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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1403.6779 | Leon Avery | Leon Avery | A model of the effect of uncertainty on the C elegans L2/L2d decision | null | PLoS ONE 9(7): e100580 | 10.1371/journal.pone.0100580 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | At the end of the first larval stage, the C elegans larva chooses between two
developmental pathways, an L2 committed to reproductive development and an L2d,
which has the option of undergoing reproductive development or entering the
dauer diapause. I develop a quantitative model of this choice using
mathematical tools developed for pricing financial options. The model predicts
that the optimal decision must take into account not only the expected
potential for reproductive growth, but also the uncertainty in that expected
potential. Because the L2d has more flexibility than the L2, it is favored in
unpredictable environments. I estimate that the ability to take uncertainty
into account may increase reproductive value by as much as 5%, and discuss
possible experimental tests for this ability.
| [
{
"created": "Wed, 26 Mar 2014 18:13:48 GMT",
"version": "v1"
},
{
"created": "Fri, 16 May 2014 14:34:34 GMT",
"version": "v2"
},
{
"created": "Fri, 25 Jul 2014 17:58:17 GMT",
"version": "v3"
}
] | 2014-07-29 | [
[
"Avery",
"Leon",
""
]
] | At the end of the first larval stage, the C elegans larva chooses between two developmental pathways, an L2 committed to reproductive development and an L2d, which has the option of undergoing reproductive development or entering the dauer diapause. I develop a quantitative model of this choice using mathematical tools developed for pricing financial options. The model predicts that the optimal decision must take into account not only the expected potential for reproductive growth, but also the uncertainty in that expected potential. Because the L2d has more flexibility than the L2, it is favored in unpredictable environments. I estimate that the ability to take uncertainty into account may increase reproductive value by as much as 5%, and discuss possible experimental tests for this ability. |
2301.00548 | Niv DeMalach | David Sampson Issaka, Or Gross, Itunuoluwa Ayilara, Talia Schabes, Niv
DeMalach | Density-dependent and independent mechanisms jointly reduce species
performance under nitrogen enrichment | null | null | 10.1111/oik.09838 | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Nitrogen (N) deposition is a primary driver of species loss in plant
communities globally. However, the mechanisms by which high N availability
causes species loss remain unclear. Many hypotheses for species loss with
increasing N availability highlight density-dependent mechanisms, i.e., changes
in species interactions. However, an alternative set of hypotheses highlights
density-independent detrimental effects of nitrogen (e.g., N toxicity). We
tested the role of density-dependent and density-independent mechanisms in
reducing species performance. For this aim, we used 120 experimental plant
communities comprised of annual species growing together in containers under
four fertilization treatments: (1) no nutrient addition(, (2) all nutrients
except N (P, K, and micronutrients), (3) Low N, and (4) high N. Each
fertilization treatment included two sowing densities to differentiate between
the effects of competition (N * density interactions) and other detrimental
effects of N. We focused on three performance attributes: the probability of
reaching the reproduction period, biomass growth, and population growth. We
found that individual biomass and population growth rates decreased with
increasing sowing density in all nutrient treatments, implying that species
interactions were predominantly negative. The common grass had a higher biomass
and population growth under N enrichment, regardless of sowing density. In
contrast, the legume showed a density-independent reduction in biomass growth
with increasing N. Lastly, the small forb showed a density-dependent reduction
in population growth, i.e., the decline occurred only under high density. Our
results demonstrate that density-dependent and density-independent mechanisms
operate simultaneously to reduce species performance under high N availability.
Yet, their relative importance varies among species and life stages.
| [
{
"created": "Mon, 2 Jan 2023 07:24:22 GMT",
"version": "v1"
}
] | 2023-04-18 | [
[
"Issaka",
"David Sampson",
""
],
[
"Gross",
"Or",
""
],
[
"Ayilara",
"Itunuoluwa",
""
],
[
"Schabes",
"Talia",
""
],
[
"DeMalach",
"Niv",
""
]
] | Nitrogen (N) deposition is a primary driver of species loss in plant communities globally. However, the mechanisms by which high N availability causes species loss remain unclear. Many hypotheses for species loss with increasing N availability highlight density-dependent mechanisms, i.e., changes in species interactions. However, an alternative set of hypotheses highlights density-independent detrimental effects of nitrogen (e.g., N toxicity). We tested the role of density-dependent and density-independent mechanisms in reducing species performance. For this aim, we used 120 experimental plant communities comprised of annual species growing together in containers under four fertilization treatments: (1) no nutrient addition(, (2) all nutrients except N (P, K, and micronutrients), (3) Low N, and (4) high N. Each fertilization treatment included two sowing densities to differentiate between the effects of competition (N * density interactions) and other detrimental effects of N. We focused on three performance attributes: the probability of reaching the reproduction period, biomass growth, and population growth. We found that individual biomass and population growth rates decreased with increasing sowing density in all nutrient treatments, implying that species interactions were predominantly negative. The common grass had a higher biomass and population growth under N enrichment, regardless of sowing density. In contrast, the legume showed a density-independent reduction in biomass growth with increasing N. Lastly, the small forb showed a density-dependent reduction in population growth, i.e., the decline occurred only under high density. Our results demonstrate that density-dependent and density-independent mechanisms operate simultaneously to reduce species performance under high N availability. Yet, their relative importance varies among species and life stages. |
1902.00483 | Stefan Bornholdt | Stefan Bornholdt and Stuart Kauffman | Ensembles, Dynamics, and Cell Types: Revisiting the Statistical
Mechanics Perspective on Cellular Regulation | 22 pages, article will be included in a special issue of J. Theor.
Biol. dedicated to the memory of Prof. Rene Thomas | null | null | null | q-bio.MN cond-mat.dis-nn physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Genetic regulatory networks control ontogeny. For fifty years Boolean
networks have served as models of such systems, ranging from ensembles of
random Boolean networks as models for generic properties of gene regulation to
working dynamical models of a growing number of sub-networks of real cells. At
the same time, their statistical mechanics has been thoroughly studied. Here we
recapitulate their original motivation in the context of current theoretical
and empirical research. We discuss ensembles of random Boolean networks whose
dynamical attractors model cell types. A sub-ensemble is the critical ensemble.
There is now strong evidence that genetic regulatory networks are dynamically
critical, and that evolution is exploring the critical sub-ensemble. The
generic properties of this sub-ensemble predict essential features of cell
differentiation. In particular, the number of attractors in such networks
scales as the DNA content raised to the 0.63 power. Data on the number of cell
types as a function of the DNA content per cell shows a scaling relationship of
0.88. Thus, the theory correctly predicts a power law relationship between the
number of cell types and the DNA contents per cell, and a comparable slope. We
discuss these new scaling values and show prospects for new research lines for
Boolean networks as a base model for systems biology.
| [
{
"created": "Fri, 1 Feb 2019 17:58:35 GMT",
"version": "v1"
}
] | 2019-02-04 | [
[
"Bornholdt",
"Stefan",
""
],
[
"Kauffman",
"Stuart",
""
]
] | Genetic regulatory networks control ontogeny. For fifty years Boolean networks have served as models of such systems, ranging from ensembles of random Boolean networks as models for generic properties of gene regulation to working dynamical models of a growing number of sub-networks of real cells. At the same time, their statistical mechanics has been thoroughly studied. Here we recapitulate their original motivation in the context of current theoretical and empirical research. We discuss ensembles of random Boolean networks whose dynamical attractors model cell types. A sub-ensemble is the critical ensemble. There is now strong evidence that genetic regulatory networks are dynamically critical, and that evolution is exploring the critical sub-ensemble. The generic properties of this sub-ensemble predict essential features of cell differentiation. In particular, the number of attractors in such networks scales as the DNA content raised to the 0.63 power. Data on the number of cell types as a function of the DNA content per cell shows a scaling relationship of 0.88. Thus, the theory correctly predicts a power law relationship between the number of cell types and the DNA contents per cell, and a comparable slope. We discuss these new scaling values and show prospects for new research lines for Boolean networks as a base model for systems biology. |
1211.6644 | Yuri Shestopaloff | Yu. K. Shestopaloff | General law of growth and replication. Growth equation and its
applications | 53 pages, 17 figures, 4 tables | Biophysical Reviews and Letters, 2012 Vol. 7, No. 1-2, p. 71-120 | 10.1142/S1793048012500051 | null | q-bio.OT physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present significantly advanced studies of the previously introduced
physical growth mechanism and unite it with biochemical growth factors.
Obtained results allowed formulating the general growth law which governs
growth and evolutional development of all living organisms, their organs and
systems. It was discovered that the growth cycle is predefined by the
distribution of nutritional resources between maintenance needs and biomass
production. This distribution is quantitatively defined by the growth ratio
parameter, which depends on the geometry of an organism, phase of growth and,
indirectly, organism's biochemical machinery. The amount of produced biomass,
in turn, defines the composition of biochemical reactions. Changing amount of
nutrients diverted to biomass production is what forces organisms to proceed
through the whole growth and replication cycle. The growth law can be
formulated as follows: the rate of growth is proportional to influx of
nutrients and growth ratio. Considering specific biochemical components of
different organisms, we find influxes of required nutrients and substitute them
into the growth equation; then, we compute growth curves for amoeba, wild type
fission yeast, fission yeast's mutant. In all cases, predicted growth curves
correspond very well to experimental data. Obtained results prove validity and
fundamental scientific value of the discovery.
| [
{
"created": "Wed, 28 Nov 2012 16:16:37 GMT",
"version": "v1"
}
] | 2012-11-29 | [
[
"Shestopaloff",
"Yu. K.",
""
]
] | We present significantly advanced studies of the previously introduced physical growth mechanism and unite it with biochemical growth factors. Obtained results allowed formulating the general growth law which governs growth and evolutional development of all living organisms, their organs and systems. It was discovered that the growth cycle is predefined by the distribution of nutritional resources between maintenance needs and biomass production. This distribution is quantitatively defined by the growth ratio parameter, which depends on the geometry of an organism, phase of growth and, indirectly, organism's biochemical machinery. The amount of produced biomass, in turn, defines the composition of biochemical reactions. Changing amount of nutrients diverted to biomass production is what forces organisms to proceed through the whole growth and replication cycle. The growth law can be formulated as follows: the rate of growth is proportional to influx of nutrients and growth ratio. Considering specific biochemical components of different organisms, we find influxes of required nutrients and substitute them into the growth equation; then, we compute growth curves for amoeba, wild type fission yeast, fission yeast's mutant. In all cases, predicted growth curves correspond very well to experimental data. Obtained results prove validity and fundamental scientific value of the discovery. |
2101.00823 | Philip Gerlee | Philip Gerlee, Julia Karlsson, Ingrid Fritzell, Thomas Brezicka, Armin
Spreco, Toomas Timpka, Anna J\"oud, Torbj\"orn Lundh | Predicting regional COVID-19 hospital admissions in Sweden using
mobility data | null | null | null | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The transmission of COVID-19 is dependent on social contacts, the rate of
which have varied during the pandemic due to mandated and voluntary social
distancing. Changes in transmission dynamics eventually affect hospital
admissions and we have used this connection in order to model and predict
regional hospital admissions in Sweden during the COVID-19 pandemic. We use an
SEIR-model for each region in Sweden in which the infectivity is assumed to
depend on mobility data in terms of public transport utilisation and mobile
phone usage. The results show that the model can capture the timing of the
first and beginning of the second wave of the pandemic. Further, we show that
for two major regions of Sweden models with public transport data outperform
models using mobile phone usage. The model assumes a three week delay from
disease transmission to hospitalisation which makes it possible to use current
mobility data to predict future admissions.
| [
{
"created": "Mon, 4 Jan 2021 08:18:53 GMT",
"version": "v1"
}
] | 2021-01-05 | [
[
"Gerlee",
"Philip",
""
],
[
"Karlsson",
"Julia",
""
],
[
"Fritzell",
"Ingrid",
""
],
[
"Brezicka",
"Thomas",
""
],
[
"Spreco",
"Armin",
""
],
[
"Timpka",
"Toomas",
""
],
[
"Jöud",
"Anna",
""
],
[
"Lundh",
"Torbjörn",
""
]
] | The transmission of COVID-19 is dependent on social contacts, the rate of which have varied during the pandemic due to mandated and voluntary social distancing. Changes in transmission dynamics eventually affect hospital admissions and we have used this connection in order to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the infectivity is assumed to depend on mobility data in terms of public transport utilisation and mobile phone usage. The results show that the model can capture the timing of the first and beginning of the second wave of the pandemic. Further, we show that for two major regions of Sweden models with public transport data outperform models using mobile phone usage. The model assumes a three week delay from disease transmission to hospitalisation which makes it possible to use current mobility data to predict future admissions. |
1901.03596 | Francisco Herrer\'ias-Azcu\'e Mr. | Francisco Herrer\'ias-Azcu\'e, Vicente P\'erez-Mu\~nuzuri and Tobias
Galla | Motion, fixation probability and the choice of an evolutionary process | null | null | 10.1371/journal.pcbi.1007238 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Different evolutionary models are known to make disparate predictions for the
success of an invading mutant in some situations. For example, some
evolutionary mechanics lead to amplification of selection in structured
populations, while others suppress it. Here, we use computer simulations to
study evolutionary populations moved by flows, and show how the speed of this
motion impacts the fixation probability of an invading mutant. Flows of
different speeds interpolate between evolutionary dynamics on fixed
heterogeneous graphs and in well-stirred populations. We find that the motion
has an active role in amplifying or suppressing selection, accomplished by
fragmenting and reconnecting the interaction graph. While increasing flow
speeds suppress selection for most evolutionary models, we identify
characteristic responses to flow for the different update rules we test. We
suggest these responses as a potential aid for choosing the most suitable
update rule for a given biological system.
| [
{
"created": "Fri, 11 Jan 2019 14:36:52 GMT",
"version": "v1"
}
] | 2020-07-01 | [
[
"Herrerías-Azcué",
"Francisco",
""
],
[
"Pérez-Muñuzuri",
"Vicente",
""
],
[
"Galla",
"Tobias",
""
]
] | Different evolutionary models are known to make disparate predictions for the success of an invading mutant in some situations. For example, some evolutionary mechanics lead to amplification of selection in structured populations, while others suppress it. Here, we use computer simulations to study evolutionary populations moved by flows, and show how the speed of this motion impacts the fixation probability of an invading mutant. Flows of different speeds interpolate between evolutionary dynamics on fixed heterogeneous graphs and in well-stirred populations. We find that the motion has an active role in amplifying or suppressing selection, accomplished by fragmenting and reconnecting the interaction graph. While increasing flow speeds suppress selection for most evolutionary models, we identify characteristic responses to flow for the different update rules we test. We suggest these responses as a potential aid for choosing the most suitable update rule for a given biological system. |
2301.01110 | Jacob Rast | Jacob Rast | Causal Discovery for Gene Regulatory Network Prediction | null | null | null | null | q-bio.MN cs.AI | http://creativecommons.org/licenses/by/4.0/ | Biological systems and processes are networks of complex nonlinear regulatory
interactions between nucleic acids, proteins, and metabolites. A natural way in
which to represent these interaction networks is through the use of a graph. In
this formulation, each node represents a nucleic acid, protein, or metabolite
and edges represent intermolecular interactions (inhibition, regulation,
promotion, coexpression, etc.). In this work, a novel algorithm for the
discovery of latent graph structures given experimental data is presented.
| [
{
"created": "Tue, 3 Jan 2023 14:11:00 GMT",
"version": "v1"
}
] | 2023-01-04 | [
[
"Rast",
"Jacob",
""
]
] | Biological systems and processes are networks of complex nonlinear regulatory interactions between nucleic acids, proteins, and metabolites. A natural way in which to represent these interaction networks is through the use of a graph. In this formulation, each node represents a nucleic acid, protein, or metabolite and edges represent intermolecular interactions (inhibition, regulation, promotion, coexpression, etc.). In this work, a novel algorithm for the discovery of latent graph structures given experimental data is presented. |
2110.03907 | Albert Christian Soewongsono | Albert Ch. Soewongsono (1), Barbara R. Holland (1), Ma{\l}gorzata M.
O'Reilly (1) (School of Natural Sciences, Discipline of Mathematics,
University of Tasmania) | The Shape of Phylogenies Under Phase-Type Distributed Times to
Speciation and Extinction | 32 pages, 14 figures, 2 tables | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | Phylogenetic trees are widely used to understand the evolutionary history of
organisms. Tree shapes provide information about macroevolutionary processes.
However, macroevolutionary models are unreliable for inferring the true
processes underlying empirical trees. Here, we propose a flexible and
biologically plausible macroevolutionary model for phylogenetic trees where
times to speciation or extinction events are drawn from a Coxian phase-type
(PH) distribution. First, we show that different choices of parameters in our
model lead to a range of tree balances as measured by Aldous' $\beta$
statistic. In particular, we demonstrate that it is possible to find parameters
that correspond well to empirical tree balance. Next, we provide a natural
extension of the $\beta$ statistic to sets of trees. This extension produces
less biased estimates of $\beta$ compared to using the median $\beta$ values
from individual trees. Furthermore, we derive a likelihood expression for the
probability of observing any tree with branch lengths under a model with
speciation but no extinction. Finally, we illustrate the application of our
model by performing both absolute and relative goodness-of-fit tests for two
large empirical phylogenies (squamates and angiosperms) that compare models
with Coxian PH distributed times to speciation with models that assume
exponential or Weibull distributed waiting times. In our numerical analysis, we
found that, in most cases, models assuming a Coxian PH distribution provided
the best fit.
| [
{
"created": "Fri, 8 Oct 2021 06:01:36 GMT",
"version": "v1"
}
] | 2021-10-11 | [
[
"Soewongsono",
"Albert Ch.",
""
],
[
"Holland",
"Barbara R.",
""
],
[
"O'Reilly",
"Małgorzata M.",
""
]
] | Phylogenetic trees are widely used to understand the evolutionary history of organisms. Tree shapes provide information about macroevolutionary processes. However, macroevolutionary models are unreliable for inferring the true processes underlying empirical trees. Here, we propose a flexible and biologically plausible macroevolutionary model for phylogenetic trees where times to speciation or extinction events are drawn from a Coxian phase-type (PH) distribution. First, we show that different choices of parameters in our model lead to a range of tree balances as measured by Aldous' $\beta$ statistic. In particular, we demonstrate that it is possible to find parameters that correspond well to empirical tree balance. Next, we provide a natural extension of the $\beta$ statistic to sets of trees. This extension produces less biased estimates of $\beta$ compared to using the median $\beta$ values from individual trees. Furthermore, we derive a likelihood expression for the probability of observing any tree with branch lengths under a model with speciation but no extinction. Finally, we illustrate the application of our model by performing both absolute and relative goodness-of-fit tests for two large empirical phylogenies (squamates and angiosperms) that compare models with Coxian PH distributed times to speciation with models that assume exponential or Weibull distributed waiting times. In our numerical analysis, we found that, in most cases, models assuming a Coxian PH distribution provided the best fit. |
1509.01663 | Louxin Zhang | Louxin Zhang | On Tree Based Phylogenetic Networks | 17 pages, 6 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A large class of phylogenetic networks can be obtained from trees by the
addition of horizontal edges between the tree edges. These networks are called
tree based networks. Reticulation-visible networks and child-sibling networks
are all tree based. In this work, we present a simply necessary and sufficient
condition for tree-based networks and prove that there is a universal tree
based network for each set of species such that every phylogenetic tree on the
same species is a base of this network. The existence of universal tree based
network implies that for any given set of phylogenetic trees (resp. clusters)
on the same species there exists a tree base network that display all of them.
| [
{
"created": "Sat, 5 Sep 2015 04:50:39 GMT",
"version": "v1"
},
{
"created": "Tue, 8 Sep 2015 10:23:02 GMT",
"version": "v2"
}
] | 2015-09-09 | [
[
"Zhang",
"Louxin",
""
]
] | A large class of phylogenetic networks can be obtained from trees by the addition of horizontal edges between the tree edges. These networks are called tree based networks. Reticulation-visible networks and child-sibling networks are all tree based. In this work, we present a simply necessary and sufficient condition for tree-based networks and prove that there is a universal tree based network for each set of species such that every phylogenetic tree on the same species is a base of this network. The existence of universal tree based network implies that for any given set of phylogenetic trees (resp. clusters) on the same species there exists a tree base network that display all of them. |
0707.1295 | Riccardo Zecchina | Carlo Baldassi, Alfredo Braunstein, Nicolas Brunel, Riccardo Zecchina | Efficient supervised learning in networks with binary synapses | 10 pages, 4 figures | PNAS 104, 11079-11084 (2007) | 10.1073/pnas.0700324104 | null | q-bio.NC cond-mat.stat-mech cs.NE q-bio.QM | null | Recent experimental studies indicate that synaptic changes induced by
neuronal activity are discrete jumps between a small number of stable states.
Learning in systems with discrete synapses is known to be a computationally
hard problem. Here, we study a neurobiologically plausible on-line learning
algorithm that derives from Belief Propagation algorithms. We show that it
performs remarkably well in a model neuron with binary synapses, and a finite
number of `hidden' states per synapse, that has to learn a random
classification task. Such system is able to learn a number of associations
close to the theoretical limit, in time which is sublinear in system size. This
is to our knowledge the first on-line algorithm that is able to achieve
efficiently a finite number of patterns learned per binary synapse.
Furthermore, we show that performance is optimal for a finite number of hidden
states which becomes very small for sparse coding. The algorithm is similar to
the standard `perceptron' learning algorithm, with an additional rule for
synaptic transitions which occur only if a currently presented pattern is
`barely correct'. In this case, the synaptic changes are meta-plastic only
(change in hidden states and not in actual synaptic state), stabilizing the
synapse in its current state. Finally, we show that a system with two visible
states and K hidden states is much more robust to noise than a system with K
visible states. We suggest this rule is sufficiently simple to be easily
implemented by neurobiological systems or in hardware.
| [
{
"created": "Mon, 9 Jul 2007 16:23:55 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Baldassi",
"Carlo",
""
],
[
"Braunstein",
"Alfredo",
""
],
[
"Brunel",
"Nicolas",
""
],
[
"Zecchina",
"Riccardo",
""
]
] | Recent experimental studies indicate that synaptic changes induced by neuronal activity are discrete jumps between a small number of stable states. Learning in systems with discrete synapses is known to be a computationally hard problem. Here, we study a neurobiologically plausible on-line learning algorithm that derives from Belief Propagation algorithms. We show that it performs remarkably well in a model neuron with binary synapses, and a finite number of `hidden' states per synapse, that has to learn a random classification task. Such system is able to learn a number of associations close to the theoretical limit, in time which is sublinear in system size. This is to our knowledge the first on-line algorithm that is able to achieve efficiently a finite number of patterns learned per binary synapse. Furthermore, we show that performance is optimal for a finite number of hidden states which becomes very small for sparse coding. The algorithm is similar to the standard `perceptron' learning algorithm, with an additional rule for synaptic transitions which occur only if a currently presented pattern is `barely correct'. In this case, the synaptic changes are meta-plastic only (change in hidden states and not in actual synaptic state), stabilizing the synapse in its current state. Finally, we show that a system with two visible states and K hidden states is much more robust to noise than a system with K visible states. We suggest this rule is sufficiently simple to be easily implemented by neurobiological systems or in hardware. |
1511.00921 | \'Etienne Fodor | \'Etienne Fodor, Wylie W. Ahmed, Maria Almonacid, Matthias Bussonnier,
Nir S. Gov, Marie-H\'el\`ene Verlhac, Timo Betz, Paolo Visco, Fr\'ed\'eric
van Wijland | Nonequilibrium dissipation in living oocytes | 5 pages, 2 figures | EPL 116, 30008 (2016) | 10.1209/0295-5075/116/30008 | null | q-bio.SC cond-mat.soft cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Living organisms are inherently out-of-equilibrium systems. We employ new
developments in stochastic energetics and rely on a minimal microscopic model
to predict the amount of mechanical energy dissipated by such dynamics. Our
model includes complex rheological effects and nonequilibrium stochastic
forces. By performing active microrheology and tracking micron-sized vesicles
in the cytoplasm of living oocytes, we provide unprecedented measurements of
the spectrum of dissipated energy. We show that our model is fully consistent
with the experimental data, and we use it to offer predictions for the
injection and dissipation energy scales involved in active fluctuations.
| [
{
"created": "Tue, 3 Nov 2015 14:31:13 GMT",
"version": "v1"
},
{
"created": "Thu, 22 Dec 2016 14:42:27 GMT",
"version": "v2"
}
] | 2016-12-23 | [
[
"Fodor",
"Étienne",
""
],
[
"Ahmed",
"Wylie W.",
""
],
[
"Almonacid",
"Maria",
""
],
[
"Bussonnier",
"Matthias",
""
],
[
"Gov",
"Nir S.",
""
],
[
"Verlhac",
"Marie-Hélène",
""
],
[
"Betz",
"Timo",
""
],
[
"Visco",
"Paolo",
""
],
[
"van Wijland",
"Frédéric",
""
]
] | Living organisms are inherently out-of-equilibrium systems. We employ new developments in stochastic energetics and rely on a minimal microscopic model to predict the amount of mechanical energy dissipated by such dynamics. Our model includes complex rheological effects and nonequilibrium stochastic forces. By performing active microrheology and tracking micron-sized vesicles in the cytoplasm of living oocytes, we provide unprecedented measurements of the spectrum of dissipated energy. We show that our model is fully consistent with the experimental data, and we use it to offer predictions for the injection and dissipation energy scales involved in active fluctuations. |
2008.03165 | Giannis Koutsou | Constantia Alexandrou, Vangelis Harmandaris, Anastasios Irakleous,
Giannis Koutsou, and Nikos Savva | Modeling the evolution of COVID-19 via compartmental and particle-based
approaches: application to the Cyprus case | Changes in v2: Updated to match published version; 21 pages, 8
figures, 1 table | PLOS ONE 16(5): e0250709 (2021) | 10.1371/journal.pone.0250709 10.17605/OSF.IO/BP79H | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present two different approaches for modeling the spread of the COVID-19
pandemic. Both approaches are based on the population classes susceptible,
exposed, infectious, quarantined, and recovered and allow for an arbitrary
number of subgroups with different infection rates and different levels of
testing. The first model is derived from a set of ordinary differential
equations that incorporates the rates at which population transitions take
place among classes. The other is a particle model, which is a specific case of
crowd simulation model, in which the disease is transmitted through particle
collisions and infection rates are varied by adjusting the particle velocities.
