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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2012.15418 | Shuai Liu | Shuai Liu, Xinran Xu, Zhihao Yang, Xiaohan Zhao, Wen Zhang | EPIHC: Improving Enhancer-Promoter Interaction Prediction by using
Hybrid features and Communicative learning | 7 pages, 9 figures, 2 tables | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Enhancer-promoter interactions (EPIs) regulate the expression of specific
genes in cells, and EPIs are important for understanding gene regulation, cell
differentiation and disease mechanisms. EPI identification through the wet
experiments is costly and time-consuming, and computational methods are in
demand. In this paper, we propose a deep neural network-based method EPIHC
based on sequence-derived features and genomic features for the EPI prediction.
EPIHC extracts features from enhancer and promoter sequences respectively using
convolutional neural networks (CNN), and then design a communicative learning
module to captures the communicative information between enhancer and promoter
sequences. EPIHC also take the genomic features of enhancers and promoters into
account. At last, EPIHC combines sequence-derived features and genomic features
to predict EPIs. The computational experiments show that EPIHC outperforms the
existing state-of-the-art EPI prediction methods on the benchmark datasets and
chromosome-split datasets, and the study reveal that the communicative learning
module can bring explicit information about EPIs, which is ignore by CNN.
Moreover, we consider two strategies to improve performances of EPIHC in the
cross-cell line prediction, and experimental results show that EPIHC
constructed on training cell lines exhibit improved performances for the other
cell lines.
| [
{
"created": "Thu, 31 Dec 2020 03:08:34 GMT",
"version": "v1"
}
] | 2021-01-01 | [
[
"Liu",
"Shuai",
""
],
[
"Xu",
"Xinran",
""
],
[
"Yang",
"Zhihao",
""
],
[
"Zhao",
"Xiaohan",
""
],
[
"Zhang",
"Wen",
""
]
] | Enhancer-promoter interactions (EPIs) regulate the expression of specific genes in cells, and EPIs are important for understanding gene regulation, cell differentiation and disease mechanisms. EPI identification through the wet experiments is costly and time-consuming, and computational methods are in demand. In this paper, we propose a deep neural network-based method EPIHC based on sequence-derived features and genomic features for the EPI prediction. EPIHC extracts features from enhancer and promoter sequences respectively using convolutional neural networks (CNN), and then design a communicative learning module to captures the communicative information between enhancer and promoter sequences. EPIHC also take the genomic features of enhancers and promoters into account. At last, EPIHC combines sequence-derived features and genomic features to predict EPIs. The computational experiments show that EPIHC outperforms the existing state-of-the-art EPI prediction methods on the benchmark datasets and chromosome-split datasets, and the study reveal that the communicative learning module can bring explicit information about EPIs, which is ignore by CNN. Moreover, we consider two strategies to improve performances of EPIHC in the cross-cell line prediction, and experimental results show that EPIHC constructed on training cell lines exhibit improved performances for the other cell lines. |
1012.5909 | Antonio Deiana | Antonio Deiana, Andrea Giansanti | Is the unfoldome widespread in proteomes? | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The term unfoldome has been recently used to indicate the universe of
intrinsically disordered proteins. These proteins are characterized by an
ensemble of high-flexible interchangeable conformations and therefore they can
interact with many targets without requiring pre-existing stereo-chemical
complementarity. It has been suggested that intrinsically disordered proteins
are frequent in proteomes and disorder is widespread also in structured
proteins. However, several studies raise some doubt about these views. It this
paper we estimate the frequency of intrinsically disordered proteins in several
living organisms by using the ratio S between the likelihood, for a protein
sequence, of being composed mainly by order-promoting or disorder-promoting
residues. We scan several proteomes from Archaea, Bacteria and Eukarya. We find
the following figures: 1.63% for Archaea, 3.91% for Bacteria, 16.35% for
Eukarya. The frequencies we found can be considered an upper bound to the real
frequency of intrinsically disordered proteins in proteomes. Our estimates are
lower than those previously reported in several studies. A scanning of proteins
in the Protein Data Bank (PDB) searching for segments of non-observed residues
reveals that segments of non-observed residues longer than 30 amino acids, are
rare. Our observations support the idea that the spread of the unfoldome has
been often overestimated. If we exclude some exceptions, the structure-function
paradigm is generally valid and pre-existing stereo-chemical complementarity
among structures remains an important requisite for interactions between
biological macromolecules.
| [
{
"created": "Wed, 29 Dec 2010 11:15:53 GMT",
"version": "v1"
}
] | 2010-12-30 | [
[
"Deiana",
"Antonio",
""
],
[
"Giansanti",
"Andrea",
""
]
] | The term unfoldome has been recently used to indicate the universe of intrinsically disordered proteins. These proteins are characterized by an ensemble of high-flexible interchangeable conformations and therefore they can interact with many targets without requiring pre-existing stereo-chemical complementarity. It has been suggested that intrinsically disordered proteins are frequent in proteomes and disorder is widespread also in structured proteins. However, several studies raise some doubt about these views. It this paper we estimate the frequency of intrinsically disordered proteins in several living organisms by using the ratio S between the likelihood, for a protein sequence, of being composed mainly by order-promoting or disorder-promoting residues. We scan several proteomes from Archaea, Bacteria and Eukarya. We find the following figures: 1.63% for Archaea, 3.91% for Bacteria, 16.35% for Eukarya. The frequencies we found can be considered an upper bound to the real frequency of intrinsically disordered proteins in proteomes. Our estimates are lower than those previously reported in several studies. A scanning of proteins in the Protein Data Bank (PDB) searching for segments of non-observed residues reveals that segments of non-observed residues longer than 30 amino acids, are rare. Our observations support the idea that the spread of the unfoldome has been often overestimated. If we exclude some exceptions, the structure-function paradigm is generally valid and pre-existing stereo-chemical complementarity among structures remains an important requisite for interactions between biological macromolecules. |
2005.13689 | Benjamin Althouse | Benjamin M. Althouse, Edward A. Wenger, Joel C. Miller, Samuel V.
Scarpino, Antoine Allard, Laurent H\'ebert-Dufresne, Hao Hu | Stochasticity and heterogeneity in the transmission dynamics of
SARS-CoV-2 | 10 pages, 3 figures | null | null | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | SARS-CoV-2 causing COVID-19 disease has moved rapidly around the globe,
infecting millions and killing hundreds of thousands. The basic reproduction
number, which has been widely used and misused to characterize the
transmissibility of the virus, hides the fact that transmission is stochastic,
is dominated by a small number of individuals, and is driven by super-spreading
events (SSEs). The distinct transmission features, such as high stochasticity
under low prevalence, and the central role played by SSEs on transmission
dynamics, should not be overlooked. Many explosive SSEs have occurred in indoor
settings stoking the pandemic and shaping its spread, such as long-term care
facilities, prisons, meat-packing plants, fish factories, cruise ships, family
gatherings, parties and night clubs. These SSEs demonstrate the urgent need to
understand routes of transmission, while posing an opportunity that outbreak
can be effectively contained with targeted interventions to eliminate SSEs.
Here, we describe the potential types of SSEs, how they influence transmission,
and give recommendations for control of SARS-CoV-2.
| [
{
"created": "Wed, 27 May 2020 22:37:18 GMT",
"version": "v1"
}
] | 2020-05-29 | [
[
"Althouse",
"Benjamin M.",
""
],
[
"Wenger",
"Edward A.",
""
],
[
"Miller",
"Joel C.",
""
],
[
"Scarpino",
"Samuel V.",
""
],
[
"Allard",
"Antoine",
""
],
[
"Hébert-Dufresne",
"Laurent",
""
],
[
"Hu",
"Hao",
""
]
] | SARS-CoV-2 causing COVID-19 disease has moved rapidly around the globe, infecting millions and killing hundreds of thousands. The basic reproduction number, which has been widely used and misused to characterize the transmissibility of the virus, hides the fact that transmission is stochastic, is dominated by a small number of individuals, and is driven by super-spreading events (SSEs). The distinct transmission features, such as high stochasticity under low prevalence, and the central role played by SSEs on transmission dynamics, should not be overlooked. Many explosive SSEs have occurred in indoor settings stoking the pandemic and shaping its spread, such as long-term care facilities, prisons, meat-packing plants, fish factories, cruise ships, family gatherings, parties and night clubs. These SSEs demonstrate the urgent need to understand routes of transmission, while posing an opportunity that outbreak can be effectively contained with targeted interventions to eliminate SSEs. Here, we describe the potential types of SSEs, how they influence transmission, and give recommendations for control of SARS-CoV-2. |
2003.07828 | Augusto Gonzalez | Augusto Gonzalez, Yasser Perera, Rolando Perez | On the gene expression landscape of cancer | null | PLoS ONE 18(2): e0277786 (2023) | 10.1371/journal.pone.0277786 | null | q-bio.TO q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A principal component analysis of the TCGA data for 15 cancer localizations
unveils the following qualitative facts about tumors: 1) The state of a tissue
in gene expression space may be described by a few variables. In particular,
there is a single variable describing the progression from a normal tissue to a
tumor. 2) Each cancer localization is characterized by a gene expression
profile, in which genes have specific weights in the definition of the cancer
state. There are no less than 2500 differentially-expressed genes, which lead
to power-like tails in the expression distribution functions. 3) Tumors in
different localizations share hundreds or even thousands of differentially
expressed genes. There are 6 genes common to the 15 studied tumor
localizations. 4) The tumor region is a kind of attractor. Tumors in advanced
stages converge to this region independently of patient age or genetic
variability. 5) There is a landscape of cancer in gene expression space with an
approximate border separating normal tissues from tumors.
| [
{
"created": "Tue, 17 Mar 2020 17:24:36 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Apr 2020 16:59:24 GMT",
"version": "v2"
},
{
"created": "Fri, 31 Jul 2020 22:27:40 GMT",
"version": "v3"
}
] | 2023-05-10 | [
[
"Gonzalez",
"Augusto",
""
],
[
"Perera",
"Yasser",
""
],
[
"Perez",
"Rolando",
""
]
] | A principal component analysis of the TCGA data for 15 cancer localizations unveils the following qualitative facts about tumors: 1) The state of a tissue in gene expression space may be described by a few variables. In particular, there is a single variable describing the progression from a normal tissue to a tumor. 2) Each cancer localization is characterized by a gene expression profile, in which genes have specific weights in the definition of the cancer state. There are no less than 2500 differentially-expressed genes, which lead to power-like tails in the expression distribution functions. 3) Tumors in different localizations share hundreds or even thousands of differentially expressed genes. There are 6 genes common to the 15 studied tumor localizations. 4) The tumor region is a kind of attractor. Tumors in advanced stages converge to this region independently of patient age or genetic variability. 5) There is a landscape of cancer in gene expression space with an approximate border separating normal tissues from tumors. |
1701.04675 | Alessandro Fontana | Alessandro Fontana | VOCSMAT: a connectionist-inspired treatment proposal for relational
traumas | 23 pages, 12 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Psychological traumas are thought to be present in a wide range of
conditions, including post-traumatic stress disorder, disorganised attachment,
personality disorders, dissociative identity disorder and psychosis. This work
presents a new psychotherapy for psychological traumas, based on a functional
model of the mind, built with elements borrowed from the fields of computer
science, artificial intelligence and neural networks. The model revolves around
the concept of hierarchical value and explains the emergence of dissociation
and splitting in response to emotional pain. The key intuition is that traumas
are caused by too strong negative emotions, which are in turn made possible by
a low-value self, which is in turn determined by low-value self-associated
ideas. The therapeutic method compiles a list of patient's traumas, identifies
for each trauma a list of low-value self-associated ideas, and provides for
each idea a list of counterexamples, to raise the self value and solve the
trauma. Since the psychotherapy proposed has not been clinically tested,
statements on its effectiveness are premature. However, since the conceptual
basis is solid and traumas are hypothesised to be present in many psychological
disorders, the potential gain may be substantial.
| [
{
"created": "Tue, 17 Jan 2017 14:01:49 GMT",
"version": "v1"
},
{
"created": "Wed, 7 Mar 2018 13:20:49 GMT",
"version": "v2"
}
] | 2018-03-08 | [
[
"Fontana",
"Alessandro",
""
]
] | Psychological traumas are thought to be present in a wide range of conditions, including post-traumatic stress disorder, disorganised attachment, personality disorders, dissociative identity disorder and psychosis. This work presents a new psychotherapy for psychological traumas, based on a functional model of the mind, built with elements borrowed from the fields of computer science, artificial intelligence and neural networks. The model revolves around the concept of hierarchical value and explains the emergence of dissociation and splitting in response to emotional pain. The key intuition is that traumas are caused by too strong negative emotions, which are in turn made possible by a low-value self, which is in turn determined by low-value self-associated ideas. The therapeutic method compiles a list of patient's traumas, identifies for each trauma a list of low-value self-associated ideas, and provides for each idea a list of counterexamples, to raise the self value and solve the trauma. Since the psychotherapy proposed has not been clinically tested, statements on its effectiveness are premature. However, since the conceptual basis is solid and traumas are hypothesised to be present in many psychological disorders, the potential gain may be substantial. |
0802.0048 | Liaofu Luo | Liaofu Luo | Entropy Production in a Cell and Reversal of Entropy Flow as an
Anticancer Therapy | 24 pages | null | 10.1007/s11467-009-0007-9 | null | q-bio.CB | null | The entropy production rate of cancer cell is always higher than healthy cell
under the case of no external field applied. Different entropy production
between two kinds of cells determines the direction of entropy flow among
cells. The entropy flow is the carrier of information flow. The entropy flow
from cancer to healthy cell takes along the harmful information of cancerous
cell, propagating its toxic action to healthy tissues. We demonstrate that a
low-frequency and low-intensity electromagnetic field or ultrasound irradiation
may increase the entropy production rate of a cell in normal tissue than that
in cancer, consequently reverse the direction of entropy current between two
kinds of cells. The modification of PH value of cells may also cause the
reversal of the direction of entropy flow between healthy and cancerous cells.
So, the biological tissue under the irradiation of electromagnetic field or
ultrasound or under the appropriate change of cell acidity can avoid the
propagation of harmful information from cancer cells. We suggest that this
entropy mechanism possibly provides a basis for a novel approach to anticancer
therapy.
| [
{
"created": "Fri, 1 Feb 2008 02:24:41 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Luo",
"Liaofu",
""
]
] | The entropy production rate of cancer cell is always higher than healthy cell under the case of no external field applied. Different entropy production between two kinds of cells determines the direction of entropy flow among cells. The entropy flow is the carrier of information flow. The entropy flow from cancer to healthy cell takes along the harmful information of cancerous cell, propagating its toxic action to healthy tissues. We demonstrate that a low-frequency and low-intensity electromagnetic field or ultrasound irradiation may increase the entropy production rate of a cell in normal tissue than that in cancer, consequently reverse the direction of entropy current between two kinds of cells. The modification of PH value of cells may also cause the reversal of the direction of entropy flow between healthy and cancerous cells. So, the biological tissue under the irradiation of electromagnetic field or ultrasound or under the appropriate change of cell acidity can avoid the propagation of harmful information from cancer cells. We suggest that this entropy mechanism possibly provides a basis for a novel approach to anticancer therapy. |
1609.05735 | Ulrich Behn | Stefan Landmann, Nicolas Preuss, Ulrich Behn | Self-tolerance and autoimmunity in a minimal model of the idiotypic
network | 30 pages, 27 figures | null | null | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider self-tolerance and its failure -autoimmunity- in a minimal
mathematical model of the idiotypic network. A node in the network represents a
clone of B-lymphocytes and its antibodies of the same idiotype which is encoded
by a bitstring. The links between nodes represent possible interactions between
clones of almost complementary idiotype. A clone survives only if the number of
populated neighbored nodes is neither too small nor too large. The dynamics is
driven by the influx of lymphocytes with randomly generated idiotype from the
bone marrow. Previous work has revealed that the network evolves towards a
highly organized modular architecture, characterized by groups of nodes which
share statistical properties. The structural properties of the architecture can
be described analytically, the statistical properties determined from
simulations are confirmed by a modular mean-field theory. To model the presence
of self we permanently occupy one or several nodes. These nodes influence their
linked neighbors, the autoreactive clones, but are themselves not affected by
idiotypic interactions. The architecture is very similar to the case without
self, but organized such that the neighbors of self are only weakly occupied,
thus providing self-tolerance. This supports the perspective that self-reactive
clones, which regularly occur in healthy organisms, are controlled by
anti-idiotypic clones. We discuss how perturbations, like an infection with
foreign antigen, a change in the influx of new idiotypes, or the random removal
of idiotypes, may lead to autoreactivity and devise protocols which cause a
reconstitution of the self-tolerant state. The results could be helpful to
understand network and probabilistic aspects of autoimmune disorders.
| [
{
"created": "Mon, 19 Sep 2016 14:09:08 GMT",
"version": "v1"
}
] | 2016-09-20 | [
[
"Landmann",
"Stefan",
""
],
[
"Preuss",
"Nicolas",
""
],
[
"Behn",
"Ulrich",
""
]
] | We consider self-tolerance and its failure -autoimmunity- in a minimal mathematical model of the idiotypic network. A node in the network represents a clone of B-lymphocytes and its antibodies of the same idiotype which is encoded by a bitstring. The links between nodes represent possible interactions between clones of almost complementary idiotype. A clone survives only if the number of populated neighbored nodes is neither too small nor too large. The dynamics is driven by the influx of lymphocytes with randomly generated idiotype from the bone marrow. Previous work has revealed that the network evolves towards a highly organized modular architecture, characterized by groups of nodes which share statistical properties. The structural properties of the architecture can be described analytically, the statistical properties determined from simulations are confirmed by a modular mean-field theory. To model the presence of self we permanently occupy one or several nodes. These nodes influence their linked neighbors, the autoreactive clones, but are themselves not affected by idiotypic interactions. The architecture is very similar to the case without self, but organized such that the neighbors of self are only weakly occupied, thus providing self-tolerance. This supports the perspective that self-reactive clones, which regularly occur in healthy organisms, are controlled by anti-idiotypic clones. We discuss how perturbations, like an infection with foreign antigen, a change in the influx of new idiotypes, or the random removal of idiotypes, may lead to autoreactivity and devise protocols which cause a reconstitution of the self-tolerant state. The results could be helpful to understand network and probabilistic aspects of autoimmune disorders. |
2202.01968 | Joshua Stevenson | Joshua Stevenson, Venta Terauds, Jeremy Sumner | Rearrangement Events on Circular Genomes | 22 pages, 3 figures | null | null | null | q-bio.PE math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Early literature on genome rearrangement modelling views the problem of
computing evolutionary distances as an inherently combinatorial one. In
particular, attention was given to estimating distances using the minimum
number of events required to transform one genome into another. In hindsight,
this approach is analogous to early methods for inferring phylogenetic trees
from DNA sequences such as maximum parsimony -- both are motivated by the
principle that the true distance minimises evolutionary change, and both are
effective if this principle is a true reflection of reality. Recent literature
considers genome rearrangement under statistical models, continuing this
parallel with DNA-based methods; the goal here is to use model-based methods
(for example maximum likelihood techniques) to compute distance estimates that
incorporate the large number of rearrangement paths that can transform one
genome into another. Crucially, this approach requires one to decide upon a set
of feasible rearrangement events and, in this paper, we focus on characterising
well-motivated models for signed, uni-chromosomal circular genomes, where the
number of regions remains fixed. Since rearrangements are often mathematically
described using permutations, we isolate the sets of permutations representing
rearrangements that are biologically reasonable in this context, for example
inversions and translocations. We provide precise mathematical expressions for
these rearrangements, and then describe them in terms of the set of cuts made
in the genome when they are applied. We directly compare cuts to breakpoints,
and use this concept to count the distinct rearrangement actions which apply a
given number of cuts. Finally, we provide some examples of rearrangement
models, and include a discussion of some questions that arise when defining
plausible models.
| [
{
"created": "Fri, 4 Feb 2022 04:54:52 GMT",
"version": "v1"
},
{
"created": "Wed, 11 Jan 2023 03:17:20 GMT",
"version": "v2"
}
] | 2023-01-12 | [
[
"Stevenson",
"Joshua",
""
],
[
"Terauds",
"Venta",
""
],
[
"Sumner",
"Jeremy",
""
]
] | Early literature on genome rearrangement modelling views the problem of computing evolutionary distances as an inherently combinatorial one. In particular, attention was given to estimating distances using the minimum number of events required to transform one genome into another. In hindsight, this approach is analogous to early methods for inferring phylogenetic trees from DNA sequences such as maximum parsimony -- both are motivated by the principle that the true distance minimises evolutionary change, and both are effective if this principle is a true reflection of reality. Recent literature considers genome rearrangement under statistical models, continuing this parallel with DNA-based methods; the goal here is to use model-based methods (for example maximum likelihood techniques) to compute distance estimates that incorporate the large number of rearrangement paths that can transform one genome into another. Crucially, this approach requires one to decide upon a set of feasible rearrangement events and, in this paper, we focus on characterising well-motivated models for signed, uni-chromosomal circular genomes, where the number of regions remains fixed. Since rearrangements are often mathematically described using permutations, we isolate the sets of permutations representing rearrangements that are biologically reasonable in this context, for example inversions and translocations. We provide precise mathematical expressions for these rearrangements, and then describe them in terms of the set of cuts made in the genome when they are applied. We directly compare cuts to breakpoints, and use this concept to count the distinct rearrangement actions which apply a given number of cuts. Finally, we provide some examples of rearrangement models, and include a discussion of some questions that arise when defining plausible models. |
1705.01856 | Ricardo Ruiz Baier I | Christian Cherubini, Simonetta Filippi, Alessio Gizzi, Ricardo
Ruiz-Baier | A note on stress-driven anisotropic diffusion and its role in active
deformable media | null | Journal of Theoretical Biology (2017) | 10.1016/j.jtbi.2017.07.013 | null | q-bio.TO math.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new model to describe diffusion processes within active
deformable media. Our general theoretical framework is based on physical and
mathematical considerations, and it suggests to use diffusion tensors directly
coupled to mechanical stress. A proof-of-concept experiment and the proposed
generalised reaction-diffusion-mechanics model reveal that initially isotropic
and homogeneous diffusion tensors turn into inhomogeneous and anisotropic
quantities due to the intrinsic structure of the nonlinear coupling. We study
the physical properties leading to these effects, and investigate mathematical
conditions for its occurrence. Together, the experiment, the model, and the
numerical results obtained using a mixed-primal finite element method, clearly
support relevant consequences of stress-assisted diffusion into anisotropy
patterns, drifting, and conduction velocity of the resulting excitation waves.
Our findings also indicate the applicability of this novel approach in the
description of mechano-electrical feedback in actively deforming bio-materials
such as the heart.
| [
{
"created": "Thu, 4 May 2017 14:26:39 GMT",
"version": "v1"
}
] | 2021-03-03 | [
[
"Cherubini",
"Christian",
""
],
[
"Filippi",
"Simonetta",
""
],
[
"Gizzi",
"Alessio",
""
],
[
"Ruiz-Baier",
"Ricardo",
""
]
] | We propose a new model to describe diffusion processes within active deformable media. Our general theoretical framework is based on physical and mathematical considerations, and it suggests to use diffusion tensors directly coupled to mechanical stress. A proof-of-concept experiment and the proposed generalised reaction-diffusion-mechanics model reveal that initially isotropic and homogeneous diffusion tensors turn into inhomogeneous and anisotropic quantities due to the intrinsic structure of the nonlinear coupling. We study the physical properties leading to these effects, and investigate mathematical conditions for its occurrence. Together, the experiment, the model, and the numerical results obtained using a mixed-primal finite element method, clearly support relevant consequences of stress-assisted diffusion into anisotropy patterns, drifting, and conduction velocity of the resulting excitation waves. Our findings also indicate the applicability of this novel approach in the description of mechano-electrical feedback in actively deforming bio-materials such as the heart. |
2206.09621 | Tristan Manfred St\"ober | Tristan Manfred St\"ober, Danylo Batulin, Jochen Triesch, Rishikesh
Narayanan, Peter Jedlicka | Degeneracy in epilepsy: Multiple Routes to Hyperexcitable Brain Circuits
and their Repair | 66 pages, 4 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Developing effective therapies against epilepsy remains a challenge. The
complex and multifaceted nature of this disease still fuels controversies about
its origin. In this perspective article, we argue that conflicting hypotheses
can be reconciled by taking into account the degeneracy of the brain, which
manifests in multiple routes leading to similar function or dysfunction. We
exemplify degeneracy at three different levels, ranging from the cellular to
the network and systems level. First, at the cellular level, we describe the
relevance of ion channel degeneracy for epilepsy and discuss its interplay with
dendritic morphology. Second, at the network level, we provide examples for the
degeneracy of synaptic and intrinsic neuronal properties that supports the
robustness of neuronal networks but also leads to diverse responses to
ictogenic and epileptogenic perturbations. Third, at the system level, we
provide examples for degeneracy in the intricate interactions between the
immune and nervous system. Finally, we show that computational approaches
including multiscale and so called population neural circuit models help
disentangle the complex web of physiological and pathological adaptations. Such
models may contribute to identifying the best personalized multitarget
strategies for directing the system towards a physiological state.
| [
{
"created": "Mon, 20 Jun 2022 08:13:40 GMT",
"version": "v1"
}
] | 2022-06-22 | [
[
"Stöber",
"Tristan Manfred",
""
],
[
"Batulin",
"Danylo",
""
],
[
"Triesch",
"Jochen",
""
],
[
"Narayanan",
"Rishikesh",
""
],
[
"Jedlicka",
"Peter",
""
]
] | Developing effective therapies against epilepsy remains a challenge. The complex and multifaceted nature of this disease still fuels controversies about its origin. In this perspective article, we argue that conflicting hypotheses can be reconciled by taking into account the degeneracy of the brain, which manifests in multiple routes leading to similar function or dysfunction. We exemplify degeneracy at three different levels, ranging from the cellular to the network and systems level. First, at the cellular level, we describe the relevance of ion channel degeneracy for epilepsy and discuss its interplay with dendritic morphology. Second, at the network level, we provide examples for the degeneracy of synaptic and intrinsic neuronal properties that supports the robustness of neuronal networks but also leads to diverse responses to ictogenic and epileptogenic perturbations. Third, at the system level, we provide examples for degeneracy in the intricate interactions between the immune and nervous system. Finally, we show that computational approaches including multiscale and so called population neural circuit models help disentangle the complex web of physiological and pathological adaptations. Such models may contribute to identifying the best personalized multitarget strategies for directing the system towards a physiological state. |
1706.08499 | Md Zulfikar Ali | Md. Zulfikar Ali, Ned S. Wingreen, Ranjan Mukhopadhyay | Hidden long evolutionary memory in a model biochemical network | 20 Pages, 14 Figures | Phys. Rev. E 97, 040401 (2018) | 10.1103/PhysRevE.97.040401 | null | q-bio.MN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a minimal model for the evolution of functional
protein-interaction networks using a sequence-based mutational algorithm, and
apply the model to study neutral drift in networks that yield oscillatory
dynamics. Starting with a functional core module, random evolutionary drift
increases network complexity even in the absence of specific selective
pressures. Surprisingly, we uncover a hidden order in sequence space that gives
rise to long-term evolutionary memory, implying strong constraints on network
evolution due to the topology of accessible sequence space.
| [
{
"created": "Mon, 26 Jun 2017 17:44:58 GMT",
"version": "v1"
}
] | 2018-04-25 | [
[
"Ali",
"Md. Zulfikar",
""
],
[
"Wingreen",
"Ned S.",
""
],
[
"Mukhopadhyay",
"Ranjan",
""
]
] | We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space. |
1009.2243 | Maurizio De Pitta' | Mati Goldberg and Maurizio De Pitt\`a and Vladislav Volman and Hugues
Berry and Eshel Ben-Jacob | Nonlinear gap junctions enable long-distance propagation of pulsating
calcium waves in astrocyte networks | Article: 30 pages, 7 figures. Supplementary Material: 11 pages, 6
figures | PLoS Comput Biol 6(8): e1000909 | 10.1371/journal.pcbi.1000909 | null | q-bio.NC nlin.CD q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A new paradigm has recently emerged in brain science whereby communications
between glial cells and neuron-glia interactions should be considered together
with neurons and their networks to understand higher brain functions. In
particular, astrocytes, the main type of glial cells in the cortex, have been
shown to communicate with neurons and with each other. They are thought to form
a gap-junction-coupled syncytium supporting cell-cell communication via
propagating Ca2+ waves. An identified mode of propagation is based on
cytoplasm-to-cytoplasm transport of inositol trisphosphate (IP3) through gap
junctions that locally trigger Ca2+ pulses via IP3-dependent Ca2+-induced Ca2+
release. It is, however, currently unknown whether this intracellular route is
able to support the propagation of long-distance regenerative Ca2+ waves or is
restricted to short-distance signaling. Furthermore, the influence of the
intracellular signaling dynamics on intercellular propagation remains to be
understood. In this work, we propose a model of the gap-junctional route for
intercellular Ca2+ wave propagation in astrocytes showing that: (1)
long-distance regenerative signaling requires nonlinear coupling in the gap
junctions, and (2) even with nonlinear gap junctions, long-distance
regenerative signaling is favored when the internal Ca2+ dynamics implements
frequency modulation-encoding oscillations with pulsating dynamics, while
amplitude modulation-encoding dynamics tends to restrict the propagation range.
