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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0802.1056
|
Miloje M. Rakocevic
|
Miloje M. Rakocevic
|
Genetic Code: Four-Codon and Non-Four-Codon Degeneracy
|
The 18 Pages, 16 Tables, 1 Figure and 5 Surveys. The paper represents
a step within further investigations of harmonic structure of the genetic
code (Rakocevic, 2004)
| null | null | null |
q-bio.BM q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work it is shown that 20 canonical amino acids (AAs) within genetic
code appear to be a whole system with strict distinction in Genetic Code Table
(GCT) into some different quantums: 20, 23, 61 amino acid molecules. These
molecules distinction is followed by specific balanced atom number and/or
nucleon number distinctions within those molecules. In this second version two
appendices are added; also a new version of Periodic system of numbers, whose
first verson is given in arXiv:1107.1998 [q-bio.OT].
|
[
{
"created": "Thu, 7 Feb 2008 20:00:52 GMT",
"version": "v1"
},
{
"created": "Fri, 20 Sep 2019 12:23:53 GMT",
"version": "v2"
}
] |
2019-09-23
|
[
[
"Rakocevic",
"Miloje M.",
""
]
] |
In this work it is shown that 20 canonical amino acids (AAs) within genetic code appear to be a whole system with strict distinction in Genetic Code Table (GCT) into some different quantums: 20, 23, 61 amino acid molecules. These molecules distinction is followed by specific balanced atom number and/or nucleon number distinctions within those molecules. In this second version two appendices are added; also a new version of Periodic system of numbers, whose first verson is given in arXiv:1107.1998 [q-bio.OT].
|
2203.14870
|
Nelson Niemeyer
|
Nelson Niemeyer, Jan-Hendrik Schleimer, Susanne Schreiber
|
Biophysical models of intrinsic homeostasis: Firing rates and beyond
| null |
Current Opinion in Neurobiology, Volume 70, October 2021, Pages
81-88
|
10.1016/j.conb.2021.07.011
| null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In view of ever-changing conditions both in the external world and in
intrinsic brain states, maintaining the robustness of computations poses a
challenge, adequate solutions to which we are only beginning to understand. At
the level of cell-intrinsic properties, biophysical models of neurons permit
one to identify relevant physiological substrates that can serve as regulators
of neuronal excitability and to test how feedback loops can stabilize crucial
variables such as long-term calcium levels and firing rates. Mathematical
theory has also revealed a rich set of complementary computational properties
arising from distinct cellular dynamics and even shaping processing at the
network level. Here, we provide an overview over recently explored homeostatic
mechanisms derived from biophysical models and hypothesize how multiple
dynamical characteristics of cells, including their intrinsic neuronal
excitability classes, can be stably controlled.
|
[
{
"created": "Mon, 28 Mar 2022 16:25:20 GMT",
"version": "v1"
}
] |
2022-03-29
|
[
[
"Niemeyer",
"Nelson",
""
],
[
"Schleimer",
"Jan-Hendrik",
""
],
[
"Schreiber",
"Susanne",
""
]
] |
In view of ever-changing conditions both in the external world and in intrinsic brain states, maintaining the robustness of computations poses a challenge, adequate solutions to which we are only beginning to understand. At the level of cell-intrinsic properties, biophysical models of neurons permit one to identify relevant physiological substrates that can serve as regulators of neuronal excitability and to test how feedback loops can stabilize crucial variables such as long-term calcium levels and firing rates. Mathematical theory has also revealed a rich set of complementary computational properties arising from distinct cellular dynamics and even shaping processing at the network level. Here, we provide an overview over recently explored homeostatic mechanisms derived from biophysical models and hypothesize how multiple dynamical characteristics of cells, including their intrinsic neuronal excitability classes, can be stably controlled.
|
q-bio/0510007
|
Michael Lachmann
|
Carl T. Bergstrom and Michael Lachmann
|
The fitness value of information
|
18 pages, 3 figures (included in text). Submitted to PNAS
| null | null | null |
q-bio.PE cs.IT math.IT q-bio.NC
| null |
Biologists measure information in different ways. Neurobiologists and
researchers in bioinformatics often measure information using
information-theoretic measures such as Shannon's entropy or mutual information.
Behavioral biologists and evolutionary ecologists more commonly use
decision-theoretic measures, such the value of information, which assess the
worth of information to a decision maker. Here we show that these two kinds of
measures are intimately related in the context of biological evolution. We
present a simple model of evolution in an uncertain environment, and calculate
the increase in Darwinian fitness that is made possible by information about
the environmental state. This fitness increase -- the fitness value of
information -- is a composite of both Shannon's mutual information and the
decision-theoretic value of information. Furthermore, we show that in certain
cases the fitness value of responding to a cue is exactly equal to the mutual
information between the cue and the environment. In general the Shannon entropy
of the environment, which seemingly fails to take anything about organismal
fitness into account, nonetheless imposes an upper bound on the fitness value
of information.
|
[
{
"created": "Mon, 3 Oct 2005 22:24:48 GMT",
"version": "v1"
}
] |
2007-07-13
|
[
[
"Bergstrom",
"Carl T.",
""
],
[
"Lachmann",
"Michael",
""
]
] |
Biologists measure information in different ways. Neurobiologists and researchers in bioinformatics often measure information using information-theoretic measures such as Shannon's entropy or mutual information. Behavioral biologists and evolutionary ecologists more commonly use decision-theoretic measures, such the value of information, which assess the worth of information to a decision maker. Here we show that these two kinds of measures are intimately related in the context of biological evolution. We present a simple model of evolution in an uncertain environment, and calculate the increase in Darwinian fitness that is made possible by information about the environmental state. This fitness increase -- the fitness value of information -- is a composite of both Shannon's mutual information and the decision-theoretic value of information. Furthermore, we show that in certain cases the fitness value of responding to a cue is exactly equal to the mutual information between the cue and the environment. In general the Shannon entropy of the environment, which seemingly fails to take anything about organismal fitness into account, nonetheless imposes an upper bound on the fitness value of information.
|
2203.02568
|
Arnaud Delorme
|
Arnaud Delorme, Dung Truong, Choonhan Youn, Subha Sivagnanam, Kenneth
Yoshimoto, Russell A. Poldrack, Amit Majumdar, Scott Makeig
|
NEMAR: An open access data, tools, and compute resource operating on
NeuroElectroMagnetic data
| null | null | null | null |
q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
To take advantage of recent and ongoing advances in large-scale computational
methods, and to preserve the scientific data created by publicly funded
research projects, data archives must be created as well as standards for
specifying, identifying, and annotating deposited data. The OpenNeuro.org
archive, begun as a repository for magnetic resonance imaging (MRI) data, is
such an archive. We present a gateway to OpenNeuro for human electrophysiology
data (BIDS-formatted EEG and MEG, as well as intracranial data). The NEMAR
gateway allows users to visualize electrophysiological data, including
time-domain and frequency-domain dynamics time locked to sets of experimental
events recorded using BIDS- and HED-formatted data annotation. In addition,
NEMAR allows users to process archived EEG data on the XSEDE high-performance
resources at SDSC in conjunction with the Neuroscience Gateway (nsgportal.org),
a freely available and easy to use portal to leverage high-performance
computing resources for neuroscience research.
|
[
{
"created": "Fri, 4 Mar 2022 20:47:26 GMT",
"version": "v1"
}
] |
2022-03-08
|
[
[
"Delorme",
"Arnaud",
""
],
[
"Truong",
"Dung",
""
],
[
"Youn",
"Choonhan",
""
],
[
"Sivagnanam",
"Subha",
""
],
[
"Yoshimoto",
"Kenneth",
""
],
[
"Poldrack",
"Russell A.",
""
],
[
"Majumdar",
"Amit",
""
],
[
"Makeig",
"Scott",
""
]
] |
To take advantage of recent and ongoing advances in large-scale computational methods, and to preserve the scientific data created by publicly funded research projects, data archives must be created as well as standards for specifying, identifying, and annotating deposited data. The OpenNeuro.org archive, begun as a repository for magnetic resonance imaging (MRI) data, is such an archive. We present a gateway to OpenNeuro for human electrophysiology data (BIDS-formatted EEG and MEG, as well as intracranial data). The NEMAR gateway allows users to visualize electrophysiological data, including time-domain and frequency-domain dynamics time locked to sets of experimental events recorded using BIDS- and HED-formatted data annotation. In addition, NEMAR allows users to process archived EEG data on the XSEDE high-performance resources at SDSC in conjunction with the Neuroscience Gateway (nsgportal.org), a freely available and easy to use portal to leverage high-performance computing resources for neuroscience research.
|
1507.00041
|
Luca Ferretti
|
Luca Ferretti, Daniel Weinreich, Benjamin Schmiegelt, Atsushi
Yamauchi, Yutaka Kobayashi, Fumio Tajima and Guillaume Achaz
|
Epistasis and constraints in fitness landscapes
|
37 pages, 8 figures
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Genotypic fitness landscapes are constructed by assessing the fitness of all
possible combinations of a given number of mutations. In the last years,
several experimental fitness landscapes have been completely resolved. As
fitness landscapes are high-dimensional, their characterization relies on
simple measures of their structure, which can be used as statistics in
empirical applications. Here we propose two new sets of measures that
explicitly capture two relevant features of fitness landscapes: epistasis and
constraints. The first set contains new measures for epistasis based on the
correlation of fitness effects of mutations. They have a natural
interpretation, capture well the interaction between mutations, can be obtained
analytically for most landscape models and can therefore be used to
discriminate between different models. The second set contains measures of
evolutionary constraints based on "chains" of forced mutations along
fitness-increasing paths. Some of these measures are non-monotonic in the
amount of epistatic interactions, but have instead a maximum for intermediate
values. We further characterize the relationships of these measures to the ones
that were previous proposed (e.g. number of peaks, roughness/slope, fraction of
non-additive components, etc). Finally, we show how these measures can help
uncovering the amount and the nature of epistatic interactions in two
experimental landscapes.
|
[
{
"created": "Tue, 30 Jun 2015 21:26:42 GMT",
"version": "v1"
}
] |
2015-07-02
|
[
[
"Ferretti",
"Luca",
""
],
[
"Weinreich",
"Daniel",
""
],
[
"Schmiegelt",
"Benjamin",
""
],
[
"Yamauchi",
"Atsushi",
""
],
[
"Kobayashi",
"Yutaka",
""
],
[
"Tajima",
"Fumio",
""
],
[
"Achaz",
"Guillaume",
""
]
] |
Genotypic fitness landscapes are constructed by assessing the fitness of all possible combinations of a given number of mutations. In the last years, several experimental fitness landscapes have been completely resolved. As fitness landscapes are high-dimensional, their characterization relies on simple measures of their structure, which can be used as statistics in empirical applications. Here we propose two new sets of measures that explicitly capture two relevant features of fitness landscapes: epistasis and constraints. The first set contains new measures for epistasis based on the correlation of fitness effects of mutations. They have a natural interpretation, capture well the interaction between mutations, can be obtained analytically for most landscape models and can therefore be used to discriminate between different models. The second set contains measures of evolutionary constraints based on "chains" of forced mutations along fitness-increasing paths. Some of these measures are non-monotonic in the amount of epistatic interactions, but have instead a maximum for intermediate values. We further characterize the relationships of these measures to the ones that were previous proposed (e.g. number of peaks, roughness/slope, fraction of non-additive components, etc). Finally, we show how these measures can help uncovering the amount and the nature of epistatic interactions in two experimental landscapes.
|
1507.02940
|
Fabio Chalub
|
Paulo Doutor, Paula Rodrigues, Maria do C\'eu Soares, Fabio A. C. C.
Chalub
|
Optimal Vaccination Strategies and Rational Behaviour in Seasonal
Epidemics
|
27 pages, 4 figures
| null |
10.1007/s00285-016-0997-1
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider a SIR model with temporary immunity and time dependent
transmission rate. We assume time dependent vaccination which confers the same
immunity as natural infection. We study two types of vaccination strategies: i)
optimal vaccination, in the sense that it minimizes the effort of vaccination
in the set of vaccination strategies for which, for any sufficiently small
perturbation of the disease free state, the number of infectious individuals is
monotonically decreasing; ii) Nash-equilibria strategies where all individuals
simultaneously minimize the joint risk of vaccination versus the risk of the
disease. The former case corresponds to an optimal solution for mandatory
vaccinations, while the second correspond to the equilibrium to be expected if
vaccination is fully voluntary. We are able to show the existence of both an
optimal and Nash strategies in a general setting. In general, these strategies
will not be functions but Radon measures. For specific forms of the
transmission rate, we provide explicit formulas for the optimal and the Nash
vaccination strategies.
|
[
{
"created": "Fri, 10 Jul 2015 15:35:42 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Mar 2016 17:30:43 GMT",
"version": "v2"
}
] |
2016-04-06
|
[
[
"Doutor",
"Paulo",
""
],
[
"Rodrigues",
"Paula",
""
],
[
"Soares",
"Maria do Céu",
""
],
[
"Chalub",
"Fabio A. C. C.",
""
]
] |
We consider a SIR model with temporary immunity and time dependent transmission rate. We assume time dependent vaccination which confers the same immunity as natural infection. We study two types of vaccination strategies: i) optimal vaccination, in the sense that it minimizes the effort of vaccination in the set of vaccination strategies for which, for any sufficiently small perturbation of the disease free state, the number of infectious individuals is monotonically decreasing; ii) Nash-equilibria strategies where all individuals simultaneously minimize the joint risk of vaccination versus the risk of the disease. The former case corresponds to an optimal solution for mandatory vaccinations, while the second correspond to the equilibrium to be expected if vaccination is fully voluntary. We are able to show the existence of both an optimal and Nash strategies in a general setting. In general, these strategies will not be functions but Radon measures. For specific forms of the transmission rate, we provide explicit formulas for the optimal and the Nash vaccination strategies.
|
2405.18768
|
Divya Nori
|
Divya Nori, Wengong Jin
|
RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow
Matching
|
Accepted to ICML 2024
| null | null | null |
q-bio.BM cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The growing significance of RNA engineering in diverse biological
applications has spurred interest in developing AI methods for structure-based
RNA design. While diffusion models have excelled in protein design, adapting
them for RNA presents new challenges due to RNA's conformational flexibility
and the computational cost of fine-tuning large structure prediction models. To
this end, we propose RNAFlow, a flow matching model for protein-conditioned RNA
sequence-structure design. Its denoising network integrates an RNA inverse
folding model and a pre-trained RosettaFold2NA network for generation of RNA
sequences and structures. The integration of inverse folding in the structure
denoising process allows us to simplify training by fixing the structure
prediction network. We further enhance the inverse folding model by
conditioning it on inferred conformational ensembles to model dynamic RNA
conformations. Evaluation on protein-conditioned RNA structure and sequence
generation tasks demonstrates RNAFlow's advantage over existing RNA design
methods.
|
[
{
"created": "Wed, 29 May 2024 05:10:25 GMT",
"version": "v1"
},
{
"created": "Sun, 9 Jun 2024 16:13:02 GMT",
"version": "v2"
}
] |
2024-06-11
|
[
[
"Nori",
"Divya",
""
],
[
"Jin",
"Wengong",
""
]
] |
The growing significance of RNA engineering in diverse biological applications has spurred interest in developing AI methods for structure-based RNA design. While diffusion models have excelled in protein design, adapting them for RNA presents new challenges due to RNA's conformational flexibility and the computational cost of fine-tuning large structure prediction models. To this end, we propose RNAFlow, a flow matching model for protein-conditioned RNA sequence-structure design. Its denoising network integrates an RNA inverse folding model and a pre-trained RosettaFold2NA network for generation of RNA sequences and structures. The integration of inverse folding in the structure denoising process allows us to simplify training by fixing the structure prediction network. We further enhance the inverse folding model by conditioning it on inferred conformational ensembles to model dynamic RNA conformations. Evaluation on protein-conditioned RNA structure and sequence generation tasks demonstrates RNAFlow's advantage over existing RNA design methods.
|
2008.09927
|
Chang Sub Kim
|
Chang Sub Kim
|
Bayesian mechanics of perceptual inference and motor control in the
brain
|
28 pages, 7 figures
| null |
10.1007/s00422-021-00859-9
| null |
q-bio.NC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The free energy principle (FEP) in the neurosciences stipulates that all
viable agents induce and minimize informational free energy in the brain to fit
their environmental niche. In this study, we continue our effort to make the
FEP a more physically principled formalism by implementing free energy
minimization based on the principle of least action. We build a Bayesian
mechanics (BM) by casting the formulation reported in the earlier publication
(Kim 2018) to considering active inference beyond passive perception. The BM is
a neural implementation of variational Bayes under the FEP in continuous time.
The resulting BM is provided as an effective Hamilton's equation of motion and
subject to the control signal arising from the brain's prediction errors at the
proprioceptive level. To demonstrate the utility of our approach, we adopt a
simple agent-based model and present a concrete numerical illustration of the
brain performing recognition dynamics by integrating BM in neural phase space.
Furthermore, we recapitulate the major theoretical architectures in the FEP by
comparing our approach with the common state-space formulations.
|
[
{
"created": "Sat, 22 Aug 2020 23:20:37 GMT",
"version": "v1"
},
{
"created": "Sat, 14 Nov 2020 08:15:48 GMT",
"version": "v2"
},
{
"created": "Thu, 21 Jan 2021 23:52:45 GMT",
"version": "v3"
}
] |
2021-01-25
|
[
[
"Kim",
"Chang Sub",
""
]
] |
The free energy principle (FEP) in the neurosciences stipulates that all viable agents induce and minimize informational free energy in the brain to fit their environmental niche. In this study, we continue our effort to make the FEP a more physically principled formalism by implementing free energy minimization based on the principle of least action. We build a Bayesian mechanics (BM) by casting the formulation reported in the earlier publication (Kim 2018) to considering active inference beyond passive perception. The BM is a neural implementation of variational Bayes under the FEP in continuous time. The resulting BM is provided as an effective Hamilton's equation of motion and subject to the control signal arising from the brain's prediction errors at the proprioceptive level. To demonstrate the utility of our approach, we adopt a simple agent-based model and present a concrete numerical illustration of the brain performing recognition dynamics by integrating BM in neural phase space. Furthermore, we recapitulate the major theoretical architectures in the FEP by comparing our approach with the common state-space formulations.
|
2407.06295
|
Ramya Deshpande
|
Ramya Deshpande, Francesco Mottes, Ariana-Dalia Vlad, Michael P.
Brenner, Alma dal Co
|
Engineering morphogenesis of cell clusters with differentiable
programming
|
8 pages, 6 figures
| null | null | null |
q-bio.CB cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Understanding the rules underlying organismal development is a major unsolved
problem in biology. Each cell in a developing organism responds to signals in
its local environment by dividing, excreting, consuming, or reorganizing, yet
how these individual actions coordinate over a macroscopic number of cells to
grow complex structures with exquisite functionality is unknown. Here we use
recent advances in automatic differentiation to discover local interaction
rules and genetic networks that yield emergent, systems-level characteristics
in a model of development. We consider a growing tissue with cellular
interactions are mediated by morphogen diffusion, differential cell adhesion
and mechanical stress. Each cell has an internal genetic network that it uses
to make decisions based on its local environment. We show that one can
simultaneously learn parameters governing the cell interactions and the genetic
network for complex developmental scenarios, including the symmetry breaking of
an embryo from an initial cell, the creation of emergent chemical
gradients,homogenization of growth via mechanical stress, programmed growth
into a prespecified shape, and the ability to repair from damage. When combined
with recent experimental advances measuring spatio-temporal dynamics and gene
expression of cells in a growing tissue, the methodology outlined here offers a
promising path to unravelling the cellular basis of development.
|
[
{
"created": "Mon, 8 Jul 2024 18:05:11 GMT",
"version": "v1"
}
] |
2024-07-10
|
[
[
"Deshpande",
"Ramya",
""
],
[
"Mottes",
"Francesco",
""
],
[
"Vlad",
"Ariana-Dalia",
""
],
[
"Brenner",
"Michael P.",
""
],
[
"Co",
"Alma dal",
""
]
] |
Understanding the rules underlying organismal development is a major unsolved problem in biology. Each cell in a developing organism responds to signals in its local environment by dividing, excreting, consuming, or reorganizing, yet how these individual actions coordinate over a macroscopic number of cells to grow complex structures with exquisite functionality is unknown. Here we use recent advances in automatic differentiation to discover local interaction rules and genetic networks that yield emergent, systems-level characteristics in a model of development. We consider a growing tissue with cellular interactions are mediated by morphogen diffusion, differential cell adhesion and mechanical stress. Each cell has an internal genetic network that it uses to make decisions based on its local environment. We show that one can simultaneously learn parameters governing the cell interactions and the genetic network for complex developmental scenarios, including the symmetry breaking of an embryo from an initial cell, the creation of emergent chemical gradients,homogenization of growth via mechanical stress, programmed growth into a prespecified shape, and the ability to repair from damage. When combined with recent experimental advances measuring spatio-temporal dynamics and gene expression of cells in a growing tissue, the methodology outlined here offers a promising path to unravelling the cellular basis of development.
|
2206.08796
|
Alexandre Triay Bagur
|
Alexandre Triay Bagur, Darryl McClymont, Chloe Hutton, Andrea
Borghetto, Michael L Gyngell, Paul Aljabar, Matthew D Robson, Michael Brady,
Daniel P Bulte
|
Estimation of Field Inhomogeneity Map Following Magnitude-Based
Ambiguity-Resolved Water-Fat Separation
|
14 pages, 11 figures
| null | null | null |
q-bio.QM
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
PURPOSE: To extend magnitude-based PDFF (Proton Density Fat Fraction) and
$R_2^*$ mapping with resolved water-fat ambiguity to calculate field
inhomogeneity (field map) using the phase images.
THEORY: The estimation is formulated in matrix form, resolving the field map
in a least-squares sense. PDFF and $R_2^*$ from magnitude fitting may be
updated using the estimated field maps.
METHODS: The limits of quantification of our voxel-independent implementation
were assessed. Bland-Altman was used to compare PDFF and field maps from our
method against a reference complex-based method on 152 UK Biobank subjects (1.5
T Siemens). A separate acquisition (3 T Siemens) presenting field
inhomogeneities was also used.
RESULTS: The proposed field mapping was accurate beyond double the
complex-based limit range. High agreement was obtained between the proposed
method and the reference in UK Biobank (PDFF bias = -0.03 %, LoA (limits of
agreement) [-0.1,0.1] %; Field map bias = 0.06 Hz, LoA = [-0.2,0.3] Hz). Robust
field mapping was observed at 3 T, for inhomogeneities over 300 Hz including
rapid variation across edges.
CONCLUSION: Field mapping following magnitude-based water-fat separation with
resolved water-fat ambiguity was demonstrated in-vivo and showed potential at
high field.
|
[
{
"created": "Fri, 17 Jun 2022 14:18:40 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Aug 2022 16:10:34 GMT",
"version": "v2"
},
{
"created": "Thu, 1 Sep 2022 12:03:27 GMT",
"version": "v3"
},
{
"created": "Wed, 14 Sep 2022 18:50:06 GMT",
"version": "v4"
}
] |
2022-09-16
|
[
[
"Bagur",
"Alexandre Triay",
""
],
[
"McClymont",
"Darryl",
""
],
[
"Hutton",
"Chloe",
""
],
[
"Borghetto",
"Andrea",
""
],
[
"Gyngell",
"Michael L",
""
],
[
"Aljabar",
"Paul",
""
],
[
"Robson",
"Matthew D",
""
],
[
"Brady",
"Michael",
""
],
[
"Bulte",
"Daniel P",
""
]
] |
PURPOSE: To extend magnitude-based PDFF (Proton Density Fat Fraction) and $R_2^*$ mapping with resolved water-fat ambiguity to calculate field inhomogeneity (field map) using the phase images. THEORY: The estimation is formulated in matrix form, resolving the field map in a least-squares sense. PDFF and $R_2^*$ from magnitude fitting may be updated using the estimated field maps. METHODS: The limits of quantification of our voxel-independent implementation were assessed. Bland-Altman was used to compare PDFF and field maps from our method against a reference complex-based method on 152 UK Biobank subjects (1.5 T Siemens). A separate acquisition (3 T Siemens) presenting field inhomogeneities was also used. RESULTS: The proposed field mapping was accurate beyond double the complex-based limit range. High agreement was obtained between the proposed method and the reference in UK Biobank (PDFF bias = -0.03 %, LoA (limits of agreement) [-0.1,0.1] %; Field map bias = 0.06 Hz, LoA = [-0.2,0.3] Hz). Robust field mapping was observed at 3 T, for inhomogeneities over 300 Hz including rapid variation across edges. CONCLUSION: Field mapping following magnitude-based water-fat separation with resolved water-fat ambiguity was demonstrated in-vivo and showed potential at high field.
|
2010.02555
|
Jacob Moss
|
Jacob Moss, Pietro Li\'o
|
Gene Regulatory Network Inference with Latent Force Models
| null | null | null | null |
q-bio.MN cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Delays in protein synthesis cause a confounding effect when constructing Gene
Regulatory Networks (GRNs) from RNA-sequencing time-series data. Accurate GRNs
can be very insightful when modelling development, disease pathways, and drug
side-effects. We present a model which incorporates translation delays by
combining mechanistic equations and Bayesian approaches to fit to experimental
data. This enables greater biological interpretability, and the use of Gaussian
processes enables non-linear expressivity through kernels as well as naturally
accounting for biological variation.
|
[
{
"created": "Tue, 6 Oct 2020 09:03:34 GMT",
"version": "v1"
}
] |
2020-10-07
|
[
[
"Moss",
"Jacob",
""
],
[
"Lió",
"Pietro",
""
]
] |
Delays in protein synthesis cause a confounding effect when constructing Gene Regulatory Networks (GRNs) from RNA-sequencing time-series data. Accurate GRNs can be very insightful when modelling development, disease pathways, and drug side-effects. We present a model which incorporates translation delays by combining mechanistic equations and Bayesian approaches to fit to experimental data. This enables greater biological interpretability, and the use of Gaussian processes enables non-linear expressivity through kernels as well as naturally accounting for biological variation.
|
1507.06303
|
Eugene Rosenfeld
|
Eugene V. Rosenfeld
|
Bond rupture mechanism enables to explain in block asymmetry of
elaxation, force-velocity curve and the path of energy dissipation in muscle
|
in Russian, 11 pages
| null | null | null |
q-bio.SC physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Bond rupture mechanism enables to explain in block asymmetry of elaxation,
force-velocity curve and the path of energy dissipation in muscle
|
[
{
"created": "Wed, 22 Jul 2015 10:40:18 GMT",
"version": "v1"
}
] |
2015-07-24
|
[
[
"Rosenfeld",
"Eugene V.",
""
]
] |
Bond rupture mechanism enables to explain in block asymmetry of elaxation, force-velocity curve and the path of energy dissipation in muscle
|
2003.11859
|
Chiara De Luca
|
Bruno Golosio, Chiara De Luca, Cristiano Capone, Elena Pastorelli,
Giovanni Stegel, Gianmarco Tiddia, Giulia De Bonis and Pier Stanislao
Paolucci
|
Thalamo-cortical spiking model of incremental learning combining
perception, context and NREM-sleep-mediated noise-resilience
| null |
PLOS Computational Biology 17(6): e1009045 (2021)
|
10.1371/journal.pcbi.1009045
| null |
q-bio.NC cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The brain exhibits capabilities of fast incremental learning from few noisy
examples, as well as the ability to associate similar memories in
autonomously-created categories and to combine contextual hints with sensory
perceptions. Together with sleep, these mechanisms are thought to be key
components of many high-level cognitive functions. Yet, little is known about
the underlying processes and the specific roles of different brain states. In
this work, we exploited the combination of context and perception in a
thalamo-cortical model based on a soft winner-take-all circuit of excitatory
and inhibitory spiking neurons. After calibrating this model to express awake
and deep-sleep states with features comparable with biological measures, we
demonstrate the model capability of fast incremental learning from few
examples, its resilience when proposed with noisy perceptions and contextual
signals, and an improvement in visual classification after sleep due to induced
synaptic homeostasis and association of similar memories.
|
[
{
"created": "Thu, 26 Mar 2020 12:18:35 GMT",
"version": "v1"
},
{
"created": "Tue, 26 Jan 2021 07:42:16 GMT",
"version": "v2"
},
{
"created": "Fri, 19 Mar 2021 10:30:45 GMT",
"version": "v3"
},
{
"created": "Thu, 5 Aug 2021 07:15:18 GMT",
"version": "v4"
}
] |
2021-08-31
|
[
[
"Golosio",
"Bruno",
""
],
[
"De Luca",
"Chiara",
""
],
[
"Capone",
"Cristiano",
""
],
[
"Pastorelli",
"Elena",
""
],
[
"Stegel",
"Giovanni",
""
],
[
"Tiddia",
"Gianmarco",
""
],
[
"De Bonis",
"Giulia",
""
],
[
"Paolucci",
"Pier Stanislao",
""
]
] |
The brain exhibits capabilities of fast incremental learning from few noisy examples, as well as the ability to associate similar memories in autonomously-created categories and to combine contextual hints with sensory perceptions. Together with sleep, these mechanisms are thought to be key components of many high-level cognitive functions. Yet, little is known about the underlying processes and the specific roles of different brain states. In this work, we exploited the combination of context and perception in a thalamo-cortical model based on a soft winner-take-all circuit of excitatory and inhibitory spiking neurons. After calibrating this model to express awake and deep-sleep states with features comparable with biological measures, we demonstrate the model capability of fast incremental learning from few examples, its resilience when proposed with noisy perceptions and contextual signals, and an improvement in visual classification after sleep due to induced synaptic homeostasis and association of similar memories.
|
1512.09233
|
Yougan Cheng
|
Yougan Cheng and Hans Othmer
|
A Model for Direction Sensing in Dictyostelium Discoideum: Ras Activity
and Symmetry Breaking Driven by a Gbetagamma- Mediated, Galpha2-Ric8 --
Dependent Signal Transduction Network
| null | null |
10.1371/journal.pcbi.1004900
| null |
q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many eukaryotic cells, including Dictyostelium discoideum (Dicty),
neutrophils and other cells of the immune system, can detect and reliably
orient themselves in chemoattractant gradients. In Dicty, signal detection and
transduction involves a G-protein-coupled receptor (GPCR) through which
extracellular cAMP signals are transduced into Ras activation via an
intermediate heterotrimeric G-protein (G2). Ras activation is the first
polarized response to cAMP gradients in Dicty. Recent work has revealed mutiple
new characteristics of Ras activation in Dicty, thereby providing new insights
into direction sensing mechanisms and pointing to the need for new models of
chemotaxis. Here we propose a novel reaction-diffusion model of Ras activation
based on three major components: one involving the GPCR, one centered on G2,
and one involving the monomeric G protein Ras. In contrast to existing local
excitation, global inhibition (LEGI) models of direction sensing, in which a
fast-responding but slowly-diffusing activator and a slow-acting rapidly
diffusing inhibitor set up an internal gradient of activity, our model is based
on equal diffusion coefficients for all cytosolic species, and the unbalanced
local sequestration of some species leads to gradient sensing and
amplification. We show that Ric8-modulated G2 cycling between the cytosol and
membrane can account for many of the observed responses in Dicty. including
imperfect adaptation, multiple phases of Ras activity in a cAMP gradient,
rectified directional sensing, and cellular memory.
|
[
{
"created": "Thu, 31 Dec 2015 07:26:07 GMT",
"version": "v1"
}
] |
2016-09-28
|
[
[
"Cheng",
"Yougan",
""
],
[
"Othmer",
"Hans",
""
]
] |
Many eukaryotic cells, including Dictyostelium discoideum (Dicty), neutrophils and other cells of the immune system, can detect and reliably orient themselves in chemoattractant gradients. In Dicty, signal detection and transduction involves a G-protein-coupled receptor (GPCR) through which extracellular cAMP signals are transduced into Ras activation via an intermediate heterotrimeric G-protein (G2). Ras activation is the first polarized response to cAMP gradients in Dicty. Recent work has revealed mutiple new characteristics of Ras activation in Dicty, thereby providing new insights into direction sensing mechanisms and pointing to the need for new models of chemotaxis. Here we propose a novel reaction-diffusion model of Ras activation based on three major components: one involving the GPCR, one centered on G2, and one involving the monomeric G protein Ras. In contrast to existing local excitation, global inhibition (LEGI) models of direction sensing, in which a fast-responding but slowly-diffusing activator and a slow-acting rapidly diffusing inhibitor set up an internal gradient of activity, our model is based on equal diffusion coefficients for all cytosolic species, and the unbalanced local sequestration of some species leads to gradient sensing and amplification. We show that Ric8-modulated G2 cycling between the cytosol and membrane can account for many of the observed responses in Dicty. including imperfect adaptation, multiple phases of Ras activity in a cAMP gradient, rectified directional sensing, and cellular memory.
|
2110.01554
|
Sebastian Contreras
|
Sebastian Contreras, Philipp D\"onges, Joel Wagner, Simon Bauer,
Sebastian B. Mohr, Emil N. Iftekhar, Mirjam Kretzschmar, Michael Maes, Kai
Nagel, Andr\'e Calero Valdez, Viola Priesemann
|
The winter dilemma
|
Estimation of COVID-19 case numbers for the coming winter
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With winter coming in the northern hemisphere, disadvantageous seasonality of
SARS-CoV-2 requires high immunity levels in the population or increasing
non-pharmaceutical interventions (NPIs), compared to summer. Otherwise
intensive care units (ICUs) might fill up. However, compliance with mandatory
NPIs, vaccine uptake, and individual protective measures depend on individuals'
opinions and behavior. Opinions, in turn, depend on information, e.g., about
vaccine safety or current infection levels. Therefore, understanding how
information about the pandemic affects its spread through the modulation of
voluntary protection-seeking behaviors is crucial for better preparedness this
winter and for future crises.
|
[
{
"created": "Mon, 4 Oct 2021 16:47:06 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Oct 2021 17:50:43 GMT",
"version": "v2"
}
] |
2021-10-18
|
[
[
"Contreras",
"Sebastian",
""
],
[
"Dönges",
"Philipp",
""
],
[
"Wagner",
"Joel",
""
],
[
"Bauer",
"Simon",
""
],
[
"Mohr",
"Sebastian B.",
""
],
[
"Iftekhar",
"Emil N.",
""
],
[
"Kretzschmar",
"Mirjam",
""
],
[
"Maes",
"Michael",
""
],
[
"Nagel",
"Kai",
""
],
[
"Valdez",
"André Calero",
""
],
[
"Priesemann",
"Viola",
""
]
] |
With winter coming in the northern hemisphere, disadvantageous seasonality of SARS-CoV-2 requires high immunity levels in the population or increasing non-pharmaceutical interventions (NPIs), compared to summer. Otherwise intensive care units (ICUs) might fill up. However, compliance with mandatory NPIs, vaccine uptake, and individual protective measures depend on individuals' opinions and behavior. Opinions, in turn, depend on information, e.g., about vaccine safety or current infection levels. Therefore, understanding how information about the pandemic affects its spread through the modulation of voluntary protection-seeking behaviors is crucial for better preparedness this winter and for future crises.
|
2205.03920
|
Wei Xie
|
Wei Xie, Giulia Pedrielli
|
From Discovery to Production: Challenges and Novel Methodologies for
Next Generation Biomanufacturing
|
15 pages, 5 figures
| null | null | null |
q-bio.QM cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
The increasingly pressing demand of novel drugs (e.g., gene therapies for
personalized cancer care, ever evolving vaccines) with unprecedented levels of
personalization, has put a remarkable pressure on the traditionally long time
required by the pharma R&D and manufacturing to go from design to production of
new products. The revolution has already brought important changes in the
technologies used within the industry. In fact, practitioners are increasingly
moving away from the classical paradigm of large-scale batch production to
continuous biomanufacturing with flexible and modular design, which is further
supported by the recent technology advance in single-use equipment. In contrast
to long design processes, low product variability (one-fits-all), and highly
rigid systems, modern pharma players are answering the question: can we bring
design and process control up to the speed that novel production technologies
give us to quickly set up a flexible production run?