The parameters of these two models are tuned using information on COVID-19 from
the literature and country-specific data, including the effect of restrictions
as they were imposed and lifted. We demonstrate the applicability of both
models using data from Cyprus, for which we find that both models yield very
similar results, giving confidence in the predictions.
| [
{
"created": "Fri, 7 Aug 2020 13:16:49 GMT",
"version": "v1"
},
{
"created": "Mon, 10 May 2021 15:41:17 GMT",
"version": "v2"
}
] | 2021-05-11 | [
[
"Alexandrou",
"Constantia",
""
],
[
"Harmandaris",
"Vangelis",
""
],
[
"Irakleous",
"Anastasios",
""
],
[
"Koutsou",
"Giannis",
""
],
[
"Savva",
"Nikos",
""
]
] | We present two different approaches for modeling the spread of the COVID-19 pandemic. Both approaches are based on the population classes susceptible, exposed, infectious, quarantined, and recovered and allow for an arbitrary number of subgroups with different infection rates and different levels of testing. The first model is derived from a set of ordinary differential equations that incorporates the rates at which population transitions take place among classes. The other is a particle model, which is a specific case of crowd simulation model, in which the disease is transmitted through particle collisions and infection rates are varied by adjusting the particle velocities. The parameters of these two models are tuned using information on COVID-19 from the literature and country-specific data, including the effect of restrictions as they were imposed and lifted. We demonstrate the applicability of both models using data from Cyprus, for which we find that both models yield very similar results, giving confidence in the predictions. |
q-bio/0502014 | Edward Lyman . D. | Edward Lyman, F. Marty Ytreberg, and Daniel M. Zuckerman | Resolution exchange simulation | revised manuscript: 4.2 pages, 3 figures | Phys. Rev. Lett. v.96:028105(2006) | 10.1103/PhysRevLett.96.028105 | null | q-bio.BM physics.bio-ph | null | We extend replica exchange simulation in two ways, and apply our approaches
to biomolecules. The first generalization permits exchange simulation between
models of differing resolution -- i.e., between detailed and coarse-grained
models. Such ``resolution exchange'' can be applied to molecular systems or
spin systems. The second extension is to ``pseudo-exchange'' simulations, which
require little CPU usage for most levels of the exchange ladder and also
substantially reduces the need for overlap between levels. Pseudo exchanges can
be used in either replica or resolution exchange simulations. We perform
efficient, converged simulations of a 50-atom peptide to illustrate the new
approaches.
| [
{
"created": "Sun, 13 Feb 2005 19:52:26 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Jun 2005 19:47:55 GMT",
"version": "v2"
},
{
"created": "Mon, 15 Aug 2005 17:28:36 GMT",
"version": "v3"
},
{
"created": "Tue, 22 Nov 2005 21:42:39 GMT",
"version": "v4"
}
] | 2009-11-11 | [
[
"Lyman",
"Edward",
""
],
[
"Ytreberg",
"F. Marty",
""
],
[
"Zuckerman",
"Daniel M.",
""
]
] | We extend replica exchange simulation in two ways, and apply our approaches to biomolecules. The first generalization permits exchange simulation between models of differing resolution -- i.e., between detailed and coarse-grained models. Such ``resolution exchange'' can be applied to molecular systems or spin systems. The second extension is to ``pseudo-exchange'' simulations, which require little CPU usage for most levels of the exchange ladder and also substantially reduces the need for overlap between levels. Pseudo exchanges can be used in either replica or resolution exchange simulations. We perform efficient, converged simulations of a 50-atom peptide to illustrate the new approaches. |
2310.16908 | Arvid Ernst Gollwitzer | Maximilian-David Rumpf, Mohammed Alser, Arvid E. Gollwitzer, Joel
Lindegger, Nour Almadhoun, Can Firtina, Serghei Mangul, Onur Mutlu | SequenceLab: A Comprehensive Benchmark of Computational Methods for
Comparing Genomic Sequences | null | null | null | null | q-bio.GN cs.AR q-bio.QM | http://creativecommons.org/licenses/by-sa/4.0/ | Computational complexity is a key limitation of genomic analyses. Thus, over
the last 30 years, researchers have proposed numerous fast heuristic methods
that provide computational relief. Comparing genomic sequences is one of the
most fundamental computational steps in most genomic analyses. Due to its high
computational complexity, optimized exact and heuristic algorithms are still
being developed. We find that these methods are highly sensitive to the
underlying data, its quality, and various hyperparameters. Despite their wide
use, no in-depth analysis has been performed, potentially falsely discarding
genetic sequences from further analysis and unnecessarily inflating
computational costs. We provide the first analysis and benchmark of this
heterogeneity. We deliver an actionable overview of the 11 most widely used
state-of-the-art methods for comparing genomic sequences. We also inform
readers about their advantages and downsides using thorough experimental
evaluation and different real datasets from all major manufacturers (i.e.,
Illumina, ONT, and PacBio). SequenceLab is publicly available at
https://github.com/CMU-SAFARI/SequenceLab.
| [
{
"created": "Wed, 25 Oct 2023 18:17:46 GMT",
"version": "v1"
},
{
"created": "Sun, 12 Nov 2023 16:07:25 GMT",
"version": "v2"
},
{
"created": "Sun, 7 Jan 2024 16:04:16 GMT",
"version": "v3"
},
{
"created": "Sun, 21 Jan 2024 15:14:32 GMT",
"version": "v4"
}
] | 2024-01-23 | [
[
"Rumpf",
"Maximilian-David",
""
],
[
"Alser",
"Mohammed",
""
],
[
"Gollwitzer",
"Arvid E.",
""
],
[
"Lindegger",
"Joel",
""
],
[
"Almadhoun",
"Nour",
""
],
[
"Firtina",
"Can",
""
],
[
"Mangul",
"Serghei",
""
],
[
"Mutlu",
"Onur",
""
]
] | Computational complexity is a key limitation of genomic analyses. Thus, over the last 30 years, researchers have proposed numerous fast heuristic methods that provide computational relief. Comparing genomic sequences is one of the most fundamental computational steps in most genomic analyses. Due to its high computational complexity, optimized exact and heuristic algorithms are still being developed. We find that these methods are highly sensitive to the underlying data, its quality, and various hyperparameters. Despite their wide use, no in-depth analysis has been performed, potentially falsely discarding genetic sequences from further analysis and unnecessarily inflating computational costs. We provide the first analysis and benchmark of this heterogeneity. We deliver an actionable overview of the 11 most widely used state-of-the-art methods for comparing genomic sequences. We also inform readers about their advantages and downsides using thorough experimental evaluation and different real datasets from all major manufacturers (i.e., Illumina, ONT, and PacBio). SequenceLab is publicly available at https://github.com/CMU-SAFARI/SequenceLab. |
1809.01127 | Roman Kaplan | Roman Kaplan, Leonid Yavits and Ran Ginosar | RASSA: Resistive Pre-Alignment Accelerator for Approximate DNA Long Read
Mapping | null | null | null | null | q-bio.GN cs.ET | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | DNA read mapping is a computationally expensive bioinformatics task, required
for genome assembly and consensus polishing. It requires to find the
best-fitting location for each DNA read on a long reference sequence. A novel
resistive approximate similarity search accelerator, RASSA, exploits charge
distribution and parallel in-memory processing to reflect a mismatch count
between DNA sequences. RASSA implementation of DNA long read pre-alignment
outperforms the state-of-art solution, minimap2, by 16-77x with comparable
accuracy and provides two orders of magnitude higher throughput than
GateKeeper, a short-read pre-alignment hardware architecture implemented in
FPGA.
| [
{
"created": "Sun, 2 Sep 2018 17:33:47 GMT",
"version": "v1"
},
{
"created": "Sun, 7 Oct 2018 08:04:12 GMT",
"version": "v2"
},
{
"created": "Mon, 28 Jan 2019 12:58:32 GMT",
"version": "v3"
}
] | 2019-01-29 | [
[
"Kaplan",
"Roman",
""
],
[
"Yavits",
"Leonid",
""
],
[
"Ginosar",
"Ran",
""
]
] | DNA read mapping is a computationally expensive bioinformatics task, required for genome assembly and consensus polishing. It requires to find the best-fitting location for each DNA read on a long reference sequence. A novel resistive approximate similarity search accelerator, RASSA, exploits charge distribution and parallel in-memory processing to reflect a mismatch count between DNA sequences. RASSA implementation of DNA long read pre-alignment outperforms the state-of-art solution, minimap2, by 16-77x with comparable accuracy and provides two orders of magnitude higher throughput than GateKeeper, a short-read pre-alignment hardware architecture implemented in FPGA. |
q-bio/0412009 | Krzysztof Kulakowski | M. J. Krawczyk and K. Kulakowski | Off-lattice simulation of the solid phase DNA amplification | 8 pages, 5 figures | Comp. Phys. Commun. 170 (2005) 131 | 10.1016/j.cpc.2005.03.108 | null | q-bio.BM q-bio.QM | null | Recent simulations of the solid phase DNA amplification (SPA) by J.-F.
Mercier et al (Biophys. J. 85 (2003) 2075) are generalized to include two kinds
of primers and the off-lattice character of the primer distribution on the
surface. The sigmoidal character of the primer occupation by DNA, observed
experimentally, is reproduced in the simulation. We discuss an influence of two
parameters on the efficience of the amplification process: the initial density
p_0 of the occupied primers from the interfacial amplification and the ratio r
of the molecule length to the average distance between primers. The number of
cycles till the saturation decreases with p_0 roughly as p_0^{-0.26}. For
r=1.5, the number of occupied primers is reduced by a factor two, when compared
to the case of longer molecules. Below r=1.4, the effectivity of SPA is reduced
by a factor 100.
| [
{
"created": "Sun, 5 Dec 2004 20:59:59 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Krawczyk",
"M. J.",
""
],
[
"Kulakowski",
"K.",
""
]
] | Recent simulations of the solid phase DNA amplification (SPA) by J.-F. Mercier et al (Biophys. J. 85 (2003) 2075) are generalized to include two kinds of primers and the off-lattice character of the primer distribution on the surface. The sigmoidal character of the primer occupation by DNA, observed experimentally, is reproduced in the simulation. We discuss an influence of two parameters on the efficience of the amplification process: the initial density p_0 of the occupied primers from the interfacial amplification and the ratio r of the molecule length to the average distance between primers. The number of cycles till the saturation decreases with p_0 roughly as p_0^{-0.26}. For r=1.5, the number of occupied primers is reduced by a factor two, when compared to the case of longer molecules. Below r=1.4, the effectivity of SPA is reduced by a factor 100. |
2104.07059 | SueYeon Chung | SueYeon Chung, L. F. Abbott | Neural population geometry: An approach for understanding biological and
artificial neural networks | 8 pages | Current Opinion in Neurobiology, Volume 70, October 2021, Pages
137-144 | 10.1016/j.conb.2021.10.010 | null | q-bio.NC cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Advances in experimental neuroscience have transformed our ability to explore
the structure and function of neural circuits. At the same time, advances in
machine learning have unleashed the remarkable computational power of
artificial neural networks (ANNs). While these two fields have different tools
and applications, they present a similar challenge: namely, understanding how
information is embedded and processed through high-dimensional representations
to solve complex tasks. One approach to addressing this challenge is to utilize
mathematical and computational tools to analyze the geometry of these
high-dimensional representations, i.e., neural population geometry. We review
examples of geometrical approaches providing insight into the function of
biological and artificial neural networks: representation untangling in
perception, a geometric theory of classification capacity, disentanglement and
abstraction in cognitive systems, topological representations underlying
cognitive maps, dynamic untangling in motor systems, and a dynamical approach
to cognition. Together, these findings illustrate an exciting trend at the
intersection of machine learning, neuroscience, and geometry, in which neural
population geometry provides a useful population-level mechanistic descriptor
underlying task implementation. Importantly, geometric descriptions are
applicable across sensory modalities, brain regions, network architectures and
timescales. Thus, neural population geometry has the potential to unify our
understanding of structure and function in biological and artificial neural
networks, bridging the gap between single neurons, populations and behavior.
| [
{
"created": "Wed, 14 Apr 2021 18:10:34 GMT",
"version": "v1"
},
{
"created": "Sat, 17 Apr 2021 03:30:26 GMT",
"version": "v2"
},
{
"created": "Sat, 20 Nov 2021 02:42:15 GMT",
"version": "v3"
}
] | 2021-11-23 | [
[
"Chung",
"SueYeon",
""
],
[
"Abbott",
"L. F.",
""
]
] | Advances in experimental neuroscience have transformed our ability to explore the structure and function of neural circuits. At the same time, advances in machine learning have unleashed the remarkable computational power of artificial neural networks (ANNs). While these two fields have different tools and applications, they present a similar challenge: namely, understanding how information is embedded and processed through high-dimensional representations to solve complex tasks. One approach to addressing this challenge is to utilize mathematical and computational tools to analyze the geometry of these high-dimensional representations, i.e., neural population geometry. We review examples of geometrical approaches providing insight into the function of biological and artificial neural networks: representation untangling in perception, a geometric theory of classification capacity, disentanglement and abstraction in cognitive systems, topological representations underlying cognitive maps, dynamic untangling in motor systems, and a dynamical approach to cognition. Together, these findings illustrate an exciting trend at the intersection of machine learning, neuroscience, and geometry, in which neural population geometry provides a useful population-level mechanistic descriptor underlying task implementation. Importantly, geometric descriptions are applicable across sensory modalities, brain regions, network architectures and timescales. Thus, neural population geometry has the potential to unify our understanding of structure and function in biological and artificial neural networks, bridging the gap between single neurons, populations and behavior. |
2106.08150 | Robin Kobus | Robin Kobus (1), Andr\'e M\"uller (1), Daniel J\"unger (1), Christian
Hundt (2) and Bertil Schmidt (1) ((1) Johannes Gutenberg University Mainz,
Germany, (2) NVIDIA AI Technology Center Luxembourg) | MetaCache-GPU: Ultra-Fast Metagenomic Classification | 11 pages. To be published in ICPP 2021 | null | null | null | q-bio.GN cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The cost of DNA sequencing has dropped exponentially over the past decade,
making genomic data accessible to a growing number of scientists. In
bioinformatics, localization of short DNA sequences (reads) within large
genomic sequences is commonly facilitated by constructing index data structures
which allow for efficient querying of substrings. Recent metagenomic
classification pipelines annotate reads with taxonomic labels by analyzing
their $k$-mer histograms with respect to a reference genome database. CPU-based
index construction is often performed in a preprocessing phase due to the
relatively high cost of building irregular data structures such as hash maps.
However, the rapidly growing amount of available reference genomes establishes
the need for index construction and querying at interactive speeds. In this
paper, we introduce MetaCache-GPU -- an ultra-fast metagenomic short read
classifier specifically tailored to fit the characteristics of CUDA-enabled
accelerators. Our approach employs a novel hash table variant featuring
efficient minhash fingerprinting of reads for locality-sensitive hashing and
their rapid insertion using warp-aggregated operations. Our performance
evaluation shows that MetaCache-GPU is able to build large reference databases
in a matter of seconds, enabling instantaneous operability, while popular
CPU-based tools such as Kraken2 require over an hour for index construction on
the same data. In the context of an ever-growing number of reference genomes,
MetaCache-GPU is the first metagenomic classifier that makes analysis pipelines
with on-demand composition of large-scale reference genome sets practical. The
source code is publicly available at https://github.com/muellan/metacache .
| [
{
"created": "Mon, 14 Jun 2021 14:31:07 GMT",
"version": "v1"
}
] | 2021-06-16 | [
[
"Kobus",
"Robin",
""
],
[
"Müller",
"André",
""
],
[
"Jünger",
"Daniel",
""
],
[
"Hundt",
"Christian",
""
],
[
"Schmidt",
"Bertil",
""
]
] | The cost of DNA sequencing has dropped exponentially over the past decade, making genomic data accessible to a growing number of scientists. In bioinformatics, localization of short DNA sequences (reads) within large genomic sequences is commonly facilitated by constructing index data structures which allow for efficient querying of substrings. Recent metagenomic classification pipelines annotate reads with taxonomic labels by analyzing their $k$-mer histograms with respect to a reference genome database. CPU-based index construction is often performed in a preprocessing phase due to the relatively high cost of building irregular data structures such as hash maps. However, the rapidly growing amount of available reference genomes establishes the need for index construction and querying at interactive speeds. In this paper, we introduce MetaCache-GPU -- an ultra-fast metagenomic short read classifier specifically tailored to fit the characteristics of CUDA-enabled accelerators. Our approach employs a novel hash table variant featuring efficient minhash fingerprinting of reads for locality-sensitive hashing and their rapid insertion using warp-aggregated operations. Our performance evaluation shows that MetaCache-GPU is able to build large reference databases in a matter of seconds, enabling instantaneous operability, while popular CPU-based tools such as Kraken2 require over an hour for index construction on the same data. In the context of an ever-growing number of reference genomes, MetaCache-GPU is the first metagenomic classifier that makes analysis pipelines with on-demand composition of large-scale reference genome sets practical. The source code is publicly available at https://github.com/muellan/metacache . |
2008.11546 | Yu Li | Yu Li | Towards Structured Prediction in Bioinformatics with Deep Learning | PhD dissertatation | null | null | null | q-bio.QM cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Using machine learning, especially deep learning, to facilitate biological
research is a fascinating research direction. However, in addition to the
standard classification or regression problems, in bioinformatics, we often
need to predict more complex structured targets, such as 2D images and 3D
molecular structures. The above complex prediction tasks are referred to as
structured prediction. Structured prediction is more complicated than the
traditional classification but has much broader applications, considering that
most of the original bioinformatics problems have complex output objects. Due
to the properties of those structured prediction problems, such as having
problem-specific constraints and dependency within the labeling space, the
straightforward application of existing deep learning models can lead to
unsatisfactory results. Here, we argue that the following ideas can help
resolve structured prediction problems in bioinformatics. Firstly, we can
combine deep learning with other classic algorithms, such as probabilistic
graphical models, which model the problem structure explicitly. Secondly, we
can design the problem-specific deep learning architectures or methods by
considering the structured labeling space and problem constraints, either
explicitly or implicitly. We demonstrate our ideas with six projects from four
bioinformatics subfields, including sequencing analysis, structure prediction,
function annotation, and network analysis. The structured outputs cover 1D
signals, 2D images, 3D structures, hierarchical labeling, and heterogeneous
networks. With the help of the above ideas, all of our methods can achieve SOTA
performance on the corresponding problems. The success of these projects
motivates us to extend our work towards other more challenging but important
problems, such as health-care problems, which can directly benefit people's
health and wellness.
| [
{
"created": "Tue, 25 Aug 2020 02:52:18 GMT",
"version": "v1"
}
] | 2020-08-31 | [
[
"Li",
"Yu",
""
]
] | Using machine learning, especially deep learning, to facilitate biological research is a fascinating research direction. However, in addition to the standard classification or regression problems, in bioinformatics, we often need to predict more complex structured targets, such as 2D images and 3D molecular structures. The above complex prediction tasks are referred to as structured prediction. Structured prediction is more complicated than the traditional classification but has much broader applications, considering that most of the original bioinformatics problems have complex output objects. Due to the properties of those structured prediction problems, such as having problem-specific constraints and dependency within the labeling space, the straightforward application of existing deep learning models can lead to unsatisfactory results. Here, we argue that the following ideas can help resolve structured prediction problems in bioinformatics. Firstly, we can combine deep learning with other classic algorithms, such as probabilistic graphical models, which model the problem structure explicitly. Secondly, we can design the problem-specific deep learning architectures or methods by considering the structured labeling space and problem constraints, either explicitly or implicitly. We demonstrate our ideas with six projects from four bioinformatics subfields, including sequencing analysis, structure prediction, function annotation, and network analysis. The structured outputs cover 1D signals, 2D images, 3D structures, hierarchical labeling, and heterogeneous networks. With the help of the above ideas, all of our methods can achieve SOTA performance on the corresponding problems. The success of these projects motivates us to extend our work towards other more challenging but important problems, such as health-care problems, which can directly benefit people's health and wellness. |
1706.06481 | Hyun Youk | Eduardo P. Olimpio, Yiteng Dang, Hyun Youk | Statistical dynamics of spatial-order formation by communicating cells | null | iScience 2, 27-40 (2018) | 10.1016/j.isci.2018.03.013 | null | q-bio.QM cond-mat.stat-mech nlin.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Communicating cells can coordinate their gene expressions to form spatial
patterns. 'Secrete-and-sense cells' secrete and sense the same molecule to do
so and are ubiquitous. Here we address why and how these cells, from disordered
beginnings, can form spatial order through a statistical mechanics-type
framework for cellular communication. Classifying cellular lattices by
'macrostate' variables - 'spatial order paramete' and average gene-expression
level - reveals a conceptual picture: cellular lattices act as particles
rolling down on 'pseudo-energy landscapes' shaped by a 'Hamiltonian' for
cellular communication. Particles rolling down represent cells' spatial order
increasing. Particles trapped on the landscapes represent metastable spatial
configurations. The gradient of the Hamiltonian and a 'trapping probability'
determine the particle's equation of motion. This framework is extendable to
more complex forms of cellular communication.
| [
{
"created": "Fri, 16 Jun 2017 15:19:49 GMT",
"version": "v1"
},
{
"created": "Sun, 23 Jul 2017 21:00:05 GMT",
"version": "v2"
},
{
"created": "Wed, 2 Aug 2017 00:12:41 GMT",
"version": "v3"
},
{
"created": "Wed, 1 Nov 2017 16:47:53 GMT",
"version": "v4"
}
] | 2018-06-05 | [
[
"Olimpio",
"Eduardo P.",
""
],
[
"Dang",
"Yiteng",
""
],
[
"Youk",
"Hyun",
""
]
] | Communicating cells can coordinate their gene expressions to form spatial patterns. 'Secrete-and-sense cells' secrete and sense the same molecule to do so and are ubiquitous. Here we address why and how these cells, from disordered beginnings, can form spatial order through a statistical mechanics-type framework for cellular communication. Classifying cellular lattices by 'macrostate' variables - 'spatial order paramete' and average gene-expression level - reveals a conceptual picture: cellular lattices act as particles rolling down on 'pseudo-energy landscapes' shaped by a 'Hamiltonian' for cellular communication. Particles rolling down represent cells' spatial order increasing. Particles trapped on the landscapes represent metastable spatial configurations. The gradient of the Hamiltonian and a 'trapping probability' determine the particle's equation of motion. This framework is extendable to more complex forms of cellular communication. |
2106.15244 | Malcolm Hillebrand | M Hillebrand, G Kalosakas, A R Bishop, Ch Skokos | Bubble lifetimes in DNA gene promoters and their mutations affecting
transcription | 6 pages, 4 figures | null | 10.1063/5.0060335 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Relative lifetimes of inherent double stranded DNA openings with lengths up
to ten base pairs are presented for different gene promoters and corresponding
mutants that either increase or decrease transcriptional activity, in the
framework of the Peyrard-Bishop-Dauxois model. Extensive microcanonical
simulations are used, with energies corresponding to physiological temperature.
The bubble lifetime profiles along the DNA sequences demonstrate a significant
reduction of the average lifetime at the mutation sites when the mutated
promoter decreases transcription, while a corresponding enhancement of the
bubble lifetime is observed in the case of mutations leading to increased
transcription. The relative difference of bubble lifetimes between the mutated
and the wild type promoters at the position of mutation varies from 20% to more
than 30% as the bubble length is decreasing
| [
{
"created": "Tue, 29 Jun 2021 10:56:05 GMT",
"version": "v1"
}
] | 2021-09-15 | [
[
"Hillebrand",
"M",
""
],
[
"Kalosakas",
"G",
""
],
[
"Bishop",
"A R",
""
],
[
"Skokos",
"Ch",
""
]
] | Relative lifetimes of inherent double stranded DNA openings with lengths up to ten base pairs are presented for different gene promoters and corresponding mutants that either increase or decrease transcriptional activity, in the framework of the Peyrard-Bishop-Dauxois model. Extensive microcanonical simulations are used, with energies corresponding to physiological temperature. The bubble lifetime profiles along the DNA sequences demonstrate a significant reduction of the average lifetime at the mutation sites when the mutated promoter decreases transcription, while a corresponding enhancement of the bubble lifetime is observed in the case of mutations leading to increased transcription. The relative difference of bubble lifetimes between the mutated and the wild type promoters at the position of mutation varies from 20% to more than 30% as the bubble length is decreasing |
2208.13675 | Thomas Schmidt | Melanie Biafora, Thomas Schmidt | Juggling too many balls at once: Qualitatively different effects when
measuring priming and masking in single, dual, and triple tasks | v1: initial upload. v2: adds arxiv reference. Manuscript is under
review, still subject to changes | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Dissociation paradigms examine dissociations between indirect measures of
prime processing and direct measures of prime awareness. It is debated whether
direct measures should be objective or subjective, and whether these measures
should be obtained on the same or separate trials. In two metacontrast
experiments, we measured prime discrimination, PAS ratings, and response
priming either separately or in multiple tasks. Single tasks show the fastest
responses in priming and therefore most likely meet the assumption of
feedforward processing as assumed under Rapid-Chase Theory. Similarly, dual
tasks allow for a fast response activation by the prime; nevertheless,
prolonged responses and slower errors occur more often. In contrast, triple
tasks have a negative effect on response activation: responses are massively
slowed and fast prime-locked errors are lost. Moreover, decreasing priming
effects and prime identification performance result in a loss of a double
dissociation. Here, a necessary condition for unconscious response priming,
feedforward processing, is violated.
| [
{
"created": "Mon, 29 Aug 2022 15:24:48 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Aug 2022 12:22:04 GMT",
"version": "v2"
}
] | 2022-08-31 | [
[
"Biafora",
"Melanie",
""
],
[
"Schmidt",
"Thomas",
""
]
] | Dissociation paradigms examine dissociations between indirect measures of prime processing and direct measures of prime awareness. It is debated whether direct measures should be objective or subjective, and whether these measures should be obtained on the same or separate trials. In two metacontrast experiments, we measured prime discrimination, PAS ratings, and response priming either separately or in multiple tasks. Single tasks show the fastest responses in priming and therefore most likely meet the assumption of feedforward processing as assumed under Rapid-Chase Theory. Similarly, dual tasks allow for a fast response activation by the prime; nevertheless, prolonged responses and slower errors occur more often. In contrast, triple tasks have a negative effect on response activation: responses are massively slowed and fast prime-locked errors are lost. Moreover, decreasing priming effects and prime identification performance result in a loss of a double dissociation. Here, a necessary condition for unconscious response priming, feedforward processing, is violated. |
1311.2554 | Christoph Adami | B. Patra, Y. Kon, G. Yadav, A.W. Sevold, J. P. Frumkin, R. R.
Vallabhajosyula, A. Hintze, B. {\O}stman, J. Schossau, A. Bhan, B. Marzolf,
J. K. Tamashiro, A. Kaur, N. S. Baliga, E. J. Grayhack, C. Adami, D. J.
Galas, A. Raval, E. M. Phizicky, and A. Ray | A genome wide dosage suppressor network reveals genetic robustness and a
novel mechanism for Huntington's disease | 42 pages, 2 tables, 6 Figures. Supplementary Tables S1-S12 and
Supplementary Figures S1-S8 at http://dx.doi.org/10.6084/m9.figshare.844761 | Nucleic Acids Research 45 (2017) 255-270 | 10.1093/nar/gkw1148 | null | q-bio.MN q-bio.QM q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mutational robustness is the extent to which an organism has evolved to
withstand the effects of deleterious mutations. We explored the extent of
mutational robustness in the budding yeast by genome wide dosage suppressor
analysis of 53 conditional lethal mutations in cell division cycle and RNA
synthesis related genes, revealing 660 suppressor interactions of which 642 are
novel. This collection has several distinctive features, including high
co-occurrence of mutant-suppressor pairs within protein modules, highly
correlated functions between the pairs, and higher diversity of functions among
the co-suppressors than previously observed. Dosage suppression of essential
genes encoding RNA polymerase subunits and chromosome cohesion complex suggest
a surprising degree of functional plasticity of macromolecular complexes and
the existence od degenerate pathways for circumventing potentially lethal
mutations. The utility of dosage-suppressor networks is illustrated by the
discovery of a novel connection between chromosome cohesion-condensation
pathways involving homologous recombination, and Huntington's disease.
| [
{
"created": "Mon, 11 Nov 2013 20:00:14 GMT",
"version": "v1"
}
] | 2020-02-04 | [
[
"Patra",
"B.",
""
],
[
"Kon",
"Y.",
""
],
[
"Yadav",
"G.",
""
],
[
"Sevold",
"A. W.",
""
],
[
"Frumkin",
"J. P.",
""
],
[
"Vallabhajosyula",
"R. R.",
""
],
[
"Hintze",
"A.",
""
],
[
"Østman",
"B.",
""
],
[
"Schossau",
"J.",
""
],
[
"Bhan",
"A.",
""
],
[
"Marzolf",
"B.",
""
],
[
"Tamashiro",
"J. K.",
""
],
[
"Kaur",
"A.",
""
],
[
"Baliga",
"N. S.",
""
],
[
"Grayhack",
"E. J.",
""
],
[
"Adami",
"C.",
""
],
[
"Galas",
"D. J.",
""
],
[
"Raval",
"A.",
""
],
[
"Phizicky",
"E. M.",
""
],
[
"Ray",
"A.",
""
]
] | Mutational robustness is the extent to which an organism has evolved to withstand the effects of deleterious mutations. We explored the extent of mutational robustness in the budding yeast by genome wide dosage suppressor analysis of 53 conditional lethal mutations in cell division cycle and RNA synthesis related genes, revealing 660 suppressor interactions of which 642 are novel. This collection has several distinctive features, including high co-occurrence of mutant-suppressor pairs within protein modules, highly correlated functions between the pairs, and higher diversity of functions among the co-suppressors than previously observed. Dosage suppression of essential genes encoding RNA polymerase subunits and chromosome cohesion complex suggest a surprising degree of functional plasticity of macromolecular complexes and the existence od degenerate pathways for circumventing potentially lethal mutations. The utility of dosage-suppressor networks is illustrated by the discovery of a novel connection between chromosome cohesion-condensation pathways involving homologous recombination, and Huntington's disease. |
2005.04937 | Christopher Overton | Christopher E. Overton, Helena B. Stage, Shazaad Ahmad, Jacob
Curran-Sebastian, Paul Dark, Rajenki Das, Elizabeth Fearon, Timothy Felton,
Martyn Fyles, Nick Gent, Ian Hall, Thomas House, Hugo Lewkowicz, Xiaoxi Pang,
Lorenzo Pellis, Robert Sawko, Andrew Ustianowski, Bindu Vekaria, Luke Webb | Using statistics and mathematical modelling to understand infectious
disease outbreaks: COVID-19 as an example | null | Infectious Disease Modelling, Volume 5 (2020), 409-441 | 10.1016/j.idm.2020.06.008 | null | q-bio.PE physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | During an infectious disease outbreak, biases in the data and complexities of
the underlying dynamics pose significant challenges in mathematically modelling
the outbreak and designing policy. Motivated by the ongoing response to
COVID-19, we provide a toolkit of statistical and mathematical models beyond
the simple SIR-type differential equation models for analysing the early stages
of an outbreak and assessing interventions. In particular, we focus on
parameter estimation in the presence of known biases in the data, and the
effect of non-pharmaceutical interventions in enclosed subpopulations, such as
households and care homes. We illustrate these methods by applying them to the
COVID-19 pandemic.
| [
{
"created": "Mon, 11 May 2020 09:06:43 GMT",
"version": "v1"
}
] | 2020-09-22 | [
[
"Overton",
"Christopher E.",
""
],
[
"Stage",
"Helena B.",
""
],
[
"Ahmad",
"Shazaad",
""
],
[
"Curran-Sebastian",
"Jacob",
""
],
[
"Dark",
"Paul",
""
],
[
"Das",
"Rajenki",
""
],
[
"Fearon",
"Elizabeth",
""
],
[
"Felton",
"Timothy",
""
],
[
"Fyles",
"Martyn",
""
],
[
"Gent",
"Nick",
""
],
[
"Hall",
"Ian",
""
],
[
"House",
"Thomas",
""
],
[
"Lewkowicz",
"Hugo",
""
],
[
"Pang",
"Xiaoxi",
""
],
[
"Pellis",
"Lorenzo",
""
],
[
"Sawko",
"Robert",
""
],
[
"Ustianowski",
"Andrew",
""
],
[
"Vekaria",
"Bindu",
""
],
[
"Webb",
"Luke",
""
]
] | During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy. Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions. In particular, we focus on parameter estimation in the presence of known biases in the data, and the effect of non-pharmaceutical interventions in enclosed subpopulations, such as households and care homes. We illustrate these methods by applying them to the COVID-19 pandemic. |
1711.04950 | Jeroen Van Boxtel | Jeroen J.A. van Boxtel | Modelling stochastic resonance in humans: the influence of lapse rate | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Adding noise to a sensory signal generally decreases human performance.
However noise can improve performance too, due to a process called stochastic
resonance (SR). This paradoxical effect may be exploited in psychophysical
experiments, to provide additional insights into how the sensory system deals
with noise. Here, I develop a model for stochastic resonance to study the
influence of noise on human perception, in which the biological parameter of
`lapse rate' was included. I show that the inclusion of lapse rate allows for
the occurrence of stochastic resonance in terms of the performance metric d'.
At the same time, I show that high levels of lapse rate cause stochastic
resonance to disappear. It is also shown that noise generated in the brain
(i.e., internal noise) may obscure any effect of stochastic resonance in
experimental settings. I further relate the model to a standard equivalent
noise model, the linear amplifier model, and show that the lapse rate can
function to scale the threshold versus noise (TvN) curve, similar to the
efficiency parameter in equivalent noise (EN) models. Therefore, lapse rate
provides a psychophysical explanation for reduced efficiency in EN paradigms.