As a result, spatially heterogeneous molecular properties and/or weak couplings
are shown to give rise to rich spatiotemporal dynamics that support complex
propagation behaviors. These results shed new light on the mechanisms
implicated in the propagation of Ca2+ waves across astrocytes and precise the
conditions under which glial cells may participate in information processing in
the brain.
| [
{
"created": "Sun, 12 Sep 2010 15:44:03 GMT",
"version": "v1"
}
] | 2010-09-14 | [
[
"Goldberg",
"Mati",
""
],
[
"De Pittà",
"Maurizio",
""
],
[
"Volman",
"Vladislav",
""
],
[
"Berry",
"Hugues",
""
],
[
"Ben-Jacob",
"Eshel",
""
]
] | A new paradigm has recently emerged in brain science whereby communications between glial cells and neuron-glia interactions should be considered together with neurons and their networks to understand higher brain functions. In particular, astrocytes, the main type of glial cells in the cortex, have been shown to communicate with neurons and with each other. They are thought to form a gap-junction-coupled syncytium supporting cell-cell communication via propagating Ca2+ waves. An identified mode of propagation is based on cytoplasm-to-cytoplasm transport of inositol trisphosphate (IP3) through gap junctions that locally trigger Ca2+ pulses via IP3-dependent Ca2+-induced Ca2+ release. It is, however, currently unknown whether this intracellular route is able to support the propagation of long-distance regenerative Ca2+ waves or is restricted to short-distance signaling. Furthermore, the influence of the intracellular signaling dynamics on intercellular propagation remains to be understood. In this work, we propose a model of the gap-junctional route for intercellular Ca2+ wave propagation in astrocytes showing that: (1) long-distance regenerative signaling requires nonlinear coupling in the gap junctions, and (2) even with nonlinear gap junctions, long-distance regenerative signaling is favored when the internal Ca2+ dynamics implements frequency modulation-encoding oscillations with pulsating dynamics, while amplitude modulation-encoding dynamics tends to restrict the propagation range. As a result, spatially heterogeneous molecular properties and/or weak couplings are shown to give rise to rich spatiotemporal dynamics that support complex propagation behaviors. These results shed new light on the mechanisms implicated in the propagation of Ca2+ waves across astrocytes and precise the conditions under which glial cells may participate in information processing in the brain. |
2202.12992 | Thomas F. Varley | Thomas F. Varley | Decomposing past and future: Integrated information decomposition based
on shared probability mass exclusions | 19 pages, 8 figures | null | 10.1371/journal.pone.0282950 | null | q-bio.NC math.PR | http://creativecommons.org/licenses/by-nc-sa/4.0/ | A core feature of complex systems is that the interactions between elements
in the present causally constrain each-other as the system evolves through
time. To fully model all of these interactions (between elements, as well as
ensembles of elements), we can decompose the total information flowing from
past to future into a set of non-overlapping temporal interactions that
describe all the different modes by which information can flow. To achieve
this, we propose a novel information-theoretic measure of temporal dependency
($I_{\tau sx}$) based on informative and misinformative local probability mass
exclusions. To demonstrate the utility of this framework, we apply the
decomposition to spontaneous spiking activity recorded from dissociated neural
cultures of rat cerebral cortex to show how different modes of information
processing are distributed over the system. Furthermore, being a localizable
analysis, we show that $I_{\tau sx}$ can provide insight into the computational
structure of single moments. We explore the time-resolved computational
structure of neuronal avalanches and find that different types of information
atoms have distinct profiles over the course of an avalanche, with the majority
of non-trivial information dynamics happening before the first half of the
cascade is completed. These analyses allow us to move beyond the historical
focus on single measures of dependency such as information transfer or
information integration, and explore a panoply of different relationships
between elements (and groups of elements) in complex systems.
| [
{
"created": "Fri, 25 Feb 2022 21:46:51 GMT",
"version": "v1"
},
{
"created": "Wed, 2 Mar 2022 02:15:36 GMT",
"version": "v2"
}
] | 2023-04-26 | [
[
"Varley",
"Thomas F.",
""
]
] | A core feature of complex systems is that the interactions between elements in the present causally constrain each-other as the system evolves through time. To fully model all of these interactions (between elements, as well as ensembles of elements), we can decompose the total information flowing from past to future into a set of non-overlapping temporal interactions that describe all the different modes by which information can flow. To achieve this, we propose a novel information-theoretic measure of temporal dependency ($I_{\tau sx}$) based on informative and misinformative local probability mass exclusions. To demonstrate the utility of this framework, we apply the decomposition to spontaneous spiking activity recorded from dissociated neural cultures of rat cerebral cortex to show how different modes of information processing are distributed over the system. Furthermore, being a localizable analysis, we show that $I_{\tau sx}$ can provide insight into the computational structure of single moments. We explore the time-resolved computational structure of neuronal avalanches and find that different types of information atoms have distinct profiles over the course of an avalanche, with the majority of non-trivial information dynamics happening before the first half of the cascade is completed. These analyses allow us to move beyond the historical focus on single measures of dependency such as information transfer or information integration, and explore a panoply of different relationships between elements (and groups of elements) in complex systems. |
1309.1373 | Michael Green | Alisher M. Kariev and Michael E. Green | Quantum Calculations Show Caution Is Needed In Interpreting
Methanethiosulfonate Accessibility Experiments On Ion Channels | 11 pages, includes 3 part figure | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Standard models of ion channel voltage gating require substantial movement of
one transmembrane segment, S4, of the voltage sensing domain. Evidence comes
from the accessibility to external methanethiosulfonate (MTS) reagents of the
positively charged arginine residues (R) on this segment. These are first
mutated to cysteines (C), which in turn react with MTS reagents; it is assumed
that the C is passively carried in the S4 movement, becoming accessible on one
side or the other of the membrane. However, the Rs were salt bridged to
negatively charged residues on other transmembrane segments, or hydrogen
bonded, while C reacts as a negative ion. The space available for MTS is fairly
close to the difference in volume between the large R residue and much smaller
C, so the MTS is not severely sterically hindered. A reagent molecule can reach
a C side chain; the C can react if not repelled by a negative charge from the
amino acid to which the R had been salt bridged. Nearby protons may also make
reaction possible unless the C itself is protonated. Therefore interpretation
of the C substitution results requires reconsideration. To test the idea we
have done quantum calculations on part of a mutated S4 and the nearby section
of the channel. The mutation is R300C of the 2A79/3Lut structure, a mutation
that would be done to test MTS reagent access; there is a large cavity where
the R is replaced by C. Two quantum calculations show a substantial difference
in the structure of this cavity with 2 water molecules compared to 4. This
suggests that the structure, and presumably reaction probability, could depend
on water molecules, very likely also protons, in or near the cavity that the
R300C mutation produces.
| [
{
"created": "Thu, 5 Sep 2013 15:24:45 GMT",
"version": "v1"
}
] | 2013-09-06 | [
[
"Kariev",
"Alisher M.",
""
],
[
"Green",
"Michael E.",
""
]
] | Standard models of ion channel voltage gating require substantial movement of one transmembrane segment, S4, of the voltage sensing domain. Evidence comes from the accessibility to external methanethiosulfonate (MTS) reagents of the positively charged arginine residues (R) on this segment. These are first mutated to cysteines (C), which in turn react with MTS reagents; it is assumed that the C is passively carried in the S4 movement, becoming accessible on one side or the other of the membrane. However, the Rs were salt bridged to negatively charged residues on other transmembrane segments, or hydrogen bonded, while C reacts as a negative ion. The space available for MTS is fairly close to the difference in volume between the large R residue and much smaller C, so the MTS is not severely sterically hindered. A reagent molecule can reach a C side chain; the C can react if not repelled by a negative charge from the amino acid to which the R had been salt bridged. Nearby protons may also make reaction possible unless the C itself is protonated. Therefore interpretation of the C substitution results requires reconsideration. To test the idea we have done quantum calculations on part of a mutated S4 and the nearby section of the channel. The mutation is R300C of the 2A79/3Lut structure, a mutation that would be done to test MTS reagent access; there is a large cavity where the R is replaced by C. Two quantum calculations show a substantial difference in the structure of this cavity with 2 water molecules compared to 4. This suggests that the structure, and presumably reaction probability, could depend on water molecules, very likely also protons, in or near the cavity that the R300C mutation produces. |
1312.3919 | Istvan Sugar | Istvan P. Sugar and Istvan Simon | Self-regulating genes. Exact steady state solution by using Poisson
Representation | 10 pages, 2 figures, 1 table, 1 supplemental material (9 pages);
additional reference to the work of Grima et al | null | 10.2478/s11534-014-0497-0 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Systems biology studies the structure and behavior of complex gene regulatory
networks. One of its aims is to develop a quantitative understanding of the
modular components that constitute such networks. The self-regulating gene is a
type of auto regulatory genetic modules which appears in over 40% of known
transcription factors in E. coli. In this work, using the technique of Poisson
Representation, we are able to provide exact steady state solutions for this
feedback model. By using the methods of synthetic biology (P.E.M. Purnick and
Weiss, R., Nature Reviews, Molecular Cell Biology, 2009, 10: 410-422) one can
build the system itself from modules like this.
| [
{
"created": "Fri, 13 Dec 2013 19:34:34 GMT",
"version": "v1"
},
{
"created": "Wed, 18 Dec 2013 17:19:21 GMT",
"version": "v2"
}
] | 2015-06-18 | [
[
"Sugar",
"Istvan P.",
""
],
[
"Simon",
"Istvan",
""
]
] | Systems biology studies the structure and behavior of complex gene regulatory networks. One of its aims is to develop a quantitative understanding of the modular components that constitute such networks. The self-regulating gene is a type of auto regulatory genetic modules which appears in over 40% of known transcription factors in E. coli. In this work, using the technique of Poisson Representation, we are able to provide exact steady state solutions for this feedback model. By using the methods of synthetic biology (P.E.M. Purnick and Weiss, R., Nature Reviews, Molecular Cell Biology, 2009, 10: 410-422) one can build the system itself from modules like this. |
1705.06144 | Armen Allahverdyan | S.G. Gevorkian, A.E. Allahverdyan, S. Gevorgyan, Wen-Jong Ma, Chin-Kun
Hu | Can morphological changes of erythrocytes be driven by hemoglobin? | 4 pages, 1 figure | null | 10.1016/j.physa.2018.05.118 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | At 49 C erythrocytes undergo morphological changes due to an internal force,
but the origin of the force that drives changes is not clear. Here we point out
that our recent experiments on thermally induced force-release in hemoglobin
can provide an explanation for the morphological changes of erythrocytes.
| [
{
"created": "Wed, 17 May 2017 13:30:45 GMT",
"version": "v1"
}
] | 2018-07-04 | [
[
"Gevorkian",
"S. G.",
""
],
[
"Allahverdyan",
"A. E.",
""
],
[
"Gevorgyan",
"S.",
""
],
[
"Ma",
"Wen-Jong",
""
],
[
"Hu",
"Chin-Kun",
""
]
] | At 49 C erythrocytes undergo morphological changes due to an internal force, but the origin of the force that drives changes is not clear. Here we point out that our recent experiments on thermally induced force-release in hemoglobin can provide an explanation for the morphological changes of erythrocytes. |
1301.3109 | Philipp Messer | Philipp W. Messer | SLiM: Simulating Evolution with Selection and Linkage | SLiM is a C++ command line program freely available under the GNU GPL
license from http://www.stanford.edu/~messer/software. A comprehensive
documentation for SLiM can be downloaded from the program website | null | null | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | SLiM is an efficient forward population genetic simulation designed for
studying the effects of linkage and selection on a chromosome-wide scale. The
program can incorporate complex scenarios of demography and population
substructure, various models for selection and dominance of new mutations,
arbitrary gene and chromosomal structure, and user-defined recombination maps.
| [
{
"created": "Mon, 14 Jan 2013 20:17:07 GMT",
"version": "v1"
}
] | 2013-01-15 | [
[
"Messer",
"Philipp W.",
""
]
] | SLiM is an efficient forward population genetic simulation designed for studying the effects of linkage and selection on a chromosome-wide scale. The program can incorporate complex scenarios of demography and population substructure, various models for selection and dominance of new mutations, arbitrary gene and chromosomal structure, and user-defined recombination maps. |
1310.7083 | Andrew Teschendorff | Christopher R.S. Banerji, Diego Miranda-Saavedra, Simone Severini,
Martin Widschwendter, Tariq Enver, Joseph X. Zhou, Andrew E. Teschendorff | Cellular network entropy as the energy potential in Waddington's
differentiation landscape | 27 pages, 5 figures | Scientific Reports (2013) 3, 3039 | 10.1038/srep03039 | null | q-bio.MN cond-mat.stat-mech q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Differentiation is a key cellular process in normal tissue development that
is significantly altered in cancer. Although molecular signatures
characterising pluripotency and multipotency exist, there is, as yet, no single
quantitative mark of a cellular sample's position in the global differentiation
hierarchy. Here we adopt a systems view and consider the sample's network
entropy, a measure of signaling pathway promiscuity, computable from a sample's
genome-wide expression profile. We demonstrate that network entropy provides a
quantitative, in-silico, readout of the average undifferentiated state of the
profiled cells, recapitulating the known hierarchy of pluripotent, multipotent
and differentiated cell types. Network entropy further exhibits dynamic changes
in time course differentiation data, and in line with a sample's
differentiation stage. In disease, network entropy predicts a higher level of
cellular plasticity in cancer stem cell populations compared to ordinary cancer
cells. Importantly, network entropy also allows identification of key
differentiation pathways. Our results are consistent with the view that
pluripotency is a statistical property defined at the cellular population
level, correlating with intra-sample heterogeneity, and driven by the degree of
signaling promiscuity in cells. In summary, network entropy provides a
quantitative measure of a cell's undifferentiated state, defining its elevation
in Waddington's landscape.
| [
{
"created": "Sat, 26 Oct 2013 08:36:12 GMT",
"version": "v1"
}
] | 2013-10-29 | [
[
"Banerji",
"Christopher R. S.",
""
],
[
"Miranda-Saavedra",
"Diego",
""
],
[
"Severini",
"Simone",
""
],
[
"Widschwendter",
"Martin",
""
],
[
"Enver",
"Tariq",
""
],
[
"Zhou",
"Joseph X.",
""
],
[
"Teschendorff",
"Andrew E.",
""
]
] | Differentiation is a key cellular process in normal tissue development that is significantly altered in cancer. Although molecular signatures characterising pluripotency and multipotency exist, there is, as yet, no single quantitative mark of a cellular sample's position in the global differentiation hierarchy. Here we adopt a systems view and consider the sample's network entropy, a measure of signaling pathway promiscuity, computable from a sample's genome-wide expression profile. We demonstrate that network entropy provides a quantitative, in-silico, readout of the average undifferentiated state of the profiled cells, recapitulating the known hierarchy of pluripotent, multipotent and differentiated cell types. Network entropy further exhibits dynamic changes in time course differentiation data, and in line with a sample's differentiation stage. In disease, network entropy predicts a higher level of cellular plasticity in cancer stem cell populations compared to ordinary cancer cells. Importantly, network entropy also allows identification of key differentiation pathways. Our results are consistent with the view that pluripotency is a statistical property defined at the cellular population level, correlating with intra-sample heterogeneity, and driven by the degree of signaling promiscuity in cells. In summary, network entropy provides a quantitative measure of a cell's undifferentiated state, defining its elevation in Waddington's landscape. |
1407.7080 | Liang Ding | Liang Ding, Xingran Xue, Sal LaMarca, Mohammad Mohebbi, Abdul Samad,
Russell L. Malmberg and Liming Cai | Ab initio Prediction of RNA Nucleotide Interactions with Backbone k-Tree
Model | Accepted by Computational Methods for Structural RNAs (CMSR'14) | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given the importance of non-coding RNAs to cellular regulatory functions and
rapid growth of RNA transcripts, computational prediction of RNA tertiary
structure remains highly demanded yet significantly challenging. Even for a
short RNA sequence, the space of tertiary conformations is immense; existing
methods to identify native-like conformations mostly resort to random sampling
of conformations to gain computational feasibility. However native
conformations may not be examined and prediction accuracy may be compromised
due to sampling. In particular, the state-of-the-art methods have yet to
deliver the desired prediction performance for RNAs of length beyond 50.
This paper presents the work to tackle a key step in the RNA tertiary
structure prediction problem, the prediction of the nucleotide interactions
that constitute the desired tertiary structure. The research is established
upon a novel graph model, called backbone k-tree, to markably constrain
nucleotide interaction relationships in RNA tertiary structure. It is shown
that the new model makes it possible to efficiently predict the optimal set of
nucleotide interactions from the query sequence, including the interactions in
all recently revealed families. Evident by the preliminary results, the new
method can predict with a high accuracy the nucleotide interactions that
constitute the tertiary structure of the query sequence, thus providing a
viable solution towards ab initio prediction of RNA tertiary structure.
| [
{
"created": "Sat, 26 Jul 2014 02:32:47 GMT",
"version": "v1"
}
] | 2014-07-29 | [
[
"Ding",
"Liang",
""
],
[
"Xue",
"Xingran",
""
],
[
"LaMarca",
"Sal",
""
],
[
"Mohebbi",
"Mohammad",
""
],
[
"Samad",
"Abdul",
""
],
[
"Malmberg",
"Russell L.",
""
],
[
"Cai",
"Liming",
""
]
] | Given the importance of non-coding RNAs to cellular regulatory functions and rapid growth of RNA transcripts, computational prediction of RNA tertiary structure remains highly demanded yet significantly challenging. Even for a short RNA sequence, the space of tertiary conformations is immense; existing methods to identify native-like conformations mostly resort to random sampling of conformations to gain computational feasibility. However native conformations may not be examined and prediction accuracy may be compromised due to sampling. In particular, the state-of-the-art methods have yet to deliver the desired prediction performance for RNAs of length beyond 50. This paper presents the work to tackle a key step in the RNA tertiary structure prediction problem, the prediction of the nucleotide interactions that constitute the desired tertiary structure. The research is established upon a novel graph model, called backbone k-tree, to markably constrain nucleotide interaction relationships in RNA tertiary structure. It is shown that the new model makes it possible to efficiently predict the optimal set of nucleotide interactions from the query sequence, including the interactions in all recently revealed families. Evident by the preliminary results, the new method can predict with a high accuracy the nucleotide interactions that constitute the tertiary structure of the query sequence, thus providing a viable solution towards ab initio prediction of RNA tertiary structure. |
2109.11425 | Fabio Sanchez PhD | Yury E. Garc\'ia, Gustavo Mery, Paola V\'asquez, Juan G. Calvo, Luis
A. Barboza, Tania Rivas, Fabio Sanchez | Projecting the Impact of Covid-19 Variants and Vaccination Strategies in
Disease Transmission using a Multilayer Network Model in Costa Rica | 17 pages, 1 figure | null | null | null | q-bio.PE math.DS | http://creativecommons.org/licenses/by/4.0/ | For countries starting to receive steady supplies of vaccines against
SARS-CoV-2, the course of Covid-19 for the following months will be determined
by the emergence of new variants and successful roll-out of vaccination
campaigns. To anticipate this scenario, we used a multilayer network model
developed to forecast the transmission dynamics of Covid-19 in Costa Rica, and
to estimate the impact of the introduction of the Delta variant in the country,
under two plausible vaccination scenarios, one sustaining Costa Rica's July
2021 vaccination pace of 30,000 doses per day and with high acceptance from the
population and another with declining vaccination pace to 13,000 doses per day
and with lower acceptance. Results suggest that the introduction and gradual
dominance of the Delta variant would increase Covid-19 hospitalizations and ICU
admissions between $35\%$ and $33.25\%$, from August 2021 to December 2021,
depending on vaccine administration and acceptance. In the presence of the
Delta variant, new Covid-19 hospitalizations and ICU admission would experience
an average increase of $24.26\%$ and $27.19\%$ respectively in the same period
if the vaccination pace drops. Our results can help decision-makers better
prepare for the COVID-19 pandemic in the months to come.
| [
{
"created": "Tue, 7 Sep 2021 19:48:29 GMT",
"version": "v1"
},
{
"created": "Fri, 24 Sep 2021 00:51:24 GMT",
"version": "v2"
}
] | 2021-09-27 | [
[
"García",
"Yury E.",
""
],
[
"Mery",
"Gustavo",
""
],
[
"Vásquez",
"Paola",
""
],
[
"Calvo",
"Juan G.",
""
],
[
"Barboza",
"Luis A.",
""
],
[
"Rivas",
"Tania",
""
],
[
"Sanchez",
"Fabio",
""
]
] | For countries starting to receive steady supplies of vaccines against SARS-CoV-2, the course of Covid-19 for the following months will be determined by the emergence of new variants and successful roll-out of vaccination campaigns. To anticipate this scenario, we used a multilayer network model developed to forecast the transmission dynamics of Covid-19 in Costa Rica, and to estimate the impact of the introduction of the Delta variant in the country, under two plausible vaccination scenarios, one sustaining Costa Rica's July 2021 vaccination pace of 30,000 doses per day and with high acceptance from the population and another with declining vaccination pace to 13,000 doses per day and with lower acceptance. Results suggest that the introduction and gradual dominance of the Delta variant would increase Covid-19 hospitalizations and ICU admissions between $35\%$ and $33.25\%$, from August 2021 to December 2021, depending on vaccine administration and acceptance. In the presence of the Delta variant, new Covid-19 hospitalizations and ICU admission would experience an average increase of $24.26\%$ and $27.19\%$ respectively in the same period if the vaccination pace drops. Our results can help decision-makers better prepare for the COVID-19 pandemic in the months to come. |
1506.04611 | Nicholas Guttenberg | Cameron Smith, Matthieu Laneuville, Nicholas Guttenberg | Emergence of self-reinforcing information bottlenecks in multilevel
selection | 8 pages, 6 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explain how hierarchical organization of biological systems emerges
naturally during evolution, through a transition in the units of individuality.
We will show how these transitions are the result of competing selective forces
operating at different levels of organization, each level having different
units of individuality. Such a transition represents a singular point in the
evolutionary process, which we will show corresponds to a phase transition in
the way information is encoded, with the formation of self-reinforcing
information bottlenecks. We present an abstract model for characterizing these
transitions that is quite general, applicable to many different versions of
such transitions. As a concrete example, we consider the transition to
multicellularity. Specifically, we study a stochastic model where isolated
communities of interacting individuals (e.g. cells) undergo a transition to
higher-order individuality (e.g. multicellularity). This transition is
indicated by the marked decrease in the number of cells utilized to generate
new communities from pre-existing ones. In this sense, the community begins to
reproduce as a whole via a decreasing number of cells. We show that the fitness
barrier to this transition is strongly reduced by horizontal gene transfer.
These features capture two of the most prominent aspects of the transition to
multicellularity: the evolution of a developmental process and reproduction
through a unicellular bottleneck.
| [
{
"created": "Mon, 15 Jun 2015 14:29:50 GMT",
"version": "v1"
}
] | 2015-06-16 | [
[
"Smith",
"Cameron",
""
],
[
"Laneuville",
"Matthieu",
""
],
[
"Guttenberg",
"Nicholas",
""
]
] | We explain how hierarchical organization of biological systems emerges naturally during evolution, through a transition in the units of individuality. We will show how these transitions are the result of competing selective forces operating at different levels of organization, each level having different units of individuality. Such a transition represents a singular point in the evolutionary process, which we will show corresponds to a phase transition in the way information is encoded, with the formation of self-reinforcing information bottlenecks. We present an abstract model for characterizing these transitions that is quite general, applicable to many different versions of such transitions. As a concrete example, we consider the transition to multicellularity. Specifically, we study a stochastic model where isolated communities of interacting individuals (e.g. cells) undergo a transition to higher-order individuality (e.g. multicellularity). This transition is indicated by the marked decrease in the number of cells utilized to generate new communities from pre-existing ones. In this sense, the community begins to reproduce as a whole via a decreasing number of cells. We show that the fitness barrier to this transition is strongly reduced by horizontal gene transfer. These features capture two of the most prominent aspects of the transition to multicellularity: the evolution of a developmental process and reproduction through a unicellular bottleneck. |
1801.04498 | Louxin Zhang | Andreas D. M. Gunawan, Bingxin Lu, Louxin Zhang | Fast Methods for Solving the Cluster Containment Problem for
Phylogenetic Networks | 8 figure, 19 pages | null | null | null | q-bio.QM cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Genetic and comparative genomic studies indicate that extant genomes are more
properly considered to be a fusion product of random mutations over generations
and genomic material transfers between individuals of different lineages. This
has motivated researchers to adopt phylogenetic networks and other general
models to study genome evolution. One important problem arising from
reconstruction and verification of phylogenetic networks is the cluster
containment problem, namely determining whether or not a cluster of taxa is
displayed in a phylogenetic network. In this work, a new upper bound for this
NP-complete problem is established through an efficient reduction to the SAT
problem. Two efficient (albeit exponential time) methods are also implemented.
It is developed on the basis of generalization of the so-called
reticulation-visible property of phylogenetic networks.
| [
{
"created": "Sun, 14 Jan 2018 02:11:23 GMT",
"version": "v1"
}
] | 2018-01-16 | [
[
"Gunawan",
"Andreas D. M.",
""
],
[
"Lu",
"Bingxin",
""
],
[
"Zhang",
"Louxin",
""
]
] | Genetic and comparative genomic studies indicate that extant genomes are more properly considered to be a fusion product of random mutations over generations and genomic material transfers between individuals of different lineages. This has motivated researchers to adopt phylogenetic networks and other general models to study genome evolution. One important problem arising from reconstruction and verification of phylogenetic networks is the cluster containment problem, namely determining whether or not a cluster of taxa is displayed in a phylogenetic network. In this work, a new upper bound for this NP-complete problem is established through an efficient reduction to the SAT problem. Two efficient (albeit exponential time) methods are also implemented. It is developed on the basis of generalization of the so-called reticulation-visible property of phylogenetic networks. |
1407.2088 | Burak Erman | Burak Erman | Fractal Structure of Shortest Interaction Paths in Native Proteins and
Determination of Residues on a Given Shortest Path | null | null | null | null | q-bio.BM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fractal structure of shortest paths depends strongly on interresidue
interaction cutoff distance. The dimensionality of shortest paths is calculated
as a function of interaction cutoff distance. Shortest paths are self similar
with a fractal dimension of 1.12 when calculated with step lengths larger than
6.8 {\AA}. Paths are multifractal below 6.8 {\AA}. The number of steps to
traverse a shortest path is a discontinuous function of cutoff size at short
cutoff values, showing abrupt decreases to smaller values as cutoff distance
increases. As information progresses along the direction of a shortest path a
large set of residues are affected because they are interacting neighbors to
the residues of the shortest path. Thus, several residues are involved
diffusively in information transport which may be identified with the present
model. An algorithm is introduced to determine the residues of a given shortest
path. The shortest path residues are the highly visited residues during
information transport. These paths are shown to lie on the high entropy
landscape of the protein where entropy is taken to increase with abundance of
visits to nodes during signal transport.
| [
{
"created": "Sun, 6 Jul 2014 08:20:55 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Jul 2014 13:11:46 GMT",
"version": "v2"
}
] | 2014-07-28 | [
[
"Erman",
"Burak",
""
]
] | Fractal structure of shortest paths depends strongly on interresidue interaction cutoff distance. The dimensionality of shortest paths is calculated as a function of interaction cutoff distance. Shortest paths are self similar with a fractal dimension of 1.12 when calculated with step lengths larger than 6.8 {\AA}. Paths are multifractal below 6.8 {\AA}. The number of steps to traverse a shortest path is a discontinuous function of cutoff size at short cutoff values, showing abrupt decreases to smaller values as cutoff distance increases. As information progresses along the direction of a shortest path a large set of residues are affected because they are interacting neighbors to the residues of the shortest path. Thus, several residues are involved diffusively in information transport which may be identified with the present model. An algorithm is introduced to determine the residues of a given shortest path. The shortest path residues are the highly visited residues during information transport. These paths are shown to lie on the high entropy landscape of the protein where entropy is taken to increase with abundance of visits to nodes during signal transport. |
2109.04509 | Rui Wang | Rui Wang, Jiahui Chen, Yuta Hozumi, Changchuan Yin, Guo-Wei Wei | Emerging vaccine-breakthrough SARS-CoV-2 variants | 15 pages, 5 figures | null | null | null | q-bio.BM q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | The recent global surge in COVID-19 infections has been fueled by new
SARS-CoV-2 variants, namely Alpha, Beta, Gamma, Delta, etc. The molecular
mechanism underlying such surge is elusive due to 4,653 non-degenerate
mutations on the spike protein, which is the target of most COVID-19 vaccines.
The understanding of the molecular mechanism of transmission and evolution is a
prerequisite to foresee the trend of emerging vaccine-breakthrough variants and
the design of mutation-proof vaccines and monoclonal antibodies. We integrate
the genotyping of 1,489,884 SARS-CoV-2 genomes isolates, 130 human antibodies,
tens of thousands of mutational data points, topological data analysis, and
deep learning to reveal SARS-CoV-2 evolution mechanism and forecast emerging
vaccine-escape variants. We show that infectivity-strengthening and
antibody-disruptive co-mutations on the S protein RBD can quantitatively
explain the infectivity and virulence of all prevailing variants. We
demonstrate that Lambda is as infectious as Delta but is more
vaccine-resistant. We analyze emerging vaccine-breakthrough co-mutations in 20
countries, including the United Kingdom, the United States, Denmark, Brazil,
and Germany, etc. We envision that natural selection through infectivity will
continue to be the main mechanism for viral evolution among unvaccinated
populations, while antibody disruptive co-mutations will fuel the future growth
of vaccine-breakthrough variants among fully vaccinated populations. Finally,
we have identified the co-mutations that have the great likelihood of becoming
dominant: [A411S, L452R, T478K], [L452R, T478K, N501Y], [V401L, L452R, T478K],
[K417N, L452R, T478K], [L452R, T478K, E484K, N501Y], and [P384L, K417N, E484K,
N501Y]. We predict they, particularly the last four, will break through
existing vaccines. We foresee an urgent need to develop new vaccines that
target these co-mutations.
| [
{
"created": "Thu, 9 Sep 2021 18:51:43 GMT",
"version": "v1"
}
] | 2021-09-13 | [
[
"Wang",
"Rui",
""
],
[
"Chen",
"Jiahui",
""
],
[
"Hozumi",
"Yuta",
""
],
[
"Yin",
"Changchuan",
""
],
[
"Wei",
"Guo-Wei",
""
]
] | The recent global surge in COVID-19 infections has been fueled by new SARS-CoV-2 variants, namely Alpha, Beta, Gamma, Delta, etc. The molecular mechanism underlying such surge is elusive due to 4,653 non-degenerate mutations on the spike protein, which is the target of most COVID-19 vaccines. The understanding of the molecular mechanism of transmission and evolution is a prerequisite to foresee the trend of emerging vaccine-breakthrough variants and the design of mutation-proof vaccines and monoclonal antibodies. We integrate the genotyping of 1,489,884 SARS-CoV-2 genomes isolates, 130 human antibodies, tens of thousands of mutational data points, topological data analysis, and deep learning to reveal SARS-CoV-2 evolution mechanism and forecast emerging vaccine-escape variants. We show that infectivity-strengthening and antibody-disruptive co-mutations on the S protein RBD can quantitatively explain the infectivity and virulence of all prevailing variants. We demonstrate that Lambda is as infectious as Delta but is more vaccine-resistant. We analyze emerging vaccine-breakthrough co-mutations in 20 countries, including the United Kingdom, the United States, Denmark, Brazil, and Germany, etc. We envision that natural selection through infectivity will continue to be the main mechanism for viral evolution among unvaccinated populations, while antibody disruptive co-mutations will fuel the future growth of vaccine-breakthrough variants among fully vaccinated populations. Finally, we have identified the co-mutations that have the great likelihood of becoming dominant: [A411S, L452R, T478K], [L452R, T478K, N501Y], [V401L, L452R, T478K], [K417N, L452R, T478K], [L452R, T478K, E484K, N501Y], and [P384L, K417N, E484K, N501Y]. We predict they, particularly the last four, will break through existing vaccines. We foresee an urgent need to develop new vaccines that target these co-mutations. |
2111.04385 | Mareike Fischer | Mareike Fischer | Defining binary phylogenetic trees using parsimony | arXiv admin note: text overlap with arXiv:1808.07098 | null | null | null | q-bio.PE math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Phylogenetic (i.e. leaf-labeled) trees play a fundamental role in
evolutionary research. A typical problem is to reconstruct such trees from data
like DNA alignments (whose columns are often referred to as characters), and a
simple optimization criterion for such reconstructions is maximum parsimony. It
is generally assumed that this criterion works well for data in which state
changes are rare. In the present manuscript, we prove that each phylogenetic
tree $T$ with $n\geq 20 k$ leaves is uniquely defined by the set $A_k(T)$,
which consists of all characters with parsimony score $k$ on $T$. This can be
considered as a promising first step towards showing that maximum parsimony as
a tree reconstruction criterion is justified when the number of changes in the
data is relatively small.
| [
{
"created": "Mon, 8 Nov 2021 11:14:07 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Sep 2022 18:59:32 GMT",
"version": "v2"
}
] | 2022-09-08 | [
[
"Fischer",
"Mareike",
""
]
] | Phylogenetic (i.e. leaf-labeled) trees play a fundamental role in evolutionary research. A typical problem is to reconstruct such trees from data like DNA alignments (whose columns are often referred to as characters), and a simple optimization criterion for such reconstructions is maximum parsimony. It is generally assumed that this criterion works well for data in which state changes are rare. In the present manuscript, we prove that each phylogenetic tree $T$ with $n\geq 20 k$ leaves is uniquely defined by the set $A_k(T)$, which consists of all characters with parsimony score $k$ on $T$. This can be considered as a promising first step towards showing that maximum parsimony as a tree reconstruction criterion is justified when the number of changes in the data is relatively small. |
1505.06067 | Bastien Boussau | Gergely J. Sz\"oll\H{o}si, Adri\'an Arellano Dav\'in, Eric Tannier,
Vincent Daubin, Bastien Boussau | Genome-scale phylogenetic analysis finds extensive gene transfer among
Fungi | null | null | null | null | q-bio.PE q-bio.GN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although the role of lateral gene transfer is well recognized in the
evolution of bacteria, it is generally assumed that it has had less influence
among eukaryotes. To explore this hypothesis we compare the dynamics of genome
evolution in two groups of organisms: Cyanobacteria and Fungi. Ancestral
genomes are inferred in both clades using two types of methods. First, Count, a
gene tree unaware method that models gene duplications, gains and losses to
explain the observed numbers of genes present in a genome. Second, ALE, a more
recent gene tree-aware method that reconciles gene trees with a species tree
using a model of gene duplication, loss, and transfer. We compare their merits
and their ability to quantify the role of transfers, and assess the impact of
taxonomic sampling on their inferences. We present what we believe is
compelling evidence that gene transfer plays a significant role in the
evolution of Fungi.
| [
{
"created": "Fri, 22 May 2015 13:30:04 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Jul 2015 13:08:08 GMT",
"version": "v2"
}
] | 2015-07-16 | [
[
"Szöllősi",
"Gergely J.",
""
],
[
"Davín",
"Adrián Arellano",
""
],
[
"Tannier",
"Eric",
""
],
[
"Daubin",
"Vincent",
""
],
[
"Boussau",
"Bastien",
""
]
] | Although the role of lateral gene transfer is well recognized in the evolution of bacteria, it is generally assumed that it has had less influence among eukaryotes. To explore this hypothesis we compare the dynamics of genome evolution in two groups of organisms: Cyanobacteria and Fungi. Ancestral genomes are inferred in both clades using two types of methods. First, Count, a gene tree unaware method that models gene duplications, gains and losses to explain the observed numbers of genes present in a genome. Second, ALE, a more recent gene tree-aware method that reconciles gene trees with a species tree using a model of gene duplication, loss, and transfer. We compare their merits and their ability to quantify the role of transfers, and assess the impact of taxonomic sampling on their inferences. We present what we believe is compelling evidence that gene transfer plays a significant role in the evolution of Fungi. |
2310.18053 | Jes\'us Fern\'andez-S\'anchez | Marta Casanellas and Jes\'us Fern\'andez-S\'anchez | Phylogenetic invariants: straightforward from the general Markov to
equivariant models | 30 pages, 4 figures | null | null | null | q-bio.PE math.AG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the last decade, some algebraic tools have been successfully applied to
phylogenetic reconstruction. These tools are mainly based on the knowledge of
equations describing algebraic varieties associated to phylogenetic trees
evolving under Markov processes of molecular substitution, the so called
phylogenetic invariants. Although the theory involved allows to explicitly
obtain these equations for all equivariant models (which include some of the
most popular nucleotide substitution models), practical uses of these algebraic
tools have been restricted to the case of the general Markov model. Arguably,
one of the reasons for this restriction is that knowledge of linear
representation theory is required before making these equations explicit.
