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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1303.3868 | Andrea Zoia | Eric Dumonteil, Satya N. Majumdar, Alberto Rosso, Andrea Zoia | Spatial extent of an outbreak in animal epidemics | 6 pages, 4 figures (main text) + 4 pages, 3 figures (supplementary
material) | Proceedings of the National Academy of Sciences, 110 (11)
4239-4244 (2013) | 10.1073/pnas.1213237110 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Characterizing the spatial extent of epidemics at the outbreak stage is key
to controlling the evolution of the disease. At the outbreak, the number of
infected individuals is typically small, so that fluctuations around their
average are important: then, it is commonly assumed that the
susceptible-infected-recovered (SIR) mechanism can be described by a stochastic
birth-death process of Galton-Watson type. The displacements of the infected
individuals can be modelled by resorting to Brownian motion, which is
applicable when long-range movements and complex network interactions can be
safely neglected, as in case of animal epidemics. In this context, the spatial
extent of an epidemic can be assessed by computing the convex hull enclosing
the infected individuals at a given time. We derive the exact evolution
equations for the mean perimeter and the mean area of the convex hull, and
compare them with Monte Carlo simulations.
| [
{
"created": "Wed, 13 Mar 2013 21:47:25 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Mar 2013 21:36:43 GMT",
"version": "v2"
}
] | 2013-03-21 | [
[
"Dumonteil",
"Eric",
""
],
[
"Majumdar",
"Satya N.",
""
],
[
"Rosso",
"Alberto",
""
],
[
"Zoia",
"Andrea",
""
]
] | Characterizing the spatial extent of epidemics at the outbreak stage is key to controlling the evolution of the disease. At the outbreak, the number of infected individuals is typically small, so that fluctuations around their average are important: then, it is commonly assumed that the susceptible-infected-recovered (SIR) mechanism can be described by a stochastic birth-death process of Galton-Watson type. The displacements of the infected individuals can be modelled by resorting to Brownian motion, which is applicable when long-range movements and complex network interactions can be safely neglected, as in case of animal epidemics. In this context, the spatial extent of an epidemic can be assessed by computing the convex hull enclosing the infected individuals at a given time. We derive the exact evolution equations for the mean perimeter and the mean area of the convex hull, and compare them with Monte Carlo simulations. |
1606.08094 | Michael Margaliot | Yoram Zarai and Michael Margaliot and Tamir Tuller | Optimal Down Regulation of mRNA Translation | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Down regulation of mRNA translation is an important problem in various
bio-medical domains ranging from developing effective medicines for tumors and
for viral diseases to developing attenuated virus strains that can be used for
vaccination. Here, we study the problem of down regulation of mRNA translation
using a mathematical model called the ribosome flow model (RFM). In the RFM,
the mRNA molecule is modeled as a chain of $n$ sites. The flow of ribosomes
between consecutive sites is regulated by $n+1$ transition rates. Given a set
of feasible transition rates, that models the outcome of all possible
mutations, we consider the problem of maximally down regulating the translation
rate by altering the rates within this set of feasible rates. Under certain
conditions on the feasible set, we show that an optimal solution can be
determined efficiently. We also rigorously analyze two special cases of the
down regulation optimization problem. Our results suggest that one must focus
on the position along the mRNA molecule where the transition rate has the
strongest effect on the protein production rate. However, this rate is not
necessarily the slowest transition rate along the mRNA molecule. We discuss
some of the biological implications of these results.
| [
{
"created": "Sun, 26 Jun 2016 22:23:08 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Jun 2016 06:26:10 GMT",
"version": "v2"
}
] | 2016-06-29 | [
[
"Zarai",
"Yoram",
""
],
[
"Margaliot",
"Michael",
""
],
[
"Tuller",
"Tamir",
""
]
] | Down regulation of mRNA translation is an important problem in various bio-medical domains ranging from developing effective medicines for tumors and for viral diseases to developing attenuated virus strains that can be used for vaccination. Here, we study the problem of down regulation of mRNA translation using a mathematical model called the ribosome flow model (RFM). In the RFM, the mRNA molecule is modeled as a chain of $n$ sites. The flow of ribosomes between consecutive sites is regulated by $n+1$ transition rates. Given a set of feasible transition rates, that models the outcome of all possible mutations, we consider the problem of maximally down regulating the translation rate by altering the rates within this set of feasible rates. Under certain conditions on the feasible set, we show that an optimal solution can be determined efficiently. We also rigorously analyze two special cases of the down regulation optimization problem. Our results suggest that one must focus on the position along the mRNA molecule where the transition rate has the strongest effect on the protein production rate. However, this rate is not necessarily the slowest transition rate along the mRNA molecule. We discuss some of the biological implications of these results. |
q-bio/0703031 | Dietrich Stauffer | Ana Proykova and Dietrich Stauffer | Monte Carlo Simulation of Medical Resource Allocation | 4 pages including all figures | null | null | null | q-bio.PE | null | Computer simulations prove that we should spend more money on red wine than
on medication. We optimise the problem of how to distribute a fixed amount of
money between medication and food spendings, using the Penna ageing model.
| [
{
"created": "Wed, 14 Mar 2007 15:57:35 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Proykova",
"Ana",
""
],
[
"Stauffer",
"Dietrich",
""
]
] | Computer simulations prove that we should spend more money on red wine than on medication. We optimise the problem of how to distribute a fixed amount of money between medication and food spendings, using the Penna ageing model. |
2305.17756 | Ashik Saha | Ashik Saha | The use of Ethnomedicinal plants in Indigenous Health Care Practice of
the Hajong Tribe community in Durgapur, Bangladesh | null | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | The Garo Hills have always been fascination to the naked human eyes. The
hills are the shelter of the earliest human habitation of Bangladesh. It is a
place of ancient cultures and many botanical wonders. It is situated in the
most northern part of Durgapur sub-district having border with Meghalaya of
India. Durgapur is rich with ethnic diversity with Hajong and Garo as the major
ethnic groups along with some Bengali settlers from the common population.
Present survey was undertaken to compile the medicinal plant usage among the
Hajong Tribe of Durgapur.
| [
{
"created": "Sun, 28 May 2023 15:42:55 GMT",
"version": "v1"
}
] | 2023-05-30 | [
[
"Saha",
"Ashik",
""
]
] | The Garo Hills have always been fascination to the naked human eyes. The hills are the shelter of the earliest human habitation of Bangladesh. It is a place of ancient cultures and many botanical wonders. It is situated in the most northern part of Durgapur sub-district having border with Meghalaya of India. Durgapur is rich with ethnic diversity with Hajong and Garo as the major ethnic groups along with some Bengali settlers from the common population. Present survey was undertaken to compile the medicinal plant usage among the Hajong Tribe of Durgapur. |
q-bio/0505038 | Michael Gudo | Michael Gudo | Hydromechanical considerations on the origin of the pentaradial body
structure of echinoderms | 8 pages, 3 figures | null | null | null | q-bio.PE q-bio.OT | null | When echinoderms are conceptualized as hydraulic entities, the early
evolution of this group can be presented in a scenario which describes how a
bilateral ancestor (an enteropneust-like organism) gradually evolved into a
pentaradial echinoderm. According to this scenario, the arms are outgrowths
from the anterior/posterior body axis of the bilateral pterobranchia-like
intermediate. These outgrowths developed when the originally U-shaped mesentery
of the intestinal tract formed loops, and correspondingly, the tensile chords
of the mesentery were attached to the body wall in five loops. The wall faces
between these regions of tensile chords could bulge out under the hydraulic
pressure of the body coelom. The originally more or less round body cavity was
deformed into a pneu with five bulges. The loops of the gut forced a roughly
symmetric arrangement, which was enhanced by a physical fact: five pneus as
well as one pneu with five internal tethers, naturally adopt a pentaradial
pattern of "minimum contact surfaces", as the most economic arrangement. These
evolutionary transformations were accompanied by certain histological
modifications, such as the development of mutable connective tissues and
skeletal elements that fused to ossicles and provided shape stabilization in
the form of a calcareous skeleton in the tissues of the body wall. The
resultant organism was an ancestral eleutherozoan echinoderm (Ur-Echinoderm).
| [
{
"created": "Thu, 19 May 2005 14:32:39 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Gudo",
"Michael",
""
]
] | When echinoderms are conceptualized as hydraulic entities, the early evolution of this group can be presented in a scenario which describes how a bilateral ancestor (an enteropneust-like organism) gradually evolved into a pentaradial echinoderm. According to this scenario, the arms are outgrowths from the anterior/posterior body axis of the bilateral pterobranchia-like intermediate. These outgrowths developed when the originally U-shaped mesentery of the intestinal tract formed loops, and correspondingly, the tensile chords of the mesentery were attached to the body wall in five loops. The wall faces between these regions of tensile chords could bulge out under the hydraulic pressure of the body coelom. The originally more or less round body cavity was deformed into a pneu with five bulges. The loops of the gut forced a roughly symmetric arrangement, which was enhanced by a physical fact: five pneus as well as one pneu with five internal tethers, naturally adopt a pentaradial pattern of "minimum contact surfaces", as the most economic arrangement. These evolutionary transformations were accompanied by certain histological modifications, such as the development of mutable connective tissues and skeletal elements that fused to ossicles and provided shape stabilization in the form of a calcareous skeleton in the tissues of the body wall. The resultant organism was an ancestral eleutherozoan echinoderm (Ur-Echinoderm). |
1909.06176 | Lorenzo Fassina PhD | Lorenzo Fassina, Giovanni Magenes, Roberto Gimmelli, Fabio Naro | Modulation of the cardiomyocyte contraction inside a hydrostatic
pressure bioreactor: $\textit{in vitro}$ verification of the Frank-Starling
law | null | BioMed Research International 2015;2015:542105 | 10.1155/2015/542105 | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | We have studied beating mouse cardiac syncytia $\textit{in vitro}$ in order
to assess the inotropic, ergotropic, and chronotropic effects of both
increasing and decreasing hydrostatic pressures. In particular, we have
performed an image processing analysis to evaluate the kinematics and the
dynamics of those pressure-loaded beating syncytia starting from the video
registration of their contraction movement. By this analysis, we have verified
the Frank-Starling law of the heart in $\textit{in vitro}$ beating cardiac
syncytia and we have obtained their geometrical-functional classification.
| [
{
"created": "Thu, 12 Sep 2019 13:27:48 GMT",
"version": "v1"
}
] | 2019-09-16 | [
[
"Fassina",
"Lorenzo",
""
],
[
"Magenes",
"Giovanni",
""
],
[
"Gimmelli",
"Roberto",
""
],
[
"Naro",
"Fabio",
""
]
] | We have studied beating mouse cardiac syncytia $\textit{in vitro}$ in order to assess the inotropic, ergotropic, and chronotropic effects of both increasing and decreasing hydrostatic pressures. In particular, we have performed an image processing analysis to evaluate the kinematics and the dynamics of those pressure-loaded beating syncytia starting from the video registration of their contraction movement. By this analysis, we have verified the Frank-Starling law of the heart in $\textit{in vitro}$ beating cardiac syncytia and we have obtained their geometrical-functional classification. |
1811.04581 | Shigeko Takahashi | Shigeko Takahashi | Topographic maps in the brain are fundamental to processing of causality | 7 pages | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ubiquity of topographic maps in the brain has long been known, and
molecular mechanisms for the formation of topographic organization of neural
systems have been revealed. Less attention has been given to the question of
why are the maps topographical and why so ubiquitous. In this study, I explore
the implications of the topographic maps for brain function, by employing the
mathematical framework of the Zeeman topology. I propose the notion about the
meaning of topographic order as generic mechanisms for the representation and
analysis of causal structure, implemented by the neural systems. This leads to
a much improved understanding of the division of labour between chemical
systems and neural systems in the formation of maps to deal with causality.
| [
{
"created": "Mon, 12 Nov 2018 06:42:42 GMT",
"version": "v1"
}
] | 2018-11-13 | [
[
"Takahashi",
"Shigeko",
""
]
] | The ubiquity of topographic maps in the brain has long been known, and molecular mechanisms for the formation of topographic organization of neural systems have been revealed. Less attention has been given to the question of why are the maps topographical and why so ubiquitous. In this study, I explore the implications of the topographic maps for brain function, by employing the mathematical framework of the Zeeman topology. I propose the notion about the meaning of topographic order as generic mechanisms for the representation and analysis of causal structure, implemented by the neural systems. This leads to a much improved understanding of the division of labour between chemical systems and neural systems in the formation of maps to deal with causality. |
1811.09851 | Xue Teng | Xue Teng, Fuad Gwadry, Haley McConkey, Scott Ernst, Femida
Gwadry-Sridhar | An adaptive treatment recommendation and outcome prediction model for
metastatic melanoma | Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
arXiv:1811.07216 | null | null | null | q-bio.QM physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Melanoma is a type of skin cancer developed from melanocytes. It is one of
the most lethal types of cancer, accounting for approximately 75% of skin
cancer deaths. Late stage melanoma is very difficult to treat, since the cancer
cells are deranged, may be genetically linked and can be unresponsive to
therapy. Therefore, determining how to effectively make use of different
treatment regimens is of vital importance to survival. In this analysis, we
propose an adaptive treatment recommendation system based on a hybrid
cluster-classification (CC) structure. Our proposed system consists of two
parts,1) distribution based clustering and 2) classification. Our
recommendation system can help to identify high-risk melanoma patients and
suggest the best approach to treatment, which enables clinicians and patients
to make decisions on the basis of real-world data. Our data came from the
Canadian Melanoma Research Network (CMRN) database, a pan-Canadian multi-year
observational database, which is part of Global Melanoma Registry Network
(GMRN). Training/testing sets are generated based on data from different
sources, leading to cross cohort analysis tasks. Experimental results show that
our proposed system achieves very promising results with an overall accuracy of
up to 80%.
| [
{
"created": "Sat, 24 Nov 2018 16:24:01 GMT",
"version": "v1"
}
] | 2018-11-28 | [
[
"Teng",
"Xue",
""
],
[
"Gwadry",
"Fuad",
""
],
[
"McConkey",
"Haley",
""
],
[
"Ernst",
"Scott",
""
],
[
"Gwadry-Sridhar",
"Femida",
""
]
] | Melanoma is a type of skin cancer developed from melanocytes. It is one of the most lethal types of cancer, accounting for approximately 75% of skin cancer deaths. Late stage melanoma is very difficult to treat, since the cancer cells are deranged, may be genetically linked and can be unresponsive to therapy. Therefore, determining how to effectively make use of different treatment regimens is of vital importance to survival. In this analysis, we propose an adaptive treatment recommendation system based on a hybrid cluster-classification (CC) structure. Our proposed system consists of two parts,1) distribution based clustering and 2) classification. Our recommendation system can help to identify high-risk melanoma patients and suggest the best approach to treatment, which enables clinicians and patients to make decisions on the basis of real-world data. Our data came from the Canadian Melanoma Research Network (CMRN) database, a pan-Canadian multi-year observational database, which is part of Global Melanoma Registry Network (GMRN). Training/testing sets are generated based on data from different sources, leading to cross cohort analysis tasks. Experimental results show that our proposed system achieves very promising results with an overall accuracy of up to 80%. |
q-bio/0312024 | Giuseppe Gaeta | G. Gaeta | Results and Limitations of the Soliton Theory of DNA Transcription | An older review paper (NOT UPDATED), posted here for archival
purposes | Journal of Biological Physics 24 (1999), 81-96 | null | null | q-bio.OT | null | It has been suggested by several authors that nonlinear excitations, in
particular solitary waves, could play a fundamental functional role in the
process of DNA transcription, effecting the opening of the double chain needed
for RNA Polymerase to be able to copy the genetic code. Some models have been
proposed to model the relevant DNA dynamics in terms of a reduced number of
effective degrees of freedom. Here I discuss advantages and disadvantages of
such an approach, and discuss in more detail one of the models, i.e. the one
proposed by Yakushevich.
| [
{
"created": "Tue, 16 Dec 2003 15:24:26 GMT",
"version": "v1"
}
] | 2016-09-08 | [
[
"Gaeta",
"G.",
""
]
] | It has been suggested by several authors that nonlinear excitations, in particular solitary waves, could play a fundamental functional role in the process of DNA transcription, effecting the opening of the double chain needed for RNA Polymerase to be able to copy the genetic code. Some models have been proposed to model the relevant DNA dynamics in terms of a reduced number of effective degrees of freedom. Here I discuss advantages and disadvantages of such an approach, and discuss in more detail one of the models, i.e. the one proposed by Yakushevich. |
1502.01236 | Luiz Max Carvalho | Flavio Coelho and Luiz Max Carvalho | Estimating the Attack Ratio of Dengue Epidemics under Time-varying Force
of Infection using Aggregated Notification Data | 11 pages, 3 figures | null | null | null | q-bio.PE stat.AP | http://creativecommons.org/licenses/by/3.0/ | Quantifying the attack ratio of disease is key to epidemiological inference
and Public Health planning. For multi-serotype pathogens, however, different
levels of serotype-specific immunity make it difficult to assess the population
at risk. In this paper we propose a Bayesian method for estimation of the
attack ratio of an epidemic and the initial fraction of susceptibles using
aggregated incidence data. We derive the probability distribution of the
effective reproductive number, R t , and use MCMC to obtain posterior
distributions of the parameters of a single-strain SIR transmission model with
time-varying force of infection. Our method is showcased in a data set
consisting of 18 years of dengue incidence in the city of Rio de Janeiro,
Brazil. We demonstrate that it is possible to learn about the initial fraction
of susceptibles and the attack ratio even in the absence of serotype specific
data. On the other hand, the information provided by this approach is limited,
stressing the need for detailed serological surveys to characterise the
distribution of serotype-specific immunity in the population.
| [
{
"created": "Wed, 4 Feb 2015 15:43:51 GMT",
"version": "v1"
}
] | 2015-02-05 | [
[
"Coelho",
"Flavio",
""
],
[
"Carvalho",
"Luiz Max",
""
]
] | Quantifying the attack ratio of disease is key to epidemiological inference and Public Health planning. For multi-serotype pathogens, however, different levels of serotype-specific immunity make it difficult to assess the population at risk. In this paper we propose a Bayesian method for estimation of the attack ratio of an epidemic and the initial fraction of susceptibles using aggregated incidence data. We derive the probability distribution of the effective reproductive number, R t , and use MCMC to obtain posterior distributions of the parameters of a single-strain SIR transmission model with time-varying force of infection. Our method is showcased in a data set consisting of 18 years of dengue incidence in the city of Rio de Janeiro, Brazil. We demonstrate that it is possible to learn about the initial fraction of susceptibles and the attack ratio even in the absence of serotype specific data. On the other hand, the information provided by this approach is limited, stressing the need for detailed serological surveys to characterise the distribution of serotype-specific immunity in the population. |
1003.5007 | Dimitris Papamichail | Dimitris Papamichail, Celine C. Lesaulnier, Steven Skiena, Sean R.
McCorkle, Bernard Ollivier, Daniel van der Lelie | Towards a Taxonomical Consensus: Diversity and Richness Inference from
Large Scale rRNA gene Analysis | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Population analysis is persistently challenging but important, leading to the
determination of diversity and function prediction of microbial community
members. Here we detail our bioinformatics methods for analyzing population
distribution and diversity in large microbial communities. This was achieved
via (i) a homology based method for robust phylotype determination, equaling
the classification accuracy of the Ribosomal Database Project (RDP) classifier,
but providing improved associations of closely related sequences; (ii) a
comparison of different clustering methods for achieving more accurate richness
estimations. Our methodology, which we developed using the RDP vetted 16S rRNA
gene sequence set, was validated by testing it on a large 16S rRNA gene dataset
of approximately 2300 sequences, which we obtained from a soil microbial
community study. We concluded that the best approach to obtain accurate
phylogenetics profile of large microbial communities, based on 16S rRNA gene
sequence information, is to apply an optimized blast classifier. This approach
is complemented by the grouping of closely related sequences, using complete
linkage clustering, in order to calculate richness and evenness indices for the
communities.
| [
{
"created": "Thu, 25 Mar 2010 21:21:57 GMT",
"version": "v1"
}
] | 2010-03-29 | [
[
"Papamichail",
"Dimitris",
""
],
[
"Lesaulnier",
"Celine C.",
""
],
[
"Skiena",
"Steven",
""
],
[
"McCorkle",
"Sean R.",
""
],
[
"Ollivier",
"Bernard",
""
],
[
"van der Lelie",
"Daniel",
""
]
] | Population analysis is persistently challenging but important, leading to the determination of diversity and function prediction of microbial community members. Here we detail our bioinformatics methods for analyzing population distribution and diversity in large microbial communities. This was achieved via (i) a homology based method for robust phylotype determination, equaling the classification accuracy of the Ribosomal Database Project (RDP) classifier, but providing improved associations of closely related sequences; (ii) a comparison of different clustering methods for achieving more accurate richness estimations. Our methodology, which we developed using the RDP vetted 16S rRNA gene sequence set, was validated by testing it on a large 16S rRNA gene dataset of approximately 2300 sequences, which we obtained from a soil microbial community study. We concluded that the best approach to obtain accurate phylogenetics profile of large microbial communities, based on 16S rRNA gene sequence information, is to apply an optimized blast classifier. This approach is complemented by the grouping of closely related sequences, using complete linkage clustering, in order to calculate richness and evenness indices for the communities. |
2301.09472 | Cecile Fauvelot | Florentine Riquet (ENTROPIE [Nouvelle-Cal\'edonie], LOV), C\'ecile
Fauvelot (ENTROPIE [Nouvelle-Cal\'edonie], LOV), Pauline Fey (ENTROPIE
[Nouvelle-Cal\'edonie]), Daphn\'e Grulois (ENTROPIE [Nouvelle-Cal\'edonie]),
Marc Leopold (ENTROPIE [Nouvelle-Cal\'edonie]) | Hatchery-produced sandfish (Holothuria scabra) show altered genetic
diversity in New Caledonia | null | Fisheries Research, 2022, 252, pp.106343 | 10.1016/j.fishres.2022.106343 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Facing an alarming continuing decline of wild sea cucumber resources,
management strategies were developed over the past three decades to sustainably
promote development, maintenance, or regeneration of wild sea cucumber
fisheries. In New Caledonia (South Pacific), dedicated management efforts via
restocking and sea ranching programs were implemented to cope with the
overharvesting of the sandfish Holothuria scabra and the recent loss of known
populations. In order to investigate genetic implications of a major H. scabra
restocking program, we assessed the genetic diversity and structure of wild
stocks and hatchery-produced sandfish and compared the genetic outcomes of
consecutive spawning and juvenile production events. For this, 1358 sandfish
collected at four sites along the northwestern coasts of New Caledonia, as well
as during five different restocking events in the Tiabet Bay, were genotyped
using nine polymorphic microsatellite markers. We found that wild H. scabra
populations from the northwestern coast of New Caledonia likely belonged to one
panmictic population with high level of gene flow observed along the study
scale. Further, this panmictic population displayed an effective size of
breeders large enough to ensure the feasibility of appropriate breeding
programs for restocking. In contrast, hatchery-produced samples did suffer from
an important reduction in the effective population size: the effective
population size were so small that genetic drift was detectable over one
generation, with the presence of inbred individuals, as well as more related
dyads than in wild populations. All these results suggest that dedicated
efforts in hatcheries are further needed to maintain genetic diversity of
hatchery-produced individuals in order to unbalance any negative impact during
this artificial selection.
| [
{
"created": "Mon, 23 Jan 2023 15:09:30 GMT",
"version": "v1"
}
] | 2023-01-24 | [
[
"Riquet",
"Florentine",
"",
"ENTROPIE [Nouvelle-Calédonie], LOV"
],
[
"Fauvelot",
"Cécile",
"",
"ENTROPIE [Nouvelle-Calédonie], LOV"
],
[
"Fey",
"Pauline",
"",
"ENTROPIE\n [Nouvelle-Calédonie]"
],
[
"Grulois",
"Daphné",
"",
"ENTROPIE [Nouvelle-Calédonie]"
],
[
"Leopold",
"Marc",
"",
"ENTROPIE [Nouvelle-Calédonie]"
]
] | Facing an alarming continuing decline of wild sea cucumber resources, management strategies were developed over the past three decades to sustainably promote development, maintenance, or regeneration of wild sea cucumber fisheries. In New Caledonia (South Pacific), dedicated management efforts via restocking and sea ranching programs were implemented to cope with the overharvesting of the sandfish Holothuria scabra and the recent loss of known populations. In order to investigate genetic implications of a major H. scabra restocking program, we assessed the genetic diversity and structure of wild stocks and hatchery-produced sandfish and compared the genetic outcomes of consecutive spawning and juvenile production events. For this, 1358 sandfish collected at four sites along the northwestern coasts of New Caledonia, as well as during five different restocking events in the Tiabet Bay, were genotyped using nine polymorphic microsatellite markers. We found that wild H. scabra populations from the northwestern coast of New Caledonia likely belonged to one panmictic population with high level of gene flow observed along the study scale. Further, this panmictic population displayed an effective size of breeders large enough to ensure the feasibility of appropriate breeding programs for restocking. In contrast, hatchery-produced samples did suffer from an important reduction in the effective population size: the effective population size were so small that genetic drift was detectable over one generation, with the presence of inbred individuals, as well as more related dyads than in wild populations. All these results suggest that dedicated efforts in hatcheries are further needed to maintain genetic diversity of hatchery-produced individuals in order to unbalance any negative impact during this artificial selection. |
2205.01753 | David Graff | David E. Graff, Matteo Aldeghi, Joseph A. Morrone, Kirk E. Jordan,
Edward O. Pyzer-Knapp and Connor W. Coley | Self-focusing virtual screening with active design space pruning | 47 pages, 26 figures, 3 tables | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | High-throughput virtual screening is an indispensable technique utilized in
the discovery of small molecules. In cases where the library of molecules is
exceedingly large, the cost of an exhaustive virtual screen may be prohibitive.
Model-guided optimization has been employed to lower these costs through
dramatic increases in sample efficiency compared to random selection. However,
these techniques introduce new costs to the workflow through the surrogate
model training and inference steps. In this study, we propose an extension to
the framework of model-guided optimization that mitigates inferences costs
using a technique we refer to as design space pruning (DSP), which irreversibly
removes poor-performing candidates from consideration. We study the application
of DSP to a variety of optimization tasks and observe significant reductions in
overhead costs while exhibiting similar performance to the baseline
optimization. DSP represents an attractive extension of model-guided
optimization that can limit overhead costs in optimization settings where these
costs are non-negligible relative to objective costs, such as docking.
| [
{
"created": "Tue, 3 May 2022 19:47:30 GMT",
"version": "v1"
}
] | 2022-05-05 | [
[
"Graff",
"David E.",
""
],
[
"Aldeghi",
"Matteo",
""
],
[
"Morrone",
"Joseph A.",
""
],
[
"Jordan",
"Kirk E.",
""
],
[
"Pyzer-Knapp",
"Edward O.",
""
],
[
"Coley",
"Connor W.",
""
]
] | High-throughput virtual screening is an indispensable technique utilized in the discovery of small molecules. In cases where the library of molecules is exceedingly large, the cost of an exhaustive virtual screen may be prohibitive. Model-guided optimization has been employed to lower these costs through dramatic increases in sample efficiency compared to random selection. However, these techniques introduce new costs to the workflow through the surrogate model training and inference steps. In this study, we propose an extension to the framework of model-guided optimization that mitigates inferences costs using a technique we refer to as design space pruning (DSP), which irreversibly removes poor-performing candidates from consideration. We study the application of DSP to a variety of optimization tasks and observe significant reductions in overhead costs while exhibiting similar performance to the baseline optimization. DSP represents an attractive extension of model-guided optimization that can limit overhead costs in optimization settings where these costs are non-negligible relative to objective costs, such as docking. |
1410.8768 | Arne B. Gjuvsland | Arne B. Gjuvsland, Erik Plahte | Reconstruction of gene regulatory networks from steady state data | 29 pages, 10 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Genes are connected in regulatory networks, often modelled by ordinary
differential equations. Changes in expression of a gene propagate to other
genes along paths in the network. At a stable state, the system's Jacobian
matrix confers information about network connectivity. To disclose the
functional properties of genes, knowledge of network connections is essential.
We present a new method to reconstruct the Jacobian matrix of models for gene
regulatory systems from equilibrium protein concentrations. In a recent paper
we defined propagation and feedback functions describing how genetic variation
at one gene propagates to the other genes in the network and possibly also back
to itself. Here we show how propagation and feedback functions provide
relations between equilibrium protein levels which are in principle observable,
and Jacobi elements which are not directly observable. We establish exact
formulae expressing the Jacobian in terms of derivatives of propagation and
feedback functions. Approximating these derivatives from perturbed and
unperturbed protein levels, we derive formulae for estimating the Jacobian. We
apply the method to models of the Drosophila segment polarity network and
randomly generated gene networks. Genes could be perturbed in two ways: by
modifying mRNA degradation rates, or by allele knockout in diploid models.
Comparison with the true Jacobians shows that for noiseless data we obtain hit
rates close to 100% in the former case and in the range 80-90% in the latter.
