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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2309.13129 | Jordan Venderley | Jordan Venderley | AntiBARTy Diffusion for Property Guided Antibody Design | null | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Over the past decade, antibodies have steadily grown in therapeutic
importance thanks to their high specificity and low risk of adverse effects
compared to other drug modalities. While traditional antibody discovery is
primarily wet lab driven, the rapid improvement of ML-based generative modeling
has made in-silico approaches an increasingly viable route for discovery and
engineering. To this end, we train an antibody-specific language model,
AntiBARTy, based on BART (Bidirectional and Auto-Regressive Transformer) and
use its latent space to train a property-conditional diffusion model for guided
IgG de novo design. As a test case, we show that we can effectively generate
novel antibodies with improved in-silico solubility while maintaining antibody
validity and controlling sequence diversity.
| [
{
"created": "Fri, 22 Sep 2023 18:30:50 GMT",
"version": "v1"
}
] | 2023-09-26 | [
[
"Venderley",
"Jordan",
""
]
] | Over the past decade, antibodies have steadily grown in therapeutic importance thanks to their high specificity and low risk of adverse effects compared to other drug modalities. While traditional antibody discovery is primarily wet lab driven, the rapid improvement of ML-based generative modeling has made in-silico approaches an increasingly viable route for discovery and engineering. To this end, we train an antibody-specific language model, AntiBARTy, based on BART (Bidirectional and Auto-Regressive Transformer) and use its latent space to train a property-conditional diffusion model for guided IgG de novo design. As a test case, we show that we can effectively generate novel antibodies with improved in-silico solubility while maintaining antibody validity and controlling sequence diversity. |
q-bio/0403034 | Francesco Rao | Francesco Rao and Amedeo Caflisch | The protein folding network | null | J. Mol. Biol. 342, 299-306, (2004) | null | null | q-bio.BM cond-mat.dis-nn | null | The conformation space of a 20-residue antiparallel $\beta$-sheet peptide,
sampled by molecular dynamics simulations, is mapped to a network.
Conformations are nodes of the network, and the transitions between them are
links. The conformation space network describes the significant free energy
minima and their dynamic connectivity without projections into arbitrarily
chosen reaction coordinates. As previously found for the Internet and the
World-Wide Web as well as for social and biological networks, the conformation
space network is scale-free and contains highly connected hubs like the native
state which is the most populated free energy basin. Furthermore, the native
basin exhibits a hierarchical organization which is not found for a random
heteropolymer lacking a predominant free-energy minimum. The network topology
is used to identify conformations in the folding transition state ensemble, and
provides a basis for understanding the heterogeneity of the transition state
and denaturated state ensemble as well as the existence of multiple pathway
| [
{
"created": "Tue, 23 Mar 2004 16:34:10 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Rao",
"Francesco",
""
],
[
"Caflisch",
"Amedeo",
""
]
] | The conformation space of a 20-residue antiparallel $\beta$-sheet peptide, sampled by molecular dynamics simulations, is mapped to a network. Conformations are nodes of the network, and the transitions between them are links. The conformation space network describes the significant free energy minima and their dynamic connectivity without projections into arbitrarily chosen reaction coordinates. As previously found for the Internet and the World-Wide Web as well as for social and biological networks, the conformation space network is scale-free and contains highly connected hubs like the native state which is the most populated free energy basin. Furthermore, the native basin exhibits a hierarchical organization which is not found for a random heteropolymer lacking a predominant free-energy minimum. The network topology is used to identify conformations in the folding transition state ensemble, and provides a basis for understanding the heterogeneity of the transition state and denaturated state ensemble as well as the existence of multiple pathway |
2112.02361 | Pavan Ramdya | Adam Gosztolai and Pavan Ramdya | Connecting the dots in ethology: applying network theory to understand
neural and animal collectives | 12 pages, 3 figures | null | null | null | q-bio.QM q-bio.NC q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A major goal shared by neuroscience and collective behavior is to understand
how dynamic interactions between individual elements give rise to behaviors in
populations of neurons and animals, respectively. This goal has recently become
within reach thanks to techniques providing access to the connectivity and
activity of neuronal ensembles as well as to behaviors among animal
collectives. The next challenge using these datasets is to unravel network
mechanisms generating population behaviors. This is aided by network theory, a
field that studies structure-function relationships in interconnected systems.
Here we review studies that have taken a network view on modern datasets to
provide unique insights into individual and collective animal behaviors.
Specifically, we focus on how analyzing signal propagation, controllability,
symmetry, and geometry of networks can tame the complexity of collective system
dynamics. These studies illustrate the potential of network theory to
accelerate our understanding of behavior across ethological scales.
| [
{
"created": "Sat, 4 Dec 2021 15:36:12 GMT",
"version": "v1"
}
] | 2021-12-07 | [
[
"Gosztolai",
"Adam",
""
],
[
"Ramdya",
"Pavan",
""
]
] | A major goal shared by neuroscience and collective behavior is to understand how dynamic interactions between individual elements give rise to behaviors in populations of neurons and animals, respectively. This goal has recently become within reach thanks to techniques providing access to the connectivity and activity of neuronal ensembles as well as to behaviors among animal collectives. The next challenge using these datasets is to unravel network mechanisms generating population behaviors. This is aided by network theory, a field that studies structure-function relationships in interconnected systems. Here we review studies that have taken a network view on modern datasets to provide unique insights into individual and collective animal behaviors. Specifically, we focus on how analyzing signal propagation, controllability, symmetry, and geometry of networks can tame the complexity of collective system dynamics. These studies illustrate the potential of network theory to accelerate our understanding of behavior across ethological scales. |
1601.02397 | Alok Ranjan Nayak Nayak | Alok Ranjan Nayak and Rahul Pandit | The effects of fibroblasts on wave dynamics in a mathematical model for
human ventricular tissue | Submitted to BIOMAT 2015 | null | null | null | q-bio.TO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present systematic numerical studies of electrical-wave propagation in
two-dimensional (2D) and three-dimensional (3D) mathematical models, for human,
ventricular tissue with myocyte cells that are attached (a) regularly and (b)
randomly to distributed fibroblasts. In both these cases we show that there is
a parameter regime in which single rotating spiral- and scroll-wave states (RS)
retain their integrity and do not evolve to a state ST that displays
spatiotemporal chaos and turbulence. However, in another range of parameters,
we observe a transition from ST to RS states in both 2D or 3D domains and for
both cases (a) and (b). Our studies show that the ST-RS transition and rotation
period of a spiral or scroll wave in the RS state depends on (i) the coupling
strength between myocytes and fibroblasts and (ii) the number of fibroblasts
attached to myocytes. We conclude that myocyte-fibroblast coupling strength and
the number of fibroblasts are more important for the ST-RS transition than the
precise way in which fibroblasts are distributed over myocyte tissue.
| [
{
"created": "Mon, 11 Jan 2016 11:01:11 GMT",
"version": "v1"
}
] | 2016-01-12 | [
[
"Nayak",
"Alok Ranjan",
""
],
[
"Pandit",
"Rahul",
""
]
] | We present systematic numerical studies of electrical-wave propagation in two-dimensional (2D) and three-dimensional (3D) mathematical models, for human, ventricular tissue with myocyte cells that are attached (a) regularly and (b) randomly to distributed fibroblasts. In both these cases we show that there is a parameter regime in which single rotating spiral- and scroll-wave states (RS) retain their integrity and do not evolve to a state ST that displays spatiotemporal chaos and turbulence. However, in another range of parameters, we observe a transition from ST to RS states in both 2D or 3D domains and for both cases (a) and (b). Our studies show that the ST-RS transition and rotation period of a spiral or scroll wave in the RS state depends on (i) the coupling strength between myocytes and fibroblasts and (ii) the number of fibroblasts attached to myocytes. We conclude that myocyte-fibroblast coupling strength and the number of fibroblasts are more important for the ST-RS transition than the precise way in which fibroblasts are distributed over myocyte tissue. |
1701.07847 | Dmitry Petrov | Dmitry Petrov, Boris Gutman, Alexander Ivanov, Joshua Faskowitz, Neda
Jahanshad, Mikhail Belyaev, Paul Thompson | Structural Connectome Validation Using Pairwise Classification | Accepted for IEEE International Symposium on Biomedical Imaging 2017 | null | null | null | q-bio.NC cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we study the extent to which structural connectomes and
topological derivative measures are unique to individual changes within human
brains. To do so, we classify structural connectome pairs from two large
longitudinal datasets as either belonging to the same individual or not. Our
data is comprised of 227 individuals from the Alzheimer's Disease Neuroimaging
Initiative (ADNI) and 226 from the Parkinson's Progression Markers Initiative
(PPMI). We achieve 0.99 area under the ROC curve score for features which
represent either weights or network structure of the connectomes (node degrees,
PageRank and local efficiency). Our approach may be useful for eliminating
noisy features as a preprocessing step in brain aging studies and early
diagnosis classification problems.
| [
{
"created": "Thu, 26 Jan 2017 19:13:36 GMT",
"version": "v1"
},
{
"created": "Mon, 30 Jan 2017 19:55:15 GMT",
"version": "v2"
}
] | 2017-02-01 | [
[
"Petrov",
"Dmitry",
""
],
[
"Gutman",
"Boris",
""
],
[
"Ivanov",
"Alexander",
""
],
[
"Faskowitz",
"Joshua",
""
],
[
"Jahanshad",
"Neda",
""
],
[
"Belyaev",
"Mikhail",
""
],
[
"Thompson",
"Paul",
""
]
] | In this work, we study the extent to which structural connectomes and topological derivative measures are unique to individual changes within human brains. To do so, we classify structural connectome pairs from two large longitudinal datasets as either belonging to the same individual or not. Our data is comprised of 227 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 226 from the Parkinson's Progression Markers Initiative (PPMI). We achieve 0.99 area under the ROC curve score for features which represent either weights or network structure of the connectomes (node degrees, PageRank and local efficiency). Our approach may be useful for eliminating noisy features as a preprocessing step in brain aging studies and early diagnosis classification problems. |
1401.2413 | Filipe Monteiro-Silva | Filipe Monteiro-Silva | Olive oil's polyphenolic metabolites - from their influence on human
health to their chemical synthesis | 44 pages, 24 figures, in Portuguese | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/3.0/ | English version of abstract
A growing number of scientific researches have been demonstrating that olive
oil operates a crucial role on the prevention of cardiovascular and tumoral
diseases, being related with low mortality and morbidity in populations that
tend to follow a Mediterranean diet. Amongst its minor components, polyphenols
have been subject to several clinical trials that have established health
benefits associated to their antioxidant, antitumoral and anti-atherosclerotic
activity. However the biological activity of polyphenols is dependent not only
of their absorption but also of their metabolization. Bioavailability studies
have demonstrated that after olive oil intake, not only hydroxytyrosol but also
its metabolites can be found in plasma, such as
3,4-dihydroxyphenylacetaldehyde, 3,4-dihydroxyphenylacetic acid, homovanillyl
alcohol and its glucoronides.
-----
Portuguese version of abstract
Um numero crescente de pesquisas cientificas demonstram que o azeite opera um
papel crucial na preven\c{c}ao de doen\c{c}as cardiovasculares e doen\c{c}as
tumorais, estando relacionado com a baixa mortalidade e morbilidade em
popula\c{c}oes que tradicionalmente seguem uma dieta Mediterranica. De entre os
seus componentes minorit\'arios, os polifenois t\^em vindo a ser alvo de
estudos clinicos que demonstraram o seu benef\'icio para a saude pela atividade
antioxidante, anti-tumoral e anti-aterosclerotica que possuem. No entanto a
atividade biol\'ogica destes polifenois \'e dependente nao s\'o da sua
absor\c{c}ao como poder\'a tambem depender da sua metaboliza\c{c}ao. Estudos de
biodisponibilidade demonstraram que ap\'os o consumo de azeite encontram-se no
plasma nao s\'o hidroxitirosol, mas igualmente os seus metabolitos, tais como o
\'acido 3,4-dihidroxifenilacetico, o 3,4-dihidroxifenilacetaldeido, o \'alcool
homovanilico e os respectivos glucoronideos.
| [
{
"created": "Fri, 10 Jan 2014 17:49:58 GMT",
"version": "v1"
}
] | 2014-01-13 | [
[
"Monteiro-Silva",
"Filipe",
""
]
] | English version of abstract A growing number of scientific researches have been demonstrating that olive oil operates a crucial role on the prevention of cardiovascular and tumoral diseases, being related with low mortality and morbidity in populations that tend to follow a Mediterranean diet. Amongst its minor components, polyphenols have been subject to several clinical trials that have established health benefits associated to their antioxidant, antitumoral and anti-atherosclerotic activity. However the biological activity of polyphenols is dependent not only of their absorption but also of their metabolization. Bioavailability studies have demonstrated that after olive oil intake, not only hydroxytyrosol but also its metabolites can be found in plasma, such as 3,4-dihydroxyphenylacetaldehyde, 3,4-dihydroxyphenylacetic acid, homovanillyl alcohol and its glucoronides. ----- Portuguese version of abstract Um numero crescente de pesquisas cientificas demonstram que o azeite opera um papel crucial na preven\c{c}ao de doen\c{c}as cardiovasculares e doen\c{c}as tumorais, estando relacionado com a baixa mortalidade e morbilidade em popula\c{c}oes que tradicionalmente seguem uma dieta Mediterranica. De entre os seus componentes minorit\'arios, os polifenois t\^em vindo a ser alvo de estudos clinicos que demonstraram o seu benef\'icio para a saude pela atividade antioxidante, anti-tumoral e anti-aterosclerotica que possuem. No entanto a atividade biol\'ogica destes polifenois \'e dependente nao s\'o da sua absor\c{c}ao como poder\'a tambem depender da sua metaboliza\c{c}ao. Estudos de biodisponibilidade demonstraram que ap\'os o consumo de azeite encontram-se no plasma nao s\'o hidroxitirosol, mas igualmente os seus metabolitos, tais como o \'acido 3,4-dihidroxifenilacetico, o 3,4-dihidroxifenilacetaldeido, o \'alcool homovanilico e os respectivos glucoronideos. |
1011.0449 | Raul Isea | Raul Isea | Identificaci\'on de nuevos medicamentos a trav\'es de m\'etodos
computacionales | 20 pages, 4 figures, Spanish paper | Revista del Instituto Nacional de Higiene Rafael Rangel (2010)
Vol. 41(1), pp: 43-49 | null | null | q-bio.OT | http://creativecommons.org/licenses/by/3.0/ | Resumen: El desarrollo de nuevos medicamentos es un problema complejo que
carece de una soluci\'on \'unica y autom\'atica desde un punto de vista
computacional, debido a la carencia de programas que permitan manejar grandes
vol\'umenes de informaci\'on que est\'an distribuidos a lo largo de todo el
mundo entre m\'ultiples bases de datos. Por ello se describe una metodolog\'ia
que permita realizar experimentos in silico para la identificaci\'on actual de
nuevos medicamentos.
Abstract: The development of new drugs is a problem that nowadays has no
solution in terms of computational power due to the lack of software for
handling the big volume of available information; besides, these data are
stored in multiple formats and are distributed all around the world. To resolve
that, a development of an in silico drug design methodology.
| [
{
"created": "Mon, 1 Nov 2010 20:57:56 GMT",
"version": "v1"
}
] | 2010-11-03 | [
[
"Isea",
"Raul",
""
]
] | Resumen: El desarrollo de nuevos medicamentos es un problema complejo que carece de una soluci\'on \'unica y autom\'atica desde un punto de vista computacional, debido a la carencia de programas que permitan manejar grandes vol\'umenes de informaci\'on que est\'an distribuidos a lo largo de todo el mundo entre m\'ultiples bases de datos. Por ello se describe una metodolog\'ia que permita realizar experimentos in silico para la identificaci\'on actual de nuevos medicamentos. Abstract: The development of new drugs is a problem that nowadays has no solution in terms of computational power due to the lack of software for handling the big volume of available information; besides, these data are stored in multiple formats and are distributed all around the world. To resolve that, a development of an in silico drug design methodology. |
2106.06377 | Fatima-Zahra Jaouimaa | Fatima-Zahra Jaouimaa, Daniel Dempsey, Suzanne van Osch, Stephen
Kinsella, Kevin Burke, Jason Wyse and James Sweeney | An age-structured SEIR model for COVID--19 incidence in Dublin, Ireland
with framework for evaluating health intervention cost | null | null | 10.1371/journal.pone.0260632 | null | q-bio.PE econ.GN physics.soc-ph q-fin.EC | http://creativecommons.org/licenses/by/4.0/ | Strategies adopted globally to mitigate the threat of COVID-19 have primarily
involved lockdown measures with substantial economic and social costs with
varying degrees of success. Morbidity patterns of COVID-19 variants have a
strong association with age, while restrictive lockdown measures have
association with negative mental health outcomes in some age groups. Reduced
economic prospects may also afflict some age cohorts more than others.
Motivated by this, we propose a model to describe COVID-19 community spread
incorporating the role of age-specific social interactions. Through a flexible
parameterisation of an age-structured deterministic Susceptible Exposed
Infectious Removed (SEIR) model, we provide a means for characterising
different forms of lockdown which may impact specific age groups differently.
Social interactions are represented through age group to age group contact
matrices, which can be trained using available data and are thus locally
adapted. This framework is easy to interpret and suitable for describing
counterfactual scenarios, which could assist policy makers with regard to
minimising morbidity balanced with the costs of prospective suppression
strategies. Our work originates from an Irish context and we use disease
monitoring data from February 29th 2020 to January 31st 2021 gathered by Irish
governmental agencies. We demonstrate how Irish lockdown scenarios can be
constructed using the proposed model formulation and show results of
retrospective fitting to incidence rates and forward planning with relevant
``what if/instead of'' lockdown counterfactuals with uncertainty
quantification. Our formulation is agnostic to a specific locale, in that
lockdown strategies in other regions can be straightforwardly encoded using
this model. The methods we describe are made publicly available online through
an accessible and easy to use web interface.
| [
{
"created": "Fri, 11 Jun 2021 13:30:45 GMT",
"version": "v1"
}
] | 2022-01-19 | [
[
"Jaouimaa",
"Fatima-Zahra",
""
],
[
"Dempsey",
"Daniel",
""
],
[
"van Osch",
"Suzanne",
""
],
[
"Kinsella",
"Stephen",
""
],
[
"Burke",
"Kevin",
""
],
[
"Wyse",
"Jason",
""
],
[
"Sweeney",
"James",
""
]
] | Strategies adopted globally to mitigate the threat of COVID-19 have primarily involved lockdown measures with substantial economic and social costs with varying degrees of success. Morbidity patterns of COVID-19 variants have a strong association with age, while restrictive lockdown measures have association with negative mental health outcomes in some age groups. Reduced economic prospects may also afflict some age cohorts more than others. Motivated by this, we propose a model to describe COVID-19 community spread incorporating the role of age-specific social interactions. Through a flexible parameterisation of an age-structured deterministic Susceptible Exposed Infectious Removed (SEIR) model, we provide a means for characterising different forms of lockdown which may impact specific age groups differently. Social interactions are represented through age group to age group contact matrices, which can be trained using available data and are thus locally adapted. This framework is easy to interpret and suitable for describing counterfactual scenarios, which could assist policy makers with regard to minimising morbidity balanced with the costs of prospective suppression strategies. Our work originates from an Irish context and we use disease monitoring data from February 29th 2020 to January 31st 2021 gathered by Irish governmental agencies. We demonstrate how Irish lockdown scenarios can be constructed using the proposed model formulation and show results of retrospective fitting to incidence rates and forward planning with relevant ``what if/instead of'' lockdown counterfactuals with uncertainty quantification. Our formulation is agnostic to a specific locale, in that lockdown strategies in other regions can be straightforwardly encoded using this model. The methods we describe are made publicly available online through an accessible and easy to use web interface. |
1611.03361 | Carlos Martinez Mr. | Carlos Alberto Mart\'inez, Kshitij Khare, Syed Rahman, Mauricio A.
Elzo | Modelling correlated marker effects in genome-wide prediction via
Gaussian concentration graph models | null | null | null | null | q-bio.QM stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In genome-wide prediction, independence of marker allele substitution effects
is typically assumed; however, since early stages of this technology it has
been known that nature points to correlated effects. In statistics, graphical
models have been identified as a useful and powerful tool for covariance
estimation in high dimensional problems and it is an area that has recently
experienced a great expansion. In particular, Gaussian concentration graph
models (GCGM) have been widely studied. These are models in which the
distribution of a set of random variables, the marker effects in this case, is
assumed to be Markov with respect to an undirected graph G. In this paper,
Bayesian (Bayes G and Bayes G-D) and frequentist (GML-BLUP) methods adapting
the theory of GCGM to genome-wide prediction were developed. Different
approaches to define the graph G based on domain-specific knowledge were
proposed, and two propositions and a corollary establishing conditions to find
decomposable graphs were proven. These methods were implemented in small
simulated and real datasets. In our simulations, scenarios where correlations
among allelic substitution effects were expected to arise due to various causes
were considered, and graphs were defined on the basis of physical marker
positions. Results showed improvements in correlation between phenotypes and
predicted breeding values and accuracies of predicted breeding values when
accounting for partially correlated allele substitution effects. Extensions to
the multiallelic loci case were described and some possible refinements
incorporating more flexible priors in the Bayesian setting were discussed. Our
models are promising because they allow incorporation of biological information
in the prediction process, and because they are more flexible and general than
other models accounting for correlated marker effects that have been proposed
previously.
| [
{
"created": "Thu, 10 Nov 2016 15:50:53 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Apr 2017 21:13:07 GMT",
"version": "v2"
},
{
"created": "Wed, 20 Sep 2017 15:35:09 GMT",
"version": "v3"
}
] | 2017-09-21 | [
[
"Martínez",
"Carlos Alberto",
""
],
[
"Khare",
"Kshitij",
""
],
[
"Rahman",
"Syed",
""
],
[
"Elzo",
"Mauricio A.",
""
]
] | In genome-wide prediction, independence of marker allele substitution effects is typically assumed; however, since early stages of this technology it has been known that nature points to correlated effects. In statistics, graphical models have been identified as a useful and powerful tool for covariance estimation in high dimensional problems and it is an area that has recently experienced a great expansion. In particular, Gaussian concentration graph models (GCGM) have been widely studied. These are models in which the distribution of a set of random variables, the marker effects in this case, is assumed to be Markov with respect to an undirected graph G. In this paper, Bayesian (Bayes G and Bayes G-D) and frequentist (GML-BLUP) methods adapting the theory of GCGM to genome-wide prediction were developed. Different approaches to define the graph G based on domain-specific knowledge were proposed, and two propositions and a corollary establishing conditions to find decomposable graphs were proven. These methods were implemented in small simulated and real datasets. In our simulations, scenarios where correlations among allelic substitution effects were expected to arise due to various causes were considered, and graphs were defined on the basis of physical marker positions. Results showed improvements in correlation between phenotypes and predicted breeding values and accuracies of predicted breeding values when accounting for partially correlated allele substitution effects. Extensions to the multiallelic loci case were described and some possible refinements incorporating more flexible priors in the Bayesian setting were discussed. Our models are promising because they allow incorporation of biological information in the prediction process, and because they are more flexible and general than other models accounting for correlated marker effects that have been proposed previously. |
1802.10176 | Arran Hodgkinson | Arran Hodgkinson, Giles Uz\'e, Ovidiu Radulescu, Dumitru Trucu | Signal propagation in sensing and reciprocating cellular systems with
spatial and structural heterogeneity | 34 pages | null | null | null | q-bio.CB math.DS q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sensing and reciprocating cellular systems (SARs) are important for the
operation of many biological systems. Production in interferon (IFN) SARs is
achieved through activation of the Jak-Stat pathway, and downstream
upregulation of IFN regulatory factor (IRF)-3 and IFN transcription, but the
role that high and low affinity IFNs play in this process remains unclear. We
present a comparative between a minimal spatio-temporal partial differential
equation (PDE) model and a novel spatio-structural-temporal (SST) model for the
consideration of receptor, binding, and metabolic aspects of SAR behaviour.
Using the SST framework, we simulate single- and multi-cluster paradigms of IFN
communication. Simulations reveal a cyclic process between the binding of IFN
to the receptor, and the consequent increase in metabolism, decreasing the
propensity for binding due to the internal feed-back mechanism. One observes
the effect of heterogeneity between cellular clusters, allowing them to
individualise and increase local production, and within clusters, where we
observe `sub popular quiescence'; a process whereby intra-cluster
subpopulations reduce their binding and metabolism such that other such
subpopulations may augment their production. Finally, we observe the ability
for low affinity IFN to communicate a long range signal, where high affinity
cannot, and the breakdown of this relationship through the introduction of cell
motility. Biological systems may utilise cell motility where environments are
unrestrictive and may use fixed system, with low affinity communication, where
a localised response is desirable.
| [
{
"created": "Sun, 25 Feb 2018 11:09:55 GMT",
"version": "v1"
}
] | 2018-03-01 | [
[
"Hodgkinson",
"Arran",
""
],
[
"Uzé",
"Giles",
""
],
[
"Radulescu",
"Ovidiu",
""
],
[
"Trucu",
"Dumitru",
""
]
] | Sensing and reciprocating cellular systems (SARs) are important for the operation of many biological systems. Production in interferon (IFN) SARs is achieved through activation of the Jak-Stat pathway, and downstream upregulation of IFN regulatory factor (IRF)-3 and IFN transcription, but the role that high and low affinity IFNs play in this process remains unclear. We present a comparative between a minimal spatio-temporal partial differential equation (PDE) model and a novel spatio-structural-temporal (SST) model for the consideration of receptor, binding, and metabolic aspects of SAR behaviour. Using the SST framework, we simulate single- and multi-cluster paradigms of IFN communication. Simulations reveal a cyclic process between the binding of IFN to the receptor, and the consequent increase in metabolism, decreasing the propensity for binding due to the internal feed-back mechanism. One observes the effect of heterogeneity between cellular clusters, allowing them to individualise and increase local production, and within clusters, where we observe `sub popular quiescence'; a process whereby intra-cluster subpopulations reduce their binding and metabolism such that other such subpopulations may augment their production. Finally, we observe the ability for low affinity IFN to communicate a long range signal, where high affinity cannot, and the breakdown of this relationship through the introduction of cell motility. Biological systems may utilise cell motility where environments are unrestrictive and may use fixed system, with low affinity communication, where a localised response is desirable. |
2110.12873 | Milena \v{C}uki\'c Radenkovi\'c Dr | Milena B. \v{C}uki\'c | Godot is not coming: when we will let innovations enter psychiatry? | 35 pages, 4 pictures | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Current diagnostic practice in psychiatry is not relying on objective
biophysical evidence. Recent pandemic emphasized the need to address the rising
number of mood disorders (in particular, depression) cases in a more efficient
way. We are proposing several already developed practices that can help improve
that diagnostic process: detection based on electrophysiological signals (both
electroencephalogram and electrocardiogram based) that were shown to be
accurate for clinical practice and several modalities of electromagnetic
stimulation that were proven to ameliorate symptoms of depression. In this
work, we are connecting the two with explanations coming from physiological
complexity studies (and our own work) as well as advanced statistical methods
like machine learning and the Bayesian inference approach. It is shown that
fractal and nonlinear measures can adequately quantify previously undetected
changes in intrinsic dynamics of physiological systems, providing the basis for
early detection of depression. We are also advocating for early screening of
cardiovascular risks in depression which is in connection to previously
described decomplexification of the autonomous nervous system resulting in
symptoms recognized clinically. All that said, additional information about the
level of complexity can help clinicians make a better decisions in the
therapeutic process, increase the overall effectiveness of the treatment, and
finally increase the quality of life of the patient.
| [
{
"created": "Sat, 16 Oct 2021 18:26:33 GMT",
"version": "v1"
}
] | 2021-10-26 | [
[
"Čukić",
"Milena B.",
""
]
] | Current diagnostic practice in psychiatry is not relying on objective biophysical evidence. Recent pandemic emphasized the need to address the rising number of mood disorders (in particular, depression) cases in a more efficient way. We are proposing several already developed practices that can help improve that diagnostic process: detection based on electrophysiological signals (both electroencephalogram and electrocardiogram based) that were shown to be accurate for clinical practice and several modalities of electromagnetic stimulation that were proven to ameliorate symptoms of depression. In this work, we are connecting the two with explanations coming from physiological complexity studies (and our own work) as well as advanced statistical methods like machine learning and the Bayesian inference approach. It is shown that fractal and nonlinear measures can adequately quantify previously undetected changes in intrinsic dynamics of physiological systems, providing the basis for early detection of depression. We are also advocating for early screening of cardiovascular risks in depression which is in connection to previously described decomplexification of the autonomous nervous system resulting in symptoms recognized clinically. All that said, additional information about the level of complexity can help clinicians make a better decisions in the therapeutic process, increase the overall effectiveness of the treatment, and finally increase the quality of life of the patient. |
1612.07712 | Thierry Mora | Ulisse Ferrari, Christophe Gardella, Olivier Marre, Thierry Mora | Closed-loop estimation of retinal network sensitivity reveals signature
of efficient coding | null | eNeuro 4 (6) ENEURO.0166-17.2017 (2018) | 10.1523/ENEURO.0166-17.2017 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | According to the theory of efficient coding, sensory systems are adapted to
represent natural scenes with high fidelity and at minimal metabolic cost.
