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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1307.6914 | Artem Kaznatcheev | Artem Kaznatcheev, Jacob G. Scott, David Basanta | Edge effects in game theoretic dynamics of spatially structured tumours | 14 pages, 3 figures; restructured abstract, added histology to fig.
1, added fig. 3, discussion of EMT introduced and cancer biology expanded | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Analysing tumour architecture for metastatic potential usually
focuses on phenotypic differences due to cellular morphology or specific
genetic mutations, but often ignore the cell's position within the
heterogeneous substructure. Similar disregard for local neighborhood structure
is common in mathematical models.
Methods: We view the dynamics of disease progression as an evolutionary game
between cellular phenotypes. A typical assumption in this modeling paradigm is
that the probability of a given phenotypic strategy interacting with another
depends exclusively on the abundance of those strategies without regard local
heterogeneities. We address this limitation by using the Ohtsuki-Nowak
transform to introduce spatial structure to the go vs. grow game.
Results: We show that spatial structure can promote the invasive (go)
strategy. By considering the change in neighbourhood size at a static boundary
-- such as a blood-vessel, organ capsule, or basement membrane -- we show an
edge effect that allows a tumour without invasive phenotypes in the bulk to
have a polyclonal boundary with invasive cells. We present an example of this
promotion of invasive (EMT positive) cells in a metastatic colony of prostate
adenocarcinoma in bone marrow.
Interpretation: Pathologic analyses that do not distinguish between cells in
the bulk and cells at a static edge of a tumour can underestimate the number of
invasive cells. We expect our approach to extend to other evolutionary game
models where interaction neighborhoods change at fixed system boundaries.
| [
{
"created": "Fri, 26 Jul 2013 03:12:34 GMT",
"version": "v1"
},
{
"created": "Fri, 10 Jan 2014 19:36:48 GMT",
"version": "v2"
},
{
"created": "Wed, 21 Jan 2015 21:10:00 GMT",
"version": "v3"
}
] | 2015-01-23 | [
[
"Kaznatcheev",
"Artem",
""
],
[
"Scott",
"Jacob G.",
""
],
[
"Basanta",
"David",
""
]
] | Background: Analysing tumour architecture for metastatic potential usually focuses on phenotypic differences due to cellular morphology or specific genetic mutations, but often ignore the cell's position within the heterogeneous substructure. Similar disregard for local neighborhood structure is common in mathematical models. Methods: We view the dynamics of disease progression as an evolutionary game between cellular phenotypes. A typical assumption in this modeling paradigm is that the probability of a given phenotypic strategy interacting with another depends exclusively on the abundance of those strategies without regard local heterogeneities. We address this limitation by using the Ohtsuki-Nowak transform to introduce spatial structure to the go vs. grow game. Results: We show that spatial structure can promote the invasive (go) strategy. By considering the change in neighbourhood size at a static boundary -- such as a blood-vessel, organ capsule, or basement membrane -- we show an edge effect that allows a tumour without invasive phenotypes in the bulk to have a polyclonal boundary with invasive cells. We present an example of this promotion of invasive (EMT positive) cells in a metastatic colony of prostate adenocarcinoma in bone marrow. Interpretation: Pathologic analyses that do not distinguish between cells in the bulk and cells at a static edge of a tumour can underestimate the number of invasive cells. We expect our approach to extend to other evolutionary game models where interaction neighborhoods change at fixed system boundaries. |
1501.00910 | Clinton Goss Ph.D. | Clinton F. Goss | Native American Flute Ergonomics | revised version, 13 pages, 9 figures, 1 table | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by-nc-sa/3.0/ | This study surveyed ergonomic issues in 308 Native American flute players. It
also correlated the physical measurements of a subgroup of 33 participants with
the largest flute they found comfortable. The data was used to derive a
predictive formula for the largest comfortable flute based on physical
measurements. The median age of players was 63 years with a mean of 6.9 years
playing Native American flute. Females reported significantly less time playing
the instrument (p = .004), but significantly faster self-reported progress
rates (p = .001). Physical discomfort was experienced by 47-64% of players at
least some of the time. Over 10% of players reported moderate discomfort on an
average basis. Females report significantly higher maximum and average physical
discomfort than males (p < .001 and p = .015, respectively). Height, arm span,
hand span, and reported length of time playing and experience level all
correlated with the largest flute that the player found comfortable.
Multivariate coefficient analysis on those factors yielded a formula with a
strong correlation to the largest comfortable flute (r = +.650). However, the
formula does not have sufficient correlation to have value in predicting flute
design. Customization of Native American flutes with the goal of improving
ergonomics is proposed as a worthwhile goal.
| [
{
"created": "Fri, 2 Jan 2015 13:41:33 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Jan 2015 10:21:25 GMT",
"version": "v2"
}
] | 2015-01-07 | [
[
"Goss",
"Clinton F.",
""
]
] | This study surveyed ergonomic issues in 308 Native American flute players. It also correlated the physical measurements of a subgroup of 33 participants with the largest flute they found comfortable. The data was used to derive a predictive formula for the largest comfortable flute based on physical measurements. The median age of players was 63 years with a mean of 6.9 years playing Native American flute. Females reported significantly less time playing the instrument (p = .004), but significantly faster self-reported progress rates (p = .001). Physical discomfort was experienced by 47-64% of players at least some of the time. Over 10% of players reported moderate discomfort on an average basis. Females report significantly higher maximum and average physical discomfort than males (p < .001 and p = .015, respectively). Height, arm span, hand span, and reported length of time playing and experience level all correlated with the largest flute that the player found comfortable. Multivariate coefficient analysis on those factors yielded a formula with a strong correlation to the largest comfortable flute (r = +.650). However, the formula does not have sufficient correlation to have value in predicting flute design. Customization of Native American flutes with the goal of improving ergonomics is proposed as a worthwhile goal. |
1407.1135 | Arnaud Poret | Arnaud Poret, Claudio Monteiro Sousa, Jean-Pierre Boissel | Enhancing Boolean networks with continuous logical operators and edge
tuning | null | null | null | null | q-bio.MN q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Due to the scarcity of quantitative details about biological phenomena,
quantitative modeling in systems biology can be compromised, especially at the
subcellular scale. One way to get around this is qualitative modeling because
it requires few to no quantitative information. One of the most popular
qualitative modeling approaches is the Boolean network formalism. However,
Boolean models allow variables to take only two values, which can be too
simplistic in some cases. The present work proposes a modeling approach derived
from Boolean networks where continuous logical operators are used and where
edges can be tuned. Using continuous logical operators allows variables to be
more finely valued while remaining qualitative. To consider that some
biological interactions can be slower or weaker than other ones, edge states
are also computed in order to modulate in speed and strength the signal they
convey. The proposed formalism is illustrated on a toy network coming from the
epidermal growth factor receptor signaling pathway. The obtained simulations
show that continuous results are produced, thus allowing finer analysis. The
simulations also show that modulating the signal conveyed by the edges allows
to incorporate knowledge about the interactions they model. The goal is to
provide enhancements in the ability of qualitative models to simulate the
dynamics of biological networks while limiting the need of quantitative
information.
| [
{
"created": "Fri, 4 Jul 2014 06:53:53 GMT",
"version": "v1"
},
{
"created": "Tue, 12 Aug 2014 19:24:35 GMT",
"version": "v2"
},
{
"created": "Sat, 16 Aug 2014 07:23:48 GMT",
"version": "v3"
},
{
"created": "Tue, 26 Aug 2014 12:06:41 GMT",
"version": "v4"
},
{
"created": "Wed, 27 May 2015 07:31:38 GMT",
"version": "v5"
},
{
"created": "Wed, 20 Mar 2019 19:25:03 GMT",
"version": "v6"
}
] | 2019-03-22 | [
[
"Poret",
"Arnaud",
""
],
[
"Sousa",
"Claudio Monteiro",
""
],
[
"Boissel",
"Jean-Pierre",
""
]
] | Due to the scarcity of quantitative details about biological phenomena, quantitative modeling in systems biology can be compromised, especially at the subcellular scale. One way to get around this is qualitative modeling because it requires few to no quantitative information. One of the most popular qualitative modeling approaches is the Boolean network formalism. However, Boolean models allow variables to take only two values, which can be too simplistic in some cases. The present work proposes a modeling approach derived from Boolean networks where continuous logical operators are used and where edges can be tuned. Using continuous logical operators allows variables to be more finely valued while remaining qualitative. To consider that some biological interactions can be slower or weaker than other ones, edge states are also computed in order to modulate in speed and strength the signal they convey. The proposed formalism is illustrated on a toy network coming from the epidermal growth factor receptor signaling pathway. The obtained simulations show that continuous results are produced, thus allowing finer analysis. The simulations also show that modulating the signal conveyed by the edges allows to incorporate knowledge about the interactions they model. The goal is to provide enhancements in the ability of qualitative models to simulate the dynamics of biological networks while limiting the need of quantitative information. |
1311.3932 | Amin Emad | Minji Kim, Jonathan G. Ligo, Amin Emad, Farzad Farnoud (Hassanzadeh),
Olgica Milenkovic and Venugopal V. Veeravalli | MetaPar: Metagenomic Sequence Assembly via Iterative Reclassification | null | null | null | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a parallel algorithmic architecture for metagenomic sequence
assembly, termed MetaPar, which allows for significant reductions in assembly
time and consequently enables the processing of large genomic datasets on
computers with low memory usage. The gist of the approach is to iteratively
perform read (re)classification based on phylogenetic marker genes and
assembler outputs generated from random subsets of metagenomic reads. Once a
sufficiently accurate classification within genera is performed, de novo
metagenomic assemblers (such as Velvet or IDBA-UD) or reference based
assemblers may be used for contig construction. We analyze the performance of
MetaPar on synthetic data consisting of 15 randomly chosen species from the
NCBI database through the effective gap and effective coverage metrics.
| [
{
"created": "Fri, 15 Nov 2013 17:31:42 GMT",
"version": "v1"
}
] | 2013-11-18 | [
[
"Kim",
"Minji",
"",
"Hassanzadeh"
],
[
"Ligo",
"Jonathan G.",
"",
"Hassanzadeh"
],
[
"Emad",
"Amin",
"",
"Hassanzadeh"
],
[
"Farnoud",
"Farzad",
"",
"Hassanzadeh"
],
[
"Milenkovic",
"Olgica",
""
],
[
"Veeravalli",
"Venugopal V.",
""
]
] | We introduce a parallel algorithmic architecture for metagenomic sequence assembly, termed MetaPar, which allows for significant reductions in assembly time and consequently enables the processing of large genomic datasets on computers with low memory usage. The gist of the approach is to iteratively perform read (re)classification based on phylogenetic marker genes and assembler outputs generated from random subsets of metagenomic reads. Once a sufficiently accurate classification within genera is performed, de novo metagenomic assemblers (such as Velvet or IDBA-UD) or reference based assemblers may be used for contig construction. We analyze the performance of MetaPar on synthetic data consisting of 15 randomly chosen species from the NCBI database through the effective gap and effective coverage metrics. |
1510.02707 | Eduardo Eyras | Mireya Plass and Eduardo Eyras | Approaches to link RNA secondary structures with splicing regulation | 21 pages, 7 figures | Methods Mol Biol. 2014;1126:341-56 | 10.1007/978-1-62703-980-2_25 | null | q-bio.QM q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | In higher eukaryotes, alternative splicing is usually regulated by protein
factors, which bind to the pre-mRNA and affect the recognition of splicing
signals. There is recent evidence that the secondary structure of the pre-mRNA
may also play an important role in this process, either by facilitating or by
hindering the interaction with factors and small nuclear ribonucleoproteins
(snRNPs) that regulate splicing. Moreover, the secondary structure could play a
fundamental role in the splicing of yeast species, which lack many of the
regulatory splicing factors present in metazoans. This review describes the
steps in the analysis of the secondary structure of the pre-mRNA and its
possible relation to splicing. As a working example, we use the case of yeast
and the problem of the recognition of the 3-prime splice site.
| [
{
"created": "Fri, 9 Oct 2015 15:41:30 GMT",
"version": "v1"
}
] | 2015-10-12 | [
[
"Plass",
"Mireya",
""
],
[
"Eyras",
"Eduardo",
""
]
] | In higher eukaryotes, alternative splicing is usually regulated by protein factors, which bind to the pre-mRNA and affect the recognition of splicing signals. There is recent evidence that the secondary structure of the pre-mRNA may also play an important role in this process, either by facilitating or by hindering the interaction with factors and small nuclear ribonucleoproteins (snRNPs) that regulate splicing. Moreover, the secondary structure could play a fundamental role in the splicing of yeast species, which lack many of the regulatory splicing factors present in metazoans. This review describes the steps in the analysis of the secondary structure of the pre-mRNA and its possible relation to splicing. As a working example, we use the case of yeast and the problem of the recognition of the 3-prime splice site. |
2402.16638 | Aaron Meyer | Zhixin Cyrillus Tan, Aaron S. Meyer | The structure is the message: preserving experimental context through
tensor decomposition | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-sa/4.0/ | Recent biological studies have been revolutionized in scale and granularity
by multiplex and high-throughput assays. Profiling cell responses across
several experimental parameters, such as perturbations, time, and genetic
contexts, leads to richer and more generalizable findings. However, these
multidimensional datasets necessitate a reevaluation of the conventional
methods for their representation and analysis. Traditionally, experimental
parameters are merged to flatten the data into a two-dimensional matrix,
sacrificing crucial experiment context reflected by the structure. As Marshall
McLuhan famously stated, "The medium is the message." In this work, we propose
that the experiment structure is the medium in which subsequent analysis is
performed, and the optimal choice of data representation must reflect the
experiment structure. We introduce tensor-structured analyses and
decompositions to preserve this information. We contend that tensor methods are
poised to become integral to the biomedical data sciences toolkit.
| [
{
"created": "Mon, 26 Feb 2024 15:09:17 GMT",
"version": "v1"
}
] | 2024-02-27 | [
[
"Tan",
"Zhixin Cyrillus",
""
],
[
"Meyer",
"Aaron S.",
""
]
] | Recent biological studies have been revolutionized in scale and granularity by multiplex and high-throughput assays. Profiling cell responses across several experimental parameters, such as perturbations, time, and genetic contexts, leads to richer and more generalizable findings. However, these multidimensional datasets necessitate a reevaluation of the conventional methods for their representation and analysis. Traditionally, experimental parameters are merged to flatten the data into a two-dimensional matrix, sacrificing crucial experiment context reflected by the structure. As Marshall McLuhan famously stated, "The medium is the message." In this work, we propose that the experiment structure is the medium in which subsequent analysis is performed, and the optimal choice of data representation must reflect the experiment structure. We introduce tensor-structured analyses and decompositions to preserve this information. We contend that tensor methods are poised to become integral to the biomedical data sciences toolkit. |
1601.07830 | Vaibhav Madhok | Vaibhav Madhok | Efficient Simulations of Individual Based Models for Adaptive Dynamics
and the Canonical Equation | 9 pages, 2 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a faster algorithm for individual based simulations for adaptive
dynamics based on a simple modification to the standard Gillespie Algorithm for
simulating stochastic birth-death processes. We provide an analytical
explanation that shows that simulations based on the modified algorithm, in the
deterministic limit, lead to the same equations of adaptive dynamics as well as
same conditions for evolutionary branching as those obtained from the standard
Gillespie algorithm. Based on this algorithm, we provide an intuitive and
simple interpretation of the canonical equation of adaptive dynamics. With the
help of examples we compare the performance of this algorithm to the standard
Gillespie algorithm and demonstrate its efficiency. We also study an example
using this algorithm to study evolutionary dynamics in a multi-dimensional
phenotypic space and study the question of predictability of evolution.
| [
{
"created": "Thu, 28 Jan 2016 16:58:22 GMT",
"version": "v1"
}
] | 2016-01-29 | [
[
"Madhok",
"Vaibhav",
""
]
] | We propose a faster algorithm for individual based simulations for adaptive dynamics based on a simple modification to the standard Gillespie Algorithm for simulating stochastic birth-death processes. We provide an analytical explanation that shows that simulations based on the modified algorithm, in the deterministic limit, lead to the same equations of adaptive dynamics as well as same conditions for evolutionary branching as those obtained from the standard Gillespie algorithm. Based on this algorithm, we provide an intuitive and simple interpretation of the canonical equation of adaptive dynamics. With the help of examples we compare the performance of this algorithm to the standard Gillespie algorithm and demonstrate its efficiency. We also study an example using this algorithm to study evolutionary dynamics in a multi-dimensional phenotypic space and study the question of predictability of evolution. |
2110.12969 | Mahmoud Kargar | Mahmoud Kargar, Alireza Mohammadi | Auditory verbal learning disabilities in patients with mild cog
impairment and mild Alzheimer's disease: A clinical study | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Learning and memory impairments are common characteristics of individuals
with mild cognitive impairment (MCI) and mild Alzheimer's disease (miAD). Early
diagnosis of MCI is necessary to prevent recurrence of the disease and
developing of miAD. For this purpose, we investigated the components of the Rey
Auditory Verbal Learning Test (RAVLT) to explore the auditory-verbal learning
(AVL) disabilities in these patients. The AVL of 20 patients with miAD and 30
patients with MCI were compared with 30 cognitively normal controls (CN) using
the RAVLT. General cognitive performance assessment was carried out based on
the Mini-Mental State Examination (MMSE) score. Finally, Pearson's correlation
coefficients were used to evaluate the correlation between the MMSE scores and
immediate and delayed recalls, verbal learning and forgetting, and memory
recognition. We found that both miAD and MCI subjects were significantly
impaired in all components of the RAVLT. Compared to the MCI subjects, miAD
patients performed worse on all components of the test. The MCI subjects had
significantly lower scores than the CN group. The AVL analysis showed that
there were significant differences between the CN and other groups, but the
difference between MCI and miAD subjects was not significant. However, there
was no difference among the groups in their verbal forgetting scores. It can be
concluded that both patients with miAD and MCI were impaired in AVL and our
findings confirm that the RAVLT can take a part in the prediction of probable
miAD and early evaluation of individuals with subjective memory complaints.
| [
{
"created": "Fri, 22 Oct 2021 10:42:58 GMT",
"version": "v1"
}
] | 2021-10-26 | [
[
"Kargar",
"Mahmoud",
""
],
[
"Mohammadi",
"Alireza",
""
]
] | Learning and memory impairments are common characteristics of individuals with mild cognitive impairment (MCI) and mild Alzheimer's disease (miAD). Early diagnosis of MCI is necessary to prevent recurrence of the disease and developing of miAD. For this purpose, we investigated the components of the Rey Auditory Verbal Learning Test (RAVLT) to explore the auditory-verbal learning (AVL) disabilities in these patients. The AVL of 20 patients with miAD and 30 patients with MCI were compared with 30 cognitively normal controls (CN) using the RAVLT. General cognitive performance assessment was carried out based on the Mini-Mental State Examination (MMSE) score. Finally, Pearson's correlation coefficients were used to evaluate the correlation between the MMSE scores and immediate and delayed recalls, verbal learning and forgetting, and memory recognition. We found that both miAD and MCI subjects were significantly impaired in all components of the RAVLT. Compared to the MCI subjects, miAD patients performed worse on all components of the test. The MCI subjects had significantly lower scores than the CN group. The AVL analysis showed that there were significant differences between the CN and other groups, but the difference between MCI and miAD subjects was not significant. However, there was no difference among the groups in their verbal forgetting scores. It can be concluded that both patients with miAD and MCI were impaired in AVL and our findings confirm that the RAVLT can take a part in the prediction of probable miAD and early evaluation of individuals with subjective memory complaints. |
2403.06251 | Bin Duan | Bin Duan, Yuzhang Shang, Dawen Cai, and Yan Yan | Online Multi-spectral Neuron Tracing | null | null | null | null | q-bio.NC cs.CV cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper, we propose an online multi-spectral neuron tracing method with
uniquely designed modules, where no offline training are required. Our method
is trained online to update our enhanced discriminative correlation filter to
conglutinate the tracing process. This distinctive offline-training-free schema
differentiates us from other training-dependent tracing approaches like deep
learning methods since no annotation is needed for our method. Besides,
compared to other tracing methods requiring complicated set-up such as for
clustering and graph multi-cut, our approach is much easier to be applied to
new images. In fact, it only needs a starting bounding box of the tracing
neuron, significantly reducing users' configuration effort. Our extensive
experiments show that our training-free and easy-configured methodology allows
fast and accurate neuron reconstructions in multi-spectral images.
| [
{
"created": "Sun, 10 Mar 2024 16:34:21 GMT",
"version": "v1"
}
] | 2024-03-12 | [
[
"Duan",
"Bin",
""
],
[
"Shang",
"Yuzhang",
""
],
[
"Cai",
"Dawen",
""
],
[
"Yan",
"Yan",
""
]
] | In this paper, we propose an online multi-spectral neuron tracing method with uniquely designed modules, where no offline training are required. Our method is trained online to update our enhanced discriminative correlation filter to conglutinate the tracing process. This distinctive offline-training-free schema differentiates us from other training-dependent tracing approaches like deep learning methods since no annotation is needed for our method. Besides, compared to other tracing methods requiring complicated set-up such as for clustering and graph multi-cut, our approach is much easier to be applied to new images. In fact, it only needs a starting bounding box of the tracing neuron, significantly reducing users' configuration effort. Our extensive experiments show that our training-free and easy-configured methodology allows fast and accurate neuron reconstructions in multi-spectral images. |
1110.1114 | Katherine St. John | Alan Joseph J. Caceres, Juan Castillo, Jinnie Lee, and Katherine St.
John | Walks on SPR Neighborhoods | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A nearest-neighbor-interchange (NNI) walk is a sequence of unrooted
phylogenetic trees, T_0, T_1, T_2,... where each consecutive pair of trees
differ by a single NNI move. We give tight bounds on the length of the shortest
NNI-walks that visit all trees in an subtree-prune-and-regraft (SPR)
neighborhood of a given tree. For any unrooted, binary tree, T, on n leaves,
the shortest walk takes {\theta}(n^2) additional steps than the number of trees
in the SPR neighborhood. This answers Bryant's Second Combinatorial Conjecture
from the Phylogenetics Challenges List, the Isaac Newton Institute, 2011, and
the Penny Ante Problem List, 2009.
| [
{
"created": "Wed, 5 Oct 2011 22:50:59 GMT",
"version": "v1"
}
] | 2011-10-07 | [
[
"Caceres",
"Alan Joseph J.",
""
],
[
"Castillo",
"Juan",
""
],
[
"Lee",
"Jinnie",
""
],
[
"John",
"Katherine St.",
""
]
] | A nearest-neighbor-interchange (NNI) walk is a sequence of unrooted phylogenetic trees, T_0, T_1, T_2,... where each consecutive pair of trees differ by a single NNI move. We give tight bounds on the length of the shortest NNI-walks that visit all trees in an subtree-prune-and-regraft (SPR) neighborhood of a given tree. For any unrooted, binary tree, T, on n leaves, the shortest walk takes {\theta}(n^2) additional steps than the number of trees in the SPR neighborhood. This answers Bryant's Second Combinatorial Conjecture from the Phylogenetics Challenges List, the Isaac Newton Institute, 2011, and the Penny Ante Problem List, 2009. |
2206.13574 | Haowen Zhang | Haowen Zhang, Shiqi Wu, Srinivas Aluru, Heng Li | Fast sequence to graph alignment using the graph wavefront algorithm | null | null | null | null | q-bio.GN q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Motivation: A pan-genome graph represents a collection of genomes and encodes
sequence variations between them. It is a powerful data structure for studying
multiple similar genomes. Sequence-to-graph alignment is an essential step for
the construction and the analysis of pan-genome graphs. However, existing
algorithms incur runtime proportional to the product of sequence length and
graph size, making them inefficient for aligning long sequences against large
graphs. Results: We propose the graph wavefront alignment algorithm (Gwfa), a
new method for aligning a sequence to a sequence graph. Although the worst-case
time complexity of Gwfa is the same as the existing algorithms, it is designed
to run faster for closely matching sequences, and its runtime in practice often
increases only moderately with the edit distance of the optimal alignment. On
four real datasets, Gwfa is up to four orders of magnitude faster than other
exact sequence-to-graph alignment algorithms. We also propose a graph pruning
heuristic on top of Gwfa, which can achieve an additional $\sim$10-fold speedup
on large graphs. Availability: Gwfa code is accessible at
https://github.com/lh3/gwfa.
| [
{
"created": "Mon, 27 Jun 2022 18:20:44 GMT",
"version": "v1"
}
] | 2022-06-29 | [
[
"Zhang",
"Haowen",
""
],
[
"Wu",
"Shiqi",
""
],
[
"Aluru",
"Srinivas",
""
],
[
"Li",
"Heng",
""
]
] | Motivation: A pan-genome graph represents a collection of genomes and encodes sequence variations between them. It is a powerful data structure for studying multiple similar genomes. Sequence-to-graph alignment is an essential step for the construction and the analysis of pan-genome graphs. However, existing algorithms incur runtime proportional to the product of sequence length and graph size, making them inefficient for aligning long sequences against large graphs. Results: We propose the graph wavefront alignment algorithm (Gwfa), a new method for aligning a sequence to a sequence graph. Although the worst-case time complexity of Gwfa is the same as the existing algorithms, it is designed to run faster for closely matching sequences, and its runtime in practice often increases only moderately with the edit distance of the optimal alignment. On four real datasets, Gwfa is up to four orders of magnitude faster than other exact sequence-to-graph alignment algorithms. We also propose a graph pruning heuristic on top of Gwfa, which can achieve an additional $\sim$10-fold speedup on large graphs. Availability: Gwfa code is accessible at https://github.com/lh3/gwfa. |
1911.08646 | Gennadi Glinsky | Gennadi V. Glinsky | A genomic dominion with regulatory dependencies on human-specific
single-nucleotide changes in Modern Humans | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene set enrichment analyses of 8,405 genes linked with 35,074 human-specific
(hs) regulatory single-nucleotide changes (SNCs) revealed the staggering
breadth of significant associations with morphological structures,
physiological processes, and pathological conditions of Modern Humans.
Significant enrichment traits include more than 1,000 anatomically-distinct
regions of the adult human brain, many different types of human cells and
tissues, more than 200 common human disorders and more than 1,000 records of
rare diseases. Thousands of genes connected with regulatory hsSNCs have been
identified in this contribution, which represent essential genetic elements of
the autosomal inheritance and survival of species phenotypes: a total of 1,494
genes linked with either autosomal dominant or recessive inheritance as well as
2,273 genes associated with premature death, embryonic lethality, as well as
pre-, peri-, neo-, and post-natal lethality of both complete and incomplete
penetrance. Therefore, thousands of heritable traits and critical genes
impacting the offspring survival appear under the human-specific regulatory
control in genomes of Modern Humans. These observations highlight the
remarkable translational opportunities afforded by the discovery of genetic
regulatory loci harboring hsSNCs that are fixed in humans, distinct from other
primates, and located in differentially-accessible (DA) chromatin regions
during human brain development.
| [
{
"created": "Wed, 20 Nov 2019 00:47:34 GMT",
"version": "v1"
}
] | 2019-11-21 | [
[
"Glinsky",
"Gennadi V.",
""
]
] | Gene set enrichment analyses of 8,405 genes linked with 35,074 human-specific (hs) regulatory single-nucleotide changes (SNCs) revealed the staggering breadth of significant associations with morphological structures, physiological processes, and pathological conditions of Modern Humans. Significant enrichment traits include more than 1,000 anatomically-distinct regions of the adult human brain, many different types of human cells and tissues, more than 200 common human disorders and more than 1,000 records of rare diseases. Thousands of genes connected with regulatory hsSNCs have been identified in this contribution, which represent essential genetic elements of the autosomal inheritance and survival of species phenotypes: a total of 1,494 genes linked with either autosomal dominant or recessive inheritance as well as 2,273 genes associated with premature death, embryonic lethality, as well as pre-, peri-, neo-, and post-natal lethality of both complete and incomplete penetrance. Therefore, thousands of heritable traits and critical genes impacting the offspring survival appear under the human-specific regulatory control in genomes of Modern Humans. These observations highlight the remarkable translational opportunities afforded by the discovery of genetic regulatory loci harboring hsSNCs that are fixed in humans, distinct from other primates, and located in differentially-accessible (DA) chromatin regions during human brain development. |
q-bio/0408010 | Manuel Middendorf | Manuel Middendorf, Etay Ziv, Chris Wiggins | Inferring Network Mechanisms: The Drosophila melanogaster Protein
Interaction Network | 19 pages, 5 figures | PNAS, Vol. 102, No. 9, pp. 3192-3197 (March 1, 2005) | 10.1073/pnas.0409515102 | null | q-bio.QM q-bio.MN | null | Naturally occurring networks exhibit quantitative features revealing
underlying growth mechanisms. Numerous network mechanisms have recently been
proposed to reproduce specific properties such as degree distributions or
clustering coefficients. We present a method for inferring the mechanism most
accurately capturing a given network topology, exploiting discriminative tools
from machine learning. The Drosophila melanogaster protein network is
confidently and robustly (to noise and training data subsampling) classified as
a duplication-mutation-complementation network over preferential attachment,
small-world, and other duplication-mutation mechanisms. Systematic
classification, rather than statistical study of specific properties, provides
a discriminative approach to understand the design of complex networks.
| [
{
"created": "Sun, 15 Aug 2004 17:09:47 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Middendorf",
"Manuel",
""
],
[
"Ziv",
"Etay",
""
],
[
"Wiggins",
"Chris",
""
]
] | Naturally occurring networks exhibit quantitative features revealing underlying growth mechanisms. Numerous network mechanisms have recently been proposed to reproduce specific properties such as degree distributions or clustering coefficients. We present a method for inferring the mechanism most accurately capturing a given network topology, exploiting discriminative tools from machine learning. The Drosophila melanogaster protein network is confidently and robustly (to noise and training data subsampling) classified as a duplication-mutation-complementation network over preferential attachment, small-world, and other duplication-mutation mechanisms. Systematic classification, rather than statistical study of specific properties, provides a discriminative approach to understand the design of complex networks. |
1912.04246 | Santosh Manicka | Santosh Manicka and Michael Levin | Modeling somatic computation with non-neural bioelectric networks | 30 pages, 8 figures in main article | Sci Rep 9, 18612 (2019) | 10.1038/s41598-019-54859-8 | null | q-bio.NC cs.NE nlin.AO | http://creativecommons.org/licenses/by/4.0/ | The field of basal cognition seeks to understand how adaptive,
context-specific behavior occurs in non-neural biological systems.