Our method adds to the network interference toolbox and provides a sign
estimate of the Jacobian from steady state data, and a value estimate of the
Jacobian if protein degradation rates are known. Also the approach identifies
some predicted connections as much more reliable than others, and could point
to further experiments for resolving uncertainties in the less dependable
Jacobian elements.
| [
{
"created": "Fri, 31 Oct 2014 15:17:39 GMT",
"version": "v1"
}
] | 2014-11-03 | [
[
"Gjuvsland",
"Arne B.",
""
],
[
"Plahte",
"Erik",
""
]
] | Genes are connected in regulatory networks, often modelled by ordinary differential equations. Changes in expression of a gene propagate to other genes along paths in the network. At a stable state, the system's Jacobian matrix confers information about network connectivity. To disclose the functional properties of genes, knowledge of network connections is essential. We present a new method to reconstruct the Jacobian matrix of models for gene regulatory systems from equilibrium protein concentrations. In a recent paper we defined propagation and feedback functions describing how genetic variation at one gene propagates to the other genes in the network and possibly also back to itself. Here we show how propagation and feedback functions provide relations between equilibrium protein levels which are in principle observable, and Jacobi elements which are not directly observable. We establish exact formulae expressing the Jacobian in terms of derivatives of propagation and feedback functions. Approximating these derivatives from perturbed and unperturbed protein levels, we derive formulae for estimating the Jacobian. We apply the method to models of the Drosophila segment polarity network and randomly generated gene networks. Genes could be perturbed in two ways: by modifying mRNA degradation rates, or by allele knockout in diploid models. Comparison with the true Jacobians shows that for noiseless data we obtain hit rates close to 100% in the former case and in the range 80-90% in the latter. Our method adds to the network interference toolbox and provides a sign estimate of the Jacobian from steady state data, and a value estimate of the Jacobian if protein degradation rates are known. Also the approach identifies some predicted connections as much more reliable than others, and could point to further experiments for resolving uncertainties in the less dependable Jacobian elements. |
2006.11609 | Marcelo Briones | Taima N. Furuyama, Fernando Antoneli, Isabel M. V. G. Carvalho,
Marcelo R. S. Briones, Luiz M. R. Janini | Temporal data series of COVID-19 epidemics in the USA, Asia and Europe
suggests a selective sweep of SARS-CoV-2 Spike D614G variant | 11 pages, 2 figures, 2 supplementary tables | Genet. Mol. Res. 20(4) 2021: GMR18960 | 10.4238/gmr18960 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The COVID-19 pandemic started in Wuhan, China, and caused the worldwide
spread of the RNA virus SARS-CoV-2, the causative agent of COVID-19. Because of
its mutational rate, wide geographical distribution, and host response variance
this coronavirus is currently evolving into an array of strains with increasing
genetic diversity. Most variants apparently have neutral effects for disease
spread and symptoms severity. However, in the viral Spike protein, which is
responsible for host cell attachment and invasion, an emergent variant,
containing the amino acid substitution D to G in position 614 (D614G), was
suggested to increase viral infection capability. To test whether this variant
has epidemiological impact, the temporal distributions of the SARS-CoV-2
samples bearing D or G at position 614 were compared in the USA, Asia and
Europe. The epidemiological curves were compared at early and late epidemic
stages. At early stages, where containment measures were still not fully
implemented, the viral variants are supposed to be unconstrained and its growth
curves might approximate the free viral dynamics. Our analysis shows that the
D614G prevalence and the growth rates of COVID-19 epidemic curves are
correlated in the USA, Asia and Europe. Our results suggest a selective sweep
that can be explained, at least in part, by a propagation advantage of this
variant, in other words, that the molecular level effects of D614G have
sufficient impact on population transmission dynamics as to be detected by
differences in rate coefficients of epidemic growth curves.
| [
{
"created": "Sat, 20 Jun 2020 16:30:10 GMT",
"version": "v1"
}
] | 2021-12-23 | [
[
"Furuyama",
"Taima N.",
""
],
[
"Antoneli",
"Fernando",
""
],
[
"Carvalho",
"Isabel M. V. G.",
""
],
[
"Briones",
"Marcelo R. S.",
""
],
[
"Janini",
"Luiz M. R.",
""
]
] | The COVID-19 pandemic started in Wuhan, China, and caused the worldwide spread of the RNA virus SARS-CoV-2, the causative agent of COVID-19. Because of its mutational rate, wide geographical distribution, and host response variance this coronavirus is currently evolving into an array of strains with increasing genetic diversity. Most variants apparently have neutral effects for disease spread and symptoms severity. However, in the viral Spike protein, which is responsible for host cell attachment and invasion, an emergent variant, containing the amino acid substitution D to G in position 614 (D614G), was suggested to increase viral infection capability. To test whether this variant has epidemiological impact, the temporal distributions of the SARS-CoV-2 samples bearing D or G at position 614 were compared in the USA, Asia and Europe. The epidemiological curves were compared at early and late epidemic stages. At early stages, where containment measures were still not fully implemented, the viral variants are supposed to be unconstrained and its growth curves might approximate the free viral dynamics. Our analysis shows that the D614G prevalence and the growth rates of COVID-19 epidemic curves are correlated in the USA, Asia and Europe. Our results suggest a selective sweep that can be explained, at least in part, by a propagation advantage of this variant, in other words, that the molecular level effects of D614G have sufficient impact on population transmission dynamics as to be detected by differences in rate coefficients of epidemic growth curves. |
2008.02388 | Miguel Ramos-Pascual | Miguel Ramos Pascual | Dynamic model of HIV infection with immune system response of
T-lymphocytes, B-cells and dendritic cells: a review | null | null | null | null | q-bio.CB q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A dynamic model of non-lineal time-dependent ordinary differential equations
(ODE) has been applied to the interactions of a HIV infection with the immune
system cells. This model has been simplified into two compartments: lymph node
and peripheral blood. The model includes CD4 T-lymphocytes in several states
(quiescent Q, naive N and activated T), cytotoxic CD8 T-cells, B-cells and
dendritic cells. Cytokines and immunoglobulins specific for each antigen (i.e.
gp41 or p24) have been also included in the model, modelling the atraction
effect of CD4 T-cells to the infected area and the reduction of virus
concentration by immunoglobulins. HIV virus infection of CD4 T-lymphocytes is
modelled in several stages: before fusion as HIV-attached (H) and after fusion
as non-permissive / abortively infected (M), and permissive / latently infected
(L) and permissive / actively infected (I). These equations have been
implemented in a C++/Python interface application, called Immune System app,
which runs Open Modelica software to solve the ODE system through a 4th order
Runge-Kutta numerical approximation. Results of the simulation show that
although HIV virus concentration in both compartments is lower than $10^{-10}$
virus/$\mu L$ after t=2 years, quiescent lymphocytes reach an equilibrium with
a concentration lower than the initial conditions, due to the latency state,
which serves as a reservoir in time of virus production. As a conclusion, this
model can provide reliable results in other conditions, such as antiviral
therapies.
| [
{
"created": "Wed, 5 Aug 2020 22:51:35 GMT",
"version": "v1"
}
] | 2020-08-07 | [
[
"Pascual",
"Miguel Ramos",
""
]
] | A dynamic model of non-lineal time-dependent ordinary differential equations (ODE) has been applied to the interactions of a HIV infection with the immune system cells. This model has been simplified into two compartments: lymph node and peripheral blood. The model includes CD4 T-lymphocytes in several states (quiescent Q, naive N and activated T), cytotoxic CD8 T-cells, B-cells and dendritic cells. Cytokines and immunoglobulins specific for each antigen (i.e. gp41 or p24) have been also included in the model, modelling the atraction effect of CD4 T-cells to the infected area and the reduction of virus concentration by immunoglobulins. HIV virus infection of CD4 T-lymphocytes is modelled in several stages: before fusion as HIV-attached (H) and after fusion as non-permissive / abortively infected (M), and permissive / latently infected (L) and permissive / actively infected (I). These equations have been implemented in a C++/Python interface application, called Immune System app, which runs Open Modelica software to solve the ODE system through a 4th order Runge-Kutta numerical approximation. Results of the simulation show that although HIV virus concentration in both compartments is lower than $10^{-10}$ virus/$\mu L$ after t=2 years, quiescent lymphocytes reach an equilibrium with a concentration lower than the initial conditions, due to the latency state, which serves as a reservoir in time of virus production. As a conclusion, this model can provide reliable results in other conditions, such as antiviral therapies. |
2211.05644 | Paulo Protachevicz R. | P. R. Protachevicz, F. S. Borges, A. M. Batista, M. S. Baptista, I. L.
Caldas, E. E. N. Macau, E. L. Lameu | Plastic neural network with transmission delays promotes equivalence
between function and structure | null | null | 10.1016/j.chaos.2023.113480 | null | q-bio.NC physics.app-ph | http://creativecommons.org/licenses/by/4.0/ | The brain is formed by cortical regions that are associated with different
cognitive functions. Neurons within the same region are more likely to connect
than neurons in distinct regions, making the brain network to have
characteristics of a network of subnetworks. The values of synaptic delays
between neurons of different subnetworks are greater than those of the same
subnetworks. This difference in communication time between neurons has
consequences on the firing patterns observed in the brain, which is directly
related to changes in neural connectivity, known as synaptic plasticity. In
this work, we build a plastic network of Hodgkin-Huxley neurons in which the
connectivity modifications follow a spike-time dependent rule. We define an
internal-delay among neurons communicating within the same subnetwork, an
external-delay for neurons belonging to distinct subnetworks, and study how
these communicating delays affect the entire network dynamics. We observe that
the neuronal network exhibits a specific connectivity configuration for each
synchronised pattern. Our results show how synaptic delays and plasticity work
together to promote the formation of structural coupling among the neuronal
subnetworks. We conclude that plastic neuronal networks are able to promote
equivalence between function and structure meaning that topology emerges from
behaviour and behaviour emerges from topology, creating a complex dynamical
process where topology adapts to conform with the plastic rules and firing
patterns reflect the changes on the synaptic weights.
| [
{
"created": "Thu, 10 Nov 2022 15:18:55 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Apr 2023 15:19:53 GMT",
"version": "v2"
}
] | 2023-05-17 | [
[
"Protachevicz",
"P. R.",
""
],
[
"Borges",
"F. S.",
""
],
[
"Batista",
"A. M.",
""
],
[
"Baptista",
"M. S.",
""
],
[
"Caldas",
"I. L.",
""
],
[
"Macau",
"E. E. N.",
""
],
[
"Lameu",
"E. L.",
""
]
] | The brain is formed by cortical regions that are associated with different cognitive functions. Neurons within the same region are more likely to connect than neurons in distinct regions, making the brain network to have characteristics of a network of subnetworks. The values of synaptic delays between neurons of different subnetworks are greater than those of the same subnetworks. This difference in communication time between neurons has consequences on the firing patterns observed in the brain, which is directly related to changes in neural connectivity, known as synaptic plasticity. In this work, we build a plastic network of Hodgkin-Huxley neurons in which the connectivity modifications follow a spike-time dependent rule. We define an internal-delay among neurons communicating within the same subnetwork, an external-delay for neurons belonging to distinct subnetworks, and study how these communicating delays affect the entire network dynamics. We observe that the neuronal network exhibits a specific connectivity configuration for each synchronised pattern. Our results show how synaptic delays and plasticity work together to promote the formation of structural coupling among the neuronal subnetworks. We conclude that plastic neuronal networks are able to promote equivalence between function and structure meaning that topology emerges from behaviour and behaviour emerges from topology, creating a complex dynamical process where topology adapts to conform with the plastic rules and firing patterns reflect the changes on the synaptic weights. |
1306.5914 | Alessandro Sarti | Giovanna Citti and Alessandro Sarti | A Gauge Field Model of Modal Completion | null | null | null | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Perceptual completion of figures is a basic process revealing the deep
architecture of low level vision. In this paper a complete gauge field
Lagrangian is proposed allowing to couple the retinex equation with
neurogeometrical models and to solve the problem of modal completion, i.e. the
pop up of the Kanizsa triangle. Euler-Lagrange equations are derived by
variational calculus and numerically solved. Plausible neurophysiological
implementations of the particle and field equations are discussed and a model
of the interaction between LGN and visual cortex is proposed.
| [
{
"created": "Tue, 25 Jun 2013 10:59:58 GMT",
"version": "v1"
}
] | 2013-06-26 | [
[
"Citti",
"Giovanna",
""
],
[
"Sarti",
"Alessandro",
""
]
] | Perceptual completion of figures is a basic process revealing the deep architecture of low level vision. In this paper a complete gauge field Lagrangian is proposed allowing to couple the retinex equation with neurogeometrical models and to solve the problem of modal completion, i.e. the pop up of the Kanizsa triangle. Euler-Lagrange equations are derived by variational calculus and numerically solved. Plausible neurophysiological implementations of the particle and field equations are discussed and a model of the interaction between LGN and visual cortex is proposed. |
1805.04566 | Zachary Vesoulis | Z.A. Vesoulis, P.G. Gamble, S. Jain, N.M. El Ters, S.M. Liao and A.M.
Mathur | WU-NEAT: A clinically validated, open- source MATLAB toolbox for
limited-channel neonatal EEG analysis | 6 pages, 4 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Goal: Limited-channel EEG research in neonates is hindered by lack of open,
accessible analytic tools. To overcome this limitation, we have created the
Washington University- Neonatal EEG Analysis Toolbox (WU-NEAT), containing two
of the most commonly used tools, provided in an open-source,
clinically-validated package running within MATLAB. Methods: The first
algorithm is the amplitude-integrated EEG (aEEG), which is generated by
filtering, rectifying and time-compressing the original EEG recording, with
subsequent semi-logarithmic display. The second algorithm is the spectral edge
frequency (SEF), calculated as the critical frequency below which a user-
defined proportion of the EEG spectral power is located. The aEEG algorithm was
validated by three experienced reviewers. Reviewers evaluated aEEG recordings
of fourteen preterm/term infants, displayed twice in random order, once using a
reference algorithm and again using the WU-NEAT aEEG algorithm. Using standard
methodology, reviewers assigned a background pattern classification.
Inter/intra-rater reliability was assessed. For the SEF, calculations were made
using the same fourteen recordings, first with the reference and then with the
WU-NEAT algorithm. Results were compared using Pearson's correlation
coefficient. Results: For the aEEG algorithm, intra- and inter-rater
reliability was 100% and 98%, respectively. For the SEF, the mean (SD) Pearson
correlation coefficient between algorithms was 0.96 (0.04). Conclusion: We have
demonstrated a clinically-validated toolbox for generating the aEEG as well as
calculating the SEF from EEG data. Open-source access will enable widespread
use of common analytic algorithms which are device-independent and not subject
to obsolescence, thereby facilitating future collaborative research in neonatal
EEG.
| [
{
"created": "Fri, 11 May 2018 19:17:34 GMT",
"version": "v1"
}
] | 2018-05-15 | [
[
"Vesoulis",
"Z. A.",
""
],
[
"Gamble",
"P. G.",
""
],
[
"Jain",
"S.",
""
],
[
"Ters",
"N. M. El",
""
],
[
"Liao",
"S. M.",
""
],
[
"Mathur",
"A. M.",
""
]
] | Goal: Limited-channel EEG research in neonates is hindered by lack of open, accessible analytic tools. To overcome this limitation, we have created the Washington University- Neonatal EEG Analysis Toolbox (WU-NEAT), containing two of the most commonly used tools, provided in an open-source, clinically-validated package running within MATLAB. Methods: The first algorithm is the amplitude-integrated EEG (aEEG), which is generated by filtering, rectifying and time-compressing the original EEG recording, with subsequent semi-logarithmic display. The second algorithm is the spectral edge frequency (SEF), calculated as the critical frequency below which a user- defined proportion of the EEG spectral power is located. The aEEG algorithm was validated by three experienced reviewers. Reviewers evaluated aEEG recordings of fourteen preterm/term infants, displayed twice in random order, once using a reference algorithm and again using the WU-NEAT aEEG algorithm. Using standard methodology, reviewers assigned a background pattern classification. Inter/intra-rater reliability was assessed. For the SEF, calculations were made using the same fourteen recordings, first with the reference and then with the WU-NEAT algorithm. Results were compared using Pearson's correlation coefficient. Results: For the aEEG algorithm, intra- and inter-rater reliability was 100% and 98%, respectively. For the SEF, the mean (SD) Pearson correlation coefficient between algorithms was 0.96 (0.04). Conclusion: We have demonstrated a clinically-validated toolbox for generating the aEEG as well as calculating the SEF from EEG data. Open-source access will enable widespread use of common analytic algorithms which are device-independent and not subject to obsolescence, thereby facilitating future collaborative research in neonatal EEG. |
1812.09218 | Paul Manz | Paul Manz, Sven Goedeke, Raoul-Martin Memmesheimer | Dynamics and computation in mixed networks containing neurons that
accelerate towards spiking | v2, 21 pages, 11 figures | Phys. Rev. E 100, 042404 (2019) | 10.1103/PhysRevE.100.042404 | null | q-bio.NC cond-mat.dis-nn nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Networks in the brain consist of different types of neurons. Here we
investigate the influence of neuron diversity on the dynamics, phase space
structure and computational capabilities of spiking neural networks. We find
that already a single neuron of a different type can qualitatively change the
network dynamics and that mixed networks may combine the computational
capabilities of ones with a single neuron type. We study inhibitory networks of
concave leaky (LIF) and convex "anti-leaky" (XIF) integrate-and-fire neurons
that generalize irregularly spiking non-chaotic LIF neuron networks. Endowed
with simple conductance-based synapses for XIF neurons, our networks can
generate a balanced state of irregular asynchronous spiking as well. We
determine the voltage probability distributions and self-consistent firing
rates assuming Poisson input with finite size spike impacts. Further, we
compute the full spectrum of Lyapunov exponents (LEs) and the covariant
Lyapunov vectors (CLVs) specifying the corresponding perturbation directions.
We find that there is approximately one positive LE for each XIF neuron. This
indicates in particular that a single XIF neuron renders the network dynamics
chaotic. A simple mean-field approach, which can be justified by properties of
the CLVs, explains the finding. As an application, we propose a spike-based
computing scheme where our networks serve as computational reservoirs and their
different stability properties yield different computational capabilities.
| [
{
"created": "Fri, 21 Dec 2018 16:02:32 GMT",
"version": "v1"
},
{
"created": "Thu, 15 Aug 2019 16:43:33 GMT",
"version": "v2"
}
] | 2019-10-09 | [
[
"Manz",
"Paul",
""
],
[
"Goedeke",
"Sven",
""
],
[
"Memmesheimer",
"Raoul-Martin",
""
]
] | Networks in the brain consist of different types of neurons. Here we investigate the influence of neuron diversity on the dynamics, phase space structure and computational capabilities of spiking neural networks. We find that already a single neuron of a different type can qualitatively change the network dynamics and that mixed networks may combine the computational capabilities of ones with a single neuron type. We study inhibitory networks of concave leaky (LIF) and convex "anti-leaky" (XIF) integrate-and-fire neurons that generalize irregularly spiking non-chaotic LIF neuron networks. Endowed with simple conductance-based synapses for XIF neurons, our networks can generate a balanced state of irregular asynchronous spiking as well. We determine the voltage probability distributions and self-consistent firing rates assuming Poisson input with finite size spike impacts. Further, we compute the full spectrum of Lyapunov exponents (LEs) and the covariant Lyapunov vectors (CLVs) specifying the corresponding perturbation directions. We find that there is approximately one positive LE for each XIF neuron. This indicates in particular that a single XIF neuron renders the network dynamics chaotic. A simple mean-field approach, which can be justified by properties of the CLVs, explains the finding. As an application, we propose a spike-based computing scheme where our networks serve as computational reservoirs and their different stability properties yield different computational capabilities. |
2208.13317 | Alexander Kaiser | Alexander D. Kaiser | Modeling the Mitral Valve | PhD Thesis, 2017, Department of Mathematics, Courant Institute of
Mathematical Sciences, New York University. Condensed version:
arXiv:1902.00018, https://doi.org/10.1002/cnm.3240 | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This thesis is concerned with modeling and simulation of the mitral valve,
one of the four valves in the human heart. The valve is composed of leaflets
attached to a ring, the free edges of which are supported by a system of
chordae, which themselves are anchored to muscles inside the heart. First, we
examine valve anatomy and show the results of original dissections. These
display the gross anatomy and information on fiber structure of the mitral
valve. Next, we build a model valve following a design-based approach to
elasticity. We incorporate information from the dissections to specify the
fiber topology of this model. We assume the valve achieves mechanical
equilibrium while supporting a static pressure load. The solution to the
resulting differential equations determines the pressurized configuration of
the valve model. To complete the model we then specify a constitutive law based
on experimental stress-strain relations from the literature. Finally, using the
immersed boundary method, we simulate the model valve in fluid in a computer
test chamber. The aim of this work is to determine the basic principles and
mechanisms underlying the anatomy and function of the mitral valve.
| [
{
"created": "Mon, 29 Aug 2022 00:21:52 GMT",
"version": "v1"
}
] | 2022-08-30 | [
[
"Kaiser",
"Alexander D.",
""
]
] | This thesis is concerned with modeling and simulation of the mitral valve, one of the four valves in the human heart. The valve is composed of leaflets attached to a ring, the free edges of which are supported by a system of chordae, which themselves are anchored to muscles inside the heart. First, we examine valve anatomy and show the results of original dissections. These display the gross anatomy and information on fiber structure of the mitral valve. Next, we build a model valve following a design-based approach to elasticity. We incorporate information from the dissections to specify the fiber topology of this model. We assume the valve achieves mechanical equilibrium while supporting a static pressure load. The solution to the resulting differential equations determines the pressurized configuration of the valve model. To complete the model we then specify a constitutive law based on experimental stress-strain relations from the literature. Finally, using the immersed boundary method, we simulate the model valve in fluid in a computer test chamber. The aim of this work is to determine the basic principles and mechanisms underlying the anatomy and function of the mitral valve. |
1410.8040 | Alexander Teplukhin | Alexander V. Teplukhin, Yulia S. Lemesheva | The study of water shell structure of double-helical B-DNA fragments
using parallel computing | Translated from Russian. See original text published in: ISBN
5-93972-188-5, "Computers and Supercomputers in Biology" / Lakhno V.D.,
Ustinin M.N. Eds., Moskva-Izhevsk: IKI, 2002, Part 1, Chapter 8, pp. 234-240 | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Something about structure of the water shell of DNA and how it can help to
design of novel biologically-active molecules and potential drugs with
sequence-specific binding to nucleic acids.
| [
{
"created": "Wed, 29 Oct 2014 16:04:55 GMT",
"version": "v1"
}
] | 2014-10-30 | [
[
"Teplukhin",
"Alexander V.",
""
],
[
"Lemesheva",
"Yulia S.",
""
]
] | Something about structure of the water shell of DNA and how it can help to design of novel biologically-active molecules and potential drugs with sequence-specific binding to nucleic acids. |
q-bio/0403024 | Bernard Leong | Thomas A. Down, Bernard Leong, Tim J.P. Hubbard | A Machine Learning Strategy to Identity Exonic Splice Enhancers in Human
Protein-coding Sequence | Submitted to Genome Research, 16 pages and 5 figures | null | null | null | q-bio.GN | null | Background: Exonic splice enhancers are sequences embedded within exons which
promote and regulate the splicing of the transcript in which they are located.
A class of exonic splice enhancers are the SR proteins, which are thought to
mediate interactions between splicing factors bound to the 5' and 3' splice
sites. Method and results: We present a novel strategy for analysing
protein-coding sequence by first randomizing the codons used at each position
within the coding sequence, then applying a motif-based machine learning
algorithm to compare the true and randomized sequences. This strategy
identified a collection of motifs which can successfully discriminate between
real and randomized coding sequence, including -- but not restricted to --
several previously reported splice enhancer elements. As well as successfully
distinguishing coding exons from randomized sequences, we show that our model
is able to recognize non-coding exons. Conclusions: Our strategy succeeded in
detecting signals in coding exons which seem to be orthogonal to the sequences'
primary function of coding for proteins. We believe that many of the motifs
detected here may represent binding sites for previously unrecognized proteins
which influence RNA splicing. We hope that this development will lead to
improved knowledge of exonic splice enhancers, and new developments in the
field of computational gene prediction.
| [
{
"created": "Tue, 16 Mar 2004 13:43:32 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Down",
"Thomas A.",
""
],
[
"Leong",
"Bernard",
""
],
[
"Hubbard",
"Tim J. P.",
""
]
] | Background: Exonic splice enhancers are sequences embedded within exons which promote and regulate the splicing of the transcript in which they are located. A class of exonic splice enhancers are the SR proteins, which are thought to mediate interactions between splicing factors bound to the 5' and 3' splice sites. Method and results: We present a novel strategy for analysing protein-coding sequence by first randomizing the codons used at each position within the coding sequence, then applying a motif-based machine learning algorithm to compare the true and randomized sequences. This strategy identified a collection of motifs which can successfully discriminate between real and randomized coding sequence, including -- but not restricted to -- several previously reported splice enhancer elements. As well as successfully distinguishing coding exons from randomized sequences, we show that our model is able to recognize non-coding exons. Conclusions: Our strategy succeeded in detecting signals in coding exons which seem to be orthogonal to the sequences' primary function of coding for proteins. We believe that many of the motifs detected here may represent binding sites for previously unrecognized proteins which influence RNA splicing. We hope that this development will lead to improved knowledge of exonic splice enhancers, and new developments in the field of computational gene prediction. |
2307.09391 | Stanislav Burov | Aviv Arcobi and Stanislav Burov | Continuous Approximation of Stochastic Maps for Modeling Asymmetric Cell
Division | null | null | null | null | q-bio.CB cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cell size control and homeostasis is a major topic in cell biology yet to be
fully understood. Several growth laws like the timer, adder, and sizer were
proposed, and mathematical approaches that model cell growth and division were
developed. This study focuses on utilizing stochastic map modeling for
investigating asymmetric cell division. We establish a mapping between the
description of cell growth and division and the Ornstein-Uhlenbeck process with
dichotomous noise. We leverage this mapping to achieve analytical solutions and
derive a closed-form expression for the stable cell size distribution under
asymmetric division. To validate our findings, we conduct numerical simulations
encompassing several cell growth scenarios. Our approach allows us to obtain a
precise criterion for a bi-phasic behavior of the cell size. While for the case
of the sizer scenario, a transition from the uni-modal phase to bi-modal is
always possible, given sufficiently large asymmetry at the division, the
affine-linear approximation of the adder scenario invariably yields uni-modal
distribution.
| [
{
"created": "Tue, 18 Jul 2023 16:15:06 GMT",
"version": "v1"
}
] | 2023-07-19 | [
[
"Arcobi",
"Aviv",
""
],
[
"Burov",
"Stanislav",
""
]
] | Cell size control and homeostasis is a major topic in cell biology yet to be fully understood. Several growth laws like the timer, adder, and sizer were proposed, and mathematical approaches that model cell growth and division were developed. This study focuses on utilizing stochastic map modeling for investigating asymmetric cell division. We establish a mapping between the description of cell growth and division and the Ornstein-Uhlenbeck process with dichotomous noise. We leverage this mapping to achieve analytical solutions and derive a closed-form expression for the stable cell size distribution under asymmetric division. To validate our findings, we conduct numerical simulations encompassing several cell growth scenarios. Our approach allows us to obtain a precise criterion for a bi-phasic behavior of the cell size. While for the case of the sizer scenario, a transition from the uni-modal phase to bi-modal is always possible, given sufficiently large asymmetry at the division, the affine-linear approximation of the adder scenario invariably yields uni-modal distribution. |
1605.05587 | Ruben Perez-Carrasco | Ruben Perez-Carrasco, Pilar Guerrero, James Briscoe, Karen Page | Intrinsic noise profoundly alters the dynamics and steady state of
morphogen-controlled bistable genetic switches | null | null | 10.1371/journal.pcbi.1005154 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | During tissue development, patterns of gene expression determine the spatial
arrangement of cell types. In many cases, gradients of secreted signaling
molecules - morphogens - guide this process. The continuous positional
information provided by the gradient is converted into discrete cell types by
the downstream transcriptional network that responds to the morphogen. A
mechanism commonly used to implement a sharp transition between two adjacent
cell fates is the genetic toggle switch, composed of cross-repressing
transcriptional determinants. Previous analyses emphasize the steady state
output of these mechanisms. Here, we explore the dynamics of the toggle switch
and use exact numerical simulations of the kinetic reactions, the Chemical
Langevin Equation, and Minimum Action Path theory to establish a framework for
studying the effect of gene expression noise on patterning time and boundary
position. This provides insight into the time scale, gene expression
trajectories and directionality of stochastic switching events between cell
states. Taking gene expression noise into account predicts that the final
boundary position of a morphogen-induced toggle switch, although robust to
changes in the details of the noise, is distinct from that of the deterministic
system. Moreover, stochastic switching introduces differences in patterning
time along the morphogen gradient that result in a patterning wave propagating
away from the morphogen source. The velocity of this wave is influenced by
noise; the wave sharpens and slows as it advances and may never reach steady
state in a biologically relevant time. This could explain experimentally
observed dynamics of pattern formation. Together the analysis reveals the
importance of dynamical transients for understanding morphogen-driven
transcriptional networks and indicates that gene expression noise can
qualitatively alter developmental patterning.