Testing this hypothesis for sensory structures performing non-linear
computations on high dimensional stimuli is still an open challenge. Here we
develop a method to characterize the sensitivity of the retinal network to
perturbations of a stimulus. Using closed-loop experiments, we explore
selectively the space of possible perturbations around a given stimulus. We
then show that the response of the retinal population to these small
perturbations can be described by a local linear model. Using this model, we
computed the sensitivity of the neural response to arbitrary temporal
perturbations of the stimulus, and found a peak in the sensitivity as a
function of the frequency of the perturbations. Based on a minimal theory of
sensory processing, we argue that this peak is set to maximize information
transmission. Our approach is relevant to testing the efficient coding
hypothesis locally in any context where no reliable encoding model is known.
| [
{
"created": "Thu, 22 Dec 2016 17:23:31 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Jan 2017 16:24:33 GMT",
"version": "v2"
}
] | 2018-04-13 | [
[
"Ferrari",
"Ulisse",
""
],
[
"Gardella",
"Christophe",
""
],
[
"Marre",
"Olivier",
""
],
[
"Mora",
"Thierry",
""
]
] | According to the theory of efficient coding, sensory systems are adapted to represent natural scenes with high fidelity and at minimal metabolic cost. Testing this hypothesis for sensory structures performing non-linear computations on high dimensional stimuli is still an open challenge. Here we develop a method to characterize the sensitivity of the retinal network to perturbations of a stimulus. Using closed-loop experiments, we explore selectively the space of possible perturbations around a given stimulus. We then show that the response of the retinal population to these small perturbations can be described by a local linear model. Using this model, we computed the sensitivity of the neural response to arbitrary temporal perturbations of the stimulus, and found a peak in the sensitivity as a function of the frequency of the perturbations. Based on a minimal theory of sensory processing, we argue that this peak is set to maximize information transmission. Our approach is relevant to testing the efficient coding hypothesis locally in any context where no reliable encoding model is known. |
2401.07473 | Eleanor Dunlop Dr | Eleanor Dunlop, Judy Cunningham, Paul Adorno, Georgios Dabos, Stuart K
Johnson, Lucinda J Black | Vitamin K content of Australian-grown horticultural commodities | 22 pages, 2 tables | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Vitamin K is emerging as a multi-function vitamin that plays a role in bone,
brain and vascular health. Vitamin K composition data remain limited globally
and Australia has lacked nationally representative data for vitamin K1
(phylloquinone, PK) in horticultural commodities. Primary samples (n = 927) of
90 different Australian-grown fruit, vegetable and nut commodities were
purchased in three Australian cities. We measured PK in duplicate in 95
composite samples using liquid chromatography with electrospray
ionisation-tandem mass spectrometry. The greatest mean concentrations of PK
were found in kale (565 ug/100 g), baby spinach (255 ug/100 g) and Brussels
sprouts (195 ug/100 g). The data contribute to the global collection of vitamin
K food composition data. They add to the evidence that PK concentrations vary
markedly between geographic regions, supporting development of region-specific
datasets for national food composition databases that do not yet contain data
for vitamin K.
| [
{
"created": "Mon, 15 Jan 2024 05:01:16 GMT",
"version": "v1"
}
] | 2024-01-17 | [
[
"Dunlop",
"Eleanor",
""
],
[
"Cunningham",
"Judy",
""
],
[
"Adorno",
"Paul",
""
],
[
"Dabos",
"Georgios",
""
],
[
"Johnson",
"Stuart K",
""
],
[
"Black",
"Lucinda J",
""
]
] | Vitamin K is emerging as a multi-function vitamin that plays a role in bone, brain and vascular health. Vitamin K composition data remain limited globally and Australia has lacked nationally representative data for vitamin K1 (phylloquinone, PK) in horticultural commodities. Primary samples (n = 927) of 90 different Australian-grown fruit, vegetable and nut commodities were purchased in three Australian cities. We measured PK in duplicate in 95 composite samples using liquid chromatography with electrospray ionisation-tandem mass spectrometry. The greatest mean concentrations of PK were found in kale (565 ug/100 g), baby spinach (255 ug/100 g) and Brussels sprouts (195 ug/100 g). The data contribute to the global collection of vitamin K food composition data. They add to the evidence that PK concentrations vary markedly between geographic regions, supporting development of region-specific datasets for national food composition databases that do not yet contain data for vitamin K. |
1812.00143 | Liane Gabora | Cameron M. Smith, Liane Gabora, and William Gardner-O'Kearney | The Extended Evolutionary Synthesis Facilitates Evolutionary Models of
Culture Change | 19 pages, 1 figure, 3 tables; in Cliodynamics: The Journal of
Quantitative History and Cultural Evolution, 9(2), 84-107 | Cliodynamics: The Journal of Quantitative History and Cultural
Evolution, 9(2), 84-107 (2018) | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Extended Evolutionary Synthesis (EES) is beginning to fulfill the whole
promise of Darwinian insight through its extension of evolutionary
understanding from the biological domain to include cultural information
evolution. Several decades of important foundation-laying work took a social
Darwinist approach and exhibited and ecologically-deterministic elements. This
is not the case with more recent developments to the evolutionary study of
culture, which emphasize non-Darwinian processes such as self-organization,
potentiality, and epigenetic change.
| [
{
"created": "Sat, 1 Dec 2018 04:44:27 GMT",
"version": "v1"
},
{
"created": "Wed, 13 Mar 2019 22:48:37 GMT",
"version": "v2"
},
{
"created": "Fri, 5 Jul 2019 21:50:47 GMT",
"version": "v3"
},
{
"created": "Mon, 15 Jul 2019 18:38:00 GMT",
"version": "v4"
}
] | 2019-07-17 | [
[
"Smith",
"Cameron M.",
""
],
[
"Gabora",
"Liane",
""
],
[
"Gardner-O'Kearney",
"William",
""
]
] | The Extended Evolutionary Synthesis (EES) is beginning to fulfill the whole promise of Darwinian insight through its extension of evolutionary understanding from the biological domain to include cultural information evolution. Several decades of important foundation-laying work took a social Darwinist approach and exhibited and ecologically-deterministic elements. This is not the case with more recent developments to the evolutionary study of culture, which emphasize non-Darwinian processes such as self-organization, potentiality, and epigenetic change. |
2002.11612 | Cecilia Hern\'andez | Daniel Inostroza, Cecilia Hern\'andez, Diego Seco, Gonzalo Navarro,
and Alvaro Olivera-Nappa | Cell cycle and protein complex dynamics in discovering signaling
pathways | null | Journal of Bioinformatics and Computational Biology 2019 | 10.1142/S0219720019500112 | null | q-bio.MN cs.CE q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Signaling pathways are responsible for the regulation of cell processes, such
as monitoring the external environment, transmitting information across
membranes, and making cell fate decisions. Given the increasing amount of
biological data available and the recent discoveries showing that many diseases
are related to the disruption of cellular signal transduction cascades, in
silico discovery of signaling pathways in cell biology has become an active
research topic in past years. However, reconstruction of signaling pathways
remains a challenge mainly because of the need for systematic approaches for
predicting causal relationships, like edge direction and activation/inhibition
among interacting proteins in the signal flow. We propose an approach for
predicting signaling pathways that integrates protein interactions, gene
expression, phenotypes, and protein complex information. Our method first finds
candidate pathways using a directed-edge-based algorithm and then defines a
graph model to include causal activation relationships among proteins, in
candidate pathways using cell cycle gene expression and phenotypes to infer
consistent pathways in yeast. Then, we incorporate protein complex coverage
information for deciding on the final predicted signaling pathways. We show
that our approach improves the predictive results of the state of the art using
different ranking metrics.
| [
{
"created": "Wed, 26 Feb 2020 16:55:24 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Apr 2020 17:07:05 GMT",
"version": "v2"
}
] | 2020-04-07 | [
[
"Inostroza",
"Daniel",
""
],
[
"Hernández",
"Cecilia",
""
],
[
"Seco",
"Diego",
""
],
[
"Navarro",
"Gonzalo",
""
],
[
"Olivera-Nappa",
"Alvaro",
""
]
] | Signaling pathways are responsible for the regulation of cell processes, such as monitoring the external environment, transmitting information across membranes, and making cell fate decisions. Given the increasing amount of biological data available and the recent discoveries showing that many diseases are related to the disruption of cellular signal transduction cascades, in silico discovery of signaling pathways in cell biology has become an active research topic in past years. However, reconstruction of signaling pathways remains a challenge mainly because of the need for systematic approaches for predicting causal relationships, like edge direction and activation/inhibition among interacting proteins in the signal flow. We propose an approach for predicting signaling pathways that integrates protein interactions, gene expression, phenotypes, and protein complex information. Our method first finds candidate pathways using a directed-edge-based algorithm and then defines a graph model to include causal activation relationships among proteins, in candidate pathways using cell cycle gene expression and phenotypes to infer consistent pathways in yeast. Then, we incorporate protein complex coverage information for deciding on the final predicted signaling pathways. We show that our approach improves the predictive results of the state of the art using different ranking metrics. |
0808.0630 | Kavita Jain | Apoorva Nagar and Kavita Jain | Exact phase diagram of quasispecies model with mutation rate modifier | Revised version | Phys. Rev. Lett. 102, 038101 (2009) | 10.1103/PhysRevLett.102.038101 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider an infinite asexual population with a mutator allele which can
elevate mutation rates. With probability $f$, a transition from nonmutator to
mutator state occurs but the reverse transition is forbidden. We find that at
$f=0$, the population is in the state with minimum mutation rate and at
$f=f_c$, a phase transition occurs between a mixed phase with both nonmutators
and mutators and a pure mutator phase. We calculate the critical probability
$f_c$ and the total mutator fraction $Q$ in the mixed phase exactly. Our
predictions for $Q$ are in agreement with those seen in microbial populations
in static environments.
| [
{
"created": "Tue, 5 Aug 2008 12:27:39 GMT",
"version": "v1"
},
{
"created": "Fri, 23 Jan 2009 09:49:06 GMT",
"version": "v2"
}
] | 2009-11-13 | [
[
"Nagar",
"Apoorva",
""
],
[
"Jain",
"Kavita",
""
]
] | We consider an infinite asexual population with a mutator allele which can elevate mutation rates. With probability $f$, a transition from nonmutator to mutator state occurs but the reverse transition is forbidden. We find that at $f=0$, the population is in the state with minimum mutation rate and at $f=f_c$, a phase transition occurs between a mixed phase with both nonmutators and mutators and a pure mutator phase. We calculate the critical probability $f_c$ and the total mutator fraction $Q$ in the mixed phase exactly. Our predictions for $Q$ are in agreement with those seen in microbial populations in static environments. |
2106.05191 | Andrei Khrennikov Yu | Andrei Khrennikov | Quantum-like model for unconscious-conscious interaction and emotional
coloring of perceptions and other conscious experiences | submitted to BioSystems | Biosystems 208, 2021, 104471 | 10.1016/j.biosystems.2021.104471 | null | q-bio.NC quant-ph | http://creativecommons.org/licenses/by/4.0/ | Quantum measurement theory is applied to quantum-like modeling of coherent
generation of perceptions and emotions and generally for emotional coloring of
conscious experiences. In quantum theory, a system should be separated from an
observer. The brain performs self-measurements. To model them, we split the
brain into two subsystems, unconsciousness and consciousness. They correspond
to a system and an observer. The states of perceptions and emotions are
described through the tensor product decomposition of the unconscious state
space; similarly, there are two classes of observables, for conscious
experiencing of perceptions and emotions, respectively. Emotional coloring is
coupled to quantum contextuality: emotional observables determine contexts.
Such contextualization reduces degeneration of unconscious states. The
quantum-like approach should be distinguished from consideration of the genuine
quantum physical processes in the brain (cf. Penrose and Hameroff). In our
approach the brain is a macroscopic system which information processing can be
described by the formalism of quantum theory.
| [
{
"created": "Sun, 6 Jun 2021 17:40:07 GMT",
"version": "v1"
}
] | 2021-12-08 | [
[
"Khrennikov",
"Andrei",
""
]
] | Quantum measurement theory is applied to quantum-like modeling of coherent generation of perceptions and emotions and generally for emotional coloring of conscious experiences. In quantum theory, a system should be separated from an observer. The brain performs self-measurements. To model them, we split the brain into two subsystems, unconsciousness and consciousness. They correspond to a system and an observer. The states of perceptions and emotions are described through the tensor product decomposition of the unconscious state space; similarly, there are two classes of observables, for conscious experiencing of perceptions and emotions, respectively. Emotional coloring is coupled to quantum contextuality: emotional observables determine contexts. Such contextualization reduces degeneration of unconscious states. The quantum-like approach should be distinguished from consideration of the genuine quantum physical processes in the brain (cf. Penrose and Hameroff). In our approach the brain is a macroscopic system which information processing can be described by the formalism of quantum theory. |
2002.03420 | Omri Barak | Omri Barak and Sandro Romani | Mapping low-dimensional dynamics to high-dimensional neural activity: A
derivation of the ring model from the neural engineering framework | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Empirical estimates of the dimensionality of neural population activity are
often much lower than the population size. Similar phenomena are also observed
in trained and designed neural network models. These experimental and
computational results suggest that mapping low-dimensional dynamics to
high-dimensional neural space is a common feature of cortical computation.
Despite the ubiquity of this observation, the constraints arising from such
mapping are poorly understood. Here we consider a specific example of mapping
low-dimensional dynamics to high-dimensional neural activity -- the neural
engineering framework. We analytically solve the framework for the classic ring
model -- a neural network encoding a static or dynamic angular variable. Our
results provide a complete characterization of the success and failure modes
for this model. Based on similarities between this and other frameworks, we
speculate that these results could apply to more general scenarios.
| [
{
"created": "Sun, 9 Feb 2020 18:34:08 GMT",
"version": "v1"
}
] | 2020-02-11 | [
[
"Barak",
"Omri",
""
],
[
"Romani",
"Sandro",
""
]
] | Empirical estimates of the dimensionality of neural population activity are often much lower than the population size. Similar phenomena are also observed in trained and designed neural network models. These experimental and computational results suggest that mapping low-dimensional dynamics to high-dimensional neural space is a common feature of cortical computation. Despite the ubiquity of this observation, the constraints arising from such mapping are poorly understood. Here we consider a specific example of mapping low-dimensional dynamics to high-dimensional neural activity -- the neural engineering framework. We analytically solve the framework for the classic ring model -- a neural network encoding a static or dynamic angular variable. Our results provide a complete characterization of the success and failure modes for this model. Based on similarities between this and other frameworks, we speculate that these results could apply to more general scenarios. |
1103.3886 | Ozgur Doruk R | Resat Ozgur Doruk | Control of the repetitive firing in the squid giant axon using
electrical fields | 14 pages, 3 figures, 2 tables, submitted to journal of Computer
Methods and Programs in Biomedicine | null | null | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this research, the aim is to develop a repetitive firing stopper mechanism
using electrical fields exerted on the fiber. The Hodgkin - Huxley nerve fiber
model is used for modeling the membrane potential behavior. The repetitive
firing of the nerve fiber can be stopped using approaches based on the control
theory where the nonlinear Hodgkin - Huxley model is used to achieve this goal.
The effects of the electrical field are considered as an additive quantity over
the equilibrium potentials of the cell membrane channels. The study is a
representative of an experimental application.
| [
{
"created": "Sun, 20 Mar 2011 20:46:29 GMT",
"version": "v1"
}
] | 2015-03-19 | [
[
"Doruk",
"Resat Ozgur",
""
]
] | In this research, the aim is to develop a repetitive firing stopper mechanism using electrical fields exerted on the fiber. The Hodgkin - Huxley nerve fiber model is used for modeling the membrane potential behavior. The repetitive firing of the nerve fiber can be stopped using approaches based on the control theory where the nonlinear Hodgkin - Huxley model is used to achieve this goal. The effects of the electrical field are considered as an additive quantity over the equilibrium potentials of the cell membrane channels. The study is a representative of an experimental application. |
1610.02227 | Werner Ehm | Werner Ehm and Jiri Wackermann | Geometric-optical illusions and Riemannian geometry | Preprint version of journal article | Journal of Mathematical Psychology 71 (2016) 28-28 | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Geometric-optical illusions (GOI) are a subclass of a vast variety of visual
illusions. A special class of GOIs originates from the superposition of a
simple geometric figure ("target") with an array of non-intersecting
curvilinear elements ("context") that elicits a perceptual distortion of the
target element. Here we specifically deal with the case of circular targets.
Starting from the fact that (half)circles are geodesics in a model of
hyperbolic geometry, we conceive of the deformations of the target as resulting
from a context-induced perturbation of that "base" geometry. We present
computational methods for predicting distorted shapes of the target in
different contexts, and we report the results of a psychophysical pilot
experiment with eight subjects and four contexts to test the predictions.
Finally, we propose a common scheme for modeling GOIs associated with more
general types of target curves, subsuming those studied previously.
| [
{
"created": "Fri, 7 Oct 2016 11:08:48 GMT",
"version": "v1"
}
] | 2016-10-10 | [
[
"Ehm",
"Werner",
""
],
[
"Wackermann",
"Jiri",
""
]
] | Geometric-optical illusions (GOI) are a subclass of a vast variety of visual illusions. A special class of GOIs originates from the superposition of a simple geometric figure ("target") with an array of non-intersecting curvilinear elements ("context") that elicits a perceptual distortion of the target element. Here we specifically deal with the case of circular targets. Starting from the fact that (half)circles are geodesics in a model of hyperbolic geometry, we conceive of the deformations of the target as resulting from a context-induced perturbation of that "base" geometry. We present computational methods for predicting distorted shapes of the target in different contexts, and we report the results of a psychophysical pilot experiment with eight subjects and four contexts to test the predictions. Finally, we propose a common scheme for modeling GOIs associated with more general types of target curves, subsuming those studied previously. |
2207.10405 | Raymond Goldstein | K.C. Leptos, M. Chioccioli, S. Furlan, A.I. Pesci, and R.E. Goldstein | Adaptive phototaxis of Chlamydomonas and the evolutionary transition to
multicellularity in Volvocine green algae | 24 pages, 21 figures, 3 supplementary videos (available from REG) | null | null | null | q-bio.CB cond-mat.soft physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | A fundamental issue in biology is the nature of evolutionary transitions from
unicellular to multicellular organisms. Volvocine algae are models for this
transition, as they span from the unicellular biflagellate Chlamydomonas to
multicellular species of Volvox with up to 50,000 Chlamydomonas-like cells on
the surface of a spherical extracellular matrix. The mechanism of phototaxis in
these species is of particular interest since they lack a nervous system and
intercellular connections; steering is a consequence of the response of
individual cells to light. Studies of Volvox and Gonium, a 16-cell organism
with a plate-like structure, have shown that the flagellar response to changing
illumination of the cellular photosensor is adaptive, with a recovery time
tuned to the rotation period of the colony around its primary axis. Here,
combining high-resolution studies of the flagellar photoresponse with 3D
tracking of freely-swimming cells, we show that such tuning also underlies
phototaxis of Chlamydomonas. A mathematical model is developed based on the
rotations around an axis perpendicular to the flagellar beat plane that occur
through the adaptive response to oscillating light levels as the organism
spins. Exploiting a separation of time scales between the flagellar
photoresponse and phototurning, we develop an equation of motion that
accurately describes the observed photoalignment. In showing that the adaptive
time scale is tuned to the organisms' rotational period across three orders of
magnitude in cell number, our results suggest a unified picture of phototaxis
in green algae in which the asymmetry in torques that produce phototurns arise
from the individual flagella of Chlamydomonas, the flagellated edges of Gonium
and the flagellated hemispheres of Volvox.
| [
{
"created": "Thu, 21 Jul 2022 10:51:31 GMT",
"version": "v1"
}
] | 2022-07-22 | [
[
"Leptos",
"K. C.",
""
],
[
"Chioccioli",
"M.",
""
],
[
"Furlan",
"S.",
""
],
[
"Pesci",
"A. I.",
""
],
[
"Goldstein",
"R. E.",
""
]
] | A fundamental issue in biology is the nature of evolutionary transitions from unicellular to multicellular organisms. Volvocine algae are models for this transition, as they span from the unicellular biflagellate Chlamydomonas to multicellular species of Volvox with up to 50,000 Chlamydomonas-like cells on the surface of a spherical extracellular matrix. The mechanism of phototaxis in these species is of particular interest since they lack a nervous system and intercellular connections; steering is a consequence of the response of individual cells to light. Studies of Volvox and Gonium, a 16-cell organism with a plate-like structure, have shown that the flagellar response to changing illumination of the cellular photosensor is adaptive, with a recovery time tuned to the rotation period of the colony around its primary axis. Here, combining high-resolution studies of the flagellar photoresponse with 3D tracking of freely-swimming cells, we show that such tuning also underlies phototaxis of Chlamydomonas. A mathematical model is developed based on the rotations around an axis perpendicular to the flagellar beat plane that occur through the adaptive response to oscillating light levels as the organism spins. Exploiting a separation of time scales between the flagellar photoresponse and phototurning, we develop an equation of motion that accurately describes the observed photoalignment. In showing that the adaptive time scale is tuned to the organisms' rotational period across three orders of magnitude in cell number, our results suggest a unified picture of phototaxis in green algae in which the asymmetry in torques that produce phototurns arise from the individual flagella of Chlamydomonas, the flagellated edges of Gonium and the flagellated hemispheres of Volvox. |
0904.4643 | Alessandro Fontana | Alessandro Fontana | Epigenetic Tracking: Towards a Project for an Artificial Biology | 16 pages, 19 figures | null | null | null | q-bio.CB nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper deals with a model of cellular growth called "Epigenetic
Tracking", whose key features are: i) distinction bewteen "normal" and "driver"
cells; ii) presence in driver cells of an epigenetic memory, that holds the
position of the cell in the driver cell lineage tree and represents the source
of differentiation during development. In the first part of the paper the model
is proved able to generate arbitrary target shapes of unmatched size and
variety by means of evo-devo techniques, thus being validated as a model of
embryogenesis and cellular differentiation. In the second part of the paper it
is shown how the model can produce artificial counterparts for some key aspects
of multicellular biology, such as junk DNA, ageing and carcinogenesis. If
individually each of these topics has been the subject of intense investigation
and modelling effort, to our knowledge no single model or theory seeking to
cover all of them under a unified framework has been put forward as yet: this
work contains such a theory, which makes Epigenetic Tracking a potential basis
for a project of Artificial Biology.
| [
{
"created": "Wed, 29 Apr 2009 15:33:15 GMT",
"version": "v1"
}
] | 2009-05-01 | [
[
"Fontana",
"Alessandro",
""
]
] | This paper deals with a model of cellular growth called "Epigenetic Tracking", whose key features are: i) distinction bewteen "normal" and "driver" cells; ii) presence in driver cells of an epigenetic memory, that holds the position of the cell in the driver cell lineage tree and represents the source of differentiation during development. In the first part of the paper the model is proved able to generate arbitrary target shapes of unmatched size and variety by means of evo-devo techniques, thus being validated as a model of embryogenesis and cellular differentiation. In the second part of the paper it is shown how the model can produce artificial counterparts for some key aspects of multicellular biology, such as junk DNA, ageing and carcinogenesis. If individually each of these topics has been the subject of intense investigation and modelling effort, to our knowledge no single model or theory seeking to cover all of them under a unified framework has been put forward as yet: this work contains such a theory, which makes Epigenetic Tracking a potential basis for a project of Artificial Biology. |
q-bio/0502041 | Rui Dilao | Rui Dilao | The reaction-diffusion approach to morphogenesis | 22 pages, 11 figures | null | null | null | q-bio.CB | null | Morphogenesis is the ensemble of processes that determines form, shape and
patterns in organisms. Based on a reaction-diffusion theoretical setting and
some prototype reaction schemes, we make a review of the models and experiments
that support possible mechanisms of morphogenesis. We present specific case
studies from chemistry (Belousov-Zhabotinsky reaction) and biology (formation
of wing eyespots patterns in butterflies). We show the importance of
conservation laws in the establishment of patterning in biological systems, and
their relevance to explain phenotypic plasticity in living organisms. Mass
conservation introduces a memory effect in biological development and
phenotypic plasticity in patterns of living organisms can be explained by
differences on the initial conditions occurring during development.
| [
{
"created": "Fri, 25 Feb 2005 22:28:36 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Dilao",
"Rui",
""
]
] | Morphogenesis is the ensemble of processes that determines form, shape and patterns in organisms. Based on a reaction-diffusion theoretical setting and some prototype reaction schemes, we make a review of the models and experiments that support possible mechanisms of morphogenesis. We present specific case studies from chemistry (Belousov-Zhabotinsky reaction) and biology (formation of wing eyespots patterns in butterflies). We show the importance of conservation laws in the establishment of patterning in biological systems, and their relevance to explain phenotypic plasticity in living organisms. Mass conservation introduces a memory effect in biological development and phenotypic plasticity in patterns of living organisms can be explained by differences on the initial conditions occurring during development. |
2301.07179 | Timothy Kline | Timothy L. Kline | Modeling Vascular Branching Alterations in Polycystic Kidney Disease | null | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | The analysis of biological networks encompasses a wide variety of fields from
genomic research of protein-protein interaction networks, to the physiological
study of biologically optimized tree-like vascular networks. It is certain that
different biological networks have different optimization criteria and we are
interested in those networks optimized for fluid transport within the
circulatory system. Many theories currently exist. For instance, distributive
vascular geometry data is typically consistent with a theoretical model that
requires simultaneous minimization of both the power loss of laminar flow and a
cost function proportional to the total volume of material needed to maintain
the system (Murray's law). However, how this optimized system breaks down (or
is altered) due to disease has yet to be characterized in detail in terms of
branching geometry and geometric interrelationships. This is important for
understanding how vasculature remodels under changes of functional demands. For
instance, in polycystic kidney disease (PKD), drastic cyst development may lead
to a significant alteration of the vascular geometry (or vascular changes may
be a preceding event). Understanding these changes could lead to a better
understanding of early disease as well as development and characterization of
treatment interventions. We have developed an optimal transport network model
which simulates distributive vascular systems in health as well as disease in
order to better understand changes that may occur due to PKD. We found that
reduced perfusion territories, dilated distributive vasculature, and vessel
rarefaction are all consequences of cyst development derived from this
theoretical model and are a direct result of the increased heterogeneity of
local renal tissue perfusion demands.
| [
{
"created": "Tue, 20 Dec 2022 14:12:56 GMT",
"version": "v1"
}
] | 2023-01-19 | [
[
"Kline",
"Timothy L.",
""
]
] | The analysis of biological networks encompasses a wide variety of fields from genomic research of protein-protein interaction networks, to the physiological study of biologically optimized tree-like vascular networks. It is certain that different biological networks have different optimization criteria and we are interested in those networks optimized for fluid transport within the circulatory system. Many theories currently exist. For instance, distributive vascular geometry data is typically consistent with a theoretical model that requires simultaneous minimization of both the power loss of laminar flow and a cost function proportional to the total volume of material needed to maintain the system (Murray's law). However, how this optimized system breaks down (or is altered) due to disease has yet to be characterized in detail in terms of branching geometry and geometric interrelationships. This is important for understanding how vasculature remodels under changes of functional demands. For instance, in polycystic kidney disease (PKD), drastic cyst development may lead to a significant alteration of the vascular geometry (or vascular changes may be a preceding event). Understanding these changes could lead to a better understanding of early disease as well as development and characterization of treatment interventions. We have developed an optimal transport network model which simulates distributive vascular systems in health as well as disease in order to better understand changes that may occur due to PKD. We found that reduced perfusion territories, dilated distributive vasculature, and vessel rarefaction are all consequences of cyst development derived from this theoretical model and are a direct result of the increased heterogeneity of local renal tissue perfusion demands. |
0710.2555 | Aleksandar Stojmirovi\'c | Aleksandar Stojmirovi\'c and Yi-Kuo Yu | Geometric Aspects of Biological Sequence Comparison | 55 pages, 2 figures, 2 tables, plain LaTex format. Added additional
references, removed Appendix B, changed abstract, minor changes in text | Journal of Computational Biology. April 2009, 16(4): 579-610. | 10.1089/cmb.2008.0100 | null | q-bio.QM | null | We propose a general framework for converting global and local similarities
between biological sequences to quasi-metrics. In contrast to previous works,
our formulation allows asymmetric distances, originating from uneven weighting
of strings, that may induce non-trivial partial orders on sets of biosequences.
Furthermore, the $\ell^p$-type distances considered are more general than
traditional generalized string edit distances corresponding to the $\ell^1$
case, and enable conversion of sequence similarities to distances for a much
wider class of scoring schemes. Our constructions require much less restrictive
gap penalties than the ones regularly used. Numerous examples are provided to
illustrate the concepts introduced and their potential applications.
| [
{
"created": "Fri, 12 Oct 2007 21:37:53 GMT",
"version": "v1"
},
{
"created": "Thu, 8 Nov 2007 22:43:50 GMT",
"version": "v2"
}
] | 2009-04-17 | [
[
"Stojmirović",
"Aleksandar",
""
],
[
"Yu",
"Yi-Kuo",
""
]
] | We propose a general framework for converting global and local similarities between biological sequences to quasi-metrics. In contrast to previous works, our formulation allows asymmetric distances, originating from uneven weighting of strings, that may induce non-trivial partial orders on sets of biosequences. Furthermore, the $\ell^p$-type distances considered are more general than traditional generalized string edit distances corresponding to the $\ell^1$ case, and enable conversion of sequence similarities to distances for a much wider class of scoring schemes. Our constructions require much less restrictive gap penalties than the ones regularly used. Numerous examples are provided to illustrate the concepts introduced and their potential applications. |
1709.10243 | Seung Ki Baek | Seung Ki Baek, Su Do Yi, and Hyeong-Chai Jeong | Duality between cooperation and defection in the presence of tit-for-tat
in replicator dynamics | 11 pages, 8 figures | J. Theor. Biol. 430, 215 (2017) | 10.1016/j.jtbi.2017.07.026 | null | q-bio.PE nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The prisoner's dilemma describes a conflict between a pair of players, in
which defection is a dominant strategy whereas cooperation is collectively
optimal. The iterated version of the dilemma has been extensively studied to
understand the emergence of cooperation. In the evolutionary context, the
iterated prisoner's dilemma is often combined with population dynamics, in
which a more successful strategy replicates itself with a higher growth rate.
