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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2404.16357 | Zhichao Liang | Zhichao Liang, Yinuo Zhang, Jushen Wu, and Quanying Liu | Reverse engineering the brain input: Network control theory to identify
cognitive task-related control nodes | null | null | null | null | q-bio.NC cs.SY eess.SY | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The human brain receives complex inputs when performing cognitive tasks,
which range from external inputs via the senses to internal inputs from other
brain regions. However, the explicit inputs to the brain during a cognitive
task remain unclear. Here, we present an input identification framework for
reverse engineering the control nodes and the corresponding inputs to the
brain. The framework is verified with synthetic data generated by a predefined
linear system, indicating it can robustly reconstruct data and recover the
inputs. Then we apply the framework to the real motor-task fMRI data from 200
human subjects. Our results show that the model with sparse inputs can
reconstruct neural dynamics in motor tasks ($EV=0.779$) and the identified 28
control nodes largely overlap with the motor system. Underpinned by network
control theory, our framework offers a general tool for understanding brain
inputs.
| [
{
"created": "Thu, 25 Apr 2024 06:36:00 GMT",
"version": "v1"
}
] | 2024-04-26 | [
[
"Liang",
"Zhichao",
""
],
[
"Zhang",
"Yinuo",
""
],
[
"Wu",
"Jushen",
""
],
[
"Liu",
"Quanying",
""
]
] | The human brain receives complex inputs when performing cognitive tasks, which range from external inputs via the senses to internal inputs from other brain regions. However, the explicit inputs to the brain during a cognitive task remain unclear. Here, we present an input identification framework for reverse engineering the control nodes and the corresponding inputs to the brain. The framework is verified with synthetic data generated by a predefined linear system, indicating it can robustly reconstruct data and recover the inputs. Then we apply the framework to the real motor-task fMRI data from 200 human subjects. Our results show that the model with sparse inputs can reconstruct neural dynamics in motor tasks ($EV=0.779$) and the identified 28 control nodes largely overlap with the motor system. Underpinned by network control theory, our framework offers a general tool for understanding brain inputs. |
2003.14360 | Ryan Renslow | Katherine J. Schultz, Sean M. Colby, Yasemin Yesiltepe, Jamie R.
Nu\~nez, Monee Y. McGrady, Ryan R. Renslow | Application and Assessment of Deep Learning for the Generation of
Potential NMDA Receptor Antagonists | null | null | 10.1039/D0CP03620J | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Uncompetitive antagonists of the N-methyl D-aspartate receptor (NMDAR) have
demonstrated therapeutic benefit in the treatment of neurological diseases such
as Parkinson's and Alzheimer's, but some also cause dissociative effects that
have led to the synthesis of illicit drugs. The ability to generate NMDAR
antagonists in silico is therefore desirable both for new medication
development and for preempting and identifying new designer drugs. Recently,
generative deep learning models have been applied to de novo drug design as a
means to expand the amount of chemical space that can be explored for potential
drug-like compounds. In this study, we assess the application of a generative
model to the NMDAR to achieve two primary objectives: (i) the creation and
release of a comprehensive library of experimentally validated NMDAR
phencyclidine (PCP) site antagonists to assist the drug discovery community and
(ii) an analysis of both the advantages conferred by applying such generative
artificial intelligence models to drug design and the current limitations of
the approach. We apply, and provide source code for, a variety of ligand- and
structure-based assessment techniques used in standard drug discovery analyses
to the deep learning-generated compounds. We present twelve candidate
antagonists that are not available in existing chemical databases to provide an
example of what this type of workflow can achieve, though synthesis and
experimental validation of these compounds is still required.
| [
{
"created": "Tue, 31 Mar 2020 16:41:18 GMT",
"version": "v1"
}
] | 2021-01-14 | [
[
"Schultz",
"Katherine J.",
""
],
[
"Colby",
"Sean M.",
""
],
[
"Yesiltepe",
"Yasemin",
""
],
[
"Nuñez",
"Jamie R.",
""
],
[
"McGrady",
"Monee Y.",
""
],
[
"Renslow",
"Ryan R.",
""
]
] | Uncompetitive antagonists of the N-methyl D-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists in silico is therefore desirable both for new medication development and for preempting and identifying new designer drugs. Recently, generative deep learning models have been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds. In this study, we assess the application of a generative model to the NMDAR to achieve two primary objectives: (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages conferred by applying such generative artificial intelligence models to drug design and the current limitations of the approach. We apply, and provide source code for, a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds. We present twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve, though synthesis and experimental validation of these compounds is still required. |
1609.08108 | Joel Miller | Joel C. Miller | Mathematical models of SIR disease spread with combined non-sexual and
sexual transmission routes | null | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The emergence of diseases such as Zika and Ebola has highlighted the need to
understand the role of sexual transmission in the spread of diseases with a
primarily non-sexual transmission route. In this paper we develop a number of
low-dimensional models which are appropriate for a range of assumptions for how
a disease will spread if it has sexual transmission through a sexual contact
network combined with some other transmission mechanism, such as direct contact
or vectors. The equations derived provide exact predictions for the dynamics of
the corresponding simulations in the large population limit.
| [
{
"created": "Mon, 26 Sep 2016 18:32:21 GMT",
"version": "v1"
},
{
"created": "Wed, 2 Nov 2016 03:38:25 GMT",
"version": "v2"
}
] | 2016-11-03 | [
[
"Miller",
"Joel C.",
""
]
] | The emergence of diseases such as Zika and Ebola has highlighted the need to understand the role of sexual transmission in the spread of diseases with a primarily non-sexual transmission route. In this paper we develop a number of low-dimensional models which are appropriate for a range of assumptions for how a disease will spread if it has sexual transmission through a sexual contact network combined with some other transmission mechanism, such as direct contact or vectors. The equations derived provide exact predictions for the dynamics of the corresponding simulations in the large population limit. |
0910.1219 | EPTCS | Roberto Barbuti (University of Pisa), Giulio Caravagna (University of
Pisa), Paolo Milazzo (University of Pisa), Andrea Maggiolo-Schettini
(University of Pisa) | On the Interpretation of Delays in Delay Stochastic Simulation of
Biological Systems | null | EPTCS 6, 2009, pp. 17-29 | 10.4204/EPTCS.6.2 | null | q-bio.QM cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Delays in biological systems may be used to model events for which the
underlying dynamics cannot be precisely observed. Mathematical modeling of
biological systems with delays is usually based on Delay Differential Equations
(DDEs), a kind of differential equations in which the derivative of the unknown
function at a certain time is given in terms of the values of the function at
previous times. In the literature, delay stochastic simulation algorithms have
been proposed. These algorithms follow a "delay as duration" approach, namely
they are based on an interpretation of a delay as the elapsing time between the
start and the termination of a chemical reaction. This interpretation is not
suitable for some classes of biological systems in which species involved in a
delayed interaction can be involved at the same time in other interactions. We
show on a DDE model of tumor growth that the delay as duration approach for
stochastic simulation is not precise, and we propose a simulation algorithm
based on a ``purely delayed'' interpretation of delays which provides better
results on the considered model.
| [
{
"created": "Wed, 7 Oct 2009 11:25:03 GMT",
"version": "v1"
}
] | 2009-10-08 | [
[
"Barbuti",
"Roberto",
"",
"University of Pisa"
],
[
"Caravagna",
"Giulio",
"",
"University of\n Pisa"
],
[
"Milazzo",
"Paolo",
"",
"University of Pisa"
],
[
"Maggiolo-Schettini",
"Andrea",
"",
"University of Pisa"
]
] | Delays in biological systems may be used to model events for which the underlying dynamics cannot be precisely observed. Mathematical modeling of biological systems with delays is usually based on Delay Differential Equations (DDEs), a kind of differential equations in which the derivative of the unknown function at a certain time is given in terms of the values of the function at previous times. In the literature, delay stochastic simulation algorithms have been proposed. These algorithms follow a "delay as duration" approach, namely they are based on an interpretation of a delay as the elapsing time between the start and the termination of a chemical reaction. This interpretation is not suitable for some classes of biological systems in which species involved in a delayed interaction can be involved at the same time in other interactions. We show on a DDE model of tumor growth that the delay as duration approach for stochastic simulation is not precise, and we propose a simulation algorithm based on a ``purely delayed'' interpretation of delays which provides better results on the considered model. |
2206.05354 | Fei He | Dominik Klepl, Fei He, Min Wu, Daniel J. Blackburn, Ptolemaios G.
Sarrigiannis | Bispectrum-based Cross-frequency Functional Connectivity: Classification
of Alzheimer's disease | 5 pages, 4 figures, conference | null | null | null | q-bio.NC eess.SP q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Alzheimer's disease (AD) is a neurodegenerative disease known to affect brain
functional connectivity (FC). Linear FC measures have been applied to study the
differences in AD by splitting neurophysiological signals such as
electroencephalography (EEG) recordings into discrete frequency bands and
analysing them in isolation. We address this limitation by quantifying
cross-frequency FC in addition to the traditional within-band approach.
Cross-bispectrum, a higher-order spectral analysis, is used to measure the
nonlinear FC and is compared with the cross-spectrum, which only measures the
linear FC within bands. Each frequency coupling is then used to construct an FC
network, which is in turn vectorised and used to train a classifier. We show
that fusing features from networks improves classification accuracy. Although
both within-frequency and cross-frequency networks can be used to predict AD
with high accuracy, our results show that bispectrum-based FC outperforms
cross-spectrum suggesting an important role of cross-frequency FC.
| [
{
"created": "Fri, 10 Jun 2022 20:53:15 GMT",
"version": "v1"
}
] | 2022-06-14 | [
[
"Klepl",
"Dominik",
""
],
[
"He",
"Fei",
""
],
[
"Wu",
"Min",
""
],
[
"Blackburn",
"Daniel J.",
""
],
[
"Sarrigiannis",
"Ptolemaios G.",
""
]
] | Alzheimer's disease (AD) is a neurodegenerative disease known to affect brain functional connectivity (FC). Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals such as electroencephalography (EEG) recordings into discrete frequency bands and analysing them in isolation. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. Each frequency coupling is then used to construct an FC network, which is in turn vectorised and used to train a classifier. We show that fusing features from networks improves classification accuracy. Although both within-frequency and cross-frequency networks can be used to predict AD with high accuracy, our results show that bispectrum-based FC outperforms cross-spectrum suggesting an important role of cross-frequency FC. |
2109.05019 | Sarwan Ali | Sarwan Ali, Murray Patterson | Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19
Spike Sequences | Accepted at IEEE International Conference on Big Data (IEEE Big Data) | null | null | null | q-bio.GN cs.LG | http://creativecommons.org/publicdomain/zero/1.0/ | With the rapid global spread of COVID-19, more and more data related to this
virus is becoming available, including genomic sequence data. The total number
of genomic sequences that are publicly available on platforms such as GISAID is
currently several million, and is increasing with every day. The availability
of such \emph{Big Data} creates a new opportunity for researchers to study this
virus in detail. This is particularly important with all of the dynamics of the
COVID-19 variants which emerge and circulate. This rich data source will give
us insights on the best ways to perform genomic surveillance for this and
future pandemic threats, with the ultimate goal of mitigating or eliminating
such threats. Analyzing and processing the several million genomic sequences is
a challenging task. Although traditional methods for sequence classification
are proven to be effective, they are not designed to deal with these specific
types of genomic sequences. Moreover, most of the existing methods also face
the issue of scalability. Previous studies which were tailored to coronavirus
genomic data proposed to use spike sequences (corresponding to a subsequence of
the genome), rather than using the complete genomic sequence, to perform
different machine learning (ML) tasks such as classification and clustering.
However, those methods suffer from scalability issues. In this paper, we
propose an approach called Spike2Vec, an efficient and scalable feature vector
representation for each spike sequence that can be used for downstream ML
tasks. Through experiments, we show that Spike2Vec is not only scalable on
several million spike sequences, but also outperforms the baseline models in
terms of prediction accuracy, F1 score, etc.
| [
{
"created": "Sun, 12 Sep 2021 03:16:27 GMT",
"version": "v1"
},
{
"created": "Sat, 9 Oct 2021 13:07:23 GMT",
"version": "v2"
},
{
"created": "Mon, 18 Oct 2021 19:33:46 GMT",
"version": "v3"
},
{
"created": "Mon, 15 Nov 2021 16:25:07 GMT",
"version": "v4"
}
] | 2021-11-16 | [
[
"Ali",
"Sarwan",
""
],
[
"Patterson",
"Murray",
""
]
] | With the rapid global spread of COVID-19, more and more data related to this virus is becoming available, including genomic sequence data. The total number of genomic sequences that are publicly available on platforms such as GISAID is currently several million, and is increasing with every day. The availability of such \emph{Big Data} creates a new opportunity for researchers to study this virus in detail. This is particularly important with all of the dynamics of the COVID-19 variants which emerge and circulate. This rich data source will give us insights on the best ways to perform genomic surveillance for this and future pandemic threats, with the ultimate goal of mitigating or eliminating such threats. Analyzing and processing the several million genomic sequences is a challenging task. Although traditional methods for sequence classification are proven to be effective, they are not designed to deal with these specific types of genomic sequences. Moreover, most of the existing methods also face the issue of scalability. Previous studies which were tailored to coronavirus genomic data proposed to use spike sequences (corresponding to a subsequence of the genome), rather than using the complete genomic sequence, to perform different machine learning (ML) tasks such as classification and clustering. However, those methods suffer from scalability issues. In this paper, we propose an approach called Spike2Vec, an efficient and scalable feature vector representation for each spike sequence that can be used for downstream ML tasks. Through experiments, we show that Spike2Vec is not only scalable on several million spike sequences, but also outperforms the baseline models in terms of prediction accuracy, F1 score, etc. |
1905.02613 | Sarah McIntyre | Sarah McIntyre, Athanasia Moungou, Rebecca Boehme, Peder M. Isager,
Frances Lau, Ali Israr, Ellen A. Lumpkin, Freddy Abnousi, H{\aa}kan Olausson | Affective touch communication in close adult relationships | Technical paper accepted for presentation at World Haptics 2019. Data
and materials available: https://doi.org/10.17605/OSF.IO/7XRWC | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inter-personal touch is a powerful aspect of social interaction that we
expect to be particularly important for emotional communication. We studied the
capacity of closely acquainted humans to signal the meaning of several word
cues (e.g. gratitude, sadness) using touch sensation alone. Participants
communicated all cues with above chance performance. We show that emotionally
close people can accurately signal the meaning of different words through
touch, and that performance is affected by the amount of contextual information
available. Even with minimal context and feedback, both attention-getting and
love were communicated surprisingly well. Neither the type of close
relationship, nor self-reported comfort with touch significantly affected
performance.
| [
{
"created": "Tue, 7 May 2019 14:33:59 GMT",
"version": "v1"
}
] | 2019-05-08 | [
[
"McIntyre",
"Sarah",
""
],
[
"Moungou",
"Athanasia",
""
],
[
"Boehme",
"Rebecca",
""
],
[
"Isager",
"Peder M.",
""
],
[
"Lau",
"Frances",
""
],
[
"Israr",
"Ali",
""
],
[
"Lumpkin",
"Ellen A.",
""
],
[
... | Inter-personal touch is a powerful aspect of social interaction that we expect to be particularly important for emotional communication. We studied the capacity of closely acquainted humans to signal the meaning of several word cues (e.g. gratitude, sadness) using touch sensation alone. Participants communicated all cues with above chance performance. We show that emotionally close people can accurately signal the meaning of different words through touch, and that performance is affected by the amount of contextual information available. Even with minimal context and feedback, both attention-getting and love were communicated surprisingly well. Neither the type of close relationship, nor self-reported comfort with touch significantly affected performance. |
1407.4656 | Pierangelo Lombardo | Pierangelo Lombardo, Andrea Gambassi, Luca Dall'Asta | Fixation properties of subdivided populations with balancing selection | 19 pages, 13 figures | Phys. Rev. E 91, 032130 (2015) | 10.1103/PhysRevE.91.032130 | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In subdivided populations, migration acts together with selection and genetic
drift and determines their evolution. Building up on a recently proposed
method, which hinges on the emergence of a time scale separation between local
and global dynamics, we study the fixation properties of subdivided populations
in the presence of balancing selection. The approximation implied by the method
is accurate when the effective selection strength is small and the number of
subpopulations is large. In particular, it predicts a phase transition between
species coexistence and biodiversity loss in the infinite-size limit and, in
finite populations, a nonmonotonic dependence of the mean fixation time on the
migration rate. In order to investigate the fixation properties of the
subdivided population for stronger selection, we introduce an effective coarser
description of the dynamics in terms of a voter model with intermediate states,
which highlights the basic mechanisms driving the evolutionary process.
| [
{
"created": "Thu, 17 Jul 2014 12:48:59 GMT",
"version": "v1"
},
{
"created": "Fri, 20 Mar 2015 21:09:18 GMT",
"version": "v2"
}
] | 2015-03-24 | [
[
"Lombardo",
"Pierangelo",
""
],
[
"Gambassi",
"Andrea",
""
],
[
"Dall'Asta",
"Luca",
""
]
] | In subdivided populations, migration acts together with selection and genetic drift and determines their evolution. Building up on a recently proposed method, which hinges on the emergence of a time scale separation between local and global dynamics, we study the fixation properties of subdivided populations in the presence of balancing selection. The approximation implied by the method is accurate when the effective selection strength is small and the number of subpopulations is large. In particular, it predicts a phase transition between species coexistence and biodiversity loss in the infinite-size limit and, in finite populations, a nonmonotonic dependence of the mean fixation time on the migration rate. In order to investigate the fixation properties of the subdivided population for stronger selection, we introduce an effective coarser description of the dynamics in terms of a voter model with intermediate states, which highlights the basic mechanisms driving the evolutionary process. |
q-bio/0505009 | Yong-Yeol Ahn | Yong-Yeol Ahn, Beom Jun Kim, Hawoong Jeong | Wiring cost in the organization of a biological network | null | null | 10.1016/j.physa.2005.12.013 | null | q-bio.NC | null | To find out the role of the wiring cost in the organization of the neural
network of the nematode \textit{Caenorhapditis elegans} (\textit{C. elegans}),
we build the neuronal map of \textit{C. elegans} based on geometrical positions
of neurons and define the cost as inter-neuronal Euclidean distance \textit{d}.
We show that the wiring probability decays exponentially as a function of
\textit{d}. Using the edge exchanging method and the component placement
optimization scheme, we show that positions of neurons are not randomly
distributed but organized to reduce the total wiring cost. Furthermore, we
numerically study the trade-off between the wiring cost and the performance of
the Hopfield model on the neural network.
| [
{
"created": "Wed, 4 May 2005 20:05:49 GMT",
"version": "v1"
}
] | 2009-11-11 | [
[
"Ahn",
"Yong-Yeol",
""
],
[
"Kim",
"Beom Jun",
""
],
[
"Jeong",
"Hawoong",
""
]
] | To find out the role of the wiring cost in the organization of the neural network of the nematode \textit{Caenorhapditis elegans} (\textit{C. elegans}), we build the neuronal map of \textit{C. elegans} based on geometrical positions of neurons and define the cost as inter-neuronal Euclidean distance \textit{d}. We show that the wiring probability decays exponentially as a function of \textit{d}. Using the edge exchanging method and the component placement optimization scheme, we show that positions of neurons are not randomly distributed but organized to reduce the total wiring cost. Furthermore, we numerically study the trade-off between the wiring cost and the performance of the Hopfield model on the neural network. |
1604.07176 | Zhen Li | Zhen Li and Yizhou Yu | Protein Secondary Structure Prediction Using Cascaded Convolutional and
Recurrent Neural Networks | 8 pages, 3 figures, Accepted by International Joint Conferences on
Artificial Intelligence (IJCAI) | null | null | null | q-bio.BM cs.AI cs.LG cs.NE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Protein secondary structure prediction is an important problem in
bioinformatics. Inspired by the recent successes of deep neural networks, in
this paper, we propose an end-to-end deep network that predicts protein
secondary structures from integrated local and global contextual features. Our
deep architecture leverages convolutional neural networks with different kernel
sizes to extract multiscale local contextual features. In addition, considering
long-range dependencies existing in amino acid sequences, we set up a
bidirectional neural network consisting of gated recurrent unit to capture
global contextual features. Furthermore, multi-task learning is utilized to
predict secondary structure labels and amino-acid solvent accessibility
simultaneously. Our proposed deep network demonstrates its effectiveness by
achieving state-of-the-art performance, i.e., 69.7% Q8 accuracy on the public
benchmark CB513, 76.9% Q8 accuracy on CASP10 and 73.1% Q8 accuracy on CASP11.
Our model and results are publicly available.
| [
{
"created": "Mon, 25 Apr 2016 09:17:18 GMT",
"version": "v1"
}
] | 2016-04-27 | [
[
"Li",
"Zhen",
""
],
[
"Yu",
"Yizhou",
""
]
] | Protein secondary structure prediction is an important problem in bioinformatics. Inspired by the recent successes of deep neural networks, in this paper, we propose an end-to-end deep network that predicts protein secondary structures from integrated local and global contextual features. Our deep architecture leverages convolutional neural networks with different kernel sizes to extract multiscale local contextual features. In addition, considering long-range dependencies existing in amino acid sequences, we set up a bidirectional neural network consisting of gated recurrent unit to capture global contextual features. Furthermore, multi-task learning is utilized to predict secondary structure labels and amino-acid solvent accessibility simultaneously. Our proposed deep network demonstrates its effectiveness by achieving state-of-the-art performance, i.e., 69.7% Q8 accuracy on the public benchmark CB513, 76.9% Q8 accuracy on CASP10 and 73.1% Q8 accuracy on CASP11. Our model and results are publicly available. |
1301.2528 | Jose Manuel Mas | Albert Pujol, Raquel Valls, Vesna Radovanovic, Emre Guney, Javier
Garcia-Garcia, Victor Codony Domenech, Laura Corredor Gonzalez, J .M. Mas,
Baldo Oliva | Virtual-organism toy-model as a tool to develop bioinformatics
approaches of Systems Biology for medical-target discovery | KEY WORDS: Systems Biology Functional Analysis Topological Analysis
Algorithm Protein network | null | null | null | q-bio.MN q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Systems Biology has emerged in the last years as a new holistic approach
based on the global understanding of cells instead of only being focused on
their individual parts (genes or proteins), to better understand the complexity
of human cells. Since the Systems Biology still does not provide the most
accurate answers to our questions due to the complexity of cells and the
limited quality of available information to perform a good gene/protein map
analysis, we have created simpler models to ensure easier analysis of the map
that represents the human cell. Therefore, a virtual organism has been designed
according to the main physiological rules for humans in order to replicate the
human organism and its vital functions. This toy model was constructed by
defining the topology of its genes/proteins and the biological functions
associated to it. There are several examples of these toy models that emulate
natural processes to perform analysis of the virtual life in order to design
the best strategy to understand real life. The strategy applied in this study
combines topological and functional analysis integrating the knowledge about
the relative position of a node among the others in the map with the
conclusions generated by mathematical models that reproduce functional data of
the virtual organism. Our results demonstrate that the combination of both
strategies allows better understanding of our virtual organism even with the
lower input of information needed and therefore it can be a potential tool to
better understand the real life.
| [
{
"created": "Fri, 11 Jan 2013 16:00:30 GMT",
"version": "v1"
}
] | 2013-01-14 | [
[
"Pujol",
"Albert",
""
],
[
"Valls",
"Raquel",
""
],
[
"Radovanovic",
"Vesna",
""
],
[
"Guney",
"Emre",
""
],
[
"Garcia-Garcia",
"Javier",
""
],
[
"Domenech",
"Victor Codony",
""
],
[
"Gonzalez",
"Laura Corredor... | Systems Biology has emerged in the last years as a new holistic approach based on the global understanding of cells instead of only being focused on their individual parts (genes or proteins), to better understand the complexity of human cells. Since the Systems Biology still does not provide the most accurate answers to our questions due to the complexity of cells and the limited quality of available information to perform a good gene/protein map analysis, we have created simpler models to ensure easier analysis of the map that represents the human cell. Therefore, a virtual organism has been designed according to the main physiological rules for humans in order to replicate the human organism and its vital functions. This toy model was constructed by defining the topology of its genes/proteins and the biological functions associated to it. There are several examples of these toy models that emulate natural processes to perform analysis of the virtual life in order to design the best strategy to understand real life. The strategy applied in this study combines topological and functional analysis integrating the knowledge about the relative position of a node among the others in the map with the conclusions generated by mathematical models that reproduce functional data of the virtual organism. Our results demonstrate that the combination of both strategies allows better understanding of our virtual organism even with the lower input of information needed and therefore it can be a potential tool to better understand the real life. |
1101.4265 | Kevin E. Cahill | Kevin Cahill | Models of Membrane Electrostatics | Minor changes, 12 pages, 8 figures | Physical Review E 85(5), 051921 (2012) | 10.1103/PhysRevE.85.051921 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | I derive formulas for the electrostatic potential of a charge in or near a
membrane modeled as one or more dielectric slabs lying between two
semi-infinite dielectrics. One can use these formulas in Monte Carlo codes to
compute the distribution of ions near cell membranes more accurately than by
using Poisson-Boltzmann theory or its linearized version. Here I use them to
discuss the electric field of a uniformly charged membrane, the image charges
of an ion, the distribution of salt ions near a charged membrane, the energy of
a zwitterion near a lipid slab, and the effect of including the phosphate head
groups as thin layers of high electric permittivity.
| [
{
"created": "Sat, 22 Jan 2011 06:30:58 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Jun 2011 04:56:35 GMT",
"version": "v2"
},
{
"created": "Tue, 18 Oct 2011 13:50:54 GMT",
"version": "v3"
},
{
"created": "Wed, 19 Oct 2011 02:33:27 GMT",
"version": "v4"
},
{
"c... | 2015-05-27 | [
[
"Cahill",
"Kevin",
""
]
] | I derive formulas for the electrostatic potential of a charge in or near a membrane modeled as one or more dielectric slabs lying between two semi-infinite dielectrics. One can use these formulas in Monte Carlo codes to compute the distribution of ions near cell membranes more accurately than by using Poisson-Boltzmann theory or its linearized version. Here I use them to discuss the electric field of a uniformly charged membrane, the image charges of an ion, the distribution of salt ions near a charged membrane, the energy of a zwitterion near a lipid slab, and the effect of including the phosphate head groups as thin layers of high electric permittivity. |
1908.05923 | Josefine Bohr Brask | Josefine Bohr Brask and Jonatan Bohr Brask | Connected cooperators and Trojan horses: How correlations between
cooperativeness and social connectedness affect the evolution of cooperation | 13 pages, 6 figures | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cooperative behaviour constitutes a key aspect of both human society and
non-human animal systems, but explaining how cooperation evolves represents a
major scientific challenge. It is now well established that social network
structure plays a central role for the viability of cooperation. However, not
much is known about the importance of the positions of cooperators in the
networks for the evolution of cooperation. Here, we investigate how cooperation
is affected by correlations between cooperativeness and individual social
connectedness. Using simulation models, we find that the effect of correlation
between cooperativeness and connectedness (degree) depends on the social
network structure, with positive effect in standard scale-free networks and no
effect in standard Poisson networks. Furthermore, when degree assortativity is
increased such that individuals cluster with others of similar social
connectedness, we find that bridge areas between social clusters can act as
barriers to the spread of defection, leading to strong enhancement of
cooperation in particular in Poisson networks. But this effect is sensitive to
the presence of Trojan horses (defectors placed within cooperator clusters).
The study provides new knowledge about the conditions under which cooperation
may evolve and persist, and the results are also relevant to consider in regard
to human cooperation experiments.
| [
{
"created": "Fri, 16 Aug 2019 10:33:14 GMT",
"version": "v1"
},
{
"created": "Mon, 27 Jan 2020 14:55:07 GMT",
"version": "v2"
},
{
"created": "Mon, 8 Mar 2021 15:43:36 GMT",
"version": "v3"
}
] | 2021-03-09 | [
[
"Brask",
"Josefine Bohr",
""
],
[
"Brask",
"Jonatan Bohr",
""
]
] | Cooperative behaviour constitutes a key aspect of both human society and non-human animal systems, but explaining how cooperation evolves represents a major scientific challenge. It is now well established that social network structure plays a central role for the viability of cooperation. However, not much is known about the importance of the positions of cooperators in the networks for the evolution of cooperation. Here, we investigate how cooperation is affected by correlations between cooperativeness and individual social connectedness. Using simulation models, we find that the effect of correlation between cooperativeness and connectedness (degree) depends on the social network structure, with positive effect in standard scale-free networks and no effect in standard Poisson networks. Furthermore, when degree assortativity is increased such that individuals cluster with others of similar social connectedness, we find that bridge areas between social clusters can act as barriers to the spread of defection, leading to strong enhancement of cooperation in particular in Poisson networks. But this effect is sensitive to the presence of Trojan horses (defectors placed within cooperator clusters). The study provides new knowledge about the conditions under which cooperation may evolve and persist, and the results are also relevant to consider in regard to human cooperation experiments. |
1910.07263 | Min Yan | Min Yan, Wen-Hao Zhang, He Wang, K. Y. Michael Wong | Bimodular continuous attractor neural networks with static and moving
stimuli | 15 pages, 11 figures, journal paper | Physical Review E, 107(6), 064302 (2023) | 10.1103/PhysRevE.107.064302 | null | q-bio.NC nlin.AO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigated the dynamical behaviors of bimodular continuous attractor
neural networks, each processing a modality of sensory input and interacting
with each other. We found that when bumps coexist in both modules, the position
of each bump is shifted towards the other input when the intermodular couplings
are excitatory and is shifted away when inhibitory. When one intermodular
coupling is excitatory while another is moderately inhibitory, temporally
modulated population spikes can be generated. On further increase of the
inhibitory coupling, momentary spikes will emerge. In the regime of bump
coexistence, bump heights are primarily strengthened by excitatory intermodular
couplings, but there is a lesser weakening effect due to a bump being displaced
from the direct input. When bimodular networks serve as decoders of
multisensory integration, we extend the Bayesian framework to show that
excitatory and inhibitory couplings encode attractive and repulsive priors,
respectively. At low disparity, the bump positions decode the posterior means
in the Bayesian framework, whereas at high disparity, multiple steady states
exist. In the regime of multiple steady states, the less stable state can be
accessed if the input causing the more stable state arrives after a
sufficiently long delay. When one input is moving, the bump in the
corresponding module is pinned when the moving stimulus is weak, unpinned at
intermediate stimulus strength, and tracks the input at strong stimulus
strength, and the stimulus strengths for these transitions increase with the
velocity of the moving stimulus. These results are important to understanding
multisensory integration of static and dynamic stimuli.
| [
{
"created": "Wed, 16 Oct 2019 10:13:49 GMT",
"version": "v1"
},
{
"created": "Sun, 16 Jul 2023 08:31:00 GMT",
"version": "v2"
}
] | 2023-07-18 | [
[
"Yan",
"Min",
""
],
[
"Zhang",
"Wen-Hao",
""
],
[
"Wang",
"He",
""
],
[
"Wong",
"K. Y. Michael",
""
]
] | We investigated the dynamical behaviors of bimodular continuous attractor neural networks, each processing a modality of sensory input and interacting with each other. We found that when bumps coexist in both modules, the position of each bump is shifted towards the other input when the intermodular couplings are excitatory and is shifted away when inhibitory. When one intermodular coupling is excitatory while another is moderately inhibitory, temporally modulated population spikes can be generated. On further increase of the inhibitory coupling, momentary spikes will emerge. In the regime of bump coexistence, bump heights are primarily strengthened by excitatory intermodular couplings, but there is a lesser weakening effect due to a bump being displaced from the direct input. When bimodular networks serve as decoders of multisensory integration, we extend the Bayesian framework to show that excitatory and inhibitory couplings encode attractive and repulsive priors, respectively. At low disparity, the bump positions decode the posterior means in the Bayesian framework, whereas at high disparity, multiple steady states exist. In the regime of multiple steady states, the less stable state can be accessed if the input causing the more stable state arrives after a sufficiently long delay. When one input is moving, the bump in the corresponding module is pinned when the moving stimulus is weak, unpinned at intermediate stimulus strength, and tracks the input at strong stimulus strength, and the stimulus strengths for these transitions increase with the velocity of the moving stimulus. These results are important to understanding multisensory integration of static and dynamic stimuli. |
2403.00716 | Leandro Okimoto | Leandro Y. S. Okimoto, Rayol Mendonca-Neto, Fab\'iola G. Nakamura,
Eduardo F. Nakamura, David Feny\"o and Claudio T. Silva | Few-shot genes selection: subset of PAM50 genes for breast cancer
subtypes classification | null | BMC Bioinformatics 25, 92 (2024) | 10.1186/s12859-024-05715-8 | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Background: In recent years, researchers have made significant strides in
understanding the heterogeneity of breast cancer and its various subtypes.
