuronal component of the network. Further, we mathematically derived the timing of the transitions that was governed by the dynamics of the calcium-dependent slow-currents in the astrocytic processes. Overall, we provide a brain-morphic mechanism for sequence learning that is inspired by, and aligns with, recent experimental findings. To evaluate our model, we emulated astrocytic atrophy and showed that memory recall becomes significantly impaired after a critical point of affected astrocytes was reached. This brain-inspired and brain-validated approach supports our ongoing efforts to incorporate non-neuronal computing elements in neuromorphic information processing. |
1704.02351 | Brenton Maisel | Brenton Maisel and Katja Lindenberg | Channel Noise Effects on Neural Synchronization | 7 Figures | null | 10.1016/j.physa.2019.123186 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Synchronization in neural networks is strongly tied to the implementation of
cognitive processes, but abnormal neuronal synchronization has been linked to a
number of brain disorders such as epilepsy and schizophrenia. Here we examine
the effects of channel noise on the synchronization of small Hodgkin-Huxley
neuronal networks. The principal feature of a Hodgkin-Huxley neuron is the
existence of protein channels that transition between open and closed states
with voltage dependent rate constants. The Hodgkin-Huxley model assumes
infinitely many channels, so fluctuations in the number of open channels do not
affect the voltage. However, real neurons have finitely many channels which
lead to fluctuations in the membrane voltage and modify the timing of the
spikes, which may in turn lead to large changes in the degree of
synchronization. We demonstrate that under mild conditions, neurons in the
network reach a steady state synchronization level that depends only on the
number of neurons in the network. The channel noise only affects the time it
takes to reach the steady state synchronization level.
| [
{
"created": "Fri, 7 Apr 2017 19:26:50 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Feb 2020 15:29:49 GMT",
"version": "v2"
}
] | 2020-02-27 | [
[
"Maisel",
"Brenton",
""
],
[
"Lindenberg",
"Katja",
""
]
] | Synchronization in neural networks is strongly tied to the implementation of cognitive processes, but abnormal neuronal synchronization has been linked to a number of brain disorders such as epilepsy and schizophrenia. Here we examine the effects of channel noise on the synchronization of small Hodgkin-Huxley neuronal networks. The principal feature of a Hodgkin-Huxley neuron is the existence of protein channels that transition between open and closed states with voltage dependent rate constants. The Hodgkin-Huxley model assumes infinitely many channels, so fluctuations in the number of open channels do not affect the voltage. However, real neurons have finitely many channels which lead to fluctuations in the membrane voltage and modify the timing of the spikes, which may in turn lead to large changes in the degree of synchronization. We demonstrate that under mild conditions, neurons in the network reach a steady state synchronization level that depends only on the number of neurons in the network. The channel noise only affects the time it takes to reach the steady state synchronization level. |
2407.06920 | Philippe Marcq | Nastassia Pricoupenko, Flavia Marsigliesi, Philippe Marcq, Carles
Blanch-Mercader, Isabelle Bonnet | Src Kinase Slows Collective Rotation of Confined Epithelial Cell
Monolayers | 41 pages, 16 figures | null | null | null | q-bio.TO physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Collective cell migration is key during development, wound healing and
metastasis and relies on coordinated cell behaviors at the group level. Src
kinase is a signalling enzyme regulating many cellular processes including
adhesion and motility and its deregulated activation has been associated to
aggressiveness of different cancers. Here, we take advantage of optogenetics to
precisely control Src activation in time to study the effect of its over
activation on collective rotation of confined monolayers. We show that Src
activation slows down collective rotation of epithelial cells confined into
circular adhesive patches. We interpret velocity, force and stress data during
period of non-activation and period of activation of Src thanks to an
hydrodynamic description of the cell assembly as a polar active fluid. Src
activation leads to a 2-fold decrease in the ratio of polar angle to friction,
which could result from increased adhesive bonds at the cell-substrate
interface. Our work reveals the importance of fine-tuning the level of Src
activity for coordinated collective behaviors.
| [
{
"created": "Tue, 9 Jul 2024 14:56:25 GMT",
"version": "v1"
}
] | 2024-07-10 | [
[
"Pricoupenko",
"Nastassia",
""
],
[
"Marsigliesi",
"Flavia",
""
],
[
"Marcq",
"Philippe",
""
],
[
"Blanch-Mercader",
"Carles",
""
],
[
"Bonnet",
"Isabelle",
""
]
] | Collective cell migration is key during development, wound healing and metastasis and relies on coordinated cell behaviors at the group level. Src kinase is a signalling enzyme regulating many cellular processes including adhesion and motility and its deregulated activation has been associated to aggressiveness of different cancers. Here, we take advantage of optogenetics to precisely control Src activation in time to study the effect of its over activation on collective rotation of confined monolayers. We show that Src activation slows down collective rotation of epithelial cells confined into circular adhesive patches. We interpret velocity, force and stress data during period of non-activation and period of activation of Src thanks to an hydrodynamic description of the cell assembly as a polar active fluid. Src activation leads to a 2-fold decrease in the ratio of polar angle to friction, which could result from increased adhesive bonds at the cell-substrate interface. Our work reveals the importance of fine-tuning the level of Src activity for coordinated collective behaviors. |
2202.11240 | Fintan Costello | Fintan Costello, Paul Watts, Rita Howe | Homeostatic behavioural response to COVID-19 infections returns R to a
set-point of 1 | 9 pages, submitted | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | One clear aspect of behaviour in the COVID-19 pandemic has been people's
focus on, and response to, reported or observed infection numbers in their
community. We describe a simple model of infectious disease spread in a
pandemic situation where people's behaviour is influenced by the current risk
of infection and where this behavioural response acts homeostatically to return
infection risk to a certain preferred level. This model predicts that the
reproduction rate $R$ will be centered around a median value of 1, and that a
related measure of relative change in the number of new infections will follow
the standard Cauchy distribution. Analysis of worldwide COVID-19 data shows
that the estimated reproduction rate has a median of 1, and that this measure
of relative change calculated from reported numbers of new infections closely
follows the standard Cauchy distribution at both an overall and an individual
country level.
| [
{
"created": "Wed, 23 Feb 2022 00:18:38 GMT",
"version": "v1"
}
] | 2022-02-24 | [
[
"Costello",
"Fintan",
""
],
[
"Watts",
"Paul",
""
],
[
"Howe",
"Rita",
""
]
] | One clear aspect of behaviour in the COVID-19 pandemic has been people's focus on, and response to, reported or observed infection numbers in their community. We describe a simple model of infectious disease spread in a pandemic situation where people's behaviour is influenced by the current risk of infection and where this behavioural response acts homeostatically to return infection risk to a certain preferred level. This model predicts that the reproduction rate $R$ will be centered around a median value of 1, and that a related measure of relative change in the number of new infections will follow the standard Cauchy distribution. Analysis of worldwide COVID-19 data shows that the estimated reproduction rate has a median of 1, and that this measure of relative change calculated from reported numbers of new infections closely follows the standard Cauchy distribution at both an overall and an individual country level. |
2202.07367 | Chun Tung Chou | Chun Tung Chou | Using transcription-based detectors to emulate the behaviour of
sequential probability ratio-based concentration detectors | null | Physical Review E, 2022 | 10.1103/PhysRevE.106.054403 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The sequential probability ratio test (SPRT) from statistics is known to have
the least mean decision time compared to other sequential or fixed-time tests
for given error rates. In some circumstances, cells need to make decisions
accurately and quickly, therefore it has been suggested the SPRT may be used to
understand the speed-accuracy tradeoff in cellular decision making. It is
generally thought that in order for cells to make use of the SPRT, it is
necessary to find biochemical circuits that can compute the log-likelihood
ratio needed for the SPRT. However, this paper takes a different approach. We
recognise that the high-level behaviour of the SPRT is defined by its positive
detection or hit rate, and the computation of the log-likelihood ratio is just
one way to realise this behaviour. In this paper, we will present a method
which uses a transcription-based detector to emulate the hit rate of the SPRT
without computing the exact log-likelihood ratio. We consider the problem of
using a promoter with multiple binding sites to accurately and quickly detect
whether the concentration of a transcription factor is above a target level. We
show that it is possible to find binding and unbinding rates of the
transcription factor to the promoter's binding sites so that the probability
that the amount of mRNA produced will be higher than a threshold is
approximately equal to the hit rate of the SPRT detector. Moreover, we show
that the average time that this transcription-based detector needs to make a
positive detection is less than or equal to that of the SPRT for a wide range
of concentrations. We remark that the last statement does not contradict Wald's
optimality result because our transcription-based detector uses an open-ended
test.
| [
{
"created": "Tue, 15 Feb 2022 12:49:06 GMT",
"version": "v1"
},
{
"created": "Sun, 20 Feb 2022 23:02:55 GMT",
"version": "v2"
},
{
"created": "Wed, 10 Aug 2022 01:43:29 GMT",
"version": "v3"
},
{
"created": "Tue, 4 Oct 2022 11:44:47 GMT",
"version": "v4"
}
] | 2022-11-04 | [
[
"Chou",
"Chun Tung",
""
]
] | The sequential probability ratio test (SPRT) from statistics is known to have the least mean decision time compared to other sequential or fixed-time tests for given error rates. In some circumstances, cells need to make decisions accurately and quickly, therefore it has been suggested the SPRT may be used to understand the speed-accuracy tradeoff in cellular decision making. It is generally thought that in order for cells to make use of the SPRT, it is necessary to find biochemical circuits that can compute the log-likelihood ratio needed for the SPRT. However, this paper takes a different approach. We recognise that the high-level behaviour of the SPRT is defined by its positive detection or hit rate, and the computation of the log-likelihood ratio is just one way to realise this behaviour. In this paper, we will present a method which uses a transcription-based detector to emulate the hit rate of the SPRT without computing the exact log-likelihood ratio. We consider the problem of using a promoter with multiple binding sites to accurately and quickly detect whether the concentration of a transcription factor is above a target level. We show that it is possible to find binding and unbinding rates of the transcription factor to the promoter's binding sites so that the probability that the amount of mRNA produced will be higher than a threshold is approximately equal to the hit rate of the SPRT detector. Moreover, we show that the average time that this transcription-based detector needs to make a positive detection is less than or equal to that of the SPRT for a wide range of concentrations. We remark that the last statement does not contradict Wald's optimality result because our transcription-based detector uses an open-ended test. |
2007.10486 | Christian Samuel Perone | Christian S. Perone | Analysis of the SARS-CoV-2 outbreak in Rio Grande do Sul / Brazil | 15 pages, 11 figures; added new ICU simulation scenarios,
hospitalization graphs and city-level mobility analyses | null | null | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | This article contains a series of analyses done for the SARS-CoV-2 outbreak
in Rio Grande do Sul (RS) in the south of Brazil. These analyses are focused on
the high-incidence cities such as the state capital Porto Alegre and at the
state level. We provide methodological details and estimates for the effective
reproduction number $R_t$, a joint analysis of the mobility data together with
the estimated $R_t$ as well as ICU simulations and ICU LoS (length of stay)
estimation for hospitalizations in Porto Alegre/RS.
| [
{
"created": "Mon, 20 Jul 2020 21:28:19 GMT",
"version": "v1"
},
{
"created": "Sun, 26 Jul 2020 21:46:03 GMT",
"version": "v2"
}
] | 2020-07-28 | [
[
"Perone",
"Christian S.",
""
]
] | This article contains a series of analyses done for the SARS-CoV-2 outbreak in Rio Grande do Sul (RS) in the south of Brazil. These analyses are focused on the high-incidence cities such as the state capital Porto Alegre and at the state level. We provide methodological details and estimates for the effective reproduction number $R_t$, a joint analysis of the mobility data together with the estimated $R_t$ as well as ICU simulations and ICU LoS (length of stay) estimation for hospitalizations in Porto Alegre/RS. |
2307.08463 | Yasin Uzun | Yasin Uzun | Approaches for benchmarking single-cell gene regulatory network
inference methods | 17 pages, 4 figures | null | null | null | q-bio.MN q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Gene regulatory networks are powerful tools for modeling interactions among
genes to regulate their expression for homeostasis and differentiation.
Single-cell sequencing offers a unique opportunity to build these networks with
high-resolution data. There are many proposed computational methods to build
these networks using single-cell data and different approaches are followed to
benchmark these methods. In this review, we lay the basic terminology in the
field and define the success metrics. Next, we present an overview of
approaches for benchmarking computational gene regulatory network approaches
for building gene regulatory networks and point out gaps and future directions
in this regard.
| [
{
"created": "Mon, 17 Jul 2023 13:10:15 GMT",
"version": "v1"
}
] | 2023-07-18 | [
[
"Uzun",
"Yasin",
""
]
] | Gene regulatory networks are powerful tools for modeling interactions among genes to regulate their expression for homeostasis and differentiation. Single-cell sequencing offers a unique opportunity to build these networks with high-resolution data. There are many proposed computational methods to build these networks using single-cell data and different approaches are followed to benchmark these methods. In this review, we lay the basic terminology in the field and define the success metrics. Next, we present an overview of approaches for benchmarking computational gene regulatory network approaches for building gene regulatory networks and point out gaps and future directions in this regard. |
1505.02710 | Lior Pachter | Nicolas Bray, Harold Pimentel, P\'all Melsted and Lior Pachter | Near-optimal RNA-Seq quantification | - Added some results (paralog analysis, allele specific expression
analysis, alignment comparison, accuracy analysis with TPMs) - Switched
bootstrap analysis to human sample from SEQC-MAQCIII - Provided link to a
snakefile that allows for reproducibility of all results and figures in the
paper | null | null | null | q-bio.QM cs.CE cs.DS q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel approach to RNA-Seq quantification that is near optimal in
speed and accuracy. Software implementing the approach, called kallisto, can be
used to analyze 30 million unaligned paired-end RNA-Seq reads in less than 5
minutes on a standard laptop computer while providing results as accurate as
those of the best existing tools. This removes a major computational bottleneck
in RNA-Seq analysis.