With the aim of enlarging the practical uses of algebraic phylogenetics, in
this paper we prove that phylogenetic invariants for trees evolving under
equivariant models can be derived from phylogenetic invariants for the general
Markov model, without the need of representation theory. Our main result states
that the algebraic variety corresponding to a phylogenetic tree evolving under
an equivariant model is an irreducible component of the variety corresponding
to the same tree under the general Markov model cut with the linear space
defined by the model. We also prove that, for any equivariant model, those
phylogenetic invariants that are relevant for practical uses (e.g. tree
reconstruction) can be simply deduced from a single rank constraint on the
matrices obtained by flattening the joint distribution at the leaves of the
tree. This condition can be easily tested from singular values of the matrices
and extends our results from trees to phylogenetic networks.
| [
{
"created": "Fri, 27 Oct 2023 11:03:39 GMT",
"version": "v1"
},
{
"created": "Thu, 9 Nov 2023 06:48:48 GMT",
"version": "v2"
},
{
"created": "Wed, 28 Feb 2024 19:18:06 GMT",
"version": "v3"
}
] | 2024-03-01 | [
[
"Casanellas",
"Marta",
""
],
[
"Fernández-Sánchez",
"Jesús",
""
]
] | In the last decade, some algebraic tools have been successfully applied to phylogenetic reconstruction. These tools are mainly based on the knowledge of equations describing algebraic varieties associated to phylogenetic trees evolving under Markov processes of molecular substitution, the so called phylogenetic invariants. Although the theory involved allows to explicitly obtain these equations for all equivariant models (which include some of the most popular nucleotide substitution models), practical uses of these algebraic tools have been restricted to the case of the general Markov model. Arguably, one of the reasons for this restriction is that knowledge of linear representation theory is required before making these equations explicit. With the aim of enlarging the practical uses of algebraic phylogenetics, in this paper we prove that phylogenetic invariants for trees evolving under equivariant models can be derived from phylogenetic invariants for the general Markov model, without the need of representation theory. Our main result states that the algebraic variety corresponding to a phylogenetic tree evolving under an equivariant model is an irreducible component of the variety corresponding to the same tree under the general Markov model cut with the linear space defined by the model. We also prove that, for any equivariant model, those phylogenetic invariants that are relevant for practical uses (e.g. tree reconstruction) can be simply deduced from a single rank constraint on the matrices obtained by flattening the joint distribution at the leaves of the tree. This condition can be easily tested from singular values of the matrices and extends our results from trees to phylogenetic networks. |
1209.1467 | Quang-Cuong Pham | Quang-Cuong Pham and Daniel Bennequin | Affine invariance of human hand movements: a direct test | 23 pages, 3 figures | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Geometrical invariance, in particular affine invariance, has been recently
proposed as an important principle underlying the production of hand movements.
However, tests of affine invariance have traditionally been applied to the
consequences of this principle rather to the principle itself. Here, we
designed and performed an original, direct, test of affine invariance in a
scribbling experiment. In each of the 10800 pairs of randomly-selected
scribbling segments, we compared the time parameterizations obtained by
transforming the first segment using four different transportation rules -
affine, equi-affine, Euclidian and constant - with the experimentally-observed
parameterization of the second segment. We observed that, when the two paths
are affinely-similar, the affine transportation of the first segment yields the
time parameterization that best matches the experimental parameterization of
the second segment, which directly demonstrates the existence of affine
invariance in the production of hand movements.
| [
{
"created": "Fri, 7 Sep 2012 09:13:12 GMT",
"version": "v1"
}
] | 2012-09-10 | [
[
"Pham",
"Quang-Cuong",
""
],
[
"Bennequin",
"Daniel",
""
]
] | Geometrical invariance, in particular affine invariance, has been recently proposed as an important principle underlying the production of hand movements. However, tests of affine invariance have traditionally been applied to the consequences of this principle rather to the principle itself. Here, we designed and performed an original, direct, test of affine invariance in a scribbling experiment. In each of the 10800 pairs of randomly-selected scribbling segments, we compared the time parameterizations obtained by transforming the first segment using four different transportation rules - affine, equi-affine, Euclidian and constant - with the experimentally-observed parameterization of the second segment. We observed that, when the two paths are affinely-similar, the affine transportation of the first segment yields the time parameterization that best matches the experimental parameterization of the second segment, which directly demonstrates the existence of affine invariance in the production of hand movements. |
1404.1587 | Ulrich S. Schwarz | Philipp J. Albert, Thorsten Erdmann and Ulrich S. Schwarz (Heidelberg
University) | Stochastic dynamics and mechanosensitivity of myosin II minifilaments | Revtex, 27 pages, 6 figures | Philipp J Albert et al 2014 New J. Phys. 16 093019 | 10.1088/1367-2630/16/9/093019 | null | q-bio.SC cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tissue cells are in a state of permanent mechanical tension that is
maintained mainly by myosin II minifilaments, which are bipolar assemblies of
tens of myosin II molecular motors contracting actin networks and bundles. Here
we introduce a stochastic model for myosin II minifilaments as two small myosin
II motor ensembles engaging in a stochastic tug-of-war. Each of the two
ensembles is described by the parallel cluster model that allows us to use
exact stochastic simulations and at the same time to keep important molecular
details of the myosin II cross-bridge cycle. Our simulation and analytical
results reveal a strong dependence of myosin II minifilament dynamics on
environmental stiffness that is reminiscent of the cellular response to
substrate stiffness. For small stiffness, minifilaments form transient
crosslinks exerting short spikes of force with negligible mean. For large
stiffness, minifilaments form near permanent crosslinks exerting a mean force
which hardly depends on environmental elasticity. This functional switch arises
because dissociation after the power stroke is suppressed by force (catch
bonding) and because ensembles can no longer perform the power stroke at large
forces. Symmetric myosin II minifilaments perform a random walk with an
effective diffusion constant which decreases with increasing ensemble size, as
demonstrated for rigid substrates with an analytical treatment.
| [
{
"created": "Sun, 6 Apr 2014 15:33:24 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Sep 2014 09:36:24 GMT",
"version": "v2"
}
] | 2014-09-16 | [
[
"Albert",
"Philipp J.",
"",
"Heidelberg\n University"
],
[
"Erdmann",
"Thorsten",
"",
"Heidelberg\n University"
],
[
"Schwarz",
"Ulrich S.",
"",
"Heidelberg\n University"
]
] | Tissue cells are in a state of permanent mechanical tension that is maintained mainly by myosin II minifilaments, which are bipolar assemblies of tens of myosin II molecular motors contracting actin networks and bundles. Here we introduce a stochastic model for myosin II minifilaments as two small myosin II motor ensembles engaging in a stochastic tug-of-war. Each of the two ensembles is described by the parallel cluster model that allows us to use exact stochastic simulations and at the same time to keep important molecular details of the myosin II cross-bridge cycle. Our simulation and analytical results reveal a strong dependence of myosin II minifilament dynamics on environmental stiffness that is reminiscent of the cellular response to substrate stiffness. For small stiffness, minifilaments form transient crosslinks exerting short spikes of force with negligible mean. For large stiffness, minifilaments form near permanent crosslinks exerting a mean force which hardly depends on environmental elasticity. This functional switch arises because dissociation after the power stroke is suppressed by force (catch bonding) and because ensembles can no longer perform the power stroke at large forces. Symmetric myosin II minifilaments perform a random walk with an effective diffusion constant which decreases with increasing ensemble size, as demonstrated for rigid substrates with an analytical treatment. |
1306.4420 | Jian Peng | Jian Peng | Statistical inference for template-based protein structure prediction | null | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Protein structure prediction is one of the most important problems in
computational biology. The most successful computational approach, also called
template-based modeling, identifies templates with solved crystal structures
for the query proteins and constructs three dimensional models based on
sequence/structure alignments. Although substantial effort has been made to
improve protein sequence alignment, the accuracy of alignments between
distantly related proteins is still unsatisfactory. In this thesis, I will
introduce a number of statistical machine learning methods to build accurate
alignments between a protein sequence and its template structures, especially
for proteins having only distantly related templates. For a protein with only
one good template, we develop a regression-tree based Conditional Random Fields
(CRF) model for pairwise protein sequence/structure alignment. By learning a
nonlinear threading scoring function, we are able to leverage the correlation
among different sequence and structural features. We also introduce an
information-theoretic measure to guide the learning algorithm to better exploit
the structural features for low-homology proteins with little evolutionary
information in their sequence profile. For a protein with multiple good
templates, we design a probabilistic consistency approach to thread the protein
to all templates simultaneously. By minimizing the discordance between the
pairwise alignments of the protein and templates, we are able to construct a
multiple sequence/structure alignment, which leads to better structure
predictions than any single-template based prediction.
| [
{
"created": "Wed, 19 Jun 2013 04:42:34 GMT",
"version": "v1"
}
] | 2013-06-20 | [
[
"Peng",
"Jian",
""
]
] | Protein structure prediction is one of the most important problems in computational biology. The most successful computational approach, also called template-based modeling, identifies templates with solved crystal structures for the query proteins and constructs three dimensional models based on sequence/structure alignments. Although substantial effort has been made to improve protein sequence alignment, the accuracy of alignments between distantly related proteins is still unsatisfactory. In this thesis, I will introduce a number of statistical machine learning methods to build accurate alignments between a protein sequence and its template structures, especially for proteins having only distantly related templates. For a protein with only one good template, we develop a regression-tree based Conditional Random Fields (CRF) model for pairwise protein sequence/structure alignment. By learning a nonlinear threading scoring function, we are able to leverage the correlation among different sequence and structural features. We also introduce an information-theoretic measure to guide the learning algorithm to better exploit the structural features for low-homology proteins with little evolutionary information in their sequence profile. For a protein with multiple good templates, we design a probabilistic consistency approach to thread the protein to all templates simultaneously. By minimizing the discordance between the pairwise alignments of the protein and templates, we are able to construct a multiple sequence/structure alignment, which leads to better structure predictions than any single-template based prediction. |
1011.3928 | Petter Holme | Sungmin Lee, Luis E. C. Rocha, Fredrik Liljeros, Petter Holme | Exploiting temporal network structures of human interaction to
effectively immunize populations | null | PLoS ONE 7(5): e36439 (2012) | 10.1371/journal.pone.0036439 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | If we can lower the number of people needed to vaccinate for a community to
be immune against contagious diseases, we can save resources and life. A key to
reach such a lower threshold of immunization is to find and vaccinate people
who, through their behavior, are more likely to become infected and effective
to spread the disease than the average. Fortunately, the very behavior that
makes these people important to vaccinate can help us finding them. People you
have met recently are more likely to be socially active and thus central in the
contact pattern, and important to vaccinate. We propose two immunization
schemes exploiting temporal contact patterns. Both of these rely only on
obtainable, local information and could implemented in practice. We show that
these schemes outperform benchmark protocols in four real data sets under
various epidemic scenarios. The data sets are dynamic, which enables us to make
more realistic evaluations than other studies - we use information only about
the past to perform the vaccination and the future to simulate disease
outbreaks. We also use models to elucidate the mechanisms behind how the
temporal structures make our immunization protocols efficient.
| [
{
"created": "Wed, 17 Nov 2010 10:18:38 GMT",
"version": "v1"
}
] | 2012-10-10 | [
[
"Lee",
"Sungmin",
""
],
[
"Rocha",
"Luis E. C.",
""
],
[
"Liljeros",
"Fredrik",
""
],
[
"Holme",
"Petter",
""
]
] | If we can lower the number of people needed to vaccinate for a community to be immune against contagious diseases, we can save resources and life. A key to reach such a lower threshold of immunization is to find and vaccinate people who, through their behavior, are more likely to become infected and effective to spread the disease than the average. Fortunately, the very behavior that makes these people important to vaccinate can help us finding them. People you have met recently are more likely to be socially active and thus central in the contact pattern, and important to vaccinate. We propose two immunization schemes exploiting temporal contact patterns. Both of these rely only on obtainable, local information and could implemented in practice. We show that these schemes outperform benchmark protocols in four real data sets under various epidemic scenarios. The data sets are dynamic, which enables us to make more realistic evaluations than other studies - we use information only about the past to perform the vaccination and the future to simulate disease outbreaks. We also use models to elucidate the mechanisms behind how the temporal structures make our immunization protocols efficient. |
1901.10962 | Joaquin Goni | Duy Duong-Tran, Kausar Abbas, Enrico Amico, Bernat Corominas-Murtra,
Mario Dzemidzic, David Kareken, Mario Ventresca and Joaqu\'in Go\~ni | A morphospace of functional configuration to assess configural breadth
based on brain functional networks | main article: 24 pages, 8 figures, 2 tables. supporting information:
11 pages, 5 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The best approach to quantify human brain functional reconfigurations in
response to varying cognitive demands remains an unresolved topic in network
neuroscience. We propose that such functional reconfigurations may be
categorized into three different types: i) Network Configural Breadth, ii)
Task-to-Task transitional reconfiguration, and iii) Within-Task
reconfiguration. In order to quantify these reconfigurations, we propose a
mesoscopic framework focused on functional networks (FNs) or communities. To do
so, we introduce a 2D network morphospace that relies on two novel mesoscopic
metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology
and integration of information within and between a reference set of FNs. In
this study, we use this framework to quantify the Network Configural Breadth
across different tasks. We show that the metrics defining this morphospace can
differentiate FNs, cognitive tasks and subjects. We also show that network
configural breadth significantly predicts behavioral measures, such as episodic
memory, verbal episodic memory, fluid intelligence and general intelligence. In
essence, we put forth a framework to explore the cognitive space in a
comprehensive manner, for each individual separately, and at different levels
of granularity. This tool that can also quantify the FN reconfigurations that
result from the brain switching between mental states.
| [
{
"created": "Wed, 30 Jan 2019 17:27:28 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Sep 2019 19:09:28 GMT",
"version": "v2"
},
{
"created": "Fri, 3 Jan 2020 18:54:36 GMT",
"version": "v3"
},
{
"created": "Fri, 6 Nov 2020 16:34:52 GMT",
"version": "v4"
}
] | 2020-11-09 | [
[
"Duong-Tran",
"Duy",
""
],
[
"Abbas",
"Kausar",
""
],
[
"Amico",
"Enrico",
""
],
[
"Corominas-Murtra",
"Bernat",
""
],
[
"Dzemidzic",
"Mario",
""
],
[
"Kareken",
"David",
""
],
[
"Ventresca",
"Mario",
""
],
[
"Goñi",
"Joaquín",
""
]
] | The best approach to quantify human brain functional reconfigurations in response to varying cognitive demands remains an unresolved topic in network neuroscience. We propose that such functional reconfigurations may be categorized into three different types: i) Network Configural Breadth, ii) Task-to-Task transitional reconfiguration, and iii) Within-Task reconfiguration. In order to quantify these reconfigurations, we propose a mesoscopic framework focused on functional networks (FNs) or communities. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology and integration of information within and between a reference set of FNs. In this study, we use this framework to quantify the Network Configural Breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states. |
1406.1975 | Wenlian Lu | Xiaohong Gong, Wenlian Lu, Keith M. Kendrick, Weidan Pu, Chu Wang, Li
Jin, Guangmin Lu, Zhening Liu, Haihong Liu, Jianfeng Feng | A brain-wide association study of DISC1 genetic variants reveals a
relationship with the structure and functional connectivity of the precuneus
in schizophrenia | 43 pages, 8 figures, 3 tables | null | 10.1002/hbm.22560 | null | q-bio.NC q-bio.GN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Disrupted in Schizophrenia Gene 1 (DISC1) plays a role in both neural
signalling and development and is associated with schizophrenia, although its
links to altered brain structure and function in this disorder are not fully
established. Here we have used structural and functional MRI to investigate
links with six DISC1 single nucleotide polymorphisms (SNPs). We employed a
brain-wide association analysis (BWAS) together with a Jacknife internal
validation approach in 46 schizophrenia patients and 24 matched healthy control
subjects. Results from structural MRI showed significant associations between
all six DISC1 variants and gray matter volume in the precuneus, post-central
gyrus and middle cingulate gyrus. Associations with specific SNPs were found
for rs2738880 in the left precuneus and right post-central gyrus, and rs1535530
in the right precuneus and middle cingulate gyrus. Using regions showing
structural associations as seeds a resting-state functional connectivity
analysis revealed significant associations between all 6 SNPS and connectivity
between the right precuneus and inferior frontal gyrus. The connection between
the right precuneus and inferior frontal gyrus was also specifically associated
with rs821617. Importantly schizophrenia patients showed positive correlations
between the six DISC-1 SNPs associated gray matter volume in the left precuneus
and right post-central gyrus and negative symptom severity. No correlations
with illness duration were found. Our results provide the first evidence
suggesting a key role for structural and functional connectivity associations
between DISC1 polymorphisms and the precuneus in schizophrenia.
| [
{
"created": "Sun, 8 Jun 2014 11:56:14 GMT",
"version": "v1"
}
] | 2014-06-10 | [
[
"Gong",
"Xiaohong",
""
],
[
"Lu",
"Wenlian",
""
],
[
"Kendrick",
"Keith M.",
""
],
[
"Pu",
"Weidan",
""
],
[
"Wang",
"Chu",
""
],
[
"Jin",
"Li",
""
],
[
"Lu",
"Guangmin",
""
],
[
"Liu",
"Zhening",
""
],
[
"Liu",
"Haihong",
""
],
[
"Feng",
"Jianfeng",
""
]
] | The Disrupted in Schizophrenia Gene 1 (DISC1) plays a role in both neural signalling and development and is associated with schizophrenia, although its links to altered brain structure and function in this disorder are not fully established. Here we have used structural and functional MRI to investigate links with six DISC1 single nucleotide polymorphisms (SNPs). We employed a brain-wide association analysis (BWAS) together with a Jacknife internal validation approach in 46 schizophrenia patients and 24 matched healthy control subjects. Results from structural MRI showed significant associations between all six DISC1 variants and gray matter volume in the precuneus, post-central gyrus and middle cingulate gyrus. Associations with specific SNPs were found for rs2738880 in the left precuneus and right post-central gyrus, and rs1535530 in the right precuneus and middle cingulate gyrus. Using regions showing structural associations as seeds a resting-state functional connectivity analysis revealed significant associations between all 6 SNPS and connectivity between the right precuneus and inferior frontal gyrus. The connection between the right precuneus and inferior frontal gyrus was also specifically associated with rs821617. Importantly schizophrenia patients showed positive correlations between the six DISC-1 SNPs associated gray matter volume in the left precuneus and right post-central gyrus and negative symptom severity. No correlations with illness duration were found. Our results provide the first evidence suggesting a key role for structural and functional connectivity associations between DISC1 polymorphisms and the precuneus in schizophrenia. |
1807.10595 | Thorsten Emig | Matthew Mulligan, Guillaume Adam, Thorsten Emig | A Minimal Power Model for Human Running Performance | 29 pages, 5 figures | null | 10.1371/journal.pone.0206645 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Models for human running performances of various complexities and underlying
principles have been proposed, often combining data from world record
performances and bio-energetic facts of human physiology. Here we present a
novel, minimal and universal model for human running performance that employs a
relative metabolic power scale. The main component is a self-consistency
relation for the time dependent maximal power output. The analytic approach
presented here is the first to derive the observed logarithmic scaling between
world (and other) record running speeds and times from basic principles of
metabolic power supply. Various female and male record performances (world,
national) and also personal best performances of individual runners for
distances from 800m to the marathon are excellently described by this model,
with mean errors of (often much) less than 1%. The model defines endurance in a
way that demonstrates symmetry between long and short racing events that are
separated by a characteristic time scale comparable to the time over which a
runner can sustain maximal oxygen uptake. As an application of our model, we
derive personalized characteristic race speeds for different durations and
distances.
| [
{
"created": "Mon, 23 Jul 2018 15:22:51 GMT",
"version": "v1"
}
] | 2019-03-06 | [
[
"Mulligan",
"Matthew",
""
],
[
"Adam",
"Guillaume",
""
],
[
"Emig",
"Thorsten",
""
]
] | Models for human running performances of various complexities and underlying principles have been proposed, often combining data from world record performances and bio-energetic facts of human physiology. Here we present a novel, minimal and universal model for human running performance that employs a relative metabolic power scale. The main component is a self-consistency relation for the time dependent maximal power output. The analytic approach presented here is the first to derive the observed logarithmic scaling between world (and other) record running speeds and times from basic principles of metabolic power supply. Various female and male record performances (world, national) and also personal best performances of individual runners for distances from 800m to the marathon are excellently described by this model, with mean errors of (often much) less than 1%. The model defines endurance in a way that demonstrates symmetry between long and short racing events that are separated by a characteristic time scale comparable to the time over which a runner can sustain maximal oxygen uptake. As an application of our model, we derive personalized characteristic race speeds for different durations and distances. |
1210.2363 | Alan Bergland | Alison F. Feder, Dmitri A. Petrov and Alan O. Bergland | LDx: estimation of linkage disequilibrium from high-throughput pooled
resequencing data | 10 pages, 5 figures | null | 10.1371/journal.pone.0048588 | null | q-bio.GN q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | High-throughput pooled resequencing offers significant potential for whole
genome population sequencing. However, its main drawback is the loss of
haplotype information. In order to regain some of this information, we present
LDx, a computational tool for estimating linkage disequilibrium (LD) from
pooled resequencing data. LDx uses an approximate maximum likelihood approach
to estimate LD (r2) between pairs of SNPs that can be observed within and among
single reads. LDx also reports r2 estimates derived solely from observed
genotype counts. We demonstrate that the LDx estimates are highly correlated
with r2 estimated from individually resequenced strains. We discuss the
performance of LDx using more stringent quality conditions and infer via
simulation the degree to which performance can improve based on read depth.
Finally we demonstrate two possible uses of LDx with real and simulated pooled
resequencing data. First, we use LDx to infer genomewide patterns of decay of
LD with physical distance in D. melanogaster population resequencing data.
Second, we demonstrate that r2 estimates from LDx are capable of distinguishing
alternative demographic models representing plausible demographic histories of
D. melanogaster.
| [
{
"created": "Mon, 8 Oct 2012 17:50:37 GMT",
"version": "v1"
},
{
"created": "Mon, 29 Oct 2012 19:04:46 GMT",
"version": "v2"
}
] | 2015-06-11 | [
[
"Feder",
"Alison F.",
""
],
[
"Petrov",
"Dmitri A.",
""
],
[
"Bergland",
"Alan O.",
""
]
] | High-throughput pooled resequencing offers significant potential for whole genome population sequencing. However, its main drawback is the loss of haplotype information. In order to regain some of this information, we present LDx, a computational tool for estimating linkage disequilibrium (LD) from pooled resequencing data. LDx uses an approximate maximum likelihood approach to estimate LD (r2) between pairs of SNPs that can be observed within and among single reads. LDx also reports r2 estimates derived solely from observed genotype counts. We demonstrate that the LDx estimates are highly correlated with r2 estimated from individually resequenced strains. We discuss the performance of LDx using more stringent quality conditions and infer via simulation the degree to which performance can improve based on read depth. Finally we demonstrate two possible uses of LDx with real and simulated pooled resequencing data. First, we use LDx to infer genomewide patterns of decay of LD with physical distance in D. melanogaster population resequencing data. Second, we demonstrate that r2 estimates from LDx are capable of distinguishing alternative demographic models representing plausible demographic histories of D. melanogaster. |
1612.05321 | Hannah Choi | Hannah Choi, Anitha Pasupathy, Eric Shea-Brown | Predictive coding in area V4: dynamic shape discrimination under partial
occlusion | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The primate visual system has an exquisite ability to discriminate partially
occluded shapes. Recent electrophysiological recordings suggest that response
dynamics in intermediate visual cortical area V4, shaped by feedback from
prefrontal cortex (PFC), may play a key role. To probe the algorithms that may
underlie these findings, we build and test a model of V4 and PFC interactions
based on a hierarchical predictive coding framework. We propose that
probabilistic inference occurs in two steps. Initially, V4 responses are driven
solely by bottom-up sensory input and are thus strongly influenced by the level
of occlusion. After a delay, V4 responses combine both feedforward input and
feedback signals from the PFC; the latter reflect predictions made by PFC about
the visual stimulus underlying V4 activity. We find that this model captures
key features of V4 and PFC dynamics observed in experiments. Specifically, PFC
responses are strongest for occluded stimuli and delayed responses in V4 are
less sensitive to occlusion, supporting our hypothesis that the feedback
signals from PFC underlie robust discrimination of occluded shapes. Thus, our
study proposes that area V4 and PFC participate in hierarchical inference, with
feedback signals encoding top-down predictions about occluded shapes.
| [
{
"created": "Fri, 16 Dec 2016 00:31:58 GMT",
"version": "v1"
}
] | 2016-12-19 | [
[
"Choi",
"Hannah",
""
],
[
"Pasupathy",
"Anitha",
""
],
[
"Shea-Brown",
"Eric",
""
]
] | The primate visual system has an exquisite ability to discriminate partially occluded shapes. Recent electrophysiological recordings suggest that response dynamics in intermediate visual cortical area V4, shaped by feedback from prefrontal cortex (PFC), may play a key role. To probe the algorithms that may underlie these findings, we build and test a model of V4 and PFC interactions based on a hierarchical predictive coding framework. We propose that probabilistic inference occurs in two steps. Initially, V4 responses are driven solely by bottom-up sensory input and are thus strongly influenced by the level of occlusion. After a delay, V4 responses combine both feedforward input and feedback signals from the PFC; the latter reflect predictions made by PFC about the visual stimulus underlying V4 activity. We find that this model captures key features of V4 and PFC dynamics observed in experiments. Specifically, PFC responses are strongest for occluded stimuli and delayed responses in V4 are less sensitive to occlusion, supporting our hypothesis that the feedback signals from PFC underlie robust discrimination of occluded shapes. Thus, our study proposes that area V4 and PFC participate in hierarchical inference, with feedback signals encoding top-down predictions about occluded shapes. |
1611.09520 | Yukiyasu Kamitani | Tomoyasu Horikawa and Yukiyasu Kamitani | Hierarchical Neural Representation of Dreamed Objects Revealed by Brain
Decoding with Deep Neural Network Features | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Dreaming is generally thought to be generated by spontaneous brain activity
during sleep with patterns common to waking experience. This view is supported
by a recent study demonstrating that dreamed objects can be predicted from
brain activity during sleep using statistical decoders trained with
stimulus-induced brain activity. However, it remains unclear whether and how
visual image features associated with dreamed objects are represented in the
brain. In this study, we used a deep neural network (DNN) model for object
recognition as a proxy for hierarchical visual feature representation, and DNN
features for dreamed objects were analyzed with brain decoding of fMRI data
collected during dreaming. The decoders were first trained with
stimulus-induced brain activity labeled with the feature values of the stimulus
image from multiple DNN layers. The decoders were then used to decode DNN
features from the dream fMRI data, and the decoded features were compared with
the averaged features of each object category calculated from a large-scale
image database. We found that the feature values decoded from the dream fMRI
data positively correlated with those associated with dreamed object categories
at mid- to high-level DNN layers. Using the decoded features, the dreamed
object category could be identified at above-chance levels by matching them to
the averaged features for candidate categories. The results suggest that
dreaming recruits hierarchical visual feature representations associated with
objects, which may support phenomenal aspects of dream experience.
| [
{
"created": "Tue, 29 Nov 2016 08:18:38 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Jan 2017 06:54:20 GMT",
"version": "v2"
}
] | 2017-01-24 | [
[
"Horikawa",
"Tomoyasu",
""
],
[
"Kamitani",
"Yukiyasu",
""
]
] | Dreaming is generally thought to be generated by spontaneous brain activity during sleep with patterns common to waking experience. This view is supported by a recent study demonstrating that dreamed objects can be predicted from brain activity during sleep using statistical decoders trained with stimulus-induced brain activity. However, it remains unclear whether and how visual image features associated with dreamed objects are represented in the brain. In this study, we used a deep neural network (DNN) model for object recognition as a proxy for hierarchical visual feature representation, and DNN features for dreamed objects were analyzed with brain decoding of fMRI data collected during dreaming. The decoders were first trained with stimulus-induced brain activity labeled with the feature values of the stimulus image from multiple DNN layers. The decoders were then used to decode DNN features from the dream fMRI data, and the decoded features were compared with the averaged features of each object category calculated from a large-scale image database. We found that the feature values decoded from the dream fMRI data positively correlated with those associated with dreamed object categories at mid- to high-level DNN layers. Using the decoded features, the dreamed object category could be identified at above-chance levels by matching them to the averaged features for candidate categories. The results suggest that dreaming recruits hierarchical visual feature representations associated with objects, which may support phenomenal aspects of dream experience. |
1502.07250 | James Kaufman | Barbara A. Jones, Justin Lessler, Simone Bianco, James H. Kaufman | Statistical mechanics and thermodynamics of viral evolution | 39 pages (55 pages including supplement), 9 figures, 11 supplemental
figures | PLoS ONE 10(9) (2015) | 10.1371/journal.pone.0137482 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper analyzes a simplified model of viral infection and evolution using
the 'grand canonical ensemble' and formalisms from statistical mechanics and
thermodynamics to enumerate all possible viruses and to derive thermodynamic
variables for the system. We model the infection process as a series of energy
barriers determined by the genetic states of the virus and host as a function
of immune response and system temperature. We find a phase transition between a
positive temperature regime of normal replication and a negative temperature
'disordered' phase of the virus. These phases define different regimes in which
different genetic strategies are favored. Perhaps most importantly, it
demonstrates that the system has a real thermodynamic temperature. For normal
replication, this temperature is linearly related to effective temperature. The
strength of immune response rescales temperature but does not change the
observed linear relationship. For all temperatures and immunities studied, we
find a universal curve relating the order parameter to viral evolvability. Real
viruses have finite length RNA segments that encode for proteins which
determine their fitness; hence the methods put forth here could be refined to
apply to real biological systems, perhaps providing insight into immune escape,
the emergence of novel pathogens and other results of viral evolution.
