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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2306.10017 | Ahmad Qazza | Ahmad Qazza and Rania Saadeh | On the Analytical Solution of Fractional SIR Epidemic Model | null | Appl. Comput. Intell. Soft Comput. (2023) 2023 1-16 | 10.1155/2023/6973734 | null | q-bio.QM physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | This article presents the solution of the fractional SIR epidemic model using
the Laplace residual power series method. We introduce the fractional SIR model
in the sense of Caputo's derivative, it is presented by three fractional
differential equations, in which the third one depends on the first coupled
equations. The Laplace residual power series method is implemented in this
research to solve the proposed model, in which we present the solution in a
form of convergent series expansion that converges rapidly to the exact one. We
analyze the results and compare the obtained approximate solutions to those
obtained from other methods. Figures and tables are illustrated to show the
efficiency of the LRPSM in handling the proposed SIR model.
| [
{
"created": "Mon, 22 May 2023 16:53:37 GMT",
"version": "v1"
}
] | 2023-06-21 | [
[
"Qazza",
"Ahmad",
""
],
[
"Saadeh",
"Rania",
""
]
] | This article presents the solution of the fractional SIR epidemic model using the Laplace residual power series method. We introduce the fractional SIR model in the sense of Caputo's derivative, it is presented by three fractional differential equations, in which the third one depends on the first coupled equations. The Laplace residual power series method is implemented in this research to solve the proposed model, in which we present the solution in a form of convergent series expansion that converges rapidly to the exact one. We analyze the results and compare the obtained approximate solutions to those obtained from other methods. Figures and tables are illustrated to show the efficiency of the LRPSM in handling the proposed SIR model. |
0901.3025 | Claudius Gros | Claudius Gros | Emotions, diffusive emotional control and the motivational problem for
autonomous cognitive systems | Chapter contribution | Handbook of Research on Synthetic Emotions and Sociable Robotics:
New Applications in Affective Computing and Artificial Intelligence, J.
Vallverdu, D. Casacuberta (Eds), IGI-Global (in print, 2009) | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | All self-active living beings need to solve the motivational problem: The
question what to do at any moment of their live. For humans and non-human
animals at least two distinct layers of motivational drives are known, the
primary needs for survival and the emotional drives leading to a wide range of
sophisticated strategies, such as explorative learning and socializing. Part of
the emotional layer of drives has universal facets, being beneficial in an
extended range of environmental settings. Emotions are triggered in the brain
by the release of neuromodulators, which are, at the same time, the agents for
meta-learning. This intrinsic relation between emotions, meta-learning and
universal action strategies suggests a central importance for emotional control
for the design of artificial intelligences and synthetic cognitive systems. An
implementation of this concept is proposed in terms of a dense and homogeneous
associative network (dHan).
| [
{
"created": "Tue, 20 Jan 2009 10:51:26 GMT",
"version": "v1"
}
] | 2009-01-21 | [
[
"Gros",
"Claudius",
""
]
] | All self-active living beings need to solve the motivational problem: The question what to do at any moment of their live. For humans and non-human animals at least two distinct layers of motivational drives are known, the primary needs for survival and the emotional drives leading to a wide range of sophisticated strategies, such as explorative learning and socializing. Part of the emotional layer of drives has universal facets, being beneficial in an extended range of environmental settings. Emotions are triggered in the brain by the release of neuromodulators, which are, at the same time, the agents for meta-learning. This intrinsic relation between emotions, meta-learning and universal action strategies suggests a central importance for emotional control for the design of artificial intelligences and synthetic cognitive systems. An implementation of this concept is proposed in terms of a dense and homogeneous associative network (dHan). |
2405.08849 | Aram Mohammed | Kocher Omer Salih, Aram Akram Mohammed, Ibrahim Maaroof Noori | Rooting of thornless blackberry cuttings as induced by the extract of
white willow (Salix alba L.) shoots collected in different times | null | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | The aqueous extract of Salix spp contains many compounds which may act as
root-promoting agents in cuttings. S. alba is a deciduous tree containing
variable phytochemicals which are variable throughout the year. So, in this
study, one- and two-year-old shoots of S. alba were collected on the 15th of
each month in the year 2022, extracted in 2% ethanol at 9 g.L-1, and placed in
a water bath at 35 {\deg}C, then they applied to thornless blackberry cuttings
for 1.5 hr. The results explained that the highest rooting percentage (66.67%)
was obtained in the cuttings soaked in the extract of willow shoots collected
on 15th of January. They were not significantly different from control
cuttings, but they were different from the cuttings soaked in the extract of
willow shoots collected on 15th of August and October (33.33%). The majority of
other shoot and root traits were high in the cuttings soaked in the extract of
willow shoots collected on 15th of December. The willow shoots collected on
15th of January contained the lowest total phenols (51.4 {\mu}g.mL-1) and total
flavonoids (29.07 {\mu}g.mL-1). Moreover, the highest total phenols (57
{\mu}g.mL-1) and IAA (365.17 {\mu}g.mL-1) were recorded in the willow shoots
collected on 15th of March, however each total flavonoids (44.96 {\mu}g.mL-1)
and salicylic acid (492.61 {\mu}g.mL-1) were the highest in the willow shoots
collected on 15th of April. Generally, based on rooting percentage, it is
advisable to collect willow shoots on 15th of January and February for
extraction and application to the thornless blackberry cuttings.
| [
{
"created": "Tue, 14 May 2024 12:48:03 GMT",
"version": "v1"
}
] | 2024-05-16 | [
[
"Salih",
"Kocher Omer",
""
],
[
"Mohammed",
"Aram Akram",
""
],
[
"Noori",
"Ibrahim Maaroof",
""
]
] | The aqueous extract of Salix spp contains many compounds which may act as root-promoting agents in cuttings. S. alba is a deciduous tree containing variable phytochemicals which are variable throughout the year. So, in this study, one- and two-year-old shoots of S. alba were collected on the 15th of each month in the year 2022, extracted in 2% ethanol at 9 g.L-1, and placed in a water bath at 35 {\deg}C, then they applied to thornless blackberry cuttings for 1.5 hr. The results explained that the highest rooting percentage (66.67%) was obtained in the cuttings soaked in the extract of willow shoots collected on 15th of January. They were not significantly different from control cuttings, but they were different from the cuttings soaked in the extract of willow shoots collected on 15th of August and October (33.33%). The majority of other shoot and root traits were high in the cuttings soaked in the extract of willow shoots collected on 15th of December. The willow shoots collected on 15th of January contained the lowest total phenols (51.4 {\mu}g.mL-1) and total flavonoids (29.07 {\mu}g.mL-1). Moreover, the highest total phenols (57 {\mu}g.mL-1) and IAA (365.17 {\mu}g.mL-1) were recorded in the willow shoots collected on 15th of March, however each total flavonoids (44.96 {\mu}g.mL-1) and salicylic acid (492.61 {\mu}g.mL-1) were the highest in the willow shoots collected on 15th of April. Generally, based on rooting percentage, it is advisable to collect willow shoots on 15th of January and February for extraction and application to the thornless blackberry cuttings. |
2105.09086 | Genki Ichinose | Genki Ichinose, Daiki Miyagawa, Erika Chiba, Hiroki Sayama | How L\'evy flights triggered by presence of defectors affect evolution
of cooperation in spatial games | 8 pages, 5 figures | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cooperation among individuals has been key to sustaining societies. However,
natural selection favors defection over cooperation. Cooperation can be favored
when the mobility of individuals allows cooperators to form a cluster (or
group). Mobility patterns of animals sometimes follow a L\'evy flight. A L\'evy
flight is a kind of random walk but it is composed of many small movements with
a few big movements. The role of L\'evy flights for cooperation has been
studied by Antonioni and Tomassini. They showed that L\'evy flights promoted
cooperation combined with conditional movements triggered by neighboring
defectors. However, the optimal condition for neighboring defectors and how the
condition changes by the intensity of L\'evy flights are still unclear. Here,
we developed an agent-based model in a square lattice where agents perform
L\'evy flights depending on the fraction of neighboring defectors. We
systematically studied the relationships among three factors for cooperation:
sensitivity to defectors, the intensity of L\'evy flights, and population
density. Results of evolutionary simulations showed that moderate sensitivity
most promoted cooperation. Then, we found that the shortest movements were best
for cooperation when the sensitivity to defectors was high. In contrast, when
the sensitivity was low, longer movements were best for cooperation. Thus,
L\'evy flights, the balance between short and long jumps, promoted cooperation
in any sensitivity, which was confirmed by evolutionary simulations. Finally,
as the population density became larger, higher sensitivity was more beneficial
for cooperation to evolve. Our study highlights that L\'evy flights are an
optimal searching strategy not only for foraging but also for constructing
cooperative relationships with others.
| [
{
"created": "Wed, 19 May 2021 12:17:47 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Jul 2022 03:57:24 GMT",
"version": "v2"
}
] | 2022-07-21 | [
[
"Ichinose",
"Genki",
""
],
[
"Miyagawa",
"Daiki",
""
],
[
"Chiba",
"Erika",
""
],
[
"Sayama",
"Hiroki",
""
]
] | Cooperation among individuals has been key to sustaining societies. However, natural selection favors defection over cooperation. Cooperation can be favored when the mobility of individuals allows cooperators to form a cluster (or group). Mobility patterns of animals sometimes follow a L\'evy flight. A L\'evy flight is a kind of random walk but it is composed of many small movements with a few big movements. The role of L\'evy flights for cooperation has been studied by Antonioni and Tomassini. They showed that L\'evy flights promoted cooperation combined with conditional movements triggered by neighboring defectors. However, the optimal condition for neighboring defectors and how the condition changes by the intensity of L\'evy flights are still unclear. Here, we developed an agent-based model in a square lattice where agents perform L\'evy flights depending on the fraction of neighboring defectors. We systematically studied the relationships among three factors for cooperation: sensitivity to defectors, the intensity of L\'evy flights, and population density. Results of evolutionary simulations showed that moderate sensitivity most promoted cooperation. Then, we found that the shortest movements were best for cooperation when the sensitivity to defectors was high. In contrast, when the sensitivity was low, longer movements were best for cooperation. Thus, L\'evy flights, the balance between short and long jumps, promoted cooperation in any sensitivity, which was confirmed by evolutionary simulations. Finally, as the population density became larger, higher sensitivity was more beneficial for cooperation to evolve. Our study highlights that L\'evy flights are an optimal searching strategy not only for foraging but also for constructing cooperative relationships with others. |
0810.2152 | Kresimir Josic | Kresimir Josic, Eric Shea-Brown, Brent Doiron, Jaime de la Rocha | Stimulus-dependent correlations and population codes | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The magnitude of correlations between stimulus-driven responses of pairs of
neurons can itself be stimulus-dependent. We examine how this dependence
impacts the information carried by neural populations about the stimuli that
drive them. Stimulus-dependent changes in correlations can both carry
information directly and modulate the information separately carried by the
firing rates and variances. We use Fisher information to quantify these effects
and show that, although stimulus dependent correlations often carry little
information directly, their modulatory effects on the overall information can
be large. In particular, if the stimulus-dependence is such that correlations
increase with stimulus-induced firing rates, this can significantly enhance the
information of the population when the structure of correlations is determined
solely by the stimulus. However, in the presence of additional strong spatial
decay of correlations, such stimulus-dependence may have a negative impact.
Opposite relationships hold when correlations decrease with firing rates.
| [
{
"created": "Mon, 13 Oct 2008 04:45:47 GMT",
"version": "v1"
}
] | 2008-10-14 | [
[
"Josic",
"Kresimir",
""
],
[
"Shea-Brown",
"Eric",
""
],
[
"Doiron",
"Brent",
""
],
[
"de la Rocha",
"Jaime",
""
]
] | The magnitude of correlations between stimulus-driven responses of pairs of neurons can itself be stimulus-dependent. We examine how this dependence impacts the information carried by neural populations about the stimuli that drive them. Stimulus-dependent changes in correlations can both carry information directly and modulate the information separately carried by the firing rates and variances. We use Fisher information to quantify these effects and show that, although stimulus dependent correlations often carry little information directly, their modulatory effects on the overall information can be large. In particular, if the stimulus-dependence is such that correlations increase with stimulus-induced firing rates, this can significantly enhance the information of the population when the structure of correlations is determined solely by the stimulus. However, in the presence of additional strong spatial decay of correlations, such stimulus-dependence may have a negative impact. Opposite relationships hold when correlations decrease with firing rates. |
q-bio/0703052 | Sophie Querouil | Sophie Qu\'erouil (EEE, IMAR-DOP) | Int\'er\^ets et limites de l'approche mol\'eculaire pour aborder la
biog\'eographie et la sp\'eciation : l'exemple de quelques Mammif\`eres
d'Afrique tropicale | null | Bulletin de la soci\'et\'e zoologique de France 127 (2002) 359-370 | null | null | q-bio.PE | null | We attempted to test biogeographic hypotheses proposed for the evolution of
tropical faunas using mitochondrial DNA sequences of selected African mammalian
taxa (Insectivora, Rodentia and Primates). 1/ we constructed a molecular
phylogeny of taxa in order to ascertain their monophyly and calibrate a
molecular clock; 2/ we analysed and compared the phylogeographic patterns of
five forest-dwelling taxa; 3/ we evaluated the evolutionary processes
potentially involved in the speciation of cercopithecine Primates. Phylogenetic
results confirm that gene history is not necessarily the same as organism
history. Phylogeographic analyses reveal distinct patterns for each model
species, suggesting differences in initial distributions or different responses
to the same events. They indicate a role of Plio-Pleistocene vicariance events
in the intra-specific diversification of small mammals. Thus, genetic
divergence would be much older than the last glacial cycles. In cercopithecine
Primates, speciation would have been predominantly allopatric and driven by
Miocene and Pliocene vicariance events. Altogether, results give support to the
refuge hypothesis, without excluding the riverine barrier nor the
paleogrographic ones. They emphasize the role of paleo-ecological changes in
generating diversity and of the main riverine barriers in shaping the present
distribution of that diversity.
| [
{
"created": "Fri, 23 Mar 2007 16:10:17 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Quérouil",
"Sophie",
"",
"EEE, IMAR-DOP"
]
] | We attempted to test biogeographic hypotheses proposed for the evolution of tropical faunas using mitochondrial DNA sequences of selected African mammalian taxa (Insectivora, Rodentia and Primates). 1/ we constructed a molecular phylogeny of taxa in order to ascertain their monophyly and calibrate a molecular clock; 2/ we analysed and compared the phylogeographic patterns of five forest-dwelling taxa; 3/ we evaluated the evolutionary processes potentially involved in the speciation of cercopithecine Primates. Phylogenetic results confirm that gene history is not necessarily the same as organism history. Phylogeographic analyses reveal distinct patterns for each model species, suggesting differences in initial distributions or different responses to the same events. They indicate a role of Plio-Pleistocene vicariance events in the intra-specific diversification of small mammals. Thus, genetic divergence would be much older than the last glacial cycles. In cercopithecine Primates, speciation would have been predominantly allopatric and driven by Miocene and Pliocene vicariance events. Altogether, results give support to the refuge hypothesis, without excluding the riverine barrier nor the paleogrographic ones. They emphasize the role of paleo-ecological changes in generating diversity and of the main riverine barriers in shaping the present distribution of that diversity. |
2004.02632 | Sara Sommariva | Sara Sommariva, Giacomo Caviglia, Michele Piana | Gain and Loss of Function mutations in biological chemical reaction
networks: a mathematical model with application to colorectal cancer cells | 21 pages, 5 figures | null | null | null | q-bio.MN math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies a system of Ordinary Differential Equations modeling a
chemical reaction network and derives from it a simulation tool mimicking Loss
of Function and Gain of Function mutations found in cancer cells. More
specifically, from a theoretical perspective, our approach focuses on the
determination of moiety conservation laws for the system and their relation
with the corresponding stoichiometric surfaces. Then we show that Loss of
Function mutations can be implemented in the model via modification of the
initial conditions in the system, while Gain of Function mutations can be
implemented by eliminating specific reactions. Finally, the model is utilized
to examine in detail the G1-S phase of a colorectal cancer cell.
| [
{
"created": "Fri, 3 Apr 2020 15:42:52 GMT",
"version": "v1"
}
] | 2020-04-07 | [
[
"Sommariva",
"Sara",
""
],
[
"Caviglia",
"Giacomo",
""
],
[
"Piana",
"Michele",
""
]
] | This paper studies a system of Ordinary Differential Equations modeling a chemical reaction network and derives from it a simulation tool mimicking Loss of Function and Gain of Function mutations found in cancer cells. More specifically, from a theoretical perspective, our approach focuses on the determination of moiety conservation laws for the system and their relation with the corresponding stoichiometric surfaces. Then we show that Loss of Function mutations can be implemented in the model via modification of the initial conditions in the system, while Gain of Function mutations can be implemented by eliminating specific reactions. Finally, the model is utilized to examine in detail the G1-S phase of a colorectal cancer cell. |
1002.3035 | Claudius Gros | Claudius Gros | Cognition and Emotion: Perspectives of a Closing Gap | A review. Cognitive Computation (in press) | Cognitive Computation 2, 78 (2010) | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The primary tasks of a cognitive system is to survive and to maximize a
life-long utility function, like the number of offsprings. A direct
computational maximization of life-long utility is however not possible in
complex environments, especially in the context, of real-world time
constraints. The central role of emotions is to serve as an intermediate layer
in the space of policies available to agents and animals, leading to a large
dimensional reduction of complexity.
We review our current understanding of the functional role of emotions,
stressing the role of the neuromodulators mediating emotions for the diffusive
homeostatic control system of the brain. We discuss a recent proposal, that
emotional diffusive control is characterized, in contrast to neutral diffusive
control, by interaction effects, viz by interferences between emotional arousal
and reward signaling. Several proposals for the realization of synthetic
emotions are discussed in this context, together with key open issues regarding
the interplay between emotional motivational drives and diffusive control.
| [
{
"created": "Tue, 16 Feb 2010 09:06:58 GMT",
"version": "v1"
}
] | 2010-05-25 | [
[
"Gros",
"Claudius",
""
]
] | The primary tasks of a cognitive system is to survive and to maximize a life-long utility function, like the number of offsprings. A direct computational maximization of life-long utility is however not possible in complex environments, especially in the context, of real-world time constraints. The central role of emotions is to serve as an intermediate layer in the space of policies available to agents and animals, leading to a large dimensional reduction of complexity. We review our current understanding of the functional role of emotions, stressing the role of the neuromodulators mediating emotions for the diffusive homeostatic control system of the brain. We discuss a recent proposal, that emotional diffusive control is characterized, in contrast to neutral diffusive control, by interaction effects, viz by interferences between emotional arousal and reward signaling. Several proposals for the realization of synthetic emotions are discussed in this context, together with key open issues regarding the interplay between emotional motivational drives and diffusive control. |
q-bio/0401012 | Eduard Schreiner | Roger Rousseau, Eduard Schreiner, Axel Kohlmeyer, and Dominik Marx | Temperature Dependent Conformational Transitions and Hydrogen Bond
Dynamics of the Elastin-Like Octapeptide GVG(VPGVG): a Molecular Dynamics
Study | 15 pages, 1 table, 8 figures | null | 10.1016/S0006-3495(04)74210-1 | null | q-bio.BM | null | A joint experimental / theoretical investigation of the elastin-like
octapeptide GVG(VPGVG) was carried out. In this paper a comprehensive molecular
dynamics study of the temperature dependent folding and unfolding of the
octapeptide is presented. The current study, as well as its experimental
counterpart find that this peptide undergoes an "inverse temperature
transition", ITT, leading to a folding at about 310-330 K. In addition, an
unfolding transition is identified at unusually high temperatures approaching
the boiling point of water. Due to the small size of the system two broad
temperature regimes are found: the "ITT regime" (at about 280-320 K) and the
"unfolding regime" at about T > 330 K, where the peptide has a maximum
probability of being folded at approximately 330 K. A detailed molecular
picture involving a thermodynamic order parameter, or reaction coordinate, for
this process is presented along with a time-correlation function analysis of
the hydrogen bond dynamics within the peptide as well as between the peptide
and solvating water molecules. Correlation with experimental evidence and
ramifications on the properties of elastin are discussed.
| [
{
"created": "Thu, 8 Jan 2004 17:50:55 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Rousseau",
"Roger",
""
],
[
"Schreiner",
"Eduard",
""
],
[
"Kohlmeyer",
"Axel",
""
],
[
"Marx",
"Dominik",
""
]
] | A joint experimental / theoretical investigation of the elastin-like octapeptide GVG(VPGVG) was carried out. In this paper a comprehensive molecular dynamics study of the temperature dependent folding and unfolding of the octapeptide is presented. The current study, as well as its experimental counterpart find that this peptide undergoes an "inverse temperature transition", ITT, leading to a folding at about 310-330 K. In addition, an unfolding transition is identified at unusually high temperatures approaching the boiling point of water. Due to the small size of the system two broad temperature regimes are found: the "ITT regime" (at about 280-320 K) and the "unfolding regime" at about T > 330 K, where the peptide has a maximum probability of being folded at approximately 330 K. A detailed molecular picture involving a thermodynamic order parameter, or reaction coordinate, for this process is presented along with a time-correlation function analysis of the hydrogen bond dynamics within the peptide as well as between the peptide and solvating water molecules. Correlation with experimental evidence and ramifications on the properties of elastin are discussed. |
1006.3627 | Manoj Gopalkrishnan | Manoj Gopalkrishnan | Catalysis in Reaction Networks | 19 pages, 1 figure | Bull Math Biol (2011) 73:2962 -- 2982 | 10.1007/s11538-011-9655-3 | null | q-bio.MN math.AC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We define catalytic networks as chemical reaction networks with an
essentially catalytic reaction pathway: one which is on in the presence of
certain catalysts and off in their absence. We show that examples of catalytic
networks include synthetic DNA molecular circuits that have been shown to
perform signal amplification and molecular logic. Recall that a critical siphon
is a subset of the species in a chemical reaction network whose absence is
forward invariant and stoichiometrically compatible with a positive point. Our
main theorem is that all weakly-reversible networks with critical siphons are
catalytic. Consequently, we obtain new proofs for the persistence of atomic
event-systems of Adleman et al., and normal networks of Gnacadja. We define
autocatalytic networks, and conjecture that a weakly-reversible reaction
network has critical siphons if and only if it is autocatalytic.
| [
{
"created": "Fri, 18 Jun 2010 08:09:26 GMT",
"version": "v1"
},
{
"created": "Mon, 17 Jan 2011 09:22:02 GMT",
"version": "v2"
},
{
"created": "Wed, 11 May 2011 03:59:18 GMT",
"version": "v3"
}
] | 2011-11-18 | [
[
"Gopalkrishnan",
"Manoj",
""
]
] | We define catalytic networks as chemical reaction networks with an essentially catalytic reaction pathway: one which is on in the presence of certain catalysts and off in their absence. We show that examples of catalytic networks include synthetic DNA molecular circuits that have been shown to perform signal amplification and molecular logic. Recall that a critical siphon is a subset of the species in a chemical reaction network whose absence is forward invariant and stoichiometrically compatible with a positive point. Our main theorem is that all weakly-reversible networks with critical siphons are catalytic. Consequently, we obtain new proofs for the persistence of atomic event-systems of Adleman et al., and normal networks of Gnacadja. We define autocatalytic networks, and conjecture that a weakly-reversible reaction network has critical siphons if and only if it is autocatalytic. |
2003.10650 | Ngoc Hieu Tran | Rui Qiao, Ngoc Hieu Tran, Baozhen Shan, Ali Ghodsi, Ming Li | Personalized workflow to identify optimal T-cell epitopes for
peptide-based vaccines against COVID-19 | null | null | null | null | q-bio.PE q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditional vaccines against viruses are designed to target their surface
proteins, i.e., antigens, which can trigger the immune system to produce
specific antibodies to capture and neutralize the viruses. However, viruses
often evolve quickly, and their antigens are prone to mutations to avoid
recognition by the antibodies (antigenic drift). This limitation of the
antibody-mediated immunity could be addressed by the T-cell mediated immunity,
which is able to recognize conserved viral HLA peptides presented on
virus-infected cells. Thus, by targeting conserved regions on the genome of a
virus, T-cell epitope-based vaccines are less subjected to mutations and may
work effectively on different strains of the virus. Here we propose a
personalized workflow to identify an optimal set of T-cell epitopes based on
the HLA alleles and the immunopeptidome of an individual person. Specifically,
our workflow trains a machine learning model on the immunopeptidome and then
predicts HLA peptides from conserved regions of a virus that are most likely to
trigger responses from the person T cells. We applied the workflow to identify
T-cell epitopes for the SARS-COV-2 virus, which has caused the recent COVID-19
pandemic in more than 100 countries across the globe.
| [
{
"created": "Tue, 24 Mar 2020 04:11:05 GMT",
"version": "v1"
}
] | 2020-03-25 | [
[
"Qiao",
"Rui",
""
],
[
"Tran",
"Ngoc Hieu",
""
],
[
"Shan",
"Baozhen",
""
],
[
"Ghodsi",
"Ali",
""
],
[
"Li",
"Ming",
""
]
] | Traditional vaccines against viruses are designed to target their surface proteins, i.e., antigens, which can trigger the immune system to produce specific antibodies to capture and neutralize the viruses. However, viruses often evolve quickly, and their antigens are prone to mutations to avoid recognition by the antibodies (antigenic drift). This limitation of the antibody-mediated immunity could be addressed by the T-cell mediated immunity, which is able to recognize conserved viral HLA peptides presented on virus-infected cells. Thus, by targeting conserved regions on the genome of a virus, T-cell epitope-based vaccines are less subjected to mutations and may work effectively on different strains of the virus. Here we propose a personalized workflow to identify an optimal set of T-cell epitopes based on the HLA alleles and the immunopeptidome of an individual person. Specifically, our workflow trains a machine learning model on the immunopeptidome and then predicts HLA peptides from conserved regions of a virus that are most likely to trigger responses from the person T cells. We applied the workflow to identify T-cell epitopes for the SARS-COV-2 virus, which has caused the recent COVID-19 pandemic in more than 100 countries across the globe. |
1610.10021 | Longfei Li | Longfei Li and R. J. Braun and W. D. Henshaw and P. E. King-Smith | Computed Flow and Fluorescence Over the Ocular Surface | null | null | null | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fluorescein is perhaps the most commonly used substance to visualize tear
film thickness and dynamics; better understanding of this process aids
understanding of dry eye syndrome which afflicts millions of people. We study a
mathematical model for tear film flow, evaporation, solutal transport and
fluorescence over the exposed ocular surface during the interblink. Transport
of the fluorescein ion by fluid flow in the tear film affects the intensity of
fluorescence via changes in concentration and tear film thickness. Evaporation
causes increased osmolarity and potential irritation over the ocular surface;
it also alters fluorescein concentration and thus fluorescence. Using thinning
rates from in vivo measurements together with thin film equations for flow and
transport of multiple solutes, we compute dynamic results for tear film
quantities of interest. We compare our computed intensity distributions with in
vivo observations. A number of experimental features are recovered by the
model.
| [
{
"created": "Mon, 31 Oct 2016 17:14:47 GMT",
"version": "v1"
}
] | 2016-11-01 | [
[
"Li",
"Longfei",
""
],
[
"Braun",
"R. J.",
""
],
[
"Henshaw",
"W. D.",
""
],
[
"King-Smith",
"P. E.",
""
]
] | Fluorescein is perhaps the most commonly used substance to visualize tear film thickness and dynamics; better understanding of this process aids understanding of dry eye syndrome which afflicts millions of people. We study a mathematical model for tear film flow, evaporation, solutal transport and fluorescence over the exposed ocular surface during the interblink. Transport of the fluorescein ion by fluid flow in the tear film affects the intensity of fluorescence via changes in concentration and tear film thickness. Evaporation causes increased osmolarity and potential irritation over the ocular surface; it also alters fluorescein concentration and thus fluorescence. Using thinning rates from in vivo measurements together with thin film equations for flow and transport of multiple solutes, we compute dynamic results for tear film quantities of interest. We compare our computed intensity distributions with in vivo observations. A number of experimental features are recovered by the model. |
1704.06831 | Shubhanshu Shekhar | Shubhanshu Shekhar, Sebastien Roch and Siavash Mirarab | Species tree estimation using ASTRAL: how many genes are enough? | 22 pages, 2 figures, Accepted for oral presentation at RECOMB 2017;
Under review at IEEE TCBB | null | 10.1109/TCBB.2017.2757930 | null | q-bio.PE cs.CE math.PR math.ST stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Species tree reconstruction from genomic data is increasingly performed using
methods that account for sources of gene tree discordance such as incomplete
lineage sorting. One popular method for reconstructing species trees from
unrooted gene tree topologies is ASTRAL. In this paper, we derive theoretical
sample complexity results for the number of genes required by ASTRAL to
guarantee reconstruction of the correct species tree with high probability. We
also validate those theoretical bounds in a simulation study. Our results
indicate that ASTRAL requires $\mathcal{O}(f^{-2} \log n)$ gene trees to
reconstruct the species tree correctly with high probability where n is the
number of species and f is the length of the shortest branch in the species
tree. Our simulations, which are the first to test ASTRAL explicitly under the
anomaly zone, show trends consistent with the theoretical bounds and also
provide some practical insights on the conditions where ASTRAL works well.
| [
{
"created": "Sat, 22 Apr 2017 18:25:44 GMT",
"version": "v1"
},
{
"created": "Sat, 16 Sep 2017 03:04:45 GMT",
"version": "v2"
}
] | 2017-12-06 | [
[
"Shekhar",
"Shubhanshu",
""
],
[
"Roch",
"Sebastien",
""
],
[
"Mirarab",
"Siavash",
""
]
] | Species tree reconstruction from genomic data is increasingly performed using methods that account for sources of gene tree discordance such as incomplete lineage sorting. One popular method for reconstructing species trees from unrooted gene tree topologies is ASTRAL. In this paper, we derive theoretical sample complexity results for the number of genes required by ASTRAL to guarantee reconstruction of the correct species tree with high probability. We also validate those theoretical bounds in a simulation study. Our results indicate that ASTRAL requires $\mathcal{O}(f^{-2} \log n)$ gene trees to reconstruct the species tree correctly with high probability where n is the number of species and f is the length of the shortest branch in the species tree. Our simulations, which are the first to test ASTRAL explicitly under the anomaly zone, show trends consistent with the theoretical bounds and also provide some practical insights on the conditions where ASTRAL works well. |
1212.3908 | Alireza Valizadeh | Sadjad Sadeghi and Alireza Valizadeh | Synchronization of delayed coupled neurons in presence of inhomogeneity | null | null | null | null | q-bio.NC nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In principle, while coupled limit cycle oscillators can overcome mismatch in
intrinsic rates and match their frequencies, but zero phase lag synchronization
is just achievable in the limit of zero mismatch, i.e., with identical
oscillators. Delay in communication, on the other hand, can exert phase shift
in the activity of the coupled oscillators. In this study, we address the
question of how phase locked, and in particular zero phase lag synchronization,
can be achieved for a heterogeneous system of two delayed coupled neurons. We
have analytically studied the possibility of inphase synchronization and near
inphase synchronization when the neurons are not identical or the connections
are not exactly symmetric. We have shown that while any single source of
inhomogeneity can violate isochronous synchrony, multiple sources of
inhomogeneity can compensate for each other and maintain synchrony. Numeric
studies on biologically plausible models also support the analytic results.
| [
{
"created": "Mon, 17 Dec 2012 07:18:56 GMT",
"version": "v1"
},
{
"created": "Wed, 19 Dec 2012 16:47:22 GMT",
"version": "v2"
},
{
"created": "Sun, 10 Feb 2013 11:32:50 GMT",
"version": "v3"
}
] | 2013-02-12 | [
[
"Sadeghi",
"Sadjad",
""
],
[
"Valizadeh",
"Alireza",
""
]
] | In principle, while coupled limit cycle oscillators can overcome mismatch in intrinsic rates and match their frequencies, but zero phase lag synchronization is just achievable in the limit of zero mismatch, i.e., with identical oscillators. Delay in communication, on the other hand, can exert phase shift in the activity of the coupled oscillators. In this study, we address the question of how phase locked, and in particular zero phase lag synchronization, can be achieved for a heterogeneous system of two delayed coupled neurons. We have analytically studied the possibility of inphase synchronization and near inphase synchronization when the neurons are not identical or the connections are not exactly symmetric. We have shown that while any single source of inhomogeneity can violate isochronous synchrony, multiple sources of inhomogeneity can compensate for each other and maintain synchrony. Numeric studies on biologically plausible models also support the analytic results. |
1502.03188 | Zvi Rosen | Elizabeth Gross, Heather A. Harrington, Zvi Rosen, and Bernd Sturmfels | Algebraic Systems Biology: A Case Study for the Wnt Pathway | 24 pages, 2 figures | null | null | null | q-bio.MN math.AG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Steady state analysis of dynamical systems for biological networks give rise
to algebraic varieties in high-dimensional spaces whose study is of interest in
their own right. We demonstrate this for the shuttle model of the Wnt signaling
pathway. Here the variety is described by a polynomial system in 19 unknowns
and 36 parameters. Current methods from computational algebraic geometry and
combinatorics are applied to analyze this model.
| [
{
"created": "Wed, 11 Feb 2015 03:44:19 GMT",
"version": "v1"
}
] | 2015-02-12 | [
[
"Gross",
"Elizabeth",
""
],
[
"Harrington",
"Heather A.",
""
],
[
"Rosen",
"Zvi",
""
],
[
"Sturmfels",
"Bernd",
""
]
] | Steady state analysis of dynamical systems for biological networks give rise to algebraic varieties in high-dimensional spaces whose study is of interest in their own right. We demonstrate this for the shuttle model of the Wnt signaling pathway. Here the variety is described by a polynomial system in 19 unknowns and 36 parameters. Current methods from computational algebraic geometry and combinatorics are applied to analyze this model. |
1407.4033 | Benjamin M. Friedrich | Steffen Werner, Jochen C. Rink, Ingmar H. Riedel-Kruse, Benjamin M.
