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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1603.03162 | Aurelio Cortese | Aurelio Cortese, Kaoru Amano, Ai Koizumi, Hakwan Lau and Mitsuo Kawato | Decoded fMRI neurofeedback can induce bidirectional behavioral changes
within single participants | 31 pages, 6 figures, 1 table | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Studies using real-time functional magnetic resonance imaging (rt-fMRI) have
recently incorporated the decoding approach, allowing for fMRI to be used as a
tool for manipulation of fine-grained neural activity. Because of the
tremendous potential for clinical applications, certain questions regarding
decoded neurofeedback (DecNef) must be addressed. Neurofeedback effects can
last for months, but the short- to mid-term dynamics are not known.
Specifically, can the same subjects learn to induce neural patterns in two
opposite directions in different sessions? This leads to a further question,
whether learning to reverse a neural pattern may be less effective after
training to induce it in a previous session. Here we employed a
within-subjects' design, with subjects undergoing DecNef training sequentially
in opposite directions (up or down regulation of confidence judgements in a
perceptual task), with the order counterbalanced across subjects. Behavioral
results indicated that the manipulation was strongly influenced by the order
and direction of neurofeedback. We therefore applied nonlinear mathematical
modeling to parametrize four main consequences of DecNef: main effect of change
in behavior, strength of down-regulation effect relative to up-regulation,
maintenance of learning over sessions, and anterograde learning interference.
Modeling results revealed that DecNef successfully induced bidirectional
behavioral changes in different sessions. Furthermore, up-regulation was more
sizable, and the effect was largely preserved even after an interval of
one-week. Lastly, the second week effect was diminished as compared to the
first week effect, indicating strong anterograde learning interference. These
results suggest reinforcement learning characteristics of DecNef, and provide
important constraints on its application to basic neuroscience, occupational
and sports trainings, and therapies.
| [
{
"created": "Thu, 10 Mar 2016 06:54:01 GMT",
"version": "v1"
}
] | 2016-03-11 | [
[
"Cortese",
"Aurelio",
""
],
[
"Amano",
"Kaoru",
""
],
[
"Koizumi",
"Ai",
""
],
[
"Lau",
"Hakwan",
""
],
[
"Kawato",
"Mitsuo",
""
]
] | Studies using real-time functional magnetic resonance imaging (rt-fMRI) have recently incorporated the decoding approach, allowing for fMRI to be used as a tool for manipulation of fine-grained neural activity. Because of the tremendous potential for clinical applications, certain questions regarding decoded neurofeedback (DecNef) must be addressed. Neurofeedback effects can last for months, but the short- to mid-term dynamics are not known. Specifically, can the same subjects learn to induce neural patterns in two opposite directions in different sessions? This leads to a further question, whether learning to reverse a neural pattern may be less effective after training to induce it in a previous session. Here we employed a within-subjects' design, with subjects undergoing DecNef training sequentially in opposite directions (up or down regulation of confidence judgements in a perceptual task), with the order counterbalanced across subjects. Behavioral results indicated that the manipulation was strongly influenced by the order and direction of neurofeedback. We therefore applied nonlinear mathematical modeling to parametrize four main consequences of DecNef: main effect of change in behavior, strength of down-regulation effect relative to up-regulation, maintenance of learning over sessions, and anterograde learning interference. Modeling results revealed that DecNef successfully induced bidirectional behavioral changes in different sessions. Furthermore, up-regulation was more sizable, and the effect was largely preserved even after an interval of one-week. Lastly, the second week effect was diminished as compared to the first week effect, indicating strong anterograde learning interference. These results suggest reinforcement learning characteristics of DecNef, and provide important constraints on its application to basic neuroscience, occupational and sports trainings, and therapies. |
1510.00953 | Zixuan Cang | Zixuan Cang, Lin Mu, Kedi Wu, Kristopher Opron, Kelin Xia and Guo-Wei
Wei | A topological approach for protein classification | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Protein function and dynamics are closely related to its sequence and
structure. However prediction of protein function and dynamics from its
sequence and structure is still a fundamental challenge in molecular biology.
Protein classification, which is typically done through measuring the
similarity be- tween proteins based on protein sequence or physical
information, serves as a crucial step toward the understanding of protein
function and dynamics. Persistent homology is a new branch of algebraic
topology that has found its success in the topological data analysis in a
variety of disciplines, including molecular biology. The present work explores
the potential of using persistent homology as an indepen- dent tool for protein
classification. To this end, we propose a molecular topological fingerprint
based support vector machine (MTF-SVM) classifier. Specifically, we construct
machine learning feature vectors solely from protein topological fingerprints,
which are topological invariants generated during the filtration process. To
validate the present MTF-SVM approach, we consider four types of problems.
First, we study protein-drug binding by using the M2 channel protein of
influenza A virus. We achieve 96% accuracy in discriminating drug bound and
unbound M2 channels. Additionally, we examine the use of MTF-SVM for the
classification of hemoglobin molecules in their relaxed and taut forms and
obtain about 80% accuracy. The identification of all alpha, all beta, and
alpha-beta protein domains is carried out in our next study using 900 proteins.
We have found a 85% success in this identifica- tion. Finally, we apply the
present technique to 55 classification tasks of protein superfamilies over 1357
samples. An average accuracy of 82% is attained. The present study establishes
computational topology as an independent and effective alternative for protein
classification.
| [
{
"created": "Sun, 4 Oct 2015 17:12:13 GMT",
"version": "v1"
}
] | 2015-10-06 | [
[
"Cang",
"Zixuan",
""
],
[
"Mu",
"Lin",
""
],
[
"Wu",
"Kedi",
""
],
[
"Opron",
"Kristopher",
""
],
[
"Xia",
"Kelin",
""
],
[
"Wei",
"Guo-Wei",
""
]
] | Protein function and dynamics are closely related to its sequence and structure. However prediction of protein function and dynamics from its sequence and structure is still a fundamental challenge in molecular biology. Protein classification, which is typically done through measuring the similarity be- tween proteins based on protein sequence or physical information, serves as a crucial step toward the understanding of protein function and dynamics. Persistent homology is a new branch of algebraic topology that has found its success in the topological data analysis in a variety of disciplines, including molecular biology. The present work explores the potential of using persistent homology as an indepen- dent tool for protein classification. To this end, we propose a molecular topological fingerprint based support vector machine (MTF-SVM) classifier. Specifically, we construct machine learning feature vectors solely from protein topological fingerprints, which are topological invariants generated during the filtration process. To validate the present MTF-SVM approach, we consider four types of problems. First, we study protein-drug binding by using the M2 channel protein of influenza A virus. We achieve 96% accuracy in discriminating drug bound and unbound M2 channels. Additionally, we examine the use of MTF-SVM for the classification of hemoglobin molecules in their relaxed and taut forms and obtain about 80% accuracy. The identification of all alpha, all beta, and alpha-beta protein domains is carried out in our next study using 900 proteins. We have found a 85% success in this identifica- tion. Finally, we apply the present technique to 55 classification tasks of protein superfamilies over 1357 samples. An average accuracy of 82% is attained. The present study establishes computational topology as an independent and effective alternative for protein classification. |
2404.17601 | Bahman Moraffah | Bahman Moraffah | Nested Inheritance Dynamics | null | null | null | null | q-bio.PE cs.LG | http://creativecommons.org/licenses/by/4.0/ | The idea of the inheritance of biological processes, such as the
developmental process or the life cycle of an organism, has been discussed in
the biology literature, but formal mathematical descriptions and plausible data
analysis frameworks are lacking. We introduce an extension of the nested
Dirichlet Process (nDP) to a multiscale model to aid in understanding the
mechanisms by which biological processes are inherited, remain stable, and are
modified across generations. To address these issues, we introduce Nested
Inheritance Dynamics Algorithm (NIDA). At its primary level, NIDA encompasses
all processes unfolding within an individual organism's lifespan. The secondary
level delineates the dynamics through which these processes evolve or remain
stable over time. This framework allows for the specification of a physical
system model at either scale, thus promoting seamless integration with
established models of development and heredity.
| [
{
"created": "Tue, 23 Apr 2024 23:10:08 GMT",
"version": "v1"
}
] | 2024-04-30 | [
[
"Moraffah",
"Bahman",
""
]
] | The idea of the inheritance of biological processes, such as the developmental process or the life cycle of an organism, has been discussed in the biology literature, but formal mathematical descriptions and plausible data analysis frameworks are lacking. We introduce an extension of the nested Dirichlet Process (nDP) to a multiscale model to aid in understanding the mechanisms by which biological processes are inherited, remain stable, and are modified across generations. To address these issues, we introduce Nested Inheritance Dynamics Algorithm (NIDA). At its primary level, NIDA encompasses all processes unfolding within an individual organism's lifespan. The secondary level delineates the dynamics through which these processes evolve or remain stable over time. This framework allows for the specification of a physical system model at either scale, thus promoting seamless integration with established models of development and heredity. |
0910.2741 | Gerhard Werner MD | Gerhard Werner | Fractals in the Nervous System: conceptual Implications for Theoretical
Neuroscience | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review.
| [
{
"created": "Wed, 14 Oct 2009 22:26:12 GMT",
"version": "v1"
},
{
"created": "Wed, 7 Apr 2010 22:23:55 GMT",
"version": "v2"
}
] | 2010-04-09 | [
[
"Werner",
"Gerhard",
""
]
] | This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review. |
1705.06993 | Seth Sullivant | Chris Durden and Seth Sullivant | Identifiability of phylogenetic parameters from k-mer data under the
coalescent | 21 pages | null | null | null | q-bio.PE math.AG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Distances between sequences based on their $k$-mer frequency counts can be
used to reconstruct phylogenies without first computing a sequence alignment.
Past work has shown that effective use of k-mer methods depends on 1)
model-based corrections to distances based on $k$-mers and 2) breaking long
sequences into blocks to obtain repeated trials from the sequence-generating
process. Good performance of such methods is based on having many high-quality
blocks with many homologous sites, which can be problematic to guarantee a
priori.
Nature provides natural blocks of sequences into homologous regions---namely,
the genes. However, directly using past work in this setting is problematic
because of possible discordance between different gene trees and the underlying
species tree. Using the multispecies coalescent model as a basis, we derive
model-based moment formulas that involve the divergence times and the
coalescent parameters. From this setting, we prove identifiability results for
the tree and branch length parameters under the Jukes-Cantor model of sequence
mutations.
| [
{
"created": "Fri, 19 May 2017 13:50:08 GMT",
"version": "v1"
}
] | 2017-05-22 | [
[
"Durden",
"Chris",
""
],
[
"Sullivant",
"Seth",
""
]
] | Distances between sequences based on their $k$-mer frequency counts can be used to reconstruct phylogenies without first computing a sequence alignment. Past work has shown that effective use of k-mer methods depends on 1) model-based corrections to distances based on $k$-mers and 2) breaking long sequences into blocks to obtain repeated trials from the sequence-generating process. Good performance of such methods is based on having many high-quality blocks with many homologous sites, which can be problematic to guarantee a priori. Nature provides natural blocks of sequences into homologous regions---namely, the genes. However, directly using past work in this setting is problematic because of possible discordance between different gene trees and the underlying species tree. Using the multispecies coalescent model as a basis, we derive model-based moment formulas that involve the divergence times and the coalescent parameters. From this setting, we prove identifiability results for the tree and branch length parameters under the Jukes-Cantor model of sequence mutations. |
1208.2636 | Corey S. O'Hern | W. Wendell Smith, Carl F. Schreck, Nabeem Hashem, Sherwin Soltani,
Abhinav Nath, Elizabeth Rhoades, and Corey S. O'Hern | Molecular Simulations of the Fluctuating Conformational Dynamics of
Intrinsically Disordered Proteins | 14 pages, 12 figures, 2 tables | Phys. Rev. E 86 (2012) 041910 | null | null | q-bio.BM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intrinsically disordered proteins (IDPs) do not possess well-defined
three-dimensional structures in solution under physiological conditions. We
develop all-atom, united-atom, and coarse-grained Langevin dynamics simulations
for the IDP alpha-synuclein that include geometric, attractive hydrophobic, and
screened electrostatic interactions and are calibrated to the inter-residue
separations measured in recent smFRET experiments. We find that alpha-synuclein
is disordered with conformational statistics that are intermediate between
random walk and collapsed globule behavior. An advantage of calibrated
molecular simulations over constraint methods is that physical forces act on
all residues, not only on residue pairs that are monitored experimentally, and
these simulations can be used to study oligomerization and aggregation of
multiple alpha-synuclein proteins that may precede amyloid formation.
| [
{
"created": "Mon, 13 Aug 2012 16:47:39 GMT",
"version": "v1"
}
] | 2012-10-16 | [
[
"Smith",
"W. Wendell",
""
],
[
"Schreck",
"Carl F.",
""
],
[
"Hashem",
"Nabeem",
""
],
[
"Soltani",
"Sherwin",
""
],
[
"Nath",
"Abhinav",
""
],
[
"Rhoades",
"Elizabeth",
""
],
[
"O'Hern",
"Corey S.",
""
]
] | Intrinsically disordered proteins (IDPs) do not possess well-defined three-dimensional structures in solution under physiological conditions. We develop all-atom, united-atom, and coarse-grained Langevin dynamics simulations for the IDP alpha-synuclein that include geometric, attractive hydrophobic, and screened electrostatic interactions and are calibrated to the inter-residue separations measured in recent smFRET experiments. We find that alpha-synuclein is disordered with conformational statistics that are intermediate between random walk and collapsed globule behavior. An advantage of calibrated molecular simulations over constraint methods is that physical forces act on all residues, not only on residue pairs that are monitored experimentally, and these simulations can be used to study oligomerization and aggregation of multiple alpha-synuclein proteins that may precede amyloid formation. |
q-bio/0402038 | Franco Bagnoli | Franco Bagnoli, Carlo Guardiani | A model of sympatric speciation through assortative mating | new version, to appear in physica A | Physica A 347, 534-574 (2005) | 10.1016/j.physa.2004.08.068 | null | q-bio.PE | null | A microscopic model is developed, within the frame of the theory of
quantitative traits, to study both numerically and analytically the combined
effect of competition and assortativity on the sympatric speciation process,
i.e. speciation in the absence of geographical barriers. Two components of
fitness are considered: a static one that describes adaptation to environmental
factors not related to the population itself, and a dynamic one that accounts
for interactions between organisms, e.g. competition. The effects of finiteness
of population size on survival of coexisting species are also accounted for.
The simulations show that both in the case of flat and ripid static fitness
landscapes, competition and assortativity do exert a synergistic effect on
speciation. We also show that competition acts as a stabilizing force against
extinction due to random sampling in a finite population. Finally, evidence is
shown that speciation can be seen as a phase transition.
| [
{
"created": "Wed, 18 Feb 2004 18:23:55 GMT",
"version": "v1"
},
{
"created": "Wed, 25 Aug 2004 10:29:14 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Bagnoli",
"Franco",
""
],
[
"Guardiani",
"Carlo",
""
]
] | A microscopic model is developed, within the frame of the theory of quantitative traits, to study both numerically and analytically the combined effect of competition and assortativity on the sympatric speciation process, i.e. speciation in the absence of geographical barriers. Two components of fitness are considered: a static one that describes adaptation to environmental factors not related to the population itself, and a dynamic one that accounts for interactions between organisms, e.g. competition. The effects of finiteness of population size on survival of coexisting species are also accounted for. The simulations show that both in the case of flat and ripid static fitness landscapes, competition and assortativity do exert a synergistic effect on speciation. We also show that competition acts as a stabilizing force against extinction due to random sampling in a finite population. Finally, evidence is shown that speciation can be seen as a phase transition. |
1302.4000 | Micha{\l} Jamr\'oz | Jamr\'oz Micha{\l}, Koli\'nski Andrzej | ClusCo: clustering and comparison of protein models | null | null | 10.1186/1471-2105-14-62 | null | q-bio.BM cs.CE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: The development, optimization and validation of protein modeling
methods require efficient tools for structural comparison. Frequently, a large
number of models need to be compared with the target native structure. The main
reason for the development of Clusco software was to create a high-throughput
tool for all-versus-all comparison, because calculating similarity matrix is
the one of the bottlenecks in the protein modeling pipeline. Results: Clusco is
fast and easy-to-use software for high-throughput comparison of protein models
with different similarity measures (cRMSD, dRMSD, GDT_TS, TM-Score, MaxSub,
Contact Map Overlap) and clustering of the comparison results with standard
methods: K-means Clustering or Hierarchical Agglomerative Clustering.
Conclusions: The application was highly optimized and written in C/C++,
including the code for parallel execution on CPU and GPU version of cRMSD,
which resulted in a significant speedup over similar clustering and scoring
computation programs.
| [
{
"created": "Sat, 16 Feb 2013 21:11:35 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Feb 2013 14:55:55 GMT",
"version": "v2"
}
] | 2013-03-04 | [
[
"Michał",
"Jamróz",
""
],
[
"Andrzej",
"Koliński",
""
]
] | Background: The development, optimization and validation of protein modeling methods require efficient tools for structural comparison. Frequently, a large number of models need to be compared with the target native structure. The main reason for the development of Clusco software was to create a high-throughput tool for all-versus-all comparison, because calculating similarity matrix is the one of the bottlenecks in the protein modeling pipeline. Results: Clusco is fast and easy-to-use software for high-throughput comparison of protein models with different similarity measures (cRMSD, dRMSD, GDT_TS, TM-Score, MaxSub, Contact Map Overlap) and clustering of the comparison results with standard methods: K-means Clustering or Hierarchical Agglomerative Clustering. Conclusions: The application was highly optimized and written in C/C++, including the code for parallel execution on CPU and GPU version of cRMSD, which resulted in a significant speedup over similar clustering and scoring computation programs. |
2012.11665 | Venta Terauds | Venta Terauds and Jeremy Sumner | A new algebraic approach to genome rearrangement models | 32 pages. v2 more concise (former Sec. 4 removed) | J. Math. Biol. 84, 49 (2022) | 10.1007/s00285-022-01744-0 | null | q-bio.PE math.RA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a unified framework for modelling genomes and their rearrangements
in a genome algebra, as elements that simultaneously incorporate all physical
symmetries. Building on previous work utilising the group algebra of the
symmetric group, we explicitly construct the genome algebra for the case of
unsigned circular genomes with dihedral symmetry and show that the maximum
likelihood estimate (MLE) of genome rearrangement distance can be validly and
more efficiently performed in this setting. We then construct the genome
algebra for a more general case, that is, for genomes that may be represented
by elements of an arbitrary group and symmetry group, and show that the MLE
computations can be performed entirely within this framework. There is no
prescribed model in this framework; that is, it allows any choice of
rearrangements that preserve the set of regions, along with arbitrary weights.
Further, since the likelihood function is built from path probabilities -- a
generalisation of path counts -- the framework may be utilised for any distance
measure that is based on path probabilities.
| [
{
"created": "Mon, 21 Dec 2020 20:26:43 GMT",
"version": "v1"
},
{
"created": "Mon, 16 May 2022 00:42:21 GMT",
"version": "v2"
}
] | 2022-05-17 | [
[
"Terauds",
"Venta",
""
],
[
"Sumner",
"Jeremy",
""
]
] | We present a unified framework for modelling genomes and their rearrangements in a genome algebra, as elements that simultaneously incorporate all physical symmetries. Building on previous work utilising the group algebra of the symmetric group, we explicitly construct the genome algebra for the case of unsigned circular genomes with dihedral symmetry and show that the maximum likelihood estimate (MLE) of genome rearrangement distance can be validly and more efficiently performed in this setting. We then construct the genome algebra for a more general case, that is, for genomes that may be represented by elements of an arbitrary group and symmetry group, and show that the MLE computations can be performed entirely within this framework. There is no prescribed model in this framework; that is, it allows any choice of rearrangements that preserve the set of regions, along with arbitrary weights. Further, since the likelihood function is built from path probabilities -- a generalisation of path counts -- the framework may be utilised for any distance measure that is based on path probabilities. |
2302.11576 | Ralph Brinks | Ralph Brinks | Estimation of age-specific excess mortality of men and women with
rheumatoid arthritis (RA) in Germany | 5 pages, 1 figure | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A MCMC approach is used to estimate the age-specific mortality rate ratio for
German men and women with RA. For constructing priors, we calculate a range of
admissible values from prevalence and incidence data based on about 60 million
people in Germany. Using these priors, MCMC mimics and compares estimated
mortality to the findings of a recent register study from Denmark. It is
estimated that the mortality rate ratio is highest in the young ages (4.0 and
3.5 for men and women aged 17.5 years, respectively) and declines towards
higher ages (1.0 and 1.2 for men and women aged 92.5 years, respectively). The
lengths of the credibility intervals decrease from younger towards older ages.
| [
{
"created": "Wed, 22 Feb 2023 17:11:50 GMT",
"version": "v1"
}
] | 2023-02-24 | [
[
"Brinks",
"Ralph",
""
]
] | A MCMC approach is used to estimate the age-specific mortality rate ratio for German men and women with RA. For constructing priors, we calculate a range of admissible values from prevalence and incidence data based on about 60 million people in Germany. Using these priors, MCMC mimics and compares estimated mortality to the findings of a recent register study from Denmark. It is estimated that the mortality rate ratio is highest in the young ages (4.0 and 3.5 for men and women aged 17.5 years, respectively) and declines towards higher ages (1.0 and 1.2 for men and women aged 92.5 years, respectively). The lengths of the credibility intervals decrease from younger towards older ages. |
2309.13484 | Md Masud Rana | Md Masud Rana and Duc Duy Nguyen | GGL-PPI: Geometric Graph Learning to Predict Mutation-Induced Binding
Free Energy Changes | null | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Protein-protein interactions (PPIs) are critical for various biological
processes, and understanding their dynamics is essential for decoding molecular
mechanisms and advancing fields such as cancer research and drug discovery.
Mutations in PPIs can disrupt protein binding affinity and lead to functional
changes and disease. Predicting the impact of mutations on binding affinity is
valuable but experimentally challenging. Computational methods, including
physics-based and machine learning-based approaches, have been developed to
address this challenge. Machine learning-based methods, fueled by extensive PPI
datasets such as Ab-Bind, PINT, SKEMPI, and others, have shown promise in
predicting binding affinity changes. However, accurate predictions and
generalization of these models across different datasets remain challenging.
Geometric graph learning has emerged as a powerful approach, combining graph
theory and machine learning, to capture structural features of biomolecules. We
present GGL-PPI, a novel method that integrates geometric graph learning and
machine learning to predict mutation-induced binding free energy changes.
GGL-PPI leverages atom-level graph coloring and multi-scale weighted colored
geometric subgraphs to extract informative features, demonstrating superior
performance on three validation datasets, namely AB-Bind, SKEMPI 1.0, and
SKEMPI 2.0 datasets. Evaluation on a blind test set highlights the unbiased
predictions of GGL-PPI for both direct and reverse mutations. The findings
underscore the potential of GGL-PPI in accurately predicting binding free
energy changes, contributing to our understanding of PPIs and aiding drug
design efforts.
| [
{
"created": "Sat, 23 Sep 2023 22:01:00 GMT",
"version": "v1"
}
] | 2023-09-26 | [
[
"Rana",
"Md Masud",
""
],
[
"Nguyen",
"Duc Duy",
""
]
] | Protein-protein interactions (PPIs) are critical for various biological processes, and understanding their dynamics is essential for decoding molecular mechanisms and advancing fields such as cancer research and drug discovery. Mutations in PPIs can disrupt protein binding affinity and lead to functional changes and disease. Predicting the impact of mutations on binding affinity is valuable but experimentally challenging. Computational methods, including physics-based and machine learning-based approaches, have been developed to address this challenge. Machine learning-based methods, fueled by extensive PPI datasets such as Ab-Bind, PINT, SKEMPI, and others, have shown promise in predicting binding affinity changes. However, accurate predictions and generalization of these models across different datasets remain challenging. Geometric graph learning has emerged as a powerful approach, combining graph theory and machine learning, to capture structural features of biomolecules. We present GGL-PPI, a novel method that integrates geometric graph learning and machine learning to predict mutation-induced binding free energy changes. GGL-PPI leverages atom-level graph coloring and multi-scale weighted colored geometric subgraphs to extract informative features, demonstrating superior performance on three validation datasets, namely AB-Bind, SKEMPI 1.0, and SKEMPI 2.0 datasets. Evaluation on a blind test set highlights the unbiased predictions of GGL-PPI for both direct and reverse mutations. The findings underscore the potential of GGL-PPI in accurately predicting binding free energy changes, contributing to our understanding of PPIs and aiding drug design efforts. |
2109.10258 | Yu-Ting Lin | Shen-Chih Wang, Chien-Kun Ting, Cheng-Yen Chen, Chin-Su Liu,
Niang-Cheng Lin, Che-Chuan Loon, Hau-Tieng Wu, Yu-Ting Lin | Arterial blood pressure waveform in liver transplant surgery possesses
variability of morphology reflecting recipients' acuity and predicting short
term outcomes | 5 figures and 1 table | null | null | null | q-bio.QM cs.LG physics.med-ph | http://creativecommons.org/licenses/by/4.0/ | Background: We investigated clinical information underneath the beat-to-beat
fluctuation of the arterial blood pressure (ABP) waveform morphology. We
proposed the Dynamical Diffusion Map algorithm (DDMap) to quantify the
variability of morphology. The underlying physiology could be the compensatory
mechanisms involving complex interactions between various physiological
mechanisms to regulate the cardiovascular system. As a liver transplant surgery
contains distinct periods, we investigated its clinical behavior in different
surgical steps. Methods: Our study used DDmap algorithm, based on unsupervised
manifold learning, to obtain a quantitative index for the beat-to-beat
variability of morphology. We examined the correlation between the variability
of ABP morphology and disease acuity as indicated by Model for End-Stage Liver
Disease (MELD) scores, the postoperative laboratory data, and 4 early allograft
failure (EAF) scores. Results: Among the 85 enrolled patients, the variability
of morphology obtained during the presurgical phase was best correlated with
MELD-Na scores. The neohepatic phase variability of morphology was associated
with EAF scores as well as postoperative bilirubin levels, international
normalized ratio, aspartate aminotransferase levels, and platelet count.
Furthermore, variability of morphology presents more associations with the
above clinical conditions than the common BP measures and their BP variability
indices. Conclusions: The variability of morphology obtained during the
presurgical phase is indicative of patient acuity, whereas those during the
neohepatic phase are indicative of short-term surgical outcomes.
| [
{
"created": "Tue, 21 Sep 2021 15:33:58 GMT",
"version": "v1"
},
{
"created": "Sat, 1 Jul 2023 05:03:24 GMT",
"version": "v2"
}
] | 2023-07-04 | [
[
"Wang",
"Shen-Chih",
""
],
[
"Ting",
"Chien-Kun",
""
],
[
"Chen",
"Cheng-Yen",
""
],
[
"Liu",
"Chin-Su",
""
],
[
"Lin",
"Niang-Cheng",
""
],
[
"Loon",
"Che-Chuan",
""
],
[
"Wu",
"Hau-Tieng",
""
],
[
"Lin",
"Yu-Ting",
""
]
] | Background: We investigated clinical information underneath the beat-to-beat fluctuation of the arterial blood pressure (ABP) waveform morphology. We proposed the Dynamical Diffusion Map algorithm (DDMap) to quantify the variability of morphology. The underlying physiology could be the compensatory mechanisms involving complex interactions between various physiological mechanisms to regulate the cardiovascular system. As a liver transplant surgery contains distinct periods, we investigated its clinical behavior in different surgical steps. Methods: Our study used DDmap algorithm, based on unsupervised manifold learning, to obtain a quantitative index for the beat-to-beat variability of morphology. We examined the correlation between the variability of ABP morphology and disease acuity as indicated by Model for End-Stage Liver Disease (MELD) scores, the postoperative laboratory data, and 4 early allograft failure (EAF) scores. Results: Among the 85 enrolled patients, the variability of morphology obtained during the presurgical phase was best correlated with MELD-Na scores. The neohepatic phase variability of morphology was associated with EAF scores as well as postoperative bilirubin levels, international normalized ratio, aspartate aminotransferase levels, and platelet count. Furthermore, variability of morphology presents more associations with the above clinical conditions than the common BP measures and their BP variability indices. Conclusions: The variability of morphology obtained during the presurgical phase is indicative of patient acuity, whereas those during the neohepatic phase are indicative of short-term surgical outcomes. |
2011.13557 | Stephan Eismann | Stephan Eismann, Patricia Suriana, Bowen Jing, Raphael J.L. Townshend,
Ron O. Dror | Protein model quality assessment using rotation-equivariant,
hierarchical neural networks | null | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proteins are miniature machines whose function depends on their
three-dimensional (3D) structure. Determining this structure computationally
remains an unsolved grand challenge. A major bottleneck involves selecting the
most accurate structural model among a large pool of candidates, a task
addressed in model quality assessment. Here, we present a novel deep learning
approach to assess the quality of a protein model. Our network builds on a
point-based representation of the atomic structure and rotation-equivariant
convolutions at different levels of structural resolution. These combined
aspects allow the network to learn end-to-end from entire protein structures.
Our method achieves state-of-the-art results in scoring protein models
submitted to recent rounds of CASP, a blind prediction community experiment.