In this tutorial, we present key challenges and potential solutions from the
world of operations research that can support answering such question. We first
present technical challenges and novel methods for the design of next
generation drugs, followed by the process modeling and control approaches to
successfully and efficiently manufacture them.
|
[
{
"created": "Sun, 8 May 2022 17:32:17 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Jun 2022 05:50:08 GMT",
"version": "v2"
}
] |
2022-06-29
|
[
[
"Xie",
"Wei",
""
],
[
"Pedrielli",
"Giulia",
""
]
] |
The increasingly pressing demand of novel drugs (e.g., gene therapies for personalized cancer care, ever evolving vaccines) with unprecedented levels of personalization, has put a remarkable pressure on the traditionally long time required by the pharma R&D and manufacturing to go from design to production of new products. The revolution has already brought important changes in the technologies used within the industry. In fact, practitioners are increasingly moving away from the classical paradigm of large-scale batch production to continuous biomanufacturing with flexible and modular design, which is further supported by the recent technology advance in single-use equipment. In contrast to long design processes, low product variability (one-fits-all), and highly rigid systems, modern pharma players are answering the question: can we bring design and process control up to the speed that novel production technologies give us to quickly set up a flexible production run? In this tutorial, we present key challenges and potential solutions from the world of operations research that can support answering such question. We first present technical challenges and novel methods for the design of next generation drugs, followed by the process modeling and control approaches to successfully and efficiently manufacture them.
|
2004.02944
|
Bo Zhang
|
Bo Zhang, Ting Ye, Siyu Heng, Michael Z. Levy, Dylan S. Small
|
Protocol for an Observational Study on the Effects of Social Distancing
on Influenza-Like Illness and COVID-19
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The novel coronavirus disease (COVID-19) is a highly contagious respiratory
disease that was first detected in Wuhan, China in December 2019, and has since
spread around the globe, claiming more than 69,000 lives by the time this
protocol is written. It has been widely acknowledged that the most effective
public policy to mitigate the pandemic is \emph{social and physical
distancing}: keeping at least six feet away from people, working from home,
closing non-essential businesses, etc. There have been a lot of anecdotal
evidences suggesting that social distancing has a causal effect on disease
mitigation; however, few studies have investigated the effect of social
distancing on disease mitigation in a transparent and statistically-sound
manner.
We propose to perform an optimal non-bipartite matching to pair counties with
similar observed covariates but vastly different average social distancing
scores during the first week (March 16th through Match 22nd) of President's
\emph{15 Days to Slow the Spread} campaign. We have produced a total of $302$
pairs of two U.S. counties with good covariate balance on a total of $16$
important variables. Our primary outcome will be the average observed illness
collected by Kinsa Inc. two weeks after the intervention period. Although the
observed illness does not directly measure COVID-19, it reflects a real-time
aspect of the pandemic, and unlike confirmed cases, it is much less confounded
by counties' testing capabilities. We also consider observed illness three
weeks after the intervention period as a secondary outcome. We will test a
proportional treatment effect using a randomization-based test with covariance
adjustment and conduct a sensitivity analysis.
|
[
{
"created": "Mon, 6 Apr 2020 19:11:58 GMT",
"version": "v1"
}
] |
2020-04-08
|
[
[
"Zhang",
"Bo",
""
],
[
"Ye",
"Ting",
""
],
[
"Heng",
"Siyu",
""
],
[
"Levy",
"Michael Z.",
""
],
[
"Small",
"Dylan S.",
""
]
] |
The novel coronavirus disease (COVID-19) is a highly contagious respiratory disease that was first detected in Wuhan, China in December 2019, and has since spread around the globe, claiming more than 69,000 lives by the time this protocol is written. It has been widely acknowledged that the most effective public policy to mitigate the pandemic is \emph{social and physical distancing}: keeping at least six feet away from people, working from home, closing non-essential businesses, etc. There have been a lot of anecdotal evidences suggesting that social distancing has a causal effect on disease mitigation; however, few studies have investigated the effect of social distancing on disease mitigation in a transparent and statistically-sound manner. We propose to perform an optimal non-bipartite matching to pair counties with similar observed covariates but vastly different average social distancing scores during the first week (March 16th through Match 22nd) of President's \emph{15 Days to Slow the Spread} campaign. We have produced a total of $302$ pairs of two U.S. counties with good covariate balance on a total of $16$ important variables. Our primary outcome will be the average observed illness collected by Kinsa Inc. two weeks after the intervention period. Although the observed illness does not directly measure COVID-19, it reflects a real-time aspect of the pandemic, and unlike confirmed cases, it is much less confounded by counties' testing capabilities. We also consider observed illness three weeks after the intervention period as a secondary outcome. We will test a proportional treatment effect using a randomization-based test with covariance adjustment and conduct a sensitivity analysis.
|
1606.02151
|
Carsten Baldauf
|
Matti Ropo and Volker Blum and Carsten Baldauf
|
Trends for isolated amino acids and dipeptides: Conformation, divalent
ion binding, and remarkable similarity of binding to calcium and lead
|
submitted, underlying data can be found here:
http://aminoaciddb.rz-berlin.mpg.de/
| null | null | null |
q-bio.BM physics.atm-clus physics.chem-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We derive structural and binding energy trends for twenty amino acids, their
dipeptides, and their interactions with the divalent cations Ca$^{2+}$,
Ba$^{2+}$, Sr$^{2+}$, Cd$^{2+}$, Pb$^{2+}$, and Hg$^{2+}$. The underlying data
set consists of 45,892 first-principles predicted conformers with relative
energies up to about 4 eV (about 400kJ/mol). We show that only very few
distinct backbone structures of isolated amino acids and their dipeptides
emerge as lowest-energy conformers. The isolated amino acids predominantly
adopt structures that involve an acidic proton shared between the carboxy and
amino function. Dipeptides adopt one of two intramolecular-hydrogen bonded
conformations C$_5$ or equatorial C$_7$. Upon complexation with a divalent
cation, the accessible conformational space shrinks and intramolecular hydrogen
bonding is prevented due to strong electrostatic interaction of backbone and
side chain functional groups with cations. Clear correlations emerge from the
binding energies of the six divalent ions with amino acids and dipeptides.
Cd$^{2+}$ and Hg$^{2+}$ show the largest binding energies - a potential
correlation with their known high acute toxicities. Ca$^{2+}$ and Pb$^{2+}$
reveal almost identical binding energies across the entire series of amino
acids and dipeptides. This observation validates past indications that
ion-mimicry of calcium and lead should play an important role in a
toxicological context.
|
[
{
"created": "Tue, 7 Jun 2016 14:24:21 GMT",
"version": "v1"
},
{
"created": "Wed, 14 Sep 2016 09:20:23 GMT",
"version": "v2"
}
] |
2016-09-15
|
[
[
"Ropo",
"Matti",
""
],
[
"Blum",
"Volker",
""
],
[
"Baldauf",
"Carsten",
""
]
] |
We derive structural and binding energy trends for twenty amino acids, their dipeptides, and their interactions with the divalent cations Ca$^{2+}$, Ba$^{2+}$, Sr$^{2+}$, Cd$^{2+}$, Pb$^{2+}$, and Hg$^{2+}$. The underlying data set consists of 45,892 first-principles predicted conformers with relative energies up to about 4 eV (about 400kJ/mol). We show that only very few distinct backbone structures of isolated amino acids and their dipeptides emerge as lowest-energy conformers. The isolated amino acids predominantly adopt structures that involve an acidic proton shared between the carboxy and amino function. Dipeptides adopt one of two intramolecular-hydrogen bonded conformations C$_5$ or equatorial C$_7$. Upon complexation with a divalent cation, the accessible conformational space shrinks and intramolecular hydrogen bonding is prevented due to strong electrostatic interaction of backbone and side chain functional groups with cations. Clear correlations emerge from the binding energies of the six divalent ions with amino acids and dipeptides. Cd$^{2+}$ and Hg$^{2+}$ show the largest binding energies - a potential correlation with their known high acute toxicities. Ca$^{2+}$ and Pb$^{2+}$ reveal almost identical binding energies across the entire series of amino acids and dipeptides. This observation validates past indications that ion-mimicry of calcium and lead should play an important role in a toxicological context.
|
1004.2020
|
Chris Adami
|
Dimitris Iliopoulos, Arend Hintze, and Christoph Adami
|
Critical dynamics in the evolution of stochastic strategies for the
iterated Prisoner's Dilemma
|
27 pages, including supplementary information. 5 figures, 4 suppl.
figures. Version accepted for publication in PLoS Comp. Biol
|
PLos Computational Biology 6 (2010) e1000948
|
10.1371/journal.pcbi.1000948
| null |
q-bio.PE q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The observed cooperation on the level of genes, cells, tissues, and
individuals has been the object of intense study by evolutionary biologists,
mainly because cooperation often flourishes in biological systems in apparent
contradiction to the selfish goal of survival inherent in Darwinian evolution.
In order to resolve this paradox, evolutionary game theory has focused on the
Prisoner's Dilemma (PD), which incorporates the essence of this conflict. Here,
we encode strategies for the iterated Prisoner's Dilemma (IPD) in terms of
conditional probabilities that represent the response of decision pathways
given previous plays. We find that if these stochastic strategies are encoded
as genes that undergo Darwinian evolution, the environmental conditions that
the strategies are adapting to determine the fixed point of the evolutionary
trajectory, which could be either cooperation or defection. A transition
between cooperative and defective attractors occurs as a function of different
parameters such a mutation rate, replacement rate, and memory, all of which
affect a player's ability to predict an opponent's behavior.
|
[
{
"created": "Mon, 12 Apr 2010 18:21:04 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Sep 2010 16:38:58 GMT",
"version": "v2"
}
] |
2010-10-21
|
[
[
"Iliopoulos",
"Dimitris",
""
],
[
"Hintze",
"Arend",
""
],
[
"Adami",
"Christoph",
""
]
] |
The observed cooperation on the level of genes, cells, tissues, and individuals has been the object of intense study by evolutionary biologists, mainly because cooperation often flourishes in biological systems in apparent contradiction to the selfish goal of survival inherent in Darwinian evolution. In order to resolve this paradox, evolutionary game theory has focused on the Prisoner's Dilemma (PD), which incorporates the essence of this conflict. Here, we encode strategies for the iterated Prisoner's Dilemma (IPD) in terms of conditional probabilities that represent the response of decision pathways given previous plays. We find that if these stochastic strategies are encoded as genes that undergo Darwinian evolution, the environmental conditions that the strategies are adapting to determine the fixed point of the evolutionary trajectory, which could be either cooperation or defection. A transition between cooperative and defective attractors occurs as a function of different parameters such a mutation rate, replacement rate, and memory, all of which affect a player's ability to predict an opponent's behavior.
|
2003.13997
|
Min Lu
|
Min Lu and Hemant Ishwaran
|
Dynamic Competing Risk Modeling COVID-19 in a Pandemic Scenario
|
https://minlu.shinyapps.io/killCOVID19/
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The emergence of coronavirus disease 2019 (COVID-19) in the United States has
forced federal and local governments to implement containment measures.
Moreover, the severity of the situation has sparked engagement by both the
research and clinical community with the goal of developing effective
treatments for the disease. This article proposes a time dynamic prediction
model with competing risks for the infected individual and develops a simple
tool for policy makers to compare different strategies in terms of when to
implement the strictest containment measures and how different treatments can
increase or suppress infected cases. Two types of containment strategies are
compared: (1) a constant containment strategy that could satisfy the needs of
citizens for a long period; and (2) an adaptive containment strategy whose
strict level changes across time. We consider how an effective treatment of the
disease can affect the dynamics in a pandemic scenario. For illustration we
consider a region with population 2.8 million and 200 initial infectious cases
assuming a 4% mortality rate compared with a 2% mortality rate if a new drug is
available. Our results show compared with a constant containment strategy,
adaptive containment strategies shorten the outbreak length and reduce maximum
daily number of cases. This, along with an effective treatment plan for the
disease can minimize death rate.
|
[
{
"created": "Tue, 31 Mar 2020 07:30:40 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Apr 2020 02:12:07 GMT",
"version": "v2"
},
{
"created": "Thu, 9 Apr 2020 21:35:22 GMT",
"version": "v3"
},
{
"created": "Wed, 1 Jul 2020 22:48:58 GMT",
"version": "v4"
}
] |
2020-07-03
|
[
[
"Lu",
"Min",
""
],
[
"Ishwaran",
"Hemant",
""
]
] |
The emergence of coronavirus disease 2019 (COVID-19) in the United States has forced federal and local governments to implement containment measures. Moreover, the severity of the situation has sparked engagement by both the research and clinical community with the goal of developing effective treatments for the disease. This article proposes a time dynamic prediction model with competing risks for the infected individual and develops a simple tool for policy makers to compare different strategies in terms of when to implement the strictest containment measures and how different treatments can increase or suppress infected cases. Two types of containment strategies are compared: (1) a constant containment strategy that could satisfy the needs of citizens for a long period; and (2) an adaptive containment strategy whose strict level changes across time. We consider how an effective treatment of the disease can affect the dynamics in a pandemic scenario. For illustration we consider a region with population 2.8 million and 200 initial infectious cases assuming a 4% mortality rate compared with a 2% mortality rate if a new drug is available. Our results show compared with a constant containment strategy, adaptive containment strategies shorten the outbreak length and reduce maximum daily number of cases. This, along with an effective treatment plan for the disease can minimize death rate.
|
1705.07614
|
Robert Legenstein
|
Zeno Jonke, Robert Legenstein, Stefan Habenschuss, Wolfgang Maass
|
Feedback inhibition shapes emergent computational properties of cortical
microcircuit motifs
|
25 pages, 6 figures
| null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cortical microcircuits are very complex networks, but they are composed of a
relatively small number of stereotypical motifs. Hence one strategy for
throwing light on the computational function of cortical microcircuits is to
analyze emergent computational properties of these stereotypical microcircuit
motifs. We are addressing here the question how spike-timing dependent
plasticity (STDP) shapes the computational properties of one motif that has
frequently been studied experimentally: interconnected populations of pyramidal
cells and parvalbumin-positive inhibitory cells in layer 2/3. Experimental
studies suggest that these inhibitory neurons exert some form of divisive
inhibition on the pyramidal cells. We show that this data-based form of
feedback inhibition, which is softer than that of winner-take-all models that
are commonly considered in theoretical analyses, contributes to the emergence
of an important computational function through STDP: The capability to
disentangle superimposed firing patterns in upstream networks, and to represent
their information content through a sparse assembly code.
|
[
{
"created": "Mon, 22 May 2017 08:52:31 GMT",
"version": "v1"
}
] |
2017-05-23
|
[
[
"Jonke",
"Zeno",
""
],
[
"Legenstein",
"Robert",
""
],
[
"Habenschuss",
"Stefan",
""
],
[
"Maass",
"Wolfgang",
""
]
] |
Cortical microcircuits are very complex networks, but they are composed of a relatively small number of stereotypical motifs. Hence one strategy for throwing light on the computational function of cortical microcircuits is to analyze emergent computational properties of these stereotypical microcircuit motifs. We are addressing here the question how spike-timing dependent plasticity (STDP) shapes the computational properties of one motif that has frequently been studied experimentally: interconnected populations of pyramidal cells and parvalbumin-positive inhibitory cells in layer 2/3. Experimental studies suggest that these inhibitory neurons exert some form of divisive inhibition on the pyramidal cells. We show that this data-based form of feedback inhibition, which is softer than that of winner-take-all models that are commonly considered in theoretical analyses, contributes to the emergence of an important computational function through STDP: The capability to disentangle superimposed firing patterns in upstream networks, and to represent their information content through a sparse assembly code.
|
1211.6179
|
Chao Yang Mr.
|
Chao Yang, Zengyou He, and Weichuan Yu
|
A Combinatorial Perspective of the Protein Inference Problem
| null | null | null | null |
q-bio.QM q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In a shotgun proteomics experiment, proteins are the most biologically
meaningful output. The success of proteomics studies depends on the ability to
accurately and efficiently identify proteins. Many methods have been proposed
to facilitate the identification of proteins from the results of peptide
identification. However, the relationship between protein identification and
peptide identification has not been thoroughly explained before.
In this paper, we are devoted to a combinatorial perspective of the protein
inference problem. We employ combinatorial mathematics to calculate the
conditional protein probabilities (Protein probability means the probability
that a protein is correctly identified) under three assumptions, which lead to
a lower bound, an upper bound and an empirical estimation of protein
probabilities, respectively. The combinatorial perspective enables us to obtain
a closed-form formulation for protein inference.
Based on our model, we study the impact of unique peptides and degenerate
peptides on protein probabilities. Here, degenerate peptides are peptides
shared by at least two proteins. Meanwhile, we also study the relationship of
our model with other methods such as ProteinProphet. A probability confidence
interval can be calculated and used together with probability to filter the
protein identification result. Our method achieves competitive results with
ProteinProphet in a more efficient manner in the experiment based on two
datasets of standard protein mixtures and two datasets of real samples.
We name our program ProteinInfer. Its Java source code is available at
http://bioinformatics.ust.hk/proteininfer
|
[
{
"created": "Tue, 27 Nov 2012 02:14:18 GMT",
"version": "v1"
},
{
"created": "Thu, 29 Nov 2012 02:19:55 GMT",
"version": "v2"
}
] |
2012-11-30
|
[
[
"Yang",
"Chao",
""
],
[
"He",
"Zengyou",
""
],
[
"Yu",
"Weichuan",
""
]
] |
In a shotgun proteomics experiment, proteins are the most biologically meaningful output. The success of proteomics studies depends on the ability to accurately and efficiently identify proteins. Many methods have been proposed to facilitate the identification of proteins from the results of peptide identification. However, the relationship between protein identification and peptide identification has not been thoroughly explained before. In this paper, we are devoted to a combinatorial perspective of the protein inference problem. We employ combinatorial mathematics to calculate the conditional protein probabilities (Protein probability means the probability that a protein is correctly identified) under three assumptions, which lead to a lower bound, an upper bound and an empirical estimation of protein probabilities, respectively. The combinatorial perspective enables us to obtain a closed-form formulation for protein inference. Based on our model, we study the impact of unique peptides and degenerate peptides on protein probabilities. Here, degenerate peptides are peptides shared by at least two proteins. Meanwhile, we also study the relationship of our model with other methods such as ProteinProphet. A probability confidence interval can be calculated and used together with probability to filter the protein identification result. Our method achieves competitive results with ProteinProphet in a more efficient manner in the experiment based on two datasets of standard protein mixtures and two datasets of real samples. We name our program ProteinInfer. Its Java source code is available at http://bioinformatics.ust.hk/proteininfer
|
2103.08247
|
Pawel Kulakowski
|
Jakub Kmiecik, Pawel Kulakowski, Krzysztof Wojcik, Andrzej Jajszczyk
|
Transmitting FRET signals to nerve cells
| null |
5th ACM/IEEE International Conference on Nanoscale Computing and
Communication, September 2018
|
10.1145/3233188.3233223
| null |
q-bio.MN cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper is concerned with a novel method allowing communication between
FRET nanonetworks and nerve cells. It is focused on two system components:
fluorophores and channelrhodopsins which serve as transmitters and receivers,
respectively. Channelrhodopsins are used here also as a FRET signal-to-voltage
converter. The trade-off between throughput and bit error rate is also
investigated.
|
[
{
"created": "Mon, 15 Mar 2021 09:57:33 GMT",
"version": "v1"
}
] |
2021-03-16
|
[
[
"Kmiecik",
"Jakub",
""
],
[
"Kulakowski",
"Pawel",
""
],
[
"Wojcik",
"Krzysztof",
""
],
[
"Jajszczyk",
"Andrzej",
""
]
] |
This paper is concerned with a novel method allowing communication between FRET nanonetworks and nerve cells. It is focused on two system components: fluorophores and channelrhodopsins which serve as transmitters and receivers, respectively. Channelrhodopsins are used here also as a FRET signal-to-voltage converter. The trade-off between throughput and bit error rate is also investigated.
|
2001.08346
|
Charles Fieseler
|
Charles Fieseler, Manuel Zimmer, J. Nathan Kutz
|
Unsupervised learning of control signals and their encodings in
$\textit{C. elegans}$ whole-brain recordings
|
10 pages, 5 figures
| null | null | null |
q-bio.QM q-bio.NC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Recent whole brain imaging experiments on $\textit{C. elegans}$ has revealed
that the neural population dynamics encode motor commands and stereotyped
transitions between behaviors on low dimensional manifolds. Efforts to
characterize the dynamics on this manifold have used piecewise linear models to
describe the entire state space, but it is unknown how a single, global
dynamical model can generate the observed dynamics. Here, we propose a control
framework to achieve such a global model of the dynamics, whereby underlying
linear dynamics is actuated by sparse control signals. This method learns the
control signals in an unsupervised way from data, then uses $\textit{ Dynamic
Mode Decomposition with control}$ (DMDc) to create the first global, linear
dynamical system that can reconstruct whole-brain imaging data. These control
signals are shown to be implicated in transitions between behaviors. In
addition, we analyze the time-delay encoding of these control signals, showing
that these transitions can be predicted from neurons previously implicated in
behavioral transitions, but also additional neurons previously unidentified.
Moreover, our decomposition method allows one to understand the observed
nonlinear global dynamics instead as linear dynamics with control. The proposed
mathematical framework is generic and can be generalized to other neurosensory
systems, potentially revealing transitions and their encodings in a completely
unsupervised way.
|
[
{
"created": "Thu, 23 Jan 2020 02:37:34 GMT",
"version": "v1"
},
{
"created": "Tue, 11 Feb 2020 19:50:02 GMT",
"version": "v2"
},
{
"created": "Mon, 13 Apr 2020 21:33:46 GMT",
"version": "v3"
}
] |
2020-04-15
|
[
[
"Fieseler",
"Charles",
""
],
[
"Zimmer",
"Manuel",
""
],
[
"Kutz",
"J. Nathan",
""
]
] |
Recent whole brain imaging experiments on $\textit{C. elegans}$ has revealed that the neural population dynamics encode motor commands and stereotyped transitions between behaviors on low dimensional manifolds. Efforts to characterize the dynamics on this manifold have used piecewise linear models to describe the entire state space, but it is unknown how a single, global dynamical model can generate the observed dynamics. Here, we propose a control framework to achieve such a global model of the dynamics, whereby underlying linear dynamics is actuated by sparse control signals. This method learns the control signals in an unsupervised way from data, then uses $\textit{ Dynamic Mode Decomposition with control}$ (DMDc) to create the first global, linear dynamical system that can reconstruct whole-brain imaging data. These control signals are shown to be implicated in transitions between behaviors. In addition, we analyze the time-delay encoding of these control signals, showing that these transitions can be predicted from neurons previously implicated in behavioral transitions, but also additional neurons previously unidentified. Moreover, our decomposition method allows one to understand the observed nonlinear global dynamics instead as linear dynamics with control. The proposed mathematical framework is generic and can be generalized to other neurosensory systems, potentially revealing transitions and their encodings in a completely unsupervised way.
|
0805.2298
|
Luciano da Fontoura Costa
|
Matheus P. Viana, Bruno A. N. Travencolo, E. Tanck and Luciano da F.
Costa
|
Characterizing the Diversity of Dynamics in Complex Networks Without
Border Effects
|
15 pages, 7 figures. A working manuscript
| null | null | null |
q-bio.TO q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The importance of structured, complex connectivity patterns found in several
real-world systems is to a great extent related to their respective effects in
constraining and even defining the respective dynamics. Yet, while complex
networks have been comprehensively investigated along the last decade in terms
of their topological measurements, relatively less attention has been focused
on the characterization of the respective dynamics. Introduced recently, the
diversity entropy of complex systems can provide valuable information about the
respective possible unfolding of dynamics. In the case of self-avoiding random
walks, the situation assumed here, the diversity measurement allows one to
quantify in how many different places an agent may effectively arrive after a
given number of steps from its initial activity. Because this measurement is
highly affected by border effects frequently found as a consequence of network
sampling, it becomes critical to devise means for sound estimation of the
diversity without being affected by this type of artifacts. We describe such an
algorithm and illustrate its potential with respect to the characterization of
the self-avoiding random walk dynamics in two real-world networks, namely bone
canals and air transportation.
|
[
{
"created": "Thu, 15 May 2008 13:14:46 GMT",
"version": "v1"
}
] |
2008-05-16
|
[
[
"Viana",
"Matheus P.",
""
],
[
"Travencolo",
"Bruno A. N.",
""
],
[
"Tanck",
"E.",
""
],
[
"Costa",
"Luciano da F.",
""
]
] |
The importance of structured, complex connectivity patterns found in several real-world systems is to a great extent related to their respective effects in constraining and even defining the respective dynamics. Yet, while complex networks have been comprehensively investigated along the last decade in terms of their topological measurements, relatively less attention has been focused on the characterization of the respective dynamics. Introduced recently, the diversity entropy of complex systems can provide valuable information about the respective possible unfolding of dynamics. In the case of self-avoiding random walks, the situation assumed here, the diversity measurement allows one to quantify in how many different places an agent may effectively arrive after a given number of steps from its initial activity. Because this measurement is highly affected by border effects frequently found as a consequence of network sampling, it becomes critical to devise means for sound estimation of the diversity without being affected by this type of artifacts. We describe such an algorithm and illustrate its potential with respect to the characterization of the self-avoiding random walk dynamics in two real-world networks, namely bone canals and air transportation.
|
1809.07275
|
Elisenda Feliu
|
Elisenda Feliu
|
On the reaction rate constants that enable multistationarity in the
two-site phosphorylation cycle
| null | null | null | null |
q-bio.MN math.AG math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Parametrized polynomial ordinary differential equation systems are broadly
used for modeling, specially in the study of biochemical reaction networks
under the assumption of mass-action kinetics. Understanding the qualitative
behavior of the solutions with respect to the parameter values gives rise to
complex problems within real algebraic geometry, concerning the study of the
signs of multivariate polynomials over the positive orthant. In this work we
provide further insight into the number of positive steady states of a
benchmark model, namely the two-site phosphorylation cycle. In particular, we
provide new conditions on the reaction rate constants for the existence of one
or three positive steady states, partially filling a gap left in previous
works.
|
[
{
"created": "Tue, 18 Sep 2018 08:13:39 GMT",
"version": "v1"
}
] |
2018-10-15
|
[
[
"Feliu",
"Elisenda",
""
]
] |
Parametrized polynomial ordinary differential equation systems are broadly used for modeling, specially in the study of biochemical reaction networks under the assumption of mass-action kinetics. Understanding the qualitative behavior of the solutions with respect to the parameter values gives rise to complex problems within real algebraic geometry, concerning the study of the signs of multivariate polynomials over the positive orthant. In this work we provide further insight into the number of positive steady states of a benchmark model, namely the two-site phosphorylation cycle. In particular, we provide new conditions on the reaction rate constants for the existence of one or three positive steady states, partially filling a gap left in previous works.
|
0710.2301
|
Ruben Moreno Bote
|
Ruben Moreno-Bote, Nestor Parga
|
Auto and crosscorrelograms for the spike response of LIF neurons with
slow synapses
|
5 pages, 3 figures
|
PRL 96, 028101 (2006)
|
10.1103/PhysRevLett.96.028101
| null |
q-bio.NC physics.bio-ph
| null |
An analytical description of the response properties of simple but realistic
neuron models in the presence of noise is still lacking. We determine
completely up to the second order the firing statistics of a single and a pair
of leaky integrate-and-fire neurons (LIFs) receiving some common slowly
filtered white noise. In particular, the auto- and cross-correlation functions
of the output spike trains of pairs of cells are obtained from an improvement
of the adiabatic approximation introduced in \cite{Mor+04}. These two functions
define the firing variability and firing synchronization between neurons, and
are of much importance for understanding neuron communication.
|
[
{
"created": "Thu, 11 Oct 2007 17:33:25 GMT",
"version": "v1"
}
] |
2009-11-13
|
[
[
"Moreno-Bote",
"Ruben",
""
],
[
"Parga",
"Nestor",
""
]
] |
An analytical description of the response properties of simple but realistic neuron models in the presence of noise is still lacking. We determine completely up to the second order the firing statistics of a single and a pair of leaky integrate-and-fire neurons (LIFs) receiving some common slowly filtered white noise. In particular, the auto- and cross-correlation functions of the output spike trains of pairs of cells are obtained from an improvement of the adiabatic approximation introduced in \cite{Mor+04}. These two functions define the firing variability and firing synchronization between neurons, and are of much importance for understanding neuron communication.
|
1112.3082
|
Alexander Peyser
|
Alexander Peyser
|
Theoretical Studies of Structure-Function Relationships in Kv Channels:
Electrostatics of the Voltage Sensor
| null | null | null | null |
q-bio.BM physics.bio-ph q-bio.QM
|
http://creativecommons.org/licenses/by-nc-sa/3.0/
|
Voltage-gated ion channels mediate electrical excitability of cellular
membranes. Reduced models of the voltage sensor (VS) of Kv channels produce
insight into the electrostatic physics underlying the response of the highly
positively charged S4 transmembrane domain to changes in membrane potential and
other electrostatic parameters. By calculating the partition function computed
from the electrostatic energy over translational and/or rotational degrees of
freedom, I compute expectations of charge displacement, energetics, probability
distributions of translation & rotation and Maxwell stress for arrangements of
S4 positively charged residues; these computations can then be compared with
experimental results to elucidate the role of various putative atomic level
features of the VS. A `paddle' model is rejected on electrostatic grounds,
owing to unfavorable energetics, insufficient charge displacement and excessive
Maxwell stress. On the other hand, a `sliding helix' model with three local
counter-charges, a protein dielectric coefficient of 4 and a 2/3 interval of
counter-charge positioning relative to the S4-helix period of positive residues
is electrostatically reasonable, comparing well with Shaker (Seoh et al.,
1996). Lack of counter-charges destabilizes the S4 in the membrane;
counter-charge interval helps determine the number and shape of energy barriers
and troughs over the range of motion of the S4; and the local dielectric
coefficient of the protein constrains the height of energy maxima. These
`sliding helix' models compare favorably with experimental results for single &
double mutant charge experiments on Shaker. Single S4 positive charge mutants
are predicted quite well by this model; single counter-charge mutants are
predicted less well; and double mutants for both an S4 charge and a
counter-charge are characterized least well.
|
[
{
"created": "Wed, 14 Dec 2011 00:38:39 GMT",
"version": "v1"
}
] |
2015-03-13
|
[
[
"Peyser",
"Alexander",
""
]
] |
Voltage-gated ion channels mediate electrical excitability of cellular membranes. Reduced models of the voltage sensor (VS) of Kv channels produce insight into the electrostatic physics underlying the response of the highly positively charged S4 transmembrane domain to changes in membrane potential and other electrostatic parameters. By calculating the partition function computed from the electrostatic energy over translational and/or rotational degrees of freedom, I compute expectations of charge displacement, energetics, probability distributions of translation & rotation and Maxwell stress for arrangements of S4 positively charged residues; these computations can then be compared with experimental results to elucidate the role of various putative atomic level features of the VS. A `paddle' model is rejected on electrostatic grounds, owing to unfavorable energetics, insufficient charge displacement and excessive Maxwell stress. On the other hand, a `sliding helix' model with three local counter-charges, a protein dielectric coefficient of 4 and a 2/3 interval of counter-charge positioning relative to the S4-helix period of positive residues is electrostatically reasonable, comparing well with Shaker (Seoh et al., 1996). Lack of counter-charges destabilizes the S4 in the membrane; counter-charge interval helps determine the number and shape of energy barriers and troughs over the range of motion of the S4; and the local dielectric coefficient of the protein constrains the height of energy maxima. These `sliding helix' models compare favorably with experimental results for single & double mutant charge experiments on Shaker. Single S4 positive charge mutants are predicted quite well by this model; single counter-charge mutants are predicted less well; and double mutants for both an S4 charge and a counter-charge are characterized least well.
|
1810.13302
|
Nicole Voges
|
Jeyathevy Sukiban, Nicole Voges, Till A. Dembek, Robin Pauli, Michael
Denker, Immo Weber, Lars Timmermann, Sonja Gr\"un
|
Evaluation of spike sorting algorithms: Simulations and application to
human Subthalamic Nucleus recordings
|
9 figures
| null | null | null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An important prerequisite for the analysis of spike synchrony in
extracellular recordings is the extraction of single unit activity from the
recorded multi unit signal. To identify single units (SUs), potential spikes
are detected and separated with respect to their potential neuronal origins
('spike sorting'). However, different sorting algorithms yield inconsistent
unit assignments which seriously influences the subsequent analyses of the
spiking activity. To evaluate the quality of spike sortings performed with
different prevalent algorithms offered by the 'Plexon Offline Sorter' we first
apply these algorithms to experimental data (ED), namely recordings in the
Subthalamic Nucleus of patients with Parkinson's disease, obtained during Deep
Brain Stimulation surgery. Since this procedure leaves us unsure about the best
sorting result we then apply all methods again to artificial data (AD) with
known ground truth (GT). AD consists of pairs of SUs with different shape
similarity embedded in the background noise of the ED. The sorting evaluation
is based on the influence of the respective methods on the SU assignments and
its effect on the resulting firing characteristics. We find a high variability
in the sorting results obtained by different algorithms that increases with SU
shape similarity. We also find significant differences in the resulting firing
characteristics of the ED. We conclude that Valley-Seeking produces the most
accurate results if the exclusion of artifacts as unsorted events is important.