Friedrich | Shape mode analysis exposes movement patterns in biology: flagella and
flatworms as case studies | 20 pages, 6 figures, accepted for publication in PLoS One | null | 10.1371/journal.pone.0113083 | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We illustrate shape mode analysis as a simple, yet powerful technique to
concisely describe complex biological shapes and their dynamics. We
characterize undulatory bending waves of beating flagella and reconstruct a
limit cycle of flagellar oscillations, paying particular attention to the
periodicity of angular data. As a second example, we analyze non-convex
boundary outlines of gliding flatworms, which allows us to expose stereotypic
body postures that can be related to two different locomotion mechanisms.
Further, shape mode analysis based on principal component analysis allows to
discriminate different flatworm species, despite large motion-associated shape
variability. Thus, complex shape dynamics is characterized by a small number of
shape scores that change in time. We present this method using descriptive
examples, explaining abstract mathematics in a graphic way.
| [
{
"created": "Tue, 15 Jul 2014 16:00:12 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Oct 2014 15:53:35 GMT",
"version": "v2"
}
] | 2015-06-22 | [
[
"Werner",
"Steffen",
""
],
[
"Rink",
"Jochen C.",
""
],
[
"Riedel-Kruse",
"Ingmar H.",
""
],
[
"Friedrich",
"Benjamin M.",
""
]
] | We illustrate shape mode analysis as a simple, yet powerful technique to concisely describe complex biological shapes and their dynamics. We characterize undulatory bending waves of beating flagella and reconstruct a limit cycle of flagellar oscillations, paying particular attention to the periodicity of angular data. As a second example, we analyze non-convex boundary outlines of gliding flatworms, which allows us to expose stereotypic body postures that can be related to two different locomotion mechanisms. Further, shape mode analysis based on principal component analysis allows to discriminate different flatworm species, despite large motion-associated shape variability. Thus, complex shape dynamics is characterized by a small number of shape scores that change in time. We present this method using descriptive examples, explaining abstract mathematics in a graphic way. |
2003.05776 | Huitong Ding | Ning An, Liuqi Jin, Huitong Ding, Jiaoyun Yang, Jing Yuan | A deep belief network-based method to identify proteomic risk markers
for Alzheimer disease | null | null | null | null | q-bio.QM cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While a large body of research has formally identified apolipoprotein E
(APOE) as a major genetic risk marker for Alzheimer disease, accumulating
evidence supports the notion that other risk markers may exist. The traditional
Alzheimer-specific signature analysis methods, however, have not been able to
make full use of rich protein expression data, especially the interaction
between attributes. This paper develops a novel feature selection method to
identify pathogenic factors of Alzheimer disease using the proteomic and
clinical data. This approach has taken the weights of network nodes as the
importance order of signaling protein expression values. After generating and
evaluating the candidate subset, the method helps to select an optimal subset
of proteins that achieved an accuracy greater than 90%, which is superior to
traditional machine learning methods for clinical Alzheimer disease diagnosis.
Besides identifying a proteomic risk marker and further reinforce the link
between metabolic risk factors and Alzheimer disease, this paper also suggests
that apidonectin-linked pathways are a possible therapeutic drug target.
| [
{
"created": "Wed, 11 Mar 2020 10:37:30 GMT",
"version": "v1"
}
] | 2020-03-13 | [
[
"An",
"Ning",
""
],
[
"Jin",
"Liuqi",
""
],
[
"Ding",
"Huitong",
""
],
[
"Yang",
"Jiaoyun",
""
],
[
"Yuan",
"Jing",
""
]
] | While a large body of research has formally identified apolipoprotein E (APOE) as a major genetic risk marker for Alzheimer disease, accumulating evidence supports the notion that other risk markers may exist. The traditional Alzheimer-specific signature analysis methods, however, have not been able to make full use of rich protein expression data, especially the interaction between attributes. This paper develops a novel feature selection method to identify pathogenic factors of Alzheimer disease using the proteomic and clinical data. This approach has taken the weights of network nodes as the importance order of signaling protein expression values. After generating and evaluating the candidate subset, the method helps to select an optimal subset of proteins that achieved an accuracy greater than 90%, which is superior to traditional machine learning methods for clinical Alzheimer disease diagnosis. Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target. |
2308.01988 | Pablo Aguirre | Sof\'ia Guarello, Pablo Aguirre, Isabel Flores | Quantifying the spread of communicable diseases with immigration of
infectious individuals | 33 pages, 8 figures | null | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We construct a set of new epidemiological thresholds to address the general
problem of spreading and containment of a disease with influx of infected
individuals when the classic $\mathcal R_0$ is no longer meaningful. We provide
analytical properties of these indices and illustrate their usefulness in a
compartmental model of COVID-19 with data taken from Chile showing a good
predictive potential when contrasted with the recorded disease behaviour. This
approach and the associated analytical and numerical results allow us to
quantify the severity of an immigration of infectious individuals into a
community, and identification of the key parameters that are capable of
changing or reversing the spread of an infectious disease in specific models.
| [
{
"created": "Thu, 3 Aug 2023 18:52:27 GMT",
"version": "v1"
}
] | 2023-08-07 | [
[
"Guarello",
"Sofía",
""
],
[
"Aguirre",
"Pablo",
""
],
[
"Flores",
"Isabel",
""
]
] | We construct a set of new epidemiological thresholds to address the general problem of spreading and containment of a disease with influx of infected individuals when the classic $\mathcal R_0$ is no longer meaningful. We provide analytical properties of these indices and illustrate their usefulness in a compartmental model of COVID-19 with data taken from Chile showing a good predictive potential when contrasted with the recorded disease behaviour. This approach and the associated analytical and numerical results allow us to quantify the severity of an immigration of infectious individuals into a community, and identification of the key parameters that are capable of changing or reversing the spread of an infectious disease in specific models. |
q-bio/0607025 | Garegin Papoian | Yueheng Lan, Peter G. Wolynes, Garegin A. Papoian | A variational approach to the stochastic aspects of cellular signal
transduction | 15 pages, 11 figures | J. Chem. Phys. 125, 124106 (2006) | 10.1063/1.2353835 | null | q-bio.QM q-bio.MN | null | Cellular signaling networks have evolved to cope with intrinsic fluctuations,
coming from the small numbers of constituents, and the environmental noise.
Stochastic chemical kinetics equations govern the way biochemical networks
process noisy signals. The essential difficulty associated with the master
equation approach to solving the stochastic chemical kinetics problem is the
enormous number of ordinary differential equations involved. In this work, we
show how to achieve tremendous reduction in the dimensionality of specific
reaction cascade dynamics by solving variationally an equivalent quantum field
theoretic formulation of stochastic chemical kinetics. The present formulation
avoids cumbersome commutator computations in the derivation of evolution
equations, making more transparent the physical significance of the variational
method. We propose novel time-dependent basis functions which work well over a
wide range of rate parameters. We apply the new basis functions to describe
stochastic signaling in several enzymatic cascades and compare the results so
obtained with those from alternative solution techniques. The variational
ansatz gives probability distributions that agree well with the exact ones,
even when fluctuations are large and discreteness and nonlinearity are
important. A numerical implementation of our technique is many orders of
magnitude more efficient computationally compared with the traditional Monte
Carlo simulation algorithms or the Langevin simulations.
| [
{
"created": "Tue, 18 Jul 2006 21:55:06 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Lan",
"Yueheng",
""
],
[
"Wolynes",
"Peter G.",
""
],
[
"Papoian",
"Garegin A.",
""
]
] | Cellular signaling networks have evolved to cope with intrinsic fluctuations, coming from the small numbers of constituents, and the environmental noise. Stochastic chemical kinetics equations govern the way biochemical networks process noisy signals. The essential difficulty associated with the master equation approach to solving the stochastic chemical kinetics problem is the enormous number of ordinary differential equations involved. In this work, we show how to achieve tremendous reduction in the dimensionality of specific reaction cascade dynamics by solving variationally an equivalent quantum field theoretic formulation of stochastic chemical kinetics. The present formulation avoids cumbersome commutator computations in the derivation of evolution equations, making more transparent the physical significance of the variational method. We propose novel time-dependent basis functions which work well over a wide range of rate parameters. We apply the new basis functions to describe stochastic signaling in several enzymatic cascades and compare the results so obtained with those from alternative solution techniques. The variational ansatz gives probability distributions that agree well with the exact ones, even when fluctuations are large and discreteness and nonlinearity are important. A numerical implementation of our technique is many orders of magnitude more efficient computationally compared with the traditional Monte Carlo simulation algorithms or the Langevin simulations. |
1710.07585 | Salil Bhate | Salil Bhate | Theoretical foundations for the Human Cell Atlas | null | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In Schiebinger et al. (2017), the authors use optimal transport of measures
on empirical distributions arising from biological experiments to relate the
single cell RNA sequencing profiles for induced pluripotent stem cells
differentiating. But such algorithms could be arbitrarily applied to any
datasets from any collection of experiments. We consider here a natural
question that arises: in a manner consistent with conventionally accepted
assumptions about biology, in which cases can the results of two experiments be
mapped to each other in this manner? The answer to this question is of
fundamental practical importance in developing algorithms that use this method
for analysing and integrating complex datasets collected as part of the Human
Cell Atlas. Here, we develop a formulation of biology in terms of sheaves of
$C^*(X)$-modules for a smooth manifold $X$ equipped with certain structures,
that enables this question to be formally answered, leading to formal
statements about experimental inference and phenotypic identifiability. These
structures capture a perspective on biology that is consistent with a standard,
widely accepted biological perspective and is mathematically intuitive. Our
methods provide a framework in which to design complex experiments and the
algorithms to analyse them in a way that their conclusions can be believed.
| [
{
"created": "Fri, 20 Oct 2017 15:59:21 GMT",
"version": "v1"
}
] | 2017-10-23 | [
[
"Bhate",
"Salil",
""
]
] | In Schiebinger et al. (2017), the authors use optimal transport of measures on empirical distributions arising from biological experiments to relate the single cell RNA sequencing profiles for induced pluripotent stem cells differentiating. But such algorithms could be arbitrarily applied to any datasets from any collection of experiments. We consider here a natural question that arises: in a manner consistent with conventionally accepted assumptions about biology, in which cases can the results of two experiments be mapped to each other in this manner? The answer to this question is of fundamental practical importance in developing algorithms that use this method for analysing and integrating complex datasets collected as part of the Human Cell Atlas. Here, we develop a formulation of biology in terms of sheaves of $C^*(X)$-modules for a smooth manifold $X$ equipped with certain structures, that enables this question to be formally answered, leading to formal statements about experimental inference and phenotypic identifiability. These structures capture a perspective on biology that is consistent with a standard, widely accepted biological perspective and is mathematically intuitive. Our methods provide a framework in which to design complex experiments and the algorithms to analyse them in a way that their conclusions can be believed. |
2310.10895 | Luis Alvarez Garcia | Luis A. \'Alvarez-Garc\'ia, Wolfram Liebermeister, Ian Leifer,
Hern\'an A. Makse | Fibration symmetry uncovers minimal regulatory networks for logical
computation in bacteria | null | null | null | null | q-bio.CB q-bio.MN | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Symmetry principles have proven important in physics, deep learning and
geometry, allowing for the reduction of complicated systems to simpler, more
comprehensible models that preserve the system's features of interest.
Biological systems often show a high level of complexity and consist of a high
number of interacting parts. Using symmetry fibrations, the relevant symmetries
for biological 'message-passing' networks, we reduced the gene regulatory
networks of E. coli and B. subtilis bacteria in a way that preserves
information flow and highlights the computational capabilities of the network.
Nodes that share isomorphic input trees are grouped into equivalence classes
called fibers, whereby genes that receive signals with the same 'history'
belong to one fiber and synchronize. We further reduce the networks to its
computational core by removing 'dangling ends' via k-core decomposition. The
computational core of the network consists of a few strongly connected
components in which signals can cycle while signals are transmitted between
these 'information vortices' in a linear feed-forward manner. These components
are in charge of decision making in the bacterial cell by employing a series of
genetic toggle-switch circuits that store memory, and oscillator circuits.
These circuits act as the central computation machine of the network, whose
output signals then spread to the rest of the network.
| [
{
"created": "Tue, 17 Oct 2023 00:05:01 GMT",
"version": "v1"
}
] | 2023-10-18 | [
[
"Álvarez-García",
"Luis A.",
""
],
[
"Liebermeister",
"Wolfram",
""
],
[
"Leifer",
"Ian",
""
],
[
"Makse",
"Hernán A.",
""
]
] | Symmetry principles have proven important in physics, deep learning and geometry, allowing for the reduction of complicated systems to simpler, more comprehensible models that preserve the system's features of interest. Biological systems often show a high level of complexity and consist of a high number of interacting parts. Using symmetry fibrations, the relevant symmetries for biological 'message-passing' networks, we reduced the gene regulatory networks of E. coli and B. subtilis bacteria in a way that preserves information flow and highlights the computational capabilities of the network. Nodes that share isomorphic input trees are grouped into equivalence classes called fibers, whereby genes that receive signals with the same 'history' belong to one fiber and synchronize. We further reduce the networks to its computational core by removing 'dangling ends' via k-core decomposition. The computational core of the network consists of a few strongly connected components in which signals can cycle while signals are transmitted between these 'information vortices' in a linear feed-forward manner. These components are in charge of decision making in the bacterial cell by employing a series of genetic toggle-switch circuits that store memory, and oscillator circuits. These circuits act as the central computation machine of the network, whose output signals then spread to the rest of the network. |
1212.3827 | Inti Pedroso | Inti Pedroso, Mark J. F. Brown, Seirian Sumner | Detecting gene innovations for phenotypic diversity across multiple
genomes | 28 pages, 4 tables and 3 figures. Includes Supp Material | null | null | null | q-bio.PE q-bio.GN q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene innovation is a key mechanism on the evolution and phenotypic diversity
of life forms. There is a need for tools able to study gene innovation across
an increasingly large number of genomic sequences to maximally capitalise our
understanding of biological systems. Here we present
Comparative-Phylostratigraphy, an open-source software suite that enables to
time the emergence of new genes across evolutionary time and to correlate
patterns of gene emergence with species traits simultaneously across whole
genomes from multiple species. Such a comparative strategy is a new powerful
tool for starting to dissect the relationship between gene innovation and
phenotypic diversity. We describe and showcase our method by analysing recently
published ant genomes. This new methodology identified significant bouts of new
gene evolution in ant clades, that are associated with shifts in life-history
traits. Our method allows easy integration of new genomic data as it becomes
available, and thus will be a valuable analytical tool for evolutionary
biologists interested in explaining the evolution of diversity of life at the
level of the genes.
| [
{
"created": "Sun, 16 Dec 2012 20:29:54 GMT",
"version": "v1"
}
] | 2012-12-18 | [
[
"Pedroso",
"Inti",
""
],
[
"Brown",
"Mark J. F.",
""
],
[
"Sumner",
"Seirian",
""
]
] | Gene innovation is a key mechanism on the evolution and phenotypic diversity of life forms. There is a need for tools able to study gene innovation across an increasingly large number of genomic sequences to maximally capitalise our understanding of biological systems. Here we present Comparative-Phylostratigraphy, an open-source software suite that enables to time the emergence of new genes across evolutionary time and to correlate patterns of gene emergence with species traits simultaneously across whole genomes from multiple species. Such a comparative strategy is a new powerful tool for starting to dissect the relationship between gene innovation and phenotypic diversity. We describe and showcase our method by analysing recently published ant genomes. This new methodology identified significant bouts of new gene evolution in ant clades, that are associated with shifts in life-history traits. Our method allows easy integration of new genomic data as it becomes available, and thus will be a valuable analytical tool for evolutionary biologists interested in explaining the evolution of diversity of life at the level of the genes. |
2104.02672 | Farid Manuchehrfar | Anna Terebus, Farid Manuchehrfar, Youfang Cao, and Jie Liang | Exact Probability Landscapes of Stochastic Phenotype Switching in
Feed-Forward Loops: Phase Diagrams of Multimodality | 25 pages, 9 figures | null | null | null | q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | Feed-forward loops (FFLs) are among the most ubiquitously found motifs of
reaction networks in nature. However, little is known about their stochastic
behavior and the variety of network phenotypes they can exhibit. In this study,
we provide full characterizations of the properties of stochastic multimodality
of FFLs, and how switching between different network phenotypes are controlled.
We have computed the exact steady state probability landscapes of all eight
types of coherent and incoherent FFLs using the finite-butter ACME algorithm,
and quantified the exact topological features of their high-dimensional
probability landscapes using persistent homology. Through analysis of the
degree of multimodality for each of a set of 10,812 probability landscapes,
where each landscape resides over 10^5-10^6 microstates, we have constructed
comprehensive phase diagrams of all relevant behavior of FFL multimodality over
broad ranges of input and regulation intensities, as well as different regimes
of promoter binding dynamics. Our results show that with slow binding and
unbinding dynamics of transcription factor to promoter, FFLs exhibit strong
stochastic behavior that is very different from what would be inferred from
deterministic models. In addition, input intensity play major roles in the
phenotypes of FFLs: At weak input intensity, FFL exhibit monomodality, but
strong input intensity may result in up to 6 stable phenotypes. Furthermore, we
found that gene duplication can enlarge stable regions of specific
multimodalities and enrich the phenotypic diversity of FFL networks, providing
means for cells towards better adaptation to changing environment. Our results
are directly applicable to analysis of behavior of FFLs in biological processes
such as stem cell differentiation and for design of synthetic networks when
certain phenotypic behavior is desired.
| [
{
"created": "Tue, 6 Apr 2021 16:59:29 GMT",
"version": "v1"
},
{
"created": "Wed, 7 Apr 2021 13:37:07 GMT",
"version": "v2"
}
] | 2021-04-08 | [
[
"Terebus",
"Anna",
""
],
[
"Manuchehrfar",
"Farid",
""
],
[
"Cao",
"Youfang",
""
],
[
"Liang",
"Jie",
""
]
] | Feed-forward loops (FFLs) are among the most ubiquitously found motifs of reaction networks in nature. However, little is known about their stochastic behavior and the variety of network phenotypes they can exhibit. In this study, we provide full characterizations of the properties of stochastic multimodality of FFLs, and how switching between different network phenotypes are controlled. We have computed the exact steady state probability landscapes of all eight types of coherent and incoherent FFLs using the finite-butter ACME algorithm, and quantified the exact topological features of their high-dimensional probability landscapes using persistent homology. Through analysis of the degree of multimodality for each of a set of 10,812 probability landscapes, where each landscape resides over 10^5-10^6 microstates, we have constructed comprehensive phase diagrams of all relevant behavior of FFL multimodality over broad ranges of input and regulation intensities, as well as different regimes of promoter binding dynamics. Our results show that with slow binding and unbinding dynamics of transcription factor to promoter, FFLs exhibit strong stochastic behavior that is very different from what would be inferred from deterministic models. In addition, input intensity play major roles in the phenotypes of FFLs: At weak input intensity, FFL exhibit monomodality, but strong input intensity may result in up to 6 stable phenotypes. Furthermore, we found that gene duplication can enlarge stable regions of specific multimodalities and enrich the phenotypic diversity of FFL networks, providing means for cells towards better adaptation to changing environment. Our results are directly applicable to analysis of behavior of FFLs in biological processes such as stem cell differentiation and for design of synthetic networks when certain phenotypic behavior is desired. |
2005.13656 | Eduardo Moreno | E. Moreno (1), S. Flemming (2), F. Font (1 and 4), M. Holschneider
(3), C. Beta (2 and 5), S. Alonso (1) ((1) Department of Physics, Universitat
Polit\`ecnica de Catalunya, (2) Institute of Physics and Astronomy,
University of Potsdam, (3) Institute of Mathematics, University of Potsdam,
(4) Centre de Recerca Matem\`atica, (5) Max Planck Institute for Dynamics and
Self-Organization) | Modeling cell crawling strategies with a bistable model: From amoeboid
to fan-shaped cell motion | null | Physica D: Nonlinear Phenomena 412, 132591 (2020) | 10.1016/j.physd.2020.132591 | null | q-bio.CB nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Eukaryotic cell motility involves a complex network of interactions between
biochemical components and mechanical processes. The cell employs this network
to polarize and induce shape changes that give rise to membrane protrusions and
retractions, ultimately leading to locomotion of the entire cell body. The
combination of a nonlinear reaction-diffusion model of cell polarization, noisy
bistable kinetics, and a dynamic phase field for the cell shape permits us to
capture the key features of this complex system to investigate several motility
scenarios, including amoeboid and fan-shaped forms as well as intermediate
states with distinct displacement mechanisms. We compare the numerical
simulations of our model to live cell imaging experiments of motile {\it
Dictyostelium discoideum} cells under different developmental conditions. The
dominant parameters of the mathematical model that determine the different
motility regimes are identified and discussed.
| [
{
"created": "Wed, 27 May 2020 21:09:39 GMT",
"version": "v1"
}
] | 2022-01-03 | [
[
"Moreno",
"E.",
"",
"1 and 4"
],
[
"Flemming",
"S.",
"",
"1 and 4"
],
[
"Font",
"F.",
"",
"1 and 4"
],
[
"Holschneider",
"M.",
"",
"2 and 5"
],
[
"Beta",
"C.",
"",
"2 and 5"
],
[
"Alonso",
"S.",
""
]
] | Eukaryotic cell motility involves a complex network of interactions between biochemical components and mechanical processes. The cell employs this network to polarize and induce shape changes that give rise to membrane protrusions and retractions, ultimately leading to locomotion of the entire cell body. The combination of a nonlinear reaction-diffusion model of cell polarization, noisy bistable kinetics, and a dynamic phase field for the cell shape permits us to capture the key features of this complex system to investigate several motility scenarios, including amoeboid and fan-shaped forms as well as intermediate states with distinct displacement mechanisms. We compare the numerical simulations of our model to live cell imaging experiments of motile {\it Dictyostelium discoideum} cells under different developmental conditions. The dominant parameters of the mathematical model that determine the different motility regimes are identified and discussed. |
1510.03119 | Radostin Simitev | Maryam Argungu, Saziye Bayram, Bindi Brook, Buddhapriya Chakrabarti,
Richard H Clayton, Donna M Daly, Rosemary J Dyson, Craig Holloway, Varun
Manhas, Shailesh Naire, Tom Shearer, Radostin D. Simitev | Modelling afferent nerve responses to bladder filling | This report summarises the outcomes from a problem posed at the UK
MMSG NC3R's \& POEMS Study Group meeting, 8--12 Sept 2014, Cambridge | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A sensation of fullness in the bladder is a regular experience, yet the
mechanisms that act to generate this sensation remain poorly understood. This
is an important issue because of the clinical problems that can result when
this system does not function properly. The aim of the study group activity was
to develop mathematical models that describe the mechanics of bladder filling,
and how stretch modulates the firing rate of afferent nerves. Several models
were developed, which were qualitatively consistent with experimental data
obtained from a mouse model.
| [
{
"created": "Mon, 12 Oct 2015 01:21:55 GMT",
"version": "v1"
}
] | 2015-10-13 | [
[
"Argungu",
"Maryam",
""
],
[
"Bayram",
"Saziye",
""
],
[
"Brook",
"Bindi",
""
],
[
"Chakrabarti",
"Buddhapriya",
""
],
[
"Clayton",
"Richard H",
""
],
[
"Daly",
"Donna M",
""
],
[
"Dyson",
"Rosemary J",
""
],
[
"Holloway",
"Craig",
""
],
[
"Manhas",
"Varun",
""
],
[
"Naire",
"Shailesh",
""
],
[
"Shearer",
"Tom",
""
],
[
"Simitev",
"Radostin D.",
""
]
] | A sensation of fullness in the bladder is a regular experience, yet the mechanisms that act to generate this sensation remain poorly understood. This is an important issue because of the clinical problems that can result when this system does not function properly. The aim of the study group activity was to develop mathematical models that describe the mechanics of bladder filling, and how stretch modulates the firing rate of afferent nerves. Several models were developed, which were qualitatively consistent with experimental data obtained from a mouse model. |
2402.18489 | Om Roy | Om Roy, Yashar Moshfeghi, Agustin Ibanez, Francisco Lopera, Mario A
Parra, Keith M Smith | FAST functional connectivity implicates P300 connectivity in working
memory deficits in Alzheimer's disease | null | null | null | null | q-bio.NC eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Measuring transient functional connectivity is an important challenge in
Electroencephalogram (EEG) research. Here, the rich potential for insightful,
discriminative information of brain activity offered by high temporal
resolution is confounded by the inherent noise of the medium and the spurious
nature of correlations computed over short temporal windows. We propose a novel
methodology to overcome these problems called Filter Average Short-Term (FAST)
functional connectivity. First, long-term, stable, functional connectivity is
averaged across an entire study cohort for a given pair of Visual Short Term
Memory (VSTM) tasks. The resulting average connectivity matrix, containing
information on the strongest general connections for the tasks, is used as a
filter to analyse the transient high temporal resolution functional
connectivity of individual subjects. In simulations, we show that this method
accurately discriminates differences in noisy Event-Related Potentials (ERPs)
between two conditions where standard connectivity and other comparable methods
fail. We then apply this to analyse activity related to visual short-term
memory binding deficits in two cohorts of familial and sporadic Alzheimer's
disease. Reproducible significant differences were found in the binding task
with no significant difference in the shape task in the P300 ERP range. This
allows new sensitive measurements of transient functional connectivity, which
can be implemented to obtain results of clinical significance.
| [
{
"created": "Wed, 28 Feb 2024 17:15:33 GMT",
"version": "v1"
}
] | 2024-02-29 | [
[
"Roy",
"Om",
""
],
[
"Moshfeghi",
"Yashar",
""
],
[
"Ibanez",
"Agustin",
""
],
[
"Lopera",
"Francisco",
""
],
[
"Parra",
"Mario A",
""
],
[
"Smith",
"Keith M",
""
]
] | Measuring transient functional connectivity is an important challenge in Electroencephalogram (EEG) research. Here, the rich potential for insightful, discriminative information of brain activity offered by high temporal resolution is confounded by the inherent noise of the medium and the spurious nature of correlations computed over short temporal windows. We propose a novel methodology to overcome these problems called Filter Average Short-Term (FAST) functional connectivity. First, long-term, stable, functional connectivity is averaged across an entire study cohort for a given pair of Visual Short Term Memory (VSTM) tasks. The resulting average connectivity matrix, containing information on the strongest general connections for the tasks, is used as a filter to analyse the transient high temporal resolution functional connectivity of individual subjects. In simulations, we show that this method accurately discriminates differences in noisy Event-Related Potentials (ERPs) between two conditions where standard connectivity and other comparable methods fail. We then apply this to analyse activity related to visual short-term memory binding deficits in two cohorts of familial and sporadic Alzheimer's disease. Reproducible significant differences were found in the binding task with no significant difference in the shape task in the P300 ERP range. This allows new sensitive measurements of transient functional connectivity, which can be implemented to obtain results of clinical significance. |
1907.11297 | Emma Towlson | Emma K. Towlson and Albert-L\'aszl\'o Barab\'asi | Synthetic ablations in the C. elegans nervous system | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Synthetic lethality, the finding that the simultaneous knockout of two or
more individually non-essential genes leads to cell or organism death, has
offered a systematic framework to explore cellular function, and also offered
therapeutic applications. Yet, the concept lacks its parallel in neuroscience -
a systematic knowledge base on the role of double or higher order ablations in
the functioning of a neural system. Here, we use the framework of network
control to systematically predict the ablation of neuron pairs and triplets. We
find that surprisingly small sets of 58 pairs and 46 triplets can reduce muscle
controllability, and that these sets are localised in the nervous system in
distinct groups. Further, they lead to highly specific experimentally testable
predictions about mechanisms of loss of control, and which muscle cells are
expected to experience this loss.
| [
{
"created": "Thu, 25 Jul 2019 20:08:15 GMT",
"version": "v1"
}
] | 2019-07-29 | [
[
"Towlson",
"Emma K.",
""
],
[
"Barabási",
"Albert-László",
""
]
] | Synthetic lethality, the finding that the simultaneous knockout of two or more individually non-essential genes leads to cell or organism death, has offered a systematic framework to explore cellular function, and also offered therapeutic applications. Yet, the concept lacks its parallel in neuroscience - a systematic knowledge base on the role of double or higher order ablations in the functioning of a neural system. Here, we use the framework of network control to systematically predict the ablation of neuron pairs and triplets. We find that surprisingly small sets of 58 pairs and 46 triplets can reduce muscle controllability, and that these sets are localised in the nervous system in distinct groups. Further, they lead to highly specific experimentally testable predictions about mechanisms of loss of control, and which muscle cells are expected to experience this loss. |
1407.2425 | Matteo Adorisio | Matteo Adorisio, Jacopo Grilli, Samir Suweis, Sandro Azaele, Jayanth
R. Banavar and Amos Maritan | Spatial maximum entropy modeling from presence/absence tropical forest
data | null | null | null | null | q-bio.PE cond-mat.stat-mech q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the assembly of ecosystems to estimate the number of species at
different spatial scales is a challenging problem. Until now, maximum entropy
approaches have lacked the important feature of considering space in an
explicit manner. We propose a spatially explicit maximum entropy model suitable
to describe spatial patterns such as the species area relationship and the
endemic area relationship. Starting from the minimal information extracted from
presence/absence data, we compare the behavior of two models considering the
occurrence or lack thereof of each species and information on spatial
correlations. Our approach uses the information at shorter spatial scales to
infer the spatial organization at larger ones. We also hypothesize a possible
ecological interpretation of the effective interaction we use to characterize
spatial clustering.
| [
{
"created": "Wed, 9 Jul 2014 10:30:58 GMT",
"version": "v1"
}
] | 2014-07-10 | [
[
"Adorisio",
"Matteo",
""
],
[
"Grilli",
"Jacopo",
""
],
[
"Suweis",
"Samir",
""
],
[
"Azaele",
"Sandro",
""
],
[
"Banavar",
"Jayanth R.",
""
],
[
"Maritan",
"Amos",
""
]
] | Understanding the assembly of ecosystems to estimate the number of species at different spatial scales is a challenging problem. Until now, maximum entropy approaches have lacked the important feature of considering space in an explicit manner. We propose a spatially explicit maximum entropy model suitable to describe spatial patterns such as the species area relationship and the endemic area relationship. Starting from the minimal information extracted from presence/absence data, we compare the behavior of two models considering the occurrence or lack thereof of each species and information on spatial correlations. Our approach uses the information at shorter spatial scales to infer the spatial organization at larger ones. We also hypothesize a possible ecological interpretation of the effective interaction we use to characterize spatial clustering. |
2104.09431 | Magdalena Djordjevic | Ognjen Milicevic, Igor Salom, Andjela Rodic, Sofija Markovic, Marko
Tumbas, Dusan Zigic, Magdalena Djordjevic and Marko Djordjevic | PM$_{2.5}$ as a major predictor of COVID-19 basic reproduction number in
the USA | 25 pages, 4 figures. Environmental Research, in press (2021) | Environ Res. 2021 Oct; 201: 111526 | 10.1016/j.envres.2021.111526 | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Many studies have proposed a relationship between COVID-19 transmissibility
and ambient pollution levels. However, a major limitation in establishing such
associations is to adequately account for complex disease dynamics, influenced
by e.g. significant differences in control measures and testing policies.