| [
{
"created": "Wed, 18 May 2016 14:08:11 GMT",
"version": "v1"
}
] | 2017-02-08 | [
[
"Perez-Carrasco",
"Ruben",
""
],
[
"Guerrero",
"Pilar",
""
],
[
"Briscoe",
"James",
""
],
[
"Page",
"Karen",
""
]
] | During tissue development, patterns of gene expression determine the spatial arrangement of cell types. In many cases, gradients of secreted signaling molecules - morphogens - guide this process. The continuous positional information provided by the gradient is converted into discrete cell types by the downstream transcriptional network that responds to the morphogen. A mechanism commonly used to implement a sharp transition between two adjacent cell fates is the genetic toggle switch, composed of cross-repressing transcriptional determinants. Previous analyses emphasize the steady state output of these mechanisms. Here, we explore the dynamics of the toggle switch and use exact numerical simulations of the kinetic reactions, the Chemical Langevin Equation, and Minimum Action Path theory to establish a framework for studying the effect of gene expression noise on patterning time and boundary position. This provides insight into the time scale, gene expression trajectories and directionality of stochastic switching events between cell states. Taking gene expression noise into account predicts that the final boundary position of a morphogen-induced toggle switch, although robust to changes in the details of the noise, is distinct from that of the deterministic system. Moreover, stochastic switching introduces differences in patterning time along the morphogen gradient that result in a patterning wave propagating away from the morphogen source. The velocity of this wave is influenced by noise; the wave sharpens and slows as it advances and may never reach steady state in a biologically relevant time. This could explain experimentally observed dynamics of pattern formation. Together the analysis reveals the importance of dynamical transients for understanding morphogen-driven transcriptional networks and indicates that gene expression noise can qualitatively alter developmental patterning. |
1911.00978 | John Vastola | John J. Vastola | Solving the chemical master equation for monomolecular reaction systems
analytically: a Doi-Peliti path integral view | 61 pages | null | null | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The chemical master equation (CME) is a fundamental description of
interacting molecules commonly used to model chemical kinetics and noisy gene
regulatory networks. Exact time-dependent solutions of the CME -- which
typically consists of infinitely many coupled differential equations -- are
rare, and are valuable for numerical benchmarking and getting intuition for the
behavior of more complicated systems. Jahnke and Huisinga's landmark
calculation of the exact time-dependent solution of the CME for monomolecular
reaction systems is one of the most general analytic results known; however, it
is hard to generalize, because it relies crucially on properties of
monomolecular reactions. In this paper, we rederive Jahnke and Huisinga's
result on the time-dependent probability distribution and moments of
monomolecular reaction systems using the Doi-Peliti path integral approach,
which reduces solving the CME to evaluating many integrals. While the
Doi-Peliti approach is less intuitive, it is also more mechanical, and hence
easier to generalize. To illustrate how the Doi-Peliti approach can go beyond
the method of Jahnke and Huisinga, we also find an explicit and exact
time-dependent solution to a problem involving an autocatalytic reaction that
Jahnke and Huisinga identified as not solvable using their method. We also find
a formal exact time-dependent solution for any CME whose list of reactions
involves only zero and first order reactions, which may be the most general
result currently known.
| [
{
"created": "Sun, 3 Nov 2019 21:45:07 GMT",
"version": "v1"
},
{
"created": "Tue, 2 Feb 2021 22:40:40 GMT",
"version": "v2"
}
] | 2021-02-04 | [
[
"Vastola",
"John J.",
""
]
] | The chemical master equation (CME) is a fundamental description of interacting molecules commonly used to model chemical kinetics and noisy gene regulatory networks. Exact time-dependent solutions of the CME -- which typically consists of infinitely many coupled differential equations -- are rare, and are valuable for numerical benchmarking and getting intuition for the behavior of more complicated systems. Jahnke and Huisinga's landmark calculation of the exact time-dependent solution of the CME for monomolecular reaction systems is one of the most general analytic results known; however, it is hard to generalize, because it relies crucially on properties of monomolecular reactions. In this paper, we rederive Jahnke and Huisinga's result on the time-dependent probability distribution and moments of monomolecular reaction systems using the Doi-Peliti path integral approach, which reduces solving the CME to evaluating many integrals. While the Doi-Peliti approach is less intuitive, it is also more mechanical, and hence easier to generalize. To illustrate how the Doi-Peliti approach can go beyond the method of Jahnke and Huisinga, we also find an explicit and exact time-dependent solution to a problem involving an autocatalytic reaction that Jahnke and Huisinga identified as not solvable using their method. We also find a formal exact time-dependent solution for any CME whose list of reactions involves only zero and first order reactions, which may be the most general result currently known. |
1102.2402 | Yves-Henri Sanejouand | Yves-Henri Sanejouand | Elastic Network Models: Theoretical and Empirical Foundations | 26 pages, 1 Figure | in "Biomolecular Simulations", Monticelli and Salonen eds, Humana
Press, p601, 2012 | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fifteen years ago Monique Tirion showed that the low-frequency normal modes
of a protein are not significantly altered when non-bonded interactions are
replaced by Hookean springs, for all atom pairs whose distance is smaller than
a given cutoff value. Since then, it has been shown that coarse-grained
versions of Tirion's model are able to provide fair insights on many dynamical
properties of biological macromolecules. In this text, theoretical tools
required for studying these so-called Elastic Network Models are described,
focusing on practical issues and, in particular, on possible artifacts. Then,
an overview of some typical results that have been obtained by studying such
models is given.
| [
{
"created": "Fri, 11 Feb 2011 17:50:03 GMT",
"version": "v1"
}
] | 2012-10-19 | [
[
"Sanejouand",
"Yves-Henri",
""
]
] | Fifteen years ago Monique Tirion showed that the low-frequency normal modes of a protein are not significantly altered when non-bonded interactions are replaced by Hookean springs, for all atom pairs whose distance is smaller than a given cutoff value. Since then, it has been shown that coarse-grained versions of Tirion's model are able to provide fair insights on many dynamical properties of biological macromolecules. In this text, theoretical tools required for studying these so-called Elastic Network Models are described, focusing on practical issues and, in particular, on possible artifacts. Then, an overview of some typical results that have been obtained by studying such models is given. |
1707.03864 | Rebecca Harris | Rebecca B. Harris, Per Alstr\"om, Anders \"Odeen, Adam D. Leach\'e | Discordance between genomic divergence and phenotypic variation in a
rapidly evolving avian genus (Motacilla) | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generally, genotypes and phenotypes are expected to be spatially congruent,
however, in widespread species complexes with few barriers to dispersal,
multiple contact zones, and limited reproductive isolation, discordance between
phenotypes and phylogeographic groups is more probable. Wagtails (Aves:
Motacilla) are a genus of birds with striking plumage pattern variation across
Eurasia. Up to 13 subspecies are recognized within a single species, yet
previous studies using mitochondrial DNA have supported phylogeographic groups
that are inconsistent with subspecies plumage characteristics. In this study,
we investigate the link between phenotypes and genotype by comparing
populations thought to be at different stages along the speciation continuum.
We take a phylogeographic approach by estimating population structure, testing
for isolation by distance, conducting demographic modeling, and estimating the
first time-calibrated species tree for the genus. Our study provides strong
evidence for species-level patterns of differentiation in wagtails, however
population-level differentiation is less pronounced. We find evidence that
three of four widespread Eurasian species exhibit an east-west divide that
contradicts both subspecies taxonomy and phenotypic variation. Both the
geographic location of this divide and time estimates from demographic models
are overlapping in two sympatric species, indicating that coincident
Pleistocene events shaped their histories.
| [
{
"created": "Wed, 12 Jul 2017 18:48:50 GMT",
"version": "v1"
}
] | 2017-07-14 | [
[
"Harris",
"Rebecca B.",
""
],
[
"Alström",
"Per",
""
],
[
"Ödeen",
"Anders",
""
],
[
"Leaché",
"Adam D.",
""
]
] | Generally, genotypes and phenotypes are expected to be spatially congruent, however, in widespread species complexes with few barriers to dispersal, multiple contact zones, and limited reproductive isolation, discordance between phenotypes and phylogeographic groups is more probable. Wagtails (Aves: Motacilla) are a genus of birds with striking plumage pattern variation across Eurasia. Up to 13 subspecies are recognized within a single species, yet previous studies using mitochondrial DNA have supported phylogeographic groups that are inconsistent with subspecies plumage characteristics. In this study, we investigate the link between phenotypes and genotype by comparing populations thought to be at different stages along the speciation continuum. We take a phylogeographic approach by estimating population structure, testing for isolation by distance, conducting demographic modeling, and estimating the first time-calibrated species tree for the genus. Our study provides strong evidence for species-level patterns of differentiation in wagtails, however population-level differentiation is less pronounced. We find evidence that three of four widespread Eurasian species exhibit an east-west divide that contradicts both subspecies taxonomy and phenotypic variation. Both the geographic location of this divide and time estimates from demographic models are overlapping in two sympatric species, indicating that coincident Pleistocene events shaped their histories. |
2302.08071 | Lev Tsimring | Arkady Pikovsky and Lev S. Tsimring | Statistical Theory of Asymmetric Damage Segregation in Clonal Cell
Populations | Mathematical Biosciences, 2023 (in press) | null | null | null | q-bio.PE nlin.AO physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Asymmetric damage segregation (ADS) is ubiquitous among unicellular
organisms: After a mother cell divides, its two daughter cells receive
sometimes slightly, sometimes strongly different fractions of damaged proteins
accumulated in the mother cell. Previous studies demonstrated that ADS provides
a selective advantage over symmetrically dividing cells by rejuvenating and
perpetuating the population as a whole. In this work we focus on the
statistical properties of damage in individual lineages and the overall damage
distributions in growing populations for a variety of ADS models with different
rules governing damage accumulation, segregation, and the lifetime dependence
on damage. We show that for a large class of deterministic ADS rules the
trajectories of damage along the lineages are chaotic, and the distributions of
damage in cells born at a given time asymptotically becomes fractal. By
exploiting the analogy of linear ADS models with the Iterated Function Systems
known in chaos theory, we derive the Frobenius-Perron equation for the
stationary damage density distribution and analytically compute the damage
distribution moments and fractal dimensions. We also investigate nonlinear and
stochastic variants of ADS models and show the robustness of the salient
features of the damage distributions.
| [
{
"created": "Thu, 16 Feb 2023 04:17:32 GMT",
"version": "v1"
}
] | 2023-02-17 | [
[
"Pikovsky",
"Arkady",
""
],
[
"Tsimring",
"Lev S.",
""
]
] | Asymmetric damage segregation (ADS) is ubiquitous among unicellular organisms: After a mother cell divides, its two daughter cells receive sometimes slightly, sometimes strongly different fractions of damaged proteins accumulated in the mother cell. Previous studies demonstrated that ADS provides a selective advantage over symmetrically dividing cells by rejuvenating and perpetuating the population as a whole. In this work we focus on the statistical properties of damage in individual lineages and the overall damage distributions in growing populations for a variety of ADS models with different rules governing damage accumulation, segregation, and the lifetime dependence on damage. We show that for a large class of deterministic ADS rules the trajectories of damage along the lineages are chaotic, and the distributions of damage in cells born at a given time asymptotically becomes fractal. By exploiting the analogy of linear ADS models with the Iterated Function Systems known in chaos theory, we derive the Frobenius-Perron equation for the stationary damage density distribution and analytically compute the damage distribution moments and fractal dimensions. We also investigate nonlinear and stochastic variants of ADS models and show the robustness of the salient features of the damage distributions. |
1802.06456 | Wei Ji Ma | Nuwan de Silva and Wei Ji Ma | Optimal allocation of attentional resource to multiple items with
unequal relevance | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In natural perception, different items (objects) in a scene are rarely
equally relevant to the observer. The brain improves performance by directing
attention to the most relevant items, for example the ones most likely to be
probed. For a general set of probing probabilities, it is not known how
attentional resources should be allocated to maximize performance. Here, we
investigate the optimal strategy for allocating a fixed resource budget E among
N items when on each trial, only one item gets probed. We develop an efficient
algorithm that, for any concave utility function, reduces the N-dimensional
problem to a set of N one-dimensional problems that the brain could plausibly
solve. We find that the intuitive strategy of allocating resource in proportion
to the probing probabilities is in general not optimal. In particular, in some
tasks, if resource is low, the optimal strategy involves allocating zero
resource to items with a nonzero probability of being probed. Our work opens
the door to normatively guided studies of attentional allocation.
| [
{
"created": "Sun, 18 Feb 2018 22:23:48 GMT",
"version": "v1"
}
] | 2018-02-20 | [
[
"de Silva",
"Nuwan",
""
],
[
"Ma",
"Wei Ji",
""
]
] | In natural perception, different items (objects) in a scene are rarely equally relevant to the observer. The brain improves performance by directing attention to the most relevant items, for example the ones most likely to be probed. For a general set of probing probabilities, it is not known how attentional resources should be allocated to maximize performance. Here, we investigate the optimal strategy for allocating a fixed resource budget E among N items when on each trial, only one item gets probed. We develop an efficient algorithm that, for any concave utility function, reduces the N-dimensional problem to a set of N one-dimensional problems that the brain could plausibly solve. We find that the intuitive strategy of allocating resource in proportion to the probing probabilities is in general not optimal. In particular, in some tasks, if resource is low, the optimal strategy involves allocating zero resource to items with a nonzero probability of being probed. Our work opens the door to normatively guided studies of attentional allocation. |
0804.3666 | Gareth Hughes | Gareth Hughes | Applications of information theory in plant disease management | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Information theory is a branch of probability and statistics involving the
analysis of communications. Information theory enables us to analyze and
quantify the information content of predictions made in the context of plant
disease management and related disciplines. In this article, some applications
of information theory in plant disease management are outlined.
| [
{
"created": "Wed, 23 Apr 2008 09:00:10 GMT",
"version": "v1"
}
] | 2008-04-24 | [
[
"Hughes",
"Gareth",
""
]
] | Information theory is a branch of probability and statistics involving the analysis of communications. Information theory enables us to analyze and quantify the information content of predictions made in the context of plant disease management and related disciplines. In this article, some applications of information theory in plant disease management are outlined. |
q-bio/0410016 | Thomas Bishop | Thomas C. Bishop | Molecular Dynamics Simulations of a Nucleosome and Free DNA | null | null | null | null | q-bio.BM | null | Nucleosomes organize the folding of DNA into chromatin and significantly
influence transcription, replication, regulation and repair. All atom molecular
dynamics simulations of a nucleosome and of its 146 basepairs of DNA free in
solution have been conducted. DNA helical parameters are extracted from each
trajectory to compare the conformation, effective force constants, persistence
length measures, and fluctuations of nucleosomal DNA to free DNA. A method for
disassembling and reconstructing the conformation and dynamics of the
nucleosome using Fourier analysis is presented. Results indicate that the
superhelical path of DNA in the nucleosome is irregular. Long length variations
in the conformation of nucleosomal DNA are identified other than those
associated with helix repeat. These variations are required to create a
proposed tetrasome conformation or to qualitatively reconstruct the 1.75 turns
of the nuclesomal superhelix. Free DNA achieves enough bend and shear in
solution to create an ideal nucleosome superhelix, but these deformations are
not organized so the conformation is essentially linear. Reconstruction of free
DNA using selected long wavelength variations in conformation can produce
either a left-handed or a right-handed superhelix. DNA is less flexible in the
nucleosome than when free in solution, however such measures are length scale
dependent.
| [
{
"created": "Thu, 14 Oct 2004 19:28:57 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Bishop",
"Thomas C.",
""
]
] | Nucleosomes organize the folding of DNA into chromatin and significantly influence transcription, replication, regulation and repair. All atom molecular dynamics simulations of a nucleosome and of its 146 basepairs of DNA free in solution have been conducted. DNA helical parameters are extracted from each trajectory to compare the conformation, effective force constants, persistence length measures, and fluctuations of nucleosomal DNA to free DNA. A method for disassembling and reconstructing the conformation and dynamics of the nucleosome using Fourier analysis is presented. Results indicate that the superhelical path of DNA in the nucleosome is irregular. Long length variations in the conformation of nucleosomal DNA are identified other than those associated with helix repeat. These variations are required to create a proposed tetrasome conformation or to qualitatively reconstruct the 1.75 turns of the nuclesomal superhelix. Free DNA achieves enough bend and shear in solution to create an ideal nucleosome superhelix, but these deformations are not organized so the conformation is essentially linear. Reconstruction of free DNA using selected long wavelength variations in conformation can produce either a left-handed or a right-handed superhelix. DNA is less flexible in the nucleosome than when free in solution, however such measures are length scale dependent. |
2204.06614 | Bhanushee Sharma | Bhanushee Sharma, Vijil Chenthamarakshan, Amit Dhurandhar, Shiranee
Pereira, James A. Hendler, Jonathan S. Dordick, Payel Das | Accurate Clinical Toxicity Prediction using Multi-task Deep Neural Nets
and Contrastive Molecular Explanations | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Explainable ML for molecular toxicity prediction is a promising approach for
efficient drug development and chemical safety. A predictive ML model of
toxicity can reduce experimental cost and time while mitigating ethical
concerns by significantly reducing animal and clinical testing. Herein, we use
a deep learning framework for simultaneously modeling in vitro, in vivo, and
clinical toxicity data. Two different molecular input representations are used:
Morgan fingerprints and pre-training SMILES embeddings. A multi-task deep
learning model accurately predicts toxicity for all endpoints, including
clinical, as indicated by AUROC and balanced accuracy. In particular, SMILES
embeddings as input to the multi-task model improved clinical toxicity
predictions compared to existing models in MoleculeNet benchmark. Additionally,
our multi-task approach is comprehensive in the sense that it is comparable to
state-of-the-art approaches for specific endpoints in in vitro, in vivo and
clinical platforms. Through both the multi-task model and transfer learning, we
were able to indicate the minimal need of in vivo data for clinical toxicity
predictions. To provide confidence and explain the model's predictions, we
adapt a post-hoc contrastive explanation method that returns pertinent positive
and pertinent negative features, which correspond well to known mutagenic and
reactive toxicophores, such as unsubstituted bonded heteroatoms, aromatic
amines, and Michael receptors. Furthermore, toxicophore recovery by pertinent
feature analysis captures more of the in vitro (53%) and in vivo (56%), rather
than of the clinical (8%), endpoints, and indeed uncovers a preference in known
toxicophore data towards in vitro and in vivo experimental data. To our
knowledge, this is the first contrastive explanation, using both present and
absent substructures, for predictions of clinical and in vivo molecular
toxicity.
| [
{
"created": "Wed, 13 Apr 2022 19:13:12 GMT",
"version": "v1"
}
] | 2022-04-15 | [
[
"Sharma",
"Bhanushee",
""
],
[
"Chenthamarakshan",
"Vijil",
""
],
[
"Dhurandhar",
"Amit",
""
],
[
"Pereira",
"Shiranee",
""
],
[
"Hendler",
"James A.",
""
],
[
"Dordick",
"Jonathan S.",
""
],
[
"Das",
"Payel",
""
]
] | Explainable ML for molecular toxicity prediction is a promising approach for efficient drug development and chemical safety. A predictive ML model of toxicity can reduce experimental cost and time while mitigating ethical concerns by significantly reducing animal and clinical testing. Herein, we use a deep learning framework for simultaneously modeling in vitro, in vivo, and clinical toxicity data. Two different molecular input representations are used: Morgan fingerprints and pre-training SMILES embeddings. A multi-task deep learning model accurately predicts toxicity for all endpoints, including clinical, as indicated by AUROC and balanced accuracy. In particular, SMILES embeddings as input to the multi-task model improved clinical toxicity predictions compared to existing models in MoleculeNet benchmark. Additionally, our multi-task approach is comprehensive in the sense that it is comparable to state-of-the-art approaches for specific endpoints in in vitro, in vivo and clinical platforms. Through both the multi-task model and transfer learning, we were able to indicate the minimal need of in vivo data for clinical toxicity predictions. To provide confidence and explain the model's predictions, we adapt a post-hoc contrastive explanation method that returns pertinent positive and pertinent negative features, which correspond well to known mutagenic and reactive toxicophores, such as unsubstituted bonded heteroatoms, aromatic amines, and Michael receptors. Furthermore, toxicophore recovery by pertinent feature analysis captures more of the in vitro (53%) and in vivo (56%), rather than of the clinical (8%), endpoints, and indeed uncovers a preference in known toxicophore data towards in vitro and in vivo experimental data. To our knowledge, this is the first contrastive explanation, using both present and absent substructures, for predictions of clinical and in vivo molecular toxicity. |
2307.14043 | Christopher Duffy | Callum Gray, Lekshmi Kailas, Peter G. Adams, Christopher D. P. Duffy | Unravelling the fluorescence kinetics of light-harvesting proteins with
simulated measurements | 5 figures, original research article submitted to BBA-Bioenergetics | null | null | null | q-bio.MN q-bio.BM q-bio.SC | http://creativecommons.org/licenses/by/4.0/ | The plant light-harvesting pigment-protein complex LHCII is the major antenna
sub-unit of PSII and is generally (though not universally) accepted to play a
role in photoprotective energy dissipation under high light conditions, a
process known Non-Photochemical Quenching (NPQ). The underlying mechanisms of
energy trapping and dissipation within LHCII are still debated. Various
proposed models differ considerably in their molecular and kinetic detail, but
are often based on different interpretations of very similar transient
absorption measurements of isolated complexes. Here we present a simulated
measurement of the fluorescence decay kinetics of quenched LHCII aggregates to
determine whether this relatively simple measurement can discriminate between
different potential NPQ mechanisms. We simulate not just the underlying physics
(excitation, energy migration, quenching and singlet-singlet annihilation) but
also the signal detection and typical experimental data analysis. Comparing
this to a selection of published fluorescence decay kinetics we find that: (1)
Different proposed quenching mechanisms produce noticeably different
fluorescence kinetics even at low (annihilation free) excitation density,
though the degree of difference is dependent on pulse width. (2) Measured decay
kinetics are consistent with most LHCII trimers becoming relatively slow
excitation quenchers. A small sub-population of very fast quenchers produces
kinetics which do not resemble any observed measurement. (3) It is necessary to
consider at least two distinct quenching mechanisms in order to accurately
reproduce experimental kinetics, supporting the idea that NPQ is not a simple
binary switch switch.
| [
{
"created": "Wed, 26 Jul 2023 08:49:13 GMT",
"version": "v1"
}
] | 2023-07-27 | [
[
"Gray",
"Callum",
""
],
[
"Kailas",
"Lekshmi",
""
],
[
"Adams",
"Peter G.",
""
],
[
"Duffy",
"Christopher D. P.",
""
]
] | The plant light-harvesting pigment-protein complex LHCII is the major antenna sub-unit of PSII and is generally (though not universally) accepted to play a role in photoprotective energy dissipation under high light conditions, a process known Non-Photochemical Quenching (NPQ). The underlying mechanisms of energy trapping and dissipation within LHCII are still debated. Various proposed models differ considerably in their molecular and kinetic detail, but are often based on different interpretations of very similar transient absorption measurements of isolated complexes. Here we present a simulated measurement of the fluorescence decay kinetics of quenched LHCII aggregates to determine whether this relatively simple measurement can discriminate between different potential NPQ mechanisms. We simulate not just the underlying physics (excitation, energy migration, quenching and singlet-singlet annihilation) but also the signal detection and typical experimental data analysis. Comparing this to a selection of published fluorescence decay kinetics we find that: (1) Different proposed quenching mechanisms produce noticeably different fluorescence kinetics even at low (annihilation free) excitation density, though the degree of difference is dependent on pulse width. (2) Measured decay kinetics are consistent with most LHCII trimers becoming relatively slow excitation quenchers. A small sub-population of very fast quenchers produces kinetics which do not resemble any observed measurement. (3) It is necessary to consider at least two distinct quenching mechanisms in order to accurately reproduce experimental kinetics, supporting the idea that NPQ is not a simple binary switch switch. |
2305.03799 | Yasaman Moradi | Yasaman Moradi, Jerry SH Lee, Andrea M. Armani | Detecting disruption of HER2 membrane protein organization in cell
membranes with nanoscale precision | null | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | The spatio-temporal organization of proteins within the cell membrane can
affect numerous biological functions, including cell signaling, communication,
and transportation. Deviations from normal spatial arrangements have been
observed in various diseases, and better understanding this process is a key
stepping-stone to advancing development of clinical interventions. However,
given the nanometer length scales involved, detecting these subtle changes has
primarily relied on complex super resolution and single molecule imaging
methods. In this work, we demonstrate an alternative fluorescent imaging
strategy for detecting protein organization based on a material that exhibits a
unique photophysical behavior known as aggregation induced emission (AIE).
Organic AIE molecules have an increase in emission signal when they are in
close proximity and the molecular motion is restricted. This property
simultaneously addresses the high background noise and low detection signal
that limit conventional widefield fluorescent imaging. To demonstrate the
potential of this approach, the fluorescent molecule sensor is conjugated to a
human epidermal growth factor receptor 2 (HER2) specific antibody and used to
investigate the spatio-temporal behavior of HER2 clustering in the membrane of
HER2-overexpressing breast cancer cells. Notably, the disruption of HER2
clusters in response to an FDA-approved monoclonal antibody therapeutic
(Trastuzumab) is successfully detected using a simple widefield fluorescent
microscope. While the sensor demonstrated here is optimized for sensing HER2
clustering, it is an easily adaptable platform. Moreover, given the
compatibility with widefield imaging, the system has the potential to be used
with high-throughput imaging techniques, accelerating investigations into
membrane protein spatio-temporal organization.
| [
{
"created": "Fri, 5 May 2023 19:01:40 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Oct 2023 23:55:21 GMT",
"version": "v2"
}
] | 2023-10-25 | [
[
"Moradi",
"Yasaman",
""
],
[
"Lee",
"Jerry SH",
""
],
[
"Armani",
"Andrea M.",
""
]
] | The spatio-temporal organization of proteins within the cell membrane can affect numerous biological functions, including cell signaling, communication, and transportation. Deviations from normal spatial arrangements have been observed in various diseases, and better understanding this process is a key stepping-stone to advancing development of clinical interventions. However, given the nanometer length scales involved, detecting these subtle changes has primarily relied on complex super resolution and single molecule imaging methods. In this work, we demonstrate an alternative fluorescent imaging strategy for detecting protein organization based on a material that exhibits a unique photophysical behavior known as aggregation induced emission (AIE). Organic AIE molecules have an increase in emission signal when they are in close proximity and the molecular motion is restricted. This property simultaneously addresses the high background noise and low detection signal that limit conventional widefield fluorescent imaging. To demonstrate the potential of this approach, the fluorescent molecule sensor is conjugated to a human epidermal growth factor receptor 2 (HER2) specific antibody and used to investigate the spatio-temporal behavior of HER2 clustering in the membrane of HER2-overexpressing breast cancer cells. Notably, the disruption of HER2 clusters in response to an FDA-approved monoclonal antibody therapeutic (Trastuzumab) is successfully detected using a simple widefield fluorescent microscope. While the sensor demonstrated here is optimized for sensing HER2 clustering, it is an easily adaptable platform. Moreover, given the compatibility with widefield imaging, the system has the potential to be used with high-throughput imaging techniques, accelerating investigations into membrane protein spatio-temporal organization. |
0810.4683 | Ganna Rozhnova | Ganna Rozhnova and Ana Nunes | Fluctuations and oscillations in a simple epidemic model | acknowledgments added; a typo in the discussion that follows Eq. (3)
is corrected; | Phys. Rev. E 79, 041922 (2009) | 10.1103/PhysRevE.79.041922 | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that the simplest stochastic epidemiological models with spatial
correlations exhibit two types of oscillatory behaviour in the endemic phase.