If the latter is less important ('clean' data) K-Means is a better option. Our
results strongly argue for the need of standardized validation procedures for
spike sorting based on GT data. The recipe suggested here is simple enough to
become a standard procedure.
|
[
{
"created": "Wed, 31 Oct 2018 14:32:45 GMT",
"version": "v1"
}
] |
2018-11-01
|
[
[
"Sukiban",
"Jeyathevy",
""
],
[
"Voges",
"Nicole",
""
],
[
"Dembek",
"Till A.",
""
],
[
"Pauli",
"Robin",
""
],
[
"Denker",
"Michael",
""
],
[
"Weber",
"Immo",
""
],
[
"Timmermann",
"Lars",
""
],
[
"Grün",
"Sonja",
""
]
] |
An important prerequisite for the analysis of spike synchrony in extracellular recordings is the extraction of single unit activity from the recorded multi unit signal. To identify single units (SUs), potential spikes are detected and separated with respect to their potential neuronal origins ('spike sorting'). However, different sorting algorithms yield inconsistent unit assignments which seriously influences the subsequent analyses of the spiking activity. To evaluate the quality of spike sortings performed with different prevalent algorithms offered by the 'Plexon Offline Sorter' we first apply these algorithms to experimental data (ED), namely recordings in the Subthalamic Nucleus of patients with Parkinson's disease, obtained during Deep Brain Stimulation surgery. Since this procedure leaves us unsure about the best sorting result we then apply all methods again to artificial data (AD) with known ground truth (GT). AD consists of pairs of SUs with different shape similarity embedded in the background noise of the ED. The sorting evaluation is based on the influence of the respective methods on the SU assignments and its effect on the resulting firing characteristics. We find a high variability in the sorting results obtained by different algorithms that increases with SU shape similarity. We also find significant differences in the resulting firing characteristics of the ED. We conclude that Valley-Seeking produces the most accurate results if the exclusion of artifacts as unsorted events is important. If the latter is less important ('clean' data) K-Means is a better option. Our results strongly argue for the need of standardized validation procedures for spike sorting based on GT data. The recipe suggested here is simple enough to become a standard procedure.
|
2302.07134
|
Shuqi Lu
|
Yuejiang Yu, Shuqi Lu, Zhifeng Gao, Hang Zheng and Guolin Ke
|
Do Deep Learning Models Really Outperform Traditional Approaches in
Molecular Docking?
| null | null | null | null |
q-bio.BM cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Molecular docking, given a ligand molecule and a ligand binding site (called
``pocket'') on a protein, predicting the binding mode of the protein-ligand
complex, is a widely used technique in drug design. Many deep learning models
have been developed for molecular docking, while most existing deep learning
models perform docking on the whole protein, rather than on a given pocket as
the traditional molecular docking approaches, which does not match common
needs. What's more, they claim to perform better than traditional molecular
docking, but the approach of comparison is not fair, since traditional methods
are not designed for docking on the whole protein without a given pocket. In
this paper, we design a series of experiments to examine the actual performance
of these deep learning models and traditional methods. For a fair comparison,
we decompose the docking on the whole protein into two steps, pocket searching
and docking on a given pocket, and build pipelines to evaluate traditional
methods and deep learning methods respectively. We find that deep learning
models are actually good at pocket searching, but traditional methods are
better than deep learning models at docking on given pockets. Overall, our work
explicitly reveals some potential problems in current deep learning models for
molecular docking and provides several suggestions for future works.
|
[
{
"created": "Tue, 14 Feb 2023 15:45:45 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Feb 2023 05:47:01 GMT",
"version": "v2"
},
{
"created": "Thu, 23 Feb 2023 09:09:30 GMT",
"version": "v3"
}
] |
2023-02-24
|
[
[
"Yu",
"Yuejiang",
""
],
[
"Lu",
"Shuqi",
""
],
[
"Gao",
"Zhifeng",
""
],
[
"Zheng",
"Hang",
""
],
[
"Ke",
"Guolin",
""
]
] |
Molecular docking, given a ligand molecule and a ligand binding site (called ``pocket'') on a protein, predicting the binding mode of the protein-ligand complex, is a widely used technique in drug design. Many deep learning models have been developed for molecular docking, while most existing deep learning models perform docking on the whole protein, rather than on a given pocket as the traditional molecular docking approaches, which does not match common needs. What's more, they claim to perform better than traditional molecular docking, but the approach of comparison is not fair, since traditional methods are not designed for docking on the whole protein without a given pocket. In this paper, we design a series of experiments to examine the actual performance of these deep learning models and traditional methods. For a fair comparison, we decompose the docking on the whole protein into two steps, pocket searching and docking on a given pocket, and build pipelines to evaluate traditional methods and deep learning methods respectively. We find that deep learning models are actually good at pocket searching, but traditional methods are better than deep learning models at docking on given pockets. Overall, our work explicitly reveals some potential problems in current deep learning models for molecular docking and provides several suggestions for future works.
|
1405.6466
|
Dante Chialvo
|
Enzo Tagliazucchi, Robin Carhart-Harris, Robert Leech, David Nutt,
Dante R. Chialvo
|
Enhanced repertoire of brain dynamical states during the psychedelic
experience
| null |
Hum. Brain Mapp., 35: 5442-5456, 2014
|
10.1002/hbm.22562
| null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The study of rapid changes in brain dynamics and functional connectivity (FC)
is of increasing interest in neuroimaging. Brain states departing from normal
waking consciousness are expected to be accompanied by alterations in the
aforementioned dynamics. In particular, the psychedelic experience produced by
psilocybin (a substance found in `magic mushrooms`) is characterized by
unconstrained cognition and profound alterations in the perception of time,
space and selfhood. Considering the spontaneous and subjective manifestation of
these effects, we hypothesize that neural correlates of the psychedelic
experience can be found in the dynamics and variability of spontaneous brain
activity fluctuations and connectivity, measurable with functional Magnetic
Resonance Imaging (fMRI). Fifteen healthy subjects were scanned before, during
and after intravenous infusion of psilocybin and an inert placebo. Blood-Oxygen
Level Dependent (BOLD) temporal variability was assessed computing the variance
and total spectral power, resulting in increased signal variability bilaterally
in the hippocampi and anterior cingulate cortex. Changes in BOLD signal
spectral behavior (including spectral scaling exponents) affected exclusively
higher brain systems such as the default mode, executive control and dorsal
attention networks. A novel framework enabled us to track different
connectivity states explored by the brain during rest. This approach revealed a
wider repertoire of connectivity states post-psilocybin than during control
conditions. Together, the present results provide a comprehensive account of
the effects of psilocybin on dynamical behaviour in the human brain at a
macroscopic level and may have implications for our understanding of the
unconstrained, hyper-associative quality of consciousness in the psychedelic
state.
|
[
{
"created": "Mon, 26 May 2014 05:50:15 GMT",
"version": "v1"
}
] |
2014-11-03
|
[
[
"Tagliazucchi",
"Enzo",
""
],
[
"Carhart-Harris",
"Robin",
""
],
[
"Leech",
"Robert",
""
],
[
"Nutt",
"David",
""
],
[
"Chialvo",
"Dante R.",
""
]
] |
The study of rapid changes in brain dynamics and functional connectivity (FC) is of increasing interest in neuroimaging. Brain states departing from normal waking consciousness are expected to be accompanied by alterations in the aforementioned dynamics. In particular, the psychedelic experience produced by psilocybin (a substance found in `magic mushrooms`) is characterized by unconstrained cognition and profound alterations in the perception of time, space and selfhood. Considering the spontaneous and subjective manifestation of these effects, we hypothesize that neural correlates of the psychedelic experience can be found in the dynamics and variability of spontaneous brain activity fluctuations and connectivity, measurable with functional Magnetic Resonance Imaging (fMRI). Fifteen healthy subjects were scanned before, during and after intravenous infusion of psilocybin and an inert placebo. Blood-Oxygen Level Dependent (BOLD) temporal variability was assessed computing the variance and total spectral power, resulting in increased signal variability bilaterally in the hippocampi and anterior cingulate cortex. Changes in BOLD signal spectral behavior (including spectral scaling exponents) affected exclusively higher brain systems such as the default mode, executive control and dorsal attention networks. A novel framework enabled us to track different connectivity states explored by the brain during rest. This approach revealed a wider repertoire of connectivity states post-psilocybin than during control conditions. Together, the present results provide a comprehensive account of the effects of psilocybin on dynamical behaviour in the human brain at a macroscopic level and may have implications for our understanding of the unconstrained, hyper-associative quality of consciousness in the psychedelic state.
|
1912.03395
|
Jakub Otwinowski
|
Jakub Otwinowski, Colin LaMont
|
Information-geometric optimization with natural selection
|
changed title
| null |
10.3390/e22090967
| null |
q-bio.PE cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Evolutionary algorithms, inspired by natural evolution, aim to optimize
difficult objective functions without computing derivatives. Here we detail the
relationship between population genetics and evolutionary optimization and
formulate a new evolutionary algorithm. Optimization of a continuous objective
function is analogous to searching for high fitness phenotypes on a fitness
landscape. We summarize how natural selection moves a population along the
non-euclidean gradient that is induced by the population on the fitness
landscape (the natural gradient). Under normal approximations common in
quantitative genetics, we show how selection is related to Newton's method in
optimization. We find that intermediate selection is most informative of the
fitness landscape. We describe the generation of new phenotypes and introduce
an operator that recombines the whole population to generate variants that
preserve normal statistics. Finally, we introduce a proof-of-principle
algorithm that combines natural selection, our recombination operator, and an
adaptive method to increase selection. Our algorithm is similar to covariance
matrix adaptation and natural evolutionary strategies in optimization, and has
similar performance. The algorithm is extremely simple in implementation with
no matrix inversion or factorization, does not require storing a covariance
matrix, and may form the basis of more general model-based optimization
algorithms with natural gradient updates.
|
[
{
"created": "Fri, 6 Dec 2019 23:57:16 GMT",
"version": "v1"
},
{
"created": "Wed, 19 Feb 2020 23:52:24 GMT",
"version": "v2"
}
] |
2023-07-19
|
[
[
"Otwinowski",
"Jakub",
""
],
[
"LaMont",
"Colin",
""
]
] |
Evolutionary algorithms, inspired by natural evolution, aim to optimize difficult objective functions without computing derivatives. Here we detail the relationship between population genetics and evolutionary optimization and formulate a new evolutionary algorithm. Optimization of a continuous objective function is analogous to searching for high fitness phenotypes on a fitness landscape. We summarize how natural selection moves a population along the non-euclidean gradient that is induced by the population on the fitness landscape (the natural gradient). Under normal approximations common in quantitative genetics, we show how selection is related to Newton's method in optimization. We find that intermediate selection is most informative of the fitness landscape. We describe the generation of new phenotypes and introduce an operator that recombines the whole population to generate variants that preserve normal statistics. Finally, we introduce a proof-of-principle algorithm that combines natural selection, our recombination operator, and an adaptive method to increase selection. Our algorithm is similar to covariance matrix adaptation and natural evolutionary strategies in optimization, and has similar performance. The algorithm is extremely simple in implementation with no matrix inversion or factorization, does not require storing a covariance matrix, and may form the basis of more general model-based optimization algorithms with natural gradient updates.
|
2212.04535
|
Yizi Zhang
|
Yizi Zhang, Meimei Liu, Zhengwu Zhang, and David Dunson
|
Motion-Invariant Variational Auto-Encoding of Brain Structural
Connectomes
| null | null | null | null |
q-bio.NC
|
http://creativecommons.org/licenses/by/4.0/
|
Mapping of human brain structural connectomes via diffusion MRI offers a
unique opportunity to understand brain structural connectivity and relate it to
various human traits, such as cognition. However, the presence of motion
artifacts during image acquisition can compromise the accuracy of connectome
reconstructions and subsequent inference results. We develop a generative model
to learn low-dimensional representations of structural connectomes that are
invariant to motion artifacts, so that we can link brain networks and human
traits more accurately, and generate motion-adjusted connectomes. We applied
the proposed model to data from the Adolescent Brain Cognitive Development
(ABCD) study and the Human Connectome Project (HCP) to investigate how our
motion-invariant connectomes facilitate understanding of the brain network and
its relationship with cognition. Empirical results demonstrate that the
proposed motion-invariant variational auto-encoder (inv-VAE) outperforms its
competitors in various aspects. In particular, motion-adjusted structural
connectomes are more strongly associated with a wide array of cognition-related
traits than other approaches without motion adjustment. Open source code is
available at https://github.com/yzhang511/inv-vae.
|
[
{
"created": "Thu, 8 Dec 2022 19:54:25 GMT",
"version": "v1"
},
{
"created": "Mon, 10 Jul 2023 19:48:29 GMT",
"version": "v2"
}
] |
2023-07-12
|
[
[
"Zhang",
"Yizi",
""
],
[
"Liu",
"Meimei",
""
],
[
"Zhang",
"Zhengwu",
""
],
[
"Dunson",
"David",
""
]
] |
Mapping of human brain structural connectomes via diffusion MRI offers a unique opportunity to understand brain structural connectivity and relate it to various human traits, such as cognition. However, the presence of motion artifacts during image acquisition can compromise the accuracy of connectome reconstructions and subsequent inference results. We develop a generative model to learn low-dimensional representations of structural connectomes that are invariant to motion artifacts, so that we can link brain networks and human traits more accurately, and generate motion-adjusted connectomes. We applied the proposed model to data from the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP) to investigate how our motion-invariant connectomes facilitate understanding of the brain network and its relationship with cognition. Empirical results demonstrate that the proposed motion-invariant variational auto-encoder (inv-VAE) outperforms its competitors in various aspects. In particular, motion-adjusted structural connectomes are more strongly associated with a wide array of cognition-related traits than other approaches without motion adjustment. Open source code is available at https://github.com/yzhang511/inv-vae.
|
2208.05019
|
Stephen Turner
|
VP Nagraj, Chris Hulme-Lowe, Shakeel Jessa, Stephen D. Turner
|
Automated Infectious Disease Forecasting: Use-cases and Practical
Considerations for Pipeline Implementation
|
Submitted to epiDAMIK 5.0: The 5th International workshop on
Epidemiology meets Data Mining and Knowledge discovery
| null | null | null |
q-bio.OT
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Real-time forecasting of disease outbreaks requires standardized outputs
generated in a timely manner. Development of pipelines to automate infectious
disease forecasts can ensure that parameterization and software dependencies
are common to any execution of the forecasting code. Here we present our
implementation of an automated cloud computing pipeline to forecast infectious
disease outcomes, with examples of usage to forecast COVID-19 and influenza
targets. We also offer our perspective on the limits of automation and
importance of human-in-the-loop automated infectious disease forecasting.
|
[
{
"created": "Tue, 9 Aug 2022 19:50:13 GMT",
"version": "v1"
}
] |
2022-08-11
|
[
[
"Nagraj",
"VP",
""
],
[
"Hulme-Lowe",
"Chris",
""
],
[
"Jessa",
"Shakeel",
""
],
[
"Turner",
"Stephen D.",
""
]
] |
Real-time forecasting of disease outbreaks requires standardized outputs generated in a timely manner. Development of pipelines to automate infectious disease forecasts can ensure that parameterization and software dependencies are common to any execution of the forecasting code. Here we present our implementation of an automated cloud computing pipeline to forecast infectious disease outcomes, with examples of usage to forecast COVID-19 and influenza targets. We also offer our perspective on the limits of automation and importance of human-in-the-loop automated infectious disease forecasting.
|
1907.05017
|
Anne Modat
|
Simon Dittami (LBI2M), Enrique Arboleda (SBR), Jean-Christophe Auguet
(UMR MARBEC), Arite Bigalke, Enora Briand (IFREMER Nantes), Paco C\'ardenas,
Ulisse Cardini, Johan Decelle, Ashwin Engelen (CCMAR), Damien Eveillard
(LS2N), Claire Gachon, Sarah Griffiths, Tilmann Harder, Ehsan Kayal (FR2424),
Elena Kazamia (IBENS), Fran\c{c}ois Lallier (ABICE), M\'onica Medina (PSU),
Ezequiel Marzinelli (SCELSE, USIMS), Teresa Morganti, Laura Pons, Soizic
Prado (MCAM), Jos\'e Pintado Valverde, Mahasweta Saha, Marc-Andre Selosse
(OSEB), Derek Skillings, Willem Stock, Shinichi Sunagawa, Eve Toulza (IHPE),
Alexey Vorobev (CEA), Catherine Leblanc (LBI2M), Fabrice Not
|
A community perspective on the concept of marine holobionts:
state-of-the-art, challenges, and future directions
|
PeerJ Preprints, Computer Science Preprints., 2019
| null |
10.7287/peerj.preprints.27519v1
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Host-microbe interactions play crucial roles in marine ecosystems, but we
still have very little understanding of the mechanisms that govern these
relationships, the evolutionary processes that shape them, and their ecological
consequences. The holobiont concept is a renewed paradigm in biology that can
help describe and understand these complex systems. It posits that a host and
its associated microbiota, living together in a long-lasting relationship, form
the holobiont, and have to be studied together, as a coherent biological and
functional unit, in order to understand the biology, ecology and evolution of
the organisms. Here we discuss critical concepts and opportunities in marine
holobiont research and identify key challenges in the field. We highlight the
potential economic, sociological, and environmental impacts of the holobiont
concept in marine biological, evolutionary, and environmental sciences with
comparisons to terrestrial science whenever appropriate. A deeper understanding
of such complex systems, however, will require further technological and
conceptual advances. The most significant challenge will be to bridge
functional research on simple and tractable model systems and global
approaches. This will require scientists to work together as an (inter)active
community in order to address, for instance, ecological and evolutionary
questions and the roles of holobionts in biogeochemical cycles.
|
[
{
"created": "Thu, 11 Jul 2019 06:40:52 GMT",
"version": "v1"
}
] |
2019-07-12
|
[
[
"Dittami",
"Simon",
"",
"LBI2M"
],
[
"Arboleda",
"Enrique",
"",
"SBR"
],
[
"Auguet",
"Jean-Christophe",
"",
"UMR MARBEC"
],
[
"Bigalke",
"Arite",
"",
"IFREMER Nantes"
],
[
"Briand",
"Enora",
"",
"IFREMER Nantes"
],
[
"Cárdenas",
"Paco",
"",
"CCMAR"
],
[
"Cardini",
"Ulisse",
"",
"CCMAR"
],
[
"Decelle",
"Johan",
"",
"CCMAR"
],
[
"Engelen",
"Ashwin",
"",
"CCMAR"
],
[
"Eveillard",
"Damien",
"",
"LS2N"
],
[
"Gachon",
"Claire",
"",
"FR2424"
],
[
"Griffiths",
"Sarah",
"",
"FR2424"
],
[
"Harder",
"Tilmann",
"",
"FR2424"
],
[
"Kayal",
"Ehsan",
"",
"FR2424"
],
[
"Kazamia",
"Elena",
"",
"IBENS"
],
[
"Lallier",
"François",
"",
"ABICE"
],
[
"Medina",
"Mónica",
"",
"PSU"
],
[
"Marzinelli",
"Ezequiel",
"",
"SCELSE, USIMS"
],
[
"Morganti",
"Teresa",
"",
"MCAM"
],
[
"Pons",
"Laura",
"",
"MCAM"
],
[
"Prado",
"Soizic",
"",
"MCAM"
],
[
"Valverde",
"José Pintado",
"",
"OSEB"
],
[
"Saha",
"Mahasweta",
"",
"OSEB"
],
[
"Selosse",
"Marc-Andre",
"",
"OSEB"
],
[
"Skillings",
"Derek",
"",
"IHPE"
],
[
"Stock",
"Willem",
"",
"IHPE"
],
[
"Sunagawa",
"Shinichi",
"",
"IHPE"
],
[
"Toulza",
"Eve",
"",
"IHPE"
],
[
"Vorobev",
"Alexey",
"",
"CEA"
],
[
"Leblanc",
"Catherine",
"",
"LBI2M"
],
[
"Not",
"Fabrice",
""
]
] |
Host-microbe interactions play crucial roles in marine ecosystems, but we still have very little understanding of the mechanisms that govern these relationships, the evolutionary processes that shape them, and their ecological consequences. The holobiont concept is a renewed paradigm in biology that can help describe and understand these complex systems. It posits that a host and its associated microbiota, living together in a long-lasting relationship, form the holobiont, and have to be studied together, as a coherent biological and functional unit, in order to understand the biology, ecology and evolution of the organisms. Here we discuss critical concepts and opportunities in marine holobiont research and identify key challenges in the field. We highlight the potential economic, sociological, and environmental impacts of the holobiont concept in marine biological, evolutionary, and environmental sciences with comparisons to terrestrial science whenever appropriate. A deeper understanding of such complex systems, however, will require further technological and conceptual advances. The most significant challenge will be to bridge functional research on simple and tractable model systems and global approaches. This will require scientists to work together as an (inter)active community in order to address, for instance, ecological and evolutionary questions and the roles of holobionts in biogeochemical cycles.
|
1705.01568
|
Artem Novozhilov
|
Alexander S. Bratus, Artem S. Novozhilov, and Yuri S. Semenov
|
Adaptive Fitness Landscape for Replicator Systems: To Maximize or not to
Maximize
|
13 pages, 4 figures
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Sewall Wright's adaptive landscape metaphor penetrates a significant part of
evolutionary thinking. Supplemented with Fisher's fundamental theorem of
natural selection and Kimura's maximum principle, it provides a unifying and
intuitive representation of the evolutionary process under the influence of
natural selection as the hill climbing on the surface of mean population
fitness. On the other hand, it is also well known that for many more or less
realistic mathematical models this picture is a sever misrepresentation of what
actually occurs. Therefore, we are faced with two questions. First, it is
important to identify the cases in which adaptive landscape metaphor actually
holds exactly in the models, that is, to identify the conditions under which
system's dynamics coincides with the process of searching for a (local) fitness
maximum. Second, even if the mean fitness is not maximized in the process of
evolution, it is still important to understand the structure of the mean
fitness manifold and see the implications of this structure on the system's
dynamics. Using as a basic model the classical replicator equation, in this
note we attempt to answer these two questions and illustrate our results with
simple well studied systems.
|
[
{
"created": "Wed, 3 May 2017 18:12:01 GMT",
"version": "v1"
}
] |
2017-05-05
|
[
[
"Bratus",
"Alexander S.",
""
],
[
"Novozhilov",
"Artem S.",
""
],
[
"Semenov",
"Yuri S.",
""
]
] |
Sewall Wright's adaptive landscape metaphor penetrates a significant part of evolutionary thinking. Supplemented with Fisher's fundamental theorem of natural selection and Kimura's maximum principle, it provides a unifying and intuitive representation of the evolutionary process under the influence of natural selection as the hill climbing on the surface of mean population fitness. On the other hand, it is also well known that for many more or less realistic mathematical models this picture is a sever misrepresentation of what actually occurs. Therefore, we are faced with two questions. First, it is important to identify the cases in which adaptive landscape metaphor actually holds exactly in the models, that is, to identify the conditions under which system's dynamics coincides with the process of searching for a (local) fitness maximum. Second, even if the mean fitness is not maximized in the process of evolution, it is still important to understand the structure of the mean fitness manifold and see the implications of this structure on the system's dynamics. Using as a basic model the classical replicator equation, in this note we attempt to answer these two questions and illustrate our results with simple well studied systems.
|
1104.4304
|
Jason Graham
|
Jason M. Graham and Bruce P. Ayati and Prem S. Ramakrishnan and James
A. Martin
|
Towards a New Spatial Representation of Bone Remodeling
|
Math. Biosci. Eng., 9(2), 2012
| null |
10.3934/mbe.2012.9.281
| null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Irregular bone remodeling is associated with a number of bone diseases such
as osteoporosis and multiple myeloma.
Computational and mathematical modeling can aid in therapy and treatment as
well as understanding fundamental biology. Different approaches to modeling
give insight into different aspects of a phenomena so it is useful to have an
arsenal of various computational and mathematical models.
Here we develop a mathematical representation of bone remodeling that can
effectively describe many aspects of the complicated geometries and spatial
behavior observed.
There is a sharp interface between bone and marrow regions. Also the surface
of bone moves in and out, i.e. in the normal direction, due to remodeling.
Based on these observations we employ the use of a level-set function to
represent the spatial behavior of remodeling. We elaborate on a temporal model
for osteoclast and osteoblast population dynamics to determine the change in
bone mass which influences how the interface between bone and marrow changes.
We exhibit simulations based on our computational model that show the motion
of the interface between bone and marrow as a consequence of bone remodeling.
The simulations show that it is possible to capture spatial behavior of bone
remodeling in complicated geometries as they occur \emph{in vitro} and \emph{in
vivo}.
By employing the level set approach it is possible to develop computational
and mathematical representations of the spatial behavior of bone remodeling. By
including in this formalism further details, such as more complex cytokine
interactions and accurate parameter values, it is possible to obtain
simulations of phenomena related to bone remodeling with spatial behavior much
as \emph{in vitro} and \emph{in vivo}. This makes it possible to perform
\emph{in silica} experiments more closely resembling experimental observations.
|
[
{
"created": "Thu, 21 Apr 2011 16:25:42 GMT",
"version": "v1"
},
{
"created": "Mon, 12 Mar 2012 15:24:19 GMT",
"version": "v2"
}
] |
2023-02-14
|
[
[
"Graham",
"Jason M.",
""
],
[
"Ayati",
"Bruce P.",
""
],
[
"Ramakrishnan",
"Prem S.",
""
],
[
"Martin",
"James A.",
""
]
] |
Irregular bone remodeling is associated with a number of bone diseases such as osteoporosis and multiple myeloma. Computational and mathematical modeling can aid in therapy and treatment as well as understanding fundamental biology. Different approaches to modeling give insight into different aspects of a phenomena so it is useful to have an arsenal of various computational and mathematical models. Here we develop a mathematical representation of bone remodeling that can effectively describe many aspects of the complicated geometries and spatial behavior observed. There is a sharp interface between bone and marrow regions. Also the surface of bone moves in and out, i.e. in the normal direction, due to remodeling. Based on these observations we employ the use of a level-set function to represent the spatial behavior of remodeling. We elaborate on a temporal model for osteoclast and osteoblast population dynamics to determine the change in bone mass which influences how the interface between bone and marrow changes. We exhibit simulations based on our computational model that show the motion of the interface between bone and marrow as a consequence of bone remodeling. The simulations show that it is possible to capture spatial behavior of bone remodeling in complicated geometries as they occur \emph{in vitro} and \emph{in vivo}. By employing the level set approach it is possible to develop computational and mathematical representations of the spatial behavior of bone remodeling. By including in this formalism further details, such as more complex cytokine interactions and accurate parameter values, it is possible to obtain simulations of phenomena related to bone remodeling with spatial behavior much as \emph{in vitro} and \emph{in vivo}. This makes it possible to perform \emph{in silica} experiments more closely resembling experimental observations.
|
q-bio/0505045
|
Georgy Karev
|
Faina S. Berezovskaya, Georgy P. Karev, and Terry W. Snell
|
Modeling the dynamics of natural rotifer populations: phase-parametric
analysis
|
27 pages, 6 figures; submitted to "Ecological Complexity"
| null | null | null |
q-bio.SC q-bio.OT
| null |
A model of the dynamics of natural rotifer populations is described as a
discrete nonlinear map depending on three parameters, which reflect
characteristics of the population and environment. Model dynamics and their
change by variation of these parameters were investigated by methods of
bifurcation theory. A phase-parametric portrait of the model was constructed
and domains of population persistence (stable equilibrium, periodic and
a-periodic oscillations of population size) as well as population extinction
were identified and investigated. The criteria for population persistence and
approaches to determining critical parameter values are described. The results
identify parameter values that lead to population extinction under various
environmental conditions. They further illustrate that the likelihood of
extinction can be substantially increased by small changes in environmental
quality, which shifts populations into new dynamical regimes.
|
[
{
"created": "Tue, 24 May 2005 21:52:20 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Berezovskaya",
"Faina S.",
""
],
[
"Karev",
"Georgy P.",
""
],
[
"Snell",
"Terry W.",
""
]
] |
A model of the dynamics of natural rotifer populations is described as a discrete nonlinear map depending on three parameters, which reflect characteristics of the population and environment. Model dynamics and their change by variation of these parameters were investigated by methods of bifurcation theory. A phase-parametric portrait of the model was constructed and domains of population persistence (stable equilibrium, periodic and a-periodic oscillations of population size) as well as population extinction were identified and investigated. The criteria for population persistence and approaches to determining critical parameter values are described. The results identify parameter values that lead to population extinction under various environmental conditions. They further illustrate that the likelihood of extinction can be substantially increased by small changes in environmental quality, which shifts populations into new dynamical regimes.
|
1609.04314
|
Sara Bernardi
|
Sara Bernardi, Ezio Venturino
|
An epidemiological model of viral infections in a Varroa-infested bee
colony: the case of a bee-dependent mite population size
| null | null | null | null |
q-bio.PE math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years the spread of the ectoparasitic mite Varroa destructor has
become the most serious threat to worldwide apiculture. In the model presented
here we extend the bee population dynamics with mite viral epidemiology
examined in an earlier paper by allowing a bee-dependent mite population size.