Embryogenesis and regeneration require plasticity in many tissue types to
achieve structural and functional goals in diverse circumstances. Thus,
advances in both evolutionary cell biology and regenerative medicine require an
understanding of how non-neural tissues could process information. Neurons
evolved from ancient cell types that used bioelectric signaling to perform
computation. However, it has not been shown whether or how non-neural
bioelectric cell networks can support computation. We generalize connectionist
methods to non-neural tissue architectures, showing that a minimal non-neural
Bio-Electric Network (BEN) model that utilizes the general principles of
bioelectricity (electrodiffusion and gating) can compute. We characterize BEN
behaviors ranging from elementary logic gates to pattern detectors, using both
fixed and transient inputs to recapitulate various biological scenarios. We
characterize the mechanisms of such networks using dynamical-systems and
information-theory tools, demonstrating that logic can manifest in
bidirectional, continuous, and relatively slow bioelectrical systems,
complementing conventional neural-centric architectures. Our results reveal a
variety of non-neural decision-making processes as manifestations of general
cellular biophysical mechanisms and suggest novel bioengineering approaches to
construct functional tissues for regenerative medicine and synthetic biology as
well as new machine learning architectures.
| [
{
"created": "Mon, 9 Dec 2019 18:36:14 GMT",
"version": "v1"
}
] | 2019-12-10 | [
[
"Manicka",
"Santosh",
""
],
[
"Levin",
"Michael",
""
]
] | The field of basal cognition seeks to understand how adaptive, context-specific behavior occurs in non-neural biological systems. Embryogenesis and regeneration require plasticity in many tissue types to achieve structural and functional goals in diverse circumstances. Thus, advances in both evolutionary cell biology and regenerative medicine require an understanding of how non-neural tissues could process information. Neurons evolved from ancient cell types that used bioelectric signaling to perform computation. However, it has not been shown whether or how non-neural bioelectric cell networks can support computation. We generalize connectionist methods to non-neural tissue architectures, showing that a minimal non-neural Bio-Electric Network (BEN) model that utilizes the general principles of bioelectricity (electrodiffusion and gating) can compute. We characterize BEN behaviors ranging from elementary logic gates to pattern detectors, using both fixed and transient inputs to recapitulate various biological scenarios. We characterize the mechanisms of such networks using dynamical-systems and information-theory tools, demonstrating that logic can manifest in bidirectional, continuous, and relatively slow bioelectrical systems, complementing conventional neural-centric architectures. Our results reveal a variety of non-neural decision-making processes as manifestations of general cellular biophysical mechanisms and suggest novel bioengineering approaches to construct functional tissues for regenerative medicine and synthetic biology as well as new machine learning architectures. |
2304.06731 | Taslim Murad | Sarwan Ali, Taslim Murad, Murray Patterson | PCD2Vec: A Poisson Correction Distance-Based Approach for Viral Host
Classification | Accepted at International Joint Conference on Neural Networks (IJCNN)
2023 | null | null | null | q-bio.QM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Coronaviruses are membrane-enveloped, non-segmented positive-strand RNA
viruses belonging to the Coronaviridae family. Various animal species, mainly
mammalian and avian, are severely infected by various coronaviruses, causing
serious concerns like the recent pandemic (COVID-19). Therefore, building a
deeper understanding of these viruses is essential to devise prevention and
mitigation mechanisms. In the Coronavirus genome, an essential structural
region is the spike region, and it's responsible for attaching the virus to the
host cell membrane. Therefore, the usage of only the spike protein, instead of
the full genome, provides most of the essential information for performing
analyses such as host classification. In this paper, we propose a novel method
for predicting the host specificity of coronaviruses by analyzing spike protein
sequences from different viral subgenera and species. Our method involves using
the Poisson correction distance to generate a distance matrix, followed by
using a radial basis function (RBF) kernel and kernel principal component
analysis (PCA) to generate a low-dimensional embedding. Finally, we apply
classification algorithms to the low-dimensional embedding to generate the
resulting predictions of the host specificity of coronaviruses. We provide
theoretical proofs for the non-negativity, symmetry, and triangle inequality
properties of the Poisson correction distance metric, which are important
properties in a machine-learning setting. By encoding the spike protein
structure and sequences using this comprehensive approach, we aim to uncover
hidden patterns in the biological sequences to make accurate predictions about
host specificity. Finally, our classification results illustrate that our
method can achieve higher predictive accuracy and improve performance over
existing baselines.
| [
{
"created": "Thu, 13 Apr 2023 03:02:22 GMT",
"version": "v1"
}
] | 2023-04-17 | [
[
"Ali",
"Sarwan",
""
],
[
"Murad",
"Taslim",
""
],
[
"Patterson",
"Murray",
""
]
] | Coronaviruses are membrane-enveloped, non-segmented positive-strand RNA viruses belonging to the Coronaviridae family. Various animal species, mainly mammalian and avian, are severely infected by various coronaviruses, causing serious concerns like the recent pandemic (COVID-19). Therefore, building a deeper understanding of these viruses is essential to devise prevention and mitigation mechanisms. In the Coronavirus genome, an essential structural region is the spike region, and it's responsible for attaching the virus to the host cell membrane. Therefore, the usage of only the spike protein, instead of the full genome, provides most of the essential information for performing analyses such as host classification. In this paper, we propose a novel method for predicting the host specificity of coronaviruses by analyzing spike protein sequences from different viral subgenera and species. Our method involves using the Poisson correction distance to generate a distance matrix, followed by using a radial basis function (RBF) kernel and kernel principal component analysis (PCA) to generate a low-dimensional embedding. Finally, we apply classification algorithms to the low-dimensional embedding to generate the resulting predictions of the host specificity of coronaviruses. We provide theoretical proofs for the non-negativity, symmetry, and triangle inequality properties of the Poisson correction distance metric, which are important properties in a machine-learning setting. By encoding the spike protein structure and sequences using this comprehensive approach, we aim to uncover hidden patterns in the biological sequences to make accurate predictions about host specificity. Finally, our classification results illustrate that our method can achieve higher predictive accuracy and improve performance over existing baselines. |
2110.12040 | Daisuke Kihara | Lyman Monroe and Daisuke Kihara | Using Steered Molecular Dynamic Tension for Assessing Quality of
Computational Protein Structure Models | null | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The native structures of proteins, except for notable exceptions of
intrinsically disordered proteins, in general take their most stable
conformation in the physiological condition to maintain their structural
framework so that their biological function can be properly carried out.
Experimentally, the stability of a protein can be measured by several means,
among which the pulling experiment using the atomic force microscope (AFM)
stands as a unique method. AFM directly measures the resistance from unfolding,
which can be quantified from the observed force-extension profile. It has been
shown that key features observed in an AFM pulling experiment can be well
reproduced by computational molecular dynamics simulations. Here, we applied
computational pulling for estimating the accuracy of computational protein
structure models under the hypothesis that the structural stability would
positively correlated with the accuracy, i.e. the closeness to the native, of a
model. We used in total 4,929 structure models for 24 target proteins from the
Critical Assessment of Techniques of Structure Prediction (CASP) and
investigated if the magnitude of the break force, i.e., the force required to
rearrange the model structure, from the force profile was sufficient
information for selecting near-native models. We found that near-native models
can be successfully selected by examining their break forces suggesting that
high break force indeed indicates high stability of models. On the other hand,
there were also near-native models that had relatively low peak forces. The
mechanisms of the stability exhibited by the break forces were explored and
discussed.
| [
{
"created": "Fri, 22 Oct 2021 20:06:01 GMT",
"version": "v1"
}
] | 2021-10-26 | [
[
"Monroe",
"Lyman",
""
],
[
"Kihara",
"Daisuke",
""
]
] | The native structures of proteins, except for notable exceptions of intrinsically disordered proteins, in general take their most stable conformation in the physiological condition to maintain their structural framework so that their biological function can be properly carried out. Experimentally, the stability of a protein can be measured by several means, among which the pulling experiment using the atomic force microscope (AFM) stands as a unique method. AFM directly measures the resistance from unfolding, which can be quantified from the observed force-extension profile. It has been shown that key features observed in an AFM pulling experiment can be well reproduced by computational molecular dynamics simulations. Here, we applied computational pulling for estimating the accuracy of computational protein structure models under the hypothesis that the structural stability would positively correlated with the accuracy, i.e. the closeness to the native, of a model. We used in total 4,929 structure models for 24 target proteins from the Critical Assessment of Techniques of Structure Prediction (CASP) and investigated if the magnitude of the break force, i.e., the force required to rearrange the model structure, from the force profile was sufficient information for selecting near-native models. We found that near-native models can be successfully selected by examining their break forces suggesting that high break force indeed indicates high stability of models. On the other hand, there were also near-native models that had relatively low peak forces. The mechanisms of the stability exhibited by the break forces were explored and discussed. |
2201.06478 | Sitabhra Sinha | Chandrashekar Kuyyamudi, Shakti N. Menon and Sitabhra Sinha | Contact-mediated signaling enables disorder-driven transitions in
cellular assemblies | 6 pages, 3 figures + 5 pages Supplementary Information | Phys. Rev. E 106, L022401 (2022) | 10.1103/PhysRevE.106.L022401 | null | q-bio.TO nlin.AO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that when cells communicate by contact-mediated interactions,
heterogeneity in cell shapes and sizes leads to qualitatively distinct
collective behavior in the tissue. For inter-cellular coupling that implements
lateral inhibition, such disorder-driven transitions can substantially alter
the asymptotic pattern of differentiated cells by modulating their fate choice
through changes in the neighborhood geometry. In addition, when contact-induced
signals influence inherent cellular oscillations, disorder leads to the
emergence of functionally relevant partially-ordered dynamical states.
| [
{
"created": "Mon, 17 Jan 2022 15:44:12 GMT",
"version": "v1"
}
] | 2023-01-10 | [
[
"Kuyyamudi",
"Chandrashekar",
""
],
[
"Menon",
"Shakti N.",
""
],
[
"Sinha",
"Sitabhra",
""
]
] | We show that when cells communicate by contact-mediated interactions, heterogeneity in cell shapes and sizes leads to qualitatively distinct collective behavior in the tissue. For inter-cellular coupling that implements lateral inhibition, such disorder-driven transitions can substantially alter the asymptotic pattern of differentiated cells by modulating their fate choice through changes in the neighborhood geometry. In addition, when contact-induced signals influence inherent cellular oscillations, disorder leads to the emergence of functionally relevant partially-ordered dynamical states. |
2211.09906 | Thomas Papouin PhD | Ciaran Murphy-Royal, ShiNung Ching, Thomas Papouin | Contextual guidance: An integrated theory for astrocytes function in
brain circuits and behavior | 14 pages of main text, 5 figures, 1 box, 2 Tables | null | 10.1038/s41593-023-01448-8 | null | q-bio.NC q-bio.CB q-bio.SC | http://creativecommons.org/licenses/by/4.0/ | The participation of astrocytes in brain computation was formally
hypothesized in 1992, coinciding with the discovery that these glial cells
display a complex form of Ca2+ excitability. This fostered conceptual advances
centered on the notion of reciprocal interactions between neurons and
astrocytes, which permitted a critical leap forward in uncovering many roles of
astrocytes in brain circuits, and signaled the rise of a major new force in
neuroscience: that of glial biology. In the past decade, a multitude of
unconventional and disparate functions of astrocytes have been documented that
are not predicted by these canonical models and that are challenging to piece
together into a holistic and parsimonious picture. This highlights a disconnect
between the rapidly evolving field of astrocyte biology and the conceptual
frameworks guiding it, and emphasizes the need for a careful reconsideration of
how we theorize the functional position of astrocytes in brain circuitry. Here,
we propose a unifying, highly transferable, data-driven, and
computationally-relevant conceptual framework for astrocyte biology, which we
coin contextual guidance. It describes astrocytes as contextual gates that
decode multiple environmental factors to shape neural circuitry in an adaptive,
state-dependent fashion. This paradigm is organically inclusive of all
fundamental features of astrocytes, many of which have remained unaccounted for
in previous theories. We find that this new concept provides an intuitive and
powerful theoretical space to improve our understanding of brain function and
computational models thereof across scales because it depicts astrocytes as a
hub for circumstantial inputs into relevant specialized circuits that permits
adaptive behaviors at the network and organism level.
| [
{
"created": "Thu, 17 Nov 2022 21:38:00 GMT",
"version": "v1"
}
] | 2023-10-23 | [
[
"Murphy-Royal",
"Ciaran",
""
],
[
"Ching",
"ShiNung",
""
],
[
"Papouin",
"Thomas",
""
]
] | The participation of astrocytes in brain computation was formally hypothesized in 1992, coinciding with the discovery that these glial cells display a complex form of Ca2+ excitability. This fostered conceptual advances centered on the notion of reciprocal interactions between neurons and astrocytes, which permitted a critical leap forward in uncovering many roles of astrocytes in brain circuits, and signaled the rise of a major new force in neuroscience: that of glial biology. In the past decade, a multitude of unconventional and disparate functions of astrocytes have been documented that are not predicted by these canonical models and that are challenging to piece together into a holistic and parsimonious picture. This highlights a disconnect between the rapidly evolving field of astrocyte biology and the conceptual frameworks guiding it, and emphasizes the need for a careful reconsideration of how we theorize the functional position of astrocytes in brain circuitry. Here, we propose a unifying, highly transferable, data-driven, and computationally-relevant conceptual framework for astrocyte biology, which we coin contextual guidance. It describes astrocytes as contextual gates that decode multiple environmental factors to shape neural circuitry in an adaptive, state-dependent fashion. This paradigm is organically inclusive of all fundamental features of astrocytes, many of which have remained unaccounted for in previous theories. We find that this new concept provides an intuitive and powerful theoretical space to improve our understanding of brain function and computational models thereof across scales because it depicts astrocytes as a hub for circumstantial inputs into relevant specialized circuits that permits adaptive behaviors at the network and organism level. |
2012.05038 | Zhengjia Dai | Junji Ma, Jinbo Zhang, Ying Lin and Zhengjia Dai | Cost-efficiency trade-offs of the human brain network revealed by a
multiobjective evolutionary algorithm | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | It is widely believed that the formation of brain network structure is under
the pressure of optimal trade-off between reducing wiring cost and promoting
communication efficiency. However, the question of whether this trade-off
exists in empirical human brain networks and, if so, how it takes effect is
still not well understood. Here, we employed a multiobjective evolutionary
algorithm to directly and quantitatively explore the cost-efficiency trade-off
in human brain networks. Using this algorithm, we generated a population of
synthetic networks with optimal but diverse cost-efficiency trade-offs. It was
found that these synthetic networks could not only reproduce a large portion of
connections in the empirical brain networks but also embed a resembling
small-world structure. Moreover, the synthetic and empirical brain networks
were found similar in terms of the spatial arrangement of hub regions and the
modular structure, which are two important topological features widely assumed
to be outcomes of cost-efficiency trade-offs. The synthetic networks had high
robustness against random attack as the empirical brain networks did.
Additionally, we also revealed some differences of the synthetic networks from
the empirical brain networks, including lower segregated processing capacity
and weaker robustness against targeted attack. These findings provide direct
and quantitative evidence that the structure of human brain networks is indeed
largely influenced by optimal cost-efficiency trade-offs. We also suggest that
some additional factors (e.g., segregated processing capacity) might jointly
determine the network organization with cost and efficiency.
| [
{
"created": "Wed, 9 Dec 2020 13:37:05 GMT",
"version": "v1"
}
] | 2020-12-10 | [
[
"Ma",
"Junji",
""
],
[
"Zhang",
"Jinbo",
""
],
[
"Lin",
"Ying",
""
],
[
"Dai",
"Zhengjia",
""
]
] | It is widely believed that the formation of brain network structure is under the pressure of optimal trade-off between reducing wiring cost and promoting communication efficiency. However, the question of whether this trade-off exists in empirical human brain networks and, if so, how it takes effect is still not well understood. Here, we employed a multiobjective evolutionary algorithm to directly and quantitatively explore the cost-efficiency trade-off in human brain networks. Using this algorithm, we generated a population of synthetic networks with optimal but diverse cost-efficiency trade-offs. It was found that these synthetic networks could not only reproduce a large portion of connections in the empirical brain networks but also embed a resembling small-world structure. Moreover, the synthetic and empirical brain networks were found similar in terms of the spatial arrangement of hub regions and the modular structure, which are two important topological features widely assumed to be outcomes of cost-efficiency trade-offs. The synthetic networks had high robustness against random attack as the empirical brain networks did. Additionally, we also revealed some differences of the synthetic networks from the empirical brain networks, including lower segregated processing capacity and weaker robustness against targeted attack. These findings provide direct and quantitative evidence that the structure of human brain networks is indeed largely influenced by optimal cost-efficiency trade-offs. We also suggest that some additional factors (e.g., segregated processing capacity) might jointly determine the network organization with cost and efficiency. |
0907.4119 | Lei-Han Tang | Li-ping Xiong, Yu-qiang Ma, and Lei-Han Tang | Attenuation of transcriptional bursting in mRNA transport | 18 pages, 3 figures | null | 10.1088/1478-3975/7/1/016005 | null | q-bio.SC cond-mat.stat-mech q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to the stochastic nature of biochemical processes, the copy number of any
given type of molecule inside a living cell often exhibits large temporal
fluctuations. Here, we develop analytic methods to investigate how the noise
arising from a bursting input is reshaped by a transport reaction which is
either linear or of the Michaelis-Menten type. A slow transport rate smoothes
out fluctuations at the output end and minimizes the impact of bursting on the
downstream cellular activities. In the context of gene expression in eukaryotic
cells, our results indicate that transcriptional bursting can be substantially
attenuated by the transport of mRNA from nucleus to cytoplasm. Saturation of
the transport mediators or nuclear pores contributes further to the noise
reduction. We suggest that the mRNA transport should be taken into account in
the interpretation of relevant experimental data on transcriptional bursting.
| [
{
"created": "Thu, 23 Jul 2009 16:49:11 GMT",
"version": "v1"
}
] | 2015-05-13 | [
[
"Xiong",
"Li-ping",
""
],
[
"Ma",
"Yu-qiang",
""
],
[
"Tang",
"Lei-Han",
""
]
] | Due to the stochastic nature of biochemical processes, the copy number of any given type of molecule inside a living cell often exhibits large temporal fluctuations. Here, we develop analytic methods to investigate how the noise arising from a bursting input is reshaped by a transport reaction which is either linear or of the Michaelis-Menten type. A slow transport rate smoothes out fluctuations at the output end and minimizes the impact of bursting on the downstream cellular activities. In the context of gene expression in eukaryotic cells, our results indicate that transcriptional bursting can be substantially attenuated by the transport of mRNA from nucleus to cytoplasm. Saturation of the transport mediators or nuclear pores contributes further to the noise reduction. We suggest that the mRNA transport should be taken into account in the interpretation of relevant experimental data on transcriptional bursting. |
1910.10968 | Alexander L\"uck | Alexander L\"uck, Verena Wolf | A Stochastic Automata Network Description for Spatial DNA-Methylation
Models | 10 pages, 3 figures, 1 table | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | DNA methylation is an important biological mechanism to regulate gene
expression and control cell development. Mechanistic modeling has become a
popular approach to enhance our understanding of the dynamics of methylation
pattern formation in living cells. Recent findings suggest that the methylation
state of a cytosine base can be influenced by its DNA neighborhood. Therefore,
it is necessary to generalize existing mathematical models that consider only
one cytosine and its partner on the opposite DNA-strand (CpG), in order to
include such neighborhood dependencies. One approach is to describe the system
as a stochastic automata network (SAN) with functional transitions. We show
that single-CpG models can successfully be generalized to multiple CpGs using
the SAN description and verify the results by comparing them to results from
extensive Monte-Carlo simulations.
| [
{
"created": "Thu, 24 Oct 2019 08:10:45 GMT",
"version": "v1"
}
] | 2019-10-25 | [
[
"Lück",
"Alexander",
""
],
[
"Wolf",
"Verena",
""
]
] | DNA methylation is an important biological mechanism to regulate gene expression and control cell development. Mechanistic modeling has become a popular approach to enhance our understanding of the dynamics of methylation pattern formation in living cells. Recent findings suggest that the methylation state of a cytosine base can be influenced by its DNA neighborhood. Therefore, it is necessary to generalize existing mathematical models that consider only one cytosine and its partner on the opposite DNA-strand (CpG), in order to include such neighborhood dependencies. One approach is to describe the system as a stochastic automata network (SAN) with functional transitions. We show that single-CpG models can successfully be generalized to multiple CpGs using the SAN description and verify the results by comparing them to results from extensive Monte-Carlo simulations. |
1512.05220 | Luke Bashford | Luke Bashford and Carsten Mehring | Ownership and Agency of an Independent Supernumerary Hand Induced by an
Imitation Brain-Computer Interface | null | null | 10.1371/journal.pone.0156591 | null | q-bio.NC cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To study body ownership and control, illusions that elicit these feelings in
non-body objects are widely used. Classically introduced with the Rubber Hand
Illusion, these illusions have been replicated more recently in virtual reality
and by using brain-computer interfaces. Traditionally these illusions
investigate the replacement of a body part by an artificial counterpart,
however as brain-computer interface research develops it offers us the
possibility to explore the case where non-body objects are controlled in
addition to movements of our own limbs. Therefore we propose a new illusion
designed to test the feeling of ownership and control of an independent
supernumerary hand. Subjects are under the impression they control a virtual
reality hand via a brain-computer interface, but in reality there is no causal
connection between brain activity and virtual hand movement but correct
movements are observed with 80% probability. These imitation brain-computer
interface trials are interspersed with movements in both the subjects' real
hands, which are in view throughout the experiment. We show that subjects
develop strong feelings of ownership and control over the third hand, despite
only receiving visual feedback with no causal link to the actual brain signals.
Our illusion is crucially different from previously reported studies as we
demonstrate independent ownership and control of the third hand without loss of
ownership in the real hands.
| [
{
"created": "Wed, 16 Dec 2015 15:55:14 GMT",
"version": "v1"
},
{
"created": "Fri, 27 May 2016 14:47:34 GMT",
"version": "v2"
}
] | 2016-06-17 | [
[
"Bashford",
"Luke",
""
],
[
"Mehring",
"Carsten",
""
]
] | To study body ownership and control, illusions that elicit these feelings in non-body objects are widely used. Classically introduced with the Rubber Hand Illusion, these illusions have been replicated more recently in virtual reality and by using brain-computer interfaces. Traditionally these illusions investigate the replacement of a body part by an artificial counterpart, however as brain-computer interface research develops it offers us the possibility to explore the case where non-body objects are controlled in addition to movements of our own limbs. Therefore we propose a new illusion designed to test the feeling of ownership and control of an independent supernumerary hand. Subjects are under the impression they control a virtual reality hand via a brain-computer interface, but in reality there is no causal connection between brain activity and virtual hand movement but correct movements are observed with 80% probability. These imitation brain-computer interface trials are interspersed with movements in both the subjects' real hands, which are in view throughout the experiment. We show that subjects develop strong feelings of ownership and control over the third hand, despite only receiving visual feedback with no causal link to the actual brain signals. Our illusion is crucially different from previously reported studies as we demonstrate independent ownership and control of the third hand without loss of ownership in the real hands. |
2309.00311 | Utku Ozbulak | Woowon Jang, Shiwoo Koak, Jiwon Im, Utku Ozbulak, Joris Vankerschaver | BRCA Gene Mutations in dbSNP: A Visual Exploration of Genetic Variants | null | null | null | null | q-bio.GN cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | BRCA genes, comprising BRCA1 and BRCA2 play indispensable roles in preserving
genomic stability and facilitating DNA repair mechanisms. The presence of
germline mutations in these genes has been associated with increased
susceptibility to various cancers, notably breast and ovarian cancers. Recent
advancements in cost-effective sequencing technologies have revolutionized the
landscape of cancer genomics, leading to a notable rise in the number of
sequenced cancer patient genomes, enabling large-scale computational studies.
In this study, we delve into the BRCA mutations in the dbSNP, housing an
extensive repository of 41,177 and 44,205 genetic mutations for BRCA1 and
BRCA2, respectively. Employing meticulous computational analysis from an
umbrella perspective, our research unveils intriguing findings pertaining to a
number of critical aspects. Namely, we discover that the majority of BRCA
mutations in dbSNP have unknown clinical significance. We find that, although
exon 11 for both genes contains the majority of the mutations and may seem as
if it is a mutation hot spot, upon analyzing mutations per base pair, we find
that all exons exhibit similar levels of mutations. Investigating mutations
within introns, while we observe that the recorded mutations are generally
uniformly distributed, almost all of the pathogenic mutations in introns are
located close to splicing regions (at the beginning or the end). In addition to
the findings mentioned earlier, we have also made other discoveries concerning
mutation types and the level of confidence in observations within the dbSNP
database.
| [
{
"created": "Fri, 1 Sep 2023 07:52:37 GMT",
"version": "v1"
}
] | 2023-09-04 | [
[
"Jang",
"Woowon",
""
],
[
"Koak",
"Shiwoo",
""
],
[
"Im",
"Jiwon",
""
],
[
"Ozbulak",
"Utku",
""
],
[
"Vankerschaver",
"Joris",
""
]
] | BRCA genes, comprising BRCA1 and BRCA2 play indispensable roles in preserving genomic stability and facilitating DNA repair mechanisms. The presence of germline mutations in these genes has been associated with increased susceptibility to various cancers, notably breast and ovarian cancers. Recent advancements in cost-effective sequencing technologies have revolutionized the landscape of cancer genomics, leading to a notable rise in the number of sequenced cancer patient genomes, enabling large-scale computational studies. In this study, we delve into the BRCA mutations in the dbSNP, housing an extensive repository of 41,177 and 44,205 genetic mutations for BRCA1 and BRCA2, respectively. Employing meticulous computational analysis from an umbrella perspective, our research unveils intriguing findings pertaining to a number of critical aspects. Namely, we discover that the majority of BRCA mutations in dbSNP have unknown clinical significance. We find that, although exon 11 for both genes contains the majority of the mutations and may seem as if it is a mutation hot spot, upon analyzing mutations per base pair, we find that all exons exhibit similar levels of mutations. Investigating mutations within introns, while we observe that the recorded mutations are generally uniformly distributed, almost all of the pathogenic mutations in introns are located close to splicing regions (at the beginning or the end). In addition to the findings mentioned earlier, we have also made other discoveries concerning mutation types and the level of confidence in observations within the dbSNP database. |
2305.06160 | Daniel Barabasi | D\'aniel L Barab\'asi, Ginestra Bianconi, Ed Bullmore, Mark Burgess,
SueYeon Chung, Tina Eliassi-Rad, Dileep George, Istv\'an A. Kov\'acs,
Hern\'an Makse, Christos Papadimitriou, Thomas E. Nichols, Olaf Sporns, Kim
Stachenfeld, Zolt\'an Toroczkai, Emma K. Towlson, Anthony M Zador, Hongkui
Zeng, Albert-L\'aszl\'o Barab\'asi, Amy Bernard, Gy\"orgy Buzs\'aki | Neuroscience needs Network Science | 19 pages, 1 figure, 1 box | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The brain is a complex system comprising a myriad of interacting elements,
posing significant challenges in understanding its structure, function, and
dynamics. Network science has emerged as a powerful tool for studying such
intricate systems, offering a framework for integrating multiscale data and
complexity. Here, we discuss the application of network science in the study of
the brain, addressing topics such as network models and metrics, the
connectome, and the role of dynamics in neural networks. We explore the
challenges and opportunities in integrating multiple data streams for
understanding the neural transitions from development to healthy function to
disease, and discuss the potential for collaboration between network science
and neuroscience communities. We underscore the importance of fostering
interdisciplinary opportunities through funding initiatives, workshops, and
conferences, as well as supporting students and postdoctoral fellows with
interests in both disciplines. By uniting the network science and neuroscience
communities, we can develop novel network-based methods tailored to neural
circuits, paving the way towards a deeper understanding of the brain and its
functions.
| [
{
"created": "Wed, 10 May 2023 13:53:17 GMT",
"version": "v1"
},
{
"created": "Thu, 11 May 2023 08:41:13 GMT",
"version": "v2"
}
] | 2023-05-12 | [
[
"Barabási",
"Dániel L",
""
],
[
"Bianconi",
"Ginestra",
""
],
[
"Bullmore",
"Ed",
""
],
[
"Burgess",
"Mark",
""
],
[
"Chung",
"SueYeon",
""
],
[
"Eliassi-Rad",
"Tina",
""
],
[
"George",
"Dileep",
""
],
[
"Kovács",
"István A.",
""
],
[
"Makse",
"Hernán",
""
],
[
"Papadimitriou",
"Christos",
""
],
[
"Nichols",
"Thomas E.",
""
],
[
"Sporns",
"Olaf",
""
],
[
"Stachenfeld",
"Kim",
""
],
[
"Toroczkai",
"Zoltán",
""
],
[
"Towlson",
"Emma K.",
""
],
[
"Zador",
"Anthony M",
""
],
[
"Zeng",
"Hongkui",
""
],
[
"Barabási",
"Albert-László",
""
],
[
"Bernard",
"Amy",
""
],
[
"Buzsáki",
"György",
""
]
] | The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network science in the study of the brain, addressing topics such as network models and metrics, the connectome, and the role of dynamics in neural networks. We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities. We underscore the importance of fostering interdisciplinary opportunities through funding initiatives, workshops, and conferences, as well as supporting students and postdoctoral fellows with interests in both disciplines. By uniting the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way towards a deeper understanding of the brain and its functions. |
1501.05842 | Christian Gold PhD | Christian Gold and J\"org Assmus | Heart rate and its variability as an indicator of mental health in male
prisoners | null | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Heart rate (HR) and its variability (HRV) has been proposed as a marker for
depressive symptoms and other aspects of mental health. However, the real
correlation between them is presently uncertain, as previous studies have
generally been conducted on the basis of small samples. In a sample of 113
adult male prisoners, we analyzed correlations between five measures of HR/HRV
and five psychological measures of mental health aspects (depression, state and
trait anxiety, and social relationships). We used Nadaraya-Watson
non-parametric regression in both directions and age-stratified Spearman
correlation to detect possible relations. Despite strong correlations among
HR/HRV measures and among psychological measures, correlations between HR/HRV
and psychological measures were low and non-significant for the overall sample.