| [
{
"created": "Mon, 11 May 2015 17:42:04 GMT",
"version": "v1"
},
{
"created": "Fri, 15 May 2015 17:12:58 GMT",
"version": "v2"
}
] | 2015-05-18 | [
[
"Bray",
"Nicolas",
""
],
[
"Pimentel",
"Harold",
""
],
[
"Melsted",
"Páll",
""
],
[
"Pachter",
"Lior",
""
]
] | We present a novel approach to RNA-Seq quantification that is near optimal in speed and accuracy. Software implementing the approach, called kallisto, can be used to analyze 30 million unaligned paired-end RNA-Seq reads in less than 5 minutes on a standard laptop computer while providing results as accurate as those of the best existing tools. This removes a major computational bottleneck in RNA-Seq analysis. |
2108.13237 | Zhaojun Wang | Zhaojun Wang, Mandana Saebi, Erin K. Grey, James J. Corbett | Ballast water-mediated species spread risk dynamics and policy
implications to reduce the invasion risk to the Mediterranean Sea | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The Mediterranean Sea is one of the most heavily invaded marine regions. This
work focuses on the dynamics and potential policy options for ballast
water-mediated nonindigenous species to the Mediterranean. Specifically, we (1)
estimated port risks in years 2012, 2015, and 2018, (2) identified hub ports
that connect many clusters, and (3) evaluated four regulatory scenarios. The
risk results show that Gibraltar, Suez, and Istanbul remained high-risk ports
from 2012-2018, and they served as hub ports that connected several spread
clusters. With policy scenario analysis, we found that regulating the high-risk
hub ports can disproportionately reduce the overall risk to the Mediterranean:
the average risk to all ports was reduced by 5-10% by regulating one high-risk
hub port, while the average risk to all ports was only reduced by 0.2% by
regulating one average-risk Mediterranean port. We also found that only
regulating high-risk ports cannot reduce their risks effectively.
| [
{
"created": "Mon, 30 Aug 2021 13:46:12 GMT",
"version": "v1"
}
] | 2021-08-31 | [
[
"Wang",
"Zhaojun",
""
],
[
"Saebi",
"Mandana",
""
],
[
"Grey",
"Erin K.",
""
],
[
"Corbett",
"James J.",
""
]
] | The Mediterranean Sea is one of the most heavily invaded marine regions. This work focuses on the dynamics and potential policy options for ballast water-mediated nonindigenous species to the Mediterranean. Specifically, we (1) estimated port risks in years 2012, 2015, and 2018, (2) identified hub ports that connect many clusters, and (3) evaluated four regulatory scenarios. The risk results show that Gibraltar, Suez, and Istanbul remained high-risk ports from 2012-2018, and they served as hub ports that connected several spread clusters. With policy scenario analysis, we found that regulating the high-risk hub ports can disproportionately reduce the overall risk to the Mediterranean: the average risk to all ports was reduced by 5-10% by regulating one high-risk hub port, while the average risk to all ports was only reduced by 0.2% by regulating one average-risk Mediterranean port. We also found that only regulating high-risk ports cannot reduce their risks effectively. |
2206.04603 | Madhur Mangalam | Damian G. Kelty-Stephen, Paul E. Cisek, Benjamin De Bari, James Dixon,
Luis H. Favela, Fred Hasselman, Fred Keijzer, Vicente Raja, Jeffrey B.
Wagman, Brandon J. Thomas, Madhur Mangalam | In search for an alternative to the computer metaphor of the mind and
brain | 157 pages, 19 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The brain-as-computer metaphor has anchored the professed computational
nature of the mind, wresting it down from the intangible logic of Platonic
philosophy to a material basis for empirical science. However, as with many
long-lasting metaphors in science, the computer metaphor has been explored and
stretched long enough to reveal its boundaries. These boundaries highlight
widening gaps in our understanding of the brain's role in an organism's
goal-directed, intelligent behaviors and thoughts. In search of a more
appropriate metaphor that reflects the potentially noncomputable functions of
mind and brain, eight author groups answer the following questions: (1) What do
we understand by the computer metaphor of the brain and cognition? (2) What are
some of the limitations of this computer metaphor? (3) What metaphor should
replace the computational metaphor? (4) What findings support alternative
metaphors? Despite agreeing about feeling the strain of the strictures of
computer metaphors, the authors suggest an exciting diversity of possible
metaphoric options for future research into the mind and brain.
| [
{
"created": "Thu, 9 Jun 2022 16:44:30 GMT",
"version": "v1"
}
] | 2022-06-10 | [
[
"Kelty-Stephen",
"Damian G.",
""
],
[
"Cisek",
"Paul E.",
""
],
[
"De Bari",
"Benjamin",
""
],
[
"Dixon",
"James",
""
],
[
"Favela",
"Luis H.",
""
],
[
"Hasselman",
"Fred",
""
],
[
"Keijzer",
"Fred",
""
]... | The brain-as-computer metaphor has anchored the professed computational nature of the mind, wresting it down from the intangible logic of Platonic philosophy to a material basis for empirical science. However, as with many long-lasting metaphors in science, the computer metaphor has been explored and stretched long enough to reveal its boundaries. These boundaries highlight widening gaps in our understanding of the brain's role in an organism's goal-directed, intelligent behaviors and thoughts. In search of a more appropriate metaphor that reflects the potentially noncomputable functions of mind and brain, eight author groups answer the following questions: (1) What do we understand by the computer metaphor of the brain and cognition? (2) What are some of the limitations of this computer metaphor? (3) What metaphor should replace the computational metaphor? (4) What findings support alternative metaphors? Despite agreeing about feeling the strain of the strictures of computer metaphors, the authors suggest an exciting diversity of possible metaphoric options for future research into the mind and brain. |
1808.05755 | Fabian Filipp | Neil F. Box, Lionel Larue, Prashiela Manga, Lluis Montoliu, Richard A.
Spritz, Fabian V. Filipp | The triennial International Pigment Cell Conference (IPCC) | null | null | null | null | q-bio.OT q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | The International Federation of Pigment Cell Societies (IFPCS) held its XXIII
triennial International Pigment Cell Conference (IPCC) in Denver, Colorado in
August 2017. The goal of the summit was to provide a venue promoting a vibrant
interchange among leading basic and clinical researchers working on
leading-edge aspects of melanocyte biology and disease. The philosophy of the
meeting, entitled Breakthroughs in Pigment Cell and Melanoma Research, was to
deliver a comprehensive program in an inclusive environment fostering
scientific exchange and building new academic bridges. This document provides
an outlook on the history, accomplishments, and sustainability of the pigment
cell and melanoma research community. Shared progress in the understanding of
cellular homeostasis of pigment cells but also clinical successes and hurdles
in the treatment of melanoma and dermatological disorders continue to drive
future research activities. A sustainable direction of the societies creates an
international forum identifying key areas of imminent needs in laboratory
research and clinical care and ensures the future of this vibrant, diverse and
unique research community at the same time. Important advances showcase wealth
and breadth of the field in melanocyte and melanoma research and include
emerging frontiers in melanoma immunotherapy, medical and surgical oncology,
dermatology, vitiligo, albinism, genomics and systems biology, precision
bench-to-bedside approaches, epidemiology, pigment biophysics and chemistry,
and evolution. This report recapitulates highlights of the federate meeting
agenda designed to advance clinical and basic research frontiers from melanoma
and dermatological sciences followed by a historical perspective of the
associated societies and conferences.
| [
{
"created": "Wed, 15 Aug 2018 19:00:00 GMT",
"version": "v1"
},
{
"created": "Fri, 24 Aug 2018 14:22:22 GMT",
"version": "v2"
}
] | 2018-08-27 | [
[
"Box",
"Neil F.",
""
],
[
"Larue",
"Lionel",
""
],
[
"Manga",
"Prashiela",
""
],
[
"Montoliu",
"Lluis",
""
],
[
"Spritz",
"Richard A.",
""
],
[
"Filipp",
"Fabian V.",
""
]
] | The International Federation of Pigment Cell Societies (IFPCS) held its XXIII triennial International Pigment Cell Conference (IPCC) in Denver, Colorado in August 2017. The goal of the summit was to provide a venue promoting a vibrant interchange among leading basic and clinical researchers working on leading-edge aspects of melanocyte biology and disease. The philosophy of the meeting, entitled Breakthroughs in Pigment Cell and Melanoma Research, was to deliver a comprehensive program in an inclusive environment fostering scientific exchange and building new academic bridges. This document provides an outlook on the history, accomplishments, and sustainability of the pigment cell and melanoma research community. Shared progress in the understanding of cellular homeostasis of pigment cells but also clinical successes and hurdles in the treatment of melanoma and dermatological disorders continue to drive future research activities. A sustainable direction of the societies creates an international forum identifying key areas of imminent needs in laboratory research and clinical care and ensures the future of this vibrant, diverse and unique research community at the same time. Important advances showcase wealth and breadth of the field in melanocyte and melanoma research and include emerging frontiers in melanoma immunotherapy, medical and surgical oncology, dermatology, vitiligo, albinism, genomics and systems biology, precision bench-to-bedside approaches, epidemiology, pigment biophysics and chemistry, and evolution. This report recapitulates highlights of the federate meeting agenda designed to advance clinical and basic research frontiers from melanoma and dermatological sciences followed by a historical perspective of the associated societies and conferences. |
1910.04590 | Taiping Zeng | Taiping Zeng, XiaoLi Li, and Bailu Si | Learning Sparse Spatial Codes for Cognitive Mapping Inspired by
Entorhinal-Hippocampal Neurocircuit | null | null | null | null | q-bio.NC cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The entorhinal-hippocampal circuit plays a critical role in higher brain
functions, especially spatial cognition. Grid cells in the medial entorhinal
cortex (MEC) periodically fire with different grid spacing and orientation,
which makes a contribution that place cells in the hippocampus can uniquely
encode locations in an environment. But how sparse firing granule cells in the
dentate gyrus are formed from grid cells in the MEC remains to be determined.
Recently, the fruit fly olfactory circuit provides a variant algorithm (called
locality-sensitive hashing) to solve this problem. To investigate how the
sparse place firing generates in the dentate gyrus can help animals to break
the perception ambiguity during environment exploration, we build a
biologically relevant, computational model from grid cells to place cells. The
weight from grid cells to dentate gyrus granule cells is learned by competitive
Hebbian learning. We resorted to the robot system for demonstrating our
cognitive mapping model on the KITTI odometry benchmark dataset. The
experimental results show that our model is able to stably, robustly build a
coherent semi-metric topological map in the large-scale outdoor environment.
The experimental results suggest that the entorhinal-hippocampal circuit as a
variant locality-sensitive hashing algorithm is capable of generating sparse
encoding for easily distinguishing different locations in the environment. Our
experiments also provide theoretical supports that this analogous hashing
algorithm may be a general principle of computation in different brain regions
and species.
| [
{
"created": "Thu, 10 Oct 2019 14:18:08 GMT",
"version": "v1"
}
] | 2019-10-11 | [
[
"Zeng",
"Taiping",
""
],
[
"Li",
"XiaoLi",
""
],
[
"Si",
"Bailu",
""
]
] | The entorhinal-hippocampal circuit plays a critical role in higher brain functions, especially spatial cognition. Grid cells in the medial entorhinal cortex (MEC) periodically fire with different grid spacing and orientation, which makes a contribution that place cells in the hippocampus can uniquely encode locations in an environment. But how sparse firing granule cells in the dentate gyrus are formed from grid cells in the MEC remains to be determined. Recently, the fruit fly olfactory circuit provides a variant algorithm (called locality-sensitive hashing) to solve this problem. To investigate how the sparse place firing generates in the dentate gyrus can help animals to break the perception ambiguity during environment exploration, we build a biologically relevant, computational model from grid cells to place cells. The weight from grid cells to dentate gyrus granule cells is learned by competitive Hebbian learning. We resorted to the robot system for demonstrating our cognitive mapping model on the KITTI odometry benchmark dataset. The experimental results show that our model is able to stably, robustly build a coherent semi-metric topological map in the large-scale outdoor environment. The experimental results suggest that the entorhinal-hippocampal circuit as a variant locality-sensitive hashing algorithm is capable of generating sparse encoding for easily distinguishing different locations in the environment. Our experiments also provide theoretical supports that this analogous hashing algorithm may be a general principle of computation in different brain regions and species. |
2306.15710 | Harry Saxton Mr | Harry Saxton, Xu Xu, Ian Halliday and Torsten Schenkel | New Perspectives on Sensitivity and Identifiability Analysis using the
Unscented Kalman Filter | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Detailed dynamical systems' models used in the life sciences may include
hundreds of state variables and many input parameters, often with physical
meaning. Therefore, efficient and unique input parameter identification, from
experimental data, is an essential but challenging task for this class of
model. To clarify our understating of the process (which within a clinical
context amounts to a personalisation), we utilise the computational methods of
Unscented Kalman filtration (UKF), sensitivity and orthogonality analysis. We
have applied these three techniques to a test-bench model of a single
ventricle, coupled, via Ohmic valves, to a Compliance-Resistor-Compliance (CRC)
Windkessel electrical analogue model of the systemic circulation, chosen in
view of its relative simplicity, interpretability and prior art. Utilising an
efficient, novel and real-time implementation of the UKF (Code available at
https://github.com/H-Sax/CMSB-2023), we show how, counter-intuitively, input
parameters are efficiently recovered from experimental data \emph{even if they
are not sensitive parameters in the currently accepted sense}. This result (i)
exposes potential limitations in the standard interpretation of what it means
for an input parameter to be designated identifiable and (ii) suggests that the
concepts of sensitivity and identifiability may have a weaker relationship than
commonly thought - at least in the presence of an appropriate data set. We
rationalise these observations.
Practically, we present results which show the UKF to be an efficient method
for assigning personalised input parameters from experimental data in
real-time, which enhances the clinical significance of our approach.
| [
{
"created": "Tue, 27 Jun 2023 12:35:23 GMT",
"version": "v1"
}
] | 2023-06-29 | [
[
"Saxton",
"Harry",
""
],
[
"Xu",
"Xu",
""
],
[
"Halliday",
"Ian",
""
],
[
"Schenkel",
"Torsten",
""
]
] | Detailed dynamical systems' models used in the life sciences may include hundreds of state variables and many input parameters, often with physical meaning. Therefore, efficient and unique input parameter identification, from experimental data, is an essential but challenging task for this class of model. To clarify our understating of the process (which within a clinical context amounts to a personalisation), we utilise the computational methods of Unscented Kalman filtration (UKF), sensitivity and orthogonality analysis. We have applied these three techniques to a test-bench model of a single ventricle, coupled, via Ohmic valves, to a Compliance-Resistor-Compliance (CRC) Windkessel electrical analogue model of the systemic circulation, chosen in view of its relative simplicity, interpretability and prior art. Utilising an efficient, novel and real-time implementation of the UKF (Code available at https://github.com/H-Sax/CMSB-2023), we show how, counter-intuitively, input parameters are efficiently recovered from experimental data \emph{even if they are not sensitive parameters in the currently accepted sense}. This result (i) exposes potential limitations in the standard interpretation of what it means for an input parameter to be designated identifiable and (ii) suggests that the concepts of sensitivity and identifiability may have a weaker relationship than commonly thought - at least in the presence of an appropriate data set. We rationalise these observations. Practically, we present results which show the UKF to be an efficient method for assigning personalised input parameters from experimental data in real-time, which enhances the clinical significance of our approach. |
1403.5615 | Feng Gao | Feng Gao, Alon Keinan | High burden of private mutations due to explosive human population
growth and purifying selection | 23 pages, 2 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent studies have shown that human populations have experienced a complex
demographic history, including a recent epoch of rapid population growth that
led to an excess in the proportion of rare genetic variants in humans today.