The results of the analysis match field observations well and give a clear
explanation of how Varroa affects the epidemiology of certain naturally
occurring bee viruses, causing considerable damages to colonies. The model
allows only four possible stable equilibria, using known field parameters. The
first one contains only the thriving healthy bees. Here the disease is
eradicated and also the mites are wiped out. Alternatively, we find the
equilibrium still with no mite population, but with endemic disease among the
thriving bee population. Thirdly, infected bees coexist with the mites in the
Varroa invasion scenario; in this situation the disease invades the hive,
driving the healthy bees to extinction and therefore affecting all the bees.
Coexistence is also possible, with both populations of bees and mites thriving
and with the disease endemically affecting both species. The analysis is in
line with field observations in natural honey bee colonies. Namely, these
diseases are endemic and if the mite population is present, necessarily the
viral infection occurs. Further, in agreement with the fact that the presence
of Varroa increments the viral transmission, the whole bee population may
become infected when the disease vector is present in the beehive. Also, a low
horizontal transmission rate of the virus among the honey bees will help in
protecting the bee colonies from Varroa infestation and viral epidemics.
|
[
{
"created": "Wed, 14 Sep 2016 15:35:02 GMT",
"version": "v1"
}
] |
2016-09-15
|
[
[
"Bernardi",
"Sara",
""
],
[
"Venturino",
"Ezio",
""
]
] |
In recent years the spread of the ectoparasitic mite Varroa destructor has become the most serious threat to worldwide apiculture. In the model presented here we extend the bee population dynamics with mite viral epidemiology examined in an earlier paper by allowing a bee-dependent mite population size. The results of the analysis match field observations well and give a clear explanation of how Varroa affects the epidemiology of certain naturally occurring bee viruses, causing considerable damages to colonies. The model allows only four possible stable equilibria, using known field parameters. The first one contains only the thriving healthy bees. Here the disease is eradicated and also the mites are wiped out. Alternatively, we find the equilibrium still with no mite population, but with endemic disease among the thriving bee population. Thirdly, infected bees coexist with the mites in the Varroa invasion scenario; in this situation the disease invades the hive, driving the healthy bees to extinction and therefore affecting all the bees. Coexistence is also possible, with both populations of bees and mites thriving and with the disease endemically affecting both species. The analysis is in line with field observations in natural honey bee colonies. Namely, these diseases are endemic and if the mite population is present, necessarily the viral infection occurs. Further, in agreement with the fact that the presence of Varroa increments the viral transmission, the whole bee population may become infected when the disease vector is present in the beehive. Also, a low horizontal transmission rate of the virus among the honey bees will help in protecting the bee colonies from Varroa infestation and viral epidemics.
|
2205.12334
|
Shahab Mahdian
|
Shahab Mahdian, Michael A. Nitsche, Vahid Nejati
|
Gender differences in the neural structures of (non) emotional
perspective-taking: A tDCS study
|
this is an undergraduate research project
| null | null | null |
q-bio.NC
|
http://creativecommons.org/publicdomain/zero/1.0/
|
The perspective-taking in social cognition is an ability that makes
third-person judgments about the intentions, beliefs and thoughts of others. We
aimed to investigate gender differences in the neural structure differences in
(non) emotional perspective-taking ability.Thirty healthy adults (15 females
and 15 males) received anodal and sham transcranial direct current stimulation
(tDCS) (2 mA, 15 min) over the right temporoparietal junction (rTPJ) and
ventromedial prefrontal cortex (vmPFC).Participants underwent a pain visual
analog scale, a visuospatial perspective-taking task (VPT) and a
self-referential attribution task during stimulation.Findings revealed
significant differences between vmPFC and rTPJ anodal tDCS stimulation in
emotional perspective-taking.Primarily the vmPFC increased emotional perception
in comparison with the rTPJ cite.This emotional perception pattern was
significant only for men. The rTPJ enhanced the males' non-emotional
perspective ability performance compared to their female contributors.The
results indicated that both the rTPJ and vmPFC are associated with
self-referential processing (SRP).The vmPFC increased Self Emotional bias
negatively while rTPJ increased it positively.The vmPFC and rTPJ play a role in
(non) emotional perspective-taking in men.While the vmPFC increased men's
emotional perspective-taking compared to rTPJ.The rTPJ excitatory stimulation
enhanced males' performance compared to females.Moreover, it indicated that the
rTPJ posterior cluster activation in men is higher than in women.
|
[
{
"created": "Tue, 24 May 2022 19:34:34 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Jul 2022 20:23:03 GMT",
"version": "v2"
}
] |
2022-07-22
|
[
[
"Mahdian",
"Shahab",
""
],
[
"Nitsche",
"Michael A.",
""
],
[
"Nejati",
"Vahid",
""
]
] |
The perspective-taking in social cognition is an ability that makes third-person judgments about the intentions, beliefs and thoughts of others. We aimed to investigate gender differences in the neural structure differences in (non) emotional perspective-taking ability.Thirty healthy adults (15 females and 15 males) received anodal and sham transcranial direct current stimulation (tDCS) (2 mA, 15 min) over the right temporoparietal junction (rTPJ) and ventromedial prefrontal cortex (vmPFC).Participants underwent a pain visual analog scale, a visuospatial perspective-taking task (VPT) and a self-referential attribution task during stimulation.Findings revealed significant differences between vmPFC and rTPJ anodal tDCS stimulation in emotional perspective-taking.Primarily the vmPFC increased emotional perception in comparison with the rTPJ cite.This emotional perception pattern was significant only for men. The rTPJ enhanced the males' non-emotional perspective ability performance compared to their female contributors.The results indicated that both the rTPJ and vmPFC are associated with self-referential processing (SRP).The vmPFC increased Self Emotional bias negatively while rTPJ increased it positively.The vmPFC and rTPJ play a role in (non) emotional perspective-taking in men.While the vmPFC increased men's emotional perspective-taking compared to rTPJ.The rTPJ excitatory stimulation enhanced males' performance compared to females.Moreover, it indicated that the rTPJ posterior cluster activation in men is higher than in women.
|
2006.16326
|
Babacar Mbaye Ndiaye
|
Vieux Medoune Ndiaye, Serigne Omar Sarr, Babacar Mbaye Ndiaye
|
Impact of contamination factors on the COVID-19 evolution in Senegal
| null | null | null | null |
q-bio.PE physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this article, we perform an analysis of COVID-19 on one of the South
Saharan countries (hot zone), the Senegal (West Africa). Many questions remain
unanswered: why the African continent is not very contaminated compared to
other continents. Factors of cross immunity, temperature, population density,
youth, etc. are taken into account for an analysis of the contamination
factors. Numerical simulations are carried out for a prediction over the coming
week.
|
[
{
"created": "Mon, 29 Jun 2020 19:16:43 GMT",
"version": "v1"
}
] |
2020-07-01
|
[
[
"Ndiaye",
"Vieux Medoune",
""
],
[
"Sarr",
"Serigne Omar",
""
],
[
"Ndiaye",
"Babacar Mbaye",
""
]
] |
In this article, we perform an analysis of COVID-19 on one of the South Saharan countries (hot zone), the Senegal (West Africa). Many questions remain unanswered: why the African continent is not very contaminated compared to other continents. Factors of cross immunity, temperature, population density, youth, etc. are taken into account for an analysis of the contamination factors. Numerical simulations are carried out for a prediction over the coming week.
|
2208.08361
|
Tulio Pascoal
|
T\'ulio Pascoal, J\'er\'emie Decouchant, Antoine Boutet, Marcus V\"olp
|
I-GWAS: Privacy-Preserving Interdependent Genome-Wide Association
Studies
| null | null | null | null |
q-bio.GN cs.CR cs.DC cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
Genome-wide Association Studies (GWASes) identify genomic variations that are
statistically associated with a trait, such as a disease, in a group of
individuals. Unfortunately, careless sharing of GWAS statistics might give rise
to privacy attacks. Several works attempted to reconcile secure processing with
privacy-preserving releases of GWASes. However, we highlight that these
approaches remain vulnerable if GWASes utilize overlapping sets of individuals
and genomic variations. In such conditions, we show that even when relying on
state-of-the-art techniques for protecting releases, an adversary could
reconstruct the genomic variations of up to 28.6% of participants, and that the
released statistics of up to 92.3% of the genomic variations would enable
membership inference attacks. We introduce I-GWAS, a novel framework that
securely computes and releases the results of multiple possibly interdependent
GWASes. I-GWAS continuously releases privacy-preserving and noise-free GWAS
results as new genomes become available.
|
[
{
"created": "Wed, 17 Aug 2022 15:52:43 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Sep 2022 09:38:31 GMT",
"version": "v2"
}
] |
2022-09-21
|
[
[
"Pascoal",
"Túlio",
""
],
[
"Decouchant",
"Jérémie",
""
],
[
"Boutet",
"Antoine",
""
],
[
"Völp",
"Marcus",
""
]
] |
Genome-wide Association Studies (GWASes) identify genomic variations that are statistically associated with a trait, such as a disease, in a group of individuals. Unfortunately, careless sharing of GWAS statistics might give rise to privacy attacks. Several works attempted to reconcile secure processing with privacy-preserving releases of GWASes. However, we highlight that these approaches remain vulnerable if GWASes utilize overlapping sets of individuals and genomic variations. In such conditions, we show that even when relying on state-of-the-art techniques for protecting releases, an adversary could reconstruct the genomic variations of up to 28.6% of participants, and that the released statistics of up to 92.3% of the genomic variations would enable membership inference attacks. We introduce I-GWAS, a novel framework that securely computes and releases the results of multiple possibly interdependent GWASes. I-GWAS continuously releases privacy-preserving and noise-free GWAS results as new genomes become available.
|
1507.02583
|
Elsa Guillot
|
Elsa G. Guillot, Murray P. Cox
|
High Frequency Haplotypes are Expected Events, not Historical Figures
|
8 pages, 1 figure, reply letter
| null | null | null |
q-bio.PE
|
http://creativecommons.org/licenses/by/4.0/
|
Cultural transmission of reproductive success states that successful men have
more children and pass this raised fecundity to their offspring. Balaresque and
colleagues found high frequency haplotypes in a Central Asian Y chromosome
dataset, which they attribute to cultural transmission of reproductive success
by prominent historical men, including Genghis Khan. Using coalescent
simulation, we show that these high frequency haplotypes are consistent with a
neutral model, where they commonly appear simply by chance. Hence, explanations
invoking cultural transmission of reproductive success are statistically
unnecessary.
|
[
{
"created": "Wed, 8 Jul 2015 09:43:28 GMT",
"version": "v1"
},
{
"created": "Thu, 17 Sep 2015 15:49:58 GMT",
"version": "v2"
}
] |
2015-09-18
|
[
[
"Guillot",
"Elsa G.",
""
],
[
"Cox",
"Murray P.",
""
]
] |
Cultural transmission of reproductive success states that successful men have more children and pass this raised fecundity to their offspring. Balaresque and colleagues found high frequency haplotypes in a Central Asian Y chromosome dataset, which they attribute to cultural transmission of reproductive success by prominent historical men, including Genghis Khan. Using coalescent simulation, we show that these high frequency haplotypes are consistent with a neutral model, where they commonly appear simply by chance. Hence, explanations invoking cultural transmission of reproductive success are statistically unnecessary.
|
1206.4361
|
Stephen Wirkus
|
Erika T. Camacho, Stephen Wirkus
|
Tracing the Progression of Retinitis Pigmentosa via Photoreceptor
Interactions
|
35 pages, 6 figures
| null | null | null |
q-bio.QM q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Retinitis pigmentosa (RP) is a group of inherited degenerative eye diseases
characterized by mutations in the genetic structure of the photoreceptors that
leads to the premature death of both rod and cone photoreceptors. Defects in
particular genes encoding proteins that are involved in either the
photoreceptor structure, phototransduction cascades, or visual cycle are
expressed in the rods but ultimately affect both types of cells. RP is
"typically" manifested by a steady death of rods followed by a period of
stability in which cones survive initially and then inevitably die too. In some
RP cases, rods and cones die off simultaneously or even cone death precedes rod
death (reverse RP). The mechanisms and factors involved in the development of
the different types of RP are not well understood nor have researchers been
able to provide more than a limited number of short-term therapies. In this
work we trace the progression of RP to complete blindness through each subtype
via bifurcation theory. We show that the evolution of RP from one stage to
another often requires the failure of multiple components. Our results indicate
that a delicate balance between the availability of nutrients and the rates of
shedding and renewal of photoreceptors is needed at every stage of RP to halt
its progression. This work provides a framework for future physiological
investigations potentially leading to long-term targeted multi-facet
interventions and therapies dependent on the particular stage and subtype of RP
under consideration. The results of this mathematical model may also give
insight into the progression of many other degenerative eye diseases involving
genetic mutations or secondary photoreceptor death and potential ways to
circumvent these diseases.
|
[
{
"created": "Tue, 19 Jun 2012 23:26:15 GMT",
"version": "v1"
}
] |
2012-06-21
|
[
[
"Camacho",
"Erika T.",
""
],
[
"Wirkus",
"Stephen",
""
]
] |
Retinitis pigmentosa (RP) is a group of inherited degenerative eye diseases characterized by mutations in the genetic structure of the photoreceptors that leads to the premature death of both rod and cone photoreceptors. Defects in particular genes encoding proteins that are involved in either the photoreceptor structure, phototransduction cascades, or visual cycle are expressed in the rods but ultimately affect both types of cells. RP is "typically" manifested by a steady death of rods followed by a period of stability in which cones survive initially and then inevitably die too. In some RP cases, rods and cones die off simultaneously or even cone death precedes rod death (reverse RP). The mechanisms and factors involved in the development of the different types of RP are not well understood nor have researchers been able to provide more than a limited number of short-term therapies. In this work we trace the progression of RP to complete blindness through each subtype via bifurcation theory. We show that the evolution of RP from one stage to another often requires the failure of multiple components. Our results indicate that a delicate balance between the availability of nutrients and the rates of shedding and renewal of photoreceptors is needed at every stage of RP to halt its progression. This work provides a framework for future physiological investigations potentially leading to long-term targeted multi-facet interventions and therapies dependent on the particular stage and subtype of RP under consideration. The results of this mathematical model may also give insight into the progression of many other degenerative eye diseases involving genetic mutations or secondary photoreceptor death and potential ways to circumvent these diseases.
|
1105.6282
|
Davron Matrasulov
|
E.K. Ivanova, N.N. Turaeva, B.L. Oksengendler
|
Quantum theory of hydrogen key of point mutation in DNA
| null |
BioPhysics, Issue 2, (2011) 3
| null | null |
q-bio.OT physics.bio-ph quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Quantum theory of hydrogen atoms distribution between two complementary
nucleotide bases in DNA double helix at moment of replication has been proposed
in this work. It bases on two mechanisms of proton tunneling: the
Andreev-Meyerovich mechanism with spontaneous phonon radiation and the
Kagan-Maximov (Flynn-Stoneham) mechanism at phonon scattering. According to the
presented model, the probability of proton location in shallow potential well
(point mutation form) is directly proportional to temperature. It was shown
that the point mutation probability decreases with increasing replication
velocity.
|
[
{
"created": "Sat, 28 May 2011 02:41:02 GMT",
"version": "v1"
}
] |
2011-06-01
|
[
[
"Ivanova",
"E. K.",
""
],
[
"Turaeva",
"N. N.",
""
],
[
"Oksengendler",
"B. L.",
""
]
] |
Quantum theory of hydrogen atoms distribution between two complementary nucleotide bases in DNA double helix at moment of replication has been proposed in this work. It bases on two mechanisms of proton tunneling: the Andreev-Meyerovich mechanism with spontaneous phonon radiation and the Kagan-Maximov (Flynn-Stoneham) mechanism at phonon scattering. According to the presented model, the probability of proton location in shallow potential well (point mutation form) is directly proportional to temperature. It was shown that the point mutation probability decreases with increasing replication velocity.
|
1404.0444
|
Stefano Schivo
|
Stefano Schivo (1), Jetse Scholma (2), Marcel Karperien (2), Janine N.
Post (2), Jaco van de Pol (1) and Rom Langerak (1) ((1) Formal Methods and
Tools, Faculty of EEMCS, University of Twente, Enschede, The Netherlands, (2)
Developmental BioEngineering, MIRA Institute for Biomedical Technology and
Technical Medicine, University of Twente, Enschede, The Netherlands)
|
Setting Parameters for Biological Models With ANIMO
| null |
EPTCS 145, 2014, pp. 35-47
|
10.4204/EPTCS.145.5
| null |
q-bio.MN cs.CE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
ANIMO (Analysis of Networks with Interactive MOdeling) is a software for
modeling biological networks, such as e.g. signaling, metabolic or gene
networks. An ANIMO model is essentially the sum of a network topology and a
number of interaction parameters. The topology describes the interactions
between biological entities in form of a graph, while the parameters determine
the speed of occurrence of such interactions. When a mismatch is observed
between the behavior of an ANIMO model and experimental data, we want to update
the model so that it explains the new data. In general, the topology of a model
can be expanded with new (known or hypothetical) nodes, and enables it to match
experimental data. However, the unrestrained addition of new parts to a model
causes two problems: models can become too complex too fast, to the point of
being intractable, and too many parts marked as "hypothetical" or "not known"
make a model unrealistic. Even if changing the topology is normally the easier
task, these problems push us to try a better parameter fit as a first step, and
resort to modifying the model topology only as a last resource. In this paper
we show the support added in ANIMO to ease the task of expanding the knowledge
on biological networks, concentrating in particular on the parameter settings.
|
[
{
"created": "Wed, 2 Apr 2014 03:38:20 GMT",
"version": "v1"
}
] |
2014-04-03
|
[
[
"Schivo",
"Stefano",
""
],
[
"Scholma",
"Jetse",
""
],
[
"Karperien",
"Marcel",
""
],
[
"Post",
"Janine N.",
""
],
[
"van de Pol",
"Jaco",
""
],
[
"Langerak",
"Rom",
""
]
] |
ANIMO (Analysis of Networks with Interactive MOdeling) is a software for modeling biological networks, such as e.g. signaling, metabolic or gene networks. An ANIMO model is essentially the sum of a network topology and a number of interaction parameters. The topology describes the interactions between biological entities in form of a graph, while the parameters determine the speed of occurrence of such interactions. When a mismatch is observed between the behavior of an ANIMO model and experimental data, we want to update the model so that it explains the new data. In general, the topology of a model can be expanded with new (known or hypothetical) nodes, and enables it to match experimental data. However, the unrestrained addition of new parts to a model causes two problems: models can become too complex too fast, to the point of being intractable, and too many parts marked as "hypothetical" or "not known" make a model unrealistic. Even if changing the topology is normally the easier task, these problems push us to try a better parameter fit as a first step, and resort to modifying the model topology only as a last resource. In this paper we show the support added in ANIMO to ease the task of expanding the knowledge on biological networks, concentrating in particular on the parameter settings.
|
2111.07336
|
Markus Kirkilionis
|
David Alonso, Steffen Bauer, Markus Kirkilionis, Lisa Maria Kreusser,
Luca Sbano
|
A Rule-Based Epidemiological Modelling Framework
|
67 pages, 23 figures
| null | null | null |
q-bio.PE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Motivated by chemical reaction rules, we introduce a rule-based
epidemiological framework for the systematic mathematical modelling of future
pandemics. Here we stress that we do not have a specific model in mind, but a
whole collection of models which can be transformed into each other, or
represent different aspects of a pandemic, and these aspects can change during
the course of the emergency, as happened during the Covid-19 pandemic. As
conditions for outbreaks in the modern world change on different time-scales,
some rapidly, epidemiology has few 'laws', besides perhaps the fundamental
infection process described by Kermack-McKendrick. Each single of our variety
of models, called framework, is based on a mathematical formulation that we
call a rule-based system. They have several advantages, for example that they
can be both interpreted stochastically and deterministically, without changing
the model structure. Rule-based systems should be easier to communicate to
non-specialists, when compared to differential equations. Due to their
combinatorial nature, the rule-based model framework we propose is ideal for
systematic mathematical modelling, systematic links to statistics, data
analysis in general and also machine learning leading to artificial
intelligence.
|
[
{
"created": "Sun, 14 Nov 2021 13:11:38 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Nov 2021 16:42:09 GMT",
"version": "v2"
},
{
"created": "Wed, 22 May 2024 13:58:23 GMT",
"version": "v3"
}
] |
2024-05-24
|
[
[
"Alonso",
"David",
""
],
[
"Bauer",
"Steffen",
""
],
[
"Kirkilionis",
"Markus",
""
],
[
"Kreusser",
"Lisa Maria",
""
],
[
"Sbano",
"Luca",
""
]
] |
Motivated by chemical reaction rules, we introduce a rule-based epidemiological framework for the systematic mathematical modelling of future pandemics. Here we stress that we do not have a specific model in mind, but a whole collection of models which can be transformed into each other, or represent different aspects of a pandemic, and these aspects can change during the course of the emergency, as happened during the Covid-19 pandemic. As conditions for outbreaks in the modern world change on different time-scales, some rapidly, epidemiology has few 'laws', besides perhaps the fundamental infection process described by Kermack-McKendrick. Each single of our variety of models, called framework, is based on a mathematical formulation that we call a rule-based system. They have several advantages, for example that they can be both interpreted stochastically and deterministically, without changing the model structure. Rule-based systems should be easier to communicate to non-specialists, when compared to differential equations. Due to their combinatorial nature, the rule-based model framework we propose is ideal for systematic mathematical modelling, systematic links to statistics, data analysis in general and also machine learning leading to artificial intelligence.
|
1512.00163
|
Yoav Ram
|
Yoav Ram and Lilach Hadany
|
The probability of improvement in Fisher's geometric model: a
probabilistic approach
|
Post-print
|
Theoretical Population Biology 99 (February): 1-6 (2015)
|
10.1016/j.tpb.2014.10.004
| null |
q-bio.PE
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Fisher developed his geometric model to support the micro-mutationalism
hypothesis which claims that small mutations are more likely to be beneficial
and therefore to contribute to evolution and adaptation. While others have
provided a general solution to the model using geometric approaches, we derive
an equivalent general solution using a probabilistic approach. Our approach to
Fisher's geometric model provides alternative intuition and interpretation of
the solution in terms of the model's parameters: for mutation to improve a
phenotype, its relative beneficial effect must be larger than the ratio of its
total effect and twice the difference between the current phenotype and the
optimal one. Our approach provides new insight into this classical model of
adaptive evolution.
|
[
{
"created": "Tue, 1 Dec 2015 07:04:10 GMT",
"version": "v1"
}
] |
2015-12-02
|
[
[
"Ram",
"Yoav",
""
],
[
"Hadany",
"Lilach",
""
]
] |
Fisher developed his geometric model to support the micro-mutationalism hypothesis which claims that small mutations are more likely to be beneficial and therefore to contribute to evolution and adaptation. While others have provided a general solution to the model using geometric approaches, we derive an equivalent general solution using a probabilistic approach. Our approach to Fisher's geometric model provides alternative intuition and interpretation of the solution in terms of the model's parameters: for mutation to improve a phenotype, its relative beneficial effect must be larger than the ratio of its total effect and twice the difference between the current phenotype and the optimal one. Our approach provides new insight into this classical model of adaptive evolution.
|
1004.4528
|
Bernat Corominas-Murtra BCM
|
Ricard V. Sol\'e, Bernat Corominas-Murtra and Jordi Fortuny
|
Diversity, competition, extinction: the ecophysics of language change
|
17 Pages. A review on current models from statistical Physics and
Theoretical Ecology applied to study language dynamics
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As early indicated by Charles Darwin, languages behave and change very much
like living species. They display high diversity, differentiate in space and
time, emerge and disappear. A large body of literature has explored the role of
information exchanges and communicative constraints in groups of agents under
selective scenarios. These models have been very helpful in providing a
rationale on how complex forms of communication emerge under evolutionary
pressures. However, other patterns of large-scale organization can be described
using mathematical methods ignoring communicative traits. These approaches
consider shorter time scales and have been developed by exploiting both
theoretical ecology and statistical physics methods. The models are reviewed
here and include extinction, invasion, origination, spatial organization,
coexistence and diversity as key concepts and are very simple in their defining
rules. Such simplicity is used in order to catch the most fundamental laws of
organization and those universal ingredients responsible for qualitative
traits. The similarities between observed and predicted patterns indicate that
an ecological theory of language is emerging, supporting (on a quantitative
basis) its ecological nature, although key differences are also present. Here
we critically review some recent advances lying and outline their implications
and limitations as well as open problems for future research.
|
[
{
"created": "Wed, 21 Apr 2010 14:23:30 GMT",
"version": "v1"
}
] |
2010-04-27
|
[
[
"Solé",
"Ricard V.",
""
],
[
"Corominas-Murtra",
"Bernat",
""
],
[
"Fortuny",
"Jordi",
""
]
] |
As early indicated by Charles Darwin, languages behave and change very much like living species. They display high diversity, differentiate in space and time, emerge and disappear. A large body of literature has explored the role of information exchanges and communicative constraints in groups of agents under selective scenarios. These models have been very helpful in providing a rationale on how complex forms of communication emerge under evolutionary pressures. However, other patterns of large-scale organization can be described using mathematical methods ignoring communicative traits. These approaches consider shorter time scales and have been developed by exploiting both theoretical ecology and statistical physics methods. The models are reviewed here and include extinction, invasion, origination, spatial organization, coexistence and diversity as key concepts and are very simple in their defining rules. Such simplicity is used in order to catch the most fundamental laws of organization and those universal ingredients responsible for qualitative traits. The similarities between observed and predicted patterns indicate that an ecological theory of language is emerging, supporting (on a quantitative basis) its ecological nature, although key differences are also present. Here we critically review some recent advances lying and outline their implications and limitations as well as open problems for future research.
|
2003.07283
|
Tam\'as R\'obert Mezei
|
P\'eter L. Erd\H{o}s, Andrew Francis, Tam\'as R\'obert Mezei
|
Rooted NNI moves on tree-based phylogenetic networks
|
Fixed typos and references to labels in the last subsection
|
Discrete Applied Mathematics, Volume 294, 15 May 2021, Pages
205-213
|
10.1016/j.dam.2021.02.016
| null |
q-bio.PE math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We show that the space of rooted tree-based phylogenetic networks is
connected under rooted nearest-neighbour interchange (rNNI) moves.
|
[
{
"created": "Mon, 16 Mar 2020 15:41:49 GMT",
"version": "v1"
},
{
"created": "Tue, 17 Mar 2020 16:57:56 GMT",
"version": "v2"
}
] |
2021-07-06
|
[
[
"Erdős",
"Péter L.",
""
],
[
"Francis",
"Andrew",
""
],
[
"Mezei",
"Tamás Róbert",
""
]
] |
We show that the space of rooted tree-based phylogenetic networks is connected under rooted nearest-neighbour interchange (rNNI) moves.
|
2110.12723
|
Lizhen Huang
|
C He (1), C Lin (1), G Mo (1), B Xi (1), A Li (3), D Huang (1), Y Wan
(1), F Chen (1), Y Liang (3), Q Zuo (1), W Xu (1), D Feng (1), G Zhang (4), L
Han (1), C Ke (2), H Du (1), L Huang (1)
|
Rapid and Accurate Detection of SARS-CoV-2 Mutations using a
Cas12a-based Sensing Platform
|
39 pages, 6 figures
| null | null | null |
q-bio.QM q-bio.BM
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The increasing prevalence of SARS-CoV-2 variants with spike mutations has
raised concerns owing to higher transmission rates, disease severity, and
escape from neutralizing antibodies. Rapid and accurate detection of SARS-CoV-2
variants provides crucial information concerning the outbreaks of SARS-CoV-2
variants and possible lines of transmission. This information is vital for
infection prevention and control. We used a Cas12a-based RT-PCR combined with
CRISPR on-site rapid detection system (RT-CORDS) platform to detect the key
mutations in SARS-COV-2 variants, such as 69/70 deletion, N501Y, and D614G. We
used type-specific CRISPR RNAs (crRNAs) to identify wild-type (crRNA-W) and
mutant (crRNA-M) sequences of SARS-CoV-2. We successfully differentiated mutant
variants from wild-type SARS-CoV-2 with a sensitivity of $10^{-17}$ M
(approximately 6 copies/$\mu$L). The assay took just 10 min with the
Cas12a/crRNA reaction after a simple RT-PCR using a fluorescence reporting
system. In addition, a sensitivity of $10^{-16}$ M could be achieved when
lateral flow strips were used as readouts. The accuracy of RT-CORDS for
SARS-CoV-2 variant detection was 100% consistent with the sequencing data. In
conclusion, using the RT-CORDS platform, we accurately, sensitively,
specifically, and rapidly detected SARS-CoV-2 variants. This method may be used
in clinical diagnosis.
|
[
{
"created": "Mon, 25 Oct 2021 08:19:21 GMT",
"version": "v1"
}
] |
2021-10-26
|
[
[
"He",
"C",
""
],
[
"Lin",
"C",
""
],
[
"Mo",
"G",
""
],
[
"Xi",
"B",
""
],
[
"Li",
"A",
""
],
[
"Huang",
"D",
""
],
[
"Wan",
"Y",
""
],
[
"Chen",
"F",
""
],
[
"Liang",
"Y",
""
],
[
"Zuo",
"Q",
""
],
[
"Xu",
"W",
""
],
[
"Feng",
"D",
""
],
[
"Zhang",
"G",
""
],
[
"Han",
"L",
""
],
[
"Ke",
"C",
""
],
[
"Du",
"H",
""
],
[
"Huang",
"L",
""
]
] |
The increasing prevalence of SARS-CoV-2 variants with spike mutations has raised concerns owing to higher transmission rates, disease severity, and escape from neutralizing antibodies. Rapid and accurate detection of SARS-CoV-2 variants provides crucial information concerning the outbreaks of SARS-CoV-2 variants and possible lines of transmission. This information is vital for infection prevention and control. We used a Cas12a-based RT-PCR combined with CRISPR on-site rapid detection system (RT-CORDS) platform to detect the key mutations in SARS-COV-2 variants, such as 69/70 deletion, N501Y, and D614G. We used type-specific CRISPR RNAs (crRNAs) to identify wild-type (crRNA-W) and mutant (crRNA-M) sequences of SARS-CoV-2. We successfully differentiated mutant variants from wild-type SARS-CoV-2 with a sensitivity of $10^{-17}$ M (approximately 6 copies/$\mu$L). The assay took just 10 min with the Cas12a/crRNA reaction after a simple RT-PCR using a fluorescence reporting system. In addition, a sensitivity of $10^{-16}$ M could be achieved when lateral flow strips were used as readouts. The accuracy of RT-CORDS for SARS-CoV-2 variant detection was 100% consistent with the sequencing data. In conclusion, using the RT-CORDS platform, we accurately, sensitively, specifically, and rapidly detected SARS-CoV-2 variants. This method may be used in clinical diagnosis.
|
2010.06477
|
Vitali Nesterov
|
Vitali Nesterov, Mario Wieser, Volker Roth
|
3DMolNet: A Generative Network for Molecular Structures
| null | null | null | null |
q-bio.BM cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
With the recent advances in machine learning for quantum chemistry, it is now
possible to predict the chemical properties of compounds and to generate novel
molecules. Existing generative models mostly use a string- or graph-based
representation, but the precise three-dimensional coordinates of the atoms are
usually not encoded. First attempts in this direction have been proposed, where
autoregressive or GAN-based models generate atom coordinates. Those either lack
a latent space in the autoregressive setting, such that a smooth exploration of
the compound space is not possible, or cannot generalize to varying chemical
compositions. We propose a new approach to efficiently generate molecular
structures that are not restricted to a fixed size or composition. Our model is
based on the variational autoencoder which learns a translation-, rotation-,
and permutation-invariant low-dimensional representation of molecules. Our
experiments yield a mean reconstruction error below 0.05 Angstrom,
outperforming the current state-of-the-art methods by a factor of four, and
which is even lower than the spatial quantization error of most chemical
descriptors. The compositional and structural validity of newly generated
molecules has been confirmed by quantum chemical methods in a set of
experiments.
|
[
{
"created": "Thu, 8 Oct 2020 13:04:36 GMT",
"version": "v1"
}
] |
2020-10-14
|
[
[
"Nesterov",
"Vitali",
""
],
[
"Wieser",
"Mario",
""
],
[
"Roth",
"Volker",
""
]
] |
With the recent advances in machine learning for quantum chemistry, it is now possible to predict the chemical properties of compounds and to generate novel molecules. Existing generative models mostly use a string- or graph-based representation, but the precise three-dimensional coordinates of the atoms are usually not encoded. First attempts in this direction have been proposed, where autoregressive or GAN-based models generate atom coordinates. Those either lack a latent space in the autoregressive setting, such that a smooth exploration of the compound space is not possible, or cannot generalize to varying chemical compositions. We propose a new approach to efficiently generate molecular structures that are not restricted to a fixed size or composition. Our model is based on the variational autoencoder which learns a translation-, rotation-, and permutation-invariant low-dimensional representation of molecules. Our experiments yield a mean reconstruction error below 0.05 Angstrom, outperforming the current state-of-the-art methods by a factor of four, and which is even lower than the spatial quantization error of most chemical descriptors. The compositional and structural validity of newly generated molecules has been confirmed by quantum chemical methods in a set of experiments.
|
2011.03443
|
Amir Shanehsazzadeh
|
Amir Shanehsazzadeh, David Belanger, David Dohan
|
Is Transfer Learning Necessary for Protein Landscape Prediction?
| null | null | null | null |
q-bio.BM cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, there has been great interest in learning how to best represent
proteins, specifically with fixed-length embeddings. Deep learning has become a
popular tool for protein representation learning as a model's hidden layers
produce potentially useful vector embeddings. TAPE introduced a number of
benchmark tasks and showed that semi-supervised learning, via pretraining
language models on a large protein corpus, improved performance on downstream
tasks. Two of the tasks (fluorescence prediction and stability prediction)
involve learning fitness landscapes. In this paper, we show that CNN models
trained solely using supervised learning both compete with and sometimes
outperform the best models from TAPE that leverage expensive pretraining on
large protein datasets. These CNN models are sufficiently simple and small that
they can be trained using a Google Colab notebook. We also find for the
fluorescence task that linear regression outperforms our models and the TAPE
models. The benchmarking tasks proposed by TAPE are excellent measures of a
model's ability to predict protein function and should be used going forward.