Furthermore, I note that ignoring lapse rate may lead to an overestimation of
internal noise in equivalent noise paradigms. Overall, describing stochastic
resonance in terms of signal detection theory, with the inclusion of lapse
rate, may provide valuable new insights into how human performance depends on
internal and external noise.
| [
{
"created": "Tue, 14 Nov 2017 04:50:23 GMT",
"version": "v1"
}
] | 2017-11-15 | [
[
"van Boxtel",
"Jeroen J. A.",
""
]
] | Adding noise to a sensory signal generally decreases human performance. However noise can improve performance too, due to a process called stochastic resonance (SR). This paradoxical effect may be exploited in psychophysical experiments, to provide additional insights into how the sensory system deals with noise. Here, I develop a model for stochastic resonance to study the influence of noise on human perception, in which the biological parameter of `lapse rate' was included. I show that the inclusion of lapse rate allows for the occurrence of stochastic resonance in terms of the performance metric d'. At the same time, I show that high levels of lapse rate cause stochastic resonance to disappear. It is also shown that noise generated in the brain (i.e., internal noise) may obscure any effect of stochastic resonance in experimental settings. I further relate the model to a standard equivalent noise model, the linear amplifier model, and show that the lapse rate can function to scale the threshold versus noise (TvN) curve, similar to the efficiency parameter in equivalent noise (EN) models. Therefore, lapse rate provides a psychophysical explanation for reduced efficiency in EN paradigms. Furthermore, I note that ignoring lapse rate may lead to an overestimation of internal noise in equivalent noise paradigms. Overall, describing stochastic resonance in terms of signal detection theory, with the inclusion of lapse rate, may provide valuable new insights into how human performance depends on internal and external noise. |
2005.02261 | D K K Vamsi | Bishal Chhetri, D. K. K. Vamsi, Vijay M. Bhagat, Ananth V. S., Bhanu
Prakash, Roshan Mandale, Swapna Muthusamy, Carani B Sanjeevi | Crucial Inflammatory Mediators and Efficacy of Drug Interventions in
Pneumonia Inflated COVID-19: An Invivo Mathematical Modelling Study | 50 pages, 37 figures | null | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The virus SARS-COV-2 caused disease COVID-19 has been declared a pandemic by
WHO. Currently, over 210 countries and territories have been affected. Careful,
well-designed drugs and vaccine for the total elimination of this virus seem to
be the need of the hour. In this context, the invivo mathematical modelling
studies can be extremely helpful in understanding the efficacy of the drug
interventions. These studies can also help understand the role of the crucial
inflammatory mediators and the behaviour of immune response towards this novel
coronavirus. Motivated by these facts, in this paper, we study the invivo
dynamics of Covid-19. The results obtained here are inline with some of the
clinical findings for Covid-19. This invivo modelling study involving the
crucial biomarkers of Covid-19 is the first of its kind and the results
obtained from this can be helpful to researchers, epidemiologists, clinicians
and doctors who are working in this field.
| [
{
"created": "Sun, 3 May 2020 19:17:29 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Oct 2020 15:18:05 GMT",
"version": "v2"
}
] | 2020-10-07 | [
[
"Chhetri",
"Bishal",
""
],
[
"Vamsi",
"D. K. K.",
""
],
[
"Bhagat",
"Vijay M.",
""
],
[
"S.",
"Ananth V.",
""
],
[
"Prakash",
"Bhanu",
""
],
[
"Mandale",
"Roshan",
""
],
[
"Muthusamy",
"Swapna",
""
],
[
"Sanjeevi",
"Carani B",
""
]
] | The virus SARS-COV-2 caused disease COVID-19 has been declared a pandemic by WHO. Currently, over 210 countries and territories have been affected. Careful, well-designed drugs and vaccine for the total elimination of this virus seem to be the need of the hour. In this context, the invivo mathematical modelling studies can be extremely helpful in understanding the efficacy of the drug interventions. These studies can also help understand the role of the crucial inflammatory mediators and the behaviour of immune response towards this novel coronavirus. Motivated by these facts, in this paper, we study the invivo dynamics of Covid-19. The results obtained here are inline with some of the clinical findings for Covid-19. This invivo modelling study involving the crucial biomarkers of Covid-19 is the first of its kind and the results obtained from this can be helpful to researchers, epidemiologists, clinicians and doctors who are working in this field. |
2007.11957 | Kok Yew Ng Dr | Ton Duc Do, Meei Mei Gui and Kok Yew Ng | Assessing the effects of time-dependent restrictions and control actions
to flatten the curve of COVID-19 in Kazakhstan | 35 pages, 7 figures, To appear in PeerJ | PeerJ 2021 | 10.7717/peerj.10806 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents the assessment of time-dependent national-level
restrictions and control actions and their effects in fighting the COVID-19
pandemic. By analysing the transmission dynamics during the first wave of
COVID-19 in the country, the effectiveness of the various levels of control
actions taken to flatten the curve can be better quantified and understood.
This in turn can help the relevant authorities to better plan for and control
the subsequent waves of the pandemic. To achieve this, a deterministic
population model for the pandemic is firstly developed to take into
consideration the time-dependent characteristics of the model parameters,
especially on the ever-evolving value of the reproduction number, which is one
of the critical measures used to describe the transmission dynamics of this
pandemic. The reproduction number alongside other key parameters of the model
can then be estimated by fitting the model to real-world data using numerical
optimisation techniques or by inducing ad-hoc control actions as recorded in
the news platforms. In this paper, the model is verified using a case study
based on the data from the first wave of COVID-19 in the Republic of
Kazakhstan. The model is fitted to provide estimates for two settings in
simulations; time-invariant and time-varying (with bounded constraints)
parameters. Finally, some forecasts are made using four scenarios with
time-dependent control measures so as to determine which would reflect on the
actual situations better.
| [
{
"created": "Tue, 21 Jul 2020 10:45:03 GMT",
"version": "v1"
},
{
"created": "Mon, 24 Aug 2020 11:15:15 GMT",
"version": "v2"
},
{
"created": "Tue, 12 Jan 2021 10:40:20 GMT",
"version": "v3"
}
] | 2021-02-04 | [
[
"Do",
"Ton Duc",
""
],
[
"Gui",
"Meei Mei",
""
],
[
"Ng",
"Kok Yew",
""
]
] | This paper presents the assessment of time-dependent national-level restrictions and control actions and their effects in fighting the COVID-19 pandemic. By analysing the transmission dynamics during the first wave of COVID-19 in the country, the effectiveness of the various levels of control actions taken to flatten the curve can be better quantified and understood. This in turn can help the relevant authorities to better plan for and control the subsequent waves of the pandemic. To achieve this, a deterministic population model for the pandemic is firstly developed to take into consideration the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measures used to describe the transmission dynamics of this pandemic. The reproduction number alongside other key parameters of the model can then be estimated by fitting the model to real-world data using numerical optimisation techniques or by inducing ad-hoc control actions as recorded in the news platforms. In this paper, the model is verified using a case study based on the data from the first wave of COVID-19 in the Republic of Kazakhstan. The model is fitted to provide estimates for two settings in simulations; time-invariant and time-varying (with bounded constraints) parameters. Finally, some forecasts are made using four scenarios with time-dependent control measures so as to determine which would reflect on the actual situations better. |
2204.01700 | Xin Gao | Xin Gao, Jianwei Li, Dianjie Li | Modeling COVID-19 vaccine-induced immunological memory development and
its links to antibody level and infectiousness | 23 pages, 5 figures | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | COVID-19 vaccines have proven to be effective against SARS-CoV-2 infection.
However, the dynamics of vaccine-induced immunological memory development and
neutralizing antibodies generation are not fully understood, limiting vaccine
development and vaccination regimen determination. Herein, we constructed a
mathematical model to characterize the vaccine-induced immune response based on
fitting the viral infection and vaccination datasets. With the example of
CoronaVac, we revealed the association between vaccine-induced immunological
memory development and neutralizing antibody levels. The establishment of the
intact immunological memory requires more than 6 months after the first and
second doses, after that a booster shot can induce high levels neutralizing
antibodies. By introducing the maximum viral load and recovery time after viral
infection, we quantitatively studied the protective effect of vaccines against
viral infection. Accordingly, we optimized the vaccination regimen, including
dose and vaccination timing, and predicted the effect of the fourth dose. Last,
by combining the viral transmission model, we showed the suppression of virus
transmission by vaccination, which may be instructive for the development of
public health policies.
| [
{
"created": "Tue, 5 Apr 2022 09:53:38 GMT",
"version": "v1"
}
] | 2022-04-06 | [
[
"Gao",
"Xin",
""
],
[
"Li",
"Jianwei",
""
],
[
"Li",
"Dianjie",
""
]
] | COVID-19 vaccines have proven to be effective against SARS-CoV-2 infection. However, the dynamics of vaccine-induced immunological memory development and neutralizing antibodies generation are not fully understood, limiting vaccine development and vaccination regimen determination. Herein, we constructed a mathematical model to characterize the vaccine-induced immune response based on fitting the viral infection and vaccination datasets. With the example of CoronaVac, we revealed the association between vaccine-induced immunological memory development and neutralizing antibody levels. The establishment of the intact immunological memory requires more than 6 months after the first and second doses, after that a booster shot can induce high levels neutralizing antibodies. By introducing the maximum viral load and recovery time after viral infection, we quantitatively studied the protective effect of vaccines against viral infection. Accordingly, we optimized the vaccination regimen, including dose and vaccination timing, and predicted the effect of the fourth dose. Last, by combining the viral transmission model, we showed the suppression of virus transmission by vaccination, which may be instructive for the development of public health policies. |
1105.1483 | Gyorgy Korniss | Lauren O'Malley, G. Korniss, Sai Satya Praveen Mungara, and Thomas
Caraco | Spatial competition and the dynamics of rarity in a temporally varying
environment | The original article is available at
www.evolutionary-ecology.com/issues/v12/n03/ccar2546.pdf | Evolutionary Ecology Research 12: 279-305 (2010) | null | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given an endogenous timescale set by invasion in a constant environment, we
introduced periodic temporal variation in competitive superiority by
alternating the species' propagation rates. By manipulating habitat size and
introduction rate, we simulated environments where successful invasion proceeds
through growth of many spatial clusters, and where invasion can occur only as a
single-cluster process. In the multi-cluster invasion regime, rapid
environmental variation produced spatial mixing of the species and
non-equilibrium coexistence. The dynamics' dominant response effectively
averaged environmental fluctuation, so that each species could avoid
competitive exclusion. Increasing the environment's half-period to match the
population-dynamic timescale let the (initially) more abundant resident
repeatedly repel the invader. Periodic transition in propagation-rate advantage
rarely interrupted the exclusion process when the more abundant species had
competitive advantage. However, at infrequent and randomly occurring times, the
rare species could invade and reverse the density pattern by rapidly eroding
the resident's preemption of space. In the single-cluster invasion regime,
environmental variation occurring faster than the population-dynamic timescale
prohibited successful invasion; the first species to reach its stationary
density (calculated for a constant environment) continued to repel the other
during long simulations. When the endogenous and exogenous timescales matched,
the species randomly reversed roles of resident and invader; the waiting times
for reversal of abundances indicate stochastic resonance. For both invasion
regimes, environmental fluctuation occurring much slower than the endogenous
dynamics produced symmetric limit cycles, alternations of the
constant-environment pattern.
| [
{
"created": "Sun, 8 May 2011 00:26:21 GMT",
"version": "v1"
}
] | 2011-05-10 | [
[
"O'Malley",
"Lauren",
""
],
[
"Korniss",
"G.",
""
],
[
"Mungara",
"Sai Satya Praveen",
""
],
[
"Caraco",
"Thomas",
""
]
] | Given an endogenous timescale set by invasion in a constant environment, we introduced periodic temporal variation in competitive superiority by alternating the species' propagation rates. By manipulating habitat size and introduction rate, we simulated environments where successful invasion proceeds through growth of many spatial clusters, and where invasion can occur only as a single-cluster process. In the multi-cluster invasion regime, rapid environmental variation produced spatial mixing of the species and non-equilibrium coexistence. The dynamics' dominant response effectively averaged environmental fluctuation, so that each species could avoid competitive exclusion. Increasing the environment's half-period to match the population-dynamic timescale let the (initially) more abundant resident repeatedly repel the invader. Periodic transition in propagation-rate advantage rarely interrupted the exclusion process when the more abundant species had competitive advantage. However, at infrequent and randomly occurring times, the rare species could invade and reverse the density pattern by rapidly eroding the resident's preemption of space. In the single-cluster invasion regime, environmental variation occurring faster than the population-dynamic timescale prohibited successful invasion; the first species to reach its stationary density (calculated for a constant environment) continued to repel the other during long simulations. When the endogenous and exogenous timescales matched, the species randomly reversed roles of resident and invader; the waiting times for reversal of abundances indicate stochastic resonance. For both invasion regimes, environmental fluctuation occurring much slower than the endogenous dynamics produced symmetric limit cycles, alternations of the constant-environment pattern. |
1511.08260 | Nancy (Xin Ru) Wang | Nancy X. R. Wang, Jared D. Olson, Jeffrey G. Ojemann, Rajesh P.N. Rao,
Bingni W. Brunton | Unsupervised decoding of long-term, naturalistic human neural recordings
with automated video and audio annotations | null | Frontiers in human neuroscience 2016 | 10.3389/fnhum.2016.00165 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fully automated decoding of human activities and intentions from direct
neural recordings is a tantalizing challenge in brain-computer interfacing.
Most ongoing efforts have focused on training decoders on specific, stereotyped
tasks in laboratory settings. Implementing brain-computer interfaces (BCIs) in
natural settings requires adaptive strategies and scalable algorithms that
require minimal supervision. Here we propose an unsupervised approach to
decoding neural states from human brain recordings acquired in a naturalistic
context. We demonstrate our approach on continuous long-term
electrocorticographic (ECoG) data recorded over many days from the brain
surface of subjects in a hospital room, with simultaneous audio and video
recordings. We first discovered clusters in high-dimensional ECoG recordings
and then annotated coherent clusters using speech and movement labels extracted
automatically from audio and video recordings. To our knowledge, this
represents the first time techniques from computer vision and speech processing
have been used for natural ECoG decoding. Our results show that our
unsupervised approach can discover distinct behaviors from ECoG data, including
moving, speaking and resting. We verify the accuracy of our approach by
comparing to manual annotations. Projecting the discovered cluster centers back
onto the brain, this technique opens the door to automated functional brain
mapping in natural settings.
| [
{
"created": "Thu, 26 Nov 2015 01:02:03 GMT",
"version": "v1"
},
{
"created": "Tue, 8 Dec 2015 06:44:07 GMT",
"version": "v2"
}
] | 2018-01-22 | [
[
"Wang",
"Nancy X. R.",
""
],
[
"Olson",
"Jared D.",
""
],
[
"Ojemann",
"Jeffrey G.",
""
],
[
"Rao",
"Rajesh P. N.",
""
],
[
"Brunton",
"Bingni W.",
""
]
] | Fully automated decoding of human activities and intentions from direct neural recordings is a tantalizing challenge in brain-computer interfacing. Most ongoing efforts have focused on training decoders on specific, stereotyped tasks in laboratory settings. Implementing brain-computer interfaces (BCIs) in natural settings requires adaptive strategies and scalable algorithms that require minimal supervision. Here we propose an unsupervised approach to decoding neural states from human brain recordings acquired in a naturalistic context. We demonstrate our approach on continuous long-term electrocorticographic (ECoG) data recorded over many days from the brain surface of subjects in a hospital room, with simultaneous audio and video recordings. We first discovered clusters in high-dimensional ECoG recordings and then annotated coherent clusters using speech and movement labels extracted automatically from audio and video recordings. To our knowledge, this represents the first time techniques from computer vision and speech processing have been used for natural ECoG decoding. Our results show that our unsupervised approach can discover distinct behaviors from ECoG data, including moving, speaking and resting. We verify the accuracy of our approach by comparing to manual annotations. Projecting the discovered cluster centers back onto the brain, this technique opens the door to automated functional brain mapping in natural settings. |
2104.05923 | Farshad Shirani | Farshad Shirani and Judith R. Miller | Competition, Trait Variance Dynamics, and the Evolution of a Species'
Range | null | Bulletin of Mathematical Biology, vol. 84, no. 3, 2022 | 10.1007/s11538-022-00990-z | null | q-bio.PE math.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Geographic ranges of communities of species evolve in response to
environmental, ecological, and evolutionary forces. Understanding the effects
of these forces on species' range dynamics is a major goal of spatial ecology.
Previous mathematical models have jointly captured the dynamic changes in
species' population distributions and the selective evolution of
fitness-related phenotypic traits in the presence of an environmental gradient.
These models inevitably include some unrealistic assumptions, and biologically
reasonable ranges of values for their parameters are not easy to specify. As a
result, simulations of the seminal models of this type can lead to markedly
different conclusions about the behavior of such populations, including the
possibility of maladaptation setting stable range boundaries. Here, we
harmonize such results by developing and simulating a continuum model of range
evolution in a community of species that interact competitively while diffusing
over an environmental gradient. Our model extends existing models by
incorporating both competition and freely changing intraspecific trait
variance. Simulations of this model predict a spatial profile of species' trait
variance that is consistent with experimental measurements available in the
literature. Moreover, they reaffirm interspecific competition as an effective
factor in limiting species' ranges, even when trait variance is not
artificially constrained. These theoretical results can inform the design of,
as yet rare, empirical studies to clarify the evolutionary causes of range
stabilization.
| [
{
"created": "Tue, 13 Apr 2021 03:54:00 GMT",
"version": "v1"
},
{
"created": "Thu, 25 Nov 2021 03:12:37 GMT",
"version": "v2"
}
] | 2022-02-02 | [
[
"Shirani",
"Farshad",
""
],
[
"Miller",
"Judith R.",
""
]
] | Geographic ranges of communities of species evolve in response to environmental, ecological, and evolutionary forces. Understanding the effects of these forces on species' range dynamics is a major goal of spatial ecology. Previous mathematical models have jointly captured the dynamic changes in species' population distributions and the selective evolution of fitness-related phenotypic traits in the presence of an environmental gradient. These models inevitably include some unrealistic assumptions, and biologically reasonable ranges of values for their parameters are not easy to specify. As a result, simulations of the seminal models of this type can lead to markedly different conclusions about the behavior of such populations, including the possibility of maladaptation setting stable range boundaries. Here, we harmonize such results by developing and simulating a continuum model of range evolution in a community of species that interact competitively while diffusing over an environmental gradient. Our model extends existing models by incorporating both competition and freely changing intraspecific trait variance. Simulations of this model predict a spatial profile of species' trait variance that is consistent with experimental measurements available in the literature. Moreover, they reaffirm interspecific competition as an effective factor in limiting species' ranges, even when trait variance is not artificially constrained. These theoretical results can inform the design of, as yet rare, empirical studies to clarify the evolutionary causes of range stabilization. |
1511.01062 | Edward Rusu | Edward Rusu | Network Models in Epidemiology: Considering Discrete and Continuous
Dynamics | 11 pages, 11 figures, matlab code | null | null | null | q-bio.PE math.DS q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Discrete and Continuous Dynamics is the first in a series of articles on
Network Models for Epidemiology. This project began in the Fall quarter of 2014
in my continuous modeling course. Since then, it has taken off and turned into
a series of articles, which I hope to compile into a single report. The purpose
of the report is to explore mathematical epidemiology. In this article, we
discuss the historical approach to disease modeling with compartmental models.
We discuss the issues and benefits of using network models. We build a discrete
dynamical system to describe infection and recovery of individuals in the
population. Lastly, we detail the computational scheme for iterating this
model.
| [
{
"created": "Mon, 19 Oct 2015 23:51:02 GMT",
"version": "v1"
}
] | 2015-11-04 | [
[
"Rusu",
"Edward",
""
]
] | Discrete and Continuous Dynamics is the first in a series of articles on Network Models for Epidemiology. This project began in the Fall quarter of 2014 in my continuous modeling course. Since then, it has taken off and turned into a series of articles, which I hope to compile into a single report. The purpose of the report is to explore mathematical epidemiology. In this article, we discuss the historical approach to disease modeling with compartmental models. We discuss the issues and benefits of using network models. We build a discrete dynamical system to describe infection and recovery of individuals in the population. Lastly, we detail the computational scheme for iterating this model. |
2012.06482 | Yannick Drif | Yannick Drif, Benjamin Roche (IRD), Pierre Valade | Cons{\'e}quences du changement climatique pour les maladies {\`a}
transmission vectorielle et impact en assurance de personnes | in French | null | null | null | q-bio.PE q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Climate change, which is largely linked to human activities, is already
having a considerable impact on our societies. Based on current trends, climate
change is expected to accelerate in the coming decades. Beyond its impact on
the pace of natural disasters (floods, hurricanes, etc.), climate change may
have catastrophic consequences for human life and health. One of the concerns
is the increase in the transmission of viruses spread by mosquitoes. Indeed,
rising temperatures have a direct positive impact on the viability of
mosquitoes in ecosystems, leading to their abundance and thus the risk of
exposure of human populations to these pathogens. This study quantifies the
consequences of global warming on the risk of epidemics of viruses transmitted
by the Aedes Albopictus mosquito in metropolitan France. This mosquito, which
is a vector for the Dengue, Chikungunya and Zika viruses, among others, arrived
in mainland France in 2004 and has since spread throughout the country. Thanks
to the association previously established between the probability of the
presence of the mosquito and the average temperature combined with a
mathematical model, the probability of an epidemic and the number of people who
could be infected and die during a season in each department are estimated. If
there is a high degree of heterogeneity in metropolitan France, nearly 2,000
deaths per year could be expected by 2040.
| [
{
"created": "Fri, 13 Nov 2020 15:05:14 GMT",
"version": "v1"
}
] | 2020-12-14 | [
[
"Drif",
"Yannick",
"",
"IRD"
],
[
"Roche",
"Benjamin",
"",
"IRD"
],
[
"Valade",
"Pierre",
""
]
] | Climate change, which is largely linked to human activities, is already having a considerable impact on our societies. Based on current trends, climate change is expected to accelerate in the coming decades. Beyond its impact on the pace of natural disasters (floods, hurricanes, etc.), climate change may have catastrophic consequences for human life and health. One of the concerns is the increase in the transmission of viruses spread by mosquitoes. Indeed, rising temperatures have a direct positive impact on the viability of mosquitoes in ecosystems, leading to their abundance and thus the risk of exposure of human populations to these pathogens. This study quantifies the consequences of global warming on the risk of epidemics of viruses transmitted by the Aedes Albopictus mosquito in metropolitan France. This mosquito, which is a vector for the Dengue, Chikungunya and Zika viruses, among others, arrived in mainland France in 2004 and has since spread throughout the country. Thanks to the association previously established between the probability of the presence of the mosquito and the average temperature combined with a mathematical model, the probability of an epidemic and the number of people who could be infected and die during a season in each department are estimated. If there is a high degree of heterogeneity in metropolitan France, nearly 2,000 deaths per year could be expected by 2040. |
2112.02097 | Claire Nedellec | Anfu Tang (LISN), Claire N\'edellec, Pierre Zweigenbaum (LISN), Louise
Del\'eger, Robert Bossy | Global alignment for relation extraction in Microbiology | null | Junior Conference on Data Science and Engineering, Feb 2021,
Orsay, France | null | null | q-bio.OT cs.CL cs.LG q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate a method to extract relations from texts based on global
alignment and syntactic information. Combined with SVM, this method is shown to
have a performance comparable or even better than LSTM on two RE tasks.
| [
{
"created": "Thu, 25 Nov 2021 10:19:05 GMT",
"version": "v1"
}
] | 2021-12-07 | [
[
"Tang",
"Anfu",
"",
"LISN"
],
[
"Nédellec",
"Claire",
"",
"LISN"
],
[
"Zweigenbaum",
"Pierre",
"",
"LISN"
],
[
"Deléger",
"Louise",
""
],
[
"Bossy",
"Robert",
""
]
] | We investigate a method to extract relations from texts based on global alignment and syntactic information. Combined with SVM, this method is shown to have a performance comparable or even better than LSTM on two RE tasks. |
2311.12570 | Frederikke Isa Marin | Frederikke Isa Marin, Felix Teufel, Marc Horlacher, Dennis Madsen,
Dennis Pultz, Ole Winther, Wouter Boomsma | BEND: Benchmarking DNA Language Models on biologically meaningful tasks | 9 pages, 1 figure, 3 tables, code available at
https://github.com/frederikkemarin/BEND, to be published in ICLR 2024 | null | null | null | q-bio.GN cs.LG | http://creativecommons.org/licenses/by/4.0/ | The genome sequence contains the blueprint for governing cellular processes.
While the availability of genomes has vastly increased over the last decades,
experimental annotation of the various functional, non-coding and regulatory
elements encoded in the DNA sequence remains both expensive and challenging.
This has sparked interest in unsupervised language modeling of genomic DNA, a
paradigm that has seen great success for protein sequence data. Although
various DNA language models have been proposed, evaluation tasks often differ
between individual works, and might not fully recapitulate the fundamental
challenges of genome annotation, including the length, scale and sparsity of
the data. In this study, we introduce BEND, a Benchmark for DNA language
models, featuring a collection of realistic and biologically meaningful
downstream tasks defined on the human genome. We find that embeddings from
current DNA LMs can approach performance of expert methods on some tasks, but
only capture limited information about long-range features. BEND is available
at https://github.com/frederikkemarin/BEND.
| [
{
"created": "Tue, 21 Nov 2023 12:34:00 GMT",
"version": "v1"
},
{
"created": "Sat, 25 Nov 2023 07:24:40 GMT",
"version": "v2"
},
{
"created": "Mon, 11 Mar 2024 09:49:06 GMT",
"version": "v3"
},
{
"created": "Tue, 9 Apr 2024 09:35:08 GMT",
"version": "v4"
}
] | 2024-04-10 | [
[
"Marin",
"Frederikke Isa",
""
],
[
"Teufel",
"Felix",
""
],
[
"Horlacher",
"Marc",
""
],
[
"Madsen",
"Dennis",
""
],
[
"Pultz",
"Dennis",
""
],
[
"Winther",
"Ole",
""
],
[
"Boomsma",
"Wouter",
""
]
] | The genome sequence contains the blueprint for governing cellular processes. While the availability of genomes has vastly increased over the last decades, experimental annotation of the various functional, non-coding and regulatory elements encoded in the DNA sequence remains both expensive and challenging. This has sparked interest in unsupervised language modeling of genomic DNA, a paradigm that has seen great success for protein sequence data. Although various DNA language models have been proposed, evaluation tasks often differ between individual works, and might not fully recapitulate the fundamental challenges of genome annotation, including the length, scale and sparsity of the data. In this study, we introduce BEND, a Benchmark for DNA language models, featuring a collection of realistic and biologically meaningful downstream tasks defined on the human genome. We find that embeddings from current DNA LMs can approach performance of expert methods on some tasks, but only capture limited information about long-range features. BEND is available at https://github.com/frederikkemarin/BEND. |
2009.04438 | Varsha Subramanyan | Anushka Halder, Arinnia Anto, Varsha Subramanyan, Moitrayee
Bhattacharyya, Smitha Vishveshwara, Saraswathi Vishveshwara | Surveying the side-chain network approach to protein structure and
dynamics: The SARS-CoV-2 spike protein as an illustrative case | 35 pages, 6 figures | Front Mol Biosci . 2020 Dec 18;7:596945 | 10.3389/fmolb.2020.596945 | null | q-bio.BM cond-mat.other physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network theory-based approaches provide valuable insights into the variations
in global structural connectivity between differing dynamical states of
proteins. Our objective is to review network-based analyses to elucidate such
variations, especially in the context of subtle conformational changes. We
present technical details of the construction and analyses of protein structure
networks, encompassing both the non-covalent connectivity and dynamics. We
examine the selection of optimal criteria for connectivity based on the
physical concept of percolation. We highlight the advantages of using
side-chain based network metrics in contrast to backbone measurements. As an
illustrative example, we apply the described network approach to investigate
the global conformational change between the closed and partially open states
of the SARS-CoV-2 spike protein. This conformational change in the spike
protein is crucial for coronavirus entry and fusion into human cells. Our
analysis reveals global structural reorientations between the two states of the
spike protein despite small changes between the two states at the backbone
level. We also observe some differences at strategic locations in the
structures, correlating with their functions, asserting the advantages of the
side-chain network analysis. Finally we present a view of allostery as a subtle
synergistic-global change between the ligand and the receptor, the
incorporation of which would enhance the drug design strategies.
| [
{
"created": "Wed, 9 Sep 2020 17:33:16 GMT",
"version": "v1"
},
{
"created": "Sat, 31 Oct 2020 05:42:26 GMT",
"version": "v2"
}
] | 2021-12-15 | [
[
"Halder",
"Anushka",
""
],
[
"Anto",
"Arinnia",
""
],
[
"Subramanyan",
"Varsha",
""
],
[
"Bhattacharyya",
"Moitrayee",
""
],
[
"Vishveshwara",
"Smitha",
""
],
[
"Vishveshwara",
"Saraswathi",
""
]
] | Network theory-based approaches provide valuable insights into the variations in global structural connectivity between differing dynamical states of proteins. Our objective is to review network-based analyses to elucidate such variations, especially in the context of subtle conformational changes. We present technical details of the construction and analyses of protein structure networks, encompassing both the non-covalent connectivity and dynamics. We examine the selection of optimal criteria for connectivity based on the physical concept of percolation. We highlight the advantages of using side-chain based network metrics in contrast to backbone measurements. As an illustrative example, we apply the described network approach to investigate the global conformational change between the closed and partially open states of the SARS-CoV-2 spike protein. This conformational change in the spike protein is crucial for coronavirus entry and fusion into human cells. Our analysis reveals global structural reorientations between the two states of the spike protein despite small changes between the two states at the backbone level. We also observe some differences at strategic locations in the structures, correlating with their functions, asserting the advantages of the side-chain network analysis. Finally we present a view of allostery as a subtle synergistic-global change between the ligand and the receptor, the incorporation of which would enhance the drug design strategies. |
2402.01056 | Sushrut Thorat | Lotta Piefke, Adrien Doerig, Tim Kietzmann, Sushrut Thorat | Computational characterization of the role of an attention schema in
controlling visuospatial attention | 7 pages, 3 figures; Accepted at CogSci 2024 | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | How does the brain control attention? The Attention Schema Theory suggests
that the brain explicitly models its state of attention, termed an attention
schema, for its control. However, it remains unclear under which circumstances
an attention schema is computationally useful, and whether it can emerge in a
learning system without hard-wiring. To address these questions, we trained a
reinforcement learning agent with attention to track and catch a ball in a
noisy environment. Crucially, the agent had additional resources that it could
freely use. We asked under which conditions these additional resources develop
an attention schema to track attention. We found that the more uncertain the
agent was about the location of its attentional window, the more it benefited
from these additional resources, which developed an attention schema. Together,
these results indicate that an attention schema emerges in simple learning
systems where attention is important and difficult to track.
| [
{
"created": "Thu, 1 Feb 2024 23:03:55 GMT",
"version": "v1"
},
{
"created": "Wed, 8 May 2024 14:56:29 GMT",
"version": "v2"
}
] | 2024-05-09 | [
[
"Piefke",
"Lotta",
""
],
[
"Doerig",
"Adrien",
""
],
[
"Kietzmann",
"Tim",
""
],
[
"Thorat",
"Sushrut",
""
]
] | How does the brain control attention? The Attention Schema Theory suggests that the brain explicitly models its state of attention, termed an attention schema, for its control. However, it remains unclear under which circumstances an attention schema is computationally useful, and whether it can emerge in a learning system without hard-wiring. To address these questions, we trained a reinforcement learning agent with attention to track and catch a ball in a noisy environment. Crucially, the agent had additional resources that it could freely use. We asked under which conditions these additional resources develop an attention schema to track attention. We found that the more uncertain the agent was about the location of its attentional window, the more it benefited from these additional resources, which developed an attention schema. Together, these results indicate that an attention schema emerges in simple learning systems where attention is important and difficult to track. |
1601.01358 | Leyla Isik | Leyla Isik, Andrea Tacchetti, and Tomaso Poggio | Fast, invariant representation for human action in the visual system | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans can effortlessly recognize others' actions in the presence of complex
transformations, such as changes in viewpoint. Several studies have located the
regions in the brain involved in invariant action recognition, however, the
underlying neural computations remain poorly understood. We use
magnetoencephalography (MEG) decoding and a dataset of well-controlled,
naturalistic videos of five actions (run, walk, jump, eat, drink) performed by
different actors at different viewpoints to study the computational steps used
to recognize actions across complex transformations. In particular, we ask when
the brain discounts changes in 3D viewpoint relative to when it initially
discriminates between actions. We measure the latency difference between
invariant and non-invariant action decoding when subjects view full videos as
well as form-depleted and motion-depleted stimuli. Our results show no
difference in decoding latency or temporal profile between invariant and
non-invariant action recognition in full videos. However, when either form or
motion information is removed from the stimulus set, we observe a decrease and
delay in invariant action decoding. Our results suggest that the brain
recognizes actions and builds invariance to complex transformations at the same
time, and that both form and motion information are crucial for fast, invariant
action recognition.
| [
{
"created": "Thu, 7 Jan 2016 00:28:06 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Aug 2017 14:46:56 GMT",
"version": "v2"
}
] | 2017-08-16 | [
[
"Isik",
"Leyla",
""
],
[
"Tacchetti",
"Andrea",
""
],
[
"Poggio",
"Tomaso",
""
]
] | Humans can effortlessly recognize others' actions in the presence of complex transformations, such as changes in viewpoint. Several studies have located the regions in the brain involved in invariant action recognition, however, the underlying neural computations remain poorly understood. We use magnetoencephalography (MEG) decoding and a dataset of well-controlled, naturalistic videos of five actions (run, walk, jump, eat, drink) performed by different actors at different viewpoints to study the computational steps used to recognize actions across complex transformations. In particular, we ask when the brain discounts changes in 3D viewpoint relative to when it initially discriminates between actions. We measure the latency difference between invariant and non-invariant action decoding when subjects view full videos as well as form-depleted and motion-depleted stimuli. Our results show no difference in decoding latency or temporal profile between invariant and non-invariant action recognition in full videos. However, when either form or motion information is removed from the stimulus set, we observe a decrease and delay in invariant action decoding. Our results suggest that the brain recognizes actions and builds invariance to complex transformations at the same time, and that both form and motion information are crucial for fast, invariant action recognition. |
1712.00683 | Peter Helfer | Peter Helfer and Thomas R. Shultz | Coupled feedback loops maintain synaptic long-term potentiation: A
computational model of PKMzeta synthesis and AMPA receptor trafficking | null | null | 10.1371/journal.pcbi.1006147 | null | q-bio.NC q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In long-term potentiation (LTP), one of the most studied types of neural
plasticity, synaptic strength is persistently increased in response to
stimulation. Although a number of different proteins have been implicated in
the sub-cellular molecular processes underlying induction and maintenance of
LTP, the precise mechanisms remain unknown. A particular challenge is to
demonstrate that a proposed molecular mechanism can provide the level of
stability needed to maintain memories for months or longer, in spite of the
fact that many of the participating molecules have much shorter life spans.