However, the wealth of genomic and proteomic data available today necessitates
efficient frameworks, instruments, and computational tools for meaningful
analysis. Despite its success as a prognostic tool, the PAM50 gene signature's
reliance on many genes presents challenges in terms of cost and complexity.
Consequently, there is a need for more efficient methods to classify breast
cancer subtypes using a reduced gene set accurately.
Results: This study explores the potential of achieving precise breast cancer
subtype categorization using a reduced gene set derived from the PAM50 gene
signature. By employing a "Few-Shot Genes Selection" method, we randomly select
smaller subsets from PAM50 and evaluate their performance using metrics and a
linear model, specifically the Support Vector Machine (SVM) classifier. In
addition, we aim to assess whether a more compact gene set can maintain
performance while simplifying the classification process. Our findings
demonstrate that certain reduced gene subsets can perform comparable or
superior to the full PAM50 gene signature.
Conclusions: The identified gene subsets, with 36 genes, have the potential
to contribute to the development of more cost-effective and streamlined
diagnostic tools in breast cancer research and clinical settings.
| [
{
"created": "Fri, 1 Mar 2024 18:04:54 GMT",
"version": "v1"
}
] | 2024-03-04 | [
[
"Okimoto",
"Leandro Y. S.",
""
],
[
"Mendonca-Neto",
"Rayol",
""
],
[
"Nakamura",
"Fabíola G.",
""
],
[
"Nakamura",
"Eduardo F.",
""
],
[
"Fenyö",
"David",
""
],
[
"Silva",
"Claudio T.",
""
]
] | Background: In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature's reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately. Results: This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a "Few-Shot Genes Selection" method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature. Conclusions: The identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings. |
2003.11668 | Marc Howard | Marc W. Howard and Michael E. Hasselmo | Cognitive computation using neural representations of time and space in
the Laplace domain | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Memory for the past makes use of a record of what happened when---a function
over past time. Time cells in the hippocampus and temporal context cells in the
entorhinal cortex both code for events as a function of past time, but with
very different receptive fields. Time cells in the hippocampus can be
understood as a compressed estimate of events as a function of the past.
Temporal context cells in the entorhinal cortex can be understood as the
Laplace transform of that function, respectively. Other functional cell types
in the hippocampus and related regions, including border cells, place cells,
trajectory coding, splitter cells, can be understood as coding for functions
over space or past movements or their Laplace transforms. More abstract
quantities, like distance in an abstract conceptual space or numerosity could
also be mapped onto populations of neurons coding for the Laplace transform of
functions over those variables. Quantitative cognitive models of memory and
evidence accumulation can also be specified in this framework allowing
constraints from both behavior and neurophysiology. More generally, the
computational power of the Laplace domain could be important for efficiently
implementing data-independent operators, which could serve as a basis for
neural models of a very broad range of cognitive computations.
| [
{
"created": "Wed, 25 Mar 2020 22:40:49 GMT",
"version": "v1"
}
] | 2020-03-27 | [
[
"Howard",
"Marc W.",
""
],
[
"Hasselmo",
"Michael E.",
""
]
] | Memory for the past makes use of a record of what happened when---a function over past time. Time cells in the hippocampus and temporal context cells in the entorhinal cortex both code for events as a function of past time, but with very different receptive fields. Time cells in the hippocampus can be understood as a compressed estimate of events as a function of the past. Temporal context cells in the entorhinal cortex can be understood as the Laplace transform of that function, respectively. Other functional cell types in the hippocampus and related regions, including border cells, place cells, trajectory coding, splitter cells, can be understood as coding for functions over space or past movements or their Laplace transforms. More abstract quantities, like distance in an abstract conceptual space or numerosity could also be mapped onto populations of neurons coding for the Laplace transform of functions over those variables. Quantitative cognitive models of memory and evidence accumulation can also be specified in this framework allowing constraints from both behavior and neurophysiology. More generally, the computational power of the Laplace domain could be important for efficiently implementing data-independent operators, which could serve as a basis for neural models of a very broad range of cognitive computations. |
2407.19066 | Xuesong Bai | Xuesong Bai, Thomas G. Fai | Stochastic Gene Expression Model of Nuclear-to-Cell Ratio Homeostasis | null | null | null | null | q-bio.CB | http://creativecommons.org/licenses/by/4.0/ | Cell size varies between different cell types, and between different growth
and osmotic conditions. However, the nuclear-to-cell volume ratio (N/C ratio)
remains nearly constant. In this paper, we build on existing deterministic
models of N/C ratio homeostasis and develop a simplified gene translation model
to study the effect of stochasticity on the N/C ratio homeostasis. We solve the
corresponding chemical master equation and obtain the mean and variance of the
N/C ratio. We also use a Taylor expansion approximation to study the effects of
the system size on the fluctuations of the N/C ratio. We then combine the
translation model with a cell division model to study the effects of extrinsic
noises from cell division on the N/C ratio. Our model demonstrates that the N/C
ratio homeostasis is maintained when the stochasticity in cell growth is taken
into account, that the N/C ratio is largely determined by the gene fraction of
nuclear proteins, and that the fluctuations in the N/C ratio diminish as the
system size increases.
| [
{
"created": "Fri, 26 Jul 2024 20:17:17 GMT",
"version": "v1"
}
] | 2024-07-30 | [
[
"Bai",
"Xuesong",
""
],
[
"Fai",
"Thomas G.",
""
]
] | Cell size varies between different cell types, and between different growth and osmotic conditions. However, the nuclear-to-cell volume ratio (N/C ratio) remains nearly constant. In this paper, we build on existing deterministic models of N/C ratio homeostasis and develop a simplified gene translation model to study the effect of stochasticity on the N/C ratio homeostasis. We solve the corresponding chemical master equation and obtain the mean and variance of the N/C ratio. We also use a Taylor expansion approximation to study the effects of the system size on the fluctuations of the N/C ratio. We then combine the translation model with a cell division model to study the effects of extrinsic noises from cell division on the N/C ratio. Our model demonstrates that the N/C ratio homeostasis is maintained when the stochasticity in cell growth is taken into account, that the N/C ratio is largely determined by the gene fraction of nuclear proteins, and that the fluctuations in the N/C ratio diminish as the system size increases. |
2308.11809 | Shirui Chen | Shirui Chen, Linxing Preston Jiang, Rajesh P. N. Rao, Eric Shea-Brown | Expressive probabilistic sampling in recurrent neural networks | null | null | null | null | q-bio.NC cs.AI cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In sampling-based Bayesian models of brain function, neural activities are
assumed to be samples from probability distributions that the brain uses for
probabilistic computation. However, a comprehensive understanding of how
mechanistic models of neural dynamics can sample from arbitrary distributions
is still lacking. We use tools from functional analysis and stochastic
differential equations to explore the minimum architectural requirements for
$\textit{recurrent}$ neural circuits to sample from complex distributions. We
first consider the traditional sampling model consisting of a network of
neurons whose outputs directly represent the samples (sampler-only network). We
argue that synaptic current and firing-rate dynamics in the traditional model
have limited capacity to sample from a complex probability distribution. We
show that the firing rate dynamics of a recurrent neural circuit with a
separate set of output units can sample from an arbitrary probability
distribution. We call such circuits reservoir-sampler networks (RSNs). We
propose an efficient training procedure based on denoising score matching that
finds recurrent and output weights such that the RSN implements Langevin
sampling. We empirically demonstrate our model's ability to sample from several
complex data distributions using the proposed neural dynamics and discuss its
applicability to developing the next generation of sampling-based brain models.
| [
{
"created": "Tue, 22 Aug 2023 22:20:39 GMT",
"version": "v1"
},
{
"created": "Wed, 1 Nov 2023 20:37:34 GMT",
"version": "v2"
},
{
"created": "Tue, 14 Nov 2023 21:07:33 GMT",
"version": "v3"
}
] | 2023-11-16 | [
[
"Chen",
"Shirui",
""
],
[
"Jiang",
"Linxing Preston",
""
],
[
"Rao",
"Rajesh P. N.",
""
],
[
"Shea-Brown",
"Eric",
""
]
] | In sampling-based Bayesian models of brain function, neural activities are assumed to be samples from probability distributions that the brain uses for probabilistic computation. However, a comprehensive understanding of how mechanistic models of neural dynamics can sample from arbitrary distributions is still lacking. We use tools from functional analysis and stochastic differential equations to explore the minimum architectural requirements for $\textit{recurrent}$ neural circuits to sample from complex distributions. We first consider the traditional sampling model consisting of a network of neurons whose outputs directly represent the samples (sampler-only network). We argue that synaptic current and firing-rate dynamics in the traditional model have limited capacity to sample from a complex probability distribution. We show that the firing rate dynamics of a recurrent neural circuit with a separate set of output units can sample from an arbitrary probability distribution. We call such circuits reservoir-sampler networks (RSNs). We propose an efficient training procedure based on denoising score matching that finds recurrent and output weights such that the RSN implements Langevin sampling. We empirically demonstrate our model's ability to sample from several complex data distributions using the proposed neural dynamics and discuss its applicability to developing the next generation of sampling-based brain models. |
2312.16624 | Luca Cattelani | Luca Cattelani and Vittorio Fortino | Dual-stage optimizer for systematic overestimation adjustment applied to
multi-objective genetic algorithms for biomarker selection | Added link to source code repository | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The challenge in biomarker discovery using machine learning from omics data
lies in the abundance of molecular features but scarcity of samples. Most
feature selection methods in machine learning require evaluating various sets
of features (models) to determine the most effective combination. This process,
typically conducted using a validation dataset, involves testing different
feature sets to optimize the model's performance. Evaluations have performance
estimation error and when the selection involves many models the best ones are
almost certainly overestimated. Biomarker identification with feature selection
methods can be addressed as a multi-objective problem with trade-offs between
predictive ability and parsimony in the number of features. Genetic algorithms
are a popular tool for multi-objective optimization but they evolve numerous
solutions thus are prone to overestimation. Methods have been proposed to
reduce the overestimation after a model has already been selected in
single-objective problems, but no algorithm existed capable of reducing the
overestimation during the optimization, improving model selection, or applied
in the more general multi-objective domain. We propose DOSA-MO, a novel
multi-objective optimization wrapper algorithm that learns how the original
estimation, its variance, and the feature set size of the solutions predict the
overestimation. DOSA-MO adjusts the expectation of the performance during the
optimization, improving the composition of the solution set. We verify that
DOSA-MO improves the performance of a state-of-the-art genetic algorithm on
left-out or external sample sets, when predicting cancer subtypes and/or
patient overall survival, using three transcriptomics datasets for kidney and
breast cancer.
| [
{
"created": "Wed, 27 Dec 2023 16:13:14 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Feb 2024 14:57:31 GMT",
"version": "v2"
},
{
"created": "Thu, 29 Feb 2024 15:40:34 GMT",
"version": "v3"
}
] | 2024-03-01 | [
[
"Cattelani",
"Luca",
""
],
[
"Fortino",
"Vittorio",
""
]
] | The challenge in biomarker discovery using machine learning from omics data lies in the abundance of molecular features but scarcity of samples. Most feature selection methods in machine learning require evaluating various sets of features (models) to determine the most effective combination. This process, typically conducted using a validation dataset, involves testing different feature sets to optimize the model's performance. Evaluations have performance estimation error and when the selection involves many models the best ones are almost certainly overestimated. Biomarker identification with feature selection methods can be addressed as a multi-objective problem with trade-offs between predictive ability and parsimony in the number of features. Genetic algorithms are a popular tool for multi-objective optimization but they evolve numerous solutions thus are prone to overestimation. Methods have been proposed to reduce the overestimation after a model has already been selected in single-objective problems, but no algorithm existed capable of reducing the overestimation during the optimization, improving model selection, or applied in the more general multi-objective domain. We propose DOSA-MO, a novel multi-objective optimization wrapper algorithm that learns how the original estimation, its variance, and the feature set size of the solutions predict the overestimation. DOSA-MO adjusts the expectation of the performance during the optimization, improving the composition of the solution set. We verify that DOSA-MO improves the performance of a state-of-the-art genetic algorithm on left-out or external sample sets, when predicting cancer subtypes and/or patient overall survival, using three transcriptomics datasets for kidney and breast cancer. |
2404.11761 | Jinzhi Lei | Yakun Li, Xiyin Liang, Jinzhi Lei | A computational scheme connecting gene regulatory network dynamics with
heterogeneous stem cell regeneration | 27 pages, 9 figures | null | null | null | q-bio.MN q-bio.CB | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Stem cell regeneration is a vital biological process in self-renewing
tissues, governing development and tissue homeostasis. Gene regulatory network
dynamics are pivotal in controlling stem cell regeneration and cell type
transitions. However, integrating the quantitative dynamics of gene regulatory
networks at the single-cell level with stem cell regeneration at the population
level poses significant challenges. This study presents a computational
framework connecting gene regulatory network dynamics with stem cell
regeneration through a data-driven formulation of the inheritance function. The
inheritance function captures epigenetic state transitions during cell division
in heterogeneous stem cell populations. Our scheme allows the derivation of the
inheritance function based on a hybrid model of cross-cell-cycle gene
regulation network dynamics. The proposed scheme enables us to derive the
inheritance function based on the hybrid model of cross-cell-cycle gene
regulation network dynamics. By explicitly incorporating gene regulatory
network structure, it replicates cross-cell-cycling gene regulation dynamics
through individual-cell-based modeling. The numerical scheme holds the
potential for extension to diverse gene regulatory networks, facilitating a
deeper understanding of the connection between gene regulation dynamics and
stem cell regeneration.
| [
{
"created": "Wed, 17 Apr 2024 21:38:42 GMT",
"version": "v1"
}
] | 2024-04-19 | [
[
"Li",
"Yakun",
""
],
[
"Liang",
"Xiyin",
""
],
[
"Lei",
"Jinzhi",
""
]
] | Stem cell regeneration is a vital biological process in self-renewing tissues, governing development and tissue homeostasis. Gene regulatory network dynamics are pivotal in controlling stem cell regeneration and cell type transitions. However, integrating the quantitative dynamics of gene regulatory networks at the single-cell level with stem cell regeneration at the population level poses significant challenges. This study presents a computational framework connecting gene regulatory network dynamics with stem cell regeneration through a data-driven formulation of the inheritance function. The inheritance function captures epigenetic state transitions during cell division in heterogeneous stem cell populations. Our scheme allows the derivation of the inheritance function based on a hybrid model of cross-cell-cycle gene regulation network dynamics. The proposed scheme enables us to derive the inheritance function based on the hybrid model of cross-cell-cycle gene regulation network dynamics. By explicitly incorporating gene regulatory network structure, it replicates cross-cell-cycling gene regulation dynamics through individual-cell-based modeling. The numerical scheme holds the potential for extension to diverse gene regulatory networks, facilitating a deeper understanding of the connection between gene regulation dynamics and stem cell regeneration. |
2012.06222 | Laurent Janniere | Steff Horemans, Matthaios Pitoulias, Alexandria Holland, Panos
Soultanas (UON), Laurent Janniere (UMR 8030) | Glycolytic pyruvate kinase moonlighting activities in DNA replication
initiation and elongation | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cells have evolved a metabolic control of DNA replication to respond to a
wide range of nutritional conditions. Accumulating data suggest that this
poorly understood control depends, at least in part, on Central Carbon
Metabolism (CCM). In Bacillus subtilis , the glycolytic pyruvate kinase (PykA)
is intricately linked to replication. This 585 amino-acid-long enzyme comprises
a catalytic (Cat) domain that binds to phosphoenolpyruvate (PEP) and ADP to
produce pyruvate and ATP, and a C-terminal domain of unknown function.
Interestingly, the C-terminal domain termed PEPut interacts with Cat and is
homologous a domain that, in other metabolic enzymes, are phosphorylated at a
conserved TSH motif at the expense of PEP and ATP to drive sugar import and
catalytic or regulatory activities. To gain insights into the role of PykA in
replication, DNA synthesis was analyzed in various Cat and PEPut mutants grown
in a medium where the metabolic activity of PykA is dispensable for growth.
Measurements of replication parameters ( ori/ter ratio, C period and fork
speed) and of the pyruvate kinase activity showed that PykA mutants exhibit
replication defects resulting from side chain modifications in the PykA protein
rather than from a reduction of its metabolic activity. Interestingly, Cat and
PEPut have distinct commitments in replication: while Cat impacts positively
and negatively replication fork speed, PEPut stimulates initiation through a
process depending on Cat-PEPut interaction and growth conditions. Residues
binding to PEP and ADP in Cat, stabilizing the Cat-PEPut interaction and
belonging to the TSH motif of PEPut were found important for the commitment of
PykA in replication. In vitro , PykA affects the activities of replication
enzymes (the polymerase DnaE, helicase DnaC and primase DnaG) essential for
initiation and elongation and genetically linked to pykA . Our results thus
connect replication initiation and elongation to CCM metabolites (PEP, ATP and
ADP), critical Cat and PEPut residues and to multiple links between PykA and
the replication enzymes DnaE, DnaC and DnaG. We propose that PykA is endowed
with a moonlighting activity that senses the concentration of signaling
metabolites and interacts with replication enzymes to convey information on the
cellular metabolic state to the replication machinery and adjust replication
initiation and elongation to metabolism. This defines a new type of replication
regulator proposed to be part of the metabolic control that gates replication
in the cell cycle.
| [
{
"created": "Fri, 11 Dec 2020 10:10:33 GMT",
"version": "v1"
}
] | 2020-12-14 | [
[
"Horemans",
"Steff",
"",
"UON"
],
[
"Pitoulias",
"Matthaios",
"",
"UON"
],
[
"Holland",
"Alexandria",
"",
"UON"
],
[
"Soultanas",
"Panos",
"",
"UON"
],
[
"Janniere",
"Laurent",
"",
"UMR 8030"
]
] | Cells have evolved a metabolic control of DNA replication to respond to a wide range of nutritional conditions. Accumulating data suggest that this poorly understood control depends, at least in part, on Central Carbon Metabolism (CCM). In Bacillus subtilis , the glycolytic pyruvate kinase (PykA) is intricately linked to replication. This 585 amino-acid-long enzyme comprises a catalytic (Cat) domain that binds to phosphoenolpyruvate (PEP) and ADP to produce pyruvate and ATP, and a C-terminal domain of unknown function. Interestingly, the C-terminal domain termed PEPut interacts with Cat and is homologous a domain that, in other metabolic enzymes, are phosphorylated at a conserved TSH motif at the expense of PEP and ATP to drive sugar import and catalytic or regulatory activities. To gain insights into the role of PykA in replication, DNA synthesis was analyzed in various Cat and PEPut mutants grown in a medium where the metabolic activity of PykA is dispensable for growth. Measurements of replication parameters ( ori/ter ratio, C period and fork speed) and of the pyruvate kinase activity showed that PykA mutants exhibit replication defects resulting from side chain modifications in the PykA protein rather than from a reduction of its metabolic activity. Interestingly, Cat and PEPut have distinct commitments in replication: while Cat impacts positively and negatively replication fork speed, PEPut stimulates initiation through a process depending on Cat-PEPut interaction and growth conditions. Residues binding to PEP and ADP in Cat, stabilizing the Cat-PEPut interaction and belonging to the TSH motif of PEPut were found important for the commitment of PykA in replication. In vitro , PykA affects the activities of replication enzymes (the polymerase DnaE, helicase DnaC and primase DnaG) essential for initiation and elongation and genetically linked to pykA . Our results thus connect replication initiation and elongation to CCM metabolites (PEP, ATP and ADP), critical Cat and PEPut residues and to multiple links between PykA and the replication enzymes DnaE, DnaC and DnaG. We propose that PykA is endowed with a moonlighting activity that senses the concentration of signaling metabolites and interacts with replication enzymes to convey information on the cellular metabolic state to the replication machinery and adjust replication initiation and elongation to metabolism. This defines a new type of replication regulator proposed to be part of the metabolic control that gates replication in the cell cycle. |
2402.03823 | Alexander Yermanos | Andreas Dounas, Tudor-Stefan Cotet, Alexander Yermanos | Learning immune receptor representations with protein language models | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Protein language models (PLMs) learn contextual representations from protein
sequences and are profoundly impacting various scientific disciplines spanning
protein design, drug discovery, and structural predictions. One particular
research area where PLMs have gained considerable attention is adaptive immune
receptors, whose tremendous sequence diversity dictates the functional
recognition of the adaptive immune system. The self-supervised nature
underlying the training of PLMs has been recently leveraged to implement a
variety of immune receptor-specific PLMs. These models have demonstrated
promise in tasks such as predicting antigen-specificity and structure,
computationally engineering therapeutic antibodies, and diagnostics. However,
challenges including insufficient training data and considerations related to
model architecture, training strategies, and data and model availability must
be addressed before fully unlocking the potential of PLMs in understanding,
translating, and engineering immune receptors.
| [
{
"created": "Tue, 6 Feb 2024 09:10:44 GMT",
"version": "v1"
}
] | 2024-02-07 | [
[
"Dounas",
"Andreas",
""
],
[
"Cotet",
"Tudor-Stefan",
""
],
[
"Yermanos",
"Alexander",
""
]
] | Protein language models (PLMs) learn contextual representations from protein sequences and are profoundly impacting various scientific disciplines spanning protein design, drug discovery, and structural predictions. One particular research area where PLMs have gained considerable attention is adaptive immune receptors, whose tremendous sequence diversity dictates the functional recognition of the adaptive immune system. The self-supervised nature underlying the training of PLMs has been recently leveraged to implement a variety of immune receptor-specific PLMs. These models have demonstrated promise in tasks such as predicting antigen-specificity and structure, computationally engineering therapeutic antibodies, and diagnostics. However, challenges including insufficient training data and considerations related to model architecture, training strategies, and data and model availability must be addressed before fully unlocking the potential of PLMs in understanding, translating, and engineering immune receptors. |
1603.00397 | Carlos Eduardo Cardoso Galhardo | C.E.C. Galhardo, B. C. Coutinho, T.J.P.Penna, M.A. de Menezes and
P.P.S. Soares | A Langevin model for complex cardiological time series | null | null | null | null | q-bio.NC cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There has been considerable efforts to understand the underlying complex
dynamics in physiological time series. Methods originated from statistical
physics revealed a non-Gaussian statistics and long range correlations in those
signals. This suggests that the regulatory system operates out of equilibrium.
Herein the complex fluctuations in blood pressure time series were successful
described by physiological motivated Langevin equation under a sigmoid
restoring force with multiplicative noise.
| [
{
"created": "Mon, 29 Feb 2016 18:44:23 GMT",
"version": "v1"
}
] | 2016-03-02 | [
[
"Galhardo",
"C. E. C.",
""
],
[
"Coutinho",
"B. C.",
""
],
[
"Penna",
"T. J. P.",
""
],
[
"de Menezes",
"M. A.",
""
],
[
"Soares",
"P. P. S.",
""
]
] | There has been considerable efforts to understand the underlying complex dynamics in physiological time series. Methods originated from statistical physics revealed a non-Gaussian statistics and long range correlations in those signals. This suggests that the regulatory system operates out of equilibrium. Herein the complex fluctuations in blood pressure time series were successful described by physiological motivated Langevin equation under a sigmoid restoring force with multiplicative noise. |
q-bio/0612016 | Michael Desai | Michael M. Desai, Daniel S. Fisher | Beneficial mutation-selection balance and the effect of linkage on
positive selection | 7 Figures, submitted to Genetics | null | null | null | q-bio.PE q-bio.GN | null | When beneficial mutations are rare, they accumulate by a series of selective
sweeps. But when they are common, many beneficial mutations will occur before
any can fix, so there will be many different mutant lineages in the population
concurrently. In an asexual population, these different mutant lineages
interfere and not all can fix simultaneously. In addition, further beneficial
mutations can accumulate in mutant lineages while these are still a minority of
the population. In this paper, we analyze the dynamics of such multiple
mutations and the interplay between multiple mutations and interference between
clones. These result in substantial variation in fitness accumulating within a
single asexual population. The amount of variation is determined by a balance
between selection, which destroys variation, and beneficial mutations, which
create more. The behavior depends in a subtle way on the population parameters:
the population size, the beneficial mutation rate, and the distribution of the
fitness increments of the potential beneficial mutations. The
mutation-selection balance leads to a continually evolving population with a
steady-state fitness variation. This variation increases logarithmically with
both population size and mutation rate and sets the rate at which the
population accumulates beneficial mutations, which thus also grows only
logarithmically with population size and mutation rate. These results imply
that mutator phenotypes are less effective in larger asexual populations. They
also have consequences for the advantages (or disadvantages) of sex via the
Fisher-Muller effect; these are discussed briefly.
| [
{
"created": "Mon, 11 Dec 2006 00:01:40 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Desai",
"Michael M.",
""
],
[
"Fisher",
"Daniel S.",
""
]
] | When beneficial mutations are rare, they accumulate by a series of selective sweeps. But when they are common, many beneficial mutations will occur before any can fix, so there will be many different mutant lineages in the population concurrently. In an asexual population, these different mutant lineages interfere and not all can fix simultaneously. In addition, further beneficial mutations can accumulate in mutant lineages while these are still a minority of the population. In this paper, we analyze the dynamics of such multiple mutations and the interplay between multiple mutations and interference between clones. These result in substantial variation in fitness accumulating within a single asexual population. The amount of variation is determined by a balance between selection, which destroys variation, and beneficial mutations, which create more. The behavior depends in a subtle way on the population parameters: the population size, the beneficial mutation rate, and the distribution of the fitness increments of the potential beneficial mutations. The mutation-selection balance leads to a continually evolving population with a steady-state fitness variation. This variation increases logarithmically with both population size and mutation rate and sets the rate at which the population accumulates beneficial mutations, which thus also grows only logarithmically with population size and mutation rate. These results imply that mutator phenotypes are less effective in larger asexual populations. They also have consequences for the advantages (or disadvantages) of sex via the Fisher-Muller effect; these are discussed briefly. |
2001.06718 | Christoph Leitner | Christoph Leitner, Christian Baumgartner, Christian Peham and Markus
Tilp | Ultrasound in Locomotion Research -- The Quest for Wider Views | Accepted for publication to CAMS-Knee OpenSim 2020, (4 pages, 2
figures, 1 table) | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a systematic review, we investigate current applications of ultrasound in
locomotion research. Shortcomings in the range of view of ultrasound systems
affect the direct validation of musculoskeletal simulations as inverse
approaches have to be applied. We present currently used methods to estimate
muscle and tendon length in human plantarflexors.
| [
{
"created": "Sat, 18 Jan 2020 19:51:54 GMT",
"version": "v1"
}
] | 2020-01-22 | [
[
"Leitner",
"Christoph",
""
],
[
"Baumgartner",
"Christian",
""
],
[
"Peham",
"Christian",
""
],
[
"Tilp",
"Markus",
""
]
] | In a systematic review, we investigate current applications of ultrasound in locomotion research. Shortcomings in the range of view of ultrasound systems affect the direct validation of musculoskeletal simulations as inverse approaches have to be applied. We present currently used methods to estimate muscle and tendon length in human plantarflexors. |
1705.08217 | Juan Seoane-Sepulveda | Per H. Enflo, Gustavo A. Mu\~noz-Fern\'andez, Juan B.
Seoane-Sep\'ulveda | A simple unified explanation of several genetic issues on today's human
population and on archaic humans | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We will give a simple, unified, possible explanation of several debated
genetic issues on today's humans, Neandertals and Denisovans. In particular it
is shown by means of a simple mathematical model why there is little genetic
variation in todays's human population or in Western Neandertal population, why
all mtDNA and y-chromosomes in today's humans seem to have African origin with
no trace of Neandertal nor Denosovan mtDNA or y-chromosomes, why a big part of
the European gene pool is young (from Neolitic time), and why today's East
Asians have mode Neandertal genes than today's Europeans.
| [
{
"created": "Tue, 23 May 2017 12:49:18 GMT",
"version": "v1"
},
{
"created": "Mon, 22 Mar 2021 15:09:58 GMT",
"version": "v2"
},
{
"created": "Sun, 25 Jul 2021 16:36:17 GMT",
"version": "v3"
}
] | 2021-07-27 | [
[
"Enflo",
"Per H.",
""
],
[
"Muñoz-Fernández",
"Gustavo A.",
""
],
[
"Seoane-Sepúlveda",
"Juan B.",
""
]
] | We will give a simple, unified, possible explanation of several debated genetic issues on today's humans, Neandertals and Denisovans. In particular it is shown by means of a simple mathematical model why there is little genetic variation in todays's human population or in Western Neandertal population, why all mtDNA and y-chromosomes in today's humans seem to have African origin with no trace of Neandertal nor Denosovan mtDNA or y-chromosomes, why a big part of the European gene pool is young (from Neolitic time), and why today's East Asians have mode Neandertal genes than today's Europeans. |
2312.12525 | Josiah Couch | Josiah Couch, Rohit Arora, Jasper Braun, Joesph Kaplinsky, Elliot
Hill, Anthony Li, Brett Altschul, Ramy Arnaout | Scaling Monte-Carlo-Based Inference on Antibody and TCR Repertoires | 11 pages, 2 tables, 4 figures | null | null | null | q-bio.QM q-bio.BM q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Previously, it has been shown that maximum-entropy models of
immune-repertoire sequence can be used to determine a person's vaccination
status. However, this approach has the drawback of requiring a computationally
intensive method to compute each model's partition function ($Z$), the
normalization constant required for calculating the probability that the model
will generate a given sequence. Specifically, the method required generating
approximately $10^{10}$ sequences via Monte-Carlo simulations for each model.