However, we believe it is important to add baselines from simple models to put
the performance of the semi-supervised models that have been reported so far
into perspective.
|
[
{
"created": "Sat, 31 Oct 2020 20:41:36 GMT",
"version": "v1"
}
] |
2020-11-09
|
[
[
"Shanehsazzadeh",
"Amir",
""
],
[
"Belanger",
"David",
""
],
[
"Dohan",
"David",
""
]
] |
Recently, there has been great interest in learning how to best represent proteins, specifically with fixed-length embeddings. Deep learning has become a popular tool for protein representation learning as a model's hidden layers produce potentially useful vector embeddings. TAPE introduced a number of benchmark tasks and showed that semi-supervised learning, via pretraining language models on a large protein corpus, improved performance on downstream tasks. Two of the tasks (fluorescence prediction and stability prediction) involve learning fitness landscapes. In this paper, we show that CNN models trained solely using supervised learning both compete with and sometimes outperform the best models from TAPE that leverage expensive pretraining on large protein datasets. These CNN models are sufficiently simple and small that they can be trained using a Google Colab notebook. We also find for the fluorescence task that linear regression outperforms our models and the TAPE models. The benchmarking tasks proposed by TAPE are excellent measures of a model's ability to predict protein function and should be used going forward. However, we believe it is important to add baselines from simple models to put the performance of the semi-supervised models that have been reported so far into perspective.
|
1402.5338
|
Jian-Jun Shu
|
Jian-Jun Shu and Yajing Li
|
A statistical fat-tail test of predicting regulatory regions in the
Drosophila genome
| null |
Computers in Biology and Medicine, Vol. 42, No. 9, pp. 935-941,
2012
|
10.1016/j.compbiomed.2012.07.007
| null |
q-bio.QM q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A statistical study of cis-regulatory modules (CRMs) is presented based on
the estimation of similar-word set distribution. It is observed that CRMs tend
to have a fat-tail distribution. A new statistical fat-tail test with two
kurtosis-based fatness coefficients is proposed to distinguish CRMs from
non-CRMs. As compared with the existing fluffy-tail test, the first fatness
coefficient is designed to reduce computational time, making the novel fat-tail
test very suitable for long sequences and large database analysis in the
post-genome time and the second one to improve separation accuracy between CRMs
and non-CRMs. These two fatness coefficients may be served as valuable
filtering indexes to predict CRMs experimentally.
|
[
{
"created": "Fri, 21 Feb 2014 16:10:59 GMT",
"version": "v1"
},
{
"created": "Fri, 7 Mar 2014 15:09:14 GMT",
"version": "v2"
},
{
"created": "Wed, 2 Aug 2023 12:31:30 GMT",
"version": "v3"
}
] |
2023-08-03
|
[
[
"Shu",
"Jian-Jun",
""
],
[
"Li",
"Yajing",
""
]
] |
A statistical study of cis-regulatory modules (CRMs) is presented based on the estimation of similar-word set distribution. It is observed that CRMs tend to have a fat-tail distribution. A new statistical fat-tail test with two kurtosis-based fatness coefficients is proposed to distinguish CRMs from non-CRMs. As compared with the existing fluffy-tail test, the first fatness coefficient is designed to reduce computational time, making the novel fat-tail test very suitable for long sequences and large database analysis in the post-genome time and the second one to improve separation accuracy between CRMs and non-CRMs. These two fatness coefficients may be served as valuable filtering indexes to predict CRMs experimentally.
|
1312.2942
|
Wes Maciejewski
|
Wes Maciejewski, Feng Fu, and Christoph Hauert
|
Evolutionary Game Dynamics in Populations with Heterogeneous Structures
| null |
PLOS Comp. Biol. 10 (4) e1003567 (2014)
|
10.1371/journal.pcbi.1003567
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Evolutionary graph theory is a well established framework for modelling the
evolution of social behaviours in structured populations. An emerging consensus
in this field is that graphs that exhibit heterogeneity in the number of
connections between individuals are more conducive to the spread of cooperative
behaviours. In this article we show that such a conclusion largely depends on
the individual-level interactions that take place. In particular, averaging
payoffs garnered through game interactions rather than accumulating the payoffs
can altogether remove the cooperative advantage of heterogeneous graphs while
such a difference does not affect the outcome on homogeneous structures. In
addition, the rate at which game interactions occur can alter the evolutionary
outcome. Less interactions allow heterogeneous graphs to support more
cooperation than homogeneous graphs, while higher rates of interactions make
homogeneous and heterogeneous graphs virtually indistinguishable in their
ability to support cooperation. Most importantly, we show that common measures
of evolutionary advantage used in homogeneous populations, such as a comparison
of the fixation probability of a rare mutant to that of the resident type, are
no longer valid in heterogeneous populations. Heterogeneity causes a bias in
where mutations occur in the population which affects the mutant's fixation
probability. We derive the appropriate measures for heterogeneous populations
that account for this bias.
|
[
{
"created": "Tue, 10 Dec 2013 20:46:39 GMT",
"version": "v1"
}
] |
2018-11-20
|
[
[
"Maciejewski",
"Wes",
""
],
[
"Fu",
"Feng",
""
],
[
"Hauert",
"Christoph",
""
]
] |
Evolutionary graph theory is a well established framework for modelling the evolution of social behaviours in structured populations. An emerging consensus in this field is that graphs that exhibit heterogeneity in the number of connections between individuals are more conducive to the spread of cooperative behaviours. In this article we show that such a conclusion largely depends on the individual-level interactions that take place. In particular, averaging payoffs garnered through game interactions rather than accumulating the payoffs can altogether remove the cooperative advantage of heterogeneous graphs while such a difference does not affect the outcome on homogeneous structures. In addition, the rate at which game interactions occur can alter the evolutionary outcome. Less interactions allow heterogeneous graphs to support more cooperation than homogeneous graphs, while higher rates of interactions make homogeneous and heterogeneous graphs virtually indistinguishable in their ability to support cooperation. Most importantly, we show that common measures of evolutionary advantage used in homogeneous populations, such as a comparison of the fixation probability of a rare mutant to that of the resident type, are no longer valid in heterogeneous populations. Heterogeneity causes a bias in where mutations occur in the population which affects the mutant's fixation probability. We derive the appropriate measures for heterogeneous populations that account for this bias.
|
2302.04200
|
Abdul Majiid
|
Madiha Hameed, Muhammad Bilal, Tuba Majid, Abdul Majid and Asifullah
Khan
|
Early Risk Prediction of Chronic Myeloid Leukemia with Protein Sequences
using Machine Learning-based Meta-Ensemble
| null | null | null | null |
q-bio.GN
|
http://creativecommons.org/licenses/by/4.0/
|
Leukemia, the cancer of blood cells, originates in the blood-forming cells of
the bone marrow. In Chronic Myeloid Leukemia (CML) conditions, the cells
partially become mature that look like normal white blood cells but do not
resist infection effectively. Early detection of CML is important for effective
treatment, but there is a lack of routine screening tests. Regular check-ups
and monitoring of symptoms are the best way to detect CML in the early stages.
In the study, we developed a multi-layer-perception-based meta-ensemble system
using protein amino acid sequences for early risk prediction of CML. The
deleterious mutation analysis of protein sequences provides 7discriminant
information in amino acid sequences causing CML. The protein sequences are
expressed into molecular descriptors using the values of hydrophobicity and
hydrophilicity of the amino acids. 9 These descriptors are transformed in
various statistical and correlation-based feature spaces. These 10 features
information is given to several diverse types of base learners. The preliminary
predictions of 11 base-learners are employed to develop Multi-Layered
Perceptron (MLP) based meta-ensemble. The 12 proposed learning approach
effectively utilizes the discriminant information to classify CML/non- 13 CML
protein sequences. The proposed prediction system has given improved results
and it can be 14 employed as a potential biomarker for early diagnosis of CML.
|
[
{
"created": "Wed, 8 Feb 2023 17:12:51 GMT",
"version": "v1"
}
] |
2023-02-09
|
[
[
"Hameed",
"Madiha",
""
],
[
"Bilal",
"Muhammad",
""
],
[
"Majid",
"Tuba",
""
],
[
"Majid",
"Abdul",
""
],
[
"Khan",
"Asifullah",
""
]
] |
Leukemia, the cancer of blood cells, originates in the blood-forming cells of the bone marrow. In Chronic Myeloid Leukemia (CML) conditions, the cells partially become mature that look like normal white blood cells but do not resist infection effectively. Early detection of CML is important for effective treatment, but there is a lack of routine screening tests. Regular check-ups and monitoring of symptoms are the best way to detect CML in the early stages. In the study, we developed a multi-layer-perception-based meta-ensemble system using protein amino acid sequences for early risk prediction of CML. The deleterious mutation analysis of protein sequences provides 7discriminant information in amino acid sequences causing CML. The protein sequences are expressed into molecular descriptors using the values of hydrophobicity and hydrophilicity of the amino acids. 9 These descriptors are transformed in various statistical and correlation-based feature spaces. These 10 features information is given to several diverse types of base learners. The preliminary predictions of 11 base-learners are employed to develop Multi-Layered Perceptron (MLP) based meta-ensemble. The 12 proposed learning approach effectively utilizes the discriminant information to classify CML/non- 13 CML protein sequences. The proposed prediction system has given improved results and it can be 14 employed as a potential biomarker for early diagnosis of CML.
|
2112.00360
|
Jana Massing
|
Jana C. Massing and Thilo Gross
|
Generalized Modeling: A survey and guide
|
46 pages, 6 figures Subsection 6.3 (Normalization): minor corrections
in normalization of second equation of the two-variable system
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many current challenges involve understanding the complex dynamical interplay
between the constituents of systems. Typically, the number of such constituents
is high, but only limited data sources on them are available. Conventional
dynamical models of complex systems are rarely mathematically tractable and
their numerical exploration suffers both from computational and data
limitations. Here we review generalized modeling, an alternative approach to
formulating dynamical models. We argue that this approach deals elegantly with
the uncertainties that exist in real world data and enables analytical insight
or highly efficient numerical investigation. We provide a survey of recent
successes of generalized modeling and a guide to the application of this
modeling approach in future studies such as complex integrative ecological
models.
|
[
{
"created": "Wed, 1 Dec 2021 09:13:06 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Dec 2021 11:10:59 GMT",
"version": "v2"
}
] |
2021-12-17
|
[
[
"Massing",
"Jana C.",
""
],
[
"Gross",
"Thilo",
""
]
] |
Many current challenges involve understanding the complex dynamical interplay between the constituents of systems. Typically, the number of such constituents is high, but only limited data sources on them are available. Conventional dynamical models of complex systems are rarely mathematically tractable and their numerical exploration suffers both from computational and data limitations. Here we review generalized modeling, an alternative approach to formulating dynamical models. We argue that this approach deals elegantly with the uncertainties that exist in real world data and enables analytical insight or highly efficient numerical investigation. We provide a survey of recent successes of generalized modeling and a guide to the application of this modeling approach in future studies such as complex integrative ecological models.
|
0802.3380
|
Anthony Coolen
|
S. Rabello, A.C.C. Coolen, C.J. Perez-Vicente, and F. Fraternali
|
A solvable model of the genesis of amino-acid sequences via coupled
dynamics of folding and slow genetic variation
|
51 pages, 13 figures, submitted to J. Phys. A
| null |
10.1088/1751-8113/41/28/285004
| null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study the coupled dynamics of primary and secondary structure formation
(i.e. slow genetic sequence selection and fast folding) in the context of a
solvable microscopic model that includes both short-range steric forces and and
long-range polarity-driven forces. Our solution is based on the diagonalization
of replicated transfer matrices, and leads in the thermodynamic limit to
explicit predictions regarding phase transitions and phase diagrams at genetic
equilibrium. The predicted phenomenology allows for natural physical
interpretations, and finds satisfactory support in numerical simulations.
|
[
{
"created": "Fri, 22 Feb 2008 20:29:07 GMT",
"version": "v1"
}
] |
2009-11-13
|
[
[
"Rabello",
"S.",
""
],
[
"Coolen",
"A. C. C.",
""
],
[
"Perez-Vicente",
"C. J.",
""
],
[
"Fraternali",
"F.",
""
]
] |
We study the coupled dynamics of primary and secondary structure formation (i.e. slow genetic sequence selection and fast folding) in the context of a solvable microscopic model that includes both short-range steric forces and and long-range polarity-driven forces. Our solution is based on the diagonalization of replicated transfer matrices, and leads in the thermodynamic limit to explicit predictions regarding phase transitions and phase diagrams at genetic equilibrium. The predicted phenomenology allows for natural physical interpretations, and finds satisfactory support in numerical simulations.
|
2009.08868
|
Qi Zhao
|
Qi Zhao, Zheng Zhao, Xiaoya Fan, Zhengwei Yuan, Qian Mao, Yudong Yao
|
Review of Machine-Learning Methods for RNA Secondary Structure
Prediction
|
25 pages, 5 figures, 1 table
| null |
10.1371/journal.pcbi.1009291
| null |
q-bio.BM cs.LG stat.ML
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Secondary structure plays an important role in determining the function of
non-coding RNAs. Hence, identifying RNA secondary structures is of great value
to research. Computational prediction is a mainstream approach for predicting
RNA secondary structure. Unfortunately, even though new methods have been
proposed over the past 40 years, the performance of computational prediction
methods has stagnated in the last decade. Recently, with the increasing
availability of RNA structure data, new methods based on machine-learning
technologies, especially deep learning, have alleviated the issue. In this
review, we provide a comprehensive overview of RNA secondary structure
prediction methods based on machine-learning technologies and a tabularized
summary of the most important methods in this field. The current pending issues
in the field of RNA secondary structure prediction and future trends are also
discussed.
|
[
{
"created": "Tue, 1 Sep 2020 03:17:15 GMT",
"version": "v1"
}
] |
2021-09-15
|
[
[
"Zhao",
"Qi",
""
],
[
"Zhao",
"Zheng",
""
],
[
"Fan",
"Xiaoya",
""
],
[
"Yuan",
"Zhengwei",
""
],
[
"Mao",
"Qian",
""
],
[
"Yao",
"Yudong",
""
]
] |
Secondary structure plays an important role in determining the function of non-coding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine-learning technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on machine-learning technologies and a tabularized summary of the most important methods in this field. The current pending issues in the field of RNA secondary structure prediction and future trends are also discussed.
|
1502.03011
|
Namiko Mitarai
|
Namiko Mitarai, Szabolcs Semsey, Kim Sneppen
|
Dynamic competition between transcription initiation and repression:
Role of nonequilibrium steps in cell-to-cell heterogeneity
|
5 pages, 3 fiugres. Figure and text updated
|
Phys. Rev. E 92, 022710 (2015)
|
10.1103/PhysRevE.92.022710
| null |
q-bio.MN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Transcriptional repression may cause transcriptional noise by a competition
between repressor and RNA polymerase binding. Although promoter activity is
often governed by a single limiting step, we argue here that the size of the
noise strongly depends on whether this step is the initial equilibrium binding
or one of the subsequent unidirectional steps. Overall, we show that
nonequilibrium steps of transcription initiation systematically increase the
cell-to-cell heterogeneity in bacterial populations. In particular, this allows
also weak promoters to give substantial transcriptional noise.
|
[
{
"created": "Tue, 10 Feb 2015 17:43:53 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Apr 2015 16:37:56 GMT",
"version": "v2"
},
{
"created": "Thu, 13 Aug 2015 14:57:13 GMT",
"version": "v3"
}
] |
2015-08-14
|
[
[
"Mitarai",
"Namiko",
""
],
[
"Semsey",
"Szabolcs",
""
],
[
"Sneppen",
"Kim",
""
]
] |
Transcriptional repression may cause transcriptional noise by a competition between repressor and RNA polymerase binding. Although promoter activity is often governed by a single limiting step, we argue here that the size of the noise strongly depends on whether this step is the initial equilibrium binding or one of the subsequent unidirectional steps. Overall, we show that nonequilibrium steps of transcription initiation systematically increase the cell-to-cell heterogeneity in bacterial populations. In particular, this allows also weak promoters to give substantial transcriptional noise.
|
2001.06948
|
Thomas Caraco
|
Thomas Caraco
|
Antibiotics, duration of infectiousness, and transmission of disease
|
22 pages of text, 5 figures
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Humans, domestic animals, orchard crops, and ornamental plants are commonly
treated with antibiotics in response to bacterial infection. By curing
infectious individuals, antibiotic therapy might limit the spread of contagious
disease among hosts. But antibiotic suppression of within-host pathogen density
might also reduce the probability that the host is otherwise removed from
infectious status before recovery. When rates of both recovery and removal
(isolation or mortality) depend directly on within-host density, antibiotic
therapy can relax the removal rate and so increase between-host disease
transmission. In this paper a deterministic within-host dynamics drives the
infectious host's probability of infection transmission, as well as the host's
time-dependent probability of surviving to recovery. The model varies (1)
inoculum size, (2) the time elapsing between infection and initiation of
therapy, (2) antibiotic efficacy, and (3) the size/susceptibility of groups
encountered by an infectious host. Results identify conditions where antibiotic
treatment simultaneously increases host survival and increases the expected
number of new infections. That is, antibiotics might convert a rare, serious
bacterial disease into a common, but treatable infection.
|
[
{
"created": "Mon, 20 Jan 2020 02:44:05 GMT",
"version": "v1"
}
] |
2020-01-22
|
[
[
"Caraco",
"Thomas",
""
]
] |
Humans, domestic animals, orchard crops, and ornamental plants are commonly treated with antibiotics in response to bacterial infection. By curing infectious individuals, antibiotic therapy might limit the spread of contagious disease among hosts. But antibiotic suppression of within-host pathogen density might also reduce the probability that the host is otherwise removed from infectious status before recovery. When rates of both recovery and removal (isolation or mortality) depend directly on within-host density, antibiotic therapy can relax the removal rate and so increase between-host disease transmission. In this paper a deterministic within-host dynamics drives the infectious host's probability of infection transmission, as well as the host's time-dependent probability of surviving to recovery. The model varies (1) inoculum size, (2) the time elapsing between infection and initiation of therapy, (2) antibiotic efficacy, and (3) the size/susceptibility of groups encountered by an infectious host. Results identify conditions where antibiotic treatment simultaneously increases host survival and increases the expected number of new infections. That is, antibiotics might convert a rare, serious bacterial disease into a common, but treatable infection.
|
1302.6952
|
Jonathan Touboul
|
Mathieu Galtier and Jonathan Touboul
|
Macroscopic equations governing noisy spiking neuronal populations
| null | null |
10.1371/journal.pone.0078917
| null |
q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
At functional scales, cortical behavior results from the complex interplay of
a large number of excitable cells operating in noisy environments. Such systems
resist to mathematical analysis, and computational neurosciences have largely
relied on heuristic partial (and partially justified) macroscopic models, which
successfully reproduced a number of relevant phenomena. The relationship
between these macroscopic models and the spiking noisy dynamics of the
underlying cells has since then been a great endeavor. Based on recent
mean-field reductions for such spiking neurons, we present here {a principled
reduction of large biologically plausible neuronal networks to firing-rate
models, providing a rigorous} relationship between the macroscopic activity of
populations of spiking neurons and popular macroscopic models, under a few
assumptions (mainly linearity of the synapses). {The reduced model we derive
consists of simple, low-dimensional ordinary differential equations with}
parameters and {nonlinearities derived from} the underlying properties of the
cells, and in particular the noise level. {These simple reduced models are
shown to reproduce accurately the dynamics of large networks in numerical
simulations}. Appropriate parameters and functions are made available {online}
for different models of neurons: McKean, Fitzhugh-Nagumo and Hodgkin-Huxley
models.
|
[
{
"created": "Wed, 27 Feb 2013 18:33:10 GMT",
"version": "v1"
}
] |
2014-03-05
|
[
[
"Galtier",
"Mathieu",
""
],
[
"Touboul",
"Jonathan",
""
]
] |
At functional scales, cortical behavior results from the complex interplay of a large number of excitable cells operating in noisy environments. Such systems resist to mathematical analysis, and computational neurosciences have largely relied on heuristic partial (and partially justified) macroscopic models, which successfully reproduced a number of relevant phenomena. The relationship between these macroscopic models and the spiking noisy dynamics of the underlying cells has since then been a great endeavor. Based on recent mean-field reductions for such spiking neurons, we present here {a principled reduction of large biologically plausible neuronal networks to firing-rate models, providing a rigorous} relationship between the macroscopic activity of populations of spiking neurons and popular macroscopic models, under a few assumptions (mainly linearity of the synapses). {The reduced model we derive consists of simple, low-dimensional ordinary differential equations with} parameters and {nonlinearities derived from} the underlying properties of the cells, and in particular the noise level. {These simple reduced models are shown to reproduce accurately the dynamics of large networks in numerical simulations}. Appropriate parameters and functions are made available {online} for different models of neurons: McKean, Fitzhugh-Nagumo and Hodgkin-Huxley models.
|
2405.14139
|
Xinhao Fan
|
Xinhao Fan, Shreesh P Mysore
|
Contribute to balance, wire in accordance: Emergence of backpropagation
from a simple, bio-plausible neuroplasticity rule
| null | null | null | null |
q-bio.NC cs.LG cs.NE
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Backpropagation (BP) has been pivotal in advancing machine learning and
remains essential in computational applications and comparative studies of
biological and artificial neural networks. Despite its widespread use, the
implementation of BP in the brain remains elusive, and its biological
plausibility is often questioned due to inherent issues such as the need for
symmetry of weights between forward and backward connections, and the
requirement of distinct forward and backward phases of computation. Here, we
introduce a novel neuroplasticity rule that offers a potential mechanism for
implementing BP in the brain. Similar in general form to the classical Hebbian
rule, this rule is based on the core principles of maintaining the balance of
excitatory and inhibitory inputs as well as on retrograde signaling, and
operates over three progressively slower timescales: neural firing, retrograde
signaling, and neural plasticity. We hypothesize that each neuron possesses an
internal state, termed credit, in addition to its firing rate. After achieving
equilibrium in firing rates, neurons receive credits based on their
contribution to the E-I balance of postsynaptic neurons through retrograde
signaling. As the network's credit distribution stabilizes, connections from
those presynaptic neurons are strengthened that significantly contribute to the
balance of postsynaptic neurons. We demonstrate mathematically that our
learning rule precisely replicates BP in layered neural networks without any
approximations. Simulations on artificial neural networks reveal that this rule
induces varying community structures in networks, depending on the learning
rate. This simple theoretical framework presents a biologically plausible
implementation of BP, with testable assumptions and predictions that may be
evaluated through biological experiments.
|
[
{
"created": "Thu, 23 May 2024 03:28:52 GMT",
"version": "v1"
}
] |
2024-05-24
|
[
[
"Fan",
"Xinhao",
""
],
[
"Mysore",
"Shreesh P",
""
]
] |
Backpropagation (BP) has been pivotal in advancing machine learning and remains essential in computational applications and comparative studies of biological and artificial neural networks. Despite its widespread use, the implementation of BP in the brain remains elusive, and its biological plausibility is often questioned due to inherent issues such as the need for symmetry of weights between forward and backward connections, and the requirement of distinct forward and backward phases of computation. Here, we introduce a novel neuroplasticity rule that offers a potential mechanism for implementing BP in the brain. Similar in general form to the classical Hebbian rule, this rule is based on the core principles of maintaining the balance of excitatory and inhibitory inputs as well as on retrograde signaling, and operates over three progressively slower timescales: neural firing, retrograde signaling, and neural plasticity. We hypothesize that each neuron possesses an internal state, termed credit, in addition to its firing rate. After achieving equilibrium in firing rates, neurons receive credits based on their contribution to the E-I balance of postsynaptic neurons through retrograde signaling. As the network's credit distribution stabilizes, connections from those presynaptic neurons are strengthened that significantly contribute to the balance of postsynaptic neurons. We demonstrate mathematically that our learning rule precisely replicates BP in layered neural networks without any approximations. Simulations on artificial neural networks reveal that this rule induces varying community structures in networks, depending on the learning rate. This simple theoretical framework presents a biologically plausible implementation of BP, with testable assumptions and predictions that may be evaluated through biological experiments.
|
1507.03972
|
Sergei Maslov
|
Purushottam Dixit, Tin Yau Pang, F. William Studier and Sergei Maslov
|
Recombinant transfer in the basic genome of E. coli
|
29 pages (including SI), 4 figures
| null |
10.1073/pnas.1510839112
| null |
q-bio.GN q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An approximation to the ~4 Mbp basic genome shared by 32 strains of E. coli
representing six evolutionary groups has been derived and analyzed
computationally. A multiple-alignment of the 32 complete genome sequences was
filtered to remove mobile elements and identify the most reliable ~90% of the
aligned length of each of the resulting 496 basic-genome pairs. Patterns of
single-bp mutations (SNPs) in aligned pairs distinguish clonally inherited
regions from regions where either genome has acquired DNA fragments from
diverged genomes by homologous recombination since their last common ancestor.
Such recombinant transfer is pervasive across the basic genome, mostly between
genomes in the same evolutionary group, and generates many unique mosaic
patterns. The six least-diverged genome-pairs have one or two recombinant
transfers of length ~40-115 kbp (and few if any other transfers), each
containing one or more gene clusters known to confer strong selective advantage
in some environments. Moderately diverged genome-pairs (0.4-1% SNPs ) show
mosaic patterns of interspersed clonal and recombinant regions of varying
lengths throughout the basic genome, whereas more highly diverged pairs within
an evolutionary group or pairs between evolutionary groups having >1.3% SNPs
have few clonal matches longer than a few kbp. Many recombinant transfers
appear to incorporate fragments of the entering DNA produced by restriction
systems of the recipient cell. A simple computational model can closely fit the
data. Most recombinant transfers seem likely to be due to generalized
transduction by co-evolving populations of phages, which could efficiently
distribute variability throughout bacterial genomes.
|
[
{
"created": "Tue, 14 Jul 2015 19:23:29 GMT",
"version": "v1"
}
] |
2016-02-17
|
[
[
"Dixit",
"Purushottam",
""
],
[
"Pang",
"Tin Yau",
""
],
[
"Studier",
"F. William",
""
],
[
"Maslov",
"Sergei",
""
]
] |
An approximation to the ~4 Mbp basic genome shared by 32 strains of E. coli representing six evolutionary groups has been derived and analyzed computationally. A multiple-alignment of the 32 complete genome sequences was filtered to remove mobile elements and identify the most reliable ~90% of the aligned length of each of the resulting 496 basic-genome pairs. Patterns of single-bp mutations (SNPs) in aligned pairs distinguish clonally inherited regions from regions where either genome has acquired DNA fragments from diverged genomes by homologous recombination since their last common ancestor. Such recombinant transfer is pervasive across the basic genome, mostly between genomes in the same evolutionary group, and generates many unique mosaic patterns. The six least-diverged genome-pairs have one or two recombinant transfers of length ~40-115 kbp (and few if any other transfers), each containing one or more gene clusters known to confer strong selective advantage in some environments. Moderately diverged genome-pairs (0.4-1% SNPs ) show mosaic patterns of interspersed clonal and recombinant regions of varying lengths throughout the basic genome, whereas more highly diverged pairs within an evolutionary group or pairs between evolutionary groups having >1.3% SNPs have few clonal matches longer than a few kbp. Many recombinant transfers appear to incorporate fragments of the entering DNA produced by restriction systems of the recipient cell. A simple computational model can closely fit the data. Most recombinant transfers seem likely to be due to generalized transduction by co-evolving populations of phages, which could efficiently distribute variability throughout bacterial genomes.
|
q-bio/0607038
|
Mathias Baumert
|
M Baumert, LM Brechtel, J Lock, A Voss, D Abbott
|
Scaling graphs of heart rate time series in athletes demonstrate the
VLF, LF and HF regions
| null |
2006 Physiol. Meas. 27 N35-N39
|
10.1088/0967-3334/27/9/N01
| null |
q-bio.QM
| null |
Scaling analysis of heart rate time series has emerged as an useful tool for
assessment of autonomic cardiac control. We investigate the heart rate time
series of ten athletes (five males and five females), by applying detrended
fluctuation analysis (DFA). High resolution ECGs are recorded under
standardized resting conditions over 30 minutes and subsequently heart rate
time series are extracted and artefacts filtered. We find three distinct
regions of scale-invariance, which correspond to the well-known VLF, LF, and HF
bands in the power spectra of heart rate variability. The scaling exponents
alpha are alphaHF: 1.15 [0.96-1.22], alphaLF: 0.68 [0.57-0.84], alphaVLF:
0.83[0.82-0.99]; p<10^-5). In conclusion, DFA scaling exponents of heart rate
time series should be fitted to the VLF, LF, and HF ranges, respectively.
|
[
{
"created": "Sat, 22 Jul 2006 00:44:22 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Baumert",
"M",
""
],
[
"Brechtel",
"LM",
""
],
[
"Lock",
"J",
""
],
[
"Voss",
"A",
""
],
[
"Abbott",
"D",
""
]
] |
Scaling analysis of heart rate time series has emerged as an useful tool for assessment of autonomic cardiac control. We investigate the heart rate time series of ten athletes (five males and five females), by applying detrended fluctuation analysis (DFA). High resolution ECGs are recorded under standardized resting conditions over 30 minutes and subsequently heart rate time series are extracted and artefacts filtered. We find three distinct regions of scale-invariance, which correspond to the well-known VLF, LF, and HF bands in the power spectra of heart rate variability. The scaling exponents alpha are alphaHF: 1.15 [0.96-1.22], alphaLF: 0.68 [0.57-0.84], alphaVLF: 0.83[0.82-0.99]; p<10^-5). In conclusion, DFA scaling exponents of heart rate time series should be fitted to the VLF, LF, and HF ranges, respectively.
|
1304.3752
|
Sergey Koren
|
Sergey Koren, Gregory P Harhay, Timothy PL Smith, James L Bono, Dayna
M Harhay, D. Scott Mcvey, Diana Radune, Nicholas H Bergman, and Adam M
Phillippy
|
Reducing assembly complexity of microbial genomes with single-molecule
sequencing
|
Published in Genome Biology: http://genomebiology.com/2013/14/9/R101/
Supplementary materials available from:
http://www.cbcb.umd.edu/software/PBcR/closure/index.html Reducing assembly
complexity of microbial genomes with single-molecule sequencing. Koren S,
Harhay GP, Smith TPL, Bono JL, Harhay DM, Mcvey SD, Radune D, Bergman NH,
Phillippy AM. Genome Biology 14:R101 2013
| null |
10.1186/gb-2013-14-9-r101
| null |
q-bio.GN
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Background: The short reads output by first- and second-generation DNA
sequencing instruments cannot completely reconstruct microbial chromosomes.