Here, we investigate the replicator dynamics of three representative
strategies, i.e., unconditional cooperation, unconditional defection, and
tit-for-tat, which prescribes reciprocal cooperation by mimicking the
opponent's previous move. Our finding is that the dynamics is self-dual in the
sense that it remains invariant when we apply time reversal and exchange the
fractions of unconditional cooperators and defectors in the population. The
duality implies that the fractions can be equalized by tit-for-tat players,
although unconditional cooperation is still dominated by defection.
Furthermore, we find that mutation among the strategies breaks the exact
duality in such a way that cooperation is more favored than defection, as long
as the cost-to-benefit ratio of cooperation is small.
| [
{
"created": "Fri, 29 Sep 2017 05:50:35 GMT",
"version": "v1"
}
] | 2017-10-02 | [
[
"Baek",
"Seung Ki",
""
],
[
"Yi",
"Su Do",
""
],
[
"Jeong",
"Hyeong-Chai",
""
]
] | The prisoner's dilemma describes a conflict between a pair of players, in which defection is a dominant strategy whereas cooperation is collectively optimal. The iterated version of the dilemma has been extensively studied to understand the emergence of cooperation. In the evolutionary context, the iterated prisoner's dilemma is often combined with population dynamics, in which a more successful strategy replicates itself with a higher growth rate. Here, we investigate the replicator dynamics of three representative strategies, i.e., unconditional cooperation, unconditional defection, and tit-for-tat, which prescribes reciprocal cooperation by mimicking the opponent's previous move. Our finding is that the dynamics is self-dual in the sense that it remains invariant when we apply time reversal and exchange the fractions of unconditional cooperators and defectors in the population. The duality implies that the fractions can be equalized by tit-for-tat players, although unconditional cooperation is still dominated by defection. Furthermore, we find that mutation among the strategies breaks the exact duality in such a way that cooperation is more favored than defection, as long as the cost-to-benefit ratio of cooperation is small. |
2309.13083 | John Stewart Fabila-Carrasco | John Stewart Fabila-Carrasco, Avalon Campbell-Cousins, Mario A.
Parra-Rodriguez, Javier Escudero | Graph-Based Permutation Patterns for the Analysis of Task-Related fMRI
Signals on DTI Networks in Mild Cognitive Impairment | 5 pages, 5 figures, 1 table | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Permutation Entropy ($PE$) is a powerful nonlinear analysis technique for
univariate time series. Recently, Permutation Entropy for Graph signals ($PEG$)
has been proposed to extend PE to data residing on irregular domains. However,
$PEG$ is limited as it provides a single value to characterise a whole graph
signal. Here, we introduce a novel approach to evaluate graph signals \emph{at
the vertex level}: graph-based permutation patterns. Synthetic datasets show
the efficacy of our method. We reveal that dynamics in graph signals,
undetectable with $PEG$, can be discerned using our graph-based patterns. These
are then validated in DTI and fMRI data acquired during a working memory task
in mild cognitive impairment, where we explore functional brain signals on
structural white matter networks. Our findings suggest that graph-based
permutation patterns in individual brain regions change as the disease
progresses, demonstrating potential as a method of analyzing graph-signals at a
granular scale.
| [
{
"created": "Thu, 21 Sep 2023 19:53:53 GMT",
"version": "v1"
},
{
"created": "Tue, 16 Jan 2024 12:22:34 GMT",
"version": "v2"
}
] | 2024-01-17 | [
[
"Fabila-Carrasco",
"John Stewart",
""
],
[
"Campbell-Cousins",
"Avalon",
""
],
[
"Parra-Rodriguez",
"Mario A.",
""
],
[
"Escudero",
"Javier",
""
]
] | Permutation Entropy ($PE$) is a powerful nonlinear analysis technique for univariate time series. Recently, Permutation Entropy for Graph signals ($PEG$) has been proposed to extend PE to data residing on irregular domains. However, $PEG$ is limited as it provides a single value to characterise a whole graph signal. Here, we introduce a novel approach to evaluate graph signals \emph{at the vertex level}: graph-based permutation patterns. Synthetic datasets show the efficacy of our method. We reveal that dynamics in graph signals, undetectable with $PEG$, can be discerned using our graph-based patterns. These are then validated in DTI and fMRI data acquired during a working memory task in mild cognitive impairment, where we explore functional brain signals on structural white matter networks. Our findings suggest that graph-based permutation patterns in individual brain regions change as the disease progresses, demonstrating potential as a method of analyzing graph-signals at a granular scale. |
2404.04755 | Tom Chou | Sayun Mao, Tom Chou, Maria D'Orsogna | A probabilistic model of relapse in drug addiction | 19 pages, 12 figures | null | null | null | q-bio.QM q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | More than 60% of individuals recovering from substance use disorder relapse
within one year. Some will resume drug consumption even after decades of
abstinence. The cognitive and psychological mechanisms that lead to relapse are
not completely understood, but stressful life experiences and external stimuli
that are associated with past drug-taking are known to play a primary role.
Stressors and cues elicit memories of drug-induced euphoria and the expectation
of relief from current anxiety, igniting an intense craving to use again;
positive experiences and supportive environments may mitigate relapse. We
present a mathematical model of relapse in drug addiction that draws on known
psychiatric concepts such as the "positive activation; negative activation"
paradigm and the "peak-end" rule to construct a relapse rate that depends on
external factors (intensity and timing of life events) and individual traits
(mental responses to these events). We analyze which combinations and ordering
of stressors, cues, and positive events lead to the largest relapse probability
and propose interventions to minimize the likelihood of relapse. We find that
the best protective factor is exposure to a mild, yet continuous, source of
contentment, rather than large, episodic jolts of happiness.
| [
{
"created": "Sat, 6 Apr 2024 23:31:21 GMT",
"version": "v1"
}
] | 2024-04-09 | [
[
"Mao",
"Sayun",
""
],
[
"Chou",
"Tom",
""
],
[
"D'Orsogna",
"Maria",
""
]
] | More than 60% of individuals recovering from substance use disorder relapse within one year. Some will resume drug consumption even after decades of abstinence. The cognitive and psychological mechanisms that lead to relapse are not completely understood, but stressful life experiences and external stimuli that are associated with past drug-taking are known to play a primary role. Stressors and cues elicit memories of drug-induced euphoria and the expectation of relief from current anxiety, igniting an intense craving to use again; positive experiences and supportive environments may mitigate relapse. We present a mathematical model of relapse in drug addiction that draws on known psychiatric concepts such as the "positive activation; negative activation" paradigm and the "peak-end" rule to construct a relapse rate that depends on external factors (intensity and timing of life events) and individual traits (mental responses to these events). We analyze which combinations and ordering of stressors, cues, and positive events lead to the largest relapse probability and propose interventions to minimize the likelihood of relapse. We find that the best protective factor is exposure to a mild, yet continuous, source of contentment, rather than large, episodic jolts of happiness. |
1712.04377 | Max Allen | Po-Jen Chiang and Maximilian L Allen | A review of our current knowledge of clouded leopards (Neofelis
nebulosa) | 8 pages, 1 figure, 3 tables | International Journal of Avian & Wildlife Biology 2017, 2(5):
00032 | 10.15406/ijawb.2017.02.00032 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Little is known about clouded leopards (Neofelis nebulosa), who have a
vulnerable population that extends across southern Asia. We reviewed the
literature and synthesized what is known about their ecology and behavior. Much
of the published literature either note detections within and on the edges of
their range, or are anecdotal observations, many of which are decades if not
over a century old. Clouded leopards are a medium-sized felid, with distinctive
cloud-shape markings, and notably long canines relative to skull size.
Estimates for population densities range from 0.58 to 6.53 individuals per 100
km2. Only 7 clouded leopards have been tracked via radio-collars, and home
range estimates range from 33.6-39.7 km2 for females and 35.5-43.5 km2 for
males. Most accounts describe clouded leopards as nocturnal, but radio
telemetry studies showed that clouded leopards have arrhythmic activity
patterns, with highest activity in the morning followed by evening crepuscular
hours. There has never been a targeted study of clouded leopard diet, but
observations show that they consume a variety of animals, including ungulates,
primates, and rodents. We encourage future study of their population density
and range to inform conservation efforts, and ecological studies in order to
understand the species and its ecological niche.
| [
{
"created": "Tue, 12 Dec 2017 16:27:28 GMT",
"version": "v1"
}
] | 2017-12-13 | [
[
"Chiang",
"Po-Jen",
""
],
[
"Allen",
"Maximilian L",
""
]
] | Little is known about clouded leopards (Neofelis nebulosa), who have a vulnerable population that extends across southern Asia. We reviewed the literature and synthesized what is known about their ecology and behavior. Much of the published literature either note detections within and on the edges of their range, or are anecdotal observations, many of which are decades if not over a century old. Clouded leopards are a medium-sized felid, with distinctive cloud-shape markings, and notably long canines relative to skull size. Estimates for population densities range from 0.58 to 6.53 individuals per 100 km2. Only 7 clouded leopards have been tracked via radio-collars, and home range estimates range from 33.6-39.7 km2 for females and 35.5-43.5 km2 for males. Most accounts describe clouded leopards as nocturnal, but radio telemetry studies showed that clouded leopards have arrhythmic activity patterns, with highest activity in the morning followed by evening crepuscular hours. There has never been a targeted study of clouded leopard diet, but observations show that they consume a variety of animals, including ungulates, primates, and rodents. We encourage future study of their population density and range to inform conservation efforts, and ecological studies in order to understand the species and its ecological niche. |
1407.2210 | Simon DeDeo | Simon DeDeo | Group Minds and the Case of Wikipedia | 21 pages, 6 figures; matches published version | Human Computation (2014) 1:1:5-29 | 10.15346/hc.v1i1.2 | SFI Working Paper #14-10-037 | q-bio.NC cs.GT cs.SI physics.soc-ph q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Group-level cognitive states are widely observed in human social systems, but
their discussion is often ruled out a priori in quantitative approaches. In
this paper, we show how reference to the irreducible mental states and
psychological dynamics of a group is necessary to make sense of large scale
social phenomena. We introduce the problem of mental boundaries by reference to
a classic problem in the evolution of cooperation. We then provide an explicit
quantitative example drawn from ongoing work on cooperation and conflict among
Wikipedia editors, showing how some, but not all, effects of individual
experience persist in the aggregate. We show the limitations of methodological
individualism, and the substantial benefits that come from being able to refer
to collective intentions, and attributions of cognitive states of the form
"what the group believes" and "what the group values".
| [
{
"created": "Tue, 8 Jul 2014 18:46:21 GMT",
"version": "v1"
},
{
"created": "Mon, 13 Oct 2014 21:34:32 GMT",
"version": "v2"
}
] | 2014-10-15 | [
[
"DeDeo",
"Simon",
""
]
] | Group-level cognitive states are widely observed in human social systems, but their discussion is often ruled out a priori in quantitative approaches. In this paper, we show how reference to the irreducible mental states and psychological dynamics of a group is necessary to make sense of large scale social phenomena. We introduce the problem of mental boundaries by reference to a classic problem in the evolution of cooperation. We then provide an explicit quantitative example drawn from ongoing work on cooperation and conflict among Wikipedia editors, showing how some, but not all, effects of individual experience persist in the aggregate. We show the limitations of methodological individualism, and the substantial benefits that come from being able to refer to collective intentions, and attributions of cognitive states of the form "what the group believes" and "what the group values". |
q-bio/0507029 | Vittoria Colizza | Vittoria Colizza, Alain Barrat, Marc Barthelemy and Alessandro
Vespignani | Prediction and predictability of global epidemics: the role of the
airline transportation network | 20 pages, 5 figures | Proc. Natl. Acad. Sci. USA 103, 2015 (2006) | 10.1073/pnas.0510525103 | null | q-bio.OT physics.bio-ph | null | The systematic study of large-scale networks has unveiled the ubiquitous
presence of connectivity patterns characterized by large scale heterogeneities
and unbounded statistical fluctuations. These features affect dramatically the
behavior of the diffusion processes occurring on networks, determining the
ensuing statistical properties of their evolution pattern and dynamics. In this
paper, we investigate the role of the large scale properties of the airline
transportation network in determining the global evolution of emerging disease.
We present a stochastic computational framework for the forecast of global
epidemics that considers the complete world-wide air travel infrastructure
complemented with census population data. We address two basic issues in global
epidemic modeling: i) We study the role of the large scale properties of the
airline transportation network in determining the global diffusion pattern of
emerging diseases; ii) We evaluate the reliability of forecasts and outbreak
scenarios with respect to the intrinsic stochasticity of disease transmission
and traffic flows. In order to address these issues we define a set of novel
quantitative measures able to characterize the level of heterogeneity and
predictability of the epidemic pattern. These measures may be used for the
analysis of containment policies and epidemic risk assessment.
| [
{
"created": "Mon, 18 Jul 2005 18:29:05 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Colizza",
"Vittoria",
""
],
[
"Barrat",
"Alain",
""
],
[
"Barthelemy",
"Marc",
""
],
[
"Vespignani",
"Alessandro",
""
]
] | The systematic study of large-scale networks has unveiled the ubiquitous presence of connectivity patterns characterized by large scale heterogeneities and unbounded statistical fluctuations. These features affect dramatically the behavior of the diffusion processes occurring on networks, determining the ensuing statistical properties of their evolution pattern and dynamics. In this paper, we investigate the role of the large scale properties of the airline transportation network in determining the global evolution of emerging disease. We present a stochastic computational framework for the forecast of global epidemics that considers the complete world-wide air travel infrastructure complemented with census population data. We address two basic issues in global epidemic modeling: i) We study the role of the large scale properties of the airline transportation network in determining the global diffusion pattern of emerging diseases; ii) We evaluate the reliability of forecasts and outbreak scenarios with respect to the intrinsic stochasticity of disease transmission and traffic flows. In order to address these issues we define a set of novel quantitative measures able to characterize the level of heterogeneity and predictability of the epidemic pattern. These measures may be used for the analysis of containment policies and epidemic risk assessment. |
1212.0447 | Iddo Friedberg | Andrew T. Oberlin, Dominika A. Jurkovic, Mitchell F. Balish and Iddo
Friedberg | Biological Database of Images and Genomes: tools for community
annotations linking image and genomic information | This article has been submitted for publication in Database
\copyright: 2012-2013 Iddo Friedberg Published by Oxford University Press on
behalf of all authors. All rights reserved | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Genomic data and biomedical imaging data are undergoing exponential growth.
However, our understanding of the phenotype-genotype connection linking the two
types of data is lagging behind. While there are many types of software that
enable the manipulation and analysis of image data and genomic data as separate
entities, there is no framework established for linking the two. We present a
generic set of software tools, BioDIG, that allows linking of image data to
genomic data. BioDIG tools can be applied to a wide range of research problems
that require linking images to genomes. BioDIG features the following: rapid
construction of web-based workbenches, community-based annotation, user
management, and web-services. By using BioDIG to create websites, researchers
and curators can rapidly annotate large number of images with genomic
information. Here we present the BioDIG software tools that include an image
module, a genome module and a user management module. We also introduce a
BioDIG-based website, MyDIG, which is being used to annotate images of
Mycoplasma.
| [
{
"created": "Mon, 3 Dec 2012 16:52:41 GMT",
"version": "v1"
}
] | 2013-01-09 | [
[
"Oberlin",
"Andrew T.",
""
],
[
"Jurkovic",
"Dominika A.",
""
],
[
"Balish",
"Mitchell F.",
""
],
[
"Friedberg",
"Iddo",
""
]
] | Genomic data and biomedical imaging data are undergoing exponential growth. However, our understanding of the phenotype-genotype connection linking the two types of data is lagging behind. While there are many types of software that enable the manipulation and analysis of image data and genomic data as separate entities, there is no framework established for linking the two. We present a generic set of software tools, BioDIG, that allows linking of image data to genomic data. BioDIG tools can be applied to a wide range of research problems that require linking images to genomes. BioDIG features the following: rapid construction of web-based workbenches, community-based annotation, user management, and web-services. By using BioDIG to create websites, researchers and curators can rapidly annotate large number of images with genomic information. Here we present the BioDIG software tools that include an image module, a genome module and a user management module. We also introduce a BioDIG-based website, MyDIG, which is being used to annotate images of Mycoplasma. |
0810.1307 | Carla Goldman | Carla Goldman, Elisa T. Sena | The dynamics of cargo driven by molecular motors in the context of
asymmetric simple exclusion processes | 20 pages, 2 figures | null | 10.1016/j.physa.2009.04.038 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the dynamics of cargo driven by a collection of interacting
molecular motors in the context of an asymmetric simple exclusion processes
(ASEP). The model is formulated to account for i) excluded volume interactions,
ii) the observed asymmetry of the stochastic movement of individual motors and
iii) interactions between motors and cargo. Items (i) and (ii) form the basis
of ASEP models and have already been considered in the literature to study the
behavior of motor density profile [Parmeggiani 03]. Item (iii) is new. It is
introduced here as an attempt to describe explicitly the dependence of cargo
movement on the dynamics of motors. The steady-state solutions of the model
indicate that the system undergoes a phase transition of condensation type as
the motor density varies. We study the consequences of this transition to the
properties of cargo velocity.
| [
{
"created": "Tue, 7 Oct 2008 21:55:04 GMT",
"version": "v1"
}
] | 2015-05-13 | [
[
"Goldman",
"Carla",
""
],
[
"Sena",
"Elisa T.",
""
]
] | We consider the dynamics of cargo driven by a collection of interacting molecular motors in the context of an asymmetric simple exclusion processes (ASEP). The model is formulated to account for i) excluded volume interactions, ii) the observed asymmetry of the stochastic movement of individual motors and iii) interactions between motors and cargo. Items (i) and (ii) form the basis of ASEP models and have already been considered in the literature to study the behavior of motor density profile [Parmeggiani 03]. Item (iii) is new. It is introduced here as an attempt to describe explicitly the dependence of cargo movement on the dynamics of motors. The steady-state solutions of the model indicate that the system undergoes a phase transition of condensation type as the motor density varies. We study the consequences of this transition to the properties of cargo velocity. |
2312.14220 | Yixuan Wang | Yixuan Wang and Shuangyin Li and Shimin DI and Lei Chen | Single-Cell RNA-seq Synthesis with Latent Diffusion Model | 13 pages, 5 figures | null | null | null | q-bio.GN cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | The single-cell RNA sequencing (scRNA-seq) technology enables researchers to
study complex biological systems and diseases with high resolution. The central
challenge is synthesizing enough scRNA-seq samples; insufficient samples can
impede downstream analysis and reproducibility. While various methods have been
attempted in past research, the resulting scRNA-seq samples were often of poor
quality or limited in terms of useful specific cell subpopulations. To address
these issues, we propose a novel method called Single-Cell Latent Diffusion
(SCLD) based on the Diffusion Model. This method is capable of synthesizing
large-scale, high-quality scRNA-seq samples, including both 'holistic' or
targeted specific cellular subpopulations within a unified framework. A
pre-guidance mechanism is designed for synthesizing specific cellular
subpopulations, while a post-guidance mechanism aims to enhance the quality of
scRNA-seq samples. The SCLD can synthesize large-scale and high-quality
scRNA-seq samples for various downstream tasks. Our experimental results
demonstrate state-of-the-art performance in cell classification and data
distribution distances when evaluated on two scRNA-seq benchmarks.
Additionally, visualization experiments show the SCLD's capability in
synthesizing specific cellular subpopulations.
| [
{
"created": "Thu, 21 Dec 2023 13:15:16 GMT",
"version": "v1"
}
] | 2023-12-25 | [
[
"Wang",
"Yixuan",
""
],
[
"Li",
"Shuangyin",
""
],
[
"DI",
"Shimin",
""
],
[
"Chen",
"Lei",
""
]
] | The single-cell RNA sequencing (scRNA-seq) technology enables researchers to study complex biological systems and diseases with high resolution. The central challenge is synthesizing enough scRNA-seq samples; insufficient samples can impede downstream analysis and reproducibility. While various methods have been attempted in past research, the resulting scRNA-seq samples were often of poor quality or limited in terms of useful specific cell subpopulations. To address these issues, we propose a novel method called Single-Cell Latent Diffusion (SCLD) based on the Diffusion Model. This method is capable of synthesizing large-scale, high-quality scRNA-seq samples, including both 'holistic' or targeted specific cellular subpopulations within a unified framework. A pre-guidance mechanism is designed for synthesizing specific cellular subpopulations, while a post-guidance mechanism aims to enhance the quality of scRNA-seq samples. The SCLD can synthesize large-scale and high-quality scRNA-seq samples for various downstream tasks. Our experimental results demonstrate state-of-the-art performance in cell classification and data distribution distances when evaluated on two scRNA-seq benchmarks. Additionally, visualization experiments show the SCLD's capability in synthesizing specific cellular subpopulations. |
q-bio/0605002 | Michael H\"ohl | Michael H\"ohl, Isidore Rigoutsos, Mark A. Ragan | Pattern-based phylogenetic distance estimation and tree reconstruction | 21 pages, 3 figures, 2 tables | null | null | null | q-bio.QM q-bio.PE | null | We have developed an alignment-free method that calculates phylogenetic
distances using a maximum likelihood approach for a model of sequence change on
patterns that are discovered in unaligned sequences. To evaluate the
phylogenetic accuracy of our method, and to conduct a comprehensive comparison
of existing alignment-free methods (freely available as Python package decaf+py
at http://www.bioinformatics.org.au), we have created a dataset of reference
trees covering a wide range of phylogenetic distances. Amino acid sequences
were evolved along the trees and input to the tested methods; from their
calculated distances we infered trees whose topologies we compared to the
reference trees.
We find our pattern-based method statistically superior to all other tested
alignment-free methods on this dataset. We also demonstrate the general
advantage of alignment-free methods over an approach based on automated
alignments when sequences violate the assumption of collinearity. Similarly, we
compare methods on empirical data from an existing alignment benchmark set that
we used to derive reference distances and trees. Our pattern-based approach
yields distances that show a linear relationship to reference distances over a
substantially longer range than other alignment-free methods. The pattern-based
approach outperforms alignment-free methods and its phylogenetic accuracy is
statistically indistinguishable from alignment-based distances.
| [
{
"created": "Sun, 30 Apr 2006 01:47:32 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Höhl",
"Michael",
""
],
[
"Rigoutsos",
"Isidore",
""
],
[
"Ragan",
"Mark A.",
""
]
] | We have developed an alignment-free method that calculates phylogenetic distances using a maximum likelihood approach for a model of sequence change on patterns that are discovered in unaligned sequences. To evaluate the phylogenetic accuracy of our method, and to conduct a comprehensive comparison of existing alignment-free methods (freely available as Python package decaf+py at http://www.bioinformatics.org.au), we have created a dataset of reference trees covering a wide range of phylogenetic distances. Amino acid sequences were evolved along the trees and input to the tested methods; from their calculated distances we infered trees whose topologies we compared to the reference trees. We find our pattern-based method statistically superior to all other tested alignment-free methods on this dataset. We also demonstrate the general advantage of alignment-free methods over an approach based on automated alignments when sequences violate the assumption of collinearity. Similarly, we compare methods on empirical data from an existing alignment benchmark set that we used to derive reference distances and trees. Our pattern-based approach yields distances that show a linear relationship to reference distances over a substantially longer range than other alignment-free methods. The pattern-based approach outperforms alignment-free methods and its phylogenetic accuracy is statistically indistinguishable from alignment-based distances. |
1512.05656 | Benjamin Albrecht | Benjamin Albrecht | Fast computation of all maximum acyclic agreement forests for two rooted
binary phylogenetic trees | 28 pages | null | null | null | q-bio.PE cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evolutionary scenarios displaying reticulation events are often represented
by rooted phylogenetic networks. Due to biological reasons, those events occur
very rarely, and, thus, networks containing a minimum number of such events,
so-called minimum hybridization networks, are of particular interest for
research. Moreover, to study reticulate evolution, biologist need not only a
subset but all of those networks. To achieve this goal, the less complex
concept of rooted phylogenetic trees can be used as building block. Here, as a
first important step, the trees are disjoint into common parts, so-called
maximum acyclic agreement forests, which can then be turned into minimum
hybridization networks by applying further network building algorithms. In this
paper, we present two modifications of the first non-naive algorithm --- called
allMAAFs --- computing all maximum acyclic agreement forests for two rooted
binary phylogenetic trees on the same set of taxa. By a simulation study, we
indicate that through these modifications the algorithm is on average 8 times
faster than the original algorithm making this algorithm accessible to larger
input trees and, thus, to a wider range of biological problems.
| [
{
"created": "Thu, 17 Dec 2015 16:21:47 GMT",
"version": "v1"
}
] | 2015-12-18 | [
[
"Albrecht",
"Benjamin",
""
]
] | Evolutionary scenarios displaying reticulation events are often represented by rooted phylogenetic networks. Due to biological reasons, those events occur very rarely, and, thus, networks containing a minimum number of such events, so-called minimum hybridization networks, are of particular interest for research. Moreover, to study reticulate evolution, biologist need not only a subset but all of those networks. To achieve this goal, the less complex concept of rooted phylogenetic trees can be used as building block. Here, as a first important step, the trees are disjoint into common parts, so-called maximum acyclic agreement forests, which can then be turned into minimum hybridization networks by applying further network building algorithms. In this paper, we present two modifications of the first non-naive algorithm --- called allMAAFs --- computing all maximum acyclic agreement forests for two rooted binary phylogenetic trees on the same set of taxa. By a simulation study, we indicate that through these modifications the algorithm is on average 8 times faster than the original algorithm making this algorithm accessible to larger input trees and, thus, to a wider range of biological problems. |
2003.08976 | Steven Frank | Steven A. Frank and William Godsoe | The generalized Price equation: forces that change population statistics | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | The Price equation partitions the change in the expected value of a
population measure. The first component describes the partial change caused by
altered frequencies. The second component describes the partial change caused
by altered measurements. In biology, frequency changes often associate with the
direct effect of natural selection. Measure changes reflect processes during
transmission that alter trait values. More broadly, the two components describe
the direct forces that change population composition and the altered frame of
reference that changes measured values. The classic Price equation is limited
to population statistics that can expressed as the expected value of a measure.
Many statistics cannot be expressed as expected values, such as the harmonic
mean and the family of rescaled diversity measures. We generalize the Price
equation to any population statistic that can be expressed as a function of
frequencies and measurements. We obtain the generalized partition between the
direct forces that cause frequency change and the altered frame of reference
that changes measurements.
| [
{
"created": "Thu, 19 Mar 2020 18:43:59 GMT",
"version": "v1"
}
] | 2020-03-23 | [
[
"Frank",
"Steven A.",
""
],
[
"Godsoe",
"William",
""
]
] | The Price equation partitions the change in the expected value of a population measure. The first component describes the partial change caused by altered frequencies. The second component describes the partial change caused by altered measurements. In biology, frequency changes often associate with the direct effect of natural selection. Measure changes reflect processes during transmission that alter trait values. More broadly, the two components describe the direct forces that change population composition and the altered frame of reference that changes measured values. The classic Price equation is limited to population statistics that can expressed as the expected value of a measure. Many statistics cannot be expressed as expected values, such as the harmonic mean and the family of rescaled diversity measures. We generalize the Price equation to any population statistic that can be expressed as a function of frequencies and measurements. We obtain the generalized partition between the direct forces that cause frequency change and the altered frame of reference that changes measurements. |
1907.01573 | Masoud Farahmand | Masoud Farahmand, Minoo N. Kavarana, Phillip M. Trusty, Ethan O. Kung | Target Flow-Pressure Operating Range for Designing a Failing Fontan
Cavopulmonary Support Device | null | null | 10.1109/TBME.2020.2974098 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fontan operation as the current standard of care for the palliation of single
ventricle defects results in significant late complications. Using a mechanical
circulatory device for the right circulation to serve the function of the
missing subpulmonary ventricle could potentially stabilize the failing Fontan
circulation. This study aims to elucidate the hydraulic operating regions that
should be targeted for designing cavopulmonary blood pumps. By integrating
numerical analysis and available clinical information, the interaction of the
cavopulmonary support via the IVC and full assist configurations with a wide
range of simulated adult failing scenarios was investigated; with IVC and full
assist corresponding to the inferior venous return or the entire venous return,
respectively, being routed through the device. We identified the desired
hydraulic operating regions for a cavopulmonary assist device by clustering all
head pressures and corresponding pump flows that result in hemodynamic
improvement for each simulated failing Fontan physiology. Results show that IVC
support can produce beneficial hemodynamics in only a small fraction of failing
Fontan scenarios. Cavopulmonary assist device could increase cardiac index by
35% and decrease the inferior vena cava pressure by 45% depending on the
patient's pre-support hemodynamic state and surgical configuration of the
cavopulmonary assist device (IVC or full support). The desired flow-pressure
operating regions we identified can serve as the performance criteria for
designing cavopulmonary assist devices as well as evaluating off-label use of
commercially available left-side blood pumps for failing Fontan cavopulmonary
support.
| [
{
"created": "Tue, 2 Jul 2019 18:20:34 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Aug 2019 16:14:41 GMT",
"version": "v2"
},
{
"created": "Sun, 10 Nov 2019 17:16:00 GMT",
"version": "v3"
},
{
"created": "Sat, 15 Feb 2020 16:33:10 GMT",
"version": "v4"
}
] | 2020-02-18 | [
[
"Farahmand",
"Masoud",
""
],
[
"Kavarana",
"Minoo N.",
""
],
[
"Trusty",
"Phillip M.",
""
],
[
"Kung",
"Ethan O.",
""
]
] | Fontan operation as the current standard of care for the palliation of single ventricle defects results in significant late complications. Using a mechanical circulatory device for the right circulation to serve the function of the missing subpulmonary ventricle could potentially stabilize the failing Fontan circulation. This study aims to elucidate the hydraulic operating regions that should be targeted for designing cavopulmonary blood pumps. By integrating numerical analysis and available clinical information, the interaction of the cavopulmonary support via the IVC and full assist configurations with a wide range of simulated adult failing scenarios was investigated; with IVC and full assist corresponding to the inferior venous return or the entire venous return, respectively, being routed through the device. We identified the desired hydraulic operating regions for a cavopulmonary assist device by clustering all head pressures and corresponding pump flows that result in hemodynamic improvement for each simulated failing Fontan physiology. Results show that IVC support can produce beneficial hemodynamics in only a small fraction of failing Fontan scenarios. Cavopulmonary assist device could increase cardiac index by 35% and decrease the inferior vena cava pressure by 45% depending on the patient's pre-support hemodynamic state and surgical configuration of the cavopulmonary assist device (IVC or full support). The desired flow-pressure operating regions we identified can serve as the performance criteria for designing cavopulmonary assist devices as well as evaluating off-label use of commercially available left-side blood pumps for failing Fontan cavopulmonary support. |
2307.03308 | Grace Sun | Grace Sun, Sandip Patel | Exploring The Contribution of Innate Immune Cells to Breast Cancer
Immunotherapy | null | null | null | null | q-bio.CB | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Breast cancer is the leading type of cancer in women. About 10-15% of breast
cancers are triple-negative breast cancer (TNBC), a subtype with the worst
prognosis. Due to the lack of estrogen, progesterone and HER2 receptor
expression, chemotherapies have been the standard of care for decades.