In a large parameter range, the oscillations are due to resonant amplification
of stochastic fluctuations, a general mechanism first reported for
predator-prey dynamics. In a narrow range of parameters that includes many
infectious diseases which confer long lasting immunity the oscillations persist
for infinite populations. This effect is apparent in simulations of the
stochastic process in systems of variable size, and can be understood from the
phase diagram of the deterministic pair approximation equations. The two
mechanisms combined play a central role in explaining the ubiquity of
oscillatory behaviour in real data and in simulation results of epidemic and
other related models.
| [
{
"created": "Sun, 26 Oct 2008 11:24:43 GMT",
"version": "v1"
},
{
"created": "Wed, 7 Jan 2009 00:35:25 GMT",
"version": "v2"
},
{
"created": "Tue, 2 Jun 2009 17:18:11 GMT",
"version": "v3"
}
] | 2009-06-02 | [
[
"Rozhnova",
"Ganna",
""
],
[
"Nunes",
"Ana",
""
]
] | We show that the simplest stochastic epidemiological models with spatial correlations exhibit two types of oscillatory behaviour in the endemic phase. In a large parameter range, the oscillations are due to resonant amplification of stochastic fluctuations, a general mechanism first reported for predator-prey dynamics. In a narrow range of parameters that includes many infectious diseases which confer long lasting immunity the oscillations persist for infinite populations. This effect is apparent in simulations of the stochastic process in systems of variable size, and can be understood from the phase diagram of the deterministic pair approximation equations. The two mechanisms combined play a central role in explaining the ubiquity of oscillatory behaviour in real data and in simulation results of epidemic and other related models. |
2403.05497 | Pankaj Mehta | Wenping Cui, Robert Marsland III, and Pankaj Mehta | Les Houches Lectures on Community Ecology: From Niche Theory to
Statistical Mechanics | 48 pages, 9 figures, Les Houches Theoretical Biophysics Summer School
2023 | null | null | null | q-bio.PE cond-mat.dis-nn cond-mat.stat-mech | http://creativecommons.org/licenses/by/4.0/ | Ecosystems are among the most interesting and well-studied examples of
self-organized complex systems. Community ecology, the study of how species
interact with each other and the environment, has a rich tradition. Over the
last few years, there has been a growing theoretical and experimental interest
in these problems from the physics and quantitative biology communities. Here,
we give an overview of community ecology, highlighting the deep connections
between ecology and statistical physics. We start by introducing the two
classes of mathematical models that have served as the workhorses of community
ecology: Consumer Resource Models (CRM) and the generalized Lotka-Volterra
models (GLV). We place a special emphasis on graphical methods and general
principles. We then review recent works showing a deep and surprising
connection between ecological dynamics and constrained optimization. We then
shift our focus by analyzing these same models in "high-dimensions" (i.e. in
the limit where the number of species and resources in the ecosystem becomes
large) and discuss how such complex ecosystems can be analyzed using methods
from the statistical physics of disordered systems such as the cavity method
and Random Matrix Theory.
| [
{
"created": "Fri, 8 Mar 2024 18:13:06 GMT",
"version": "v1"
}
] | 2024-03-11 | [
[
"Cui",
"Wenping",
""
],
[
"Marsland",
"Robert",
"III"
],
[
"Mehta",
"Pankaj",
""
]
] | Ecosystems are among the most interesting and well-studied examples of self-organized complex systems. Community ecology, the study of how species interact with each other and the environment, has a rich tradition. Over the last few years, there has been a growing theoretical and experimental interest in these problems from the physics and quantitative biology communities. Here, we give an overview of community ecology, highlighting the deep connections between ecology and statistical physics. We start by introducing the two classes of mathematical models that have served as the workhorses of community ecology: Consumer Resource Models (CRM) and the generalized Lotka-Volterra models (GLV). We place a special emphasis on graphical methods and general principles. We then review recent works showing a deep and surprising connection between ecological dynamics and constrained optimization. We then shift our focus by analyzing these same models in "high-dimensions" (i.e. in the limit where the number of species and resources in the ecosystem becomes large) and discuss how such complex ecosystems can be analyzed using methods from the statistical physics of disordered systems such as the cavity method and Random Matrix Theory. |
1212.0658 | Diana David-Rus | Diana David-Rus | Protein Synthesis- Degradation, A Stochastic Approach-part I | add references, update text | null | null | null | q-bio.QM math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we study a protein synthesis degradation process by defining a
general mathematical model. Using generating function technique we present a
method that allows exact calculation of joint probability distribution of
protein copies in a cell for a two dimensional birth-death process with
interaction. We discuss the model in steady state for a particular choice of
transition rules and find exact solutions.
| [
{
"created": "Tue, 4 Dec 2012 10:01:46 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Nov 2013 18:08:26 GMT",
"version": "v2"
}
] | 2013-11-11 | [
[
"David-Rus",
"Diana",
""
]
] | In this work, we study a protein synthesis degradation process by defining a general mathematical model. Using generating function technique we present a method that allows exact calculation of joint probability distribution of protein copies in a cell for a two dimensional birth-death process with interaction. We discuss the model in steady state for a particular choice of transition rules and find exact solutions. |
1808.00675 | Zhaofei Yu | Zhaofei Yu, Yonghong Tian, Tiejun Huang, Jian K. Liu | Winner-Take-All as Basic Probabilistic Inference Unit of Neuronal
Circuits | 10 pages, 4 figures | null | null | null | q-bio.NC cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Experimental observations of neuroscience suggest that the brain is working a
probabilistic way when computing information with uncertainty. This processing
could be modeled as Bayesian inference. However, it remains unclear how
Bayesian inference could be implemented at the level of neuronal circuits of
the brain. In this study, we propose a novel general-purpose neural
implementation of probabilistic inference based on a ubiquitous network of
cortical microcircuits, termed winner-take-all (WTA) circuit. We show that each
WTA circuit could encode the distribution of states defined on a variable. By
connecting multiple WTA circuits together, the joint distribution can be
represented for arbitrary probabilistic graphical models. Moreover, we prove
that the neural dynamics of WTA circuit is able to implement one of the most
powerful inference methods in probabilistic graphical models, mean-field
inference. We show that the synaptic drive of each spiking neuron in the WTA
circuit encodes the marginal probability of the variable in each state, and the
firing probability (or firing rate) of each neuron is proportional to the
marginal probability. Theoretical analysis and experimental results demonstrate
that the WTA circuits can get comparable inference result as mean-field
approximation. Taken together, our results suggest that the WTA circuit could
be seen as the minimal inference unit of neuronal circuits.
| [
{
"created": "Thu, 2 Aug 2018 05:56:30 GMT",
"version": "v1"
}
] | 2018-08-03 | [
[
"Yu",
"Zhaofei",
""
],
[
"Tian",
"Yonghong",
""
],
[
"Huang",
"Tiejun",
""
],
[
"Liu",
"Jian K.",
""
]
] | Experimental observations of neuroscience suggest that the brain is working a probabilistic way when computing information with uncertainty. This processing could be modeled as Bayesian inference. However, it remains unclear how Bayesian inference could be implemented at the level of neuronal circuits of the brain. In this study, we propose a novel general-purpose neural implementation of probabilistic inference based on a ubiquitous network of cortical microcircuits, termed winner-take-all (WTA) circuit. We show that each WTA circuit could encode the distribution of states defined on a variable. By connecting multiple WTA circuits together, the joint distribution can be represented for arbitrary probabilistic graphical models. Moreover, we prove that the neural dynamics of WTA circuit is able to implement one of the most powerful inference methods in probabilistic graphical models, mean-field inference. We show that the synaptic drive of each spiking neuron in the WTA circuit encodes the marginal probability of the variable in each state, and the firing probability (or firing rate) of each neuron is proportional to the marginal probability. Theoretical analysis and experimental results demonstrate that the WTA circuits can get comparable inference result as mean-field approximation. Taken together, our results suggest that the WTA circuit could be seen as the minimal inference unit of neuronal circuits. |
1612.07430 | William McFadden | William M. McFadden, Patrick M. McCall, Edwin M. Munro | Filament turnover is essential for continuous long range contractile
flow in a model actomyosin cortex | null | null | null | null | q-bio.SC cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we develop and analyze a minimal model for a 2D network of
cross-linked actin filaments and myosin motors, representing the cortical
cytoskeleton of eukaryotic cells. We implement coarse-grained representations
of force production by myosin motors and stress dissipation through an
effective cross-link friction and filament turnover. We use this model to
characterize how the sustained production of active stress, and the steady
dissipation of elastic stress, depend individually on motor activity, effective
cross-link friction and filament turnover. Then we combine these results to
gain insights into how microscopic network parameters control steady state flow
produced by asymmetric distributions of motor activity. Our results provide a
framework for understanding how local modulation of microscopic interactions
within contractile networks control macroscopic quantities like active stress
and effective viscosity to control cortical deformation and flow at cellular
scales.
| [
{
"created": "Thu, 22 Dec 2016 03:34:07 GMT",
"version": "v1"
}
] | 2016-12-23 | [
[
"McFadden",
"William M.",
""
],
[
"McCall",
"Patrick M.",
""
],
[
"Munro",
"Edwin M.",
""
]
] | In this paper, we develop and analyze a minimal model for a 2D network of cross-linked actin filaments and myosin motors, representing the cortical cytoskeleton of eukaryotic cells. We implement coarse-grained representations of force production by myosin motors and stress dissipation through an effective cross-link friction and filament turnover. We use this model to characterize how the sustained production of active stress, and the steady dissipation of elastic stress, depend individually on motor activity, effective cross-link friction and filament turnover. Then we combine these results to gain insights into how microscopic network parameters control steady state flow produced by asymmetric distributions of motor activity. Our results provide a framework for understanding how local modulation of microscopic interactions within contractile networks control macroscopic quantities like active stress and effective viscosity to control cortical deformation and flow at cellular scales. |
2012.13405 | Jiseon Min | Jiseon Min and Ariel Amir | A transport approach to relate asymmetric protein segregation and
population growth | 21 pages, 11 figures, submitted to JStat | null | 10.1088/1742-5468/ac1262 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many unicellular organisms allocate their key proteins asymmetrically between
the mother and daughter cells, especially in a stressed environment. A recent
theoretical model is able to predict when the asymmetry in segregation of key
proteins enhances the population fitness, extrapolating the solution at two
limits where the segregation is perfectly asymmetric (asymmetry $a$ = 1) and
when the asymmetry is small ($0 \leq a \ll 1$). We generalize the model by
introducing stochasticity and use a transport equation to obtain a
self-consistent equation for the population growth rate and the distribution of
the amount of key proteins. We provide two ways of solving the self-consistent
equation: numerically by updating the solution for the self-consistent equation
iteratively and analytically by expanding moments of the distribution. With
these more powerful tools, we can extend the previous model by Lin et al. to
include stochasticity to the segregation asymmetry. We show the stochastic
model is equivalent to the deterministic one with a modified effective
asymmetry parameter ($a_{\rm eff}$). We discuss the biological implication of
our models and compare with other theoretical models.
| [
{
"created": "Thu, 24 Dec 2020 19:00:00 GMT",
"version": "v1"
},
{
"created": "Wed, 30 Dec 2020 01:24:20 GMT",
"version": "v2"
},
{
"created": "Sat, 1 May 2021 19:10:00 GMT",
"version": "v3"
}
] | 2021-08-11 | [
[
"Min",
"Jiseon",
""
],
[
"Amir",
"Ariel",
""
]
] | Many unicellular organisms allocate their key proteins asymmetrically between the mother and daughter cells, especially in a stressed environment. A recent theoretical model is able to predict when the asymmetry in segregation of key proteins enhances the population fitness, extrapolating the solution at two limits where the segregation is perfectly asymmetric (asymmetry $a$ = 1) and when the asymmetry is small ($0 \leq a \ll 1$). We generalize the model by introducing stochasticity and use a transport equation to obtain a self-consistent equation for the population growth rate and the distribution of the amount of key proteins. We provide two ways of solving the self-consistent equation: numerically by updating the solution for the self-consistent equation iteratively and analytically by expanding moments of the distribution. With these more powerful tools, we can extend the previous model by Lin et al. to include stochasticity to the segregation asymmetry. We show the stochastic model is equivalent to the deterministic one with a modified effective asymmetry parameter ($a_{\rm eff}$). We discuss the biological implication of our models and compare with other theoretical models. |
1503.00385 | Guo-Wei Wei | Jin Kyoung Park, Kelin Xia and Guo-Wei We | Atomic Scale Design and Three-Dimensional Simulation of Ionic Diffusive
Nanofluidic Channels | 20 figures. arXiv admin note: text overlap with arXiv:1412.0176 by
other authors | null | null | null | q-bio.QM cond-mat.soft physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advance in nanotechnology has led to rapid advances in nanofluidics,
which has been established as a reliable means for a wide variety of
applications, including molecular separation, detection, crystallization and
biosynthesis. Although atomic and molecular level consideration is a key
ingredient in experimental design and fabrication of nanfluidic systems, atomic
and molecular modeling of nanofluidics is rare and most simulations at
nanoscale are restricted to one- or two-dimensions in the literature, to our
best knowledge. The present work introduces atomic scale design and
three-dimensional (3D) simulation of ionic diffusive nanofluidic systems. We
propose a variational multiscale framework to represent the nanochannel in
discrete atomic and/or molecular detail while describe the ionic solution by
continuum. Apart from the major electrostatic and entropic effects, the
non-electrostatic interactions between the channel and solution, and among
solvent molecules are accounted in our modeling. We derive generalized
Poisson-Nernst-Planck (PNP) equations for nanofluidic systems. Mathematical
algorithms, such as Dirichlet to Neumann mapping and the matched interface and
boundary (MIB) methods are developed to rigorously solve the aforementioned
equations to the second-order accuracy in 3D realistic settings. Three ionic
diffusive nanofluidic systems, including a negatively charged nanochannel, a
bipolar nanochannel and a double-well nanochannel are designed to investigate
the impact of atomic charges to channel current, density distribution and
electrostatic potential. Numerical findings, such as gating, ion depletion and
inversion, are in good agreements with those from experimental measurements and
numerical simulations in the literature.
| [
{
"created": "Mon, 2 Mar 2015 01:11:59 GMT",
"version": "v1"
}
] | 2015-03-03 | [
[
"Park",
"Jin Kyoung",
""
],
[
"Xia",
"Kelin",
""
],
[
"We",
"Guo-Wei",
""
]
] | Recent advance in nanotechnology has led to rapid advances in nanofluidics, which has been established as a reliable means for a wide variety of applications, including molecular separation, detection, crystallization and biosynthesis. Although atomic and molecular level consideration is a key ingredient in experimental design and fabrication of nanfluidic systems, atomic and molecular modeling of nanofluidics is rare and most simulations at nanoscale are restricted to one- or two-dimensions in the literature, to our best knowledge. The present work introduces atomic scale design and three-dimensional (3D) simulation of ionic diffusive nanofluidic systems. We propose a variational multiscale framework to represent the nanochannel in discrete atomic and/or molecular detail while describe the ionic solution by continuum. Apart from the major electrostatic and entropic effects, the non-electrostatic interactions between the channel and solution, and among solvent molecules are accounted in our modeling. We derive generalized Poisson-Nernst-Planck (PNP) equations for nanofluidic systems. Mathematical algorithms, such as Dirichlet to Neumann mapping and the matched interface and boundary (MIB) methods are developed to rigorously solve the aforementioned equations to the second-order accuracy in 3D realistic settings. Three ionic diffusive nanofluidic systems, including a negatively charged nanochannel, a bipolar nanochannel and a double-well nanochannel are designed to investigate the impact of atomic charges to channel current, density distribution and electrostatic potential. Numerical findings, such as gating, ion depletion and inversion, are in good agreements with those from experimental measurements and numerical simulations in the literature. |
0706.1102 | Piotr Szymczak | P. Szymczak and Marek Cieplak | Influence of Hydrodynamic Interactions on Mechanical Unfolding of
Proteins | to be published in Journal of Physics: Condensed Matter | null | 10.1088/0953-8984/19/28/285224 | null | q-bio.BM | null | We incorporate hydrodynamic interactions in a structure-based model of
ubiquitin and demonstrate that the hydrodynamic coupling may reduce the peak
force when stretching the protein at constant speed, especially at larger
speeds. Hydrodynamic interactions are also shown to facilitate unfolding at
constant force and inhibit stretching by fluid flows.
| [
{
"created": "Fri, 8 Jun 2007 01:04:03 GMT",
"version": "v1"
}
] | 2015-05-13 | [
[
"Szymczak",
"P.",
""
],
[
"Cieplak",
"Marek",
""
]
] | We incorporate hydrodynamic interactions in a structure-based model of ubiquitin and demonstrate that the hydrodynamic coupling may reduce the peak force when stretching the protein at constant speed, especially at larger speeds. Hydrodynamic interactions are also shown to facilitate unfolding at constant force and inhibit stretching by fluid flows. |
1807.04334 | Vernon Lawhern | Jonathan R. McDaniel, Stephen M. Gordon, Amelia J. Solon, Vernon J.
Lawhern | Analyzing P300 Distractors for Target Reconstruction | 4 pages, 3 figures | 40th Annual International Conference of the IEEE Engineering in
Medicine and Biology Society (EMBC), Honolulu, HI, USA, 2018, pp. 2543-2546 | 10.1109/EMBC.2018.8512854 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | P300-based brain-computer interfaces (BCIs) are often trained per-user and
per-application space. Training such models requires ground truth knowledge of
target and non-target stimulus categories during model training, which imparts
bias into the model. Additionally, not all non-targets are created equal; some
may contain visual features that resemble targets or may otherwise be visually
salient. Current research has indicated that non-target distractors may elicit
attenuated P300 responses based on the perceptual similarity of these
distractors to the target category. To minimize this bias, and enable a more
nuanced analysis, we use a generalized BCI approach that is fit to neither user
nor task. We do not seek to improve the overall accuracy of the BCI with our
generalized approach; we instead demonstrate the utility of our approach for
identifying target-related image features. When combined with other intelligent
agents, such as computer vision systems, the performance of the generalized
model equals that of the user-specific models, without any user specific data.
| [
{
"created": "Wed, 11 Jul 2018 20:07:27 GMT",
"version": "v1"
}
] | 2018-10-31 | [
[
"McDaniel",
"Jonathan R.",
""
],
[
"Gordon",
"Stephen M.",
""
],
[
"Solon",
"Amelia J.",
""
],
[
"Lawhern",
"Vernon J.",
""
]
] | P300-based brain-computer interfaces (BCIs) are often trained per-user and per-application space. Training such models requires ground truth knowledge of target and non-target stimulus categories during model training, which imparts bias into the model. Additionally, not all non-targets are created equal; some may contain visual features that resemble targets or may otherwise be visually salient. Current research has indicated that non-target distractors may elicit attenuated P300 responses based on the perceptual similarity of these distractors to the target category. To minimize this bias, and enable a more nuanced analysis, we use a generalized BCI approach that is fit to neither user nor task. We do not seek to improve the overall accuracy of the BCI with our generalized approach; we instead demonstrate the utility of our approach for identifying target-related image features. When combined with other intelligent agents, such as computer vision systems, the performance of the generalized model equals that of the user-specific models, without any user specific data. |
1511.00411 | Pete Latham | Alexander Lerchner and Peter E. Latham | A unifying framework for understanding state-dependent network dynamics
in cortex | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Activity in neocortex exhibits a range of behaviors, from irregular to
temporally precise, and from weakly to strongly correlated. So far there has
been no single theoretical framework that could explain all these behaviors,
leaving open the possibility that they are a signature of radically different
mechanisms. Here, we suggest that this is not the case. Instead, we show that a
single theory can account for a broad spectrum of experimental observations,
including specifics such as the fine temporal details of subthreshold
cross-correlations. For the model underlying our theory, we need only assume a
small number of well-established properties common to all local cortical
networks. When these assumptions are combined with realistically structured
input, they produce exactly the repertoire of behaviors that is observed
experimentally, and lead to a number of testable predictions.
| [
{
"created": "Mon, 2 Nov 2015 08:47:17 GMT",
"version": "v1"
}
] | 2015-11-03 | [
[
"Lerchner",
"Alexander",
""
],
[
"Latham",
"Peter E.",
""
]
] | Activity in neocortex exhibits a range of behaviors, from irregular to temporally precise, and from weakly to strongly correlated. So far there has been no single theoretical framework that could explain all these behaviors, leaving open the possibility that they are a signature of radically different mechanisms. Here, we suggest that this is not the case. Instead, we show that a single theory can account for a broad spectrum of experimental observations, including specifics such as the fine temporal details of subthreshold cross-correlations. For the model underlying our theory, we need only assume a small number of well-established properties common to all local cortical networks. When these assumptions are combined with realistically structured input, they produce exactly the repertoire of behaviors that is observed experimentally, and lead to a number of testable predictions. |
1803.07312 | Simon Pearce | Simon P Pearce and Matthias Heil and Oliver E Jensen and Gareth W
Jones and Andreas Prokop | Curvature-sensitive kinesin binding can explain microtubule ring
formation and reveals chaotic dynamics in a mathematical model | 24 pages, 6 figures. Accepted for publication in the Bulletin of
Mathematical Biology | null | null | null | q-bio.SC nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Microtubules are filamentous tubular protein polymers which are essential for
a range of cellular behaviour, and are generally straight over micron length
scales. However, in some gliding assays, where microtubules move over a carpet
of molecular motors, individual microtubules can also form tight arcs or rings,
even in the absence of crosslinking proteins. Understanding this phenomenon may
provide important explanations for similar highly curved microtubules which can
be found in nerve cells undergoing neurodegeneration. We propose a model for
gliding assays where the kinesins moving the microtubules over the surface
induce ring formation through differential binding, substantiated by recent
findings that a mutant version of the motor protein kinesin applied in solution
is able to lock-in microtubule curvature. For certain parameter regimes, our
model predicts that both straight and curved microtubules can exist
simultaneously as stable steady-states, as has been seen experimentally.
Additionally, unsteady solutions are found, where a wave of differential
binding propagates down the microtubule as it glides across the surface, which
can lead to chaotic motion. Whilst this model explains two-dimensional
microtubule behaviour in an experimental gliding assay, it has the potential to
be adapted to explain pathological curling in nerve cells.
| [
{
"created": "Tue, 20 Mar 2018 09:10:15 GMT",
"version": "v1"
},
{
"created": "Wed, 1 Aug 2018 12:07:14 GMT",
"version": "v2"
}
] | 2018-08-02 | [
[
"Pearce",
"Simon P",
""
],
[
"Heil",
"Matthias",
""
],
[
"Jensen",
"Oliver E",
""
],
[
"Jones",
"Gareth W",
""
],
[
"Prokop",
"Andreas",
""
]
] | Microtubules are filamentous tubular protein polymers which are essential for a range of cellular behaviour, and are generally straight over micron length scales. However, in some gliding assays, where microtubules move over a carpet of molecular motors, individual microtubules can also form tight arcs or rings, even in the absence of crosslinking proteins. Understanding this phenomenon may provide important explanations for similar highly curved microtubules which can be found in nerve cells undergoing neurodegeneration. We propose a model for gliding assays where the kinesins moving the microtubules over the surface induce ring formation through differential binding, substantiated by recent findings that a mutant version of the motor protein kinesin applied in solution is able to lock-in microtubule curvature. For certain parameter regimes, our model predicts that both straight and curved microtubules can exist simultaneously as stable steady-states, as has been seen experimentally. Additionally, unsteady solutions are found, where a wave of differential binding propagates down the microtubule as it glides across the surface, which can lead to chaotic motion. Whilst this model explains two-dimensional microtubule behaviour in an experimental gliding assay, it has the potential to be adapted to explain pathological curling in nerve cells. |
1806.07903 | Gino Del Ferraro | Gino Del Ferraro, Andrea Moreno, Byungjoon Min, Flaviano Morone,
\'Ursula P\'erez-Ram\'irez, Laura P\'erez-Cervera, Lucas C. Parra, Andrei
Holodny, Santiago Canals, Hern\'an A. Makse | Finding influential nodes for integration in brain networks using
optimal percolation theory | 20 pages, 6 figures, Supplementary Info | Nature Communications, 9, 2274, (2018) | 10.1038/s41467-018-04718-3 | null | q-bio.NC cond-mat.dis-nn physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Global integration of information in the brain results from complex
interactions of segregated brain networks. Identifying the most influential
neuronal populations that efficiently bind these networks is a fundamental
problem of systems neuroscience. Here we apply optimal percolation theory and
pharmacogenetic interventions in-vivo to predict and subsequently target nodes
that are essential for global integration of a memory network in rodents. The
theory predicts that integration in the memory network is mediated by a set of
low-degree nodes located in the nucleus accumbens. This result is confirmed
with pharmacogenetic inactivation of the nucleus accumbens, which eliminates
the formation of the memory network, while inactivations of other brain areas
leave the network intact. Thus, optimal percolation theory predicts essential
nodes in brain networks. This could be used to identify targets of
interventions to modulate brain function.
| [
{
"created": "Wed, 20 Jun 2018 18:01:10 GMT",
"version": "v1"
}
] | 2018-06-22 | [
[
"Del Ferraro",
"Gino",
""
],
[
"Moreno",
"Andrea",
""
],
[
"Min",
"Byungjoon",
""
],
[
"Morone",
"Flaviano",
""
],
[
"Pérez-Ramírez",
"Úrsula",
""
],
[
"Pérez-Cervera",
"Laura",
""
],
[
"Parra",
"Lucas C.",
""
],
[
"Holodny",
"Andrei",
""
],
[
"Canals",
"Santiago",
""
],
[
"Makse",
"Hernán A.",
""
]
] | Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here we apply optimal percolation theory and pharmacogenetic interventions in-vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function. |
q-bio/0406040 | Yamir Moreno Vega | Jesus Gomez-Gardenes, Yamir Moreno, and Luis M. Floria | Michaelis-Menten Dynamics in Complex Heterogeneous Networks | Paper enlarged and modified, including the title. Some problems with
the pdf were detected in the past. If they persist, please ask for the pdf by
e-mailing yamir(at_no_spam)unizar.es. Version to appear in Physica A | Physica A, 352, 265-81 (2005) | 10.1016/j.physa.2005.01.016 | null | q-bio.MN cond-mat.stat-mech | null | Biological networks have been recently found to exhibit many topological
properties of the so-called complex networks. It has been reported that they
are, in general, both highly skewed and directed. In this paper, we report on
the dynamics of a Michaelis-Menten like model when the topological features of
the underlying network resemble those of real biological networks.
Specifically, instead of using a random graph topology, we deal with a complex
heterogeneous network characterized by a power-law degree distribution coupled
to a continuous dynamics for each network's component. The dynamics of the
model is very rich and stationary, periodic and chaotic states are observed
upon variation of the model's parameters. We characterize these states
numerically and report on several quantities such as the system's phase diagram
and size distributions of clusters of stationary, periodic and chaotic nodes.
The results are discussed in view of recent debate about the ubiquity of
complex networks in nature and on the basis of several biological processes
that can be well described by the dynamics studied.
| [
{
"created": "Fri, 18 Jun 2004 20:07:13 GMT",
"version": "v1"
},
{
"created": "Wed, 23 Jun 2004 15:35:02 GMT",
"version": "v2"
},
{
"created": "Mon, 31 Jan 2005 18:01:21 GMT",
"version": "v3"
}
] | 2007-05-23 | [
[
"Gomez-Gardenes",
"Jesus",
""
],
[
"Moreno",
"Yamir",
""
],
[
"Floria",
"Luis M.",
""
]
] | Biological networks have been recently found to exhibit many topological properties of the so-called complex networks. It has been reported that they are, in general, both highly skewed and directed. In this paper, we report on the dynamics of a Michaelis-Menten like model when the topological features of the underlying network resemble those of real biological networks. Specifically, instead of using a random graph topology, we deal with a complex heterogeneous network characterized by a power-law degree distribution coupled to a continuous dynamics for each network's component. The dynamics of the model is very rich and stationary, periodic and chaotic states are observed upon variation of the model's parameters. We characterize these states numerically and report on several quantities such as the system's phase diagram and size distributions of clusters of stationary, periodic and chaotic nodes. The results are discussed in view of recent debate about the ubiquity of complex networks in nature and on the basis of several biological processes that can be well described by the dynamics studied. |
0912.5500 | William Bialek | Aleksandra M. Walczak, Gapser Tkacik and William Bialek | Optimizing information flow in small genetic networks. II: Feed forward
interactions | null | null | 10.1103/PhysRevE.81.041905 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Central to the functioning of a living cell is its ability to control the
readout or expression of information encoded in the genome. In many cases, a
single transcription factor protein activates or represses the expression of
many genes. As the concentration of the transcription factor varies, the target
genes thus undergo correlated changes, and this redundancy limits the ability
of the cell to transmit information about input signals. We explore how
interactions among the target genes can reduce this redundancy and optimize
information transmission. Our discussion builds on recent work [Tkacik et al,
Phys Rev E 80, 031920 (2009)], and there are connections to much earlier work
on the role of lateral inhibition in enhancing the efficiency of information
transmission in neural circuits; for simplicity we consider here the case where
the interactions have a feed forward structure, with no loops. Even with this
limitation, the networks that optimize information transmission have a
structure reminiscent of the networks found in real biological systems.
| [
{
"created": "Wed, 30 Dec 2009 18:14:33 GMT",
"version": "v1"
}
] | 2013-05-29 | [
[
"Walczak",
"Aleksandra M.",
""
],
[
"Tkacik",
"Gapser",
""
],
[
"Bialek",
"William",
""
]
] | Central to the functioning of a living cell is its ability to control the readout or expression of information encoded in the genome. In many cases, a single transcription factor protein activates or represses the expression of many genes. As the concentration of the transcription factor varies, the target genes thus undergo correlated changes, and this redundancy limits the ability of the cell to transmit information about input signals. We explore how interactions among the target genes can reduce this redundancy and optimize information transmission. Our discussion builds on recent work [Tkacik et al, Phys Rev E 80, 031920 (2009)], and there are connections to much earlier work on the role of lateral inhibition in enhancing the efficiency of information transmission in neural circuits; for simplicity we consider here the case where the interactions have a feed forward structure, with no loops. Even with this limitation, the networks that optimize information transmission have a structure reminiscent of the networks found in real biological systems. |
1406.5211 | Charles Davis | Charles C. Davis, Hanno Schaefer, Brad R. Ruhfel, Michael J. Donoghue,
and Erika J. Edwards | Climates and clades: biased methods, biased results | 7 pages, 1 figure | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Thuiller et al. analyzed the consequences of anticipated climate change on
plant, bird, and mammal phylogenetic diversity (PD) across Europe. They
concluded that species loss will not be clade specific across the Tree of Life,
and that there will not be an overall decline in PD across the whole of Europe.
We applaud their attempt to integrate phylogenetic knowledge into scenarios of
future extinction but their analyses raise a series of concerns. We focus here
on their analyses of plants.
| [
{
"created": "Thu, 19 Jun 2014 20:53:13 GMT",
"version": "v1"
}
] | 2014-06-23 | [
[
"Davis",
"Charles C.",
""
],
[
"Schaefer",
"Hanno",
""
],
[
"Ruhfel",
"Brad R.",
""
],
[
"Donoghue",
"Michael J.",
""
],
[
"Edwards",
"Erika J.",
""
]
] | Thuiller et al. analyzed the consequences of anticipated climate change on plant, bird, and mammal phylogenetic diversity (PD) across Europe. They concluded that species loss will not be clade specific across the Tree of Life, and that there will not be an overall decline in PD across the whole of Europe. We applaud their attempt to integrate phylogenetic knowledge into scenarios of future extinction but their analyses raise a series of concerns. We focus here on their analyses of plants. |
q-bio/0611092 | Wentian Li | Young Ju Suh, Wentian Li | Genotype-based Case-Control Analysis, Violation of Hardy-Weinberg
Equilibrium, and Phase Diagrams | 10 pages, 2 figures | Proceedings of the 5th Asia-Pacific Bioinformatics Conference,
eds. David Shankoff, Lusheng Wang, Francis Chin, pp.185-194 (Imperial College
Press, 2007) | null | null | q-bio.QM q-bio.SC | null | We study in detail a particular statistical method in genetic case-control
analysis, labeled "genotype-based association", in which the two test results
from assuming dominant and recessive model are combined in one optimal output.