This excess can impact the burden of private mutations for each individual,
defined here as the proportion of heterozygous variants in each newly sequenced
individual that are novel compared to another large sample of sequenced
individuals. We calculated the burden of private mutations predicted by
different demographic models, and compared with empirical estimates based on
data from the NHLBI Exome Sequencing Project and data from the Neutral Regions
(NR) dataset. We observed a significant excess in the proportion of private
mutations in the empirical data compared with models of demographic history
without a recent epoch of population growth. Incorporating recent growth into
the model provides a much improved fit to empirical observations. This
phenomenon becomes more marked for larger sample sizes. The proportion of
private mutations is additionally increased by purifying selection, which
differentially affect mutations of different functional annotations. These
results have important implications to the design and analysis of
sequencing-based association studies of complex human disease as they pertain
to private and very rare variants.
| [
{
"created": "Sat, 22 Mar 2014 04:41:29 GMT",
"version": "v1"
}
] | 2014-03-25 | [
[
"Gao",
"Feng",
""
],
[
"Keinan",
"Alon",
""
]
] | Recent studies have shown that human populations have experienced a complex demographic history, including a recent epoch of rapid population growth that led to an excess in the proportion of rare genetic variants in humans today. This excess can impact the burden of private mutations for each individual, defined here as the proportion of heterozygous variants in each newly sequenced individual that are novel compared to another large sample of sequenced individuals. We calculated the burden of private mutations predicted by different demographic models, and compared with empirical estimates based on data from the NHLBI Exome Sequencing Project and data from the Neutral Regions (NR) dataset. We observed a significant excess in the proportion of private mutations in the empirical data compared with models of demographic history without a recent epoch of population growth. Incorporating recent growth into the model provides a much improved fit to empirical observations. This phenomenon becomes more marked for larger sample sizes. The proportion of private mutations is additionally increased by purifying selection, which differentially affect mutations of different functional annotations. These results have important implications to the design and analysis of sequencing-based association studies of complex human disease as they pertain to private and very rare variants. |
1704.08933 | Ankit Gupta | Ankit Gupta, Andreas Milias-Argeitis and Mustafa Khammash | Dynamic disorder in simple enzymatic reactions induces stochastic
amplification of substrate | 7 Figures | null | 10.1098/rsif.2017.0311 | null | q-bio.QM math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A growing amount of evidence points to the fact that many enzymes exhibit
fluctuations in their catalytic activity, which are associated with
conformational changes on a broad range of timescales. The experimental study
of this phenomenon, termed dynamic disorder, has become possible due to
advances in single-molecule enzymology measurement techniques, through which
the catalytic activity of individual enzyme molecules can be tracked in time.
The biological role and importance of these fluctuations in a system with a
small number of enzymes such as a living cell have only recently started being
explored. In this work, we examine a simple stochastic reaction system
consisting of an inflowing substrate and an enzyme with a randomly fluctuating
catalytic reaction rate that converts the substrate into an outflowing product.
To describe analytically the effect of rate fluctuations on the average
substrate abundance at steady-state, we derive an explicit formula that
connects the relative speed of enzymatic fluctuations with the mean substrate
level. We demonstrate that the relative speed of rate fluctuations can have a
dramatic effect on the mean substrate, and lead to large positive deviations
from predictions based on the assumption of deterministic enzyme activity. Our
results also establish an interesting connection between the amplification
effect and the mixing properties of the Markov process describing the enzymatic
activity fluctuations, which can be used to easily predict the fluctuation
speed above which such deviations become negligible. As the techniques of
single-molecule enzymology continuously evolve, it may soon be possible to
study the stochastic phenomena due to enzymatic activity fluctuations within
living cells. Our work can be used to formulate experimentally testable
hypotheses regarding the magnitude of these fluctuations, as well as their
phenotypic consequences.
| [
{
"created": "Fri, 28 Apr 2017 13:51:49 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Jul 2017 09:13:20 GMT",
"version": "v2"
}
] | 2017-07-28 | [
[
"Gupta",
"Ankit",
""
],
[
"Milias-Argeitis",
"Andreas",
""
],
[
"Khammash",
"Mustafa",
""
]
] | A growing amount of evidence points to the fact that many enzymes exhibit fluctuations in their catalytic activity, which are associated with conformational changes on a broad range of timescales. The experimental study of this phenomenon, termed dynamic disorder, has become possible due to advances in single-molecule enzymology measurement techniques, through which the catalytic activity of individual enzyme molecules can be tracked in time. The biological role and importance of these fluctuations in a system with a small number of enzymes such as a living cell have only recently started being explored. In this work, we examine a simple stochastic reaction system consisting of an inflowing substrate and an enzyme with a randomly fluctuating catalytic reaction rate that converts the substrate into an outflowing product. To describe analytically the effect of rate fluctuations on the average substrate abundance at steady-state, we derive an explicit formula that connects the relative speed of enzymatic fluctuations with the mean substrate level. We demonstrate that the relative speed of rate fluctuations can have a dramatic effect on the mean substrate, and lead to large positive deviations from predictions based on the assumption of deterministic enzyme activity. Our results also establish an interesting connection between the amplification effect and the mixing properties of the Markov process describing the enzymatic activity fluctuations, which can be used to easily predict the fluctuation speed above which such deviations become negligible. As the techniques of single-molecule enzymology continuously evolve, it may soon be possible to study the stochastic phenomena due to enzymatic activity fluctuations within living cells. Our work can be used to formulate experimentally testable hypotheses regarding the magnitude of these fluctuations, as well as their phenotypic consequences. |
0901.4850 | Ralf Bundschuh | Malcolm McCauley, Robert Forties, Ulrich Gerland and Ralf Bundschuh | Anomalous scaling in nanopore translocation of structured heteropolymers | 21 pages, 7 figures, 1 table | Phys. Biol. 6 (2009) 036006 | 10.1088/1478-3975/6/3/036006 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Translocation through a nanopore is a new experimental technique to probe
physical properties of biomolecules. A bulk of theoretical and computational
work exists on the dependence of the time to translocate a single unstructured
molecule on the length of the molecule. Here, we study the same problem but for
RNA molecules for which the breaking of the secondary structure is the main
barrier for translocation. To this end, we calculate the mean translocation
time of single-stranded RNA through a nanopore of zero thickness and at zero
voltage for many randomly chosen RNA sequences. We find the translocation time
to depend on the length of the RNA molecule with a power law. The exponent
changes as a function of temperature and exceeds the naively expected exponent
of two for purely diffusive transport at all temperatures. We interpret the
power law scaling in terms of diffusion in a one-dimensional energy landscape
with a logarithmic barrier.
| [
{
"created": "Fri, 30 Jan 2009 09:45:42 GMT",
"version": "v1"
}
] | 2009-05-04 | [
[
"McCauley",
"Malcolm",
""
],
[
"Forties",
"Robert",
""
],
[
"Gerland",
"Ulrich",
""
],
[
"Bundschuh",
"Ralf",
""
]
] | Translocation through a nanopore is a new experimental technique to probe physical properties of biomolecules. A bulk of theoretical and computational work exists on the dependence of the time to translocate a single unstructured molecule on the length of the molecule. Here, we study the same problem but for RNA molecules for which the breaking of the secondary structure is the main barrier for translocation. To this end, we calculate the mean translocation time of single-stranded RNA through a nanopore of zero thickness and at zero voltage for many randomly chosen RNA sequences. We find the translocation time to depend on the length of the RNA molecule with a power law. The exponent changes as a function of temperature and exceeds the naively expected exponent of two for purely diffusive transport at all temperatures. We interpret the power law scaling in terms of diffusion in a one-dimensional energy landscape with a logarithmic barrier. |
q-bio/0411029 | Scott A. Hill | Scott A. Hill, Xiao-Ping Liu, Melissa A. Borla, Jorge V. Jose and
Donald M. O'Malley | Neurokinematic Modeling of Complex Swimming Patterns of the Larval
Zebrafish | 7 pages with five embedded figures. Presented at "Computational
Neuroscience 2004" conference in Baltimore, Maryland. To be published in
conference proceedings in Neurocomputing | null | null | null | q-bio.NC | null | Larval zebrafish exhibit a variety of complex undulatory swimming patterns.
This repertoire is controlled by the 300 neurons projecting from brain into
spinal cord. Understanding how descending control signals shape the output of
spinal circuits, however, is nontrivial. We have therefore developed a
segmental oscillator model (using NEURON) to investigate this system. We found
that adjusting the strength of NMDA and glycinergic synapses enabled the
generation of oscillation (tail-beat) frequencies over the range exhibited in
different larval swim patterns. In addition, we developed a kinematic model to
visualize the more complex axial bending patterns used during prey capture.
| [
{
"created": "Fri, 12 Nov 2004 23:50:27 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Hill",
"Scott A.",
""
],
[
"Liu",
"Xiao-Ping",
""
],
[
"Borla",
"Melissa A.",
""
],
[
"Jose",
"Jorge V.",
""
],
[
"O'Malley",
"Donald M.",
""
]
] | Larval zebrafish exhibit a variety of complex undulatory swimming patterns. This repertoire is controlled by the 300 neurons projecting from brain into spinal cord. Understanding how descending control signals shape the output of spinal circuits, however, is nontrivial. We have therefore developed a segmental oscillator model (using NEURON) to investigate this system. We found that adjusting the strength of NMDA and glycinergic synapses enabled the generation of oscillation (tail-beat) frequencies over the range exhibited in different larval swim patterns. In addition, we developed a kinematic model to visualize the more complex axial bending patterns used during prey capture. |
2007.09704 | Adeel Razi | Karl J. Friston, Erik D. Fagerholm, Tahereh S. Zarghami, Thomas Parr,
In\^es Hip\'olito, Lo\"ic Magrou, Adeel Razi | Parcels and particles: Markov blankets in the brain | null | null | null | null | q-bio.NC q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | At the inception of human brain mapping, two principles of functional anatomy
underwrote most conceptions - and analyses - of distributed brain responses:
namely functional segregation and integration. There are currently two main
approaches to characterising functional integration. The first is a mechanistic
modelling of connectomics in terms of directed effective connectivity that
mediates neuronal message passing and dynamics on neuronal circuits. The second
phenomenological approach usually characterises undirected functional
connectivity (i.e., measurable correlations), in terms of intrinsic brain
networks, self-organised criticality, dynamical instability, etc. This paper
describes a treatment of effective connectivity that speaks to the emergence of
intrinsic brain networks and critical dynamics. It is predicated on the notion
of Markov blankets that play a fundamental role in the self-organisation of far
from equilibrium systems. Using the apparatus of the renormalisation group, we
show that much of the phenomenology found in network neuroscience is an
emergent property of a particular partition of neuronal states, over
progressively larger scales. As such, it offers a way of linking dynamics on
directed graphs to the phenomenology of intrinsic brain networks.
| [
{
"created": "Sun, 19 Jul 2020 16:18:41 GMT",
"version": "v1"
}
] | 2020-07-21 | [
[
"Friston",
"Karl J.",
""
],
[
"Fagerholm",
"Erik D.",
""
],
[
"Zarghami",
"Tahereh S.",
""
],
[
"Parr",
"Thomas",
""
],
[
"Hipólito",
"Inês",
""
],
[
"Magrou",
"Loïc",
""
],
[
"Razi",
"Adeel",
""
]
] | At the inception of human brain mapping, two principles of functional anatomy underwrote most conceptions - and analyses - of distributed brain responses: namely functional segregation and integration. There are currently two main approaches to characterising functional integration. The first is a mechanistic modelling of connectomics in terms of directed effective connectivity that mediates neuronal message passing and dynamics on neuronal circuits. The second phenomenological approach usually characterises undirected functional connectivity (i.e., measurable correlations), in terms of intrinsic brain networks, self-organised criticality, dynamical instability, etc. This paper describes a treatment of effective connectivity that speaks to the emergence of intrinsic brain networks and critical dynamics. It is predicated on the notion of Markov blankets that play a fundamental role in the self-organisation of far from equilibrium systems. Using the apparatus of the renormalisation group, we show that much of the phenomenology found in network neuroscience is an emergent property of a particular partition of neuronal states, over progressively larger scales. As such, it offers a way of linking dynamics on directed graphs to the phenomenology of intrinsic brain networks. |
2204.07678 | Omar Saucedo | Omar Saucedo, Joseph H. Tien | Host movement, transmission hot spots, and vector-borne disease dynamics
on spatial networks | A few minor notation typos. 1) Figure 1, N_{i} corrected to
N_{i}^{h}. 2) Typo in vector equations, system 2.1. N_{i} corrected to
N_{i}^{h} and I_{i} corrected to I_{i}^{h} 3) On page 10, \mu_{v,i} corrected
to \mu{i}^{v} | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine how spatial heterogeneity combines with mobility network structure
to influence vector-borne disease dynamics. Specifically, we consider a
Ross-Macdonald-type disease model on $n$ spatial locations that are coupled by
host movement on a strongly connected, weighted, directed graph. We derive a
closed form approximation to the domain reproduction number using a Laurent
series expansion, and use this approximation to compute sensitivities of the
basic reproduction number to model parameters. To illustrate how these results
can be used to help inform mitigation strategies, as a case study we apply
these results to malaria dynamics in Namibia, using published cell phone data
and estimates for local disease transmission. Our analytical results are
particularly useful for understanding drivers of transmission when mobility
sinks and transmission hot spots do not coincide.
