id stringlengths 9 13 | submitter stringlengths 4 48 | authors stringlengths 4 9.62k | title stringlengths 4 343 | comments stringlengths 2 480 ⌀ | journal-ref stringlengths 9 309 ⌀ | doi stringlengths 12 138 ⌀ | report-no stringclasses 277 values | categories stringlengths 8 87 | license stringclasses 9 values | orig_abstract stringlengths 27 3.76k | versions listlengths 1 15 | update_date stringlengths 10 10 | authors_parsed listlengths 1 147 | abstract stringlengths 24 3.75k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1606.03059 | Vu Dinh | Vu Dinh, Lam Si Tung Ho, Marc A. Suchard, Frederick A. Matsen IV | Consistency and convergence rate of phylogenetic inference via
regularization | 34 pages, 5 figures. To appear on The Annals of Statistics | null | null | null | q-bio.PE math.ST stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is common in phylogenetics to have some, perhaps partial, information
about the overall evolutionary tree of a group of organisms and wish to find an
evolutionary tree of a specific gene for those organisms. There may not be
enough information in the gene sequences alone to accurately reconstruct the
correct "gene tree." Although the gene tree may deviate from the "species tree"
due to a variety of genetic processes, in the absence of evidence to the
contrary it is parsimonious to assume that they agree. A common statistical
approach in these situations is to develop a likelihood penalty to incorporate
such additional information. Recent studies using simulation and empirical data
suggest that a likelihood penalty quantifying concordance with a species tree
can significantly improve the accuracy of gene tree reconstruction compared to
using sequence data alone. However, the consistency of such an approach has not
yet been established, nor have convergence rates been bounded. Because
phylogenetics is a non-standard inference problem, the standard theory does not
apply. In this paper, we propose a penalized maximum likelihood estimator for
gene tree reconstruction, where the penalty is the square of the
Billera-Holmes-Vogtmann geodesic distance from the gene tree to the species
tree. We prove that this method is consistent, and derive its convergence rate
for estimating the discrete gene tree structure and continuous edge lengths
(representing the amount of evolution that has occurred on that branch)
simultaneously. We find that the regularized estimator is "adaptive fast
converging," meaning that it can reconstruct all edges of length greater than
any given threshold from gene sequences of polynomial length. Our method does
not require the species tree to be known exactly; in fact, our asymptotic
theory holds for any such guide tree.
| [
{
"created": "Thu, 9 Jun 2016 18:45:54 GMT",
"version": "v1"
},
{
"created": "Sat, 6 Jan 2018 00:40:09 GMT",
"version": "v2"
}
] | 2018-01-09 | [
[
"Dinh",
"Vu",
""
],
[
"Ho",
"Lam Si Tung",
""
],
[
"Suchard",
"Marc A.",
""
],
[
"Matsen",
"Frederick A.",
"IV"
]
] | It is common in phylogenetics to have some, perhaps partial, information about the overall evolutionary tree of a group of organisms and wish to find an evolutionary tree of a specific gene for those organisms. There may not be enough information in the gene sequences alone to accurately reconstruct the correct "gene tree." Although the gene tree may deviate from the "species tree" due to a variety of genetic processes, in the absence of evidence to the contrary it is parsimonious to assume that they agree. A common statistical approach in these situations is to develop a likelihood penalty to incorporate such additional information. Recent studies using simulation and empirical data suggest that a likelihood penalty quantifying concordance with a species tree can significantly improve the accuracy of gene tree reconstruction compared to using sequence data alone. However, the consistency of such an approach has not yet been established, nor have convergence rates been bounded. Because phylogenetics is a non-standard inference problem, the standard theory does not apply. In this paper, we propose a penalized maximum likelihood estimator for gene tree reconstruction, where the penalty is the square of the Billera-Holmes-Vogtmann geodesic distance from the gene tree to the species tree. We prove that this method is consistent, and derive its convergence rate for estimating the discrete gene tree structure and continuous edge lengths (representing the amount of evolution that has occurred on that branch) simultaneously. We find that the regularized estimator is "adaptive fast converging," meaning that it can reconstruct all edges of length greater than any given threshold from gene sequences of polynomial length. Our method does not require the species tree to be known exactly; in fact, our asymptotic theory holds for any such guide tree. |
2305.05406 | Arthur Genthon | Arthur Genthon, Takashi Nozoe, Luca Peliti, David Lacoste | Cell lineage statistics with incomplete population trees | null | PRX Life 1, 013014 (2023) | 10.1103/PRXLife.1.013014 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cell lineage statistics is a powerful tool for inferring cellular parameters,
such as division rate, death rate or the population growth rate. Yet, in
practice such an analysis suffers from a basic problem: how should we treat
incomplete lineages that do not survive until the end of the experiment? Here,
we develop a model-independent theoretical framework to address this issue. We
show how to quantify fitness landscape, survivor bias and selection for
arbitrary cell traits from cell lineage statistics in the presence of death,
and we test this method using an experimental data set in which a cell
population is exposed to a drug that kills a large fraction of the population.
This analysis reveals that failing to properly account for dead lineages can
lead to misleading fitness estimations. For simple trait dynamics, we prove and
illustrate numerically that the fitness landscape and the survivor bias can in
addition be used for the non-parametric estimation of the division and death
rates, using only lineage histories. Our framework provides universal bounds on
the population growth rate, and a fluctuation-response relation which
quantifies the reduction of population growth rate due to the variability in
death rate. Further, in the context of cell size control, we obtain
generalizations of Powell's relation that link the distributions of generation
times with the population growth rate, and show that the survivor bias can
sometimes conceal the adder property, namely the constant increment of volume
between birth and division.
| [
{
"created": "Tue, 9 May 2023 12:54:01 GMT",
"version": "v1"
},
{
"created": "Wed, 13 Sep 2023 06:30:35 GMT",
"version": "v2"
}
] | 2023-09-14 | [
[
"Genthon",
"Arthur",
""
],
[
"Nozoe",
"Takashi",
""
],
[
"Peliti",
"Luca",
""
],
[
"Lacoste",
"David",
""
]
] | Cell lineage statistics is a powerful tool for inferring cellular parameters, such as division rate, death rate or the population growth rate. Yet, in practice such an analysis suffers from a basic problem: how should we treat incomplete lineages that do not survive until the end of the experiment? Here, we develop a model-independent theoretical framework to address this issue. We show how to quantify fitness landscape, survivor bias and selection for arbitrary cell traits from cell lineage statistics in the presence of death, and we test this method using an experimental data set in which a cell population is exposed to a drug that kills a large fraction of the population. This analysis reveals that failing to properly account for dead lineages can lead to misleading fitness estimations. For simple trait dynamics, we prove and illustrate numerically that the fitness landscape and the survivor bias can in addition be used for the non-parametric estimation of the division and death rates, using only lineage histories. Our framework provides universal bounds on the population growth rate, and a fluctuation-response relation which quantifies the reduction of population growth rate due to the variability in death rate. Further, in the context of cell size control, we obtain generalizations of Powell's relation that link the distributions of generation times with the population growth rate, and show that the survivor bias can sometimes conceal the adder property, namely the constant increment of volume between birth and division. |
2404.17305 | Babacar Mbaye Ndiaye | Karam Allali, Mouhamadou A.M.T. Balde, Babacar M. Ndiaye | An optimal control study for a two-strain SEIR epidemic model with
saturated incidence rates and treatment | null | null | null | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This work will study an optimal control problem describing the two-strain
SEIR epidemic model. The studied model is in the form of six nonlinear
differential equations illustrating the dynamics of the susceptibles and the
exposed, the infected, and the recovered individuals. The exposed and the
infected compartments are each divided into two sub-classes representing the
first and the second strain. The model includes two saturated rates and two
treatments for each strain. We begin our study by showing the well-posedness of
our problem. The basic reproduction number is calculated and depends mainly on
the reproduction numbers of the first and second strains. The global stability
of the disease-free equilibrium is fulfilled. The optimal control study is
achieved by using the Pontryagin minimum principle. Numerical simulations have
shown the importance of therapy in minimizing the infection's effect. By
administrating suitable therapies, the disease's severity decreases
considerably. The estimation of parameters as well as a comparison study with
COVID-19 clinical data are fulfilled. It was shown that the mathematical model
results fits well the clinical data.
| [
{
"created": "Fri, 26 Apr 2024 10:31:38 GMT",
"version": "v1"
}
] | 2024-04-29 | [
[
"Allali",
"Karam",
""
],
[
"Balde",
"Mouhamadou A. M. T.",
""
],
[
"Ndiaye",
"Babacar M.",
""
]
] | This work will study an optimal control problem describing the two-strain SEIR epidemic model. The studied model is in the form of six nonlinear differential equations illustrating the dynamics of the susceptibles and the exposed, the infected, and the recovered individuals. The exposed and the infected compartments are each divided into two sub-classes representing the first and the second strain. The model includes two saturated rates and two treatments for each strain. We begin our study by showing the well-posedness of our problem. The basic reproduction number is calculated and depends mainly on the reproduction numbers of the first and second strains. The global stability of the disease-free equilibrium is fulfilled. The optimal control study is achieved by using the Pontryagin minimum principle. Numerical simulations have shown the importance of therapy in minimizing the infection's effect. By administrating suitable therapies, the disease's severity decreases considerably. The estimation of parameters as well as a comparison study with COVID-19 clinical data are fulfilled. It was shown that the mathematical model results fits well the clinical data. |
2210.14508 | Jumpei Yamagishi | Jumpei F. Yamagishi, Tetsuhiro S. Hatakeyama | Linear Response Theory of Evolved Metabolic Systems | 6+6 pages, 3+4 figures, 1 table | null | 10.1103/PhysRevLett.131.028401 | null | q-bio.MN physics.bio-ph q-bio.QM | http://creativecommons.org/licenses/by-sa/4.0/ | Predicting cellular metabolic states is a central problem in biophysics.
Conventional approaches, however, sensitively depend on the microscopic details
of individual metabolic systems. In this Letter, we derived a universal linear
relationship between the metabolic responses against nutrient conditions and
metabolic inhibition, with the aid of a microeconomic theory. The relationship
holds in arbitrary metabolic systems as long as the law of mass conservation
stands, as supported by extensive numerical calculations. It offers
quantitative predictions without prior knowledge of systems.
| [
{
"created": "Wed, 26 Oct 2022 06:38:52 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Jul 2023 03:48:52 GMT",
"version": "v2"
}
] | 2023-07-26 | [
[
"Yamagishi",
"Jumpei F.",
""
],
[
"Hatakeyama",
"Tetsuhiro S.",
""
]
] | Predicting cellular metabolic states is a central problem in biophysics. Conventional approaches, however, sensitively depend on the microscopic details of individual metabolic systems. In this Letter, we derived a universal linear relationship between the metabolic responses against nutrient conditions and metabolic inhibition, with the aid of a microeconomic theory. The relationship holds in arbitrary metabolic systems as long as the law of mass conservation stands, as supported by extensive numerical calculations. It offers quantitative predictions without prior knowledge of systems. |
2406.16453 | Raffaele Marino | Raffaele Marino, Lorenzo Buffoni, Lorenzo Chicchi, Francesca Di Patti,
Diego Febbe, Lorenzo Giambagli, Duccio Fanelli | Learning in Wilson-Cowan model for metapopulation | null | null | null | null | q-bio.NC cond-mat.dis-nn cond-mat.stat-mech cs.AI cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Wilson-Cowan model for metapopulation, a Neural Mass Network Model,
treats different subcortical regions of the brain as connected nodes, with
connections representing various types of structural, functional, or effective
neuronal connectivity between these regions. Each region comprises interacting
populations of excitatory and inhibitory cells, consistent with the standard
Wilson-Cowan model. By incorporating stable attractors into such a
metapopulation model's dynamics, we transform it into a learning algorithm
capable of achieving high image and text classification accuracy. We test it on
MNIST and Fashion MNIST, in combination with convolutional neural networks, on
CIFAR-10 and TF-FLOWERS, and, in combination with a transformer architecture
(BERT), on IMDB, always showing high classification accuracy. These numerical
evaluations illustrate that minimal modifications to the Wilson-Cowan model for
metapopulation can reveal unique and previously unobserved dynamics.
| [
{
"created": "Mon, 24 Jun 2024 08:45:03 GMT",
"version": "v1"
}
] | 2024-06-25 | [
[
"Marino",
"Raffaele",
""
],
[
"Buffoni",
"Lorenzo",
""
],
[
"Chicchi",
"Lorenzo",
""
],
[
"Di Patti",
"Francesca",
""
],
[
"Febbe",
"Diego",
""
],
[
"Giambagli",
"Lorenzo",
""
],
[
"Fanelli",
"Duccio",
""
]
] | The Wilson-Cowan model for metapopulation, a Neural Mass Network Model, treats different subcortical regions of the brain as connected nodes, with connections representing various types of structural, functional, or effective neuronal connectivity between these regions. Each region comprises interacting populations of excitatory and inhibitory cells, consistent with the standard Wilson-Cowan model. By incorporating stable attractors into such a metapopulation model's dynamics, we transform it into a learning algorithm capable of achieving high image and text classification accuracy. We test it on MNIST and Fashion MNIST, in combination with convolutional neural networks, on CIFAR-10 and TF-FLOWERS, and, in combination with a transformer architecture (BERT), on IMDB, always showing high classification accuracy. These numerical evaluations illustrate that minimal modifications to the Wilson-Cowan model for metapopulation can reveal unique and previously unobserved dynamics. |
1810.04793 | Kamran Kowsari | Jinghe Zhang, Kamran Kowsari, James H. Harrison, Jennifer M. Lobo,
Laura E. Barnes | Patient2Vec: A Personalized Interpretable Deep Representation of the
Longitudinal Electronic Health Record | Accepted by IEEE Access | null | 10.1109/ACCESS.2018.2875677 | null | q-bio.QM cs.AI cs.IR cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The wide implementation of electronic health record (EHR) systems facilitates
the collection of large-scale health data from real clinical settings. Despite
the significant increase in adoption of EHR systems, this data remains largely
unexplored, but presents a rich data source for knowledge discovery from
patient health histories in tasks such as understanding disease correlations
and predicting health outcomes. However, the heterogeneity, sparsity, noise,
and bias in this data present many complex challenges. This complexity makes it
difficult to translate potentially relevant information into machine learning
algorithms. In this paper, we propose a computational framework, Patient2Vec,
to learn an interpretable deep representation of longitudinal EHR data which is
personalized for each patient. To evaluate this approach, we apply it to the
prediction of future hospitalizations using real EHR data and compare its
predictive performance with baseline methods. Patient2Vec produces a vector
space with meaningful structure and it achieves an AUC around 0.799
outperforming baseline methods. In the end, the learned feature importance can
be visualized and interpreted at both the individual and population levels to
bring clinical insights.
| [
{
"created": "Wed, 10 Oct 2018 16:41:05 GMT",
"version": "v1"
},
{
"created": "Mon, 22 Oct 2018 15:13:16 GMT",
"version": "v2"
},
{
"created": "Thu, 25 Oct 2018 13:38:34 GMT",
"version": "v3"
}
] | 2018-10-26 | [
[
"Zhang",
"Jinghe",
""
],
[
"Kowsari",
"Kamran",
""
],
[
"Harrison",
"James H.",
""
],
[
"Lobo",
"Jennifer M.",
""
],
[
"Barnes",
"Laura E.",
""
]
] | The wide implementation of electronic health record (EHR) systems facilitates the collection of large-scale health data from real clinical settings. Despite the significant increase in adoption of EHR systems, this data remains largely unexplored, but presents a rich data source for knowledge discovery from patient health histories in tasks such as understanding disease correlations and predicting health outcomes. However, the heterogeneity, sparsity, noise, and bias in this data present many complex challenges. This complexity makes it difficult to translate potentially relevant information into machine learning algorithms. In this paper, we propose a computational framework, Patient2Vec, to learn an interpretable deep representation of longitudinal EHR data which is personalized for each patient. To evaluate this approach, we apply it to the prediction of future hospitalizations using real EHR data and compare its predictive performance with baseline methods. Patient2Vec produces a vector space with meaningful structure and it achieves an AUC around 0.799 outperforming baseline methods. In the end, the learned feature importance can be visualized and interpreted at both the individual and population levels to bring clinical insights. |
2103.09178 | Giovanni Nastasi | Fabiana Calleri, Giovanni Nastasi, Vittorio Romano | Continuous-time stochastic processes for the spread of COVID-19 disease
simulated via a Monte Carlo approach and comparison with deterministic models | null | Journal of Mathematical Biology, vol. 83, art. no. 34 (2021) | 10.1007/s00285-021-01657-4 | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two stochastic models are proposed to describe the evolution of the COVID-19
pandemic. In the first model the population is partitioned into four
compartments: susceptible $S$, infected $I$, removed $R$ and dead people $D$.
In order to have a cross validation, a deterministic version of such a model is
also devised which is represented by a system of ordinary differential
equations with delays. In the second stochastic model two further compartments
are added: the class $A$ of asymptomatic individuals and the class $L$ of
isolated infected people. Effects such as social distancing measures are easily
included and the consequences are analyzed. Numerical solutions are obtained
with Monte Carlo simulations. Quantitative predictions are provided which can
be useful for the evaluation of political measures, e.g. the obtained results
suggest that strategies based on herd immunity are too risky.
| [
{
"created": "Tue, 16 Mar 2021 16:27:01 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Sep 2021 07:38:51 GMT",
"version": "v2"
}
] | 2021-09-16 | [
[
"Calleri",
"Fabiana",
""
],
[
"Nastasi",
"Giovanni",
""
],
[
"Romano",
"Vittorio",
""
]
] | Two stochastic models are proposed to describe the evolution of the COVID-19 pandemic. In the first model the population is partitioned into four compartments: susceptible $S$, infected $I$, removed $R$ and dead people $D$. In order to have a cross validation, a deterministic version of such a model is also devised which is represented by a system of ordinary differential equations with delays. In the second stochastic model two further compartments are added: the class $A$ of asymptomatic individuals and the class $L$ of isolated infected people. Effects such as social distancing measures are easily included and the consequences are analyzed. Numerical solutions are obtained with Monte Carlo simulations. Quantitative predictions are provided which can be useful for the evaluation of political measures, e.g. the obtained results suggest that strategies based on herd immunity are too risky. |
0905.2145 | Peter Borowski | Peter Borowski, Eric N. Cytrynbaum | Predictions from a stochastic polymer model for the MinDE dynamics in
E.coli | 16 pages | Phys. Rev. E 80, 041916 (2009) | 10.1103/PhysRevE.80.041916 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The spatiotemporal oscillations of the Min proteins in the bacterium
Escherichia coli play an important role in cell division. A number of different
models have been proposed to explain the dynamics from the underlying
biochemistry. Here, we extend a previously described discrete polymer model
from a deterministic to a stochastic formulation. We express the stochastic
evolution of the oscillatory system as a map from the probability distribution
of maximum polymer length in one period of the oscillation to the probability
distribution of maximum polymer length half a period later and solve for the
fixed point of the map with a combined analytical and numerical technique. This
solution gives a theoretical prediction of the distributions of both lengths of
the polar MinD zones and periods of oscillations -- both of which are
experimentally measurable. The model provides an interesting example of a
stochastic hybrid system that is, in some limits, analytically tractable.
| [
{
"created": "Wed, 13 May 2009 19:06:35 GMT",
"version": "v1"
}
] | 2013-05-29 | [
[
"Borowski",
"Peter",
""
],
[
"Cytrynbaum",
"Eric N.",
""
]
] | The spatiotemporal oscillations of the Min proteins in the bacterium Escherichia coli play an important role in cell division. A number of different models have been proposed to explain the dynamics from the underlying biochemistry. Here, we extend a previously described discrete polymer model from a deterministic to a stochastic formulation. We express the stochastic evolution of the oscillatory system as a map from the probability distribution of maximum polymer length in one period of the oscillation to the probability distribution of maximum polymer length half a period later and solve for the fixed point of the map with a combined analytical and numerical technique. This solution gives a theoretical prediction of the distributions of both lengths of the polar MinD zones and periods of oscillations -- both of which are experimentally measurable. The model provides an interesting example of a stochastic hybrid system that is, in some limits, analytically tractable. |
2103.03724 | Jinjiang Guo Ph.D. | Yue Kang, Dawei Leng, Jinjiang Guo, Lurong Pan | Sequence-based deep learning antibody design for in silico antibody
affinity maturation | null | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Antibody therapeutics has been extensively studied in drug discovery and
development within the past decades. One increasingly popular focus in the
antibody discovery pipeline is the optimization step for therapeutic leads.
Both traditional methods and in silico approaches aim to generate candidates
with high binding affinity against specific target antigens. Traditional in
vitro approaches use hybridoma or phage display for candidate selection, and
surface plasmon resonance (SPR) for evaluation, while in silico computational
approaches aim to reduce the high cost and improve efficiency by incorporating
mathematical algorithms and computational processing power in the design
process. In the present study, we investigated different graph-based designs
for depicting antibody-antigen interactions in terms of antibody affinity
prediction using deep learning techniques. While other in silico computations
require experimentally determined crystal structures, our study took interest
in the capability of sequence-based models for in silico antibody maturation.
Our preliminary studies achieved satisfying prediction accuracy on binding
affinities comparing to conventional approaches and other deep learning
approaches. To further study the antibody-antigen binding specificity, and to
simulate the optimization process in real-world scenario, we introduced
pairwise prediction strategy. We performed analysis based on both baseline and
pairwise prediction results. The resulting prediction and efficiency prove the
feasibility and computational efficiency of sequence-based method to be adapted
as a scalable industry practice.
| [
{
"created": "Sun, 21 Feb 2021 02:48:31 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Aug 2022 01:57:42 GMT",
"version": "v2"
}
] | 2022-08-16 | [
[
"Kang",
"Yue",
""
],
[
"Leng",
"Dawei",
""
],
[
"Guo",
"Jinjiang",
""
],
[
"Pan",
"Lurong",
""
]
] | Antibody therapeutics has been extensively studied in drug discovery and development within the past decades. One increasingly popular focus in the antibody discovery pipeline is the optimization step for therapeutic leads. Both traditional methods and in silico approaches aim to generate candidates with high binding affinity against specific target antigens. Traditional in vitro approaches use hybridoma or phage display for candidate selection, and surface plasmon resonance (SPR) for evaluation, while in silico computational approaches aim to reduce the high cost and improve efficiency by incorporating mathematical algorithms and computational processing power in the design process. In the present study, we investigated different graph-based designs for depicting antibody-antigen interactions in terms of antibody affinity prediction using deep learning techniques. While other in silico computations require experimentally determined crystal structures, our study took interest in the capability of sequence-based models for in silico antibody maturation. Our preliminary studies achieved satisfying prediction accuracy on binding affinities comparing to conventional approaches and other deep learning approaches. To further study the antibody-antigen binding specificity, and to simulate the optimization process in real-world scenario, we introduced pairwise prediction strategy. We performed analysis based on both baseline and pairwise prediction results. The resulting prediction and efficiency prove the feasibility and computational efficiency of sequence-based method to be adapted as a scalable industry practice. |
2407.05173 | Hoa Trinh | Hoa Trinh, Satish Kumar Thittamaranahalli | Single-Sequence-Based Protein Secondary Structure Prediction using
One-Hot and Chemical Encodings of Amino Acids | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In protein secondary structure prediction, each amino acid in sequence is
typically treated as a distinct category and represented by a one-hot vector.
In this study, we developed two novel chemical representations for amino acids
utilizing molecular fingerprints and the dimensionality reduction algorithm
FastMap. We demonstrate that the two new chemical encodings can provide
additional information about the interactions of amino acids in sequences that
an LSTM-based model cannot capture with one-hot encoding alone. Compared to the
latest LSTM-based model used in the single-sequence-based method
SPOT-1D-Single, our ensemble model utilizing one-hot and chemical encodings
achieves better accuracy across most test sets while requiring approximately
nine times fewer trainable parameters for each encoding model. Our
single-sequence-based method is valuable for its simplicity, lower resource
requirements, and independence from external sequence data. It is beneficial
when quick or preliminary predictions are needed or when data on homologous
sequences is scarce.
| [
{
"created": "Sat, 6 Jul 2024 20:31:12 GMT",
"version": "v1"
}
] | 2024-07-09 | [
[
"Trinh",
"Hoa",
""
],
[
"Thittamaranahalli",
"Satish Kumar",
""
]
] | In protein secondary structure prediction, each amino acid in sequence is typically treated as a distinct category and represented by a one-hot vector. In this study, we developed two novel chemical representations for amino acids utilizing molecular fingerprints and the dimensionality reduction algorithm FastMap. We demonstrate that the two new chemical encodings can provide additional information about the interactions of amino acids in sequences that an LSTM-based model cannot capture with one-hot encoding alone. Compared to the latest LSTM-based model used in the single-sequence-based method SPOT-1D-Single, our ensemble model utilizing one-hot and chemical encodings achieves better accuracy across most test sets while requiring approximately nine times fewer trainable parameters for each encoding model. Our single-sequence-based method is valuable for its simplicity, lower resource requirements, and independence from external sequence data. It is beneficial when quick or preliminary predictions are needed or when data on homologous sequences is scarce. |
2207.10861 | Zi Chen | Joseph Sutlive, Hamed Seyyedhosseinzadeh, Zheng Ao, Haning Xiu, Kun
Gou, Feng Guo, and Zi Chen | Mechanics of Morphogenesis in Neural Development: in vivo, in vitro, and
in silico | null | null | null | null | q-bio.NC physics.bio-ph physics.med-ph | http://creativecommons.org/licenses/by/4.0/ | Morphogenesis in the central nervous system has received intensive attention
as elucidating fundamental mechanisms of morphogenesis will shed light on the
physiology and pathophysiology of the developing central nervous system.
Morphogenesis of the central nervous system is of a vast topic that includes
important morphogenetic events such as neurulation and cortical folding. Here
we review three types of methods used to improve our understanding of
morphogenesis of the central nervous system: in vivo experiments, organoids (in
vitro), and computational models (in silico). The in vivo experiments are used
to explore cellular- and tissue-level mechanics and interpret them on the roles
of neurulation morphogenesis. Recent advances in human brain organoids have
provided new opportunities to study morphogenesis and neurogenesis to
compensate for the limitations of in vivo experiments, as organoid models are
able to recapitulate some critical neural morphogenetic processes during early
human brain development. Due to the complexity and costs of in vivo and in
vitro studies, a variety of computational models have been developed and used
to explain the formation and morphogenesis of brain structures. We review and
discuss the Pros and Cons of these methods and their usage in the studies on
morphogenesis of the central nervous system. Notably, none of these methods
alone is sufficient to unveil the biophysical mechanisms of morphogenesis, thus
calling for the interdisciplinary approaches using a combination of these
methods in order to test hypotheses and generate new insights on both normal
and abnormal development of the central nervous system.
| [
{
"created": "Fri, 22 Jul 2022 03:48:03 GMT",
"version": "v1"
}
] | 2022-07-25 | [
[
"Sutlive",
"Joseph",
""
],
[
"Seyyedhosseinzadeh",
"Hamed",
""
],
[
"Ao",
"Zheng",
""
],
[
"Xiu",
"Haning",
""
],
[
"Gou",
"Kun",
""
],
[
"Guo",
"Feng",
""
],
[
"Chen",
"Zi",
""
]
] | Morphogenesis in the central nervous system has received intensive attention as elucidating fundamental mechanisms of morphogenesis will shed light on the physiology and pathophysiology of the developing central nervous system. Morphogenesis of the central nervous system is of a vast topic that includes important morphogenetic events such as neurulation and cortical folding. Here we review three types of methods used to improve our understanding of morphogenesis of the central nervous system: in vivo experiments, organoids (in vitro), and computational models (in silico). The in vivo experiments are used to explore cellular- and tissue-level mechanics and interpret them on the roles of neurulation morphogenesis. Recent advances in human brain organoids have provided new opportunities to study morphogenesis and neurogenesis to compensate for the limitations of in vivo experiments, as organoid models are able to recapitulate some critical neural morphogenetic processes during early human brain development. Due to the complexity and costs of in vivo and in vitro studies, a variety of computational models have been developed and used to explain the formation and morphogenesis of brain structures. We review and discuss the Pros and Cons of these methods and their usage in the studies on morphogenesis of the central nervous system. Notably, none of these methods alone is sufficient to unveil the biophysical mechanisms of morphogenesis, thus calling for the interdisciplinary approaches using a combination of these methods in order to test hypotheses and generate new insights on both normal and abnormal development of the central nervous system. |
1409.2584 | Thomas R. Weikl | Thomas R. Weikl and Fabian Paul | Conformational selection in protein binding and function | review article; 10 pages, 4 figures, Protein Sci. 2014 | null | 10.1002/pro.2539 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Protein binding and function often involves conformational changes. Advanced
NMR experiments indicate that these conformational changes can occur in the
absence of ligand molecules (or with bound ligands), and that the ligands may
'select' protein conformations for binding (or unbinding). In this review, we
argue that this conformational selection requires transition times for ligand
binding and unbinding that are small compared to the dwell times of proteins in
different conformations, which is plausible for small ligand molecules. Such a
separation of timescales leads to a decoupling and temporal ordering of
binding/unbinding events and conformational changes. We propose that
conformational-selection and induced-change processes (such as induced fit) are
two sides of the same coin, because the temporal ordering is reversed in
binding and unbinding direction. Conformational-selection processes can be
characterized by a conformational excitation that occurs prior to a binding or
unbinding event, while induced-change processes exhibit a characteristic
conformational relaxation that occurs after a binding or unbinding event. We
discuss how the ordering of events can be determined from relaxation rates and
effective on- and off-rates determined in mixing experiments, and from the
conformational exchange rates measured in advanced NMR or single-molecule FRET
experiments. For larger ligand molecules such as peptides, conformational
changes and binding events can be intricately coupled and exhibit aspects of
conformational-selection and induced-change processes in both binding and
unbinding direction.
| [
{
"created": "Tue, 9 Sep 2014 03:36:46 GMT",
"version": "v1"
}
] | 2014-09-10 | [
[
"Weikl",
"Thomas R.",
""
],
[
"Paul",
"Fabian",
""
]
] | Protein binding and function often involves conformational changes. Advanced NMR experiments indicate that these conformational changes can occur in the absence of ligand molecules (or with bound ligands), and that the ligands may 'select' protein conformations for binding (or unbinding). In this review, we argue that this conformational selection requires transition times for ligand binding and unbinding that are small compared to the dwell times of proteins in different conformations, which is plausible for small ligand molecules. Such a separation of timescales leads to a decoupling and temporal ordering of binding/unbinding events and conformational changes. We propose that conformational-selection and induced-change processes (such as induced fit) are two sides of the same coin, because the temporal ordering is reversed in binding and unbinding direction. Conformational-selection processes can be characterized by a conformational excitation that occurs prior to a binding or unbinding event, while induced-change processes exhibit a characteristic conformational relaxation that occurs after a binding or unbinding event. We discuss how the ordering of events can be determined from relaxation rates and effective on- and off-rates determined in mixing experiments, and from the conformational exchange rates measured in advanced NMR or single-molecule FRET experiments. For larger ligand molecules such as peptides, conformational changes and binding events can be intricately coupled and exhibit aspects of conformational-selection and induced-change processes in both binding and unbinding direction. |
q-bio/0512008 | Lior Pachter | Colin Dewey, Peter Huggins, Kevin Woods, Bernd Sturmfels and Lior
Pachter | Parametric Alignment of Drosophila Genomes | 19 pages, 3 figures | null | 10.1371/journal.pcbi.0020073 | null | q-bio.GN math.CO q-bio.QM | null | The classic algorithms of Needleman--Wunsch and Smith--Waterman find a
maximum a posteriori probability alignment for a pair hidden Markov model
(PHMM). In order to process large genomes that have undergone complex genome
rearrangements, almost all existing whole genome alignment methods apply fast
heuristics to divide genomes into small pieces which are suitable for
Needleman--Wunsch alignment. In these alignment methods, it is standard
practice to fix the parameters and to produce a single alignment for subsequent
analysis by biologists.