Immunotherapy has emerged as promising for TNBC treatment. In 2020, the Food
and Drug Administration (FDA) granted approval to pembrolizumab in combination
with chemotherapy for patients with advanced triple-negative breast cancer.
However, only a subgroup of advanced TNBC patients live longer whose tumors
have a PD-L1 Combined Positive Score of at least 10 (CPS>=10). There is still
an unmet medical need to provide alternative treatment for the rest of
patients. Interestingly, a few of patients at UCSD Moores Cancer Center were
found to have had excellent responses to pembrolizumab despite low CPS scores
(termed Elite Responders). The hypothesis of this project is that there may be
an alternative immune response mechanism and/or crosstalk happening between the
innate and adaptive immune systems, especially in Natural Killer Cells and
Macrophages, that contributed to this unexpected excellent response. Our
procedure used ACDBio RNAscope Multiplex Fluorescence v2 method to spatially
analyze innate immune cells (Natural Killer cells and macrophages) and adaptive
immune cells (T-cells) in the Tumor Micro Environment. Our data demonstrated
increased tumor infiltration of innate immune cells (macrophage and Natural
Killer cells) in the Elite Responders. This conclusion indicated the joint
effort of two immune systems (innate and adaptive) which eventually led to
increased survival.
| [
{
"created": "Thu, 6 Jul 2023 21:42:02 GMT",
"version": "v1"
}
] | 2023-07-10 | [
[
"Sun",
"Grace",
""
],
[
"Patel",
"Sandip",
""
]
] | Breast cancer is the leading type of cancer in women. About 10-15% of breast cancers are triple-negative breast cancer (TNBC), a subtype with the worst prognosis. Due to the lack of estrogen, progesterone and HER2 receptor expression, chemotherapies have been the standard of care for decades. Immunotherapy has emerged as promising for TNBC treatment. In 2020, the Food and Drug Administration (FDA) granted approval to pembrolizumab in combination with chemotherapy for patients with advanced triple-negative breast cancer. However, only a subgroup of advanced TNBC patients live longer whose tumors have a PD-L1 Combined Positive Score of at least 10 (CPS>=10). There is still an unmet medical need to provide alternative treatment for the rest of patients. Interestingly, a few of patients at UCSD Moores Cancer Center were found to have had excellent responses to pembrolizumab despite low CPS scores (termed Elite Responders). The hypothesis of this project is that there may be an alternative immune response mechanism and/or crosstalk happening between the innate and adaptive immune systems, especially in Natural Killer Cells and Macrophages, that contributed to this unexpected excellent response. Our procedure used ACDBio RNAscope Multiplex Fluorescence v2 method to spatially analyze innate immune cells (Natural Killer cells and macrophages) and adaptive immune cells (T-cells) in the Tumor Micro Environment. Our data demonstrated increased tumor infiltration of innate immune cells (macrophage and Natural Killer cells) in the Elite Responders. This conclusion indicated the joint effort of two immune systems (innate and adaptive) which eventually led to increased survival. |
2110.11237 | Islem Rekik | Alpay Tekin, Ahmed Nebli and Islem Rekik | Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph
Evaluation Trajectory | null | null | null | null | q-bio.NC cs.CV cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Several brain disorders can be detected by observing alterations in the
brain's structural and functional connectivities. Neurological findings suggest
that early diagnosis of brain disorders, such as mild cognitive impairment
(MCI), can prevent and even reverse its development into Alzheimer's disease
(AD). In this context, recent studies aimed to predict the evolution of brain
connectivities over time by proposing machine learning models that work on
brain images. However, such an approach is costly and time-consuming. Here, we
propose to use brain connectivities as a more efficient alternative for
time-dependent brain disorder diagnosis by regarding the brain as instead a
large interconnected graph characterizing the interconnectivity scheme between
several brain regions. We term our proposed method Recurrent Brain Graph Mapper
(RBGM), a novel efficient edge-based recurrent graph neural network that
predicts the time-dependent evaluation trajectory of a brain graph from a
single baseline. Our RBGM contains a set of recurrent neural network-inspired
mappers for each time point, where each mapper aims to project the ground-truth
brain graph onto its next time point. We leverage the teacher forcing method to
boost training and improve the evolved brain graph quality. To maintain the
topological consistency between the predicted brain graphs and their
corresponding ground-truth brain graphs at each time point, we further
integrate a topological loss. We also use l1 loss to capture time-dependency
and minimize the distance between the brain graph at consecutive time points
for regularization. Benchmarks against several variants of RBGM and
state-of-the-art methods prove that we can achieve the same accuracy in
predicting brain graph evolution more efficiently, paving the way for novel
graph neural network architecture and a highly efficient training scheme.
| [
{
"created": "Wed, 6 Oct 2021 09:25:55 GMT",
"version": "v1"
}
] | 2021-10-22 | [
[
"Tekin",
"Alpay",
""
],
[
"Nebli",
"Ahmed",
""
],
[
"Rekik",
"Islem",
""
]
] | Several brain disorders can be detected by observing alterations in the brain's structural and functional connectivities. Neurological findings suggest that early diagnosis of brain disorders, such as mild cognitive impairment (MCI), can prevent and even reverse its development into Alzheimer's disease (AD). In this context, recent studies aimed to predict the evolution of brain connectivities over time by proposing machine learning models that work on brain images. However, such an approach is costly and time-consuming. Here, we propose to use brain connectivities as a more efficient alternative for time-dependent brain disorder diagnosis by regarding the brain as instead a large interconnected graph characterizing the interconnectivity scheme between several brain regions. We term our proposed method Recurrent Brain Graph Mapper (RBGM), a novel efficient edge-based recurrent graph neural network that predicts the time-dependent evaluation trajectory of a brain graph from a single baseline. Our RBGM contains a set of recurrent neural network-inspired mappers for each time point, where each mapper aims to project the ground-truth brain graph onto its next time point. We leverage the teacher forcing method to boost training and improve the evolved brain graph quality. To maintain the topological consistency between the predicted brain graphs and their corresponding ground-truth brain graphs at each time point, we further integrate a topological loss. We also use l1 loss to capture time-dependency and minimize the distance between the brain graph at consecutive time points for regularization. Benchmarks against several variants of RBGM and state-of-the-art methods prove that we can achieve the same accuracy in predicting brain graph evolution more efficiently, paving the way for novel graph neural network architecture and a highly efficient training scheme. |
1909.03986 | Akihiko Wada | Akihiko Wada, Yuya Saito, Ryusuke Irie, Koji Kamagata, Tomoko Maekawa,
Shohei Fujita, Akifumi Hagiwara, Kanako Kumamaru, Michimasa Suzuki, Atsushi
Nakanishi, Masaaki Hori, Toshiaki Shimizu, Shigeki Aoki | Convolutional Neural Networks for Estimation of Myelin Maturation in
Infant Brain | 7 pages, 4 figures, 1 table | null | null | null | q-bio.QM eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Myelination plays an important role in the neurological development of infant
brain and MRI can visualize the myelination extension as T1 high and T2 low
signal intensity at white matter. We tried to construct a convolutional neural
network machine learning model to estimate the myelination. Eight layers CNN
architecture was constructed to estimate the subjects age with T1 and T2
weighted image at 5 levels associated with myelin maturation in 119 subjects up
to 24 months. CNN model learned with all age dataset revealed a strong
correlation between the estimated age and the corrected age and the coefficient
of correlation, root mean square error and mean absolute error was 0. 81, 3. 40
and 2. 28. Moreover, the adaptation of ensemble learning models with two
datasets 0 to 16 months and 8 to 24 months improved that to 0. 93, 2. 12 and 1.
34. Deep learning can be adaptable to myelination estimation in infant brain.
| [
{
"created": "Mon, 9 Sep 2019 16:54:42 GMT",
"version": "v1"
}
] | 2019-09-10 | [
[
"Wada",
"Akihiko",
""
],
[
"Saito",
"Yuya",
""
],
[
"Irie",
"Ryusuke",
""
],
[
"Kamagata",
"Koji",
""
],
[
"Maekawa",
"Tomoko",
""
],
[
"Fujita",
"Shohei",
""
],
[
"Hagiwara",
"Akifumi",
""
],
[
"Kumamaru",
"Kanako",
""
],
[
"Suzuki",
"Michimasa",
""
],
[
"Nakanishi",
"Atsushi",
""
],
[
"Hori",
"Masaaki",
""
],
[
"Shimizu",
"Toshiaki",
""
],
[
"Aoki",
"Shigeki",
""
]
] | Myelination plays an important role in the neurological development of infant brain and MRI can visualize the myelination extension as T1 high and T2 low signal intensity at white matter. We tried to construct a convolutional neural network machine learning model to estimate the myelination. Eight layers CNN architecture was constructed to estimate the subjects age with T1 and T2 weighted image at 5 levels associated with myelin maturation in 119 subjects up to 24 months. CNN model learned with all age dataset revealed a strong correlation between the estimated age and the corrected age and the coefficient of correlation, root mean square error and mean absolute error was 0. 81, 3. 40 and 2. 28. Moreover, the adaptation of ensemble learning models with two datasets 0 to 16 months and 8 to 24 months improved that to 0. 93, 2. 12 and 1. 34. Deep learning can be adaptable to myelination estimation in infant brain. |
1102.4015 | Yupeng Wang | Yupeng Wang, Xinyu Liu, Michael Kelley and Romdhane Rekaya | dMotifGreedy: a novel tool for de novo discovery of DNA motifs with
enhanced power of reporting distinct motifs | null | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by/3.0/ | De novo discovery of over-represented DNA motifs is one of the major
challenges in computational biology. Although numerous tools have been
available for de novo motif discovery, many of these tools are subject to local
optima phenomena, which may hinder detection of multiple distinct motifs. A
greedy algorithm based tool named dMotifGreedy was developed. dMotifGreedy
begins by searching for candidate motifs from pair-wise local alignments of
input sequences and then computes an optimal global solution for each candidate
motif through a greedy algorithm. dMotifGreedy has competitive performance in
detecting a true motif and greatly enhanced performance in detecting multiple
distinct true motifs. dMotifGreedy is freely available via a stand-alone
program at http://lambchop.ads.uga.edu/dmotifgreedy/download.php.
| [
{
"created": "Sat, 19 Feb 2011 19:07:52 GMT",
"version": "v1"
}
] | 2011-02-22 | [
[
"Wang",
"Yupeng",
""
],
[
"Liu",
"Xinyu",
""
],
[
"Kelley",
"Michael",
""
],
[
"Rekaya",
"Romdhane",
""
]
] | De novo discovery of over-represented DNA motifs is one of the major challenges in computational biology. Although numerous tools have been available for de novo motif discovery, many of these tools are subject to local optima phenomena, which may hinder detection of multiple distinct motifs. A greedy algorithm based tool named dMotifGreedy was developed. dMotifGreedy begins by searching for candidate motifs from pair-wise local alignments of input sequences and then computes an optimal global solution for each candidate motif through a greedy algorithm. dMotifGreedy has competitive performance in detecting a true motif and greatly enhanced performance in detecting multiple distinct true motifs. dMotifGreedy is freely available via a stand-alone program at http://lambchop.ads.uga.edu/dmotifgreedy/download.php. |
1007.2787 | Jason Prentice | Jason S. Prentice, Jan Homann, Kristina D. Simmons, Ga\v{s}per
Tka\v{c}ik, Vijay Balasubramanian, and Philip C. Nelson | Fast, scalable, Bayesian spike identification for multi-electrode arrays | null | PLoS ONE 6: e19884 (2011) | 10.1371/journal.pone.0019884 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an algorithm to identify individual neural spikes observed on
high-density multi-electrode arrays (MEAs). Our method can distinguish large
numbers of distinct neural units, even when spikes overlap, and accounts for
intrinsic variability of spikes from each unit. As MEAs grow larger, it is
important to find spike-identification methods that are scalable, that is, the
computational cost of spike fitting should scale well with the number of units
observed. Our algorithm accomplishes this goal, and is fast, because it
exploits the spatial locality of each unit and the basic biophysics of
extracellular signal propagation. Human intervention is minimized and
streamlined via a graphical interface. We illustrate our method on data from a
mammalian retina preparation and document its performance on simulated data
consisting of spikes added to experimentally measured background noise. The
algorithm is highly accurate.
| [
{
"created": "Fri, 16 Jul 2010 14:57:07 GMT",
"version": "v1"
}
] | 2013-08-01 | [
[
"Prentice",
"Jason S.",
""
],
[
"Homann",
"Jan",
""
],
[
"Simmons",
"Kristina D.",
""
],
[
"Tkačik",
"Gašper",
""
],
[
"Balasubramanian",
"Vijay",
""
],
[
"Nelson",
"Philip C.",
""
]
] | We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human intervention is minimized and streamlined via a graphical interface. We illustrate our method on data from a mammalian retina preparation and document its performance on simulated data consisting of spikes added to experimentally measured background noise. The algorithm is highly accurate. |
1904.02866 | Sushrut Thorat | Sushrut Thorat, Daria Proklova, Marius V. Peelen | The nature of the animacy organization in human ventral temporal cortex | 16 pages, 5 figures, code+data at -
https://doi.org/10.17605/OSF.IO/VXWG9 Update - added supplementary results
and edited abstract | null | 10.7554/eLife.47142 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | The principles underlying the animacy organization of the ventral temporal
cortex (VTC) remain hotly debated, with recent evidence pointing to an animacy
continuum rather than a dichotomy. What drives this continuum? According to the
visual categorization hypothesis, the continuum reflects the degree to which
animals contain animal-diagnostic features. By contrast, the agency hypothesis
posits that the continuum reflects the degree to which animals are perceived as
(social) agents. Here, we tested both hypotheses with a stimulus set in which
visual categorizability and agency were dissociated based on representations in
convolutional neural networks and behavioral experiments. Using fMRI, we found
that visual categorizability and agency explained independent components of the
animacy continuum in VTC. Modeled together, they fully explained the animacy
continuum. Finally, clusters explained by visual categorizability were
localized posterior to clusters explained by agency. These results show that
multiple organizing principles, including agency, underlie the animacy
continuum in VTC.
| [
{
"created": "Fri, 5 Apr 2019 04:30:02 GMT",
"version": "v1"
},
{
"created": "Wed, 10 Apr 2019 15:28:54 GMT",
"version": "v2"
},
{
"created": "Fri, 12 Jul 2019 13:00:17 GMT",
"version": "v3"
},
{
"created": "Mon, 15 Jul 2019 08:16:12 GMT",
"version": "v4"
}
] | 2019-09-11 | [
[
"Thorat",
"Sushrut",
""
],
[
"Proklova",
"Daria",
""
],
[
"Peelen",
"Marius V.",
""
]
] | The principles underlying the animacy organization of the ventral temporal cortex (VTC) remain hotly debated, with recent evidence pointing to an animacy continuum rather than a dichotomy. What drives this continuum? According to the visual categorization hypothesis, the continuum reflects the degree to which animals contain animal-diagnostic features. By contrast, the agency hypothesis posits that the continuum reflects the degree to which animals are perceived as (social) agents. Here, we tested both hypotheses with a stimulus set in which visual categorizability and agency were dissociated based on representations in convolutional neural networks and behavioral experiments. Using fMRI, we found that visual categorizability and agency explained independent components of the animacy continuum in VTC. Modeled together, they fully explained the animacy continuum. Finally, clusters explained by visual categorizability were localized posterior to clusters explained by agency. These results show that multiple organizing principles, including agency, underlie the animacy continuum in VTC. |
2003.05331 | Maximilian Pichler | Maximilian Pichler, Florian Hartig | A new method for faster and more accurate inference of species
associations from big community data | 65 pages, 5 figures | Methods in ecology and evolution (2013), 12(11), 2159-2173 | 10.1111/2041-210X.13687 | null | q-bio.QM q-bio.PE stat.AP stat.CO | http://creativecommons.org/licenses/by/4.0/ | 1. Joint Species Distribution models (JSDMs) explain spatial variation in
community composition by contributions of the environment, biotic associations,
and possibly spatially structured residual covariance. They show great promise
as a general analytical framework for community ecology and macroecology, but
current JSDMs, even when approximated by latent variables, scale poorly on
large datasets, limiting their usefulness for currently emerging big (e.g.,
metabarcoding and metagenomics) community datasets. 2. Here, we present a
novel, more scalable JSDM (sjSDM) that circumvents the need to use latent
variables by using a Monte-Carlo integration of the joint JSDM likelihood and
allows flexible elastic net regularization on all model components. We
implemented sjSDM in PyTorch, a modern machine learning framework that can make
use of CPU and GPU calculations. Using simulated communities with known
species-species associations and different number of species and sites, we
compare sjSDM with state-of-the-art JSDM implementations to determine
computational runtimes and accuracy of the inferred species-species and
species-environmental associations. 3. We find that sjSDM is orders of
magnitude faster than existing JSDM algorithms (even when run on the CPU) and
can be scaled to very large datasets. Despite the dramatically improved speed,
sjSDM produces more accurate estimates of species association structures than
alternative JSDM implementations. We demonstrate the applicability of sjSDM to
big community data using eDNA case study with thousands of fungi operational
taxonomic units (OTU). 4. Our sjSDM approach makes the analysis of JSDMs to
large community datasets with hundreds or thousands of species possible,
substantially extending the applicability of JSDMs in ecology. We provide our
method in an R package to facilitate its applicability for practical data
analysis.
| [
{
"created": "Wed, 11 Mar 2020 14:37:02 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Mar 2020 08:11:01 GMT",
"version": "v2"
},
{
"created": "Tue, 16 Jun 2020 13:26:47 GMT",
"version": "v3"
},
{
"created": "Mon, 12 Oct 2020 11:46:42 GMT",
"version": "v4"
},
{
"created": "Fri, 2 Jul 2021 09:24:25 GMT",
"version": "v5"
}
] | 2023-03-28 | [
[
"Pichler",
"Maximilian",
""
],
[
"Hartig",
"Florian",
""
]
] | 1. Joint Species Distribution models (JSDMs) explain spatial variation in community composition by contributions of the environment, biotic associations, and possibly spatially structured residual covariance. They show great promise as a general analytical framework for community ecology and macroecology, but current JSDMs, even when approximated by latent variables, scale poorly on large datasets, limiting their usefulness for currently emerging big (e.g., metabarcoding and metagenomics) community datasets. 2. Here, we present a novel, more scalable JSDM (sjSDM) that circumvents the need to use latent variables by using a Monte-Carlo integration of the joint JSDM likelihood and allows flexible elastic net regularization on all model components. We implemented sjSDM in PyTorch, a modern machine learning framework that can make use of CPU and GPU calculations. Using simulated communities with known species-species associations and different number of species and sites, we compare sjSDM with state-of-the-art JSDM implementations to determine computational runtimes and accuracy of the inferred species-species and species-environmental associations. 3. We find that sjSDM is orders of magnitude faster than existing JSDM algorithms (even when run on the CPU) and can be scaled to very large datasets. Despite the dramatically improved speed, sjSDM produces more accurate estimates of species association structures than alternative JSDM implementations. We demonstrate the applicability of sjSDM to big community data using eDNA case study with thousands of fungi operational taxonomic units (OTU). 4. Our sjSDM approach makes the analysis of JSDMs to large community datasets with hundreds or thousands of species possible, substantially extending the applicability of JSDMs in ecology. We provide our method in an R package to facilitate its applicability for practical data analysis. |
2308.06294 | Jingye Yang | Jingye Yang, Cong Liu, Wendy Deng, Da Wu, Chunhua Weng, Yunyun Zhou,
Kai Wang | Enhancing Phenotype Recognition in Clinical Notes Using Large Language
Models: PhenoBCBERT and PhenoGPT | null | null | null | null | q-bio.QM cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We hypothesize that large language models (LLMs) based on the transformer
architecture can enable automated detection of clinical phenotype terms,
including terms not documented in the HPO. In this study, we developed two
types of models: PhenoBCBERT, a BERT-based model, utilizing Bio+Clinical BERT
as its pre-trained model, and PhenoGPT, a GPT-based model that can be
initialized from diverse GPT models, including open-source versions such as
GPT-J, Falcon, and LLaMA, as well as closed-source versions such as GPT-3 and
GPT-3.5. We compared our methods with PhenoTagger, a recently developed HPO
recognition tool that combines rule-based and deep learning methods. We found
that our methods can extract more phenotype concepts, including novel ones not
characterized by HPO. We also performed case studies on biomedical literature
to illustrate how new phenotype information can be recognized and extracted. We
compared current BERT-based versus GPT-based models for phenotype tagging, in
multiple aspects including model architecture, memory usage, speed, accuracy,
and privacy protection. We also discussed the addition of a negation step and
an HPO normalization layer to the transformer models for improved HPO term
tagging. In conclusion, PhenoBCBERT and PhenoGPT enable the automated discovery
of phenotype terms from clinical notes and biomedical literature, facilitating
automated downstream tasks to derive new biological insights on human diseases.
| [
{
"created": "Fri, 11 Aug 2023 03:40:22 GMT",
"version": "v1"
},
{
"created": "Thu, 9 Nov 2023 15:18:38 GMT",
"version": "v2"
}
] | 2023-11-10 | [
[
"Yang",
"Jingye",
""
],
[
"Liu",
"Cong",
""
],
[
"Deng",
"Wendy",
""
],
[
"Wu",
"Da",
""
],
[
"Weng",
"Chunhua",
""
],
[
"Zhou",
"Yunyun",
""
],
[
"Wang",
"Kai",
""
]
] | We hypothesize that large language models (LLMs) based on the transformer architecture can enable automated detection of clinical phenotype terms, including terms not documented in the HPO. In this study, we developed two types of models: PhenoBCBERT, a BERT-based model, utilizing Bio+Clinical BERT as its pre-trained model, and PhenoGPT, a GPT-based model that can be initialized from diverse GPT models, including open-source versions such as GPT-J, Falcon, and LLaMA, as well as closed-source versions such as GPT-3 and GPT-3.5. We compared our methods with PhenoTagger, a recently developed HPO recognition tool that combines rule-based and deep learning methods. We found that our methods can extract more phenotype concepts, including novel ones not characterized by HPO. We also performed case studies on biomedical literature to illustrate how new phenotype information can be recognized and extracted. We compared current BERT-based versus GPT-based models for phenotype tagging, in multiple aspects including model architecture, memory usage, speed, accuracy, and privacy protection. We also discussed the addition of a negation step and an HPO normalization layer to the transformer models for improved HPO term tagging. In conclusion, PhenoBCBERT and PhenoGPT enable the automated discovery of phenotype terms from clinical notes and biomedical literature, facilitating automated downstream tasks to derive new biological insights on human diseases. |
1202.2268 | Celia Blanco | Celia Blanco and David Hochberg | Homochiral oligopeptides by chiral amplification: Interpretation of
experimental data with a copolymerization model | 18 pages, 12 figures, 9 tables | Phys. Chem. Chem. Phys., 2012, 14, 2301-2311 | 10.1039/C2CP22813K | null | q-bio.QM cond-mat.soft physics.chem-ph q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a differential rate equation model of chiral polymerization based
on a simple copolymerization scheme in which the enantiomers are added to, or
removed from, the homochiral or heterochiral chains (reversible stepwise
isodesmic growth or dissociation). The model is set up for closed systems and
takes into account the corresponding thermodynamic constraints implied by the
reversible monomer attachments, while obeying a constant mass constraint. In
its simplest form, the model depends on a single variable rate constant, the
maximum chain length N, and the initial concentrations. We have fit the model
to the experimental data from the Rehovot group on lattice-controlled chiral
amplification of oligopeptides. We find in all the chemical systems employed
except for one, that the model fits the measured relative abundances of the
oligopetides with higher degrees of correlation than from a purely random
polymerization process.
| [
{
"created": "Fri, 10 Feb 2012 14:36:23 GMT",
"version": "v1"
}
] | 2017-08-23 | [
[
"Blanco",
"Celia",
""
],
[
"Hochberg",
"David",
""
]
] | We present a differential rate equation model of chiral polymerization based on a simple copolymerization scheme in which the enantiomers are added to, or removed from, the homochiral or heterochiral chains (reversible stepwise isodesmic growth or dissociation). The model is set up for closed systems and takes into account the corresponding thermodynamic constraints implied by the reversible monomer attachments, while obeying a constant mass constraint. In its simplest form, the model depends on a single variable rate constant, the maximum chain length N, and the initial concentrations. We have fit the model to the experimental data from the Rehovot group on lattice-controlled chiral amplification of oligopeptides. We find in all the chemical systems employed except for one, that the model fits the measured relative abundances of the oligopetides with higher degrees of correlation than from a purely random polymerization process. |
1309.1837 | Paolo Sibani | Nikolaj Becker and Paolo Sibani | Evolution and non-equilibrium physics. A study of the Tangled Nature
Model | 6 pages, 6 figures | EPL 105, Number 1, January 2014, 18005 | 10.1209/0295-5075/105/18005 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We argue that the stochastic dynamics of interacting agents which replicate,
mutate and die constitutes a non-equilibrium physical process akin to aging in
complex materials. Specifically, our study uses extensive computer simulations
of the Tangled Nature Model (TNM) of biological evolution to show that
punctuated equilibria successively generated by the model's dynamics have
increasing entropy and are separated by increasing entropic barriers. We
further show that these states are organized in a hierarchy and that limiting
the values of possible interactions to a finite interval leads to stationary
fluctuations within a component of the latter. A coarse-grained description
based on the temporal statistics of quakes, the events leading from one
component of the hierarchy to the next, accounts for the logarithmic growth of
the population and the decaying rate of change of macroscopic variables.