| [
{
"created": "Wed, 25 Feb 2015 17:08:39 GMT",
"version": "v1"
}
] | 2016-01-08 | [
[
"Jones",
"Barbara A.",
""
],
[
"Lessler",
"Justin",
""
],
[
"Bianco",
"Simone",
""
],
[
"Kaufman",
"James H.",
""
]
] | This paper analyzes a simplified model of viral infection and evolution using the 'grand canonical ensemble' and formalisms from statistical mechanics and thermodynamics to enumerate all possible viruses and to derive thermodynamic variables for the system. We model the infection process as a series of energy barriers determined by the genetic states of the virus and host as a function of immune response and system temperature. We find a phase transition between a positive temperature regime of normal replication and a negative temperature 'disordered' phase of the virus. These phases define different regimes in which different genetic strategies are favored. Perhaps most importantly, it demonstrates that the system has a real thermodynamic temperature. For normal replication, this temperature is linearly related to effective temperature. The strength of immune response rescales temperature but does not change the observed linear relationship. For all temperatures and immunities studied, we find a universal curve relating the order parameter to viral evolvability. Real viruses have finite length RNA segments that encode for proteins which determine their fitness; hence the methods put forth here could be refined to apply to real biological systems, perhaps providing insight into immune escape, the emergence of novel pathogens and other results of viral evolution. |
2302.02416 | Michael B\"orsch | Lukas Spantzel, Iv\'an P\'erez, Thomas Heitkamp, Anika Westphal,
Stefanie Reuter, Ralf Mrowka, Michael B\"orsch | Monitoring oligomerization dynamics of individual human neurotensin
receptors 1 in living cells and in SMALP nanodiscs | 13 pages, 4 figures | null | null | null | q-bio.BM q-bio.SC | http://creativecommons.org/licenses/by/4.0/ | The human neurotensin receptor 1 (NTSR1) is a G protein-coupled receptor. The
receptor is activated by a small peptide ligand neurotensin. NTSR1 can be
expressed in HEK cells by stable transfection. Previously we used the
fluorescent protein markers mRuby3 or mNeonGreen fused to NTSR1 for EMCCD-based
structured illumination microscopy (SIM) in living HEK cells. Ligand binding
induced conformational changes in NTSR1 which triggered the intracellular
signaling processes. Recent single-molecule studies revealed a dynamic
monomer/dimer equilibrium of this receptor in artificial lipid bilayers. Here
we report on the oligomerization state of human NTSR1 from living cells by
trapping them into lipid nanodiscs. Briefly, SMALPs (styrene-maleic acid
copolymer lipid nanoparticles) were produced directly from the plasma membranes
of living HEK293T FlpIn cells. SMALPs with a diameter of 15 nm were soluble and
stable. NTSR1 in SMALPs were analyzed by single-molecule intensity measurements
one membrane patch at a time using a custom-built confocal anti-Brownian
electrokinetic trap (ABEL trap) microscope. We found oligomerization changes
before and after stimulation of the receptor with its ligand neurotensin.
| [
{
"created": "Sun, 5 Feb 2023 16:00:48 GMT",
"version": "v1"
}
] | 2023-02-07 | [
[
"Spantzel",
"Lukas",
""
],
[
"Pérez",
"Iván",
""
],
[
"Heitkamp",
"Thomas",
""
],
[
"Westphal",
"Anika",
""
],
[
"Reuter",
"Stefanie",
""
],
[
"Mrowka",
"Ralf",
""
],
[
"Börsch",
"Michael",
""
]
] | The human neurotensin receptor 1 (NTSR1) is a G protein-coupled receptor. The receptor is activated by a small peptide ligand neurotensin. NTSR1 can be expressed in HEK cells by stable transfection. Previously we used the fluorescent protein markers mRuby3 or mNeonGreen fused to NTSR1 for EMCCD-based structured illumination microscopy (SIM) in living HEK cells. Ligand binding induced conformational changes in NTSR1 which triggered the intracellular signaling processes. Recent single-molecule studies revealed a dynamic monomer/dimer equilibrium of this receptor in artificial lipid bilayers. Here we report on the oligomerization state of human NTSR1 from living cells by trapping them into lipid nanodiscs. Briefly, SMALPs (styrene-maleic acid copolymer lipid nanoparticles) were produced directly from the plasma membranes of living HEK293T FlpIn cells. SMALPs with a diameter of 15 nm were soluble and stable. NTSR1 in SMALPs were analyzed by single-molecule intensity measurements one membrane patch at a time using a custom-built confocal anti-Brownian electrokinetic trap (ABEL trap) microscope. We found oligomerization changes before and after stimulation of the receptor with its ligand neurotensin. |
1207.4436 | Daniel Soudry | Daniel Soudry and Ron Meir | An exact reduction of the master equation to a strictly stable system
with an explicit expression for the stationary distribution | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evolution of a continuous time Markov process with a finite number of
states is usually calculated by the Master equation - a linear differential
equations with a singular generator matrix. We derive a general method for
reducing the dimensionality of the Master equation by one by using the
probability normalization constraint, thus obtaining a affine differential
equation with a (non-singular) stable generator matrix. Additionally, the
reduced form yields a simple explicit expression for the stationary probability
distribution, which is usually derived implicitly. Finally, we discuss the
application of this method to stochastic differential equations.
| [
{
"created": "Wed, 18 Jul 2012 18:37:08 GMT",
"version": "v1"
}
] | 2012-07-19 | [
[
"Soudry",
"Daniel",
""
],
[
"Meir",
"Ron",
""
]
] | The evolution of a continuous time Markov process with a finite number of states is usually calculated by the Master equation - a linear differential equations with a singular generator matrix. We derive a general method for reducing the dimensionality of the Master equation by one by using the probability normalization constraint, thus obtaining a affine differential equation with a (non-singular) stable generator matrix. Additionally, the reduced form yields a simple explicit expression for the stationary probability distribution, which is usually derived implicitly. Finally, we discuss the application of this method to stochastic differential equations. |
1402.1781 | Daniel Schrider | Daniel R. Schrider and Andrew D. Kern | Discovering functional DNA elements using population genomic
information: A proof of concept using human mtDNA | null | null | 10.1093/gbe/evu116 | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Identifying the complete set of functional elements within the human genome
would be a windfall for multiple areas of biological research including
medicine, molecular biology, and evolution. Complete knowledge of function
would aid in the prioritization of loci when searching for the genetic bases of
disease or adaptive phenotypes. Because mutations that disrupt function are
disfavored by natural selection, purifying selection leaves a detectable
signature within functional elements; accordingly this signal has been
exploited through the use of genomic comparisons of distantly related species.
However, the functional complement of the genome changes extensively across
time and between lineages, therefore, evidence of the current action of
purifying selection is essential. Because the removal of deleterious mutations
by natural selection also reduces within-species genetic diversity within
functional loci, dense population genetic data have the potential to reveal
genomic elements that are currently functional. Here we assess the potential of
this approach using 16,411 human mitochondrial genomes. We show that the high
density of polymorphism in this dataset precisely delineates regions
experiencing purifying selection. Further, we show that the number of
segregating alleles at a site is strongly correlated with its divergence across
species after accounting for known mutational biases in human mtDNA. These two
measures track one another at a remarkably fine scale across many loci--a
correlation that is purely the result of natural selection. Our results
demonstrate that genetic variation has the potential to reveal exactly which
nucleotides in the genome are currently performing important functions and
likely to have deleterious fitness effects when mutated. As more complete
genomes are sequenced, similar power to reveal purifying selection may be
achievable in the human nuclear genome.
| [
{
"created": "Fri, 7 Feb 2014 21:56:43 GMT",
"version": "v1"
}
] | 2015-11-24 | [
[
"Schrider",
"Daniel R.",
""
],
[
"Kern",
"Andrew D.",
""
]
] | Identifying the complete set of functional elements within the human genome would be a windfall for multiple areas of biological research including medicine, molecular biology, and evolution. Complete knowledge of function would aid in the prioritization of loci when searching for the genetic bases of disease or adaptive phenotypes. Because mutations that disrupt function are disfavored by natural selection, purifying selection leaves a detectable signature within functional elements; accordingly this signal has been exploited through the use of genomic comparisons of distantly related species. However, the functional complement of the genome changes extensively across time and between lineages, therefore, evidence of the current action of purifying selection is essential. Because the removal of deleterious mutations by natural selection also reduces within-species genetic diversity within functional loci, dense population genetic data have the potential to reveal genomic elements that are currently functional. Here we assess the potential of this approach using 16,411 human mitochondrial genomes. We show that the high density of polymorphism in this dataset precisely delineates regions experiencing purifying selection. Further, we show that the number of segregating alleles at a site is strongly correlated with its divergence across species after accounting for known mutational biases in human mtDNA. These two measures track one another at a remarkably fine scale across many loci--a correlation that is purely the result of natural selection. Our results demonstrate that genetic variation has the potential to reveal exactly which nucleotides in the genome are currently performing important functions and likely to have deleterious fitness effects when mutated. As more complete genomes are sequenced, similar power to reveal purifying selection may be achievable in the human nuclear genome. |
0810.0868 | Mike Steel Prof. | David Bryant and Mike Steel | Computing the Distribution of a Tree Metric | 16 pages, 3 figures | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Robinson-Foulds (RF) distance is by far the most widely used measure of
dissimilarity between trees. Although the distribution of these distances has
been investigated for twenty years, an algorithm that is explicitly polynomial
time has yet to be described for computing this distribution (which is also the
distribution of trees around a given tree under the popular Robinson-Foulds
metric). In this paper we derive a polynomial-time algorithm for this
distribution. We show how the distribution can be approximated by a Poisson
distribution determined by the proportion of leaves that lie in `cherries' of
the given tree. We also describe how our results can be used to derive
normalization constants that are required in a recently-proposed maximum
likelihood approach to supertree construction.
| [
{
"created": "Mon, 6 Oct 2008 02:21:41 GMT",
"version": "v1"
}
] | 2008-10-07 | [
[
"Bryant",
"David",
""
],
[
"Steel",
"Mike",
""
]
] | The Robinson-Foulds (RF) distance is by far the most widely used measure of dissimilarity between trees. Although the distribution of these distances has been investigated for twenty years, an algorithm that is explicitly polynomial time has yet to be described for computing this distribution (which is also the distribution of trees around a given tree under the popular Robinson-Foulds metric). In this paper we derive a polynomial-time algorithm for this distribution. We show how the distribution can be approximated by a Poisson distribution determined by the proportion of leaves that lie in `cherries' of the given tree. We also describe how our results can be used to derive normalization constants that are required in a recently-proposed maximum likelihood approach to supertree construction. |
2207.02629 | Louxin Zhang | Yufeng Wu and Louxin Zhang | Can Multiple Phylogenetic Trees Be Displayed in a Tree-Child Network
Simultaneously? | 17 pages, 7 figures | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | A binary phylogenetic network on a taxon set $X$ is a rooted acyclic digraph
in which the degree of each nonleaf node is three and its leaves
(i.e.degree-one nodes) are uniquely labeled with the taxa of $X$. It is
tree-child if each nonleaf node has at least one child of indegree one. A set
of binary phylogenetic trees may or may not be simultaneously displayed in a
binary tree-child network. Necessary conditions for multiple phylogenetic trees
being simultaneously displayed in a tree-child network are given here. In
particular, it is proved that any two phylogenetic trees can always
simultaneously be displayed in some tree-child network on the same taxa set. It
is also proved that any set of multiple binary phylogenetic trees can always
simultaneously be displayed in some non-binary tree-child network on the same
taxa set, where each nonleaf node is of either indegree one and outdegree two
or indegree at least two and outdegree out.
| [
{
"created": "Wed, 6 Jul 2022 12:40:57 GMT",
"version": "v1"
}
] | 2022-07-07 | [
[
"Wu",
"Yufeng",
""
],
[
"Zhang",
"Louxin",
""
]
] | A binary phylogenetic network on a taxon set $X$ is a rooted acyclic digraph in which the degree of each nonleaf node is three and its leaves (i.e.degree-one nodes) are uniquely labeled with the taxa of $X$. It is tree-child if each nonleaf node has at least one child of indegree one. A set of binary phylogenetic trees may or may not be simultaneously displayed in a binary tree-child network. Necessary conditions for multiple phylogenetic trees being simultaneously displayed in a tree-child network are given here. In particular, it is proved that any two phylogenetic trees can always simultaneously be displayed in some tree-child network on the same taxa set. It is also proved that any set of multiple binary phylogenetic trees can always simultaneously be displayed in some non-binary tree-child network on the same taxa set, where each nonleaf node is of either indegree one and outdegree two or indegree at least two and outdegree out. |
2211.08084 | Meng Huang | Meng Huang, Jiangtao Ma, Changzhou Long, Junpeng Zhang, Xiucai Ye,
Tetsuya Sakurai | Inferring cell-specific lncRNA regulation with single-cell
RNA-sequencing data in the developing human neocortex | null | null | null | null | q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | Long non-coding RNAs (lncRNAs) are important regulators to modulate gene
expression and cell proliferation in the developing human brain. Previous
methods mainly use bulk lncRNA and mRNA expression data to study lncRNA
regulation. However, to analyze lncRNA regulation regarding individual cells,
we focus on single-cell RNA-sequencing (scRNA-seq) data instead of bulk data.
Recent advance in scRNA-seq has provided a way to investigate lncRNA regulation
at single-cell level. We will propose a novel computational method, CSlncR
(cell-specific lncRNA regulation), which combines putative lncRNA-mRNA binding
information with scRNA-seq data including lncRNAs and mRNAs to identify
cell-specific lncRNA-mRNA regulation networks at individual cells. To
understand lncRNA regulation at different development stages, we apply CSlncR
to the scRNA-seq data of human neocortex. Network analysis shows that the
lncRNA regulation is unique in each cell from the different human neocortex
development stages. The comparison results indicate that CSlncR is also an
effective tool for predicting cell-specific lncRNA targets and clustering
single cells, which helps understand cell-cell communication.
| [
{
"created": "Tue, 15 Nov 2022 12:11:18 GMT",
"version": "v1"
},
{
"created": "Tue, 22 Nov 2022 06:08:45 GMT",
"version": "v2"
},
{
"created": "Wed, 30 Nov 2022 03:34:32 GMT",
"version": "v3"
}
] | 2022-12-01 | [
[
"Huang",
"Meng",
""
],
[
"Ma",
"Jiangtao",
""
],
[
"Long",
"Changzhou",
""
],
[
"Zhang",
"Junpeng",
""
],
[
"Ye",
"Xiucai",
""
],
[
"Sakurai",
"Tetsuya",
""
]
] | Long non-coding RNAs (lncRNAs) are important regulators to modulate gene expression and cell proliferation in the developing human brain. Previous methods mainly use bulk lncRNA and mRNA expression data to study lncRNA regulation. However, to analyze lncRNA regulation regarding individual cells, we focus on single-cell RNA-sequencing (scRNA-seq) data instead of bulk data. Recent advance in scRNA-seq has provided a way to investigate lncRNA regulation at single-cell level. We will propose a novel computational method, CSlncR (cell-specific lncRNA regulation), which combines putative lncRNA-mRNA binding information with scRNA-seq data including lncRNAs and mRNAs to identify cell-specific lncRNA-mRNA regulation networks at individual cells. To understand lncRNA regulation at different development stages, we apply CSlncR to the scRNA-seq data of human neocortex. Network analysis shows that the lncRNA regulation is unique in each cell from the different human neocortex development stages. The comparison results indicate that CSlncR is also an effective tool for predicting cell-specific lncRNA targets and clustering single cells, which helps understand cell-cell communication. |
q-bio/0611006 | Elena Diaz Almela | Elena Diaz-Almela, Nuria Marba, Elvira Alvarez, Rocio Santiago,
Marianne Holmer, Antoni Grau, Roberto Danovaro, Marina Argyrou, Ioannis
Karakasis, Carlos Manuel Duarte | Benthic inputs as predictors of seagrass (Posidonia oceanica) fish
farm-induced decline | submitted to Biological Conservation Journal (Ed. Elsevier), 36 pages | null | null | null | q-bio.QM q-bio.PE | null | Fish farms represent a growing source of disturbance to shallow benthic
ecosystems like seagrass meadows. Despite some existing insights on the
mechanisms underlying decline, efficient tools to quantitatively predict the
response of benthic communities to fish farm effluents have not yet been
developed. We explored relationships of fish farm organic and nutrient input
rates to the sediments with population dynamics of the key seagrass species
(Posidonia oceanica) in deep meadows growing around four Mediterranean Sea
bream and Sea bass fish farms. We performed 2 annual shoot censuses on
permanent plots at increasing distance from cages. Before each census we
measured sedimentation rates adjacent to the plots using benthic sediment
traps. High shoot mortality rates were recorded near the cages, up to 20 times
greater than at control sites. Recruitment rates remained similar to
undisturbed meadows and could not compensate mortality, leading to rapid
seagrass decline within the first 100 meters from cages. Seagrass mortality
increased with total (R2= 0.47, p< 0.0002), organic matter (R2= 0.36, p=
0.001), nitrogen (R2= 0.34, p= 0.002) and phosphorus (R2= 0.58, p< 3 x 10-5)
sedimentation rates. P. oceanica decline accelerated above a phosphorus loading
threshold of 50 mg m-2 day-1. Benthic sedimentation rates seem a powerful
predictor of seagrass mortality from fish farming, integrating local
hydrodynamics, waste effluents variability and several environmental
mechanisms, fuelled by organic inputs and leading to seagrass loss. Coupling
direct measurements of benthic sedimentation rates with dynamics of key species
is proposed as an efficient way to predict and minimize fish farm impacts to
benthic communities.
| [
{
"created": "Thu, 2 Nov 2006 16:21:10 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Diaz-Almela",
"Elena",
""
],
[
"Marba",
"Nuria",
""
],
[
"Alvarez",
"Elvira",
""
],
[
"Santiago",
"Rocio",
""
],
[
"Holmer",
"Marianne",
""
],
[
"Grau",
"Antoni",
""
],
[
"Danovaro",
"Roberto",
""
],
[
"Argyrou",
"Marina",
""
],
[
"Karakasis",
"Ioannis",
""
],
[
"Duarte",
"Carlos Manuel",
""
]
] | Fish farms represent a growing source of disturbance to shallow benthic ecosystems like seagrass meadows. Despite some existing insights on the mechanisms underlying decline, efficient tools to quantitatively predict the response of benthic communities to fish farm effluents have not yet been developed. We explored relationships of fish farm organic and nutrient input rates to the sediments with population dynamics of the key seagrass species (Posidonia oceanica) in deep meadows growing around four Mediterranean Sea bream and Sea bass fish farms. We performed 2 annual shoot censuses on permanent plots at increasing distance from cages. Before each census we measured sedimentation rates adjacent to the plots using benthic sediment traps. High shoot mortality rates were recorded near the cages, up to 20 times greater than at control sites. Recruitment rates remained similar to undisturbed meadows and could not compensate mortality, leading to rapid seagrass decline within the first 100 meters from cages. Seagrass mortality increased with total (R2= 0.47, p< 0.0002), organic matter (R2= 0.36, p= 0.001), nitrogen (R2= 0.34, p= 0.002) and phosphorus (R2= 0.58, p< 3 x 10-5) sedimentation rates. P. oceanica decline accelerated above a phosphorus loading threshold of 50 mg m-2 day-1. Benthic sedimentation rates seem a powerful predictor of seagrass mortality from fish farming, integrating local hydrodynamics, waste effluents variability and several environmental mechanisms, fuelled by organic inputs and leading to seagrass loss. Coupling direct measurements of benthic sedimentation rates with dynamics of key species is proposed as an efficient way to predict and minimize fish farm impacts to benthic communities. |
2311.03654 | Maryam Golchin | Maryam Golchin, Moreno Di Marco, Paul Horwood, Dean Paini, Andrew
Hoskins, R.I. Hickson | Prediction of viral spillover risk based on the mass action principle | null | null | null | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | Infectious zoonotic disease emergence, through spillover events, is of global
concern and has the potential to cause significant harm to society, as recently
demonstrated by COVID-19. More than 70% of the 400 infectious diseases that
emerged in the past five decades have a zoonotic origin, including all recent
pandemics. There have been several approaches used to predict the risk of
spillover through some of the known or suspected infectious disease emergence
drivers, largely using correlative approaches. Here, we predict the spatial
distribution of spillover risk by approximating general transmission through
animal and human interactions. These mass action interactions are approximated
through the multiplication of the spatial distribution of zoonotic viral
diversity and human population density. Although our results indicate higher
risk in regions along the equator and in Southeast Asia where both viral
diversity and human population density are high, it should be noted that this
is primarily a conceptual exercise. We compared our spillover risk map to key
factors, including the model inputs of zoonotic viral diversity estimate map,
human population density map, and the spatial distribution of species richness.
Despite the limitations of this approach, this viral spillover map is a step
towards developing a more comprehensive spillover risk prediction system to
inform global monitoring.
| [
{
"created": "Tue, 7 Nov 2023 01:48:49 GMT",
"version": "v1"
}
] | 2023-11-08 | [
[
"Golchin",
"Maryam",
""
],
[
"Di Marco",
"Moreno",
""
],
[
"Horwood",
"Paul",
""
],
[
"Paini",
"Dean",
""
],
[
"Hoskins",
"Andrew",
""
],
[
"Hickson",
"R. I.",
""
]
] | Infectious zoonotic disease emergence, through spillover events, is of global concern and has the potential to cause significant harm to society, as recently demonstrated by COVID-19. More than 70% of the 400 infectious diseases that emerged in the past five decades have a zoonotic origin, including all recent pandemics. There have been several approaches used to predict the risk of spillover through some of the known or suspected infectious disease emergence drivers, largely using correlative approaches. Here, we predict the spatial distribution of spillover risk by approximating general transmission through animal and human interactions. These mass action interactions are approximated through the multiplication of the spatial distribution of zoonotic viral diversity and human population density. Although our results indicate higher risk in regions along the equator and in Southeast Asia where both viral diversity and human population density are high, it should be noted that this is primarily a conceptual exercise. We compared our spillover risk map to key factors, including the model inputs of zoonotic viral diversity estimate map, human population density map, and the spatial distribution of species richness. Despite the limitations of this approach, this viral spillover map is a step towards developing a more comprehensive spillover risk prediction system to inform global monitoring. |
1511.03011 | Ranjan Mannige | Ranjan V. Mannige and Joyjit Kundu and Stephen Whitelam | The Ramachandran number: an order parameter for protein geometry | null | null | 10.1371/journal.pone.0160023 | null | q-bio.BM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Three-dimensional protein structures usually contain regions of local order,
called secondary structure, such as $\alpha$-helices and $\beta$-sheets.
Secondary structure is characterized by the local rotational state of the
protein backbone, quantified by two dihedral angles called $\phi$ and $\psi$.
Particular types of secondary structure can generally be described by a single
(diffuse) location on a two-dimensional plot drawn in the space of the angles
$\phi$ and $\psi$, called a Ramachandran plot. By contrast, a
recently-discovered nanomaterial made from peptoids, structural isomers of
peptides, displays a secondary-structure motif corresponding to two regions on
the Ramachandran plot [Mannige et al., Nature 526, 415 (2015)]. In order to
describe such `higher-order' secondary structure in a compact way we introduce
here a means of describing regions on the Ramachandran plot in terms of a
single Ramachandran number, ${\mathcal{R}}$, which is a structurally meaningful
combination of $\phi$ and $\psi$. We show that the potential applications of
${\mathcal{R}}$ are numerous: it can be used to describe the geometric content
of protein structures, and can be used to draw diagrams that reveal, at a
glance, the frequency of occurrence of regular secondary structures and
disordered regions in large protein datasets. We propose that ${\mathcal{R}}$
might be used as an order parameter for protein geometry for a wide range of
applications.
| [
{
"created": "Tue, 10 Nov 2015 08:10:51 GMT",
"version": "v1"
},
{
"created": "Tue, 24 Nov 2015 22:03:55 GMT",
"version": "v2"
}
] | 2016-08-10 | [
[
"Mannige",
"Ranjan V.",
""
],
[
"Kundu",
"Joyjit",
""
],
[
"Whitelam",
"Stephen",
""
]
] | Three-dimensional protein structures usually contain regions of local order, called secondary structure, such as $\alpha$-helices and $\beta$-sheets. Secondary structure is characterized by the local rotational state of the protein backbone, quantified by two dihedral angles called $\phi$ and $\psi$. Particular types of secondary structure can generally be described by a single (diffuse) location on a two-dimensional plot drawn in the space of the angles $\phi$ and $\psi$, called a Ramachandran plot. By contrast, a recently-discovered nanomaterial made from peptoids, structural isomers of peptides, displays a secondary-structure motif corresponding to two regions on the Ramachandran plot [Mannige et al., Nature 526, 415 (2015)]. In order to describe such `higher-order' secondary structure in a compact way we introduce here a means of describing regions on the Ramachandran plot in terms of a single Ramachandran number, ${\mathcal{R}}$, which is a structurally meaningful combination of $\phi$ and $\psi$. We show that the potential applications of ${\mathcal{R}}$ are numerous: it can be used to describe the geometric content of protein structures, and can be used to draw diagrams that reveal, at a glance, the frequency of occurrence of regular secondary structures and disordered regions in large protein datasets. We propose that ${\mathcal{R}}$ might be used as an order parameter for protein geometry for a wide range of applications. |
1907.12082 | Sergei Koniakhin | Sergei V. Koniakhin | Effects of resource competition on evolution and adaptive radiation | 12 pages, 10 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The entanglement of population dynamics, evolution, and adaptive radiation
for species competing for resources is studied. For resource harvesting, we
modify the model used in Ref. Phys. Rev. Lett. 118 048103 and introduce new
resource contest principles. We realistically implement the effects of
beneficial and deleterious mutations on the coefficients in the equations
governing the population dynamics and consider the emergence of reproductive
isolation. The proposed model is shown to be in agreement with the competitive
exclusion principle and no vacant niche principle. We establish a mechanism
that contributes to preventing the accumulation of irreversible deleterious
mutations: competition between recently diverged species/subpopulations. The
proposed model is applicable for descriptions of more complex systems. In case
of many constants in time resources, one observes very rapid specialization, a
feature not reproducible by the common model.
| [
{
"created": "Sun, 28 Jul 2019 13:58:43 GMT",
"version": "v1"
},
{
"created": "Sat, 30 Sep 2023 09:33:13 GMT",
"version": "v2"
}
] | 2023-10-03 | [
[
"Koniakhin",
"Sergei V.",
""
]
] | The entanglement of population dynamics, evolution, and adaptive radiation for species competing for resources is studied. For resource harvesting, we modify the model used in Ref. Phys. Rev. Lett. 118 048103 and introduce new resource contest principles. We realistically implement the effects of beneficial and deleterious mutations on the coefficients in the equations governing the population dynamics and consider the emergence of reproductive isolation. The proposed model is shown to be in agreement with the competitive exclusion principle and no vacant niche principle. We establish a mechanism that contributes to preventing the accumulation of irreversible deleterious mutations: competition between recently diverged species/subpopulations. The proposed model is applicable for descriptions of more complex systems. In case of many constants in time resources, one observes very rapid specialization, a feature not reproducible by the common model. |
1506.01752 | Mikhail Tikhonov | Mikhail Tikhonov | Community-level cohesion without cooperation | 9 pages, 5 figures + supplementary material. Revised and updated | eLife 2016;5:e15747 | 10.7554/eLife.15747 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work draws attention to community-community encounters ("coalescence")
as likely an important factor shaping natural ecosystems. This work builds on
MacArthur's classic model of competitive coexistence to investigate such
community-level competition in a minimal theoretical setting. It is shown that
the ability of a species to survive a coalescence event is best predicted by a
community-level "fitness" of its native community rather than the intrinsic
performance of the species itself. The model presented here allows formalizing
a macroscopic perspective whereby a community harboring organisms at varying
abundances becomes equivalent to a single organism expressing genes at
different levels. While most natural communities do not satisfy the strict
criteria of multicellularity developed by multi-level selection theory, the
effective cohesion described here is a generic consequence of division of
labor, requires no cooperative interactions, and can be expected to be
widespread in microbial ecosystems.
| [
{
"created": "Thu, 4 Jun 2015 23:52:49 GMT",
"version": "v1"
},
{
"created": "Fri, 20 Nov 2015 00:03:09 GMT",
"version": "v2"
},
{
"created": "Thu, 10 Mar 2016 22:12:01 GMT",
"version": "v3"
}
] | 2016-11-28 | [
[
"Tikhonov",
"Mikhail",
""
]
] | Recent work draws attention to community-community encounters ("coalescence") as likely an important factor shaping natural ecosystems. This work builds on MacArthur's classic model of competitive coexistence to investigate such community-level competition in a minimal theoretical setting. It is shown that the ability of a species to survive a coalescence event is best predicted by a community-level "fitness" of its native community rather than the intrinsic performance of the species itself. The model presented here allows formalizing a macroscopic perspective whereby a community harboring organisms at varying abundances becomes equivalent to a single organism expressing genes at different levels. While most natural communities do not satisfy the strict criteria of multicellularity developed by multi-level selection theory, the effective cohesion described here is a generic consequence of division of labor, requires no cooperative interactions, and can be expected to be widespread in microbial ecosystems. |
2403.06496 | Arkady Zgonnikov | Floor Bontje, Arkady Zgonnikov | Are you sure? Modelling Drivers' Confidence Judgments in Left-Turn Gap
Acceptance Decisions | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When a person makes a decision, it is automatically accompanied by a
subjective probability judgment of the decision being correct, in other words,
a confidence judgment. A better understanding of the mechanisms responsible for
these confidence judgments could provide novel insights into human behavior.