Our main result is the construction of a whole genome parametric alignment of
Drosophila melanogaster and Drosophila pseudoobscura. Parametric alignment
resolves the issue of robustness to changes in parameters by finding all
optimal alignments for all possible parameters in a PHMM. Our alignment draws
on existing heuristics for dividing whole genomes into small pieces for
alignment, and it relies on advances we have made in computing convex polytopes
that allow us to parametrically align non-coding regions using biologically
realistic models. We demonstrate the utility of our parametric alignment for
biological inference by showing that cis-regulatory elements are more conserved
between Drosophila melanogaster and Drosophila pseudoobscura than previously
thought. We also show how whole genome parametric alignment can be used to
quantitatively assess the dependence of branch length estimates on alignment
parameters.
The alignment polytopes, software, and supplementary material can be
downloaded at http://bio.math.berkeley.edu/parametric/.
| [
{
"created": "Fri, 2 Dec 2005 21:24:37 GMT",
"version": "v1"
}
] | 2015-06-26 | [
[
"Dewey",
"Colin",
""
],
[
"Huggins",
"Peter",
""
],
[
"Woods",
"Kevin",
""
],
[
"Sturmfels",
"Bernd",
""
],
[
"Pachter",
"Lior",
""
]
] | The classic algorithms of Needleman--Wunsch and Smith--Waterman find a maximum a posteriori probability alignment for a pair hidden Markov model (PHMM). In order to process large genomes that have undergone complex genome rearrangements, almost all existing whole genome alignment methods apply fast heuristics to divide genomes into small pieces which are suitable for Needleman--Wunsch alignment. In these alignment methods, it is standard practice to fix the parameters and to produce a single alignment for subsequent analysis by biologists. Our main result is the construction of a whole genome parametric alignment of Drosophila melanogaster and Drosophila pseudoobscura. Parametric alignment resolves the issue of robustness to changes in parameters by finding all optimal alignments for all possible parameters in a PHMM. Our alignment draws on existing heuristics for dividing whole genomes into small pieces for alignment, and it relies on advances we have made in computing convex polytopes that allow us to parametrically align non-coding regions using biologically realistic models. We demonstrate the utility of our parametric alignment for biological inference by showing that cis-regulatory elements are more conserved between Drosophila melanogaster and Drosophila pseudoobscura than previously thought. We also show how whole genome parametric alignment can be used to quantitatively assess the dependence of branch length estimates on alignment parameters. The alignment polytopes, software, and supplementary material can be downloaded at http://bio.math.berkeley.edu/parametric/. |
1003.4624 | Suan Li Mai | Mai Suan Li, Nguyen Truong Co, Govardhan Reddy, C-K Hu, and D.
Thirumalai | Determination of factors governing fibrillogenesis of polypeptide chains
using lattice models | 7 pages, 4 figures, submitted to Phys. Rev. Lett. | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Using lattice models we explore the factors that determine the tendencies of
polypeptide chains to aggregate by exhaustively sampling the sequence and
conformational space. The morphologies of the fibril-like structures and the
time scales ($\tau_{fib}$) for their formation depend on a subtle balance
between hydrophobic and coulomb interactions. The extent of population of a
fibril-prone structure in the spectrum of monomer conformations is the major
determinant of $\tau_{fib}$. This observation is used to determine the
aggregation-prone consensus sequences by exhaustively exploring the sequence
space. Our results provide a basis for genome wide search of fragments that are
aggregation prone.
| [
{
"created": "Wed, 24 Mar 2010 12:28:37 GMT",
"version": "v1"
}
] | 2010-03-25 | [
[
"Li",
"Mai Suan",
""
],
[
"Co",
"Nguyen Truong",
""
],
[
"Reddy",
"Govardhan",
""
],
[
"Hu",
"C-K",
""
],
[
"Thirumalai",
"D.",
""
]
] | Using lattice models we explore the factors that determine the tendencies of polypeptide chains to aggregate by exhaustively sampling the sequence and conformational space. The morphologies of the fibril-like structures and the time scales ($\tau_{fib}$) for their formation depend on a subtle balance between hydrophobic and coulomb interactions. The extent of population of a fibril-prone structure in the spectrum of monomer conformations is the major determinant of $\tau_{fib}$. This observation is used to determine the aggregation-prone consensus sequences by exhaustively exploring the sequence space. Our results provide a basis for genome wide search of fragments that are aggregation prone. |
1809.06199 | Amirhossein Hajiaghajani | Soheil Hashemi, Amirhossein Hajiaghajani, and Ali Abdolali | Noninvasive Blockade of Action Potential by Electromagnetic Induction | 6 pages, 4 figures, 1 table | null | null | null | q-bio.NC eess.SP q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conventional anesthesia methods such as injective anesthetic agents may cause
various side effects such as injuries, allergies, and infections. We aim to
investigate a noninvasive scheme of an electromagnetic radiator system to block
action potential (AP) in neuron fibers. We achieved a high-gradient and
unipolar tangential electric field by designing circular geometric coils on an
electric rectifier filter layer. An asymmetric sawtooth pulse shape supplied
the coils in order to create an effective blockage. The entire setup was placed
5 cm above 50 motor and sensory neurons of the spinal cord. A validated
time-domain full-wave analysis code Based on cable model of the neurons and the
electric and magnetic potentials is used to simulate and investigate the
proposed scheme. We observed action potential blockage on both motor and
sensory neurons. In addition, the introduced approach shows promising potential
for AP manipulation in the spinal cord.
| [
{
"created": "Wed, 29 Aug 2018 06:42:41 GMT",
"version": "v1"
}
] | 2018-09-18 | [
[
"Hashemi",
"Soheil",
""
],
[
"Hajiaghajani",
"Amirhossein",
""
],
[
"Abdolali",
"Ali",
""
]
] | Conventional anesthesia methods such as injective anesthetic agents may cause various side effects such as injuries, allergies, and infections. We aim to investigate a noninvasive scheme of an electromagnetic radiator system to block action potential (AP) in neuron fibers. We achieved a high-gradient and unipolar tangential electric field by designing circular geometric coils on an electric rectifier filter layer. An asymmetric sawtooth pulse shape supplied the coils in order to create an effective blockage. The entire setup was placed 5 cm above 50 motor and sensory neurons of the spinal cord. A validated time-domain full-wave analysis code Based on cable model of the neurons and the electric and magnetic potentials is used to simulate and investigate the proposed scheme. We observed action potential blockage on both motor and sensory neurons. In addition, the introduced approach shows promising potential for AP manipulation in the spinal cord. |
q-bio/0702014 | David J. Aldous | David Aldous, Maxim Krikun, and Lea Popovic | Stochastic Models for Phylogenetic Trees on Higher-order Taxa | 41 pages. Minor revisions | null | null | null | q-bio.PE math.PR | null | Simple stochastic models for phylogenetic trees on species have been well
studied. But much paleontology data concerns time series or trees on
higher-order taxa, and any broad picture of relationships between extant groups
requires use of higher-order taxa. A coherent model for trees on (say) genera
should involve both a species-level model and a model for the classification
scheme by which species are assigned to genera. We present a general framework
for such models, and describe three alternate classification schemes. Combining
with the species-level model of Aldous-Popovic (2005), one gets models for
higher-order trees, and we initiate analytic study of such models. In
particular we derive formulas for the lifetime of genera, for the distribution
of number of species per genus, and for the offspring structure of the tree on
genera.
| [
{
"created": "Thu, 8 Feb 2007 00:53:12 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Aug 2007 18:59:55 GMT",
"version": "v2"
}
] | 2007-08-28 | [
[
"Aldous",
"David",
""
],
[
"Krikun",
"Maxim",
""
],
[
"Popovic",
"Lea",
""
]
] | Simple stochastic models for phylogenetic trees on species have been well studied. But much paleontology data concerns time series or trees on higher-order taxa, and any broad picture of relationships between extant groups requires use of higher-order taxa. A coherent model for trees on (say) genera should involve both a species-level model and a model for the classification scheme by which species are assigned to genera. We present a general framework for such models, and describe three alternate classification schemes. Combining with the species-level model of Aldous-Popovic (2005), one gets models for higher-order trees, and we initiate analytic study of such models. In particular we derive formulas for the lifetime of genera, for the distribution of number of species per genus, and for the offspring structure of the tree on genera. |
1412.5773 | Kotaro Konno | Kotaro Konno | A general parameterized mathematical food web model that predicts a
stable green world in the terrestrial ecosystem | 68 pages, 2 figure, 3 Tables | Ecological Monographs 2016, Vol 86, Issue 2, 190-204 | 10.1890/15-1420 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Terrestrial ecosystems are generally green and only a small part (<10%) of
the plant matter is consumed by herbivores annually,but the reason has been
unclear due to the lack of food web models for predicting the absolute
herbivore biomass in physical units. Here, I present a simple parameterized
mathematical food web model that can predict the biomass density of herbivores
h (kg protein/m3) and carnivores c from ecological factors such as the
nutritive values of plants np (kg protein/m3), herbivores nh, and carnivores
nc, searching efficiency (volume) of carnivores S (/day), eating efficiency
(speed) of herbivores eh (/day) and carnivores ec, respiratory decrease in
herbivore and carnivore biomasses, dh (/day) and dc, absorption efficiency of
herbivores and carnivores h (ratio) and c, and probabilities of carnivores
preying on herbivores or carnivores, Phc (ratio) and Pcc.The model predicts a
stable equilibrium with low herbivore biomass h sufficient to keep the world
green if the food web consists of the three trophic levels, plants, herbivores
and carnivores; intraguild predation of carnivores exists; np<nh,nc; S>>eh, and
Phc>Pcc >0,which are well-realized in above-ground terrestrial ecosystems where
plant-rich "green world" is common. The h and c calculated from our model
showed good agreement with those from empirical observations in forests, where
both h and c are ca. 100 mg (fresh biomass/m2), and in savannahs. The model
predicts that the nutritive values and digestibility of plants are positively
correlated with h and the intensity of herbivory, which theoretically explains
the out-door defensive effects of the anti-nutritive or quantitative defenses
(e.g., tannins, protease inhibitors) of plants, and predicts that c and c/h are
positively correlated with the relative growth rate of herbivores. The present
model introduced parameterized realities into food web theory.
| [
{
"created": "Thu, 18 Dec 2014 09:29:41 GMT",
"version": "v1"
},
{
"created": "Tue, 4 Aug 2015 06:53:30 GMT",
"version": "v2"
}
] | 2016-05-24 | [
[
"Konno",
"Kotaro",
""
]
] | Terrestrial ecosystems are generally green and only a small part (<10%) of the plant matter is consumed by herbivores annually,but the reason has been unclear due to the lack of food web models for predicting the absolute herbivore biomass in physical units. Here, I present a simple parameterized mathematical food web model that can predict the biomass density of herbivores h (kg protein/m3) and carnivores c from ecological factors such as the nutritive values of plants np (kg protein/m3), herbivores nh, and carnivores nc, searching efficiency (volume) of carnivores S (/day), eating efficiency (speed) of herbivores eh (/day) and carnivores ec, respiratory decrease in herbivore and carnivore biomasses, dh (/day) and dc, absorption efficiency of herbivores and carnivores h (ratio) and c, and probabilities of carnivores preying on herbivores or carnivores, Phc (ratio) and Pcc.The model predicts a stable equilibrium with low herbivore biomass h sufficient to keep the world green if the food web consists of the three trophic levels, plants, herbivores and carnivores; intraguild predation of carnivores exists; np<nh,nc; S>>eh, and Phc>Pcc >0,which are well-realized in above-ground terrestrial ecosystems where plant-rich "green world" is common. The h and c calculated from our model showed good agreement with those from empirical observations in forests, where both h and c are ca. 100 mg (fresh biomass/m2), and in savannahs. The model predicts that the nutritive values and digestibility of plants are positively correlated with h and the intensity of herbivory, which theoretically explains the out-door defensive effects of the anti-nutritive or quantitative defenses (e.g., tannins, protease inhibitors) of plants, and predicts that c and c/h are positively correlated with the relative growth rate of herbivores. The present model introduced parameterized realities into food web theory. |
2208.11700 | Louise Coppieters De Gibon | Louise Coppieters de Gibson, Philip N. Garner | Low-Level Physiological Implications of End-to-End Learning of Speech
Recognition | Submitted to INTERSPEECH 2022 | null | null | null | q-bio.NC cs.AI cs.SD eess.AS | http://creativecommons.org/licenses/by-sa/4.0/ | Current speech recognition architectures perform very well from the point of
view of machine learning, hence user interaction. This suggests that they are
emulating the human biological system well. We investigate whether the
inference can be inverted to provide insights into that biological system; in
particular the hearing mechanism. Using SincNet, we confirm that end-to-end
systems do learn well known filterbank structures. However, we also show that
wider band-width filters are important in the learned structure. Whilst some
benefits can be gained by initialising both narrow and wide-band filters,
physiological constraints suggest that such filters arise in mid-brain rather
than the cochlea. We show that standard machine learning architectures must be
modified to allow this process to be emulated neurally.
| [
{
"created": "Mon, 22 Aug 2022 13:10:36 GMT",
"version": "v1"
}
] | 2022-08-26 | [
[
"de Gibson",
"Louise Coppieters",
""
],
[
"Garner",
"Philip N.",
""
]
] | Current speech recognition architectures perform very well from the point of view of machine learning, hence user interaction. This suggests that they are emulating the human biological system well. We investigate whether the inference can be inverted to provide insights into that biological system; in particular the hearing mechanism. Using SincNet, we confirm that end-to-end systems do learn well known filterbank structures. However, we also show that wider band-width filters are important in the learned structure. Whilst some benefits can be gained by initialising both narrow and wide-band filters, physiological constraints suggest that such filters arise in mid-brain rather than the cochlea. We show that standard machine learning architectures must be modified to allow this process to be emulated neurally. |
1310.4441 | David Tourigny | David S. Tourigny | Geometric phase shifts in biological oscillators | Matches published version | J. Theor. Biol. (2014) 355, 239-242 | 10.1016/j.jtbi.2014.04.017 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many intracellular processes continue to oscillate during the cell cycle.
Although it is not well-understood how they are affected by discontinuities in
the cellular environment, the general assumption is that oscillations remain
robust provided the period of cell divisions is much larger than the period of
the oscillator. Here, I will show that under these conditions a cell will in
fact have to correct for an additional quantity added to the phase of
oscillation upon every repetition of the cell cycle. The resulting phase shift
is an analogue of the geometric phase, a curious entity first discovered in
quantum mechanics. In this Letter, I will discuss the theory of the geometric
phase shift and demonstrate its relevance to biological oscillations.
| [
{
"created": "Wed, 16 Oct 2013 16:40:06 GMT",
"version": "v1"
},
{
"created": "Thu, 30 Jan 2014 19:50:34 GMT",
"version": "v2"
},
{
"created": "Wed, 24 Sep 2014 15:14:49 GMT",
"version": "v3"
}
] | 2014-09-25 | [
[
"Tourigny",
"David S.",
""
]
] | Many intracellular processes continue to oscillate during the cell cycle. Although it is not well-understood how they are affected by discontinuities in the cellular environment, the general assumption is that oscillations remain robust provided the period of cell divisions is much larger than the period of the oscillator. Here, I will show that under these conditions a cell will in fact have to correct for an additional quantity added to the phase of oscillation upon every repetition of the cell cycle. The resulting phase shift is an analogue of the geometric phase, a curious entity first discovered in quantum mechanics. In this Letter, I will discuss the theory of the geometric phase shift and demonstrate its relevance to biological oscillations. |
0805.4017 | Alexandre Morozov V | Alexandre V. Morozov, Karissa Fortney, Daria A. Gaykalova, Vasily M.
Studitsky, Jonathan Widom, Eric D. Siggia | Extrinsic and intrinsic nucleosome positioning signals | 20 pages and 6 figures in main text, plus supporting information | null | null | null | q-bio.GN q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In eukaryotic genomes, nucleosomes function to compact DNA and to regulate
access to it both by simple physical occlusion and by providing the substrate
for numerous covalent epigenetic tags. While nucleosome positions in vitro are
determined by sequence alone, in vivo competition with other DNA-binding
factors and action of chromatin remodeling enzymes play a role that needs to be
quantified. We developed a biophysical model for the sequence dependence of DNA
bending energies, and validated it against a collection of in vitro free
energies of nucleosome formation and a nucleosome crystal structure; we also
successfully designed both strong and poor histone binding sequences ab initio.
For in vivo data from S.cerevisiae, the strongest positioning signal came from
the competition with other factors. Based on sequence alone, our model predicts
that functional transcription factor binding sites have a tendency to be
covered by nucleosomes, but are uncovered in vivo because functional sites
cluster within a single nucleosome footprint, making transcription factors bind
cooperatively. Similarly a weak enhancement of nucleosome binding in the TATA
region for naked DNA becomes a strong depletion when the TATA-binding protein
is included, in quantitative agreement with experiment. Predictions at specific
loci were also greatly enhanced by including competing factors. Our physically
grounded model distinguishes multiple ways in which genomic sequence can
influence nucleosome positions and thus provides an alternative explanation for
several important experimental findings.
| [
{
"created": "Tue, 27 May 2008 17:46:52 GMT",
"version": "v1"
}
] | 2008-05-28 | [
[
"Morozov",
"Alexandre V.",
""
],
[
"Fortney",
"Karissa",
""
],
[
"Gaykalova",
"Daria A.",
""
],
[
"Studitsky",
"Vasily M.",
""
],
[
"Widom",
"Jonathan",
""
],
[
"Siggia",
"Eric D.",
""
]
] | In eukaryotic genomes, nucleosomes function to compact DNA and to regulate access to it both by simple physical occlusion and by providing the substrate for numerous covalent epigenetic tags. While nucleosome positions in vitro are determined by sequence alone, in vivo competition with other DNA-binding factors and action of chromatin remodeling enzymes play a role that needs to be quantified. We developed a biophysical model for the sequence dependence of DNA bending energies, and validated it against a collection of in vitro free energies of nucleosome formation and a nucleosome crystal structure; we also successfully designed both strong and poor histone binding sequences ab initio. For in vivo data from S.cerevisiae, the strongest positioning signal came from the competition with other factors. Based on sequence alone, our model predicts that functional transcription factor binding sites have a tendency to be covered by nucleosomes, but are uncovered in vivo because functional sites cluster within a single nucleosome footprint, making transcription factors bind cooperatively. Similarly a weak enhancement of nucleosome binding in the TATA region for naked DNA becomes a strong depletion when the TATA-binding protein is included, in quantitative agreement with experiment. Predictions at specific loci were also greatly enhanced by including competing factors. Our physically grounded model distinguishes multiple ways in which genomic sequence can influence nucleosome positions and thus provides an alternative explanation for several important experimental findings. |
2005.09572 | Yitan Zhu | Yitan Zhu, Thomas Brettin, Yvonne A. Evrard, Alexander Partin,
Fangfang Xia, Maulik Shukla, Hyunseung Yoo, James H. Doroshow, Rick Stevens | Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug
Response | null | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transfer learning has been shown to be effective in many applications in
which training data for the target problem are limited but data for a related
(source) problem are abundant. In this paper, we apply transfer learning to the
prediction of anti-cancer drug response. Previous transfer learning studies for
drug response prediction focused on building models that predict the response
of tumor cells to a specific drug treatment. We target the more challenging
task of building general prediction models that can make predictions for both
new tumor cells and new drugs. We apply the classic transfer learning framework
that trains a prediction model on the source dataset and refines it on the
target dataset, and extends the framework through ensemble. The ensemble
transfer learning pipeline is implemented using LightGBM and two deep neural
network (DNN) models with different architectures. Uniquely, we investigate its
power for three application settings including drug repurposing, precision
oncology, and new drug development, through different data partition schemes in
cross-validation. We test the proposed ensemble transfer learning on benchmark
in vitro drug screening datasets, taking one dataset as the source domain and
another dataset as the target domain. The analysis results demonstrate the
benefit of applying ensemble transfer learning for predicting anti-cancer drug
response in all three applications with both LightGBM and DNN models. Compared
between the different prediction models, a DNN model with two subnetworks for
the inputs of tumor features and drug features separately outperforms LightGBM
and the other DNN model that concatenates tumor features and drug features for
input in the drug repurposing and precision oncology applications. In the more
challenging application of new drug development, LightGBM performs better than
the other two DNN models.
| [
{
"created": "Wed, 13 May 2020 20:29:48 GMT",
"version": "v1"
}
] | 2020-05-20 | [
[
"Zhu",
"Yitan",
""
],
[
"Brettin",
"Thomas",
""
],
[
"Evrard",
"Yvonne A.",
""
],
[
"Partin",
"Alexander",
""
],
[
"Xia",
"Fangfang",
""
],
[
"Shukla",
"Maulik",
""
],
[
"Yoo",
"Hyunseung",
""
],
[
"Doroshow",
"James H.",
""
],
[
"Stevens",
"Rick",
""
]
] | Transfer learning has been shown to be effective in many applications in which training data for the target problem are limited but data for a related (source) problem are abundant. In this paper, we apply transfer learning to the prediction of anti-cancer drug response. Previous transfer learning studies for drug response prediction focused on building models that predict the response of tumor cells to a specific drug treatment. We target the more challenging task of building general prediction models that can make predictions for both new tumor cells and new drugs. We apply the classic transfer learning framework that trains a prediction model on the source dataset and refines it on the target dataset, and extends the framework through ensemble. The ensemble transfer learning pipeline is implemented using LightGBM and two deep neural network (DNN) models with different architectures. Uniquely, we investigate its power for three application settings including drug repurposing, precision oncology, and new drug development, through different data partition schemes in cross-validation. We test the proposed ensemble transfer learning on benchmark in vitro drug screening datasets, taking one dataset as the source domain and another dataset as the target domain. The analysis results demonstrate the benefit of applying ensemble transfer learning for predicting anti-cancer drug response in all three applications with both LightGBM and DNN models. Compared between the different prediction models, a DNN model with two subnetworks for the inputs of tumor features and drug features separately outperforms LightGBM and the other DNN model that concatenates tumor features and drug features for input in the drug repurposing and precision oncology applications. In the more challenging application of new drug development, LightGBM performs better than the other two DNN models. |
1907.01575 | Dumitru Trucu | Robyn Shuttleworth and Dumitru Trucu | Cell-scale degradation of peritumoural extracellular matrix fibre
network and its role within tissue-scale cancer invasion | null | null | null | null | q-bio.TO math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Local cancer invasion of tissue is a complex, multiscale process which plays
an essential role in tumour progression. Occurring over many different temporal
and spatial scales, the first stage of invasion is the secretion of matrix
degrading enzymes (MDEs) by the cancer cells that consequently degrade the
surrounding extracellular matrix (ECM). This process is vital for creating
space in which the cancer cells can progress and it is driven by the activities
of specific matrix metalloproteinases (MMPs). In this paper, we consider the
key role of two MMPs by developing further the novel two-part multiscale model
introduced in [33] to better relate at micro-scale the two micro-scale
activities that were considered there, namely, the micro-dynamics concerning
the continuous rearrangement of the naturally oriented ECM fibres within the
bulk of the tumour and MDEs proteolytic micro-dynamics that take place in an
appropriate cell-scale neighbourhood of the tumour boundary. Focussing
primarily on the activities of the membrane-tethered MT1-MMP and the soluble
MMP-2 with the fibrous ECM phase, in this work we investigate the MT1-MMP/MMP-2
cascade and its overall effect on tumour progression. To that end, we will
propose a new multiscale modelling framework by considering the degradation of
the ECM fibres not only to take place at macro-scale in the bulk of the tumour
but also explicitly in the micro-scale neighbourhood of the tumour interface as
a consequence of the interactions with molecular fluxes of MDEs that exercise
their spatial dynamics at the invasive edge of the tumour.
| [
{
"created": "Tue, 2 Jul 2019 18:21:42 GMT",
"version": "v1"
}
] | 2019-07-04 | [
[
"Shuttleworth",
"Robyn",
""
],
[
"Trucu",
"Dumitru",
""
]
] | Local cancer invasion of tissue is a complex, multiscale process which plays an essential role in tumour progression. Occurring over many different temporal and spatial scales, the first stage of invasion is the secretion of matrix degrading enzymes (MDEs) by the cancer cells that consequently degrade the surrounding extracellular matrix (ECM). This process is vital for creating space in which the cancer cells can progress and it is driven by the activities of specific matrix metalloproteinases (MMPs). In this paper, we consider the key role of two MMPs by developing further the novel two-part multiscale model introduced in [33] to better relate at micro-scale the two micro-scale activities that were considered there, namely, the micro-dynamics concerning the continuous rearrangement of the naturally oriented ECM fibres within the bulk of the tumour and MDEs proteolytic micro-dynamics that take place in an appropriate cell-scale neighbourhood of the tumour boundary. Focussing primarily on the activities of the membrane-tethered MT1-MMP and the soluble MMP-2 with the fibrous ECM phase, in this work we investigate the MT1-MMP/MMP-2 cascade and its overall effect on tumour progression. To that end, we will propose a new multiscale modelling framework by considering the degradation of the ECM fibres not only to take place at macro-scale in the bulk of the tumour but also explicitly in the micro-scale neighbourhood of the tumour interface as a consequence of the interactions with molecular fluxes of MDEs that exercise their spatial dynamics at the invasive edge of the tumour. |
1201.1030 | Matthew Grant | Matthew A. A. Grant, Chiara Saggioro, Ulisse Ferrari, Bruno Bassetti,
Bianca Sclavi, Marco Cosentino Lagomarsino | DnaA and the timing of chromosome replication in Escherichia coli as a
function of growth rate | null | BMC Systems Biology 2011, 5:201 | 10.1186/1752-0509-5-201 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: In Escherichia coli, overlapping rounds of DNA replication allow
the bacteria to double in faster times than the time required to copy the
genome. The precise timing of initiation of DNA replication is determined by a
regulatory circuit that depends on the binding of a critical number of
ATP-bound DnaA proteins at the origin of replication. The synthesis of DnaA in
the cell is controlled by a growth-rate dependent, negatively autoregulated
gene found near the origin of replication. Both the regulatory and initiation
activity of DnaA depend on its nucleotide bound state and its availability.
Results: In order to investigate the contributions of the different
regulatory processes to the timing of initiation of DNA replication at varying
growth rates, we formulate a minimal quantitative model of the initiator
circuit that includes the key ingredients known to regulate the activity of the
DnaA protein. This model describes the average-cell oscillations in
DnaA-ATP/DNA during the cell cycle, for varying growth rates. We evaluate the
conditions under which this ratio attains the same threshold value at the time
of initiation, independently of the growth rate.
Conclusions: We find that a quantitative description of replication
initiation by DnaA must rely on the dependency of the basic parameters on
growth rate, in order to account for the timing of initiation of DNA
replication at different cell doubling times. We isolate two main possible
scenarios for this. One possibility is that the basal rate of regulatory
inactivation by ATP hydrolysis must vary with growth rate. Alternatively, some
parameters defining promoter activity need to be a function of the growth rate.
In either case, the basal rate of gene expression needs to increase with the
growth rate, in accordance with the known characteristics of the dnaA promoter.
| [
{
"created": "Wed, 4 Jan 2012 22:39:33 GMT",
"version": "v1"
}
] | 2015-03-19 | [
[
"Grant",
"Matthew A. A.",
""
],
[
"Saggioro",
"Chiara",
""
],
[
"Ferrari",
"Ulisse",
""
],
[
"Bassetti",
"Bruno",
""
],
[
"Sclavi",
"Bianca",
""
],
[
"Lagomarsino",
"Marco Cosentino",
""
]
] | Background: In Escherichia coli, overlapping rounds of DNA replication allow the bacteria to double in faster times than the time required to copy the genome. The precise timing of initiation of DNA replication is determined by a regulatory circuit that depends on the binding of a critical number of ATP-bound DnaA proteins at the origin of replication. The synthesis of DnaA in the cell is controlled by a growth-rate dependent, negatively autoregulated gene found near the origin of replication. Both the regulatory and initiation activity of DnaA depend on its nucleotide bound state and its availability. Results: In order to investigate the contributions of the different regulatory processes to the timing of initiation of DNA replication at varying growth rates, we formulate a minimal quantitative model of the initiator circuit that includes the key ingredients known to regulate the activity of the DnaA protein. This model describes the average-cell oscillations in DnaA-ATP/DNA during the cell cycle, for varying growth rates. We evaluate the conditions under which this ratio attains the same threshold value at the time of initiation, independently of the growth rate. Conclusions: We find that a quantitative description of replication initiation by DnaA must rely on the dependency of the basic parameters on growth rate, in order to account for the timing of initiation of DNA replication at different cell doubling times. We isolate two main possible scenarios for this. One possibility is that the basal rate of regulatory inactivation by ATP hydrolysis must vary with growth rate. Alternatively, some parameters defining promoter activity need to be a function of the growth rate. In either case, the basal rate of gene expression needs to increase with the growth rate, in accordance with the known characteristics of the dnaA promoter. |
1706.00804 | Marcos Amaku | Marcos Amaku, Marcelo Nascimento Burattini, Eleazar Chaib, Francisco
Antonio Bezerra Coutinho, David Greenhalgh, Luis Fernandez Lopez, Eduardo
Massad | Estimating the prevalence of infectious diseases from under-reported
age-dependent compulsorily notification databases | 22 pages, 4 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: National or local laws, norms or regulations (sometimes and in
some countries) require medical providers to report notifiable diseases to
public health authorities. Reporting, however, is almost always incomplete.
This is due to a variety of reasons, ranging from not recognizing the diseased
to failures in the technical or administrative steps leading to the final
official register in the disease notification system. The reported fraction
varies from 9% to 99% and is strongly associated with the disease being
reported.
Methods: In this paper we propose a method to approximately estimate the full
prevalence (and any other variable or parameter related to transmission
intensity) of infectious diseases. The model assumes incomplete notification of
incidence and allows the estimation of the non-notified number of infections
and it is illustrated by the case of hepatitis C in Brazil. The method has the
advantage that it can be corrected iteratively by comparing its findings with
empirical results.
Results: The application of the model for the case of hepatitis C in Brazil
resulted in a prevalence of notified cases that varied between 163,902 and
169,382 cases; a prevalence of non-notified cases that varied between 1,433,638
and 1,446,771; and a total prevalence of infections that varied between
1,597,540 and 1,616,153 cases.
Conclusions: We conclude that that the model proposed can be useful for
estimation of the actual magnitude of endemic states of infectious diseases,
particularly for those where the number of notified cases is only the tip of
the iceberg. In addition, the method can be applied to other situations, such
as the well known underreported incidence of criminality (for example rape),
among others.
| [
{
"created": "Fri, 2 Jun 2017 18:35:05 GMT",
"version": "v1"
}
] | 2017-06-06 | [
[
"Amaku",
"Marcos",
""
],
[
"Burattini",
"Marcelo Nascimento",
""
],
[
"Chaib",
"Eleazar",
""
],
[
"Coutinho",
"Francisco Antonio Bezerra",
""
],
[
"Greenhalgh",
"David",
""
],
[
"Lopez",
"Luis Fernandez",
""
],
[
"Massad",
"Eduardo",
""
]
] | Background: National or local laws, norms or regulations (sometimes and in some countries) require medical providers to report notifiable diseases to public health authorities. Reporting, however, is almost always incomplete. This is due to a variety of reasons, ranging from not recognizing the diseased to failures in the technical or administrative steps leading to the final official register in the disease notification system. The reported fraction varies from 9% to 99% and is strongly associated with the disease being reported. Methods: In this paper we propose a method to approximately estimate the full prevalence (and any other variable or parameter related to transmission intensity) of infectious diseases. The model assumes incomplete notification of incidence and allows the estimation of the non-notified number of infections and it is illustrated by the case of hepatitis C in Brazil. The method has the advantage that it can be corrected iteratively by comparing its findings with empirical results. Results: The application of the model for the case of hepatitis C in Brazil resulted in a prevalence of notified cases that varied between 163,902 and 169,382 cases; a prevalence of non-notified cases that varied between 1,433,638 and 1,446,771; and a total prevalence of infections that varied between 1,597,540 and 1,616,153 cases. Conclusions: We conclude that that the model proposed can be useful for estimation of the actual magnitude of endemic states of infectious diseases, particularly for those where the number of notified cases is only the tip of the iceberg. In addition, the method can be applied to other situations, such as the well known underreported incidence of criminality (for example rape), among others. |
1404.5324 | Thomas House | Thomas House | For principled model fitting in mathematical biology | 7 pages, 3 figures. To appear in Journal of Mathematical Biology. The
final publication is available at Springer via
http://dx.doi.org/10.1007/s00285-014-0787-6 | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The mathematical models used to capture features of complex, biological
systems are typically non-linear, meaning that there are no generally valid
simple relationships between their outputs and the data that might be used to
validate them. This invalidates the assumptions behind standard statistical
methods such as linear regression, and often the methods used to parameterise
biological models from data are ad hoc. In this perspective, I will argue for
an approach to model fitting in mathematical biology that incorporates modern
statistical methodology without losing the insights gained through non-linear
dynamic models, and will call such an approach principled model fitting.