| [
{
"created": "Fri, 15 Apr 2022 23:39:01 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Apr 2022 23:28:52 GMT",
"version": "v2"
}
] | 2022-04-22 | [
[
"Saucedo",
"Omar",
""
],
[
"Tien",
"Joseph H.",
""
]
] | We examine how spatial heterogeneity combines with mobility network structure to influence vector-borne disease dynamics. Specifically, we consider a Ross-Macdonald-type disease model on $n$ spatial locations that are coupled by host movement on a strongly connected, weighted, directed graph. We derive a closed form approximation to the domain reproduction number using a Laurent series expansion, and use this approximation to compute sensitivities of the basic reproduction number to model parameters. To illustrate how these results can be used to help inform mitigation strategies, as a case study we apply these results to malaria dynamics in Namibia, using published cell phone data and estimates for local disease transmission. Our analytical results are particularly useful for understanding drivers of transmission when mobility sinks and transmission hot spots do not coincide. |
1610.03417 | Alessandro Fontana | Alessandro Fontana | Is psychosis caused by defective dissociation? An Artificial Life model
for schizophrenia | 14 pages, 6 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Both neurobiological and environmental factors are known to play a role in
the origin of schizophrenia, but no model has been proposed that accounts for
both. This work presents a functional model of schizophrenia that merges
psychodynamic elements with ingredients borrowed from the theory of
psychological traumas, and evidences the interplay of traumatic experiences and
defective mental functions in the pathogenesis of the disorder. Our model
foresees that dissociation is a standard tool used by the mind to protect
itself from emotional pain. In case of repeated traumas, the mind learns to
adopt selective forms of dissociation to avoid pain without losing touch with
external reality. We conjecture that this process is defective in
schizophrenia, where dissociation is either too weak, giving rise to positive
symptoms, or too strong, causing negative symptoms.
| [
{
"created": "Tue, 11 Oct 2016 16:30:36 GMT",
"version": "v1"
},
{
"created": "Thu, 9 Mar 2017 13:01:28 GMT",
"version": "v2"
}
] | 2017-03-10 | [
[
"Fontana",
"Alessandro",
""
]
] | Both neurobiological and environmental factors are known to play a role in the origin of schizophrenia, but no model has been proposed that accounts for both. This work presents a functional model of schizophrenia that merges psychodynamic elements with ingredients borrowed from the theory of psychological traumas, and evidences the interplay of traumatic experiences and defective mental functions in the pathogenesis of the disorder. Our model foresees that dissociation is a standard tool used by the mind to protect itself from emotional pain. In case of repeated traumas, the mind learns to adopt selective forms of dissociation to avoid pain without losing touch with external reality. We conjecture that this process is defective in schizophrenia, where dissociation is either too weak, giving rise to positive symptoms, or too strong, causing negative symptoms. |
2009.01269 | Ilenna Jones | Ilenna Simone Jones, Konrad Paul Kording | Can Single Neurons Solve MNIST? The Computational Power of Biological
Dendritic Trees | 21 pages, 4 main figures, 1 supplementary figure, 2 tables | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Physiological experiments have highlighted how the dendrites of biological
neurons can nonlinearly process distributed synaptic inputs. This is in stark
contrast to units in artificial neural networks that are generally linear apart
from an output nonlinearity. If dendritic trees can be nonlinear, biological
neurons may have far more computational power than their artificial
counterparts. Here we use a simple model where the dendrite is implemented as a
sequence of thresholded linear units. We find that such dendrites can readily
solve machine learning problems, such as MNIST or CIFAR-10, and that they
benefit from having the same input onto several branches of the dendritic tree.
This dendrite model is a special case of sparse network. This work suggests
that popular neuron models may severely underestimate the computational power
enabled by the biological fact of nonlinear dendrites and multiple synapses per
pair of neurons. The next generation of artificial neural networks may
significantly benefit from these biologically inspired dendritic architectures.
| [
{
"created": "Wed, 2 Sep 2020 18:07:39 GMT",
"version": "v1"
}
] | 2020-09-04 | [
[
"Jones",
"Ilenna Simone",
""
],
[
"Kording",
"Konrad Paul",
""
]
] | Physiological experiments have highlighted how the dendrites of biological neurons can nonlinearly process distributed synaptic inputs. This is in stark contrast to units in artificial neural networks that are generally linear apart from an output nonlinearity. If dendritic trees can be nonlinear, biological neurons may have far more computational power than their artificial counterparts. Here we use a simple model where the dendrite is implemented as a sequence of thresholded linear units. We find that such dendrites can readily solve machine learning problems, such as MNIST or CIFAR-10, and that they benefit from having the same input onto several branches of the dendritic tree. This dendrite model is a special case of sparse network. This work suggests that popular neuron models may severely underestimate the computational power enabled by the biological fact of nonlinear dendrites and multiple synapses per pair of neurons. The next generation of artificial neural networks may significantly benefit from these biologically inspired dendritic architectures. |
1708.01871 | Mojtaba Sedigh Fazli | Mojtaba Sedigh Fazli, Stephen Andrew Vella, Silvia N.J. Moreno,
Shannon Quinn | Computational Motility Tracking of Calcium Dynamics in Toxoplasma gondii | 7 pages, 13 figures, KDDBigDas Workshop | null | null | null | q-bio.QM cs.CV q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Toxoplasma gondii is the causative agent responsible for toxoplasmosis and
serves as one of the most common parasites in the world. For a successful lytic
cycle, T. gondii must traverse biological barriers in order to invade host
cells, and as such, motility is critical for its virulence. Calcium signaling,
governed by fluctuations in cytosolic calcium (Ca2+) concentrations, is
utilized universally across life and regulates many cellular processes,
including the stimulation of T. gondii virulence factors such as motility.
Therefore, increases in cytosolic calcium, called calcium oscillations, serve
as a means to link and quantify the intracellular signaling processes that lead
to T. gondii motility and invasion. Here, we describe our work extracting,
quantifying and modeling motility patterns of T. gondii before and after the
addition of pharmacological drugs and/or extracellular calcium. We demonstrate
a computational pipeline including a robust tracking system using optical flow
and dense trajectory features to extract T. gondii motility patterns. Using
this pipeline, we were able to track changes in T.gondii motility in response
to cytosolic Ca2+ fluxes in extracellular parasites. This allows us to study
how Ca2+ signaling via release from intracellular Ca2+ stores and/or from
extracellular Ca2+ entry relates to motility patterns, a crucial first step in
developing countermeasures for T. gondii virulence.
| [
{
"created": "Tue, 1 Aug 2017 04:00:16 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Aug 2017 01:40:37 GMT",
"version": "v2"
}
] | 2017-08-21 | [
[
"Fazli",
"Mojtaba Sedigh",
""
],
[
"Vella",
"Stephen Andrew",
""
],
[
"Moreno",
"Silvia N. J.",
""
],
[
"Quinn",
"Shannon",
""
]
] | Toxoplasma gondii is the causative agent responsible for toxoplasmosis and serves as one of the most common parasites in the world. For a successful lytic cycle, T. gondii must traverse biological barriers in order to invade host cells, and as such, motility is critical for its virulence. Calcium signaling, governed by fluctuations in cytosolic calcium (Ca2+) concentrations, is utilized universally across life and regulates many cellular processes, including the stimulation of T. gondii virulence factors such as motility. Therefore, increases in cytosolic calcium, called calcium oscillations, serve as a means to link and quantify the intracellular signaling processes that lead to T. gondii motility and invasion. Here, we describe our work extracting, quantifying and modeling motility patterns of T. gondii before and after the addition of pharmacological drugs and/or extracellular calcium. We demonstrate a computational pipeline including a robust tracking system using optical flow and dense trajectory features to extract T. gondii motility patterns. Using this pipeline, we were able to track changes in T.gondii motility in response to cytosolic Ca2+ fluxes in extracellular parasites. This allows us to study how Ca2+ signaling via release from intracellular Ca2+ stores and/or from extracellular Ca2+ entry relates to motility patterns, a crucial first step in developing countermeasures for T. gondii virulence. |
0908.0146 | Luis Diambra | L. Diambra | Inferring genetic networks: An information theoretic approach | 17 pages, 4 figus | null | null | null | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the postgenome era many efforts have been dedicated to systematically
elucidate the complex web of interacting genes and proteins. These efforts
include experimental and computational methods. Microarray technology offers an
opportunity for monitoring gene expression level at the genome scale. By
recourse to information theory, this study proposes a mathematical approach to
reconstruct gene regulatory networks at coarse-grain level from high throughput
gene expression data. The method provides the {\it a posteriori} probability
that a given gene regulates positively, negatively or does not regulate each
one of the network genes. This approach also allows the introduction of prior
knowledge and the quantification of the information gain from experimental data
used in the inference procedure. This information gain can be used to chose
genes to be perturbed in subsequent experiments in order to refine the
knowledge about the architecture of an underlying gene regulatory network. The
performance of the proposed approach has been studied by {\it in numero}
experiments. Our results suggest that the approach is suitable for focusing on
size-limited problems, such as, recovering a small subnetwork of interest by
performing perturbation over selected genes.
| [
{
"created": "Sun, 2 Aug 2009 18:24:29 GMT",
"version": "v1"
}
] | 2009-08-04 | [
[
"Diambra",
"L.",
""
]
] | In the postgenome era many efforts have been dedicated to systematically elucidate the complex web of interacting genes and proteins. These efforts include experimental and computational methods. Microarray technology offers an opportunity for monitoring gene expression level at the genome scale. By recourse to information theory, this study proposes a mathematical approach to reconstruct gene regulatory networks at coarse-grain level from high throughput gene expression data. The method provides the {\it a posteriori} probability that a given gene regulates positively, negatively or does not regulate each one of the network genes. This approach also allows the introduction of prior knowledge and the quantification of the information gain from experimental data used in the inference procedure. This information gain can be used to chose genes to be perturbed in subsequent experiments in order to refine the knowledge about the architecture of an underlying gene regulatory network. The performance of the proposed approach has been studied by {\it in numero} experiments. Our results suggest that the approach is suitable for focusing on size-limited problems, such as, recovering a small subnetwork of interest by performing perturbation over selected genes. |
1806.00646 | Bob Eisenberg | Shixin Xu, Bob Eisenberg, Zilong Song, Huaxiong Huang | Osmosis through a Semi-permeable Membrane: a Consistent Approach to
Interactions | typos corrected; equations reformatted a bit; masking of part of
Fig.1 corrected | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The movement of ionic solutions is an essential part of biology and
technology. Fluidics, from nano- to micro- to microfluidics, is a burgeoning
area of technology which is all about the movement of ionic solutions, on
various scales. Many cells, tissues, and organs of animals and plants depend on
osmosis, as the movement of fluids is called in biology. Indeed, the movement
of fluids through channel proteins (that have a hole down their middle) is
fluidics on an atomic scale. Ionic fluids are complex fluids, with energy
stored in many ways. Ionic fluids flow driven by gradients of concentration,
chemical and electrical potential, and hydrostatic pressure. Each flow is
classically described by its own field theory, independent of the others, but
of course, in reality every gradient drives every kind of flow to a varying
extent. Combining field equations is tricky and so the theory of complex fluids
derives the equations, rather than assumes their interactions. When field
equations are derived, rather than assumed, their variables are consistent.
That is to say all variables satisfy all equations under all conditions with
one set of parameters. Here we treat a classical osmotic cell in this spirit,
using a sharp interface method to derive boundary conditions consistent with
all flows and fields. We allow volume to change with concentration, since
changes of volume are a property of ionic solutions known to all who make them
in the laboratory. We consider flexible and inflexible membranes. We show how
to combine the energetics of the membrane with the energetics of the
surrounding complex fluids. The results seem general but need application to
specific situations of technological, biological and experimental importance
before the consequences of consistency can be understood.
| [
{
"created": "Sat, 2 Jun 2018 14:57:17 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Jun 2018 13:17:46 GMT",
"version": "v2"
},
{
"created": "Thu, 7 Jun 2018 12:11:55 GMT",
"version": "v3"
}
] | 2018-06-08 | [
[
"Xu",
"Shixin",
""
],
[
"Eisenberg",
"Bob",
""
],
[
"Song",
"Zilong",
""
],
[
"Huang",
"Huaxiong",
""
]
] | The movement of ionic solutions is an essential part of biology and technology. Fluidics, from nano- to micro- to microfluidics, is a burgeoning area of technology which is all about the movement of ionic solutions, on various scales. Many cells, tissues, and organs of animals and plants depend on osmosis, as the movement of fluids is called in biology. Indeed, the movement of fluids through channel proteins (that have a hole down their middle) is fluidics on an atomic scale. Ionic fluids are complex fluids, with energy stored in many ways. Ionic fluids flow driven by gradients of concentration, chemical and electrical potential, and hydrostatic pressure. Each flow is classically described by its own field theory, independent of the others, but of course, in reality every gradient drives every kind of flow to a varying extent. Combining field equations is tricky and so the theory of complex fluids derives the equations, rather than assumes their interactions. When field equations are derived, rather than assumed, their variables are consistent. That is to say all variables satisfy all equations under all conditions with one set of parameters. Here we treat a classical osmotic cell in this spirit, using a sharp interface method to derive boundary conditions consistent with all flows and fields. We allow volume to change with concentration, since changes of volume are a property of ionic solutions known to all who make them in the laboratory. We consider flexible and inflexible membranes. We show how to combine the energetics of the membrane with the energetics of the surrounding complex fluids. The results seem general but need application to specific situations of technological, biological and experimental importance before the consequences of consistency can be understood. |
q-bio/0411037 | Krishnakumar Garikipati | E. Kuhl, K. Garikipati, E. M. Arruda and K. Grosh | Remodeling of biological tissue: Mechanically induced reorientation of a
transversely isotropic chain network | LaTeX2e, 19 pages, 9 figures | null | 10.1016/j.jmps.2005.03.002 | null | q-bio.QM q-bio.TO | null | A new class of micromechanically motivated chain network models for soft
biological tissues is presented. On the microlevel, it is based on the
statistics of long chain molecules. A wormlike chain model is applied to
capture the behavior of the collagen microfibrils. On the macrolevel, the
network of collagen chains is represented by a transversely isotropic eight
chain unit cell introducing one characteristic material axis. Biomechanically
induced remodeling is captured by allowing for a continuous reorientation of
the predominant unit cell axis driven by a biomechanical stimulus. To this end,
we adopt the gradual alignment of the unit cell axis with the direction of
maximum principal strain. The evolution of the unit cell axis' orientation is
governed by a first-order rate equation. For the temporal discretization of the
remodeling rate equation, we suggest an exponential update scheme of
Euler-Rodrigues type. For the spatial discretization, a finite element strategy
is applied which introduces the current individual cell orientation as an
internal variable on the integration point level. Selected model problems are
analyzed to illustrate the basic features of the new model. Finally, the
presented approach is applied to the biomechanically relevant boundary value
problem of an in vitro engineered functional tendon construct.