Here we present a computational model that combines simulations of several
biochemical reactions that have been suggested in the LTP literature and show
that the resulting system does exhibit the required stability. At the core of
the model are two interlinked feedback loops of molecular reactions, one
involving the atypical protein kinase PKM{\zeta} and its messenger RNA, the
other involving PKM{\zeta} and GluA2-containing AMPA receptors. We demonstrate
that robust bistability - stable equilibria both in the synapse's potentiated
and unpotentiated states - can arise from a set of simple molecular reactions.
The model is able to account for a wide range of empirical results, including
induction and maintenance of late-phase LTP, cellular memory reconsolidation
and the effects of different pharmaceutical interventions.
| [
{
"created": "Sat, 2 Dec 2017 23:54:01 GMT",
"version": "v1"
},
{
"created": "Wed, 28 Mar 2018 20:55:55 GMT",
"version": "v2"
},
{
"created": "Mon, 30 Apr 2018 23:12:38 GMT",
"version": "v3"
}
] | 2019-06-11 | [
[
"Helfer",
"Peter",
""
],
[
"Shultz",
"Thomas R.",
""
]
] | In long-term potentiation (LTP), one of the most studied types of neural plasticity, synaptic strength is persistently increased in response to stimulation. Although a number of different proteins have been implicated in the sub-cellular molecular processes underlying induction and maintenance of LTP, the precise mechanisms remain unknown. A particular challenge is to demonstrate that a proposed molecular mechanism can provide the level of stability needed to maintain memories for months or longer, in spite of the fact that many of the participating molecules have much shorter life spans. Here we present a computational model that combines simulations of several biochemical reactions that have been suggested in the LTP literature and show that the resulting system does exhibit the required stability. At the core of the model are two interlinked feedback loops of molecular reactions, one involving the atypical protein kinase PKM{\zeta} and its messenger RNA, the other involving PKM{\zeta} and GluA2-containing AMPA receptors. We demonstrate that robust bistability - stable equilibria both in the synapse's potentiated and unpotentiated states - can arise from a set of simple molecular reactions. The model is able to account for a wide range of empirical results, including induction and maintenance of late-phase LTP, cellular memory reconsolidation and the effects of different pharmaceutical interventions. |
1609.04136 | Chi Xue | Chi Xue and Nigel Goldenfeld | Stochastic predator-prey dynamics of transposons in the human genome | null | null | 10.1103/PhysRevLett.117.208101 | null | q-bio.PE physics.bio-ph q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transposable elements, or transposons, are DNA sequences that can jump from
site to site in the genome during the life cycle of a cell, usually encoding
the very enzymes which perform their excision. However, some transposons are
parasitic, relying on the enzymes produced by the regular transposons. In this
case, we show that a stochastic model, which takes into account the small copy
numbers of the transposons in a cell, predicts noise-induced predator-prey
oscillations with a characteristic time scale that is much longer than the cell
replication time, indicating that the state of the predator-prey oscillator is
stored in the genome and transmitted to successive generations. Our work
demonstrates the important role of number fluctuations in the expression of
mobile genetic elements, and shows explicitly how ecological concepts can be
applied to the dynamics and fluctuations of living genomes.
| [
{
"created": "Wed, 14 Sep 2016 04:50:41 GMT",
"version": "v1"
},
{
"created": "Fri, 7 Oct 2016 02:43:10 GMT",
"version": "v2"
}
] | 2016-11-16 | [
[
"Xue",
"Chi",
""
],
[
"Goldenfeld",
"Nigel",
""
]
] | Transposable elements, or transposons, are DNA sequences that can jump from site to site in the genome during the life cycle of a cell, usually encoding the very enzymes which perform their excision. However, some transposons are parasitic, relying on the enzymes produced by the regular transposons. In this case, we show that a stochastic model, which takes into account the small copy numbers of the transposons in a cell, predicts noise-induced predator-prey oscillations with a characteristic time scale that is much longer than the cell replication time, indicating that the state of the predator-prey oscillator is stored in the genome and transmitted to successive generations. Our work demonstrates the important role of number fluctuations in the expression of mobile genetic elements, and shows explicitly how ecological concepts can be applied to the dynamics and fluctuations of living genomes. |
2304.08770 | Benjamin Zoller | Po-Ta Chen, Michal Levo, Benjamin Zoller, Thomas Gregor | Gene activity fully predicts transcriptional bursting dynamics | null | null | null | null | q-bio.MN physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Transcription commonly occurs in bursts, with alternating productive (ON) and
quiescent (OFF) periods, governing mRNA production rates. Yet, how
transcription is regulated through bursting dynamics remains unresolved. Here,
we conduct real-time measurements of endogenous transcriptional bursting with
single-mRNA sensitivity. Leveraging the diverse transcriptional activities in
early fly embryos, we uncover stringent relationships between bursting
parameters. Specifically, we find that the durations of ON and OFF periods are
linked. Regardless of the developmental stage or body-axis position, gene
activity levels predict individual alleles' average ON and OFF periods. Lowly
transcribing alleles predominantly modulate OFF periods (burst frequency),
while highly transcribing alleles primarily tune ON periods (burst size). These
relationships persist even under perturbations of cis-regulatory elements or
trans-factors and account for bursting dynamics measured in other species. Our
results suggest a novel mechanistic constraint governing bursting dynamics
rather than a modular control of distinct parameters by distinct regulatory
processes.
| [
{
"created": "Tue, 18 Apr 2023 06:58:46 GMT",
"version": "v1"
},
{
"created": "Mon, 2 Oct 2023 13:45:00 GMT",
"version": "v2"
},
{
"created": "Fri, 28 Jun 2024 15:47:06 GMT",
"version": "v3"
}
] | 2024-07-01 | [
[
"Chen",
"Po-Ta",
""
],
[
"Levo",
"Michal",
""
],
[
"Zoller",
"Benjamin",
""
],
[
"Gregor",
"Thomas",
""
]
] | Transcription commonly occurs in bursts, with alternating productive (ON) and quiescent (OFF) periods, governing mRNA production rates. Yet, how transcription is regulated through bursting dynamics remains unresolved. Here, we conduct real-time measurements of endogenous transcriptional bursting with single-mRNA sensitivity. Leveraging the diverse transcriptional activities in early fly embryos, we uncover stringent relationships between bursting parameters. Specifically, we find that the durations of ON and OFF periods are linked. Regardless of the developmental stage or body-axis position, gene activity levels predict individual alleles' average ON and OFF periods. Lowly transcribing alleles predominantly modulate OFF periods (burst frequency), while highly transcribing alleles primarily tune ON periods (burst size). These relationships persist even under perturbations of cis-regulatory elements or trans-factors and account for bursting dynamics measured in other species. Our results suggest a novel mechanistic constraint governing bursting dynamics rather than a modular control of distinct parameters by distinct regulatory processes. |
1012.3623 | Woodrow L Shew | Woodrow L. Shew, Hongdian Yang, Shan Yu, Rajarshi Roy, Dietmar Plenz | Information capacity and transmission are maximized in balanced cortical
networks with neuronal avalanches | null | The Journal of Neuroscience, January 5, 2011 31(01) | null | null | q-bio.NC cond-mat.dis-nn physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The repertoire of neural activity patterns that a cortical network can
produce constrains the network's ability to transfer and process information.
Here, we measured activity patterns obtained from multi-site local field
potential (LFP) recordings in cortex cultures, urethane anesthetized rats, and
awake macaque monkeys. First, we quantified the information capacity of the
pattern repertoire of ongoing and stimulus-evoked activity using Shannon
entropy. Next, we quantified the efficacy of information transmission between
stimulus and response using mutual information. By systematically changing the
ratio of excitation/inhibition (E/I) in vitro and in a network model, we
discovered that both information capacity and information transmission are
maximized at a particular intermediate E/I, at which ongoing activity emerges
as neuronal avalanches. Next, we used our in vitro and model results to
correctly predict in vivo information capacity and interactions between
neuronal groups during ongoing activity. Close agreement between our
experiments and model suggest that neuronal avalanches and peak information
capacity arise due to criticality and are general properties of cortical
networks with balanced E/I.
| [
{
"created": "Thu, 16 Dec 2010 14:37:47 GMT",
"version": "v1"
}
] | 2010-12-17 | [
[
"Shew",
"Woodrow L.",
""
],
[
"Yang",
"Hongdian",
""
],
[
"Yu",
"Shan",
""
],
[
"Roy",
"Rajarshi",
""
],
[
"Plenz",
"Dietmar",
""
]
] | The repertoire of neural activity patterns that a cortical network can produce constrains the network's ability to transfer and process information. Here, we measured activity patterns obtained from multi-site local field potential (LFP) recordings in cortex cultures, urethane anesthetized rats, and awake macaque monkeys. First, we quantified the information capacity of the pattern repertoire of ongoing and stimulus-evoked activity using Shannon entropy. Next, we quantified the efficacy of information transmission between stimulus and response using mutual information. By systematically changing the ratio of excitation/inhibition (E/I) in vitro and in a network model, we discovered that both information capacity and information transmission are maximized at a particular intermediate E/I, at which ongoing activity emerges as neuronal avalanches. Next, we used our in vitro and model results to correctly predict in vivo information capacity and interactions between neuronal groups during ongoing activity. Close agreement between our experiments and model suggest that neuronal avalanches and peak information capacity arise due to criticality and are general properties of cortical networks with balanced E/I. |
1410.1549 | Cristian Micheletti | Cristian Micheletti, Marco Di Stefano and Henri Orland | The unknotted strands of life: knots are very rare in RNA structures | 7 pages, 5 figures, 1 table | null | null | null | q-bio.BM cond-mat.soft physics.bio-ph physics.chem-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ongoing effort to detect and characterize physical entanglement in
biopolymers has so far established that knots are present in many globular
proteins and also abound in viral DNA packaged inside bacteriophages. RNA
molecules, on the other hand, have not yet been systematically screened for the
occurrence of physical knots. We have accordingly undertaken the systematic
profiling of the ~6,000 RNA structures present in the protein data bank. The
search identified no more than three deeply-knotted RNA molecules. These are
ribosomal RNAs solved by cryo-em and consist of about 3,000 nucleotides.
Compared to the case of proteins and viral DNA, the observed incidence of RNA
knots is therefore practically negligible. This suggests that either
evolutionary selection, or thermodynamic and kinetic folding mechanisms act
towards minimizing the entanglement of RNA to an extent that is unparalleled by
other types of biomolecules. The properties of the three observed RNA knotting
patterns provide valuable clues for designing RNA sequences capable of
self-tying in a twist-knot fold.
| [
{
"created": "Mon, 6 Oct 2014 20:00:32 GMT",
"version": "v1"
}
] | 2014-10-08 | [
[
"Micheletti",
"Cristian",
""
],
[
"Di Stefano",
"Marco",
""
],
[
"Orland",
"Henri",
""
]
] | The ongoing effort to detect and characterize physical entanglement in biopolymers has so far established that knots are present in many globular proteins and also abound in viral DNA packaged inside bacteriophages. RNA molecules, on the other hand, have not yet been systematically screened for the occurrence of physical knots. We have accordingly undertaken the systematic profiling of the ~6,000 RNA structures present in the protein data bank. The search identified no more than three deeply-knotted RNA molecules. These are ribosomal RNAs solved by cryo-em and consist of about 3,000 nucleotides. Compared to the case of proteins and viral DNA, the observed incidence of RNA knots is therefore practically negligible. This suggests that either evolutionary selection, or thermodynamic and kinetic folding mechanisms act towards minimizing the entanglement of RNA to an extent that is unparalleled by other types of biomolecules. The properties of the three observed RNA knotting patterns provide valuable clues for designing RNA sequences capable of self-tying in a twist-knot fold. |
0710.4269 | Anirvan M. Sengupta | Madalena Chave, Eduardo D. Sontag and Anirvan M. Sengupta | Shape, size and robustness: feasible regions in the parameter space of
biochemical networks | 38 pages, 6 figure | null | null | null | q-bio.MN | null | The concept of robustness of regulatory networks has been closely related to
the nature of the interactions among genes, and the capability of pattern
maintenance or reproducibility. Defining this robustness property is a
challenging task, but mathematical models have often associated it to the
volume of the space of admissible parameters. Not only the volume of the space
but also its topology and geometry contain information on essential aspects of
the network, including feasible pathways, switching between two parallel
pathways or distinct/disconnected active regions of parameters. A general
method is presented here to characterize the space of admissible parameters, by
writing it as a semi-algebraic set, and then theoretically analyzing its
topology and geometry, as well as volume. This method provides a more objective
and complete measure of the robustness of a developmental module. As an
illustration, the segment polarity gene network is analyzed.
| [
{
"created": "Tue, 23 Oct 2007 13:55:12 GMT",
"version": "v1"
}
] | 2007-10-24 | [
[
"Chave",
"Madalena",
""
],
[
"Sontag",
"Eduardo D.",
""
],
[
"Sengupta",
"Anirvan M.",
""
]
] | The concept of robustness of regulatory networks has been closely related to the nature of the interactions among genes, and the capability of pattern maintenance or reproducibility. Defining this robustness property is a challenging task, but mathematical models have often associated it to the volume of the space of admissible parameters. Not only the volume of the space but also its topology and geometry contain information on essential aspects of the network, including feasible pathways, switching between two parallel pathways or distinct/disconnected active regions of parameters. A general method is presented here to characterize the space of admissible parameters, by writing it as a semi-algebraic set, and then theoretically analyzing its topology and geometry, as well as volume. This method provides a more objective and complete measure of the robustness of a developmental module. As an illustration, the segment polarity gene network is analyzed. |
1412.2368 | Guowei Wei | Bao Wang and Guo-Wei Wei | Objective-oriented Persistent Homology | 13 figures and 96 references | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Persistent homology provides a new approach for the topological
simplification of big data via measuring the life time of intrinsic topological
features in a filtration process and has found its success in scientific and
engineering applications. However, such a success is essentially limited to
qualitative data characterization, identification and analysis (CIA). In this
work, we outline a general protocol to construct objective-oriented persistent
homology methods. The minimization of the objective functional leads to a
Laplace-Beltrami operator which generates a multiscale representation of the
initial data and offers an objective oriented filtration process. The resulting
differential geometry based objective-oriented persistent homology is able to
preserve desirable geometric features in the evolutionary filtration and
enhances the corresponding topological persistence. The consistence between
Laplace-Beltrami flow based filtration and Euclidean distance based filtration
is confirmed on the Vietoris-Rips complex for a large amount of numerical
tests. The convergence and reliability of the present Laplace-Beltrami flow
based cubical complex filtration approach are analyzed over various spatial and
temporal mesh sizes. The efficiency and robustness of the present method are
verified by more than 500 fullerene molecules. It is shown that the proposed
persistent homology based quantitative model offers good predictions of total
curvature energies for ten types of fullerene isomers. The present work offers
the first example to design objective-oriented persistent homology to enhance
or preserve desirable features in the original data during the filtration
process and then automatically detect or extract the corresponding topological
traits from the data.
| [
{
"created": "Sun, 7 Dec 2014 16:56:13 GMT",
"version": "v1"
}
] | 2014-12-09 | [
[
"Wang",
"Bao",
""
],
[
"Wei",
"Guo-Wei",
""
]
] | Persistent homology provides a new approach for the topological simplification of big data via measuring the life time of intrinsic topological features in a filtration process and has found its success in scientific and engineering applications. However, such a success is essentially limited to qualitative data characterization, identification and analysis (CIA). In this work, we outline a general protocol to construct objective-oriented persistent homology methods. The minimization of the objective functional leads to a Laplace-Beltrami operator which generates a multiscale representation of the initial data and offers an objective oriented filtration process. The resulting differential geometry based objective-oriented persistent homology is able to preserve desirable geometric features in the evolutionary filtration and enhances the corresponding topological persistence. The consistence between Laplace-Beltrami flow based filtration and Euclidean distance based filtration is confirmed on the Vietoris-Rips complex for a large amount of numerical tests. The convergence and reliability of the present Laplace-Beltrami flow based cubical complex filtration approach are analyzed over various spatial and temporal mesh sizes. The efficiency and robustness of the present method are verified by more than 500 fullerene molecules. It is shown that the proposed persistent homology based quantitative model offers good predictions of total curvature energies for ten types of fullerene isomers. The present work offers the first example to design objective-oriented persistent homology to enhance or preserve desirable features in the original data during the filtration process and then automatically detect or extract the corresponding topological traits from the data. |
2210.09470 | Wei-Hsiang Lin | Wei-Hsiang Lin | Biomass transfer on autocatalytic reaction network: a delay differential
equation formulation | Error in the text | null | null | null | q-bio.MN cond-mat.soft math.DS | http://creativecommons.org/licenses/by-nc-nd/4.0/ | For a biological system to grow, the biomass must be incorporated,
transferred, and accumulated into the underlying reaction network. There are
two perspectives for studying growth dynamics of reaction networks: one way is
to focus on each node in the networks and study its associated influxes and
effluxes. The other way is to focus on a fraction of biomass and study its
trajectory along the reaction pathways. The former perspective (analogous to
the "Eulerian representation" in fluid mechanics) has been studied extensively,
while the latter perspective (analogous to the "Lagrangian representation" in
fluid mechanics) has not been systematically explored. In this work, I
characterized the biomass transfer process for autocatalytic, growing systems
with scalable reaction fluxes. Under balanced growth, the long-term growth
dynamics of the systems are described by delay differential equations (DDEs).
The kernel function of the DDE serves as a unique pattern for the catalytic
delay for a reaction network, and in frequency domain the delay spectrum
provides a geometric interpretation for long-term growth rate. The DDE
formulation provides a clear intuition on how autocatalytic reaction pathways
lead to system growth, it also enables us to classify and compare reaction
networks with different network structures.
| [
{
"created": "Mon, 17 Oct 2022 23:17:24 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Mar 2023 02:13:29 GMT",
"version": "v2"
}
] | 2023-03-03 | [
[
"Lin",
"Wei-Hsiang",
""
]
] | For a biological system to grow, the biomass must be incorporated, transferred, and accumulated into the underlying reaction network. There are two perspectives for studying growth dynamics of reaction networks: one way is to focus on each node in the networks and study its associated influxes and effluxes. The other way is to focus on a fraction of biomass and study its trajectory along the reaction pathways. The former perspective (analogous to the "Eulerian representation" in fluid mechanics) has been studied extensively, while the latter perspective (analogous to the "Lagrangian representation" in fluid mechanics) has not been systematically explored. In this work, I characterized the biomass transfer process for autocatalytic, growing systems with scalable reaction fluxes. Under balanced growth, the long-term growth dynamics of the systems are described by delay differential equations (DDEs). The kernel function of the DDE serves as a unique pattern for the catalytic delay for a reaction network, and in frequency domain the delay spectrum provides a geometric interpretation for long-term growth rate. The DDE formulation provides a clear intuition on how autocatalytic reaction pathways lead to system growth, it also enables us to classify and compare reaction networks with different network structures. |
1206.5771 | Christoph Adami | Lars Marstaller, Arend Hintze, and Christoph Adami | The evolution of representation in simple cognitive networks | 36 pages, 10 figures, one Table | Neural Computation 25 (2013) 2079-2107 | 10.1162/NECO_a_00475 | null | q-bio.NC cs.NE q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Representations are internal models of the environment that can provide
guidance to a behaving agent, even in the absence of sensory information. It is
not clear how representations are developed and whether or not they are
necessary or even essential for intelligent behavior. We argue here that the
ability to represent relevant features of the environment is the expected
consequence of an adaptive process, give a formal definition of representation
based on information theory, and quantify it with a measure R. To measure how R
changes over time, we evolve two types of networks---an artificial neural
network and a network of hidden Markov gates---to solve a categorization task
using a genetic algorithm. We find that the capacity to represent increases
during evolutionary adaptation, and that agents form representations of their
environment during their lifetime. This ability allows the agents to act on
sensorial inputs in the context of their acquired representations and enables
complex and context-dependent behavior. We examine which concepts (features of
the environment) our networks are representing, how the representations are
logically encoded in the networks, and how they form as an agent behaves to
solve a task. We conclude that R should be able to quantify the representations
within any cognitive system, and should be predictive of an agent's long-term
adaptive success.
| [
{
"created": "Mon, 25 Jun 2012 19:03:04 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Aug 2013 17:27:01 GMT",
"version": "v2"
}
] | 2013-08-07 | [
[
"Marstaller",
"Lars",
""
],
[
"Hintze",
"Arend",
""
],
[
"Adami",
"Christoph",
""
]
] | Representations are internal models of the environment that can provide guidance to a behaving agent, even in the absence of sensory information. It is not clear how representations are developed and whether or not they are necessary or even essential for intelligent behavior. We argue here that the ability to represent relevant features of the environment is the expected consequence of an adaptive process, give a formal definition of representation based on information theory, and quantify it with a measure R. To measure how R changes over time, we evolve two types of networks---an artificial neural network and a network of hidden Markov gates---to solve a categorization task using a genetic algorithm. We find that the capacity to represent increases during evolutionary adaptation, and that agents form representations of their environment during their lifetime. This ability allows the agents to act on sensorial inputs in the context of their acquired representations and enables complex and context-dependent behavior. We examine which concepts (features of the environment) our networks are representing, how the representations are logically encoded in the networks, and how they form as an agent behaves to solve a task. We conclude that R should be able to quantify the representations within any cognitive system, and should be predictive of an agent's long-term adaptive success. |
1911.09959 | Sarra Ghanjeti | Sarra Ghanjeti | Alignment of Protein-Protein Interaction Networks | 57 pages, in French, 9 figures | null | null | null | q-bio.QM q-bio.MN | http://creativecommons.org/licenses/by-nc-sa/4.0/ | PPI network alignment aims to find topological and functional similarities
between networks of different species. Several alignment approaches have been
proposed. Each of these approaches relies on a different alignment method and
uses different biological information during the alignment process such as the
topological structure of the networks and the sequence similarities between the
proteins, but less of them integrate the functional similarities between
proteins. In this context, we present our algorithm PPINA (Protein-Protein
Interaction Network Aligner), which is an extension of the NETAL algorithm. The
latter aligns two networks based on the sequence, functional and network
topology similarity of the proteins. PPINA has been tested on real PPI
networks. The results show that PPINA has outperformed other alignment
algorithms where it provides biologically meaningful results.
| [
{
"created": "Fri, 22 Nov 2019 10:32:31 GMT",
"version": "v1"
}
] | 2019-11-25 | [
[
"Ghanjeti",
"Sarra",
""
]
] | PPI network alignment aims to find topological and functional similarities between networks of different species. Several alignment approaches have been proposed. Each of these approaches relies on a different alignment method and uses different biological information during the alignment process such as the topological structure of the networks and the sequence similarities between the proteins, but less of them integrate the functional similarities between proteins. In this context, we present our algorithm PPINA (Protein-Protein Interaction Network Aligner), which is an extension of the NETAL algorithm. The latter aligns two networks based on the sequence, functional and network topology similarity of the proteins. PPINA has been tested on real PPI networks. The results show that PPINA has outperformed other alignment algorithms where it provides biologically meaningful results. |
2303.08245 | Chenyu Wu | C. Wu, E.B. Gunnarsson, E.M. Myklebust, A. K\"ohn-Luque, D.S. Tadele,
J.M. Enserink, A. Frigessi, J. Foo, K. Leder | Using birth-death processes to infer tumor subpopulation structure from
live-cell imaging drug screening data | 36 pages, 14 figures. v2: 1. Rearranged paper and figures. 2.
Modified the figures to make them easier to access; results unchanged. 3.
Revised the argument in section 3 and section 4; results unchanged. 4.