Particularly striking is that our method does not use physics-inspired energy
terms and does not rely on the availability of additional information (beyond
the atomic structure of the individual protein model), such as sequence
alignments of multiple proteins.
| [
{
"created": "Fri, 27 Nov 2020 05:03:53 GMT",
"version": "v1"
}
] | 2020-11-30 | [
[
"Eismann",
"Stephan",
""
],
[
"Suriana",
"Patricia",
""
],
[
"Jing",
"Bowen",
""
],
[
"Townshend",
"Raphael J. L.",
""
],
[
"Dror",
"Ron O.",
""
]
] | Proteins are miniature machines whose function depends on their three-dimensional (3D) structure. Determining this structure computationally remains an unsolved grand challenge. A major bottleneck involves selecting the most accurate structural model among a large pool of candidates, a task addressed in model quality assessment. Here, we present a novel deep learning approach to assess the quality of a protein model. Our network builds on a point-based representation of the atomic structure and rotation-equivariant convolutions at different levels of structural resolution. These combined aspects allow the network to learn end-to-end from entire protein structures. Our method achieves state-of-the-art results in scoring protein models submitted to recent rounds of CASP, a blind prediction community experiment. Particularly striking is that our method does not use physics-inspired energy terms and does not rely on the availability of additional information (beyond the atomic structure of the individual protein model), such as sequence alignments of multiple proteins. |
2112.14185 | Nicholas Glykos | Ioanna Gkogka and Nicholas M. Glykos | Folding molecular dynamics simulation of T-peptide, a HIV viral entry
inhibitor : Structure, dynamics, and comparison with the experimental data | null | null | 10.1002/jcc.26850 | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Peptide T is a synthetic octapeptide fragment, which corresponds to the
region 185-192 of the gp120 HIV coat protein and functions as a viral entry
inhibitor. In this work, a folding molecular dynamics simulation of peptide T
in a membrane-mimicking (DMSO) solution was performed with the aim of
characterizing the peptide's structural and dynamical properties. We show that
peptide T is highly flexible and dynamic. The main structural characteristics
observed were rapidly interconverting short helical stretches and turns, with a
notable preference for the formation of $\beta$-turns. The simulation also
indicated that the C-terminal part appears to be more stable than the rest of
the peptide, with the most preferred conformation for residues 5-8 being a
$\beta$-turn. In order to validate the accuracy of the simulations, we compared
our results with the experimental NMR data obtained for the T-peptide in the
same solvent. In agreement with the simulation, the NMR data indicated the
presence of a preferred structure in solution that was consistent with a
$\beta$-turn comprising the four C-terminal residues. An additional comparison
between the experimental and simulation-derived chemical shifts also showed a
reasonable agreement between experiment and simulation, further validating the
simulation-derived structural characterization of the T-peptide.
| [
{
"created": "Tue, 28 Dec 2021 15:25:12 GMT",
"version": "v1"
}
] | 2022-04-12 | [
[
"Gkogka",
"Ioanna",
""
],
[
"Glykos",
"Nicholas M.",
""
]
] | Peptide T is a synthetic octapeptide fragment, which corresponds to the region 185-192 of the gp120 HIV coat protein and functions as a viral entry inhibitor. In this work, a folding molecular dynamics simulation of peptide T in a membrane-mimicking (DMSO) solution was performed with the aim of characterizing the peptide's structural and dynamical properties. We show that peptide T is highly flexible and dynamic. The main structural characteristics observed were rapidly interconverting short helical stretches and turns, with a notable preference for the formation of $\beta$-turns. The simulation also indicated that the C-terminal part appears to be more stable than the rest of the peptide, with the most preferred conformation for residues 5-8 being a $\beta$-turn. In order to validate the accuracy of the simulations, we compared our results with the experimental NMR data obtained for the T-peptide in the same solvent. In agreement with the simulation, the NMR data indicated the presence of a preferred structure in solution that was consistent with a $\beta$-turn comprising the four C-terminal residues. An additional comparison between the experimental and simulation-derived chemical shifts also showed a reasonable agreement between experiment and simulation, further validating the simulation-derived structural characterization of the T-peptide. |
1407.3348 | Long Fan | Long Fan and Ka Hou Chu | LV Barcoding: locality sensitive hashing-based tool for rapid species
identification in DNA barcoding | 10 pages, 4 figures, 1 table, DNA barcoding, Locality-sensitive
hashing, Algorithm, Software | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | DNA barcoding has emerged as a cost-effective approach for species
identification. However, the scarcity of tools used for searching the booming
reference database becomes an obstacle, currently with BLAST as the only
practical choice. Here, we propose a program - LV Barcoding - based on both the
random hyperplane projection-based locality sensitive hashing method and the
composition vector-based VIP Barcoding for fast species identification. The
performance of LV Barcoding is assessed on the data release of BOLD. LV
Barcoding has higher accuracy than BLAST, and is able to match a single query
against ~114,000 reference barcodes within 10 seconds on a desktop computer.
This program is available at http://msl.sls.cuhk.edu.hk/vipbarcoding/.
| [
{
"created": "Sat, 12 Jul 2014 06:05:29 GMT",
"version": "v1"
}
] | 2014-07-15 | [
[
"Fan",
"Long",
""
],
[
"Chu",
"Ka Hou",
""
]
] | DNA barcoding has emerged as a cost-effective approach for species identification. However, the scarcity of tools used for searching the booming reference database becomes an obstacle, currently with BLAST as the only practical choice. Here, we propose a program - LV Barcoding - based on both the random hyperplane projection-based locality sensitive hashing method and the composition vector-based VIP Barcoding for fast species identification. The performance of LV Barcoding is assessed on the data release of BOLD. LV Barcoding has higher accuracy than BLAST, and is able to match a single query against ~114,000 reference barcodes within 10 seconds on a desktop computer. This program is available at http://msl.sls.cuhk.edu.hk/vipbarcoding/. |
2104.14180 | Xingjian Zhang | Gleb Pogudin and Xingjian Zhang | Interpretable exact linear reductions via positivity | null | null | null | null | q-bio.MN cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Kinetic models of biochemical systems used in the modern literature often
contain hundreds or even thousands of variables. While these models are
convenient for detailed simulations, their size is often an obstacle to
deriving mechanistic insights. One way to address this issue is to perform an
exact model reduction by finding a self-consistent lower-dimensional projection
of the corresponding dynamical system. Recently, a new algorithm CLUE has been
designed and implemented, which allows one to construct an exact linear
reduction of the smallest possible dimension such that the fixed variables of
interest are preserved. It turned out that allowing arbitrary linear
combinations (as opposed to zero-one combinations used in the prior approaches)
may yield a much smaller reduction. However, there was a drawback: some of the
new variables did not have clear physical meaning, thus making the reduced
model harder to interpret. We design and implement an algorithm that, given an
exact linear reduction, re-parametrizes it by performing an invertible
transformation of the new coordinates to improve the interpretability of the
new variables. We apply our algorithm to three case studies and show that
"uninterpretable" variables disappear entirely in all the case studies. The
implementation of the algorithm and the files for the case studies are
available at https://github.com/xjzhaang/LumpingPostiviser.
| [
{
"created": "Thu, 29 Apr 2021 07:59:21 GMT",
"version": "v1"
},
{
"created": "Fri, 30 Apr 2021 14:28:00 GMT",
"version": "v2"
},
{
"created": "Sat, 26 Jun 2021 16:17:48 GMT",
"version": "v3"
}
] | 2021-06-29 | [
[
"Pogudin",
"Gleb",
""
],
[
"Zhang",
"Xingjian",
""
]
] | Kinetic models of biochemical systems used in the modern literature often contain hundreds or even thousands of variables. While these models are convenient for detailed simulations, their size is often an obstacle to deriving mechanistic insights. One way to address this issue is to perform an exact model reduction by finding a self-consistent lower-dimensional projection of the corresponding dynamical system. Recently, a new algorithm CLUE has been designed and implemented, which allows one to construct an exact linear reduction of the smallest possible dimension such that the fixed variables of interest are preserved. It turned out that allowing arbitrary linear combinations (as opposed to zero-one combinations used in the prior approaches) may yield a much smaller reduction. However, there was a drawback: some of the new variables did not have clear physical meaning, thus making the reduced model harder to interpret. We design and implement an algorithm that, given an exact linear reduction, re-parametrizes it by performing an invertible transformation of the new coordinates to improve the interpretability of the new variables. We apply our algorithm to three case studies and show that "uninterpretable" variables disappear entirely in all the case studies. The implementation of the algorithm and the files for the case studies are available at https://github.com/xjzhaang/LumpingPostiviser. |
2404.13851 | Yuri A. Dabaghian | M. S. Zobaer, N. Lotfi, C. M. Domenico, C. Hoffman, L. Perotti, D. Ji,
Y. Dabaghian | Theta oscillons in behaving rats | 12 pages, 6 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Recently discovered constituents of the brain waves -- the oscillons --
provide high-resolution representation of the extracellular field dynamics.
Here we study the most robust, highest-amplitude oscillons that manifest in
actively behaving rats and generally correspond to the traditional theta-waves.
We show that the resemblances between theta-oscillons and the conventional
theta-waves apply to the ballpark characteristics -- mean frequencies,
amplitudes, and bandwidths. In addition, both hippocampal and cortical
oscillons exhibit a number of intricate, behavior-attuned, transient properties
that suggest a new vantage point for understanding the theta-rhythms'
structure, origins and functions. We demonstrate that oscillons are
frequency-modulated waves, with speed-controlled parameters, embedded into a
noise background. We also use a basic model of neuronal synchronization to
contextualize and to interpret the observed phenomena. In particular, we argue
that the synchronicity level in physiological networks is fairly weak and
modulated by the animal's locomotion.
| [
{
"created": "Mon, 22 Apr 2024 03:25:03 GMT",
"version": "v1"
}
] | 2024-04-23 | [
[
"Zobaer",
"M. S.",
""
],
[
"Lotfi",
"N.",
""
],
[
"Domenico",
"C. M.",
""
],
[
"Hoffman",
"C.",
""
],
[
"Perotti",
"L.",
""
],
[
"Ji",
"D.",
""
],
[
"Dabaghian",
"Y.",
""
]
] | Recently discovered constituents of the brain waves -- the oscillons -- provide high-resolution representation of the extracellular field dynamics. Here we study the most robust, highest-amplitude oscillons that manifest in actively behaving rats and generally correspond to the traditional theta-waves. We show that the resemblances between theta-oscillons and the conventional theta-waves apply to the ballpark characteristics -- mean frequencies, amplitudes, and bandwidths. In addition, both hippocampal and cortical oscillons exhibit a number of intricate, behavior-attuned, transient properties that suggest a new vantage point for understanding the theta-rhythms' structure, origins and functions. We demonstrate that oscillons are frequency-modulated waves, with speed-controlled parameters, embedded into a noise background. We also use a basic model of neuronal synchronization to contextualize and to interpret the observed phenomena. In particular, we argue that the synchronicity level in physiological networks is fairly weak and modulated by the animal's locomotion. |
0906.4279 | Vasily Ogryzko V | Vasily Ogryzko | Quantum information processing at the cellular level. Euclidean approach | 82 pages. The part on decoherence and enzymatic mechanism has been
improved | null | null | null | q-bio.MN cond-mat.soft quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Application of quantum principles to living cells requires a new
approximation of the full quantum mechanical description of intracellular
dynamics. We discuss what principal elements any such good approximation should
contain. As one such element, the notion of "Catalytic force" Cf is introduced.
Cf is the effect of the molecular target of catalysis on the catalytic
microenvironment that adjusts the microenvironment towards a state that
facilitates the catalytic act. This phenomenon is experimentally testable and
has an intriguing implication for biological organization and evolution, as it
amounts to "optimization without natural selection of replicators". Unlike the
statistical-mechanical approaches to self-organization, the Cf principle does
not encounter the problem of "tradeoff between stability and complexity" at the
level of individual cell. Physically, the Cf is considered as a harmonic-like
force of reaction, which keeps the state of the cell close to the ground state,
defined here as a state where enzymatic acts work most efficiently. Ground
state is subject to unitary evolution, and serves as a starting point in a
general strategy of quantum description of intracellular processes, termed here
"Euclidean approach". The next step of this strategy is transition from the
description of ground state to that one of growing state, and we suggest how it
can be accomplished using arguments from the fluctuation-dissipation theorem.
Finally, given that the most reliable and informative observable of an
individual cell is the sequence of its genome, we propose that the
non-classical correlations between individual molecular events at the single
cell level could be easiest to detect using high throughput DNA sequencing.
| [
{
"created": "Tue, 23 Jun 2009 14:49:17 GMT",
"version": "v1"
},
{
"created": "Wed, 7 Oct 2009 14:43:26 GMT",
"version": "v2"
},
{
"created": "Sun, 22 Nov 2009 18:36:32 GMT",
"version": "v3"
}
] | 2009-11-22 | [
[
"Ogryzko",
"Vasily",
""
]
] | Application of quantum principles to living cells requires a new approximation of the full quantum mechanical description of intracellular dynamics. We discuss what principal elements any such good approximation should contain. As one such element, the notion of "Catalytic force" Cf is introduced. Cf is the effect of the molecular target of catalysis on the catalytic microenvironment that adjusts the microenvironment towards a state that facilitates the catalytic act. This phenomenon is experimentally testable and has an intriguing implication for biological organization and evolution, as it amounts to "optimization without natural selection of replicators". Unlike the statistical-mechanical approaches to self-organization, the Cf principle does not encounter the problem of "tradeoff between stability and complexity" at the level of individual cell. Physically, the Cf is considered as a harmonic-like force of reaction, which keeps the state of the cell close to the ground state, defined here as a state where enzymatic acts work most efficiently. Ground state is subject to unitary evolution, and serves as a starting point in a general strategy of quantum description of intracellular processes, termed here "Euclidean approach". The next step of this strategy is transition from the description of ground state to that one of growing state, and we suggest how it can be accomplished using arguments from the fluctuation-dissipation theorem. Finally, given that the most reliable and informative observable of an individual cell is the sequence of its genome, we propose that the non-classical correlations between individual molecular events at the single cell level could be easiest to detect using high throughput DNA sequencing. |
2009.09702 | Andrij Rovenchak | Mykola Husev and Andrij Rovenchak | On the verge of life: Distribution of nucleotide sequences in viral RNAs | null | Biosemiotics 14, No. 2, 253-269 (2021) | 10.1007/s12304-021-09403-5 | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The aim of the study is to analyze viruses using parameters obtained from
distributions of nucleotide sequences in the viral RNA. Seeking for the input
data homogeneity, we analyze single-stranded RNA viruses only. Two approaches
are used to obtain the nucleotide sequences; In the first one, chunks of equal
length (four nucleotides) are considered. In the second approach, the whole RNA
genome is divided into parts by adenine or the most frequent nucleotide as a
"space". Rank--frequency distributions are studied in both cases. Within the
first approach, the P\'olya and the negative hypergeometric distribution yield
the best fit. For the distributions obtained within the second approach, we
have calculated a set of parameters, including entropy, mean sequence length,
and its dispersion. The calculated parameters became the basis for the
classification of viruses. We observed that proximity of viruses on planes
spanned on various pairs of parameters corresponds to related species. In
certain cases, such a proximity is observed for unrelated species as well
calling thus for the expansion of the set of parameters used in the
classification. We also observed that the fourth most frequent nucleotide
sequences obtained within the second approach are of different nature in case
of human coronaviruses (different nucleotides for MERS, SARS-CoV, and
SARS-CoV-2 versus identical nucleotides for four other coronaviruses). We
expect that our findings will be useful as a supplementary tool in the
classification of diseases caused by RNA viruses with respect to severity and
contagiousness.
| [
{
"created": "Mon, 21 Sep 2020 09:17:23 GMT",
"version": "v1"
}
] | 2022-04-25 | [
[
"Husev",
"Mykola",
""
],
[
"Rovenchak",
"Andrij",
""
]
] | The aim of the study is to analyze viruses using parameters obtained from distributions of nucleotide sequences in the viral RNA. Seeking for the input data homogeneity, we analyze single-stranded RNA viruses only. Two approaches are used to obtain the nucleotide sequences; In the first one, chunks of equal length (four nucleotides) are considered. In the second approach, the whole RNA genome is divided into parts by adenine or the most frequent nucleotide as a "space". Rank--frequency distributions are studied in both cases. Within the first approach, the P\'olya and the negative hypergeometric distribution yield the best fit. For the distributions obtained within the second approach, we have calculated a set of parameters, including entropy, mean sequence length, and its dispersion. The calculated parameters became the basis for the classification of viruses. We observed that proximity of viruses on planes spanned on various pairs of parameters corresponds to related species. In certain cases, such a proximity is observed for unrelated species as well calling thus for the expansion of the set of parameters used in the classification. We also observed that the fourth most frequent nucleotide sequences obtained within the second approach are of different nature in case of human coronaviruses (different nucleotides for MERS, SARS-CoV, and SARS-CoV-2 versus identical nucleotides for four other coronaviruses). We expect that our findings will be useful as a supplementary tool in the classification of diseases caused by RNA viruses with respect to severity and contagiousness. |
1612.06565 | Hoi-To Wai | Hoi-To Wai, Anna Scaglione, Uzi Harush, Baruch Barzel, Amir Leshem | RIDS: Robust Identification of Sparse Gene Regulatory Networks from
Perturbation Experiments | 20 pages, 6 figures | null | null | null | q-bio.QM cs.IT math.IT q-bio.MN stat.ML | http://creativecommons.org/licenses/by/4.0/ | Reconstructing the causal network in a complex dynamical system plays a
crucial role in many applications, from sub-cellular biology to economic
systems. Here we focus on inferring gene regulation networks (GRNs) from
perturbation or gene deletion experiments. Despite their scientific merit, such
perturbation experiments are not often used for such inference due to their
costly experimental procedure, requiring significant resources to complete the
measurement of every single experiment. To overcome this challenge, we develop
the Robust IDentification of Sparse networks (RIDS) method that reconstructs
the GRN from a small number of perturbation experiments. Our method uses the
gene expression data observed in each experiment and translates that into a
steady state condition of the system's nonlinear interaction dynamics. Applying
a sparse optimization criterion, we are able to extract the parameters of the
underlying weighted network, even from very few experiments. In fact, we
demonstrate analytically that, under certain conditions, the GRN can be
perfectly reconstructed using $K = \Omega (d_{max})$ perturbation experiments,
where $d_{max}$ is the maximum in-degree of the GRN, a small value for
realistic sparse networks, indicating that RIDS can achieve high performance
with a scalable number of experiments. We test our method on both synthetic and
experimental data extracted from the DREAM5 network inference challenge. We
show that the RIDS achieves superior performance compared to the
state-of-the-art methods, while requiring as few as ~60% less experimental
data. Moreover, as opposed to almost all competing methods, RIDS allows us to
infer the directionality of the GRN links, allowing us to infer empirical GRNs,
without relying on the commonly provided list of transcription factors.
| [
{
"created": "Tue, 20 Dec 2016 09:34:27 GMT",
"version": "v1"
}
] | 2016-12-21 | [
[
"Wai",
"Hoi-To",
""
],
[
"Scaglione",
"Anna",
""
],
[
"Harush",
"Uzi",
""
],
[
"Barzel",
"Baruch",
""
],
[
"Leshem",
"Amir",
""
]
] | Reconstructing the causal network in a complex dynamical system plays a crucial role in many applications, from sub-cellular biology to economic systems. Here we focus on inferring gene regulation networks (GRNs) from perturbation or gene deletion experiments. Despite their scientific merit, such perturbation experiments are not often used for such inference due to their costly experimental procedure, requiring significant resources to complete the measurement of every single experiment. To overcome this challenge, we develop the Robust IDentification of Sparse networks (RIDS) method that reconstructs the GRN from a small number of perturbation experiments. Our method uses the gene expression data observed in each experiment and translates that into a steady state condition of the system's nonlinear interaction dynamics. Applying a sparse optimization criterion, we are able to extract the parameters of the underlying weighted network, even from very few experiments. In fact, we demonstrate analytically that, under certain conditions, the GRN can be perfectly reconstructed using $K = \Omega (d_{max})$ perturbation experiments, where $d_{max}$ is the maximum in-degree of the GRN, a small value for realistic sparse networks, indicating that RIDS can achieve high performance with a scalable number of experiments. We test our method on both synthetic and experimental data extracted from the DREAM5 network inference challenge. We show that the RIDS achieves superior performance compared to the state-of-the-art methods, while requiring as few as ~60% less experimental data. Moreover, as opposed to almost all competing methods, RIDS allows us to infer the directionality of the GRN links, allowing us to infer empirical GRNs, without relying on the commonly provided list of transcription factors. |
1408.4154 | Anna Lisa Amadori | A. L. Amadori, M. Briani, R. Natalini | A non-local rare mutations model for quasispecies and Prisoner's
dilemma: numerical assessment of qualitative behaviour | null | European Journal of Applied Mathematics 27/1 (2016) , pp. 87-110 | 10.1017/S0956792515000352 | null | q-bio.PE math.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An integro-differential model for evolutionary dynamics with mutations is
investigated by improving the understanding of its behavior using numerical
simulations. The proposed numerical approach can handle also density dependent
fitness, and gives new insights about the role of mutation in the preservation
of cooperation.
| [
{
"created": "Mon, 18 Aug 2014 20:47:14 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Aug 2014 06:30:12 GMT",
"version": "v2"
}
] | 2020-01-27 | [
[
"Amadori",
"A. L.",
""
],
[
"Briani",
"M.",
""
],
[
"Natalini",
"R.",
""
]
] | An integro-differential model for evolutionary dynamics with mutations is investigated by improving the understanding of its behavior using numerical simulations. The proposed numerical approach can handle also density dependent fitness, and gives new insights about the role of mutation in the preservation of cooperation. |
1504.03202 | Guillermo Abramson | Guillermo Abramson, Mar\'ia F. Laguna, Marcelo N. Kuperman, Adri\'an
Monjeau and Jos\'e L. Lanata | On the roles of hunting and habitat size on the extinction of megafauna | Quaternary International (2015, in press) | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We study a mechanistic mathematical model of extinction and coexistence in a
generic hunter-prey ecosystem. The model represents typical scenarios of human
invasion and environmental change, characteristic of the late Pleistocene,
concomitant with the extinction of fauna in many regions of the world. As a
first approach we focus on a small trophic web of three species, including two
herbivores in asymmetric competition, in order to characterize the generic
behaviors. Specifically, we use a stochastic dynamical system, allowing the
study of the role of fluctuations and spatial correlations. We show that the
presence of hunters drives the superior herbivore to extinction even in
habitats that would allow coexistence, and even when the pressure of hunting is
lower than on the inferior one. The role of system size and fluctuating
populations is addressed, showing an ecological meltdown in small systems in
the presence of humans. The time to extinction as a function of the system
size, as calculated with the model, shows a good agreement with paleontological
data. Other findings show the intricate play of the anthropic and environmental
factors that may have caused the extinction of megafauna.
| [
{
"created": "Mon, 13 Apr 2015 14:52:18 GMT",
"version": "v1"
},
{
"created": "Fri, 14 Aug 2015 17:02:57 GMT",
"version": "v2"
}
] | 2016-08-08 | [
[
"Abramson",
"Guillermo",
""
],
[
"Laguna",
"María F.",
""
],
[
"Kuperman",
"Marcelo N.",
""
],
[
"Monjeau",
"Adrián",
""
],
[
"Lanata",
"José L.",
""
]
] | We study a mechanistic mathematical model of extinction and coexistence in a generic hunter-prey ecosystem. The model represents typical scenarios of human invasion and environmental change, characteristic of the late Pleistocene, concomitant with the extinction of fauna in many regions of the world. As a first approach we focus on a small trophic web of three species, including two herbivores in asymmetric competition, in order to characterize the generic behaviors. Specifically, we use a stochastic dynamical system, allowing the study of the role of fluctuations and spatial correlations. We show that the presence of hunters drives the superior herbivore to extinction even in habitats that would allow coexistence, and even when the pressure of hunting is lower than on the inferior one. The role of system size and fluctuating populations is addressed, showing an ecological meltdown in small systems in the presence of humans. The time to extinction as a function of the system size, as calculated with the model, shows a good agreement with paleontological data. Other findings show the intricate play of the anthropic and environmental factors that may have caused the extinction of megafauna. |
q-bio/0401001 | Wei Wang | Wei Wang and Jean-Jacques E. Slotine | K-Winners-Take-All Computation with Neural Oscillators | 9 pages, 4 figures | null | null | null | q-bio.NC | null | Artificial spike-based computation, inspired by models of computation in the
central nervous system, may present significant performance advantages over
traditional methods for specific types of large scale problems. This paper
describes very simple network architectures for k-winners-take-all and
soft-winner-take-all computation using neural oscillators. Fast convergence is
achieved from arbitrary initial conditions, which makes the networks
particularly suitable to track time-varying inputs.
| [
{
"created": "Wed, 31 Dec 2003 21:47:22 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Wang",
"Wei",
""
],
[
"Slotine",
"Jean-Jacques E.",
""
]
] | Artificial spike-based computation, inspired by models of computation in the central nervous system, may present significant performance advantages over traditional methods for specific types of large scale problems. This paper describes very simple network architectures for k-winners-take-all and soft-winner-take-all computation using neural oscillators. Fast convergence is achieved from arbitrary initial conditions, which makes the networks particularly suitable to track time-varying inputs. |
1611.01390 | Jonathan Vacher | Jonathan Vacher, Andrew Isaac Meso, Laurent U. Perrinet and Gabriel
Peyr\'e | Bayesian Modeling of Motion Perception using Dynamical Stochastic
Textures | article+supplementary, 34+5 pages, 10+1 figures, accepted to Neural
Computation. arXiv admin note: text overlap with arXiv:1511.02705 | null | null | null | q-bio.NC cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A common practice to account for psychophysical biases in vision is to frame
them as consequences of a dynamic process relying on optimal inference with
respect to a generative model. The present study details the complete
formulation of such a generative model intended to probe visual motion
perception with a dynamic texture model. It is first derived in a set of
axiomatic steps constrained by biological plausibility. We extend previous
contributions by detailing three equivalent formulations of this texture model.
First, the composite dynamic textures are constructed by the random aggregation
of warped patterns, which can be viewed as 3D Gaussian fields. Secondly, these
textures are cast as solutions to a stochastic partial differential equation
(sPDE). This essential step enables real time, on-the-fly texture synthesis
using time-discretized auto-regressive processes. It also allows for the
derivation of a local motion-energy model, which corresponds to the
log-likelihood of the probability density. The log-likelihoods are essential
for the construction of a Bayesian inference framework. We use the dynamic
texture model to psychophysically probe speed perception in humans using
zoom-like changes in the spatial frequency content of the stimulus. The human
data replicates previous findings showing perceived speed to be positively
biased by spatial frequency increments. A Bayesian observer who combines a
Gaussian likelihood centered at the true speed and a spatial frequency
dependent width with a "slow speed prior" successfully accounts for the
perceptual bias. More precisely, the bias arises from a decrease in the
observer's likelihood width estimated from the experiments as the spatial
frequency increases. Such a trend is compatible with the trend of the dynamic
texture likelihood width.
| [
{
"created": "Wed, 2 Nov 2016 21:20:03 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Aug 2018 21:02:49 GMT",
"version": "v2"
}
] | 2018-08-24 | [
[
"Vacher",
"Jonathan",
""
],
[
"Meso",
"Andrew Isaac",
""
],
[
"Perrinet",
"Laurent U.",
""
],
[
"Peyré",
"Gabriel",
""
]
] | A common practice to account for psychophysical biases in vision is to frame them as consequences of a dynamic process relying on optimal inference with respect to a generative model. The present study details the complete formulation of such a generative model intended to probe visual motion perception with a dynamic texture model. It is first derived in a set of axiomatic steps constrained by biological plausibility. We extend previous contributions by detailing three equivalent formulations of this texture model. First, the composite dynamic textures are constructed by the random aggregation of warped patterns, which can be viewed as 3D Gaussian fields. Secondly, these textures are cast as solutions to a stochastic partial differential equation (sPDE). This essential step enables real time, on-the-fly texture synthesis using time-discretized auto-regressive processes. It also allows for the derivation of a local motion-energy model, which corresponds to the log-likelihood of the probability density. The log-likelihoods are essential for the construction of a Bayesian inference framework. We use the dynamic texture model to psychophysically probe speed perception in humans using zoom-like changes in the spatial frequency content of the stimulus. The human data replicates previous findings showing perceived speed to be positively biased by spatial frequency increments. A Bayesian observer who combines a Gaussian likelihood centered at the true speed and a spatial frequency dependent width with a "slow speed prior" successfully accounts for the perceptual bias. More precisely, the bias arises from a decrease in the observer's likelihood width estimated from the experiments as the spatial frequency increases. Such a trend is compatible with the trend of the dynamic texture likelihood width. |
2112.08876 | Nicolas Mary | Nicolas Mary, Nathalie Iannuccelli, Geoffrey Petit, Nathalie Bonnet
(ENVT), Alain Pinton, Vladimir Grosbois, Bertrand Servin, Juliette Riquet,
Alain Ducos | Analysis of hybridization in French wild boar populations using
genome-wide genotyping data | in French. Journ{\'e}es de la Recherche Porcine en France, 2021 | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The "genetic purity" of French wild boar populations has been monitored since
the 1980s based on a cytogenetic difference between wild boars and domestic
pigs (36 and 38 chromosomes, respectively). This difference makes it possible
to identify any boar with 37 or 38 chromosomes as "hybrid", without however
being able to determine the origin (recent or ancient) of the hybridization,
nor guarantee the "purity" of an animal with 36 chromosomes. Analysis of
results of more than 4,600 tests performed over the last 12 years reveals an
average "hybrid" rate of 15.8%, with high variability between populations. To
analyse hybridization in greater detail and overcome inherent limitations of
the cytogenetic approach, 362 wild boars recently collected in different
regions of France were genotyped on a 70K SNP (GeneSeek GGP Porcine HD) chip.
This study showed that for 96.4% of the wild boars analysed, includingmost of
those with 37 or 38 chromosomes, the percentage of the genome of "domestic pig"
origin varied from 0 to 18%. This suggests that hybridization is a fairly
common phenomenon but of moderate intensity, and often ancient. Nevertheless,
higher rates of hybridization have been observed in some regions such as
Ard{\`e}che, and several cases of recent hybridization with domestic pigs were
found in 3.6% of the wild boars analysed, most of them with pigs of Asian
origin.
| [
{
"created": "Thu, 16 Dec 2021 13:42:09 GMT",
"version": "v1"
}
] | 2021-12-17 | [
[
"Mary",
"Nicolas",
"",
"ENVT"
],
[
"Iannuccelli",
"Nathalie",
"",
"ENVT"
],
[
"Petit",
"Geoffrey",
"",
"ENVT"
],
[
"Bonnet",
"Nathalie",
"",
"ENVT"
],
[
"Pinton",
"Alain",
""
],
[
"Grosbois",
"Vladimir",
""
],
[
"Servin",
"Bertrand",
""
],
[
"Riquet",
"Juliette",
""
],
[
"Ducos",
"Alain",
""
]
] | The "genetic purity" of French wild boar populations has been monitored since the 1980s based on a cytogenetic difference between wild boars and domestic pigs (36 and 38 chromosomes, respectively). This difference makes it possible to identify any boar with 37 or 38 chromosomes as "hybrid", without however being able to determine the origin (recent or ancient) of the hybridization, nor guarantee the "purity" of an animal with 36 chromosomes. Analysis of results of more than 4,600 tests performed over the last 12 years reveals an average "hybrid" rate of 15.8%, with high variability between populations. To analyse hybridization in greater detail and overcome inherent limitations of the cytogenetic approach, 362 wild boars recently collected in different regions of France were genotyped on a 70K SNP (GeneSeek GGP Porcine HD) chip. This study showed that for 96.4% of the wild boars analysed, includingmost of those with 37 or 38 chromosomes, the percentage of the genome of "domestic pig" origin varied from 0 to 18%. This suggests that hybridization is a fairly common phenomenon but of moderate intensity, and often ancient. Nevertheless, higher rates of hybridization have been observed in some regions such as Ard{\`e}che, and several cases of recent hybridization with domestic pigs were found in 3.6% of the wild boars analysed, most of them with pigs of Asian origin. |
2001.08285 | Elisenda Feliu | E. Feliu, N. Kaihnsa, T. de Wolff, O. Y\"ur\"uk | The kinetic space of multistationarity in dual phosphorylation | null | null | null | null | q-bio.MN math.AG math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multistationarity in molecular systems underlies switch-like responses in
cellular decision making. Determining whether and when a system displays
multistationarity is in general a difficult problem. In this work we completely
determine the set of kinetic parameters that enable multistationarity in a
ubiquitous motif involved in cell signaling, namely a dual phosphorylation
cycle. In addition we show that the regions of multistationarity and
monostationarity are both path connected.