Principled model fitting therefore involves defining likelihoods of observing
real data on the basis of models that capture key biological mechanisms.
| [
{
"created": "Mon, 21 Apr 2014 20:32:28 GMT",
"version": "v1"
}
] | 2014-04-23 | [
[
"House",
"Thomas",
""
]
] | The mathematical models used to capture features of complex, biological systems are typically non-linear, meaning that there are no generally valid simple relationships between their outputs and the data that might be used to validate them. This invalidates the assumptions behind standard statistical methods such as linear regression, and often the methods used to parameterise biological models from data are ad hoc. In this perspective, I will argue for an approach to model fitting in mathematical biology that incorporates modern statistical methodology without losing the insights gained through non-linear dynamic models, and will call such an approach principled model fitting. Principled model fitting therefore involves defining likelihoods of observing real data on the basis of models that capture key biological mechanisms. |
2001.07844 | Tandy Warnow | John A. Rhodes, Michael G. Nute, and Tandy Warnow | NJst and ASTRID are not statistically consistent under a random model of
missing data | 6 pages, no figures, provides counterexample to theorem (the first
and corresponding author are both co-authors on this paper) | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Species tree estimation from multi-locus datasets is statistically
challenging for multiple reasons, including gene tree heterogeneity across the
genome due to incomplete lineage sorting (ILS). Species tree estimation methods
have been developed that operate by estimating gene trees and then using those
gene trees to estimate the species tree. Several of these methods (e.g.,
ASTRAL, ASTRID, and NJst) are provably statistically consistent under the
multi-species coalescent (MSC) model, provided that the gene trees are
estimated correctly, and there is no missing data. Recently, Nute et al. (BMC
Genomics 2018) addressed the question of whether these methods remain
statistically consistent under random models of taxon deletion, and asserted
that they do so. Here we provide a counterexample to one of these theorems, and
establish that ASTRID and NJst are not statistically consistent under an i.i.d.
model of taxon deletion.
| [
{
"created": "Wed, 22 Jan 2020 01:50:53 GMT",
"version": "v1"
}
] | 2020-01-23 | [
[
"Rhodes",
"John A.",
""
],
[
"Nute",
"Michael G.",
""
],
[
"Warnow",
"Tandy",
""
]
] | Species tree estimation from multi-locus datasets is statistically challenging for multiple reasons, including gene tree heterogeneity across the genome due to incomplete lineage sorting (ILS). Species tree estimation methods have been developed that operate by estimating gene trees and then using those gene trees to estimate the species tree. Several of these methods (e.g., ASTRAL, ASTRID, and NJst) are provably statistically consistent under the multi-species coalescent (MSC) model, provided that the gene trees are estimated correctly, and there is no missing data. Recently, Nute et al. (BMC Genomics 2018) addressed the question of whether these methods remain statistically consistent under random models of taxon deletion, and asserted that they do so. Here we provide a counterexample to one of these theorems, and establish that ASTRID and NJst are not statistically consistent under an i.i.d. model of taxon deletion. |
1603.01351 | Kazuhisa Shibata | Kazuhisa Shibata, Takeo Watanabe, Mitsuo Kawato, Yuka Sasaki | Differential activation patterns in the same brain region led to
opposite emotional states | 49 pages, 7 figures | PLoS Biol 14(9): e1002546 (2016) | 10.1371/journal.pbio.1002546 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In human studies, how averaged activation in a brain region relates to human
behavior has been extensively investigated. This approach has led to the
finding that positive and negative facial preferences are represented by
different brain regions. However, using a multi-voxel pattern induction method
we found that different patterns of neural activations within the cingulate
cortex (CC) play roles in representing opposite emotional states. In the
present study, while neutrally-preferred faces were presented, activation
patterns in the CC that corresponded to higher (or lower) preference were
repeatedly induced by the pattern induction method. As a result, previously
neutrally-preferred faces became more (or less) preferred. We conclude that a
different activation pattern in the CC, rather than averaged activation in a
different area, represents and causally determines positive or negative facial
preference. This new approach may reveal importance in an activation pattern
within a brain region in many cognitive functions.
| [
{
"created": "Fri, 4 Mar 2016 05:41:33 GMT",
"version": "v1"
}
] | 2016-09-12 | [
[
"Shibata",
"Kazuhisa",
""
],
[
"Watanabe",
"Takeo",
""
],
[
"Kawato",
"Mitsuo",
""
],
[
"Sasaki",
"Yuka",
""
]
] | In human studies, how averaged activation in a brain region relates to human behavior has been extensively investigated. This approach has led to the finding that positive and negative facial preferences are represented by different brain regions. However, using a multi-voxel pattern induction method we found that different patterns of neural activations within the cingulate cortex (CC) play roles in representing opposite emotional states. In the present study, while neutrally-preferred faces were presented, activation patterns in the CC that corresponded to higher (or lower) preference were repeatedly induced by the pattern induction method. As a result, previously neutrally-preferred faces became more (or less) preferred. We conclude that a different activation pattern in the CC, rather than averaged activation in a different area, represents and causally determines positive or negative facial preference. This new approach may reveal importance in an activation pattern within a brain region in many cognitive functions. |
0910.2008 | Shweta Bansal | Shweta Bansal and Lauren Ancel Meyers | The Impact of Past Epidemics on Future Disease Dynamics | null | Journal of Theoretical Biology, Volume 309, 21 September 2012,
Pages 176-184 | 10.1016/j.jtbi.2012.06.012 | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many pathogens spread primarily via direct contact between infected and
susceptible hosts. Thus, the patterns of contacts or contact network of a
population fundamentally shapes the course of epidemics. While there is a
robust and growing theory for the dynamics of single epidemics in networks, we
know little about the impacts of network structure on long term epidemic or
endemic transmission. For seasonal diseases like influenza, pathogens
repeatedly return to populations with complex and changing patterns of
susceptibility and immunity acquired through prior infection. Here, we develop
two mathematical approaches for modeling consecutive seasonal outbreaks of a
partially-immunizing infection in a population with contact heterogeneity.
Using methods from percolation theory we consider both leaky immunity, where
all previously infected individuals gain partial immunity, and perfect
immunity, where a fraction of previously infected individuals are fully immune.
By restructuring the epidemiologically active portion of their host population,
such diseases limit the potential of future outbreaks. We speculate that these
dynamics can result in evolutionary pressure to increase infectiousness.
| [
{
"created": "Mon, 12 Oct 2009 18:24:34 GMT",
"version": "v1"
}
] | 2012-08-01 | [
[
"Bansal",
"Shweta",
""
],
[
"Meyers",
"Lauren Ancel",
""
]
] | Many pathogens spread primarily via direct contact between infected and susceptible hosts. Thus, the patterns of contacts or contact network of a population fundamentally shapes the course of epidemics. While there is a robust and growing theory for the dynamics of single epidemics in networks, we know little about the impacts of network structure on long term epidemic or endemic transmission. For seasonal diseases like influenza, pathogens repeatedly return to populations with complex and changing patterns of susceptibility and immunity acquired through prior infection. Here, we develop two mathematical approaches for modeling consecutive seasonal outbreaks of a partially-immunizing infection in a population with contact heterogeneity. Using methods from percolation theory we consider both leaky immunity, where all previously infected individuals gain partial immunity, and perfect immunity, where a fraction of previously infected individuals are fully immune. By restructuring the epidemiologically active portion of their host population, such diseases limit the potential of future outbreaks. We speculate that these dynamics can result in evolutionary pressure to increase infectiousness. |
2007.02712 | Matthew Merski | Natalia Blanco (1), Kristen Stafford (1 and 2), Marie-Claude Lavoie
(2), Axel Brandenburg (3), Maria W. Gorna (4), and Matthew Merski (4) ((1)
Center for International Health, Education, and Biosecurity, Institute of
Human Virology -University of Maryland School of Medicine, Baltimore,
Maryland USA, (2) Department of Epidemiology and Public Health, University of
Maryland School of Medicine, Baltimore, Maryland USA, (3) Nordita, KTH Royal
Institute of Technology and Stockholm University, Stockholm, Sweden, (4)
Biological and Chemical Research Centre, Department of Chemistry, University
of Warsaw, Warsaw, Poland) | Prospective Prediction of Future SARS-CoV-2 Infections Using Empirical
Data on a National Level to Gauge Response Effectiveness | 20 pages, 2 tables, 3 figures followed by 12 pages of supporting
information | Epidemiology & Infection, Volume 149, 2021, e80 | 10.1017/S0950268821000649 | NORDITA-2020-070 | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predicting an accurate expected number of future COVID-19 cases is essential
to properly evaluate the effectiveness of any treatment or preventive measure.
This study aimed to identify the most appropriate mathematical model to
prospectively predict the expected number of cases without any intervention.
The total number of cases for the COVID-19 epidemic in 28 countries was
analyzed and fitted to several simple rate models including the logistic,
Gompertz, quadratic, simple square, and simple exponential growth models. The
resulting model parameters were used to extrapolate predictions for more recent
data. While the Gompertz growth models (mean R2 = 0.998) best fitted the
current data, uncertainties in the eventual case limit made future predictions
with logistic models prone to errors. Of the other models, the quadratic rate
model (mean R2 = 0.992) fitted the current data best for 25 (89 %) countries as
determined by R2 values. The simple square and quadratic models accurately
predicted the number of future total cases 37 and 36 days in advance
respectively, compared to only 15 days for the simple exponential model. The
simple exponential model significantly overpredicted the total number of future
cases while the quadratic and simple square models did not. These results
demonstrated that accurate future predictions of the case load in a given
country can be made significantly in advance without the need for complicated
models of population behavior and generate a reliable assessment of the
efficacy of current prescriptive measures against disease spread.
| [
{
"created": "Mon, 6 Jul 2020 13:00:01 GMT",
"version": "v1"
}
] | 2021-04-07 | [
[
"Blanco",
"Natalia",
"",
"1 and 2"
],
[
"Stafford",
"Kristen",
"",
"1 and 2"
],
[
"Lavoie",
"Marie-Claude",
""
],
[
"Brandenburg",
"Axel",
""
],
[
"Gorna",
"Maria W.",
""
],
[
"Merski",
"Matthew",
""
]
] | Predicting an accurate expected number of future COVID-19 cases is essential to properly evaluate the effectiveness of any treatment or preventive measure. This study aimed to identify the most appropriate mathematical model to prospectively predict the expected number of cases without any intervention. The total number of cases for the COVID-19 epidemic in 28 countries was analyzed and fitted to several simple rate models including the logistic, Gompertz, quadratic, simple square, and simple exponential growth models. The resulting model parameters were used to extrapolate predictions for more recent data. While the Gompertz growth models (mean R2 = 0.998) best fitted the current data, uncertainties in the eventual case limit made future predictions with logistic models prone to errors. Of the other models, the quadratic rate model (mean R2 = 0.992) fitted the current data best for 25 (89 %) countries as determined by R2 values. The simple square and quadratic models accurately predicted the number of future total cases 37 and 36 days in advance respectively, compared to only 15 days for the simple exponential model. The simple exponential model significantly overpredicted the total number of future cases while the quadratic and simple square models did not. These results demonstrated that accurate future predictions of the case load in a given country can be made significantly in advance without the need for complicated models of population behavior and generate a reliable assessment of the efficacy of current prescriptive measures against disease spread. |
1705.07990 | Adriano De Jesus Da Silva | A. J. da Silva and E. S. Santos | Aqueous solution interactions with sex hormone-binding globulin and
estradiol: A theoretical investigation | 26 pages, 7 figures, 4 tables | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sex hormone-binding globulin (SHBG) is a binding protein that regulates
availability of steroids hormones in the plasma. Although best known as steroid
carrier, studies have associated SHBG in modulating behavioral aspects related
to sexual receptivity. Among steroids, estradiol (17\b{eta}-estradiol,
oestradiol or E2) is well recognized as the most active endogenous female
hormone, exerting important roles in reproductive and nonreproductive
functions. Thus, in this study we aimed to employ molecular dynamics (MD) and
docking techniques for quantifying the interaction energy between a complex
aqueous solution, composed by different salts, SHBG and E2. Due to glucose
concentration resembles those observed in diabetic levels, special emphasis was
devoted to uncover the main consequences of this carbohydrate on the SHBG and
E2 molecules. We also examined possible energetic changes due to solution on
the binding energy of SHBG-E2 complex. In this framework, our calculations
uncovered a remarkable interaction energy between glucose and SHBG surface.
Surprisingly, we also observed solute components movement toward SHBG yielding
clusters surrounding the protein. This finding, corroborated by the higher
energy and shorter distance found between glucose and SHBG, suggests a scenario
in favor of a detainment state. In addition, in spite of protein superficial
area increment it does not exerted modification on binding site area nor over
binding energy SHBG-E2 complex. Finally, our calculations also highlighted an
interaction between E2 and glucose when the hormone was immersed in the
solution. In summary, our findings contribute for a better comprehension of
both SHBG and E2 interplay with aqueous solution components.
| [
{
"created": "Mon, 22 May 2017 20:44:46 GMT",
"version": "v1"
}
] | 2017-05-24 | [
[
"da Silva",
"A. J.",
""
],
[
"Santos",
"E. S.",
""
]
] | Sex hormone-binding globulin (SHBG) is a binding protein that regulates availability of steroids hormones in the plasma. Although best known as steroid carrier, studies have associated SHBG in modulating behavioral aspects related to sexual receptivity. Among steroids, estradiol (17\b{eta}-estradiol, oestradiol or E2) is well recognized as the most active endogenous female hormone, exerting important roles in reproductive and nonreproductive functions. Thus, in this study we aimed to employ molecular dynamics (MD) and docking techniques for quantifying the interaction energy between a complex aqueous solution, composed by different salts, SHBG and E2. Due to glucose concentration resembles those observed in diabetic levels, special emphasis was devoted to uncover the main consequences of this carbohydrate on the SHBG and E2 molecules. We also examined possible energetic changes due to solution on the binding energy of SHBG-E2 complex. In this framework, our calculations uncovered a remarkable interaction energy between glucose and SHBG surface. Surprisingly, we also observed solute components movement toward SHBG yielding clusters surrounding the protein. This finding, corroborated by the higher energy and shorter distance found between glucose and SHBG, suggests a scenario in favor of a detainment state. In addition, in spite of protein superficial area increment it does not exerted modification on binding site area nor over binding energy SHBG-E2 complex. Finally, our calculations also highlighted an interaction between E2 and glucose when the hormone was immersed in the solution. In summary, our findings contribute for a better comprehension of both SHBG and E2 interplay with aqueous solution components. |
1404.3989 | Andrew Beam | Andrew L. Beam, Alison Motsinger-Reif, Jon Doyle | Bayesian Neural Networks for Genetic Association Studies of Complex
Disease | null | null | 10.1186/s12859-014-0368-0 | null | q-bio.GN stat.AP stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Discovering causal genetic variants from large genetic association studies
poses many difficult challenges. Assessing which genetic markers are involved
in determining trait status is a computationally demanding task, especially in
the presence of gene-gene interactions. A non-parametric Bayesian approach in
the form of a Bayesian neural network is proposed for use in analyzing genetic
association studies. Demonstrations on synthetic and real data reveal they are
able to efficiently and accurately determine which variants are involved in
determining case-control status. Using graphics processing units (GPUs) the
time needed to build these models is decreased by several orders of magnitude.
In comparison with commonly used approaches for detecting genetic interactions,
Bayesian neural networks perform very well across a broad spectrum of possible
genetic relationships while having the computational efficiency needed to
handle large datasets.
| [
{
"created": "Tue, 15 Apr 2014 17:11:53 GMT",
"version": "v1"
},
{
"created": "Wed, 16 Apr 2014 00:44:21 GMT",
"version": "v2"
}
] | 2015-04-09 | [
[
"Beam",
"Andrew L.",
""
],
[
"Motsinger-Reif",
"Alison",
""
],
[
"Doyle",
"Jon",
""
]
] | Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions. A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. Using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting genetic interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships while having the computational efficiency needed to handle large datasets. |
0909.4596 | Vladimir Ivancevic | Vladimir G. Ivancevic | New Universal Theory of Injury Prediction and Prevention | 5 pages, 3 figures | null | null | null | q-bio.TO q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The prediction and prevention of traumatic brain injury, spinal injury and
general musculo-skeletal injury is a very important aspect of preventive
medical science. Recently, in a series of papers, I have proposed a new coupled
loading-rate hypothesis as a unique cause of all above injuries. This new
hypothesis states that the main cause of all mechanical injuries is a Euclidean
Jolt, which is an impulsive loading that strikes any part of the human body
(head, spine or any bone/joint) - in several coupled degrees-of-freedom
simultaneously. It never goes in a single direction only. Also, it is never a
static force. It is always an impulsive translational and/or rotational force,
coupled to some human mass eccentricity.
Keywords: traumatic brain injury, spinal injury, musculo-skeletal injury,
coupled loading-rate hypothesis, Euclidean jolt
| [
{
"created": "Fri, 25 Sep 2009 03:43:33 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Oct 2009 02:55:58 GMT",
"version": "v2"
}
] | 2009-10-08 | [
[
"Ivancevic",
"Vladimir G.",
""
]
] | The prediction and prevention of traumatic brain injury, spinal injury and general musculo-skeletal injury is a very important aspect of preventive medical science. Recently, in a series of papers, I have proposed a new coupled loading-rate hypothesis as a unique cause of all above injuries. This new hypothesis states that the main cause of all mechanical injuries is a Euclidean Jolt, which is an impulsive loading that strikes any part of the human body (head, spine or any bone/joint) - in several coupled degrees-of-freedom simultaneously. It never goes in a single direction only. Also, it is never a static force. It is always an impulsive translational and/or rotational force, coupled to some human mass eccentricity. Keywords: traumatic brain injury, spinal injury, musculo-skeletal injury, coupled loading-rate hypothesis, Euclidean jolt |
1103.2479 | Joachim Krug | Jasper Franke, Alexander Kl\"ozer, J. Arjan G.M. de Visser and Joachim
Krug | Evolutionary accessibility of mutational pathways | 16 pages, 4 figures; supplementary material available on request | PLoS Computational Biology 7 (8) e1002134 (2011) | 10.1371/journal.pcbi.1002134 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Functional effects of different mutations are known to combine to the total
effect in highly nontrivial ways. For the trait under evolutionary selection
(`fitness'), measured values over all possible combinations of a set of
mutations yield a fitness landscape that determines which mutational states can
be reached from a given initial genotype. Understanding the accessibility
properties of fitness landscapes is conceptually important in answering
questions about the predictability and repeatability of evolutionary
adaptation. Here we theoretically investigate accessibility of the globally
optimal state on a wide variety of model landscapes, including landscapes with
tunable ruggedness as well as neutral `holey' landscapes. We define a
mutational pathway to be accessible if it contains the minimal number of
mutations required to reach the target genotype, and if fitness increases in
each mutational step. Under this definition accessibility is high, in the sense
that at least one accessible pathwayexists with a substantial probability that
approaches unity as the dimensionality of the fitness landscape (set by the
number of mutational loci) becomes large. At the same time the number of
alternative accessible pathways grows without bound. We test the model
predictions against an empirical 8-locus fitness landscape obtained for the
filamentous fungus \textit{Aspergillus niger}. By analyzing subgraphs of the
full landscape containing different subsets of mutations, we are able to probe
the mutational distance scale in the empirical data. The predicted effect of
high accessibility is supported by the empirical data and very robust, which we
argue to reflect the generic topology of sequence spaces.
| [
{
"created": "Sat, 12 Mar 2011 21:10:23 GMT",
"version": "v1"
},
{
"created": "Mon, 29 Aug 2011 15:38:26 GMT",
"version": "v2"
}
] | 2015-05-27 | [
[
"Franke",
"Jasper",
""
],
[
"Klözer",
"Alexander",
""
],
[
"de Visser",
"J. Arjan G. M.",
""
],
[
"Krug",
"Joachim",
""
]
] | Functional effects of different mutations are known to combine to the total effect in highly nontrivial ways. For the trait under evolutionary selection (`fitness'), measured values over all possible combinations of a set of mutations yield a fitness landscape that determines which mutational states can be reached from a given initial genotype. Understanding the accessibility properties of fitness landscapes is conceptually important in answering questions about the predictability and repeatability of evolutionary adaptation. Here we theoretically investigate accessibility of the globally optimal state on a wide variety of model landscapes, including landscapes with tunable ruggedness as well as neutral `holey' landscapes. We define a mutational pathway to be accessible if it contains the minimal number of mutations required to reach the target genotype, and if fitness increases in each mutational step. Under this definition accessibility is high, in the sense that at least one accessible pathwayexists with a substantial probability that approaches unity as the dimensionality of the fitness landscape (set by the number of mutational loci) becomes large. At the same time the number of alternative accessible pathways grows without bound. We test the model predictions against an empirical 8-locus fitness landscape obtained for the filamentous fungus \textit{Aspergillus niger}. By analyzing subgraphs of the full landscape containing different subsets of mutations, we are able to probe the mutational distance scale in the empirical data. The predicted effect of high accessibility is supported by the empirical data and very robust, which we argue to reflect the generic topology of sequence spaces. |
2008.01446 | Hendrik Richter | Hendrik Richter | Constructing transient amplifiers for death-Birth updating: A case study
of cubic and quartic regular graphs | null | null | null | null | q-bio.PE cs.NE math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A central question of evolutionary dynamics on graphs is whether or not a
mutation introduced in a population of residents survives and eventually even
spreads to the whole population, or gets extinct. The outcome naturally depends
on the fitness of the mutant and the rules by which mutants and residents may
propagate on the network, but arguably the most determining factor is the
network structure. Some structured networks are transient amplifiers. They
increase for a certain fitness range the fixation probability of beneficial
mutations as compared to a well-mixed population. We study a perturbation
methods for identifying transient amplifiers for death-Birth updating. The
method includes calculating the coalescence times of random walks on graphs and
finding the vertex with the largest remeeting time. If the graph is perturbed
by removing an edge from this vertex, there is a certain likelihood that the
resulting perturbed graph is a transient amplifier. We test all pairwise
nonisomorphic cubic and quartic regular graphs up to a certain size and thus
cover the whole structural range expressible by these graphs. We carry out a
spectral analysis and show that the graphs from which the transient amplifiers
can be constructed share certain structural properties. The graphs are
path-like, have low conductance and are rather easy to divide into subgraphs by
removing edges and/or vertices. This is connected with the subgraphs being
identical (or almost identical) building blocks and the frequent occurrence of
cut and/or hinge vertices. Identifying spectral and structural properties may
promote finding and designing such networks.
| [
{
"created": "Tue, 4 Aug 2020 10:37:09 GMT",
"version": "v1"
}
] | 2020-08-05 | [
[
"Richter",
"Hendrik",
""
]
] | A central question of evolutionary dynamics on graphs is whether or not a mutation introduced in a population of residents survives and eventually even spreads to the whole population, or gets extinct. The outcome naturally depends on the fitness of the mutant and the rules by which mutants and residents may propagate on the network, but arguably the most determining factor is the network structure. Some structured networks are transient amplifiers. They increase for a certain fitness range the fixation probability of beneficial mutations as compared to a well-mixed population. We study a perturbation methods for identifying transient amplifiers for death-Birth updating. The method includes calculating the coalescence times of random walks on graphs and finding the vertex with the largest remeeting time. If the graph is perturbed by removing an edge from this vertex, there is a certain likelihood that the resulting perturbed graph is a transient amplifier. We test all pairwise nonisomorphic cubic and quartic regular graphs up to a certain size and thus cover the whole structural range expressible by these graphs. We carry out a spectral analysis and show that the graphs from which the transient amplifiers can be constructed share certain structural properties. The graphs are path-like, have low conductance and are rather easy to divide into subgraphs by removing edges and/or vertices. This is connected with the subgraphs being identical (or almost identical) building blocks and the frequent occurrence of cut and/or hinge vertices. Identifying spectral and structural properties may promote finding and designing such networks. |
2204.04030 | Guido Tiana | M. Tajana, A. Trovato, G. Tiana | Key interaction patterns in proteins revealed by cluster expansion of
the partition function | null | null | null | null | q-bio.BM cond-mat.stat-mech | http://creativecommons.org/licenses/by/4.0/ | The native conformation of structured proteins is stabilized by a complex
network of interactions. We analyzed the elementary patterns that constitute
such network and ranked them according to their importance in shaping protein
sequence design. To achieve this goal, we employed a cluster expansion of the
partition function in the space of sequences and evaluated numerically the
statistical importance of each cluster. An important feature of this procedure
is that it is applied to a dense, finite system. We found that patterns that
contribute most to the partition function are cycles with even numbers of
nodes, while cliques are typically detrimental. Each cluster also gives a
contribute to the sequence entropy, which is a measure of the evolutionary
designability of a fold. We compared the entropies associated with different
interaction patterns to their abundances in the native structures of real
proteins.
| [
{
"created": "Fri, 8 Apr 2022 12:39:04 GMT",
"version": "v1"
}
] | 2022-04-11 | [
[
"Tajana",
"M.",
""
],
[
"Trovato",
"A.",
""
],
[
"Tiana",
"G.",
""
]
] | The native conformation of structured proteins is stabilized by a complex network of interactions. We analyzed the elementary patterns that constitute such network and ranked them according to their importance in shaping protein sequence design. To achieve this goal, we employed a cluster expansion of the partition function in the space of sequences and evaluated numerically the statistical importance of each cluster. An important feature of this procedure is that it is applied to a dense, finite system. We found that patterns that contribute most to the partition function are cycles with even numbers of nodes, while cliques are typically detrimental. Each cluster also gives a contribute to the sequence entropy, which is a measure of the evolutionary designability of a fold. We compared the entropies associated with different interaction patterns to their abundances in the native structures of real proteins. |
2002.12784 | Alexander Kholmanskiy | Alexander Kholmanskiy, Nataliya Zaytseva | Dependence of chlorophyll content in leaves from light regime,
electromagnetic fields and plant species | null | JOJ Hortic Arboric. 2020; 3(1): 555602 | 10.19080/JOJHA.2020.03.555602 | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The regularity of the distribution of chlorophylls content in a series of 30
cultivated plants and 75 steppe grasses was studied. The increased content of
chlorophyll and magnesium in vegetables and grains compared with greens and
steppe grasses is associated with more complex genetics of metabolism, which
has stages of flowering and fruiting. The chlorophyll content increases with
the use of LED phytoirradiators with an emission band coinciding with the first
absorption band of chlorophyll. Industrial electromagnetic fields can affect
the biosynthesis of pigments in deciduous trees, but cultivated herbaceous
plants are not sensitive to them.
| [
{
"created": "Thu, 27 Feb 2020 15:01:52 GMT",
"version": "v1"
}
] | 2021-03-30 | [
[
"Kholmanskiy",
"Alexander",
""
],
[
"Zaytseva",
"Nataliya",
""
]
] | The regularity of the distribution of chlorophylls content in a series of 30 cultivated plants and 75 steppe grasses was studied. The increased content of chlorophyll and magnesium in vegetables and grains compared with greens and steppe grasses is associated with more complex genetics of metabolism, which has stages of flowering and fruiting. The chlorophyll content increases with the use of LED phytoirradiators with an emission band coinciding with the first absorption band of chlorophyll. Industrial electromagnetic fields can affect the biosynthesis of pigments in deciduous trees, but cultivated herbaceous plants are not sensitive to them. |
2402.00246 | Albert Christian Soewongsono | Albert C. Soewongsono, Michael J. Landis | A Diffusion-Based Approach for Simulating Forward-in-Time
State-Dependent Speciation and Extinction Dynamics | Minor typo fixes, figure aesthetics improvements, and some new
analyses. 47 pages, 12 figures, 2 tables | null | null | null | q-bio.PE math.PR | http://creativecommons.org/licenses/by/4.0/ | We establish a general framework using a diffusion approximation to simulate
forward-in-time state counts or frequencies for cladogenetic state-dependent
speciation-extinction (ClaSSE) models. We apply the framework to various two-
and three-region geographic-state speciation-extinction (GeoSSE) models. We
show that the species range state dynamics simulated under tree-based and
diffusion-based processes are comparable. We derive a method to infer rate
parameters that are compatible with given observed stationary state frequencies
and obtain an analytical result to compute stationary state frequencies for a
given set of rate parameters. We also describe a procedure to find the time to
reach the stationary frequencies of a ClaSSE model using our diffusion-based
approach, which we demonstrate using a worked example for a two-region GeoSSE
model. Finally, we discuss how the diffusion framework can be applied to
formalize relationships between evolutionary patterns and processes under
state-dependent diversification scenarios.
| [
{
"created": "Thu, 1 Feb 2024 00:06:59 GMT",
"version": "v1"
},
{
"created": "Mon, 24 Jun 2024 20:43:47 GMT",
"version": "v2"
}
] | 2024-06-26 | [
[
"Soewongsono",
"Albert C.",
""
],
[
"Landis",
"Michael J.",
""
]
] | We establish a general framework using a diffusion approximation to simulate forward-in-time state counts or frequencies for cladogenetic state-dependent speciation-extinction (ClaSSE) models. We apply the framework to various two- and three-region geographic-state speciation-extinction (GeoSSE) models. We show that the species range state dynamics simulated under tree-based and diffusion-based processes are comparable. We derive a method to infer rate parameters that are compatible with given observed stationary state frequencies and obtain an analytical result to compute stationary state frequencies for a given set of rate parameters. We also describe a procedure to find the time to reach the stationary frequencies of a ClaSSE model using our diffusion-based approach, which we demonstrate using a worked example for a two-region GeoSSE model. Finally, we discuss how the diffusion framework can be applied to formalize relationships between evolutionary patterns and processes under state-dependent diversification scenarios. |
q-bio/0511049 | Filipe Tostevin | Filipe Tostevin and Martin Howard | A stochastic model of Min oscillations in Escherichia coli and Min
protein segregation during cell division | 19 pages, 12 figures (25 figure files); published at
http://www.iop.org/EJ/journal/physbio | Phys. Biol. 3 (2006) 1-12 | 10.1088/1478-3975/3/1/001 | null | q-bio.SC cond-mat.stat-mech | null | The Min system in Escherichia coli directs division to the centre of the cell
through pole-to-pole oscillations of the MinCDE proteins. We present a one
dimensional stochastic model of these oscillations which incorporates membrane
polymerisation of MinD into linear chains. This model reproduces much of the
observed phenomenology of the Min system, including pole-to-pole oscillations
of the Min proteins. We then apply this model to investigate the Min system
during cell division. Oscillations continue initially unaffected by the closing
septum, before cutting off rapidly. The fractions of Min proteins in the
daughter cells vary widely, from 50%-50% up to 85%-15% of the total from the
parent cell, suggesting that there may be another mechanism for regulating
these levels in vivo.
| [
{
"created": "Tue, 29 Nov 2005 19:34:53 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Tostevin",
"Filipe",
""
],
[
"Howard",
"Martin",
""
]
] | The Min system in Escherichia coli directs division to the centre of the cell through pole-to-pole oscillations of the MinCDE proteins. We present a one dimensional stochastic model of these oscillations which incorporates membrane polymerisation of MinD into linear chains. This model reproduces much of the observed phenomenology of the Min system, including pole-to-pole oscillations of the Min proteins. We then apply this model to investigate the Min system during cell division. Oscillations continue initially unaffected by the closing septum, before cutting off rapidly. The fractions of Min proteins in the daughter cells vary widely, from 50%-50% up to 85%-15% of the total from the parent cell, suggesting that there may be another mechanism for regulating these levels in vivo. |
1501.04406 | Liane Gabora | Liane Gabora | How Creative Ideas Take Shape | Psychology Today (online). (2011)
http://www.psychologytoday.com/blog/mindbloggling/201109/how-creative-ideas-take-shape.
arXiv admin note: substantial text overlap with arXiv:1308.4241 | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | According to the honing theory of creativity, creative thought works not on
individually considered, discrete, predefined representations but on a
contextually-elicited amalgam of items which exist in a state of potentiality
and may not be readily separable. This leads to the prediction that analogy
making proceeds not by mapping correspondences from candidate sources to
target, as predicted by the structure mapping theory of analogy, but by weeding
out non-correspondences, thereby whittling away at potentiality. Participants
were given an analogy problem, interrupted before they had time to solve it,
and asked to write down what they had by way of a solution. Na\"ive judges
categorized responses as significantly more supportive of the predictions of
honing theory than those of structure mapping.
| [
{
"created": "Mon, 19 Jan 2015 06:43:03 GMT",
"version": "v1"
}
] | 2015-01-20 | [
[
"Gabora",
"Liane",
""
]
] | According to the honing theory of creativity, creative thought works not on individually considered, discrete, predefined representations but on a contextually-elicited amalgam of items which exist in a state of potentiality and may not be readily separable. This leads to the prediction that analogy making proceeds not by mapping correspondences from candidate sources to target, as predicted by the structure mapping theory of analogy, but by weeding out non-correspondences, thereby whittling away at potentiality. Participants were given an analogy problem, interrupted before they had time to solve it, and asked to write down what they had by way of a solution. Na\"ive judges categorized responses as significantly more supportive of the predictions of honing theory than those of structure mapping. |
1804.05219 | Petter Holme | Sang Hoon Lee, Petter Holme | Navigating temporal networks | null | null | 10.1016/j.physa.2018.09.036 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Navigation on graphs is the problem how an agent walking on the graph can get
from a source to a target with limited information about the graph. The
information and the way to exploit it can vary. In this paper, we study
navigation on temporal networks -- networks where we have explicit information
about the time of the interaction, not only who interacts with whom. We
contrast a type of greedy navigation -- where agents follow paths that would
have worked well in the past -- with two strategies that do not exploit the
additional information. We test these on empirical temporal network data sets.