This is impractical for large numbers of models. Here we propose an alternative
method that requires estimating $Z$ this way for only a few models: it then
uses these expensive estimates to estimate $Z$ more efficiently for the
remaining models. We demonstrate that this new method enables the generation of
accurate estimates for 27 models using only three expensive estimates, thereby
reducing the computational cost by an order of magnitude. Importantly, this
gain in efficiency is achieved with only minimal impact on classification
accuracy. Thus, this new method enables larger-scale investigations in
computational immunology and represents a useful contribution to energy-based
modeling more generally.
| [
{
"created": "Tue, 19 Dec 2023 19:01:27 GMT",
"version": "v1"
}
] | 2023-12-21 | [
[
"Couch",
"Josiah",
""
],
[
"Arora",
"Rohit",
""
],
[
"Braun",
"Jasper",
""
],
[
"Kaplinsky",
"Joesph",
""
],
[
"Hill",
"Elliot",
""
],
[
"Li",
"Anthony",
""
],
[
"Altschul",
"Brett",
""
],
[
"Arnaou... | Previously, it has been shown that maximum-entropy models of immune-repertoire sequence can be used to determine a person's vaccination status. However, this approach has the drawback of requiring a computationally intensive method to compute each model's partition function ($Z$), the normalization constant required for calculating the probability that the model will generate a given sequence. Specifically, the method required generating approximately $10^{10}$ sequences via Monte-Carlo simulations for each model. This is impractical for large numbers of models. Here we propose an alternative method that requires estimating $Z$ this way for only a few models: it then uses these expensive estimates to estimate $Z$ more efficiently for the remaining models. We demonstrate that this new method enables the generation of accurate estimates for 27 models using only three expensive estimates, thereby reducing the computational cost by an order of magnitude. Importantly, this gain in efficiency is achieved with only minimal impact on classification accuracy. Thus, this new method enables larger-scale investigations in computational immunology and represents a useful contribution to energy-based modeling more generally. |
1309.5840 | Huilin Zhu | Huilin Zhu, Yuebo Fan, Huan Guo, Dan Huang and Sailing He | Reduced interhemispheric functional connectivity of children with
autism: evidence from functional near infrared spectroscopy studies | 7 pages, 3 figures, fields: autism spectrum disorder, near inferred
spectroscopy, functional connectivity | null | null | null | q-bio.NC physics.med-ph physics.optics | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Autism spectrum disorder is a neuro-developmental disorder characterized by
abnormalities of neural synchronization. In this study, functional near
infrared spectroscopy (fNIRS) is used to study the difference in functional
connectivity in left and right inferior frontal cortices (IFC) and temporal
cortices (TC) between autistic and typically developing children between 8-11
years of age. 10 autistic children and 10 typical ones were recruited in our
study for 8-min resting state measurement. Results show that the overall
interhemispheric correlation of HbO was significantly lower in autistic
children than in the controls. In particular, reduced connectivity was found to
be most significant in TC area of autism. Autistic children lose the symmetry
in the patterns of correlation maps. These results suggest the feasibility of
using the fNIRS method to assess abnormal functional connectivity of the
autistic brain and its potential application in autism diagnosis.
| [
{
"created": "Mon, 23 Sep 2013 15:21:59 GMT",
"version": "v1"
}
] | 2013-09-24 | [
[
"Zhu",
"Huilin",
""
],
[
"Fan",
"Yuebo",
""
],
[
"Guo",
"Huan",
""
],
[
"Huang",
"Dan",
""
],
[
"He",
"Sailing",
""
]
] | Autism spectrum disorder is a neuro-developmental disorder characterized by abnormalities of neural synchronization. In this study, functional near infrared spectroscopy (fNIRS) is used to study the difference in functional connectivity in left and right inferior frontal cortices (IFC) and temporal cortices (TC) between autistic and typically developing children between 8-11 years of age. 10 autistic children and 10 typical ones were recruited in our study for 8-min resting state measurement. Results show that the overall interhemispheric correlation of HbO was significantly lower in autistic children than in the controls. In particular, reduced connectivity was found to be most significant in TC area of autism. Autistic children lose the symmetry in the patterns of correlation maps. These results suggest the feasibility of using the fNIRS method to assess abnormal functional connectivity of the autistic brain and its potential application in autism diagnosis. |
2202.02520 | Maik Sch\"unemann | Maik Sch\"unemann, Udo Ernst and Marc Kesseb\"ohmer | A rigorous stochastic theory for spike pattern formation in recurrent
neural networks with arbitrary connection topologies | 88 pages, 7 figures | null | null | null | q-bio.NC math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cortical networks exhibit synchronized activity which often occurs in
spontaneous events in the form of spike avalanches. Since synchronization has
been causally linked to central aspects of brain function such as selective
signal processing and integration of stimulus information, participating in an
avalanche is a form of a transient synchrony which temporarily creates neural
assemblies and hence might especially be useful for implementing flexible
information processing. For understanding how assembly formation supports
neural computation, it is therefore essential to establish a comprehensive
theory of how network structure and dynamics interact to generate specific
avalanche patterns and sequences. Here we derive exact avalanche distributions
for a finite network of recurrently coupled spiking neurons with arbitrary
non-negative interaction weights, which is made possible by formally mapping
the model dynamics to a linear, random dynamical system on the $N$-torus and by
exploiting self-similarities inherent in the phase space. We introduce the
notion of relative unique ergodicity and show that this property is guaranteed
if the system is driven by a time-invariant Bernoulli process. This approach
allows us not only to provide closed-form analytical expressions for avalanche
size, but also to determine the detailed set(s) of units firing in an avalanche
(i.e., the avalanche assembly). The underlying dependence between network
structure and dynamics is made transparent by expressing the distribution of
avalanche assemblies in terms of the induced graph Laplacian. We explore
analytical consequences of this dependence and provide illustrating examples.
| [
{
"created": "Sat, 5 Feb 2022 09:28:40 GMT",
"version": "v1"
}
] | 2022-02-08 | [
[
"Schünemann",
"Maik",
""
],
[
"Ernst",
"Udo",
""
],
[
"Kesseböhmer",
"Marc",
""
]
] | Cortical networks exhibit synchronized activity which often occurs in spontaneous events in the form of spike avalanches. Since synchronization has been causally linked to central aspects of brain function such as selective signal processing and integration of stimulus information, participating in an avalanche is a form of a transient synchrony which temporarily creates neural assemblies and hence might especially be useful for implementing flexible information processing. For understanding how assembly formation supports neural computation, it is therefore essential to establish a comprehensive theory of how network structure and dynamics interact to generate specific avalanche patterns and sequences. Here we derive exact avalanche distributions for a finite network of recurrently coupled spiking neurons with arbitrary non-negative interaction weights, which is made possible by formally mapping the model dynamics to a linear, random dynamical system on the $N$-torus and by exploiting self-similarities inherent in the phase space. We introduce the notion of relative unique ergodicity and show that this property is guaranteed if the system is driven by a time-invariant Bernoulli process. This approach allows us not only to provide closed-form analytical expressions for avalanche size, but also to determine the detailed set(s) of units firing in an avalanche (i.e., the avalanche assembly). The underlying dependence between network structure and dynamics is made transparent by expressing the distribution of avalanche assemblies in terms of the induced graph Laplacian. We explore analytical consequences of this dependence and provide illustrating examples. |
1705.05570 | Damien Chablat | Jing Chang (IRCCyN), Damien Chablat (IRCCyN), Fouad Bennis (IRCCyN),
Liang Ma | Muscle Fatigue Analysis Using OpenSim | null | 19th International Conference on Human-Computer Interaction, Jul
2017, Vancouver, Canada. 2017 | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this research, attempts are made to conduct concrete muscle fatigue
analysis of arbitrary motions on OpenSim, a digital human modeling platform. A
plug-in is written on the base of a muscle fatigue model, which makes it
possible to calculate the decline of force-output capability of each muscle
along time. The plug-in is tested on a three-dimensional, 29 degree-of-freedom
human model. Motion data is obtained by motion capturing during an arbitrary
running at a speed of 3.96 m/s. Ten muscles are selected for concrete analysis.
As a result, the force-output capability of these muscles reduced to 60%-70%
after 10 minutes' running, on a general basis. Erector spinae, which loses
39.2% of its maximal capability, is found to be more fatigue-exposed than the
others. The influence of subject attributes (fatigability) is evaluated and
discussed.
| [
{
"created": "Tue, 16 May 2017 08:01:16 GMT",
"version": "v1"
}
] | 2017-05-17 | [
[
"Chang",
"Jing",
"",
"IRCCyN"
],
[
"Chablat",
"Damien",
"",
"IRCCyN"
],
[
"Bennis",
"Fouad",
"",
"IRCCyN"
],
[
"Ma",
"Liang",
""
]
] | In this research, attempts are made to conduct concrete muscle fatigue analysis of arbitrary motions on OpenSim, a digital human modeling platform. A plug-in is written on the base of a muscle fatigue model, which makes it possible to calculate the decline of force-output capability of each muscle along time. The plug-in is tested on a three-dimensional, 29 degree-of-freedom human model. Motion data is obtained by motion capturing during an arbitrary running at a speed of 3.96 m/s. Ten muscles are selected for concrete analysis. As a result, the force-output capability of these muscles reduced to 60%-70% after 10 minutes' running, on a general basis. Erector spinae, which loses 39.2% of its maximal capability, is found to be more fatigue-exposed than the others. The influence of subject attributes (fatigability) is evaluated and discussed. |
2210.06804 | Leonardo Trujillo | Leonardo Trujillo, Paul Banse, Guillaume Beslon | Getting higher on rugged landscapes: Inversion mutations open access to
fitter adaptive peaks in NK fitness landscapes | 35 pages, 10 figures, accepted for publication in PLoS Computational
Biology | PLoS Computational Biology October 31, 2022 | 10.1371/journal.pcbi.1010647 | null | q-bio.PE nlin.AO physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Molecular evolution is often conceptualised as adaptive walks on rugged
fitness landscapes, driven by mutations and constrained by incremental fitness
selection. It is well known that epistasis shapes the ruggedness of the
landscape's surface, outlining their topography (with high-fitness peaks
separated by valleys of lower fitness genotypes). However, within the strong
selection weak mutation (SSWM) limit, once an adaptive walk reaches a local
peak, natural selection restricts passage through downstream paths and hampers
any possibility of reaching higher fitness values. Here, in addition to the
widely used point mutations, we introduce a minimal model of sequence
inversions to simulate adaptive walks. We use the well known NK model to
instantiate rugged landscapes. We show that adaptive walks can reach higher
fitness values through inversion mutations, which, compared to point mutations,
allows the evolutionary process to escape local fitness peaks. To elucidate the
effects of this chromosomal rearrangement, we use a graph-theoretical
representation of accessible mutants and show how new evolutionary paths are
uncovered. The present model suggests a simple mechanistic rationale to analyse
escapes from local fitness peaks in molecular evolution driven by (intragenic)
structural inversions and reveals some consequences of the limits of point
mutations for simulations of molecular evolution.
| [
{
"created": "Thu, 13 Oct 2022 07:34:36 GMT",
"version": "v1"
}
] | 2022-11-03 | [
[
"Trujillo",
"Leonardo",
""
],
[
"Banse",
"Paul",
""
],
[
"Beslon",
"Guillaume",
""
]
] | Molecular evolution is often conceptualised as adaptive walks on rugged fitness landscapes, driven by mutations and constrained by incremental fitness selection. It is well known that epistasis shapes the ruggedness of the landscape's surface, outlining their topography (with high-fitness peaks separated by valleys of lower fitness genotypes). However, within the strong selection weak mutation (SSWM) limit, once an adaptive walk reaches a local peak, natural selection restricts passage through downstream paths and hampers any possibility of reaching higher fitness values. Here, in addition to the widely used point mutations, we introduce a minimal model of sequence inversions to simulate adaptive walks. We use the well known NK model to instantiate rugged landscapes. We show that adaptive walks can reach higher fitness values through inversion mutations, which, compared to point mutations, allows the evolutionary process to escape local fitness peaks. To elucidate the effects of this chromosomal rearrangement, we use a graph-theoretical representation of accessible mutants and show how new evolutionary paths are uncovered. The present model suggests a simple mechanistic rationale to analyse escapes from local fitness peaks in molecular evolution driven by (intragenic) structural inversions and reveals some consequences of the limits of point mutations for simulations of molecular evolution. |
2004.05635 | Fred Vermolen | Fred Vermolen | A Spatial Markov Chain Cellular Automata Model for the Spread of Viruses | 13 pages, 10 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a Spatial Markov Chain model for the spread of viruses. The model
is based on the principle to represent a graph connecting nodes, which
represent humans. The vertices between the nodes represent relations between
humans. In this way, a graph is connected in which the likelihood of infectious
spread from person to person is determined by the intensity of interpersonal
contact. Infectious transfer is determined by chance. The model is extended to
incorporate various lockdown scenarios.
| [
{
"created": "Sun, 12 Apr 2020 15:43:43 GMT",
"version": "v1"
}
] | 2020-04-14 | [
[
"Vermolen",
"Fred",
""
]
] | We consider a Spatial Markov Chain model for the spread of viruses. The model is based on the principle to represent a graph connecting nodes, which represent humans. The vertices between the nodes represent relations between humans. In this way, a graph is connected in which the likelihood of infectious spread from person to person is determined by the intensity of interpersonal contact. Infectious transfer is determined by chance. The model is extended to incorporate various lockdown scenarios. |
2305.00296 | Xerxes D. Arsiwalla | Xerxes D. Arsiwalla | A Cognitive Account of the Puzzle of Ideography | 4 pages. Invited commentary. Accepted for publication in Behavioral
and Brain Sciences, Cambridge University Press | null | null | null | q-bio.NC cs.CL | http://creativecommons.org/licenses/by/4.0/ | In this commentary article to 'The Puzzle of Ideography' by Morin, we put
forth a new cognitive account of the puzzle of ideography, that complements the
standardization account of Morin. Efficient standardization of spoken language
is phenomenologically attributed to a modality effect coupled with chunking of
cognitive representations, further aided by multi-sensory integration and the
serialized nature of attention. These cognitive mechanisms are crucial for
explaining why languages dominate graphic codes for general-purpose human
communication.
| [
{
"created": "Sat, 29 Apr 2023 16:13:13 GMT",
"version": "v1"
}
] | 2023-05-02 | [
[
"Arsiwalla",
"Xerxes D.",
""
]
] | In this commentary article to 'The Puzzle of Ideography' by Morin, we put forth a new cognitive account of the puzzle of ideography, that complements the standardization account of Morin. Efficient standardization of spoken language is phenomenologically attributed to a modality effect coupled with chunking of cognitive representations, further aided by multi-sensory integration and the serialized nature of attention. These cognitive mechanisms are crucial for explaining why languages dominate graphic codes for general-purpose human communication. |
1207.1478 | Aaron Clauset | Aaron Clauset | How large should whales be? | 7 pages, 3 figures, 2 data tables | PLOS ONE 8(1), e53967 (2013) | 10.1371/journal.pone.0053967 | null | q-bio.PE physics.bio-ph physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evolution and distribution of species body sizes for terrestrial mammals
is well-explained by a macroevolutionary tradeoff between short-term selective
advantages and long-term extinction risks from increased species body size,
unfolding above the 2g minimum size induced by thermoregulation in air. Here,
we consider whether this same tradeoff, formalized as a constrained
convection-reaction-diffusion system, can also explain the sizes of fully
aquatic mammals, which have not previously been considered. By replacing the
terrestrial minimum with a pelagic one, at roughly 7000g, the terrestrial
mammal tradeoff model accurately predicts, with no tunable parameters, the
observed body masses of all extant cetacean species, including the 175,000,000g
Blue Whale. This strong agreement between theory and data suggests that a
universal macroevolutionary tradeoff governs body size evolution for all
mammals, regardless of their habitat. The dramatic sizes of cetaceans can thus
be attributed mainly to the increased convective heat loss is water, which
shifts the species size distribution upward and pushes its right tail into
ranges inaccessible to terrestrial mammals. Under this macroevolutionary
tradeoff, the largest expected species occurs where the rate at which
smaller-bodied species move up into large-bodied niches approximately equals
the rate at which extinction removes them.
| [
{
"created": "Thu, 5 Jul 2012 22:30:08 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Jan 2013 23:51:23 GMT",
"version": "v2"
}
] | 2013-01-14 | [
[
"Clauset",
"Aaron",
""
]
] | The evolution and distribution of species body sizes for terrestrial mammals is well-explained by a macroevolutionary tradeoff between short-term selective advantages and long-term extinction risks from increased species body size, unfolding above the 2g minimum size induced by thermoregulation in air. Here, we consider whether this same tradeoff, formalized as a constrained convection-reaction-diffusion system, can also explain the sizes of fully aquatic mammals, which have not previously been considered. By replacing the terrestrial minimum with a pelagic one, at roughly 7000g, the terrestrial mammal tradeoff model accurately predicts, with no tunable parameters, the observed body masses of all extant cetacean species, including the 175,000,000g Blue Whale. This strong agreement between theory and data suggests that a universal macroevolutionary tradeoff governs body size evolution for all mammals, regardless of their habitat. The dramatic sizes of cetaceans can thus be attributed mainly to the increased convective heat loss is water, which shifts the species size distribution upward and pushes its right tail into ranges inaccessible to terrestrial mammals. Under this macroevolutionary tradeoff, the largest expected species occurs where the rate at which smaller-bodied species move up into large-bodied niches approximately equals the rate at which extinction removes them. |
1203.6372 | Heng Li | Heng Li | A statistical framework for SNP calling, mutation discovery, association
mapping and population genetical parameter estimation from sequencing data | The published version (open access now) with addition of a test for
multi-allelic sites | Bioinformatics (2011) 27:2987-93 | 10.1093/bioinformatics/btr509 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Most existing methods for DNA sequence analysis rely on accurate
sequences or genotypes. However, in applications of the next-generation
sequencing (NGS), accurate genotypes may not be easily obtained (e.g.
multi-sample low-coverage sequencing or somatic mutation discovery). These
applications press for the development of new methods for analyzing sequence
data with uncertainty.
Results: We present a statistical framework for calling SNPs, discovering
somatic mutations, inferring population genetical parameters and performing
association tests directly based on sequencing data without explicit genotyping
or linkage-based imputation. On real data, we demonstrate that our method
achieves comparable accuracy to alternative methods for estimating site allele
count, for inferring allele frequency spectrum and for association mapping. We
also highlight the necessity of using symmetric datasets for finding somatic
mutations and confirm that for discovering rare events, mismapping is
frequently the leading source of errors.
Availability: http://samtools.sourceforge.net.
Contact: hengli@broadinstitute.org.
| [
{
"created": "Wed, 28 Mar 2012 20:36:28 GMT",
"version": "v1"
},
{
"created": "Tue, 3 Apr 2012 14:21:44 GMT",
"version": "v2"
},
{
"created": "Sat, 16 Mar 2013 14:58:31 GMT",
"version": "v3"
}
] | 2013-03-19 | [
[
"Li",
"Heng",
""
]
] | Motivation: Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. Results: We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. Availability: http://samtools.sourceforge.net. Contact: hengli@broadinstitute.org. |
1210.2944 | Tomasz Rutkowski | Zhenyu Cai, Shoji Makino, Takeshi Yamada, and Tomasz M. Rutkowski | Spatial Auditory BCI Paradigm Utilizing N200 and P300 Responses | APSIPA ASC 2012 | null | null | null | q-bio.NC cs.SD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper presents our recent results obtained with a new auditory spatial
localization based BCI paradigm in which the ERP shape differences at early
latencies are employed to enhance the traditional P300 responses in an oddball
experimental setting. The concept relies on the recent results in auditory
neuroscience showing a possibility to differentiate early anterior
contralateral responses to attended spatial sources. Contemporary
stimuli-driven BCI paradigms benefit mostly from the P300 ERP latencies in so
called "aha-response" settings. We show the further enhancement of the
classification results in spatial auditory paradigms by incorporating the N200
latencies, which differentiate the brain responses to lateral, in relation to
the subject head, sound locations in the auditory space. The results reveal
that those early spatial auditory ERPs boost online classification results of
the BCI application. The online BCI experiments with the multi-command BCI
prototype support our research hypothesis with the higher classification
results and the improved information-transfer-rates.
| [
{
"created": "Wed, 10 Oct 2012 14:59:26 GMT",
"version": "v1"
}
] | 2012-10-11 | [
[
"Cai",
"Zhenyu",
""
],
[
"Makino",
"Shoji",
""
],
[
"Yamada",
"Takeshi",
""
],
[
"Rutkowski",
"Tomasz M.",
""
]
] | The paper presents our recent results obtained with a new auditory spatial localization based BCI paradigm in which the ERP shape differences at early latencies are employed to enhance the traditional P300 responses in an oddball experimental setting. The concept relies on the recent results in auditory neuroscience showing a possibility to differentiate early anterior contralateral responses to attended spatial sources. Contemporary stimuli-driven BCI paradigms benefit mostly from the P300 ERP latencies in so called "aha-response" settings. We show the further enhancement of the classification results in spatial auditory paradigms by incorporating the N200 latencies, which differentiate the brain responses to lateral, in relation to the subject head, sound locations in the auditory space. The results reveal that those early spatial auditory ERPs boost online classification results of the BCI application. The online BCI experiments with the multi-command BCI prototype support our research hypothesis with the higher classification results and the improved information-transfer-rates. |
2004.02789 | Thomas Larsen Dr. | Jamileh Javidpour, Eduardo Ramirez-Romero, and Thomas Larsen | The effect of temperature on basal metabolism of Mnemiopsis leidyi | 6 pages, 3 figures | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To evaluate the influence of temperature on metabolic performance on the
invasive ctenophore Mnemiopsis leidyi, we exposed fully acclimatized adults to
conditions typical for the annual variability of the Western Baltic Sea region.
We derived basal metabolic rates from oxygen consumption rates of adult M.
leidyi specimens exposed to temperatures between 3.5{\deg}C to 20.5{\deg}C at a
salinity of 22. We found a Q10 value of 3.67, which means that the carbon
specific respiration rates are about 9 times greater at 20{\deg}C than
3{\deg}C. According to this rate, a small-sized individual 20 mm in oral-aboral
length, would without feeding have enough nutrient reserves to survive 80 days
at 3{\deg}C, but only 9 days at 20{\deg}C. Thus, prey availability during late
summer is critical for M. leidyi population survival.
| [
{
"created": "Mon, 6 Apr 2020 16:31:21 GMT",
"version": "v1"
}
] | 2020-04-07 | [
[
"Javidpour",
"Jamileh",
""
],
[
"Ramirez-Romero",
"Eduardo",
""
],
[
"Larsen",
"Thomas",
""
]
] | To evaluate the influence of temperature on metabolic performance on the invasive ctenophore Mnemiopsis leidyi, we exposed fully acclimatized adults to conditions typical for the annual variability of the Western Baltic Sea region. We derived basal metabolic rates from oxygen consumption rates of adult M. leidyi specimens exposed to temperatures between 3.5{\deg}C to 20.5{\deg}C at a salinity of 22. We found a Q10 value of 3.67, which means that the carbon specific respiration rates are about 9 times greater at 20{\deg}C than 3{\deg}C. According to this rate, a small-sized individual 20 mm in oral-aboral length, would without feeding have enough nutrient reserves to survive 80 days at 3{\deg}C, but only 9 days at 20{\deg}C. Thus, prey availability during late summer is critical for M. leidyi population survival. |
q-bio/0508014 | Emmanuel Tannenbaum | Emmanuel Tannenbaum | Selective advantage for multicellular replicative strategies: A two-cell
example | 4 pages, 2 figures, to be submitted to Physical Review Letters | null | 10.1103/PhysRevE.73.010904 | null | q-bio.PE q-bio.CB | null | This paper develops a quasispecies model where cells can adopt a two-cell
survival strategy. Within this strategy, pairs of cells join together, at which
point one of the cells sacrifices its own replicative ability for the sake of
the other cell. We develop a simplified model for the evolutionary dynamics of
this process, allowing us to solve for the steady-state using standard
approaches from quasispecies theory. We find that our model exhibits two
distinct regimes of behavior: At low concentrations of limiting resource, the
two-cell strategy outcompetes the single-cell survival strategy, while at high
concentrations of limiting resource, the single-cell survival strategy
dominates. Associated with the two solution regimes of our model is a
localization to delocalization transition over the portion of the genome coding
for the multicell strategy, analogous to the error catastrophe in standard
quasispecies models. The existence of such a transition indicates that
multicellularity can emerge because natural selection does not act on specific
cells, but rather on replicative strategies. Within this framework, individual
cells become the means by which replicative strategies are propagated. Such a
framework is therefore consistent with the concept that natural selection does
not act on individuals, but rather on populations.
| [
{
"created": "Sat, 13 Aug 2005 17:28:44 GMT",
"version": "v1"
}
] | 2009-11-11 | [
[
"Tannenbaum",
"Emmanuel",
""
]
] | This paper develops a quasispecies model where cells can adopt a two-cell survival strategy. Within this strategy, pairs of cells join together, at which point one of the cells sacrifices its own replicative ability for the sake of the other cell. We develop a simplified model for the evolutionary dynamics of this process, allowing us to solve for the steady-state using standard approaches from quasispecies theory. We find that our model exhibits two distinct regimes of behavior: At low concentrations of limiting resource, the two-cell strategy outcompetes the single-cell survival strategy, while at high concentrations of limiting resource, the single-cell survival strategy dominates. Associated with the two solution regimes of our model is a localization to delocalization transition over the portion of the genome coding for the multicell strategy, analogous to the error catastrophe in standard quasispecies models. The existence of such a transition indicates that multicellularity can emerge because natural selection does not act on specific cells, but rather on replicative strategies. Within this framework, individual cells become the means by which replicative strategies are propagated. Such a framework is therefore consistent with the concept that natural selection does not act on individuals, but rather on populations. |
1709.03000 | Ari Kahn | Ari E. Kahn, Elisabeth A. Karuza, Jean M. Vettel, Danielle S. Bassett | Network constraints on learnability of probabilistic motor sequences | 29 pages, 4 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human learners are adept at grasping the complex relationships underlying
incoming sequential input. In the present work, we formalize complex
relationships as graph structures derived from temporal associations in motor
sequences. Next, we explore the extent to which learners are sensitive to key
variations in the topological properties inherent to those graph structures.
Participants performed a probabilistic motor sequence task in which the order
of button presses was determined by the traversal of graphs with modular,
lattice-like, or random organization. Graph nodes each represented a unique
button press and edges represented a transition between button presses. Results
indicate that learning, indexed here by participants' response times, was
strongly mediated by the graph's meso-scale organization, with modular graphs
being associated with shorter response times than random and lattice graphs.
Moreover, variations in a node's number of connections (degree) and a node's
role in mediating long-distance communication (betweenness centrality) impacted
graph learning, even after accounting for level of practice on that node. These
results demonstrate that the graph architecture underlying temporal sequences
of stimuli fundamentally constrains learning, and moreover that tools from
network science provide a valuable framework for assessing how learners encode
complex, temporally structured information.
| [
{
"created": "Sat, 9 Sep 2017 20:13:18 GMT",
"version": "v1"
},
{
"created": "Wed, 25 Apr 2018 20:51:44 GMT",
"version": "v2"
},
{
"created": "Tue, 30 Oct 2018 15:34:12 GMT",
"version": "v3"
}
] | 2018-10-31 | [
[
"Kahn",
"Ari E.",
""
],
[
"Karuza",
"Elisabeth A.",
""
],
[
"Vettel",
"Jean M.",
""
],
[
"Bassett",
"Danielle S.",
""
]
] | Human learners are adept at grasping the complex relationships underlying incoming sequential input. In the present work, we formalize complex relationships as graph structures derived from temporal associations in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties inherent to those graph structures. Participants performed a probabilistic motor sequence task in which the order of button presses was determined by the traversal of graphs with modular, lattice-like, or random organization. Graph nodes each represented a unique button press and edges represented a transition between button presses. Results indicate that learning, indexed here by participants' response times, was strongly mediated by the graph's meso-scale organization, with modular graphs being associated with shorter response times than random and lattice graphs. Moreover, variations in a node's number of connections (degree) and a node's role in mediating long-distance communication (betweenness centrality) impacted graph learning, even after accounting for level of practice on that node. These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information. |
2306.01403 | Tuan Minh Pham | Tuan Minh Pham and Kunihiko Kaneko | Dynamical Theory for Adaptive Systems | 30 pages and 2 figures | null | null | null | q-bio.PE cond-mat.dis-nn nlin.AO physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | The study of adaptive dynamics, involving many degrees of freedom on two
separated timescales, one for fast changes of state variables and another for
the slow adaptation of parameters controlling the former's dynamics is crucial
for understanding feedback mechanisms underlying evolution and learning. We
present a path-integral approach \`a la Martin-Siggia-Rose-De Dominicis-Janssen
(MSRDJ) to analyse nonequilibrium phase transitions in such dynamical systems.
As an illustration, we apply our framework to the adaptation of gene-regulatory
networks under a dynamic genotype-phenotype map: phenotypic variations are
shaped by the fast stochastic gene-expression dynamics and are coupled to the
slowly evolving distribution of genotypes, each encoded by a network structure.