Revised the abstract | null | null | null | q-bio.PE q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Tumor heterogeneity is a complex and widely recognized trait that poses
significant challenges in developing effective cancer therapies. In particular,
many tumors harbor a variety of subpopulations with distinct therapeutic
response characteristics. Characterizing this heterogeneity by determining the
subpopulation structure within a tumor enables more precise and successful
treatment strategies. In our prior work, we developed PhenoPop, a computational
framework for unravelling the drug-response subpopulation structure within a
tumor from bulk high-throughput drug screening data. However, the deterministic
nature of the underlying models driving PhenoPop restricts the model fit and
the information it can extract from the data. As an advancement, we propose a
stochastic model based on the linear birth-death process to address this
limitation. Our model can formulate a dynamic variance along the horizon of the
experiment so that the model uses more information from the data to provide a
more robust estimation. In addition, the newly proposed model can be readily
adapted to situations where the experimental data exhibits a positive time
correlation. We test our model on simulated data (in silico) and experimental
data (in vitro), which supports our argument about its advantages.
| [
{
"created": "Tue, 14 Mar 2023 21:39:19 GMT",
"version": "v1"
},
{
"created": "Tue, 13 Jun 2023 17:07:15 GMT",
"version": "v2"
}
] | 2023-06-14 | [
[
"Wu",
"C.",
""
],
[
"Gunnarsson",
"E. B.",
""
],
[
"Myklebust",
"E. M.",
""
],
[
"Köhn-Luque",
"A.",
""
],
[
"Tadele",
"D. S.",
""
],
[
"Enserink",
"J. M.",
""
],
[
"Frigessi",
"A.",
""
],
[
"Foo",
"J.",
""
],
[
"Leder",
"K.",
""
]
] | Tumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages. |
2007.08523 | Paolo Pin | Matteo Bizzarri, Fabrizio Panebianco, Paolo Pin | Epidemic dynamics with homophily, vaccination choices, and pseudoscience
attitudes | null | null | null | null | q-bio.PE econ.GN physics.soc-ph q-fin.EC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We interpret attitudes towards science and pseudosciences as cultural traits
that diffuse in society through communication efforts exerted by agents. We
present a tractable model that allows us to study the interaction among the
diffusion of an epidemic, vaccination choices, and the dynamics of cultural
traits. We apply it to study the impact of homophily between pro-vaxxers and
anti-vaxxers on the total number of cases (the cumulative infection). We show
that, during the outbreak of a disease, homophily has the direct effect of
decreasing the speed of recovery. Hence, it may increase the number of cases
and make the disease endemic. The dynamics of the shares of the two cultural
traits in the population is crucial in determining the sign of the total effect
on the cumulative infection: more homophily is beneficial if agents are not too
flexible in changing their cultural trait, is detrimental otherwise.
| [
{
"created": "Thu, 16 Jul 2020 09:23:02 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Sep 2020 08:56:06 GMT",
"version": "v2"
},
{
"created": "Fri, 11 Jun 2021 08:04:31 GMT",
"version": "v3"
}
] | 2021-06-14 | [
[
"Bizzarri",
"Matteo",
""
],
[
"Panebianco",
"Fabrizio",
""
],
[
"Pin",
"Paolo",
""
]
] | We interpret attitudes towards science and pseudosciences as cultural traits that diffuse in society through communication efforts exerted by agents. We present a tractable model that allows us to study the interaction among the diffusion of an epidemic, vaccination choices, and the dynamics of cultural traits. We apply it to study the impact of homophily between pro-vaxxers and anti-vaxxers on the total number of cases (the cumulative infection). We show that, during the outbreak of a disease, homophily has the direct effect of decreasing the speed of recovery. Hence, it may increase the number of cases and make the disease endemic. The dynamics of the shares of the two cultural traits in the population is crucial in determining the sign of the total effect on the cumulative infection: more homophily is beneficial if agents are not too flexible in changing their cultural trait, is detrimental otherwise. |
1911.04040 | Petter Holme | Naoki Masuda, Petter Holme | Small inter-event times govern epidemic spreading on temporal networks | null | Phys. Rev. Research 2, 023163 (2020) | 10.1103/PhysRevResearch.2.023163 | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Just like the degrees of human and animal interaction networks, the
distribution of the times between interactions is known to often be
right-skewed and fat-tailed. Both these distributions affect epidemic dynamics
strongly, but, as we show in this Letter, for very different reasons. Whereas
the high degrees of the tail are critical for facilitating epidemics, it is the
small interevent times that control the dynamics of epidemics. We investigate
this effect both analytically and numerically for different versions of the
Susceptible-Infected-Recovered model on different types of networks.
| [
{
"created": "Mon, 11 Nov 2019 02:34:50 GMT",
"version": "v1"
},
{
"created": "Thu, 9 Apr 2020 05:54:12 GMT",
"version": "v2"
}
] | 2020-05-14 | [
[
"Masuda",
"Naoki",
""
],
[
"Holme",
"Petter",
""
]
] | Just like the degrees of human and animal interaction networks, the distribution of the times between interactions is known to often be right-skewed and fat-tailed. Both these distributions affect epidemic dynamics strongly, but, as we show in this Letter, for very different reasons. Whereas the high degrees of the tail are critical for facilitating epidemics, it is the small interevent times that control the dynamics of epidemics. We investigate this effect both analytically and numerically for different versions of the Susceptible-Infected-Recovered model on different types of networks. |
1811.12153 | Diego Fasoli | Diego Fasoli, Stefano Panzeri | Stationary-State Statistics of a Binary Neural Network Model with
Quenched Disorder | 30 pages, 6 figures, 2 supplemental Python scripts | null | 10.3390/e21070630 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the statistical properties of the stationary firing-rate states of a
neural network model with quenched disorder. The model has arbitrary size,
discrete-time evolution equations and binary firing rates, while the topology
and the strength of the synaptic connections are randomly generated from known,
generally arbitrary, probability distributions. We derived semi-analytical
expressions of the occurrence probability of the stationary states and the mean
multistability diagram of the model, in terms of the distribution of the
synaptic connections and of the external stimuli to the network. Our
calculations rely on the probability distribution of the bifurcation points of
the stationary states with respect to the external stimuli, which can be
calculated in terms of the permanent of special matrices, according to extreme
value theory. While our semi-analytical expressions are exact for any size of
the network and for any distribution of the synaptic connections, we also
specialized our calculations to the case of statistically-homogeneous
multi-population networks. In the specific case of this network topology, we
calculated analytically the permanent, obtaining a compact formula that
outperforms of several orders of magnitude the
Balasubramanian-Bax-Franklin-Glynn algorithm. To conclude, by applying the
Fisher-Tippett-Gnedenko theorem, we derived asymptotic expressions of the
stationary-state statistics of multi-population networks in the
large-network-size limit, in terms of the Gumbel (double exponential)
distribution. We also provide a Python implementation of our formulas and some
examples of the results generated by the code.
| [
{
"created": "Thu, 29 Nov 2018 14:11:24 GMT",
"version": "v1"
}
] | 2019-07-24 | [
[
"Fasoli",
"Diego",
""
],
[
"Panzeri",
"Stefano",
""
]
] | We study the statistical properties of the stationary firing-rate states of a neural network model with quenched disorder. The model has arbitrary size, discrete-time evolution equations and binary firing rates, while the topology and the strength of the synaptic connections are randomly generated from known, generally arbitrary, probability distributions. We derived semi-analytical expressions of the occurrence probability of the stationary states and the mean multistability diagram of the model, in terms of the distribution of the synaptic connections and of the external stimuli to the network. Our calculations rely on the probability distribution of the bifurcation points of the stationary states with respect to the external stimuli, which can be calculated in terms of the permanent of special matrices, according to extreme value theory. While our semi-analytical expressions are exact for any size of the network and for any distribution of the synaptic connections, we also specialized our calculations to the case of statistically-homogeneous multi-population networks. In the specific case of this network topology, we calculated analytically the permanent, obtaining a compact formula that outperforms of several orders of magnitude the Balasubramanian-Bax-Franklin-Glynn algorithm. To conclude, by applying the Fisher-Tippett-Gnedenko theorem, we derived asymptotic expressions of the stationary-state statistics of multi-population networks in the large-network-size limit, in terms of the Gumbel (double exponential) distribution. We also provide a Python implementation of our formulas and some examples of the results generated by the code. |
2001.11437 | Vu Anh Truong Nguyen | Vu AT Nguyen and Dervis Can Vural | Theoretical guidelines for editing ecological communities | 10 pages, 8 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Having control over species abundances and community resilience is of great
interest for experimental, agricultural, industrial and conservation purposes.
Here, we theoretically explore the possibility of manipulating ecological
communities by modifying pairwise interactions. Specifically, we establish
which interaction values should be modified, and by how much, in order to alter
the composition or resilience of a community towards a favorable direction.
While doing so, we also take into account the experimental difficulties in
making such modifications by including in our optimization process, a cost
parameter, which penalizes large modifications. In addition to prescribing what
changes should be made to interspecies interactions given some modification
cost, our approach also serves to establish the limits of community control,
i.e. how well can one approach an ecological goal at best, even when not
constrained by cost.
| [
{
"created": "Thu, 30 Jan 2020 16:36:40 GMT",
"version": "v1"
}
] | 2020-01-31 | [
[
"Nguyen",
"Vu AT",
""
],
[
"Vural",
"Dervis Can",
""
]
] | Having control over species abundances and community resilience is of great interest for experimental, agricultural, industrial and conservation purposes. Here, we theoretically explore the possibility of manipulating ecological communities by modifying pairwise interactions. Specifically, we establish which interaction values should be modified, and by how much, in order to alter the composition or resilience of a community towards a favorable direction. While doing so, we also take into account the experimental difficulties in making such modifications by including in our optimization process, a cost parameter, which penalizes large modifications. In addition to prescribing what changes should be made to interspecies interactions given some modification cost, our approach also serves to establish the limits of community control, i.e. how well can one approach an ecological goal at best, even when not constrained by cost. |
1108.2840 | Miguel \'A. Carreira-Perpi\~n\'an | Miguel \'A. Carreira-Perpi\~n\'an, Geoffrey J. Goodhill | Generalised elastic nets | 52 pages, 16 figures. Original manuscript dated August 14, 2003 and
not updated since. Current authors' email addresses:
mcarreira-perpinan@ucmerced.edu, g.goodhill@uq.edu.au | null | null | null | q-bio.NC cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The elastic net was introduced as a heuristic algorithm for combinatorial
optimisation and has been applied, among other problems, to biological
modelling. It has an energy function which trades off a fitness term against a
tension term. In the original formulation of the algorithm the tension term was
implicitly based on a first-order derivative. In this paper we generalise the
elastic net model to an arbitrary quadratic tension term, e.g. derived from a
discretised differential operator, and give an efficient learning algorithm. We
refer to these as generalised elastic nets (GENs). We give a theoretical
analysis of the tension term for 1D nets with periodic boundary conditions, and
show that the model is sensitive to the choice of finite difference scheme that
represents the discretised derivative. We illustrate some of these issues in
the context of cortical map models, by relating the choice of tension term to a
cortical interaction function. In particular, we prove that this interaction
takes the form of a Mexican hat for the original elastic net, and of
progressively more oscillatory Mexican hats for higher-order derivatives. The
results apply not only to generalised elastic nets but also to other methods
using discrete differential penalties, and are expected to be useful in other
areas, such as data analysis, computer graphics and optimisation problems.
| [
{
"created": "Sun, 14 Aug 2011 03:47:14 GMT",
"version": "v1"
}
] | 2011-08-16 | [
[
"Carreira-Perpiñán",
"Miguel Á.",
""
],
[
"Goodhill",
"Geoffrey J.",
""
]
] | The elastic net was introduced as a heuristic algorithm for combinatorial optimisation and has been applied, among other problems, to biological modelling. It has an energy function which trades off a fitness term against a tension term. In the original formulation of the algorithm the tension term was implicitly based on a first-order derivative. In this paper we generalise the elastic net model to an arbitrary quadratic tension term, e.g. derived from a discretised differential operator, and give an efficient learning algorithm. We refer to these as generalised elastic nets (GENs). We give a theoretical analysis of the tension term for 1D nets with periodic boundary conditions, and show that the model is sensitive to the choice of finite difference scheme that represents the discretised derivative. We illustrate some of these issues in the context of cortical map models, by relating the choice of tension term to a cortical interaction function. In particular, we prove that this interaction takes the form of a Mexican hat for the original elastic net, and of progressively more oscillatory Mexican hats for higher-order derivatives. The results apply not only to generalised elastic nets but also to other methods using discrete differential penalties, and are expected to be useful in other areas, such as data analysis, computer graphics and optimisation problems. |
2304.09566 | Giuseppe de Vito | Giuseppe de Vito, Lapo Turrini, Chiara Fornetto, Elena Trabalzini,
Pietro Ricci, Duccio Fanelli, Francesco Vanzi, Francesco Saverio Pavone | Brain-wide functional imaging to highlight differences between the
diurnal and nocturnal neuronal activity in zebrafish larvae | 22 pages, 8 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most living organisms show highly conserved physiological changes following a
24-hour cycle which goes by the name of circadian rhythm. Among experimental
models, the effects of light-dark cycle have been recently investigated in the
larval zebrafish. Owing to its small size and transparency, this vertebrate
enables optical access to the entire brain. Indeed, the combination of this
organism with light-sheet imaging grants high spatio-temporal resolution
volumetric recording of neuronal activity. This imaging technique, in its
multiphoton variant, allows functional investigations without unwanted visual
stimulation. Here, we employed a custom two-photon light-sheet microscope to
study brain-wide differences in neuronal activity between diurnal and nocturnal
periods in larval zebrafish assessed at the transition between day and night.
We describe for the first time an activity increase in the low frequency domain
of the pretectum and a frequency-localized activity decrease of the anterior
rhombencephalic turning region during the nocturnal period. Moreover, our data
confirm a nocturnal reduction in habenular activity. Furthermore, brain-wide
detrended fluctuation analysis revealed a nocturnal decrease in the
self-affinity of the neuronal signals in parts of the dorsal thalamus and the
medulla oblongata and an increase in the pretectum. Our data show that
brain-wide nonlinear light-sheet imaging represents a useful tool to
investigate circadian rhythm effects on neuronal activity.
| [
{
"created": "Wed, 19 Apr 2023 11:09:50 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Aug 2023 17:16:27 GMT",
"version": "v2"
}
] | 2023-08-28 | [
[
"de Vito",
"Giuseppe",
""
],
[
"Turrini",
"Lapo",
""
],
[
"Fornetto",
"Chiara",
""
],
[
"Trabalzini",
"Elena",
""
],
[
"Ricci",
"Pietro",
""
],
[
"Fanelli",
"Duccio",
""
],
[
"Vanzi",
"Francesco",
""
],
[
"Pavone",
"Francesco Saverio",
""
]
] | Most living organisms show highly conserved physiological changes following a 24-hour cycle which goes by the name of circadian rhythm. Among experimental models, the effects of light-dark cycle have been recently investigated in the larval zebrafish. Owing to its small size and transparency, this vertebrate enables optical access to the entire brain. Indeed, the combination of this organism with light-sheet imaging grants high spatio-temporal resolution volumetric recording of neuronal activity. This imaging technique, in its multiphoton variant, allows functional investigations without unwanted visual stimulation. Here, we employed a custom two-photon light-sheet microscope to study brain-wide differences in neuronal activity between diurnal and nocturnal periods in larval zebrafish assessed at the transition between day and night. We describe for the first time an activity increase in the low frequency domain of the pretectum and a frequency-localized activity decrease of the anterior rhombencephalic turning region during the nocturnal period. Moreover, our data confirm a nocturnal reduction in habenular activity. Furthermore, brain-wide detrended fluctuation analysis revealed a nocturnal decrease in the self-affinity of the neuronal signals in parts of the dorsal thalamus and the medulla oblongata and an increase in the pretectum. Our data show that brain-wide nonlinear light-sheet imaging represents a useful tool to investigate circadian rhythm effects on neuronal activity. |
2007.09466 | Sergio L\'opez Bernal | Sergio L\'opez Bernal, Alberto Huertas Celdr\'an, Lorenzo Fern\'andez
Maim\'o, Michael Taynnan Barros, Sasitharan Balasubramaniam, Gregorio
Mart\'inez P\'erez | Cyberattacks on Miniature Brain Implants to Disrupt Spontaneous Neural
Signaling | null | null | null | null | q-bio.NC cs.CR | http://creativecommons.org/publicdomain/zero/1.0/ | Brain-Computer Interfaces (BCI) arose as systems that merge computing systems
with the human brain to facilitate recording, stimulation, and inhibition of
neural activity. Over the years, the development of BCI technologies has
shifted towards miniaturization of devices that can be seamlessly embedded into
the brain and can target single neuron or small population sensing and control.
We present a motivating example highlighting vulnerabilities of two promising
micron-scale BCI technologies, demonstrating the lack of security and privacy
principles in existing solutions. This situation opens the door to a novel
family of cyberattacks, called neuronal cyberattacks, affecting neuronal
signaling. This paper defines the first two neural cyberattacks, Neuronal
Flooding (FLO) and Neuronal Scanning (SCA), where each threat can affect the
natural activity of neurons. This work implements these attacks in a neuronal
simulator to determine their impact over the spontaneous neuronal behavior,
defining three metrics: number of spikes, percentage of shifts, and dispersion
of spikes. Several experiments demonstrate that both cyberattacks produce a
reduction of spikes compared to spontaneous behavior, generating a rise in
temporal shifts and a dispersion increase. Mainly, SCA presents a higher impact
than FLO in the metrics focused on the number of spikes and dispersion, where
FLO is slightly more damaging, considering the percentage of shifts.
Nevertheless, the intrinsic behavior of each attack generates a differentiation
on how they alter neuronal signaling. FLO is adequate to generate an immediate
impact on the neuronal activity, whereas SCA presents higher effectiveness for
damages to the neural signaling in the long-term.
| [
{
"created": "Sat, 18 Jul 2020 16:25:46 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Sep 2020 14:55:42 GMT",
"version": "v2"
}
] | 2020-09-11 | [
[
"Bernal",
"Sergio López",
""
],
[
"Celdrán",
"Alberto Huertas",
""
],
[
"Maimó",
"Lorenzo Fernández",
""
],
[
"Barros",
"Michael Taynnan",
""
],
[
"Balasubramaniam",
"Sasitharan",
""
],
[
"Pérez",
"Gregorio Martínez",
""
]
] | Brain-Computer Interfaces (BCI) arose as systems that merge computing systems with the human brain to facilitate recording, stimulation, and inhibition of neural activity. Over the years, the development of BCI technologies has shifted towards miniaturization of devices that can be seamlessly embedded into the brain and can target single neuron or small population sensing and control. We present a motivating example highlighting vulnerabilities of two promising micron-scale BCI technologies, demonstrating the lack of security and privacy principles in existing solutions. This situation opens the door to a novel family of cyberattacks, called neuronal cyberattacks, affecting neuronal signaling. This paper defines the first two neural cyberattacks, Neuronal Flooding (FLO) and Neuronal Scanning (SCA), where each threat can affect the natural activity of neurons. This work implements these attacks in a neuronal simulator to determine their impact over the spontaneous neuronal behavior, defining three metrics: number of spikes, percentage of shifts, and dispersion of spikes. Several experiments demonstrate that both cyberattacks produce a reduction of spikes compared to spontaneous behavior, generating a rise in temporal shifts and a dispersion increase. Mainly, SCA presents a higher impact than FLO in the metrics focused on the number of spikes and dispersion, where FLO is slightly more damaging, considering the percentage of shifts. Nevertheless, the intrinsic behavior of each attack generates a differentiation on how they alter neuronal signaling. FLO is adequate to generate an immediate impact on the neuronal activity, whereas SCA presents higher effectiveness for damages to the neural signaling in the long-term. |
1505.02928 | Srinandan Dasmahapatra | An Nguyen, Adam Prugel-Bennett and Srinandan Dasmahapatra | A Low Dimensional Approximation For Competence In Bacillus Subtilis | 12 pages, to be published in IEEE/ACM Transactions on Computational
Biology and Bioinformatics | null | 10.1109/TCBB.2015.2440275 | null | q-bio.QM physics.bio-ph q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The behaviour of a high dimensional stochastic system described by a Chemical
Master Equation (CME) depends on many parameters, rendering explicit simulation
an inefficient method for exploring the properties of such models. Capturing
their behaviour by low-dimensional models makes analysis of system behaviour
tractable. In this paper, we present low dimensional models for the
noise-induced excitable dynamics in Bacillus subtilis, whereby a key protein
ComK, which drives a complex chain of reactions leading to bacterial
competence, gets expressed rapidly in large quantities (competent state) before
subsiding to low levels of expression (vegetative state). These rapid reactions
suggest the application of an adiabatic approximation of the dynamics of the
regulatory model that, however, lead to competence durations that are incorrect
by a factor of 2. We apply a modified version of an iterative functional
procedure that faithfully approximates the time-course of the trajectories in
terms of a 2-dimensional model involving proteins ComK and ComS. Furthermore,
in order to describe the bimodal bivariate marginal probability distribution
obtained from the Gillespie simulations of the CME, we introduce a tunable
multiplicative noise term in a 2-dimensional Langevin model whose stationary
state is described by the time-independent solution of the corresponding
Fokker-Planck equation.
| [
{
"created": "Tue, 12 May 2015 09:30:47 GMT",
"version": "v1"
}
] | 2016-11-17 | [
[
"Nguyen",
"An",
""
],
[
"Prugel-Bennett",
"Adam",
""
],
[
"Dasmahapatra",
"Srinandan",
""
]
] | The behaviour of a high dimensional stochastic system described by a Chemical Master Equation (CME) depends on many parameters, rendering explicit simulation an inefficient method for exploring the properties of such models. Capturing their behaviour by low-dimensional models makes analysis of system behaviour tractable. In this paper, we present low dimensional models for the noise-induced excitable dynamics in Bacillus subtilis, whereby a key protein ComK, which drives a complex chain of reactions leading to bacterial competence, gets expressed rapidly in large quantities (competent state) before subsiding to low levels of expression (vegetative state). These rapid reactions suggest the application of an adiabatic approximation of the dynamics of the regulatory model that, however, lead to competence durations that are incorrect by a factor of 2. We apply a modified version of an iterative functional procedure that faithfully approximates the time-course of the trajectories in terms of a 2-dimensional model involving proteins ComK and ComS. Furthermore, in order to describe the bimodal bivariate marginal probability distribution obtained from the Gillespie simulations of the CME, we introduce a tunable multiplicative noise term in a 2-dimensional Langevin model whose stationary state is described by the time-independent solution of the corresponding Fokker-Planck equation. |
2306.14707 | Danko Georgiev | Danko D. Georgiev | Causal potency of consciousness in the physical world | 47 pages, 7 figures. International Journal of Modern Physics B (2023) | International Journal of Modern Physics B 2024; 38 (19): 2450256 | 10.1142/s0217979224502564 | null | q-bio.NC quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evolution of the human mind through natural selection mandates that our
conscious experiences are causally potent in order to leave a tangible impact
upon the surrounding physical world. Any attempt to construct a functional
theory of the conscious mind within the framework of classical physics,
however, inevitably leads to causally impotent conscious experiences in direct
contradiction to evolution theory. Here, we derive several rigorous theorems
that identify the origin of the latter impasse in the mathematical properties
of ordinary differential equations employed in combination with the alleged
functional production of the mind by the brain. Then, we demonstrate that a
mind--brain theory consistent with causally potent conscious experiences is
provided by modern quantum physics, in which the unobservable conscious mind is
reductively identified with the quantum state of the brain and the observable
brain is constructed by the physical measurement of quantum brain observables.
The resulting quantum stochastic dynamics obtained from sequential quantum
measurements of the brain is governed by stochastic differential equations,
which permit genuine free will exercised through sequential conscious choices
of future courses of action. Thus, quantum reductionism provides a solid
theoretical foundation for the causal potency of consciousness, free will and
cultural transmission.
| [
{
"created": "Mon, 26 Jun 2023 13:55:33 GMT",
"version": "v1"
}
] | 2024-05-10 | [
[
"Georgiev",
"Danko D.",
""
]
] | The evolution of the human mind through natural selection mandates that our conscious experiences are causally potent in order to leave a tangible impact upon the surrounding physical world. Any attempt to construct a functional theory of the conscious mind within the framework of classical physics, however, inevitably leads to causally impotent conscious experiences in direct contradiction to evolution theory. Here, we derive several rigorous theorems that identify the origin of the latter impasse in the mathematical properties of ordinary differential equations employed in combination with the alleged functional production of the mind by the brain. Then, we demonstrate that a mind--brain theory consistent with causally potent conscious experiences is provided by modern quantum physics, in which the unobservable conscious mind is reductively identified with the quantum state of the brain and the observable brain is constructed by the physical measurement of quantum brain observables. The resulting quantum stochastic dynamics obtained from sequential quantum measurements of the brain is governed by stochastic differential equations, which permit genuine free will exercised through sequential conscious choices of future courses of action. Thus, quantum reductionism provides a solid theoretical foundation for the causal potency of consciousness, free will and cultural transmission. |
2212.07695 | Vittorio Lippi | Vittorio Lippi, Christoph Maurer, Thomas Mergner | Human body-sway steady-state responses to small amplitude tilts and
translations of the support surface -- Effects of superposition of the two
stimuli | 10 pages, 7 figures | Gait & Posture, Volume 100, 2023, Pages 139-148, ISSN 0966-6362 | 10.1016/j.gaitpost.2022.12.003 | null | q-bio.NC stat.AP | http://creativecommons.org/licenses/by/4.0/ | Upright stance tested with a superposition of support surface tilt and
translation. Steady state response is characterized by frequency response
function. Interaction between two stimuli absent in most of the cases. Larger
stimuli may create interaction. Simulations suggest that the observed effects
can be due to joint stiffness modulation.
| [
{
"created": "Thu, 15 Dec 2022 10:13:32 GMT",
"version": "v1"
}
] | 2022-12-16 | [
[
"Lippi",
"Vittorio",
""
],
[
"Maurer",
"Christoph",
""
],
[
"Mergner",
"Thomas",
""
]
] | Upright stance tested with a superposition of support surface tilt and translation. Steady state response is characterized by frequency response function. Interaction between two stimuli absent in most of the cases. Larger stimuli may create interaction. Simulations suggest that the observed effects can be due to joint stiffness modulation. |
2012.08671 | Aaron Wang | Wei Cheng, Ghulam Murtaza, Aaron Wang | SimpleChrome: Encoding of Combinatorial Effects for Predicting Gene
Expression | null | null | null | null | q-bio.GN cs.LG | http://creativecommons.org/licenses/by/4.0/ | Due to recent breakthroughs in state-of-the-art DNA sequencing technology,
genomics data sets have become ubiquitous. The emergence of large-scale data
sets provides great opportunities for better understanding of genomics,
especially gene regulation. Although each cell in the human body contains the
same set of DNA information, gene expression controls the functions of these
cells by either turning genes on or off, known as gene expression levels. There
are two important factors that control the expression level of each gene: (1)
Gene regulation such as histone modifications can directly regulate gene
expression. (2) Neighboring genes that are functionally related to or interact
with each other that can also affect gene expression level. Previous efforts
have tried to address the former using Attention-based model. However,
addressing the second problem requires the incorporation of all potentially
related gene information into the model. Though modern machine learning and
deep learning models have been able to capture gene expression signals when
applied to moderately sized data, they have struggled to recover the underlying
signals of the data due to the nature of the data's higher dimensionality. To
remedy this issue, we present SimpleChrome, a deep learning model that learns
the latent histone modification representations of genes. The features learned
from the model allow us to better understand the combinatorial effects of
cross-gene interactions and direct gene regulation on the target gene
expression. The results of this paper show outstanding improvements on the
predictive capabilities of downstream models and greatly relaxes the need for a
large data set to learn a robust, generalized neural network. These results
have immediate downstream effects in epigenomics research and drug development.
| [
{
"created": "Tue, 15 Dec 2020 23:30:36 GMT",
"version": "v1"
},
{
"created": "Thu, 17 Dec 2020 05:58:21 GMT",
"version": "v2"
}
] | 2020-12-18 | [
[
"Cheng",
"Wei",
""
],
[
"Murtaza",
"Ghulam",
""
],
[
"Wang",
"Aaron",
""
]
] | Due to recent breakthroughs in state-of-the-art DNA sequencing technology, genomics data sets have become ubiquitous. The emergence of large-scale data sets provides great opportunities for better understanding of genomics, especially gene regulation. Although each cell in the human body contains the same set of DNA information, gene expression controls the functions of these cells by either turning genes on or off, known as gene expression levels. There are two important factors that control the expression level of each gene: (1) Gene regulation such as histone modifications can directly regulate gene expression. (2) Neighboring genes that are functionally related to or interact with each other that can also affect gene expression level. Previous efforts have tried to address the former using Attention-based model. However, addressing the second problem requires the incorporation of all potentially related gene information into the model. Though modern machine learning and deep learning models have been able to capture gene expression signals when applied to moderately sized data, they have struggled to recover the underlying signals of the data due to the nature of the data's higher dimensionality. To remedy this issue, we present SimpleChrome, a deep learning model that learns the latent histone modification representations of genes. The features learned from the model allow us to better understand the combinatorial effects of cross-gene interactions and direct gene regulation on the target gene expression. The results of this paper show outstanding improvements on the predictive capabilities of downstream models and greatly relaxes the need for a large data set to learn a robust, generalized neural network. These results have immediate downstream effects in epigenomics research and drug development. |
1309.7414 | Liane Gabora | Liane Gabora | The Beer Can Theory of Creativity | 25 pages | In P. Bentley & D. Corne (Eds.), Creative Evolutionary Systems
(pp. 147-161). San Francisco: Morgan Kauffman. (2000) | null | null | q-bio.NC nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This chapter explores the cognitive mechanisms underlying the emergence and
evolution of cultural novelty. Section Two summarizes the rationale for viewing
the process by which the fruits of the mind take shape as they spread from one
individual to another as a form of evolution, and briefly discusses a computer
model of this process. Section Three presents theoretical and empirical
evidence that the sudden proliferation of human culture approximately two
million years ago began with the capacity for creativity: that is, the ability
to generate novelty strategically and contextually. The next two sections take
a closer look at the creative process. Section Four examines the mechanisms
underlying the fluid, associative thought that constitutes the inspirational
component of creativity. Section Five explores how that initial flicker of
inspiration crystallizes into a solid, workable idea as it gets mulled over in
light of the various constraints and affordances of the world into which it
will be born. Finally, Section Six wraps things up with a few speculative
thoughts about the overall unfolding of this evolutionary process.
| [
{
"created": "Sat, 28 Sep 2013 03:07:53 GMT",
"version": "v1"
},
{
"created": "Fri, 5 Jul 2019 20:01:35 GMT",
"version": "v2"
},
{
"created": "Tue, 9 Jul 2019 20:04:24 GMT",
"version": "v3"
}
] | 2019-07-11 | [
[
"Gabora",
"Liane",
""
]
] | This chapter explores the cognitive mechanisms underlying the emergence and evolution of cultural novelty. Section Two summarizes the rationale for viewing the process by which the fruits of the mind take shape as they spread from one individual to another as a form of evolution, and briefly discusses a computer model of this process. Section Three presents theoretical and empirical evidence that the sudden proliferation of human culture approximately two million years ago began with the capacity for creativity: that is, the ability to generate novelty strategically and contextually. The next two sections take a closer look at the creative process. Section Four examines the mechanisms underlying the fluid, associative thought that constitutes the inspirational component of creativity. Section Five explores how that initial flicker of inspiration crystallizes into a solid, workable idea as it gets mulled over in light of the various constraints and affordances of the world into which it will be born. Finally, Section Six wraps things up with a few speculative thoughts about the overall unfolding of this evolutionary process. |
1706.03013 | Genki Ichinose | Genki Ichinose, Yoshiki Satotani, Hiroki Sayama | How mutation alters fitness of cooperation in networked evolutionary
games | 6 pages, 5 figures | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cooperation is ubiquitous in every level of living organisms. It is known
that spatial (network) structure is a viable mechanism for cooperation to
evolve. Until recently, it has been difficult to predict whether cooperation
can evolve at a network (population) level. To address this problem, Pinheiro
et al. proposed a numerical metric, called Average Gradient of Selection (AGoS)
in 2012. AGoS can characterize and forecast the evolutionary fate of
cooperation at a population level. However, stochastic mutation of strategies
was not considered in the analysis of AGoS. Here we analyzed the evolution of
cooperation using AGoS where mutation may occur to strategies of individuals in
networks. Our analyses revealed that mutation always has a negative effect on
the evolution of cooperation regardless of the fraction of cooperators and
network structures. Moreover, we found that mutation affects the fitness of
cooperation differently on different social network structures.
| [
{
"created": "Fri, 9 Jun 2017 15:57:02 GMT",
"version": "v1"
}
] | 2017-06-12 | [
[
"Ichinose",
"Genki",
""
],
[
"Satotani",
"Yoshiki",
""
],
[
"Sayama",
"Hiroki",
""
]
] | Cooperation is ubiquitous in every level of living organisms. It is known that spatial (network) structure is a viable mechanism for cooperation to evolve. Until recently, it has been difficult to predict whether cooperation can evolve at a network (population) level. To address this problem, Pinheiro et al. proposed a numerical metric, called Average Gradient of Selection (AGoS) in 2012. AGoS can characterize and forecast the evolutionary fate of cooperation at a population level. However, stochastic mutation of strategies was not considered in the analysis of AGoS. Here we analyzed the evolution of cooperation using AGoS where mutation may occur to strategies of individuals in networks. Our analyses revealed that mutation always has a negative effect on the evolution of cooperation regardless of the fraction of cooperators and network structures. Moreover, we found that mutation affects the fitness of cooperation differently on different social network structures. |
2307.06732 | Robert Rosenbaum | Vicky Zhu and Robert Rosenbaum | Learning fixed points of recurrent neural networks by reparameterizing
the network model | null | null | null | null | q-bio.NC cs.NE | http://creativecommons.org/licenses/by/4.0/ | In computational neuroscience, fixed points of recurrent neural networks are
commonly used to model neural responses to static or slowly changing stimuli.