However, we found an age dependency, suggesting some correlations in younger
people (HR with STAI-State, r = 0.39; with HADS-Anxiety, r = 0.52; both p <
.005). Overall, the general utility of HR/HRV as a marker for mental health
across populations remains unclear. Future research should address age and
other potential confounders more consistently.
| [
{
"created": "Thu, 22 Jan 2015 14:49:24 GMT",
"version": "v1"
}
] | 2015-01-26 | [
[
"Gold",
"Christian",
""
],
[
"Assmus",
"Jörg",
""
]
] | Heart rate (HR) and its variability (HRV) has been proposed as a marker for depressive symptoms and other aspects of mental health. However, the real correlation between them is presently uncertain, as previous studies have generally been conducted on the basis of small samples. In a sample of 113 adult male prisoners, we analyzed correlations between five measures of HR/HRV and five psychological measures of mental health aspects (depression, state and trait anxiety, and social relationships). We used Nadaraya-Watson non-parametric regression in both directions and age-stratified Spearman correlation to detect possible relations. Despite strong correlations among HR/HRV measures and among psychological measures, correlations between HR/HRV and psychological measures were low and non-significant for the overall sample. However, we found an age dependency, suggesting some correlations in younger people (HR with STAI-State, r = 0.39; with HADS-Anxiety, r = 0.52; both p < .005). Overall, the general utility of HR/HRV as a marker for mental health across populations remains unclear. Future research should address age and other potential confounders more consistently. |
2006.16648 | Jian Ma | Jian Ma | Associations between finger tapping, gait and fall risk with application
to fall risk assessment | null | null | null | null | q-bio.QM cs.LG stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As the world ages, elderly care becomes a big concern of the society. To
address the elderly's issues on dementia and fall risk, we have investigated
smart cognitive and fall risk assessment with machine learning methodology
based on the data collected from finger tapping test and Timed Up and Go (TUG)
test. Meanwhile, we have discovered the associations between cognition and
finger motion from finger tapping data and the association between fall risk
and gait characteristics from TUG data. In this paper, we jointly analyze the
finger tapping and gait characteristics data with copula entropy. We find that
the associations between certain finger tapping characteristics ('number of
taps', 'average interval of tapping', 'frequency of tapping' of both hands of
bimanual inphase and those of left hand of bimanual untiphase) and TUG score
are relatively high. According to this finding, we propose to utilize this
associations to improve the predictive models of automatic fall risk assessment
we developed previously. Experimental results show that using the
characteristics of both finger tapping and gait as inputs of the predictive
models of predicting TUG score can considerably improve the prediction
performance in terms of MAE compared with using only one type of
characteristics.
| [
{
"created": "Tue, 30 Jun 2020 10:17:41 GMT",
"version": "v1"
},
{
"created": "Mon, 22 Feb 2021 10:50:59 GMT",
"version": "v2"
}
] | 2021-02-23 | [
[
"Ma",
"Jian",
""
]
] | As the world ages, elderly care becomes a big concern of the society. To address the elderly's issues on dementia and fall risk, we have investigated smart cognitive and fall risk assessment with machine learning methodology based on the data collected from finger tapping test and Timed Up and Go (TUG) test. Meanwhile, we have discovered the associations between cognition and finger motion from finger tapping data and the association between fall risk and gait characteristics from TUG data. In this paper, we jointly analyze the finger tapping and gait characteristics data with copula entropy. We find that the associations between certain finger tapping characteristics ('number of taps', 'average interval of tapping', 'frequency of tapping' of both hands of bimanual inphase and those of left hand of bimanual untiphase) and TUG score are relatively high. According to this finding, we propose to utilize this associations to improve the predictive models of automatic fall risk assessment we developed previously. Experimental results show that using the characteristics of both finger tapping and gait as inputs of the predictive models of predicting TUG score can considerably improve the prediction performance in terms of MAE compared with using only one type of characteristics. |
2404.16696 | Andres Campero | Andres Campero | Report on Candidate Computational Indicators for Conscious Valenced
Experience | null | null | null | null | q-bio.NC cs.AI | http://creativecommons.org/licenses/by/4.0/ | This report enlists 13 functional conditions cashed out in computational
terms that have been argued to be constituent of conscious valenced experience.
These are extracted from existing empirical and theoretical literature on,
among others, animal sentience, medical disorders, anaesthetics, philosophy,
evolution, neuroscience, and artificial intelligence.
| [
{
"created": "Thu, 25 Apr 2024 15:58:09 GMT",
"version": "v1"
}
] | 2024-04-26 | [
[
"Campero",
"Andres",
""
]
] | This report enlists 13 functional conditions cashed out in computational terms that have been argued to be constituent of conscious valenced experience. These are extracted from existing empirical and theoretical literature on, among others, animal sentience, medical disorders, anaesthetics, philosophy, evolution, neuroscience, and artificial intelligence. |
1703.07454 | James Kaufman | James H. Kaufman, Christopher A. Elkins, Matthew Davis, Allison M
Weis, Bihua C. Huang, Mark K Mammel, Isha R. Patel, Kristen L. Beck, Stefan
Edlund, David Chambliss, Simone Bianco, Mark Kunitomi, Bart C. Weimer | Insular microbiogeography | 23 pages, 7 figures, including Appendices | null | null | null | q-bio.GN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The diversity revealed by large scale genomics in microbiology is calling
into question long held beliefs about genome stability, evolutionary rate, even
the definition of a species. MacArthur and Wilson's theory of insular
biogeography provides an explanation for the diversity of macroscopic animal
and plant species as a consequence of the associated hierarchical web of
species interdependence. We report a large scale study of microbial diversity
that reveals that the cumulative number of genes discovered increases with the
number of genomes studied as a simple power law. This result is demonstrated
for three different genera comparing over 15,000 isolates. We show that this
power law is formally related to the MacArthur-Wilson exponent, suggesting the
emerging diversity of microbial genotypes arises because the scale independent
behavior first reported by MacArthur and Wilson extends down to the scale of
microbes and their genes. Assessing the depth of available whole genome
sequences implies a dynamically changing core genome, suggesting that
traditional taxonomic classifications should be replaced with a quasispecies
model that captures the diversity and dynamic exchange of genes. We report
Species population "clouds" in a defined microbiome, with scale invariance
extending down to the level of single-nucleotide polymorphisms (SNPs).
| [
{
"created": "Tue, 21 Mar 2017 22:18:29 GMT",
"version": "v1"
}
] | 2017-03-23 | [
[
"Kaufman",
"James H.",
""
],
[
"Elkins",
"Christopher A.",
""
],
[
"Davis",
"Matthew",
""
],
[
"Weis",
"Allison M",
""
],
[
"Huang",
"Bihua C.",
""
],
[
"Mammel",
"Mark K",
""
],
[
"Patel",
"Isha R.",
""
],
[
"Beck",
"Kristen L.",
""
],
[
"Edlund",
"Stefan",
""
],
[
"Chambliss",
"David",
""
],
[
"Bianco",
"Simone",
""
],
[
"Kunitomi",
"Mark",
""
],
[
"Weimer",
"Bart C.",
""
]
] | The diversity revealed by large scale genomics in microbiology is calling into question long held beliefs about genome stability, evolutionary rate, even the definition of a species. MacArthur and Wilson's theory of insular biogeography provides an explanation for the diversity of macroscopic animal and plant species as a consequence of the associated hierarchical web of species interdependence. We report a large scale study of microbial diversity that reveals that the cumulative number of genes discovered increases with the number of genomes studied as a simple power law. This result is demonstrated for three different genera comparing over 15,000 isolates. We show that this power law is formally related to the MacArthur-Wilson exponent, suggesting the emerging diversity of microbial genotypes arises because the scale independent behavior first reported by MacArthur and Wilson extends down to the scale of microbes and their genes. Assessing the depth of available whole genome sequences implies a dynamically changing core genome, suggesting that traditional taxonomic classifications should be replaced with a quasispecies model that captures the diversity and dynamic exchange of genes. We report Species population "clouds" in a defined microbiome, with scale invariance extending down to the level of single-nucleotide polymorphisms (SNPs). |
q-bio/0512026 | Emmanuel Tannenbaum | Emmanuel Tannenbaum | A hypothesis on the selective advantage for sleep | 3 pages, no figures. This paper is a speculative note. I am still
considering whether to submit it for publication | null | null | null | q-bio.NC q-bio.CB | null | In this note, we present a hypothesis for the emergence of the phenomenon of
sleep in organisms with sufficiently developed central nervous systems. We
argue that sleep emerges because individual neurons must periodically enter a
resting state and perform various ``garbage collection'' activities. Because
the proper functioning of the central nervous system is dependent on the
interconnections amongst a large collection of individual neurons, it becomes
optimal, from the standpoint of the organism, for these garbage collection
activities to occur simultaneously. We present analogies with complex
structures in modern economies to make our case, although we emphasize that our
hypothesis is purely speculative at this time. Although the ``garbage
collection'' hypothesis has been previously advanced, we believe that our
speculation is useful because it illustrates the power of a general paradigm
for understanding the emergence of collective behavior in agent-built systems.
| [
{
"created": "Mon, 12 Dec 2005 11:40:35 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Tannenbaum",
"Emmanuel",
""
]
] | In this note, we present a hypothesis for the emergence of the phenomenon of sleep in organisms with sufficiently developed central nervous systems. We argue that sleep emerges because individual neurons must periodically enter a resting state and perform various ``garbage collection'' activities. Because the proper functioning of the central nervous system is dependent on the interconnections amongst a large collection of individual neurons, it becomes optimal, from the standpoint of the organism, for these garbage collection activities to occur simultaneously. We present analogies with complex structures in modern economies to make our case, although we emphasize that our hypothesis is purely speculative at this time. Although the ``garbage collection'' hypothesis has been previously advanced, we believe that our speculation is useful because it illustrates the power of a general paradigm for understanding the emergence of collective behavior in agent-built systems. |
1204.3198 | Ramon Ferrer i Cancho | Ramon Ferrer-i-Cancho and Antoni Hern\'andez-Fern\'andez | The failure of the law of brevity in two New World primates. Statistical
caveats | Little improvements in the statistical arguments | Statistical caveats. Glottotheory 4 (1), 45-55 (2013) | 10.1524/glot.2013.0004 | null | q-bio.NC cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Parallels of Zipf's law of brevity, the tendency of more frequent words to be
shorter, have been found in bottlenose dolphins and Formosan macaques. Although
these findings suggest that behavioral repertoires are shaped by a general
principle of compression, common marmosets and golden-backed uakaris do not
exhibit the law. However, we argue that the law may be impossible or difficult
to detect statistically in a given species if the repertoire is too small, a
problem that could be affecting golden backed uakaris, and show that the law is
present in a subset of the repertoire of common marmosets. We suggest that the
visibility of the law will depend on the subset of the repertoire under
consideration or the repertoire size.
| [
{
"created": "Sat, 14 Apr 2012 19:25:54 GMT",
"version": "v1"
},
{
"created": "Fri, 28 Sep 2012 17:26:00 GMT",
"version": "v2"
}
] | 2014-12-03 | [
[
"Ferrer-i-Cancho",
"Ramon",
""
],
[
"Hernández-Fernández",
"Antoni",
""
]
] | Parallels of Zipf's law of brevity, the tendency of more frequent words to be shorter, have been found in bottlenose dolphins and Formosan macaques. Although these findings suggest that behavioral repertoires are shaped by a general principle of compression, common marmosets and golden-backed uakaris do not exhibit the law. However, we argue that the law may be impossible or difficult to detect statistically in a given species if the repertoire is too small, a problem that could be affecting golden backed uakaris, and show that the law is present in a subset of the repertoire of common marmosets. We suggest that the visibility of the law will depend on the subset of the repertoire under consideration or the repertoire size. |
2407.01648 | Minkai Xu | Siyi Gu, Minkai Xu, Alexander Powers, Weili Nie, Tomas Geffner,
Karsten Kreis, Jure Leskovec, Arash Vahdat, Stefano Ermon | Aligning Target-Aware Molecule Diffusion Models with Exact Energy
Optimization | null | null | null | null | q-bio.BM cs.LG q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Generating ligand molecules for specific protein targets, known as
structure-based drug design, is a fundamental problem in therapeutics
development and biological discovery. Recently, target-aware generative models,
especially diffusion models, have shown great promise in modeling
protein-ligand interactions and generating candidate drugs. However, existing
models primarily focus on learning the chemical distribution of all drug
candidates, which lacks effective steerability on the chemical quality of model
generations. In this paper, we propose a novel and general alignment framework
to align pretrained target diffusion models with preferred functional
properties, named AliDiff. AliDiff shifts the target-conditioned chemical
distribution towards regions with higher binding affinity and structural
rationality, specified by user-defined reward functions, via the preference
optimization approach. To avoid the overfitting problem in common preference
optimization objectives, we further develop an improved Exact Energy Preference
Optimization method to yield an exact and efficient alignment of the diffusion
models, and provide the closed-form expression for the converged distribution.
Empirical studies on the CrossDocked2020 benchmark show that AliDiff can
generate molecules with state-of-the-art binding energies with up to -7.07 Avg.
Vina Score, while maintaining strong molecular properties.
| [
{
"created": "Mon, 1 Jul 2024 06:10:29 GMT",
"version": "v1"
}
] | 2024-07-03 | [
[
"Gu",
"Siyi",
""
],
[
"Xu",
"Minkai",
""
],
[
"Powers",
"Alexander",
""
],
[
"Nie",
"Weili",
""
],
[
"Geffner",
"Tomas",
""
],
[
"Kreis",
"Karsten",
""
],
[
"Leskovec",
"Jure",
""
],
[
"Vahdat",
"Arash",
""
],
[
"Ermon",
"Stefano",
""
]
] | Generating ligand molecules for specific protein targets, known as structure-based drug design, is a fundamental problem in therapeutics development and biological discovery. Recently, target-aware generative models, especially diffusion models, have shown great promise in modeling protein-ligand interactions and generating candidate drugs. However, existing models primarily focus on learning the chemical distribution of all drug candidates, which lacks effective steerability on the chemical quality of model generations. In this paper, we propose a novel and general alignment framework to align pretrained target diffusion models with preferred functional properties, named AliDiff. AliDiff shifts the target-conditioned chemical distribution towards regions with higher binding affinity and structural rationality, specified by user-defined reward functions, via the preference optimization approach. To avoid the overfitting problem in common preference optimization objectives, we further develop an improved Exact Energy Preference Optimization method to yield an exact and efficient alignment of the diffusion models, and provide the closed-form expression for the converged distribution. Empirical studies on the CrossDocked2020 benchmark show that AliDiff can generate molecules with state-of-the-art binding energies with up to -7.07 Avg. Vina Score, while maintaining strong molecular properties. |
1803.02197 | Maurizio Serva | Maurizio Serva | The origins of the Malagasy people, some certainties and a few mysteries | arXiv admin note: substantial text overlap with arXiv:1102.2180,
arXiv:1107.4218 | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Malagasy language belongs to the Greater Barito East group of the
Austronesian family, the language most closely connected to Malagasy dialects
is Maanyan (Kalimantan), but Malay as well other Indonesian and Philippine
languages are also related. The African contribution is very high in the
Malagasy genetic make-up (about 50%) but negligible in the language. Because of
the linguistic link, it is widely accepted that the island was settled by
Indonesian sailors after a maritime trek but date and place of landing are
still debated. The 50% Indonesian genetic contribution to present Malagasy
points in a different direction then Maanyan for the Asian ancestry, therefore,
the ethnic composition of the Austronesian settlers is also still debated. In
this talk I mainly review the joint research of Filippo Petroni, Dima
Volchenkov, S\"oren Wichmann and myself which tries to shed new light on these
problems. The key point is the application of a new quantitative methodology
which is able to find out the kinship relations among languages (or dialects).
New techniques are also introduced in order to extract the maximum information
from these relations concerning time and space patterns.
| [
{
"created": "Sat, 3 Mar 2018 11:50:25 GMT",
"version": "v1"
}
] | 2018-03-07 | [
[
"Serva",
"Maurizio",
""
]
] | The Malagasy language belongs to the Greater Barito East group of the Austronesian family, the language most closely connected to Malagasy dialects is Maanyan (Kalimantan), but Malay as well other Indonesian and Philippine languages are also related. The African contribution is very high in the Malagasy genetic make-up (about 50%) but negligible in the language. Because of the linguistic link, it is widely accepted that the island was settled by Indonesian sailors after a maritime trek but date and place of landing are still debated. The 50% Indonesian genetic contribution to present Malagasy points in a different direction then Maanyan for the Asian ancestry, therefore, the ethnic composition of the Austronesian settlers is also still debated. In this talk I mainly review the joint research of Filippo Petroni, Dima Volchenkov, S\"oren Wichmann and myself which tries to shed new light on these problems. The key point is the application of a new quantitative methodology which is able to find out the kinship relations among languages (or dialects). New techniques are also introduced in order to extract the maximum information from these relations concerning time and space patterns. |
1612.01561 | Konstantin Blyuss | G. Neofytou, Y.N. Kyrychko, K.B. Blyuss | Mathematical model of plant-virus interactions mediated by RNA
interference | 28 pages, 7 figures | J. Theor. Biol. 403, 129-142 (2016) | 10.1016/j.jtbi.2016.05.018 | null | q-bio.PE nlin.CD q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cross-protection, which refers to a process whereby artificially inoculating
a plant with a mild strain provides protection against a more aggressive
isolate of the virus, is known to be an effective tool of disease control in
plants. In this paper we derive and analyse a new mathematical model of the
interactions between two competing viruses with particular account for RNA
interference. Our results show that co-infection of the host can either
increase or decrease the potency of individual infections depending on the
levels of cross-protection or cross-enhancement between different viruses.
Analytical and numerical bifurcation analyses are employed to investigate the
stability of all steady states of the model in order to identify parameter
regions where the system exhibits synergistic or antagonistic behaviour between
viral strains, as well as different types of host recovery. We show that not
only viral attributes but also the propagating component of RNA-interference in
plants can play an important role in determining the dynamics.
| [
{
"created": "Mon, 5 Dec 2016 21:42:25 GMT",
"version": "v1"
}
] | 2016-12-07 | [
[
"Neofytou",
"G.",
""
],
[
"Kyrychko",
"Y. N.",
""
],
[
"Blyuss",
"K. B.",
""
]
] | Cross-protection, which refers to a process whereby artificially inoculating a plant with a mild strain provides protection against a more aggressive isolate of the virus, is known to be an effective tool of disease control in plants. In this paper we derive and analyse a new mathematical model of the interactions between two competing viruses with particular account for RNA interference. Our results show that co-infection of the host can either increase or decrease the potency of individual infections depending on the levels of cross-protection or cross-enhancement between different viruses. Analytical and numerical bifurcation analyses are employed to investigate the stability of all steady states of the model in order to identify parameter regions where the system exhibits synergistic or antagonistic behaviour between viral strains, as well as different types of host recovery. We show that not only viral attributes but also the propagating component of RNA-interference in plants can play an important role in determining the dynamics. |
2010.07473 | Andrea Arnold | Sara Amato, Andrea Arnold | Modeling Microglia Activation and Inflammation-Based Neuroprotectant
Strategies During Ischemic Stroke | 21 pages, 5 figures | Bulletin of Mathematical Biology 83 (2021) 72 | 10.1007/s11538-021-00905-4 | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural inflammation immediately follows the onset of ischemic stroke. During
this process, microglial cells can be activated into two different phenotypes:
the M1 phenotype, which can worsen brain injury by producing pro-inflammatory
cytokines; or the M2 phenotype, which can aid in long term recovery by
producing anti-inflammatory cytokines. In this study, we formulate a nonlinear
system of differential equations to model the activation of microglia
post-ischemic stroke, which includes bidirectional switching between the
microglia phenotypes, as well as the interactions between these cells and the
cytokines that they produce. Further, we explore neuroprotectant-based modeling
strategies to suppress the activation of the detrimental M1 phenotype, while
promoting activation of the beneficial M2 phenotype. Through use of global
sensitivity techniques, we analyze the effects of the model parameters on the
ratio of M1 to M2 microglia and the total number of activated microglial cells
in the system over time. Results demonstrate the significance of bidirectional
microglia phenotype switching on the ratio of M1 to M2 microglia, in both the
absence and presence of neuroprotectant terms. Simulations further suggest that
early inhibition of M1 activation and support of M2 activation leads to a
decreased minimum ratio of M1 to M2 microglia and allows for a larger number of
M2 than M1 cells for a longer time period.
| [
{
"created": "Thu, 15 Oct 2020 02:27:19 GMT",
"version": "v1"
}
] | 2021-07-23 | [
[
"Amato",
"Sara",
""
],
[
"Arnold",
"Andrea",
""
]
] | Neural inflammation immediately follows the onset of ischemic stroke. During this process, microglial cells can be activated into two different phenotypes: the M1 phenotype, which can worsen brain injury by producing pro-inflammatory cytokines; or the M2 phenotype, which can aid in long term recovery by producing anti-inflammatory cytokines. In this study, we formulate a nonlinear system of differential equations to model the activation of microglia post-ischemic stroke, which includes bidirectional switching between the microglia phenotypes, as well as the interactions between these cells and the cytokines that they produce. Further, we explore neuroprotectant-based modeling strategies to suppress the activation of the detrimental M1 phenotype, while promoting activation of the beneficial M2 phenotype. Through use of global sensitivity techniques, we analyze the effects of the model parameters on the ratio of M1 to M2 microglia and the total number of activated microglial cells in the system over time. Results demonstrate the significance of bidirectional microglia phenotype switching on the ratio of M1 to M2 microglia, in both the absence and presence of neuroprotectant terms. Simulations further suggest that early inhibition of M1 activation and support of M2 activation leads to a decreased minimum ratio of M1 to M2 microglia and allows for a larger number of M2 than M1 cells for a longer time period. |
1607.08444 | Charles E. Hall | Charles Hall, Vishal Koparde, Max Jameson-Lee, Abdelrhman Elnasseh,
Allison Scalora, Jared Kobulnicky, Myrna Serrano, Catherine Roberts, Gregory
Buck, Micheal Neale, Daniel Nixon, and Amir Toor | Cytomegalovirus Antigenic Mimicry of Human Alloreactive Peptides: A
Potential Trigger for Graft versus Host Disease | Pre-submission manuscript, 4 tables, 5 figures, 2 supplements & 2
Appendices-available upon request from first author | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The association between human cytomegalovirus (hCMV) reactivation and the
development of graft-versus-host-disease (GVHD) has been observed in stem cell
transplantation (SCT). Seventy seven SCT donor-recipient pairs (DRP) (HLA
matched unrelated donor (MUD), n=50; matched related donor (MRD), n=27)
underwent whole exome sequencing to identify single nucleotide polymorphisms
(SNPs) generating alloreactive peptide libraries for each DRP (9-mer
peptide-HLA complexes); Human CMV CROSS (Cross-Reactive Open Source Sequence)
Database was compiled from NCBI; HLA class I binding affinity for each DRPs HLA
was calculated by NetMHCpan 2.8 and hCMV- derived 9-mers algorithmically
compared to the alloreactive peptide-HLA complex libraries. Short consecutive
(6 or greater) amino acid (AA) sequence homology matching hCMV to recipient
peptides was considered for HLA-bound-peptide (IC50<500 nM) cross reactivity.
Of the 70,686 hCMV 9-mers contained within the hCMV CROSS database, 29,658.8
+/- 9038.5 were found to match MRD DRP alloreactive peptides and 52,910.2 +/-
16121.8 matched MUD DRP peptides (Student's T-test, p<0.001). In silico
analysis revealed multiple high affinity, immunogenic CMV-Human peptide matches
(IC50<500 nM) expressed in GVHD-affected tissue-specific manner (proteins
expressed at 10 RPKM or greater). hCMV+GVHD was found in 18 patients, 13
developing hCMV viremia before GVHD onset with a subset analysis of 7 instances
of hCMV viremia prior to acute GVHD onset (n=3), chronic GVHD (n=2) and acute +
chronic GVHD (n=2) indicating cross reactive peptide expression within affected
organs. We propose that based on our analysis and preliminary clinical
correlations that hCMV immune cross-reactivity may cause antigenic mimicry of
human alloreactive peptides triggering GVHD.
| [
{
"created": "Thu, 28 Jul 2016 13:19:03 GMT",
"version": "v1"
}
] | 2016-07-29 | [
[
"Hall",
"Charles",
""
],
[
"Koparde",
"Vishal",
""
],
[
"Jameson-Lee",
"Max",
""
],
[
"Elnasseh",
"Abdelrhman",
""
],
[
"Scalora",
"Allison",
""
],
[
"Kobulnicky",
"Jared",
""
],
[
"Serrano",
"Myrna",
""
],
[
"Roberts",
"Catherine",
""
],
[
"Buck",
"Gregory",
""
],
[
"Neale",
"Micheal",
""
],
[
"Nixon",
"Daniel",
""
],
[
"Toor",
"Amir",
""
]
] | The association between human cytomegalovirus (hCMV) reactivation and the development of graft-versus-host-disease (GVHD) has been observed in stem cell transplantation (SCT). Seventy seven SCT donor-recipient pairs (DRP) (HLA matched unrelated donor (MUD), n=50; matched related donor (MRD), n=27) underwent whole exome sequencing to identify single nucleotide polymorphisms (SNPs) generating alloreactive peptide libraries for each DRP (9-mer peptide-HLA complexes); Human CMV CROSS (Cross-Reactive Open Source Sequence) Database was compiled from NCBI; HLA class I binding affinity for each DRPs HLA was calculated by NetMHCpan 2.8 and hCMV- derived 9-mers algorithmically compared to the alloreactive peptide-HLA complex libraries. Short consecutive (6 or greater) amino acid (AA) sequence homology matching hCMV to recipient peptides was considered for HLA-bound-peptide (IC50<500 nM) cross reactivity. Of the 70,686 hCMV 9-mers contained within the hCMV CROSS database, 29,658.8 +/- 9038.5 were found to match MRD DRP alloreactive peptides and 52,910.2 +/- 16121.8 matched MUD DRP peptides (Student's T-test, p<0.001). In silico analysis revealed multiple high affinity, immunogenic CMV-Human peptide matches (IC50<500 nM) expressed in GVHD-affected tissue-specific manner (proteins expressed at 10 RPKM or greater). hCMV+GVHD was found in 18 patients, 13 developing hCMV viremia before GVHD onset with a subset analysis of 7 instances of hCMV viremia prior to acute GVHD onset (n=3), chronic GVHD (n=2) and acute + chronic GVHD (n=2) indicating cross reactive peptide expression within affected organs. We propose that based on our analysis and preliminary clinical correlations that hCMV immune cross-reactivity may cause antigenic mimicry of human alloreactive peptides triggering GVHD. |
1807.10556 | Ralph Brinks | Ralph Brinks | Morbidity, mortality and the illness-death model | 5 pages, 2 figures | null | null | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article, we use the illness-death model to present a mathematical
framework for studying the compression of morbidity (COM) hypothesis. It turns
out that questions about COM are completely determined by the transition rates
in the illness-death model and a closely related partial differential equation.
By this, the COM hypothesis is analytically tractable. To demonstrate the
usefulness of the mathematical framework, an example is given, which has been
motivated by empirical findings from Germany.
| [
{
"created": "Fri, 27 Jul 2018 12:42:14 GMT",
"version": "v1"
},
{
"created": "Mon, 30 Jul 2018 10:27:46 GMT",
"version": "v2"
}
] | 2018-07-31 | [
[
"Brinks",
"Ralph",
""
]
] | In this article, we use the illness-death model to present a mathematical framework for studying the compression of morbidity (COM) hypothesis. It turns out that questions about COM are completely determined by the transition rates in the illness-death model and a closely related partial differential equation. By this, the COM hypothesis is analytically tractable. To demonstrate the usefulness of the mathematical framework, an example is given, which has been motivated by empirical findings from Germany. |
1804.04585 | Stefano Casagranda | Stefano Casagranda, Marco Pizzolato (EPFL), Francisco Torrealdea
(UCL), Xavier Golay (UCL), Timoth\'e Boutelier | Principal Process Analysis of dynamic GlucoCEST MRI data | null | ISMRM 2018, Jun 2018, Paris, France. Proc. Int. Soc. Mag. Reson.
Med. 27 (2018) | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | GlucoCEST is an MRI contrast enhancement technique sensitive to the
concentration of sugar in the tissue. Because of a differencein metabolism, it
is thought that tumors consume more sugar than normal tissue. However, glucose
metabolism is complex and depends onmany processes, which are all important to
understand the origin of the measured signal. To achieve this goal we apply
here a process analysismethod to a deterministic system describing the
metabolism of glucose in the tissue.
| [
{
"created": "Tue, 13 Mar 2018 13:05:11 GMT",
"version": "v1"
}
] | 2018-04-13 | [
[
"Casagranda",
"Stefano",
"",
"EPFL"
],
[
"Pizzolato",
"Marco",
"",
"EPFL"
],
[
"Torrealdea",
"Francisco",
"",
"UCL"
],
[
"Golay",
"Xavier",
"",
"UCL"
],
[
"Boutelier",
"Timothé",
""
]
] | GlucoCEST is an MRI contrast enhancement technique sensitive to the concentration of sugar in the tissue. Because of a differencein metabolism, it is thought that tumors consume more sugar than normal tissue. However, glucose metabolism is complex and depends onmany processes, which are all important to understand the origin of the measured signal. To achieve this goal we apply here a process analysismethod to a deterministic system describing the metabolism of glucose in the tissue. |
1504.06045 | Yutaka Hori | Yutaka Hori, Hiroki Miyazako, Soichiro Kumagai, Shinji Hara | Coordinated Spatial Pattern Formation in Biomolecular Communication
Networks | null | IEEE Transactions on Molecular, Biological and Multi-Scale
Communications, vol. 1, No. 2, pp.111-121, 2015 | 10.1109/TMBMC.2015.2500567 | null | q-bio.MN cs.SY nlin.PS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a control theoretic framework to model and analyze the
self-organized pattern formation of molecular concentrations in biomolecular
communication networks, emerging applications in synthetic biology. In
biomolecular communication networks, bionanomachines, or biological cells,
communicate with each other using a cell-to-cell communication mechanism
mediated by a diffusible signaling molecule, thereby the dynamics of molecular
concentrations are approximately modeled as a reaction-diffusion system with a
single diffuser. We first introduce a feedback model representation of the
reaction-diffusion system and provide a systematic local stability/instability
analysis tool using the root locus of the feedback system. The instability
analysis then allows us to analytically derive the conditions for the
self-organized spatial pattern formation, or Turing pattern formation, of the
bionanomachines. We propose a novel synthetic biocircuit motif called
activator-repressor-diffuser system and show that it is one of the minimum
biomolecular circuits that admit self-organized patterns over cell population.
| [
{
"created": "Thu, 23 Apr 2015 05:46:56 GMT",
"version": "v1"
},
{
"created": "Wed, 13 May 2015 19:02:10 GMT",
"version": "v2"
},
{
"created": "Mon, 21 Sep 2015 18:40:12 GMT",
"version": "v3"
},
{
"created": "Sun, 6 Jan 2019 01:31:07 GMT",
"version": "v4"
}
] | 2019-01-08 | [
[
"Hori",
"Yutaka",
""
],
[
"Miyazako",
"Hiroki",
""
],
[
"Kumagai",
"Soichiro",
""
],
[
"Hara",
"Shinji",
""
]
] | This paper proposes a control theoretic framework to model and analyze the self-organized pattern formation of molecular concentrations in biomolecular communication networks, emerging applications in synthetic biology. In biomolecular communication networks, bionanomachines, or biological cells, communicate with each other using a cell-to-cell communication mechanism mediated by a diffusible signaling molecule, thereby the dynamics of molecular concentrations are approximately modeled as a reaction-diffusion system with a single diffuser. We first introduce a feedback model representation of the reaction-diffusion system and provide a systematic local stability/instability analysis tool using the root locus of the feedback system. The instability analysis then allows us to analytically derive the conditions for the self-organized spatial pattern formation, or Turing pattern formation, of the bionanomachines. We propose a novel synthetic biocircuit motif called activator-repressor-diffuser system and show that it is one of the minimum biomolecular circuits that admit self-organized patterns over cell population. |
1102.2331 | Gergely J Sz\"oll\H{o}si | Gergely J Sz\"oll\H{o}si and Vincent Daubin | The pattern and process of gene family evolution | Book chapter, to appear in "Evolutionary Genomics: statistical and
computational methods", edited by Dr Maria Anisimova | null | null | null | q-bio.GN q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large scale databases are available that contain homologous gene families
constructed from hundreds of complete genome sequences from across the three
domains of Life. Here we discuss approches of increasing complexity aimed at
extracting information on the pattern and process of gene family evolution from
such datasets. In particular, we consider models that invoke processes of gene
birth (duplication and transfer) and death (loss) to explain the evolution of
gene families. First, we review birth-and-death models of family size evolution
and their implications in light of the universal features of family size
distribution observed across different species and the three domains of life.