Another difficulty is appropriately controlling the effects of other
potentially important factors, due to both their mutual correlations and a
limited dataset. To overcome these difficulties, we will here use the basic
reproduction number ($R_0$) that we estimate for USA states using non-linear
dynamics methods. To account for a large number of predictors (many of which
are mutually strongly correlated), combined with a limited dataset, we employ
machine-learning methods. Specifically, to reduce dimensionality without
complicating the variable interpretation, we employ Principal Component
Analysis on subsets of mutually related (and correlated) predictors. Methods
that allow feature (predictor) selection, and ranking their importance, are
then used, including both linear regressions with regularization and feature
selection (Lasso and Elastic Net) and non-parametric methods based on ensembles
of weak-learners (Random Forest and Gradient Boost). Through these
substantially different approaches, we robustly obtain that PM$_{2.5}$ is a
major predictor of $R_0$ in USA states, with corrections from factors such as
other pollutants, prosperity measures, population density, chronic disease
levels, and possibly racial composition. As a rough magnitude estimate, we
obtain that a relative change in $R_0$, with variations in pollution levels
observed in the USA, is typically ~30%, which further underscores the
importance of pollution in COVID-19 transmissibility.
| [
{
"created": "Mon, 19 Apr 2021 16:22:22 GMT",
"version": "v1"
},
{
"created": "Mon, 14 Jun 2021 13:26:23 GMT",
"version": "v2"
}
] | 2021-09-07 | [
[
"Milicevic",
"Ognjen",
""
],
[
"Salom",
"Igor",
""
],
[
"Rodic",
"Andjela",
""
],
[
"Markovic",
"Sofija",
""
],
[
"Tumbas",
"Marko",
""
],
[
"Zigic",
"Dusan",
""
],
[
"Djordjevic",
"Magdalena",
""
],
[
"Djordjevic",
"Marko",
""
]
] | Many studies have proposed a relationship between COVID-19 transmissibility and ambient pollution levels. However, a major limitation in establishing such associations is to adequately account for complex disease dynamics, influenced by e.g. significant differences in control measures and testing policies. Another difficulty is appropriately controlling the effects of other potentially important factors, due to both their mutual correlations and a limited dataset. To overcome these difficulties, we will here use the basic reproduction number ($R_0$) that we estimate for USA states using non-linear dynamics methods. To account for a large number of predictors (many of which are mutually strongly correlated), combined with a limited dataset, we employ machine-learning methods. Specifically, to reduce dimensionality without complicating the variable interpretation, we employ Principal Component Analysis on subsets of mutually related (and correlated) predictors. Methods that allow feature (predictor) selection, and ranking their importance, are then used, including both linear regressions with regularization and feature selection (Lasso and Elastic Net) and non-parametric methods based on ensembles of weak-learners (Random Forest and Gradient Boost). Through these substantially different approaches, we robustly obtain that PM$_{2.5}$ is a major predictor of $R_0$ in USA states, with corrections from factors such as other pollutants, prosperity measures, population density, chronic disease levels, and possibly racial composition. As a rough magnitude estimate, we obtain that a relative change in $R_0$, with variations in pollution levels observed in the USA, is typically ~30%, which further underscores the importance of pollution in COVID-19 transmissibility. |
1702.07652 | Mahdi Imani | Mahdi Imani and Ulisses Braga-Neto | Control of Gene Regulatory Networks with Noisy Measurements and
Uncertain Inputs | null | null | null | null | q-bio.MN cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper is concerned with the problem of stochastic control of gene
regulatory networks (GRNs) observed indirectly through noisy measurements and
with uncertainty in the intervention inputs. The partial observability of the
gene states and uncertainty in the intervention process are accounted for by
modeling GRNs using the partially-observed Boolean dynamical system (POBDS)
signal model with noisy gene expression measurements. Obtaining the optimal
infinite-horizon control strategy for this problem is not attainable in
general, and we apply reinforcement learning and Gaussian process techniques to
find a near-optimal solution. The POBDS is first transformed to a
directly-observed Markov Decision Process in a continuous belief space, and the
Gaussian process is used for modeling the cost function over the belief and
intervention spaces. Reinforcement learning then is used to learn the cost
function from the available gene expression data. In addition, we employ
sparsification, which enables the control of large partially-observed GRNs. The
performance of the resulting algorithm is studied through a comprehensive set
of numerical experiments using synthetic gene expression data generated from a
melanoma gene regulatory network.
| [
{
"created": "Fri, 24 Feb 2017 16:32:57 GMT",
"version": "v1"
}
] | 2017-02-27 | [
[
"Imani",
"Mahdi",
""
],
[
"Braga-Neto",
"Ulisses",
""
]
] | This paper is concerned with the problem of stochastic control of gene regulatory networks (GRNs) observed indirectly through noisy measurements and with uncertainty in the intervention inputs. The partial observability of the gene states and uncertainty in the intervention process are accounted for by modeling GRNs using the partially-observed Boolean dynamical system (POBDS) signal model with noisy gene expression measurements. Obtaining the optimal infinite-horizon control strategy for this problem is not attainable in general, and we apply reinforcement learning and Gaussian process techniques to find a near-optimal solution. The POBDS is first transformed to a directly-observed Markov Decision Process in a continuous belief space, and the Gaussian process is used for modeling the cost function over the belief and intervention spaces. Reinforcement learning then is used to learn the cost function from the available gene expression data. In addition, we employ sparsification, which enables the control of large partially-observed GRNs. The performance of the resulting algorithm is studied through a comprehensive set of numerical experiments using synthetic gene expression data generated from a melanoma gene regulatory network. |
1408.6184 | Vladimir Teif | Vladimir B. Teif, Nick Kepper, Klaus Yserentant, Gero Wedemann and
Karsten Rippe | Affinity, stoichiometry and cooperativity of heterochromatin protein 1
(HP1) binding to nucleosomal arrays | To appear in Journal of Physics: Condensed Matter | null | 10.1088/0953-8984/27/6/064110 | null | q-bio.BM physics.bio-ph physics.data-an q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Heterochromatin protein 1 (HP1) participates in establishing and maintaining
heterochromatin via its histone modification dependent chromatin interactions.
In recent papers HP1 binding to nucleosomal arrays was measured in vitro and
interpreted in terms of nearest-neighbor cooperative binding. This mode of
chromatin interactions could lead to spreading of HP1 along the nucleosome
chain. Here, we reanalyzed previous data by representing the nucleosome chain
as a one-dimensional binding lattice, and show how the experimental HP1 binding
isotherms can be explained by a simpler model without cooperative interactions
between neighboring HP1 dimers. Based on these calculations and spatial models
of dinucleosomes and nucleosome chains, we propose that binding stoichiometry
is dependent of the nucleosome repeat length (NRL) rather than protein
interactions between HP1 dimers. According to our calculations, more open
nucleosome arrays with long DNA linkers are characterized by a larger number of
binding sites in comparison to chains with short NRL. Furthermore, we
demonstrate by Monte Carlo simulations that the NRL dependent folding of the
nucleosome chain can induce allosteric changes of HP1 binding sites. Thus, HP1
chromatin interactions can be modulated by the change of binding stoichiometry
and type of binding to condensed (methylated) and non-condensed (unmethylated)
nucleosome arrays in the absence of direct interactions between HP1 dimers.
| [
{
"created": "Tue, 26 Aug 2014 16:56:25 GMT",
"version": "v1"
},
{
"created": "Wed, 3 Sep 2014 12:59:55 GMT",
"version": "v2"
},
{
"created": "Thu, 2 Oct 2014 14:37:19 GMT",
"version": "v3"
}
] | 2015-06-22 | [
[
"Teif",
"Vladimir B.",
""
],
[
"Kepper",
"Nick",
""
],
[
"Yserentant",
"Klaus",
""
],
[
"Wedemann",
"Gero",
""
],
[
"Rippe",
"Karsten",
""
]
] | Heterochromatin protein 1 (HP1) participates in establishing and maintaining heterochromatin via its histone modification dependent chromatin interactions. In recent papers HP1 binding to nucleosomal arrays was measured in vitro and interpreted in terms of nearest-neighbor cooperative binding. This mode of chromatin interactions could lead to spreading of HP1 along the nucleosome chain. Here, we reanalyzed previous data by representing the nucleosome chain as a one-dimensional binding lattice, and show how the experimental HP1 binding isotherms can be explained by a simpler model without cooperative interactions between neighboring HP1 dimers. Based on these calculations and spatial models of dinucleosomes and nucleosome chains, we propose that binding stoichiometry is dependent of the nucleosome repeat length (NRL) rather than protein interactions between HP1 dimers. According to our calculations, more open nucleosome arrays with long DNA linkers are characterized by a larger number of binding sites in comparison to chains with short NRL. Furthermore, we demonstrate by Monte Carlo simulations that the NRL dependent folding of the nucleosome chain can induce allosteric changes of HP1 binding sites. Thus, HP1 chromatin interactions can be modulated by the change of binding stoichiometry and type of binding to condensed (methylated) and non-condensed (unmethylated) nucleosome arrays in the absence of direct interactions between HP1 dimers. |
2201.04983 | Gustavo Machado | Jason A. Galvis, Cesar A. Corzo, Joaquin M. Prada, Gustavo Machado | Modeling between-farm transmission dynamics of porcine epidemic diarrhea
virus: characterizing the dominant transmission routes | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | The role of transportation vehicles, pig movement between farms, proximity to
infected premises, and feed deliveries has not been fully considered in the
dissemination dynamics of porcine epidemic diarrhea virus (PEDV). This has
limited efforts for disease control and elimination restricting the development
of risk-based resource allocation to the most relevant modes of PEDV
dissemination. Here, we modeled nine modes of between-farm transmission
pathways including farm-to-farm proximity (local transmission), contact network
of pig farm movements between sites, four different contact networks of
transportation vehicles (vehicles that transport pigs from farm-to-farm, pigs
to markets, feed distribution and crew), the volume of animal by-products
within feed diets (e.g. animal fat and meat and bone meal) to reproduce PEDV
transmission dynamics. The model was calibrated in space and time with weekly
PEDV outbreaks. We investigated the model performance to identify outbreak
locations and the contribution of each route in the dissemination of PEDV. The
model estimated that 42.7% of the infections in sow farms were related to
vehicles transporting feed, 34.5% of infected nurseries were associated with
vehicles transporting pigs to farms, and for both farm types, pig movements or
local transmission were the next most relevant routes. On the other hand,
finishers were most often (31.4%) infected via local transmission, followed by
the vehicles transporting feed and pigs to farm networks. Feed ingredients did
not significantly improve model calibration metrics. The proposed modeling
framework provides an evaluation of PEDV transmission dynamics, ranking the
most important routes of PEDV dissemination and granting the swine industry
valuable information to focus efforts and resources on the most important
transmission routes.
| [
{
"created": "Thu, 13 Jan 2022 13:41:03 GMT",
"version": "v1"
},
{
"created": "Tue, 25 Jan 2022 17:53:02 GMT",
"version": "v2"
}
] | 2022-01-26 | [
[
"Galvis",
"Jason A.",
""
],
[
"Corzo",
"Cesar A.",
""
],
[
"Prada",
"Joaquin M.",
""
],
[
"Machado",
"Gustavo",
""
]
] | The role of transportation vehicles, pig movement between farms, proximity to infected premises, and feed deliveries has not been fully considered in the dissemination dynamics of porcine epidemic diarrhea virus (PEDV). This has limited efforts for disease control and elimination restricting the development of risk-based resource allocation to the most relevant modes of PEDV dissemination. Here, we modeled nine modes of between-farm transmission pathways including farm-to-farm proximity (local transmission), contact network of pig farm movements between sites, four different contact networks of transportation vehicles (vehicles that transport pigs from farm-to-farm, pigs to markets, feed distribution and crew), the volume of animal by-products within feed diets (e.g. animal fat and meat and bone meal) to reproduce PEDV transmission dynamics. The model was calibrated in space and time with weekly PEDV outbreaks. We investigated the model performance to identify outbreak locations and the contribution of each route in the dissemination of PEDV. The model estimated that 42.7% of the infections in sow farms were related to vehicles transporting feed, 34.5% of infected nurseries were associated with vehicles transporting pigs to farms, and for both farm types, pig movements or local transmission were the next most relevant routes. On the other hand, finishers were most often (31.4%) infected via local transmission, followed by the vehicles transporting feed and pigs to farm networks. Feed ingredients did not significantly improve model calibration metrics. The proposed modeling framework provides an evaluation of PEDV transmission dynamics, ranking the most important routes of PEDV dissemination and granting the swine industry valuable information to focus efforts and resources on the most important transmission routes. |
1207.0298 | Moritz Helias | Moritz Helias, Tom Tetzlaff, Markus Diesmann | Echoes in correlated neural systems | null | M Helias, T Tetzlaff, M Diesmann (2013). Echoes in correlated
neural systems. New J. Phys. 15 023002 | 10.1088/1367-2630/15/2/023002 | null | q-bio.NC cond-mat.dis-nn cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Correlations are employed in modern physics to explain microscopic and
macroscopic phenomena, like the fractional quantum Hall effect and the Mott
insulator state in high temperature superconductors and ultracold atoms.
Simultaneously probed neurons in the intact brain reveal correlations between
their activity, an important measure to study information processing in the
brain that also influences macroscopic signals of neural activity, like the
electro encephalogram (EEG). Networks of spiking neurons differ from most
physical systems: The interaction between elements is directed, time delayed,
mediated by short pulses, and each neuron receives events from thousands of
neurons. Even the stationary state of the network cannot be described by
equilibrium statistical mechanics. Here we develop a quantitative theory of
pairwise correlations in finite sized random networks of spiking neurons. We
derive explicit analytic expressions for the population averaged cross
correlation functions. Our theory explains why the intuitive mean field
description fails, how the echo of single action potentials causes an apparent
lag of inhibition with respect to excitation, and how the size of the network
can be scaled while maintaining its dynamical state. Finally, we derive a new
criterion for the emergence of collective oscillations from the spectrum of the
time-evolution propagator.
| [
{
"created": "Mon, 2 Jul 2012 07:35:27 GMT",
"version": "v1"
},
{
"created": "Thu, 19 Jul 2012 13:32:15 GMT",
"version": "v2"
},
{
"created": "Thu, 6 Dec 2012 22:25:28 GMT",
"version": "v3"
},
{
"created": "Tue, 19 Feb 2013 17:21:22 GMT",
"version": "v4"
}
] | 2013-02-20 | [
[
"Helias",
"Moritz",
""
],
[
"Tetzlaff",
"Tom",
""
],
[
"Diesmann",
"Markus",
""
]
] | Correlations are employed in modern physics to explain microscopic and macroscopic phenomena, like the fractional quantum Hall effect and the Mott insulator state in high temperature superconductors and ultracold atoms. Simultaneously probed neurons in the intact brain reveal correlations between their activity, an important measure to study information processing in the brain that also influences macroscopic signals of neural activity, like the electro encephalogram (EEG). Networks of spiking neurons differ from most physical systems: The interaction between elements is directed, time delayed, mediated by short pulses, and each neuron receives events from thousands of neurons. Even the stationary state of the network cannot be described by equilibrium statistical mechanics. Here we develop a quantitative theory of pairwise correlations in finite sized random networks of spiking neurons. We derive explicit analytic expressions for the population averaged cross correlation functions. Our theory explains why the intuitive mean field description fails, how the echo of single action potentials causes an apparent lag of inhibition with respect to excitation, and how the size of the network can be scaled while maintaining its dynamical state. Finally, we derive a new criterion for the emergence of collective oscillations from the spectrum of the time-evolution propagator. |
2109.07540 | Garri Davydyan | Garri Davydyan | Feedback patterns in simulating intestinal wall motions:
interdisciplinary approach to the motility mechanisms | 23 pages, 10 figures | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | Ability of smooth muscles to contract in response to distension plays a
crucial role in motor function of intestine. Qualitative analysis of dynamical
models using myogenic active property of smooth muscles has shown well
agreement with physiologic data. Considered as a self-regulatory unit, function
of gastrointestinal (GI) segment is assumed to be regulated by integration of
basis patterns providing accumulation and propagation of intestinal content. By
implementing external, depending on neural system, variable to the previous
model, and considering two attaches to one another reservoirs as a physical
analogue of the segmental partition of intestine, a system of six ODE
equations, three for each reservoir, describes coordinated wall motions and
propagation of the content from one reservoir to another. It was shown that
besides negative feedback (NFB), other functional patterns, namely positive
feedback (PFB) and reciprocal links (RL) are involved in regulations of
filling-emptying cycle. Being integrated in a whole functional system these
three patterns expressed in a matrix form represent basis elements of imaginary
part of coquaternion which with identity basis component is an algebraically
closed structure under addition and multiplication of its elements. A
coquaternion ring may be considered as a model of inner self-regulatory
functional structure providing coordinated wall motions of GI tract portions.
| [
{
"created": "Wed, 15 Sep 2021 19:12:59 GMT",
"version": "v1"
}
] | 2021-09-17 | [
[
"Davydyan",
"Garri",
""
]
] | Ability of smooth muscles to contract in response to distension plays a crucial role in motor function of intestine. Qualitative analysis of dynamical models using myogenic active property of smooth muscles has shown well agreement with physiologic data. Considered as a self-regulatory unit, function of gastrointestinal (GI) segment is assumed to be regulated by integration of basis patterns providing accumulation and propagation of intestinal content. By implementing external, depending on neural system, variable to the previous model, and considering two attaches to one another reservoirs as a physical analogue of the segmental partition of intestine, a system of six ODE equations, three for each reservoir, describes coordinated wall motions and propagation of the content from one reservoir to another. It was shown that besides negative feedback (NFB), other functional patterns, namely positive feedback (PFB) and reciprocal links (RL) are involved in regulations of filling-emptying cycle. Being integrated in a whole functional system these three patterns expressed in a matrix form represent basis elements of imaginary part of coquaternion which with identity basis component is an algebraically closed structure under addition and multiplication of its elements. A coquaternion ring may be considered as a model of inner self-regulatory functional structure providing coordinated wall motions of GI tract portions. |
0805.3691 | Zoltan Dezso | Z. Dezso, R. Welch, V. Kazandaev, A. Naito, J. Fuscoe, C. Melvin, Y.
Dragan, Y. Nikolsky, T. Nikolskaya, A. Bugrim | Elucidation of differential response networks from toxicogenomics data | 28 pages, 3 figures and 3 tables | null | null | null | q-bio.MN q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe a novel approach to the analysis of toxicogenomics data and
elucidation of biological networks affected by drug treatments. In this method
approximately 15,000 linear pathway modules were generated from manually
assembled pathway maps from MetaCore (GeneGo, Inc.). Microarray expression data
from livers of rat exposed to phenobarbital, mestranol and tamoxifen were
mapped onto these modules. Using different analytical techniques we have
identified sets of "differential" pathways featuring highly correlated
expression among multiple repeats of the same treatment while showing strong
anti-correlation across different treatments. Network modules distinguishing
chemical treatments were re-assembled based on these pathways. Unlike
traditional statistical and clustering procedures in expression profiling, our
method takes into account both network connectivity and gene expression in the
course of the analysis. We demonstrate that it enables identification of
important cellular mechanisms involved in drug response that would have been
missed by the analysis based on individual gene expression profiles.
| [
{
"created": "Fri, 23 May 2008 19:51:09 GMT",
"version": "v1"
}
] | 2008-05-26 | [
[
"Dezso",
"Z.",
""
],
[
"Welch",
"R.",
""
],
[
"Kazandaev",
"V.",
""
],
[
"Naito",
"A.",
""
],
[
"Fuscoe",
"J.",
""
],
[
"Melvin",
"C.",
""
],
[
"Dragan",
"Y.",
""
],
[
"Nikolsky",
"Y.",
""
],
[
"Nikolskaya",
"T.",
""
],
[
"Bugrim",
"A.",
""
]
] | We describe a novel approach to the analysis of toxicogenomics data and elucidation of biological networks affected by drug treatments. In this method approximately 15,000 linear pathway modules were generated from manually assembled pathway maps from MetaCore (GeneGo, Inc.). Microarray expression data from livers of rat exposed to phenobarbital, mestranol and tamoxifen were mapped onto these modules. Using different analytical techniques we have identified sets of "differential" pathways featuring highly correlated expression among multiple repeats of the same treatment while showing strong anti-correlation across different treatments. Network modules distinguishing chemical treatments were re-assembled based on these pathways. Unlike traditional statistical and clustering procedures in expression profiling, our method takes into account both network connectivity and gene expression in the course of the analysis. We demonstrate that it enables identification of important cellular mechanisms involved in drug response that would have been missed by the analysis based on individual gene expression profiles. |
1801.04597 | Ozgur Aydogmus | Ozgur Aydogmus | Discovering the effect of nonlocal payoff calculation on the stabilty of
ESS: Spatial patterns of Hawk-Dove game in metapopulations | null | null | 10.1016/j.jtbi.2018.01.016 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The classical idea of evolutionarily stable strategy (ESS) modeling animal
behavior does not involve any spatial dependence. We considered a spatial
Hawk-Dove game played by animals in a patchy environment with wrap around
boundaries. We posit that each site contains the same number of individuals. An
evolution equation for analyzing the stability of the ESS is found as the mean
dynamics of the classical frequency dependent Moran process coupled via
migration and nonlocal payoff calculation in 1D and 2D habitats. The linear
stability analysis of the model is performed and conditions to observe spatial
patterns are investigated. For the nearest neighbor interactions (including von
Neumann and Moore neighborhoods in 2D) we concluded that it is possible to
destabilize the ESS of the game and observe pattern formation when the
dispersal rate is small enough. We numerically investigate the spatial patterns
arising from the replicator equations coupled via nearest neighbor payoff
calculation and dispersal.
| [
{
"created": "Sun, 14 Jan 2018 19:08:30 GMT",
"version": "v1"
}
] | 2020-07-01 | [
[
"Aydogmus",
"Ozgur",
""
]
] | The classical idea of evolutionarily stable strategy (ESS) modeling animal behavior does not involve any spatial dependence. We considered a spatial Hawk-Dove game played by animals in a patchy environment with wrap around boundaries. We posit that each site contains the same number of individuals. An evolution equation for analyzing the stability of the ESS is found as the mean dynamics of the classical frequency dependent Moran process coupled via migration and nonlocal payoff calculation in 1D and 2D habitats. The linear stability analysis of the model is performed and conditions to observe spatial patterns are investigated. For the nearest neighbor interactions (including von Neumann and Moore neighborhoods in 2D) we concluded that it is possible to destabilize the ESS of the game and observe pattern formation when the dispersal rate is small enough. We numerically investigate the spatial patterns arising from the replicator equations coupled via nearest neighbor payoff calculation and dispersal. |
1212.4379 | Celia Blanco | Celia Blanco and David Hochberg | Models for Mirror Symmetry Breaking via {\beta}-Sheet-Controlled
Copolymerization: (i) Mass Balance and (ii) Probabilistic Treatment | null | J. Phys. Chem. B, 2012, 116, 13953-13967 | 10.1021/jp305627m | null | q-bio.QM physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Experimental mechanisms that yield the growth of homochiral copolymers over
their heterochiral counterparts have been advocated by Lahav and co-workers.
These chiral amplification mechanisms proceed through racemic
{\beta}-sheet-controlled polymerization operative in both surface crystallites
as well as in solution. We develop two complementary theoretical models for
these template-induced desymmetrization processes leading to multicomponent
homochiral copolymers. First, assuming reversible {\beta}-sheet formation, the
equilibrium between the free monomer pool and the polymer strand within the
template is assumed. This yields coupled nonlinear mass balance equations whose
solutions are used to calculate enantiomeric excesses and average lengths of
the homochiral chains formed. The second approach is a probabilistic treatment
based on random polymerization. The occlusion probabilities depend on the
polymerization activation energies for each monomer species and are
proportional to the concentrations of the monomers in solution in the constant
pool approximation. The monomer occlusion probabilities are represented
geometrically in terms of unit simplexes from which conditions for maximizing
or minimizing the likelihood for mirror symmetry breaking can be determined.
| [
{
"created": "Tue, 18 Dec 2012 15:24:13 GMT",
"version": "v1"
}
] | 2012-12-19 | [
[
"Blanco",
"Celia",
""
],
[
"Hochberg",
"David",
""
]
] | Experimental mechanisms that yield the growth of homochiral copolymers over their heterochiral counterparts have been advocated by Lahav and co-workers. These chiral amplification mechanisms proceed through racemic {\beta}-sheet-controlled polymerization operative in both surface crystallites as well as in solution. We develop two complementary theoretical models for these template-induced desymmetrization processes leading to multicomponent homochiral copolymers. First, assuming reversible {\beta}-sheet formation, the equilibrium between the free monomer pool and the polymer strand within the template is assumed. This yields coupled nonlinear mass balance equations whose solutions are used to calculate enantiomeric excesses and average lengths of the homochiral chains formed. The second approach is a probabilistic treatment based on random polymerization. The occlusion probabilities depend on the polymerization activation energies for each monomer species and are proportional to the concentrations of the monomers in solution in the constant pool approximation. The monomer occlusion probabilities are represented geometrically in terms of unit simplexes from which conditions for maximizing or minimizing the likelihood for mirror symmetry breaking can be determined. |
2403.03229 | Tuan Aqeel Bohoran | Tuan A. Bohoran, Polydoros N. Kampaktsis, Laura McLaughlin, Jay Leb,
Gerry P. McCann, Archontis Giannakidis | Embracing Uncertainty Flexibility: Harnessing a Supervised Tree Kernel
to Empower Ensemble Modelling for 2D Echocardiography-Based Prediction of
Right Ventricular Volume | In the Proceedings of the 16th International Conference of Machine
Vision (ICMV 2023), November 15-18, Yerevan, Armenia | null | null | null | q-bio.TO cs.LG eess.IV math.AP | http://creativecommons.org/licenses/by/4.0/ | The right ventricular (RV) function deterioration strongly predicts clinical
outcomes in numerous circumstances. To boost the clinical deployment of
ensemble regression methods that quantify RV volumes using tabular data from
the widely available two-dimensional echocardiography (2DE), we propose to
complement the volume predictions with uncertainty scores. To this end, we
employ an instance-based method which uses the learned tree structure to
identify the nearest training samples to a target instance and then uses a
number of distribution types to more flexibly model the output. The
probabilistic and point-prediction performances of the proposed framework are
evaluated on a relatively small-scale dataset, comprising 100 end-diastolic and
end-systolic RV volumes. The reference values for point performance were
obtained from MRI. The results demonstrate that our flexible approach yields
improved probabilistic and point performances over other state-of-the-art
methods. The appropriateness of the proposed framework is showcased by
providing exemplar cases. The estimated uncertainty embodies both aleatoric and
epistemic types. This work aligns with trustworthy artificial intelligence
since it can be used to enhance the decision-making process and reduce risks.