The greedy navigation is indeed more efficient than the reference strategies,
meaning that there are correlations in the real temporal networks that can be
exploited. We find that the navigability for individual nodes is most strongly
correlated with degree and burstiness, i.e., both topological and temporal
structures affect the navigation efficiency.
| [
{
"created": "Sat, 14 Apr 2018 13:16:02 GMT",
"version": "v1"
}
] | 2018-10-17 | [
[
"Lee",
"Sang Hoon",
""
],
[
"Holme",
"Petter",
""
]
] | Navigation on graphs is the problem how an agent walking on the graph can get from a source to a target with limited information about the graph. The information and the way to exploit it can vary. In this paper, we study navigation on temporal networks -- networks where we have explicit information about the time of the interaction, not only who interacts with whom. We contrast a type of greedy navigation -- where agents follow paths that would have worked well in the past -- with two strategies that do not exploit the additional information. We test these on empirical temporal network data sets. The greedy navigation is indeed more efficient than the reference strategies, meaning that there are correlations in the real temporal networks that can be exploited. We find that the navigability for individual nodes is most strongly correlated with degree and burstiness, i.e., both topological and temporal structures affect the navigation efficiency. |
1412.4416 | Amir Toor | Amir A. Toor, Roy T. Sabo, Catherine H. Roberts, Bonny L. Moore,
Salman R. Salman, Allison F. Scalora, May T. Aziz, Ali S. Shubar Ali, Charles
E. Hall, Jeremy Meier, Radhika M. Thorn, Elaine Wang, Shiyu Song, Kristin
Miller, Kathryn Rizzo, William B. Clark, John M. McCarty, Harold M. Chung,
Masoud H. Manjili and Michael C. Neale | Dynamical System Modeling Of Immune Reconstitution Following Allogeneic
Stem Cell Transplantation Identifies Patients At Risk For Adverse Outcomes | 17 pages, 4 tables, 4 Figures, 4 Supplementary figures | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Systems that evolve over time and follow mathematical laws as they do so, are
called dynamical systems. Lymphocyte recovery and clinical outcomes in 41
allograft recipients conditioned using anti-thymocyte globulin (ATG) and 4.5
Gray total-body-irradiation were studied to determine if immune reconstitution
could be described as a dynamical system. Survival, relapse, and graft vs. host
disease (GVHD) were not significantly different in two cohorts of patients
receiving different doses of ATG. However, donor-derived CD3+ (ddCD3) cell
reconstitution was superior in the lower ATG dose cohort, and there were fewer
instances of donor lymphocyte infusion (DLI). Lymphoid recovery was plotted in
each individual over time and demonstrated one of three sigmoid growth
patterns; Pattern A (n=15), had rapid growth with high lymphocyte counts,
pattern B (n=14), slower growth with intermediate recovery and pattern C, poor
lymphocyte reconstitution (n=10). There was a significant association between
lymphocyte recovery patterns and both the rate of change of ddCD3 at day 30
post-SCT and the clinical outcomes. GVHD was observed more frequently with
pattern A; relapse and DLI more so with pattern C, with a consequent survival
advantage in patients with patterns A and B. We conclude that evaluating immune
reconstitution following SCT as a dynamical system may differentiate patients
at risk of adverse outcomes and allow early intervention to modulate that risk.
| [
{
"created": "Sun, 14 Dec 2014 21:57:21 GMT",
"version": "v1"
}
] | 2014-12-16 | [
[
"Toor",
"Amir A.",
""
],
[
"Sabo",
"Roy T.",
""
],
[
"Roberts",
"Catherine H.",
""
],
[
"Moore",
"Bonny L.",
""
],
[
"Salman",
"Salman R.",
""
],
[
"Scalora",
"Allison F.",
""
],
[
"Aziz",
"May T.",
""
],
[
"Ali",
"Ali S. Shubar",
""
],
[
"Hall",
"Charles E.",
""
],
[
"Meier",
"Jeremy",
""
],
[
"Thorn",
"Radhika M.",
""
],
[
"Wang",
"Elaine",
""
],
[
"Song",
"Shiyu",
""
],
[
"Miller",
"Kristin",
""
],
[
"Rizzo",
"Kathryn",
""
],
[
"Clark",
"William B.",
""
],
[
"McCarty",
"John M.",
""
],
[
"Chung",
"Harold M.",
""
],
[
"Manjili",
"Masoud H.",
""
],
[
"Neale",
"Michael C.",
""
]
] | Systems that evolve over time and follow mathematical laws as they do so, are called dynamical systems. Lymphocyte recovery and clinical outcomes in 41 allograft recipients conditioned using anti-thymocyte globulin (ATG) and 4.5 Gray total-body-irradiation were studied to determine if immune reconstitution could be described as a dynamical system. Survival, relapse, and graft vs. host disease (GVHD) were not significantly different in two cohorts of patients receiving different doses of ATG. However, donor-derived CD3+ (ddCD3) cell reconstitution was superior in the lower ATG dose cohort, and there were fewer instances of donor lymphocyte infusion (DLI). Lymphoid recovery was plotted in each individual over time and demonstrated one of three sigmoid growth patterns; Pattern A (n=15), had rapid growth with high lymphocyte counts, pattern B (n=14), slower growth with intermediate recovery and pattern C, poor lymphocyte reconstitution (n=10). There was a significant association between lymphocyte recovery patterns and both the rate of change of ddCD3 at day 30 post-SCT and the clinical outcomes. GVHD was observed more frequently with pattern A; relapse and DLI more so with pattern C, with a consequent survival advantage in patients with patterns A and B. We conclude that evaluating immune reconstitution following SCT as a dynamical system may differentiate patients at risk of adverse outcomes and allow early intervention to modulate that risk. |
1802.00317 | Yukihiro Murakami | Leo van Iersel, Remie Janssen, Mark Jones, Yukihiro Murakami and
Norbert Zeh | Polynomial-Time Algorithms for Phylogenetic Inference Problems involving
duplication and reticulation | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A common problem in phylogenetics is to try to infer a species phylogeny from
gene trees. We consider different variants of this problem. The first variant,
called Unrestricted Minimal Episodes Inference, aims at inferring a species
tree based on a model with speciation and duplication where duplications are
clustered in duplication episodes. The goal is to minimize the number of such
episodes. The second variant, Parental Hybridization, aims at inferring a
species \emph{network} based on a model with speciation and reticulation. The
goal is to minimize the number of reticulation events. It is a variant of the
well-studied Hybridization Number problem with a more generous view on which
gene trees are consistent with a given species network. We show that these
seemingly different problems are in fact closely related and can, surprisingly,
both be solved in polynomial time, using a structure we call "beaded trees".
However, we also show that methods based on these problems have to be used with
care because the optimal species phylogenies always have a restricted form. To
mitigate this problem, we introduce a new variant of Unrestricted Minimal
Episodes Inference that minimizes the duplication episode depth. We prove that
this new variant of the problem can also be solved in polynomial time
| [
{
"created": "Thu, 1 Feb 2018 14:58:15 GMT",
"version": "v1"
},
{
"created": "Fri, 9 Aug 2019 13:11:26 GMT",
"version": "v2"
}
] | 2019-08-12 | [
[
"van Iersel",
"Leo",
""
],
[
"Janssen",
"Remie",
""
],
[
"Jones",
"Mark",
""
],
[
"Murakami",
"Yukihiro",
""
],
[
"Zeh",
"Norbert",
""
]
] | A common problem in phylogenetics is to try to infer a species phylogeny from gene trees. We consider different variants of this problem. The first variant, called Unrestricted Minimal Episodes Inference, aims at inferring a species tree based on a model with speciation and duplication where duplications are clustered in duplication episodes. The goal is to minimize the number of such episodes. The second variant, Parental Hybridization, aims at inferring a species \emph{network} based on a model with speciation and reticulation. The goal is to minimize the number of reticulation events. It is a variant of the well-studied Hybridization Number problem with a more generous view on which gene trees are consistent with a given species network. We show that these seemingly different problems are in fact closely related and can, surprisingly, both be solved in polynomial time, using a structure we call "beaded trees". However, we also show that methods based on these problems have to be used with care because the optimal species phylogenies always have a restricted form. To mitigate this problem, we introduce a new variant of Unrestricted Minimal Episodes Inference that minimizes the duplication episode depth. We prove that this new variant of the problem can also be solved in polynomial time |
1707.08252 | Ewan Colman | Ewan Colman, Kristen Spies and Shweta Bansal | The reachability of contagion in temporal contact networks: how disease
latency can exploit the rhythm of human behavior | 9 Pages, 5 figures | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The symptoms of many infectious diseases influence their host to withdraw
from social activity limiting their own potential to spread. Successful
transmission therefore requires the onset of infectiousness to coincide with a
time when its host is socially active. Since social activity and infectiousness
are both temporal phenomena, we hypothesize that diseases are most pervasive
when these two processes are synchronized. We consider disease dynamics that
incorporate a behavioral response that effectively shortens the infectious
period of the disease. We apply this model to data collected from face-to-face
social interactions and look specifically at how the duration of the latent
period effects the reachability of the disease. We then simulate the spread of
the model disease on the network to test the robustness of our results.
Diseases with latent periods that synchronize with the temporal social behavior
of people, i.e. latent periods of 24 hours or 7 days, correspond to peaks in
the number of individuals who are potentially at risk of becoming infected. The
effect of this synchronization is present for a range of disease models with
realistic parameters. The relationship between the latent period of an
infectious disease and its pervasiveness is non-linear and depends strongly on
the social context in which the disease is spreading.
| [
{
"created": "Tue, 25 Jul 2017 23:55:20 GMT",
"version": "v1"
}
] | 2017-07-27 | [
[
"Colman",
"Ewan",
""
],
[
"Spies",
"Kristen",
""
],
[
"Bansal",
"Shweta",
""
]
] | The symptoms of many infectious diseases influence their host to withdraw from social activity limiting their own potential to spread. Successful transmission therefore requires the onset of infectiousness to coincide with a time when its host is socially active. Since social activity and infectiousness are both temporal phenomena, we hypothesize that diseases are most pervasive when these two processes are synchronized. We consider disease dynamics that incorporate a behavioral response that effectively shortens the infectious period of the disease. We apply this model to data collected from face-to-face social interactions and look specifically at how the duration of the latent period effects the reachability of the disease. We then simulate the spread of the model disease on the network to test the robustness of our results. Diseases with latent periods that synchronize with the temporal social behavior of people, i.e. latent periods of 24 hours or 7 days, correspond to peaks in the number of individuals who are potentially at risk of becoming infected. The effect of this synchronization is present for a range of disease models with realistic parameters. The relationship between the latent period of an infectious disease and its pervasiveness is non-linear and depends strongly on the social context in which the disease is spreading. |
1404.2724 | Branislav Brutovsky | Branislav Brutovsky and Denis Horvath | Towards Inverse Modeling of Intratumoral Heterogeneity | 9 pages, 3 figures | null | null | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Development of resistance limits efficiency of present anticancer therapies
and preventing it remains big challenge in cancer research. It is accepted, at
intuitive level, that the resistance emerges as a consequence of cancer cells
heterogeneity at molecular, genetic and cellular levels. Produced by many
sources, tumor heterogeneity is extremely complex time dependent statistical
characteristics which may be quantified by the measures defined in many
different ways, most of them coming from statistical mechanics. In the paper we
apply Markovian framework to relate population heterogeneity with the
statistics of environment. As, from the evolutionary viewpoint, therapy
corresponds to a purposeful modification of the cells fitness landscape, we
assume that understanding general relation between spatiotemporal statistics of
tumor microenvironment and intratumor heterogeneity enables to conceive the
therapy as the inverse problem and solve it by optimization techniques. To
account for the inherent stochasticity of biological processes at cellular
scale, the generalized distance-based concept was applied to express distances
between probabilistically described cell states and environmental conditions,
respectively.
| [
{
"created": "Thu, 10 Apr 2014 08:11:58 GMT",
"version": "v1"
},
{
"created": "Fri, 9 Jan 2015 11:10:27 GMT",
"version": "v2"
},
{
"created": "Sat, 30 May 2015 06:45:55 GMT",
"version": "v3"
}
] | 2015-06-02 | [
[
"Brutovsky",
"Branislav",
""
],
[
"Horvath",
"Denis",
""
]
] | Development of resistance limits efficiency of present anticancer therapies and preventing it remains big challenge in cancer research. It is accepted, at intuitive level, that the resistance emerges as a consequence of cancer cells heterogeneity at molecular, genetic and cellular levels. Produced by many sources, tumor heterogeneity is extremely complex time dependent statistical characteristics which may be quantified by the measures defined in many different ways, most of them coming from statistical mechanics. In the paper we apply Markovian framework to relate population heterogeneity with the statistics of environment. As, from the evolutionary viewpoint, therapy corresponds to a purposeful modification of the cells fitness landscape, we assume that understanding general relation between spatiotemporal statistics of tumor microenvironment and intratumor heterogeneity enables to conceive the therapy as the inverse problem and solve it by optimization techniques. To account for the inherent stochasticity of biological processes at cellular scale, the generalized distance-based concept was applied to express distances between probabilistically described cell states and environmental conditions, respectively. |
1202.6388 | Jose Fontanari | Jose F. Fontanari, Marie-Claude Bonniot-Cabanac, Michel Cabanac and
Leonid I. Perlovsky | A structural model of emotions of cognitive dissonances | null | Neural Networks 32 (2012) 57-64 | 10.1016/j.neunet.2012.04.007 | null | q-bio.NC cs.HC physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cognitive dissonance is the stress that comes from holding two conflicting
thoughts simultaneously in the mind, usually arising when people are asked to
choose between two detrimental or two beneficial options. In view of the
well-established role of emotions in decision making, here we investigate
whether the conventional structural models used to represent the relationships
among basic emotions, such as the Circumplex model of affect, can describe the
emotions of cognitive dissonance as well. We presented a questionnaire to 34
anonymous participants, where each question described a decision to be made
among two conflicting motivations and asked the participants to rate
analogically the pleasantness and the intensity of the experienced emotion. We
found that the results were compatible with the predictions of the Circumplex
model for basic emotions.
| [
{
"created": "Tue, 28 Feb 2012 21:44:22 GMT",
"version": "v1"
}
] | 2012-06-05 | [
[
"Fontanari",
"Jose F.",
""
],
[
"Bonniot-Cabanac",
"Marie-Claude",
""
],
[
"Cabanac",
"Michel",
""
],
[
"Perlovsky",
"Leonid I.",
""
]
] | Cognitive dissonance is the stress that comes from holding two conflicting thoughts simultaneously in the mind, usually arising when people are asked to choose between two detrimental or two beneficial options. In view of the well-established role of emotions in decision making, here we investigate whether the conventional structural models used to represent the relationships among basic emotions, such as the Circumplex model of affect, can describe the emotions of cognitive dissonance as well. We presented a questionnaire to 34 anonymous participants, where each question described a decision to be made among two conflicting motivations and asked the participants to rate analogically the pleasantness and the intensity of the experienced emotion. We found that the results were compatible with the predictions of the Circumplex model for basic emotions. |
1708.08672 | Ernest Montbrio | Jose M. Esnaola-Acebes, Alex Roxin, Daniele Avitabile, Ernest
Montbri\'o | Synchrony-induced modes of oscillation of a neural field model | null | Phys. Rev. E 96, 052407 (2017) | 10.1103/PhysRevE.96.052407 | null | q-bio.NC nlin.CD nlin.PS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the modes of oscillation of heterogeneous ring-networks of
quadratic integrate-and-fire neurons with non-local, space-dependent coupling.
Perturbations of the equilibrium state with a particular wave number produce
transient standing waves with a specific frequency, analogous to those in a
tense string. In the neuronal network, the equilibrium corresponds to a
spatially homogeneous, asynchronous state. Perturbations of this state excite
the network's oscillatory modes, which reflect the interplay of episodes of
synchronous spiking with the excitatory-inhibitory spatial interactions. In the
thermodynamic limit, an exact low-dimensional neural field model describing the
macroscopic dynamics of the network is derived. This allows us to obtain
formulas for the Turing eigenvalues of the spatially-homogeneous state, and
hence to obtain its stability boundary. We find that the frequency of each
Turing mode depends on the corresponding Fourier coefficient of the synaptic
pattern of connectivity. The decay rate instead, is identical for all
oscillation modes as a consequence of the heterogeneity-induced
desynchronization of the neurons. Finally, we numerically compute the spectrum
of spatially-inhomogeneous solutions branching from the Turing bifurcation,
showing that similar oscillatory modes operate in neural bump states, and are
maintained away from onset.
| [
{
"created": "Tue, 29 Aug 2017 09:59:14 GMT",
"version": "v1"
}
] | 2017-11-15 | [
[
"Esnaola-Acebes",
"Jose M.",
""
],
[
"Roxin",
"Alex",
""
],
[
"Avitabile",
"Daniele",
""
],
[
"Montbrió",
"Ernest",
""
]
] | We investigate the modes of oscillation of heterogeneous ring-networks of quadratic integrate-and-fire neurons with non-local, space-dependent coupling. Perturbations of the equilibrium state with a particular wave number produce transient standing waves with a specific frequency, analogous to those in a tense string. In the neuronal network, the equilibrium corresponds to a spatially homogeneous, asynchronous state. Perturbations of this state excite the network's oscillatory modes, which reflect the interplay of episodes of synchronous spiking with the excitatory-inhibitory spatial interactions. In the thermodynamic limit, an exact low-dimensional neural field model describing the macroscopic dynamics of the network is derived. This allows us to obtain formulas for the Turing eigenvalues of the spatially-homogeneous state, and hence to obtain its stability boundary. We find that the frequency of each Turing mode depends on the corresponding Fourier coefficient of the synaptic pattern of connectivity. The decay rate instead, is identical for all oscillation modes as a consequence of the heterogeneity-induced desynchronization of the neurons. Finally, we numerically compute the spectrum of spatially-inhomogeneous solutions branching from the Turing bifurcation, showing that similar oscillatory modes operate in neural bump states, and are maintained away from onset. |
2301.06351 | Willy Kuo | Willy Kuo, Ngoc An Le, Bernhard Spingler, Georg Schulz, Bert M\"uller,
Vartan Kurtcuoglu | Tomographic imaging of microvasculature with a purpose-designed,
polymeric X-ray contrast agent | 10 pages, 6 figures | Proceedings of SPIE 12242 (2022) 1224205 | 10.1117/12.2634303 | null | q-bio.TO physics.med-ph | http://creativecommons.org/licenses/by/4.0/ | Imaging of microvasculature is primarily performed with X-ray contrast
agents, owing to the wide availability of absorption-contrast laboratory source
microCT compared to phase contrast capable devices. Standard commercial
contrast agents used in angiography are not suitable for high-resolution
imaging ex vivo, however, as they are small molecular compounds capable of
diffusing through blood vessel walls within minutes. Large nanoparticle-based
blood pool contrast agents on the other hand exhibit problems with aggregation,
resulting in clogging in the smallest blood vessels. Injection with solidifying
plastic resins has, therefore, remained the gold standard for microvascular
imaging, despite the considerable amount of training and optimization needed to
properly perfuse the viscous compounds. Even with optimization, frequent gas
and water inclusions commonly result in interrupted vessel segments. This lack
of suitable compounds has led us to develop the polymeric, cross-linkable X-ray
contrast agent XlinCA. As a water-soluble organic molecule, aggregation and
inclusions are inherently avoided. High molecular weight allows it to be
retained even in the highly fenestrated vasculature of the kidney filtration
system. It can be covalently crosslinked using the same aldehydes used in
tissue fixation protocols, leading to stable and permanent contrast. These
properties allowed us to image whole mice and individual organs in 6 to
12-month-old C57BL/6J mice without requiring lengthy optimizations of injection
rates and pressures, while at the same time achieving greatly improved filling
of the vasculature compared to resin-based vascular casting. This work aims at
illuminating the rationales, processes and challenges involved in creating this
recently developed contrast agent.
| [
{
"created": "Mon, 16 Jan 2023 10:49:41 GMT",
"version": "v1"
}
] | 2023-01-18 | [
[
"Kuo",
"Willy",
""
],
[
"Le",
"Ngoc An",
""
],
[
"Spingler",
"Bernhard",
""
],
[
"Schulz",
"Georg",
""
],
[
"Müller",
"Bert",
""
],
[
"Kurtcuoglu",
"Vartan",
""
]
] | Imaging of microvasculature is primarily performed with X-ray contrast agents, owing to the wide availability of absorption-contrast laboratory source microCT compared to phase contrast capable devices. Standard commercial contrast agents used in angiography are not suitable for high-resolution imaging ex vivo, however, as they are small molecular compounds capable of diffusing through blood vessel walls within minutes. Large nanoparticle-based blood pool contrast agents on the other hand exhibit problems with aggregation, resulting in clogging in the smallest blood vessels. Injection with solidifying plastic resins has, therefore, remained the gold standard for microvascular imaging, despite the considerable amount of training and optimization needed to properly perfuse the viscous compounds. Even with optimization, frequent gas and water inclusions commonly result in interrupted vessel segments. This lack of suitable compounds has led us to develop the polymeric, cross-linkable X-ray contrast agent XlinCA. As a water-soluble organic molecule, aggregation and inclusions are inherently avoided. High molecular weight allows it to be retained even in the highly fenestrated vasculature of the kidney filtration system. It can be covalently crosslinked using the same aldehydes used in tissue fixation protocols, leading to stable and permanent contrast. These properties allowed us to image whole mice and individual organs in 6 to 12-month-old C57BL/6J mice without requiring lengthy optimizations of injection rates and pressures, while at the same time achieving greatly improved filling of the vasculature compared to resin-based vascular casting. This work aims at illuminating the rationales, processes and challenges involved in creating this recently developed contrast agent. |
q-bio/0507016 | Luciano da Fontoura Costa | Luciano da Fontoura Costa, Ruth Caldeira de Melo, Ester da Silva,
Audrey Borghi-Silva and Aparecida Maria Catai | Spectral Detrended Fluctuation Analysis and Its Application to Heart
Rate Variability Assessment | 10 pages, 4 figures | null | null | null | q-bio.QM cond-mat.dis-nn q-bio.TO | null | Detrend fluctuation analysis (DFA) has become a choice method for effective
analysis of a broad variety of nonstationary signals. We show in the present
article that, provided the nonstationary fluctuations occur at a large enough
time scale, an alternative approach can be obtained by using the Fourier series
of the signal. More specifically, signal reconstructions considering Fourier
series with increasing number of higher spectral components are subtracted from
the signal, while the dispersion of such a difference is calculated. The slope
of the loglog representation of the dispersions in terms of the time scale
(reciprocal of the frequency) is calculated and used for the characterization
of the signal. The detrend action in this methodology is performed by the early
incorporation of the low frequency spectral components in the signal
representation. The application of the spectral DFA to the analysis of heart
rate variability data has yielded results which are similar to those obtained
by traditional DFA. Because of the direct relationship with the spectral
content of the analyzed signal, the spectral DFA may be used as a complementary
resource for characterization and analysis of some types of nonstationary
signals.
| [
{
"created": "Mon, 11 Jul 2005 00:27:19 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Costa",
"Luciano da Fontoura",
""
],
[
"de Melo",
"Ruth Caldeira",
""
],
[
"da Silva",
"Ester",
""
],
[
"Borghi-Silva",
"Audrey",
""
],
[
"Catai",
"Aparecida Maria",
""
]
] | Detrend fluctuation analysis (DFA) has become a choice method for effective analysis of a broad variety of nonstationary signals. We show in the present article that, provided the nonstationary fluctuations occur at a large enough time scale, an alternative approach can be obtained by using the Fourier series of the signal. More specifically, signal reconstructions considering Fourier series with increasing number of higher spectral components are subtracted from the signal, while the dispersion of such a difference is calculated. The slope of the loglog representation of the dispersions in terms of the time scale (reciprocal of the frequency) is calculated and used for the characterization of the signal. The detrend action in this methodology is performed by the early incorporation of the low frequency spectral components in the signal representation. The application of the spectral DFA to the analysis of heart rate variability data has yielded results which are similar to those obtained by traditional DFA. Because of the direct relationship with the spectral content of the analyzed signal, the spectral DFA may be used as a complementary resource for characterization and analysis of some types of nonstationary signals. |
2006.12618 | Aiying Zhang | Aiying Zhang, Gemeng Zhang, Biao Cai, Tony W. Wilson, Julia M.
Stephen, Vince D. Calhoun and Yu-Ping Wang | A Bayesian incorporated linear non-Gaussian acyclic model for multiple
directed graph estimation to study brain emotion circuit development in
adolescence | null | null | null | null | q-bio.NC cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Emotion perception is essential to affective and cognitive development which
involves distributed brain circuits. The ability of emotion identification
begins in infancy and continues to develop throughout childhood and
adolescence. Understanding the development of brain's emotion circuitry may
help us explain the emotional changes observed during adolescence. Our previous
study delineated the trajectory of brain functional connectivity (FC) from late
childhood to early adulthood during emotion identification tasks. In this work,
we endeavour to deepen our understanding from association to causation. We
proposed a Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM),
which incorporated our previous association model into the prior estimation
pipeline. In particular, it can jointly estimate multiple directed acyclic
graphs (DAGs) for multiple age groups at different developmental stages.
Simulation results indicated more stable and accurate performance over various
settings, especially when the sample size was small (high-dimensional cases).
We then applied to the analysis of real data from the Philadelphia
Neurodevelopmental Cohort (PNC). This included 855 individuals aged 8-22 years
who were divided into five different adolescent stages. Our network analysis
revealed the development of emotion-related intra- and inter- modular
connectivity and pinpointed several emotion-related hubs. We further
categorized the hubs into two types: in-hubs and out-hubs, as the center of
receiving and distributing information. Several unique developmental hub
structures and group-specific patterns were also discovered. Our findings help
provide a causal understanding of emotion development in the human brain.
| [
{
"created": "Tue, 16 Jun 2020 21:35:12 GMT",
"version": "v1"
}
] | 2020-06-24 | [
[
"Zhang",
"Aiying",
""
],
[
"Zhang",
"Gemeng",
""
],
[
"Cai",
"Biao",
""
],
[
"Wilson",
"Tony W.",
""
],
[
"Stephen",
"Julia M.",
""
],
[
"Calhoun",
"Vince D.",
""
],
[
"Wang",
"Yu-Ping",
""
]
] | Emotion perception is essential to affective and cognitive development which involves distributed brain circuits. The ability of emotion identification begins in infancy and continues to develop throughout childhood and adolescence. Understanding the development of brain's emotion circuitry may help us explain the emotional changes observed during adolescence. Our previous study delineated the trajectory of brain functional connectivity (FC) from late childhood to early adulthood during emotion identification tasks. In this work, we endeavour to deepen our understanding from association to causation. We proposed a Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM), which incorporated our previous association model into the prior estimation pipeline. In particular, it can jointly estimate multiple directed acyclic graphs (DAGs) for multiple age groups at different developmental stages. Simulation results indicated more stable and accurate performance over various settings, especially when the sample size was small (high-dimensional cases). We then applied to the analysis of real data from the Philadelphia Neurodevelopmental Cohort (PNC). This included 855 individuals aged 8-22 years who were divided into five different adolescent stages. Our network analysis revealed the development of emotion-related intra- and inter- modular connectivity and pinpointed several emotion-related hubs. We further categorized the hubs into two types: in-hubs and out-hubs, as the center of receiving and distributing information. Several unique developmental hub structures and group-specific patterns were also discovered. Our findings help provide a causal understanding of emotion development in the human brain. |
0909.2594 | J. M. Schwarz | K.-C. Lee, A. Gopinathan, and J. M. Schwarz | Modeling the formation of in vitro filopodia | 22 pages, 16 figures | null | null | null | q-bio.SC cond-mat.soft q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Filopodia are bundles of actin filaments that extend out ahead of the leading
edge of a crawling cell to probe its upcoming environment. {\it In vitro}
experiments [D. Vignjevic {\it et al.}, J. Cell Biol. {\bf 160}, 951 (2003)]
have determined the minimal ingredients required for the formation of filopodia
from the dendritic-like morphology of the leading edge. We model these
experiments using kinetic aggregation equations for the density of growing
bundle tips. In mean field, we determine the bundle size distribution to be
broad for bundle sizes smaller than a characteristic bundle size above which
the distribution decays exponentially. Two-dimensional simulations
incorporating both bundling and cross-linking measure a bundle size
distribution that agrees qualitatively with mean field. The simulations also
demonstrate a nonmonotonicity in the radial extent of the dendritic region as a
function of capping protein concentration, as was observed in experiments, due
to the interplay between percolation and the ratcheting of growing filaments
off a spherical obstacle.
| [
{
"created": "Mon, 14 Sep 2009 17:09:46 GMT",
"version": "v1"
},
{
"created": "Thu, 13 May 2010 19:59:16 GMT",
"version": "v2"
}
] | 2010-05-14 | [
[
"Lee",
"K. -C.",
""
],
[
"Gopinathan",
"A.",
""
],
[
"Schwarz",
"J. M.",
""
]
] | Filopodia are bundles of actin filaments that extend out ahead of the leading edge of a crawling cell to probe its upcoming environment. {\it In vitro} experiments [D. Vignjevic {\it et al.}, J. Cell Biol. {\bf 160}, 951 (2003)] have determined the minimal ingredients required for the formation of filopodia from the dendritic-like morphology of the leading edge. We model these experiments using kinetic aggregation equations for the density of growing bundle tips. In mean field, we determine the bundle size distribution to be broad for bundle sizes smaller than a characteristic bundle size above which the distribution decays exponentially. Two-dimensional simulations incorporating both bundling and cross-linking measure a bundle size distribution that agrees qualitatively with mean field. The simulations also demonstrate a nonmonotonicity in the radial extent of the dendritic region as a function of capping protein concentration, as was observed in experiments, due to the interplay between percolation and the ratcheting of growing filaments off a spherical obstacle. |
1107.2521 | Valmir Barbosa | Andre Nathan, Valmir C. Barbosa | Network algorithmics and the emergence of synchronization in cortical
models | Presentation improved | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When brain signals are recorded in an electroencephalogram or some similar
large-scale record of brain activity, oscillatory patterns are typically
observed that are thought to reflect the aggregate electrical activity of the
underlying neuronal ensemble. Although it now seems that such patterns
participate in feedback loops both temporally with the neurons' spikes and
spatially with other brain regions, the mechanisms that might explain the
existence of such loops have remained essentially unknown. Here we present a
theoretical study of these issues on a cortical model we introduced earlier
[Nathan A, Barbosa VC (2010) Network algorithmics and the emergence of the
cortical synaptic-weight distribution. Phys Rev E 81: 021916]. We start with
the definition of two synchronization measures that aim to capture the
synchronization possibilities offered by the model regarding both the overall
spiking activity of the neurons and the spiking activity that causes the
immediate firing of the postsynaptic neurons. We present computational results
on our cortical model, on a model that is random in the Erd\H{o}s-R\'enyi
sense, and on a structurally deterministic model. We have found that the
algorithmic component underlying our cortical model ultimately provides,
through the two synchronization measures, a strong quantitative basis for the
emergence of both types of synchronization in all cases. This, in turn, may
explain the rise both of temporal feedback loops in the neurons' combined
electrical activity and of spatial feedback loops as brain regions that are
spatially separated engage in rhythmic behavior.