Subsequently, we proceed to recent developments on models capable of more
completely considering information in the sequences of homologous gene families
through the probabilistic reconciliation of the phylogenetic histories of
individual genes with the phylogenetic history of the genomes in which they
have resided. To illustrate the methods and results presented, we use data from
the HOGENOM database, demonstrating that the distribution of homologous gene
family sizes in the genomes of the Eukaryota, Archaea and Bacteria exhibit
remarkably similar shapes. We shown that these distributions are best described
by models of gene family size evolution where for individual genes the death
(loss) rate is larger than the birth (duplication and transfer) rate, but new
families are continually supplied to the genome by a process of origination.
Finally, we use probabilistic reconciliation methods to take into consideration
additional information from gene phylogenies, and find that, for prokaryotes,
the majority of birth events are the result of transfer.
| [
{
"created": "Fri, 11 Feb 2011 11:45:11 GMT",
"version": "v1"
}
] | 2011-02-14 | [
[
"Szöllősi",
"Gergely J",
""
],
[
"Daubin",
"Vincent",
""
]
] | Large scale databases are available that contain homologous gene families constructed from hundreds of complete genome sequences from across the three domains of Life. Here we discuss approches of increasing complexity aimed at extracting information on the pattern and process of gene family evolution from such datasets. In particular, we consider models that invoke processes of gene birth (duplication and transfer) and death (loss) to explain the evolution of gene families. First, we review birth-and-death models of family size evolution and their implications in light of the universal features of family size distribution observed across different species and the three domains of life. Subsequently, we proceed to recent developments on models capable of more completely considering information in the sequences of homologous gene families through the probabilistic reconciliation of the phylogenetic histories of individual genes with the phylogenetic history of the genomes in which they have resided. To illustrate the methods and results presented, we use data from the HOGENOM database, demonstrating that the distribution of homologous gene family sizes in the genomes of the Eukaryota, Archaea and Bacteria exhibit remarkably similar shapes. We shown that these distributions are best described by models of gene family size evolution where for individual genes the death (loss) rate is larger than the birth (duplication and transfer) rate, but new families are continually supplied to the genome by a process of origination. Finally, we use probabilistic reconciliation methods to take into consideration additional information from gene phylogenies, and find that, for prokaryotes, the majority of birth events are the result of transfer. |
1403.7478 | Alfonso P\'erez-Escudero | Alfonso P\'erez-Escudero and Gonzalo G. de Polavieja | The Informative Herd: why humans and other animals imitate more when
conditions are adverse | To appear in the proceedings of Collective Intelligence 2014 (Boston,
June 10-12) The work regarding this document is in progress. Updates expected | null | null | null | q-bio.NC q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decisions in a group often result in imitation and aggregation, which are
enhanced in panic, dangerous, stressful or negative situations. Current
explanations of this enhancement are restricted to particular contexts, such as
anti-predatory behavior, deflection of responsibility in humans, or cases in
which the negative situation is associated with an increase in uncertainty. But
this effect is observed across taxa and in very diverse conditions, suggesting
that it may arise from a more general cause, such as a fundamental
characteristic of social decision-making. Current decision-making theories do
not explain it, but we noted that they concentrate on estimating which of the
available options is the best one, implicitly neglecting the cases in which
several options can be good at the same time. We explore a more general model
of decision-making that instead estimates the probability that each option is
good, allowing several options to be good simultaneously. This model predicts
with great generality the enhanced imitation in negative situations. Fish and
human behavioral data showing an increased imitation behavior in negative
circumstances are well described by this type of decisions to choose a good
option.
| [
{
"created": "Fri, 28 Mar 2014 18:34:13 GMT",
"version": "v1"
}
] | 2014-03-31 | [
[
"Pérez-Escudero",
"Alfonso",
""
],
[
"de Polavieja",
"Gonzalo G.",
""
]
] | Decisions in a group often result in imitation and aggregation, which are enhanced in panic, dangerous, stressful or negative situations. Current explanations of this enhancement are restricted to particular contexts, such as anti-predatory behavior, deflection of responsibility in humans, or cases in which the negative situation is associated with an increase in uncertainty. But this effect is observed across taxa and in very diverse conditions, suggesting that it may arise from a more general cause, such as a fundamental characteristic of social decision-making. Current decision-making theories do not explain it, but we noted that they concentrate on estimating which of the available options is the best one, implicitly neglecting the cases in which several options can be good at the same time. We explore a more general model of decision-making that instead estimates the probability that each option is good, allowing several options to be good simultaneously. This model predicts with great generality the enhanced imitation in negative situations. Fish and human behavioral data showing an increased imitation behavior in negative circumstances are well described by this type of decisions to choose a good option. |
1405.5617 | Alexei Koulakov | Honi Sanders, Brian Kolterman, Roman Shusterman, Dmitry Rinberg,
Alexei A. Koulakov, and John Lisman | Odors in olfactory bulb are defined by a short discrete temporal
sequence: recognition by brute-force conversion to a spatial pattern
(chunking) | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mitral cells, the principal neurons in the olfactory bulb, respond to
odorants by firing bursts of action potentials called sharp events. A given
cell produces a sharp event at a fixed phase during the sniff cycle in response
to a given odor; different cells have different phases. The olfactory bulb
response to an odor is thus a sequence of sharp events. Here, we show that
sharp event onset is biased toward certain phases of the ongoing gamma
frequency oscillation. Thus, the signature of an odor is a discrete sequence.
The fact that this sequence is relatively short suggests a new class of "brute
force" solutions to the problem of odor recognition: cortex may contain a small
number of modules, each forming a persistent snapshot of what occurs in a
certain gamma cycle. Towards the end of the sniff, the collection of these
snapshots forms a spatial pattern that could be recognized by standard
attractor-based network mechanisms. We demonstrate the feasibility of this
solution with simulations of simple network architectures having modules that
represent gamma-cycle specific information. Thus "brute force" solutions for
converting a discrete temporal sequence into a spatial pattern (chunking) are
neurally plausible.
| [
{
"created": "Thu, 22 May 2014 03:01:23 GMT",
"version": "v1"
},
{
"created": "Tue, 1 Jul 2014 04:44:18 GMT",
"version": "v2"
}
] | 2014-07-02 | [
[
"Sanders",
"Honi",
""
],
[
"Kolterman",
"Brian",
""
],
[
"Shusterman",
"Roman",
""
],
[
"Rinberg",
"Dmitry",
""
],
[
"Koulakov",
"Alexei A.",
""
],
[
"Lisman",
"John",
""
]
] | Mitral cells, the principal neurons in the olfactory bulb, respond to odorants by firing bursts of action potentials called sharp events. A given cell produces a sharp event at a fixed phase during the sniff cycle in response to a given odor; different cells have different phases. The olfactory bulb response to an odor is thus a sequence of sharp events. Here, we show that sharp event onset is biased toward certain phases of the ongoing gamma frequency oscillation. Thus, the signature of an odor is a discrete sequence. The fact that this sequence is relatively short suggests a new class of "brute force" solutions to the problem of odor recognition: cortex may contain a small number of modules, each forming a persistent snapshot of what occurs in a certain gamma cycle. Towards the end of the sniff, the collection of these snapshots forms a spatial pattern that could be recognized by standard attractor-based network mechanisms. We demonstrate the feasibility of this solution with simulations of simple network architectures having modules that represent gamma-cycle specific information. Thus "brute force" solutions for converting a discrete temporal sequence into a spatial pattern (chunking) are neurally plausible. |
1506.04797 | Alexandra Gavryushkina | Alexandra Gavryushkina, Tracy A. Heath, Daniel T. Ksepka, Tanja
Stadler, David Welch, and Alexei J. Drummond | Bayesian total evidence dating reveals the recent crown radiation of
penguins | 50 pages, 6 figures | null | 10.1093/sysbio/syw060 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The total-evidence approach to divergence-time dating uses molecular and
morphological data from extant and fossil species to infer phylogenetic
relationships, species divergence times, and macroevolutionary parameters in a
single coherent framework. Current model-based implementations of this approach
lack an appropriate model for the tree describing the diversification and
fossilization process and can produce estimates that lead to erroneous
conclusions. We address this shortcoming by providing a total-evidence method
implemented in a Bayesian framework. This approach uses a mechanistic tree
prior to describe the underlying diversification process that generated the
tree of extant and fossil taxa. Previous attempts to apply the total-evidence
approach have used tree priors that do not account for the possibility that
fossil samples may be direct ancestors of other samples. The fossilized
birth-death (FBD) process explicitly models the diversification, fossilization,
and sampling processes and naturally allows for sampled ancestors. This model
was recently applied to estimate divergence times based on molecular data and
fossil occurrence dates. We incorporate the FBD model and a model of
morphological trait evolution into a Bayesian total-evidence approach to dating
species phylogenies. We apply this method to extant and fossil penguins and
show that the modern penguins radiated much more recently than has been
previously estimated, with the basal divergence in the crown clade occurring at
~12.7 Ma and most splits leading to extant species occurring in the last 2
million years. Our results demonstrate that including stem-fossil diversity can
greatly improve the estimates of the divergence times of crown taxa. The method
is available in BEAST2 (v. 2.4) www.beast2.org with packages SA (v. at least
1.1.4) and morph-models (v. at least 1.0.4).
| [
{
"created": "Mon, 15 Jun 2015 23:10:49 GMT",
"version": "v1"
},
{
"created": "Tue, 24 Jan 2017 10:47:10 GMT",
"version": "v2"
}
] | 2017-01-25 | [
[
"Gavryushkina",
"Alexandra",
""
],
[
"Heath",
"Tracy A.",
""
],
[
"Ksepka",
"Daniel T.",
""
],
[
"Stadler",
"Tanja",
""
],
[
"Welch",
"David",
""
],
[
"Drummond",
"Alexei J.",
""
]
] | The total-evidence approach to divergence-time dating uses molecular and morphological data from extant and fossil species to infer phylogenetic relationships, species divergence times, and macroevolutionary parameters in a single coherent framework. Current model-based implementations of this approach lack an appropriate model for the tree describing the diversification and fossilization process and can produce estimates that lead to erroneous conclusions. We address this shortcoming by providing a total-evidence method implemented in a Bayesian framework. This approach uses a mechanistic tree prior to describe the underlying diversification process that generated the tree of extant and fossil taxa. Previous attempts to apply the total-evidence approach have used tree priors that do not account for the possibility that fossil samples may be direct ancestors of other samples. The fossilized birth-death (FBD) process explicitly models the diversification, fossilization, and sampling processes and naturally allows for sampled ancestors. This model was recently applied to estimate divergence times based on molecular data and fossil occurrence dates. We incorporate the FBD model and a model of morphological trait evolution into a Bayesian total-evidence approach to dating species phylogenies. We apply this method to extant and fossil penguins and show that the modern penguins radiated much more recently than has been previously estimated, with the basal divergence in the crown clade occurring at ~12.7 Ma and most splits leading to extant species occurring in the last 2 million years. Our results demonstrate that including stem-fossil diversity can greatly improve the estimates of the divergence times of crown taxa. The method is available in BEAST2 (v. 2.4) www.beast2.org with packages SA (v. at least 1.1.4) and morph-models (v. at least 1.0.4). |
1603.01077 | Pouya Ghaemmaghami Tabrizi | Pouya Ghaemmaghami | Functional Connectivity in Default Mode Network During Resting State: An
Evaluation of the Effects of Data Pre-processing | Master's thesis, July 2013 | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Resting state functional connectivity estimates from MRI measures has become
a promising tool to characterize human brain networks. There are, however,
limitations in the method since several sources of errors have been seen to
significantly affect the final estimates. This has lead to a great interest in
the field to do systematic investigations that help determine the most reliable
and robust strategies to perform functional connectivity analysis. In the
present study, we examine the influence of two aspects of data pre- processing
in resting state functional connectivity analysis: the effect of criteria used
to select nodes in the default mode network (DMN) for the computation of
connectivity, and the effect of using or not physiological noise correction.
Three different strategies of region of interest (ROI) selection were compared
to define DMN node coordinates: (1) ROIs centered on atlas-based coordinates,
(2) ROIs based on the result of group independent component analysis, and (3)
ROIs based on the estimated DMN of each individual. The study was done using
data of 15 healthy volunteers, which had resting state data available from a
separate project. We found that both effects, ROI selection criteria for DMN
nodes and physiological noise correction, have significant effects on the
functional connectivity estimates. In particular, our results show that
physiological noise correction introduces small but significant reductions in
functional connectivity, consistent with a reduction of artifactual non-neural
correlations introduced by physiological effects. Further, selecting DMN nodes
based on the single subject ICA results introduced small but significant
increases in functional connectivity, consistent with higher subject
specificity of the node selection.
| [
{
"created": "Thu, 3 Mar 2016 12:49:09 GMT",
"version": "v1"
}
] | 2016-03-04 | [
[
"Ghaemmaghami",
"Pouya",
""
]
] | Resting state functional connectivity estimates from MRI measures has become a promising tool to characterize human brain networks. There are, however, limitations in the method since several sources of errors have been seen to significantly affect the final estimates. This has lead to a great interest in the field to do systematic investigations that help determine the most reliable and robust strategies to perform functional connectivity analysis. In the present study, we examine the influence of two aspects of data pre- processing in resting state functional connectivity analysis: the effect of criteria used to select nodes in the default mode network (DMN) for the computation of connectivity, and the effect of using or not physiological noise correction. Three different strategies of region of interest (ROI) selection were compared to define DMN node coordinates: (1) ROIs centered on atlas-based coordinates, (2) ROIs based on the result of group independent component analysis, and (3) ROIs based on the estimated DMN of each individual. The study was done using data of 15 healthy volunteers, which had resting state data available from a separate project. We found that both effects, ROI selection criteria for DMN nodes and physiological noise correction, have significant effects on the functional connectivity estimates. In particular, our results show that physiological noise correction introduces small but significant reductions in functional connectivity, consistent with a reduction of artifactual non-neural correlations introduced by physiological effects. Further, selecting DMN nodes based on the single subject ICA results introduced small but significant increases in functional connectivity, consistent with higher subject specificity of the node selection. |
0901.3838 | Sachin Talathi | Sachin S. Talathi, Dong-Uk Hwang, Abraham Miliotis, Paul R. Carney,
William L. Ditto | Synchrony with Shunting Inhibition | 4 Figures | null | 10.1007/s10827-009-0210-2 | null | q-bio.NC q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spike time response curves (STRC's) are used to study the influence of
synaptic stimuli on the firing times of a neuron oscillator without the
assumption of weak coupling. They allow us to approximate the dynamics of
synchronous state in networks of neurons through a discrete map. Linearization
about the fixed point of the discrete map can then be used to predict the
stability of patterns of synchrony in the network. General theory for taking
into account the contribution from higher order STRC terms, in the
approximation of the discrete map for coupled neuronal oscillators in synchrony
is still lacking. Here we present a general framework to account for higher
order STRC corrections in the approximation of discrete map to determine the
domain of 1:1 phase locking state in the network of two interacting neurons. We
begin by demonstrating that the effect of synaptic stimuli through a shunting
synapse to a neuron firing in the gamma frequency band (20-80 Hz) last for
three consecutive firing cycles. We then show that the discrete map derived by
taking into account the higher order STRC contributions is successfully able
predict the domain of synchronous 1:1 phase locked state in a network of two
heterogeneous interneurons coupled through a shunting synapse.
| [
{
"created": "Mon, 26 Jan 2009 21:42:34 GMT",
"version": "v1"
},
{
"created": "Fri, 30 Jan 2009 20:00:15 GMT",
"version": "v2"
}
] | 2013-04-24 | [
[
"Talathi",
"Sachin S.",
""
],
[
"Hwang",
"Dong-Uk",
""
],
[
"Miliotis",
"Abraham",
""
],
[
"Carney",
"Paul R.",
""
],
[
"Ditto",
"William L.",
""
]
] | Spike time response curves (STRC's) are used to study the influence of synaptic stimuli on the firing times of a neuron oscillator without the assumption of weak coupling. They allow us to approximate the dynamics of synchronous state in networks of neurons through a discrete map. Linearization about the fixed point of the discrete map can then be used to predict the stability of patterns of synchrony in the network. General theory for taking into account the contribution from higher order STRC terms, in the approximation of the discrete map for coupled neuronal oscillators in synchrony is still lacking. Here we present a general framework to account for higher order STRC corrections in the approximation of discrete map to determine the domain of 1:1 phase locking state in the network of two interacting neurons. We begin by demonstrating that the effect of synaptic stimuli through a shunting synapse to a neuron firing in the gamma frequency band (20-80 Hz) last for three consecutive firing cycles. We then show that the discrete map derived by taking into account the higher order STRC contributions is successfully able predict the domain of synchronous 1:1 phase locked state in a network of two heterogeneous interneurons coupled through a shunting synapse. |
2212.04270 | Guillermo Lorenzo | Guillermo Lorenzo, Angela M. Jarrett, Christian T. Meyer, Vito
Quaranta, Darren R. Tyson, Thomas E. Yankeelov | Identifying mechanisms driving the early response of triple negative
breast cancer patients to neoadjuvant chemotherapy using a mechanistic model
integrating in vitro and in vivo imaging data | null | null | null | null | q-bio.TO cs.CE q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Neoadjuvant chemotherapy (NAC) is a standard-of-care treatment for locally
advanced triple negative breast cancer (TNBC) before surgery. The early
assessment of TNBC response to NAC would enable an oncologist to adapt the
therapeutic plan of a non-responding patient, thereby improving treatment
outcomes while preventing unnecessary toxicities. To this end, a promising
approach consists of obtaining \textsl{in silico} personalized forecasts of
tumor response to NAC \textsl{via} computer simulation of mechanistic models
constrained with patient-specific magnetic resonance imaging (MRI) data
acquired early during NAC. Here, we present a model featuring the essential
mechanisms of TNBC growth and response to NAC, including an explicit
description of drug pharmacodynamics and pharmacokinetics. As longitudinal
\textsl{in vivo} MRI data for model calibration is limited, we perform a
sensitivity analysis to identify the model mechanisms driving the response to
two NAC drug combinations: doxorubicin with cyclophosphamide, and paclitaxel
with carboplatin. The model parameter space is constructed by combining
patient-specific MRI-based \textsl{in silico} parameter estimates and
\textit{in vitro} measurements of pharmacodynamic parameters obtained using
time-resolved microscopy assays of several TNBC lines. The sensitivity analysis
is run in two MRI-based scenarios corresponding to a well-perfused and a
poorly-perfused tumor. Out of the 15 parameters considered herein, only the
baseline tumor cell net proliferation rate along with the maximum
concentrations and effects of doxorubicin, carboplatin, and paclitaxel exhibit
a relevant impact on model forecasts (total effect index, $S_T>$0.1). These
results dramatically limit the number of parameters that require \textsl{in
vivo} MRI-constrained calibration, thereby facilitating the clinical
application of our model.
| [
{
"created": "Thu, 8 Dec 2022 13:53:54 GMT",
"version": "v1"
}
] | 2022-12-09 | [
[
"Lorenzo",
"Guillermo",
""
],
[
"Jarrett",
"Angela M.",
""
],
[
"Meyer",
"Christian T.",
""
],
[
"Quaranta",
"Vito",
""
],
[
"Tyson",
"Darren R.",
""
],
[
"Yankeelov",
"Thomas E.",
""
]
] | Neoadjuvant chemotherapy (NAC) is a standard-of-care treatment for locally advanced triple negative breast cancer (TNBC) before surgery. The early assessment of TNBC response to NAC would enable an oncologist to adapt the therapeutic plan of a non-responding patient, thereby improving treatment outcomes while preventing unnecessary toxicities. To this end, a promising approach consists of obtaining \textsl{in silico} personalized forecasts of tumor response to NAC \textsl{via} computer simulation of mechanistic models constrained with patient-specific magnetic resonance imaging (MRI) data acquired early during NAC. Here, we present a model featuring the essential mechanisms of TNBC growth and response to NAC, including an explicit description of drug pharmacodynamics and pharmacokinetics. As longitudinal \textsl{in vivo} MRI data for model calibration is limited, we perform a sensitivity analysis to identify the model mechanisms driving the response to two NAC drug combinations: doxorubicin with cyclophosphamide, and paclitaxel with carboplatin. The model parameter space is constructed by combining patient-specific MRI-based \textsl{in silico} parameter estimates and \textit{in vitro} measurements of pharmacodynamic parameters obtained using time-resolved microscopy assays of several TNBC lines. The sensitivity analysis is run in two MRI-based scenarios corresponding to a well-perfused and a poorly-perfused tumor. Out of the 15 parameters considered herein, only the baseline tumor cell net proliferation rate along with the maximum concentrations and effects of doxorubicin, carboplatin, and paclitaxel exhibit a relevant impact on model forecasts (total effect index, $S_T>$0.1). These results dramatically limit the number of parameters that require \textsl{in vivo} MRI-constrained calibration, thereby facilitating the clinical application of our model. |
0901.1544 | Tom Michoel | Anagha Joshi, Riet De Smet, Kathleen Marchal, Yves Van de Peer, Tom
Michoel | Module networks revisited: computational assessment and prioritization
of model predictions | 8 pages REVTeX, 6 figures | null | 10.1093/bioinformatics/btn658 | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The solution of high-dimensional inference and prediction problems in
computational biology is almost always a compromise between mathematical theory
and practical constraints such as limited computational resources. As time
progresses, computational power increases but well-established inference
methods often remain locked in their initial suboptimal solution. We revisit
the approach of Segal et al. (2003) to infer regulatory modules and their
condition-specific regulators from gene expression data. In contrast to their
direct optimization-based solution we use a more representative centroid-like
solution extracted from an ensemble of possible statistical models to explain
the data. The ensemble method automatically selects a subset of most
informative genes and builds a quantitatively better model for them. Genes
which cluster together in the majority of models produce functionally more
coherent modules. Regulators which are consistently assigned to a module are
more often supported by literature, but a single model always contains many
regulator assignments not supported by the ensemble. Reliably detecting
condition-specific or combinatorial regulation is particularly hard in a single
optimum but can be achieved using ensemble averaging.
| [
{
"created": "Mon, 12 Jan 2009 10:47:03 GMT",
"version": "v1"
}
] | 2009-01-13 | [
[
"Joshi",
"Anagha",
""
],
[
"De Smet",
"Riet",
""
],
[
"Marchal",
"Kathleen",
""
],
[
"Van de Peer",
"Yves",
""
],
[
"Michoel",
"Tom",
""
]
] | The solution of high-dimensional inference and prediction problems in computational biology is almost always a compromise between mathematical theory and practical constraints such as limited computational resources. As time progresses, computational power increases but well-established inference methods often remain locked in their initial suboptimal solution. We revisit the approach of Segal et al. (2003) to infer regulatory modules and their condition-specific regulators from gene expression data. In contrast to their direct optimization-based solution we use a more representative centroid-like solution extracted from an ensemble of possible statistical models to explain the data. The ensemble method automatically selects a subset of most informative genes and builds a quantitatively better model for them. Genes which cluster together in the majority of models produce functionally more coherent modules. Regulators which are consistently assigned to a module are more often supported by literature, but a single model always contains many regulator assignments not supported by the ensemble. Reliably detecting condition-specific or combinatorial regulation is particularly hard in a single optimum but can be achieved using ensemble averaging. |
2208.04008 | Marina Zajnulina | Marina Zajnulina | Advances of Artificial Intelligence in Classical and Novel
Spectroscopy-Based Approaches for Cancer Diagnostics. A Review | null | null | null | null | q-bio.TO eess.IV stat.AP stat.ML | http://creativecommons.org/licenses/by/4.0/ | Cancer is one of the leading causes of death worldwide. Fast and safe
early-stage, pre- and intra-operative diagnostics can significantly contribute
to successful cancer identification and treatment. Artificial intelligence has
played an increasing role in the enhancement of cancer diagnostics techniques
in the last 15 years. This review covers the advances of artificial
intelligence applications in well-established techniques such as MRI and CT.
Also, it shows its high potential in combination with optical
spectroscopy-based approaches that are under development for mobile,
ultra-fast, and low-invasive diagnostics. I will show how spectroscopy-based
approaches can reduce the time of tissue preparation for pathological analysis
by making thin-slicing or haematoxylin-and-eosin staining obsolete. I will
present examples of spectroscopic tools for fast and low-invasive ex- and
in-vivo tissue classification for the determination of a tumour and its
boundaries. Also, I will discuss that, contrary to MRI and CT, spectroscopic
measurements do not require the administration of chemical agents to enhance
the quality of cancer imaging which contributes to the development of more
secure diagnostic methods. Overall, we will see that the combination of
spectroscopy and artificial intelligence constitutes a highly promising and
fast-developing field of medical technology that will soon augment available
cancer diagnostic methods.
| [
{
"created": "Mon, 8 Aug 2022 09:39:36 GMT",
"version": "v1"
}
] | 2022-08-10 | [
[
"Zajnulina",
"Marina",
""
]
] | Cancer is one of the leading causes of death worldwide. Fast and safe early-stage, pre- and intra-operative diagnostics can significantly contribute to successful cancer identification and treatment. Artificial intelligence has played an increasing role in the enhancement of cancer diagnostics techniques in the last 15 years. This review covers the advances of artificial intelligence applications in well-established techniques such as MRI and CT. Also, it shows its high potential in combination with optical spectroscopy-based approaches that are under development for mobile, ultra-fast, and low-invasive diagnostics. I will show how spectroscopy-based approaches can reduce the time of tissue preparation for pathological analysis by making thin-slicing or haematoxylin-and-eosin staining obsolete. I will present examples of spectroscopic tools for fast and low-invasive ex- and in-vivo tissue classification for the determination of a tumour and its boundaries. Also, I will discuss that, contrary to MRI and CT, spectroscopic measurements do not require the administration of chemical agents to enhance the quality of cancer imaging which contributes to the development of more secure diagnostic methods. Overall, we will see that the combination of spectroscopy and artificial intelligence constitutes a highly promising and fast-developing field of medical technology that will soon augment available cancer diagnostic methods. |
0912.3426 | Hugo Gabriel Eyherabide | Hugo Gabriel Eyherabide, Ariel Rokem, Andreas V.M. Herz, Ines Samengo | Bursts generate a non-reducible spike pattern code | null | Frontiers in Neuroscience Vol. 3 Issue 1 2009 | 10.3389/neuro.01.002.2009 | null | q-bio.NC q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | At the single-neuron level, precisely timed spikes can either constitute
firing-rate codes or spike-pattern codes that utilize the relative timing
between consecutive spikes. There has been little experimental support for the
hypothesis that such temporal patterns contribute substantially to information
transmission. By using grasshopper auditory receptors as a model system, we
show that correlations between spikes can be used to represent behaviorally
relevant stimuli. The correlations reflect the inner structure of the spike
train: a succession of burst-like patterns. We demonstrate that bursts with
different spike counts encode different stimulus features, such that about 20%
of the transmitted information corresponds to discriminating between different
features, and the remaining 80% is used to allocate these features in time. In
this spike-pattern code, the what and the when of the stimuli are encoded in
the duration of each burst and the time of burst onset, respectively. Given the
ubiquity of burst firing, we expect similar findings also for other neural
systems.
| [
{
"created": "Thu, 17 Dec 2009 15:08:58 GMT",
"version": "v1"
}
] | 2009-12-18 | [
[
"Eyherabide",
"Hugo Gabriel",
""
],
[
"Rokem",
"Ariel",
""
],
[
"Herz",
"Andreas V. M.",
""
],
[
"Samengo",
"Ines",
""
]
] | At the single-neuron level, precisely timed spikes can either constitute firing-rate codes or spike-pattern codes that utilize the relative timing between consecutive spikes. There has been little experimental support for the hypothesis that such temporal patterns contribute substantially to information transmission. By using grasshopper auditory receptors as a model system, we show that correlations between spikes can be used to represent behaviorally relevant stimuli. The correlations reflect the inner structure of the spike train: a succession of burst-like patterns. We demonstrate that bursts with different spike counts encode different stimulus features, such that about 20% of the transmitted information corresponds to discriminating between different features, and the remaining 80% is used to allocate these features in time. In this spike-pattern code, the what and the when of the stimuli are encoded in the duration of each burst and the time of burst onset, respectively. Given the ubiquity of burst firing, we expect similar findings also for other neural systems. |
2109.08945 | Alexandros Delitzas | Christodoulos Kechris, Alexandros Delitzas, Vasileios Matsoukas,
Panagiotis C. Petrantonakis | Removing Noise from Extracellular Neural Recordings Using Fully
Convolutional Denoising Autoencoders | Accepted version to be published in the 43rd Annual International
Conference of the IEEE Engineering in Medicine and Biology Society (EMBC
2021) | null | 10.1109/EMBC46164.2021.9630585 | null | q-bio.NC cs.LG eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Extracellular recordings are severely contaminated by a considerable amount
of noise sources, rendering the denoising process an extremely challenging task
that should be tackled for efficient spike sorting. To this end, we propose an
end-to-end deep learning approach to the problem, utilizing a Fully
Convolutional Denoising Autoencoder, which learns to produce a clean neuronal
activity signal from a noisy multichannel input. The experimental results on
simulated data show that our proposed method can improve significantly the
quality of noise-corrupted neural signals, outperforming widely-used wavelet
denoising techniques.
| [
{
"created": "Sat, 18 Sep 2021 14:51:24 GMT",
"version": "v1"
}
] | 2021-12-13 | [
[
"Kechris",
"Christodoulos",
""
],
[
"Delitzas",
"Alexandros",
""
],
[
"Matsoukas",
"Vasileios",
""
],
[
"Petrantonakis",
"Panagiotis C.",
""
]
] | Extracellular recordings are severely contaminated by a considerable amount of noise sources, rendering the denoising process an extremely challenging task that should be tackled for efficient spike sorting. To this end, we propose an end-to-end deep learning approach to the problem, utilizing a Fully Convolutional Denoising Autoencoder, which learns to produce a clean neuronal activity signal from a noisy multichannel input. The experimental results on simulated data show that our proposed method can improve significantly the quality of noise-corrupted neural signals, outperforming widely-used wavelet denoising techniques. |
1312.5204 | Fangting Li | Fangting Li, Mingyang Hu, Bo Zhao, Hao Yan, Bin Wu and Qi Ouyang | A globally attractive cycle driven by sequential bifurcations containing
ghost effects in a 3-node yeast cell cycle model | 14 pages, 5 figures withSupplemental information | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Yeast cells produce daughter cells through a DNA replication and mitosis
cycle associated with checkpoints and governed by the cell cycle regulatory
network. To ensure genome stability and genetic information inheritance, this
regulatory network must be dynamically robust against various fluctuations.