This method differs both from the allele-based association which artificially
doubles the sample size, and the direct chi-square test on 3-by-2 contingency
table which may overestimate the degree of freedom. We conclude that the
comparative advantage (or disadvantage) of the genotype-based test over the
allele-based test mainly depends on two parameters, the allele frequency
difference delta and the Hardy-Weinberg disequilibrium coefficient difference
delta_epsilon. Six different situations, called "phases", characterized by the
two X^2 test statistics in allele-based and genotype-based test, are well
separated in the phase diagram parameterized by delta and delta_epsilon. For
two major groups of phases, a single parameter theta = tan^-1
(delta/delta_epsilon) is able to achieves an almost perfect phase separation.
We also applied the analytic result to several types of disease models. It is
shown that for dominant and additive models, genotype-based tests are favored
over allele-based tests.
| [
{
"created": "Tue, 28 Nov 2006 17:48:30 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Mar 2008 14:52:54 GMT",
"version": "v2"
}
] | 2008-03-12 | [
[
"Suh",
"Young Ju",
""
],
[
"Li",
"Wentian",
""
]
] | We study in detail a particular statistical method in genetic case-control analysis, labeled "genotype-based association", in which the two test results from assuming dominant and recessive model are combined in one optimal output. This method differs both from the allele-based association which artificially doubles the sample size, and the direct chi-square test on 3-by-2 contingency table which may overestimate the degree of freedom. We conclude that the comparative advantage (or disadvantage) of the genotype-based test over the allele-based test mainly depends on two parameters, the allele frequency difference delta and the Hardy-Weinberg disequilibrium coefficient difference delta_epsilon. Six different situations, called "phases", characterized by the two X^2 test statistics in allele-based and genotype-based test, are well separated in the phase diagram parameterized by delta and delta_epsilon. For two major groups of phases, a single parameter theta = tan^-1 (delta/delta_epsilon) is able to achieves an almost perfect phase separation. We also applied the analytic result to several types of disease models. It is shown that for dominant and additive models, genotype-based tests are favored over allele-based tests. |
2402.19095 | Yanlin Zhou | Yanlin Zhou, Kai Tan, Xinyu Shen, Zheng He, Haotian Zheng | A Protein Structure Prediction Approach Leveraging Transformer and CNN
Integration | null | null | null | null | q-bio.BM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proteins are essential for life, and their structure determines their
function. The protein secondary structure is formed by the folding of the
protein primary structure, and the protein tertiary structure is formed by the
bending and folding of the secondary structure. Therefore, the study of protein
secondary structure is very helpful to the overall understanding of protein
structure. Although the accuracy of protein secondary structure prediction has
continuously improved with the development of machine learning and deep
learning, progress in the field of protein structure prediction, unfortunately,
remains insufficient to meet the large demand for protein information.
Therefore, based on the advantages of deep learning-based methods in feature
extraction and learning ability, this paper adopts a two-dimensional fusion
deep neural network model, DstruCCN, which uses Convolutional Neural Networks
(CCN) and a supervised Transformer protein language model for single-sequence
protein structure prediction. The training features of the two are combined to
predict the protein Transformer binding site matrix, and then the
three-dimensional structure is reconstructed using energy minimization.
| [
{
"created": "Thu, 29 Feb 2024 12:24:20 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Mar 2024 05:30:10 GMT",
"version": "v2"
}
] | 2024-03-11 | [
[
"Zhou",
"Yanlin",
""
],
[
"Tan",
"Kai",
""
],
[
"Shen",
"Xinyu",
""
],
[
"He",
"Zheng",
""
],
[
"Zheng",
"Haotian",
""
]
] | Proteins are essential for life, and their structure determines their function. The protein secondary structure is formed by the folding of the protein primary structure, and the protein tertiary structure is formed by the bending and folding of the secondary structure. Therefore, the study of protein secondary structure is very helpful to the overall understanding of protein structure. Although the accuracy of protein secondary structure prediction has continuously improved with the development of machine learning and deep learning, progress in the field of protein structure prediction, unfortunately, remains insufficient to meet the large demand for protein information. Therefore, based on the advantages of deep learning-based methods in feature extraction and learning ability, this paper adopts a two-dimensional fusion deep neural network model, DstruCCN, which uses Convolutional Neural Networks (CCN) and a supervised Transformer protein language model for single-sequence protein structure prediction. The training features of the two are combined to predict the protein Transformer binding site matrix, and then the three-dimensional structure is reconstructed using energy minimization. |
1210.6554 | Antonio Giuliano Zippo Dr. | Antonio G. Zippo, Riccardo Storchi, Giuliana Gelsomino, Sara Nencini,
Gian Carlo Caramenti, Maurizio Valente, Gabriele E. M. Biella | Neuronal functional connectivity among multiple areas of the rat
somatosensory system during spontaneous and evoked activities | null | PLoS Computational Biology 06/2013 | 10.1371/journal.pcbi.1003104 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Small-World Networks (SWNs) represent a fundamental model for the
comprehension of many complex man-made and biological networks. In the central
nervous system, SWN models have been shown to fit well both anatomical and
functional maps at the macroscopic level. However the functional microscopic
level, where the nodes of a network are composed of single neurons, is still
poorly understood. At this level, although recent evidences suggest that
functional connectivity maps exhibit small-world organization, it is not known
whether and how these maps, distributed in multiple brain regions, change
across different conditions. We addressed these questions by simultaneous
multi-array extracellular recordings in three brain regions diversely involved
in somatosensory information processing: the ventropostero-lateral thalamic
nuclei (VPL), the primary somatosensory cortex (S1) and the centro-median
thalamic nuclei (CM). From both spike and Local Field Potential (LFP)
recordings, we estimated the functional connectivity maps by using the
Normalized Compression Similarity (spikes) and the Phase Synchrony (LFPs).
Then, by using graph-theoretical statistics, we characterized the functional
map topology both during spontaneous activity and sensory stimulation. Our main
results show that: (i) spikes and LFPs show SWN organization during spontaneous
activity; (ii) After stimulation onset, while substantial functional map
reconfigurations occur both in spike and LFPs, small-worldness is nonetheless
preserved (iii) The stimulus triggers a significant increase of inter-area LFP
connections without modifying the topology of intra-area functional
connections; (iv) Through computer simulations of the fundamental concept of
cell assemblies, transient groups of activating neurons can be described by
small-world networks.
| [
{
"created": "Wed, 24 Oct 2012 14:50:34 GMT",
"version": "v1"
},
{
"created": "Thu, 17 Jan 2013 11:20:34 GMT",
"version": "v2"
}
] | 2013-06-17 | [
[
"Zippo",
"Antonio G.",
""
],
[
"Storchi",
"Riccardo",
""
],
[
"Gelsomino",
"Giuliana",
""
],
[
"Nencini",
"Sara",
""
],
[
"Caramenti",
"Gian Carlo",
""
],
[
"Valente",
"Maurizio",
""
],
[
"Biella",
"Gabriele E. M.",
""
]
] | Small-World Networks (SWNs) represent a fundamental model for the comprehension of many complex man-made and biological networks. In the central nervous system, SWN models have been shown to fit well both anatomical and functional maps at the macroscopic level. However the functional microscopic level, where the nodes of a network are composed of single neurons, is still poorly understood. At this level, although recent evidences suggest that functional connectivity maps exhibit small-world organization, it is not known whether and how these maps, distributed in multiple brain regions, change across different conditions. We addressed these questions by simultaneous multi-array extracellular recordings in three brain regions diversely involved in somatosensory information processing: the ventropostero-lateral thalamic nuclei (VPL), the primary somatosensory cortex (S1) and the centro-median thalamic nuclei (CM). From both spike and Local Field Potential (LFP) recordings, we estimated the functional connectivity maps by using the Normalized Compression Similarity (spikes) and the Phase Synchrony (LFPs). Then, by using graph-theoretical statistics, we characterized the functional map topology both during spontaneous activity and sensory stimulation. Our main results show that: (i) spikes and LFPs show SWN organization during spontaneous activity; (ii) After stimulation onset, while substantial functional map reconfigurations occur both in spike and LFPs, small-worldness is nonetheless preserved (iii) The stimulus triggers a significant increase of inter-area LFP connections without modifying the topology of intra-area functional connections; (iv) Through computer simulations of the fundamental concept of cell assemblies, transient groups of activating neurons can be described by small-world networks. |
1705.03094 | Xueyang Feng | Weihua Guo, You Xu, and Xueyang Feng | DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome
Sequencing | null | null | null | null | q-bio.GN q-bio.QM | http://creativecommons.org/licenses/by-sa/4.0/ | Life science is entering a new era of petabyte-level sequencing data.
Converting such big data to biological insights represents a huge challenge for
computational analysis. To this end, we developed DeepMetabolism, a
biology-guided deep learning system to predict cell phenotypes from
transcriptomics data. By integrating unsupervised pre-training with supervised
training, DeepMetabolism is able to predict phenotypes with high accuracy
(PCC>0.92), high speed (<30 min for >100 GB data using a single GPU), and high
robustness (tolerate up to 75% noise). We envision DeepMetabolism to bridge the
gap between genotype and phenotype and to serve as a springboard for
applications in synthetic biology and precision medicine.
| [
{
"created": "Mon, 8 May 2017 21:26:07 GMT",
"version": "v1"
}
] | 2017-05-10 | [
[
"Guo",
"Weihua",
""
],
[
"Xu",
"You",
""
],
[
"Feng",
"Xueyang",
""
]
] | Life science is entering a new era of petabyte-level sequencing data. Converting such big data to biological insights represents a huge challenge for computational analysis. To this end, we developed DeepMetabolism, a biology-guided deep learning system to predict cell phenotypes from transcriptomics data. By integrating unsupervised pre-training with supervised training, DeepMetabolism is able to predict phenotypes with high accuracy (PCC>0.92), high speed (<30 min for >100 GB data using a single GPU), and high robustness (tolerate up to 75% noise). We envision DeepMetabolism to bridge the gap between genotype and phenotype and to serve as a springboard for applications in synthetic biology and precision medicine. |
1709.00073 | Bryce Morsky | Bryce Morsky and Chris T. Bauch | Truncation selection and diffusion on lattices | null | null | null | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | Evolutionary games on graphs have been extensively studied. A variety of
graph structures, graph dynamics, and behaviours of replicators have been
explored. These models have primarily been studied in the framework of
facilitation of cooperation, and much previous research has shed light on this
field of study. However, there has been little attention devoted to truncation
selection as most models employ proportional selection (such as in the
replicator equation) or `imitate the best.' Here we systematically explore
truncation selection on periodic square lattices, where replicators below a
fitness threshold are culled and the reproduction probabilities are equal for
all survivors. We employ two variations of this method: independent truncation,
where the threshold is fixed; and dependent truncation, which is a
generalization of `imitate the best.' Further, we explore the effects of
diffusion in our networks in the following orders of operation:
contest-diffusion-offspring (CDO), and diffusion-contest-offspring (DCO). CDO
and DCO frequently facilitate and diminish cooperation, respectively. For
independent truncation, we find three regimes determined by the fitness
threshold: cooperation decreases as we raise the threshold; polymorphisms and
extinction can occur; and the entire population goes extinct. Further, we show
how an intermediate sucker's payoff maximizes cooperation in the DCO
independent truncation model. We find that dependent truncation affects games
differently; lower levels reduce cooperation for the Hawk Dove game and
increase it for the Stag Hunt, and higher levels produce the opposite effects.
We compare these truncation methods to proportional selection, and show that
they can facilitate cooperation. We conclude that truncation selection can
impact the prevalence of cooperation in complex ways, and therefore merit
further study.
| [
{
"created": "Thu, 31 Aug 2017 20:41:29 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Jun 2018 19:25:28 GMT",
"version": "v2"
}
] | 2018-06-29 | [
[
"Morsky",
"Bryce",
""
],
[
"Bauch",
"Chris T.",
""
]
] | Evolutionary games on graphs have been extensively studied. A variety of graph structures, graph dynamics, and behaviours of replicators have been explored. These models have primarily been studied in the framework of facilitation of cooperation, and much previous research has shed light on this field of study. However, there has been little attention devoted to truncation selection as most models employ proportional selection (such as in the replicator equation) or `imitate the best.' Here we systematically explore truncation selection on periodic square lattices, where replicators below a fitness threshold are culled and the reproduction probabilities are equal for all survivors. We employ two variations of this method: independent truncation, where the threshold is fixed; and dependent truncation, which is a generalization of `imitate the best.' Further, we explore the effects of diffusion in our networks in the following orders of operation: contest-diffusion-offspring (CDO), and diffusion-contest-offspring (DCO). CDO and DCO frequently facilitate and diminish cooperation, respectively. For independent truncation, we find three regimes determined by the fitness threshold: cooperation decreases as we raise the threshold; polymorphisms and extinction can occur; and the entire population goes extinct. Further, we show how an intermediate sucker's payoff maximizes cooperation in the DCO independent truncation model. We find that dependent truncation affects games differently; lower levels reduce cooperation for the Hawk Dove game and increase it for the Stag Hunt, and higher levels produce the opposite effects. We compare these truncation methods to proportional selection, and show that they can facilitate cooperation. We conclude that truncation selection can impact the prevalence of cooperation in complex ways, and therefore merit further study. |
q-bio/0611075 | Thierry Rabilloud | Pierre Lescuyer, Jean-Marc Strub, Sylvie Luche, H\'el\`ene Diemer,
Pascal Martinez, Alain Van Dorsselaer, Jo\"el Lunardi, Thierry Rabilloud | Progress in the definition of a reference human mitochondrial proteome | website publisher http://www.interscience.wiley.com | Proteomics 3 (02/2003) 157-67 | 10.1002/pmic.200390024 | null | q-bio.GN | null | Owing to the complexity of higher eukaryotic cells, a complete proteome is
likely to be very difficult to achieve. However, advantage can be taken of the
cell compartmentalization to build organelle proteomes, which can moreover be
viewed as specialized tools to study specifically the biology and "physiology"
of the target organelle. Within this frame, we report here the construction of
the human mitochondrial proteome, using placenta as the source tissue. Protein
identification was carried out mainly by peptide mass fingerprinting. The
optimization steps in two-dimensional electrophoresis needed for proteome
research are discussed. However, the relative paucity of data concerning
mitochondrial proteins is still the major limiting factor in building the
corresponding proteome, which should be a useful tool for researchers working
on human mitochondria and their deficiencies.
| [
{
"created": "Fri, 24 Nov 2006 13:01:05 GMT",
"version": "v1"
}
] | 2016-08-16 | [
[
"Lescuyer",
"Pierre",
""
],
[
"Strub",
"Jean-Marc",
""
],
[
"Luche",
"Sylvie",
""
],
[
"Diemer",
"Hélène",
""
],
[
"Martinez",
"Pascal",
""
],
[
"Van Dorsselaer",
"Alain",
""
],
[
"Lunardi",
"Joël",
""
],
[
"Rabilloud",
"Thierry",
""
]
] | Owing to the complexity of higher eukaryotic cells, a complete proteome is likely to be very difficult to achieve. However, advantage can be taken of the cell compartmentalization to build organelle proteomes, which can moreover be viewed as specialized tools to study specifically the biology and "physiology" of the target organelle. Within this frame, we report here the construction of the human mitochondrial proteome, using placenta as the source tissue. Protein identification was carried out mainly by peptide mass fingerprinting. The optimization steps in two-dimensional electrophoresis needed for proteome research are discussed. However, the relative paucity of data concerning mitochondrial proteins is still the major limiting factor in building the corresponding proteome, which should be a useful tool for researchers working on human mitochondria and their deficiencies. |
2305.10541 | Anton Orlichenko | Anton Orlichenko, Gang Qu, Ziyu Zhou, Zhengming Ding, Yu-Ping Wang | Angle Basis: a Generative Model and Decomposition for Functional
Connectivity | 8 Page Main Paper, 17 Pages with Supplemental Materials | null | null | null | q-bio.NC q-bio.PE | http://creativecommons.org/licenses/by-sa/4.0/ | Functional connectivity (FC) is one of the most common inputs to fMRI-based
predictive models, due to a combination of its simplicity and robustness.
However, there may be a lack of theoretical models for the generation of FC. In
this work, we present a straightforward decomposition of FC into a set of basis
states of sine waves with an additional jitter component. We show that the
decomposition matches the predictive ability of FC after including 5-10 bases.
We also find that both the decomposition and its residual have approximately
equal predictive value, and when combined into an ensemble, exceed the AUC of
FC-based prediction by up to 5%. Additionally, we find the residual can be used
for subject fingerprinting, with 97.3% same-subject, different-scan
identifiability, compared to 62.5% for FC. Unlike PCA or Factor Analysis
methods, our method does not require knowledge of a population to perform its
decomposition; a single subject is enough. Our decomposition of FC into two
equally-predictive components may lead to a novel appreciation of group
differences in patient populations. Additionally, we generate synthetic patient
FC based on user-specified characteristics such as age, sex, and disease
diagnosis. By creating synthetic datasets or augmentations we may reduce the
high financial burden associated with fMRI data acquisition.
| [
{
"created": "Wed, 17 May 2023 19:56:56 GMT",
"version": "v1"
}
] | 2023-05-19 | [
[
"Orlichenko",
"Anton",
""
],
[
"Qu",
"Gang",
""
],
[
"Zhou",
"Ziyu",
""
],
[
"Ding",
"Zhengming",
""
],
[
"Wang",
"Yu-Ping",
""
]
] | Functional connectivity (FC) is one of the most common inputs to fMRI-based predictive models, due to a combination of its simplicity and robustness. However, there may be a lack of theoretical models for the generation of FC. In this work, we present a straightforward decomposition of FC into a set of basis states of sine waves with an additional jitter component. We show that the decomposition matches the predictive ability of FC after including 5-10 bases. We also find that both the decomposition and its residual have approximately equal predictive value, and when combined into an ensemble, exceed the AUC of FC-based prediction by up to 5%. Additionally, we find the residual can be used for subject fingerprinting, with 97.3% same-subject, different-scan identifiability, compared to 62.5% for FC. Unlike PCA or Factor Analysis methods, our method does not require knowledge of a population to perform its decomposition; a single subject is enough. Our decomposition of FC into two equally-predictive components may lead to a novel appreciation of group differences in patient populations. Additionally, we generate synthetic patient FC based on user-specified characteristics such as age, sex, and disease diagnosis. By creating synthetic datasets or augmentations we may reduce the high financial burden associated with fMRI data acquisition. |
1811.10497 | Udit Bhatia | Udit Bhatia and Tarik Gouhier and Auroop Ratan Ganguly | Universal and generalizable restoration strategies for degraded
ecological networks | null | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans are increasingly stressing ecosystems via habitat destruction, climate
change and global population movements leading to the widespread loss of
biodiversity and the disruption of key ecological services. Ecosystems
characterized primarily by mutualistic relationships between species such as
plant-pollinator interactions may be particularly vulnerable to such
perturbations because the loss of biodiversity can cause extinction cascades
that can compromise the entire network. Here, we develop a general restoration
strategy based on network-science for degraded ecosystems. Specifically, we
show that network topology can be used to identify the optimal sequence of
species reintroductions needed to maximize biodiversity gains following partial
and full ecosystem collapse. This restoration strategy generalizes across
topologically-disparate and geographically-distributed ecosystems.
Additionally, we find that although higher connectance and diversity promote
persistence in pristine ecosystems, these attributes reduce the effectiveness
of restoration efforts in degraded networks. Hence, focusing on restoring the
factors that promote persistence in pristine ecosystems may yield suboptimal
recovery strategies for degraded ecosystems. Overall, our results have
important insights for designing effective ecosystem restoration strategies to
preserve biodiversity and ensure the delivery of critical natural services that
fuel economic development, food security and human health around the globe
| [
{
"created": "Tue, 13 Nov 2018 21:13:33 GMT",
"version": "v1"
}
] | 2018-11-27 | [
[
"Bhatia",
"Udit",
""
],
[
"Gouhier",
"Tarik",
""
],
[
"Ganguly",
"Auroop Ratan",
""
]
] | Humans are increasingly stressing ecosystems via habitat destruction, climate change and global population movements leading to the widespread loss of biodiversity and the disruption of key ecological services. Ecosystems characterized primarily by mutualistic relationships between species such as plant-pollinator interactions may be particularly vulnerable to such perturbations because the loss of biodiversity can cause extinction cascades that can compromise the entire network. Here, we develop a general restoration strategy based on network-science for degraded ecosystems. Specifically, we show that network topology can be used to identify the optimal sequence of species reintroductions needed to maximize biodiversity gains following partial and full ecosystem collapse. This restoration strategy generalizes across topologically-disparate and geographically-distributed ecosystems. Additionally, we find that although higher connectance and diversity promote persistence in pristine ecosystems, these attributes reduce the effectiveness of restoration efforts in degraded networks. Hence, focusing on restoring the factors that promote persistence in pristine ecosystems may yield suboptimal recovery strategies for degraded ecosystems. Overall, our results have important insights for designing effective ecosystem restoration strategies to preserve biodiversity and ensure the delivery of critical natural services that fuel economic development, food security and human health around the globe |
1803.05275 | Maxime Lenormand | Maxime Lenormand, Guillaume Papuga, Olivier Argagnon, Maxence
Soubeyrand, Guilhem De Barros, Samuel Alleaume and Sandra Luque | Biogeographical network analysis of plant species distribution in the
Mediterranean region | 16 pages, 7 figures + Appendix | Ecology and Evolution 9, 237-250 (2019) | 10.1002/ece3.4718 | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The delimitation of bioregions helps to understand historical and ecological
drivers of species distribution. In this work, we performed a network analysis
of the spatial distribution patterns of plants in south of France
(Languedoc-Roussillon and Provence-Alpes-C\^ote d'Azur) to analyze the
biogeographical structure of the French Mediterranean flora at different
scales. We used a network approach to identify and characterize biogeographical
regions, based on a large database containing 2.5 million of geolocalized plant
records corresponding to more than 3500 plant species. This methodology is
performed following five steps, from the biogeographical bipartite network
construction, to the identification of biogeographical regions under the form
of spatial network communities, the analysis of their interactions and the
identification of clusters of plant species based on the species contribution
to the biogeographical regions. First, we identified two sub-networks that
distinguish Mediterranean and temperate biota. Then, we separated eight
statistically significant bioregions that present a complex spatial structure.
Some of them are spatially well delimited, and match with particular geological
entities. On the other hand fuzzy transitions arise between adjacent bioregions
that share a common geological setting, but are spread along a climatic
gradient. The proposed network approach illustrates the biogeographical
structure of the flora in southern France, and provides precise insights into
the relationships between bioregions. This approach sheds light on ecological
drivers shaping the distribution of Mediterranean biota: the interplay between
a climatic gradient and geological substrate shapes biodiversity patterns.
Finally this work exemplifies why fragmented distributions are common in the
Mediterranean region, isolating groups of species that share a similar
eco-evolutionary history.
| [
{
"created": "Tue, 6 Mar 2018 20:47:45 GMT",
"version": "v1"
},
{
"created": "Thu, 30 Aug 2018 15:55:09 GMT",
"version": "v2"
},
{
"created": "Mon, 7 Jan 2019 08:32:43 GMT",
"version": "v3"
},
{
"created": "Thu, 8 Feb 2024 10:07:02 GMT",
"version": "v4"
}
] | 2024-02-09 | [
[
"Lenormand",
"Maxime",
""
],
[
"Papuga",
"Guillaume",
""
],
[
"Argagnon",
"Olivier",
""
],
[
"Soubeyrand",
"Maxence",
""
],
[
"De Barros",
"Guilhem",
""
],
[
"Alleaume",
"Samuel",
""
],
[
"Luque",
"Sandra",
""
]
] | The delimitation of bioregions helps to understand historical and ecological drivers of species distribution. In this work, we performed a network analysis of the spatial distribution patterns of plants in south of France (Languedoc-Roussillon and Provence-Alpes-C\^ote d'Azur) to analyze the biogeographical structure of the French Mediterranean flora at different scales. We used a network approach to identify and characterize biogeographical regions, based on a large database containing 2.5 million of geolocalized plant records corresponding to more than 3500 plant species. This methodology is performed following five steps, from the biogeographical bipartite network construction, to the identification of biogeographical regions under the form of spatial network communities, the analysis of their interactions and the identification of clusters of plant species based on the species contribution to the biogeographical regions. First, we identified two sub-networks that distinguish Mediterranean and temperate biota. Then, we separated eight statistically significant bioregions that present a complex spatial structure. Some of them are spatially well delimited, and match with particular geological entities. On the other hand fuzzy transitions arise between adjacent bioregions that share a common geological setting, but are spread along a climatic gradient. The proposed network approach illustrates the biogeographical structure of the flora in southern France, and provides precise insights into the relationships between bioregions. This approach sheds light on ecological drivers shaping the distribution of Mediterranean biota: the interplay between a climatic gradient and geological substrate shapes biodiversity patterns. Finally this work exemplifies why fragmented distributions are common in the Mediterranean region, isolating groups of species that share a similar eco-evolutionary history. |
0803.3047 | Sarah Feldt | S. Feldt, J. Waddell, V. L. Hetrick, J. D. Berke, M. Zochowski | A functional clustering algorithm for the analysis of dynamic network
data | 19 pages, 7 figures, significant changes to structure of paper, added
methods sections and new measures | null | 10.1103/PhysRevE.79.056104 | null | q-bio.NC physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We formulate a novel technique for the detection of functional clusters in
discrete event data. The advantage of this algorithm is that no prior knowledge
of the number of functional groups is needed, as our procedure progressively
combines data traces and derives the optimal clustering cutoff in a simple and
intuitive manner through the use of surrogate data sets. In order to
demonstrate the power of this algorithm to detect changes in network dynamics
and connectivity, we apply it to both simulated neural spike train data and
real neural data obtained from the mouse hippocampus during exploration and
slow-wave sleep. Using the simulated data, we show that our algorithm performs
better than existing methods. In the experimental data, we observe
state-dependent clustering patterns consistent with known neurophysiological
processes involved in memory consolidation.
| [
{
"created": "Thu, 20 Mar 2008 18:29:47 GMT",
"version": "v1"
},
{
"created": "Mon, 28 Apr 2008 18:36:02 GMT",
"version": "v2"
},
{
"created": "Wed, 7 Jan 2009 23:42:42 GMT",
"version": "v3"
}
] | 2015-05-13 | [
[
"Feldt",
"S.",
""
],
[
"Waddell",
"J.",
""
],
[
"Hetrick",
"V. L.",
""
],
[
"Berke",
"J. D.",
""
],
[
"Zochowski",
"M.",
""
]
] | We formulate a novel technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulated neural spike train data and real neural data obtained from the mouse hippocampus during exploration and slow-wave sleep. Using the simulated data, we show that our algorithm performs better than existing methods. In the experimental data, we observe state-dependent clustering patterns consistent with known neurophysiological processes involved in memory consolidation. |
1312.5625 | Nils Becker | Nils B. Becker and Andrew Mugler and Pieter Rein ten Wolde | Prediction and Dissipation in Biochemical Sensing | 9 pages, 5 figures | null | null | null | q-bio.MN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cells sense and predict their environment via energy-dissipating pathways.
However, it is unclear whether dissipation helps or harms prediction. Here we
study dissipation and prediction for a minimal sensory module of receptors that
reversibly bind ligand. We find that the module performs short-term prediction
optimally when operating in an adiabatic regime where dissipation vanishes. In
contrast, beyond a critical forecast interval, prediction becomes most precise
in a regime of maximal dissipation, suggesting that dissipative sensing in
biological systems can serve to enhance prediction performance.
| [
{
"created": "Thu, 19 Dec 2013 16:33:25 GMT",
"version": "v1"
}
] | 2013-12-20 | [
[
"Becker",
"Nils B.",
""
],
[
"Mugler",
"Andrew",
""
],
[
"Wolde",
"Pieter Rein ten",
""
]
] | Cells sense and predict their environment via energy-dissipating pathways. However, it is unclear whether dissipation helps or harms prediction. Here we study dissipation and prediction for a minimal sensory module of receptors that reversibly bind ligand. We find that the module performs short-term prediction optimally when operating in an adiabatic regime where dissipation vanishes. In contrast, beyond a critical forecast interval, prediction becomes most precise in a regime of maximal dissipation, suggesting that dissipative sensing in biological systems can serve to enhance prediction performance. |
2103.00640 | Martin Frasch | Martin G. Frasch, Bernd Walter, Christoph Anders and Reinhard Bauer | Update on the multimodal pathophysiological dataset of gradual cerebral
ischemia in a cohort of juvenile pigs: auditory, sensory and high-frequency
sensory evoked potentials | Accompanying data repository:
https://doi.org/10.6084/m9.figshare.14122049.v3. arXiv admin note: text
overlap with arXiv:2002.09154 | null | 10.1038/s41597-021-01029-z | null | q-bio.NC q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We expand from a spontaneous to an evoked potentials (EP) data set of brain
electrical activities as electrocorticogram (ECoG) and electrothalamogram
(EThG) in juvenile pig under various sedation, ischemia and recovery states.