The feature importance scores of our framework can be exploited to reduce the
number of required 2DE views which could enhance the proposed pipeline's
clinical application.
| [
{
"created": "Mon, 4 Mar 2024 12:36:31 GMT",
"version": "v1"
},
{
"created": "Tue, 12 Mar 2024 16:03:03 GMT",
"version": "v2"
}
] | 2024-03-13 | [
[
"Bohoran",
"Tuan A.",
""
],
[
"Kampaktsis",
"Polydoros N.",
""
],
[
"McLaughlin",
"Laura",
""
],
[
"Leb",
"Jay",
""
],
[
"McCann",
"Gerry P.",
""
],
[
"Giannakidis",
"Archontis",
""
]
] | The right ventricular (RV) function deterioration strongly predicts clinical outcomes in numerous circumstances. To boost the clinical deployment of ensemble regression methods that quantify RV volumes using tabular data from the widely available two-dimensional echocardiography (2DE), we propose to complement the volume predictions with uncertainty scores. To this end, we employ an instance-based method which uses the learned tree structure to identify the nearest training samples to a target instance and then uses a number of distribution types to more flexibly model the output. The probabilistic and point-prediction performances of the proposed framework are evaluated on a relatively small-scale dataset, comprising 100 end-diastolic and end-systolic RV volumes. The reference values for point performance were obtained from MRI. The results demonstrate that our flexible approach yields improved probabilistic and point performances over other state-of-the-art methods. The appropriateness of the proposed framework is showcased by providing exemplar cases. The estimated uncertainty embodies both aleatoric and epistemic types. This work aligns with trustworthy artificial intelligence since it can be used to enhance the decision-making process and reduce risks. The feature importance scores of our framework can be exploited to reduce the number of required 2DE views which could enhance the proposed pipeline's clinical application. |
2005.04527 | Jesus Cuevas | P.G. Kevrekidis, J. Cuevas-Maraver, Y. Drossinos, Z. Rapti, G.A.
Kevrekidis | Reaction-diffusion spatial modeling of COVID-19: Greece and Andalusia as
case examples | 28 pages, 16 figures and 2 movies | Phys. Rev. E 104, 024412 (2021) | 10.1103/PhysRevE.104.024412 | null | q-bio.PE nlin.PS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine the spatial modeling of the outbreak of COVID-19 in two regions:
the autonomous community of Andalusia in Spain and the mainland of Greece. We
start with a 0D compartmental epidemiological model consisting of Susceptible,
Exposed, Asymptomatic, (symptomatically) Infected, Hospitalized, Recovered, and
deceased populations. We emphasize the importance of the viral latent period
and the key role of an asymptomatic population. We optimize model parameters
for both regions by comparing predictions to the cumulative number of infected
and total number of deaths via minimizing the $\ell^2$ norm of the difference
between predictions and observed data. We consider the sensitivity of model
predictions on reasonable variations of model parameters and initial
conditions, addressing issues of parameter identifiability. We model both
pre-quarantine and post-quarantine evolution of the epidemic by a
time-dependent change of the viral transmission rates that arises in response
to containment measures. Subsequently, a spatially distributed version of the
0D model in the form of reaction-diffusion equations is developed. We consider
that, after an initial localized seeding of the infection, its spread is
governed by the diffusion (and 0D model "reactions") of the asymptomatic and
symptomatically infected populations, which decrease with the imposed
restrictive measures. We inserted the maps of the two regions, and we imported
population-density data into COMSOL, which was subsequently used to solve
numerically the model PDEs. Upon discussing how to adapt the 0D model to this
spatial setting, we show that these models bear significant potential towards
capturing both the well-mixed, 0D description and the spatial expansion of the
pandemic in the two regions. Veins of potential refinement of the model
assumptions towards future work are also explored.
| [
{
"created": "Sat, 9 May 2020 23:07:50 GMT",
"version": "v1"
},
{
"created": "Sun, 24 May 2020 23:11:16 GMT",
"version": "v2"
},
{
"created": "Wed, 14 Apr 2021 22:19:02 GMT",
"version": "v3"
},
{
"created": "Thu, 1 Jul 2021 22:12:26 GMT",
"version": "v4"
}
] | 2021-08-18 | [
[
"Kevrekidis",
"P. G.",
""
],
[
"Cuevas-Maraver",
"J.",
""
],
[
"Drossinos",
"Y.",
""
],
[
"Rapti",
"Z.",
""
],
[
"Kevrekidis",
"G. A.",
""
]
] | We examine the spatial modeling of the outbreak of COVID-19 in two regions: the autonomous community of Andalusia in Spain and the mainland of Greece. We start with a 0D compartmental epidemiological model consisting of Susceptible, Exposed, Asymptomatic, (symptomatically) Infected, Hospitalized, Recovered, and deceased populations. We emphasize the importance of the viral latent period and the key role of an asymptomatic population. We optimize model parameters for both regions by comparing predictions to the cumulative number of infected and total number of deaths via minimizing the $\ell^2$ norm of the difference between predictions and observed data. We consider the sensitivity of model predictions on reasonable variations of model parameters and initial conditions, addressing issues of parameter identifiability. We model both pre-quarantine and post-quarantine evolution of the epidemic by a time-dependent change of the viral transmission rates that arises in response to containment measures. Subsequently, a spatially distributed version of the 0D model in the form of reaction-diffusion equations is developed. We consider that, after an initial localized seeding of the infection, its spread is governed by the diffusion (and 0D model "reactions") of the asymptomatic and symptomatically infected populations, which decrease with the imposed restrictive measures. We inserted the maps of the two regions, and we imported population-density data into COMSOL, which was subsequently used to solve numerically the model PDEs. Upon discussing how to adapt the 0D model to this spatial setting, we show that these models bear significant potential towards capturing both the well-mixed, 0D description and the spatial expansion of the pandemic in the two regions. Veins of potential refinement of the model assumptions towards future work are also explored. |
1809.03968 | Svitlana Braichenko | Svitlana Braichenko, Atul Bhaskar, and Srinandan Dasmahapatra | A phenomenological cluster-based model of Ca2+ waves and oscillations
for Inositol 1,4,5-trisphosphate receptor (IP3R) channels | 18 pages, 10 figures | null | 10.1103/PhysRevE.98.032413 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Clusters of IP3 receptor channels in the membranes of the endoplasmic
reticulum (ER) of many non-excitable cells release calcium ions in a
cooperative manner giving rise to dynamical patterns such as Ca2+ puffs, waves,
and oscillations that occur on multiple spatial and temporal scales. We
introduce a minimal yet descriptive reaction-diffusion model of IP3 receptors
for a saturating concentration of IP3 using a principled reduction of a
detailed Markov chain description of individual channels. A dynamical systems
analysis reveals the possibility of excitable, bistable and oscillatory
dynamics of this model that correspond to three types of observed patterns of
calcium release -- puffs, waves, and oscillations respectively. We explain the
emergence of these patterns via a bifurcation analysis of a coupled two-cluster
model, compute the phase diagram and quantify the speed of the waves and period
of oscillations in terms of system parameters. We connect the termination of
large-scale Ca2+ release events to IP3 unbinding or stochasticity.
| [
{
"created": "Tue, 11 Sep 2018 15:20:15 GMT",
"version": "v1"
},
{
"created": "Thu, 13 Sep 2018 10:35:53 GMT",
"version": "v2"
}
] | 2018-10-17 | [
[
"Braichenko",
"Svitlana",
""
],
[
"Bhaskar",
"Atul",
""
],
[
"Dasmahapatra",
"Srinandan",
""
]
] | Clusters of IP3 receptor channels in the membranes of the endoplasmic reticulum (ER) of many non-excitable cells release calcium ions in a cooperative manner giving rise to dynamical patterns such as Ca2+ puffs, waves, and oscillations that occur on multiple spatial and temporal scales. We introduce a minimal yet descriptive reaction-diffusion model of IP3 receptors for a saturating concentration of IP3 using a principled reduction of a detailed Markov chain description of individual channels. A dynamical systems analysis reveals the possibility of excitable, bistable and oscillatory dynamics of this model that correspond to three types of observed patterns of calcium release -- puffs, waves, and oscillations respectively. We explain the emergence of these patterns via a bifurcation analysis of a coupled two-cluster model, compute the phase diagram and quantify the speed of the waves and period of oscillations in terms of system parameters. We connect the termination of large-scale Ca2+ release events to IP3 unbinding or stochasticity. |
2310.15779 | Jorge Vila | Jorge A. Vila | Analysis of proteins in the light of mutations | Manuscript of 10 pages and 5 figures | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Proteins have evolved through mutations, amino acid substitutions, since life
appeared on Earth, some 109 years ago. The study of these phenomena has been of
particular significance because of their impact on protein stability, function,
and structure. Three of the most recent findings in these areas deserve to be
highlighted. First, an innovative method has made it feasible to massively
determine the impact of mutations on protein stability. Second, a theoretical
analysis showed how mutations impact the evolution of protein folding rates.
Lastly, it has been shown that native-state structural changes brought on by
mutations can be explained in detail by the amide hydrogen exchange protection
factors. This study offers a new perspective on how those findings can be used
to analyze proteins in the light of mutations. The preliminary results indicate
that: (i) mutations can be viewed as sensitive probes to identify "typos" in
the amino-acid sequence and also to assess the resistance of naturally
occurring proteins to unwanted sequence alterations; (ii) the presence of
"typos" in the amino acid sequence, rather than being an evolutionary obstacle,
could promote faster evolvability and, in turn, increase the likelihood of
higher protein stability; (iii) the mutation site is far more important than
the substituted amino acid in terms of the protein's marginal stability
changes, and (iv) the protein evolution unpredictability at the molecular level
by mutations exists even in the absence of epistasis effects. Finally, the
study results support the Darwinian concept of evolution as "descent with
modification" by demonstrating that some regions of any protein sequence are
susceptible to mutations while others are not.
| [
{
"created": "Tue, 24 Oct 2023 12:27:45 GMT",
"version": "v1"
}
] | 2023-10-25 | [
[
"Vila",
"Jorge A.",
""
]
] | Proteins have evolved through mutations, amino acid substitutions, since life appeared on Earth, some 109 years ago. The study of these phenomena has been of particular significance because of their impact on protein stability, function, and structure. Three of the most recent findings in these areas deserve to be highlighted. First, an innovative method has made it feasible to massively determine the impact of mutations on protein stability. Second, a theoretical analysis showed how mutations impact the evolution of protein folding rates. Lastly, it has been shown that native-state structural changes brought on by mutations can be explained in detail by the amide hydrogen exchange protection factors. This study offers a new perspective on how those findings can be used to analyze proteins in the light of mutations. The preliminary results indicate that: (i) mutations can be viewed as sensitive probes to identify "typos" in the amino-acid sequence and also to assess the resistance of naturally occurring proteins to unwanted sequence alterations; (ii) the presence of "typos" in the amino acid sequence, rather than being an evolutionary obstacle, could promote faster evolvability and, in turn, increase the likelihood of higher protein stability; (iii) the mutation site is far more important than the substituted amino acid in terms of the protein's marginal stability changes, and (iv) the protein evolution unpredictability at the molecular level by mutations exists even in the absence of epistasis effects. Finally, the study results support the Darwinian concept of evolution as "descent with modification" by demonstrating that some regions of any protein sequence are susceptible to mutations while others are not. |
1505.01144 | Vicente M. Reyes Ph.D. | Vicente M. Reyes | A Global and Local Structure-Based Method for Predicting Binary
Protein-Protein Interaction Partners: Proof of Principle and Feasibility | 14 pages txt; 37 pages total (incl. figures & tables); 6 figures
(some multi-panel); 6 tables (some multi-panel); 8603 words text; 8711 words
total (incl. figures & tables) | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We report a 3D structure-based method of predicting protein-protein
interaction partners. It involves screening for pairs of tetrahedra
representing interacting amino acids at the interface of the protein-protein
complex, with one tetrahedron on each protomer. H-bonds and VDW interactions at
their interface are first determined and then interacting tetrahedral motifs
(one from each protomer) representing backbone or side chain centroids of the
interacting amino acids, are then built. The method requires that the protein
protomers be transformed first into double-centroid reduced representation
(Reyes, V.M. & Sheth, V.N., 2011; Reyes, V.M., 2015a). The method is applied to
a set of 801 protein structures in the PDB with unknown functions, which were
screened for pairs of tetrahedral motifs characteristic of nine binary
complexes, namely: (1.) RAP-Gmppnp-cRAF1 Ras-binding domain; (2.) RHOA-protein
kinase PKN/PRK1 effector domain; (3.) RAC-HOGD1; (4.) RAC-P67PHOX; (5.)
kinase-associated phosphatase (KAP)-phosphoCDK2; (6.) Ig Fc-protein A fragment
B; (7.) Ig light chain dimers; (8.) beta catenin-HTCF-4; and (9.) IL-2
homodimers. Our search method found 33, 297, 62, 63, 120, 0, 108, 16 and 504
putative complexes, respectively. After considering the degree of interface
overlap between the protomers, these numbers were significantly trimmed down to
4, 2, 1, 8, 3, 0, 1, 1 and 1, respectively. Negative and positive control
experiments indicate that the screening process has acceptable specificity and
sensitivity. The results were further validated by applying the CP and TS
methods (Reyes, V.M., 2015b) for the quantitative determination of interface
burial and inter-protomer overlap in the complex. Our method is simple, fast
and scalable, and once the partner interface 3D SMs are identified, they can be
used to computationally dock the two protomers together to form the complex.
| [
{
"created": "Tue, 24 Mar 2015 01:57:08 GMT",
"version": "v1"
}
] | 2015-05-06 | [
[
"Reyes",
"Vicente M.",
""
]
] | We report a 3D structure-based method of predicting protein-protein interaction partners. It involves screening for pairs of tetrahedra representing interacting amino acids at the interface of the protein-protein complex, with one tetrahedron on each protomer. H-bonds and VDW interactions at their interface are first determined and then interacting tetrahedral motifs (one from each protomer) representing backbone or side chain centroids of the interacting amino acids, are then built. The method requires that the protein protomers be transformed first into double-centroid reduced representation (Reyes, V.M. & Sheth, V.N., 2011; Reyes, V.M., 2015a). The method is applied to a set of 801 protein structures in the PDB with unknown functions, which were screened for pairs of tetrahedral motifs characteristic of nine binary complexes, namely: (1.) RAP-Gmppnp-cRAF1 Ras-binding domain; (2.) RHOA-protein kinase PKN/PRK1 effector domain; (3.) RAC-HOGD1; (4.) RAC-P67PHOX; (5.) kinase-associated phosphatase (KAP)-phosphoCDK2; (6.) Ig Fc-protein A fragment B; (7.) Ig light chain dimers; (8.) beta catenin-HTCF-4; and (9.) IL-2 homodimers. Our search method found 33, 297, 62, 63, 120, 0, 108, 16 and 504 putative complexes, respectively. After considering the degree of interface overlap between the protomers, these numbers were significantly trimmed down to 4, 2, 1, 8, 3, 0, 1, 1 and 1, respectively. Negative and positive control experiments indicate that the screening process has acceptable specificity and sensitivity. The results were further validated by applying the CP and TS methods (Reyes, V.M., 2015b) for the quantitative determination of interface burial and inter-protomer overlap in the complex. Our method is simple, fast and scalable, and once the partner interface 3D SMs are identified, they can be used to computationally dock the two protomers together to form the complex. |
1802.02701 | Shiang Hu | Shiang Hu, Esin Karahan, Pedro A. Valdes-Sosa | Restate the reference for EEG microstate analysis | null | null | null | null | q-bio.QM q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the decades of efforts, the choice of EEG reference is still a
debated fundamental issue. Non-neutral reference can inevitably inject the
uncontrolled temporal biases into all EEG recordings, which may influence the
spatiotemporal analysis of brain activity. A method, termed microstates,
identifying spatiotemporal EEG features as the quasi-stable topography states
in milliseconds, suggests its potential as biomarkers of neurophysiological
disease. As reference electrode standardization technique (REST) could
reconstruct an infinity reference approximately, it is a question whether REST
or the other references will be more reliable than average reference (AR) for
the microstates analysis. In this study, we design the microstate-based EEG
forward model, and apply different references for microstates analysis. The
spatial similarity between the generated and assumed cluster maps is mainly
investigated. Furthermore, the real EEG data by the parametric bootstrap method
is used to validate the performance of the references. Finally, we find that
REST is robust to recover more similar cluster maps to the assumption than AR
in the simulation, and the cluster maps between REST and AR on the real EEG
data are quite different. This study may indicate that REST contributes to
identifying more objective microstates features than AR.
| [
{
"created": "Thu, 8 Feb 2018 03:25:50 GMT",
"version": "v1"
}
] | 2018-02-09 | [
[
"Hu",
"Shiang",
""
],
[
"Karahan",
"Esin",
""
],
[
"Valdes-Sosa",
"Pedro A.",
""
]
] | Despite the decades of efforts, the choice of EEG reference is still a debated fundamental issue. Non-neutral reference can inevitably inject the uncontrolled temporal biases into all EEG recordings, which may influence the spatiotemporal analysis of brain activity. A method, termed microstates, identifying spatiotemporal EEG features as the quasi-stable topography states in milliseconds, suggests its potential as biomarkers of neurophysiological disease. As reference electrode standardization technique (REST) could reconstruct an infinity reference approximately, it is a question whether REST or the other references will be more reliable than average reference (AR) for the microstates analysis. In this study, we design the microstate-based EEG forward model, and apply different references for microstates analysis. The spatial similarity between the generated and assumed cluster maps is mainly investigated. Furthermore, the real EEG data by the parametric bootstrap method is used to validate the performance of the references. Finally, we find that REST is robust to recover more similar cluster maps to the assumption than AR in the simulation, and the cluster maps between REST and AR on the real EEG data are quite different. This study may indicate that REST contributes to identifying more objective microstates features than AR. |
2101.07764 | Sisi Jia | Sisi Jia (1), Siew Cheng Phua (2), Yuta Nihongaki (2), Yizeng Li (3
and 5), Michael Pacella (1), Yi Li (1), Abdul M. Mohammed (1), Sean Sun (3),
Takanari Inoue (2), Rebecca Schulman (1 and 4) ((1) Chemical and Biomolecular
Engineering, Johns Hopkins University, Baltimore, USA, (2) Cell Biology,
Johns Hopkins University School of Medicine, Baltimore, USA, (3) Mechanical
Engineering, Johns Hopkins University, Baltimore, USA, (4) Computer Science,
Johns Hopkins University, Baltimore, USA, (5) Department of Mechanical
Engineering, Kennesaw State University, Marietta, USA) | Growth and site-specific organization of micron-scale biomolecular
devices on living mammalian cells | 20 pages, 5 figures | null | 10.1038/s41467-021-25890-z | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Mesoscale molecular assemblies on the cell surface, such as cilia and
filopodia, integrate information, control transport and amplify signals.
Synthetic devices mimicking these structures could sensitively monitor these
cellular functions and direct new ones. The challenges in creating such
devices, however are that they must be integrated with cells in a precise
kinetically controlled process and a device's structure and its precisely
structured cell interface must then be maintained during active cellular
function. Here we report the ability to integrate synthetic micro-scale
filaments, DNA nanotubes, into a cell's architecture by anchoring them by their
ends to specific receptors on the surfaces of mammalian cells. These filaments
can act as shear stress meters: how anchored nanotubes bend at the cell surface
quantitatively indicates the magnitude of shear stresses between 0-2 dyn per
cm2, a regime important for cell signaling. Nanotubes can also grow while
anchored to cells, thus acting as dynamic components of cells. This approach to
cell surface engineering, in which synthetic biomolecular assemblies are
organized within existing cellular architecture, could make it possible to
build new types of sensors, machines and scaffolds that can interface with,
control and measure properties of cells.
| [
{
"created": "Tue, 19 Jan 2021 18:17:39 GMT",
"version": "v1"
}
] | 2021-10-27 | [
[
"Jia",
"Sisi",
"",
"3\n and 5"
],
[
"Phua",
"Siew Cheng",
"",
"3\n and 5"
],
[
"Nihongaki",
"Yuta",
"",
"3\n and 5"
],
[
"Li",
"Yizeng",
"",
"3\n and 5"
],
[
"Pacella",
"Michael",
"",
"1 and 4"
],
[
"Li",
"Yi",
"",
"1 and 4"
],
[
"Mohammed",
"Abdul M.",
"",
"1 and 4"
],
[
"Sun",
"Sean",
"",
"1 and 4"
],
[
"Inoue",
"Takanari",
"",
"1 and 4"
],
[
"Schulman",
"Rebecca",
"",
"1 and 4"
]
] | Mesoscale molecular assemblies on the cell surface, such as cilia and filopodia, integrate information, control transport and amplify signals. Synthetic devices mimicking these structures could sensitively monitor these cellular functions and direct new ones. The challenges in creating such devices, however are that they must be integrated with cells in a precise kinetically controlled process and a device's structure and its precisely structured cell interface must then be maintained during active cellular function. Here we report the ability to integrate synthetic micro-scale filaments, DNA nanotubes, into a cell's architecture by anchoring them by their ends to specific receptors on the surfaces of mammalian cells. These filaments can act as shear stress meters: how anchored nanotubes bend at the cell surface quantitatively indicates the magnitude of shear stresses between 0-2 dyn per cm2, a regime important for cell signaling. Nanotubes can also grow while anchored to cells, thus acting as dynamic components of cells. This approach to cell surface engineering, in which synthetic biomolecular assemblies are organized within existing cellular architecture, could make it possible to build new types of sensors, machines and scaffolds that can interface with, control and measure properties of cells. |
2010.06409 | Thomas Wiehe | Thomas Wiehe | Counting, grafting and evolving binary trees | to appear in: Probabilistic Structures in Evolution, E.~Baake and
A.~Wakolbinger (eds.), EMS Publishing House, Zurich | Probabilistic Structures in Evolution (E. Baake and A.
Wakolbinger, eds.), EMS Press, Berlin, 2021, pp. 427-450 | 10.4171/ECR/17-1/20 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Binary trees are fundamental objects in models of evolutionary biology and
population genetics. Here, we discuss some of their combinatorial and
structural properties as they depend on the tree class considered. Furthermore,
the process by which trees are generated determines the probability
distribution in tree space. Yule trees, for instance, are generated by a pure
birth process. When considered as unordered, they have neither a closed-form
enumeration nor a simple probability distribution. But their ordered siblings
have both. They present the object of choice when studying tree structure in
the framework of evolving genealogies.
| [
{
"created": "Tue, 13 Oct 2020 14:10:45 GMT",
"version": "v1"
}
] | 2021-06-30 | [
[
"Wiehe",
"Thomas",
""
]
] | Binary trees are fundamental objects in models of evolutionary biology and population genetics. Here, we discuss some of their combinatorial and structural properties as they depend on the tree class considered. Furthermore, the process by which trees are generated determines the probability distribution in tree space. Yule trees, for instance, are generated by a pure birth process. When considered as unordered, they have neither a closed-form enumeration nor a simple probability distribution. But their ordered siblings have both. They present the object of choice when studying tree structure in the framework of evolving genealogies. |
1311.4843 | Joseph Pickrell | Joseph K. Pickrell | Joint analysis of functional genomic data and genome-wide association
studies of 18 human traits | Fixed typos, included minor clarifications | Am J Hum Genet. 2014 Apr 3;94(4):559-73 | 10.1016/j.ajhg.2014.03.004 | null | q-bio.GN | http://creativecommons.org/licenses/by/3.0/ | Annotations of gene structures and regulatory elements can inform genome-wide
association studies (GWAS). However, choosing the relevant annotations for
interpreting an association study of a given trait remains challenging. We
describe a statistical model that uses association statistics computed across
the genome to identify classes of genomic element that are enriched or depleted
for loci that influence a trait. The model naturally incorporates multiple
types of annotations. We applied the model to GWAS of 18 human traits,
including red blood cell traits, platelet traits, glucose levels, lipid levels,
height, BMI, and Crohn's disease. For each trait, we evaluated the relevance of
450 different genomic annotations, including protein-coding genes, enhancers,
and DNase-I hypersensitive sites in over a hundred tissues and cell lines. We
show that the fraction of phenotype-associated SNPs that influence protein
sequence ranges from around 2% (for platelet volume) up to around 20% (for LDL
cholesterol); that repressed chromatin is significantly depleted for SNPs
associated with several traits; and that cell type-specific DNase-I
hypersensitive sites are enriched for SNPs associated with several traits (for
example, the spleen in platelet volume). Finally, by re-weighting each GWAS
using information from functional genomics, we increase the number of loci with
high-confidence associations by around 5%.
| [
{
"created": "Tue, 19 Nov 2013 19:12:08 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Nov 2013 04:58:12 GMT",
"version": "v2"
},
{
"created": "Tue, 21 Jan 2014 21:46:57 GMT",
"version": "v3"
},
{
"created": "Tue, 25 Feb 2014 16:57:43 GMT",
"version": "v4"
}
] | 2014-04-24 | [
[
"Pickrell",
"Joseph K.",
""
]
] | Annotations of gene structures and regulatory elements can inform genome-wide association studies (GWAS). However, choosing the relevant annotations for interpreting an association study of a given trait remains challenging. We describe a statistical model that uses association statistics computed across the genome to identify classes of genomic element that are enriched or depleted for loci that influence a trait. The model naturally incorporates multiple types of annotations. We applied the model to GWAS of 18 human traits, including red blood cell traits, platelet traits, glucose levels, lipid levels, height, BMI, and Crohn's disease. For each trait, we evaluated the relevance of 450 different genomic annotations, including protein-coding genes, enhancers, and DNase-I hypersensitive sites in over a hundred tissues and cell lines. We show that the fraction of phenotype-associated SNPs that influence protein sequence ranges from around 2% (for platelet volume) up to around 20% (for LDL cholesterol); that repressed chromatin is significantly depleted for SNPs associated with several traits; and that cell type-specific DNase-I hypersensitive sites are enriched for SNPs associated with several traits (for example, the spleen in platelet volume). Finally, by re-weighting each GWAS using information from functional genomics, we increase the number of loci with high-confidence associations by around 5%. |
2303.05242 | Ghazwan Hasan | Shimal Yonuis Abdulhadi, Ghazwan Qasim Hasan and Raghad Nawaf Gergees | Molecular detection and antimicrobial activity of Endophytic fungi
isolated from a medical plant Rosmarinus officinalis | 14 pages, 4 figures, 1 table, September 2020 Vol. 23 Issue 13B | Vol 23, 2020, 14 | 10.36295/ASRO.2020.231384 | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Endophytes are tiny organisms present in living tissues of distinct plants
and have been extensively studied for their endophytic microbial complement.
Roots of Rosmarinus officinalis were subjected to the isolation of endophytic
fungi and screened for antimicrobial activity against Gram-positive
(Staphylococcus aureus and Bacillus subtilis) and Gram-negative (Escherichia
coli, Pseudomonas aeruginosa, Klebsiella pneumoniae) bacteria. Genomic DNA from
active fungal strain of Trichoderma harzianum was isolated, and the internal
transcribed spacer (ITS) region was amplified using ITS4 and ITS5 primers and
sequenced for genetic inference in fungus. The crude extract of T. harzianum
isolate with Ethyl acetate was showed significant antimicrobial activity
against P. aeruginosa, S. aureus, K. pneumonia, B. subtilis and E. coli. The
antimicrobial activity was highest against P. aeruginosa at concentration of 40
microgram/ ml, followed by S. aureus and K. pneumonia at the same
concentration. The lowest antimicrobial activity was against by S. aureus at
concentration of 60 microgram/ ml. The current study is confirmed that the
antimicrobial activity is due to bioactive compounds founded in endophytic
fungi.
| [
{
"created": "Thu, 9 Mar 2023 13:26:12 GMT",
"version": "v1"
}
] | 2023-03-10 | [
[
"Abdulhadi",
"Shimal Yonuis",
""
],
[
"Hasan",
"Ghazwan Qasim",
""
],
[
"Gergees",
"Raghad Nawaf",
""
]
] | Endophytes are tiny organisms present in living tissues of distinct plants and have been extensively studied for their endophytic microbial complement. Roots of Rosmarinus officinalis were subjected to the isolation of endophytic fungi and screened for antimicrobial activity against Gram-positive (Staphylococcus aureus and Bacillus subtilis) and Gram-negative (Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae) bacteria. Genomic DNA from active fungal strain of Trichoderma harzianum was isolated, and the internal transcribed spacer (ITS) region was amplified using ITS4 and ITS5 primers and sequenced for genetic inference in fungus. The crude extract of T. harzianum isolate with Ethyl acetate was showed significant antimicrobial activity against P. aeruginosa, S. aureus, K. pneumonia, B. subtilis and E. coli. The antimicrobial activity was highest against P. aeruginosa at concentration of 40 microgram/ ml, followed by S. aureus and K. pneumonia at the same concentration. The lowest antimicrobial activity was against by S. aureus at concentration of 60 microgram/ ml. The current study is confirmed that the antimicrobial activity is due to bioactive compounds founded in endophytic fungi. |
2007.15507 | Emer Duffy | Emer Duffy, Aoife Morrin | Endogenous and microbial volatile organic compounds in cutaneous health
and disease | null | null | 10.1016/j.trac.2018.12.012 | null | q-bio.OT | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Human skin is a region of high metabolic activity where a rich variety of
biomarkers are secreted from the stratum corneum. The skin is a constant source
of volatile organic compounds (VOCs) derived from skin glands and resident
microbiota. Skin VOCs contain the footprints of cellular activities and thus
offer unique insights into the intricate processes of cutaneous physiology.