| [
{
"created": "Thu, 18 Nov 2004 20:12:17 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Kuhl",
"E.",
""
],
[
"Garikipati",
"K.",
""
],
[
"Arruda",
"E. M.",
""
],
[
"Grosh",
"K.",
""
]
] | A new class of micromechanically motivated chain network models for soft biological tissues is presented. On the microlevel, it is based on the statistics of long chain molecules. A wormlike chain model is applied to capture the behavior of the collagen microfibrils. On the macrolevel, the network of collagen chains is represented by a transversely isotropic eight chain unit cell introducing one characteristic material axis. Biomechanically induced remodeling is captured by allowing for a continuous reorientation of the predominant unit cell axis driven by a biomechanical stimulus. To this end, we adopt the gradual alignment of the unit cell axis with the direction of maximum principal strain. The evolution of the unit cell axis' orientation is governed by a first-order rate equation. For the temporal discretization of the remodeling rate equation, we suggest an exponential update scheme of Euler-Rodrigues type. For the spatial discretization, a finite element strategy is applied which introduces the current individual cell orientation as an internal variable on the integration point level. Selected model problems are analyzed to illustrate the basic features of the new model. Finally, the presented approach is applied to the biomechanically relevant boundary value problem of an in vitro engineered functional tendon construct. |
2212.05313 | \`Alex Gim\'enez-Romero | \`Alex Gim\'enez-Romero, Eduardo Moralejo, Manuel A. Mat\'ias | A compartmental model for Xylella fastidiosa diseases with explicit
vector seasonal dynamics | 18 pages, 8 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | The bacterium Xylella fastidiosa (Xf) is mainly transmitted by the
spittlebug, Philaenus spumarius, in Europe, where it has caused significant
economic damage to olive and almond trees. Understanding the factors that
determine disease dynamics in pathosystems that share similarities can help
design control strategies focused on minimizing transmission chains. Here we
introduce a compartmental model for Xf-caused diseases in Europe that accounts
for the main relevant epidemiological processes, including the seasonal
dynamics of P. spumarius. The model was confronted with epidemiological data
from the two major outbreaks of Xf in Europe, the olive quick disease syndrome
(OQDS) in Apulia, Italy, caused by the subspecies pauca, and the almond leaf
scorch disease (ALSD) in Majorca, Spain, caused by subspecies multiplex and
fastidiosa. Using a Bayesian inference framework, we show how the model
successfully reproduces the general field data in both diseases. In a global
sensitivity analysis, the vector-plant and plant-vector transmission rates,
together with the vector removal rate, were the most influential parameters in
determining the time of the infected host population peak, the incidence peak
and the final number of dead hosts. We also used our model to check different
vector-based control strategies, showing that a joint strategy focused on
increasing the rate of vector removal while lowering the number of annual
newborn vectors is optimal for disease control.
| [
{
"created": "Sat, 10 Dec 2022 14:38:13 GMT",
"version": "v1"
}
] | 2022-12-13 | [
[
"Giménez-Romero",
"Àlex",
""
],
[
"Moralejo",
"Eduardo",
""
],
[
"Matías",
"Manuel A.",
""
]
] | The bacterium Xylella fastidiosa (Xf) is mainly transmitted by the spittlebug, Philaenus spumarius, in Europe, where it has caused significant economic damage to olive and almond trees. Understanding the factors that determine disease dynamics in pathosystems that share similarities can help design control strategies focused on minimizing transmission chains. Here we introduce a compartmental model for Xf-caused diseases in Europe that accounts for the main relevant epidemiological processes, including the seasonal dynamics of P. spumarius. The model was confronted with epidemiological data from the two major outbreaks of Xf in Europe, the olive quick disease syndrome (OQDS) in Apulia, Italy, caused by the subspecies pauca, and the almond leaf scorch disease (ALSD) in Majorca, Spain, caused by subspecies multiplex and fastidiosa. Using a Bayesian inference framework, we show how the model successfully reproduces the general field data in both diseases. In a global sensitivity analysis, the vector-plant and plant-vector transmission rates, together with the vector removal rate, were the most influential parameters in determining the time of the infected host population peak, the incidence peak and the final number of dead hosts. We also used our model to check different vector-based control strategies, showing that a joint strategy focused on increasing the rate of vector removal while lowering the number of annual newborn vectors is optimal for disease control. |
0705.3869 | Eugene Shakhnovich | Konstantin Zeldovich, Peiqiu Chen, Boris Shakhnovich, Eugene
Shakhnovich | A first-principles model of early evolution: Emergence of gene families,
species and preferred protein folds | In press, PLoS Computational Biology | null | 10.1371/journal.pcbi.0030139 | null | q-bio.BM q-bio.PE | null | In this work we develop a microscopic physical model of early evolution,
where phenotype,organism life expectancy, is directly related to genotype, the
stability of its proteins in their native conformations which can be determined
exactly in the model. Simulating the model on a computer, we consistently
observe the Big Bang scenario whereby exponential population growth ensues as
soon as favorable sequence-structure combinations (precursors of stable
proteins) are discovered. Upon that, random diversity of the structural space
abruptly collapses into a small set of preferred proteins. We observe that
protein folds remain stable and abundant in the population at time scales much
greater than mutation or organism lifetime, and the distribution of the
lifetimes of dominant folds in a population approximately follows a power law.
The separation of evolutionary time scales between discovery of new folds and
generation of new sequences gives rise to emergence of protein families and
superfamilies whose sizes are power-law distributed, closely matching the same
distributions for real proteins. On the population level we observe emergence
of species, subpopulations which carry similar genomes. Further we present a
simple theory that relates stability of evolving proteins to the sizes of
emerging genomes. Together, these results provide a microscopic first
principles picture of how first gene families developed in the course of early
evolution
| [
{
"created": "Sat, 26 May 2007 02:26:55 GMT",
"version": "v1"
}
] | 2015-05-13 | [
[
"Zeldovich",
"Konstantin",
""
],
[
"Chen",
"Peiqiu",
""
],
[
"Shakhnovich",
"Boris",
""
],
[
"Shakhnovich",
"Eugene",
""
]
] | In this work we develop a microscopic physical model of early evolution, where phenotype,organism life expectancy, is directly related to genotype, the stability of its proteins in their native conformations which can be determined exactly in the model. Simulating the model on a computer, we consistently observe the Big Bang scenario whereby exponential population growth ensues as soon as favorable sequence-structure combinations (precursors of stable proteins) are discovered. Upon that, random diversity of the structural space abruptly collapses into a small set of preferred proteins. We observe that protein folds remain stable and abundant in the population at time scales much greater than mutation or organism lifetime, and the distribution of the lifetimes of dominant folds in a population approximately follows a power law. The separation of evolutionary time scales between discovery of new folds and generation of new sequences gives rise to emergence of protein families and superfamilies whose sizes are power-law distributed, closely matching the same distributions for real proteins. On the population level we observe emergence of species, subpopulations which carry similar genomes. Further we present a simple theory that relates stability of evolving proteins to the sizes of emerging genomes. Together, these results provide a microscopic first principles picture of how first gene families developed in the course of early evolution |
0709.1152 | Radhakrishnan Nagarajan | Radhakrishnan Nagarajan | Power-law Signatures and Patchiness in Genechip Oligonucleotide
Microarrays | 21 Pages, 6 Figures | null | null | null | q-bio.GN q-bio.QM | null | . Genechip oligonucleotide microarrays have been used widely for
transcriptional profiling of a large number of genes in a given paradigm. Gene
expression estimation precedes biological inference and is given as a complex
combination of atomic entities on the array called probes. These probe
intensities are further classified into perfect-match (PM) and mis-match (MM)
probes. While former is a measure of specific binding, the lat-ter is a measure
of non-specific binding. The behavior of the MM probes has especially proven to
be elusive. The present study investigates qualita-tive similarities in the
distributional signatures and local correlation struc-tures/patchiness between
the PM and MM probe intensities. These qualita-tive similarities are
established on publicly available microarrays generated across laboratories
investigating the same paradigm. Persistence of these similarities across raw
as well as background subtracted probe intensities is also investigated. The
results presented raise fundamental concerns in inter-preting Genechip
oligonucleotide microarray data.
| [
{
"created": "Fri, 7 Sep 2007 20:17:55 GMT",
"version": "v1"
},
{
"created": "Wed, 31 Oct 2007 21:32:34 GMT",
"version": "v2"
}
] | 2007-11-01 | [
[
"Nagarajan",
"Radhakrishnan",
""
]
] | . Genechip oligonucleotide microarrays have been used widely for transcriptional profiling of a large number of genes in a given paradigm. Gene expression estimation precedes biological inference and is given as a complex combination of atomic entities on the array called probes. These probe intensities are further classified into perfect-match (PM) and mis-match (MM) probes. While former is a measure of specific binding, the lat-ter is a measure of non-specific binding. The behavior of the MM probes has especially proven to be elusive. The present study investigates qualita-tive similarities in the distributional signatures and local correlation struc-tures/patchiness between the PM and MM probe intensities. These qualita-tive similarities are established on publicly available microarrays generated across laboratories investigating the same paradigm. Persistence of these similarities across raw as well as background subtracted probe intensities is also investigated. The results presented raise fundamental concerns in inter-preting Genechip oligonucleotide microarray data. |
2106.03688 | Giovanni Granato G | Giovanni Granato, Emilio Cartoni, Federico Da Rold, Andrea Mattera,
Gianluca Baldassarre | A Computational Model of Representation Learning in the Brain Cortex,
Integrating Unsupervised and Reinforcement Learning | null | null | null | null | q-bio.NC cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A common view on the brain learning processes proposes that the three classic
learning paradigms -- unsupervised, reinforcement, and supervised -- take place
in respectively the cortex, the basal-ganglia, and the cerebellum. However,
dopamine outbursts, usually assumed to encode reward, are not limited to the
basal ganglia but also reach prefrontal, motor, and higher sensory cortices. We
propose that in the cortex the same reward-based trial-and-error processes
might support not only the acquisition of motor representations but also of
sensory representations. In particular, reward signals might guide
trial-and-error processes that mix with associative learning processes to
support the acquisition of representations better serving downstream action
selection. We tested the soundness of this hypothesis with a computational
model that integrates unsupervised learning (Contrastive Divergence) and
reinforcement learning (REINFORCE). The model was tested with a task requiring
different responses to different visual images grouped in categories involving
either colour, shape, or size. Results show that a balanced mix of unsupervised
and reinforcement learning processes leads to the best performance. Indeed,
excessive unsupervised learning tends to under-represent task-relevant features
while excessive reinforcement learning tends to initially learn slowly and then
to incur in local minima. These results stimulate future empirical studies on
category learning directed to investigate similar effects in the extrastriate
visual cortices. Moreover, they prompt further computational investigations
directed to study the possible advantages of integrating unsupervised and
reinforcement learning processes.
| [
{
"created": "Mon, 7 Jun 2021 15:03:02 GMT",
"version": "v1"
}
] | 2021-06-08 | [
[
"Granato",
"Giovanni",
""
],
[
"Cartoni",
"Emilio",
""
],
[
"Da Rold",
"Federico",
""
],
[
"Mattera",
"Andrea",
""
],
[
"Baldassarre",
"Gianluca",
""
]
] | A common view on the brain learning processes proposes that the three classic learning paradigms -- unsupervised, reinforcement, and supervised -- take place in respectively the cortex, the basal-ganglia, and the cerebellum. However, dopamine outbursts, usually assumed to encode reward, are not limited to the basal ganglia but also reach prefrontal, motor, and higher sensory cortices. We propose that in the cortex the same reward-based trial-and-error processes might support not only the acquisition of motor representations but also of sensory representations. In particular, reward signals might guide trial-and-error processes that mix with associative learning processes to support the acquisition of representations better serving downstream action selection. We tested the soundness of this hypothesis with a computational model that integrates unsupervised learning (Contrastive Divergence) and reinforcement learning (REINFORCE). The model was tested with a task requiring different responses to different visual images grouped in categories involving either colour, shape, or size. Results show that a balanced mix of unsupervised and reinforcement learning processes leads to the best performance. Indeed, excessive unsupervised learning tends to under-represent task-relevant features while excessive reinforcement learning tends to initially learn slowly and then to incur in local minima. These results stimulate future empirical studies on category learning directed to investigate similar effects in the extrastriate visual cortices. Moreover, they prompt further computational investigations directed to study the possible advantages of integrating unsupervised and reinforcement learning processes. |
2407.14063 | Tsvi Tlusty | John M. McBride, Aleksei Koshevarnikov, Marta Siek, Bartosz A.
Grzybowski and Tsvi Tlusty | Statistical Survey of Chemical and Geometric Patterns on Protein
Surfaces as a Blueprint for Protein-mimicking Nanoparticles | null | null | null | null | q-bio.BM physics.bio-ph physics.chem-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Despite recent breakthroughs in understanding how protein sequence relates to
structure and function, considerably less attention has been paid to the
general features of protein surfaces beyond those regions involved in binding
and catalysis. This paper provides a systematic survey of the universe of
protein surfaces and quantifies the sizes, shapes, and curvatures of the
positively/negatively charged and hydrophobic/hydrophilic surface patches as
well as correlations between such patches. It then compares these statistics
with the metrics characterizing nanoparticles functionalized with ligands
terminated with positively and negatively charged ligands. These particles are
of particular interest because they are also surface-patchy and have been shown
to exhibit both antibiotic and anticancer activities - via selective
interactions against various cellular structures - prompting loose analogies to
proteins. Our analyses support such analogies in several respects (e.g.,
patterns of charged protrusions and hydrophobic niches similar to those
observed in proteins), although there are also significant differences. Looking
forward, this work provides a blueprint for the rational design of synthetic
nanoobjects with further enhanced mimicry of proteins' surface properties.