Finally, we question the role of fitness in large scale evolution models and
speculate on the possible evolutionary role of rejuvenation and memory effects.
| [
{
"created": "Sat, 7 Sep 2013 08:26:35 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Aug 2014 10:23:10 GMT",
"version": "v2"
}
] | 2014-08-19 | [
[
"Becker",
"Nikolaj",
""
],
[
"Sibani",
"Paolo",
""
]
] | We argue that the stochastic dynamics of interacting agents which replicate, mutate and die constitutes a non-equilibrium physical process akin to aging in complex materials. Specifically, our study uses extensive computer simulations of the Tangled Nature Model (TNM) of biological evolution to show that punctuated equilibria successively generated by the model's dynamics have increasing entropy and are separated by increasing entropic barriers. We further show that these states are organized in a hierarchy and that limiting the values of possible interactions to a finite interval leads to stationary fluctuations within a component of the latter. A coarse-grained description based on the temporal statistics of quakes, the events leading from one component of the hierarchy to the next, accounts for the logarithmic growth of the population and the decaying rate of change of macroscopic variables. Finally, we question the role of fitness in large scale evolution models and speculate on the possible evolutionary role of rejuvenation and memory effects. |
2004.11677 | Sourendu Gupta | Sourendu Gupta | Inferring epidemic parameters for COVID-19 from fatality counts in
Mumbai | null | null | null | TIFR/TH/20-11 | q-bio.PE physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Epidemic parameters are estimated through Bayesian inference using the daily
fatality counts in Mumbai during the period from March 31 to April 14. A
doubling time of 5.5 days (median with 95% CrI of 4.6-6.9 days) is observed. In
the SEIR model this gives the basic reproduction rate R_0 of 3.4 (median with
95% CrI of 2.4-4.8). Using as input the infection fatality rate and the
interval between infection and death, the number of infections in Mumbai is
inferred. It is found that the ratio of the number of test positives to the
total infections is 0.13\% (median), implying that tests are currently finding
1 out of 750 cases of infection. After correcting for different testing rates,
this result is compatible with a measurement of the ratio made recently via
serological testing in the USA. From the estimates of the number of infections
we infer that the first COVID-19 cases were seeded in Mumbai between late
December 2019 and early February 2020. provided the doubling times remained
unchanged since then. We remark on some public health implications if the rate
of growth cannot be controlled in about a week.
| [
{
"created": "Thu, 23 Apr 2020 16:42:42 GMT",
"version": "v1"
}
] | 2020-04-27 | [
[
"Gupta",
"Sourendu",
""
]
] | Epidemic parameters are estimated through Bayesian inference using the daily fatality counts in Mumbai during the period from March 31 to April 14. A doubling time of 5.5 days (median with 95% CrI of 4.6-6.9 days) is observed. In the SEIR model this gives the basic reproduction rate R_0 of 3.4 (median with 95% CrI of 2.4-4.8). Using as input the infection fatality rate and the interval between infection and death, the number of infections in Mumbai is inferred. It is found that the ratio of the number of test positives to the total infections is 0.13\% (median), implying that tests are currently finding 1 out of 750 cases of infection. After correcting for different testing rates, this result is compatible with a measurement of the ratio made recently via serological testing in the USA. From the estimates of the number of infections we infer that the first COVID-19 cases were seeded in Mumbai between late December 2019 and early February 2020. provided the doubling times remained unchanged since then. We remark on some public health implications if the rate of growth cannot be controlled in about a week. |
1708.00731 | Maurizio De Pitt\`a | Maurizio De Pitt\`a | Glia | 13 pages. Submitted as contributed section to the chapter on
"Neurophysiology" of the book "Da\~no cerebral" (Brain damage), JC
Arango-Asprilla \& L Olabarrieta-Landa eds. Manual Moderno Editions | null | null | null | q-bio.NC q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Essential introduction to glial cells with emphasis on astrocytes, microglia
and their interplay in reactive astrogliosis.
| [
{
"created": "Wed, 2 Aug 2017 13:01:08 GMT",
"version": "v1"
}
] | 2017-08-03 | [
[
"De Pittà",
"Maurizio",
""
]
] | Essential introduction to glial cells with emphasis on astrocytes, microglia and their interplay in reactive astrogliosis. |
0910.1167 | Jean-Philippe Vert | Kevin Bleakley (CBIO), Jean-Philippe Vert (CBIO) | Joint segmentation of many aCGH profiles using fast group LARS | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Array-Based Comparative Genomic Hybridization (aCGH) is a method used to
search for genomic regions with copy numbers variations. For a given aCGH
profile, one challenge is to accurately segment it into regions of constant
copy number. Subjects sharing the same disease status, for example a type of
cancer, often have aCGH profiles with similar copy number variations, due to
duplications and deletions relevant to that particular disease. We introduce a
constrained optimization algorithm that jointly segments aCGH profiles of many
subjects. It simultaneously penalizes the amount of freedom the set of profiles
have to jump from one level of constant copy number to another, at genomic
locations known as breakpoints. We show that breakpoints shared by many
different profiles tend to be found first by the algorithm, even in the
presence of significant amounts of noise. The algorithm can be formulated as a
group LARS problem. We propose an extremely fast way to find the solution path,
i.e., a sequence of shared breakpoints in order of importance. For no extra
cost the algorithm smoothes all of the aCGH profiles into piecewise-constant
regions of equal copy number, giving low-dimensional versions of the original
data. These can be shown for all profiles on a single graph, allowing for
intuitive visual interpretation. Simulations and an implementation of the
algorithm on bladder cancer aCGH profiles are provided.
| [
{
"created": "Wed, 7 Oct 2009 06:50:53 GMT",
"version": "v1"
}
] | 2009-10-08 | [
[
"Bleakley",
"Kevin",
"",
"CBIO"
],
[
"Vert",
"Jean-Philippe",
"",
"CBIO"
]
] | Array-Based Comparative Genomic Hybridization (aCGH) is a method used to search for genomic regions with copy numbers variations. For a given aCGH profile, one challenge is to accurately segment it into regions of constant copy number. Subjects sharing the same disease status, for example a type of cancer, often have aCGH profiles with similar copy number variations, due to duplications and deletions relevant to that particular disease. We introduce a constrained optimization algorithm that jointly segments aCGH profiles of many subjects. It simultaneously penalizes the amount of freedom the set of profiles have to jump from one level of constant copy number to another, at genomic locations known as breakpoints. We show that breakpoints shared by many different profiles tend to be found first by the algorithm, even in the presence of significant amounts of noise. The algorithm can be formulated as a group LARS problem. We propose an extremely fast way to find the solution path, i.e., a sequence of shared breakpoints in order of importance. For no extra cost the algorithm smoothes all of the aCGH profiles into piecewise-constant regions of equal copy number, giving low-dimensional versions of the original data. These can be shown for all profiles on a single graph, allowing for intuitive visual interpretation. Simulations and an implementation of the algorithm on bladder cancer aCGH profiles are provided. |
1708.04860 | Ruben Van Bergen | R.S. van Bergen, J.F.M. Jehee | Modeling correlated noise is necessary to decode uncertainty | null | null | 10.1016/j.neuroimage.2017.08.015 | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Brain decoding algorithms form an important part of the arsenal of analysis
tools available to neuroscientists, allowing for a more detailed study of the
kind of information represented in patterns of cortical activity. While most
current decoding algorithms focus on estimating a single, most likely stimulus
from the pattern of noisy fMRI responses, the presence of noise causes this
estimate to be uncertain. This uncertainty in stimulus estimates is a
potentially highly relevant aspect of cortical stimulus processing, and
features prominently in Bayesian or probabilistic models of neural coding.
Here, we focus on sensory uncertainty and how best to extract this information
with fMRI. We first demonstrate in simulations that decoding algorithms that
take into account correlated noise between fMRI voxels better recover the
amount of uncertainty (quantified as the width of a probability distribution
over possible stimuli) associated with the decoded estimate. Furthermore, we
show that not all correlated variability should be treated equally, as modeling
tuning-dependent correlations has the greatest impact on decoding performance.
Next, we examine actual noise correlations in human visual cortex, and find
that shared variability in areas V1-V3 depends on the tuning properties of fMRI
voxels. In line with our simulations, accounting for this shared noise between
similarly tuned voxels produces important benefits in decoding. Our findings
underscore the importance of accurate noise models in fMRI decoding approaches,
and suggest a statistically feasible method to incorporate the most relevant
forms of shared noise.
| [
{
"created": "Wed, 16 Aug 2017 12:41:34 GMT",
"version": "v1"
}
] | 2017-08-17 | [
[
"van Bergen",
"R. S.",
""
],
[
"Jehee",
"J. F. M.",
""
]
] | Brain decoding algorithms form an important part of the arsenal of analysis tools available to neuroscientists, allowing for a more detailed study of the kind of information represented in patterns of cortical activity. While most current decoding algorithms focus on estimating a single, most likely stimulus from the pattern of noisy fMRI responses, the presence of noise causes this estimate to be uncertain. This uncertainty in stimulus estimates is a potentially highly relevant aspect of cortical stimulus processing, and features prominently in Bayesian or probabilistic models of neural coding. Here, we focus on sensory uncertainty and how best to extract this information with fMRI. We first demonstrate in simulations that decoding algorithms that take into account correlated noise between fMRI voxels better recover the amount of uncertainty (quantified as the width of a probability distribution over possible stimuli) associated with the decoded estimate. Furthermore, we show that not all correlated variability should be treated equally, as modeling tuning-dependent correlations has the greatest impact on decoding performance. Next, we examine actual noise correlations in human visual cortex, and find that shared variability in areas V1-V3 depends on the tuning properties of fMRI voxels. In line with our simulations, accounting for this shared noise between similarly tuned voxels produces important benefits in decoding. Our findings underscore the importance of accurate noise models in fMRI decoding approaches, and suggest a statistically feasible method to incorporate the most relevant forms of shared noise. |
2407.11103 | Nikolai Schapin | Nikolai Schapin, Maciej Majewski, Mariona Torrens-Fontanals, Gianni De
Fabritiis | PlayMolecule pKAce: Small Molecule Protonation through Equivariant
Neural Networks | 9 pages, 3 figures, 1 table | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Small molecule protonation is an important part of the preparation of small
molecules for many types of computational chemistry protocols. For this, a
correct estimation of the pKa values of the protonation sites of molecules is
required. In this work, we present pKAce, a new web application for the
prediction of micro-pKa values of the molecules' protonation sites. We adapt
the state-of-the-art, equivariant, TensorNet model originally developed for
quantum mechanics energy and force predictions to the prediction of micro-pKa
values. We show that an adapted version of this model can achieve
state-of-the-art performance comparable with established models while trained
on just a fraction of their training data.
| [
{
"created": "Mon, 15 Jul 2024 13:22:44 GMT",
"version": "v1"
}
] | 2024-07-17 | [
[
"Schapin",
"Nikolai",
""
],
[
"Majewski",
"Maciej",
""
],
[
"Torrens-Fontanals",
"Mariona",
""
],
[
"De Fabritiis",
"Gianni",
""
]
] | Small molecule protonation is an important part of the preparation of small molecules for many types of computational chemistry protocols. For this, a correct estimation of the pKa values of the protonation sites of molecules is required. In this work, we present pKAce, a new web application for the prediction of micro-pKa values of the molecules' protonation sites. We adapt the state-of-the-art, equivariant, TensorNet model originally developed for quantum mechanics energy and force predictions to the prediction of micro-pKa values. We show that an adapted version of this model can achieve state-of-the-art performance comparable with established models while trained on just a fraction of their training data. |
2007.11834 | Johannes M\"uller | Johannes M\"uller, Mirjam Kretzschmar | Contact Tracing -- Old Models and New Challenges | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Contact tracing is an effective method to control emerging diseases. Since
the 1980's, mathematical modelers are developing a consistent theory for
contact tracing, with the aim to find effective and efficient implementations
of contact tracing, and to assess the effects of contact tracing on the spread
of an infectious disease. Despite the progress made in the area, there remain
important open questions. In addition, technological developments, especially
in the field of molecular biology (genetic sequencing of pathogens) and modern
communication (digital contact tracing), have posed new challenges for the
modeling community. In the present paper, we discuss modeling approaches for
contact tracing and identify some of the current challenges for the field.
| [
{
"created": "Thu, 23 Jul 2020 07:38:15 GMT",
"version": "v1"
},
{
"created": "Sat, 15 Aug 2020 11:15:37 GMT",
"version": "v2"
}
] | 2020-08-18 | [
[
"Müller",
"Johannes",
""
],
[
"Kretzschmar",
"Mirjam",
""
]
] | Contact tracing is an effective method to control emerging diseases. Since the 1980's, mathematical modelers are developing a consistent theory for contact tracing, with the aim to find effective and efficient implementations of contact tracing, and to assess the effects of contact tracing on the spread of an infectious disease. Despite the progress made in the area, there remain important open questions. In addition, technological developments, especially in the field of molecular biology (genetic sequencing of pathogens) and modern communication (digital contact tracing), have posed new challenges for the modeling community. In the present paper, we discuss modeling approaches for contact tracing and identify some of the current challenges for the field. |
1510.06062 | Christopher Calderon | Christopher P. Calderon | Motion Blur Filtering: A Statistical Approach for Extracting Confinement
Forces and Diffusivity from a Single Blurred Trajectory | Companion Python and Jupyter / Ipython notebooks maintained at:
http://www.github.com/calderoc/MotionBlurFilter | Phys. Rev. E 93, 053303 (2016) | 10.1103/PhysRevE.93.053303 | null | q-bio.QM cond-mat.soft q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Single Particle Tracking (SPT) can aid in understanding complex
spatio-temporal processes. However, quantifying diffusivity and forces from
individual live cell trajectories is complicated by inter- & intra-trajectory
kinetic heterogeneity, thermal fluctuations, and statistical temporal
dependence inherent to the underlying molecule's time correlated confined
dynamics experienced in the cell. Experimental artifacts such as localization
uncertainty and motion blur also obscure the data. We introduce a new maximum
likelihood estimation (MLE) technique that decouples the above noise sources
and systematically treats temporal correlation via a likelihood function
(permitting more reliable extraction of effective forces from position vs. time
data). Our estimator is demonstrated to be consistent over a wide range of
exposure times, diffusion coefficients, and confinement "radii". The algorithm
and corresponding software can reliably extract motion parameters independent
of exposure time in trajectories exhibiting confined and/or non-stationary
dynamics and will aid in directly comparing trajectories obtained from
different imaging modalities.
| [
{
"created": "Tue, 20 Oct 2015 21:09:21 GMT",
"version": "v1"
},
{
"created": "Wed, 18 May 2016 17:40:41 GMT",
"version": "v2"
}
] | 2016-05-19 | [
[
"Calderon",
"Christopher P.",
""
]
] | Single Particle Tracking (SPT) can aid in understanding complex spatio-temporal processes. However, quantifying diffusivity and forces from individual live cell trajectories is complicated by inter- & intra-trajectory kinetic heterogeneity, thermal fluctuations, and statistical temporal dependence inherent to the underlying molecule's time correlated confined dynamics experienced in the cell. Experimental artifacts such as localization uncertainty and motion blur also obscure the data. We introduce a new maximum likelihood estimation (MLE) technique that decouples the above noise sources and systematically treats temporal correlation via a likelihood function (permitting more reliable extraction of effective forces from position vs. time data). Our estimator is demonstrated to be consistent over a wide range of exposure times, diffusion coefficients, and confinement "radii". The algorithm and corresponding software can reliably extract motion parameters independent of exposure time in trajectories exhibiting confined and/or non-stationary dynamics and will aid in directly comparing trajectories obtained from different imaging modalities. |
q-bio/0610015 | Evgeniy Khain | Evgeniy Khain, Leonard M. Sander, and Casey M. Schneider-Mizell | The role of cell-cell adhesion in wound healing | to appear in Journal of Statistical Physics | null | 10.1007/s10955-006-9194-8 | null | q-bio.CB | null | We present a stochastic model which describes fronts of cells invading a
wound. In the model cells can move, proliferate, and experience cell-cell
adhesion. We find several qualitatively different regimes of front motion and
analyze the transitions between them. Above a critical value of adhesion and
for small proliferation large isolated clusters are formed ahead of the front.
This is mapped onto the well-known ferromagnetic phase transition in the Ising
model. For large adhesion, and larger proliferation the clusters become
connected (at some fixed time). For adhesion below the critical value the
results are similar to our previous work which neglected adhesion. The results
are compared with experiments, and possible directions of future work are
proposed.
| [
{
"created": "Thu, 5 Oct 2006 23:07:40 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Khain",
"Evgeniy",
""
],
[
"Sander",
"Leonard M.",
""
],
[
"Schneider-Mizell",
"Casey M.",
""
]
] | We present a stochastic model which describes fronts of cells invading a wound. In the model cells can move, proliferate, and experience cell-cell adhesion. We find several qualitatively different regimes of front motion and analyze the transitions between them. Above a critical value of adhesion and for small proliferation large isolated clusters are formed ahead of the front. This is mapped onto the well-known ferromagnetic phase transition in the Ising model. For large adhesion, and larger proliferation the clusters become connected (at some fixed time). For adhesion below the critical value the results are similar to our previous work which neglected adhesion. The results are compared with experiments, and possible directions of future work are proposed. |
1104.4092 | Pablo Sartori | Pablo Sartori and Yuhai Tu | Noise Filtering Strategies of Adaptive Signaling Networks: The Case of
E. Coli Chemotaxis | 15 pages, 4 figures | Journal of Statistical Physics: Statistical Mechanics and Biology
special issue, Year 2011, Month April, Volume 142, Number 6, 1206-1217 | 10.1007/s10955-011-0169-z | null | q-bio.CB physics.bio-ph q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two distinct mechanisms for filtering noise in an input signal are identified
in a class of adaptive sensory networks. We find that the high frequency noise
is filtered by the output degradation process through time-averaging; while the
low frequency noise is damped by adaptation through negative feedback. Both
filtering processes themselves introduce intrinsic noises, which are found to
be unfiltered and can thus amount to a significant internal noise floor even
without signaling. These results are applied to E. coli chemotaxis. We show
unambiguously that the molecular mechanism for the Berg-Purcell time-averaging
scheme is the dephosphorylation of the response regulator CheY-P, not the
receptor adaptation process as previously suggested. The high frequency noise
due to the stochastic ligand binding-unbinding events and the random ligand
molecule diffusion is averaged by the CheY-P dephosphorylation process to a
negligible level in E.coli. We identify a previously unstudied noise source
caused by the random motion of the cell in a ligand gradient. We show that this
random walk induced signal noise has a divergent low frequency component, which
is only rendered finite by the receptor adaptation process. For gradients
within the E. coli sensing range, this dominant external noise can be
comparable to the significant intrinsic noise in the system. The dependence of
the response and its fluctuations on the key time scales of the system are
studied systematically. We show that the chemotaxis pathway may have evolved to
optimize gradient sensing, strong response, and noise control in different time
scales
| [
{
"created": "Wed, 20 Apr 2011 18:30:19 GMT",
"version": "v1"
}
] | 2016-11-25 | [
[
"Sartori",
"Pablo",
""
],
[
"Tu",
"Yuhai",
""
]
] | Two distinct mechanisms for filtering noise in an input signal are identified in a class of adaptive sensory networks. We find that the high frequency noise is filtered by the output degradation process through time-averaging; while the low frequency noise is damped by adaptation through negative feedback. Both filtering processes themselves introduce intrinsic noises, which are found to be unfiltered and can thus amount to a significant internal noise floor even without signaling. These results are applied to E. coli chemotaxis. We show unambiguously that the molecular mechanism for the Berg-Purcell time-averaging scheme is the dephosphorylation of the response regulator CheY-P, not the receptor adaptation process as previously suggested. The high frequency noise due to the stochastic ligand binding-unbinding events and the random ligand molecule diffusion is averaged by the CheY-P dephosphorylation process to a negligible level in E.coli. We identify a previously unstudied noise source caused by the random motion of the cell in a ligand gradient. We show that this random walk induced signal noise has a divergent low frequency component, which is only rendered finite by the receptor adaptation process. For gradients within the E. coli sensing range, this dominant external noise can be comparable to the significant intrinsic noise in the system. The dependence of the response and its fluctuations on the key time scales of the system are studied systematically. We show that the chemotaxis pathway may have evolved to optimize gradient sensing, strong response, and noise control in different time scales |
1905.10402 | Johannes Zierenberg | Johannes Zierenberg, Jens Wilting, Viola Priesemann, Anna Levina | Description of spreading dynamics by microscopic network models and
macroscopic branching processes can differ due to coalescence | 13 pages | Phys. Rev. E 101, 022301 (2020) | 10.1103/PhysRevE.101.022301 | null | q-bio.NC cond-mat.dis-nn physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spreading processes are conventionally monitored on a macroscopic level by
counting the number of incidences over time. The spreading process can then be
modeled either on the microscopic level, assuming an underlying interaction
network, or directly on the macroscopic level, assuming that microscopic
contributions are negligible. The macroscopic characteristics of both
descriptions are commonly assumed to be identical. In this work, we show that
these characteristics of microscopic and macroscopic descriptions can be
different due to coalescence, i.e., a node being activated at the same time by
multiple sources. In particular, we consider a (microscopic) branching network
(probabilistic cellular automaton) with annealed connectivity disorder, record
the macroscopic activity, and then approximate this activity by a (macroscopic)
branching process. In this framework, we analytically calculate the effect of
coalescence on the collective dynamics. We show that coalescence leads to a
universal non-linear scaling function for the conditional expectation value of
successive network activity. This allows us to quantify the difference between
the microscopic model parameter and established macroscopic estimates. To
overcome this difference, we propose a non-linear estimator that correctly
infers the model branching parameter for all system sizes.
| [
{
"created": "Fri, 24 May 2019 18:33:25 GMT",
"version": "v1"
}
] | 2020-02-12 | [
[
"Zierenberg",
"Johannes",
""
],
[
"Wilting",
"Jens",
""
],
[
"Priesemann",
"Viola",
""
],
[
"Levina",
"Anna",
""
]
] | Spreading processes are conventionally monitored on a macroscopic level by counting the number of incidences over time. The spreading process can then be modeled either on the microscopic level, assuming an underlying interaction network, or directly on the macroscopic level, assuming that microscopic contributions are negligible. The macroscopic characteristics of both descriptions are commonly assumed to be identical. In this work, we show that these characteristics of microscopic and macroscopic descriptions can be different due to coalescence, i.e., a node being activated at the same time by multiple sources. In particular, we consider a (microscopic) branching network (probabilistic cellular automaton) with annealed connectivity disorder, record the macroscopic activity, and then approximate this activity by a (macroscopic) branching process. In this framework, we analytically calculate the effect of coalescence on the collective dynamics. We show that coalescence leads to a universal non-linear scaling function for the conditional expectation value of successive network activity. This allows us to quantify the difference between the microscopic model parameter and established macroscopic estimates. To overcome this difference, we propose a non-linear estimator that correctly infers the model branching parameter for all system sizes. |
2112.08040 | Giuseppe Tronci | He Liang, Jie Yin, Kenny Man, Xuebin B. Yang, Elena Calciolari,
Nikolaos Donos, Stephen J. Russell, David J. Wood, Giuseppe Tronci | A long-lasting guided bone regeneration membrane from sequentially
functionalised photoactive atelocollagen | 11 figures, 2 tables, accepted on Acta Biomaterialia | null | null | null | q-bio.TO | http://creativecommons.org/licenses/by/4.0/ | The fast degradation of collagen-based membranes in the biological
environment remains a critical challenge, resulting in underperforming Guided
Bone Regeneration (GBR) therapy leading to compromised clinical results.
Photoactive atelocollagen (AC) systems functionalised with ethylenically
unsaturated monomers, such as 4-vinylbenzyl chloride (4VBC), have been shown to
generate mechanically competent materials for wound healing, inflammation
control and drug delivery, whereby control of the molecular architecture of the
AC network is key. Building on this platform, the sequential functionalisation
with 4VBC and methacrylic anhydride (MA) was hypothesised to generate UV-cured
AC hydrogels with reduced swelling ratio, increased proteolytic stability and
barrier functionality for GBR therapy. The sequentially functionalised
atelocollagen precursor (SAP) was characterised via TNBS and ninhydrin
colourimetric assays, circular dichroism and UV-curing rheometry, which
confirmed nearly complete consumption of collagen primary amino groups,
preserved triple helices and fast (within 180 s) gelation kinetics,
respectively. Hydrogel swelling ratio and compression modulus were adjusted
depending on the aqueous environment used for UV-curing, whilst the sequential
functionalisation of AC successfully generated hydrogels with superior
proteolytic stability in vitro compared to both 4VBC functionalised control and
the commercial dental membrane Bio-Gide. These in vitro results were confirmed
in vivo via both subcutaneous implantation and a proof-of-concept study in a
GBR calvarial model, indicating integrity of the hydrogel and barrier defect,
as well as tissue formation following 1-month implantation in rats.
| [
{
"created": "Wed, 15 Dec 2021 11:04:11 GMT",
"version": "v1"
}
] | 2021-12-16 | [
[
"Liang",
"He",
""
],
[
"Yin",
"Jie",
""
],
[
"Man",
"Kenny",
""
],
[
"Yang",
"Xuebin B.",
""
],
[
"Calciolari",
"Elena",
""
],
[
"Donos",
"Nikolaos",
""
],
[
"Russell",
"Stephen J.",
""
],
[
"Wood",
"David J.",
""
],
[
"Tronci",
"Giuseppe",
""
]
] | The fast degradation of collagen-based membranes in the biological environment remains a critical challenge, resulting in underperforming Guided Bone Regeneration (GBR) therapy leading to compromised clinical results. Photoactive atelocollagen (AC) systems functionalised with ethylenically unsaturated monomers, such as 4-vinylbenzyl chloride (4VBC), have been shown to generate mechanically competent materials for wound healing, inflammation control and drug delivery, whereby control of the molecular architecture of the AC network is key. Building on this platform, the sequential functionalisation with 4VBC and methacrylic anhydride (MA) was hypothesised to generate UV-cured AC hydrogels with reduced swelling ratio, increased proteolytic stability and barrier functionality for GBR therapy. The sequentially functionalised atelocollagen precursor (SAP) was characterised via TNBS and ninhydrin colourimetric assays, circular dichroism and UV-curing rheometry, which confirmed nearly complete consumption of collagen primary amino groups, preserved triple helices and fast (within 180 s) gelation kinetics, respectively. Hydrogel swelling ratio and compression modulus were adjusted depending on the aqueous environment used for UV-curing, whilst the sequential functionalisation of AC successfully generated hydrogels with superior proteolytic stability in vitro compared to both 4VBC functionalised control and the commercial dental membrane Bio-Gide. These in vitro results were confirmed in vivo via both subcutaneous implantation and a proof-of-concept study in a GBR calvarial model, indicating integrity of the hydrogel and barrier defect, as well as tissue formation following 1-month implantation in rats. |
1010.3063 | Jose Acacio de Barros | Patrick Suppes, Jose Acacio de Barros, and Gary Oas | Phase-Oscillator Computations as Neural Models of Stimulus-Response
Conditioning and Response Selection | null | Journal of Mathematical Psychology, Volume 56, Issue 2, April
2012, Pages 95-117 | null | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The activity of collections of synchronizing neurons can be represented by
weakly coupled nonlinear phase oscillators satisfying Kuramoto's equations. In
this article, we build such neural-oscillator models, partly based on
neurophysiological evidence, to represent approximately the learning behavior
predicted and confirmed in three experiments by well-known stochastic learning
models of behavioral stimulus-response theory. We use three Kuramoto
oscillators to model a continuum of responses, and we provide detailed
numerical simulations and analysis of the three-oscillator Kuramoto problem,
including an analysis of the stability points for different coupling
conditions. We show that the oscillator simulation data are well-matched to the
behavioral data of the three experiments.
| [
{
"created": "Fri, 15 Oct 2010 02:03:59 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Oct 2010 01:38:01 GMT",
"version": "v2"
},
{
"created": "Thu, 26 Apr 2012 17:33:37 GMT",
"version": "v3"
}
] | 2012-04-27 | [
[
"Suppes",
"Patrick",
""
],
[
"de Barros",
"Jose Acacio",
""
],
[
"Oas",
"Gary",
""
]
] | The activity of collections of synchronizing neurons can be represented by weakly coupled nonlinear phase oscillators satisfying Kuramoto's equations. In this article, we build such neural-oscillator models, partly based on neurophysiological evidence, to represent approximately the learning behavior predicted and confirmed in three experiments by well-known stochastic learning models of behavioral stimulus-response theory. We use three Kuramoto oscillators to model a continuum of responses, and we provide detailed numerical simulations and analysis of the three-oscillator Kuramoto problem, including an analysis of the stability points for different coupling conditions. We show that the oscillator simulation data are well-matched to the behavioral data of the three experiments. |
1605.08031 | Kameron Harris | Kameron Decker Harris and Stefan Mihalas and Eric Shea-Brown | High resolution neural connectivity from incomplete tracing data using
nonnegative spline regression | Supplement at
https://github.com/kharris/high-res-connectivity-nips-2016 | NIPS, 2016 | null | null | q-bio.NC physics.bio-ph physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Whole-brain neural connectivity data are now available from viral tracing
experiments, which reveal the connections between a source injection site and
elsewhere in the brain. These hold the promise of revealing spatial patterns of
connectivity throughout the mammalian brain. To achieve this goal, we seek to
fit a weighted, nonnegative adjacency matrix among 100 $\mu$m brain "voxels"
using viral tracer data. Despite a multi-year experimental effort, injections
provide incomplete coverage, and the number of voxels in our data is orders of
magnitude larger than the number of injections, making the problem severely
underdetermined. Furthermore, projection data are missing within the injection
site because local connections there are not separable from the injection
signal.
We use a novel machine-learning algorithm to meet these challenges and
develop a spatially explicit, voxel-scale connectivity map of the mouse visual
system. Our method combines three features: a matrix completion loss for
missing data, a smoothing spline penalty to regularize the problem, and
(optionally) a low rank factorization. We demonstrate the consistency of our
estimator using synthetic data and then apply it to newly available Allen Mouse
Brain Connectivity Atlas data for the visual system. Our algorithm is
significantly more predictive than current state of the art approaches which
assume regions to be homogeneous. We demonstrate the efficacy of a low rank
version on visual cortex data and discuss the possibility of extending this to
a whole-brain connectivity matrix at the voxel scale.
| [
{
"created": "Tue, 24 May 2016 21:16:19 GMT",
"version": "v1"
},
{
"created": "Fri, 27 May 2016 17:33:02 GMT",
"version": "v2"
},
{
"created": "Wed, 26 Oct 2016 19:12:02 GMT",
"version": "v3"
}
] | 2016-10-27 | [
[
"Harris",
"Kameron Decker",
""
],
[
"Mihalas",
"Stefan",
""
],
[
"Shea-Brown",
"Eric",
""
]
] | Whole-brain neural connectivity data are now available from viral tracing experiments, which reveal the connections between a source injection site and elsewhere in the brain. These hold the promise of revealing spatial patterns of connectivity throughout the mammalian brain. To achieve this goal, we seek to fit a weighted, nonnegative adjacency matrix among 100 $\mu$m brain "voxels" using viral tracer data. Despite a multi-year experimental effort, injections provide incomplete coverage, and the number of voxels in our data is orders of magnitude larger than the number of injections, making the problem severely underdetermined. Furthermore, projection data are missing within the injection site because local connections there are not separable from the injection signal. We use a novel machine-learning algorithm to meet these challenges and develop a spatially explicit, voxel-scale connectivity map of the mouse visual system. Our method combines three features: a matrix completion loss for missing data, a smoothing spline penalty to regularize the problem, and (optionally) a low rank factorization. We demonstrate the consistency of our estimator using synthetic data and then apply it to newly available Allen Mouse Brain Connectivity Atlas data for the visual system. Our algorithm is significantly more predictive than current state of the art approaches which assume regions to be homogeneous. We demonstrate the efficacy of a low rank version on visual cortex data and discuss the possibility of extending this to a whole-brain connectivity matrix at the voxel scale. |
1203.4365 | Pietro Faccioli | T. Skrbic, C. Micheletti and P. Faccioli | The Role of Non-native Interactions in the Folding of Knotted Proteins | Accepted for publication on PLOS Computational Biology | null | 10.1371/journal.pcbi.1002504 | null | q-bio.BM cond-mat.mes-hall cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stochastic simulations of coarse-grained protein models are used to
investigate the propensity to form knots in early stages of protein folding.