We model the dynamics of the concentrations of the proteins over time by
means of a parametrized polynomial ordinary differential equation (ODE) system
arising from the mass-action assumption. Since this system has three linear
first integrals defined by the total amounts of the substrate and the two
enzymes, we study for what parameter values the ODE system has at least two
positive steady states after suitably choosing the total amounts. We employ a
suite of techniques from (real) algebraic geometry, which in particular concern
the study of the signs of a multivariate polynomial over the positive orthant
and sums of nonnegative circuit polynomials.
| [
{
"created": "Wed, 22 Jan 2020 21:25:10 GMT",
"version": "v1"
},
{
"created": "Tue, 14 Apr 2020 08:09:11 GMT",
"version": "v2"
}
] | 2020-04-15 | [
[
"Feliu",
"E.",
""
],
[
"Kaihnsa",
"N.",
""
],
[
"de Wolff",
"T.",
""
],
[
"Yürük",
"O.",
""
]
] | Multistationarity in molecular systems underlies switch-like responses in cellular decision making. Determining whether and when a system displays multistationarity is in general a difficult problem. In this work we completely determine the set of kinetic parameters that enable multistationarity in a ubiquitous motif involved in cell signaling, namely a dual phosphorylation cycle. In addition we show that the regions of multistationarity and monostationarity are both path connected. We model the dynamics of the concentrations of the proteins over time by means of a parametrized polynomial ordinary differential equation (ODE) system arising from the mass-action assumption. Since this system has three linear first integrals defined by the total amounts of the substrate and the two enzymes, we study for what parameter values the ODE system has at least two positive steady states after suitably choosing the total amounts. We employ a suite of techniques from (real) algebraic geometry, which in particular concern the study of the signs of a multivariate polynomial over the positive orthant and sums of nonnegative circuit polynomials. |
1907.06305 | Justin Yeakel | Justin D. Yeakel and Uttam Bhat and Seth D. Newsome | Caching in or falling back at the Sevilleta | 13 pages, 6 figures, 2 tables, Appendices | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Foraging in uncertain environments requires balancing the risks associated
with finding alternative resources against potential gains. In aridland
environments characterized by extreme variation in the amount and seasonal
timing of primary production, consumer communities must weigh the risks
associated with foraging for preferred seeds that can be cached against
fallback foods of low nutritional quality (e.g., leaves) that must be consumed
immediately. Here we explore the influence of resource-scarcity, body size, and
seasonal uncertainty on the expected foraging behaviors of caching rodents in
the northern Chihuahaun Desert by integrating these elements with a Stochastic
Dynamic Program (SDP) to determine fitness-maximizing foraging strategies. We
demonstrate that resource-limited environments promote dependence on fallback
foods, reducing the likelihood of starvation while increasing future risk
exposure. Our results point to a qualitative difference in the use of fallback
foods and the fitness benefits of caching at the threshold body size of 50 g.
Above this threshold the fitness benefits are greater for consumers with
smaller caches, affirming empirical observations of cache use among rodents in
such dynamic environments. This suggests that larger-bodied consumers with
larger caches may be less sensitive to the future uncertainties in monsoonal
onset predicted by global climate scenarios.
| [
{
"created": "Mon, 15 Jul 2019 01:19:46 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Aug 2019 16:29:20 GMT",
"version": "v2"
}
] | 2019-08-22 | [
[
"Yeakel",
"Justin D.",
""
],
[
"Bhat",
"Uttam",
""
],
[
"Newsome",
"Seth D.",
""
]
] | Foraging in uncertain environments requires balancing the risks associated with finding alternative resources against potential gains. In aridland environments characterized by extreme variation in the amount and seasonal timing of primary production, consumer communities must weigh the risks associated with foraging for preferred seeds that can be cached against fallback foods of low nutritional quality (e.g., leaves) that must be consumed immediately. Here we explore the influence of resource-scarcity, body size, and seasonal uncertainty on the expected foraging behaviors of caching rodents in the northern Chihuahaun Desert by integrating these elements with a Stochastic Dynamic Program (SDP) to determine fitness-maximizing foraging strategies. We demonstrate that resource-limited environments promote dependence on fallback foods, reducing the likelihood of starvation while increasing future risk exposure. Our results point to a qualitative difference in the use of fallback foods and the fitness benefits of caching at the threshold body size of 50 g. Above this threshold the fitness benefits are greater for consumers with smaller caches, affirming empirical observations of cache use among rodents in such dynamic environments. This suggests that larger-bodied consumers with larger caches may be less sensitive to the future uncertainties in monsoonal onset predicted by global climate scenarios. |
0805.1395 | Reinhard Laubenbacher | Paola Vera-Licona and Reinhard Laubenbacher | Inference of ecological interaction networks | To appear in Annales Zoologici Fennici | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The inference of the interactions between organisms in an ecosystem from
observational data is an important problem in ecology. This paper presents a
mathematical inference method, originally developed for the inference of
biochemical networks in molecular biology, adapted for the inference of
networks of ecological interactions. The method is applied to a network of
invertebrate families (taxa) in a rice field.
| [
{
"created": "Fri, 9 May 2008 18:41:21 GMT",
"version": "v1"
}
] | 2008-05-12 | [
[
"Vera-Licona",
"Paola",
""
],
[
"Laubenbacher",
"Reinhard",
""
]
] | The inference of the interactions between organisms in an ecosystem from observational data is an important problem in ecology. This paper presents a mathematical inference method, originally developed for the inference of biochemical networks in molecular biology, adapted for the inference of networks of ecological interactions. The method is applied to a network of invertebrate families (taxa) in a rice field. |
q-bio/0610016 | Sergei Fedotov | Sergei Fedotov and Alexander Iomin | Migration and proliferation dichotomy in tumor cell invasion | 9 pages | null | 10.1103/PhysRevLett.98.118101 | null | q-bio.CB q-bio.QM | null | We propose a two-component reaction-transport model for the
migration-proliferation dichotomy in the spreading of tumor cells. By using a
continuous time random walk (CTRW) we formulate a system of the balance
equations for the cancer cells of two phenotypes with random switching between
cell proliferation and migration. The transport process is formulated in terms
of the CTRW with an arbitrary waiting time distribution law. Proliferation is
modeled by a standard logistic growth. We apply hyperbolic scaling and
Hamilton-Jacobi formalism to determine the overall rate of tumor cell invasion.
In particular, we take into account both normal diffusion and anomalous
transport (subdiffusion) in order to show that the standard diffusion
approximation for migration leads to overestimation of the overall cancer
spreading rate.
| [
{
"created": "Sat, 7 Oct 2006 00:14:00 GMT",
"version": "v1"
},
{
"created": "Tue, 17 Oct 2006 03:05:19 GMT",
"version": "v2"
}
] | 2015-06-26 | [
[
"Fedotov",
"Sergei",
""
],
[
"Iomin",
"Alexander",
""
]
] | We propose a two-component reaction-transport model for the migration-proliferation dichotomy in the spreading of tumor cells. By using a continuous time random walk (CTRW) we formulate a system of the balance equations for the cancer cells of two phenotypes with random switching between cell proliferation and migration. The transport process is formulated in terms of the CTRW with an arbitrary waiting time distribution law. Proliferation is modeled by a standard logistic growth. We apply hyperbolic scaling and Hamilton-Jacobi formalism to determine the overall rate of tumor cell invasion. In particular, we take into account both normal diffusion and anomalous transport (subdiffusion) in order to show that the standard diffusion approximation for migration leads to overestimation of the overall cancer spreading rate. |
1909.12449 | Pilhwa Lee | Pilhwa Lee | Electrodiffusion with calcium-activated potassium channels in dendritic
spine | 19 pages, 7 figures | null | null | null | q-bio.SC q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | We investigate calcium signaling feedback through calcium-activated potassium
channels of a dendritic spine by applying the immersed boundary method with
electrodiffusion. We simulate the stochastic gating of such ion channels and
the resulting spatial distribution of concentration, current, and membrane
voltage within the dendritic spine. In this simulation, the permeability to
ionic flow across the membrane is regulated by the amplitude of chemical
potential barriers. With spatially localized ion channels, chemical potential
barriers are locally and stochastically regulated. This regulation represents
the ion channel gating with multiple subunits, the open and closed states
governed by a continuous-time Markov process. The model simulation
recapitulates an inhibitory action on voltage-sensitive calcium channels by the
calcium-activated potassium channels in a stochastic manner as a
\emph{non-local} feedback loop. The model predicts amplified calcium influx
with more closely placed channel complexes, proposing a potential mechanism of
differential calcium handling by channel distributions. This work provides a
foundation for future computer simulation studies of dendritic spine motility
and structural plasticity.
| [
{
"created": "Fri, 27 Sep 2019 00:39:48 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Sep 2020 18:52:57 GMT",
"version": "v2"
}
] | 2020-09-28 | [
[
"Lee",
"Pilhwa",
""
]
] | We investigate calcium signaling feedback through calcium-activated potassium channels of a dendritic spine by applying the immersed boundary method with electrodiffusion. We simulate the stochastic gating of such ion channels and the resulting spatial distribution of concentration, current, and membrane voltage within the dendritic spine. In this simulation, the permeability to ionic flow across the membrane is regulated by the amplitude of chemical potential barriers. With spatially localized ion channels, chemical potential barriers are locally and stochastically regulated. This regulation represents the ion channel gating with multiple subunits, the open and closed states governed by a continuous-time Markov process. The model simulation recapitulates an inhibitory action on voltage-sensitive calcium channels by the calcium-activated potassium channels in a stochastic manner as a \emph{non-local} feedback loop. The model predicts amplified calcium influx with more closely placed channel complexes, proposing a potential mechanism of differential calcium handling by channel distributions. This work provides a foundation for future computer simulation studies of dendritic spine motility and structural plasticity. |
2301.03388 | Jean-Louis Dessalles | Julien Lie-Panis, Jean-Louis Dessalles | Runaway signals: Exaggerated displays of commitment may result from
second-order signaling | 15 pages, 4 figures. Supplementary Information: 20 pages, 7 figures
More illustrations on this Website: https://evolife.telecom-paris.fr/outrage | Journal of Theoretical Biology (2023) | 10.1016/j.jtbi.2023.111586 | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | To demonstrate their commitment, for instance during wartime, members of a
group will sometimes all engage in the same ruinous display. Such uniform,
high-cost signals are hard to reconcile with standard models of signaling. For
signals to be stable, they should honestly inform their audience; yet, uniform
signals are trivially uninformative. To explain this phenomenon, we design a
simple model, which we call the signal runaway game. In this game, senders can
express outrage at non-senders. Outrage functions as a second-order signal. By
expressing outrage at non-senders, senders draw attention to their own signal,
and benefit from its increased visibility. Using our model and a simulation, we
show that outrage can stabilize uniform signals, and can lead signal costs to
run away. Second-order signaling may explain why groups sometimes demand
displays of commitment from all their members, and why these displays can
entail extreme costs.
| [
{
"created": "Mon, 9 Jan 2023 14:44:45 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Aug 2023 08:08:11 GMT",
"version": "v2"
}
] | 2023-08-10 | [
[
"Lie-Panis",
"Julien",
""
],
[
"Dessalles",
"Jean-Louis",
""
]
] | To demonstrate their commitment, for instance during wartime, members of a group will sometimes all engage in the same ruinous display. Such uniform, high-cost signals are hard to reconcile with standard models of signaling. For signals to be stable, they should honestly inform their audience; yet, uniform signals are trivially uninformative. To explain this phenomenon, we design a simple model, which we call the signal runaway game. In this game, senders can express outrage at non-senders. Outrage functions as a second-order signal. By expressing outrage at non-senders, senders draw attention to their own signal, and benefit from its increased visibility. Using our model and a simulation, we show that outrage can stabilize uniform signals, and can lead signal costs to run away. Second-order signaling may explain why groups sometimes demand displays of commitment from all their members, and why these displays can entail extreme costs. |
2109.12404 | Mario Flores | Mario Flores, Zhentao Liu, Ting-He Zhang, Md Musaddaqui Hasib,
Yu-Chiao Chiu, Zhenqing Ye, Karla Paniagua, Sumin Jo, Jianqiu Zhang,
Shou-Jiang Gao, Yu-Fang Jin, Yidong Chen, and Yufei Huang | Deep learning tackles single-cell analysis A survey of deep learning for
scRNA-seq analysis | 74 pages | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Since its selection as the method of the year in 2013, single-cell
technologies have become mature enough to provide answers to complex research
questions. With the growth of single-cell profiling technologies, there has
also been a significant increase in data collected from single-cell profilings,
resulting in computational challenges to process these massive and complicated
datasets. To address these challenges, deep learning (DL) is positioning as a
competitive alternative for single-cell analyses besides the traditional
machine learning approaches. Here we present a processing pipeline of
single-cell RNA-seq data, survey a total of 25 DL algorithms and their
applicability for a specific step in the processing pipeline. Specifically, we
establish a unified mathematical representation of all variational autoencoder,
autoencoder, and generative adversarial network models, compare the training
strategies and loss functions for these models, and relate the loss functions
of these models to specific objectives of the data processing step. Such
presentation will allow readers to choose suitable algorithms for their
particular objective at each step in the pipeline. We envision that this survey
will serve as an important information portal for learning the application of
DL for scRNA-seq analysis and inspire innovative use of DL to address a broader
range of new challenges in emerging multi-omics and spatial single-cell
sequencing.
| [
{
"created": "Sat, 25 Sep 2021 17:08:06 GMT",
"version": "v1"
}
] | 2021-09-28 | [
[
"Flores",
"Mario",
""
],
[
"Liu",
"Zhentao",
""
],
[
"Zhang",
"Ting-He",
""
],
[
"Hasib",
"Md Musaddaqui",
""
],
[
"Chiu",
"Yu-Chiao",
""
],
[
"Ye",
"Zhenqing",
""
],
[
"Paniagua",
"Karla",
""
],
[
"Jo",
"Sumin",
""
],
[
"Zhang",
"Jianqiu",
""
],
[
"Gao",
"Shou-Jiang",
""
],
[
"Jin",
"Yu-Fang",
""
],
[
"Chen",
"Yidong",
""
],
[
"Huang",
"Yufei",
""
]
] | Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant increase in data collected from single-cell profilings, resulting in computational challenges to process these massive and complicated datasets. To address these challenges, deep learning (DL) is positioning as a competitive alternative for single-cell analyses besides the traditional machine learning approaches. Here we present a processing pipeline of single-cell RNA-seq data, survey a total of 25 DL algorithms and their applicability for a specific step in the processing pipeline. Specifically, we establish a unified mathematical representation of all variational autoencoder, autoencoder, and generative adversarial network models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. Such presentation will allow readers to choose suitable algorithms for their particular objective at each step in the pipeline. We envision that this survey will serve as an important information portal for learning the application of DL for scRNA-seq analysis and inspire innovative use of DL to address a broader range of new challenges in emerging multi-omics and spatial single-cell sequencing. |
1702.05083 | Sonia Pinto-de-Carvalho | Roberta Oliveira-Prado, M\'aderson Alvares de Souza Cabral, Sylvie
Oliffson Kamphorst, S\^onia Pinto-de-Carvalho, Rodrigo Corr\^ea-Oliveira and
Andrea Gazzinelli | Modeling the prevalence of Schistosoma mansoni infection in an endemic
population | the pdf file has 10 pages. Includes 3 figures | null | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate theoretically if treatment alone can reduce the
schistosomiasis's prevalence in an infected population, in a long-lasting
sustainable way. We use a non-linear system of ordinary differential equations
(a SI system combined with a logistic population growth) which describes the
time evolution of the non-infected and infected populations, in terms of the
recovering, infection, and demographic rates.
Our model leads to the conclusion that the only way to eliminate this endemic
disease is to implement public health policies aimed at both treatment and
environment.
We apply our model to the endemic area of Virgem das Gra\c{c}as, in Brazil,
where the prevalence of Schistosiamisis in 2001 was greater than 50\%. The
epidemiological data are extracted from a longitudinal study carried on the
region between 2001 and 2010 and the demographic parameter from official
Brazilian population data. When these estimated parameters are entered, our
model gives a limit prevalence for Virgem das Gra\c{c}as of 11\%, which is
still significantly high even though treatment in accordance to the government
regulations is systematically performed. This estimative reinforces once again,
that in order to eliminate Schistomiasis, public health policies aimed at
treatment, sanitation, including snail control, and health education programs
are mandatory.
| [
{
"created": "Thu, 16 Feb 2017 18:41:07 GMT",
"version": "v1"
}
] | 2017-02-17 | [
[
"Oliveira-Prado",
"Roberta",
""
],
[
"Cabral",
"Máderson Alvares de Souza",
""
],
[
"Kamphorst",
"Sylvie Oliffson",
""
],
[
"Pinto-de-Carvalho",
"Sônia",
""
],
[
"Corrêa-Oliveira",
"Rodrigo",
""
],
[
"Gazzinelli",
"Andrea",
""
]
] | We investigate theoretically if treatment alone can reduce the schistosomiasis's prevalence in an infected population, in a long-lasting sustainable way. We use a non-linear system of ordinary differential equations (a SI system combined with a logistic population growth) which describes the time evolution of the non-infected and infected populations, in terms of the recovering, infection, and demographic rates. Our model leads to the conclusion that the only way to eliminate this endemic disease is to implement public health policies aimed at both treatment and environment. We apply our model to the endemic area of Virgem das Gra\c{c}as, in Brazil, where the prevalence of Schistosiamisis in 2001 was greater than 50\%. The epidemiological data are extracted from a longitudinal study carried on the region between 2001 and 2010 and the demographic parameter from official Brazilian population data. When these estimated parameters are entered, our model gives a limit prevalence for Virgem das Gra\c{c}as of 11\%, which is still significantly high even though treatment in accordance to the government regulations is systematically performed. This estimative reinforces once again, that in order to eliminate Schistomiasis, public health policies aimed at treatment, sanitation, including snail control, and health education programs are mandatory. |
2004.06482 | Ibrahima Gueye | Lynda Bouzid Khiri, Ibrahima Gueye, Hubert Naacke, Idrissa Sarr and
St\'ephane Gan\c{c}arski | A Malaria Control Model Using Mobility Data: An Early Explanation of
Kedougou's Case in Senegal | null | null | null | null | q-bio.PE cs.CY cs.SY eess.SY | http://creativecommons.org/publicdomain/zero/1.0/ | Studies in malaria control cover many areas such as medicine, sociology,
biology, mathematic, physic, computer science and so forth. Researches in the
realm of mathematic are conducted to predict the occurrence of the disease and
to support the eradication process. Basically, the modeling methodology is
predominantly deterministic and differential equation based while selecting
clinical and biological features that seem to be important. Yet, if the
individual characteristics matter when modeling the disease, the overall
estimation of the malaria is not done based on the health status of each
individual but in a non-specified percentage of the global population. The goal
of this paper is to propose a model that relies on a daily evolution of the
individual's state, which depends on their mobility and the characteristics of
the area they visit. Thus, the mobility data of a single person moving from one
area to another, gathered thanks to mobile networks, is the essential building
block to predict the outcome of the disease. We implement our solution and
demonstrate its effectiveness through empirical experiments. The results show
how promising the model is in providing possible insights into the failure of
the disease control in the Kedougou region.
| [
{
"created": "Sun, 12 Apr 2020 12:44:45 GMT",
"version": "v1"
}
] | 2020-04-15 | [
[
"Khiri",
"Lynda Bouzid",
""
],
[
"Gueye",
"Ibrahima",
""
],
[
"Naacke",
"Hubert",
""
],
[
"Sarr",
"Idrissa",
""
],
[
"Gançarski",
"Stéphane",
""
]
] | Studies in malaria control cover many areas such as medicine, sociology, biology, mathematic, physic, computer science and so forth. Researches in the realm of mathematic are conducted to predict the occurrence of the disease and to support the eradication process. Basically, the modeling methodology is predominantly deterministic and differential equation based while selecting clinical and biological features that seem to be important. Yet, if the individual characteristics matter when modeling the disease, the overall estimation of the malaria is not done based on the health status of each individual but in a non-specified percentage of the global population. The goal of this paper is to propose a model that relies on a daily evolution of the individual's state, which depends on their mobility and the characteristics of the area they visit. Thus, the mobility data of a single person moving from one area to another, gathered thanks to mobile networks, is the essential building block to predict the outcome of the disease. We implement our solution and demonstrate its effectiveness through empirical experiments. The results show how promising the model is in providing possible insights into the failure of the disease control in the Kedougou region. |
1809.05196 | Anna Miller | Anna Miller, Dawei Li, Jason Platt, Arij Daou, Daniel Margoliash,
Henry Abarbanel | Statistical Data Assimilation: Formulation and Examples from
Neurobiology | 28 pages, 13 figuress | null | null | null | q-bio.NC physics.bio-ph | http://creativecommons.org/publicdomain/zero/1.0/ | For the Research Topic Data Assimilation and Control: Theory and Applications
in Life Sciences we first review the formulation of statistical data
assimilation (SDA) and discuss algorithms for exploring variational
approximations to the conditional expected values of biophysical aspects of
functional neural circuits. Then we report on the application of SDA to (1) the
exploration of properties of individual neurons in the HVC nucleus of the avian
song system, and (2) characterizing individual neurons formulated as very large
scale integration (VLSI) analog circuits with a goal of building functional,
biophysically realistic, VLSI representations of functional nervous systems.
Networks of neurons pose a substantially greater challenge, and we comment on
formulating experiments to probe the properties, especially the functional
connectivity, in song command circuits within HVC.
| [
{
"created": "Thu, 13 Sep 2018 22:27:24 GMT",
"version": "v1"
}
] | 2018-09-17 | [
[
"Miller",
"Anna",
""
],
[
"Li",
"Dawei",
""
],
[
"Platt",
"Jason",
""
],
[
"Daou",
"Arij",
""
],
[
"Margoliash",
"Daniel",
""
],
[
"Abarbanel",
"Henry",
""
]
] | For the Research Topic Data Assimilation and Control: Theory and Applications in Life Sciences we first review the formulation of statistical data assimilation (SDA) and discuss algorithms for exploring variational approximations to the conditional expected values of biophysical aspects of functional neural circuits. Then we report on the application of SDA to (1) the exploration of properties of individual neurons in the HVC nucleus of the avian song system, and (2) characterizing individual neurons formulated as very large scale integration (VLSI) analog circuits with a goal of building functional, biophysically realistic, VLSI representations of functional nervous systems. Networks of neurons pose a substantially greater challenge, and we comment on formulating experiments to probe the properties, especially the functional connectivity, in song command circuits within HVC. |
1111.2796 | Alexandre Souto Martinez PhD | Brenno Caetano Troca Cabella, Fabiano Ribeiro and Alexandre Souto
Martinez | Effective carrying capacity and analytical solution of a particular case
of the Richards-like two-species population dynamics model | To appear in Physica A (6 pages, 5 figures) | Physica A 391 (2012) 1281-1286 | 10.1016/j.physa.2011.11.018 | null | q-bio.PE cond-mat.dis-nn physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a generalized two-species population dynamic model and
analytically solve it for the amensalism and commensalism ecological
interactions. These two-species models can be simplified to a one-species model
with a time dependent extrinsic growth factor. With a one-species model with an
effective carrying capacity one is able to retrieve the steady state solutions
of the previous one-species model. The equivalence obtained between the
effective carrying capacity and the extrinsic growth factor is complete only
for a particular case, the Gompertz model. Here we unveil important aspects of
sigmoid growth curves, which are relevant to growth processes and population
dynamics.
| [
{
"created": "Fri, 11 Nov 2011 16:44:09 GMT",
"version": "v1"
}
] | 2012-07-05 | [
[
"Cabella",
"Brenno Caetano Troca",
""
],
[
"Ribeiro",
"Fabiano",
""
],
[
"Martinez",
"Alexandre Souto",
""
]
] | We consider a generalized two-species population dynamic model and analytically solve it for the amensalism and commensalism ecological interactions. These two-species models can be simplified to a one-species model with a time dependent extrinsic growth factor. With a one-species model with an effective carrying capacity one is able to retrieve the steady state solutions of the previous one-species model. The equivalence obtained between the effective carrying capacity and the extrinsic growth factor is complete only for a particular case, the Gompertz model. Here we unveil important aspects of sigmoid growth curves, which are relevant to growth processes and population dynamics. |
1812.02105 | Cinzia Soresina | Sara Pasquali and Cinzia Soresina and Enrico Marchesini | Estimation of the mortality rate functions from time series field data
in a stage-structured demographic model for Lobesia botrana | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Simulating the population dynamics of a stage-structured population requires
the knowledge of the biodemographic functions characterizing the species,
namely development, mortality and fecundity. In general, development and
fecundity can satisfactorily be estimated starting from literature data.
Unfortunately, this is often not the case of the mortality function, because of
the lack of experimental data. To overcome this problem we estimate the
mortality rate function from field data on the abundance of the species. The
mortality is expressed as a linear combination of cubic splines and the
estimation method allows to determine its coefficients taking into account the
observations measurement error. Moreover, the variability in the estimate is
quantified by means of the confidence bands for both mortality and dynamics.
The presented method allows to obtain a more flexible shape for the mortality
rate functions compared with previous methods applied to the same pest. The
method is applied to the case of Lobesia botrana, the main pest in the European
vineyards, with abundance data collected in a location in the North of Italy
for five consecutive years. Data collected over three years are used to
estimate the mortality and to analyze the variability in the estimate and its
effects on the population dynamics. Other two datasets are used to validate the
model simulating the dynamics using the estimated mortality.
| [
{
"created": "Wed, 5 Dec 2018 16:49:54 GMT",
"version": "v1"
},
{
"created": "Sun, 25 Oct 2020 08:49:27 GMT",
"version": "v2"
}
] | 2020-10-27 | [
[
"Pasquali",
"Sara",
""
],
[
"Soresina",
"Cinzia",
""
],
[
"Marchesini",
"Enrico",
""
]
] | Simulating the population dynamics of a stage-structured population requires the knowledge of the biodemographic functions characterizing the species, namely development, mortality and fecundity. In general, development and fecundity can satisfactorily be estimated starting from literature data. Unfortunately, this is often not the case of the mortality function, because of the lack of experimental data. To overcome this problem we estimate the mortality rate function from field data on the abundance of the species. The mortality is expressed as a linear combination of cubic splines and the estimation method allows to determine its coefficients taking into account the observations measurement error. Moreover, the variability in the estimate is quantified by means of the confidence bands for both mortality and dynamics. The presented method allows to obtain a more flexible shape for the mortality rate functions compared with previous methods applied to the same pest. The method is applied to the case of Lobesia botrana, the main pest in the European vineyards, with abundance data collected in a location in the North of Italy for five consecutive years. Data collected over three years are used to estimate the mortality and to analyze the variability in the estimate and its effects on the population dynamics. Other two datasets are used to validate the model simulating the dynamics using the estimated mortality. |
1005.1589 | Kalyan Vinnakota | Kalyan C. Vinnakota, David A. Mitchell, Robert J. Deschenes, Tetsuro
Wakatsuki and Daniel A. Beard | Analysis of Diffusion of Ras2 in Saccharomyces cerevisiae Using
Fluorescence Recovery after Photobleaching | Accepted for publication in Physical Biology (2010). 28 pages, 7
figures, 3 tables | Kalyan C Vinnakota et al 2010 Phys. Biol. 7 026011 | 10.1088/1478-3975/7/2/026011 | null | q-bio.SC q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Binding, lateral diffusion and exchange are fundamental dynamic processes
involved in protein association with cellular membranes. In this study, we
developed numerical simulations of lateral diffusion and exchange of
fluorophores in membranes with arbitrary bleach geometry and exchange of the
membrane localized fluorophore with the cytosol during Fluorescence Recovery
after Photobleaching (FRAP) experiments. The model simulations were used to
design FRAP experiments with varying bleach region sizes on plasma-membrane
localized wild type GFP-Ras2 with a dual lipid anchor and mutant GFP-Ras2C318S
with a single lipid anchor in live yeast cells to investigate diffusional
mobility and the presence of any exchange processes operating in the time scale
of our experiments. Model parameters estimated using data from FRAP experiments
with a 1 micron x 1 micron bleach region-of-interest (ROI) and a 0.5 micron x
0.5 micron bleach ROI showed that GFP-Ras2, single or dual lipid modified,
diffuses as single species with no evidence of exchange with a cytoplasmic
pool. This is the first report of Ras2 mobility in yeast plasma membrane. The
methods developed in this study are generally applicable for studying diffusion
and exchange of membrane associated fluorophores using FRAP on commercial
confocal laser scanning microscopes.