| [
{
"created": "Fri, 19 Jul 2024 06:41:26 GMT",
"version": "v1"
},
{
"created": "Thu, 25 Jul 2024 07:19:17 GMT",
"version": "v2"
}
] | 2024-07-26 | [
[
"McBride",
"John M.",
""
],
[
"Koshevarnikov",
"Aleksei",
""
],
[
"Siek",
"Marta",
""
],
[
"Grzybowski",
"Bartosz A.",
""
],
[
"Tlusty",
"Tsvi",
""
]
] | Despite recent breakthroughs in understanding how protein sequence relates to structure and function, considerably less attention has been paid to the general features of protein surfaces beyond those regions involved in binding and catalysis. This paper provides a systematic survey of the universe of protein surfaces and quantifies the sizes, shapes, and curvatures of the positively/negatively charged and hydrophobic/hydrophilic surface patches as well as correlations between such patches. It then compares these statistics with the metrics characterizing nanoparticles functionalized with ligands terminated with positively and negatively charged ligands. These particles are of particular interest because they are also surface-patchy and have been shown to exhibit both antibiotic and anticancer activities - via selective interactions against various cellular structures - prompting loose analogies to proteins. Our analyses support such analogies in several respects (e.g., patterns of charged protrusions and hydrophobic niches similar to those observed in proteins), although there are also significant differences. Looking forward, this work provides a blueprint for the rational design of synthetic nanoobjects with further enhanced mimicry of proteins' surface properties. |
2001.06545 | Sebastian Raschka | Sebastian Raschka and Benjamin Kaufman | Machine learning and AI-based approaches for bioactive ligand discovery
and GPCR-ligand recognition | 2nd submission fixed the mis-formatted quotation characters (i.e.,
\^a) | Elsevier Methods, Volume 180, 1 August 2020, Pages 89-110 | 10.1016/j.ymeth.2020.06.016 | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the last decade, machine learning and artificial intelligence applications
have received a significant boost in performance and attention in both academic
research and industry. The success behind most of the recent state-of-the-art
methods can be attributed to the latest developments in deep learning. When
applied to various scientific domains that are concerned with the processing of
non-tabular data, for example, image or text, deep learning has been shown to
outperform not only conventional machine learning but also highly specialized
tools developed by domain experts. This review aims to summarize AI-based
research for GPCR bioactive ligand discovery with a particular focus on the
most recent achievements and research trends. To make this article accessible
to a broad audience of computational scientists, we provide instructive
explanations of the underlying methodology, including overviews of the most
commonly used deep learning architectures and feature representations of
molecular data. We highlight the latest AI-based research that has led to the
successful discovery of GPCR bioactive ligands. However, an equal focus of this
review is on the discussion of machine learning-based technology that has been
applied to ligand discovery in general and has the potential to pave the way
for successful GPCR bioactive ligand discovery in the future. This review
concludes with a brief outlook highlighting the recent research trends in deep
learning, such as active learning and semi-supervised learning, which have
great potential for advancing bioactive ligand discovery.
| [
{
"created": "Fri, 17 Jan 2020 22:01:26 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Jan 2020 23:57:00 GMT",
"version": "v2"
},
{
"created": "Sat, 6 Jun 2020 04:08:39 GMT",
"version": "v3"
}
] | 2020-12-21 | [
[
"Raschka",
"Sebastian",
""
],
[
"Kaufman",
"Benjamin",
""
]
] | In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be attributed to the latest developments in deep learning. When applied to various scientific domains that are concerned with the processing of non-tabular data, for example, image or text, deep learning has been shown to outperform not only conventional machine learning but also highly specialized tools developed by domain experts. This review aims to summarize AI-based research for GPCR bioactive ligand discovery with a particular focus on the most recent achievements and research trends. To make this article accessible to a broad audience of computational scientists, we provide instructive explanations of the underlying methodology, including overviews of the most commonly used deep learning architectures and feature representations of molecular data. We highlight the latest AI-based research that has led to the successful discovery of GPCR bioactive ligands. However, an equal focus of this review is on the discussion of machine learning-based technology that has been applied to ligand discovery in general and has the potential to pave the way for successful GPCR bioactive ligand discovery in the future. This review concludes with a brief outlook highlighting the recent research trends in deep learning, such as active learning and semi-supervised learning, which have great potential for advancing bioactive ligand discovery. |
1610.05564 | Laurent Perrinet | Laurent Perrinet (INT), Rick Adams, Karl Friston | Active inference, eye movements and oculomotor delays | null | Biological Cybernetics (Modeling), Springer Verlag, 2014, 106 (8),
pp.777-801 | 10.1007/s00422-014-0620-8 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper considers the problem of sensorimotor delays in the optimal
control of (smooth) eye movements under uncertainty. Specifically, we consider
delays in the visuo-oculomotor loop and their implications for active
inference. Active inference uses a generalisation of Kalman filtering to
provide Bayes optimal estimates of hidden states and action in generalized
coordinates of motion. Representing hidden states in generalized coordinates
provides a simple way of compensating for both sensory and oculomotor delays.
The efficacy of this scheme is illustrated using neuronal simulations of
pursuit initiation responses, with and without compensation. We then consider
an extension of the gener-ative model to simulate smooth pursuit eye movements
- in which the visuo-oculomotor system believes both the target and its centre
of gaze are attracted to a (hidden) point moving in the visual field. Finally,
the generative model is equipped with a hierarchical structure, so that it can
recognise and remember unseen (occluded) trajectories and emit anticipatory
responses. These simulations speak to a straightforward and neurobiologically
plausible solution to the generic problem of integrating information from
different sources with different temporal delays and the particular
difficulties encountered when a system - like the oculomotor system - tries to
control its environment with delayed signals.
| [
{
"created": "Tue, 18 Oct 2016 12:25:00 GMT",
"version": "v1"
}
] | 2016-10-19 | [
[
"Perrinet",
"Laurent",
"",
"INT"
],
[
"Adams",
"Rick",
""
],
[
"Friston",
"Karl",
""
]
] | This paper considers the problem of sensorimotor delays in the optimal control of (smooth) eye movements under uncertainty. Specifically, we consider delays in the visuo-oculomotor loop and their implications for active inference. Active inference uses a generalisation of Kalman filtering to provide Bayes optimal estimates of hidden states and action in generalized coordinates of motion. Representing hidden states in generalized coordinates provides a simple way of compensating for both sensory and oculomotor delays. The efficacy of this scheme is illustrated using neuronal simulations of pursuit initiation responses, with and without compensation. We then consider an extension of the gener-ative model to simulate smooth pursuit eye movements - in which the visuo-oculomotor system believes both the target and its centre of gaze are attracted to a (hidden) point moving in the visual field. Finally, the generative model is equipped with a hierarchical structure, so that it can recognise and remember unseen (occluded) trajectories and emit anticipatory responses. These simulations speak to a straightforward and neurobiologically plausible solution to the generic problem of integrating information from different sources with different temporal delays and the particular difficulties encountered when a system - like the oculomotor system - tries to control its environment with delayed signals. |
1902.01393 | Yuanyuan Han | Yuanyuan Han, Rui Tang, Yi Gu, Alex Ce Zhang, Wei Cai, Violet Castor,
Sung Hwan Cho, William Alaynick, Yu-Hwa Lo | Cameraless High-throughput 3D Imaging Flow Cytometry | null | null | null | null | q-bio.QM physics.optics | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Increasing demand for understanding the vast heterogeneity of cellular
phenotypes has driven the development of imaging flow cytometry (IFC), that
combines features of flow cytometry with fluorescence and bright field
microscopy. IFC combines the throughput and statistical advantage of flow
cytometry with the ability to discretely measure events based on a real or
computational image, as well as conventional flow cytometry metrics. A
limitation of existing IFC systems is that, regardless of detection
methodology, only two-dimensional (2D) cell images are obtained. Without
tomographic three-dimensional (3D) resolution the projection problem remains:
collapsing 3D information onto a 2D image, limiting the reliability of spot
counting or co-localization crucial to cell phenotyping. Here we present a
solution to the projection problem: three-dimensional imaging flow cytometry
(3D-IFC), a high-throughput 3D cell imager based on optical sectioning
microscopy. We combine orthogonal light-sheet scanning illumination with our
previous spatiotemporal transformation detection to produce 3D cell image
reconstruction from a cameraless single-pixel photodetector readout. We further
demonstrate this capability by co-capturing 3D fluorescence and label-free
side-scattering images of single cells in flow at a velocity of 0.2 m s-1,
corresponding to a throughput of approximately 500 cells per second with 60,000
voxels (resized subsequently to 106 voxels) for each cell image at a resolution
of less than 1 micron in X, Y, and Z dimensions. Improved high-throughput
imaging tools are needed to phenotype-genotype recognized heterogeneity in the
fields of immunology, oncology, cell- and gene- therapy, and drug discovery.
| [
{
"created": "Sat, 2 Feb 2019 21:30:57 GMT",
"version": "v1"
}
] | 2019-02-06 | [
[
"Han",
"Yuanyuan",
""
],
[
"Tang",
"Rui",
""
],
[
"Gu",
"Yi",
""
],
[
"Zhang",
"Alex Ce",
""
],
[
"Cai",
"Wei",
""
],
[
"Castor",
"Violet",
""
],
[
"Cho",
"Sung Hwan",
""
],
[
"Alaynick",
"Willi... | Increasing demand for understanding the vast heterogeneity of cellular phenotypes has driven the development of imaging flow cytometry (IFC), that combines features of flow cytometry with fluorescence and bright field microscopy. IFC combines the throughput and statistical advantage of flow cytometry with the ability to discretely measure events based on a real or computational image, as well as conventional flow cytometry metrics. A limitation of existing IFC systems is that, regardless of detection methodology, only two-dimensional (2D) cell images are obtained. Without tomographic three-dimensional (3D) resolution the projection problem remains: collapsing 3D information onto a 2D image, limiting the reliability of spot counting or co-localization crucial to cell phenotyping. Here we present a solution to the projection problem: three-dimensional imaging flow cytometry (3D-IFC), a high-throughput 3D cell imager based on optical sectioning microscopy. We combine orthogonal light-sheet scanning illumination with our previous spatiotemporal transformation detection to produce 3D cell image reconstruction from a cameraless single-pixel photodetector readout. We further demonstrate this capability by co-capturing 3D fluorescence and label-free side-scattering images of single cells in flow at a velocity of 0.2 m s-1, corresponding to a throughput of approximately 500 cells per second with 60,000 voxels (resized subsequently to 106 voxels) for each cell image at a resolution of less than 1 micron in X, Y, and Z dimensions. Improved high-throughput imaging tools are needed to phenotype-genotype recognized heterogeneity in the fields of immunology, oncology, cell- and gene- therapy, and drug discovery. |
1707.02365 | Joaquin Goni | Enrico Amico, Joaqu\'in Go\~ni | The quest for identifiability in human functional connectomes | 31 pages, 11 figures | Scientific Reports, 2018 | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evaluation of the individual 'fingerprint' of a human functional
connectome (FC) is becoming a promising avenue for neuroscientific research,
due to its enormous potential inherent to drawing single subject inferences
from functional connectivity profiles. Here we show that the individual
fingerprint of a human functional connectome can be maximized from a
reconstruction procedure based on group-wise decomposition in a finite number
of brain connectivity modes. We use data from the Human Connectome Project to
demonstrate that the optimal reconstruction of the individual FCs through
connectivity eigenmodes maximizes subject identifiability across resting-state
and all seven tasks evaluated. The identifiability of the optimally
reconstructed individual connectivity profiles increases both at the global and
edgewise level, also when the reconstruction is imposed on additional
functional data of the subjects. Furthermore, reconstructed FC data provide
more robust associations with task-behavioral measurements. Finally, we extend
this approach to also map the most task-sensitive functional connections.
Results show that is possible to maximize individual fingerprinting in the
functional connectivity domain regardless of the task, a crucial next step in
the area of brain connectivity towards individualized connectomics.
| [
{
"created": "Fri, 7 Jul 2017 21:34:14 GMT",
"version": "v1"
},
{
"created": "Mon, 27 Nov 2017 20:54:45 GMT",
"version": "v2"
},
{
"created": "Thu, 12 Apr 2018 16:14:19 GMT",
"version": "v3"
}
] | 2018-04-13 | [
[
"Amico",
"Enrico",
""
],
[
"Goñi",
"Joaquín",
""
]
] | The evaluation of the individual 'fingerprint' of a human functional connectome (FC) is becoming a promising avenue for neuroscientific research, due to its enormous potential inherent to drawing single subject inferences from functional connectivity profiles. Here we show that the individual fingerprint of a human functional connectome can be maximized from a reconstruction procedure based on group-wise decomposition in a finite number of brain connectivity modes. We use data from the Human Connectome Project to demonstrate that the optimal reconstruction of the individual FCs through connectivity eigenmodes maximizes subject identifiability across resting-state and all seven tasks evaluated. The identifiability of the optimally reconstructed individual connectivity profiles increases both at the global and edgewise level, also when the reconstruction is imposed on additional functional data of the subjects. Furthermore, reconstructed FC data provide more robust associations with task-behavioral measurements. Finally, we extend this approach to also map the most task-sensitive functional connections. Results show that is possible to maximize individual fingerprinting in the functional connectivity domain regardless of the task, a crucial next step in the area of brain connectivity towards individualized connectomics. |
1312.6310 | Rembrandt Bakker | Rembrandt Bakker, Paul Tiesinga, Rolf K\"otter | The Scalable Brain Atlas: instant web-based access to public brain
atlases and related content | Rolf K\"otter sadly passed away on June 9th, 2010. He co-initiated
this project and played a crucial role in the design and quality assurance of
the Scalable Brain Atlas | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Scalable Brain Atlas (SBA) is a collection of web services that provide
unified access to a large collection of brain atlas templates for different
species. Its main component is an atlas viewer that displays brain atlas data
as a stack of slices in which stereotaxic coordinates and brain regions can be
selected. These are subsequently used to launch web queries to resources that
require coordinates or region names as input. It supports plugins which run
inside the viewer and respond when a new slice, coordinate or region is
selected. It contains 20 atlas templates in six species, and plugins to compute
coordinate transformations, display anatomical connectivity and fiducial
points, and retrieve properties, descriptions, definitions and 3d
reconstructions of brain regions. The ambition of SBA is to provide a unified
representation of all publicly available brain atlases directly in the web
browser, while remaining a responsive and light weight resource that
specializes in atlas comparisons, searches, coordinate transformations and
interactive displays.
| [
{
"created": "Sat, 21 Dec 2013 22:03:24 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Dec 2014 15:27:48 GMT",
"version": "v2"
}
] | 2014-12-12 | [
[
"Bakker",
"Rembrandt",
""
],
[
"Tiesinga",
"Paul",
""
],
[
"Kötter",
"Rolf",
""
]
] | The Scalable Brain Atlas (SBA) is a collection of web services that provide unified access to a large collection of brain atlas templates for different species. Its main component is an atlas viewer that displays brain atlas data as a stack of slices in which stereotaxic coordinates and brain regions can be selected. These are subsequently used to launch web queries to resources that require coordinates or region names as input. It supports plugins which run inside the viewer and respond when a new slice, coordinate or region is selected. It contains 20 atlas templates in six species, and plugins to compute coordinate transformations, display anatomical connectivity and fiducial points, and retrieve properties, descriptions, definitions and 3d reconstructions of brain regions. The ambition of SBA is to provide a unified representation of all publicly available brain atlases directly in the web browser, while remaining a responsive and light weight resource that specializes in atlas comparisons, searches, coordinate transformations and interactive displays. |
1009.5956 | Raul Isea | Raul Isea | Identification of 11 potential malaria vaccine candidates using
Bioinformatics | 5 pages | VacciMonitor (2010), volumen 19(3), pp. 15-19 | null | null | q-bio.GN | http://creativecommons.org/licenses/by/3.0/ | In this paper, we suggested eleven protein targets to be used as possible
vaccines against Plasmodium falciparum causative agent of almost two to three
million deaths per year. A comprehensive analysis of protein target have been
selected from the small experimental fragment of antigen in the P. falciparum
genome, all of them common to the four stages of the parasite life cycle (i.e.,
sporozoites, merozoites, trophozoites and gametocytes). The potential vaccine
candidates should be analyzed in silico technique using various bioinformatics
tools. Finally, the possible protein target according to PlasmoDB gene ID are
PFC0975c, PFE0660c, PF08_0071, PF10_0084, PFI0180w, MAL13P1.56, PF14_0192,
PF13_0141, PF14_0425, PF13_0322, y PF14_0598.