The study is carried out comparatively for two homologous
carbamoyltransferases, a natively-knotted N-acetylornithine
carbamoyltransferase (AOTCase) and an unknotted ornithine carbamoyltransferase
(OTCase). In addition, two different sets of pairwise amino acid interactions
are considered: one promoting exclusively native interactions, and the other
additionally including non-native quasi-chemical and electrostatic
interactions. With the former model neither protein show a propensity to form
knots. With the additional non-native interactions, knotting propensity remains
negligible for the natively-unknotted OTCase while for AOTCase it is much
enhanced. Analysis of the trajectories suggests that the different entanglement
of the two transcarbamylases follows from the tendency of the C-terminal to
point away from (for OTCase) or approach and eventually thread (for AOTCase)
other regions of partly-folded protein. The analysis of the OTCase/AOTCase pair
clarifies that natively-knotted proteins can spontaneously knot during early
folding stages and that non-native sequence-dependent interactions are
important for promoting and disfavoring early knotting events.
| [
{
"created": "Tue, 20 Mar 2012 10:02:46 GMT",
"version": "v1"
}
] | 2015-06-04 | [
[
"Skrbic",
"T.",
""
],
[
"Micheletti",
"C.",
""
],
[
"Faccioli",
"P.",
""
]
] | Stochastic simulations of coarse-grained protein models are used to investigate the propensity to form knots in early stages of protein folding. The study is carried out comparatively for two homologous carbamoyltransferases, a natively-knotted N-acetylornithine carbamoyltransferase (AOTCase) and an unknotted ornithine carbamoyltransferase (OTCase). In addition, two different sets of pairwise amino acid interactions are considered: one promoting exclusively native interactions, and the other additionally including non-native quasi-chemical and electrostatic interactions. With the former model neither protein show a propensity to form knots. With the additional non-native interactions, knotting propensity remains negligible for the natively-unknotted OTCase while for AOTCase it is much enhanced. Analysis of the trajectories suggests that the different entanglement of the two transcarbamylases follows from the tendency of the C-terminal to point away from (for OTCase) or approach and eventually thread (for AOTCase) other regions of partly-folded protein. The analysis of the OTCase/AOTCase pair clarifies that natively-knotted proteins can spontaneously knot during early folding stages and that non-native sequence-dependent interactions are important for promoting and disfavoring early knotting events. |
0708.3499 | Francesc Rossell\'o | Gabriel Cardona, Francesc Rossello, Gabriel Valiente | Comparison of Tree-Child Phylogenetic Networks | 37 pages | null | null | null | q-bio.PE cs.CE cs.DM | null | Phylogenetic networks are a generalization of phylogenetic trees that allow
for the representation of non-treelike evolutionary events, like recombination,
hybridization, or lateral gene transfer. In this paper, we present and study a
new class of phylogenetic networks, called tree-child phylogenetic networks,
where every non-extant species has some descendant through mutation. We provide
an injective representation of these networks as multisets of vectors of
natural numbers, their path multiplicity vectors, and we use this
representation to define a distance on this class and to give an alignment
method for pairs of these networks. To the best of our knowledge, they are
respectively the first true distance and the first alignment method defined on
a meaningful class of phylogenetic networks strictly extending the class of
phylogenetic trees. Simple, polynomial algorithms for reconstructing a
tree-child phylogenetic network from its path multiplicity vectors, for
computing the distance between two tree-child phylogenetic networks, and for
aligning a pair of tree-child phylogenetic networks, are provided, and they
have been implemented as a Perl package and a Java applet, and they are
available at http://bioinfo.uib.es/~recerca/phylonetworks/mudistance
| [
{
"created": "Mon, 27 Aug 2007 09:37:55 GMT",
"version": "v1"
}
] | 2007-08-28 | [
[
"Cardona",
"Gabriel",
""
],
[
"Rossello",
"Francesc",
""
],
[
"Valiente",
"Gabriel",
""
]
] | Phylogenetic networks are a generalization of phylogenetic trees that allow for the representation of non-treelike evolutionary events, like recombination, hybridization, or lateral gene transfer. In this paper, we present and study a new class of phylogenetic networks, called tree-child phylogenetic networks, where every non-extant species has some descendant through mutation. We provide an injective representation of these networks as multisets of vectors of natural numbers, their path multiplicity vectors, and we use this representation to define a distance on this class and to give an alignment method for pairs of these networks. To the best of our knowledge, they are respectively the first true distance and the first alignment method defined on a meaningful class of phylogenetic networks strictly extending the class of phylogenetic trees. Simple, polynomial algorithms for reconstructing a tree-child phylogenetic network from its path multiplicity vectors, for computing the distance between two tree-child phylogenetic networks, and for aligning a pair of tree-child phylogenetic networks, are provided, and they have been implemented as a Perl package and a Java applet, and they are available at http://bioinfo.uib.es/~recerca/phylonetworks/mudistance |
2211.07285 | Jacob Nobel | Jacob de Nobel, Anna V. Kononova, Jeroen Briaire, Johan Frijns, Thomas
B\"ack | Optimizing Stimulus Energy for Cochlear Implants with a Machine Learning
Model of the Auditory Nerve | 39 pages, 10 figures | null | null | null | q-bio.NC cs.NE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Performing simulations with a realistic biophysical auditory nerve fiber
model can be very time consuming, due to the complex nature of the calculations
involved. Here, a surrogate (approximate) model of such an auditory nerve fiber
model was developed using machine learning methods, to perform simulations more
efficiently. Several machine learning models were compared, of which a
Convolutional Neural Network showed the best performance. In fact, the
Convolutional Neural Network was able to emulate the behavior of the auditory
nerve fiber model with extremely high similarity ($R^2 > 0.99$), tested under a
wide range of experimental conditions, whilst reducing the simulation time by
five orders of magnitude. In addition, we introduce a method for randomly
generating charge-balanced waveforms using hyperplane projection. In the second
part of this paper, the Convolutional Neural Network surrogate model was used
by an Evolutionary Algorithm to optimize the shape of the stimulus waveform in
terms energy efficiency. The resulting waveforms resemble a positive
Gaussian-like peak, preceded by an elongated negative phase. When comparing the
energy of the waveforms generated by the Evolutionary Algorithm with the
commonly used square wave, energy decreases of 8% - 45% were observed for
different pulse durations. These results were validated with the original
auditory nerve fiber model, which demonstrates that our proposed surrogate
model can be used as its accurate and efficient replacement.
| [
{
"created": "Mon, 14 Nov 2022 11:28:39 GMT",
"version": "v1"
}
] | 2022-11-15 | [
[
"de Nobel",
"Jacob",
""
],
[
"Kononova",
"Anna V.",
""
],
[
"Briaire",
"Jeroen",
""
],
[
"Frijns",
"Johan",
""
],
[
"Bäck",
"Thomas",
""
]
] | Performing simulations with a realistic biophysical auditory nerve fiber model can be very time consuming, due to the complex nature of the calculations involved. Here, a surrogate (approximate) model of such an auditory nerve fiber model was developed using machine learning methods, to perform simulations more efficiently. Several machine learning models were compared, of which a Convolutional Neural Network showed the best performance. In fact, the Convolutional Neural Network was able to emulate the behavior of the auditory nerve fiber model with extremely high similarity ($R^2 > 0.99$), tested under a wide range of experimental conditions, whilst reducing the simulation time by five orders of magnitude. In addition, we introduce a method for randomly generating charge-balanced waveforms using hyperplane projection. In the second part of this paper, the Convolutional Neural Network surrogate model was used by an Evolutionary Algorithm to optimize the shape of the stimulus waveform in terms energy efficiency. The resulting waveforms resemble a positive Gaussian-like peak, preceded by an elongated negative phase. When comparing the energy of the waveforms generated by the Evolutionary Algorithm with the commonly used square wave, energy decreases of 8% - 45% were observed for different pulse durations. These results were validated with the original auditory nerve fiber model, which demonstrates that our proposed surrogate model can be used as its accurate and efficient replacement. |
1901.06114 | Swathi Tej | Swathi Tej, Kumar Gaurav and Sutapa Mukherji | Small RNA driven feed-forward loop: critical role of sRNA in noise
filtering | 17 pages, 11 figures | IOP Physical Biology online 2019 | 10.1088/1478-3975/ab1563 | null | q-bio.MN cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A feed-forward loop (FFL) is a common gene-regulatory motif in which usually
the upstream regulator is a protein, a transcription factor, that regulates the
expression of the target protein in two parallel pathways. Here, we study a
distinct sRNA-driven FFL (sFFL) discovered recently in Salmonella enterica.
Unlike previously studied transcriptional FFLs (tFFL) and sRNA-mediated FFLs
(smFFL), here the upstream regulator is an sRNA that activates the target
protein and its transcriptional activator. Such sFFL has not been subjected to
rigorous analysis. We, therefore, set out to understand two aspects. First is a
quantitative comparison of the regulatory response of sFFL with tFFL and smFFL
using a differential equation framework. Since the process of gene expression
is inherently stochastic, the second objective is to find how the noise affects
the functionality of sFFL. We find the response of sFFLto be stronger, faster,
and more sensitive to the initial concentration of the upstream regulator than
tFFL and smFFL. Further, a generating function based analysis and stochastic
simulations lead to a non-trivial prediction that an optimal noise filtration
can be attained depending on the synthesis rate of the sRNA and the degradation
rate of the transcriptional activator. We conclude that in sFFL, sRNA plays a
critical role not only in driving a rapid and strong response, but also a
reliable response that depends critically on its concentration. Given the
advantages of sFFL brought out in this work, it should not be surprising if
future work reveals their employment in different biological contexts.
| [
{
"created": "Fri, 18 Jan 2019 07:27:34 GMT",
"version": "v1"
},
{
"created": "Fri, 17 May 2019 07:01:18 GMT",
"version": "v2"
}
] | 2019-05-20 | [
[
"Tej",
"Swathi",
""
],
[
"Gaurav",
"Kumar",
""
],
[
"Mukherji",
"Sutapa",
""
]
] | A feed-forward loop (FFL) is a common gene-regulatory motif in which usually the upstream regulator is a protein, a transcription factor, that regulates the expression of the target protein in two parallel pathways. Here, we study a distinct sRNA-driven FFL (sFFL) discovered recently in Salmonella enterica. Unlike previously studied transcriptional FFLs (tFFL) and sRNA-mediated FFLs (smFFL), here the upstream regulator is an sRNA that activates the target protein and its transcriptional activator. Such sFFL has not been subjected to rigorous analysis. We, therefore, set out to understand two aspects. First is a quantitative comparison of the regulatory response of sFFL with tFFL and smFFL using a differential equation framework. Since the process of gene expression is inherently stochastic, the second objective is to find how the noise affects the functionality of sFFL. We find the response of sFFLto be stronger, faster, and more sensitive to the initial concentration of the upstream regulator than tFFL and smFFL. Further, a generating function based analysis and stochastic simulations lead to a non-trivial prediction that an optimal noise filtration can be attained depending on the synthesis rate of the sRNA and the degradation rate of the transcriptional activator. We conclude that in sFFL, sRNA plays a critical role not only in driving a rapid and strong response, but also a reliable response that depends critically on its concentration. Given the advantages of sFFL brought out in this work, it should not be surprising if future work reveals their employment in different biological contexts. |
1102.0030 | Michael B\"orsch | Torsten Rendler, Marc Renz, Eva Hammann, Stefan Ernst, Nawid Zarrabi,
Michael Boersch | Monitoring single membrane protein dynamics in a liposome manipulated in
solution by the ABELtrap | 12 pages, 10 figures | null | 10.1117/12.873069 | null | q-bio.QM cond-mat.soft physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | FoF1-ATP synthase is the essential membrane enzyme maintaining the cellular
level of adenosine triphosphate (ATP) and comprises two rotary motors. We
measure subunit rotation in FoF1-ATP synthase by intramolecular Foerster
resonance energy transfer (FRET) between two fluorophores at the rotor and at
the stator of the enzyme. Confocal FRET measurements of freely diffusing single
enzymes in lipid vesicles are limited to hundreds of milliseconds by the
transit times through the laser focus. We evaluate two different methods to
trap the enzyme inside the confocal volume in order to extend the observation
times. Monte Carlo simulations show that optical tweezers with low laser power
are not suitable for lipid vesicles with a diameter of 130 nm. A. E. Cohen
(Harvard) and W. E. Moerner (Stanford) have recently developed an Anti-Brownian
electrokinetic trap (ABELtrap) which is capable to apparently immobilize single
molecules, proteins, viruses or vesicles in solution. Trapping of fluorescent
particles is achieved by applying a real time, position-dependent feedback to
four electrodes in a microfluidic device. The standard deviation from a given
target position in the ABELtrap is smaller than 200 nm. We develop a
combination of the ABELtrap with confocal FRET measurements to monitor single
membrane enzyme dynamics by FRET for more than 10 seconds in solution.
| [
{
"created": "Mon, 31 Jan 2011 23:06:48 GMT",
"version": "v1"
}
] | 2015-05-27 | [
[
"Rendler",
"Torsten",
""
],
[
"Renz",
"Marc",
""
],
[
"Hammann",
"Eva",
""
],
[
"Ernst",
"Stefan",
""
],
[
"Zarrabi",
"Nawid",
""
],
[
"Boersch",
"Michael",
""
]
] | FoF1-ATP synthase is the essential membrane enzyme maintaining the cellular level of adenosine triphosphate (ATP) and comprises two rotary motors. We measure subunit rotation in FoF1-ATP synthase by intramolecular Foerster resonance energy transfer (FRET) between two fluorophores at the rotor and at the stator of the enzyme. Confocal FRET measurements of freely diffusing single enzymes in lipid vesicles are limited to hundreds of milliseconds by the transit times through the laser focus. We evaluate two different methods to trap the enzyme inside the confocal volume in order to extend the observation times. Monte Carlo simulations show that optical tweezers with low laser power are not suitable for lipid vesicles with a diameter of 130 nm. A. E. Cohen (Harvard) and W. E. Moerner (Stanford) have recently developed an Anti-Brownian electrokinetic trap (ABELtrap) which is capable to apparently immobilize single molecules, proteins, viruses or vesicles in solution. Trapping of fluorescent particles is achieved by applying a real time, position-dependent feedback to four electrodes in a microfluidic device. The standard deviation from a given target position in the ABELtrap is smaller than 200 nm. We develop a combination of the ABELtrap with confocal FRET measurements to monitor single membrane enzyme dynamics by FRET for more than 10 seconds in solution. |
q-bio/0703027 | Alexei V. Tkachenko | Alexei V. Tkachenko (University of Michigan) | Elasticity of strongly stretched ssDNA | 5 pages, 2 figures | null | 10.1016/j.physa.2007.04.119 | null | q-bio.BM cond-mat.soft | null | We present a simple model which describes elastic response of single-stranded
DNA (ssDNA) to stretching, including the regime of very high force (up to 1000
pN). ssDNA is modelled as a discreet persistent chain, whose ground state is a
zigzag rather than a straight line configuration. This mimics the underlying
molecular architecture and helps to explain the experimentally observed
staturation of the stretching curve at very high force.
| [
{
"created": "Mon, 12 Mar 2007 02:53:20 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Tkachenko",
"Alexei V.",
"",
"University of Michigan"
]
] | We present a simple model which describes elastic response of single-stranded DNA (ssDNA) to stretching, including the regime of very high force (up to 1000 pN). ssDNA is modelled as a discreet persistent chain, whose ground state is a zigzag rather than a straight line configuration. This mimics the underlying molecular architecture and helps to explain the experimentally observed staturation of the stretching curve at very high force. |
2212.12440 | Gregory Kyro | Gregory W. Kyro, Rafael I. Brent, Victor S. Batista | HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for
Highly Accurate Protein-Ligand Binding Affinity Prediction | null | null | 10.1021/acs.jcim.3c00251 | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Applying deep learning concepts from image detection and graph theory has
greatly advanced protein-ligand binding affinity prediction, a challenge with
enormous ramifications for both drug discovery and protein engineering. We
build upon these advances by designing a novel deep learning architecture
consisting of a 3-dimensional convolutional neural network utilizing
channel-wise attention and two graph convolutional networks utilizing
attention-based aggregation of node features. HAC-Net (Hybrid Attention-Based
Convolutional Neural Network) obtains state-of-the-art results on the PDBbind
v.2016 core set, the most widely recognized benchmark in the field. We
extensively assess the generalizability of our model using multiple train-test
splits, each of which maximizes differences between either protein structures,
protein sequences, or ligand extended-connectivity fingerprints of complexes in
the training and test sets. Furthermore, we perform 10-fold cross-validation
with a similarity cutoff between SMILES strings of ligands in the training and
test sets, and also evaluate the performance of HAC-Net on lower-quality data.
We envision that this model can be extended to a broad range of supervised
learning problems related to structure-based biomolecular property prediction.
All of our software is available as open source at
https://github.com/gregory-kyro/HAC-Net/, and the HACNet Python package is
available through PyPI.
| [
{
"created": "Fri, 23 Dec 2022 16:14:53 GMT",
"version": "v1"
},
{
"created": "Sat, 18 Feb 2023 16:54:07 GMT",
"version": "v2"
},
{
"created": "Thu, 23 Mar 2023 01:07:14 GMT",
"version": "v3"
},
{
"created": "Tue, 28 Mar 2023 21:07:31 GMT",
"version": "v4"
}
] | 2023-12-05 | [
[
"Kyro",
"Gregory W.",
""
],
[
"Brent",
"Rafael I.",
""
],
[
"Batista",
"Victor S.",
""
]
] | Applying deep learning concepts from image detection and graph theory has greatly advanced protein-ligand binding affinity prediction, a challenge with enormous ramifications for both drug discovery and protein engineering. We build upon these advances by designing a novel deep learning architecture consisting of a 3-dimensional convolutional neural network utilizing channel-wise attention and two graph convolutional networks utilizing attention-based aggregation of node features. HAC-Net (Hybrid Attention-Based Convolutional Neural Network) obtains state-of-the-art results on the PDBbind v.2016 core set, the most widely recognized benchmark in the field. We extensively assess the generalizability of our model using multiple train-test splits, each of which maximizes differences between either protein structures, protein sequences, or ligand extended-connectivity fingerprints of complexes in the training and test sets. Furthermore, we perform 10-fold cross-validation with a similarity cutoff between SMILES strings of ligands in the training and test sets, and also evaluate the performance of HAC-Net on lower-quality data. We envision that this model can be extended to a broad range of supervised learning problems related to structure-based biomolecular property prediction. All of our software is available as open source at https://github.com/gregory-kyro/HAC-Net/, and the HACNet Python package is available through PyPI. |
2302.02470 | Istvan Kiss Z | Istv\'an Z. Kiss and Luc Berthouze and Wasiur R. KhudaBukhsh | Towards inferring network properties from epidemic data | 23 pages, 15 figures | null | null | null | q-bio.QM q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Epidemic propagation on networks represents an important departure from
traditional massaction models. However, the high-dimensionality of the exact
models poses a challenge to both mathematical analysis and parameter inference.
By using mean-field models, such as the pairwise model (PWM), the complexity
becomes tractable. While such models have been used extensively for model
analysis, there is limited work in the context of statistical inference. In
this paper, we explore the extent to which the PWM with the
susceptible-infected-recovered (SIR) epidemic can be used to infer disease- and
network-related parameters. The widely-used MLE approach exhibits several
issues pertaining to parameter unidentifiability and a lack of robustness to
exact knowledge about key quantities such as population size and/or proportion
of under reporting. As an alternative, we considered the recently developed
dynamical survival analysis (DSA). For scenarios in which there is no model
mismatch, such as when data are generated via simulations, both methods perform
well despite strong dependence between parameters. However, for real-world
data, such as foot-and-mouth, H1N1 and COVID19, the DSA method appears more
robust to potential model mismatch and the parameter estimates appear more
epidemiologically plausible. Taken together, however, our findings suggest that
network-based mean-field models can be used to formulate approximate
likelihoods which, coupled with an efficient inference scheme, make it possible
to not only learn about the parameters of the disease dynamics but also that of
the underlying network.
| [
{
"created": "Sun, 5 Feb 2023 19:59:33 GMT",
"version": "v1"
}
] | 2023-02-07 | [
[
"Kiss",
"István Z.",
""
],
[
"Berthouze",
"Luc",
""
],
[
"KhudaBukhsh",
"Wasiur R.",
""
]
] | Epidemic propagation on networks represents an important departure from traditional massaction models. However, the high-dimensionality of the exact models poses a challenge to both mathematical analysis and parameter inference. By using mean-field models, such as the pairwise model (PWM), the complexity becomes tractable. While such models have been used extensively for model analysis, there is limited work in the context of statistical inference. In this paper, we explore the extent to which the PWM with the susceptible-infected-recovered (SIR) epidemic can be used to infer disease- and network-related parameters. The widely-used MLE approach exhibits several issues pertaining to parameter unidentifiability and a lack of robustness to exact knowledge about key quantities such as population size and/or proportion of under reporting. As an alternative, we considered the recently developed dynamical survival analysis (DSA). For scenarios in which there is no model mismatch, such as when data are generated via simulations, both methods perform well despite strong dependence between parameters. However, for real-world data, such as foot-and-mouth, H1N1 and COVID19, the DSA method appears more robust to potential model mismatch and the parameter estimates appear more epidemiologically plausible. Taken together, however, our findings suggest that network-based mean-field models can be used to formulate approximate likelihoods which, coupled with an efficient inference scheme, make it possible to not only learn about the parameters of the disease dynamics but also that of the underlying network. |
1801.06079 | Antoine Allard | Antoine Allard and M. \'Angeles Serrano | Navigable maps of structural brain networks across species | 20 pages, 5 figures, 2 supp. tables, 3 supp. appendices, 10 supp.
figures | PLOS Computational Biology 16, e1007584 (2020) | 10.1371/journal.pcbi.1007584 | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Connectomes are spatially embedded networks whose architecture has been
shaped by physical constraints and communication needs throughout evolution.
Using a decentralized navigation protocol, we investigate the relationship
between the structure of the connectomes of different species and their spatial
layout. As a navigation strategy, we use greedy routing where nearest
neighbors, in terms of geometric distance, are visited. We measure the fraction
of successful greedy paths and their length as compared to shortest paths in
the topology of connectomes. In Euclidean space, we find a striking difference
between the navigability properties of mammalian and non-mammalian species,
which implies the inability of Euclidean distances to fully explain the
structural organization of their connectomes. In contrast, we find that
hyperbolic space, the effective geometry of complex networks, provides almost
perfectly navigable maps of connectomes for all species, meaning that
hyperbolic distances are exceptionally congruent with the structure of
connectomes. Hyperbolic maps therefore offer a quantitative meaningful
representation of connectomes that suggests a new cartography of the brain
based on the combination of its connectivity with its effective geometry rather
than on its anatomy only. Hyperbolic maps also provide a universal framework to
study decentralized communication processes in connectomes of different species
and at different scales on an equal footing.
| [
{
"created": "Thu, 18 Jan 2018 15:02:42 GMT",
"version": "v1"
},
{
"created": "Thu, 6 Feb 2020 13:21:34 GMT",
"version": "v2"
}
] | 2020-02-07 | [
[
"Allard",
"Antoine",
""
],
[
"Serrano",
"M. Ángeles",
""
]
] | Connectomes are spatially embedded networks whose architecture has been shaped by physical constraints and communication needs throughout evolution. Using a decentralized navigation protocol, we investigate the relationship between the structure of the connectomes of different species and their spatial layout. As a navigation strategy, we use greedy routing where nearest neighbors, in terms of geometric distance, are visited. We measure the fraction of successful greedy paths and their length as compared to shortest paths in the topology of connectomes. In Euclidean space, we find a striking difference between the navigability properties of mammalian and non-mammalian species, which implies the inability of Euclidean distances to fully explain the structural organization of their connectomes. In contrast, we find that hyperbolic space, the effective geometry of complex networks, provides almost perfectly navigable maps of connectomes for all species, meaning that hyperbolic distances are exceptionally congruent with the structure of connectomes. Hyperbolic maps therefore offer a quantitative meaningful representation of connectomes that suggests a new cartography of the brain based on the combination of its connectivity with its effective geometry rather than on its anatomy only. Hyperbolic maps also provide a universal framework to study decentralized communication processes in connectomes of different species and at different scales on an equal footing. |
1307.1382 | Pradeep Kumar Mohanty | Mahashweta Basu, Nitai P. Bhattacharyya, and P. K. Mohanty | Link-weight distribution of microRNA co-target networks exhibit
universality | 6 pages, 8 eps figures | null | null | null | q-bio.MN physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | MicroRNAs (miRNAs) are small non-coding RNAs which regulate gene expression
by binding to the 3' UTR of the corresponding messenger RNAs. We construct
miRNA co-target networks for 22 different species using a target prediction
database, MicroCosm Tagets. The miRNA pairs of individual species having one or
more common target genes are connected and the number of co-targets are
assigned as the weight of these links. We show that the link-weight
distributions of all the species collapse remarkably onto each other when
scaled suitably. It turns out that the scale-factor is a measure of complexity
of the species. A simple model, where targets are chosen randomly by miRNAs,
could provide the correct scaling function and explain the universality.
| [
{
"created": "Thu, 4 Jul 2013 15:48:33 GMT",
"version": "v1"
}
] | 2013-07-05 | [
[
"Basu",
"Mahashweta",
""
],
[
"Bhattacharyya",
"Nitai P.",
""
],
[
"Mohanty",
"P. K.",
""
]
] | MicroRNAs (miRNAs) are small non-coding RNAs which regulate gene expression by binding to the 3' UTR of the corresponding messenger RNAs. We construct miRNA co-target networks for 22 different species using a target prediction database, MicroCosm Tagets. The miRNA pairs of individual species having one or more common target genes are connected and the number of co-targets are assigned as the weight of these links. We show that the link-weight distributions of all the species collapse remarkably onto each other when scaled suitably. It turns out that the scale-factor is a measure of complexity of the species. A simple model, where targets are chosen randomly by miRNAs, could provide the correct scaling function and explain the universality. |
1310.6934 | Carson C. Chow | Michael A. Buice and Carson C. Chow | Generalized activity equations for spiking neural network dynamics | null | Front. Comput. Neurosci. 7:162 (2013) | 10.3389/fncom.2013.00162 | null | q-bio.NC | http://creativecommons.org/licenses/publicdomain/ | Much progress has been made in uncovering the computational capabilities of
spiking neural networks. However, spiking neurons will always be more expensive
to simulate compared to rate neurons because of the inherent disparity in time
scales - the spike duration time is much shorter than the inter-spike time,
which is much shorter than any learning time scale. In numerical analysis, this
is a classic stiff problem. Spiking neurons are also much more difficult to
study analytically. One possible approach to making spiking networks more
tractable is to augment mean field activity models with some information about
spiking correlations. For example, such a generalized activity model could
carry information about spiking rates and correlations between spikes
self-consistently. Here, we will show how this can be accomplished by
constructing a complete formal probabilistic description of the network and
then expanding around a small parameter such as the inverse of the number of
neurons in the network. The mean field theory of the system gives a rate-like
description. The first order terms in the perturbation expansion keep track of
covariances.
| [
{
"created": "Fri, 25 Oct 2013 14:32:44 GMT",
"version": "v1"
},
{
"created": "Wed, 30 Oct 2013 03:17:29 GMT",
"version": "v2"
}
] | 2013-10-31 | [
[
"Buice",
"Michael A.",
""
],
[
"Chow",
"Carson C.",
""
]
] | Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to rate neurons because of the inherent disparity in time scales - the spike duration time is much shorter than the inter-spike time, which is much shorter than any learning time scale. In numerical analysis, this is a classic stiff problem. Spiking neurons are also much more difficult to study analytically. One possible approach to making spiking networks more tractable is to augment mean field activity models with some information about spiking correlations. For example, such a generalized activity model could carry information about spiking rates and correlations between spikes self-consistently. Here, we will show how this can be accomplished by constructing a complete formal probabilistic description of the network and then expanding around a small parameter such as the inverse of the number of neurons in the network. The mean field theory of the system gives a rate-like description. The first order terms in the perturbation expansion keep track of covariances. |
1801.01019 | Nghia (Andy) Nguyen | Christine A. Liang, Lei Chen, Amer Wahed, Andy N.D. Nguyen | Proteomics Analysis of FLT3-ITD Mutation in Acute Myeloid Leukemia Using
Deep Learning Neural Network | 12 pages, 4 figures, 2 tables | null | null | null | q-bio.QM cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep Learning can significantly benefit cancer proteomics and genomics. In
this study, we attempt to determine a set of critical proteins that are
associated with the FLT3-ITD mutation in newly-diagnosed acute myeloid leukemia
patients. A Deep Learning network consisting of autoencoders forming a
hierarchical model from which high-level features are extracted without labeled
training data. Dimensional reduction reduced the number of critical proteins
from 231 to 20. Deep Learning found an excellent correlation between FLT3-ITD
mutation with the levels of these 20 critical proteins (accuracy 97%,
sensitivity 90%, specificity 100%). Our Deep Learning network could hone in on
20 proteins with the strongest association with FLT3-ITD. The results of this
study allow a novel approach to determine critical protein pathways in the
FLT3-ITD mutation, and provide proof-of-concept for an accurate approach to
model big data in cancer proteomics and genomics.
| [
{
"created": "Fri, 29 Dec 2017 13:05:30 GMT",
"version": "v1"
}
] | 2018-01-04 | [
[
"Liang",
"Christine A.",
""
],
[
"Chen",
"Lei",
""
],
[
"Wahed",
"Amer",
""
],
[
"Nguyen",
"Andy N. D.",
""
]
] | Deep Learning can significantly benefit cancer proteomics and genomics. In this study, we attempt to determine a set of critical proteins that are associated with the FLT3-ITD mutation in newly-diagnosed acute myeloid leukemia patients. A Deep Learning network consisting of autoencoders forming a hierarchical model from which high-level features are extracted without labeled training data. Dimensional reduction reduced the number of critical proteins from 231 to 20. Deep Learning found an excellent correlation between FLT3-ITD mutation with the levels of these 20 critical proteins (accuracy 97%, sensitivity 90%, specificity 100%). Our Deep Learning network could hone in on 20 proteins with the strongest association with FLT3-ITD. The results of this study allow a novel approach to determine critical protein pathways in the FLT3-ITD mutation, and provide proof-of-concept for an accurate approach to model big data in cancer proteomics and genomics. |
1509.01225 | Armando G. M. Neves | Irene N\'u\~nez Rodr\'iguez, Armando G. M. Neves | Evolution of cooperation in a particular case of the infinitely repeated
Prisoner's Dilemma with three strategies | 1 figure | null | null | null | q-bio.PE math.CA physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We will study a population of individuals playing the infinitely repeated
Prisoner's Dilemma under replicator dynamics. The population consists of three
kinds of individuals using the following reactive strategies: ALLD (individuals
which always defect), ATFT (almost tit-for-tat: individuals which almost always
repeat the opponent's last move) and G (generous individuals, which always
cooperate when the opponent cooperated in the last move and have a positive
probability $q$ of cooperating when they are defected). Our aim is studying in
a mathematically rigorous fashion the dynamics of a simplified version for the
computer experiment in [Nowak, Sigmund, Nature, 355, pp. 250--53, 1992]
involving 100 reactive strategies. We will see that as the generosity degree of
the G individuals varies, equilibria (rest points) of the dynamics appear or
disappear, and the dynamics changes accordingly. Not only we will prove that
the results of the experiment are true in our simplified version, but we will
have complete control on the existence or non-existence of the equilbria for
the dynamics for all possible values of the parameters, given that ATFT
individuals are close enough to TFT. For most values of the parameters the
dynamics will be completely determined.