Therefore, most genomes have been left unfinished due to the significant
resources required to manually close gaps in draft assemblies.
Third-generation, single-molecule sequencing addresses this problem by greatly
increasing sequencing read length, which simplifies the assembly problem.
Results: To measure the benefit of single-molecule sequencing on microbial
genome assembly, we sequenced and assembled the genomes of six bacteria and
analyzed the repeat complexity of 2,267 complete bacteria and archaea. Our
results indicate that the majority of known bacterial and archaeal genomes can
be assembled without gaps, at finished-grade quality, using a single PacBio RS
sequencing library. These single-library assemblies are also more accurate than
typical short-read assemblies and hybrid assemblies of short and long reads.
Conclusions: Automated assembly of long, single-molecule sequencing data
reduces the cost of microbial finishing to $1,000 for most genomes, and future
advances in this technology are expected to drive the cost lower. This is
expected to increase the number of completed genomes, improve the quality of
microbial genome databases, and enable high-fidelity, population-scale studies
of pan-genomes and chromosomal organization.
|
[
{
"created": "Sat, 13 Apr 2013 00:25:43 GMT",
"version": "v1"
},
{
"created": "Thu, 2 May 2013 17:10:52 GMT",
"version": "v2"
},
{
"created": "Tue, 30 Jul 2013 20:50:29 GMT",
"version": "v3"
},
{
"created": "Fri, 2 Aug 2013 20:50:26 GMT",
"version": "v4"
},
{
"created": "Fri, 15 Nov 2013 15:31:36 GMT",
"version": "v5"
}
] |
2013-11-18
|
[
[
"Koren",
"Sergey",
""
],
[
"Harhay",
"Gregory P",
""
],
[
"Smith",
"Timothy PL",
""
],
[
"Bono",
"James L",
""
],
[
"Harhay",
"Dayna M",
""
],
[
"Mcvey",
"D. Scott",
""
],
[
"Radune",
"Diana",
""
],
[
"Bergman",
"Nicholas H",
""
],
[
"Phillippy",
"Adam M",
""
]
] |
Background: The short reads output by first- and second-generation DNA sequencing instruments cannot completely reconstruct microbial chromosomes. Therefore, most genomes have been left unfinished due to the significant resources required to manually close gaps in draft assemblies. Third-generation, single-molecule sequencing addresses this problem by greatly increasing sequencing read length, which simplifies the assembly problem. Results: To measure the benefit of single-molecule sequencing on microbial genome assembly, we sequenced and assembled the genomes of six bacteria and analyzed the repeat complexity of 2,267 complete bacteria and archaea. Our results indicate that the majority of known bacterial and archaeal genomes can be assembled without gaps, at finished-grade quality, using a single PacBio RS sequencing library. These single-library assemblies are also more accurate than typical short-read assemblies and hybrid assemblies of short and long reads. Conclusions: Automated assembly of long, single-molecule sequencing data reduces the cost of microbial finishing to $1,000 for most genomes, and future advances in this technology are expected to drive the cost lower. This is expected to increase the number of completed genomes, improve the quality of microbial genome databases, and enable high-fidelity, population-scale studies of pan-genomes and chromosomal organization.
|
1809.00083
|
Haicang Zhang
|
Haicang Zhang, Qi Zhang, Fusong Ju, Jianwei Zhu, Shiwei Sun, Yujuan
Gao, Ziwei Xie, Minghua Deng, Shiwei Sun, Wei-Mou Zheng, Dongbo Bu
|
Predicting protein inter-residue contacts using composite likelihood
maximization and deep learning
| null | null | null | null |
q-bio.BM cs.LG stat.ME
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Accurate prediction of inter-residue contacts of a protein is important to
calcu- lating its tertiary structure. Analysis of co-evolutionary events among
residues has been proved effective to inferring inter-residue contacts. The
Markov ran- dom field (MRF) technique, although being widely used for contact
prediction, suffers from the following dilemma: the actual likelihood function
of MRF is accurate but time-consuming to calculate, in contrast, approximations
to the actual likelihood, say pseudo-likelihood, are efficient to calculate but
inaccu- rate. Thus, how to achieve both accuracy and efficiency simultaneously
remains a challenge. In this study, we present such an approach (called clmDCA)
for contact prediction. Unlike plmDCA using pseudo-likelihood, i.e., the
product of conditional probability of individual residues, our approach uses
composite- likelihood, i.e., the product of conditional probability of all
residue pairs. Com- posite likelihood has been theoretically proved as a better
approximation to the actual likelihood function than pseudo-likelihood.
Meanwhile, composite likelihood is still efficient to maximize, thus ensuring
the efficiency of clmDCA. We present comprehensive experiments on popular
benchmark datasets, includ- ing PSICOV dataset and CASP-11 dataset, to show
that: i) clmDCA alone outperforms the existing MRF-based approaches in
prediction accuracy. ii) When equipped with deep learning technique for
refinement, the prediction ac- curacy of clmDCA was further significantly
improved, suggesting the suitability of clmDCA for subsequent refinement
procedure. We further present successful application of the predicted contacts
to accurately build tertiary structures for proteins in the PSICOV dataset.
Accessibility: The software clmDCA and a server are publicly accessible
through http://protein.ict.ac.cn/clmDCA/.
|
[
{
"created": "Fri, 31 Aug 2018 23:38:41 GMT",
"version": "v1"
}
] |
2018-09-05
|
[
[
"Zhang",
"Haicang",
""
],
[
"Zhang",
"Qi",
""
],
[
"Ju",
"Fusong",
""
],
[
"Zhu",
"Jianwei",
""
],
[
"Sun",
"Shiwei",
""
],
[
"Gao",
"Yujuan",
""
],
[
"Xie",
"Ziwei",
""
],
[
"Deng",
"Minghua",
""
],
[
"Sun",
"Shiwei",
""
],
[
"Zheng",
"Wei-Mou",
""
],
[
"Bu",
"Dongbo",
""
]
] |
Accurate prediction of inter-residue contacts of a protein is important to calcu- lating its tertiary structure. Analysis of co-evolutionary events among residues has been proved effective to inferring inter-residue contacts. The Markov ran- dom field (MRF) technique, although being widely used for contact prediction, suffers from the following dilemma: the actual likelihood function of MRF is accurate but time-consuming to calculate, in contrast, approximations to the actual likelihood, say pseudo-likelihood, are efficient to calculate but inaccu- rate. Thus, how to achieve both accuracy and efficiency simultaneously remains a challenge. In this study, we present such an approach (called clmDCA) for contact prediction. Unlike plmDCA using pseudo-likelihood, i.e., the product of conditional probability of individual residues, our approach uses composite- likelihood, i.e., the product of conditional probability of all residue pairs. Com- posite likelihood has been theoretically proved as a better approximation to the actual likelihood function than pseudo-likelihood. Meanwhile, composite likelihood is still efficient to maximize, thus ensuring the efficiency of clmDCA. We present comprehensive experiments on popular benchmark datasets, includ- ing PSICOV dataset and CASP-11 dataset, to show that: i) clmDCA alone outperforms the existing MRF-based approaches in prediction accuracy. ii) When equipped with deep learning technique for refinement, the prediction ac- curacy of clmDCA was further significantly improved, suggesting the suitability of clmDCA for subsequent refinement procedure. We further present successful application of the predicted contacts to accurately build tertiary structures for proteins in the PSICOV dataset. Accessibility: The software clmDCA and a server are publicly accessible through http://protein.ict.ac.cn/clmDCA/.
|
2007.14391
|
Jialei Chen
|
Jialei Chen, Zhaonan Liu, Kan Wang, Chen Jiang, Chuck Zhang, Ben Wang
|
A calibration-free method for biosensing in cell manufacturing
| null |
IISE Transactions, 2020
|
10.1080/24725854.2020.1856982
| null |
q-bio.QM stat.ME
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Chimeric antigen receptor T cell therapy has demonstrated innovative
therapeutic effectiveness in fighting cancers; however, it is extremely
expensive due to the intrinsic patient-to-patient variability in cell
manufacturing. We propose in this work a novel calibration-free statistical
framework to effectively recover critical quality attributes under the
patient-to-patient variability. Specifically, we model this variability via a
patient-specific calibration parameter, and use readings from multiple
biosensors to construct a patient-invariance statistic, thereby alleviating the
effect of the calibration parameter. A carefully formulated optimization
problem and an algorithmic framework are presented to find the best
patient-invariance statistic and the model parameters. Using the
patient-invariance statistic, we can recover the critical quality attribute of
interest, free from the calibration parameter. We demonstrate improvements of
the proposed calibration-free method in different simulation experiments. In
the cell manufacturing case study, our method not only effectively recovers
viable cell concentration for monitoring, but also reveals insights for the
cell manufacturing process.
|
[
{
"created": "Mon, 27 Jul 2020 22:37:56 GMT",
"version": "v1"
}
] |
2021-05-18
|
[
[
"Chen",
"Jialei",
""
],
[
"Liu",
"Zhaonan",
""
],
[
"Wang",
"Kan",
""
],
[
"Jiang",
"Chen",
""
],
[
"Zhang",
"Chuck",
""
],
[
"Wang",
"Ben",
""
]
] |
Chimeric antigen receptor T cell therapy has demonstrated innovative therapeutic effectiveness in fighting cancers; however, it is extremely expensive due to the intrinsic patient-to-patient variability in cell manufacturing. We propose in this work a novel calibration-free statistical framework to effectively recover critical quality attributes under the patient-to-patient variability. Specifically, we model this variability via a patient-specific calibration parameter, and use readings from multiple biosensors to construct a patient-invariance statistic, thereby alleviating the effect of the calibration parameter. A carefully formulated optimization problem and an algorithmic framework are presented to find the best patient-invariance statistic and the model parameters. Using the patient-invariance statistic, we can recover the critical quality attribute of interest, free from the calibration parameter. We demonstrate improvements of the proposed calibration-free method in different simulation experiments. In the cell manufacturing case study, our method not only effectively recovers viable cell concentration for monitoring, but also reveals insights for the cell manufacturing process.
|
1807.01479
|
Johannes Zierenberg
|
Johannes Zierenberg and Jens Wilting and Viola Priesemann
|
Homeostatic plasticity and external input shape neural network dynamics
|
14 pages, 8 figures, accepted at Phys. Rev. X
|
Phys. Rev. X 8, 031018 (2018)
|
10.1103/PhysRevX.8.031018
| null |
q-bio.NC cond-mat.dis-nn nlin.AO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In vitro and in vivo spiking activity clearly differ. Whereas networks in
vitro develop strong bursts separated by periods of very little spiking
activity, in vivo cortical networks show continuous activity. This is puzzling
considering that both networks presumably share similar single-neuron dynamics
and plasticity rules. We propose that the defining difference between in vitro
and in vivo dynamics is the strength of external input. In vitro, networks are
virtually isolated, whereas in vivo every brain area receives continuous input.
We analyze a model of spiking neurons in which the input strength, mediated by
spike rate homeostasis, determines the characteristics of the dynamical state.
In more detail, our analytical and numerical results on various network
topologies show consistently that under increasing input, homeostatic
plasticity generates distinct dynamic states, from bursting, to
close-to-critical, reverberating and irregular states. This implies that the
dynamic state of a neural network is not fixed but can readily adapt to the
input strengths. Indeed, our results match experimental spike recordings in
vitro and in vivo: the in vitro bursting behavior is consistent with a state
generated by very low network input (< 0.1%), whereas in vivo activity suggests
that on the order of 1% recorded spikes are input-driven, resulting in
reverberating dynamics. Importantly, this predicts that one can abolish the
ubiquitous bursts of in vitro preparations, and instead impose dynamics
comparable to in vivo activity by exposing the system to weak long-term
stimulation, thereby opening new paths to establish an in vivo-like assay in
vitro for basic as well as neurological studies.
|
[
{
"created": "Wed, 4 Jul 2018 08:30:38 GMT",
"version": "v1"
}
] |
2018-07-24
|
[
[
"Zierenberg",
"Johannes",
""
],
[
"Wilting",
"Jens",
""
],
[
"Priesemann",
"Viola",
""
]
] |
In vitro and in vivo spiking activity clearly differ. Whereas networks in vitro develop strong bursts separated by periods of very little spiking activity, in vivo cortical networks show continuous activity. This is puzzling considering that both networks presumably share similar single-neuron dynamics and plasticity rules. We propose that the defining difference between in vitro and in vivo dynamics is the strength of external input. In vitro, networks are virtually isolated, whereas in vivo every brain area receives continuous input. We analyze a model of spiking neurons in which the input strength, mediated by spike rate homeostasis, determines the characteristics of the dynamical state. In more detail, our analytical and numerical results on various network topologies show consistently that under increasing input, homeostatic plasticity generates distinct dynamic states, from bursting, to close-to-critical, reverberating and irregular states. This implies that the dynamic state of a neural network is not fixed but can readily adapt to the input strengths. Indeed, our results match experimental spike recordings in vitro and in vivo: the in vitro bursting behavior is consistent with a state generated by very low network input (< 0.1%), whereas in vivo activity suggests that on the order of 1% recorded spikes are input-driven, resulting in reverberating dynamics. Importantly, this predicts that one can abolish the ubiquitous bursts of in vitro preparations, and instead impose dynamics comparable to in vivo activity by exposing the system to weak long-term stimulation, thereby opening new paths to establish an in vivo-like assay in vitro for basic as well as neurological studies.
|
2404.05732
|
Ehud Ahissar
|
Ehud Ahissar, Daniel Polani, Merav Ahissar
|
Mapping the Mind-Brain Duality to a Digital-Analog Perceptual Duality
| null | null | null | null |
q-bio.NC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Could the abstract ideas of our minds originate from neuronal interactions
within our brains? To address this question, we examine interactions within
'brain-world' (BW) and 'brain-brain' (BB) domains, which represent the brain's
physical interactions with its environment and the mental interactions between
brains, respectively. BW interactions are characterized as analog - dynamic and
continuous, whereas BB interactions are digital - non-dynamic and discrete.
This distinction allows BB interactions to facilitate effective, albeit
information-limited, communication through categorization. We review existing
data showing that cascades of neural circuits can convert between analog and
digital signals, thereby linking physical and mental processes. Importantly, we
argue that these circuits cannot fully reduce one domain to the other,
suggesting that the mind-brain duality can be mapped to the BB-BW duality. Such
mapping suggests that the mind's foundation is inherently social, offering a
novel explanation for the physical-mental gap while acknowledging the
coexistence of the physical body and the non-physical mind.
|
[
{
"created": "Tue, 20 Feb 2024 19:01:44 GMT",
"version": "v1"
}
] |
2024-04-10
|
[
[
"Ahissar",
"Ehud",
""
],
[
"Polani",
"Daniel",
""
],
[
"Ahissar",
"Merav",
""
]
] |
Could the abstract ideas of our minds originate from neuronal interactions within our brains? To address this question, we examine interactions within 'brain-world' (BW) and 'brain-brain' (BB) domains, which represent the brain's physical interactions with its environment and the mental interactions between brains, respectively. BW interactions are characterized as analog - dynamic and continuous, whereas BB interactions are digital - non-dynamic and discrete. This distinction allows BB interactions to facilitate effective, albeit information-limited, communication through categorization. We review existing data showing that cascades of neural circuits can convert between analog and digital signals, thereby linking physical and mental processes. Importantly, we argue that these circuits cannot fully reduce one domain to the other, suggesting that the mind-brain duality can be mapped to the BB-BW duality. Such mapping suggests that the mind's foundation is inherently social, offering a novel explanation for the physical-mental gap while acknowledging the coexistence of the physical body and the non-physical mind.
|
2401.07719
|
Sebastien Benzekry
|
Benjamin Schneider (ISU), S\'ebastien Benzekry (COMPO), Jonathan
Mochel (ISU)
|
Comprehensive Joint Modeling of First-Line Therapeutics in Non-Small
Cell Lung Cancer
| null | null | null | null |
q-bio.CB q-bio.TO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
First-line antiproliferatives for non-small cell lung cancer (NSCLC) have a
relatively high failure rate due to high intrinsic resistance rates and
acquired resistance rates to therapy. 57% patients are diagnosed in late-stage
disease due to the tendency of early-stage NSCLC to be asymptomatic. For
patients first diagnosed with metastatic disease the 5-year survival rate is
approximately 5%. To help accelerate the development of novel therapeutics and
computer-based tools for optimizing individual therapy, we have collated data
from 11 different clinical trials in NSCLC and developed a semi-mechanistic,
clinical model of NSCLC growth and pharmacodynamics relative to the various
therapeutics represented in the study. In this study, we have produced
extremely precise estimates of clinical parameters fundamental to cancer
modeling such as the rate of acquired resistance to various pharmaceuticals,
the relationship between drug concentration and rate of cancer cell death, as
well as the fine temporal dynamics of anti-VEGF therapy. In the simulation sets
documented in this study, we have used the model to make meaningful
descriptions of efficacy gain in making bevacizumab-antiproliferative
combination therapy sequential, over a series of days, rather than concurrent.
|
[
{
"created": "Mon, 15 Jan 2024 14:35:45 GMT",
"version": "v1"
}
] |
2024-01-17
|
[
[
"Schneider",
"Benjamin",
"",
"ISU"
],
[
"Benzekry",
"Sébastien",
"",
"COMPO"
],
[
"Mochel",
"Jonathan",
"",
"ISU"
]
] |
First-line antiproliferatives for non-small cell lung cancer (NSCLC) have a relatively high failure rate due to high intrinsic resistance rates and acquired resistance rates to therapy. 57% patients are diagnosed in late-stage disease due to the tendency of early-stage NSCLC to be asymptomatic. For patients first diagnosed with metastatic disease the 5-year survival rate is approximately 5%. To help accelerate the development of novel therapeutics and computer-based tools for optimizing individual therapy, we have collated data from 11 different clinical trials in NSCLC and developed a semi-mechanistic, clinical model of NSCLC growth and pharmacodynamics relative to the various therapeutics represented in the study. In this study, we have produced extremely precise estimates of clinical parameters fundamental to cancer modeling such as the rate of acquired resistance to various pharmaceuticals, the relationship between drug concentration and rate of cancer cell death, as well as the fine temporal dynamics of anti-VEGF therapy. In the simulation sets documented in this study, we have used the model to make meaningful descriptions of efficacy gain in making bevacizumab-antiproliferative combination therapy sequential, over a series of days, rather than concurrent.
|
2403.03000
|
Niklas Hohmann
|
Niklas Hohmann
|
Possible and Impossible Inferences From Reconstructed Evolutionary
Processes using Phylogenies as an Example
| null | null | null | null |
q-bio.QM q-bio.PE
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Our understanding of past evolutionary change is often based on
reconstructions based on incomplete data, raising fundamental questions about
the degree to which we can make reliable inferences about past evolutionary
processes. This was demonstrated by Louca and Pennell (2020), who showed that
each pure-birth process can be generated by an infinite number of birth-death
processes. Here, I explore what it means to reconstruct past evolutionary
change with three approaches from measure theory, group theory, and homotopy
theory to better understand structural constraint and origins of
(non)identifiability. As an example, the developed framework is applied to the
case of birth-death processes.
|
[
{
"created": "Mon, 4 Mar 2024 16:48:14 GMT",
"version": "v1"
}
] |
2024-03-06
|
[
[
"Hohmann",
"Niklas",
""
]
] |
Our understanding of past evolutionary change is often based on reconstructions based on incomplete data, raising fundamental questions about the degree to which we can make reliable inferences about past evolutionary processes. This was demonstrated by Louca and Pennell (2020), who showed that each pure-birth process can be generated by an infinite number of birth-death processes. Here, I explore what it means to reconstruct past evolutionary change with three approaches from measure theory, group theory, and homotopy theory to better understand structural constraint and origins of (non)identifiability. As an example, the developed framework is applied to the case of birth-death processes.
|
1809.01538
|
Adeshina Adekunle
|
Adeshina I. Adekunle, Michael T. Meehan, Emma S. McBryde
|
Mathematical analysis of a Wolbachia invasive model with imperfect
maternal transmission and loss of Wolbachia infection
| null |
https://doi.org/10.1016/j.idm.2019.10.001
| null | null |
q-bio.PE math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Arboviral infections, especially dengue, continue to cause significant health
burden in their endemic regions. One of the strategies to tackle these
infections is to replace the main vector agent, Ae. aegypti, with the ones
incapable of transmitting the virus. Wolbachia, an intracellular bacterium, has
shown promise in achieving this goal. However, key factors such as imperfect
maternal transmission, loss of Wolbachia infection, reduced reproductive
capacity and shortened life-span affect the dynamics of Wolbachia in different
forms in the Ae. aegypti population. In this study, we developed a Wolbachia
transmission dynamic model adjusting for imperfect maternal transmission and
loss of Wolbachia infection. The invasive reproductive number that determines
the likelihood of replacement of the Wolbachia-uninfected (WU) population is
derived and with it, we established the local and global stability of the
equilibrium points. This analysis clearly shows that cytoplasmic
incompatibility (CI) does not guarantee establishment of the Wolbachia-infected
(WI) mosquitoes as imperfect maternal transmission and loss of Wolbachia
infection could outweigh the gains from CI. Optimal release programs depending
on the level of imperfect maternal transmission and loss of Wolbachia infection
are shown. Hence, it is left to decision makers to either aim for replacement
or co-existence of both populations.
|
[
{
"created": "Tue, 4 Sep 2018 05:56:04 GMT",
"version": "v1"
},
{
"created": "Tue, 4 Jun 2019 01:46:42 GMT",
"version": "v2"
}
] |
2019-10-22
|
[
[
"Adekunle",
"Adeshina I.",
""
],
[
"Meehan",
"Michael T.",
""
],
[
"McBryde",
"Emma S.",
""
]
] |
Arboviral infections, especially dengue, continue to cause significant health burden in their endemic regions. One of the strategies to tackle these infections is to replace the main vector agent, Ae. aegypti, with the ones incapable of transmitting the virus. Wolbachia, an intracellular bacterium, has shown promise in achieving this goal. However, key factors such as imperfect maternal transmission, loss of Wolbachia infection, reduced reproductive capacity and shortened life-span affect the dynamics of Wolbachia in different forms in the Ae. aegypti population. In this study, we developed a Wolbachia transmission dynamic model adjusting for imperfect maternal transmission and loss of Wolbachia infection. The invasive reproductive number that determines the likelihood of replacement of the Wolbachia-uninfected (WU) population is derived and with it, we established the local and global stability of the equilibrium points. This analysis clearly shows that cytoplasmic incompatibility (CI) does not guarantee establishment of the Wolbachia-infected (WI) mosquitoes as imperfect maternal transmission and loss of Wolbachia infection could outweigh the gains from CI. Optimal release programs depending on the level of imperfect maternal transmission and loss of Wolbachia infection are shown. Hence, it is left to decision makers to either aim for replacement or co-existence of both populations.
|
1412.7972
|
Antti Niemi
|
Jianfeng He, Jin Dai, Jing Li, Xubiao Peng, Antti J. Niemi
|
Aspects of structural landscape of human islet amyloid polypeptide
| null | null |
10.1063/1.4905586
| null |
q-bio.BM cond-mat.soft nlin.PS physics.med-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The human islet amyloid polypeptide (hIAPP) co-operates with insulin to
maintain glycemic balance. It also constitutes the amyloid plaques that
aggregate in the pancreas of type-II diabetic patients. We have performed
extensive in silico investigations to analyse the structural landscape of
monomeric hIAPP, which is presumed to be intrinsically disordered. For this we
construct from first principles a highly predictive energy function that
describes a monomeric hIAPP observed in a NMR experiment, as a local energy
minimum. We subject our theoretical model of hIAPP to repeated heating and
cooling simulations, back and forth between a high temperature regime where the
conformation resembles a random walker and a low temperature limit where no
thermal motions prevail. We find that the final low temperature conformations
display a high level of degeneracy, in a manner which is fully in line with the
presumed intrinsically disordered character of hIAPP. In particular, we
identify an isolated family of alpha-helical conformations that might cause the
transition to amyloidosis, by nucleation.
|
[
{
"created": "Fri, 26 Dec 2014 19:07:18 GMT",
"version": "v1"
}
] |
2015-06-23
|
[
[
"He",
"Jianfeng",
""
],
[
"Dai",
"Jin",
""
],
[
"Li",
"Jing",
""
],
[
"Peng",
"Xubiao",
""
],
[
"Niemi",
"Antti J.",
""
]
] |
The human islet amyloid polypeptide (hIAPP) co-operates with insulin to maintain glycemic balance. It also constitutes the amyloid plaques that aggregate in the pancreas of type-II diabetic patients. We have performed extensive in silico investigations to analyse the structural landscape of monomeric hIAPP, which is presumed to be intrinsically disordered. For this we construct from first principles a highly predictive energy function that describes a monomeric hIAPP observed in a NMR experiment, as a local energy minimum. We subject our theoretical model of hIAPP to repeated heating and cooling simulations, back and forth between a high temperature regime where the conformation resembles a random walker and a low temperature limit where no thermal motions prevail. We find that the final low temperature conformations display a high level of degeneracy, in a manner which is fully in line with the presumed intrinsically disordered character of hIAPP. In particular, we identify an isolated family of alpha-helical conformations that might cause the transition to amyloidosis, by nucleation.
|
1405.6411
|
Artem Novozhilov S
|
Yuri S. Semenov, Alexander S. Bratus, Artem S. Novozhilov
|
On the behavior of the leading eigenvalue of Eigen's evolutionary
matrices
|
37 pages
| null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study general properties of the leading eigenvalue $\overline{w}(q)$ of
Eigen's evolutionary matrices depending on the probability $q$ of faithful
reproduction. This is a linear algebra problem that has various applications in
theoretical biology, including such diverse fields as the origin of life,
evolution of cancer progression, and virus evolution. We present the exact
expressions for $\overline{w}(q),\overline{w}'(q),\overline{w}''(q)$ for
$q=0,0.5,1$ and prove that the absolute minimum of $\overline{w}(q)$, which
always exists, belongs to the interval $[0,0.5]$. For the specific case of a
single peaked landscape we also find lower and upper bounds on
$\overline{w}(q)$, which are used to estimate the critical mutation rate, after
which the distribution of the types of individuals in the population becomes
almost uniform. This estimate is used as a starting point to conjecture another
estimate, valid for any fitness landscape, and which is checked by numerical
calculations. The last estimate stresses the fact that the inverse dependence
of the critical mutation rate on the sequence length is not a generally valid
fact. Therefore, the discussions of the error threshold applied to biological
systems must take this fact into account.
|
[
{
"created": "Sun, 25 May 2014 18:02:25 GMT",
"version": "v1"
}
] |
2014-05-27
|
[
[
"Semenov",
"Yuri S.",
""
],
[
"Bratus",
"Alexander S.",
""
],
[
"Novozhilov",
"Artem S.",
""
]
] |
We study general properties of the leading eigenvalue $\overline{w}(q)$ of Eigen's evolutionary matrices depending on the probability $q$ of faithful reproduction. This is a linear algebra problem that has various applications in theoretical biology, including such diverse fields as the origin of life, evolution of cancer progression, and virus evolution. We present the exact expressions for $\overline{w}(q),\overline{w}'(q),\overline{w}''(q)$ for $q=0,0.5,1$ and prove that the absolute minimum of $\overline{w}(q)$, which always exists, belongs to the interval $[0,0.5]$. For the specific case of a single peaked landscape we also find lower and upper bounds on $\overline{w}(q)$, which are used to estimate the critical mutation rate, after which the distribution of the types of individuals in the population becomes almost uniform. This estimate is used as a starting point to conjecture another estimate, valid for any fitness landscape, and which is checked by numerical calculations. The last estimate stresses the fact that the inverse dependence of the critical mutation rate on the sequence length is not a generally valid fact. Therefore, the discussions of the error threshold applied to biological systems must take this fact into account.
|
2306.04582
|
Carla Goldman
|
Pedro Ribeiro de Almeida, Vitor Hirata Sanches, Carla Goldman
|
Balancing the Benefits of Vaccination: an Envy-Free Strategy
|
28 pages, 11 figures
| null | null | null |
q-bio.QM physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Covid-19 pandemic revealed the difficulties of vaccinating a population
under the circumstances marked by urgency and limited availability of doses
while balancing benefits associated with distinct guidelines satisfying
specific ethical criteria (J.W. Wu, S.D. John, E.Y. Adashi, Allocating Vaccines
in the Pandemic: The Ethical Dimension, The Am. J. of Medicine V.33(11): 1241 -
1242 (2020)). We offer a vaccination strategy that may be useful in this
regard. It relies on the mathematical concept of envy-freeness. We consider
finding balance by allocating the resource among individuals that seem to be
heterogeneous concerning the direct and indirect benefits of vaccination,
depending on age. The proposed strategy adapts a constructive approach in the
literature based on Sperner`s Lemma to point out an approximate division of
doses guaranteeing that both benefits are optimized each time a batch becomes
available. Applications using data about population age distributions from
diverse countries suggest that, among other features, this strategy maintains
the desired balance throughout the entire vaccination period.
|
[
{
"created": "Wed, 7 Jun 2023 16:34:15 GMT",
"version": "v1"
}
] |
2023-06-08
|
[
[
"de Almeida",
"Pedro Ribeiro",
""
],
[
"Sanches",
"Vitor Hirata",
""
],
[
"Goldman",
"Carla",
""
]
] |
The Covid-19 pandemic revealed the difficulties of vaccinating a population under the circumstances marked by urgency and limited availability of doses while balancing benefits associated with distinct guidelines satisfying specific ethical criteria (J.W. Wu, S.D. John, E.Y. Adashi, Allocating Vaccines in the Pandemic: The Ethical Dimension, The Am. J. of Medicine V.33(11): 1241 - 1242 (2020)). We offer a vaccination strategy that may be useful in this regard. It relies on the mathematical concept of envy-freeness. We consider finding balance by allocating the resource among individuals that seem to be heterogeneous concerning the direct and indirect benefits of vaccination, depending on age. The proposed strategy adapts a constructive approach in the literature based on Sperner`s Lemma to point out an approximate division of doses guaranteeing that both benefits are optimized each time a batch becomes available. Applications using data about population age distributions from diverse countries suggest that, among other features, this strategy maintains the desired balance throughout the entire vaccination period.
|
1512.00424
|
Vicente M. Reyes Ph.D.
|
Vicente M. Reyes
|
Implementation of the Spherical Coordinate Representation of Protein 3D
Structures and its Applications Using FORTRAN 77/90 Language
|
36 pages, 10228 words total (27 pages/9384 words text, 9 pages/844
words figures+tables+legends), 7 figures total (fig. 1: panels A, B & C, fig.