| [
{
"created": "Mon, 10 May 2010 15:25:47 GMT",
"version": "v1"
}
] | 2010-06-08 | [
[
"Vinnakota",
"Kalyan C.",
""
],
[
"Mitchell",
"David A.",
""
],
[
"Deschenes",
"Robert J.",
""
],
[
"Wakatsuki",
"Tetsuro",
""
],
[
"Beard",
"Daniel A.",
""
]
] | Binding, lateral diffusion and exchange are fundamental dynamic processes involved in protein association with cellular membranes. In this study, we developed numerical simulations of lateral diffusion and exchange of fluorophores in membranes with arbitrary bleach geometry and exchange of the membrane localized fluorophore with the cytosol during Fluorescence Recovery after Photobleaching (FRAP) experiments. The model simulations were used to design FRAP experiments with varying bleach region sizes on plasma-membrane localized wild type GFP-Ras2 with a dual lipid anchor and mutant GFP-Ras2C318S with a single lipid anchor in live yeast cells to investigate diffusional mobility and the presence of any exchange processes operating in the time scale of our experiments. Model parameters estimated using data from FRAP experiments with a 1 micron x 1 micron bleach region-of-interest (ROI) and a 0.5 micron x 0.5 micron bleach ROI showed that GFP-Ras2, single or dual lipid modified, diffuses as single species with no evidence of exchange with a cytoplasmic pool. This is the first report of Ras2 mobility in yeast plasma membrane. The methods developed in this study are generally applicable for studying diffusion and exchange of membrane associated fluorophores using FRAP on commercial confocal laser scanning microscopes. |
1704.01205 | Wayne Hayes | Wayne B. Hayes, Nil Mamano | SANA NetGO: A combinatorial approach to using Gene Ontology (GO) terms
to score network alignments | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene Ontology (GO) terms are frequently used to score alignments between
protein-protein interaction (PPI) networks. Methods exist to measure the GO
similarity between two proteins in isolation, but pairs of proteins in a
network alignment are not isolated: each pairing is implicitly dependent upon
every other pairing via the alignment itself. Current methods fail to take into
account the frequency of GO terms across the networks, and attempt to account
for common GO terms in an ad hoc fashion by imposing arbitrary rules on when to
"allow" GO terms based on their location in the GO hierarchy, rather than using
readily available frequency information in the PPI networks themselves. Here we
develop a new measure, NetGO, that naturally weighs infrequent, informative GO
terms more heavily than frequent, less informative GO terms, without requiring
arbitrary cutoffs. In particular, NetGO down-weights the score of frequent GO
terms according to their frequency in the networks being aligned. This is a
global measure applicable only to alignments, independent of pairwise GO
measures, in the same sense that the edge-based EC or S3 scores are global
measures of topological similarity independent of pairwise topological
similarities. We demonstrate the superiority of NetGO by creating alignments of
predetermined quality based on homologous pairs of nodes and show that NetGO
correlates with alignment quality much better than any existing GO-based
alignment measures. We also demonstrate that NetGO provides a measure of
taxonomic similarity between species, consistent with existing taxonomic
measures--a feature not shared with existing GO-based network alignment
measures. Finally, we re-score alignments produced by almost a dozen aligners
from a previous study and show that NetGO does a better job than existing
measures at separating good alignments from bad ones.
| [
{
"created": "Tue, 4 Apr 2017 22:22:37 GMT",
"version": "v1"
}
] | 2017-04-06 | [
[
"Hayes",
"Wayne B.",
""
],
[
"Mamano",
"Nil",
""
]
] | Gene Ontology (GO) terms are frequently used to score alignments between protein-protein interaction (PPI) networks. Methods exist to measure the GO similarity between two proteins in isolation, but pairs of proteins in a network alignment are not isolated: each pairing is implicitly dependent upon every other pairing via the alignment itself. Current methods fail to take into account the frequency of GO terms across the networks, and attempt to account for common GO terms in an ad hoc fashion by imposing arbitrary rules on when to "allow" GO terms based on their location in the GO hierarchy, rather than using readily available frequency information in the PPI networks themselves. Here we develop a new measure, NetGO, that naturally weighs infrequent, informative GO terms more heavily than frequent, less informative GO terms, without requiring arbitrary cutoffs. In particular, NetGO down-weights the score of frequent GO terms according to their frequency in the networks being aligned. This is a global measure applicable only to alignments, independent of pairwise GO measures, in the same sense that the edge-based EC or S3 scores are global measures of topological similarity independent of pairwise topological similarities. We demonstrate the superiority of NetGO by creating alignments of predetermined quality based on homologous pairs of nodes and show that NetGO correlates with alignment quality much better than any existing GO-based alignment measures. We also demonstrate that NetGO provides a measure of taxonomic similarity between species, consistent with existing taxonomic measures--a feature not shared with existing GO-based network alignment measures. Finally, we re-score alignments produced by almost a dozen aligners from a previous study and show that NetGO does a better job than existing measures at separating good alignments from bad ones. |
2206.05540 | Chantal Nguyen | Chantal Nguyen, Imri Dromi, Aharon Kempinski, Gabriella E. C. Gall,
Orit Peleg, Yasmine Meroz | Noisy circumnutations facilitate self-organized shade avoidance in
sunflowers | null | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Circumnutations are widespread in plants and typically associated with
exploratory movements, however a quantitative understanding of their role
remains elusive. In this study we report, for the first time, the role of noisy
circumnutations in facilitating an optimal growth pattern within a crowded
group of mutually shading plants. We revisit the problem of self-organization
observed for sunflowers, mediated by shade response interactions. Our analysis
reveals that circumnutation movements conform to a bounded random walk
characterized by a remarkably broad distribution of velocities, covering three
orders of magnitude. In motile animal systems such wide distributions of
movement velocities are frequently identified with enhancement of behavioral
processes, suggesting that circumnutations may serve as a source of functional
noise. To test our hypothesis, we developed a parsimonious model of interacting
growing disks, informed by experiments, successfully capturing the
characteristic dynamics of individual and multiple interacting plants.
Employing our simulation framework we examine the role of circumnutations in
the system, and find that the observed breadth of the velocity distribution
confers advantageous effects by facilitating exploration of potential
configurations, leading to an optimized arrangement with minimal shading. These
findings represent the first report of functional noise in plant movements, and
establishes a theoretical foundation for investigating how plants navigate
their environment by employing computational processes such as task-oriented
processes, optimization, and active sensing.
| [
{
"created": "Sat, 11 Jun 2022 14:47:11 GMT",
"version": "v1"
},
{
"created": "Tue, 1 Aug 2023 00:41:34 GMT",
"version": "v2"
}
] | 2023-08-02 | [
[
"Nguyen",
"Chantal",
""
],
[
"Dromi",
"Imri",
""
],
[
"Kempinski",
"Aharon",
""
],
[
"Gall",
"Gabriella E. C.",
""
],
[
"Peleg",
"Orit",
""
],
[
"Meroz",
"Yasmine",
""
]
] | Circumnutations are widespread in plants and typically associated with exploratory movements, however a quantitative understanding of their role remains elusive. In this study we report, for the first time, the role of noisy circumnutations in facilitating an optimal growth pattern within a crowded group of mutually shading plants. We revisit the problem of self-organization observed for sunflowers, mediated by shade response interactions. Our analysis reveals that circumnutation movements conform to a bounded random walk characterized by a remarkably broad distribution of velocities, covering three orders of magnitude. In motile animal systems such wide distributions of movement velocities are frequently identified with enhancement of behavioral processes, suggesting that circumnutations may serve as a source of functional noise. To test our hypothesis, we developed a parsimonious model of interacting growing disks, informed by experiments, successfully capturing the characteristic dynamics of individual and multiple interacting plants. Employing our simulation framework we examine the role of circumnutations in the system, and find that the observed breadth of the velocity distribution confers advantageous effects by facilitating exploration of potential configurations, leading to an optimized arrangement with minimal shading. These findings represent the first report of functional noise in plant movements, and establishes a theoretical foundation for investigating how plants navigate their environment by employing computational processes such as task-oriented processes, optimization, and active sensing. |
1405.7089 | Christof Koch | Giulio Tononi, and Christof Koch | Consciousness: Here, There but Not Everywhere | 15 pages, 5 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The science of consciousness has made great strides by focusing on the
behavioral and neuronal correlates of experience. However, correlates are not
enough if we are to understand even basic neurological fact; nor are they of
much help in cases where we would like to know if consciousness is present:
patients with a few remaining islands of functioning cortex, pre-term infants,
non-mammalian species, and machines that are rapidly outperforming people at
driving, recognizing faces and objects, and answering difficult questions. To
address these issues, we need a theory of consciousness that specifies what
experience is and what type of physical systems can have it. Integrated
Information Theory (IIT) does so by starting from conscious experience via five
phenomenological axioms of existence, composition, information, integration,
and exclusion. From these it derives five postulates about the properties
required of physical mechanisms to support consciousness. The theory provides a
principled account of both the quantity and the quality of an individual
experience, and a calculus to evaluate whether or not a particular system of
mechanisms is conscious and of what. IIT explains a range of clinical and
laboratory findings, makes testable predictions, and extrapolates to unusual
conditions. The theory vindicates some panpsychist intuitions -- consciousness
is an intrinsic, fundamental property, is graded, is common among biological
organisms, and even some very simple systems have some. However, unlike
panpsychism, IIT implies that not everything is conscious, for example group of
individuals or feed forward networks. In sharp contrast with widespread
functionalist beliefs, IIT implies that digital computers, even if their
behavior were to be functionally equivalent to ours, and even if they were to
run faithful simulations of the human brain, would experience next to nothing.
| [
{
"created": "Tue, 27 May 2014 23:40:43 GMT",
"version": "v1"
}
] | 2023-10-15 | [
[
"Tononi",
"Giulio",
""
],
[
"Koch",
"Christof",
""
]
] | The science of consciousness has made great strides by focusing on the behavioral and neuronal correlates of experience. However, correlates are not enough if we are to understand even basic neurological fact; nor are they of much help in cases where we would like to know if consciousness is present: patients with a few remaining islands of functioning cortex, pre-term infants, non-mammalian species, and machines that are rapidly outperforming people at driving, recognizing faces and objects, and answering difficult questions. To address these issues, we need a theory of consciousness that specifies what experience is and what type of physical systems can have it. Integrated Information Theory (IIT) does so by starting from conscious experience via five phenomenological axioms of existence, composition, information, integration, and exclusion. From these it derives five postulates about the properties required of physical mechanisms to support consciousness. The theory provides a principled account of both the quantity and the quality of an individual experience, and a calculus to evaluate whether or not a particular system of mechanisms is conscious and of what. IIT explains a range of clinical and laboratory findings, makes testable predictions, and extrapolates to unusual conditions. The theory vindicates some panpsychist intuitions -- consciousness is an intrinsic, fundamental property, is graded, is common among biological organisms, and even some very simple systems have some. However, unlike panpsychism, IIT implies that not everything is conscious, for example group of individuals or feed forward networks. In sharp contrast with widespread functionalist beliefs, IIT implies that digital computers, even if their behavior were to be functionally equivalent to ours, and even if they were to run faithful simulations of the human brain, would experience next to nothing. |
2305.10606 | Maciej Trusiak | Julianna Winnik, Damian Suski, Piotr Zda\'nkowski, Luiza Stanaszek,
Vicente Mic\'o and Maciej Trusiak | Versatile optimization-based speed-up method for autofocusing in digital
holographic microscopy | null | null | null | null | q-bio.QM eess.IV physics.optics | http://creativecommons.org/licenses/by/4.0/ | We propose a speed-up method for the in-focus plane detection in digital
holographic microscopy that can be applied to a broad class of autofocusing
algorithms that involve repetitive propagation of an object wave to various
axial locations to decide the in-focus position. The classical autofocusing
algorithms apply a uniform search strategy, i.e., they probe multiple,
uniformly distributed axial locations, which leads to heavy computational
overhead. Our method substantially reduces the computational load, without
sacrificing the accuracy, by skillfully selecting the next location to
investigate, which results in a decreased total number of probed propagation
distances. This is achieved by applying the golden selection search with
parabolic interpolation, which is the gold standard for tackling
single-variable optimization problems. The proposed approach is successfully
applied to three diverse autofocusing cases, providing up to 136-fold speed-up.
| [
{
"created": "Wed, 17 May 2023 23:23:04 GMT",
"version": "v1"
}
] | 2023-05-19 | [
[
"Winnik",
"Julianna",
""
],
[
"Suski",
"Damian",
""
],
[
"Zdańkowski",
"Piotr",
""
],
[
"Stanaszek",
"Luiza",
""
],
[
"Micó",
"Vicente",
""
],
[
"Trusiak",
"Maciej",
""
]
] | We propose a speed-up method for the in-focus plane detection in digital holographic microscopy that can be applied to a broad class of autofocusing algorithms that involve repetitive propagation of an object wave to various axial locations to decide the in-focus position. The classical autofocusing algorithms apply a uniform search strategy, i.e., they probe multiple, uniformly distributed axial locations, which leads to heavy computational overhead. Our method substantially reduces the computational load, without sacrificing the accuracy, by skillfully selecting the next location to investigate, which results in a decreased total number of probed propagation distances. This is achieved by applying the golden selection search with parabolic interpolation, which is the gold standard for tackling single-variable optimization problems. The proposed approach is successfully applied to three diverse autofocusing cases, providing up to 136-fold speed-up. |
2110.01225 | Guillermo Gallardo | Guillermo Gallardo (IMPNSC, ATHENA), Gaston Zanitti (PARIETAL), Mat
Higger (PNL), Sylvain Bouix (PNL), Demian Wassermann (PARIETAL) | Inferring the Localization of White-Matter Tracts using Diffusion Driven
Label Fusion | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inferring which pathways are affected by a brain lesion is key for both pre
and post-treatment planning. However, many disruptive lesions cause changes in
the tissue that interrupt tractography algorithms. In such cases, aggregating
information from healthy subjects can provide a solution to inferring the
affected pathways. In this paper, we introduce a novel label fusion technique
that leverages diffusion information to locate brain pathways. Through
simulations and experiments in publicly available data we show that our method
is able to correctly reconstruct brain pathways, even if they are affected by a
focal lesion.
| [
{
"created": "Mon, 4 Oct 2021 07:40:35 GMT",
"version": "v1"
}
] | 2021-10-05 | [
[
"Gallardo",
"Guillermo",
"",
"IMPNSC, ATHENA"
],
[
"Zanitti",
"Gaston",
"",
"PARIETAL"
],
[
"Higger",
"Mat",
"",
"PNL"
],
[
"Bouix",
"Sylvain",
"",
"PNL"
],
[
"Wassermann",
"Demian",
"",
"PARIETAL"
]
] | Inferring which pathways are affected by a brain lesion is key for both pre and post-treatment planning. However, many disruptive lesions cause changes in the tissue that interrupt tractography algorithms. In such cases, aggregating information from healthy subjects can provide a solution to inferring the affected pathways. In this paper, we introduce a novel label fusion technique that leverages diffusion information to locate brain pathways. Through simulations and experiments in publicly available data we show that our method is able to correctly reconstruct brain pathways, even if they are affected by a focal lesion. |
2003.00612 | Andreas Buttensch\"on | Katerina Kaouri, Vasiliki Bitsouni, Andreas Buttensch\"on, R\"udiger
Thul | Adhesion-driven patterns in a calcium-dependent model of cancer cell
movement | 27 pages; 8 figures | null | null | null | q-bio.CB math.DS q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cancer cells exhibit increased motility and proliferation, which are
instrumental in the formation of tumours and metastases. These pathological
changes can be traced back to malfunctions of cellular signalling pathways, and
calcium signalling plays a prominent role in these. We formulate a new model
for cancer cell movement which for the first time explicitly accounts for the
dependence of cell proliferation and cell-cell adhesion on calcium. At the
heart of our work is a non-linear, integro-differential (non-local) equation
for cancer cell movement, accounting for cell diffusion, advection and
proliferation. We also employ an established model of cellular calcium
signalling with a rich dynamical repertoire that includes experimentally
observed periodic wave trains and solitary pulses. The cancer cell density
exhibits travelling fronts and complex spatial patterns arising from an
adhesion-driven instability (ADI). We show how the different calcium signals
and variations in the strengths of cell-cell attraction and repulsion shape the
emergent cellular aggregation patterns, which are a key component of the
metastatic process. Performing a linear stability analysis, we identify
parameter regions corresponding to ADI. These regions are confirmed by
numerical simulations, which also reveal different types of aggregation
patterns and these patterns are significantly affected by \ca. Our study
demonstrates that the maximal cell density decreases with calcium
concentration, while the frequencies of the calcium oscillations and the cell
density oscillations are approximately equal in many cases. Furthermore, as the
calcium levels increase the speed of the travelling fronts increases, which is
related to a higher cancer invasion potential. These novel insights provide a
step forward in the design of new cancer treatments that may rely on
controlling the dynamics of cellular calcium.
| [
{
"created": "Sun, 1 Mar 2020 23:39:10 GMT",
"version": "v1"
}
] | 2020-03-03 | [
[
"Kaouri",
"Katerina",
""
],
[
"Bitsouni",
"Vasiliki",
""
],
[
"Buttenschön",
"Andreas",
""
],
[
"Thul",
"Rüdiger",
""
]
] | Cancer cells exhibit increased motility and proliferation, which are instrumental in the formation of tumours and metastases. These pathological changes can be traced back to malfunctions of cellular signalling pathways, and calcium signalling plays a prominent role in these. We formulate a new model for cancer cell movement which for the first time explicitly accounts for the dependence of cell proliferation and cell-cell adhesion on calcium. At the heart of our work is a non-linear, integro-differential (non-local) equation for cancer cell movement, accounting for cell diffusion, advection and proliferation. We also employ an established model of cellular calcium signalling with a rich dynamical repertoire that includes experimentally observed periodic wave trains and solitary pulses. The cancer cell density exhibits travelling fronts and complex spatial patterns arising from an adhesion-driven instability (ADI). We show how the different calcium signals and variations in the strengths of cell-cell attraction and repulsion shape the emergent cellular aggregation patterns, which are a key component of the metastatic process. Performing a linear stability analysis, we identify parameter regions corresponding to ADI. These regions are confirmed by numerical simulations, which also reveal different types of aggregation patterns and these patterns are significantly affected by \ca. Our study demonstrates that the maximal cell density decreases with calcium concentration, while the frequencies of the calcium oscillations and the cell density oscillations are approximately equal in many cases. Furthermore, as the calcium levels increase the speed of the travelling fronts increases, which is related to a higher cancer invasion potential. These novel insights provide a step forward in the design of new cancer treatments that may rely on controlling the dynamics of cellular calcium. |
2006.09275 | Stephan Eismann | Stephan Eismann, Raphael J.L. Townshend, Nathaniel Thomas, Milind
Jagota, Bowen Jing, Ron O. Dror | Hierarchical, rotation-equivariant neural networks to select structural
models of protein complexes | 11 pages, 5 figures + SI: Updated based on the published version in
PROTEINS. Presented at NeurIPS 2019 workshop Learning Meaningful
Representations of Life | null | 10.1002/prot.26033 | null | q-bio.BM cs.CV cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predicting the structure of multi-protein complexes is a grand challenge in
biochemistry, with major implications for basic science and drug discovery.
Computational structure prediction methods generally leverage pre-defined
structural features to distinguish accurate structural models from less
accurate ones. This raises the question of whether it is possible to learn
characteristics of accurate models directly from atomic coordinates of protein
complexes, with no prior assumptions. Here we introduce a machine learning
method that learns directly from the 3D positions of all atoms to identify
accurate models of protein complexes, without using any pre-computed
physics-inspired or statistical terms. Our neural network architecture combines
multiple ingredients that together enable end-to-end learning from molecular
structures containing tens of thousands of atoms: a point-based representation
of atoms, equivariance with respect to rotation and translation, local
convolutions, and hierarchical subsampling operations. When used in combination
with previously developed scoring functions, our network substantially improves
the identification of accurate structural models among a large set of possible
models. Our network can also be used to predict the accuracy of a given
structural model in absolute terms. The architecture we present is readily
applicable to other tasks involving learning on 3D structures of large atomic
systems.
| [
{
"created": "Fri, 5 Jun 2020 20:17:12 GMT",
"version": "v1"
},
{
"created": "Sat, 23 Jan 2021 00:47:10 GMT",
"version": "v2"
}
] | 2021-01-26 | [
[
"Eismann",
"Stephan",
""
],
[
"Townshend",
"Raphael J. L.",
""
],
[
"Thomas",
"Nathaniel",
""
],
[
"Jagota",
"Milind",
""
],
[
"Jing",
"Bowen",
""
],
[
"Dror",
"Ron O.",
""
]
] | Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage pre-defined structural features to distinguish accurate structural models from less accurate ones. This raises the question of whether it is possible to learn characteristics of accurate models directly from atomic coordinates of protein complexes, with no prior assumptions. Here we introduce a machine learning method that learns directly from the 3D positions of all atoms to identify accurate models of protein complexes, without using any pre-computed physics-inspired or statistical terms. Our neural network architecture combines multiple ingredients that together enable end-to-end learning from molecular structures containing tens of thousands of atoms: a point-based representation of atoms, equivariance with respect to rotation and translation, local convolutions, and hierarchical subsampling operations. When used in combination with previously developed scoring functions, our network substantially improves the identification of accurate structural models among a large set of possible models. Our network can also be used to predict the accuracy of a given structural model in absolute terms. The architecture we present is readily applicable to other tasks involving learning on 3D structures of large atomic systems. |
1212.1715 | Jayajit Das | Jayajit Das | Positive feedback produces broad distributions in maximum activation
attained within a narrow time window in stochastic biochemical reactions | accepted for publication in Journal of Chemical Physics. Details of
the calculations are provided in the supplementary document | null | 10.1063/1.4772583 | null | q-bio.MN cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How do single cell fate decisions induced by activation of key signaling
proteins above threshold concentrations within a time interval are affected by
stochastic fluctuations in biochemical reactions? We address this question
using minimal models of stochastic chemical reactions commonly found in cell
signaling and gene regulatory systems. Employing exact solutions and
semi-analytical methods we calculate distributions of the maximum value ($N$)
of activated species concentrations ($P_{max}(N)$) and the time ($t$) taken to
reach the maximum value ($P_{max}(t)$) within a time window in the minimal
models. We find, the presence of positive feedback interactions make
$P_{max}(N)$ more spread out with a higher "peakedness" in $P_{max}(t)$. Thus
positive feedback interactions may help single cells to respond sensitively to
a stimulus when cell decision processes require upregulation of activated forms
of key proteins to a threshold number within a time window.
| [
{
"created": "Sat, 8 Dec 2012 19:53:57 GMT",
"version": "v1"
}
] | 2015-06-12 | [
[
"Das",
"Jayajit",
""
]
] | How do single cell fate decisions induced by activation of key signaling proteins above threshold concentrations within a time interval are affected by stochastic fluctuations in biochemical reactions? We address this question using minimal models of stochastic chemical reactions commonly found in cell signaling and gene regulatory systems. Employing exact solutions and semi-analytical methods we calculate distributions of the maximum value ($N$) of activated species concentrations ($P_{max}(N)$) and the time ($t$) taken to reach the maximum value ($P_{max}(t)$) within a time window in the minimal models. We find, the presence of positive feedback interactions make $P_{max}(N)$ more spread out with a higher "peakedness" in $P_{max}(t)$. Thus positive feedback interactions may help single cells to respond sensitively to a stimulus when cell decision processes require upregulation of activated forms of key proteins to a threshold number within a time window. |
2005.09687 | Jesse Livezey | Jesse A. Livezey and Joshua I. Glaser | Deep learning approaches for neural decoding: from CNNs to LSTMs and
spikes to fMRI | 22 pages, 3 figures | null | null | null | q-bio.NC cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decoding behavior, perception, or cognitive state directly from neural
signals has applications in brain-computer interface research as well as
implications for systems neuroscience. In the last decade, deep learning has
become the state-of-the-art method in many machine learning tasks ranging from
speech recognition to image segmentation. The success of deep networks in other
domains has led to a new wave of applications in neuroscience. In this article,
we review deep learning approaches to neural decoding. We describe the
architectures used for extracting useful features from neural recording
modalities ranging from spikes to EEG. Furthermore, we explore how deep
learning has been leveraged to predict common outputs including movement,
speech, and vision, with a focus on how pretrained deep networks can be
incorporated as priors for complex decoding targets like acoustic speech or
images. Deep learning has been shown to be a useful tool for improving the
accuracy and flexibility of neural decoding across a wide range of tasks, and
we point out areas for future scientific development.
| [
{
"created": "Tue, 19 May 2020 18:10:35 GMT",
"version": "v1"
}
] | 2020-05-21 | [
[
"Livezey",
"Jesse A.",
""
],
[
"Glaser",
"Joshua I.",
""
]
] | Decoding behavior, perception, or cognitive state directly from neural signals has applications in brain-computer interface research as well as implications for systems neuroscience. In the last decade, deep learning has become the state-of-the-art method in many machine learning tasks ranging from speech recognition to image segmentation. The success of deep networks in other domains has led to a new wave of applications in neuroscience. In this article, we review deep learning approaches to neural decoding. We describe the architectures used for extracting useful features from neural recording modalities ranging from spikes to EEG. Furthermore, we explore how deep learning has been leveraged to predict common outputs including movement, speech, and vision, with a focus on how pretrained deep networks can be incorporated as priors for complex decoding targets like acoustic speech or images. Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks, and we point out areas for future scientific development. |
1704.02004 | John Rhodes | John A. Rhodes | Topological metrizations of trees, and new quartet methods of tree
inference | Final version | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Topological phylogenetic trees can be assigned edge weights in several
natural ways, highlighting different aspects of the tree. Here the rooted
triple and quartet metrizations are introduced, and applied to formulate novel
fast methods of inferring large trees from rooted triple and quartet data.
These methods can be applied in new statistically consistent procedures for
inference of a species tree from gene trees under the multispecies coalescent
model.
| [
{
"created": "Thu, 6 Apr 2017 19:52:20 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Apr 2017 22:31:34 GMT",
"version": "v2"
},
{
"created": "Wed, 24 Oct 2018 19:51:51 GMT",
"version": "v3"
},
{
"created": "Mon, 13 May 2019 22:53:13 GMT",
"version": "v4"
}
] | 2019-05-15 | [
[
"Rhodes",
"John A.",
""
]
] | Topological phylogenetic trees can be assigned edge weights in several natural ways, highlighting different aspects of the tree. Here the rooted triple and quartet metrizations are introduced, and applied to formulate novel fast methods of inferring large trees from rooted triple and quartet data. These methods can be applied in new statistically consistent procedures for inference of a species tree from gene trees under the multispecies coalescent model. |
2310.10063 | Quratul Ain Dr. | Shahida Perveen, Qurat-ul-Ain, Sarosh Iqbal, Sheeba Wajid, Khalid
Muhammad khan, and Muhammad Iqbal Choudhary | 1,1-Diphenyl-2-picrylhydrazyl and superoxide anion radical scavenging 1
activities of heterocyclic 2-oxo-1,2,3,4-tetrahydropyrimidines | 11 pages,1 scheme, 1 table | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | To investigate1,1-Diphenyl-2-picrylhydrazyl (DPPH) and superoxide radical
(SOR) 17 scavenging activities of 2-oxo-1,2,3,4-tetrahydropyrimidines
derivatives. Free radicals are 18 highly unstable and reactive molecules/atoms.
In the body, free radicals form during 19 normal and abnormal metabolism in the
body and cause serious damage to other 20 biomolecules through generating
oxidative stress (OS). If free radicals induced OS does 21 not neutralized
properly, host multiple pathologies including several types of cancers.
| [
{
"created": "Mon, 16 Oct 2023 04:57:17 GMT",
"version": "v1"
}
] | 2023-10-17 | [
[
"Perveen",
"Shahida",
""
],
[
"Qurat-ul-Ain",
"",
""
],
[
"Iqbal",
"Sarosh",
""
],
[
"Wajid",
"Sheeba",
""
],
[
"khan",
"Khalid Muhammad",
""
],
[
"Choudhary",
"Muhammad Iqbal",
""
]
] | To investigate1,1-Diphenyl-2-picrylhydrazyl (DPPH) and superoxide radical (SOR) 17 scavenging activities of 2-oxo-1,2,3,4-tetrahydropyrimidines derivatives. Free radicals are 18 highly unstable and reactive molecules/atoms. In the body, free radicals form during 19 normal and abnormal metabolism in the body and cause serious damage to other 20 biomolecules through generating oxidative stress (OS). If free radicals induced OS does 21 not neutralized properly, host multiple pathologies including several types of cancers. |
1704.03548 | Peng Yang | Peng Yang | Computational Approaches for Disease Gene Identification | PhD thesis, Nanyang Technological University, May 2013 | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Identifying disease genes from human genome is an important and fundamental
problem in biomedical research. Despite many publications of machine learning
methods applied to discover new disease genes, it still remains a challenge
because of the pleiotropy of genes, the limited number of confirmed disease
genes among whole genome and the genetic heterogeneity of diseases. Recent
approaches have applied the concept of 'guilty by association' to investigate
the association between a disease phenotype and its causative genes, which
means that candidate genes with similar characteristics as known disease genes
are more likely to be associated with diseases. However, due to the imbalance
issues (few genes are experimentally confirmed as disease related genes within
human genome) in disease gene identification, semi-supervised approaches, like
label propagation approaches and positive-unlabeled learning, are used to
identify candidate disease genes via making use of unknown genes for training -
typically in the scenario of a small amount of confirmed disease genes (labeled
data) with a large amount of unknown genome (unlabeled data). The performance
of Disease gene prediction models are limited by potential bias of single
learning models and incompleteness and noise of single biological data sources,
therefore ensemble learning models are applied via combining multiple diverse
biological sources and learning models to obtain better predictive performance.