Here we construct a simplified cell cycle model for a budding yeast to
investigate the underlying mechanism that ensures robustness in this process
containing sequential tasks (DNA replication and mitosis). We first establish a
three-variable model and select a parameter set that qualitatively describes
the yeast cell cycle process. Then, through nonlinear dynamic analysis, we
demonstrate that the yeast cell cycle process is an excitable system driven by
a sequence of saddle-node bifurcations with ghost effects. We further show that
the yeast cell cycle trajectory is globally attractive with modularity in both
state and parameter space, while the convergent manifold provides a suitable
control state for cell cycle checkpoints. These results not only highlight a
regulatory mechanism for executing successive cell cycle processes, but also
provide a possible strategy for the synthetic network design of sequential-task
processes.
| [
{
"created": "Wed, 18 Dec 2013 16:24:07 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Sep 2014 08:39:49 GMT",
"version": "v2"
}
] | 2014-09-15 | [
[
"Li",
"Fangting",
""
],
[
"Hu",
"Mingyang",
""
],
[
"Zhao",
"Bo",
""
],
[
"Yan",
"Hao",
""
],
[
"Wu",
"Bin",
""
],
[
"Ouyang",
"Qi",
""
]
] | Yeast cells produce daughter cells through a DNA replication and mitosis cycle associated with checkpoints and governed by the cell cycle regulatory network. To ensure genome stability and genetic information inheritance, this regulatory network must be dynamically robust against various fluctuations. Here we construct a simplified cell cycle model for a budding yeast to investigate the underlying mechanism that ensures robustness in this process containing sequential tasks (DNA replication and mitosis). We first establish a three-variable model and select a parameter set that qualitatively describes the yeast cell cycle process. Then, through nonlinear dynamic analysis, we demonstrate that the yeast cell cycle process is an excitable system driven by a sequence of saddle-node bifurcations with ghost effects. We further show that the yeast cell cycle trajectory is globally attractive with modularity in both state and parameter space, while the convergent manifold provides a suitable control state for cell cycle checkpoints. These results not only highlight a regulatory mechanism for executing successive cell cycle processes, but also provide a possible strategy for the synthetic network design of sequential-task processes. |
2301.08436 | Brian Long | Brian Long, Jeremy Miller and The SpaceTx Consortium | SpaceTx: A Roadmap for Benchmarking Spatial Transcriptomics Exploration
of the Brain | null | null | null | null | q-bio.NC q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Mapping spatial distributions of transcriptomic cell types is essential to
understanding the brain, with its exceptional cellular heterogeneity and the
functional significance of its spatial organization. Spatial transcriptomics
techniques are hoped to accomplish these measurements, but each method uses
different experimental and computational protocols, with different trade-offs
and optimizations. In 2017, the SpaceTx Consortium was formed to compare these
methods and determine their suitability for large-scale spatial transcriptomic
atlases. SpaceTx work included progress in tissue processing, taxonomy
development, gene selection, image processing and data standardization, cell
segmentation, cell type assignments, and visualization. Although the landscape
of experimental methods has changed dramatically since the beginning of
SpaceTx, the need for quantitative and detailed benchmarking of spatial
transcriptomics methods in the brain is still unmet. Here, we summarize the
work of SpaceTx and highlight outstanding challenges as spatial transcriptomics
grows into a mature field. We also discuss how our progress provides a roadmap
for benchmarking spatial transcriptomics methods in the future. Data and
analyses from this consortium, along with code and methods are publicly
available at https://spacetx.github.io/.
| [
{
"created": "Fri, 20 Jan 2023 06:20:45 GMT",
"version": "v1"
}
] | 2023-01-23 | [
[
"Long",
"Brian",
""
],
[
"Miller",
"Jeremy",
""
],
[
"Consortium",
"The SpaceTx",
""
]
] | Mapping spatial distributions of transcriptomic cell types is essential to understanding the brain, with its exceptional cellular heterogeneity and the functional significance of its spatial organization. Spatial transcriptomics techniques are hoped to accomplish these measurements, but each method uses different experimental and computational protocols, with different trade-offs and optimizations. In 2017, the SpaceTx Consortium was formed to compare these methods and determine their suitability for large-scale spatial transcriptomic atlases. SpaceTx work included progress in tissue processing, taxonomy development, gene selection, image processing and data standardization, cell segmentation, cell type assignments, and visualization. Although the landscape of experimental methods has changed dramatically since the beginning of SpaceTx, the need for quantitative and detailed benchmarking of spatial transcriptomics methods in the brain is still unmet. Here, we summarize the work of SpaceTx and highlight outstanding challenges as spatial transcriptomics grows into a mature field. We also discuss how our progress provides a roadmap for benchmarking spatial transcriptomics methods in the future. Data and analyses from this consortium, along with code and methods are publicly available at https://spacetx.github.io/. |
2202.01079 | Zhangyang Gao | Zhangyang Gao, Cheng Tan, Stan Z. Li | AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB | null | null | null | null | q-bio.QM cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While DeepMind has tentatively solved protein folding, its inverse problem --
protein design which predicts protein sequences from their 3D structures --
still faces significant challenges. Particularly, the lack of large-scale
standardized benchmark and poor accuray hinder the research progress. In order
to standardize comparisons and draw more research interest, we use AlphaFold
DB, one of the world's largest protein structure databases, to establish a new
graph-based benchmark -- AlphaDesign. Based on AlphaDesign, we propose a new
method called ADesign to improve accuracy by introducing protein angles as new
features, using a simplified graph transformer encoder (SGT), and proposing a
confidence-aware protein decoder (CPD). Meanwhile, SGT and CPD also improve
model efficiency by simplifying the training and testing procedures.
Experiments show that ADesign significantly outperforms previous graph models,
e.g., the average accuracy is improved by 8\%, and the inference speed is 40+
times faster than before.
| [
{
"created": "Tue, 1 Feb 2022 08:28:24 GMT",
"version": "v1"
},
{
"created": "Sat, 12 Feb 2022 00:55:09 GMT",
"version": "v2"
}
] | 2022-02-15 | [
[
"Gao",
"Zhangyang",
""
],
[
"Tan",
"Cheng",
""
],
[
"Li",
"Stan Z.",
""
]
] | While DeepMind has tentatively solved protein folding, its inverse problem -- protein design which predicts protein sequences from their 3D structures -- still faces significant challenges. Particularly, the lack of large-scale standardized benchmark and poor accuray hinder the research progress. In order to standardize comparisons and draw more research interest, we use AlphaFold DB, one of the world's largest protein structure databases, to establish a new graph-based benchmark -- AlphaDesign. Based on AlphaDesign, we propose a new method called ADesign to improve accuracy by introducing protein angles as new features, using a simplified graph transformer encoder (SGT), and proposing a confidence-aware protein decoder (CPD). Meanwhile, SGT and CPD also improve model efficiency by simplifying the training and testing procedures. Experiments show that ADesign significantly outperforms previous graph models, e.g., the average accuracy is improved by 8\%, and the inference speed is 40+ times faster than before. |
1309.3788 | Wiet de Ronde | Wiet de Ronde and Pieter Rein ten Wolde | Multiplexing oscillatory biochemical signals | Includes SI | null | 10.1088/1478-3975/11/2/026004 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years it is increasingly being recognized that biochemical signals
are not necessarily constant in time and that the temporal dynamics of a signal
can be the information carrier. Moreover, it is now well established that
components are often shared between signaling pathways. Here we show by
mathematical modeling that living cells can multiplex a constant and an
oscillatory signal: they can transmit these two signals through the same
signaling pathway simultaneously, and yet respond to them specifically and
reliably. We find that information transmission is reduced not only by noise
arising from the intrinsic stochasticity of biochemical reactions, but also by
crosstalk between the different channels. Yet, under biologically relevant
conditions more than 2 bits of information can be transmitted per channel, even
when the two signals are transmitted simultaneously. These observations suggest
that oscillatory signals are ideal for multiplexing signals.
| [
{
"created": "Sun, 15 Sep 2013 19:03:48 GMT",
"version": "v1"
}
] | 2015-06-17 | [
[
"de Ronde",
"Wiet",
""
],
[
"Wolde",
"Pieter Rein ten",
""
]
] | In recent years it is increasingly being recognized that biochemical signals are not necessarily constant in time and that the temporal dynamics of a signal can be the information carrier. Moreover, it is now well established that components are often shared between signaling pathways. Here we show by mathematical modeling that living cells can multiplex a constant and an oscillatory signal: they can transmit these two signals through the same signaling pathway simultaneously, and yet respond to them specifically and reliably. We find that information transmission is reduced not only by noise arising from the intrinsic stochasticity of biochemical reactions, but also by crosstalk between the different channels. Yet, under biologically relevant conditions more than 2 bits of information can be transmitted per channel, even when the two signals are transmitted simultaneously. These observations suggest that oscillatory signals are ideal for multiplexing signals. |
1801.02549 | Mahmoud Hassan | M. Hassan and F. Wendling | Electroencephalography source connectivity: toward high time/space
resolution brain networks | Accepted in the IEEE Signal Processing Magazine | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The human brain is a large-scale network which function depends on dynamic
interactions between spatially-distributed regions. In the rapidly-evolving
field of network neuroscience, two yet unresolved challenges are potential
breakthroughs. First, functional brain networks should be estimated from
noninvasive and easy to use neuroimaging techniques. Second, the time/space
resolution of these techniques should be high enough to assess the dynamics of
identified networks. Emerging evidence suggests that Electroencephalography
(EEG) source connectivity method may offer solutions to both issues provided
that scalp EEG signals are appropriately processed. Therefore, the performance
of EEG source connectivity method strongly depends on signal processing (SP)
that involves various methods such as preprocessing techniques, inverse
solutions, statistical couplings between signals and network science. The main
objective of this tutorial-like review is to provide an overview on EEG source
connectivity. We describe the major contributions that the SP community brought
to this research field. We emphasize the methodological issues that need to be
carefully addressed to obtain relevant results and we stress the current
limitations that need further investigation. We also report results obtained in
concrete applications, in both normal and pathological brain states. Future
directions in term of signal processing methods and applications are eventually
provided
| [
{
"created": "Mon, 8 Jan 2018 16:47:59 GMT",
"version": "v1"
}
] | 2018-01-09 | [
[
"Hassan",
"M.",
""
],
[
"Wendling",
"F.",
""
]
] | The human brain is a large-scale network which function depends on dynamic interactions between spatially-distributed regions. In the rapidly-evolving field of network neuroscience, two yet unresolved challenges are potential breakthroughs. First, functional brain networks should be estimated from noninvasive and easy to use neuroimaging techniques. Second, the time/space resolution of these techniques should be high enough to assess the dynamics of identified networks. Emerging evidence suggests that Electroencephalography (EEG) source connectivity method may offer solutions to both issues provided that scalp EEG signals are appropriately processed. Therefore, the performance of EEG source connectivity method strongly depends on signal processing (SP) that involves various methods such as preprocessing techniques, inverse solutions, statistical couplings between signals and network science. The main objective of this tutorial-like review is to provide an overview on EEG source connectivity. We describe the major contributions that the SP community brought to this research field. We emphasize the methodological issues that need to be carefully addressed to obtain relevant results and we stress the current limitations that need further investigation. We also report results obtained in concrete applications, in both normal and pathological brain states. Future directions in term of signal processing methods and applications are eventually provided |
q-bio/0404024 | Dimitris Kugiumtzis | D. Kugiumtzis and A. Provata | Statistical analysis of Gene and Intergenic DNA Sequences | 18 pages, to appear in Physica A | null | 10.1016/j.physa.2004.05.070 | null | q-bio.GN | null | Much of the on-going statistical analysis of DNA sequences is focused on the
estimation of characteristics of coding and non-coding regions that would
possibly allow discrimination of these regions. In the current approach, we
concentrate specifically on genes and intergenic regions. To estimate the level
and type of correlation in these regions we apply various statistical methods
inspired from nonlinear time series analysis, namely the probability
distribution of tuplets, the Mutual Information and the Identical Neighbour
Fit. The methods are suitably modified to work on symbolic sequences and they
are first tested for validity on sequences obtained from well--known simple
deterministic and stochastic models. Then they are applied to the DNA sequence
of chromosome 1 of {\em arabidopsis thaliana}. The results suggest that
correlations do exist in the DNA sequence but they are weak and that intergenic
sequences tend to be more correlated than gene sequences. The use of
statistical tests with surrogate data establish these findings in a rigorous
statistical manner.
| [
{
"created": "Wed, 21 Apr 2004 08:33:41 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Kugiumtzis",
"D.",
""
],
[
"Provata",
"A.",
""
]
] | Much of the on-going statistical analysis of DNA sequences is focused on the estimation of characteristics of coding and non-coding regions that would possibly allow discrimination of these regions. In the current approach, we concentrate specifically on genes and intergenic regions. To estimate the level and type of correlation in these regions we apply various statistical methods inspired from nonlinear time series analysis, namely the probability distribution of tuplets, the Mutual Information and the Identical Neighbour Fit. The methods are suitably modified to work on symbolic sequences and they are first tested for validity on sequences obtained from well--known simple deterministic and stochastic models. Then they are applied to the DNA sequence of chromosome 1 of {\em arabidopsis thaliana}. The results suggest that correlations do exist in the DNA sequence but they are weak and that intergenic sequences tend to be more correlated than gene sequences. The use of statistical tests with surrogate data establish these findings in a rigorous statistical manner. |
2404.15369 | Cosmin Badea | Nur Aizaan Anwar, Cosmin Badea | Can a Machine be Conscious? Towards Universal Criteria for Machine
Consciousness | This work was supported by the UKRI CDT in AI for Healthcare,
http://ai4health.io (Grant No. EP/S023283/1) | null | null | null | q-bio.NC cs.AI cs.CY | http://creativecommons.org/licenses/by-nc-nd/4.0/ | As artificially intelligent systems become more anthropomorphic and
pervasive, and their potential impact on humanity more urgent, discussions
about the possibility of machine consciousness have significantly intensified,
and it is sometimes seen as 'the holy grail'. Many concerns have been voiced
about the ramifications of creating an artificial conscious entity. This is
compounded by a marked lack of consensus around what constitutes consciousness
and by an absence of a universal set of criteria for determining consciousness.
By going into depth on the foundations and characteristics of consciousness, we
propose five criteria for determining whether a machine is conscious, which can
also be applied more generally to any entity. This paper aims to serve as a
primer and stepping stone for researchers of consciousness, be they in
philosophy, computer science, medicine, or any other field, to further pursue
this holy grail of philosophy, neuroscience and artificial intelligence.
| [
{
"created": "Fri, 19 Apr 2024 18:38:22 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Apr 2024 17:28:30 GMT",
"version": "v2"
}
] | 2024-05-01 | [
[
"Anwar",
"Nur Aizaan",
""
],
[
"Badea",
"Cosmin",
""
]
] | As artificially intelligent systems become more anthropomorphic and pervasive, and their potential impact on humanity more urgent, discussions about the possibility of machine consciousness have significantly intensified, and it is sometimes seen as 'the holy grail'. Many concerns have been voiced about the ramifications of creating an artificial conscious entity. This is compounded by a marked lack of consensus around what constitutes consciousness and by an absence of a universal set of criteria for determining consciousness. By going into depth on the foundations and characteristics of consciousness, we propose five criteria for determining whether a machine is conscious, which can also be applied more generally to any entity. This paper aims to serve as a primer and stepping stone for researchers of consciousness, be they in philosophy, computer science, medicine, or any other field, to further pursue this holy grail of philosophy, neuroscience and artificial intelligence. |
q-bio/0511020 | Eytan Domany | Joseph Lotem, Hila Benjamin, Dvir Netaneli, Eytan Domany and Leo Sachs | Induction in myeloid leukemic cells of genes that are expressed in
different normal tissues | null | PNAS 101, 16022 (2004) | 10.1073/pnas.0406966101 | null | q-bio.TO q-bio.QM | null | Using DNA microarray and cluster analysis of expressed genes in a cloned line
(M1-t-p53) of myeloid leukemic cells, we have analyzed the expression of genes
that are preferentially expressed in different normal tissues. Clustering of
547 highly expressed genes in these leukemic cells showed 38 genes
preferentially expressed in normal hematopoietic tissues and 122 other genes
preferentially expressed in different normal non-hematopoietic tissues
including neuronal tissues, muscle, liver and testis. We have also analyzed the
genes whose expression in the leukemic cells changed after activation of
wild-type p53 and treatment with the cytokine interleukin 6 (IL-6) or the
calcium mobilizer thapsigargin (TG). Out of 620 such genes in the leukemic
cells that were differentially expressed in normal tissues, clustering showed
80 genes that were preferentially expressed in hematopoietic tissues and 132
genes in different normal non-hematopietic tissues that also included neuronal
tissues, muscle, liver and testis. Activation of p53 and treatment with IL-6 or
TG induced different changes in the genes preferentially expressed in these
normal tissues. These myeloid leukemic cells thus express genes that are
expressed in normal non-hematopoietic tissues, and various treatments can
reprogram these cells to induce other such non-hematopoietic genes. The results
indicate that these leukemic cells share with normal hematopoietic stem cells
the plasticity of differentiation to different cell types. It is suggested that
this reprogramming to induce in malignant cells genes that are expressed in
different normal tissues may be of clinical value in therapy.
| [
{
"created": "Tue, 15 Nov 2005 07:47:51 GMT",
"version": "v1"
}
] | 2009-11-11 | [
[
"Lotem",
"Joseph",
""
],
[
"Benjamin",
"Hila",
""
],
[
"Netaneli",
"Dvir",
""
],
[
"Domany",
"Eytan",
""
],
[
"Sachs",
"Leo",
""
]
] | Using DNA microarray and cluster analysis of expressed genes in a cloned line (M1-t-p53) of myeloid leukemic cells, we have analyzed the expression of genes that are preferentially expressed in different normal tissues. Clustering of 547 highly expressed genes in these leukemic cells showed 38 genes preferentially expressed in normal hematopoietic tissues and 122 other genes preferentially expressed in different normal non-hematopoietic tissues including neuronal tissues, muscle, liver and testis. We have also analyzed the genes whose expression in the leukemic cells changed after activation of wild-type p53 and treatment with the cytokine interleukin 6 (IL-6) or the calcium mobilizer thapsigargin (TG). Out of 620 such genes in the leukemic cells that were differentially expressed in normal tissues, clustering showed 80 genes that were preferentially expressed in hematopoietic tissues and 132 genes in different normal non-hematopietic tissues that also included neuronal tissues, muscle, liver and testis. Activation of p53 and treatment with IL-6 or TG induced different changes in the genes preferentially expressed in these normal tissues. These myeloid leukemic cells thus express genes that are expressed in normal non-hematopoietic tissues, and various treatments can reprogram these cells to induce other such non-hematopoietic genes. The results indicate that these leukemic cells share with normal hematopoietic stem cells the plasticity of differentiation to different cell types. It is suggested that this reprogramming to induce in malignant cells genes that are expressed in different normal tissues may be of clinical value in therapy. |
2212.13168 | Johannes Berg | Niklas Bonacker and Johannes Berg | Inferring stochastic regulatory networks from perturbations of the
non-equilibrium steady state | 9 pages | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Regulatory networks describe the interactions between molecular or cellular
regulators, like transcription factors and genes in gene regulatory networks,
kinases and their receptors in signalling networks, or neurons in neural
networks. A long-standing aim of quantitative biology is to reconstruct such
networks on the basis of large-scale data. Our aim is to leverage fluctuations
around the non-equilibrium steady state for network inference. To this end, we
use a stochastic model of gene regulation or neural dynamics and solve it
approximately within a Gaussian mean-field theory. We develop a likelihood
estimate based on this stochastic theory to infer regulatory interactions from
perturbation data on the network nodes. We apply this approach to artificial
perturbation data as well as to phospho-proteomic data from cell-line
experiments and compare our results to inference schemes restricted to mean
activities in the steady state.
| [
{
"created": "Mon, 26 Dec 2022 14:07:14 GMT",
"version": "v1"
},
{
"created": "Tue, 27 Dec 2022 16:38:05 GMT",
"version": "v2"
}
] | 2022-12-29 | [
[
"Bonacker",
"Niklas",
""
],
[
"Berg",
"Johannes",
""
]
] | Regulatory networks describe the interactions between molecular or cellular regulators, like transcription factors and genes in gene regulatory networks, kinases and their receptors in signalling networks, or neurons in neural networks. A long-standing aim of quantitative biology is to reconstruct such networks on the basis of large-scale data. Our aim is to leverage fluctuations around the non-equilibrium steady state for network inference. To this end, we use a stochastic model of gene regulation or neural dynamics and solve it approximately within a Gaussian mean-field theory. We develop a likelihood estimate based on this stochastic theory to infer regulatory interactions from perturbation data on the network nodes. We apply this approach to artificial perturbation data as well as to phospho-proteomic data from cell-line experiments and compare our results to inference schemes restricted to mean activities in the steady state. |
2308.02995 | Aaron Ge | Aaron Ge, Yasmmin C\^ortes Martins, Tongwu Zhang, Kailing Chen, Maria
Teresa Landi, Brian Park, Jeya Balasubramanian, Jonas S Almeida | mSigSDK -- private, at scale, computation of mutation signatures | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | In our previous work, we demonstrated that it is feasible to perform analysis
on mutation signature data without the need for downloads or installations and
analyze individual patient data at scale without compromising privacy. Building
on this foundation, we developed a Software Development Kit (SDK) called
mSigSDK to facilitate the orchestration of distributed data processing
workflows and graphic visualization of mutational signature analysis results.
We strictly adhered to modern web computing standards, particularly the
modularization standards set by the ECMAScript ES6 framework (JavaScript
modules). Our approach allows for computation to be entirely performed by
secure delegation to the computational resources of the user's own machine
(in-browser), without any downloads or installations. The mSigSDK was developed
primarily as a companion library to the mSig Portal resource of the National
Cancer Institute Division of Cancer Epidemiology and Genetics (NIH/NCI/DCEG),
with a focus on its FAIR extensibility as components of other researchers'
computational constructs. Anticipated extensions include the programmatic
operation of other mutation signature API ecosystems such as SIGNAL and COSMIC,
advancing towards a data commons for mutational signature research (Grossman et
al., 2016).
| [
{
"created": "Sun, 6 Aug 2023 02:41:49 GMT",
"version": "v1"
},
{
"created": "Fri, 19 Jan 2024 19:30:23 GMT",
"version": "v2"
}
] | 2024-01-23 | [
[
"Ge",
"Aaron",
""
],
[
"Martins",
"Yasmmin Côrtes",
""
],
[
"Zhang",
"Tongwu",
""
],
[
"Chen",
"Kailing",
""
],
[
"Landi",
"Maria Teresa",
""
],
[
"Park",
"Brian",
""
],
[
"Balasubramanian",
"Jeya",
""
],
[
"Almeida",
"Jonas S",
""
]
] | In our previous work, we demonstrated that it is feasible to perform analysis on mutation signature data without the need for downloads or installations and analyze individual patient data at scale without compromising privacy. Building on this foundation, we developed a Software Development Kit (SDK) called mSigSDK to facilitate the orchestration of distributed data processing workflows and graphic visualization of mutational signature analysis results. We strictly adhered to modern web computing standards, particularly the modularization standards set by the ECMAScript ES6 framework (JavaScript modules). Our approach allows for computation to be entirely performed by secure delegation to the computational resources of the user's own machine (in-browser), without any downloads or installations. The mSigSDK was developed primarily as a companion library to the mSig Portal resource of the National Cancer Institute Division of Cancer Epidemiology and Genetics (NIH/NCI/DCEG), with a focus on its FAIR extensibility as components of other researchers' computational constructs. Anticipated extensions include the programmatic operation of other mutation signature API ecosystems such as SIGNAL and COSMIC, advancing towards a data commons for mutational signature research (Grossman et al., 2016). |
1408.1580 | Ethan Bolker | Ethan D. Bolker, Jeremy J. Hatch, Catalin Zara | Modeling how windfarm geometry affects bird mortality | null | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Birds flying across a region containing a windfarm risk death from turbine
encounters. This paper describes a geometric model that helps estimate that
risk and a spreadsheet that implements the model.
| [
{
"created": "Wed, 6 Aug 2014 16:02:22 GMT",
"version": "v1"
}
] | 2014-08-08 | [
[
"Bolker",
"Ethan D.",
""
],
[
"Hatch",
"Jeremy J.",
""
],
[
"Zara",
"Catalin",
""
]
] | Birds flying across a region containing a windfarm risk death from turbine encounters. This paper describes a geometric model that helps estimate that risk and a spreadsheet that implements the model. |
2004.14580 | Md Navid Akbar | Md Navid Akbar, Marianna La Rocca, Rachael Garner, Dominique Duncan,
Deniz Erdo\u{g}mu\c{s} | Prediction of Epilepsy Development in Traumatic Brain Injury Patients
from Diffusion Weighted MRI | 2 pages, 3 figures, 1 table | null | null | null | q-bio.QM cs.LG eess.IV q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Post-traumatic epilepsy (PTE) is a life-long complication of traumatic brain
injury (TBI) and is a major public health problem that has an estimated
incidence that ranges from 2%-50%, depending on the severity of the TBI.
Currently, the pathomechanism that in-duces epileptogenesis in TBI patients is
unclear, and one of the most challenging goals in the epilepsy community is to
predict which TBI patients will develop epilepsy. In this work, we used
diffusion-weighted imaging (DWI) of 14 TBI patients recruited in the Epilepsy
Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx)to measure and
analyze fractional anisotropy (FA), obtained from tract-based spatial statistic
(TBSS) analysis. Then we used these measurements to train two support vector
machine (SVM) models to predict which TBI patients have developed epilepsy. Our
approach, tested on these 14 patients with a leave-two-out cross-validation,
allowed us to obtain an accuracy of 0.857 $\pm$ 0.18 (with a 95% level of
confidence), demonstrating it to be potentially promising for the early
characterization of PTE.
| [
{
"created": "Thu, 30 Apr 2020 04:06:24 GMT",
"version": "v1"
},
{
"created": "Fri, 1 May 2020 22:22:57 GMT",
"version": "v2"
}
] | 2020-11-17 | [
[
"Akbar",
"Md Navid",
""
],
[
"La Rocca",
"Marianna",
""
],
[
"Garner",
"Rachael",
""
],
[
"Duncan",
"Dominique",
""
],
[
"Erdoğmuş",
"Deniz",
""
]
] | Post-traumatic epilepsy (PTE) is a life-long complication of traumatic brain injury (TBI) and is a major public health problem that has an estimated incidence that ranges from 2%-50%, depending on the severity of the TBI. Currently, the pathomechanism that in-duces epileptogenesis in TBI patients is unclear, and one of the most challenging goals in the epilepsy community is to predict which TBI patients will develop epilepsy. In this work, we used diffusion-weighted imaging (DWI) of 14 TBI patients recruited in the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx)to measure and analyze fractional anisotropy (FA), obtained from tract-based spatial statistic (TBSS) analysis. Then we used these measurements to train two support vector machine (SVM) models to predict which TBI patients have developed epilepsy. Our approach, tested on these 14 patients with a leave-two-out cross-validation, allowed us to obtain an accuracy of 0.857 $\pm$ 0.18 (with a 95% level of confidence), demonstrating it to be potentially promising for the early characterization of PTE. |
2208.10661 | Jialong Jiang | Jialong Jiang, Sisi Chen, Tiffany Tsou, Christopher S. McGinnis,
Tahmineh Khazaei, Qin Zhu, Jong H. Park, Paul Rivaud, Inna-Marie Strazhnik,
Eric D. Chow, David A. Sivak, Zev J. Gartner, Matt Thomson | Therapeutic algebra of immunomodulatory drug responses at single-cell
resolution | 19 pages, 5 figures | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Therapeutic modulation of immune states is central to the treatment of human
disease. However, how drugs and drug combinations impact the diverse cell types
in the human immune system remains poorly understood at the transcriptome
scale. Here, we apply single-cell mRNA-seq to profile the response of human
immune cells to 502 immunomodulatory drugs alone and in combination. We develop
a unified mathematical model that quantitatively describes the transcriptome
scale response of myeloid and lymphoid cell types to individual drugs and drug
combinations through a single inferred regulatory network. The mathematical
model reveals how drug combinations generate novel, macrophage and T-cell
states by recruiting combinations of gene expression programs through both
additive and non-additive drug interactions. A simplified drug response algebra
allows us to predict the continuous modulation of immune cell populations
between activated, resting and hyper-inhibited states through combinatorial
drug dose titrations. Our results suggest that transcriptome-scale mathematical
models could enable the design of therapeutic strategies for programming the
human immune system using combinations of therapeutics.
| [
{
"created": "Tue, 23 Aug 2022 00:43:32 GMT",
"version": "v1"
}
] | 2022-08-24 | [
[
"Jiang",
"Jialong",
""
],
[
"Chen",
"Sisi",
""
],
[
"Tsou",
"Tiffany",
""
],
[
"McGinnis",
"Christopher S.",
""
],
[
"Khazaei",
"Tahmineh",
""
],
[
"Zhu",
"Qin",
""
],
[
"Park",
"Jong H.",
""
],
[
"Rivaud",
"Paul",
""
],
[
"Strazhnik",
"Inna-Marie",
""
],
[
"Chow",
"Eric D.",
""
],
[
"Sivak",
"David A.",
""
],
[
"Gartner",
"Zev J.",
""
],
[
"Thomson",
"Matt",
""
]
] | Therapeutic modulation of immune states is central to the treatment of human disease. However, how drugs and drug combinations impact the diverse cell types in the human immune system remains poorly understood at the transcriptome scale. Here, we apply single-cell mRNA-seq to profile the response of human immune cells to 502 immunomodulatory drugs alone and in combination. We develop a unified mathematical model that quantitatively describes the transcriptome scale response of myeloid and lymphoid cell types to individual drugs and drug combinations through a single inferred regulatory network. The mathematical model reveals how drug combinations generate novel, macrophage and T-cell states by recruiting combinations of gene expression programs through both additive and non-additive drug interactions. A simplified drug response algebra allows us to predict the continuous modulation of immune cell populations between activated, resting and hyper-inhibited states through combinatorial drug dose titrations. Our results suggest that transcriptome-scale mathematical models could enable the design of therapeutic strategies for programming the human immune system using combinations of therapeutics. |
2205.09757 | Devin Goodsman | Devin W. Goodsman | Quantifying Population Movement Using a Novel Implementation of Digital
Image Correlation in the ICvectorfields package | null | null | null | null | q-bio.QM q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Movements in imagery captivate the human eye and imagination. They are also
of interest in variety of scientific disciplines that study spatiotemporal
dynamics. Popular methods for quantifying movement in imagery include particle
image velocimetry and digital image correlation. Both methods are widely
applied in engineering and materials science, but less applied in other
disciplines. This paper describes an implementation of a basic digital image
correlation algorithm in R open source software as well as an extension
designed to quantify persistent movement velocities in sequences of three or
more images. Algorithms are applied in the novel arena of landscape ecology to
quantify population movement and to produce vector fields for easy
visualization of complex movement patterns across space. Functions to
facilitate analyses are available in the ICvectorfields software package. These
methods and functions are likely to produce novel insights in theoretical and
landscape ecology because they facilitate visualization and comparison of
theoretical and observed data in complex and heterogeneous environments.
| [
{
"created": "Wed, 18 May 2022 22:45:08 GMT",
"version": "v1"
}
] | 2022-05-23 | [
[
"Goodsman",
"Devin W.",
""
]
] | Movements in imagery captivate the human eye and imagination. They are also of interest in variety of scientific disciplines that study spatiotemporal dynamics. Popular methods for quantifying movement in imagery include particle image velocimetry and digital image correlation. Both methods are widely applied in engineering and materials science, but less applied in other disciplines. This paper describes an implementation of a basic digital image correlation algorithm in R open source software as well as an extension designed to quantify persistent movement velocities in sequences of three or more images. Algorithms are applied in the novel arena of landscape ecology to quantify population movement and to produce vector fields for easy visualization of complex movement patterns across space. Functions to facilitate analyses are available in the ICvectorfields software package. These methods and functions are likely to produce novel insights in theoretical and landscape ecology because they facilitate visualization and comparison of theoretical and observed data in complex and heterogeneous environments. |
2309.13326 | Viorel Munteanu | Viorel Munteanu, Michael Saldana, Dumitru Ciorba, Viorel Bostan,
Justin Maine Su, Nadiia Kasianchuk, Nitesh Kumar Sharma, Sergey Knyazev,
Victor Gordeev, Eva A{\ss}mann, Andrei Lobiuc, Mihai Covasa, Keith A.