However, so far confidence judgments have been mostly studied in simplistic
laboratory tasks while little is known about confidence in naturalistic dynamic
tasks such as driving. In this study, we made a first attempt of connecting
fundamental research on confidence with naturalistic driver behavior. We
investigated the confidence of drivers in left-turn gap acceptance decisions in
a driver simulator experiment (N=17). We found that confidence in these
decisions depends on the size of the gap to the oncoming vehicle. Specifically,
confidence increased with the gap size for trials in which the gap was
accepted, and decreased with the gap size for rejected gaps. Similarly to more
basic tasks, confidence was negatively related to the response times and
correlated with action dynamics during decision execution. Finally, we found
that confidence judgments can be captured with an extended dynamic
drift-diffusion model. In the model, the drift rate of the evidence accumulator
as well as the decision boundaries are functions of the gap size. Furthermore,
we demonstrated that allowing for post-decision evidence accumulation in the
model increases its ability to describe confidence judgments in rejected gap
decisions. Overall, our study confirmed that principles known from fundamental
confidence research extend to confidence judgments in dynamic decisions during
a naturalistic task.
| [
{
"created": "Mon, 11 Mar 2024 08:09:13 GMT",
"version": "v1"
},
{
"created": "Sat, 16 Mar 2024 07:33:55 GMT",
"version": "v2"
}
] | 2024-03-19 | [
[
"Bontje",
"Floor",
""
],
[
"Zgonnikov",
"Arkady",
""
]
] | When a person makes a decision, it is automatically accompanied by a subjective probability judgment of the decision being correct, in other words, a confidence judgment. A better understanding of the mechanisms responsible for these confidence judgments could provide novel insights into human behavior. However, so far confidence judgments have been mostly studied in simplistic laboratory tasks while little is known about confidence in naturalistic dynamic tasks such as driving. In this study, we made a first attempt of connecting fundamental research on confidence with naturalistic driver behavior. We investigated the confidence of drivers in left-turn gap acceptance decisions in a driver simulator experiment (N=17). We found that confidence in these decisions depends on the size of the gap to the oncoming vehicle. Specifically, confidence increased with the gap size for trials in which the gap was accepted, and decreased with the gap size for rejected gaps. Similarly to more basic tasks, confidence was negatively related to the response times and correlated with action dynamics during decision execution. Finally, we found that confidence judgments can be captured with an extended dynamic drift-diffusion model. In the model, the drift rate of the evidence accumulator as well as the decision boundaries are functions of the gap size. Furthermore, we demonstrated that allowing for post-decision evidence accumulation in the model increases its ability to describe confidence judgments in rejected gap decisions. Overall, our study confirmed that principles known from fundamental confidence research extend to confidence judgments in dynamic decisions during a naturalistic task. |
0709.3931 | Laurent Jacob | Laurent Jacob (CB), Jean-Philippe Vert (CB) | Kernel methods for in silico chemogenomics | null | null | null | null | q-bio.QM | null | Predicting interactions between small molecules and proteins is a crucial
ingredient of the drug discovery process. In particular, accurate predictive
models are increasingly used to preselect potential lead compounds from large
molecule databases, or to screen for side-effects. While classical in silico
approaches focus on predicting interactions with a given specific target, new
chemogenomics approaches adopt cross-target views. Building on recent
developments in the use of kernel methods in bio- and chemoinformatics, we
present a systematic framework to screen the chemical space of small molecules
for interaction with the biological space of proteins. We show that this
framework allows information sharing across the targets, resulting in a
dramatic improvement of ligand prediction accuracy for three important classes
of drug targets: enzymes, GPCR and ion channels.
| [
{
"created": "Tue, 25 Sep 2007 12:09:08 GMT",
"version": "v1"
}
] | 2007-09-26 | [
[
"Jacob",
"Laurent",
"",
"CB"
],
[
"Vert",
"Jean-Philippe",
"",
"CB"
]
] | Predicting interactions between small molecules and proteins is a crucial ingredient of the drug discovery process. In particular, accurate predictive models are increasingly used to preselect potential lead compounds from large molecule databases, or to screen for side-effects. While classical in silico approaches focus on predicting interactions with a given specific target, new chemogenomics approaches adopt cross-target views. Building on recent developments in the use of kernel methods in bio- and chemoinformatics, we present a systematic framework to screen the chemical space of small molecules for interaction with the biological space of proteins. We show that this framework allows information sharing across the targets, resulting in a dramatic improvement of ligand prediction accuracy for three important classes of drug targets: enzymes, GPCR and ion channels. |
2205.01858 | Xiaofan Jia | Xiaofan Jia, Sadeed Bin Sayed, Nahian Ibn Hasan, Luis J. Gomez,
Guang-Bin Huang, and Abdulkadir C. Yucel | DeeptDCS: Deep Learning-Based Estimation of Currents Induced During
Transcranial Direct Current Stimulation | null | null | null | null | q-bio.QM cs.LG eess.SP | http://creativecommons.org/licenses/by/4.0/ | Objective: Transcranial direct current stimulation (tDCS) is a non-invasive
brain stimulation technique used to generate conduction currents in the head
and disrupt brain functions. To rapidly evaluate the tDCS-induced current
density in near real-time, this paper proposes a deep learning-based emulator,
named DeeptDCS. Methods: The emulator leverages Attention U-net taking the
volume conductor models (VCMs) of head tissues as inputs and outputting the
three-dimensional current density distribution across the entire head. The
electrode configurations are also incorporated into VCMs without increasing the
number of input channels; this enables the straightforward incorporation of the
non-parametric features of electrodes (e.g., thickness, shape, size, and
position) in the training and testing of the proposed emulator. Results:
Attention U-net outperforms standard U-net and its other three variants
(Residual U-net, Attention Residual U-net, and Multi-scale Residual U-net) in
terms of accuracy. The generalization ability of DeeptDCS to non-trained
electrode configurations can be greatly enhanced through fine-tuning the model.
The computational time required by one emulation via DeeptDCS is a fraction of
a second. Conclusion: DeeptDCS is at least two orders of magnitudes faster than
a physics-based open-source simulator, while providing satisfactorily accurate
results. Significance: The high computational efficiency permits the use of
DeeptDCS in applications requiring its repetitive execution, such as
uncertainty quantification and optimization studies of tDCS.
| [
{
"created": "Wed, 4 May 2022 02:25:39 GMT",
"version": "v1"
},
{
"created": "Thu, 6 Oct 2022 04:22:58 GMT",
"version": "v2"
}
] | 2022-10-07 | [
[
"Jia",
"Xiaofan",
""
],
[
"Sayed",
"Sadeed Bin",
""
],
[
"Hasan",
"Nahian Ibn",
""
],
[
"Gomez",
"Luis J.",
""
],
[
"Huang",
"Guang-Bin",
""
],
[
"Yucel",
"Abdulkadir C.",
""
]
] | Objective: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique used to generate conduction currents in the head and disrupt brain functions. To rapidly evaluate the tDCS-induced current density in near real-time, this paper proposes a deep learning-based emulator, named DeeptDCS. Methods: The emulator leverages Attention U-net taking the volume conductor models (VCMs) of head tissues as inputs and outputting the three-dimensional current density distribution across the entire head. The electrode configurations are also incorporated into VCMs without increasing the number of input channels; this enables the straightforward incorporation of the non-parametric features of electrodes (e.g., thickness, shape, size, and position) in the training and testing of the proposed emulator. Results: Attention U-net outperforms standard U-net and its other three variants (Residual U-net, Attention Residual U-net, and Multi-scale Residual U-net) in terms of accuracy. The generalization ability of DeeptDCS to non-trained electrode configurations can be greatly enhanced through fine-tuning the model. The computational time required by one emulation via DeeptDCS is a fraction of a second. Conclusion: DeeptDCS is at least two orders of magnitudes faster than a physics-based open-source simulator, while providing satisfactorily accurate results. Significance: The high computational efficiency permits the use of DeeptDCS in applications requiring its repetitive execution, such as uncertainty quantification and optimization studies of tDCS. |
1107.4499 | Iain Johnston | Iain G. Johnston, Bernadett Gaal, Ricardo Pires das Neves, Tariq
Enver, Francisco J. Iborra and Nick S. Jones | Mitochondrial Variability as a Source of Extrinsic Cellular Noise | null | null | 10.1371/journal.pcbi.1002416 | null | q-bio.CB physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a study investigating the role of mitochondrial variability in
generating noise in eukaryotic cells. Noise in cellular physiology plays an
important role in many fundamental cellular processes, including transcription,
translation, stem cell differentiation and response to medication, but the
specific random influences that affect these processes have yet to be clearly
elucidated. Here we present a mechanism by which variability in mitochondrial
volume and functionality, along with cell cycle dynamics, is linked to
variability in transcription rate and hence has a profound effect on downstream
cellular processes. Our model mechanism is supported by an appreciable volume
of recent experimental evidence, and we present the results of several new
experiments with which our model is also consistent. We find that noise due to
mitochondrial variability can sometimes dominate over other extrinsic noise
sources (such as cell cycle asynchronicity) and can significantly affect
large-scale observable properties such as cell cycle length and gene expression
levels. We also explore two recent regulatory network-based models for stem
cell differentiation, and find that extrinsic noise in transcription rate
causes appreciable variability in the behaviour of these model systems. These
results suggest that mitochondrial and transcriptional variability may be an
important mechanism influencing a large variety of cellular processes and
properties.
| [
{
"created": "Fri, 22 Jul 2011 12:40:50 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Dec 2011 13:46:11 GMT",
"version": "v2"
}
] | 2015-05-28 | [
[
"Johnston",
"Iain G.",
""
],
[
"Gaal",
"Bernadett",
""
],
[
"Neves",
"Ricardo Pires das",
""
],
[
"Enver",
"Tariq",
""
],
[
"Iborra",
"Francisco J.",
""
],
[
"Jones",
"Nick S.",
""
]
] | We present a study investigating the role of mitochondrial variability in generating noise in eukaryotic cells. Noise in cellular physiology plays an important role in many fundamental cellular processes, including transcription, translation, stem cell differentiation and response to medication, but the specific random influences that affect these processes have yet to be clearly elucidated. Here we present a mechanism by which variability in mitochondrial volume and functionality, along with cell cycle dynamics, is linked to variability in transcription rate and hence has a profound effect on downstream cellular processes. Our model mechanism is supported by an appreciable volume of recent experimental evidence, and we present the results of several new experiments with which our model is also consistent. We find that noise due to mitochondrial variability can sometimes dominate over other extrinsic noise sources (such as cell cycle asynchronicity) and can significantly affect large-scale observable properties such as cell cycle length and gene expression levels. We also explore two recent regulatory network-based models for stem cell differentiation, and find that extrinsic noise in transcription rate causes appreciable variability in the behaviour of these model systems. These results suggest that mitochondrial and transcriptional variability may be an important mechanism influencing a large variety of cellular processes and properties. |
1312.2411 | Timur Sadykov | Timur Sadykov | Human blood genotypes dynamics | 7 pages, 1 table | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We give a complete closed form description of the evolution of human blood
genotypes frequencies (in the ABO and Rh classification) after any (finite or
infinite) number of generations and for any initial distribution.
| [
{
"created": "Mon, 9 Dec 2013 12:39:48 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Nov 2014 15:01:51 GMT",
"version": "v2"
}
] | 2014-12-01 | [
[
"Sadykov",
"Timur",
""
]
] | We give a complete closed form description of the evolution of human blood genotypes frequencies (in the ABO and Rh classification) after any (finite or infinite) number of generations and for any initial distribution. |
2002.04136 | Martin Frasch | Aude Castel, Patrick Burns, Colin Wakefield, Keven. J. Jean, Yael S.
Frank, Mingju Cao, Andre Desrochers, Gilles Fecteau, Christophe Faure,
Christophe L. Herry, Martin G. Frasch | The neonatal sepsis is diminished by cervical vagus nerve stimulation
and tracked non-invasively by ECG: a preliminary report in the piglet model | updated manuscript with VENG analyses | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | An electrocardiogram (ECG)-derived heart rate variability (HRV) index
reliably tracks the inflammatory response induced by low-dose
lipopolysaccharide (LPS) in near-term sheep fetuses. We evaluated the effect of
vagus nerve stimulation (VNS) on vagus nerve electroneurogram (VENG) and the
systemic inflammatory response induced by a high dose of LPS in neonatal
piglets to mimic late-onset neonatal sepsis. We tested if our HRV inflammatory
index tracks inflammation in piglets and its relationship to VENG. Following
anesthesia, electrodes were attached to the left vagal nerve; ECG and blood
pressure (BP) were recorded throughout the experiment. Following baseline, the
piglets were administered LPS as 2mg/kg IV bolus. In the VNS treated piglet,
the vagus nerve was stimulated for 10 minutes prior to and 10 min after the
injection of LPS. In both groups, every 15 min post LPS, the arterial blood
sample was drawn for blood gas, metabolites, and inflammatory cytokines. At the
end of the experiment, the piglets were euthanized. BP and HRV measures were
calculated. The piglets developed a potent inflammatory response to the LPS
injection with TNF-alpha, IL-1beta, IL-6 and IL-8 peaking between 45 and 90 min
post-injection. VNS diminished the LPS-induced systemic inflammatory response
varying across the measured cytokines from two to ten-fold. The HRV index
tracked accurately the temporal profile of cytokines and VENG changes. This
novel model allows manipulating and tracking neonatal sepsis: The HRV
inflammatory index 1) applies across species pre- and postnatally and 2)
performs well at different degrees of sepsis (i.e., nanogram and milligram
doses of LPS); 3) the present VNS paradigm effectively suppresses LPS-induced
inflammation, even at high doses of LPS. The potential of early postnatal VNS
to counteract sepsis and of HRV monitoring to early detect and track it deserve
further study.
| [
{
"created": "Mon, 10 Feb 2020 23:43:19 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Dec 2022 18:54:08 GMT",
"version": "v2"
}
] | 2022-12-21 | [
[
"Castel",
"Aude",
""
],
[
"Burns",
"Patrick",
""
],
[
"Wakefield",
"Colin",
""
],
[
"Jean",
"Keven. J.",
""
],
[
"Frank",
"Yael S.",
""
],
[
"Cao",
"Mingju",
""
],
[
"Desrochers",
"Andre",
""
],
[
"Fecteau",
"Gilles",
""
],
[
"Faure",
"Christophe",
""
],
[
"Herry",
"Christophe L.",
""
],
[
"Frasch",
"Martin G.",
""
]
] | An electrocardiogram (ECG)-derived heart rate variability (HRV) index reliably tracks the inflammatory response induced by low-dose lipopolysaccharide (LPS) in near-term sheep fetuses. We evaluated the effect of vagus nerve stimulation (VNS) on vagus nerve electroneurogram (VENG) and the systemic inflammatory response induced by a high dose of LPS in neonatal piglets to mimic late-onset neonatal sepsis. We tested if our HRV inflammatory index tracks inflammation in piglets and its relationship to VENG. Following anesthesia, electrodes were attached to the left vagal nerve; ECG and blood pressure (BP) were recorded throughout the experiment. Following baseline, the piglets were administered LPS as 2mg/kg IV bolus. In the VNS treated piglet, the vagus nerve was stimulated for 10 minutes prior to and 10 min after the injection of LPS. In both groups, every 15 min post LPS, the arterial blood sample was drawn for blood gas, metabolites, and inflammatory cytokines. At the end of the experiment, the piglets were euthanized. BP and HRV measures were calculated. The piglets developed a potent inflammatory response to the LPS injection with TNF-alpha, IL-1beta, IL-6 and IL-8 peaking between 45 and 90 min post-injection. VNS diminished the LPS-induced systemic inflammatory response varying across the measured cytokines from two to ten-fold. The HRV index tracked accurately the temporal profile of cytokines and VENG changes. This novel model allows manipulating and tracking neonatal sepsis: The HRV inflammatory index 1) applies across species pre- and postnatally and 2) performs well at different degrees of sepsis (i.e., nanogram and milligram doses of LPS); 3) the present VNS paradigm effectively suppresses LPS-induced inflammation, even at high doses of LPS. The potential of early postnatal VNS to counteract sepsis and of HRV monitoring to early detect and track it deserve further study. |
0909.2682 | Wentian Li | Wentian Li, Jan Freudenberg | Two-Parameter Characterization of Chromosome-Scale Recombination Rate | null | Genome Research, 19:2300-2307 (2009) | 10.1101/gr.092676.109 | null | q-bio.GN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The genome-wide recombination rate ($RR$) of a species is often described by
one parameter, the ratio between total genetic map length ($G$) and physical
map length ($P$), measured in centiMorgans per Megabase (cM/Mb). The value of
this parameter varies greatly between species, but the cause for these
differences is not entirely clear. A constraining factor of overall $RR$ in a
species, which may cause increased $RR$ for smaller chromosomes, is the
requirement of at least one chiasma per chromosome (or chromosome-arm) per
meiosis. In the present study, we quantify the relative excess of recombination
events on smaller chromosomes by a linear regression model, which relates the
genetic length of chromosomes to their physical length. We find for several
species that the two-parameter regression, $G= G_0 + k \cdot P$ provides a
better characterization of the relationship between genetic and physical map
length than the one-parameter regression that runs through the origin. A
non-zero intercept ($G_0$) indicates a relative excess of recombination on
smaller chromosomes in a genome. Given $G_0$, the parameter $k$ predicts the
increase of genetic map length over the increase of physical map length. The
observed values of $G_0$ have a similar magnitude for diverse species, whereas
$k$ varies by two orders of magnitude. The implications of this strategy for
the genetic maps of human, mouse, rat, chicken, honeybee, worm and yeast are
discussed.
| [
{
"created": "Mon, 14 Sep 2009 22:26:43 GMT",
"version": "v1"
}
] | 2011-03-16 | [
[
"Li",
"Wentian",
""
],
[
"Freudenberg",
"Jan",
""
]
] | The genome-wide recombination rate ($RR$) of a species is often described by one parameter, the ratio between total genetic map length ($G$) and physical map length ($P$), measured in centiMorgans per Megabase (cM/Mb). The value of this parameter varies greatly between species, but the cause for these differences is not entirely clear. A constraining factor of overall $RR$ in a species, which may cause increased $RR$ for smaller chromosomes, is the requirement of at least one chiasma per chromosome (or chromosome-arm) per meiosis. In the present study, we quantify the relative excess of recombination events on smaller chromosomes by a linear regression model, which relates the genetic length of chromosomes to their physical length. We find for several species that the two-parameter regression, $G= G_0 + k \cdot P$ provides a better characterization of the relationship between genetic and physical map length than the one-parameter regression that runs through the origin. A non-zero intercept ($G_0$) indicates a relative excess of recombination on smaller chromosomes in a genome. Given $G_0$, the parameter $k$ predicts the increase of genetic map length over the increase of physical map length. The observed values of $G_0$ have a similar magnitude for diverse species, whereas $k$ varies by two orders of magnitude. The implications of this strategy for the genetic maps of human, mouse, rat, chicken, honeybee, worm and yeast are discussed. |
1511.06353 | Matjaz Perc | Xiaojie Chen, Tatsuya Sasaki, Matjaz Perc | Evolution of public cooperation in a monitored society with implicated
punishment and within-group enforcement | 9 two-column pages, 5 figures; accepted for publication in Scientific
Reports | Sci. Rep. 5 (2015) 17050 | 10.1038/srep17050 | null | q-bio.PE cs.GT physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Monitoring with implicated punishment is common in human societies to avert
freeriding on common goods. But is it effective in promoting public
cooperation? We show that the introduction of monitoring and implicated
punishment is indeed effective, as it transforms the public goods game to a
coordination game, thus rendering cooperation viable in infinite and finite
well-mixed populations. We also show that the addition of within-group
enforcement further promotes the evolution of public cooperation. However,
although the group size in this context has nonlinear effects on collective
action, an intermediate group size is least conductive to cooperative
behaviour. This contradicts recent field observations, where an intermediate
group size was declared optimal with the conjecture that group-size effects and
within-group enforcement are responsible. Our theoretical research thus
clarifies key aspects of monitoring with implicated punishment in human
societies, and additionally, it reveals fundamental group-size effects that
facilitate prosocial collective action.
| [
{
"created": "Thu, 19 Nov 2015 20:46:29 GMT",
"version": "v1"
}
] | 2015-11-20 | [
[
"Chen",
"Xiaojie",
""
],
[
"Sasaki",
"Tatsuya",
""
],
[
"Perc",
"Matjaz",
""
]
] | Monitoring with implicated punishment is common in human societies to avert freeriding on common goods. But is it effective in promoting public cooperation? We show that the introduction of monitoring and implicated punishment is indeed effective, as it transforms the public goods game to a coordination game, thus rendering cooperation viable in infinite and finite well-mixed populations. We also show that the addition of within-group enforcement further promotes the evolution of public cooperation. However, although the group size in this context has nonlinear effects on collective action, an intermediate group size is least conductive to cooperative behaviour. This contradicts recent field observations, where an intermediate group size was declared optimal with the conjecture that group-size effects and within-group enforcement are responsible. Our theoretical research thus clarifies key aspects of monitoring with implicated punishment in human societies, and additionally, it reveals fundamental group-size effects that facilitate prosocial collective action. |
2401.07023 | Francesco Fumarola | Francesco Fumarola, Lukasz Kusmierz, Ronald B. Dekker | "Bayesian anchoring" and the fourfold pattern of risk attitudes | 26 pages, 7 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Experiments on decision making under uncertainty are known to display a
classical pattern of risk aversion and risk seeking referred to as "fourfold
pattern" (or "reflection effect") , but recent experiments varying the speed
and order of mental processing have brought to light a more nuanced
phenomenology. We model experiments though a Bayesian formalization of the
anchor-and-adjust heuristic observed in empirical studies on cognitive bias.
Using only elementary assumptions on constrained information processing, we are
able to infer three separate effects found in recent observations: (1) the
reported enhancement of the fourfold pattern for quicker decision processes;
(2) the observed decrease of fluctuations for slower decision-making trials;
(3) the reported dependence of the outcome on the order in which options are
processed. The application of Bayesian modeling offers a solution to recent
empirical riddles by bridging two heretofore separate domains of experimental
inquiry on bounded rationality.
| [
{
"created": "Sat, 13 Jan 2024 09:31:37 GMT",
"version": "v1"
}
] | 2024-01-17 | [
[
"Fumarola",
"Francesco",
""
],
[
"Kusmierz",
"Lukasz",
""
],
[
"Dekker",
"Ronald B.",
""
]
] | Experiments on decision making under uncertainty are known to display a classical pattern of risk aversion and risk seeking referred to as "fourfold pattern" (or "reflection effect") , but recent experiments varying the speed and order of mental processing have brought to light a more nuanced phenomenology. We model experiments though a Bayesian formalization of the anchor-and-adjust heuristic observed in empirical studies on cognitive bias. Using only elementary assumptions on constrained information processing, we are able to infer three separate effects found in recent observations: (1) the reported enhancement of the fourfold pattern for quicker decision processes; (2) the observed decrease of fluctuations for slower decision-making trials; (3) the reported dependence of the outcome on the order in which options are processed. The application of Bayesian modeling offers a solution to recent empirical riddles by bridging two heretofore separate domains of experimental inquiry on bounded rationality. |
1606.09284 | Jacopo Grilli | Jacopo Grilli, Matteo Osella, Andrew S. Kennard, Marco Cosentino
Lagomarsino | Relevant parameters in models of cell division control | 15 pages, 5 figures | Phys. Rev. E 95, 032411 (2017) | 10.1103/PhysRevE.95.032411 | null | q-bio.CB cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A recent burst of dynamic single-cell growth-division data makes it possible
to characterize the stochastic dynamics of cell division control in bacteria.
Different modeling frameworks were used to infer specific mechanisms from such
data, but the links between frameworks are poorly explored, with relevant
consequences for how well any particular mechanism can be supported by the
data. Here, we describe a simple and generic framework in which two common
formalisms can be used interchangeably: (i) a continuous-time division process
described by a hazard function and (ii) a discrete-time equation describing
cell size across generations (where the unit of time is a cell cycle). In our
framework, this second process is a discrete-time Langevin equation with a
simple physical analogue. By perturbative expansion around the mean initial
size (or inter-division time), we show explicitly how this framework describes
a wide range of division control mechanisms, including combinations of time and
size control, as well as the constant added size mechanism recently found to
capture several aspects of the cell division behavior of different bacteria. As
we show by analytical estimates and numerical simulation, the available data
are characterized with great precision by the first-order approximation of this
expansion. Hence, a single dimensionless parameter defines the strength and the
action of the division control. However, this parameter may emerge from several
mechanisms, which are distinguished only by higher-order terms in our
perturbative expansion. An analytical estimate of the sample size needed to
distinguish between second-order effects shows that this is larger than what is
available in the current datasets. These results provide a unified framework
for future studies and clarify the relevant parameters at play in the control
of cell division.
| [
{
"created": "Wed, 29 Jun 2016 20:58:36 GMT",
"version": "v1"
}
] | 2017-03-22 | [
[
"Grilli",
"Jacopo",
""
],
[
"Osella",
"Matteo",
""
],
[
"Kennard",
"Andrew S.",
""
],
[
"Lagomarsino",
"Marco Cosentino",
""
]
] | A recent burst of dynamic single-cell growth-division data makes it possible to characterize the stochastic dynamics of cell division control in bacteria. Different modeling frameworks were used to infer specific mechanisms from such data, but the links between frameworks are poorly explored, with relevant consequences for how well any particular mechanism can be supported by the data. Here, we describe a simple and generic framework in which two common formalisms can be used interchangeably: (i) a continuous-time division process described by a hazard function and (ii) a discrete-time equation describing cell size across generations (where the unit of time is a cell cycle). In our framework, this second process is a discrete-time Langevin equation with a simple physical analogue. By perturbative expansion around the mean initial size (or inter-division time), we show explicitly how this framework describes a wide range of division control mechanisms, including combinations of time and size control, as well as the constant added size mechanism recently found to capture several aspects of the cell division behavior of different bacteria. As we show by analytical estimates and numerical simulation, the available data are characterized with great precision by the first-order approximation of this expansion. Hence, a single dimensionless parameter defines the strength and the action of the division control. However, this parameter may emerge from several mechanisms, which are distinguished only by higher-order terms in our perturbative expansion. An analytical estimate of the sample size needed to distinguish between second-order effects shows that this is larger than what is available in the current datasets. These results provide a unified framework for future studies and clarify the relevant parameters at play in the control of cell division. |
2202.11690 | Daniel Taylor | Daniel Taylor, Jonathan Shock, Deshendran Moodley, Jonathan Ipser,
Matthias Treder | Brain Structural Saliency Over The Ages | Submission to ACAIN 2022 conference (Sept. 2022); 20 pages including
acknowledgements, references, appendices; 14 figures | null | null | null | q-bio.NC cs.LG eess.IV | http://creativecommons.org/licenses/by/4.0/ | Brain Age (BA) estimation via Deep Learning has become a strong and reliable
bio-marker for brain health, but the black-box nature of Neural Networks does
not easily allow insight into the features of brain ageing.We trained a ResNet
model as a BA regressor on T1 structural MRI volumes from a small
cross-sectional cohort of 524 individuals. Using Layer-wise Relevance
Propagation (LRP) and DeepLIFT saliency mapping techniques, we analysed the
trained model to determine the most relevant structures for brain ageing for
the network, and compare these between the saliency mapping techniques. We show
the change in attribution of relevance to different brain regions through the
course of ageing. A tripartite pattern of relevance attribution to brain
regions emerges. Some regions increase in relevance with age (e.g. the right
Transverse Temporal Gyrus); some decrease in relevance with age (e.g. the right
Fourth Ventricle); and others are consistently relevant across ages. We also
examine the effect of the Brain Age Gap (BAG) on the distribution of relevance
within the brain volume. It is hoped that these findings will provide
clinically relevant region-wise trajectories for normal brain ageing, and a
baseline against which to compare brain ageing trajectories.
| [
{
"created": "Wed, 12 Jan 2022 09:50:29 GMT",
"version": "v1"
},
{
"created": "Mon, 28 Feb 2022 17:36:45 GMT",
"version": "v2"
},
{
"created": "Sat, 23 Jul 2022 12:00:21 GMT",
"version": "v3"
}
] | 2022-07-26 | [
[
"Taylor",
"Daniel",
""
],
[
"Shock",
"Jonathan",
""
],
[
"Moodley",
"Deshendran",
""
],
[
"Ipser",
"Jonathan",
""
],
[
"Treder",
"Matthias",
""
]
] | Brain Age (BA) estimation via Deep Learning has become a strong and reliable bio-marker for brain health, but the black-box nature of Neural Networks does not easily allow insight into the features of brain ageing.We trained a ResNet model as a BA regressor on T1 structural MRI volumes from a small cross-sectional cohort of 524 individuals. Using Layer-wise Relevance Propagation (LRP) and DeepLIFT saliency mapping techniques, we analysed the trained model to determine the most relevant structures for brain ageing for the network, and compare these between the saliency mapping techniques. We show the change in attribution of relevance to different brain regions through the course of ageing. A tripartite pattern of relevance attribution to brain regions emerges. Some regions increase in relevance with age (e.g. the right Transverse Temporal Gyrus); some decrease in relevance with age (e.g. the right Fourth Ventricle); and others are consistently relevant across ages. We also examine the effect of the Brain Age Gap (BAG) on the distribution of relevance within the brain volume. It is hoped that these findings will provide clinically relevant region-wise trajectories for normal brain ageing, and a baseline against which to compare brain ageing trajectories. |
2009.02310 | Jack Cook | Jack A. Cook | A Differential Topological Model for Olfactory Learning and
Representation | 167 pages, 4 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This thesis is designed to be a self-contained exposition of the
neurobiological and mathematical aspects of sensory perception, memory, and
learning with a bias towards olfaction. The final chapters introduce a new
approach to modeling focusing more on the geometry of the system as opposed to
element wise dynamics. Additionally, we construct an organism independent model
for olfactory processing: something which is currently missing from the
literature.
| [
{
"created": "Fri, 4 Sep 2020 17:20:14 GMT",
"version": "v1"
}
] | 2020-09-07 | [
[
"Cook",
"Jack A.",
""
]
] | This thesis is designed to be a self-contained exposition of the neurobiological and mathematical aspects of sensory perception, memory, and learning with a bias towards olfaction. The final chapters introduce a new approach to modeling focusing more on the geometry of the system as opposed to element wise dynamics. Additionally, we construct an organism independent model for olfactory processing: something which is currently missing from the literature. |
1608.05832 | Christophe Guyeux | Jacques M. Bahi and Christophe Guyeux and Antoine Perasso | Chaos in DNA Evolution | arXiv admin note: text overlap with arXiv:1105.1512 | Int. J. Biomath. 09, 1650076 (2016) | 10.1142/S1793524516500765 | null | q-bio.GN math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we explain why the chaotic model (CM) of Bahi and Michel
(2008) accurately simulates gene mutations over time. First, we demonstrate
that the CM model is a truly chaotic one, as defined by Devaney. Then, we show
that mutations occurring in gene mutations have the same chaotic dynamic, thus
making the use of chaotic models relevant for genome evolution.
| [
{
"created": "Sat, 20 Aug 2016 14:44:08 GMT",
"version": "v1"
}
] | 2016-08-23 | [
[
"Bahi",
"Jacques M.",
""
],
[
"Guyeux",
"Christophe",
""
],
[
"Perasso",
"Antoine",
""
]
] | In this paper, we explain why the chaotic model (CM) of Bahi and Michel (2008) accurately simulates gene mutations over time. First, we demonstrate that the CM model is a truly chaotic one, as defined by Devaney. Then, we show that mutations occurring in gene mutations have the same chaotic dynamic, thus making the use of chaotic models relevant for genome evolution. |
2401.07379 | Stefan Semrau | Maria Mircea, Diego Garlaschelli, Stefan Semrau | Inference of dynamical gene regulatory networks from single-cell data
with physics informed neural networks | 25 pages, 8 figures | null | null | null | q-bio.QM cs.AI cs.LG physics.bio-ph q-bio.MN | http://creativecommons.org/licenses/by-nc-nd/4.0/ | One of the main goals of developmental biology is to reveal the gene
regulatory networks (GRNs) underlying the robust differentiation of multipotent
progenitors into precisely specified cell types. Most existing methods to infer
GRNs from experimental data have limited predictive power as the inferred GRNs
merely reflect gene expression similarity or correlation. Here, we demonstrate,
how physics-informed neural networks (PINNs) can be used to infer the
parameters of predictive, dynamical GRNs that provide mechanistic understanding
of biological processes. Specifically we study GRNs that exhibit bifurcation
behavior and can therefore model cell differentiation. We show that PINNs
outperform regular feed-forward neural networks on the parameter inference task
and analyze two relevant experimental scenarios: 1. a system with cell
communication for which gene expression trajectories are available and 2.