We establish that under this map, genotypes corresponding to reciprocal
networks of coherent feedback loops are selected within an intermediate range
of environmental noise, leading to phenotypic robustness.
| [
{
"created": "Fri, 2 Jun 2023 09:49:20 GMT",
"version": "v1"
},
{
"created": "Thu, 21 Sep 2023 15:52:58 GMT",
"version": "v2"
},
{
"created": "Fri, 5 Jan 2024 15:33:34 GMT",
"version": "v3"
},
{
"created": "Fri, 24 May 2024 22:01:54 GMT",
"version": "v4"
},
{
"cre... | 2024-08-06 | [
[
"Pham",
"Tuan Minh",
""
],
[
"Kaneko",
"Kunihiko",
""
]
] | The study of adaptive dynamics, involving many degrees of freedom on two separated timescales, one for fast changes of state variables and another for the slow adaptation of parameters controlling the former's dynamics is crucial for understanding feedback mechanisms underlying evolution and learning. We present a path-integral approach \`a la Martin-Siggia-Rose-De Dominicis-Janssen (MSRDJ) to analyse nonequilibrium phase transitions in such dynamical systems. As an illustration, we apply our framework to the adaptation of gene-regulatory networks under a dynamic genotype-phenotype map: phenotypic variations are shaped by the fast stochastic gene-expression dynamics and are coupled to the slowly evolving distribution of genotypes, each encoded by a network structure. We establish that under this map, genotypes corresponding to reciprocal networks of coherent feedback loops are selected within an intermediate range of environmental noise, leading to phenotypic robustness. |
1908.03348 | Giulio Biroli | Felix Roy, Matthieu Barbier, Giulio Biroli, Guy Bunin | Can endogenous fluctuations persist in high-diversity ecosystems? | null | null | null | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When can complex ecological interactions drive an entire ecosystem into a
persistent non-equilibrium state, where species abundances keep fluctuating
without going to extinction? We show that high-diversity spatially-extended
systems, in which conditions vary somewhat between spatial locations, can
exhibit chaotic dynamics which persist for extremely long times. We develop a
theoretical framework, based on dynamical mean-field theory, to quantify the
conditions under which these fluctuating states exist, and predict their
properties. We uncover parallels with the persistence of externally-perturbed
ecosystems, such as the role of perturbation strength, synchrony and
correlation time. But uniquely to endogenous fluctuations, these properties
arise from the species dynamics themselves, creating feedback loops between
perturbation and response. A key result is that the fluctuation amplitude and
species diversity are tightly linked, in particular fluctuations enable
dramatically more species to coexist than at equilibrium in the very same
system. Our findings highlight crucial differences between well-mixed and
spatially-extended systems, with implications for experiments and their ability
to reproduce natural dynamics. They shed light on the maintenance of
biodiversity, and the strength and synchrony of fluctuations observed in
natural systems.
| [
{
"created": "Fri, 9 Aug 2019 07:36:19 GMT",
"version": "v1"
},
{
"created": "Mon, 26 Aug 2019 08:54:40 GMT",
"version": "v2"
}
] | 2019-08-27 | [
[
"Roy",
"Felix",
""
],
[
"Barbier",
"Matthieu",
""
],
[
"Biroli",
"Giulio",
""
],
[
"Bunin",
"Guy",
""
]
] | When can complex ecological interactions drive an entire ecosystem into a persistent non-equilibrium state, where species abundances keep fluctuating without going to extinction? We show that high-diversity spatially-extended systems, in which conditions vary somewhat between spatial locations, can exhibit chaotic dynamics which persist for extremely long times. We develop a theoretical framework, based on dynamical mean-field theory, to quantify the conditions under which these fluctuating states exist, and predict their properties. We uncover parallels with the persistence of externally-perturbed ecosystems, such as the role of perturbation strength, synchrony and correlation time. But uniquely to endogenous fluctuations, these properties arise from the species dynamics themselves, creating feedback loops between perturbation and response. A key result is that the fluctuation amplitude and species diversity are tightly linked, in particular fluctuations enable dramatically more species to coexist than at equilibrium in the very same system. Our findings highlight crucial differences between well-mixed and spatially-extended systems, with implications for experiments and their ability to reproduce natural dynamics. They shed light on the maintenance of biodiversity, and the strength and synchrony of fluctuations observed in natural systems. |
0802.3926 | Amy Bauer | Amy L. Bauer, Trachette L. Jackson, Yi Jiang, Thimo Rohlf | Stochastic Network Model of Receptor Cross-Talk Predicts Anti-Angiogenic
Effects | 17 pages, 4 figures, 1 table | null | null | LA-UR 08-0706 | q-bio.MN cond-mat.dis-nn nlin.CG q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cancer invasion and metastasis depend on angiogenesis. The cellular processes
(growth, migration, and apoptosis) that occur during angiogenesis are tightly
regulated by signaling molecules. Thus, understanding how cells synthesize
multiple biochemical signals initiated by key external stimuli can lead to the
development of novel therapeutic strategies to combat cancer. In the face of
large amounts of disjoint experimental data generated from multitudes of
laboratories using various assays, theoretical signal transduction models
provide a framework to distill this vast amount of data. Such models offer an
opportunity to formulate and test new hypotheses, and can be used to make
experimentally verifiable predictions. This study is the first to propose a
network model that highlights the cross-talk between the key receptors involved
in angiogenesis, namely growth factor, integrin, and cadherin receptors. From
available experimental data, we construct a stochastic Boolean network model of
receptor cross-talk, and systematically analyze the dynamical stability of the
network under continuous-time Boolean dynamics with a noisy production
function. We find that the signal transduction network exhibits a robust and
fast response to external signals, independent of the internal cell state. We
derive an input-output table that maps external stimuli to cell phenotypes,
which is extraordinarily stable against molecular noise with one important
exception: an oscillatory feedback loop between the key signaling molecules
RhoA and Rac1 is unstable under arbitrarily low noise, leading to erratic,
dysfunctional cell motion. Finally, we show that the network exhibits an
apoptotic response rate that increases with noise, suggesting that the
probability of programmed cell death depends on cell health.
| [
{
"created": "Tue, 26 Feb 2008 22:23:06 GMT",
"version": "v1"
}
] | 2008-03-05 | [
[
"Bauer",
"Amy L.",
""
],
[
"Jackson",
"Trachette L.",
""
],
[
"Jiang",
"Yi",
""
],
[
"Rohlf",
"Thimo",
""
]
] | Cancer invasion and metastasis depend on angiogenesis. The cellular processes (growth, migration, and apoptosis) that occur during angiogenesis are tightly regulated by signaling molecules. Thus, understanding how cells synthesize multiple biochemical signals initiated by key external stimuli can lead to the development of novel therapeutic strategies to combat cancer. In the face of large amounts of disjoint experimental data generated from multitudes of laboratories using various assays, theoretical signal transduction models provide a framework to distill this vast amount of data. Such models offer an opportunity to formulate and test new hypotheses, and can be used to make experimentally verifiable predictions. This study is the first to propose a network model that highlights the cross-talk between the key receptors involved in angiogenesis, namely growth factor, integrin, and cadherin receptors. From available experimental data, we construct a stochastic Boolean network model of receptor cross-talk, and systematically analyze the dynamical stability of the network under continuous-time Boolean dynamics with a noisy production function. We find that the signal transduction network exhibits a robust and fast response to external signals, independent of the internal cell state. We derive an input-output table that maps external stimuli to cell phenotypes, which is extraordinarily stable against molecular noise with one important exception: an oscillatory feedback loop between the key signaling molecules RhoA and Rac1 is unstable under arbitrarily low noise, leading to erratic, dysfunctional cell motion. Finally, we show that the network exhibits an apoptotic response rate that increases with noise, suggesting that the probability of programmed cell death depends on cell health. |
2003.12667 | Silvia Licciardi | Giuseppe Dattoli, Emanuele Di Palma, Silvia Licciardi, Elio Sabia | On the Evolution of Covid-19 in Italy: a Follow up Note | 10 pages, 28 figures | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a previous note we made an analysis of the spreading of the COVID disease
in Italy. We used a model based on the logistic and Hubbert functions, the
analysis we exploited has shown limited usefulness in terms of predictions and
failed in fixing fundamental indications like the point of inflection of the
disease growth. In this note we elaborate on the previous model, using
multi-logistic models and attempt a more realistic analysis.
| [
{
"created": "Fri, 27 Mar 2020 23:58:22 GMT",
"version": "v1"
}
] | 2020-03-31 | [
[
"Dattoli",
"Giuseppe",
""
],
[
"Di Palma",
"Emanuele",
""
],
[
"Licciardi",
"Silvia",
""
],
[
"Sabia",
"Elio",
""
]
] | In a previous note we made an analysis of the spreading of the COVID disease in Italy. We used a model based on the logistic and Hubbert functions, the analysis we exploited has shown limited usefulness in terms of predictions and failed in fixing fundamental indications like the point of inflection of the disease growth. In this note we elaborate on the previous model, using multi-logistic models and attempt a more realistic analysis. |
2207.08456 | Sara Parmigiani PhD | Sara Parmigiani, Jessica M. Ross, Christopher Cline, Christopher B.
Minasi, Juha Gogulski, Corey J Keller | Reliability and validity of TMS-EEG biomarkers | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Noninvasive brain stimulation and neuroimaging have revolutionized human
neuroscience, with a multitude of applications including diagnostic subtyping,
treatment optimization, and relapse prediction. It is therefore particularly
relevant to identify robust and clinically valuable brain biomarkers linking
symptoms to their underlying neural mechanisms. Brain biomarkers must be
reproducible (i.e., have internal reliability) across similar experiments
within a laboratory and be generalizable (i.e., have external reliability)
across experimental setups, laboratories, brain regions, and disease states.
However, reliability (internal and external) is not alone sufficient;
biomarkers also must have validity. Validity describes closeness to a true
measure of the underlying neural signal or disease state. We propose that these
two metrics, reliability and validity, should be evaluated and optimized before
any biomarker is used to inform treatment decisions. Here, we discuss these
metrics with respect to causal brain connectivity biomarkers from coupling
transcranial magnetic stimulation (TMS) with electroencephalography (EEG). We
discuss controversies around TMS-EEG stemming from the multiple large
off-target components (noise) and relatively weak genuine brain responses
(signal), as is unfortunately often the case with human neuroscience. We review
the current state of TMS-EEG recordings, which consist of a mix of reliable
noise and unreliable signal. We describe methods for evaluating TMS-EEG
biomarkers, including how to assess internal and external reliability across
facilities, cognitive states, brain networks, and disorders, and how to
validate these biomarkers using invasive neural recordings or treatment
response. We provide recommendations to increase reliability and validity,
discuss lessons learned, and suggest future directions for the field.
| [
{
"created": "Mon, 18 Jul 2022 09:23:19 GMT",
"version": "v1"
}
] | 2022-07-19 | [
[
"Parmigiani",
"Sara",
""
],
[
"Ross",
"Jessica M.",
""
],
[
"Cline",
"Christopher",
""
],
[
"Minasi",
"Christopher B.",
""
],
[
"Gogulski",
"Juha",
""
],
[
"Keller",
"Corey J",
""
]
] | Noninvasive brain stimulation and neuroimaging have revolutionized human neuroscience, with a multitude of applications including diagnostic subtyping, treatment optimization, and relapse prediction. It is therefore particularly relevant to identify robust and clinically valuable brain biomarkers linking symptoms to their underlying neural mechanisms. Brain biomarkers must be reproducible (i.e., have internal reliability) across similar experiments within a laboratory and be generalizable (i.e., have external reliability) across experimental setups, laboratories, brain regions, and disease states. However, reliability (internal and external) is not alone sufficient; biomarkers also must have validity. Validity describes closeness to a true measure of the underlying neural signal or disease state. We propose that these two metrics, reliability and validity, should be evaluated and optimized before any biomarker is used to inform treatment decisions. Here, we discuss these metrics with respect to causal brain connectivity biomarkers from coupling transcranial magnetic stimulation (TMS) with electroencephalography (EEG). We discuss controversies around TMS-EEG stemming from the multiple large off-target components (noise) and relatively weak genuine brain responses (signal), as is unfortunately often the case with human neuroscience. We review the current state of TMS-EEG recordings, which consist of a mix of reliable noise and unreliable signal. We describe methods for evaluating TMS-EEG biomarkers, including how to assess internal and external reliability across facilities, cognitive states, brain networks, and disorders, and how to validate these biomarkers using invasive neural recordings or treatment response. We provide recommendations to increase reliability and validity, discuss lessons learned, and suggest future directions for the field. |
1411.1917 | Vladimir Privman | Vladimir Privman, Evgeny Katz | Can bio-inspired information processing steps be realized as synthetic
biochemical processes? | null | Physica Status Solidi A 212, 219-228 (2015) | 10.1002/pssa.201400131 | VP-260 | q-bio.MN cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider possible designs and experimental realiza-tions in synthesized
rather than naturally occurring bio-chemical systems of a selection of basic
bio-inspired information processing steps. These include feed-forward loops,
which have been identified as the most common information processing motifs in
many natural pathways in cellular functioning, and memory-involving processes,
specifically, associative memory. Such systems should not be designed to
literally mimic nature. Rather, we can be guided by nature's mechanisms for
experimenting with new information/signal processing steps which are based on
coupled biochemical reactions, but are vastly simpler than natural processes,
and which will provide tools for the long-term goal of understanding and
harnessing nature's information processing paradigm. Our biochemical processes
of choice are enzymatic cascades because of their compatibility with
physiological processes in vivo and with electronics (e.g., electrodes) in
vitro allowing for networking and interfacing of enzyme-catalyzed processes
with other chemical and biochemical reactions. In addition to designing and
realizing feed-forward loops and other processes, one has to develop approaches
to probe their response to external control of the time-dependence of the
input(s), by measuring the resulting time-dependence of the output. The goal
will be to demonstrate the expected features, for example, the delayed response
and stabilizing effect of the feed-forward loops.
| [
{
"created": "Fri, 7 Nov 2014 13:58:31 GMT",
"version": "v1"
}
] | 2015-02-09 | [
[
"Privman",
"Vladimir",
""
],
[
"Katz",
"Evgeny",
""
]
] | We consider possible designs and experimental realiza-tions in synthesized rather than naturally occurring bio-chemical systems of a selection of basic bio-inspired information processing steps. These include feed-forward loops, which have been identified as the most common information processing motifs in many natural pathways in cellular functioning, and memory-involving processes, specifically, associative memory. Such systems should not be designed to literally mimic nature. Rather, we can be guided by nature's mechanisms for experimenting with new information/signal processing steps which are based on coupled biochemical reactions, but are vastly simpler than natural processes, and which will provide tools for the long-term goal of understanding and harnessing nature's information processing paradigm. Our biochemical processes of choice are enzymatic cascades because of their compatibility with physiological processes in vivo and with electronics (e.g., electrodes) in vitro allowing for networking and interfacing of enzyme-catalyzed processes with other chemical and biochemical reactions. In addition to designing and realizing feed-forward loops and other processes, one has to develop approaches to probe their response to external control of the time-dependence of the input(s), by measuring the resulting time-dependence of the output. The goal will be to demonstrate the expected features, for example, the delayed response and stabilizing effect of the feed-forward loops. |
0708.0342 | Tomoshiro Ochiai | J.C. Nacher and T. Ochiai | Transcription and noise in negative feedback loops | Latex, 13 pages, 4 figures | null | null | null | q-bio.MN | null | Recently, several studies have investigated the transcription process
associated to specific genetic regulatory networks. In this work, we present a
stochastic approach for analyzing the dynamics and effect of negative feedback
loops (FBL) on the transcriptional noise. First, our analysis allows us to
identify a bimodal activity depending of the strength of self-repression
coupling D. In the strong coupling region D>>1, the variance of the
transcriptional noise is found to be reduced a 28 % more than described
earlier. Secondly, the contribution of the noise effect to the abundance of
regulating protein becomes manifest when the coefficient of variation is
computed. In the strong coupling region, this coefficient is found to be
independent of all parameters and in fair agreement with the experimentally
observed values. Finally, our analysis reveals that the regulating protein is
significantly induced by the intrinsic and external noise in the strong
coupling region. In short, it indicates that the existence of inherent noise in
FBL makes it possible to produce a basal amount of proteins even though the
repression level D is very strong.
| [
{
"created": "Thu, 2 Aug 2007 13:25:21 GMT",
"version": "v1"
}
] | 2007-08-20 | [
[
"Nacher",
"J. C.",
""
],
[
"Ochiai",
"T.",
""
]
] | Recently, several studies have investigated the transcription process associated to specific genetic regulatory networks. In this work, we present a stochastic approach for analyzing the dynamics and effect of negative feedback loops (FBL) on the transcriptional noise. First, our analysis allows us to identify a bimodal activity depending of the strength of self-repression coupling D. In the strong coupling region D>>1, the variance of the transcriptional noise is found to be reduced a 28 % more than described earlier. Secondly, the contribution of the noise effect to the abundance of regulating protein becomes manifest when the coefficient of variation is computed. In the strong coupling region, this coefficient is found to be independent of all parameters and in fair agreement with the experimentally observed values. Finally, our analysis reveals that the regulating protein is significantly induced by the intrinsic and external noise in the strong coupling region. In short, it indicates that the existence of inherent noise in FBL makes it possible to produce a basal amount of proteins even though the repression level D is very strong. |
1304.3160 | Rori Rohlfs | Rori V. Rohlfs, Erin Murphy, Yun S. Song, Montgomery Slatkin | The influence of relatives on the efficiency and error rate of familial
searching | main text: 19 pages, 4 tables, 2 figures supplemental text: 2 pages,
5 tables all together as single file | PLoS ONE 8(8): e70495 (2013) | 10.1371/journal.pone.0070495 | null | q-bio.GN stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the consequences of adopting the criteria used by the state of
California, as described by Myers et al. (2011), for conducting familial
searches. We carried out a simulation study of randomly generated profiles of
related and unrelated individuals with 13-locus CODIS genotypes and YFiler
Y-chromosome haplotypes, on which the Myers protocol for relative
identification was carried out. For Y-chromosome sharing first degree
relatives, the Myers protocol has a high probability (80 - 99%) of identifying
their relationship. For unrelated individuals, there is a low probability that
an unrelated person in the database will be identified as a first-degree
relative. For more distant Y-haplotype sharing relatives (half-siblings, first
cousins, half-first cousins or second cousins) there is a substantial
probability that the more distant relative will be incorrectly identified as a
first-degree relative. For example, there is a 3 - 18% probability that a first
cousin will be identified as a full sibling, with the probability depending on
the population background. Although the California familial search policy is
likely to identify a first degree relative if his profile is in the database,
and it poses little risk of falsely identifying an unrelated individual in a
database as a first-degree relative, there is a substantial risk of falsely
identifying a more distant Y-haplotype sharing relative in the database as a
first-degree relative, with the consequence that their immediate family may
become the target for further investigation. This risk falls disproportionately
on those ethnic groups that are currently overrepresented in state and federal
databases.
| [
{
"created": "Wed, 10 Apr 2013 22:53:47 GMT",
"version": "v1"
},
{
"created": "Wed, 14 Aug 2013 22:16:03 GMT",
"version": "v2"
}
] | 2015-06-15 | [
[
"Rohlfs",
"Rori V.",
""
],
[
"Murphy",
"Erin",
""
],
[
"Song",
"Yun S.",
""
],
[
"Slatkin",
"Montgomery",
""
]
] | We investigate the consequences of adopting the criteria used by the state of California, as described by Myers et al. (2011), for conducting familial searches. We carried out a simulation study of randomly generated profiles of related and unrelated individuals with 13-locus CODIS genotypes and YFiler Y-chromosome haplotypes, on which the Myers protocol for relative identification was carried out. For Y-chromosome sharing first degree relatives, the Myers protocol has a high probability (80 - 99%) of identifying their relationship. For unrelated individuals, there is a low probability that an unrelated person in the database will be identified as a first-degree relative. For more distant Y-haplotype sharing relatives (half-siblings, first cousins, half-first cousins or second cousins) there is a substantial probability that the more distant relative will be incorrectly identified as a first-degree relative. For example, there is a 3 - 18% probability that a first cousin will be identified as a full sibling, with the probability depending on the population background. Although the California familial search policy is likely to identify a first degree relative if his profile is in the database, and it poses little risk of falsely identifying an unrelated individual in a database as a first-degree relative, there is a substantial risk of falsely identifying a more distant Y-haplotype sharing relative in the database as a first-degree relative, with the consequence that their immediate family may become the target for further investigation. This risk falls disproportionately on those ethnic groups that are currently overrepresented in state and federal databases. |
1908.00723 | Jin Jun Li | Jin Li | Universal Transforming Geometric Network | null | null | null | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The recurrent geometric network (RGN), the first end-to-end differentiable
neural architecture for protein structure prediction, is a competitive
alternative to existing models. However, the RGN's use of recurrent neural
networks (RNNs) as internal representations results in long training time and
unstable gradients. And because of its sequential nature, it is less effective
at learning global dependencies among amino acids than existing transformer
architectures. We propose the Universal Transforming Geometric Network (UTGN),
an end-to-end differentiable model that uses the encoder portion of the
Universal Transformer architecture as an alternative for internal
representations. Our experiments show that compared to RGN, UTGN achieve a
$1.7$ \si{\angstrom} improvement on the free modeling portion and a $0.7$
\si{\angstrom} improvement on the template based modeling of the CASP12
competition.
| [
{
"created": "Fri, 2 Aug 2019 07:14:08 GMT",
"version": "v1"
}
] | 2019-08-05 | [
[
"Li",
"Jin",
""
]
] | The recurrent geometric network (RGN), the first end-to-end differentiable neural architecture for protein structure prediction, is a competitive alternative to existing models. However, the RGN's use of recurrent neural networks (RNNs) as internal representations results in long training time and unstable gradients. And because of its sequential nature, it is less effective at learning global dependencies among amino acids than existing transformer architectures. We propose the Universal Transforming Geometric Network (UTGN), an end-to-end differentiable model that uses the encoder portion of the Universal Transformer architecture as an alternative for internal representations. Our experiments show that compared to RGN, UTGN achieve a $1.7$ \si{\angstrom} improvement on the free modeling portion and a $0.7$ \si{\angstrom} improvement on the template based modeling of the CASP12 competition. |
1710.02431 | Jonathan Karr | Arthur P. Goldberg, Bal\'azs Szigeti, Yin Hoon Chew, John A. P. Sekar,
Yosef D. Roth, Jonathan R. Karr | Emerging whole-cell modeling principles and methods | 10 pages, 2 figures, 7 supplementary tables | null | 10.1016/j.copbio.2017.12.013 | null | q-bio.QM | http://creativecommons.org/publicdomain/zero/1.0/ | Whole-cell computational models aim to predict cellular phenotypes from
genotype by representing the entire genome, the structure and concentration of
each molecular species, each molecular interaction, and the extracellular
environment. Whole-cell models have great potential to transform bioscience,
bioengineering, and medicine. However, numerous challenges remain to achieve
whole-cell models. Nevertheless, researchers are beginning to leverage recent
progress in measurement technology, bioinformatics, data sharing, rule-based
modeling, and multi-algorithmic simulation to build the first whole-cell
models. We anticipate that ongoing efforts to develop scalable whole-cell
modeling tools will enable dramatically more comprehensive and more accurate
models, including models of human cells.
| [
{
"created": "Fri, 6 Oct 2017 14:41:05 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Dec 2017 17:37:24 GMT",
"version": "v2"
}
] | 2019-09-05 | [
[
"Goldberg",
"Arthur P.",
""
],
[
"Szigeti",
"Balázs",
""
],
[
"Chew",
"Yin Hoon",
""
],
[
"Sekar",
"John A. P.",
""
],
[
"Roth",
"Yosef D.",
""
],
[
"Karr",
"Jonathan R.",
""
]
] | Whole-cell computational models aim to predict cellular phenotypes from genotype by representing the entire genome, the structure and concentration of each molecular species, each molecular interaction, and the extracellular environment. Whole-cell models have great potential to transform bioscience, bioengineering, and medicine. However, numerous challenges remain to achieve whole-cell models. Nevertheless, researchers are beginning to leverage recent progress in measurement technology, bioinformatics, data sharing, rule-based modeling, and multi-algorithmic simulation to build the first whole-cell models. We anticipate that ongoing efforts to develop scalable whole-cell modeling tools will enable dramatically more comprehensive and more accurate models, including models of human cells. |
2306.14200 | Hon-Cheong So | Hon-Cheong So, Xiao Xue, Pak-Chung Sham | SumVg: Total heritability explained by all variants in genome-wide
association studies based on summary statistics with standard error estimates | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Genome-wide association studies (GWAS) are commonly employed to study the
genetic basis of complex traits and diseases, and a key question is how much
heritability could be explained by all variants in GWAS. One widely used
approach that relies on summary statistics only is LD score regression (LDSC),
however the approach requires certain assumptions on the SNP effects (all SNPs
contribute to heritability and each SNP contributes equal variance). More
flexible modeling methods may be useful. We previously developed an approach
recovering the true z-statistics from a set of observed z-statistics with an
empirical Bayes approach, using only summary statistics. However, methods for
standard error (SE) estimation are not available yet, limiting the
interpretation of results and applicability of the approach. In this study we
developed several resampling-based approaches to estimate the SE of SNP-based
heritability, including two jackknife and three parametric bootstrap methods.
Simulations showed that delete-d-jackknife and parametric bootstrap approaches
provide good estimates of the SE. Particularly, the parametric bootstrap
approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. In
addition, we applied our method to estimate SNP-based heritability of 12
immune-related traits (levels of cytokines and growth factors) to shed light on
their genetic architecture. We also implemented the methods to compute the sum
of heritability explained and the corresponding SE in an R package SumVg,
available at https://github.com/lab-hcso/Estimating-SE-of-total-heritability/ .
In conclusion, SumVg may provide a useful alternative tool for SNP heritability
and SE estimates, which does not rely on distributional assumptions of SNP
effects.
| [
{
"created": "Sun, 25 Jun 2023 10:32:56 GMT",
"version": "v1"
}
] | 2023-06-27 | [
[
"So",
"Hon-Cheong",
""
],
[
"Xue",
"Xiao",
""
],
[
"Sham",
"Pak-Chung",
""
]
] | Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits and diseases, and a key question is how much heritability could be explained by all variants in GWAS. One widely used approach that relies on summary statistics only is LD score regression (LDSC), however the approach requires certain assumptions on the SNP effects (all SNPs contribute to heritability and each SNP contributes equal variance). More flexible modeling methods may be useful. We previously developed an approach recovering the true z-statistics from a set of observed z-statistics with an empirical Bayes approach, using only summary statistics. However, methods for standard error (SE) estimation are not available yet, limiting the interpretation of results and applicability of the approach. In this study we developed several resampling-based approaches to estimate the SE of SNP-based heritability, including two jackknife and three parametric bootstrap methods. Simulations showed that delete-d-jackknife and parametric bootstrap approaches provide good estimates of the SE. Particularly, the parametric bootstrap approaches yield the lowest root-mean-squared-error (RMSE) of the true SE. In addition, we applied our method to estimate SNP-based heritability of 12 immune-related traits (levels of cytokines and growth factors) to shed light on their genetic architecture. We also implemented the methods to compute the sum of heritability explained and the corresponding SE in an R package SumVg, available at https://github.com/lab-hcso/Estimating-SE-of-total-heritability/ . In conclusion, SumVg may provide a useful alternative tool for SNP heritability and SE estimates, which does not rely on distributional assumptions of SNP effects. |
1609.00900 | Ta\c{s}k{\i}n Deniz | Taskin Deniz, Stefan Rotter | Joint Statistics of Strongly Correlated Neurons via Dimensional
Reduction | 40 pages, 11 figures. Submitted to IOP-Journal of Physics A | null | 10.1088/1751-8121/aa677e | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The relative timing of action potentials in neurons recorded from local
cortical networks often shows a non-trivial dependence, which is then
quantified by cross-correlation functions. Theoretical models emphasize that
such spike train correlations are an inevitable consequence of two neurons
being part of the same network and sharing some synaptic input. For non-linear
neuron models, however, explicit correlation functions are difficult to compute
analytically, and perturbative methods work only for weak shared input. In
order to treat strong correlations, we suggest here an alternative
non-perturbative method. Specifically, we study the case of two leaky
integrate-and-fire neurons with strong shared input. Correlation functions
derived from simulated spike trains fit our theoretical predictions very
accurately. Using our method, we computed the non-linear correlation transfer
as well as correlation functions that are asymmetric due to inhomogeneous
intrinsic parameters or unequal input.
| [
{
"created": "Sun, 4 Sep 2016 08:12:11 GMT",
"version": "v1"
}
] | 2017-06-28 | [
[
"Deniz",
"Taskin",
""
],
[
"Rotter",
"Stefan",
""
]
] | The relative timing of action potentials in neurons recorded from local cortical networks often shows a non-trivial dependence, which is then quantified by cross-correlation functions. Theoretical models emphasize that such spike train correlations are an inevitable consequence of two neurons being part of the same network and sharing some synaptic input. For non-linear neuron models, however, explicit correlation functions are difficult to compute analytically, and perturbative methods work only for weak shared input. In order to treat strong correlations, we suggest here an alternative non-perturbative method. Specifically, we study the case of two leaky integrate-and-fire neurons with strong shared input. Correlation functions derived from simulated spike trains fit our theoretical predictions very accurately. Using our method, we computed the non-linear correlation transfer as well as correlation functions that are asymmetric due to inhomogeneous intrinsic parameters or unequal input. |
1805.08626 | Stefano De Blasi | Stefano De Blasi | Simulation of Large Scale Neural Networks for Evaluation Applications | Poster 2018, Prague May 10 | null | null | null | q-bio.NC eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the complexity of biological neural networks like the human
brain is one of the scientific challenges of our century. The organization of
the brain can be described at different levels, ranging from small neural
networks to entire brain regions. Existing methods for the description of
functionally or effective connectivity are based on the analysis of relations
between the activities of different neural units by detecting correlations or
information flow. This is a crucial step in understanding neural disorders like
Alzheimers disease and their causative factors. To evaluate these estimation
methods, it is necessary to refer to a neural network with known connectivity,
which is typically unknown for natural biological neural networks. Therefore,
network simulations, also in silico, are available. In this work, the in silico
simulation of large scale neural networks is established and the influence of
different topologies on the generated patterns of neuronal signals is
investigated. The goal is to develop standard evaluation methods for
neurocomputational algorithms with a realistic large scale model to enable
benchmarking and comparability of different studies.