This EP data set includes three stimulation paradigms: auditory (AEP, 40 and
2000 Hz), sensory (SEP, left and right maxillary nerve) and high-frequency
oscillations (HFO) SEP. This permits derivation of electroencephalogram (EEG)
biomarkers of corticothalamic communication under these conditions. The data
set is presented in full band sampled at 2000 Hz. We provide technical
validation of the evoked responses for the states of sedation, ischemia and
recovery. This extended data set now permits mutual inferences between
spontaneous and evoked activities across the recorded modalities. Future
studies on the dataset may contribute to the development of new brain
monitoring technologies, which will facilitate the prevention of neurological
injuries.
| [
{
"created": "Sun, 28 Feb 2021 21:58:49 GMT",
"version": "v1"
}
] | 2021-12-06 | [
[
"Frasch",
"Martin G.",
""
],
[
"Walter",
"Bernd",
""
],
[
"Anders",
"Christoph",
""
],
[
"Bauer",
"Reinhard",
""
]
] | We expand from a spontaneous to an evoked potentials (EP) data set of brain electrical activities as electrocorticogram (ECoG) and electrothalamogram (EThG) in juvenile pig under various sedation, ischemia and recovery states. This EP data set includes three stimulation paradigms: auditory (AEP, 40 and 2000 Hz), sensory (SEP, left and right maxillary nerve) and high-frequency oscillations (HFO) SEP. This permits derivation of electroencephalogram (EEG) biomarkers of corticothalamic communication under these conditions. The data set is presented in full band sampled at 2000 Hz. We provide technical validation of the evoked responses for the states of sedation, ischemia and recovery. This extended data set now permits mutual inferences between spontaneous and evoked activities across the recorded modalities. Future studies on the dataset may contribute to the development of new brain monitoring technologies, which will facilitate the prevention of neurological injuries. |
2004.12779 | Alessandra Micheletti | Luisa Ferrari, Giuseppe Gerardi, Giancarlo Manzi, Alessandra
Micheletti, Federica Nicolussi, Silvia Salini | COVID-Pro in Italy: a dashboard for a province-based analysis | null | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an dashboard developed to analyse the outbreak of the
Covid-19 infection in Italy considering daily NUTS-3 data on positive cases
provided by the Italian Ministry of Health and on deaths derived from Italian
regional authorities' official press conferences. Descriptive time series plots
are provided together with a map describing the spatial distribution of
province cumulative cases and rates. A section on a proposed time-dependent
adjusted SIRD model for NUTS-3 regions is also provided in the dashboard.
| [
{
"created": "Fri, 24 Apr 2020 12:48:32 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Apr 2020 08:09:32 GMT",
"version": "v2"
},
{
"created": "Mon, 1 Jun 2020 18:23:45 GMT",
"version": "v3"
}
] | 2020-06-03 | [
[
"Ferrari",
"Luisa",
""
],
[
"Gerardi",
"Giuseppe",
""
],
[
"Manzi",
"Giancarlo",
""
],
[
"Micheletti",
"Alessandra",
""
],
[
"Nicolussi",
"Federica",
""
],
[
"Salini",
"Silvia",
""
]
] | This paper presents an dashboard developed to analyse the outbreak of the Covid-19 infection in Italy considering daily NUTS-3 data on positive cases provided by the Italian Ministry of Health and on deaths derived from Italian regional authorities' official press conferences. Descriptive time series plots are provided together with a map describing the spatial distribution of province cumulative cases and rates. A section on a proposed time-dependent adjusted SIRD model for NUTS-3 regions is also provided in the dashboard. |
1903.11915 | Karolina Finc | Kamil Bonna, Karolina Finc, Maria Zimmermann, {\L}ukasz Bola, Piotr
Mostowski, Maciej Szul, Pawe{\l} Rutkowski, W{\l}odzis{\l}aw Duch, Artur
Marchewka, Katarzyna Jednor\'og, Marcin Szwed | Early deafness leads to re-shaping of global functional connectivity
beyond the auditory cortex | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Early sensory deprivation such as blindness or deafness shapes brain
development in multiple ways. While it is established that deprived brain areas
start to be engaged in the processing of stimuli from the remaining modalities
and in high-level cognitive tasks, some reports have also suggested the
possibility of structural and functional changes in non-deprived brain areas.
We compared resting-state functional network organization of the brain in
early-deaf adults and hearing controls by examining global network segregation
and integration. Relative to hearing controls, deaf adults exhibited an altered
modular organization, with regions of the salience network coupled with the
fronto-parietal network. They showed weaker connections between auditory and
somatomotor regions, stronger coupling between the fronto-parietal network and
several other large-scale networks (visual, memory, cingulo-opercular and
somatomotor), and an enlargement of the default mode network. Their overall
functional segregation of brain networks was also lower. Our findings suggest
that brain plasticity in deaf adults is not limited to changes in auditory
cortex but additionally alters the coupling between other large-scale networks.
These widespread functional connectivity changes may provide a mechanism for
the superior behavioral performance of the deaf in visual and attentional
tasks.
| [
{
"created": "Thu, 28 Mar 2019 12:27:32 GMT",
"version": "v1"
}
] | 2019-03-29 | [
[
"Bonna",
"Kamil",
""
],
[
"Finc",
"Karolina",
""
],
[
"Zimmermann",
"Maria",
""
],
[
"Bola",
"Łukasz",
""
],
[
"Mostowski",
"Piotr",
""
],
[
"Szul",
"Maciej",
""
],
[
"Rutkowski",
"Paweł",
""
],
[
"Duch",
"Włodzisław",
""
],
[
"Marchewka",
"Artur",
""
],
[
"Jednoróg",
"Katarzyna",
""
],
[
"Szwed",
"Marcin",
""
]
] | Early sensory deprivation such as blindness or deafness shapes brain development in multiple ways. While it is established that deprived brain areas start to be engaged in the processing of stimuli from the remaining modalities and in high-level cognitive tasks, some reports have also suggested the possibility of structural and functional changes in non-deprived brain areas. We compared resting-state functional network organization of the brain in early-deaf adults and hearing controls by examining global network segregation and integration. Relative to hearing controls, deaf adults exhibited an altered modular organization, with regions of the salience network coupled with the fronto-parietal network. They showed weaker connections between auditory and somatomotor regions, stronger coupling between the fronto-parietal network and several other large-scale networks (visual, memory, cingulo-opercular and somatomotor), and an enlargement of the default mode network. Their overall functional segregation of brain networks was also lower. Our findings suggest that brain plasticity in deaf adults is not limited to changes in auditory cortex but additionally alters the coupling between other large-scale networks. These widespread functional connectivity changes may provide a mechanism for the superior behavioral performance of the deaf in visual and attentional tasks. |
2305.05163 | Lu Ling | Lu Ling, Washim Uddin Mondal, Satish V, Ukkusuri | Cooperating Graph Neural Networks with Deep Reinforcement Learning for
Vaccine Prioritization | null | null | null | null | q-bio.PE cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This study explores the vaccine prioritization strategy to reduce the overall
burden of the pandemic when the supply is limited. Existing methods conduct
macro-level or simplified micro-level vaccine distribution by assuming the
homogeneous behavior within subgroup populations and lacking mobility dynamics
integration. Directly applying these models for micro-level vaccine allocation
leads to sub-optimal solutions due to the lack of behavioral-related details.
To address the issue, we first incorporate the mobility heterogeneity in
disease dynamics modeling and mimic the disease evolution process using a
Trans-vaccine-SEIR model. Then we develop a novel deep reinforcement learning
to seek the optimal vaccine allocation strategy for the high-degree
spatial-temporal disease evolution system. The graph neural network is used to
effectively capture the structural properties of the mobility contact network
and extract the dynamic disease features. In our evaluation, the proposed
framework reduces 7% - 10% of infections and deaths than the baseline
strategies. Extensive evaluation shows that the proposed framework is robust to
seek the optimal vaccine allocation with diverse mobility patterns in the
micro-level disease evolution system. In particular, we find the optimal
vaccine allocation strategy in the transit usage restriction scenario is
significantly more effective than restricting cross-zone mobility for the top
10% age-based and income-based zones. These results provide valuable insights
for areas with limited vaccines and low logistic efficacy.
| [
{
"created": "Tue, 9 May 2023 04:19:10 GMT",
"version": "v1"
}
] | 2023-05-10 | [
[
"Ling",
"Lu",
""
],
[
"Mondal",
"Washim Uddin",
""
],
[
"V",
"Satish",
""
],
[
"Ukkusuri",
"",
""
]
] | This study explores the vaccine prioritization strategy to reduce the overall burden of the pandemic when the supply is limited. Existing methods conduct macro-level or simplified micro-level vaccine distribution by assuming the homogeneous behavior within subgroup populations and lacking mobility dynamics integration. Directly applying these models for micro-level vaccine allocation leads to sub-optimal solutions due to the lack of behavioral-related details. To address the issue, we first incorporate the mobility heterogeneity in disease dynamics modeling and mimic the disease evolution process using a Trans-vaccine-SEIR model. Then we develop a novel deep reinforcement learning to seek the optimal vaccine allocation strategy for the high-degree spatial-temporal disease evolution system. The graph neural network is used to effectively capture the structural properties of the mobility contact network and extract the dynamic disease features. In our evaluation, the proposed framework reduces 7% - 10% of infections and deaths than the baseline strategies. Extensive evaluation shows that the proposed framework is robust to seek the optimal vaccine allocation with diverse mobility patterns in the micro-level disease evolution system. In particular, we find the optimal vaccine allocation strategy in the transit usage restriction scenario is significantly more effective than restricting cross-zone mobility for the top 10% age-based and income-based zones. These results provide valuable insights for areas with limited vaccines and low logistic efficacy. |
1307.5364 | Dominique Gravel | Claire Jacquet, Charlotte Moritz, Lyne Morissette, Pierre Legagneux,
Fran\c{c}ois Massol, Phillippe Archambault, Dominique Gravel | No complexity-stability relationship in natural communities | Main text: 9 pages, 4 figures. Supplementary Information: 14 pages, 3
figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We performed a stability analysis of 119 quantitative food webs which were
compiled using a standard methodology to build Ecopath mass-balance models. Our
analysis reveals that classic descriptors of complexity do not affect stability
in natural food webs. Food web structure, which is non-random in real
communities, reflects another form of complexity that we found influences
dramatically the stability of real communities. We conclude that the occurrence
of complex communities in nature is possible owing to their trophic structure.
| [
{
"created": "Sat, 20 Jul 2013 01:51:27 GMT",
"version": "v1"
}
] | 2013-07-23 | [
[
"Jacquet",
"Claire",
""
],
[
"Moritz",
"Charlotte",
""
],
[
"Morissette",
"Lyne",
""
],
[
"Legagneux",
"Pierre",
""
],
[
"Massol",
"François",
""
],
[
"Archambault",
"Phillippe",
""
],
[
"Gravel",
"Dominique",
""
]
] | We performed a stability analysis of 119 quantitative food webs which were compiled using a standard methodology to build Ecopath mass-balance models. Our analysis reveals that classic descriptors of complexity do not affect stability in natural food webs. Food web structure, which is non-random in real communities, reflects another form of complexity that we found influences dramatically the stability of real communities. We conclude that the occurrence of complex communities in nature is possible owing to their trophic structure. |
2311.05862 | Shi Gu | Shi Gu, Marcelo G Mattar, Huajin Tang, Gang Pan | Emergence and reconfiguration of modular structure for synaptic neural
networks during continual familiarity detection | null | null | null | null | q-bio.NC q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | While advances in artificial intelligence and neuroscience have enabled the
emergence of neural networks capable of learning a wide variety of tasks, our
understanding of the temporal dynamics of these networks remains limited. Here,
we study the temporal dynamics during learning of Hebbian Feedforward (HebbFF)
neural networks in tasks of continual familiarity detection. Drawing
inspiration from the field of network neuroscience, we examine the network's
dynamic reconfiguration, focusing on how network modules evolve throughout
learning. Through a comprehensive assessment involving metrics like network
accuracy, modular flexibility, and distribution entropy across diverse learning
modes, our approach reveals various previously unknown patterns of network
reconfiguration. In particular, we find that the emergence of network
modularity is a salient predictor of performance, and that modularization
strengthens with increasing flexibility throughout learning. These insights not
only elucidate the nuanced interplay of network modularity, accuracy, and
learning dynamics but also bridge our understanding of learning in artificial
and biological realms.
| [
{
"created": "Fri, 10 Nov 2023 04:16:38 GMT",
"version": "v1"
}
] | 2023-11-13 | [
[
"Gu",
"Shi",
""
],
[
"Mattar",
"Marcelo G",
""
],
[
"Tang",
"Huajin",
""
],
[
"Pan",
"Gang",
""
]
] | While advances in artificial intelligence and neuroscience have enabled the emergence of neural networks capable of learning a wide variety of tasks, our understanding of the temporal dynamics of these networks remains limited. Here, we study the temporal dynamics during learning of Hebbian Feedforward (HebbFF) neural networks in tasks of continual familiarity detection. Drawing inspiration from the field of network neuroscience, we examine the network's dynamic reconfiguration, focusing on how network modules evolve throughout learning. Through a comprehensive assessment involving metrics like network accuracy, modular flexibility, and distribution entropy across diverse learning modes, our approach reveals various previously unknown patterns of network reconfiguration. In particular, we find that the emergence of network modularity is a salient predictor of performance, and that modularization strengthens with increasing flexibility throughout learning. These insights not only elucidate the nuanced interplay of network modularity, accuracy, and learning dynamics but also bridge our understanding of learning in artificial and biological realms. |
2308.07992 | Anton Orlichenko | Anton Orlichenko and Kuan-Jui Su and Qing Tian and Hui Shen and
Hong-Wen Deng and Yu-Ping Wang | Somatomotor-Visual Resting State Functional Connectivity Increases After
Two Years in the UK Biobank Longitudinal Cohort | 11 pages, 12 figures, 4 tables | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-sa/4.0/ | Purpose: Functional magnetic resonance imaging (fMRI) and functional
connectivity (FC) have been used to follow aging in both children and older
adults. Robust changes have been observed in children, where high connectivity
among all brain regions changes to a more modular structure with maturation. In
this work, we examine changes in FC in older adults after two years of aging in
the UK Biobank longitudinal cohort. Approach: We process data using the
Power264 atlas, then test whether FC changes in the 2,722-subject longitudinal
cohort are statistically significant using a Bonferroni-corrected t-test. We
also compare the ability of Power264 and UKB-provided, ICA-based FC to
determine which of a longitudinal scan pair is older. Results: We find a 6.8\%
average increase in SMT-VIS connectivity from younger to older scan (from
$\rho=0.39$ to $\rho=0.42$) that occurs in male, female, older subject ($>65$
years old), and younger subject ($<55$ years old) groups. Among all
inter-network connections, this average SMT-VIS connectivity is the best
predictor of relative scan age, accurately predicting which scan is older 57\%
of the time. Using the full FC and a training set of 2,000 subjects, one is
able to predict which scan is older 82.5\% of the time using either the full
Power264 FC or the UKB-provided ICA-based FC. Conclusions: We conclude that
SMT-VIS connectivity increases in the longitudinal cohort, while resting state
FC increases generally with age in the cross-sectional cohort. However, we
consider the possibility of a change in resting state scanner task between UKB
longitudinal data acquisitions.
| [
{
"created": "Tue, 15 Aug 2023 18:47:59 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Aug 2023 12:44:01 GMT",
"version": "v2"
}
] | 2023-08-28 | [
[
"Orlichenko",
"Anton",
""
],
[
"Su",
"Kuan-Jui",
""
],
[
"Tian",
"Qing",
""
],
[
"Shen",
"Hui",
""
],
[
"Deng",
"Hong-Wen",
""
],
[
"Wang",
"Yu-Ping",
""
]
] | Purpose: Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, where high connectivity among all brain regions changes to a more modular structure with maturation. In this work, we examine changes in FC in older adults after two years of aging in the UK Biobank longitudinal cohort. Approach: We process data using the Power264 atlas, then test whether FC changes in the 2,722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, ICA-based FC to determine which of a longitudinal scan pair is older. Results: We find a 6.8\% average increase in SMT-VIS connectivity from younger to older scan (from $\rho=0.39$ to $\rho=0.42$) that occurs in male, female, older subject ($>65$ years old), and younger subject ($<55$ years old) groups. Among all inter-network connections, this average SMT-VIS connectivity is the best predictor of relative scan age, accurately predicting which scan is older 57\% of the time. Using the full FC and a training set of 2,000 subjects, one is able to predict which scan is older 82.5\% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions: We conclude that SMT-VIS connectivity increases in the longitudinal cohort, while resting state FC increases generally with age in the cross-sectional cohort. However, we consider the possibility of a change in resting state scanner task between UKB longitudinal data acquisitions. |
2109.02962 | Hong-Li Zeng | Hong-Li Zeng, Yue Liu, Vito Dichio, Kaisa Thorell, Rickard Nord\'en,
and Erik Aurell | Mutation frequency time series reveal complex mixtures of clones in the
world-wide SARS-CoV-2 viral population | null | null | null | null | q-bio.PE q-bio.GN stat.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We compute the allele frequencies of the alpha (B.1.1.7), beta (B.1.351) and
delta (B.167.2) variants of SARS-CoV-2 from almost two million genome sequences
on the GISAID repository. We find that the frequencies of a majority of the
defining mutations in alpha rose towards the end of 2020 but drifted apart
during spring 2021, a similar pattern being followed by delta during summer of
2021. For beta we find a more complex scenario with frequencies of some
mutations rising and some remaining close to zero. Our results point to that
what is generally reported as single variants is in fact a collection of
variants with different genetic characteristics. For all three variants we
further find some alleles with a clearly deviating time series.
| [
{
"created": "Tue, 7 Sep 2021 09:32:47 GMT",
"version": "v1"
}
] | 2021-09-08 | [
[
"Zeng",
"Hong-Li",
""
],
[
"Liu",
"Yue",
""
],
[
"Dichio",
"Vito",
""
],
[
"Thorell",
"Kaisa",
""
],
[
"Nordén",
"Rickard",
""
],
[
"Aurell",
"Erik",
""
]
] | We compute the allele frequencies of the alpha (B.1.1.7), beta (B.1.351) and delta (B.167.2) variants of SARS-CoV-2 from almost two million genome sequences on the GISAID repository. We find that the frequencies of a majority of the defining mutations in alpha rose towards the end of 2020 but drifted apart during spring 2021, a similar pattern being followed by delta during summer of 2021. For beta we find a more complex scenario with frequencies of some mutations rising and some remaining close to zero. Our results point to that what is generally reported as single variants is in fact a collection of variants with different genetic characteristics. For all three variants we further find some alleles with a clearly deviating time series. |
2406.01167 | Carmen Oana Tarniceriu | Gregory Dumont and Carmen Oana Tarniceriu | Pattern Formation in a Spiking Neural-Field of Renewal Neurons | 23 pages, 10 figures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Elucidating the neurophysiological mechanisms underlying neural pattern
formation remains an outstanding challenge in Computational Neuroscience. In
this paper, we address the issue of understanding the emergence of neural
patterns by considering a network of renewal neurons, a well-established class
of spiking cells. Taking the thermodynamics limit, the network's dynamics can
be accurately represented by a partial differential equation coupled with a
nonlocal differential equation. The stationary state of the nonlocal system is
determined, and a perturbation analysis is performed to analytically
characterize the conditions for the occurrence of Turing instabilities.
Considering neural network parameters such as the synaptic coupling and the
external drive, we numerically obtain the bifurcation line that separates the
asynchronous regime from the emergence of patterns. Our theoretical findings
provide a new and insightful perspective on the emergence of Turing patterns in
spiking neural networks. In the long term, our formalism will enable the study
of neural patterns while maintaining the connections between microscopic
cellular properties, network coupling, and the emergence of Turing
instabilities.
| [
{
"created": "Mon, 3 Jun 2024 10:04:47 GMT",
"version": "v1"
}
] | 2024-06-04 | [
[
"Dumont",
"Gregory",
""
],
[
"Tarniceriu",
"Carmen Oana",
""
]
] | Elucidating the neurophysiological mechanisms underlying neural pattern formation remains an outstanding challenge in Computational Neuroscience. In this paper, we address the issue of understanding the emergence of neural patterns by considering a network of renewal neurons, a well-established class of spiking cells. Taking the thermodynamics limit, the network's dynamics can be accurately represented by a partial differential equation coupled with a nonlocal differential equation. The stationary state of the nonlocal system is determined, and a perturbation analysis is performed to analytically characterize the conditions for the occurrence of Turing instabilities. Considering neural network parameters such as the synaptic coupling and the external drive, we numerically obtain the bifurcation line that separates the asynchronous regime from the emergence of patterns. Our theoretical findings provide a new and insightful perspective on the emergence of Turing patterns in spiking neural networks. In the long term, our formalism will enable the study of neural patterns while maintaining the connections between microscopic cellular properties, network coupling, and the emergence of Turing instabilities. |
1412.7560 | Franck Jabot | Franck Jabot, Guillaume Lagarrigues, Beno\^it Courbaud, Nicolas
Dumoulin | A comparison of emulation methods for Approximate Bayesian Computation | null | null | null | null | q-bio.QM q-bio.PE stat.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Approximate Bayesian Computation (ABC) is a family of statistical inference
techniques, which is increasingly used in biology and other scientific fields.
Its main benefit is to be applicable to models for which the computation of the
model likelihood is intractable. The basic idea of ABC is to empirically
approximate the model likelihood by using intensive realizations of model runs.
Due to computing time limitations, ABC has thus been mainly applied to models
that are relatively quick to simulate. We here aim at briefly introducing the
field of statistical emulation of computer code outputs and to demonstrate its
potential for ABC applications. Emulation consists in replacing the costly to
simulate model by another (quick to simulate) statistical model called emulator
or meta-model. This emulator is fitted to a small number of outputs of the
original model, and is subsequently used as a surrogate during the inference
procedure. In this contribution, we first detail the principles of model
emulation, with a special reference to the ABC context in which the description
of the stochasticity of model realizations is as important as the description
of the trends linking model parameters and outputs. We then compare several
emulation strategies in an ABC context, using as case study a stochastic
ecological model of community dynamics. We finally describe a novel
emulation-based sequential ABC algorithm which is shown to decrease computing
time by a factor of two on the studied example, compared to previous sequential
ABC algorithms. Routines to perform emulation-based ABC were made available
within the R package EasyABC.
| [
{
"created": "Tue, 16 Dec 2014 08:18:48 GMT",
"version": "v1"
}
] | 2014-12-25 | [
[
"Jabot",
"Franck",
""
],
[
"Lagarrigues",
"Guillaume",
""
],
[
"Courbaud",
"Benoît",
""
],
[
"Dumoulin",
"Nicolas",
""
]
] | Approximate Bayesian Computation (ABC) is a family of statistical inference techniques, which is increasingly used in biology and other scientific fields. Its main benefit is to be applicable to models for which the computation of the model likelihood is intractable. The basic idea of ABC is to empirically approximate the model likelihood by using intensive realizations of model runs. Due to computing time limitations, ABC has thus been mainly applied to models that are relatively quick to simulate. We here aim at briefly introducing the field of statistical emulation of computer code outputs and to demonstrate its potential for ABC applications. Emulation consists in replacing the costly to simulate model by another (quick to simulate) statistical model called emulator or meta-model. This emulator is fitted to a small number of outputs of the original model, and is subsequently used as a surrogate during the inference procedure. In this contribution, we first detail the principles of model emulation, with a special reference to the ABC context in which the description of the stochasticity of model realizations is as important as the description of the trends linking model parameters and outputs. We then compare several emulation strategies in an ABC context, using as case study a stochastic ecological model of community dynamics. We finally describe a novel emulation-based sequential ABC algorithm which is shown to decrease computing time by a factor of two on the studied example, compared to previous sequential ABC algorithms. Routines to perform emulation-based ABC were made available within the R package EasyABC. |
2004.00056 | Kashif Zia Dr. | Kashif Zia, Umar Farooq | COVID-19 Outbreak in Pakistan: Model-Driven Impact Analysis and
Guidelines | 12 pages, 6 figures | null | null | null | q-bio.PE math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivated by the rapid spread of COVID-19 all across the globe, we have
performed simulations of a system dynamic epidemic spread model in different
possible situations. The simulation, not only captures the model dynamic of the
spread of the virus, but also, takes care of population and mobility data. The
model is calibrated based on epidemic data and events specifically of Pakistan,
which can easily be generalized. The simulation results are quite disturbing,
indicating that, even with stringent social distancing and testing strategies
and for a quite long time (even beyond one year), the spread would be
significant (in tens of thousands). The real alarm is when some of these
measures got leaked for a short time within this duration, which may result in
catastrophic situation when millions of people would be infected.
| [
{
"created": "Tue, 31 Mar 2020 18:40:57 GMT",
"version": "v1"
}
] | 2020-04-02 | [
[
"Zia",
"Kashif",
""
],
[
"Farooq",
"Umar",
""
]
] | Motivated by the rapid spread of COVID-19 all across the globe, we have performed simulations of a system dynamic epidemic spread model in different possible situations. The simulation, not only captures the model dynamic of the spread of the virus, but also, takes care of population and mobility data. The model is calibrated based on epidemic data and events specifically of Pakistan, which can easily be generalized. The simulation results are quite disturbing, indicating that, even with stringent social distancing and testing strategies and for a quite long time (even beyond one year), the spread would be significant (in tens of thousands). The real alarm is when some of these measures got leaked for a short time within this duration, which may result in catastrophic situation when millions of people would be infected. |
1606.00309 | Claude Pasquier | Claude Pasquier, Julien Gard\`es | Prediction of miRNA-disease associations with a vector space model | null | Scientific Reports, Nature Publishing Group, 2016, 6, pp.27036 | 10.1038/srep27036 | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | MicroRNAs play critical roles in many physiological processes. Their
dysregulations are also closely related to the development and progression of
various human diseases, including cancer. Therefore, identifying new microRNAs
that are associated with diseases contributes to a better understanding of
pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in
biotechnology for early diagnosis. To date, several in silico methods have been
developed to address the issue of microRNA-disease association prediction.