These applications raise the question of how to train the weights in a
recurrent neural network to minimize a loss function evaluated on fixed points.
A natural approach is to use gradient descent on the Euclidean space of
synaptic weights. We show that this approach can lead to poor learning
performance due, in part, to singularities that arise in the loss surface. We
use a reparameterization of the recurrent network model to derive two
alternative learning rules that produces more robust learning dynamics. We show
that these learning rules can be interpreted as steepest descent and gradient
descent, respectively, under a non-Euclidean metric on the space of recurrent
weights. Our results question the common, implicit assumption that learning in
the brain should be expected to follow the negative Euclidean gradient of
synaptic weights.
| [
{
"created": "Thu, 13 Jul 2023 13:09:11 GMT",
"version": "v1"
},
{
"created": "Thu, 27 Jul 2023 09:23:48 GMT",
"version": "v2"
}
] | 2023-07-28 | [
[
"Zhu",
"Vicky",
""
],
[
"Rosenbaum",
"Robert",
""
]
] | In computational neuroscience, fixed points of recurrent neural networks are commonly used to model neural responses to static or slowly changing stimuli. These applications raise the question of how to train the weights in a recurrent neural network to minimize a loss function evaluated on fixed points. A natural approach is to use gradient descent on the Euclidean space of synaptic weights. We show that this approach can lead to poor learning performance due, in part, to singularities that arise in the loss surface. We use a reparameterization of the recurrent network model to derive two alternative learning rules that produces more robust learning dynamics. We show that these learning rules can be interpreted as steepest descent and gradient descent, respectively, under a non-Euclidean metric on the space of recurrent weights. Our results question the common, implicit assumption that learning in the brain should be expected to follow the negative Euclidean gradient of synaptic weights. |
1101.1858 | Kate Inasaridze | Ketevan Inasaridze, Vera Bzhalava | Dual-task Coordination in Children and Adolescents with Attention
Deficit Hyperactivity Disorder (ADHD) | 31 pages, 9 figures, 7 tables | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The deficit of executive functioning was found to be associated with
attention deficit hyperactivity disorder (ADHD) in general and its subtypes.
One of the important functions of central executive is the ability
simultaneously coordinate two tasks. The study aimed at defining the dual-task
performance characteristics in healthy children and adolescents on the
computerised and the paper and pencil dual-task methods; investigating the
effect of task difficulty on dual-task performance in ADHD in comparison to age
and years of education matched healthy controls; testing if the paper and
pencil version of the dual-task method is giving the same results in ADHD and
healthy controls; investigating whether the dual-task functioning in ADHD is
defined by the deficits in the general motor functioning and comorbidity
factors. The study investigated dual task functioning in 6-16 years old 91
typically developing controls and 91 children with ADHD. It was found that: (1)
the dual-task coordination is available in children and adolescents with ADHD
in general and in its subtypes and not significantly different from performance
of age and years of education matched healthy controls; (2) Increase of the
task difficulty in dual-task paradigm don't affect disproportionately children
and adolescents with ADHD in comparison to age and years of education matched
healthy controls; (3) The paper and pencil version of the dual-task method is
giving the same results in ADHD and healthy controls as computerised version;
(4) The dual-task functioning in ADHD in general and in its subtypes is not
defined by the general motor functioning while in healthy controls dual task
performance is associated with the general motor functioning level; (5) The
dual-task functioning in ADHD in general and in its subtypes is not defined by
the comorbidity factors.
| [
{
"created": "Mon, 10 Jan 2011 16:08:00 GMT",
"version": "v1"
}
] | 2011-01-11 | [
[
"Inasaridze",
"Ketevan",
""
],
[
"Bzhalava",
"Vera",
""
]
] | The deficit of executive functioning was found to be associated with attention deficit hyperactivity disorder (ADHD) in general and its subtypes. One of the important functions of central executive is the ability simultaneously coordinate two tasks. The study aimed at defining the dual-task performance characteristics in healthy children and adolescents on the computerised and the paper and pencil dual-task methods; investigating the effect of task difficulty on dual-task performance in ADHD in comparison to age and years of education matched healthy controls; testing if the paper and pencil version of the dual-task method is giving the same results in ADHD and healthy controls; investigating whether the dual-task functioning in ADHD is defined by the deficits in the general motor functioning and comorbidity factors. The study investigated dual task functioning in 6-16 years old 91 typically developing controls and 91 children with ADHD. It was found that: (1) the dual-task coordination is available in children and adolescents with ADHD in general and in its subtypes and not significantly different from performance of age and years of education matched healthy controls; (2) Increase of the task difficulty in dual-task paradigm don't affect disproportionately children and adolescents with ADHD in comparison to age and years of education matched healthy controls; (3) The paper and pencil version of the dual-task method is giving the same results in ADHD and healthy controls as computerised version; (4) The dual-task functioning in ADHD in general and in its subtypes is not defined by the general motor functioning while in healthy controls dual task performance is associated with the general motor functioning level; (5) The dual-task functioning in ADHD in general and in its subtypes is not defined by the comorbidity factors. |
1510.09155 | Momoko Hayamizu | Momoko Hayamizu, Hiroshi Endo, and Kenji Fukumizu | A characterization of minimum spanning tree-like metric spaces | 9 pages, 2 figures | null | null | null | q-bio.QM cs.DM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent years have witnessed a surge of biological interest in the minimum
spanning tree (MST) problem for its relevance to automatic model construction
using the distances between data points. Despite the increasing use of MST
algorithms for this purpose, the goodness-of-fit of an MST to the data is often
elusive because no quantitative criteria have been developed to measure it.
Motivated by this, we provide a necessary and sufficient condition to ensure
that a metric space on n points can be represented by a fully labeled tree on n
vertices, and thereby determine when an MST preserves all pairwise distances
between points in a finite metric space.
| [
{
"created": "Fri, 30 Oct 2015 16:57:08 GMT",
"version": "v1"
}
] | 2015-11-02 | [
[
"Hayamizu",
"Momoko",
""
],
[
"Endo",
"Hiroshi",
""
],
[
"Fukumizu",
"Kenji",
""
]
] | Recent years have witnessed a surge of biological interest in the minimum spanning tree (MST) problem for its relevance to automatic model construction using the distances between data points. Despite the increasing use of MST algorithms for this purpose, the goodness-of-fit of an MST to the data is often elusive because no quantitative criteria have been developed to measure it. Motivated by this, we provide a necessary and sufficient condition to ensure that a metric space on n points can be represented by a fully labeled tree on n vertices, and thereby determine when an MST preserves all pairwise distances between points in a finite metric space. |
1509.01697 | Alan D. Rendall | Dorothea M\"ohring and Alan D. Rendall | Overload breakdown in models for photosynthesis | null | null | null | null | q-bio.MN math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many models of the Calvin cycle of photosynthesis it is observed that
there are solutions where concentrations of key substances belonging to the
cycle tend to zero at late times, a phenomenon known as overload breakdown. In
this paper we prove theorems about the existence and non-existence of solutions
of this type and obtain information on which concentrations tend to zero when
overload breakdown occurs. As a starting point we take a model of Pettersson
and Ryde-Pettersson which seems to be prone to overload breakdown and a
modification of it due to Poolman which was intended to avoid this effect.
| [
{
"created": "Sat, 5 Sep 2015 12:46:35 GMT",
"version": "v1"
}
] | 2015-09-08 | [
[
"Möhring",
"Dorothea",
""
],
[
"Rendall",
"Alan D.",
""
]
] | In many models of the Calvin cycle of photosynthesis it is observed that there are solutions where concentrations of key substances belonging to the cycle tend to zero at late times, a phenomenon known as overload breakdown. In this paper we prove theorems about the existence and non-existence of solutions of this type and obtain information on which concentrations tend to zero when overload breakdown occurs. As a starting point we take a model of Pettersson and Ryde-Pettersson which seems to be prone to overload breakdown and a modification of it due to Poolman which was intended to avoid this effect. |
q-bio/0311039 | Alexander Kraskov | Alexander Kraskov, Harald St\"ogbauer, Ralph G. Andrzejak, and Peter
Grassberger | Hierarchical Clustering Based on Mutual Information | 11 pages, 5 figures | null | null | null | q-bio.QM cs.CC physics.bio-ph | null | Motivation: Clustering is a frequently used concept in variety of
bioinformatical applications. We present a new method for hierarchical
clustering of data called mutual information clustering (MIC) algorithm. It
uses mutual information (MI) as a similarity measure and exploits its grouping
property: The MI between three objects X, Y, and Z is equal to the sum of the
MI between X and Y, plus the MI between Z and the combined object (XY).
Results: We use this both in the Shannon (probabilistic) version of
information theory, where the "objects" are probability distributions
represented by random samples, and in the Kolmogorov (algorithmic) version,
where the "objects" are symbol sequences. We apply our method to the
construction of mammal phylogenetic trees from mitochondrial DNA sequences and
we reconstruct the fetal ECG from the output of independent components analysis
(ICA) applied to the ECG of a pregnant woman.
Availability: The programs for estimation of MI and for clustering
(probabilistic version) are available at
http://www.fz-juelich.de/nic/cs/software
| [
{
"created": "Fri, 28 Nov 2003 17:04:26 GMT",
"version": "v1"
},
{
"created": "Mon, 1 Dec 2003 07:37:34 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Kraskov",
"Alexander",
""
],
[
"Stögbauer",
"Harald",
""
],
[
"Andrzejak",
"Ralph G.",
""
],
[
"Grassberger",
"Peter",
""
]
] | Motivation: Clustering is a frequently used concept in variety of bioinformatical applications. We present a new method for hierarchical clustering of data called mutual information clustering (MIC) algorithm. It uses mutual information (MI) as a similarity measure and exploits its grouping property: The MI between three objects X, Y, and Z is equal to the sum of the MI between X and Y, plus the MI between Z and the combined object (XY). Results: We use this both in the Shannon (probabilistic) version of information theory, where the "objects" are probability distributions represented by random samples, and in the Kolmogorov (algorithmic) version, where the "objects" are symbol sequences. We apply our method to the construction of mammal phylogenetic trees from mitochondrial DNA sequences and we reconstruct the fetal ECG from the output of independent components analysis (ICA) applied to the ECG of a pregnant woman. Availability: The programs for estimation of MI and for clustering (probabilistic version) are available at http://www.fz-juelich.de/nic/cs/software |
q-bio/0511002 | Simone Pigolotti | S. Pigolotti, A. Flammini, M.Marsili, and A.Maritan | Species lifetime distribution for simple models of ecologies | 19 pages, 2 figures | PNAS (2005) 102: pp. 15747-15751 | 10.1073/pnas.0502648102 | null | q-bio.PE | null | Interpretation of empirical results based on a taxa's lifetime distribution
shows apparently conflicting results. Species' lifetime is reported to be
exponentially distributed, whereas higher order taxa, such as families or
genera, follow a broader distribution, compatible with power law decay. We show
that both these evidences are consistent with a simple evolutionary model that
does not require specific assumptions on species interaction. The model
provides a zero-order description of the dynamics of ecological communities and
its species lifetime distribution can be computed exactly. Different behaviors
are found: an initial $t^{-3/2}$ power law, emerging from a random walk type of
dynamics, which crosses over to a steeper $t^{-2}$ branching process-like
regime and finally is cutoff by an exponential decay which becomes weaker and
weaker as the total population increases. Sampling effects can also be taken
into account and shown to be relevant: if species in the fossil record were
sampled according to the Fisher log-series distribution, lifetime should be
distributed according to a $t^{-1}$ power law. Such variability of behaviors in
a simple model, combined with the scarcity of data available, cast serious
doubts on the possibility to validate theories of evolution on the basis of
species lifetime data.
| [
{
"created": "Wed, 2 Nov 2005 15:15:26 GMT",
"version": "v1"
}
] | 2009-11-11 | [
[
"Pigolotti",
"S.",
""
],
[
"Flammini",
"A.",
""
],
[
"Marsili",
"M.",
""
],
[
"Maritan",
"A.",
""
]
] | Interpretation of empirical results based on a taxa's lifetime distribution shows apparently conflicting results. Species' lifetime is reported to be exponentially distributed, whereas higher order taxa, such as families or genera, follow a broader distribution, compatible with power law decay. We show that both these evidences are consistent with a simple evolutionary model that does not require specific assumptions on species interaction. The model provides a zero-order description of the dynamics of ecological communities and its species lifetime distribution can be computed exactly. Different behaviors are found: an initial $t^{-3/2}$ power law, emerging from a random walk type of dynamics, which crosses over to a steeper $t^{-2}$ branching process-like regime and finally is cutoff by an exponential decay which becomes weaker and weaker as the total population increases. Sampling effects can also be taken into account and shown to be relevant: if species in the fossil record were sampled according to the Fisher log-series distribution, lifetime should be distributed according to a $t^{-1}$ power law. Such variability of behaviors in a simple model, combined with the scarcity of data available, cast serious doubts on the possibility to validate theories of evolution on the basis of species lifetime data. |
0908.0657 | Ranjith Padinhateeri | Padinhateeri Ranjith, Kirone Mallick, Jean-Francois Joanny, David
Lacoste | Role of ATP-hydrolysis in the dynamics of a single actin filament | To appear in Biophysical Journal (2010) | Biophys. J, 98, 1418 (2010) | 10.1016/j.bpj.2009.12.4306 | null | q-bio.BM q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the stochastic dynamics of growth and shrinkage of single actin
filaments taking into account insertion, removal, and ATP hydrolysis of
subunits either according to the vectorial mechanism or to the random
mechanism. In a previous work, we developed a model for a single actin or
microtubule filament where hydrolysis occurred according to the vectorial
mechanism: the filament could grow only from one end, and was in contact with a
reservoir of monomers. Here we extend this approach in several ways, by
including the dynamics of both ends and by comparing two possible mechanisms of
ATP hydrolysis. Our emphasis is mainly on two possible limiting models for the
mechanism of hydrolysis within a single filament, namely the vectorial or the
random model. We propose a set of experiments to test the nature of the precise
mechanism of hydrolysis within actin filaments.
| [
{
"created": "Wed, 5 Aug 2009 12:39:40 GMT",
"version": "v1"
},
{
"created": "Thu, 7 Jan 2010 07:04:51 GMT",
"version": "v2"
}
] | 2015-05-13 | [
[
"Ranjith",
"Padinhateeri",
""
],
[
"Mallick",
"Kirone",
""
],
[
"Joanny",
"Jean-Francois",
""
],
[
"Lacoste",
"David",
""
]
] | We study the stochastic dynamics of growth and shrinkage of single actin filaments taking into account insertion, removal, and ATP hydrolysis of subunits either according to the vectorial mechanism or to the random mechanism. In a previous work, we developed a model for a single actin or microtubule filament where hydrolysis occurred according to the vectorial mechanism: the filament could grow only from one end, and was in contact with a reservoir of monomers. Here we extend this approach in several ways, by including the dynamics of both ends and by comparing two possible mechanisms of ATP hydrolysis. Our emphasis is mainly on two possible limiting models for the mechanism of hydrolysis within a single filament, namely the vectorial or the random model. We propose a set of experiments to test the nature of the precise mechanism of hydrolysis within actin filaments. |
1805.05453 | Marina Voinova V | Marina V Voinova | Modeling water transport processes in dialysis | 60 pages, review | null | null | null | q-bio.TO cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mathematical modeling is an important theoretical tool which provides
researchers with quantification of the permeability of dialyzing systems in
renal replacement therapy. In the paper we provide a short review of the most
successful theoretical approaches and refer to the corresponding experimental
methods studying these phenomena in both biological and synthetic filters in
dialysis. Two levels of modeling of fluid and solute transport are considered
in the review: thermodynamic and kinetic modeling of hemodialysis and
peritoneal dialysis. A brief account for hindered diffusion across cake layers
formed due to membrane filters fouling is given, too.
| [
{
"created": "Fri, 11 May 2018 16:34:45 GMT",
"version": "v1"
}
] | 2018-05-16 | [
[
"Voinova",
"Marina V",
""
]
] | Mathematical modeling is an important theoretical tool which provides researchers with quantification of the permeability of dialyzing systems in renal replacement therapy. In the paper we provide a short review of the most successful theoretical approaches and refer to the corresponding experimental methods studying these phenomena in both biological and synthetic filters in dialysis. Two levels of modeling of fluid and solute transport are considered in the review: thermodynamic and kinetic modeling of hemodialysis and peritoneal dialysis. A brief account for hindered diffusion across cake layers formed due to membrane filters fouling is given, too. |
2403.18862 | Sebastien Dam | S\'ebastien Dam (UR, Inria, CNRS, IRISA, EMPENN), Jean-Marie Batail
(CHGR), Gabriel H Robert (UR, Inria, CNRS, IRISA, EMPENN, CHGR), Dominique
Drapier (CHGR), Pierre Maurel (UR, Inria, CNRS, IRISA, EMPENN), Julie
Coloigner (UR, Inria, CNRS, IRISA, EMPENN) | Structural Brain Connectivity and Treatment Improvement in Mood Disorder | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: The treatment of depressive episodes is well established, with
clearly demonstrated effectiveness of antidepressants and psychotherapies.
However, more than one-third of depressed patients do not respond to treatment.
Identifying the brain structural basis of treatment-resistant depression could
prevent useless pharmacological prescriptions,adverse events, and lost
therapeutic opportunities.Methods: Using diffusion magnetic resonance imaging,
we performed structural connectivity analyses on a cohort of 154 patients with
mood disorder (MD) -- and 77 sex- and age-matched healthy control (HC)
participants. To assess illness improvement, the MD patients went through two
clinical interviews at baseline and at 6-month follow-up and were classified
based on the Clinical Global Impression-Improvement score into improved or
not-improved. First, the threshold-free network-based statistics was conducted
to measure the differences in regional network architecture. Second,
nonparametric permutations tests were performed on topological metrics based on
graph theory to examine differences in connectome organization. Results: The
threshold-free network-based statistics revealed impaired connections
involvingregions of the basal ganglia in MD patients compared to HC.
Significant increase of local efficiency and clustering coefficient was found
in the lingual gyrus, insula and amygdala in the MD group. Compared with the
not-improved, the improved displayed significantly reduced network integration
and segregation, predominately in the default-mode regions, including the
precuneus, middle temporal lobe and rostral anterior cingulate.Conclusions:
This study highlights the involvement of regions belonging to the basal
ganglia, the fronto-limbic network and the default mode network, leading to a
better understanding of MD disease and its unfavorable outcome.
| [
{
"created": "Fri, 22 Mar 2024 08:20:00 GMT",
"version": "v1"
}
] | 2024-03-29 | [
[
"Dam",
"Sébastien",
"",
"UR, Inria, CNRS, IRISA, EMPENN"
],
[
"Batail",
"Jean-Marie",
"",
"CHGR"
],
[
"Robert",
"Gabriel H",
"",
"UR, Inria, CNRS, IRISA, EMPENN, CHGR"
],
[
"Drapier",
"Dominique",
"",
"CHGR"
],
[
"Maurel",
"Pierre",
"",
"UR, Inria, CNRS, IRISA, EMPENN"
],
[
"Coloigner",
"Julie",
"",
"UR, Inria, CNRS, IRISA, EMPENN"
]
] | Background: The treatment of depressive episodes is well established, with clearly demonstrated effectiveness of antidepressants and psychotherapies. However, more than one-third of depressed patients do not respond to treatment. Identifying the brain structural basis of treatment-resistant depression could prevent useless pharmacological prescriptions,adverse events, and lost therapeutic opportunities.Methods: Using diffusion magnetic resonance imaging, we performed structural connectivity analyses on a cohort of 154 patients with mood disorder (MD) -- and 77 sex- and age-matched healthy control (HC) participants. To assess illness improvement, the MD patients went through two clinical interviews at baseline and at 6-month follow-up and were classified based on the Clinical Global Impression-Improvement score into improved or not-improved. First, the threshold-free network-based statistics was conducted to measure the differences in regional network architecture. Second, nonparametric permutations tests were performed on topological metrics based on graph theory to examine differences in connectome organization. Results: The threshold-free network-based statistics revealed impaired connections involvingregions of the basal ganglia in MD patients compared to HC. Significant increase of local efficiency and clustering coefficient was found in the lingual gyrus, insula and amygdala in the MD group. Compared with the not-improved, the improved displayed significantly reduced network integration and segregation, predominately in the default-mode regions, including the precuneus, middle temporal lobe and rostral anterior cingulate.Conclusions: This study highlights the involvement of regions belonging to the basal ganglia, the fronto-limbic network and the default mode network, leading to a better understanding of MD disease and its unfavorable outcome. |
1906.05584 | Antonio de Candia | S. Scarpetta, A. de Candia | Information capacity of a network of spiking neurons | Accepted for publication in Physica A | null | 10.1016/j.physa.2019.123681 | null | q-bio.NC cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study a model of spiking neurons, with recurrent connections that result
from learning a set of spatio-temporal patterns with a spike-timing dependent
plasticity rule and a global inhibition. We investigate the ability of the
network to store and selectively replay multiple patterns of spikes, with a
combination of spatial population and phase-of-spike code. Each neuron in a
pattern is characterized by a binary variable determining if the neuron is
active in the pattern, and a phase-lag variable representing the spike-timing
order among the active units. After the learning stage, we study the dynamics
of the network induced by a brief cue stimulation, and verify that the network
is able to selectively replay the pattern correctly and persistently. We
calculate the information capacity of the network, defined as the maximum
number of patterns that can be encoded in the network times the number of bits
carried by each pattern, normalized by the number of synapses, and find that it
can reach a value $\alpha_\text{max}\simeq 0.27$, similar to the one of
sequence processing neural networks, and almost double of the capacity of the
static Hopfield model. We study the dependence of the capacity on the global
inhibition, connection strength (or neuron threshold) and fraction of neurons
participating to the patterns. The results show that a dual population and
temporal coding can be optimal for the capacity of an associative memory.
| [
{
"created": "Thu, 13 Jun 2019 09:56:33 GMT",
"version": "v1"
},
{
"created": "Sun, 24 Nov 2019 21:34:39 GMT",
"version": "v2"
}
] | 2020-04-22 | [
[
"Scarpetta",
"S.",
""
],
[
"de Candia",
"A.",
""
]
] | We study a model of spiking neurons, with recurrent connections that result from learning a set of spatio-temporal patterns with a spike-timing dependent plasticity rule and a global inhibition. We investigate the ability of the network to store and selectively replay multiple patterns of spikes, with a combination of spatial population and phase-of-spike code. Each neuron in a pattern is characterized by a binary variable determining if the neuron is active in the pattern, and a phase-lag variable representing the spike-timing order among the active units. After the learning stage, we study the dynamics of the network induced by a brief cue stimulation, and verify that the network is able to selectively replay the pattern correctly and persistently. We calculate the information capacity of the network, defined as the maximum number of patterns that can be encoded in the network times the number of bits carried by each pattern, normalized by the number of synapses, and find that it can reach a value $\alpha_\text{max}\simeq 0.27$, similar to the one of sequence processing neural networks, and almost double of the capacity of the static Hopfield model. We study the dependence of the capacity on the global inhibition, connection strength (or neuron threshold) and fraction of neurons participating to the patterns. The results show that a dual population and temporal coding can be optimal for the capacity of an associative memory. |
1905.10441 | Japan Patel | Japan K. Patel, Richard Vasques, Barry D. Ganapol | Towards a Multiphysics Model for Tumor Response to
Combined-Hyperthermia-Radiotherapy Treatment | 10 pages, 3 figures, submitted to ANS Topical Meeting | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop a multiphysics-based model to predict the response of localized
tumors to combined-hyperthermia-radiotherapy (CHR) treatment. This procedure
combines hyperthermia (tumor heating) with standard radiotherapy to improve
efficacy of the overall treatment. In addition to directly killing tumor cells,
tumor heating amends several parameters within the tumor microenvironment. This
leads to radiosensitization, which improves the performance of radiotherapy
while reducing the side-effects of excess radiation in the surrounding normal
tissue. Existing tools to model this kind of treatment consider each of the
physics separately. The model presented in this paper accounts for the synergy
between hyperthermia and radiotherapy providing a more realistic and holistic
approach to simulate CHR treatment. Our model couples radiation transport and
heat-transfer with cell population dynamics.
| [
{
"created": "Fri, 10 May 2019 20:46:27 GMT",
"version": "v1"
},
{
"created": "Wed, 3 Jul 2019 18:36:12 GMT",
"version": "v2"
}
] | 2019-07-05 | [
[
"Patel",
"Japan K.",
""
],
[
"Vasques",
"Richard",
""
],
[
"Ganapol",
"Barry D.",
""
]
] | We develop a multiphysics-based model to predict the response of localized tumors to combined-hyperthermia-radiotherapy (CHR) treatment. This procedure combines hyperthermia (tumor heating) with standard radiotherapy to improve efficacy of the overall treatment. In addition to directly killing tumor cells, tumor heating amends several parameters within the tumor microenvironment. This leads to radiosensitization, which improves the performance of radiotherapy while reducing the side-effects of excess radiation in the surrounding normal tissue. Existing tools to model this kind of treatment consider each of the physics separately. The model presented in this paper accounts for the synergy between hyperthermia and radiotherapy providing a more realistic and holistic approach to simulate CHR treatment. Our model couples radiation transport and heat-transfer with cell population dynamics. |
1501.00421 | Arianna Bianchi | Arianna Bianchi, Kevin J. Painter, Jonathan A. Sherratt | A Mathematical Model for Lymphangiogenesis in Normal and Diabetic Wounds | null | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Several studies suggest that one possible cause of impaired wound healing is
failed or insufficient lymphangiogenesis, that is the formation of new
lymphatic capillaries. Although many mathematical models have been developed to
describe the formation of blood capillaries (angiogenesis) very few have been
proposed for the regeneration of the lymphatic network. Moreover,
lymphangiogenesis is markedly distinct from angiogenesis, occurring at
different times and in a different manner. Here a model of five ordinary
differential equations is presented to describe the formation of lymphatic
capillaries following a skin wound. The variables represent different cell
densities and growth factor concentrations, and where possible the parameters
are estimated from experimental and clinical data. The system is then solved
numerically and the results are compared with the available biological
literature. Finally, a parameter sensitivity analysis of the model is taken as
a starting point for suggesting new therapeutic approaches targeting the
enhancement of lymphangiogenesis in diabetic wounds. The work provides a deeper
understanding of the phenomenon in question, clarifying the main factors
involved. In particular, the balance between TGF-$\beta$ and VEGF levels,
rather than their absolute values, is identified as crucial to effective
lymphangiogenesis. In addition, the results indicate lowering the
macrophage-mediated activation of TGF-$\beta$ and increasing the basal
lymphatic endothelial cell growth rate, \emph{inter alia}, as potential
treatments. It is hoped the findings of this paper may be considered in the
development of future experiments investigating novel lymphangiogenic
therapies.
| [
{
"created": "Fri, 2 Jan 2015 15:03:47 GMT",
"version": "v1"
}
] | 2015-01-05 | [
[
"Bianchi",
"Arianna",
""
],
[
"Painter",
"Kevin J.",
""
],
[
"Sherratt",
"Jonathan A.",
""
]
] | Several studies suggest that one possible cause of impaired wound healing is failed or insufficient lymphangiogenesis, that is the formation of new lymphatic capillaries. Although many mathematical models have been developed to describe the formation of blood capillaries (angiogenesis) very few have been proposed for the regeneration of the lymphatic network. Moreover, lymphangiogenesis is markedly distinct from angiogenesis, occurring at different times and in a different manner. Here a model of five ordinary differential equations is presented to describe the formation of lymphatic capillaries following a skin wound. The variables represent different cell densities and growth factor concentrations, and where possible the parameters are estimated from experimental and clinical data. The system is then solved numerically and the results are compared with the available biological literature. Finally, a parameter sensitivity analysis of the model is taken as a starting point for suggesting new therapeutic approaches targeting the enhancement of lymphangiogenesis in diabetic wounds. The work provides a deeper understanding of the phenomenon in question, clarifying the main factors involved. In particular, the balance between TGF-$\beta$ and VEGF levels, rather than their absolute values, is identified as crucial to effective lymphangiogenesis. In addition, the results indicate lowering the macrophage-mediated activation of TGF-$\beta$ and increasing the basal lymphatic endothelial cell growth rate, \emph{inter alia}, as potential treatments. It is hoped the findings of this paper may be considered in the development of future experiments investigating novel lymphangiogenic therapies. |
0811.0115 | German Andres Enciso | Winfried Just, German Enciso | Extremely chaotic Boolean networks | 10 pages for the main article, 33 pages for detailed proofs of the
main results, 4 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is an increasingly important problem to study conditions on the structure
of a network that guarantee a given behavior for its underlying dynamical
system. In this paper we report that a Boolean network may fall within the
chaotic regime, even under the simultaneous assumption of several conditions
which in randomized studies have been separately shown to correlate with
ordered behavior. These properties include using at most two inputs for every
variable, using biased and canalyzing regulatory functions, and restricting the
number of negative feedback loops.