This review examines the growing body of research on skin VOC markers as they
relate to skin physiology, whereby variations in skin-intrinsic and microbial
metabolic processes give rise to unique volatile profiles. Emerging evidence
for volatile biomarkers linked to skin perturbations and skin cancer are
examined. Microbial-derived VOCs are also investigated as prospective
diagnostic markers, and their potential to shape the composition of the local
skin microbiota, and consequently cutaneous health, is considered. Finally, a
brief outlook on emerging analytical challenges and opportunities for skin
VOC-based research and diagnostics is presented.
| [
{
"created": "Thu, 30 Jul 2020 14:59:51 GMT",
"version": "v1"
},
{
"created": "Fri, 31 Jul 2020 11:26:07 GMT",
"version": "v2"
}
] | 2020-08-03 | [
[
"Duffy",
"Emer",
""
],
[
"Morrin",
"Aoife",
""
]
] | Human skin is a region of high metabolic activity where a rich variety of biomarkers are secreted from the stratum corneum. The skin is a constant source of volatile organic compounds (VOCs) derived from skin glands and resident microbiota. Skin VOCs contain the footprints of cellular activities and thus offer unique insights into the intricate processes of cutaneous physiology. This review examines the growing body of research on skin VOC markers as they relate to skin physiology, whereby variations in skin-intrinsic and microbial metabolic processes give rise to unique volatile profiles. Emerging evidence for volatile biomarkers linked to skin perturbations and skin cancer are examined. Microbial-derived VOCs are also investigated as prospective diagnostic markers, and their potential to shape the composition of the local skin microbiota, and consequently cutaneous health, is considered. Finally, a brief outlook on emerging analytical challenges and opportunities for skin VOC-based research and diagnostics is presented. |
1111.2323 | John Schreck | John S. Schreck and Jian-Min Yuan | A Statistical Mechanical Approach to Protein Aggregation | 13 pages, 8 figures, accepted to J. Chem. Phys | J. Chem. Phys. 135, 235102 (2011) | 10.1016/j.bpj.2011.11.1400 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop a theory of aggregation using statistical mechanical methods. An
example of a complicated aggregation system with several levels of structures
is peptide/protein self-assembly. The problem of protein aggregation is
important for the understanding and treatment of neurodegenerative diseases and
also for the development of bio-macromolecules as new materials. We write the
effective Hamiltonian in terms of interaction energies between protein
monomers, protein and solvent, as well as between protein filaments. The grand
partition function can be expressed in terms of a Zimm-Bragg-like transfer
matrix, which is calculated exactly and all thermodynamic properties can be
obtained. We start with two-state and three-state descriptions of protein
monomers using Potts models that can be generalized to include q-states, for
which the exactly solvable feature of the model remains. We focus on n X N
lattice systems, corresponding to the ordered structures observed in some real
fibrils. We have obtained results on nucleation processes and phase diagrams,
in which a protein property such as the sheet content of aggregates is
expressed as a function of the number of proteins on the lattice and
inter-protein or interfacial interaction energies. We have applied our methods
to A{\beta}(1-40) and Curli fibrils and obtained results in good agreement with
experiments.
| [
{
"created": "Wed, 9 Nov 2011 20:38:06 GMT",
"version": "v1"
}
] | 2023-07-19 | [
[
"Schreck",
"John S.",
""
],
[
"Yuan",
"Jian-Min",
""
]
] | We develop a theory of aggregation using statistical mechanical methods. An example of a complicated aggregation system with several levels of structures is peptide/protein self-assembly. The problem of protein aggregation is important for the understanding and treatment of neurodegenerative diseases and also for the development of bio-macromolecules as new materials. We write the effective Hamiltonian in terms of interaction energies between protein monomers, protein and solvent, as well as between protein filaments. The grand partition function can be expressed in terms of a Zimm-Bragg-like transfer matrix, which is calculated exactly and all thermodynamic properties can be obtained. We start with two-state and three-state descriptions of protein monomers using Potts models that can be generalized to include q-states, for which the exactly solvable feature of the model remains. We focus on n X N lattice systems, corresponding to the ordered structures observed in some real fibrils. We have obtained results on nucleation processes and phase diagrams, in which a protein property such as the sheet content of aggregates is expressed as a function of the number of proteins on the lattice and inter-protein or interfacial interaction energies. We have applied our methods to A{\beta}(1-40) and Curli fibrils and obtained results in good agreement with experiments. |
2311.04425 | Franco Pestilli | Eberechi Wogu, Patrick Filima, Bradley Caron, Daniel Levitas, Peer
Herholz, Catherine Leal, Mohammed F. Mehboob, Soichi Hayashi, Simisola
Akintoye, George Ogoh, Tawe Godwin, Damian Eke, Franco Pestilli | A labeled Clinical-MRI dataset of Nigerian brains | null | null | null | null | q-bio.NC eess.IV | http://creativecommons.org/licenses/by/4.0/ | We describe a Magnetic Resonance Imaging (MRI) dataset from individuals from
the African nation of Nigeria. The dataset contains pseudonymized structural
MRI (T1w, T2w, FLAIR) data of clinical quality. The dataset contains data from
36 images from healthy control subjects, 32 images from individuals diagnosed
with age-related dementia and 20 from individuals with Parkinson's disease.
There is currently a paucity of data from the African continent. Given the
potential for Africa to contribute to the global neuroscience community, this
first MRI dataset represents both an opportunity and benchmark for future
studies to share data from the African continent.
| [
{
"created": "Wed, 8 Nov 2023 01:41:17 GMT",
"version": "v1"
}
] | 2023-11-09 | [
[
"Wogu",
"Eberechi",
""
],
[
"Filima",
"Patrick",
""
],
[
"Caron",
"Bradley",
""
],
[
"Levitas",
"Daniel",
""
],
[
"Herholz",
"Peer",
""
],
[
"Leal",
"Catherine",
""
],
[
"Mehboob",
"Mohammed F.",
""
],
[
"Hayashi",
"Soichi",
""
],
[
"Akintoye",
"Simisola",
""
],
[
"Ogoh",
"George",
""
],
[
"Godwin",
"Tawe",
""
],
[
"Eke",
"Damian",
""
],
[
"Pestilli",
"Franco",
""
]
] | We describe a Magnetic Resonance Imaging (MRI) dataset from individuals from the African nation of Nigeria. The dataset contains pseudonymized structural MRI (T1w, T2w, FLAIR) data of clinical quality. The dataset contains data from 36 images from healthy control subjects, 32 images from individuals diagnosed with age-related dementia and 20 from individuals with Parkinson's disease. There is currently a paucity of data from the African continent. Given the potential for Africa to contribute to the global neuroscience community, this first MRI dataset represents both an opportunity and benchmark for future studies to share data from the African continent. |
1908.04758 | Philippe Terrier PhD | Philippe Terrier | Gait recognition via deep learning of the center-of-pressure trajectory | A revised and augmented version of this preprint has been published
in the journal Applied Sciences in January 2020 | Appl. Sci. 2020, 10, 774 | 10.3390/app10030774 | null | q-bio.QM cs.LG q-bio.NC stat.ML | http://creativecommons.org/licenses/by/4.0/ | The fact that every human has a distinctive walking style has prompted a
proposal to use gait recognition as an identification criterion. Using
end-to-end learning, I investigated whether the center-of-pressure trajectory
is sufficiently unique to identify a person with a high certainty. Thirty-six
adults walked on a treadmill equipped with a force platform that recorded the
positions of the center of pressure. The raw two-dimensional signals were
sliced into segments of two gait cycles. A set of 20,250 segments from 30
subjects was used to configure and train convolutional neural networks (CNNs).
The best CNN classified a separate set containing 2,250 segments with 99.9%
overall accuracy. A second set of 4,500 segments from the six remaining
subjects was then used for transfer learning. Several small subsamples of this
set were selected randomly and used for fine tuning. Training with two segments
per subject was sufficient to achieve 100% accuracy. The results suggest that
every person produces a unique trajectory of underfoot pressures and that CNNs
can learn the distinctive features of these trajectories. Using transfer
learning, a few strides could be sufficient to learn and identify new gaits.
| [
{
"created": "Wed, 24 Jul 2019 09:49:57 GMT",
"version": "v1"
},
{
"created": "Wed, 2 Oct 2019 13:47:44 GMT",
"version": "v2"
},
{
"created": "Thu, 6 Feb 2020 13:06:26 GMT",
"version": "v3"
}
] | 2020-08-10 | [
[
"Terrier",
"Philippe",
""
]
] | The fact that every human has a distinctive walking style has prompted a proposal to use gait recognition as an identification criterion. Using end-to-end learning, I investigated whether the center-of-pressure trajectory is sufficiently unique to identify a person with a high certainty. Thirty-six adults walked on a treadmill equipped with a force platform that recorded the positions of the center of pressure. The raw two-dimensional signals were sliced into segments of two gait cycles. A set of 20,250 segments from 30 subjects was used to configure and train convolutional neural networks (CNNs). The best CNN classified a separate set containing 2,250 segments with 99.9% overall accuracy. A second set of 4,500 segments from the six remaining subjects was then used for transfer learning. Several small subsamples of this set were selected randomly and used for fine tuning. Training with two segments per subject was sufficient to achieve 100% accuracy. The results suggest that every person produces a unique trajectory of underfoot pressures and that CNNs can learn the distinctive features of these trajectories. Using transfer learning, a few strides could be sufficient to learn and identify new gaits. |
1802.10361 | M Rule | M. E. Rule and M. Sorbaro and M. H. Hennig | Optimal encoding in stochastic latent-variable Models | null | null | 10.3390/e22070714 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we explore encoding strategies learned by statistical models of
sensory coding in noisy spiking networks. Early stages of sensory communication
in neural systems can be viewed as encoding channels in the
information-theoretic sense. However, neural populations face constraints not
commonly considered in communications theory. Using restricted Boltzmann
machines as a model of sensory encoding, we find that networks with sufficient
capacity learn to balance precision and noise-robustness in order to adaptively
communicate stimuli with varying information content. Mirroring variability
suppression observed in sensory systems, informative stimuli are encoded with
high precision, at the cost of more variable responses to frequent, hence less
informative stimuli. Curiously, we also find that statistical criticality in
the neural population code emerges at model sizes where the input statistics
are well captured. These phenomena have well-defined thermodynamic
interpretations, and we discuss their connection to prevailing theories of
coding and statistical criticality in neural populations.
| [
{
"created": "Wed, 28 Feb 2018 11:17:13 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Mar 2018 14:28:59 GMT",
"version": "v2"
},
{
"created": "Mon, 25 May 2020 10:33:20 GMT",
"version": "v3"
},
{
"created": "Wed, 24 Jun 2020 06:16:13 GMT",
"version": "v4"
}
] | 2020-06-30 | [
[
"Rule",
"M. E.",
""
],
[
"Sorbaro",
"M.",
""
],
[
"Hennig",
"M. H.",
""
]
] | In this work we explore encoding strategies learned by statistical models of sensory coding in noisy spiking networks. Early stages of sensory communication in neural systems can be viewed as encoding channels in the information-theoretic sense. However, neural populations face constraints not commonly considered in communications theory. Using restricted Boltzmann machines as a model of sensory encoding, we find that networks with sufficient capacity learn to balance precision and noise-robustness in order to adaptively communicate stimuli with varying information content. Mirroring variability suppression observed in sensory systems, informative stimuli are encoded with high precision, at the cost of more variable responses to frequent, hence less informative stimuli. Curiously, we also find that statistical criticality in the neural population code emerges at model sizes where the input statistics are well captured. These phenomena have well-defined thermodynamic interpretations, and we discuss their connection to prevailing theories of coding and statistical criticality in neural populations. |
2004.11633 | Giuseppe Gaeta | Mariano Cadoni and Giuseppe Gaeta | How long does a lockdown need to be? | 11 pages, 8 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Social distancing, often in the form of lockdown, has been adopted by many
countries as a way to contrast the spreading of COVID-19. We discuss the
temporal aspects of social distancing in contrasting an epidemic diffusion. We
argue that a strategy based uniquely on social distancing requires to maintain
the relative measures for a very long time, while a more articulate strategy,
which also uses early detection and prompt isolation, can be both more
efficient on reducing the epidemic peak and allow to relax the social
distancing measures after a much shorter time. We consider in more detail the
situation in Italy, simulating the effect of different strategies through a
recently introduced SIR-type epidemiological model. The short answer to the
question in the title is: "it depends on what else you do".
| [
{
"created": "Fri, 24 Apr 2020 10:08:15 GMT",
"version": "v1"
}
] | 2020-04-27 | [
[
"Cadoni",
"Mariano",
""
],
[
"Gaeta",
"Giuseppe",
""
]
] | Social distancing, often in the form of lockdown, has been adopted by many countries as a way to contrast the spreading of COVID-19. We discuss the temporal aspects of social distancing in contrasting an epidemic diffusion. We argue that a strategy based uniquely on social distancing requires to maintain the relative measures for a very long time, while a more articulate strategy, which also uses early detection and prompt isolation, can be both more efficient on reducing the epidemic peak and allow to relax the social distancing measures after a much shorter time. We consider in more detail the situation in Italy, simulating the effect of different strategies through a recently introduced SIR-type epidemiological model. The short answer to the question in the title is: "it depends on what else you do". |
1205.2469 | Stefania Scarsoglio | Stefania Scarsoglio, Paolo D'Odorico, Francesco Laio, Luca Ridolfi | Spatio-temporal stochastic resonance induces patterns in wetland
vegetation dynamics | 9 pages, 7 figures | Ecological Complexity, Vol. 10, Pages 93-101 (2012) | 10.1016/j.ecocom.2011.11.003 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Water availability is a major environmental driver affecting riparian and
wetland vegetation. The interaction between water table fluctuations and
vegetation in a stochastic environment contributes to the complexity of the
dynamics of these ecosystems. We investigate the possible emergence of spatial
patterns induced by spatio-temporal stochastic resonance in a simple model of
groundwater-dependent ecosystems. These spatio-temporal dynamics are driven by
the combined effect of three components: (i) an additive white Gaussian noise,
accounting for external random disturbances such as fires or fluctuations in
rain water availability, (ii) a weak periodic modulation in time, describing
hydrological drivers such as seasonal fluctuations of water table depth, and
(iii) a spatial coupling term, which takes into account the ability of
vegetation to spread and colonize other parts of the landscape. A suitable
cooperation between these three terms is able to give rise to ordered
structures which show spatial and temporal coherence, and are statistically
steady in time.
| [
{
"created": "Fri, 11 May 2012 09:48:09 GMT",
"version": "v1"
}
] | 2012-05-14 | [
[
"Scarsoglio",
"Stefania",
""
],
[
"D'Odorico",
"Paolo",
""
],
[
"Laio",
"Francesco",
""
],
[
"Ridolfi",
"Luca",
""
]
] | Water availability is a major environmental driver affecting riparian and wetland vegetation. The interaction between water table fluctuations and vegetation in a stochastic environment contributes to the complexity of the dynamics of these ecosystems. We investigate the possible emergence of spatial patterns induced by spatio-temporal stochastic resonance in a simple model of groundwater-dependent ecosystems. These spatio-temporal dynamics are driven by the combined effect of three components: (i) an additive white Gaussian noise, accounting for external random disturbances such as fires or fluctuations in rain water availability, (ii) a weak periodic modulation in time, describing hydrological drivers such as seasonal fluctuations of water table depth, and (iii) a spatial coupling term, which takes into account the ability of vegetation to spread and colonize other parts of the landscape. A suitable cooperation between these three terms is able to give rise to ordered structures which show spatial and temporal coherence, and are statistically steady in time. |
1707.06017 | Shervine Amidi | Afshine Amidi, Shervine Amidi, Dimitrios Vlachakis, Vasileios
Megalooikonomou, Nikos Paragios and Evangelia I. Zacharaki | EnzyNet: enzyme classification using 3D convolutional neural networks on
spatial representation | 11 pages, 6 figures | null | null | null | q-bio.QM cs.CV stat.ML | http://creativecommons.org/licenses/by/4.0/ | During the past decade, with the significant progress of computational power
as well as ever-rising data availability, deep learning techniques became
increasingly popular due to their excellent performance on computer vision
problems. The size of the Protein Data Bank has increased more than 15 fold
since 1999, which enabled the expansion of models that aim at predicting
enzymatic function via their amino acid composition. Amino acid sequence
however is less conserved in nature than protein structure and therefore
considered a less reliable predictor of protein function. This paper presents
EnzyNet, a novel 3D-convolutional neural networks classifier that predicts the
Enzyme Commission number of enzymes based only on their voxel-based spatial
structure. The spatial distribution of biochemical properties was also examined
as complementary information. The 2-layer architecture was investigated on a
large dataset of 63,558 enzymes from the Protein Data Bank and achieved an
accuracy of 78.4% by exploiting only the binary representation of the protein
shape. Code and datasets are available at https://github.com/shervinea/enzynet.
| [
{
"created": "Wed, 19 Jul 2017 10:59:29 GMT",
"version": "v1"
}
] | 2017-07-20 | [
[
"Amidi",
"Afshine",
""
],
[
"Amidi",
"Shervine",
""
],
[
"Vlachakis",
"Dimitrios",
""
],
[
"Megalooikonomou",
"Vasileios",
""
],
[
"Paragios",
"Nikos",
""
],
[
"Zacharaki",
"Evangelia I.",
""
]
] | During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank has increased more than 15 fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence however is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D-convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The 2-layer architecture was investigated on a large dataset of 63,558 enzymes from the Protein Data Bank and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet. |
1611.04743 | Ronan M.T. Fleming Dr | Laurent Heirendt, Ronan M.T. Fleming, Ines Thiele | DistributedFBA.jl: High-level, high-performance flux balance analysis in
Julia | 2 pages, 1 figure. Supplementary Material: 1 page | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivation:
Flux balance analysis, and its variants, are widely used methods for
predicting steady-state reaction rates in biochemical reaction networks. The
exploration of high dimensional networks with such methods is currently
hampered by software performance limitations.
Results:
DistributedFBA.jl is a high-level, high-performance, open-source
implementation of flux balance analysis in Julia. It is tailored to solve
multiple flux balance analyses on a subset or all the reactions of large and
huge-scale networks, on any number of threads or nodes.
Availability:
The code and benchmark data are freely available on
http://github.com/opencobra/COBRA.jl. The documentation can be found at
http://opencobra.github.io/COBRA.jl
| [
{
"created": "Tue, 15 Nov 2016 08:53:45 GMT",
"version": "v1"
}
] | 2016-11-17 | [
[
"Heirendt",
"Laurent",
""
],
[
"Fleming",
"Ronan M. T.",
""
],
[
"Thiele",
"Ines",
""
]
] | Motivation: Flux balance analysis, and its variants, are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks with such methods is currently hampered by software performance limitations. Results: DistributedFBA.jl is a high-level, high-performance, open-source implementation of flux balance analysis in Julia. It is tailored to solve multiple flux balance analyses on a subset or all the reactions of large and huge-scale networks, on any number of threads or nodes. Availability: The code and benchmark data are freely available on http://github.com/opencobra/COBRA.jl. The documentation can be found at http://opencobra.github.io/COBRA.jl |
q-bio/0511042 | Gasper Tkacik | Noam Slonim, Gurinder Singh Atwal, Gasper Tkacik, William Bialek | Information based clustering: Supplementary material | Supplementary material for the article to be published in Proceedings
of the National Academy of Sciences USA, 47 pages, 21 figures | null | null | null | q-bio.QM | null | This technical report provides the supplementary material for a paper
entitled "Information based clustering", to appear shortly in Proceedings of
the National Academy of Sciences (USA). In Section I we present in detail the
iterative clustering algorithm used in our experiments and in Section II we
describe the validation scheme used to determine the statistical significance
of our results. Then in subsequent sections we provide all the experimental
results for three very different applications: the response of gene expression
in yeast to different forms of environmental stress, the dynamics of stock
prices in the Standard and Poor's 500, and viewer ratings of popular movies. In
particular, we highlight some of the results that seem to deserve special
attention. All the experimental results and relevant code, including a freely
available web application, can be found at
http://www.genomics.princeton.edu/biophysics-theory .
| [
{
"created": "Fri, 25 Nov 2005 20:39:05 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Nov 2005 21:47:13 GMT",
"version": "v2"
}
] | 2009-09-29 | [
[
"Slonim",
"Noam",
""
],
[
"Atwal",
"Gurinder Singh",
""
],
[
"Tkacik",
"Gasper",
""
],
[
"Bialek",
"William",
""
]
] | This technical report provides the supplementary material for a paper entitled "Information based clustering", to appear shortly in Proceedings of the National Academy of Sciences (USA). In Section I we present in detail the iterative clustering algorithm used in our experiments and in Section II we describe the validation scheme used to determine the statistical significance of our results. Then in subsequent sections we provide all the experimental results for three very different applications: the response of gene expression in yeast to different forms of environmental stress, the dynamics of stock prices in the Standard and Poor's 500, and viewer ratings of popular movies. In particular, we highlight some of the results that seem to deserve special attention. All the experimental results and relevant code, including a freely available web application, can be found at http://www.genomics.princeton.edu/biophysics-theory . |
2002.06196 | Javier Jaimes | Javier A. Jaimes, Nicole M. Andre, Jean K. Millet and Gary R.
Whittaker | Structural modeling of 2019-novel coronavirus (nCoV) spike protein
reveals a proteolytically-sensitive activation loop as a distinguishing
feature compared to SARS-CoV and related SARS-like coronaviruses | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The 2019 novel coronavirus (2019-nCoV) is currently causing a widespread
outbreak centered on Hubei province, China and is a major public health
concern. Taxonomically 2019-nCoV is closely related to SARS-CoV and
SARS-related bat coronaviruses, and it appears to share a common receptor with
SARS-CoV (ACE-2). Here, we perform structural modeling of the 2019-nCoV spike
glycoprotein. Our data provide support for the similar receptor utilization
between 2019-nCoV and SARS-CoV, despite a relatively low amino acid similarity
in the receptor binding module. Compared to SARS-CoV, we identify an extended
structural loop containing basic amino acids at the interface of the receptor
binding (S1) and fusion (S2) domains, which we predict to be
proteolytically-sensitive. We suggest this loop confers fusion activation and
entry properties more in line with MERS-CoV and other coronaviruses, and that
the presence of this structural loop in 2019-nCoV may affect virus stability
and transmission.
| [
{
"created": "Fri, 14 Feb 2020 00:52:04 GMT",
"version": "v1"
}
] | 2020-02-18 | [
[
"Jaimes",
"Javier A.",
""
],
[
"Andre",
"Nicole M.",
""
],
[
"Millet",
"Jean K.",
""
],
[
"Whittaker",
"Gary R.",
""
]
] | The 2019 novel coronavirus (2019-nCoV) is currently causing a widespread outbreak centered on Hubei province, China and is a major public health concern. Taxonomically 2019-nCoV is closely related to SARS-CoV and SARS-related bat coronaviruses, and it appears to share a common receptor with SARS-CoV (ACE-2). Here, we perform structural modeling of the 2019-nCoV spike glycoprotein. Our data provide support for the similar receptor utilization between 2019-nCoV and SARS-CoV, despite a relatively low amino acid similarity in the receptor binding module. Compared to SARS-CoV, we identify an extended structural loop containing basic amino acids at the interface of the receptor binding (S1) and fusion (S2) domains, which we predict to be proteolytically-sensitive. We suggest this loop confers fusion activation and entry properties more in line with MERS-CoV and other coronaviruses, and that the presence of this structural loop in 2019-nCoV may affect virus stability and transmission. |
1709.01746 | Sourabh Lahiri | Sourabh Lahiri, Philippe Nghe, Sander J. Tans, Martin Luc Rosinberg,
David Lacoste | Information-theoretic analysis of the directional influence between
cellular processes | 24 pages, 7 figures | PLoS ONE 12(11): e0187431 (2017) | 10.1371/journal.pone.0187431 | null | q-bio.QM cond-mat.stat-mech cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inferring the directionality of interactions between cellular processes is a
major challenge in systems biology. Time-lagged correlations allow to
discriminate between alternative models, but they still rely on assumed
underlying interactions. Here, we use the transfer entropy (TE), an
information-theoretic quantity that quantifies the directional influence
between fluctuating variables in a model-free way. We present a theoretical
approach to compute the transfer entropy, even when the noise has an extrinsic
component or in the presence of feedback. We re-analyze the experimental data
from Kiviet et al. (2014) where fluctuations in gene expression of metabolic
enzymes and growth rate have been measured in single cells of E. coli. We
confirm the formerly detected modes between growth and gene expression, while
prescribing more stringent conditions on the structure of noise sources. We
furthermore point out practical requirements in terms of length of time series
and sampling time which must be satisfied in order to infer optimally transfer
entropy from times series of fluctuations.
| [
{
"created": "Wed, 6 Sep 2017 09:54:30 GMT",
"version": "v1"
},
{
"created": "Sun, 12 Nov 2017 10:31:33 GMT",
"version": "v2"
}
] | 2017-11-15 | [
[
"Lahiri",
"Sourabh",
""
],
[
"Nghe",
"Philippe",
""
],
[
"Tans",
"Sander J.",
""
],
[
"Rosinberg",
"Martin Luc",
""
],
[
"Lacoste",
"David",
""
]
] | Inferring the directionality of interactions between cellular processes is a major challenge in systems biology. Time-lagged correlations allow to discriminate between alternative models, but they still rely on assumed underlying interactions. Here, we use the transfer entropy (TE), an information-theoretic quantity that quantifies the directional influence between fluctuating variables in a model-free way. We present a theoretical approach to compute the transfer entropy, even when the noise has an extrinsic component or in the presence of feedback. We re-analyze the experimental data from Kiviet et al. (2014) where fluctuations in gene expression of metabolic enzymes and growth rate have been measured in single cells of E. coli. We confirm the formerly detected modes between growth and gene expression, while prescribing more stringent conditions on the structure of noise sources. We furthermore point out practical requirements in terms of length of time series and sampling time which must be satisfied in order to infer optimally transfer entropy from times series of fluctuations. |
2210.14491 | Shin-Ichi Inage | Hana Hebishima, Mina Arakaki, Chikako Dozono, Hanna Frolova, Shinichi
Inage | Mathematical definition of public language, and modeling of will and
consciousness based on the public language | 21 pages, 4 figures, 4 tables | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To propose a mathematical model of consciousness and will, we first simulated
the inverted qualia with a toy model of a neural network. As a result, we
confirmed that there can be an inverted qualia on the neural network. In other
words, the qualia were individual-dependent and considered difficult as an
indicator of consciousness and will. To solve that difficulty, we introduce a
probability space and a random variable into a set of qualia and define a
public language for events. Based on this idea of public language,
consciousness and will are modeled. In this proposal, future actions are
randomly selected from the comparison between "recognition of events" by
external observation and past episodic memory, and the actual "recognition of
actions" is regarded as the occurrence of consciousness. The basic formula is
also derived. This proposal is compared with other past philosophical
discussions.
| [
{
"created": "Wed, 26 Oct 2022 05:32:27 GMT",
"version": "v1"
}
] | 2022-10-27 | [
[
"Hebishima",
"Hana",
""
],
[
"Arakaki",
"Mina",
""
],
[
"Dozono",
"Chikako",
""
],
[
"Frolova",
"Hanna",
""
],
[
"Inage",
"Shinichi",
""
]
] | To propose a mathematical model of consciousness and will, we first simulated the inverted qualia with a toy model of a neural network. As a result, we confirmed that there can be an inverted qualia on the neural network. In other words, the qualia were individual-dependent and considered difficult as an indicator of consciousness and will. To solve that difficulty, we introduce a probability space and a random variable into a set of qualia and define a public language for events. Based on this idea of public language, consciousness and will are modeled. In this proposal, future actions are randomly selected from the comparison between "recognition of events" by external observation and past episodic memory, and the actual "recognition of actions" is regarded as the occurrence of consciousness. The basic formula is also derived. This proposal is compared with other past philosophical discussions. |
1704.02741 | Roseli Suzi Wedemann | Maheen Siddiqui, Roseli S. Wedemann, Henrik Jensen | Avalanches and Generalized Memory Associativity in a Network Model for
Conscious and Unconscious Mental Functioning | null | null | 10.1016/j.physa.2017.08.011 | null | q-bio.NC cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore statistical characteristics of avalanches associated with the
dynamics of a complex-network model, where two modules corresponding to
sensorial and symbolic memories interact, representing unconscious and
conscious mental processes. The model illustrates Freud's ideas regarding the
neuroses and that consciousness is related with symbolic and linguistic memory
activity in the brain. It incorporates the Stariolo-Tsallis generalization of
the Boltzmann Machine in order to model memory retrieval and associativity. In
the present work, we define and measure avalanche size distributions during
memory retrieval, in order to gain insight regarding basic aspects of the
functioning of these complex networks. The avalanche sizes defined for our
model should be related to the time consumed and also to the size of the
neuronal region which is activated, during memory retrieval. This allows the
qualitative comparison of the behaviour of the distribution of cluster sizes,
obtained during fMRI measurements of the propagation of signals in the brain,
with the distribution of avalanche sizes obtained in our simulation
experiments. This comparison corroborates the indication that the Nonextensive
Statistical Mechanics formalism may indeed be more well suited to model the
complex networks which constitute brain and mental structure.
| [
{
"created": "Mon, 10 Apr 2017 07:38:28 GMT",
"version": "v1"
}
] | 2017-11-22 | [
[
"Siddiqui",
"Maheen",
""
],
[
"Wedemann",
"Roseli S.",
""
],
[
"Jensen",
"Henrik",
""
]
] | We explore statistical characteristics of avalanches associated with the dynamics of a complex-network model, where two modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's ideas regarding the neuroses and that consciousness is related with symbolic and linguistic memory activity in the brain. It incorporates the Stariolo-Tsallis generalization of the Boltzmann Machine in order to model memory retrieval and associativity. In the present work, we define and measure avalanche size distributions during memory retrieval, in order to gain insight regarding basic aspects of the functioning of these complex networks. The avalanche sizes defined for our model should be related to the time consumed and also to the size of the neuronal region which is activated, during memory retrieval. This allows the qualitative comparison of the behaviour of the distribution of cluster sizes, obtained during fMRI measurements of the propagation of signals in the brain, with the distribution of avalanche sizes obtained in our simulation experiments. This comparison corroborates the indication that the Nonextensive Statistical Mechanics formalism may indeed be more well suited to model the complex networks which constitute brain and mental structure. |
1505.00655 | Maurizio De Pitta' | Maurizio De Pitt\`a, Nicolas Brunel and Andrea Volterra | Astrocytes: orchestrating synaptic plasticity? | 63 pages, 4 figures | null | 10.1016/j.neuroscience.2015.04.001 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Synaptic plasticity is the capacity of a preexisting connection between two
neurons to change in strength as a function of neural activity. Because
synaptic plasticity is the major candidate mechanism for learning and memory,
the elucidation of its constituting mechanisms is of crucial importance in many
aspects of normal and pathological brain function. In particular, a prominent
aspect that remains debated is how the plasticity mechanisms, that encompass a
broad spectrum of temporal and spatial scales, come to play together in a
concerted fashion. Here we review and discuss evidence that pinpoints to a
possible non-neuronal, glial candidate for such orchestration: the regulation
of synaptic plasticity by astrocytes.
| [
{
"created": "Mon, 4 May 2015 14:32:54 GMT",
"version": "v1"
}
] | 2015-05-05 | [
[
"De Pittà",
"Maurizio",
""
],
[
"Brunel",
"Nicolas",
""
],
[
"Volterra",
"Andrea",
""
]
] | Synaptic plasticity is the capacity of a preexisting connection between two neurons to change in strength as a function of neural activity. Because synaptic plasticity is the major candidate mechanism for learning and memory, the elucidation of its constituting mechanisms is of crucial importance in many aspects of normal and pathological brain function. In particular, a prominent aspect that remains debated is how the plasticity mechanisms, that encompass a broad spectrum of temporal and spatial scales, come to play together in a concerted fashion. Here we review and discuss evidence that pinpoints to a possible non-neuronal, glial candidate for such orchestration: the regulation of synaptic plasticity by astrocytes. |
2103.05781 | Moo K. Chung | Moo K. Chung | Graph Theory in Brain Networks | null | null | null | null | q-bio.NC q-bio.QM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Recent developments in graph theoretic analysis of complex networks have led
to deeper understanding of brain networks. Many complex networks show similar
macroscopic behaviors despite differences in the microscopic details. Probably
two most often observed characteristics of complex networks are scale-free and
small-world properties. In this paper, we will explore whether brain networks
follow scale-free and small-worldness among other graph theory properties.
| [
{
"created": "Tue, 9 Mar 2021 23:16:53 GMT",
"version": "v1"
}
] | 2021-03-11 | [
[
"Chung",
"Moo K.",
""
]
] | Recent developments in graph theoretic analysis of complex networks have led to deeper understanding of brain networks. Many complex networks show similar macroscopic behaviors despite differences in the microscopic details. Probably two most often observed characteristics of complex networks are scale-free and small-world properties. In this paper, we will explore whether brain networks follow scale-free and small-worldness among other graph theory properties. |
1804.05003 | Thomas Gaudelet | Thomas Gaudelet, Noel Malod-Dognin and Natasa Przulj | Higher order molecular organisation as a source of biological function | This article has been accepted for publication in Bioinformatics
Published by Oxford University Press,
https://academic.oup.com/bioinformatics/article/34/17/i944/5093209 | null | 10.1093/bioinformatics/bty570 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Molecular interactions have widely been modelled as networks. The local
wiring patterns around molecules in molecular networks are linked with their
biological functions. However, networks model only pairwise interactions
between molecules and cannot explicitly and directly capture the higher order
molecular organisation, such as protein complexes and pathways. Hence, we ask
if hypergraphs (hypernetworks), that directly capture entire complexes and
pathways along with protein-protein interactions (PPIs), carry additional
functional information beyond what can be uncovered from networks of pairwise
molecular interactions. The mathematical formalism of a hypergraph has long
been known, but not often used in studying molecular networks due to the lack
of sophisticated algorithms for mining the underlying biological information
hidden in the wiring patterns of molecular systems modelled as hypernetworks.