| [
{
"created": "Wed, 29 Sep 2010 17:55:46 GMT",
"version": "v1"
}
] | 2010-09-30 | [
[
"Isea",
"Raul",
""
]
] | In this paper, we suggested eleven protein targets to be used as possible vaccines against Plasmodium falciparum causative agent of almost two to three million deaths per year. A comprehensive analysis of protein target have been selected from the small experimental fragment of antigen in the P. falciparum genome, all of them common to the four stages of the parasite life cycle (i.e., sporozoites, merozoites, trophozoites and gametocytes). The potential vaccine candidates should be analyzed in silico technique using various bioinformatics tools. Finally, the possible protein target according to PlasmoDB gene ID are PFC0975c, PFE0660c, PF08_0071, PF10_0084, PFI0180w, MAL13P1.56, PF14_0192, PF13_0141, PF14_0425, PF13_0322, y PF14_0598. |
1906.00092 | Anik Chattopadhyay | Anik Chattopadhyay, Arunava Banerjee | Signal Coding and Perfect Reconstruction using Spike Trains | null | null | null | null | q-bio.NC cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many animal sensory pathways, the transformation from external stimuli to
spike trains is essentially deterministic. In this context, a new mathematical
framework for coding and reconstruction, based on a biologically plausible
model of the spiking neuron, is presented. The framework considers encoding of
a signal through spike trains generated by an ensemble of neurons via a
standard convolve-then-threshold mechanism. Neurons are distinguished by their
convolution kernels and threshold values. Reconstruction is posited as a convex
optimization minimizing energy. Formal conditions under which perfect
reconstruction of the signal from the spike trains is possible are then
identified in this setup. Finally, a stochastic gradient descent mechanism is
proposed to achieve these conditions. Simulation experiments are presented to
demonstrate the strength and efficacy of the framework
| [
{
"created": "Fri, 31 May 2019 21:53:42 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Jul 2019 19:40:29 GMT",
"version": "v2"
}
] | 2019-08-01 | [
[
"Chattopadhyay",
"Anik",
""
],
[
"Banerjee",
"Arunava",
""
]
] | In many animal sensory pathways, the transformation from external stimuli to spike trains is essentially deterministic. In this context, a new mathematical framework for coding and reconstruction, based on a biologically plausible model of the spiking neuron, is presented. The framework considers encoding of a signal through spike trains generated by an ensemble of neurons via a standard convolve-then-threshold mechanism. Neurons are distinguished by their convolution kernels and threshold values. Reconstruction is posited as a convex optimization minimizing energy. Formal conditions under which perfect reconstruction of the signal from the spike trains is possible are then identified in this setup. Finally, a stochastic gradient descent mechanism is proposed to achieve these conditions. Simulation experiments are presented to demonstrate the strength and efficacy of the framework |
1402.3845 | Michael B\"orsch | Thomas M. Duncan, Monika G. Dueser, Thomas Heitkamp, Duncan G. G.
McMillan, Michael Boersch | Regulatory conformational changes of the epsilon subunit in single
FRET-labeled FoF1-ATP synthase | 15 pages, 6 figures | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Subunit epsilon is an intrinsic regulator of the bacterial FoF1-ATP synthase,
the ubiquitous membrane-embedded enzyme that utilizes a proton motive force in
most organisms to synthesize adenosine triphosphate (ATP). The C-terminal
domain of epsilon can extend into the central cavity formed by the alpha and
beta subunits, as revealed by the recent X-ray structure of the F1 portion of
the Escherichia coli enzyme. This insertion blocks the rotation of the central
gamma subunit and, thereby, prevents wasteful ATP hydrolysis. Here we aim to
develop an experimental system that can reveal conditions under which epsilon
inhibits the holoenzyme FoF1-ATP synthase in vitro. Labeling the C-terminal
domain of epsilon and the gamma subunit specifically with two different
fluorophores for single-molecule Foerster resonance energy transfer (smFRET)
allowed monitoring of the conformation of epsilon in the reconstituted enzyme
in real time. New mutants were made for future three-color smFRET experiments
to unravel the details of regulatory conformational changes in epsilon.
| [
{
"created": "Sun, 16 Feb 2014 21:46:12 GMT",
"version": "v1"
}
] | 2014-02-18 | [
[
"Duncan",
"Thomas M.",
""
],
[
"Dueser",
"Monika G.",
""
],
[
"Heitkamp",
"Thomas",
""
],
[
"McMillan",
"Duncan G. G.",
""
],
[
"Boersch",
"Michael",
""
]
] | Subunit epsilon is an intrinsic regulator of the bacterial FoF1-ATP synthase, the ubiquitous membrane-embedded enzyme that utilizes a proton motive force in most organisms to synthesize adenosine triphosphate (ATP). The C-terminal domain of epsilon can extend into the central cavity formed by the alpha and beta subunits, as revealed by the recent X-ray structure of the F1 portion of the Escherichia coli enzyme. This insertion blocks the rotation of the central gamma subunit and, thereby, prevents wasteful ATP hydrolysis. Here we aim to develop an experimental system that can reveal conditions under which epsilon inhibits the holoenzyme FoF1-ATP synthase in vitro. Labeling the C-terminal domain of epsilon and the gamma subunit specifically with two different fluorophores for single-molecule Foerster resonance energy transfer (smFRET) allowed monitoring of the conformation of epsilon in the reconstituted enzyme in real time. New mutants were made for future three-color smFRET experiments to unravel the details of regulatory conformational changes in epsilon. |
2203.00133 | Josinaldo Menezes | E. Rangel, B. Moura, J. Menezes | Combination of survival movement strategies in cyclic game systems
during an epidemic | 8 pages, 7 figures | Biosystems 217, 104689 (2022) | 10.1016/j.biosystems.2022.104689 | null | q-bio.PE nlin.AO nlin.PS physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Disease outbreaks affect many ecosystems threatening species that also fight
against other natural enemies. We investigate a cyclic game system with $5$
species, whose organisms outcompete according to the rules of a generalised
spatial rock-paper-scissors game, during an epidemic. We study the effects of
behavioural movement strategies that allow individuals of one out of the
species to move towards areas with a low density of disease vectors and a high
concentration of enemies of their enemies. We perform a series of stochastic
simulations to discover the impact of self-preservation strategies in pattern
formation, calculating the species' spatial autocorrelation functions.
Considering organisms with different physical and cognitive abilities, we
compute the benefits of each movement tactic to reduce selection and infection
risks. Our findings show that the maximum profit in terms of territorial
dominance in the cyclic game is achieved if both survival movement strategies
are combined, with individuals prioritising social distancing. In the case of
an epidemic causing symptomatic illness, the drop in infection risk when
organisms identify and avoid disease vectors does not render a rise in the
species population because many refuges are disregarded, limiting the benefits
of safeguarding against natural enemies. Our results may be helpful to the
understanding of the behavioural strategies in ecosystems where organisms adapt
to face living conditions changes.
| [
{
"created": "Mon, 28 Feb 2022 23:05:07 GMT",
"version": "v1"
},
{
"created": "Wed, 11 May 2022 21:11:10 GMT",
"version": "v2"
}
] | 2022-05-13 | [
[
"Rangel",
"E.",
""
],
[
"Moura",
"B.",
""
],
[
"Menezes",
"J.",
""
]
] | Disease outbreaks affect many ecosystems threatening species that also fight against other natural enemies. We investigate a cyclic game system with $5$ species, whose organisms outcompete according to the rules of a generalised spatial rock-paper-scissors game, during an epidemic. We study the effects of behavioural movement strategies that allow individuals of one out of the species to move towards areas with a low density of disease vectors and a high concentration of enemies of their enemies. We perform a series of stochastic simulations to discover the impact of self-preservation strategies in pattern formation, calculating the species' spatial autocorrelation functions. Considering organisms with different physical and cognitive abilities, we compute the benefits of each movement tactic to reduce selection and infection risks. Our findings show that the maximum profit in terms of territorial dominance in the cyclic game is achieved if both survival movement strategies are combined, with individuals prioritising social distancing. In the case of an epidemic causing symptomatic illness, the drop in infection risk when organisms identify and avoid disease vectors does not render a rise in the species population because many refuges are disregarded, limiting the benefits of safeguarding against natural enemies. Our results may be helpful to the understanding of the behavioural strategies in ecosystems where organisms adapt to face living conditions changes. |
1609.02545 | Wilten Nicola | Wilten Nicola and Claudia Clopath | Supervised Learning in Spiking Neural Networks with FORCE Training | null | Nicola, W., & Clopath, C. (2017). Supervised learning in spiking
neural networks with FORCE training. Nature communications, 8(1), 2208 | 10.1038/s41467-017-01827-3 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Populations of neurons display an extraordinary diversity in the behaviors
they affect and display. Machine learning techniques have recently emerged that
allow us to create networks of model neurons that display behaviours of similar
complexity. Here, we demonstrate the direct applicability of one such
technique, the FORCE method, to spiking neural networks. We train these
networks to mimic dynamical systems, classify inputs, and store discrete
sequences that correspond to the notes of a song. Finally, we use FORCE
training to create two biologically motivated model circuits. One is inspired
by the zebra-finch and successfully reproduces songbird singing. The second
network is motivated by the hippocampus and is trained to store and replay a
movie scene. FORCE trained networks reproduce behaviors comparable in
complexity to their inspired circuits and yield information not easily
obtainable with other techniques such as behavioral responses to
pharmacological manipulations and spike timing statistics.
| [
{
"created": "Thu, 8 Sep 2016 19:43:30 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Nov 2016 15:53:32 GMT",
"version": "v2"
},
{
"created": "Thu, 4 Jan 2018 17:02:11 GMT",
"version": "v3"
}
] | 2018-02-07 | [
[
"Nicola",
"Wilten",
""
],
[
"Clopath",
"Claudia",
""
]
] | Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviours of similar complexity. Here, we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra-finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques such as behavioral responses to pharmacological manipulations and spike timing statistics. |
1307.8143 | Sivan Leviyang | Sivan Leviyang | Constructing Lower-Bounds for CTL Escape Rates in Early HIV and SIV
Infection | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intrahost simian immunodeficiency virus (SIV) evolution is marked by repeated
viral escape from cytotoxic T-lymphocyte (CTLs) response. Typically, the first
such CTL escape occurs in a matter of days, starting around the time of peak
viral load. Many authors have developed methods to quantify the strength of CTL
response by measuring the rate at which CTL escape occurs, but such methods
usually depend on sampling at two or more timepoints, while many datasets
capture the dynamics of the first CTL escape at only a single timepoint. Here,
we develop inference methods for CTL escape rates applicable to single
timepoint datasets. Through a model of early infection dynamics, we construct
confidence intervals for escape rates, but since early infection dynamics are
not completely understood, we also develop a one-sided confidence interval
serving as a lower bound for escape rates over a collection of early infection
models. We apply our methods to two SIV datasets, using our lower bounds and
existing methods to show that escape rates are relatively high during the
initial days of the first CTL escape and then drop to lower levels as the
escape proceeds. We also compare escape in the lymph nodes and the rectal
mucosa, showing that escape in the lymph nodes is initially faster, but as the
first escape proceeds, the rate of escape in the lymph nodes drops below the
rate seen in the rectal mucosa.
| [
{
"created": "Tue, 30 Jul 2013 20:51:55 GMT",
"version": "v1"
}
] | 2013-08-01 | [
[
"Leviyang",
"Sivan",
""
]
] | Intrahost simian immunodeficiency virus (SIV) evolution is marked by repeated viral escape from cytotoxic T-lymphocyte (CTLs) response. Typically, the first such CTL escape occurs in a matter of days, starting around the time of peak viral load. Many authors have developed methods to quantify the strength of CTL response by measuring the rate at which CTL escape occurs, but such methods usually depend on sampling at two or more timepoints, while many datasets capture the dynamics of the first CTL escape at only a single timepoint. Here, we develop inference methods for CTL escape rates applicable to single timepoint datasets. Through a model of early infection dynamics, we construct confidence intervals for escape rates, but since early infection dynamics are not completely understood, we also develop a one-sided confidence interval serving as a lower bound for escape rates over a collection of early infection models. We apply our methods to two SIV datasets, using our lower bounds and existing methods to show that escape rates are relatively high during the initial days of the first CTL escape and then drop to lower levels as the escape proceeds. We also compare escape in the lymph nodes and the rectal mucosa, showing that escape in the lymph nodes is initially faster, but as the first escape proceeds, the rate of escape in the lymph nodes drops below the rate seen in the rectal mucosa. |
2401.04444 | Vitaly Vanchurin | Artem Romanenko and Vitaly Vanchurin | Quasi-equilibrium states and phase transitions in biological evolution | 20 pages, 10 figures, 2 tables, accepted for publication in Entropy | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop a macroscopic description of the evolutionary dynamics by
following the temporal dynamics of the total Shannon entropy of sequences,
denoted by $S$, and the average Hamming distance between them, denoted by $H$.