However, these methods have various limitations. In this study, we investigate
the hypothesis that information attached to miRNAs and diseases can be revealed
by distributional semantics. Our basic approach is to represent distributional
information on miRNAs and diseases in a high-dimensional vector space and to
define associations between miRNAs and diseases in terms of their vector
similarity. Cross validations performed on a dataset of known miRNA-disease
associations demonstrate the excellent performance of our method. Moreover, the
case study focused on breast cancer confirms the ability of our method to
discover new disease-miRNA associations and to identify putative false
associations reported in databases.
| [
{
"created": "Wed, 1 Jun 2016 14:36:08 GMT",
"version": "v1"
}
] | 2016-06-02 | [
[
"Pasquier",
"Claude",
""
],
[
"Gardès",
"Julien",
""
]
] | MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in biotechnology for early diagnosis. To date, several in silico methods have been developed to address the issue of microRNA-disease association prediction. However, these methods have various limitations. In this study, we investigate the hypothesis that information attached to miRNAs and diseases can be revealed by distributional semantics. Our basic approach is to represent distributional information on miRNAs and diseases in a high-dimensional vector space and to define associations between miRNAs and diseases in terms of their vector similarity. Cross validations performed on a dataset of known miRNA-disease associations demonstrate the excellent performance of our method. Moreover, the case study focused on breast cancer confirms the ability of our method to discover new disease-miRNA associations and to identify putative false associations reported in databases. |
q-bio/0402035 | Kenji Morita | Kenji Morita, Kazuyuki Aihara | Fine Discrimination of Analog Patterns by Nonlinear Dendritic Inhibition | 4 pages, 3 figures | null | null | null | q-bio.NC | null | Recent experiments revealed that a certain class of inhibitory neurons in the
cerebral cortex make synapses not onto cell bodies but at distal parts of
dendrites of the target neurons, mediating highly nonlinear dendritic
inhibition. We propose a novel form of competitive neural network model that
realizes such dendritic inhibition. Contrary to the conventional lateral
inhibition in neural networks, our dendritic inhibition models don't always
show winner-take-all behaviors; instead, they converge to "I don't know" states
when unknown input patterns are presented. We derive reduced two-dimensional
dynamics for the network, showing that a drastic shift of the fixed point from
a winner-take-all state to an "I don't know" state occurs in accordance with
the increase in noise added to the stored patterns. By preventing
misrecognition in such a way, dendritic inhibition networks achieve fine
pattern discrimination, which could be one of the basic computations by
inhibitory connected recurrent neural networks in the brain.
| [
{
"created": "Tue, 17 Feb 2004 20:52:15 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Morita",
"Kenji",
""
],
[
"Aihara",
"Kazuyuki",
""
]
] | Recent experiments revealed that a certain class of inhibitory neurons in the cerebral cortex make synapses not onto cell bodies but at distal parts of dendrites of the target neurons, mediating highly nonlinear dendritic inhibition. We propose a novel form of competitive neural network model that realizes such dendritic inhibition. Contrary to the conventional lateral inhibition in neural networks, our dendritic inhibition models don't always show winner-take-all behaviors; instead, they converge to "I don't know" states when unknown input patterns are presented. We derive reduced two-dimensional dynamics for the network, showing that a drastic shift of the fixed point from a winner-take-all state to an "I don't know" state occurs in accordance with the increase in noise added to the stored patterns. By preventing misrecognition in such a way, dendritic inhibition networks achieve fine pattern discrimination, which could be one of the basic computations by inhibitory connected recurrent neural networks in the brain. |
2311.06357 | Filippo Utro | Davide Gurnari, Aldo Guzm\'an-S\'aenz, Filippo Utro, Aritra Bose,
Saugata Basu, Laxmi Parida | Probing omics data via harmonic persistent homology | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Identifying molecular signatures from complex disease patients with
underlying symptomatic similarities is a significant challenge in the analysis
of high dimensional multi-omics data. Topological data analysis (TDA) provides
a way of extracting such information from the geometric structure of the data
and identifying multiway higher-order relationships. Here, we propose an
application of Harmonic persistent homology, which overcomes the limitations of
ambiguous assignment of the topological information to the original elements in
a representative topological cycle from the data. When applied to multi-omics
data, this leads to the discovery of hidden patterns highlighting the
relationships between different omic profiles, while allowing for common tasks
in multi-omics analyses, such as disease subtyping, and most importantly
biomarker identification for similar latent biological pathways that are
associated with complex diseases. Our experiments on multiple cancer data show
that harmonic persistent homology effectively dissects multi-omics data to
identify biomarkers by detecting representative cycles predictive of disease
subtypes.
| [
{
"created": "Fri, 10 Nov 2023 19:11:03 GMT",
"version": "v1"
},
{
"created": "Sat, 20 Apr 2024 12:39:20 GMT",
"version": "v2"
}
] | 2024-04-23 | [
[
"Gurnari",
"Davide",
""
],
[
"Guzmán-Sáenz",
"Aldo",
""
],
[
"Utro",
"Filippo",
""
],
[
"Bose",
"Aritra",
""
],
[
"Basu",
"Saugata",
""
],
[
"Parida",
"Laxmi",
""
]
] | Identifying molecular signatures from complex disease patients with underlying symptomatic similarities is a significant challenge in the analysis of high dimensional multi-omics data. Topological data analysis (TDA) provides a way of extracting such information from the geometric structure of the data and identifying multiway higher-order relationships. Here, we propose an application of Harmonic persistent homology, which overcomes the limitations of ambiguous assignment of the topological information to the original elements in a representative topological cycle from the data. When applied to multi-omics data, this leads to the discovery of hidden patterns highlighting the relationships between different omic profiles, while allowing for common tasks in multi-omics analyses, such as disease subtyping, and most importantly biomarker identification for similar latent biological pathways that are associated with complex diseases. Our experiments on multiple cancer data show that harmonic persistent homology effectively dissects multi-omics data to identify biomarkers by detecting representative cycles predictive of disease subtypes. |
0709.2247 | Alessandro Pelizzola | P. Bruscolini, A. Pelizzola and M. Zamparo | Downhill versus two-state protein folding in a statistical mechanical
model | 20 pages, 13 figures | J. Chem. Phys. 126, 215103 (2007) | 10.1063/1.2738473 | null | q-bio.BM cond-mat.stat-mech | null | The authors address the problem of downhill protein folding in the framework
of a simple statistical mechanical model, which allows an exact solution for
the equilibrium and a semianalytical treatment of the kinetics. Focusing on
protein 1BBL, a candidate for downhill folding behavior, and comparing it to
the WW domain of protein PIN1, a two-state folder of comparable size, the
authors show that there are qualitative differences in both the equilibrium and
kinetic properties of the two molecules. However, the barrierless scenario
which would be expected if 1BBL were a true downhill folder is observed only at
low enough temperature.
| [
{
"created": "Fri, 14 Sep 2007 10:05:24 GMT",
"version": "v1"
}
] | 2007-09-17 | [
[
"Bruscolini",
"P.",
""
],
[
"Pelizzola",
"A.",
""
],
[
"Zamparo",
"M.",
""
]
] | The authors address the problem of downhill protein folding in the framework of a simple statistical mechanical model, which allows an exact solution for the equilibrium and a semianalytical treatment of the kinetics. Focusing on protein 1BBL, a candidate for downhill folding behavior, and comparing it to the WW domain of protein PIN1, a two-state folder of comparable size, the authors show that there are qualitative differences in both the equilibrium and kinetic properties of the two molecules. However, the barrierless scenario which would be expected if 1BBL were a true downhill folder is observed only at low enough temperature. |
1404.1152 | Josh Moles | Josh Moles, Peter Banda, Christof Teuscher | Delay Line as a Chemical Reaction Network | 9 pages, 11 figures, 6 tables | Parallel Process. Lett. 25, 1540002 (2015) | 10.1142/S0129626415400022 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Chemistry as an unconventional computing medium presently lacks a systematic
approach to gather, store, and sort data over time. To build more complicated
systems in chemistries, the ability to look at data in the past would be a
valuable tool to perform complex calculations. In this paper we present the
first implementation of a chemical delay line providing information storage in
a chemistry that can reliably capture information over an extended period of
time. The delay line is capable of parallel operations in a single instruction,
multiple data (SIMD) fashion.
Using Michaelis-Menten kinetics, we describe the chemical delay line
implementation featuring an enzyme acting as a means to reduce copy errors. We
also discuss how information is randomly accessible from any element on the
delay line. Our work shows how the chemical delay line retains and provides a
value from a previous cycle. The system's modularity allows for integration
with existing chemical systems. We exemplify the delay line capabilities by
integration with a threshold asymmetric signal perceptron to demonstrate how it
learns all 14 linearly separable binary functions over a size two sliding
window. The delay line has applications in biomedical diagnosis and treatment,
such as smart drug delivery.
| [
{
"created": "Fri, 4 Apr 2014 04:43:14 GMT",
"version": "v1"
},
{
"created": "Tue, 31 Mar 2015 22:37:41 GMT",
"version": "v2"
}
] | 2015-04-02 | [
[
"Moles",
"Josh",
""
],
[
"Banda",
"Peter",
""
],
[
"Teuscher",
"Christof",
""
]
] | Chemistry as an unconventional computing medium presently lacks a systematic approach to gather, store, and sort data over time. To build more complicated systems in chemistries, the ability to look at data in the past would be a valuable tool to perform complex calculations. In this paper we present the first implementation of a chemical delay line providing information storage in a chemistry that can reliably capture information over an extended period of time. The delay line is capable of parallel operations in a single instruction, multiple data (SIMD) fashion. Using Michaelis-Menten kinetics, we describe the chemical delay line implementation featuring an enzyme acting as a means to reduce copy errors. We also discuss how information is randomly accessible from any element on the delay line. Our work shows how the chemical delay line retains and provides a value from a previous cycle. The system's modularity allows for integration with existing chemical systems. We exemplify the delay line capabilities by integration with a threshold asymmetric signal perceptron to demonstrate how it learns all 14 linearly separable binary functions over a size two sliding window. The delay line has applications in biomedical diagnosis and treatment, such as smart drug delivery. |
1604.02699 | Yunan Luo | Yunan Luo, Jianyang Zeng, Bonnie Berger, Jian Peng | Low-density locality-sensitive hashing boosts metagenomic binning | RECOMB 2016. Due to the limitation "The abstract field cannot be
longer than 1,920 characters", the abstract appearing here is slightly
shorter than the one in the PDF file | null | null | null | q-bio.QM q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Metagenomic binning is an essential task in analyzing metagenomic sequence
datasets. To analyze structure or function of microbial communities from
environmental samples, metagenomic sequence fragments are assigned to their
taxonomic origins. Although sequence alignment algorithms can readily be used
and usually provide high-resolution alignments and accurate binning results,
the computational cost of such alignment-based methods becomes prohibitive as
metagenomic datasets continue to grow. Alternative compositional-based methods,
which exploit sequence composition by profiling local short k-mers in
fragments, are often faster but less accurate than alignment-based methods.
Inspired by the success of linear error correcting codes in noisy channel
communication, we introduce Opal, a fast and accurate novel compositional-based
binning method. It incorporates ideas from Gallager's low-density parity-check
code to design a family of compact and discriminative locality-sensitive
hashing functions that encode long-range compositional dependencies in long
fragments. By incorporating the Gallager LSH functions as features in a simple
linear SVM, Opal provides fast, accurate and robust binning for datasets
consisting of a large number of species, even with mutations and sequencing
errors. Opal not only performs up to two orders of magnitude faster than BWA,
an alignment-based binning method, but also achieves improved binning accuracy
and robustness to sequencing errors. Opal also outperforms models built on
traditional k-mer profiles in terms of robustness and accuracy. Finally, we
demonstrate that we can effectively use Opal in the "coarse search" stage of a
compressive genomics pipeline to identify a much smaller candidate set of
taxonomic origins for a subsequent alignment-based method to analyze, thus
providing metagenomic binning with high scalability, high accuracy and high
resolution.
| [
{
"created": "Sun, 10 Apr 2016 14:57:58 GMT",
"version": "v1"
}
] | 2016-04-12 | [
[
"Luo",
"Yunan",
""
],
[
"Zeng",
"Jianyang",
""
],
[
"Berger",
"Bonnie",
""
],
[
"Peng",
"Jian",
""
]
] | Metagenomic binning is an essential task in analyzing metagenomic sequence datasets. To analyze structure or function of microbial communities from environmental samples, metagenomic sequence fragments are assigned to their taxonomic origins. Although sequence alignment algorithms can readily be used and usually provide high-resolution alignments and accurate binning results, the computational cost of such alignment-based methods becomes prohibitive as metagenomic datasets continue to grow. Alternative compositional-based methods, which exploit sequence composition by profiling local short k-mers in fragments, are often faster but less accurate than alignment-based methods. Inspired by the success of linear error correcting codes in noisy channel communication, we introduce Opal, a fast and accurate novel compositional-based binning method. It incorporates ideas from Gallager's low-density parity-check code to design a family of compact and discriminative locality-sensitive hashing functions that encode long-range compositional dependencies in long fragments. By incorporating the Gallager LSH functions as features in a simple linear SVM, Opal provides fast, accurate and robust binning for datasets consisting of a large number of species, even with mutations and sequencing errors. Opal not only performs up to two orders of magnitude faster than BWA, an alignment-based binning method, but also achieves improved binning accuracy and robustness to sequencing errors. Opal also outperforms models built on traditional k-mer profiles in terms of robustness and accuracy. Finally, we demonstrate that we can effectively use Opal in the "coarse search" stage of a compressive genomics pipeline to identify a much smaller candidate set of taxonomic origins for a subsequent alignment-based method to analyze, thus providing metagenomic binning with high scalability, high accuracy and high resolution. |
1510.05849 | John Vandermeer | John Vandermeer, Pej Rohani, Ivette Perfecto | Local dynamics of the coffee rust disease and the potential effect of
shade | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present a mode that incorporates two levels of dynamic
structuring of the coffee rust disease (caused by the rust fungus, Hemileia
vastatrix). First, two distinct spatial scales of transmission dynamics are
interrogated with respect to the resultant structure of catastrophic
transitions of epidemics. Second the effect of management style, especially the
well-known issue of shade management, is added to the base-line model. The
final structure includes a simple one-to-one functional structure of disease
incidence as a function as well as a classical critical transition structure,
and finally a hysteretic loop. These qualitative structures accord well with
the recent history of the coffee rust disease.
| [
{
"created": "Tue, 20 Oct 2015 12:10:05 GMT",
"version": "v1"
}
] | 2019-10-31 | [
[
"Vandermeer",
"John",
""
],
[
"Rohani",
"Pej",
""
],
[
"Perfecto",
"Ivette",
""
]
] | In this paper we present a mode that incorporates two levels of dynamic structuring of the coffee rust disease (caused by the rust fungus, Hemileia vastatrix). First, two distinct spatial scales of transmission dynamics are interrogated with respect to the resultant structure of catastrophic transitions of epidemics. Second the effect of management style, especially the well-known issue of shade management, is added to the base-line model. The final structure includes a simple one-to-one functional structure of disease incidence as a function as well as a classical critical transition structure, and finally a hysteretic loop. These qualitative structures accord well with the recent history of the coffee rust disease. |
2106.04005 | Margaret Cheung | Chengxuan Li, James Liman, Yossi Eliaz, Margaret S Cheung | Forecasting Avalanches in Branched Actomyosin Networks with Network
Science and Machine Learning | Submitted | null | null | null | q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | We explored the dynamical and structural effects of actin-related proteins
2/3 (Arp2/3) on actomyosin networks using mechanochemical simulations of active
matter networks. At a nanoscale, the Arp2/3 complex alters the topology of
actomyosin by nucleating a daughter filament at an angle to a mother filament.
At a subcellular scale, they orchestrate the formation of branched actomyosin
network. Using a coarse-grained approach, we sought to understand how an
actomyosin network temporally and spatially reorganizes itself by varying the
concentration of the Arp2/3 complexes. Driven by the motor dynamics, the
network stalls at a high concentration of Arp2/3 and contracts at a low Arp2/3
concentration. At an intermediate Arp2/3 concentration, however, the actomyosin
network is formed by loosely connected clusters that may collapse suddenly when
driven by motors. This physical phenomenon is called an "avalanche" largely due
to the marginal instability inherent from the morphology of a branched
actomyosin network when the Arp2/3 complex is present. While embracing the data
science approaches, we unveiled the higher-order patterns in the branched
actomyosin networks and discovered a sudden change in the "social" network
topology of the actomyosin. This is a new type of avalanches in addition to the
two types of avalanches associated with a sudden change in the size or the
shape of the whole actomyosin network as shown in the previous investigation.
Our new finding promotes the importance of using network theory and machine
learning models to forecast avalanches in actomyosin networks. The mechanisms
of the Arp2/3 complexes in shaping the architecture of branched actomyosin
networks obtained in this paper will help us better understand the emergent
reorganization of the topology in dense actomyosin networks that are difficult
to detect in experiments.
| [
{
"created": "Mon, 7 Jun 2021 23:15:13 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Sep 2021 17:43:41 GMT",
"version": "v2"
}
] | 2021-09-08 | [
[
"Li",
"Chengxuan",
""
],
[
"Liman",
"James",
""
],
[
"Eliaz",
"Yossi",
""
],
[
"Cheung",
"Margaret S",
""
]
] | We explored the dynamical and structural effects of actin-related proteins 2/3 (Arp2/3) on actomyosin networks using mechanochemical simulations of active matter networks. At a nanoscale, the Arp2/3 complex alters the topology of actomyosin by nucleating a daughter filament at an angle to a mother filament. At a subcellular scale, they orchestrate the formation of branched actomyosin network. Using a coarse-grained approach, we sought to understand how an actomyosin network temporally and spatially reorganizes itself by varying the concentration of the Arp2/3 complexes. Driven by the motor dynamics, the network stalls at a high concentration of Arp2/3 and contracts at a low Arp2/3 concentration. At an intermediate Arp2/3 concentration, however, the actomyosin network is formed by loosely connected clusters that may collapse suddenly when driven by motors. This physical phenomenon is called an "avalanche" largely due to the marginal instability inherent from the morphology of a branched actomyosin network when the Arp2/3 complex is present. While embracing the data science approaches, we unveiled the higher-order patterns in the branched actomyosin networks and discovered a sudden change in the "social" network topology of the actomyosin. This is a new type of avalanches in addition to the two types of avalanches associated with a sudden change in the size or the shape of the whole actomyosin network as shown in the previous investigation. Our new finding promotes the importance of using network theory and machine learning models to forecast avalanches in actomyosin networks. The mechanisms of the Arp2/3 complexes in shaping the architecture of branched actomyosin networks obtained in this paper will help us better understand the emergent reorganization of the topology in dense actomyosin networks that are difficult to detect in experiments. |
2310.03035 | Maitham Yousif | Fadhil G. Al-Amran, Salman Rawaf, Maitham G. Yousif | Early Detection of Post-COVID-19 Fatigue Syndrome Using Deep Learning
Models | null | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | The research titled "Early Detection of Post-COVID-19 Fatigue Syndrome using
Deep Learning Models" addresses a pressing concern arising from the COVID-19
pandemic. Post-COVID-19 Fatigue Syndrome (PCFS) has become a significant health
issue affecting individuals who have recovered from COVID-19 infection. This
study harnesses a robust dataset comprising 940 patients from diverse age
groups, whose medical records were collected from various hospitals in Iraq
over the years 2022, 2022, and 2023. The primary objective of this research is
to develop and evaluate deep learning models for the early detection of PCFS.
Leveraging the power of deep learning, these models are trained on a
comprehensive set of clinical and demographic features extracted from the
dataset. The goal is to enable timely identification of PCFS symptoms in
post-COVID-19 patients, which can lead to more effective interventions and
improved patient outcomes. The study's findings underscore the potential of
deep learning in healthcare, particularly in the context of COVID-19 recovery.
Early detection of PCFS can aid healthcare professionals in providing timely
care and support to affected individuals, potentially reducing the long-term
impact of this syndrome on their quality of life. This research contributes to
the growing body of knowledge surrounding COVID-19-related health complications
and highlights the importance of leveraging advanced machine learning
techniques for early diagnosis and intervention. Keywords: Early Detection,
Post-COVID-19 Fatigue Syndrome, Deep Learning Models, Healthcare, COVID-19
Recovery, Medical Data Analysis, Machine Learning, Health Interventions.
| [
{
"created": "Tue, 26 Sep 2023 17:44:17 GMT",
"version": "v1"
}
] | 2023-10-06 | [
[
"Al-Amran",
"Fadhil G.",
""
],
[
"Rawaf",
"Salman",
""
],
[
"Yousif",
"Maitham G.",
""
]
] | The research titled "Early Detection of Post-COVID-19 Fatigue Syndrome using Deep Learning Models" addresses a pressing concern arising from the COVID-19 pandemic. Post-COVID-19 Fatigue Syndrome (PCFS) has become a significant health issue affecting individuals who have recovered from COVID-19 infection. This study harnesses a robust dataset comprising 940 patients from diverse age groups, whose medical records were collected from various hospitals in Iraq over the years 2022, 2022, and 2023. The primary objective of this research is to develop and evaluate deep learning models for the early detection of PCFS. Leveraging the power of deep learning, these models are trained on a comprehensive set of clinical and demographic features extracted from the dataset. The goal is to enable timely identification of PCFS symptoms in post-COVID-19 patients, which can lead to more effective interventions and improved patient outcomes. The study's findings underscore the potential of deep learning in healthcare, particularly in the context of COVID-19 recovery. Early detection of PCFS can aid healthcare professionals in providing timely care and support to affected individuals, potentially reducing the long-term impact of this syndrome on their quality of life. This research contributes to the growing body of knowledge surrounding COVID-19-related health complications and highlights the importance of leveraging advanced machine learning techniques for early diagnosis and intervention. Keywords: Early Detection, Post-COVID-19 Fatigue Syndrome, Deep Learning Models, Healthcare, COVID-19 Recovery, Medical Data Analysis, Machine Learning, Health Interventions. |
1509.02483 | Richard A Neher | Fabio Zanini, Johanna Brodin, Lina Thebo, Christa Lanz, G\"oran Bratt,
Jan Albert and Richard A. Neher | Population genomics of intrapatient HIV-1 evolution | null | null | 10.7554/eLife.11282 | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many microbial populations rapidly adapt to changing environments with
multiple variants competing for survival. To quantify such complex evolutionary
dynamics in vivo, time resolved and genome wide data including rare variants
are essential. We performed whole-genome deep sequencing of HIV-1 populations
in 9 untreated patients, with 6-12 longitudinal samples per patient spanning
5-8 years of infection. We show that patterns of minor diversity are
reproducible between patients and mirror global HIV-1 diversity, suggesting a
universal landscape of fitness costs that control diversity. Reversions towards
the ancestral HIV-1 sequence are observed throughout infection and account for
almost one third of all sequence changes. Reversion rates depend strongly on
conservation. Frequent recombination limits linkage disequilibrium to about
100bp in most of the genome, but strong hitch-hiking due to short range linkage
limits diversity.
| [
{
"created": "Tue, 8 Sep 2015 18:25:12 GMT",
"version": "v1"
}
] | 2016-01-25 | [
[
"Zanini",
"Fabio",
""
],
[
"Brodin",
"Johanna",
""
],
[
"Thebo",
"Lina",
""
],
[
"Lanz",
"Christa",
""
],
[
"Bratt",
"Göran",
""
],
[
"Albert",
"Jan",
""
],
[
"Neher",
"Richard A.",
""
]
] | Many microbial populations rapidly adapt to changing environments with multiple variants competing for survival. To quantify such complex evolutionary dynamics in vivo, time resolved and genome wide data including rare variants are essential. We performed whole-genome deep sequencing of HIV-1 populations in 9 untreated patients, with 6-12 longitudinal samples per patient spanning 5-8 years of infection. We show that patterns of minor diversity are reproducible between patients and mirror global HIV-1 diversity, suggesting a universal landscape of fitness costs that control diversity. Reversions towards the ancestral HIV-1 sequence are observed throughout infection and account for almost one third of all sequence changes. Reversion rates depend strongly on conservation. Frequent recombination limits linkage disequilibrium to about 100bp in most of the genome, but strong hitch-hiking due to short range linkage limits diversity. |
2204.05522 | Baihan Lin | Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf | Deep Annotation of Therapeutic Working Alliance in Psychotherapy | null | null | null | null | q-bio.NC cs.AI cs.CL cs.HC cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The therapeutic working alliance is an important predictor of the outcome of
the psychotherapy treatment. In practice, the working alliance is estimated
from a set of scoring questionnaires in an inventory that both the patient and
the therapists fill out. In this work, we propose an analytical framework of
directly inferring the therapeutic working alliance from the natural language
within the psychotherapy sessions in a turn-level resolution with deep
embeddings such as the Doc2Vec and SentenceBERT models. The transcript of each
psychotherapy session can be transcribed and generated in real-time from the
session speech recordings, and these embedded dialogues are compared with the
distributed representations of the statements in the working alliance
inventory. We demonstrate, in a real-world dataset with over 950 sessions of
psychotherapy treatments in anxiety, depression, schizophrenia and suicidal
patients, the effectiveness of this method in mapping out trajectories of
patient-therapist alignment and the interpretability that can offer insights in
clinical psychiatry. We believe such a framework can be provide timely feedback
to the therapist regarding the quality of the conversation in interview
sessions.
| [
{
"created": "Tue, 12 Apr 2022 04:42:51 GMT",
"version": "v1"
}
] | 2022-04-14 | [
[
"Lin",
"Baihan",
""
],
[
"Cecchi",
"Guillermo",
""
],
[
"Bouneffouf",
"Djallel",
""
]
] | The therapeutic working alliance is an important predictor of the outcome of the psychotherapy treatment. In practice, the working alliance is estimated from a set of scoring questionnaires in an inventory that both the patient and the therapists fill out. In this work, we propose an analytical framework of directly inferring the therapeutic working alliance from the natural language within the psychotherapy sessions in a turn-level resolution with deep embeddings such as the Doc2Vec and SentenceBERT models. The transcript of each psychotherapy session can be transcribed and generated in real-time from the session speech recordings, and these embedded dialogues are compared with the distributed representations of the statements in the working alliance inventory. We demonstrate, in a real-world dataset with over 950 sessions of psychotherapy treatments in anxiety, depression, schizophrenia and suicidal patients, the effectiveness of this method in mapping out trajectories of patient-therapist alignment and the interpretability that can offer insights in clinical psychiatry. We believe such a framework can be provide timely feedback to the therapist regarding the quality of the conversation in interview sessions. |
1804.10518 | Sergei Kozyrev | S.V. Kozyrev | Biology is a constructive physics | 9 pages, commentaries added | null | null | null | q-bio.PE cond-mat.stat-mech cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Yuri Manin's approach to Zipf's law (Kolmogorov complexity as energy) is
applied to investigation of biological evolution. Model of constructive
statistical mechanics where complexity is a contribution to energy is proposed
to model genomics. Scaling laws in genomics are discussed in relation to Zipf's
law. This gives a model of Eugene Koonin's Third Evolutionary Synthesis
(physical model which should describe scaling in genomics).
| [
{
"created": "Thu, 26 Apr 2018 11:51:30 GMT",
"version": "v1"
},
{
"created": "Sat, 30 Jun 2018 16:16:25 GMT",
"version": "v2"
},
{
"created": "Wed, 4 Jul 2018 11:40:30 GMT",
"version": "v3"
},
{
"created": "Sat, 11 Aug 2018 17:05:09 GMT",
"version": "v4"
}
] | 2018-08-14 | [
[
"Kozyrev",
"S. V.",
""
]
] | Yuri Manin's approach to Zipf's law (Kolmogorov complexity as energy) is applied to investigation of biological evolution. Model of constructive statistical mechanics where complexity is a contribution to energy is proposed to model genomics. Scaling laws in genomics are discussed in relation to Zipf's law. This gives a model of Eugene Koonin's Third Evolutionary Synthesis (physical model which should describe scaling in genomics). |
1105.3580 | Chris Brackley | Chris A. Brackley, David Broomhead, M. Carmen Romano, and Marco Thiel | A Max-Plus Model of Ribosome Dynamics During mRNA Translation | null | null | null | null | q-bio.QM math.RA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine the dynamics of the translation stage of cellular protein
production, in which ribosomes move uni-directionally along mRNA strands
building an amino acid chain as they go. We describe the system using a timed
event graph - a class of Petri net useful for studying discrete events which
take a finite time. We use max-plus algebra to describe a deterministic version
of the model, calculating the protein production rate and density of ribosomes
on the mRNA. We find exact agreement between these analytical results and
numerical simulations of the deterministic case.
| [
{
"created": "Wed, 18 May 2011 10:07:30 GMT",
"version": "v1"
}
] | 2015-03-19 | [
[
"Brackley",
"Chris A.",
""
],
[
"Broomhead",
"David",
""
],
[
"Romano",
"M. Carmen",
""
],
[
"Thiel",
"Marco",
""
]
] | We examine the dynamics of the translation stage of cellular protein production, in which ribosomes move uni-directionally along mRNA strands building an amino acid chain as they go. We describe the system using a timed event graph - a class of Petri net useful for studying discrete events which take a finite time. We use max-plus algebra to describe a deterministic version of the model, calculating the protein production rate and density of ribosomes on the mRNA. We find exact agreement between these analytical results and numerical simulations of the deterministic case. |
2305.16714 | Mike Steel Prof. | Mike Steel | Interior operators and their relationship to autocatalytic networks | 13 pages, 1 figure | null | null | null | q-bio.MN | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The emergence of an autocatalytic network from an available set of elements
is a fundamental step in early evolutionary processes, such as the origin of
metabolism. Given a set of elements, the reactions between them (chemical or
otherwise), and certain elements catalysing certain reactions, a Reflexively
Autocatalytic F-generated (RAF) set is a subset $R'$ of reactions that is
self-generating from a given food set, and with each reaction in $R'$ being
catalysed from within $R'$. RAF theory has been applied to various phenomena in
theoretical biology, and a key feature of the approach is that it is possible
to efficiently identify and classify RAFs within large systems. This is
possible because RAFs can be described as the (nonempty) subsets of the
reactions that are the fixed points of an (efficiently computable) interior map
that operates on subsets of reactions. Although the main generic results
concerning RAFs can be derived using just this property, we show that for
systems with at least 12 reactions there are generic results concerning RAFs
that cannot be proven using the interior operator property alone.
| [
{
"created": "Fri, 26 May 2023 07:59:36 GMT",
"version": "v1"
},
{
"created": "Wed, 7 Jun 2023 20:42:38 GMT",
"version": "v2"
},
{
"created": "Tue, 13 Jun 2023 03:09:49 GMT",
"version": "v3"
},
{
"created": "Tue, 29 Aug 2023 05:13:31 GMT",
"version": "v4"
}
] | 2023-08-30 | [
[
"Steel",
"Mike",
""
]
] | The emergence of an autocatalytic network from an available set of elements is a fundamental step in early evolutionary processes, such as the origin of metabolism. Given a set of elements, the reactions between them (chemical or otherwise), and certain elements catalysing certain reactions, a Reflexively Autocatalytic F-generated (RAF) set is a subset $R'$ of reactions that is self-generating from a given food set, and with each reaction in $R'$ being catalysed from within $R'$. RAF theory has been applied to various phenomena in theoretical biology, and a key feature of the approach is that it is possible to efficiently identify and classify RAFs within large systems. This is possible because RAFs can be described as the (nonempty) subsets of the reactions that are the fixed points of an (efficiently computable) interior map that operates on subsets of reactions. Although the main generic results concerning RAFs can be derived using just this property, we show that for systems with at least 12 reactions there are generic results concerning RAFs that cannot be proven using the interior operator property alone. |
1407.1794 | Frederick Matsen IV | Frederick A Matsen IV | Phylogenetics and the human microbiome | to appear in Systematic Biology | null | null | null | q-bio.PE q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The human microbiome is the ensemble of genes in the microbes that live
inside and on the surface of humans. Because microbial sequencing information
is now much easier to come by than phenotypic information, there has been an
explosion of sequencing and genetic analysis of microbiome samples. Much of the
analytical work for these sequences involves phylogenetics, at least
indirectly, but methodology has developed in a somewhat different direction
than for other applications of phylogenetics. In this paper I review the field
and its methods from the perspective of a phylogeneticist, as well as
describing current challenges for phylogenetics coming from this type of work.
| [
{
"created": "Mon, 7 Jul 2014 18:33:08 GMT",
"version": "v1"
}
] | 2014-07-08 | [
[
"Matsen",
"Frederick A",
"IV"
]
] | The human microbiome is the ensemble of genes in the microbes that live inside and on the surface of humans. Because microbial sequencing information is now much easier to come by than phenotypic information, there has been an explosion of sequencing and genetic analysis of microbiome samples. Much of the analytical work for these sequences involves phylogenetics, at least indirectly, but methodology has developed in a somewhat different direction than for other applications of phylogenetics. In this paper I review the field and its methods from the perspective of a phylogeneticist, as well as describing current challenges for phylogenetics coming from this type of work. |
1206.1311 | Gyorgy Korniss | F. Molnar Jr, T. Caraco, G. Korniss | Extraordinary Sex Ratios: Cultural Effects on Ecological Consequences | final version, reflecting changes in response to referees' comments | PLoS ONE 7(8): e43364 (2012) | 10.1371/journal.pone.0043364 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We model sex-structured population dynamics to analyze pairwise competition
between groups differing both genetically and culturally. A sex-ratio allele is
expressed in the heterogametic sex only, so that assumptions of Fisher's
analysis do not apply. Sex-ratio evolution drives cultural evolution of a
group-associated trait governing mortality in the homogametic sex. The two-sex
dynamics under resource limitation induces a strong Allee effect that depends
on both sex ratio and cultural trait values. We describe the resulting
threshold, separating extinction from positive growth, as a function of female
and male densities. When initial conditions avoid extinction due to the Allee
effect, different sex ratios cannot coexist; in our model, greater female
allocation always invades and excludes a lesser allocation. But the culturally
transmitted trait interacts with the sex ratio to determine the ecological
consequences of successful invasion. The invading female allocation may permit
population persistence at self-regulated equilibrium. For this case, the
resident culture may be excluded, or may coexist with the invader culture. That
is, a single sex-ratio allele in females and a cultural dimorphism in male
mortality can persist; a low-mortality resident trait is maintained by
father-to-son cultural transmission. Otherwise, the successfully invading
female allocation excludes the resident allele and culture, and then drives the
population to extinction via a shortage of males. Finally, we show that the
results obtained under homogeneous mixing hold, with caveats, in a spatially
explicit model with local mating and diffusive dispersal in both sexes.
| [
{
"created": "Wed, 6 Jun 2012 19:37:04 GMT",
"version": "v1"
},
{
"created": "Fri, 7 Sep 2012 14:23:02 GMT",
"version": "v2"
}
] | 2012-09-10 | [
[
"Molnar",
"F.",
"Jr"
],
[
"Caraco",
"T.",
""
],
[
"Korniss",
"G.",
""
]
] | We model sex-structured population dynamics to analyze pairwise competition between groups differing both genetically and culturally. A sex-ratio allele is expressed in the heterogametic sex only, so that assumptions of Fisher's analysis do not apply. Sex-ratio evolution drives cultural evolution of a group-associated trait governing mortality in the homogametic sex. The two-sex dynamics under resource limitation induces a strong Allee effect that depends on both sex ratio and cultural trait values. We describe the resulting threshold, separating extinction from positive growth, as a function of female and male densities. When initial conditions avoid extinction due to the Allee effect, different sex ratios cannot coexist; in our model, greater female allocation always invades and excludes a lesser allocation. But the culturally transmitted trait interacts with the sex ratio to determine the ecological consequences of successful invasion. The invading female allocation may permit population persistence at self-regulated equilibrium. For this case, the resident culture may be excluded, or may coexist with the invader culture. That is, a single sex-ratio allele in females and a cultural dimorphism in male mortality can persist; a low-mortality resident trait is maintained by father-to-son cultural transmission. Otherwise, the successfully invading female allocation excludes the resident allele and culture, and then drives the population to extinction via a shortage of males. Finally, we show that the results obtained under homogeneous mixing hold, with caveats, in a spatially explicit model with local mating and diffusive dispersal in both sexes. |
1509.05163 | Ganesh Bagler Dr | Reena Yadav, Mayur Ghatge, Kirankumar Hiremath, Ganesh Bagler | Numerical study of variable lung ventilation strategies | 6 pages, 7 figures, 37th National Systems Conference (NSC 2013;
Theme: Systems Thinking in Social Innovation and Emerging Technologies).
appears in Chapter 26, pp 299-306, Systems Thinking Approach for Social
Problems, Lecture Notes in Electrical Engineering, 327, 2015 | null | null | null | q-bio.QM physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mechanical ventilation is used for patients with a variety of lung diseases.