| [
{
"created": "Wed, 13 Jul 2011 10:59:42 GMT",
"version": "v1"
},
{
"created": "Fri, 22 Jul 2011 13:31:09 GMT",
"version": "v2"
},
{
"created": "Sat, 1 Jun 2013 14:49:02 GMT",
"version": "v3"
}
] | 2013-06-04 | [
[
"Nathan",
"Andre",
""
],
[
"Barbosa",
"Valmir C.",
""
]
] | When brain signals are recorded in an electroencephalogram or some similar large-scale record of brain activity, oscillatory patterns are typically observed that are thought to reflect the aggregate electrical activity of the underlying neuronal ensemble. Although it now seems that such patterns participate in feedback loops both temporally with the neurons' spikes and spatially with other brain regions, the mechanisms that might explain the existence of such loops have remained essentially unknown. Here we present a theoretical study of these issues on a cortical model we introduced earlier [Nathan A, Barbosa VC (2010) Network algorithmics and the emergence of the cortical synaptic-weight distribution. Phys Rev E 81: 021916]. We start with the definition of two synchronization measures that aim to capture the synchronization possibilities offered by the model regarding both the overall spiking activity of the neurons and the spiking activity that causes the immediate firing of the postsynaptic neurons. We present computational results on our cortical model, on a model that is random in the Erd\H{o}s-R\'enyi sense, and on a structurally deterministic model. We have found that the algorithmic component underlying our cortical model ultimately provides, through the two synchronization measures, a strong quantitative basis for the emergence of both types of synchronization in all cases. This, in turn, may explain the rise both of temporal feedback loops in the neurons' combined electrical activity and of spatial feedback loops as brain regions that are spatially separated engage in rhythmic behavior. |
1710.01128 | Saurabh Shanu Mr. | Saurabh Shanu, Sudeepto Bhattacharya | A Computational Approach for Designing Tiger Corridors in India | 12 pages, 5 figures, 6 tables, NGCT conference 2017 | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wildlife corridors are components of landscapes, which facilitate the
movement of organisms and processes between intact habitat areas, and thus
provide connectivity between the habitats within the landscapes. Corridors are
thus regions within a given landscape that connect fragmented habitat patches
within the landscape. The major concern of designing corridors as a
conservation strategy is primarily to counter, and to the extent possible,
mitigate the effects of habitat fragmentation and loss on the biodiversity of
the landscape, as well as support continuance of land use for essential local
and global economic activities in the region of reference. In this paper, we
use game theory, graph theory, membership functions and chain code algorithm to
model and design a set of wildlife corridors with tiger (Panthera tigris
tigris) as the focal species. We identify the parameters which would affect the
tiger population in a landscape complex and using the presence of these
identified parameters construct a graph using the habitat patches supporting
tiger presence in the landscape complex as vertices and the possible paths
between them as edges. The passage of tigers through the possible paths have
been modelled as an Assurance game, with tigers as an individual player. The
game is played recursively as the tiger passes through each grid considered for
the model. The iteration causes the tiger to choose the most suitable path
signifying the emergence of adaptability. As a formal explanation of the game,
we model this interaction of tiger with the parameters as deterministic finite
automata, whose transition function is obtained by the game payoff.
| [
{
"created": "Mon, 2 Oct 2017 14:15:32 GMT",
"version": "v1"
}
] | 2017-10-04 | [
[
"Shanu",
"Saurabh",
""
],
[
"Bhattacharya",
"Sudeepto",
""
]
] | Wildlife corridors are components of landscapes, which facilitate the movement of organisms and processes between intact habitat areas, and thus provide connectivity between the habitats within the landscapes. Corridors are thus regions within a given landscape that connect fragmented habitat patches within the landscape. The major concern of designing corridors as a conservation strategy is primarily to counter, and to the extent possible, mitigate the effects of habitat fragmentation and loss on the biodiversity of the landscape, as well as support continuance of land use for essential local and global economic activities in the region of reference. In this paper, we use game theory, graph theory, membership functions and chain code algorithm to model and design a set of wildlife corridors with tiger (Panthera tigris tigris) as the focal species. We identify the parameters which would affect the tiger population in a landscape complex and using the presence of these identified parameters construct a graph using the habitat patches supporting tiger presence in the landscape complex as vertices and the possible paths between them as edges. The passage of tigers through the possible paths have been modelled as an Assurance game, with tigers as an individual player. The game is played recursively as the tiger passes through each grid considered for the model. The iteration causes the tiger to choose the most suitable path signifying the emergence of adaptability. As a formal explanation of the game, we model this interaction of tiger with the parameters as deterministic finite automata, whose transition function is obtained by the game payoff. |
2104.03080 | Harvey Devereux | Harvey L. Devereux, Colin R. Twomey, Matthew S. Turner, Shashi
Thutupalli | Whirligig Beetles as Corralled Active Brownian Particles | To be published in the Journal of the Royal Society Interface on 14
April 2021 - Accepted 23 March 2021 | J. R. Soc. Interface, 2021 | 10.1098/rsif.2021.0114 | null | q-bio.QM cond-mat.soft cond-mat.stat-mech physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | We study the collective dynamics of groups of whirligig beetles Dineutus
discolor (Coleoptera: Gyrinidae) swimming freely on the surface of water. We
extract individual trajectories for each beetle, including positions and
orientations, and use this to discover (i) a density dependent speed scaling
like $v\sim\rho^{-\nu}$ with $\nu\approx0.4$ over two orders of magnitude in
density (ii) an inertial delay for velocity alignment of $\sim 13$ ms and (iii)
coexisting high and low density phases, consistent with motility induced phase
separation (MIPS). We modify a standard active brownian particle (ABP) model to
a Corralled ABP (CABP) model that functions in open space by incorporating a
density-dependent reorientation of the beetles, towards the cluster. We use our
new model to test our hypothesis that a MIPS (or a MIPS like effect) can
explain the co-occurrence of high and low density phases we see in our data.
The fitted model then successfully recovers a MIPS-like condensed phase for
$N=200$ and the absence of such a phase for smaller group sizes $N=50,100$.
| [
{
"created": "Wed, 7 Apr 2021 12:03:38 GMT",
"version": "v1"
}
] | 2021-04-15 | [
[
"Devereux",
"Harvey L.",
""
],
[
"Twomey",
"Colin R.",
""
],
[
"Turner",
"Matthew S.",
""
],
[
"Thutupalli",
"Shashi",
""
]
] | We study the collective dynamics of groups of whirligig beetles Dineutus discolor (Coleoptera: Gyrinidae) swimming freely on the surface of water. We extract individual trajectories for each beetle, including positions and orientations, and use this to discover (i) a density dependent speed scaling like $v\sim\rho^{-\nu}$ with $\nu\approx0.4$ over two orders of magnitude in density (ii) an inertial delay for velocity alignment of $\sim 13$ ms and (iii) coexisting high and low density phases, consistent with motility induced phase separation (MIPS). We modify a standard active brownian particle (ABP) model to a Corralled ABP (CABP) model that functions in open space by incorporating a density-dependent reorientation of the beetles, towards the cluster. We use our new model to test our hypothesis that a MIPS (or a MIPS like effect) can explain the co-occurrence of high and low density phases we see in our data. The fitted model then successfully recovers a MIPS-like condensed phase for $N=200$ and the absence of such a phase for smaller group sizes $N=50,100$. |
1012.3430 | Alfred Bennun Dr. | Alfred Bennun | Characterization of the norepinephrine-activation of adenylate cyclase
suggests a role in memory affirmation pathways. Overexposure to epinephrine
inactivates adenylate-cyclase,a causal pathway for stress-pathologies | 14 pages,4 figures,3 tables | BioSystems 100 (2010) p. 87-93 | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Incubation with noradrenaline (norepinephrine) of isolated membranes of rat's
brain corpus striatum and cortex, showed that ionic-magnesium (Mg2+) is
required for the neurotransmitter activatory response of Adenylate Cyclase [ATP
pyrophosphate-lyase (cyclizing), (EC 4.6.1.1)], AC.An Mg2+-dependent response
to the activatory effects of adrenaline, and subsequent inhibition by calcium,
suggest capability for a turnover, associated with cyclic changes in membrane
potential and participation in a short term-memory pathway.In the cell, the
neurotransmitter by activating AC generates intracellular cyclic AMP. Calcium
entrance in the cell inhibits the enzyme. The increment of cyclic AMP activates
kinaseA and their protein phosphorylating activity, allowing a long term memory
pathway. Hence, consolidating neuronal circuits, related to emotional learning
and memory affirmation.The activatory effect relates to an enzyme-noradrenaline
complex which may participate on the physiology of the fight or flight
response, by prolonged exposure. However, the persistence of an unstable enzyme
complex turns the enzyme inactive. Effect concordant, with the observation that
prolonged exposure to adrenaline, participate in the etiology of stress
triggered pathologies. At the cell physiological level AC responsiveness to
hormones could be modulated by the concentration of Chelating Metabolites.
These ones produce the release of free ATP4-, a negative modulator of AC and
the Mg2+ activated insulin receptor tyrosine kinase (IRTK). Thus, allowing an
integration of the hormonal response of both enzymes by ionic controls. This
effect could supersede the metabolic feedback control by energy-charge.
Accordingly, maximum hormonal response of both enzymes, to high Mg2+ and low
free ATP4-, allows a correlation with the known effects of low caloric intake
increasing average life expectancy.
| [
{
"created": "Wed, 15 Dec 2010 19:25:54 GMT",
"version": "v1"
}
] | 2010-12-16 | [
[
"Bennun",
"Alfred",
""
]
] | Incubation with noradrenaline (norepinephrine) of isolated membranes of rat's brain corpus striatum and cortex, showed that ionic-magnesium (Mg2+) is required for the neurotransmitter activatory response of Adenylate Cyclase [ATP pyrophosphate-lyase (cyclizing), (EC 4.6.1.1)], AC.An Mg2+-dependent response to the activatory effects of adrenaline, and subsequent inhibition by calcium, suggest capability for a turnover, associated with cyclic changes in membrane potential and participation in a short term-memory pathway.In the cell, the neurotransmitter by activating AC generates intracellular cyclic AMP. Calcium entrance in the cell inhibits the enzyme. The increment of cyclic AMP activates kinaseA and their protein phosphorylating activity, allowing a long term memory pathway. Hence, consolidating neuronal circuits, related to emotional learning and memory affirmation.The activatory effect relates to an enzyme-noradrenaline complex which may participate on the physiology of the fight or flight response, by prolonged exposure. However, the persistence of an unstable enzyme complex turns the enzyme inactive. Effect concordant, with the observation that prolonged exposure to adrenaline, participate in the etiology of stress triggered pathologies. At the cell physiological level AC responsiveness to hormones could be modulated by the concentration of Chelating Metabolites. These ones produce the release of free ATP4-, a negative modulator of AC and the Mg2+ activated insulin receptor tyrosine kinase (IRTK). Thus, allowing an integration of the hormonal response of both enzymes by ionic controls. This effect could supersede the metabolic feedback control by energy-charge. Accordingly, maximum hormonal response of both enzymes, to high Mg2+ and low free ATP4-, allows a correlation with the known effects of low caloric intake increasing average life expectancy. |
1404.2917 | Christopher Chatham H | Christopher H. Chatham and David Badre | How to test cognitive theory with fMRI | 40 pages, 6 figures, draft chapter for forthcoming book | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The objective of this chapter is to provide a guide to using functional
magnetic resonance imaging (fMRI) to inform cognitive theory. This is, of
course, a daunting task, as the premise itself - that fMRI data can inform
cognitive theory - is still actively debated. Below, we touch on this debate as
a means of framing our guide. In particular, we argue that cognitive theories
can be constrained by neuroscientific data, including that offered by fMRI, but
to do so requires embellishing the cognitive theory so that it can make
predictions for neuroscience; much the same as how testing a cognitive theory
using behavior requires embellishing that theory to make experimentally
realizable behavioral predictions (i.e., the process of generating operational
definitions). Moreover, recent years have seen the development of several new
approaches that allow fMRI to better test neurally-embellished models. Along
with a review of several ways of testing neurally-embellished cognitive theory
using fMRI, we also consider the inferential challenges that can accompany
these approaches. Readers of this chapter should gain an understanding of both
of the potential power and the challenges associated with fMRI as a cognitive
neuroscience methodology.
| [
{
"created": "Thu, 10 Apr 2014 19:41:14 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Jul 2015 11:17:15 GMT",
"version": "v2"
}
] | 2015-07-08 | [
[
"Chatham",
"Christopher H.",
""
],
[
"Badre",
"David",
""
]
] | The objective of this chapter is to provide a guide to using functional magnetic resonance imaging (fMRI) to inform cognitive theory. This is, of course, a daunting task, as the premise itself - that fMRI data can inform cognitive theory - is still actively debated. Below, we touch on this debate as a means of framing our guide. In particular, we argue that cognitive theories can be constrained by neuroscientific data, including that offered by fMRI, but to do so requires embellishing the cognitive theory so that it can make predictions for neuroscience; much the same as how testing a cognitive theory using behavior requires embellishing that theory to make experimentally realizable behavioral predictions (i.e., the process of generating operational definitions). Moreover, recent years have seen the development of several new approaches that allow fMRI to better test neurally-embellished models. Along with a review of several ways of testing neurally-embellished cognitive theory using fMRI, we also consider the inferential challenges that can accompany these approaches. Readers of this chapter should gain an understanding of both of the potential power and the challenges associated with fMRI as a cognitive neuroscience methodology. |
1807.11862 | Braslav Rabar | Braslav Rabar, Strahil Ristov, Maja Zagor\v{s}\v{c}ak, Martin
Rosenzweig and Pavle Goldstein | IGLOSS: iterative gapless local similarity search | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Searching for local sequence patterns is one of the basic tasks in
bioinformatics. Sequence patterns might have structural, functional or some
other relevance, and numerous methods have been developed to detect and analyze
them. These methods often depend on the wealth of information already
collected. The explosion in the number of newly available sequences calls for
novel methods to explore local sequence similarity. We have developed a high
sensitivity web-based iterative local similarity scanner, that finds sequence
patterns similar to a submitted query.
| [
{
"created": "Tue, 31 Jul 2018 15:15:32 GMT",
"version": "v1"
}
] | 2018-08-01 | [
[
"Rabar",
"Braslav",
""
],
[
"Ristov",
"Strahil",
""
],
[
"Zagorščak",
"Maja",
""
],
[
"Rosenzweig",
"Martin",
""
],
[
"Goldstein",
"Pavle",
""
]
] | Searching for local sequence patterns is one of the basic tasks in bioinformatics. Sequence patterns might have structural, functional or some other relevance, and numerous methods have been developed to detect and analyze them. These methods often depend on the wealth of information already collected. The explosion in the number of newly available sequences calls for novel methods to explore local sequence similarity. We have developed a high sensitivity web-based iterative local similarity scanner, that finds sequence patterns similar to a submitted query. |
1807.00082 | Thomas Dean | Thomas Dean, Maurice Chiang, Marcus Gomez, Nate Gruver, Yousef Hindy,
Michelle Lam, Peter Lu, Sophia Sanchez, Rohun Saxena, Michael Smith, Lucy
Wang, Catherine Wong | Amanuensis: The Programmer's Apprentice | null | null | null | null | q-bio.NC cs.AI | http://creativecommons.org/licenses/by/4.0/ | This document provides an overview of the material covered in a course taught
at Stanford in the spring quarter of 2018. The course draws upon insight from
cognitive and systems neuroscience to implement hybrid connectionist and
symbolic reasoning systems that leverage and extend the state of the art in
machine learning by integrating human and machine intelligence. As a concrete
example we focus on digital assistants that learn from continuous dialog with
an expert software engineer while providing initial value as powerful
analytical, computational and mathematical savants. Over time these savants
learn cognitive strategies (domain-relevant problem solving skills) and develop
intuitions (heuristics and the experience necessary for applying them) by
learning from their expert associates. By doing so these savants elevate their
innate analytical skills allowing them to partner on an equal footing as
versatile collaborators - effectively serving as cognitive extensions and
digital prostheses, thereby amplifying and emulating their human partner's
conceptually-flexible thinking patterns and enabling improved access to and
control over powerful computing resources.
| [
{
"created": "Fri, 29 Jun 2018 22:59:08 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Nov 2018 13:33:18 GMT",
"version": "v2"
}
] | 2018-11-09 | [
[
"Dean",
"Thomas",
""
],
[
"Chiang",
"Maurice",
""
],
[
"Gomez",
"Marcus",
""
],
[
"Gruver",
"Nate",
""
],
[
"Hindy",
"Yousef",
""
],
[
"Lam",
"Michelle",
""
],
[
"Lu",
"Peter",
""
],
[
"Sanchez",
"Sophia",
""
],
[
"Saxena",
"Rohun",
""
],
[
"Smith",
"Michael",
""
],
[
"Wang",
"Lucy",
""
],
[
"Wong",
"Catherine",
""
]
] | This document provides an overview of the material covered in a course taught at Stanford in the spring quarter of 2018. The course draws upon insight from cognitive and systems neuroscience to implement hybrid connectionist and symbolic reasoning systems that leverage and extend the state of the art in machine learning by integrating human and machine intelligence. As a concrete example we focus on digital assistants that learn from continuous dialog with an expert software engineer while providing initial value as powerful analytical, computational and mathematical savants. Over time these savants learn cognitive strategies (domain-relevant problem solving skills) and develop intuitions (heuristics and the experience necessary for applying them) by learning from their expert associates. By doing so these savants elevate their innate analytical skills allowing them to partner on an equal footing as versatile collaborators - effectively serving as cognitive extensions and digital prostheses, thereby amplifying and emulating their human partner's conceptually-flexible thinking patterns and enabling improved access to and control over powerful computing resources. |
2008.09000 | Yuemin Bian | Yuemin Bian and Xiang-Qun Xie | Generative chemistry: drug discovery with deep learning generative
models | 29 pages, 4 tables, 5 figures | null | 10.1007/s00894-021-04674-8 | null | q-bio.BM cs.LG q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The de novo design of molecular structures using deep learning generative
models introduces an encouraging solution to drug discovery in the face of the
continuously increased cost of new drug development. From the generation of
original texts, images, and videos, to the scratching of novel molecular
structures, the incredible creativity of deep learning generative models
surprised us about the height machine intelligence can achieve. The purpose of
this paper is to review the latest advances in generative chemistry which
relies on generative modeling to expedite the drug discovery process. This
review starts with a brief history of artificial intelligence in drug discovery
to outline this emerging paradigm. Commonly used chemical databases, molecular
representations, and tools in cheminformatics and machine learning are covered
as the infrastructure for the generative chemistry. The detailed discussions on
utilizing cutting-edge generative architectures, including recurrent neural
network, variational autoencoder, adversarial autoencoder, and generative
adversarial network for compound generation are focused. Challenges and future
perspectives follow.
| [
{
"created": "Thu, 20 Aug 2020 14:38:21 GMT",
"version": "v1"
}
] | 2021-02-08 | [
[
"Bian",
"Yuemin",
""
],
[
"Xie",
"Xiang-Qun",
""
]
] | The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures, the incredible creativity of deep learning generative models surprised us about the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for the generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow. |
q-bio/0606025 | Kavita Jain | Kavita Jain and Joachim Krug | Deterministic and stochastic regimes of asexual evolution on rugged
fitness landscapes | Revised version, to appear in Genetics. Note on the role of selection
in defining d_eff added; new figure 4 included | Genetics 175, 1275 (2007) | null | null | q-bio.PE cond-mat.stat-mech | null | We study the adaptation dynamics of an initially maladapted asexual
population with genotypes represented by binary sequences of length $L$. The
population evolves in a maximally rugged fitness landscape with a large number
of local optima. We find that whether the evolutionary trajectory is
deterministic or stochastic depends on the effective mutational distance
$d_{\mathrm{eff}}$ upto which the population can spread in genotype space. For
$d_{\mathrm{eff}}=L$, the deterministic quasispecies theory operates while for
$d_{\mathrm{eff}} < 1$, the evolution is completely stochastic. Between these
two limiting cases, the dynamics are described by a local quasispecies theory
below a crossover time $T_{\times}$ while above $T_{\times}$, the population
gets trapped at a local fitness peak and manages to find a better peak either
via stochastic tunneling or double mutations. In the stochastic regime
$d_\mathrm{eff} < 1$, we identify two subregimes associated with clonal
interference and uphill adaptive walks, respectively. We argue that our
findings are relevant to the interepretation of evolution experiments with
microbial populations.
| [
{
"created": "Mon, 19 Jun 2006 17:20:05 GMT",
"version": "v1"
},
{
"created": "Thu, 30 Nov 2006 09:51:32 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Jain",
"Kavita",
""
],
[
"Krug",
"Joachim",
""
]
] | We study the adaptation dynamics of an initially maladapted asexual population with genotypes represented by binary sequences of length $L$. The population evolves in a maximally rugged fitness landscape with a large number of local optima. We find that whether the evolutionary trajectory is deterministic or stochastic depends on the effective mutational distance $d_{\mathrm{eff}}$ upto which the population can spread in genotype space. For $d_{\mathrm{eff}}=L$, the deterministic quasispecies theory operates while for $d_{\mathrm{eff}} < 1$, the evolution is completely stochastic. Between these two limiting cases, the dynamics are described by a local quasispecies theory below a crossover time $T_{\times}$ while above $T_{\times}$, the population gets trapped at a local fitness peak and manages to find a better peak either via stochastic tunneling or double mutations. In the stochastic regime $d_\mathrm{eff} < 1$, we identify two subregimes associated with clonal interference and uphill adaptive walks, respectively. We argue that our findings are relevant to the interepretation of evolution experiments with microbial populations. |
1304.7212 | David Tourigny | David S Tourigny | Geometry of the Energy Landscape for a Protein Folding on the Ribosome | Expanded version contained within arXiv:1307.6801 | null | null | null | q-bio.BM cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Energy landscape theory describes how a full-length protein can attain its
native fold by sampling only a tiny fraction of all possible structures.
Although protein folding is now understood to be concomitant with synthesis on
the ribosome, there have been few attempts to modify energy landscape theory by
accounting for cotranslational folding. Here we provide a model for
cotranslational folding that leads to a natural definition of a nested energy
landscape. By applying concepts drawn from submanifold differential geometry,
the physics of protein folding on the ribosome can be explored in a
quantitative manner and conditions on the nested energy landscapes for a good
cotranslational folder are derived.
| [
{
"created": "Fri, 26 Apr 2013 16:07:31 GMT",
"version": "v1"
},
{
"created": "Wed, 24 Sep 2014 13:06:47 GMT",
"version": "v2"
}
] | 2014-09-25 | [
[
"Tourigny",
"David S",
""
]
] | Energy landscape theory describes how a full-length protein can attain its native fold by sampling only a tiny fraction of all possible structures. Although protein folding is now understood to be concomitant with synthesis on the ribosome, there have been few attempts to modify energy landscape theory by accounting for cotranslational folding. Here we provide a model for cotranslational folding that leads to a natural definition of a nested energy landscape. By applying concepts drawn from submanifold differential geometry, the physics of protein folding on the ribosome can be explored in a quantitative manner and conditions on the nested energy landscapes for a good cotranslational folder are derived. |
2212.06873 | Casey Barkan | Casey Barkan and Shenshen Wang | Multiple Phase Transitions Shape Biodiversity of a Migrating Population | 8 pages, 3 figures | null | 10.1103/PhysRevE.107.034405 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a wide variety of natural systems, closely-related microbial strains
coexist stably, resulting in high levels of fine-scale biodiversity. However,
the mechanisms that stabilize this coexistence are not fully understood.
Spatial heterogeneity is one common stabilizing mechanism, but the rate at
which organisms disperse throughout the heterogeneous environment may strongly
impact the stabilizing effect that heterogeneity can provide. An intriguing
example is the gut microbiome, where active mechanisms exist to control the
movement of microbes and potentially maintain diversity. We investigate how
biodiversity is affected by migration rate using a simple evolutionary model
with heterogeneous selection pressure. We find that the biodiversity-migration
rate relationship is shaped by multiple phase transitions, including a
reentrant phase transition to coexistence. At each transition, an ecotype goes
extinct and dynamics exhibit critical slowing down (CSD). CSD is encoded in the
statistics of fluctuations due to demographic noise -- this may provide an
experimental means for detecting and altering impending extinction.
| [
{
"created": "Tue, 13 Dec 2022 19:38:29 GMT",
"version": "v1"
}
] | 2023-03-29 | [
[
"Barkan",
"Casey",
""
],
[
"Wang",
"Shenshen",
""
]
] | In a wide variety of natural systems, closely-related microbial strains coexist stably, resulting in high levels of fine-scale biodiversity. However, the mechanisms that stabilize this coexistence are not fully understood. Spatial heterogeneity is one common stabilizing mechanism, but the rate at which organisms disperse throughout the heterogeneous environment may strongly impact the stabilizing effect that heterogeneity can provide. An intriguing example is the gut microbiome, where active mechanisms exist to control the movement of microbes and potentially maintain diversity. We investigate how biodiversity is affected by migration rate using a simple evolutionary model with heterogeneous selection pressure. We find that the biodiversity-migration rate relationship is shaped by multiple phase transitions, including a reentrant phase transition to coexistence. At each transition, an ecotype goes extinct and dynamics exhibit critical slowing down (CSD). CSD is encoded in the statistics of fluctuations due to demographic noise -- this may provide an experimental means for detecting and altering impending extinction. |
1505.04660 | Susmita Roy | Susmita Roy and Biman Bagchi | Control of human immune response function by T-cell population
fluctuation and relaxation dynamics | 6 Figures. arXiv admin note: text overlap with arXiv:1404.5111 | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Clinical studies have indicated that in malignant surveillances fluctuations
in the population of certain effector T-cell repertoire become suppressed.
Motivated by such observations and in an attempt to quantify adaptive human
response to pathogens, we define an immune response function (IMRF) in terms of
mean square fluctuations of T-cell concentrations. We employ a recently
developed kinetic model of T-cell regulation that contains the essential
immunosuppressive effects of vitamin-D. We employ Gillespie algorithm to make
the first study of fluctuations along the stochastic trajectories. This
fluctuation-based IMRF can differentiate responses of different individuals
after pathogenic incursion both under healthy and disease conditions. We find
that relative fluctuations in T-cells (and hence IMRF) are different in
strongly regulated (malignant prone) and weakly regulated (autoimmune prone)
regions. The cross-over from one steady state (weakly regulated) to the other
(strongly regulated) is accompanied by a divergence-like growth in the
fluctuation of both the effector and regulatory T-cell concentration over a
wide range of pathogenic stimulation, displaying a dynamical phase transition
like behavior. The growth in fluctuation in this desired immune response regime
is found to arise from an intermittent fluctuation between regulatory and
effector T-cells that results in a bimodal distribution of population of each,
indicating bistability. The signature of intermittent behavior is further
confirmed by calculating the power spectrum of the corresponding fluctuation of
time correlation function. The calculated time correlation functions of
fluctuations show that the slow fluctuation causes the bistabilty in healthy
state. Thus, in diseases diagnosis process, such steady state response
parameters can provide immense information which might become helpful to define
an immune status.
| [
{
"created": "Mon, 18 May 2015 14:31:12 GMT",
"version": "v1"
}
] | 2015-05-19 | [
[
"Roy",
"Susmita",
""
],
[
"Bagchi",
"Biman",
""
]
] | Clinical studies have indicated that in malignant surveillances fluctuations in the population of certain effector T-cell repertoire become suppressed. Motivated by such observations and in an attempt to quantify adaptive human response to pathogens, we define an immune response function (IMRF) in terms of mean square fluctuations of T-cell concentrations. We employ a recently developed kinetic model of T-cell regulation that contains the essential immunosuppressive effects of vitamin-D. We employ Gillespie algorithm to make the first study of fluctuations along the stochastic trajectories. This fluctuation-based IMRF can differentiate responses of different individuals after pathogenic incursion both under healthy and disease conditions. We find that relative fluctuations in T-cells (and hence IMRF) are different in strongly regulated (malignant prone) and weakly regulated (autoimmune prone) regions. The cross-over from one steady state (weakly regulated) to the other (strongly regulated) is accompanied by a divergence-like growth in the fluctuation of both the effector and regulatory T-cell concentration over a wide range of pathogenic stimulation, displaying a dynamical phase transition like behavior. The growth in fluctuation in this desired immune response regime is found to arise from an intermittent fluctuation between regulatory and effector T-cells that results in a bimodal distribution of population of each, indicating bistability. The signature of intermittent behavior is further confirmed by calculating the power spectrum of the corresponding fluctuation of time correlation function. The calculated time correlation functions of fluctuations show that the slow fluctuation causes the bistabilty in healthy state. Thus, in diseases diagnosis process, such steady state response parameters can provide immense information which might become helpful to define an immune status. |
1412.6566 | James Moore | James R. Moore and Fred Adler | Mathematical modeling of type 1 diabetes in the NOD mouse: separating
incidence and age of onset | 29 pages, 12 figures | null | null | null | q-bio.TO q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Type 1 diabetes (T1D) is an autoimmune disease of the beta cells of the
pancreas. The nonobese diabetic (NOD) mouse is a commonly used animal model,
with roughly an 80% incidence rate of T1D among females. In 100% of NOD mice,
macrophages and T-cells invade the islets in a process called insulitis. It can
be several weeks between insulitis and T1D, and some mice do not progress at
all. It is thought that this delay is mediated by regulatory T-cells (Tregs)
and that a gradual loss of effectiveness in this population leads to T1D.