Crandall, Wenhao O. Ouyang, Nicholas C. Wu, Christopher Mason, Braden T
Tierney, Alexander G Lucaci, Alex Zelikovsky, Fatemeh Mohebbi, Pavel Skums,
Cynthia Gibas, Jessica Schlueter, Piotr Rzymski, Helena Solo-Gabriele, Martin
H\"olzer, Adam Smith, Serghei Mangul | SARS-CoV-2 Wastewater Genomic Surveillance: Approaches, Challenges, and
Opportunities | V Munteanu and M Saldana contributed equally to this work. M
H\"olzer, A Smith and S Mangul jointly supervised this work. For
correspondence: serghei.mangul@gmail.com | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | During the SARS-CoV-2 pandemic, wastewater-based genomic surveillance (WWGS)
emerged as an efficient viral surveillance tool that takes into account
asymptomatic cases and can identify known and novel mutations and offers the
opportunity to assign known virus lineages based on the detected mutations
profiles. WWGS can also hint towards novel or cryptic lineages, but it is
difficult to clearly identify and define novel lineages from wastewater (WW)
alone. While WWGS has significant advantages in monitoring SARS-CoV-2 viral
spread, technical challenges remain, including poor sequencing coverage and
quality due to viral RNA degradation. As a result, the viral RNAs in wastewater
have low concentrations and are often fragmented, making sequencing difficult.
WWGS analysis requires advanced computational tools that are yet to be
developed and benchmarked. The existing bioinformatics tools used to analyze
wastewater sequencing data are often based on previously developed methods for
quantifying the expression of transcripts or viral diversity. Those methods
were not developed for wastewater sequencing data specifically, and are not
optimized to address unique challenges associated with wastewater. While
specialized tools for analysis of wastewater sequencing data have also been
developed recently, it remains to be seen how they will perform given the
ongoing evolution of SARS-CoV-2 and the decline in testing and patient-based
genomic surveillance. Here, we discuss opportunities and challenges associated
with WWGS, including sample preparation, sequencing technology, and
bioinformatics methods.
| [
{
"created": "Sat, 23 Sep 2023 10:10:00 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Jan 2024 07:54:18 GMT",
"version": "v2"
}
] | 2024-01-31 | [
[
"Munteanu",
"Viorel",
""
],
[
"Saldana",
"Michael",
""
],
[
"Ciorba",
"Dumitru",
""
],
[
"Bostan",
"Viorel",
""
],
[
"Su",
"Justin Maine",
""
],
[
"Kasianchuk",
"Nadiia",
""
],
[
"Sharma",
"Nitesh Kumar",
""
],
[
"Knyazev",
"Sergey",
""
],
[
"Gordeev",
"Victor",
""
],
[
"Aßmann",
"Eva",
""
],
[
"Lobiuc",
"Andrei",
""
],
[
"Covasa",
"Mihai",
""
],
[
"Crandall",
"Keith A.",
""
],
[
"Ouyang",
"Wenhao O.",
""
],
[
"Wu",
"Nicholas C.",
""
],
[
"Mason",
"Christopher",
""
],
[
"Tierney",
"Braden T",
""
],
[
"Lucaci",
"Alexander G",
""
],
[
"Zelikovsky",
"Alex",
""
],
[
"Mohebbi",
"Fatemeh",
""
],
[
"Skums",
"Pavel",
""
],
[
"Gibas",
"Cynthia",
""
],
[
"Schlueter",
"Jessica",
""
],
[
"Rzymski",
"Piotr",
""
],
[
"Solo-Gabriele",
"Helena",
""
],
[
"Hölzer",
"Martin",
""
],
[
"Smith",
"Adam",
""
],
[
"Mangul",
"Serghei",
""
]
] | During the SARS-CoV-2 pandemic, wastewater-based genomic surveillance (WWGS) emerged as an efficient viral surveillance tool that takes into account asymptomatic cases and can identify known and novel mutations and offers the opportunity to assign known virus lineages based on the detected mutations profiles. WWGS can also hint towards novel or cryptic lineages, but it is difficult to clearly identify and define novel lineages from wastewater (WW) alone. While WWGS has significant advantages in monitoring SARS-CoV-2 viral spread, technical challenges remain, including poor sequencing coverage and quality due to viral RNA degradation. As a result, the viral RNAs in wastewater have low concentrations and are often fragmented, making sequencing difficult. WWGS analysis requires advanced computational tools that are yet to be developed and benchmarked. The existing bioinformatics tools used to analyze wastewater sequencing data are often based on previously developed methods for quantifying the expression of transcripts or viral diversity. Those methods were not developed for wastewater sequencing data specifically, and are not optimized to address unique challenges associated with wastewater. While specialized tools for analysis of wastewater sequencing data have also been developed recently, it remains to be seen how they will perform given the ongoing evolution of SARS-CoV-2 and the decline in testing and patient-based genomic surveillance. Here, we discuss opportunities and challenges associated with WWGS, including sample preparation, sequencing technology, and bioinformatics methods. |
1712.06756 | Gustavo Rodrigues Ferreira | Gustavo Rodrigues Ferreira, Helder Imoto Nakaya, Luciano da Fontoura
Costa | Gene regulatory and signalling networks exhibit distinct topological
distributions of motifs | 7 pages, 7 figures. Submitted to PRE | Phys. Rev. E 97, 042417 (2018) | 10.1103/PhysRevE.97.042417 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The biological processes of cellular decision making and differentiation
involve a plethora of signalling pathways and gene regulatory circuits. These
networks, in their turn, exhibit a multitude of motifs playing crucial parts in
regulating network activity. Here, we compare the topological placement of
motifs in gene regulatory and signalling networks and find that it suggests
different evolutionary strategies in motif distribution for distinct cellular
subnetworks.
| [
{
"created": "Tue, 19 Dec 2017 02:40:47 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Dec 2017 01:41:13 GMT",
"version": "v2"
},
{
"created": "Thu, 12 Apr 2018 15:18:04 GMT",
"version": "v3"
}
] | 2018-05-02 | [
[
"Ferreira",
"Gustavo Rodrigues",
""
],
[
"Nakaya",
"Helder Imoto",
""
],
[
"Costa",
"Luciano da Fontoura",
""
]
] | The biological processes of cellular decision making and differentiation involve a plethora of signalling pathways and gene regulatory circuits. These networks, in their turn, exhibit a multitude of motifs playing crucial parts in regulating network activity. Here, we compare the topological placement of motifs in gene regulatory and signalling networks and find that it suggests different evolutionary strategies in motif distribution for distinct cellular subnetworks. |
2212.07702 | Arne Elofsson | Arne Elofsson | Protein Structure Prediction until CASP15 | 10 pages 2 figures | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | In Dec 2020, the results of AlphaFold2 were presented at CASP14, sparking a
revolution in the field of protein structure predictions. For the first time, a
purely computational method could challenge experimental accuracy for structure
prediction of single protein domains. The code of AlphaFold2 was released in
the summer of 2021, and since then, it has been shown that it can be used to
accurately predict the structure of most (ordered) proteins and many
protein-protein interactions. It has also sparked an explosion of development
in the field, improving AI-based methods to predict protein complexes,
disordered regions, and protein design. Here I will review some of the
inventions sparked by the release of AlphaFold.
| [
{
"created": "Thu, 15 Dec 2022 10:25:44 GMT",
"version": "v1"
}
] | 2022-12-16 | [
[
"Elofsson",
"Arne",
""
]
] | In Dec 2020, the results of AlphaFold2 were presented at CASP14, sparking a revolution in the field of protein structure predictions. For the first time, a purely computational method could challenge experimental accuracy for structure prediction of single protein domains. The code of AlphaFold2 was released in the summer of 2021, and since then, it has been shown that it can be used to accurately predict the structure of most (ordered) proteins and many protein-protein interactions. It has also sparked an explosion of development in the field, improving AI-based methods to predict protein complexes, disordered regions, and protein design. Here I will review some of the inventions sparked by the release of AlphaFold. |
2006.13841 | Todd R Lewis PhD | Todd R. Lewis, Alex Ramsay, Arnold Sciberras and Colin Bailey | Kleptoparasitism on carpenter ants (Camponotus spp.) by Podarcis
tiliguerta (Gmelin, 1789) in Corsica and Podarcis filfolensis (Bedriaga,
1876) on the Maltese islands | null | Herpetozoa 27 3/4 (2015) 175-176 | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Kleptoparasitic behavior was observed by the authors in P. tiliguerta and P.
filfolensis. That this behavior has been observed in two mediterranean species
of Podarcis, independently parasitizing on species of Camponotus ant, suggests
there is benefit to kleptoparasitism on Camponotus spp. in Mediterranean
ecosystems.
| [
{
"created": "Wed, 24 Jun 2020 16:12:12 GMT",
"version": "v1"
}
] | 2020-06-25 | [
[
"Lewis",
"Todd R.",
""
],
[
"Ramsay",
"Alex",
""
],
[
"Sciberras",
"Arnold",
""
],
[
"Bailey",
"Colin",
""
]
] | Kleptoparasitic behavior was observed by the authors in P. tiliguerta and P. filfolensis. That this behavior has been observed in two mediterranean species of Podarcis, independently parasitizing on species of Camponotus ant, suggests there is benefit to kleptoparasitism on Camponotus spp. in Mediterranean ecosystems. |
1510.07699 | Kelin Xia | Kelin Xia, Kristopher Opron and Guo-Wei Wei | Multiscale Gaussian network model (mGNM) and multiscale anisotropic
network model (mANM) | 21 pages,16 figures | null | 10.1063/1.4936132 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gaussian network model(GNM) and anisotropic network model(ANM) are some of
the most popular methods for the study of protein flexibility and related
functions. In this work, we propose generalized GNM(gGNM) and ANM methods and
show that the GNM Kirchhoff matrix can be built from the ideal low-pass filter,
which is a special case of a wide class of correlation functions underpinning
the linear scaling flexibility-rigidity index(FRI) method. Based on the
mathematical structure of correlation functions, we propose a unified framework
to construct generalized Kirchhoff matrices whose matrix inverse leads to
gGNMs, whereas, the direct inverse of its diagonal elements gives rise to FRI
method.With this connection,we further introduce two multiscale elastic network
models, namely, multiscale GNM(mGNM) and multiscale ANM(mANM), which are able
to incorporate different scales into the generalized Kirchkoff matrices or
generalized Hessian matrices.We validate our new multiscale methods with
extensive numerical experiments. We illustrate that gGNMs outperform the
original GNM method in the B-factor prediction of a set of 364 proteins.We
demonstrate that for a given correlation function, FRI and gGNM methods provide
essentially identical B-factor predictions when the scale value in the
correlation function is sufficiently large.More importantly,we reveal intrinsic
multiscale behavior in protein structures. The proposed mGNM and mANM are able
to capture this multiscale behavior and thus give rise to a significant
improvement of more than 11% in B-factor predictions over the original GNM and
ANM methods. We further demonstrate benefit of our mGNM in the B-factor
predictions on many proteins that fail the original GNM method. We show that
the present mGNM can also be used to analyze protein domain separations.
Finally, we showcase the ability of our mANM for the simulation of protein
collective motions.
| [
{
"created": "Mon, 26 Oct 2015 21:47:17 GMT",
"version": "v1"
}
] | 2016-01-20 | [
[
"Xia",
"Kelin",
""
],
[
"Opron",
"Kristopher",
""
],
[
"Wei",
"Guo-Wei",
""
]
] | Gaussian network model(GNM) and anisotropic network model(ANM) are some of the most popular methods for the study of protein flexibility and related functions. In this work, we propose generalized GNM(gGNM) and ANM methods and show that the GNM Kirchhoff matrix can be built from the ideal low-pass filter, which is a special case of a wide class of correlation functions underpinning the linear scaling flexibility-rigidity index(FRI) method. Based on the mathematical structure of correlation functions, we propose a unified framework to construct generalized Kirchhoff matrices whose matrix inverse leads to gGNMs, whereas, the direct inverse of its diagonal elements gives rise to FRI method.With this connection,we further introduce two multiscale elastic network models, namely, multiscale GNM(mGNM) and multiscale ANM(mANM), which are able to incorporate different scales into the generalized Kirchkoff matrices or generalized Hessian matrices.We validate our new multiscale methods with extensive numerical experiments. We illustrate that gGNMs outperform the original GNM method in the B-factor prediction of a set of 364 proteins.We demonstrate that for a given correlation function, FRI and gGNM methods provide essentially identical B-factor predictions when the scale value in the correlation function is sufficiently large.More importantly,we reveal intrinsic multiscale behavior in protein structures. The proposed mGNM and mANM are able to capture this multiscale behavior and thus give rise to a significant improvement of more than 11% in B-factor predictions over the original GNM and ANM methods. We further demonstrate benefit of our mGNM in the B-factor predictions on many proteins that fail the original GNM method. We show that the present mGNM can also be used to analyze protein domain separations. Finally, we showcase the ability of our mANM for the simulation of protein collective motions. |
1902.10270 | Carina Curto | Carina Curto, Christopher Langdon, Katherine Morrison | Robust motifs of threshold-linear networks | 24 pages, 7 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To any inhibition-dominated threshold-linear network (TLN) we can associate a
directed graph that captures the pattern of strong and weak inhibition between
neurons. Robust motifs are graphs for which the structure of fixed points in
the network is independent of the choice of connectivity matrix $W$, and whose
dynamics are thus greatly constrained. This makes them ideal building blocks
for constructing larger networks whose behaviors are robust to changes in
connection strengths. In contrast, flexible motifs correspond to networks with
multiple dynamic regimes. In this work, we give a purely graphical
characterization of both flexible and robust motifs of any size. We find that
all but a few robust motifs fall into two infinite families of graphs with
simple feedforward architectures.
| [
{
"created": "Wed, 27 Feb 2019 00:09:43 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Aug 2019 17:36:36 GMT",
"version": "v2"
},
{
"created": "Mon, 16 Dec 2019 23:32:06 GMT",
"version": "v3"
}
] | 2019-12-18 | [
[
"Curto",
"Carina",
""
],
[
"Langdon",
"Christopher",
""
],
[
"Morrison",
"Katherine",
""
]
] | To any inhibition-dominated threshold-linear network (TLN) we can associate a directed graph that captures the pattern of strong and weak inhibition between neurons. Robust motifs are graphs for which the structure of fixed points in the network is independent of the choice of connectivity matrix $W$, and whose dynamics are thus greatly constrained. This makes them ideal building blocks for constructing larger networks whose behaviors are robust to changes in connection strengths. In contrast, flexible motifs correspond to networks with multiple dynamic regimes. In this work, we give a purely graphical characterization of both flexible and robust motifs of any size. We find that all but a few robust motifs fall into two infinite families of graphs with simple feedforward architectures. |
1901.11160 | Esther Lopez Montalvo | Clodoaldo Rold\'an, Sonia Murcia-Mascar\'os, Esther L\'opez-Montalvo
(TRACES), Cristina Vilanova, Manuel Porcar (ICBiBE) | Proteomic and metagenomic insights into prehistoric Spanish Levantine
Rock Art | null | Scientific Reports, Nature Publishing Group, 2018, 8 (1) | 10.1038/s41598-018-28121-6 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Iberian Mediterranean Basin is home to one of the largest groups of
prehistoric rock art sites in Europe. Despite the cultural relevance of
prehistoric Spanish Levantine rock art, pigment composition remains partially
unknown, and the nature of the binders used for painting has yet to be
disclosed. In this work, we present the first omic analysis applied to one of
the flagship Levantine rock art sites: the Valltorta ravine (Castell{\'o}n,
Spain). We used high-throughput sequencing to provide the first description of
the bacterial communities colonizing the rock art patina, which proved to be
dominated by Firmicutes species and might have a protective effect on the
paintings. Proteomic analysis was also performed on rock art microsamples in
order to determine the organic binders present in Levantine prehistoric rock
art pigments. This information could shed light on the controversial dating of
this UNESCO Cultural Heritage, and contribute to defining the chrono-cultural
framework of the societies responsible for these paintings.
| [
{
"created": "Thu, 24 Jan 2019 10:12:36 GMT",
"version": "v1"
}
] | 2019-02-01 | [
[
"Roldán",
"Clodoaldo",
"",
"TRACES"
],
[
"Murcia-Mascarós",
"Sonia",
"",
"TRACES"
],
[
"López-Montalvo",
"Esther",
"",
"TRACES"
],
[
"Vilanova",
"Cristina",
"",
"ICBiBE"
],
[
"Porcar",
"Manuel",
"",
"ICBiBE"
]
] | The Iberian Mediterranean Basin is home to one of the largest groups of prehistoric rock art sites in Europe. Despite the cultural relevance of prehistoric Spanish Levantine rock art, pigment composition remains partially unknown, and the nature of the binders used for painting has yet to be disclosed. In this work, we present the first omic analysis applied to one of the flagship Levantine rock art sites: the Valltorta ravine (Castell{\'o}n, Spain). We used high-throughput sequencing to provide the first description of the bacterial communities colonizing the rock art patina, which proved to be dominated by Firmicutes species and might have a protective effect on the paintings. Proteomic analysis was also performed on rock art microsamples in order to determine the organic binders present in Levantine prehistoric rock art pigments. This information could shed light on the controversial dating of this UNESCO Cultural Heritage, and contribute to defining the chrono-cultural framework of the societies responsible for these paintings. |
1801.00674 | Emily Evans | Emily Jennings Evans and John C. Dallon | A Three-Dimensional Mathematical Model of Collagen Contraction | null | null | null | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we introduce a three-dimensional mathematical model of
collagen contraction with microbuckling based on the two-dimensional model
previously developed by the authors. The model both qualitatively and
quantitatively replicates experimental data including lattice contraction over
a time course of 40 hours for lattices with various cell densities, cell
density profiles within contracted lattices, radial cut angles in lattices, and
cell force propagation within a lattice. The importance of the model lattice
formation and the crucial nature of its connectivity are discussed including
differences with models which do not include microbuckling. The model suggests
that most cells within contracting lattices are engaged in directed motion.
| [
{
"created": "Tue, 2 Jan 2018 14:50:17 GMT",
"version": "v1"
}
] | 2018-01-03 | [
[
"Evans",
"Emily Jennings",
""
],
[
"Dallon",
"John C.",
""
]
] | In this paper, we introduce a three-dimensional mathematical model of collagen contraction with microbuckling based on the two-dimensional model previously developed by the authors. The model both qualitatively and quantitatively replicates experimental data including lattice contraction over a time course of 40 hours for lattices with various cell densities, cell density profiles within contracted lattices, radial cut angles in lattices, and cell force propagation within a lattice. The importance of the model lattice formation and the crucial nature of its connectivity are discussed including differences with models which do not include microbuckling. The model suggests that most cells within contracting lattices are engaged in directed motion. |
2010.09932 | Sophia Shatek | Amanda K. Robinson, Tijl Grootswagers, Sophia M. Shatek, Jack Gerboni,
Alex Holcombe, Thomas A. Carlson | Overlapping neural representations for the position of visible and
imagined objects | All data and analysis code for this study are available at
https://osf.io/8v47t/ | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Humans can covertly track the position of an object, even if the object is
temporarily occluded. What are the neural mechanisms underlying our capacity to
track moving objects when there is no physical stimulus for the brain to track?
One possibility is that the brain 'fills-in' information about imagined objects
using internally generated representations similar to those generated by
feed-forward perceptual mechanisms. Alternatively, the brain might deploy a
higher order mechanism, for example using an object tracking model that
integrates visual signals and motion dynamics. In the present study, we used
EEG and time-resolved multivariate pattern analyses to investigate the spatial
processing of visible and imagined objects. Participants tracked an object that
moved in discrete steps around fixation, occupying six consecutive locations.
They were asked to imagine that the object continued on the same trajectory
after it disappeared and move their attention to the corresponding positions.
Time-resolved decoding of EEG data revealed that the location of the visible
stimuli could be decoded shortly after image onset, consistent with early
retinotopic visual processes. For processing of unseen/imagined positions, the
patterns of neural activity resembled stimulus-driven mid-level visual
processes, but were detected earlier than perceptual mechanisms, implicating an
anticipatory and more variable tracking mechanism. Encoding models revealed
that spatial representations were much weaker for imagined than visible
stimuli. Monitoring the position of imagined objects thus utilises similar
perceptual and attentional processes as monitoring objects that are actually
present, but with different temporal dynamics. These results indicate that
internally generated representations rely on top-down processes, and their
timing is influenced by the predictability of the stimulus.
| [
{
"created": "Tue, 20 Oct 2020 00:09:06 GMT",
"version": "v1"
},
{
"created": "Wed, 11 Nov 2020 23:42:35 GMT",
"version": "v2"
}
] | 2020-11-13 | [
[
"Robinson",
"Amanda K.",
""
],
[
"Grootswagers",
"Tijl",
""
],
[
"Shatek",
"Sophia M.",
""
],
[
"Gerboni",
"Jack",
""
],
[
"Holcombe",
"Alex",
""
],
[
"Carlson",
"Thomas A.",
""
]
] | Humans can covertly track the position of an object, even if the object is temporarily occluded. What are the neural mechanisms underlying our capacity to track moving objects when there is no physical stimulus for the brain to track? One possibility is that the brain 'fills-in' information about imagined objects using internally generated representations similar to those generated by feed-forward perceptual mechanisms. Alternatively, the brain might deploy a higher order mechanism, for example using an object tracking model that integrates visual signals and motion dynamics. In the present study, we used EEG and time-resolved multivariate pattern analyses to investigate the spatial processing of visible and imagined objects. Participants tracked an object that moved in discrete steps around fixation, occupying six consecutive locations. They were asked to imagine that the object continued on the same trajectory after it disappeared and move their attention to the corresponding positions. Time-resolved decoding of EEG data revealed that the location of the visible stimuli could be decoded shortly after image onset, consistent with early retinotopic visual processes. For processing of unseen/imagined positions, the patterns of neural activity resembled stimulus-driven mid-level visual processes, but were detected earlier than perceptual mechanisms, implicating an anticipatory and more variable tracking mechanism. Encoding models revealed that spatial representations were much weaker for imagined than visible stimuli. Monitoring the position of imagined objects thus utilises similar perceptual and attentional processes as monitoring objects that are actually present, but with different temporal dynamics. These results indicate that internally generated representations rely on top-down processes, and their timing is influenced by the predictability of the stimulus. |
1902.06553 | Benedetta Franceschiello Dr. | Fabio Anselmi, Micah M. Murray and Benedetta Franceschiello | A computational model for grid maps in neural populations | 16 pages, 4 figures | null | null | null | q-bio.NC cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Grid cells in the entorhinal cortex, together with head direction, place,
speed and border cells, are major contributors to the organization of spatial
representations in the brain. In this work we introduce a novel theoretical and
algorithmic framework able to explain the emergence of hexagonal grid-like
response patterns from head direction cells' responses. We show that this
pattern is a result of minimal variance encoding of neurons. The novelty lies
into the formulation of the encoding problem through the modern Frame Theory
language, specifically that of equiangular Frames, providing new insights about
the optimality of hexagonal grid receptive fields. The model proposed overcomes
some crucial limitations of the current attractor and oscillatory models. It is
based on the well-accepted and tested hypothesis of Hebbian learning, providing
a simplified cortical-based framework that does not require the presence of
theta velocity-driven oscillations (oscillatory model) or translational
symmetries in the synaptic connections (attractor model). We moreover
demonstrate that the proposed encoding mechanism naturally explains axis
alignment of neighbor grid cells and maps shifts, rotations and scaling of the
stimuli onto the shape of grid cells' receptive fields, giving a
straightforward explanation of the experimental evidence of grid cells
remapping under transformations of environmental cues.
| [
{
"created": "Mon, 18 Feb 2019 13:02:04 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Feb 2019 07:56:22 GMT",
"version": "v2"
},
{
"created": "Wed, 24 Jul 2019 08:12:59 GMT",
"version": "v3"
}
] | 2019-07-25 | [
[
"Anselmi",
"Fabio",
""
],
[
"Murray",
"Micah M.",
""
],
[
"Franceschiello",
"Benedetta",
""
]
] | Grid cells in the entorhinal cortex, together with head direction, place, speed and border cells, are major contributors to the organization of spatial representations in the brain. In this work we introduce a novel theoretical and algorithmic framework able to explain the emergence of hexagonal grid-like response patterns from head direction cells' responses. We show that this pattern is a result of minimal variance encoding of neurons. The novelty lies into the formulation of the encoding problem through the modern Frame Theory language, specifically that of equiangular Frames, providing new insights about the optimality of hexagonal grid receptive fields. The model proposed overcomes some crucial limitations of the current attractor and oscillatory models. It is based on the well-accepted and tested hypothesis of Hebbian learning, providing a simplified cortical-based framework that does not require the presence of theta velocity-driven oscillations (oscillatory model) or translational symmetries in the synaptic connections (attractor model). We moreover demonstrate that the proposed encoding mechanism naturally explains axis alignment of neighbor grid cells and maps shifts, rotations and scaling of the stimuli onto the shape of grid cells' receptive fields, giving a straightforward explanation of the experimental evidence of grid cells remapping under transformations of environmental cues. |
q-bio/0508036 | Eugene Shakhnovich | Konstantin B. Zeldovich, Igor N. Berezovsky, Eugene I. Sha | Physical origins of protein superfamilies | null | null | null | null | q-bio.GN q-bio.BM q-bio.PE | null | In this work, we discovered a fundamental connection between selection for
protein stability and emergence of preferred structures of proteins. Using
standard exact 3-dimensional lattice model we evolve sequences starting from
random ones and determining exact native structure after each mutation.
Acceptance of mutations is biased to select for stable proteins. We found that
certain structures, wonderfold, are independently discovered numerous times as
native states of stable proteins in many unrelated runs of selection. Diversity
of sequences that fold into wonderfold structures gives rise to superfamilies,
i.e. sets of dissimilar sequences that fold into the same or very similar
structures. Wonderfolds appear to be the most designable structures out of
complete set of compact lattice proteins. Furthermore, proteins having
wondefolds as their native structure tend to be most thermostable among all
evolved proteins. This effect is purely due to the favorable geometric
properties of wonderfolds and, thus, dominates any dependence on sequences. The
present work establishes a model of prebiotic structure selection, which
identifies dominant structural patterns emerging upon optimization of proteins
for survival in hot environment. Convergently discovered prebiotic initial
superfamilies with wonderfold structures could have served as a seed for
subsequent biological evolution involving gene duplications and divergence.
| [
{
"created": "Thu, 25 Aug 2005 17:15:15 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Zeldovich",
"Konstantin B.",
""
],
[
"Berezovsky",
"Igor N.",
""
],
[
"Sha",
"Eugene I.",
""
]
] | In this work, we discovered a fundamental connection between selection for protein stability and emergence of preferred structures of proteins. Using standard exact 3-dimensional lattice model we evolve sequences starting from random ones and determining exact native structure after each mutation. Acceptance of mutations is biased to select for stable proteins. We found that certain structures, wonderfold, are independently discovered numerous times as native states of stable proteins in many unrelated runs of selection. Diversity of sequences that fold into wonderfold structures gives rise to superfamilies, i.e. sets of dissimilar sequences that fold into the same or very similar structures. Wonderfolds appear to be the most designable structures out of complete set of compact lattice proteins. Furthermore, proteins having wondefolds as their native structure tend to be most thermostable among all evolved proteins. This effect is purely due to the favorable geometric properties of wonderfolds and, thus, dominates any dependence on sequences. The present work establishes a model of prebiotic structure selection, which identifies dominant structural patterns emerging upon optimization of proteins for survival in hot environment. Convergently discovered prebiotic initial superfamilies with wonderfold structures could have served as a seed for subsequent biological evolution involving gene duplications and divergence. |
2304.07205 | Detao Ji | Ji Detao, Liu Weier | A Method for Improving the Detection Accura-cy of MSIsensor Based on
Downsampling | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Motivation: Microsatellite instability (MSI) is a cancer biomarker associated
with cancer prognosis and chemotherapy sensitivity. Since the discovery of MSI,
polymerase chain reaction (PCR)-based testing has been considered the gold
standard for MSI detection. However, with the decrease in sequencing costs,
software that calculates MSI based on next-generation sequencing (NGS) data has
been widely applied. Results: In this study, we evaluated the performance of
the MSIsensor detection software, focusing on the limitations of the chi-square
test algorithm in determining microsatellite stability under high-depth
sequencing data. We demonstrated that the chi-square test algorithm is
insufficient for accurately as-sessing microsatellite stability in this
context. Furthermore, we explored the application of downsampling techniques to
enhance the accuracy of MSIsensor detection. Our findings provide insight into
the limita-tions of current methods and offer potential improvements for more
reliable MSI detection based on NGS data.
| [
{
"created": "Fri, 14 Apr 2023 15:41:19 GMT",
"version": "v1"
}
] | 2023-04-17 | [
[
"Detao",
"Ji",
""
],
[
"Weier",
"Liu",
""
]
] | Motivation: Microsatellite instability (MSI) is a cancer biomarker associated with cancer prognosis and chemotherapy sensitivity. Since the discovery of MSI, polymerase chain reaction (PCR)-based testing has been considered the gold standard for MSI detection. However, with the decrease in sequencing costs, software that calculates MSI based on next-generation sequencing (NGS) data has been widely applied. Results: In this study, we evaluated the performance of the MSIsensor detection software, focusing on the limitations of the chi-square test algorithm in determining microsatellite stability under high-depth sequencing data. We demonstrated that the chi-square test algorithm is insufficient for accurately as-sessing microsatellite stability in this context. Furthermore, we explored the application of downsampling techniques to enhance the accuracy of MSIsensor detection. Our findings provide insight into the limita-tions of current methods and offer potential improvements for more reliable MSI detection based on NGS data. |
1712.05005 | Jean-Louis Dessalles | Jean-Louis Dessalles | Language: The missing selection pressure | 34 pages, 3 figures | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human beings are talkative. What advantage did their ancestors find in
communicating so much? Numerous authors consider this advantage to be "obvious"
and "enormous". If so, the problem of the evolutionary emergence of language
amounts to explaining why none of the other primate species evolved anything
even remotely similar to language. What I propose here is to reverse the
picture. On closer examination, language resembles a losing strategy. Competing
for providing other individuals with information, sometimes striving to be
heard, makes apparently no sense within a Darwinian framework. At face value,
language as we can observe it should never have existed or should have been
counter-selected. In other words, the selection pressure that led to language
is still missing. The solution I propose consists in regarding language as a
social signaling device that developed in a context of generalized insecurity
that is unique to our species. By talking, individuals advertise their
alertness and their ability to get informed. This hypothesis is shown to be
compatible with many characteristics of language that otherwise are left
unexplained.
| [
{
"created": "Wed, 13 Dec 2017 20:46:49 GMT",
"version": "v1"
}
] | 2017-12-15 | [
[
"Dessalles",
"Jean-Louis",
""
]
] | Human beings are talkative. What advantage did their ancestors find in communicating so much? Numerous authors consider this advantage to be "obvious" and "enormous". If so, the problem of the evolutionary emergence of language amounts to explaining why none of the other primate species evolved anything even remotely similar to language. What I propose here is to reverse the picture. On closer examination, language resembles a losing strategy. Competing for providing other individuals with information, sometimes striving to be heard, makes apparently no sense within a Darwinian framework. At face value, language as we can observe it should never have existed or should have been counter-selected. In other words, the selection pressure that led to language is still missing. The solution I propose consists in regarding language as a social signaling device that developed in a context of generalized insecurity that is unique to our species. By talking, individuals advertise their alertness and their ability to get informed. This hypothesis is shown to be compatible with many characteristics of language that otherwise are left unexplained. |
2208.07455 | Thomas Cleland | Jack A. Cook, Thomas A. Cleland | A Geometric Framework for Odor Representation | 33 pages, 4 figures. arXiv admin note: substantial text overlap with
arXiv:2009.02310 | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We present a generalized theoretical framework for olfactory representation
and plasticity, using the theory of smooth manifolds and sheaves to depict
categorical odor learning via distributed neural computation. Beginning with
the space of all possible inputs to the olfactory system, we develop a dynamic
model for odor learning that culminates in a perceptual space in which
categorical odor representations are hierarchically constructed through
experience, exhibiting statistically appropriate consequential regions and
clear relationships between the broader and narrower identities to which a
given odor might be assigned. The model reflects both the sampling-based
physical similarity relationships among odorants, as observed in physiological
receptor response profiles, and the acquired, learning-dependent perceptual
similarity relationships among odors that can be measured behaviorally, and
defines the relationship between them. Individual training and experience
generates correspondingly more sophisticated odor identification capabilities.