We propose a new, multi-scale, protein interaction hypernetwork model that
utilizes hypergraphs to capture different scales of protein organization,
including PPIs, protein complexes and pathways. In analogy to graphlets, we
introduce hypergraphlets, small, connected, non-isomorphic, induced
sub-hypergraphs of a hypergraph, to quantify the local wiring patterns of these
multi-scale molecular hypergraphs and to mine them for new biological
information. We apply them to model the multi-scale protein networks of baker
yeast and human and show that the higher order molecular organisation captured
by these hypergraphs is strongly related to the underlying biology.
Importantly, we demonstrate that our new models and data mining tools reveal
different, but complementary biological information compared to classical PPI
networks. We apply our hypergraphlets to successfully predict biological
functions of uncharacterised proteins.
| [
{
"created": "Fri, 13 Apr 2018 15:49:53 GMT",
"version": "v1"
},
{
"created": "Thu, 20 Sep 2018 15:20:29 GMT",
"version": "v2"
}
] | 2018-09-21 | [
[
"Gaudelet",
"Thomas",
""
],
[
"Malod-Dognin",
"Noel",
""
],
[
"Przulj",
"Natasa",
""
]
] | Molecular interactions have widely been modelled as networks. The local wiring patterns around molecules in molecular networks are linked with their biological functions. However, networks model only pairwise interactions between molecules and cannot explicitly and directly capture the higher order molecular organisation, such as protein complexes and pathways. Hence, we ask if hypergraphs (hypernetworks), that directly capture entire complexes and pathways along with protein-protein interactions (PPIs), carry additional functional information beyond what can be uncovered from networks of pairwise molecular interactions. The mathematical formalism of a hypergraph has long been known, but not often used in studying molecular networks due to the lack of sophisticated algorithms for mining the underlying biological information hidden in the wiring patterns of molecular systems modelled as hypernetworks. We propose a new, multi-scale, protein interaction hypernetwork model that utilizes hypergraphs to capture different scales of protein organization, including PPIs, protein complexes and pathways. In analogy to graphlets, we introduce hypergraphlets, small, connected, non-isomorphic, induced sub-hypergraphs of a hypergraph, to quantify the local wiring patterns of these multi-scale molecular hypergraphs and to mine them for new biological information. We apply them to model the multi-scale protein networks of baker yeast and human and show that the higher order molecular organisation captured by these hypergraphs is strongly related to the underlying biology. Importantly, we demonstrate that our new models and data mining tools reveal different, but complementary biological information compared to classical PPI networks. We apply our hypergraphlets to successfully predict biological functions of uncharacterised proteins. |
1905.02636 | Sam Blakeman | Sam Blakeman and Denis Mareschal | A Complementary Learning Systems Approach to Temporal Difference
Learning | null | null | null | null | q-bio.NC cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Complementary Learning Systems (CLS) theory suggests that the brain uses a
'neocortical' and a 'hippocampal' learning system to achieve complex behavior.
These two systems are complementary in that the 'neocortical' system relies on
slow learning of distributed representations while the 'hippocampal' system
relies on fast learning of pattern-separated representations. Both of these
systems project to the striatum, which is a key neural structure in the brain's
implementation of Reinforcement Learning (RL). Current deep RL approaches share
similarities with a 'neocortical' system because they slowly learn distributed
representations through backpropagation in Deep Neural Networks (DNNs). An
ongoing criticism of such approaches is that they are data inefficient and lack
flexibility. CLS theory suggests that the addition of a 'hippocampal' system
could address these criticisms. In the present study we propose a novel
algorithm known as Complementary Temporal Difference Learning (CTDL), which
combines a DNN with a Self-Organising Map (SOM) to obtain the benefits of both
a 'neocortical' and a 'hippocampal' system. Key features of CTDL include the
use of Temporal Difference (TD) error to update a SOM and the combination of a
SOM and DNN to calculate action values. We evaluate CTDL on grid worlds and the
Cart-Pole environment, and show several benefits over the classic Deep
Q-Network (DQN) approach. These results demonstrate (1) the utility of
complementary learning systems for the evaluation of actions, (2) that the TD
error signal is a useful form of communication between the two systems and (3)
the biological plausibility of the proposed approach.
| [
{
"created": "Tue, 7 May 2019 15:17:20 GMT",
"version": "v1"
}
] | 2019-05-08 | [
[
"Blakeman",
"Sam",
""
],
[
"Mareschal",
"Denis",
""
]
] | Complementary Learning Systems (CLS) theory suggests that the brain uses a 'neocortical' and a 'hippocampal' learning system to achieve complex behavior. These two systems are complementary in that the 'neocortical' system relies on slow learning of distributed representations while the 'hippocampal' system relies on fast learning of pattern-separated representations. Both of these systems project to the striatum, which is a key neural structure in the brain's implementation of Reinforcement Learning (RL). Current deep RL approaches share similarities with a 'neocortical' system because they slowly learn distributed representations through backpropagation in Deep Neural Networks (DNNs). An ongoing criticism of such approaches is that they are data inefficient and lack flexibility. CLS theory suggests that the addition of a 'hippocampal' system could address these criticisms. In the present study we propose a novel algorithm known as Complementary Temporal Difference Learning (CTDL), which combines a DNN with a Self-Organising Map (SOM) to obtain the benefits of both a 'neocortical' and a 'hippocampal' system. Key features of CTDL include the use of Temporal Difference (TD) error to update a SOM and the combination of a SOM and DNN to calculate action values. We evaluate CTDL on grid worlds and the Cart-Pole environment, and show several benefits over the classic Deep Q-Network (DQN) approach. These results demonstrate (1) the utility of complementary learning systems for the evaluation of actions, (2) that the TD error signal is a useful form of communication between the two systems and (3) the biological plausibility of the proposed approach. |
1109.1296 | Jason Graham | Jason M. Graham, Bruce P. Ayati, Lei Ding, Prem S. Ramakrishnan, James
A. Martin | Reaction-Diffusion-Delay Model for EPO/TNF-$\alpha$? Interaction in
Articular Cartilage Lesion Abatement | 14 pages, 6 figures | null | null | null | q-bio.TO q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Injuries to articular cartilage result in the development of lesions that
form on the surface of the cartilage. Such lesions are associated with
articular cartilage degeneration and osteoarthritis. The typical injury
response often causes collateral damage, primarily an effect of inflammation,
which results in the spread of lesions beyond the region where the initial
injury occurs. We present a minimal mathematical model based on known
mechanisms to investigate the spread and abatement of such lesions. In
particular we represent the "balancing act" between pro-inflammatory and
anti-inflammatory cytokines that is hypothesized to be a principal mechanism in
the expansion properties of cartilage damage during the typical injury
response. We present preliminary results of in vitro studies that confirm the
anti-inflammatory activities of the cytokine erythropoietin (EPO). We assume
that the diffusion of cytokines determine the spatial behaviour of injury
response and lesion expansion so that a reaction-diffusion system involving
chemical species and chondrocyte cell state population densities is a natural
way to represent cartilage injury response. We present computational results
using the mathematical model showing that our representation is successful in
capturing much of the interesting spatial behaviour of injury associated lesion
development and abatement in articular cartilage. Further, we discuss the use
of this model to study the possibility of using EPO as a therapy for reducing
the amount of inflammation induced collateral damage to cartilage during the
typical injury response. The mathematical model presented herein suggests that
not only are anti-inflammatory cytokines, such as EPO necessary to prevent
chondrocytes signaled by pro-inflammatory cytokines from entering apoptosis,
they may also influence how chondrocytes respond to signaling by
pro-inflammatory cytokines.
| [
{
"created": "Tue, 6 Sep 2011 20:36:34 GMT",
"version": "v1"
}
] | 2011-09-08 | [
[
"Graham",
"Jason M.",
""
],
[
"Ayati",
"Bruce P.",
""
],
[
"Ding",
"Lei",
""
],
[
"Ramakrishnan",
"Prem S.",
""
],
[
"Martin",
"James A.",
""
]
] | Injuries to articular cartilage result in the development of lesions that form on the surface of the cartilage. Such lesions are associated with articular cartilage degeneration and osteoarthritis. The typical injury response often causes collateral damage, primarily an effect of inflammation, which results in the spread of lesions beyond the region where the initial injury occurs. We present a minimal mathematical model based on known mechanisms to investigate the spread and abatement of such lesions. In particular we represent the "balancing act" between pro-inflammatory and anti-inflammatory cytokines that is hypothesized to be a principal mechanism in the expansion properties of cartilage damage during the typical injury response. We present preliminary results of in vitro studies that confirm the anti-inflammatory activities of the cytokine erythropoietin (EPO). We assume that the diffusion of cytokines determine the spatial behaviour of injury response and lesion expansion so that a reaction-diffusion system involving chemical species and chondrocyte cell state population densities is a natural way to represent cartilage injury response. We present computational results using the mathematical model showing that our representation is successful in capturing much of the interesting spatial behaviour of injury associated lesion development and abatement in articular cartilage. Further, we discuss the use of this model to study the possibility of using EPO as a therapy for reducing the amount of inflammation induced collateral damage to cartilage during the typical injury response. The mathematical model presented herein suggests that not only are anti-inflammatory cytokines, such as EPO necessary to prevent chondrocytes signaled by pro-inflammatory cytokines from entering apoptosis, they may also influence how chondrocytes respond to signaling by pro-inflammatory cytokines. |
2008.03972 | Audrey B\"urki | Audrey B\"urki, F.-Xavier Alario, Shravan Vasishth | When words collide: Bayesian meta-analyses of distractor and target
properties in the picture-word interference paradigm | null | null | null | null | q-bio.NC stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the picture-word interference paradigm, participants name pictures while
ignoring a written or spoken distractor word. Naming times to the pictures are
slowed down by the presence of the distractor word. Various properties of the
distractor modulate this slow down, for example naming times are shorter with
frequent vs. infrequent distractors. Building on this line of research, the
present study investigates in more detail the impact of distractor and target
word properties on picture naming times. We report the results of several
Bayesian meta-analyses, based on 35 datasets. The aim of the first analysis was
to obtain an estimation of the size of the distractor frequency effect, and of
its precision, in typical picture-word interference experiments where this
variable is not manipulated. The analysis shows that a one-unit increase in log
frequency results in response times to the pictures decreasing by about 4ms
(95% Credible Interval: [-6, -2]). With the second and third analyses, we show
that after accounting for the effect of frequency, two variables known to
influence processing times in visual word processing tasks also influence
picture naming times: distractor length and orthographic neighborhood. Finally,
we found that distractor word frequency and target word frequency interact; the
effect of distractor frequency decreases as the frequency of the target word
increases. We discuss the theoretical and methodological implications of these
findings, as well as the importance of obtaining high-precision estimates of
experimental effects.
| [
{
"created": "Mon, 10 Aug 2020 09:13:57 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Mar 2023 10:11:23 GMT",
"version": "v2"
}
] | 2023-03-17 | [
[
"Bürki",
"Audrey",
""
],
[
"Alario",
"F. -Xavier",
""
],
[
"Vasishth",
"Shravan",
""
]
] | In the picture-word interference paradigm, participants name pictures while ignoring a written or spoken distractor word. Naming times to the pictures are slowed down by the presence of the distractor word. Various properties of the distractor modulate this slow down, for example naming times are shorter with frequent vs. infrequent distractors. Building on this line of research, the present study investigates in more detail the impact of distractor and target word properties on picture naming times. We report the results of several Bayesian meta-analyses, based on 35 datasets. The aim of the first analysis was to obtain an estimation of the size of the distractor frequency effect, and of its precision, in typical picture-word interference experiments where this variable is not manipulated. The analysis shows that a one-unit increase in log frequency results in response times to the pictures decreasing by about 4ms (95% Credible Interval: [-6, -2]). With the second and third analyses, we show that after accounting for the effect of frequency, two variables known to influence processing times in visual word processing tasks also influence picture naming times: distractor length and orthographic neighborhood. Finally, we found that distractor word frequency and target word frequency interact; the effect of distractor frequency decreases as the frequency of the target word increases. We discuss the theoretical and methodological implications of these findings, as well as the importance of obtaining high-precision estimates of experimental effects. |
2112.14852 | Pablo Jos\'e Francisco Pena Rodrigues | Miguel da Silva Pinheiro and Pablo Jos\'e Francisco Pena Rodrigues | Human Niche Evolution: pathways, choices and outcomes | 24 pages | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humankind has spread worldwide supported by cultural and technological
knowledge, but the environmental sustainability on the human niche evolution
depends on a new human beings relationship with the biosphere. Human lifestyles
nowadays are very Antropocentric and in many ways deleterious to the other life
forms. Here we try to identify future scenarios, where the less deleterious is
the Natural-Technological Model that points the urgent need to change the
evolutionary direction of the human niche seeking the resumption of original
ecological relations. New cultural habits and novel technologies, thereby,
would reverse the current anthropogenic impacts. The middle way is the
Bio-Anthropogenic Model that predicts the success of the emerging ecosystems
and the symbiotic relationship of humans and anthropogenic-favored species,
hybrids, aliens and genetically modified organisms. For such, we must also
change our way of life and adopt new conscious ways of consumption aiming at
the socio-environmental good. Lastly, the Wear Out Model, which depends only on
maintaining current patterns of human expansion. The lack of investments on new
technologies and new cultural habits, added to the current patterns of human
niche evolution that are based on the massive exploitation of world resources,
will lead to a fearsome scenario with a precarious global health, biodiversity
losses and food scarcity. This theoretical models indicates some pathways and
can help us to choose a better future.
| [
{
"created": "Wed, 29 Dec 2021 22:21:46 GMT",
"version": "v1"
}
] | 2022-01-03 | [
[
"Pinheiro",
"Miguel da Silva",
""
],
[
"Rodrigues",
"Pablo José Francisco Pena",
""
]
] | Humankind has spread worldwide supported by cultural and technological knowledge, but the environmental sustainability on the human niche evolution depends on a new human beings relationship with the biosphere. Human lifestyles nowadays are very Antropocentric and in many ways deleterious to the other life forms. Here we try to identify future scenarios, where the less deleterious is the Natural-Technological Model that points the urgent need to change the evolutionary direction of the human niche seeking the resumption of original ecological relations. New cultural habits and novel technologies, thereby, would reverse the current anthropogenic impacts. The middle way is the Bio-Anthropogenic Model that predicts the success of the emerging ecosystems and the symbiotic relationship of humans and anthropogenic-favored species, hybrids, aliens and genetically modified organisms. For such, we must also change our way of life and adopt new conscious ways of consumption aiming at the socio-environmental good. Lastly, the Wear Out Model, which depends only on maintaining current patterns of human expansion. The lack of investments on new technologies and new cultural habits, added to the current patterns of human niche evolution that are based on the massive exploitation of world resources, will lead to a fearsome scenario with a precarious global health, biodiversity losses and food scarcity. This theoretical models indicates some pathways and can help us to choose a better future. |
2111.06340 | Bozitao Zhong | Bozitao Zhong, Xiaoming Su, Minhua Wen, Sichen Zuo, Liang Hong and
James Lin | ParaFold: Paralleling AlphaFold for Large-Scale Predictions | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | AlphaFold predicts protein structures from the amino acid sequence at or near
experimental resolution, solving the 50-year-old protein folding challenge,
leading to progress by transforming large-scale genomics data into protein
structures. AlphaFold will also greatly change the scientific research model
from low-throughput to high-throughput manner. The AlphaFold framework is a
mixture of two types of workloads: MSA construction based on CPUs and model
inference on GPUs. The first CPU stage dominates the overall runtime, taking
hours for a single protein due to the large database sizes and I/O bottlenecks.
However, GPUs in this CPU stage remain idle, resulting in low GPU utilization
and restricting the capacity of large-scale structure predictions. Therefore,
we proposed ParaFold, an open-source parallel version of AlphaFold for high
throughput protein structure predictions. ParaFold separates the CPU and GPU
parts to enable large-scale structure predictions. ParaFold also effectively
reduces the CPU and GPU runtime with two optimizations without compromising the
quality of prediction results: using multi-threaded parallelism on CPUs and
using optimized JAX compilation on GPUs. We evaluated ParaFold with three
datasets of different size and protein lengths. We evaluated the accuracy and
efficiency of optimizations on CPUs and GPUs, and showed the large-scale
prediction capability by running ParaFold inferences of 19,704 small proteins
in five hours on one NVIDIA DGX-2. Using the JAX compile optimization, ParaFold
attained a 13.8X average speedup over AlphaFold. ParaFold offers a rapid and
effective approach for high-throughput structure predictions, leveraging the
predictive power by running on supercomputers, with shorter time, and at a
lower cost. The development of ParaFold will greatly speed up high-throughput
studies and render the protein "structure-omics" feasible.
| [
{
"created": "Thu, 11 Nov 2021 17:53:37 GMT",
"version": "v1"
},
{
"created": "Sat, 13 Nov 2021 15:21:13 GMT",
"version": "v2"
}
] | 2021-11-16 | [
[
"Zhong",
"Bozitao",
""
],
[
"Su",
"Xiaoming",
""
],
[
"Wen",
"Minhua",
""
],
[
"Zuo",
"Sichen",
""
],
[
"Hong",
"Liang",
""
],
[
"Lin",
"James",
""
]
] | AlphaFold predicts protein structures from the amino acid sequence at or near experimental resolution, solving the 50-year-old protein folding challenge, leading to progress by transforming large-scale genomics data into protein structures. AlphaFold will also greatly change the scientific research model from low-throughput to high-throughput manner. The AlphaFold framework is a mixture of two types of workloads: MSA construction based on CPUs and model inference on GPUs. The first CPU stage dominates the overall runtime, taking hours for a single protein due to the large database sizes and I/O bottlenecks. However, GPUs in this CPU stage remain idle, resulting in low GPU utilization and restricting the capacity of large-scale structure predictions. Therefore, we proposed ParaFold, an open-source parallel version of AlphaFold for high throughput protein structure predictions. ParaFold separates the CPU and GPU parts to enable large-scale structure predictions. ParaFold also effectively reduces the CPU and GPU runtime with two optimizations without compromising the quality of prediction results: using multi-threaded parallelism on CPUs and using optimized JAX compilation on GPUs. We evaluated ParaFold with three datasets of different size and protein lengths. We evaluated the accuracy and efficiency of optimizations on CPUs and GPUs, and showed the large-scale prediction capability by running ParaFold inferences of 19,704 small proteins in five hours on one NVIDIA DGX-2. Using the JAX compile optimization, ParaFold attained a 13.8X average speedup over AlphaFold. ParaFold offers a rapid and effective approach for high-throughput structure predictions, leveraging the predictive power by running on supercomputers, with shorter time, and at a lower cost. The development of ParaFold will greatly speed up high-throughput studies and render the protein "structure-omics" feasible. |
2406.02522 | Congrui Jin | Nisha Rokaya, Erin C. Carr, Richard A. Wilson, Congrui Jin | Lichen-Mediated Self-Growing Construction Materials for Habitat
Outfitting on Mars | null | null | null | null | q-bio.CB astro-ph.EP astro-ph.IM physics.pop-ph | http://creativecommons.org/licenses/by/4.0/ | As its next step in space exploration, the National Aeronautics and Space
Administration (NASA) revealed plans to establish a permanent human presence on
Mars. Habitat outfitting, i.e., the technology to provide the crew with the
necessary equipment to perform mission tasks as well as a comfortable, safe,
and livable habitable volume, has not been fully explored yet. This study
proposes that, rather than shipping prefabricated outfitting elements to Mars,
habitat outfitting can be realized by in-situ construction using cyanobacteria
and fungi as building agents. A synthetic lichen system, composed of
diazotrophic cyanobacteria and filamentous fungi, can be created to produce
abundant biominerals (CaCO3) and biopolymers, which will glue Martian regolith
into consolidated building blocks. These self-growing building blocks can be
assembled into various structures, such as floors, walls, partitions, and
furniture.
| [
{
"created": "Tue, 4 Jun 2024 17:41:25 GMT",
"version": "v1"
},
{
"created": "Fri, 7 Jun 2024 20:01:31 GMT",
"version": "v2"
},
{
"created": "Thu, 13 Jun 2024 19:55:48 GMT",
"version": "v3"
}
] | 2024-06-17 | [
[
"Rokaya",
"Nisha",
""
],
[
"Carr",
"Erin C.",
""
],
[
"Wilson",
"Richard A.",
""
],
[
"Jin",
"Congrui",
""
]
] | As its next step in space exploration, the National Aeronautics and Space Administration (NASA) revealed plans to establish a permanent human presence on Mars. Habitat outfitting, i.e., the technology to provide the crew with the necessary equipment to perform mission tasks as well as a comfortable, safe, and livable habitable volume, has not been fully explored yet. This study proposes that, rather than shipping prefabricated outfitting elements to Mars, habitat outfitting can be realized by in-situ construction using cyanobacteria and fungi as building agents. A synthetic lichen system, composed of diazotrophic cyanobacteria and filamentous fungi, can be created to produce abundant biominerals (CaCO3) and biopolymers, which will glue Martian regolith into consolidated building blocks. These self-growing building blocks can be assembled into various structures, such as floors, walls, partitions, and furniture. |
1601.03060 | Emin Orhan | A. Emin Orhan, Wei Ji Ma | Efficient Probabilistic Inference in Generic Neural Networks Trained
with Non-Probabilistic Feedback | 30 pages, 10 figures, 6 supplementary figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Animals perform near-optimal probabilistic inference in a wide range of
psychophysical tasks. Probabilistic inference requires trial-to-trial
representation of the uncertainties associated with task variables and
subsequent use of this representation. Previous work has implemented such
computations using neural networks with hand-crafted and task-dependent
operations. We show that generic neural networks trained with a simple
error-based learning rule perform near-optimal probabilistic inference in nine
common psychophysical tasks. In a probabilistic categorization task,
error-based learning in a generic network simultaneously explains a monkey's
learning curve and the evolution of qualitative aspects of its choice behavior.
In all tasks, the number of neurons required for a given level of performance
grows sub-linearly with the input population size, a substantial improvement on
previous implementations of probabilistic inference. The trained networks
develop a novel sparsity-based probabilistic population code. Our results
suggest that probabilistic inference emerges naturally in generic neural
networks trained with error-based learning rules.
| [
{
"created": "Tue, 12 Jan 2016 21:16:35 GMT",
"version": "v1"
},
{
"created": "Fri, 27 May 2016 17:01:52 GMT",
"version": "v2"
},
{
"created": "Mon, 5 Dec 2016 01:49:45 GMT",
"version": "v3"
},
{
"created": "Fri, 21 Apr 2017 21:22:34 GMT",
"version": "v4"
}
] | 2017-04-25 | [
[
"Orhan",
"A. Emin",
""
],
[
"Ma",
"Wei Ji",
""
]
] | Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sub-linearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules. |
1406.6040 | David Wick | W. David Wick | Stopping the SuperSpreader Epidemic, Part III: Prediction | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In two previous papers, I introduced SuperSpreader (SS) epidemic models,
offered some theoretical discussion of prevention issues, and fitted some
models to data derived from published accounts of the ongoing MERS epidemic
(concluding that a pandemic is likely). Continuing on this theme, here I
discuss prediction: whether, in a disease outbreak driven by superspreader
events, a rigorous decision point---meaning a declaration that a pandemic is
imminent---can be defined. I show that all sources of prediction bias
contribute to generating false negatives (i.e., discounting the chance of a
pandemic when it is looming or has already started). Nevertheless, the
statistical difficulties can be overcome by improved data gathering and use of
known techniques that decrease bias. One peculiarity of the SS epidemic is that
the prediction can sometimes be made long before the actual pandemic onset,
generating lead time to alert the medical community and the public. Thus
modeling is useful to overcome a false sense of security arising from the long
"kindling times" characteristic of SS epidemics and certain
political/psychological factors, as well as improve the public health response.
| [
{
"created": "Mon, 23 Jun 2014 19:47:33 GMT",
"version": "v1"
}
] | 2014-06-24 | [
[
"Wick",
"W. David",
""
]
] | In two previous papers, I introduced SuperSpreader (SS) epidemic models, offered some theoretical discussion of prevention issues, and fitted some models to data derived from published accounts of the ongoing MERS epidemic (concluding that a pandemic is likely). Continuing on this theme, here I discuss prediction: whether, in a disease outbreak driven by superspreader events, a rigorous decision point---meaning a declaration that a pandemic is imminent---can be defined. I show that all sources of prediction bias contribute to generating false negatives (i.e., discounting the chance of a pandemic when it is looming or has already started). Nevertheless, the statistical difficulties can be overcome by improved data gathering and use of known techniques that decrease bias. One peculiarity of the SS epidemic is that the prediction can sometimes be made long before the actual pandemic onset, generating lead time to alert the medical community and the public. Thus modeling is useful to overcome a false sense of security arising from the long "kindling times" characteristic of SS epidemics and certain political/psychological factors, as well as improve the public health response. |
0711.3628 | Francesc Rossell\'o | Gabriel Cardona, Francesc Rossello, Gabriel Valiente | A Perl Package and an Alignment Tool for Phylogenetic Networks | 5 pages | null | null | null | q-bio.PE cs.CE | null | Phylogenetic networks are a generalization of phylogenetic trees that allow
for the representation of evolutionary events acting at the population level,
like recombination between genes, hybridization between lineages, and lateral
gene transfer. While most phylogenetics tools implement a wide range of
algorithms on phylogenetic trees, there exist only a few applications to work
with phylogenetic networks, and there are no open-source libraries either.
In order to improve this situation, we have developed a Perl package that
relies on the BioPerl bundle and implements many algorithms on phylogenetic
networks. We have also developed a Java applet that makes use of the
aforementioned Perl package and allows the user to make simple experiments with
phylogenetic networks without having to develop a program or Perl script by
herself.
The Perl package has been accepted as part of the BioPerl bundle. It can be
downloaded from http://dmi.uib.es/~gcardona/BioInfo/Bio-PhyloNetwork.tgz. The
web-based application is available at http://dmi.uib.es/~gcardona/BioInfo/. The
Perl package includes full documentation of all its features.
| [
{
"created": "Thu, 22 Nov 2007 18:05:49 GMT",
"version": "v1"
}
] | 2007-11-26 | [
[
"Cardona",
"Gabriel",
""
],
[
"Rossello",
"Francesc",
""
],
[
"Valiente",
"Gabriel",
""
]
] | Phylogenetic networks are a generalization of phylogenetic trees that allow for the representation of evolutionary events acting at the population level, like recombination between genes, hybridization between lineages, and lateral gene transfer. While most phylogenetics tools implement a wide range of algorithms on phylogenetic trees, there exist only a few applications to work with phylogenetic networks, and there are no open-source libraries either. In order to improve this situation, we have developed a Perl package that relies on the BioPerl bundle and implements many algorithms on phylogenetic networks. We have also developed a Java applet that makes use of the aforementioned Perl package and allows the user to make simple experiments with phylogenetic networks without having to develop a program or Perl script by herself. The Perl package has been accepted as part of the BioPerl bundle. It can be downloaded from http://dmi.uib.es/~gcardona/BioInfo/Bio-PhyloNetwork.tgz. The web-based application is available at http://dmi.uib.es/~gcardona/BioInfo/. The Perl package includes full documentation of all its features. |
1007.4583 | Martyn Amos | Angel Goni-Moreno and Martyn Amos | A population-based microbial oscillator | Submitted | null | null | null | q-bio.CB nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Genetic oscillators are a major theme of interest in the emerging field of
synthetic biology. Until recently, most work has been carried out using
intra-cellular oscillators, but this approach restricts the broader
applicability of such systems. Motivated by a desire to develop large-scale,
spatially-distributed cell-based computational systems, we present an initial
design for a population-level oscillator which uses three different bacterial
strains. Our system is based on the client-server model familiar to computer
science, and uses quorum sensing for communication between nodes. We present
the results of extensive in silico simulation tests, which confirm that our
design is both feasible and robust.
| [
{
"created": "Mon, 26 Jul 2010 22:36:56 GMT",
"version": "v1"
}
] | 2010-07-28 | [
[
"Goni-Moreno",
"Angel",
""
],
[
"Amos",
"Martyn",
""
]
] | Genetic oscillators are a major theme of interest in the emerging field of synthetic biology. Until recently, most work has been carried out using intra-cellular oscillators, but this approach restricts the broader applicability of such systems. Motivated by a desire to develop large-scale, spatially-distributed cell-based computational systems, we present an initial design for a population-level oscillator which uses three different bacterial strains. Our system is based on the client-server model familiar to computer science, and uses quorum sensing for communication between nodes. We present the results of extensive in silico simulation tests, which confirm that our design is both feasible and robust. |
1811.12315 | Diane Peurichard | Diane Peurichard, Marielle Ousset, Jenny Paupert, Benjamin Aymard,
Anne Lorsignol, Louis Casteilla, Pierre Degond | Extra-cellular matrix rigidity may dictate the fate of injury outcome | null | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | After injury, if regeneration can be observed in hydra, planaria and some
vertebrates, regeneration is rare in mammals and particularly in humans. In
this paper, we investigate the mechanisms by which biological tissues recover
after injury. We explore this question on adipose tissue, using the
mathematical framework recently developed in Peurichard et al, J. Theoret.
Biol. 429 (2017), pp. 61-81. Our assumption is that simple mechanical cues
between the Extra-Cellular Matrix (ECM) and differentiated cells can explain
adipose tissue morphogenesis and that regeneration requires after injury the
same mechanisms. We validate this hypothesis by means of a two-dimensional
Individual Based Model (IBM) of interacting adipocytes and ECM fiber elements.