| [
{
"created": "Thu, 3 Sep 2015 19:22:36 GMT",
"version": "v1"
}
] | 2015-09-04 | [
[
"Rodríguez",
"Irene Núñez",
""
],
[
"Neves",
"Armando G. M.",
""
]
] | We will study a population of individuals playing the infinitely repeated Prisoner's Dilemma under replicator dynamics. The population consists of three kinds of individuals using the following reactive strategies: ALLD (individuals which always defect), ATFT (almost tit-for-tat: individuals which almost always repeat the opponent's last move) and G (generous individuals, which always cooperate when the opponent cooperated in the last move and have a positive probability $q$ of cooperating when they are defected). Our aim is studying in a mathematically rigorous fashion the dynamics of a simplified version for the computer experiment in [Nowak, Sigmund, Nature, 355, pp. 250--53, 1992] involving 100 reactive strategies. We will see that as the generosity degree of the G individuals varies, equilibria (rest points) of the dynamics appear or disappear, and the dynamics changes accordingly. Not only we will prove that the results of the experiment are true in our simplified version, but we will have complete control on the existence or non-existence of the equilbria for the dynamics for all possible values of the parameters, given that ATFT individuals are close enough to TFT. For most values of the parameters the dynamics will be completely determined. |
1002.4835 | Jesus Gomez-Gardenes | Jesus Gomez-Gardenes, Gorka Zamora-Lopez, Yamir Moreno, Alex Arenas | From modular to centralized organization of synchronization in
functional areas of the cat cerebral cortex | 24 pages, 8 figures. Final version published in PLoS One | PLoS ONE 5, e12313 (2010) | 10.1371/journal.pone.0012313 | null | q-bio.NC nlin.AO physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent studies have pointed out the importance of transient synchronization
between widely distributed neural assemblies to understand conscious
perception. These neural assemblies form intricate networks of neurons and
synapses whose detailed map for mammals is still unknown and far from our
experimental capabilities. Only in a few cases, for example the C. elegans, we
know the complete mapping of the neuronal tissue or its mesoscopic level of
description provided by cortical areas. Here we study the process of transient
and global synchronization using a simple model of phase-coupled oscillators
assigned to cortical areas in the cerebral cat cortex. Our results highlight
the impact of the topological connectivity in the developing of
synchronization, revealing a transition in the synchronization organization
that goes from a modular decentralized coherence to a centralized synchronized
regime controlled by a few cortical areas forming a Rich-Club connectivity
pattern.
| [
{
"created": "Thu, 25 Feb 2010 17:21:58 GMT",
"version": "v1"
},
{
"created": "Sat, 6 Nov 2010 11:59:27 GMT",
"version": "v2"
}
] | 2010-11-09 | [
[
"Gomez-Gardenes",
"Jesus",
""
],
[
"Zamora-Lopez",
"Gorka",
""
],
[
"Moreno",
"Yamir",
""
],
[
"Arenas",
"Alex",
""
]
] | Recent studies have pointed out the importance of transient synchronization between widely distributed neural assemblies to understand conscious perception. These neural assemblies form intricate networks of neurons and synapses whose detailed map for mammals is still unknown and far from our experimental capabilities. Only in a few cases, for example the C. elegans, we know the complete mapping of the neuronal tissue or its mesoscopic level of description provided by cortical areas. Here we study the process of transient and global synchronization using a simple model of phase-coupled oscillators assigned to cortical areas in the cerebral cat cortex. Our results highlight the impact of the topological connectivity in the developing of synchronization, revealing a transition in the synchronization organization that goes from a modular decentralized coherence to a centralized synchronized regime controlled by a few cortical areas forming a Rich-Club connectivity pattern. |
1905.03182 | Breno de Oliveira Ferraz | P.P. Avelino and B.F. de Oliveira | Death by starvation in May-Leonard models | 7 pages, 9 figures | null | 10.1209/0295-5075/126/68002 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the dynamics of spatial stochastic May-Leonard models with mutual
predation interactions of equal strength between any two individuals of
different species. Using two-dimensional simulations, with two and three
pecies, we investigate the dynamical impact of the death of individuals after a
given threshold number of successive unsuccessful predation attempts. We find
that the death of these individuals can have a strong impact on the dynamics of
population networks and provide a crucial contribution to the preservation of
coexistence.
| [
{
"created": "Wed, 8 May 2019 16:10:44 GMT",
"version": "v1"
}
] | 2019-07-25 | [
[
"Avelino",
"P. P.",
""
],
[
"de Oliveira",
"B. F.",
""
]
] | We consider the dynamics of spatial stochastic May-Leonard models with mutual predation interactions of equal strength between any two individuals of different species. Using two-dimensional simulations, with two and three pecies, we investigate the dynamical impact of the death of individuals after a given threshold number of successive unsuccessful predation attempts. We find that the death of these individuals can have a strong impact on the dynamics of population networks and provide a crucial contribution to the preservation of coexistence. |
1804.11075 | Jicun Wang-Michelitsch | Jicun Wang-Michelitsch, Thomas M Michelitsch | Acute lymphoblastic leukemia may develop as a result of rapid
transformation of a lymphoblast triggered by repeated bone-remodeling during
bone-growth | 16 pages, 2 figures | null | null | null | q-bio.CB q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Acute lymphoblastic leukemia (ALL) and chronic lymphocytic leukemia (CLL) are
two major forms of leukemia that arise from lymphoid cells (LCs). ALL occurs
mostly in children and CLL occurs mainly in old people. However, the
Philadelphia-chromosome-positive ALL (Ph+-ALL) and the Ph-like ALL occur in
both children and adults. To understand childhood leukemia/lymphoma, we have
recently proposed two hypotheses on the causes and the mechanism of cell
transformation of a LC. Hypothesis A is: repeated bone-remodeling during
bone-growth and bone-repair may be a source of cell injuries of marrow cells
including hematopoietic stem cells (HSCs), myeloid cells, and LCs. Hypothesis B
is: a LC may have three pathways on transformation: a slow, a rapid, and an
accelerated. We discuss in the present paper the developing mechanisms of ALL
and CLL by these hypotheses. Having a peak incidence in young children, ALL may
develop mainly as a result of rapid cell transformation of a lymphoblast (or
pro-lymphocyte). Differently, Ph+-ALL and Ph-like ALL may develop as results of
transformation of a lymphoblast via accelerated pathway. Occurring mainly in
adults, CLL may be a result of transformation of a memory B-cell via slow
pathway. By causing cell injuries of HSCs and LCs, repeated bone-remodeling
during bone-growth and bone-repair may be related to the cell transformation of
a LC. In conclusion, ALL may develop as a result of cell transformation of a
lymphoblast via rapid or accelerated pathway; and repeated bone-remodeling
during bone-growth may be a trigger for the cell transformation of a
lymphoblast in a child.
| [
{
"created": "Mon, 30 Apr 2018 08:15:57 GMT",
"version": "v1"
}
] | 2018-05-01 | [
[
"Wang-Michelitsch",
"Jicun",
""
],
[
"Michelitsch",
"Thomas M",
""
]
] | Acute lymphoblastic leukemia (ALL) and chronic lymphocytic leukemia (CLL) are two major forms of leukemia that arise from lymphoid cells (LCs). ALL occurs mostly in children and CLL occurs mainly in old people. However, the Philadelphia-chromosome-positive ALL (Ph+-ALL) and the Ph-like ALL occur in both children and adults. To understand childhood leukemia/lymphoma, we have recently proposed two hypotheses on the causes and the mechanism of cell transformation of a LC. Hypothesis A is: repeated bone-remodeling during bone-growth and bone-repair may be a source of cell injuries of marrow cells including hematopoietic stem cells (HSCs), myeloid cells, and LCs. Hypothesis B is: a LC may have three pathways on transformation: a slow, a rapid, and an accelerated. We discuss in the present paper the developing mechanisms of ALL and CLL by these hypotheses. Having a peak incidence in young children, ALL may develop mainly as a result of rapid cell transformation of a lymphoblast (or pro-lymphocyte). Differently, Ph+-ALL and Ph-like ALL may develop as results of transformation of a lymphoblast via accelerated pathway. Occurring mainly in adults, CLL may be a result of transformation of a memory B-cell via slow pathway. By causing cell injuries of HSCs and LCs, repeated bone-remodeling during bone-growth and bone-repair may be related to the cell transformation of a LC. In conclusion, ALL may develop as a result of cell transformation of a lymphoblast via rapid or accelerated pathway; and repeated bone-remodeling during bone-growth may be a trigger for the cell transformation of a lymphoblast in a child. |
2209.07756 | Takuya Sato U. | Takuya U. Sato, Chikara Furusawa and Kunihiko Kaneko | Prediction of Cross-Fitness for Adaptive Evolution to Different
Environmental Conditions: Consequence of Phenotypic Dimensional Reduction | 21pages, 12figures | null | null | null | q-bio.PE q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How adaptive evolution to one environmental stress improves or suppresses
adaptation to another is an important problem in evolutionary biology. For
instance, in microbiology, the evolution of bacteria to be resistant to
different antibiotics is a critical issue that has been investigated as
cross-resistance. In fact, recent experiments on bacteria have suggested that
the cross-resistance of their evolution to various stressful environments can
be predicted by the changes to their transcriptome upon application of stress.
However, there are no studies so far that explain a possible theoretical
relationship between cross-resistance and changes in the transcriptome, which
causes high-dimensional changes to cell phenotype. Here, we show that a
correlation exists between fitness change in stress tolerance evolution and
response to the environment, using a cellular model with a high-dimensional
phenotype and establishing the relationship theoretically. The present results
allow for the prediction of evolution from transcriptome information in
response to different stresses before evolution. The relevance of this to
microbiological evolution experiments is discussed.
| [
{
"created": "Fri, 16 Sep 2022 07:32:16 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Feb 2023 07:49:03 GMT",
"version": "v2"
}
] | 2023-02-23 | [
[
"Sato",
"Takuya U.",
""
],
[
"Furusawa",
"Chikara",
""
],
[
"Kaneko",
"Kunihiko",
""
]
] | How adaptive evolution to one environmental stress improves or suppresses adaptation to another is an important problem in evolutionary biology. For instance, in microbiology, the evolution of bacteria to be resistant to different antibiotics is a critical issue that has been investigated as cross-resistance. In fact, recent experiments on bacteria have suggested that the cross-resistance of their evolution to various stressful environments can be predicted by the changes to their transcriptome upon application of stress. However, there are no studies so far that explain a possible theoretical relationship between cross-resistance and changes in the transcriptome, which causes high-dimensional changes to cell phenotype. Here, we show that a correlation exists between fitness change in stress tolerance evolution and response to the environment, using a cellular model with a high-dimensional phenotype and establishing the relationship theoretically. The present results allow for the prediction of evolution from transcriptome information in response to different stresses before evolution. The relevance of this to microbiological evolution experiments is discussed. |
1704.01693 | JinSeok Park | JinSeok Park, Deok-Ho Kim, Sagar R. Shah, Hong-Nam Kim, Kshitiz, David
Ellison, Peter Kim, Kahp-Yang Suh, Alfredo Qui\~nones-Hinojosa, Andre
Levchenko | Switch-like enhancement of epithelial-mesenchymal transition by YAP
through feedback regulation of WT1 and small Rho-family GTPases | null | null | 10.1038/s41467-019-10729-5 | null | q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Collective cell migration is a hallmark of developmental and
patho-physiological states, including wound healing and invasive cancer growth.
The integrity of the expanding epithelial sheets can be influenced by
extracellular cues, including cell-cell and cell-matrix interactions. We show
the nano-scale topography of the extracellular matrix underlying epithelial
cell layers can have a strong effect on the speed and morphology of the fronts
of the expanding sheet triggering epithelial-mesenchymal transition (EMT). We
further demonstrate that this behavior depends on the mechano-sensitivity of
the transcription regulator YAP and two new feedback cross-regulation
mechanisms: through Wilms Tumor-1 and E-cadherin, loosening cell-cell contacts,
and through Rho GTPase family proteins, enhancing cell migration. These
YAP-dependent regulatory feedback loops result in a switch-like change in the
signaling and expression of EMT-related markers, leading to a robust
enhancement in invasive epithelial sheet expansion, which might lead to a
poorer clinical outcome in renal and other cancers.
| [
{
"created": "Thu, 6 Apr 2017 02:51:47 GMT",
"version": "v1"
}
] | 2019-07-03 | [
[
"Park",
"JinSeok",
""
],
[
"Kim",
"Deok-Ho",
""
],
[
"Shah",
"Sagar R.",
""
],
[
"Kim",
"Hong-Nam",
""
],
[
"Kshitiz",
"",
""
],
[
"Ellison",
"David",
""
],
[
"Kim",
"Peter",
""
],
[
"Suh",
"Kahp-Yang",
""
],
[
"Quiñones-Hinojosa",
"Alfredo",
""
],
[
"Levchenko",
"Andre",
""
]
] | Collective cell migration is a hallmark of developmental and patho-physiological states, including wound healing and invasive cancer growth. The integrity of the expanding epithelial sheets can be influenced by extracellular cues, including cell-cell and cell-matrix interactions. We show the nano-scale topography of the extracellular matrix underlying epithelial cell layers can have a strong effect on the speed and morphology of the fronts of the expanding sheet triggering epithelial-mesenchymal transition (EMT). We further demonstrate that this behavior depends on the mechano-sensitivity of the transcription regulator YAP and two new feedback cross-regulation mechanisms: through Wilms Tumor-1 and E-cadherin, loosening cell-cell contacts, and through Rho GTPase family proteins, enhancing cell migration. These YAP-dependent regulatory feedback loops result in a switch-like change in the signaling and expression of EMT-related markers, leading to a robust enhancement in invasive epithelial sheet expansion, which might lead to a poorer clinical outcome in renal and other cancers. |
1301.5187 | W B Langdon | W. B. Langdon | Which is faster: Bowtie2GP > Bowtie > Bowtie2 > BWA | 4 pages, 2 tables | null | null | RN/13/03 | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We have recently used genetic programming to automatically generate an
improved version of Langmead's DNA read alignment tool Bowtie2 Sect.5.3
RN/12/09. We find it runs more than four times faster than the Bioinformatics
sequencing tool (BWA) currently used with short next generation paired end DNA
sequences by the Cancer Institute, takes less memory and yet finds similar
matches in the human genome.
| [
{
"created": "Tue, 22 Jan 2013 14:04:33 GMT",
"version": "v1"
}
] | 2013-01-23 | [
[
"Langdon",
"W. B.",
""
]
] | We have recently used genetic programming to automatically generate an improved version of Langmead's DNA read alignment tool Bowtie2 Sect.5.3 RN/12/09. We find it runs more than four times faster than the Bioinformatics sequencing tool (BWA) currently used with short next generation paired end DNA sequences by the Cancer Institute, takes less memory and yet finds similar matches in the human genome. |
1401.0478 | Sungwoo Ahn | Sungwoo Ahn, Jessica Solfest1, and Leonid L Rubchinsky | Fine temporal structure of cardiorespiratory synchronization | 33 pages, 5 figures. In press at Am J Physiol-Heart C | Am J Physiol Heart Circ Physiol 306:H755, 2014 | 10.1152/ajpheart.00314.2013 | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cardiac and respiratory rhythms are known to exhibit a modest degree of phase
synchronization, which is affected by age, diseases, and other factors. We
study the fine temporal structure of this synchrony in healthy young, healthy
elderly, and elderly subjects with coronary artery disease. We employ novel
time-series analysis to explore how phases of oscillations go in and out of the
phase-locked state at each cycle of oscillations. For the first time we show
that cardiorespiratory system is engaged in weakly synchronized dynamics with a
very specific temporal patterning of synchrony: the oscillations go out of
synchrony frequently, but return to the synchronous state very quickly (usually
within just one cycle of oscillations). Properties of synchrony depended on the
age and disease status. Healthy subjects exhibited more synchrony at the higher
(1:4) frequency-locking ratio between respiratory and cardiac rhythms, while
subjects with coronary artery disease exhibited relatively more 1:2 synchrony.
However, multiple short desynchronization episodes prevailed regardless of age
and disease status. The same average synchrony level could alternatively be
achieved with few long desynchronizations, but this was not observed in the
data. This implies functional importance of short desynchronizations dynamics.
These dynamics suggest that a synchronous state is easy to create if needed,
but is also easy to break. Short desynchronizations dynamics may facilitate the
mutual coordination of cardiac and respiratory rhythms by creating intermittent
synchronous episodes. It may be an efficient background dynamics to promote
adaptation of cardiorespiratory coordination to various external and internal
factors.
| [
{
"created": "Thu, 2 Jan 2014 17:31:56 GMT",
"version": "v1"
}
] | 2021-04-26 | [
[
"Ahn",
"Sungwoo",
""
],
[
"Solfest1",
"Jessica",
""
],
[
"Rubchinsky",
"Leonid L",
""
]
] | Cardiac and respiratory rhythms are known to exhibit a modest degree of phase synchronization, which is affected by age, diseases, and other factors. We study the fine temporal structure of this synchrony in healthy young, healthy elderly, and elderly subjects with coronary artery disease. We employ novel time-series analysis to explore how phases of oscillations go in and out of the phase-locked state at each cycle of oscillations. For the first time we show that cardiorespiratory system is engaged in weakly synchronized dynamics with a very specific temporal patterning of synchrony: the oscillations go out of synchrony frequently, but return to the synchronous state very quickly (usually within just one cycle of oscillations). Properties of synchrony depended on the age and disease status. Healthy subjects exhibited more synchrony at the higher (1:4) frequency-locking ratio between respiratory and cardiac rhythms, while subjects with coronary artery disease exhibited relatively more 1:2 synchrony. However, multiple short desynchronization episodes prevailed regardless of age and disease status. The same average synchrony level could alternatively be achieved with few long desynchronizations, but this was not observed in the data. This implies functional importance of short desynchronizations dynamics. These dynamics suggest that a synchronous state is easy to create if needed, but is also easy to break. Short desynchronizations dynamics may facilitate the mutual coordination of cardiac and respiratory rhythms by creating intermittent synchronous episodes. It may be an efficient background dynamics to promote adaptation of cardiorespiratory coordination to various external and internal factors. |
1712.07443 | Eli Kinney-Lang | Eli Kinney-Lang, Loukianos Spyrou, Ahmed Ebied, Richard FM Chin,
Javier Escudero | Tensor-driven extraction of developmental features from varying
paediatric EEG datasets | 16 pages, 6 figures, pre-print, under consideration for publication.
Reduced figure resolution due to size limit-please contact corresponding
author for full figure resolution | null | null | null | q-bio.NC eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objective. Consistently changing physiological properties in developing
children's brains challenges new data heavy technologies, like brain-computer
interfaces (BCI). Advancing signal processing methods in such technologies to
be more sensitive to developmental changes could help improve their function
and usability in paediatric populations. Taking advantage of the
multi-dimensional structure of EEG data through tensor analysis offers a
framework to extract relevant developmental features present in paediatric
resting-state EEG datasets. Methods. Three paediatric datasets from varying
developmental states and populations were analyzed using a developed two-step
constrained Parallel Factor (PARAFAC) tensor decomposition. The datasets
included the Muir Maxwell Epilepsy Centre, Children's Hospital Boston-MIT and
the Child Mind Institute, outlining two impaired and one healthy population,
respectively. Within dataset cross-validation used support vector machines
(SVM) for classification of out-of-fold data predicting subject age as a proxy
measure of development. t-distributed Stochastic Neighbour Embedding (t-SNE)
maps complemented classification analysis through visualization of the
high-dimensional feature structures. Main Results. Development-sensitive
features were successfully identified for the developmental conditions of each
dataset. SVM classification accuracy and misclassification costs were improved
significantly for both healthy and impaired paediatric populations. t-SNE maps
revealed suitable tensor factorization was key in extracting developmental
features. Significance. The described methods are a promising tool for
incorporating the unique developmental features present throughout childhood
EEG into new technologies like BCI and its applications.
| [
{
"created": "Wed, 20 Dec 2017 12:28:22 GMT",
"version": "v1"
}
] | 2017-12-21 | [
[
"Kinney-Lang",
"Eli",
""
],
[
"Spyrou",
"Loukianos",
""
],
[
"Ebied",
"Ahmed",
""
],
[
"Chin",
"Richard FM",
""
],
[
"Escudero",
"Javier",
""
]
] | Objective. Consistently changing physiological properties in developing children's brains challenges new data heavy technologies, like brain-computer interfaces (BCI). Advancing signal processing methods in such technologies to be more sensitive to developmental changes could help improve their function and usability in paediatric populations. Taking advantage of the multi-dimensional structure of EEG data through tensor analysis offers a framework to extract relevant developmental features present in paediatric resting-state EEG datasets. Methods. Three paediatric datasets from varying developmental states and populations were analyzed using a developed two-step constrained Parallel Factor (PARAFAC) tensor decomposition. The datasets included the Muir Maxwell Epilepsy Centre, Children's Hospital Boston-MIT and the Child Mind Institute, outlining two impaired and one healthy population, respectively. Within dataset cross-validation used support vector machines (SVM) for classification of out-of-fold data predicting subject age as a proxy measure of development. t-distributed Stochastic Neighbour Embedding (t-SNE) maps complemented classification analysis through visualization of the high-dimensional feature structures. Main Results. Development-sensitive features were successfully identified for the developmental conditions of each dataset. SVM classification accuracy and misclassification costs were improved significantly for both healthy and impaired paediatric populations. t-SNE maps revealed suitable tensor factorization was key in extracting developmental features. Significance. The described methods are a promising tool for incorporating the unique developmental features present throughout childhood EEG into new technologies like BCI and its applications. |
1307.7803 | Aaron Darling | Arnon Mazza, Irit Gat-Viks, Hesso Farhan, and Roded Sharan | A Minimum-Labeling Approach for Reconstructing Protein Networks across
Multiple Conditions | Peer-reviewed and presented as part of the 13th Workshop on
Algorithms in Bioinformatics (WABI2013) | null | null | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The sheer amounts of biological data that are generated in recent years have
driven the development of network analysis tools to facilitate the
interpretation and representation of these data. A fundamental challenge in
this domain is the reconstruction of a protein-protein subnetwork that
underlies a process of interest from a genome-wide screen of associated genes.
Despite intense work in this area, current algorithmic approaches are largely
limited to analyzing a single screen and are, thus, unable to account for
information on condition-specific genes, or reveal the dynamics (over time or
condition) of the process in question. Here we propose a novel formulation for
network reconstruction from multiple-condition data and devise an efficient
integer program solution for it. We apply our algorithm to analyze the response
to influenza infection in humans over time as well as to analyze a pair of ER
export related screens in humans. By comparing to an extant, single-condition
tool we demonstrate the power of our new approach in integrating data from
multiple conditions in a compact and coherent manner, capturing the dynamics of
the underlying processes.
| [
{
"created": "Tue, 30 Jul 2013 03:57:31 GMT",
"version": "v1"
}
] | 2013-07-31 | [
[
"Mazza",
"Arnon",
""
],
[
"Gat-Viks",
"Irit",
""
],
[
"Farhan",
"Hesso",
""
],
[
"Sharan",
"Roded",
""
]
] | The sheer amounts of biological data that are generated in recent years have driven the development of network analysis tools to facilitate the interpretation and representation of these data. A fundamental challenge in this domain is the reconstruction of a protein-protein subnetwork that underlies a process of interest from a genome-wide screen of associated genes. Despite intense work in this area, current algorithmic approaches are largely limited to analyzing a single screen and are, thus, unable to account for information on condition-specific genes, or reveal the dynamics (over time or condition) of the process in question. Here we propose a novel formulation for network reconstruction from multiple-condition data and devise an efficient integer program solution for it. We apply our algorithm to analyze the response to influenza infection in humans over time as well as to analyze a pair of ER export related screens in humans. By comparing to an extant, single-condition tool we demonstrate the power of our new approach in integrating data from multiple conditions in a compact and coherent manner, capturing the dynamics of the underlying processes. |
q-bio/0311004 | Thomas R. Weikl | Thomas R. Weikl, Matteo Palassini, and Ken A. Dill | Cooperativity in two-state protein folding kinetics | 9 pages, 6 figures, 1 table | null | null | null | q-bio.BM cond-mat.stat-mech | null | We present a solvable model that predicts the folding kinetics of two-state
proteins from their native structures. The model is based on conditional chain
entropies. It assumes that folding processes are dominated by small-loop
closure events that can be inferred from native structures. For CI2, the src
SH3 domain, TNfn3, and protein L, the model reproduces two-state kinetics, and
it predicts well the average Phi-values for secondary structures. The barrier
to folding is the formation of predominantly local structures such as helices
and hairpins, which are needed to bring nonlocal pairs of amino acids into
contact.
| [
{
"created": "Thu, 6 Nov 2003 16:45:19 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Weikl",
"Thomas R.",
""
],
[
"Palassini",
"Matteo",
""
],
[
"Dill",
"Ken A.",
""
]
] | We present a solvable model that predicts the folding kinetics of two-state proteins from their native structures. The model is based on conditional chain entropies. It assumes that folding processes are dominated by small-loop closure events that can be inferred from native structures. For CI2, the src SH3 domain, TNfn3, and protein L, the model reproduces two-state kinetics, and it predicts well the average Phi-values for secondary structures. The barrier to folding is the formation of predominantly local structures such as helices and hairpins, which are needed to bring nonlocal pairs of amino acids into contact. |
2208.06382 | Bradly Alicea | Bradly Alicea, Jesse Parent | Layers, Folds, and Semi-Neuronal Information Processing | 15 Pages, 4 Figures. Conference: 13th Annual Meeting of the BICA
Society (BICA*AI 2022) | null | null | null | q-bio.NC cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | What role does phenotypic complexity play in the systems-level function of an
embodied agent? The organismal phenotype is a topologically complex structure
that interacts with a genotype, developmental physics, and an informational
environment. Using this observation as inspiration, we utilize a type of
embodied agent that exhibits layered representational capacity: meta-brain
models. Meta-brains are used to demonstrate how phenotypes process information
and exhibit self-regulation from development to maturity. We focus on two
candidate structures that potentially explain this capacity: folding and
layering. As layering and folding can be observed in a host of biological
contexts, they form the basis for our representational investigations. First,
an innate starting point (genomic encoding) is described. The generative output
of this encoding is a differentiation tree, which results in a layered
phenotypic representation. Then we specify a formal meta-brain model of the
gut, which exhibits folding and layering in development in addition to
different degrees of representation of processed information. This organ
topology is retained in maturity, with the potential for additional folding and
representational drift in response to inflammation. Next, we consider
topological remapping using the developmental Braitenberg Vehicle (dBV) as a
toy model. During topological remapping, it is shown that folding of a layered
neural network can introduce a number of distortions to the original model,
some with functional implications. The paper concludes with a discussion on how
the meta-brains method can assist us in the investigation of enactivism,
holism, and cognitive processing in the context of biological simulation.
| [
{
"created": "Thu, 7 Jul 2022 21:47:23 GMT",
"version": "v1"
}
] | 2022-08-15 | [
[
"Alicea",
"Bradly",
""
],
[
"Parent",
"Jesse",
""
]
] | What role does phenotypic complexity play in the systems-level function of an embodied agent? The organismal phenotype is a topologically complex structure that interacts with a genotype, developmental physics, and an informational environment. Using this observation as inspiration, we utilize a type of embodied agent that exhibits layered representational capacity: meta-brain models. Meta-brains are used to demonstrate how phenotypes process information and exhibit self-regulation from development to maturity. We focus on two candidate structures that potentially explain this capacity: folding and layering. As layering and folding can be observed in a host of biological contexts, they form the basis for our representational investigations. First, an innate starting point (genomic encoding) is described. The generative output of this encoding is a differentiation tree, which results in a layered phenotypic representation. Then we specify a formal meta-brain model of the gut, which exhibits folding and layering in development in addition to different degrees of representation of processed information. This organ topology is retained in maturity, with the potential for additional folding and representational drift in response to inflammation. Next, we consider topological remapping using the developmental Braitenberg Vehicle (dBV) as a toy model. During topological remapping, it is shown that folding of a layered neural network can introduce a number of distortions to the original model, some with functional implications. The paper concludes with a discussion on how the meta-brains method can assist us in the investigation of enactivism, holism, and cognitive processing in the context of biological simulation. |
q-bio/0602015 | Anna Ochab-Marcinek | A. Fiasconaro, A. Ochab-Marcinek, B. Spagnolo, E. Gudowska-Nowak | Co-occurrence of resonant activation and noise-enhanced stability in a
model of cancer growth in the presence of immune response | 18 pages, 11 figures, published in Physical Review E 74, 041904
(2006) | Physical Review E 74, 041904 (2006) | 10.1103/PhysRevE.74.041904 | null | q-bio.PE | null | We investigate a stochastic version of a simple enzymatic reaction which
follows the generic Michaelis-Menten kinetics. At sufficiently high
concentrations of reacting species, the molecular fluctuations can be
approximated as a realization of a Brownian dynamics for which the model
reaction kinetics takes on the form of a stochastic differential equation.