However, this does not explain why some mice progress and others do not. We
propose a simple mathematical model of the interaction between beta cells and
the immune populations, including regulatory T-cells. We find that individual
mice may enter one of two stable steady states: a `mild' insulitis state that
does not progress to T1D and a `severe' insulitis state that does. We then run
a sensitivity analysis to identify which parameters affect incidence of T1D
versus those that affect age of onset. We also test the model by simulating
several experimental manipulations found in the literature that modify
insulitis severity and/or Treg activity. Notably, we are able to match a
reproduce a large number of phenomena using a relatively small number of
equations. We finish by proposing experiments that could help validate or
refine the model.
| [
{
"created": "Sat, 20 Dec 2014 00:57:07 GMT",
"version": "v1"
}
] | 2014-12-23 | [
[
"Moore",
"James R.",
""
],
[
"Adler",
"Fred",
""
]
] | Type 1 diabetes (T1D) is an autoimmune disease of the beta cells of the pancreas. The nonobese diabetic (NOD) mouse is a commonly used animal model, with roughly an 80% incidence rate of T1D among females. In 100% of NOD mice, macrophages and T-cells invade the islets in a process called insulitis. It can be several weeks between insulitis and T1D, and some mice do not progress at all. It is thought that this delay is mediated by regulatory T-cells (Tregs) and that a gradual loss of effectiveness in this population leads to T1D. However, this does not explain why some mice progress and others do not. We propose a simple mathematical model of the interaction between beta cells and the immune populations, including regulatory T-cells. We find that individual mice may enter one of two stable steady states: a `mild' insulitis state that does not progress to T1D and a `severe' insulitis state that does. We then run a sensitivity analysis to identify which parameters affect incidence of T1D versus those that affect age of onset. We also test the model by simulating several experimental manipulations found in the literature that modify insulitis severity and/or Treg activity. Notably, we are able to match a reproduce a large number of phenomena using a relatively small number of equations. We finish by proposing experiments that could help validate or refine the model. |
2205.02150 | Niket Thakkar | Niket Thakkar and Mike Famulare | COVID-19 epidemiology as emergent behavior on a dynamic transmission
forest | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | In this paper we create a compartmental, stochastic process model of
SARS-CoV-2 transmission, where the process's mean and variance have distinct
dynamics. The model is fit to time series data from Washington from January
2020 to March 2021 using a deterministic, biologically-motivated signal
processing approach, and we show that the model's hidden states, like
population prevalence, agree with survey and other estimates. Then, in the
paper's second half, we demonstrate that the same model can be reframed as a
branching process with a dynamic degree distribution. This perspective allows
us to generate approximate transmission trees and estimate some higher order
statistics, like the clustering of cases as outbreaks, which we find to be
consistent with related observations from contact tracing and phylogenetics.
| [
{
"created": "Wed, 4 May 2022 16:10:34 GMT",
"version": "v1"
}
] | 2022-05-05 | [
[
"Thakkar",
"Niket",
""
],
[
"Famulare",
"Mike",
""
]
] | In this paper we create a compartmental, stochastic process model of SARS-CoV-2 transmission, where the process's mean and variance have distinct dynamics. The model is fit to time series data from Washington from January 2020 to March 2021 using a deterministic, biologically-motivated signal processing approach, and we show that the model's hidden states, like population prevalence, agree with survey and other estimates. Then, in the paper's second half, we demonstrate that the same model can be reframed as a branching process with a dynamic degree distribution. This perspective allows us to generate approximate transmission trees and estimate some higher order statistics, like the clustering of cases as outbreaks, which we find to be consistent with related observations from contact tracing and phylogenetics. |
0803.3591 | Simon Flyvbjerg Norrelykke | Liang Li, Simon F. Norrelykke and Edward C. Cox | Persistent Cell Motion in the Absence of External Signals: A Search
Strategy for Eukaryotic Cells | 15 pages, 11 figures, accepted for publication in PLOS One | PLoS ONE 3(5): e2093 | 10.1371/journal.pone.0002093 | null | q-bio.CB q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Eukaryotic cells are large enough to detect signals and then orient to them
by differentiating the signal strength across the length and breadth of the
cell. Amoebae, fibroblasts, neutrophils and growth cones all behave in this
way. Little is known however about cell motion and searching behavior in the
absence of a signal. Is individual cell motion best characterized as a random
walk? Do individual cells have a search strategy when they are beyond the range
of the signal they would otherwise move toward? Here we ask if single,
isolated, Dictyostelium and Polysphondylium amoebae bias their motion in the
absence of external cues. We placed single well-isolated Dictyostelium and
Polysphondylium cells on a nutrient-free agar surface and followed them at 10
sec intervals for ~10 hr, then analyzed their motion with respect to velocity,
turning angle, persistence length, and persistence time, comparing the results
to the expectation for a variety of different types of random motion. We find
that amoeboid behavior is well described by a special kind of random motion:
Amoebae show a long persistence time (~10 min) beyond which they start to lose
their direction; they move forward in a zig-zag manner; and they make turns
every 1-2 min on average. They bias their motion by remembering the last turn
and turning away from it. Interpreting the motion as consisting of runs and
turns, the duration of a run and the amplitude of a turn are both found to be
exponentially distributed. We show that this behavior greatly improves their
chances of finding a target relative to performing a random walk. We believe
that other eukaryotic cells may employ a strategy similar to Dictyostelium when
seeking conditions or signal sources not yet within range of their detection
system.
| [
{
"created": "Tue, 25 Mar 2008 16:31:14 GMT",
"version": "v1"
}
] | 2008-05-19 | [
[
"Li",
"Liang",
""
],
[
"Norrelykke",
"Simon F.",
""
],
[
"Cox",
"Edward C.",
""
]
] | Eukaryotic cells are large enough to detect signals and then orient to them by differentiating the signal strength across the length and breadth of the cell. Amoebae, fibroblasts, neutrophils and growth cones all behave in this way. Little is known however about cell motion and searching behavior in the absence of a signal. Is individual cell motion best characterized as a random walk? Do individual cells have a search strategy when they are beyond the range of the signal they would otherwise move toward? Here we ask if single, isolated, Dictyostelium and Polysphondylium amoebae bias their motion in the absence of external cues. We placed single well-isolated Dictyostelium and Polysphondylium cells on a nutrient-free agar surface and followed them at 10 sec intervals for ~10 hr, then analyzed their motion with respect to velocity, turning angle, persistence length, and persistence time, comparing the results to the expectation for a variety of different types of random motion. We find that amoeboid behavior is well described by a special kind of random motion: Amoebae show a long persistence time (~10 min) beyond which they start to lose their direction; they move forward in a zig-zag manner; and they make turns every 1-2 min on average. They bias their motion by remembering the last turn and turning away from it. Interpreting the motion as consisting of runs and turns, the duration of a run and the amplitude of a turn are both found to be exponentially distributed. We show that this behavior greatly improves their chances of finding a target relative to performing a random walk. We believe that other eukaryotic cells may employ a strategy similar to Dictyostelium when seeking conditions or signal sources not yet within range of their detection system. |
1801.07938 | Caio Seguin | Caio Seguin, Martijn P. van den Heuvel, Andrew Zalesky | Navigation of brain networks | null | null | 10.1073/pnas.1801351115 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the mechanisms of neural communication in large-scale brain
networks remains a major goal in neuroscience. We investigated whether
navigation is a parsimonious routing model for connectomics. Navigating a
network involves progressing to the next node that is closest in distance to a
desired destination. We developed a measure to quantify navigation efficiency
and found that connectomes in a range of mammalian species (human, mouse and
macaque) can be successfully navigated with near-optimal efficiency (>80% of
optimal efficiency for typical connection densities). Rewiring network topology
or repositioning network nodes resulted in 45%-60% reductions in navigation
performance. Specifically, we found that brain networks cannot be progressively
rewired (randomized or clusterized) to result in topologies with significantly
improved navigation performance. Navigation was also found to: i) promote a
resource-efficient distribution of the information traffic load, potentially
relieving communication bottlenecks; and, ii) explain significant variation in
functional connectivity. Unlike prevalently studied communication strategies in
connectomics, navigation does not mandate biologically unrealistic assumptions
about global knowledge of network topology. We conclude that the wiring and
spatial embedding of brain networks is conducive to effective decentralized
communication. Graph-theoretic studies of the connectome should consider
measures of network efficiency and centrality that are consistent with
decentralized models of neural communication.
| [
{
"created": "Wed, 24 Jan 2018 11:45:20 GMT",
"version": "v1"
}
] | 2018-06-05 | [
[
"Seguin",
"Caio",
""
],
[
"Heuvel",
"Martijn P. van den",
""
],
[
"Zalesky",
"Andrew",
""
]
] | Understanding the mechanisms of neural communication in large-scale brain networks remains a major goal in neuroscience. We investigated whether navigation is a parsimonious routing model for connectomics. Navigating a network involves progressing to the next node that is closest in distance to a desired destination. We developed a measure to quantify navigation efficiency and found that connectomes in a range of mammalian species (human, mouse and macaque) can be successfully navigated with near-optimal efficiency (>80% of optimal efficiency for typical connection densities). Rewiring network topology or repositioning network nodes resulted in 45%-60% reductions in navigation performance. Specifically, we found that brain networks cannot be progressively rewired (randomized or clusterized) to result in topologies with significantly improved navigation performance. Navigation was also found to: i) promote a resource-efficient distribution of the information traffic load, potentially relieving communication bottlenecks; and, ii) explain significant variation in functional connectivity. Unlike prevalently studied communication strategies in connectomics, navigation does not mandate biologically unrealistic assumptions about global knowledge of network topology. We conclude that the wiring and spatial embedding of brain networks is conducive to effective decentralized communication. Graph-theoretic studies of the connectome should consider measures of network efficiency and centrality that are consistent with decentralized models of neural communication. |
2006.13131 | Maroussia Bojkova Prof. | Maroussia Slavtchova-Bojkova, Kaloyan Vitanov | Computational modelling of cancer evolution by multi-type branching
processes | 7 pages, 4 figures, 62nd ISI world statistics congress | null | null | null | q-bio.PE stat.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Metastasis, the spread of cancer cells from a primary tumor to secondary
location(s) in the human organism, is the ultimate cause of death for the
majority of cancer patients. That is why, it is crucial to understand
metastases evolution in order to successfully combat the disease. We consider a
metastasized cancer cell population after medical treatment (e.g.
chemotherapy). Arriving in a different environment the cancer cells may change
their lifespan and reproduction, thus they may proliferate into different
types. If the treatment is effective, in the context of branching processes it
means, the reproduction of cancer cells is such that the mean offspring of each
cell is less than one. However, it is possible mutations to occur during cell
division cycle. These mutations can produce a new cancer cell type, which is
resistant to the treatment. Cancer cells from this new type may lead to the
rise of a non-extinction branching process. The above scenario leads us to the
choice of a reducible multi-type age-dependent branching process as a relevant
framework for studying the asymptotic behavior of such complex structures. Our
previous theoretical results are related to the asymptotic behavior of the
waiting time until the first occurrence of a mutant starting a non-extinction
process and the modified hazard function as a measure of immediate recurrence
of cancer disease. In the present paper these asymptotic results are used for
developing numerical schemes and algorithms implemented in Python via the NumPy
package for approximate calculation of the corresponding quantities. In
conclusion, our conjecture is that this methodology can be advantageous in
revealing the role of the lifespan distribution of the cancer cells in the
context of cancer disease evolution and other complex cell population systems,
in general.
| [
{
"created": "Tue, 23 Jun 2020 16:24:35 GMT",
"version": "v1"
}
] | 2020-06-24 | [
[
"Slavtchova-Bojkova",
"Maroussia",
""
],
[
"Vitanov",
"Kaloyan",
""
]
] | Metastasis, the spread of cancer cells from a primary tumor to secondary location(s) in the human organism, is the ultimate cause of death for the majority of cancer patients. That is why, it is crucial to understand metastases evolution in order to successfully combat the disease. We consider a metastasized cancer cell population after medical treatment (e.g. chemotherapy). Arriving in a different environment the cancer cells may change their lifespan and reproduction, thus they may proliferate into different types. If the treatment is effective, in the context of branching processes it means, the reproduction of cancer cells is such that the mean offspring of each cell is less than one. However, it is possible mutations to occur during cell division cycle. These mutations can produce a new cancer cell type, which is resistant to the treatment. Cancer cells from this new type may lead to the rise of a non-extinction branching process. The above scenario leads us to the choice of a reducible multi-type age-dependent branching process as a relevant framework for studying the asymptotic behavior of such complex structures. Our previous theoretical results are related to the asymptotic behavior of the waiting time until the first occurrence of a mutant starting a non-extinction process and the modified hazard function as a measure of immediate recurrence of cancer disease. In the present paper these asymptotic results are used for developing numerical schemes and algorithms implemented in Python via the NumPy package for approximate calculation of the corresponding quantities. In conclusion, our conjecture is that this methodology can be advantageous in revealing the role of the lifespan distribution of the cancer cells in the context of cancer disease evolution and other complex cell population systems, in general. |
1503.01216 | Manisha Bhardwaj | Manisha Bhardwaj, Sam Carroll, Wei Ji Ma, Kresimir Josic | Visual Decisions in the Presence of Measurement and Stimulus
Correlations | 30 pages | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans and other animals base their decisions on noisy sensory input. Much
work has therefore been devoted to understanding the computations that underly
such decisions. The problem has been studied in a variety of tasks and with
stimuli of differing complexity. However, the impact of correlations in sensory
noise on perceptual judgments is not well understood. Here we examine how
stimulus correlations together with correlations in sensory noise impact
decision making. As an example, we consider the task of detecting the presence
of a single or multiple targets amongst distractors. We assume that both the
distractors and the observer's measurements of the stimuli are correlated. The
computations of an optimal observer in this task are nontrivial, yet can be
analyzed and understood intuitively. We find that when distractors are strongly
correlated, measurement correlations can have a strong impact on performance.
When distractor correlations are weak, measurement correlations have little
impact, unless the number of stimuli is large. Correlations in neural responses
to structured stimuli can therefore strongly impact perceptual judgments.
| [
{
"created": "Wed, 4 Mar 2015 04:37:26 GMT",
"version": "v1"
}
] | 2015-03-05 | [
[
"Bhardwaj",
"Manisha",
""
],
[
"Carroll",
"Sam",
""
],
[
"Ma",
"Wei Ji",
""
],
[
"Josic",
"Kresimir",
""
]
] | Humans and other animals base their decisions on noisy sensory input. Much work has therefore been devoted to understanding the computations that underly such decisions. The problem has been studied in a variety of tasks and with stimuli of differing complexity. However, the impact of correlations in sensory noise on perceptual judgments is not well understood. Here we examine how stimulus correlations together with correlations in sensory noise impact decision making. As an example, we consider the task of detecting the presence of a single or multiple targets amongst distractors. We assume that both the distractors and the observer's measurements of the stimuli are correlated. The computations of an optimal observer in this task are nontrivial, yet can be analyzed and understood intuitively. We find that when distractors are strongly correlated, measurement correlations can have a strong impact on performance. When distractor correlations are weak, measurement correlations have little impact, unless the number of stimuli is large. Correlations in neural responses to structured stimuli can therefore strongly impact perceptual judgments. |
2108.07839 | Stefano Fusi | Stefano Fusi | Memory capacity of neural network models | This is a chapter of the forthcoming book "Human memory", Oxford
University Press, Edited by M. Kahana and A. Wagner. arXiv admin note:
substantial text overlap with arXiv:1706.04946 | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Memory is a complex phenomenon that involves several distinct mechanisms.
These mechanisms operate at different spatial and temporal levels. This chapter
focuses on the theoretical framework and the mathematical models that have been
developed to understand how these mechanisms are orchestrated to store,
preserve and retrieve a large number of memories. In particular, this chapter
reviews the theoretical studies on memory capacity, in which the investigators
estimated how the number of storable memories scales with the number of neurons
and synapses in the neural circuitry. The memory capacity depends on the
complexity of the synapses, the sparseness of the representations, the spatial
and temporal correlations between memories and the specific way memories are
retrieved. Complexity is important when the synapses can only be modified with
a limited precision, as in the case of biological synapses, and sparseness can
greatly increase memory capacity and be particularly beneficial when memories
are structured (correlated to each other). The theoretical tools discussed by
this chapter can be harnessed to identify the important computational
principles that underlie memory storage, preservation and retrieval and provide
guidance in designing and interpreting memory experiments.
| [
{
"created": "Tue, 17 Aug 2021 19:08:25 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Dec 2021 01:26:35 GMT",
"version": "v2"
}
] | 2021-12-22 | [
[
"Fusi",
"Stefano",
""
]
] | Memory is a complex phenomenon that involves several distinct mechanisms. These mechanisms operate at different spatial and temporal levels. This chapter focuses on the theoretical framework and the mathematical models that have been developed to understand how these mechanisms are orchestrated to store, preserve and retrieve a large number of memories. In particular, this chapter reviews the theoretical studies on memory capacity, in which the investigators estimated how the number of storable memories scales with the number of neurons and synapses in the neural circuitry. The memory capacity depends on the complexity of the synapses, the sparseness of the representations, the spatial and temporal correlations between memories and the specific way memories are retrieved. Complexity is important when the synapses can only be modified with a limited precision, as in the case of biological synapses, and sparseness can greatly increase memory capacity and be particularly beneficial when memories are structured (correlated to each other). The theoretical tools discussed by this chapter can be harnessed to identify the important computational principles that underlie memory storage, preservation and retrieval and provide guidance in designing and interpreting memory experiments. |
1201.0153 | David R. Bickel | Zhenyu Yang, Zuojing Li, David R. Bickel | Empirical Bayes estimation of posterior probabilities of enrichment | exhaustive revision of Zhenyu Yang and David R. Bickel, "Minimum
Description Length Measures of Evidence for Enrichment" (December 2010).
COBRA Preprint Series. Article 76. http://biostats.bepress.com/cobra/ps/art76 | A comparative study of five estimators of the local false
discovery rate," BMC Bioinformatics 14, art. 87 (2013) | 10.1186/1471-2105-14-87 | null | q-bio.GN stat.AP stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To interpret differentially expressed genes or other discovered features,
researchers conduct hypothesis tests to determine which biological categories
such as those of the Gene Ontology (GO) are enriched in the sense of having
differential representation among the discovered features. We study application
of better estimators of the local false discovery rate (LFDR), a probability
that the biological category has equivalent representation among the
preselected features.
We identified three promising estimators of the LFDR for detecting
differential representation: a semiparametric estimator (SPE), a normalized
maximum likelihood estimator (NMLE), and a maximum likelihood estimator (MLE).
We found that the MLE performs at least as well as the SPE for on the order of
100 of GO categories even when the ideal number of components in its underlying
mixture model is unknown. However, the MLE is unreliable when the number of GO
categories is small compared to the number of PMM components. Thus, if the
number of categories is on the order of 10, the SPE is a more reliable LFDR
estimator. The NMLE depends not only on the data but also on a specified value
of the prior probability of differential representation. It is therefore an
appropriate LFDR estimator only when the number of GO categories is too small
for application of the other methods.
For enrichment detection, we recommend estimating the LFDR by the MLE given
at least a medium number (~100) of GO categories, by the SPE given a small
number of GO categories (~10), and by the NMLE given a very small number (~1)
of GO categories.
| [
{
"created": "Fri, 30 Dec 2011 16:59:25 GMT",
"version": "v1"
}
] | 2013-09-03 | [
[
"Yang",
"Zhenyu",
""
],
[
"Li",
"Zuojing",
""
],
[
"Bickel",
"David R.",
""
]
] | To interpret differentially expressed genes or other discovered features, researchers conduct hypothesis tests to determine which biological categories such as those of the Gene Ontology (GO) are enriched in the sense of having differential representation among the discovered features. We study application of better estimators of the local false discovery rate (LFDR), a probability that the biological category has equivalent representation among the preselected features. We identified three promising estimators of the LFDR for detecting differential representation: a semiparametric estimator (SPE), a normalized maximum likelihood estimator (NMLE), and a maximum likelihood estimator (MLE). We found that the MLE performs at least as well as the SPE for on the order of 100 of GO categories even when the ideal number of components in its underlying mixture model is unknown. However, the MLE is unreliable when the number of GO categories is small compared to the number of PMM components. Thus, if the number of categories is on the order of 10, the SPE is a more reliable LFDR estimator. The NMLE depends not only on the data but also on a specified value of the prior probability of differential representation. It is therefore an appropriate LFDR estimator only when the number of GO categories is too small for application of the other methods. For enrichment detection, we recommend estimating the LFDR by the MLE given at least a medium number (~100) of GO categories, by the SPE given a small number of GO categories (~10), and by the NMLE given a very small number (~1) of GO categories. |
1708.03056 | Daniel Juliano Pamplona da Silva | Daniel Juliano Pamplona da Silva | Crossing-effect in non-isolated and non-symmetric systems of patches | null | null | null | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The main result of this article is the determination of the minimal size for
the general case of problems with two identical patches. This solution is
presented in the explicit form, which allows to recuperate all the cases found
in the literature as particular cases, namely, one isolated fragment, one
single fragment communicating with its neighborhood, a system with two
identical fragments isolated from the matrix but mutually communicating and a
system of two identical fragments inserted in a homogeneous matrix. It is also
addressed the new problem of a single fragment communicating with the matrix,
with different life difficulty of each side. As application, it is found that
the internal condition $a_{0}$ can set which system is the worst to life. This
prediction confirms and extends the prediction already found in the literature
between isolated and non-isolated systems.
| [
{
"created": "Thu, 10 Aug 2017 02:36:51 GMT",
"version": "v1"
}
] | 2017-08-11 | [
[
"da Silva",
"Daniel Juliano Pamplona",
""
]
] | The main result of this article is the determination of the minimal size for the general case of problems with two identical patches. This solution is presented in the explicit form, which allows to recuperate all the cases found in the literature as particular cases, namely, one isolated fragment, one single fragment communicating with its neighborhood, a system with two identical fragments isolated from the matrix but mutually communicating and a system of two identical fragments inserted in a homogeneous matrix. It is also addressed the new problem of a single fragment communicating with the matrix, with different life difficulty of each side. As application, it is found that the internal condition $a_{0}$ can set which system is the worst to life. This prediction confirms and extends the prediction already found in the literature between isolated and non-isolated systems. |
q-bio/0502046 | Per Arne Rikvold | Per Arne Rikvold | Fluctuations in models of biological macroevolution | 7 pages, 5 figures | Proceedings of SPIE -- Volume 5845, Noise in Complex Systems and
Stochastic Dynamics III, edited by L.B. Kish, K. Lindenberg, and Z. Gingl
(SPIE, Bellingham, WA, 2005), pp. 148-155. | 10.1117/12.609762 | null | q-bio.PE cond-mat.stat-mech | null | Fluctuations in diversity and extinction sizes are discussed and compared for
two different, individual-based models of biological coevolution. Both models
display power-law distributions for various quantities of evolutionary
interest, such as the lifetimes of individual species, the quiet periods
between evolutionary upheavals larger than a given cutoff, and the sizes of
extinction events. Time series of the diversity and measures of the size of
extinctions give rise to flicker noise. Surprisingly, the power-law behaviors
of the probability densities of quiet periods in the two models differ, while
the distributions of the lifetimes of individual species are the same.
| [
{
"created": "Mon, 28 Feb 2005 16:33:33 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Rikvold",
"Per Arne",
""
]
] | Fluctuations in diversity and extinction sizes are discussed and compared for two different, individual-based models of biological coevolution. Both models display power-law distributions for various quantities of evolutionary interest, such as the lifetimes of individual species, the quiet periods between evolutionary upheavals larger than a given cutoff, and the sizes of extinction events. Time series of the diversity and measures of the size of extinctions give rise to flicker noise. Surprisingly, the power-law behaviors of the probability densities of quiet periods in the two models differ, while the distributions of the lifetimes of individual species are the same. |
1508.03097 | Tatiana Tatarinova | Pavel Flegontov, Piya Changmai, Anastassiya Zidkova, Maria D.
Logacheva, Olga Flegontova, Mikhail S. Gelfand, Evgeny S. Gerasimov,
Ekaterina E. Khrameeva, Olga P. Konovalova, Tatiana Neretina, Yuri V.
Nikolsky, George Starostin, Vita V. Stepanova, Igor V. Travinsky, Martin
T\v{r}\'iska, Petr T\v{r}\'iska, Tatiana V. Tatarinova | Genomic study of the Ket: a Paleo-Eskimo-related ethnic group with
significant ancient North Eurasian ancestry | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Kets, an ethnic group in the Yenisei River basin, Russia, are considered
the last nomadic hunter-gatherers of Siberia, and Ket language has no
transparent affiliation with any language family. We investigated connections
between the Kets and Siberian and North American populations, with emphasis on
the Mal'ta and Paleo-Eskimo ancient genomes using original data from 46
unrelated samples of Kets and 42 samples of their neighboring ethnic groups
(Uralic-speaking Nganasans, Enets, and Selkups). We genotyped over 130,000
autosomal SNPs, determined mitochondrial and Y-chromosomal haplogroups, and
performed high-coverage genome sequencing of two Ket individuals. We
established that the Kets belong to the cluster of Siberian populations related
to Paleo-Eskimos. Unlike other members of this cluster (Nganasans, Ulchi,
Yukaghirs, and Evens), Kets and closely related Selkups have a high degree of
Mal'ta ancestry. Implications of these findings for the linguistic hypothesis
uniting Ket and Na-Dene languages into a language macrofamily are discussed.
| [
{
"created": "Thu, 13 Aug 2015 01:32:13 GMT",
"version": "v1"
}
] | 2015-08-14 | [
[
"Flegontov",
"Pavel",
""
],
[
"Changmai",
"Piya",
""
],
[
"Zidkova",
"Anastassiya",
""
],
[
"Logacheva",
"Maria D.",
""
],
[
"Flegontova",
"Olga",
""
],
[
"Gelfand",
"Mikhail S.",
""
],
[
"Gerasimov",
"Evgeny S.",
""
],
[
"Khrameeva",
"Ekaterina E.",
""
],
[
"Konovalova",
"Olga P.",
""
],
[
"Neretina",
"Tatiana",
""
],
[
"Nikolsky",
"Yuri V.",
""
],
[
"Starostin",
"George",
""
],
[
"Stepanova",
"Vita V.",
""
],
[
"Travinsky",
"Igor V.",
""
],
[
"Tříska",
"Martin",
""
],
[
"Tříska",
"Petr",
""
],
[
"Tatarinova",
"Tatiana V.",
""
]
] | The Kets, an ethnic group in the Yenisei River basin, Russia, are considered the last nomadic hunter-gatherers of Siberia, and Ket language has no transparent affiliation with any language family. We investigated connections between the Kets and Siberian and North American populations, with emphasis on the Mal'ta and Paleo-Eskimo ancient genomes using original data from 46 unrelated samples of Kets and 42 samples of their neighboring ethnic groups (Uralic-speaking Nganasans, Enets, and Selkups). We genotyped over 130,000 autosomal SNPs, determined mitochondrial and Y-chromosomal haplogroups, and performed high-coverage genome sequencing of two Ket individuals. We established that the Kets belong to the cluster of Siberian populations related to Paleo-Eskimos. Unlike other members of this cluster (Nganasans, Ulchi, Yukaghirs, and Evens), Kets and closely related Selkups have a high degree of Mal'ta ancestry. Implications of these findings for the linguistic hypothesis uniting Ket and Na-Dene languages into a language macrofamily are discussed. |
1210.6605 | Iannis Matsoukas Ph.D | I. G. Matsoukas, A. J. Massiah, B. Thomas | Florigenic and antiflorigenic signalling in plants | null | null | 10.1093/pcp/pcs130 | null | q-bio.BM q-bio.GN q-bio.MN q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evidence that Flowering Locus T (FT) protein and its paralog Twin Sister
of FT, act as the long distance floral stimulus, or at least that they are part
of it in diverse plant species, has attracted much attention in recent years.
Studies to understand the physiological and molecular apparatuses that
integrate spatial and temporal signals to regulate developmental transition in
plants have occupied countless scientists and have resulted in an unmanageably
large amount of research data. Analysis of these data has helped to identify
multiple systemic florigenic and antiflorigenic regulators. This study gives an
overview of the recent research on gene products, phytohormones and other
metabolites that have been demonstrated to have florigenic or antiflorigenic
functions in plants.
| [
{
"created": "Wed, 24 Oct 2012 16:59:04 GMT",
"version": "v1"
}
] | 2012-10-25 | [
[
"Matsoukas",
"I. G.",
""
],
[
"Massiah",
"A. J.",
""
],
[
"Thomas",
"B.",
""
]
] | The evidence that Flowering Locus T (FT) protein and its paralog Twin Sister of FT, act as the long distance floral stimulus, or at least that they are part of it in diverse plant species, has attracted much attention in recent years. Studies to understand the physiological and molecular apparatuses that integrate spatial and temporal signals to regulate developmental transition in plants have occupied countless scientists and have resulted in an unmanageably large amount of research data. Analysis of these data has helped to identify multiple systemic florigenic and antiflorigenic regulators. This study gives an overview of the recent research on gene products, phytohormones and other metabolites that have been demonstrated to have florigenic or antiflorigenic functions in plants. |
2204.04614 | Patrick Vincent Lubenia | Patrick Vincent N. Lubenia, Eduardo R. Mendoza, Angelyn R. Lao | Reaction Network Analysis of Metabolic Insulin Signaling | 34 pages, 1 figure | null | null | null | q-bio.MN math.AG | http://creativecommons.org/publicdomain/zero/1.0/ | Absolute concentration robustness (ACR) and concordance are novel concepts in
the theory of robustness and stability within Chemical Reaction Network Theory.