In this thesis, we propose three computational models for identifying candidate
disease genes.
| [
{
"created": "Tue, 11 Apr 2017 21:52:39 GMT",
"version": "v1"
},
{
"created": "Sun, 21 May 2017 22:11:31 GMT",
"version": "v2"
}
] | 2017-05-23 | [
[
"Yang",
"Peng",
""
]
] | Identifying disease genes from human genome is an important and fundamental problem in biomedical research. Despite many publications of machine learning methods applied to discover new disease genes, it still remains a challenge because of the pleiotropy of genes, the limited number of confirmed disease genes among whole genome and the genetic heterogeneity of diseases. Recent approaches have applied the concept of 'guilty by association' to investigate the association between a disease phenotype and its causative genes, which means that candidate genes with similar characteristics as known disease genes are more likely to be associated with diseases. However, due to the imbalance issues (few genes are experimentally confirmed as disease related genes within human genome) in disease gene identification, semi-supervised approaches, like label propagation approaches and positive-unlabeled learning, are used to identify candidate disease genes via making use of unknown genes for training - typically in the scenario of a small amount of confirmed disease genes (labeled data) with a large amount of unknown genome (unlabeled data). The performance of Disease gene prediction models are limited by potential bias of single learning models and incompleteness and noise of single biological data sources, therefore ensemble learning models are applied via combining multiple diverse biological sources and learning models to obtain better predictive performance. In this thesis, we propose three computational models for identifying candidate disease genes. |
1209.0364 | Brian Williams Dr | Brian G. Williams, Eleanor Gouws, John Hargrove, Cari van Schalkwyk,
and Hilmarie Brand | Pre-exposure prophylaxis (PrEP) versus treatment-as-prevention (TasP)
for the control of HIV: Where does the balance lie? | 4 pages | null | null | null | q-bio.PE stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Anti-retroviral drugs can reduce the infectiousness of people living with HIV
by about 96%--treatment as prevention or TasP--and can reduce the risk of being
infected by an HIV positive person by about 70%--pre-exposure prophylaxis or
PrEP--raising the prospect of using anti-retroviral drugs to stop the epidemic
of HIV. The question as to which is more effective, more affordable and more
cost effective, and under what conditions, continues to be debated in the
scientific literature. Here we compare TasP and PreP in order to determine the
conditions under which each strategy is favourable. This analysis suggests that
where the incidence of HIV is less than 5% or the risk-reduction under PrEP is
less than 50%, TasP is favoured over PrEP; otherwise PrEP is favoured over
TasP. The potential for using PreP should therefore be restricted to those
among whom the annual incidence of HIV is greater than 5% and TasP reduces
transmission by more than 50%. PreP should be considered for commercial sex
workers, young women aged about 20 to 25 years, men-who-have-sex with men, or
intravenous drug users, but only where the incidence of HIV is high.
| [
{
"created": "Mon, 27 Aug 2012 11:46:15 GMT",
"version": "v1"
}
] | 2012-09-04 | [
[
"Williams",
"Brian G.",
""
],
[
"Gouws",
"Eleanor",
""
],
[
"Hargrove",
"John",
""
],
[
"van Schalkwyk",
"Cari",
""
],
[
"Brand",
"Hilmarie",
""
]
] | Anti-retroviral drugs can reduce the infectiousness of people living with HIV by about 96%--treatment as prevention or TasP--and can reduce the risk of being infected by an HIV positive person by about 70%--pre-exposure prophylaxis or PrEP--raising the prospect of using anti-retroviral drugs to stop the epidemic of HIV. The question as to which is more effective, more affordable and more cost effective, and under what conditions, continues to be debated in the scientific literature. Here we compare TasP and PreP in order to determine the conditions under which each strategy is favourable. This analysis suggests that where the incidence of HIV is less than 5% or the risk-reduction under PrEP is less than 50%, TasP is favoured over PrEP; otherwise PrEP is favoured over TasP. The potential for using PreP should therefore be restricted to those among whom the annual incidence of HIV is greater than 5% and TasP reduces transmission by more than 50%. PreP should be considered for commercial sex workers, young women aged about 20 to 25 years, men-who-have-sex with men, or intravenous drug users, but only where the incidence of HIV is high. |
1911.09948 | Dominique Beroule | Dominique B\'eroule (LIMSI), Pascale Gisquet-Verrier (Neuro-PSI) | Decision Making guided by Emotion A computational architecture | null | WCCI 2012 IEEE world congress on computational intelligence, Jun
2012, Brisbane, France | null | null | q-bio.NC cs.LG cs.NE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A computational architecture is presented, in which "swift and fuzzy"
emotional channels guide a "slow and precise" decision-making channel. Reported
neurobiological studies first provide hints on the representation of both
emotional and cognitive dimensions across brain structures, mediated by the
neuromodulation system. The related model is based on Guided Propagation
Networks, the inner flows of which can be guided through modulation. A
key-channel of this model grows from a few emotional cues, and is aimed at
anticipating the consequences of ongoing possible actions. Current experimental
results of a computer simulation show the integrated contribution of several
emotional influences, as well as issues of accidental all-out emotions.
| [
{
"created": "Fri, 22 Nov 2019 09:49:34 GMT",
"version": "v1"
}
] | 2019-11-25 | [
[
"Béroule",
"Dominique",
"",
"LIMSI"
],
[
"Gisquet-Verrier",
"Pascale",
"",
"Neuro-PSI"
]
] | A computational architecture is presented, in which "swift and fuzzy" emotional channels guide a "slow and precise" decision-making channel. Reported neurobiological studies first provide hints on the representation of both emotional and cognitive dimensions across brain structures, mediated by the neuromodulation system. The related model is based on Guided Propagation Networks, the inner flows of which can be guided through modulation. A key-channel of this model grows from a few emotional cues, and is aimed at anticipating the consequences of ongoing possible actions. Current experimental results of a computer simulation show the integrated contribution of several emotional influences, as well as issues of accidental all-out emotions. |
1112.0465 | Alberto Puliafito Dr | Alberto Puliafito, Lars Hufnagel, Pierre Neveu, Sebastian Streichan,
Alex Sigal, Deborah K. Fygenson and Boris I. Shraiman | Collective and single cell behavior in epithelial contact inhibition | null | null | 10.1073/pnas.1007809109 | null | q-bio.TO q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Control of cell proliferation is a fundamental aspect of tissue physiology
central to morphogenesis, wound healing and cancer. Although many of the
molecular genetic factors are now known, the system level regulation of growth
is still poorly understood. A simple form of inhibition of cell proliferation
is encountered in vitro in normally differentiating epithelial cell cultures
and is known as "contact inhibition". The study presented here provides a
quantitative characterization of contact inhibition dynamics on tissue-wide and
single cell levels. Using long-term tracking of cultured MDCK cells we
demonstrate that inhibition of cell division in a confluent monolayer follows
inhibition of cell motility and sets in when mechanical constraint on local
expansion causes divisions to reduce cell area. We quantify cell motility and
cell cycle statistics in the low density confluent regime and their change
across the transition to epithelial morphology which occurs with increasing
cell density. We then study the dynamics of cell area distribution arising
through reductive division, determine the average mitotic rate as a function of
cell size and demonstrate that complete arrest of mitosis occurs when cell area
falls below a critical value. We also present a simple computational model of
growth mechanics which captures all aspects of the observed behavior. Our
measurements and analysis show that contact inhibition is a consequence of
mechanical interaction and constraint rather than interfacial contact alone,
and define quantitative phenotypes that can guide future studies of molecular
mechanisms underlying contact inhibition.
| [
{
"created": "Fri, 2 Dec 2011 14:04:37 GMT",
"version": "v1"
}
] | 2015-06-03 | [
[
"Puliafito",
"Alberto",
""
],
[
"Hufnagel",
"Lars",
""
],
[
"Neveu",
"Pierre",
""
],
[
"Streichan",
"Sebastian",
""
],
[
"Sigal",
"Alex",
""
],
[
"Fygenson",
"Deborah K.",
""
],
[
"Shraiman",
"Boris I.",
""
]
] | Control of cell proliferation is a fundamental aspect of tissue physiology central to morphogenesis, wound healing and cancer. Although many of the molecular genetic factors are now known, the system level regulation of growth is still poorly understood. A simple form of inhibition of cell proliferation is encountered in vitro in normally differentiating epithelial cell cultures and is known as "contact inhibition". The study presented here provides a quantitative characterization of contact inhibition dynamics on tissue-wide and single cell levels. Using long-term tracking of cultured MDCK cells we demonstrate that inhibition of cell division in a confluent monolayer follows inhibition of cell motility and sets in when mechanical constraint on local expansion causes divisions to reduce cell area. We quantify cell motility and cell cycle statistics in the low density confluent regime and their change across the transition to epithelial morphology which occurs with increasing cell density. We then study the dynamics of cell area distribution arising through reductive division, determine the average mitotic rate as a function of cell size and demonstrate that complete arrest of mitosis occurs when cell area falls below a critical value. We also present a simple computational model of growth mechanics which captures all aspects of the observed behavior. Our measurements and analysis show that contact inhibition is a consequence of mechanical interaction and constraint rather than interfacial contact alone, and define quantitative phenotypes that can guide future studies of molecular mechanisms underlying contact inhibition. |
2005.07791 | Sebastian Goncalves Dr | Ben-Hur Francisco Cardoso, Sebasti\'an Gon\c{c}alves | Urban Scaling of COVID-19 epidemics | 10 pages, 14 figures | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Susceptible-Invective-Recovered (SIR) mathematical models are in high demand
due to the COVID-19 pandemic. They are used in their standard formulation, or
through the many variants, trying to fit and hopefully predict the number of
new cases for the next days or weeks, in any place, city, or country. Such is
key knowledge for the authorities to prepare for the health systems demand or
to apply restrictions to slow down the infectives curve. Even when the model
can be easily solved ---by the use of specialized software or by programming
the numerical solution of the differential equations that represent the
model---, the prediction is a non-easy task, because the behavioral change of
people is reflected in a continuous change of the parameters. A relevant
question is what we can use of one city to another; if what happened in Madrid
could have been applied to New York and then, if what we have learned from this
city would be of use for S\~ao Paulo. With this idea in mind, we present an
analysis of a spreading-rate related measure of COVID-19 as a function of
population density and population size for all US counties, as long as for
Brazilian cities and German cities. Contrary to what is the common hypothesis
in epidemics modeling, we observe a higher {\em per-capita} contact rate for
higher city's population density and population size. Also, we find that the
population size has a more explanatory effect than the population density. A
contact rate scaling theory is proposed to explain the results.
| [
{
"created": "Fri, 15 May 2020 21:26:55 GMT",
"version": "v1"
}
] | 2020-05-19 | [
[
"Cardoso",
"Ben-Hur Francisco",
""
],
[
"Gonçalves",
"Sebastián",
""
]
] | Susceptible-Invective-Recovered (SIR) mathematical models are in high demand due to the COVID-19 pandemic. They are used in their standard formulation, or through the many variants, trying to fit and hopefully predict the number of new cases for the next days or weeks, in any place, city, or country. Such is key knowledge for the authorities to prepare for the health systems demand or to apply restrictions to slow down the infectives curve. Even when the model can be easily solved ---by the use of specialized software or by programming the numerical solution of the differential equations that represent the model---, the prediction is a non-easy task, because the behavioral change of people is reflected in a continuous change of the parameters. A relevant question is what we can use of one city to another; if what happened in Madrid could have been applied to New York and then, if what we have learned from this city would be of use for S\~ao Paulo. With this idea in mind, we present an analysis of a spreading-rate related measure of COVID-19 as a function of population density and population size for all US counties, as long as for Brazilian cities and German cities. Contrary to what is the common hypothesis in epidemics modeling, we observe a higher {\em per-capita} contact rate for higher city's population density and population size. Also, we find that the population size has a more explanatory effect than the population density. A contact rate scaling theory is proposed to explain the results. |
1308.5254 | Daniel Larremore | Daniel B. Larremore, Aaron Clauset, Caroline O. Buckee | A network approach to analyzing highly recombinant malaria parasite
genes | 19 pages, 8 figures, 6 supplemental figures, 4 supplemental texts. To
appear in PLoS Computational Biology | PLoS Comput Biol 9(10): e1003268, 2013 | 10.1371/journal.pcbi.1003268 | null | q-bio.GN physics.data-an q-bio.MN q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The var genes of the human malaria parasite Plasmodium falciparum present a
challenge to population geneticists due to their extreme diversity, which is
generated by high rates of recombination. These genes encode a primary antigen
protein called PfEMP1, which is expressed on the surface of infected red blood
cells and elicits protective immune responses. Var gene sequences are
characterized by pronounced mosaicism, precluding the use of traditional
phylogenetic tools that require bifurcating tree-like evolutionary
relationships. We present a new method that identifies highly variable regions
(HVRs), and then maps each HVR to a complex network in which each sequence is a
node and two nodes are linked if they share an exact match of significant
length. Here, networks of var genes that recombine freely are expected to have
a uniformly random structure, but constraints on recombination will produce
network communities that we identify using a stochastic block model. We
validate this method on synthetic data, showing that it correctly recovers
populations of constrained recombination, before applying it to the Duffy
Binding Like-{\alpha} (DBL{\alpha}) domain of var genes. We find nine HVRs
whose network communities map in distinctive ways to known DBL{\alpha}
classifications and clinical phenotypes. We show that the recombinational
constraints of some HVRs are correlated, while others are independent. These
findings suggest that this micromodular structuring facilitates independent
evolutionary trajectories of neighboring mosaic regions, allowing the parasite
to retain protein function while generating enormous sequence diversity. Our
approach therefore offers a rigorous method for analyzing evolutionary
constraints in var genes, and is also flexible enough to be easily applied more
generally to any highly recombinant sequences.
| [
{
"created": "Fri, 23 Aug 2013 22:02:58 GMT",
"version": "v1"
}
] | 2013-10-22 | [
[
"Larremore",
"Daniel B.",
""
],
[
"Clauset",
"Aaron",
""
],
[
"Buckee",
"Caroline O.",
""
]
] | The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs), and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-{\alpha} (DBL{\alpha}) domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBL{\alpha} classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences. |
1904.03675 | Tarik Gouhier | Tarik C. Gouhier and Pradeep Pillai | Trivial pursuits | null | Frontiers in Ecology and Evolution 7 (2019) 236 | 10.3389/fevo.2019.00236 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We demonstrate that the conclusions drawn by Bernhard et al. (2018) regarding
the ability of nonlinear averaging to accurately predict organismal performance
under fluctuating temperatures are flawed because of a series of experimental
and statistical issues that include the presence of a hidden treatment effect,
the use of a single low frequency temperature fluctuation that could easily be
tracked by the fast growing organism, and the decision to quantify performance
via population growth rate, a metric that can mask significant variation in
population size.
| [
{
"created": "Sun, 7 Apr 2019 15:55:14 GMT",
"version": "v1"
}
] | 2019-06-14 | [
[
"Gouhier",
"Tarik C.",
""
],
[
"Pillai",
"Pradeep",
""
]
] | We demonstrate that the conclusions drawn by Bernhard et al. (2018) regarding the ability of nonlinear averaging to accurately predict organismal performance under fluctuating temperatures are flawed because of a series of experimental and statistical issues that include the presence of a hidden treatment effect, the use of a single low frequency temperature fluctuation that could easily be tracked by the fast growing organism, and the decision to quantify performance via population growth rate, a metric that can mask significant variation in population size. |
0809.1460 | Simant Dube | Simant Dube, Alain Mir, Robert C. Jones, Ramesh Ramakrishnan, Gang Sun | Computation of Maximal Resolution of Copy Number Variation on a
Nanofluidic Device using Digital PCR | Preliminary draft. Ongoing work to be submitted in refereed
conferences and/or journals | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Copy Number Variations (CNVs) of regions of the human genome are important in
disease association studies.The digital array is a nanofluidic biochip which
utilizes integrated channels and valves that partition mixtures of sample and
reagents into 765 nanovolume reaction chambers. It was recently shown how one
can perform statistical analysis of CNV in a DNA sample the digital array. In
particular, it was shown how one can accurately estimate the true concentration
of the molecules in the DNA sample and then determine the ratios of different
sequences along with statistical confidence intervals on these estimations. In
this paper we perform computation of maximum number of copies which can be
distinguished using the digital array which gives its resolution in terms of
its ability to determine CNV. Then, we demonstrate the usefulness of the
mathematical analysis to solve an important real-world problem of determination
of the copy number of X chromosome as our example application.
| [
{
"created": "Mon, 8 Sep 2008 23:04:01 GMT",
"version": "v1"
},
{
"created": "Mon, 27 Oct 2008 16:52:42 GMT",
"version": "v2"
}
] | 2008-10-27 | [
[
"Dube",
"Simant",
""
],
[
"Mir",
"Alain",
""
],
[
"Jones",
"Robert C.",
""
],
[
"Ramakrishnan",
"Ramesh",
""
],
[
"Sun",
"Gang",
""
]
] | Copy Number Variations (CNVs) of regions of the human genome are important in disease association studies.The digital array is a nanofluidic biochip which utilizes integrated channels and valves that partition mixtures of sample and reagents into 765 nanovolume reaction chambers. It was recently shown how one can perform statistical analysis of CNV in a DNA sample the digital array. In particular, it was shown how one can accurately estimate the true concentration of the molecules in the DNA sample and then determine the ratios of different sequences along with statistical confidence intervals on these estimations. In this paper we perform computation of maximum number of copies which can be distinguished using the digital array which gives its resolution in terms of its ability to determine CNV. Then, we demonstrate the usefulness of the mathematical analysis to solve an important real-world problem of determination of the copy number of X chromosome as our example application. |
2112.14774 | Eduardo Moreno | Eduardo Moreno, Robert Gro{\ss}mann, Carsten Beta and Sergio Alonso | From single to collective motion of social amoebae: a computational
study of interacting cells | 25 pages, 12 Figures and 1 Table | Front. Phys. 9 (2022) 750187 | 10.3389/fphy.2021.750187 | null | q-bio.CB nlin.PS physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | The coupling of the internal mechanisms of cell polarization to cell shape
deformations and subsequent cell crawling poses many interdisciplinary
scientific challenges. Several mathematical approaches have been proposed to
model the coupling of both processes, where one of the most successful methods
relies on a phase field that encodes the morphology of the cell, together with
the integration of partial differential equations that account for the
polarization mechanism inside the cell domain as defined by the phase field.
This approach has been previously employed to model the motion of single cells
of the social amoeba Dictyostelium discoideum, a widely used model organism to
study actin-driven motility and chemotaxis of eukaryotic cells. Besides single
cell motility, Dictyostelium discoideum is also well-known for its collective
behavior. Here, we extend the previously introduced model for single cell
motility to describe the collective motion of large populations of interacting
amoebae by including repulsive interactions between the cells. We performed
numerical simulations of this model, first characterizing the motion of single
cells in terms of their polarity and velocity vectors. We then systematically
studied the collisions between two cells that provided the basic interaction
scenarios also observed in larger ensembles of interacting amoebae. Finally,
the relevance of the cell density was analyzed, revealing a systematic decrease
of the motility with density, associated with the formation of transient cell
clusters that emerge in this system. This model is a prototypical active matter
system for the investigation of the emergent collective dynamics of deformable,
self-driven cells with a highly complex, nonlinear coupling of cell shape
deformations, self-propulsion and repulsive cell-cell interactions.
| [
{
"created": "Wed, 29 Dec 2021 05:31:46 GMT",
"version": "v1"
}
] | 2023-11-08 | [
[
"Moreno",
"Eduardo",
""
],
[
"Großmann",
"Robert",
""
],
[
"Beta",
"Carsten",
""
],
[
"Alonso",
"Sergio",
""
]
] | The coupling of the internal mechanisms of cell polarization to cell shape deformations and subsequent cell crawling poses many interdisciplinary scientific challenges. Several mathematical approaches have been proposed to model the coupling of both processes, where one of the most successful methods relies on a phase field that encodes the morphology of the cell, together with the integration of partial differential equations that account for the polarization mechanism inside the cell domain as defined by the phase field. This approach has been previously employed to model the motion of single cells of the social amoeba Dictyostelium discoideum, a widely used model organism to study actin-driven motility and chemotaxis of eukaryotic cells. Besides single cell motility, Dictyostelium discoideum is also well-known for its collective behavior. Here, we extend the previously introduced model for single cell motility to describe the collective motion of large populations of interacting amoebae by including repulsive interactions between the cells. We performed numerical simulations of this model, first characterizing the motion of single cells in terms of their polarity and velocity vectors. We then systematically studied the collisions between two cells that provided the basic interaction scenarios also observed in larger ensembles of interacting amoebae. Finally, the relevance of the cell density was analyzed, revealing a systematic decrease of the motility with density, associated with the formation of transient cell clusters that emerge in this system. This model is a prototypical active matter system for the investigation of the emergent collective dynamics of deformable, self-driven cells with a highly complex, nonlinear coupling of cell shape deformations, self-propulsion and repulsive cell-cell interactions. |
2311.18222 | Tsvi Tlusty | John M Mcbride and Tsvi Tlusty | AI-predicted protein deformation encodes energy landscape | null | null | null | null | q-bio.BM physics.bio-ph q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | AI algorithms have proven to be excellent predictors of protein structure,
but whether and how much these algorithms can capture the underlying physics
remains an open question. Here, we aim to test this question using the
Alphafold2 (AF) algorithm: We use AF to predict the subtle structural
deformation induced by single mutations, quantified by strain, and compare with
experimental datasets of corresponding perturbations in folding free energy
$\Delta\Delta G$. Unexpectedly, we find that physical strain alone -- without
any additional data or computation -- correlates almost as well with
$\Delta\Delta G$ as state-of-the-art energy-based and machine-learning
predictors. This indicates that the AF-predicted structures alone encode fine
details about the energy landscape. In particular, the structures encode
significant information on stability, enough to estimate (de-)stabilizing
effects of mutations, thus paving the way for the development of novel,
structure-based stability predictors for protein design and evolution.
| [
{
"created": "Thu, 30 Nov 2023 03:33:05 GMT",
"version": "v1"
},
{
"created": "Fri, 19 Jul 2024 15:35:52 GMT",
"version": "v2"
}
] | 2024-07-22 | [
[
"Mcbride",
"John M",
""
],
[
"Tlusty",
"Tsvi",
""
]
] | AI algorithms have proven to be excellent predictors of protein structure, but whether and how much these algorithms can capture the underlying physics remains an open question. Here, we aim to test this question using the Alphafold2 (AF) algorithm: We use AF to predict the subtle structural deformation induced by single mutations, quantified by strain, and compare with experimental datasets of corresponding perturbations in folding free energy $\Delta\Delta G$. Unexpectedly, we find that physical strain alone -- without any additional data or computation -- correlates almost as well with $\Delta\Delta G$ as state-of-the-art energy-based and machine-learning predictors. This indicates that the AF-predicted structures alone encode fine details about the energy landscape. In particular, the structures encode significant information on stability, enough to estimate (de-)stabilizing effects of mutations, thus paving the way for the development of novel, structure-based stability predictors for protein design and evolution. |
2004.05726 | Irina Kareva | Irina Kareva and Georgy Karev | Linear rather than exponential decay: a mathematical model and
underlying mechanisms | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Some populations, such as red blood cells (RBCs), exhibit a pattern of
population decline that is closer to linear rather than exponential, which has
proven to be unexpectedly challenging to describe with a single simple
mathematical model. Here we show that a sub-exponential model of population
extinction can approximate very well the experimental curves of RBC extinction,
and that one possible mechanism underlying sub-exponential decay is population
heterogeneity with respect to death rates of individuals. We further show that
a sub-exponential model of population decline can be derived within the
frameworks of frequency-dependent model of population extinction if there
exists heterogeneity with respect to mortality rates such that their initial
distribution is the Gamma distribution. Notably, specific biological mechanisms
that may result in linear pattern of population decay may be different
depending on the specific biological system; however, in the end they must
converge to individual death rates being different within the population, since
uniform death rates would result in exponential population decline. As such,
the proposed model is not intended to describe the complex dynamics of RBC
biology but instead can provide a way to phenomenologically describe linear
decay of population size. We briefly discuss the potential utility of this
model for describing effects of drugs that may cause RBC depletion and conclude
with a suggestion that this tool can provide a lens for discovering linear
extinction patterns in other populations, which may have previously been
overlooked.
| [
{
"created": "Mon, 13 Apr 2020 00:11:44 GMT",
"version": "v1"
}
] | 2020-04-14 | [
[
"Kareva",
"Irina",
""
],
[
"Karev",
"Georgy",
""
]
] | Some populations, such as red blood cells (RBCs), exhibit a pattern of population decline that is closer to linear rather than exponential, which has proven to be unexpectedly challenging to describe with a single simple mathematical model. Here we show that a sub-exponential model of population extinction can approximate very well the experimental curves of RBC extinction, and that one possible mechanism underlying sub-exponential decay is population heterogeneity with respect to death rates of individuals. We further show that a sub-exponential model of population decline can be derived within the frameworks of frequency-dependent model of population extinction if there exists heterogeneity with respect to mortality rates such that their initial distribution is the Gamma distribution. Notably, specific biological mechanisms that may result in linear pattern of population decay may be different depending on the specific biological system; however, in the end they must converge to individual death rates being different within the population, since uniform death rates would result in exponential population decline. As such, the proposed model is not intended to describe the complex dynamics of RBC biology but instead can provide a way to phenomenologically describe linear decay of population size. We briefly discuss the potential utility of this model for describing effects of drugs that may cause RBC depletion and conclude with a suggestion that this tool can provide a lens for discovering linear extinction patterns in other populations, which may have previously been overlooked. |
2012.06537 | William Winlow Professor | Andrew Simon Johnson and William Winlow | Does the brain function as a quantum phase computer using phase ternary
computation? | 16 pages, 7 figures. Key Words: Plasticity; Action potential; Timing;
Error redaction; Synchronization; Quantum phase computation; Phase ternary
computation; Retinal model | null | null | null | q-bio.NC cs.AI | http://creativecommons.org/licenses/by/4.0/ | Here we provide evidence that the fundamental basis of nervous communication
is derived from a pressure pulse/soliton capable of computation with sufficient
temporal precision to overcome any processing errors. Signalling and computing
within the nervous system are complex and different phenomena. Action
potentials are plastic and this makes the action potential peak an
inappropriate fixed point for neural computation, but the action potential
threshold is suitable for this purpose. Furthermore, neural models timed by
spiking neurons operate below the rate necessary to overcome processing error.
Using retinal processing as our example, we demonstrate that the contemporary
theory of nerve conduction based on cable theory is inappropriate to account
for the short computational time necessary for the full functioning of the
retina and by implication the rest of the brain. Moreover, cable theory cannot
be instrumental in the propagation of the action potential because at the
activation-threshold there is insufficient charge at the activation site for
successive ion channels to be electrostatically opened. Deconstruction of the
brain neural network suggests that it is a member of a group of Quantum phase
computers of which the Turing machine is the simplest: the brain is another
based upon phase ternary computation. However, attempts to use Turing based
mechanisms cannot resolve the coding of the retina or the computation of
intelligence, as the technology of Turing based computers is fundamentally
different. We demonstrate that that coding in the brain neural network is
quantum based, where the quanta have a temporal variable and a phase-base
variable enabling phase ternary computation as previously demonstrated in the
retina.
| [
{
"created": "Fri, 4 Dec 2020 08:00:23 GMT",
"version": "v1"
}
] | 2020-12-14 | [
[
"Johnson",
"Andrew Simon",
""
],
[
"Winlow",
"William",
""
]
] | Here we provide evidence that the fundamental basis of nervous communication is derived from a pressure pulse/soliton capable of computation with sufficient temporal precision to overcome any processing errors. Signalling and computing within the nervous system are complex and different phenomena. Action potentials are plastic and this makes the action potential peak an inappropriate fixed point for neural computation, but the action potential threshold is suitable for this purpose. Furthermore, neural models timed by spiking neurons operate below the rate necessary to overcome processing error. Using retinal processing as our example, we demonstrate that the contemporary theory of nerve conduction based on cable theory is inappropriate to account for the short computational time necessary for the full functioning of the retina and by implication the rest of the brain. Moreover, cable theory cannot be instrumental in the propagation of the action potential because at the activation-threshold there is insufficient charge at the activation site for successive ion channels to be electrostatically opened. Deconstruction of the brain neural network suggests that it is a member of a group of Quantum phase computers of which the Turing machine is the simplest: the brain is another based upon phase ternary computation. However, attempts to use Turing based mechanisms cannot resolve the coding of the retina or the computation of intelligence, as the technology of Turing based computers is fundamentally different. We demonstrate that that coding in the brain neural network is quantum based, where the quanta have a temporal variable and a phase-base variable enabling phase ternary computation as previously demonstrated in the retina. |
1812.07430 | Bharat Adkar | Bharat V. Adkar, Sanchari Bhattacharyya, Amy I. Gilson, Wenli Zhang,
Eugene I. Shakhnovich | Substrate inhibition imposes fitness penalty at high protein stability | 30 pages, 6 figures, 1 table, Supplementary figures and tables - 6
pages | null | 10.1073/pnas.1821447116 | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Proteins are only moderately stable. It has long been debated whether this
narrow range of stabilities is solely a result of neutral drift towards lower
stability or purifying selection against excess stability is also at work - for
which no experimental evidence was found so far. Here we show that mutations
outside the active site in the essential E. coli enzyme adenylate kinase result
in stability-dependent increase in substrate inhibition by AMP, thereby
impairing overall enzyme activity at high stability. Such inhibition caused
substantial fitness defects not only in the presence of excess substrate but
also under physiological conditions. In the latter case, substrate inhibition
caused differential accumulation of AMP in the stationary phase for the
inhibition prone mutants. Further, we show that changes in flux through Adk
could accurately describe the variation in fitness effects. Taken together,
these data suggest that selection against substrate inhibition and hence excess
stability may have resulted in a narrow range of optimal stability observed for
modern proteins.
| [
{
"created": "Tue, 18 Dec 2018 15:24:09 GMT",
"version": "v1"
}
] | 2022-10-12 | [
[
"Adkar",
"Bharat V.",
""
],
[
"Bhattacharyya",
"Sanchari",
""
],
[
"Gilson",
"Amy I.",
""
],
[
"Zhang",
"Wenli",
""
],
[
"Shakhnovich",
"Eugene I.",
""
]
] | Proteins are only moderately stable. It has long been debated whether this narrow range of stabilities is solely a result of neutral drift towards lower stability or purifying selection against excess stability is also at work - for which no experimental evidence was found so far. Here we show that mutations outside the active site in the essential E. coli enzyme adenylate kinase result in stability-dependent increase in substrate inhibition by AMP, thereby impairing overall enzyme activity at high stability. Such inhibition caused substantial fitness defects not only in the presence of excess substrate but also under physiological conditions. In the latter case, substrate inhibition caused differential accumulation of AMP in the stationary phase for the inhibition prone mutants. Further, we show that changes in flux through Adk could accurately describe the variation in fitness effects. Taken together, these data suggest that selection against substrate inhibition and hence excess stability may have resulted in a narrow range of optimal stability observed for modern proteins. |
2306.13438 | Yao Li | Ning Jiang, Charles Kolozsvary, Yao Li | Artificial Neural Network Prediction of COVID-19 Daily Infection Count | null | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is well known that the confirmed COVID-19 infection is only a fraction of
the true fraction. In this paper we use an artificial neural network to learn
the connection between the confirmed infection count, the testing data, and the
true infection count. The true infection count in the training set is obtained
by backcasting from the death count and the infection fatality ratio (IFR).
Multiple factors are taken into consideration in the estimation of IFR. We also
calibrate the recovered true COVID-19 case count with an SEIR model.
| [
{
"created": "Fri, 23 Jun 2023 11:06:36 GMT",
"version": "v1"
}
] | 2023-06-26 | [
[
"Jiang",
"Ning",
""
],
[
"Kolozsvary",
"Charles",
""
],
[
"Li",
"Yao",
""
]
] | It is well known that the confirmed COVID-19 infection is only a fraction of the true fraction. In this paper we use an artificial neural network to learn the connection between the confirmed infection count, the testing data, and the true infection count. The true infection count in the training set is obtained by backcasting from the death count and the infection fatality ratio (IFR). Multiple factors are taken into consideration in the estimation of IFR. We also calibrate the recovered true COVID-19 case count with an SEIR model. |
2306.03109 | Alexander Bukharin | Alexander Bukharin, Tianyi Liu, Shengjie Wang, Simiao Zuo, Weihao Gao,
Wen Yan, Tuo Zhao | Machine Learning Force Fields with Data Cost Aware Training | null | null | null | null | q-bio.QM cs.LG physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine learning force fields (MLFF) have been proposed to accelerate
molecular dynamics (MD) simulation, which finds widespread applications in
chemistry and biomedical research. Even for the most data-efficient MLFFs,
reaching chemical accuracy can require hundreds of frames of force and energy
labels generated by expensive quantum mechanical algorithms, which may scale as
$O(n^3)$ to $O(n^7)$, with $n$ proportional to the number of basis functions.