The model successfully generates regeneration or scar formation as functions of
few key parameters, and seems to indicate that the fate of injury outcome could
be mainly due to ECM rigidity.
| [
{
"created": "Mon, 12 Nov 2018 11:37:19 GMT",
"version": "v1"
},
{
"created": "Sun, 3 Mar 2019 23:06:49 GMT",
"version": "v2"
}
] | 2019-03-05 | [
[
"Peurichard",
"Diane",
""
],
[
"Ousset",
"Marielle",
""
],
[
"Paupert",
"Jenny",
""
],
[
"Aymard",
"Benjamin",
""
],
[
"Lorsignol",
"Anne",
""
],
[
"Casteilla",
"Louis",
""
],
[
"Degond",
"Pierre",
""
]
] | After injury, if regeneration can be observed in hydra, planaria and some vertebrates, regeneration is rare in mammals and particularly in humans. In this paper, we investigate the mechanisms by which biological tissues recover after injury. We explore this question on adipose tissue, using the mathematical framework recently developed in Peurichard et al, J. Theoret. Biol. 429 (2017), pp. 61-81. Our assumption is that simple mechanical cues between the Extra-Cellular Matrix (ECM) and differentiated cells can explain adipose tissue morphogenesis and that regeneration requires after injury the same mechanisms. We validate this hypothesis by means of a two-dimensional Individual Based Model (IBM) of interacting adipocytes and ECM fiber elements. The model successfully generates regeneration or scar formation as functions of few key parameters, and seems to indicate that the fate of injury outcome could be mainly due to ECM rigidity. |
2005.05403 | Anand Bihari Dr. | Kailsah Nath Tripathi, Anand Bihari, Sudhakar Tripathi, R. B. Mishra | A Review on Brain Mechanisms for Language Acquisition and Comprehension | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper reviews the main perspectives of language acquisition and language
comprehension. In language acquisition, we have reviewed the different types of
language acquisitions like first language acquisition, second language
acquisition, sign language acquisition and skill acquisition. The experimental
techniques for neurolinguistic acquisition detection is also discussed. The
findings of experiments for acquisition detection is also discussed, it
includes the region of brain activated after acquisition. Findings shows that
the different types of acquisition involve different regions of the brain. In
language comprehension, native language comprehension and bilingual's
comprehension has been considered. Comprehension involve different brain
regions for different sentence or word comprehension depending upon their
semantic and syntax. The different fMRIEEG analysis techniques (statistical or
graph theoretical) are also discoursed in our review. Tools for
neurolinguistics computations are also discussed.
| [
{
"created": "Sun, 12 Apr 2020 16:54:34 GMT",
"version": "v1"
}
] | 2020-05-13 | [
[
"Tripathi",
"Kailsah Nath",
""
],
[
"Bihari",
"Anand",
""
],
[
"Tripathi",
"Sudhakar",
""
],
[
"Mishra",
"R. B.",
""
]
] | This paper reviews the main perspectives of language acquisition and language comprehension. In language acquisition, we have reviewed the different types of language acquisitions like first language acquisition, second language acquisition, sign language acquisition and skill acquisition. The experimental techniques for neurolinguistic acquisition detection is also discussed. The findings of experiments for acquisition detection is also discussed, it includes the region of brain activated after acquisition. Findings shows that the different types of acquisition involve different regions of the brain. In language comprehension, native language comprehension and bilingual's comprehension has been considered. Comprehension involve different brain regions for different sentence or word comprehension depending upon their semantic and syntax. The different fMRIEEG analysis techniques (statistical or graph theoretical) are also discoursed in our review. Tools for neurolinguistics computations are also discussed. |
1809.04025 | David Steinsaltz | David Steinsaltz and Shripad Tuljapurkar | Stochastic growth rates for populations in random environments with rare
migration | 19 pages, 5 figures. This article and "Stability of fixed life
histories to perturbation by rare diapause" supersede the earlier paper
"Stochastic growth rates for life histories with rare migration or diapause"
arXiv:1505.00116 | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The growth of a population divided among spatial sites, with migration
between the sites, is sometimes modelled by a product of random matrices, with
each diagonal elements representing the growth rate in a given time period, and
off-diagonal elements the migration rate. The randomness of the matrices then
represents stochasticity of environmental conditions. We consider the case
where the off-diagonal elements are small, representing a situation where
migration has been introduced into an otherwise sessile meta-population. We
examine the asymptotic behaviour of the long-term growth rate. When there is a
single site with the highest growth rate, under the assumption of Gaussian log
growth rates at the individual sites (or having Gaussian-like tails) we show
that the behavior near zero is like a power of $\epsilon$, and derive upper and
lower bounds for the power in terms of the difference in the growth rates and
the distance between the sites. In particular, when the difference in mean log
growth rate between two sites is sufficiently small, or the variance of the
difference between the sites sufficiently large, migration will always be
favored by natural selection, in the sense that introducing a small amount of
migration will increase the growth rate of the population relative to the
zero-migration case.
| [
{
"created": "Tue, 11 Sep 2018 16:49:13 GMT",
"version": "v1"
}
] | 2018-09-12 | [
[
"Steinsaltz",
"David",
""
],
[
"Tuljapurkar",
"Shripad",
""
]
] | The growth of a population divided among spatial sites, with migration between the sites, is sometimes modelled by a product of random matrices, with each diagonal elements representing the growth rate in a given time period, and off-diagonal elements the migration rate. The randomness of the matrices then represents stochasticity of environmental conditions. We consider the case where the off-diagonal elements are small, representing a situation where migration has been introduced into an otherwise sessile meta-population. We examine the asymptotic behaviour of the long-term growth rate. When there is a single site with the highest growth rate, under the assumption of Gaussian log growth rates at the individual sites (or having Gaussian-like tails) we show that the behavior near zero is like a power of $\epsilon$, and derive upper and lower bounds for the power in terms of the difference in the growth rates and the distance between the sites. In particular, when the difference in mean log growth rate between two sites is sufficiently small, or the variance of the difference between the sites sufficiently large, migration will always be favored by natural selection, in the sense that introducing a small amount of migration will increase the growth rate of the population relative to the zero-migration case. |
2402.16894 | Jiachen Yao | Jiachen Yao, Nina Hagemann, Qiaojie Xiong, Jianxu Chen, Dirk M.
Hermann, Chao Chen | Topological Analysis of Mouse Brain Vasculature via 3D Light-sheet
Microscopy Images | null | null | null | null | q-bio.NC eess.IV | http://creativecommons.org/licenses/by/4.0/ | Vascular networks play a crucial role in understanding brain functionalities.
Brain integrity and function, neuronal activity and plasticity, which are
crucial for learning, are actively modulated by their local environments,
specifically vascular networks. With recent developments in high-resolution 3D
light-sheet microscopy imaging together with tissue processing techniques, it
becomes feasible to obtain and examine large-scale brain vasculature in mice.
To establish a structural foundation for functional study, however, we need
advanced image analysis and structural modeling methods.
Existing works use geometric features such as thickness, tortuosity, etc.
However, geometric features cannot fully capture structural characteristics
such as the richness of branches, connectivity, etc. In this paper, we study
the morphology of brain vasculature through a topological lens. We extract
topological features based on the theory of topological data analysis.
Comparing of these robust and multi-scale topological structural features
across different brain anatomical structures and between normal and obese
populations sheds light on their promising future in studying neurological
diseases.
| [
{
"created": "Fri, 23 Feb 2024 19:46:36 GMT",
"version": "v1"
}
] | 2024-02-28 | [
[
"Yao",
"Jiachen",
""
],
[
"Hagemann",
"Nina",
""
],
[
"Xiong",
"Qiaojie",
""
],
[
"Chen",
"Jianxu",
""
],
[
"Hermann",
"Dirk M.",
""
],
[
"Chen",
"Chao",
""
]
] | Vascular networks play a crucial role in understanding brain functionalities. Brain integrity and function, neuronal activity and plasticity, which are crucial for learning, are actively modulated by their local environments, specifically vascular networks. With recent developments in high-resolution 3D light-sheet microscopy imaging together with tissue processing techniques, it becomes feasible to obtain and examine large-scale brain vasculature in mice. To establish a structural foundation for functional study, however, we need advanced image analysis and structural modeling methods. Existing works use geometric features such as thickness, tortuosity, etc. However, geometric features cannot fully capture structural characteristics such as the richness of branches, connectivity, etc. In this paper, we study the morphology of brain vasculature through a topological lens. We extract topological features based on the theory of topological data analysis. Comparing of these robust and multi-scale topological structural features across different brain anatomical structures and between normal and obese populations sheds light on their promising future in studying neurological diseases. |
2401.11784 | James Isbister | James B Isbister | Full-dimensional characterisation of time-warped spike-time
stimulus-response distribution geometries | Accepted as an extended abstract at the NeurReps workshop at NeurIPS
2023. The workshop doesn't publish extended abstracts so submitting here | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Characterising the representation of sensory stimuli in the brain is a
fundamental scientific endeavor, which can illuminate principles of information
coding. Most characterizations reduce the dimensionality of neural data by
converting spike trains to firing rates or binned spike counts, applying
explicitly named methods of ``dimensionality reduction'', or collapsing
trial-to-trial variability. Characterisation of the full-dimensional geometry
of timing-based representations may provide unexpected insights into how
complex high-dimensional information is encoded. Recent research shows that the
distribution of representations elicited over trials of a single stimulus can
be geometrically characterized without the application of dimensionality
reduction, maintaining the temporal spiking information of individual neurons
in a cell assembly and illuminating rich geometric structure. We extend these
results, showing that precise spike time patterns for larger cell assemblies
are time-warped (i.e. stretched or compressed) on each trial. Moreover, by
geometrically characterizing distributions of large spike time patterns, our
analysis supports the hypothesis that the degree to which a spike time pattern
is time-warped depends on the cortical area's background activity level on a
single trial. Finally, we suggest that the proliferation of large
electrophysiology datasets and the increasing concentration of ``neural
geometrists'', creates ideal conditions for characterization of
full-dimensional spike time representations, in complement to dimensionality
reduction approaches.
| [
{
"created": "Mon, 22 Jan 2024 09:30:08 GMT",
"version": "v1"
}
] | 2024-01-23 | [
[
"Isbister",
"James B",
""
]
] | Characterising the representation of sensory stimuli in the brain is a fundamental scientific endeavor, which can illuminate principles of information coding. Most characterizations reduce the dimensionality of neural data by converting spike trains to firing rates or binned spike counts, applying explicitly named methods of ``dimensionality reduction'', or collapsing trial-to-trial variability. Characterisation of the full-dimensional geometry of timing-based representations may provide unexpected insights into how complex high-dimensional information is encoded. Recent research shows that the distribution of representations elicited over trials of a single stimulus can be geometrically characterized without the application of dimensionality reduction, maintaining the temporal spiking information of individual neurons in a cell assembly and illuminating rich geometric structure. We extend these results, showing that precise spike time patterns for larger cell assemblies are time-warped (i.e. stretched or compressed) on each trial. Moreover, by geometrically characterizing distributions of large spike time patterns, our analysis supports the hypothesis that the degree to which a spike time pattern is time-warped depends on the cortical area's background activity level on a single trial. Finally, we suggest that the proliferation of large electrophysiology datasets and the increasing concentration of ``neural geometrists'', creates ideal conditions for characterization of full-dimensional spike time representations, in complement to dimensionality reduction approaches. |
1008.0724 | Bartlomiej Waclaw Dr | Bartlomiej Waclaw, Rosalind J. Allen, and Martin R. Evans | A dynamical phase transition in a model for evolution with migration | 4+ pages, 4 figures | Phys. Rev. Lett. 105, 268101 (2010) | 10.1103/PhysRevLett.105.268101 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Migration between different habitats is ubiquitous among biological
populations. In this Letter, we study a simple quasispecies model for evolution
in two different habitats, with different fitness landscapes, coupled through
one-way migration. Our model applies to asexual, rapidly evolving organisms
such as microbes. Our key finding is a dynamical phase transition at a critical
value of the migration rate. The time to reach steady state diverges at this
critical migration rate. Above the transition, the population is dominated by
immigrants from the primary habitat. Below the transition, the genetic
composition of the population is highly non-trivial, with multiple coexisting
quasispecies which are not native to either habitat. Using results from
localization theory, we show that the critical migration rate may be very small
--- demonstrating that evolutionary outcomes can be very sensitive to even a
small amount of migration.
| [
{
"created": "Wed, 4 Aug 2010 09:29:07 GMT",
"version": "v1"
}
] | 2010-12-23 | [
[
"Waclaw",
"Bartlomiej",
""
],
[
"Allen",
"Rosalind J.",
""
],
[
"Evans",
"Martin R.",
""
]
] | Migration between different habitats is ubiquitous among biological populations. In this Letter, we study a simple quasispecies model for evolution in two different habitats, with different fitness landscapes, coupled through one-way migration. Our model applies to asexual, rapidly evolving organisms such as microbes. Our key finding is a dynamical phase transition at a critical value of the migration rate. The time to reach steady state diverges at this critical migration rate. Above the transition, the population is dominated by immigrants from the primary habitat. Below the transition, the genetic composition of the population is highly non-trivial, with multiple coexisting quasispecies which are not native to either habitat. Using results from localization theory, we show that the critical migration rate may be very small --- demonstrating that evolutionary outcomes can be very sensitive to even a small amount of migration. |
2006.09747 | Qifeng Bai | Qifeng Bai | Research and development of MolAICal for drug design via deep learning
and classical programming | null | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Deep learning methods have permeated into the research area of computer-aided
drug design. The deep learning generative model and classical algorithm can be
simultaneously used for three-dimensional (3D) drug design in the 3D pocket of
the receptor. Here, three aspects of MolAICal are illustrated for drug design:
in the first part, the MolAICal uses the genetic algorithm, Vinardo score and
deep learning generative model trained by generative adversarial net (GAN) for
drug design. In the second part, the deep learning generative model is trained
by drug-like molecules from the drug database such as ZINC database. The
MolAICal invokes the deep learning generative model and molecular docking for
drug virtual screening automatically. In the third part, the useful drug tools
are added for calculating the relative properties such as Pan-assay
interference compounds (PAINS), Lipinski's rule of five, synthetic
accessibility (SA), and so on. Besides, the structural similarity search and
quantitative structure-activity relationship (QSAR), etc are also embedded for
the calculations of drug properties in the MolAICal. MolAICal will constantly
optimize and develop the current and new modules for drug design. The MolAICal
can help the scientists, pharmacists and biologists to design the rational 3D
drugs in the receptor pocket through the deep learning model and classical
programming. MolAICal is free of charge for any academic and educational
purposes, and it can be downloaded from the website https://molaical.github.io.
| [
{
"created": "Wed, 17 Jun 2020 09:39:47 GMT",
"version": "v1"
}
] | 2020-06-18 | [
[
"Bai",
"Qifeng",
""
]
] | Deep learning methods have permeated into the research area of computer-aided drug design. The deep learning generative model and classical algorithm can be simultaneously used for three-dimensional (3D) drug design in the 3D pocket of the receptor. Here, three aspects of MolAICal are illustrated for drug design: in the first part, the MolAICal uses the genetic algorithm, Vinardo score and deep learning generative model trained by generative adversarial net (GAN) for drug design. In the second part, the deep learning generative model is trained by drug-like molecules from the drug database such as ZINC database. The MolAICal invokes the deep learning generative model and molecular docking for drug virtual screening automatically. In the third part, the useful drug tools are added for calculating the relative properties such as Pan-assay interference compounds (PAINS), Lipinski's rule of five, synthetic accessibility (SA), and so on. Besides, the structural similarity search and quantitative structure-activity relationship (QSAR), etc are also embedded for the calculations of drug properties in the MolAICal. MolAICal will constantly optimize and develop the current and new modules for drug design. The MolAICal can help the scientists, pharmacists and biologists to design the rational 3D drugs in the receptor pocket through the deep learning model and classical programming. MolAICal is free of charge for any academic and educational purposes, and it can be downloaded from the website https://molaical.github.io. |
0810.0029 | Guillermo Cecchi | James R. Kozloski and Guillermo A. Cecchi | Topological Effects of Synaptic Time Dependent Plasticity | 26 pages, 5 figures | Frontiers in Neural Circuits, March 2010, Volume 4, Article 7 | 10.3389/fncir.2010.00007 | null | q-bio.NC cond-mat.dis-nn math-ph math.MP q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that the local Spike Timing-Dependent Plasticity (STDP) rule has the
effect of regulating the trans-synaptic weights of loops of any length within a
simulated network of neurons. We show that depending on STDP's polarity,
functional loops are formed or eliminated in networks driven to normal spiking
conditions by random, partially correlated inputs, where functional loops
comprise weights that exceed a non-zero threshold. We further prove that STDP
is a form of loop-regulating plasticity for the case of a linear network
comprising random weights drawn from certain distributions. Thus a notable
local synaptic learning rule makes a specific prediction about synapses in the
brain in which standard STDP is present: that under normal spiking conditions,
they should participate in predominantly feed-forward connections at all
scales. Our model implies that any deviations from this prediction would
require a substantial modification to the hypothesized role for standard STDP.
Given its widespread occurrence in the brain, we predict that STDP could also
regulate long range synaptic loops among individual neurons across all brain
scales, up to, and including, the scale of global brain network topology.
| [
{
"created": "Tue, 30 Sep 2008 21:16:26 GMT",
"version": "v1"
},
{
"created": "Fri, 24 Apr 2009 18:24:48 GMT",
"version": "v2"
},
{
"created": "Fri, 19 Mar 2010 23:12:26 GMT",
"version": "v3"
}
] | 2010-03-23 | [
[
"Kozloski",
"James R.",
""
],
[
"Cecchi",
"Guillermo A.",
""
]
] | We show that the local Spike Timing-Dependent Plasticity (STDP) rule has the effect of regulating the trans-synaptic weights of loops of any length within a simulated network of neurons. We show that depending on STDP's polarity, functional loops are formed or eliminated in networks driven to normal spiking conditions by random, partially correlated inputs, where functional loops comprise weights that exceed a non-zero threshold. We further prove that STDP is a form of loop-regulating plasticity for the case of a linear network comprising random weights drawn from certain distributions. Thus a notable local synaptic learning rule makes a specific prediction about synapses in the brain in which standard STDP is present: that under normal spiking conditions, they should participate in predominantly feed-forward connections at all scales. Our model implies that any deviations from this prediction would require a substantial modification to the hypothesized role for standard STDP. Given its widespread occurrence in the brain, we predict that STDP could also regulate long range synaptic loops among individual neurons across all brain scales, up to, and including, the scale of global brain network topology. |
2302.00587 | Masatsugu Yamada | Masatsugu Yamada and Mahito Sugiyama | Molecular Graph Generation by Decomposition and Reassembling | null | null | null | null | q-bio.BM cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Designing molecular structures with desired chemical properties is an
essential task in drug discovery and material design. However, finding
molecules with the optimized desired properties is still a challenging task due
to combinatorial explosion of candidate space of molecules. Here we propose a
novel \emph{decomposition-and-reassembling} based approach, which does not
include any optimization in hidden space and our generation process is highly
interpretable. Our method is a two-step procedure: In the first decomposition
step, we apply frequent subgraph mining to a molecular database to collect
smaller size of subgraphs as building blocks of molecules. In the second
reassembling step, we search desirable building blocks guided via reinforcement
learning and combine them to generate new molecules. Our experiments show that
not only can our method find better molecules in terms of two standard
criteria, the penalized $\log P$ and drug-likeness, but also generate drug
molecules with showing the valid intermediate molecules.
| [
{
"created": "Sun, 11 Dec 2022 06:12:04 GMT",
"version": "v1"
}
] | 2023-02-02 | [
[
"Yamada",
"Masatsugu",
""
],
[
"Sugiyama",
"Mahito",
""
]
] | Designing molecular structures with desired chemical properties is an essential task in drug discovery and material design. However, finding molecules with the optimized desired properties is still a challenging task due to combinatorial explosion of candidate space of molecules. Here we propose a novel \emph{decomposition-and-reassembling} based approach, which does not include any optimization in hidden space and our generation process is highly interpretable. Our method is a two-step procedure: In the first decomposition step, we apply frequent subgraph mining to a molecular database to collect smaller size of subgraphs as building blocks of molecules. In the second reassembling step, we search desirable building blocks guided via reinforcement learning and combine them to generate new molecules. Our experiments show that not only can our method find better molecules in terms of two standard criteria, the penalized $\log P$ and drug-likeness, but also generate drug molecules with showing the valid intermediate molecules. |
1404.0372 | Mirko Maraldi | Mirko Maraldi, Clara Valero, Krishna Garikipati | A computational study of stress fiber-focal adhesion dynamics governing
cell contractility | 23 pages, 12 figures, 1 table. Accepted for publication in
Biophysical Journal | null | 10.1016/j.bpj.2014.03.027 | null | q-bio.SC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We apply a recently developed model of cytoskeletal force generation to study
a cell intrinsic contractility, as well as its response to external loading.
The model is based on a non-equilibrium thermodynamic treatment of the
mechano-chemistry governing force in the stress fiber-focal adhesion system.
Our computational study suggests that the mechanical coupling between the
stress fibers and focal adhesions leads to a complex, dynamic, mechano-chemical
response. We collect the results in response maps whose regimes are
distinguished by the initial geometry of the stress fiber-focal adhesion
system, and by the external load on the cell. The results from our model
connect qualitatively with recent studies on the force response of smooth
muscle cells on arrays of polymeric microposts (Mann et al., Lab. on a Chip,
12, 731-740, 2012).
| [
{
"created": "Tue, 1 Apr 2014 19:57:16 GMT",
"version": "v1"
}
] | 2015-06-19 | [
[
"Maraldi",
"Mirko",
""
],
[
"Valero",
"Clara",
""
],
[
"Garikipati",
"Krishna",
""
]
] | We apply a recently developed model of cytoskeletal force generation to study a cell intrinsic contractility, as well as its response to external loading. The model is based on a non-equilibrium thermodynamic treatment of the mechano-chemistry governing force in the stress fiber-focal adhesion system. Our computational study suggests that the mechanical coupling between the stress fibers and focal adhesions leads to a complex, dynamic, mechano-chemical response. We collect the results in response maps whose regimes are distinguished by the initial geometry of the stress fiber-focal adhesion system, and by the external load on the cell. The results from our model connect qualitatively with recent studies on the force response of smooth muscle cells on arrays of polymeric microposts (Mann et al., Lab. on a Chip, 12, 731-740, 2012). |
1505.04139 | Shaon Chakrabarti | David L. Pincus, Shaon Chakrabarti, D. Thirumalai | Helicase processivity and not the unwinding velocity exhibits universal
increase with force | null | null | 10.1016/j.bpj.2015.05.020 | null | q-bio.BM cond-mat.stat-mech physics.bio-ph q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Helicases, involved in a number of cellular functions, are motors that
translocate along singlestranded nucleic acid and couple the motion to
unwinding double-strands of a duplex nucleic acid. The junction between double
and single strands creates a barrier to the movement of the helicase, which can
be manipulated in vitro by applying mechanical forces directly on the nucleic
acid strands. Single molecule experiments have demonstrated that the unwinding
velocities of some helicases increase dramatically with increase in the
external force, while others show little response. In contrast, the unwinding
processivity always increases when the force increases. The differing responses
of the unwinding velocity and processivity to force has lacked explanation. By
generalizing a previous model of processive unwinding by helicases, we provide
a unified framework for understanding the dependence of velocity and
processivity on force and the nucleic acid sequence. We predict that the
sensitivity of unwinding processivity to external force is a universal feature
that should be observed in all helicases. Our prediction is illustrated using
T7 and NS3 helicases as case studies. Interestingly, the increase in unwinding
processivity with force depends on whether the helicase forces base pair
opening by direct interaction or if such a disruption occurs spontaneously due
to thermal uctuations. Based on the theoretical results, we propose that
proteins like single-strand binding proteins associated with helicases in the
replisome, may have co-evolved with helicases to increase the unwinding
processivity even if the velocity remains unaffected.
| [
{
"created": "Fri, 15 May 2015 17:59:09 GMT",
"version": "v1"
}
] | 2023-07-19 | [
[
"Pincus",
"David L.",
""
],
[
"Chakrabarti",
"Shaon",
""
],
[
"Thirumalai",
"D.",
""
]
] | Helicases, involved in a number of cellular functions, are motors that translocate along singlestranded nucleic acid and couple the motion to unwinding double-strands of a duplex nucleic acid. The junction between double and single strands creates a barrier to the movement of the helicase, which can be manipulated in vitro by applying mechanical forces directly on the nucleic acid strands. Single molecule experiments have demonstrated that the unwinding velocities of some helicases increase dramatically with increase in the external force, while others show little response. In contrast, the unwinding processivity always increases when the force increases. The differing responses of the unwinding velocity and processivity to force has lacked explanation. By generalizing a previous model of processive unwinding by helicases, we provide a unified framework for understanding the dependence of velocity and processivity on force and the nucleic acid sequence. We predict that the sensitivity of unwinding processivity to external force is a universal feature that should be observed in all helicases. Our prediction is illustrated using T7 and NS3 helicases as case studies. Interestingly, the increase in unwinding processivity with force depends on whether the helicase forces base pair opening by direct interaction or if such a disruption occurs spontaneously due to thermal uctuations. Based on the theoretical results, we propose that proteins like single-strand binding proteins associated with helicases in the replisome, may have co-evolved with helicases to increase the unwinding processivity even if the velocity remains unaffected. |
1308.1319 | Richard A Neher | Taylor A. Kessinger, Alan S. Perelson, Richard A. Neher | Inferring HIV escape rates from multi-locus genotype data | null | Front. Immunol. 4:252. 2013 | 10.3389/fimmu.2013.00252 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cytotoxic T-lymphocytes (CTLs) recognize viral protein fragments displayed by
major histocompatibility complex (MHC) molecules on the surface of virally
infected cells and generate an anti-viral response that can kill the infected
cells. Virus variants whose protein fragments are not efficiently presented on
infected cells or whose fragments are presented but not recognized by CTLs
therefore have a competitive advantage and spread rapidly through the
population. We present a method that allows a more robust estimation of these
escape rates from serially sampled sequence data. The proposed method accounts
for competition between multiple escapes by explicitly modeling the
accumulation of escape mutations and the stochastic effects of rare multiple
mutants. Applying our method to serially sampled HIV sequence data, we estimate
rates of HIV escape that are substantially larger than those previously
reported. The method can be extended to complex escapes that require
compensatory mutations. We expect our method to be applicable in other contexts
such as cancer evolution where time series data is also available.
| [
{
"created": "Tue, 6 Aug 2013 15:50:27 GMT",
"version": "v1"
}
] | 2014-03-25 | [
[
"Kessinger",
"Taylor A.",
""
],
[
"Perelson",
"Alan S.",
""
],
[
"Neher",
"Richard A.",
""
]
] | Cytotoxic T-lymphocytes (CTLs) recognize viral protein fragments displayed by major histocompatibility complex (MHC) molecules on the surface of virally infected cells and generate an anti-viral response that can kill the infected cells. Virus variants whose protein fragments are not efficiently presented on infected cells or whose fragments are presented but not recognized by CTLs therefore have a competitive advantage and spread rapidly through the population. We present a method that allows a more robust estimation of these escape rates from serially sampled sequence data. The proposed method accounts for competition between multiple escapes by explicitly modeling the accumulation of escape mutations and the stochastic effects of rare multiple mutants. Applying our method to serially sampled HIV sequence data, we estimate rates of HIV escape that are substantially larger than those previously reported. The method can be extended to complex escapes that require compensatory mutations. We expect our method to be applicable in other contexts such as cancer evolution where time series data is also available. |
1312.5403 | Boleslaw Szymanski | Konrad R. Fialkowski | Lack of water and endurance running could have caused the exponential
growth in human brain: Point of no return model | arXiv admin note: text overlap with arXiv:1309.5614 | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Growth in brain volume is one of the most spectacular changes in the hominid
lineage. The anthropological community agrees on that point. No consensus,
however, has been reached on selection pressures contributing to that growth.
In that respect Martin (1984) can be invoked. In his review of size
relationships among primates he stated that despite the relationship between
brain size, body size and feeding behavior no single interpretation could be
provided that revealed the causality of such relationship.
This paper deals with one specific aspect of hominid brain growth; the fact
that for most of the hominid period, growth in brain volume was exponential in
character. To the best of our knowledge, no attempt has been made to identify a
selection mechanism that can facilitate just the exponential features of that
growth (as distinct from any of its other characteristics). It is broadly
accepted that the dynamics of this growth were peculiar. Growth was very fast,
or even rapid in the evolutionary scale of time. The most profound evidence of
that opinion was expressed by Haldane that this dramatic increase in brain size
was the most rapid evolutionary change known to him.
| [
{
"created": "Thu, 19 Dec 2013 04:26:05 GMT",
"version": "v1"
}
] | 2013-12-20 | [
[
"Fialkowski",
"Konrad R.",
""
]
] | Growth in brain volume is one of the most spectacular changes in the hominid lineage. The anthropological community agrees on that point. No consensus, however, has been reached on selection pressures contributing to that growth. In that respect Martin (1984) can be invoked. In his review of size relationships among primates he stated that despite the relationship between brain size, body size and feeding behavior no single interpretation could be provided that revealed the causality of such relationship. This paper deals with one specific aspect of hominid brain growth; the fact that for most of the hominid period, growth in brain volume was exponential in character. To the best of our knowledge, no attempt has been made to identify a selection mechanism that can facilitate just the exponential features of that growth (as distinct from any of its other characteristics). It is broadly accepted that the dynamics of this growth were peculiar. Growth was very fast, or even rapid in the evolutionary scale of time. The most profound evidence of that opinion was expressed by Haldane that this dramatic increase in brain size was the most rapid evolutionary change known to him. |
2101.03784 | Hansen Zhao | Hansen Zhao, Xu Zhao, Huan Yao, Jiaxin Feng, Sichun Zhang, Xinrong
Zhang | Estimate Metabolite Taxonomy and Structure with a Fragment-Centered
Database and Fragment Network | null | null | null | null | q-bio.QM q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | Metabolite structure identification has become the major bottleneck of the
mass spectrometry based metabolomics research. Till now, number of mass spectra
databases and search algorithms have been developed to address this issue.