We argue that a biological system can persist in the so-called
quasi-equilibrium state for an extended period, characterized by strong
correlations between $S$ and $H$, before undergoing a phase transition to
another quasi-equilibrium state. To demonstrate the results, we conducted a
statistical analysis of SARS-CoV-2 data from the United Kingdom during the
period between March, 2020 and December, 2023. From a purely theoretical
perspective, this allows us to systematically study various types of phase
transitions described by a discontinuous change in the thermodynamic
parameters. From a more practical point of view, the analysis can be used, for
example, as an early warning system for pandemics.
| [
{
"created": "Tue, 9 Jan 2024 09:17:16 GMT",
"version": "v1"
},
{
"created": "Thu, 22 Feb 2024 01:23:14 GMT",
"version": "v2"
}
] | 2024-02-23 | [
[
"Romanenko",
"Artem",
""
],
[
"Vanchurin",
"Vitaly",
""
]
] | We develop a macroscopic description of the evolutionary dynamics by following the temporal dynamics of the total Shannon entropy of sequences, denoted by $S$, and the average Hamming distance between them, denoted by $H$. We argue that a biological system can persist in the so-called quasi-equilibrium state for an extended period, characterized by strong correlations between $S$ and $H$, before undergoing a phase transition to another quasi-equilibrium state. To demonstrate the results, we conducted a statistical analysis of SARS-CoV-2 data from the United Kingdom during the period between March, 2020 and December, 2023. From a purely theoretical perspective, this allows us to systematically study various types of phase transitions described by a discontinuous change in the thermodynamic parameters. From a more practical point of view, the analysis can be used, for example, as an early warning system for pandemics. |
q-bio/0511047 | Ala Trusina | Ala Trusina, Kim Sneppen, Ian B. Dodd, Keith E. Shearwin, J. Barry
Egan | Functional alignment of regulatory networks: A study of temperate phages | accepted in Plos Computational Biology | null | 10.1371/journal.pcbi.0010074 | null | q-bio.MN cond-mat.other q-bio.GN q-bio.OT | null | The relationship between the design and functionality of molecular networks
is now a key issue in biology. Comparison of regulatory networks performing
similar tasks can give insights into how network architecture is constrained by
the functions it directs. We here discuss methods of network comparison based
on network architecture and signaling logic. Introducing local and global
signaling scores for the difference between two networks we quantify
similarities between evolutionary closely and distantly related bacteriophages.
Despite the large evolutionary separation between phage $\lambda$ and 186 their
networks are found to be similar when difference is measured in terms of global
signaling. We finally discuss how network alignment can be used to to pinpoint
protein similarities viewed from the network perspective.
| [
{
"created": "Mon, 28 Nov 2005 20:06:27 GMT",
"version": "v1"
}
] | 2015-06-26 | [
[
"Trusina",
"Ala",
""
],
[
"Sneppen",
"Kim",
""
],
[
"Dodd",
"Ian B.",
""
],
[
"Shearwin",
"Keith E.",
""
],
[
"Egan",
"J. Barry",
""
]
] | The relationship between the design and functionality of molecular networks is now a key issue in biology. Comparison of regulatory networks performing similar tasks can give insights into how network architecture is constrained by the functions it directs. We here discuss methods of network comparison based on network architecture and signaling logic. Introducing local and global signaling scores for the difference between two networks we quantify similarities between evolutionary closely and distantly related bacteriophages. Despite the large evolutionary separation between phage $\lambda$ and 186 their networks are found to be similar when difference is measured in terms of global signaling. We finally discuss how network alignment can be used to to pinpoint protein similarities viewed from the network perspective. |
2102.13300 | Yi-Ling Chen | Yi-Ling Chen and Chun-Chung Chen and Yu-Ying Mei and Ning Zhou and
Dongchuan Wu and Ting-Kuo Lee | Ubiquitous proximity to a critical state for collective neural activity
in the CA1 region of freely moving mice | 19 pages, 20 figures | null | 10.1016/j.cjph.2021.12.010 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Using miniscope recordings of calcium fluorescence signals in the CA1 region
of the hippocampus of mice, we monitor the neural activity of hippocampal
regions while the animals are freely moving in an open chamber. Using a
data-driven statistical modeling approach, the statistical properties of the
recorded data are mapped to spin-glass models with pairwise interactions.
Considering the parameter space of the model, the observed system is generally
near a critical state between two distinct phases. The close proximity to the
criticality is found to be robust against different ways of sampling and
segmentation of the measured data. By independently altering the coupling
distribution and the network structure of the statistical model, the network
structures are found to be vital to maintain the proximity to the critical
state. We further find the observed assignment of the coupling strengths makes
the net coupling at each site more balanced with slight variation, which likely
helps the maintenance of the critical state. Network analysis on the
connectivity obtained by thresholding the coupling strengths find the
connectivity of the networks to be well described by a random network model.
These results are consistent across different experiments, sampling and
segmentation choices in our analysis.
| [
{
"created": "Fri, 26 Feb 2021 04:37:57 GMT",
"version": "v1"
}
] | 2022-04-06 | [
[
"Chen",
"Yi-Ling",
""
],
[
"Chen",
"Chun-Chung",
""
],
[
"Mei",
"Yu-Ying",
""
],
[
"Zhou",
"Ning",
""
],
[
"Wu",
"Dongchuan",
""
],
[
"Lee",
"Ting-Kuo",
""
]
] | Using miniscope recordings of calcium fluorescence signals in the CA1 region of the hippocampus of mice, we monitor the neural activity of hippocampal regions while the animals are freely moving in an open chamber. Using a data-driven statistical modeling approach, the statistical properties of the recorded data are mapped to spin-glass models with pairwise interactions. Considering the parameter space of the model, the observed system is generally near a critical state between two distinct phases. The close proximity to the criticality is found to be robust against different ways of sampling and segmentation of the measured data. By independently altering the coupling distribution and the network structure of the statistical model, the network structures are found to be vital to maintain the proximity to the critical state. We further find the observed assignment of the coupling strengths makes the net coupling at each site more balanced with slight variation, which likely helps the maintenance of the critical state. Network analysis on the connectivity obtained by thresholding the coupling strengths find the connectivity of the networks to be well described by a random network model. These results are consistent across different experiments, sampling and segmentation choices in our analysis. |
1012.2242 | Thierry Mora | Thierry Mora and William Bialek | Are biological systems poised at criticality? | 21 pages | J Stat Phys (2011) 144:268-302 | 10.1007/s10955-011-0229-4 | null | q-bio.QM cond-mat.dis-nn cond-mat.stat-mech nlin.AO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many of life's most fascinating phenomena emerge from interactions among many
elements--many amino acids determine the structure of a single protein, many
genes determine the fate of a cell, many neurons are involved in shaping our
thoughts and memories. Physicists have long hoped that these collective
behaviors could be described using the ideas and methods of statistical
mechanics. In the past few years, new, larger scale experiments have made it
possible to construct statistical mechanics models of biological systems
directly from real data. We review the surprising successes of this "inverse"
approach, using examples form families of proteins, networks of neurons, and
flocks of birds. Remarkably, in all these cases the models that emerge from the
data are poised at a very special point in their parameter space--a critical
point. This suggests there may be some deeper theoretical principle behind the
behavior of these diverse systems.
| [
{
"created": "Fri, 10 Dec 2010 11:55:04 GMT",
"version": "v1"
}
] | 2011-11-28 | [
[
"Mora",
"Thierry",
""
],
[
"Bialek",
"William",
""
]
] | Many of life's most fascinating phenomena emerge from interactions among many elements--many amino acids determine the structure of a single protein, many genes determine the fate of a cell, many neurons are involved in shaping our thoughts and memories. Physicists have long hoped that these collective behaviors could be described using the ideas and methods of statistical mechanics. In the past few years, new, larger scale experiments have made it possible to construct statistical mechanics models of biological systems directly from real data. We review the surprising successes of this "inverse" approach, using examples form families of proteins, networks of neurons, and flocks of birds. Remarkably, in all these cases the models that emerge from the data are poised at a very special point in their parameter space--a critical point. This suggests there may be some deeper theoretical principle behind the behavior of these diverse systems. |
1605.03373 | Vicente Botella-Soler | Vicente Botella-Soler, St\'ephane Deny, Olivier Marre, Ga\v{s}per
Tka\v{c}ik | Nonlinear decoding of a complex movie from the mammalian retina | 24 pages, 21 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Retinal circuitry transforms spatiotemporal patterns of light into spiking
activity of ganglion cells, which provide the sole visual input to the brain.
Recent advances have led to a detailed characterization of retinal activity and
stimulus encoding by large neural populations. The inverse problem of decoding,
where the stimulus is reconstructed from spikes, has received less attention,
in particular for complex input movies that should be reconstructed
"pixel-by-pixel". We recorded around a hundred neurons from a dense patch in a
rat retina and decoded movies of multiple small discs executing
mutually-avoiding random motions. We constructed nonlinear (kernelized)
decoders that improved significantly over linear decoding results, mostly due
to their ability to reliably separate between neural responses driven by
locally fluctuating light signals, and responses at locally constant light
driven by spontaneous or network activity. This improvement crucially depended
on the precise, non-Poisson temporal structure of individual spike trains,
which originated in the spike-history dependence of neural responses. Our
results suggest a general paradigm in which downstream neural circuitry could
discriminate between spontaneous and stimulus-driven activity on the basis of
higher-order statistical structure intrinsic to the incoming spike trains.
| [
{
"created": "Wed, 11 May 2016 11:05:07 GMT",
"version": "v1"
}
] | 2016-05-12 | [
[
"Botella-Soler",
"Vicente",
""
],
[
"Deny",
"Stéphane",
""
],
[
"Marre",
"Olivier",
""
],
[
"Tkačik",
"Gašper",
""
]
] | Retinal circuitry transforms spatiotemporal patterns of light into spiking activity of ganglion cells, which provide the sole visual input to the brain. Recent advances have led to a detailed characterization of retinal activity and stimulus encoding by large neural populations. The inverse problem of decoding, where the stimulus is reconstructed from spikes, has received less attention, in particular for complex input movies that should be reconstructed "pixel-by-pixel". We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small discs executing mutually-avoiding random motions. We constructed nonlinear (kernelized) decoders that improved significantly over linear decoding results, mostly due to their ability to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous or network activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. Our results suggest a general paradigm in which downstream neural circuitry could discriminate between spontaneous and stimulus-driven activity on the basis of higher-order statistical structure intrinsic to the incoming spike trains. |
2105.03330 | Evangelos Matsinos | Evangelos Matsinos | COVID-19: The extraction of the effective reproduction number from the
time series of new cases | 40 pages, 7 figures, 5 tables | null | null | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Addressed in this work is the performance of five popular algorithms, which
aim at assessing the dissemination dynamics of the COVID-19 disease on the
basis of the time series of new confirmed cases. The tests are based on
simulated data, generated by means of a deterministic compartmental
epidemiological model \cite{Matsinos2020a}, adapted herein to also include the
possibility of the loss of immunity by the group of the recovered (or
vaccinated) subjects. Assuming a simple temporal dependence of the effective
reproduction number (the exact details are of no relevance as far as the
conclusions of this work are concerned), time series of new cases were
generated in a time domain of nearly one year for the five top-ranking
countries in the cumulative number of infections by January 1, 2021. These
countries are (in descending order of infections): the United States of
America, India, Brazil, Russia, and the United Kingdom. The processing of each
simulated time series led to the establishment of relations between the input
(actual) and the reconstructed values of the effective reproduction number for
each country and algorithm, separately; this work argues that all five
algorithms underestimate the effective reproduction number when the latter
exceeds the critical value of $1$. The five algorithms were subsequently
applied to the real-life time series of new cases for the aforementioned five
countries, which also span a temporal interval of nearly one year; corrected
values of the effective reproduction number are obtained for these countries in
2020.
| [
{
"created": "Fri, 7 May 2021 15:29:03 GMT",
"version": "v1"
}
] | 2021-05-10 | [
[
"Matsinos",
"Evangelos",
""
]
] | Addressed in this work is the performance of five popular algorithms, which aim at assessing the dissemination dynamics of the COVID-19 disease on the basis of the time series of new confirmed cases. The tests are based on simulated data, generated by means of a deterministic compartmental epidemiological model \cite{Matsinos2020a}, adapted herein to also include the possibility of the loss of immunity by the group of the recovered (or vaccinated) subjects. Assuming a simple temporal dependence of the effective reproduction number (the exact details are of no relevance as far as the conclusions of this work are concerned), time series of new cases were generated in a time domain of nearly one year for the five top-ranking countries in the cumulative number of infections by January 1, 2021. These countries are (in descending order of infections): the United States of America, India, Brazil, Russia, and the United Kingdom. The processing of each simulated time series led to the establishment of relations between the input (actual) and the reconstructed values of the effective reproduction number for each country and algorithm, separately; this work argues that all five algorithms underestimate the effective reproduction number when the latter exceeds the critical value of $1$. The five algorithms were subsequently applied to the real-life time series of new cases for the aforementioned five countries, which also span a temporal interval of nearly one year; corrected values of the effective reproduction number are obtained for these countries in 2020. |
1211.2356 | Chris Greenman | CD Greenman, SL Cooke, J Marshall, MR Stratton, and PJ Campbell | Modelling Breakage-Fusion-Bridge Cycles as a Stochastic Paper Folding
Process | 34 pages, 11 figures | null | null | null | q-bio.GN math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Breakage-Fusion-Bridge cycles in cancer arise when a broken segment of DNA is
duplicated and an end from each copy joined together. This structure then
`unfolds' into a new piece of palindromic DNA. This is one mechanism
responsible for the localised amplicons observed in cancer genome data. The
process has parallels with paper folding sequences that arise when a piece of
paper is folded several times and then unfolded. Here we adapt such methods to
study the breakage-fusion-bridge structures in detail. We firstly consider
discrete representations of this space with 2-d trees to demonstrate that there
are 2^(n(n-1)/2) qualitatively distinct evolutions involving n
breakage-fusion-bridge cycles. Secondly we consider the stochastic nature of
the fold positions, to determine evolution likelihoods, and also describe how
amplicons become localised. Finally we highlight these methods by inferring the
evolution of breakage-fusion-bridge cycles with data from primary tissue cancer
samples.
| [
{
"created": "Sat, 10 Nov 2012 22:34:10 GMT",
"version": "v1"
}
] | 2012-11-13 | [
[
"Greenman",
"CD",
""
],
[
"Cooke",
"SL",
""
],
[
"Marshall",
"J",
""
],
[
"Stratton",
"MR",
""
],
[
"Campbell",
"PJ",
""
]
] | Breakage-Fusion-Bridge cycles in cancer arise when a broken segment of DNA is duplicated and an end from each copy joined together. This structure then `unfolds' into a new piece of palindromic DNA. This is one mechanism responsible for the localised amplicons observed in cancer genome data. The process has parallels with paper folding sequences that arise when a piece of paper is folded several times and then unfolded. Here we adapt such methods to study the breakage-fusion-bridge structures in detail. We firstly consider discrete representations of this space with 2-d trees to demonstrate that there are 2^(n(n-1)/2) qualitatively distinct evolutions involving n breakage-fusion-bridge cycles. Secondly we consider the stochastic nature of the fold positions, to determine evolution likelihoods, and also describe how amplicons become localised. Finally we highlight these methods by inferring the evolution of breakage-fusion-bridge cycles with data from primary tissue cancer samples. |
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