To address this issue, we propose a multi-stage computational framework --
ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of
cheap inaccurate data and expensive accurate data. The motivation behind
ASTEROID is that inaccurate data, though incurring large bias, can help capture
the sophisticated structures of the underlying force field. Therefore, we first
train a MLFF model on a large amount of inaccurate training data, employing a
bias-aware loss function to prevent the model from overfitting tahe potential
bias of this data. We then fine-tune the obtained model using a small amount of
accurate training data, which preserves the knowledge learned from the
inaccurate training data while significantly improving the model's accuracy.
Moreover, we propose a variant of ASTEROID based on score matching for the
setting where the inaccurate training data are unlabeled. Extensive experiments
on MD datasets and downstream tasks validate the efficacy of ASTEROID. Our code
and data are available at https://github.com/abukharin3/asteroid.
| [
{
"created": "Mon, 5 Jun 2023 04:34:54 GMT",
"version": "v1"
}
] | 2023-06-07 | [
[
"Bukharin",
"Alexander",
""
],
[
"Liu",
"Tianyi",
""
],
[
"Wang",
"Shengjie",
""
],
[
"Zuo",
"Simiao",
""
],
[
"Gao",
"Weihao",
""
],
[
"Yan",
"Wen",
""
],
[
"Zhao",
"Tuo",
""
]
] | Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation, which finds widespread applications in chemistry and biomedical research. Even for the most data-efficient MLFFs, reaching chemical accuracy can require hundreds of frames of force and energy labels generated by expensive quantum mechanical algorithms, which may scale as $O(n^3)$ to $O(n^7)$, with $n$ proportional to the number of basis functions. To address this issue, we propose a multi-stage computational framework -- ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of cheap inaccurate data and expensive accurate data. The motivation behind ASTEROID is that inaccurate data, though incurring large bias, can help capture the sophisticated structures of the underlying force field. Therefore, we first train a MLFF model on a large amount of inaccurate training data, employing a bias-aware loss function to prevent the model from overfitting tahe potential bias of this data. We then fine-tune the obtained model using a small amount of accurate training data, which preserves the knowledge learned from the inaccurate training data while significantly improving the model's accuracy. Moreover, we propose a variant of ASTEROID based on score matching for the setting where the inaccurate training data are unlabeled. Extensive experiments on MD datasets and downstream tasks validate the efficacy of ASTEROID. Our code and data are available at https://github.com/abukharin3/asteroid. |
1302.3757 | Helge Aufderheide | Helge Aufderheide, Lars Rudolf, Thilo Gross and Kevin D. Lafferty | How to predict community responses to perturbations in the face of
imperfect knowledge and network complexity | 9 pages, 5 figures, Supplementary information PDF included | Proc. R. Soc. B 280 1773 (2013) | 10.1098/rspb.2013.2355 | null | q-bio.PE physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is a challenge to predict the response of a large, complex system to a
perturbation. Recent attempts to predict the behaviour of food webs have
revealed that the effort needed to understand a system grows quickly with its
complexity, because increasingly precise information on the elements of the
system is required. Here, we show that not all elements of the system need to
be measured equally well. This suggests that a more efficient allocation of
effort to understand a complex systems is possible. We develop an iterative
technique for determining an efficient measurement strategy. Finally, in our
assessment of model food webs, we find that it is most important to precisely
measure the mortality and predation rates of long-lived, generalist, top
predators. Prioritizing the study of such species will make it easier to
understand the response of complex food webs to perturbations.
| [
{
"created": "Fri, 15 Feb 2013 14:12:27 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Feb 2013 18:32:20 GMT",
"version": "v2"
},
{
"created": "Wed, 15 May 2013 10:58:19 GMT",
"version": "v3"
},
{
"created": "Fri, 13 Sep 2013 14:15:59 GMT",
"version": "v4"
},
{
"created": "Mon, 16 Sep 2013 12:54:41 GMT",
"version": "v5"
}
] | 2013-11-07 | [
[
"Aufderheide",
"Helge",
""
],
[
"Rudolf",
"Lars",
""
],
[
"Gross",
"Thilo",
""
],
[
"Lafferty",
"Kevin D.",
""
]
] | It is a challenge to predict the response of a large, complex system to a perturbation. Recent attempts to predict the behaviour of food webs have revealed that the effort needed to understand a system grows quickly with its complexity, because increasingly precise information on the elements of the system is required. Here, we show that not all elements of the system need to be measured equally well. This suggests that a more efficient allocation of effort to understand a complex systems is possible. We develop an iterative technique for determining an efficient measurement strategy. Finally, in our assessment of model food webs, we find that it is most important to precisely measure the mortality and predation rates of long-lived, generalist, top predators. Prioritizing the study of such species will make it easier to understand the response of complex food webs to perturbations. |
2110.13298 | Xiang Ji | Xiang Ji, Alexander A. Fisher, Shuo Su, Jeffrey L. Thorne, Barney
Potter, Philippe Lemey, Guy Baele, Marc A. Suchard | Scalable Bayesian divergence time estimation with ratio transformations | 34 pages, 6 figures | null | null | null | q-bio.PE stat.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Divergence time estimation is crucial to provide temporal signals for dating
biologically important events, from species divergence to viral transmissions
in space and time. With the advent of high-throughput sequencing, recent
Bayesian phylogenetic studies have analyzed hundreds to thousands of sequences.
Such large-scale analyses challenge divergence time reconstruction by requiring
inference on highly-correlated internal node heights that often become
computationally infeasible. To overcome this limitation, we explore a ratio
transformation that maps the original N - 1 internal node heights into a space
of one height parameter and N - 2 ratio parameters. To make analyses scalable,
we develop a collection of linear-time algorithms to compute the gradient and
Jacobian-associated terms of the log-likelihood with respect to these ratios.
We then apply Hamiltonian Monte Carlo sampling with the ratio transform in a
Bayesian framework to learn the divergence times in four pathogenic virus
phylogenies: West Nile virus, rabies virus, Lassa virus and Ebola virus. Our
method both resolves a mixing issue in the West Nile virus example and improves
inference efficiency by at least 5-fold for the Lassa and rabies virus
examples. Our method also makes it now computationally feasible to incorporate
mixed-effects molecular clock models for the Ebola virus example, confirms the
findings from the original study and reveals clearer multimodal distributions
of the divergence times of some clades of interest.
| [
{
"created": "Mon, 25 Oct 2021 22:17:38 GMT",
"version": "v1"
}
] | 2021-10-27 | [
[
"Ji",
"Xiang",
""
],
[
"Fisher",
"Alexander A.",
""
],
[
"Su",
"Shuo",
""
],
[
"Thorne",
"Jeffrey L.",
""
],
[
"Potter",
"Barney",
""
],
[
"Lemey",
"Philippe",
""
],
[
"Baele",
"Guy",
""
],
[
"Suchard",
"Marc A.",
""
]
] | Divergence time estimation is crucial to provide temporal signals for dating biologically important events, from species divergence to viral transmissions in space and time. With the advent of high-throughput sequencing, recent Bayesian phylogenetic studies have analyzed hundreds to thousands of sequences. Such large-scale analyses challenge divergence time reconstruction by requiring inference on highly-correlated internal node heights that often become computationally infeasible. To overcome this limitation, we explore a ratio transformation that maps the original N - 1 internal node heights into a space of one height parameter and N - 2 ratio parameters. To make analyses scalable, we develop a collection of linear-time algorithms to compute the gradient and Jacobian-associated terms of the log-likelihood with respect to these ratios. We then apply Hamiltonian Monte Carlo sampling with the ratio transform in a Bayesian framework to learn the divergence times in four pathogenic virus phylogenies: West Nile virus, rabies virus, Lassa virus and Ebola virus. Our method both resolves a mixing issue in the West Nile virus example and improves inference efficiency by at least 5-fold for the Lassa and rabies virus examples. Our method also makes it now computationally feasible to incorporate mixed-effects molecular clock models for the Ebola virus example, confirms the findings from the original study and reveals clearer multimodal distributions of the divergence times of some clades of interest. |
1309.1077 | Thomas Montenegro-Johnson | Thomas D. Montenegro-Johnson, Andrew A. Smith, David J. Smith, Daniel
Loghin and John R. Blake | Modelling the Fluid Mechanics of Cilia and Flagella in Reproduction and
Development | 20 pages, 24 figures | Montenegro-Johnson et al, Eur. Phys. J. E, 35 10 (2012) 111 | 10.1140/epje/i2012-12111-1 | null | q-bio.QM cond-mat.soft physics.bio-ph physics.flu-dyn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cilia and flagella are actively bending slender organelles, performing
functions such as motility, feeding and embryonic symmetry breaking. We review
the mechanics of viscous-dominated microscale flow, including time-reversal
symmetry, drag anisotropy of slender bodies, and wall effects. We focus on the
fundamental force singularity, higher order multipoles, and the method of
images, providing physical insight and forming a basis for computational
approaches. Two biological problems are then considered in more detail: (1)
left-right symmetry breaking flow in the node, a microscopic structure in
developing vertebrate embryos, and (2) motility of microswimmers through
non-Newtonian fluids. Our model of the embryonic node reveals how particle
transport associated with morphogenesis is modulated by the gradual emergence
of cilium posterior tilt. Our model of swimming makes use of force
distributions within a body-conforming finite element framework, allowing the
solution of nonlinear inertialess Carreau flow. We find that a three-sphere
model swimmer and a model sperm are similarly affected by shear-thinning; in
both cases swimming due to a prescribed beat is enhanced by shear-thinning,
with optimal Deborah number around 0.8. The sperm exhibits an almost perfect
linear relationship between velocity and the logarithm of the ratio of zero to
infinite shear viscosity, with shear-thickening hindering cell progress.
| [
{
"created": "Wed, 4 Sep 2013 15:42:39 GMT",
"version": "v1"
}
] | 2013-09-06 | [
[
"Montenegro-Johnson",
"Thomas D.",
""
],
[
"Smith",
"Andrew A.",
""
],
[
"Smith",
"David J.",
""
],
[
"Loghin",
"Daniel",
""
],
[
"Blake",
"John R.",
""
]
] | Cilia and flagella are actively bending slender organelles, performing functions such as motility, feeding and embryonic symmetry breaking. We review the mechanics of viscous-dominated microscale flow, including time-reversal symmetry, drag anisotropy of slender bodies, and wall effects. We focus on the fundamental force singularity, higher order multipoles, and the method of images, providing physical insight and forming a basis for computational approaches. Two biological problems are then considered in more detail: (1) left-right symmetry breaking flow in the node, a microscopic structure in developing vertebrate embryos, and (2) motility of microswimmers through non-Newtonian fluids. Our model of the embryonic node reveals how particle transport associated with morphogenesis is modulated by the gradual emergence of cilium posterior tilt. Our model of swimming makes use of force distributions within a body-conforming finite element framework, allowing the solution of nonlinear inertialess Carreau flow. We find that a three-sphere model swimmer and a model sperm are similarly affected by shear-thinning; in both cases swimming due to a prescribed beat is enhanced by shear-thinning, with optimal Deborah number around 0.8. The sperm exhibits an almost perfect linear relationship between velocity and the logarithm of the ratio of zero to infinite shear viscosity, with shear-thickening hindering cell progress. |
2004.14838 | Dimitar Atanasov | Nikolay M. Yanev, Vessela K. Stoimenova, Dimitar V. Atanasov | Branching stochastic processes as models of Covid-19 epidemic
development | arXiv admin note: substantial text overlap with arXiv:2004.00941 | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The aim of the paper is to describe two models of Covid-19 infection
dynamics. For this purpose a special class of branching processes with two
types of individuals is considered. These models are intended to use only the
observed daily statistics to estimate the main parameter of the infection and
to give a prediction of the mean value of the non-observed population of the
infected individuals. Similar problems are considered also in the case when the
processes admit an immigration component. This is a serious advantage in
comparison with other more complicated models where the officially reported
data are not sufficient for estimation of the model parameters. In this way the
specific development of the Covid-19 epidemics is considered also for all
countries as it is given in the specially created site
http://ir-statistics.net/covid-19 where the obtained results are updated daily.
| [
{
"created": "Wed, 29 Apr 2020 09:41:58 GMT",
"version": "v1"
},
{
"created": "Mon, 4 May 2020 11:17:13 GMT",
"version": "v2"
}
] | 2020-05-05 | [
[
"Yanev",
"Nikolay M.",
""
],
[
"Stoimenova",
"Vessela K.",
""
],
[
"Atanasov",
"Dimitar V.",
""
]
] | The aim of the paper is to describe two models of Covid-19 infection dynamics. For this purpose a special class of branching processes with two types of individuals is considered. These models are intended to use only the observed daily statistics to estimate the main parameter of the infection and to give a prediction of the mean value of the non-observed population of the infected individuals. Similar problems are considered also in the case when the processes admit an immigration component. This is a serious advantage in comparison with other more complicated models where the officially reported data are not sufficient for estimation of the model parameters. In this way the specific development of the Covid-19 epidemics is considered also for all countries as it is given in the specially created site http://ir-statistics.net/covid-19 where the obtained results are updated daily. |
0802.1903 | Nicolas Vuillerme | Nicolas Vuillerme (TIMC), Herv\'e Vincent (LMAS) | How performing a mental arithmetic task modify the regulation of centre
of foot pressure displacements during bipedal quiet standing | null | Experimental Brain Research 169, 1 (2006) 130-4 | 10.1007/s00221-005-0124-9 | null | q-bio.NC | null | We investigated the effect of performing a mental arithmetic task with two
levels of difficulty on the regulation of centre of foot pressure (COP)
displacements during bipedal quiet standing in young healthy individuals. There
was also a control condition in which no concurrent task was required. A
space-time-domain analysis showed decreased COP displacements, along the
antero-posterior axis, when participants concurrently performed the most
difficult mental arithmetic task. Frequency-domain and stabilogram-diffusion
analyses further suggested these decreased COP displacements to be associated
with an increased stiffness and a reduction of the exploratory behaviours in
the short term, respectively.
| [
{
"created": "Wed, 13 Feb 2008 20:10:29 GMT",
"version": "v1"
}
] | 2008-02-14 | [
[
"Vuillerme",
"Nicolas",
"",
"TIMC"
],
[
"Vincent",
"Hervé",
"",
"LMAS"
]
] | We investigated the effect of performing a mental arithmetic task with two levels of difficulty on the regulation of centre of foot pressure (COP) displacements during bipedal quiet standing in young healthy individuals. There was also a control condition in which no concurrent task was required. A space-time-domain analysis showed decreased COP displacements, along the antero-posterior axis, when participants concurrently performed the most difficult mental arithmetic task. Frequency-domain and stabilogram-diffusion analyses further suggested these decreased COP displacements to be associated with an increased stiffness and a reduction of the exploratory behaviours in the short term, respectively. |
2009.12248 | Marco Saltini | Marco Saltini and Bela M. Mulder | Microtubule-based actin transport and localization in a spherical cell | null | null | null | null | q-bio.SC q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The interaction between actin filaments and microtubules is crucial for many
eukaryotic cellular processes, such as, among others, cell polarization, cell
motility and cellular wound healing. The importance of this interaction has
long been recognised, yet very little is understood about both the underlying
mechanisms and the consequences for the spatial (re)organization of the
cellular cytoskeleton. At the same time, understanding the causes and the
consequences of the interaction between different biomolecular components are
key questions for \emph{in vitro} research involving reconstituted biomolecular
systems, especially in the light of current interest in creating minimal
synthetic cells. In this light, recent \emph{in vitro} experiments have shown
that the actin-microtubule interaction mediated by the cytolinker TipAct, which
binds to actin lattice and microtubule tip, causes the directed transport of
actin filaments. We develop an analytical theory of dynamically unstable
microtubules, nucleated from the center of a spherical cell, in interaction
with actin filaments. We show that, depending on the balance between the
diffusion of unbound actin filaments and propensity to bind microtubules, actin
is either concentrated in the center of the cell, where the density of
microtubules is highest, or becomes localized to the cell cortex.
| [
{
"created": "Fri, 25 Sep 2020 14:02:50 GMT",
"version": "v1"
}
] | 2020-09-28 | [
[
"Saltini",
"Marco",
""
],
[
"Mulder",
"Bela M.",
""
]
] | The interaction between actin filaments and microtubules is crucial for many eukaryotic cellular processes, such as, among others, cell polarization, cell motility and cellular wound healing. The importance of this interaction has long been recognised, yet very little is understood about both the underlying mechanisms and the consequences for the spatial (re)organization of the cellular cytoskeleton. At the same time, understanding the causes and the consequences of the interaction between different biomolecular components are key questions for \emph{in vitro} research involving reconstituted biomolecular systems, especially in the light of current interest in creating minimal synthetic cells. In this light, recent \emph{in vitro} experiments have shown that the actin-microtubule interaction mediated by the cytolinker TipAct, which binds to actin lattice and microtubule tip, causes the directed transport of actin filaments. We develop an analytical theory of dynamically unstable microtubules, nucleated from the center of a spherical cell, in interaction with actin filaments. We show that, depending on the balance between the diffusion of unbound actin filaments and propensity to bind microtubules, actin is either concentrated in the center of the cell, where the density of microtubules is highest, or becomes localized to the cell cortex. |
2206.10612 | Alex Bogatu | Alex Bogatu, Magdalena Wysocka, Oskar Wysocki, Holly Butterworth,
Donal Landers, Elaine Kilgour, Andre Freitas | Metareview-informed Explainable Cytokine Storm Detection during CAR-T
cell Therapy | null | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by-sa/4.0/ | Cytokine release syndrome (CRS), also known as cytokine storm, is one of the
most consequential adverse effects of chimeric antigen receptor therapies that
have shown promising results in cancer treatment. When emerging, CRS could be
identified by the analysis of specific cytokine and chemokine profiles that
tend to exhibit similarities across patients. In this paper, we exploit these
similarities using machine learning algorithms and set out to pioneer a
meta--review informed method for the identification of CRS based on specific
cytokine peak concentrations and evidence from previous clinical studies. We
argue that such methods could support clinicians in analyzing suspect cytokine
profiles by matching them against CRS knowledge from past clinical studies,
with the ultimate aim of swift CRS diagnosis. During evaluation with
real--world CRS clinical data, we emphasize the potential of our proposed
method of producing interpretable results, in addition to being effective in
identifying the onset of cytokine storm.
| [
{
"created": "Mon, 20 Jun 2022 12:45:57 GMT",
"version": "v1"
}
] | 2022-06-23 | [
[
"Bogatu",
"Alex",
""
],
[
"Wysocka",
"Magdalena",
""
],
[
"Wysocki",
"Oskar",
""
],
[
"Butterworth",
"Holly",
""
],
[
"Landers",
"Donal",
""
],
[
"Kilgour",
"Elaine",
""
],
[
"Freitas",
"Andre",
""
]
] | Cytokine release syndrome (CRS), also known as cytokine storm, is one of the most consequential adverse effects of chimeric antigen receptor therapies that have shown promising results in cancer treatment. When emerging, CRS could be identified by the analysis of specific cytokine and chemokine profiles that tend to exhibit similarities across patients. In this paper, we exploit these similarities using machine learning algorithms and set out to pioneer a meta--review informed method for the identification of CRS based on specific cytokine peak concentrations and evidence from previous clinical studies. We argue that such methods could support clinicians in analyzing suspect cytokine profiles by matching them against CRS knowledge from past clinical studies, with the ultimate aim of swift CRS diagnosis. During evaluation with real--world CRS clinical data, we emphasize the potential of our proposed method of producing interpretable results, in addition to being effective in identifying the onset of cytokine storm. |
1403.7668 | William David Pearse | William D. Pearse, Andy Purvis, David B. Roy, and Alexandros
Stamatakis | Modelling ecological communities as if they were DNA | null | null | null | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ecologists are interested in understanding and predicting how ecological
communities change through time. While it might seem natural to measure this
through changes in species' abundances, computational limitations mean
transitions between community types are often modelled instead. We present an
approach inspired by DNA substitution models that attempts to estimate historic
interactions between species, and thus estimate turnover rates in ecological
communities. Although our simulations show that the method has some
limitations, our application to butterfly community data shows the method can
detect signal in real data. Open source C++ code implementing the method is
available at http://www.github.com/willpearse/lotto.
| [
{
"created": "Sat, 29 Mar 2014 20:48:19 GMT",
"version": "v1"
}
] | 2014-04-01 | [
[
"Pearse",
"William D.",
""
],
[
"Purvis",
"Andy",
""
],
[
"Roy",
"David B.",
""
],
[
"Stamatakis",
"Alexandros",
""
]
] | Ecologists are interested in understanding and predicting how ecological communities change through time. While it might seem natural to measure this through changes in species' abundances, computational limitations mean transitions between community types are often modelled instead. We present an approach inspired by DNA substitution models that attempts to estimate historic interactions between species, and thus estimate turnover rates in ecological communities. Although our simulations show that the method has some limitations, our application to butterfly community data shows the method can detect signal in real data. Open source C++ code implementing the method is available at http://www.github.com/willpearse/lotto. |
1810.11192 | Tianming Wang | Rongna Feng, Xinyue Lu, Tianming Wang, Jiawei Feng, Yifei Sun, Wenhong
Xiao, Yu Guan, Limin Feng, James L. D. Smith and Jianping Ge | Effects of free-ranging livestock on sympatric herbivores at fine
spatiotemporal scales | 43 pages, 4 figures and 3 tables | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding wildlife-livestock interactions is crucial for the design and
management of protected areas that aim to conserve large mammal communities
undergoing conflicts with humans worldwide. An example of the need to quantify
the strength and direction of species interactions is the conservation of big
cats in newly established protected areas in China. Currently, free-ranging
livestock degrade the food and habitat of the endangered Amur tiger and Amur
leopard in the forest landscapes of Northeast China, but quantitative
assessments of how livestock affect the use of habitat by the major ungulate
prey of these predators are very limited. Here, we examined livestock-ungulate
interactions using large-scale camera-trap data in the newly established Tiger
and Leopard National Park in Northeast China, which borders Russia. We used
N-mixture models, two-species occupancy models and activity pattern overlap to
understand the effects of cattle grazing on three ungulate species (wild boar,
roe deer and sika deer) at a fine spatiotemporal scale. Our results showed that
incorporating the biotic interactions with cattle had significant negative
effects on encounters with three ungulates; sika deer were particularly
displaced as more cattle encroached on forest habitat, as they exhibited low
levels of co-occurrence with cattle in terms of habitat use. These results,
combined with spatiotemporal overlap, suggested fine-scale avoidance
behaviours, and they can help to refine strategies for the conservation of
tigers, leopards and their prey in human-dominated transboundary landscapes.
Progressively controlling cattle and the impact of cattle on biodiversity while
simultaneously addressing the economic needs of local communities should be key
priority actions for the Chinese government.
| [
{
"created": "Fri, 26 Oct 2018 05:32:59 GMT",
"version": "v1"
},
{
"created": "Fri, 24 Jan 2020 01:16:42 GMT",
"version": "v2"
}
] | 2020-01-27 | [
[
"Feng",
"Rongna",
""
],
[
"Lu",
"Xinyue",
""
],
[
"Wang",
"Tianming",
""
],
[
"Feng",
"Jiawei",
""
],
[
"Sun",
"Yifei",
""
],
[
"Xiao",
"Wenhong",
""
],
[
"Guan",
"Yu",
""
],
[
"Feng",
"Limin",
""
],
[
"Smith",
"James L. D.",
""
],
[
"Ge",
"Jianping",
""
]
] | Understanding wildlife-livestock interactions is crucial for the design and management of protected areas that aim to conserve large mammal communities undergoing conflicts with humans worldwide. An example of the need to quantify the strength and direction of species interactions is the conservation of big cats in newly established protected areas in China. Currently, free-ranging livestock degrade the food and habitat of the endangered Amur tiger and Amur leopard in the forest landscapes of Northeast China, but quantitative assessments of how livestock affect the use of habitat by the major ungulate prey of these predators are very limited. Here, we examined livestock-ungulate interactions using large-scale camera-trap data in the newly established Tiger and Leopard National Park in Northeast China, which borders Russia. We used N-mixture models, two-species occupancy models and activity pattern overlap to understand the effects of cattle grazing on three ungulate species (wild boar, roe deer and sika deer) at a fine spatiotemporal scale. Our results showed that incorporating the biotic interactions with cattle had significant negative effects on encounters with three ungulates; sika deer were particularly displaced as more cattle encroached on forest habitat, as they exhibited low levels of co-occurrence with cattle in terms of habitat use. These results, combined with spatiotemporal overlap, suggested fine-scale avoidance behaviours, and they can help to refine strategies for the conservation of tigers, leopards and their prey in human-dominated transboundary landscapes. Progressively controlling cattle and the impact of cattle on biodiversity while simultaneously addressing the economic needs of local communities should be key priority actions for the Chinese government. |
2405.13239 | Domenic Germano | Domenic P.J. Germano, Alexander E. Zarebski, Sophie Hautphenne, Robert
Moss, Jennifer A. Flegg, and Mark B. Flegg | A hybrid framework for compartmental models enabling simulation-based
inference | null | null | null | null | q-bio.PE math.DS | http://creativecommons.org/licenses/by/4.0/ | Multi-scale systems often exhibit stochastic and deterministic dynamics.
Capturing these aspects in a compartmental model is challenging. Notably, low
occupancy compartments exhibit stochastic dynamics and high occupancy
compartments exhibit deterministic dynamics. Failing to account for
stochasticity in small populations can produce 'atto-foxes', e.g. in the
Lotka-Volterra ordinary differential equation (ODE) model. This limitation
becomes problematic when studying extinction of species or the clearance of
infection, but it can be resolved by using discrete stochastic models e.g.
continuous time Markov chains (CTMCs). Unfortunately, simulating CTMCs is
infeasible for most realistic populations.
We develop a novel mathematical framework, to couple continuous ODEs and
discrete CTMCs: 'Jump-Switch-Flow' (JSF). In this framework populations can
reach extinct states ("absorbing states"), thereby resolving atto-fox-type
problems. JSF has the desired behaviours of exact CTMC simulation, but is
substantially faster than existing alternatives.
JSF's utility for simulation-based inference, particularly multi-scale
problems, is demonstrated by several case-studies. In a simulation study, we
demonstrate how JSF can enable a more nuanced analysis of the efficacy of
public health interventions. We also carry out a novel analysis of longitudinal
within-host data from SARS-CoV-2 infections to quantify the timing of viral
clearance. JSF offers a novel approach to compartmental model development and
simulation.
| [
{
"created": "Tue, 21 May 2024 22:54:00 GMT",
"version": "v1"
},
{
"created": "Tue, 13 Aug 2024 01:01:02 GMT",
"version": "v2"
}
] | 2024-08-14 | [
[
"Germano",
"Domenic P. J.",
""
],
[
"Zarebski",
"Alexander E.",
""
],
[
"Hautphenne",
"Sophie",
""
],
[
"Moss",
"Robert",
""
],
[
"Flegg",
"Jennifer A.",
""
],
[
"Flegg",
"Mark B.",
""
]
] | Multi-scale systems often exhibit stochastic and deterministic dynamics. Capturing these aspects in a compartmental model is challenging. Notably, low occupancy compartments exhibit stochastic dynamics and high occupancy compartments exhibit deterministic dynamics. Failing to account for stochasticity in small populations can produce 'atto-foxes', e.g. in the Lotka-Volterra ordinary differential equation (ODE) model. This limitation becomes problematic when studying extinction of species or the clearance of infection, but it can be resolved by using discrete stochastic models e.g. continuous time Markov chains (CTMCs). Unfortunately, simulating CTMCs is infeasible for most realistic populations. We develop a novel mathematical framework, to couple continuous ODEs and discrete CTMCs: 'Jump-Switch-Flow' (JSF). In this framework populations can reach extinct states ("absorbing states"), thereby resolving atto-fox-type problems. JSF has the desired behaviours of exact CTMC simulation, but is substantially faster than existing alternatives. JSF's utility for simulation-based inference, particularly multi-scale problems, is demonstrated by several case-studies. In a simulation study, we demonstrate how JSF can enable a more nuanced analysis of the efficacy of public health interventions. We also carry out a novel analysis of longitudinal within-host data from SARS-CoV-2 infections to quantify the timing of viral clearance. JSF offers a novel approach to compartmental model development and simulation. |
0801.2447 | Diego Oyarz\'un | Diego Oyarz\'un, Brian Ingalls, Richard Middleton and Dimitrios
Kalamatianos | Optimal metabolic pathway activation | 14 pages, 3 figures. Paper to be presented at the 17th IFAC World
Congress, Seoul, Korea, July 2008 | null | null | null | q-bio.QM math.OC q-bio.MN | null | This paper deals with temporal enzyme distribution in the activation of
biochemical pathways. Pathway activation arises when production of a certain
biomolecule is required due to changing environmental conditions. Under the
premise that biological systems have been optimized through evolutionary
processes, a biologically meaningful optimal control problem is posed. In this
setup, the enzyme concentrations are assumed to be time dependent and
constrained by a limited overall enzyme production capacity, while the
optimization criterion accounts for both time and resource usage.