We also prove for n-dimensional Boolean networks that if in addition the
number of outputs for each variable is bounded and there exist periodic orbits
of length c^n for c sufficiently close to 2, any network with these properties
must have a large proportion of variables that simply copy previous values of
other variables. Such systems share a structural similarity to a relatively
small Turing machine acting on one or several tapes.
| [
{
"created": "Sat, 1 Nov 2008 21:57:49 GMT",
"version": "v1"
}
] | 2008-11-04 | [
[
"Just",
"Winfried",
""
],
[
"Enciso",
"German",
""
]
] | It is an increasingly important problem to study conditions on the structure of a network that guarantee a given behavior for its underlying dynamical system. In this paper we report that a Boolean network may fall within the chaotic regime, even under the simultaneous assumption of several conditions which in randomized studies have been separately shown to correlate with ordered behavior. These properties include using at most two inputs for every variable, using biased and canalyzing regulatory functions, and restricting the number of negative feedback loops. We also prove for n-dimensional Boolean networks that if in addition the number of outputs for each variable is bounded and there exist periodic orbits of length c^n for c sufficiently close to 2, any network with these properties must have a large proportion of variables that simply copy previous values of other variables. Such systems share a structural similarity to a relatively small Turing machine acting on one or several tapes. |
0908.3037 | Luis David Garcia-Puente | Elena Dimitrova, Luis David Garcia-Puente, Franziska Hinkelmann, Abdul
S. Jarrah, Reinhard Laubenbacher, Brandilyn Stigler, Michael Stillman, and
Paola Vera-Licona | Parameter estimation for Boolean models of biological networks | Web interface of the software is available at
http://polymath.vbi.vt.edu/polynome/ | Theoretical Computer Science 412 (2011) 2816-2826 | 10.1016/j.tcs.2010.04.034 | null | q-bio.MN q-bio.OT q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Boolean networks have long been used as models of molecular networks and play
an increasingly important role in systems biology. This paper describes a
software package, Polynome, offered as a web service, that helps users
construct Boolean network models based on experimental data and biological
input. The key feature is a discrete analog of parameter estimation for
continuous models. With only experimental data as input, the software can be
used as a tool for reverse-engineering of Boolean network models from
experimental time course data.
| [
{
"created": "Fri, 21 Aug 2009 01:13:13 GMT",
"version": "v1"
}
] | 2019-07-10 | [
[
"Dimitrova",
"Elena",
""
],
[
"Garcia-Puente",
"Luis David",
""
],
[
"Hinkelmann",
"Franziska",
""
],
[
"Jarrah",
"Abdul S.",
""
],
[
"Laubenbacher",
"Reinhard",
""
],
[
"Stigler",
"Brandilyn",
""
],
[
"Stillman",
"Michael",
""
],
[
"Vera-Licona",
"Paola",
""
]
] | Boolean networks have long been used as models of molecular networks and play an increasingly important role in systems biology. This paper describes a software package, Polynome, offered as a web service, that helps users construct Boolean network models based on experimental data and biological input. The key feature is a discrete analog of parameter estimation for continuous models. With only experimental data as input, the software can be used as a tool for reverse-engineering of Boolean network models from experimental time course data. |
0711.3456 | Eugene Shakhnovich | Muyoung Heo, Konstantin B. Zeldovich, Eugene I. Shakhnovich | Emergence of clonal selection and affinity maturation in an ab initio
microscopic model of immunity | null | null | null | null | q-bio.BM q-bio.PE | null | Mechanisms of immunity, and of the host-pathogen interactions in general are
among the most fundamental problems of medicine, ecology, and evolution
studies. Here, we present a microscopic, protein-level, sequence-based model of
immune system, with explicitly defined interactions between host and pathogen
proteins.. Simulations of this model show that possible outcomes of the
infection (extinction of cells, survival with complete elimination of viruses,
or chronic infection with continuous coexistence of cells and viruses)
crucially depend on mutation rates of the viral and immunoglobulin proteins.
Infection is always lethal if the virus mutation rate exceeds a certain
threshold. Potent immunoglobulins are discovered in this model via clonal
selection and affinity maturation. Surviving cells acquire lasting immunity
against subsequent infection by the same virus strain. As a second line of
defense cells develop apoptosis-like behavior by reducing their lifetimes to
eliminate viruses. These results demonstrate the feasibility of microscopic
sequence-based models of immune system, where population dynamics of the
evolving B-cells is explicitly tied to the molecular properties of their
proteins.
| [
{
"created": "Wed, 21 Nov 2007 20:46:58 GMT",
"version": "v1"
}
] | 2007-11-22 | [
[
"Heo",
"Muyoung",
""
],
[
"Zeldovich",
"Konstantin B.",
""
],
[
"Shakhnovich",
"Eugene I.",
""
]
] | Mechanisms of immunity, and of the host-pathogen interactions in general are among the most fundamental problems of medicine, ecology, and evolution studies. Here, we present a microscopic, protein-level, sequence-based model of immune system, with explicitly defined interactions between host and pathogen proteins.. Simulations of this model show that possible outcomes of the infection (extinction of cells, survival with complete elimination of viruses, or chronic infection with continuous coexistence of cells and viruses) crucially depend on mutation rates of the viral and immunoglobulin proteins. Infection is always lethal if the virus mutation rate exceeds a certain threshold. Potent immunoglobulins are discovered in this model via clonal selection and affinity maturation. Surviving cells acquire lasting immunity against subsequent infection by the same virus strain. As a second line of defense cells develop apoptosis-like behavior by reducing their lifetimes to eliminate viruses. These results demonstrate the feasibility of microscopic sequence-based models of immune system, where population dynamics of the evolving B-cells is explicitly tied to the molecular properties of their proteins. |
q-bio/0504029 | Pablo Echenique | J. L. Alonso and Pablo Echenique | A physically meaningful method for the comparison of potential energy
functions | 30 pages, 7 figures, LaTeX, BibTeX. v2: A misspelling in the author's
name has been corrected. v3: A new application of the method has been added
at the end of section 9 and minor modifications have also been made in other
sections. v4: Journal reference and minor corrections added | J. Comp. Chem. 27 (2006) 238-252 | 10.1002/jcc.20337 | null | q-bio.QM cond-mat.soft q-bio.BM | null | In the study of the conformational behavior of complex systems, such as
proteins, several related statistical measures are commonly used to compare two
different potential energy functions. Among them, the Pearson's correlation
coefficient r has no units and allows only semi-quantitative statements to be
made. Those that do have units of energy and whose value may be compared to a
physically relevant scale, such as the root mean square deviation (RMSD), the
mean error of the energies (ER), the standard deviation of the error (SDER) or
the mean absolute error (AER), overestimate the distance between potentials.
Moreover, their precise statistical meaning is far from clear. In this article,
a new measure of the distance between potential energy functions is defined
which overcomes the aforementioned difficulties. In addition, its precise
physical meaning is discussed, the important issue of its additivity is
investigated and some possible applications are proposed. Finally, two of these
applications are illustrated with practical examples: the study of the van der
Waals energy, as implemented in CHARMM, in the Trp-Cage protein (PDB code 1L2Y)
and the comparison of different levels of the theory in the ab initio study of
the Ramachandran map of the model peptide HCO-L-Ala-NH2.
| [
{
"created": "Thu, 21 Apr 2005 12:05:58 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Apr 2005 16:03:26 GMT",
"version": "v2"
},
{
"created": "Thu, 14 Jul 2005 10:39:15 GMT",
"version": "v3"
},
{
"created": "Mon, 18 Jul 2005 09:28:25 GMT",
"version": "v4"
},
{
"created": "Wed, 7 Dec 2005 12:43:53 GMT",
"version": "v5"
}
] | 2007-12-19 | [
[
"Alonso",
"J. L.",
""
],
[
"Echenique",
"Pablo",
""
]
] | In the study of the conformational behavior of complex systems, such as proteins, several related statistical measures are commonly used to compare two different potential energy functions. Among them, the Pearson's correlation coefficient r has no units and allows only semi-quantitative statements to be made. Those that do have units of energy and whose value may be compared to a physically relevant scale, such as the root mean square deviation (RMSD), the mean error of the energies (ER), the standard deviation of the error (SDER) or the mean absolute error (AER), overestimate the distance between potentials. Moreover, their precise statistical meaning is far from clear. In this article, a new measure of the distance between potential energy functions is defined which overcomes the aforementioned difficulties. In addition, its precise physical meaning is discussed, the important issue of its additivity is investigated and some possible applications are proposed. Finally, two of these applications are illustrated with practical examples: the study of the van der Waals energy, as implemented in CHARMM, in the Trp-Cage protein (PDB code 1L2Y) and the comparison of different levels of the theory in the ab initio study of the Ramachandran map of the model peptide HCO-L-Ala-NH2. |
0805.4087 | Stefan Braunewell | Stefan Braunewell, Stefan Bornholdt | Reliability of regulatory networks and its evolution | 11 pages, 12 figures | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of reliability of the dynamics in biological regulatory networks
is studied in the framework of a generalized Boolean network model with
continuous timing and noise. Using well-known artificial genetic networks such
as the repressilator, we discuss concepts of reliability of rhythmic
attractors. In a simple evolution process we investigate how overall network
structure affects the reliability of the dynamics. In the course of the
evolution, networks are selected for reliable dynamics. We find that most
networks can be easily evolved towards reliable functioning while preserving
the original function.
| [
{
"created": "Tue, 27 May 2008 10:17:21 GMT",
"version": "v1"
}
] | 2008-05-28 | [
[
"Braunewell",
"Stefan",
""
],
[
"Bornholdt",
"Stefan",
""
]
] | The problem of reliability of the dynamics in biological regulatory networks is studied in the framework of a generalized Boolean network model with continuous timing and noise. Using well-known artificial genetic networks such as the repressilator, we discuss concepts of reliability of rhythmic attractors. In a simple evolution process we investigate how overall network structure affects the reliability of the dynamics. In the course of the evolution, networks are selected for reliable dynamics. We find that most networks can be easily evolved towards reliable functioning while preserving the original function. |
2101.08346 | Neda Shafiee | Neda Shafiee, Mahsa Dadar, Simon Ducharme, D. Louis Collins | Automatic prediction of cognitive and functional decline can
significantly decrease the number of subjects required for clinical trials in
early Alzheimer's disease | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | INTRODUCTION: Heterogeneity in the progression of Alzheimer's disease makes
it challenging to predict the rate of cognitive and functional decline for
individual patients. Tools for short-term prediction could help enrich clinical
trial designs and focus prevention strategies on the most at-risk patients.
METHOD: We built a prognostic model using baseline cognitive scores and
MRI-based features to determine which subjects with mild cognitive impairment
remained stable and which functionally declined (measured by a two-point
increase in CDR-SB) over 2 and 3-year follow-up periods, periods typical of the
length of clinical trials. RESULTS: Combining both sets of features yields 77%
accuracy (81% sensitivity and 75% specificity) to predict cognitive decline at
2 years (74% accuracy at 3 years with 75% sensitivity and 73% specificity).
Using this tool to select trial participants yields a 3.8-fold decrease in the
required sample size for a 2-year study (2.8-fold decrease for a 3-year study)
for a hypothesized 25% treatment effect to reduce cognitive decline.
DISCUSSION: This cohort enrichment tool could accelerate treatment development
by increasing power in clinical trials.
| [
{
"created": "Wed, 20 Jan 2021 22:31:25 GMT",
"version": "v1"
}
] | 2021-01-22 | [
[
"Shafiee",
"Neda",
""
],
[
"Dadar",
"Mahsa",
""
],
[
"Ducharme",
"Simon",
""
],
[
"Collins",
"D. Louis",
""
]
] | INTRODUCTION: Heterogeneity in the progression of Alzheimer's disease makes it challenging to predict the rate of cognitive and functional decline for individual patients. Tools for short-term prediction could help enrich clinical trial designs and focus prevention strategies on the most at-risk patients. METHOD: We built a prognostic model using baseline cognitive scores and MRI-based features to determine which subjects with mild cognitive impairment remained stable and which functionally declined (measured by a two-point increase in CDR-SB) over 2 and 3-year follow-up periods, periods typical of the length of clinical trials. RESULTS: Combining both sets of features yields 77% accuracy (81% sensitivity and 75% specificity) to predict cognitive decline at 2 years (74% accuracy at 3 years with 75% sensitivity and 73% specificity). Using this tool to select trial participants yields a 3.8-fold decrease in the required sample size for a 2-year study (2.8-fold decrease for a 3-year study) for a hypothesized 25% treatment effect to reduce cognitive decline. DISCUSSION: This cohort enrichment tool could accelerate treatment development by increasing power in clinical trials. |
2307.02287 | Cyril Rauch | Cyril Rauch, Panagiota Kyratzi and Andras Paldi | Genomic Informational Field Theory (GIFT) to characterize genotypes
involved in large phenotypic fluctuations | 51 pages (Main Text: pages 1-35 inc. references. Appendices: pages
35-51), 4 figures in the emain text, 2 figures in the appendices | null | null | null | q-bio.PE physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | Based on the normal distribution and its properties, i.e., average and
variance, Fisher works have provided a conceptual framework to identify
genotype-phenotype associations. While Fisher intuition has proved fruitful
over the past century, the current demands for higher mapping precisions have
led to the formulation of a new genotype-phenotype association method a.k.a.
GIFT (Genomic Informational Field Theory). Not only is the method more powerful
in extracting information from genotype and phenotype datasets, GIFT can also
deal with any phenotype distribution density function. Here we apply GIFT to a
hypothetical Cauchy-distributed phenotype. As opposed to the normal
distribution that restricts fluctuations to a finite variance defined by the
bulk of the distribution, Cauchy distribution embraces large phenotypic
fluctuations and as a result, averages and variances from Cauchy-distributed
phenotypes cannot be defined mathematically. While classic genotype-phenotype
association methods (GWAS) are unable to function without proper average and
variance, it is demonstrated here that GIFT can associate genotype to phenotype
in this case. As phenotypic plasticity, i.e., phenotypic fluctuation, is
central to surviving sudden environmental changes, by applying GIFT the unique
characteristic of the genotype permitting evolution of biallelic organisms to
take place is determined in this case.
| [
{
"created": "Wed, 5 Jul 2023 13:40:53 GMT",
"version": "v1"
}
] | 2023-07-06 | [
[
"Rauch",
"Cyril",
""
],
[
"Kyratzi",
"Panagiota",
""
],
[
"Paldi",
"Andras",
""
]
] | Based on the normal distribution and its properties, i.e., average and variance, Fisher works have provided a conceptual framework to identify genotype-phenotype associations. While Fisher intuition has proved fruitful over the past century, the current demands for higher mapping precisions have led to the formulation of a new genotype-phenotype association method a.k.a. GIFT (Genomic Informational Field Theory). Not only is the method more powerful in extracting information from genotype and phenotype datasets, GIFT can also deal with any phenotype distribution density function. Here we apply GIFT to a hypothetical Cauchy-distributed phenotype. As opposed to the normal distribution that restricts fluctuations to a finite variance defined by the bulk of the distribution, Cauchy distribution embraces large phenotypic fluctuations and as a result, averages and variances from Cauchy-distributed phenotypes cannot be defined mathematically. While classic genotype-phenotype association methods (GWAS) are unable to function without proper average and variance, it is demonstrated here that GIFT can associate genotype to phenotype in this case. As phenotypic plasticity, i.e., phenotypic fluctuation, is central to surviving sudden environmental changes, by applying GIFT the unique characteristic of the genotype permitting evolution of biallelic organisms to take place is determined in this case. |
2309.11513 | Zohreh Shams | Zohreh Shams | Gene Expression Patterns of CsZCD and Apocarotenoid Accumulation during
Saffron Stigma Development | null | null | 10.47191/ijpbms/v3-i9-04 | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | Crocus sativus L., otherwise known as saffron, is a highly prized plant due
to its unique triploid capability and elongated stigmas, contributing to its
status as the costly spice globally. The color and taste properties of saffron
are linked to carotenoid elements including cis- and trans-crocin, picrocrocin,
and safranal. In the research carried out, we dedicated our attention to the
gene CsZCD, an important player in the formation of apocarotenoids. Through the
application of real-time polymerase chain reaction to RNA purified from saffron
stigmas at various growth phases, it was determined that the peak expression of
the CsZCD gene coincided with the red stage, which is associated with the
highest concentration of apocarotenoids. The data showed a 2.69-fold
enhancement in CsZCD gene expression during the red phase, whereas a 0.90-fold
and 0.69-fold reduction was noted at the stages characterized by orange and
yellow hues, respectively. A noteworthy observation was that CsZCD's expression
was three times that of the CsTUB gene. Additionally, relative to CsTUB, CsLYC
displayed 0.7-fold and 0.3-times expression. Our investigation provides insight
into the governance of CsZCD during stigma maturation and its possible
influence on the fluctuation in apocarotenoid content. These discoveries carry
significance for the industrial production of saffron spice and underscore the
importance of additional studies on pivotal genes participating in the
synthesis of apocarotenoids.
| [
{
"created": "Fri, 15 Sep 2023 16:10:41 GMT",
"version": "v1"
}
] | 2023-09-22 | [
[
"Shams",
"Zohreh",
""
]
] | Crocus sativus L., otherwise known as saffron, is a highly prized plant due to its unique triploid capability and elongated stigmas, contributing to its status as the costly spice globally. The color and taste properties of saffron are linked to carotenoid elements including cis- and trans-crocin, picrocrocin, and safranal. In the research carried out, we dedicated our attention to the gene CsZCD, an important player in the formation of apocarotenoids. Through the application of real-time polymerase chain reaction to RNA purified from saffron stigmas at various growth phases, it was determined that the peak expression of the CsZCD gene coincided with the red stage, which is associated with the highest concentration of apocarotenoids. The data showed a 2.69-fold enhancement in CsZCD gene expression during the red phase, whereas a 0.90-fold and 0.69-fold reduction was noted at the stages characterized by orange and yellow hues, respectively. A noteworthy observation was that CsZCD's expression was three times that of the CsTUB gene. Additionally, relative to CsTUB, CsLYC displayed 0.7-fold and 0.3-times expression. Our investigation provides insight into the governance of CsZCD during stigma maturation and its possible influence on the fluctuation in apocarotenoid content. These discoveries carry significance for the industrial production of saffron spice and underscore the importance of additional studies on pivotal genes participating in the synthesis of apocarotenoids. |
2402.17621 | Natasha K. Dudek | Natasha K. Dudek, Mariam Chakhvadze, Saba Kobakhidze, Omar Kantidze,
Yuriy Gankin | Supervised machine learning for microbiomics: bridging the gap between
current and best practices | 25 pages, 5 figures | null | null | null | q-bio.GN cs.LG | http://creativecommons.org/licenses/by/4.0/ | Machine learning (ML) is set to accelerate innovations in clinical
microbiomics, such as in disease diagnostics and prognostics. This will require
high-quality, reproducible, interpretable workflows whose predictive
capabilities meet or exceed the high thresholds set for clinical tools by
regulatory agencies. Here, we capture a snapshot of current practices in the
application of supervised ML to microbiomics data, through an in-depth analysis
of 100 peer-reviewed journal articles published in 2021-2022. We apply a
data-driven approach to steer discussion of the merits of varied approaches to
experimental design, including key considerations such as how to mitigate the
effects of small dataset size while avoiding data leakage. We further provide
guidance on how to avoid common experimental design pitfalls that can hurt
model performance, trustworthiness, and reproducibility. Discussion is
accompanied by an interactive online tutorial that demonstrates foundational
principles of ML experimental design, tailored to the microbiomics community.
Formalizing community best practices for supervised ML in microbiomics is an
important step towards improving the success and efficiency of clinical
research, to the benefit of patients and other stakeholders.
| [
{
"created": "Tue, 27 Feb 2024 15:49:26 GMT",
"version": "v1"
},
{
"created": "Tue, 23 Jul 2024 16:39:05 GMT",
"version": "v2"
}
] | 2024-07-25 | [
[
"Dudek",
"Natasha K.",
""
],
[
"Chakhvadze",
"Mariam",
""
],
[
"Kobakhidze",
"Saba",
""
],
[
"Kantidze",
"Omar",
""
],
[
"Gankin",
"Yuriy",
""
]
] | Machine learning (ML) is set to accelerate innovations in clinical microbiomics, such as in disease diagnostics and prognostics. This will require high-quality, reproducible, interpretable workflows whose predictive capabilities meet or exceed the high thresholds set for clinical tools by regulatory agencies. Here, we capture a snapshot of current practices in the application of supervised ML to microbiomics data, through an in-depth analysis of 100 peer-reviewed journal articles published in 2021-2022. We apply a data-driven approach to steer discussion of the merits of varied approaches to experimental design, including key considerations such as how to mitigate the effects of small dataset size while avoiding data leakage. We further provide guidance on how to avoid common experimental design pitfalls that can hurt model performance, trustworthiness, and reproducibility. Discussion is accompanied by an interactive online tutorial that demonstrates foundational principles of ML experimental design, tailored to the microbiomics community. Formalizing community best practices for supervised ML in microbiomics is an important step towards improving the success and efficiency of clinical research, to the benefit of patients and other stakeholders. |
2203.00628 | Zhenyu Yang | Zhenyu Yang, Zongsheng Hu, Hangjie Ji, Kyle Lafata, Scott Floyd,
Fang-Fang Yin, Chunhao Wang | A Neural Ordinary Differential Equation Model for Visualizing Deep
Neural Network Behaviors in Multi-Parametric MRI based Glioma Segmentation | 30 pages, 7 figures, 2 tables | null | null | null | q-bio.QM cs.LG eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Purpose: To develop a neural ordinary differential equation (ODE) model for
visualizing deep neural network (DNN) behavior during multi-parametric MRI
(mp-MRI) based glioma segmentation as a method to enhance deep learning
explainability. Methods: By hypothesizing that deep feature extraction can be
modeled as a spatiotemporally continuous process, we designed a novel deep
learning model, neural ODE, in which deep feature extraction was governed by an
ODE without explicit expression. The dynamics of 1) MR images after
interactions with DNN and 2) segmentation formation can be visualized after
solving ODE. An accumulative contribution curve (ACC) was designed to
quantitatively evaluate the utilization of each MRI by DNN towards the final
segmentation results. The proposed neural ODE model was demonstrated using 369
glioma patients with a 4-modality mp-MRI protocol: T1, contrast-enhanced T1
(T1-Ce), T2, and FLAIR. Three neural ODE models were trained to segment
enhancing tumor (ET), tumor core (TC), and whole tumor (WT). The key MR
modalities with significant utilization by DNN were identified based on ACC
analysis. Segmentation results by DNN using only the key MR modalities were
compared to the ones using all 4 MR modalities. Results: All neural ODE models
successfully illustrated image dynamics as expected. ACC analysis identified
T1-Ce as the only key modality in ET and TC segmentations, while both FLAIR and
T2 were key modalities in WT segmentation. Compared to the U-Net results using
all 4 MR modalities, Dice coefficient of ET (0.784->0.775), TC (0.760->0.758),
and WT (0.841->0.837) using the key modalities only had minimal differences
without significance. Conclusion: The neural ODE model offers a new tool for
optimizing the deep learning model inputs with enhanced explainability. The
presented methodology can be generalized to other medical image-related deep
learning applications.
| [
{
"created": "Tue, 1 Mar 2022 17:16:41 GMT",
"version": "v1"
},
{
"created": "Wed, 23 Mar 2022 23:26:25 GMT",
"version": "v2"
}
] | 2022-03-25 | [
[
"Yang",
"Zhenyu",
""
],
[
"Hu",
"Zongsheng",
""
],
[
"Ji",
"Hangjie",
""
],
[
"Lafata",
"Kyle",
""
],
[
"Floyd",
"Scott",
""
],
[
"Yin",
"Fang-Fang",
""
],
[
"Wang",
"Chunhao",
""
]
] | Purpose: To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network (DNN) behavior during multi-parametric MRI (mp-MRI) based glioma segmentation as a method to enhance deep learning explainability. Methods: By hypothesizing that deep feature extraction can be modeled as a spatiotemporally continuous process, we designed a novel deep learning model, neural ODE, in which deep feature extraction was governed by an ODE without explicit expression. The dynamics of 1) MR images after interactions with DNN and 2) segmentation formation can be visualized after solving ODE. An accumulative contribution curve (ACC) was designed to quantitatively evaluate the utilization of each MRI by DNN towards the final segmentation results. The proposed neural ODE model was demonstrated using 369 glioma patients with a 4-modality mp-MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. Three neural ODE models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT). The key MR modalities with significant utilization by DNN were identified based on ACC analysis. Segmentation results by DNN using only the key MR modalities were compared to the ones using all 4 MR modalities. Results: All neural ODE models successfully illustrated image dynamics as expected. ACC analysis identified T1-Ce as the only key modality in ET and TC segmentations, while both FLAIR and T2 were key modalities in WT segmentation. Compared to the U-Net results using all 4 MR modalities, Dice coefficient of ET (0.784->0.775), TC (0.760->0.758), and WT (0.841->0.837) using the key modalities only had minimal differences without significance. Conclusion: The neural ODE model offers a new tool for optimizing the deep learning model inputs with enhanced explainability. The presented methodology can be generalized to other medical image-related deep learning applications. |
1504.00525 | Jean-Baptiste Masson dr. | Mohamed El Beheiry, Maxime Dahan and Jean-Baptiste Masson | InferenceMAP: Mapping of Single-Molecule Dynamics with Bayesian
Inference | 56 pages | null | 10.1016/j.bpj.2014.11.2580 | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Single-particle tracking (SPT) grants unprecedented insight into cellular
function at the molecular scale [1]. Throughout the cell, the movement of
single-molecules is generally heterogeneous and complex. Hence, there is an
imperative to understand the multi-scale nature of single-molecule dynamics in
biological systems. We have previously shown that with high-density SPT,
spatial maps of the parameters that dictate molecule motion can be generated to
intricately describe cellular environments [2,3,4]. To date, however, there
exist no publically available tools that reconcile trajectory data to generate
the aforementioned maps. We address this void in the SPT community with
InferenceMAP: an interactive software package that uses a powerful Bayesian
method to map the dynamic cellular space experienced by individual
biomolecules.
| [
{
"created": "Thu, 2 Apr 2015 12:32:19 GMT",
"version": "v1"
}
] | 2015-06-24 | [
[
"Beheiry",
"Mohamed El",
""
],
[
"Dahan",
"Maxime",
""
],
[
"Masson",
"Jean-Baptiste",
""
]
] | Single-particle tracking (SPT) grants unprecedented insight into cellular function at the molecular scale [1]. Throughout the cell, the movement of single-molecules is generally heterogeneous and complex. Hence, there is an imperative to understand the multi-scale nature of single-molecule dynamics in biological systems. We have previously shown that with high-density SPT, spatial maps of the parameters that dictate molecule motion can be generated to intricately describe cellular environments [2,3,4]. To date, however, there exist no publically available tools that reconcile trajectory data to generate the aforementioned maps. We address this void in the SPT community with InferenceMAP: an interactive software package that uses a powerful Bayesian method to map the dynamic cellular space experienced by individual biomolecules. |
1303.0103 | Jonathan Crofts | Jonathan J Crofts and Ernesto Estrada | A statistical mechanics description of environmental variability in
metabolic networks | null | null | null | null | q-bio.MN cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many of the chemical reactions that take place within a living cell are
irreversible. Due to evolutionary pressures, the number of allowable reactions
within these systems are highly constrained and thus the resulting metabolic
networks display considerable asymmetry. In this paper, we explore possible
evolutionary factors pertaining to the reduced symmetry observed in these
networks, and demonstrate the important role environmental variability plays in
shaping their structural organization. Interpreting the returnability index as
an equilibrium constant for a reaction network in equilibrium with a
hypothetical reference system, enables us to quantify the extent to which a
metabolic network is in disequilibrium. Further, by introducing a new directed
centrality measure via an extension of the subgraph centrality metric to
directed networks, we are able to characterise individual metabolites by their
participation within metabolic pathways. To demonstrate these ideas, we study
116 metabolic networks of bacteria. In particular, we find that the equilibrium
constant for the metabolic networks decreases significantly in-line with
variability in bacterial habitats, supporting the view that environmental
variability promotes disequilibrium within these biochemical reaction systems.
| [
{
"created": "Fri, 1 Mar 2013 07:18:09 GMT",
"version": "v1"
}
] | 2013-03-04 | [
[
"Crofts",
"Jonathan J",
""
],
[
"Estrada",
"Ernesto",
""
]
] | Many of the chemical reactions that take place within a living cell are irreversible. Due to evolutionary pressures, the number of allowable reactions within these systems are highly constrained and thus the resulting metabolic networks display considerable asymmetry. In this paper, we explore possible evolutionary factors pertaining to the reduced symmetry observed in these networks, and demonstrate the important role environmental variability plays in shaping their structural organization. Interpreting the returnability index as an equilibrium constant for a reaction network in equilibrium with a hypothetical reference system, enables us to quantify the extent to which a metabolic network is in disequilibrium. Further, by introducing a new directed centrality measure via an extension of the subgraph centrality metric to directed networks, we are able to characterise individual metabolites by their participation within metabolic pathways. To demonstrate these ideas, we study 116 metabolic networks of bacteria. In particular, we find that the equilibrium constant for the metabolic networks decreases significantly in-line with variability in bacterial habitats, supporting the view that environmental variability promotes disequilibrium within these biochemical reaction systems. |
1612.02744 | Igor Kaufman | I.Kh. Kaufman, O.A. Fedorenko, D.G. Luchinsky, W.A.T. Gibby, S.K.
Roberts. P.V.E. McClintock, R.S. Eisenberg | Ionic Coulomb blockade and anomalous mole fraction effect in NaChBac
bacterial ion channels | 12 pages, 5 figures, 32 references, submitted to EPJ | null | null | null | q-bio.SC physics.bio-ph q-bio.CB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We report an experimental study of the influences of the fixed charge and
bulk ionic concentrations on the conduction of biological ion channels, and we
consider the results within the framework of the ionic Coulomb blockade model
of permeation and selectivity. Voltage clamp recordings were used to
investigate the Na$^+$/Ca$^{2+}$ anomalous mole fraction effect (AMFE)
exhibited by the bacterial sodium channel NaChBac and its mutants.