snapshot measurements of a cell population in which cell communication is
absent. Our analysis will inform the design of future experiments to be
analyzed with PINNs and provides a starting point to explore this powerful
class of neural network models further.
| [
{
"created": "Sun, 14 Jan 2024 21:43:10 GMT",
"version": "v1"
}
] | 2024-01-23 | [
[
"Mircea",
"Maria",
""
],
[
"Garlaschelli",
"Diego",
""
],
[
"Semrau",
"Stefan",
""
]
] | One of the main goals of developmental biology is to reveal the gene regulatory networks (GRNs) underlying the robust differentiation of multipotent progenitors into precisely specified cell types. Most existing methods to infer GRNs from experimental data have limited predictive power as the inferred GRNs merely reflect gene expression similarity or correlation. Here, we demonstrate, how physics-informed neural networks (PINNs) can be used to infer the parameters of predictive, dynamical GRNs that provide mechanistic understanding of biological processes. Specifically we study GRNs that exhibit bifurcation behavior and can therefore model cell differentiation. We show that PINNs outperform regular feed-forward neural networks on the parameter inference task and analyze two relevant experimental scenarios: 1. a system with cell communication for which gene expression trajectories are available and 2. snapshot measurements of a cell population in which cell communication is absent. Our analysis will inform the design of future experiments to be analyzed with PINNs and provides a starting point to explore this powerful class of neural network models further. |
q-bio/0604005 | Sorin Tanase-Nicola | Jeroen S. van Zon, Marco J. Morelli, Sorin Tanase-Nicola and Pieter
Rein ten Wolde | Diffusion of transcription factors can drastically enhance the noise in
gene expression | 15 pages, 6 figures, REVTeX4 | null | null | null | q-bio.MN | null | We study by simulation the effect of the diffusive motion of repressor
molecules on the noise in mRNA and protein levels in the case of a repressed
gene. We find that spatial fluctuations due to diffusion can drastically
enhance the noise in gene expression. For a fixed repressor strength, the noise
due to diffusion can be minimized by increasing the number of repressors or by
decreasing the rate of the open complex formation. We also show that the effect
of spatial fluctuations can be well described by a two-step kinetic scheme,
where formation of an encounter complex by diffusion and the subsequent
association reaction are treated separately. Our results also emphasize that
power spectra are a highly useful tool for studying the propagation of noise
through the different stages of gene expression.
| [
{
"created": "Tue, 4 Apr 2006 16:02:14 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"van Zon",
"Jeroen S.",
""
],
[
"Morelli",
"Marco J.",
""
],
[
"Tanase-Nicola",
"Sorin",
""
],
[
"Wolde",
"Pieter Rein ten",
""
]
] | We study by simulation the effect of the diffusive motion of repressor molecules on the noise in mRNA and protein levels in the case of a repressed gene. We find that spatial fluctuations due to diffusion can drastically enhance the noise in gene expression. For a fixed repressor strength, the noise due to diffusion can be minimized by increasing the number of repressors or by decreasing the rate of the open complex formation. We also show that the effect of spatial fluctuations can be well described by a two-step kinetic scheme, where formation of an encounter complex by diffusion and the subsequent association reaction are treated separately. Our results also emphasize that power spectra are a highly useful tool for studying the propagation of noise through the different stages of gene expression. |
2105.02869 | Nicha Dvornek | Nicha C. Dvornek, Pamela Ventola, and James S. Duncan | Estimating Reproducible Functional Networks Associated with Task
Dynamics using Unsupervised LSTMs | IEEE International Symposium on Biomedical Imaging (ISBI) 2020 | 2020 IEEE 17th International Symposium on Biomedical Imaging
(ISBI), 2020, p. 1395-1398 | 10.1109/ISBI45749.2020.9098377 | null | q-bio.QM cs.LG eess.IV stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a method for estimating more reproducible functional networks that
are more strongly associated with dynamic task activity by using recurrent
neural networks with long short term memory (LSTMs). The LSTM model is trained
in an unsupervised manner to learn to generate the functional magnetic
resonance imaging (fMRI) time-series data in regions of interest. The learned
functional networks can then be used for further analysis, e.g., correlation
analysis to determine functional networks that are strongly associated with an
fMRI task paradigm. We test our approach and compare to other methods for
decomposing functional networks from fMRI activity on 2 related but separate
datasets that employ a biological motion perception task. We demonstrate that
the functional networks learned by the LSTM model are more strongly associated
with the task activity and dynamics compared to other approaches. Furthermore,
the patterns of network association are more closely replicated across subjects
within the same dataset as well as across datasets. More reproducible
functional networks are essential for better characterizing the neural
correlates of a target task.
| [
{
"created": "Thu, 6 May 2021 17:53:22 GMT",
"version": "v1"
}
] | 2021-05-07 | [
[
"Dvornek",
"Nicha C.",
""
],
[
"Ventola",
"Pamela",
""
],
[
"Duncan",
"James S.",
""
]
] | We propose a method for estimating more reproducible functional networks that are more strongly associated with dynamic task activity by using recurrent neural networks with long short term memory (LSTMs). The LSTM model is trained in an unsupervised manner to learn to generate the functional magnetic resonance imaging (fMRI) time-series data in regions of interest. The learned functional networks can then be used for further analysis, e.g., correlation analysis to determine functional networks that are strongly associated with an fMRI task paradigm. We test our approach and compare to other methods for decomposing functional networks from fMRI activity on 2 related but separate datasets that employ a biological motion perception task. We demonstrate that the functional networks learned by the LSTM model are more strongly associated with the task activity and dynamics compared to other approaches. Furthermore, the patterns of network association are more closely replicated across subjects within the same dataset as well as across datasets. More reproducible functional networks are essential for better characterizing the neural correlates of a target task. |
1512.07404 | Demian Wassermann | Demian Wassermann (ATHENA, HMS, PNL), Makris Nikos (CMA, HMS), Yogesh
Rathi (PNL, HMS), Shenton Martha (HMS, PNL), Ron Kikinis (HMS), Marek Kubicki
(HMS, PNL), Carl-Fredrik Westin (HMS) | The White Matter Query Language: A Novel Approach for Describing Human
White Matter Anatomy | Brain Structure and Function, Springer Verlag, 2016 | null | 10.1007/s00429-015-1179-4 | null | q-bio.NC q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We have developed a novel method to describe human white matter anatomy using
an approach that is both intuitive and simple to use, and which automatically
extracts white matter tracts from diffusion MRI volumes. Further, our method
simplifies the quantification and statistical analysis of white matter tracts
on large diffusion MRI databases. This work reflects the careful syntactical
definition of major white matter fiber tracts in the human brain based on a
neuroanatomist's expert knowledge. The framework is based on a novel query
language with a near-to-English textual syntax. This query language makes it
possible to construct a dictionary of anatomical definitions that describe
white matter tracts. The definitions include adjacent gray and white matter
regions, and rules for spatial relations. This novel method makes it possible
to automatically label white matter anatomy across subjects. After describing
this method, we provide an example of its implementation where we encode
anatomical knowledge in human white matter for 10 association and 15 projection
tracts per hemisphere, along with 7 commissural tracts. Importantly, this novel
method is comparable in accuracy to manual labeling. Finally, we present
results applying this method to create a white matter atlas from 77 healthy
subjects, and we use this atlas in a small proof-of-concept study to detect
changes in association tracts that characterize schizophrenia.
| [
{
"created": "Wed, 23 Dec 2015 09:25:13 GMT",
"version": "v1"
}
] | 2015-12-24 | [
[
"Wassermann",
"Demian",
"",
"ATHENA, HMS, PNL"
],
[
"Nikos",
"Makris",
"",
"CMA, HMS"
],
[
"Rathi",
"Yogesh",
"",
"PNL, HMS"
],
[
"Martha",
"Shenton",
"",
"HMS, PNL"
],
[
"Kikinis",
"Ron",
"",
"HMS"
],
[
"Kubicki",
"Marek",
"",
"HMS, PNL"
],
[
"Westin",
"Carl-Fredrik",
"",
"HMS"
]
] | We have developed a novel method to describe human white matter anatomy using an approach that is both intuitive and simple to use, and which automatically extracts white matter tracts from diffusion MRI volumes. Further, our method simplifies the quantification and statistical analysis of white matter tracts on large diffusion MRI databases. This work reflects the careful syntactical definition of major white matter fiber tracts in the human brain based on a neuroanatomist's expert knowledge. The framework is based on a novel query language with a near-to-English textual syntax. This query language makes it possible to construct a dictionary of anatomical definitions that describe white matter tracts. The definitions include adjacent gray and white matter regions, and rules for spatial relations. This novel method makes it possible to automatically label white matter anatomy across subjects. After describing this method, we provide an example of its implementation where we encode anatomical knowledge in human white matter for 10 association and 15 projection tracts per hemisphere, along with 7 commissural tracts. Importantly, this novel method is comparable in accuracy to manual labeling. Finally, we present results applying this method to create a white matter atlas from 77 healthy subjects, and we use this atlas in a small proof-of-concept study to detect changes in association tracts that characterize schizophrenia. |
2202.02958 | Nikita Bhandari | Nikita Bhandari, Rahee Walambe, Ketan Kotecha, Satyajeet Khare | A comprehensive survey on computational learning methods for analysis of
gene expression data | 43 pages, 8 figures, 5 tables | null | null | null | q-bio.GN cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Computational analysis methods including machine learning have a significant
impact in the fields of genomics and medicine. High-throughput gene expression
analysis methods such as microarray technology and RNA sequencing produce
enormous amounts of data. Traditionally, statistical methods are used for
comparative analysis of gene expression data. However, more complex analysis
for classification of sample observations, or discovery of feature genes
requires sophisticated computational approaches. In this review, we compile
various statistical and computational tools used in analysis of expression
microarray data. Even though the methods are discussed in the context of
expression microarrays, they can also be applied for the analysis of RNA
sequencing and quantitative proteomics datasets. We discuss the types of
missing values, and the methods and approaches usually employed in their
imputation. We also discuss methods of data normalization, feature selection,
and feature extraction. Lastly, methods of classification and class discovery
along with their evaluation parameters are described in detail. We believe that
this detailed review will help the users to select appropriate methods for
preprocessing and analysis of their data based on the expected outcome.
| [
{
"created": "Mon, 7 Feb 2022 05:53:13 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Feb 2022 15:51:09 GMT",
"version": "v2"
},
{
"created": "Tue, 15 Feb 2022 15:47:29 GMT",
"version": "v3"
},
{
"created": "Wed, 16 Feb 2022 01:59:35 GMT",
"version": "v4"
},
{
"created": "Tue, 27 Sep 2022 16:44:40 GMT",
"version": "v5"
}
] | 2022-09-28 | [
[
"Bhandari",
"Nikita",
""
],
[
"Walambe",
"Rahee",
""
],
[
"Kotecha",
"Ketan",
""
],
[
"Khare",
"Satyajeet",
""
]
] | Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome. |
1011.5466 | Ilya M. Nemenman | Ilya Nemenman | Information theory and adaptation | To appear as Chapter 5 of "Quantitative Biology: From Molecular to
Cellular Systems", ME Wall, ed. (Taylor and Francis, 2011) | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this Chapter, we ask questions (1) What is the right way to measure the
quality of information processing in a biological system? and (2) What can
real-life organisms do in order to improve their performance in
information-processing tasks? We then review the body of work that investigates
these questions experimentally, computationally, and theoretically in
biological domains as diverse as cell biology, population biology, and
computational neuroscience
| [
{
"created": "Wed, 24 Nov 2010 19:49:58 GMT",
"version": "v1"
}
] | 2010-11-25 | [
[
"Nemenman",
"Ilya",
""
]
] | In this Chapter, we ask questions (1) What is the right way to measure the quality of information processing in a biological system? and (2) What can real-life organisms do in order to improve their performance in information-processing tasks? We then review the body of work that investigates these questions experimentally, computationally, and theoretically in biological domains as diverse as cell biology, population biology, and computational neuroscience |
1712.05788 | Gerald Teschl | Clemens Ager, Karl Unterkofler, Pawel Mochalski, Susanne Teschl,
Gerald Teschl, Chris A. Mayhew, and Julian King | Modeling-based determination of physiological parameters of systemic
VOCs by breath gas analysis, part 2 | null | J. Breath Res. 12, 036011 (2018) | 10.1088/1752-7163/aab2b6 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a recent paper we presented a simple two compartment model which describes
the influence of inhaled concentrations on exhaled breath concentrations for
volatile organic compounds (VOCs) with small Henry constants. In this paper we
extend this investigation concerning the influence of inhaled concentrations on
exhaled breath concentrations for VOCs with higher Henry constants.
To this end we extend our model with an additional compartment which takes
into account the influence of the upper airways on exhaled breath VOC
concentrations.
| [
{
"created": "Fri, 15 Dec 2017 18:51:51 GMT",
"version": "v1"
},
{
"created": "Thu, 5 Apr 2018 14:54:39 GMT",
"version": "v2"
}
] | 2018-04-06 | [
[
"Ager",
"Clemens",
""
],
[
"Unterkofler",
"Karl",
""
],
[
"Mochalski",
"Pawel",
""
],
[
"Teschl",
"Susanne",
""
],
[
"Teschl",
"Gerald",
""
],
[
"Mayhew",
"Chris A.",
""
],
[
"King",
"Julian",
""
]
] | In a recent paper we presented a simple two compartment model which describes the influence of inhaled concentrations on exhaled breath concentrations for volatile organic compounds (VOCs) with small Henry constants. In this paper we extend this investigation concerning the influence of inhaled concentrations on exhaled breath concentrations for VOCs with higher Henry constants. To this end we extend our model with an additional compartment which takes into account the influence of the upper airways on exhaled breath VOC concentrations. |
1507.02699 | Giacomo Plazzotta | Giacomo Plazzotta and Caroline Colijn | Asymptotic frequency of shapes in supercritical branching trees | 12 pages, 4 figures | null | null | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The shapes of branching trees have been linked to disease transmission
patterns. In this paper we use the general Crump-Mode-Jagers branching process
to model an outbreak of an infectious disease under mild assumptions.
Introducing a new class of characteristic functions, we are able to derive a
formula for the limit of the frequency of the occurrences of a given shape in a
general tree. The computational challenges concerning the evaluation of this
formula are in part overcome using the Jumping Chronological Contour Process.
We apply the formula to derive the limit of the frequency of cherries,
pitchforks and double cherries in the constant rate birth-death model, and the
frequency of cherries under a non-constant death rate.
| [
{
"created": "Thu, 9 Jul 2015 20:32:53 GMT",
"version": "v1"
}
] | 2015-07-13 | [
[
"Plazzotta",
"Giacomo",
""
],
[
"Colijn",
"Caroline",
""
]
] | The shapes of branching trees have been linked to disease transmission patterns. In this paper we use the general Crump-Mode-Jagers branching process to model an outbreak of an infectious disease under mild assumptions. Introducing a new class of characteristic functions, we are able to derive a formula for the limit of the frequency of the occurrences of a given shape in a general tree. The computational challenges concerning the evaluation of this formula are in part overcome using the Jumping Chronological Contour Process. We apply the formula to derive the limit of the frequency of cherries, pitchforks and double cherries in the constant rate birth-death model, and the frequency of cherries under a non-constant death rate. |
0711.0455 | Jacob Bock Axelsen | Jacob Bock Axelsen, Sandeep Krishna and Kim Sneppen | Cost and Capacity of Signaling in the Escherichia coli Protein Reaction
Network | 21 pages, 6 figures | Axelsen JB, Krishna S & Sneppen K, Stat. Mech. (2008) P01018 | 10.1088/1742-5468/2008/01/P01018 | null | q-bio.MN cond-mat.soft | null | In systems biology new ways are required to analyze the large amount of
existing data on regulation of cellular processes. Recent work can be roughly
classified into either dynamical models of well-described subsystems, or
coarse-grained descriptions of the topology of the molecular networks at the
scale of the whole organism. In order to bridge these two disparate approaches
one needs to develop simplified descriptions of dynamics and topological
measures which address the propagation of signals in molecular networks. Here,
we consider the directed network of protein regulation in E. coli,
characterizing its modularity in terms of its potential to transmit signals. We
demonstrate that the simplest measure based on identifying sub-networks of
strong components, within which each node could send a signal to every other
node, indeed partitions the network into functional modules. We then suggest
measures to quantify the cost and spread associated with sending a signal
between any particular pair of proteins. Thereby, we address the signalling
specificity within and between modules, and show that in the regulation of
E.coli there is a systematic reduction of the cost and spread for signals
traveling over more than two intermediate reactions.
| [
{
"created": "Sat, 3 Nov 2007 14:05:15 GMT",
"version": "v1"
}
] | 2008-02-01 | [
[
"Axelsen",
"Jacob Bock",
""
],
[
"Krishna",
"Sandeep",
""
],
[
"Sneppen",
"Kim",
""
]
] | In systems biology new ways are required to analyze the large amount of existing data on regulation of cellular processes. Recent work can be roughly classified into either dynamical models of well-described subsystems, or coarse-grained descriptions of the topology of the molecular networks at the scale of the whole organism. In order to bridge these two disparate approaches one needs to develop simplified descriptions of dynamics and topological measures which address the propagation of signals in molecular networks. Here, we consider the directed network of protein regulation in E. coli, characterizing its modularity in terms of its potential to transmit signals. We demonstrate that the simplest measure based on identifying sub-networks of strong components, within which each node could send a signal to every other node, indeed partitions the network into functional modules. We then suggest measures to quantify the cost and spread associated with sending a signal between any particular pair of proteins. Thereby, we address the signalling specificity within and between modules, and show that in the regulation of E.coli there is a systematic reduction of the cost and spread for signals traveling over more than two intermediate reactions. |
1807.02349 | Alberto Alonso-Izquierdo Dr | A. Alonso-Izquierdo, M. A. Gonzalez Leon and M. de la Torre Mayado | A generalized Holling type II model for the interaction between
dextral-sinistral snails and Pareas snakes | 27 pages, 7 figures | Applied Mathematical Modelling 73 (2019) 459-472 | 10.1016/j.apm.2019.04.005 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pareatic snakes possess outstanding asymmetry in the mandibular tooth number,
which has probably been caused by its evolution to improve the feeding on the
predominant dextral snails. Gene mutation can generate chiral inversion on the
snail body. A sinistral snail population can thrive in this ecological context.
The interactions between dextral/sinistral snails and Pareas snakes are modeled
in this paper by using a new generalized functional response of Holling type
II. Distinct Pareas species show different bilateral asymmetry degrees. This
parameter plays an essential role in our model and determines the evolution of
the populations. Stability of the solutions is also analyzed for different
regimes in the space of parameters.
| [
{
"created": "Fri, 6 Jul 2018 10:44:53 GMT",
"version": "v1"
}
] | 2019-04-25 | [
[
"Alonso-Izquierdo",
"A.",
""
],
[
"Leon",
"M. A. Gonzalez",
""
],
[
"Mayado",
"M. de la Torre",
""
]
] | Pareatic snakes possess outstanding asymmetry in the mandibular tooth number, which has probably been caused by its evolution to improve the feeding on the predominant dextral snails. Gene mutation can generate chiral inversion on the snail body. A sinistral snail population can thrive in this ecological context. The interactions between dextral/sinistral snails and Pareas snakes are modeled in this paper by using a new generalized functional response of Holling type II. Distinct Pareas species show different bilateral asymmetry degrees. This parameter plays an essential role in our model and determines the evolution of the populations. Stability of the solutions is also analyzed for different regimes in the space of parameters. |
1809.00807 | Biman Bagchi - | Puja Banerjee, Sayantan Mondal and Biman Bagchi | Adverse effect of ethanol on insulin dimer stability | 40 pages, 10 figures | null | null | null | q-bio.BM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Alcohol is widely believed to have an effect on diabetes, often considered
beneficial in small amounts but detrimental in excess. The reasons are not
fully known but questions have been asked about the stability of insulin
oligomers in the presence of ethanol. We compute the free energy surface (FES)
and the pathway of insulin dimer dissociation in water and in 5% and 10%
water-ethanol mixture. We find that in the presence of ethanol the barrier
energy of dissociation reaction decreases by about 40% even in 5% water-ethanol
solution. In addition, ethanol induces a significant change in the reaction
pathway. We obtain estimates of the rate of reaction and binding energy for all
the three systems and those agree well with the previous experimental results
for the insulin dimer dissociation in water. The computed FES in water exhibits
ruggedness due to the existence of a number of intermediate states surrounded
by high and broad transition state region. However, the presence of ethanol
smoothens out the ruggedness. We extracted the conformations of the
intermediate states along the minimum energy pathway in all the three systems
and analyzed the change in microscopic structures in the presence of ethanol.
Interestingly, we discover a stable intermediate state in water-ethanol
mixtures where the monomers are separated (center-to-center) by about 3 nm and
the contact order parameter is close to zero. This intermediate is stabilized
by the distribution of ethanol and water molecules at the interface and which,
significantly, serves to reduce the dissociation rate constant .The solvation
of the two monomers during the dissociation and proteins' departure from native
state configuration are analyzed to obtain insight into the dimer dissociation
processes.
| [
{
"created": "Tue, 4 Sep 2018 06:42:55 GMT",
"version": "v1"
}
] | 2018-09-05 | [
[
"Banerjee",
"Puja",
""
],
[
"Mondal",
"Sayantan",
""
],
[
"Bagchi",
"Biman",
""
]
] | Alcohol is widely believed to have an effect on diabetes, often considered beneficial in small amounts but detrimental in excess. The reasons are not fully known but questions have been asked about the stability of insulin oligomers in the presence of ethanol. We compute the free energy surface (FES) and the pathway of insulin dimer dissociation in water and in 5% and 10% water-ethanol mixture. We find that in the presence of ethanol the barrier energy of dissociation reaction decreases by about 40% even in 5% water-ethanol solution. In addition, ethanol induces a significant change in the reaction pathway. We obtain estimates of the rate of reaction and binding energy for all the three systems and those agree well with the previous experimental results for the insulin dimer dissociation in water. The computed FES in water exhibits ruggedness due to the existence of a number of intermediate states surrounded by high and broad transition state region. However, the presence of ethanol smoothens out the ruggedness. We extracted the conformations of the intermediate states along the minimum energy pathway in all the three systems and analyzed the change in microscopic structures in the presence of ethanol. Interestingly, we discover a stable intermediate state in water-ethanol mixtures where the monomers are separated (center-to-center) by about 3 nm and the contact order parameter is close to zero. This intermediate is stabilized by the distribution of ethanol and water molecules at the interface and which, significantly, serves to reduce the dissociation rate constant .The solvation of the two monomers during the dissociation and proteins' departure from native state configuration are analyzed to obtain insight into the dimer dissociation processes. |
1401.2811 | Frantisek Slanina | Jan Geryk, Frantisek Slanina | Modules in the metabolic network of E.coli with regulatory interactions | null | Int. J. Data Mining and Bioinformatics, Vol. 8, No. 2, 2013, pages
188-202 | 10.1504/IJDMB.2013.055500 | null | q-bio.MN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine the modular structure of the metabolic network when combined with
the regulatory network representing direct regulation of enzymes by small
metabolites in E.coli. In order to identify the modular structure we introduce
clustering algorithm based on a novel vertex similarity measure for bipartite
graphs. We also apply a standard module identification method based on
simulated annealing. Both methods identify the same modular core each of them
with different resolution. We observe slight but still statistically
significant increase of modularity after regulatory interactions addition.
Enrichment of the metabolic network with the regulatory information leads to
identification of new functional modules, which cannot be detected in the
metabolic network only. Regulatory loops in the modules are the source of their
self-control, i.e. autonomy, and allow to make hypothesis about module
function. This study demonstrates that incorporation of regulatory information
is important component in defining functional units of the metabolic network.
| [
{
"created": "Mon, 13 Jan 2014 12:24:43 GMT",
"version": "v1"
}
] | 2014-01-14 | [
[
"Geryk",
"Jan",
""
],
[
"Slanina",
"Frantisek",
""
]
] | We examine the modular structure of the metabolic network when combined with the regulatory network representing direct regulation of enzymes by small metabolites in E.coli. In order to identify the modular structure we introduce clustering algorithm based on a novel vertex similarity measure for bipartite graphs. We also apply a standard module identification method based on simulated annealing. Both methods identify the same modular core each of them with different resolution. We observe slight but still statistically significant increase of modularity after regulatory interactions addition. Enrichment of the metabolic network with the regulatory information leads to identification of new functional modules, which cannot be detected in the metabolic network only. Regulatory loops in the modules are the source of their self-control, i.e. autonomy, and allow to make hypothesis about module function. This study demonstrates that incorporation of regulatory information is important component in defining functional units of the metabolic network. |
q-bio/0408018 | B. J. Powell | B. J. Powell | 5,6-dihydroxyindole-2-carboxylic acid (DHICA): a First Principles
Density-Functional Study | 5 pages, 2 figures | Chem. Phys. Lett. 402, 111 (2005). | 10.1016/j.cplett.2004.12.010 | null | q-bio.BM cond-mat.mtrl-sci cond-mat.soft physics.bio-ph physics.chem-ph | null | We report first principles density functional calculations for
5,6-dihydroxyindole-2-carboxylic acid (DHICA) and several reduced forms. DHICA
and 5,6-dihydroxyindole (DHI) are believed to be the basic building blocks of
the eumelanins. Our results show that carboxylation has a significant effect on
the physical properties of the molecules. In particular, the relative
stabilities and the HOMO-LUMO gaps (calculated with the $\Delta$SCF method) of
the various redox forms are strongly affected. We predict that, in contrast to
DHI, the density of unpaired electrons, and hence the ESR signal, in DHICA is
negligibly small.
| [
{
"created": "Tue, 24 Aug 2004 00:04:44 GMT",
"version": "v1"
}
] | 2016-09-08 | [
[
"Powell",
"B. J.",
""
]
] | We report first principles density functional calculations for 5,6-dihydroxyindole-2-carboxylic acid (DHICA) and several reduced forms. DHICA and 5,6-dihydroxyindole (DHI) are believed to be the basic building blocks of the eumelanins. Our results show that carboxylation has a significant effect on the physical properties of the molecules. In particular, the relative stabilities and the HOMO-LUMO gaps (calculated with the $\Delta$SCF method) of the various redox forms are strongly affected. We predict that, in contrast to DHI, the density of unpaired electrons, and hence the ESR signal, in DHICA is negligibly small. |
1911.00034 | Yves Dumont YD | Pierre-Alexandre Bliman, Daiver Cardona-Salgado, Yves Dumont, and Olga
Vasilieva | Optimal Control Approach for Implementation of Sterile Insect Techniques | 16 pages, 3 figures | null | null | null | q-bio.PE math.DS math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Vector or pest control is essential to reduce the risk of vector-borne
diseases or crop losses. Among the available biological control tools, the
Sterile Insect Technique (SIT) is one of the most promising. However,
SIT-control campaigns must be carefully planned in advance in order to render
desirable outcomes. In this paper, we design SIT-control intervention programs
that can avoid the real-time monitoring of the wild population and require to
mass-rear a minimal overall number of sterile insects, in order to induce a
local elimination of the wild population in the shortest time. Continuous-time
release programs are obtained by applying an optimal control approach, and then
laying the groundwork of more practical SIT-control programs consisting of
periodic impulsive releases.
| [
{
"created": "Thu, 31 Oct 2019 18:10:57 GMT",
"version": "v1"
}
] | 2019-11-04 | [
[
"Bliman",
"Pierre-Alexandre",
""
],
[
"Cardona-Salgado",
"Daiver",
""
],
[
"Dumont",
"Yves",
""
],
[
"Vasilieva",
"Olga",
""
]
] | Vector or pest control is essential to reduce the risk of vector-borne diseases or crop losses. Among the available biological control tools, the Sterile Insect Technique (SIT) is one of the most promising. However, SIT-control campaigns must be carefully planned in advance in order to render desirable outcomes. In this paper, we design SIT-control intervention programs that can avoid the real-time monitoring of the wild population and require to mass-rear a minimal overall number of sterile insects, in order to induce a local elimination of the wild population in the shortest time. Continuous-time release programs are obtained by applying an optimal control approach, and then laying the groundwork of more practical SIT-control programs consisting of periodic impulsive releases. |
1902.09746 | Martin Frasch | Silvia M. Lobmaier, Alexander Mueller, Camilla Zelgert, Chao Shen,
Pei-Chun Su, Georg Schmidt, Bernd Haller, Gabriela Berg, Bibiana Fabre, Joyce
Weyrich, Hau-tieng Wu, Martin G. Frasch, Marta C. Antonelli | Fetus: the radar of maternal stress, a cohort study | null | Archives of Gynecology and Obstetrics 2019 | 10.1007/s00404-019-05390-8 | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Objective: We hypothesized that prenatal stress (PS) exerts lasting impact on
fetal heart rate (fHR). We sought to validate the presence of such PS signature
in fHR by measuring coupling between maternal HR (mHR) and fHR. Study design:
Prospective observational cohort study in stressed group (SG) mothers with
controls matched for gestational age during screening at third trimester using
Cohen Perceived Stress Scale (PSS) questionnaire with PSS-10 equal or above 19
classified as SG. Women with PSS-10 less than 19 served as control group (CG).
Setting: Klinikum rechts der Isar of the Technical University of Munich.
Population: Singleton 3rd trimester pregnant women. Methods: Transabdominal
fetal electrocardiograms (fECG) were recorded. We deployed a signal processing
algorithm termed bivariate phase-rectified signal averaging (BPRSA) to quantify
coupling between mHR and fHR resulting in a fetal stress index (FSI). Maternal
hair cortisol was measured at birth. Differences were assumed to be significant
for p value less than 0.05. Main Outcome Measures: Differences for FSI between
both groups. Results: We screened 1500 women enrolling 538 of which 16.5 %
showed a PSS-10 score equal or above 19 at 34+0 weeks. Fifty five women
eventually comprised the SG and n=55 served as CG. Median PSS was 22.0 (IQR
21.0-24.0) in the SG and 9.0 (6.0-12.0) in the CG, respectively. Maternal hair
cortisol was higher in SG than CG at 86.6 (48.0-169.2) versus 53.0 (34.4-105.9)
pg/mg. At 36+5 weeks, FSI was significantly higher in fetuses of stressed
mothers when compared to controls [0.43 (0.18-0.85) versus 0.00 (-0.49-0.18)].