After eliminating a fast kinetics, the model can be rephrased into a form of a
one-dimensional overdamped Langevin equation. We discuss physical aspects of
environmental noises acting in such a reduced system, pointing out the
possibility of coexistence of dynamical regimes where noise-enhanced stability
and resonant activation phenomena can be observed together.
| [
{
"created": "Mon, 13 Feb 2006 15:22:27 GMT",
"version": "v1"
},
{
"created": "Thu, 24 May 2007 12:31:55 GMT",
"version": "v2"
}
] | 2009-11-13 | [
[
"Fiasconaro",
"A.",
""
],
[
"Ochab-Marcinek",
"A.",
""
],
[
"Spagnolo",
"B.",
""
],
[
"Gudowska-Nowak",
"E.",
""
]
] | We investigate a stochastic version of a simple enzymatic reaction which follows the generic Michaelis-Menten kinetics. At sufficiently high concentrations of reacting species, the molecular fluctuations can be approximated as a realization of a Brownian dynamics for which the model reaction kinetics takes on the form of a stochastic differential equation. After eliminating a fast kinetics, the model can be rephrased into a form of a one-dimensional overdamped Langevin equation. We discuss physical aspects of environmental noises acting in such a reduced system, pointing out the possibility of coexistence of dynamical regimes where noise-enhanced stability and resonant activation phenomena can be observed together. |
1607.06824 | Fariborz Taherkhani | Fariborz Taherkhani and Farid Taherkhani | Permutation, Multiscale and Modified Multiscale Entropies a Natural
Complexity for Low-High Infection Level Intracellular Viral Reaction Kinetics | null | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Viral infectious diseases, such as HIV virus growth, cause an important
health concern. Study of intracellular viral processes can provide us to
develop drug and understanding the drug dose to decrease the HIV virus in
during growth. Kinetics Monte Carlo simulation has been done for solving Master
equation about dynamics of intracellular viral reaction kinetics. Scaling
relationship between equilibrium time and initial population of template has
been found as power low, , where N , are the number of initial population of
template species , equilibrium time, a = 163.1 , b = -0.1429 respectively.
Stochastic dynamics shows that increasing initial population of template
decreases the time of equilibrium. Entropy generation has been considered in
low, intermediate and high infection level of intracellular viral kinetics
reaction in during dynamical process. Permutation, multi scaling and modified
multiscaling entropies have been calculated for three kinds of species in
intracellular reaction dynamics, genome, structural protein, and template. Our
result shows that presence of noise in dynamical process of intracellular
reaction will change order of permutation entropy for the mentioned of three
species. In addition to multiscaling entropy is computed for mentioned model
and it has the following order: template > structural protein> genome.
Dependency of permutation entropy result to permutation order becomes small in
high infection level in intracellular viral kinetics dynamics. At short time
scale in intracellular reaction dynamics, convergency of permutation entropy
occurs with medium permutation order value. In the big time scale of
intracellular dynamics, permutation entropy scale with permutation order n as a
scaling relation .
| [
{
"created": "Fri, 22 Jul 2016 20:04:36 GMT",
"version": "v1"
},
{
"created": "Wed, 3 Aug 2016 19:39:28 GMT",
"version": "v2"
},
{
"created": "Wed, 5 Oct 2016 06:19:53 GMT",
"version": "v3"
},
{
"created": "Tue, 28 Mar 2017 08:22:08 GMT",
"version": "v4"
}
] | 2017-03-29 | [
[
"Taherkhani",
"Fariborz",
""
],
[
"Taherkhani",
"Farid",
""
]
] | Viral infectious diseases, such as HIV virus growth, cause an important health concern. Study of intracellular viral processes can provide us to develop drug and understanding the drug dose to decrease the HIV virus in during growth. Kinetics Monte Carlo simulation has been done for solving Master equation about dynamics of intracellular viral reaction kinetics. Scaling relationship between equilibrium time and initial population of template has been found as power low, , where N , are the number of initial population of template species , equilibrium time, a = 163.1 , b = -0.1429 respectively. Stochastic dynamics shows that increasing initial population of template decreases the time of equilibrium. Entropy generation has been considered in low, intermediate and high infection level of intracellular viral kinetics reaction in during dynamical process. Permutation, multi scaling and modified multiscaling entropies have been calculated for three kinds of species in intracellular reaction dynamics, genome, structural protein, and template. Our result shows that presence of noise in dynamical process of intracellular reaction will change order of permutation entropy for the mentioned of three species. In addition to multiscaling entropy is computed for mentioned model and it has the following order: template > structural protein> genome. Dependency of permutation entropy result to permutation order becomes small in high infection level in intracellular viral kinetics dynamics. At short time scale in intracellular reaction dynamics, convergency of permutation entropy occurs with medium permutation order value. In the big time scale of intracellular dynamics, permutation entropy scale with permutation order n as a scaling relation . |
0907.2714 | Vladislav Volman | Vladislav Volman, Herbert Levine | Signal processing in local neuronal circuits based on activity-dependent
noise and competition | 15 pages, 4 pages, in press for "Chaos" | null | 10.1063/1.3184806 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the characteristics of weak signal detection by a recurrent neuronal
network with plastic synaptic coupling. It is shown that in the presence of an
asynchronous component in synaptic transmission, the network acquires
selectivity with respect to the frequency of weak periodic stimuli. For
non-periodic frequency-modulated stimuli, the response is quantified by the
mutual information between input (signal) and output (network's activity), and
is optimized by synaptic depression. Introducing correlations in signal
structure resulted in the decrease of input-output mutual information. Our
results suggest that in neural systems with plastic connectivity, information
is not merely carried passively by the signal; rather, the information content
of the signal itself might determine the mode of its processing by a local
neuronal circuit.
| [
{
"created": "Wed, 15 Jul 2009 21:50:05 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Jul 2009 18:26:24 GMT",
"version": "v2"
}
] | 2015-05-13 | [
[
"Volman",
"Vladislav",
""
],
[
"Levine",
"Herbert",
""
]
] | We study the characteristics of weak signal detection by a recurrent neuronal network with plastic synaptic coupling. It is shown that in the presence of an asynchronous component in synaptic transmission, the network acquires selectivity with respect to the frequency of weak periodic stimuli. For non-periodic frequency-modulated stimuli, the response is quantified by the mutual information between input (signal) and output (network's activity), and is optimized by synaptic depression. Introducing correlations in signal structure resulted in the decrease of input-output mutual information. Our results suggest that in neural systems with plastic connectivity, information is not merely carried passively by the signal; rather, the information content of the signal itself might determine the mode of its processing by a local neuronal circuit. |
q-bio/0312048 | Anders Irb\"ack | Nitin Gupta, Anders Irb\"ack | Coupled folding-binding versus docking: A lattice model study | 17 pages, 7 figures (to appear in J. Chem. Phys.) | J. Chem. Phys. 120 (2004) 3983-3989 | 10.1063/1.1643900 | LU TP 03-44 | q-bio.BM cond-mat.soft | null | Using a simple hydrophobic/polar protein model, we perform a Monte Carlo
study of the thermodynamics and kinetics of binding to a target structure for
two closely related sequences, one of which has a unique folded state while the
other is unstructured. We obtain significant differences in their binding
behavior. The stable sequence has rigid docking as its preferred binding mode,
while the unstructured chain tends to first attach to the target and then fold.
The free-energy profiles associated with these two binding modes are compared.
| [
{
"created": "Tue, 30 Dec 2003 21:11:50 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Gupta",
"Nitin",
""
],
[
"Irbäck",
"Anders",
""
]
] | Using a simple hydrophobic/polar protein model, we perform a Monte Carlo study of the thermodynamics and kinetics of binding to a target structure for two closely related sequences, one of which has a unique folded state while the other is unstructured. We obtain significant differences in their binding behavior. The stable sequence has rigid docking as its preferred binding mode, while the unstructured chain tends to first attach to the target and then fold. The free-energy profiles associated with these two binding modes are compared. |
2406.15155 | Tiziana Cattai | Tiziana Cattai, Camilla Caporali, Marie-Constance Corsi, Stefania
Colonnese | Introducing the modularity graph: an application to brain functional
networks | This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In signal processing, exploring complex systems through network
representations has become an area of growing interest. This study introduces
the modularity graph, a new graph-based feature, to highlight the relationship
across the graph communities. After showing an application to the random graph
class known as Stochastic Block Model, we consider the brain functional
connectivity network estimated from real EEG data. The modularity graph
provides a quantitative framework for examining the interactions between neuron
clusters within the brain's network. The modularity graph works alongside
multiscale community detection algorithms, thereby enabling the identification
of community structures at various scales. After introducing the modularity
graph, we apply it to the brain functional connectivity network, estimated from
publicly available EEG recordings of motor imagery experiments. Statistical
analysis across multiple scales shows that the modularity graph differs for the
distinct brain connectivity states associated with various motor imagery tasks.
This work emphasizes the application of signal on graph processing techniques
to understand brain behavior during specific cognitive tasks, leveraging the
novel modularity graph to identify patterns of brain connectivity in different
cognitive conditions. This approach sets the stage for further signal on graph
analysis to devise brain network modularity, and to gain insights into the
motor imagery mechanisms.
| [
{
"created": "Fri, 21 Jun 2024 14:01:12 GMT",
"version": "v1"
}
] | 2024-06-24 | [
[
"Cattai",
"Tiziana",
""
],
[
"Caporali",
"Camilla",
""
],
[
"Corsi",
"Marie-Constance",
""
],
[
"Colonnese",
"Stefania",
""
]
] | In signal processing, exploring complex systems through network representations has become an area of growing interest. This study introduces the modularity graph, a new graph-based feature, to highlight the relationship across the graph communities. After showing an application to the random graph class known as Stochastic Block Model, we consider the brain functional connectivity network estimated from real EEG data. The modularity graph provides a quantitative framework for examining the interactions between neuron clusters within the brain's network. The modularity graph works alongside multiscale community detection algorithms, thereby enabling the identification of community structures at various scales. After introducing the modularity graph, we apply it to the brain functional connectivity network, estimated from publicly available EEG recordings of motor imagery experiments. Statistical analysis across multiple scales shows that the modularity graph differs for the distinct brain connectivity states associated with various motor imagery tasks. This work emphasizes the application of signal on graph processing techniques to understand brain behavior during specific cognitive tasks, leveraging the novel modularity graph to identify patterns of brain connectivity in different cognitive conditions. This approach sets the stage for further signal on graph analysis to devise brain network modularity, and to gain insights into the motor imagery mechanisms. |
2002.08975 | Aria Wang | Aria Yuan Wang and Michael J. Tarr | Learning Intermediate Features of Object Affordances with a
Convolutional Neural Network | Published on 2018 Conference on Cognitive Computational Neuroscience.
See <https://ccneuro.org/2018/Papers/ViewPapers.asp?PaperNum=1134> | null | 10.32470/CCN.2018.1134-0 | null | q-bio.NC cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Our ability to interact with the world around us relies on being able to
infer what actions objects afford -- often referred to as affordances. The
neural mechanisms of object-action associations are realized in the visuomotor
pathway where information about both visual properties and actions is
integrated into common representations. However, explicating these mechanisms
is particularly challenging in the case of affordances because there is hardly
any one-to-one mapping between visual features and inferred actions. To better
understand the nature of affordances, we trained a deep convolutional neural
network (CNN) to recognize affordances from images and to learn the underlying
features or the dimensionality of affordances. Such features form an underlying
compositional structure for the general representation of affordances which can
then be tested against human neural data. We view this representational
analysis as the first step towards a more formal account of how humans perceive
and interact with the environment.
| [
{
"created": "Thu, 20 Feb 2020 19:04:40 GMT",
"version": "v1"
}
] | 2020-02-24 | [
[
"Wang",
"Aria Yuan",
""
],
[
"Tarr",
"Michael J.",
""
]
] | Our ability to interact with the world around us relies on being able to infer what actions objects afford -- often referred to as affordances. The neural mechanisms of object-action associations are realized in the visuomotor pathway where information about both visual properties and actions is integrated into common representations. However, explicating these mechanisms is particularly challenging in the case of affordances because there is hardly any one-to-one mapping between visual features and inferred actions. To better understand the nature of affordances, we trained a deep convolutional neural network (CNN) to recognize affordances from images and to learn the underlying features or the dimensionality of affordances. Such features form an underlying compositional structure for the general representation of affordances which can then be tested against human neural data. We view this representational analysis as the first step towards a more formal account of how humans perceive and interact with the environment. |
2111.02465 | Jacob Remington | Jacob M. Remington, Jonathon B. Ferrell, and Jianing Li | Concerted Rolling and Membrane Penetration Revealed by Atomistic
Simulations of Antimicrobial Peptides | null | null | null | null | q-bio.BM physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Short peptides with antimicrobial activity have therapeutic potential for
treating bacterial infections. Mechanisms of actions for antimicrobial peptides
require binding the biological membrane of their target, which often represents
a key mechanistic step. A multitude of data-driven approaches have been
developed to predict potential antimicrobial peptide sequences; however, these
methods are usually agnostic to the physical interactions between the peptide
and the membrane. Towards developing higher throughput screening methodologies,
here we use Markov State Modeling and all-atom molecular dynamics simulations
to quantify the membrane binding and insertion kinetics of three prototypical
and antimicrobial peptides (alpha-helical magainin 2 and PGLa and beta-hairpin
tachyplesin 1). By leveraging a set of collective variables that capture the
essential physics of the amphiphilic and cationic peptide-membrane interactions
we reveal how the slowest kinetic process of membrane insertion is the dynamic
rolling of the peptide from a prebound to fully inserted state. These results
add critical details to how antimicrobial peptides insert into bacterial
membranes.
| [
{
"created": "Wed, 3 Nov 2021 18:42:31 GMT",
"version": "v1"
}
] | 2021-11-05 | [
[
"Remington",
"Jacob M.",
""
],
[
"Ferrell",
"Jonathon B.",
""
],
[
"Li",
"Jianing",
""
]
] | Short peptides with antimicrobial activity have therapeutic potential for treating bacterial infections. Mechanisms of actions for antimicrobial peptides require binding the biological membrane of their target, which often represents a key mechanistic step. A multitude of data-driven approaches have been developed to predict potential antimicrobial peptide sequences; however, these methods are usually agnostic to the physical interactions between the peptide and the membrane. Towards developing higher throughput screening methodologies, here we use Markov State Modeling and all-atom molecular dynamics simulations to quantify the membrane binding and insertion kinetics of three prototypical and antimicrobial peptides (alpha-helical magainin 2 and PGLa and beta-hairpin tachyplesin 1). By leveraging a set of collective variables that capture the essential physics of the amphiphilic and cationic peptide-membrane interactions we reveal how the slowest kinetic process of membrane insertion is the dynamic rolling of the peptide from a prebound to fully inserted state. These results add critical details to how antimicrobial peptides insert into bacterial membranes. |
2311.02177 | Devin Greene | Devin Greene | A Primer for the Walsh Transform | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-sa/4.0/ | A mathematical development of the Walsh transform, Walsh basis, and Walsh
coefficients is given. The author was prompted to write this by a wish to give
a unified treatment of epistatic coordinates as they are used in evolutionary
biology. At the end of the article, opinions are expressed regarding the
usefulness of these concepts for the practical researcher.
| [
{
"created": "Fri, 3 Nov 2023 18:08:24 GMT",
"version": "v1"
}
] | 2023-11-07 | [
[
"Greene",
"Devin",
""
]
] | A mathematical development of the Walsh transform, Walsh basis, and Walsh coefficients is given. The author was prompted to write this by a wish to give a unified treatment of epistatic coordinates as they are used in evolutionary biology. At the end of the article, opinions are expressed regarding the usefulness of these concepts for the practical researcher. |
1111.3126 | Sergey Murik E | Sergey E. Murik | Polarization Theory of Motivations, Emotions and Attention | 12 pages, 2 figures | Bulletin of Eastern-Siberian Scientific Center SB RAMS, 2005, No.
7, p.167-174 (in Russian) | null | null | q-bio.NC q-bio.TO | http://creativecommons.org/licenses/by-nc-sa/3.0/ | A new theory of motivations, emotions and attention is suggested, considering
them as functions of sensory systems. The theory connects neurophysiological
mechanisms of mental phenomena with the change of metabolic and functional
state of perceptive neurons, which is reflected in the degree of polarization
of a cell membrane.
| [
{
"created": "Mon, 14 Nov 2011 08:03:32 GMT",
"version": "v1"
}
] | 2011-11-15 | [
[
"Murik",
"Sergey E.",
""
]
] | A new theory of motivations, emotions and attention is suggested, considering them as functions of sensory systems. The theory connects neurophysiological mechanisms of mental phenomena with the change of metabolic and functional state of perceptive neurons, which is reflected in the degree of polarization of a cell membrane. |
1502.00544 | Michael B\"orsch | Anja Renz, Marc Renz, Diana Kluetsch, Gabriele Deckers-Hebestreit,
Michael B\"orsch | 3D-localization microscopy and tracking of FoF1-ATP synthases in living
bacteria | 13 pages, 5 figures | null | 10.1117/12.2080981 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | FoF1-ATP synthases are membrane-embedded protein machines that catalyze the
synthesis of adenosine triphosphate. Using photoactivation-based localization
microscopy (PALM) in TIR-illumination as well as structured illumination
microscopy (SIM), we explore the spatial distribution and track single FoF1-ATP
synthases in living E. coli cells under physiological conditions at different
temperatures. For quantitative diffusion analysis by mean-squared-displacement
measurements, the limited size of the observation area in the membrane with its
significant membrane curvature has to be considered. Therefore, we applied a
'sliding observation window' approach (M. Renz et al., Proc. SPIE 8225, 2012)
and obtained the one-dimensional diffusion coefficient of FoF1-ATP synthase
diffusing on the long axis in living E. coli cells.
| [
{
"created": "Mon, 2 Feb 2015 16:56:22 GMT",
"version": "v1"
}
] | 2015-06-11 | [
[
"Renz",
"Anja",
""
],
[
"Renz",
"Marc",
""
],
[
"Kluetsch",
"Diana",
""
],
[
"Deckers-Hebestreit",
"Gabriele",
""
],
[
"Börsch",
"Michael",
""
]
] | FoF1-ATP synthases are membrane-embedded protein machines that catalyze the synthesis of adenosine triphosphate. Using photoactivation-based localization microscopy (PALM) in TIR-illumination as well as structured illumination microscopy (SIM), we explore the spatial distribution and track single FoF1-ATP synthases in living E. coli cells under physiological conditions at different temperatures. For quantitative diffusion analysis by mean-squared-displacement measurements, the limited size of the observation area in the membrane with its significant membrane curvature has to be considered. Therefore, we applied a 'sliding observation window' approach (M. Renz et al., Proc. SPIE 8225, 2012) and obtained the one-dimensional diffusion coefficient of FoF1-ATP synthase diffusing on the long axis in living E. coli cells. |
q-bio/0410031 | Marek Cieplak | Jayanth R. Banavar, Marek Cieplak, and Amos Maritan | Lattice tube model of proteins | Phys. Rev. Lett. in press; 4 figures | null | 10.1103/PhysRevLett.93.238101 | null | q-bio.BM cond-mat.stat-mech | null | We present a new lattice model for proteins that incorporates a tube-like
anisotropy by introducing a preference for mutually parallel alignments in the
conformations. The model is demonstrated to capture many aspects of real
proteins.
| [
{
"created": "Tue, 26 Oct 2004 20:37:21 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Banavar",
"Jayanth R.",
""
],
[
"Cieplak",
"Marek",
""
],
[
"Maritan",
"Amos",
""
]
] | We present a new lattice model for proteins that incorporates a tube-like anisotropy by introducing a preference for mutually parallel alignments in the conformations. The model is demonstrated to capture many aspects of real proteins. |
q-bio/0404038 | Eugene Shakhnovich | Isaac Hubner, Mikael Oliveberg, and Eugene Shakhnovich | Simulation, Experiment, and Evolution: Understanding Nucleation in
Protein S6 Folding | PNAS in press | null | 10.1073/pnas.0401672101 | null | q-bio.BM cond-mat.soft | null | In this study, we explore nucleation and the transition state ensemble of the
ribosomal protein S6 using a Monte Carlo Go model in conjunction with
restraints from experiment. The results are analyzed in the context of
extensive experimental and evolutionary data. The roles of individual residues
in the folding nucleus are identified and the order of events in the S6 folding
mechanism is explored in detail. Interpretation of our results agrees with, and
extends the utility of, experiments that shift f-values by modulating
denaturant concentration and presents strong evidence for the realism of the
mechanistic details in our Monte Carlo Go model and the structural
interpretation of experimental f-values. We also observe plasticity in the
contacts of the hydrophobic core that support the specific nucleus. For S6,
which binds to RNA and protein after folding, this plasticity may result from
the conformational flexibility required to achieve biological function. These
results present a theoretical and conceptual picture that is relevant in
understanding the mechanism of nucleation in protein folding.
| [
{
"created": "Mon, 26 Apr 2004 23:07:28 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Hubner",
"Isaac",
""
],
[
"Oliveberg",
"Mikael",
""
],
[
"Shakhnovich",
"Eugene",
""
]
] | In this study, we explore nucleation and the transition state ensemble of the ribosomal protein S6 using a Monte Carlo Go model in conjunction with restraints from experiment. The results are analyzed in the context of extensive experimental and evolutionary data. The roles of individual residues in the folding nucleus are identified and the order of events in the S6 folding mechanism is explored in detail. Interpretation of our results agrees with, and extends the utility of, experiments that shift f-values by modulating denaturant concentration and presents strong evidence for the realism of the mechanistic details in our Monte Carlo Go model and the structural interpretation of experimental f-values. We also observe plasticity in the contacts of the hydrophobic core that support the specific nucleus. For S6, which binds to RNA and protein after folding, this plasticity may result from the conformational flexibility required to achieve biological function. These results present a theoretical and conceptual picture that is relevant in understanding the mechanism of nucleation in protein folding. |
2003.01513 | Javier Sagastuy-Brena | Daniel Kunin, Aran Nayebi, Javier Sagastuy-Brena, Surya Ganguli,
Jonathan M. Bloom, Daniel L. K. Yamins | Two Routes to Scalable Credit Assignment without Weight Symmetry | ICML 2020 Camera Ready Version, 19 pages including supplementary
information, 10 figures | null | null | null | q-bio.NC cs.LG cs.NE stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The neural plausibility of backpropagation has long been disputed, primarily
for its use of non-local weight transport $-$ the biologically dubious
requirement that one neuron instantaneously measure the synaptic weights of
another. Until recently, attempts to create local learning rules that avoid
weight transport have typically failed in the large-scale learning scenarios
where backpropagation shines, e.g. ImageNet categorization with deep
convolutional networks. Here, we investigate a recently proposed local learning
rule that yields competitive performance with backpropagation and find that it
is highly sensitive to metaparameter choices, requiring laborious tuning that
does not transfer across network architecture. Our analysis indicates the
underlying mathematical reason for this instability, allowing us to identify a
more robust local learning rule that better transfers without metaparameter
tuning. Nonetheless, we find a performance and stability gap between this local
rule and backpropagation that widens with increasing model depth. We then
investigate several non-local learning rules that relax the need for
instantaneous weight transport into a more biologically-plausible "weight
estimation" process, showing that these rules match state-of-the-art
performance on deep networks and operate effectively in the presence of noisy
updates. Taken together, our results suggest two routes towards the discovery
of neural implementations for credit assignment without weight symmetry:
further improvement of local rules so that they perform consistently across
architectures and the identification of biological implementations for
non-local learning mechanisms.
| [
{
"created": "Fri, 28 Feb 2020 18:39:16 GMT",
"version": "v1"
},
{
"created": "Thu, 25 Jun 2020 03:55:29 GMT",
"version": "v2"
}
] | 2020-06-26 | [
[
"Kunin",
"Daniel",
""
],
[
"Nayebi",
"Aran",
""
],
[
"Sagastuy-Brena",
"Javier",
""
],
[
"Ganguli",
"Surya",
""
],
[
"Bloom",
"Jonathan M.",
""
],
[
"Yamins",
"Daniel L. K.",
""
]
] | The neural plausibility of backpropagation has long been disputed, primarily for its use of non-local weight transport $-$ the biologically dubious requirement that one neuron instantaneously measure the synaptic weights of another. Until recently, attempts to create local learning rules that avoid weight transport have typically failed in the large-scale learning scenarios where backpropagation shines, e.g. ImageNet categorization with deep convolutional networks. Here, we investigate a recently proposed local learning rule that yields competitive performance with backpropagation and find that it is highly sensitive to metaparameter choices, requiring laborious tuning that does not transfer across network architecture. Our analysis indicates the underlying mathematical reason for this instability, allowing us to identify a more robust local learning rule that better transfers without metaparameter tuning. Nonetheless, we find a performance and stability gap between this local rule and backpropagation that widens with increasing model depth. We then investigate several non-local learning rules that relax the need for instantaneous weight transport into a more biologically-plausible "weight estimation" process, showing that these rules match state-of-the-art performance on deep networks and operate effectively in the presence of noisy updates. Taken together, our results suggest two routes towards the discovery of neural implementations for credit assignment without weight symmetry: further improvement of local rules so that they perform consistently across architectures and the identification of biological implementations for non-local learning mechanisms. |
2404.12141 | Keyue Qiu | Yanru Qu, Keyue Qiu, Yuxuan Song, Jingjing Gong, Jiawei Han, Mingyue
Zheng, Hao Zhou, Wei-Ying Ma | MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space | Accepted to ICML 2024 | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | Generative models for structure-based drug design (SBDD) have shown promising
results in recent years. Existing works mainly focus on how to generate
molecules with higher binding affinity, ignoring the feasibility prerequisites
for generated 3D poses and resulting in false positives. We conduct thorough
studies on key factors of ill-conformational problems when applying
autoregressive methods and diffusion to SBDD, including mode collapse and
hybrid continuous-discrete space. In this paper, we introduce MolCRAFT, the
first SBDD model that operates in the continuous parameter space, together with
a novel noise reduced sampling strategy. Empirical results show that our model
consistently achieves superior performance in binding affinity with more stable
3D structure, demonstrating our ability to accurately model interatomic
interactions. To our best knowledge, MolCRAFT is the first to achieve
reference-level Vina Scores (-6.59 kcal/mol) with comparable molecular size,
outperforming other strong baselines by a wide margin (-0.84 kcal/mol). Code is
available at https://github.com/AlgoMole/MolCRAFT.
| [
{
"created": "Thu, 18 Apr 2024 12:43:39 GMT",
"version": "v1"
},
{
"created": "Tue, 23 Apr 2024 02:59:42 GMT",
"version": "v2"
},
{
"created": "Wed, 15 May 2024 09:26:38 GMT",
"version": "v3"
},
{
"created": "Tue, 28 May 2024 03:48:38 GMT",
"version": "v4"
}
] | 2024-05-29 | [
[
"Qu",
"Yanru",
""
],
[
"Qiu",
"Keyue",
""
],
[
"Song",
"Yuxuan",
""
],
[
"Gong",
"Jingjing",
""
],
[
"Han",
"Jiawei",
""
],
[
"Zheng",
"Mingyue",
""
],
[
"Zhou",
"Hao",
""
],
[
"Ma",
"Wei-Ying",
""
]
] | Generative models for structure-based drug design (SBDD) have shown promising results in recent years. Existing works mainly focus on how to generate molecules with higher binding affinity, ignoring the feasibility prerequisites for generated 3D poses and resulting in false positives. We conduct thorough studies on key factors of ill-conformational problems when applying autoregressive methods and diffusion to SBDD, including mode collapse and hybrid continuous-discrete space. In this paper, we introduce MolCRAFT, the first SBDD model that operates in the continuous parameter space, together with a novel noise reduced sampling strategy. Empirical results show that our model consistently achieves superior performance in binding affinity with more stable 3D structure, demonstrating our ability to accurately model interatomic interactions. To our best knowledge, MolCRAFT is the first to achieve reference-level Vina Scores (-6.59 kcal/mol) with comparable molecular size, outperforming other strong baselines by a wide margin (-0.84 kcal/mol). Code is available at https://github.com/AlgoMole/MolCRAFT. |
1106.5313 | Siddhartha Chakrabarty | Gaurav Pachpute and Siddhartha P. Chakrabarty | Analysis of Hepatitis C Viral Dynamics Using Latin Hypercube Sampling | null | null | 10.1016/j.cnsns.2012.03.035 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a mathematical model comprising of four coupled ordinary
differential equations (ODEs) for studying the hepatitis C (HCV) viral
dynamics. The model embodies the efficacies of a combination therapy of
interferon and ribavirin. A condition for the stability of the uninfected and
the infected steady states is presented. A large number of sample points for
the model parameters (which were physiologically feasible) were generated using
Latin hypercube sampling. Analysis of our simulated values indicated
approximately 24% cases as having an uninfected steady state. Statistical tests
like the chi-square-test and the Spearman's test were also done on the sample
values. The results of these tests indicate a distinctly differently
distribution of certain parameter values and not in case of others, vis-a-vis,
the stability of the uninfected and the infected steady states.
| [
{
"created": "Mon, 27 Jun 2011 06:44:53 GMT",
"version": "v1"
}
] | 2015-05-28 | [
[
"Pachpute",
"Gaurav",
""
],
[
"Chakrabarty",
"Siddhartha P.",
""
]
] | We consider a mathematical model comprising of four coupled ordinary differential equations (ODEs) for studying the hepatitis C (HCV) viral dynamics. The model embodies the efficacies of a combination therapy of interferon and ribavirin. A condition for the stability of the uninfected and the infected steady states is presented. A large number of sample points for the model parameters (which were physiologically feasible) were generated using Latin hypercube sampling. Analysis of our simulated values indicated approximately 24% cases as having an uninfected steady state. Statistical tests like the chi-square-test and the Spearman's test were also done on the sample values. The results of these tests indicate a distinctly differently distribution of certain parameter values and not in case of others, vis-a-vis, the stability of the uninfected and the infected steady states. |
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