Because these odor representations are constructed from experience and depend
on local, distributed plasticity mechanisms, geometries that fix curvature are
insufficient to describe the capabilities of the system. This generative
framework also encompasses hypotheses explaining representational drift in
postbulbar circuits and the context-dependent remapping of perceptual
similarity relationships.
| [
{
"created": "Mon, 15 Aug 2022 22:23:46 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Oct 2023 16:50:38 GMT",
"version": "v2"
}
] | 2023-10-13 | [
[
"Cook",
"Jack A.",
""
],
[
"Cleland",
"Thomas A.",
""
]
] | We present a generalized theoretical framework for olfactory representation and plasticity, using the theory of smooth manifolds and sheaves to depict categorical odor learning via distributed neural computation. Beginning with the space of all possible inputs to the olfactory system, we develop a dynamic model for odor learning that culminates in a perceptual space in which categorical odor representations are hierarchically constructed through experience, exhibiting statistically appropriate consequential regions and clear relationships between the broader and narrower identities to which a given odor might be assigned. The model reflects both the sampling-based physical similarity relationships among odorants, as observed in physiological receptor response profiles, and the acquired, learning-dependent perceptual similarity relationships among odors that can be measured behaviorally, and defines the relationship between them. Individual training and experience generates correspondingly more sophisticated odor identification capabilities. Because these odor representations are constructed from experience and depend on local, distributed plasticity mechanisms, geometries that fix curvature are insufficient to describe the capabilities of the system. This generative framework also encompasses hypotheses explaining representational drift in postbulbar circuits and the context-dependent remapping of perceptual similarity relationships. |
2404.02465 | Deng Luo | Deng Luo, Zainab Alsuwaykit, Dawar Khan, Ond\v{r}ej Strnad, Tobias
Isenberg, and Ivan Viola | DiffFit: Visually-Guided Differentiable Fitting of Molecule Structures
to a Cryo-EM Map | 16 pages, 7 figures, 3 tables. IEEE VIS 2024 | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | We introduce DiffFit, a differentiable algorithm for fitting protein
atomistic structures into an experimental reconstructed Cryo-Electron
Microscopy (cryo-EM) volume map. In structural biology, this process is
necessary to semi-automatically composite large mesoscale models of complex
protein assemblies and complete cellular structures that are based on measured
cryo-EM data. The current approaches require manual fitting in three dimensions
to start, resulting in approximately aligned structures followed by an
automated fine-tuning of the alignment. The DiffFit approach enables domain
scientists to fit new structures automatically and visualize the results for
inspection and interactive revision. The fitting begins with differentiable
three-dimensional (3D) rigid transformations of the protein atom coordinates
followed by sampling the density values at the atom coordinates from the target
cryo-EM volume. To ensure a meaningful correlation between the sampled
densities and the protein structure, we proposed a novel loss function based on
a multi-resolution volume-array approach and the exploitation of the negative
space. This loss function serves as a critical metric for assessing the fitting
quality, ensuring the fitting accuracy and an improved visualization of the
results. We assessed the placement quality of DiffFit with several large,
realistic datasets and found it to be superior to that of previous methods. We
further evaluated our method in two use cases: automating the integration of
known composite structures into larger protein complexes and facilitating the
fitting of predicted protein domains into volume densities to aid researchers
in identifying unknown proteins. We implemented our algorithm as an open-source
plugin (github.com/nanovis/DiffFit) in ChimeraX, a leading visualization
software in the field. All supplemental materials are available at
osf.io/5tx4q.
| [
{
"created": "Wed, 3 Apr 2024 05:08:46 GMT",
"version": "v1"
},
{
"created": "Thu, 4 Apr 2024 06:09:30 GMT",
"version": "v2"
},
{
"created": "Wed, 3 Jul 2024 11:54:58 GMT",
"version": "v3"
},
{
"created": "Wed, 31 Jul 2024 08:12:20 GMT",
"version": "v4"
},
{
"created": "Thu, 1 Aug 2024 14:08:29 GMT",
"version": "v5"
}
] | 2024-08-02 | [
[
"Luo",
"Deng",
""
],
[
"Alsuwaykit",
"Zainab",
""
],
[
"Khan",
"Dawar",
""
],
[
"Strnad",
"Ondřej",
""
],
[
"Isenberg",
"Tobias",
""
],
[
"Viola",
"Ivan",
""
]
] | We introduce DiffFit, a differentiable algorithm for fitting protein atomistic structures into an experimental reconstructed Cryo-Electron Microscopy (cryo-EM) volume map. In structural biology, this process is necessary to semi-automatically composite large mesoscale models of complex protein assemblies and complete cellular structures that are based on measured cryo-EM data. The current approaches require manual fitting in three dimensions to start, resulting in approximately aligned structures followed by an automated fine-tuning of the alignment. The DiffFit approach enables domain scientists to fit new structures automatically and visualize the results for inspection and interactive revision. The fitting begins with differentiable three-dimensional (3D) rigid transformations of the protein atom coordinates followed by sampling the density values at the atom coordinates from the target cryo-EM volume. To ensure a meaningful correlation between the sampled densities and the protein structure, we proposed a novel loss function based on a multi-resolution volume-array approach and the exploitation of the negative space. This loss function serves as a critical metric for assessing the fitting quality, ensuring the fitting accuracy and an improved visualization of the results. We assessed the placement quality of DiffFit with several large, realistic datasets and found it to be superior to that of previous methods. We further evaluated our method in two use cases: automating the integration of known composite structures into larger protein complexes and facilitating the fitting of predicted protein domains into volume densities to aid researchers in identifying unknown proteins. We implemented our algorithm as an open-source plugin (github.com/nanovis/DiffFit) in ChimeraX, a leading visualization software in the field. All supplemental materials are available at osf.io/5tx4q. |
q-bio/0311017 | Chao Tang | Morten Kloster, Chao Tang, Ned Wingreen | Finding regulatory modules through large-scale gene-expression data
analysis | 7 pages, 6 figures in main text; 2 text pages, 7 figures, 1 table in
supplement; rewritten version | Bioinformatics 21, 1172 (2005). | null | null | q-bio.QM q-bio.GN | null | The use of gene microchips has enabled a rapid accumulation of
gene-expression data. One of the major challenges of analyzing this data is the
diversity, in both size and signal strength, of the various modules in the gene
regulatory networks of organisms. Based on the Iterative Signature Algorithm
[Bergmann, S., Ihmels, J. and Barkai, N. (2002) Phys. Rev. E 67, 031902], we
present an algorithm - the Progressive Iterative Signature Algorithm (PISA) -
that, by sequentially eliminating modules, allows unsupervised identification
of both large and small regulatory modules. We applied PISA to a large set of
yeast gene-expression data, and, using the Gene Ontology annotation database as
a reference, found that our algorithm is much better able to identify
regulatory modules than methods based on high-throughput transcription-factor
binding experiments or on comparative genomics.
| [
{
"created": "Thu, 13 Nov 2003 00:31:58 GMT",
"version": "v1"
},
{
"created": "Mon, 19 Jan 2004 20:27:45 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Kloster",
"Morten",
""
],
[
"Tang",
"Chao",
""
],
[
"Wingreen",
"Ned",
""
]
] | The use of gene microchips has enabled a rapid accumulation of gene-expression data. One of the major challenges of analyzing this data is the diversity, in both size and signal strength, of the various modules in the gene regulatory networks of organisms. Based on the Iterative Signature Algorithm [Bergmann, S., Ihmels, J. and Barkai, N. (2002) Phys. Rev. E 67, 031902], we present an algorithm - the Progressive Iterative Signature Algorithm (PISA) - that, by sequentially eliminating modules, allows unsupervised identification of both large and small regulatory modules. We applied PISA to a large set of yeast gene-expression data, and, using the Gene Ontology annotation database as a reference, found that our algorithm is much better able to identify regulatory modules than methods based on high-throughput transcription-factor binding experiments or on comparative genomics. |
1812.11184 | Carl Boettiger | Milad Memarzadeh, Carl Boettiger | Resolving the measurement uncertainty paradox in ecological management | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Ecological management and decision-making typically focus on uncertainty
about the future, but surprisingly little is known about how to account for
uncertainty of the present: that is, the realities of having only partial or
imperfect measurements. Our primary paradigms for handling decisions under
uncertainty -- the precautionary principle and optimal control -- have so far
given contradictory results. This paradox is best illustrated in the example of
fisheries management, where many ideas that guide thinking about ecological
decision making were first developed. We find that simplistic optimal control
approaches have repeatedly concluded that a manager should increase catch
quotas when faced with greater uncertainty about the fish biomass. Current best
practices take a more precautionary approach, decreasing catch quotas by a
fixed amount to account for uncertainty. Using comparisons to both simulated
and historical catch data, we find that neither approach is sufficient to avoid
stock collapses under moderate observational uncertainty. Using partially
observed Markov decision process (POMDP) methods, we demonstrate how this
paradox arises from flaws in the standard theory, which contributes to
over-exploitation of fisheries and increased probability of economic and
ecological collapse. In contrast, we find POMDP-based management avoids such
over-exploitation while also generating higher economic value. These results
have significant implications for how we handle uncertainty in both fisheries
and ecological management more generally.
| [
{
"created": "Fri, 28 Dec 2018 18:07:13 GMT",
"version": "v1"
}
] | 2019-01-01 | [
[
"Memarzadeh",
"Milad",
""
],
[
"Boettiger",
"Carl",
""
]
] | Ecological management and decision-making typically focus on uncertainty about the future, but surprisingly little is known about how to account for uncertainty of the present: that is, the realities of having only partial or imperfect measurements. Our primary paradigms for handling decisions under uncertainty -- the precautionary principle and optimal control -- have so far given contradictory results. This paradox is best illustrated in the example of fisheries management, where many ideas that guide thinking about ecological decision making were first developed. We find that simplistic optimal control approaches have repeatedly concluded that a manager should increase catch quotas when faced with greater uncertainty about the fish biomass. Current best practices take a more precautionary approach, decreasing catch quotas by a fixed amount to account for uncertainty. Using comparisons to both simulated and historical catch data, we find that neither approach is sufficient to avoid stock collapses under moderate observational uncertainty. Using partially observed Markov decision process (POMDP) methods, we demonstrate how this paradox arises from flaws in the standard theory, which contributes to over-exploitation of fisheries and increased probability of economic and ecological collapse. In contrast, we find POMDP-based management avoids such over-exploitation while also generating higher economic value. These results have significant implications for how we handle uncertainty in both fisheries and ecological management more generally. |
2401.10144 | Yiqun Lin | Yiqun Lin, Liang Pan, Yi Li, Ziwei Liu, and Xiaomeng Li | Exploiting Hierarchical Interactions for Protein Surface Learning | Accepted to J-BHI | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Predicting interactions between proteins is one of the most important yet
challenging problems in structural bioinformatics. Intrinsically, potential
function sites in protein surfaces are determined by both geometric and
chemical features. However, existing works only consider handcrafted or
individually learned chemical features from the atom type and extract geometric
features independently. Here, we identify two key properties of effective
protein surface learning: 1) relationship among atoms: atoms are linked with
each other by covalent bonds to form biomolecules instead of appearing alone,
leading to the significance of modeling the relationship among atoms in
chemical feature learning. 2) hierarchical feature interaction: the neighboring
residue effect validates the significance of hierarchical feature interaction
among atoms and between surface points and atoms (or residues). In this paper,
we present a principled framework based on deep learning techniques, namely
Hierarchical Chemical and Geometric Feature Interaction Network (HCGNet), for
protein surface analysis by bridging chemical and geometric features with
hierarchical interactions. Extensive experiments demonstrate that our method
outperforms the prior state-of-the-art method by 2.3% in site prediction task
and 3.2% in interaction matching task, respectively. Our code is available at
https://github.com/xmed-lab/HCGNet.
| [
{
"created": "Wed, 17 Jan 2024 14:10:40 GMT",
"version": "v1"
}
] | 2024-01-19 | [
[
"Lin",
"Yiqun",
""
],
[
"Pan",
"Liang",
""
],
[
"Li",
"Yi",
""
],
[
"Liu",
"Ziwei",
""
],
[
"Li",
"Xiaomeng",
""
]
] | Predicting interactions between proteins is one of the most important yet challenging problems in structural bioinformatics. Intrinsically, potential function sites in protein surfaces are determined by both geometric and chemical features. However, existing works only consider handcrafted or individually learned chemical features from the atom type and extract geometric features independently. Here, we identify two key properties of effective protein surface learning: 1) relationship among atoms: atoms are linked with each other by covalent bonds to form biomolecules instead of appearing alone, leading to the significance of modeling the relationship among atoms in chemical feature learning. 2) hierarchical feature interaction: the neighboring residue effect validates the significance of hierarchical feature interaction among atoms and between surface points and atoms (or residues). In this paper, we present a principled framework based on deep learning techniques, namely Hierarchical Chemical and Geometric Feature Interaction Network (HCGNet), for protein surface analysis by bridging chemical and geometric features with hierarchical interactions. Extensive experiments demonstrate that our method outperforms the prior state-of-the-art method by 2.3% in site prediction task and 3.2% in interaction matching task, respectively. Our code is available at https://github.com/xmed-lab/HCGNet. |
1502.06629 | Brian Enquist Prof. | Brian J. Enquist, Jon Norberg, Stephen P. Bonser, Cyrille Violle,
Colleen T. Webb, Amanda Henderson, Lindsey L. Sloat, Van M. Savage | Scaling from traits to ecosystems: Developing a general Trait Driver
Theory via integrating trait-based and metabolic scaling theories | 96 pages, 7 Figures, Appendix with 5 figures | 2015, Advances in Ecological Research, 52 | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rise of trait-based ecology has led to an increased focus on the
distribution and dynamics of traits in communities. However, a general theory
of trait-based ecology, that can apply across different scales (e.g., species
that differ in size) and gradients (e.g., temperature), has yet to be
formulated. While research focused on metabolic and allometric scaling theory
provides the basis for such a theory it does not explicitly account for
differences traits within and across taxa, such as variation in the optimal
temperature for growth. Here we synthesize trait-based and metabolic scaling
approaches into a framework that we term Trait Drivers Theory or TDT. It shows
that the shape and dynamics of trait distributions can be uniquely linked to
fundamental drivers of community assembly and how the community will respond to
future drivers. To assess predictions and assumptions of TDT, we review several
theoretical studies, recent empirical studies spanning local and biogeographic
gradients. Further, we analyze how the shift in trait distributions influences
ecosystem productivity across an elevational gradient and a 140-year long
ecological experiment. We argue that our general TDT provides a baseline for
(i) recasting the predictions of ecological theories based on species richness
in terms of the shape of trait distributions; and (ii) integrating how specific
traits, including body size, and functional diversity scale up to influence the
dynamics of species assemblages across climatic gradients and how shifts in
functional composition influences ecosystem functioning. Further, it offers a
novel framework to integrate trait, metabolic/allometric, and species-richness
based approaches in order to build a more predictive functional biogeography to
show how assemblages of species have and will respond to climate change.
| [
{
"created": "Mon, 23 Feb 2015 21:09:28 GMT",
"version": "v1"
}
] | 2015-02-25 | [
[
"Enquist",
"Brian J.",
""
],
[
"Norberg",
"Jon",
""
],
[
"Bonser",
"Stephen P.",
""
],
[
"Violle",
"Cyrille",
""
],
[
"Webb",
"Colleen T.",
""
],
[
"Henderson",
"Amanda",
""
],
[
"Sloat",
"Lindsey L.",
""
],
[
"Savage",
"Van M.",
""
]
] | The rise of trait-based ecology has led to an increased focus on the distribution and dynamics of traits in communities. However, a general theory of trait-based ecology, that can apply across different scales (e.g., species that differ in size) and gradients (e.g., temperature), has yet to be formulated. While research focused on metabolic and allometric scaling theory provides the basis for such a theory it does not explicitly account for differences traits within and across taxa, such as variation in the optimal temperature for growth. Here we synthesize trait-based and metabolic scaling approaches into a framework that we term Trait Drivers Theory or TDT. It shows that the shape and dynamics of trait distributions can be uniquely linked to fundamental drivers of community assembly and how the community will respond to future drivers. To assess predictions and assumptions of TDT, we review several theoretical studies, recent empirical studies spanning local and biogeographic gradients. Further, we analyze how the shift in trait distributions influences ecosystem productivity across an elevational gradient and a 140-year long ecological experiment. We argue that our general TDT provides a baseline for (i) recasting the predictions of ecological theories based on species richness in terms of the shape of trait distributions; and (ii) integrating how specific traits, including body size, and functional diversity scale up to influence the dynamics of species assemblages across climatic gradients and how shifts in functional composition influences ecosystem functioning. Further, it offers a novel framework to integrate trait, metabolic/allometric, and species-richness based approaches in order to build a more predictive functional biogeography to show how assemblages of species have and will respond to climate change. |
2211.12083 | Yoshihiko Takase | Yoshihiko Takase | Investigating factors behind the outbreak of the 6th and the 7th waves
of COVID-19 in Tokyo | 10 pages, 15 PDF figures | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The 6th wave of COVID-19 in Tokyo continued for the longest period of
infection (about 190 days from late Nov. 2021), and the 7th wave, which
occurred in mid-May 2022, was the largest wave ever (cumulative 1.7 million
people). In order to elucidate their factors, the infection wave was analyzed
by using the Avrami equation. The main component of the 6th wave was formed by
the coupling of increased human interaction due to the New Year holidays and
the invasion of the new virus variant Omicron BA.1. After that, side waves were
formed by the coupling of the invasion of the new virus variant Omicron BA.2
and the human interaction in the consecutive holidays in February, March, and
May. These side waves caused the 6th wave not to converge for a long time. The
outbreak of the main component of the 7th wave occurred by the coupling of the
invasion of the new virus variant Omicron BA.5 and multiple social factors,
followed by human interaction during the July holidays. Based on the results
that the domain growth rate $K$ and the infection rise time $t_\mathrm{on}$
were almost independent of the initial susceptible $D_\mathrm{s}$, dense
nucleation followed by a near growth model was deduced. The quantity $K \cdot
t_\mathrm{on}$ was considered to represent the infectivity of the virus.
| [
{
"created": "Tue, 22 Nov 2022 08:27:47 GMT",
"version": "v1"
}
] | 2022-11-23 | [
[
"Takase",
"Yoshihiko",
""
]
] | The 6th wave of COVID-19 in Tokyo continued for the longest period of infection (about 190 days from late Nov. 2021), and the 7th wave, which occurred in mid-May 2022, was the largest wave ever (cumulative 1.7 million people). In order to elucidate their factors, the infection wave was analyzed by using the Avrami equation. The main component of the 6th wave was formed by the coupling of increased human interaction due to the New Year holidays and the invasion of the new virus variant Omicron BA.1. After that, side waves were formed by the coupling of the invasion of the new virus variant Omicron BA.2 and the human interaction in the consecutive holidays in February, March, and May. These side waves caused the 6th wave not to converge for a long time. The outbreak of the main component of the 7th wave occurred by the coupling of the invasion of the new virus variant Omicron BA.5 and multiple social factors, followed by human interaction during the July holidays. Based on the results that the domain growth rate $K$ and the infection rise time $t_\mathrm{on}$ were almost independent of the initial susceptible $D_\mathrm{s}$, dense nucleation followed by a near growth model was deduced. The quantity $K \cdot t_\mathrm{on}$ was considered to represent the infectivity of the virus. |
2104.10082 | Lana Descheemaeker | Lana Descheemaeker | Modeling biological networks: from single gene systems to large
microbial communities | Doctor of Sciences thesis | null | null | null | q-bio.PE nlin.AO | http://creativecommons.org/licenses/by-sa/4.0/ | In this research, we study biological networks at different scales: a gene
autoregulatory network at the single-cell level and the gut microbiota at the
population level.
Proteins are the main actors in cells, they are the building blocks, act as
enzymes and antibodies. The production of proteins is mediated by transcription
factors. In some cases, a protein acts as its own transcription factor, this is
called autoregulation. It is known that autorepression speeds up the response
and that autoactivation can lead to multiple stable equilibria. In this thesis,
we study the effects of the combination of activation and repression in
autoregulation, as a case study we investigate the possible dynamics of the
leucine responsive protein B of the archaeon Sulfolobus solfataricus (Ss-LrpB),
a protein that regulates itself in a unique and non-monotonic way via three
binding boxes. We examine for which conditions this type of network leads to
oscillations or bistability.
In the second part, much larger biological systems are considered. Ecological
systems, among which the human gut microbiome, are characterized by
heavy-tailed abundance profiles. We study how these distributions can arise
from population-based models by adding saturation effects and linear noise.
Moreover, we examine different characteristics of experimental time series of
microbial communities, such as the noise color and neutrality of the
biodiversity, and look at the influence of the parameters on these
characteristics. With the first research topic we want to lay a foundation for
the understanding of non-monotonic gene regulation and take the first steps
toward synthetic biology in archaea. In the second part of the thesis, we
investigate experimental time series from complex ecosystems and seek
theoretical models reproducing all observed characteristics in view of building
predictive models.
| [
{
"created": "Tue, 20 Apr 2021 16:04:36 GMT",
"version": "v1"
}
] | 2021-04-21 | [
[
"Descheemaeker",
"Lana",
""
]
] | In this research, we study biological networks at different scales: a gene autoregulatory network at the single-cell level and the gut microbiota at the population level. Proteins are the main actors in cells, they are the building blocks, act as enzymes and antibodies. The production of proteins is mediated by transcription factors. In some cases, a protein acts as its own transcription factor, this is called autoregulation. It is known that autorepression speeds up the response and that autoactivation can lead to multiple stable equilibria. In this thesis, we study the effects of the combination of activation and repression in autoregulation, as a case study we investigate the possible dynamics of the leucine responsive protein B of the archaeon Sulfolobus solfataricus (Ss-LrpB), a protein that regulates itself in a unique and non-monotonic way via three binding boxes. We examine for which conditions this type of network leads to oscillations or bistability. In the second part, much larger biological systems are considered. Ecological systems, among which the human gut microbiome, are characterized by heavy-tailed abundance profiles. We study how these distributions can arise from population-based models by adding saturation effects and linear noise. Moreover, we examine different characteristics of experimental time series of microbial communities, such as the noise color and neutrality of the biodiversity, and look at the influence of the parameters on these characteristics. With the first research topic we want to lay a foundation for the understanding of non-monotonic gene regulation and take the first steps toward synthetic biology in archaea. In the second part of the thesis, we investigate experimental time series from complex ecosystems and seek theoretical models reproducing all observed characteristics in view of building predictive models. |
2308.11936 | Won Kyu Kim | Won Kyu Kim, Kiri Choi, Changbong Hyeon, Seogjoo J. Jang | General Chemical Reaction Network Theory for Olfactory Sensing Based on
G-Protein-Coupled Receptors : Elucidation of Odorant Mixture Effects and
Agonist-Synergist Threshold | null | null | null | null | q-bio.MN physics.bio-ph physics.chem-ph | http://creativecommons.org/licenses/by/4.0/ | This work presents a general chemical reaction network theory for olfactory
sensing processes that employ G-protein-coupled receptors as olfactory
receptors (ORs). The theory is applicable to general mixtures of odorants and
an arbitrary number of ORs. Reactions of ORs with G-proteins, both in the
presence and the absence of odorants, are explicitly considered. A unique
feature of the theory is the definition of an odor activity vector consisting
of strengths of odorant-induced signals from ORs relative to those due to
background G-protein activity in the absence of odorants. It is demonstrated
that each component of the odor activity defined this way reduces to a
Michaelis-Menten form capable of accounting for cooperation or competition
effects between different odorants. The main features of the theory are
illustrated for a two-odorant mixture. Known and potential mixture effects,
such as suppression, shadowing, inhibition, and synergy are quantitatively
described. Effects of relative values of rate constants, basal activity, and
G-protein concentration are also demonstrated.
| [
{
"created": "Wed, 23 Aug 2023 05:59:45 GMT",
"version": "v1"
},
{
"created": "Tue, 5 Sep 2023 03:44:58 GMT",
"version": "v2"
}
] | 2023-09-06 | [
[
"Kim",
"Won Kyu",
""
],
[
"Choi",
"Kiri",
""
],
[
"Hyeon",
"Changbong",
""
],
[
"Jang",
"Seogjoo J.",
""
]
] | This work presents a general chemical reaction network theory for olfactory sensing processes that employ G-protein-coupled receptors as olfactory receptors (ORs). The theory is applicable to general mixtures of odorants and an arbitrary number of ORs. Reactions of ORs with G-proteins, both in the presence and the absence of odorants, are explicitly considered. A unique feature of the theory is the definition of an odor activity vector consisting of strengths of odorant-induced signals from ORs relative to those due to background G-protein activity in the absence of odorants. It is demonstrated that each component of the odor activity defined this way reduces to a Michaelis-Menten form capable of accounting for cooperation or competition effects between different odorants. The main features of the theory are illustrated for a two-odorant mixture. Known and potential mixture effects, such as suppression, shadowing, inhibition, and synergy are quantitatively described. Effects of relative values of rate constants, basal activity, and G-protein concentration are also demonstrated. |
2301.00483 | Vladimir Makarenkov | Vladimir Makarenkov, Gayane S. Barseghyan and Nadia Tahiri | Inferring multiple consensus trees and supertrees using clustering: a
review | null | null | null | null | q-bio.PE cs.DS q-bio.GN stat.AP | http://creativecommons.org/licenses/by/4.0/ | Phylogenetic trees (i.e. evolutionary trees, additive trees or X-trees) play
a key role in the processes of modeling and representing species evolution.
Genome evolution of a given group of species is usually modeled by a species
phylogenetic tree that represents the main patterns of vertical descent.
However, the evolution of each gene is unique. It can be represented by its own
gene tree which can differ substantially from a general species tree
representation. Consensus trees and supertrees have been widely used in
evolutionary studies to combine phylogenetic information contained in
individual gene trees. Nevertheless, if the available gene trees are quite
different from each other, then the resulting consensus tree or supertree can
either include many unresolved subtrees corresponding to internal nodes of high
degree or can simply be a star tree. This may happen if the available gene
trees have been affected by different reticulate evolutionary events, such as
horizontal gene transfer, hybridization or genetic recombination. Thus, the
problem of inferring multiple alternative consensus trees or supertrees, using
clustering, becomes relevant since it allows one to regroup in different
clusters gene trees having similar evolutionary patterns (e.g. gene trees
representing genes that have undergone the same horizontal gene transfer or
recombination events). We critically review recent advances and methods in the
field of phylogenetic tree clustering, discuss the methods' mathematical
properties, and describe the main advantages and limitations of multiple
consensus tree and supertree approaches. In the application section, we show
how the multiple supertree clustering approach can be used to cluster aaRS gene
trees according to their evolutionary patterns.
| [
{
"created": "Sun, 1 Jan 2023 22:26:55 GMT",
"version": "v1"
}
] | 2023-01-03 | [
[
"Makarenkov",
"Vladimir",
""
],
[
"Barseghyan",
"Gayane S.",
""
],
[
"Tahiri",
"Nadia",
""
]
] | Phylogenetic trees (i.e. evolutionary trees, additive trees or X-trees) play a key role in the processes of modeling and representing species evolution. Genome evolution of a given group of species is usually modeled by a species phylogenetic tree that represents the main patterns of vertical descent. However, the evolution of each gene is unique. It can be represented by its own gene tree which can differ substantially from a general species tree representation. Consensus trees and supertrees have been widely used in evolutionary studies to combine phylogenetic information contained in individual gene trees. Nevertheless, if the available gene trees are quite different from each other, then the resulting consensus tree or supertree can either include many unresolved subtrees corresponding to internal nodes of high degree or can simply be a star tree. This may happen if the available gene trees have been affected by different reticulate evolutionary events, such as horizontal gene transfer, hybridization or genetic recombination. Thus, the problem of inferring multiple alternative consensus trees or supertrees, using clustering, becomes relevant since it allows one to regroup in different clusters gene trees having similar evolutionary patterns (e.g. gene trees representing genes that have undergone the same horizontal gene transfer or recombination events). We critically review recent advances and methods in the field of phylogenetic tree clustering, discuss the methods' mathematical properties, and describe the main advantages and limitations of multiple consensus tree and supertree approaches. In the application section, we show how the multiple supertree clustering approach can be used to cluster aaRS gene trees according to their evolutionary patterns. |
q-bio/0611012 | Andras Lorincz | A. Lorincz, V. Gyenes, M. Kiszlinger, I. Szita | Mind model seems necessary for the emergence of communication | 22 pages, 3 figures, New Ties (http://www.new-ties.org/) EU FP6
discussion paper | null | null | null | q-bio.NC q-bio.PE | null | We consider communication when there is no agreement about symbols and
meanings. We treat it within the framework of reinforcement learning. We apply
different reinforcement learning models in our studies and simplify the problem
as much as possible. We show that the modelling of the other agent is
insufficient in the simplest possible case, unless the intentions can also be
modelled. The model of the agent and its intentions enable quick agreements
about symbol-meaning association. We show that when both agents assume an
`intention model' about the other agent then the symbol-meaning association
process can be spoiled and symbol meaning association may become hard.
| [
{
"created": "Fri, 3 Nov 2006 16:17:15 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Lorincz",
"A.",
""
],
[
"Gyenes",
"V.",
""
],
[
"Kiszlinger",
"M.",
""
],
[
"Szita",
"I.",
""
]
] | We consider communication when there is no agreement about symbols and meanings. We treat it within the framework of reinforcement learning. We apply different reinforcement learning models in our studies and simplify the problem as much as possible. We show that the modelling of the other agent is insufficient in the simplest possible case, unless the intentions can also be modelled. The model of the agent and its intentions enable quick agreements about symbol-meaning association. We show that when both agents assume an `intention model' about the other agent then the symbol-meaning association process can be spoiled and symbol meaning association may become hard. |
1101.3215 | Emmanuele DiBenedetto | Leonardo Lenoci, Heidi E. Hamm, and Emmanuele DiBenedetto | Identification of the key parameters in a mathematical model of
PAR1-mediated signaling in endothelial cells | 25 pages, 4 figures, 8 tables | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biophysical models are often populated by a large number of input parameters
that are difficult to predict or measure experimentally. The validity and
robustness of a given model can be evaluated by a sensitivity test to its input
parameters. In this study, we performed local (based on a Taylor-like method)
and global sensitivity (based on Monte Carlo filtering techniques) analyses of
a previously derived PAR1-mediated activation model of endothelial cells. This
activation model previously demonstrated that peptide-activated PAR1 has a
different receptor/G-protein binding affinity that favors Galpha_q activation
over Galpha_12/13 by approximately 800-fold. Interestingly, the present study
shows that the parameter regulating the binding rate of activated PAR1 to
Galpha_12/13 is indeed important to obtain the expected RhoGTP response.