Using geometric arguments we establish the bang-bang nature of the solution
and reveal that each reaction must be sequentially activated in the same order
as they appear in the pathway. The results hold for a broad range of enzyme
dynamics which includes, but is not limited to, Mass Action, Michaelis-Menten
and Hill Equation kinetics.
| [
{
"created": "Wed, 16 Jan 2008 18:06:51 GMT",
"version": "v1"
}
] | 2008-01-17 | [
[
"Oyarzún",
"Diego",
""
],
[
"Ingalls",
"Brian",
""
],
[
"Middleton",
"Richard",
""
],
[
"Kalamatianos",
"Dimitrios",
""
]
] | This paper deals with temporal enzyme distribution in the activation of biochemical pathways. Pathway activation arises when production of a certain biomolecule is required due to changing environmental conditions. Under the premise that biological systems have been optimized through evolutionary processes, a biologically meaningful optimal control problem is posed. In this setup, the enzyme concentrations are assumed to be time dependent and constrained by a limited overall enzyme production capacity, while the optimization criterion accounts for both time and resource usage. Using geometric arguments we establish the bang-bang nature of the solution and reveal that each reaction must be sequentially activated in the same order as they appear in the pathway. The results hold for a broad range of enzyme dynamics which includes, but is not limited to, Mass Action, Michaelis-Menten and Hill Equation kinetics. |
1811.02526 | Les Hatton | Les Hatton, Gregory Warr | CoHSI IV: Unifying Horizontal and Vertical Gene Transfer - is Mechanism
Irrelevant ? | 16 pages, 8 figures, 8 tables, 37 references | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In previous papers we have described with strong experimental support, the
organising role that CoHSI (Conservation of Hartley-Shannon Information) plays
in determining important global properties of all known proteins, from defining
the length distribution, to the natural emergence of very long proteins and
their relationship to evolutionary time. Here we consider the insight that
CoHSI might bring to a different problem, the distribution of identical
proteins across species. Horizontal and Vertical Gene Transfer (HGT/VGT) both
lead to the replication of protein sequences across species through a diversity
of mechanisms some of which remain unknown. In contrast, CoHSI predicts from
fundamental theory that such systems will demonstrate power law behavior
independently of any mechanisms, and using the Uniprot database we show that
the global pattern of protein re-use is emphatically linear on a log-log plot
(adj. $R^{2} = 0.99, p < 2.2 \times 10^{-16}$ over 4 decades); i.e. it is
extremely close to the predicted power law. Specifically we show that over 6.9
million proteins in TrEMBL 18-02 are re-used, i.e. their sequence appears
identically in between 2 and 9,812 species, with re-used proteins varying in
length from 7 to as long as 14,596 amino acids. Using (DL+V) to denote the
three domains of life plus viruses, 21,676 proteins are shared between two
(DL+V); 22 between three (DL+V) and 5 are shared in all four (DL+V). Although
the majority of protein re-use occurs between bacterial species those proteins
most frequently re-used occur disproportionately in viruses, which play a
fundamental role in this distribution.
These results suggest that diverse mechanisms of gene transfer (including
traditional inheritance) are irrelevant in determining the global distribution
of protein re-use.
| [
{
"created": "Mon, 5 Nov 2018 17:40:05 GMT",
"version": "v1"
}
] | 2018-11-07 | [
[
"Hatton",
"Les",
""
],
[
"Warr",
"Gregory",
""
]
] | In previous papers we have described with strong experimental support, the organising role that CoHSI (Conservation of Hartley-Shannon Information) plays in determining important global properties of all known proteins, from defining the length distribution, to the natural emergence of very long proteins and their relationship to evolutionary time. Here we consider the insight that CoHSI might bring to a different problem, the distribution of identical proteins across species. Horizontal and Vertical Gene Transfer (HGT/VGT) both lead to the replication of protein sequences across species through a diversity of mechanisms some of which remain unknown. In contrast, CoHSI predicts from fundamental theory that such systems will demonstrate power law behavior independently of any mechanisms, and using the Uniprot database we show that the global pattern of protein re-use is emphatically linear on a log-log plot (adj. $R^{2} = 0.99, p < 2.2 \times 10^{-16}$ over 4 decades); i.e. it is extremely close to the predicted power law. Specifically we show that over 6.9 million proteins in TrEMBL 18-02 are re-used, i.e. their sequence appears identically in between 2 and 9,812 species, with re-used proteins varying in length from 7 to as long as 14,596 amino acids. Using (DL+V) to denote the three domains of life plus viruses, 21,676 proteins are shared between two (DL+V); 22 between three (DL+V) and 5 are shared in all four (DL+V). Although the majority of protein re-use occurs between bacterial species those proteins most frequently re-used occur disproportionately in viruses, which play a fundamental role in this distribution. These results suggest that diverse mechanisms of gene transfer (including traditional inheritance) are irrelevant in determining the global distribution of protein re-use. |
1902.02784 | Erivelton Geraldo Nepomuceno | E. G. Nepomuceno, D. F. Resende, M. J. Lacerda | A Survey of the Individual-Based Model applied in Biomedical and
Epidemiology | null | Journal of Biomedical Research and Reviews, vol. 1, no. 1, pp.
11-24, 2018 | null | null | q-bio.PE nlin.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Individual-based model (IBM) has been used to simulate and to design control
strategies for dynamic systems that are subject to stochasticity and
heterogeneity, such as infectious diseases. In the IBM, an individual is
represented by a set of specific characteristics that may change dynamically
over time. This feature allows a more realistic analysis of the spread of an
epidemic. This paper presents a literature survey of IBM applied to biomedical
and epidemiology research. The main goal is to present existing techniques,
advantages and future perspectives in the development of the model. We
evaluated 89 articles, which mostly analyze interventions aimed at endemic
infections. In addition to the review, an overview of IBM is presented as an
alternative to complement or replace compartmental models, such as the SIR
(Susceptible-Infected-Recovered) model. Numerical simulations also illustrate
the capabilities of IBM, as well as some limitations regarding the effects of
discretization. We show that similar side-effects of discretization scheme for
compartmental models may also occur in IBM, which requires careful attention.
| [
{
"created": "Thu, 7 Feb 2019 15:36:07 GMT",
"version": "v1"
}
] | 2019-02-11 | [
[
"Nepomuceno",
"E. G.",
""
],
[
"Resende",
"D. F.",
""
],
[
"Lacerda",
"M. J.",
""
]
] | Individual-based model (IBM) has been used to simulate and to design control strategies for dynamic systems that are subject to stochasticity and heterogeneity, such as infectious diseases. In the IBM, an individual is represented by a set of specific characteristics that may change dynamically over time. This feature allows a more realistic analysis of the spread of an epidemic. This paper presents a literature survey of IBM applied to biomedical and epidemiology research. The main goal is to present existing techniques, advantages and future perspectives in the development of the model. We evaluated 89 articles, which mostly analyze interventions aimed at endemic infections. In addition to the review, an overview of IBM is presented as an alternative to complement or replace compartmental models, such as the SIR (Susceptible-Infected-Recovered) model. Numerical simulations also illustrate the capabilities of IBM, as well as some limitations regarding the effects of discretization. We show that similar side-effects of discretization scheme for compartmental models may also occur in IBM, which requires careful attention. |
0706.3647 | Vittoria Colizza | Vittoria Colizza, Alessandro Vespignani | Epidemic modeling in metapopulation systems with heterogeneous coupling
pattern: theory and simulations | 18 pages, 8 figures, minor corrections, inclusion of subleading terms | Journal of Theoretical Biology 251, 450-467 (2008) | null | null | q-bio.PE physics.soc-ph | null | The spatial structure of populations is a key element in the understanding of
the large scale spreading of epidemics. Motivated by the recent empirical
evidence on the heterogeneous properties of transportation and commuting
patterns among urban areas, we present a thorough analysis of the behavior of
infectious diseases in metapopulation models characterized by heterogeneous
connectivity and mobility patterns. We derive the basic reaction-diffusion
equation describing the metapopulation system at the mechanistic level and
derive an early stage dynamics approximation for the subpopulation invasion
dynamics. The analytical description uses degree block variables that allows us
to take into account arbitrary degree distribution of the metapopulation
network. We show that along with the usual single population epidemic threshold
the metapopulation network exhibits a global threshold for the subpopulation
invasion. We find an explicit analytic expression for the invasion threshold
that determines the minimum number of individuals traveling among
subpopulations in order to have the infection of a macroscopic number of
subpopulations. The invasion threshold is a function of factors such as the
basic reproductive number, the infectious period and the mobility process and
it is found to decrease for increasing network heterogeneity. We provide
extensive mechanistic numerical Monte Carlo simulations that recover the
analytical finding in a wide range of metapopulation network connectivity
patterns. The results can be useful in the understanding of recent data driven
computational approaches to disease spreading in large transportation networks
and the effect of containment measures such as travel restrictions.
| [
{
"created": "Mon, 25 Jun 2007 14:05:24 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Aug 2007 18:41:08 GMT",
"version": "v2"
}
] | 2008-03-19 | [
[
"Colizza",
"Vittoria",
""
],
[
"Vespignani",
"Alessandro",
""
]
] | The spatial structure of populations is a key element in the understanding of the large scale spreading of epidemics. Motivated by the recent empirical evidence on the heterogeneous properties of transportation and commuting patterns among urban areas, we present a thorough analysis of the behavior of infectious diseases in metapopulation models characterized by heterogeneous connectivity and mobility patterns. We derive the basic reaction-diffusion equation describing the metapopulation system at the mechanistic level and derive an early stage dynamics approximation for the subpopulation invasion dynamics. The analytical description uses degree block variables that allows us to take into account arbitrary degree distribution of the metapopulation network. We show that along with the usual single population epidemic threshold the metapopulation network exhibits a global threshold for the subpopulation invasion. We find an explicit analytic expression for the invasion threshold that determines the minimum number of individuals traveling among subpopulations in order to have the infection of a macroscopic number of subpopulations. The invasion threshold is a function of factors such as the basic reproductive number, the infectious period and the mobility process and it is found to decrease for increasing network heterogeneity. We provide extensive mechanistic numerical Monte Carlo simulations that recover the analytical finding in a wide range of metapopulation network connectivity patterns. The results can be useful in the understanding of recent data driven computational approaches to disease spreading in large transportation networks and the effect of containment measures such as travel restrictions. |
q-bio/0604018 | Jesus M. Cortes | J.M. Cortes, P.L. Garrido, H.J. Kappen, J. Marro, C. Morillas, D.
Navidad and J.J. Torres | Algorithms for identification and categorization | 6 pages, 5 figures | AIP Conference Proceedings 779: 178-184, 2005 | 10.1063/1.2008611 | null | q-bio.NC | null | The main features of a family of efficient algorithms for recognition and
classification of complex patterns are briefly reviewed. They are inspired in
the observation that fast synaptic noise is essential for some of the
processing of information in the brain.
| [
{
"created": "Sun, 16 Apr 2006 19:09:59 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Cortes",
"J. M.",
""
],
[
"Garrido",
"P. L.",
""
],
[
"Kappen",
"H. J.",
""
],
[
"Marro",
"J.",
""
],
[
"Morillas",
"C.",
""
],
[
"Navidad",
"D.",
""
],
[
"Torres",
"J. J.",
""
]
] | The main features of a family of efficient algorithms for recognition and classification of complex patterns are briefly reviewed. They are inspired in the observation that fast synaptic noise is essential for some of the processing of information in the brain. |
1002.1600 | Namiko Mitarai | Mille A. Micheelsen, Namiko Mitarai, Kim Sneppen, and Ian. B. Dodd | Theory for stability and regulation of epigenetic landscapes | references added | Phys. Biol. 7, 026010 (2010) | 10.1088/1478-3975/7/2/026010 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cells can often choose among several stably heritable phenotypes. Examples
are the expression of genes in eukaryotic cells where long chromosomal regions
can adopt persistent and heritable silenced or active states, that may be
associated with positive feedback in dynamic modification of nucleosomes. We
generalize this mechanism in terms of bistability associated with valleys in an
epigenetic landscape. A transfer matrix method was used to rigorously follow
the system through the disruptive process of cell division. This combined
treatment of noisy dynamics both between and during cell division provides an
efficient way to calculate the stability of alternative states in a broad range
of epigenetic systems.
| [
{
"created": "Mon, 8 Feb 2010 13:18:58 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Sep 2010 11:42:18 GMT",
"version": "v2"
}
] | 2015-05-18 | [
[
"Micheelsen",
"Mille A.",
""
],
[
"Mitarai",
"Namiko",
""
],
[
"Sneppen",
"Kim",
""
],
[
"Dodd",
"Ian. B.",
""
]
] | Cells can often choose among several stably heritable phenotypes. Examples are the expression of genes in eukaryotic cells where long chromosomal regions can adopt persistent and heritable silenced or active states, that may be associated with positive feedback in dynamic modification of nucleosomes. We generalize this mechanism in terms of bistability associated with valleys in an epigenetic landscape. A transfer matrix method was used to rigorously follow the system through the disruptive process of cell division. This combined treatment of noisy dynamics both between and during cell division provides an efficient way to calculate the stability of alternative states in a broad range of epigenetic systems. |
0902.0970 | Mikl\'os Cs\H{u}r\"os | Mikl\'os Cs\H{u}r\"os, Istv\'an Mikl\'os | Mathematical Framework for Phylogenetic Birth-And-Death Models | null | null | null | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A phylogenetic birth-and-death model is a probabilistic graphical model for a
so-called phylogenetic profile, i.e., the size distribution for a homolog gene
family at the terminal nodes of a phylogeny. Profile datasets are used in
bioinformatics analyses for the inference of evolutionary trees, and of
functional associations between gene families, as well as for the
quantification of various processes guiding genome evolution. Here we describe
the mathematical formalism for phylogenetic birth-and-death models. We also
present an algorithm for computing the likelihood in a gain-loss-duplication
model.
| [
{
"created": "Thu, 5 Feb 2009 20:22:27 GMT",
"version": "v1"
}
] | 2009-02-06 | [
[
"Csűrös",
"Miklós",
""
],
[
"Miklós",
"István",
""
]
] | A phylogenetic birth-and-death model is a probabilistic graphical model for a so-called phylogenetic profile, i.e., the size distribution for a homolog gene family at the terminal nodes of a phylogeny. Profile datasets are used in bioinformatics analyses for the inference of evolutionary trees, and of functional associations between gene families, as well as for the quantification of various processes guiding genome evolution. Here we describe the mathematical formalism for phylogenetic birth-and-death models. We also present an algorithm for computing the likelihood in a gain-loss-duplication model. |
1509.06111 | Alessandro Fontana | Alessandro Fontana | A theoretical model of soma-to-germline transmission of transposable
elements to build new gene regulatory sequences | 9 pages, 5 figures. arXiv admin note: substantial text overlap with
arXiv:1304.2174 | null | null | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transposable elements are DNA sequences that can move around to different
positions in the genome. During this process, they can cause mutations, and
lead to an increase in genome size. Despite representing a large genomic
fraction, transposable elements have no clear biological function. This work
builds upon a previous model, to propose a new concept of natural selection
which combines Lamarckian and Darwinian elements. Transposable elements are
hypothesised to be the vector of a flow of genetic information from soma to
germline, that shapes gene regulatory regions across the genome. The paper
introduces the concept, presents and discusses the body of evidence in support
of this hypothesis, and suggests an experiment to test it.
| [
{
"created": "Mon, 21 Sep 2015 05:46:57 GMT",
"version": "v1"
},
{
"created": "Tue, 3 Nov 2015 08:04:41 GMT",
"version": "v2"
}
] | 2015-11-04 | [
[
"Fontana",
"Alessandro",
""
]
] | Transposable elements are DNA sequences that can move around to different positions in the genome. During this process, they can cause mutations, and lead to an increase in genome size. Despite representing a large genomic fraction, transposable elements have no clear biological function. This work builds upon a previous model, to propose a new concept of natural selection which combines Lamarckian and Darwinian elements. Transposable elements are hypothesised to be the vector of a flow of genetic information from soma to germline, that shapes gene regulatory regions across the genome. The paper introduces the concept, presents and discusses the body of evidence in support of this hypothesis, and suggests an experiment to test it. |
0904.2676 | Zhou Tianshou | Jiajun Zhang, Zhanjiang Yuan, Tianshou Zhou | Geometric Characteristics of Dynamic Correlations for Combinatorial
Regulation in Gene Expression Noise | 4 pages, 3 figures, and supporting material | null | 10.1103/PhysRevE.80.021905 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowing which mode of combinatorial regulation (typically, AND or OR logic
operation) that a gene employs is important for determining its function in
regulatory networks. Here, we introduce a dynamic cross-correlation function
between the output of a gene and its upstream regulator concentrations for
signatures of combinatorial regulation in gene expression noise. We find that
the correlation function is always upwards convex for the AND operation whereas
downwards convex for the OR operation, whichever sources of noise (intrinsic or
extrinsic or both). In turn, this fact implies a means for inferring regulatory
synergies from available experimental data. The extensions and applications are
discussed.
| [
{
"created": "Fri, 17 Apr 2009 12:07:06 GMT",
"version": "v1"
},
{
"created": "Sat, 18 Apr 2009 02:22:21 GMT",
"version": "v2"
}
] | 2015-05-13 | [
[
"Zhang",
"Jiajun",
""
],
[
"Yuan",
"Zhanjiang",
""
],
[
"Zhou",
"Tianshou",
""
]
] | Knowing which mode of combinatorial regulation (typically, AND or OR logic operation) that a gene employs is important for determining its function in regulatory networks. Here, we introduce a dynamic cross-correlation function between the output of a gene and its upstream regulator concentrations for signatures of combinatorial regulation in gene expression noise. We find that the correlation function is always upwards convex for the AND operation whereas downwards convex for the OR operation, whichever sources of noise (intrinsic or extrinsic or both). In turn, this fact implies a means for inferring regulatory synergies from available experimental data. The extensions and applications are discussed. |
2304.11920 | Tony Lindeberg | Tony Lindeberg | Orientation selectivity properties for the affine Gaussian derivative
and the affine Gabor models for visual receptive fields | 30 pages, 12 figures, 1 table. Note: From version 3 of this preprint,
the previous manuscript in 2304.11920v2 has been split into two papers, with
the part regarding biological interpretations and biological hypotheses moved
to arXiv:2404.04858 | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a theoretical analysis of the orientation selectivity of
simple and complex cells that can be well modelled by the generalized Gaussian
derivative model for visual receptive fields, with the purely spatial component
of the receptive fields determined by oriented affine Gaussian derivatives for
different orders of spatial differentiation.
A detailed mathematical analysis is presented for the three different cases
of either: (i) purely spatial receptive fields, (ii) space-time separable
spatio-temporal receptive fields and (iii) velocity-adapted spatio-temporal
receptive fields. Closed-form theoretical expressions for the orientation
selectivity curves for idealized models of simple and complex cells are derived
for all these main cases, and it is shown that the orientation selectivity of
the receptive fields becomes more narrow, as a scale parameter ratio $\kappa$,
defined as the ratio between the scale parameters in the directions
perpendicular to vs. parallel with the preferred orientation of the receptive
field, increases. It is also shown that the orientation selectivity becomes
more narrow with increasing order of spatial differentiation in the underlying
affine Gaussian derivative operators over the spatial domain.
For comparison, we also present a corresponding theoretical orientation
selectivity analysis for purely spatial receptive fields according to an affine
Gabor model. The results from that analysis are consistent with the results
obtained from the affine Gaussian derivative model,in the respect that the
orientation selectivity becomes more narrow when making the receptive fields
wider in the direction perpendicular to the preferred orientation of the
receptive field.
| [
{
"created": "Mon, 24 Apr 2023 08:57:59 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Dec 2023 13:35:15 GMT",
"version": "v2"
},
{
"created": "Sat, 6 Apr 2024 12:05:25 GMT",
"version": "v3"
},
{
"created": "Tue, 9 Apr 2024 05:20:40 GMT",
"version": "v4"
},
{
"created": "Sat, 13 Apr 2024 09:49:38 GMT",
"version": "v5"
},
{
"created": "Mon, 20 May 2024 09:35:56 GMT",
"version": "v6"
},
{
"created": "Thu, 23 May 2024 11:39:20 GMT",
"version": "v7"
},
{
"created": "Mon, 10 Jun 2024 06:46:23 GMT",
"version": "v8"
}
] | 2024-06-11 | [
[
"Lindeberg",
"Tony",
""
]
] | This paper presents a theoretical analysis of the orientation selectivity of simple and complex cells that can be well modelled by the generalized Gaussian derivative model for visual receptive fields, with the purely spatial component of the receptive fields determined by oriented affine Gaussian derivatives for different orders of spatial differentiation. A detailed mathematical analysis is presented for the three different cases of either: (i) purely spatial receptive fields, (ii) space-time separable spatio-temporal receptive fields and (iii) velocity-adapted spatio-temporal receptive fields. Closed-form theoretical expressions for the orientation selectivity curves for idealized models of simple and complex cells are derived for all these main cases, and it is shown that the orientation selectivity of the receptive fields becomes more narrow, as a scale parameter ratio $\kappa$, defined as the ratio between the scale parameters in the directions perpendicular to vs. parallel with the preferred orientation of the receptive field, increases. It is also shown that the orientation selectivity becomes more narrow with increasing order of spatial differentiation in the underlying affine Gaussian derivative operators over the spatial domain. For comparison, we also present a corresponding theoretical orientation selectivity analysis for purely spatial receptive fields according to an affine Gabor model. The results from that analysis are consistent with the results obtained from the affine Gaussian derivative model,in the respect that the orientation selectivity becomes more narrow when making the receptive fields wider in the direction perpendicular to the preferred orientation of the receptive field. |
1711.09199 | Bruno. Cessac | Dora Matzakou-Karvouniari, Lionel Gil, Elaine Orendorff, Olivier
Marre, Serge Picaud, Bruno Cessac | A biophysical model explains the spontaneous bursting behavior in the
developing retina | 25 pages, 13 figures, submitted | null | null | null | q-bio.NC math.DS physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | During early development, waves of activity propagate across the retina and
play a key role in the proper wiring of the early visual system. During the
stage II these waves are triggered by a transient network of neurons, called
Starburst Amacrine Cells (SACs), showing a bursting activity which disappears
upon further maturation. While several models have attempted to reproduce
retinal waves, none of them is able to mimic the rhythmic autonomous bursting
of individual SACs and reveal how these cells change their intrinsic properties
during development. Here, we introduce a mathematical model, grounded on
biophysics, which enables us to reproduce the bursting activity of SACs and to
propose a plausible, generic and robust, mechanism that generates it. The core
parameters controlling repetitive firing are fast depolarizing $V$-gated
calcium channels and hyperpolarizing $V$-gated potassium channels. The
quiescent phase of bursting is controlled by a slow after hyperpolarization
(sAHP), mediated by calcium-dependent potassium channels. Based on a
bifurcation analysis we show how biophysical parameters, regulating calcium and
potassium activity, control the spontaneously occurring fast oscillatory
activity followed by long refractory periods in individual SACs. We make a
testable experimental prediction on the role of voltage-dependent potassium
channels on the excitability properties of SACs and on the evolution of this
excitability along development. We also propose an explanation on how SACs can
exhibit a large variability in their bursting periods, as observed
experimentally within a SACs network as well as across different species, yet
based on a simple, unique, mechanism. As we discuss, these observations at the
cellular level have a deep impact on the retinal waves description.
| [
{
"created": "Sat, 25 Nov 2017 06:23:35 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Dec 2018 14:10:39 GMT",
"version": "v2"
},
{
"created": "Tue, 18 Dec 2018 07:19:16 GMT",
"version": "v3"
}
] | 2018-12-19 | [
[
"Matzakou-Karvouniari",
"Dora",
""
],
[
"Gil",
"Lionel",
""
],
[
"Orendorff",
"Elaine",
""
],
[
"Marre",
"Olivier",
""
],
[
"Picaud",
"Serge",
""
],
[
"Cessac",
"Bruno",
""
]
] | During early development, waves of activity propagate across the retina and play a key role in the proper wiring of the early visual system. During the stage II these waves are triggered by a transient network of neurons, called Starburst Amacrine Cells (SACs), showing a bursting activity which disappears upon further maturation. While several models have attempted to reproduce retinal waves, none of them is able to mimic the rhythmic autonomous bursting of individual SACs and reveal how these cells change their intrinsic properties during development. Here, we introduce a mathematical model, grounded on biophysics, which enables us to reproduce the bursting activity of SACs and to propose a plausible, generic and robust, mechanism that generates it. The core parameters controlling repetitive firing are fast depolarizing $V$-gated calcium channels and hyperpolarizing $V$-gated potassium channels. The quiescent phase of bursting is controlled by a slow after hyperpolarization (sAHP), mediated by calcium-dependent potassium channels. Based on a bifurcation analysis we show how biophysical parameters, regulating calcium and potassium activity, control the spontaneously occurring fast oscillatory activity followed by long refractory periods in individual SACs. We make a testable experimental prediction on the role of voltage-dependent potassium channels on the excitability properties of SACs and on the evolution of this excitability along development. We also propose an explanation on how SACs can exhibit a large variability in their bursting periods, as observed experimentally within a SACs network as well as across different species, yet based on a simple, unique, mechanism. As we discuss, these observations at the cellular level have a deep impact on the retinal waves description. |
q-bio/0312039 | Ying Lu | Zhanjun Lu, Ying Lu, Shuxia Song, Zhai Yu, Xiufang Wang | RNA Binding Density on X-chromosome Differing from that on 22 Autosomes
in Human | null | null | null | null | q-bio.GN | null | To test whether X-chromosome has unique genomic characteristics, X-chromosome
and 22 autosomes were compared for RNA binding density. Nucleotide sequences on
the chromosomes were divided into 50kb per segment that was recoded as a set of
frequency values of 7-nucleotide (7nt) strings using all possible 7nt strings
(47=16384). 120 genes highly expressed in tonsil germinal center B cells were
selected for calculating 7nt string frequency values of all introns (RNAs). The
binding density of DNA segments and RNAs was determined by the amount of
complement sequences. It was shown for the first time that gene-poor and low
gene expression X-chromosome had the lowest percentage of the DNA segments that
can highly bind RNAs, whereas gene-rich and high gene expression chromosome 19
had the highest percentage of the segments. On the basis of these results, it
is proposed that the nonrandom properties of distribution of RNA highly binding
DNA segments on the chromosomes provide strong evidence that lack of RNA highly
binding segments may be a cause of X-chromosome inactivation
| [
{
"created": "Wed, 24 Dec 2003 05:16:22 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Lu",
"Zhanjun",
""
],
[
"Lu",
"Ying",
""
],
[
"Song",
"Shuxia",
""
],
[
"Yu",
"Zhai",
""
],
[
"Wang",
"Xiufang",
""
]
] | To test whether X-chromosome has unique genomic characteristics, X-chromosome and 22 autosomes were compared for RNA binding density. Nucleotide sequences on the chromosomes were divided into 50kb per segment that was recoded as a set of frequency values of 7-nucleotide (7nt) strings using all possible 7nt strings (47=16384). 120 genes highly expressed in tonsil germinal center B cells were selected for calculating 7nt string frequency values of all introns (RNAs). The binding density of DNA segments and RNAs was determined by the amount of complement sequences. It was shown for the first time that gene-poor and low gene expression X-chromosome had the lowest percentage of the DNA segments that can highly bind RNAs, whereas gene-rich and high gene expression chromosome 19 had the highest percentage of the segments. On the basis of these results, it is proposed that the nonrandom properties of distribution of RNA highly binding DNA segments on the chromosomes provide strong evidence that lack of RNA highly binding segments may be a cause of X-chromosome inactivation |
1611.00054 | Eugene Postnikov | Anastasia I. Lavrova, Eugene B. Postnikov, Andrey Yu. Zyubin, Svetlana
V. Babak | ODE and Random Boolean networks in application to modelling of
6-mercaptopurine metabolism | 9 pages; 2 figure; 2 tables | null | null | null | q-bio.MN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider two approaches to modelling of cell metabolism of
6-mercaptopurine, which is one of the important chemotherapy drugs used for
treating of acute lymphocytic leukemia: kinetic ordinary differential equations
and random Boolean networks, and analyse their interplay with respect to taking
into account ATP concentration as a key parameter of switching between
different pathways. It is shown that Boolean networks, which allow for avoiding
complexity of general kinetic modelling, preserve an opportunity to the
reproduction of the principal switching mechanism. To keep a detailed
quantitative measure of the control parameter, a combined Boolean-ODE method is
proposed.
| [
{
"created": "Mon, 31 Oct 2016 21:34:46 GMT",
"version": "v1"
}
] | 2016-11-02 | [
[
"Lavrova",
"Anastasia I.",
""
],
[
"Postnikov",
"Eugene B.",
""
],
[
"Zyubin",
"Andrey Yu.",
""
],
[
"Babak",
"Svetlana V.",
""
]
] | We consider two approaches to modelling of cell metabolism of 6-mercaptopurine, which is one of the important chemotherapy drugs used for treating of acute lymphocytic leukemia: kinetic ordinary differential equations and random Boolean networks, and analyse their interplay with respect to taking into account ATP concentration as a key parameter of switching between different pathways. It is shown that Boolean networks, which allow for avoiding complexity of general kinetic modelling, preserve an opportunity to the reproduction of the principal switching mechanism. To keep a detailed quantitative measure of the control parameter, a combined Boolean-ODE method is proposed. |
0905.2294 | Arijit Bhattacharyay | A. Bhattacharyay | Volume exclusion and elasticity driven directional transport: an
alternative model for bacterium motility | 16 pages and 4 figures | null | 10.1088/1751-8113/43/31/315003 | null | q-bio.CB cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | On the basis of a model we capture the role of strong attractive interaction
in suppressing the rotational degrees of freedom of the system and volume
exclusion in keeping microscopic symmetry-breaking intact to result in
super-diffusive transport of small systems in a thermal atmosphere over a large
time scale. Our results, characterize such systems on the basis of having a
super-diffusive intermediate regime in between a very small and large time
scales of diffusive regimes. Although, the Brownian ratchet model fails to
account for the origin of motility in actin polymerization propelled
directional motion of bacterium like Listeria Monocytogene (LM) and similar
bio-mimetic systems due to the presence of strong attractive forces, our model
can account for the origin of directional transport in such systems on the
basis of the same interactions.
| [
{
"created": "Thu, 14 May 2009 09:55:40 GMT",
"version": "v1"
}
] | 2015-05-13 | [
[
"Bhattacharyay",
"A.",
""
]
] | On the basis of a model we capture the role of strong attractive interaction in suppressing the rotational degrees of freedom of the system and volume exclusion in keeping microscopic symmetry-breaking intact to result in super-diffusive transport of small systems in a thermal atmosphere over a large time scale. Our results, characterize such systems on the basis of having a super-diffusive intermediate regime in between a very small and large time scales of diffusive regimes. Although, the Brownian ratchet model fails to account for the origin of motility in actin polymerization propelled directional motion of bacterium like Listeria Monocytogene (LM) and similar bio-mimetic systems due to the presence of strong attractive forces, our model can account for the origin of directional transport in such systems on the basis of the same interactions. |
1701.02572 | Jos\'e Halloy | Axel S\'eguret, Bertrand Collignon, Leo Cazenille, Yohann Chemtob,
Jos\'e Halloy | Loose social organisation of AB strain zebrafish groups in a two-patch
environment | 26 pages, 22 figures | null | 10.1371/journal.pone.0206193 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore the collective behaviours of 7 group sizes: 1, 2, 3, 5, 7, 10 and
20 AB zebrafish (Danio rerio) in a constraint environment composed of two
identical squared rooms connected by a corridor. This simple set-up is similar
to a natural patchy environment. We track the positions and the identities of
the fish and compute the metrics at the group and at the individual levels.