| [
{
"created": "Sun, 20 May 2018 15:03:04 GMT",
"version": "v1"
}
] | 2018-05-23 | [
[
"De Blasi",
"Stefano",
""
]
] | Understanding the complexity of biological neural networks like the human brain is one of the scientific challenges of our century. The organization of the brain can be described at different levels, ranging from small neural networks to entire brain regions. Existing methods for the description of functionally or effective connectivity are based on the analysis of relations between the activities of different neural units by detecting correlations or information flow. This is a crucial step in understanding neural disorders like Alzheimers disease and their causative factors. To evaluate these estimation methods, it is necessary to refer to a neural network with known connectivity, which is typically unknown for natural biological neural networks. Therefore, network simulations, also in silico, are available. In this work, the in silico simulation of large scale neural networks is established and the influence of different topologies on the generated patterns of neuronal signals is investigated. The goal is to develop standard evaluation methods for neurocomputational algorithms with a realistic large scale model to enable benchmarking and comparability of different studies. |
2110.07117 | Richard Reeve | Sonia Natalie Mitchell, Andrew Lahiff, Nathan Cummings, Jonathan
Hollocombe, Bram Boskamp, Ryan Field, Dennis Reddyhoff, Kristian Zarebski,
Antony Wilson, Bruno Viola, Martin Burke, Blair Archibald, Paul Bessell,
Richard Blackwell, Lisa A Boden, Alys Brett, Sam Brett, Ruth Dundas, Jessica
Enright, Alejandra N. Gonzalez-Beltran, Claire Harris, Ian Hinder,
Christopher David Hughes, Martin Knight, Vino Mano, Ciaran McMonagle, Dominic
Mellor, Sibylle Mohr, Glenn Marion, Louise Matthews, Iain J. McKendrick,
Christopher Mark Pooley, Thibaud Porphyre, Aaron Reeves, Edward Townsend,
Robert Turner, Jeremy Walton, Richard Reeve | FAIR Data Pipeline: provenance-driven data management for traceable
scientific workflows | null | null | 10.1098/rsta.2021.0300 | null | q-bio.QM cs.DL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modern epidemiological analyses to understand and combat the spread of
disease depend critically on access to, and use of, data. Rapidly evolving
data, such as data streams changing during a disease outbreak, are particularly
challenging. Data management is further complicated by data being imprecisely
identified when used. Public trust in policy decisions resulting from such
analyses is easily damaged and is often low, with cynicism arising where claims
of "following the science" are made without accompanying evidence. Tracing the
provenance of such decisions back through open software to primary data would
clarify this evidence, enhancing the transparency of the decision-making
process. Here, we demonstrate a Findable, Accessible, Interoperable and
Reusable (FAIR) data pipeline developed during the COVID-19 pandemic that
allows easy annotation of data as they are consumed by analyses, while tracing
the provenance of scientific outputs back through the analytical source code to
data sources. Such a tool provides a mechanism for the public, and fellow
scientists, to better assess the trust that should be placed in scientific
evidence, while allowing scientists to support policy-makers in openly
justifying their decisions. We believe that tools such as this should be
promoted for use across all areas of policy-facing research.
| [
{
"created": "Thu, 14 Oct 2021 02:05:16 GMT",
"version": "v1"
},
{
"created": "Wed, 4 May 2022 22:44:17 GMT",
"version": "v2"
}
] | 2022-10-12 | [
[
"Mitchell",
"Sonia Natalie",
""
],
[
"Lahiff",
"Andrew",
""
],
[
"Cummings",
"Nathan",
""
],
[
"Hollocombe",
"Jonathan",
""
],
[
"Boskamp",
"Bram",
""
],
[
"Field",
"Ryan",
""
],
[
"Reddyhoff",
"Dennis",
""... | Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of "following the science" are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline developed during the COVID-19 pandemic that allows easy annotation of data as they are consumed by analyses, while tracing the provenance of scientific outputs back through the analytical source code to data sources. Such a tool provides a mechanism for the public, and fellow scientists, to better assess the trust that should be placed in scientific evidence, while allowing scientists to support policy-makers in openly justifying their decisions. We believe that tools such as this should be promoted for use across all areas of policy-facing research. |
0707.1503 | Ricardo V\^encio | Ricardo V\^encio and Ilya Shmulevich | ProbCD: enrichment analysis accounting for categorization uncertainty | 16 pages, 3 figures, submitted to a journal in the Bioinformatics
field | BMC Bioinformatics 2007, 8:383 | 10.1186/1471-2105-8-383 | null | q-bio.QM q-bio.GN | null | As in many other areas of science, systems biology makes extensive use of
statistical association and significance estimates in contingency tables, a
type of categorical data analysis known in this field as enrichment (also
over-representation or enhancement) analysis. In spite of efforts to create
probabilistic annotations, especially in the Gene Ontology context, or to deal
with uncertainty in high throughput-based datasets, current enrichment methods
largely ignore this probabilistic information since they are mainly based on
variants of the Fisher Exact Test. We developed an open-source R package to
deal with probabilistic categorical data analysis, ProbCD, that does not
require a static contingency table. The contingency table for the enrichment
problem is built using the expectation of a Bernoulli Scheme stochastic process
given the categorization probabilities. An on-line interface was created to
allow usage by non-programmers and is available at:
http://xerad.systemsbiology.net/ProbCD/ . We present an analysis framework and
software tools to address the issue of uncertainty in categorical data
analysis. In particular, concerning the enrichment analysis, ProbCD can
accommodate: (i) the stochastic nature of the high-throughput experimental
techniques and (ii) probabilistic gene annotation.
| [
{
"created": "Tue, 10 Jul 2007 17:42:34 GMT",
"version": "v1"
}
] | 2011-11-10 | [
[
"Vêncio",
"Ricardo",
""
],
[
"Shmulevich",
"Ilya",
""
]
] | As in many other areas of science, systems biology makes extensive use of statistical association and significance estimates in contingency tables, a type of categorical data analysis known in this field as enrichment (also over-representation or enhancement) analysis. In spite of efforts to create probabilistic annotations, especially in the Gene Ontology context, or to deal with uncertainty in high throughput-based datasets, current enrichment methods largely ignore this probabilistic information since they are mainly based on variants of the Fisher Exact Test. We developed an open-source R package to deal with probabilistic categorical data analysis, ProbCD, that does not require a static contingency table. The contingency table for the enrichment problem is built using the expectation of a Bernoulli Scheme stochastic process given the categorization probabilities. An on-line interface was created to allow usage by non-programmers and is available at: http://xerad.systemsbiology.net/ProbCD/ . We present an analysis framework and software tools to address the issue of uncertainty in categorical data analysis. In particular, concerning the enrichment analysis, ProbCD can accommodate: (i) the stochastic nature of the high-throughput experimental techniques and (ii) probabilistic gene annotation. |
2201.11164 | Julian Gendreau | Garrett Garner, Daniel Streetman, Joshua Fricker, Neal Patel, Nolan
Brown, Shane Shahrestani, Julian Gendreau | Focal cortical dysplasia as a cause of epilepsy: the current evidence of
associated genes and future therapeutic treatments | null | null | null | null | q-bio.NC q-bio.GN q-bio.SC | http://creativecommons.org/licenses/by/4.0/ | Focal cortical dysplasias (FCDs) are the most common cause of treatment
resistant epilepsy affecting the pediatric population. Most individuals with
FCD have seizure onset during the first five years of life and the majority
will have seizures by the age of sixteen. Many cases of FCD are postulated to
be the result of abnormal brain development in utero by germline or somatic
gene mutations regulating neuronal growth and migration during corticogenesis.
Other cases of FCD are thought to be related to infections during brain
development, or even other causes still unable to be fully determined. Typical
anti-seizure medications are oftentimes ineffective in FCD as well as surgery
is unable to be successfully performed due to the involvement of eloquent areas
of the brain or insufficient resection of the epileptogenic focus, posing a
challenge for physicians. The genetic nature of FCD provides an avenue for drug
development with several genetic and molecular targets undergoing study over
the last two decades.
| [
{
"created": "Wed, 26 Jan 2022 20:04:09 GMT",
"version": "v1"
}
] | 2022-01-28 | [
[
"Garner",
"Garrett",
""
],
[
"Streetman",
"Daniel",
""
],
[
"Fricker",
"Joshua",
""
],
[
"Patel",
"Neal",
""
],
[
"Brown",
"Nolan",
""
],
[
"Shahrestani",
"Shane",
""
],
[
"Gendreau",
"Julian",
""
]
] | Focal cortical dysplasias (FCDs) are the most common cause of treatment resistant epilepsy affecting the pediatric population. Most individuals with FCD have seizure onset during the first five years of life and the majority will have seizures by the age of sixteen. Many cases of FCD are postulated to be the result of abnormal brain development in utero by germline or somatic gene mutations regulating neuronal growth and migration during corticogenesis. Other cases of FCD are thought to be related to infections during brain development, or even other causes still unable to be fully determined. Typical anti-seizure medications are oftentimes ineffective in FCD as well as surgery is unable to be successfully performed due to the involvement of eloquent areas of the brain or insufficient resection of the epileptogenic focus, posing a challenge for physicians. The genetic nature of FCD provides an avenue for drug development with several genetic and molecular targets undergoing study over the last two decades. |
1808.08820 | Jianqiang Lin | J. Lin, S. Guha, S. Ramanathan | Vanadium dioxide circuits emulate neurological disorders | 25 pages, 14 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Information in the central nervous system (CNS) is conducted via electrical
signals known as action potentials and is encoded in time. Several neurological
disorders including depression, Attention Deficit Hyperactivity Disorder
(ADHD), originate in faulty brain signaling frequencies. Here, we present a
Hodgkin-Huxley model analog for a strongly correlated VO2 artificial neuron
system that undergoes an electrically-driven insulator-metal transition. We
demonstrate that tuning of the insulating phase resistance in VO2 threshold
switch circuits can enable direct mimicry of neuronal origins of disorders in
the central nervous system. The results introduce use of circuits based on
quantum materials as complementary to model animal studies for neuroscience,
especially when precise measurements of local electrical properties or
competing parallel paths for conduction in complex neural circuits can be a
challenge to identify onset of breakdown or diagnose early symptoms of disease.
| [
{
"created": "Mon, 27 Aug 2018 12:40:31 GMT",
"version": "v1"
}
] | 2018-08-28 | [
[
"Lin",
"J.",
""
],
[
"Guha",
"S.",
""
],
[
"Ramanathan",
"S.",
""
]
] | Information in the central nervous system (CNS) is conducted via electrical signals known as action potentials and is encoded in time. Several neurological disorders including depression, Attention Deficit Hyperactivity Disorder (ADHD), originate in faulty brain signaling frequencies. Here, we present a Hodgkin-Huxley model analog for a strongly correlated VO2 artificial neuron system that undergoes an electrically-driven insulator-metal transition. We demonstrate that tuning of the insulating phase resistance in VO2 threshold switch circuits can enable direct mimicry of neuronal origins of disorders in the central nervous system. The results introduce use of circuits based on quantum materials as complementary to model animal studies for neuroscience, especially when precise measurements of local electrical properties or competing parallel paths for conduction in complex neural circuits can be a challenge to identify onset of breakdown or diagnose early symptoms of disease. |
1305.6202 | Mark Robinson | Mark D. Robinson | Agreeing to disagree, some ironies, disappointing scientific practice
and a call for better: reply to <<The poor performance of TMM on
microRNA-Seq>> | 5 pages, 3 Supplemental PDFs | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This letter is a response to a Divergent Views article entitled <<The poor
performance of TMM on microRNA-Seq>> (Garmire and Subramaniam 2013), which was
a response to our Divergent Views article entitled <<miRNA-seq normalization
comparisons need improvement>> (Zhou et al. 2013). Using reproducible code
examples, we showed that they incorrectly used our normalization method and
highlighted additional concerns with their study. Here, I wish to debunk
several untrue or misleading statements made by the authors (hereafter referred
to as GS) in their response. Unlike GSs, my claims are supported by R code,
citations and email correspondences. I finish by making a call for better
practice.
| [
{
"created": "Mon, 27 May 2013 13:00:07 GMT",
"version": "v1"
},
{
"created": "Mon, 10 Jun 2013 07:29:38 GMT",
"version": "v2"
}
] | 2013-06-11 | [
[
"Robinson",
"Mark D.",
""
]
] | This letter is a response to a Divergent Views article entitled <<The poor performance of TMM on microRNA-Seq>> (Garmire and Subramaniam 2013), which was a response to our Divergent Views article entitled <<miRNA-seq normalization comparisons need improvement>> (Zhou et al. 2013). Using reproducible code examples, we showed that they incorrectly used our normalization method and highlighted additional concerns with their study. Here, I wish to debunk several untrue or misleading statements made by the authors (hereafter referred to as GS) in their response. Unlike GSs, my claims are supported by R code, citations and email correspondences. I finish by making a call for better practice. |
2301.08559 | Linn\'ea Gyllingberg | Linn\'ea Gyllingberg, Abeba Birhane, and David J.T. Sumpter | The Lost Art of Mathematical Modelling | null | null | null | null | q-bio.OT cs.LG nlin.AO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We provide a critique of mathematical biology in light of rapid developments
in modern machine learning. We argue that out of the three modelling activities
-- (1) formulating models; (2) analysing models; and (3) fitting or comparing
models to data -- inherent to mathematical biology, researchers currently focus
too much on activity (2) at the cost of (1). This trend, we propose, can be
reversed by realising that any given biological phenomena can be modelled in an
infinite number of different ways, through the adoption of an open/pluralistic
approach. We explain the open approach using fish locomotion as a case study
and illustrate some of the pitfalls -- universalism, creating models of models,
etc. -- that hinder mathematical biology. We then ask how we might rediscover a
lost art: that of creative mathematical modelling.
This article is dedicated to the memory of Edmund Crampin.
| [
{
"created": "Thu, 19 Jan 2023 13:16:31 GMT",
"version": "v1"
},
{
"created": "Fri, 2 Jun 2023 09:03:19 GMT",
"version": "v2"
}
] | 2023-06-05 | [
[
"Gyllingberg",
"Linnéa",
""
],
[
"Birhane",
"Abeba",
""
],
[
"Sumpter",
"David J. T.",
""
]
] | We provide a critique of mathematical biology in light of rapid developments in modern machine learning. We argue that out of the three modelling activities -- (1) formulating models; (2) analysing models; and (3) fitting or comparing models to data -- inherent to mathematical biology, researchers currently focus too much on activity (2) at the cost of (1). This trend, we propose, can be reversed by realising that any given biological phenomena can be modelled in an infinite number of different ways, through the adoption of an open/pluralistic approach. We explain the open approach using fish locomotion as a case study and illustrate some of the pitfalls -- universalism, creating models of models, etc. -- that hinder mathematical biology. We then ask how we might rediscover a lost art: that of creative mathematical modelling. This article is dedicated to the memory of Edmund Crampin. |
1304.7782 | Geir Halnes | Geir Halnes, Ivar {\O}stby, Klas H. Pettersen, Stig W. Omholt, Gaute
T. Einevoll | Electrodiffusive model for astrocytic and neuronal ion concentration
dynamics | 19 pages, 5 figures, 1 table (Equations 37 & 38 and the two first
equations in Figure 2 were corrected May 30th 2013) | null | 10.1371/journal.pcbi.1003386 | null | q-bio.CB q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Electrical neural signalling typically takes place at the time-scale of
milliseconds, and is typically modeled using the cable equation. This is a good
approximation for processes when ionic concentrations vary little during the
time course of a simulation. During periods of intense neural signalling,
however, the local extracellular K+ concentration may increase by several
millimolars. Clearance of excess K+ likely depends partly on diffusion in the
extracellular space, partly on local uptake by- and intracellular transport
within astrocytes. This process takes place at the time scale of seconds, and
can not be modeled accurately without accounting for the spatiotemporal
variations in ion concentrations. The work presented here consists of two main
parts: First, we developed a general electrodiffusive formalism for modeling
ion concentration dynamics in a one-dimensional geometry, including both an
intra- and extracellular domain. The formalism was based on the Nernst-Planck
equations. It ensures (i) consistency between the membrane potential and ion
concentrations, (ii) global particle/charge conservation, and (iii) accounts
for diffusion and concentration dependent variations in resistivities. Second,
we applied the formalism to model how astrocytes exchange ions with the ECS,
and identified the key astrocytic mechanisms involved in K+ removal from high
concentration regions. We found that a local increase in extracellular
K\textsuperscript{+} evoked a local depolarization of the astrocyte membrane,
which at the same time (i) increased the local astrocytic uptake of
K\textsuperscript{+}, (ii) suppressed extracellular transport of K+, (iii)
increased transport of K+ within astrocytes, and (iv) facilitated astrocytic
relase of K+ in extracellular low concentration regions. In summary, these
mechanisms seem optimal for shielding the extracellular space from excess K+.
| [
{
"created": "Mon, 29 Apr 2013 20:02:20 GMT",
"version": "v1"
},
{
"created": "Thu, 30 May 2013 12:56:08 GMT",
"version": "v2"
}
] | 2014-03-05 | [
[
"Halnes",
"Geir",
""
],
[
"Østby",
"Ivar",
""
],
[
"Pettersen",
"Klas H.",
""
],
[
"Omholt",
"Stig W.",
""
],
[
"Einevoll",
"Gaute T.",
""
]
] | Electrical neural signalling typically takes place at the time-scale of milliseconds, and is typically modeled using the cable equation. This is a good approximation for processes when ionic concentrations vary little during the time course of a simulation. During periods of intense neural signalling, however, the local extracellular K+ concentration may increase by several millimolars. Clearance of excess K+ likely depends partly on diffusion in the extracellular space, partly on local uptake by- and intracellular transport within astrocytes. This process takes place at the time scale of seconds, and can not be modeled accurately without accounting for the spatiotemporal variations in ion concentrations. The work presented here consists of two main parts: First, we developed a general electrodiffusive formalism for modeling ion concentration dynamics in a one-dimensional geometry, including both an intra- and extracellular domain. The formalism was based on the Nernst-Planck equations. It ensures (i) consistency between the membrane potential and ion concentrations, (ii) global particle/charge conservation, and (iii) accounts for diffusion and concentration dependent variations in resistivities. Second, we applied the formalism to model how astrocytes exchange ions with the ECS, and identified the key astrocytic mechanisms involved in K+ removal from high concentration regions. We found that a local increase in extracellular K\textsuperscript{+} evoked a local depolarization of the astrocyte membrane, which at the same time (i) increased the local astrocytic uptake of K\textsuperscript{+}, (ii) suppressed extracellular transport of K+, (iii) increased transport of K+ within astrocytes, and (iv) facilitated astrocytic relase of K+ in extracellular low concentration regions. In summary, these mechanisms seem optimal for shielding the extracellular space from excess K+. |
1212.1200 | John Rhodes | Elizabeth S. Allman, John A. Rhodes, Amelia Taylor | A semialgebraic description of the general Markov model on phylogenetic
trees | 29 pages, 0 figures; Mittag-Leffler Institute, Spring 2011 | null | null | null | q-bio.PE math.AG math.ST stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many of the stochastic models used in inference of phylogenetic trees from
biological sequence data have polynomial parameterization maps. The image of
such a map --- the collection of joint distributions for a model --- forms the
model space. Since the parameterization is polynomial, the Zariski closure of
the model space is an algebraic variety which is typically much larger than the
model space, but has been usefully studied with algebraic methods. Of ultimate
interest, however, is not the full variety, but only the model space. Here we
develop complete semialgebraic descriptions of the model space arising from the
k-state general Markov model on a tree, with slightly restricted parameters.
Our approach depends upon both recently-formulated analogs of Cayley's
hyperdeterminant, and the construction of certain quadratic forms from the
joint distribution whose positive (semi-)definiteness encodes information about
parameter values. We additionally investigate the use of Sturm sequences for
obtaining similar results.
| [
{
"created": "Wed, 5 Dec 2012 23:03:29 GMT",
"version": "v1"
}
] | 2012-12-07 | [
[
"Allman",
"Elizabeth S.",
""
],
[
"Rhodes",
"John A.",
""
],
[
"Taylor",
"Amelia",
""
]
] | Many of the stochastic models used in inference of phylogenetic trees from biological sequence data have polynomial parameterization maps. The image of such a map --- the collection of joint distributions for a model --- forms the model space. Since the parameterization is polynomial, the Zariski closure of the model space is an algebraic variety which is typically much larger than the model space, but has been usefully studied with algebraic methods. Of ultimate interest, however, is not the full variety, but only the model space. Here we develop complete semialgebraic descriptions of the model space arising from the k-state general Markov model on a tree, with slightly restricted parameters. Our approach depends upon both recently-formulated analogs of Cayley's hyperdeterminant, and the construction of certain quadratic forms from the joint distribution whose positive (semi-)definiteness encodes information about parameter values. We additionally investigate the use of Sturm sequences for obtaining similar results. |
1610.07278 | Oscar Garc\'ia | Oscar Garc\'ia | Cohort aggregation modelling for complex forest stands: Spruce-aspen
mixtures in British Columbia | Accepted manuscript, to appear in Ecological Modelling | Ecological Modelling 343: 109-122, 2017 | 10.1016/j.ecolmodel.2016.10.020 | null | q-bio.QM q-bio.PE stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mixed-species growth models are needed as a synthesis of ecological knowledge
and for guiding forest management. Individual-tree models have been commonly
used, but the difficulties of reliably scaling from the individual to the stand
level are often underestimated. Emergent properties and statistical issues
limit their effectiveness. A more holistic modelling of aggregates at the whole
stand level is a potentially attractive alternative. This work explores
methodology for developing biologically consistent dynamic mixture models where
the state is described by aggregate stand-level variables for species or
age/size cohorts. The methods are demonstrated and tested with a two-cohort
model for spruce-aspen mixtures named SAM. The models combine single-species
submodels and submodels for resource partitioning among the cohorts. The
partitioning allows for differences in competitive strength among species and
size classes, and for complementarity effects. Height growth reduction in
suppressed cohorts is also modelled. SAM fits well the available data, and
exhibits behaviors consistent with current ecological knowledge. The general
framework can be applied to any number of cohorts, and should be useful as a
basis for modelling other mixed-species or uneven-aged stands.
| [
{
"created": "Mon, 24 Oct 2016 04:43:40 GMT",
"version": "v1"
},
{
"created": "Tue, 25 Oct 2016 03:03:37 GMT",
"version": "v2"
}
] | 2016-11-08 | [
[
"García",
"Oscar",
""
]
] | Mixed-species growth models are needed as a synthesis of ecological knowledge and for guiding forest management. Individual-tree models have been commonly used, but the difficulties of reliably scaling from the individual to the stand level are often underestimated. Emergent properties and statistical issues limit their effectiveness. A more holistic modelling of aggregates at the whole stand level is a potentially attractive alternative. This work explores methodology for developing biologically consistent dynamic mixture models where the state is described by aggregate stand-level variables for species or age/size cohorts. The methods are demonstrated and tested with a two-cohort model for spruce-aspen mixtures named SAM. The models combine single-species submodels and submodels for resource partitioning among the cohorts. The partitioning allows for differences in competitive strength among species and size classes, and for complementarity effects. Height growth reduction in suppressed cohorts is also modelled. SAM fits well the available data, and exhibits behaviors consistent with current ecological knowledge. The general framework can be applied to any number of cohorts, and should be useful as a basis for modelling other mixed-species or uneven-aged stands. |
1310.0736 | Liane Gabora | Liane Gabora | Toward a Theory of Creative Inklings | arXiv admin note: substantial text overlap with arXiv:1309.7414 | In R. Ascott (Ed.), Art, technology, consciousness (pp. 159-164).
Bristol UK: Intellect Press (2000) | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is perhaps not so baffling that we have the ability to develop, refine,
and manifest a creative idea, once it has been conceived. But what sort of a
system could spawn the initial seed of creativity from which an idea grows?
This paper looks at how the mind is structured in such a way that we can
experience a glimmer of insight or inkling of artistic inspiration.
| [
{
"created": "Sat, 28 Sep 2013 03:27:25 GMT",
"version": "v1"
},
{
"created": "Fri, 5 Jul 2019 20:26:52 GMT",
"version": "v2"
}
] | 2019-07-09 | [
[
"Gabora",
"Liane",
""
]
] | It is perhaps not so baffling that we have the ability to develop, refine, and manifest a creative idea, once it has been conceived. But what sort of a system could spawn the initial seed of creativity from which an idea grows? This paper looks at how the mind is structured in such a way that we can experience a glimmer of insight or inkling of artistic inspiration. |
2209.11953 | Baibhab Chatterjee | Baibhab Chatterjee, K Gaurav Kumar, Shulan Xiao, Gourab Barik, Krishna
Jayant and Shreyas Sen | TD-BPQBC: A 1.8{\mu}W 5.5mm3 ADC-less Neural Implant SoC utilizing
13.2pJ/Sample Time-domain Bi-phasic Quasi-static Brain Communication | 4 pages, 6 figures, presented in ESSCIRC 2022 conference | null | null | null | q-bio.NC eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Untethered miniaturized wireless neural sensor nodes with data transmission
and energy harvesting capabilities call for circuit and system-level
innovations to enable ultra-low energy deep implants for brain-machine
interfaces. Realizing that the energy and size constraints of a neural implant
motivate highly asymmetric system design (a small, low-power sensor and
transmitter at the implant, with a relatively higher power receiver at a
body-worn hub), we present Time-Domain Bi-Phasic Quasi-static Brain
Communication (TD- BPQBC), offloading the burden of analog to digital
conversion (ADC) and digital signal processing (DSP) to the receiver. The input
analog signal is converted to time-domain pulse-width modulated (PWM)
waveforms, and transmitted using the recently developed BPQBC method for
reducing communication power in implants. The overall SoC consumes only
1.8{\mu}W power while sensing and communicating at 800kSps. The transmitter
energy efficiency is only 1.1pJ/b, which is >30X better than the
state-of-the-art, enabling a fully-electrical, energy-harvested, and connected
in-brain sensor/stimulator node.
| [
{
"created": "Sat, 24 Sep 2022 08:09:18 GMT",
"version": "v1"
},
{
"created": "Wed, 19 Oct 2022 19:41:56 GMT",
"version": "v2"
}
] | 2022-10-21 | [
[
"Chatterjee",
"Baibhab",
""
],
[
"Kumar",
"K Gaurav",
""
],
[
"Xiao",
"Shulan",
""
],
[
"Barik",
"Gourab",
""
],
[
"Jayant",
"Krishna",
""
],
[
"Sen",
"Shreyas",
""
]
] | Untethered miniaturized wireless neural sensor nodes with data transmission and energy harvesting capabilities call for circuit and system-level innovations to enable ultra-low energy deep implants for brain-machine interfaces. Realizing that the energy and size constraints of a neural implant motivate highly asymmetric system design (a small, low-power sensor and transmitter at the implant, with a relatively higher power receiver at a body-worn hub), we present Time-Domain Bi-Phasic Quasi-static Brain Communication (TD- BPQBC), offloading the burden of analog to digital conversion (ADC) and digital signal processing (DSP) to the receiver. The input analog signal is converted to time-domain pulse-width modulated (PWM) waveforms, and transmitted using the recently developed BPQBC method for reducing communication power in implants. The overall SoC consumes only 1.8{\mu}W power while sensing and communicating at 800kSps. The transmitter energy efficiency is only 1.1pJ/b, which is >30X better than the state-of-the-art, enabling a fully-electrical, energy-harvested, and connected in-brain sensor/stimulator node. |
1407.2488 | Franz Baumdicker | Franz Baumdicker | The site frequency spectrum of dispensable genes | 24 pages, 8 figures | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The differences between DNA-sequences within a population are the basis to
infer the ancestral relationship of the individuals. Within the classical
infinitely many sites model, it is possible to estimate the mutation rate based
on the site frequency spectrum, which is comprised by the numbers
$C_1,...,C_{n-1}$, where n is the sample size and $C_s$ is the number of site
mutations (Single Nucleotide Polymorphisms, SNPs) which are seen in $s$
genomes. Classical results can be used to compare the observed site frequency
spectrum with its neutral expectation, $E[C_s]= \theta_2/s$, where $\theta_2$
is the scaled site mutation rate. In this paper, we will relax the assumption
of the infinitely many sites model that all individuals only carry homologous
genetic material. Especially, it is today well-known that bacterial genomes
have the ability to gain and lose genes, such that every single genome is a
mosaic of genes, and genes are present and absent in a random fashion, giving
rise to the dispensable genome. While this presence and absence has been
modeled under neutral evolution within the infinitely many genes model in
previous papers, we link presence and absence of genes with the numbers of site
mutations seen within each gene. In this work we derive a formula for the
expectation of the joint gene and site frequency spectrum, denotes $G_{k,s}$
the number of mutated sites occurring in exactly $s$ gene sequences, while the
corresponding gene is present in exactly $k$ individuals. We show that standard
estimators of $\theta_2$ for dispensable genes are biased and that the site
frequency spectrum for dispensable genes differs from the classical result.
| [
{
"created": "Wed, 9 Jul 2014 14:19:54 GMT",
"version": "v1"
}
] | 2014-07-10 | [
[
"Baumdicker",
"Franz",
""
]
] | The differences between DNA-sequences within a population are the basis to infer the ancestral relationship of the individuals. Within the classical infinitely many sites model, it is possible to estimate the mutation rate based on the site frequency spectrum, which is comprised by the numbers $C_1,...,C_{n-1}$, where n is the sample size and $C_s$ is the number of site mutations (Single Nucleotide Polymorphisms, SNPs) which are seen in $s$ genomes. Classical results can be used to compare the observed site frequency spectrum with its neutral expectation, $E[C_s]= \theta_2/s$, where $\theta_2$ is the scaled site mutation rate. In this paper, we will relax the assumption of the infinitely many sites model that all individuals only carry homologous genetic material. Especially, it is today well-known that bacterial genomes have the ability to gain and lose genes, such that every single genome is a mosaic of genes, and genes are present and absent in a random fashion, giving rise to the dispensable genome. While this presence and absence has been modeled under neutral evolution within the infinitely many genes model in previous papers, we link presence and absence of genes with the numbers of site mutations seen within each gene. In this work we derive a formula for the expectation of the joint gene and site frequency spectrum, denotes $G_{k,s}$ the number of mutated sites occurring in exactly $s$ gene sequences, while the corresponding gene is present in exactly $k$ individuals. We show that standard estimators of $\theta_2$ for dispensable genes are biased and that the site frequency spectrum for dispensable genes differs from the classical result. |
1109.0615 | Siddhartha Chakrabarty | Gaurav Pachpute and Siddhartha P. Chakrabarty | Optimal Therapy of Hepatitis C Dynamics and Sampling Based Analysis | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine two models for hepatitis C viral (HCV) dynamics, one for
monotherapy with interferon (IFN) and the other for combination therapy with
IFN and ribavirin. Optimal therapy for both the models is determined using the
steepest gradient method, by defining an objective functional which minimizes
the infected hepatocyte levels, virion population and the side-effects of the
drug(s). The optimal therapy for both the models shows an initial period of
high efficacy, followed by a gradual decline. The period of high efficacy
coincides with a significant decrease in the infected hepatocyte levels as well
as viral load, whereas the efficacy drops after liver regeneration through
restored hepatocyte levels. The period of high efficacy is not altered
significantly when the cost coefficients are varied, as long as the side
effects are relatively small. This suggests a higher dependence of the optimal
therapy on the model parameters in case of drugs with minimal side effects.