In this paper, we have extended Shinar and Feinberg's reaction network analysis
approach to the insulin signaling system based on recent advances in
decomposing reaction networks. We have shown that the network with 20 species,
35 complexes, and 35 reactions is concordant, implying at most one positive
equilibrium in each of its stoichiometric compatibility class. We have obtained
the system's finest independent decomposition consisting of 10 subnetworks, a
coarsening of which reveals three subnetworks which are not only functionally
but also structurally important. Utilizing the network's deficiency-oriented
coarsening, we have developed a method to determine positive equilibria for the
entire network. Our analysis has also shown that the system has ACR in 8
species all coming from a deficiency zero subnetwork. Interestingly, we have
shown that, for a set of rate constants, the insulin-regulated glucose
transporter GLUT4 (important in glucose energy metabolism), has stable ACR.
| [
{
"created": "Sun, 10 Apr 2022 06:28:33 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Aug 2022 14:05:19 GMT",
"version": "v2"
},
{
"created": "Tue, 13 Sep 2022 03:04:42 GMT",
"version": "v3"
}
] | 2022-09-14 | [
[
"Lubenia",
"Patrick Vincent N.",
""
],
[
"Mendoza",
"Eduardo R.",
""
],
[
"Lao",
"Angelyn R.",
""
]
] | Absolute concentration robustness (ACR) and concordance are novel concepts in the theory of robustness and stability within Chemical Reaction Network Theory. In this paper, we have extended Shinar and Feinberg's reaction network analysis approach to the insulin signaling system based on recent advances in decomposing reaction networks. We have shown that the network with 20 species, 35 complexes, and 35 reactions is concordant, implying at most one positive equilibrium in each of its stoichiometric compatibility class. We have obtained the system's finest independent decomposition consisting of 10 subnetworks, a coarsening of which reveals three subnetworks which are not only functionally but also structurally important. Utilizing the network's deficiency-oriented coarsening, we have developed a method to determine positive equilibria for the entire network. Our analysis has also shown that the system has ACR in 8 species all coming from a deficiency zero subnetwork. Interestingly, we have shown that, for a set of rate constants, the insulin-regulated glucose transporter GLUT4 (important in glucose energy metabolism), has stable ACR. |
q-bio/0403027 | German Andres Enciso | G.A. Enciso, E.D. Sontag | On the stability of Murray's testosterone model | 10 pages, no figures. Submitted to the Journal of Theoretical Biology | null | null | null | q-bio.MN | null | We prove the global asymptotic stability of a well-known delayed
negative-feedback model of testosterone dynamics, which has been proposed as a
model of oscillatory behavior. We establish stability (and hence the
impossibility of oscillations) even in the presence of delays of arbitrary
length.
| [
{
"created": "Fri, 19 Mar 2004 05:47:12 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Enciso",
"G. A.",
""
],
[
"Sontag",
"E. D.",
""
]
] | We prove the global asymptotic stability of a well-known delayed negative-feedback model of testosterone dynamics, which has been proposed as a model of oscillatory behavior. We establish stability (and hence the impossibility of oscillations) even in the presence of delays of arbitrary length. |
1301.3110 | Jeffrey Shaman | Jeffrey Shaman, Alicia Karspeck and Marc Lipsitch | Week 1 Influenza Forecast for the 2012-2013 U.S. Season | null | null | null | null | q-bio.PE stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is part of a series of weekly influenza forecasts made during the
2012-2013 influenza season. Here we present results of forecasts initiated
following assimilation of observations for Week 1 (i.e. the forecast begins
January 6, 2013) for municipalities in the United States. These forecasts were
performed on January 11, 2013. Results from forecasts initiated the six
previous weeks (Weeks 47-52) are also presented. The accuracy of these
predictions will not be known for certain until the conclusion of the current
influenza season; however, at the moment a number of the forecasted peaks
appear to be inaccurate. This inaccuracy may be due to the virulence of
influenza this season, which appears to be sending more influenza-infected
persons to seek medical attention and inflates ILI levels (and possibly the
proportion testing influenza positive) relative to years with milder flu
strains. New forecasts that adjust, or scale, for this difference and match the
two focus cities that appear to have already peaked are identified. These new
forecasts will be used, in addition to the previously scaled forms, to make
influenza predictions for the remainder of the season.
| [
{
"created": "Mon, 14 Jan 2013 20:18:47 GMT",
"version": "v1"
},
{
"created": "Sun, 20 Jan 2013 01:57:53 GMT",
"version": "v2"
}
] | 2013-01-22 | [
[
"Shaman",
"Jeffrey",
""
],
[
"Karspeck",
"Alicia",
""
],
[
"Lipsitch",
"Marc",
""
]
] | This is part of a series of weekly influenza forecasts made during the 2012-2013 influenza season. Here we present results of forecasts initiated following assimilation of observations for Week 1 (i.e. the forecast begins January 6, 2013) for municipalities in the United States. These forecasts were performed on January 11, 2013. Results from forecasts initiated the six previous weeks (Weeks 47-52) are also presented. The accuracy of these predictions will not be known for certain until the conclusion of the current influenza season; however, at the moment a number of the forecasted peaks appear to be inaccurate. This inaccuracy may be due to the virulence of influenza this season, which appears to be sending more influenza-infected persons to seek medical attention and inflates ILI levels (and possibly the proportion testing influenza positive) relative to years with milder flu strains. New forecasts that adjust, or scale, for this difference and match the two focus cities that appear to have already peaked are identified. These new forecasts will be used, in addition to the previously scaled forms, to make influenza predictions for the remainder of the season. |
1409.2839 | Fang-Cheng Yeh | Fang-Cheng Yeh and Timothy D. Verstynen | Increasing the Analytical Accessibility of Multishell and Diffusion
Spectrum Imaging Data Using Generalized Q-Sampling Conversion | null | https://www.frontiersin.org/articles/10.3389/fnins.2016.00418/full | null | null | q-bio.NC physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many diffusion MRI researchers, including the Human Connectome Project (HCP),
acquire data using multishell (e.g., WU-Minn consortium) and diffusion spectrum
imaging (DSI) schemes (e.g., USC-Harvard consortium). However, these data sets
are not readily accessible to high angular resolution diffusion imaging (HARDI)
analysis methods that are popular in connectomics analysis. Here we introduce a
scheme conversion approach that transforms multishell and DSI data into their
corresponding HARDI representations, thereby empowering HARDI-based analytical
methods to make use of data acquired using non-HARDI approaches. This method
was evaluated on both phantom and in-vivo human data sets by acquiring
multishell, DSI, and HARDI data simultaneously, and comparing the converted
HARDI, from non-HARDI methods, with the original HARDI data. Analysis on the
phantom shows that the converted HARDI from DSI and multishell data strongly
predicts the original HARDI (correlation coefficient > 0.9). Our in-vivo study
shows that the converted HARDI can be reconstructed by constrained spherical
deconvolution, and the fiber orientation distributions are consistent with
those from the original HARDI. We further illustrate that our scheme conversion
method can be applied to HCP data, and the converted HARDI do not appear to
sacrifice angular resolution. Thus this novel approach can benefit all
HARDI-based analysis approaches, allowing greater analytical accessibility to
non-HARDI data, including data from the HCP.
| [
{
"created": "Tue, 9 Sep 2014 18:41:48 GMT",
"version": "v1"
}
] | 2023-07-28 | [
[
"Yeh",
"Fang-Cheng",
""
],
[
"Verstynen",
"Timothy D.",
""
]
] | Many diffusion MRI researchers, including the Human Connectome Project (HCP), acquire data using multishell (e.g., WU-Minn consortium) and diffusion spectrum imaging (DSI) schemes (e.g., USC-Harvard consortium). However, these data sets are not readily accessible to high angular resolution diffusion imaging (HARDI) analysis methods that are popular in connectomics analysis. Here we introduce a scheme conversion approach that transforms multishell and DSI data into their corresponding HARDI representations, thereby empowering HARDI-based analytical methods to make use of data acquired using non-HARDI approaches. This method was evaluated on both phantom and in-vivo human data sets by acquiring multishell, DSI, and HARDI data simultaneously, and comparing the converted HARDI, from non-HARDI methods, with the original HARDI data. Analysis on the phantom shows that the converted HARDI from DSI and multishell data strongly predicts the original HARDI (correlation coefficient > 0.9). Our in-vivo study shows that the converted HARDI can be reconstructed by constrained spherical deconvolution, and the fiber orientation distributions are consistent with those from the original HARDI. We further illustrate that our scheme conversion method can be applied to HCP data, and the converted HARDI do not appear to sacrifice angular resolution. Thus this novel approach can benefit all HARDI-based analysis approaches, allowing greater analytical accessibility to non-HARDI data, including data from the HCP. |
1511.04643 | Satohiro Tajima | Satohiro Tajima | Sensory Polymorphism and Behavior: When Machine Vision Meets Monkey Eyes | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Polymorphism in the peripheral sensory system (e.g., congenital individual
differences in photopigment configuration) is important in diverse research
fields, ranging from evolutionary biology to engineering, because of its
potential relationship to the cognitive and behavioral variability among
individuals. However, there is a gap between the current understanding of
sensory polymorphism and the behavioral variability that is an outcome of
potentially complex cognitive processes in natural environments. Linking
peripheral sensor properties to behavior requires computational models of
nervous processes transforming sensory representations into actions, based on
quantitative data from physiological and behavioral studies. Recently, studies
based on machine vision approaches are shedding light on the quantitative
relationships between sensory polymorphisms and the resulting behavioral
variability. To reach a convergent understanding of the functional impacts of
sensory polymorphisms in realistic environments, a close coordination among
physiological, behavioral, and computational approaches is required. Aiming at
enhancing such integrative researches, this paper provides an overview for the
recent progresses in those interdisciplinary approaches, and suggests effective
strategies for such integrative paradigms.
| [
{
"created": "Sun, 15 Nov 2015 02:19:05 GMT",
"version": "v1"
},
{
"created": "Fri, 6 Jan 2017 20:47:40 GMT",
"version": "v2"
}
] | 2017-01-10 | [
[
"Tajima",
"Satohiro",
""
]
] | Polymorphism in the peripheral sensory system (e.g., congenital individual differences in photopigment configuration) is important in diverse research fields, ranging from evolutionary biology to engineering, because of its potential relationship to the cognitive and behavioral variability among individuals. However, there is a gap between the current understanding of sensory polymorphism and the behavioral variability that is an outcome of potentially complex cognitive processes in natural environments. Linking peripheral sensor properties to behavior requires computational models of nervous processes transforming sensory representations into actions, based on quantitative data from physiological and behavioral studies. Recently, studies based on machine vision approaches are shedding light on the quantitative relationships between sensory polymorphisms and the resulting behavioral variability. To reach a convergent understanding of the functional impacts of sensory polymorphisms in realistic environments, a close coordination among physiological, behavioral, and computational approaches is required. Aiming at enhancing such integrative researches, this paper provides an overview for the recent progresses in those interdisciplinary approaches, and suggests effective strategies for such integrative paradigms. |
0706.3177 | Laurent Perrinet | Laurent Perrinet (INT, INCM) | Role of homeostasis in learning sparse representations | null | Neural Computation, Massachusetts Institute of Technology Press
(MIT Press), 2010, 22 (7), pp.1812-36 | 10.1162/neco.2010.05-08-795 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neurons in the input layer of primary visual cortex in primates develop
edge-like receptive fields. One approach to understanding the emergence of this
response is to state that neural activity has to efficiently represent sensory
data with respect to the statistics of natural scenes. Furthermore, it is
believed that such an efficient coding is achieved using a competition across
neurons so as to generate a sparse representation, that is, where a relatively
small number of neurons are simultaneously active. Indeed, different models of
sparse coding, coupled with Hebbian learning and homeostasis, have been
proposed that successfully match the observed emergent response. However, the
specific role of homeostasis in learning such sparse representations is still
largely unknown. By quantitatively assessing the efficiency of the neural
representation during learning, we derive a cooperative homeostasis mechanism
that optimally tunes the competition between neurons within the sparse coding
algorithm. We apply this homeostasis while learning small patches taken from
natural images and compare its efficiency with state-of-the-art algorithms.
Results show that while different sparse coding algorithms give similar coding
results, the homeostasis provides an optimal balance for the representation of
natural images within the population of neurons. Competition in sparse coding
is optimized when it is fair. By contributing to optimizing statistical
competition across neurons, homeostasis is crucial in providing a more
efficient solution to the emergence of independent components.
| [
{
"created": "Thu, 21 Jun 2007 15:32:54 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Sep 2007 12:44:09 GMT",
"version": "v2"
},
{
"created": "Wed, 6 Feb 2008 08:10:52 GMT",
"version": "v3"
},
{
"created": "Wed, 19 Mar 2008 08:00:43 GMT",
"version": "v4"
},
{
"created": "Fri, 19 Sep 2008 19:26:23 GMT",
"version": "v5"
},
{
"created": "Fri, 25 Jun 2010 13:33:29 GMT",
"version": "v6"
},
{
"created": "Thu, 8 Dec 2016 12:52:51 GMT",
"version": "v7"
}
] | 2016-12-09 | [
[
"Perrinet",
"Laurent",
"",
"INT, INCM"
]
] | Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the emergence of this response is to state that neural activity has to efficiently represent sensory data with respect to the statistics of natural scenes. Furthermore, it is believed that such an efficient coding is achieved using a competition across neurons so as to generate a sparse representation, that is, where a relatively small number of neurons are simultaneously active. Indeed, different models of sparse coding, coupled with Hebbian learning and homeostasis, have been proposed that successfully match the observed emergent response. However, the specific role of homeostasis in learning such sparse representations is still largely unknown. By quantitatively assessing the efficiency of the neural representation during learning, we derive a cooperative homeostasis mechanism that optimally tunes the competition between neurons within the sparse coding algorithm. We apply this homeostasis while learning small patches taken from natural images and compare its efficiency with state-of-the-art algorithms. Results show that while different sparse coding algorithms give similar coding results, the homeostasis provides an optimal balance for the representation of natural images within the population of neurons. Competition in sparse coding is optimized when it is fair. By contributing to optimizing statistical competition across neurons, homeostasis is crucial in providing a more efficient solution to the emergence of independent components. |
1802.02678 | Charles Delahunt | Charles B. Delahunt, Jeffrey A. Riffell, J. Nathan Kutz | Biological Mechanisms for Learning: A Computational Model of Olfactory
Learning in the Manduca sexta Moth, with Applications to Neural Nets | 35 pages, 10 figures | null | null | null | q-bio.NC cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The insect olfactory system, which includes the antennal lobe (AL), mushroom
body (MB), and ancillary structures, is a relatively simple neural system
capable of learning. Its structural features, which are widespread in
biological neural systems, process olfactory stimuli through a cascade of
networks where large dimension shifts occur from stage to stage and where
sparsity and randomness play a critical role in coding. Learning is partly
enabled by a neuromodulatory reward mechanism of octopamine stimulation of the
AL, whose increased activity induces rewiring of the MB through Hebbian
plasticity. Enforced sparsity in the MB focuses Hebbian growth on neurons that
are the most important for the representation of the learned odor. Based upon
current biophysical knowledge, we have constructed an end-to-end computational
model of the Manduca sexta moth olfactory system which includes the interaction
of the AL and MB under octopamine stimulation. Our model is able to robustly
learn new odors, and our simulations of integrate-and-fire neurons match the
statistical features of in-vivo firing rate data. From a biological
perspective, the model provides a valuable tool for examining the role of
neuromodulators, like octopamine, in learning, and gives insight into critical
interactions between sparsity, Hebbian growth, and stimulation during learning.
Our simulations also inform predictions about structural details of the
olfactory system that are not currently well-characterized. From a machine
learning perspective, the model yields bio-inspired mechanisms that are
potentially useful in constructing neural nets for rapid learning from very few
samples. These mechanisms include high-noise layers, sparse layers as noise
filters, and a biologically-plausible optimization method to train the network
based on octopamine stimulation, sparse layers, and Hebbian growth.
| [
{
"created": "Thu, 8 Feb 2018 00:16:31 GMT",
"version": "v1"
}
] | 2018-02-09 | [
[
"Delahunt",
"Charles B.",
""
],
[
"Riffell",
"Jeffrey A.",
""
],
[
"Kutz",
"J. Nathan",
""
]
] | The insect olfactory system, which includes the antennal lobe (AL), mushroom body (MB), and ancillary structures, is a relatively simple neural system capable of learning. Its structural features, which are widespread in biological neural systems, process olfactory stimuli through a cascade of networks where large dimension shifts occur from stage to stage and where sparsity and randomness play a critical role in coding. Learning is partly enabled by a neuromodulatory reward mechanism of octopamine stimulation of the AL, whose increased activity induces rewiring of the MB through Hebbian plasticity. Enforced sparsity in the MB focuses Hebbian growth on neurons that are the most important for the representation of the learned odor. Based upon current biophysical knowledge, we have constructed an end-to-end computational model of the Manduca sexta moth olfactory system which includes the interaction of the AL and MB under octopamine stimulation. Our model is able to robustly learn new odors, and our simulations of integrate-and-fire neurons match the statistical features of in-vivo firing rate data. From a biological perspective, the model provides a valuable tool for examining the role of neuromodulators, like octopamine, in learning, and gives insight into critical interactions between sparsity, Hebbian growth, and stimulation during learning. Our simulations also inform predictions about structural details of the olfactory system that are not currently well-characterized. From a machine learning perspective, the model yields bio-inspired mechanisms that are potentially useful in constructing neural nets for rapid learning from very few samples. These mechanisms include high-noise layers, sparse layers as noise filters, and a biologically-plausible optimization method to train the network based on octopamine stimulation, sparse layers, and Hebbian growth. |
1811.00590 | Jorge Ramirez | Jorge M Ramirez, Sara M Vallejo, Yurani Villa, Sara Gaona, Sarai
Quintero | Modeling tropotaxis in ant colonies: recruitment and trail formation | Submitted to Journal of Insect Behavior | null | null | null | q-bio.PE nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose an active walker model for the motion of individual ants
communicating via chemical signals. It is assumed that communication takes the
form of a time-dependent pheromone field that feedbacks into the motion ants
through tropotaxis: individuals can sense the gradient of the pheromone
concentration field and adjust their orientation accordingly. The individual
model takes the form of a Langevin system of equations in polar coordinates
driven by two-dimensional Gaussian fluctuations and with orientation changes in
response to two pheromone fields: one emanating from the nest, and other
actively produced by ants in their nest-bound journey after finding a food
source. We explicitly track the evolution of both fields in three dimensions.
The proposed tropotaxis model relating the pheromone field to the orientation
changes is similar to Weber's law, but depends explicitly only on the gradient
of the pheromone concentration. We identify ranges of values for the model
parameters that yield the emergence of two key foraging patterns: successful
recruitment to newly found sources, and colony-wide trail networks.
| [
{
"created": "Thu, 1 Nov 2018 18:57:49 GMT",
"version": "v1"
},
{
"created": "Fri, 16 Aug 2019 12:40:40 GMT",
"version": "v2"
}
] | 2019-08-19 | [
[
"Ramirez",
"Jorge M",
""
],
[
"Vallejo",
"Sara M",
""
],
[
"Villa",
"Yurani",
""
],
[
"Gaona",
"Sara",
""
],
[
"Quintero",
"Sarai",
""
]
] | We propose an active walker model for the motion of individual ants communicating via chemical signals. It is assumed that communication takes the form of a time-dependent pheromone field that feedbacks into the motion ants through tropotaxis: individuals can sense the gradient of the pheromone concentration field and adjust their orientation accordingly. The individual model takes the form of a Langevin system of equations in polar coordinates driven by two-dimensional Gaussian fluctuations and with orientation changes in response to two pheromone fields: one emanating from the nest, and other actively produced by ants in their nest-bound journey after finding a food source. We explicitly track the evolution of both fields in three dimensions. The proposed tropotaxis model relating the pheromone field to the orientation changes is similar to Weber's law, but depends explicitly only on the gradient of the pheromone concentration. We identify ranges of values for the model parameters that yield the emergence of two key foraging patterns: successful recruitment to newly found sources, and colony-wide trail networks. |
1610.05715 | Charo del Genio | Veselina V. Uzunova, Mussa Quareshy, Charo I. del Genio and Richard M.
Napier | Tomographic docking suggests the mechanism of auxin receptor TIR1
selectivity | 11 pages, 7 figures | Open Biol. 6, 160139 (2016) | 10.1098/rsob.160139 | null | q-bio.BM q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the binding of plant hormone IAA on its receptor TIR1 introducing a
novel computational method that we call tomographic docking and that accounts
for interactions occurring along the depth of the binding pocket. Our results
suggest that selectivity is related to constraints that potential ligands
encounter on their way from the surface of the protein to their final position
at the pocket bottom. Tomographic docking helps develop specific hypotheses
about ligand binding, distinguishing binders from non-binders, and suggests
that binding is a three-step mechanism, consisting of engagement with a niche
in the back wall of the pocket, interaction with a molecular filter which
allows or precludes further descent of ligands, and binding on the pocket base.
Only molecules that are able to descend the pocket and bind at its base allow
the co-receptor IAA7 to bind on the complex, thus behaving as active auxins.
Analyzing the interactions at different depths, our new method helps in
identifying critical residues that constitute preferred future study targets
and in the quest for safe and effective herbicides. Also, it has the potential
to extend the utility of docking from ligand searches to the study of processes
contributing to selectivity.
| [
{
"created": "Tue, 18 Oct 2016 17:33:22 GMT",
"version": "v1"
}
] | 2016-10-20 | [
[
"Uzunova",
"Veselina V.",
""
],
[
"Quareshy",
"Mussa",
""
],
[
"del Genio",
"Charo I.",
""
],
[
"Napier",
"Richard M.",
""
]
] | We study the binding of plant hormone IAA on its receptor TIR1 introducing a novel computational method that we call tomographic docking and that accounts for interactions occurring along the depth of the binding pocket. Our results suggest that selectivity is related to constraints that potential ligands encounter on their way from the surface of the protein to their final position at the pocket bottom. Tomographic docking helps develop specific hypotheses about ligand binding, distinguishing binders from non-binders, and suggests that binding is a three-step mechanism, consisting of engagement with a niche in the back wall of the pocket, interaction with a molecular filter which allows or precludes further descent of ligands, and binding on the pocket base. Only molecules that are able to descend the pocket and bind at its base allow the co-receptor IAA7 to bind on the complex, thus behaving as active auxins. Analyzing the interactions at different depths, our new method helps in identifying critical residues that constitute preferred future study targets and in the quest for safe and effective herbicides. Also, it has the potential to extend the utility of docking from ligand searches to the study of processes contributing to selectivity. |
1411.0721 | Ariel Rokem | Ariel Rokem, Jason D. Yeatman, Franco Pestilli, Kendrick N. Kay, Aviv
Mezer, Stefan van der Walt, and Brian A. Wandell | Evaluating the accuracy of diffusion MRI models in white matter | null | null | 10.1371/journal.pone.0123272 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Models of diffusion MRI within a voxel are useful for making inferences about
the properties of the tissue and inferring fiber orientation distribution used
by tractography algorithms. A useful model must fit the data accurately.
However, evaluations of model-accuracy of some of the models that are commonly
used in analyzing human white matter have not been published before. Here, we
evaluate model-accuracy of the two main classes of diffusion MRI models. The
diffusion tensor model (DTM) summarizes diffusion as a 3-dimensional Gaussian
distribution. Sparse fascicle models (SFM) summarize the signal as a linear sum
of signals originating from a collection of fascicles oriented in different
directions. We use cross-validation to assess model-accuracy at different
gradient amplitudes (b-values) throughout the white matter. Specifically, we
fit each model to all the white matter voxels in one data set and then use the
model to predict a second, independent data set. This is the first evaluation
of model-accuracy of these models. In most of the white matter the DTM predicts
the data more accurately than test-retest reliability; SFM model-accuracy is
higher than test-retest reliability and also higher than the DTM, particularly
for measurements with (a) a b-value above 1000 in locations containing fiber
crossings, and (b) in the regions of the brain surrounding the optic
radiations. The SFM also has better parameter-validity: it more accurately
estimates the fiber orientation distribution function (fODF) in each voxel,
which is useful for fiber tracking.
| [
{
"created": "Mon, 3 Nov 2014 22:25:33 GMT",
"version": "v1"
},
{
"created": "Mon, 10 Nov 2014 20:58:41 GMT",
"version": "v2"
},
{
"created": "Sat, 14 Mar 2015 20:00:29 GMT",
"version": "v3"
}
] | 2017-02-08 | [
[
"Rokem",
"Ariel",
""
],
[
"Yeatman",
"Jason D.",
""
],
[
"Pestilli",
"Franco",
""
],
[
"Kay",
"Kendrick N.",
""
],
[
"Mezer",
"Aviv",
""
],
[
"van der Walt",
"Stefan",
""
],
[
"Wandell",
"Brian A.",
""
]
] | Models of diffusion MRI within a voxel are useful for making inferences about the properties of the tissue and inferring fiber orientation distribution used by tractography algorithms. A useful model must fit the data accurately. However, evaluations of model-accuracy of some of the models that are commonly used in analyzing human white matter have not been published before. Here, we evaluate model-accuracy of the two main classes of diffusion MRI models. The diffusion tensor model (DTM) summarizes diffusion as a 3-dimensional Gaussian distribution. Sparse fascicle models (SFM) summarize the signal as a linear sum of signals originating from a collection of fascicles oriented in different directions. We use cross-validation to assess model-accuracy at different gradient amplitudes (b-values) throughout the white matter. Specifically, we fit each model to all the white matter voxels in one data set and then use the model to predict a second, independent data set. This is the first evaluation of model-accuracy of these models. In most of the white matter the DTM predicts the data more accurately than test-retest reliability; SFM model-accuracy is higher than test-retest reliability and also higher than the DTM, particularly for measurements with (a) a b-value above 1000 in locations containing fiber crossings, and (b) in the regions of the brain surrounding the optic radiations. The SFM also has better parameter-validity: it more accurately estimates the fiber orientation distribution function (fODF) in each voxel, which is useful for fiber tracking. |
1909.13141 | Nancy Horton | Chad K. Park and Nancy C. Horton | Structures, Functions, and Mechanisms of Filament Forming Enzymes: A
Renaissance of Enzyme Filamentation | A comprehensive review, 90 pages, 31 figures | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Filament formation by non-cytoskeletal enzymes has been known for decades,
yet only relatively recently has its wide-spread role in enzyme regulation and
biology come to be appreciated. This comprehensive review summarizes what is
known for each enzyme confirmed to form filamentous structures in vitro, and
for the many that are known only to form large self-assemblies within cells.
For some enzymes, studies describing both the in vitro filamentous structures
and cellular self-assembly formation are also known and described. Special
attention is paid to the detailed structures of each type of enzyme filament,
as well as the roles the structures play in enzyme regulation and in biology.
Where it is known or hypothesized, the advantages conferred by enzyme
filamentation are reviewed. Finally, the similarities, differences, and
comparison to the SgrAI system are also highlighted.
| [
{
"created": "Sat, 28 Sep 2019 19:40:13 GMT",
"version": "v1"
}
] | 2019-10-01 | [
[
"Park",
"Chad K.",
""
],
[
"Horton",
"Nancy C.",
""
]
] | Filament formation by non-cytoskeletal enzymes has been known for decades, yet only relatively recently has its wide-spread role in enzyme regulation and biology come to be appreciated. This comprehensive review summarizes what is known for each enzyme confirmed to form filamentous structures in vitro, and for the many that are known only to form large self-assemblies within cells. For some enzymes, studies describing both the in vitro filamentous structures and cellular self-assembly formation are also known and described. Special attention is paid to the detailed structures of each type of enzyme filament, as well as the roles the structures play in enzyme regulation and in biology. Where it is known or hypothesized, the advantages conferred by enzyme filamentation are reviewed. Finally, the similarities, differences, and comparison to the SgrAI system are also highlighted. |
2212.09923 | Alexander Mayer | Alex Mayer, Grace McLaughlin, Sierra Cole, Amy Gladfelter, Marcus
Roper | The Role of RNA Condensation in Reducing Gene Expression Noise | null | null | 10.1016/j.bpj.2022.11.2256 | null | q-bio.SC q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biomolecular condensates have been shown to play a fundamental role in
localizing biochemistry in a cell. RNA is a common constituent of condensates,
and can determine their biophysical properties. Functions of biomolecular
condensates are varied including activating, inhibiting, and localizing
reactions. Recent theoretical work has shown that the phase separation of
proteins into droplets can diminish cell to cell variability in protein
abundance. However, the extent to which phase separation involving mRNAs may
also buffer noise has yet to be explored. In this paper, we introduce a
phenomenological model for the phase separation of mRNAs into RNP condensates,
and quantify noise suppression as a function of gene expression kinetic
parameters. Through stochastic simulations, we highlight the ability for
condensates formed from just a handful of mRNAs to regulate the abundance and
suppress the fluctuations of proteins. We place particular emphasis on how this
mechanism can facilitate efficient transcription by reducing noise even in the
situation of infrequent bursts of transcription by exploiting the physics of a
concentration-dependent, deterministic phase separation threshold. We
investigate two biologically relevant models in which phase separation acts to
either "buffer" noise by storing mRNA in inert droplets, or "filter" phase
separated mRNAs by accelerating their decay, and quantify expression noise as a
function of kinetic parameters. In either case the most efficient expression
occurs when bursts produce mRNAs close the phase separation threshold, which we
find to be broadly consistent with observations of an RNP-droplet forming
cyclinin multinucleate Ashbya gossypii cells. We finally consider the
contribution of noise in the phase separation threshold, and show that protein
copy number noise can be suppressed by phase separation threshold fluctuations
in certain conditions.
| [
{
"created": "Tue, 20 Dec 2022 00:16:33 GMT",
"version": "v1"
}
] | 2023-02-22 | [
[
"Mayer",
"Alex",
""
],
[
"McLaughlin",
"Grace",
""
],
[
"Cole",
"Sierra",
""
],
[
"Gladfelter",
"Amy",
""
],
[
"Roper",
"Marcus",
""
]
] | Biomolecular condensates have been shown to play a fundamental role in localizing biochemistry in a cell. RNA is a common constituent of condensates, and can determine their biophysical properties. Functions of biomolecular condensates are varied including activating, inhibiting, and localizing reactions. Recent theoretical work has shown that the phase separation of proteins into droplets can diminish cell to cell variability in protein abundance. However, the extent to which phase separation involving mRNAs may also buffer noise has yet to be explored. In this paper, we introduce a phenomenological model for the phase separation of mRNAs into RNP condensates, and quantify noise suppression as a function of gene expression kinetic parameters. Through stochastic simulations, we highlight the ability for condensates formed from just a handful of mRNAs to regulate the abundance and suppress the fluctuations of proteins. We place particular emphasis on how this mechanism can facilitate efficient transcription by reducing noise even in the situation of infrequent bursts of transcription by exploiting the physics of a concentration-dependent, deterministic phase separation threshold. We investigate two biologically relevant models in which phase separation acts to either "buffer" noise by storing mRNA in inert droplets, or "filter" phase separated mRNAs by accelerating their decay, and quantify expression noise as a function of kinetic parameters. In either case the most efficient expression occurs when bursts produce mRNAs close the phase separation threshold, which we find to be broadly consistent with observations of an RNP-droplet forming cyclinin multinucleate Ashbya gossypii cells. We finally consider the contribution of noise in the phase separation threshold, and show that protein copy number noise can be suppressed by phase separation threshold fluctuations in certain conditions. |
1912.00091 | Liane Gabora | Liane Gabora | Creativity | Reference: Gabora, L. (in press). Creativity. Oxford Research
Encyclopedia of Psychology. Oxford UK: Oxford University Press | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Creativity is perhaps what most differentiates humans from other species. It
involves the capacity to shift between divergent and convergent modes of
thought in response to task demands. Divergent thought has been characterized
as the kind of thinking needed to generate multiple solutions, while convergent
thought has been characterized as the kind of thinking needed for tasks in with
one solution. Divergent thought has been conceived of as reflecting on the task
from unconventional perspectives, while convergent thought has been conceived
of as reflecting on it from conventional perspectives. Personality traits
correlated with creativity include openness to experience, tolerance of
ambiguity, and self-confidence. Evidence that creativity is linked with
affective disorders is mixed. Neuroscientific research using
electroencephalography (EEG) or functional magnetic resonance imaging (fMRI)
suggests that creativity is associated with a loosening of cognitive control
and decreased arousal. The distributed, content-addressable structure of
associative memory is conducive to bringing task-relevant items to mind without
the need for explicit search. Human creativity dates back to the earliest stone
tools over three million years ago, with the Paleolithic marking the onset of
art, science, and religion. Areas of controversy concern the relative
contributions of expertise, chance, and intuition, the importance of process
versus product, whether creativity is domain-specific versus domain-general,
the extent to which creativity is correlated with affective disorders, and
whether divergent thought entails the generation of multiple ideas or the
honing of a single initially ambiguous mental representation that may manifest
as different external outputs. Areas for further research include computational
modeling, the biological basis of creativity, and studies that track ideation
processes over time.
| [
{
"created": "Fri, 29 Nov 2019 23:17:03 GMT",
"version": "v1"
}
] | 2019-12-03 | [
[
"Gabora",
"Liane",
""
]
] | Creativity is perhaps what most differentiates humans from other species. It involves the capacity to shift between divergent and convergent modes of thought in response to task demands. Divergent thought has been characterized as the kind of thinking needed to generate multiple solutions, while convergent thought has been characterized as the kind of thinking needed for tasks in with one solution. Divergent thought has been conceived of as reflecting on the task from unconventional perspectives, while convergent thought has been conceived of as reflecting on it from conventional perspectives. Personality traits correlated with creativity include openness to experience, tolerance of ambiguity, and self-confidence. Evidence that creativity is linked with affective disorders is mixed. Neuroscientific research using electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) suggests that creativity is associated with a loosening of cognitive control and decreased arousal. The distributed, content-addressable structure of associative memory is conducive to bringing task-relevant items to mind without the need for explicit search. Human creativity dates back to the earliest stone tools over three million years ago, with the Paleolithic marking the onset of art, science, and religion. Areas of controversy concern the relative contributions of expertise, chance, and intuition, the importance of process versus product, whether creativity is domain-specific versus domain-general, the extent to which creativity is correlated with affective disorders, and whether divergent thought entails the generation of multiple ideas or the honing of a single initially ambiguous mental representation that may manifest as different external outputs. Areas for further research include computational modeling, the biological basis of creativity, and studies that track ideation processes over time. |
1808.00998 | Laura Ellwein Fix | Laura Ellwein Fix | Parameter identifiability of a respiratory mechanics model in an
idealized preterm infant | 15 figures (6 in main body, 9 in appendix). Changes to content and
format based on reviewer comments. 26 pages, p.27 is extraneous | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The complexity of mathematical models describing respiratory mechanics has
grown in recent years to integrate with cardiovascular models and incorporate
nonlinear dynamics. However, additional model complexity has rarely been
studied in the context of patient-specific observable data. This study
investigates parameter identification of a previously developed nonlinear
respiratory mechanics model (Ellwein Fix, PLoS ONE 2018) tuned to the
physiology of 1 kg preterm infant, using local deterministic sensitivity
analysis, subset selection, and gradient-based optimization. The model consists
of 4 differential state equations with 31 parameters to predict airflow and
dynamic pulmonary volumes and pressures generated under six simulation
conditions. The relative sensitivity solutions of the model state equations
with respect to each of the parameters were calculated with finite differences
and a sensitivity ranking was created for each parameter and simulation. Subset
selection identified a set of independent parameters that could be estimated
for all six simulations. The combination of these analyses produced a subset of
6 independent sensitive parameters that could be estimated given idealized
clinical data. All optimizations performed using pseudo-data with perturbed
nominal parameters converged within 40 iterations and estimated parameters
within ~8% of nominal values on average. This analysis indicates the
feasibility of performing parameter estimation on real patient-specific data
set described by a nonlinear respiratory mechanics model for studying dynamics
in preterm infants.
| [
{
"created": "Thu, 2 Aug 2018 19:30:01 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Jan 2020 19:57:28 GMT",
"version": "v2"
}
] | 2020-01-17 | [
[
"Fix",
"Laura Ellwein",
""
]
] | The complexity of mathematical models describing respiratory mechanics has grown in recent years to integrate with cardiovascular models and incorporate nonlinear dynamics. However, additional model complexity has rarely been studied in the context of patient-specific observable data. This study investigates parameter identification of a previously developed nonlinear respiratory mechanics model (Ellwein Fix, PLoS ONE 2018) tuned to the physiology of 1 kg preterm infant, using local deterministic sensitivity analysis, subset selection, and gradient-based optimization. The model consists of 4 differential state equations with 31 parameters to predict airflow and dynamic pulmonary volumes and pressures generated under six simulation conditions. The relative sensitivity solutions of the model state equations with respect to each of the parameters were calculated with finite differences and a sensitivity ranking was created for each parameter and simulation. Subset selection identified a set of independent parameters that could be estimated for all six simulations. The combination of these analyses produced a subset of 6 independent sensitive parameters that could be estimated given idealized clinical data. All optimizations performed using pseudo-data with perturbed nominal parameters converged within 40 iterations and estimated parameters within ~8% of nominal values on average. This analysis indicates the feasibility of performing parameter estimation on real patient-specific data set described by a nonlinear respiratory mechanics model for studying dynamics in preterm infants. |
1305.4337 | Abhishek Dey | Abhishek Dey, Shaunak Sen | Describing Function-based Approximations of Biomolecular Systems | 11 pages, 7 figures | null | 10.1049/iet-syb.2017.0026 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mathematical methods provide useful framework for the analysis and design of
complex systems. In newer contexts such as biology, however, there is a need to
both adapt existing methods as well as to develop new ones. Using a combination
of analytical and computational approaches, we adapt and develop the method of
describing functions to represent the input-output responses of biomolecular
signalling systems. We approximate representative systems exhibiting various
saturating and hysteretic dynamics in a way that is better than the standard
linearization. Further, we develop analytical upper bounds for the
computational error estimates. Finally, we use these error estimates to augment
the limit cycle analysis with a simple and quick way to bound the predicted
oscillation amplitude. These results provide system approximations that can add
more insight into the local behaviour of these systems than standard
linearization, compute responses to other periodic inputs, and to analyze limit
cycles.
| [
{
"created": "Sun, 19 May 2013 07:38:46 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Feb 2017 12:38:38 GMT",
"version": "v2"
},
{
"created": "Mon, 19 Jun 2017 18:43:46 GMT",
"version": "v3"
},
{
"created": "Tue, 5 Dec 2017 13:03:16 GMT",
"version": "v4"
}
] | 2017-12-06 | [
[
"Dey",
"Abhishek",
""
],
[
"Sen",
"Shaunak",
""
]
] | Mathematical methods provide useful framework for the analysis and design of complex systems. In newer contexts such as biology, however, there is a need to both adapt existing methods as well as to develop new ones. Using a combination of analytical and computational approaches, we adapt and develop the method of describing functions to represent the input-output responses of biomolecular signalling systems. We approximate representative systems exhibiting various saturating and hysteretic dynamics in a way that is better than the standard linearization. Further, we develop analytical upper bounds for the computational error estimates. Finally, we use these error estimates to augment the limit cycle analysis with a simple and quick way to bound the predicted oscillation amplitude. These results provide system approximations that can add more insight into the local behaviour of these systems than standard linearization, compute responses to other periodic inputs, and to analyze limit cycles. |
1907.05060 | Fabian Pallasdies | Fabian Pallasdies, Sven Goedeke, Wilhelm Braun and Raoul-Martin
Memmesheimer | From Single Neurons to Behavior in the Jellyfish Aurelia aurita | null | eLife 2019;8:e50084 | 10.7554/eLife.50084 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Jellyfish nerve nets provide insight into the origins of nervous systems, as
both their taxonomic position and their evolutionary age imply that jellyfish
resemble some of the earliest neuron-bearing, actively-swimming animals. Here
we develop the first neuronal network model for the nerve nets of jellyfish.