2: panels A, B, C & D), 6 tables total (tbl. 1, tbl. 2, tbl. 3: panels A, B &
C, tbl. 4)
| null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We previously described the representation of protein 3D structures in
spherical coordinates (rho, phi, theta) and two of its applications: separation
of the outer layer (OL) from the inner core (IC) of proteins, and assessment of
protein surface protrusions and invaginations (Reyes, V.M., 2011& 2009). Here
we present results demonstrating the performance success of the FORTRAN 77 and
90 programs used in the implementation of the two said applications, and how to
implement both applications. In particular, we show here data that demonstrate
the success of our OL-IC separation procedure using a subset of the Laskowski
et al. (1996) dataset. Using a theoretical model protein in the form of a
scalene ellipsoid grid of points with and without an artificially constructed
protrusion or invagination, we also show results demonstrating that protrusions
and invaginations on the protein surface maybe predicted. The nine programs we
present here and their respective functions are: find_molec_centr.f: finds the
x-, y- and z-coordinates of the protein molecular geometric centroid,
cart2sphere_degrees.f90: converts PDB protein coordinates to spherical, with
phi and theta in degrees, cart2sphere_radians.f90: does the same thing as the
second program, but with phi and theta in radians, spher2cart_degrees.f90:
converts the coordinates from spherical back to PDB, where input phi and theta
are in degrees, spher2cart_radians.f90: does the same thing as the fourth
program, but with phi and theta in radians, find_rho_cutoff.f: determines the
rho cut-off for finding the boundary between OL and IC,
phi6_theta8_binning.f90: performs the binning of phi in six- and theta in
eight-degree increments, phi10_theta10_binning.f90: performs the binning of phi
and theta both in ten-degree increments, and bin_rho.f90: performs the binning
of rho values for plotting the frequency distribution of maximum rho values.
|
[
{
"created": "Mon, 30 Nov 2015 08:12:59 GMT",
"version": "v1"
}
] |
2015-12-02
|
[
[
"Reyes",
"Vicente M.",
""
]
] |
We previously described the representation of protein 3D structures in spherical coordinates (rho, phi, theta) and two of its applications: separation of the outer layer (OL) from the inner core (IC) of proteins, and assessment of protein surface protrusions and invaginations (Reyes, V.M., 2011& 2009). Here we present results demonstrating the performance success of the FORTRAN 77 and 90 programs used in the implementation of the two said applications, and how to implement both applications. In particular, we show here data that demonstrate the success of our OL-IC separation procedure using a subset of the Laskowski et al. (1996) dataset. Using a theoretical model protein in the form of a scalene ellipsoid grid of points with and without an artificially constructed protrusion or invagination, we also show results demonstrating that protrusions and invaginations on the protein surface maybe predicted. The nine programs we present here and their respective functions are: find_molec_centr.f: finds the x-, y- and z-coordinates of the protein molecular geometric centroid, cart2sphere_degrees.f90: converts PDB protein coordinates to spherical, with phi and theta in degrees, cart2sphere_radians.f90: does the same thing as the second program, but with phi and theta in radians, spher2cart_degrees.f90: converts the coordinates from spherical back to PDB, where input phi and theta are in degrees, spher2cart_radians.f90: does the same thing as the fourth program, but with phi and theta in radians, find_rho_cutoff.f: determines the rho cut-off for finding the boundary between OL and IC, phi6_theta8_binning.f90: performs the binning of phi in six- and theta in eight-degree increments, phi10_theta10_binning.f90: performs the binning of phi and theta both in ten-degree increments, and bin_rho.f90: performs the binning of rho values for plotting the frequency distribution of maximum rho values.
|
1304.6966
|
Zhandong Liu
|
Ying-Wooi Wan, Claire M. Mach, Genevera Allen, Matthew L. Anderson,
Zhandong Liu
|
On the Reproducibility of TCGA Ovarian Cancer MicroRNA Profiles
| null | null |
10.1371/journal.pone.0087782
| null |
q-bio.GN stat.AP
|
http://creativecommons.org/licenses/publicdomain/
|
Dysregulated microRNA (miRNA) expression is a well-established feature of
human cancer. However, the role of specific miRNAs in determining cancer
outcomes remains unclear. Using Level 3 expression data from the Cancer Genome
Atlas (TCGA), we identified 61 miRNAs that are associated with overall survival
in 469 ovarian cancers profiled by microarray (p<0.01). We also identified 12
miRNAs that are associated with survival when miRNAs were profiled in the same
specimens using Next Generation Sequencing (miRNA-Seq) (p<0.01). Surprisingly,
only 1 miRNA transcript is associated with ovarian cancer survival in both
datasets. Our analyses indicate that this discrepancy is due to the fact that
miRNA levels reported by the two platforms correlate poorly, even after
correcting for potential issues inherent to signal detection algorithms.
Further investigation is warranted.
|
[
{
"created": "Thu, 25 Apr 2013 17:34:52 GMT",
"version": "v1"
},
{
"created": "Wed, 13 Nov 2013 22:47:49 GMT",
"version": "v2"
}
] |
2014-03-05
|
[
[
"Wan",
"Ying-Wooi",
""
],
[
"Mach",
"Claire M.",
""
],
[
"Allen",
"Genevera",
""
],
[
"Anderson",
"Matthew L.",
""
],
[
"Liu",
"Zhandong",
""
]
] |
Dysregulated microRNA (miRNA) expression is a well-established feature of human cancer. However, the role of specific miRNAs in determining cancer outcomes remains unclear. Using Level 3 expression data from the Cancer Genome Atlas (TCGA), we identified 61 miRNAs that are associated with overall survival in 469 ovarian cancers profiled by microarray (p<0.01). We also identified 12 miRNAs that are associated with survival when miRNAs were profiled in the same specimens using Next Generation Sequencing (miRNA-Seq) (p<0.01). Surprisingly, only 1 miRNA transcript is associated with ovarian cancer survival in both datasets. Our analyses indicate that this discrepancy is due to the fact that miRNA levels reported by the two platforms correlate poorly, even after correcting for potential issues inherent to signal detection algorithms. Further investigation is warranted.
|
2103.00867
|
Tatjana Pyragiene
|
Kestutis Pyragas, Augustinas P. Fedaravi\v{c}ius, Tatjana Pyragien\.e
|
Suppression of synchronous spiking in two interacting populations of
excitatory and inhibitory quadratic integrate-and-fire neurons
|
11 figures
|
Phys. Rev. E 104, 014203 (2021)
|
10.1103/PhysRevE.104.014203
| null |
q-bio.NC nlin.CD
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Collective oscillations and their suppression by external stimulation are
analyzed in a large-scale neural network consisting of two interacting
populations of excitatory and inhibitory quadratic integrate-and-fire neurons.
In the limit of an infinite number of neurons, the microscopic model of this
network can be reduced to an exact low-dimensional system of mean-field
equations. Bifurcation analysis of these equations reveals three different
dynamic modes in a free network: a stable resting state, a stable limit cycle,
and bistability with a coexisting resting state and a limit cycle. We show that
in the limit cycle mode, high-frequency stimulation of an inhibitory population
can stabilize an unstable resting state and effectively suppress collective
oscillations. We also show that in the bistable mode, the dynamics of the
network can be switched from a stable limit cycle to a stable resting state by
applying an inhibitory pulse to the excitatory population. The results obtained
from the mean-field equations are confirmed by numerical simulation of the
microscopic model.
|
[
{
"created": "Mon, 1 Mar 2021 09:36:54 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Jul 2021 08:00:33 GMT",
"version": "v2"
}
] |
2021-07-14
|
[
[
"Pyragas",
"Kestutis",
""
],
[
"Fedaravičius",
"Augustinas P.",
""
],
[
"Pyragienė",
"Tatjana",
""
]
] |
Collective oscillations and their suppression by external stimulation are analyzed in a large-scale neural network consisting of two interacting populations of excitatory and inhibitory quadratic integrate-and-fire neurons. In the limit of an infinite number of neurons, the microscopic model of this network can be reduced to an exact low-dimensional system of mean-field equations. Bifurcation analysis of these equations reveals three different dynamic modes in a free network: a stable resting state, a stable limit cycle, and bistability with a coexisting resting state and a limit cycle. We show that in the limit cycle mode, high-frequency stimulation of an inhibitory population can stabilize an unstable resting state and effectively suppress collective oscillations. We also show that in the bistable mode, the dynamics of the network can be switched from a stable limit cycle to a stable resting state by applying an inhibitory pulse to the excitatory population. The results obtained from the mean-field equations are confirmed by numerical simulation of the microscopic model.
|
2407.00040
|
Alwani Liyana Ahmad
|
Alwani Liyana Ahmad, Jose Sanchez-Bornot, Roberto C. Sotero, Damien
Coyle, Zamzuri Idris, and Ibrahima Faye
|
A Machine Learning Approach for Identifying Anatomical Biomarkers of
Early Mild Cognitive Impairment
|
34 pages, 6 figures, 7 tables
| null | null | null |
q-bio.NC cs.LG eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
Alzheimer Disease poses a significant challenge, necessitating early
detection for effective intervention. MRI is a key neuroimaging tool due to its
ease of use and cost effectiveness. This study analyzes machine learning
methods for MRI based biomarker selection and classification to distinguish
between healthy controls and those who develop mild cognitive impairment within
five years. Using 3 Tesla MRI data from ADNI and OASIS 3, we applied various
machine learning techniques, including MATLAB Classification Learner app,
nested cross validation, and Bayesian optimization. Data harmonization with
polynomial regression improved performance. Consistent features identified were
the entorhinal, hippocampus, lateral ventricle, and lateral orbitofrontal
regions. For balanced ADNI data, Naive Bayes with z score harmonization
performed best. For balanced OASIS 3, SVM with z score correction excelled. In
imbalanced data, RUSBoost showed strong performance on ADNI and OASIS 3. Z
score harmonization highlighted the potential of a semi automatic pipeline for
early AD detection using MRI.
|
[
{
"created": "Wed, 29 May 2024 06:12:05 GMT",
"version": "v1"
},
{
"created": "Fri, 9 Aug 2024 02:00:05 GMT",
"version": "v2"
}
] |
2024-08-12
|
[
[
"Ahmad",
"Alwani Liyana",
""
],
[
"Sanchez-Bornot",
"Jose",
""
],
[
"Sotero",
"Roberto C.",
""
],
[
"Coyle",
"Damien",
""
],
[
"Idris",
"Zamzuri",
""
],
[
"Faye",
"Ibrahima",
""
]
] |
Alzheimer Disease poses a significant challenge, necessitating early detection for effective intervention. MRI is a key neuroimaging tool due to its ease of use and cost effectiveness. This study analyzes machine learning methods for MRI based biomarker selection and classification to distinguish between healthy controls and those who develop mild cognitive impairment within five years. Using 3 Tesla MRI data from ADNI and OASIS 3, we applied various machine learning techniques, including MATLAB Classification Learner app, nested cross validation, and Bayesian optimization. Data harmonization with polynomial regression improved performance. Consistent features identified were the entorhinal, hippocampus, lateral ventricle, and lateral orbitofrontal regions. For balanced ADNI data, Naive Bayes with z score harmonization performed best. For balanced OASIS 3, SVM with z score correction excelled. In imbalanced data, RUSBoost showed strong performance on ADNI and OASIS 3. Z score harmonization highlighted the potential of a semi automatic pipeline for early AD detection using MRI.
|
q-bio/0612039
|
Maksim Kouza M
|
Maksim Kouza, Mai Suan Li, Edward P. O'Brien Jr., Chin-Kun Hu and D.
Thirumalai
|
Effect of finite size on cooperativity and rates of protein folding
|
17 pages, 3 figures, 2 tables
|
J. Phys. Chem. A 110, 671 (2006)
|
10.1021/jp053770b
| null |
q-bio.BM
| null |
We analyze the dependence of cooperativity of the thermal denaturation
transition and folding rates of globular proteins on the number of amino acid
residues, $N$, using lattice models with side chains,off-lattice Go models and
the available experimental data. A dimensionless measure of cooperativity,
$\Omega_c$ ($0 < \Omega_c < \infty$), scales as $\Omega_c \sim N^{\zeta}$. The
results of simulations and the analysis of experimental data further confirm
the earlier prediction that $\zeta$ is universal with $\zeta = 1 +\gamma$,
where exponent $\gamma$ characterizes the susceptibility of a self-avoiding
walk. This finding suggests that the structural characteristics in the
denaturated state are manifested in the folding cooperativity at the transition
temperature. The folding rates $k_F$ for the Go models and a dataset of 69
proteins can be fit using $k_F = k_F^0 \exp(-cN^\beta)$. Both $\beta = 1/2$ and
2/3 provide a good fit of the data. We find that $k_F = k_F^0
\exp(-cN^{{1/2}})$, with the average (over the dataset of proteins) $k_F^0
\approx (0.2\mu s)^{-1}$ and $c \approx 1.1$, can be used to estimate folding
rates to within an order of magnitude in most cases. The minimal models give
identical $N$ dependence with $c \approx 1$. The prefactor for off-lattice Go
models is nearly four orders of magnitude larger than the experimental value.
|
[
{
"created": "Thu, 21 Dec 2006 15:14:45 GMT",
"version": "v1"
}
] |
2016-09-28
|
[
[
"Kouza",
"Maksim",
""
],
[
"Li",
"Mai Suan",
""
],
[
"O'Brien",
"Edward P.",
"Jr."
],
[
"Hu",
"Chin-Kun",
""
],
[
"Thirumalai",
"D.",
""
]
] |
We analyze the dependence of cooperativity of the thermal denaturation transition and folding rates of globular proteins on the number of amino acid residues, $N$, using lattice models with side chains,off-lattice Go models and the available experimental data. A dimensionless measure of cooperativity, $\Omega_c$ ($0 < \Omega_c < \infty$), scales as $\Omega_c \sim N^{\zeta}$. The results of simulations and the analysis of experimental data further confirm the earlier prediction that $\zeta$ is universal with $\zeta = 1 +\gamma$, where exponent $\gamma$ characterizes the susceptibility of a self-avoiding walk. This finding suggests that the structural characteristics in the denaturated state are manifested in the folding cooperativity at the transition temperature. The folding rates $k_F$ for the Go models and a dataset of 69 proteins can be fit using $k_F = k_F^0 \exp(-cN^\beta)$. Both $\beta = 1/2$ and 2/3 provide a good fit of the data. We find that $k_F = k_F^0 \exp(-cN^{{1/2}})$, with the average (over the dataset of proteins) $k_F^0 \approx (0.2\mu s)^{-1}$ and $c \approx 1.1$, can be used to estimate folding rates to within an order of magnitude in most cases. The minimal models give identical $N$ dependence with $c \approx 1$. The prefactor for off-lattice Go models is nearly four orders of magnitude larger than the experimental value.
|
2308.08524
|
Carsten Conradi
|
Carsten Conradi and Maya Mincheva
|
In distributive phosphorylation catalytic constants enable non-trivial
dynamics
| null | null | null | null |
q-bio.MN math.DS
|
http://creativecommons.org/licenses/by/4.0/
|
Ordered distributive double phosphorylation is a recurrent motif in
intracellular signaling and control. It is either sequential (where the site
phosphorylated last is dephosphorylated first) or cyclic (where the site
phosphorylated first is dephosphorylated first). Sequential distributive double
phosphorylation has been extensively studied and an inequality involving only
the catalytic constants of kinase and phosphatase is known to be sufficient for
multistationarity. As multistationarity is necessary for bistability it has
been argued that these constants enable bistability.
Here we show for cyclic distributive double phosphorylation that if its
catalytic constants satisfy the very same inequality, then Hopf bifurcations
and hence sustained oscillations can occur. Hence we argue that in distributive
double phosphorylation (sequential or distributive) the catalytic constants
enable non-trivial dynamics.
In fact, if the rate constant values in a network of cyclic distributive
double phosphorylation are such that Hopf bifurcations and sustained
oscillations can occur, then a network of sequential distributive double
phosphorylation with the same rate constant values will show multistationarity
-- albeit for different values of the total concentrations. For cyclic
distributive double phosphorylation we further describe a procedure to generate
rate constant values where Hopf bifurcations and hence sustained oscillations
can occur. This may, for example, allow for an efficient sampling of
oscillatory regions in parameter space.
Our analysis is greatly simplified by the fact that it is possible to reduce
the network of cyclic distributive double phosphorylation to what we call a
network with a single extreme ray. We summarize key properties of these
networks.
|
[
{
"created": "Wed, 16 Aug 2023 17:20:52 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Feb 2024 18:05:49 GMT",
"version": "v2"
}
] |
2024-02-09
|
[
[
"Conradi",
"Carsten",
""
],
[
"Mincheva",
"Maya",
""
]
] |
Ordered distributive double phosphorylation is a recurrent motif in intracellular signaling and control. It is either sequential (where the site phosphorylated last is dephosphorylated first) or cyclic (where the site phosphorylated first is dephosphorylated first). Sequential distributive double phosphorylation has been extensively studied and an inequality involving only the catalytic constants of kinase and phosphatase is known to be sufficient for multistationarity. As multistationarity is necessary for bistability it has been argued that these constants enable bistability. Here we show for cyclic distributive double phosphorylation that if its catalytic constants satisfy the very same inequality, then Hopf bifurcations and hence sustained oscillations can occur. Hence we argue that in distributive double phosphorylation (sequential or distributive) the catalytic constants enable non-trivial dynamics. In fact, if the rate constant values in a network of cyclic distributive double phosphorylation are such that Hopf bifurcations and sustained oscillations can occur, then a network of sequential distributive double phosphorylation with the same rate constant values will show multistationarity -- albeit for different values of the total concentrations. For cyclic distributive double phosphorylation we further describe a procedure to generate rate constant values where Hopf bifurcations and hence sustained oscillations can occur. This may, for example, allow for an efficient sampling of oscillatory regions in parameter space. Our analysis is greatly simplified by the fact that it is possible to reduce the network of cyclic distributive double phosphorylation to what we call a network with a single extreme ray. We summarize key properties of these networks.
|
1903.07551
|
Eryu Xia
|
Eryu Xia, Yiqin Yu, Enliang Xu, Jing Mei, Wen Sun
|
From Risk Prediction Models to Risk Assessment Service: A Formulation of
Development Paradigm
| null | null | null | null |
q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Risk assessment services fulfil the task of generating a risk report from
personal information and are developed for purposes like disease prognosis,
resource utilization prioritization, and informing clinical interventions. A
major component of a risk assessment service is a risk prediction model. For a
model to be easily integrated into risk assessment services, efforts are needed
to design a detailed development roadmap for the intended service at the time
of model development. However, methodology for such design is less described.
We thus reviewed existing literature and formulated a six-stage risk assessment
service development paradigm, from requirements analysis, service development,
model validation, pilot study, to iterative service deployment and assessment
and refinement. The study aims at providing a prototypic development roadmap
with checkpoints for the design of risk assessment services.
|
[
{
"created": "Fri, 1 Mar 2019 02:54:46 GMT",
"version": "v1"
}
] |
2019-03-19
|
[
[
"Xia",
"Eryu",
""
],
[
"Yu",
"Yiqin",
""
],
[
"Xu",
"Enliang",
""
],
[
"Mei",
"Jing",
""
],
[
"Sun",
"Wen",
""
]
] |
Risk assessment services fulfil the task of generating a risk report from personal information and are developed for purposes like disease prognosis, resource utilization prioritization, and informing clinical interventions. A major component of a risk assessment service is a risk prediction model. For a model to be easily integrated into risk assessment services, efforts are needed to design a detailed development roadmap for the intended service at the time of model development. However, methodology for such design is less described. We thus reviewed existing literature and formulated a six-stage risk assessment service development paradigm, from requirements analysis, service development, model validation, pilot study, to iterative service deployment and assessment and refinement. The study aims at providing a prototypic development roadmap with checkpoints for the design of risk assessment services.
|
1807.06740
|
Zachary Kilpatrick PhD
|
Nikhil Krishnan and Zachary P. Kilpatrick
|
Optimizing a jump-diffusion model of a starving forager
|
8 pages, 5 figures
|
Phys. Rev. E 98, 052406 (2018)
|
10.1103/PhysRevE.98.052406
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We analyze the movement of a starving forager on a one-dimensional periodic
lattice, where each location contains one unit of food. As the forager lands on
sites with food, it consumes the food, leaving the sites empty. If the forager
lands consecutively on $s$ empty sites, then it will starve. The forager has
two modes of movement: it can either diffuse, by moving with equal probability
to adjacent sites on the lattice, or it can jump to a uniformly randomly chosen
site on the lattice. We show that the lifetime $T$ of the forager in either
paradigm can be approximated by the sum of the cover time $\tau_{\rm cover}$
and the starvation time $s$, when $s$ far exceeds the number $n$ of lattice
sites. Our main findings focus on the hybrid model, where the forager has a
probability of either jumping or diffusing. The lifetime of the forager varies
non-monotonically according to $p_j$, the probability of jumping. By examining
a small system, analyzing a heuristic model, and using direct numerical
simulation, we explore the tradeoff between jumps and diffusion, and show that
the strategy that maximizes the forager lifetime is a mixture of both modes of
movement.
|
[
{
"created": "Wed, 18 Jul 2018 02:23:27 GMT",
"version": "v1"
}
] |
2018-11-28
|
[
[
"Krishnan",
"Nikhil",
""
],
[
"Kilpatrick",
"Zachary P.",
""
]
] |
We analyze the movement of a starving forager on a one-dimensional periodic lattice, where each location contains one unit of food. As the forager lands on sites with food, it consumes the food, leaving the sites empty. If the forager lands consecutively on $s$ empty sites, then it will starve. The forager has two modes of movement: it can either diffuse, by moving with equal probability to adjacent sites on the lattice, or it can jump to a uniformly randomly chosen site on the lattice. We show that the lifetime $T$ of the forager in either paradigm can be approximated by the sum of the cover time $\tau_{\rm cover}$ and the starvation time $s$, when $s$ far exceeds the number $n$ of lattice sites. Our main findings focus on the hybrid model, where the forager has a probability of either jumping or diffusing. The lifetime of the forager varies non-monotonically according to $p_j$, the probability of jumping. By examining a small system, analyzing a heuristic model, and using direct numerical simulation, we explore the tradeoff between jumps and diffusion, and show that the strategy that maximizes the forager lifetime is a mixture of both modes of movement.
|
2202.07473
|
Ahmed Hafez
|
Ahmed Hafez, Beatriz Soriano, Aya A. Elsayed, Ricardo Futami, Raquel
Ceprian, Ricardo Ramos-Ruiz, Genis Martinez, Francisco J. Roig, Miguel A.
Torres-Font, Fernando Naya-Catal\`a, Josep Alvar Calduch-Giner, Lucia
Trilla-Fuertes, Angelo Gamez-Pozo, Vicente Arnau, Jose M. Sempere, Jaume
Perez-S\'anchez, Toni Gabald\'on, Carlos Llorens
|
Client applications and Server Side docker for management of RNASeq
and/or VariantSeq workflows and pipelines of the GPRO Suite
| null | null | null | null |
q-bio.GN
|
http://creativecommons.org/licenses/by/4.0/
|
The GPRO suite is an in-progress bioinformatic project for -omic data
analyses. As part of the continued growth of this project, we introduce a
client side & server side solution for comparative transcriptomics and analysis
of variants. The client side consists of two Java applications called "RNASeq"
and "VariantSeq" to manage workflows for RNA-seq and Variant-seq analysis,
respectively, based on the most common command line interface tools for each
topic. Both applications are coupled with a Linux server infrastructure (named
GPRO Server Side) that hosts all dependencies of each application (scripts,
databases, and command line interface tools). Implementation of the server side
requires a Linux operating system, PHP, SQL, Python, bash scripting, and
third-party software. The GPRO Server Side can be deployed via a Docker
container that can be installed in the user's PC using any operating system or
on remote servers as a cloud solution. The two applications are available as
desktop and cloud applications and provide two execution modes: a Step-by-Step
mode enables each step of a workflow to be executed independently and a
Pipeline mode allows all steps to be run sequentially. The two applications
also feature an experimental support system called GENIE that consists of a
virtual chatbot/assistant and a pipeline jobs panel coupled with an expert
system. The chatbot can troubleshoot issues with the usage of each tool, the
pipeline job panel provides information about the status of each task executed
in the GPRO Server Side, and the expert provides the user with a potential
recommendation to identify or fix failed analyses. The two applications and the
GPRO Server Side combine the user-friendliness and security of client software
with the efficiency of front-end & back-end solutions to manage command line
interface software for RNA-seq and variant-seq analysis via interface
environments.
|
[
{
"created": "Mon, 14 Feb 2022 12:00:35 GMT",
"version": "v1"
},
{
"created": "Mon, 23 May 2022 13:28:45 GMT",
"version": "v2"
},
{
"created": "Mon, 8 Aug 2022 06:39:41 GMT",
"version": "v3"
},
{
"created": "Sat, 19 Nov 2022 12:29:58 GMT",
"version": "v4"
}
] |
2022-11-22
|
[
[
"Hafez",
"Ahmed",
""
],
[
"Soriano",
"Beatriz",
""
],
[
"Elsayed",
"Aya A.",
""
],
[
"Futami",
"Ricardo",
""
],
[
"Ceprian",
"Raquel",
""
],
[
"Ramos-Ruiz",
"Ricardo",
""
],
[
"Martinez",
"Genis",
""
],
[
"Roig",
"Francisco J.",
""
],
[
"Torres-Font",
"Miguel A.",
""
],
[
"Naya-Català",
"Fernando",
""
],
[
"Calduch-Giner",
"Josep Alvar",
""
],
[
"Trilla-Fuertes",
"Lucia",
""
],
[
"Gamez-Pozo",
"Angelo",
""
],
[
"Arnau",
"Vicente",
""
],
[
"Sempere",
"Jose M.",
""
],
[
"Perez-Sánchez",
"Jaume",
""
],
[
"Gabaldón",
"Toni",
""
],
[
"Llorens",
"Carlos",
""
]
] |
The GPRO suite is an in-progress bioinformatic project for -omic data analyses. As part of the continued growth of this project, we introduce a client side & server side solution for comparative transcriptomics and analysis of variants. The client side consists of two Java applications called "RNASeq" and "VariantSeq" to manage workflows for RNA-seq and Variant-seq analysis, respectively, based on the most common command line interface tools for each topic. Both applications are coupled with a Linux server infrastructure (named GPRO Server Side) that hosts all dependencies of each application (scripts, databases, and command line interface tools). Implementation of the server side requires a Linux operating system, PHP, SQL, Python, bash scripting, and third-party software. The GPRO Server Side can be deployed via a Docker container that can be installed in the user's PC using any operating system or on remote servers as a cloud solution. The two applications are available as desktop and cloud applications and provide two execution modes: a Step-by-Step mode enables each step of a workflow to be executed independently and a Pipeline mode allows all steps to be run sequentially. The two applications also feature an experimental support system called GENIE that consists of a virtual chatbot/assistant and a pipeline jobs panel coupled with an expert system. The chatbot can troubleshoot issues with the usage of each tool, the pipeline job panel provides information about the status of each task executed in the GPRO Server Side, and the expert provides the user with a potential recommendation to identify or fix failed analyses. The two applications and the GPRO Server Side combine the user-friendliness and security of client software with the efficiency of front-end & back-end solutions to manage command line interface software for RNA-seq and variant-seq analysis via interface environments.
|
2104.02794
|
Janaki Raghavan
|
R. Janaki and A. S. Vytheeswaran
|
The interplay of inhibitory and electrical synapses results in complex
persistent activity
|
Main text: 10 pages, 5 figures; Supplementary Information: 2 pages, 3
figures
| null | null | null |
q-bio.NC nlin.CD
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Inhibitory neurons play a crucial role in maintaining persistent neuronal
activity. Although connected extensively through electrical synapses
(gap-junctions), these neurons also exhibit interactions through chemical
synapses in certain regions of the brain. When the coupling is sufficiently
strong, the effects of these two synaptic modalities combine in a nonlinear
way. Hence, in this work, we focus on the strong inhibition regime and identify
the parametric conditions that result in the emergence of self-sustained
oscillations in systems of coupled excitable neurons, in the presence of a
brief sub-threshold stimulus. Our investigation on the dynamics in a minimal
network of two neurons reveals a rich set of dynamical behaviors viz., periodic
and various complex oscillations including period-n (n=2,4,8...) dynamics and
chaos. We further extend our study by considering a system of inhibitory
neurons arranged in a one-dimensional ring topology and determine the optimal
conditions for sustained activity. Our work highlights the nonlinear dynamical
behavior arising due to the combined effects of gap-junctions and strong
synaptic inhibition, which can have potential implications in maintaining
robust memory patterns.
|
[
{
"created": "Tue, 6 Apr 2021 21:25:58 GMT",
"version": "v1"
}
] |
2021-04-08
|
[
[
"Janaki",
"R.",
""
],
[
"Vytheeswaran",
"A. S.",
""
]
] |
Inhibitory neurons play a crucial role in maintaining persistent neuronal activity. Although connected extensively through electrical synapses (gap-junctions), these neurons also exhibit interactions through chemical synapses in certain regions of the brain. When the coupling is sufficiently strong, the effects of these two synaptic modalities combine in a nonlinear way. Hence, in this work, we focus on the strong inhibition regime and identify the parametric conditions that result in the emergence of self-sustained oscillations in systems of coupled excitable neurons, in the presence of a brief sub-threshold stimulus. Our investigation on the dynamics in a minimal network of two neurons reveals a rich set of dynamical behaviors viz., periodic and various complex oscillations including period-n (n=2,4,8...) dynamics and chaos. We further extend our study by considering a system of inhibitory neurons arranged in a one-dimensional ring topology and determine the optimal conditions for sustained activity. Our work highlights the nonlinear dynamical behavior arising due to the combined effects of gap-junctions and strong synaptic inhibition, which can have potential implications in maintaining robust memory patterns.
|
1502.00156
|
Ulrich S. Schwarz
|
Johanna E. Baschek, Heinrich C. R. Klein and Ulrich S. Schwarz
(Heidelberg University)
|
Stochastic dynamics of virus capsid formation: direct versus
hierarchical self-assembly
|
Revtex, 31 pages, 9 EPS figures
|
BMC Biophysics 5:22 (2012)
|
10.1186/2046-1682-5-22
| null |
q-bio.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In order to replicate within their cellular host, many viruses have developed
self-assembly strategies for their capsids which are sufficiently robust as to
be reconstituted in vitro. Mathematical models for virus self-assembly usually
assume that the bonds leading to cluster formation have constant reactivity
over the time course of assembly (direct assembly). In some cases, however,
binding sites between the capsomers have been reported to be activated during
the self-assembly process (hierarchical assembly). In order to study possible
advantages of such hierarchical schemes for icosahedral virus capsid assembly,
we use Brownian dynamics simulations of a patchy particle model that allows us
to switch binding sites on and off during assembly. For T1 viruses, we
implement a hierarchical assembly scheme where inter-capsomer bonds become
active only if a complete pentamer has been assembled. We find direct assembly
to be favorable for reversible bonds allowing for repeated structural
reorganizations, while hierarchical assembly is favorable for strong bonds with
small dissociation rate, as this situation is less prone to kinetic trapping.