Conclusion: Our findings show a persistent effect of PS affecting fetuses in
the last trimester.
| [
{
"created": "Tue, 26 Feb 2019 05:58:29 GMT",
"version": "v1"
}
] | 2019-11-21 | [
[
"Lobmaier",
"Silvia M.",
""
],
[
"Mueller",
"Alexander",
""
],
[
"Zelgert",
"Camilla",
""
],
[
"Shen",
"Chao",
""
],
[
"Su",
"Pei-Chun",
""
],
[
"Schmidt",
"Georg",
""
],
[
"Haller",
"Bernd",
""
],
[
"Berg",
"Gabriela",
""
],
[
"Fabre",
"Bibiana",
""
],
[
"Weyrich",
"Joyce",
""
],
[
"Wu",
"Hau-tieng",
""
],
[
"Frasch",
"Martin G.",
""
],
[
"Antonelli",
"Marta C.",
""
]
] | Objective: We hypothesized that prenatal stress (PS) exerts lasting impact on fetal heart rate (fHR). We sought to validate the presence of such PS signature in fHR by measuring coupling between maternal HR (mHR) and fHR. Study design: Prospective observational cohort study in stressed group (SG) mothers with controls matched for gestational age during screening at third trimester using Cohen Perceived Stress Scale (PSS) questionnaire with PSS-10 equal or above 19 classified as SG. Women with PSS-10 less than 19 served as control group (CG). Setting: Klinikum rechts der Isar of the Technical University of Munich. Population: Singleton 3rd trimester pregnant women. Methods: Transabdominal fetal electrocardiograms (fECG) were recorded. We deployed a signal processing algorithm termed bivariate phase-rectified signal averaging (BPRSA) to quantify coupling between mHR and fHR resulting in a fetal stress index (FSI). Maternal hair cortisol was measured at birth. Differences were assumed to be significant for p value less than 0.05. Main Outcome Measures: Differences for FSI between both groups. Results: We screened 1500 women enrolling 538 of which 16.5 % showed a PSS-10 score equal or above 19 at 34+0 weeks. Fifty five women eventually comprised the SG and n=55 served as CG. Median PSS was 22.0 (IQR 21.0-24.0) in the SG and 9.0 (6.0-12.0) in the CG, respectively. Maternal hair cortisol was higher in SG than CG at 86.6 (48.0-169.2) versus 53.0 (34.4-105.9) pg/mg. At 36+5 weeks, FSI was significantly higher in fetuses of stressed mothers when compared to controls [0.43 (0.18-0.85) versus 0.00 (-0.49-0.18)]. Conclusion: Our findings show a persistent effect of PS affecting fetuses in the last trimester. |
1802.08841 | Armita Nourmohammad | Armita Nourmohammad, Jakub Otwinowski, Marta {\L}uksza, Thierry Mora,
Aleksandra M Walczak | Fierce selection and interference in B-cell repertoire response to
chronic HIV-1 | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | During chronic infection, HIV-1 engages in a rapid coevolutionary arms race
with the host's adaptive immune system. While it is clear that HIV exerts
strong selection on the adaptive immune system, the characteristics of the
somatic evolution that shape the immune response are still unknown. Traditional
population genetics methods fail to distinguish chronic immune response from
healthy repertoire evolution. Here, we infer the evolutionary modes of B-cell
repertoires and identify complex dynamics with a constant production of better
B-cell receptor mutants that compete, maintaining large clonal diversity and
potentially slowing down adaptation. A substantial fraction of mutations that
rise to high frequencies in pathogen engaging CDRs of B-cell receptors (BCRs)
are beneficial, in contrast to many such changes in structurally relevant
frameworks that are deleterious and circulate by hitchhiking. We identify a
pattern where BCRs in patients who experience larger viral expansions undergo
stronger selection with a rapid turnover of beneficial mutations due to clonal
interference in their CDR3 regions. Using population genetics modeling, we show
that the extinction of these beneficial mutations can be attributed to the rise
of competing beneficial alleles and clonal interference. The picture is of a
dynamic repertoire, where better clones may be outcompeted by new mutants
before they fix.
| [
{
"created": "Sat, 24 Feb 2018 12:40:13 GMT",
"version": "v1"
},
{
"created": "Sat, 25 Jul 2020 18:18:24 GMT",
"version": "v2"
}
] | 2020-07-28 | [
[
"Nourmohammad",
"Armita",
""
],
[
"Otwinowski",
"Jakub",
""
],
[
"Łuksza",
"Marta",
""
],
[
"Mora",
"Thierry",
""
],
[
"Walczak",
"Aleksandra M",
""
]
] | During chronic infection, HIV-1 engages in a rapid coevolutionary arms race with the host's adaptive immune system. While it is clear that HIV exerts strong selection on the adaptive immune system, the characteristics of the somatic evolution that shape the immune response are still unknown. Traditional population genetics methods fail to distinguish chronic immune response from healthy repertoire evolution. Here, we infer the evolutionary modes of B-cell repertoires and identify complex dynamics with a constant production of better B-cell receptor mutants that compete, maintaining large clonal diversity and potentially slowing down adaptation. A substantial fraction of mutations that rise to high frequencies in pathogen engaging CDRs of B-cell receptors (BCRs) are beneficial, in contrast to many such changes in structurally relevant frameworks that are deleterious and circulate by hitchhiking. We identify a pattern where BCRs in patients who experience larger viral expansions undergo stronger selection with a rapid turnover of beneficial mutations due to clonal interference in their CDR3 regions. Using population genetics modeling, we show that the extinction of these beneficial mutations can be attributed to the rise of competing beneficial alleles and clonal interference. The picture is of a dynamic repertoire, where better clones may be outcompeted by new mutants before they fix. |
2102.03180 | Erik Fagerholm | Erik D. Fagerholm, Robert Leech, Steven Williams, Carlos A. Zarate
Jr., Rosalyn J. Moran, Jessica R. Gilbert | Fine-tuning neural excitation/inhibition for tailored ketamine use in
treatment-resistant depression | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | The glutamatergic modulator ketamine has been shown to rapidly reduce
depressive symptoms in patients with treatment-resistant major depressive
disorder (TRD). Although its mechanisms of action are not fully understood,
changes in cortical excitation/inhibition (E/I) following ketamine
administration are well documented in animal models and could represent a
potential biomarker of treatment response. Here, we analyse neuromagnetic
virtual electrode timeseries collected from the primary somatosensory cortex in
18 unmedicated patients with TRD and in an equal number of age-matched healthy
controls during a somatosensory 'airpuff' stimulation task. These two groups
were scanned as part of a clinical trial of ketamine efficacy under three
conditions: a) baseline; b) 6-9 hours following subanesthetic ketamine
infusion; and c) 6-9 hours following placebo-saline infusion. We obtained
estimates of E/I interaction strengths by using Dynamic Causal Modelling (DCM)
on the timeseries, thereby allowing us to pinpoint, under each scanning
condition, where each subject's dynamics lie within the Poincar\'e diagram - as
defined in dynamical systems theory. We demonstrate that the Poincar\'e diagram
offers classification capability for TRD patients, in that the further the
patients' coordinates were shifted (by virtue of ketamine) toward the stable
(top-left) quadrant of the Poincar\'e diagram, the more their depressive
symptoms improved. The same relationship was not observed by virtue of a
placebo effect - thereby verifying the drug-specific nature of the results. We
show that the shift in neural dynamics required for symptom improvement
necessitates an increase in both excitatory and inhibitory coupling. We present
accompanying MATLAB code made available in a public repository, thereby
allowing for future studies to assess individually-tailored treatments of TRD.
| [
{
"created": "Thu, 4 Feb 2021 17:12:20 GMT",
"version": "v1"
}
] | 2021-02-08 | [
[
"Fagerholm",
"Erik D.",
""
],
[
"Leech",
"Robert",
""
],
[
"Williams",
"Steven",
""
],
[
"Zarate",
"Carlos A.",
"Jr."
],
[
"Moran",
"Rosalyn J.",
""
],
[
"Gilbert",
"Jessica R.",
""
]
] | The glutamatergic modulator ketamine has been shown to rapidly reduce depressive symptoms in patients with treatment-resistant major depressive disorder (TRD). Although its mechanisms of action are not fully understood, changes in cortical excitation/inhibition (E/I) following ketamine administration are well documented in animal models and could represent a potential biomarker of treatment response. Here, we analyse neuromagnetic virtual electrode timeseries collected from the primary somatosensory cortex in 18 unmedicated patients with TRD and in an equal number of age-matched healthy controls during a somatosensory 'airpuff' stimulation task. These two groups were scanned as part of a clinical trial of ketamine efficacy under three conditions: a) baseline; b) 6-9 hours following subanesthetic ketamine infusion; and c) 6-9 hours following placebo-saline infusion. We obtained estimates of E/I interaction strengths by using Dynamic Causal Modelling (DCM) on the timeseries, thereby allowing us to pinpoint, under each scanning condition, where each subject's dynamics lie within the Poincar\'e diagram - as defined in dynamical systems theory. We demonstrate that the Poincar\'e diagram offers classification capability for TRD patients, in that the further the patients' coordinates were shifted (by virtue of ketamine) toward the stable (top-left) quadrant of the Poincar\'e diagram, the more their depressive symptoms improved. The same relationship was not observed by virtue of a placebo effect - thereby verifying the drug-specific nature of the results. We show that the shift in neural dynamics required for symptom improvement necessitates an increase in both excitatory and inhibitory coupling. We present accompanying MATLAB code made available in a public repository, thereby allowing for future studies to assess individually-tailored treatments of TRD. |
2202.12715 | Nicha Dvornek | Xueqi Guo, Sule Tinaz, Nicha C. Dvornek | Early Disease Stage Characterization in Parkinson's Disease from
Resting-state fMRI Data Using a Long Short-term Memory Network | Submitted to IEEE Journal of Biomedical and Health Informatics | null | 10.3389/fnimg.2022.952084 | null | q-bio.NC cs.LG eess.SP q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Parkinson's disease (PD) is a common and complex neurodegenerative disorder
with 5 stages in the Hoehn and Yahr scaling. Given the heterogeneity of PD, it
is challenging to classify early stages 1 and 2 and detect brain function
alterations. Functional magnetic resonance imaging (fMRI) is a promising tool
in revealing functional connectivity (FC) differences and developing biomarkers
in PD. Some machine learning approaches like support vector machine and
logistic regression have been successfully applied in the early diagnosis of PD
using fMRI data, which outperform classifiers based on manually selected
morphological features. However, the early-stage characterization in FC changes
has not been fully investigated. Given the complexity and non-linearity of fMRI
data, we propose the use of a long short-term memory (LSTM) network to
characterize the early stages of PD. The study included 84 subjects (56 in
stage 2 and 28 in stage 1) from the Parkinson's Progression Markers Initiative
(PPMI), the largest available public PD dataset. Under a repeated 10-fold
stratified cross-validation, the LSTM model reached an accuracy of 71.63%,
13.52% higher than the best traditional machine learning method, indicating
significantly better robustness and accuracy compared with other machine
learning classifiers. We used the learned LSTM model weights to select the top
brain regions that contributed to model prediction and performed FC analyses to
characterize functional changes with disease stage and motor impairment to gain
better insight into the brain mechanisms of PD.
| [
{
"created": "Fri, 11 Feb 2022 18:34:11 GMT",
"version": "v1"
}
] | 2022-07-22 | [
[
"Guo",
"Xueqi",
""
],
[
"Tinaz",
"Sule",
""
],
[
"Dvornek",
"Nicha C.",
""
]
] | Parkinson's disease (PD) is a common and complex neurodegenerative disorder with 5 stages in the Hoehn and Yahr scaling. Given the heterogeneity of PD, it is challenging to classify early stages 1 and 2 and detect brain function alterations. Functional magnetic resonance imaging (fMRI) is a promising tool in revealing functional connectivity (FC) differences and developing biomarkers in PD. Some machine learning approaches like support vector machine and logistic regression have been successfully applied in the early diagnosis of PD using fMRI data, which outperform classifiers based on manually selected morphological features. However, the early-stage characterization in FC changes has not been fully investigated. Given the complexity and non-linearity of fMRI data, we propose the use of a long short-term memory (LSTM) network to characterize the early stages of PD. The study included 84 subjects (56 in stage 2 and 28 in stage 1) from the Parkinson's Progression Markers Initiative (PPMI), the largest available public PD dataset. Under a repeated 10-fold stratified cross-validation, the LSTM model reached an accuracy of 71.63%, 13.52% higher than the best traditional machine learning method, indicating significantly better robustness and accuracy compared with other machine learning classifiers. We used the learned LSTM model weights to select the top brain regions that contributed to model prediction and performed FC analyses to characterize functional changes with disease stage and motor impairment to gain better insight into the brain mechanisms of PD. |
1301.3325 | David Lawrie | David S. Lawrie, Philipp W. Messer, Ruth Hershberg, and Dmitri A.
Petrov | Strong Purifying Selection at Synonymous Sites in D. melanogaster | null | null | 10.1371/journal.pgen.1003527 | null | q-bio.PE q-bio.GN | http://creativecommons.org/licenses/by/3.0/ | Synonymous sites are generally assumed to be subject to weak selective
constraint. For this reason, they are often neglected as a possible source of
important functional variation. We use site frequency spectra from deep
population sequencing data to show that, contrary to this expectation, 22% of
four-fold synonymous (4D) sites in D. melanogaster evolve under very strong
selective constraint while few, if any, appear to be under weak constraint.
Linking polymorphism with divergence data, we further find that the fraction of
synonymous sites exposed to strong purifying selection is higher for those
positions that show slower evolution on the Drosophila phylogeny. The function
underlying the inferred strong constraint appears to be separate from splicing
enhancers, nucleosome positioning, and the translational optimization
generating canonical codon bias. The fraction of synonymous sites under strong
constraint within a gene correlates well with gene expression, particularly in
the mid-late embryo, pupae, and adult developmental stages. Genes enriched in
strongly constrained synonymous sites tend to be particularly functionally
important and are often involved in key developmental pathways. Given that the
observed widespread constraint acting on synonymous sites is likely not limited
to Drosophila, the role of synonymous sites in genetic disease and adaptation
should be reevaluated.
| [
{
"created": "Tue, 15 Jan 2013 12:49:27 GMT",
"version": "v1"
}
] | 2013-06-14 | [
[
"Lawrie",
"David S.",
""
],
[
"Messer",
"Philipp W.",
""
],
[
"Hershberg",
"Ruth",
""
],
[
"Petrov",
"Dmitri A.",
""
]
] | Synonymous sites are generally assumed to be subject to weak selective constraint. For this reason, they are often neglected as a possible source of important functional variation. We use site frequency spectra from deep population sequencing data to show that, contrary to this expectation, 22% of four-fold synonymous (4D) sites in D. melanogaster evolve under very strong selective constraint while few, if any, appear to be under weak constraint. Linking polymorphism with divergence data, we further find that the fraction of synonymous sites exposed to strong purifying selection is higher for those positions that show slower evolution on the Drosophila phylogeny. The function underlying the inferred strong constraint appears to be separate from splicing enhancers, nucleosome positioning, and the translational optimization generating canonical codon bias. The fraction of synonymous sites under strong constraint within a gene correlates well with gene expression, particularly in the mid-late embryo, pupae, and adult developmental stages. Genes enriched in strongly constrained synonymous sites tend to be particularly functionally important and are often involved in key developmental pathways. Given that the observed widespread constraint acting on synonymous sites is likely not limited to Drosophila, the role of synonymous sites in genetic disease and adaptation should be reevaluated. |
1707.05678 | Valerey Grytsay Dr | V.I. Grytsay | Self-organization and fractality created by gluconeogenesis in the
metabolic process | 11 pages, 6 figures | Chaotic Modeling and Simulation, 2, pp.113-127 (2016) | null | null | q-bio.OT nlin.AO nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Within a mathematical model, the process of interaction of the metabolic
processes such as glycolysis and gluconeogenesis is studied. As a result of the
running of two opposite processes in a cell, the conditions for their
interaction and the self-organization in a single dissipative system are
created.
| [
{
"created": "Fri, 14 Jul 2017 11:38:15 GMT",
"version": "v1"
}
] | 2017-07-19 | [
[
"Grytsay",
"V. I.",
""
]
] | Within a mathematical model, the process of interaction of the metabolic processes such as glycolysis and gluconeogenesis is studied. As a result of the running of two opposite processes in a cell, the conditions for their interaction and the self-organization in a single dissipative system are created. |
0706.1017 | Yves-Henri Sanejouand | Brice Juanico, Yves-Henri Sanejouand, Francesco Piazza, Paolo de los
Rios | Discrete breathers in nonlinear network models of proteins | 4 pages, 5 figures. Minor changes | Physical review letters vol. 99, 238104 (2007) | 10.1103/PhysRevLett.99.238104 | null | q-bio.BM | null | We introduce a topology-based nonlinear network model of protein dynamics
with the aim of investigating the interplay of spatial disorder and
nonlinearity. We show that spontaneous localization of energy occurs
generically and is a site-dependent process. Localized modes of nonlinear
origin form spontaneously in the stiffest parts of the structure and display
site-dependent activation energies. Our results provide a straightforward way
for understanding the recently discovered link between protein local stiffness
and enzymatic activity. They strongly suggest that nonlinear phenomena may play
an important role in enzyme function, allowing for energy storage during the
catalytic process.
| [
{
"created": "Thu, 7 Jun 2007 15:01:29 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Nov 2007 18:07:57 GMT",
"version": "v2"
},
{
"created": "Fri, 21 Dec 2007 18:23:10 GMT",
"version": "v3"
}
] | 2011-11-10 | [
[
"Juanico",
"Brice",
""
],
[
"Sanejouand",
"Yves-Henri",
""
],
[
"Piazza",
"Francesco",
""
],
[
"Rios",
"Paolo de los",
""
]
] | We introduce a topology-based nonlinear network model of protein dynamics with the aim of investigating the interplay of spatial disorder and nonlinearity. We show that spontaneous localization of energy occurs generically and is a site-dependent process. Localized modes of nonlinear origin form spontaneously in the stiffest parts of the structure and display site-dependent activation energies. Our results provide a straightforward way for understanding the recently discovered link between protein local stiffness and enzymatic activity. They strongly suggest that nonlinear phenomena may play an important role in enzyme function, allowing for energy storage during the catalytic process. |
2405.14904 | Brandon Legried | Brandon Legried | Large deviation principles and evolutionary multiple structure alignment
of non-coding RNA | 25 pages main document, 31 pages total with references and appendix,
1 figure | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | Non-coding RNA are functional molecules that are not translated into
proteins. Their function comes as important regulators of biological function.
Because they are not translated, they need not be as stable as other types of
RNA. The TKF91 Structure Tree from Holmes 2004 is a probability model that
effectively describes correlated substitution, insertion, and deletion of base
pairs, and found to have some worth in understanding dynamic folding patterns.
In this paper, we provide a new probabilistic analysis of the TKF91 Structure
Tree. Large deviation principles on stem lengths, helix lengths, and tree size
are proved. Additionally, we give a new alignment procedure that constructs
accurate sequence and structural alignments for sequences with low identity for
a dense enough phylogeny.
| [
{
"created": "Wed, 22 May 2024 23:08:40 GMT",
"version": "v1"
}
] | 2024-05-27 | [
[
"Legried",
"Brandon",
""
]
] | Non-coding RNA are functional molecules that are not translated into proteins. Their function comes as important regulators of biological function. Because they are not translated, they need not be as stable as other types of RNA. The TKF91 Structure Tree from Holmes 2004 is a probability model that effectively describes correlated substitution, insertion, and deletion of base pairs, and found to have some worth in understanding dynamic folding patterns. In this paper, we provide a new probabilistic analysis of the TKF91 Structure Tree. Large deviation principles on stem lengths, helix lengths, and tree size are proved. Additionally, we give a new alignment procedure that constructs accurate sequence and structural alignments for sequences with low identity for a dense enough phylogeny. |
1701.08663 | Franziska Oschmann | Konstantin Mergenthaler, Franziska Oschmann, Jeremy Petravicz,
Dipanjan Roy, Mriganka Sur, Klaus Obermayer | A computational study on synaptic and extrasynaptic effects of astrocyte
glutamate uptake on orientation tuning in V1 | null | null | null | null | q-bio.NC q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Astrocytes affect neural transmission by a tight control via glutamate
transporters on glutamate concentrations in direct vicinity to the synaptic
cleft and by extracellular glutamate. Their relevance for information
representation has been supported by in-vivo studies in ferret and mouse
primary visual cortex. In ferret blocking glutamate transport pharmacologically
broadened tuning curves and enhanced the response at preferred orientation. In
knock-out mice with reduced expression of glutamate transporters sharpened
tuning was observed. It is however unclear how focal and ambient changes in
glutamate concentration affect stimulus representation. Here we develop a
computational framework, which allows the investigation of synaptic and
extrasynaptic effects of glutamate uptake on orientation tuning in recurrently
connected network models with pinwheel-domain (ferret) or salt-and-pepper
(mouse) organization. This model proposed that glutamate uptake shapes
information representation when it affects the contribution of excitatory and
inhibitory neurons to the network activity. Namely, strengthening the
contribution of excitatory neurons generally broadens tuning and elevates the
response. In contrast, strengthening the contribution of inhibitory neurons can
have a sharpening effect on tuning. In addition local representational topology
also plays a role: In the pinwheel-domain model effects were strongest within
domains - regions where neighboring neurons share preferred orientations.
Around pinwheels but also within salt-and-pepper networks the effects were less
strong. Our model proposes that the pharmacological intervention in ferret
increases the contribution of excitatory cells, while the reduced expression in
mouse increases the contribution of inhibitory cells to network activity.
| [
{
"created": "Mon, 30 Jan 2017 15:52:45 GMT",
"version": "v1"
}
] | 2017-01-31 | [
[
"Mergenthaler",
"Konstantin",
""
],
[
"Oschmann",
"Franziska",
""
],
[
"Petravicz",
"Jeremy",
""
],
[
"Roy",
"Dipanjan",
""
],
[
"Sur",
"Mriganka",
""
],
[
"Obermayer",
"Klaus",
""
]
] | Astrocytes affect neural transmission by a tight control via glutamate transporters on glutamate concentrations in direct vicinity to the synaptic cleft and by extracellular glutamate. Their relevance for information representation has been supported by in-vivo studies in ferret and mouse primary visual cortex. In ferret blocking glutamate transport pharmacologically broadened tuning curves and enhanced the response at preferred orientation. In knock-out mice with reduced expression of glutamate transporters sharpened tuning was observed. It is however unclear how focal and ambient changes in glutamate concentration affect stimulus representation. Here we develop a computational framework, which allows the investigation of synaptic and extrasynaptic effects of glutamate uptake on orientation tuning in recurrently connected network models with pinwheel-domain (ferret) or salt-and-pepper (mouse) organization. This model proposed that glutamate uptake shapes information representation when it affects the contribution of excitatory and inhibitory neurons to the network activity. Namely, strengthening the contribution of excitatory neurons generally broadens tuning and elevates the response. In contrast, strengthening the contribution of inhibitory neurons can have a sharpening effect on tuning. In addition local representational topology also plays a role: In the pinwheel-domain model effects were strongest within domains - regions where neighboring neurons share preferred orientations. Around pinwheels but also within salt-and-pepper networks the effects were less strong. Our model proposes that the pharmacological intervention in ferret increases the contribution of excitatory cells, while the reduced expression in mouse increases the contribution of inhibitory cells to network activity. |
2303.06763 | Elsa Gomes | Nirbhaya Shajia, Florbela Nunes, M.Ines Rocha, Elsa Ferreira Gomes and
Helena Castro | MigraR: an open-source, R-based application for analysis and
quantification of cell migration parameters | null | Computer Methods and Programs in Biomedicine, Volume 213, 2022,
106529, ISSN 0169-2607 | 10.1016/j.cmpb.2021.106529 | null | q-bio.QM q-bio.CB | http://creativecommons.org/licenses/by/4.0/ | Background and objective: Cell migration is essential for many biological
phenomena with direct impact on human health and disease. One conventional
approach to study cell migration involves the quantitative analysis of
individual cell trajectories recorded by time-lapse video microscopy. Dedicated
software tools exist to assist the automated or semi-automated tracking of
cells and translate these into coordinate positions along time. However, cell
biologists usually bump into the difficulty of plotting and computing these
data sets into biologically meaningful figures and metrics. Methods: This
report describes MigraR, an intuitive graphical user interface executed from
the RStudioTM (via the R package Shiny), which greatly simplifies the task of
translating coordinate positions of moving cells into measurable parameters of
cell migration (velocity, straightness, and direction of movement), as well as
of plotting cell trajectories and migration metrics. One innovative function of
this interface is that it allows users to refine their data sets by setting
limits based on time, velocity and straightness. Results: MigraR was tested on
different data to assess its applicability. Intended users of MigraR are cell
biologists with no prior knowledge of data analysis, seeking to accelerate the
quantification and visualization of cell migration data sets delivered in the
format of Excel files by available cell-tracking software. Conclusions: Through
the graphics it provides, MigraR is an useful tool for the analysis of
migration parameters and cellular trajectories. Since its source code is open,
it can be subject of refinement by expert users to best suit the needs of other
researchers. It is available at GitHub and can be easily reproduced.
| [
{
"created": "Sun, 12 Mar 2023 21:59:19 GMT",
"version": "v1"
}
] | 2023-03-14 | [
[
"Shajia",
"Nirbhaya",
""
],
[
"Nunes",
"Florbela",
""
],
[
"Rocha",
"M. Ines",
""
],
[
"Gomes",
"Elsa Ferreira",
""
],
[
"Castro",
"Helena",
""
]
] | Background and objective: Cell migration is essential for many biological phenomena with direct impact on human health and disease. One conventional approach to study cell migration involves the quantitative analysis of individual cell trajectories recorded by time-lapse video microscopy. Dedicated software tools exist to assist the automated or semi-automated tracking of cells and translate these into coordinate positions along time. However, cell biologists usually bump into the difficulty of plotting and computing these data sets into biologically meaningful figures and metrics. Methods: This report describes MigraR, an intuitive graphical user interface executed from the RStudioTM (via the R package Shiny), which greatly simplifies the task of translating coordinate positions of moving cells into measurable parameters of cell migration (velocity, straightness, and direction of movement), as well as of plotting cell trajectories and migration metrics. One innovative function of this interface is that it allows users to refine their data sets by setting limits based on time, velocity and straightness. Results: MigraR was tested on different data to assess its applicability. Intended users of MigraR are cell biologists with no prior knowledge of data analysis, seeking to accelerate the quantification and visualization of cell migration data sets delivered in the format of Excel files by available cell-tracking software. Conclusions: Through the graphics it provides, MigraR is an useful tool for the analysis of migration parameters and cellular trajectories. Since its source code is open, it can be subject of refinement by expert users to best suit the needs of other researchers. It is available at GitHub and can be easily reproduced. |
1403.7556 | Eric Libby | Eric Libby, William Ratcliff, Michael Travisano, Ben Kerr | Geometry shapes evolution of early multicellularity | 7 figures | null | 10.1371/journal.pcbi.1003803 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Organisms have increased in complexity through a series of major evolutionary
transitions, in which formerly autonomous entities become parts of a novel
higher-level entity. One intriguing feature of the higher-level entity after
some major transitions is a division of reproductive labor among its
lower-level units. Although it can have clear benefits once established, it is
unknown how such reproductive division of labor originates. We consider a
recent evolution experiment on the yeast Saccharomyces cerevisiae as a unique
platform to address the issue of reproductive differentiation during an
evolutionary transition in individuality. In the experiment, independent yeast
lineages evolved a multicellular "snowflake-like'' cluster form in response to
gravity selection. Shortly after the evolution of clusters, the yeast evolved
higher rates of cell death. While cell death enables clusters to split apart
and form new groups, it also reduces their performance in the face of gravity
selection. To understand the selective value of increased cell death, we create
a mathematical model of the cellular arrangement within snowflake yeast
clusters. The model reveals that the mechanism of cell death and the geometry
of the snowflake interact in complex, evolutionarily important ways. We find
that the organization of snowflake yeast imposes powerful limitations on the
available space for new cell growth. By dying more frequently, cells in
clusters avoid encountering space limitations, and, paradoxically, reach higher
numbers. In addition, selection for particular group sizes can explain the
increased rate of apoptosis both in terms of total cell number and total
numbers of collectives. Thus, by considering the geometry of a primitive
multicellular organism we can gain insight into the initial emergence of
reproductive division of labor during an evolutionary transition in
individuality.
| [
{
"created": "Fri, 28 Mar 2014 22:18:47 GMT",
"version": "v1"
}
] | 2014-11-13 | [
[
"Libby",
"Eric",
""
],
[
"Ratcliff",
"William",
""
],
[
"Travisano",
"Michael",
""
],
[
"Kerr",
"Ben",
""
]
] | Organisms have increased in complexity through a series of major evolutionary transitions, in which formerly autonomous entities become parts of a novel higher-level entity. One intriguing feature of the higher-level entity after some major transitions is a division of reproductive labor among its lower-level units. Although it can have clear benefits once established, it is unknown how such reproductive division of labor originates. We consider a recent evolution experiment on the yeast Saccharomyces cerevisiae as a unique platform to address the issue of reproductive differentiation during an evolutionary transition in individuality. In the experiment, independent yeast lineages evolved a multicellular "snowflake-like'' cluster form in response to gravity selection. Shortly after the evolution of clusters, the yeast evolved higher rates of cell death. While cell death enables clusters to split apart and form new groups, it also reduces their performance in the face of gravity selection. To understand the selective value of increased cell death, we create a mathematical model of the cellular arrangement within snowflake yeast clusters. The model reveals that the mechanism of cell death and the geometry of the snowflake interact in complex, evolutionarily important ways. We find that the organization of snowflake yeast imposes powerful limitations on the available space for new cell growth. By dying more frequently, cells in clusters avoid encountering space limitations, and, paradoxically, reach higher numbers. In addition, selection for particular group sizes can explain the increased rate of apoptosis both in terms of total cell number and total numbers of collectives. Thus, by considering the geometry of a primitive multicellular organism we can gain insight into the initial emergence of reproductive division of labor during an evolutionary transition in individuality. |
1907.07774 | Klaudius Scheufele | Klaudius Scheufele, Shashank Subramanian, Andreas Mang, George Biros,
Miriam Mehl | Image-Driven Biophysical Tumor Growth Model Calibration | 24 pages, 8 figures | SIAM Journal on Scientific Computing, 42(3):B549-B580, 2020 | 10.1137/19M1275280 | null | q-bio.QM eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel formulation for the calibration of a biophysical tumor
growth model from a single-time snapshot, MRI scan of a glioblastoma patient.
Tumor growth models are typically nonlinear parabolic partial differential
equations (PDEs). Thus, we have to generate a second snapshot to be able to
extract significant information from a single patient snapshot. We create this
two-snapshot scenario as follows. We use an atlas (an average of several scans
of healthy individuals) as a substitute for an earlier, pretumor, MRI scan of
the patient. Then, using the patient scan and the atlas, we combine
image-registration algorithms and parameter estimation algorithms to achieve a
better estimate of the healthy patient scan and the tumor growth parameters
that are consistent with the data. Our scheme is based on our recent work
(Scheufele et al, "Biophysically constrained diffeomorphic image registration,
Tumor growth, Atlas registration, Adjoint-based methods, Parallel algorithms",
CMAME, 2018), but apply a different and novel scheme where the tumor growth
simulation in contrast to the previous work is executed in the patient brain
domain and not in the atlas domain yielding more meaningful patient-specific
results. As a basis, we use a PDE-constrained optimization framework. We derive
a modified Picard-iteration-type solution strategy in which we alternate
between registration and tumor parameter estimation in a new way. In addition,
we consider an $\ell_1$ sparsity constraint on the initial condition for the
tumor and integrate it with the new joint inversion scheme. We solve the
subproblems with a reduced-space, inexact Gauss-Newton-Krylov/quasi-Newton
methods. We present results using real brain data with synthetic tumor data
that show that the new scheme reconstructs the tumor parameters in a more
accurate and reliable way compared to our earlier scheme.
| [
{
"created": "Tue, 16 Jul 2019 08:21:55 GMT",
"version": "v1"
}
] | 2020-06-30 | [
[
"Scheufele",
"Klaudius",
""
],
[
"Subramanian",
"Shashank",
""
],
[
"Mang",
"Andreas",
""
],
[
"Biros",
"George",
""
],
[
"Mehl",
"Miriam",
""
]
] | We present a novel formulation for the calibration of a biophysical tumor growth model from a single-time snapshot, MRI scan of a glioblastoma patient. Tumor growth models are typically nonlinear parabolic partial differential equations (PDEs). Thus, we have to generate a second snapshot to be able to extract significant information from a single patient snapshot. We create this two-snapshot scenario as follows. We use an atlas (an average of several scans of healthy individuals) as a substitute for an earlier, pretumor, MRI scan of the patient. Then, using the patient scan and the atlas, we combine image-registration algorithms and parameter estimation algorithms to achieve a better estimate of the healthy patient scan and the tumor growth parameters that are consistent with the data. Our scheme is based on our recent work (Scheufele et al, "Biophysically constrained diffeomorphic image registration, Tumor growth, Atlas registration, Adjoint-based methods, Parallel algorithms", CMAME, 2018), but apply a different and novel scheme where the tumor growth simulation in contrast to the previous work is executed in the patient brain domain and not in the atlas domain yielding more meaningful patient-specific results. As a basis, we use a PDE-constrained optimization framework. We derive a modified Picard-iteration-type solution strategy in which we alternate between registration and tumor parameter estimation in a new way. In addition, we consider an $\ell_1$ sparsity constraint on the initial condition for the tumor and integrate it with the new joint inversion scheme. We solve the subproblems with a reduced-space, inexact Gauss-Newton-Krylov/quasi-Newton methods. We present results using real brain data with synthetic tumor data that show that the new scheme reconstructs the tumor parameters in a more accurate and reliable way compared to our earlier scheme. |
0801.2587 | Aleksandar Stojmirovi\'c | Aleksandar Stojmirovi\'c, E. Michael Gertz, Stephen F. Altschul and
Yi-Kuo Yu | The effectiveness of position- and composition-specific gap costs for
protein similarity searches | 17 pages, 4 figures, 2 tables | Bioinformatics. 2008 Jul 1;24(13):i15-23. | 10.1093/bioinformatics/btn171 | null | q-bio.BM q-bio.QM | null | The flexibility in gap cost enjoyed by Hidden Markov Models (HMMs) is
expected to afford them better retrieval accuracy than position-specific
scoring matrices (PSSMs). We attempt to quantify the effect of more general gap
parameters by separately examining the influence of position- and
composition-specific gap scores, as well as by comparing the retrieval accuracy
of the PSSMs constructed using an iterative procedure to that of the HMMs
provided by Pfam and SUPERFAMILY, curated ensembles of multiple alignments.