Specifically, we focus on the moon jelly Aurelia aurita and the control of its
energy-efficient swimming motion. The proposed single neuron model disentangles
the contributions of different currents to a spike. The network model
identifies factors ensuring non-pathological activity and suggests an
optimization for the transmission of signals. After modeling the jellyfish's
muscle system and its bell in a hydrodynamic environment, we explore the
swimming elicited by neural activity. We find that different delays between
nerve net activations lead to well-controlled, differently directed movements.
Our model bridges the scales from single neurons to behavior, allowing for a
comprehensive understanding of jellyfish neural control.
| [
{
"created": "Thu, 11 Jul 2019 08:57:27 GMT",
"version": "v1"
}
] | 2020-02-25 | [
[
"Pallasdies",
"Fabian",
""
],
[
"Goedeke",
"Sven",
""
],
[
"Braun",
"Wilhelm",
""
],
[
"Memmesheimer",
"Raoul-Martin",
""
]
] | Jellyfish nerve nets provide insight into the origins of nervous systems, as both their taxonomic position and their evolutionary age imply that jellyfish resemble some of the earliest neuron-bearing, actively-swimming animals. Here we develop the first neuronal network model for the nerve nets of jellyfish. Specifically, we focus on the moon jelly Aurelia aurita and the control of its energy-efficient swimming motion. The proposed single neuron model disentangles the contributions of different currents to a spike. The network model identifies factors ensuring non-pathological activity and suggests an optimization for the transmission of signals. After modeling the jellyfish's muscle system and its bell in a hydrodynamic environment, we explore the swimming elicited by neural activity. We find that different delays between nerve net activations lead to well-controlled, differently directed movements. Our model bridges the scales from single neurons to behavior, allowing for a comprehensive understanding of jellyfish neural control. |
2312.07590 | Carsten Wiuf | Carsten Wiuf and Chuang Xu | Any Stochastic Reaction Network has a Stationary Measure | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this note, we use a result by Harris (1957) to show that there always
exists a stationary measure (not necessarily a distribution) on a closed
irreducible component of a stochastic reaction network. This measure might not
be unique. In particular, any weakly reversible stochastic reaction network has
a stationary measure on all closed irreducibe components, irrespective whether
it is compelx balanced or not.
| [
{
"created": "Mon, 11 Dec 2023 08:09:14 GMT",
"version": "v1"
}
] | 2023-12-14 | [
[
"Wiuf",
"Carsten",
""
],
[
"Xu",
"Chuang",
""
]
] | In this note, we use a result by Harris (1957) to show that there always exists a stationary measure (not necessarily a distribution) on a closed irreducible component of a stochastic reaction network. This measure might not be unique. In particular, any weakly reversible stochastic reaction network has a stationary measure on all closed irreducibe components, irrespective whether it is compelx balanced or not. |
2006.00397 | Christopher Griffin | Christopher Griffin and Riley Mummah and Russ deForest | A Finite Population Destroys a Traveling Wave in Spatial Replicator
Dynamics | 17 pages, 7 figures | null | 10.1016/j.chaos.2021.110847 | null | q-bio.PE cs.GT math.AP nlin.PS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We derive both the finite and infinite population spatial replicator dynamics
as the fluid limit of a stochastic cellular automaton. The infinite population
spatial replicator is identical to the model used by Vickers and our derivation
justifies the addition of a diffusion to the replicator. The finite population
form generalizes the results by Durett and Levin on finite spatial replicator
games. We study the differences in the two equations as they pertain to a
one-dimensional rock-paper-scissors game.
| [
{
"created": "Sun, 31 May 2020 01:11:44 GMT",
"version": "v1"
},
{
"created": "Fri, 27 Nov 2020 22:06:45 GMT",
"version": "v2"
},
{
"created": "Thu, 4 Mar 2021 21:11:59 GMT",
"version": "v3"
}
] | 2021-04-28 | [
[
"Griffin",
"Christopher",
""
],
[
"Mummah",
"Riley",
""
],
[
"deForest",
"Russ",
""
]
] | We derive both the finite and infinite population spatial replicator dynamics as the fluid limit of a stochastic cellular automaton. The infinite population spatial replicator is identical to the model used by Vickers and our derivation justifies the addition of a diffusion to the replicator. The finite population form generalizes the results by Durett and Levin on finite spatial replicator games. We study the differences in the two equations as they pertain to a one-dimensional rock-paper-scissors game. |
1901.05537 | Momiao Xiong | Zixin Hu, Rong Jiao, Jiucun Wang, Panpan Wang, Yun Zhu, Jinying Zhao,
Phil De Jager, David A Bennett, Li Jin and Momiao Xiong | Shared Causal Paths underlying Alzheimer's dementia and Type 2 Diabetes | 53 pages | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Although Alzheimer's disease (AD) is a central nervous system
disease and type 2 diabetes mellitus (T2DM) is a metabolic disorder, an
increasing number of genetic epidemiological studies show clear link between AD
and T2DM. The current approach to uncovering the shared pathways between AD and
T2DM involves association analysis; however, such analyses lack power to
discover the mechanisms of the diseases. Methods: We develop novel statistical
methods to shift the current paradigm of genetic analysis from association
analysis to deep causal inference for uncovering the shared mechanisms between
AD and T2DM, and develop pipelines to infer multilevel omics causal networks
which lead to shifting the current paradigm of genetic analysis from genetic
analysis alone to integrated causal genomic, epigenomic, transcriptional and
phenotypic data analysis. To discover common causal paths from genetic variants
to AD and T2DM, we also develop algorithms that can automatically search the
causal paths from genetic variants to diseases and Results: The proposed
methods and algorithms are applied to ROSMAP dataset with 432 individuals who
simultaneously had genotype, RNA-seq, DNA methylation and some phenotypes. We
construct multi-omics causal networks and identify 13 shared causal genes, 16
shared causal pathways between AD and T2DM, and 754 gene expression and 101
gene methylation nodes that were connected to both AD and T2DM in multi-omics
causal networks. Conclusions: The results of application of the proposed
pipelines for identifying causal paths to real data analysis of AD and T2DM
provided strong evidence to support the link between AD and T2DM and unraveled
causal mechanism to explain this link.
| [
{
"created": "Wed, 16 Jan 2019 21:40:03 GMT",
"version": "v1"
}
] | 2019-01-18 | [
[
"Hu",
"Zixin",
""
],
[
"Jiao",
"Rong",
""
],
[
"Wang",
"Jiucun",
""
],
[
"Wang",
"Panpan",
""
],
[
"Zhu",
"Yun",
""
],
[
"Zhao",
"Jinying",
""
],
[
"De Jager",
"Phil",
""
],
[
"Bennett",
"David A",
""
],
[
"Jin",
"Li",
""
],
[
"Xiong",
"Momiao",
""
]
] | Background: Although Alzheimer's disease (AD) is a central nervous system disease and type 2 diabetes mellitus (T2DM) is a metabolic disorder, an increasing number of genetic epidemiological studies show clear link between AD and T2DM. The current approach to uncovering the shared pathways between AD and T2DM involves association analysis; however, such analyses lack power to discover the mechanisms of the diseases. Methods: We develop novel statistical methods to shift the current paradigm of genetic analysis from association analysis to deep causal inference for uncovering the shared mechanisms between AD and T2DM, and develop pipelines to infer multilevel omics causal networks which lead to shifting the current paradigm of genetic analysis from genetic analysis alone to integrated causal genomic, epigenomic, transcriptional and phenotypic data analysis. To discover common causal paths from genetic variants to AD and T2DM, we also develop algorithms that can automatically search the causal paths from genetic variants to diseases and Results: The proposed methods and algorithms are applied to ROSMAP dataset with 432 individuals who simultaneously had genotype, RNA-seq, DNA methylation and some phenotypes. We construct multi-omics causal networks and identify 13 shared causal genes, 16 shared causal pathways between AD and T2DM, and 754 gene expression and 101 gene methylation nodes that were connected to both AD and T2DM in multi-omics causal networks. Conclusions: The results of application of the proposed pipelines for identifying causal paths to real data analysis of AD and T2DM provided strong evidence to support the link between AD and T2DM and unraveled causal mechanism to explain this link. |
2111.13964 | Mufei Li | Fabio Broccatelli, Richard Trager, Michael Reutlinger, George Karypis,
Mufei Li | Benchmarking Accuracy and Generalizability of Four Graph Neural Networks
Using Large In Vitro ADME Datasets from Different Chemical Spaces | null | Molecular Informatics 2022 | 10.1002/minf.202100321 | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we benchmark a variety of single- and multi-task graph neural
network (GNN) models against lower-bar and higher-bar traditional machine
learning approaches employing human engineered molecular features. We consider
four GNN variants -- Graph Convolutional Network (GCN), Graph Attention Network
(GAT), Message Passing Neural Network (MPNN), and Attentive Fingerprint
(AttentiveFP). So far deep learning models have been primarily benchmarked
using lower-bar traditional models solely based on fingerprints, while more
realistic benchmarks employing fingerprints, whole-molecule descriptors and
predictions from other related endpoints (e.g., LogD7.4) appear to be scarce
for industrial ADME datasets. In addition to time-split test sets based on
Genentech data, this study benefits from the availability of measurements from
an external chemical space (Roche data). We identify GAT as a promising
approach to implementing deep learning models. While all GNN models
significantly outperform lower-bar benchmark traditional models solely based on
fingerprints, only GATs seem to offer a small but consistent improvement over
higher-bar benchmark traditional models. Finally, the accuracy of in vitro
assays from different laboratories predicting the same experimental endpoints
appears to be comparable with the accuracy of GAT single-task models,
suggesting that most of the observed error from the models is a function of the
experimental error propagation.
| [
{
"created": "Sat, 27 Nov 2021 18:54:38 GMT",
"version": "v1"
}
] | 2022-02-28 | [
[
"Broccatelli",
"Fabio",
""
],
[
"Trager",
"Richard",
""
],
[
"Reutlinger",
"Michael",
""
],
[
"Karypis",
"George",
""
],
[
"Li",
"Mufei",
""
]
] | In this work, we benchmark a variety of single- and multi-task graph neural network (GNN) models against lower-bar and higher-bar traditional machine learning approaches employing human engineered molecular features. We consider four GNN variants -- Graph Convolutional Network (GCN), Graph Attention Network (GAT), Message Passing Neural Network (MPNN), and Attentive Fingerprint (AttentiveFP). So far deep learning models have been primarily benchmarked using lower-bar traditional models solely based on fingerprints, while more realistic benchmarks employing fingerprints, whole-molecule descriptors and predictions from other related endpoints (e.g., LogD7.4) appear to be scarce for industrial ADME datasets. In addition to time-split test sets based on Genentech data, this study benefits from the availability of measurements from an external chemical space (Roche data). We identify GAT as a promising approach to implementing deep learning models. While all GNN models significantly outperform lower-bar benchmark traditional models solely based on fingerprints, only GATs seem to offer a small but consistent improvement over higher-bar benchmark traditional models. Finally, the accuracy of in vitro assays from different laboratories predicting the same experimental endpoints appears to be comparable with the accuracy of GAT single-task models, suggesting that most of the observed error from the models is a function of the experimental error propagation. |
2308.04610 | Hiba Kobeissi | Hiba Kobeissi, Javiera Jilberto, M. \c{C}a\u{g}atay Karakan, Xining
Gao, Samuel J. DePalma, Shoshana L. Das, Lani Quach, Jonathan Urquia, Brendon
M. Baker, Christopher S. Chen, David Nordsletten, Emma Lejeune | MicroBundleCompute: Automated segmentation, tracking, and analysis of
subdomain deformation in cardiac microbundles | 16 main pages, 7 main figures, Supplementary Information included as
appendices | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-sa/4.0/ | Advancing human induced pluripotent stem cell derived cardiomyocyte
(hiPSC-CM) technology will lead to significant progress ranging from disease
modeling, to drug discovery, to regenerative tissue engineering. Yet, alongside
these potential opportunities comes a critical challenge: attaining mature
hiPSC-CM tissues. At present, there are multiple techniques to promote maturity
of hiPSC-CMs including physical platforms and cell culture protocols. However,
when it comes to making quantitative comparisons of functional behavior, there
are limited options for reliably and reproducibly computing functional metrics
that are suitable for direct cross-system comparison. In addition, the current
standard functional metrics obtained from time-lapse images of cardiac
microbundle contraction reported in the field (i.e., post forces, average
tissue stress) do not take full advantage of the available information present
in these data (i.e., full-field tissue displacements and strains). Thus, we
present "MicroBundleCompute," a computational framework for automatic
quantification of morphology-based mechanical metrics from movies of cardiac
microbundles. Briefly, this computational framework offers tools for automatic
tissue segmentation, tracking, and analysis of brightfield and phase contrast
movies of beating cardiac microbundles. It is straightforward to implement,
requires little to no parameter tuning, and runs quickly on a personal
computer. In this paper, we describe the methods underlying this computational
framework, show the results of our extensive validation studies, and
demonstrate the utility of exploring heterogeneous tissue deformations and
strains as functional metrics. With this manuscript, we disseminate
"MicroBundleCompute" as an open-source computational tool with the aim of
making automated quantitative analysis of beating cardiac microbundles more
accessible to the community.
| [
{
"created": "Tue, 8 Aug 2023 22:27:45 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Feb 2024 20:38:05 GMT",
"version": "v2"
}
] | 2024-02-22 | [
[
"Kobeissi",
"Hiba",
""
],
[
"Jilberto",
"Javiera",
""
],
[
"Karakan",
"M. Çağatay",
""
],
[
"Gao",
"Xining",
""
],
[
"DePalma",
"Samuel J.",
""
],
[
"Das",
"Shoshana L.",
""
],
[
"Quach",
"Lani",
""
],
[
"Urquia",
"Jonathan",
""
],
[
"Baker",
"Brendon M.",
""
],
[
"Chen",
"Christopher S.",
""
],
[
"Nordsletten",
"David",
""
],
[
"Lejeune",
"Emma",
""
]
] | Advancing human induced pluripotent stem cell derived cardiomyocyte (hiPSC-CM) technology will lead to significant progress ranging from disease modeling, to drug discovery, to regenerative tissue engineering. Yet, alongside these potential opportunities comes a critical challenge: attaining mature hiPSC-CM tissues. At present, there are multiple techniques to promote maturity of hiPSC-CMs including physical platforms and cell culture protocols. However, when it comes to making quantitative comparisons of functional behavior, there are limited options for reliably and reproducibly computing functional metrics that are suitable for direct cross-system comparison. In addition, the current standard functional metrics obtained from time-lapse images of cardiac microbundle contraction reported in the field (i.e., post forces, average tissue stress) do not take full advantage of the available information present in these data (i.e., full-field tissue displacements and strains). Thus, we present "MicroBundleCompute," a computational framework for automatic quantification of morphology-based mechanical metrics from movies of cardiac microbundles. Briefly, this computational framework offers tools for automatic tissue segmentation, tracking, and analysis of brightfield and phase contrast movies of beating cardiac microbundles. It is straightforward to implement, requires little to no parameter tuning, and runs quickly on a personal computer. In this paper, we describe the methods underlying this computational framework, show the results of our extensive validation studies, and demonstrate the utility of exploring heterogeneous tissue deformations and strains as functional metrics. With this manuscript, we disseminate "MicroBundleCompute" as an open-source computational tool with the aim of making automated quantitative analysis of beating cardiac microbundles more accessible to the community. |
1208.2612 | Marcelo Briones | Francisco Bosco, Diogo Castro and Marcelo R. S. Briones | Neutral and Stable Equilibria of Genetic Systems and The Hardy-Weinberg
Principle: Limitations of the Chi-Square Test and Advantages of
Auto-Correlation Functions of Allele Frequencies | 14 pages, 6 figures | null | 10.3389/fgene.2012.00276 | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since the foundations of Population Genetics the notion of genetic
equilibrium (in close analogy to Classical Mechanics) has been associated to
the Hardy-Weinberg (HW) Principle and the identification of equilibrium is
currently assumed by stating that the HW axioms are valid if appropriate values
of Chi-Square (p<0.05) are observed in experiments. Here we show by numerical
experiments with the genetic system of one locus/two alleles that considering
large ensembles of populations the Chi-Square test is not decisive and may lead
to false negatives in random mating populations and false positives in
nonrandom mating populations. As a result we confirm the logical statement that
statistical tests can not be used to deduce if the genetic population is under
the HW conditions. Furthermore, we show that under the HW conditions
populations of any finite size evolve in time according to what can be
identified as neutral dynamics to which the very notion of equilibrium is
unattainable for any practical purpose. Therefore, under the HW conditions
equilibrium properties are not observable. We also show that by relaxing the
condition of random mating the dynamics acquires all the characteristics of
asymptotic stable equilibrium. As a consequence our results show that the
question of equilibrium in genetic systems should be approached in close
analogy to non-equilibrium statistical physics and its observability should be
focused on dynamical quantities like the typical decay properties of the
allelic auto correlation function in time. In this perspective one should
abandon the classical notion of genetic equilibrium and its relation to the HW
proportions and open investigations in the direction of searching for unifying
general principles of population genetic transformations capable to take in
consideration these systems in their full complexity.
| [
{
"created": "Mon, 13 Aug 2012 15:25:04 GMT",
"version": "v1"
},
{
"created": "Tue, 14 Aug 2012 16:07:47 GMT",
"version": "v2"
}
] | 2013-01-01 | [
[
"Bosco",
"Francisco",
""
],
[
"Castro",
"Diogo",
""
],
[
"Briones",
"Marcelo R. S.",
""
]
] | Since the foundations of Population Genetics the notion of genetic equilibrium (in close analogy to Classical Mechanics) has been associated to the Hardy-Weinberg (HW) Principle and the identification of equilibrium is currently assumed by stating that the HW axioms are valid if appropriate values of Chi-Square (p<0.05) are observed in experiments. Here we show by numerical experiments with the genetic system of one locus/two alleles that considering large ensembles of populations the Chi-Square test is not decisive and may lead to false negatives in random mating populations and false positives in nonrandom mating populations. As a result we confirm the logical statement that statistical tests can not be used to deduce if the genetic population is under the HW conditions. Furthermore, we show that under the HW conditions populations of any finite size evolve in time according to what can be identified as neutral dynamics to which the very notion of equilibrium is unattainable for any practical purpose. Therefore, under the HW conditions equilibrium properties are not observable. We also show that by relaxing the condition of random mating the dynamics acquires all the characteristics of asymptotic stable equilibrium. As a consequence our results show that the question of equilibrium in genetic systems should be approached in close analogy to non-equilibrium statistical physics and its observability should be focused on dynamical quantities like the typical decay properties of the allelic auto correlation function in time. In this perspective one should abandon the classical notion of genetic equilibrium and its relation to the HW proportions and open investigations in the direction of searching for unifying general principles of population genetic transformations capable to take in consideration these systems in their full complexity. |
1110.0800 | Rhiju Das | Wipapat Kladwang, Fang-Chieh Chou, and Rhiju Das | Automated RNA structure prediction uncovers a missing link in double
glycine riboswitches | null | null | null | null | q-bio.BM q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The tertiary structures of functional RNA molecules remain difficult to
decipher. A new generation of automated RNA structure prediction methods may
help address these challenges but have not yet been experimentally validated.
Here we apply four prediction tools to a remarkable class of double glycine
riboswitches that exhibit ligand-binding cooperativity. A novel method
(BPPalign), RMdetect, JAR3D, and Rosetta 3D modeling give consistent
predictions for a new stem P0 and kink-turn motif. These elements structure the
linker between the RNAs' double aptamers. Chemical mapping on the F. nucleatum
riboswitch with SHAPE, DMS, and CMCT probing, mutate-and-map studies, and
mutation/rescue experiments all provide strong evidence for the structured
linker. Under solution conditions that separate two glycine binding
transitions, disrupting this helix-junction-helix structure gives 120-fold and
6- to 30-fold poorer association constants for the two transitions,
corresponding to an overall energetic impact of 4.3 \pm 0.5 kcal/mol. Prior
biochemical and crystallography studies from several labs did not include this
critical element due to over-truncation of the RNA. We argue that several
further undiscovered elements are likely to exist in the flanking regions of
this and other RNA switches, and automated prediction tools can now play a
powerful role in their detection and dissection.
| [
{
"created": "Tue, 4 Oct 2011 19:01:43 GMT",
"version": "v1"
}
] | 2011-10-05 | [
[
"Kladwang",
"Wipapat",
""
],
[
"Chou",
"Fang-Chieh",
""
],
[
"Das",
"Rhiju",
""
]
] | The tertiary structures of functional RNA molecules remain difficult to decipher. A new generation of automated RNA structure prediction methods may help address these challenges but have not yet been experimentally validated. Here we apply four prediction tools to a remarkable class of double glycine riboswitches that exhibit ligand-binding cooperativity. A novel method (BPPalign), RMdetect, JAR3D, and Rosetta 3D modeling give consistent predictions for a new stem P0 and kink-turn motif. These elements structure the linker between the RNAs' double aptamers. Chemical mapping on the F. nucleatum riboswitch with SHAPE, DMS, and CMCT probing, mutate-and-map studies, and mutation/rescue experiments all provide strong evidence for the structured linker. Under solution conditions that separate two glycine binding transitions, disrupting this helix-junction-helix structure gives 120-fold and 6- to 30-fold poorer association constants for the two transitions, corresponding to an overall energetic impact of 4.3 \pm 0.5 kcal/mol. Prior biochemical and crystallography studies from several labs did not include this critical element due to over-truncation of the RNA. We argue that several further undiscovered elements are likely to exist in the flanking regions of this and other RNA switches, and automated prediction tools can now play a powerful role in their detection and dissection. |
0707.1977 | Levent Kurnaz | E. Gultepe and M. L. Kurnaz | Monte Carlo Simulation and Statistical Analysis of the Effect of Coding
Table Specificity on Genetic Information Coding | null | null | null | null | q-bio.PE q-bio.GN | null | We present a computer simulation, which is inspired by Penna model, to help
understanding the effect of genetic coding tables on population dynamics. To
represent populations we used real and artificial gene sequences in this model.
We coded these sequences using different amino acid tables in Nature, the
standard table as well as the tables which are used by mithocondria and some
eukaryotes. Contrary to common belief we find that the standard code table
which is used in most organisms in Nature, does not give the most resilient
coding against point mutations.
| [
{
"created": "Fri, 13 Jul 2007 11:22:35 GMT",
"version": "v1"
}
] | 2007-07-16 | [
[
"Gultepe",
"E.",
""
],
[
"Kurnaz",
"M. L.",
""
]
] | We present a computer simulation, which is inspired by Penna model, to help understanding the effect of genetic coding tables on population dynamics. To represent populations we used real and artificial gene sequences in this model. We coded these sequences using different amino acid tables in Nature, the standard table as well as the tables which are used by mithocondria and some eukaryotes. Contrary to common belief we find that the standard code table which is used in most organisms in Nature, does not give the most resilient coding against point mutations. |
2005.07567 | Guangcun Shan Prof. | Lu Han, G.C. Shan, B.F. Chu, H.Y. Wang, Z.J. Wang, S.Q. Gao, W.X. Zhou | Accelerating drug repurposing for COVID-19 via modeling drug mechanism
of action with large scale gene-expression profiles | 22 pages, 4 figures. Cognitive Neurodynamics (2021) | null | null | null | q-bio.QM cs.LG stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The novel coronavirus disease, named COVID-19, emerged in China in December
2019, and has rapidly spread around the world. It is clearly urgent to fight
COVID-19 at global scale. The development of methods for identifying drug uses
based on phenotypic data can improve the efficiency of drug development.
However, there are still many difficulties in identifying drug applications
based on cell picture data. This work reported one state-of-the-art machine
learning method to identify drug uses based on the cell image features of 1024
drugs generated in the LINCS program. Because the multi-dimensional features of
the image are affected by non-experimental factors, the characteristics of
similar drugs vary greatly, and the current sample number is not enough to use
deep learning and other methods are used for learning optimization. As a
consequence, this study is based on the supervised ITML algorithm to convert
the characteristics of drugs. The results show that the characteristics of ITML
conversion are more conducive to the recognition of drug functions. The
analysis of feature conversion shows that different features play important
roles in identifying different drug functions. For the current COVID-19,
Chloroquine and Hydroxychloroquine achieve antiviral effects by inhibiting
endocytosis, etc., and were classified to the same community. And Clomiphene in
the same community inibited the entry of Ebola Virus, indicated a similar MoAs
that could be reflected by cell image.
| [
{
"created": "Fri, 15 May 2020 14:28:56 GMT",
"version": "v1"
},
{
"created": "Tue, 5 Oct 2021 15:31:18 GMT",
"version": "v2"
}
] | 2021-10-06 | [
[
"Han",
"Lu",
""
],
[
"Shan",
"G. C.",
""
],
[
"Chu",
"B. F.",
""
],
[
"Wang",
"H. Y.",
""
],
[
"Wang",
"Z. J.",
""
],
[
"Gao",
"S. Q.",
""
],
[
"Zhou",
"W. X.",
""
]
] | The novel coronavirus disease, named COVID-19, emerged in China in December 2019, and has rapidly spread around the world. It is clearly urgent to fight COVID-19 at global scale. The development of methods for identifying drug uses based on phenotypic data can improve the efficiency of drug development. However, there are still many difficulties in identifying drug applications based on cell picture data. This work reported one state-of-the-art machine learning method to identify drug uses based on the cell image features of 1024 drugs generated in the LINCS program. Because the multi-dimensional features of the image are affected by non-experimental factors, the characteristics of similar drugs vary greatly, and the current sample number is not enough to use deep learning and other methods are used for learning optimization. As a consequence, this study is based on the supervised ITML algorithm to convert the characteristics of drugs. The results show that the characteristics of ITML conversion are more conducive to the recognition of drug functions. The analysis of feature conversion shows that different features play important roles in identifying different drug functions. For the current COVID-19, Chloroquine and Hydroxychloroquine achieve antiviral effects by inhibiting endocytosis, etc., and were classified to the same community. And Clomiphene in the same community inibited the entry of Ebola Virus, indicated a similar MoAs that could be reflected by cell image. |
2407.00033 | Youzhi Qu | Youzhi Qu, Junfeng Xia, Xinyao Jian, Wendu Li, Kaining Peng, Zhichao
Liang, Haiyan Wu, Quanying Liu | Uncovering cognitive taskonomy through transfer learning in masked
autoencoder-based fMRI reconstruction | null | null | null | null | q-bio.NC cs.AI | http://creativecommons.org/licenses/by/4.0/ | Data reconstruction is a widely used pre-training task to learn the
generalized features for many downstream tasks. Although reconstruction tasks
have been applied to neural signal completion and denoising, neural signal
reconstruction is less studied. Here, we employ the masked autoencoder (MAE)
model to reconstruct functional magnetic resonance imaging (fMRI) data, and
utilize a transfer learning framework to obtain the cognitive taskonomy, a
matrix to quantify the similarity between cognitive tasks. Our experimental
results demonstrate that the MAE model effectively captures the temporal
dynamics patterns and interactions within the brain regions, enabling robust
cross-subject fMRI signal reconstruction. The cognitive taskonomy derived from
the transfer learning framework reveals the relationships among cognitive
tasks, highlighting subtask correlations within motor tasks and similarities
between emotion, social, and gambling tasks. Our study suggests that the fMRI
reconstruction with MAE model can uncover the latent representation and the
obtained taskonomy offers guidance for selecting source tasks in neural
decoding tasks for improving the decoding performance on target tasks.
| [
{
"created": "Fri, 24 May 2024 09:29:16 GMT",
"version": "v1"
}
] | 2024-07-02 | [
[
"Qu",
"Youzhi",
""
],
[
"Xia",
"Junfeng",
""
],
[
"Jian",
"Xinyao",
""
],
[
"Li",
"Wendu",
""
],
[
"Peng",
"Kaining",
""
],
[
"Liang",
"Zhichao",
""
],
[
"Wu",
"Haiyan",
""
],
[
"Liu",
"Quanying",
""
]
] | Data reconstruction is a widely used pre-training task to learn the generalized features for many downstream tasks. Although reconstruction tasks have been applied to neural signal completion and denoising, neural signal reconstruction is less studied. Here, we employ the masked autoencoder (MAE) model to reconstruct functional magnetic resonance imaging (fMRI) data, and utilize a transfer learning framework to obtain the cognitive taskonomy, a matrix to quantify the similarity between cognitive tasks. Our experimental results demonstrate that the MAE model effectively captures the temporal dynamics patterns and interactions within the brain regions, enabling robust cross-subject fMRI signal reconstruction. The cognitive taskonomy derived from the transfer learning framework reveals the relationships among cognitive tasks, highlighting subtask correlations within motor tasks and similarities between emotion, social, and gambling tasks. Our study suggests that the fMRI reconstruction with MAE model can uncover the latent representation and the obtained taskonomy offers guidance for selecting source tasks in neural decoding tasks for improving the decoding performance on target tasks. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.