We use the Latin hypercube sampling technique to randomly generate a large
number of patient scenarios (i.e, model parameter sets) and study the dynamics
of each set under the optimal therapy already determined. Results show an
increase in the percentage of responders (as indicated by drop in viral load
below detection levels) in case of combination therapy as compared to
monotherapy. Statistical tests performed to study the correlations between
sample parameters and the time required for the viral load to fall below
detection level, show a strong monotonic correlation with the death rate of
infected hepatocytes, identifying it to be an important factor in deciding
individual drug regimens.
| [
{
"created": "Sat, 3 Sep 2011 11:17:16 GMT",
"version": "v1"
}
] | 2011-09-06 | [
[
"Pachpute",
"Gaurav",
""
],
[
"Chakrabarty",
"Siddhartha P.",
""
]
] | We examine two models for hepatitis C viral (HCV) dynamics, one for monotherapy with interferon (IFN) and the other for combination therapy with IFN and ribavirin. Optimal therapy for both the models is determined using the steepest gradient method, by defining an objective functional which minimizes the infected hepatocyte levels, virion population and the side-effects of the drug(s). The optimal therapy for both the models shows an initial period of high efficacy, followed by a gradual decline. The period of high efficacy coincides with a significant decrease in the infected hepatocyte levels as well as viral load, whereas the efficacy drops after liver regeneration through restored hepatocyte levels. The period of high efficacy is not altered significantly when the cost coefficients are varied, as long as the side effects are relatively small. This suggests a higher dependence of the optimal therapy on the model parameters in case of drugs with minimal side effects. We use the Latin hypercube sampling technique to randomly generate a large number of patient scenarios (i.e, model parameter sets) and study the dynamics of each set under the optimal therapy already determined. Results show an increase in the percentage of responders (as indicated by drop in viral load below detection levels) in case of combination therapy as compared to monotherapy. Statistical tests performed to study the correlations between sample parameters and the time required for the viral load to fall below detection level, show a strong monotonic correlation with the death rate of infected hepatocytes, identifying it to be an important factor in deciding individual drug regimens. |
2305.00165 | Wei Xie | Keqi Wang, Wei Xie, Sarah W. Harcum | Metabolic Regulatory Network Kinetic Modeling with Multiple Isotopic
Tracers for iPSCs | 26 pages, 16 figures | null | null | null | q-bio.MN q-bio.CB | http://creativecommons.org/licenses/by/4.0/ | The rapidly expanding market for regenerative medicines and cell therapies
highlights the need to advance the understanding of cellular metabolisms and
improve the prediction of cultivation production process for human induced
pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was
developed to characterize underlying mechanisms of iPSC culture process, which
can predict cell response to environmental perturbation and support process
control. This model focuses on the central carbon metabolic network, including
glycolysis, pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle,
and amino acid metabolism, which plays a crucial role to support iPSC
proliferation. Heterogeneous measures of extracellular metabolites and multiple
isotopic tracers collected under multiple conditions were used to learn
metabolic regulatory mechanisms. Systematic cross-validation confirmed the
model's performance in terms of providing reliable predictions on cellular
metabolism and culture process dynamics under various culture conditions. Thus,
the developed mechanistic kinetic model can support process control strategies
to strategically select optimal cell culture conditions at different times,
ensure cell product functionality, and facilitate large-scale manufacturing of
regenerative medicines and cell therapies.
| [
{
"created": "Sat, 29 Apr 2023 04:12:42 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Oct 2023 00:59:57 GMT",
"version": "v2"
}
] | 2023-10-27 | [
[
"Wang",
"Keqi",
""
],
[
"Xie",
"Wei",
""
],
[
"Harcum",
"Sarah W.",
""
]
] | The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross-validation confirmed the model's performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large-scale manufacturing of regenerative medicines and cell therapies. |
1702.00633 | James Barrett | James E. Barrett, Andrew Feber, Javier Herrero, Miljana Tanic, Gareth
Wilson, Charles Swanton, Stephan Beck | Quantification of tumour evolution and heterogeneity via Bayesian
epiallele detection | null | null | null | null | q-bio.QM math.ST q-bio.GN stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Epigenetic heterogeneity within a tumour can play an important
role in tumour evolution and the emergence of resistance to treatment. It is
increasingly recognised that the study of DNA methylation (DNAm) patterns along
the genome -- so-called `epialleles' -- offers greater insight into epigenetic
dynamics than conventional analyses which examine DNAm marks individually.
Results: We have developed a Bayesian model to infer which epialleles are
present in multiple regions of the same tumour. We apply our method to reduced
representation bisulfite sequencing (RRBS) data from multiple regions of one
lung cancer tumour and a matched normal sample. The model borrows information
from all tumour regions to leverage greater statistical power. The total number
of epialleles, the epiallele DNAm patterns, and a noise hyperparameter are all
automatically inferred from the data. Uncertainty as to which epiallele an
observed sequencing read originated from is explicitly incorporated by
marginalising over the appropriate posterior densities. The degree to which
tumour samples are contaminated with normal tissue can be estimated and
corrected for. By tracing the distribution of epialleles throughout the tumour
we can infer the phylogenetic history of the tumour, identify epialleles that
differ between normal and cancer tissue, and define a measure of global
epigenetic disorder.
| [
{
"created": "Thu, 2 Feb 2017 12:00:08 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Feb 2017 16:26:20 GMT",
"version": "v2"
}
] | 2017-02-21 | [
[
"Barrett",
"James E.",
""
],
[
"Feber",
"Andrew",
""
],
[
"Herrero",
"Javier",
""
],
[
"Tanic",
"Miljana",
""
],
[
"Wilson",
"Gareth",
""
],
[
"Swanton",
"Charles",
""
],
[
"Beck",
"Stephan",
""
]
] | Motivation: Epigenetic heterogeneity within a tumour can play an important role in tumour evolution and the emergence of resistance to treatment. It is increasingly recognised that the study of DNA methylation (DNAm) patterns along the genome -- so-called `epialleles' -- offers greater insight into epigenetic dynamics than conventional analyses which examine DNAm marks individually. Results: We have developed a Bayesian model to infer which epialleles are present in multiple regions of the same tumour. We apply our method to reduced representation bisulfite sequencing (RRBS) data from multiple regions of one lung cancer tumour and a matched normal sample. The model borrows information from all tumour regions to leverage greater statistical power. The total number of epialleles, the epiallele DNAm patterns, and a noise hyperparameter are all automatically inferred from the data. Uncertainty as to which epiallele an observed sequencing read originated from is explicitly incorporated by marginalising over the appropriate posterior densities. The degree to which tumour samples are contaminated with normal tissue can be estimated and corrected for. By tracing the distribution of epialleles throughout the tumour we can infer the phylogenetic history of the tumour, identify epialleles that differ between normal and cancer tissue, and define a measure of global epigenetic disorder. |
2002.03638 | Axel Brandenburg | Axel Brandenburg | Piecewise quadratic growth during the 2019 novel coronavirus epidemic | 9 pages, 14 figures, 2 tables, published in Infectious Disease
Modelling | Infectious Disease Modelling 5, 681-690 (2020) | 10.1016/j.idm.2020.08.014 | Nordita-2020-015 | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | The temporal growth in the number of deaths in the COVID-19 epidemic is
subexponential. Here we show that a piecewise quadratic law provides an
excellent fit during the thirty days after the first three fatalities on
January 20 and later since the end of March 2020. There is also a brief
intermediate period of exponential growth. During the second quadratic growth
phase, the characteristic time of the growth is about eight times shorter than
in the beginning, which can be understood as the occurrence of separate
hotspots. Quadratic behavior can be motivated by peripheral growth when further
spreading occurs only on the outskirts of an infected region. We also study
numerical solutions of a simple epidemic model, where the spatial extend of the
system is taken into account. To model the delayed onset outside China together
with the early one in China within a single model with minimal assumptions, we
adopt an initial condition of several hotspots, of which one reaches saturation
much earlier than the others. At each site, quadratic growth commences when the
local number of infections has reached a certain saturation level. The total
number of deaths does then indeed follow a piecewise quadratic behavior.
| [
{
"created": "Mon, 10 Feb 2020 10:36:26 GMT",
"version": "v1"
},
{
"created": "Fri, 14 Feb 2020 18:57:22 GMT",
"version": "v2"
},
{
"created": "Mon, 20 Apr 2020 15:41:11 GMT",
"version": "v3"
},
{
"created": "Mon, 21 Sep 2020 06:37:34 GMT",
"version": "v4"
}
] | 2020-09-22 | [
[
"Brandenburg",
"Axel",
""
]
] | The temporal growth in the number of deaths in the COVID-19 epidemic is subexponential. Here we show that a piecewise quadratic law provides an excellent fit during the thirty days after the first three fatalities on January 20 and later since the end of March 2020. There is also a brief intermediate period of exponential growth. During the second quadratic growth phase, the characteristic time of the growth is about eight times shorter than in the beginning, which can be understood as the occurrence of separate hotspots. Quadratic behavior can be motivated by peripheral growth when further spreading occurs only on the outskirts of an infected region. We also study numerical solutions of a simple epidemic model, where the spatial extend of the system is taken into account. To model the delayed onset outside China together with the early one in China within a single model with minimal assumptions, we adopt an initial condition of several hotspots, of which one reaches saturation much earlier than the others. At each site, quadratic growth commences when the local number of infections has reached a certain saturation level. The total number of deaths does then indeed follow a piecewise quadratic behavior. |
2302.01140 | Martin Johnsson | M. Johnsson (Swedish University of Agricultural Sciences) | The big challenge for livestock genomics is to make sequence data pay | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | This paper will argue that one of the biggest challenges for livestock
genomics is to make whole-genome sequencing and functional genomics applicable
to breeding practice. It discusses potential explanations for why it is so
difficult to consistently improve the accuracy of genomic prediction by means
of whole-genome sequence data, and three potential attacks on the problem.
| [
{
"created": "Thu, 2 Feb 2023 14:55:51 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Feb 2023 11:41:42 GMT",
"version": "v2"
},
{
"created": "Mon, 5 Jun 2023 10:57:21 GMT",
"version": "v3"
},
{
"created": "Mon, 24 Jul 2023 09:32:42 GMT",
"version": "v4"
}
] | 2023-07-25 | [
[
"Johnsson",
"M.",
"",
"Swedish University of Agricultural Sciences"
]
] | This paper will argue that one of the biggest challenges for livestock genomics is to make whole-genome sequencing and functional genomics applicable to breeding practice. It discusses potential explanations for why it is so difficult to consistently improve the accuracy of genomic prediction by means of whole-genome sequence data, and three potential attacks on the problem. |
1903.12155 | Akihiko Akao | Akihiko Akao, Sho Shirasaka, Yasuhiko Jimbo, Bard Ermentrout and
Kiyoshi Kotani | Theta-gamma cross-frequency coupling enables covariance between distant
brain regions | 13 pages, 4 figures | null | null | null | q-bio.NC nlin.PS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cross-frequency coupling (CFC) is thought to play an important role in
communication across distant brain regions. However, neither the mechanism of
its generation nor the influence on the underlying spiking dynamics is well
understood. Here, we investigate the dynamics of two interacting distant
neuronal modules coupled by inter-regional long-range connections. Each
neuronal module comprises an excitatory and inhibitory population of quadratic
integrate-and-fire neurons connected locally with conductance-based synapses.
The two modules are coupled reciprocally with delays that represent the
long-range conduction time. We applied the Ott-Antonsen ansatz to reduce the
spiking dynamics to the corresponding mean field equations as a small set of
delay differential equations. Bifurcation analysis on these mean field
equations shows inter-regional conduction delay is sufficient to produce CFC
via a torus bifurcation. Spike correlation analysis during the CFC revealed
that several local clusters exhibit synchronized firing in gamma-band
frequencies. These clusters exhibit locally decorrelated firings between the
cluster pairs within the same population. In contrast, the clusters exhibit
long-range gamma-band cross-covariance between the distant clusters that have
similar firing frequency. The interactions of the different gamma frequencies
produce a beat leading to population-level CFC. We analyzed spike counts in
relation to the phases of the macroscopic fast and slow oscillations and found
population spike counts vary with respect to macroscopic phases. Such firing
phase preference accompanies a phase window with high spike count and low Fano
factor, which is suitable for a population rate code. Our work suggests the
inter-regional conduction delay plays a significant role in the emergence of
CFC and the underlying spiking dynamics may support long-range communication
and neural coding.
| [
{
"created": "Thu, 28 Mar 2019 17:42:05 GMT",
"version": "v1"
}
] | 2019-03-29 | [
[
"Akao",
"Akihiko",
""
],
[
"Shirasaka",
"Sho",
""
],
[
"Jimbo",
"Yasuhiko",
""
],
[
"Ermentrout",
"Bard",
""
],
[
"Kotani",
"Kiyoshi",
""
]
] | Cross-frequency coupling (CFC) is thought to play an important role in communication across distant brain regions. However, neither the mechanism of its generation nor the influence on the underlying spiking dynamics is well understood. Here, we investigate the dynamics of two interacting distant neuronal modules coupled by inter-regional long-range connections. Each neuronal module comprises an excitatory and inhibitory population of quadratic integrate-and-fire neurons connected locally with conductance-based synapses. The two modules are coupled reciprocally with delays that represent the long-range conduction time. We applied the Ott-Antonsen ansatz to reduce the spiking dynamics to the corresponding mean field equations as a small set of delay differential equations. Bifurcation analysis on these mean field equations shows inter-regional conduction delay is sufficient to produce CFC via a torus bifurcation. Spike correlation analysis during the CFC revealed that several local clusters exhibit synchronized firing in gamma-band frequencies. These clusters exhibit locally decorrelated firings between the cluster pairs within the same population. In contrast, the clusters exhibit long-range gamma-band cross-covariance between the distant clusters that have similar firing frequency. The interactions of the different gamma frequencies produce a beat leading to population-level CFC. We analyzed spike counts in relation to the phases of the macroscopic fast and slow oscillations and found population spike counts vary with respect to macroscopic phases. Such firing phase preference accompanies a phase window with high spike count and low Fano factor, which is suitable for a population rate code. Our work suggests the inter-regional conduction delay plays a significant role in the emergence of CFC and the underlying spiking dynamics may support long-range communication and neural coding. |
1603.06335 | Philippe Robert S. | Marie Doumic and Sarah Eugene and Philippe Robert | Asymptotics of Stochastic Protein Assembly Models | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Self-assembly of proteins is a biological phenomenon which gives rise to
spontaneous formation of amyloid fibrils or polymers. The starting point of
this phase, called nucleation exhibits an important variability among
replicated experiments.To analyse the stochastic nature of this phenomenon, one
of the simplest models considers two populations of chemical components:
monomers and polymerised monomers. Initially there are only monomers. There are
two reactions for the polymerization of a monomer: either two monomers collide
to combine into two polymerised monomers or a monomer is polymerised after the
encounter of a polymerised monomer. It turns out that this simple model does
not explain completely the variability observed in the experiments. This paper
investigates extensions of this model to take into account other mechanisms of
the polymerization process that may have impact an impact on fluctuations.The
first variant consists in introducing a preliminary conformation step to take
into account the biological fact that, before being polymerised, a monomer has
two states, regular or misfolded. Only misfolded monomers can be polymerised so
that the fluctuations of the number of misfolded monomers can be also a source
of variability of the number of polymerised monomers. The second variant
represents the reaction rate $\alpha$ of spontaneous formation of a polymer as
of the order of $N^{-\nu}$, with $\nu$ some positive constant. First and second
order results for the starting instant of nucleation are derived from these
limit theorems. The proofs of the results rely on a study of a stochastic
averaging principle for a model related to an Ehrenfest urn model, and also on
a scaling analysis of a population model.
| [
{
"created": "Mon, 21 Mar 2016 06:37:01 GMT",
"version": "v1"
}
] | 2016-03-22 | [
[
"Doumic",
"Marie",
""
],
[
"Eugene",
"Sarah",
""
],
[
"Robert",
"Philippe",
""
]
] | Self-assembly of proteins is a biological phenomenon which gives rise to spontaneous formation of amyloid fibrils or polymers. The starting point of this phase, called nucleation exhibits an important variability among replicated experiments.To analyse the stochastic nature of this phenomenon, one of the simplest models considers two populations of chemical components: monomers and polymerised monomers. Initially there are only monomers. There are two reactions for the polymerization of a monomer: either two monomers collide to combine into two polymerised monomers or a monomer is polymerised after the encounter of a polymerised monomer. It turns out that this simple model does not explain completely the variability observed in the experiments. This paper investigates extensions of this model to take into account other mechanisms of the polymerization process that may have impact an impact on fluctuations.The first variant consists in introducing a preliminary conformation step to take into account the biological fact that, before being polymerised, a monomer has two states, regular or misfolded. Only misfolded monomers can be polymerised so that the fluctuations of the number of misfolded monomers can be also a source of variability of the number of polymerised monomers. The second variant represents the reaction rate $\alpha$ of spontaneous formation of a polymer as of the order of $N^{-\nu}$, with $\nu$ some positive constant. First and second order results for the starting instant of nucleation are derived from these limit theorems. The proofs of the results rely on a study of a stochastic averaging principle for a model related to an Ehrenfest urn model, and also on a scaling analysis of a population model. |
1602.07183 | John Helliwell R | Simon W. M. Tanley, Loes M. J. Kroon-Batenburg, Antoine M. M. Schreurs
and John R. Helliwell | Re-refinement of 4xan: hen egg white lysozyme with carboplatin in sodium
bromide solution including details of solute, solvent, ion and split
occupancy amino acid electron density evidence | 43 pages | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A re-refinement of 4xan, hen egg white lysozyme (HEWL) with carboplatin
crystallised in NaBr solution, has been made (Tanley et al 2016). This follows
our Response article (Tanley et al 2015) to the Critique article of Shabalin et
al 2015, suggesting the need for corrections to some solute molecule
interpretations of electron density in 4xan and removal of an organic moiety as
a ligand to the platinum ion coordinated to His15. We note Shabalin et al
(2015) model of a chlorine in that density and a close by bromine at partial
occupancy to explain the shape. However, as the bromide concentration is in
huge excess over chloride (by 20 fold), we think that the 4yem Shabalin et al
2015 interpretation highly unlikely, but nevertheless we still cannot offer an
explanation for that shape, confirming our earlier analysis described in Tanley
et al (2014). Following Shabalin et al (2015) reprocessing of the raw
diffraction data for 4g4a, we also redid the diffraction data processing for
4xan to a higher resolution using EVAL (Schreurs et al 2010) concluding in
favour of 1.3 Angstrom as the resolution limit and which is the basis for our
revised PDB file for 4xan (5HMJ). It is very interesting that there is extra
X-ray diffraction data from 1.47 to 1.30 Angstrom resolution e.g. with
<I/sigma(I)> =0.39 and CC1/2 = 0.181 in the final shell (1.30 to 1.322
Angstrom). In this arXiv article we document in detail our different solvent
and split occupancy side chain electron density interpretations as evidence for
our statement of approach in our Response article (Tanley et al 2015). Our
critical re-examination includes comparisons based on the 4xan diffraction data
images reprocessing with three different software packages so as to evaluate
the possibility of variations in electron density interpretations due to that.
Overall our finalised model (PDB code 5HMJ) is now improved over 4xan.
| [
{
"created": "Tue, 23 Feb 2016 15:04:24 GMT",
"version": "v1"
}
] | 2016-02-24 | [
[
"Tanley",
"Simon W. M.",
""
],
[
"Kroon-Batenburg",
"Loes M. J.",
""
],
[
"Schreurs",
"Antoine M. M.",
""
],
[
"Helliwell",
"John R.",
""
]
] | A re-refinement of 4xan, hen egg white lysozyme (HEWL) with carboplatin crystallised in NaBr solution, has been made (Tanley et al 2016). This follows our Response article (Tanley et al 2015) to the Critique article of Shabalin et al 2015, suggesting the need for corrections to some solute molecule interpretations of electron density in 4xan and removal of an organic moiety as a ligand to the platinum ion coordinated to His15. We note Shabalin et al (2015) model of a chlorine in that density and a close by bromine at partial occupancy to explain the shape. However, as the bromide concentration is in huge excess over chloride (by 20 fold), we think that the 4yem Shabalin et al 2015 interpretation highly unlikely, but nevertheless we still cannot offer an explanation for that shape, confirming our earlier analysis described in Tanley et al (2014). Following Shabalin et al (2015) reprocessing of the raw diffraction data for 4g4a, we also redid the diffraction data processing for 4xan to a higher resolution using EVAL (Schreurs et al 2010) concluding in favour of 1.3 Angstrom as the resolution limit and which is the basis for our revised PDB file for 4xan (5HMJ). It is very interesting that there is extra X-ray diffraction data from 1.47 to 1.30 Angstrom resolution e.g. with <I/sigma(I)> =0.39 and CC1/2 = 0.181 in the final shell (1.30 to 1.322 Angstrom). In this arXiv article we document in detail our different solvent and split occupancy side chain electron density interpretations as evidence for our statement of approach in our Response article (Tanley et al 2015). Our critical re-examination includes comparisons based on the 4xan diffraction data images reprocessing with three different software packages so as to evaluate the possibility of variations in electron density interpretations due to that. Overall our finalised model (PDB code 5HMJ) is now improved over 4xan. |
2205.12432 | Nana Wei | Nana Wei, Yating Nie, Lin Liu, Xiaoqi Zheng, Hua-Jun Wu4 | Secuer: ultrafast, scalable and accurate clustering of single-cell
RNA-seq data | null | null | 10.1371/journal.pcbi.1010753 | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Identifying cell clusters is a critical step for single-cell transcriptomics
study. Despite the numerous clustering tools developed recently, the rapid
growth of scRNA-seq volumes prompts for a more (computationally) efficient
clustering method. Here, we introduce Secuer, a Scalable and Efficient speCtral
clUstERing algorithm for scRNA-seq data. By employing an anchor-based bipartite
graph representation algorithm, Secuer enjoys reduced runtime and memory usage
by orders of magnitude, especially for ultra-large datasets profiling over 1 or
even 10 million cells. Meanwhile, Secuer also achieves better or comparable
accuracy than competing methods in small and moderate benchmark datasets.
Furthermore, we showcase that Secuer can also serve as a building block for a
new consensus clustering method, Secuer-consensus, which again greatly improves
the runtime and scalability of state-of-the-art consensus clustering methods
while also maintaining the accuracy. Overall, Secuer is a versatile, accurate,
and scalable clustering framework suitable for small to ultra-large single-cell
clustering tasks.
| [
{
"created": "Wed, 25 May 2022 01:40:41 GMT",
"version": "v1"
},
{
"created": "Thu, 7 Jul 2022 13:27:03 GMT",
"version": "v2"
}
] | 2023-01-11 | [
[
"Wei",
"Nana",
""
],
[
"Nie",
"Yating",
""
],
[
"Liu",
"Lin",
""
],
[
"Zheng",
"Xiaoqi",
""
],
[
"Wu4",
"Hua-Jun",
""
]
] | Identifying cell clusters is a critical step for single-cell transcriptomics study. Despite the numerous clustering tools developed recently, the rapid growth of scRNA-seq volumes prompts for a more (computationally) efficient clustering method. Here, we introduce Secuer, a Scalable and Efficient speCtral clUstERing algorithm for scRNA-seq data. By employing an anchor-based bipartite graph representation algorithm, Secuer enjoys reduced runtime and memory usage by orders of magnitude, especially for ultra-large datasets profiling over 1 or even 10 million cells. Meanwhile, Secuer also achieves better or comparable accuracy than competing methods in small and moderate benchmark datasets. Furthermore, we showcase that Secuer can also serve as a building block for a new consensus clustering method, Secuer-consensus, which again greatly improves the runtime and scalability of state-of-the-art consensus clustering methods while also maintaining the accuracy. Overall, Secuer is a versatile, accurate, and scalable clustering framework suitable for small to ultra-large single-cell clustering tasks. |
1804.04428 | No\"el Malod-Dognin Ph.D | Noel Malod-Dognin and Natasa Przulj | Functional geometry of protein-protein interaction networks | 16 pages, 11 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation: Protein-protein interactions (PPIs) are usually modelled as
networks. These networks have extensively been studied using graphlets, small
induced subgraphs capturing the local wiring patterns around nodes in networks.
They revealed that proteins involved in similar functions tend to be similarly
wired. However, such simple models can only represent pairwise relationships
and cannot fully capture the higher-order organization of protein interactions,
including protein complexes. Results: To model the multi-sale organization of
these complex biological systems, we utilize simplicial complexes from
computational geometry. The question is how to mine these new representations
of PPI networks to reveal additional biological information. To address this,
we define simplets, a generalization of graphlets to simplicial complexes. By
using simplets, we define a sensitive measure of similarity between simplicial
complex network representations that allows for clustering them according to
their data types better than clustering them by using other state-of-the-art
measures, e.g., spectral distance, or facet distribution distance. We model
human and baker's yeast PPI networks as simplicial complexes that capture PPIs
and protein complexes as simplices. On these models, we show that our newly
introduced simplet-based methods cluster proteins by function better than the
clustering methods that use the standard PPI networks, uncovering the new
underlying functional organization of the cell. We demonstrate the existence of
the functional geometry in the PPI data and the superiority of our
simplet-based methods to effectively mine for new biological information hidden
in the complexity of the higher order organization of PPI networks.
| [
{
"created": "Thu, 12 Apr 2018 11:08:05 GMT",
"version": "v1"
}
] | 2018-04-13 | [
[
"Malod-Dognin",
"Noel",
""
],
[
"Przulj",
"Natasa",
""
]
] | Motivation: Protein-protein interactions (PPIs) are usually modelled as networks. These networks have extensively been studied using graphlets, small induced subgraphs capturing the local wiring patterns around nodes in networks. They revealed that proteins involved in similar functions tend to be similarly wired. However, such simple models can only represent pairwise relationships and cannot fully capture the higher-order organization of protein interactions, including protein complexes. Results: To model the multi-sale organization of these complex biological systems, we utilize simplicial complexes from computational geometry. The question is how to mine these new representations of PPI networks to reveal additional biological information. To address this, we define simplets, a generalization of graphlets to simplicial complexes. By using simplets, we define a sensitive measure of similarity between simplicial complex network representations that allows for clustering them according to their data types better than clustering them by using other state-of-the-art measures, e.g., spectral distance, or facet distribution distance. We model human and baker's yeast PPI networks as simplicial complexes that capture PPIs and protein complexes as simplices. On these models, we show that our newly introduced simplet-based methods cluster proteins by function better than the clustering methods that use the standard PPI networks, uncovering the new underlying functional organization of the cell. We demonstrate the existence of the functional geometry in the PPI data and the superiority of our simplet-based methods to effectively mine for new biological information hidden in the complexity of the higher order organization of PPI networks. |
2103.07233 | Mar\'ia Vallet-Regi | Marina Martinez-Carmona, Isabel Izquierdo-Barba, Montserrat Colilla,
Maria Vallet-Regi | Concanavalin A-targeted mesoporous silica nanoparticles for infection
treatment | 27 pages, 9 figures | Acta Biomaterialia. 96, 547-556 (2019) | 10.1016/j.actbio.2019.07.001 | null | q-bio.TO physics.bio-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The ability of bacteria to form biofilms hinders any conventional treatment
against chronic infections and has serious socio-economic implications. In this
sense, a nanocarrier capable of overcoming the barrier of the
mucopolysaccharide matrix of the biofilm and releasing its loaded-antibiotic
within would be desirable. Herein, a new nanosystem based on levofloxacin
(LEVO)-loaded mesoporous silica nanoparticles (MSNs) decorated with lectin
Concanavalin A (ConA) has been developed. The presence of ConA promotes its
internalization into the biofilm matrix, which increases the antimicrobial
efficacy of the antibiotic hosted within the mesopores. This nanodevice is
envisioned as a promising alternative to conventional infection treatments by
improving the antimicrobial efficacy and reducing side effects.
| [
{
"created": "Fri, 12 Mar 2021 12:20:28 GMT",
"version": "v1"
}
] | 2021-03-15 | [
[
"Martinez-Carmona",
"Marina",
""
],
[
"Izquierdo-Barba",
"Isabel",
""
],
[
"Colilla",
"Montserrat",
""
],
[
"Vallet-Regi",
"Maria",
""
]
] | The ability of bacteria to form biofilms hinders any conventional treatment against chronic infections and has serious socio-economic implications. In this sense, a nanocarrier capable of overcoming the barrier of the mucopolysaccharide matrix of the biofilm and releasing its loaded-antibiotic within would be desirable. Herein, a new nanosystem based on levofloxacin (LEVO)-loaded mesoporous silica nanoparticles (MSNs) decorated with lectin Concanavalin A (ConA) has been developed. The presence of ConA promotes its internalization into the biofilm matrix, which increases the antimicrobial efficacy of the antibiotic hosted within the mesopores. This nanodevice is envisioned as a promising alternative to conventional infection treatments by improving the antimicrobial efficacy and reducing side effects. |
1405.0114 | Wolfgang Kaisers | Wolfgang Kaisers and Holger Schwender and Heiner Schaal | Hierarchical clustering of DNA k-mer counts in RNA-seq fastq files
reveals batch effects | 5 pages, 6 figures | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Batch effects, artificial sources of variation due to experimental design,
are a widespread phenomenon in high throughput data. Therefore, mechanisms for
detection of batch effects are needed requiring comparison of multiple samples.