First, we show that the size of the population affects the behaviour of each
individual in a group, the cohesion of the groups, the preferential
interactions and the transition dynamics between the two rooms. Second, during
collective departures, we show that the rankings of exit correspond to the
topological organisations of the fish prior to their collective departure with
no leadership. This spatial organisation emerge in the group a few seconds
before a collective departure. These results provide new evidences on the
spatial organisation of the groups and the effect of the population size on
individual and collective behaviours in a patchy environment.
| [
{
"created": "Tue, 10 Jan 2017 13:10:05 GMT",
"version": "v1"
}
] | 2019-02-12 | [
[
"Séguret",
"Axel",
""
],
[
"Collignon",
"Bertrand",
""
],
[
"Cazenille",
"Leo",
""
],
[
"Chemtob",
"Yohann",
""
],
[
"Halloy",
"José",
""
]
] | We explore the collective behaviours of 7 group sizes: 1, 2, 3, 5, 7, 10 and 20 AB zebrafish (Danio rerio) in a constraint environment composed of two identical squared rooms connected by a corridor. This simple set-up is similar to a natural patchy environment. We track the positions and the identities of the fish and compute the metrics at the group and at the individual levels. First, we show that the size of the population affects the behaviour of each individual in a group, the cohesion of the groups, the preferential interactions and the transition dynamics between the two rooms. Second, during collective departures, we show that the rankings of exit correspond to the topological organisations of the fish prior to their collective departure with no leadership. This spatial organisation emerge in the group a few seconds before a collective departure. These results provide new evidences on the spatial organisation of the groups and the effect of the population size on individual and collective behaviours in a patchy environment. |
1711.07918 | Andrew Mugler | Shivam Gupta, Julien Varennes, Hendrik C. Korswagen, Andrew Mugler | Temporal precision of regulated gene expression | 8 pages, 4 figures | null | 10.1371/journal.pcbi.1006201 | null | q-bio.MN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Important cellular processes such as migration, differentiation, and
development often rely on precise timing. Yet, the molecular machinery that
regulates timing is inherently noisy. How do cells achieve precise timing with
noisy components? We investigate this question using a first-passage-time
approach, for an event triggered by a molecule that crosses an abundance
threshold and that is regulated by either an accumulating activator or a
diminishing repressor. We find that the optimal strategy corresponds to a
nonlinear increase in the amount of the target molecule over time. Optimality
arises from a tradeoff between minimizing the extrinsic timing noise of the
regulator, and minimizing the intrinsic timing noise of the target molecule
itself. Although either activation or repression outperforms an unregulated
strategy, when we consider the effects of cell division, we find that
repression outperforms activation if division occurs late in the process. Our
results explain the nonlinear increase and low noise of mig-1 gene expression
in migrating neuroblast cells during Caenorhabditis elegans development, and
suggest that mig-1 regulation is dominated by repression for maximal temporal
precision. These findings suggest that dynamic regulation may be a simple and
powerful strategy for precise cellular timing.
| [
{
"created": "Tue, 21 Nov 2017 17:07:39 GMT",
"version": "v1"
}
] | 2018-07-04 | [
[
"Gupta",
"Shivam",
""
],
[
"Varennes",
"Julien",
""
],
[
"Korswagen",
"Hendrik C.",
""
],
[
"Mugler",
"Andrew",
""
]
] | Important cellular processes such as migration, differentiation, and development often rely on precise timing. Yet, the molecular machinery that regulates timing is inherently noisy. How do cells achieve precise timing with noisy components? We investigate this question using a first-passage-time approach, for an event triggered by a molecule that crosses an abundance threshold and that is regulated by either an accumulating activator or a diminishing repressor. We find that the optimal strategy corresponds to a nonlinear increase in the amount of the target molecule over time. Optimality arises from a tradeoff between minimizing the extrinsic timing noise of the regulator, and minimizing the intrinsic timing noise of the target molecule itself. Although either activation or repression outperforms an unregulated strategy, when we consider the effects of cell division, we find that repression outperforms activation if division occurs late in the process. Our results explain the nonlinear increase and low noise of mig-1 gene expression in migrating neuroblast cells during Caenorhabditis elegans development, and suggest that mig-1 regulation is dominated by repression for maximal temporal precision. These findings suggest that dynamic regulation may be a simple and powerful strategy for precise cellular timing. |
2101.09774 | James Tee | James Tee, Desmond P. Taylor | What If Memory Information is Stored Inside the Neuron, Instead of in
the Synapse? | 8 pages, 9 figures | null | null | null | q-bio.NC cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Memory information in the brain is commonly believed to be stored in the
synapse. However, a recent groundbreaking electrophysiology research has raised
the possibility that memory information may actually be stored inside the
neuron itself. Drawing on information theory and communications system
engineering perspectives, we examine the problem of how memory information
might be transmitted reliably between neurons. We identify 2 types of errors
that affect neuronal communications (i.e., channel error and source error),
along with plausible error mitigation solutions. We confirm the feasibility of
these solutions using simulations. Four alternative hypotheses of the synapse's
function are also proposed. We conclude by highlighting some research
directions, along with potential areas of application.
| [
{
"created": "Sun, 24 Jan 2021 18:54:56 GMT",
"version": "v1"
}
] | 2021-01-26 | [
[
"Tee",
"James",
""
],
[
"Taylor",
"Desmond P.",
""
]
] | Memory information in the brain is commonly believed to be stored in the synapse. However, a recent groundbreaking electrophysiology research has raised the possibility that memory information may actually be stored inside the neuron itself. Drawing on information theory and communications system engineering perspectives, we examine the problem of how memory information might be transmitted reliably between neurons. We identify 2 types of errors that affect neuronal communications (i.e., channel error and source error), along with plausible error mitigation solutions. We confirm the feasibility of these solutions using simulations. Four alternative hypotheses of the synapse's function are also proposed. We conclude by highlighting some research directions, along with potential areas of application. |
1505.02831 | Ilya Nemenman | Lina Merchan and Ilya Nemenman | On the sufficiency of pairwise interactions in maximum entropy models of
biological networks | null | null | 10.1007/s10955-016-1456-5 | null | q-bio.QM cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biological information processing networks consist of many components, which
are coupled by an even larger number of complex multivariate interactions.
However, analyses of data sets from fields as diverse as neuroscience,
molecular biology, and behavior have reported that observed statistics of
states of some biological networks can be approximated well by maximum entropy
models with only pairwise interactions among the components. Based on
simulations of random Ising spin networks with $p$-spin ($p>2$) interactions,
here we argue that this reduction in complexity can be thought of as a natural
property of densely interacting networks in certain regimes, and not
necessarily as a special property of living systems. By connecting our analysis
to the theory of random constraint satisfaction problems, we suggest a reason
for why some biological systems may operate in this regime.
| [
{
"created": "Mon, 11 May 2015 23:04:43 GMT",
"version": "v1"
}
] | 2016-03-23 | [
[
"Merchan",
"Lina",
""
],
[
"Nemenman",
"Ilya",
""
]
] | Biological information processing networks consist of many components, which are coupled by an even larger number of complex multivariate interactions. However, analyses of data sets from fields as diverse as neuroscience, molecular biology, and behavior have reported that observed statistics of states of some biological networks can be approximated well by maximum entropy models with only pairwise interactions among the components. Based on simulations of random Ising spin networks with $p$-spin ($p>2$) interactions, here we argue that this reduction in complexity can be thought of as a natural property of densely interacting networks in certain regimes, and not necessarily as a special property of living systems. By connecting our analysis to the theory of random constraint satisfaction problems, we suggest a reason for why some biological systems may operate in this regime. |
1811.08763 | Fani Deligianni Dr | Fani Deligianni, Jonathan D. Clayden and Guang-Zhong Yang | Comparison of Brain Networks based on Predictive Models of Connectivity | 7 pages, 4 figures | 19th IEEE International Conference on Bioinformatics and
Bioengineering (IEEE BIBE, 2019) | 10.1109/BIBE.2019.00029 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this study we adopt predictive modelling to identify simultaneously
commonalities and differences in multi-modal brain networks acquired within
subjects. Typically, predictive modelling of functional connectomes from
structural connectomes explores commonalities across multimodal imaging data.
However, direct application of multivariate approaches such as sparse Canonical
Correlation Analysis (sCCA) applies on the vectorised elements of functional
connectivity across subjects and it does not guarantee that the predicted
models of functional connectivity are Symmetric Positive Matrices (SPD). We
suggest an elegant solution based on the transportation of the connectivity
matrices on a Riemannian manifold, which notably improves the prediction
performance of the model. Randomised lasso is used to alleviate the dependency
of the sCCA on the lasso parameters and control the false positive rate.
Subsequently, the binomial distribution is exploited to set a threshold
statistic that reflects whether a connection is selected or rejected by chance.
Finally, we estimate the sCCA loadings based on a de-noising approach that
improves the estimation of the coefficients. We validate our approach based on
resting-state fMRI and diffusion weighted MRI data. Quantitative validation of
the prediction performance shows superior performance, whereas qualitative
results of the identification process are promising.
| [
{
"created": "Mon, 19 Nov 2018 19:43:12 GMT",
"version": "v1"
},
{
"created": "Tue, 5 Nov 2019 14:38:10 GMT",
"version": "v2"
}
] | 2019-11-06 | [
[
"Deligianni",
"Fani",
""
],
[
"Clayden",
"Jonathan D.",
""
],
[
"Yang",
"Guang-Zhong",
""
]
] | In this study we adopt predictive modelling to identify simultaneously commonalities and differences in multi-modal brain networks acquired within subjects. Typically, predictive modelling of functional connectomes from structural connectomes explores commonalities across multimodal imaging data. However, direct application of multivariate approaches such as sparse Canonical Correlation Analysis (sCCA) applies on the vectorised elements of functional connectivity across subjects and it does not guarantee that the predicted models of functional connectivity are Symmetric Positive Matrices (SPD). We suggest an elegant solution based on the transportation of the connectivity matrices on a Riemannian manifold, which notably improves the prediction performance of the model. Randomised lasso is used to alleviate the dependency of the sCCA on the lasso parameters and control the false positive rate. Subsequently, the binomial distribution is exploited to set a threshold statistic that reflects whether a connection is selected or rejected by chance. Finally, we estimate the sCCA loadings based on a de-noising approach that improves the estimation of the coefficients. We validate our approach based on resting-state fMRI and diffusion weighted MRI data. Quantitative validation of the prediction performance shows superior performance, whereas qualitative results of the identification process are promising. |
1612.04110 | Renata Rychtarikova | Renata Rychtarikova and Dalibor Stys | Observation of dynamics inside an unlabeled live cell using bright-field
photon microscopy: Evaluation of organelles' trajectories | 12 pages, 5 figures, supplementary data | null | null | null | q-bio.QM cs.CV q-bio.CB q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article presents an algorithm for the evaluation of organelles'
movements inside of an unmodified live cell. We used a time-lapse image series
obtained using wide-field bright-field photon transmission microscopy as an
algorithm input. The benefit of the algorithm is the application of the R\'enyi
information entropy, namely a variable called a point information gain, which
enables to highlight the borders of the intracellular organelles and to
localize the organelles' centers of mass with the precision of one pixel.
| [
{
"created": "Tue, 13 Dec 2016 11:59:57 GMT",
"version": "v1"
}
] | 2016-12-14 | [
[
"Rychtarikova",
"Renata",
""
],
[
"Stys",
"Dalibor",
""
]
] | This article presents an algorithm for the evaluation of organelles' movements inside of an unmodified live cell. We used a time-lapse image series obtained using wide-field bright-field photon transmission microscopy as an algorithm input. The benefit of the algorithm is the application of the R\'enyi information entropy, namely a variable called a point information gain, which enables to highlight the borders of the intracellular organelles and to localize the organelles' centers of mass with the precision of one pixel. |
2403.10863 | Wenwen Min | Xiaoyu Li, Wenwen Min, Shunfang Wang, Changmiao Wang, Taosheng Xu | stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for
Spatial Transcriptomics Data Imputation | Submitted to IJCAI2024 | null | null | null | q-bio.GN cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spatially resolved transcriptomics represents a significant advancement in
single-cell analysis by offering both gene expression data and their
corresponding physical locations. However, this high degree of spatial
resolution entails a drawback, as the resulting spatial transcriptomic data at
the cellular level is notably plagued by a high incidence of missing values.
Furthermore, most existing imputation methods either overlook the spatial
information between spots or compromise the overall gene expression data
distribution. To address these challenges, our primary focus is on effectively
utilizing the spatial location information within spatial transcriptomic data
to impute missing values, while preserving the overall data distribution. We
introduce \textbf{stMCDI}, a novel conditional diffusion model for spatial
transcriptomics data imputation, which employs a denoising network trained
using randomly masked data portions as guidance, with the unmasked data serving
as conditions. Additionally, it utilizes a GNN encoder to integrate the spatial
position information, thereby enhancing model performance. The results obtained
from spatial transcriptomics datasets elucidate the performance of our methods
relative to existing approaches.
| [
{
"created": "Sat, 16 Mar 2024 09:06:38 GMT",
"version": "v1"
}
] | 2024-03-19 | [
[
"Li",
"Xiaoyu",
""
],
[
"Min",
"Wenwen",
""
],
[
"Wang",
"Shunfang",
""
],
[
"Wang",
"Changmiao",
""
],
[
"Xu",
"Taosheng",
""
]
] | Spatially resolved transcriptomics represents a significant advancement in single-cell analysis by offering both gene expression data and their corresponding physical locations. However, this high degree of spatial resolution entails a drawback, as the resulting spatial transcriptomic data at the cellular level is notably plagued by a high incidence of missing values. Furthermore, most existing imputation methods either overlook the spatial information between spots or compromise the overall gene expression data distribution. To address these challenges, our primary focus is on effectively utilizing the spatial location information within spatial transcriptomic data to impute missing values, while preserving the overall data distribution. We introduce \textbf{stMCDI}, a novel conditional diffusion model for spatial transcriptomics data imputation, which employs a denoising network trained using randomly masked data portions as guidance, with the unmasked data serving as conditions. Additionally, it utilizes a GNN encoder to integrate the spatial position information, thereby enhancing model performance. The results obtained from spatial transcriptomics datasets elucidate the performance of our methods relative to existing approaches. |
0901.3198 | Laurent Noe | Mikhail A. Roytberg (IMPB), Anna Gambin, Laurent No\'e (LIFL, INRIA
Lille - Nord Europe), Slawomir Lasota, Eugenia Furletova (IMPB), Ewa Szczurek
(MPI), Gregory Kucherov (LIFL, INRIA Lille - Nord Europe) | On subset seeds for protein alignment | IEEE/ACM Transactions on Computational Biology and Bioinformatics
(2009) | IEEE/ACM Transactions on Computational Biology and Bioinformatics,
6 (3) : 483-494, 2009 | 10.1109/TCBB.2009.4 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We apply the concept of subset seeds proposed in [1] to similarity search in
protein sequences. The main question studied is the design of efficient seed
alphabets to construct seeds with optimal sensitivity/selectivity trade-offs.
We propose several different design methods and use them to construct several
alphabets. We then perform a comparative analysis of seeds built over those
alphabets and compare them with the standard BLASTP seeding method [2], [3], as
well as with the family of vector seeds proposed in [4]. While the formalism of
subset seeds is less expressive (but less costly to implement) than the
cumulative principle used in BLASTP and vector seeds, our seeds show a similar
or even better performance than BLASTP on Bernoulli models of proteins
compatible with the common BLOSUM62 matrix. Finally, we perform a large-scale
benchmarking of our seeds against several main databases of protein alignments.
Here again, the results show a comparable or better performance of our seeds
vs. BLASTP.
| [
{
"created": "Wed, 21 Jan 2009 07:59:27 GMT",
"version": "v1"
}
] | 2011-01-18 | [
[
"Roytberg",
"Mikhail A.",
"",
"IMPB"
],
[
"Gambin",
"Anna",
"",
"LIFL, INRIA\n Lille - Nord Europe"
],
[
"Noé",
"Laurent",
"",
"LIFL, INRIA\n Lille - Nord Europe"
],
[
"Lasota",
"Slawomir",
"",
"IMPB"
],
[
"Furletova",
"Eugenia",
"",
"IMPB"
],
[
"Szczurek",
"Ewa",
"",
"MPI"
],
[
"Kucherov",
"Gregory",
"",
"LIFL, INRIA Lille - Nord Europe"
]
] | We apply the concept of subset seeds proposed in [1] to similarity search in protein sequences. The main question studied is the design of efficient seed alphabets to construct seeds with optimal sensitivity/selectivity trade-offs. We propose several different design methods and use them to construct several alphabets. We then perform a comparative analysis of seeds built over those alphabets and compare them with the standard BLASTP seeding method [2], [3], as well as with the family of vector seeds proposed in [4]. While the formalism of subset seeds is less expressive (but less costly to implement) than the cumulative principle used in BLASTP and vector seeds, our seeds show a similar or even better performance than BLASTP on Bernoulli models of proteins compatible with the common BLOSUM62 matrix. Finally, we perform a large-scale benchmarking of our seeds against several main databases of protein alignments. Here again, the results show a comparable or better performance of our seeds vs. BLASTP. |
2206.08122 | Roberto Goya-Maldonado | Vladimir Belov, Tracy Erwin-Grabner, Ali Saffet Gonul, Alyssa R. Amod,
Amar Ojha, Andre Aleman, Annemiek Dols, Anouk Scharntee, Aslihan Uyar-Demir,
Ben J Harrison, Benson M. Irungu, Bianca Besteher, Bonnie Klimes-Dougan,
Brenda W. J. H. Penninx, Bryon A. Mueller, Carlos Zarate, Christopher G.
Davey, Christopher R. K. Ching, Colm G. Connolly, Cynthia H. Y. Fu, Dan J.
Stein, Danai Dima, David E. J. Linden, David M. A. Mehler, Edith
Pomarol-Clotet, Elena Pozzi, Elisa Melloni, Francesco Benedetti, Frank P.
MacMaster, Hans J. Grabe, Henry V\"olzke, Ian H. Gotlib, Jair C. Soares,
Jennifer W. Evans, Kang Sim, Katharina Wittfeld, Kathryn Cullen, Liesbeth
Reneman, Mardien L. Oudega, Margaret J. Wright, Maria J. Portella, Matthew D.
Sacchet, Meng Li, Moji Aghajani, Mon-Ju Wu, Natalia Jaworska, Neda Jahanshad,
Nic J. A. van der Wee, Nynke Groenewold, Paul J. Hamilton, Philipp Saemann,
Robin B\"ulow, Sara Poletti, Sarah Whittle, Sophia I. Thomopoulos, Steven
J.A. van, der Werff, Sheri-Michelle Koopowitz, Thomas Lancaster, Tiffany C.
Ho, Tony T. Yang, Zeynep Basgoze, Dick J. Veltman, Lianne Schmaal, Paul M.
Thompson, and Roberto Goya-Maldonado | Multi-site benchmark classification of major depressive disorder using
machine learning on cortical and subcortical measures | main document 37 pages; supplementary material 24 pages | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Machine learning (ML) techniques have gained popularity in the neuroimaging
field due to their potential for classifying neuropsychiatric disorders.
However, the diagnostic predictive power of the existing algorithms has been
limited by small sample sizes, lack of representativeness, data leakage, and/or
overfitting. Here, we overcome these limitations with the largest multi-site
sample size to date (n=5,356) to provide a generalizable ML classification
benchmark of major depressive disorder (MDD). Using brain measures from
standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify
MDD vs healthy controls (HC) with around 62% balanced accuracy, but when
harmonizing the data using ComBat balanced accuracy dropped to approximately
52%. Similar results were observed in stratified groups according to age of
onset, antidepressant use, number of episodes and sex. Future studies
incorporating higher dimensional brain imaging/phenotype features, and/or using
more advanced machine and deep learning methods may achieve more encouraging
prospects.
| [
{
"created": "Thu, 16 Jun 2022 12:34:29 GMT",
"version": "v1"
},
{
"created": "Thu, 30 Jun 2022 12:15:34 GMT",
"version": "v2"
},
{
"created": "Tue, 25 Oct 2022 12:59:15 GMT",
"version": "v3"
}
] | 2022-10-26 | [
[
"Belov",
"Vladimir",
""
],
[
"Erwin-Grabner",
"Tracy",
""
],
[
"Gonul",
"Ali Saffet",
""
],
[
"Amod",
"Alyssa R.",
""
],
[
"Ojha",
"Amar",
""
],
[
"Aleman",
"Andre",
""
],
[
"Dols",
"Annemiek",
""
],
[
"Scharntee",
"Anouk",
""
],
[
"Uyar-Demir",
"Aslihan",
""
],
[
"Harrison",
"Ben J",
""
],
[
"Irungu",
"Benson M.",
""
],
[
"Besteher",
"Bianca",
""
],
[
"Klimes-Dougan",
"Bonnie",
""
],
[
"Penninx",
"Brenda W. J. H.",
""
],
[
"Mueller",
"Bryon A.",
""
],
[
"Zarate",
"Carlos",
""
],
[
"Davey",
"Christopher G.",
""
],
[
"Ching",
"Christopher R. K.",
""
],
[
"Connolly",
"Colm G.",
""
],
[
"Fu",
"Cynthia H. Y.",
""
],
[
"Stein",
"Dan J.",
""
],
[
"Dima",
"Danai",
""
],
[
"Linden",
"David E. J.",
""
],
[
"Mehler",
"David M. A.",
""
],
[
"Pomarol-Clotet",
"Edith",
""
],
[
"Pozzi",
"Elena",
""
],
[
"Melloni",
"Elisa",
""
],
[
"Benedetti",
"Francesco",
""
],
[
"MacMaster",
"Frank P.",
""
],
[
"Grabe",
"Hans J.",
""
],
[
"Völzke",
"Henry",
""
],
[
"Gotlib",
"Ian H.",
""
],
[
"Soares",
"Jair C.",
""
],
[
"Evans",
"Jennifer W.",
""
],
[
"Sim",
"Kang",
""
],
[
"Wittfeld",
"Katharina",
""
],
[
"Cullen",
"Kathryn",
""
],
[
"Reneman",
"Liesbeth",
""
],
[
"Oudega",
"Mardien L.",
""
],
[
"Wright",
"Margaret J.",
""
],
[
"Portella",
"Maria J.",
""
],
[
"Sacchet",
"Matthew D.",
""
],
[
"Li",
"Meng",
""
],
[
"Aghajani",
"Moji",
""
],
[
"Wu",
"Mon-Ju",
""
],
[
"Jaworska",
"Natalia",
""
],
[
"Jahanshad",
"Neda",
""
],
[
"van der Wee",
"Nic J. A.",
""
],
[
"Groenewold",
"Nynke",
""
],
[
"Hamilton",
"Paul J.",
""
],
[
"Saemann",
"Philipp",
""
],
[
"Bülow",
"Robin",
""
],
[
"Poletti",
"Sara",
""
],
[
"Whittle",
"Sarah",
""
],
[
"Thomopoulos",
"Sophia I.",
""
],
[
"van",
"Steven J. A.",
""
],
[
"Werff",
"der",
""
],
[
"Koopowitz",
"Sheri-Michelle",
""
],
[
"Lancaster",
"Thomas",
""
],
[
"Ho",
"Tiffany C.",
""
],
[
"Yang",
"Tony T.",
""
],
[
"Basgoze",
"Zeynep",
""
],
[
"Veltman",
"Dick J.",
""
],
[
"Schmaal",
"Lianne",
""
],
[
"Thompson",
"Paul M.",
""
],
[
"Goya-Maldonado",
"Roberto",
""
]
] | Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (n=5,356) to provide a generalizable ML classification benchmark of major depressive disorder (MDD). Using brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD vs healthy controls (HC) with around 62% balanced accuracy, but when harmonizing the data using ComBat balanced accuracy dropped to approximately 52%. Similar results were observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may achieve more encouraging prospects. |
0804.4218 | Anna Ochab-Marcinek | Anna Ochab-Marcinek | Predicting the asymmetric response of a genetic switch to noise | 23 pages, 7 figures, accepted for publication in Journal of
Theoretical Biology. Version 3: corrected typos, corrected the Fig. 3 caption | null | 10.1016/j.jtbi.2008.04.032 | null | q-bio.QM q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a simple analytical tool which gives an approximate insight into
the stationary behavior of nonlinear systems undergoing the influence of a weak
and rapid noise from one dominating source, e.g. the kinetic equations
describing a genetic switch with the concentration of one substrate fluctuating
around a constant mean. The proposed method allows for predicting the
asymmetric response of the genetic switch to noise, arising from the
noise-induced shift of stationary states. The method has been tested on an
example model of the lac operon regulatory network: a reduced Yildirim-Mackey
model with fluctuating extracellular lactose concentration. We calculate
analytically the shift of the system's stationary states in the presence of
noise. The results of the analytical calculation are in excellent agreement
with the results of numerical simulation of the noisy system. The simulation
results suggest that the structure of the kinetics of the underlying
biochemical reactions protects the bistability of the lactose utilization
mechanism from environmental fluctuations. We also show that, in the
consequence of the noise-induced shift of stationary states, the presence of
fluctuations stabilizes the behavior of the system in a selective way: although
the extrinsic noise facilitates, to some extent, switching off the lactose
metabolism, the same noise prevents it from switching on.
| [
{
"created": "Sat, 26 Apr 2008 11:09:25 GMT",
"version": "v1"
},
{
"created": "Wed, 28 May 2008 14:57:12 GMT",
"version": "v2"
},
{
"created": "Wed, 13 Aug 2008 10:08:21 GMT",
"version": "v3"
}
] | 2008-08-13 | [
[
"Ochab-Marcinek",
"Anna",
""
]
] | We present a simple analytical tool which gives an approximate insight into the stationary behavior of nonlinear systems undergoing the influence of a weak and rapid noise from one dominating source, e.g. the kinetic equations describing a genetic switch with the concentration of one substrate fluctuating around a constant mean. The proposed method allows for predicting the asymmetric response of the genetic switch to noise, arising from the noise-induced shift of stationary states. The method has been tested on an example model of the lac operon regulatory network: a reduced Yildirim-Mackey model with fluctuating extracellular lactose concentration. We calculate analytically the shift of the system's stationary states in the presence of noise. The results of the analytical calculation are in excellent agreement with the results of numerical simulation of the noisy system. The simulation results suggest that the structure of the kinetics of the underlying biochemical reactions protects the bistability of the lactose utilization mechanism from environmental fluctuations. We also show that, in the consequence of the noise-induced shift of stationary states, the presence of fluctuations stabilizes the behavior of the system in a selective way: although the extrinsic noise facilitates, to some extent, switching off the lactose metabolism, the same noise prevents it from switching on. |
q-bio/0509020 | Yves-Henri Sanejouand | Samuel Nicolay and Yves-Henri Sanejouand | Functional modes of proteins are among the most robust ones | 4 pages, 5 figures | Phys. Rev. Letters vol. 96 078104 (2006) | 10.1103/PhysRevLett.96.078104 | null | q-bio.BM | null | It is shown that a small subset of modes which are likely to be involved in
protein functional motions of large amplitude can be determined by retaining
the most robust normal modes obtained using different protein models. This
result should prove helpful in the context of several applications proposed
recently, like for solving difficult molecular replacement problems or for
fitting atomic structures into low-resolution electron density maps. Moreover,
it may also pave the way for the development of methods allowing to predict
such motions accurately.
| [
{
"created": "Thu, 15 Sep 2005 08:54:10 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Nicolay",
"Samuel",
""
],
[
"Sanejouand",
"Yves-Henri",
""
]
] | It is shown that a small subset of modes which are likely to be involved in protein functional motions of large amplitude can be determined by retaining the most robust normal modes obtained using different protein models. This result should prove helpful in the context of several applications proposed recently, like for solving difficult molecular replacement problems or for fitting atomic structures into low-resolution electron density maps. Moreover, it may also pave the way for the development of methods allowing to predict such motions accurately. |
1506.06845 | Oleksandr Yakovenko | Oleksandr Ya Yakovenko, Sreeja Leelakumari, Ganna Vashchenko, Albert
Badiong and Steven J.M. Jones | New class of compounds - variators - are reprogramming substrate
specificity of H4K12Ac, H4K16Ac and H4K20Ac epigenetic marks reading
bromodomain of BPTF protein | 13 pages 1 figure. The paper is about the second example of
developing of protein reprogramming compounds to illustrate systematic and
reproducible approach for their rational design. arXiv admin note:
substantial text overlap with arXiv:1506.06433 | null | null | bptf2 1 2015 | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Previously reported [http://arxiv.org/abs/1506.06433] reprogramming of
substrate specificity of H3K4Me3 epigenetic marks reading PHD domain of BPTF
protein illustrates therapeutic potential of a new class of non-inhibitor small
organic compounds - variators. Here we address the question about
reproducibility of rational design of variators by reprogramming of the second
epigenetic marks reading domain of BPTF protein - bromodomain. Bromodomain of
BPTF binds to epigenetic marks in form of acetylated lysine of histone H4
(H4K12Ac, H4K16Ac and H4K20Ac), which physicochemical properties and binding
mode differs considerably from those of methylated H3K4 marks. Thus, detailed
description of computational approach for reprogramming of bromodomain
substrate specificity illustrates both general and target specific attributes
of computer aided variators design.
| [
{
"created": "Tue, 23 Jun 2015 02:30:01 GMT",
"version": "v1"
}
] | 2015-06-24 | [
[
"Yakovenko",
"Oleksandr Ya",
""
],
[
"Leelakumari",
"Sreeja",
""
],
[
"Vashchenko",
"Ganna",
""
],
[
"Badiong",
"Albert",
""
],
[
"Jones",
"Steven J. M.",
""
]
] | Previously reported [http://arxiv.org/abs/1506.06433] reprogramming of substrate specificity of H3K4Me3 epigenetic marks reading PHD domain of BPTF protein illustrates therapeutic potential of a new class of non-inhibitor small organic compounds - variators. Here we address the question about reproducibility of rational design of variators by reprogramming of the second epigenetic marks reading domain of BPTF protein - bromodomain. Bromodomain of BPTF binds to epigenetic marks in form of acetylated lysine of histone H4 (H4K12Ac, H4K16Ac and H4K20Ac), which physicochemical properties and binding mode differs considerably from those of methylated H3K4 marks. Thus, detailed description of computational approach for reprogramming of bromodomain substrate specificity illustrates both general and target specific attributes of computer aided variators design. |
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