We found that position-specific gap penalties have an advantage over uniform
gap costs. We did not explore optimizing distinct uniform gap costs for each
query. For Pfam, PSSMs iteratively constructed from seeds based on HMM
consensus sequences perform equivalently to HMMs that were adjusted to have
constant gap transition probabilities, albeit with much greater variance. We
observed no effect of composition-specific gap costs on retrieval performance.
| [
{
"created": "Wed, 16 Jan 2008 23:02:46 GMT",
"version": "v1"
}
] | 2008-10-31 | [
[
"Stojmirović",
"Aleksandar",
""
],
[
"Gertz",
"E. Michael",
""
],
[
"Altschul",
"Stephen F.",
""
],
[
"Yu",
"Yi-Kuo",
""
]
] | The flexibility in gap cost enjoyed by Hidden Markov Models (HMMs) is expected to afford them better retrieval accuracy than position-specific scoring matrices (PSSMs). We attempt to quantify the effect of more general gap parameters by separately examining the influence of position- and composition-specific gap scores, as well as by comparing the retrieval accuracy of the PSSMs constructed using an iterative procedure to that of the HMMs provided by Pfam and SUPERFAMILY, curated ensembles of multiple alignments. We found that position-specific gap penalties have an advantage over uniform gap costs. We did not explore optimizing distinct uniform gap costs for each query. For Pfam, PSSMs iteratively constructed from seeds based on HMM consensus sequences perform equivalently to HMMs that were adjusted to have constant gap transition probabilities, albeit with much greater variance. We observed no effect of composition-specific gap costs on retrieval performance. |
1205.2721 | Pascal Grange | Pascal Grange and Partha P. Mitra | Computational neuroanatomy and gene expression: optimal sets of marker
genes for brain regions | 6 pages, 5 figures | IEEE, 46th annual Conference on Information Sciences and Systems,
Princeton 2012 | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The three-dimensional data-driven Allen Gene Expression Atlas of the adult
mouse brain consists of numerized in-situ hybridization data for thousands of
genes, co-registered to the Allen Reference Atlas. We propose quantitative
criteria to rank genes as markers of a brain region, based on the localization
of the gene expression and on its functional fitting to the shape of the
region. These criteria lead to natural generalizations to sets of genes. We
find sets of genes weighted with coefficients of both signs with almost perfect
localization in all major regions of the left hemisphere of the brain, except
the pallidum. Generalization of the fitting criterion with positivity
constraint provides a lesser improvement of the markers, but requires sparser
sets of genes.
| [
{
"created": "Fri, 11 May 2012 21:04:11 GMT",
"version": "v1"
}
] | 2012-05-15 | [
[
"Grange",
"Pascal",
""
],
[
"Mitra",
"Partha P.",
""
]
] | The three-dimensional data-driven Allen Gene Expression Atlas of the adult mouse brain consists of numerized in-situ hybridization data for thousands of genes, co-registered to the Allen Reference Atlas. We propose quantitative criteria to rank genes as markers of a brain region, based on the localization of the gene expression and on its functional fitting to the shape of the region. These criteria lead to natural generalizations to sets of genes. We find sets of genes weighted with coefficients of both signs with almost perfect localization in all major regions of the left hemisphere of the brain, except the pallidum. Generalization of the fitting criterion with positivity constraint provides a lesser improvement of the markers, but requires sparser sets of genes. |
2011.00659 | Navodini Wijethilake | Navodini Wijethilake, Dulani Meedeniya, Charith Chitraranjan, Indika
Perera | Survival prediction and risk estimation of Glioma patients using mRNA
expressions | Presented at the 20th IEEE International Conference on BioInformatics
And BioEngineering (BIBE 2020) | null | null | null | q-bio.GN cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gliomas are lethal type of central nervous system tumors with a poor
prognosis. Recently, with the advancements in the micro-array technologies
thousands of gene expression related data of glioma patients are acquired,
leading for salient analysis in many aspects. Thus, genomics are been emerged
into the field of prognosis analysis. In this work, we identify survival
related 7 gene signature and explore two approaches for survival prediction and
risk estimation. For survival prediction, we propose a novel probabilistic
programming based approach, which outperforms the existing traditional machine
learning algorithms. An average 4 fold accuracy of 74% is obtained with the
proposed algorithm. Further, we construct a prognostic risk model for risk
estimation of glioma patients. This model reflects the survival of glioma
patients, with high risk for low survival patients.
| [
{
"created": "Mon, 2 Nov 2020 00:39:04 GMT",
"version": "v1"
}
] | 2020-11-03 | [
[
"Wijethilake",
"Navodini",
""
],
[
"Meedeniya",
"Dulani",
""
],
[
"Chitraranjan",
"Charith",
""
],
[
"Perera",
"Indika",
""
]
] | Gliomas are lethal type of central nervous system tumors with a poor prognosis. Recently, with the advancements in the micro-array technologies thousands of gene expression related data of glioma patients are acquired, leading for salient analysis in many aspects. Thus, genomics are been emerged into the field of prognosis analysis. In this work, we identify survival related 7 gene signature and explore two approaches for survival prediction and risk estimation. For survival prediction, we propose a novel probabilistic programming based approach, which outperforms the existing traditional machine learning algorithms. An average 4 fold accuracy of 74% is obtained with the proposed algorithm. Further, we construct a prognostic risk model for risk estimation of glioma patients. This model reflects the survival of glioma patients, with high risk for low survival patients. |
1605.02060 | Kwame Kutten | Kwame S. Kutten, Joshua T. Vogelstein, Nicolas Charon, Li Ye, Karl
Deisseroth, Michael I. Miller | Deformably Registering and Annotating Whole CLARITY Brains to an Atlas
via Masked LDDMM | null | Proc. SPIE 9896 Optics, Photonics and Digital Technologies for
Imaging Applications IV (2016) | 10.1117/12.2227444 | null | q-bio.QM cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The CLARITY method renders brains optically transparent to enable
high-resolution imaging in the structurally intact brain. Anatomically
annotating CLARITY brains is necessary for discovering which regions contain
signals of interest. Manually annotating whole-brain, terabyte CLARITY images
is difficult, time-consuming, subjective, and error-prone. Automatically
registering CLARITY images to a pre-annotated brain atlas offers a solution,
but is difficult for several reasons. Removal of the brain from the skull and
subsequent storage and processing cause variable non-rigid deformations, thus
compounding inter-subject anatomical variability. Additionally, the signal in
CLARITY images arises from various biochemical contrast agents which only
sparsely label brain structures. This sparse labeling challenges the most
commonly used registration algorithms that need to match image histogram
statistics to the more densely labeled histological brain atlases. The standard
method is a multiscale Mutual Information B-spline algorithm that dynamically
generates an average template as an intermediate registration target. We
determined that this method performs poorly when registering CLARITY brains to
the Allen Institute's Mouse Reference Atlas (ARA), because the image histogram
statistics are poorly matched. Therefore, we developed a method (Mask-LDDMM)
for registering CLARITY images, that automatically find the brain boundary and
learns the optimal deformation between the brain and atlas masks. Using
Mask-LDDMM without an average template provided better results than the
standard approach when registering CLARITY brains to the ARA. The LDDMM
pipelines developed here provide a fast automated way to anatomically annotate
CLARITY images. Our code is available as open source software at
http://NeuroData.io.
| [
{
"created": "Fri, 6 May 2016 19:51:27 GMT",
"version": "v1"
}
] | 2016-05-09 | [
[
"Kutten",
"Kwame S.",
""
],
[
"Vogelstein",
"Joshua T.",
""
],
[
"Charon",
"Nicolas",
""
],
[
"Ye",
"Li",
""
],
[
"Deisseroth",
"Karl",
""
],
[
"Miller",
"Michael I.",
""
]
] | The CLARITY method renders brains optically transparent to enable high-resolution imaging in the structurally intact brain. Anatomically annotating CLARITY brains is necessary for discovering which regions contain signals of interest. Manually annotating whole-brain, terabyte CLARITY images is difficult, time-consuming, subjective, and error-prone. Automatically registering CLARITY images to a pre-annotated brain atlas offers a solution, but is difficult for several reasons. Removal of the brain from the skull and subsequent storage and processing cause variable non-rigid deformations, thus compounding inter-subject anatomical variability. Additionally, the signal in CLARITY images arises from various biochemical contrast agents which only sparsely label brain structures. This sparse labeling challenges the most commonly used registration algorithms that need to match image histogram statistics to the more densely labeled histological brain atlases. The standard method is a multiscale Mutual Information B-spline algorithm that dynamically generates an average template as an intermediate registration target. We determined that this method performs poorly when registering CLARITY brains to the Allen Institute's Mouse Reference Atlas (ARA), because the image histogram statistics are poorly matched. Therefore, we developed a method (Mask-LDDMM) for registering CLARITY images, that automatically find the brain boundary and learns the optimal deformation between the brain and atlas masks. Using Mask-LDDMM without an average template provided better results than the standard approach when registering CLARITY brains to the ARA. The LDDMM pipelines developed here provide a fast automated way to anatomically annotate CLARITY images. Our code is available as open source software at http://NeuroData.io. |
2004.13178 | Ali Demirci | Semra Ahmetolan, Ayse Humeyra Bilge, Ali Demirci, Ayse Peker-Dobie,
Onder Ergonul | What Can We Estimate from Fatality and Infectious Case Data using the
Susceptible-Infected-Removed (SIR) model? A case Study of Covid-19 Pandemic | null | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rapidly spreading Covid-19 that affected almost all countries, was first
reported at the end of 2019. As a consequence of its highly infectious nature,
countries all over the world have imposed extremely strict measures to control
its spread. Since the earliest stages of this major pandemic, academics have
done a huge amount of research in order to understand the disease, develop
medication, vaccines and tests, and model its spread. Among these studies, a
great deal of effort has been invested in the estimation of epidemic parameters
in the early stage, for the countries affected by Covid-19, hence to predict
the course of the epidemic but the variability of the controls over the course
of the epidemic complicated the modeling processes. In this article, the
determination of the basic reproduction number, the mean duration of the
infectious period, the estimation of the timing of the peak of the epidemic
wave is discussed using early phase data. Daily case reports and daily
fatalities for ten countries over the period January 22, 2020 - April 18, 2020
are evaluated using the Susceptible-Infected-Removed (SIR) model. For each
country, the SIR models fitting cumulative infective case data within 5% error
are analysed. It is observed that the basic reproduction number and the mean
duration of the infectious period can be estimated only in cases where the
spread of the epidemic is over (for China and South Korea in the present case).
Nevertheless, it is shown that the timing of the maximum and timings of the
inflection points of the proportion of infected individuals can be robustly
estimated from the normalized data. The validation of the estimates by
comparing the predictions with actual data has shown that the predictions were
realised for all countries except USA, as long as lock-down measures were
retained.
| [
{
"created": "Mon, 27 Apr 2020 21:08:51 GMT",
"version": "v1"
},
{
"created": "Fri, 10 Jul 2020 09:57:01 GMT",
"version": "v2"
}
] | 2020-07-13 | [
[
"Ahmetolan",
"Semra",
""
],
[
"Bilge",
"Ayse Humeyra",
""
],
[
"Demirci",
"Ali",
""
],
[
"Peker-Dobie",
"Ayse",
""
],
[
"Ergonul",
"Onder",
""
]
] | The rapidly spreading Covid-19 that affected almost all countries, was first reported at the end of 2019. As a consequence of its highly infectious nature, countries all over the world have imposed extremely strict measures to control its spread. Since the earliest stages of this major pandemic, academics have done a huge amount of research in order to understand the disease, develop medication, vaccines and tests, and model its spread. Among these studies, a great deal of effort has been invested in the estimation of epidemic parameters in the early stage, for the countries affected by Covid-19, hence to predict the course of the epidemic but the variability of the controls over the course of the epidemic complicated the modeling processes. In this article, the determination of the basic reproduction number, the mean duration of the infectious period, the estimation of the timing of the peak of the epidemic wave is discussed using early phase data. Daily case reports and daily fatalities for ten countries over the period January 22, 2020 - April 18, 2020 are evaluated using the Susceptible-Infected-Removed (SIR) model. For each country, the SIR models fitting cumulative infective case data within 5% error are analysed. It is observed that the basic reproduction number and the mean duration of the infectious period can be estimated only in cases where the spread of the epidemic is over (for China and South Korea in the present case). Nevertheless, it is shown that the timing of the maximum and timings of the inflection points of the proportion of infected individuals can be robustly estimated from the normalized data. The validation of the estimates by comparing the predictions with actual data has shown that the predictions were realised for all countries except USA, as long as lock-down measures were retained. |
1509.07258 | Tuomo M\"aki-Marttunen | Tuomo M\"aki-Marttunen, Geir Halnes, Anna Devor, Aree Witoelar,
Francesco Bettella, Srdjan Djurovic, Yunpeng Wang, Gaute T. Einevoll, Ole A.
Andreassen, Anders M. Dale | Functional effects of schizophrenia-linked genetic variants on intrinsic
single-neuron excitability: A modeling study | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Recent genome-wide association studies (GWAS) have identified a
large number of genetic risk factors for schizophrenia (SCZ) featuring ion
channels and calcium transporters. For some of these risk factors, independent
prior investigations have examined the effects of genetic alterations on the
cellular electrical excitability and calcium homeostasis. In the present
proof-of-concept study, we harnessed these experimental results for modeling of
computational properties on layer V cortical pyramidal cell and identify
possible common alterations in behavior across SCZ-related genes.
Methods: We applied a biophysically detailed multi-compartmental model to
study the excitability of a layer V pyramidal cell. We reviewed the literature
on functional genomics for variants of genes associated with SCZ, and used
changes in neuron model parameters to represent the effects of these variants.
Results: We present and apply a framework for examining the effects of subtle
single nucleotide polymorphisms in ion channel and Ca2+ transporter-encoding
genes on neuron excitability. Our analysis indicates that most of the
considered SCZ- related genetic variants affect the spiking behavior and
intracellular calcium dynamics resulting from summation of inputs across the
dendritic tree.
Conclusions: Our results suggest that alteration in the ability of a single
neuron to integrate the inputs and scale its excitability may constitute a
fundamental mechanistic contributor to mental disease, alongside with the
previously proposed deficits in synaptic communication and network behavior.
| [
{
"created": "Thu, 24 Sep 2015 07:12:16 GMT",
"version": "v1"
}
] | 2015-09-25 | [
[
"Mäki-Marttunen",
"Tuomo",
""
],
[
"Halnes",
"Geir",
""
],
[
"Devor",
"Anna",
""
],
[
"Witoelar",
"Aree",
""
],
[
"Bettella",
"Francesco",
""
],
[
"Djurovic",
"Srdjan",
""
],
[
"Wang",
"Yunpeng",
""
],
[
"Einevoll",
"Gaute T.",
""
],
[
"Andreassen",
"Ole A.",
""
],
[
"Dale",
"Anders M.",
""
]
] | Background: Recent genome-wide association studies (GWAS) have identified a large number of genetic risk factors for schizophrenia (SCZ) featuring ion channels and calcium transporters. For some of these risk factors, independent prior investigations have examined the effects of genetic alterations on the cellular electrical excitability and calcium homeostasis. In the present proof-of-concept study, we harnessed these experimental results for modeling of computational properties on layer V cortical pyramidal cell and identify possible common alterations in behavior across SCZ-related genes. Methods: We applied a biophysically detailed multi-compartmental model to study the excitability of a layer V pyramidal cell. We reviewed the literature on functional genomics for variants of genes associated with SCZ, and used changes in neuron model parameters to represent the effects of these variants. Results: We present and apply a framework for examining the effects of subtle single nucleotide polymorphisms in ion channel and Ca2+ transporter-encoding genes on neuron excitability. Our analysis indicates that most of the considered SCZ- related genetic variants affect the spiking behavior and intracellular calcium dynamics resulting from summation of inputs across the dendritic tree. Conclusions: Our results suggest that alteration in the ability of a single neuron to integrate the inputs and scale its excitability may constitute a fundamental mechanistic contributor to mental disease, alongside with the previously proposed deficits in synaptic communication and network behavior. |
q-bio/0605022 | Kazuya Ishibashi | Kazuya Ishibashi, Kosuke Hamaguchi, and Masato Okada | Theory of Interaction of Memory Patterns in Layered Associative Networks | null | null | 10.1143/JPSJ.75.114803 | null | q-bio.NC | null | A synfire chain is a network that can generate repeated spike patterns with
millisecond precision. Although synfire chains with only one activity
propagation mode have been intensively analyzed with several neuron models,
those with several stable propagation modes have not been thoroughly
investigated. By using the leaky integrate-and-fire neuron model, we
constructed a layered associative network embedded with memory patterns. We
analyzed the network dynamics with the Fokker-Planck equation. First, we
addressed the stability of one memory pattern as a propagating spike volley. We
showed that memory patterns propagate as pulse packets. Second, we investigated
the activity when we activated two different memory patterns. Simultaneous
activation of two memory patterns with the same strength led the propagating
pattern to a mixed state. In contrast, when the activations had different
strengths, the pulse packet converged to a two-peak state. Finally, we studied
the effect of the preceding pulse packet on the following pulse packet. The
following pulse packet was modified from its original activated memory pattern,
and it converged to a two-peak state, mixed state or non-spike state depending
on the time interval.
| [
{
"created": "Mon, 15 May 2006 03:17:48 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Ishibashi",
"Kazuya",
""
],
[
"Hamaguchi",
"Kosuke",
""
],
[
"Okada",
"Masato",
""
]
] | A synfire chain is a network that can generate repeated spike patterns with millisecond precision. Although synfire chains with only one activity propagation mode have been intensively analyzed with several neuron models, those with several stable propagation modes have not been thoroughly investigated. By using the leaky integrate-and-fire neuron model, we constructed a layered associative network embedded with memory patterns. We analyzed the network dynamics with the Fokker-Planck equation. First, we addressed the stability of one memory pattern as a propagating spike volley. We showed that memory patterns propagate as pulse packets. Second, we investigated the activity when we activated two different memory patterns. Simultaneous activation of two memory patterns with the same strength led the propagating pattern to a mixed state. In contrast, when the activations had different strengths, the pulse packet converged to a two-peak state. Finally, we studied the effect of the preceding pulse packet on the following pulse packet. The following pulse packet was modified from its original activated memory pattern, and it converged to a two-peak state, mixed state or non-spike state depending on the time interval. |
2311.04232 | Siddhartha Srivastava | Siddhartha Srivastava and Krishna Garikipati | Pattern formation in dense populations studied by inference of nonlinear
diffusion-reaction mechanisms | null | null | null | null | q-bio.QM physics.bio-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Reaction-diffusion systems have been proposed as a model for pattern
formation and morphogenesis. The Fickian diffusion typically employed in these
constructions model the Brownian motion of particles. The biological and
chemical elements that form the basis of this process, like cells and proteins,
occupy finite mass and volume and interact during migration. We propose a
Reaction-diffusion system with Maxwell-Stefan formulation to construct the
diffusive flux. This formulation relies on inter-species force balance and
provides a more realistic model for interacting elements. We also present a
variational system inference-based technique to extract these models from
spatiotemporal data for these processes. We show that the inferred models can
capture the characteristics of local Turing instability that instigates the
pattern formation process. Moreover, the equilibrium solutions of the inferred
models form similar patterns to the observed data.
| [
{
"created": "Sat, 4 Nov 2023 17:17:10 GMT",
"version": "v1"
}
] | 2023-11-09 | [
[
"Srivastava",
"Siddhartha",
""
],
[
"Garikipati",
"Krishna",
""
]
] | Reaction-diffusion systems have been proposed as a model for pattern formation and morphogenesis. The Fickian diffusion typically employed in these constructions model the Brownian motion of particles. The biological and chemical elements that form the basis of this process, like cells and proteins, occupy finite mass and volume and interact during migration. We propose a Reaction-diffusion system with Maxwell-Stefan formulation to construct the diffusive flux. This formulation relies on inter-species force balance and provides a more realistic model for interacting elements. We also present a variational system inference-based technique to extract these models from spatiotemporal data for these processes. We show that the inferred models can capture the characteristics of local Turing instability that instigates the pattern formation process. Moreover, the equilibrium solutions of the inferred models form similar patterns to the observed data. |
1910.14452 | Xiaoxian Tang | Carsten Conradi, Nida Obatake, Anne Shiu, Xiaoxian Tang | Dynamics of ERK regulation in the processive limit | 22 pages, 2 figures, 3 tables, builds on arXiv:1903.02617 | null | null | null | q-bio.MN math.AG math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a model of extracellular signal-regulated kinase (ERK) regulation
by dual-site phosphorylation and dephosphorylation, which exhibits bistability
and oscillations, but loses these properties in the limit in which the
mechanisms underlying phosphorylation and dephosphorylation become processive.
Our results suggest that anywhere along the way to becoming processive, the
model remains bistable and oscillatory. More precisely, in simplified versions
of the model, precursors to bistability and oscillations (specifically,
multistationarity and Hopf bifurcations, respectively) exist at all
"processivity levels". Finally, we investigate whether bistability and
oscillations can exist together.
| [
{
"created": "Thu, 31 Oct 2019 13:20:54 GMT",
"version": "v1"
},
{
"created": "Thu, 3 Sep 2020 08:58:37 GMT",
"version": "v2"
}
] | 2020-09-04 | [
[
"Conradi",
"Carsten",
""
],
[
"Obatake",
"Nida",
""
],
[
"Shiu",
"Anne",
""
],
[
"Tang",
"Xiaoxian",
""
]
] | We consider a model of extracellular signal-regulated kinase (ERK) regulation by dual-site phosphorylation and dephosphorylation, which exhibits bistability and oscillations, but loses these properties in the limit in which the mechanisms underlying phosphorylation and dephosphorylation become processive. Our results suggest that anywhere along the way to becoming processive, the model remains bistable and oscillatory. More precisely, in simplified versions of the model, precursors to bistability and oscillations (specifically, multistationarity and Hopf bifurcations, respectively) exist at all "processivity levels". Finally, we investigate whether bistability and oscillations can exist together. |
q-bio/0503006 | Gerardo Chowell | Gerardo Chowell, Nick W. Hengartner, Carlos Castillo-Chavez, and Paul
W. Fenimore, J. M. Hyman | The Basic Reproductive Number of Ebola and the Effects of Public Health
Measures: The Cases of Congo and Uganda | 27 pages, 7 figures | Journal of Theoretical Biology 229 (2004) | null | LA-UR-03-8189 | q-bio.OT | null | Despite improved control measures, Ebola remains a serious public health risk
in African regions where recurrent outbreaks have been observed since the
initial epidemic in 1976. Using epidemic modeling and data from two
well-documented Ebola outbreaks (Congo 1995 and Uganda 2000), we estimate the
number of secondary cases generated by an index case in the absence of control
interventions ($R_0$). Our estimate of $R_0$ is 1.83 (SD 0.06) for Congo (1995)
and 1.34 (SD 0.03) for Uganda (2000). We model the course of the outbreaks via
an SEIR (susceptible-exposed-infectious-removed) epidemic model that includes a
smooth transition in the transmission rate after control interventions are put
in place. We perform an uncertainty analysis of the basic reproductive number
$R_0$ to quantify its sensitivity to other disease-related parameters. We also
analyze the sensitivity of the final epidemic size to the time interventions
begin and provide a distribution for the final epidemic size. The control
measures implemented during these two outbreaks (including education and
contact tracing followed by quarantine) reduce the final epidemic size by a
factor of 2 relative the final size with a two-week delay in their
implementation.
| [
{
"created": "Tue, 1 Mar 2005 23:52:47 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Chowell",
"Gerardo",
""
],
[
"Hengartner",
"Nick W.",
""
],
[
"Castillo-Chavez",
"Carlos",
""
],
[
"Fenimore",
"Paul W.",
""
],
[
"Hyman",
"J. M.",
""
]
] | Despite improved control measures, Ebola remains a serious public health risk in African regions where recurrent outbreaks have been observed since the initial epidemic in 1976. Using epidemic modeling and data from two well-documented Ebola outbreaks (Congo 1995 and Uganda 2000), we estimate the number of secondary cases generated by an index case in the absence of control interventions ($R_0$). Our estimate of $R_0$ is 1.83 (SD 0.06) for Congo (1995) and 1.34 (SD 0.03) for Uganda (2000). We model the course of the outbreaks via an SEIR (susceptible-exposed-infectious-removed) epidemic model that includes a smooth transition in the transmission rate after control interventions are put in place. We perform an uncertainty analysis of the basic reproductive number $R_0$ to quantify its sensitivity to other disease-related parameters. We also analyze the sensitivity of the final epidemic size to the time interventions begin and provide a distribution for the final epidemic size. The control measures implemented during these two outbreaks (including education and contact tracing followed by quarantine) reduce the final epidemic size by a factor of 2 relative the final size with a two-week delay in their implementation. |
0812.0622 | Eduardo D. Sontag | Eduardo D. Sontag | Remarks on Feedforward Circuits, Adaptation, and Pulse Memory | Updates version 1; added many references, simulations, examples, and
also more comments on approximate adaptation | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This note studies feedforward circuits as models for perfect adaptation to
step signals in biological systems. A global convergence theorem is proved in a
general framework, which includes examples from the literature as particular
cases. A notable aspect of these circuits is that they do not adapt to pulse
signals, because they display a memory phenomenon. Estimates are given of the
magnitude of this effect.
| [
{
"created": "Wed, 3 Dec 2008 02:57:01 GMT",
"version": "v1"
},
{
"created": "Sun, 7 Jun 2009 15:31:05 GMT",
"version": "v2"
}
] | 2009-06-07 | [
[
"Sontag",
"Eduardo D.",
""
]
] | This note studies feedforward circuits as models for perfect adaptation to step signals in biological systems. A global convergence theorem is proved in a general framework, which includes examples from the literature as particular cases. A notable aspect of these circuits is that they do not adapt to pulse signals, because they display a memory phenomenon. Estimates are given of the magnitude of this effect. |
1801.08722 | Nathalie Gon | Sylvie Luche (1), Elise Eymard-Vernain (1), H\'el\`ene Diemer (2),
Alain Van Dorsselaer (2), Thierry Rabilloud (1), C\'ecile Lelong (1) ((1)
LCBM - UMR 5249, (2) LSMBO-DSA-IPHC) | Zinc oxide induces the stringent response and major reorientations in
the central metabolism of Bacillus subtilis | null | Journal of Proteomics, Elsevier, 2016, 135 (Sp{\'e}ciale Issue),
pp.170-180 | 10.1016/j.jprot.2015.07.018 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Microorganisms, such as bacteria, are one of the first targets of
nanoparticles in the environment. In this study, we tested the effect of two
nanoparticles, ZnO and TiO2, with the salt ZnSO4 as the control, on the
Gram-positive bacterium Bacillus subtilis by 2D gel electrophoresis-based
proteomics. Despite a significant effect on viability (LD50), TiO2 NPs had no
detectable effect on the proteomic pattern, while ZnO NPs and ZnSO4
significantly modified B. subtilis metabolism. These results allowed us to
conclude that the effects of ZnO observed in this work were mainly attributable
to Zn dissolution in the culture media. Proteomic analysis highlighted twelve
modulated proteins related to central metabolism: MetE and MccB (cysteine
metabolism), OdhA, AspB, IolD, AnsB, PdhB and YtsJ (Krebs cycle) and XylA,
YqjI, Drm and Tal (pentose phosphate pathway). Biochemical assays, such as free
sulfhydryl, CoA-SH and malate dehydrogenase assays corroborated the observed
central metabolism reorientation and showed that Zn stress induced oxidative
stress, probably as a consequence of thiol chelation stress by Zn ions. The
other patterns affected by ZnO and ZnSO4 were the stringent response and the
general stress response. Nine proteins involved in or controlled by the
stringent response showed a modified expression profile in the presence of ZnO
NPs or ZnSO4: YwaC, SigH, YtxH, YtzB, TufA, RplJ, RpsB, PdhB and Mbl. An
increase in the ppGpp concentration confirmed the involvement of the stringent
response during a Zn stress. All these metabolic reorientations in response to
Zn stress were probably the result of complex regulatory mechanisms including
at least the stringent response via YwaC.
| [
{
"created": "Fri, 26 Jan 2018 09:22:53 GMT",
"version": "v1"
}
] | 2018-06-22 | [
[
"Luche",
"Sylvie",
""
],
[
"Eymard-Vernain",
"Elise",
""
],
[
"Diemer",
"Hélène",
""
],
[
"Van Dorsselaer",
"Alain",
""
],
[
"Rabilloud",
"Thierry",
""
],
[
"Lelong",
"Cécile",
""
]
] | Microorganisms, such as bacteria, are one of the first targets of nanoparticles in the environment. In this study, we tested the effect of two nanoparticles, ZnO and TiO2, with the salt ZnSO4 as the control, on the Gram-positive bacterium Bacillus subtilis by 2D gel electrophoresis-based proteomics. Despite a significant effect on viability (LD50), TiO2 NPs had no detectable effect on the proteomic pattern, while ZnO NPs and ZnSO4 significantly modified B. subtilis metabolism. These results allowed us to conclude that the effects of ZnO observed in this work were mainly attributable to Zn dissolution in the culture media. Proteomic analysis highlighted twelve modulated proteins related to central metabolism: MetE and MccB (cysteine metabolism), OdhA, AspB, IolD, AnsB, PdhB and YtsJ (Krebs cycle) and XylA, YqjI, Drm and Tal (pentose phosphate pathway). Biochemical assays, such as free sulfhydryl, CoA-SH and malate dehydrogenase assays corroborated the observed central metabolism reorientation and showed that Zn stress induced oxidative stress, probably as a consequence of thiol chelation stress by Zn ions. The other patterns affected by ZnO and ZnSO4 were the stringent response and the general stress response. Nine proteins involved in or controlled by the stringent response showed a modified expression profile in the presence of ZnO NPs or ZnSO4: YwaC, SigH, YtxH, YtzB, TufA, RplJ, RpsB, PdhB and Mbl. An increase in the ppGpp concentration confirmed the involvement of the stringent response during a Zn stress. All these metabolic reorientations in response to Zn stress were probably the result of complex regulatory mechanisms including at least the stringent response via YwaC. |
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