We apply hierarchical clustering (HC) on DNA k-mer counts of multiple RNA-seq
derived Fastq files. Ideally, HC generated trees reflect experimental treatment
groups and thus may indicate experimental effects, but clustering of
preparation groups indicates the presence of batch effects. In order to provide
a simple applicable tool we implemented sequential analysis of Fastq reads with
low memory usage in an R package (seqTools) available on Bioconductor. DNA
k-mer counts were analysed on 61 Fastq files containing RNA-seq data from two
cell types (dermal fibroblasts and Jurkat cells) sequenced on 8 different
Illumina Flowcells. Results: Pairwise comparison of all Flowcells with
hierarchical clustering revealed strong Flowcell based tree separation in 6 (21
%) and detectable Flowcell based clustering in 17 (60.7 %) of 28 Flowcell
comparisons. In our samples, batch effects were also present in reads mapped to
the human genome. Filtering reads for high quality (Phred >30) did not remove
the batch effects. Conclusions: Hierarchical clustering of DNA k-mer counts
provides a quality criterion and an unspecific diagnostic tool for RNA-seq
experiments.
| [
{
"created": "Thu, 1 May 2014 08:53:46 GMT",
"version": "v1"
},
{
"created": "Sat, 3 May 2014 12:25:21 GMT",
"version": "v2"
},
{
"created": "Tue, 6 May 2014 15:16:50 GMT",
"version": "v3"
},
{
"created": "Tue, 13 May 2014 17:41:39 GMT",
"version": "v4"
},
{
"crea... | 2017-07-24 | [
[
"Kaisers",
"Wolfgang",
""
],
[
"Schwender",
"Holger",
""
],
[
"Schaal",
"Heiner",
""
]
] | Batch effects, artificial sources of variation due to experimental design, are a widespread phenomenon in high throughput data. Therefore, mechanisms for detection of batch effects are needed requiring comparison of multiple samples. We apply hierarchical clustering (HC) on DNA k-mer counts of multiple RNA-seq derived Fastq files. Ideally, HC generated trees reflect experimental treatment groups and thus may indicate experimental effects, but clustering of preparation groups indicates the presence of batch effects. In order to provide a simple applicable tool we implemented sequential analysis of Fastq reads with low memory usage in an R package (seqTools) available on Bioconductor. DNA k-mer counts were analysed on 61 Fastq files containing RNA-seq data from two cell types (dermal fibroblasts and Jurkat cells) sequenced on 8 different Illumina Flowcells. Results: Pairwise comparison of all Flowcells with hierarchical clustering revealed strong Flowcell based tree separation in 6 (21 %) and detectable Flowcell based clustering in 17 (60.7 %) of 28 Flowcell comparisons. In our samples, batch effects were also present in reads mapped to the human genome. Filtering reads for high quality (Phred >30) did not remove the batch effects. Conclusions: Hierarchical clustering of DNA k-mer counts provides a quality criterion and an unspecific diagnostic tool for RNA-seq experiments. |
0909.4239 | Rui Dilao | Rui Dil\~ao, Daniele Muraro | mRNA diffusion explains protein gradients in \textit{Drosophila} early
development | 14 pages, 2 figures | null | null | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new model describing the production and the establishment of the
stable gradient of the Bicoid protein along the antero-posterior axis of the
embryo of \textit{Drosophila}. In this model, we consider that \textit{bicoid}
mRNA diffuses along the antero-posterior axis of the embryo and the protein is
produced in the ribosomes localized near the syncytial nuclei. Bicoid protein
stays localized near the syncytial nuclei as observed in experiments. We
calibrate the parameters of the mathematical model with experimental data taken
during the cleavage stages 11 to 14 of the developing embryo of
\textit{Drosophila}. We obtain good agreement between the experimental and the
model gradients, with relative errors in the range 5-8%. The inferred diffusion
coefficient of \textit{bicoid} mRNA is in the range 4.6\times
10^{-12}-1.5\times 10^{-11}m^2s^{-1}, in agreement with the theoretical
predictions and experimental measurements for the diffusion of macromolecules
in the cytoplasm. We show that the model based on the mRNA diffusion hypothesis
is consistent with the known observational data, supporting the recent
experimental findings of the gradient of \textit{bicoid} mRNA in
\textit{Drosophila} [Spirov et al. (2009) Development 136:605-614].
| [
{
"created": "Wed, 23 Sep 2009 15:58:57 GMT",
"version": "v1"
}
] | 2009-09-24 | [
[
"Dilão",
"Rui",
""
],
[
"Muraro",
"Daniele",
""
]
] | We propose a new model describing the production and the establishment of the stable gradient of the Bicoid protein along the antero-posterior axis of the embryo of \textit{Drosophila}. In this model, we consider that \textit{bicoid} mRNA diffuses along the antero-posterior axis of the embryo and the protein is produced in the ribosomes localized near the syncytial nuclei. Bicoid protein stays localized near the syncytial nuclei as observed in experiments. We calibrate the parameters of the mathematical model with experimental data taken during the cleavage stages 11 to 14 of the developing embryo of \textit{Drosophila}. We obtain good agreement between the experimental and the model gradients, with relative errors in the range 5-8%. The inferred diffusion coefficient of \textit{bicoid} mRNA is in the range 4.6\times 10^{-12}-1.5\times 10^{-11}m^2s^{-1}, in agreement with the theoretical predictions and experimental measurements for the diffusion of macromolecules in the cytoplasm. We show that the model based on the mRNA diffusion hypothesis is consistent with the known observational data, supporting the recent experimental findings of the gradient of \textit{bicoid} mRNA in \textit{Drosophila} [Spirov et al. (2009) Development 136:605-614]. |
1608.03425 | Haiguang Wen | Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao,
Zhongming Liu | Neural Encoding and Decoding with Deep Learning for Dynamic Natural
Vision | 27 pages, 10 figures, 1 table | Cerebral Cortex. 2017 pp.1-25 | 10.1093/cercor/bhx268 | null | q-bio.NC q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Convolutional neural network (CNN) driven by image recognition has been shown
to be able to explain cortical responses to static pictures at ventral-stream
areas. Here, we further showed that such CNN could reliably predict and decode
functional magnetic resonance imaging data from humans watching natural movies,
despite its lack of any mechanism to account for temporal dynamics or feedback
processing. Using separate data, encoding and decoding models were developed
and evaluated for describing the bi-directional relationships be-tween the CNN
and the brain. Through the encoding models, the CNN-predicted areas covered not
only the ventral stream, but also the dorsal stream, albe-it to a lesser
degree; single-voxel response was visualized as the specific pixel pattern that
drove the response, revealing the distinct representation of individual
cortical location; cortical activation was synthesized from natural images with
high-throughput to map category representation, con-trast, and selectivity.
Through the decoding models, fMRI signals were directly decoded to estimate the
feature representations in both visual and semantic spaces, for direct visual
reconstruction and seman-tic categorization, respectively. These results
cor-roborate, generalize, and extend previous findings, and highlight the value
of using deep learning, as an all-in-one model of the visual cortex, to
understand and decode natural vision.
| [
{
"created": "Thu, 11 Aug 2016 11:51:21 GMT",
"version": "v1"
},
{
"created": "Tue, 14 Nov 2017 17:35:51 GMT",
"version": "v2"
}
] | 2017-11-15 | [
[
"Wen",
"Haiguang",
""
],
[
"Shi",
"Junxing",
""
],
[
"Zhang",
"Yizhen",
""
],
[
"Lu",
"Kun-Han",
""
],
[
"Cao",
"Jiayue",
""
],
[
"Liu",
"Zhongming",
""
]
] | Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-stream areas. Here, we further showed that such CNN could reliably predict and decode functional magnetic resonance imaging data from humans watching natural movies, despite its lack of any mechanism to account for temporal dynamics or feedback processing. Using separate data, encoding and decoding models were developed and evaluated for describing the bi-directional relationships be-tween the CNN and the brain. Through the encoding models, the CNN-predicted areas covered not only the ventral stream, but also the dorsal stream, albe-it to a lesser degree; single-voxel response was visualized as the specific pixel pattern that drove the response, revealing the distinct representation of individual cortical location; cortical activation was synthesized from natural images with high-throughput to map category representation, con-trast, and selectivity. Through the decoding models, fMRI signals were directly decoded to estimate the feature representations in both visual and semantic spaces, for direct visual reconstruction and seman-tic categorization, respectively. These results cor-roborate, generalize, and extend previous findings, and highlight the value of using deep learning, as an all-in-one model of the visual cortex, to understand and decode natural vision. |
2401.13023 | Antje Keppler | Peter Bajcsy, Sreenivas Bhattiprolu, Katy Boerner, Beth A Cimini, Lucy
Collinson, Jan Ellenberg, Reto Fiolka, Maryellen Giger, Wojtek Goscinski,
Matthew Hartley, Nathan Hotaling, Rick Horwitz, Florian Jug, Anna Kreshuk,
Emma Lundberg, Aastha Mathur, Kedar Narayan, Shuichi Onami, Anne L. Plant,
Fred Prior, Jason Swedlow, Adam Taylor, and Antje Keppler | Enabling Global Image Data Sharing in the Life Sciences | This manuscript (arXiv:2401.13023) is published with a closely
related companion entitled, Harmonizing the Generation and Pre-publication
Stewardship of FAIR Image Data, which can be found at the following link,
arXiv:2401.13022 | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Coordinated collaboration is essential to realize the added value of and
infrastructure requirements for global image data sharing in the life sciences.
In this White Paper, we take a first step at presenting some of the most common
use cases as well as critical/emerging use cases of (including the use of
artificial intelligence for) biological and medical image data, which would
benefit tremendously from better frameworks for sharing (including technical,
resourcing, legal, and ethical aspects). In the second half of this paper, we
paint an ideal world scenario for how global image data sharing could work and
benefit all life sciences and beyond. As this is still a long way off, we
conclude by suggesting several concrete measures directed toward our
institutions, existing imaging communities and data initiatives, and national
funders, as well as publishers. Our vision is that within the next ten years,
most researchers in the world will be able to make their datasets openly
available and use quality image data of interest to them for their research and
benefit. This paper is published in parallel with a companion White Paper
entitled Harmonizing the Generation and Pre-publication Stewardship of FAIR
Image Data, which addresses challenges and opportunities related to producing
well-documented and high-quality image data that is ready to be shared. The
driving goal is to address remaining challenges and democratize access to
everyday practices and tools for a spectrum of biomedical researchers,
regardless of their expertise, access to resources, and geographical location.
| [
{
"created": "Tue, 23 Jan 2024 18:47:52 GMT",
"version": "v1"
},
{
"created": "Wed, 31 Jan 2024 15:55:19 GMT",
"version": "v2"
},
{
"created": "Fri, 2 Feb 2024 09:45:11 GMT",
"version": "v3"
},
{
"created": "Fri, 9 Aug 2024 06:09:57 GMT",
"version": "v4"
}
] | 2024-08-12 | [
[
"Bajcsy",
"Peter",
""
],
[
"Bhattiprolu",
"Sreenivas",
""
],
[
"Boerner",
"Katy",
""
],
[
"Cimini",
"Beth A",
""
],
[
"Collinson",
"Lucy",
""
],
[
"Ellenberg",
"Jan",
""
],
[
"Fiolka",
"Reto",
""
],
[
... | Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we take a first step at presenting some of the most common use cases as well as critical/emerging use cases of (including the use of artificial intelligence for) biological and medical image data, which would benefit tremendously from better frameworks for sharing (including technical, resourcing, legal, and ethical aspects). In the second half of this paper, we paint an ideal world scenario for how global image data sharing could work and benefit all life sciences and beyond. As this is still a long way off, we conclude by suggesting several concrete measures directed toward our institutions, existing imaging communities and data initiatives, and national funders, as well as publishers. Our vision is that within the next ten years, most researchers in the world will be able to make their datasets openly available and use quality image data of interest to them for their research and benefit. This paper is published in parallel with a companion White Paper entitled Harmonizing the Generation and Pre-publication Stewardship of FAIR Image Data, which addresses challenges and opportunities related to producing well-documented and high-quality image data that is ready to be shared. The driving goal is to address remaining challenges and democratize access to everyday practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location. |
2109.04105 | Anna Paola Muntoni | Anna Paola Muntoni, Andrea Pagnani, Martin Weigt and Francesco Zamponi | adabmDCA: Adaptive Boltzmann machine learning for biological sequences | null | BMC Bioinformatics 22, 528 (2021) | 10.1186/s12859-021-04441-9 | null | q-bio.QM cond-mat.dis-nn q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Boltzmann machines are energy-based models that have been shown to provide an
accurate statistical description of domains of evolutionary-related protein and
RNA families. They are parametrized in terms of local biases accounting for
residue conservation, and pairwise terms to model epistatic coevolution between
residues. From the model parameters, it is possible to extract an accurate
prediction of the three-dimensional contact map of the target domain. More
recently, the accuracy of these models has been also assessed in terms of their
ability in predicting mutational effects and generating in silico functional
sequences. Our adaptive implementation of Boltzmann machine learning, adabmDCA,
can be generally applied to both protein and RNA families and accomplishes
several learning set-ups, depending on the complexity of the input data and on
the user requirements. The code is fully available at
https://github.com/anna-pa-m/adabmDCA. As an example, we have performed the
learning of three Boltzmann machines modeling the Kunitz and Beta-lactamase2
protein domains and TPP-riboswitch RNA domain. The models learned by adabmDCA
are comparable to those obtained by state-of-the-art techniques for this task,
in terms of the quality of the inferred contact map as well as of the
synthetically generated sequences. In addition, the code implements both
equilibrium and out-of-equilibrium learning, which allows for an accurate and
lossless training when the equilibrium one is prohibitive in terms of
computational time, and allows for pruning irrelevant parameters using an
information-based criterion.
| [
{
"created": "Thu, 9 Sep 2021 08:58:25 GMT",
"version": "v1"
},
{
"created": "Tue, 2 Nov 2021 08:24:05 GMT",
"version": "v2"
}
] | 2021-11-03 | [
[
"Muntoni",
"Anna Paola",
""
],
[
"Pagnani",
"Andrea",
""
],
[
"Weigt",
"Martin",
""
],
[
"Zamponi",
"Francesco",
""
]
] | Boltzmann machines are energy-based models that have been shown to provide an accurate statistical description of domains of evolutionary-related protein and RNA families. They are parametrized in terms of local biases accounting for residue conservation, and pairwise terms to model epistatic coevolution between residues. From the model parameters, it is possible to extract an accurate prediction of the three-dimensional contact map of the target domain. More recently, the accuracy of these models has been also assessed in terms of their ability in predicting mutational effects and generating in silico functional sequences. Our adaptive implementation of Boltzmann machine learning, adabmDCA, can be generally applied to both protein and RNA families and accomplishes several learning set-ups, depending on the complexity of the input data and on the user requirements. The code is fully available at https://github.com/anna-pa-m/adabmDCA. As an example, we have performed the learning of three Boltzmann machines modeling the Kunitz and Beta-lactamase2 protein domains and TPP-riboswitch RNA domain. The models learned by adabmDCA are comparable to those obtained by state-of-the-art techniques for this task, in terms of the quality of the inferred contact map as well as of the synthetically generated sequences. In addition, the code implements both equilibrium and out-of-equilibrium learning, which allows for an accurate and lossless training when the equilibrium one is prohibitive in terms of computational time, and allows for pruning irrelevant parameters using an information-based criterion. |
q-bio/0507036 | Tom Michoel | Tom Michoel, Yves Van de Peer | A helicoidal transfer matrix model for inhomogeneous DNA melting | v3: Matlab toolbox included with source file; article unchanged, 12
pages, 11 figures, RevTeX | Phys. Rev. E 73, 011908 (2006) | 10.1103/PhysRevE.73.011908 | null | q-bio.BM q-bio.QM | null | An inhomogeneous helicoidal nearest-neighbor model with continuous degrees of
freedom is shown to predict the same DNA melting properties as traditional
long-range Ising models, for free DNA molecules in solution, as well as
superhelically stressed DNA with a fixed linking number constraint. Without
loss of accuracy, the continuous degrees of freedom can be discretized using a
minimal number of discretization points, yielding an effective transfer matrix
model of modest dimension (d=36). The resulting algorithms to compute DNA
melting profiles are both simple and efficient.
| [
{
"created": "Mon, 25 Jul 2005 14:14:14 GMT",
"version": "v1"
},
{
"created": "Tue, 8 Nov 2005 10:06:10 GMT",
"version": "v2"
},
{
"created": "Fri, 13 Oct 2006 08:05:41 GMT",
"version": "v3"
}
] | 2007-05-23 | [
[
"Michoel",
"Tom",
""
],
[
"Van de Peer",
"Yves",
""
]
] | An inhomogeneous helicoidal nearest-neighbor model with continuous degrees of freedom is shown to predict the same DNA melting properties as traditional long-range Ising models, for free DNA molecules in solution, as well as superhelically stressed DNA with a fixed linking number constraint. Without loss of accuracy, the continuous degrees of freedom can be discretized using a minimal number of discretization points, yielding an effective transfer matrix model of modest dimension (d=36). The resulting algorithms to compute DNA melting profiles are both simple and efficient. |
2306.06407 | Jacek Mi\c{e}kisz | Javad Mohamadichamgavi and Jacek Mi\c{e}kisz | Effect of the degree of an initial mutant in Moran processes in
structured populations | 10 pages, 11 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | We study the effect of the mutant's degree on the fixation probability,
extinction and fixation times, in the Moran process on Erd\"{o}s-R\'{e}nyi and
Barab\'{a}si-Albert graphs. We performed stochastic simulations and use
mean-field type approximations to obtain analytical formulas. We showed that
the initial placement of a mutant has a significant impact on the fixation
probability and extinction time, while it has no effect on the fixation time.
In both types of graphs, an increase in the degree of a initial mutant results
in a decreased fixation probability and a shorter time to extinction.
| [
{
"created": "Sat, 10 Jun 2023 10:42:13 GMT",
"version": "v1"
}
] | 2023-06-13 | [
[
"Mohamadichamgavi",
"Javad",
""
],
[
"Miȩkisz",
"Jacek",
""
]
] | We study the effect of the mutant's degree on the fixation probability, extinction and fixation times, in the Moran process on Erd\"{o}s-R\'{e}nyi and Barab\'{a}si-Albert graphs. We performed stochastic simulations and use mean-field type approximations to obtain analytical formulas. We showed that the initial placement of a mutant has a significant impact on the fixation probability and extinction time, while it has no effect on the fixation time. In both types of graphs, an increase in the degree of a initial mutant results in a decreased fixation probability and a shorter time to extinction. |
2005.05606 | Gianni De Fabritiis | Alejandro Varela-Rial, Maciej Majewski, Alberto Cuzzolin, Gerard
Mart\'inez-Rosell and Gianni De Fabritiis | SkeleDock: A Web Application for Scaffold Docking in PlayMolecule | null | null | null | null | q-bio.QM q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | SkeleDock is a scaffold docking algorithm which uses the structure of a
protein-ligand complex as a template to model the binding mode of a chemically
similar system. This algorithm was evaluated in the D3R Grand Challenge 4 pose
prediction challenge, where it achieved competitive performance. Furthermore,
we show that, if crystallized fragments of the target ligand are available,
SkeleDock can outperform rDock docking software at predicting the binding mode.
This article also addresses the capacity of this algorithm to model macrocycles
and deal with scaffold hopping. SkeleDock can be accessed at
https://playmolecule.org/SkeleDock/.
| [
{
"created": "Tue, 12 May 2020 08:24:52 GMT",
"version": "v1"
}
] | 2020-05-13 | [
[
"Varela-Rial",
"Alejandro",
""
],
[
"Majewski",
"Maciej",
""
],
[
"Cuzzolin",
"Alberto",
""
],
[
"Martínez-Rosell",
"Gerard",
""
],
[
"De Fabritiis",
"Gianni",
""
]
] | SkeleDock is a scaffold docking algorithm which uses the structure of a protein-ligand complex as a template to model the binding mode of a chemically similar system. This algorithm was evaluated in the D3R Grand Challenge 4 pose prediction challenge, where it achieved competitive performance. Furthermore, we show that, if crystallized fragments of the target ligand are available, SkeleDock can outperform rDock docking software at predicting the binding mode. This article also addresses the capacity of this algorithm to model macrocycles and deal with scaffold hopping. SkeleDock can be accessed at https://playmolecule.org/SkeleDock/. |
2209.13014 | Yang Zhang | Yang Zhang, Gengmo Zhou, Zhewei Wei, Hongteng Xu | Predicting Protein-Ligand Binding Affinity via Joint Global-Local
Interaction Modeling | null | null | null | null | q-bio.BM cs.AI cs.LG q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The prediction of protein-ligand binding affinity is of great significance
for discovering lead compounds in drug research. Facing this challenging task,
most existing prediction methods rely on the topological and/or spatial
structure of molecules and the local interactions while ignoring the
multi-level inter-molecular interactions between proteins and ligands, which
often lead to sub-optimal performance. To solve this issue, we propose a novel
global-local interaction (GLI) framework to predict protein-ligand binding
affinity. In particular, our GLI framework considers the inter-molecular
interactions between proteins and ligands, which involve not only the
high-energy short-range interactions between closed atoms but also the
low-energy long-range interactions between non-bonded atoms. For each pair of
protein and ligand, our GLI embeds the long-range interactions globally and
aggregates local short-range interactions, respectively. Such a joint
global-local interaction modeling strategy helps to improve prediction
accuracy, and the whole framework is compatible with various neural
network-based modules. Experiments demonstrate that our GLI framework
outperforms state-of-the-art methods with simple neural network architectures
and moderate computational costs.
| [
{
"created": "Sun, 18 Sep 2022 10:17:05 GMT",
"version": "v1"
}
] | 2022-09-28 | [
[
"Zhang",
"Yang",
""
],
[
"Zhou",
"Gengmo",
""
],
[
"Wei",
"Zhewei",
""
],
[
"Xu",
"Hongteng",
""
]
] | The prediction of protein-ligand binding affinity is of great significance for discovering lead compounds in drug research. Facing this challenging task, most existing prediction methods rely on the topological and/or spatial structure of molecules and the local interactions while ignoring the multi-level inter-molecular interactions between proteins and ligands, which often lead to sub-optimal performance. To solve this issue, we propose a novel global-local interaction (GLI) framework to predict protein-ligand binding affinity. In particular, our GLI framework considers the inter-molecular interactions between proteins and ligands, which involve not only the high-energy short-range interactions between closed atoms but also the low-energy long-range interactions between non-bonded atoms. For each pair of protein and ligand, our GLI embeds the long-range interactions globally and aggregates local short-range interactions, respectively. Such a joint global-local interaction modeling strategy helps to improve prediction accuracy, and the whole framework is compatible with various neural network-based modules. Experiments demonstrate that our GLI framework outperforms state-of-the-art methods with simple neural network architectures and moderate computational costs. |
q-bio/0506017 | Bruce Hoeneisen | B. Hoeneisen and G. Trueba | Built to evolve | 10 pages, 4 figures | null | null | null | q-bio.PE | null | We study the probabilities of evolution based on random mutations and natural
selection. We conclude that evolution to multicellular eukaryots, or even
prokaryots, is unlikely to be the result of only random mutations. Complex
organisms have evolved through several mechanisms besides random mutations,
namely DNA recombination, adaptive mutations, and acquisition of foreign DNA.
We conclude that all living organisms, in addition to being self-organizing and
reproducing (autopoyetic), have built-in mechanisms of evolution, some of which
respond in very specific ways to environmental stress.
| [
{
"created": "Wed, 15 Jun 2005 19:47:26 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Hoeneisen",
"B.",
""
],
[
"Trueba",
"G.",
""
]
] | We study the probabilities of evolution based on random mutations and natural selection. We conclude that evolution to multicellular eukaryots, or even prokaryots, is unlikely to be the result of only random mutations. Complex organisms have evolved through several mechanisms besides random mutations, namely DNA recombination, adaptive mutations, and acquisition of foreign DNA. We conclude that all living organisms, in addition to being self-organizing and reproducing (autopoyetic), have built-in mechanisms of evolution, some of which respond in very specific ways to environmental stress. |
2301.05321 | Philip Greulich | Cristina Parigini, Philip Greulich | Homeostatic regulation of renewing tissue cell populations via crowding
control | null | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | To maintain renewing epithelial tissues in a healthy, homeostatic state,
(stem) cell divisions and differentiation need to be tightly regulated.
Mechanisms of homeostatic control often rely on crowding control: cells are
able to sense the cell density in their environment (via various molecular and
mechanosensing pathways) and respond by adjusting division, differentiation,
and cell state transitions appropriately. Here we determine, via a
mathematically rigorous framework, which general conditions for the crowding
feedback regulation (i) must be minimally met, and (ii) are sufficient, to
allow the maintenance of homeostasis in renewing tissues. We show that those
conditions naturally allow for a degree of robustness toward disruption of
regulation. Furthermore, intrinsic to this feedback regulation is that stem
cell identity is established collectively by the cell population, not by
individual cells, which implies the possibility of `quasi-dedifferentiation',
in which cells committed to differentiation may reacquire stem cell properties
upon depletion of the stem cell pool. These findings can guide future
experimental campaigns to identify specific crowding feedback mechanisms.
| [
{
"created": "Thu, 12 Jan 2023 22:26:24 GMT",
"version": "v1"
}
] | 2023-01-16 | [
[
"Parigini",
"Cristina",
""
],
[
"Greulich",
"Philip",
""
]
] | To maintain renewing epithelial tissues in a healthy, homeostatic state, (stem) cell divisions and differentiation need to be tightly regulated. Mechanisms of homeostatic control often rely on crowding control: cells are able to sense the cell density in their environment (via various molecular and mechanosensing pathways) and respond by adjusting division, differentiation, and cell state transitions appropriately. Here we determine, via a mathematically rigorous framework, which general conditions for the crowding feedback regulation (i) must be minimally met, and (ii) are sufficient, to allow the maintenance of homeostasis in renewing tissues. We show that those conditions naturally allow for a degree of robustness toward disruption of regulation. Furthermore, intrinsic to this feedback regulation is that stem cell identity is established collectively by the cell population, not by individual cells, which implies the possibility of `quasi-dedifferentiation', in which cells committed to differentiation may reacquire stem cell properties upon depletion of the stem cell pool. These findings can guide future experimental campaigns to identify specific crowding feedback mechanisms. |
1202.2491 | Henry Tuckwell | Henry C. Tuckwell, J\"urgen Jost | Analysis of inverse stochastic resonance and the long-term firing of
Hodgkin-Huxley neurons with Gaussian white noise | 27 pages, 16 figures | null | 10.1016/j.physa.2012.06.019 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In previous articles we have investigated the firing properties of the
standard Hodgkin-Huxley (HH) systems of ordinary and partial differential
equations in response to input currents composed of a drift (mean) and additive
Gaussian white noise. For certain values of the mean current, as the noise
amplitude increased from zero, the firing rate exhibited a minimum and this
phenomenon was called inverse stochastic resonance (ISR). Here we analyse the
underlying transitions from a stable equilibrium point to the limit cycle and
vice-versa. Focusing on the case of a mean input current density $\mu=6.8$ at
which repetitive firing occurs and ISR had been found to be pronounced, some of
the properties of the corresponding stable equilibrium point are found. A
linearized approximation around this point has oscillatory solutions from whose
maxima spikes tend to occur. A one dimensional diffusion is also constructed
for small noise based on the correlations between the pairs of HH variables and
the small magnitudes of the fluctuations in two of them.
Properties of the basin of attraction of the limit cycle (spike) are
investigated heuristically and also the nature of distribution of spikes at
very small noise corresponding to trajectories which do not ever enter the
basin of attraction of the equilibrium point. Long term trials of duration
500000 ms are carried out for values of the noise parameter $\sigma$ from 0 to
2.0, with results appearing in Section 3. The graph of mean spike count versus
$\sigma$ is divided into 4 regions $R_1,...,R_4,$ where $R_3$ contains the
minimum associated with ISR.
| [
{
"created": "Sun, 12 Feb 2012 06:22:12 GMT",
"version": "v1"
}
] | 2015-06-04 | [
[
"Tuckwell",
"Henry C.",
""
],
[
"Jost",
"Jürgen",
""
]
] | In previous articles we have investigated the firing properties of the standard Hodgkin-Huxley (HH) systems of ordinary and partial differential equations in response to input currents composed of a drift (mean) and additive Gaussian white noise. For certain values of the mean current, as the noise amplitude increased from zero, the firing rate exhibited a minimum and this phenomenon was called inverse stochastic resonance (ISR). Here we analyse the underlying transitions from a stable equilibrium point to the limit cycle and vice-versa. Focusing on the case of a mean input current density $\mu=6.8$ at which repetitive firing occurs and ISR had been found to be pronounced, some of the properties of the corresponding stable equilibrium point are found. A linearized approximation around this point has oscillatory solutions from whose maxima spikes tend to occur. A one dimensional diffusion is also constructed for small noise based on the correlations between the pairs of HH variables and the small magnitudes of the fluctuations in two of them. Properties of the basin of attraction of the limit cycle (spike) are investigated heuristically and also the nature of distribution of spikes at very small noise corresponding to trajectories which do not ever enter the basin of attraction of the equilibrium point. Long term trials of duration 500000 ms are carried out for values of the noise parameter $\sigma$ from 0 to 2.0, with results appearing in Section 3. The graph of mean spike count versus $\sigma$ is divided into 4 regions $R_1,...,R_4,$ where $R_3$ contains the minimum associated with ISR. |
1510.00386 | Kamran Kaveh | Venkata. S. K. Manem, Kamran Kaveh, Mohammad Kohandel, Siv
Sivaloganathan | Modelling Invasion Dynamics with Spatial Random-Fitness due to
Microenvironment | 23 pages, 11 figures. PLoS One (2015) | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Numerous experimental studies have demonstrated that the microenvironment is
a key regulator influencing the proliferative and migrative potentials of
species. Spatial and temporal disturbances lead to adverse and hazardous
microenvironments for cellular systems that is reflected in the phenotypic
heterogeneity within the system. In this paper, we study the effect of
microenvironment on the invasive capability of species, or mutants, on
structured grids under the influence of site-dependent random proliferation in
addition to a migration potential. We discuss both continuous and discrete
fitness distributions. Our results suggest that the invasion probability is
negatively correlated with the variance of fitness distribution of mutants (for
both advantageous and neutral mutants) in the absence of migration of both
types of cells. A similar behaviour is observed even in the presence of a
random fitness distribution of host cells in the system with neutral fitness
rate. In the case of a bimodal distribution, we observe zero invasion
probability until the system reaches a (specific) proportion of advantageous
phenotypes. Also, we find that the migrative potential amplifies the invasion
probability as the variance of fitness of mutants increases in the system,
which is the exact opposite in the absence of migration. Our computational
framework captures the harsh microenvironmental conditions through quenched
random fitness distributions and migration of cells, and our analysis shows
that they play an important role in the invasion dynamics of several biological
systems such as bacterial micro-habitats, epithelial dysplasia, and metastasis.