However, at the same time it is more vulnerable to monomer starvation during
the final phase. Increasing the number of initial monomers does have only a
weak effect on these general features. The differences between the two assembly
schemes become more pronounced for more complex virus geometries, as shown here
for T3 viruses, which assemble through homogeneous pentamers and heterogeneous
hexamers in the hierarchical scheme. In order to complement the simulations for
this more complicated case, we introduce a master equation approach that agrees
well with the simulation results.
|
[
{
"created": "Sat, 31 Jan 2015 19:47:54 GMT",
"version": "v1"
}
] |
2015-02-03
|
[
[
"Baschek",
"Johanna E.",
"",
"Heidelberg University"
],
[
"Klein",
"Heinrich C. R.",
"",
"Heidelberg University"
],
[
"Schwarz",
"Ulrich S.",
"",
"Heidelberg University"
]
] |
In order to replicate within their cellular host, many viruses have developed self-assembly strategies for their capsids which are sufficiently robust as to be reconstituted in vitro. Mathematical models for virus self-assembly usually assume that the bonds leading to cluster formation have constant reactivity over the time course of assembly (direct assembly). In some cases, however, binding sites between the capsomers have been reported to be activated during the self-assembly process (hierarchical assembly). In order to study possible advantages of such hierarchical schemes for icosahedral virus capsid assembly, we use Brownian dynamics simulations of a patchy particle model that allows us to switch binding sites on and off during assembly. For T1 viruses, we implement a hierarchical assembly scheme where inter-capsomer bonds become active only if a complete pentamer has been assembled. We find direct assembly to be favorable for reversible bonds allowing for repeated structural reorganizations, while hierarchical assembly is favorable for strong bonds with small dissociation rate, as this situation is less prone to kinetic trapping. However, at the same time it is more vulnerable to monomer starvation during the final phase. Increasing the number of initial monomers does have only a weak effect on these general features. The differences between the two assembly schemes become more pronounced for more complex virus geometries, as shown here for T3 viruses, which assemble through homogeneous pentamers and heterogeneous hexamers in the hierarchical scheme. In order to complement the simulations for this more complicated case, we introduce a master equation approach that agrees well with the simulation results.
|
q-bio/0505003
|
William Bialek
|
William Bialek and Rob R. de Ruyter van Steveninck
|
Features and dimensions: Motion estimation in fly vision
|
18 pages, 11 figures, some in color
| null | null | null |
q-bio.NC
| null |
We characterize the computation of motion in the fly visual system as a
mapping from the high dimensional space of signals in the retinal photodetector
array to the probability of generating an action potential in a motion
sensitive neuron. Our approach to this problem identifies a low dimensional
subspace of signals within which the neuron is most sensitive, and then samples
this subspace to visualize the nonlinear structure of the mapping. The results
illustrate the computational strategies predicted for a system that makes
optimal motion estimates given the physical noise sources in the detector
array. More generally, the hypothesis that neurons are sensitive to low
dimensional subspaces of their inputs formalizes the intuitive notion of
feature selectivity and suggests a strategy for characterizing the neural
processing of complex, naturalistic sensory inputs.
|
[
{
"created": "Mon, 2 May 2005 22:02:58 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Bialek",
"William",
""
],
[
"van Steveninck",
"Rob R. de Ruyter",
""
]
] |
We characterize the computation of motion in the fly visual system as a mapping from the high dimensional space of signals in the retinal photodetector array to the probability of generating an action potential in a motion sensitive neuron. Our approach to this problem identifies a low dimensional subspace of signals within which the neuron is most sensitive, and then samples this subspace to visualize the nonlinear structure of the mapping. The results illustrate the computational strategies predicted for a system that makes optimal motion estimates given the physical noise sources in the detector array. More generally, the hypothesis that neurons are sensitive to low dimensional subspaces of their inputs formalizes the intuitive notion of feature selectivity and suggests a strategy for characterizing the neural processing of complex, naturalistic sensory inputs.
|
1606.01319
|
Vladimir Privman
|
Sergii Domanskyi, Vladimir Privman
|
Modeling and Modifying Response of Biochemical Processes for
Biocomputing and Biosensing Signal Processing
|
arXiv admin note: text overlap with arXiv:1305.5803, arXiv:1411.2185
|
Ch. 3 in Advances in Unconventional Computing, Vol. 2: Prototypes,
Models and Algorithms, pages 61-83, edited by A. Adamatzky, Vol. 23 of
Emergence, Complexity and Computation (Springer Nature, Basel, Switzerland,
2017)
|
10.1007/978-3-319-33921-4_3
|
VP-271
|
q-bio.MN cond-mat.soft
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Processes involving multi-input multi-step reaction cascades are used in
developing novel biosensing, biocomputing, and decision making systems. In
various applications different changes in responses of the constituent
processing steps (reactions) in a cascade are desirable in order to allow
control of the system's response. Here we consider conversion of convex
response to sigmoid by "intensity filtering," as well as "threshold filtering,"
and we offer a general overview of this field of research. Specifically, we
survey rate equation modelling that has been used for enzymatic reactions. This
allows us to design modified biochemical processes as "network components" with
responses desirable in applications.
|
[
{
"created": "Sat, 4 Jun 2016 02:28:35 GMT",
"version": "v1"
}
] |
2016-08-16
|
[
[
"Domanskyi",
"Sergii",
""
],
[
"Privman",
"Vladimir",
""
]
] |
Processes involving multi-input multi-step reaction cascades are used in developing novel biosensing, biocomputing, and decision making systems. In various applications different changes in responses of the constituent processing steps (reactions) in a cascade are desirable in order to allow control of the system's response. Here we consider conversion of convex response to sigmoid by "intensity filtering," as well as "threshold filtering," and we offer a general overview of this field of research. Specifically, we survey rate equation modelling that has been used for enzymatic reactions. This allows us to design modified biochemical processes as "network components" with responses desirable in applications.
|
1903.02890
|
Kelin Xia
|
Zhenyu Meng, D Vijay Anand, Yunpeng Lu, Jie Wu, Kelin Xia
|
Weighted persistent homology for biomolecular data analysis
|
27 pages; 18 figures
| null | null | null |
q-bio.BM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we systematically review weighted persistent homology (WPH)
models and their applications in biomolecular data analysis. Essentially, the
weight value, which reflects physical, chemical and biological properties, can
be assigned to vertices (atom centers), edges (bonds), or higher order
simplexes (cluster of atoms), depending on the biomolecular structure,
function, and dynamics properties. Further, we propose the first localized
weighted persistent homology (LWPH). Inspired by the great success of element
specific persistent homology (ESPH), we do not treat biomolecules as an
inseparable system like all previous weighted models, instead we decompose them
into a series of local domains, which may be overlapped with each other. The
general persistent homology or weighted persistent homology analysis is then
applied on each of these local domains. In this way, functional properties,
that are embedded in local structures, can be revealed. Our model has been
applied to systematically studying DNA structures. It has been found that our
LWPH based features can be used to successfully discriminate the A-, B-, and
Z-types of DNA. More importantly, our LWPH based PCA model can identify two
configurational states of DNA structure in ion liquid environment, which can be
revealed only by the complicated helical coordinate system. The great
consistence with the helical-coordinate model demonstrates that our model
captures local structure variations so well that it is comparable with
geometric models. Moreover, geometric measurements are usually defined in very
local regions. For instance, the helical-coordinate system is limited to one or
two basepairs. However, our LWPH can quantitatively characterize structure
information in local regions or domains with arbitrary sizes and shapes, where
traditional geometrical measurements fail.
|
[
{
"created": "Thu, 7 Mar 2019 13:09:33 GMT",
"version": "v1"
}
] |
2019-03-08
|
[
[
"Meng",
"Zhenyu",
""
],
[
"Anand",
"D Vijay",
""
],
[
"Lu",
"Yunpeng",
""
],
[
"Wu",
"Jie",
""
],
[
"Xia",
"Kelin",
""
]
] |
In this paper, we systematically review weighted persistent homology (WPH) models and their applications in biomolecular data analysis. Essentially, the weight value, which reflects physical, chemical and biological properties, can be assigned to vertices (atom centers), edges (bonds), or higher order simplexes (cluster of atoms), depending on the biomolecular structure, function, and dynamics properties. Further, we propose the first localized weighted persistent homology (LWPH). Inspired by the great success of element specific persistent homology (ESPH), we do not treat biomolecules as an inseparable system like all previous weighted models, instead we decompose them into a series of local domains, which may be overlapped with each other. The general persistent homology or weighted persistent homology analysis is then applied on each of these local domains. In this way, functional properties, that are embedded in local structures, can be revealed. Our model has been applied to systematically studying DNA structures. It has been found that our LWPH based features can be used to successfully discriminate the A-, B-, and Z-types of DNA. More importantly, our LWPH based PCA model can identify two configurational states of DNA structure in ion liquid environment, which can be revealed only by the complicated helical coordinate system. The great consistence with the helical-coordinate model demonstrates that our model captures local structure variations so well that it is comparable with geometric models. Moreover, geometric measurements are usually defined in very local regions. For instance, the helical-coordinate system is limited to one or two basepairs. However, our LWPH can quantitatively characterize structure information in local regions or domains with arbitrary sizes and shapes, where traditional geometrical measurements fail.
|
1710.10080
|
Daan Brinks
|
Miao-Ping Chien, Daan Brinks, Yoav Adam, William Bloxham, Simon
Kheifets, Adam E. Cohen
|
Two-photon photoactivated voltage imaging in tissue with an
Archaerhodopsin-derived reporter
| null | null | null | null |
q-bio.QM physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Robust voltage imaging in tissue remains a technical challenge. Existing
combinations of genetically encoded voltage indicators (GEVIs) and microscopy
techniques cannot simultaneously achieve sufficiently high voltage sensitivity,
background rejection, and time resolution for high-resolution mapping of
sub-cellular voltage dynamics in intact brain tissue. We developed a pooled
high-throughput screening approach to identify Archaerhodopsin mutants with
unusual photophysical properties. After screening ~105 cells, we identified a
novel GEVI, NovArch, whose 1-photon near infrared fluorescence is reversibly
enhanced by weak 2-photon excitation. Because the 2-photon excitation acts
catalytically rather than stoichiometrically, high fluorescence signals,
optical sectioning, and high time resolution are achieved simultaneously, at
modest 2- photon laser power. We developed a microscopy system optimized for
NovArch imaging in tissue. The combination of protein and optical engineering
enhanced signal contrast sufficiently to enable optical mapping of
back-propagating action potentials in dendrites in acute mouse brain slice.
|
[
{
"created": "Fri, 27 Oct 2017 11:21:30 GMT",
"version": "v1"
}
] |
2017-10-30
|
[
[
"Chien",
"Miao-Ping",
""
],
[
"Brinks",
"Daan",
""
],
[
"Adam",
"Yoav",
""
],
[
"Bloxham",
"William",
""
],
[
"Kheifets",
"Simon",
""
],
[
"Cohen",
"Adam E.",
""
]
] |
Robust voltage imaging in tissue remains a technical challenge. Existing combinations of genetically encoded voltage indicators (GEVIs) and microscopy techniques cannot simultaneously achieve sufficiently high voltage sensitivity, background rejection, and time resolution for high-resolution mapping of sub-cellular voltage dynamics in intact brain tissue. We developed a pooled high-throughput screening approach to identify Archaerhodopsin mutants with unusual photophysical properties. After screening ~105 cells, we identified a novel GEVI, NovArch, whose 1-photon near infrared fluorescence is reversibly enhanced by weak 2-photon excitation. Because the 2-photon excitation acts catalytically rather than stoichiometrically, high fluorescence signals, optical sectioning, and high time resolution are achieved simultaneously, at modest 2- photon laser power. We developed a microscopy system optimized for NovArch imaging in tissue. The combination of protein and optical engineering enhanced signal contrast sufficiently to enable optical mapping of back-propagating action potentials in dendrites in acute mouse brain slice.
|
0805.4316
|
Vasily Ogryzko V
|
Vasily Ogryzko
|
On two quantum approaches to adaptive mutations in bacteria
|
35 pages, 6 figures, 3 tables, 1 Appendix. This version was accepted
in the NeuroQuantology Journal. Format has been adjusted accordingly,
together with a number of clarifying modifications
| null | null | null |
q-bio.PE q-bio.CB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
I compare two quantum-theoretical approaches to the phenomenon of adaptive
mutations, termed here Q-cell and Q-genome. I use 'fluctuation trapping' model
as a general framework. I introduce notions of R-error and D-error and argue
that the 'fluctuation trapping' model has to employ a correlation between the
R- and D- errors. Further, I compare how the two approaches can justify the
R-D-error correlation, focusing on the advantages of the Q-cell approach. The
positive role of environmentally induced decoherence (EID) on both steps of the
adaptation process is emphasized. A starving bacterial cell is proposed to be
in an einselected state. The intracellular dynamics in this state has a unitary
character and I propose to interpret it as 'exponential growth in imaginary
time', analogously to the commonly considered 'diffusion' interpretation of the
Schroedinger equation. Addition of a substrate leads to Wick rotation and a
switch from 'imaginary time' reproduction to a 'real time' reproduction regime.
Due to the variations at the genomic level (such as base tautomery), the
starving cell has to be represented as a superposition of different components,
all 'reproducing in imaginary time'. Adidtion of a selective substrate,
allowing only one of these components to amplify, will cause Wick rotation and
amplification of this component, thus justifying the occurence of the R-D-error
correlation. Further ramifications of the proposed ideas for evolutionary
theory are discussed.
|
[
{
"created": "Wed, 28 May 2008 11:59:22 GMT",
"version": "v1"
},
{
"created": "Mon, 28 Jul 2008 11:15:56 GMT",
"version": "v2"
},
{
"created": "Thu, 31 Jul 2008 14:36:46 GMT",
"version": "v3"
},
{
"created": "Mon, 7 Sep 2009 15:33:37 GMT",
"version": "v4"
}
] |
2009-09-07
|
[
[
"Ogryzko",
"Vasily",
""
]
] |
I compare two quantum-theoretical approaches to the phenomenon of adaptive mutations, termed here Q-cell and Q-genome. I use 'fluctuation trapping' model as a general framework. I introduce notions of R-error and D-error and argue that the 'fluctuation trapping' model has to employ a correlation between the R- and D- errors. Further, I compare how the two approaches can justify the R-D-error correlation, focusing on the advantages of the Q-cell approach. The positive role of environmentally induced decoherence (EID) on both steps of the adaptation process is emphasized. A starving bacterial cell is proposed to be in an einselected state. The intracellular dynamics in this state has a unitary character and I propose to interpret it as 'exponential growth in imaginary time', analogously to the commonly considered 'diffusion' interpretation of the Schroedinger equation. Addition of a substrate leads to Wick rotation and a switch from 'imaginary time' reproduction to a 'real time' reproduction regime. Due to the variations at the genomic level (such as base tautomery), the starving cell has to be represented as a superposition of different components, all 'reproducing in imaginary time'. Adidtion of a selective substrate, allowing only one of these components to amplify, will cause Wick rotation and amplification of this component, thus justifying the occurence of the R-D-error correlation. Further ramifications of the proposed ideas for evolutionary theory are discussed.
|
2210.01546
|
Jai Sharma
|
Jai Sharma, Vidhyacharan Bhaskar
|
An RNA Sequencing Analysis of Glaucoma Genesis in Mice
|
6 pages, 6 figures
| null | null | null |
q-bio.GN
|
http://creativecommons.org/licenses/by/4.0/
|
Glaucoma is the leading cause of irreversible blindness in people over the
age of 60, accounting for 6.6 to 8% of all blindness in 2010, but there is
still much to be learned about the genetic origins of the eye disease. With the
modern development of Next-Generation Sequencing (NGS) technologies, scientists
are starting to learn more about the genetic origins of Glaucoma. This research
uses differential expression (DE) and gene ontology (GO) analyses to study the
genetic differences between mice with severe Glaucoma and multiple control
groups. Optical nerve head (ONH) and retina data samples of genome-wide RNA
expression from NCBI (NIH) are used for pairwise comparison experimentation. In
addition, principal component analysis (PCA) and dispersion visualization
methods are employed to perform quality control tests of the sequenced data.
Genes with skewed gene counts are also identified, as they may be marker genes
for a particular severity of Glaucoma. The gene ontologies found in this
experiment support existing knowledge of Glaucoma genesis, providing confidence
that the results were valid. Future researchers can thoroughly study the gene
lists generated by the DE and GO analyses to find potential activator or
protector genes for Glaucoma in mice to develop drug treatments or gene
therapies to slow or stop the progression of the disease. The overall goal is
that in the future, such treatments can be made for humans as well to improve
the quality of life for human patients with Glaucoma and reduce Glaucoma
blindness rates.
|
[
{
"created": "Sun, 2 Oct 2022 10:16:31 GMT",
"version": "v1"
}
] |
2022-10-05
|
[
[
"Sharma",
"Jai",
""
],
[
"Bhaskar",
"Vidhyacharan",
""
]
] |
Glaucoma is the leading cause of irreversible blindness in people over the age of 60, accounting for 6.6 to 8% of all blindness in 2010, but there is still much to be learned about the genetic origins of the eye disease. With the modern development of Next-Generation Sequencing (NGS) technologies, scientists are starting to learn more about the genetic origins of Glaucoma. This research uses differential expression (DE) and gene ontology (GO) analyses to study the genetic differences between mice with severe Glaucoma and multiple control groups. Optical nerve head (ONH) and retina data samples of genome-wide RNA expression from NCBI (NIH) are used for pairwise comparison experimentation. In addition, principal component analysis (PCA) and dispersion visualization methods are employed to perform quality control tests of the sequenced data. Genes with skewed gene counts are also identified, as they may be marker genes for a particular severity of Glaucoma. The gene ontologies found in this experiment support existing knowledge of Glaucoma genesis, providing confidence that the results were valid. Future researchers can thoroughly study the gene lists generated by the DE and GO analyses to find potential activator or protector genes for Glaucoma in mice to develop drug treatments or gene therapies to slow or stop the progression of the disease. The overall goal is that in the future, such treatments can be made for humans as well to improve the quality of life for human patients with Glaucoma and reduce Glaucoma blindness rates.
|
0708.1598
|
HC Paul Lee
|
Sing-Guan Kong, Hong-Da Chen, Wen-Lang Fan, Jan Wigger, Andrew Torda,
and HC Lee
|
Genomes: at the edge of chaos with maximum information capacity
|
4 pages, 3 figures, paper
| null | null | null |
q-bio.GN
| null |
We propose an order index, phi, which quantifies the notion of ``life at the
edge of chaos'' when applied to genome sequences. It maps genomes to a number
from 0 (random and of infinite length) to 1 (fully ordered) and applies
regardless of sequence length. The 786 complete genomic sequences in GenBank
were found to have phi values in a very narrow range, 0.037+/-0.027. We show
this implies that genomes are halfway towards being completely random, namely,
at the edge of chaos. We argue that this narrow range represents the
neighborhood of a fixed-point in the space of sequences, and genomes are driven
there by the dynamics of a robust, predominantly neutral evolution process.
|
[
{
"created": "Sun, 12 Aug 2007 07:45:19 GMT",
"version": "v1"
}
] |
2007-08-14
|
[
[
"Kong",
"Sing-Guan",
""
],
[
"Chen",
"Hong-Da",
""
],
[
"Fan",
"Wen-Lang",
""
],
[
"Wigger",
"Jan",
""
],
[
"Torda",
"Andrew",
""
],
[
"Lee",
"HC",
""
]
] |
We propose an order index, phi, which quantifies the notion of ``life at the edge of chaos'' when applied to genome sequences. It maps genomes to a number from 0 (random and of infinite length) to 1 (fully ordered) and applies regardless of sequence length. The 786 complete genomic sequences in GenBank were found to have phi values in a very narrow range, 0.037+/-0.027. We show this implies that genomes are halfway towards being completely random, namely, at the edge of chaos. We argue that this narrow range represents the neighborhood of a fixed-point in the space of sequences, and genomes are driven there by the dynamics of a robust, predominantly neutral evolution process.
|
2305.06808
|
Glen Pridham
|
Rebecca Tobin, Glen Pridham, Andrew D. Rutenberg
|
Modelling disease impact: lifespan reduction is greatest for young
adults in an exogenous damage model of disease
| null |
Sci. Rep. 13 (2023) 1-10
|
10.1038/s41598-023-43005-0
| null |
q-bio.PE physics.bio-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We model the effects of disease and other exogenous damage during human
aging. Even when the exogenous damage is repaired at the end of acute disease,
propagated secondary damage remains. We consider both short-term mortality
effects due to (acute) exogenous damage and long-term mortality effects due to
propagated damage within the context of a generic network model (GNM) of
individual aging that simulates a U.S. population. Across a wide range of
disease durations and severities we find that while excess short-term mortality
is highest for the oldest individuals, the long-term years of life lost are
highest for the youngest individuals. These appear to be universal effects of
human disease. We support this conclusion with a phenomenological model
coupling damage and mortality. Our results are consistent with previous
lifetime mortality studies of atom bomb survivors and post-recovery health
studies of COVID-19. We suggest that short-term health impact studies could
complement lifetime mortality studies to better characterize the lifetime
impacts of disease on both individuals and populations.
|
[
{
"created": "Thu, 11 May 2023 14:01:41 GMT",
"version": "v1"
},
{
"created": "Sat, 29 Jul 2023 13:59:37 GMT",
"version": "v2"
}
] |
2023-09-29
|
[
[
"Tobin",
"Rebecca",
""
],
[
"Pridham",
"Glen",
""
],
[
"Rutenberg",
"Andrew D.",
""
]
] |
We model the effects of disease and other exogenous damage during human aging. Even when the exogenous damage is repaired at the end of acute disease, propagated secondary damage remains. We consider both short-term mortality effects due to (acute) exogenous damage and long-term mortality effects due to propagated damage within the context of a generic network model (GNM) of individual aging that simulates a U.S. population. Across a wide range of disease durations and severities we find that while excess short-term mortality is highest for the oldest individuals, the long-term years of life lost are highest for the youngest individuals. These appear to be universal effects of human disease. We support this conclusion with a phenomenological model coupling damage and mortality. Our results are consistent with previous lifetime mortality studies of atom bomb survivors and post-recovery health studies of COVID-19. We suggest that short-term health impact studies could complement lifetime mortality studies to better characterize the lifetime impacts of disease on both individuals and populations.
|
0805.0819
|
Luis Trevisan
|
Luis Augusto Trevisan and Fabiano Meira de Moura Luz
|
Prey-predator modeling of CO2 atmospheric concentration
|
Key words:Prey Predator model, Photosynthesis rate, CO2 concentration
| null | null | null |
q-bio.OT q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work we propose a mathematical model, based in a modified version of
the Lotka-Volterra prey-predator equations, to predict the increasing in CO2
atmospheric concentration. We consider how the photosynthesis rate has changed
with the increase of CO2 and how this affects plant reproduction and CO2
absorptions rates. Total CO2 emissions (natural and manmade) and biomass
numerical parameter changes are considered. It is shown that the atmospheric
system can be in equilibrium under some specific conditions, and also some
comparisons with historical are done.
|
[
{
"created": "Wed, 7 May 2008 01:13:32 GMT",
"version": "v1"
}
] |
2008-05-08
|
[
[
"Trevisan",
"Luis Augusto",
""
],
[
"Luz",
"Fabiano Meira de Moura",
""
]
] |
In this work we propose a mathematical model, based in a modified version of the Lotka-Volterra prey-predator equations, to predict the increasing in CO2 atmospheric concentration. We consider how the photosynthesis rate has changed with the increase of CO2 and how this affects plant reproduction and CO2 absorptions rates. Total CO2 emissions (natural and manmade) and biomass numerical parameter changes are considered. It is shown that the atmospheric system can be in equilibrium under some specific conditions, and also some comparisons with historical are done.
|
1602.06584
|
Lionel Roques
|
Mamadou Ciss, Sylvain Poggi, Mohamed-Mahmoud Memmah, Pierre Franck,
Marie Gosme, Nicolas Parisey and Lionel Roques
|
A model-based approach to assess the effectiveness of pest biocontrol by
natural enemies
| null | null | null | null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Main goal: The aim of this note is to propose a modeling approach for
assessing the effectiveness of pest biocontrol by natural enemies in
diversified agricultural landscapes including several pesticide-based
management strategies. Our approach combines a stochastic landscape model with
a spatially-explicit model of population dynamics. It enables us to analyze the
effect of the landscape composition (proportion of semi-natural habitat,
non-treated crops, slightly treated crops and conventionally treated crops) on
the effectiveness of pest biocontrol. Effectiveness is measured through
environmental and agronomical descriptors, measuring respectively the impact of
the pesticides on the environment and the average agronomic productivity of the
whole landscape taking into account losses caused by pests.
Conclusions: The effectiveness of the pesticide, the intensity of the
treatment and the pest intrinsic growth rate are found to be the main drivers
of landscape productivity. The loss in productivity due to a reduced use of
pesticide can be partly compensated by biocontrol. However, the model suggests
that it is not possible to maintain a constant level of productivity while
reducing the use of pesticides, even with highly efficient natural enemies.
Fragmentation of the semi-natural habitats and increased crop rotation tend to
slightly enhance the effectiveness of biocontrol but have a marginal effect
compared to the predation rate by natural enemies.
This note was written in the framework of the ANR project PEERLESS
"Predictive Ecological Engineering for Landscape Ecosystem Services and
Sustainability"(ANR-12-AGRO-0006).
|
[
{
"created": "Sun, 21 Feb 2016 21:28:05 GMT",
"version": "v1"
}
] |
2016-02-23
|
[
[
"Ciss",
"Mamadou",
""
],
[
"Poggi",
"Sylvain",
""
],
[
"Memmah",
"Mohamed-Mahmoud",
""
],
[
"Franck",
"Pierre",
""
],
[
"Gosme",
"Marie",
""
],
[
"Parisey",
"Nicolas",
""
],
[
"Roques",
"Lionel",
""
]
] |
Main goal: The aim of this note is to propose a modeling approach for assessing the effectiveness of pest biocontrol by natural enemies in diversified agricultural landscapes including several pesticide-based management strategies. Our approach combines a stochastic landscape model with a spatially-explicit model of population dynamics. It enables us to analyze the effect of the landscape composition (proportion of semi-natural habitat, non-treated crops, slightly treated crops and conventionally treated crops) on the effectiveness of pest biocontrol. Effectiveness is measured through environmental and agronomical descriptors, measuring respectively the impact of the pesticides on the environment and the average agronomic productivity of the whole landscape taking into account losses caused by pests. Conclusions: The effectiveness of the pesticide, the intensity of the treatment and the pest intrinsic growth rate are found to be the main drivers of landscape productivity. The loss in productivity due to a reduced use of pesticide can be partly compensated by biocontrol. However, the model suggests that it is not possible to maintain a constant level of productivity while reducing the use of pesticides, even with highly efficient natural enemies. Fragmentation of the semi-natural habitats and increased crop rotation tend to slightly enhance the effectiveness of biocontrol but have a marginal effect compared to the predation rate by natural enemies. This note was written in the framework of the ANR project PEERLESS "Predictive Ecological Engineering for Landscape Ecosystem Services and Sustainability"(ANR-12-AGRO-0006).
|
2010.07437
|
Axel Wism\"uller
|
Axel Wism\"uller and Larry Stockmaster
|
Tracking Results and Utilization of Artificial Intelligence (tru-AI) in
Radiology: Early-Stage COVID-19 Pandemic Observations
|
9 pages, 1 figure, 1 table
| null | null | null |
q-bio.QM cs.LG q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Objective: To introduce a method for tracking results and utilization of
Artificial Intelligence (tru-AI) in radiology. By tracking both large-scale
utilization and AI results data, the tru-AI approach is designed to calculate
surrogates for measuring important disease-related observational quantities
over time, such as the prevalence of intracranial hemorrhage during the
COVID-19 pandemic outbreak. Methods: To quantitatively investigate the clinical
applicability of the tru-AI approach, we analyzed service requests for
automatically identifying intracranial hemorrhage (ICH) on head CT using a
commercial AI solution. This software is typically used for AI-based
prioritization of radiologists' reading lists for reducing turnaround times in
patients with emergent clinical findings, such as ICH or pulmonary embolism.We
analyzed data of N=9,421 emergency-setting non-contrast head CT studies at a
major US healthcare system acquired from November 1, 2019 through June 2, 2020,
and compared two observation periods, namely (i) a pre-pandemic epoch from
November 1, 2019 through February 29, 2020, and (ii) a period during the
COVID-19 pandemic outbreak, April 1-30, 2020. Results: Although daily CT scan
counts were significantly lower during (40.1 +/- 7.9) than before (44.4 +/-
7.6) the COVID-19 outbreak, we found that ICH was more likely to be observed by
AI during than before the COVID-19 outbreak (p<0.05), with approximately one
daily ICH+ case more than statistically expected. Conclusion: Our results
suggest that, by tracking both large-scale utilization and AI results data in
radiology, the tru-AI approach can contribute clinical value as a versatile
exploratory tool, aiming at a better understanding of pandemic-related effects
on healthcare.
|
[
{
"created": "Wed, 14 Oct 2020 23:37:15 GMT",
"version": "v1"
}
] |
2020-10-16
|
[
[
"Wismüller",
"Axel",
""
],
[
"Stockmaster",
"Larry",
""
]
] |
Objective: To introduce a method for tracking results and utilization of Artificial Intelligence (tru-AI) in radiology. By tracking both large-scale utilization and AI results data, the tru-AI approach is designed to calculate surrogates for measuring important disease-related observational quantities over time, such as the prevalence of intracranial hemorrhage during the COVID-19 pandemic outbreak. Methods: To quantitatively investigate the clinical applicability of the tru-AI approach, we analyzed service requests for automatically identifying intracranial hemorrhage (ICH) on head CT using a commercial AI solution. This software is typically used for AI-based prioritization of radiologists' reading lists for reducing turnaround times in patients with emergent clinical findings, such as ICH or pulmonary embolism.We analyzed data of N=9,421 emergency-setting non-contrast head CT studies at a major US healthcare system acquired from November 1, 2019 through June 2, 2020, and compared two observation periods, namely (i) a pre-pandemic epoch from November 1, 2019 through February 29, 2020, and (ii) a period during the COVID-19 pandemic outbreak, April 1-30, 2020. Results: Although daily CT scan counts were significantly lower during (40.1 +/- 7.9) than before (44.4 +/- 7.6) the COVID-19 outbreak, we found that ICH was more likely to be observed by AI during than before the COVID-19 outbreak (p<0.05), with approximately one daily ICH+ case more than statistically expected. Conclusion: Our results suggest that, by tracking both large-scale utilization and AI results data in radiology, the tru-AI approach can contribute clinical value as a versatile exploratory tool, aiming at a better understanding of pandemic-related effects on healthcare.
|
1211.0937
|
Bernhard Mehlig
|
M. Rafajlovic, A. Eriksson, A. Rimark, S. H. Saltin, G. Charrier, M.
Panova, C. Andr\'e, K. Johannesson, and B. Mehlig
|
The effect of multiple paternity on genetic diversity during and after
colonisation
|
7 pages, 5 figures, electronic supplementary material
|
PLoS ONE 8(10) e75587 (2013)
|
10.1371/journal.pone.0075587
| null |
q-bio.PE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In metapopulations, genetic variation of local populations is influenced by
the genetic content of the founders, and of migrants following establishment.
We analyse the effect of multiple paternity on genetic diversity using a model
in which the highly promiscuous marine snail Littorina saxatilis expands from a
mainland to colonise initially empty islands of an archipelago. Migrant females
carry a large number of eggs fertilised by 1 - 10 mates. We quantify the
genetic diversity of the population in terms of its heterozygosity: initially
during the transient colonisation process, and at long times when the
population has reached an equilibrium state with migration. During
colonisation, multiple paternity increases the heterozygosity by 10 - 300 % in
comparison with the case of single paternity. The equilibrium state, by
contrast, is less strongly affected: multiple paternity gives rise to 10 - 50 %
higher heterozygosity compared with single paternity. Further we find that far
from the mainland, new mutations spreading from the mainland cause bursts of
high genetic diversity separated by long periods of low diversity. This effect
is boosted by multiple paternity. We conclude that multiple paternity
facilitates colonisation and maintenance of small populations, whether or not
this is the main cause for the evolution of extreme promiscuity in Littorina
saxatilis.
|
[
{
"created": "Mon, 5 Nov 2012 17:23:36 GMT",
"version": "v1"
}
] |
2013-10-30
|
[
[
"Rafajlovic",
"M.",
""
],
[
"Eriksson",
"A.",
""
],
[
"Rimark",
"A.",
""
],
[
"Saltin",
"S. H.",
""
],
[
"Charrier",
"G.",
""
],
[
"Panova",
"M.",
""
],
[
"André",
"C.",
""
],
[
"Johannesson",
"K.",
""
],
[
"Mehlig",
"B.",
""
]
] |
In metapopulations, genetic variation of local populations is influenced by the genetic content of the founders, and of migrants following establishment. We analyse the effect of multiple paternity on genetic diversity using a model in which the highly promiscuous marine snail Littorina saxatilis expands from a mainland to colonise initially empty islands of an archipelago. Migrant females carry a large number of eggs fertilised by 1 - 10 mates. We quantify the genetic diversity of the population in terms of its heterozygosity: initially during the transient colonisation process, and at long times when the population has reached an equilibrium state with migration. During colonisation, multiple paternity increases the heterozygosity by 10 - 300 % in comparison with the case of single paternity. The equilibrium state, by contrast, is less strongly affected: multiple paternity gives rise to 10 - 50 % higher heterozygosity compared with single paternity. Further we find that far from the mainland, new mutations spreading from the mainland cause bursts of high genetic diversity separated by long periods of low diversity. This effect is boosted by multiple paternity. We conclude that multiple paternity facilitates colonisation and maintenance of small populations, whether or not this is the main cause for the evolution of extreme promiscuity in Littorina saxatilis.
|
2006.16735
|
Erik Aurell
|
Hong-Li Zeng, Eugenio Mauri, Vito Dichio, Simona Cocco, Remi Monasson,
Erik Aurell
|
Inferring epistasis from genomic data with comparable mutation and
outcrossing rate
|
16 pages, 9 figures. Substantial revision from second version,
previous suggestions and comments gratefully acknowledged
| null |
10.1088/1742-5468/ac0f64
| null |
q-bio.PE cond-mat.stat-mech
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider a population evolving due to mutation, selection and
recombination, where selection includes single-locus terms (additive fitness)
and two-loci terms (pairwise epistatic fitness). We further consider the
problem of inferring fitness in the evolutionary dynamics from one or several
snap-shots of the distribution of genotypes in the population. In the recent
literature this has been done by applying the Quasi-Linkage Equilibrium (QLE)
regime first obtained by Kimura in the limit of high recombination. Here we
show that the approach also works in the interesting regime where the effects
of mutations are comparable to or larger than recombination. This leads to a
modified main epistatic fitness inference formula where the rates of mutation
and recombination occur together. We also derive this formula using by a
previously developed Gaussian closure that formally remains valid when
recombination is absent. The findings are validated through numerical
simulations.
|
[
{
"created": "Tue, 30 Jun 2020 12:45:19 GMT",
"version": "v1"
},
{
"created": "Wed, 30 Dec 2020 15:21:04 GMT",
"version": "v2"
},
{
"created": "Tue, 4 May 2021 07:50:49 GMT",
"version": "v3"
}
] |
2021-09-01
|
[
[
"Zeng",
"Hong-Li",
""
],
[
"Mauri",
"Eugenio",
""
],
[
"Dichio",
"Vito",
""
],
[
"Cocco",
"Simona",
""
],
[
"Monasson",
"Remi",
""
],
[
"Aurell",
"Erik",
""
]
] |
We consider a population evolving due to mutation, selection and recombination, where selection includes single-locus terms (additive fitness) and two-loci terms (pairwise epistatic fitness). We further consider the problem of inferring fitness in the evolutionary dynamics from one or several snap-shots of the distribution of genotypes in the population. In the recent literature this has been done by applying the Quasi-Linkage Equilibrium (QLE) regime first obtained by Kimura in the limit of high recombination. Here we show that the approach also works in the interesting regime where the effects of mutations are comparable to or larger than recombination. This leads to a modified main epistatic fitness inference formula where the rates of mutation and recombination occur together. We also derive this formula using by a previously developed Gaussian closure that formally remains valid when recombination is absent. The findings are validated through numerical simulations.
|
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