However, two critical problems still exist: the low chemical component record
coverage in databases and significant MS/MS spectra variations related to
experiment equipment and parameter settings. In this work, we considered the
molecule fragment as basic building blocks of the metabolic components which
had relatively consistent signatures in MS/MS spectra. And from a bottom-up
point of view, we built a fragment centered database, MSFragDB, by reorganizing
the data from the Human Metabolome Database (HMDB) and developed an
intensity-free searching algorithm to search and rank the most relative
metabolite according to the users' input. We also proposed the concept of
fragment network, a graph structure that encoded the relationship between the
molecule fragments to find close motif that indicated a specific chemical
structure. Although based on the same dataset as the HMDB, validation results
implied that the MSFragDB had a higher hit ratio and furthermore, estimated
possible taxonomy that a query spectrum belongs to when the corresponding
chemical component was missing in the database. Aid by the Fragment Network,
the MSFragDB was also proved to be able to estimate the right structure while
the MS/MS spectrum suffers from the precursor-contamination. The strategy
proposed is general and can be adopted in existing databases. We believe
MSFragDB and Fragment Network can improve the performance of structure
identification with existing data. The beta version of the database is freely
available at www.xrzhanglab.com/msfragdb/.
| [
{
"created": "Mon, 11 Jan 2021 09:39:34 GMT",
"version": "v1"
}
] | 2021-01-12 | [
[
"Zhao",
"Hansen",
""
],
[
"Zhao",
"Xu",
""
],
[
"Yao",
"Huan",
""
],
[
"Feng",
"Jiaxin",
""
],
[
"Zhang",
"Sichun",
""
],
[
"Zhang",
"Xinrong",
""
]
] | Metabolite structure identification has become the major bottleneck of the mass spectrometry based metabolomics research. Till now, number of mass spectra databases and search algorithms have been developed to address this issue. However, two critical problems still exist: the low chemical component record coverage in databases and significant MS/MS spectra variations related to experiment equipment and parameter settings. In this work, we considered the molecule fragment as basic building blocks of the metabolic components which had relatively consistent signatures in MS/MS spectra. And from a bottom-up point of view, we built a fragment centered database, MSFragDB, by reorganizing the data from the Human Metabolome Database (HMDB) and developed an intensity-free searching algorithm to search and rank the most relative metabolite according to the users' input. We also proposed the concept of fragment network, a graph structure that encoded the relationship between the molecule fragments to find close motif that indicated a specific chemical structure. Although based on the same dataset as the HMDB, validation results implied that the MSFragDB had a higher hit ratio and furthermore, estimated possible taxonomy that a query spectrum belongs to when the corresponding chemical component was missing in the database. Aid by the Fragment Network, the MSFragDB was also proved to be able to estimate the right structure while the MS/MS spectrum suffers from the precursor-contamination. The strategy proposed is general and can be adopted in existing databases. We believe MSFragDB and Fragment Network can improve the performance of structure identification with existing data. The beta version of the database is freely available at www.xrzhanglab.com/msfragdb/. |
1609.06843 | Stefano Zapperi | Giulio Costantini, Zoe Budrikis, Alessandro Taloni, Alexander K.
Buell, Stefano Zapperi, Caterina A. M. La Porta | Fluctuations in protein aggregation: Design of preclinical screening for
early diagnosis of neurodegenerative disease | null | Phys. Rev. Applied 6, 034012 (2016) | null | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Autocatalytic fibril nucleation has recently been proposed to be a
determining factor for the spread of neurodegenerative diseases, but the same
process could also be exploited to amplify minute quantities of protein
aggregates in a diagnostic context. Recent advances in microfluidic technology
allow analysis of protein aggregation in micron-scale samples potentially
enabling such diagnostic approaches, but the theoretical foundations for the
analysis and interpretation of such data are so far lacking. Here we study
computationally the onset of protein aggregation in small volumes and show that
the process is ruled by intrinsic fluctuations whose volume dependent
distribution we also estimate theoretically. Based on these results, we develop
a strategy to quantify in silico the statistical errors associated with the
detection of aggregate containing samples. Our work opens a new perspective on
the forecasting of protein aggregation in asymptomatic subjects.
| [
{
"created": "Thu, 22 Sep 2016 07:36:28 GMT",
"version": "v1"
}
] | 2016-09-23 | [
[
"Costantini",
"Giulio",
""
],
[
"Budrikis",
"Zoe",
""
],
[
"Taloni",
"Alessandro",
""
],
[
"Buell",
"Alexander K.",
""
],
[
"Zapperi",
"Stefano",
""
],
[
"La Porta",
"Caterina A. M.",
""
]
] | Autocatalytic fibril nucleation has recently been proposed to be a determining factor for the spread of neurodegenerative diseases, but the same process could also be exploited to amplify minute quantities of protein aggregates in a diagnostic context. Recent advances in microfluidic technology allow analysis of protein aggregation in micron-scale samples potentially enabling such diagnostic approaches, but the theoretical foundations for the analysis and interpretation of such data are so far lacking. Here we study computationally the onset of protein aggregation in small volumes and show that the process is ruled by intrinsic fluctuations whose volume dependent distribution we also estimate theoretically. Based on these results, we develop a strategy to quantify in silico the statistical errors associated with the detection of aggregate containing samples. Our work opens a new perspective on the forecasting of protein aggregation in asymptomatic subjects. |
2308.08561 | Yifan Zhou | Yifan Zhou, Yew Kee Wong, Yan Shing Liang, Haichuan Qiu, Yu Xi Wu, Bin
He | Implementation of The Future of Drug Discovery: QuantumBased Machine
Learning Simulation (QMLS) | 13 pages, 6 figures | International Journal of Computer Science and Mobile Applications,
Vol 11 Issue 5,May- 2023 | 10.5281/zenodo.7983561 | null | q-bio.BM cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | The Research & Development (R&D) phase of drug development is a lengthy and
costly process. To revolutionize this process, we introduce our new concept
QMLS to shorten the whole R&D phase to three to six months and decrease the
cost to merely fifty to eighty thousand USD. For Hit Generation, Machine
Learning Molecule Generation (MLMG) generates possible hits according to the
molecular structure of the target protein while the Quantum Simulation (QS)
filters molecules from the primary essay based on the reaction and binding
effectiveness with the target protein. Then, For Lead Optimization, the
resultant molecules generated and filtered from MLMG and QS are compared, and
molecules that appear as a result of both processes will be made into dozens of
molecular variations through Machine Learning Molecule Variation (MLMV), while
others will only be made into a few variations. Lastly, all optimized molecules
would undergo multiple rounds of QS filtering with a high standard for reaction
effectiveness and safety, creating a few dozen pre-clinical-trail-ready drugs.
This paper is based on our first paper, where we pitched the concept of machine
learning combined with quantum simulations. In this paper we will go over the
detailed design and framework of QMLS, including MLMG, MLMV, and QS.
| [
{
"created": "Mon, 14 Aug 2023 13:18:40 GMT",
"version": "v1"
},
{
"created": "Wed, 25 Oct 2023 13:13:01 GMT",
"version": "v2"
}
] | 2023-10-26 | [
[
"Zhou",
"Yifan",
""
],
[
"Wong",
"Yew Kee",
""
],
[
"Liang",
"Yan Shing",
""
],
[
"Qiu",
"Haichuan",
""
],
[
"Wu",
"Yu Xi",
""
],
[
"He",
"Bin",
""
]
] | The Research & Development (R&D) phase of drug development is a lengthy and costly process. To revolutionize this process, we introduce our new concept QMLS to shorten the whole R&D phase to three to six months and decrease the cost to merely fifty to eighty thousand USD. For Hit Generation, Machine Learning Molecule Generation (MLMG) generates possible hits according to the molecular structure of the target protein while the Quantum Simulation (QS) filters molecules from the primary essay based on the reaction and binding effectiveness with the target protein. Then, For Lead Optimization, the resultant molecules generated and filtered from MLMG and QS are compared, and molecules that appear as a result of both processes will be made into dozens of molecular variations through Machine Learning Molecule Variation (MLMV), while others will only be made into a few variations. Lastly, all optimized molecules would undergo multiple rounds of QS filtering with a high standard for reaction effectiveness and safety, creating a few dozen pre-clinical-trail-ready drugs. This paper is based on our first paper, where we pitched the concept of machine learning combined with quantum simulations. In this paper we will go over the detailed design and framework of QMLS, including MLMG, MLMV, and QS. |
q-bio/0703056 | Thomas Kuhlman | T. E. Kuhlman, Z. Zhang, M. H. Saier Jr., T. Hwa | Quantitative Characterization of Combinatorial Transcriptional Control
of the Lactose Operon of E. coli | null | Proc Natl Acad Sci 104(14): pp. 6043-6048 (2007) | 10.1073/pnas.0606717104 | null | q-bio.MN | null | It is the goal of systems biology to understand the behavior of the whole in
terms of the knowledge of the parts. This is hard to achieve in many cases due
to the difficulty of characterizing the many constituents and their complex web
of interactions involved in a biological system. The lac promoter of E. coli
offers a possibility of confronting system-leve properties of transcriptional
regulation with the known biochemistry of the molecular constituents and their
mutual interactions. Such confrontations can reveal previously unknown
constituents and interactions, as well as offering new insight into how the
components work together as a whole. Here we study the combinatorial control of
the lac promoter by the regulators LacR and CRP. A previous in vivo study
[Setty et al., PNAS 100: 7702-7 (2003)] found gross disagreement between the
observed promoter activites and the expected behavior based on the known
molecular mechanisms. We repeated the study by identifying and removing several
extraneous factors which significantly modulated the expression of the lac
promoter. Through quantitative, systematic characterization of promoter
activity for a number of key mutants and guided by the thermodynamic model of
transcriptional gene regulation, we are able to account for the combinatorial
control of the lac promoter quantitatively, in terms of a cooperative
interaction between CRP and LacR-mediated DNA looping. Specifically, our
analysis indicates that the sensitivity of the inducer response results from
LacR-mediated DNA looping, which is significantly enhanced by CRP.
| [
{
"created": "Mon, 26 Mar 2007 20:22:39 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Kuhlman",
"T. E.",
""
],
[
"Zhang",
"Z.",
""
],
[
"Saier",
"M. H.",
"Jr."
],
[
"Hwa",
"T.",
""
]
] | It is the goal of systems biology to understand the behavior of the whole in terms of the knowledge of the parts. This is hard to achieve in many cases due to the difficulty of characterizing the many constituents and their complex web of interactions involved in a biological system. The lac promoter of E. coli offers a possibility of confronting system-leve properties of transcriptional regulation with the known biochemistry of the molecular constituents and their mutual interactions. Such confrontations can reveal previously unknown constituents and interactions, as well as offering new insight into how the components work together as a whole. Here we study the combinatorial control of the lac promoter by the regulators LacR and CRP. A previous in vivo study [Setty et al., PNAS 100: 7702-7 (2003)] found gross disagreement between the observed promoter activites and the expected behavior based on the known molecular mechanisms. We repeated the study by identifying and removing several extraneous factors which significantly modulated the expression of the lac promoter. Through quantitative, systematic characterization of promoter activity for a number of key mutants and guided by the thermodynamic model of transcriptional gene regulation, we are able to account for the combinatorial control of the lac promoter quantitatively, in terms of a cooperative interaction between CRP and LacR-mediated DNA looping. Specifically, our analysis indicates that the sensitivity of the inducer response results from LacR-mediated DNA looping, which is significantly enhanced by CRP. |
2004.06111 | Martina Mammarella Dr. | Teodoro Alamo and Daniel G. Reina and Martina Mammarella and Alberto
Abella | Open Data Resources for Fighting COVID-19 | 30 pages Minor improvements | null | null | null | q-bio.OT stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We provide an insight into the open data resources pertinent to the study of
the spread of Covid-19 pandemic and its control. We identify the variables
required to analyze fundamental aspects like seasonal behaviour, regional
mortality rates, and effectiveness of government measures. Open data resources,
along with data-driven methodologies, provide many opportunities to improve the
response of the different administrations to the virus. We describe the present
limitations and difficulties encountered in most of the open-data resources. To
facilitate the access to the main open-data portals and resources, we identify
the most relevant institutions, at a world scale, providing Covid-19
information and/or auxiliary variables (demographics, mobility, etc.). We also
describe several open resources to access Covid-19 data-sets at a country-wide
level (i.e. China, Italy, Spain, France, Germany, U.S., etc.). In an attempt to
facilitate the rapid response to the study of the seasonal behaviour of
Covid-19, we enumerate the main open resources in terms of weather and climate
variables. CONCO-Team: The authors of this paper belong to the CONtrol COvid-19
Team, which is composed of different researches from universities of Spain,
Italy, France, Germany, United Kingdom and Argentina. The main goal of
CONCO-Team is to develop data-driven methods for the better understanding and
control of the pandemic.
| [
{
"created": "Mon, 13 Apr 2020 09:52:53 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Apr 2020 09:56:29 GMT",
"version": "v2"
},
{
"created": "Mon, 11 May 2020 13:38:06 GMT",
"version": "v3"
}
] | 2020-05-12 | [
[
"Alamo",
"Teodoro",
""
],
[
"Reina",
"Daniel G.",
""
],
[
"Mammarella",
"Martina",
""
],
[
"Abella",
"Alberto",
""
]
] | We provide an insight into the open data resources pertinent to the study of the spread of Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behaviour, regional mortality rates, and effectiveness of government measures. Open data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, at a world scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 data-sets at a country-wide level (i.e. China, Italy, Spain, France, Germany, U.S., etc.). In an attempt to facilitate the rapid response to the study of the seasonal behaviour of Covid-19, we enumerate the main open resources in terms of weather and climate variables. CONCO-Team: The authors of this paper belong to the CONtrol COvid-19 Team, which is composed of different researches from universities of Spain, Italy, France, Germany, United Kingdom and Argentina. The main goal of CONCO-Team is to develop data-driven methods for the better understanding and control of the pandemic. |
1706.05451 | Amanda Parker | Amanda S. Parker, Krishnakumar M. Ravikumar, Daniel L. Cox | Molecular Dynamics-Based Strength Estimates of Beta-Solenoid Proteins | null | null | null | null | q-bio.BM q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The use of beta-solenoid proteins as functionalizable, nanoscale,
self-assembling molecular building blocks may have many applications, including
templating the growth of wires or higher-dimensional structures. By
understanding their mechanical strengths, we can efficiently design the
proteins for specific functions. We present a study of the mechanical
properties of seven beta-solenoid proteins using GROMACS molecular dynamics
software to produce force/torque-displacement data, implement umbrella sampling
of bending/twisting trajectories, produce Potentials of Mean Force (PMFs),
extract effective spring constants, and calculate rigidities for two bending
and two twisting directions for each protein. We examine the differences
between computing the strength values from force/torque-displacement data alone
and PMF data, and show how higher precision estimates can be obtained from the
former. In addition to the analysis of the methods, we report estimates for the
bend/twist persistence lengths for each protein, which range from 0.5-3.4
$\mu$m. We note that beta-solenoid proteins with internal disulfide bridges do
not enjoy enhanced bending or twisting strength, and that the strongest
correlate with bend/twist rigidity is the number of hydrogen bonds per turn. In
addition, we compute estimates of the Young's modulus ($Y$) for each protein,
which range from $Y$ = 3.5 to 7.2 GPa.
| [
{
"created": "Fri, 16 Jun 2017 23:02:50 GMT",
"version": "v1"
}
] | 2017-06-20 | [
[
"Parker",
"Amanda S.",
""
],
[
"Ravikumar",
"Krishnakumar M.",
""
],
[
"Cox",
"Daniel L.",
""
]
] | The use of beta-solenoid proteins as functionalizable, nanoscale, self-assembling molecular building blocks may have many applications, including templating the growth of wires or higher-dimensional structures. By understanding their mechanical strengths, we can efficiently design the proteins for specific functions. We present a study of the mechanical properties of seven beta-solenoid proteins using GROMACS molecular dynamics software to produce force/torque-displacement data, implement umbrella sampling of bending/twisting trajectories, produce Potentials of Mean Force (PMFs), extract effective spring constants, and calculate rigidities for two bending and two twisting directions for each protein. We examine the differences between computing the strength values from force/torque-displacement data alone and PMF data, and show how higher precision estimates can be obtained from the former. In addition to the analysis of the methods, we report estimates for the bend/twist persistence lengths for each protein, which range from 0.5-3.4 $\mu$m. We note that beta-solenoid proteins with internal disulfide bridges do not enjoy enhanced bending or twisting strength, and that the strongest correlate with bend/twist rigidity is the number of hydrogen bonds per turn. In addition, we compute estimates of the Young's modulus ($Y$) for each protein, which range from $Y$ = 3.5 to 7.2 GPa. |
2107.13537 | Carlos Diego Nascimento Damasceno | Fabio M. F. Lobato, Carlos D. N. Damasceno, P\'ericles L. Machado,
Nandamudi L. Vijaykumar, Andr\'e R. dos Santos, Sylvain H. Darnet, Andr\'e N.
A. Gon\c{c}alves, Dayse O. de Alencar, \'Adamo L. de Santana | Abordagem probabil\'istica para an\'alise de confiabilidade de dados
gerados em sequenciamentos multiplex na plataforma ABI SOLiD | 8 pages, 4 figures, 2 tables, Published in Portuguese in the Anais of
the XLIII Simp\'osio Brasileiro de Pesquisa Operacional (SBPO 2011), 2011.
URL: http://www.din.uem.br/sbpo/sbpo2011/pdf/87903.pdf | null | null | null | q-bio.GN cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The next-generation sequencers such as Illumina and SOLiD platforms generate
a large amount of data, commonly above 10 Gigabytes of text files.
Particularly, the SOLiD platform allows the sequencing of multiple samples in a
single run, called multiplex run, through a tagging system called Barcode. This
feature requires a computational process for separation of the data sample
because the sequencer provides a mixture of all samples in a single output.
This process must be secure to avoid any harm that may scramble further
analysis. In this context, realized the need to develop a probabilistic model
capable of assigning a degree of confidence in the marking system used in
multiplex sequencing. The results confirmed the adequacy of the model obtained,
which allows, among other things, to guide a process of filtering the data and
evaluation of the sequencing protocol used.
| [
{
"created": "Tue, 27 Jul 2021 15:33:42 GMT",
"version": "v1"
},
{
"created": "Wed, 11 Aug 2021 09:32:11 GMT",
"version": "v2"
}
] | 2021-08-12 | [
[
"Lobato",
"Fabio M. F.",
""
],
[
"Damasceno",
"Carlos D. N.",
""
],
[
"Machado",
"Péricles L.",
""
],
[
"Vijaykumar",
"Nandamudi L.",
""
],
[
"Santos",
"André R. dos",
""
],
[
"Darnet",
"Sylvain H.",
""
],
[
"Gonçalves",
"André N. A.",
""
],
[
"de Alencar",
"Dayse O.",
""
],
[
"de Santana",
"Ádamo L.",
""
]
] | The next-generation sequencers such as Illumina and SOLiD platforms generate a large amount of data, commonly above 10 Gigabytes of text files. Particularly, the SOLiD platform allows the sequencing of multiple samples in a single run, called multiplex run, through a tagging system called Barcode. This feature requires a computational process for separation of the data sample because the sequencer provides a mixture of all samples in a single output. This process must be secure to avoid any harm that may scramble further analysis. In this context, realized the need to develop a probabilistic model capable of assigning a degree of confidence in the marking system used in multiplex sequencing. The results confirmed the adequacy of the model obtained, which allows, among other things, to guide a process of filtering the data and evaluation of the sequencing protocol used. |
1310.6689 | James Kunert | James Kunert, Eli Shlizerman, J. Nathan Kutz | Low-dimensional functionality of complex network dynamics: Neuro-sensory
integration in the Caenorhabditis elegans connectome | null | null | 10.1103/PhysRevE.89.052805 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop a biophysical model of neuro-sensory integration in the model
organism Caenorhabditis elegans. Building on recent experimental findings of
the neuron conductances and their resolved connectome, we posit the first full
dynamic model of the neural voltage excitations that allows for a
characterization of input stimuli to behavioral responses. Thus a clear
connection between receptory cell inputs to downstream motor-responses is
illustrated, showing that robust, low-dimensional bifurcation structures
dominate neural pathways of activity. The underlying bifurcation structures
discovered, i.e. an induced Hopf bifurcation, are critical in explaining
behavioral responses such as swimming and crawling. More broadly, we
demonstrate that complex dynamical networks can produce robust functionality
from underlying low-dimensional bifurcations.
| [
{
"created": "Thu, 24 Oct 2013 18:10:40 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Feb 2014 19:11:09 GMT",
"version": "v2"
}
] | 2015-06-17 | [
[
"Kunert",
"James",
""
],
[
"Shlizerman",
"Eli",
""
],
[
"Kutz",
"J. Nathan",
""
]
] | We develop a biophysical model of neuro-sensory integration in the model organism Caenorhabditis elegans. Building on recent experimental findings of the neuron conductances and their resolved connectome, we posit the first full dynamic model of the neural voltage excitations that allows for a characterization of input stimuli to behavioral responses. Thus a clear connection between receptory cell inputs to downstream motor-responses is illustrated, showing that robust, low-dimensional bifurcation structures dominate neural pathways of activity. The underlying bifurcation structures discovered, i.e. an induced Hopf bifurcation, are critical in explaining behavioral responses such as swimming and crawling. More broadly, we demonstrate that complex dynamical networks can produce robust functionality from underlying low-dimensional bifurcations. |
1705.01079 | Delfim F. M. Torres | Amira Rachah, Delfim F. M. Torres | Analysis, simulation and optimal control of a SEIR model for Ebola virus
with demographic effects | This is a preprint of a paper whose final and definite form is with
'Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat.', ISSN: 1303-5991.
Submitted 27 Sept 2016; Article revised 05 Apr 2017; Article accepted for
publication 01 May 2017. arXiv admin note: text overlap with arXiv:1603.05794 | Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 67 (2018), no. 1,
179--197 | 10.1501/Commua1_0000000841 | null | q-bio.PE math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ebola virus is one of the most virulent pathogens for humans. We present a
mathematical description of different Susceptible-Exposed-Infectious-Recovered
(SEIR) models. By using mathematical modeling and analysis, the latest major
outbreak of Ebola virus in West Africa is described. Our aim is to study and
discuss the properties of SEIR models with respect to Ebola virus, the
information they provide, and when the models make sense. We added to the basic
SEIR model demographic effects in order to analyze the equilibria with vital
dynamics. Numerical simulations confirm the theoretical analysis. The control
of the propagation of the virus through vaccination is investigated and the
case study of Liberia is discussed in detail.
| [
{
"created": "Tue, 2 May 2017 17:22:48 GMT",
"version": "v1"
}
] | 2017-08-01 | [
[
"Rachah",
"Amira",
""
],
[
"Torres",
"Delfim F. M.",
""
]
] | Ebola virus is one of the most virulent pathogens for humans. We present a mathematical description of different Susceptible-Exposed-Infectious-Recovered (SEIR) models. By using mathematical modeling and analysis, the latest major outbreak of Ebola virus in West Africa is described. Our aim is to study and discuss the properties of SEIR models with respect to Ebola virus, the information they provide, and when the models make sense. We added to the basic SEIR model demographic effects in order to analyze the equilibria with vital dynamics. Numerical simulations confirm the theoretical analysis. The control of the propagation of the virus through vaccination is investigated and the case study of Liberia is discussed in detail. |
1611.04364 | Laurent Perrinet | Wahiba Taouali (INT), Giacomo Benvenuti (INT), Pascal Wallisch,
Fr\'ed\'eric Chavane (INT), Laurent Perrinet (INT) | Testing the Odds of Inherent versus Observed Over-dispersion in Neural
Spike Counts Odds of Inherent versus Observed Over-dispersion | null | Journal of Neurophysiology, American Physiological Society, 2016,
115 (1), pp.434-444 | 10.1152/jn.00194.2015 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The repeated presentation of an identical visual stimulus in the receptive
field of a neuron may evoke different spiking patterns at each trial.
Probabilistic methods are essential to understand the functional role of this
variance within the neural activity. In that case, a Poisson process is the
most common model of trial-to-trial variability. For a Poisson process, the
variance of the spike count is constrained to be equal to the mean,
irrespective of the duration of measurements. Numerous studies have shown that
this relationship does not generally hold. Specifically, a majority of
electrophysiological recordings show an " over-dispersion " effect: Responses
that exhibit more inter-trial variability than expected from a Poisson process
alone. A model that is particularly well suited to quantify over-dispersion is
the Negative-Binomial distribution model. This model is well-studied and widely
used but has only recently been applied to neuroscience. In this paper, we
address three main issues. First, we describe how the Negative-Binomial
distribution provides a model apt to account for overdispersed spike counts.
Second, we quantify the significance of this model for any neurophysiological
data by proposing a statistical test, which quantifies the odds that
over-dispersion could be due to the limited number of repetitions (trials). We
apply this test to three neurophysiological tests along the visual pathway.
Finally, we compare the performance of this model to the Poisson model on a
population decoding task. We show that the decoding accuracy is improved when
accounting for over-dispersion, especially under the hypothesis of tuned
over-dispersion.
| [
{
"created": "Mon, 14 Nov 2016 12:50:47 GMT",
"version": "v1"
}
] | 2016-11-15 | [
[
"Taouali",
"Wahiba",
"",
"INT"
],
[
"Benvenuti",
"Giacomo",
"",
"INT"
],
[
"Wallisch",
"Pascal",
"",
"INT"
],
[
"Chavane",
"Frédéric",
"",
"INT"
],
[
"Perrinet",
"Laurent",
"",
"INT"
]
] | The repeated presentation of an identical visual stimulus in the receptive field of a neuron may evoke different spiking patterns at each trial. Probabilistic methods are essential to understand the functional role of this variance within the neural activity. In that case, a Poisson process is the most common model of trial-to-trial variability. For a Poisson process, the variance of the spike count is constrained to be equal to the mean, irrespective of the duration of measurements. Numerous studies have shown that this relationship does not generally hold. Specifically, a majority of electrophysiological recordings show an " over-dispersion " effect: Responses that exhibit more inter-trial variability than expected from a Poisson process alone. A model that is particularly well suited to quantify over-dispersion is the Negative-Binomial distribution model. This model is well-studied and widely used but has only recently been applied to neuroscience. In this paper, we address three main issues. First, we describe how the Negative-Binomial distribution provides a model apt to account for overdispersed spike counts. Second, we quantify the significance of this model for any neurophysiological data by proposing a statistical test, which quantifies the odds that over-dispersion could be due to the limited number of repetitions (trials). We apply this test to three neurophysiological tests along the visual pathway. Finally, we compare the performance of this model to the Poisson model on a population decoding task. We show that the decoding accuracy is improved when accounting for over-dispersion, especially under the hypothesis of tuned over-dispersion. |
q-bio/0406014 | Ted Theodosopoulos | Patricia Theodosopoulos and Ted Theodosopoulos | Evolution at the Edge of Chaos: A Paradigm for the Maturation of the
Humoral Immune Response | 26 pages, 8 figures, presented at the DIMACS Workshop on Evolution as
Computation, 1998 | Evolution as Computation, Springer Natural Computing Series,
Landweber and Winfree eds., 2002, pp. 41-66 | null | null | q-bio.QM q-bio.PE | null | We study the maturation of the antibody population following primary antigen
presentation as a global optimization problem. Emphasis is placed on the
trade-off between the safety of mutations that lead to local improvements to
the antibody's affinity and the necessity of eventual mutations that result in
global reconfigurations in the antibody's shape. The model described herein
gives evidence of the underlying optimization process from which the rapidity
and consistency of the biologic response could be derived.
| [
{
"created": "Sun, 6 Jun 2004 01:24:53 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Theodosopoulos",
"Patricia",
""
],
[
"Theodosopoulos",
"Ted",
""
]
] | We study the maturation of the antibody population following primary antigen presentation as a global optimization problem. Emphasis is placed on the trade-off between the safety of mutations that lead to local improvements to the antibody's affinity and the necessity of eventual mutations that result in global reconfigurations in the antibody's shape. The model described herein gives evidence of the underlying optimization process from which the rapidity and consistency of the biologic response could be derived. |
1305.3244 | Edward Aboufadel | Edward Aboufadel | A Scoring System for Continuous Glucose Monitor Data | 12 pages, 10 figures, 1 table | null | null | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As continuous glucose monitors (CGMs) are used increasingly by diabetic
patients, new and intuitive tools are needed to help patients and their
physicians use these streams of data to improve blood glucose management. In
this paper, we propose a daily CGM Score which can be calculated from CGM data.
The calculation involves assigning grades and scores to 80-minute periods of
CGM data, and then aggregating the results. Scores for an individual patient,
or among a set of patients, can then be compared and contrasted, and
longitudinal studies of CGM data can also be accomplished.
| [
{
"created": "Sun, 12 May 2013 13:04:18 GMT",
"version": "v1"
}
] | 2013-05-15 | [
[
"Aboufadel",
"Edward",
""
]
] | As continuous glucose monitors (CGMs) are used increasingly by diabetic patients, new and intuitive tools are needed to help patients and their physicians use these streams of data to improve blood glucose management. In this paper, we propose a daily CGM Score which can be calculated from CGM data. The calculation involves assigning grades and scores to 80-minute periods of CGM data, and then aggregating the results. Scores for an individual patient, or among a set of patients, can then be compared and contrasted, and longitudinal studies of CGM data can also be accomplished. |
1712.09403 | Sergey Bereg | Sruthi Chappidi, Sergey Bereg | DCJVis: visualization of genome rearrangements using DCJ operations | null | null | null | null | q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The {\em double-cut-and-join} (DCJ) operation, introduced by Yancopoulos
\emph{et al.}, allows minimum edit distance to be computed by modeling all
possible classical rearrangement operations, such as inversions, fusions,
fissions, translocations, and transpositions, in linear-time between two
genomes. However, there is lack of visualization tool that can effectively
present DCJ operations that will help biologists to use DCJ operation. In this
paper, a new visualization program is introduced, DCJVis, to create a diagram
of each DCJ operation necessary to transform between the genomes of two
distinct organisms by describing a possible sequence of genome graphs based on
the selected gene adjacency on the source genome for the DCJ operation. Our
program is the first visualization tool for DCJ operations using circular
layout. Specifically, the genomes of \textit{Saccharomyces cerevisiae} and
\textit{Candida albicans} are used to demonstrate the functionality of this
program and provide an example of the type of problem this program can solve
for biologists.
| [
{
"created": "Tue, 5 Dec 2017 20:13:56 GMT",
"version": "v1"
}
] | 2017-12-29 | [
[
"Chappidi",
"Sruthi",
""
],
[
"Bereg",
"Sergey",
""
]
] | The {\em double-cut-and-join} (DCJ) operation, introduced by Yancopoulos \emph{et al.}, allows minimum edit distance to be computed by modeling all possible classical rearrangement operations, such as inversions, fusions, fissions, translocations, and transpositions, in linear-time between two genomes. However, there is lack of visualization tool that can effectively present DCJ operations that will help biologists to use DCJ operation. In this paper, a new visualization program is introduced, DCJVis, to create a diagram of each DCJ operation necessary to transform between the genomes of two distinct organisms by describing a possible sequence of genome graphs based on the selected gene adjacency on the source genome for the DCJ operation. Our program is the first visualization tool for DCJ operations using circular layout. Specifically, the genomes of \textit{Saccharomyces cerevisiae} and \textit{Candida albicans} are used to demonstrate the functionality of this program and provide an example of the type of problem this program can solve for biologists. |
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