Traditionally, ventilators have been designed to monotonously deliver equal
sized breaths. While it may seem intuitive that lungs may benefit from
unvarying and stable ventilation pressure strategy, recently it has been
reported that variable lung ventilation is advantageous. In this study, we
analyze the mean tidal volume in response to different `variable ventilation
pressure' strategies. We found that uniformly distributed variability in
pressure gives the best tidal volume as compared to that of normal, scale-
free, log normal and linear distributions.
| [
{
"created": "Thu, 17 Sep 2015 08:48:11 GMT",
"version": "v1"
}
] | 2015-09-18 | [
[
"Yadav",
"Reena",
""
],
[
"Ghatge",
"Mayur",
""
],
[
"Hiremath",
"Kirankumar",
""
],
[
"Bagler",
"Ganesh",
""
]
] | Mechanical ventilation is used for patients with a variety of lung diseases. Traditionally, ventilators have been designed to monotonously deliver equal sized breaths. While it may seem intuitive that lungs may benefit from unvarying and stable ventilation pressure strategy, recently it has been reported that variable lung ventilation is advantageous. In this study, we analyze the mean tidal volume in response to different `variable ventilation pressure' strategies. We found that uniformly distributed variability in pressure gives the best tidal volume as compared to that of normal, scale- free, log normal and linear distributions. |
2404.09089 | Steven Frank | Steven A. Frank | A biological circuit to anticipate trend | null | null | null | null | q-bio.PE q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | Organisms gain by anticipating future changes in the environment. Those
environmental changes often follow stochastic trends. The greater the slope of
the trend, the more likely the trend's momentum carries the future trend in the
same direction. This article presents a simple biological circuit that measures
the momentum, providing a prediction about future trend. The circuit calculates
the momentum by the difference between a short-term and a long-term exponential
moving average. The time lengths of the two moving averages can be adjusted by
changing the decay rates of state variables. Different time lengths for those
averages trade off between errors caused by noise and errors caused by lags in
predicting a change in the direction of the trend. Prior studies have
emphasized circuits that make similar calculations about trends. However, those
prior studies embedded their analyses in the details of particular
applications, obscuring the simple generality and wide applicability of the
approach. The model here contributes to the topic by clarifying the great
simplicity and generality of anticipation for stochastic trends. This article
also notes that, in financial analysis, the difference between moving averages
is widely used to predict future trends in asset prices. The financial measure
is called the moving average convergence-divergence (MACD) indicator.
Connecting the biological problem to financial analysis opens the way for
future studies in biology to exploit the variety of highly developed trend
models in finance.
| [
{
"created": "Sat, 13 Apr 2024 22:02:33 GMT",
"version": "v1"
}
] | 2024-04-16 | [
[
"Frank",
"Steven A.",
""
]
] | Organisms gain by anticipating future changes in the environment. Those environmental changes often follow stochastic trends. The greater the slope of the trend, the more likely the trend's momentum carries the future trend in the same direction. This article presents a simple biological circuit that measures the momentum, providing a prediction about future trend. The circuit calculates the momentum by the difference between a short-term and a long-term exponential moving average. The time lengths of the two moving averages can be adjusted by changing the decay rates of state variables. Different time lengths for those averages trade off between errors caused by noise and errors caused by lags in predicting a change in the direction of the trend. Prior studies have emphasized circuits that make similar calculations about trends. However, those prior studies embedded their analyses in the details of particular applications, obscuring the simple generality and wide applicability of the approach. The model here contributes to the topic by clarifying the great simplicity and generality of anticipation for stochastic trends. This article also notes that, in financial analysis, the difference between moving averages is widely used to predict future trends in asset prices. The financial measure is called the moving average convergence-divergence (MACD) indicator. Connecting the biological problem to financial analysis opens the way for future studies in biology to exploit the variety of highly developed trend models in finance. |
1612.03649 | Francesco Fumarola | Francesco Fumarola | Hierarchical searching in episodic memory | 22 pages, 10 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An analysis of free-recall datasets from two independent experiments allows
to identify two anomalous instances of non-monotonicity in free recall: a
maximum in the dependence of the inter-response intervals on the
serial-position lags, and a minimum in the rate of contiguous recall near the
beginning of the recall process. Both effects, it is argued, may stem from a
hierarchical search protocol in the space of memories. An elementary
random-walk model on binary strings is used to test this hypothesis.
| [
{
"created": "Mon, 12 Dec 2016 12:42:35 GMT",
"version": "v1"
},
{
"created": "Tue, 9 May 2017 17:59:43 GMT",
"version": "v2"
}
] | 2017-05-10 | [
[
"Fumarola",
"Francesco",
""
]
] | An analysis of free-recall datasets from two independent experiments allows to identify two anomalous instances of non-monotonicity in free recall: a maximum in the dependence of the inter-response intervals on the serial-position lags, and a minimum in the rate of contiguous recall near the beginning of the recall process. Both effects, it is argued, may stem from a hierarchical search protocol in the space of memories. An elementary random-walk model on binary strings is used to test this hypothesis. |
2210.09842 | Faramarz Valafar | Afif Elghraoui, and Faramarz Valafar | MVP: Detection of motif-making and -breaking mutations | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Background: DNA, RNA, and protein sequence motifs can be recognition sites
for biological functions such as regulation, DNA base modification, and
molecular binding in general. The gain and loss of such motifs can carry
important consequences. When comparing sequences, the analysis of individual
variants does not impart an understanding of the impact on these sites. Rather,
only when these variants considered together with their neighbors and the
original sequence context does this become possible.
Results: The motif-variant probe (mvp) makes this consideration, counting
instances of user-specified sequence motifs before and after mutation and
reports those that result in motif gain or loss. mvp can perform a similar
analysis for proteins with amino acid variant data. The software is freely
available at https://lpcdrp.gitlab.io/mvp and also installable with the conda
package manager.
Conclusions: The ability to easily search for variants affecting any motif,
together with the simultaneous consideration of neighboring variants makes mvp
a versatile tool to aid in a less-frequented dimension of comparative genomics.
| [
{
"created": "Sat, 15 Oct 2022 18:16:58 GMT",
"version": "v1"
}
] | 2022-10-19 | [
[
"Elghraoui",
"Afif",
""
],
[
"Valafar",
"Faramarz",
""
]
] | Background: DNA, RNA, and protein sequence motifs can be recognition sites for biological functions such as regulation, DNA base modification, and molecular binding in general. The gain and loss of such motifs can carry important consequences. When comparing sequences, the analysis of individual variants does not impart an understanding of the impact on these sites. Rather, only when these variants considered together with their neighbors and the original sequence context does this become possible. Results: The motif-variant probe (mvp) makes this consideration, counting instances of user-specified sequence motifs before and after mutation and reports those that result in motif gain or loss. mvp can perform a similar analysis for proteins with amino acid variant data. The software is freely available at https://lpcdrp.gitlab.io/mvp and also installable with the conda package manager. Conclusions: The ability to easily search for variants affecting any motif, together with the simultaneous consideration of neighboring variants makes mvp a versatile tool to aid in a less-frequented dimension of comparative genomics. |
0806.1649 | Carlos Roca P. | Carlos P. Roca, Jos\'e A. Cuesta, and Angel S\'anchez | Effect of spatial structure on the evolution of cooperation | Final version, 20 pages, 14 figures | Phys. Rev. E 80, 046106 (2009) | 10.1103/PhysRevE.80.046106 | null | q-bio.PE cs.GT physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spatial structure is known to have an impact on the evolution of cooperation,
and so it has been intensively studied during recent years. Previous work has
shown the relevance of some features, such as the synchronicity of the
updating, the clustering of the network or the influence of the update rule.
This has been done, however, for concrete settings with particular games,
networks and update rules, with the consequence that some contradictions have
arisen and a general understanding of these topics is missing in the broader
context of the space of 2x2 games. To address this issue, we have performed a
systematic and exhaustive simulation in the different degrees of freedom of the
problem. In some cases, we generalize previous knowledge to the broader context
of our study and explain the apparent contradictions. In other cases, however,
our conclusions refute what seems to be established opinions in the field, as
for example the robustness of the effect of spatial structure against changes
in the update rule, or offer new insights into the subject, e.g. the relation
between the intensity of selection and the asymmetry between the effects on
games with mixed equilibria.
| [
{
"created": "Tue, 10 Jun 2008 15:01:55 GMT",
"version": "v1"
},
{
"created": "Fri, 4 Jul 2008 19:29:19 GMT",
"version": "v2"
},
{
"created": "Thu, 30 Apr 2009 11:36:19 GMT",
"version": "v3"
},
{
"created": "Sat, 7 Nov 2009 18:57:01 GMT",
"version": "v4"
}
] | 2009-11-07 | [
[
"Roca",
"Carlos P.",
""
],
[
"Cuesta",
"José A.",
""
],
[
"Sánchez",
"Angel",
""
]
] | Spatial structure is known to have an impact on the evolution of cooperation, and so it has been intensively studied during recent years. Previous work has shown the relevance of some features, such as the synchronicity of the updating, the clustering of the network or the influence of the update rule. This has been done, however, for concrete settings with particular games, networks and update rules, with the consequence that some contradictions have arisen and a general understanding of these topics is missing in the broader context of the space of 2x2 games. To address this issue, we have performed a systematic and exhaustive simulation in the different degrees of freedom of the problem. In some cases, we generalize previous knowledge to the broader context of our study and explain the apparent contradictions. In other cases, however, our conclusions refute what seems to be established opinions in the field, as for example the robustness of the effect of spatial structure against changes in the update rule, or offer new insights into the subject, e.g. the relation between the intensity of selection and the asymmetry between the effects on games with mixed equilibria. |
1704.08087 | Andrea De Martino | Daniele De Martino, Andrea De Martino | Constraint-based inverse modeling of metabolic networks: a proof of
concept | 4 pages, comments welcome | null | null | null | q-bio.MN cond-mat.dis-nn cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of inferring the probability distribution of flux
configurations in metabolic network models from empirical flux data. For the
simple case in which experimental averages are to be retrieved, data are
described by a Boltzmann-like distribution ($\propto e^{F/T}$) where $F$ is a
linear combination of fluxes and the `temperature' parameter $T\geq 0$ allows
for fluctuations. The zero-temperature limit corresponds to a Flux Balance
Analysis scenario, where an objective function ($F$) is maximized. As a test,
we have inverse modeled, by means of Boltzmann learning, the catabolic core of
Escherichia coli in glucose-limited aerobic stationary growth conditions.
Empirical means are best reproduced when $F$ is a simple combination of biomass
production and glucose uptake and the temperature is finite, implying the
presence of fluctuations. The scheme presented here has the potential to
deliver new quantitative insight on cellular metabolism. Our implementation is
however computationally intensive, and highlights the major role that effective
algorithms to sample the high-dimensional solution space of metabolic networks
can play in this field.
| [
{
"created": "Wed, 26 Apr 2017 13:02:58 GMT",
"version": "v1"
}
] | 2017-04-27 | [
[
"De Martino",
"Daniele",
""
],
[
"De Martino",
"Andrea",
""
]
] | We consider the problem of inferring the probability distribution of flux configurations in metabolic network models from empirical flux data. For the simple case in which experimental averages are to be retrieved, data are described by a Boltzmann-like distribution ($\propto e^{F/T}$) where $F$ is a linear combination of fluxes and the `temperature' parameter $T\geq 0$ allows for fluctuations. The zero-temperature limit corresponds to a Flux Balance Analysis scenario, where an objective function ($F$) is maximized. As a test, we have inverse modeled, by means of Boltzmann learning, the catabolic core of Escherichia coli in glucose-limited aerobic stationary growth conditions. Empirical means are best reproduced when $F$ is a simple combination of biomass production and glucose uptake and the temperature is finite, implying the presence of fluctuations. The scheme presented here has the potential to deliver new quantitative insight on cellular metabolism. Our implementation is however computationally intensive, and highlights the major role that effective algorithms to sample the high-dimensional solution space of metabolic networks can play in this field. |
q-bio/0610031 | Andrew Stein | A M Stein, D A Vader, T S Deisboeck, E A Chiocca, L M Sander and D A
Weitz | Directionality of glioblastoma invasion in a 3d in vitro experiment | null | null | null | null | q-bio.CB | null | Glioblastoma is the most malignant form of brain cancer. It is extremely
invasive; the mechanisms that govern invasion are not well understood. To
better understand the process of invasion, we conducted an in vitro experiment
in which a 3d tumour spheroid is implanted into a collagen gel. The paths of
individual invasive cells were tracked. These cells were modeled as radially
biased, persistent random walkers. The radial velocity bias was found to be 20
microns/hr on day one, but decayed significantly by day two. The cause of this
bias is thought to be due to chemotactic factors and contact guidance along
collagen fibers.
| [
{
"created": "Tue, 17 Oct 2006 17:15:23 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Stein",
"A M",
""
],
[
"Vader",
"D A",
""
],
[
"Deisboeck",
"T S",
""
],
[
"Chiocca",
"E A",
""
],
[
"Sander",
"L M",
""
],
[
"Weitz",
"D A",
""
]
] | Glioblastoma is the most malignant form of brain cancer. It is extremely invasive; the mechanisms that govern invasion are not well understood. To better understand the process of invasion, we conducted an in vitro experiment in which a 3d tumour spheroid is implanted into a collagen gel. The paths of individual invasive cells were tracked. These cells were modeled as radially biased, persistent random walkers. The radial velocity bias was found to be 20 microns/hr on day one, but decayed significantly by day two. The cause of this bias is thought to be due to chemotactic factors and contact guidance along collagen fibers. |
q-bio/0504002 | Arnaud Buhot | A. Halperin, A. Buhot, and E. B. Zhulina | Brush Effects on DNA Chips: Thermodynamics, Kinetics and Design
Guidlines | 20 pages, 5 figures (one figure in PNG format : figure1) | null | 10.1529/biophysj.105.063479 | null | q-bio.GN cond-mat.soft q-bio.QM | null | In biology experiments, oligonucleotide microarrays are contacted with a
solution of long nucleic acid (NA) targets. The hybridized probes thus carry
long tails. When the surface density of the oligonucleotide probes is high
enough, the progress of hybridization leads to the formation of a
polyelectrolyte brush due to mutual crowding of the NA tails. The free energy
penalty associated with the brush modifies both the hybridization isotherms and
the rate equations: the attainable hybridization is lowered significantly as is
the hybridization rate. While the equilibrium hybridization fraction, $x_{eq}$,
is low, the hybridization follows a Langmuir type isotherm, $x_{eq}/(1-x_{eq})
= c_t K$ where $c_t$ is the target concentration and $K$ is the equilibrium
constant smaller than its bulk value by a factor $(n/N)^{2/5}$ due to wall
effects where $n$ and $N$ denote the number of bases in the probe and the
target. At higher $x_{eq}$, when the brush is formed, the leading correction is
$x_{eq}/(1-x_{eq}) = c_t K \exp [ - const' (x_{eq}^{2/3} - x_B^{2/3})]$ where
$x_B$ corresponds to the onset of the brush regime. The denaturation rate
constant in the two regimes are identical. However, the hybridization rate
constant in the brush regime is lower, the leading correction being $\exp [-
const' (x^{2/3} - x_B^{2/3})]$.
| [
{
"created": "Mon, 4 Apr 2005 09:44:39 GMT",
"version": "v1"
}
] | 2016-09-08 | [
[
"Halperin",
"A.",
""
],
[
"Buhot",
"A.",
""
],
[
"Zhulina",
"E. B.",
""
]
] | In biology experiments, oligonucleotide microarrays are contacted with a solution of long nucleic acid (NA) targets. The hybridized probes thus carry long tails. When the surface density of the oligonucleotide probes is high enough, the progress of hybridization leads to the formation of a polyelectrolyte brush due to mutual crowding of the NA tails. The free energy penalty associated with the brush modifies both the hybridization isotherms and the rate equations: the attainable hybridization is lowered significantly as is the hybridization rate. While the equilibrium hybridization fraction, $x_{eq}$, is low, the hybridization follows a Langmuir type isotherm, $x_{eq}/(1-x_{eq}) = c_t K$ where $c_t$ is the target concentration and $K$ is the equilibrium constant smaller than its bulk value by a factor $(n/N)^{2/5}$ due to wall effects where $n$ and $N$ denote the number of bases in the probe and the target. At higher $x_{eq}$, when the brush is formed, the leading correction is $x_{eq}/(1-x_{eq}) = c_t K \exp [ - const' (x_{eq}^{2/3} - x_B^{2/3})]$ where $x_B$ corresponds to the onset of the brush regime. The denaturation rate constant in the two regimes are identical. However, the hybridization rate constant in the brush regime is lower, the leading correction being $\exp [- const' (x^{2/3} - x_B^{2/3})]$. |
0908.2025 | Wilfred Ndifon | Asamoah Nkwanta, Wilfred Ndifon | A contact-waiting-time metric and RNA folding rates | 15 pages, 1 figure, 3 tables, matlab code. Published in FEBS Letters
(June '09) | null | null | null | q-bio.BM q-bio.GN q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Metrics for indirectly predicting the folding rates of RNA sequences are of
interest. In this letter, we introduce a simple metric of RNA structural
complexity, which accounts for differences in the energetic contributions of
RNA base contacts toward RNA structure formation. We apply the metric to RNA
sequences whose folding rates were previously determined experimentally. We
find that the metric has good correlation (correlation coefficient: -0.95, p <<
0.01) with the logarithmically transformed folding rates of those RNA
sequences. This suggests that the metric can be useful for predicting RNA
folding rates. We use the metric to predict the folding rates of bacterial and
eukaryotic group II introns. Future applications of the metric (e.g., to
predict structural RNAs) could prove fruitful.
| [
{
"created": "Fri, 14 Aug 2009 08:37:27 GMT",
"version": "v1"
}
] | 2009-08-17 | [
[
"Nkwanta",
"Asamoah",
""
],
[
"Ndifon",
"Wilfred",
""
]
] | Metrics for indirectly predicting the folding rates of RNA sequences are of interest. In this letter, we introduce a simple metric of RNA structural complexity, which accounts for differences in the energetic contributions of RNA base contacts toward RNA structure formation. We apply the metric to RNA sequences whose folding rates were previously determined experimentally. We find that the metric has good correlation (correlation coefficient: -0.95, p << 0.01) with the logarithmically transformed folding rates of those RNA sequences. This suggests that the metric can be useful for predicting RNA folding rates. We use the metric to predict the folding rates of bacterial and eukaryotic group II introns. Future applications of the metric (e.g., to predict structural RNAs) could prove fruitful. |
2301.03782 | Yue Wang | Yue Wang, Joseph X. Zhou, Edoardo Pedrini, Irit Rubin, May Khalil,
Roberto Taramelli, Hong Qian, Sui Huang | Cell Population Growth Kinetics in the Presence of Stochastic
Heterogeneity of Cell Phenotype | null | null | 10.1016/j.jtbi.2023.111645 | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Recent studies at individual cell resolution have revealed phenotypic
heterogeneity in nominally clonal tumor cell populations. The heterogeneity
affects cell growth behaviors, which can result in departure from the idealized
uniform exponential growth of the cell population. Here we measured the
stochastic time courses of growth of an ensemble of populations of HL60
leukemia cells in cultures, starting with distinct initial cell numbers to
capture a departure from the {uniform exponential growth model for the initial
growth (``take-off'')}. Despite being derived from the same cell clone, we
observed significant variations in the early growth patterns of individual
cultures with statistically significant differences in growth dynamics, which
could be explained by the presence of inter-converting subpopulations with
different growth rates, and which could last for many generations. Based on the
hypothesis of existence of multiple subpopulations, we developed a branching
process model that was consistent with the experimental observations.
| [
{
"created": "Tue, 10 Jan 2023 04:21:34 GMT",
"version": "v1"
},
{
"created": "Thu, 19 Oct 2023 01:53:37 GMT",
"version": "v2"
}
] | 2023-10-20 | [
[
"Wang",
"Yue",
""
],
[
"Zhou",
"Joseph X.",
""
],
[
"Pedrini",
"Edoardo",
""
],
[
"Rubin",
"Irit",
""
],
[
"Khalil",
"May",
""
],
[
"Taramelli",
"Roberto",
""
],
[
"Qian",
"Hong",
""
],
[
"Huang",
"Sui",
""
]
] | Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations. The heterogeneity affects cell growth behaviors, which can result in departure from the idealized uniform exponential growth of the cell population. Here we measured the stochastic time courses of growth of an ensemble of populations of HL60 leukemia cells in cultures, starting with distinct initial cell numbers to capture a departure from the {uniform exponential growth model for the initial growth (``take-off'')}. Despite being derived from the same cell clone, we observed significant variations in the early growth patterns of individual cultures with statistically significant differences in growth dynamics, which could be explained by the presence of inter-converting subpopulations with different growth rates, and which could last for many generations. Based on the hypothesis of existence of multiple subpopulations, we developed a branching process model that was consistent with the experimental observations. |
2301.06981 | Josinaldo Menezes | J. Menezes, R. Barbalho | How multiple weak species jeopardise biodiversity in spatial
rock-paper-scissors models | 10 pages, 10 figures | Chaos, Solitons & Fractals 169, 113290 (2023) | 10.1016/j.chaos.2023.113290 | null | q-bio.PE math.DS nlin.AO nlin.PS physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study generalised rock-paper-scissors models with an arbitrary odd number
N \geq 5 of species, among which n are weak, with 2 \leq n \leq (N-1)/2.
Because of the species' weakness, the probability of individuals conquering
territory in the cyclic spatial game is low. Running stochastic simulations, we
study the role of unevenness in the rock-paper-scissors game in spatial
patterns and population dynamics, considering diverse models where the weak
species are in different positions in the cyclic game order. Studying systems
with five and seven species, we discover that the individuals' spatial
organisation arising from the pattern formation process determines the
stability of the cyclic game with multiple weak species. Our outcomes show that
the presence of species unbalances the spatial distribution of organisms of the
same species bringing consequences on territorial dominance, with the
predominant species being determined by the position in the cyclic game order.
Our simulations elucidate that, in general, the further apart the regions
inhabited by different weak species are, the less the coexistence between the
species is jeopardised. We show that if multiple weak species occupy adjacent
spatial domains, the unevenness in the cyclic game is reinforced, maximising
the chances of biodiversity loss. Our discoveries may also be helpful to
biologists in comprehending systems where weak species unbalance biodiversity
stability.
| [
{
"created": "Tue, 17 Jan 2023 16:06:08 GMT",
"version": "v1"
}
] | 2023-06-06 | [
[
"Menezes",
"J.",
""
],
[
"Barbalho",
"R.",
""
]
] | We study generalised rock-paper-scissors models with an arbitrary odd number N \geq 5 of species, among which n are weak, with 2 \leq n \leq (N-1)/2. Because of the species' weakness, the probability of individuals conquering territory in the cyclic spatial game is low. Running stochastic simulations, we study the role of unevenness in the rock-paper-scissors game in spatial patterns and population dynamics, considering diverse models where the weak species are in different positions in the cyclic game order. Studying systems with five and seven species, we discover that the individuals' spatial organisation arising from the pattern formation process determines the stability of the cyclic game with multiple weak species. Our outcomes show that the presence of species unbalances the spatial distribution of organisms of the same species bringing consequences on territorial dominance, with the predominant species being determined by the position in the cyclic game order. Our simulations elucidate that, in general, the further apart the regions inhabited by different weak species are, the less the coexistence between the species is jeopardised. We show that if multiple weak species occupy adjacent spatial domains, the unevenness in the cyclic game is reinforced, maximising the chances of biodiversity loss. Our discoveries may also be helpful to biologists in comprehending systems where weak species unbalance biodiversity stability. |
q-bio/0506008 | Jean-Michel Claverie | Jean-Michel Claverie (IGS) | Giant viruses in the oceans : the 4th Algal Virus Workshop | Submitted to Virology J | null | null | null | q-bio.PE | null | Giant double-stranded DNA viruses (such as record breaking Acanthamoeba
polyphaga Mimivirus), with particle sizes of 0.2 to 0.6 micron, genomes of 300
kbp to 1.200 kbp, and commensurate complex gene contents, constitute an
evolutionary mystery. They challenge the common vision of viruses,
traditionally seen as highly streamlined genomes optimally fitted to the
smallest possible -filterable- package. Such giant viruses are now discovered
in increasing numbers through the systematic sampling of ocean waters as well
as freshwater aquatic environments, where they play a significant role in
controlling phyto- and bacterio- plankton populations. The 4th algal virus
workshop showed that the study of these ecologically important viruses is now
massively entering the genomic era, promising a better understanding of their
diversity and, hopefully, some insights on their origin and the evolutionary
forces that shaped their genomes.
| [
{
"created": "Tue, 7 Jun 2005 20:16:27 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Claverie",
"Jean-Michel",
"",
"IGS"
]
] | Giant double-stranded DNA viruses (such as record breaking Acanthamoeba polyphaga Mimivirus), with particle sizes of 0.2 to 0.6 micron, genomes of 300 kbp to 1.200 kbp, and commensurate complex gene contents, constitute an evolutionary mystery. They challenge the common vision of viruses, traditionally seen as highly streamlined genomes optimally fitted to the smallest possible -filterable- package. Such giant viruses are now discovered in increasing numbers through the systematic sampling of ocean waters as well as freshwater aquatic environments, where they play a significant role in controlling phyto- and bacterio- plankton populations. The 4th algal virus workshop showed that the study of these ecologically important viruses is now massively entering the genomic era, promising a better understanding of their diversity and, hopefully, some insights on their origin and the evolutionary forces that shaped their genomes. |
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