We believe that our results may lead to more experimental studies, which can in
turn provide further insights into the role and impact of heterogeneous
environments on invasion dynamics.
| [
{
"created": "Thu, 24 Sep 2015 23:49:53 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Oct 2015 22:23:54 GMT",
"version": "v2"
}
] | 2015-10-07 | [
[
"Manem",
"Venkata. S. K.",
""
],
[
"Kaveh",
"Kamran",
""
],
[
"Kohandel",
"Mohammad",
""
],
[
"Sivaloganathan",
"Siv",
""
]
] | Numerous experimental studies have demonstrated that the microenvironment is a key regulator influencing the proliferative and migrative potentials of species. Spatial and temporal disturbances lead to adverse and hazardous microenvironments for cellular systems that is reflected in the phenotypic heterogeneity within the system. In this paper, we study the effect of microenvironment on the invasive capability of species, or mutants, on structured grids under the influence of site-dependent random proliferation in addition to a migration potential. We discuss both continuous and discrete fitness distributions. Our results suggest that the invasion probability is negatively correlated with the variance of fitness distribution of mutants (for both advantageous and neutral mutants) in the absence of migration of both types of cells. A similar behaviour is observed even in the presence of a random fitness distribution of host cells in the system with neutral fitness rate. In the case of a bimodal distribution, we observe zero invasion probability until the system reaches a (specific) proportion of advantageous phenotypes. Also, we find that the migrative potential amplifies the invasion probability as the variance of fitness of mutants increases in the system, which is the exact opposite in the absence of migration. Our computational framework captures the harsh microenvironmental conditions through quenched random fitness distributions and migration of cells, and our analysis shows that they play an important role in the invasion dynamics of several biological systems such as bacterial micro-habitats, epithelial dysplasia, and metastasis. We believe that our results may lead to more experimental studies, which can in turn provide further insights into the role and impact of heterogeneous environments on invasion dynamics. |
1710.00349 | Nathalie Balaban Q | Noga Mosheiff, Bruno M.C. Martins, Sivan Pearl-Mizrahi, Alexander
Gruenberger, Stefan Helfrich, Irina Mihalcescu, Dietrich Kohlheyer, James
C.W. Locke, Leon Glass and Nathalie Q. Balaban | Correlations of single-cell division times with and without periodic
forcing | null | Phys. Rev. X 8, 021035 (2018) | 10.1103/PhysRevX.8.021035 | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Periodic forcing of nonlinear oscillators leads to a large number of dynamic
behaviors. The coupling of the cell-cycle to the circadian clock provides a
biological realization of such forcing. Using high throughput single-cell
microscopy, we have studied the correlations between cell cycle duration in
discrete lineages of several different organisms including those with known
coupling to a circadian clock and those without known coupling to a circadian
clock. Correlations between cell cycles duration in discrete lineages observed
in the organisms with a circadian clock cannot be explained by a simple
statistical model but are consistent with predictions of a biologically
plausible two dimensional nonlinear map. Surprisingly, the nonlinear map is
equivalent to a classic nonlinear map called the fattened Arnold map. The model
predicts that circadian coupling may increase cell to cell variability in a
clonal population of cells. In agreement with this prediction, deletion of the
circadian clock reduces variability. Our results show that simple correlations
can identify systems under periodic forcing and that studies of nonlinear
coupling of biological oscillators provide insight into basic cellular
processes of growth.
| [
{
"created": "Sun, 1 Oct 2017 13:49:30 GMT",
"version": "v1"
}
] | 2018-05-16 | [
[
"Mosheiff",
"Noga",
""
],
[
"Martins",
"Bruno M. C.",
""
],
[
"Pearl-Mizrahi",
"Sivan",
""
],
[
"Gruenberger",
"Alexander",
""
],
[
"Helfrich",
"Stefan",
""
],
[
"Mihalcescu",
"Irina",
""
],
[
"Kohlheyer",
"Die... | Periodic forcing of nonlinear oscillators leads to a large number of dynamic behaviors. The coupling of the cell-cycle to the circadian clock provides a biological realization of such forcing. Using high throughput single-cell microscopy, we have studied the correlations between cell cycle duration in discrete lineages of several different organisms including those with known coupling to a circadian clock and those without known coupling to a circadian clock. Correlations between cell cycles duration in discrete lineages observed in the organisms with a circadian clock cannot be explained by a simple statistical model but are consistent with predictions of a biologically plausible two dimensional nonlinear map. Surprisingly, the nonlinear map is equivalent to a classic nonlinear map called the fattened Arnold map. The model predicts that circadian coupling may increase cell to cell variability in a clonal population of cells. In agreement with this prediction, deletion of the circadian clock reduces variability. Our results show that simple correlations can identify systems under periodic forcing and that studies of nonlinear coupling of biological oscillators provide insight into basic cellular processes of growth. |
2310.19614 | Julian Rossbroich | Julian Rossbroich, Friedemann Zenke | Dis-inhibitory neuronal circuits can control the sign of synaptic
plasticity | Accepted at NeurIPS 2023; fixed error in Figure S2 | null | null | null | q-bio.NC cs.LG cs.NE | http://creativecommons.org/licenses/by/4.0/ | How neuronal circuits achieve credit assignment remains a central unsolved
question in systems neuroscience. Various studies have suggested plausible
solutions for back-propagating error signals through multi-layer networks.
These purely functionally motivated models assume distinct neuronal
compartments to represent local error signals that determine the sign of
synaptic plasticity. However, this explicit error modulation is inconsistent
with phenomenological plasticity models in which the sign depends primarily on
postsynaptic activity. Here we show how a plausible microcircuit model and
Hebbian learning rule derived within an adaptive control theory framework can
resolve this discrepancy. Assuming errors are encoded in top-down
dis-inhibitory synaptic afferents, we show that error-modulated learning
emerges naturally at the circuit level when recurrent inhibition explicitly
influences Hebbian plasticity. The same learning rule accounts for
experimentally observed plasticity in the absence of inhibition and performs
comparably to back-propagation of error (BP) on several non-linearly separable
benchmarks. Our findings bridge the gap between functional and experimentally
observed plasticity rules and make concrete predictions on inhibitory
modulation of excitatory plasticity.
| [
{
"created": "Mon, 30 Oct 2023 15:06:19 GMT",
"version": "v1"
},
{
"created": "Mon, 11 Dec 2023 18:04:14 GMT",
"version": "v2"
}
] | 2023-12-12 | [
[
"Rossbroich",
"Julian",
""
],
[
"Zenke",
"Friedemann",
""
]
] | How neuronal circuits achieve credit assignment remains a central unsolved question in systems neuroscience. Various studies have suggested plausible solutions for back-propagating error signals through multi-layer networks. These purely functionally motivated models assume distinct neuronal compartments to represent local error signals that determine the sign of synaptic plasticity. However, this explicit error modulation is inconsistent with phenomenological plasticity models in which the sign depends primarily on postsynaptic activity. Here we show how a plausible microcircuit model and Hebbian learning rule derived within an adaptive control theory framework can resolve this discrepancy. Assuming errors are encoded in top-down dis-inhibitory synaptic afferents, we show that error-modulated learning emerges naturally at the circuit level when recurrent inhibition explicitly influences Hebbian plasticity. The same learning rule accounts for experimentally observed plasticity in the absence of inhibition and performs comparably to back-propagation of error (BP) on several non-linearly separable benchmarks. Our findings bridge the gap between functional and experimentally observed plasticity rules and make concrete predictions on inhibitory modulation of excitatory plasticity. |
1410.6153 | Benjamin Ivorra Prof. | Benjamin Ivorra, Di\`ene Ngom, \'Angel Manuel Ramos | Be-CoDiS: A mathematical model to predict the risk of human diseases
spread between countries. Validation and application to the 2014-15 Ebola
Virus Disease epidemic | 34 pages; Version 5; Work in Progress | null | null | null | q-bio.PE cs.CE math.DS physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ebola virus disease is a lethal human and primate disease that currently
requires a particular attention from the international health authorities due
to important outbreaks in some Western African countries and isolated cases in
the United Kingdom, the USA and Spain. Regarding the emergency of this
situation, there is a need of development of decision tools, such as
mathematical models, to assist the authorities to focus their efforts in
important factors to eradicate Ebola. In this work, we propose a novel
deterministic spatial-temporal model, called Be-CoDiS (Between-Countries
Disease Spread), to study the evolution of human diseases within and between
countries. The main interesting characteristics of Be-CoDiS are the
consideration of the movement of people between countries, the control measure
effects and the use of time dependent coefficients adapted to each country.
First, we focus on the mathematical formulation of each component of the model
and explain how its parameters and inputs are obtained. Then, in order to
validate our approach, we consider two numerical experiments regarding the
2014-15 Ebola epidemic. The first one studies the ability of the model in
predicting the EVD evolution between countries starting from the index cases in
Guinea in December 2013. The second one consists of forecasting the evolution
of the epidemic by using some recent data. The results obtained with Be-CoDiS
are compared to real data and other models outputs found in the literature.
Finally, a brief parameter sensitivity analysis is done. A free Matlab version
of Be-CoDiS is available at: http://www.mat.ucm.es/momat/software.htm
| [
{
"created": "Wed, 22 Oct 2014 19:52:56 GMT",
"version": "v1"
},
{
"created": "Sat, 8 Nov 2014 22:06:05 GMT",
"version": "v2"
},
{
"created": "Thu, 20 Nov 2014 12:58:15 GMT",
"version": "v3"
},
{
"created": "Mon, 15 Dec 2014 16:33:56 GMT",
"version": "v4"
},
{
"cr... | 2015-05-13 | [
[
"Ivorra",
"Benjamin",
""
],
[
"Ngom",
"Diène",
""
],
[
"Ramos",
"Ángel Manuel",
""
]
] | Ebola virus disease is a lethal human and primate disease that currently requires a particular attention from the international health authorities due to important outbreaks in some Western African countries and isolated cases in the United Kingdom, the USA and Spain. Regarding the emergency of this situation, there is a need of development of decision tools, such as mathematical models, to assist the authorities to focus their efforts in important factors to eradicate Ebola. In this work, we propose a novel deterministic spatial-temporal model, called Be-CoDiS (Between-Countries Disease Spread), to study the evolution of human diseases within and between countries. The main interesting characteristics of Be-CoDiS are the consideration of the movement of people between countries, the control measure effects and the use of time dependent coefficients adapted to each country. First, we focus on the mathematical formulation of each component of the model and explain how its parameters and inputs are obtained. Then, in order to validate our approach, we consider two numerical experiments regarding the 2014-15 Ebola epidemic. The first one studies the ability of the model in predicting the EVD evolution between countries starting from the index cases in Guinea in December 2013. The second one consists of forecasting the evolution of the epidemic by using some recent data. The results obtained with Be-CoDiS are compared to real data and other models outputs found in the literature. Finally, a brief parameter sensitivity analysis is done. A free Matlab version of Be-CoDiS is available at: http://www.mat.ucm.es/momat/software.htm |
1806.10764 | John Baez | John C. Baez, Blake S. Pollard, Jonathan Lorand and Maru Sarazola | Biochemical Coupling Through Emergent Conservation Laws | 13 pages | null | null | null | q-bio.MN physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bazhin has analyzed ATP coupling in terms of quasiequilibrium states where
fast reactions have reached an approximate steady state while slow reactions
have not yet reached equilibrium. After an expository introduction to the
relevant aspects of reaction network theory, we review his work and explain the
role of emergent conserved quantities in coupling. These are quantities, left
unchanged by fast reactions, whose conservation forces exergonic processes such
as ATP hydrolysis to drive desired endergonic processes.
| [
{
"created": "Thu, 28 Jun 2018 04:16:03 GMT",
"version": "v1"
}
] | 2018-06-29 | [
[
"Baez",
"John C.",
""
],
[
"Pollard",
"Blake S.",
""
],
[
"Lorand",
"Jonathan",
""
],
[
"Sarazola",
"Maru",
""
]
] | Bazhin has analyzed ATP coupling in terms of quasiequilibrium states where fast reactions have reached an approximate steady state while slow reactions have not yet reached equilibrium. After an expository introduction to the relevant aspects of reaction network theory, we review his work and explain the role of emergent conserved quantities in coupling. These are quantities, left unchanged by fast reactions, whose conservation forces exergonic processes such as ATP hydrolysis to drive desired endergonic processes. |
1006.4911 | Fabio Pichierri | Fabio Pichierri | A quantum mechanical analysis of the light-harvesting complex 2 from
purple photosynthetic bacteria. Insights into the electrostatic effects of
transmembrane helices | 14 pages, 7 figures | BioSystems 103 (2011) 132-137 | 10.1016/j.biosystems.2010.08.006 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We perform a quantum mechanical study of the peptides that are part of the
LH2 complex from Rhodopseudomonas acidophila, a non-sulfur purple bacteria that
has the ability of producing chemical energy from photosynthesis. The
electronic structure calculations indicate that the transmembrane helices of
these peptides are characterized by dipole moments with a magnitude of ~150 D.
When the full nonamer assembly made of eighteen peptides is considered, then a
macrodipole of magnitude 704 D is built up from the vector sum of each monomer
dipole. The macrodipole is oriented normal to the membrane plane and with the
positive tip toward the cytoplasm thereby indicating that the electronic charge
of the protein scaffold is polarized toward the periplasm. The results obtained
here suggest that the asymmetric charge distribution of the protein scaffold
contributes an anisotropic electrostatic environment which differentiates the
absorption properties of the bacteriochlorophyll pigments, B800 and B850,
embedded in the LH2 complex.
| [
{
"created": "Fri, 25 Jun 2010 05:24:08 GMT",
"version": "v1"
}
] | 2011-02-03 | [
[
"Pichierri",
"Fabio",
""
]
] | We perform a quantum mechanical study of the peptides that are part of the LH2 complex from Rhodopseudomonas acidophila, a non-sulfur purple bacteria that has the ability of producing chemical energy from photosynthesis. The electronic structure calculations indicate that the transmembrane helices of these peptides are characterized by dipole moments with a magnitude of ~150 D. When the full nonamer assembly made of eighteen peptides is considered, then a macrodipole of magnitude 704 D is built up from the vector sum of each monomer dipole. The macrodipole is oriented normal to the membrane plane and with the positive tip toward the cytoplasm thereby indicating that the electronic charge of the protein scaffold is polarized toward the periplasm. The results obtained here suggest that the asymmetric charge distribution of the protein scaffold contributes an anisotropic electrostatic environment which differentiates the absorption properties of the bacteriochlorophyll pigments, B800 and B850, embedded in the LH2 complex. |
1206.5092 | Andrea De Martino | Daniele De Martino, Matteo Figliuzzi, Andrea De Martino, Enzo Marinari | A scalable algorithm to explore the Gibbs energy landscape of
genome-scale metabolic networks | 11 pages, 6 figures, 1 table; for associated supporting material see
http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002562 | PLoS Comput Biol 8(6): e1002562 (2012) | 10.1371/journal.pcbi.1002562 | null | q-bio.MN cond-mat.dis-nn physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The integration of various types of genomic data into predictive models of
biological networks is one of the main challenges currently faced by
computational biology. Constraint-based models in particular play a key role in
the attempt to obtain a quantitative understanding of cellular metabolism at
genome scale. In essence, their goal is to frame the metabolic capabilities of
an organism based on minimal assumptions that describe the steady states of the
underlying reaction network via suitable stoichiometric constraints,
specifically mass balance and energy balance (i.e. thermodynamic feasibility).
The implementation of these requirements to generate viable configurations of
reaction fluxes and/or to test given flux profiles for thermodynamic
feasibility can however prove to be computationally intensive. We propose here
a fast and scalable stoichiometry-based method to explore the Gibbs energy
landscape of a biochemical network at steady state. The method is applied to
the problem of reconstructing the Gibbs energy landscape underlying metabolic
activity in the human red blood cell, and to that of identifying and removing
thermodynamically infeasible reaction cycles in the Escherichia coli metabolic
network (iAF1260). In the former case, we produce consistent predictions for
chemical potentials (or log-concentrations) of intracellular metabolites; in
the latter, we identify a restricted set of loops (23 in total) in the
periplasmic and cytoplasmic core as the origin of thermodynamic infeasibility
in a large sample ($10^6$) of flux configurations generated randomly and
compatibly with the prior information available on reaction reversibility.
| [
{
"created": "Fri, 22 Jun 2012 09:47:44 GMT",
"version": "v1"
}
] | 2012-06-25 | [
[
"De Martino",
"Daniele",
""
],
[
"Figliuzzi",
"Matteo",
""
],
[
"De Martino",
"Andrea",
""
],
[
"Marinari",
"Enzo",
""
]
] | The integration of various types of genomic data into predictive models of biological networks is one of the main challenges currently faced by computational biology. Constraint-based models in particular play a key role in the attempt to obtain a quantitative understanding of cellular metabolism at genome scale. In essence, their goal is to frame the metabolic capabilities of an organism based on minimal assumptions that describe the steady states of the underlying reaction network via suitable stoichiometric constraints, specifically mass balance and energy balance (i.e. thermodynamic feasibility). The implementation of these requirements to generate viable configurations of reaction fluxes and/or to test given flux profiles for thermodynamic feasibility can however prove to be computationally intensive. We propose here a fast and scalable stoichiometry-based method to explore the Gibbs energy landscape of a biochemical network at steady state. The method is applied to the problem of reconstructing the Gibbs energy landscape underlying metabolic activity in the human red blood cell, and to that of identifying and removing thermodynamically infeasible reaction cycles in the Escherichia coli metabolic network (iAF1260). In the former case, we produce consistent predictions for chemical potentials (or log-concentrations) of intracellular metabolites; in the latter, we identify a restricted set of loops (23 in total) in the periplasmic and cytoplasmic core as the origin of thermodynamic infeasibility in a large sample ($10^6$) of flux configurations generated randomly and compatibly with the prior information available on reaction reversibility. |
2101.02097 | Victor Riquelme | Pedro Gajardo and Victor Riquelme | Inmate population models with nonhomogeneous sentence lengths and their
effects in an epidemiological model | 24 pages, 3 figures, 7 tables | null | null | null | q-bio.PE math.DS q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we develop an inmate population model with a sentencing length
structure. The sentence length structure of new inmates represents the problem
data and can usually be estimated from the histograms corresponding to the
conviction times that are sentenced in a given population. We obtain a
transport equation, typically known as the McKendrick equation, the homogenous
version of which is included in population models with age structures. Using
this equation, we compute the inmate population and entry/exit rates in
equilibrium, which are the values to consider in the design of a penitentiary
system. With data from the Chilean penitentiary system, we illustrate how to
perform these computations. In classifying the inmate population into two
groups of sentence lengths (short and long), we incorporate the SIS
(susceptible-infected-susceptible) epidemiological model, which considers the
entry of infective individuals. We show that a failure to consider the
structure of the sentence lengths -- as is common in epidemiological models
developed for inmate populations -- for prevalences of new inmates below a
certain threshold induces an underestimation of the prevalence in the prison
population at steady state. The threshold depends on the basic reproduction
number associated with the nonstructured SIS model with no entry of new
inmates. We illustrate our findings with analytical and numerical examples for
different distributions of sentencing lengths.
| [
{
"created": "Wed, 6 Jan 2021 15:40:15 GMT",
"version": "v1"
},
{
"created": "Fri, 29 Jan 2021 15:26:01 GMT",
"version": "v2"
}
] | 2021-02-01 | [
[
"Gajardo",
"Pedro",
""
],
[
"Riquelme",
"Victor",
""
]
] | In this work, we develop an inmate population model with a sentencing length structure. The sentence length structure of new inmates represents the problem data and can usually be estimated from the histograms corresponding to the conviction times that are sentenced in a given population. We obtain a transport equation, typically known as the McKendrick equation, the homogenous version of which is included in population models with age structures. Using this equation, we compute the inmate population and entry/exit rates in equilibrium, which are the values to consider in the design of a penitentiary system. With data from the Chilean penitentiary system, we illustrate how to perform these computations. In classifying the inmate population into two groups of sentence lengths (short and long), we incorporate the SIS (susceptible-infected-susceptible) epidemiological model, which considers the entry of infective individuals. We show that a failure to consider the structure of the sentence lengths -- as is common in epidemiological models developed for inmate populations -- for prevalences of new inmates below a certain threshold induces an underestimation of the prevalence in the prison population at steady state. The threshold depends on the basic reproduction number associated with the nonstructured SIS model with no entry of new inmates. We illustrate our findings with analytical and numerical examples for different distributions of sentencing lengths. |
1810.04069 | Ines Abdeljaoued Tej PhD | Ines Abdeljaoued-Tej and Alia BenKahla and Ghassen Haddad and Annick
Valibouze | A linear algorithm for computing Polynomial Dynamical System | 11 pages, 3 figures | null | null | null | q-bio.MN math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computation biology helps to understand all processes in organisms from
interaction of molecules to complex functions of whole organs. Therefore, there
is a need for mathematical methods and models that deliver logical explanations
in a reasonable time. For the last few years there has been a growing interest
in biological theory connected to finite fields: the algebraic modeling tools
used up to now are based on Gr\"obner bases or Boolean group. Let $n$ variables
representing gene products, changing over the time on $p$ values. A Polynomial
dynamical system (PDS) is a function which has several components, each one is
a polynom with $n$ variables and coefficient in the finite field $Z/pZ$ that
model the evolution of gene products. We propose herein a method using
algebraic separators, which are special polynomials abundantly studied in
effective Galois theory. This approach avoids heavy calculations and provides a
first Polynomial model in linear time.
| [
{
"created": "Mon, 8 Oct 2018 11:57:28 GMT",
"version": "v1"
}
] | 2018-10-10 | [
[
"Abdeljaoued-Tej",
"Ines",
""
],
[
"BenKahla",
"Alia",
""
],
[
"Haddad",
"Ghassen",
""
],
[
"Valibouze",
"Annick",
""
]
] | Computation biology helps to understand all processes in organisms from interaction of molecules to complex functions of whole organs. Therefore, there is a need for mathematical methods and models that deliver logical explanations in a reasonable time. For the last few years there has been a growing interest in biological theory connected to finite fields: the algebraic modeling tools used up to now are based on Gr\"obner bases or Boolean group. Let $n$ variables representing gene products, changing over the time on $p$ values. A Polynomial dynamical system (PDS) is a function which has several components, each one is a polynom with $n$ variables and coefficient in the finite field $Z/pZ$ that model the evolution of gene products. We propose herein a method using algebraic separators, which are special polynomials abundantly studied in effective Galois theory. This approach avoids heavy calculations and provides a first Polynomial model in linear time. |
2306.06065 | Alexander Gower | Alexander H. Gower, Konstantin Korovin, Daniel Brunns{\aa}ker,
Ievgeniia A. Tiukova and Ross D. King | LGEM$^\text{+}$: a first-order logic framework for automated improvement
of metabolic network models through abduction | 15 pages, one figure, two tables, two algorithms | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Scientific discovery in biology is difficult due to the complexity of the
systems involved and the expense of obtaining high quality experimental data.
Automated techniques are a promising way to make scientific discoveries at the
scale and pace required to model large biological systems. A key problem for
21st century biology is to build a computational model of the eukaryotic cell.
The yeast Saccharomyces cerevisiae is the best understood eukaryote, and
genome-scale metabolic models (GEMs) are rich sources of background knowledge
that we can use as a basis for automated inference and investigation.
We present LGEM+, a system for automated abductive improvement of GEMs
consisting of: a compartmentalised first-order logic framework for describing
biochemical pathways (using curated GEMs as the expert knowledge source); and a
two-stage hypothesis abduction procedure.
We demonstrate that deductive inference on logical theories created using
LGEM+, using the automated theorem prover iProver, can predict growth/no-growth
of S. cerevisiae strains in minimal media. LGEM+ proposed 2094 unique candidate
hypotheses for model improvement. We assess the value of the generated
hypotheses using two criteria: (a) genome-wide single-gene essentiality
prediction, and (b) constraint of flux-balance analysis (FBA) simulations. For
(b) we developed an algorithm to integrate FBA with the logic model. We rank
and filter the hypotheses using these assessments. We intend to test these
hypotheses using the robot scientist Genesis, which is based around chemostat
cultivation and high-throughput metabolomics.
| [
{
"created": "Fri, 9 Jun 2023 17:39:44 GMT",
"version": "v1"
}
] | 2023-06-12 | [
[
"Gower",
"Alexander H.",
""
],
[
"Korovin",
"Konstantin",
""
],
[
"Brunnsåker",
"Daniel",
""
],
[
"Tiukova",
"Ievgeniia A.",
""
],
[
"King",
"Ross D.",
""
]
] | Scientific discovery in biology is difficult due to the complexity of the systems involved and the expense of obtaining high quality experimental data. Automated techniques are a promising way to make scientific discoveries at the scale and pace required to model large biological systems. A key problem for 21st century biology is to build a computational model of the eukaryotic cell. The yeast Saccharomyces cerevisiae is the best understood eukaryote, and genome-scale metabolic models (GEMs) are rich sources of background knowledge that we can use as a basis for automated inference and investigation. We present LGEM+, a system for automated abductive improvement of GEMs consisting of: a compartmentalised first-order logic framework for describing biochemical pathways (using curated GEMs as the expert knowledge source); and a two-stage hypothesis abduction procedure. We demonstrate that deductive inference on logical theories created using LGEM+, using the automated theorem prover iProver, can predict growth/no-growth of S. cerevisiae strains in minimal media. LGEM+ proposed 2094 unique candidate hypotheses for model improvement. We assess the value of the generated hypotheses using two criteria: (a) genome-wide single-gene essentiality prediction, and (b) constraint of flux-balance analysis (FBA) simulations. For (b) we developed an algorithm to integrate FBA with the logic model. We rank and filter the hypotheses using these assessments. We intend to test these hypotheses using the robot scientist Genesis, which is based around chemostat cultivation and high-throughput metabolomics. |
1511.08230 | Thierry Emonet | Nicholas W Frankel, William Pontius, Yann S Dufour, Junjiajia Long,
Luis Hernandez- Nunez, Thierry Emonet | Adaptability of non-genetic diversity in bacterial chemotaxis | Journal link: http://elifesciences.org/content/3/e03526 | eLife 3, e03526 (2014) | 10.7554/eLife.03526.001 | null | q-bio.PE q-bio.CB q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | Bacterial chemotaxis systems are as diverse as the environments that bacteria
inhabit, but how much environmental variation can cells tolerate with a single
system? Diversification of a single chemotaxis system could serve as an
alternative, or even evolutionary stepping-stone, to switching between multiple
systems. We hypothesized that mutations in gene regulation could lead to
heritable control of chemotactic diversity. By simulating foraging and
colonization of E. coli using a single-cell chemotaxis model, we found that
different environments selected for different behaviors. The resulting
trade-offs show that populations facing diverse environments would ideally
diversify behaviors when time for navigation is limited. We show that
advantageous diversity can arise from changes in the distribution of protein
levels among individuals, which could occur through mutations in gene
regulation. We propose experiments to test our prediction that chemotactic
diversity in a clonal population could be a selectable trait that enables
adaptation to environmental variability.
| [
{
"created": "Wed, 25 Nov 2015 21:19:23 GMT",
"version": "v1"
}
] | 2015-11-30 | [
[
"Frankel",
"Nicholas W",
""
],
[
"Pontius",
"William",
""
],
[
"Dufour",
"Yann S",
""
],
[
"Long",
"Junjiajia",
""
],
[
"Nunez",
"Luis Hernandez-",
""
],
[
"Emonet",
"Thierry",
""
]
] | Bacterial chemotaxis systems are as diverse as the environments that bacteria inhabit, but how much environmental variation can cells tolerate with a single system? Diversification of a single chemotaxis system could serve as an alternative, or even evolutionary stepping-stone, to switching between multiple systems. We hypothesized that mutations in gene regulation could lead to heritable control of chemotactic diversity. By simulating foraging and colonization of E. coli using a single-cell chemotaxis model, we found that different environments selected for different behaviors. The resulting trade-offs show that populations facing diverse environments would ideally diversify behaviors when time for navigation is limited. We show that advantageous diversity can arise from changes in the distribution of protein levels among individuals, which could occur through mutations in gene regulation. We propose experiments to test our prediction that chemotactic diversity in a clonal population could be a selectable trait that enables adaptation to environmental variability. |
1606.00632 | Kenshi Sakai | Nina Sviridova and Kenshi Sakai | Noise Induced Synchronization on Collective Dynamics of Citrus
Production | 6 pages, 9 figures | Journal of the Japanese Society of Agricultural Machinery and food
Engineers,Volume78(3),221-226,2016 | null | null | q-bio.PE nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is very common to observe nonlinear features in agricultural and
ecological systems. For example, in tree crop production, alternate bearing is
well known phenomena caused by nonlinear dynamics. Production of single tree of
Citrus Unshiu trees is recognized to be driven by a mechanistic process modeled
with the so-called "resource budget model", which demonstrates phenomenon of
alternate bearing. However, the term of alternative bearing is used not only
for an individual tree's production but also for total production of a large
sized population of trees. In this paper, we developed noise induced uncoupled
dynamics model for population alternate bearing based on Isagi's resource
budget model. Based on numerical experiments with the developed model,
theoretical possibility of a substantial alternate bearing effect even in a
national market was proposed.
| [
{
"created": "Thu, 2 Jun 2016 11:34:15 GMT",
"version": "v1"
}
] | 2016-06-03 | [
[
"Sviridova",
"Nina",
""
],
[
"Sakai",
"Kenshi",
""
]
] | It is very common to observe nonlinear features in agricultural and ecological systems. For example, in tree crop production, alternate bearing is well known phenomena caused by nonlinear dynamics. Production of single tree of Citrus Unshiu trees is recognized to be driven by a mechanistic process modeled with the so-called "resource budget model", which demonstrates phenomenon of alternate bearing. However, the term of alternative bearing is used not only for an individual tree's production but also for total production of a large sized population of trees. In this paper, we developed noise induced uncoupled dynamics model for population alternate bearing based on Isagi's resource budget model. Based on numerical experiments with the developed model, theoretical possibility of a substantial alternate bearing effect even in a national market was proposed. |
2308.11309 | Maur\'icio Moreira-Soares | Maur\'icio Moreira-Soares, Eduardo Mossmann, Rui D. M. Travasso and
Jos\'e Rafael Bordin | TrajPy: empowering feature engineering for trajectory analysis across
domains | 4 pages, 1 figure | null | null | null | q-bio.QM physics.bio-ph physics.data-an | http://creativecommons.org/licenses/by/4.0/ | Trajectories, sequentially measured quantities that form a path, are an
important presence in many different fields, from hadronic beams in physics to
electrocardiograms in medicine. Trajectory anal-ysis requires the
quantification and classification of curves either using statistical
descriptors or physics-based features. To date, there is no extensive and
user-friendly package for trajectory anal-ysis available, despite its
importance and potential application across domains. We developed a free
open-source python package named TrajPy as a complementary tool to empower
trajectory analysis. The package showcases a friendly graphic user interface
and provides a set of physical descriptors that help characterizing these
intricate structures. In combina-tion with image analysis, it was already
successfully applied to the study of mitochondrial motility in neuroblastoma
cell lines and to the analysis of in silico models for cell migration. The
TrajPy package was developed in Python 3 and released under the GNU GPL-3
license. Easy installation is available through PyPi and the development source
code can be found in the repository https://github.com/ocbe-uio/TrajPy/. The
package release is automatically archived under the DOI 10.5281/zenodo.3656044.
| [
{
"created": "Tue, 22 Aug 2023 09:37:48 GMT",
"version": "v1"
}
] | 2023-08-23 | [
[
"Moreira-Soares",
"Maurício",
""
],
[
"Mossmann",
"Eduardo",
""
],
[
"Travasso",
"Rui D. M.",
""
],
[
"Bordin",
"José Rafael",
""
]
] | Trajectories, sequentially measured quantities that form a path, are an important presence in many different fields, from hadronic beams in physics to electrocardiograms in medicine. Trajectory anal-ysis requires the quantification and classification of curves either using statistical descriptors or physics-based features. To date, there is no extensive and user-friendly package for trajectory anal-ysis available, despite its importance and potential application across domains. We developed a free open-source python package named TrajPy as a complementary tool to empower trajectory analysis. The package showcases a friendly graphic user interface and provides a set of physical descriptors that help characterizing these intricate structures. In combina-tion with image analysis, it was already successfully applied to the study of mitochondrial motility in neuroblastoma cell lines and to the analysis of in silico models for cell migration. The TrajPy package was developed in Python 3 and released under the GNU GPL-3 license. Easy installation is available through PyPi and the development source code can be found in the repository https://github.com/ocbe-uio/TrajPy/. The package release is automatically archived under the DOI 10.5281/zenodo.3656044. |
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