Site-directed mutagenesis was used to study the effect of either increasing or
decreasing the fixed charge in their selectivity filters for comparison with
the predictions of the Coulomb blockade model. The model was found to describe
well some aspects of the experimental (divalent blockade and AMFE) and
simulated (discrete multi-ion conduction and occupancy band) phenomena,
including a concentration-dependent shift of the Coulomb staircase. These
results substantially extend the understanding of ion channel selectivity and
may also be applicable to biomimetic nanopores with charged walls.
| [
{
"created": "Thu, 8 Dec 2016 17:59:51 GMT",
"version": "v1"
}
] | 2016-12-09 | [
[
"Kaufman",
"I. Kh.",
""
],
[
"Fedorenko",
"O. A.",
""
],
[
"Luchinsky",
"D. G.",
""
],
[
"Gibby",
"W. A. T.",
""
],
[
"McClintock",
"S. K. Roberts. P. V. E.",
""
],
[
"Eisenberg",
"R. S.",
""
]
] | We report an experimental study of the influences of the fixed charge and bulk ionic concentrations on the conduction of biological ion channels, and we consider the results within the framework of the ionic Coulomb blockade model of permeation and selectivity. Voltage clamp recordings were used to investigate the Na$^+$/Ca$^{2+}$ anomalous mole fraction effect (AMFE) exhibited by the bacterial sodium channel NaChBac and its mutants. Site-directed mutagenesis was used to study the effect of either increasing or decreasing the fixed charge in their selectivity filters for comparison with the predictions of the Coulomb blockade model. The model was found to describe well some aspects of the experimental (divalent blockade and AMFE) and simulated (discrete multi-ion conduction and occupancy band) phenomena, including a concentration-dependent shift of the Coulomb staircase. These results substantially extend the understanding of ion channel selectivity and may also be applicable to biomimetic nanopores with charged walls. |
1801.09372 | Tzvetomir Tzvetanov | Christian Beste and Daniel Kaping and Tzvetomir Tzvetanov | Extension of the non-parametric cluster-based time-frequency statistics
to the full time windows and to single condition tests | 14 pages, 5 figures | null | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Oscillatory processes are central for the understanding of the neural bases
of cognition and behaviour. To analyse these processes, time-frequency (TF)
decomposition methods are applied and non-parametric cluster-based statistical
procedure are used for comparing two or more conditions. While this combination
is a powerful method, it has two drawbacks. One the unreliable estimation of
signals outside the cone-of-influence and the second relates to the length of
the time frequency window used for the analysis. Both impose constrains on the
non-parametric statistical procedure for inferring an effect in the TF domain.
Here we extend the method to reliably infer oscillatory differences within the
full TF map and to test single conditions. We show that it can be applied in
small time windows irrespective of the cone-of-influence and we further develop
its application to single-condition case for testing the hypothesis of the
presence or not of time-varying signals. We present tests of this new method on
real EEG and behavioural data and show that its sensitivity to single-condition
tests is at least as good as classic Fourier analysis. Statistical inference in
the full TF map is available and efficient in detecting differences between
conditions as well as the presence of time-varying signal in single condition.
| [
{
"created": "Mon, 29 Jan 2018 06:27:08 GMT",
"version": "v1"
}
] | 2018-01-30 | [
[
"Beste",
"Christian",
""
],
[
"Kaping",
"Daniel",
""
],
[
"Tzvetanov",
"Tzvetomir",
""
]
] | Oscillatory processes are central for the understanding of the neural bases of cognition and behaviour. To analyse these processes, time-frequency (TF) decomposition methods are applied and non-parametric cluster-based statistical procedure are used for comparing two or more conditions. While this combination is a powerful method, it has two drawbacks. One the unreliable estimation of signals outside the cone-of-influence and the second relates to the length of the time frequency window used for the analysis. Both impose constrains on the non-parametric statistical procedure for inferring an effect in the TF domain. Here we extend the method to reliably infer oscillatory differences within the full TF map and to test single conditions. We show that it can be applied in small time windows irrespective of the cone-of-influence and we further develop its application to single-condition case for testing the hypothesis of the presence or not of time-varying signals. We present tests of this new method on real EEG and behavioural data and show that its sensitivity to single-condition tests is at least as good as classic Fourier analysis. Statistical inference in the full TF map is available and efficient in detecting differences between conditions as well as the presence of time-varying signal in single condition. |
2208.05770 | Qiang Li | Qiang Li, Greg Ver Steeg, Jesus Malo | Functional Connectivity via Total Correlation: Analytical results in
Visual Areas | 31 pages, 14 figures, Accepted to Neurocomputing Journal | Neurocomputing 2023, 127143 | 10.1016/j.neucom.2023.127143 | null | q-bio.NC math.PR | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Recent studies invoke the superiority of the multivariate Total Correlation
concept over the conventional pairwise measures of functional connectivity in
biological networks. Those seminal works certainly show that empirical measures
of Total Correlation lead to connectivity patterns that differ from what is
obtained using the most popular measure, linear correlation, or its higher
order and nonlinear alternative Mutual Information. However, they do not
provide analytical results that explain the differences beyond the obvious
multivariate versus bivariate definitions. Moreover, the accuracy of the
empirical estimators could not be addressed directly because no controlled
scenario with known analytical result was provided either. This point is
critical because empirical estimation of information theory measures is always
challenging. As opposed to previous empirical approaches, in this work we
present analytical results to prove the advantages of Total Correlation over
Mutual Information to describe the functional connectivity. In particular, we
do it in neural networks for early vision (retina-LGN-cortex) which are
realistic but simple enough to get analytical results. The presented analytical
setting is also useful to check empirical estimates of Total Correlation.
Therefore, once certain estimate can be trusted, one can explore the behavior
with natural signals where the analytical results (that assume Gaussian
signals), may not be valid. In this regard, as applications (a) we explore the
effect of connectivity and feedback in the analytical retina-LGN-cortex network
with natural images, and (b) we assess the functional connectivity in visual
areas V1-V2-V3-V4 from actual fMRI recordings.
| [
{
"created": "Thu, 11 Aug 2022 12:01:26 GMT",
"version": "v1"
},
{
"created": "Mon, 11 Dec 2023 14:23:33 GMT",
"version": "v2"
}
] | 2023-12-25 | [
[
"Li",
"Qiang",
""
],
[
"Steeg",
"Greg Ver",
""
],
[
"Malo",
"Jesus",
""
]
] | Recent studies invoke the superiority of the multivariate Total Correlation concept over the conventional pairwise measures of functional connectivity in biological networks. Those seminal works certainly show that empirical measures of Total Correlation lead to connectivity patterns that differ from what is obtained using the most popular measure, linear correlation, or its higher order and nonlinear alternative Mutual Information. However, they do not provide analytical results that explain the differences beyond the obvious multivariate versus bivariate definitions. Moreover, the accuracy of the empirical estimators could not be addressed directly because no controlled scenario with known analytical result was provided either. This point is critical because empirical estimation of information theory measures is always challenging. As opposed to previous empirical approaches, in this work we present analytical results to prove the advantages of Total Correlation over Mutual Information to describe the functional connectivity. In particular, we do it in neural networks for early vision (retina-LGN-cortex) which are realistic but simple enough to get analytical results. The presented analytical setting is also useful to check empirical estimates of Total Correlation. Therefore, once certain estimate can be trusted, one can explore the behavior with natural signals where the analytical results (that assume Gaussian signals), may not be valid. In this regard, as applications (a) we explore the effect of connectivity and feedback in the analytical retina-LGN-cortex network with natural images, and (b) we assess the functional connectivity in visual areas V1-V2-V3-V4 from actual fMRI recordings. |
2211.11346 | Kazuyoshi Tsutsumi | Kazuyoshi Tsutsumi and Ernst Niebur | Hierarchically Modular Dynamical Neural Network Relaxing in a Warped
Space: Basic Model and its Characteristics | 44 pages, 22 EPS figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a hierarchically modular, dynamical neural network model whose
architecture minimizes a specifically designed energy function and defines its
temporal characteristics. The model has an internal and an external space that
are connected with a layered internetwork that consists of a pair of forward
and backward subnets composed of static neurons (with an instantaneous
time-course). Dynamical neurons with large time constants in the internal space
determine the overall time-course. The model offers a framework in which state
variables in the network relax in a warped space, due to the cooperation
between dynamic and static neurons. We assume that the system operates in
either a learning or an association mode, depending on the presence or absence
of feedback paths and input ports. In the learning mode, synaptic weights in
the internetwork are modified by strong inputs corresponding to repetitive
neuronal bursting, which represents sinusoidal or quasi-sinusoidal waves in the
short-term average density of nerve impulses or in the membrane potential. A
two-dimensional mapping relationship can be formed by employing signals with
different frequencies based on the same mechanism as Lissajous curves. In the
association mode, the speed of convergence to a goal point greatly varies with
the mapping relationship of the previously trained internetwork, and owing to
this property, the convergence trajectory in the two-dimensional model with the
non-linear mapping internetwork cannot go straight but instead must curve. We
further introduce a constrained association mode with a given target trajectory
and elucidate that in the internal space, an output trajectory is generated,
which is mapped from the external space according to the inverse of the mapping
relationship of the forward subnet.
| [
{
"created": "Mon, 21 Nov 2022 10:53:46 GMT",
"version": "v1"
}
] | 2022-11-22 | [
[
"Tsutsumi",
"Kazuyoshi",
""
],
[
"Niebur",
"Ernst",
""
]
] | We propose a hierarchically modular, dynamical neural network model whose architecture minimizes a specifically designed energy function and defines its temporal characteristics. The model has an internal and an external space that are connected with a layered internetwork that consists of a pair of forward and backward subnets composed of static neurons (with an instantaneous time-course). Dynamical neurons with large time constants in the internal space determine the overall time-course. The model offers a framework in which state variables in the network relax in a warped space, due to the cooperation between dynamic and static neurons. We assume that the system operates in either a learning or an association mode, depending on the presence or absence of feedback paths and input ports. In the learning mode, synaptic weights in the internetwork are modified by strong inputs corresponding to repetitive neuronal bursting, which represents sinusoidal or quasi-sinusoidal waves in the short-term average density of nerve impulses or in the membrane potential. A two-dimensional mapping relationship can be formed by employing signals with different frequencies based on the same mechanism as Lissajous curves. In the association mode, the speed of convergence to a goal point greatly varies with the mapping relationship of the previously trained internetwork, and owing to this property, the convergence trajectory in the two-dimensional model with the non-linear mapping internetwork cannot go straight but instead must curve. We further introduce a constrained association mode with a given target trajectory and elucidate that in the internal space, an output trajectory is generated, which is mapped from the external space according to the inverse of the mapping relationship of the forward subnet. |
1810.00613 | Joana Ribeiro | Joana P.C. Ribeiro, Bjarki {\TH}. Elvarsson, Erla Sturlud\'ottir and
Gunnar Stef\'ansson | An overview of the marine food web in Icelandic waters using Ecopath
with Ecosim | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fishing activities have broad impacts that affect, although not exclusively,
the targeted stocks. These impacts affect predators and prey of the harvested
species, as well as the whole ecosystem it inhabits. Ecosystem models can be
used to study the interactions that occur within a system, including those
between different organisms and those between fisheries and targeted species.
Trophic web models like Ecopath with Ecosim (EwE) can handle fishing fleets as
a top predator, with top-down impact on harvested organisms. The aim of this
study was to better understand the Icelandic marine ecosystem and the
interactions within. This was done by constructing an EwE model of Icelandic
waters. The model was run from 1984 to 2013 and was fitted to time series of
biomass estimates, landings data and mean annual temperature. The final model
was chosen by selecting the model with the lowest Akaike information criterion.
A skill assessment was performed using the Pearson's correlation coefficient,
the coefficient of determination, the modelling efficiency and the reliability
index to evaluate the model performance. The model performed satisfactorily
when simulating previously estimated biomass and known landings. Most of the
groups with time series were estimated to have top-down control over their
prey. These are harvested species with direct and/or indirect links to lower
trophic levels and future fishing policies should take this into account. This
model could be used as a tool to investigate how such policies could impact the
marine ecosystem in Icelandic waters.
| [
{
"created": "Mon, 1 Oct 2018 10:45:08 GMT",
"version": "v1"
},
{
"created": "Fri, 1 Feb 2019 13:54:13 GMT",
"version": "v2"
}
] | 2019-02-04 | [
[
"Ribeiro",
"Joana P. C.",
""
],
[
"Elvarsson",
"Bjarki Þ.",
""
],
[
"Sturludóttir",
"Erla",
""
],
[
"Stefánsson",
"Gunnar",
""
]
] | Fishing activities have broad impacts that affect, although not exclusively, the targeted stocks. These impacts affect predators and prey of the harvested species, as well as the whole ecosystem it inhabits. Ecosystem models can be used to study the interactions that occur within a system, including those between different organisms and those between fisheries and targeted species. Trophic web models like Ecopath with Ecosim (EwE) can handle fishing fleets as a top predator, with top-down impact on harvested organisms. The aim of this study was to better understand the Icelandic marine ecosystem and the interactions within. This was done by constructing an EwE model of Icelandic waters. The model was run from 1984 to 2013 and was fitted to time series of biomass estimates, landings data and mean annual temperature. The final model was chosen by selecting the model with the lowest Akaike information criterion. A skill assessment was performed using the Pearson's correlation coefficient, the coefficient of determination, the modelling efficiency and the reliability index to evaluate the model performance. The model performed satisfactorily when simulating previously estimated biomass and known landings. Most of the groups with time series were estimated to have top-down control over their prey. These are harvested species with direct and/or indirect links to lower trophic levels and future fishing policies should take this into account. This model could be used as a tool to investigate how such policies could impact the marine ecosystem in Icelandic waters. |
2301.07568 | Abbi Abdel-Rehim | Abbi Abdel-Rehim, Oghenejokpeme Orhobor, Hang Lou, Hao Ni and Ross D.
King | Beating the Best: Improving on AlphaFold2 at Protein Structure
Prediction | 12 pages | null | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The goal of Protein Structure Prediction (PSP) problem is to predict a
protein's 3D structure (confirmation) from its amino acid sequence. The problem
has been a 'holy grail' of science since the Noble prize-winning work of
Anfinsen demonstrated that protein conformation was determined by sequence. A
recent and important step towards this goal was the development of AlphaFold2,
currently the best PSP method. AlphaFold2 is probably the highest profile
application of AI to science. Both AlphaFold2 and RoseTTAFold (another
impressive PSP method) have been published and placed in the public domain
(code & models). Stacking is a form of ensemble machine learning ML in which
multiple baseline models are first learnt, then a meta-model is learnt using
the outputs of the baseline level model to form a model that outperforms the
base models. Stacking has been successful in many applications. We developed
the ARStack PSP method by stacking AlphaFold2 and RoseTTAFold. ARStack
significantly outperforms AlphaFold2. We rigorously demonstrate this using two
sets of non-homologous proteins, and a test set of protein structures published
after that of AlphaFold2 and RoseTTAFold. As more high quality prediction
methods are published it is likely that ensemble methods will increasingly
outperform any single method.
| [
{
"created": "Wed, 18 Jan 2023 14:39:34 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Jan 2023 09:54:01 GMT",
"version": "v2"
}
] | 2023-01-24 | [
[
"Abdel-Rehim",
"Abbi",
""
],
[
"Orhobor",
"Oghenejokpeme",
""
],
[
"Lou",
"Hang",
""
],
[
"Ni",
"Hao",
""
],
[
"King",
"Ross D.",
""
]
] | The goal of Protein Structure Prediction (PSP) problem is to predict a protein's 3D structure (confirmation) from its amino acid sequence. The problem has been a 'holy grail' of science since the Noble prize-winning work of Anfinsen demonstrated that protein conformation was determined by sequence. A recent and important step towards this goal was the development of AlphaFold2, currently the best PSP method. AlphaFold2 is probably the highest profile application of AI to science. Both AlphaFold2 and RoseTTAFold (another impressive PSP method) have been published and placed in the public domain (code & models). Stacking is a form of ensemble machine learning ML in which multiple baseline models are first learnt, then a meta-model is learnt using the outputs of the baseline level model to form a model that outperforms the base models. Stacking has been successful in many applications. We developed the ARStack PSP method by stacking AlphaFold2 and RoseTTAFold. ARStack significantly outperforms AlphaFold2. We rigorously demonstrate this using two sets of non-homologous proteins, and a test set of protein structures published after that of AlphaFold2 and RoseTTAFold. As more high quality prediction methods are published it is likely that ensemble methods will increasingly outperform any single method. |
1411.0291 | Li Zhaoping | Li Zhaoping and Li Zhe | Primary visual cortex as a saliency map: parameter-free prediction of
behavior from V1 physiology | 11 figures, 66 pages | PLoS Comput Biol 11(10): e1004375 (2015) | 10.1371/journal.pcbi.1004375 | null | q-bio.NC physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It has been hypothesized that neural activities in the primary visual cortex
(V1) represent a saliency map of the visual field to exogenously guide
attention. This hypothesis has so far provided only qualitative predictions and
their confirmations. We report this hypothesis' first quantitative prediction,
derived without free parameters, and its confirmation by human behavioral data.
The hypothesis provides a direct link between V1 neural responses to a visual
location and the saliency of that location to guide attention exogenously. In a
visual input containing many bars, one of them saliently different from all the
other bars which are identical to each other, saliency at the singleton's
location can be measured by the shortness of the reaction time in a visual
search task to find the singleton. The hypothesis predicts quantitatively the
whole distribution of the reaction times to find a singleton unique in color,
orientation, and motion direction from the reaction times to find other types
of singletons. The predicted distribution matches the experimentally observed
distribution in all six human observers. A requirement for this successful
prediction is a data-motivated assumption that V1 lacks neurons tuned
simultaneously to color, orientation, and motion direction of visual inputs.
Since evidence suggests that extrastriate cortices do have such neurons, we
discuss the possibility that the extrastriate cortices play no role in guiding
exogenous attention so that they can be devoted to other functional roles like
visual decoding or endogenous attention.
| [
{
"created": "Sun, 2 Nov 2014 18:21:39 GMT",
"version": "v1"
}
] | 2015-10-08 | [
[
"Zhaoping",
"Li",
""
],
[
"Zhe",
"Li",
""
]
] | It has been hypothesized that neural activities in the primary visual cortex (V1) represent a saliency map of the visual field to exogenously guide attention. This hypothesis has so far provided only qualitative predictions and their confirmations. We report this hypothesis' first quantitative prediction, derived without free parameters, and its confirmation by human behavioral data. The hypothesis provides a direct link between V1 neural responses to a visual location and the saliency of that location to guide attention exogenously. In a visual input containing many bars, one of them saliently different from all the other bars which are identical to each other, saliency at the singleton's location can be measured by the shortness of the reaction time in a visual search task to find the singleton. The hypothesis predicts quantitatively the whole distribution of the reaction times to find a singleton unique in color, orientation, and motion direction from the reaction times to find other types of singletons. The predicted distribution matches the experimentally observed distribution in all six human observers. A requirement for this successful prediction is a data-motivated assumption that V1 lacks neurons tuned simultaneously to color, orientation, and motion direction of visual inputs. Since evidence suggests that extrastriate cortices do have such neurons, we discuss the possibility that the extrastriate cortices play no role in guiding exogenous attention so that they can be devoted to other functional roles like visual decoding or endogenous attention. |
q-bio/0402046 | Giulia Menconi | Giulia Menconi | Sublinear Growth of Information in DNA Sequences | 30 pages, 13 figures, submitted (Oct. 2003) | null | null | null | q-bio.GN cond-mat.stat-mech physics.data-an | null | We introduce a novel method to analyse complete genomes and recognise some
distinctive features by means of an adaptive compression algorithm, which is
not DNA-oriented. We study the Information Content as a function of the number
of symbols encoded by the algorithm. Preliminar results are shown concerning
regions having a sublinear type of information growth, which is strictly
connected to the presence of highly repetitive subregions that might be
supposed to have a regulatory function within the genome.
| [
{
"created": "Fri, 27 Feb 2004 21:30:59 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Menconi",
"Giulia",
""
]
] | We introduce a novel method to analyse complete genomes and recognise some distinctive features by means of an adaptive compression algorithm, which is not DNA-oriented. We study the Information Content as a function of the number of symbols encoded by the algorithm. Preliminar results are shown concerning regions having a sublinear type of information growth, which is strictly connected to the presence of highly repetitive subregions that might be supposed to have a regulatory function within the genome. |
1907.05395 | Prasanna Parvathaneni | Prasanna Parvathaneni, Shunxing Bao, Vishwesh Nath, Neil D. Woodward,
Daniel O. Claassen, Carissa J. Cascio, David H. Zald, Yuankai Huo, Bennett A.
Landman, Ilwoo Lyu | Cortical Surface Parcellation using Spherical Convolutional Neural
Networks | null | null | null | null | q-bio.NC eess.IV q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present cortical surface parcellation using spherical deep convolutional
neural networks. Traditional multi-atlas cortical surface parcellation requires
inter-subject surface registration using geometric features with high
processing time on a single subject (2-3 hours). Moreover, even optimal surface
registration does not necessarily produce optimal cortical parcellation as
parcel boundaries are not fully matched to the geometric features. In this
context, a choice of training features is important for accurate cortical
parcellation. To utilize the networks efficiently, we propose cortical
parcellation-specific input data from an irregular and complicated structure of
cortical surfaces. To this end, we align ground-truth cortical parcel
boundaries and use their resulting deformation fields to generate new pairs of
deformed geometric features and parcellation maps. To extend the capability of
the networks, we then smoothly morph cortical geometric features and
parcellation maps using the intermediate deformation fields. We validate our
method on 427 adult brains for 49 labels. The experimental results show that
our method out-performs traditional multi-atlas and naive spherical U-Net
approaches, while achieving full cortical parcellation in less than a minute.
| [
{
"created": "Thu, 11 Jul 2019 17:20:00 GMT",
"version": "v1"
}
] | 2019-07-12 | [
[
"Parvathaneni",
"Prasanna",
""
],
[
"Bao",
"Shunxing",
""
],
[
"Nath",
"Vishwesh",
""
],
[
"Woodward",
"Neil D.",
""
],
[
"Claassen",
"Daniel O.",
""
],
[
"Cascio",
"Carissa J.",
""
],
[
"Zald",
"David H.",
""
],
[
"Huo",
"Yuankai",
""
],
[
"Landman",
"Bennett A.",
""
],
[
"Lyu",
"Ilwoo",
""
]
] | We present cortical surface parcellation using spherical deep convolutional neural networks. Traditional multi-atlas cortical surface parcellation requires inter-subject surface registration using geometric features with high processing time on a single subject (2-3 hours). Moreover, even optimal surface registration does not necessarily produce optimal cortical parcellation as parcel boundaries are not fully matched to the geometric features. In this context, a choice of training features is important for accurate cortical parcellation. To utilize the networks efficiently, we propose cortical parcellation-specific input data from an irregular and complicated structure of cortical surfaces. To this end, we align ground-truth cortical parcel boundaries and use their resulting deformation fields to generate new pairs of deformed geometric features and parcellation maps. To extend the capability of the networks, we then smoothly morph cortical geometric features and parcellation maps using the intermediate deformation fields. We validate our method on 427 adult brains for 49 labels. The experimental results show that our method out-performs traditional multi-atlas and naive spherical U-Net approaches, while achieving full cortical parcellation in less than a minute. |
0705.3895 | Apoorva Patel | Apoorva D. Patel | Towards Understanding the Origin of Genetic Languages | (v1) 33 pages, contributed chapter to "Quantum Aspects of Life",
edited by D. Abbott, P. Davies and A. Pati, (v2) published version with some
editing | null | 10.1142/9781848162556_0010 | null | q-bio.GN cs.IT math.IT physics.bio-ph quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Molecular biology is a nanotechnology that works--it has worked for billions
of years and in an amazing variety of circumstances. At its core is a system
for acquiring, processing and communicating information that is universal, from
viruses and bacteria to human beings. Advances in genetics and experience in
designing computers have taken us to a stage where we can understand the
optimisation principles at the root of this system, from the availability of
basic building blocks to the execution of tasks. The languages of DNA and
proteins are argued to be the optimal solutions to the information processing
tasks they carry out. The analysis also suggests simpler predecessors to these
languages, and provides fascinating clues about their origin. Obviously, a
comprehensive unraveling of the puzzle of life would have a lot to say about
what we may design or convert ourselves into.
| [
{
"created": "Sat, 26 May 2007 13:01:20 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Oct 2008 11:37:41 GMT",
"version": "v2"
}
] | 2016-12-21 | [
[
"Patel",
"Apoorva D.",
""
]
] | Molecular biology is a nanotechnology that works--it has worked for billions of years and in an amazing variety of circumstances. At its core is a system for acquiring, processing and communicating information that is universal, from viruses and bacteria to human beings. Advances in genetics and experience in designing computers have taken us to a stage where we can understand the optimisation principles at the root of this system, from the availability of basic building blocks to the execution of tasks. The languages of DNA and proteins are argued to be the optimal solutions to the information processing tasks they carry out. The analysis also suggests simpler predecessors to these languages, and provides fascinating clues about their origin. Obviously, a comprehensive unraveling of the puzzle of life would have a lot to say about what we may design or convert ourselves into. |
q-bio/0407028 | Arnaud Buhot | A. Buhot and A. Halperin | The Effects of Stacking on the Configurations and Elasticity of Single
Stranded Nucleic Acids | 4 pages and 2 figures. Accepted in Phys. Rev. E Rapid Comm | Phys. Rev. E 70 020902(R) (2004) | 10.1103/PhysRevE.70.020902 | null | q-bio.BM cond-mat.stat-mech | null | Stacking interactions in single stranded nucleic acids give rise to
configurations of an annealed rod-coil multiblock copolymer. Theoretical
analysis identifies the resulting signatures for long homopolynucleotides: A
non monotonous dependence of size on temperature, corresponding effects on
cyclization and a plateau in the extension force law. Explicit numerical
results for poly(dA) and poly(rU) are presented.
| [
{
"created": "Wed, 21 Jul 2004 13:50:44 GMT",
"version": "v1"
}
] | 2009-11-10 | [
[
"Buhot",
"A.",
""
],
[
"Halperin",
"A.",
""
]
] | Stacking interactions in single stranded nucleic acids give rise to configurations of an annealed rod-coil multiblock copolymer. Theoretical analysis identifies the resulting signatures for long homopolynucleotides: A non monotonous dependence of size on temperature, corresponding effects on cyclization and a plateau in the extension force law. Explicit numerical results for poly(dA) and poly(rU) are presented. |
2403.19844 | Prakash Chourasia | Sarwan Ali, Prakash Chourasia, Murray Patterson | Expanding Chemical Representation with k-mers and Fragment-based
Fingerprints for Molecular Fingerprinting | 12 Pages, 3 tables, Accepted at SimBig2023 | SimBig2023 | null | null | q-bio.BM cs.LG physics.chem-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This study introduces a novel approach, combining substruct counting,
$k$-mers, and Daylight-like fingerprints, to expand the representation of
chemical structures in SMILES strings. The integrated method generates
comprehensive molecular embeddings that enhance discriminative power and
information content. Experimental evaluations demonstrate its superiority over
traditional Morgan fingerprinting, MACCS, and Daylight fingerprint alone,
improving chemoinformatics tasks such as drug classification. The proposed
method offers a more informative representation of chemical structures,
advancing molecular similarity analysis and facilitating applications in
molecular design and drug discovery. It presents a promising avenue for
molecular structure analysis and design, with significant potential for
practical implementation.
| [
{
"created": "Thu, 28 Mar 2024 21:36:07 GMT",
"version": "v1"
}
] | 2024-04-01 | [
[
"Ali",
"Sarwan",
""
],
[
"Chourasia",
"Prakash",
""
],
[
"Patterson",
"Murray",
""
]
] | This study introduces a novel approach, combining substruct counting, $k$-mers, and Daylight-like fingerprints, to expand the representation of chemical structures in SMILES strings. The integrated method generates comprehensive molecular embeddings that enhance discriminative power and information content. Experimental evaluations demonstrate its superiority over traditional Morgan fingerprinting, MACCS, and Daylight fingerprint alone, improving chemoinformatics tasks such as drug classification. The proposed method offers a more informative representation of chemical structures, advancing molecular similarity analysis and facilitating applications in molecular design and drug discovery. It presents a promising avenue for molecular structure analysis and design, with significant potential for practical implementation. |
0908.1310 | Denis Semenov A. | Denis A. Semenov | Reasons underlying certain tendencies in the data on the frequency of
codon usage | 3 pages | null | null | null | q-bio.OT q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The tendencies described in this work were revealed in the course of
examination of adenine and uracil distribution in the mRNA encoding sequence.
The study also discusses the usage of codons occupied by the amino acid
arginine in the table of the universal genetic code. All of the described
tendencies are qualitative, so neither sophisticated methods nor cumbersome
calculations are necessary to reveal and interpret them.
| [
{
"created": "Mon, 10 Aug 2009 12:25:04 GMT",
"version": "v1"
}
] | 2009-08-11 | [
[
"Semenov",
"Denis A.",
""
]
] | The tendencies described in this work were revealed in the course of examination of adenine and uracil distribution in the mRNA encoding sequence. The study also discusses the usage of codons occupied by the amino acid arginine in the table of the universal genetic code. All of the described tendencies are qualitative, so neither sophisticated methods nor cumbersome calculations are necessary to reveal and interpret them. |
1902.07283 | Hamid Behjat | Sevil Maghsadhagh, Anders Eklund, Hamid Behjat | Graph Spectral Characterization of Brain Cortical Morphology | arXiv admin note: substantial text overlap with arXiv:1810.10339 | null | null | null | q-bio.NC cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The human brain cortical layer has a convoluted morphology that is unique to
each individual. Characterization of the cortical morphology is necessary in
longitudinal studies of structural brain change, as well as in discriminating
individuals in health and disease. A method for encoding the cortical
morphology in the form of a graph is presented. The design of graphs that
encode the global cerebral hemisphere cortices as well as localized cortical
regions is proposed. Spectral metrics derived from these graphs are then
studied and proposed as descriptors of cortical morphology. As proof-of-concept
of their applicability in characterizing cortical morphology, the metrics are
studied in the context of hemispheric asymmetry as well as gender dependent
discrimination of cortical morphology.
| [
{
"created": "Tue, 19 Feb 2019 21:04:26 GMT",
"version": "v1"
}
] | 2019-02-21 | [
[
"Maghsadhagh",
"Sevil",
""
],
[
"Eklund",
"Anders",
""
],
[
"Behjat",
"Hamid",
""
]
] | The human brain cortical layer has a convoluted morphology that is unique to each individual. Characterization of the cortical morphology is necessary in longitudinal studies of structural brain change, as well as in discriminating individuals in health and disease. A method for encoding the cortical morphology in the form of a graph is presented. The design of graphs that encode the global cerebral hemisphere cortices as well as localized cortical regions is proposed. Spectral metrics derived from these graphs are then studied and proposed as descriptors of cortical morphology. As proof-of-concept of their applicability in characterizing cortical morphology, the metrics are studied in the context of hemispheric asymmetry as well as gender dependent discrimination of cortical morphology. |
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