Moreover, we show that the parameters representing the rate of PAR1
deactivation and the rate of PAR1 binding to G_q, are the most important
parameters in the system. Finally, we illustrate that the kinetic model
considered in this study is robust and we provide complementary insights into
the biological meaning and importance of its kinetic parameters.
| [
{
"created": "Mon, 17 Jan 2011 14:02:07 GMT",
"version": "v1"
}
] | 2015-03-17 | [
[
"Lenoci",
"Leonardo",
""
],
[
"Hamm",
"Heidi E.",
""
],
[
"DiBenedetto",
"Emmanuele",
""
]
] | Biophysical models are often populated by a large number of input parameters that are difficult to predict or measure experimentally. The validity and robustness of a given model can be evaluated by a sensitivity test to its input parameters. In this study, we performed local (based on a Taylor-like method) and global sensitivity (based on Monte Carlo filtering techniques) analyses of a previously derived PAR1-mediated activation model of endothelial cells. This activation model previously demonstrated that peptide-activated PAR1 has a different receptor/G-protein binding affinity that favors Galpha_q activation over Galpha_12/13 by approximately 800-fold. Interestingly, the present study shows that the parameter regulating the binding rate of activated PAR1 to Galpha_12/13 is indeed important to obtain the expected RhoGTP response. Moreover, we show that the parameters representing the rate of PAR1 deactivation and the rate of PAR1 binding to G_q, are the most important parameters in the system. Finally, we illustrate that the kinetic model considered in this study is robust and we provide complementary insights into the biological meaning and importance of its kinetic parameters. |
q-bio/0702045 | Ophir Flomenbom | Ophir Flomenbom, Johan Hofkens, Kelly Velonia, Frans C. de Schryver,
Alan E. Rowan, Roeland J. M. Nolte, Joseph Klafter, Robert J. Silbey | Correctly validating results from single molecule data: the case of
stretched exponential decay in the catalytic activity of single lipase B
molecules | null | Chem. Phys. Lett. 432, 371-374 (2006) | 10.1016/j.cplett.2006.10.060 | null | q-bio.SC cond-mat.soft q-bio.BM | null | The question of how to validate and interpret correctly the waiting time
probability density functions (WT-PDFs) from single molecule data is addressed.
It is shown by simulation that when a stretched exponential WT-PDF, with a
stretched exponent alfa and a time scale parameter tau, generates the off
periods of a two-state trajectory, a reliable recovery of the input WT-PDF from
the trajectory is obtained even when the bin size used to define the
trajectory, dt, is much larger than the parameter tau. This holds true as long
as the first moment of the WT-PDF is much larger than dt. Our results validate
the results in an earlier study of the activity of single Lipase B molecules
and disprove recent related critique.
| [
{
"created": "Thu, 22 Feb 2007 17:39:58 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Flomenbom",
"Ophir",
""
],
[
"Hofkens",
"Johan",
""
],
[
"Velonia",
"Kelly",
""
],
[
"de Schryver",
"Frans C.",
""
],
[
"Rowan",
"Alan E.",
""
],
[
"Nolte",
"Roeland J. M.",
""
],
[
"Klafter",
"Joseph",
""
],
[
"Silbey",
"Robert J.",
""
]
] | The question of how to validate and interpret correctly the waiting time probability density functions (WT-PDFs) from single molecule data is addressed. It is shown by simulation that when a stretched exponential WT-PDF, with a stretched exponent alfa and a time scale parameter tau, generates the off periods of a two-state trajectory, a reliable recovery of the input WT-PDF from the trajectory is obtained even when the bin size used to define the trajectory, dt, is much larger than the parameter tau. This holds true as long as the first moment of the WT-PDF is much larger than dt. Our results validate the results in an earlier study of the activity of single Lipase B molecules and disprove recent related critique. |
q-bio/0702035 | Simone Pigolotti | Simone Pigolotti, Cristobal Lopez, Emilio Hernandez-Garcia | Species clustering in competitive Lotka-Volterra models | 4 pages, 3 figures | Phys. Rev. Lett. 98, 258101 (2007) | 10.1103/PhysRevLett.98.258101 | null | q-bio.PE nlin.PS | null | We study the properties of Lotka-Volterra competitive models in which the
intensity of the interaction among species depends on their position along an
abstract niche space through a competition kernel. We show analytically and
numerically that the properties of these models change dramatically when the
Fourier transform of this kernel is not positive definite, due to a pattern
forming instability. We estimate properties of the species distributions, such
as the steady number of species and their spacings, for different types of
kernels.
| [
{
"created": "Fri, 16 Feb 2007 17:29:13 GMT",
"version": "v1"
}
] | 2008-05-15 | [
[
"Pigolotti",
"Simone",
""
],
[
"Lopez",
"Cristobal",
""
],
[
"Hernandez-Garcia",
"Emilio",
""
]
] | We study the properties of Lotka-Volterra competitive models in which the intensity of the interaction among species depends on their position along an abstract niche space through a competition kernel. We show analytically and numerically that the properties of these models change dramatically when the Fourier transform of this kernel is not positive definite, due to a pattern forming instability. We estimate properties of the species distributions, such as the steady number of species and their spacings, for different types of kernels. |
0708.2294 | Maria A. Avino-Diaz | Maria A. Avino-Diaz | A probabilistic regulatory network for the human immune system | 9 pages | null | null | null | q-bio.CB q-bio.BM | null | In this paper we made a review of some papers about probabilistic regulatory
networks (PRN), in particular we introduce our concept of homomorphisms of PRN
with an example of projection of a regulatory network to a smaller one. We
apply the model PRN (or Probabilistic Boolean Network) to the immune system,
the PRN works with two functions. The model called ""The B/T-cells
interaction"" is Boolean, so we are really working with a Probabilistic Boolean
Network. Using Markov Chains we determine the state of equilibrium of the
immune response.
| [
{
"created": "Fri, 17 Aug 2007 16:19:42 GMT",
"version": "v1"
}
] | 2007-08-20 | [
[
"Avino-Diaz",
"Maria A.",
""
]
] | In this paper we made a review of some papers about probabilistic regulatory networks (PRN), in particular we introduce our concept of homomorphisms of PRN with an example of projection of a regulatory network to a smaller one. We apply the model PRN (or Probabilistic Boolean Network) to the immune system, the PRN works with two functions. The model called ""The B/T-cells interaction"" is Boolean, so we are really working with a Probabilistic Boolean Network. Using Markov Chains we determine the state of equilibrium of the immune response. |
1601.07291 | Ron Nielsen | Ron W Nielsen | Unified Growth Theory Contradicted by the Mathematical Analysis of the
Historical Growth of Human Population | 22 pages, 16 figures, 1 table, 8691 words. arXiv admin note: text
overlap with arXiv:1603.01685 | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data describing the historical growth of human population global and regional
(Western Europe, Eastern Europe, Asia, former USSR, Africa and Latin America)
are analysed. Results are in harmony with the earlier analysis of the
historical growth of the world population in the past 12,000 years and with a
similar but limited study carried out over 50 years ago. This analysis is also
in harmony with the study of the historical economic growth. Within the range
of analysable data, there was no Malthusian stagnation. Takeoffs from
stagnation to growth, postulated by the Unified Growth Theory never happened.
There were no escapes from the Malthusian trap because there was no trap. This
analysis and the earlier studies of the Gross Domestic Product lead to the
conclusion that there were also no takeoffs in the income per capita
distributions, claimed by the Unified Growth Theory. Consequently, the claimed
in this theory differential timing in takeoffs never happened. Unified Growth
Theory is contradicted yet again by the mathematical analysis of the same data,
which were used, but never analysed, during the formulation of this theory.
However, this study, as well as the earlier publications on the related topics,
shows also that some fundamental postulates used in the economic and
demographic research are repeatedly contradicted by the mathematical analysis
of data.
| [
{
"created": "Wed, 27 Jan 2016 08:49:47 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Apr 2016 03:02:19 GMT",
"version": "v2"
}
] | 2016-04-21 | [
[
"Nielsen",
"Ron W",
""
]
] | Data describing the historical growth of human population global and regional (Western Europe, Eastern Europe, Asia, former USSR, Africa and Latin America) are analysed. Results are in harmony with the earlier analysis of the historical growth of the world population in the past 12,000 years and with a similar but limited study carried out over 50 years ago. This analysis is also in harmony with the study of the historical economic growth. Within the range of analysable data, there was no Malthusian stagnation. Takeoffs from stagnation to growth, postulated by the Unified Growth Theory never happened. There were no escapes from the Malthusian trap because there was no trap. This analysis and the earlier studies of the Gross Domestic Product lead to the conclusion that there were also no takeoffs in the income per capita distributions, claimed by the Unified Growth Theory. Consequently, the claimed in this theory differential timing in takeoffs never happened. Unified Growth Theory is contradicted yet again by the mathematical analysis of the same data, which were used, but never analysed, during the formulation of this theory. However, this study, as well as the earlier publications on the related topics, shows also that some fundamental postulates used in the economic and demographic research are repeatedly contradicted by the mathematical analysis of data. |
q-bio/0609036 | Claire Christensen | Claire Christensen and Reka Albert | Using graph concepts to understand the organization of complex systems | v(1) 38 pages, 7 figures, to appear in the International Journal of
Bifurcation and Chaos v(2) Line spacing changed; now 23 pages, 7 figures, to
appear in the Special Issue "Complex Networks' Structure and Dynamics'' of
the International Journal of Bifurcation and Chaos (Volume 17, Issue 7, July
2007) edited by S. Boccaletti and V. Latora | null | 10.1142/S021812740701835X | null | q-bio.OT cond-mat.dis-nn q-bio.MN | null | Complex networks are universal, arising in fields as disparate as sociology,
physics, and biology. In the past decade, extensive research into the
properties and behaviors of complex systems has uncovered surprising
commonalities among the topologies of different systems. Attempts to explain
these similarities have led to the ongoing development and refinement of
network models and graph-theoretical analysis techniques with which to
characterize and understand complexity. In this tutorial, we demonstrate
through illustrative examples, how network measures and models have contributed
to the elucidation of the organization of complex systems.
| [
{
"created": "Sun, 24 Sep 2006 18:29:42 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Sep 2006 00:52:03 GMT",
"version": "v2"
},
{
"created": "Tue, 7 Nov 2006 19:51:47 GMT",
"version": "v3"
}
] | 2015-06-26 | [
[
"Christensen",
"Claire",
""
],
[
"Albert",
"Reka",
""
]
] | Complex networks are universal, arising in fields as disparate as sociology, physics, and biology. In the past decade, extensive research into the properties and behaviors of complex systems has uncovered surprising commonalities among the topologies of different systems. Attempts to explain these similarities have led to the ongoing development and refinement of network models and graph-theoretical analysis techniques with which to characterize and understand complexity. In this tutorial, we demonstrate through illustrative examples, how network measures and models have contributed to the elucidation of the organization of complex systems. |
1211.2820 | Javad Noorbakhsh | Javad Noorbakhsh, Alex Lang and Pankaj Mehta | Intrinsic noise of microRNA-regulated genes and the ceRNA hypothesis | 32 pages, 11 figures | PLoS ONE 8(8): e72676 (2013) | 10.1371/journal.pone.0072676 | null | q-bio.MN q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | MicroRNAs are small noncoding RNAs that regulate genes post-transciptionally
by binding and degrading target eukaryotic mRNAs. We use a quantitative model
to study gene regulation by inhibitory microRNAs and compare it to gene
regulation by prokaryotic small non-coding RNAs (sRNAs). Our model uses a
combination of analytic techniques as well as computational simulations to
calculate the mean-expression and noise profiles of genes regulated by both
microRNAs and sRNAs. We find that despite very different molecular machinery
and modes of action (catalytic vs stoichiometric), the mean expression levels
and noise profiles of microRNA-regulated genes are almost identical to genes
regulated by prokaryotic sRNAs. This behavior is extremely robust and persists
across a wide range of biologically relevant parameters. We extend our model to
study crosstalk between multiple mRNAs that are regulated by a single microRNA
and show that noise is a sensitive measure of microRNA-mediated interaction
between mRNAs. We conclude by discussing possible experimental strategies for
uncovering the microRNA-mRNA interactions and testing the competing endogenous
RNA (ceRNA) hypothesis.
| [
{
"created": "Mon, 12 Nov 2012 21:01:30 GMT",
"version": "v1"
},
{
"created": "Mon, 9 Sep 2013 19:05:37 GMT",
"version": "v2"
}
] | 2013-09-10 | [
[
"Noorbakhsh",
"Javad",
""
],
[
"Lang",
"Alex",
""
],
[
"Mehta",
"Pankaj",
""
]
] | MicroRNAs are small noncoding RNAs that regulate genes post-transciptionally by binding and degrading target eukaryotic mRNAs. We use a quantitative model to study gene regulation by inhibitory microRNAs and compare it to gene regulation by prokaryotic small non-coding RNAs (sRNAs). Our model uses a combination of analytic techniques as well as computational simulations to calculate the mean-expression and noise profiles of genes regulated by both microRNAs and sRNAs. We find that despite very different molecular machinery and modes of action (catalytic vs stoichiometric), the mean expression levels and noise profiles of microRNA-regulated genes are almost identical to genes regulated by prokaryotic sRNAs. This behavior is extremely robust and persists across a wide range of biologically relevant parameters. We extend our model to study crosstalk between multiple mRNAs that are regulated by a single microRNA and show that noise is a sensitive measure of microRNA-mediated interaction between mRNAs. We conclude by discussing possible experimental strategies for uncovering the microRNA-mRNA interactions and testing the competing endogenous RNA (ceRNA) hypothesis. |
1507.07820 | Charles Price | Charles A. Price, Paul Drake, Erik J. Veneklaas and Michael Renton | Flow similarity, stochastic branching, and quarter power scaling in
plants | 60 pages including: 4 figures, 2 tables, 20 supplemental figures, 2
supplemental notes, 5 supplemental tables | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The origin of allometric scaling patterns that are multiples of 1/4 has long
fascinated biologists. While not universal, scaling relationships with
exponents that are close to multiples of 1/4 are common and have been described
in all major clades. Foremost among these relationships is the 3/4 scaling of
metabolism with mass which underpins the 1/4 power dependence of biological
rates and times. Several models have been advanced to explain the underlying
mechanistic drivers of such patterns, but questions regarding a disconnect
between model structures and empirical data have limited their widespread
acceptance. Notable among these is a fractal branching model which predicts
power law scaling of both metabolism and physical dimensions. While a power law
is a useful first approximation to many datasets, non-linearity in some large
data compilations suggest the possibility of more complex or alternative
mechanisms. Here, we first show that quarter power scaling can be derived using
only the preservation of volume flow rate and velocity as model constraints.
Applying our model to the specific case of land plants, we show that
incorporating biomechanical principles and allowing different parts of plant
branching networks to be optimized to serve different functions predicts
non-linearity in allometric relationships, and helps explain why interspecific
scaling exponents covary along a fractal continuum. We also demonstrate that
while branching may be a stochastic process, due to the conservation of volume,
data may still be consistent with the expectations for a fractal network when
one examines subtrees within a tree. Data from numerous sources at the level of
plant shoots, stems, petioles, and leaves show strong agreement with our model
predictions. This novel theoretical framework provides an easily testable
alternative to current general models of plant metabolic allometry.
| [
{
"created": "Fri, 24 Jul 2015 15:42:53 GMT",
"version": "v1"
}
] | 2015-07-29 | [
[
"Price",
"Charles A.",
""
],
[
"Drake",
"Paul",
""
],
[
"Veneklaas",
"Erik J.",
""
],
[
"Renton",
"Michael",
""
]
] | The origin of allometric scaling patterns that are multiples of 1/4 has long fascinated biologists. While not universal, scaling relationships with exponents that are close to multiples of 1/4 are common and have been described in all major clades. Foremost among these relationships is the 3/4 scaling of metabolism with mass which underpins the 1/4 power dependence of biological rates and times. Several models have been advanced to explain the underlying mechanistic drivers of such patterns, but questions regarding a disconnect between model structures and empirical data have limited their widespread acceptance. Notable among these is a fractal branching model which predicts power law scaling of both metabolism and physical dimensions. While a power law is a useful first approximation to many datasets, non-linearity in some large data compilations suggest the possibility of more complex or alternative mechanisms. Here, we first show that quarter power scaling can be derived using only the preservation of volume flow rate and velocity as model constraints. Applying our model to the specific case of land plants, we show that incorporating biomechanical principles and allowing different parts of plant branching networks to be optimized to serve different functions predicts non-linearity in allometric relationships, and helps explain why interspecific scaling exponents covary along a fractal continuum. We also demonstrate that while branching may be a stochastic process, due to the conservation of volume, data may still be consistent with the expectations for a fractal network when one examines subtrees within a tree. Data from numerous sources at the level of plant shoots, stems, petioles, and leaves show strong agreement with our model predictions. This novel theoretical framework provides an easily testable alternative to current general models of plant metabolic allometry. |
2407.10072 | Eric Medwedeff | Eric Medwedeff and Eric Mjolsness | Advances in the Simulation and Modeling of Complex Systems using
Dynamical Graph Grammars | 24 pages, 13 figures | null | null | null | q-bio.QM physics.bio-ph physics.chem-ph | http://creativecommons.org/licenses/by/4.0/ | The Dynamical Graph Grammar (DGG) formalism can describe complex system
dynamics with graphs that are mapped into a master equation. An exact
stochastic simulation algorithm may be used, but it is slow for large systems.
To overcome this problem, an approximate spatial stochastic/deterministic
simulation algorithm, which uses spatial decomposition of the system's
time-evolution operator through an expanded cell complex (ECC), was previously
developed and implemented for a cortical microtubule array (CMA) model. Here,
computational efficiency is improved at the cost of introducing errors confined
to interactions between adjacent subdomains of different dimensions, realized
as some events occurring out of order. A rule instances to domains mapping
function $\phi$, ensures the errors are local. This approach has been further
refined and generalized in this work. Additional efficiency is achieved by
maintaining an incrementally updated match data structure for all possible rule
matches. The API has been redesigned to support DGG rules in general, rather
than for one specific model. To demonstrate these improvements in the
algorithm, we have developed the Dynamical Graph Grammar Modeling Library
(DGGML) and a DGG model for the periclinal face of the plant cell CMA. This
model explores the effects of face shape and boundary conditions on local and
global alignment. For a rectangular face, different boundary conditions
reorient the array between the long and short axes. The periclinal CMA DGG
demonstrates the flexibility and utility of DGGML, and these new methods
highlight DGGs' potential for testing, screening, or generating hypotheses to
explain emergent phenomena.
| [
{
"created": "Sun, 14 Jul 2024 04:19:26 GMT",
"version": "v1"
}
] | 2024-07-16 | [
[
"Medwedeff",
"Eric",
""
],
[
"Mjolsness",
"Eric",
""
]
] | The Dynamical Graph Grammar (DGG) formalism can describe complex system dynamics with graphs that are mapped into a master equation. An exact stochastic simulation algorithm may be used, but it is slow for large systems. To overcome this problem, an approximate spatial stochastic/deterministic simulation algorithm, which uses spatial decomposition of the system's time-evolution operator through an expanded cell complex (ECC), was previously developed and implemented for a cortical microtubule array (CMA) model. Here, computational efficiency is improved at the cost of introducing errors confined to interactions between adjacent subdomains of different dimensions, realized as some events occurring out of order. A rule instances to domains mapping function $\phi$, ensures the errors are local. This approach has been further refined and generalized in this work. Additional efficiency is achieved by maintaining an incrementally updated match data structure for all possible rule matches. The API has been redesigned to support DGG rules in general, rather than for one specific model. To demonstrate these improvements in the algorithm, we have developed the Dynamical Graph Grammar Modeling Library (DGGML) and a DGG model for the periclinal face of the plant cell CMA. This model explores the effects of face shape and boundary conditions on local and global alignment. For a rectangular face, different boundary conditions reorient the array between the long and short axes. The periclinal CMA DGG demonstrates the flexibility and utility of DGGML, and these new methods highlight DGGs' potential for testing, screening, or generating hypotheses to explain emergent phenomena. |
2102.02412 | Emma Lejeune | Bill Zhao and Kehan Zhang and Christopher S. Chen and Emma Lejeune | Sarc-Graph: Automated segmentation, tracking, and analysis of sarcomeres
in hiPSC-derived cardiomyocytes | Link to SI:
https://github.com/elejeune11/Sarc-Graph/tree/main/Supplementary_Information | null | 10.1371/journal.pcbi.1009443 | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | A better fundamental understanding of human induced pluripotent stem
cell-derived cardiomyocytes (hiPSC-CMs) has the potential to advance
applications ranging from drug discovery to cardiac repair. Automated
quantitative analysis of beating hiPSC-CMs is an important and fast developing
component of the hiPSC-CM research pipeline. Here we introduce "Sarc-Graph," a
computational framework to segment, track, and analyze sarcomeres in
fluorescently tagged hiPSC-CMs. Our framework includes functions to segment
z-discs and sarcomeres, track z-discs and sarcomeres in beating cells, and
perform automated spatiotemporal analysis and data visualization. In addition
to reporting good performance for sarcomere segmentation and tracking with
little to no parameter tuning and a short runtime, we introduce two novel
analysis approaches. First, we construct spatial graphs where z-discs
correspond to nodes and sarcomeres correspond to edges. This makes measuring
the network distance between each sarcomere (i.e., the number of connecting
sarcomeres separating each sarcomere pair) straightforward. Second, we treat
tracked and segmented components as fiducial markers and use them to compute
the approximate deformation gradient of the entire tracked population. This
represents a new quantitative descriptor of hiPSC-CM function. We showcase and
validate our approach with both synthetic and experimental movies of beating
hiPSC-CMs. By publishing Sarc-Graph, we aim to make automated quantitative
analysis of hiPSC-CM behavior more accessible to the broader research
community.
| [
{
"created": "Thu, 4 Feb 2021 04:57:25 GMT",
"version": "v1"
}
] | 2021-11-17 | [
[
"Zhao",
"Bill",
""
],
[
"Zhang",
"Kehan",
""
],
[
"Chen",
"Christopher S.",
""
],
[
"Lejeune",
"Emma",
""
]
] | A better fundamental understanding of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) has the potential to advance applications ranging from drug discovery to cardiac repair. Automated quantitative analysis of beating hiPSC-CMs is an important and fast developing component of the hiPSC-CM research pipeline. Here we introduce "Sarc-Graph," a computational framework to segment, track, and analyze sarcomeres in fluorescently tagged hiPSC-CMs. Our framework includes functions to segment z-discs and sarcomeres, track z-discs and sarcomeres in beating cells, and perform automated spatiotemporal analysis and data visualization. In addition to reporting good performance for sarcomere segmentation and tracking with little to no parameter tuning and a short runtime, we introduce two novel analysis approaches. First, we construct spatial graphs where z-discs correspond to nodes and sarcomeres correspond to edges. This makes measuring the network distance between each sarcomere (i.e., the number of connecting sarcomeres separating each sarcomere pair) straightforward. Second, we treat tracked and segmented components as fiducial markers and use them to compute the approximate deformation gradient of the entire tracked population. This represents a new quantitative descriptor of hiPSC-CM function. We showcase and validate our approach with both synthetic and experimental movies of beating hiPSC-CMs. By publishing Sarc-Graph, we aim to make automated quantitative analysis of hiPSC-CM behavior more accessible to the broader research community. |
1809.05025 | Ricardo Castro Santis | Gustavo Ossandon and Ricardo Castro-Santis | Effects of density-dependent migration on a population subjected to
Allee effect | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This study assessed the effects of migration on the dynamics of a species
population. It was considered that the species in its natural state and without
the presence of migration exhibited Allee effect. This work also considered
migration as a density-dependent function, which, from a maximum rate,
decreases to a minimum of zero when the population reaches its carrying
capacity.
| [
{
"created": "Thu, 13 Sep 2018 15:57:56 GMT",
"version": "v1"
}
] | 2018-09-14 | [
[
"Ossandon",
"Gustavo",
""
],
[
"Castro-Santis",
"Ricardo",
""
]
] | This study assessed the effects of migration on the dynamics of a species population. It was considered that the species in its natural state and without the presence of migration exhibited Allee effect. This work also considered migration as a density-dependent function, which, from a maximum rate, decreases to a minimum of zero when the population reaches its carrying capacity. |
2211.15283 | Tuan Minh Pham | Tuan Minh Pham and Kunihiko Kaneko | Double-replica theory for evolution of genotype-phenotype
interrelationship | 15 pages, 7 figures | null | null | null | q-bio.PE cond-mat.dis-nn cond-mat.stat-mech | http://creativecommons.org/licenses/by/4.0/ | The relationship between genotype and phenotype plays a crucial role in
determining the function and robustness of biological systems. Here the
evolution progresses through the change in genotype, whereas the selection is
based on the phenotype, and genotype-phenotype relation also evolves. Theory
for such phenotypic evolution remains poorly-developed, in contrast to
evolution under the fitness landscape determined by genotypes. Here we provide
statistical-physics formulation of this problem by introducing replicas for
genotype and phenotype. We apply it to an evolution model, in which phenotypes
are given by spin configurations; genotypes are interaction matrix for spins to
give the Hamiltonian, and the fitness depends only on the configuration of a
subset of spins called target. We describe the interplay between the genetic
variations and phenotypic variances by noise in this model by our new approach
that extends the replica theory for spin-glasses to include spin-replica for
phenotypes and coupling-replica for genotypes. Within this framework we obtain
a phase diagram of the evolved phenotypes against the noise and selection
pressure, where each phase is distinguished by the fitness and overlaps for
genotypes and phenotypes. Among the phases, robust fitted phase, relevant to
biological evolution, is achieved under the intermediate level of noise
(temperature), where robustness to noise and to genetic mutation are
correlated, as a result of replica symmetry. We also find a trade-off between
maintaining a high fitness level of phenotype and acquiring a robust pattern of
genes as well as the dependence of this trade-off on the ratio between the size
of the functional (target) part to that of the remaining non-functional
(non-target) one. The selection pressure needed to achieve high fitness
increases with the fraction of target spins.
| [
{
"created": "Mon, 28 Nov 2022 13:09:36 GMT",
"version": "v1"
}
] | 2022-11-29 | [
[
"Pham",
"Tuan Minh",
""
],
[
"Kaneko",
"Kunihiko",
""
]
] | The relationship between genotype and phenotype plays a crucial role in determining the function and robustness of biological systems. Here the evolution progresses through the change in genotype, whereas the selection is based on the phenotype, and genotype-phenotype relation also evolves. Theory for such phenotypic evolution remains poorly-developed, in contrast to evolution under the fitness landscape determined by genotypes. Here we provide statistical-physics formulation of this problem by introducing replicas for genotype and phenotype. We apply it to an evolution model, in which phenotypes are given by spin configurations; genotypes are interaction matrix for spins to give the Hamiltonian, and the fitness depends only on the configuration of a subset of spins called target. We describe the interplay between the genetic variations and phenotypic variances by noise in this model by our new approach that extends the replica theory for spin-glasses to include spin-replica for phenotypes and coupling-replica for genotypes. Within this framework we obtain a phase diagram of the evolved phenotypes against the noise and selection pressure, where each phase is distinguished by the fitness and overlaps for genotypes and phenotypes. Among the phases, robust fitted phase, relevant to biological evolution, is achieved under the intermediate level of noise (temperature), where robustness to noise and to genetic mutation are correlated, as a result of replica symmetry. We also find a trade-off between maintaining a high fitness level of phenotype and acquiring a robust pattern of genes as well as the dependence of this trade-off on the ratio between the size of the functional (target) part to that of the remaining non-functional (non-target) one. The selection pressure needed to achieve high fitness increases with the fraction of target spins. |
1708.08298 | Charbel Eid | Charbel Eid and Juan G. Santiago | Isotachophoresis applied to chemical reactions | null | null | null | null | q-bio.BM physics.bio-ph physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This review discusses research developments and applications of
isotachophoresis (ITP) to the initiation, control, and acceleration of chemical
reactions, emphasizing reactions involving biomolecular reactants such as
nucleic acids, proteins, and live cells. ITP is a versatile technique which
requires no specific geometric design or material, and is compatible with a
wide range of microfluidic and automated platforms. Though ITP has
traditionally been used as a purification and separation technique, recent
years have seen its emergence as a method to automate and speed up chemical
reactions. ITP has been used to demonstrate up to 14,000-fold acceleration of
nucleic acid assays, and has been used to enhance lateral flow and other
immunoassays, and even whole bacterial cell detection assays. We here classify
these studies into two categories: homogeneous (all reactants in solution) and
heterogeneous (at least one reactant immobilized on a solid surface) assay
configurations. For each category, we review and describe physical modeling and
scaling of ITP-aided reaction assays, and elucidate key principles in ITP assay
design. We summarize experimental advances, and identify common threads and
approaches which researchers have used to optimize assay performance. Lastly,
we propose unaddressed challenges and opportunities that could further improve
these applications of ITP.
| [
{
"created": "Mon, 28 Aug 2017 13:01:51 GMT",
"version": "v1"
}
] | 2017-08-29 | [
[
"Eid",
"Charbel",
""
],
[
"Santiago",
"Juan G.",
""
]
] | This review discusses research developments and applications of isotachophoresis (ITP) to the initiation, control, and acceleration of chemical reactions, emphasizing reactions involving biomolecular reactants such as nucleic acids, proteins, and live cells. ITP is a versatile technique which requires no specific geometric design or material, and is compatible with a wide range of microfluidic and automated platforms. Though ITP has traditionally been used as a purification and separation technique, recent years have seen its emergence as a method to automate and speed up chemical reactions. ITP has been used to demonstrate up to 14,000-fold acceleration of nucleic acid assays, and has been used to enhance lateral flow and other immunoassays, and even whole bacterial cell detection assays. We here classify these studies into two categories: homogeneous (all reactants in solution) and heterogeneous (at least one reactant immobilized on a solid surface) assay configurations. For each category, we review and describe physical modeling and scaling of ITP-aided reaction assays, and elucidate key principles in ITP assay design. We summarize experimental advances, and identify common threads and approaches which researchers have used to optimize assay performance. Lastly, we propose unaddressed challenges and opportunities that could further improve these applications of ITP. |
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